From 93c3b9aac4261ee73c2af0ea2daef6f88c48b459 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Sun, 20 Oct 2024 07:47:45 +0000 Subject: [PATCH 01/13] First stage of refactoring inputs --- eo_tides/eo.py | 135 ++- eo_tides/model.py | 43 +- tests/testing.ipynb | 2287 +++++++++---------------------------------- 3 files changed, 601 insertions(+), 1864 deletions(-) diff --git a/eo_tides/eo.py b/eo_tides/eo.py index df71e93..dfb8367 100644 --- a/eo_tides/eo.py +++ b/eo_tides/eo.py @@ -2,6 +2,7 @@ from __future__ import annotations import os +import textwrap import warnings from typing import TYPE_CHECKING @@ -14,12 +15,59 @@ if TYPE_CHECKING: import numpy as np -from .model import model_tides +from .model import _standardise_time, model_tides + + +def _standardise_inputs( + ds: xr.DataArray | xr.Dataset | GeoBox, + time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None, +) -> (GeoBox, np.ndarray): + """ + Takes an xarray or GeoBox input and an optional custom times, + and returns a standardised GeoBox and + """ + + # If `ds` is an xarray object, extract its GeoBox and time + if isinstance(ds, (xr.DataArray, xr.Dataset)): + # Try to extract GeoBox + try: + gbox = ds.odc.geobox + except AttributeError: + error_msg = """ + Cannot extract a valid GeoBox for `ds`. This is required for + extracting details about `ds`'s CRS and spatial location. + + Import `odc.geo.xr` then run `ds = ds.odc.assign_crs(crs=...)` + to prepare your data before passing it to this function. + """ + raise Exception(textwrap.dedent(error_msg).strip()) + + # Use custom time by default if provided; otherwise try and extract from `ds` + if time is not None: + time = _standardise_time(time) + elif "time" in ds.coords: + time = ds.coords["time"].values + else: + raise ValueError("`ds` does not have a time dimension, and no custom times were provided via `time`.") + + # If `ds` is a GeoBox, use it directly; raise an error if no time was provided + elif isinstance(ds, GeoBox): + gbox = ds + if time is not None: + time = _standardise_time(time) + else: + raise ValueError("If `ds` is a GeoBox, `time` must be provided.") + + # Raise error if no valid inputs were provided + else: + raise TypeError("`ds` must be an xarray.DataArray, xarray.Dataset, or odc.geo.geobox.GeoBox.") + + return gbox, time def _pixel_tides_resample( tides_lowres, - ds, + gbox, resample_method="bilinear", dask_chunks="auto", dask_compute=True, @@ -32,10 +80,10 @@ def _pixel_tides_resample( ---------- tides_lowres : xarray.DataArray The low resolution tide modelling data array to be resampled. - ds : xarray.Dataset - The dataset whose geobox will be used as the template for the - resampling operation. This is typically the same satellite - dataset originally passed to `pixel_tides`. + gbox : GeoBox + The GeoBox to use as the template for the resampling operation. + This is typically comes from the same satellite dataset originally + passed to `pixel_tides` (e.g. `ds.odc.geobox`). resample_method : string, optional The resampling method to use. Defaults to "bilinear"; valid options include "nearest", "cubic", "min", "max", "average" etc. @@ -59,7 +107,7 @@ def _pixel_tides_resample( """ # Determine spatial dimensions - y_dim, x_dim = ds.odc.spatial_dims + y_dim, x_dim = gbox.dimensions # Convert array to Dask, using no chunking along y and x dims, # and a single chunk for each timestep/quantile and tide model @@ -75,13 +123,13 @@ def _pixel_tides_resample( if (y_dim in ds.chunks) & (x_dim in ds.chunks): dask_chunks = (ds.chunks[y_dim], ds.chunks[x_dim]) else: - dask_chunks = ds.odc.geobox.shape + dask_chunks = gbox.shape else: - dask_chunks = ds.odc.geobox.shape + dask_chunks = gbox.shape # Reproject into the GeoBox of `ds` using odc.geo and Dask tides_highres = tides_lowres_dask.odc.reproject( - how=ds.odc.geobox, + how=gbox, chunks=dask_chunks, resampling=resample_method, ).rename("tide_height") @@ -230,8 +278,8 @@ def tag_tides( def pixel_tides( - ds: xr.Dataset | xr.DataArray, - times=None, + ds: xr.Dataset | xr.DataArray | GeoBox, + time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None = None, model: str | list[str] = "EOT20", directory: str | os.PathLike | None = None, resample: bool = True, @@ -276,7 +324,7 @@ def pixel_tides( ds : xarray.Dataset or xarray.DataArray A multi-dimensional dataset (e.g. "x", "y", "time") that will be used to define the tide modelling grid. - times : pd.DatetimeIndex or list of pd.Timestamp, optional + time : pd.DatetimeIndex or list of pd.Timestamp, optional By default, the function will model tides using the times contained in the `time` dimension of `ds`. Alternatively, this param can be used to model tides for a custom set of times @@ -357,39 +405,42 @@ def pixel_tides( If `resample=False`, results for the intermediate low-resolution tide modelling grid will be returned instead. """ - # First test if no time dimension and nothing passed to `times` - if ("time" not in ds.dims) & (times is None): - raise ValueError( - "`ds` does not contain a 'time' dimension. Times are required " - "for modelling tides: please pass in a set of custom tides " - "using the `times` parameter. For example: " - "`times=pd.date_range(start='2000', end='2001', freq='5h')`", - ) + # # First test if no time dimension and nothing passed to `times` + # if ("time" not in ds.dims) & (times is None): + # raise ValueError( + # "`ds` does not contain a 'time' dimension. Times are required " + # "for modelling tides: please pass in a set of custom tides " + # "using the `times` parameter. For example: " + # "`times=pd.date_range(start='2000', end='2001', freq='5h')`", + # ) - # If custom times are provided, convert them to a consistent - # pandas.DatatimeIndex format - if times is not None: - if isinstance(times, list): - time_coords = pd.DatetimeIndex(times) - elif isinstance(times, pd.Timestamp): - time_coords = pd.DatetimeIndex([times]) - else: - time_coords = times + # # If custom times are provided, convert them to a consistent + # # pandas.DatatimeIndex format + # if times is not None: + # if isinstance(times, list): + # time_coords = pd.DatetimeIndex(times) + # elif isinstance(times, pd.Timestamp): + # time_coords = pd.DatetimeIndex([times]) + # else: + # time_coords = times - # Otherwise, use times from `ds` directly - else: - time_coords = ds.coords["time"] + # # Otherwise, use times from `ds` directly + # else: + # time_coords = ds.coords["time"] + + # Standardise data inputs and time + gbox, time_coords = _standardise_inputs(ds, time) # Standardise model into a list for easy handling model = [model] if isinstance(model, str) else model # Determine spatial dimensions - y_dim, x_dim = ds.odc.spatial_dims + y_dim, x_dim = gbox.dimensions # Determine resolution and buffer, using different defaults for # geographic (i.e. degrees) and projected (i.e. metres) CRSs: - crs_units = ds.odc.geobox.crs.units[0][0:6] - if ds.odc.geobox.crs.geographic: + crs_units = gbox.crs.units[0][0:6] + if gbox.crs.geographic: if resolution is None: resolution = 0.05 elif resolution > 360: @@ -417,7 +468,7 @@ def pixel_tides( buffer = 12000 # Raise error if resolution is less than dataset resolution - dataset_res = ds.odc.geobox.resolution.x + dataset_res = gbox.resolution.x if resolution < dataset_res: raise ValueError( f"The resolution of the low-resolution tide " @@ -432,20 +483,20 @@ def pixel_tides( # Create a new reduced resolution tide modelling grid after # first buffering the grid print(f"Creating reduced resolution {resolution} x {resolution} {crs_units} tide modelling array") - buffered_geobox = ds.odc.geobox.buffered(buffer) + buffered_geobox = gbox.buffered(buffer) rescaled_geobox = GeoBox.from_bbox(bbox=buffered_geobox.boundingbox, resolution=resolution) rescaled_ds = odc.geo.xr.xr_zeros(rescaled_geobox) # Flatten grid to 1D, then add time dimension flattened_ds = rescaled_ds.stack(z=(x_dim, y_dim)) - flattened_ds = flattened_ds.expand_dims(dim={"time": time_coords.values}) + flattened_ds = flattened_ds.expand_dims(dim={"time": time_coords}) # Model tides in parallel, returning a pandas.DataFrame tide_df = model_tides( x=flattened_ds[x_dim], y=flattened_ds[y_dim], time=flattened_ds.time, - crs=f"EPSG:{ds.odc.geobox.crs.epsg}", + crs=f"EPSG:{gbox.crs.epsg}", model=model, directory=directory, **model_tides_kwargs, @@ -480,14 +531,14 @@ def pixel_tides( tides_lowres = tides_lowres.squeeze("tide_model") # Ensure CRS is present before we apply any resampling - tides_lowres = tides_lowres.odc.assign_crs(ds.odc.geobox.crs) + tides_lowres = tides_lowres.odc.assign_crs(gbox.crs) # Reproject into original high resolution grid if resample: print("Reprojecting tides into original resolution") tides_highres = _pixel_tides_resample( tides_lowres, - ds, + gbox, resample_method, dask_chunks, dask_compute, diff --git a/eo_tides/model.py b/eo_tides/model.py index 0252991..64e058e 100644 --- a/eo_tides/model.py +++ b/eo_tides/model.py @@ -50,12 +50,29 @@ def _set_directory(directory): return directory +def _standardise_time( + time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None, +) -> np.ndarray | None: + """ + Accept a datetime64 ndarray, pandas.DatetimeIndex + or pandas.Timestamp, and return a datetime64 ndarray. + """ + # Return time as-is if none + if time is None: + return time + + # Convert to a 1D datetime64 array + time = np.atleast_1d(time).astype("datetime64[ns]") + + return time + + def list_models( directory: str | os.PathLike | None = None, show_available: bool = True, show_supported: bool = True, raise_error: bool = False, -) -> tuple[list[str], list[str]]: +) -> (list[str], list[str]): """ List all tide models available for tide modelling, and all models supported by `eo-tides` and `pyTMD`. @@ -289,16 +306,14 @@ def _model_tides( ) # Raise error if constituent files no not cover analysis extent - except IndexError: - error_msg = textwrap.dedent( - f""" - The {model} tide model constituent files do not cover the requested analysis extent. - This can occur if you are using clipped model files to improve run times. - Consider using model files that cover your entire analysis area, or set `crop=False` - to reduce the extent of tide model constituent files that is loaded. - """ - ).strip() - raise Exception(error_msg) + except IndexError as e: + error_msg = f""" + The {model} tide model constituent files do not cover the requested analysis extent. + This can occur if you are using clipped model files to improve run times. + Consider using model files that cover your entire analysis area, or set `crop=False` + to reduce the extent of tide model constituent files that is loaded. + """ + raise Exception(textwrap.dedent(error_msg).strip()) from None # Calculate complex phase in radians for Euler's cph = -1j * ph * np.pi / 180.0 @@ -674,7 +689,7 @@ def model_tides( models_requested = list(np.atleast_1d(model)) x = np.atleast_1d(x) y = np.atleast_1d(y) - time = np.atleast_1d(time) + time = _standardise_time(time) # Validate input arguments assert method in ("bilinear", "spline", "linear", "nearest") @@ -695,10 +710,6 @@ def model_tides( "you intended to model multiple timesteps at each point." ) - # If time passed as a single Timestamp, convert to datetime64 - if isinstance(time, pd.Timestamp): - time = time.to_datetime64() - # Set tide modelling files directory. If no custom path is # provided, try global environment variable. directory = _set_directory(directory) diff --git a/tests/testing.ipynb b/tests/testing.ipynb index 772eb58..84c2164 100644 --- a/tests/testing.ipynb +++ b/tests/testing.ipynb @@ -2,7 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -e .. --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -167,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -177,13 +186,6 @@ "Modelling tides using EOT20, GOT5.5 in parallel\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:00\n return [fn(*args) for args in chunk]\n File \"/home/jovyan/Robbi/eo-tides/eo_tides/model.py\", line 299, in _model_tides\n raise Exception(textwrap.dedent(error_msg).strip()) from None\nException: The EOT20 tide model constituent files do not cover the requested analysis extent.\nThis can occur if you are using clipped model files to improve run times.\nConsider using model files that cover your entire analysis area, or set `crop=False`\nto reduce the extent of tide model constituent files that is loaded.\n\"\"\"", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[16], line 7\u001b[0m\n\u001b[1;32m 3\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m180\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m50\u001b[39m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Run EOT20 tidal model for locations and timesteps in tide gauge data\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m modelled_tides_df \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_tides\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43my\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mEOT20\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGOT5.5\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmeasured_tides_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mdirectory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../tests/data/tide_models\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/workspaces/eo-tides/eo_tides/model.py:806\u001b[0m, in \u001b[0;36mmodel_tides\u001b[0;34m(x, y, time, model, directory, crs, crop, method, extrapolate, cutoff, mode, parallel, parallel_splits, output_units, output_format, ensemble_models, **ensemble_kwargs)\u001b[0m\n\u001b[1;32m 804\u001b[0m \u001b[38;5;66;03m# Apply func in parallel, iterating through each input param\u001b[39;00m\n\u001b[1;32m 805\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 806\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 807\u001b[0m \u001b[43m \u001b[49m\u001b[43mtqdm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 808\u001b[0m \u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43miter_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_iters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 809\u001b[0m \u001b[43m \u001b[49m\u001b[43mtotal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmodel_iters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 810\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 811\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 812\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m BrokenProcessPool:\n\u001b[1;32m 813\u001b[0m error_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 814\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mParallelised tide modelling failed, likely to to an out-of-memory error. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 815\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry reducing the size of your analysis, or set `parallel=False`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 816\u001b[0m )\n", - "File \u001b[0;32m/workspaces/eo-tides/.venv/lib/python3.12/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m 1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43miterable\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 1182\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01myield\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\n\u001b[1;32m 1183\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Update and possibly print the progressbar.\u001b[39;49;00m\n\u001b[1;32m 1184\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;49;00m\n", - "File \u001b[0;32m~/.local/share/uv/python/cpython-3.12.0-linux-x86_64-gnu/lib/python3.12/concurrent/futures/process.py:608\u001b[0m, in \u001b[0;36m_chain_from_iterable_of_lists\u001b[0;34m(iterable)\u001b[0m\n\u001b[1;32m 602\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_chain_from_iterable_of_lists\u001b[39m(iterable):\n\u001b[1;32m 603\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 604\u001b[0m \u001b[38;5;124;03m Specialized implementation of itertools.chain.from_iterable.\u001b[39;00m\n\u001b[1;32m 605\u001b[0m \u001b[38;5;124;03m Each item in *iterable* should be a list. This function is\u001b[39;00m\n\u001b[1;32m 606\u001b[0m \u001b[38;5;124;03m careful not to keep references to yielded objects.\u001b[39;00m\n\u001b[1;32m 607\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 608\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43melement\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43miterable\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 609\u001b[0m \u001b[43m \u001b[49m\u001b[43melement\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreverse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 610\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mwhile\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43melement\u001b[49m\u001b[43m:\u001b[49m\n", - "File \u001b[0;32m~/.local/share/uv/python/cpython-3.12.0-linux-x86_64-gnu/lib/python3.12/concurrent/futures/_base.py:619\u001b[0m, in \u001b[0;36mExecutor.map..result_iterator\u001b[0;34m()\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m fs:\n\u001b[1;32m 617\u001b[0m \u001b[38;5;66;03m# Careful not to keep a reference to the popped future\u001b[39;00m\n\u001b[1;32m 618\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 619\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[43m_result_or_cancel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m _result_or_cancel(fs\u001b[38;5;241m.\u001b[39mpop(), end_time \u001b[38;5;241m-\u001b[39m time\u001b[38;5;241m.\u001b[39mmonotonic())\n", - "File \u001b[0;32m~/.local/share/uv/python/cpython-3.12.0-linux-x86_64-gnu/lib/python3.12/concurrent/futures/_base.py:317\u001b[0m, in \u001b[0;36m_result_or_cancel\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 317\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfut\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 319\u001b[0m fut\u001b[38;5;241m.\u001b[39mcancel()\n", - "File \u001b[0;32m~/.local/share/uv/python/cpython-3.12.0-linux-x86_64-gnu/lib/python3.12/concurrent/futures/_base.py:456\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 455\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 456\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 458\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n", - "File \u001b[0;32m~/.local/share/uv/python/cpython-3.12.0-linux-x86_64-gnu/lib/python3.12/concurrent/futures/_base.py:401\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 401\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 403\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "Cell \u001b[0;32mIn[12], line 7\u001b[0m\n\u001b[1;32m 3\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m180\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m50\u001b[39m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Run EOT20 tidal model for locations and timesteps in tide gauge data\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m modelled_tides_df \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_tides\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43my\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mEOT20\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGOT5.5\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmeasured_tides_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mdirectory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../tests/data/tide_models\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Robbi/eo-tides/eo_tides/model.py:807\u001b[0m, in \u001b[0;36mmodel_tides\u001b[0;34m(x, y, time, model, directory, crs, crop, method, extrapolate, cutoff, mode, parallel, parallel_splits, output_units, output_format, ensemble_models, **ensemble_kwargs)\u001b[0m\n\u001b[1;32m 805\u001b[0m \u001b[38;5;66;03m# Apply func in parallel, iterating through each input param\u001b[39;00m\n\u001b[1;32m 806\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 807\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 808\u001b[0m \u001b[43m \u001b[49m\u001b[43mtqdm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 809\u001b[0m \u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43miter_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_iters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 810\u001b[0m \u001b[43m \u001b[49m\u001b[43mtotal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmodel_iters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 811\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 812\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m BrokenProcessPool:\n\u001b[1;32m 814\u001b[0m error_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 815\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mParallelised tide modelling failed, likely to to an out-of-memory error. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 816\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry reducing the size of your analysis, or set `parallel=False`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 817\u001b[0m )\n", + "File \u001b[0;32m/env/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m 1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m 1182\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m 1183\u001b[0m \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m 1184\u001b[0m \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n", + "File \u001b[0;32m/env/lib/python3.10/concurrent/futures/process.py:575\u001b[0m, in \u001b[0;36m_chain_from_iterable_of_lists\u001b[0;34m(iterable)\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_chain_from_iterable_of_lists\u001b[39m(iterable):\n\u001b[1;32m 570\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 571\u001b[0m \u001b[38;5;124;03m Specialized implementation of itertools.chain.from_iterable.\u001b[39;00m\n\u001b[1;32m 572\u001b[0m \u001b[38;5;124;03m Each item in *iterable* should be a list. This function is\u001b[39;00m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;124;03m careful not to keep references to yielded objects.\u001b[39;00m\n\u001b[1;32m 574\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 575\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m element \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m 576\u001b[0m element\u001b[38;5;241m.\u001b[39mreverse()\n\u001b[1;32m 577\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m element:\n", + "File \u001b[0;32m/env/lib/python3.10/concurrent/futures/_base.py:621\u001b[0m, in \u001b[0;36mExecutor.map..result_iterator\u001b[0;34m()\u001b[0m\n\u001b[1;32m 618\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m fs:\n\u001b[1;32m 619\u001b[0m \u001b[38;5;66;03m# Careful not to keep a reference to the popped future\u001b[39;00m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 621\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[43m_result_or_cancel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 622\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m _result_or_cancel(fs\u001b[38;5;241m.\u001b[39mpop(), end_time \u001b[38;5;241m-\u001b[39m time\u001b[38;5;241m.\u001b[39mmonotonic())\n", + "File \u001b[0;32m/env/lib/python3.10/concurrent/futures/_base.py:319\u001b[0m, in \u001b[0;36m_result_or_cancel\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 319\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfut\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 320\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 321\u001b[0m fut\u001b[38;5;241m.\u001b[39mcancel()\n", + "File \u001b[0;32m/env/lib/python3.10/concurrent/futures/_base.py:458\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 457\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 459\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 460\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n", + "File \u001b[0;32m/env/lib/python3.10/concurrent/futures/_base.py:403\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 403\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 405\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", "\u001b[0;31mException\u001b[0m: The EOT20 tide model constituent files do not cover the requested analysis extent.\nThis can occur if you are using clipped model files to improve run times.\nConsider using model files that cover your entire analysis area, or set `crop=False`\nto reduce the extent of tide model constituent files that is loaded." ] } @@ -231,166 +233,455 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from eo_tides import list_models\n", + "list_models(directory=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modelling ebb and flow tidal phases\n", + "The `tag_tides` function also allows us to determine whether each satellite observation was taken while the tide was rising/incoming (flow tide) or falling/outgoing (ebb tide) by setting `ebb_flow=True`. This is achieved by comparing tide heights 15 minutes before and after the observed satellite observation.\n", + "\n", + "Ebb and flow data can provide valuable contextual information for interpreting satellite imagery, particularly in tidal flat or mangrove forest environments where water may remain in the landscape for considerable time after the tidal peak.\n", + "\n", + "Once you run the cell below, our data will now also contain a new `ebb_flow` variable under **Data variables**:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import datacube\n", + "\n", + "dc = datacube.Datacube()\n", + "\n", + "ds = dc.load(product=\"ga_s2ls_intertidal_cyear_3\", limit=1, measurements=\"elevation\")" + ] + }, + { + "cell_type": "code", + "execution_count": 85, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "──────────────────────────────────────────────────────────────\n", - " 󠀠🌊 | Model | Expected path \n", - "──────────────────────────────────────────────────────────────\n", - " ❌ │ AODTM-5 │ aodtm5_tmd \n", - " ❌ │ AOTIM-5 │ aotim5_tmd \n", - " ❌ │ AOTIM-5-2018 │ Arc5km2018 \n", - " ❌ │ Arc2kmTM │ Arc2kmTM \n", - " ❌ │ CATS0201 │ cats0201_tmd \n", - " ❌ │ CATS2008 │ CATS2008 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " ❌ │ CATS2008-v2023 │ CATS2008_v2023 \n", - " ❌ │ CATS2008_load │ CATS2008a_SPOTL_Load \n", - " ❌ │ EOT20 │ EOT20/ocean_tides \n", - " ❌ │ EOT20_load │ EOT20/load_tides \n", - " ❌ │ FES2012 │ fes2012/data \n", - " ❌ │ FES2014 │ fes2014/ocean_tide \n", - " ❌ │ FES2014_extrapolated │ fes2014/ocean_tide_extrapolated \n", - " ❌ │ FES2014_load │ fes2014/load_tide \n", - " ❌ │ FES2022 │ fes2022b/ocean_tide \n", - " ❌ │ FES2022_extrapolated │ fes2022b/ocean_tide_extrapolated\n", - " ❌ │ FES2022_load │ fes2022b/load_tide \n", - " ❌ │ GOT4.10 │ GOT4.10c/grids_oceantide \n", - " ❌ │ GOT4.10_load │ GOT4.10c/grids_loadtide \n", - " ❌ │ GOT4.7 │ GOT4.7/grids_oceantide \n", - " ❌ │ GOT4.7_load │ GOT4.7/grids_loadtide \n", - " ❌ │ GOT4.8 │ got4.8/grids_oceantide \n", - " ❌ │ GOT4.8_load │ got4.8/grids_loadtide \n", - " ❌ │ GOT5.5 │ GOT5.5/ocean_tides \n", - " ❌ │ GOT5.5D │ GOT5.5/ocean_tides \n", - " ❌ │ GOT5.5D_extrapolated │ GOT5.5/extrapolated \n", - " ❌ │ GOT5.5_extrapolated │ GOT5.5/extrapolated \n", - " ❌ │ GOT5.5_load │ GOT5.5/load_tides \n", - " ❌ │ GOT5.6 │ GOT5.5/ocean_tides \n", - " ❌ │ GOT5.6_extrapolated │ GOT5.5/extrapolated \n", - " ❌ │ Gr1km-v2 │ greenlandTMD_v2 \n", - " ❌ │ Gr1kmTM │ Gr1kmTM \n", - " ❌ │ HAMTIDE11 │ hamtide \n", - " ❌ │ TPXO10-atlas-v2 │ TPXO10_atlas_v2 \n", - " ❌ │ TPXO10-atlas-v2-nc │ TPXO10_atlas_v2 \n", - " ❌ │ TPXO7.2 │ TPXO7.2_tmd \n", - " ❌ │ TPXO7.2_load │ TPXO7.2_load \n", - " ❌ │ TPXO8-atlas │ tpxo8_atlas \n", - " ❌ │ TPXO8-atlas-nc │ TPXO8_atlas_v1 \n", - " ❌ │ TPXO9-atlas │ TPXO9_atlas \n", - " ❌ │ TPXO9-atlas-nc │ TPXO9_atlas \n", - " ❌ │ TPXO9-atlas-v2 │ TPXO9_atlas_v2 \n", - " ❌ │ TPXO9-atlas-v2-nc │ TPXO9_atlas_v2 \n", - " ❌ │ TPXO9-atlas-v3 │ TPXO9_atlas_v3 \n", - " ❌ │ TPXO9-atlas-v3-nc │ TPXO9_atlas_v3 \n", - " ❌ │ TPXO9-atlas-v4 │ TPXO9_atlas_v4 \n", - " ❌ │ TPXO9-atlas-v4-nc │ TPXO9_atlas_v4 \n", - " ❌ │ TPXO9-atlas-v5 │ TPXO9_atlas_v5 \n", - " ❌ │ TPXO9-atlas-v5-nc │ TPXO9_atlas_v5 \n", - " ❌ │ TPXO9.1 │ TPXO9.1/DATA \n", - "──────────────────────────────────────────────────────────────\n", - "\n", - "Summary:\n", - "Available models: 0/50\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/workspaces/eo-tides/eo_tides/model.py:164: UserWarning: No valid tide models are available in `.`.\n", - "Are you sure you have provided the correct `directory` path, or set the \n", - "`EO_TIDES_TIDE_MODELS` environment variable to point to the location of your \n", - "tide model directory?\n", - " warnings.warn(warning_msg, UserWarning)\n" - ] - }, + "data": { + "text/plain": [ + "(GeoBox((3200, 3200), Affine(10.0, 0.0, 1248000.0,\n", + " 0.0, -10.0, -1184000.0), CRS('PROJCS[\"GDA94 / Australian Albers\",GEOGCS[\"GDA94\",DATUM[\"Geocentric_Datum_of_Australia_1994\",SPHEROID[\"GRS 1980\",6378137,298.257222101,AUTHORITY[\"EPSG\",\"7019\"]],AUTHORITY[\"EPSG\",\"6283\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4283\"]],PROJECTION[\"Albers_Conic_Equal_Area\"],PARAMETER[\"latitude_of_center\",0],PARAMETER[\"longitude_of_center\",132],PARAMETER[\"standard_parallel_1\",-18],PARAMETER[\"standard_parallel_2\",-36],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"3577\"]]')),\n", + " array(['2022-02-01T00:00:00.000000000'], dtype='datetime64[ns]'))" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from odc.geo.geobox import GeoBox\n", + "import xarray as xr\n", + "import textwrap\n", + "import numpy as np\n", + "\n", + "from typing import Any\n", + "\n", + "\n", + "def _standardise_time(\n", + " time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None,\n", + ") -> np.ndarray | None:\n", + " \"\"\"\n", + " Accept a datetime64 ndarray, pandas.DatetimeIndex\n", + " or pandas.Timestamp, and return a datetime64 ndarray.\n", + " \"\"\"\n", + " # Return time as-is if none\n", + " if time is None:\n", + " return time\n", + "\n", + " # Convert to a 1D datetime64 array\n", + " time = np.atleast_1d(time).astype(\"datetime64[ns]\")\n", + "\n", + " return time\n", + "\n", + "\n", + "def _standardise_inputs(\n", + " ds: xr.DataArray | xr.Dataset | GeoBox,\n", + " time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None,\n", + ") -> (GeoBox, np.ndarray):\n", + " \"\"\"\n", + " Takes an xarray or GeoBox input and an optional custom times,\n", + " and returns a standardised GeoBox and \n", + " \"\"\"\n", + "\n", + " # If `ds` is an xarray object, extract its GeoBox and time\n", + " if isinstance(ds, (xr.DataArray, xr.Dataset)):\n", + "\n", + " # Try to extract GeoBox\n", + " try:\n", + " gbox = ds.odc.geobox\n", + " except AttributeError:\n", + " error_msg = \"\"\"\n", + " Cannot extract a valid GeoBox for `ds`. This is required for\n", + " extracting details about `ds`'s CRS and spatial location.\n", + " \n", + " Import `odc.geo.xr` then run `ds = ds.odc.assign_crs(crs=...)`\n", + " to prepare your data before passing it to this function.\n", + " \"\"\"\n", + " raise Exception(textwrap.dedent(error_msg).strip())\n", + "\n", + " # Use custom time by default if provided; otherwise try and extract from `ds`\n", + " if time is not None:\n", + " time = _standardise_time(time)\n", + " elif \"time\" in ds.coords:\n", + " time = ds.coords[\"time\"].values\n", + " else:\n", + " raise ValueError(\n", + " \"`ds` does not have a time dimension, and no custom times were provided via `time`.\"\n", + " )\n", + "\n", + " # If `ds` is a GeoBox, use it directly; raise an error if no time was provided\n", + " elif isinstance(ds, GeoBox):\n", + " gbox = ds\n", + " if time is not None:\n", + " time = _standardise_time(time)\n", + " else:\n", + " raise ValueError(\"If `ds` is a GeoBox, `time` must be provided.\")\n", + "\n", + " # Raise error if no valid inputs were provided\n", + " else:\n", + " raise TypeError(\n", + " \"`ds` must be an xarray.DataArray, xarray.Dataset, or odc.geo.geobox.GeoBox.\"\n", + " )\n", + "\n", + " return gbox, time\n", + "\n", + "\n", + "time = pd.date_range(\"2021\", \"2022\").values\n", + "time = pd.date_range(\"2021\", \"2022\")\n", + "time = pd.Timestamp(\"2022-02-01\")\n", + "# time = satellite_ds.time\n", + "# time = [\"a\", \"b\"]\n", + "\n", + "\n", + "gbox, time = _standardise_inputs(ds=ds.drop_dims(\"time\").odc.geobox, time=time)\n", + "gbox, time" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ { "data": { "text/plain": [ - "([],\n", - " ['AODTM-5',\n", - " 'AOTIM-5',\n", - " 'AOTIM-5-2018',\n", - " 'Arc2kmTM',\n", - " 'CATS0201',\n", - " 'CATS2008',\n", - " 'CATS2008-v2023',\n", - " 'CATS2008_load',\n", - " 'EOT20',\n", - " 'EOT20_load',\n", - " 'FES2012',\n", - " 'FES2014',\n", - " 'FES2014_extrapolated',\n", - " 'FES2014_load',\n", - " 'FES2022',\n", - " 'FES2022_extrapolated',\n", - " 'FES2022_load',\n", - " 'GOT4.10',\n", - " 'GOT4.10_load',\n", - " 'GOT4.7',\n", - " 'GOT4.7_load',\n", - " 'GOT4.8',\n", - " 'GOT4.8_load',\n", - " 'GOT5.5',\n", - " 'GOT5.5D',\n", - " 'GOT5.5D_extrapolated',\n", - " 'GOT5.5_extrapolated',\n", - " 'GOT5.5_load',\n", - " 'GOT5.6',\n", - " 'GOT5.6_extrapolated',\n", - " 'Gr1km-v2',\n", - " 'Gr1kmTM',\n", - " 'HAMTIDE11',\n", - " 'TPXO10-atlas-v2',\n", - " 'TPXO10-atlas-v2-nc',\n", - " 'TPXO7.2',\n", - " 'TPXO7.2_load',\n", - " 'TPXO8-atlas',\n", - " 'TPXO8-atlas-nc',\n", - " 'TPXO9-atlas',\n", - " 'TPXO9-atlas-nc',\n", - " 'TPXO9-atlas-v2',\n", - " 'TPXO9-atlas-v2-nc',\n", - " 'TPXO9-atlas-v3',\n", - " 'TPXO9-atlas-v3-nc',\n", - " 'TPXO9-atlas-v4',\n", - " 'TPXO9-atlas-v4-nc',\n", - " 'TPXO9-atlas-v5',\n", - " 'TPXO9-atlas-v5-nc',\n", - " 'TPXO9.1'])" + "(609,)" ] }, - "execution_count": 35, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from eo_tides import list_models\n", - "list_models(directory=\"\")" + "satellite_ds.chunks[\"x\"]" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 74, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['2021-01-01T00:00:00.000000000', '2021-01-02T00:00:00.000000000',\n", + " '2021-01-03T00:00:00.000000000', '2021-01-04T00:00:00.000000000',\n", + " '2021-01-05T00:00:00.000000000', '2021-01-06T00:00:00.000000000',\n", + " '2021-01-07T00:00:00.000000000', '2021-01-08T00:00:00.000000000',\n", + " '2021-01-09T00:00:00.000000000', '2021-01-10T00:00:00.000000000',\n", + " '2021-01-11T00:00:00.000000000', '2021-01-12T00:00:00.000000000',\n", + " '2021-01-13T00:00:00.000000000', '2021-01-14T00:00:00.000000000',\n", + " '2021-01-15T00:00:00.000000000', '2021-01-16T00:00:00.000000000',\n", + " '2021-01-17T00:00:00.000000000', '2021-01-18T00:00:00.000000000',\n", + " 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" '2021-12-09T00:00:00.000000000', '2021-12-10T00:00:00.000000000',\n", + " '2021-12-11T00:00:00.000000000', '2021-12-12T00:00:00.000000000',\n", + " '2021-12-13T00:00:00.000000000', '2021-12-14T00:00:00.000000000',\n", + " '2021-12-15T00:00:00.000000000', '2021-12-16T00:00:00.000000000',\n", + " '2021-12-17T00:00:00.000000000', '2021-12-18T00:00:00.000000000',\n", + " '2021-12-19T00:00:00.000000000', '2021-12-20T00:00:00.000000000',\n", + " '2021-12-21T00:00:00.000000000', '2021-12-22T00:00:00.000000000',\n", + " '2021-12-23T00:00:00.000000000', '2021-12-24T00:00:00.000000000',\n", + " '2021-12-25T00:00:00.000000000', '2021-12-26T00:00:00.000000000',\n", + " '2021-12-27T00:00:00.000000000', '2021-12-28T00:00:00.000000000',\n", + " '2021-12-29T00:00:00.000000000', '2021-12-30T00:00:00.000000000',\n", + " '2021-12-31T00:00:00.000000000', '2022-01-01T00:00:00.000000000'],\n", + " dtype='datetime64[ns]')" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Modelling ebb and flow tidal phases\n", - "The `tag_tides` function also allows us to determine whether each satellite observation was taken while the tide was rising/incoming (flow tide) or falling/outgoing (ebb tide) by setting `ebb_flow=True`. This is achieved by comparing tide heights 15 minutes before and after the observed satellite observation.\n", + "import pandas as pd\n", "\n", - "Ebb and flow data can provide valuable contextual information for interpreting satellite imagery, particularly in tidal flat or mangrove forest environments where water may remain in the landscape for considerable time after the tidal peak.\n", + "time = pd.date_range(\"2021\", \"2022\").values\n", + "# time = pd.date_range(\"2021\", \"2022\")\n", + "# time = pd.Timestamp(\"2022-02-01\")\n", + "time = satellite_ds.time\n", "\n", - "Once you run the cell below, our data will now also contain a new `ebb_flow` variable under **Data variables**:" + "\n", + "def _standardise_time(\n", + " time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None,\n", + ") -> np.ndarray | None:\n", + " \"\"\"\n", + " Accept a datetime64 ndarray, pandas.DatetimeIndex\n", + " or pandas.Timestamp, and return a datetime64 ndarray.\n", + " \"\"\"\n", + " # Return time as-is if none\n", + " if time is None:\n", + " return time\n", + "\n", + " # Convert to a 1D datetime64 array\n", + " time = np.atleast_1d(time).astype(\"datetime64[ns]\")\n", + "\n", + " return time\n", + "\n", + "\n", + "time = pd.date_range(\"2021\", \"2022\").values\n", + "# time = pd.date_range(\"2021\", \"2022\")\n", + "# time = pd.Timestamp(\"2022-02-01\")\n", + "# time = satellite_ds.time\n", + "# time = [pd.Timestamp(\"2022-02-01\"), pd.Timestamp(\"2022-02-01\")]\n", + "# time = None\n", + "_standardise_time(time=time)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "test = np.atleast_1d(time).astype('datetime64[ns]')" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['2022-02-01T00:00:00.000000000', '2022-02-01T00:00:00.000000000'],\n", + " dtype='datetime64[ns]')" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = ds.odc.assign_crs(\"EPSG:3577\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test = satellite_ds.nbart_red.drop_attrs(deep=True).drop_vars(\"spatial_ref\").odc.reload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test #odc.reload()" ] }, { @@ -497,194 +788,25 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating reduced resolution 5000 x 5000 metre tide modelling array\n", - "Modelling tides using EOT20, GOT5.5 in parallel\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 10/10 [00:00<00:00, 10.10it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing tide quantiles\n", - "Returning low resolution tide array\n", - "Creating reduced resolution 5000 x 5000 metre tide modelling array\n", - "Modelling tides using EOT20, GOT5.5 in parallel\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 10/10 [00:02<00:00, 4.76it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing tide quantiles\n", - "Returning low resolution tide array\n", - " Size: 720B\n", - "array([[[-1.7344081, -1.760125 , -1.7868625, -1.8580176, nan,\n", - " nan, nan, nan, nan, nan],\n", - " [-1.7684059, -1.8007368, -1.8340894, -1.8580176, -1.8580176,\n", - " nan, nan, nan, nan, nan],\n", - " [-1.806048 , -1.8471183, -1.8895663, -1.8580176, -1.8580176,\n", - " -2.1748762, -2.1748762, nan, nan, nan],\n", - " [-1.846053 , -1.8960319, -1.9473902, -2.0383308, -2.1748762,\n", - " -2.1748762, -2.1748762, -2.1748762, nan, nan],\n", - " [-1.8846477, -1.9435406, -2.0038147, -2.0623674, -2.1157305,\n", - " -2.16796 , -2.1748762, -2.1748762, nan, nan],\n", - " [-1.9184506, -1.9783273, -2.038216 , -2.0931957, -2.139294 ,\n", - " -2.1842573, -2.1748762, nan, nan, nan],\n", - " [-1.9518493, -2.0111885, -2.0712438, -2.12515 , -2.1639793,\n", - " -2.2016718, -2.2251456, -2.2251456, nan, nan],\n", - " [-1.9854381, -2.042891 , -2.1010184, -2.1425843, -2.1425843,\n", - " -2.2251456, -2.2251456, -2.2251456, nan, nan],\n", - " [-2.0118232, -2.0632582, -2.1150248, -2.1425843, -2.1425843,\n", - " -2.2251456, -2.2251456, nan, nan, nan]],\n", - "\n", - " [[-1.6880094, -1.7164723, -1.7459012, -1.8160598, -1.9561046,\n", - " -1.9561046, -1.9561046, nan, nan, nan],\n", - " [-1.7213236, -1.7560506, -1.7917448, -1.8160598, -1.9561046,\n", - " -1.9561046, -1.9561046, nan, nan, nan],\n", - " [-1.7572768, -1.8001883, -1.8444098, -1.8970759, -1.9545422,\n", - " -2.011992 , -1.9561046, -2.1751308, -2.1751308, -2.1751308],\n", - " [-1.7948241, -1.8462056, -1.8988986, -1.9546113, -2.0121198,\n", - " -2.0696118, -2.1271808, -2.1751308, -2.1751308, -2.1751308],\n", - " [-1.8310307, -1.8908874, -1.952057 , -2.0116165, -2.0662582,\n", - " -2.11976 , -2.1449702, -2.162709 , -2.1751308, -2.1751308],\n", - " [-1.8627172, -1.9237646, -1.9848691, -2.0410962, -2.088431 ,\n", - " -2.1346242, -2.1574447, -2.1710668, -2.1911345, -2.1911345],\n", - " [-1.8940805, -1.9548151, -2.0163682, -2.071709 , -2.111732 ,\n", - " -2.1506124, -2.1705492, -2.1800516, -2.1889217, -2.1911345],\n", - " [-1.92572 , -1.9846749, -2.0445163, -2.0878263, -2.0878263,\n", - " -2.173731 , -2.173731 , -2.1911345, -2.1911345, -2.1911345],\n", - " [-1.9507298, -2.003324 , -2.0566194, -2.0878263, -2.0878263,\n", - " -2.173731 , -2.173731 , -2.1911345, -2.1911345, -2.1911345]]],\n", - " dtype=float32)\n", - "Coordinates:\n", - " * x (x) float64 80B -1.058e+06 -1.052e+06 ... -1.018e+06 -1.012e+06\n", - " * y (y) float64 72B -1.942e+06 -1.948e+06 ... -1.978e+06 -1.982e+06\n", - " * tide_model (tide_model) object 16B 'EOT20' 'GOT5.5'\n", - " quantile float64 8B 0.0\n", - " spatial_ref int32 4B 3577\n", - " Size: 7kB\n", - "Dimensions: (x: 10, y: 9, tide_model: 2)\n", - "Coordinates:\n", - " * x (x) float64 80B -1.058e+06 -1.052e+06 ... -1.018e+06 -1.012e+06\n", - " * y (y) float64 72B -1.942e+06 -1.948e+06 ... -1.978e+06 -1.982e+06\n", - " * tide_model (tide_model) object 16B 'EOT20' 'GOT5.5'\n", - " spatial_ref int32 4B 3577\n", - "Data variables:\n", - " hat (tide_model, y, x) float32 720B 3.988 4.023 ... 4.421 4.421\n", - " hot (tide_model, y, x) float32 720B 1.531 1.543 ... 1.767 1.767\n", - " lat (tide_model, y, x) float32 720B -4.03 -4.065 ... -4.664 -4.664\n", - " lot (tide_model, y, x) float32 720B -1.734 -1.76 ... -2.191 -2.191\n", - " otr (tide_model, y, x) float32 720B 3.265 3.303 ... 3.958 3.958\n", - " tr (tide_model, y, x) float32 720B 8.018 8.088 ... 9.086 9.086\n", - " spread (tide_model, y, x) float32 720B 0.4073 0.4083 ... 0.4357 0.4357\n", - " offset_low (tide_model, y, x) float32 720B 0.2863 0.285 ... 0.2722 0.2722\n", - " offset_high (tide_model, y, x) float32 720B 0.3065 0.3066 ... 0.2921 0.2921\n" - ] - } - ], + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['hat',\n", - " 'hot',\n", - " 'lat',\n", - " 'lot',\n", - " 'otr',\n", - " 'tr',\n", - " 'spread',\n", - " 'offset_low',\n", - " 'offset_high']" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "list(stats_ds.data_vars.keys())" ] }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating reduced resolution 5000 x 5000 metre tide modelling array\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Modelling tides using EOT20 in parallel\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 5/5 [00:00<00:00, 7.36it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing tide quantiles\n", - "Returning low resolution tide array\n", - "Creating reduced resolution 5000 x 5000 metre tide modelling array\n", - "Modelling tides using EOT20 in parallel\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 5/5 [00:01<00:00, 3.95it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing tide quantiles\n", - "Returning low resolution tide array\n" - ] - } - ], + "outputs": [], "source": [ "from eo_tides.stats import pixel_stats\n", "\n", @@ -719,23 +841,9 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "unsupported operand type(s) for -: 'method' and 'float'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[75], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Verify values are roughly expected\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mallclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstats_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moffset_high\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.30\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43matol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.02\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(stats_ds\u001b[38;5;241m.\u001b[39moffset_low\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;241m.\u001b[39mitem, \u001b[38;5;241m0.27\u001b[39m, atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.02\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(stats_ds\u001b[38;5;241m.\u001b[39mspread\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;241m.\u001b[39mitem, \u001b[38;5;241m0.43\u001b[39m, atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.02\u001b[39m)\n", - "File \u001b[0;32m/workspaces/eo-tides/.venv/lib/python3.12/site-packages/numpy/_core/numeric.py:2307\u001b[0m, in \u001b[0;36mallclose\u001b[0;34m(a, b, rtol, atol, equal_nan)\u001b[0m\n\u001b[1;32m 2225\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_allclose_dispatcher)\n\u001b[1;32m 2226\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mallclose\u001b[39m(a, b, rtol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.e-5\u001b[39m, atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.e-8\u001b[39m, equal_nan\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 2227\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2228\u001b[0m \u001b[38;5;124;03m Returns True if two arrays are element-wise equal within a tolerance.\u001b[39;00m\n\u001b[1;32m 2229\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2305\u001b[0m \n\u001b[1;32m 2306\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2307\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mall\u001b[39m(\u001b[43misclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrtol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43matol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43matol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mequal_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mequal_nan\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 2308\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m builtins\u001b[38;5;241m.\u001b[39mbool(res)\n", - "File \u001b[0;32m/workspaces/eo-tides/.venv/lib/python3.12/site-packages/numpy/_core/numeric.py:2417\u001b[0m, in \u001b[0;36misclose\u001b[0;34m(a, b, rtol, atol, equal_nan)\u001b[0m\n\u001b[1;32m 2414\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(y)\n\u001b[1;32m 2416\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m errstate(invalid\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m'\u001b[39m), _no_nep50_warning():\n\u001b[0;32m-> 2417\u001b[0m result \u001b[38;5;241m=\u001b[39m (less_equal(\u001b[38;5;28mabs\u001b[39m(\u001b[43mx\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43my\u001b[49m), atol \u001b[38;5;241m+\u001b[39m rtol \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mabs\u001b[39m(y))\n\u001b[1;32m 2418\u001b[0m \u001b[38;5;241m&\u001b[39m isfinite(y)\n\u001b[1;32m 2419\u001b[0m \u001b[38;5;241m|\u001b[39m (x \u001b[38;5;241m==\u001b[39m y))\n\u001b[1;32m 2420\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m equal_nan:\n\u001b[1;32m 2421\u001b[0m result \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m isnan(x) \u001b[38;5;241m&\u001b[39m isnan(y)\n", - "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'method' and 'float'" - ] - } - ], + "outputs": [], "source": [ "# Verify values are roughly expected\n", "assert np.allclose(stats_ds.offset_high.mean().item, 0.30, atol=0.02)\n", @@ -745,1093 +853,70 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3040720224380493" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "stats_ds.offset_high.mean().item()" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - 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" - ], - "text/plain": [ - " Size: 5kB\n", - "array([-1044045., -1044015., -1043985., ..., -1025865., -1025835., -1025805.])\n", - "Coordinates:\n", - " * x (x) float64 5kB -1.044e+06 -1.044e+06 ... -1.026e+06 -1.026e+06\n", - " spatial_ref int32 4B 3577\n", - "Attributes:\n", - " units: metre\n", - " resolution: 30.0\n", - " crs: EPSG:3577" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "satellite_ds.x" ] @@ -2278,7 +953,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2292,9 +967,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.10.15" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 4146d7f5ed4521684ccb8f57163faeffb716fc03 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Sun, 20 Oct 2024 07:50:25 +0000 Subject: [PATCH 02/13] Clean --- eo_tides/eo.py | 23 ----------------------- 1 file changed, 23 deletions(-) diff --git a/eo_tides/eo.py b/eo_tides/eo.py index dfb8367..0c749dd 100644 --- a/eo_tides/eo.py +++ b/eo_tides/eo.py @@ -405,29 +405,6 @@ def pixel_tides( If `resample=False`, results for the intermediate low-resolution tide modelling grid will be returned instead. """ - # # First test if no time dimension and nothing passed to `times` - # if ("time" not in ds.dims) & (times is None): - # raise ValueError( - # "`ds` does not contain a 'time' dimension. Times are required " - # "for modelling tides: please pass in a set of custom tides " - # "using the `times` parameter. For example: " - # "`times=pd.date_range(start='2000', end='2001', freq='5h')`", - # ) - - # # If custom times are provided, convert them to a consistent - # # pandas.DatatimeIndex format - # if times is not None: - # if isinstance(times, list): - # time_coords = pd.DatetimeIndex(times) - # elif isinstance(times, pd.Timestamp): - # time_coords = pd.DatetimeIndex([times]) - # else: - # time_coords = times - - # # Otherwise, use times from `ds` directly - # else: - # time_coords = ds.coords["time"] - # Standardise data inputs and time gbox, time_coords = _standardise_inputs(ds, time) From eec5009a1b472a45c6ecb629f14b82a0b1c8aff5 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 21 Oct 2024 07:19:15 +0000 Subject: [PATCH 03/13] Update case study notebook --- docs/notebooks/Case_study_intertidal.ipynb | 360 +++++++++++++-------- 1 file changed, 225 insertions(+), 135 deletions(-) diff --git a/docs/notebooks/Case_study_intertidal.ipynb b/docs/notebooks/Case_study_intertidal.ipynb index ad5aa5d..0d0d47c 100644 --- a/docs/notebooks/Case_study_intertidal.ipynb +++ b/docs/notebooks/Case_study_intertidal.ipynb @@ -46,17 +46,20 @@ "source": [ "import odc.stac\n", "import pystac_client\n", + "import planetary_computer\n", "import matplotlib.pyplot as plt\n", "\n", "from eo_tides.eo import tag_tides\n", - "from eo_tides.stats import tide_stats\n" + "from eo_tides.stats import tide_stats\n", + "from eo_tides.model import list_models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We also need to tell `eo-tides` the location of our tide model directory (if you haven't set this up, [refer to the setup instructions here](../../setup)):" + "### Tide model directory\n", + "We need to tell `eo-tides` the location of our tide model directory (if you haven't set this up, [refer to the setup instructions here](../../setup)):" ] }, { @@ -65,9 +68,66 @@ "metadata": { "tags": [] }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────\n", + " 󠀠🌊 | Model | Expected path \n", + "────────────────────────────────────────────────────────────────────────────────\n", + " ✅ │ EOT20 │ ../../tests/data/tide_models/EOT20/ocean_tides \n", + " ✅ │ GOT5.5 │ ../../tests/data/tide_models/GOT5.5/ocean_tides \n", + " ✅ │ HAMTIDE11 │ ../../tests/data/tide_models/hamtide \n", + "────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Summary:\n", + "Available models: 3/50\n" + ] + } + ], + "source": [ + "directory = \"../../tests/data/tide_models\"\n", + "\n", + "# Confirm we have model data\n", + "list_models(directory=directory, show_supported=False);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analysis parameters\n", + "To make our analysis more re-usable, we can define some important parameters up-front.\n", + "The default will load **Landsat 8 and 9** satellite data from **2022-23** over the city of **Broome, Western Australia** - a macrotidal region with extensive intertidal coastal habitats.\n", + "\n", + "
\n", + "

Tip

\n", + "

\n", + " Leave the defaults below unchanged the first time you run through this notebook.\n", + "

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ - "directory = \"../../tests/data/tide_models/\"" + "# Set the study area (xmin, ymin, xmax, ymax)\n", + "bbox = [122.12, -18.25, 122.43, -17.93]\n", + "\n", + "# Set the time period\n", + "start_date = \"2022-01-01\"\n", + "end_date = \"2023-12-31\"\n", + "\n", + "# Satellite products and bands to load\n", + "satellite_sensors = [\"landsat-c2-l2\"]\n", + "bands = [\"green\", \"nir08\"]\n", + "\n", + "# Tide model to use\n", + "tide_model = \"EOT20\"" ] }, { @@ -79,10 +139,7 @@ "Now we can load a time-series of satellite data over our area of interest using the Open Data Cube's `odc-stac` package.\n", "This powerful package allows us to load open satellite data (e.g ESA Sentinel-2 or NASA/USGS Landsat) for any time period and location on the planet, and load our data into a multi-dimensional `xarray.Dataset` format dataset.\n", "\n", - "In this example, we will load **cloud-free Landsat 8 and 9** satellite data from **2022-23** over the city of **Broome, Western Australia** - a macrotidal region with extensive intertidal coastal habitats.\n", - "We will load this data from the [Digital Earth Australia](https://knowledge.dea.ga.gov.au/guides/setup/gis/stac/) STAC catalogue.\n", - "\n", - "In this example, we will restrict our data to cloud-free images with less than 5% cloud (`filter = \"eo:cloud_cover < 5\"`), and load our data at low resolution (`resolution=50`) to improve load times.\n", + "In this example, we will load our data from the [Microsoft Planetary Computer](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/) STAC catalogue, and restrict our data to cloud-free images with less than 10% cloud (`eo:cloud_cover\": {\"lt\": 10}\"`), and load our data at low resolution (`resolution=50`) to improve load times.\n", "\n", "
\n", "

Tip

\n", @@ -94,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "tags": [] }, @@ -103,22 +160,25 @@ "name": "stdout", "output_type": "stream", "text": [ - " Size: 413MB\n", - "Dimensions: (y: 712, x: 659, time: 110)\n", + " Size: 454MB\n", + "Dimensions: (time: 121, y: 712, x: 659)\n", "Coordinates:\n", " * y (y) float64 6kB 8.017e+06 8.017e+06 ... 7.982e+06 7.982e+06\n", " * x (x) float64 5kB 4.068e+05 4.069e+05 ... 4.397e+05 4.397e+05\n", " spatial_ref int32 4B 32751\n", - " * time (time) datetime64[ns] 880B 2022-01-01T01:55:58.691030 ... 20...\n", + " * time (time) datetime64[ns] 968B 2022-01-01T01:55:34.757654 ... 20...\n", "Data variables:\n", - " nbart_green (time, y, x) float32 206MB dask.array\n", - " nbart_nir (time, y, x) float32 206MB dask.array\n" + " green (time, y, x) float32 227MB dask.array\n", + " nir08 (time, y, x) float32 227MB dask.array\n" ] } ], "source": [ "# Connect to STAC catalog\n", - "catalog = pystac_client.Client.open(\"https://explorer.dea.ga.gov.au/stac\")\n", + "catalog = pystac_client.Client.open(\n", + " \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n", + " modifier=planetary_computer.sign_inplace,\n", + ")\n", "\n", "# Set cloud access defaults\n", "odc.stac.configure_rio(\n", @@ -127,18 +187,20 @@ ")\n", "\n", "# Build a query and search the STAC catalog for all matching items\n", - "bbox = [122.12, -18.25, 122.43, -17.93]\n", "query = catalog.search(\n", " bbox=bbox,\n", - " collections=[\"ga_ls8c_ard_3\", \"ga_ls9c_ard_3\"],\n", - " datetime=\"2022-01-01/2023-12-31\",\n", - " filter = \"eo:cloud_cover < 5\" # Filter to images with <5% cloud\n", + " collections=satellite_sensors,\n", + " datetime=f\"{start_date}/{end_date}\",\n", + " query={\n", + " \"eo:cloud_cover\": {\"lt\": 10}, # Filter to images with <5% cloud\n", + " \"platform\": {\"in\": [\"landsat-8\", \"landsat-9\"]}, # No Landsat 7\n", + " },\n", ")\n", "\n", "# Load data into xarray format\n", "ds = odc.stac.load(\n", - " items=list(query.items()),\n", - " bands=[\"nbart_green\", \"nbart_nir\"],\n", + " items=query.item_collection(),\n", + " bands=bands,\n", " crs=\"utm\",\n", " resolution=50,\n", " groupby=\"solar_day\",\n", @@ -146,6 +208,11 @@ " fail_on_error=False,\n", " chunks={},\n", ")\n", + "\n", + "# Apply USGS Landsat Collection 2 scaling factors to convert\n", + "# surface reflectance to between 0.0 and 1.0. See:\n", + "# https://www.usgs.gov/faqs/how-do-i-use-a-scale-factor-landsat-level-2-science-products\n", + "ds = (ds.where(ds != 0) * 0.0000275 + -0.2).clip(0, 1)\n", "print(ds)" ] }, @@ -167,13 +234,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + "/env/lib/python3.10/site-packages/dask/core.py:133: RuntimeWarning: invalid value encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", "/env/lib/python3.10/site-packages/rasterio/warp.py:387: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n", " dest = _reproject(\n" ] @@ -208,7 +277,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_95d396e204c1c4adb3c288493d66b762 {\n", + " #map_595801a05462d8fc3ef65622aed73bb0 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -235,14 +304,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_95d396e204c1c4adb3c288493d66b762" ></div>\n", + " <div class="folium-map" id="map_595801a05462d8fc3ef65622aed73bb0" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_95d396e204c1c4adb3c288493d66b762 = L.map(\n", - " "map_95d396e204c1c4adb3c288493d66b762",\n", + " var map_595801a05462d8fc3ef65622aed73bb0 = L.map(\n", + " "map_595801a05462d8fc3ef65622aed73bb0",\n", " {\n", " center: [0.0, 0.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -256,61 +325,61 @@ "\n", " \n", " \n", - " var tile_layer_0049b73e669b37d839b5b1f7b5e37770 = L.tileLayer(\n", + " var tile_layer_73782eb3c23ff8a950351a86483267e9 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_0049b73e669b37d839b5b1f7b5e37770.addTo(map_95d396e204c1c4adb3c288493d66b762);\n", + " tile_layer_73782eb3c23ff8a950351a86483267e9.addTo(map_595801a05462d8fc3ef65622aed73bb0);\n", " \n", " \n", - " var image_overlay_902578573975f666d4cb329c76d708a0 = L.imageOverlay(\n", - " "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArsAAAL3CAYAAACK+Y2VAAAgAElEQVR4nOy9ebAl133f9+lzer37 feu82d4sGABDACQIgZu4CCQlbkWJ1GItUeSiKxYjJ463crkqVuRQKiXlVEqJpJQdWWXFiS1bYSxR 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3MAAgeWWPqgYjCJGyvFMWdSeFWFrM6Cb5hlKIMWwKQ66eabOr91PYQft52rancMc5jCHOcxhDnOY wxzmMIc5zGEOc5jDHOYwhzn8/wX/Fyc9oWFfSdveAAAAAElFTkSuQmCC ",\n", " [[-17.92813466526891, 122.11796413846858], [-18.252193254797525, 122.43192533026834]],\n", " {}\n", " );\n", " \n", " \n", - " image_overlay_902578573975f666d4cb329c76d708a0.addTo(map_95d396e204c1c4adb3c288493d66b762);\n", + " image_overlay_f59563076b3aa50fe814022f8543b742.addTo(map_595801a05462d8fc3ef65622aed73bb0);\n", " \n", " \n", - " map_95d396e204c1c4adb3c288493d66b762.fitBounds(\n", + " map_595801a05462d8fc3ef65622aed73bb0.fitBounds(\n", " [[-17.928541358818464, 122.11999572144752], [-18.251457152765816, 122.43006048571965]],\n", " {}\n", " );\n", " \n", " \n", - " var layer_control_a512678d6f5b07a286a1fc6619328fb9_layers = {\n", + " var layer_control_8f73b3f050a506402446ff042b5cb751_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_0049b73e669b37d839b5b1f7b5e37770,\n", + " "openstreetmap" : tile_layer_73782eb3c23ff8a950351a86483267e9,\n", " },\n", " overlays : {\n", - " "ndwi" : image_overlay_902578573975f666d4cb329c76d708a0,\n", + " "ndwi" : image_overlay_f59563076b3aa50fe814022f8543b742,\n", " },\n", " };\n", - " let layer_control_a512678d6f5b07a286a1fc6619328fb9 = L.control.layers(\n", - " layer_control_a512678d6f5b07a286a1fc6619328fb9_layers.base_layers,\n", - " layer_control_a512678d6f5b07a286a1fc6619328fb9_layers.overlays,\n", + " let layer_control_8f73b3f050a506402446ff042b5cb751 = L.control.layers(\n", + " layer_control_8f73b3f050a506402446ff042b5cb751_layers.base_layers,\n", + " layer_control_8f73b3f050a506402446ff042b5cb751_layers.overlays,\n", " {"autoZIndex": true, "collapsed": true, "position": "topright"}\n", - " ).addTo(map_95d396e204c1c4adb3c288493d66b762);\n", + " ).addTo(map_595801a05462d8fc3ef65622aed73bb0);\n", "\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>
" ], "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate NDWI\n", - "ds[[\"ndwi\"]] = (ds.nbart_green - ds.nbart_nir) / (ds.nbart_green + ds.nbart_nir) \n", + "ds[[\"ndwi\"]] = (ds.green - ds.nir08) / (ds.green + ds.nir08) \n", "\n", "# Plot a single timestep\n", "ds.ndwi.isel(time=1).odc.explore(vmin=-0.5, vmax=0.5, cmap=\"RdBu\")" @@ -327,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -342,6 +411,7 @@ "source": [ "ds[\"tide_height\"] = tag_tides(\n", " ds=ds,\n", + " model=tide_model,\n", " directory=directory,\n", ")" ] @@ -356,12 +426,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -394,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -406,7 +476,7 @@ "Modelling tides using EOT20\n", "\n", "\n", - "🌊 Modelled astronomical tide range: 9.45 metres.\n", + "🌊 Modelled astronomical tide range: 9.46 metres.\n", "🛰️ Observed tide range: 6.37 metres.\n", "\n", "🔴 67% of the modelled astronomical tide range was observed at this location.\n", @@ -414,14 +484,14 @@ "🔴 The lowest 21% (2.00 metres) of the tide range was never observed.\n", "\n", "🌊 Mean modelled astronomical tide height: -0.00 metres.\n", - "🛰️ Mean observed tide height: 0.63 metres.\n", + "🛰️ Mean observed tide height: 0.65 metres.\n", "\n", - "⬆️ The mean observed tide height was 0.63 metres higher than the mean modelled astronomical tide height.\n" + "⬆️ The mean observed tide height was 0.65 metres higher than the mean modelled astronomical tide height.\n" ] }, { "data": { - "image/png": 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06FEolUocOXIEjz/+OIqKigAAjz/+ON58803s2rUL6enpWL58eafoox3FxsZCpVLh4MGDqKqqglqt7nTM9ddfj5EjR2LRokU4d+4cjh49itWrV9tUPqVSiVWrVuH3339Hfn4+9u/fj8zMzB6XNrsDuVyOlStX4sMPP8TKlSud2jmtrq7Gn//8Z3z55Zc4f/48lEol/vvf/2Lt2rWm5Z/Dhw+HXq/He++9h9zcXHzxxRfYtGmT2etYel9HjBiBhQsXYtGiRfj222+hVCpx6tQp/Otf/8L//vc/u8obGxuLEydOIC8vD1VVVTAajXjkkUdQU1ODu+++GydPnkRubi7279+PBx54oMtO2lNPPYWDBw/ilVdeQWZmJrZu3YqNGzd2Ctr122+/Ye3atcjMzMS///1v/Pe//8Xjjz8OoDUK7yeffILk5GTTdQkKCsLQoUMd/tsXLlwIhUKB1157DfPnz0dgYKDpd/Hx8Thw4ACOHz+OtLQ0/P3vf+/UfrB0ney9Bt1555138NVXXyE9PR2ZmZn473//i6ioKKfkxGWMMdGJsvNVJNHR0QSAoqOjxS4KY8wJOgZ66ah9kCQioubmZnr00UcpIiKCAgICaPr06XTy5Emz5/zwww8UHx9PAQEB9Kc//Yk+/fRTswAoREQnT56kWbNmkVwup5CQEBo3bhy99tprpt93FySJiKi0tJQWLVpkKsewYcPowQcfpPr6eiJqDYr0+OOPU1hYGIWHh9OKFSto0aJF3f6tREQPPfQQ9evXjwDQmjVrLJYlIyODrr76avL396cRI0bQ3r17bSpfWVkZzZs3jwYOHEj+/v40dOhQevHFF8lgsD64lTfQaDS0cuVKmjRpEvXu3ZuCg4Np5MiR9Pzzz5NarTYd9/bbb9PAgQMpKCiIbrjhBvr88887fd4sva8tLS304osvUmxsLPXq1YuioqLotttuo/PnzxOR7UGSMjIyaOrUqRQUFEQASKlUEhFRZmYm3XbbbRQeHk5BQUE0atQoeuKJJ7oN3rNz504aM2YM9erVi4YMGUJvvfWW2e+HDh1KL7/8Mi1YsICCg4NpwIABtH79etPvd+3aRVdeeSWFhYVRSEgITZ06lX7++WfT72392zu64oorCAD98ssvZo9XV1fT3LlzSS6XU2RkJD3//POdvneWrlPHIEnWXoP230siovHjx5ve382bN9OECRMoJCSEwsLC6LrrriOFQtHl38QYY1ImI3IwyoaExMTEoLi4GNHR0aaZCcYYY4wxxhhj7sGrlvgyxhhjjDHGGHNf3EFljDHGGGOMMeYWuIPKGGOMMcYYY8wtcAeVMcYYY4wxxphb4A4qY4wxxhhjjDG3wB1UxhhjjDHGGGNugTuojDHGGGOMMcbcAndQGWOMMcYYY4y5Be6gMsYYY4wxxhhzC9xBZYwxxhhjjDHmFriDyhhjjDHGGGPMLXAHlTHGGGOMMcaYW+AOKmOMMcYYY4wxt8AdVMYYY4wxxhhjboE7qIwxxhhjzCsYjSR2ERhjPfDKDqpOpzP9f2pqKgoLCwEAGo0GCoUCjY2NAIDy8nKcO3fOdGxGRgby8/NNr6FQKFBfXw8AqKysxJkzZ0zHZmVlQalUAgAMBgMUCgVqa2sBANXV1VAoFCBqrSRzcnKQk5MDACAiKBQKVFdXAwBqa2uhUChgMBgAAEqlEllZWabznDlzBpWVlQCA+vp6KBQK09+Xn5+PjIwM07Hnzp1DeXk5AKCxsREKhQIajQYAUFhYiNTUVNOxFy5cQGlpKQCgqakJCoUCzc3NAIDi4mKkpKSYjk1JSUFRUREAoLm5GQqFAiqVCgBQWlqK8+fPm45NS0tDQUEBAECr1UKhUKChocF0vc+ePWt2vfPy8syud11dnel6KxQKs+udm5trdr1ramoAADU1NVAoFDAajQCA3NxcZGdnm56rUChQVVVldr31er3pemdmZpqOPXv2LCoqKgAADQ0NUCgUaGlpAQAUFBQgPT3ddOz58+dRVlYGAFCpVGbXu6ioyOx6Jycno6SkBACgVquhUCigVqsBACUlJUhOTjYdm5qaarrebZ/ZtutdVlZmdr3T09NN17ulpcXseldUVJhd78zMTNNnVq/Xm31mq6qqzK53dna26XobjUaL17vtM5ubm2v2mVUoFKbPbF1dndlnNi8vz+wze/bsWdNntu16a7Va0/VOS0szu95tn9m26932mS0qKur0mS0uLgZw8TPb1NQEoPUze+HCBbPrzXUE1xFcR3AdwXWE9OuIj3cdxJZDydDoDFxHcB3RqY5gboS8SHR0NAEgHx8f02Njx46lRx99lIiIsrKyCAAdOnSIiIjWrl1Lffr0MR07depUWrJkCRERlZSUEADas2cPERFt3LiR/P39Tcded911dNdddxERUX19PQGgHTt2EBHRli1bCADpdDoiIrrlllvolltuISIinU5HAGjLli1ERLRjxw4CQPX19UREdNddd9F1111nOo+/vz9t3LiRiIj27NlDAKikpISIiJYsWUJTp041HdunTx9au3YtEREdOnSIAFBWVhYRET366KM0duxYs2u1Zs0aIiI6efIkAaBz584REdHKlStp+PDhpmNHjBhBTz/9NBERJScnEwA6fvw4ERG98sorFBUVZTp24sSJtHz5ciIiUiqVBIAOHDhARETr1q2j0NBQ07HTp0+nxYsXExFRRUUFAaDvv/+eiIg2bdpEvr6+pmNnz55N8+fPJyIilUpFAGj79u1ERPTFF18QANJoNEREdNttt9GcOXNMzwVAH330ERERffPNNwSAqquriYho4cKFNGPGDNOxwcHBtGHDBiIi2rt3LwGgwsJCIiJatmwZTZ482XRsREQEvf7660REdPToUQJA6enpRET05JNP0pgxY0zHDh06lFavXk1ERElJSQSAkpKSiIho9erVNHToUNOxY8aMoSeffJKIiNLT0wkAHT16lIiIXn/9dYqIiDAdO3nyZFq2bBkRERUWFhIA2rt3LxERbdiwgYKDg03HzpgxgxYuXEhERNXV1QSAvvnmGyIi+uijj6h9dTFnzhy67bbbiIhIo9EQAPriiy+IiGj79u0EgFQqFRERzZ8/n2bPnm16rq+vL23atImIiL7//nsCQBUVFUREtHjxYpo+fbrp2NDQUFq3bh0RER04cIAAkFKpJCKi5cuX08SJE03HRkVF0SuvvEJERMePHycAlJycTERETz/9NI0YMcJ07PDhw2nlypVERHTu3DkCQCdPniQiojVr1lB0dLTpWK4juI7gOqIV1xFcR3hCHbFi7ceUXtrAdQTXEZ3qCOY+vLKDGhkZaXosJSWFCgoKiIioubmZkpKSqKGhgYiIysrK6OzZs6Zj09PTKS8vj4iIWlpaKCkpierq6oioteJTKBSmYzMzMyk3N5eIiPR6PSUlJVFNTQ0REVVVVVFSUhIZjUYiIsrOzqbs7GwiIjIajZSUlERVVVVERFRTU0NJSUmk1+uJiCg3N5cyMzNN51EoFKYvZV1dHSUlJVFLSwsREeXl5ZkqMiKis2fPUllZGRERNTQ0UFJSEjU3NxMRUUFBAaWkpJiOPX/+vOkGpVKpKCkpidRqNRERFRUVmW7qRK03k7bKVa1WU1JSEjU2NhJR6w247YZERJSamkr5+flE1FohJSUlmW6aZWVldObMGbPr3VaJtF3v2tpa0/Vuq3jbrndOTo7Z9W67OVRXV1NSUhIZDAYiIsrJyTHdUIlaK/LKykqz691208/NzaWMjAzTsWfOnKHy8nIiam0wJCUlkVarJSKi/Px8SktLMx177tw5Ki0tJSKixsZGs+tdWFhodr0vXLhAxcXFRETU1NRESUlJ1NTURERExcXFdOHCBdOxKSkppuvd9pltu96lpaVm1zstLc10vbVardn1Li8vN7veGRkZps+sTqcz+8xWVlaaXe+srCzT9TYYDBavd9tnNicnx+wzm5SUZPrM1tbWmn1mlUql2Wf2zJkzps9s2/VuayDk5+dTamqq2fVu+8y2Xe+2z2xhYWGnz2xRURERXfzMtt0IS0pK6Pz582bXm+sIriO4juA6gusI6dcRr362hzYfOE/ppQ1cR3Ad0amOYO5DRkResxg/JiYGxcXFiI6ONi1tYIwxxjwJEUFnIPj7ibOLx2gk+PjIRDk3Y93ZfqJ1qerlQ/tgZFSoyKVhjHXFK/egMsYYY57ql/QK7EwqQoNG1/PBAjueU4WvThWiSat3+bkZc1fpZQ04llXFAZoYsxJ3UBljjDEPUt7QGgBEWdnk0vNmlTcir6o1KEtWhcql52bMnSny61BQo0ZRbbPYRWFMEriDyhhjjDGHncqrFbsIjHWptF78zqHujyjAjLHucQeVMcYYY4x5tEPplWIXgTFmJe6gMqc6U1CL7ScKcDqvRuyiMMYYY4wxJrjY2FisX7/e6uPz8vIgk8nM8sja66WXXsKECRO6Pea+++7DvHnzHD6Xq3AHlTlNUa0aaaWtycozy3k/EmOMOZu6hYMTMcaYELrrRF577bV44oknTD+fOnUKy5Ytc3qZZDIZvvvuO7PHnn76aRw8eNDp53YlP7ELwDyXRmcQ7dxEBEVBHfqF+CM2IkS0cjDGmCudLagz/X+NukW8gjDGmBfp37+/aOeWy+WQy+Wind8ZeAaVeaTiumZklDXieE612EVhjDGX0bdLY1Fap0GjCKlmGGPM23Rc4pueno6rr74agYGBGDNmDH7++WeLs5+5ubmYOXMmgoODMX78ePz+++/dngMAbrvtNshkMtPPHZf4GgwGrFixAuHh4ejXrx+effZZEJmnOCIirF27FsOGDUNQUBDGjx+PnTt3OnIJBMUdVOaRNDqOlMcYY+lljWIXgTHGvIrRaMS8efMQHByMEydOYPPmzVi9erXFY1evXo2nn34aZ8+exYgRI3D33XdDr7e8VePUqVMAgC1btqC0tNT0c0fr1q3Dp59+ik8++QTHjh1DTU0Ndu3aZXbM888/jy1btuCDDz5ASkoKnnzySdx77704cuSIA3+5cHiJL2OM2cFgJPj6yMQuBmOMMYkob9BgeH/PWorpDa666ir4+JjP6TU3N3cZmGj//v3IycnB4cOHERUVBQB47bXXMGvWrE7HPv3007j55psBAC+//DIuvfRSZGdnY9SoUZ2ObVtGHB4ebnpdS9avX49Vq1bhr3/9KwBg06ZN2Ldvn+n3TU1NePvtt/HLL79g2rRpAIBhw4bh2LFj+PDDDzFjxowuX9tVuIPKGGM2OqmsQXaFCjePG4jeQb3ELg5jjDEJyKtS45JILfqHBohdFGaDr7/+GqNHjzZ7bOHChV0en5GRgcGDB5t1IqdMmWLx2HHjxpn+f+DAgQCAiooKix1Ua9TX16O0tNTU8QQAPz8/TJ482bTMNzU1FRqNplOHuaWlBRMnTrTrvELjDipjjNkou6I1KnVaaQOmDusncmkYY4xJRZWKO6hSM3jwYMTHx5s9FhQU1OXxRASZzLoVVr16XRzkbnuO0ejcbWptr//jjz8iOjra7HcBAe7x2eQ9qIwxxhhjjDEmgFGjRqGgoADl5eWmx7raL2qrXr16wWDoOktG7969MXDgQCQmJpoe0+v1SEpKMv08ZswYBAQEoKCgAPHx8Wb/Bg8eLEg5HcUzqKxbTVo9Sus1iIsI4f12jDHGGJM8AvV8EGN2mjVrFoYPH47Fixdj7dq1aGxsNAVJsnZmtSuxsbE4ePAgpk+fjoCAAPTp06fTMY8//jjefPNNXHLJJRg9ejTefvtt1NXVmX4fGhqKp59+Gk8++SSMRiOuvvpqNDQ04Pjx45DL5Vi8eLFDZRQCz6Cybv14vhQnlTVILWkQuyhWMxgJJ5U1YheDsS4ZjYQTudXIr24SuyjMSYgIuZUqTvPCWAcGIyGlpB51IubpVeTXuSxXu7KK63lv4+vri++++w4qlQpXXHEFli5diueffx4AEBgY6NBrr1u3DgcOHMDgwYO73C/61FNPYdGiRbjvvvswbdo0hIaG4rbbbjM75pVXXsGLL76IN954A6NHj8YNN9yAH374AXFxcQ6VTygy6pgYx4PFxMSguLgY0dHRKCoqErs4bi+9rAGK/DoAQP/QAMwaM8Cm52dXNOKkstb08z1XDhGyeG53XuY9tp8oAAAM6x9i1x7U7AqVaRCFP5+eSaz3+NfMShTVNpt+vmSAHFfE9nXJudu+FwAwZlAYJgwOd8l5mbScL6pDcnHroLcrvxvtP58AcFl0GMbFhLv8vBOHhGP0wDCnn5e5l99++w1XX301srOzMXz4cLGL4/Z4iS/rUlvnVGp0Bq8Zc2ES5aqReyaeykatKOc1es+YM5OomibXz5zySgbmart27YJcLscll1yC7OxsPP7445g+fTp3Tq3EHVTGmCQZjYTT+bUY2DsQg/sGi10cxtxCSZ1G7CIw5nYSc3nbD3OtxsZGPPvssygsLERERASuv/56rFu3TuxiSQZ3UJlbatDoUFavQXx/OXw4OBOzIKtChew//om1TDa3sglXxPblAGLM67XozdMiqLV6kUrCWGcdP5+MOduiRYuwaNEisYshWRwkiTlNndr+JTV7zpXidF4tUkulE5yJuZa6RZwGsN5g3tBJ488o8zAGI+F0Xg1K65t7PvgP54rqzH7Oq1aLtsyZMcaYtHEH1UuIEQsrs1zl8GtUqbiBw9xLelmj2c+1IkaiZMwZMssbkVmuwqH0Squf09DceUDS1uilWr0Bv6SXY/e5ElQ08FJlT6TRGXgZupOVN2hQ78AEAWPugDuoEuBo51JvMOKH86U4nl0lUIkYcy+uDDokpZRLjNmjSaTlucnFDSir10Kl0ePntAqbnpuUX4vvzxZDq+cAZO4sKb+254OY3Ro0OhxMq8CPF0rFLorXICIsW7YMffv2hUwmw9mzZy0+5kovvPACli1b5tJzduWKK67At99+a/PzJNtBfeONNyCTyfDEE0+IXRSn0ugM+EZRjG+SiuweETtXVAeVRo+8arXApeuaO2Uv+jm1XOwiMCc7V1jnsnPpje7z2baHO303GWvPkX2CGWWNaNIakCXAyh3mPCrem+xU7VcyiJln1pvs3bsXn332Gfbs2YPS0lJcdtllFh9zRF5entUd3fLycmzYsAH/+Mc/TI/9+uuvuOWWWzBo0CDIZDJ89913Zs/R6XR47rnnMHbsWISEhGDQoEFYtGgRSkpKzI5bsWIF+vbtiyFDhuCrr74y+92OHTtwyy23dCrPCy+8gJUrV8JotK1+l2QH9dSpU9i8eTPGjRsndlGcTlnVhBa9EVq90e4RsYwy22/YjjZiC2o6d4YL7Oggl9RpOu35s1WFi/dBGSXegZEiKaUWMhgJ54vqTT8fz3HdygaNzoBvFcU8i+FiFY28pJExS3jAzHn+d6FM7CJ4hZycHAwcOBBXXXUVoqKi4OfnZ/ExV/nkk08wbdo0xMbGmh5ramrC+PHjsXHjRovPUavVUCgUeOGFF6BQKPDtt98iMzMTt956q+mYH374Adu3b8f+/fvxr3/9C/fffz+qq6sBAHV1dVi9ejX+/e9/d3rtm2++GfX19di3b59Nf4fkOqgqlQoLFy7ERx99hD59+ohdHI/l6GyrpVHSY3YuMT5fXN/zQW5CozNgx+lC/Jpp/d4t5l0yOuxhzauy7buWX92EwxkVds02pZU2QKs3dioDEx7hYsP751TblqtKUXkDxwtgtrN1nzJjrnbkyBFMmTIFAQEBGDhwIFauXAm9vrWNe9999+HRRx9FQUEBZDIZYmNjLT4GADt37sTYsWMRFBSEfv364frrr0dT08XP/5YtWzB69GgEBgZi1KhReP/9902/i4uLAwBMnDgRMpkM1157bZfl/eqrr8w6lgBw00034dVXX8Xtt99u8Tm9e/fGgQMHsGDBAowcORJTp07Fe++9h6SkJBQUFAAA0tLScO2112Ly5Mm4++67ERYWhtzcXADAs88+i+XLl2PIkM4ZFXx9fTFnzhz85z//6eFKm5NcmplHHnkEN998M66//nq8+uqr3R6r1Wqh1V68afJInfXcKam1lCJB5lU3wUhAUa310S+ZfdJKpdnJ0jiwR65KpcVv2a0jlskl9Zg0hAfpmLCMRhIkwJ092nfqmXdQt/CeYea+iouLMWfOHNx33334/PPPkZ6ejgcffBCBgYF46aWXsGHDBgwfPhybN2/GqVOn4OvrC39//06PlZaW4u6778batWtx2223obGxEUePHjX1Sz766COsWbMGGzduxMSJE3HmzBk8+OCDCAkJweLFi3Hy5ElMmTIFP//8My699FL4+/tbLG9tbS2Sk5MxefJkh//2+vp6yGQyhIeHAwDGjx+PzZs3o7a2Frm5uWhubkZ8fDyOHTsGhUKBDz74oMvXmjJlCtauXWvT+SXVQf3qq6+gUChw6tQpq45/44038PLLLzu5VIwxsXlLw3Z/ysX91FqdY0vfj2RWYsaI/o4WiXVBBmnmxs0oNx/4SS9rwKioMJFKw5gwvOUewYT1/vvvY/Dgwdi4cSNkMhlGjRqFkpISPPfcc3jxxRfRu3dvhIaGwtfXF1FRUabndXxMoVBAr9fj9ttvx9ChQwEAY8eONR3/yiuvYN26daYZzri4OKSmpuLDDz/E4sWL0b9/6726X79+ZufpKD8/H0SEQYMGOfR3azQarFy5Evfccw/Cwlrr/xtuuAH33nsvrrjiCgQFBWHr1q0ICQnBww8/jM8++wwffPAB3nvvPURERGDz5s249NJLTa8XHR2NgoICGI1G+PhYt3hXMkt8CwsL8fjjj+PLL79EYGCgVc9ZtWoV6uvrTf8cfcM8QVa5NGedGHOW7IpGSc3SC6WYZ/mZBbVN5oFVFPl1Lju3VDv1zHvoHIyJwaQlLS0N06ZNg0x2sW6aPn06VCoVioqKrH6d8ePH47rrrsPYsWNxxx134KOPPkJtbWssiMrKShQWFmLJkiWQy+Wmf6+++ipycnJsKm9zc+t93dp+kiU6nQ533XUXjEaj2TJjAHjppZeQnZ2NCxcu4LbbbsPrr7+O66+/Hr169cKrr76KY8eOYenSpVi0aJHZ84KCgmA0Gs1WtfZEMjOoSUlJqKiowOWXX256zGAw4Ndff8XGjRuh1Wrh6+tr9pyAgAAEBASYfm7/AfNWp/JqccmAUJufV9mohVZvQICfb88HO+hCkXT2nDL3YE/DVm8wYsfpizeYe67svHfCGrbuHOCdBowx5lpCDH5UNmpxILUc8ZFyTInrK0CpXCOvqgnVTVpMGtKH28E2IqJO16xtWa4t19LX1xcHDhzA8ePHsX//frz33ntYvXo1Tpw4geDgYACty3yvvPLKTs+zRUREBIDWpb5ts6620Ol0WLBgAZRKJX755RfT7Kkl6enp2LZtG86cOYNPP/0U11xzDfr3748FCxbggQceQENDg+n5NTU1CA4ORlBQkNVlkcwM6nXXXYcLFy7g7Nmzpn+TJ0/GwoULcfbsWZvfRHvUq3UuzbcoBCH33boq8ucFCQVFYq0qG7VoduFeIiFmPLMqOB0Fcx5eUiiO80X1krtPM2lI/qNtkl2hQkWDNCJzZ1c04nhONTLKVBwbww5jxozB8ePHzdrSx48fR2hoKKKjo216LZlMhunTp+Pll1/GmTNn4O/vj127dmHAgAGIjo5Gbm4u4uPjzf61BUdq23NqMHRftw0fPhxhYWFITU218S+92DnNysrCzz//jH79+nV5bFue13Xr1kEul8NgMECn05leB4BZWpnk5GRMmjTJpvJIZgY1NDS0Ux6hkJAQ9OvXz+H8Qtaob9aZ0rzYO9MiBkvpXuxV2+Q+gZPcSaNGh9P5tRgz0Dv3abWNKgOu+250jPxosGMgRi9QahpbB6R5ANu1NDoD9qWUYXh/OS6L7i12cZwqW+KDLkJ16tNKGzCRA4gxJ/o5rcLlbcEWvRH+frbNK51UXpxY0DqQZ9hbLV++HOvXr8ejjz6KhIQEZGRkYM2aNVixYoXVeykB4MSJEzh48CBmz56NyMhInDhxApWVlRg9ejSA1qWzjz32GMLCwnDTTTdBq9Xi9OnTqK2txYoVKxAZGYmgoCDs3bsXMTExCAwMRO/ene9nPj4+uP7663Hs2DHMmzfP9LhKpUJ2drbpZ6VSibNnz5pymur1esyfPx8KhQJ79uyBwWBAWVlrmqK+fft2Csr00UcfITIy0hQtePr06XjppZeQmJiIn376CWPGjDEFVwKAo0ePYvbs2VZfL0BCHVSxVUo0j50jic+ZdX7LrkZNUwtK6zSYNDRc7OK4XLkbjCTzfkrWlZSSBjRpDThfVO/SDqoY+ynzvCxlx5HMSvTylUEeYN6U4blr1pHUVzRUNGrwc2oF4iJCMG141zNbTFjR0dH43//+h2eeeQbjx49H3759sWTJEjz//PM2vU5YWBh+/fVXrF+/Hg0NDRg6dCjWrVuHm266CQCwdOlSBAcH46233sKzzz6LkJAQjB07Fk888QQAwM/PD++++y7++c9/4sUXX8Sf/vQnHD582OK5li1bhiVLlmDt2rWmTvTp06cxc+ZM0zErVqwAACxevBifffYZioqKsHv3bgDAhAkTzF7v0KFDZmltysvL8frrr+P48eOmx6ZMmYKnnnoKN998MyIjI7F161bT74qLi3H8+HF8+eWXNl0zSXdQu3pzGHOlZl3nnK+MMffg7enFsspVuCJWGvvlbOnUN2h0gg5MNbcY8HNaOYb3l2PMIO9cDePJGpqlfZ9OKWkA0Lp6iDuorjVjxgycPHmyy98/8cQTpo5kV4+NHj0ae/fu7fY899xzD+65554uf7906VIsXbq0x/LOnj0b0dHR+Prrr3H33XcDAK699tpu74WxsbFW3ysHDBiAvLy8To+/+OKLePHFFzs9vn79etx3332IiYmx6vXbSGYPKmNS0P77bTR6d8PYW3h5/8cm3vSdMHjR3yoGob93F4rr0ajR42xhnbAvzBhjLiSTybB582bo9e4xKBMZGYlXXnnF5udxB9VK7tIILaq1bU9pmZ3LL7lt5bifksvELoJL1HRISyEVFRJdti9VzS0G7EwqQmJutdhFcbqaphZB9/9bq6tqWy+R1BgqrXgNKqO73OSZx2huMXjVNiujkfDThVIcz6kSuyheb/z48fjb3/4mdjEAAM888wwGDBhg8/O4gyox521MwVJYY98SqNQ/lpMw27QPgFPf7PlBpcobNJKNDFjeIEzuUw56ZJ3M8kbojYTcSs/fJ3m+qE7sIphJK7U+/7VWpM5scV2zTdG5+XvnGCJCcV0zRzx2Eo3OgF1nirEzyfpcmVJX1qBBrVqHvCrXD84xz8MdVCvxzZC5u+wKlctnM4vrpNk5tZdWz405WxiMhKJaNSe3t4PRSDZ3Hrq6TTVorB8sK60TZ2VBVrn1nWjmuOwKFY5kVOKHcyWiliPXQwN7NQg9QM0T/MzLSDpIEmPuQqNuwv6dW/FdQzl8wgZg9vzFCAwOcdn5C2vUOKmsASCtNEhSc8HGFQzO5IpOnz0JydtTFNQiq1yFgb0D0TfEv+cnMJOf08pRpWpB35BeuG70APTy9b7x5MIaNQb3DRa7GJ2U1jfDz8cH/UMDxC6K3dpWvugMhFN5NaIF0lJp3GOfnDWaWsQpa526BaX1vCWFeRfuoNohudi16QqYazVodCit0yA+Ug5fn54b5s3qJqxZOg/Fednw8fGB0WjEb3t34eWPv3N+Yf9Qp/b85cTtiRWZ1Z3yyBXVti6JdGYj+X8XymAgwi3jBtrVSc35Iy9nab2GG1g2qlK1roaoadIhrbQB42LCxS2QCI5mVVkccFOpVHj+1beQkpGN/oMGu3RAUN2ix6H0SgCeMxgopUjPYimsUQsWCfhMQR2G9Q9BgJ+vVce3DT67u/xqXtrLhON9Q7IAdLqLjfnU1FQUFhYCADQaDRQKBRobW5calZeX49y5c6ZjS/JzUFlahPNF9dDpdFAoFKivb51RqaysxJkzZ0zHZmVlQalUAgAMBgMUCgVqa1sTJldXV0OhUJga2Tk5OcjJyQHQ2vBWKBSorm4NJFJfVwtl+gUYDa1LvcqKCpCVlWU6z5kzZ1BZ2XqzrK+vh0KhMP19+fn5KMnPMR2bn5WK+urWYxsbG6FQKKDRtDYaCwsLkZqaajq2IDsdtVXlrdelWQ1l+gVoNa0jrsXFxUhJSTEdm5KSgqKi1n0Wzc3NUCgUUDe1Nkxrq8pRkJVmOjYtLQ0FBQUAAK1WC4VCgYaGBtP1Pnv2rPn1Lik0vWcKhQJ1dXWm661QKMyud25uLgAgSVkNZfoFqOpbj1XV10GhUMBobO1c5ObmmiUsVigUqKpq3dRfW1uL977ej5M5lUgrbYBSqURmZqbp2LNnz6KiogIA0NDQ0JrUePsnKM7LBhmNMOj1IKMRxXnZ2LHpLVOiY5VKZXa9i4qKzK53cnIySkpal1qp1erWa6hurexLSkqQnJxsOjY1NdV0vds+s23Xu666Att/Omo6Nj093XS9W1pazK53RUWF2fXOzMw0fWb1er3ZZ7aqqsrsemdnZyM3NxdEgNFoNLveNTU1UCgUMPzxmc3NzTX7zCoUCtNntq6uzuwzm5eXh4yMDLPrXV5ebna9tdrWfWpVZcUoVl583R8PJ6K0tNTsejc3N5uud8fPbE1F63vTotFAmX4BTU2tS81KS0tx4cIFs+vdVkdota3HNv9xveurK5GddvG9ycjIQH5+PgD0WEeUFShRUdL63thaRyjTL6CxvhappQ2ora01u95KpbLLOqKxoQHK9AvQ6y/WEe2v97lz50zXu7KmDmfPKFBTr4K6xdCpjrhw4YLpejc1NZld7+LiYhw/ddYUbK0oNxPVFaWm661QKKBSqUzX+/z586bXtaWOyMjIMIW776mOKCtQmuqItutdU9Pa8Gv7zFpbRygUClOEREt1RO0f9ay6qbH1eutaO5sFBQVIT083HXv+/Pku64jqilIUKVtfV91isKqOaBu2KVK2u95aDdKTz5mud1lZmdn17lhHKNMvQN3Ueg+sr6lCXubF7013dURjXQ2U6Re/N2WFeSguaH1vjEajxevdvo4oyss1PVeZfgENta33wI51REpKCsZPmICNb72KI3t24OsP1uL5+/4CjbrJdL1b/qgjCgoKkJZ28f5z/vx5i3WEzmDEqZRsFOVefB9TUlJQXFwM4OJ9rampCc0thtb7WvbF99GWdoQtdYRQ7QhLdUT7652XkdxtO6KrOqKndoSlOqKtHVFTUdbpendsRwhdR+j1uta6vrH1etdUm9/X2rcjOtYRiWn5rW2xP+qIiuKCbuuIX38/CcMfdURFSQFKC9pd78wUHE9u/bntvtbS0nUdUV3V2ubQqJugTL+AusbWe5WldkTZH9dbq2k2a7fVVpYjM+3isZbaEdbWEZbaEdUqLZRVTSgtyDXd16xtRwDW1RHOaEd0rCOYGyEvEh0dTQDIx8fH9NjYsWPp0UcfJSKirKwsAkCHDh0iIqK1a9dSnz59Wn9X3kDxl02ka2+5k7Yl5lNJSQkBoD179hAR0caNG8nf39/0utdddx3dddddRERUX19PAOjdjz+nbxWFtHHTRwSAdDodERHdcsstdMsttxARkU6nIwC0ZcsWIiJ6ecPHBIA+OphM2xLz6Zob59J1111nOo+/vz9t3LiRiIj27NlDAKikpISIiJYsWULxl02kbYn5tC0xn0LCetPdCauIiOjQoUMEgLKysoiI6NFHH6WxY8eaXrdP/yi6fckTtC0xn/756W4CQO/992ciIlq5ciUNHz7cdOyIESPo6aefJiKi5ORkAkAvffQtbUvMp/nLnqLwfv1NZZg4cSItX76ciIiUSiUBoAMHDhAR0bp16yg0NNR07Ihxk+lPc+a3Xu/SMgJA33//PRERbdq0iXx9fU1lmD17Ns2fP5+IiD45lEYA6JF/vkvbEvPp4TXvEADSaDRERHTbbbfRnDlzTM8FQB999BEREX3zzTcEgD7cd45+SSunhQsX0owZM0zHBgcH04YNG4iIaO/evQSA/nTjbeTr50do3SVCAMjXz496+QfQ66+/TkRER48eJQCUnp5ORERPPvkkjRkzxvS6Q4cOpdWrVxMRUVJSEgGgpKQkIiJavXo1DR061HTsmDFj6MknnyQiovT0dAJAn33zE21LzKcFDz9LoeF9TcdOnjyZli1bRkREhYWFBID27t1LREQbNmyg4OBg07EzZsyghQsXEhFRdXU1AaBvvvmGiIg++qj1M9tmzpw5dNttt9HpvBr67NdMAkAPr3mHiIi2b99OAEilUhER0fz582n27Nmm5/r6+tKmTZuIiOj7778nAFRRUUFERIsXL6bp06ebjg0NDaV169YREdGBAwcIACmVSjqRW03X//VvFDviUtPnpW9EJL3yyitERHT8+HECQMnJyURE9PTTT9OIESNMrzt8+HC6ZdFy2paYT2980fo+njx5koiI1qxZQ9HR0aZj29cRX/18ggDQ6n9/RdsS8+nuhFUU2jvcdOzUqVNpyZIlRERd1hHfKgppW2I+XTp5Ok2bdSttS8w31RE7duwgIqItW7ZYrCO2JebT58dyCAAte/7/6HBGBe3YsYMAUH19PRER3XXXXV3WEa9v+pIA0MY9J2lbYj4tWbKEpk6dajq2T58+tHbtWjIYjLT6318RAFr33yOk0ug61RHR0dG0Zs0aIiI6efIkAaBz584RUWsdMSBmqOm9GThkGN28cBltS8ynf21vfR+PHz9ORESvvPIKRUVFmV7XmjqizfTp02nx4sVERFRRUWGxjjilrKZtifk09sprTHWESqUiALR9+3YiIvriiy9sriOqq6uJiCzWEQn/eJW2JebTc+s/b637dycSEdGyZcto8uTJpmMjIiK6rCNuumsJRcddQtsS8+n3nCqr6oj9KWW0LTGfouMuoZvuWkLbEvPpra9/IQB09OjR1s/A669TRESEqQyW6ojn1n9O2xLz6W9PvkQBgUGmY7urI5auepMAmN7zCVfNpGtn30xERBqNhgDQF198QUSW64jJ0681PdfH15ceeO512paY36mOmDRpklldC4Agk9Gdy5+jVe9uIwD0w7GzRES0fPlymjhxoqn8UVFRFuuIpPwaunnhMho4ZBhtS8wnotY6YuXKlUREdO7cOVMdUdWooduXPEF9+l/8zFrbjiCyro5oY6kd0VMdQdS5HWGpjpg09U+m6+3Xq/t2hKU6gqjndoSlOuL9bw7StsR8umXRclMdQWS5HSFkHbEtMZ8++ElBAGjF2o9pW2I+Pf/G2122IzrWEc+8/l7rvfbXTNqWmE+TZ9xgVTtiW2I+Tb9hHo2eONV0vQMCg+jZl94koovtiMLCQiKyXEfc9/gq2paYTy9u2kkAaP1/DxGR5XbE8iefoW2J+fTqZ63v46uf7aFtifk0974Eih48xHSspXaELXVEx3aEslJF2xLzafTEqTT9hnlEZH07gsi6OkLodgRR5zqCuQ8ZkffEVo+JiUFxcTEiIyNNoympqakIDQ3F4MGDodFokJqaiksuuQShoaEoLy9HWVkZxo8fj+yKRnx3+DR6+Qeg/8AY3DFpIC5cuIDhw4ejd+/eqKysRFFRESZOnAigdSTOz88PcXFxMBgMOHfuHJKqeyEkrDdk2kaMlGsxceJEyGQy06jn8OHDQUQ4c+YMhg4din79+uGjAxdQUVKAoZeMgY+vLzQ1pfhTfF9ccsklAFpnR2JiYtC/f3/U19cjJycHY8eORa9evZCfn4//nsjFoKHDAbTOoIb37Y+H51yOxsZGZGVlYcyYMQgMDERhYSEaGxsxZswYAMCb2/YjNLwP+kQMgKZZjdL8HIwePRq3TxmG4uJi1NXV4dJLLwXQOvLZu3dvxMTEoLm5GWlpaThbH4TA4BDUVpWjsbYGQy4ZDQCYGNaEkJAQDBkyBFqtFikpKYiPj0dYWBjKy8tRWlqKVG3rUqOS/Bz06uWP/oMGY3i/APRqKMawYcMQHh6OyspKFBYWYtKkSabr7evri2HDhuHL40rkZ6Wi/8DBkPcOh6q+DpMj9JgwYQJ8fHyQm5sLo9GI+Ph4AK0jcUOGDEFERARqa2vx/u7jGBI/GjH95IgNaIJOp8OIESMAtI7EDRo0CJGRkWhoaEB2djb+75Ov8NWmdSDjxeWfMh8fzJ6/GJ9teB1RUVFQqVTIzMw0Xe+ioiI0NDSYrndycjL69u2LQYMGQa1WIz09HaNGjUJwcDBKSkpQU1ODyy67zPSZDQsLQ0xMjOkzqwsZgJw6A+qqK9BQU42V994AoHXkMzg4GEOGDEFLSwuSk5NN17uiogIlJSWYMGECgNbZkV69eiEuLg56vR7nz59HXFwc+vTpg6qqKhQUFJiud3Z263LmOr8+SC2uR35mCvoPHIxls8ehpqYGeXl5GD9+PHx9fZGbmwuDwWD6zCoUCgwePBj9+/dHXV0dcnNzTZ/ZvLw8aLVajBw50nS9Bw4ciAEDBpiu96WXXopzJU1IPJ8BbbMa0XGtr9tUloO/TBmFgQMHmq736NGjERQUhKKiItTX15t9Zg/lNqFvZBRaNBoU52Xh1hmTMTZ2AEpLS1FVVYWxY8d2qiN+SSnC76fPIWpwHIJC5KivrkSgvgH33zoTQOtofWBgIIYOHQqdTmexjkhraU2yXlaghI+fLyIHDcGdk6Nx7tw50/Wurq5Gfn4+Jk6ciP2p5SguUOLq+P44XeMPIkJeRjIiBsZgVOwgjOvvB6VSabreSqUSer3edL2PHD+JCmMIRsZG42RGEcqL8jE4fhT8/HphehRBo9GYrve5c+cQFRUFeXg/fPVbJsoKlYiOuwR3XDkMNRWlZnXEhQsXEBERgYEDB6KpqQkZGRmm611cXIxtv6YhZljr96YoNxNB8lD0ixyIFo0G40JVGDFiBORyOUpLS1FZWYlx48YBaJ0d6amOaPvMZmRkICAgALGxsabr3bGOMPaNRWa5CmUFSsy7fDCGDRtmqpNjY2PRt29f02fW2jpCqVRi3Lhx8PNrvfYd6wiluheafUOhbmpEeWEeBg8fiQVXxqGitBhqtRqjRo0C0Do7EhkZabGOeO+HE2huakRM3AgM7ReM3pqyHuuIMp/+qGjUokiZiaCQP663VgNDTSHuvP5KyOVylJWVoaKiwnS9O9YRb/3nAAYMjkVwSCjqa6pQW1WOf/ztph7riA/3nUVVWTHiRrV+b8oK8xDXX47bZkyC0WjE2bNnO13v9nXE8awK+IQPAtA6g9pvwCCE9emHOSPDzOqIe+65Bzt2/BcGw8Xllj6+vrj2ljtx96P/QHlhHq676nJMvSQKBQUFaGpqwujRo03Xu3///p3qiFOFKpzLyEWzqhExw0bgniuHICUlBeHh4YiOjjbd10aOHAkN+eGrI+fRWFeLlQtnd6ojumtHdFVH9BkQg5ioCNTVVPfYjrBUR/TUjmj7zLavI45llMO3T+v1zstIxoM3XdFlO8JSHTFgwIAe2xGW6ogi6gOV3hc1FWVQqxrMrnfHdkRXdcS+304jo0qHW6aPw6BQP6vqiOPlPtDrdSjMTkdk9BCEhPbGoMAWhBkaLLYjOtYR//0tDZnZuRg64lL4+PigorgAfxkX1WM7wtfPDxUlBTDo9Rg4ZFjr9c5MwbTL4jFz4iWm+9pll10Gf39/FBQUdKojUmoBCgyHRt06Qxkddwnuu2ZEp3bE6TPncKK4BX36D4BW04ySvGwMio1HQGAQaivLERdKmDNjSpftiLbr3VMdYakdER5zCY7nVKO0IBe+fn544rarrW5HDBs2zKo6Quh2REBAQKc6grkPr+ygRkdHm5Y2WCu7ohEnlbWmn+3Ze7L9ROuyh7AgP/xl3CCbntMmPLgX5owdaPM527Om7Jae1zuoF24eZ925LT3fkXNbe80qGjT4Oa3CrvN2PP/A3oGYOSqy5+N/y8Bzi24124MaHRuPlz/+Dg/MdF6lp9EZcKagDlUqLQJ7+ZqlaHDV3ihFQS3S26WwcNV5T+RWI6dDupKo3gH48yjrcm2VN2hw0I7PyW/ZVRb32dx1xWD4WLFfGbDtO6nVG/BNUusyw5FRocgoM490Gt0nCDNG9O/2fLvOFKG5xfLe2a7O26TV4/uzF6N7zp0wCCEBtoUs6KoO6O68QtMbjNhx+mJd76rzHs6oQEmHaLjxkXJMibN+n1/H62dN2X9OLUeFhVQtQ/oG4+pLInp8fvvPm63ntvR+2/I3W7pmls795ptvYvXq1aallkDrgOCCh57BrYuWAwBGDQzFpCF9rDovAPyaWWmWLqu7v7dapcW+lPIej7NWYY0aR7OqIA/0w63jrWsXCOFQeoXZvnBXfTf2pZShWmUebd7Wc7f/rFn7XEufz8uiw6ze2703uaxTlPzuzt1d/QcAlw4Kw/jB1p3b2muWnFeOV996B5UlhRb3Zk+J64v4SLlV57RVXlUTjudczHHtKfuzmXg4SJKVHO3G25LfjdmvxMFALLYm1dYbjIBfEF7++Dvs37m1yxuDMyTmVpsadI0iRUKU6vCW0Llb08saMWZQmKCv2VHHzqm1uuqceoPUUvfJ56w3Ov99cPTreK7QfaJUdyUhIQFbP/8CGRnpZgOCs+cvdlkZ2qK2H/m0FnFxcUhISIBcbl/Dv23AS0rRbB3haN7Vikb3CbaWWd6IEQNCxS4GgNY91bffNBPZmRmdgjU60hZp0urxW3YVRkWFYUg/10bUbtDocDijEmMGhiI+0j2uM3Md7qC6yIHUckFex9OTahuMjjWxHL0+PyWX2nR8Un7rrHpgcIhp9N5VKho8Y9Ajv7oJ2RUqTI+PQGAv66IaWiJmZ7m0vtnpHVRXsnQp69QtqGzUIj5SbnfaGVdzq0a/BAZz1CKl0bCFXC7HT7/8ipWvvOXSAcE2KpXKLGo7GY348ssvkZiYaHcn1Zs0aR27R7tTRNvTebVu00HduHEjcjIzWoM1/jEYVpyXjf07tzrUNjmdX4sqVQuOZVfhnn6unRVNyquFSqPHSWUtd1C9EHdQreQu7TGNzrNnQzIdSNZeWKNGbqVjSb/b3zxL6zVo0uq7XdqYV+2ZScatJcT34rfs1mVBZwrqMG14PyvP6yZfSCeTwX3+zv9daI026+Mjw/D+0myIG4xkVeooqfLcv8ycXC536oBgSV0zBoUHWfzdJx9+cDFq+x8dgbS0NGzcuBErV650WplYK6FSvXgapVJpmjlt4+PjY8qEAAC16hZLT+2WravKhOTohAWTNq9MMyMVxXXCLkOUAnWL/aOrZwvr7Hre7znVOJReYTG35qk89xmt9XQtButvhJbeKzH7rNbO3kplBUR3l7JEwvXSnvMlPR/EOjlTUNvt740e1pA8nFHZ5e/y8/Pg42PedGoLPGQPksLUOnN7cXFxZp1ToDV1S/9Bg00/Z5WrOu2jtYUjEwiM2Yo7qN0gIlSptNAbjKIsH6zifas20do50qesakJpvQZ1al2n39n7mt7CnfagulNZupJS4vo9fkJ33Atrmt1qH5glGp0BaaUNaO4wIODo8kJX0dkwWNOes74CaaVdN0x1BiP2p5Y56czuZ8jQ2E4dAYPBgLi4OJFKxFjr3uzhI0ZC5uMDXz8/yHx8LO7NLq23boBRb6EOOp3X9UCV0HWPVm+wGPCNeQ9e4tuNjPJGKPLrENU7QDING2sYjWR1xFGpUFY1ObwURQL9G+amrO0E6gzS+JR1LGXHn7MrVIgMDXRVcWx2PKcKZfXCNG7q1C2oaNTiEhfuvW0fQdnd/ZZdhZqmzoN7UmHr7O/Svz+Mzz7/wmwP6rBLRuLS6xZAbzDCz9e2cX93WsYvFk9feu8Kcrkc3/50qNsovtZKK23AmYI6/MmKyN/dadEb0WIwQm5jBHjgYnwP5r24g9qNzHIVAAjW0HEXZ4vqbArBLwU9LUFj7q+4tlmyDRVHZ2+Ti+txWXRvYQrDBK2zTXtvZXBZoA4x933ZylJqGCnoKiVZT3z8zaO2X3fFZYiaNg+NRj9kVagwemDPwdJa9EaU1WswKDyw0+P+ft63sC2zvNGq6yYmrd79JymCQ4QJ1nimoA5Aa6aA8GB/q57T8a5dVq/BL+mt3y970pRZWtHGvAt3UD1YeYPlhkN6aWO3HdR8Lw/8w6zXcULJ0dn51JIGjI1xfketvtn+fTjOcL6ocwdVpVLh+df+DxfSs+weDdfoDN12njU6g8XIydIbInA+sWcJrZmd6+p9432O5lQqFR5b9arNM00anQFHs6rMorYvmBxjyrVrbVCX33KqUFqnwdB+wWbvzY8XSnDbxBg7/iJpcyR69KH0CqvylVuSXNyAkVGhCPDrOXq8LavoGjTcuWrrnAJAlUprcweVMf7EiKChWe+SZbb25l4VN2er6xtSbTnt9jZXoSUowqUpC6SuY+fnt5wq/OmS/na/XlmDBmPRcwc1x8FozfbOsNkTBdEeKpUKU6dORWpamkM57X7N7DrYCwB8qyjmhOoSkVragHEx4d0e427d0OwKFcYP7m1VB0BI6aWNGBfd22KH3pHvlqXvvz0zyKV/PCe/Wo3BfS9GC/bmnMX2KnUw93lqSQMmCryirF6k2T+htyDYUp+4W93DpM/71pK4iZQS90kgLzRLEVatlVGmErAkPWvLabdj01vY/sVW7Nj0FtYsnQeNmmeR7VFYY1uE17RSaX0PXJXiYOPGjUhLS2tNZaHXg4xGU047W1SphOlQe+t+oMIatWCv5WgDrr5ZmrMyF4pcHxgMaO3QWyLUd6vNsewqR4op2T2oycX12JdSZjGYTleatOKkiOluZtsoheh6LtTsQCYFxoTEHVSRZFX0HK5bqtWmsko6nbuNGzdezGknQGOF2aZtrwszp1Qq4etrPuvUMaedKxXXip9axt7Ito44muVY54NZn7tbL3AAsa5Slrnbd0uqy6/PF9WjWtVi02qWxNxqJ5bIskaNDl+fEue9dTVHJgfa2BuRW5rDLMydcQeVWa2+WWdVxEMpjfS3JbduT8zGSn2zDmmlDZJJUC1m7lFXc+Xof1xcHAwG8wZ2x5x2nqxO3WK2TO7XzEr893SRaEvnpMLR7RnOCHhkbQfMVSklvP27JTRbZiC7GjRwZh7p/Snl3f5eChOoDS5sUzkjY0WuhCYtmPvgDqpIpFApWnKhuOflWlL60/oPiukxubUr/Xi+FGcK6kTJl2kPqX6ObZVSUu/S1B8JCQkYPXp0jzntpKq7QGx6gxH/u1CGHy+UmgbEiv6Ywc20YuWJp7J1+bwrSGUgrT1P/255Mnu2Gkg9l3l9s04S6cm6K2FpnabLoJ2MdYWDJLkxd5ycyq1SYfzgcLGL0Yk9M3lGI2HYNfMRHfulKaed0Wh0aWOlpqkFeoMRkWHm6QaqVK4NVEVE0OqNFiO6WtKk1eNcUR3UbpQf2Jmd5XOFrh0wkMvlSExMxNLnbI80KgW/ZVdjaD/Lf0tLu6W8eiPB303SDjma+kXMvW7OOrUU03u523dLqntQ29Q0uS4iuitnEt3F7znWbzMQOkiS3kA27THuTkOzDgPCXJM7u0mrh1ZvRN8Q61LkMPfEHVQ35v5jZsIxGAm/ZnUfcVRoRiIEBoeY5bRzdWNlb3Lrfo/bJka75HxdOZJZiZI6DWQyYN6EaAT5d99RPZZdhWqBAvAwy3z9gwTJaWcPb6p7rFVQo0aVSosIeYBdz3fHGVBHteUKdzVHg1fJ5XLRvlsdVTRKe2Ypv1qNSyI1nQZZPUlGmetXbhiNhPpmHVpEnj2tdeG2ioJqNU7l1SA+Uu7Q6oy21U725F9l7oPfOQkqrFFjcN/gHo/TS2j51ZmCWlPofWc4lVeDK2L7Wvxd+5x2rtS+YaJu0eOgHUnjhdK294wIOJ1f02OqGHccyfa0/bB6o3hL06pETTXVtaxyFSYN6QNfkWZV00sbcfUl9nVQmXDSSt1zqbc9jWprg0i5s9J6xzqo1l61FicESrPm3Bnlrv+8JeZWI6/atoEYIYIk2UuIGrktIrZQWS7qm3XcQZUw3oPqAo0CJ21WWLmsKlWkVDbd1ZGH0i13wpy9iT5LpJH+rhARfk69eC0I7jOg4OhSRrGIuR/WPd45YWh0BhzPcSzaplCRvC0F2Ekv675eEyuVBWMpJQ093u/VLcJ9PvOqmnAqr8ble4HFmvV1RgAfd2Vr51RsnnQPZO6BO6gu8MO5UrueJ90Joa6rKkeTansKN+mLuh1Ho5Ayx3UVadMWvzvYwW2zN7ms06xAXQ9Lzg5liLcSQUxCD4QKxZqBo5xK1w0gqlQqvPnmm/jkzVXY/fn7gue87mnZ888CrpQ5nlONrHIVvj5VaFPHV2cwOnQvbj+4ytyH0HtQGRMTz327sa7u60LMFBGRW1VmjubAa26R5qwfs02L3ogCB/efOYP7fJNcI69KjfExeqcvn2rSGlBcZ9vezYZm8WZQa5tacK6oTpRUW790sTpFCnIqXNNBValUmDp1KtLS0iD7Iyjeb3t34eWPv3NZ3AGVxjmfz+wKFcbFhFt17LnCOqeUgbU6w9eXMYfxDCqzibekFWlTrWoRLIodc9xJZQ1OKmvELkYnYn0timubRVuSbe1WA0f9mml9FEuxHUgrR0mdRpSliFJe/uiq78/GjRuRlpYGo9EIg14PMhpRnJeN/Tu3uqgE7qGKA9w5lbMGIXoi5h7UtFLnbinL6mYfsNFI2J9SZlcaIua+eAaVddLTEjqhqVQq7P78fZdH0bW2Kj9TWNdlgCVnKavXolGjQ2hgL5ee1xoqlQrfffZvlBW7Pupxd7OnNWrvbHSdK3L95xNwXg5MsSLDCsHRlSD2cuUSWWewpV3tyP1CqVTC19fXLPe1j48PKksKbS2yaJLyaxDg54vLonuLXRRJ0KibRIvS70wqrR5yNwkAVFzX7PR246m8WlwyILTL81epWlClasHlQ/s4tRzMddzj081cjshy1NOMskZUOLgP0NbGxtSpU5GalmbKQ+rqJVc9EWvf7IncGlw/ZoAo5+6KO79fYnUObJFbKXwwMFcPKDlTk1YvWnA3d6HR2T4TeiK3+1UF7vzNaG4xWJ1L09H6Jy4uDgaD+fU1Go3oP2iwXWV3tfpmHTLKWgcj7OmganStS+ZrLQzmVTZq0T9UnAjVzpr4U6lUWLN0nlme846fF3dcFWZNHvTdZ0twz5VDzB4Ta9uW2IHpxMwxzZyHl/i6KZVIDbXU0nqXnq9tyRU5sOTK3pkcd9832F2aEY3OIErUXyHeL7E5mkOROY+Ol9O7bOm0uziSaf3eWUfrn4SEBIwePRoyHx/4+vlB5uOD6Nh4zJ6/2N7iuwwRdRkF31pHMitxIrfGYqfsQGq5Q6/dEyJCo4uXvm7cuBHFedmSu1/tT3Hue8GYFPAMqp2yK1SIj5Q75bUNRsLuPxINW2Ip9YKUtA/QZO2Sq4ZmPeqbdegd1HnJqyc06GwdATyWJc6+PKVSCZmPDyDSEjmZzPER76Ja24LuCMWemTFHiLV0njlGK1JeTLH22tc0Wb8CQKlUmmbC2thS/8jlciQmJmLpc69K7ntRqdI6HGG7WsS9p93dp521d7Lt82KQ8JJuW4i5B5UxoXEHtRvdfdlTSuqd1kE9Wyheh0smwLyiLVWkLUuujmVV4eZxAzs97qoIkM5ka9h+R5dh2ysuLs6scQhIa4mcWHQGI75VFLvsfO68FFsqxIjEK6ZzRa5dPQPYPmgjRP0jl8tx66LlNp3XHUi979G2NNmVrPm8eFKnTlFQJ3YRGBMML/HtQkWDcyMxaruJvJnvxgmae6rLyxs0yKuyfp9dQkICRo4cZdWSK61eelEqf0kXZ6lOYq71eSit2e/SJiEhAdGx8U5dIidmg6G7SIH2IiL893SR4K/bHU9Yii22H8/bl79aqmypB4RyJLPSpuMTEhJwyQjr7hfMe3S1zccV96v0MvfZM+9oRHdrtr+UN7h3LnuO5Os5eAa1C7/2sIRSyiH9ga5nOR3dY3/QiiTk7QM0yeVyPP/Rt9i9fUuPS66kONBZVi/OTGduZROmDutn1bG27HeRy+V4+ePvBImK2FVHNK20EWMGhdn8ekLoLlKgvezZI61SqfD2OxvwqyLVrmusVCrhI/Fopd5KrKBsYrBmyWlqSYOpPpDL5fjhwGG88MY6yS3RZc6z53wJ5k6I7vR4cHCIIPer7pa/K/LrMCpKnPuV0I5asXXoYFpFp+BM7iSjTPhBZiYO7qAyM6J0vP2CJLnkylOVN2i7DWEfGBwiyPul7GKmPa+6SbQOqjtoW56blpYGmZ3Lc+Pi4mCUcLRSxtqcLawzqw9CJLpE15M5ujfWUV21W47nVAtyv9KItC+cMW/GS3wlyFkziSklrt+DJHWeGnT0hA1LhO2V2EVqDEc+35Ui7c0VUtvyXKMDy3MTEhJwiZVL55nnE3PZvAQXvlhFo27C7s/fxydvrsLuz9+HRi18CqnuqFvETe3RXleDjWLrLm82s0ysz3WzyIMczP3wDKqXam2wmK/nTS7mDqotciulH5ypK93tkRZbd4nXk/JrcONlnQNpSYm1ka27I5fL8Y8PvsH/dnzGSyGZ6DNcQnKHDq9G3dRlfk1A2C0CXfnujHmkfylvO8qvVmNybF/4+rh78jfP1t3n2tn3jr0p3rXnn/WMO6jM5dyhgSGErmYArZFW6j6BFaRE28MNVIr7lDuyJbJ1d/wCgz1yKaSUm7C5lSrE9AmGv59rFy/VqXXQG4zw8+VFU0LYv3Prxfyafwwkta1yGP/sc6KUSVnVhEsGyBEhD7D7NZq0eoR0sbWjJxWNGkSGBtr1XL2RcK6oDpOG9OnyGKOT8n57wC1DMN19rh29lzT0kAO3ucX+QfGUEm5PeSK+W0mQsxrhQqSYkRKhL6O1S2O0egPOi5DSob3KRi3KJBiMZd9/t7o88bqjkRFtlZCQgNGjR/Py3C7kVaud1lh1tsTcGpxQOn/5vCXn3WyFjL3Ljt3hLlVZUggfH/PmkzsEIcuvdmw55vdnS+yOZN5V9gGVSoWXXnmtx/tiemkjdN3smUnlQV2nc+bnOqOsEbVNwufhLa5rRp3au1KCeQueQWU2EaJZaGl5sRiatMLt4el+yZc5o8irZ4kIB1JbI/dOGBwubmFs1HYD7SrxujO6LUezek6FUdmohcFIgixRk8vlSExMxNLnXuXluV1ILW3AZdG9nfLa3S3dV2kdbwgV1jTb/dwWvdHu2dd6N2vE1bpZeWzRf9Bgj80HfbawTrBI5rbmYz6lrMFV8REWXyvHg7fUtOfs/eIZZY0YMzAMMgspG5z9uS6pb0afEH9BXquNWsB2HHMvPIPqpaQ5/2CZvX/LoYyLKXEcDQxgtjRGQnknzxbWiV2ETqibd1SIG2hBjW3vbXmDdYGXLgg4QyX/I1LpkpVv4NZFy7lz2oGzgmG16I1IzK3pcvl+TZO4naozBfbn+Ovue2WNBo10O5RCmz1/sdPza3oCW/MxF9XZP3jjDC16I1RO6gB1NUDu7DRTGp0ReV3MdvPnmrkTnkH1UGJGbZSKtj0PQgQG6Glmz5soq5oQFyF8h6q8QYNZ8xfj2N5dZu+VrTdQZ0VeLqlrtjgjbWmkmrkna3LWOrLXzlGNPezj6o6jt4Q950oFzX8o5W9FoED5NT2dEAHfxKLVG/BNUrHTXj+vWo34AZ3rku6WOQulvtnyYBN/rpk74Q6qiMobNBgQ5pyGTk6le4Z9dwYZHJsRFiIwgCcv+bJVRlmjUzqoB9MqeryB9tQIT3RB+pyOStxsVoA5Jrm4Hn8eJU4HtcmB1CLdfTeO51ShWmXb/rBGD59RPZpViT9d0t/ssXOFF1dJCJUPWmqOZVVZfaxQAd+cqavvha3fB3vkV6sFG+yqaBBm5tVbP9fM/XAH1YkSc6tR082m8MMZFbjzCuFGpNvraTmWMyZYrc2jau+pnTUnLMTs5+z5i/GbgzN7rFVDc/eN8O5uoD0tY8wVYeDmqA0NOiauwlr3zpvoSCqRrr4ZTVo98qps/7ubddaVRejVPK5aG9Rxr3CduqXb+7m3sCW3aEJCAr788kuzPah8X3QOKe/pZswS7qA6UU+NYaLW0Ok+NgZWsSZHpUaEvHftR5fdXftlNELMfvLSmPa8c3k5r6qXhq6CWWl0BpzOs3+Pp1QZ+YNrla6WRXZUp+ZObBsO+Ga74lpeccMYwB1UURkJ+EZRhHkTo9FLwPx0ZfWaLjfBtymoacKwCLnNnWNP2dvavoMq1OynVJbGiPkWqlQqbNy4EUqlEvV+fbixwlwutaQBY2M6RwC2ZuAPkO5AhLvV3blV9kVldfe9q+UNWhTXNSM6PMiu56eVNmD0wDCBS2U9ncG+z0lXH6+2gG+Osvb7KRSxQgf01HZjzFtwB1VkOgOhtE6DIf2CBXvNX9IrejzmpLIWJ5W1uGNyjKCdY2sI3U6yNiUiEVkMWOOus596ieZ67Or9bUs5kJaWBh9fXxgMBpuDUdl7bsba1Dhphqui0b3zCnf11RAriFdGmXPThuRWNmF8TDiC/H2deh5LCqrVFjuozVasbDpTUGexg1qlck7kakvOF9VhXEy42WN5VU0I6CVe4ge9nR1nxpg0cQfVSS4USWO5a3FtM2KdENDGVYSaFXDH2c+GZj1K6poxyM6ReEtUKhXee+89/Hwy2Wkd8Vq1DjVNLejbId9ZW8oBo9FoWlJtazAqR5zKs5w6xJnUDgS1Yc7hrJnEn1N7HhgUk6cM3tjyZ5wtrMO04f2cVhZb/ZxWbtfzGjQ6l26hSS5uMOugNmh0OJ7j+gBzwMVVNwdOXHCbwePuWDMI0cZTvpOMOQN3UJ3EkZyI3pSVwijRWUJXOZVXg7kTogV5rfYzmDI70+lY60BqWacAYK5IOdDVp0lvMCKr3LkzNo7mmXQXWr11DSy1CPvchWBNKhkx6AxGpJc2OvEM7vl3O5OllB22dCCEZm+aoHoBAuA48rkXI6YF4Jp7ltD1dkm9OHtIPeX+w1gb8dZrMAbA4OAQIo9AWq/9DKY1SdMdYSmVm5gpB/hjYr1D6ZVWHVen1qHQhoie1rA2Mqwjyhu6WCrpwg9Jx4E5g5Hw39NFDg1s9qSrulJq46GOlvd/F0oFKYfU/JYtvWjirrxnMftxO4w5A3dQu+DKgBLHsqskM5OoMxA0DjYi7R3pc/Q98fZKtG0Gsz1nJk0/V1hn9nNCQgJGjx4NmY8PfP38IPPx6TIYVZWqNdCIrdwtEIynSyttEOy1tHqD1Z1jqTvZYbm5SiveUnBH63NXc/Qb7upgO+6iSILRYZVKJXx8zJupzrxnSZlMckNNjHWPO6huIq/a9fkZ7ZWYK85eFEu8aTm0o1w9g5lSYt55kcvl+P3337HgoWcw4y8LsOChZ7pcqrU/pRxHMirR2EM+X+Y5hFjG6BAX1iVi5OO11LFr0RuxL8W+fZHVKk6nwi7KrhB+C0VcXJzDKeC8hZhLfLkdxpyBO6hdcHVkQynloitvcCxapZB/qisum0qjR5OIMxxCSUhIwKhR1s1gOuu6tqUcWLLyDdy6aHmP+4jE2Oco1izs2XYzzjwT7Fwldc3Qd1yHLuIld8XtxtJHypEBoDMFddad1+4zWOaNbWFvrQ4SEhIwYuQoq+5ZQnPFPZ/reca6xkGSumBNxdFV2hLJsvJP8cY69bfsKsy+NErsYjhELpfjyLHfkPD8G6Km09Gom9wupY8QHP1epJY0YMLgcACtkZClqkDEPH7W7ok9nFGJ2H7BuCo+wuZziF3/qbR6yANsv3WLNcNSWqdBfbMOvYN6CfJ6Ulpt5DFEaubI5XJs/W4/3t7wnvPuFxa+Fk1aPU4qa4U7hwuIucRX7DqReSbuoEpUk1aPEDsaKULwxrrI3uiL7sbapOnOGndRqVRYs3QeivOy4eOEqIxNWgPULXoE+5t/NzxoGMntHRMxGMvRLOvPnVetxlXxTiyMk+w+W4IFk2Pg90f+ainMwiTl1+DPowYI8lp5VeINgDDXqm/WIbvW4PIUcJWNrss5KxSO4ss8DS/xdYCioA71zeLMdHx/tkS0mQoJtIeYg5z1Hq985S0U52WDnBiV8Zd0x/JRevPnW8xgbV582W2mM1y8WqfzrZvpaWjWixYQqeN3SsygUMw2KpUK7779Fj55cxV2f/4+NGrXzWBzDALXcSQGgCctJGTug2dQHZBR1ojM8kbcPWVIzwfbQG+wrql2LLsK9/QT9tyejBvA4mrRG5GelQMfHx8YnJgLtaFZnMavO32+bBk4yyhrxMioUACt0ZOZ6znSvrMlv++xrCpcP0aYmUxHuGpPvzt9J6XIlbmzWev13v35+3YtZ3Z0ie+PDqRf8uZBXeY83EF1kNBfzIJqtdnoOOueq/Yj8TviOAKh/6DBHJVRYJWNWtQ2taBPiL9dz0/KrzV1UMX8nPMgvPNVdFi66KoYCtyAlab2eUjxR73dtuLFKctuO3xOXPH5LKl3LOijUNoGA1LT0uza/uKtS3y986/2DrzE10203cCP50gvmbaYEnNrej6I2ayrqNJtI7z2LveaPX8xomPjRYnKaK06kZbtO6JT6icX3bWFPI1UGhruUM7T+VzvMefzhjykLXojlFXiB95qGwxw5vYXxqSEZ1A9zDEbgoSIhUfT3V+jRo/S+mYM7B1keszREV4ACAwOwcsff+fWUXz3Jpfh9knRCOzlK3ZRrCbi1lGP0dQinX2RhTXNaNDoEBYoTGRc5hmEXn3l6jykeiNBbzCaAoDZIrWkAWMGhdl13vzqJsRFuP4e1P7dahsMMDpx+4uzGES8AXU3x857Y6WNZ1DdhFBfpAIr0yxIkc5AaNEbez6wC1KIdtmRmEU+qTSfpRFqhDcwOMSmXKhiECP/qqD4xmwTjc6AwxmVVh3rLhE+yf6q0OWkV/NKU25lE2qaWgR7vYSEBMSPGGn1ipfS+mZUOJgn/VRe56BfGnVTjyt32ueRtpU7bKty9WCAkBy59oD16cFK65s7BfLr7p2TYJOPtcMzqG7CHb5IrmrTthiMCELr7JStI2+n8mow3Y7chY6SYuc2q7wRg/sG2z0T2PFPViqVkPn4mPYiAdIZ4bWVjx1fBkufEY1OQr0IF0vKr8XlQ/uIXQwAQION0UKNRoKRyK6ZHkuyK6wPdMSsJ1a9LeZ+wPTSBrvy+1oil8vx/b7DeOlfb1u14uVQeusgz91TBtu9f1RZ1YRpw/uZftaom5yamsxdJCQk4NPPPkd2Vobp73S37S/OYm16sLbP142XRaGvnTEXmHRwB9UJimrtn8WUXjeo1YWiequP/fF8Kf56eTQC/Hw7753rgbUjbax1JDqnUoUbLxsoyOtJeYTXVkIlPT/kYMobd6BRNzllSXZGWaPbdFBt9e2ZYugMRiyYPBi+9oxmtNOo0SGttFGgkrknKQ7wsVYhVubOdpb9O7deTE3mikBNLtT+ayGXy/Hfnw7ijf/b4LbbX9zF0axKzJ0QLXYxmJNxB9UJfs20fR+oO6yVd2SZy4Vi6zuoAFCtasGg8CCU1LlHBD1XcmUqj5om+wP+qFsMKKvXIKp3IIDWEd5/f7TFbCTb2SO8ZwrqcONlUQ6/js2fbIG+j1oHlqTbouOMTYNAgZ68ZfbCVm1bDVRaPXoHObYP1N7PiLdG7WTuj0i4Nk1lSaHTU5O5i+Bg2wYDGjU6hHrhPnRrV965Q7ua2U8ye1DfeOMNXHHFFQgNDUVkZCTmzZuHjIwMsYvlMcpcHGrd3lkHqTfJtHqD1ctZ3MEv6RWmnIVyuRwvf/wdFjz0DGb8ZQEWPPSM0zsqNU0tKLdjT1N1u0EAIsKB1HKbnu/gpJjohPqMmc1euFFkydJ6DTQ6ie8T9iJSr7ddra3OVbfosS+lzGXp1NyRLanJOu5P9HQ/nLM/d6lQxF4d0V1nlRduSJtkOqhHjhzBI488gsTERBw4cAB6vR6zZ89GU5P4Fbc9DWh30fb9/cXFSxHFGNlyh7pKinsS20c3FSPAkT2BsfalXOyQqlsMqFPbNqPoqvyQ7q5t9qI9d5m9UOR3DqbiTYRahm4LsRujYnLln/792RIArStIqlUtKKpttvq57vAOCVl92pKa7PtzxcKd2Abni+pM/59S0mD360jxtpNrQ4oeIb9DLXojEnOrcaagTrgXZW5FMkt89+7da/bzli1bEBkZiaSkJFxzzTUilarVwbQK3HPlEIdeQ6z7/u851YgOD+r5QIH52FkTu0v7SKu3fvaGiC52eAQof6ONAV0c5ibX3JXEaie0fVbcpSNgy+yFqzVopJMWxlMItXRTqCXonqy8QYMWg/QGNAGgrEFjlqLMEbakJmtuMcJoJPi4eAlMcnEDxsWEAwBUDtRLblLt26Rttt8a54vqMax/CIL9u+56WBPzgKg1cnBuZfedYyl2+NlFkumgdlRf37rnsW/fvl0eo9VqodWaL/VjnTm6fMieHFi+XHNYxdI+M1uXqzrKVfso3UlhrRqjouzLqedJZs9fjN/27nLpvmPmOrbWwr+kV+D6MQNsPk/HW6+lVCLuztW3rF/SK0z7/23hDs2cQ+mVWDA5xuEo122XvG3lji2aJZgqzJbZSHdha/MvvawRk4ZYDo5nS8yDmib3SPfFnEeSHVQiwooVK3D11Vfjsssu6/K4N954Ay+//LILS2Y/e29+zS0GBPnbl0ZETGL3T6sabcsV5wb3fBN7lwmrVCq8s+FdHD6dYlOEwKNZVbh9UrRd6WrMZo8lRJFfh4G9gxDYy/4Glkqlwu7P37cpImPbDJW7XDNbZi+Y56to1KJJq0dIgCSbDg5xh46flOiNBD9f8bZAuTIYoVCqVcLlsHUVY4cvhiNR3z05YjOznSTvMgkJCTh//jyOHTvW7XGrVq3CihUrTD+PHj0aJSUlTi2bvbO09t789qeWcbhtK+kNhLY+1rFs6QQqclSL3ogWjRpTp05FWloaZHZEY61s1GJw32Cbz/17TrVgOfnsZW+7Ut2ih0xmXxWpUqkwdepUpKal2RX91tZ6xJmNZ3tmL1zDDXoMAhXBWal8nMG+sRNhLpTLtzcwhx1Mk36qLda19vceR6O+Wxux2WAkhzIUMGmQTJCkNo8++ih2796NQ4cOISYmpttjAwICEBYWZvrnLrMSQmrSSm8Zi1h2nREngIJZ50GEj6BKq8fGjRuRlpYGowPRWIvrrA/U0Sav2vG8tVKZuWhfzrbrbWv0W4n8qUxAKpUKa5bOw45Nb+HInh3YsektrFk6Dxq185f7pZfZnn9VjOBMbWydYRLy++SBzQe358gsqCP7d2vUrpnJlGqqqPbRktsPpjoa9d3amAd6L4vW7K0k00ElIiQkJODbb7/FL7/8gri4OKedS6VS4bvP/o1P3lyF3Z+/75KGghA3P95j6zo1TXbewER4i5p1BiiVSvj4mi/RtTUa65GMSqGL5rEcvd6eOJjmjkrsGHQxI8Db9MmHH4iSyqe2qQVKO/a82fPR9IRbk6v/BnvPJ9VOT0f1ah2Si+2PiHsit8bqY9Ut5oF+0kttH7gRW1qp/dfKVvvbxcFo/2mzJup7d59rWyI2M88nmSW+jzzyCLZv347vv/8eoaGhKCsrAwD07t0bQUHCRaF1dGmevVJLGxEfGeq01xdadoUK8ZFysYthM7sj/HWoVA+lS6ezdiSjEnFxcTAazGfbXR2N1d4GlxT7avZe79ZBJveJ4uvOhLhEhzMqHYvALkAZCvLzrFrWJjSNDZHIPUWdi2bGPKGPmFOpwvD+4t3jXTWLCbSuRMsqb8QlA6TTBuuoucV1wQzbBugbNTpklatMjzsa9Z1jHrD2JDOD+sEHH6C+vh7XXnstBg4caPr39ddfC3oee5fmtbG30aTS6FEgwHJIVzmptH500p103NAvNZnl9o3sJiQkYOSoUTwy6SIJCQkYMdL+662xMXKytD/V0vXjhVKz5W72GDI01qWpfBwtr10zqA6dUTiNDqQAseXvFiw9jIgXzpYZSGcQcpBOo27C7s/f73ZVnKJAepGlxdaxHSjEDKgYudaZe5LMDKqrZhSUSiV8fX3NGgyuSkzfoNF5xFIodyb1rQun7UzPIJfL8fORY1ix5l+ijUxeKK532bnEoG4xwGAk+PrIIJfLceDwUTz98lrbovj+8V9eTt0zd/kqVze1oH9ogN3PX/L3h/HZ51+4LJXP6fxaTInrOj1bT8S4R7nDip2iWuuXg5fUaVDRqEFkqO1pYhzFbQhzjgbuaXsNp8zqifReFdaoMWFwuMOv09JhINW6GVD+gDLrSKaD6ipxcXEwiLwU0l58Y+qZGDOoeiPB387E4UIu25HL5XZHY21qsX/mAWjd49N+KZCnOltYh8uHtuZ4c+R6SwXXOY6Ty+UuXdaWXaFyqIMqhpPKGvs6qAJ+PvNtXOGUVtrocAe1tF6cFC1S1vEttzV1ScdOlxAdXHfTqNEjr6oJsRGOld/SqgL3jfrOpEYyS3xdJSEhAaNHj+alkB5KjAb1zqQiGByYuk0vc13wg64o8uscer4jf7+UFNQ4FlCNO3zeQ6O7OBDq6cva3OVzLdjSWyt0XPXV/v12pqLaZvzmRWnUemJN4J728qrN63BHI9O6K7Hy0zJmLZ5B7UAulyMxMRFLn3uVN2lbSayALno7Ghti7UFt1OgQHuxv13MvFNVjVFSYwCWSDjEbt2TjR8yF7V+PUlSrxrEs72pU/5JegTljB7pN5w2QVj5We4i5r/JbhevSnOVXqzE93mWncysVjRoM7H0xcKajgXuszc3JGBMWd1At8IaleZ5ASrmw7Ioc/Afp/JXOU+1APjxHJCqrbTre0QA09qaIEGqQyNaXadY5tvS7UaNDTVMLfsu27ToD7jMrZ686tWOJ5k/m1WDWmAEClca5Sxk9JfWJLTzhL1apVPj3+rdx6kKGZAYsDqVXYsHkGNPPs+cvxm97d9m9x9vRDi4zJ/V6m7kOd1CZKHKrmjBpiH0zio4g2LfUytEGlq8juVIEqNAPZVQgPKiX4y8kkn0p5WY/u2qmp1plW6oDqUeJtlVzi9GhvUw/nCvF4L7CpAk7X1QnyOtIRWWjVtCBG1v26nXMG+muhOoY51c7Pxe60GqbWtAnxP57rEbdhCum3ITMjHTIJLb3sv3gtaOpSxzt4Lqr9t8MTmvG3BF3UAXGX3PrpJc2YkjfYFHO7aq9QO35ONBBFaKRVVqnQWmdZ+w5ceegFY5O6ovVTiAiyGQyu1KIJJfUOxRso2NQEnslF4u/V9vVhNzbbctSxrY8iPbQuXAdfFm9FvVqHXoHOzY4V1xnfQRfE5EbAz8ll+HOKwajpK4ZJXaUf//OrcjISAcZjYAVwYXcmSOBe5yZm9NdgmAV1EgnxSHzHtxBZaJxJCedN/H2wc2OnSZbozIy69nzWROtUy1QD+BbRRHCHezACEXMPaCuWspYZmej3N7l86fyanC9nUuhT+RW48ph/eDnI814knqjEUft3NvNey8vclZkWq3esRUoQmnS2jto78DKMMZ6wB1UD9Ko5Q6fu7NnCaiX90874YaT8IgspwzwBhqdEWX14uxxNiuHPSsDBHzPXLWU0d4BDXuXzztSf+ZUNmFKXF/42hFDQOr7bnnvpWsU1TYjNiIEKpUKuz9/32WDU+INLDJmHe6gegijkfDj+VJRzm1vhWNPFF6xGIxAc4sBQf6+dj2fQNDoDPgpucz253KNboYbTu6l/cez1oGln46wd1bOnYi9MsCZSxmlzp4Yd1Kvt6W891JK155AUKlUmDp1KlLT0kTZtpJd6fk5ypn0cAdVYI5sNrdrn8sfpBicRSvQ3jNbOPL+HMuucihqZnYF3wSEIOWGU0/SyxoxYXC4aOd3tB4xuLAe0uqMpkGjX9IrXHZeS+odiMhbWNu6/8sdVgZYs5TxUHoFxsb0tul1G5r10OoNCPCzb4DPERwAxj6ODliUN2gwtB8PbvSECNi4cSPS0tJEG5xS8XYr5oa4g+pGbI0YKja9wQg/X2nuzbFHZaP9ywArGsRfQigmIZuI1jac2gL/iMWednFqSQOG9RevUXc0q9Lm5zjaAbD3LdLqjdh1ptgspYQY8qubkFlu3+BTfbMO6aWNAOxcGSBC36tWrbMrRc7xnGrMHBnphBJ1j7un9nNk7+Vv2dUYEBYocImsI6XtCgRAqVSaBlvb2DI4ZU8dLPUl6MzzcQdVYEW19s+COkKMquZgegVuuDRKtL9ZSo7nVCM+Um738wuq1WgxuD76sDuwdO+1puF0QlmDqcP6OalUzqNzcGWBPcts2y6xGKvuHZ3gUosQlbs9ezunAKBqFzfAk1cGABAtirijny+9kZD2xyCCK8/rCVoktI1HLESEuLg4h7atKKuklwaJsZ5wB1UgjRodgv39cDzH9oTzzuLsiJBtM76/u9Hf7M6aHAhidSzbvkiM3iy3sglTh/WDWqTgYWUN9jXIHZ311RnFaxSm29GQL/fy1QVteA+oszjWU0wpsS99EfdPmTUIQEJCAj757HPkZGXYNThlfxRex9hzq+KBG2Yt7qAKZM/5Uvx1krhLzdpz51yRQmlukdiMooSWHbmT3CrH9u4eFGl/4pmCOrueZ8/HxNGbfusSMcc/oJxPzzHOSmfhDuwJNNSevR9xR78b1SoeQLGXFDsjLi8zAXK5HNt2H8DbG95z3eCUBN8b5l24gyoQd6uIxY4I6QqHM8UNjGIr7p/ap8TBpYHu9t3siT2j0uoWAzQ6AwJ7uT4IDWPW8BFpY6A7fP3FCtRU7gbpk7xFYY3arj23bXtBg0PkkmqbJeXXCPI6pfW8RYxZ5j0RbrxMW0TI9jwlV2RbSonmFtuXMorZWJFaR8kTiNEw1Di4J1Jm51DGSaUwDQZb8eeaWUNKgWvas/fz3b7u+TlNnMFU3hriOkezqpBV4T17lTPK7FvZ1PGefCjd9sB8zDtwB9UDqFs677Hz5FyRjqSU2JdcBqNIN4RSD8jVKDW1DqT/sJfDKU/sbMhXObAUUWHncmSAo0FKlRgNY5VKhfc3/B8+eXMVdn/+PjRq64O72N9RtO95jmp/WkciwDPpsOez5g6fT3s5kl6LsZ7wEl8PsOd8KW6bGG32mKdHhLSXzkAocSDfLGM9sScFR3v2zjQ50tDJrlBh9MBQ+19ARN7cSHKkkakoqBWsHNbQqJswdepNSEtLg8yFcREcHUCpsLNzWa1q8bpUbB2dK6wTuwguZVfQIOGL4TJ6sUb7mVfgDqoH0Bs6VxIcEdL9ODuqMvMMBgvfZ2sYHRyKl2pb42iW9y5jdGRmrqHZtdGt9+74DGlpaa0re1wYF0HMJZQnlTW4Kj5CvAKIzN4UdIUSDbZm3wyqRCtexpyMO6gezJMjQkqNN0RV7kq9Wge9iKlP7NXiYD5Se1U32dfpaGvm5Fe7tnHH7StmjfKSQvj6+pptPXFFXAQxP5551WpcFS9iASTqaFYVpg7ra/fzHamTkvJdu7KgTbGLV3a5Q70t1YEI5hreu/bEw7hDZcO6ZhZVWa8HGY2m2QNP9+OFUuxLKRe7GDb7LVti+X3/qAOyyu0LXmGU6hQqk4TIQYNhMJgHELMlLgJvzWCerC2vvKdrf5fx5tUvrGc2zaASEY4cOYKjR48iLy8ParUa/fv3x8SJE3H99ddj8GDpB+BxRGa57RHcmHdoi6pscPHsAfMeju61y62yPmCN2Xm5X+t17HnPb1xwH84f3oPUtDS74iIcz6nG5Ng+tp+YSZIjK0GkGjHaG/D9glnLqhnU5uZmvP766xg8eDBuuukm/Pjjj6irq4Ovry+ys7OxZs0axMXFYc6cOUhMTHR2md3W+aJ6sYtgF0t7WK2l1TuWUsNbeHJUZSYssaKV6g3SW4bNxHGh2PZ7XWBwCBITE7HgoWcw4y8LsOChZ2ze4pBa0mDzeZk0uTrqvcHoXXWgWNHXlVVN+DWTU8uwnlk1gzpixAhceeWV2LRpE2644Qb06tWr0zH5+fnYvn077rzzTjz//PN48MEHBS8scw5HgqtIcGuhKDiqMnM2R1fo2vt0HhAXT4uUGtQEyOVyh+IiqFt4QJQ5z6k8cfafOkInpTrgD/YGz2LexaoO6k8//YTLLrus22OGDh2KVatW4amnnkJ+fr4ghWPM3WSUNWJklO3pODiqsndRqVTY/fn7/F6zbjkawTO91L5tJRWNrs/JzPlymbtT2rnNQUz/PV2EsdG9xS4GY4KzqoPaU+e0PX9/f1xyySV2F4jZh2/+rpGUX4th/e3raHBUZe/Qmu9xjtleOylEbLZ32xanSZCeZp6JdJmiWo5U6mpcJfWMrxFzd3almdFoNDh//jwqKio67au79dZbBSkYY+5qz/kSsYvArCRG52n/zq1IS0trjdjswnyPYuP96NLhTY1TlUYPncEo2t/8ayZHKmWMMVvZ3EHdu3cvFi1ahKqqzpWuTCbrFEaeuT8e4bVNc4v09nww12mL2Gxvvkep9R10BoJGZ8CP50vFLgqz0qm8GpefU8xOcWJuNQaFB4lXAOYVxPqM8wo65olszoOakJCAO+64A6WlpTAajWb/uHMqHkcqxpomnXAFYczLeWPE5iOZldDqeeBGKnQORG63l5GAwhpxBkMLazgoC2PtcZeWuTubZ1ArKiqwYsUKDBgwwBnlYV6kSqUVuwiMCW72/MU4e+gHZGakSypic06l/QFCvCXJPHPM0Sxe7sqcT4rBjhyRXCy99EsaXdcTWhp1EweUZLZ3UOfPn4/Dhw9j+PDhzigP8yIldfZHkixwIIk3Y84UGByC//18BP947f/susF60/5Ab1en5tUrjAntbGGdKOeV0lJbe4PiCeX33GqLj2vUTVizdJ5ZSj4pBBlkwrO5g7px40bccccdOHr0KMaOHdspJ+pjjz0mWOEY68qxbPtH4rkDwJwtxMF8j8w7nC+qF7sIXkPpwAoBxjyN2M2gykbLK+j279yK4rxsrwsyyDqzuYO6fft27Nu3D0FBQTh8+DBksovjMDKZjDuoImnQ8Ei8tS4Uc6OQMca8SUUXDWLGxFSlaumys+ZMxbXNqG/WoXdQr54PdgJ9F/vg24IMGuwMMsg8h80d1Oeffx7//Oc/sXLlSvj42BxjiTnJL2kVopxXb+TAKIwxxhhj9jiSWSnKeRNzqzFzZKQo5+6KNwYZZJbZ3EFtaWnBnXfeyZ1TN2MUab3GQZE6xoxZo6uvBQdhYIwxJhSNugmfbPwEyRnZNt9TjCI14HQGIzLLG0U5d1dmz1+M3/buMtuDKoUgg0x4NndQFy9ejK+//hr/+Mc/nFEeJjHqFk4txKTF3YMwJOXX2v3cJq1ewJIwb8SDN4zZpu2eUpKXDZkd9xSZSBGL/Hxk0Is1u9GFwOAQvPzxd1wHMds7qAaDAWvXrsW+ffswbty4TkGS3n77bcEKxxhjQnNFEAaxOooHUstFOa8juEPkPtx98Mbb8HdDGtrfU2DHPaU1los4HcVatfulCAsMDuGASMz2DuqFCxcwceJEAEBycrLZ72RiDQMxUfDNk7m79NLOy5dcEYQhMbdGsNeyhdRWNHCHyL1wBE33wd8N99Jde8fRe4qYLedSB9L9MeZMNndQDx065IxyMInhmyeTgoKazvlyOQiD++AOkXvhCJrug78b7qOn9o6j9xQO6cJYZ/y1YHYxu3nq9SCj0XTzZMydzZ6/GNGx8ZD5+MDXzw8yHx8OwiCStg5Re9whEg8P3rgP/m64j57aO47eU2SizqEyTySTyfDdd9+JXQyHWNVBfeihh1BYaF2l+PXXX2Pbtm0OFYq5P755MqlqC8Kw4KFnMOMvC7DgoWd45l8k3CFyLzx44z74u+E+emrvOHpPkdrWDG/zzTff4Morr0Tv3r0RGhqKSy+9FE899ZTYxfJ4Vi3x7d+/Py677DJcddVVuPXWWzF58mQMGjQIgYGBqK2tRWpqKo4dO4avvvoK0dHR2Lx5s7PLzUTGN08mZRyEwT1wSgH3whE03Qd/N9yHNe0dvqd4pp9//hl33XUXXn/9ddx6662QyWRITU3FwYMHHXrdlpYW+Pv7C1RKzyQjIqtCh1VUVOCTTz7BV1991Sk4UmhoKK6//nosW7YMs2fPdkpBhRATE4Pi4mJER0ejqKio22O3nyhwUamkydKejOjYeJ6JYozZhIOtMWYZfzfcg6e2d/qG9EJNk07sYjjNzFH9MbB3kEOv8cQTT+DcuXPdxt956aWX8N133+Hhhx/Gq6++iurqatx888346KOPEB4eDgC47777UFdXhyuvvBLvvfce/P39kZeXh+LiYqxYsQL79++Hj48Prr76amzYsAGxsbEAgFOnTuEf//gHzpw5A51OhwkTJuCdd97BpEmTTOfPysrCkiVLcPLkSQwbNgwbNmzA7NmzsWvXLsybN8+hv19MVgdJioyMxKpVq7Bq1SrU1dUhPz8fzc3NiIiIwPDhwzmCr5fhkXbGmBB45oExy/i74R48tb1j3fSUd4uKisL27duRnJyMyy67rMvjsrOzsWPHDvzwww9oaGjAkiVL8Mgjj5hteTx48CDCwsJw4MABEBHUajVmzpyJP/3pT/j111/h5+eHV199FTfeeCPOnz8Pf39/NDY2YvHixXj33XcBAOvWrcOcOXOQlZWF0NBQGI1G3H777YiIiEBiYiIaGhrwxBNPOPuyuITNUXwBIDw83DQqwLwX3zwZY8y78Kwe80bc3vFOjz76KI4ePYqxY8di6NChmDp1KmbPno2FCxciICDAdJxGo8HWrVsRExMDAHjvvfdw8803Y926dYiKigIAhISE4OOPPzYt7f3000/h4+ODjz/+2DTJt2XLFoSHh+Pw4cOYPXs2/vznP5uV58MPP0SfPn1w5MgR/OUvf8HPP/+MtLQ05OXlmc79+uuv46abbnL6tXE2uzqojDHGGPMunF6MMeZNQkJC8OOPPyInJweHDh1CYmIinnrqKWzYsAG///47goODAQBDhgwxdRABYNq0aTAajcjIyDB1UMeOHWu27zQpKQnZ2dkIDQ01O6dGo0FOTg6A1u2VL774In755ReUl5fDYDBArVajoKB1G2JaWprFc3sC7qAyxhhjrEecm5Mx5o2GDx+O4cOHY+nSpVi9ejVGjBiBr7/+Gvfff7/F49tmRNtvfwwJMR/EMxqNuPzyyy1mPunfvz+A1r2rlZWVWL9+PYYOHYqAgABMmzYNLS0tAABLYYQ8Zcsld1AZY4wx1qO2dBuGdhFNOb0YY9JUq/bcAEnOFBsbi+DgYDQ1NZkeKygoQElJCQYNGgQA+P333+Hj44MRI0Z0+TqTJk3C119/jcjISISFhVk85ujRo3j//fcxZ84cAEBhYSGqqqpMvx8zZozFc3sCq/KgMunSqJuw+/P38cmbq7D78/ehUTf1/CTGGGOsA04vxhjzJi+99BKeffZZHD58GEqlEmfOnMEDDzwAnU6HWbNmmY4LDAzE4sWLce7cORw9ehSPPfYYFixYYFrea8nChQsRERGBuXPn4ujRo1AqlThy5Agef/xxU6aR+Ph4fPHFF0hLS8OJEyewcOFCBAVdjEx8/fXXY+TIkVi0aJHp3KtXr3beBXEhmzuof/7zn1FXV9fp8YaGhk6beZm42vYL7dj0Fo7s2YEdm97CmqXzuJPKGGPMZrPnL0Z0bDxkPj7w9fODzMeHc3MyxjzWjBkzkJubi0WLFmHUqFG46aabUFZWhv3792PkyJGm4+Lj43H77bdjzpw5mD17Ni677DK8//773b52cHAwfv31VwwZMgS33347Ro8ejQceeADNzc2mGdVPP/0UtbW1mDhxIv72t7/hscceQ2RkpOk1fHx8sGvXLmi1WkyZMgVLly7Fa6+95pyL4WJW50Ft4+Pjg7KyMrMLBLRu5I2OjoZO575LBrwtD+ruz9/Hjk1vgdqNeMt8fLDgoWd4vxBjjDGbcRRfxpgUCJEH1RpteVDPnj3r9HN5E6v3oJ4/f970/6mpqSgrKzP9bDAYsHfvXkRHRwtbOuYQ3i/EGGNMSJxugzHGmLNZ3UGdMGECZDIZZDKZxaW8QUFBeO+99wQtHHMM7xdijDHGGGOMSYnVS3zz8/NBRBg2bBhOnjxpCoEMAP7+/oiMjISvr6/TCioEb1viaylnXXRsPOesY4wxxhhjHstVS3yZc1g9gzp06FAA6DQjx9xXYHAIXv74O94vxBhjjDHGGJMEu/KgZmZm4vDhw6ioqOjUYX3xxRcFKRgTBu8XYowxxhhj3kQGmdhFYA6wuYP60Ucf4eGHH0ZERASioqIgk138AMhkMu6gMsYYY4wxxkRDsClJSbdeeOEFlJeXY/PmzYK9pr2uuOIKrFq1CrfffrvYRXEqm/Ogvvrqq3jttddQVlaGs2fP4syZM6Z/CoXCGWVkjFlBo27C7s/fxydvrsLuz9/nfLeMMcYY8yiubuuUl5djw4YN+Mc//mH2eHFxMe69917069cPwcHBmDBhApKSkky/bwss2/HfW2+9ZTpmxYoV6Nu3L4YMGYKvvvrK7PV37NiBW265pVN5XnjhBaxcudLjt1zaPINaW1uLO+64wxllYYzZyVJArN/27uKAWIwxxhjzCD21ddrnaU4ZPwqrnn4ScrncoXN+8sknmDZtGmJjY02P1dbWYvr06Zg5cyZ++uknREZGIicnB+Hh4aZjSktLzV7np59+wpIlS/DXv/4VAPDDDz9g+/bt2L9/P7KysnD//fdj1qxZ6NevH+rq6rB69WocPHiwU3luvvlmPPjgg9i3bx9uuukmh/42d2ZzB/WOO+7A/v378dBDDzmjPIx5JGcnt9+/cyuK87JBRqMp721xXjb279zKe5AZY8wDOfu+woTD75UwumvrzJ6/2Kzzeni3Ed/v/BqJiYkOdVK/+uor/P3vfzd77F//+hcGDx6MLVu2mB5r34EFgKioKLOfv//+e8ycORPDhg0DAKSlpeHaa6/F5MmTMXnyZDzxxBPIzc1Fv3798Oyzz2L58uUYMmRIp/L4+vpizpw5+M9//sMd1Hfffdf0//Hx8XjhhReQmJiIsWPHolevXmbHPvbYY8KWkDGJc8XsZmVJIXx8fEwVNgD4+PigsqRQkNdnjDHmPnjVjHTweyWc7to6ljqvaWlp2LhxI1auXGnX+Wpra5GcnIzJkyebPb57927ccMMNuOOOO3DkyBFER0dj+fLlePDBBy2+Tnl5OX788Uds3brV9Nj48eOxefNm1NbWIjc3F83NzYiPj8exY8egUCjwwQcfdFmuKVOmYO3atXb9TVJhVQf1nXfeMftZLpfjyJEjOHLkiNnjMpmMO6iMdeCK2c3+gwZ32o9gNBrRf9BgQV6fMcaY++BVM9LB75VwumvrWOq8+vr6QqlU2n2+/Px8EBEGDRpk9nhubi4++OADrFixAv/4xz9w8uRJPPbYYwgICMCiRYs6vc7WrVsRGhpqFtjohhtuwL333osrrrgCQUFB2Lp1K0JCQvDwww/js88+wwcffID33nsPERER2Lx5My699FLTc6Ojo1FQUACj0QgfH5vDCUmCVR1UR95cxrydK2Y3Z89fjN/27jIboY2Ojcfs+YsFOwdjjDH3wKtmpIPfK+F019bZv3Nrp86rwWBAXFyc3edrbm4GAAQGBpo9bjQaMXnyZLz++usAgIkTJyIlJQUffPCBxQ7qp59+ioULF3Z6nZdeegkvvfSS2c/XX389evXqhVdffRUXLlzAnj17sGjRIrMATEFBQTAajdBqtQgKCrL773NnduVBZYxZzxWzm4HBIXj54+94jwtjjHkBXjUjLGfuEeX3SjjdtXU6dl7JaMTo0aORkJBg9/kiIiIAtC717d+/v+nxgQMHYsyYMWbHjh49Gt98802n1zh69CgyMjLw9ddfd3uu9PR0bNu2DWfOnMGnn36Ka665Bv3798eCBQvwwAMPoKGhAWFhYQCAmpoaBAcHe2znFLCjg7pixQqLj8tkMgQGBiI+Ph5z585F3759HS4cY57AVbObgcEhvFyIMca8AK+aEY6z94jyeyWsrto6HTuvUwWI4jt8+HCEhYUhNTUVI0aMMD0+ffp0ZGRkmB2bmZmJoUOHdnqNTz75BJdffjnGjx/f5XmICMuWLcO6desgl8thMBig0+kAwPTf9oMcycnJmDRpkt1/lxTIiMimTLYzZ86EQqGAwWDAyJEjQUTIysqCr68vRo0ahYyMDMhkMhw7dqzT6ILYYmJiUFxcjOjoaBQVFXV77PYTBS4qlWfjyHWt+DowxhgTEt9XhLH78/exY9NboHYdAJmPDxY89Ixgg778Xrnen0dFIqp3YM8H9uCvf/0r4uLi8H//93+mx06dOoWrrroKL7/8MhYsWICTJ0/iwQcfxObNm7Fw4ULTcQ0NDRg4cCDWrVvXbfaTzZs3Y//+/di5cycA4OTJk5g1axb27duHn376CTt37kRKSorp+GuvvRbXXXcdXnjhBYf/Pndlcwd1/fr1OHr0KLZs2WKaam5oaMCSJUtw9dVX48EHH8Q999yD5uZm7Nu3zymFthd3UF3L0qhkdGw8R65jjDHGmFv45M1VOLJnBwx6vekxXz8/zPjLAixZ+YaIJWOOEKqDum/fPixZsgQFBQVmAYn27NmDVatWISsrC3FxcVixYkWnKL6bN2/GE088gdLSUvTu3dvi65eXl+PKK6/E8ePHzYIx/fOf/8SGDRsQGRmJrVu3YsqUKQCA4uJixMXFITc3FzExMQ7/fe7K5g5qdHQ0Dhw40Gl2NCUlBbNnz0ZxcTEUCgVmz56NqqoqQQvrKO6gupYrRiUZEwOPhjPGmGfgtopnmjmqPwb2dnyPJhFh6tSpeOKJJ3D33XcLUDLHPPPMM6ivr8fmzZvFLopT2RybuL6+HhUVFZ0er6ysRENDAwAgPDwcLS0tjpfOgvfffx9xcXEIDAzE5ZdfjqNHjzrlPMxxbZHr2uPIdUzq2lYG7Nj0Fo7s2YEdm97CmqXzoFE3iV00xhhjNpo9fzGiY+Mh8/GBr58fZD4+vEeUmchkMmzevBn6djPsYoqMjMQrr7widjGczuYgSXPnzsUDDzyAdevW4YorroBMJsPJkyfx9NNPY968eQBa106330wslK+//hpPPPEE3n//fUyfPh0ffvghbrrpJqSmpmLIkCGCn485hiPXMU/EOe0YY8xzcBR8zySDTLDXGj9+fLdBjlzpmWeeEbsILmFzB/XDDz/Ek08+ibvuuss0muDn54fFixfjnXfeAQCMGjUKH3/8sbAlBfD2229jyZIlWLp0KYDW/bD79u3DBx98gDfe6LxPQKvVQqvVmn62cTUzcxBHrmOeiHPaMcaYZ3H3KPi8rYR5G5s7qHK5HB999BHeeecd5ObmgogwfPhwszDOEyZMELKMAICWlhYkJSVh5cqVZo/Pnj0bx48ft/icN954Ay+//LLgZWHW4VFJ5ol4ZQBjjDFXcXYaHE9F4EkpKbO5g9pGLpdj3LhxQpalW1VVVTAYDBgwYIDZ4wMGDEBZWZnF56xatcosb+vo0aNRUlLi1HIyc+4+KsmYrXhlAGOMMVfhbSXMG1nVQb399tvx2WefISwsDLfffnu3x3777beCFKwrMpn5mnIi6vRYm4CAAAQEBHT5XMYYsxWvDGCMMeYqvK2EeSOrOqi9e/c2de66yuPjbBEREfD19e00W1pRUdFpVpUxxpyJVwYwxhhzBd5WwryRVR3ULVu2WPx/V/L398fll1+OAwcO4LbbbjM9fuDAAcydO1eUMjHGGGOMMeYsvK2EeSO79qDq9XocPnwYOTk5uOeeexAaGoqSkhKEhYWZBUsS2ooVK/C3v/0NkydPxrRp07B582YUFBTgoYcecto5GWOMMcY8DUeGlQbeVsK8kc0d1Pz8fNx4440oKCiAVqvFrFmzEBoairVr10Kj0WDTpk3OKCcA4M4770R1dTX++c9/orS0FJdddhn+97//YejQoU47J2OMMcaYJ+HIsNLC20qYt7G5g/r4449j8uTJOHfuHPr162d6/LbbbjPlJ3Wm5cuXY/ly/pIyxrrGMwPSIMT7xO81Y7bjyLCMMXdmcwf12LFj+O233+Dv72/2+NChQ1FcXCxYwRhjzB48MyANQrxP/F4zZh+ODMsYc2c+tj7BaDTCYDB0eryoqAihoaGCFIoxxuxlNjOg14OMRtPMAHMfQrxP/F4zZh+ODCscjboJuz9/H5+8uQq7P38fGnWT2EViTPJsnkGdNWsW1q9fj82bNwNozS2qUqmwZs0azJkzR/ACMsaYLXhmQBqEeJ/4vWaeytlL1zkyrDB4FQdjzmFzB/Wdd97BzJkzMWbMGGg0Gtxzzz3IyspCREQE/vOf/zijjIwxZjWeGZAGId4nfq9di/f7uoYrOj0cGVYYvJeXMeewuYM6aNAgnD17Fv/5z3+gUChgNBqxZMkSLFy4EEFBQc4oI2Oi44aZdPDMgDQI8T7xe+06PFNkG0fuGa7q9HBkWMfxKg73RSR2CZgj7MqDGhQUhAceeAAPPPCA0OVhzO1ww0xaeGZAGoR4n/i9dh2eKbKeo/cM7vRIB6/iYMw57OqgZmZm4vDhw6ioqOj0xXzxxRcFKRhj7oIbZtLDMwPSIMT7xO+1a3CnyXqO3jO40yMdvIqDMeewuYP60Ucf4eGHH0ZERASioqIgk8lMv5PJZNxBZR6HG2aMMW/HnSbrOXrP4E6PdPAqDsacw+YO6quvvorXXnsNzz33nDPKw5jb4YYZY8zbcafJeo7eM7jTIy28ioMx4cmIbNtGHBYWhrNnz2LYsGHOKpPTxMTEoLi4GNHR0SgqKur22O0nClxUKubuLO0nio6N5z2ojDGvwsHirMP3DMbEd+3I/hgUzsFbpcrmDuqSJUtwxRVX4KGHHnJWmZyGO6jMXtwwY4wxZi2+ZzAmLu6gSptVS3zfffdd0//Hx8fjhRdeQGJiIsaOHYtevXqZHfvYY48JW0LG3IA3LOHhBhVjjAnDG+4ZjDHmLFbNoMbFxVn3YjIZcnNzHS6Us/AMKmOW8ZI0xpgQeKCLMeYOZozsj2ieQZUsq2ZQlUqls8vBGBMRp9JhjDmKc0YzxhgTgo/YBWCMia8tLUJ7nEqHMWYLs4EuvR5kNJoGuhhjjDFr2ZxmhjHmeTiVDmPMUe6QM5qXGDPGmPRxB5UxxjkOGWMOE3ugi5cYM8aYZ+AOKmOME8Mzxhwm9kAX76VnjDHPwB1UxhgATovAGHOM2ANd7rDEmDHGmOPs6qAePXoUH374IXJycrBz505ER0fjiy++QFxcHK6++mqhy8gYY4wxCRBzoEvsJcaMMcaEYXMU32+++QY33HADgoKCcObMGWi1WgBAY2MjXn/9dcELyLybRt2E3Z+/j0/eXIXdn78PjbpJ7CIxxhhzQ7PnL0Z0bDxkPj7w9fODzMeH99IzxpgEyYiIbHnCxIkT8eSTT2LRokUIDQ3FuXPnMGzYMJw9exY33ngjysrKnFVWh8XExKC4uBjR0dEoKirq9tjtJwpcVCrWFUsBL6Jj4/Hyx99hUP8+qGlqEbuIjDHGrHTNiAj8mlnl1HO4QxRfdygDsx6/X55pxsj+iA4PErsYzE42L/HNyMjANddc0+nxsLAw1NXVCVEmxgB0H/DiPxvfwH9O8r4ixhiTipg+wU4/h9h76TmSsLTw+yUtPJjgPWxe4jtw4EBkZ2d3evzYsWMYNmyYIIViDLgY8KK9toAXMplMpFIx5r6uGREhdhEY82pmA6t6PchoNA2sMvfjDu+Xnw+3Z6zRNpiwY9NbOLJnB3Zsegtrls7jrV8eyuYO6t///nc8/vjjOHHiBGQyGUpKSrBt2zY8/fTTWL6cI4Ay4XDAC8Zs44oZKsbcnZixC7obWGXuxx3er5vGRrnsXFJm62CCjTsYmZuxeYnvs88+i/r6esycORMajQbXXHMNAgIC8PTTTyMhIcEZZWReSuyceowxxqRF7CWbPLAqLe7wfgX4+brsXFLGaaS8i11pZl577TWsXr0aqampMBqNGDNmDORyudBlY1YK9veFusUgdjEEJ3ZOPcbsNTIqFBlljWIXgzGLZDLAUycXuopd8Mu3n2POvQ9b/Tr27nXjgVVpaf9++f7R+eH3yz25w2ACcx27OqgAEBwcjMmTJwtZFmYngoe2NCBcwAveWM9c6fKhfbiDytzW/Mtj8N/T3Ueyl6quZlkqbJhlcWQWlgdWpaX9+9VSWwb/PlH8frkpHvzxLlZ1UG+//XarX/Dbb7+1uzBMegJ7+UCjM3Z7jNidQ7GXfDHGmDvp5Wtz+AnJ6GqWZUC09bMs3UWQt2bAVOxIwq4i9r1dKG3v18goOTLKVGIXx6X6hvRCTZNO7GJYhQd/vItVHdTevXub/p+IsGvXLvTu3ds0g5qUlIS6ujqbOrJMODKIFwHulvGDuh2Jd4fOoaONDcYYY9LQ1SzL8uWPQFnf/WBqG97r1jN3uLcLTazsANae1hkDAldf0h+7z5Y49Bqu5C2DP8zKDuqWLVtM///cc89hwYIF2LRpE3x9Wzd2GwwGLF++HGFhYc4pJeuWmEt8exqJd4fOITc2GGPMO3Q1y3LliGgoT1lX5/eJGACjwTyuA+91M+cO93ahjRwQivRS99ya4awBgUA/z11NwaTN5j2on376KY4dO2bqnAKAr68vVqxYgauuugpvvfWWoAVk0uYOnUPeWM8YY96j4yyLn68MPlbmmtSom/D7zz90SlExcMgw3uvWjjvc24UW4OTOWlczoNZ8Mj1xQMDZPDUQnLewuYOq1+uRlpaGkSNHmj2elpbWqRPAmBCdQ0uVui14Yz1jjHmvW8cPsvrY/Tu3orQg1/xBmQxTZ90i2aWrzsADv7bpbgbUGp44IMBYd2zuoN5///144IEHkJ2djalTpwIAEhMT8eabb+L+++8XvIBM2hztHHZVqS84l2R1GXhjvXfxlMAdUnP7pGh8qygWuxjMQw3uG4TCmma7nhvYy/o8k5Y6Ar6+vqirLLfr3J7KmQO/lwyQI6vcs4IVdTcDuuia13t8vjMGBG68LEq0fbeM9cTmDur//d//ISoqCu+88w5KS0sBAAMHDsSzzz6Lp556SvACMsvaN8IHDR6CmbctcstGuKOdw64q9Y0bN2LIzHtsKgcvg/F8nhi4Qyr8PTgyLDMXEuCLJq1rc28H+Pm65Lw8M2gdHvi1jaMzoM4YEOgb4g+DkdfBMvdkcwfVx8cHzz77LJ599lk0NDQAAAdHcrGOjXAyGnH4x2/dthHuSOewq0pdqVRiyEyhSsg8hSv36fBMLfNGQ/sFo75ZB8C1HVRXEbojcPnQPkjKrxW4lO7B0wZ+nTmb2N3AhzXn5QEB5m1s7qC2xx1TcXjTZvmuKvW4uDiRSgSMGRSG1JIG0c7PuuaqfTo8U9sZrxRzvf6hAahs1IpdDI8idEdgQFiAwCVkUiTEwIczBgS42mbuyqoO6qRJk3Dw4EH06dMHEydO7Ha0R6FQCFY4Zpk3bZbvqlJPSEjA7pQau17TkcTUt04YBHmAH3dQ3ZSrlud50yCRtXgvE/MUnjYzKDWeWJN0N/DhyN8r5Rl6Px8Z9LzEmHXBqg7q3LlzERDQOgo4b948Z5ZHsoL9faFucc2SJ3fZIzNigNzp5+iqUpfL5QBs76D2Dw1AL18ZAPs6qPIAhxYdMCfrOKBBRiMGOSFiszcNEtkiqncAyup5Rs8asf2CkVetFrsYNhOr8yDV8Y+wwF5iF0FyhvWXI1OEIEnO/og5Y+AjJMD6AGDu5saxUdhzrlTsYjA3ZVVre82aNXjggQewYcMGrFmzxtllkiRX3jxd1QjvyeTYvi45D49mM2u1H9BQVZbg6kljEHnlXMGX3brLIBFjzL35+Mhw3ehIHEyrELsoktE3xF/sIths4pBwnCusA08IWueaEREIC+wFXx/AwBkqmQVWTwdt3boVb775JkJDQ51ZHkm7dFAYUlyw9LPjrOKsK8c6pRHuqXgpomdrG9AYEBaA60YPwPYTBYKfo7v9RFPiXDNw05UBYQEob3DtLObN4wa69HyMScmAsEAE+PlAq+eWuKcK8JNmFHOxmkMxfYLFOTGTDKs7qEQ8LNST8YPDXdJBBcxnFe+5cohTGuHOxgE+3I8zItNK9fPZne72E8VHOn/pO9D5vXrw7w8DAMSoqnsH8TJGxroT5O/LHVRmEY+ZM9aZTRvqeOaJCSU8uBdC/H1RKXZBmAlHpjXXO6jXH+k0LBNz6bml9yr5yB4kJiaKUh5mn35yf1SrWsQuBmNMRGK2rbldz9yVTWsSRowYgb59+3b7jzFnmzdxkNhF8EhmkWn1epDRaIpMK0VxEY51qn19gMF9gwQqjbAsvVdpaWnYuHEjj8ZLyOwxA8QuAmNOIw+UVlBBMevO2H685JWx9myqPV5++WX07t3bWWVhDpg0NByK/Dqxi+ESwf7SuukJRSZz7vJNd41M28tXhsF9g5Fb2WTT84b1d3Sprfv29Cy9V76+vlAqlbjChtdxxpJuZj1vnL0I8vdBcwsvdWWeRyYDYOc9enJsX7ujel81vB+O51Tbd2I4v23BmD1saunfddddiIyMdFZZmANGRYV5TQfVW/nIAIMTbyLuGpk2oJd0w+jbw5pULZbeK4PBgLi4OAzrL7cqSBIv6RYGR2i1zdzx0fjqlHenY2LOJcWAVH4+9g9WDekb7FAHlTF3ZPUSX28c6WXubUhfXhIjpNnzFyM6Nh4yHx/4+vlB5uNjikzLXGdg756XFVt6r0aPHo2EhASrlzZ72pJusQwIC8Sg8ECXn1dqgamuGt4PQGvaFVcK8pdmdNX/b+++w6Mq07+Bf6dl0ie9V9JIqEloAkKQJoiuYFnU1zUuylpgXdvqrtgWxbJ2+YnKIlhwlVXXtoqgIqurCGQILSQQkhDSe0J6e94/hjnMpM6cmTll5v5cV65dw5k5J8+cuc9T78cebGn02MrfU5z7M9Cb/xY1YtRzRfyICJE0yuJLZGtafABKG6yfEuPnoUF5Y4cDrsixQn3dUdHU6bD3HykzLRGGl1aFlFAfHCptGvG4oT6rfzyzDt7elk9rluqUbmIZL60KPY6cUmFncTauCedr6QTX3QJp8fgw/OdIpSjnDvTWoqrFcc+r4QT7aNHW1Sv4eflaQOvQCRmSxQ3UgdPJiDm57oElZ248y1yo/WrtbcaYQHyiL3foOcTMTEsMswKUSoVF03wHflbWNE4B+03pzkoJtup4Ii6htkGSCq3atZYImJLbKLsr0qhsrzvaOvBrw9JZQhyGWlV2MjMxSOxL4C1chOlpYlLzfCBcnCTuZ+zOcy2mmNO8iGN1trfh83dew5an/4Knn34ara2tFr/WdJqwWq2GkueU7gg/8TMdjwmmUX4iPWqV+LGXGqnSJtbquaRQ1+qoIvLjmulQHcDX3faHQEqYDwqqztnhaqzjraXbwBLRNq55TQnzRkGV5Q0Iewn0drMoaQ6Rl4FJjn74/AO89957Fu+FajpNuK+5GpnjUzB2/jVo75dfhXZqXIDVWZ6NJkXrcPhss52vSLpo7b51kkK9caqaX9y+YpL4W6JlpQTjs9wKsS9DUFNiA/BTYZ3YlyEIJc8W7oRIfjty2CPzO6W0IZaglomEqGikSzY83VRo7+6z6jUZMf5o6+pDmQ3rX3UeGjR39PB+vdDCdFqb34O+FUMzS3J0fqqucS/UmHnXW/QexmnCE6N0GB+pwzfHq9De2u3Iyx5EqQD6bZhftjAtFCqlAuE6d1Q2W7fmLS7IE+MiXKuBGqZzrRkzpvw8NWhqty5+2jIFk++sF3vy0qoR4KVBQ5t8nhu2ign0hPsZJTp7pL80zfh849toE7LeaK/M78smCrMunFLnyBtN8SXikXHLI9LfummN4X7uUCgUNk+37aeIS84zJjkyZdwLlS8xvpLBPrZ1Ytj6euI6KHy6Dr45Kvji28nmY+PsO4WCX/ZhPnURe2V+t/VvJq6BGqgy566R8UfogpWFyTF+Nr2eT5HxnQJExKOwoKk40l6oRPps3d/X1bZ+C/Vx3ZFfIpzMWH9er+tnTJQEZHyf73xycQzVKUqZ34mjyLh1QwD7TCFKCKbF8kLxdLNtVj3f7Z5WZETKNpGMK9XDjY0OZkFXxMC9UJUme6HKidAjHVLBdw2YK4ry90BMoLzWzk6J49fQcSZyXLWUEubD63V9/Qzp0cJ/5kImQbRX5ndCLOGaNQM7E2tDansJ8HJDggiNF1dac2uvv5TvFDV3jQozxgTyPq8LfVSisqYDwpjk6Nrb7sfcZdfiySefxL59+6zebsbs/LxfyZ9aqcTica63F6CrNsz5sDVBna0mRlnfmZAcyq+hIzemmcQ/f+c1dLZfSFY2M0G+uxtYq58xKJUKeLjx+17znRHh5+nG63V8DMz8ruCZ+Z0QS1CSJAJAnGQOE6P8cJpn5k1XNSsxCLvzqq17kR0al0qlAv19/Jsv81ND8N2JGl6vjfL3RGePdQmp7EHMRrklU3wB871Qr58eY9U5TLMxThibhCceus/q67SXQG9aRyq0KH8PmxK2uZLxkTocKXOdRFqWGilpjkrpgwAvNwR4uaGhTdjEa3zE2ThCL3YnihBMM7+rWmvR5x3MK4svIZagLlwXpxWxF9/DTYWUMGGnF8t9IDDYRyvL/ctCffmvHxNrKuT0eP4jzgCQEetn9WuEWldorFjueP3v2PvlDrz67HrMmDHDbPSDOLc5ycFiXwKRuJkJI8fAkZLmjIvwBSCfJRq27mVvj60G5cDYKfrUC6/iit/dQY1T4jDUQJUIjUgbeou1kbjxrJRV0TJzUy5UJmXyvLcbMaaCh+vcobNx6v7YMF87XY39DaxY9vf348SJE/j8/bfEvjRCHEYujSUpSAnzQVzQyI2PkZLmSGGLHeI4/YzxmvpOiKWogSoRfEdOdB7i9NqtyIjk/n+gt3BrIFx1k/lIP+u2tSHimBYfYNPr+SbBstZwW9RUlZcKcn5nIff8A4TYwpFJc/iu5STCYACCaGkGcSCKABLBt2OXb0p0W5n2ji5M5Z/gxNp2ub8XVQjFkBYu3dFAKQn1lccDe7gtasIirVvHKjaxp7t7u7teGgd3N9caGfOgkcBhDcwkbo+kOfHnR21/MylylCOJKR67xtiEMYZQXy3CdbT9E3EMaqDKnBSm0ShllOI1Lkh+I7BXpkfY9gZ2GJQbH6nDkvFhSJbh+lch2b7pujDfpeG2qLni+t8Lcn57mRpn24i1rSZG+ol6fjGMjxB+Wp/xWyF0ZlwPNyWSRNjb0siWDi8hMmMPzCR+7W334/F/fGrTukRvraHTR071Clek89BAoVCIspadbg3X4HrdvxJGPbWOFejtxu35Gh/kheI6eSSEsXXvVHvx93KDoo6eDM7ANBtjbcVZLJg2HmvXrsXPZ1rR1so/42aAlxZVzV12vFJps3WdstxkxPqJuj3O2DAfHCptEux8U+MCZNtQEioztmkmcSJPc5KD8UNBrcXHZ8b6i5p5PdhHi+oW13nOuCpp1HwJACApxBs5ZxrFvgynFeyjFWyESlJc8E+WK08Bp08OvUVNq03vOT7CF3kVLTZeGbGEj7saze09gp7T0u2PBhotG+yo51UY/9e2YBbh54EmK8rMJZ8X57nwny4b9ppeG2Fljosof/45MeanhvB+rbWYKDt7E3uhBqqEKJUKTIrW4fBZ2m/N2VCYFNZlE8PFvgReEoPlPYVaLfRCKBc2IVKHzp4+fHG4UrBz8q3w2drxwrdhPNCESJ1VHSiu3EZLsWE6tdBLQUz3dE5LSUTyuvsFPb9YMkTKQWLK2u/IwC3nDN9tqiGRwag2ITH2ehA7K1vKh0pWfoJ4Zoi2V3Zrob+Pcp1OaMraLbO0GnoMWSs+yAsalRI+7hpE2jCawZeY6zJtYe2WVaajiGJsBecrUpZ+APDS8h+/ELKjauCezq/9/QnMmDEDHS6wp7Pp/Snks8qeo+tZKdavYbU11wORB6oZSAxNSRhZhB9ljJOTpBBhk5oMRY6JseQsXGddg0mMpDvENkmhPtbtoS3/fhdoRcgRoVIqcM2UKKtf50rPyeH2dP7yn1vFvjRigRBfd6u3FArwcsOsxEAsSBNuujARniwaqCUlJVi1ahXi4+Ph4eGBhIQEPProo+ju5p/Mg8iTnyf/PVddeT2RWKIlsG/tzIQgsS/B4eICxS9nPsZH+vJOukOVE+GZjtIE2BCLrT6vSKFbCjOaNDxGI+eKkFnVlJATQYbb07ma9nR2arGBXgjykse2boQfWaxBzc/PR39/P9544w0kJibi2LFjuPXWW9HW1obnnnvOIecM8NKgoc36BBRjw32QX3nOAVdE5Ez8ag7hQy4zGuzVEZAY4o261ga7vJclmA3FG+JjPkrEZ52jv4CNLCI/Yvdp8t1/WuzO2JQw4WbODLenc6iAezpfnOT8naCmpNBxA4j//SSOJYsR1EsvvRRbt27FokWLMGbMGFxxxRW477778MknnzjsnAk8k5VkxPBbtB4joxGQCZG2T8lL51lOcpUWwa+iIQVeWtr+yFWMEThJkz2b/5Nj/Kx+jS2JYOxlUrR8pjibdti4QuVQzD9xYpROEvuc86FV879uPyu3bppz2dXw8QsAoIBSqeT2dF523c28r8Facv2cTLlTLgAiMbK9I5ubmxEQMPIm7V1dXWhpaeF+mC3d9Q6WHu1n83vMGCPMpvW2VkxiAz0RZqf06NYQq7KRFuE7aLRHTi6fGCH2JQjPuK2FRHqKR6MdME02OsDydaBjw8VrpJnG5GAf26ZradUqxFrR0ZcS5s0lpbpkrLDThU2vc5yM1uCafh8k/Di1HxG//nyfswNjgdxYMxuks70NT629AS2N9VAoFejv70dQUBC+/fZbeHh6OfAqCSGOJstIdvr0abz66qu47bbbRjzuqaeegk6n434qKioEukLr2SPrndCjH3ypZZip9NLxYbxf6ybzrTecIbOsswsZkLrfmsaDmOt4TC9zgYD74w3Ep8NsLo/sk0Z8192KzdosuEa2dvS4wmitvSwcFyrq+W35XljLmCAJjIGdn+ZbV1eHbdu2CXYNgP3uT2vjAn0tRuYSnWhOTNSn5GOPPQaFQjHiz8GDB81eU1FRgUsvvRTXXHMNbrnllhHf/y9/+Quam5u5n4gIx4wE2ZKO3RbWZj5zdbY8RAK8bEnOxP+8ctTZ3obP33kNW57+C55++mm0traKfUlEonxNtgsQe92cNS6bGI5IKze3dwamDVRbPi5bOvyEJJcZFKZ8Rd6CQ8jvxXAJkoqLiwW7Bldktr2N/L4iRCZETZK0Zs0arFy5csRj4uLiuP9fUVGBefPm4aKLLsKbb7456vtrtVpotRdGBxxVAZoaJ8zU2oGCveU7bZQIR6MUfk+68pJCKJVK/PD5B3jvvfewb98+wa7BLpjxf+TZBSuHSkOwjxZjguQ5Dc9e++wKbZGNo2sqhX2m+Frb4Wf67OabwJAP0+9RgKcbWjt7BTkvscxwCZLi4+NFuiLbeGhU6O7tH/1AQlyAqA3UoKAgBAVZlv2svLwc8+bNQ2ZmJrZu3Tqo10wsob5aePDIHmkPmbG2JxqS06iFlKSG++JUtTxGBjNi/QQ7l9medOcrDidOnMDGjRsRM+96wa6DWE6sEDAuwpemjwssyNu26dx81wmLdY+F+tr295pe9pQ4f5Q2tPN+r0nRfthbUGvT9RBzi66+Cf/b+W+uQ5T19yM1NRVr1qzBD0UtYl+e1WYnBeE/RyrFvgzZoPqrc5PFNjMVFRXIyspCTEwMnnvuOdTWXgjyYWHymCrkCPZoGEs5cZS92XO6lrcV07rFjKGxgZ7wdBPua26cctVn0qttnHIVM8+y9+hsb8Ouj95GbcVZxMXFYeb6vzjoakcgsyRJxLXwXQtqK9NnjjVxzdY1t3z/2ik2zm5Sm3SE25qpNdLPA/6eGjS2CzP6awu+idOE3m7F3dMLj//jU+55sXD6BKxZswbe3t4AhGug2uvbKNeZGYQ4giwaqLt27UJhYSEKCwsRFRVl9m+u1MCSCuq0so5cGzkL00Kt3mLG1ilXg6YI9/fj192f4ZV//gdCpoRIlEnCMTlzpjgyJsgbFU2dgp3PHlt92cqaR69Y6yJtrfDrrNzyZDRiJcfy0qqtmhZtmthvQVoIvs2rseh19tqP2Rrunl644nd3AACuny7c3qeEEMeSxjzZUWRnZ4MxNuQPsY3cp0hE+VuekEHmf6rggn20Vo++Lrr6JkTGJUKhVEKlVnN70q1Zs8ai13/1/maUF58yTBHu7UV/fz9OnDiBj97dwudP4CUuyFOUihaRr5hAT6v3b+RrRUakaMtKxGLP2G3pNOUFaeJllR7I1r/fljwZct4ijRAiX7IYQSWDedqpghJnxb6B9mLPRrGvhwZo7LDb+xHbmE656qivxEWTU02mXDWM+NrO9jbs/PCtQR1PSqUSFWfPOPCqzYmdBZPIk5+nBk0CTN+0daqpvTh7h59UyhkArs6Mwr8OlvF+vbtGhTCdFlXNXXa8KucxfcAe8koF0G/F+IdYHf1SGGCQwjUQ50QNVJmKt1P2y/E8NomnPe2s42p/r3HKVUyAJ2abrEn6zeQIfJY7/F7Euz56G22tg9cN9fX1ISI61iHXKiXjInzt9l4agfbe9XGnR4icZcT6QX+middraQKTcIT6PruquEDz+tTVmVHYYUOHgKUuGSudUXpCpIaingR486jk2aPRE+TtJkoWTRdrr7nc3zuc0fYLHmpPOwDw8/PD1Teusvg8tu6xKFaHgj33U54c7We39xrJwjTbti1xVTESmUI+Nsx+nSKOJNd1/EQ44X78pyIPTDqmFqhDwK7LAugrQpwMdX/zYJplND4+HtP/9uD5KYz8RPIIrGI+sG2twNuzARBo5X56cjMzIVDsS7CK6XcjKWEMJj/2gMXfjeCI6EHTexUKBe666y54enkD7W0WvY+1eywO5AyVYaGmJ0ppGqSchPrSuj5ryG0WSkoYvyy4xNwVkyPw+QizbkxNjvJz7MVIHc1oIE6GGqhWGirL6L5dn2Lfvn28G6mTeARWezyw+cYzrc2ZCO1X27AmmY1pmQX7uKG4zrIGj61s+azi7DSV28iR6+SG+m78tPPfFn83TPe0U6tU6OvrQ2pqKu69915UtTMU1Ur785JCZlVChCJko9HfU14dkfbYo5xYt52bmGTWf2IzsTuMLE2OSW12eaMpvlba9dHbKC8pHJRldOPGjbzfU6jpJPYycL2GtcQObgCQYIdtRBJD5LcVyaXjHLdvsK3fDWOCpWtvux8333wznnzySa5xmxDsLdgWDab3pzXZUoXK4uosnGGk2pUJuQZVrO1ZxGDcGsfW56yrkUK9wlp2vWYZ/v18zEwIpJk7LkIe3VMSYlwn12ey16NKpUJxcbGIVyUsMdat2ps9Ms9ZPpJsfq5F40Kx63i1zefnw5GfnT2+G8YESwP3s1MoFEgM8UZeheM3XzdtOE2I1KGg6hzv97p8Uji+OFxpj8sihLiArJRgaNVK2XVcuzKxGsemp/X3dEPtOefP0uxr497GRD4oAlopOCIa/SYVcMCQZTQ+Pp7X+yWF8huFs8sUXzv2gF8xOcLiY8Vq3oo1YjPwswrytmwfPnsQMsuqvb8bfFxkhzW7pp+Xm1rJ+zsKAD5WbFljet5YEbZ/IuKJtGI/Z77snTFUjiNWckGNU+u5+oyMWYnyylfBR2asv805Joh8UBS00qKrb0JkXCIUSiXUajWUSiVSUw17PfLBdwNtqQVja9aKuNq+Wfbas9ZaWrUSaeHCZek0/W6o7PDd4GO4AeIIKxKR8b077Xlbj7HDFHQiH3OTgx1+DntPnqBtZhzDxR6PAAz5Cz5/5zVsefovePrpp9Ha2ir2JcmOp5vldbD5qbZ1VolV/7TX9opEHmiKr5WM6+R2ffQ2+pqrkTk+BWvWrDFLAjM+0hfHyh07FXG4h9i8scHYk19r4buIU8MQbQRVpBOH6xw/OjKUjFh/QXviTb8b7fUVmDk5bdB3QyxZKSF4/9dSi451xQoiIcQchQFhDEqu9/kHeO+992xKPCk0qQ0YjEShoCziRB6ogcqDcZ3cpGgdxkUMztw5McpPtAaqNY0hsTIjulID4OKkINHOLUYxG78bSaHevGcH2MIeFQV6eAtD7DigVbtYog07l7fYn5+j0QCxfUyJ9cc3I+RcMEuud36JiDG53oMPPmj5iWR4P6qH2PebDM/ZYw4xR98OG4jZa+Zuh8pVeow4qfDl1NsoRdZklhWL7VsRiceadaNEnsJ17hgr0l6VHm7y/W6YEmqKr6VbShBpCvTWInyEJRbG5Hqm7JF4MjlUwO83zyqNygkSThLiKM7xpBSJmL059kiiYpq6P0HALVOsWQ9oT87S+5Zi4YNXzL83dZi1r2Ktx5UDJ7k9JW9hWijmjQ0RLRHNcFtcXTYxXOArsU5CsDjrv9IihFtHTxxjpNg2WnK9rJRgTI72G/0cA06SHOoDtUANQB1llpUkRgvlZY0aqDJla6Kh1HDzRo63Vm3RQ8AeAk2y2GooW6HV1ColxkeOXmkTa6R6dmLQsJ/r0gnhNidokCpXS/5F7MvRlVxb40HMgE5RZ7/dnfzPG9HA0Ud/B+7xbJpcT6PRDEquF+HnYVEnxVCflz9lfBXE2HBhRqtd+Tvpiqh14IQCvUcPykNVptUq4b/+Y8Ms7x23tQffw4k2d7aksqnViPP1Hqni6qZW0hpPiXH2hoalJkYNzifgCFKLQ5mx/JZ62Do4IdaIrKVcdexlSpy/2ewqALh0fJjDpqYbk+tde9v9uPnmm/Hkk0/ySpBESzNsY0vm+AwLl4vRs4ZYgxqoNrD1y+ao6Y7z7bzfnSO5qZUWVwyDfWzbPzQmwHn2lmSjVJ8CvDRO1xC0ZLqOmA9Ae+85S3uhOsZw98j4SMc3UL3d1cNO8XW04f7uFJHW4k4fI86+jXH0vbKaQqHAbyZFYkVGpEPe35hc74033sCDDz4om+y9juAt4N7lpiZaEf/4PmcdMUvv8knSXhpB+KMGqg1snTK1ZEKYna7EnCVrq6TUkWVpT3xsoG097q40BVOsBFiuzNdJevDFWiNujZkJ4m1Kb8vI37S4ACglmBhlapz18UKu4TTRwnwLom2HJqmn8wVKpQLuEhv9d3X2/A4qlQr4eozeOA73c+e9NGu43BSZMZZl/B+qDkcj586LGqgOsiAtBGNGqciIuc3BUIFN6uvJKeOd5aT+WRLHc5ZssUOJE3HD9mnxwm+f5GhJQmY8FZkzd1TS6LD9XMJzJlp6tHN3Ds9JCub1uoHTxk3FBHo69fOK8EN3hA1GmqIb4uOOGTymMAXQon4iYRoR1inbyyVjQ5AcKt2pY/auOFuzJ7IY5Hon2fI5jfZS6oMThiOT/ohJqp0nI/WXdra34fN3XsOWp/+Cp59+Gq2trYJd10jCdPxmkgjZeebmREkm3VSjD9hQeHQtznN3C8zTTYVoB6xpDNd5yGKKHZE2Rw0SWJPUaiRiVBDDdO6YEifNChwhALCUx1YzKgu/7LaGBKlOPeXDUyvOOj9HE2vrJL4629vw6C1XYsfrf8feL3fgoYcewowZMyTTSLVEkAVJKa1haRK1JePDRPm8nScKEKmTVzSzk56eHu7/5+Xl4ezZswCAzs5O6PV6nDt3DgDQXF+LM6fyuGMrzpxGbWUZAGBsqCf0ej2am5sBALW1tTh06BB37KlTp7iNpvv7+lCcfxRtLYZjzzU3ojj/KJf05fTp0zh9+jQAQyKYlrKTONfcCABoa2lGcf5R9Pf1AQBqKkqhaq3kznPo0CHU1tYarre5GXq9nvv7aivLUHHmNHfsmVN5aK43HNve2gq9Xo/Ozk4AwNmzZ1FYkM8dW1qYj8a6akO5dLSjOP8ous8fW15ejuPHj3PHHj9+HGVlhnLp7uxEcf5RdLa3AQAa66pReuoEd2x58SmUlpYCALq6uqDX69F6roUr75KTF9634sxp1FYYPpve3h7o9Xo0NTVx5a3X67ljq0qLUVNealberc2GY1ubmwxleH6vtaKiIhQWFsJLa3gQFOcfxbmmBrPy7uvt5cq7srSIO09ubi5qamoM73uuBcX5R9Hb0w0AqKsqR0VJ4YUyPHUCNdVVhmMHlHd9TSXKik9yx549XYDGWkN5d3V2oDj/KLo6OwzlUFGBY8eOccfm5eWhurLcUN5d5uXdVF9jVt75+flceXd3d0Ov16O97fz93VBnVt6VpUWoqTAc29fbC71ej8ZGw31YV1cHvV7PJbWqOltyobz7+83Ku6mxAXq9Hn3n79mioiKcOnWKO49er8f4QMNjru2cobx7e8/fsxVnze7Z3NxcVFdXm5V3T3cXV97lxRfet/TUCVRXVZqVd0eHoQzLysrM7tmyopNoqDF8Ntw929FuKIfKShw9epQ79vTJfNRXV5iVd0eboQI1MEYUFBTgzJkzAAxxRq/Xo73VcH+3NNajpODC51hVWsyVd39fH44dzuXKu76+HgXHD3MxorrsDKrLDO/LGDPcsyYxwrS8i4uLzcq7pOAYWhrrAQDtrS3m5V1ZhtOnLtyHhw8fRkOd4f7uaGs1fO+7zt+z1RVm92xpYT4qKw3l3dbWxpW3UgE01FShrOjCsWVFJ1FfU2lW3m3nK6GVlZU4cuQId2x58SnUVRnu757uLhTnH71wzw6IEQUFBSgpKeHKu+D4YbSda+bKuzj/wudoGiP6+vqg1+vR0GD43jc0GO5ZY4yoKS9F1dkS7rWmMaKxsRF6vR6952NEcXExTp688LeWnDyO5oY6Q3m3nTOLEeVnS5GffyHOHjlyBFVVF2JEYZ5JeY8QI9rb2w33Vns70mP80FhbjbOnCy6Ud7FJeZ+/Z42V/qqqKrPyNo0RvT3d5uU9IEacPHmSe6719faaP9eaGszLe0CMGLK8jc+18lJUlRablbfxnm1uajJ7rlWXm8eIkpPHLzzXzpe3MUaUlpbixIkL8fDIkSPcPdvZ3mb2XKuvqTS7Z0/kHUd5ueE+7OjoMMTv8zGisa4apYUXPsey4pNmMcK0HlFdXY3Dhw9zx5rWI3p7ewzlbRIjcnP51SOqy86Y1SP0ej3q6w1l2NjYiJPHj5jVI0zL2zRGnGsxr0ecOXMGBQUX7i3TesTAGHH27Fnk5V2Ih0ePHr1Q3gPqEQ01VfjXm8+hvKQQrL8ffb296O/vx4kTJ/Diiy8a6gYmMWJgPWJgjGhpaeHKOzc317y8TeoRxflHh40Rp06dQlGR4XnfN0o9whgjLjq/Hl6v16OuzvC9V/a0jliPMI0RLS0t0Ov16O42xIhIdeugekRTvSEmc/dsVye8tGqUlZWZlfdI9YiBMSIvL4+rtxnrvh3D1CMqSgq58jbWI4zlXVNTY1beA+sRpvessR5hVFhYyJX3wHoEV94mMWJgPcJY97WmHmEs766uoWMEkQ6XbKAagwgArFy5En//+98BGCqxmZmZyMnJAQD8+PXHePLOldyxb6y/F5++9QoAoLGhHpmZmfjpp58AADt27MCMGTO4Y2+//Xb89a9/BQB0drZjXfYyHDtgOFb/47dYl72Mq1DefffduPvuuwEYguJvFlwM/Y/fAgCOHfgJ67KXobPT8GDcsenveOXxB7nzzJgxAzt27AAA/PTTT8jMzOT+vk/fegVvrL+XO/bJO1fix68/BgDkHc1FZmYmF6D+/ve/457bb+aOffbum/D9v98HYHgYrMtehqqzhgfaxo0b8Zvf/IY7dsWKFXj55ZcBGALxuuxlOHva8PD+4fMP8cyfbuSOfe3Ru/DMM88AMDx0MjMzcSzXELD+982nWH/btdyxm5/8Mz7+x0sAgPZzLcjMzMR///tfAMAnn3yCadOmccdue/4R/PP/ngIAdHd3YV32Mhz5dS8AIPfn77Euexn3wL3vvvtw1113IcrfMAVyXfYyHNz7jaFccn7GuuxlXAPkozeex5an/sKdZ9asWfjggw8AAEf0B7Auexmazz/YP9u2EZsev5s7dsPa67Hj/XcN15BrKG9j4+Xr9zfjlb/ewR373L2/x+6P3wFgeBisy17GPaRee+01LFu2jDv2mmuuwTtvvgbA0Ehbl70MZ04aHlJ7v/wXNqy9nluDeuONN+LJJ580fDY1NcjMzIR3k+GB8MvuL/C31Vdx77vlqb/gozeeBwAoetuRmZmJPXv2AAA+/fRTZGZmcse+++Jj2P7KEwCAvt4erMtehtyfvzdcw/ffIjMzk2uMP/DAA9yedgAwbdo0fPXFZwCA/EP7sS57GdrPd1J8/I+XsPnJP3PHzpkzB9u3bwcAHD+sx7rsZWg634D64t1NeO3Ru7hjn/nTjfjwPUMZHj16FJmZmdzD7+WXX8aKFSu4Y1/48y3Y9dHbAICqs8VYl72Ma+y+8cYbWLJkCXfsw3+8FV++9wYAQ6VqXfYyrlIzMEZkZ2dj/fr1hs+mrg6ZmZkoOHwAALDvuy/x6C3LuWPfevYh7NhkiD2dne24YsFsfPut4Xv/xRdfYNWVC7iH83sv/Q3vvfQ3AIaK6rrsZWYxIjMzE21thsrFX//6V9x+++3ceR69ZTn2ffclAKDgsOGeNTa2Pn3rFdyz5g/csfPmzcOeLw0xojj/KNZlL+Ma8l++9wY2rlvLHfvs3TfhjTcM5ZKXl4fMzEycOnUKk2P8sOujt/HCn2/hjn3pwT/gmw/fAnAhRuTlGRrrW7ZsweLFi7ljX3v0Lnzx7iYAQFNdDdZlL0PRcUMFf2CMWLVqFR577DHDsU1NWHXlAuQf2g8AOPDDTjyy6kKcMo0RnZ2dyMzMxDffGL73X331FTIzM9F3voKz/ZUn8O6Lj3GvNY0Re/bsQWZmJldBe/jhh7F69Wru2L+tvgq/7P4CAFB4VG8WI1567lnceOOFeDh//nxs3boVwIUYYawIDhUjDu/6EIChUZmZmYn8/Hx4adXI2fkBnrv399yxr/z1Dnz9/mYAF2JEUb6hobl161bMnz+fO9Y0RjQ31mNd9jIUHjXE5IExYvXq1Xj44YcBAG2t5wyfY87PAICDe7/BuuwLcco0RvT09CAzMxNfffUVAOCbb75BZmYmus83JP/5f09h2/OPcK99ZNVvcOCHnYZr+J/h/jZ2Tn721stmMWL9bdfif998CgAoOn7YLEY888wzuOGGG7hjFy9ejC1btgAAzp7Ox7rsZVxl+psP38JLD174Lqy85mps3LgRgKHhkpmZycWI7//9Pp69+ybu2I3r1prFCNN6xDvvvIN58+Zxx5rWI841NWBd9jKzGJF18SzuWEvqEaYxwrQekZmZiS++MNyH3377LW6/epFZPeKtZx/izmMaI3J+/cWsHrF+/XpkZ2dzx5rWI0xjRKC3G/7+979j5coL8XDJkiVcjBhYj9j10dvY+8UOKJXm1VCVSoUjR44gMzOT6yjcsmXLoHrEwBixf7/he799+3bMmTOHO3ZgPWJd9rJhY8SaNWvwwAMPnC/vziHrEQNjhDE3RmZmJj799FMAwJH9I9cjTGPEL78YytvY8f3y888Oqkfs/fJfAIAzJ/OwLnsZFyNeeOEFXHPNNdyxI9Ujdn/8jlmMuOaaa/DCCy8Y3vfMGcPz8nyMMNYjjDY9fjc+22b4LhjrEb/88gsA4IMPPsCsWRfuWdN6REdbq1mMGFiPuOuuu3DfffcBGFyPOPLrXqzLXmYWI9auvfD8mTZtGj755BMA1tUj9u/fj8zMTK7jZGCMINKhYJbs3eAkoqKiUF5ejpCQEK43JS8vDz4+PoiOjkZnZyfy8vKQlJSEqnbg25yTaGqoRWxSGgBDT5zGTYvg8ChkRPugvaoYCQkJ0Ol0qK2tRVlZGdLT0wEYHmhqtRq/1KjQ39eHM6fyEBIRAy9fHc41N6Kusgx//d1SKBQKrtczISEBjDF88f3/UNnnAx+dP9pamlFTUYrYpDQoVSrUVJRifLg3FsyYDMAwghoVFYXg4GA0Nzfj9OnTmDBhAv6lr0RtZRl6ursQEZsAwNDz6RcQDF1gMBL8lNC01SAtLQ3u7u44e/YsjhRVodk9FIBhdMTHzx/+QaHo7GhH5ZnTiIxLgpu7O+ZGqdDU1IRx48YBMIyg6nQ6HKpXor6pFeUlpxAeMwbunl5orKvGucYGxCSlAjA8pH47MwkxMTHo6urC8ePH0eURjOIWhub6WjTW1yAuedyF8ta4ITgiGr29PRjv0YwxY8bAz88PtbW1OHv2LDIyMvD+r4beYKVKhZDIGK68g8Oj4a3zQ2tzE8JULVg2byaUSiWKiorQ39+PZk0gCqoMPe5BYZHw8QvgyjsmMRUqtRo1FaXwUDH84YqLARgqkREREQgJCcH/8krxw4GjiE5IgVrjhrqqcnR3diAiLtFQhqdOYGFmEjJTx6C1tRUnT57kyvvVL35FR9s5RMUnAzD0fHr7+sE/OBRdnR2oKClERFwibp6bgoqKCjQ0NGD8+PHcPVvexlDb74Purk6UF18o76b6GrQ01CP78rkI07kjPz8fnp6eiImJQXd3N44dO4bExER8eaIJzQ11aKyr5sq7srQIKrUaIRExuGJiKE6eOI74+Hj4+/ujrq4OpaWlyMjIwLHyZuz65TCUSqWhvPv7cebkca68JwQp0dNcg0mTJkGlUqGoqAh9fX1ISkoCYOj5jI6Oxu6iDrSda0ZNeSmiE8dCrdagtuIsenq6cd+187jyDg8PR2hoKH48fgb/PXgMUWOSoXHToq6qHF0d7YiMTzIp70RkpiZw5Z2amgoPDw+UlZWhubkZ48aNw/u/lqKs6CQ8vX0REBKG7s5Owz0bmwB3D0/Mi9Ggrq4OEyZMAAB8sOtn1HYqERgawZV3WHQ8PLy80Vxfy8WI66fHoKCgAO7u7oiNjUVPTw+OHj0KfaMbPL190dJYj4aaSsSlGD7HqtJiKNUqhEQY7lm/jgrMykiDv78/6uvr8dlPudCGJkKhUHCjp6FRsWCMoaTgGILCo7gYkRnYw5V3cXExent7Ua8KQFFtG0oKjiEgJBy+/oFob21BddmZC+VdWYb0SC/MmToJgGEE1T8oGD+V9aKjrRVVZ4sRGZ8EN6076qsr0NHeyt2zpYX5uOmSCQgPD0dbWxsKCgq48v7yl2PIK6lC1BjDsWVFJ+Hh7YPAkHCuvK9fOB3xEUGorKxEbW0tJk6ciPd/LUV58SloPTwRFBaJnu4ulBWdRGh0HDy9fLgY8dcbDR0IBQUF0Gq1iIuLQ09PD9796r/o8wqBl48OLY31qK+uQPzYCRfKW6XC/1s0Ff4eahw+fBhxcXEICAhAQ0MDSkpKkNcVAKVSiZryUvT39yMsOg4AzGLEkmQfFBcXY+LEiVCr1SguLkZPTw/qVAEoqWtHycnj8A8KhS4gCO1t51B9toSLEWO9O+Gp6sfYsWMBGEb0QkJCEBYWxt2zR1q9DeVdUzkoRiyZkoCJyWPQ3t6O/Px8jB07Fp6envjPvuM4VlSB6IQUQ3kXn4SH1/nyPn/P3nXVXHh7e6Oqqgo1NTWYOHEiAHAx4qdKwwjq2dMFF8rbJEZcMjYELdWl0Gg0iI+Px66j5Tigz73wXGtqQF1V+YXyPlvCxYiVU6OQm5s7uLw7/Q3PtfJS9Pf1ISwmnivvwNAI+PoHYkakFk015ZgwYQI0Gg2Ki4vx8YES7rlWcvI4/ANDoAsM5srbGCNmhxtG91NTU7nyDg4Oxslzapwur0NlaRH3XKuvqURH6znunk3UNCAyNBiRkZHo6OjAiRMnkNviCXcPT8NzrakRMYljL5S3pzcXIyZ6tyIpKQk+Pj6orq5GVVUVJk2ahO9OVOPQ0TyuHtHb24OzhfkIjYrlYsSkgD5cNG2KWT0iPj4e7/1cPGQ9Ii5lPBcjrpgcwdUjDh06hNjYWAQGBqKxsREffn8Q3uGJXD2iv/dCeZvGiNkxnqirLOXK+8yZM+js7ERKiuHeeuq9nVw9wjRGrLwoAbVVFTh37hzS0gx1pqNHjyIoKAj5LSoUVzaY1SMaaqrwn/ffwDc7toGdH5UEAKVSicceewyXXXYZkpOT4e3tjcrKSrz93RGzesTAGLF2+cXw9fVFdXU1KisrMXnyZOzOq8bhY3lm9YizhfkIiYwxixEP3XQZV94qlQpjxoxBX18fnt2+E4FhF+oRtZVnEZs8zixG3HbFTHi6qaHX6xETE4OgoCB8+NMJFJ4uMqtH9PX2IjxmzIV7NigUty/JQEtLCwoLCzF+/Hi4ubmhtLQUO34pNKtH+AYEwi8wBJ3tbYZ7Nj4JN8xKRHVlBVpaWrjyfub93UPWI7TuHmisrUZrSxNiElNw3bQY5OXlwdfXF1FRUVzd93S3D3oUWq4eYSzvipJCuLl7ICgsEismhSIv7zgSExPh6+uLmpoaVFRUIK8rYFA9oq+3F6WFJxASEQP/AH9kxbpz9QjAMIKqVCoxZswYfJFbjiOHc83qbbWVZy/UfctLsXR8CJKTk83qEcHBwdj87dEh6xEr5k3FmGBvs3qEsbzHjRsHrVaL0tJSsxhBpMMlG6iRkZHcyOFwTlWfw4GSxmH/fcaYAIs2Nn7/19Jh/+366TFD/v50bSt+LWoY9nXT4gNGTZU/0nkBw6b0A/f9y69qgf5M04ivA4a/7u7efnyUM3K5DvX6o2XNOFrezPu8o/2twNCfV86ZRhRUnRv1tVdlRg6Zcfnw2SYcr2gZ8bUXJwUNuVbZkmsGhv+bj5Q14Vj58Oeenxoy4j6oo51/uPMCwLHyZhwpG/7zmpMchCj/0ddnW/vdOFTaiBOVI39elpzb2r99f3EDCmtGXxNly/0JGLZOMU2w8b/COpypbx/1dcN91vuK6lFU2zbq6+emBCPSzzyhkq33pyVltiA1BCEDrtvW81pSZovGhSLIe+g9lSubO1Dd0oW8Eb7Xw53759N1KKkb+dwL0kIQ4jNyjoGRymC41/9aVI/To3zWI32nRzsvMPg+++5ENapbukZ8zWjntuTzXjwuFIEDPi9b75O9J2tR3tgx4msvnxQ+aAsLW89rSZktT4+ExxAJGC0590if8Z6CGlQ2dQ7777GBnhgfqYPOY+T8AMNdx4jnzq9BZfPgcxvXoJaXFEKtUqGvrw+pqanYt2/foL1Q+TyzdudVo/bc6PfoSHWart4+fHG4csh/B4Ar0yPg6Wa+pvk/RyrR3NEzzCtGP68ln/U1U6IGbfdiyesUCuC6aUOf98sjFWjp6B3x9SunRg+5VdZo53ZTK3F1ZtSw/25JmV03LXrIJHXDndvSejqRJufMFEAcZqQsgSOlEXekxBBvixoQfIm5HZCjTIzS4URlC3r6XKZ/SjTjI31H7Eyw1UgdEZImUraNkU4brvNAuM5jxAYqMTBUyi1roBLxTYn1xxdNwze0psT5C/6sc/f0wuP/+BS7Pnobut5GxMfHY82aNYMap2JxUyt51WvYiHmLCV/WZlCnT0HeXHINqisItHNmOSkzJjsilhsfqRuxN1OK5PqwmRjlJ/YlyMaUONv2EIy2YOTeUSzJcmtrJlxLM/YKIT3Gz6Lj5o0dft9EZ93uZTjjInSjH+QgPu4axIyw84BYWZrdPb1wxe/uwBtvvIEHH3zQro1TSyYIjg23bQ9gZ8puTYiUUAPVSc1NHnkz5aGmENkDbZEjHwqFAvEO2LPN3vt5CnnucLp/RZUcaltlMSbQ0y6NHmMCNWtMjHJ842PgVFcxuWtUIzZ4jLxG2NJlyYRwXDYx3OZGglyE6dwR7MPvM5TZDjKyMS7CPlunmaJGq/25SowgF1DIkyB7rAp2H2UvrfjAwQ0Te5x3TtLIDWMfd+nMKlcNsY5CSmaMcfyenVLc2H3xuFDRzj3U94LIC98GgKnZiUFWv8ZLq0ZK2MiVKAkNgEqGzkODKD/rOwTkSstzKcyKDHnNeDHl7+U6M7qIY2TE2Da7hsgPNVBd1FCL3IV439GSOwkpVeI9ckIs7pdiI13MUSIxGxDUeLEPe0wFd1R8dBR7/M1qmf3NYrh8UrjDzzFcHBiYEEdOxkf4nk/MaP/RSsDy6eZCsXUNagA16AmhBipxXc6Y/MiZebmNPvpOVWzh2NqgtuTzdATHTkGX60pp4OLkkUeN6buFQVl9xaJWyevTUKuUGB+pg5/H0A0vW/8eRyxVERN1VhJCDVRiYritFxzN1t5GJUVzl5Bkwei7nJOD2bvya+m3gu+3Z+l4/qNJGbF+I65NdFZSjlSho2x/QyyTEevn8HNMcrLEa5eMDRH7EuxKtIRTmtGr9LZeGVW3iFCogSpBo+1D5ijBPlpcMjYES8aHiXJ+vqQ0bdhSi0RcZ+lojnp+KZWKUSt/ch4Vt/e0rgkWJu3hu02NzoZkROG+zrnmcLR1/I4avbVH/oBRpzU7sGLqiHFnsZKqjA1zzDRWZ+ZtQWdVkIw7H4ViyZId+c7xIK6GGqgOxmdtRLCPdsTtFhzZgxWmcx9xZEOKvWdirc3xtCETslij1XKXGOxtUS+x1EyM0sHDTdjrHrh5/FAWpoVKch2yXKllvE5wNHynZNs+YsPvHSipymBJocN35krx2W5KTqOstA+qNNij446Ix3mfphKRGs6vN9WS9P1EXAkCJDEi5tQqJeanym/0eXykDsvTpZeFU+qVUrlJGyXey7FzxWiojktLKuKWTF2n21AYob7u+M3kCMxNGTnbvhQ5qvOHtoQhRJrk+7SUEQ2PBABuTtoT70wVYqVSIbnsgcQ2VFkhtnBTK7EgdfiRHktGtQlxJC+tGjRpwrFsfY44MumTM370vh6uF1cVCgU+/fTTYf+9pKQECoUCubm5gl2TvTlnK8gJKJUKXDbB8SntpSCW9p50Ks7UCQE4ZiP3gRy1jlqO67MdzcluTwDA2FH2X5U6molnu0nRlq05t5Wc1oLK9b5KCvF22LZPUi0TWxqZS8eHI9hH65Ds1mI39B577DFMnjx50O8rKyuxZMkS4S9IQNRAlTBvd+n1Clky9fiihECr3lPnoXHY/miEDBTuZ11SIC+t2uGN1OgAxyQN4jN7g9hGjAqgv5cbpsW7zprLMJ1ta/iDZZoDIFxniF2xgYOfw+MihGmgznPAWlBbOzUdPeMsJUzYjj6FQoFgn5HvUWeL7FPjAni/VqlUYGFaKK7OkN4yGkcJCwuDVivPOGYpaqASq1iSkCiWx/pZOWdftZXcpqd42JAcSgpmJ4683yORtktFzDLuppbuI1OsZHH2IHRlW8ojziNtmzZvbAh+OzUa7hrzGGyPkSNL30GK99lwGahHWxNuKTGSGo62ZRrfjrCRPudp8aM3Eh2Vidxdoxq1UT6aUTORO0BXVxf++Mc/IiQkBO7u7pg9ezYOHDjA/XtmZiaef/557r+vvPJKqNVqtLS0AACqqqqgUChQUFAw6L23bduGxx9/HIcPH4ZCoYBCocC2bdsADJ7iu3//fqSnp8Pd3R1TpkzBoUOHBr1fXl4eli5dCm9vb4SGhuLGG29EXV2dnUrC/qQXacionK3nDADGBHvBx4YRY6EzpNrTcJuXi2W0sowP9EJKmDemDpNp2t9TWn/PQFKsYA0lRcKVaDHZezsea1iyPdRw8TnAS5ztw8hgSqVixHV+gd5udt+X2BKTo/1G7QShrNuWiw7whL8N22GJaVyEDukxfpho4XZh9hDi4y5qfJWjP//5z/j444/x9ttvQ6/XIzExEYsXL0ZDQwMAICsrCz/88AMAgDGGH3/8Ef7+/vjpp58AAHv27EFYWBhSUlIGvfdvf/tb3HvvvRg3bhwqKytRWVmJ3/72t4OOa2trw7Jly5CSkoKcnBw89thjuO+++8yOqaysxNy5czF58mQcPHgQO3fuRHV1Na699lo7l4j9yKOmRpyeRqXE5ZMieL9+QqSf/S7GxS1KG3mESqlUIDM2AFH+g0fK5yQHDerdJ/zQVkRA8gjbYojB14ZGy4wx1i19sJYYCb7stY2D1NbFZYmU5VbKI7sjEXoKrDU8LdhjVYpUSgVSw31tijl8UP+H5dra2rBp0yb8/e9/x5IlS5CWlobNmzfDw8MDW7ZsAWBooP7444/o7+/HkSNHoFKpcOONN3KN1h9++AFz584d8v09PDzg7e0NtVqNsLAwhIWFwcNj8HKg7du3o6+vD2+99RbGjRuHZcuW4f777zc7ZtOmTcjIyMCGDRswduxYpKen46233sKePXtw8uRJ+xaMnVADlSdHTXNwdpQldTCprRMcaR/c0bhq49S4Nmw0NL1YfGKFbjmPfGklPLXZVVkymi+U9Gjprn9mTroZ5lDRROjGrDUi/RyTZ0FMp0+fRk9PD2bNmsX9TqPRYNq0aThx4gQAYM6cOTh37hwOHTqEvXv3Yu7cuZg3bx727t0LYOQGqqVOnDiBSZMmwdPzwqDBRRddZHZMTk4O9uzZA29vb+5n7Nix3N8hRfLsWhIATXMgjjZ9TABO17RiUrQfYgI9sSe/VpTrmBStw+GzzXZ5L2uq4P6eGjS299jlvPYyXONltCrOOAuTfNm6xoaQ4YzU8J431rYRwYuTHduxIt+mu3ikMsNCpRRn7Z+lRhtMsLXDSqwOr6GeSTPGBOLfh8oFvxZLTBlmSZCp+CAv1J7rsuk8fp4aNAlUrzB2fgy8xxhj3O90Oh0mT56MH374AT///DMuueQSXHzxxcjNzcWpU6dw8uRJZGVl2eU6RtLf34/LL78czzzzzKB/Cw+X5o4h1C06jEBvrUO3aBhtATwRhlCp+YeSEOyNRePC4K5RIVznYfag89IKNxIpVPbHgbJS7J8N0laOSFNPhkDFPCxHfPdt3X/VWfflFkMCbf00iCNnVrnSXuVSTWDo7a62KPdDYog3FqaFWtSYHc7CtFC4a4SJV4mJiXBzc+PWkwJAT08PDh48iNTUVO53WVlZ2LNnD/773/8iKysLfn5+SEtLwxNPPIGQkBCzYwdyc3NDX1/fiNeRlpaGw4cPo6Ojg/vdvn37zI7JyMjA8ePHERcXh8TERLMfLy9pbvVIT50RhPlatx2FNRak8k+2QexHrMbZUEw/79HWgYbr7Dtdxl4d4NZMprL3w9Ta7WOG4sjvPDExwo3i6GnQYiS/scZo331XY0vyPCmK9PPAZRPDMTsxyOZ73e98AqAof+ebPmkvvu4aTB8zfHZaW5LmhevceS9rsXWQgu8jO3WUzMZiT4gO9tHatAesRqVEIDe7wH5/TUFBAXJzc81+NBoNbr/9dtx///3YuXMn8vLycOutt6K9vR2rVq3iXpuVlYWdO3dCoVAgLS2N+9327dtHnd4bFxeH4uJi5Obmoq6uDl1dg0eYr7/+eiiVSqxatQp5eXn46quv8Nxzz5kdc+edd6KhoQHXXXcd9u/fj6KiIuzatQu///3vR20Ai4UaqCKR83okMdi63kZua4ZHa7wF+2hHzELpisbbobNhuPvEkbMpHEnsygYfMYGe3BILe9/jl08Kl3zsHe67P9qogDOO/l86Pkw2WbetofPQICbQ0+ZOukvGhmBafIBNe0gS/uYk8586PydJnERcQmYFFosjIuHKlSuRnp5u9lNRUYGnn34aV111FW688UZkZGSgsLAQ33zzDfz9L4wCz5kzBwAwd+5cro4xd+5c9PX1jdpAveqqq3DppZdi3rx5CA4Oxj//+c9Bx3h7e+OLL75AXl4e0tPT8dBDDw2ayhsREYH//e9/6Ovrw+LFizF+/Hjcdddd0Ol0UCqlGWOdq2vSzphEq3dya2zZw2jrbUabFueMiRICvNxQXNdml/dKDvNBfuU5RNhhFNIZeWvVuHZKFHYcLBP0vFq1El29/YKeUwoWpYWis7eP19TUkfIHWNM4vWRsCL7Pr7H6/I6yMG3kTrowX3eolQr09g+OdY56ZDj6UeTIfWcTQ7xRXNcGpQIYoshkwV2jkm3nmasTYzquu0Y5+ppcga5FLuLi4katP77yyit45ZVXhv13nU6H3t5es99deeWVFtVLtVotPvroo0G/H/jaGTNmIDc3d8RjkpKS8Mknn4x6TqmQZrOZjMjWAYCEYOcbeQvXeWBytB/m2DmZx6xEx24NIRWTo/xwydgQm6ecOfPDTS3CSE5s4OCtfKwh2c9jlAtTKhW8102OCfLCtHh/zLVxq5AwCzMzC2W0qckKhQJZNiZDstbkaD94uCkF32tyfKTto0DBPlosT4/EJWP5r4XXeUh7ujixD6WTDApY0k8v074a4oSogToCR/be2iJ6iP0nreGsGYrTInwRwWNt5tyUYEQHDP262EDhGvMhvoZRYk8RelaVSgXCdO6iNMJMTYuX51Q1H600K6p8KxsXJci3Y0ahUCAxxAeRfh5Ii7Asu7J9L0D4U4rFx12D5elRmBTtZ9P7WJsFNsTHPp0HHm4qmz4vWzuQ5Moe6/3tZWy44/eOtfcWKXKZtT5wdF6otc7OOK2fWI/ughGE6zyQEuYz4gJ7MUg5pbtUjTStJdLPAxeLtB7E1MyEIIyP9MWCUabySZmtva9yna4m1eyJfNm69tNjtMQhPG8UbysT5ohxPwV7a+Hh5lqPVlu/90HeWi7hjyl60knTnKRgLpHNmGBxY/bkKD+Hn8PqDhRfaWwBxIe/54UBjIH7e9uy5tYaUf4eNiVKIs7BtZ6iPGTG+iNB5ABsSg4L3KU4G0YOa1DdNSpMjPKDt5aWhvMhxfvOVrbetVqRZoGk2nFUI8DrQsNlvg3TMYWiUCgwT4JbKEndRWMGj9pLP2qbk/OSEGtyW6iUClyZHok5yUHIiOG/JYi1hmqzSLHDfrKNMwocxZKPeHK0H1LDfXDpeOuyidtzJFuhUGCmjL9LxD6ogSozQ/UyS40rJnEiBmJ+8jLogxBcSqjjp78NRa1Sjjy91oobJSslBBmxfliREQkvETtvYgJcczqnJSjiGxpPti4JkdOj002tRJS/p6BZsS+bGG723xJsmwIQZoqqo8rdTa1Eeoy/1UvBhOyoIK6BGqgC0PLcJ0vOhoudUn4Aj4+0/3o1sdb7ilmRJ/ZjayVErVI6ZNZFZqxwlRF3jQpjw3x57zdoLzMkttTDEWxJGOTqpL59kTOQ+h7GQrl2ShSvgQBX6cQN8HJDuM4dHjwT7VmLMYbVq1cjICAACoUCubm5Q/5OSA8//DBWr14t6DmHM3XqVF7Zg6mBypM1oWFOkmM3nh+Or8fQX04pNxIdxZJgPtHOa1mmxQeI0lCcFK1zyWnCtt7XUhwhGydGkh8LpITZODIrw4qS2AnEhCC1xIBCPKqCvLQu+UyUEjmU/xgrdz9wZOe0K8QiW4yP1GHe2BC7J7cazs6dO7Ft2zZ8+eWXqKysxPjx44f8nS1KSkosbuhWV1fj5Zdfxl//+lfud0899RSmTp0KHx8fhISE4Morr0RBQYHZ6z755BMsXrwYQUFBw57rnnvuQUBAAGJiYvDBBx+Y/duOHTtw+eWXD3rNww8/jAcffBD9/dZtmUd3uQD8PN1G3afTEeYKtKB9KHzrn1LKDmgrXysTuthLOI9Mxnw424hBUqh01pobadUjx42LLej8cpYtEsRwcVIQ3NRKZNm4ZY3Y6A4YnlKpwBIr1tvZuhUXkSdrMzZTkh/7ESuXgqVOnz6N8PBwzJw5E2FhYVCr1UP+TihbtmzBRRddhLi4OO53e/fuxZ133ol9+/Zh9+7d6O3txaJFi9DW1sYd09bWhlmzZuHpp58e8n2/+OILvP/++9i1axeeeeYZ3HzzzaivrwcANDU14aGHHsL//d//DXrdZZddhubmZnzzzTdW/R3S/tQlTGp75A3Fx10zZI+4L89pMvZY+O82Ss/frATHPPzlkCRJbtzUSkyLt22qJ5+09WI+9+enSmsKZLQFo75Jod7w99RIqwEuk7pbdIAnrs6MQoQdeuLlsDbfXSOtKoFQUVthxQ0ZY8etZaQ2Yk2cmwxC0JCuTI8U9fx79+7FtGnToNVqER4ejgcffBC9vb0AgOzsbKxduxalpaVQKBSIi4sb8ncA8NFHH2HChAnw8PBAYGAgFixYYNZA3Lp1K1JTU+Hu7o6xY8fitdde4/4tPj4eAJCenm7Y8zora9jr/eCDD3DFFVeY/W7nzp3Izs7GuHHjMGnSJGzduhWlpaXIycnhjrnxxhvxyCOPYMGCBUO+74kTJ5CVlYUpU6bguuuug6+vL4qKigAAf/7zn3HHHXcgJiZm0OtUKhWWLl2Kf/7znyOU8mAUHXmYnRgk+nooW4T48mtc+1gxIjhcHBxtGqUzPbAdXbkylqW/iImz4oNsa/TMGCJz52h83DUI04mTxl+MPWptpVEpsWRCOCZESj8DOBGXp0BrtpyJcUuhUB7PVZ2Hxuqpo4TwJVY/fbC3bc9rMWdrlZeXY+nSpZg6dSoOHz6MTZs2YcuWLXjiiScAAC+//DL+9re/ISoqCpWVlThw4MCQv6usrMR1112H3//+9zhx4gR++OEHrFixghs82bx5Mx566CE8+eSTOHHiBDZs2ICHH34Yb7/9NgBg//79AIBvv/0WlZWVw67pbGxsxLFjxzBlypQR/67m5mYAQECA5bkVJk2ahIMHD6KxsRE5OTno6OhAYmIifvrpJ+j1evzxj38c9rXTpk3Djz/+aPG5AICeRjyoVcJ8WeTa2zUSsVLCy2H0wloebipcMyUKKoUCHxw4K/bl8OKmViIu0BMl9e3QeVje0J6ZEIRP9OUOvDLp+M3kCDS0dePHU3ViXwohZIBFaWEoqW/jveduargvimrbRj/Qjkz3ugQMuwM425INsbn6nK3MWH94uqnQ1N5je84CEb322muIjo7Gxo0boVAoMHbsWFRUVOCBBx7AI488Ap1OBx8fH6hUKoSFXVgqMPB3er0evb29WLFiBWJjYwEAEyZM4I5fv349nn/+eaxYsQKAYcQ0Ly8Pb7zxBm666SYEBxuWmQQGBpqdZ6AzZ86AMYaIiIhhj2GM4Z577sHs2bOtWhu7ePFi/L//9/8wdepUeHh44O2334aXlxduv/12bNu2DZs2bcKrr76KoKAgvPnmmxg3bhz32sjISJSWlqK/vx9KpWUDUc4zXEWICOKDxO391qiUUCoVuDozStTrMOLzUJ4aH4Cpcf6CTp+1555tjualVVs0lddR7DE1eEocbUFArCf1JpPOQwMvrRrjInSjrhcfjhjr69w1KrN11daswXUEvtuyeMhwRou1VAINiAzFlqUNKqVhicSEKJ2sZ8adOHECF110kdkgx6xZs9Da2oqysjKL32fSpEmYP38+JkyYgGuuuQabN29GY2MjAKC2thZnz57FqlWr4O3tzf088cQTOH36tFXX29HRAQBwdx9+RseaNWtw5MgRq6fcAsBjjz2GwsJCHD16FMuXL8eGDRuwYMECaDQaPPHEE/jpp59wyy234He/+53Z6zw8PNDf34+uri6LzyXfu0ZEtvaMucJ2BWIReqDUXaPCtPihP08hL0XODwCNSomkUB9Bp82nS3QjdSniO7Biuu5bihmSpUzsjq+BRstGHuIj/JR7PuvX7eXS8WGYFh9gl44jd40K81NDsDAtVNCZPqYNYznNMDKNR1kp0soJ4AgROndo7NRItfZjVikVksu7IDTG2KDvh/HZZs33RqVSYffu3fj666+RlpaGV199FSkpKSguLuay227evBm5ubncz7Fjx7Bv3z6rrjcoyJDHxdj4HWjt2rX4/PPPsWfPHkRF2TawkZ+fj+3bt2P9+vX44YcfMGfOHAQHB+Paa6+FXq9HS0sLd2xDQwM8PT3h4WF53JZvrVbGxgRLKFmJiwh3YFIrGT3bHU4uRSGnCpn4FA79/pDBpg/o9BJrP2Wj0Ua4HLXtxUidwXOSg7EiIxJLJwg/+hfg5cZ7Su9QQn3dESxCI1+OlkwIx9hwH6zIiLRqWYhcKRQKXJwkXhZxvk9KlYXTOKUuLS0NP//8s1mH688//wwfHx9ERlqXvEmhUGDWrFl4/PHHcejQIbi5ueHf//43QkNDERkZiaKiIiQmJpr9GJMjubkZngF9fX0jniMhIQG+vr7Iy8sz+z1jDGvWrMEnn3yC77//nntfvoz7vD7//PPw9vZGX18fenp6AID7X9NtZY4dO4aMjAyrzkFrUHlw9qqto0bjLkqwPiGOvc7LZ3TO1i1vXH0NCnEesxKD8FGO5dOZiG0GrtW3dZsbW5IfSXmLHXeNasjYLvVtKQh/Og8NMmKkv2RAikt6hUySFGvj7AIPiSQiveOOO/DSSy9h7dq1WLNmDQoKCvDoo4/innvusXgtJQD8+uuv+O6777Bo0SKEhITg119/RW1tLVJTUwEYps7+8Y9/hK+vL5YsWYKuri4uIdE999yDkJAQeHh4YOfOnYiKioK7uzt0usGJD5VKJRYsWICffvoJV155Jff7O++8E++//z4+++wz+Pj4oKqqCgCg0+m4Uc2GhgaUlpaioqICALh9UsPCwgate928eTNCQkK4bMGzZs3CY489hn379nGjxH5+ftzxP/74IxYtWmRxeQE0giqYWYlB57flcN3pvWLFa77TYy62cL87jZP0FBJiKjXcB1q1Emnhvrw6rYzTVAO8Bo9yjLbdFDFn6/R3WxLg+ImYJZzvVbv6tEQ5kWJDzh6mxAVwGZ7lLMDLDRqVYsg4PpzZiUE2J8QM9NYiPcbPor2+HSkyMhJfffUV9u/fj0mTJuG2227DqlWrsG7dOqvex9fXF//973+xdOlSJCcnY926dXj++eexZMkSAMAtt9yCf/zjH9i2bRsmTJiAuXPnYtu2bdxIp1qtxiuvvII33ngDERER+M1vfjPsuVavXo0PPvjAbARz06ZNaG5uRlZWFsLDw7mfDz/8kDvm888/R3p6Oi677DIAwMqVK5Geno7XX3/d7P2rq6uxYcMGvPLKK9zvpk2bhnvvvReXXXYZduzYga1bt3L/Vl5ejp9//hk333yzVWVGI6gCCfLWSiaRjSUCvDRoaOsx+501AUelVKC/z7y7Tg4jir4earR0GPa3snTaWpS/B6L8PRDkrUXu2Sbu90763HVagQJMo9SqlVAqgH4ZfBnSY/wxOdqPmw6dGeuPnDONyIj1s+j1fp5uWJERCTeVEt19Fx6U6TF+LpHchADeVmxNZk9+nrZ9l9XU6SiYSwToTBiYtdheRnrG6zw0WJ4ehfd/LXXIufngs7JFrVLiqowoUZYypYb7Cn/SIcydO5fb5mUof/rTn/CnP/1pxN+lpqZi586dI57n+uuvx/XXXz/sv99yyy245ZZbRr3eRYsWITIyEh9++CGuu+46AOY5IYaTnZ2N7OzsUY8LDQ1FSUnJoN8/8sgjeOSRRwb9/qWXXkJ2drbVa14pCpMhDbXmIdyKfd7mjZVnD/bc5GDEBHhaldVQqVRgTnIw0iKkEUxdlcrGJ6ij1tGZUioVWJEhn44q07W6KWE+uCozEmPDLL/P3TWqQR1bcYHSSgBEHOcSGT4HlowPoy1XBBTi4/j17Y5Ywz0xSifatnlCUyoVlLdBRhQKBd5880309vaKfSkAgJCQEKxfv97q19EIKhmS5xAjHNYE46AhNmaWQ3jzcddgtp2mlMhgkMyp+IucSMZSQ1V+lTJ5+PPdSoNYb2KUDkfKmpEe4yfoef09NWhs7xn9QAt4j5L9V4rkEkeIsIIH1GnGRw5e/0eIVEyaNAmTJk0S+zIAAPfffz+v19EIqoSJ2WNFvWWECMNdoxRlmw4ibeMjdVieHin4NLeZCeKu+SLOS87VCrVKiYVpoVa/Tuj10GIlozTlqaWOTGI7aqASpyKlhrV0rkQ4Gplmz7TXPnN8ZKWEiHbfBjhobRaxDzHW6qpE/C6IRWEStixZq0X4ifATb+9ae+AzozfUiqVR9iDmHsqXjA3BtHj/IWfQEWItedYmRRDqS184W/F97FNGRssEeGng4aaEn0h7wyWHesPXXZxz+9iYjGXphHA7XYn1xKoQXzYh3GXWUEkBlbR0+bprkBDshdRwH0l1cjoblVIhqXXJ1mSmJaML07kjMcRH7MsgTkJ2DdSuri5MnjwZCoUCubm5gp13fqr1UzukKNBbfiMmQvdAytXicWH4zaRI0RodKWHiPZhmW7glkClj+voZYwLgJcO1cjZzwXq4XNb6iklKJSRk1830MYFIl8H+mvYiVoZlKZmTLN09focT5S/vUWhCLCW7Buqf//xnREREiH0ZgrH3HlDzRey9lFLFRwhCj4spFAqXHRHj83dHB3hi5dRojAn2dsAVWc7SERux94OTu+RQb5v3E7WFXDLDemnVoqyJHvg10Hlo4EXbETmMVq3CsknhuDLddepTA3m6WddIl8J32JpG9eRoP7P/pv45IieyaqB+/fXX2LVrF5577jmLju/q6kJLSwv3I8e1JdEBnnZ9PyG20iBELqxt2HoM0cARavq/vWOBK/DUqODhpoSXVoUpcQGiXUd6jJ+ojWNrZaVcqAQLVSn383SDn+eFKZcL00Jpuq2D+bprrG6kuTI/TzckBHthfKSwicuM2+Sorfwuug3ICTHU9oGESJVsWivV1dW49dZb8e6778LT07KK2lNPPQWdTsf9VFRUOPgqpWdWovgZ3YQmlTUuVLVyPkN18MziMb3Y1biZlJtWwERaSqUCv5kUicsnijtKlCDyKL211ColZiUG4qKEQEG3FpprMjpEbVMiRdPHBGJilJ+g53RTK3FVZiRWZETyfg8fdzUlLyKyIosGKmMM2dnZuO222zBlyhSLX/eXv/wFzc3N3I+9pgarZZTlMFznAS+JpPwWavw6TCfemlU53RvEPmwdGZPjzA5rKZUKXJ0ZhasyhV8jrVS67tR3W8QGeomaEZQQe5oWL94MCnvQqlU0A464FFHv9sceewwKhWLEn4MHD+LVV19FS0sL/vKXv1j1/lqtFr6+vtyPrdOFpsb5IyXMGyE+4jWA+JybpkkJ56qMKLEvQVAqpQIxAZ4I93OHj0gZfIk8uKmVgo7GSQmFYELEJeTMDUKI7URdfLBmzRqsXLlyxGPi4uLwxBNPYN++fdBqzacnTJkyBTfccAPefvttR14mJylU3PTZ88YGD1pTIBQPN9vPa2kdLdDbDfWt3TafTwym67VcpTI+m5L32IRvB1JahHXroNw1KkT5e6CssYPX+QghhBBChCBqAzUoKAhBQaNXbl955RU88cQT3H9XVFRg8eLF+PDDDzF9+nRHXqKkiJlBbmaCcI2QOUnB+PehcsHOZ28XJwWhs6cPOk8aUSSOMzBDoyVmJwbhgwNn7X8xRLJcMQ8BIYQQeZNF+raYmBiz//b2NiScSEhIQFSUa02p5MPazG9DEbJp7OGmgkalQE+fPNfmUbZVQogpMZcZi7kmnxBCCOGDJuW7gJkJgfD1UFNPOiGEEMniu1Z3bLi4y28IIYTYlyxGUAeKi4tzicyX9uLn6YZlIm+zYK0of08U17VB50HTZAkh8kZJkizj6aZGhJ87VEoFNFZkLM2I8UdfP8Op6lZMi/d34BUSIh/BtK0MkTFZNlCJMLzd1Wjt7AVgaORa67pp0fjnfn7r3abE+SPYR4sofw9eryeEELF4aenRyldWCr99rKfGBWBCpM7mbZ+Ia7DH0iepWp4eiQ7Kg0Fkjp6iZFhXTIpAb18/+hl4ZQ+2ZXsbjUqJxBB5bW5PhCPnyoV8r5xYSqNSYmFaKHbnVYt9KYJJi/BFXkWLqNdAjVNiMRED8eykYIe+v4ebCh5uhu9CUqg3TlW38kqqR4iYaA0qGZFapRRtaxtChqNWKTE/ld9Ii60mRulser1SqUBcIL9EWgvSQuDnqcGCNH5/u1KpgFplqJl50yifQ3m6XWgsCbUiJYDHTBd7oQowIaPTqpWI9BNuZtjUuACsyIik5I1EdqjlIQPRAR4I8NIgyEve6wm0GrrdiP2Itb5mfKRtDVQAmJl4YdsmDytGfUJ83LF0QjhCfPhnZr0qIwrXTokSddsq4hhqlRKLx4WKfRmEkGEoRagG0cwCIkfUhS4DFzt4OogQ/Dw1CNfRelJiP0qlAtEBHujp65dlMq2FaaHo7e/npmIJxdAwpcapkIRMkqQWowZMiNyIlGeT8nsSYhlqoBJB0PQv4ghy7rwJ9pH3jAhCCLHF3BT5xm9CiGNRVyshhBBCCBGUkGsxBxFpEgltOUWIZaiBSpyej7v8pn8SaZsxJgCAYTskQoZCFVFCCCGEH5riS5zahEidLNcnEmkbE+yNmABPqFXUx0eG5ummRrifO1QKBTR0nxACAAjydoNaqYCPO1U/CSHDowhBnFqojtb5EcegxikZzbwUcbZCMqWhpElEQtQqJa7KjIKrJhGnJEmEWIYaqIQQQoiTUorUEgjwEm9PViJttMUVIWQ01LVKBCH0PlwaleEB6C/ixvWEECKmSdG279nL19xkytBKpEOrlkZ1l9amE2IZGkElDnVxUhDau/sE701fkRGFvn5Ga78IIS4rLdxXlPMqFRB8f19CRhLi6y72JRBCrEANVOJQ0QGeopxXpVTQNCJCiEtT0HANIYQQGaLhJUIIIYQQQgghkkANVEIIIYQQ4jK8tTSBkBApowYqIYQQQghxGRcnBQl2rhiRljoRImfUQCWEEEKchFp1Yd0po00XCRmSj7tGsHPNSgwU7FyEOAtqoBJCCCFOwkurxsQoHabE+QueJCkz1h8AMDNBuNEpQiw1MyEQXloVLh0fJuh5KVkZIdajSfiEEEKIExkfKc7+pylhPkgM8aYM6kSS4oK8EBfkJfZlEEIs4JIjqD09Pdz/z8vLw9mzZwEAnZ2d0Ov1OHfuHACguroahw8f5o4tKCjAmTNnuPfQ6/Vobm4GANTW1uLQoUPcsadOnUJxcTEAoK+vD3q9Ho2NjQCA+vp66PV6bvrV6dOncfr0aQCGKVl6vR719fUAgMbGRuj1evT19QEAiouLcerUKe48hw4dQm1tLQCgubkZer2e+/vOnDmDgoIC7tjDhw+juroaAHDu3Dno9Xp0dnYCAM6ePYu8vDzu2KNHj6KyshIA0NbWBr1ej46ODgBAeXk5jh8/zh17/PhxlJWVAQA6Ojqg1+vR2toKAKisrMSRI0e4Y0+cOIHS0lIAQFdXF/R6PVpaWrjyzs3NNSvvkpISs/Juamriyluv15uVd1FRkVl5NzQ0AAAaGhqg1+vR398PACgqKkJhYSH3Wr1ej7q6OrPy7u3t5cr75MmT3LG5ubmoqakBALS0tECv16O7uxsAUFpaivz8fO7YI0eOoKqqCgDQ2tpqVt5lZWVm5X3s2DFUVFQAANrb26HX69He3g4AqKiowLFjx7hj8/LyuPI23rPG8q6qqjIr7/z8fK68u7u7zcq7pqbGrLxPnjzJ3bO9vb1m92xdXZ1ZeRcWFnLl3d/fP2R5G+/ZoqIis3tWr9dz92xTU5PZPVtSUmJ2z+bm5nL3rLG8u7q6uPI+ceKEWXkb71ljeRvv2bKyskH3bHl5OYAL92xbWxsAwz179OhRs/KmGEExgmKEZTHC2DilGEExgmLEhRhRWWp4X41K6fIxwkiKMYJICHMhkZGRDABTKpXc7yZMmMDWrl3LGGPs1KlTDADbs2cPY4yxZ599lvn7+3PHzpgxg61atYoxxlhFRQUDwL788kvGGGMbN25kbm5u3LHz589nK1euZIwx1tzczACwHTt2MMYY27p1KwPAenp6GGOMXX755ezyyy9njDHW09PDALCtW7cyxhjbsWMHA8Cam5sZY4ytXLmSzZ8/nzuPm5sb27hxI2OMsS+//JIBYBUVFYwxxlatWsVmzJjBHevv78+effZZxhhje/bsYQDYqVOnGGOMrV27lk2YMMGsrB599FHGGGP79+9nANjhw4cZY4w9+OCDLCEhgTs2OTmZ3XfffYwxxo4dO8YAsJ9//pkxxtj69etZWFgYd2x6ejq74447GGOMFRcXMwBs9+7djDHGnn/+eebj48MdO2vWLHbTTTcxxhirqalhANhnn33GGGPs9ddfZyqVijt20aJF7Oqrr2aMMdba2soAsPfff58xxti7777LALDOzk7GGGPLly9nS5cu5V4LgG3evJkxxtjHH3/MALD6+nrGGGM33HADmzt3Lnesp6cne/nllxljjO3cuZMBYGfPnmWMMbZ69Wo2ZcoU7tigoCC2YcMGxhhjP/74IwPA8vPzGWOM3X333SwtLY07NjY2lj300EOMMcZycnIYAJaTk8MYY+yhhx5isbGx3LFpaWns7rvvZowxlp+fzwCwH3/8kTHG2IYNG1hQUBB37JQpU9jq1asZY4ydPXuWAWA7d+5kjDH28ssvM09PT+7YuXPnshtuuIExxlh9fT0DwD7++GPGGGObN29mpuFi6dKlbPny5Ywxxjo7OxkA9u677zLGGHv//fcZANba2soYY+zqq69mixYt4l6rUqnY66+/zhhj7LPPPmMAWE1NDWOMsZtuuonNmjWLO9bHx4c9//zzjDHGdu/ezQCw4uJixhhjd9xxB0tPT+eODQsLY+vXr2eMMfbzzz8zAOzYsWOMMcbuu+8+lpyczB2bkJDAHnzwQcYYY4cPH2YA2P79+xljjD366KMsMjKSO5ZiBMUIihEGFCMoRlCM4BcjZsy6mH19tJI1tXVTjDhPijGCSIdLNlBDQkK43x0/fpyVlpYyxhjr6OhgOTk5rKWlhTHGWFVVFcvNzeWOzc/PZyUlJYwxxrq7u1lOTg5rampijBkCn16v5449efIkKyoqYowx1tvby3JyclhDQwNjjLG6ujqWk5PD+vv7GWOMFRYWssLCQsYYY/39/SwnJ4fV1dUxxhhraGhgOTk5rLe3lzHGWFFRETt58iR3Hr1ez30pm5qaWE5ODuvu7maMMVZSUsIFMsYYy83NZVVVVYwxxlpaWlhOTg7r6OhgjDFWWlrKjh8/zh175MgR7gHV2trKcnJyWHt7O2OMsbKyMu6hzpjhYWIMru3t7SwnJ4edO3eOMWZ4ABsfSIwxlpeXx86cOcMYMwSknJwc7qFZVVXFDh06ZFbexiBiLO/GxkauvI2B11jep0+fNitv48Ohvr6e5eTksL6+PsYYY6dPn+YeqIwZAnltba1ZeRsf+kVFRaygoIA79tChQ6y6upoxZqgw5OTksK6uLsYYY2fOnGEnTpzgjj18+DCrrKxkjDF27tw5s/I+e/asWXkfPXqUlZeXM8YYa2trYzk5OaytrY0xxlh5eTk7evQod+zx48e58jbes8byrqysNCvvEydOcOXd1dVlVt7V1dVm5V1QUMDdsz09PWb3bG1trVl5nzp1iivvvr6+IcvbeM+ePn3a7J7Nycnh7tnGxkaze7a4uNjsnj106BB3zxrL21hBOHPmDMvLyzMrb+M9ayxv4z179uzZQfdsWVkZY+zCPWt8EFZUVLAjR46YlTfFCIoRFCMoRlCMoBhBMcK5YwSRDgVjrpPmLyoqCuXl5YiMjOSmNhBCCCGEEEIIkQaXXINKCCGEEEIIIUR6qIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSqIFKCCGEEEIIIUQSFIwxJvZFCMXNzQ09PT1QKpUIDw8X+3IIIYQQQgghIgsLC8PBgwfFvgxynlrsCxBSX18fAKC/vx/l5eUiXw0hhBBCCCGEEFMu1UB1d3dHZ2cnVCoVQkJCRjyWMYaKigpERERAoVAIdIWujcpcHFTu4qGyFw+VvfCozMVB5S4eKnvxWFv2YWFhAlwVsZRLTfG1RktLC3Q6HZqbm+Hr6yv25bgEKnNxULmLh8pePFT2wqMyFweVu3io7MVDZS9vlCSJEEIIIYQQQogkUAOVEEIIIYQQQogkUAN1GFqtFo8++ii0Wq3Yl+IyqMzFQeUuHip78VDZC4/KXBxU7uKhshcPlb280RpUQgghhBBCCCGSQCOohBBCCCGEEEIkgRqohBBCCCGEEEIkgRqohBBCCCGEEEIkgRqohBBCCCGEEEIkgRqohDghY+6zrVu3oqKiQuSrIYQQYk8U4wkhzsxlG6iUvFh4/f39Yl+Cy1AoFCgtLcWqVavw73//G729vWJfEiHEyVGMFw7FeEKIM3PJBmpzczP6+vq4/6bGquMxxqBUuuTtJore3l7ExMRg7dq1eOGFF1BYWCj2Jbmsnp4esS/B5dTU1KC2thbd3d0AqOEkBIrxwqIYLx0U44VXWFiI3bt3i30ZxIFc6mnS09ODO++8E0uXLsXSpUuxfv169PX1QaFQiH1pTsvYEaBQKFBRUYGsrCx89913Il+Vc+vr64NarQYAvPjii6irq8PWrVvR0dEh8pW5ls8//xy9vb3QaDQAgKamJnEvyAX09PTgtttuw5w5c3D55ZfjiiuuQFdXFzWcHIhivPAoxksDxXhxHDlyBMnJybjuuutw5swZsS+HOIjLPLV3796NtLQ0HD9+HPfffz+io6Oxfft2PPbYYwBorOhWYQAAEHtJREFUFNVRVCoVACA/Px+bNm2Cp6cnEhMTaUTDgYxlXl9fD6VSiQ0bNuD//u//cOjQIZGvzHWUlJRgxYoVeOaZZ3D48GFMnToVb775ptiX5dQ++ugjpKamcrFm1apVOHXqFO69916xL82pUYwXHsV48VGMF093dzcWL14MjUaDZ599VuzLIQ7iEg3UlpYW7NixA4sXL8bu3btx5ZVXYtOmTVi5ciUOHDiA9vZ2GkW1k4GVks7OTtx5551IS0vDgQMH8M477yA2NpZGNByooqICixYtwrZt2wAAd955J6Kjo/Hiiy+isbFR3ItzcsbRpLi4ODzyyCN48sknMXXqVCxcuBB33XWXyFfn3H744Qdcf/31+PbbbzFv3jzceuutuPjii6HVasW+NKdCMV58FOPFQzFefHq9Hv7+/ti+fTvefPNN7N+/X+xLIg7gEk+Qvr4+zJ49G7fccgs0Gg0YY3Bzc0NnZyc6Ojrg6elJI6h20NfXN6hS4u7ujksvvRRJSUnQarUICgqinnU7GW7dS0REBBQKBfbu3YsDBw4AADZt2oSPP/4Y33//Pd3rDmCstBhHNjo7O/Htt9+is7MTixcvxoYNG6ih5CDGeLJu3Trceuut3NTHM2fO4OjRo4iIiMCvv/4q5iU6DYrxwqIYLx0U48Vlek9rtVrExsbikksuwdSpU/H4448DMAxGEefhlA3UN998E5s3b8bevXsBAP7+/vjd736HyZMnA7hQoWlubsaYMWMAgEZQbWAauOvq6rBu3Tq8/fbbyM3NBQDMnTsXS5cuxffff4+Ojg4olUqqwNigv78fL7/8Mu677z4AQFdXFz7++GPU1tZyx6xfvx4nT57EN998g/b2dmRlZeGyyy7D008/jbKyMrEu3SkxxrhKy7/+9S/ccccdOHjwIHbu3Imvv/4a//nPf/Djjz8CoGQ99mIa440NptDQUERHRwMAXn31VcTHx8PT0xNffPEFlixZgscffxxdXV1iXrZsUYwXFsV4aaEYL7yB9XiFQsGVrV6vR2trKwBg+/bt2LlzJ5YsWYLFixcjPz9ftGsmdsacyPvvv89CQkLYRRddxCZPnsyCg4PZk08+yRhjrLe3lzuuv7+fMcbY9OnT2T/+8Q+z3xH+vvvuO+bl5cUyMjJYamoq8/b2Zh9++CHr6+tjx44dY4mJiWzNmjWMMcb6+vpEvlp5+8Mf/sCmT5/Ofv31V/avf/2Lubu7s48//tjsmD/+8Y9s5syZbOfOnYwxxsrLy5lWq2Uvvvgi6+rqEuOynYJprDD+//LycnbJJZewoKAg9uijj7Kvv/6a9fX1sZaWFrZ8+XKWkZEx5OuJdYaK8Rs2bGCMmcf4bdu2sf/+979cWb/33nvMw8ODlZSUiHLdzoJivHAoxouHYrx4Rorxxnt65cqV7Ntvv2WMMbZ582bm4eHBNBoN++ijj0S7bmJ/TtNA3b59O5s0aRJ7/fXXGWOGYLJx40bm5eXFWlpaBh1fXFzMgoODWX5+Pve706dPM8bowWopYxDOyclhixcvZn/729/YW2+9xRgzBJJVq1axzMxM7uH50ksvMV9fX67MTSuUxDLGMs/NzWWLFi1iN998M2OMsQULFrBrrrmGFRUVcceWlZWx+Ph4dvvtt7Pq6mrGGGNr1qxhGo2GFRcXC37tzqCrq4vV19cPunfXrVvH5s+fz6qqqga9Zv/+/czDw4Nt27aN+11zc7PDr9XZWBLjh4spJ06cYGq1mu3atUuw63UGFOOFRzFeXBTjxWNpPf6mm25iN954I5s6dSoLDg5m69evZ/7+/uy5554T69KJA8i+gWoM5tu2bWOrV69m7e3t3L/99NNPLDk5mf3666+DXrdp0yaux0uv17Np06ax4OBg1tPTI8yFy1BhYSG74oor2MaNG81+/+uvvzIfHx/m5ubG9u3bx/2+rq6OzZw5k915552so6ODFRcXs4ULF7KsrCyhL90pPf/88ywjI4N9/fXX7ODBgywsLIy9+eabZvdwdnY2S0hIYO+88w73u6+++kqMy5W9p556ik2ePJlNnz6dpaSksJdffpmdOnWKMcZYamoq++tf/2p2vLGC093dzR588EGm1WrZl19+ye666y52ySWXsDNnzgj+N8gR3xhv6qmnnmKLFi0yey0ZjGK8tFCMFxbFeHFYE+Pb29vZ8uXLWWBgILvzzjtZWVkZY4yxp59+mikUCuqYcSKybaDm5OSwxsZG7r+bmpoG9Xjl5uaysLAw1tDQwP3O+EVYu3Ytu/rqq9ndd9/NlEolW7VqFevs7BTk2uXqtddeY2FhYczHx4e9+uqr3Ihza2sre+qpp5hWq+WCeXd3N2OMsRdeeIHFxcWxnp4e1tvby95++20WERFBQcQGplOOrr76arZ06VLW09PDVq1axWbPns0OHjzIGGOso6ODXXHFFUyn07GbbrqJ1dXViXnZspWfn8+mT5/OkpOT2bvvvss2b97Mbr31Vubh4cFmzZrFWltbWXh4ONuyZQtjjA05ta63t5etWLGCTZw4kaWnp7OcnByh/wzZ4Rvjjc6cOcMKCwvZLbfcwiIiIrjRDZp+NzyK8dJAMV5YFOPFwTfG79+/nx0/ftzsuM7OTvbss8/SDEgnIrsG6kcffcSioqJYQkICi4mJYQ8//LDZlAvTm/OFF15gs2bNYoyZB5S+vj4WGxvLFAoFy8rKGnSjk6G9+OKLbN26dezTTz9lv/3tb9mcOXNYa2srY8zwII2Pj2d/+MMfzF7z5ZdfMi8vL27tV0tLC3UE2IGxArN9+3Y2ZcoU9tprr7Ha2lo2ceJENn/+fPbJJ5+wdevWsTVr1rDvv/+eNTU1iXzF8vXII4+wJUuWDCrDN954g3l6erJ7772X3XPPPSwtLW3Qaw8ePMiNOLW3t9MaSAvwjfHGBhNjjJ08eZLde++9LCoqis2bN48VFBQI9wfIGMV46aAYLxyK8cLiG+MprrgWWTVQDxw4wMaOHcteeukldvjwYfbaa6+x4OBgdvvtt7P6+nrGmOHGNk5/Wb58ObvzzjsHvU9TUxN76qmn2DfffCPo9cuV8UH5yy+/sKCgINbZ2cnq6urY1KlT2VVXXcU+//xzxphhsbpSqWQffPABq6ioYIwx9rvf/Y4tW7ZsUK8YrU2yj46ODrZ69Wo2e/ZsVlZWxvbu3cuWLVvG4uLi2MSJE82mPlLPovUqKiqYt7c3e+211xhjhjI0lmNjYyO77777mEqlYu+99x6LiopiDz/8MLf2KD8/n1199dWDpuSR4dkrxre3t7MffviB/e9//xP0+uWKYrx0UYx3LIrxwrJXjCfOTxYNVOPDc9OmTSwqKsps8fnGjRvZjBkz2Pr167nf9fX1sf7+fpaQkMC+/PJLxhhjBQUFbOXKlay0tFTYi3ciFRUVLCsri/373//mfnfdddcxnU7HvvvuO1ZWVsauvfZaplAo2PLly9lFF13EwsLCKDGJgxgfot999x2bPXs2u/vuu7l/M03+RVMa+Tty5Ajz9PRk//rXvxhjg8tyz549LCIigj3wwAPs448/Zv7+/iwpKYldeeWVzMvLi1177bVDJmkj5ijGSwPFeGmhGO94FOOFQTGeWEsW+6Aa9ygtLi5GcnIytxE7AGRnZyMzMxNff/01jh8/DgBQKpU4cOAAPD09kZGRgT/96U+YOHEi6uvrERwcLMrf4AwUCgVaW1sxbtw4AMCdd96JTz/9FGPGjMGf/vQnvPrqq7j99tvh4+OD+fPn48UXX0RlZSUWLlwo8pU7J+P+j5dccgnmzp2L/fv3Y9euXQCAlJQUAIb9C2mPX/7c3NzQ1dWFyspK9Pb2cmXJzm8aPmXKFCgUCjDGsGLFCnz++ee47777kJycjN27d+PDDz+Ej4+PmH+CLNgzxoeEhIjyNzgDivHSQjHe8SjGC4NiPLGauO3joe3atYutXbuWvfTSS2bTVz777DPm7u7OJW4wTiHatWsXmzVrFnvhhRe4Yx999FGmUCiYj48PS0tL45IKEH6MvV8LFixgycnJLCAggGVmZrI9e/Ywxhh79dVXWVhYGFu4cCG7/vrrWWJiIvda07VhxL6Mn0teXh7LyspiTz/9NPWm21lWVhabPn36kBkZu7u7WUhICPvb3/4mwpXJF8V46aEYL00U4x2PYrz9UYwntpLUCGplZSUuv/xy/L//9//Q0NCALVu2YNGiRdi/fz8AYNGiRYiLi8MzzzwD4EKPzMKFC6FUKlFYWMi9l0ajQVBQELZt24bjx48jMzNT+D/IiRjL+uKLL0ZHRweeeOIJ/PLLL8jKygJg6Gn/5JNP8OSTT2L9+vVobm7G+vXrAcCsp4zYl7FnNzU1FV1dXSguLuZ+R+zjnnvuwcGDB7FlyxbU1tYCMIxaAMAnn3yC0NBQZGdnc8dT2Q+PYrx0UYyXJorxjkcx3n4oxhO7EbN1bKqtrY3ddNNN7Le//a3ZRtRTp05l2dnZjDFDT8s777zDlErloOQXN9xwg9neazU1NcJcuIt58sknWWZmJmNs+HUv3d3d7IEHHmAJCQlDpmMn9ldQUMDS09PZK6+8IvalOKV7772XqdVqdsUVV7C9e/ey3NxctmHDBhYcHMwefvhh1tPTQ6Mao6AYLw8U46WJYrxjUYy3HcV4Yk+SaaAyxtjq1avZ119/zRhjXAavxx9/nE2fPp07prOzky1fvpylpqayH374gfX397PKyko2bdo09o9//EOU63YlR44cYW5ubqywsJAxNnwF5ty5c0JelsvbsGEDW7t2LWUSdKBnnnmGJSUlsfDwcDZ+/Hg2btw4Ll4Ry1CMlz6K8dJEMd7xKMbbjmI8sRcFY9KZq9DT0wONRgPAMIVCoVDgxhtvhIeHB958803ud52dnViyZAny8vIwefJkHDt2DDExMdixYweio6NF/iucW0FBARYsWIDXX38dl112mdiXQ87r7+/nEmoQx2lubkZXVxfKy8uRnp4u9uXIDsV46aMYL00U44VBMd42FOOJvUiqgTqUOXPm4Pe//z2ys7PBGEN/fz9UKhWqq6tx5MgRHDhwAHFxcbj++uvFvlSXwBhDXl4el+WREEJsQTFeWijGE0LsiWI84UPSDdSioiLMnDkT//nPf7jF0d3d3XBzcxP5yojxtqH09oQQvijGSxfFeEKIrSjGE74kOV/E+GD86aef4O3tzd3Ujz/+OO666y7U1NSIeXkEhkoLVVwIIXxQjJc+ivGEEL4oxhNbSTI3vPGhuH//flx11VXYvXs3Vq9ejfb2drz77ru0SS8hhMgYxXhCCHFeFOOJrSQ7xbezsxMTJkzA6dOn4ebmhscffxwPPPCA2JdFCCHEDijGE0KI86IYT2wh2QYqYNi4NykpCS+88ALc3d3FvhxCCCF2RDGeEEKcF8V4wpekG6h9fX1QqVRiXwYhhBAHoBhPCCHOi2I84UvSDVRCCCGEEEIIIa5Dkll8CSGEEEIIIYS4HmqgEkIIIYQQQgiRBGqgEkIIIYQQQgiRBGqgEkIIIYQQQgiRBGqgEkIIIYQQQgiRBGqgEkIIIYQQQgiRBGqgEkIIIYQQQgiRBGqgEkIIIYQQQgiRBGqgEkIIIYQQQgiRBGqgEkIIIYQQQgiRhP8PQwrRYzTAxZMAAAAASUVORK5CYII=", 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", 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" ] @@ -431,7 +501,11 @@ } ], "source": [ - "tide_stats(ds=ds, directory=directory);" + "tide_stats(\n", + " ds=ds,\n", + " model=tide_model,\n", + " directory=directory,\n", + ");" ] }, { @@ -455,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -463,7 +537,7 @@ "output_type": "stream", "text": [ "Low tide threshold: -1.00 metres AMSL\n", - "High tide threshold: 2.46 metres AMSL\n" + "High tide threshold: 2.51 metres AMSL\n" ] } ], @@ -485,12 +559,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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JBoN7smu7d+8ucUXVbt26Yfbs2ejSpQvi4uIwZcoUuLm54ZNPPsHp06exbt06nao3nTp1gkqlwq5du7B69Wrt/Z07d0ZCQgIUCgU6duxYZn+6d++OVatWISoqCo0bN8bRo0cxb948hIWFmbV9VapUwZQpUzBz5kyMGDEC/fv3x99//43p06eblJZjjqpVq2Ly5MmYPXs2qlSpgj59+iAtLQ2JiYkIDg7Wyakur0aNGgEAFi9ejCFDhsDV1RWRkZE6o6oagwcPxqJFizB48GC8//77qFevHrZu3Yrt27frtf3000/x5JNPomvXrhg6dChCQ0Nx8+ZN/PHHHzh27Bi++eYbg30aOXIkPD090bZtWwQHByMrKwuzZ8+Gr68vHn30UQCFn/d7772HhIQEdOjQAefPn8eMGTNQq1YtnVKSPj4+qFmzJn744Qd06tQJVatWRUBAACIiIrB48WK0a9cO7du3x+jRoxEREYF79+7h4sWL2Lx5s06FGHM1atQIKSkp2Lx5M4KDg+Hj44PIyEjMmDEDO3bsQJs2bTBhwgRERkYiNzcXly9fxtatW7Fs2TKzv7vmaNOmDapUqYJRo0YhISEBrq6uWLt2LU6ePKnTzsnJCYmJiXj55ZfRr18/DBs2DLdv3y7xu/nss89i7dq16NatGyZOnIjHHnsMrq6uSEtLQ3JyMnr16oU+ffpg2bJl2L17N5566inUqFEDubm5WLlyJYCy59oQEUmKrWf0EpVEUyXE0D9N9ZC9e/eKjh07ikqVKglPT0/RqlUrsXnzZr3nU6vVIiAgQAAQ6enp2vs1VWmaNWtmVL9u3bolhg8fLgIDA4WXl5do166d2Lt3r15VEU11mW+++Ubn70uqiKJWq8Xs2bNFeHi4cHNzE40bNxabN2/We05DAIixY8fq3V+zZk2dCiolVV5Rq9Vi5syZIiwsTPvaW7ZsETExMTrVYEzZHkOmTZsmQkJChJOTkwAgkpOThRD6FVmEECItLU307dtXeHt7Cx8fH9G3b1+xb9++El/r5MmT4plnnhGBgYHC1dVVBAUFiY4dO4ply5aV2p/Vq1eLuLg4Ub16deHm5iZCQkLEM888I37//Xdtm7y8PDFlyhQRGhoqPDw8RLNmzcT3339fYsWanTt3iqZNmwp3d3cBQOe9T01NFcOGDROhoaHC1dVVVKtWTbRp00bMnDmz1PfS2Go5J06cEG3bthVeXl56lY7++ecfMWHCBFGrVi3h6uoqqlatKpo3by7eeustcf/+/VLfI0PVcubNm6fTztD3o6SqV/v27ROtW7cWXl5eolq1amLEiBHi2LFjJX62n332mahbt65wc3MT9evXFytXrhS9evUSTZs21WlXUFAg5s+fL2JiYoSHh4fw9vYWUVFR4uWXXxZ//vmnEKKwClSfPn1EzZo1hbu7u/D39xcdOnQQP/74Y6nvARGR1CiEKGXFHiJyOKmpqYiKikJCQoLOgk5Etnb79m3Ur18fvXv3xmeffWbr7hAR2SUG90QO7OTJk1i3bh3atGmDypUr4/z585g7dy7u3r2L06dPG6yaQ2RtWVlZeP/99xEXFwd/f39cuXIFixYtwrlz53DkyBFtxR8iItLFnHsiB1apUiUcOXIEK1aswO3bt+Hr64vY2Fi8//77DOzJptzd3XH58mWMGTMGN2/ehJeXF1q1aoVly5YxsCciKgVH7omIiIiIZIKLWBERERERyQSDeyIiIiIimWBwT0REREQkEw41obZSpUrIzc2Fs7MzAgMDbd0dIiIiMkJ2djZUKhU8PDyQk5Nj6+4Q2TWHmlDr7OwMtVpt624QERGRGZycnKBSqWzdDSK75lAj95rg3snJCcHBwbbuDhERERkhMzMTarUazs7Otu4Kkd1zqOA+MDAQ6enpCA4ORlpamq27Q0REREYICwtDeno6U2qJjMAJtUREREREMsHgnoiIiIhIJhjcExERERHJBIN7IiIiIiKZYHBPRERERCQTDO6JiIiIiGSCwT0RERERkUwwuCciIiIikgkG90REREREMuFQK9SSNKiUSpw7uB0Pb6XDs0ooolp2hbMLv6pEREREZWHERHbl+PbVCNmfiEdwQ3vftR3+yGidgKZdh9iwZ0RERET2j8E92Y3j21cjZt+EwhuK/+6vJm6g2r4JOA4wwCcqA698ERE5Nh7xyS6olEqE7E8EADgpdB9zUgBqAQTvT4Sq00AGKkQG8MoXERFxQi3ZhXMHt6M6bugF9hpOCiAIN3Du4PaK7RiRRGiufFUTN3TuryZuIGbfBBzfvtpGPSMioorE4J7swsNb6RZtR+RIyrryBfx75UuprOCeERFRRWNwT3bBs0qoRdsRORJe+SIiIg0mL5NdiGrZFdd2+KOaKDlAUQsgW+GPqJZdK75zRHaOV74cAydLE5ExeFQgu+Ds4oKM1gmotm8C1EI3tUAtCv+b2ToBQfwhI9JjzStfDCjtAydLE5GxeIQmu9G06xAcBxCyPxHVi/yAZSv8kckfMCKDrHXliwGlfWCZYCIyBYN7sitNuw6BqtNAnCk2UsgReyLDrHHliwGlfWCZYCIyFY8EZHecXVzwSNunbN0NIkmx5JUvBpT249zB7YVXTsqYLH3m4HYeN4kIAIN7IiLZsNSVLwaU9oOTpYnIVAzuiYhkxBJXvhhQ2g+WCSYiU7HOPRER6WBAaT+iWnbFNfhr504UpxZAFlgmmIj+w+CeiIh0MKC0H5rJ0gD0Po+ik6U594GINBjcExGRDgaU9qVp1yE42WYJ/lH469yfrfDHyTZLWLWIiHTwyExERHq47oR9YZlgIjIWjwpEDoorjzo2Yz5/BpT2hWWCicgYPEJThWEwaT+48qhjM+XzZ0BJRCQtjKyoQjCYtB9cedSx8fMnIpI3Tqglq9MEE9XEDZ37q4kbiNk3Ace3r7ZRzxxPWSuPAv+uPKpUVnDPqCLw8ycikj8G92RVDCbsy7mD21EdN/Q+Cw3NyqPnDm6v2I5RheDnT0QkfwzuyaoYTNgXrjzq2Pj5ExHJH4N7sioGE/aFK486Nn7+RETyx+CerIrBhH3hyqOOjZ8/EZH8Mbgnq2IwYV+48qhj4+dPRCR/DO7JqhhM2B8uZe/Y+PkTEcmbQghhYExVfsLCwpCeno7Q0FCkpaXZujsORVPnvugy9lngMva2xEXFHBs/f5IS/n4TGY/BPVUYBhNERGQO/n4TGY+RFVUYLmNPREREZF3MuSciIiIikgmO3BMROSCmyRERyROP5EREDkYzwf2RIhPcr+3wRwYnuBMRSR6DeyIiB3J8+2rE7JtQeEPx3/3VxA1U2zcBxwEG+EREEiaZnPulS5eicePGqFy5MipXrozWrVvj559/tnW3ZE+lVOLMbz/hyJbPcOa3n6BSKm3dpQrl6NtP8qJSKhGyPxEA4KTQfUxzO3h/Ir/nREQSJpmR+7CwMHzwwQeoW7cuAGD16tXo1asXjh8/jkceecTGvZMnR7907+jbT9JUWi79uYPbC7/PipL/1kkBBOEGzhzczspW/+LcBCKSGskcoXr06KFz+/3338fSpUtx4MABBvdW4OiX7h19+0mayjohfXgr3ajnMbad3PEEn4ikSDLBfVEqlQrffPMNcnJy0Lp1a4Pt8vLykJeXp73tQOt1lUtZl+7V4t9L950GynIEy9G3n6TJmBNSzyqhRj2Xse3kjCf4RCRVksm5B4BTp07B29sb7u7uGDVqFDZt2oTo6GiD7WfPng1fX1/tv4yMjArsrXSdO7gd1XFDL7DV0Fy6P3dwe8V2rII4+vaT9BibS1+veSdcgz/UBsY51ALIgj+iWna1Ym/tH+cmEJGUSSq4j4yMxIkTJ3DgwAGMHj0aQ4YMwdmzZw22nzZtGu7cuaP9FxISUoG9lS5Hv3Tv6NtfFCcUS4OxJ6R/Ht2FjNYJAKAX4GtuZ7ZOcPgrUjzBJyIpk9QR3M3NTTuhtkWLFjh8+DAWL16MTz/9tMT27u7ucHd3195WKAwcqUmHo1+6d/Tt12C+sXSYckLaovtLOA4gZH8iqhf5bLMV/sjkZwuAJ/hEJG2SCu6LE0Lo5NSTZUS17IprO/xRTZQ8cqUWhYGAXC/dO/r2A8w3lhpTT0ibdh0CVaeBOFOsCkyQg4/Ya/AEn4ikTDJpOW+++Sb27t2Ly5cv49SpU3jrrbeQkpKCgQMH2rprsuPs4uLQl+4dffuZbyw9US27mpxL7+zigkfaPoUW3V/CI22fku332RzmvJ9ERPZCMsH9tWvXMGjQIERGRqJTp044ePAgtm3bhi5duti6a7LUtOsQnGyzBP8o/HXuz1b442SbJbIftZX79peWS898Y+lx9BNSS+P7SURSJpkj04oVK2zdBYfj6Jfu5br9rIUuT027DmEuvQXx/SQiqVIIByr+HhYWhvT0dISGhiItLc3W3SGqcEVz6YuOzGtGI0+2WQI37wA8suP5Mp/rTJevuIqpHeKKqpYlxfdTin0uC3+/iYwn7b2dSEJs/YNr7OJcVd846/ATiqVMk0tvKbb+3tqapd9Pa2OVKyJynCM0kQ3Zww/uuYPbC1+/jFz6M0d3Ib91AqrtmwC1KHmEP7N1guTTk6hs9vC9JeOxyhURARKaUEskVZof3Grihs791cQNxOybgOPbV1dIP0zJpZf7hGIqm718b8k4rHJFRBoceiO7I6c0AGNTYVSdBlp9G1kLnYxlT99bc8jpGGIso6/MHdwuqTQjIjKdvI92JDlySwOwpx9ccxbnklq+MVmGPX1vTSW3Y4ixWOWKiDSYlkN2Q45pAPb0g8va3WQse/remkKOxxBjcVVdItJgcE8VprSFk+SaL2pvP7jMpSdj2Nv31hhyPYYYi6vqEpEGh+ioQpR1qVzKaQClMScVxtqYS09lscfvbVnkegwxlubKHKtcERH3crI6Y8qzqQryjHoue0sDKIu9/uAyl55KY6/f29JINZXIkriqLhEBDO7JyoytuvFPpw+Nej57SgMwFn9wSYqk9r2VYiqRNfDKHBFxbyerMvZS+T8ArkFaaQCm4A8uSZGUvrdSTCWyFl6ZI3Js9neEJlkx9hJ43p0syaUBmIo/uCRFUvneSjGViIjIGlgth6zKlEvlrORCROXBYwgREaAQQhgonCU/YWFhSE9PR2hoKNLS0mzdHYegUipxfWb9Mi+VV3v7gra+utRWl5Raf4nkjvuk/PD3m8h4PNqRVZlzqVwqaQCA466GSWTPpHQMISKyNAb3ZHX2WHXDEiN7xpT4ZIBPREREFYnBPVUIe6q6YYnRdmNLfKo6DWQ6AJEdUqkFDqXeRPa9XAT6eOCxWlXhXFLuIBGRxDDqoApjD5fKLTXa7uirYRJJ2bbTmUjcfBaZd3K19wX7eiChRzTiGwbbsGdEROXnkMG9EAI5OTl69zs7O8PDw0N7u6Q2Gk5OTvD09DSr7YMHD2BoHrNCoYCXl5dZbR8+fAi1Wm2wH5UqVTKrbW5uLlQqlUXaenl5QaEojIjz8vKgVCot0tbT0xNOToXFn/Lz81FQUKDXRqVUwndPAtQuAi7/jtDlqwQK/u2uWgC+exJwt1Vv7Wi7h4cHnJ2d9Z73ZuZfyMnX/Vw8XKAd+StQCeSr/m1XwnfD3d0dLv++RkFBAfLz8w1uW9G2SqUSeXmGV/N1c3ODq6uryW1VKhVyc3MNtnV1dYWbm5vJbdVqNR4+fGiRti4uLnB3dwdQuA8/ePDAIm1N2e95jCi5rZSOEdtOZ2LU6oNQF+tDxj+5eHnlPix+tgl6tqhV5vFEw9Axoqy2puz3PEa4GXyciEogHEhoaKgAYPBft27ddNp7eXkZbNuhQwedtgEBAQbbtmjRQqdtzZo1DbaNjo7WaRsdHW2wbc2aNXXatmjRwmDbgIAAnbYdOnQw2NbLy0unbbdu3Up934rq169fqW3v37+vbTtkyJBS22ZnZ2vbjhkzptS2qamp2rZTpkwpte3p0ZWESKgsREJlkdDBrdS2hw4d0j7v3LlzS22bPMRL+7wfP+lRatstW7ZonzcpKanUths2bNC23bBhQ6ltk5KStG23bNlSatuPP/5Y2zY5ObnUtnPnztW2PXToUKltExIStG1Pnz5datspU6Zo26amppbadsyYMdq22dnZpbYdMmSItu39+/dLbduvXz+d73BpbXmMKPwn1WOEUqUWrWbtFJUfe7rUtid/P6V93oSEhFLbmnSMSE7Wtv34449LbctjROE/zTFC8/sdGhoqiKh0rHNPZAXC1h0gIj2HUm/qpOIYcirtTgX0hojIOhyyzn1ISAguXLig9zgvuZfcVkqX3AHDl8bP7t+G6N0vwtMVcFLop+Vo23VMQnTreAClX3I/uXMtGh2YAqAwx16TlqMWhWk5Rx+dj5jOA0vsLy+5m96WaTmFeIwwr62npyc2/56JietPQKgKIErpw+IXHkOfZuEAmJZjL8cI1rknMp5DBvc8ODgmcxbUKoum8k7REp9ZsF2JTyIybP+lG3hu+YEy260b2Qqt6/iX2Y4qDn+/iYznkBNqyTGZs6BWWTQlPk8d2IYrVy4hx7Uawpp0Qqu6gRbuPRGV12O1qiLY1wNZd3JLTJ1TAAjyLSyLSUQkVQzuyaFYY0GtHef+QeJeT2TeqVt4x7EjLKtHZIecnRRI6BGN0V8egwK6c2M05/oJPaJZ756IJI1pOeSQLLFCLVBYL3v0l8f0RgE1ocHSF5oxwCeyEEstPMU699LD328i43HknhySJRbUUqkFEjefLfHyvkBhgJ+4+Sy6RAdxJJConCwZkMc3DEaX6CCuUEtEssTgnshMZZXVEwAy7+TiUOpNTs4jKgdDV8iy7uRi9JfHzLpC5uyksJv90lJXJIiIAAb3RGbLvld2vWxT2hGRPrlfIWOKEBFZGhexIjJToI9H2Y1MaEdE+ky5QiY1misSxbdPc0Vi2+lMG/WMiKSMwT2RASq1wP5LN/DDiXTsv3QDKrXu2KGmrJ6hsUIFCkfgWFaPyHxyvUJW1hUJoPCKRPHjDhFRWZiWQ1QCYy6Vs6wekfXJ9QoZ5+wQkbVw5J6oGFMulcc3DMbSF5ohyFc3sAjy9WAZTCILkOsVMrlekSAi2+PIPVER5kzeY1k9Iuux5hUyW1apkesVCSKyPQb3REWYe6ncnsrqEcmN5gpZ8VS5oHJUlbF1lRrNFYmsO7klDiYoULh9UrsiQUS2x+CeqAheKjcP63STtVnyCpk16uabinN2iMhaGNwTFcFL5aaz9QgoOQ5LXCGzp7r51rgiQUTE4J6oCF4qN409jIASmcLeqtRwzg4RWRqr5RAVoblUDkCvOgcvletinW6SIntMvdNckejVJBSt6/jz+EJE5cLgnqgYlrc0jpxXDiX5YuodEcmdZNJyZs+eje+++w7nzp2Dp6cn2rRpgzlz5iAyMtLWXSMZ4qXystnjCChRWZh6R0RyJ5mR+z179mDs2LE4cOAAduzYAaVSiSeeeAI5OTm27hrJFC+Vl44joCRFTL0zjUotsP/SDfxwIh37L91gmh2RBEhm5H7btm06t5OSkhAYGIijR4/i8ccft1GviBwXR0BJqlilxjishEUkTZIJ7ou7c+cOAKBqVQYORLbAOt0kZUy9K319ClbCIpIuSQb3QghMnjwZ7dq1Q8OGDQ22y8vLQ15ens7fEcl9waWK3D6OgJKUOfLK0qWNyneJDrKbtQCIyHSSDO7HjRuH33//Hb/++mup7WbPno3ExMQK6hVJgdwvM9ti++Q8Air3E0FyTGWNyr/SuZ5drQVARKZRCIkNZ48fPx7ff/89fvnlF9SqVavUtsVH7hs0aICMjAyEhoYiLS3N2l0lO2PoB00Tqkn9MrPct6+iyf1EkByTSi3Qbs5ug8G7AoCvpytuPywo87kWP9sEvZqEWriHJQsLC0N6ejp/v4mMIJlqOUIIjBs3Dt999x12795dZmAPAO7u7qhcubL2n0Jh+RE3VhKQBrkvuCT37atomhOl4gGQZmRz2+lMG/WMqHyMWZ/CmMAeYCUsInslmbScsWPH4quvvsIPP/wAHx8fZGVlAQB8fX3h6elpkz5xZE867G3JeUuT+/ZVpLJOlJhvTFJm7LoTfp6uuPOwgJWwiCRIMiP3S5cuxZ07dxAbG4vg4GDtv6+//tom/eHInrTIfcEluW9fReLKuyRnxo62v9i28Oo41wIgkh7JjNzb09QAjuxJj9wXXJL79lUknihRcXKaWG3s+hTjOtZFZJA3K2ERSZBkgnt7whQI6ZH7gkty376KxBMlKsrY9EupnACYsj6FnCthEcmZZNJy7AlH9qRH7kvOy337KpLmRMnQO6VAYXDHEyX5Mzb9ctvpTLSbsxvPLT+AietP4LnlB9Buzm67Tc/UrE8R5Kt7ghrk66FXVUuzFkCvJqFoXcefxxAiCeDIvRk4sidNcl9wSe7bV1G48i4BxqdfqtXA2K+kt5IrR+WJ5Etyde7Lw1J1cjV1gstKgfj19Y48UNohqVw+N5fct6+isBqWY9t/6QaeW36gzHZVK7nhZk5+iY/xt8ByWOeeyHgcuTcDR/akTe5Lzst9+yqKNUY2eeIlHcamVRoK7AHOvyIi22BwbyamQBDJnyVPlHgloHwq+sTIkmmVnH9FRBWJwX05MGeRiIyhmZgptbxse2GLEyNjKlBVqeSKmzllr+bK+VdEVJFYLaecWEmAiEpT1sRMoHBipkrtMNOfTGKrBQONqUA1s1dDVlYiIrvD4J6oAqnUAvsv3cAPJ9Kx/9INBnQOgCvems/WJ0ZllYzs1jiEJWiJyO4wLYeogjDnWprKm+vNdTHMZw8LBpaVfsn5V0RkbxjcE1UA5lxLkyVOyLguhvns5cSorInVnH9FRPaEaTlEVmbr1AIyj6VyvbnirfmkdGJUnvlXTNcjIkviyD2RldlDagGZxtjVSbtEB5UZxHFdDPMZU7EmSOInRkzXIyJL48g9kZXZS2oBGc/Sk2DLmpjJIK5kxlSskfKJka0qARGRvHHknsjKpJRaQIWscULGvGzzyHXCqiWvDhERFcXgnsjKHCG1QG6sdUJmyRVvHYkcT4yYrkdE1sK0HCIrk3tqgRxxEqz9kduCgUzXIyJrYXBPVAGYcy0tPCEja2O6HhFZC9NyJKq8C+tQxZNjaoGcyTXXm+wD0/WIyFoY3EsQS6dJF3OupYUnZGQtLJFKRNbCtByJYek0ooolt1xvsh9M1yMia+DIvYSwdBoRkbzw6hARWRqDewlh6TQiIvlhuh4RWRKDewlh6TQix8VJ9EREZAwG9xLC0mlEjomT6ImIyFicUCshXFiHyPFwEj0REZmCwb2EcGEdIsdS1iR6oHASvUpdUgsiInJEDO4lxlFLp6nUAvsv3cAPJ9Kx/9INBjPkEEyZRE9ERAQw516SHK10GvONyVFxEj0REZmKwb1EOUrpNE2+cfFxek2+sZyvVhBxEj0REZmKaTlkt5hvTI6Ok+iJiMhUDO7JbjHfmBwdJ9ETEZGpGNyT3WK+MZHjTqInIiLzMOee7BbzjYkKOdokeiIiMh+De7JbmnzjrDu5JebdK1A4esl84/+o1IIBoEw5yiR6IiIqHwb3ZLc0+cajvzwGBaAT4DPfWB9LhhIRERFz7smuMd/YOJqSocUnIGtKhm47nWmjnhEREVFF4sg92T3mG5eurJKhChSWDO0SHcT3jIiISObMGrkfNmwY7t27p3d/Tk4Ohg0bVu5OERWnyTfu1SQUrev4M0gtgiVDiYiISMOs4H716tV4+PCh3v0PHz7EF198Ue5OEZHxWDKUiIiINExKy7l79y6EEBBC4N69e/Dw+C8PWqVSYevWrQgMDLR4J4nIMJYMJSIiIg2Tgns/Pz8oFAooFArUr19f73GFQoHExESLdY6IysaSoURkTSyxSyQtJgX3ycnJEEKgY8eO2LhxI6pW/S9YcHNzQ82aNRESEmLxTmr88ssvmDdvHo4ePYrMzExs2rQJvXv3ttrrEUkBS4YSkbWwxC6R9JgU3Hfo0AEAkJqaivDwcDg5VWwlzZycHMTExODFF19E3759K/S1ieyZpmRo8R/hIP4IE5GZNCV2i18R1JTYZTliIvtkVinMmjVr4vbt2zh06BCys7OhVqt1Hh88eLBFOlfck08+iSeffNIqz00kdSwZSkSWIqcSuyqVCgUFBbbuBlG5uLq6wtnZ2ai2ZgX3mzdvxsCBA5GTkwMfHx8oFP/t2AqFwmrBvany8vKQl5envS1ESYcpIvnQlAwlIioPU0rs2usxRwiBrKws3L5929ZdIbIIPz8/BAUF6cTdJTEruH/11VcxbNgwzJo1C15eXmZ1sCLMnj2bE3yJiIhMJIcSu5rAPjAwEF5eXmUGRET2SgiBBw8eIDs7GwAQHFx6OpxZwX16ejomTJhg14E9AEybNg2TJ0/W3m7QoAEyMjJs2CMiIiL7J/USuyqVShvY+/vb55UFIlN4enoCALKzsxEYGFhqio5ZwX3Xrl1x5MgR1K5d27weVhB3d3e4u7trb/OsnaSCpefki5+tY5Hq5y31EruaHHt7H4QkMoXm+1xQUGCZ4P7HH3/U/v9TTz2FqVOn4uzZs2jUqBFcXV112vbs2dPU/hLRv1h6Tr742ToWKX/ecimxy0E9khNjv88KYeQsU2PLXioUCqhUKqPamur+/fu4ePEiAKBp06ZYuHAh4uLiULVqVdSoUaPMvw8LC0N6ejpCQ0ORlpZmlT4SlYeh0nOa3VmOpeekOrJpKn628v1sSyKXz9teTlBM/f3Ozc1FamoqatWqBQ8P+0wdIjKVsd9ro0fui5e7tIUjR44gLi5Oe1uTTz9kyBCsWrXKRr0isgw5lZ4zlr0EDtbGz7aQHD/bojQnM1l3HuK9n/6QxefNErv2JyUlBXFxcbh16xb8/PywatUqvPLKK3ZXFah4v6ZPn47vv/8eJ06cAAAMHToUt2/fxvfff2/V13VEFbsKVTnFxsZCCKH3j4G9aVRqgf2XbuCHE+nYf+kGVGqWCLUHppSekwPNyGbxbdYskLPtdKaNemZ5/GwLyfGz1dh2OhPt5uzGc8sPYNKGk7iZk2+wrdQ+b02J3V5NQtG6jr9DBfa2+r3ct28fnJ2dER8fXyGvV5pVq1bBz8/P4s+7ePFinfgtNjYWr7zySrmfd8CAAbhw4UK5n0fKzJpQu2TJkhLvVygU8PDwQN26dfH4448bXWyfKo4jjqZJhRxKzxnL0Uay+dkWkuNnCxhOwSmLHD5vObPl7+XKlSsxfvx4fP7557h69apRqcdS4+vra5Xn9fT01FaWcVRmjdwvWrQIb775Jl555RUkJiZi+vTpeOWVVzBt2jS888476NSpEyIjI/H3339bur9UDo44miYlUi89ZwpHG8nmZ/sfuX22pZ3MlEUOn7dc2fL3MicnBxs2bMDo0aPRvXv3cmcn5OfnY9y4cQgODoaHhwciIiIwe/Zs7eMLFy5Eo0aNUKlSJYSHh2PMmDG4f/8+gMIUoBdffBF37tyBQqGAQqHA9OnTtc/72muvITQ0FJUqVULLli2RkpJidL+GDh2K3r17a/9/z549WLx4sfZ1Ll++DAA4e/YsunXrBm9vb1SvXh2DBg3C9evXDT5v8SsN06dPR5MmTbBy5UrUqFED3t7eGD16NFQqFebOnYugoCAEBgbi/fff13me0t4XjeXLlyM8PBxeXl7o06cPFi5cqHeVY/PmzWjevDk8PDxQu3ZtJCYmQqlU6vSvRo0acHd3R0hICCZMmGD0e2iIWcH9rFmz8Oijj+LPP//EjRs3cPPmTVy4cAEtW7bE4sWLcfXqVQQFBWHSpEnl7iBZRlmjaUDhaBpTdGxHU3rO0FimAoWjRvZaes4UjjSSDfCzLU87e1fWyUxJ5PR5y5Gtfy+//vprREZGIjIyEi+88AKSkpJgZO2TEi1ZsgQ//vgjNmzYgPPnz+PLL79ERESE9nEnJycsWbIEp0+fxurVq7F792689tprAIA2bdrgww8/ROXKlZGZmYnMzExMmTIFAPDiiy/it99+w/r16/H777+jf//+iI+Px59//mlyHxcvXozWrVtj5MiR2tcJDw9HZmYmOnTogCZNmuDIkSPYtm0brl27hmeeecak57906RJ+/vlnbNu2DevWrcPKlSvx1FNPIS0tDXv27MGcOXPw9ttv48CBA0a9LwDw22+/YdSoUZg4cSJOnDiBLl266J0gbN++HS+88AImTJiAs2fP4tNPP8WqVau07b799lssWrQIn376Kf788098//33aNSokcnvX3FmpeW8/fbb2LhxI+rUqaO9r27dupg/fz769u2Lv/76C3PnzkXfvn3L3UGyDDksJS53cik9ZwxHGskG+NmWp529M/UkRW6ftxzZ+vdyxYoVeOGFFwAA8fHxuH//Pnbt2oXOnTub9XxXr15FvXr10K5dOygUCtSsWVPn8aJ57rVq1cJ7772H0aNH45NPPoGbmxt8fX2hUCgQFBSkbXfp0iWsW7cOaWlpCAkJAQBMmTIF27ZtQ1JSEmbNmmVSH319feHm5gYvLy+d11m6dCmaNWum83wrV65EeHg4Lly4gPr16xv1/Gq1GitXroSPjw+io6MRFxeH8+fPY+vWrXByckJkZCTmzJmDlJQUtGrVqsz3BQA++ugjPPnkk9qTnfr162Pfvn3YsmWL9u/ef/99vPHGGxgyZAgAoHbt2njvvffw2muvISEhQTsY3rlzZ7i6uqJGjRp47LHHTHrvSmLWyH1mZqbOJQUNpVKJrKwsAEBISAju3btXvt6RxTjaaJpUxTcMxtIXmiHIVzfwCfL1kEzpPGM40ki2Bj/bQnL7bE09SZHb5y1Htvy9PH/+PA4dOoRnn30WAODi4oIBAwZg5cqVZj/n0KFDceLECURGRmLChAn43//+p/N4cnIyunTpgtDQUPj4+GDw4MG4ceMGcnJyDD7nsWPHIIRA/fr14e3trf23Z88eXLp0yey+Fnf06FEkJyfrvEZUVBQAmPQ6ERER8PHx0d6uXr06oqOjdcq8V69eHdnZ2drbZb0v58+f1wvEi98+evQoZsyYodN/zdWJBw8eoH///nj48CFq166NkSNHYtOmTSXG16Yya+Q+Li4OL7/8Mj7//HM0bdoUAHD8+HGMHj0aHTt2BACcOnUKtWrVKncHyTIcbTRNyhyh9JwjjWQXxc+2kJw+27JWcgWAqpVc8U73RxBUWX6ftxzZ8vdyxYoVUCqVCA0N1d4nhICrqytu3bqFKlWqmPyczZo1Q2pqKn7++Wfs3LkTzzzzDDp37oxvv/0WV65cQbdu3TBq1Ci89957qFq1Kn799VcMHz5cu8pvSdRqNZydnXH06FG94ine3t4m97G01+nRowfmzJmj91hwsPEnyMUXW1UoFCXepyn7bsz7IoTQW1SqePqUWq1GYmIinn76ab0+eXh4IDw8HOfPn8eOHTuwc+dOjBkzBvPmzcOePXv0+mcKs4L7FStWYNCgQWjevLn2xZVKJTp16oQVK1YAKPxwFyxYYHbHyLKkvpS4o9GUnpMzzUh28WoUQTKv3sTPVl6frTEnM7P6NJLVNsudrX4vlUolvvjiCyxYsABPPPGEzmN9+/bF2rVrMW7cOLOeu3LlyhgwYAAGDBiAfv36IT4+Hjdv3sSRI0egVCqxYMEC7Sj2hg0bdP7Wzc1Nb3HSpk2bQqVSITs7G+3btzerT8WV9DrNmjXDxo0bERERARcXs0JWsxjzvkRFReHQoUN6f1dUs2bNcP78edStW9fga3l6eqJnz57o2bMnxo4di6ioKJw6dQrNmjUzu/9mvVNBQUHYsWMHzp07hwsXLkAIgaioKERGRmrbFF1simzP0UbTSBocYSTbUTnSZ+tIJzOOwFa/l1u2bMGtW7cwfPhwvTKR/fr1w4oVK8wK7hctWoTg4GA0adIETk5O+OabbxAUFAQ/Pz/UqVMHSqUSH330EXr06IHffvsNy5Yt0/n7iIgIbd5/TEwMvLy8UL9+fQwcOBCDBw/GggUL0LRpU1y/fh27d+9Go0aN0K1bN5P7GRERgYMHD+Ly5cvw9vZG1apVMXbsWCxfvhzPPfccpk6dioCAAFy8eBHr16/H8uXLrVZy3Zj3Zfz48Xj88cexcOFC9OjRA7t378bPP/+sM5r/7rvvonv37ggPD0f//v3h5OSE33//HadOncLMmTOxatUqqFQqtGzZEl5eXlizZg08PT315kWYqlyLWEVFRaFnz57o1auXTmBP9slRcn5JWhx5gRwNuS4s50ifbXzDYPz6ekesG9kKi59tgnUjW+HX1zvyuCpRtvi9XLFiBTp37lxi/fe+ffvixIkTOHbsmMnP6+3tjTlz5qBFixZ49NFHcfnyZe1E0iZNmmDhwoWYM2cOGjZsiLVr1+qUyQQKK+aMGjUKAwYMQLVq1TB37lwAQFJSEgYPHoxXX30VkZGR6NmzJw4ePIjw8HCztn/KlClwdnZGdHQ0qlWrhqtXryIkJAS//fYbVCoVunbtioYNG2LixInw9fXVyZe3NGPel7Zt22LZsmVYuHAhYmJisG3bNkyaNAkeHv99Z7p27YotW7Zgx44dePTRR9GqVSssXLhQG7z7+flh+fLlaNu2LRo3boxdu3Zh8+bN8Pcv39VdhTCyvtLkyZPx3nvvoVKlSpg8eXKpbRcuXFiuTllLWFgY0tPTERoairS0NFt3x2Y0S6TLfTSNSAq4sBxR2Uz9/c7NzUVqaipq1aqlE2yZir+XZIqRI0fi3Llz2Lt3r1We39jvtdFpOcePH9dOIjh+/LjBdsUnF5D9cYScXyIpMLSyqWahHF5RI7It/l5SaebPn48uXbqgUqVK+Pnnn7F69WptqUxbMjq4T05OLvH/iYjIdGUtlKNA4UI5XaKDOFJIRGSHDh06hLlz5+LevXuoXbs2lixZghEjRti6W+ZNqNW4ePEiLl26hMcffxyenp4llgUiIiJ9tl4oh4iIyqd4BR17YdZshBs3bqBTp06oX78+unXrhszMTADAiBEj8Oqrr1q0g2SYXCfhETkCLixHRETWYNbI/aRJk+Dq6oqrV6+iQYMG2vsHDBiASZMmsb59BeAkPCJp48JyRERkDWaN3P/vf//DnDlzEBYWpnN/vXr1cOXKFYt0jAzTTMIrfklfMwlv2+lMG/WMiIylWSjHUCKjAoUn7FxYjoiITGFWcJ+TkwMvLy+9+69fvw53d/dyd4oMK2sSHlA4CY8pOkT2TbNQDgC9AJ8LyxERkbnMCu4ff/xxfPHFF9rbCoUCarUa8+bN48q0VmbKJDwism9cWI6IiCzNrJz7efPmITY2FkeOHEF+fj5ee+01nDlzBjdv3sRvv/1m6T5SEZyERyQv8Q2D0SU6iAvlEBGRRZg1ch8dHY3ff/8djz32GLp06YKcnBw8/fTTOH78OOrUqWPpPlIRnIRHJD+ahXJ6NQlF6zr+DOyJqFSrVq2Cn5+fSX8zdOhQ9O7d2yKvr1Ao8P333xt8/PLly1AoFDhx4oTZr2HONlIhs+vcBwUFITEx0ZJ9ISNoJuFl3cktMe9egcJL+pyER0REVE5qFXBlH3D/GuBdHajZBnByttrLDR06FLdv39YLnFNSUhAXF4dbt27Bz88PAwYMQLdu3azWD43p06fj+++/1wvSMzMzUaVKFau+dkVtoxyZHdzfvn0bhw4dQnZ2NtRqtc5jgwcPLnfHqGSaSXijvzwGBaAT4HMSHhERkYWc/RHY9jpwN+O/+yqHAPFzgOietusXAE9PT3h6etrs9YOCgqz+GrbeRikzKy1n8+bNqFGjBp588kmMGzcOEydO1P575ZVXLNxFKo6T8IiIiKzo7I/AhsG6gT0A3M0svP/sj7bp179KSlmZOXMmAgMD4ePjgxEjRuCNN95AkyZN9P52/vz5CA4Ohr+/P8aOHYuCggKDr5GYmIiTJ09CoVBAoVBg1apVAPTTcg4dOoSmTZvCw8MDLVq0wPHjx/We7+zZs+jWrRu8vb1RvXp1DBo0CNevXzd6G6dPn44mTZpg5cqVqFGjBry9vTF69GioVCrMnTsXQUFBCAwMxPvvv6/zPAsXLkSjRo1QqVIlhIeHY8yYMbh//75Om+XLlyM8PBxeXl7o06cPFi5cqPf+bt68Gc2bN4eHhwdq166NxMREKJVKnf7VqFED7u7uCAkJwYQJEwxum7WZNXL/6quvYtiwYZg1a1aJJTHJ+jgJj4iIyArUqsIRe4NFpxXAtjeAqKesmqJjirVr1+L999/HJ598grZt22L9+vVYsGABatWqpdMuOTkZwcHBSE5OxsWLFzFgwAA0adIEI0eO1HvOAQMG4PTp09i2bRt27twJAPD19dVrl5OTg+7du6Njx4748ssvkZqaiokTJ+q0yczMRIcOHTBy5EgsXLgQDx8+xOuvv45nnnkGu3fvNno7L126hJ9//hnbtm3DpUuX0K9fP6SmpqJ+/frYs2cP9u3bh2HDhqFTp05o1aoVAMDJyQlLlixBREQEUlNTMWbMGLz22mv45JNPAAC//fYbRo0ahTlz5qBnz57YuXMn3nnnHZ3X3b59O1544QUsWbIE7du3x6VLl/DSSy8BABISEvDtt99i0aJFWL9+PR555BFkZWXh5MmTRm+XxQkzeHl5iUuXLpnzpzYVGhoqAIjQ0FBbd4WIiIiMZOrv98OHD8XZs2fFw4cPTX+xv34RIqFy2f/++sX05y7DkCFDhLOzs6hUqZLOPw8PDwFA3Lp1SwghRFJSkvD19dX+XcuWLcXYsWN1nqtt27YiJiZG57lr1qwplEql9r7+/fuLAQMGGOxPQkKCznNoABCbNm0SQgjx6aefiqpVq4qcnBzt40uXLhUAxPHjx4UQQrzzzjviiSee0HmOv//+WwAQ58+fL/G1i29jQkKC8PLyEnfv3tXe17VrVxERESFUKpX2vsjISDF79myD27Rhwwbh7++vvT1gwADx1FNP6bQZOHCgzmu3b99ezJo1S6fNmjVrRHBwsBBCiAULFoj69euL/Px8g69rCcZ+r81Ky+natSuOHDliuTMMIiIiIntw/5pl25koLi4OJ06c0Pn3+eefl/o358+fx2OPPaZzX/HbAPDII4/A2fm/qw3BwcHIzs4uV3//+OMPxMTE6GRytG7dWqfN0aNHkZycDG9vb+2/qKgoAIWj8caKiIiAj4+P9nb16tURHR0NJycnnfuKblNycjK6dOmC0NBQ+Pj4YPDgwbhx4wZycnIAGPfeHT16FDNmzNDp/8iRI5GZmYkHDx6gf//+ePjwIWrXro2RI0di06ZNOik7Fc3otJwff/wvv+ypp57C1KlTcfbsWTRq1Aiurq46bXv2tO1EE6KiVGrB9CUiIguS9XHVu7pl25moUqVKqFu3rs59aWlpZf6dQqH7/guhn1ZUPF7TLEJaHiW9TnFqtRo9evTAnDlz9B4LDjZ+nmBJ/S9tm65cuYJu3bph1KhReO+991C1alX8+uuvGD58uHaugRCizPdOrVYjMTERTz/9tF6fPDw8EB4ejvPnz2PHjh3YuXMnxowZg3nz5mHPnj16/asIRgf3JdVGnTFjht59CoUCKpWqXJ0ispRtpzORuPmszqq+wb4eSOgRzYnHRERmKOm4GlTZHc89VgMRAZWkH+zXbFNYFeduJkrOu1cUPl6zTUX3zKDIyEgcOnQIgwYN0t5niQwLNze3MmO66OhorFmzBg8fPtRWtzlw4IBOm2bNmmHjxo2IiIiAi4vZhRpNduTIESiVSixYsEA7ur9hwwadNlFRUTh06JDe3xXVrFkznD9/Xu+kqyhPT0/07NkTPXv2xNixYxEVFYVTp06hWbNmFtoa4xn9Dpf3zI6oPMwZJdp2OhOjvzymd2jOupOL0V8eY2UhIhuQ9YivEaS+/QaPq3fzsGjnn9rbkh5EcXIuLHe5YTBgqOh0/Ad2M5kWAMaPH4+RI0eiRYsWaNOmDb7++mv8/vvvqF27drmeVzMJ9cSJEwgLC4OPjw/c3d112jz//PN46623MHz4cLz99tu4fPky5s+fr9Nm7NixWL58OZ577jlMnToVAQEBuHjxItavX4/ly5frpApZUp06daBUKvHRRx+hR48e+O2337Bs2TKdNuPHj8fjjz+OhQsXokePHti9ezd+/vlnndH8d999F927d0d4eDj69+8PJycn/P777zh16hRmzpyJVatWQaVSoWXLlvDy8sKaNWvg6emJmjVrWmW7ymJWzr2xGjVqhL///tuaL0EOYNvpTLSbsxvPLT+AietP4LnlB9Buzm5sO51p8G9UaoHEzWcN1joAgMTNZ6FSl305kYgsw5x9WU6kvv2lHVeL0wyiSGXb9ET3BJ75Aqhc7OSkckjh/Tauc1/cwIEDMW3aNEyZMgXNmjVDamoqhg4dCg+P8q1W37dvX8THxyMuLg7VqlXDunXr9Np4e3tj8+bNOHv2LJo2bYq33npLL/0mJCQEv/32G1QqFbp27YqGDRti4sSJ8PX11cmXt7QmTZpg4cKFmDNnDho2bIi1a9di9uzZOm3atm2LZcuWYeHChYiJicG2bdswadIknfeua9eu2LJlC3bs2IFHH30UrVq1wsKFC7XBu5+fH5YvX462bduicePG2LVrFzZv3gx/f3+rbVtpFMKYZCkz+fj44OTJk+U+c7SUsLAwpKenIzQ01Kj8NbI9Q6NEmvNpQ6Pv+y/dwHPLD+jdX9y6ka3Quo5tdj4iR2LuviwXcth+Y4+rGpoV0399vWO5r06Y+vudm5uL1NRU1KpVq3wBbgWvUGtJXbp0QVBQENasWWPrrkjOyJEjce7cOezdu9fWXdFh7Pe64hKfiExU1ui7AoWj712ig/R+OLLv5ZbwV/qMbUdE5ivPviwHctl+U4+XAkDmnVwcSr0p3UEUJ2egVntb96JMDx48wLJly9C1a1c4Oztj3bp12LlzJ3bs2GHrrknC/Pnz0aVLF1SqVAk///wzVq9era2DL0VWTcshKo9DqTd1JmwVV/SHo7hAH+NGaoxtR0TmK8++LAdy2X5zj5ccRLE+hUKBrVu3on379mjevDk2b96MjRs3onPnzrbumiQcOnQIXbp0QaNGjbBs2TIsWbIEI0aMsHW3zMaRe7Jb5Rl9f6xWVQT7eiDrTq6hWgcI8i2czEZE1iXHK2mmTIyVy/aXdVw1hIMo1ufp6aldRZZMV7yCjtQxuCe7VZ7Rd2cnBRJ6RGP0l8cM1TpAQo9ou74ETiQXcruSZmqJXblsf2nH1ZJwEIXINpiWQ3ZLM0pkKPxWoPAH1dAPR3zDYCx9oRmCfHV/MIN8PSQxeY1ILsq7L9sTzcTY4mk2pVWHkdP2GzquFsdBFCLbKffIfW5ursEZu59++imqV7fOCm5kWfZYe9kSo+/xDYPRJTrI7raNyJHI5UqauRNj5bL9GsWPq5evP8C6Q1eRdbfIolZSrnNPJHFmBfdqtRrvv/8+li1bhmvXruHChQuoXbs23nnnHURERGD48OEAChc2IPtnz6u4akaJ9FZDNKF/zk4K6VZqIJIJS+zLtmbKxNjixxw5bH9RxY+r4zrW5SAKkZ0wK7ifOXMmVq9ejblz52LkyJHa+xs1aoRFixZpg3uyf1JYxZWj70TyIPV9ubwTY6W+/aXhIAqR/TAruP/iiy/w2WefoVOnThg1apT2/saNG+PcuXMW6xxZl5RqL/OHQ589plIRlUXK+7IlJsZKefuJSBrMmlCbnp6OunXr6t2vVqtRUFBQ7k5RxZBL7WVHJPVl7ImkSE4TY8k4KrUKh7MOY+tfW3E46zBUapWtu1Thpk+fjiZNmpTaZujQoejdu3e5XiciIgIffvhhuZ6DCpkV3D/yyCMlLsn7zTffoGnTpuXuFFUMudRedjTmVOsgovLTTIwFoBfgS3FiLJVu55Wd6LqxK4ZtH4bX976OYduHoevGrth5xXr15C0RJJeHQqHA999/r3PflClTsGvXLqu/9uHDh/HSSy9Z/XUcgVnBfUJCAsaNG4c5c+ZArVbju+++w8iRIzFr1iy8++67lu6jjk8++QS1atWCh4cHmjdvXuJJBhlHLrWXHUlZqVRAYSqVSm3KEjNEZCyW2HUMO6/sxOSUybj24JrO/dkPsjE5ZbJVA3x74+3tDX9/66eSVatWDV5eXlZ/HUdgVnDfo0cPfP3119i6dSsUCgXeffdd/PHHH9i8eTO6dOli6T5qff3113jllVfw1ltv4fjx42jfvj2efPJJXL161WqvKWe8xCw9TKUisr34hsH49fWOWDeyFRY/2wTrRrbCr693tGhgr1IL7L90Az+cSMf+Szd4wl6BVGoVPjj0AUQJwyia++YcmmOTFJ09e/bgscceg7u7O4KDg/HGG29AqVQCADZv3gw/Pz+o1WoAwIkTJ6BQKDB16lTt37/88st47rnnSnzuiIgIAECfPn2gUCi0t4un5ahUKkyePBl+fn7w9/fHa6+9BiF03yshBObOnYvatWvD09MTMTEx+Pbbb0vdtuJpOQqFAp9++im6d+8OLy8vNGjQAPv378fFixcRGxuLSpUqoXXr1rh06ZL2by5duoRevXqhevXq8Pb2xqOPPqq3cm9mZiaeeuopeHp6olatWvjqq6/0XvvOnTt46aWXEBgYiMqVK6Njx444efKk9vGTJ08iLi4OPj4+qFy5Mpo3b44jR46Uun0VyexFrLp27Yo9e/bg/v37ePDgAX799Vc88cQTluybnoULF2L48OEYMWIEGjRogA8//BDh4eFYunSpVV9XrniJWXqYSkVkHzQTY3s1CUXrOv4WPU5yTo1tHcs+pjdiX5SAQNaDLBzLPlaBvSqc79itWzc8+uijOHnyJJYuXYoVK1Zg5syZAIDHH38c9+7dw/HjxwEUnggEBARgz5492udISUlBhw4dSnz+w4cPAwCSkpKQmZmpvV3cggULsHLlSqxYsQK//vorbt68iU2bNum0efvtt5GUlISlS5fizJkzmDRpEl544QWdvhjjvffew+DBg3HixAlERUXh+eefx8svv4xp06Zpg+lx48Zp29+/fx/dunXDzp07cfz4cXTt2hU9evTQGQQePHgwMjIykJKSgo0bN+Kzzz5Ddna29nEhBJ566ilkZWVh69atOHr0KJo1a4ZOnTrh5s3CgbOBAwciLCwMhw8fxtGjR/HGG2/A1dXVpG2zJsmsUJufn4+jR4/qnUA88cQT2LdvX4l/k5eXh7t372r/FT+zJF5ilhqmUhHJG+fU2N4/D/6xaDtL+eSTTxAeHo6PP/4YUVFR6N27NxITE7FgwQKo1Wr4+vqiSZMmSElJAVAYyE+aNAknT57EvXv3kJWVhQsXLiA2NrbE569WrRoAwM/PD0FBQdrbxX344YeYNm0a+vbtiwYNGmDZsmXw9fXVPp6Tk4OFCxdi5cqV6Nq1K2rXro2hQ4fihRdewKeffmrSNr/44ot45plnUL9+fbz++uu4fPkyBg4ciK5du6JBgwaYOHGidnsBICYmBi+//DIaNWqEevXqYebMmahduzZ+/PFHAMC5c+ewc+dOLF++HC1btkSzZs3w+eef4+HDh9rnSE5OxqlTp/DNN9+gRYsWqFevHubPnw8/Pz/t1YerV6+ic+fOiIqKQr169dC/f3/ExMSYtG3WZHQpzCpVqkChMG5kQnNmY0nXr1+HSqXSW/G2evXqyMrKKvFvZs+ejcTERIv3RW7kXHtZbjSpVFl3ckvMu1eg8MSMqVRE0iOl8sRyVs2r5KDW3HaW8scff6B169Y6sVjbtm1x//59pKWloUaNGoiNjUVKSgomT56MvXv3YubMmdi4cSN+/fVX3L59G9WrV0dUVJTZfbhz5w4yMzPRunVr7X0uLi5o0aKFdgD17NmzyM3N1UvTzs/PN7noSuPGjbX/r4n/GjVqpHNfbm4u7t69i8qVKyMnJweJiYnYsmULMjIyoFQq8fDhQ+3I/fnz5+Hi4oJmzZppn6Nu3bqoUqWK9vbRo0dx//59vXkGDx8+1KYATZ48GSNGjMCaNWvQuXNn9O/fH3Xq1DFp26zJ6OC+aC7SjRs3MHPmTHTt2lX7Ae/fvx/bt2/HO++8Y/FOFlX8BEMIYfCkY9q0aZg8ebL2doMGDZCRkWHV/kkVay9Lg9yWsSei/5RnBVyynGaBzVDdqzqyH2SXmHevgALVvaqjWWCzEv7aekqKdzQBteb+2NhYrFixAidPnoSTkxOio6PRoUMH7NmzB7du3TKYkmNJmpz/n376CaGhoTqPubu7m/RcRVNdNNtY0n2a15w6dSq2b9+O+fPno27duvD09ES/fv2Qn58PAAYzOIrer1arERwcrHNFQMPPzw9A4TyE559/Hj/99BN+/vlnJCQkYP369ejTp49J22ctRgf3Q4YM0f5/3759MWPGDJ08pwkTJuDjjz/Gzp07MWnSJMv2EkBAQACcnZ31Rumzs7P1RvM13N3ddb5Ixl55ILJnclvGnogKcU6NfXB2csYbj72BySmToYBCJ8BX/DuM8vpjr8PZyblC+xUdHY2NGzfqBPn79u2Dj4+PNojW5N1/+OGH6NChAxQKBTp06IDZs2fj1q1bmDhxYqmv4erqCpXK8ERhX19fBAcH48CBA3j88ccBAEqlUpuXrumnu7s7rl69WiEnE0Xt3bsXQ4cO1QbZ9+/fx+XLl7WPR0VFQalU4vjx42jevDkA4OLFi7h9+7a2TbNmzZCVlQUXFxftpOKS1K9fH/Xr18ekSZPw3HPPISkpSXrBfVHbt2/HnDlz9O7v2rUr3njjjXJ3qiRubm5o3rw5duzYofPm7dixA7169bLKaxLZK6ZSEckP59TYj841O2Nh7EJ8cOgDncm11b2q4/XHXkfnmp2t9tp37tzBiRMndO6rWrUqxowZgw8//BDjx4/HuHHjcP78eSQkJGDy5MlwciqcQqnJu//yyy+xePFiAIUBf//+/VFQUGAw314jIiICu3btQtu2beHu7q6TrqIxceJEfPDBB6hXrx4aNGiAhQsX6gTHPj4+mDJlCiZNmgS1Wo127drh7t272LdvH7y9vXUGiy2tbt26+O6779CjRw8oFAq888472lF9oDC479y5M1566SUsXboUrq6uePXVV+Hp6ak9YercuTNat26N3r17Y86cOYiMjERGRga2bt2K3r1745FHHsHUqVPRr18/1KpVC2lpaTh8+DD69u1rte0ylVnBvb+/PzZt2qRTXgkAvv/+e6vWQp08eTIGDRqEFi1aoHXr1vjss89w9epVjBo1ymqvSWSvmEpFJC+cU2NfOtfsjLjwOBzLPoZ/HvyDal7V0CywmdVH7FNSUvRy04cMGYJVq1Zh69atmDp1KmJiYlC1alUMHz4cb7/9tk7buLg4HDt2TBvIV6lSBdHR0cjIyECDBg1Kfe0FCxZg8uTJWL58OUJDQ3VGvTVeffVVZGZmYujQoXBycsKwYcPQp08f3LlzR9vmvffeQ2BgIGbPno2//voLfn5+aNasGd58803z3hQjLVq0CMOGDUObNm0QEBCA119/HXfv3tVp88UXX2D48OF4/PHHERQUhNmzZ+PMmTPw8Cg8aVYoFNi6dSveeustDBs2DP/88w+CgoLw+OOPo3r16nB2dsaNGzcwePBgXLt2DQEBAXj66aftao6nQphRQmbVqlUYPnw44uPjtTn3Bw4cwLZt2/D5559j6NChlu6n1ieffIK5c+ciMzMTDRs2xKJFi7SXhsoSFhaG9PR0hIaGIi0tzWp9JCIiMoemWg5Q8pwaR61iZurvd25uLlJTU7WLXhIZkpaWhvDwcOzcuROdOnWydXdKZez32qzgHgAOHjyIJUuW4I8//oAQAtHR0ZgwYQJatmxpdqetjcE9EZH9U6mFQ6ecbTudqTenJtjB59QwuCdL2b17N+7fv49GjRohMzMTr732GtLT03HhwgW7qlVfEmO/12al5QBAy5YtsXbtWnP/nIj+5eiBDFFRDGw5p4bImgoKCvDmm2/ir7/+go+PD9q0aYO1a9fafWBvCqODe00NUc3/l0bTjohKx0DGuJMbngA5Bk1KSvHLyZoFnBwpJYVzaoiso2vXrujatautu2FVJi1ilZmZicDAQPj5+ZVYVlJTnqm0MkpEVIiBjHEnNzwBcgxcwImIyDKMDu53796NqlULZ+gnJSUhPDwczs66M8bVarV2FTAiMoyBjHEnNwAc/gTIUXABJ7IGM6cVEtklY7/PRgf3RRciGDZsmHYUv6gbN26gc+fOVq1hSiQHjh7IGHNyM/3HMwAUDn0C5EjkuIAT08lsR5M//eDBA3h6etq4N0SW8eDBAwAoc36AWRNqS1oCGShcCYyz0onKJsdAxhTGnNxk3c0r9TnkfgIkdaYGtnJbwInpZLbl7OwMPz8/ZGdnAwC8vLy4Sj1JlhACDx48QHZ2Nvz8/PQyZ4ozKbifPHkyAGhX/fLy8tI+plKpcPDgQTRp0sT0XhM5GLkFMqay5EmLXE+ApMycwFZOCzhxPo19CAoKAgBtgE8kdX5+ftrvdWlMCu6PHz8OoPAM4tSpU3Bzc9M+5ubmhpiYGEyZMsXErhI5HjkFMuaw5EmL1E+ApJi6UVqfzQ1snZ0USOgRjdFfHoMCJS/glNAjWhLvjaPPp7EXCoUCwcHBCAwMREFBga27Q1Qurq6uZY7Ya5gU3CcnJwMAXnzxRSxevJglL6ncpBjYWIJcAhlzGXNyU72yOwAFrt2V7wmQFFM3Sutzl+igcgW28Q2DsfSFZnrPH2Tn70lRjj6fxh45OzsbHRQRyYFZOfdJSUmW7gc5ICkGNpYkh0DGXMac3Ezv+QgAyPYESIqpG2X1+ZXO9cod2Ep9ASdHn09DRLZn9gq1ROUhxcDGGqQeyJSHsSc3cjwBkmLqhjF9TvrtslHPVVZgK+UFnBx9Pg0R2R6De6pwUgxsrEnKgUx5GXNyI8cTICmmbhjT59sPjctrlnNg6+jzaYjI9hjcU4WTYmBD1mPMyY3cToCkmLphbF/8PF1x52GBwwa2jj6fhohsz8nWHSDHI8XAhsiSpJi6YWxfXmxbC8B/gayGIwW2mpSzIF/d9yzI18NhUg6JyHY4ck8VToqBDZElSTF1w9g+j+tYF5FB3rKbJ2EqOaaTEZE0MLinCifFwIbIkqSYumFKnxnYFpJbOhkRSQPTcqjCaYIEwLEv3ZNjk2Lqhil91gS2vZqEonUdf+7PREQVRCGEKGnwVJbCwsKQnp6O0NBQpKWl2bo7Ds+e6tw76mJaZHtS/O5Jsc8kbfz9JjIe03LIZuzl0r09nWSQ4zGUumHPATTTTYiI7BeDe7IpWwcJXEyL7BFPOImIyFzMuSeHVdZiWkDhYloqtcNkrpEd0JxwFl8LQnPCue10po16RkREUsDgnhyWKYtpEVUEnnASEVF5Mbgnh8XFtMje8ISTiIjKizn35LC4mBbZG2udcNrz5FwiIrIsBvfksLiYFtkba5xwcnIuEZFjYVoOOSwupkX2RnPCaegbp0BhYG7sCScn5xIROR4G9+TQpLhKKMmXJU84OTmXiMgxMS2HHJ69LKZFBPx3wlk8lSbIxFQaUybnckEqIiL5YHBPBNsvpkVUlCVOOFkNiojIMTG4J5Ow6gZRxSjvCSerQREROSYG92Q0Vt0gkg5rVoNy9JN8R99+IrJvDO7JKJqqG8WDBE3VDU4+JbIvmsm5o788BgWgs++WpxqUo5/kO/r2E5H9Y7UcKhOrbhBJk6WrQTl6aU1H334NlVpg/6Ub+OFEOvZfusFjP5Gd4cg9lYlVN4iky1LVoMo6yVeg8CS/S3SQLFNUHH37NXjlgsj+ceSeysSqG0TSppmc26tJKFrX8Tcr+DTlJF+OHH37AV65IJIKBvdUJlbdIJI2S6RROPpJvqNvP9MziaSDaTlUJmtW3SAi67JUGoWjn+Q7+vYzPZNIOjhyT2XSVN0A/quyoVGeqhtEZF2WTKPQnOQb2ssVKDxpkOtJvqNvv6NfuSCSEgb3ZBRTq26wmgKRbZmTRlHafuvoJ/mOvv2OfuWCSEqYlkNGM7bqBqspENmeqWkUxuy3mpP84u2CHGT/duTtZ3omkXQohBAOM6QaFhaG9PR0hIaGIi0tzdbdsTuWWHXR0GJXmmfhYldEFeOHE+mYuP5Eme0WP9sE7i5OJu239rJCq636YS/bX9E0x3eg5EXRrHl85+83kfE4ck8ALDPazjrQRPbD2PSIgErumPLtSZP2W01pTVuy5RXCith+ezyBcOQrF0RSIpng/v3338dPP/2EEydOwM3NDbdv37Z1l2TD0Gi7ZtKdsaMxrKZAZD+MTaOAAkX2WzWcvVKhcLkHofSB6kEtCDjZ3X5rqWOWvbLn1EZLLYpGRNYjmQm1+fn56N+/P0aPHm3rrsiKJWsXs5oCkf0wdgLo9ft5AAAXn9OoVHcOvGouh2foenjVXI5KdefAxec0APvZb+Veb10KC0VZYlE0IrIeyYzcJyYmAgBWrVpV7ucSQiAnJ0fvfmdnZ3h4/Hcpu6Q2Gk5OTvD09DSr7YMHD2BoqoNCoYCXl5dZbR8+fAi1Wm2wH5UqVdJre/CvG0j/57Z+n90K3wcBIOPGXew58zda1i551E7zvIE+HhDKfIhS+qBwddemC+Tl5UGpVBps6+XlBYVCYVRbT09PODkVnqvm5+ejoKDAIm09PDzg7OxsctuCggLk5+cbbOvu7g4XFxeT2yqVSuTl5Rls6+bmBldXV5PbqlQq5OYaDt5cXV3h5uZmclu1Wo2HDx9apK2Liwvc3d0BFO7DDx48sEhbU/Z7qR0jHq/tq5NGoS7IA4RAkK873nyyAdrXqoyDf92Ak/sxuAWsh8Llv/Eedb4aQn0LbgFfQJ3/LHycG+lsQ9HjSW5uLlQqlcHtM6VtWft90WOWwtVd21YoCyDUhc+b/k+u3jHLXo4RKiHw1ckUpN5MQ4inP55p9Djc/t2/VWqBdzcehSo/DwoXVyicCp9XqJRQq5RQAHh341G0qRmrE1A7yjGCiIwkJCYpKUn4+voa1TY3N1fcuXNH+y8kJESgMF4t8V+3bt10/t7Ly8tg2w4dOui0DQgIMNi2RYsWOm1r1qxpsG10dLRO2+joaINta9asqdO2RYsWBtsGBATotO3QoYPBtgpXd1Hz9S3af561DT8vAJFXUCCEEEKpUouqDR8vte2j0zcLpUothBBiyJAhpbbNzs7W9nfMmDGltk1NTdW2nTJlSqltT58+rW2bkJBQattDhw5p286dO7fUtsnJydq2H3/8caltt2zZom2blJRUatsNGzZo227YsKHUtklJSdq2W7ZsKbXtxx9/rG2bnJxcatu5c+dq2x46dKjUtgkJCdq2p0+fLrXtlClTtG1TU1NLbTtmzBht2+zs7FLbDhkyRNv2/v37pbbt16+fzr5RWlupHiOUKrXYd/G6qBsdY7Cts7ezaLiqofafV6ThbfPy8tLpQ7du3Up934rq169fqW3v37+vbVvWMSJs/Frtccq76VOltrWHY8TLixNEoxXtRMNVDUXwC8Gltq3WL0G7bf7dXim1rSMcI0JDQwUAERoaKoiodJJJyzHH7Nmz4evrq/2XkZFh6y7JUosv4jBv7zdwdlIgOrhyqW3ffLIBL+EWka9U4tfLZ2zdDZI5TRqFn5er4UbcLa1ue8ZqqJ1u27obRCRzNi2FOX36dG26jSGHDx9GixYttLdXrVqFV155xagJtXl5eTqXHBs0aICMjAyEhITgwoULeu2ldsndEmk5KrVA54UpuHYnD0VfRZOWowAQ6OWEHZPa46P9m7A29YN/X7tIP9wKzxGH1HkX4x/tgW2/p2PWz38g685/770mDaDXo7WNTrWRe1rOov2bsObPJVCLWxCqwndfofLFc3VG45W2T+u0dYRL7kzLsc0xYvaeddiUtqiwH+66aTlFDwp9wiZhWofntLdtlZZT9JiFEtJyFACq+7pj52Td1BVbHiPylUq0/+pJCPe7cHLW9Fdo93shACeVLxa2/RIjVh8HAL20HKH6731Y9eKjOilHjnCMYClMIuPZNOd+3LhxePbZZ0ttExERYfbzu7u7a3/gAWh/BBQKhc6PjSHGtDGnbdEfW2Pa5iuV+OpkCq7ezUKNykF4PiZWm6NZVNHgoCxF287o27zU2sWJTzeBh6cHvk77DE7uTjqBvYYQwJoLSzCxdR/0fqwOerSoXWY1heKfT2lMaevm5mZ0jqa12rq6ump/FEsyb+83WH1pBuBU+H1UuPz7gy/uYV3GXLgd88DU9v31/s7FxUX7I14WU9o6Ozsb/R02pa2Tk5NV2hq7D5vaFrDefm/NY4SxSjpG1K0eAad/9C/iOrk56bUztA1FT3jKYkpbQ/t9SccshYsrnOD67+PNUNnH2+DzVvQx4pujO6HwugdFkcsjCpf/9vtC95BW8BdCq/npVThSOLsU/kNhhaMOj4QbvAIq52MEERnHpsF9QEAAAgICbNkFuzdv7zdY8+cSCOfb2vsWnvTDoHoTSgz+zGFM7eJVR3dCON82eOVeoQCEy218dTIFQ5t3tos62PYqX6nEmj+X/BvY6z6mUOieKJV0EkdkSc/HxGLhST+onW4bPHF3Uvnh+ZjYCu+bIVKrt371bpZR7dLuZSGhRyeM/vIYFCh5sCWhRzRTG4moVJKJHK5evYqbN2/i6tWrUKlUOHHiBACgbt268PY2PEIjZTqju0XuVzvdLrwfsGiAX1rtYmN/nIxt58i+Opli0olSaYy9qkNkiJuLCwbVm4DVl2ZACN0TTk22z6D6E+zueyWleus1KgcZ3U5qJy5EZH/s62hdinfffRerV6/W3m7atCkAIDk5GbGxsTbqlfXYYnS3tNF2U36cqHSWOlGqiKs65Bg035fi3ycnlR8G1bff75NUrhCaenVESicuRGR/JBPcr1q1yiI17qXCkqO7liDFS/f2yhInShV5VYccw9T2/TGxdR9eCbICc66OSOXEhYjsj6xLYUqZvaXBaH6cgP9+jDTs+dK9PXo+JhYKlZ/e+6ghBKBQGj5R0l7VQclXdYDCqzr5pVQXIiqJm4sLhjbvjHfjXsDQ5p25P1vQ1Pb9MaTOu3BS++nc76Tyw5A67/JknIgshkduO2WPaTBSvXRvb8qb42xvV3WIyDi8OkJEFYFHFDtlr2kw/HGyjPKcKNnbVR0iMp7m6ggRkbUwIrNT9lzBgj9OlmHuiZI9XtUhIiIi+8Dg3o4xDUb+zDlRsterOkRERGR7DO7tHNNgKo5UasZb66qOVLafiIiIDOMvtwQwDcb6pFYz3tJXdaS2/URERFQyBvfk8KRaM95SV3Wkuv1ERESkTyGEoWrb8hMWFob09HSEhoYiLS3N1t0hO5CvVKLFF3Fl5q8fGZIsyxQVR99+IpIG/n4TGY+LWJFD09aMN1A0vmjNeDkyZ/vzlUqsOroTM5K/xKqjO7lYFhERkR3hUBw5NEevGW/q9jM3n4iIyL4xuCeH5ug1403ZfubmExER2T+m5ZBDez4mFgqVHwzNPBECUCjlWzPe2O3v90g7rPlzCQDopfBobq+5sIQpOkRERDbG4J4cmqZmPAC9ANfWKwFXBGO3/9szvzr03AQiIiKpYHBPDm9q+/4YUuddOKn9dO53UvlhSJ13ZZ9qYsz2O/rcBCIiIqmQ53AkkYkcfSXgqe37Y/SjPfD6jqX4+34awr3DMKfLaHh7eADg3AQiIiKpcIzIhcgIjrwScPEqOKk3gTZffaetgvN8TCwWnvQrsx6+XOcmEBERSQXTcogcnKYKjtrpts79mio48/Z+4/BzE4ikjutTEDkO/hITObB8pbKwCo5TyVVwhCisgjOxdR/t3IPide6dVH4YVJ917onsFdenIHIsDO6JHJh2hVoDjxetgjO0eWeHn5tAJDVcn4LI8fAXmciBmVMFx5HnJhBJiSlX5niCTiQfzLkncmCsgkMkX9orc1yfgsihMLgncmCOvkIvkZxxfQoix8TgnsiBsQoOSR2rwBjGK3NEjom/2EQOjlVwSKpYBaZ0XJ+CyDExuCcik6rg5CuVrJZDNscqMGXTXJlbfWlGYYpdkTeKV+aI5It7NBEBMK4KDkdKyR6wCozxeGWOyPE49lGPiIzGkVKyF6auz+DouD4FkWPhnk1EZeJIKdkTVoExHdenIHIcrJZDRGVivWyyJ6wCQ0RkGIN7IioTR0rJnnB9BiIiwxjcE1GZOFJK9oTrMxARGcbgnojKxJFSsjdT2/fHkDrvwkntp3O/k8oPQ+q8y8ndROSwOKxBRGVivWyyR6wCQ0Skj0dAIjIK62WTPWIVGCIiXQzuichoHCklueMKzEQkdTxiEZFJOFJKcsUVmIlIDhjcExGRw+MKzEQkF6yWQ0REDk27AjNKXoEZKFyBOV+prOCeERGZjsE9ERE5NK7ATERywuCeiIgcGldgJiI5kURwf/nyZQwfPhy1atWCp6cn6tSpg4SEBOTn59u6a0REJHFcgZmI5EQSE2rPnTsHtVqNTz/9FHXr1sXp06cxcuRI5OTkYP78+bbuHhERSdjzMbFYeNIPaqeSU3OEKFzPgSswE5EUSCK4j4+PR3x8vPZ27dq1cf78eSxdupTBPRERlYubiwuivNvj7IPNeo9xBWYikhpJpOWU5M6dO6hataqtu0FERBI3b+83JQb2GtFePVgGk4gkQ5LDEJcuXcJHH32EBQsWlNouLy8PeXl52ttCMwRDRESEImUwnfTLYGqcu7cX+UolR+6JSBJsOnI/ffp0KBSKUv8dOXJE528yMjIQHx+P/v37Y8SIEaU+/+zZs+Hr66v9l5GRYc3NIQeRr1Ri1dGdmJH8JVYd3cna10QSxjKYRCQ3Nh2GGDduHJ599tlS20RERGj/PyMjA3FxcWjdujU+++yzMp9/2rRpmDx5svZ2gwYNGOBTuXB5eiJ5YRlMIpIbmwb3AQEBCAgIMKpteno64uLi0Lx5cyQlJcHJqeyLDu7u7nB3d9feVhgamiEyApenJ5IflsEkIrmRxITajIwMxMbGIjw8HPPnz8c///yDrKwsZGVxJIUqBpenJ5Kn52NioVD5wdCULCEAhZJlMIlIOiQxO+h///sfLl68iIsXLyIsLEznMU6SpYqgzcs18HjRvNyhzTsDKDwh+OpkCq7ezUKNykF4PiaWE/KI7IybiwsG1ZuA1ZdmFAbyRXZylsEkIimSxNFq6NChGDp0qK27QQ7M1Lxc5uYTSYdmnyy+zzqp/DCoPvdZIpIWSQT3RLZmSl4uc/OJpGdq+/6Y2LoPr7YRkeTxqEVkBGOXp+/3SDu0+apLiTWzFYrCdmsuLMHE1n0YNBDZGTcXF21aHRGRVEliQi2RrWnycgHoTbwrmpf77ZlfWTObiIiIbIbBPZGRprbvjyF13oWT2k/nfieVH4bUeRdT2/fHj3/uMOq5WDObiIiIrIF5AUQmKC0vN1+pxJ8P9gDOZT8Pa2YTERGRNTC4JzKRobzcr06mAM45ZT+Byps1s4mIiMgqGNwTmaC02vXGptrU82rPybRERERkFYwwrICLF8lTWbXrjU216VnvCSv1kIiIiBwdI04L4+JF8mRM7fqJrfsYVS6TKTlERERkLayWY0GaAFDtdFvnfk0AOG/vN7bpGJVLvlKJNX8uAVBy7XqgsHY9AKPKZfIqDhEREVkLg3sLMTYAzFcqK7hnVF5fnUwxuna9MeUyiYiIiKyFQ4gWog0ADTxeNADkCojSYuxEWU07LmNPREREtsJow0JMDQBJOoydKFu0HZexJyIiIltgWo6FmBMAkjQ8HxMLhcpPL49eQwhAoeREWSJ7lq9UYtXRnZiR/CVWHd3JFEkiki2O3FvI8zGxrJQiU24uLhhUbwJWX5pRGMgX+Xw5UZbI/rGKGRE5Eo7cW4gmAARYKUWOOFGWSJpYxYyIHI1CCEPJBvITFhaG9PR0hIaGIi0tzSqvUdIIkULph0H1OUIkB1ygjEg68pVKtPgirswrqkeGJHM/tnMV8ftNJBc8mlkYK6XIGyfKEkkHq5gRkSNixGkFDACJiGyPVcyIyBEx556IiGSJVcyIyBExuCciIlliGVsickQM7omISJZYxYyIHBGPaEREJFuaKmXFq5g5qVjFjIjkicE9ERHJGquYEZEj4ZGNiIhkj1XMiMhRMOeeiIiIiEgmGNwTEREREckE03KIiEjW8pVK5tsTkcPg0Y2IiGRr3t5v9CrlLDzph0H1WCmHiOSJwT0REcnSvL3fYPWlGYAToChyv9rpduH9AAN8IpId5twTEZHs5CuVWPPnEgCAQqH7mOb2mgtLkK9UVnDPiIisi8E9ERHJzlcnUyCcb+sF9hoKBSBcbuOrkykV2i8iImtjcE9ERLJz9W6WRdsREUkFg3siIpKdGpWDLNqOiEgqGNwTEZHsPB8TC4XKD0KU/LgQgELph+djYiu0X0RE1sbgnoiIZMfNxQWD6k0AAL0AX3N7UP0JrHdPRLLDoxoREcmSpsxl8Tr3Tio/DKrPOvdEJE8M7omISLamtu+Pia37cIVaInIYPLoREZGsubm4YGjzzrbuBhFRhWDOPRERERGRTDC4JyIiIiKSCQb3REREREQyIZngvmfPnqhRowY8PDwQHByMQYMGISMjw9bdIiIiIiKyG5IJ7uPi4rBhwwacP38eGzduxKVLl9CvXz9bd4uIiIiIyG5IplrOpEmTtP9fs2ZNvPHGG+jduzcKCgrg6upqw54REREREdkHyQT3Rd28eRNr165FmzZtSg3s8/LykJeXp70tDK1DTkREREQkA5JJywGA119/HZUqVYK/vz+uXr2KH374odT2s2fPhq+vr/Yfc/SJiIiISM5sGtxPnz4dCoWi1H9HjhzRtp86dSqOHz+O//3vf3B2dsbgwYNLHY2fNm0a7ty5o/0XEhJSEZtFRERERGQTCmHDXJXr16/j+vXrpbaJiIiAh4eH3v1paWkIDw/Hvn370Lp1a6Nez83NDQUFBXByckJwcLBZfSYiIqKKlZmZCbVaDVdXV+Tn59u6O0R2zaY59wEBAQgICDDrbzXnJEVz6suiUqkAAGq1Gunp6Wa9LhEREdmG5neciAyz6ci9sQ4dOoRDhw6hXbt2qFKlCv766y+8++67yMzMxJkzZ+Du7m7U81SqVAm5ublwdnZGYGCgxfonhEBGRgZCQkKgUCgs9rz2Qu7bB3Ab5YDbJ33cRnmwxjZmZ2dDpVLBw8MDOTk5FnlOIrmSRHB/6tQpTJw4ESdPnkROTg6Cg4MRHx+Pt99+G6GhobbuHu7evQtfX1/cuXMHlStXtnV3LE7u2wdwG+WA2yd93EZ5cIRtJLJnkiiF2ahRI+zevdvW3SAiIiIismuSKoVJRERERESGMbi3AHd3dyQkJBid+y81ct8+gNsoB9w+6eM2yoMjbCORPZNEzj0REREREZWNI/dERERERDLB4J6IiIiISCYY3BMRERERyQSDe6JSKBQKfP/997buBpFD435IRGQ8Bvf/mj17Nh599FH4+PggMDAQvXv3xvnz53XaCCEwffp0hISEwNPTE7GxsThz5oz28Zs3b2L8+PGIjIyEl5cXatSogQkTJuDOnTvaNpcvX8bw4cNRq1YteHp6ok6dOkhISEB+fn6FbWtx+/btg7OzM+Lj423Wh4oydOhQ9O7d29bdsLi///4bw4cPR0hICNzc3FCzZk1MnDgRN27cMOrvU1JSoFAocPv2bet2tAwVtR8CQM+ePVGjRg14eHggODgYgwYNQkZGRoVsZ0m4H0of90PT90ONvLw8NGnSBAqFAidOnLDm5hHJHoP7f+3Zswdjx47FgQMHsGPHDiiVSjzxxBM6y1zPnTsXCxcuxMcff4zDhw8jKCgIXbp0wb179wAAGRkZyMjIwPz583Hq1CmsWrUK27Ztw/Dhw7XPce7cOajVanz66ac4c+YMFi1ahGXLluHNN9+s8G3WWLlyJcaPH49ff/0VV69eLddzqVQqqNVqC/WMjPHXX3+hRYsWuHDhAtatW4eLFy9i2bJl2LVrF1q3bo2bN2/auotGq6j9EADi4uKwYcMGnD9/Hhs3bsSlS5fQr1+/Ct3eorgfShv3Q/P2Q43XXnsNISEhFbJ9RLInqETZ2dkCgNizZ48QQgi1Wi2CgoLEBx98oG2Tm5srfH19xbJlyww+z4YNG4Sbm5soKCgw2Gbu3LmiVq1aluu8Ce7fvy98fHzEuXPnxIABA0RiYqL2seTkZAFAbNmyRTRu3Fi4u7uLxx57TPz+++/aNklJScLX11ds3rxZNGjQQDg7O4u//vrLFptilCFDhohevXoJIYSoWbOmWLRokc7jMTExIiEhQXsbgNi0aVOF9c8c8fHxIiwsTDx48EDn/szMTOHl5SVGjRolhCj8vk6dOlWEhYUJNzc3UbduXfH555+L1NRUAUDn35AhQ2ywJfoqcj/84YcfhEKhEPn5+ZbbACNxP1yk8zj3Q8faD7du3SqioqLEmTNnBABx/Phxq2wHkaPgyL0BmkuHVatWBQCkpqYiKysLTzzxhLaNu7s7OnTogH379pX6PJUrV4aLi0upbTSvU9G+/vprREZGIjIyEi+88AKSkpIgii19MHXqVMyfPx+HDx9GYGAgevbsiYKCAu3jDx48wOzZs/H555/jzJkzCAwMrOjNcFg3b97E9u3bMWbMGHh6euo8FhQUhIEDB+Lrr7+GEAKDBw/G+vXrsWTJEvzxxx9YtmwZvL29ER4ejo0bNwIAzp8/j8zMTCxevNgWm6OnovbDmzdvYu3atWjTpg1cXV0tuAXG4X4obdwPzd8Pr127hpEjR2LNmjXw8vKy0hYQORbDEacDE0Jg8uTJaNeuHRo2bAgAyMrKAgBUr15dp2316tVx5cqVEp/nxo0beO+99/Dyyy8bfK1Lly7ho48+woIFCyzUe9OsWLECL7zwAgAgPj4e9+/fx65du9C5c2dtm4SEBHTp0gUAsHr1aoSFhWHTpk145plnAAAFBQX45JNPEBMTU/Eb4OD+/PNPCCHQoEGDEh9v0KABbt26hcOHD2PDhg3YsWOH9rOtXbu2tp3mRzswMBB+fn5W77cxKmI/fP311/Hxxx/jwYMHaNWqFbZs2WLhrTAO90Np434I7W1T9kMhBIYOHYpRo0ahRYsWuHz5snU2gsjBcOS+BOPGjcPvv/+OdevW6T2mUCh0bgsh9O4DgLt37+Kpp55CdHQ0EhISSnydjIwMxMfHo3///hgxYoRlOm+C8+fP49ChQ3j22WcBAC4uLhgwYABWrlyp065169ba/69atSoiIyPxxx9/aO9zc3ND48aNK6bTZBLN6G9qaiqcnZ3RoUMHG/fIeBWxH06dOhXHjx/H//73Pzg7O2Pw4MF6I+bWxv1Q/rgflrwffvTRR7h79y6mTZtm+Y4TOTCO3Bczfvx4/Pjjj/jll18QFhamvT8oKAhA4YhFcHCw9v7s7Gy90Yt79+4hPj4e3t7e2LRpU4mX+TMyMhAXF4fWrVvjs88+s9LWlG7FihVQKpUIDQ3V3ieEgKurK27dulXq3xY9gHt6epZ4QLd3Tk5OeoFc0TQHKahbty4UCgXOnj1bYvWRc+fOoUqVKpK73F1R+2FAQAACAgJQv359NGjQAOHh4Thw4IBOIG1t3A+5H9ora++Hu3fvxoEDB+Du7q7zNy1atMDAgQOxevVqa2wWkexx5P5fQgiMGzcO3333HXbv3o1atWrpPF6rVi0EBQVhx44d2vvy8/OxZ88etGnTRnvf3bt38cQTT8DNzQ0//vgjPDw89F4rPT0dsbGxaNasGZKSkuDkVPEfg1KpxBdffIEFCxbgxIkT2n8nT55EzZo1sXbtWm3bAwcOaP//1q1buHDhAqKioiq8z5ZWrVo1ZGZmam/fvXsXqampNuyR6fz9/dGlSxd88sknePjwoc5jWVlZWLt2LQYMGIBGjRpBrVZjz549JT6Pm5sbgMIqK7ZUkfthSa8NFJbkqyjcD7kfFuVo++GSJUtw8uRJ7fd+69atAArnoLz//vtW3EIimau4ubv2bfTo0cLX11ekpKSIzMxM7b+ilQ8++OAD4evrK7777jtx6tQp8dxzz4ng4GBx9+5dIYQQd+/eFS1bthSNGjUSFy9e1HkepVIphBAiPT1d1K1bV3Ts2FGkpaXptKlImzZtEm5ubuL27dt6j7355puiSZMm2iodjzzyiNi5c6c4deqU6Nmzp6hRo4bIy8sTQvxXpUMqilbpeOONN0RQUJD45ZdfxKlTp0Tv3r2Ft7e35Kp0XLhwQQQEBIj27duLPXv2iKtXr4qff/5ZNGzYUNSrV0/cuHFDCCHE0KFDRXh4uNi0aZP466+/RHJysvj666+FEEKkpaUJhUIhVq1aJbKzs8W9e/dssi0VtR8ePHhQfPTRR+L48ePi8uXLYvfu3aJdu3aiTp06Ijc3t8K2l/sh90NH3g+L01QMYrUcovJhcP8vFCtBpvmXlJSkbaNWq0VCQoIICgoS7u7u4vHHHxenTp3SPq75ES7pX2pqqhCi8EfYUJuK1L17d9GtW7cSHzt69KgAIBYsWCAAiM2bN4tHHnlEuLm5iUcffVScOHFC21ZqQcWgQYNE3759hRBC3LlzRzzzzDOicuXKIjw8XKxatUqSJfiEEOLy5cti6NChIigoSLi6uorw8HAxfvx4cf36dW2bhw8fikmTJong4GBtCb6VK1dqH58xY4YICgoSCoXCZiX4Kmo//P3330VcXJyoWrWqcHd3FxEREWLUqFEiLS2tQreX+yH3Q0feD4tjcE9kGQohKnj2GElGSkoK4uLicOvWLbup3FBe8fHxqFu3Lj7++GNbd4XIKNwPiYjIFMy5J4dw69Yt/PTTT0hJSdEpL0hEFYf7IRGR9bFaDjmEYcOG4fDhw3j11VfRq1cvW3eHyCFxPyQisj6m5RARERERyQTTcoiIiIiIZILBPRERERGRTDC4JyIiIiKSCQb3REREREQyweCeiOxeSkoKFAoFbt++beuuEBER2TVWyyEiuxMbG4smTZrgww8/BADk5+fj5s2bqF69OhQKhW07R0REZMdY556I7J6bmxuCgoJs3Q0iIiK7x7QcIrIrQ4cOxZ49e7B48WIoFAooFAqsWrVKJy1n1apV8PPzw5YtWxAZGQkvLy/069cPOTk5WL16NSIiIlClShWMHz8eKpVK+9z5+fl47bXXEBoaikqVKqFly5ZISUmxzYYSERFZAUfuiciuLF68GBcuXEDDhg0xY8YMAMCZM2f02j148ABLlizB+vXrce/ePTz99NN4+umn4efnh61bt+Kvv/5C37590a5dOwwYMAAA8OKLL+Ly5ctYv349QkJCsGnTJsTHx+PUqVOoV69ehW4nERGRNTC4JyK74uvrCzc3N3h5eWlTcc6dO6fXrqCgAEuXLkWdOnUAAP369cOaNWtw7do1eHt7Izo6GnFxcUhOTsaAAQNw6dIlrFu3DmlpaQgJCQEATJkyBdu2bUNSUhJmzZpVcRtJRERkJQzuiUiSvLy8tIE9AFSvXh0RERHw9vbWuS87OxsAcOzYMQghUL9+fZ3nycvLg7+/f8V0moiIyMoY3BORJLm6uurcVigUJd6nVqsBAGq1Gs7Ozjh69CicnZ112hU9ISAiIpIyBvdEZHfc3Nx0JsJaQtOmTaFSqZCdnY327dtb9LmJiIjsBavlEJHdiYiIwMGDB3H58mVcv35dO/peHvXr18fAgQMxePBgfPfdd0hNTcXhw4cxZ84cbN261QK9JiIisj0G90Rkd6ZMmQJnZ2dER0ejWrVquHr1qkWeNykpCYMHD8arr76KyMhI9OzZEwcPHkR4eLhFnp+IiMjWuEItEREREZFMcOSeiIiIiEgmGNwTEREREckEg3siIiIiIplgcE9EREREJBMM7omIiIiIZILBPRERERGRTDC4JyIiIiKSCQb3REREREQyweCeiIiIiEgmGNwTEREREckEg3siIiIiIplgcE9EREREJBP/Dwn4FKb2eiTSAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
" ] @@ -505,7 +579,7 @@ "ds_hightide = ds.sel(time=ds.tide_height >= hightide_thresh)\n", "\n", "# Plot extracted images over all images\n", - "ds.tide_height.plot(marker=\"o\", linewidth=0, label=\"All satellite images\")\n", + "ds.tide_height.plot(marker=\"o\", linewidth=0, label=\"Other satellite images\")\n", "ds_hightide.tide_height.plot(marker=\"o\", linewidth=0, label=\"High tide images\")\n", "ds_lowtide.tide_height.plot(marker=\"o\", linewidth=0, label=\"Low tide images\")\n", "plt.axhline(lowtide_thresh, color=\"black\", linestyle=\"dashed\")\n", @@ -535,12 +609,19 @@ "

\n", " Note the use of .load() below. Up to this point, our entire analysis has been \"lazy\", which means we haven't loaded the majority of our satellite data - we have simply \"queued\" up our analysis to run in a single step. This makes it quick and easy to write code without having to wait for every step of our workflow to run every time, or ever worrying about running out of memory. Running .load() triggers our entire analysis to run, and then load our final outputs into memory for further use. For more information about lazy loading and processing, see Parallel processing with Dask.\n", "

\n", + "\n", + "\n", + "
\n", + "

Important

\n", + "

\n", + " Be patient; this step may take several minutes to complete.\n", + "

\n", "
" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -562,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -595,7 +676,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_a6cbda053897356d92dffeceb5104496 {\n", + " #map_af6dba06bccfa13d35d85e7bf8df6b6b {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -622,14 +703,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_a6cbda053897356d92dffeceb5104496" ></div>\n", + " <div class="folium-map" id="map_af6dba06bccfa13d35d85e7bf8df6b6b" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_a6cbda053897356d92dffeceb5104496 = L.map(\n", - " "map_a6cbda053897356d92dffeceb5104496",\n", + " var map_af6dba06bccfa13d35d85e7bf8df6b6b = L.map(\n", + " "map_af6dba06bccfa13d35d85e7bf8df6b6b",\n", " {\n", " center: [0.0, 0.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -643,54 +724,54 @@ "\n", " \n", " \n", - " var tile_layer_ee125458747045edadc95f311b312b76 = L.tileLayer(\n", + " var tile_layer_4636dc98cfec3fafdaf6152aada13234 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_ee125458747045edadc95f311b312b76.addTo(map_a6cbda053897356d92dffeceb5104496);\n", + " tile_layer_4636dc98cfec3fafdaf6152aada13234.addTo(map_af6dba06bccfa13d35d85e7bf8df6b6b);\n", " \n", " \n", - " var image_overlay_1521ccd8fdc2f8a0affdbca300b2639c = L.imageOverlay(\n", - " 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image_overlay_1521ccd8fdc2f8a0affdbca300b2639c.addTo(map_a6cbda053897356d92dffeceb5104496);\n", + " image_overlay_c44cfe00b3af3ccd0e0d590dd7a4e388.addTo(map_af6dba06bccfa13d35d85e7bf8df6b6b);\n", " \n", " \n", - " map_a6cbda053897356d92dffeceb5104496.fitBounds(\n", + " map_af6dba06bccfa13d35d85e7bf8df6b6b.fitBounds(\n", " [[-17.928541358818464, 122.11999572144752], [-18.251457152765816, 122.43006048571965]],\n", " {}\n", " );\n", " \n", " \n", - " var layer_control_f095907b6196842b073a475f9114bbf9_layers = {\n", + " var layer_control_aac12ac08ff317d38202cc66f537dd93_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_ee125458747045edadc95f311b312b76,\n", + " "openstreetmap" : tile_layer_4636dc98cfec3fafdaf6152aada13234,\n", " },\n", " overlays : {\n", - " "ndwi" : image_overlay_1521ccd8fdc2f8a0affdbca300b2639c,\n", + " "ndwi" : image_overlay_c44cfe00b3af3ccd0e0d590dd7a4e388,\n", " },\n", " };\n", - " let layer_control_f095907b6196842b073a475f9114bbf9 = L.control.layers(\n", - " layer_control_f095907b6196842b073a475f9114bbf9_layers.base_layers,\n", - " layer_control_f095907b6196842b073a475f9114bbf9_layers.overlays,\n", + " let layer_control_aac12ac08ff317d38202cc66f537dd93 = L.control.layers(\n", + " layer_control_aac12ac08ff317d38202cc66f537dd93_layers.base_layers,\n", + " layer_control_aac12ac08ff317d38202cc66f537dd93_layers.overlays,\n", " {"autoZIndex": true, "collapsed": true, "position": "topright"}\n", - " ).addTo(map_a6cbda053897356d92dffeceb5104496);\n", + " ).addTo(map_af6dba06bccfa13d35d85e7bf8df6b6b);\n", "\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -701,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -734,7 +815,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_7d782629a982d58869ffa3b724306ea3 {\n", + " #map_1d0ee3170caccef5ec9ec67541ccf528 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -761,14 +842,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_7d782629a982d58869ffa3b724306ea3" ></div>\n", + " <div class="folium-map" id="map_1d0ee3170caccef5ec9ec67541ccf528" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_7d782629a982d58869ffa3b724306ea3 = L.map(\n", - " "map_7d782629a982d58869ffa3b724306ea3",\n", + " var map_1d0ee3170caccef5ec9ec67541ccf528 = L.map(\n", + " "map_1d0ee3170caccef5ec9ec67541ccf528",\n", " {\n", " center: [0.0, 0.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -782,54 +863,54 @@ "\n", " \n", " \n", - " var tile_layer_0bd82a8626c30f1fb405c868917cd246 = L.tileLayer(\n", + " var tile_layer_a08a101aa70f1833b65f39a5f85bc875 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_0bd82a8626c30f1fb405c868917cd246.addTo(map_7d782629a982d58869ffa3b724306ea3);\n", + " tile_layer_a08a101aa70f1833b65f39a5f85bc875.addTo(map_1d0ee3170caccef5ec9ec67541ccf528);\n", " \n", " \n", - " var image_overlay_530609af2a67cf4b826186005fca7c2d = L.imageOverlay(\n", - " "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArsAAAL3CAYAAACK+Y2VAAAgAElEQVR4nOydeXgkd3nnP3V2dVef Uuu+Nfd4Lo/vC3yAsTHGBzhAgAcHNpBkgQC5CGQXyAlkl2MJCYRg2OUGx2CMbbDjYMYen+Px3Jc0 0mh0S91qqc/qOvePavWMRmN7bI5guz7PM89IXdVdperuX72/9/e+3y8EBAQEBAQEBAQEvEQRTveg 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layer_control_83005f2ed62a224177eacdd6d2ca206a_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_0bd82a8626c30f1fb405c868917cd246,\n", + " "openstreetmap" : tile_layer_a08a101aa70f1833b65f39a5f85bc875,\n", " },\n", " overlays : {\n", - " "ndwi" : image_overlay_530609af2a67cf4b826186005fca7c2d,\n", + " "ndwi" : image_overlay_6342304ee2858fb936e92824664a4852,\n", " },\n", " };\n", - " let layer_control_8835d8e5780258ca3ba11275af818725 = L.control.layers(\n", - " layer_control_8835d8e5780258ca3ba11275af818725_layers.base_layers,\n", - " layer_control_8835d8e5780258ca3ba11275af818725_layers.overlays,\n", + " let layer_control_83005f2ed62a224177eacdd6d2ca206a = L.control.layers(\n", + " layer_control_83005f2ed62a224177eacdd6d2ca206a_layers.base_layers,\n", + " layer_control_83005f2ed62a224177eacdd6d2ca206a_layers.overlays,\n", " {"autoZIndex": true, "collapsed": true, "position": "topright"}\n", - " ).addTo(map_7d782629a982d58869ffa3b724306ea3);\n", + " ).addTo(map_1d0ee3170caccef5ec9ec67541ccf528);\n", "\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -848,12 +929,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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zuWfOnInVauXCCy/c7T5dMzffDVs95ZRTcDgcPWbZBw0aRFZWVny5tLQU0NNL7HZ7j/W7SvXo+vt00fV37LrmvW3ToEGDMJvN/O53v+O5555jy5Ytu73OvWHBggU0Nzdz3nnndXt2VFXl6KOPZvHixUbYooHBL4Dfoq1//vnnWbx4cY/P3tiOefPm0b9//x7aQmecccYu909PT2f48OHd1g0YMOBHpfZ9l/Xr11NdXc2ZZ57ZLXoxLy+Pww47rNu+77//PgkJCUydOrXbu3vQoEGkp6fvVaWGLpKSkrj22mt56623WLRo0Q9u/65s/9ixY9mwYQPV1dU0NTWxatWqeKrG6NGj+fbbb2lra2Pbtm2Ul5f/oEjDDRs2sHnzZi666CKsVusu99mXfl8X360q0WXju6L1dl6/q+fgxBNP7NaerkiCzz//HEVR9qlNw4cPZ+bMmVx33XXMnTt3ryNKdsf7779P//79GTRoULfnZ9KkSXtd6cPA4OeG4TjYDwQCAQYOHMjDDz/8g49xxRVX8OSTT3Lvvfeybt063nvvvR4G9JdES0sLmqaRkZHRY1tmZiZAPPzrsMMOw263M3v2bDZt2kRFRUXccbBo0SL8fj+zZ8+msLCQgoKC/dbGpqYm0tLSeqxPT0/vttwVhjd9+nRkWe72+eMf/wjougx7i9vt5oYbbuCjjz7apeLx3tBlQLvuJUBCQgKDBg2KH3POnDmMHj06vn306NFxQ9UVyuhyufb53A0NDWRmZiKKu399NDU1YTKZSElJ6bZeEATS09N7hP4lJiZ2WzabzXtcHwqFuq03mUwkJSV1W9f1d+w61962qaioiNmzZ5Oamsqf/vQnioqKKCoq4sEHH9zt9e6Jrufn5JNP7vH83H333Wiatk+VNgwMDH4afou2vrS0lKFDh/b47I02zu5s7K7WAT3e4QAWi+VHD+C+2yboaed3ta6uro7W1lbMZnOPd3dtbe0+2X2AK6+8kszMTK655pof3P5d2f6ddQ7mzp2LJEmMGjUKIO7g+eKLL36UvkGXpsOeBIH3pd/Xxb7Y/u/afdj93zESieD3+/epTf/+97+59tpr+d///sfYsWNJTEzk+OOPZ+PGjbu95j1RV1fHihUrejw7LpcLTdP2+fkxMPg5YPqpG/BrYPLkyUyePHm32yORCDfccAMvvfQSra2t9O/fn7vvvjvuEV67di2PPfYYq1atoqSk5CC1+sDi9XoRRTGeb78z1dXVACQnJwO6QTj88MOZPXs22dnZpKenU1ZWRmFhIaAbw08//bSHZ/rHkpSU1E1DoYvviiN2tfP666/nxBNP3OWx9vXvdumll/Lggw9y7bXX7rIG8/fx7rvvAvQQexw7diz33XcfK1asYPXq1d1KQI0ePZr777+fFStWUFFRsdtZn+8jJSWFL7/8ElVVd+s8SEpKIhaL0dDQ0G2grmkatbW1DBs27Aede3fEYjGampq6dTy7/o5d6/alTUcccQRHHHEEiqKwZMkSHnroIa688krS0tI4/fTT96ltXc/PQw89tFs15t11pA0MDH4+GLZ+30hKSuqW/97F3lYXOBB02YNdtWFXtj8pKYmPPvpol8faV8e7zWbjpptu4ne/+10PYb69IRgMMnv2bIqKiroN4I888kgkSWLu3LlYLBaGDBkSn113u93xCYXm5mZMJlPcqbAvdNnMLm2FXbEv/b79xe7+jmazGafTiclk2us2ORwObr75Zm6++Wbq6uri0QdTp05l3bp1+9y25ORkbDYbTz/99G63Gxj80jAiDg4CF1xwAfPnz+fVV19lxYoVnHLKKRx99NFxL+Z7771HYWEh77//PgUFBeTn53PxxRf/omchHQ4HI0aM4O233+42W6CqKi+++CLZ2dn07t07vn7ChAl88803vPXWW/F0BIfDwciRI3nooYeorq7eqzQFi8UCsFczFGPHjqW9vT0+CO/i5Zdf7rZcUlJCcXExy5cv3+XMyw+ZuTebzdx2220sXryYN954Y5++u3z5cu644w7y8/M59dRTe1wTwM0334woit3CSbv+f/PNN3fbd1+ZPHkyoVBoj6KQ48ePB+ghavXWW28RCATi2/cnL730Urflrr9jV6f9h7RJkiRGjBjBI488AsDSpUt3e/7dPXujRo0iISGBNWvW7Pb56ZplMTAw+OXyW7T1e2L06NGsWrWKNWvWdFv/6quv7vdz7W1kQklJCRkZGbzyyivdwv63bt3arSoQ6GH0TU1NKIqyy/f2D3H+XHjhhZSWlnLdddd9b7WInVEUhcsuu4ympqYeaY4ej4fBgwfHIw6+O6HQVVVp7ty5DB8+vFvI/t7Su3dvioqKePrpp3uIG3exr/2+/cHbb7/dLRKhvb2d9957jyOOOAJJkn5wm9LS0jj//PM544wzWL9+fY8KTTuzu2fv2GOPZfPmzSQlJe3y+dm5+oKBwS8FI+LgALN582ZeeeUVKisr42FR06dP56OPPuKZZ57hjjvuYMuWLWzdupU33niD559/HkVRuOqqqzj55JN7qPz+3Pjss8+oqKjosf6YY47hzjvv5KijjmLs2LFMnz4ds9nMo48+yqpVq3jllVe65ReOHz8eRVH49NNPee655+LrJ0yYwI033oggCIwbN+5721NWVgbAgw8+yHnnnYcsy5SUlOxyYH/uuefyr3/9i3PPPZfbb7+d4uJiPvzwQz7++OMe+/7nP/9h8uTJTJo0ifPPP5+srCyam5tZu3YtS5cu3efBP+h5nvfeey8zZ87c7T7ffPMNHo+HaDRKdXU1n376KS+88AKpqam89957PQacXTMPM2bM6OHQSEhIYODAgcyYMQNZln/QrENXu5955hn+8Ic/sH79esaOHYuqqixatIjS0lJOP/10jjrqKCZNmsS1116Lz+dj1KhRrFixghtvvJHBgwdzzjnn/KBz7w6z2cx9992H3+9n2LBhLFiwgNtuu43JkyfHHSZ726bHH3+czz77jClTppCbm0soFIrPGOzJedW/f38AnnjiCVwuF1arlYKCApKSknjooYc477zzaG5u5uSTTyY1NZWGhgaWL19OQ0NDt7rcBgYGvzx+7bb+h3DllVfy9NNPM3nyZG655RbS0tJ4+eWX47O3e0p321e6yhG/9957ZGRk4HK5djmwF0WRW2+9lYsvvpgTTjiBSy65hNbWVm666aYeYe+nn346L730EscccwxXXHEFw4cPR5ZlKisrmTNnDtOmTeOEE07Yp3ZKksQdd9wR/96AAQN67FNXV8dXX32Fpmm0t7ezatUqnn/+eZYvX85VV13FJZdc0uM7Y8eO5Z577kEQBO6+++5u20aPHs2//vUvNE3roQW0LzzyyCNMnTqVkSNHctVVV5Gbm8u2bdv4+OOP4477fen37Q8kSeKoo47iL3/5C6qqcvfdd+Pz+eITJPvSphEjRnDssccyYMAAvF4va9eu5YUXXuDQQw/tprX0XcrKynj11Vd57bXXKCwsxGq1UlZWxpVXXslbb73FkUceyVVXXcWAAQNQVZVt27Yxa9Ysrr76akaMGLFf74eBwQHnp1Bk/DUDaDNmzIgvv/766xqgORyObh+TyaSdeuqpmqbtUPFfv359/HvffPONBmjr1q072JewV3yf4nJXVYMvvvhCGzdunOZwODSbzaaNHDlSe++993ocT1VVLTk5WQO0qqqq+Pr58+drQDc13e/j+uuv1zIzMzVRFLupKX+3qoKmaVplZaV20kknaU6nU3O5XNpJJ52kLViwoEdVBU3TtOXLl2unnnqqlpqaqsmyrKWnp2vjxo3THn/88e9tE7tRSp41a1b8nu2qqkLXx2KxaBkZGdrEiRO1Bx98UPP5fLs91/DhwzVAmz59eo9tV155pQZoo0aN6rFtb6sqaJpe1eH//u//tOLiYs1sNmtJSUnauHHjtAULFnTb59prr9Xy8vI0WZa1jIwM7dJLL9VaWlq6HWtXytKatut7tqs2dilCr1ixQhszZoxms9m0xMRE7dJLL9X8fn+Pdn9fmxYuXKidcMIJWl5enmaxWLSkpCRt9OjR3So5dLVv56oKmqZpDzzwgFZQUKBJktTjGZo3b542ZcoULTExUZNlWcvKytKmTJmivfHGG3u61QYGBj9Dfu22/vsqJ02ZMuV7qypomqatWrVKmzBhgma1WrXExETtoosuilf16aospGm6fe7Xr1+P85x33nk9zrMrli1bpo0aNUqz2+0aELf1362q0MWTTz4Zt1+9e/fWnn766V2eKxqNavfee682cOBAzWq1ak6nU+vTp4/2+9//Xtu4ceMe27Sn6kiHHXaYBuyyqkLXRxRFze12a2VlZdrvfvc7beHChbs914cffqgBmiRJWltbW7dtzc3N8f7QJ5980uO7e1tVQdN0+zh58mTN4/FoFotFKyoq0q666qpu++xNv293z9fu7tl329jVF7j77ru1m2++WcvOztbMZrM2ePBg7eOPP+7R7r1p03XXXacNHTpU83q9msVi0QoLC7WrrrpKa2xs7NG+namoqNAmTpyouVyuHtVG/H6/dsMNN2glJSWa2WzWPB6PVlZWpl111VVabW3tHu60gcHPE0HTjEKi+xNBEJgxYwbHH388AK+99hpnnXUWq1evRpKkbvs6nU7S09O58cYbueOOO4hGo/FtwWAQu93OrFmzOOqoow7mJRgY/KI4//zzefPNN/H7/T91UwwMDH4jGLb+h/O73/2OV155haamJiNNy+AHUVFRQUFBAffccw/Tp0//qZtjYPCbwUhVOMAMHjwYRVGor6/niCOO2OU+o0aNIhaLsXnzZoqKigC99A3oJYIMDAwMDAwMfr4Ytn7X3HLLLWRmZlJYWIjf7+f999/nySef5IYbbjCcBgYGBga/MAzHwX7A7/ezadOm+HJ5eTnLli0jMTGR3r17c9ZZZ3Huuedy3333MXjwYBobG/nss88oKyvjmGOOYcKECQwZMoQLL7yQBx54AFVV+dOf/sRRRx2134VkDAwMDAwMDPYdw9bvO7Isc88991BZWUksFqO4uJj777+fK6644qdumoGBgYHBPmKkKuwH5s6du0uF+vPOO49nn32WaDTKbbfdxvPPP09VVRVJSUkceuih3HzzzXExv+rqai6//HJmzZqFw+Fg8uTJ3HfffT1q2RoYGBgYGBgcfAxbb2BgYGDwW8ZwHBgYGBgY/CaJxWKYTEbgnYGBgYGBwa8Vw9bvP/ZfLRwDAwMDA4NfCJ988gn333//T90MAwMDAwMDgwNEc3MzZ511FsY8+f7BiDgwMDAwMPhNoSgKhxxyCBUVFWzevJmkpKSfukkGBgYGBgYG+5mrr76a+++/n5kzZ3L00Uf/1M35xWM4Dn4gDoeDUCiEJEmkpqb+1M0xMDAw+MVRX1+PoihYrVYCgcBBO+/zzz/PeeedB8CVV17Jv/71r4N2boNfFoatNzAwMPjx/BT2vry8nD59+hCJRCgrK+Pbb7/tUS7XYN8wHAc/EEmSUFX1p26GgYGBwS8eURRRFOWgnCsYDFJSUsL27dsBXfV97dq18fJ4BgY7Y9h6AwMDg/3HwbT3Z555Jq+88kp8+emnn+aCCy44KOf+tWI4Dn4gZrOZaDQKCCDbfurm/OYQBJFMrxVUlY4mH5oG9gQb/pYOzCaRYEzv6JkEEBCQZRHJYiIWjCI7LGiqiqbqj74/EOlxfJMAJlFElIQe20STiBpT0TQNURIRRAF/RxQAqyQgyRIIApLZhCCJKKEI/o4oLrcVLaagRFXQNMIxFZvVhCAK+n4RBU1VMVlkBJOEKMuo4TBKJIYgiWiqSiysIMkSgigAGmgQjSioGgiA0vlz7urmdu5lsJ8w7qeOLAhIAp3PIaiqhtD5U5HMEmpU/30JotD5W9Cfb0mWUKIKaCBKIo2RCKqmIcsykUjP3+GB4K677uL666/vtu7UU0/ltddeOyjnN/hlYdj6nwcJCW7EpkbUTlvf1tKBVRIJKbq1kwBR6LT1ZhPRYATZbkYQBGLhKKIkxu30zkiASRQQJQFBEPQ86M6XvCiLaIqGGlMRZd3WB4IxAKyigChLCCJIZhlBFIiFIgSCMdxuK0o0hhpTdTOtgWQSEWURyZ1AtLkZNaZissoIJhFRNqOEwqjRGIIooCka0YiCqfOcgiigxlSiUZVYp4037JDBwUACJEFAFAABFFXvB4kCSBYJJaygaPqyKAqIsoga0X8vXX1d0SRispmpa/OjwkGz90uWLGHYsGHd1mVmZrJx40bsdvsBP/+vFcNx8APJzs6mqqoKZDtyv9N+6ub85kjpexjbbuiN0lTLn4+4hvEpduY0dOA0iUwo8iKZRQZfOg5BFFnx1Fx6Hz+ET+77jEOmFDHnnY1MOLkUS4KTRc8vxSYJzKzTw6ZOKUvhjZUNuE0iZ5/Uh1goSt2KBpxpDpaubkDp/LUMyHWTe2Q+zzy1FJskUh2KMdBjoW/vRKKBKNFgjJHXTqb2q9VEgzEefW0NlxzbC0ES6DVtJOtenc/qxdUsbQ0R1WBKhhNXhpO0AalkjxmMpqpIVgvh1na2f7Ycq9eOOz+DaEcQTVGJ+DpwZCSx+YMVrF/XRJbXyuamIG1RBX9MpSVqzJDtDxydjqOgoqF+Z31A0RABSYDob+wtmmyWKHLIJJolUkoS8dd1EPaFqQvGyM904sxw0rypBSWiIEoiib28pJRl0Ly+Hl+lj2gwhifHxbSFi2iMRsnKyqKysvKAt7uhoYFevXrh8/kAASQZFL0D89VXXzFixIgD3gaDXxaGrf95sObjB7g3bQBDE6wsaQ2RZpEYmmjD4rYw8OLDEUWRNa9+RclJQ1n0r8/oPbmImW+uY/LJfbAkOFn4wrcEFI2lrSEATixJ4u31TVhFgVMPzyaxVxLb5m9HtplYsL6JoKJhkwSGZrnJH5vPv/67FLdJxBdTybfLjOidSLBFP9aI6ROp/2Y9qqLy0AuruGhyEZJZpPDY4Wx+ZxGN65qZsakZWYCTh2YAkDksh7ShJcRCEcwuO8H6VrbNWYnsMOPKStQnCkIRBFEk1hGi6utqVm/3YZNEVrbp5/2t2R2Dg48sQKJZorfTTG5xIrWbW4ioGi1RFbdJJD3FTlV9AJskYhYFbA6ZnEOzCLaEqF5aS0TVcDrNSGaJszavpDF2cOy9pmmMHTuWefPm6SskCyhhAG677Tb+/ve/H9Dz/5oxqioY/CJJzEoHUQKTjEMSSC/ycskJJfhiKs1NQZaXt1GzcDXPTH8Lf02A2iWbyM33sGlWOQ3hGK1bWwk1+5h4y1Rm1gU4pSwFgDdWNpBjM3HG1GLqV9az6otKrB4LVq8Vt0nk8MOyGVjgoe8Zw7j/8SW0RFXSrXqJF7dJpGmrjzfXNPJOeSvX/+EVksuKKDxWH4z89/1NPPHORhpXbMaV6WRRS4g/XzocAGeag8TiRLx98hDtduTUTJRQGGtmJtmjy4gGwqx+aRF1S7ciSCKyw0q4pV2/F50zvJv8ESKqhj9mOA32B7IAigYRtbvTACCg7Oix2SQRryz+pl6mjRGFqAYNYYVgS4hgawizwxy/T6llmUiyRCimUh+IoEQV6pdXITtkhE5nTNgXQTvIj+qtt97a6TQAIbEYMWNIfNtf//pXQ3XZwOBnSrZTz0suLvZy0eQi6sIK63xh5mxpoXrBOh77+3s0bfWxfd4aHEk2Fr+7EbdJpLWijfbKZo6+6ViWtoaYmu0G4O31TeTYTJw1qZCWLW188856nGl2HKkOUiwmjhuTx+gh6ZSdN4J//XcpQNzWm0WBiq1tvNv5+fvlb5DYr4Dc8YcA8P5nFTz5zkaaV5fjSHXyxbY2HqqZTZZNRo2qeAu9uHLTMLk92LJzCNa34ijKJ+vwUjRFZdUry2jb2oTJagZAEEUEScBtElE0jahmOA0MDg5RTX/eN/gj+OsCtERVbJJIRNVI8VhI7pMIgD+mUtERRbaZqF1WR6AugNkuYxYF1KhKQ2MHonTwekkffPDBDqeB2YWp19HosRJw9913U19ff9Da8mvjt9TXNfgVcfqk3qgdPrRQgKCi8dJXVfxnxnompznI7ZvEBn+EF19dw+GHZfNBpQ+b14bZKQNQ5DDjq2ynfM5W/vbH1xiaYOWNlQ2kWSTSLBLjRmTx+vsbCTQFKRudS0djkMZ1TQybXETYFya5JIn7bvoo3pb17WGGJlhJS3Owwb8j/Oq4PI+eYqCoXH5OfyamOgD45wMLePDZFQDc9+jXTEx14KvxEwtFueW69yj/3+dE66tRIjHCtbU4+5ZRfNFpHPp/J5E5oghfeR2R9g7qllfiyfPQb1oJskNGEqAurBgdiv1EVIOQqsXvZ6bVRKbV1O2lqaIbTJskkmiWkHtmtvxqWd8epjGi4MpwEVQ0LG4z2R4L0WCMbZ9vwZ5sw5tiZ+IVR1K/sYW27e3M/7QCe5KdvqcNIhqMxdMbDgYbN27kscce0xdEE1LGYMSkErDoA4kvvviCd9999+A1yMDAYK+RfDUAvLK4hqdmbmZIgpUBGS5aoir/mbGecYekM6s+QCwYQ3boA5aQqhFqDbH58+38/fI3yLSaeK/SR6bVhNskctQRubzzaQVNHVHKJhTQXh0gUB9gxIR8gi1BbF4rj946K96GDf4IRQ6Z0hR7D1sviiJKNMZlZ/ZjRLoTsyjwzwcW8MDTy2iMKFyeMYGRfZLoaNSjBl+49SO2ffgl4boaTA4roepqvCNGUHz2ZIb/ZTyubC9tW5tQQhEa1zeS2CuRkdN64zYZ3fYDjUMSSDYbAnpdbA/GCCoaCXkeRMDptZJslggGorRsaSU320WKRWLKGf2pqPazvcbP7FUNiJJI78m6dpDUlQZ0EIjFYlxzzTXxZSlzKIItETGpNwDt7e3cfPPNB6Utv0aMN5DBL5JzBmWCqqLFokzpnEEAfYb4+c+383+3TibXLvP0rC1M65VIe007FreFjIFpHHp6GW+vb+LdrW0ArPKFsYoCkiBQF1Z4bu5Wzj2rPwm5bsrnV/JBpQ9N1WhY00hisZfWrW2M3+mcAUUjJ9mOIAlU7JRDGQrG2PLht4h2O4l98uLrraKAVRTijoRxtx+PI8nGOx9tYWyKnZnvbGDhHR8QbvVjLy5FDQZQWuoJNTQjSBLugjTc+RmkDczG4rbiq2xDtpnwyAdn4PpbfGn0dpoJKiqSoDsL3CYRa1d+P7pHHnTj+FtBEgT8MZX2mnaSU/Xn32Q1oakaJpsJVVHxFiZQ/skaErNdxEIxilMddDR1ULd0G840O+JB7AT/7W9/IxbT85PFlP4Ish1BEJEyhsb3ufbaazvz2Q0MDH5WbF/DmOQdeclWUWDGpmb+cdNEMq0mXpxfyZQMJ0pUwewwk9c/hYlnlvHasjpm1eupiNWhWPxfX0xlxpwKTjmumLyiBMrnbmXGpmbqKtupW9lAcp8kmja2MLYksVszMq0mZJupWzqgpmhs/vBbZI+bxNJcQsEYIVUj365HRE5O02399v++Qd9TB1D5VTVDixN565VVLLzjA0JNPhwl/VAD7Wgd7diS3HjyM0gsycBTlEX6kGz9nFta8DrNB/pO/6bxyvpseqRTA0sW+E1NCOwO3db7yfRYUCIKyS4zzRGFWFBBUzQyBqZR+dV2cpLtVIdi9HaaaWwJUreiHne2i4wCD7GDlEL79NNPs3btWgAEewqCR+9/i+mDQNSjhv7zn/+wfv36g9KeXxu/xTGAwa+AiKKCqqCFQ1jcOwyp02vl3hcuIFDTxNDDsgHwFiawaH4lYV8YV6aT+x79mrNHZcc996FOI1EdijEkwcrUbDd1K/Qwpln1AVTg3a1tCKJAxZxtZA7L4d2tbSSbJU4blMZxeR5ioRhvrGzo1saMvsk8PWsL8/76MgsfnEt6Z0hXSNUwiwKDzx6M2yTSuGIzjlQHNklkbXuEQwel4clz40hPRGlvQRBFNFVBspqxpSTgykmjfVsdzRtqESSR5QurCLaEsEnCQYk22NtXf9fA+pdKmkXCKgqkWSQ2+CMUOMwEFQ2HJBBRNRRNi79AKzqiNEcUdtbS/LW/XH2dKTGiJGKymgjUd+BvCaFEFAJ1HdRXtNGypZWaDc00V7YTiqmsqGknsVcinryEeH7wwWDhwoW8+eab+oLJhpjaP75N8OQiOPQye+vXr+fJJ588aO0yMDDYO2L1Vbg6UwUA0hJt/N+tk2ndXM3EUbqtT+mbzLyltQTqA9iTbdz/+BJO7pu8y+MNSbAyvjiR2mX1BOo74jpHcxs7UCIKm2aVUzylNG7Xx6fYOSzRhtMk8uaaxm7HSuzl5dlPK/j82leY88AOW98WVShxWThm6Qy8sshpORpKNMbXDQEWbWjm8P4puDKdODISUQM+BFEEk4zJ7UF223GkJ9G6cTsNK2tQFY3F39axtTUU194x2H+I6A6ClqhKisVEsFN000gL2YESUTA7ZNraI1S2hTGLAlsaAmyp9rP121o2bPOxvbGDNIuJr5qDZOa4caQ5aK/xI5mlgyLo6ff7+b//+7/4spg1DKFzQkeQ7YipZfq1KEoPkWSDvePX3rc1+JWSK/rQwiFQFbKGZ8bXh31hquevxJ6aQMUSPbQxoTCRNIvEoD9P48FnV3DGsAxenl+JWRTiBrhrMLy0NUR7KIY92Y60U6ha10xHxiFpvPXst4Ce5519WB5mh0z9TpUZenfOCMQ6Zzfe3dqGP6by4ud6+bfxKXZ6Oc3cff+X+GIqbz71DUNf/A+HD04jomq8OL+S9CG5SE4XotmKGupg+0cLESWRSHsHmqoSbPKjRFRE2USiWSLQEf3ZaRt0eex/yYRUjbqwXjaoKhiN57hKgj7jrqJ3NnJsJlSICybCjmfql+5A2RNBRWVzRRsdTUGUiEJbVEFVNQK+MG1RFUHSoxJsDpmIqtHHbaFtaxubZ23G5rXGK5scSDRNY/r06fFlMX0QgiTHlwVBQMzcobx800030d7efsDbZWBgsA+oCjmdDgKASCBCoKYJV24qa5fWAmBxWyhymDn0plN47M11nFiS1GOQ38XS1hAtDR24s13Yk3ZUy+iKBMwelsFrjy+Kr+97dCEeWaQ5sqOMXL5df48oUX3dO+Wt+Ha29cWJuE0il2dNJKJq3JM3jjOTL2TK6Dz8MZVXFteQNSIfk9uDYJJRIyHK//e5fsxQBKlT40CJKli9djKtpk6n9Y+7lQY9UdnhIGiOKKR/Jy2xi99qqogKLK8L4GsMomga5YEILVG9okJbVCGoaLRF1XiKUF+3hVBbmLWLqnGk2gn7whwMiYP77ruPuro6AARPHqIjrdt2MaUfmPTf+4wZM/jyyy8PfKN+Zfw2fwEGv3ik9jq0WATV34rJbo2vFyWR+x9fwhN3fsqnDR0ApAwqJr3Iy19OfpjfTSvmlcU1qOgDW6dJJHOnWQyAssmF5I7rR6CuIy6aqGgamcOzqV1Wz9STSgB9wPj1ayuprg2wqHP2dGiCNZ77WL6hOd6xWNAcjA8kP23oiCs7gz5brayYhyRLNEYUTixJIqlfAZqqoPia2fa+LvBiSkymo6YJs8tONBDBnqy//CxmKR42Dj8fw7Y7N8bPo3V7h8iOgX9bVI3PQgz0WEnsdCxFNT0HsIvvXndI1Ug2S79KB4KigU0ScKTaSSr2kp/nYXFLCKvNRK/CBGJBhazOjrjbLGF2mDE7ZVr8EVRFw2SVv+cMP54ZM2awYMECfcHiiec57ozoSEVIyAegvr6ee+6554C3y8DAYO9R/a0oOw3aG0Mx7nv0a+64/bO4/S2aOpz0XDdXn/oY5x6Zw9vrm/Z4zMIRmRROGUTV1jZO6KVHCSiaRtGkXqxbWMW0U0vj+z70wiqaI0r8XKUuczw1cctaXWgRYHlbOG4D3lzTyNxGvR8SUDS2B2N8fvWhxEJRGiMK0woSSOrfaetbGyh/6xMsCQ4Es5VAbROW5CRaK9pwZThRIzEUTXdYh34FTvmfM6FOkWkV6O+2dNM78P3MJmgOJjZJwOm1kui2MDLRxtr2CF5ZpI/bgiToaTySQFwvzOyUqQvHUKMqxceVHXBxxNra2p1st4CUeUiPfQRJRsoYHF82RJH3nV9SH97AYAdqDFQVRImGVTXx1eGIwmGJNjzyjhf9W9fNwJGmRww88c7G+PquWeOgopJm2bF/7bI6ts9bgzvbRfFxZZxSlkJamoNNMzeS2MvLQy+sAmBsioPc4sRuIkmjju/NrfedQLJZYkFzcK+iAB5a+hhXHnMnz83dCsBXW9tY/t9PefKip0AUefr5FQiSSLi2FndBBtXzVxL2hZEdFiwJTtzZLhRNi+fZ/9xn+n8pZrcurKBCvJPWlfcYVDQ8skhvp5lSlxmHJJBjM5Fm0UsUdqU4dOGVRRojCiFVQxZ0R8TPxbnzYwmpGgFFw53txmSViQZjjOnlZWtriLWbW/C16RUXACSzRKg9TMuWNrYHY4RaQkSDB7aWczQa5brrrosvS5lDEYRd33sp4xDo3HbfffdRXV19QNtmYGCw92iKQrCpI75sk0SGJFi7Der+c/mr8Sim5ztn/fdE/epGtnywjIGTCik9bRBnj8pG0WDTzI0U9EuOixgP9Fg4Ls/D2vYd76ujT+/PzfdMi9v6vbG7Tw8ax5XOsnjbltcFWPHUHB46/0kEs5XXX1+LIIkEK7fjzs/QxRN9egk5S4KTlAzHz96+/1ro0rDIc8j0dprp7TQjC/rETLJZIsdm6mHHfy12fXe0RVXc2S48OS4iqsbQBCsVHVGWNAcJKBoBRcVpEuPlGjdvb6c2FMNfF2D1y0sPePtuvPFGAgE95UhM7oNg8exyPyGxGKwJgF6GOZ7GaLBX/LqfcoNfLVqHH0T98e194vD4+tn1ARY0B7sN5luiSjeHQRddQna682CHMXZluBAlgfQhOSjBCJmHZBBsCRHoiJKQlxDfz5vpxF8XoLFzFuSEXok0rmtiycNz4usad5oh2Zmp2W7+r3kVN7Ws5i+HXMqUDGd8W3UoRjQQpV+Om6X3v8vodCd5Jx+DLTcfa1oyzqwUPHkeYqEIsUCIqk0tOJ1m2joNnWJ4Tw8IKlAVjJFolggqGnn9UzCLAk6TiKIRj14pcpixSQJFDpl8u4w/psZftF1hfDvPWnQJL/1SBZi2dURJLMmgcX0TmqJhcVtIs5goznJhFgUSCxIIBvSZuYawQnsoxpFD0xEkAfUAx9w+8cQTbNyo//YFRxqCO2e3+woWN2JSHwA6Ojq65UkaGBj8dIw673ygu62f26hH7u1sYzcHory2rG6vj5uQpw8s3AVptG6qxeK2EFRU6nxhvIXe+H5eWaKxPRJ/b5/QK5HaZTUseXCHre9Kafsud/33LJ4eNI6nB43jhnWfc9qgtHg6Y0VHFE3RGJLjZtm/32HSiEyyj52ANTMT2WHDW5rXaetjRDtCVFX54xMEBgeHumCMwtIkQLffkqD/m2iWyLXLyDvNsO9s13+Ng6vqUIyMYflsWtWIWRTIz3SSZjEx0GPFKgrkdToSJEGgLaogCzAh14MoiazqFCg9UKxZs2aHPpEo60KIu+G7osjXX389kciBncT4NfFrfLYNfuUcfsEFoCoIJjNaNEL1wrX0d1uAnrPZpZ3Kr7uiMaLgkUXaokqP8DNPQTKu3DTe/e/XFE+/il6TCskqTcJX6Yvv468LxAdEsgCSWeTjpbW8U976vdfwXqUPURBo//sFRDX4oMYP6LPTXlnktWV1zFzfxEtfVcXFHwObN9G0fD2ibCKpXwGSbCLc6id/YCoVO6VCGBw4VPR8vrpwDLNTxiGJZNl050COTaYxorC4JYhNEpEEvaRTL6eZAodMkUMm0SzhNomkWSS8sv76VTR9puKXKsDkj+laG70mF5M/Nh9VUXF6rUQ70zfaa/xYzBKVbWEyPRbqwjFWrWjA7DDHRYsOBD6fr1vJpZ1FknaHmD4QRD194plnnmHVqlUHrH0GBgZ7x9sXD0VTVLbPW7VfU77MTpn0ITnYEj3M+XATg26+giHj8ykoSKBlS0t8v7pwrJvwrWQWWbm+mfd26g/sjusueQmA4KsXUR2K8dqyOjb4I5zQK5FMq4mXvqpi6XYfr39ZSdgXAVEisLmCxhWbCLe2k9Q3C1XR8Ne0UdA7kbborvszBgeGDf4INq8VRdNTW1uiKvl2meaIwipfOO7IsUliPOIwx2YipTOK1W0Sf1UDLTUSY/jxJeSM1LXFUiwSkqBPWPkCUdIsJio6oiSbJapDMZbXBbC4zSgaCAfQ6XXdddehqp2CzWllCCbrHvcX3NkIznQANm/ezOOPP37A2vZr49f0PBv8RnjxnMFosShaJIRgsTLnw02s6gzn25lTylJQNLqVTfou69sjPQZsSkThtSeX8MA1/yPNYuKy/hfQXu1n8bd1tHaWcARwZTjjTonzp/Ti7TWNNEaUbmkPe2L9xAkU/O5ibn/olPi6if1SGJOXwKUn9+HcE3QthdXbfax9/A3MLjuunDSqvlzLlg+X0bq1lcb1jWz4to6+vXUBSBFDAfhAE9X0yIPqb2qZcNUYRp/Sl+NGZOKQRaqCMQZ6rPFw0q7ZiSKHGa8s0RZV4xEhXZEIKuCR9WiFvX12fk6kW3UHVsQXpPKr7ahRFZvXiivTidVlwZ3tonBCPr0LE7An2RhYnEiW06xHHBzAsNu7776bhgZdEV1IKEC0p3zvdwSTFTFtAACqqnLttdcesPYZGBjsHY5l7yNarXz40eZd5vcfl+eJO2L3hbAvzBMPLeCW6TPIsclcPvzPhH0RPlxRrw/iO/HH1Hgpx99NK+btNY1UBaMM9Fi6Ha/3TqUSd/5/zUVJ2E5/ivtevzS+ruSE/hw7qYDLz+nPMVOLCakaGyva2Pr6e9gzkvAUZVG7uIItM9cQagnRuK6ZVWsbGZBg/cVGp/0S8cVUKhfXcMofD+XEqcWcWJKEWRTYHozF02R2jjzMt8uYRb20t4ie4rhzD/SX/reLhSK017RT9XUNkUAUZ6qdhFw3NkkkJcNB/4kFlCXaSDRLFDnM5NtlIoGoLqAYin3/CX4A8+bN47333tMXZLsugPg9CIKAtJMo8i233EJra+sBad+vDcNxYPCLI4mAXopRVUBVadhNiOD8jS3xgb0IuyxhpNIzLy0WipFvlzkswxkXNkrum45NEkkrS+m2X1cnJtgS4tjOsMddRV93DQgvObZXfN27S2vRYlEuspwUX/fpmkZmleszHavmbEUWYG17hI8/3oJot9OwbCPt1X4EUcTslDFZTRQPSEWURDyyZEQdHCSCikpLWKF9Wx2WBCdJxYkUjNWVsm2SQFan5kFdWCHTamJ5WygupNWVIuORpfhshaLpHvuuKIVfUrktp0lEjcYI+8KIkojFbcZkNeEtTCBreAa9pg6mfM5WJLNELBQjForREY6hRJQDVp6psrKS+++/X18QRF2/YC8RU/qCrAs6fvjhh3z22WcHookGBgZ7iaYqaNFINxHanXl/a1s8VW9fiPijFDjMHJ3lZlZnKLU7202WzYSjU9QVdMdBl10Pddr6lqhKcZar2/E2+CPxqgyXr38/vv6OK/Ucau8dO+rGv/rIQp55dyMRf5SVs8txSAJLW0N88tpqECVq5q+MRzjKDhmb10rvXDeSWcJ2MOTpDeLUhRWCDa3YU90kFXspHZML6E7zFIv+aQjHSLNILG8LU94Zifprq6xkFQUi7R3EgjEsbguCKGBxW0juk0TRsAwG/W4Mq2aVI8oiQUUjqKg0RhQkWRfwlg5AhKGqqt2qJknpQxBE0x6+sQPBnozgLQSgqamJu+66a7+379eI8fYx+MVhatmGFg6htrfSUVXfLefvrJFZAPz+hBKqQ7F4/mFXqTy3SezhKPDF1G6q92FfmPF/nUBKZ/3noQlWWssbulVCmFaQgMm6QxynZkMzC2r8HJFk26WuQVf+43/f3xRfF1A0EEWOG7SjnOTvrx1LicvCY2+uQxIE7nz+Au549DQuvuM41I4Oso8+gpKTDul0Gsg40xw0b2rh8+V19P9OJ8bgwBHVYHlbiCee/pb65ZV4++SRO2EIZ4zMYtgp/Tj2z0cQUbXOED79798Y0VNibJKeH6loGolmPUqkOhSjLqxQ2/nM2iQRq6iLLnY9mw5JiFd56Pr/zwGzKKCEIgiSQGpZCs4MF5qqUr+ygW3zK1n62Oc4Uu1s6lQ4b2noYHMgysaNLZgOkIPkH//4B6GQ/nsVk0sRLHv/2xBEE1LGkPjy9OnT4yGQBgYGBx+lqRZ/VfeyiieW6HnnlxzbC5UfJrobC8U44R+TSe2cECh1mQnU+1nbvqNq0XF5Hgoc5ngVnYb1zWxsDnLR5KJdlnrsckBcnjUxvi7Q6XUQTTuqyFy9eSYlLgvPvbOBBJvMLS9cxB2PnsZpNx6DFo2Qd/x4+p97KFavFbNTxuK2sHarj1nbfT0iHQwOLEtbQzz47AoaVtWQOjgfb0kmJ/dNZsyxvTj1d8Piwsmhzk/Xs+iVRVIsEiFVi/cvf8kRoZKgO9sESSCp2EtCnodYMMbKmVtYvrCKebd+iEMWWd6p/dUYUVjjC7O61s9Aj4UDYe1fe+01lixZoi9YvQiJRft2TelD4qLIDzzwANu2bdvfTfzV8XPpexoY7D3hIABaKIA9Jz1u0AFe+qqKqy4Zwuezy3f5VV9MxRdT8XaGhneVYuxSvQdQFQ1fRQ3pQwsAWNIa4u2ZWwBYO1d/qZidMt7CBDb4I4joJRYvvmIUXzQFe5zz5E4HBMDVfxzebZva0sAffncTAEMSrFgTXBxxfG9OH5LOoRcPR3S4secX4j5kBEo0RuVHXxBubSfaqUof7CwN5ZElPt7UvE+30eDHEVE1PLJEJBChabX+vHnyPDRvbGTt69+SaJYYmO3GnWxjfIqdKRlOvLKIWRTwyhLDvDYGevSyRl0OqK4OR2NEIaLq5bu6IhgCiu5oSLFIOE36cWRBD338KV/kGTYTsstOxK8LfbVtbSPijxJqC6MpGpqisWZDs15KzCzhdJqJqBq5aY7vP/gPYMWKFTz33HP6gmRGTBu4z8cQvEVg08uzffvtt7zyyiv7s4kGBgb7gOJrxZWb1i0S6+31TVx+Tn8+mF2xx+9aO9+Tu1K8jwZjtGzcTvbhuijq2vYIL3y4GYDFM/V/BVGg/4AUlraGsIoCM+sCHHfJcJ7q3N7FlAwn1/31yHgo+s2tq3ucT43pM9GnD0lH8WQx8cwyTj40i35nDEIwW7Fl5+AecgjRQIiqWfPpqG0iFooRqOsgFopiFQXMorDLfobBgaejMUj9txXIdhuyQ6b6m1pWv7YCqygwMtFGcZaLgR4LhyXqTidFQ+8HeCwM9FjiWly7Qxa6R8aK/LwGaelWE440F9HOiIqOpg46moJx0ciIqrGkOYiiaeTYTOTbZVSg2GXW99nPEwXhcJi//e1v8WUpc9huqybtDsHiQkzuGz/eP/7xj/3axl8jP6dn0sDge3nx2VvRIjtm/j+74oVd6hvsXDbpu3SJ1FWHYvG8xcM6B29dL/y2rS00rtrOhRMLOblvMsO8Vv54Wt+4QJLFbSEWinH0sAycnR0Sf1XPmtEXTizsNitx36Nfd9serd7h4Lhg1j/5x9UzSBlUTNGU/iQPKmHdcx/i37ABpa2Jze8sIvWQEiLtHahRBUESaNnSiuyQKfRaCSoaRQ4Zg++nyCH/oJzYnXGaRCRBfxbSh5fir2qkZUsLmqJh81qRrSbsyTZmbWjGYZcRO4UU+7st5BUl0HtCPlaXBZsk0Mtppr/bQo7NhFXUowmkzk6EounCSyO8VkpdZjKtukEekmClxGWhzGOlxGX+SfQRejvNiJJI7dJKEvISCPvCCJJAJBBFlES8hR6sXgtuk4hHFgm1hdncFCTFIrF0u48DUQDk2muvjddlFtMGIJj2fXbuu/mPf/vb3wiHe75nDAwMDiyZQ/SZ+/f+/HJ85r4L2WGN2/Bd4ZVFQqpGVCMugFzq0svqDU2wokZVqr+uZOtnqzlvTB7H5Xk4MtnOeWPy4qkJss2ExW3h3CNz4vY/3Orvca5x953OXfd8Hp9RvjGhe551Yq8dUUxpH8ziSnsp8y64j8Kjy0gZ3Jt1L3xMYEs5SlsTG99eRMrgYqKBEJqioUQUWrf68MhiPNrgl54rf7BIs0j77V7JDpmsw0sBaNqqp5FIZomopmG2y7y5phFFg4CiYhV1u16Y4mDkuHwUTY/Oy7TqUYS7cmR1aSXk2EyUuszk2mVy7Xo5yK6BeL79p+vjeWSJtTPW4i1MwF8XQFM0oqEYsiSQmWSPC0h6ZL3y1CpfGIcksKQ5qKcu7EV58n3h0UcfpaKiAgDBlYnozvpBxxHTBoCka5K88MILLF++fH818VfJ3iWCGBj8TDjJXYtSF9BLMZr0wdh3+dd/91wvdnOnt7SLIQlWDjtrICNCEdq2thKoD+Cv8ZNQkABA6alDmPXg5+SEojg6X9q+ynYkWWLd6kYiqsat952ghzO/vGOW4Z5nz+Ov5z+3x7aYktJZ9fFV3J82ALW9lRybiVXPz2fDllaqgnM4cWoxG97+GiXyFfZkG2/+9S2WtIa46hK9ExJqCRH2hfEFoqi7uDaDXdMWVYl0hg/uSmxrb/DHVCRB4IsvtlO9tJahl46iozGIM82Bpqq4MpxUrm+ml9OMLxClMaKQ55AJRFUiFW24s924s13kj82nozFAa3kr/roAqqrREFYIKCoOScQXU/HHVGpDepudJrGzOoOeyhBUNLKcZlY2BxmSIOM2iWwPRg/YsyAL+szDkHQnVo8FJaJgcVuoXaaXMwv7wsg2E6qisnx5PVZRd37kFieyfl0TBW4La1qCjOidiLh2G+xnkfB42KJkQUwu/cHHEV2ZqI40tEAd27Zto6Ghgezs7P3USgMDg71hw92HE/1kU1xwdmfuf3zJHr/7XWHkgR4LY08qZRLgq/TRsqUVk82EzWuldWsb/c8+hHcfmk9KKIan07HcWN+B7JD5Zm0TZlHg+vtOxDvtbHj6hPhxJ6xYwNUDDttjW5o37eiXpDn0QeDR79/Khg9WoUSXUDC+mO3zVuF/9SusXiszrnmLRS0h/vKHoUR8QepW1iPJEhs7UyF+ySHvB5OGsLJftJ+sosCMb2rpv/ETJl1+OIqm4cpwEmoNkS8KbGjsYKDHgj+mdookW1A0WFLlY1y2i5I+STgznIR9YZo3tbCxOYgnpmIWBWpDsW66FVVBvYpH1yBcEugcjKtkWk00RxRy7TJWUWCDP9KjKtj+RARGJtpIdukDa0eqnepvapHMIkpExWyXCQaiLNjaikfWJy/69/Iyf20jvTujC3MynMgOGXOTQGA/PrdxWw97LL/4fQgmC2JKP9Tab9E0jW+++YaBA/c9UvG3guE4MPhlEeoURoyEaFi8mpXNQWRhhxE9oVciMzpD9rsGVW1RZY9GdmlriKUPfYXbpA/SphUk4Mp0EmoJ4S30UrtkC6t8YbLWNcdLIyb28hL2hanuFEj8x9UzKHWZGZ9i59MGXVBRzth9zfgu1EiI37+8jJn+lVzhLGNaQQKvLq3ljGEZjB2Wg8lqJrl/LhFfB4ueXxrXWRAkEU1RUSIKSkTdZdSFwe5pjOg1hj2yhE3TaIuq+9S5kIUdNZ0BnKl2GldtQ7aZWL2giv5HZNO4rpm0bBfmaj9tUQWvLKJ26h7kDEilYU0joiTE64XbkmwoEYW6ugA2ScAm6V57r6yrNad1pigAZJUmUbtOD/93WU1Y3GYGyiJaZ+c6X4BMq6mzLKQeMlkdimEWBZojCts6oj+oMyUCWTaZUpcZNaoSDcawJ9to29pGxiHpKBEVJaqgKRqxUIyidCcNjR1IgoASUTjqDyPpqG2m44NNbN/mQzuQ0gGSea9FknaLybZ/2mJgYPDD2PAV22cvYYO/exTh1Gx3vByit1OM7fucwMvbwix/ell8eUqGE4/VDUBynyRqF1ewxhemoLKdnAGp8GkFadkuQi1htnVECakaN1z5FjnXv8PJfZPj0YR/uvENTtjVCXeB8Pr/+Ju7L5cc24vbb53NaYPSyB6ZgyibSB2YhyfPz5fPf8uizjREk9VMNBBCiaiE2sOGrd9HukxMl83+btTK3hJSNUT0wXzVgk1YTSIrl9UxaHgmvho/+XaZ6lCMiKqnFEY7BY9HD0mncX0TklnCW5iApupVhwojClsD0XiVJbModOoi6ZEKXX2L4kQb65qCnRGFuqDyyERb/HvDvNb4JEZXukBFRxSbpJca31NVsb3hyGQ7ZlFAEAVkm4mmrT7yhmcgSALhtggt5a24k22UxlRaogpBRaOjKcjZVxxOW3k9bVvbWL6mkaIkm56qcGAKKyCY7D/u+7Jh6/cWw3Fg8ItCDQVAVVFaGtj+xea4Un0XXQN7gMNKknhtWV182do50OsyHEMSrN0ED7u8tktr/Wwvb9VXzt0K6E6Ijzc1U+rS89ca1jQimSV6O83xUMm17RF+/+Y1fDrpNgBMabl7vJaHt8+k7N4NfH7LITyaPhiA5XUBTuiVyLJ1TQy9+jhEZwKCLFM98zNSMhyonVUePEVZVH25hrAvgqroM9IG+0ZXJYNSl4W6cIxMqwlFgwXN3fNHu8Icd3Y+6eKGegqB2ySSe0QBaUNLCNTPpTjVQcQfRYko9JpcQuNLy0i2mjA7zChRBassIZkliqeU0rCqGrPbTu2y2vigv6usU3NEIapp2CSR7MIEREkksdhL29Y2/HUdZPRNxuq10lreihJRsHosyA4ZR6qD5k3NhH0RTFYTZodMsCVEoU13MORHVMoCURbWB3Yp5Lk7ZAFKXBb6FSYQagvHBbs6GvX71dEYxOyQcWU4cWUnsn1+OXaHjcSIwtqGDoYMz6Z1Uy0djUH8MZVkqwmhZ8Tv/kMQEMQfmb5xAFSgDQwM9p5YTQXlcyp6vKvkndLyDku280HNjpdJV6pX1zs72Szt8l23pDlI3acV3dZlWk18Ut1OVkuQIQlWqrf7cJpEhnmtcW2B7cEYN5sHcCF6xZWW8j2HNj89aFz8/8UXnsoGYONX1Zw2KI2NFW0Mv2aqbutNMtWz5pGWaINOO+TISqFpfR1tzUGsuwhvN9g7ohoU2GQawjGKHGZCqrZXTpiuksldEw2DjutN5uFlLP7XR5Q6zHQ0dqBo0G9aCf631wJ638IhCThNEpqi0eeEvjSsqkF2WKhbWU9DTQBvghVHKEZdWO+7NYRj9HVbyEuwYnGbSeyVSP3KBgK+MANy3diTbTSsbyaiajjsMuZOW9+4Xo+EUTTwpNlpqAkwJMGK2S7r+hhRla+ag/scWemVRY7MdtPQFsZlNSHbTNTVBfA6zQRbQpgdZlLKMkgblMX6d9aQZJdpbFWo6Ihy/PgC6pZuo6Oxg1WbWpAEECWR0AHspwqi+OPs/T5qI/yWMe6UwS+Gx5+4Kf5/LRSgalNLt+1WUeDVpbXx5cPu+xOPrHuJM4ZlUOrSQ6YCikaaRSLNIrGyLdTt+3c8ehrnHpnTTWyx1GXmuDwP24MxAopGXTjGguYgH9T4SezlZX5TR7djNH/9DVMynADUvPU6d/33rF1eywm9Eul96xoUReMfnn7x2ZSKjijvb27mlGvGI5itoCq0rVhJ2sgyio8r4+Z7pnHPs+cRqGpAEEWsXguSWQ9lM37Me4+7M9w/0SxRF9Zn4oOKhqLplTdEdjgMEs36TH+XmGaXNoIekhglxSJRuWAromwi4o8S9oWRzBL9zx7K+nfWYBYFQjGVgC9MYi8vFreZ+pUNbJ27CU9eArFQBMksYU2w4s5244+ptEWVeLWQBJtMR2MQb2ECjvRErF4rOYdmkVKWgbdXGiUnDSF7ZDb544oByDysD7GggjvbhWwzYbKZMDv1jobFbSHYGsJkNTEqy9VD58EhCZS6zN1Uu62igFUUKPNYSbNImDo7EVJnWKI92UZCngfJLJI+ogTQaz0nFScSDUT5sKKVPkk2qr7azqLZFXy0sJL8PA+LmoKEo/s5T+E7CKL04z6G48DA4Cfjo9fvJuZrY/MuxADfXr9DU+ioZ6/k4VXPcEpZCkUOXZAt2jl4c0hCD6fBHY+exlkjs+IRXKDb+hN6JcajCBvCCktbQ3zRFCSxIKGHIGHgoxu48sJBAGyb8xATVizY5TWce+SOyMMLl30Wt/WbAxE+XdPItL9PQjBb0WJRWlesIm14X/qfPZQbb5/CPc+eh6+8Bsks4kk0ZkR/DG6TSG1nRMD3DaK7BIcdkhC3w1FN10ba8qmuSxX2RehoDGL1Wuk3rYRFb62NlxxMNkukJNsx22WqNrWw7YtyrF4rvso2lIiKN8GK1WvBF1ORBH0CIqrp0QKhYIzkkiQSClJwZ7voPSGfrOGZpA7MYtAFw+g9OpeiiUWoikZSnxSiiq6vYOnst7odMrLVhCvTSWVHFI/LzNiUXc/GJ5uluED4zqRZJAocelSh0ySiKvqAP7swAUeqHXuyjbyjyuio9xFs8lEwNh/ZIbPBH2F8ip31H2zk3blbeX1pLb0znJQHonyxre2A9lF/vK03etB7ixFxYPCL4dyMAFpNFDUUQFN0z+bOOeq/P7eMB59dAegv/L9NvJFRSXaCisqAIi/n/WkCtsISZp39T5Y0B8myyUiCngPni6kseXA2rgwn488oI/mtNfSf0oumjc1s3EncsMsoNEYUXJnObrPQV/9xOLf8Yyb/d+tkSqoauP3W2bhNIuds+YaB8x9m+jnPAPDZZQ/wwD8nMen99WinHt/jOqMaLHtyAYXjayg47zQ8/WSi9dUkFGXRUd+Kr6KGjvo2/DV+AvUdrGsKElA0w3GwDxQ4ZKqCeieiK50lyybHqxWkW01Igu40iKgaDkmMh/pLgr5P1yelRFfft5YdyiF/Vvnm3x8TqAuw4X/LkcwSpqhKai8vmqpSt6oBZ6qdjEPSsHlt+Gv0PNuwL0J9IMKgMXk4TSL+ztxHsygRiyok5blpXNdEyxZdB8GT48KWFCZjRG9aN2zH7NY7Bv3OOYKO2iYKJui1if01bQTqOrAn2THZTPhr/LgznKiKStin6JU8THoKhdVlQVVUXJlOAnUdZNtlTFYTsVCMaOc9cjrNxEIxZIeMO9tNoD6AO9tN2BcmoSCFYH0LYV8EV7aZ5o2N+OsCKBrM3NaGs7q98zcnsHpLKx5ZxBQT4ADl6grw4yMODkgBKQMDg73h8KYvCYtij5nh359Qwn9mrI8vXznpNg5LtBFQVPqnOjj/srE4S3rz/gUPsbglRLJZt47NET3fffH9n+DJc3P8eYP58pUVDJtcRO2yOjZWtcePaZMEFE0PbXd2TgbEz3fhIC5PH8fDW9/j33d7+bO3Hzk2E08PGsdlN11Fx/FTAT3S4Gk9m4ILl33W7Rjbg3qk4tf3f0qvSYUUnncqCQPsBLdVYE9PItzqp3HFZtqrWvT3fkuIr5qNagr7Sld/TdG0eLRpeSBClk3usV+XTe/qW4IeueLu7BdkJtkxO2XsQ45g9D1O5v/jdZo3tdDWKVypaNC7MAFBEmja6kMSoHhkZlzIuqEmQFBRqQsrHJnp6Ewl1FA04n0Pm0OmbmUDDWsaaazvIK01hOyQKTgqncZV2/AUpNJWXk/ZuSNpXreV0mOK6GjsIBqMEagP4Ei167P7LSHyEqzEQjGCisaYzpSDtqhCmtuCpmjIjs6ohI4owc5744upbPJH6O/WfzOyJODOdiFKIvZkG6qi4cry0r6tDldWIpqq0lregNkhxyuMge6ICyoqyyt92CS9T6XtXrP8xyMYEQcHC8NxYPCLwOz0IgSaUaMR1PZWBEmiLaroSu1hfTbhyRdWAvoMaZeB6KqpnNMRZdEVr3eW0BNJsZgYOiyDYU/cw8bbb2fhjHX0mlyCLcXLkofnMeiU/li8TnKPGUXJ9lrGN7fx2gNf4DSJVHREOSLJRsWcbeTb5Xi6xOyXlvP7E0q48/9mxh0KvpjKC4WH8AIwbtl8jg9+TbD/RMRvP0A7dfpur3fmtjaOnrWF1q3/oc/po7jukpf417vTCa4qRzSb8FX6qNrUgk3SVXo3+CP7RQDot4KiQb5dpi6sd94kQSDNIlEXVih1mUlJthNsDdEcUci26zP1+ZKArzFIYraLYEuIZl8YmyRSu66ZzLIUwhu+RXK6iASixEIxCscX4OvMwW2vacfm1cMHHakOSk4bDaLE/BvfxJlmx1uYgGldM2s+39bZmdA7LS6rLjIYC8b08ECnjBpVOyMbWkkoaEGymrGleGlaXUGo2Yc9PZHcE44mtK2cLIeNhm83EAtFaF5fjxJRiASiCJ2dIqdTT+3pEhm1ea1EA1Eks4gUlfD7I7RFVWySQKJZIhiI4i1MIKkkFXtqAtYkD77yGmxJEco/3UjuEQV48hJoXFNLLBRjiz+CR9YFnmyds3+1naKUiiYgisIPK8BuYGDwq0YyW4lVbUaJ6u9oryzG87V3dhp00ZVitq0jyuK/v0dE1bBJAh5Z5PD+KYx65g5W33Q3n360heJj+2BPT2Lm3Z9w+BkDMLvtFBx7KIOb2jimqoHnHvgSsyhQF1YY4bVSv7Ihbh8APnx9Dfe9+nuuLTwu/q7eHoxx4bLP6Dj+s26pCdDTabAzH9T4mTxzM/66Z8ifMIB/XD2Df707ndYNX6FEY7Rt9VG+vR0VXZTW5z+Qo69fH12lARvCO6JOJEEgqKgMSbDGNYSqQzGSzRJeiz4LbxYFXFYT7aEYLVEFhySypSFAIQ5CKxeiRmO6MHVEYeCkQnyVPkRZomVLK2anrFcY8NpI7p9NYp88PvrrGyQm2bAn20je0sr6lQ00R5R4dSaPrFd/aPaF48+UJICqqDRtaiH7sBD2VDeO9CQaVlVR+eU6PHle8s44Cf+KpcgOK/XfbkQJRaheXEVLg55C0ZWCq0dQitgkEV8giiRAVxyCP6bS0ikYLQlQ4jLTGFHom+8hfVAGZpcda5KHQG0TsUAIyWzCluIl2NBCa3kD9iQHmxdUoaJP2qVbTTg6+xRdURh5CVYk34GbKDA4ePykLpZYLMYNN9xAQUEBNpuNwsJCbrnlFl2dfg/MmzePQw45BKvVSmFhIY8//ni37atXr+akk04iPz8fQRB44IEHehzjpptuQhCEbp/09PT9eXkG+5F5L9+AFgmh+ltRAz7USJSWqBo35Jef05+/PXAymVZTjzC0yWkOzrx0JBUdURrCMRrCCs0RhZlfbufV4ecy56215GQ4yTn7LGypCfgiCutmrCFQ1ciKRz+kceVm3PkZnPnXsZz94OmcPiSdUz++lyp/hPGjd+gYHH3RMDx5SUQ1OG1QWtxj3YVt8iSeUAfRGFSgz+G7vdbDEm2kW02saQnir/Hz9FVv8OCnt6B2tOMuyMDsciDKnd5xu0xKhmM/3unfBm1RfebJJok0RpR4GaSusD1/SwhFg5wMJ42hGA2NHaiKhtWmbxclEW+nYrA72UZCXgKR5lYQJQ65bBySLFG3so5exw+jvaYdf30HwZYQ3sIEnGkO1EiU1U9/gj3ZpgsKRlTsyTbdYaHoA3UAZ+ff1pHmILVfMmaHmdSylPjgf8O7a2hYVUPb5ip8lT5MdisVn65HaarF3n8opox8bKleNEVFMouYOoUU0welkVyShNkp62kHZhElqhBsCRFqC6MqGpqi6TMgkoBNEikan8+A08pQowqxjhCeoiwCtU00b6gl0t6BO9tNQlEWrtw0IoEI9RtbqArGOCzZTkVHlOaIQmNEiUdyOCSR2A8Uqto7hB8dvmhoHOwfDFtvsK+sfO8u1GAg7tTschqcNTKLG2+fsssSe+NT7Fx4yVCqQzEaI7pQW0TV+GhZHf8d9Qfe/2ATRQ6Z7NNOAfTB/jevr6KtvJ4VT3xC5byVOLNS+N1Nk/n9/Sdz7pE5nPXhnayu9TP58B0pB1PO6E/1598SVDQGeiw9OtIbj947b2h/t4V8ux7i7a/xc8t03WkQa27AnpGE7LDGtRzSLBL90p3fc0SD79IWVQkqWlxsECDXrke+bfJHqAvrdsnZWY2oJhjrjB7QZ+RtkoDbJFIXjuGVJTIOSadlw3Ykq4WR048iwSZTu6yO4mlDadvqo9kXprk2QHqRl6RiL2okxrLHZ5Oc5SLsC+t21WGOiytbO0UR89McBBSN9EwnKRkOvAlWMnon0tEYxJZgZfnz31D+6WYaV27GX+PH4rZQMaecWE0FCUcdj334OOypXmIhXdvI7bEiCTDgkHSK8z0kW01xQcUuEcMqf4T2ztScrqgKp0lk6GHZjD+jrLPseASz206gton65VUEW4KkHTYQV26qHhnTFmHb/O1Uh2KUusz6BIOi4YvpaQ4lCVZsksC39YFuf4P9jZGWePD4SSMO7r77bh5//HGee+45+vXrx5IlS7jgggvweDxcccUVu/xOeXk5xxxzDJdccgkvvvgi8+fP549//CMpKSmcdNJJAHR0dFBYWMgpp5zCVVddtdvz9+vXj9mzZ8eXJeng10E32DsGxbagRkKgKGiKiqaqOKQdkQUmq5nar9cycVQ2Nq+VL2aVx8Mbj7z5ONo2V8XDG3fOd2yO6LMUm/wRNo7/S9wTfcJTf8CcX8ryG/7J8vc34a8L0P/SE9BiUUbeeTHRyk14ZZFAXSB+rLvv/zL+/1BbmP5uC0t2El/8oMbP03+6lb+arUy48Bze+ORGrjzq5l1eb45N5ri7T+SD695mgz/Cinufo35lA6PvPAlrSiL2FeXkH5pJoK4jnn9msPdsD8bwx1Ry7TKZVhNOk4jbYyUjzc7GNY1s8IfIscnQGMQjS/hjegWLUDBGqLIdq82EPcmm5/s7ZBrWNFBw1olo4SDOAUPof3YbtUu2sPm9JaT0TcFkbUGNqvhr/OSOKaX267VkjCikcdV2apbWIkgC5Rv0aiAeWdQjE2wmtm5upSWq4KoL0F7jx55sIxaK4W8JkVfspXZbNYnFXjbO3KTrF7SUEwsqKKEwZklCA5xZKXTUNOGvC2BPtuPKdJJQnMPmD5djduhCR8l99HSL9mo/YV8E2abrGESDMTILE7C4LSSW5mJJcOLtnUOgtomahasxWfXv161soPSkMiLt+u8h+7BCLO4qPJU+EvI8TF5cQ0TVqA7FGJHuJBSMUZDjQm09gI4DAYQf+U43OhP7B8PWG+wr2SveIhgIoYS6z7C7s100ra7gxIFpODOcfDpvWzzqb8Kt0/BV1HDhxEKenrWlm61vaQsjor/7146/Jq5AP/Xhc5Bzill09b9Y+mkFwZYQAy4/AcFkZvA1ZxLdvgGnSSQW2iEH31XyubfTjNss0ctp7lb14c675sJOUQdfXv0Qh993+S6vM80iMe32abx/w7tENfj61pfwVbYz5s6TsKck0Lqplv5WE2FfGH9NYJfHMNg9KnoVpa6JnC4nTz+PhW9bQtSGYqRbTcgCpFlMVIdiOCRBr5BQH9DTU13meLnE2mV1FJ91DKLDjcNspfjY3rRuaWbTe0vx5LmRzCKRQBRfZTtZI/OItHeQO6aYuqXbqN7uQ6rxs7E5iKLpQpxBRSPfLrOqqp0N/ggFLWFag1ESbDJNm1poCCv0ynDS1hzEbTPx1Tsb8MgSjVXtWE0i0XY/VrMVpW4btpQEAjVNBFtqcGe7SB+Uiis7kfJPN2JxW1AaOigsSyFXVfHXdVBVr1dwMosmIqpG3wEpyA4zyaUZmN12HOmJRHwdBGqbsSQ48eR5aK/2E6prxJqSiBqNkX9Uf9q31xF+Zx15R+Yy/6PNWEWh05FgocYfoa/Xxmc17T+49PXeIEjSj7L3gmikKuwtP6njYOHChUybNo0pU6YAkJ+fzyuvvNKtNud3efzxx8nNzY3PLJSWlrJkyRLuvffeeGdi2LBhDBs2DIDrrrtut8cymUzGzMMvgOGnn43WFWnQ0U4sFGHpI7PjToOLJhdhSXDiW1WFKIkIkkieQ6ZPtos31zTy1nUzKMr3MOL/TofvhDh2pRREFY01vjBRDaYVJPDy+Y/FI6gdkqC/SHsNILzuG5be/hzRoJ7vrkQVxqfYKRyRyUefVnD8yX3oaAzquWBztsFOjoPj8jycP+Nejv/ry7S0BuNOgxyb/tLuip4YdlwxDz27gmHzV9Ln0CxyG4Os+qqKUaf1x1zYj2jVZqKBSOdM9YEVl/s1k2iWqArG4rmFkUCE0KYwhb28HHfu4dgy0pj31xfxd3YI1aiKonXmI1pNlJe36g4HRcPiNrP63y+QMbw3zqxk6pdvRZAEknqlkjayjC9veIPkPomoikZHfQt555zBxkefwlfpw5XhxFuYgG1DM2ZRwOqyIEgCQqd7Ps1ior3GT0rfZH22wiljcZspPvFQGtd9gCgJWNwWooEozU0xfDGVjI+/IWlbHQl9i5BSsgi3LsHmtWH1Wqlf3cDy9zdRNCwDk1WPNgi1hBBEMe4wkB1mwr4wKX2T6WgMIkoCbZurSC4rItTaTkdNM46sZD3yojABR6oDs9uOIEk4s5IJNvlILlVxZbho3tSEbDUR6YjilSVWN3Zgk0TWfkfcdP8jIBpVFX4WGLbeYF9RmmpRQhEW3v1xfN2JJUm4c5NpWFWNxW3GZDUxIMXOYFlixqZmnrz6TQZkuDjygUtg1t09jtll07scDUck2XjykmfoCnxymkSSSlKx9B9JeNkXfH3Hm8Q6B5KBug7GJNvJ65/CZ4uqmHZCCf66AM40B4FZ5d0cB/9362SefmuHLkOX0+D3J5Sw5vNtcaHFMcf24uGXVzN84WpKR2SSWxdg/cYWxpzeH3NBH2JVm3WNmUCUQH13IWaDvadLC0sWIMViIqioVHZEGey1cviNU7EVFPHWmfexwR/BJunh/M2dkYheWeLb+gA5NhmzKBALxVh2/xtkH1GMyWqhfmUtSkQhuU8SGYf2Y/6tH2BPtukD9UiMnCmjWf/ch/gqfWTmuHFmOGlcVN1ZCUEjqmmoiu7E6u+20BGOkZXnQekUDvbIEvnje1P+5NckKrr2UVcVrbpwlKz3v8b5zXoyR5VhK+pNZP5KbF4rJpuJ2mX1LJpdQXG+B0e2g2ggSiQQiUfxFBQkYE+2o0QUTDYTSkTBnmwjUNuCPSOJcGs7mqqSPXEUgknGun4jiX1iOHqXINrdeO0uVH8rzqxkABrX1JFtl1nnC+ORJeY36ba+ulNn4UAiCuKPsveGOOLe85PeqcMPP5xPP/2UDRs2ALB8+XK+/PJLjjnmmN1+Z+HChUycOLHbukmTJrFkyRKi0ehuvrVrNm7cSGZmJgUFBZx++uls2bJlt/uGw2F8Pl/8o2lGos7BYu7pSWiREMSiaLEoZreDqio/yZ0qsgu+3E7T2mr8NX5kh0zLlhYks4Q92UZvp5klrSEqtrbxt5P/HT/md9XkYYcT4aj3/oVNEshPc8TDqze8u4ZFf/g7Kx55hzmLaxj299OZsvh1zA4zfY8uxGQ1kW/XQ9YG3nApVV/XMGL6Ud2O3//sQximbKR501K+fvVFphUkAPoMSN1O+XcPPrsCFVj1wSaCLSH8dQHOfPNv5J93FqH1y7n61MeoX91IR2MHJpsJb2HCfr3fvxX8naF0XfWTARa3hPSOYK2u2J01PJOScfmkJerRBQ67zIhzhyDb9NKNa9sjVDd1EKjvoOabOja9t4zar9dSNHU4JqtM7dLtbJ7xBaMf+SPFJx7KgOnnkXFof5SmWkRJpGBiKSarCU3RSywFFQ1Hmh2L28yGbT7MooDboYsUipKI1WulcV0zq1Y0sPr5zxk+fRKiLJE+KI30Qak4ZJGMzg5AxNcRr8xhz0gkpX8m0UAEi9uCy2rCk+dFjSok9kpCdpjjaRNWrxWT1URySTJ5E4eSPiyfguOPJHficEKt7cgOG1lHDkQJRbCnJ5E5qoy0IYV4irIwu+xEfB26SGKrnzlvraV1q48WfwSHXe7M59QFqPq7Lca4/DeCYesN9oVtcx5CDYUwu+2sbtsxAP/f+iZqlmylcX0TskOvTuMLRHX1eaeZzYEoG+sDXHXsDqeBew8lDE/79k0iqkaxy0xzRE9h3DRzAwvO/guL736L2asaOPSG4zl90bNIZpHSMblYvVYyrSaqv6llyC2XUf1NLRNvmdrtuOE//LPb8vXXjQF0bYadqzM8/PJqANZ8tIWOxg7aa/yc+8a15J1xEuHNa7j6zCc7bX2w08Hs+cH39LdMV4h8tLPMsT+msrJNd4zXf7MeLRyiaFAa4wakMjjTRUTVKHCYOe6S4UiC7lBa2x5mcyBCiz9C5eoGlj35FU2rK8g5ohf2ZDu1y+oo//Abxj90Pv3PPYwhVx1P8oAi2jduQZQE8sbo/cRYZyqEP6YSUHS9n2WtIdIsUrxPGwlE0RSN2mo/X61rouLTDRx7w2Qsbgt9Ds0iK8uJJOiTWuG2CNFAiGgghNrWpEcFFiYQDUQwO3XBQk+em1goRtFEXTTZZDXhTLNjT7YjmUUyRxbQ76LJlP1+MoWnTaHw5Aloioq3OIfs6++g7stvAD160ZmVjCmjACklCy0WRVMUKud+y9xXV8UnzTyyhKJppFhM+GMqHlnUU0ONcqK/Cn7Sv+K1117LGWecQZ8+fZBlmcGDB3PllVdyxhln7PY7tbW1pKWldVuXlpZGLBajsbFxN9/qyYgRI3j++ef5+OOP+e9//0ttbS2HHXYYTU1Nu9z/zjvvxOPxxD/V1dV7fS6DH4cYDaJFd3jzNUWhJarEwxCDiobVa8WT56ZpfROvLK7BleHUB92dntnNge4dza58yZ1Js+gvbcFkZnlbmLbmIBUdUcZPLWb0v3/PkOvOpu9ZR3D5I2fw7sX/4d99T0Ayi1QurEZVNFKS7aQPSkMoHIQoCSx7Yl6346ccOgwttGPWoGRa3z1ed3qRl+zDCul35hD8a1YSq9tG/bcbAfh4axtZI3PRFI3G9U1xg/Nz5udmMpo78+27OoxfNHQwppeXaDDGBw/PZ+7VL5A9ugxnhq6SrEQVTFYTmVMm4qvxM+r43vHw+1hUoakjyraNzWz7ohxVUSk8dgSCKGJ2mKn/fAHlHy4msH415l4DqJu3EFVRqZq/mYFXnkj1N7UIokBatp4HaXaY6ds7kfRcNwCaqhGoD+Cr9JFalkL/ASm01/hpWLaRUEuI+pX1dDQGSembjDPNgaaqmBxWRJsDwWQmedhAsk46HnduIvZkvaqDv6YNT0EySf0K0BQNJap0Og1kXNlevKV5+Cpq8BbnINrdIEokDeyDPTUB0SyT1K8Ac2ICkfYOXDmpAAiSiGg2oSkqolkPaEvs5cXrNBPoiOJJs5NjM1EbiqFocIAnIX583qNRVWG/YNh6g30hcd0sBGlH2dsuPLKILclOckkSDWsambGpGW+Clfq1jdR2phKs+U4FBt8uasc7OkeSQrSDqmCMls4896PG5TH2kUsZfvvvGXzpBK55/Ew+vvIVru9zBrJDpnZZPaIkkJ5gJXdUDkJef8K+MAvvmNnt+INO7x7tkH7tPXu83qzSJHKPKGLYFWPxr16F0lBFzULdqTB7WxvZh2aiRBQ6GoO71HYw2DMBRYtPFoU6ozvLPBYC9QFeeG45b5z/CCWnHEr6oAyqmzqIqBoZNhOZ06bQGFGY9oeRRFRNT1nU9IiVLf4Iaz/RSzPmjuuP2WFGlCW2f7yADW8tob18O/bSAbRtrkKQROqWVzPwd+NoKW+NV1HoYmSiDUkQ4lWe/C16pGpmjptSl5mG7T5qvl5P2BdmxfxKYkGFlGQ7yVYTodYQ9pQErJlZSEl6VFXJlb/HW+glqSSJkj5JaIpGUnEiyQOKsHltCJKAqmiYbBLODDf2FG98ck7ypiCYZBJHHYGUkIi6bDZpRw5Dzi/F0nc4lqJ+qAEfgiQheZJo+HYjtpQEXXg8z0PYF2F9e5g0i4mcTk2oLqHE8C5+i/uLH61nZKQq7DU/6Z167bXXePHFF3n55ZdZunQpzz33HPfeey/PPffcHr/33bzTrhmBfclHnTx5MieddBJlZWVMmDCBDz74AGC3577++utpa2uLfzIzM/f6XAY/nMIjpqG0NenCiAEf/u21hFv9rG3XHQmT0xyYRQFREtkwv4q31zRy4cRCWre24Uh1MKrYy7SCBKaNz0cSYEiClf5uC1ZRYFpBAvl2mTsf1zuvdWGFfLsMZl1UxmoSGZJgxZ7qwVw8kOC2CpY+/AnXXPgCk26bhtMkMvvrGpSogqaqBFtDuPPTicx7g9GfvEzgO3WftVAApW47ACarg38+0LPuc7JZ4vQh6dx63wnYk224CzJwpCdhy8lGECUefPgrBnosnHNMEe1VLby+qApJlkix/PwdBz83JYaoBuWBKGZRrxgw0GOhpaGDtUtrKS3yUjylhHWvLiBteF/CnZ1RZ5odpb0lHqFQ5JDJscl6vmK6gwnXTcRkNbH5nUW0bNiOt9BLy5YWNEXF4rZSOW8l1W+/jRqNkTygiKxRRVR+9AXNTUGafWH8dYG4Myjsi6CpGjavFXe2C9DTJWKhGKqiYU+2IYgiLeWteAsTiAQiRANRWqr92JMcVC3YhNLSgKYqiDYHWixKyuBiZJuJ5P455E8aQtak0XQ0tJA+LJ+EghRcGS5SBxfhyk3DluQmoU+Bfq/qq5G8KajBAEokhmB3YUpMQe3oQHZYiXaE0BQVa5IHTVGJtHfgSE9EFgRyx5TGhRZjQQV7Z4fC31nD+oAh7IfOhBESsV8wbL3B3tJn4knE6rbRsmE7gZrm+ATBkAQrADavjS8XVDJzWxunD0mnrjmIO8PJ5P4pTEx1cNqwDEAvB9flUJ9WkECaReKfT58D6ANJhySgBf1xAdhSlxlPQTJyfintK5fz1d0fcs2FLzDozIH0dpp5d2ktvrYQYV+EhrYwnqIsIgve4eglb3fTGcqxmQg2dXc2XeEd1uM60yw7bL2qaLgLMpBkE7b0ZBAlHn5yKf3dFk4alU2groNXl9YS9kXiivsG+0ZLVEVEj0DJsZlojijMWd9MqctM/yOyWfrIbLKOHEhI7RT2S3OAqsQHvbl2OV6eubfTzGl/n4Qn0UbFp+tp31aHI81O0/omBEkXIa6ct5rqdz5AU1RsSU7SBmZS9flyGtsj1IVjVHREkQX9WfTIYrwCiNeiz9aHfbrIoWw1YbfoKYVtdR0U99XTAsK+MCtaQ9iSbFQtqiCwpRzBZCbjiMEIVgeOrGSUiELGsBwKjx1GwalT8Fc1kNw3nfQhuXgLE+j3lwvxFGXhGTkKOac3mGQEiw1z78FosQiWgUcCYO03AsGViJrZBykjD0E2E2usIVpdjhKNkdSvAIckUHrmkSgRhVy7jMtqwiaJZNl03Qg4sH1AQRR/pDii4TjYW37SO/XXv/6V6667jtNPP52ysjLOOeccrrrqKu68887dfic9PZ3a2tpu6+rr6zGZTCQlJf3gtjgcDsrKyti4ceMut1ssFtxud/xjiGYdeJrnP8Kq6YXx5YZFS1EjMVY89Xl8nSfLxcghaTz47ArmNnYwJMEan7H15LhJ6ZuC2amHeo9JdeCVRRySwB9/dwjjn/8bFR1Rrv/DK7hNIvl2maCiUvv8f/SyNGaJU+85iYTeOSy/5kasmVm8urSWZLPExrcXccGsf3JEnyS2tIToaNQ7L4v/s5COqnoi817HJHcfzEspOzzCsdCuRY4aIwqONAdf3D2LiD9Kw7cbsSZ5kDxJVH3yJacdls3ytjALP9/OE+9sJKph5D7+CFIsenWKqqBepcObYic9wUp5eStb526h//lj2PzOAswOM64MJwWTBrD2yfc48pqjWPzuRkKd5T1lSUAySzR8uwnZZiLYEiJ1zJHEQlGG3PwH6pdvJ+wLkXV4P8wuB+78DNwlxZhdDmS7leKRmSTYZNzZLixuC2aHGdmmV0BIKEggqTgRW5INQC/7JAm0bfXh29aMI9VO47pmskfmYE+2kT0ojZqlNXgLE2lcpmt6CGa9023NLSD1kBJkuw1ragqCxUr6UePIOOEksqYejWQ1E2kPEKxvIRaKEKxtRJBE5OQ01EA7gmxGdlhRWptR2ppQojFkuxVHaRlyRg6iMwHJaibsC2F2ORh/4VCUSJRYVNHVq1uCbGrTZ1N0p4HxHv0tYNh6g73lm5MVqj9dSCwQYsnDO6L2spxmjh6UxgNPL2ODP8LYFAfBlhC5+R7SB+lCiZIArkwnU7Pd2CSRFIvEGcMyGP/iDdSFFa658AXkzhBvRYP6Ga9j7hTNO/uOaXh757DsmluwpXp5e70ekVK9eBvnzLiBSb0SaYwoRAIRsrKcLH3sC/xbq4nMe10fZHayPRhjb6gL67Z+xq0z0RSNqi9XI7vtmFKy2D7zC04dkcUqX5jPl9Ty9Cw9taY9FCPxFxBd+HNFEvQIlO3BGDk2mXSriaWtIZbM207pqUNZ8d/ZuDurKxUeXcrSm//LGU9cwNuPfUVQ0fDKEk6TiEmWqPhEL/8d9kVIO6SEWFDh8Af/ROsWXeQ4dXA+ks2MIIlkjjkE2WHFZLfSZ2gGXlkix6aLMiebJUyyhCfRRkKum5S+ySRnuRBlkebyVswOmY5wjOaNLTi9Vuo3tpB3ZC4JeR6G5XrYvqIeR6qDxhWbwSRjSskCIOOEk8g+vC+O9CTMiQmI3lSyTjmZnLPPIuNv92F22ZE8SYSa2tBiUaSsIiRvKlo0guprQg20o7jSkPodjmp2EE3phepMgUgYLRhAi4QwJaUTbe9AU1UmXHY4mqKypbOE88rmIEtb9ahfmyTglcX4b83gl81P6jjo6OhA/E54iCRJeyzRdOihh/LJJ590Wzdr1iyGDh2KLMs/uC3hcJi1a9eSkZHxg49hsH+xbV0MLTVowQBKQxXe4hz8VQ28tqwuvs/i9U148jz0dpo5IslGRUeUYEuIBdvbaNrYjL+mHYvbAoBklig9qoCcDCdrZ6zl9gnXU+SQKXLI+GIqYw7NYsLgdG69aRYBRWNuVTvLn/iM+bd+gKaqPHHWv8m3y5x9dhlpQ3J5dPx0Ent5GTIolUOuPR1nhpNZ9QH8VQ1Y+g6n/Dshk4JJJvXarwAQTbt/Vp+auZlJD5zBsm9rESQRNRIlsnUjyQOKeHF+JaDPanSlVqiKStsuUi9+bvwcQyzrwgoRVZ8NN4sCi8pb0VSN9AQr325t49HLXsVf005KvyQOmX6CXoIo2U7lPF3MqjDFgdkuI5klPHluis48liH/uBCAWN02OhqDKC31SGYRe6ob94gj8A4egPfoE6n5bAEpJ5+NLdWLI9WJI9WOZJZQFRVXpoNQW5hoMEagPoAjK5n0Ibkk9vJispporw6Q0jcZVdGIBqKE2sNsn78NJaI/B6llei6iMysFQZTQggHUtiaUlgasSR68gwcgJaUjOhMQrHY9FUgUUaMxbIl6Hq1st+pOgFa/frNUBcFsRZAkRKuVYH0r1twCTIkpaJEQsfoqlDa9znP26DLaKmp48z+LqPxiI9nDMvDHVBJsMl5ZItEsYZNEDmQ/QkDonIX44R/Dr7F/MGy9wd6wafa/iWxZTWJpHh0NrXxQ449vm1frx53tJtNqotRlZnZ9gEhHlPlrG9n2ZSW+Sh/OVDuSLNERjjF0dA7FiTbWbGjmylHTKeosaxjVYOrANE4anskt/5hJY0RheVuYZU/MZd6tH+KvC/DMH14gx2biqkuGkDUin38fezPpg9IYNDidIVefREKeh/cqfcQCQcx9DmFzRdsPut6nZm7m9PtPYfa3uoNMjcbwr1lJ6iElvPRVFaDb+q7UiqCi0hDeO8eEQU+6dKySzRILmoM0RxRSLLrz4IGbP6a9OkB6ppOj/nky7vwMLB4zS+9/lyG9E8mxmeJOm6SSRAZdczajH9UrwrRvr6duZT0dG9fRXtOO1Wsl7ZgpJA0soejPf6bivS/InH4LFq8Tk03C4zLjNInYLSZcVhOtwSixUIxYKEZSSSpZI3PwFiZgdelRkCk5bqxeK8HWEG1RhWXvbiAS0KNu8wan48zw4MpN1asKqCrRirVEtqzCUZRP8lGTkHN6I1j0yQOt4BBYOhNNVWGnfqhStw3lsNMhFkVtb8XcdzisX4C2dRWxDUsQV85G/OY9lJZ6olWbUduaiDXVkj1mMO3b6vj80fmsffkLxkwq7CzhrPepBnqs2CQRjyzBgSyi9CNtveEg3nt+0qoKU6dO5fbbbyc3N5d+/frx7bffcv/993PhhRfG97n++uupqqri+eefB+APf/gDDz/8MH/5y1+45JJLWLhwIU899RSvvPJK/DuRSIQ1a9bE/19VVcWyZctwOp306tULgOnTpzN16lRyc3Opr6/ntttuw+fzcd555x3EO2CwOz547S608Eq0WBS1vYWt739O7sThLHtjVbf9NgeiRPxRks0SKcl2ig/JIFAfoC6s0O+cI6j6fAWtW9vw1/ixJ9nYMLuCDf4IU08qYcPb64moGg1hhdMGpbHo62oiqoaIHlLli6kk5HnIPaKIhx+cT6JZYmiWmzXvbyS12Mtpf5/ETde9x5QMJ2ooQPrQAm48vA/ewQMof+JJipJs8ZKQAGJWMf66D8kdeSwL756E9NIt/O2Pr+3y+je88QUn/98x/OPqGXifWkqWTSbNoofTL28LM7MuwECPhbqwQktYIfgzL8nYpWr8c6Q6FKPIoYsI9XdbWFDjRxL0kpgZTj1vsde0kdR+uZS04X1x5qSSXOairaKGfhdNZvVTM3Fne8iddhQbX3iPyq+qKJ5SgpzXh/5/cCB2zvb7tjWz9YWXSSzNw9LaQN7lV4EgknLKuXi3radu5SNIsoTFbcGebMOZ4cJToGsH2BI9BJvbMNlMuLPdaEobDWsa6QjH8CTqkQhhX4QvF1QyINdNsCWILcmNZLWghYPQ2aEQrHYAYk21mJLSEV1efXssSnDTWpxZyUQ7QniKsgg1+XZoFqgKWiyKYJWJ+ALYCopw2l0oLQ0IVju1H8/FXaAPxFY9P5/CyX0RJZHxE/KJ+KOkDs7H/Pl2PHluUhxmVEWlZUsrwt6nqv8ghB9ZVcHQONg/GLbeYG9I/fYN1r42l6Kpw/n6/U3dtvliKoH6AE6TSP98D4fneWja2ML2ugC/u3Q0rZur2PZFOc2bWjCLAmu/rGSDP8LRh2azbf72uKr76UPSWbqhmbboDkHixojSaesL+NejX+ORRd0WvLmGPgPTOPOvY7n1pllMTnOgBnwk98/m74PzSRw6mPp3Z9CrMIElS7tHx+yM/X/v0XH81F1u2/ze11x68zHcdN178NoavLLIMK+NUpeZte0RvmgKxu1+9OdpQn9x+GMq+XY5Xl2ja/bf1xZi3BVT2PbpN6QOKsZblEZibxHftkaGTT+WL//vbYoGpdLrxMNZcsuzbF3XxMBJhSQddiiHJTixZefgznYTqA+w8dGnyDi0H8FZ79D7znvRRBPZ5/+OtG0baPj9A0RUjcRsF7JDxtESInN4NqJswp7qJeIL6CLGmY64Q6FtaxuKpjs/JFVj/sYWejvNBFtDWL16H0MN+EAUkVKyEDvaUVrqiWxagbnXAD3q0GSG8m9oW/Yt1iQ3kU0ryDz+OLRYlFhNBeaCGpRQAFSF2PYNqO2tmFKzUNtbUZpqMaVkUffZXGyJHlS1ljUvfcXgP01AkCQK+iUjO2TShhaT/GkFudkuTFYT9mQ7/oVVRESB6IEUmhWkH2fvjVSFveYndRz8P3vnHd9Gef/x991Jpy1LluU948TZibNDFiGBMEPKLlB2Cy2FAp20vxYooxMoBcoslFlWGGETEgjZezpx4sTx3pYla6+7+/1xjiEljBICtPX79fIrsaST7072Pc99nu/387nnnnv4zW9+w5VXXklnZyf5+flcccUV3HDDDf2vaWtro7Gxsf/7srIy3nzzTa677jr+9re/kZ+fz913390fzwTQ2trKuHHj+r+//fbbuf322zn66KNZtmwZAM3NzZx77rl0d3fj9XqZOnUqa9eupaSk5Mgf+ACfyRxjE2pvBC2VRAn24K0cQvf2fWSXZkBXFKMAuWYDsijQvrWDsopMimcPobeuE9/eHsptRn75/Wf4zvRClL5JwYYl9eTZZXJMEsGmEDkmA1XBBHFVQ1VUiixGepIKtXxopOgdW0b9u1VkyhInnVDOvpX6BGTxqmaKNrdz+YIhRDqibPjDy0iyROG0MqTsQvKOGskd960HYFqmhalnj+TMxbrnwR1XHUVmTw3ajHnAoYWDB1/eAy/v4ZqLx7B/ST2iUUSURJbu62Feto2acBJFg3Mn5eFrCB7S7PGbxKFEA6PAZ06EjAJYJPGQBldfJl19/hZNsTSzylwoSQVN1aj87jQal1Xz9x89xzCPhZVPbCEn00LxjEIsXhetH2ykcMYwRNlAxwdrGHLBfIqPbSYRCLP/kaeIdIZ1A8Lhevmg7LBhG1mJ5MlFC/lR/J2IDjcNz71COp5GtslEu6OUz59IT3UDskM3OrTmebCVFGD1umlYso3MIZk48u1Eu2N6NUOWhd6GICNz7ShJlZg/RPXCKub+4zpQFbRUCkEUibe307V1L4IoUnhStj7RUBWUXh+yN5vu7bW4K4qQnTbCLV2Ikoirohj6VoaVQA+WsnKSLQ3UvbkB79gyjFYzBpuZXU+vJm9SEel4GsloQHBYKTm2Ek1RaXx/OxV5duL+ONmjc6lbsh9rlgWt/kh+qsJhCwcDHgdfDgNj/QCfhbOgAqWrhfxpw+ipbiDHaQL/h3HG+WYDzXt6GJlrp/yE4fTUtFHbHibfbOA3P3mZ86fq19iMEif1Db3EVY0ii5F4IE65TU9XAvTrk6DH3H00zciaZcW3pxMVOP3YMrataaEjkWbdsgaMHzRw+YIhhFrDLP7J8zizLJTOHYwhtxhrbiPP9okGN9xyIld4L4QHHj7o2O696S9cyqE5MNZfvmAI1SubkUUBT4mTxRvamJdtY1tvnJ6kwoWzitizo2tAPPgS8Jok6qMpym3Gfs+IM38ym433r+KWaxYyNsNE7XPVjHeZyR7ixpFnp/6ttQw9bRSCJNKyfCsTfvFtyndU01vbws67nyTUGiZvQh32HBveUQUYbWbsoycgFZSjBdpI1WxFKB3G/icX6kbEBpFod4wJ355Kx6Y9mD26EXLCH8IzqgyTy0H9u1Vkj/aSiiTpbQqRkWPFnlSpbQ9TbtPjIXtTKuvf2c8Fi25EMBhBlFDDAVIt9bSu2oEgiQwaMRnS+rxWSyXJmDqdwNPP695H8SiRPTuxDR9NeuM7CCYzqbYm3avBk0u6vZHG1z8gf8Zoknur0RSVbY+soHTuYJSkgiiJJPwhJtz4PZSQnz2Pv0lRnh0AZ6GTPSuayJQlOhJ6hPmRMjo44HHwhbcfGOs/N1+rxOJwOLjrrrtoaGggFotRW1vLrbfeiizL/a957LHH+icABzj66KPZvHkziUSCuro6vv/97x/0fGlpKZqmfezro+/z7LPP0tra2r9K8eKLLzJixKe73A/w1TD+jHMRlBRaKokWj5IMBJGd+krpMxvaAN2NfXyunR8+fgWjvjOxP/M+0hEh3efYnmOSkIwSe/b6ySjLJlOWGHluJXFVo2TuCGrCyf6eq/f39LDKF8XeZ5xzcp6duV4r3uNPxD3ITb7ZgKvMS0amhUHD9f7ak04o56FFe3l/awftjUEm33YFG5/czD+O/QlbH/iwxPb0v36bwhNm8uZ9DwFwTKkLYiEEg5E7/vndTz0X4Y4IFacOI7cyh5f36b1zIxcMpT6awmkQ2bvXf2QN5o4QIp8tGtD3mq+iL+6AMFHmNCHJIul4mp3dUSxeFzVbOhjqMtMQiBNMq3T0xNj2+j5q366m5tVqAnubaF29m56aNlre+YBYZwBHeSmJYBxrlhVREjGYZWy5HpKhCIbCwSi+duI71iB5C0hUb8QzsoyCSQX9pojmbC+2XA/O8kIA1GQKLRlHkETcg7ORZN2ASVVU9lV1Y3GbsWZZcOTbcOTbsGZZCMXT9Kxd3y8aACQCYWLdYQxmub90UTDIaNFQf+JH15a9xDr9ANgKvCQCIVAVevc20rZ2J1oijsGZgXesbpxY9842EoEwe3b78O/rIuKLkY4nSUf0iXrM14ujwE0qkuoTOPxkDcsk2h07oq0KA3xzGBjrB/g08irnUvvsj/Rqpr4bqFcbPiz/H5th4qo7z+Co707GYJFIhqK6aCoIxBQVp0FEkkWq6nuxZTvIMEqccNkkWuNpSmaXszEQx9x3sVm4q5vqUJIsWcImCZyYY2O8y8ygS84lEUwwO8uKq8xDnl1m6mA3AOfOLOKhRXtZWdWFSZaY/oeLWffPbdx19LXsfHJF/35e4v4O7/yLaHCAxZff8annIB1TmHzOaApHenlmQxtjM0yMOGUIHQkFu0Fk27bOAWPELwmLJFJu09vmwmmV3pSC2ZPB5kCcqZkWOhIKGUaR1niaN9a3UvNBIzve2o+vupWW1bW0rm+m4ZUl9Na2kD9jNKHWcF8loIrJZcdoM+stfkOnkm6qoWvRQkzDJhBevxx7UTa5ldlEUir+QByTy47F40R22EgGo1jzPEQ7AxhtZtyD3GiKhqpopONpahqDmJwyxQ4TbruMzWnq38/u99/XDy6dglSSYH0bweZeBFHUExNECS2dAlVBDQcwue10vL8SgGBdG6FtW4g3N4KqEGrqpPn9LWCU0dIpTC47yWCU1lU7UOJJ9raE6N7Vjr8rSjqe1FsiZTOxpmbseRk0tYX1lAlJoHBoJklVY3yufaB+77+EgdqMAb5xrLwwFzUWQY2GUGMR6t7aSNuanXRX6f39WbKEChgtBlreW0/181sYd818BEkk0qmXeNVHU2TKEo8va+DM3y1g0QNr2dYb5/UH11HsMLH2nuWce87w/kicY4ZmMq/MTVMsxUknlOvl4iYD9Y89xYpXaqgKJmhcUUtjRwTJKHFqSQa7luurY63xNIqm8d5376RoTPZBvfx3vnIdlgnHIM3+Tv9jtubN0KeMmitn8ePvT/zEc/HIW7W88vgWXnurtv+xVCTBDbecSERR2RiI4yp2MjvL+mWd/q+Ef0d0PuCqfaTpSCj9xn3d8TQ9SYXOTXuIqxot4SQXPfI9iiwGJEHvNVWSKha3Gf9+P81rW+jZ66d5VT01r2ymY+UmnMVZFMwai6uiSDca9PWSDEYhEcNQVIFcOpxkzVZifZOEdDyFKIlUnDoSJRzCku1CkM24RlZgGjIaedgEIu0+jA4ryXAKg8XA/n1+hk3Mw1Howjsii8zBHjJKMrB6rGQ4ZFKRGFoyrvczShIGs4x/f4Du6jYkdzb+Nav1GCKDka5N1YSa/exfWsf+N7eQUZaHMcOJr6qOaHMbrpEVpCJxNFUhUF2LkkwRbuki2KwLeylNo35jW78pqHt4CYIkkjmynJa1jWSUOPHv76V2Qxsxf5yeYOLIxjEOpCoMMMB/BPuvL0X+4HH2v76BxiWbad+sj60HJqg2SaRry152PbeVqb9cgMXrItgcAnS3fIsk8MTyJr59w4k89+R2Vvmi3PvXVUwpdLLksc1cfcGo/rH+wKJAdSjBqeNzka1GjALs+/vTrNnZzbLuKLtf3sWOnhhWj5WZHgu7tncBuvFhXTDBe1c+RMkwDxZJROu7iL204Ee8//dHPvEYm9e/we2R6k98/tHF+1n5zHY29VUvbOtNkIwkufG2k4kpGtt6E5RVZDKqz7NpgC9OTThJbSRFUyxFa99YX/vaJgBqI0mu/vtFlFqNSAL9/kcZDpnWTe0s39hOT3OIHa/V0LC8kfb11eRW5lF20kScpXnEfEEi7T2kInGQZEKVp+KZfQyJmi1E232IfSkNiqYx+TuVJAJhjA4rjtJ8XOUFOMpLyTx6DuGWLr2dIJJElAQaIilGVGSSPdJL0fRCCqcW4B3pwWg2UG4zEvMFSTXtRVMVMMpIZhPdu310VbWBKBFe957uUWSU6Vq5lta1+1nz9/Ws/78H8U4YjiU3i+7ttcTqarEXZKGk0qihAIgi6XgSf00T9e83YvZk0BJLU725vb/9x+S2o6kKlrwcund1UFqSQdf+AI2rmol0RKmPptjWETmi4+lAHONXx8CZGuAbxQ9uuBYhFtSjC33tROv2s+K9BjRFJdwRochi6I/CW7irG3uBl3GXz+If332UhD9M0fRiurqj5JgkTrqgkjNHZPGrK5+jOpRkwQmDmD63lNeag1QFE9zzZBX10RRmUeCVqi6WNQQoshhp29xOdWuIRFLhjvvWM/9nc/nZbadQPFNfuQg09BKJpmiKpfneKYP1iMdjShh3xUwinRGKCx28vaGNLFnCNGoqiBLDf76s/xgFSUKQJP0CL0qUX3sdf9v1BPMLnYc8J7WRVH+cDUDt0npev+N9tvUmOHdSHqIkkpVt5bTBmV/4vA9cCKAxmsIm6eWDmbLE+WcNZ+NL1fSmFMZMyCXS0EJtJEVuvl6GlwgmGPfDY/Ht9VM8vZDCqYWUnzyKyb85H1dFEZ5RZbSvryZQ00TOMTOw5XkoPutUgsvfIlW7HQxGeqv3EWrqYPezqwi1hjE5ZSLtPbSvr0YyGoi1tJL0dSOYdB+D/LO+jS3PQ+GMEeRMGMrkUwbjGpSJLTeTrDGDMNj0KoJQW5iMIgeaopIO9CDaHAgGGdvgwQw9YwKJYIJ0uz4519Ipok3tmFwO4v44qXiaaHdUr1JIpwjU+bDkZiHIZoqOGUe6pxtHUTZxX5CWtU0Eu2NoiorbKKFoMOo749EUFclowGCW6Vy/k2QkRc7YfBJJhQyHjG+fn/LKHAyGI/ubd+Bv7Qt/DQgHAwxwxImuXUxv1S7eXloPQLgtrFcR9P35re6JITusjDxvPP/4/pP01rZRMquI9ngat1Hk0utmcu6kPG782SJa42m+d9FYLjxtKC/v62FfOMk9T1b1C9BvtIVZ2hUlU5Zoq+tlbasuQNzzZBUX33ACv7lpHqO/M576aIqGqi4UDToSaS6cVcR4l5kp0wsZfuYofPv8DMm2MXh+JZedWE6goepQh3YQmdN/yKOVc/jzY4f211jnjx/ki5R/1HDevn0p9dEUZ47IIuaPM8htZkpfT/sAXxyjAElVwywKXHLqENavbSGpakwfnkXj4vVs601QaNXNA5OqxowbF9AdT3PcnBKyh7iZeMF4Zv7hPNxDijB7nOx9aS2BvU2UnXEcotHAoO9djP/R32Nf9RSCyUL7snWEGjtpeK+aSEeUTKeJQF0X7et3Iztt+HbsJebrRcrwIOYPJv9nt+Isy2PItyZQMmc40+eW4ix0Ys3NJKMsG7PbSqI3iZJUyHSaSEfi9O79sNXLWTGIMRdNRkmqKP5O4r4gau4QYlUbAIj5YiRVjZg/juTMJBkI0lXVhsEsI+cWkj99NFKGp3/hoXVDE2pfRUWx1YhFEjn2utlE2nyoikq6rZ5YWwfxQJzSOUPoSSpIskQkmGD6mGwq7DLKEfS5EsTDHesHZsGfl4EzNcA3ijvHJlCjQdRIEKUvI378SC9RX4RnNrQhCQLKRwxWdr+wkWiXn3NvO5WWtY3c+/fNVEzO51dv3sgrj29hS0Mv5TYjF80uwWA2Ys2yMdNjOehGvKDPLdefUulIpEnF9aie0WeOpMIuo6kqj/z2Lf5450qyZIlFdQGm/XAGRRYDu9e0cMFT17B5aT03Xf8aRdNLCHdG6U4q3LT+fv0HGAw0r3+j/+dpgoCmKLrKqfatposSJyy+l7tX3c7Mvti9T+Ktjgib+/o1n9nQxtNrW7BmWYgEE+Sbv5htyTfNIcEo6HnLtq+wDyOYVtkciJNSNGKKRuumdk6942yu/Md3GXnB0SRDEU4+dxQtLWFKSzKQbTKBmibGXzGN8gVHUX7VFWSU5aHGI4SaOjA4M5AdVqKdvXprgi9Iz+o1xLoCGHKK8b23BJPbjrdyCJU3Xol7kBuT00TjymZaNzTRvb0Wk8uutxdsX4vS3kjHKwsJt3QRD4TQFBVnaS7O0jxsBV78uxsINfsxueyUzS0na0QOlmy91DbVXIvS6yOybx8ZQ8tw5Nmpfvglwi1daJEgRqcVz5xjcQ9y48yzY7AYSATCCFYHzsIM1HicRFszBmcGppIhegUDUDSzjKIJuXolBbrp1P63q/Hv68C/t4mOjXvo2NaMI89Ox7ZWLDYjoiSSVDX8+/UEiyOHcPirEAPFlQMMcESJv3A5qWgcyWhgfK6dUGuYhbu6P9aiVvv2bkKNnZxyxRTat3Zwz5NVzJteyA0v/pi/3b6cD3Z0kSVLnD+1gJg/hjXbybRMy0H+OFmy1J9G1JNUCKdVMowSIyflA5CKxHjo90v5w5/1yOdl3VFmXDgOSRDYsKWDC566hqq1LfzprtUMP2048VCC3toWprf9e34ZnruaeLRyzme2Kv762hd1rwf0hZKX9vgYcfZYWuPpr3Rs/G8kpYEk6NUE25Y3ceFdZ/ObZ65k9CUzMTqsnHHyYDb2xJiYacFmNdK+vpo5V0yl5NixTPnLz7Fku0kHe4m0+3AU52Bxmwm1hundWYOmqNQ9+gSRli4MxRXsf/ARTC47rooiJv7+Gux5dkRJpHZ1Cx07OumtbcHkcqCm0nS8t4zEujdp/t319O5rIdbpRzKbcA/OwTOiAINZJhWJkwzGcA9yM/j4QRTPKMRWkIWSTNGx+F2ie3cTqWvEPWY4WcM87HnwOZR4gvTKF0nHk+SedgZ54/PwWI0YLQZSna0YbWayRuRgzCsi7WvHUlyK6PaiJeIYrWZK5w7FPciFv6YJt1H3ndrx+Dqi7T2oqTRdm6pp/mAHmYPddFe3UeA2IxklnFkWGvfqZqTSEexNPOBx8MW/Bm6HPy9fqzniAAN8FNFgRIuF0RJx1EhIjxmsbSF3fAErn9mOiF7eJQkCIhqD7TIFU4sAeOPG11ndE2N2llUv63rhVcaVZLBwVzfzC508vqyBC2cV0bmjE+dHcpDNokA4rdKRUJjoMqMCvSkVRdO44771fO+UwQiiyAU/nkXtGzvpbglRG0nhnXssJe/uQpQEtt7+LMPG5rBuWQNmj5O3OiJ9ByQhaBqaIOAuG4u/bpv+uCCCUYZU8kPhQFUQjDKSJ4+z3/ojZ8YipBpr+OCXL7KvN057PP2pBohPLG8C4PShHsrmlvPwQxuPuKHg4XIgveKjmEVdGJI/8i9A5IjWtH/IUIdMa98qVlVLiHcuewJFgxFOE0YBBg/1MGxiHsVzRuIaNxZECSnDQ7K+mvaXXyTvjLPxL32L7OlT6Fi+hvzTT0PpakFTFbJmH4NgsZHct51UfTUmtx3ZYSXU2IkhqwtXRRGO4myqntpIOp6m9u3dtG9uYtDJlUQ7A8i5hbpRoSjRtmob9gIvVtmg50M7Mwi1hgk09FI4YwTRLj+yw8bWB1dw1K9OxpBdgGCQEaRWxAwPg88+BkNOMVo8AoqC0WGn+bkXcJblEfXp7Tgml51kVycFs8YimMwkO7oRzDY0VSHS1kNXVRtDFkxkz8u7UJIKDrOBLKeMklSw5ThoWLqHkRfOQIknad/aTuH0UtKxeqK+GGaDiGw3IkoCfDWdKAMMMMA3DIsnn0T1RpLBKL21LRTPKOSdl2sAkAT6W5ncRpHs0dmYXHaevnc1TbE0o5wmjBYDDa8s4cRRXp7b2sFcr5Wn17Zw+lAPzWtbsHzk5lpEXzkOplXMokCBxUhrPE1vSuHRxfu5eG4p0fYeLr1uJtsf30hbLM2O3jgFC05izMs7MZgNbL39WYry7Kzzx5EdVppiad56dCtUzvlCx+/+0/7+bSOPnkVq/04kdzY/PfaGfg+gj3o9APzhz8uZ67Uy8ydzCTV29JswD/Dv051UsEkC6/xx1l36JKAnLCia7qsxJddO2TEllJ1/GqgKhpxi0q11dL75OjkX/5CWB/5C/qmnUP/08wz53rmkmmuJ+4IUHHsUkieXjsXvkty9SRf38zLxVdWh9vpwD84ho8RD9cIqwp1R9r21F1t2C8Uzy1FSaZI9AbInDCUZitK5cQ8mlwNRNmDJzMAxfBjrbnyMSGeUsZdM0QV+UaT2jSom/fQUJE8uAIEt20AUKZ43GcmTiyCb0ZJxZEeQmr/8DVd5Pp6hAWS7EcmVSbS+kewJQ9FSKRKBMPIgD1oiTqixk8YVtYw8fxp7d+lx4maDiBuIBBMYbWY237uU8VfNJRWJ01XVRtaIHFKRJL69fqSURJZbXwxTAgPOnv8NDAgHA3xjCL98LfS2o4YDBLZVEWrqQE2meeXZnSiaRrHVSDitElNUvCaJ8288kaonVvPCjq7+97AZRUKtYQpmDIfl9YCe/5xvNtDbFKLJHyemqMwvdLLVF6UpluakUhfv7OuhvDSD57Z29L/XnQuv4vHLHsa4spkhQ9yEOyLY7TJFqkbX0iU8taqZ+YVOXmsOUm4z8tOrp3LvH5cB8JMrJyNY7aBpkE7Tcvc8rPN14SCYPRJHTy2CpkKafvFAjUUQDEZEuwvR6UG02Jj7SAFzEnG0dAp58Jj+fdOScXxL3qbqiTUs3NVNvtlAazzNklo/sZr1pDQ4a7SXYWeM45abFh/Rz+2L8lHRwGkQkUWhzz9AQ9E0Ior2lQkGBzhgmlXmtJFUNfLNFvwphaSqYZQEqnf7+M5jPwBVRbS7MGQXku5uwzzxWDyiRO+KdxEkkXhLI56RZXS8+QbeGVPpXrkWR3EntgkzdJHIYCQV0ZV82WlFyvDgOfYEUvXVVF5hofG9KlxlHow2M1avi2QoSteaDWQOL6VrUzWpUBT/7gZcFUVEOwM4rFayRuTi3x+gc+teSuZNomX5NoafU4kxOx9EiXRHI7LDim/VGkSjEWc6hWh3IZjMiHYXubOn4N+6k569fux5dpqXbaFpTQv2HCvjfn4e1hIbgiii+DtJBEKUHjeKvYs2kk4paIqGaBQpnFrIi89U4R0RZvD8SjrWV2MvyqbU4yQV0c0iu5qCxBQNOaYgHEl3ROHw4xgHPA4GGODI0fmrCnrWrqNzSy2JYJLnltYhCQJuo0hvSkVFF5Mvv3oaVc9s5Y22cP+2WbJEw/o2Jl1xFE2v7AHg/a4oZlGgxxcjmFbZGogzO8tKOK37AR2da+eN5iAnDcnkpT2+/ve6c+FV/Oacv+Fd08KUikyaoylyTAZGZ5hpWfQmrzb0ctrgTB5f1oDTIHLtpZX8/a+r6E4qXNmyjUdPvu6wz4Xt0hf6/rcLxh4sRMy/6gqeGdrAu+feyvFv/5Xdv7sdz8XXkSkauGP2M6jRKMH6tm/sWP+vHGrR4OvCazJAn+O/P6XqJfaCwOZAnF889yMARLsLKbsQ1d9J+ugL8QC9rz2FwSwT2bmF3MnDqf/nSxTPn0P3+1vIApzeAgxmE5GGFpKhCLa8TFLROGKGh6JLvkt863JEo4GGZXvJHuntr0gINXXQtXVvX5JYLel4ilBTB46iHKJdfmzlcXIr86h5vYbuqkZKjp9A45ItFE4vRbA6AEi1NWH2OOlYvgGDzYyrPIVkd4AoIZcOp2yBDd+23XTt6sZVksHOB1+ldkMbxSM8TLjlSix5OWjpFIq/hXQ8wbCzJ1P97Np+MSuYVJjz4zm88eelJAJhJvzoOHprW3AU5+AdN4RYZ4DA/h6CSQUppZKVbcSaZUGqO3ILBR9WCX7hN/jydua/nIEzNcA3gpYP7kEIdaH6u0i3N+LbWc+dD2zkrke3Ek7rF/PelEJPUiGlQanVyF2/eo0XdnQd1O9nz7GRNcxD1RNrSASTTHGb+da0AqaWZBCLpNgciFMdSmJyykwpc3HmiCxaOiPMyrJi8Vj4xY9nkCVLXHtpJQ9f8hDbehPUhJMkgklUVaMnmGCFL8bSu1dw8dxS1ndFyJIlpo7O5tV/bO5f5b/jvvUgiGiipLvhpz+MlcqZeRXdzkFoBjMYDLrbraqAKOr/Alo6qRvZGGQkTy6izQGiqD8m6zd6Waeczuxn/8i9Wx/ilyv/yl9e/Sknj8thXq6dCrvMCzu6uOWmxfzy+tlccdrQTz3/X/eF4EB1QUzRSKofCgZfx351JxWiiTT5HisWScBt1N23M2XdgTnd0028vZ3qv/wdNRJEdLiIrXodyZOLa+58JLNM3BfENHQcBrMJxd9FuKmTjvXVqP4uBKuD3p27cI2fgHnEBOwjRpPuaCS8cSUYjIiiSNnxleRMHEpGeQGCJBJu6cJRnIOxZBjFl1+JZJYxueyEW3TRTIlGUZJp8ibkYjDLGPPLSATjeEYOQrTYEESJVCSGMTsf17AynGV5aIpCrFmvVBEtNkSLjY4tdX3u0BrR7ijtXVFURUMNB/TfY4uNWGsnPTVtGK1mTE4TFpcZg8VAbyhJMpJklNNEzQd6r2VGeQFGmwVbrgdJNrB/czuj51fgTymYnDLaEcx1FhAQRemwvgY8DgYY4MgQfucGkk21tK2v4f6Fu3l08X4iil4REFO0/gjfUU4Tf75zJW+0hRmb8aExYIZDpmhCLrue2UxS1cgxSZwzKY9ZfUbBmwNxVPTFhPwME+dU5tAYSjDYLmO0GLj6glEAXHtpJX+76EEiikZ9NIWqqBRajVQFE2wOxHnmwfWcP7WAFY29GAU4friH5/+peyaMcpq4r2DsET9Xr937IPar3+a0rIlYv/Mk43flsCnmYnPETuKEq5HLhnHLTYup+sOjXDpv0BHfn8PlmyIagG50nGs2YJFEckwSsigw3CGTVDXSPd0kuzrZcdvfQFMRrQ7Edx5AzPDgPuEMbAVeAjVNmIePx+xxovT6iPoi1L2zlXRXCxlDiom0+8iqHIqlcholZ51KqmE3nS/rrQOy08awsyfjHlaCxetGTaWJtvWQNaYc57wzKfnTw0iyhNFqJtalJx0Fd+9DTaXxjshCkiW0b/8fcb+eziBabAhmG+l4ElNOHt5xQ8goy0OQRKLNbQiyGTUeQXS4aN9Yh6skA1VRiXbr7bWqoqGGAqixCFKGh0htPf6aVuwl+RjMBtxGEVESUDSItPvIs8vUf2SsN3ucqKk0mqqyd1MbE08fjiwKqIqGkjyyn7ogioc51n/ds+D/HAbO1ABfO1ZvEZk9NWiqghqP6KZr69v6n+/uU4B7U2q/WVKk7wbz7jV/4Yw7zup/bcHUIh58Zif2PDsZJU6ybTIxfxzZZiR/tBe3UeTUkgw21vfiHZHFoHkVjJleSN7YHERJ5Mn71tKdVLjr0a20xHQfhJleK7FAnOJpheTk2ADdrGnYt6fTkVCY5DZTvdtHTTjZ/wdVbjPqwoEkI8hmOjKGHHTMS+v0QQBJRpB0lVQwyPwrgmxGMFkQ7S5QVbR0X3uDqoCqIshmvSXCYMSQX8a0v/2S45+/gcljs3H2Gc81rdhH5tA8rr208hM/g3/3kv5lXzgiikZMUftdjA/wdU0wasJJ4qEErXF9JaImnCSzzMUlt5+BqWQIGSeczZDzjke0Odn9u9sxj51BaNsWWp5+jI711SQCIRJ7tuAaO4pwUztGhxXJLJNqrUMNBbB4nIgOF8l92+lctpxoc5uerNDTjSgbkMz674IkG4i096Am0yQCYXrXrkLp1VfKVEXFlqtXJaiKSt5RI9EUlURQTz4omDESyZOL0uvDv2EDprxCNFUlGQgC6GZJyTShqp0oIT+tb72HyWnCaDMiyRKpWJpgWsVgNtC7txFNVfRViFSaYFMIUTYQag3T3hUl7o/jMBsIt4VoiqXpSCioqRRGqy7qJUMRlKSeny5IIi2xdF+rwhE2RxzwOBhggG8k2q6VRNp7qF7ZfNDjRoF+0QCgKZZCBe5edTtn3nBS/+ODT6zgkbdqseVYGTLETYZRYu9ePzFFJadQX3mdl21jRVeU3MpsSmaXM6zMxfiKTExOmQee0s0M73p0K7URPeP+tMGZ1DQGyRrqYUqfWXFTLM3Yy+fQnVQY7zLT2Bzi6NFefv/AuSiaxuULDh7bvypmnfZTZp32U5pDKYSRR3Nv4xsYjBJjfnIeP7ly8teyT/+JdCQUelMqrfG07r2TUsn3WPnZX8/CVD4C+3FnM+Lyb6HaPKy+9JfIFZX4lr1P/d/upnbROgBSDbvJmXUULcu3YfXYECWBSF0jiZ4AklnGWDKM1uefo+7pl+jcuBujzUwqEkc0flj0LUgiocYOlFSa3n0tNNx9B5r44fMWrxuTy4HRZqZgli5WqYpGezhN1ohcMoeXovT68C1fjrmwGE1V6K1tASDSrkd5h/fUoIYCtC9ZjiPPgSSLSH2tu+3xNKIkENhWhSG7oH+sb9vUjiBKBJtDdCcVkuEUcVWja0cbKzvCrGoJkY7EMZhlNEVFEEUks4xFEhEkkZpwEvcgF94R3iO7UCAc3lg/sEjw+RkQDgb42un6x7mQiKJFQySb9vPSTbqRoFkUENF/SYNpvWzxQOX62BFZ/H7ZH6m55+90btnLPZvv53unDOatp7dz7swifHt6SAQTDDq2lJrGIK9t6yAdT3PShDxGfWcC888YSrA5RPaEoWRW5JI3ZRCSLPKtBRUsKHPpVQSZFk4bnMkbbWFyRnmJ+aL4u6JMdJk5d1IesieLKW5z/8oEfHijWxtJgZrWy59MVq5+aedBx3zJpTegGUyoRguaICCaLAcZJaKquiBgcyAYjAgWG6iK7nCbjKPGI3qFQjLeHyMjGIy60GBzMOUvP+e21Xdx95q/oCQVHr5zBTteq2FBmYuL55Ye9md2JG7o/Sm1P/ro66YplkYUBQr72mMmuS1klGTgmDEPgMTW5SjBAIk9W/COG4zvnVeJ+fTM5EHf0x2zI+09+LdsJ+7rpfDU4yk89Xi6t9ciGGW9BLGzhWBdG0abRU8/iCdJRfTeWTWV7v/eYJYxuR1kVFZisOkVJ8WnHkesK4B9xGgSgTDpSJzOTXq5rj3PBYC1pISulWsJ1dajKfonVvPYIkzZ2QhWB47RY/vNE0WLjUBdF0pSJaPEg6qojL3mTEYOcpEIJuiuagBAjQRpfK8KR75e7SBI+udlcpoYd8V0Ag1BnAbd/FA0GvtTHkwuB60bmigY7qF2cS2D7TKJoO4IPcAAA/xvMeuyS4lU7+Cvv3njY949qX+5t5he6uKud2+k9uEniPX0cveKP3Hx3FLuvn89Z4320tsQJNAYZN6pQ2iNp1nhiwG6CDD+ogmcO38IPXv9eCuHUDitBO/ILGzZNr57xjAmuswYBV1gmJZp4eV9PVSOy0VJKezsa3E8c0QWkl2v4st1mRk+PpdnNrTxy+8/Q3UoSd7kiq/qtB2SiSdfh/3YX2M99Q4WXzwU4agzKLv+Rv4a3MI5lTlf6779pyCLAqVWI4qmzzszh7hx9o31ya3LiDY0kFjyNOUnj6Fj4TOkojFURWX0//2QeE+QeFcPTa8vBaDo2IlUnDmdQG0LajJNtL2HxJ6tJINRTC476XiSaJefSLu+ABBt8xH39RLr1BeTJKOBnGmVmD0ZGBu3UHrmiUTa/WSf911SkRiJQJi2Nfp80uQ0M8icIHfWJLq37yO0vxlN0ReWtt/zMq4R5chFg8gYOQLZaSXWo6c2dFW1EGoLkT22ANkmM/nGC5nkNrOvqpvOLbUgSii+Nva/uZXMIW7aV24GIMdkQLYbOfeuc2je2UWZTSacVhFlA6JsRHY5MZhldj+/Hne+naXP7AD0lJSuXV2I34C53QCHz4BwMMDXiqdiImLYhxaPoEaCLLn+JcqLHLy8r6e/uuCj04rpfYkDT69t4Uczf07rpnZMLju19z6IwWxgWI4d0SgRTaTRFA1naS4VxU6OH5zJ3l3d9NQFsGRmoCkaIy+cQeuqHRhsZlKhKHvXtqL0RciMd5npTirs74pw50vX8PqKJlqqfQyakk9EUXl3Wyd/PPdecl1mmmIfJjT8+PsTAbjpD/N10UBTQVPp7ImSO2b2Qcf+ZKsVQdNAktEMJjDbPhQQDogB8sGxS3rerK6Qask4WjrZF5ej9K8Io6pgMOpfwFF3XscNH9xOTNFbLZ59v4Hrvjf+S/wUvzw6Ekp/bNbXzbLOCClFw22S6E4q+Pb4qLvvfv2zEUV2P7eSqr8txFFeiqO8FIPZhCQb6V6yGHuBF8loQEmmyJ4xhcYX38C/fiOO4hy6NlVj9mQgmMzYC7IAdJEgGO1rS+jG5OqLfAyEibT7kMwykd27MGVnk6haA4DF60JTFaKdfmSnlZxpYzFYDChJPX9ZjUfwjB1GIhDCXpCFGg2SjKRAFAns2E1oxzYkbwG28lI23vwYodYwqUiSlrUNxHwxVl3/GBaPhbJ5w8kaVUK8o5vg7n3E/XHCHRH2vbGTVCTFoPG5RLujvH/XMqweC3aDSJFFXymRZAMmlx1BEtlV04O5z2nZKICz0HFkF/SFw09VGFiFGGCAL583K1u5/ycvMjPfQVUwgfkQNxQHWhBf2uPj2uN+y9pFumli3RPPAnCM14bVY6UxpMcXmj0OphQ6OW1wJhv39tDtj2H2ZGDOMDH6kum0rtqBIInIDitL396PklLIlCVGOE0s6YxQG0nyh4fP57nljeze2c34ikzCaZWFu7q59fwHGe61sqguwGN9kZE/7tjOmSOyuHnI976CM/b5sM/9FY5ZP0FQkqzpVBj+/nv88vrZX/dufeNpjaeJKSr2PtG7e7ePbb/6PQCizcn2R5az45H3yBhaimtEOQl/GE1R2f/w42SUF5CKxBEkkbyjRlL3+ho6Nu3Bmu0iWK9XzhqycnEU56CpKpqqko7oKSKpSIyMwQVoiqqbIoaiWLJd+KtqcJbmElr1DoLRiMllQ2mvJ1jfjsXrIn/6aFwlLtRUGuWDf6Klk3gnjyXa7sNW4CXd00U6nkYwyDS/+g7t769CLigha8Jo1vzsPoLNQURJoGlFHV27ull+7cPIViPTzh9LRlk2yab9+DduIeaP07q5nf2La0jH0xRW5hDuiPLyj5/HmaH/fR7jtWK0mfsFi2QoysqqLiweC1myRKYskTnYjSSLiEcwfvnwx/qB2+HPy4A54gBfG94R02i8ZSyqrw01EqTppdfZHIjT3RnBKBzspJ8l6zdvB1YTDvDOvh5eu3UJKQ3OqcwhGUlSu7WDqmCCyy8Yj7tyJHV3LWdjIM5Mj4XiaYWY3HaGXHYOu+//J+uW1CMCoybkkumxsHxJHeNHehl94VQevfUdwmmVrXe9xCS3Gdlq5OHX93HjbSez8+l1LNvnZ1FdAICfXj2V0ssuRY1FuPui8xEdLjSDGTEZYak6iHhkKwaz7aB9v/ulKjaMz+eeo8y6wAB6lQJ9F0GTBS2VRLDpYomaiPVVIoi6B8IBb4S+x0indL+EVPLD9gdJQkun0OIRznn5BlAVYqfdysKntnPx3NL+SdA3EbMoHFSy+lUTUTQWd0Yotxn1DOKkwqbnq3jnqW2Mr8xm6FlTsVUMpWvlWiTZSNzXS+7sKYT27u+fCHgmjCaybx9ZY8oBSEXiWLLdetLA4vdJhqJkDishFYmjpNJYsl39JX9GqxlBFPurD+wjRus3/RvryXB5yZ49C8HqQHZaCTV2kAzWUX7ufLR0CsFgpPYZfQWkbVM7mUOasOdl4MhzoEVDmD1OTFmZKF0tbH/oXTxDMkmGkzgKMwm17sdgNmC0GbG4zXRta8SaZaX09HlUP7II9yAXodYwsk0mHU/jyLfjyLdjcprYsbWDE6+egdnlwGAzE2nrQXZaaXxvO+G0SjKcQkkplJW5MJgNeqtC6sh9hgPmiAMM8M0i9Jc51D7yFF0JpT8x4KPXeadBj3pb548ftN0Gf5x1t70H6NUE9dEUHVva2dab4LgLx1F44jG8/OgmaiMpKuwy5ZU52HIzcY0ZRfXDL/HKm/uwG0Smjs5muNfK3xft5VtDPYw/dSQ7/rycjoTC1vuXMt5lxmM18uzmdn5z0zyW3bOC6lCSRXUBiiwGLmzeSvncH/Ho8deCPAZ+d/dXdu4+L5bT7gFg0xt/oeBX93F59QIeWrT3a96rbzYdCX3BwmkQqfXFWLW4jkde/yWT3GZO+tXxuMZPoOW1t0nHk8T9UcpOnES4pRs1lSIejJA7eTidm/aQNWYQktGgV+SJulDV9PpSIh0hcsYPIuEPoakqqqLq0cTBKCa3A9Fo0FsNAyE8R01GtDppe2YhBW43pZfqVYwWj5NATRPxniAl35qLaHMimm3sfegJkuEUzWtbyK3swJHnwJFnR40EMbnsZE2qRPG1s+Vvb+PIt5MIJrBl2wk09GK0GdEUjZwxXto3N2PLtpFz9FF0vrmBnNHZKJvaScXS2PNsGG1Gio4qwLSzi+b9AU6/6WS9tdJuJ97RjdFmxl+j+yYlggkyMi14bUYchZkIknhEzZAPxDF+YQbiGD83A8LBAF8Lst1N420TUP3tqLEIyYa9NK+q719tTmkfOu/O9VpZ2qXnxBuFD0sZ5xc6eaM52F+RsHhnFxZJ7I9gqn5xB8HGbiySgFkU6E4qxHxRdj6xnMeXPck1F4/BaRDJ9FjIKHISbgszJFO/SW9bX0Op1UhOjo3nVzaTKUscOzST83NseM+/nORDKzlubDaRzijDThtJ4VmngyghOlz6jbwjC0FJ4ssezR+f2EI8mqR5/RsHnYPW6t0si6W4IJrPlTPLmCY0gCAiGGU0QUA1mBEUBU0yfigoWO1o8Sii1amLB6qkR+qJIoJVvykkneKg2+0+U0UAwWgjpWk0xdJ4hmZz3aBMTC47Na/upMcXY1l39Ev+pL8YNknAIonEvwHVB7WRFLIoMKhv0KvIs1O3s5vssU20rNxJ/lHDCbd04SzNQw0FiHX6cRTnkPRFiLe2Eu3yH5QRbHY5cI4bQ6KtGWOGk1RvEFE2YHI5kFxelIBueJiKxAi3dCE7bXRs3Itz6BDkEZNw9PpQAl0kgxFklxPP6CFIzkzSvnbSvnZ6a1twVRQz+OKzSXc0Ujw3xd6XV5M9YSixzgB1r6+h7FuzQFUJt3Qx/PwZGG0WECXW3vYSjc0hFA2GDHFjsBiQ7TLtWzsoPV3f/6Y1LbQH4gwd7aX85DH0VDdiMBspnj2UzCEe7AVefSUkntR9G+JJlizez9xjS4n74yxuCnLLb7/FvkVrUb/i1IwBBhjg6yP66k8IvfkUrZvaDikKf3Ss/ygzPZaDFg1e3qf3bB+oVNi/eC/+fV1IfUJfb0ohFUmx6e53eHZzOz84cxjlNhmP1UjmEDeBhl6Oz7GRiqTwVbcywmlikNvMs5vbEYHzhns4zWwg98IrqP/je9y0/n4eiVfgj6Yon/ujI3BmjgzD6GDJ2LP5+Zm3suGddkJVO6l6cjV763v72ysHOJhgWqUxmkISoMxmpDaSore2hd3Pr2fQCSNJBqNkjysnFY0Tafdhy/WQCHTTtWUvotFAtN1HMhhFdloxOqx4xg4juK8BR1FO33iYQBBFbHke1KQ+r0sD0b7Kwq6qFrKnRzAOHkvBHN2rovP1RZg9GeRMHY1odRDcs5dofSPRLj+OohwGf/d8lK4Wio5uo+6drXhGlhFp97H1gXcZ/5MzUMMB2tZUMeayYxCNBoL17ex4fB1N3VE6EmnGZdswmI048iS6dnWDqhDpjNC1p4eOhMKgfDsV3xpP55ZazJlOJl43mdLt+7Bku1GTuvJv9maSDASpXljFKScPJtIZ4b3tnfzy1V+y7/GX+/0PBvjPZ+BTHOBrIfDUhWiRAFoijuLvZOF1z/Hs5naMHxEkp3ssVNjlgyYSH+1/fK8tdFAbgz+lkm82MHlEFt9eUMGrDb3E/HHW9sQ4viSD8SO95E0swT1I7+te9UoN1aEERosBg9VMbmUOSlKhqrqbRG8SAEESOMZrZUKWlVWb2nl6bQtvTDmPgkl5lMweQkMgjqMoB8Xfpd+gm626s62aRnHkcN2iajoaA7Ttqvr4OWioIhaK8cHbm/n9OzWkckegSbLue2Byfmic2FeNIFjsIBoQ+kQDQI/2szr0jN54tO91NgSTGUGS9O1Vtd8bQUslueiR73HrXWdQdMpcPCPLqH27mrUNvSRVjZ9cOZkFZa7D/4APk4iifWNaFkCPaYz0eTAoSYXWeJqM0jz2vFdPzNeLq6II+8hRuuFgKk2osQNJNiIaDdgLvGSU5ZE5vBRNVTG57WjJOEo8SbyjGwBztlfPX5YkveQPvdfRM2E0BrOM2W3FUFgOooFYUzPN72/GlJv7YZRnPEo6nkQwysgOm26maTAiOj1IdgeOAjdb79crEMrPOwWAVChM8wc7MZhlmpZsZM8zujni+LmlBNMq6XiauD9O7tSRVH5/DsE9e/v7eYeO9pI/pYye6ka2vbOfHa/V0LhsD5ZsF5JswJyThSU/G2thHt3baznryqNwlXnxDPXyrcochD5TRFU50k7LA+aIAwzwTSG5/i3uufaFgyKUASa6zLiN4iFFA+BjlYagGxAfX5LBpfMG8dIeH70NvdSEkywoc3HcxDwKp5WRMyYbgKVv76cqmEA0ikQ6onhHZKFoUBdMEGwOIQlgcsqUWo0cm21jzdYOXt7XwzvHXMrcaYXEtq7i0sI4N/z491/+STmC2E7+AwsyxrPn3Zdw/mI1zWffyFErl/DzSeew/Lq7OWu09+vexW8kyT5RS9F0c+5kMMbW3T6UeBKzx4l70iSSwSgGs0y4pYt0PImjOAd7gRdHUQ65R41EVVRkp410sFdvS4jGSARCZA4rwV1RhDnbi5JKk4roAk7O1NEIkp5aIJcOB9FAvLOL3U++gyBJumGyqqCGAxjMMpJZ1isaMl36YpGqYC0uxDM0h7W3v4umqIy7+mS0dBLfzv10bmtBVVRqFq6k/t0qJFlk/NFF/Z5S4bYQ+UcNZ/Ql0+nZvJ1BJ1USUzSGD/NQNL0Y3856Wje1s+6JzWz8yxsYbRYE2Yx18lwMnjwkZyatq3Yw/MxROIuzMNpk5ozJRpDNxPxxGlc1o6lHbrwfaFX46hg4UwN85eSPnwfBbn11XFXo3rCNkZPygQ+FgVFOEyt8MWrCyU98n8ghVivtBpHm/QHuX7gbgO2b2vnWKC8b2sJMvu0Kin94DblHjaTcZmRkZQ4VdpmS2eX493Xz0kt7aI6m2NabYM3GNrxZVkxOE+90RNjRE6PcpjvdV14yCUmW2PfGLrb1JjB7nP2tA4gG3a8AuH9PkvbeOOmkQqSr8ZDH0Lp5MbI1g2Awzvn/3M77iTwETUWM91It5qMZTKTdxSAaUE0OXUQQRASLHcHqRHB6+nwR1IPiGgVR+vD/Jj3W8YDhohIO4Rg3ESm7EMe4icy872f89NmrOemGk3AU5zDklGFcdd7IL/z5/reyrDuqR4MGE8iiwMu3vIXZINK8Yq9ubtiuf8YWrxtBEvEefyKaovYN8ibivl4yh5UgWa3E29v1lIVIHKPDjuQtQI2E0BJxJHc2GIxE2nswePIQjQZi3WFEdy6RZa9gHzO+bxKhoikKXZuq6d60A0PfxCIZimDILdZjOy02urfsQk2lsWZZift6SbQ1I4gS3dtrKV8whWB9O+1b23EUuBn7vbnY8lzMmFaIxW3G7DZT99ZGLGXlJIMRNt77ARa3GdEokQxFMblsFA5yUToxD88wL1kTRiNZrf2DsZjhoWevH2dZHiaXnWh3hIySDARJQrYbSR/RVpQBj4MBBvimEHv2MjpWb2XWiKyDHncbRTYG4h8zSfwsZFGgqjPCo4v3A7C9MciZI7JY1RJi0s2XU/zDa8iZPBybJDBplJcym5GyY0pJRpI8trSe2kiSqmCC57Z2UOwwoSRV6qMpFndGyDHpxbjDzxiNPcdG1WPLSG1858s5EV8jU+b/GOeMa4h2NbHv/Vc4URo94IFwCOJ96QqN0RRGAf7+7C6yZIn6pTWIsgEl5EdNpRFlA2oqzeBLv40g6WkCRqceB+quKEJ2WIl2BvqFctlhw5BbTCoSR41FsHh0M8FYpx/J7cXqdaMqGoLTQ+T9l7CWDsLqsSGZZaLtPTS/v5nm9zfrngJ9XgmmYRMx5JVi8BbQsXITqUgcV0kG6XiSYG0zpFME9rUz6uLZ9Oyso2unj8zBHo664Qxkm8z4QS6sHgsGs5Fdz67FUVZEMhhl6Z+X4LQZScfThNuCxPxxjBYDhUMzyZ9URNbsYxBkM8n9O0n72hBMFnr2+nFXFCFZZDRFw55jQ/F3Yc2y4E8o/efhSCAI4uEtEgwIB5+bgVaFAb5UMgePp/rYFor/qZAIdn/s+Yuvv5r7JyZRulrRknHSbfU89OcP6E4q2CSBiKIxymmiKpj4XD9vWqaF1T0frkb8a6n95kCcmKJyyvFl7H9yIY0rmyk7dhBnXz2dtg31xD4iPhRZDEw6sZxZksCa1/ahKRqh1jAnFzrxRVN0JxUuXzCER/+ygpiicXSubmAX9wVxjZkKsi4YaJLMa72ZvLalluYaH02b3kdJfnJZYOvmxZhtC3A6zfzkkQ0MqcjCYzexu2kLyXgK2dxLhsNEd7cfo8lAKpHm9vPGYTaIVCr1uqeBbO5ffRYMRgSjDTWmtzDQl7wgmK2gKshF5Sj+zg8NFlNJRIsNe0UFSjiAozSfRE+An3ocbH9uB4s7I5/rs/h3+bo9DL4Iq3tiTHSZsUlif7VL5hAPcV+Q3toWMoeXEO3SB890Zwum7GyUYABjyTA0ZafufZGM45w2VzezXL8cefAYFF8bBm8BajyCFougRqOoqTSSJxfZaWP4dd9Fad6LnFuIaHPiGVlGvL2dVCSOpqjIDlt/bGN3VSNZRweJ7aum6rEPsGZZkIwSrkGZ5M8/gXRbPXufXYLJaSbU2EH7xjom/+pMABLdPkwuO0qyjVQsjWQ0YM8zk2xpIGvKBDwf7MG/v5fJvzqTTbe/jLPQgTXLyuAFk7GUlYOioMQjgJFQXROJwC4ySjJQk2msuR6W37GMC/54GqD3a5rNBoLxI1NZIgj0e30c1psMMMAAh0X0lWuIrXiFP9658qDH880GWuPpT9jqYIoshoOMiKtDBy8q1ISTtOzxcc7cMvY89Cxtm9opmVXM5ZeOo3ltE0lVI9gcRLbJDHfIzDi6mNmKxuJlDaiKXmFVatUNhSOKyrmT8vjznSuRRYHbtz9K16KFTDz7fDY+//Rhno1vFkPeFonVvsCbMy7hjbbw17073yhUwC6JxBSVYFqlIseGLddD56pNgG5qnDN5OGokhNmTQdzXi61sKNHavZg9GSSDETzHnoCWiKGsXkHG5KNIdzRiys1FtNhQIyHS8SSSRUby5AHVjP3VFaSbajBkZiFl5eEdN4REIEwyGCERjJNRlk339loMNjNtGxvIn99GcPMmtj2yAlu2DbPbTE5lHiXnn02qYTfbHngHe56DluXb6NzZxczbL0JLxunZsReTy4amaCgouAZlYovEiTS0kL/gZHLe3k2wO8Yx91zA2hueJHOwG2uWhUl3/YrftJZQYbJz3lwPhp5Gup++j/iGnVizLKQicaxeN23b1jHnj2diyCnCXpDFyGlx+ODQC2hfBv0Vtl90+4HEh8/NgMQywJfKiJljSYaidL7924MeNzmziC66jr8NbiXdsActGWfXXY+x5uYX6U4qOA0is7J0pfbzigbAQaLBobh4bikji5yseK+B3qYgQ04ZRtVL1WSNH0715nZGjPHSuaOVD1Y1EVc1hlx2Dvc8WUVK0+joibGoLkA0oU9W2uNpnn59Hx0J3Y25LpjgxttOxj33RERXNogG9lgGc+XqJH98pYrGPd0oikoqGvzM49i/YhFVa/fR3djM9s2trFzXRGdjLx0NAXraQ9TX+mjd10HzXh8NVbX84IG1XPXEJq7aIPBkpBTFW46WMwjB4QaT7tMgyH2VBvS1L1h0c0bBbMXgyUONBnWRoe/1AKLViWAwYi0dRMFxM5h3x9nccMuJfGd64WFHO4noHhUH2lE+TTRwG0Wy5IMHgW/KxWpjII4sCvSmFPaGkkQ6Q5jcdow2M711bRRccgXGrBzUcIBYSytKKo0aDSGXDkcNBTCNmAwGI0p7I7LLSbq1DsEgk2qq0UsUAdFqJevYeWiqgmXSsfiWva+LPTYnyZotpCPx/hJHe4GXRCCEZDTgKM5m+HXfJVG3h7o3N2Jymig7cSJDzjueglmVCLKZXU++j9ltpWjeFPa8uJXSY8eQDvaCKCE7bex5cSuJYAJHvh0llabkrFP72iCMKEmVo39/Bv6qGgqnlTH00tP09JLZp7Bg73BGvJLF46XfQa4Yhy03E9+uFiRZpGXlTrq31/LtG07EUlyKmkwRbgugpo9sq8LXxX333UdZWRlms5kJEyawYsWKz7XdqlWrMBgMVFZWHtkdHGCAr4jw/fPZeNk1LP3p84B+/Z/W5yX0eUUD4CDR4FBcPLeUY4sz+GBVE/79fgbNG8zKl3ZTPH8OtU0hxg7JREkqLHyvnrpIilE/uZhH3qolwyixsSfGqw295JsNyKLAjt4Ez21oQwVGOE2ESqZQ8kLsv040OIDljPs5I2cyj1bO4dxJeZxakvF179I3hlhfO9223gRKUtEjk502BEmk5Pqbkd1uYo319Na2kI4n0VIprEOGkQxGsE+cAapCuq0eg1kmWV+NIJuJt7ZiLKpASyeRHVZy5h2HYDDinreA5hdfIVm3G9HmJLJtHXFfL0oyhWSWyR5X3h/r6CjKYcItVxLatoX6JduxZdsYPH88Q88/jtzJ+jyi5oUVeEflUXzsOKrfrGXCj04gsGM3sdZO3BVF7H1jD6lYGqvHSqw7TMV1P9Q9mQxGBEng+Lu/Q+Nr71F+8kiGX3MpyXCKsza6ufPXt/P9y2/COf1qLn4/gqM4h56aNow2mdY1u/HXNHHKBw/hO+WnKP4u1GQak9OEkvj8f+//SfyvjfcDFQcDfKk88O2xZGoF7OtN8bPf/YzvTykiN96KkE6gNlb33azKVP35EXavaWGFL0ap1UhLLMVbHR9f2f6oGeKB70HvPfs8txwvLGvgojOH4++K8tjSei63G+lJKrS8t4G5V04ja9JYVv7iCWojusHLNXNvwG0U2darixdT3GayChws2t7Zf6N7zcVjiPljrFvWiC0vU185tmRwy7Y0+zv3sm1HO5qqEQv46aj6fBcQgPbtywAwjJlNKi4jiBJqOkk0JCKKApJs7jdYTPR2Y/Xk07a/m3dlmb/l2CkrceGyGrnnhGKkxq0IjkyIhZDc2WiCgBaPoiXiCJKEGgmCKKGGAv2tDJoS6/dOUPs8EQSTmXBLF7njC7Hlerh6dAPJcIoHX97zuY/rACrweQsMwmn1Y3ne3yRkUaDcJiMJULWimXRMYcjpU3AedzpaLIwaDfUbUpryCtEScVTANHIyqfrdGPJ1vwPR7iLd3Y7S0Yb9qGMBPf5JdLhQwwEUXztqn/AkmMyQTmEsGYarwoIaCZJuqSVQ04i7ooim97dQfu580m31mEqGkDm0iVhnAHNhsb69bCbd1cKQ06dS9+ZGgrXNTP7piYhGA9sfXkLlj8+kdtFqBp88gkh7D6XzZ2Lw5KGlk5jz80GUMLvNJENRJLMJi9mEb/0mnIVO3lLKaWvchJKM89s/L+J2byG77vwxlve3oKkqjsJMsicMpXbRGtrX7cE7tgzZJiPJkp4GckQQvpZUheeee45rr72W++67j+nTp/Pggw9y4oknsmvXLoqLiz9xu97eXi688ELmzp1LR0fH4ez1AAN8I4g8ehbrrr6N9ds6qQkncRpEwmn1MwX/L8JjS+u5dN4g7D0xntnQxsVOE91Jhb1Pv8mJV8/AXVHEB798kWCfWHnNMb8GPlyoKLcZycm0sLrPeBHg2ksrSUaSWDcv+tL395vKcanh4IbwUyfgX7uKG3/2v3Psn4Si6XPPF5Y18K1IilEXz0L+0e0IDWtJB3uRHVaSwSj2knzdrFpVsA4fQ7q9EcntRUunsJUPJtZYT9xXR8asY9GScaQMDwBaPEKioxFUFcksI7kyIZXEMng4tgkz0CIhetevIdrlJ2fiUOrf2YR7VAXpjkYsuVlklLiJdISwFuieAnKRh8TeHRTPGUPje9sRpFqOuflU1FSaXc9sYNqt51P1wGuUzysn0hli2CWnILmzSe3fiXfiKFBVLG4zvh17cZbmoakqzS++Qt6EfN6876GDzs3zd97H80DV1Ax6G/xklLgpu/5Gtl55DQazAcfRozG5HcR7gke0gu9DX6IvuP0XbFX4XxzvB4SDAb40oouuQ2tYBgYjxfte4NdWJ3JgIggiWjSIGvKDUWbltffwwo4uRGC8y/yp7r4HLtiyKJBUNRQNMox61m5M0VDR0xVea/74qr5NEsgwSrgr8pkyOJcxnQFKv/0timZtoW39Hv7xyGZ+8mc3RosBt1FE0UDRNDKMEscO0y/o0++8kpXX/I25OTb290189i+pp2RWEWf99hTMw8dTnTON/1u0i1hKIR5JYjQZqHr9+S98Hg8ICJ9GqK2WUFstoMfQNKoqHWNmY/e4mL6zE4MsIko9hANxfvHtsZydHUbSNNRe34eJC6kkgsmM5M5GjQRRoyEwGHWjRVVBjUYxDa2k5JwCfCtXYfY4sRdkkYrE+cWPZ1D94o7+OK0vm0OJBoe7Nn0gpePLYFl3lGmZFrqTCkMcMt17fHTd+gZH2w6kV8hoyTjmnCwEo1EXCNrqkdzZyINGosYjyINGElr1DubiMkxOT391iOTJJbl/J4LBqMdtAhnDB4OikOqsIx1PInuzQVUJ1rWx740dOAsbyJ82jHhLI+lIHIe3gHBLN3lHjdRjikwW0l0tiFYHstvN0ItOYuudC8mfUoYt18PYq04h3dNNpCPC4HOPR7Q5UXt99Kxdh3vyRETZjBaLoCQV4r4gjqJsRKuVwK5aJLNMSyiOr7kNNZ1EMukCV/YpG7nix7fyu/wG0u2NbLz1KWw5VkZcdR4bfvN3bDlWtCMsDh2ucCB8AXPEO++8k8suu4zvfve7ANx1112888473H///fz+959srnbFFVdw3nnnIUkSr7zyyhfd5QEG+Np54h+38K2211l86i/6x+byPof6f4d/bWf7pNSFA6/NGV/MSeOLmbavnaGXnsbgmhoa39/OHbct5f/+/C0MZkN/SyToc4TjB2cCMPOO77Ls6geZX+jkg/YwGUaRHa/VcH7BVDrGLwDe/QJn4j8X+9VvA/DP7auxnHTCIedY/wuktI/8Hqoa72/tYNvPX+JC2UAUkMwmlHgCe0EWgqzPp5J1u5EzPBiyC1BjEeTBYwiuWYalqBBDTt/NpEGfF6QadD8uwWxDi0fIqhyKIEpEavcBIDusiLKReCDEnpd34B7URM74EkJ1TajJNBlDy4j7o+RNqUBye/XqxdY6BEnE7M1k6EUnsf7mZxAlEVuBl1EXHkW0sZmYP8boq05HdLiI7tmJf9k6Ck6bjxYJ6vO/vr8RR3E2kjubzpXrPjUVYdRaJxlFU0js7uHlky8ma6iHPb9+hOD3z8bkNOHItyNKR7AdQDjMOMYvKBz8L473A8LBAIdNRtFwmq/Nwff830lHYtgKvGiKisXuIrVvG4LZhhoK8Nal91IdStAUS/d7E3xWJNABx9cDIoFR4GMmSv86oB2YGAx3mNgYiPPWPSv7f47t0S1EFI1Sq5HxLjO/vvbFg7a9cFYR5fPHU/3MOiRZ4taTb6XYaqQrkWbOlALMbjOBugCDzjwWKcPDPw0TuOueVaiqRiqRpvaDr16dP+BU2759GaLBiNVTQEbRcNq2LsHszuWn9Q3cklPAPVcdxWxjJxj0Pk7R5kSNRVAjQb2toW/gUpP6arloNpPYsxVBEskoL0Dy5GLOL0DM8KCGAiipNJfu6USSRfz7Ayzb5/9GJSF8lC9TNDjA6p4YFXaZjT0xhjtMSAIs/enzjDhjJAVzJvcbU0Zq9rD6d28x8+YFGEsh3dGIIacYNRoi0tKFubiMaNVGrKMmonS19KUniAR2VOOePBHBnY3S1ULr4g/IGlOOKScPNRpCjcfJnDqFNbctZm6WFXuBF0SJdCROcON6Kq6+nPDmNbQu/oDCM76FlkrS/PZy8mdPoPaZ9xh+3gwCNU3sfmE9giRgMBsYe+3pqKEA0fpGkqEoqUgMLRZBS6fQ4hHKThirT2TMugGnozgHW4GXZbu7UJJxRIMRNZ3CO2IaXbtW89zCzdx698kInc3E/HESwQRaLELu+AIK5kxGfG/1l/ypfATh8IWDj66QhEIhgsEPrzUmkwmTyXTQy5PJJJs2beL6668/6PF58+axevUnH+s//vEPamtreeqpp7j11lsPb58HGOArILjqHu5Y08Jvf/qHgx4P3z+fJ46Zy/+FEkQUjSluM+v88X9bNICPt7N9kmjgNor4UypP/XXlhy0NC/VJu1nUk5F+85OXD9rm0nmDqDjrKKoe+4BUJMV1p/yRcpuRpKpxyXdGMeiSc7lbmwTX/o6cmVf92/v+38J5F/4asiYS2fhrEu8+yca/vMW2vT0f85j4b+bA72FK0xMX9oWTPHHt88ycV8bgs49BskokegKk2nt49ua/cfb1x+Epl1D8nRjyylBDfkKNHZhzsvC9t4TMGTP053KKEcw2fFt2kj17FkJOEamG3TS8soSsMeVY8rOJd3STDEbJmjKBl25ZwkV5dpyleYiSSLilm+4tuxh21cV0LV1C48tvU3bphfTsbiBY18Gg04+h6r5FjL/qOBqXbGb3yzsRREH3Kvi/b6PGI/Rs3k7MF0SJ93llpVOkGmsYevokJIusGy2LEt7JYzF4C+C9tz/1XD1574/Qzn6X7j0+xuc5UEbnYC/I0lMobFaIHpkY0MOuOPiIKBKLxT5zrIf/3fF+QDgY4N8m8uhZvGcdTzSl8sq2Vh4ZE0CLR8gYOYJXLvwry7qjXL5gCIMXGJDMJjo37ubOv6wkpel5zE2x9OcuVYyrGkbhw5u+AyvR/9rCcND+KRoLylwsqgsAHCRORBSN8S4zQ0ozaG36uII++tI5/O57T3LZ9yez+ultVNhlZlw4DmdpHgD739xC9mg9Ou+22Dj++chGGte+/rmO5atATacId9QT7qgHINrVRLSrCYsrh58/vomRw/LJdpqYWZ7Fnq4wV04uxLzj7f64RjHDo7cxpFN6PKC/Ey0RR3R6EO0uNEMU0ikEk5m8meOwFzSTDEXInTgIz7YGYr4or61pYWyGmZJRXpKRJJqi0dsSOmQryld2Xo7AexoF2BdOogIdiShuo0iFXaZ1QxNFp8zFWDqcxO5N2IaNQJIX01vbgm2sQqq5FsFsQ7TYcI8ZjuTJQ2prItWwG7mikuTuTRhLhpEIbEIwGAlv3wxA1phy0vEkhmQcVAXRrLceHDUyi5GXnaivYNicRHfsxV7gpeWFhXhGlvXvr2ixUXCsvtpQvmAaGIyk47UMO2sy1oJsArvriNY3Yi0uxGIyI/UESEU+/Ds15JUiByPYRk8g1bQXRAmDK5Okr5tfHVfBsjc3oKkKosGIKEpkDh5Pb0sN4n4vaVVl6i9OQjKbaHztPTwjy/BX1XDESw6+REaMGHHQ9zfeeCM33XTTQY91d3ejKAo5OQf7geTk5NDe3n7I9927dy/XX389K1aswGAYGJIH+M/gxeKJNPlifDC7hIl/+gmpxhoa31zJj466DoDhDpnqUJJ1/iNzo3CAk/Ps/aZ+/+qDMMppYlihg6ZDmP5ljcjlV997mmu+N543nqliosvMlaXTAPjtVvhWbTnHjTQe0X3/T8J2woEbnBFQDrEXf8APy8/ixBwbl44/jxfvuQIA65Vn9ydd/DdyYHGkLpJkyNZOBp8NpmETUDavwFJYhNPwDpF2H5nJOMmWBkS7C8Eo4x1XgcFbgNzRTbJuN+ZRk4ltX4ucW0iwvh1vIkZoxzY0RSVrTHn/zxMkEVteJunuds4a7WXUxbMRJBHBZCbauZeM0jyaX3wF97ASArWtaKkknpGDyJowmo6Vmxh56XH6IoYkMPLccWTPmELT60vpWruFrDGDySgvwJLtpnt7Laq/E8FkQbTYiAeayMwrJd3eiDG/DNHmIFG781PPzUO/+w4nO7pJ/u4CIsd8F/8vLsJVUUQiECLaFUAzmo/oZ/NlcdVVV3HVVR+KhYca6+F/d7z/z9zrAb42Xnnm9yw56Xgc+XbyCp38/SffR7AXoEaCdK3Z0J9q8NCivbBoL26jSG9K5Zi+EsND5TF/FgcEApEPKxD0xz9+03Hx3FIeW1qPPccGfcLBAURgaqaF4tIMZj13F4k9W1hxyh8Pes3PLn6c+YVOal6tZtjEPDJKXOQdO1P3AZDNjJ99PJqq8KIwhoWvbPpGiQafRk/ddjR1FNtS+qD39gd1iKLA4h3t/PaUmRwV2vyhWptK6qvHZhtqX7Sj6HD1GyyqoQACRox5ZThFCdHqAMBRnEPHxj3MB0SjhGdotp4MYJYx72pngdVIkz/+mVUmn8WRqB74IvyrcOVPqSgaBJtD1D7zGhXXFJLo7MQkinSHkvTsaSNz1yYMmV60eIRkWz1aPEKqsQbzmKl0vvoyHmcm0TYfDk8AS7Ybxd9Fb20L+SfOAUVBbWlES8YJNXYgO61YhwyjYGoJ8c4uDGYZA5Dwh3GVFwCw+7mVFM4YBn1tD6JsxlpaTNMbH5AzeTj5syfoMZ2AwWbBWlxI6/vrKDj+aFpeWU7+jFFoqqK3TcQiKPEk8T3bMGbpA6Uaj2C0WRiy7Vnq/nERgy57CgCbO5OeRj/JUA9kl0FrPYIooiZTpONJLLlZxHy9h5968KkIiIfplPzRlsxdu3ZRUFDQ//2hViA+3O7gn6tp2iGjHRVF4bzzzuO3v/0tFRUVh7WvAwzwZSLb3bzY/j6jvjORslcOviEPrrqHa216ZO/jyxp4fPKP+p+b6DKzMRA/4ivS507K45kNbTjy7PAvwoBZFJjkNpOTY2PWw9ej+LtY9y9j/Z/uWs1cr5V1L1Zz7MmDmbgn96DnX7n7AV45okfwn43ljPsJr/kL9h+8Bm21zDvz533PlLL5PAur36w97LH+m4wkCOxsD5Px4FtM+P1wEv4wktVKR0Kha0cbttx1OMvydHPAoI+4rxeL3I5z+hzaXnwBb1Yu0c4Acm4hztJclGAP3dtrKTn9eLRknGhjM0o4TE91A+6KImzlg8kZnU24RU8rc40eTiqkz7dVRWXrA+8xeP5ovU1SUQjX1pN37Ex23PM8w86ZQdlpc0FV0OIRLF4XmaOHUP/aCgaddQL7nvyAnPHFIEloqoLkzsZoawBRouPYayhpXw+AMHkBdw4J8+eHV9O2denHzsnvFu7glO8PwjRsIqKk0hGM4Rw6hO4NWymeOwGWbjlin4coHt54/9Hh+d577+WCCy7o//7Txnp92/+t8f6bYlQ+wH8IqqpR3RXl1XWtPL6wmh233UNi8/sk9mzltluW9L+uyGLg1JIMMowSKp9cYvjvcKA9ShL0crGPMtdr5brvjSdzSBa/+PEMnl7b8vF9Ry8vz63Mxffqc6jhwCF/TjSRJrcyG0ESKJ4/B0N2gX5zrCpo3lIeS43gtn9uoW7Vp5dsfZOIdjXRsvEtAm1d9Hb0EPYFCPmC1G5p5ILblnDeZhei26sbIlpsGIsqEB0upAwPxkEj9dL6UIB0s+6rIPaJCGKGR/9yZyPnFlJ8ximMuGAWY35wCoUnzCJ7wlDyZo5j1MXHkDXMw4QZhVx2Yvkh99FtPPTl6NSSDBaUuSiy6DrnkRINKuxy/8/4omwMxFnaEaFzRxddb72O0WYmtHc/iqb1t5Ske7pAVTEWV2AaMRnJ7UXpasFZmscdZ/xZNzgyyKQjMXY//iaF55yF6PSQ9rUje/QM9HBLF7LDSmCTnuksiCJKMo0aCpAzcShybiF5JxzLqB+cRtzXS88H7+mVIw4X/q07yZ8+GnNBMaLZhmh3Edhdh2PIIADyZo4jWL2bknkTkR1WtGgI0eYEScLscSLZ7Yg2PX1DcmeDwUiqsxX13b+z/zKZ+399KhF/DzZvEZmDxzPlgXp2jz0XQ1Yum+5+h/zpo5HcXiyejCMegSSIwmF98RGPA4fDgdPp7P861GQiKysLSZI+ttrQ2dn5sVUJ0NsfNm7cyFVXXYXBYMBgMHDzzTezbds2DAYD77333pd+TgYY4POQVTGJ1U293HLTYi7d+h7RRXolgXfEtH7RACDHJHFynr0/BWfjEb5ZrLDLXHPxGLKGefj5tdN4dvPHV/biqsYKX4y8Cbkk9u6gt2rXId+rOpSk46XXPiYaDPD5sP/gtUM+Pn5XDleVTuPEqjWHfD7ffOhx9swRWcz0WA57HP4s3EYRp+Hwbn+CaZWacJKaLR00/fNZAAK761A0jXQ8jSXbRTKoV1kay0biqJyEmOEhtX8nrvICbj7tz9gLssAoo8ST7Hp8CaXnLkB0uFCCASz52aiKSrDRhyCJBLZXITstaKqK7LQSb20hd+pIbOWl5B8zhcnXf4tYV4CWFxaSCIRxjZ9A81vvM+KCY5BLhiBleBAdLhrfXIlnwmgE2UzpiVPpXL2RgmmDsRd40VIpBIMR0eHCWTEI04jJFO15C6WrhfSgyXSqFi4famLPvadSOuPUj52TbYuexXbi7zh3tZGVk48h76gRaKqCLc+DXHToed+XhSAe5nj/kRt9i8XymWM9/O+O9wMVBwP8W5x+/q942WXGHk6SJUs8tGgvRYvraIqlcRtFhjtMVIzLoWZLB3t7YoTTKqOcpn8rYvGTSGmHrjIAWN4dZenDm/n9A+ey+W9LDvmaA7gqinBOnEy64+OZspedWM6WNS289NZ+rvn9qfpNsdPDrY1ZvL+jnciyekI9Mdp3b0ZJ/uep6Z+U8rBOncvwjVZuuOwYThuWhXnvCrR0SjfyESV9lbvyVFxN61FDgf7yOy2V1G8iZTOCUUYwW7GarQhG3ejPnl+mxwiKEiOvcCFabLS+9R4/sBkJNocwOU0kgglsOXoUZ7Q7hn9/gPwJuRhtMqlIErPbjLM0lyGBMIlAmM4dXWzZ56cm/OWuaDkNYv97njXaSzqmUHbsIOwFXoJ1bdS9X091V5SYon5qNFhc1XhuawdXj27C7HHiGj+BCfP2kj2uHFE2YiwZhuhwgyiiJeOokRCmoeNItTXx0zdvIt3RRMd7y3AU5TDmt2eS3L8Tye1FtDmRPLkovnbyZo4j1RvEMagQ9jeTCITJGDWCWGM9L/1qEefe9x2SfW0FzrI8JLNMMhSF5lqyjjkGKcNDuquFaO0+urfrYlA6voVUJI7RZiarciii1aG3sKRT0Nf/d+Cz1hIx1GhIr0SRzRisTtRwABWYs/EB6n46neEPRUhGwdfsoz4QY+SU+cDjyE49v1qQ/vt0a1mWmTBhAu+++y6nnXZa/+PvvvsuCxYs+NjrnU4nO3bsOOix++67j/fee4+FCxdSVlb2sW0GGOCroHXzYiZ7bXzQHma8y8xVJfO50WKgaas+uZ3psVA4yMX6Xd1s7xMLckwSHYkj63NTE05S89h2brnjNLY98umpRUXHjEOQRDzzToLrD77JvXPhVbhu3QXX/u5I7u7/NGd95/+gcg4AHd/PIeeBD93jd0wKsP2V3Zz01/O4xXk6t42IYTn9b1D04fbbKrv5x1NV/UkYXxZek6F/rL/qvJEMvfOej7RiwJpX72DeFfexsO1Vntv66Y73q3tiDN7chMFmxnvUJI6buY/ssYWoqTTWIcOQPLl6QlYogBoJYhw0EmX7Wm5afDPJhr20vvEuRoeVsTdcRXLfdqQMD4IkIlqdGIHBC6aiKipmj5NYpx9BFLEU5OPbspOn7/iAH953nm5YPGoknpFlqMk06XiSnrXrKD5bNz9Uen2Eq7bR/MEOBEmkY/kGlFQayWjAmufBUVaEEg6R6mzFOn4m9XlTyW+tQ/G10TZ6AWlF49XNnRQ6zTy2uoEHzxnDnT+YyolXebm1MYvbfn5wNc9rDzzGBf44zikzSezZgtGqV+y2dYe+1M/x6+Z/dbwfEA4G+Lc5btOr3FY8lz0hXQxoiqXJNxsY5zazzhejdU0LkqCXckmC3gt2pCcUB8rGn/75S58pUrz1x3fJtn1AZ0RPSYgpKguGZ5E9OhuT04yiNXPBRWNxDB/GQsdM7niqiq76GuCTb7z/0zlQdnbZ6te4XDbjHTYV0SBjkBPkDXLTXh/kB99ux2UuJ9spk1I0Tu5di+TNB0lGSCcQzFYEqxPJ6kSVbYhhH1o8oosMJjOIIoa8MgpOs6H42lGCAeSSIfhWriIdT2Av8GIbPhr6TG7UeES/SY0EEWxOSKdId7XgLK3F9MEe5mbbaN3UzqqWEFM8Fnb2JpBFgUmjvIesOPksNgbiDHfIDM93MPayWdiHD+9fUc8WJcq/H2dOLILia+fxSx5gXzjZ7859KO55sorxr+1jwf/FqbhoAUgSXSvXkjdiMmqvD8mTixoJoUaDaIpC29qdlI2ajFE24wgE6d5eS6CmiUi7n8GXnEnS1429ohLR4UI024i+9TLm4jI0pZF0PAGqwuu/fpWTfnIMPdX1eCaMpu6xf1I4dxKgxzFq6SSJ2l3IuYUk25uxjxiNY+I0kvu2E6xvJ6tyKKgKkjOzXxhQIyHEDI8e5WmQ0RIxBJMFQVVRI/pEQIvrKytaMo4oG2l87DF23P4Ic+5aTUtNOyaDiLLmRcZ892gQJRKdnbpw8CkuzYeLwMdLCL/Qm/yb/PjHP+aCCy5g4sSJHHXUUTz00EM0Njby/e9/H4Bf/vKXtLS08MQTTyCKIqNGjTpo++zsbMxm88ceH2CAr5pzB5/M99ufY3lfC+KBBYIym8zmQJzqbZ0omp5wZDfobYlZsvSVmOTe9rNXPmae+K/8/UfPYZEEkurT/Y8tKHOxIGM8j9566CqEAY4MHxUNAEZvcEHBVPjTfuB27jzENmO3ZsGo2axcdDuBecfxWo2PE4sz2NkdJalqnHvFZP50179vsFsTTjI2w8TDV/+V8a8/DyccbFB31Kk/AeB4RkLlSC7d+ukrwU8sb8K9poUrrk0z+qrTESw26p5+mcGTZ5Furdfjl5NxYk3NGIsrqHtrA8OurUQuG0ZGKErDkq34ax4m2h1l7HXnkIrEMQ0tQEt5kS02gqvew1pSgljbQtzXizMeYdFfV3DO96fg39uEZ/QQ9jy8kEGnTEE0m5E8uWixCL3r1+AcP4F0VwuO8VMYMX0OobXLCNa14S4rQk2m8Zx6Dp0vPo3BLJMIhJDOmcnibR1815NHrPIUrnxyKw+eM4ZpRW4KnDLHfqcSZ6COfGuSxLaNnLd4FU/POI/6la/2n4/Ni/5A5lO/IdVci2ixIXlyOWlrPpp25K4LAsLhjfdfcNv/xfF+QDgY4N/GfvzNBJ77EcuueZRXG3oxiwKt8TSWYAKLJCCLQn98YjitIosCmfKRX4kA+sslP411/jj440xxmwmmVS6dN4jy+RP0+Jr8fC457XgEi40HjNN4bOEOfM1d/7WCwaFQknHaty8jr3IuAE27OxBEibsf2wDo7rUmi8zvMm1Y7DEsljTD8h1kWF2s2+dDNogcO8rND4aVIbbvQbTY0HIHY3D5IR7pL3M3FlcgGIy4x47UhQVF0VfiAVQFQ1/GsVAwCM1ghnAPGIx43V4yh5cSbunCmu0kvzNIzB/nlBwbstNCqDnAZSeWE+2O8saWjs+9WjG/0MngEwdTdPLR+r7JfdGKBhlkM6KSQnKDMb+MS1/xsuYn97K5pudTKx82B+K0/Pp1fv54LpLbS091MzmRoO6ZYdQdixFFtHSScFsvqfpqos1txHy9FJ9xCj1r11H94g6cZSvImTOb5sf/QeEFF6H42rEW5YKiUPfOdoqOHopgsVFS7qJrWx0jrr+aVGMN9oIsEj0BTC4HsZZW4r5ePJMngCRhcGYQ3LoFg82MKTsbz4xy1FAAQ24x6fZGBLMNpdcHoIsGkgRmvTIEUQRV0f9Np/p9EBAlECU9i7qngVlj8lhvk+kMJ/qjJbVUElN2NgZvAQmDFTgykZ4IHL7HwRfY5pxzzsHn83HzzTfT1tbGqFGjePPNNykpKQGgra2NxsaPVzsNMMA3jXigg6a/P8fpP7uIpV1RRHQvF3sijSQIKH0VgJIAvSkFiyRyJBPXPsoIp+kze+gPXJvLbbrJ4Z8fuwjPXU1HfN8G+HKZseCnYBkLY+FBgL5pwm+XAZVziDx6FteM/8HnbmM8f2oBc+ND4XPGZj9aOYeXuzd+aiSlP6Xyhz8v545xFYiiRLQ7ihryI9p0DyjJW4CjUk8t6NzRScHG1YRbugAYceU5tC9ZTt2yTex/7g3Kz53P/ocfY9AVl6GGAsgOK4LBSOOyvRROK0N0eiixGemqaqXyxitJtdTiGpxLIhBGMidJt3QSafeRd9I83ZNKlOh6dzGi0UjG0DJsw0eTbqsnccqP8T38f4BuwmiwWTDG/JxU4UEKlvPy7m4unlaCLAnUB2IMzTJj2/4m6uCJqFvXIE+Yh3Pvfk4ZUs69Kz88FzajiMXjREvG2TDxe/x64XbWP/so+U6ZVt/hty0fCuFL9Dj4d/hfHO8HhIMBvhCum7axbe0KyBvTP1FQNLAbRJS+yBpJ0M0Me1Mq/tSRj+75wZnDuH/h7k99jQh4TRLlNpmIonLNxWMonjcZY8kwtLTe35WqPJnpt7yP1VmH2Wqku2bDEd/3byJqOomSlikalkNzTTetmxeTO2Y27duX9b/G4skns2wsNVv1OJt0PILBbKNhfw8b9ucADsLxNPv27KS43MPF08o5w92KFgv3m/LJg8fo1QWihGDPAEFEM5gR1DRo+mvEVAzN6sRosqD0+pDc2WQOBtfEIKnmWnprW3AN08u8BLOVWFMzbWt2cZpNZtPm9s+sQrnqvJHkHTUCsydDdxC2OsEog2hAEw0HGuj6X28oGsL0B36F+0/3kXyzlvroJ0eNdSQU5IpKtFgE99B8xAwPpFM03H8v2ZNHYiwop2PhM3Rs7yL6l1conl1BsK6DzJEt2AuyGDS3jO6qJjKH1aGm0qSb9+lGlaKElJVHMpxi1zObmT1nNh/s6OLHPzqBdHsjUoaHzInj6Fy5DoNZxlJUiCCJtC1Zgaaqukmh10VmQTaGnCK0dEqvMIhFdCEHUMJhjNn5fQdthERcFwASMV0sECVEjx7PeQBNFfFOHouy/QPmDjmNX/tfQC65BKVNwlJcihoOIM0+jyUdYEodmUnEAQ7bQ+ELziauvPJKrrzyykM+99hjj33qtjfddNMhHZwHGODr4MW77ufFgqlc2vXhqmtS1cjoMz62G0TsBuhJKoTTKsFP7uL60rh8wRDdgPkzcBtFLJJITNF4tHIOjw6IBv+V2C59gdwLb+aXi25jW++nj/VXXzCKcTuy/+2fcVrWRH7Z9cFnLoAdmEtmjytFcmejpVM0PfGkHqVcNpSWl18l4oux95UN5E0qpWV1LZ6RZXhGllE4qZVQaxgl0IVkllF8bQCIViuCyUIykqTqma3knTiXpR0Rfv2LcaS7WpAyPGTPmELbkhVYvG497nHNauqeXIimqihJBUexF0e2G3nyiahmB5uLT2DyppdQSgpIB3uJdgbIGDUCMdaLYnJQb6+gp7mT85LrEfZZOG3wVIStr+vzl0QEdeb5uoh45R9Y9vsPAOi9eTzeP9WRl2hFPf1apN5WTj73VlLRINfc/GOeOOef//Z5/7x86Ev0Bbc/jGqF/7XxfkA4GOALM/bEawkFNtH0mx9y+z1r6UkqeE1Sv3CQYRSRBOErc8D/NNHAKIBFEgmmVQosRoaMyWbw/PG4556o3wDJZjbaxvLijlbeumEJwS4/+9dWEw98en/bfzMGsx3JIOFxWVi/+R2Ag0QDgJivlRZf6yG337344O9rlsLurSfzpxw7ziwrZqNeHRIMxrl0zkhmlLhY3RjAapQIJxVmlLjoiqS4f2Udp4wu4lsVLgxd+xCyChHUNJrRiiEZxpBXhiG7GjUWQZTNSFl5GPPLsI8aS7qtHutLKzk+28mjD2/kogvG8OIzVQx3mBi5YCgmlx1NUcmZNQmDJw8xw6OX9FtdaKIE0oeXSE0ygqYiqAoYzAjZZkbd+htsefew7Mmtn2gKdk5lDunWetb/9gmmP/xbtGQcpb2R/JPn9ZfqeyZPYFZFsW6C2N1O3Bdk91NLKV8whdJzTmHrn55CU1Qyygvwb9mOe+I4DAXlNDz5T3pbQpzw/K9Z/O3buO6B8zENHYcaCbLse3cy+afHkzVmMIgS/q076a5qxFGYiZJMIzusWDwZGIuGALrhpRoKoPi7EG0OtFQKyW5HMBoRJL19RMjwoKWTqIk4osWmH2BfGseBqhEtntKNRNMpZjW/DfllaDv03xvBaMQ0bAJaIsTY5/6EEj9875MBBhjgyPNo5Ry6VtzLLxwj6EroY71FEogpKhlG6VPbtr5sPo9oAJApSyy4YCyj1jqP8B4N8HXTvn0Z15RNp/fWSfx8/h+ZuWUV2yfPYpTTxNlrHsV81kMAPLrjM97oU/j98KPZMLj1E+ea87JtpDuaWHfzs0y//3q0dIp0eyMFC07SvYKAvONnkzWmHGN2PvGWRuK+IDseeY9RF89m1HUXsvb6+1HiCWy5HjqXr8Z71CSMeWXUP/08LS1hznnx/3h4wS384paTyJgxB8XXxjPfvp1Tb55PzmQ9Mrh10RsE6rpwFLpRU2ksXitGmwXnSeeRdhcibn2HCR2NJIK9KMk06T5vI9FiQwh1ce0bAd554GG2vXUXasbJCBte4Zm9EY4beRKKCu/V9TBCi/F6dSffnVjIs1dPY9/54/iHP0bmoOXsFXMY6t9P7o+WkIoGaTjLQtsj1/HEf0768gCfwn+fO9UAXymO2T+j7qq7mZ1lJZxWKbIYsUgCuWYD1aHkV9Lr+HkYbJcJplXMokBhhgmDxUDGtFmINiei2YZgtvLQmnpiSQUlrRJo/N8WDQA9vs8gkucyYzDbv5T3jAe76ekI95eURWMpREnkqTUNfPeJzTy1tpEn1zZS0xlmSW0Pe7ojSKJAU2+M+za1gyAiaB+RojQVDAYEsw2DtwDR4UawOsBg1A0brQ5seS6UeJIzjx9EpDPMifPKmHDpJDKHlaCm0pg9GQAINodebm8w6KLBgQqDf6k2QBD1nyuIYDCQP/eog2JC/xXPUA9aPILJadInD6qqOyn3+hBtejkfkkTc14sa9KGk0pg9TjIrdKdvQTbjyLcT7QpgctmxZLsQzDYEo8z+xXsRRQHJk8uyzgiSKxMMRjqWvIctx4qaShNt89G9dQ/peBJBEjG57KipNLLThmSW9f3pazHQEjG9BQFAVfQ2DUBTFD1Zoc/QEVXR98Ego/b69PMGepVGn2igRkNo6SSIov5ZGIy6J4IrB3X3Wrb9Y903PlXhcC0SBhjgvwnvzKv4zvRCZFGgwGLEIonkmg2fWnH1VfNRx/5Sq5GdP7zna9ybAb5qMn69gTubF/Odi3/DTRsfwvT+Ukp+W/2lvb//D0994nP5o71EG5v1ca1vHBUtNt37x+7Sx1lF0asAwgGMNgtGmxnPMC9KKo2mKrhKMgi3dGMvyMLqdevjqapQ/XI1iqYhZnjYFUyQMbQMLRGjadFicl1mNEWla0sNLcu3oqkqakrpXxiRnTZMLjsIIobadajxiD6/MxpIhqJIFhlLXk7feC2xfe1+ANKqhpAIY8gp5pyRXhbu6sRrlbCbDHitRnrCCZKKSk80TYbJwIqaLgaNLeHlnR3skgfR21SNJJtZ89dlWNwWtE8wN/8yOPyxfmCw/7wMVBwMcNicfM71UDiV5RtuZ/Ci2/DXNLF/8T529MY/lnV/ONyz8W88fuxP2BiIYxQ46L1PG5zJy/t6AD5mzuQ0iNj6HNyPy3cw48ZTsVROQ7Q60Qwmap0jeGZbG2+9shTRIBPpavqfFw0AEuEe1t15EW4lxCO/++Tevn+H7t3rAGhef/DjRquT7BHTGT6+AEkUiCXTPLOyjuaablo2voXZlYPBZIH/O59hXjtNvTGOHeShNL0P1DSGgkF6i4NsByWJqKng1BMfir9dQMfbb1N4zDjMo49CjejHIhhlMjNzQJJB1etrNWNfD79BRpPkD0UDJdknWIh6rKJo7LvhNmAaO4tvv1HMoJ/egW+fn7c6IgcdmygJGPJKqbz+IlLN+zDk6DGIgigh2l20vvQSOXOP1gUMUcJos5B70kn0rl2FKSePntVr0BSNQE0TttxM4r4gsq8dpasFo8XA8LPHUfvXe8iUJe4+717OvGwCstPKxBsu1j0KVJVYTy+WzAwyh5Ug2R3knH4OqApKrw815O9v09ETFCQUXzui3YXBW4DQV1mgxiIoRWOgrR7BZEbt8z/oFw0UBRS9eseQU4zi70RLxPWJkhRHU1VEs5Xgm/9k871LGXH2WHhg35fye/VJiAOTgQEG+FKZE6mAMXoG+Z1/+w2jrj6X6kNEIh4O9259iAdmXX3IFrNTSzJ4tUH3RXEbRfypg2saLX29k/MLnZyWNREu+L8vdd8G+OZjO/kPAFgvfPozXvnvM+/Mn0PlHPbMSfLHO1ce9JxsN5Ix7Wgm5WfrgrrFpnslGYwIJjPBdStxjB7b3/6X7m6n4LT5BNavRfZm07N2PSanmVBjB9bcTFKROEqvT4809FiYePZ4au99EK9J4nfn3c+55wzH4nFy9F8uIdHeTubwEtrXV2PxuskaU45oNJB3xtmosQiqv5PkhnfQknEkbwFSn+Gx3Shj8BYgVUzijV4X71Z10rN/GwDDU/W0KuU4CsZh9zdx/ugCjrr5fVb95hhm3LaMTZe4IbiLUtlGjVxMR2+cdc89R0vtcdzd9hb+64ew/g+v4D22FNFo4Iu5Bn0+RITDGu8HpgqfnwHhYIAvhatuuo5Zp/0UgEEzz+fttXM4Zs871PztH9z7z51fys/Y/+AjjJqQS+uqZlrjBzdSHhANgINEg1KrkZiiUh1KcNZoLxXfGotcpOfVI5t5IZDNX59ej78jgq9mIxZPPrFPKL3/X0M0yLh3vsXjBt2V31UyikBD1RH5WalokJaNbwEnIhkkVrXU0tv04SrBASHnhl/dR1bFJE48YRh13VHmDimgOMNMdzRFStWYobTofgiipBsqusxIDje5C07TB/CMbKQMr+5bAGiiAc1g7PdbONCaoBnMaJIRzSAjpOIIgogmyQiKvoKuiQa9XuuAB0P+YI568q+km2owXvgHZJuRhbu6AWjd1M6QdIrWN96lbulept12EeG9tdiHlJNuqyd72v+zd+bhcZV1+/+cZc6cWTOTydq0adN0L3SFQlnKjiKbAorLiwug+OIGuIH7+pPXFxEFxVfFXREXBBGUHQoUKN33pk3TJs2eSSazz1l/fzyTaUtBKyVU8Xyua65kzpw550zS5nnO97m/930MSrwOe7AbJdEApoG5ZxtVS5by8Du+xrKvvYWBte1UtVYjh6LEl85ADoQw97Yz513Hs+v+ddimzTFNURZcdTJDG9oJ1sVxS0W0qUeRevIRIpPqcUwLWfOhzVqMm8+IyUR5coPj4BTzSJqOrPtwZBm1oVnc+COKA1IwjDrSiVlu7dn3D0UB0xAFFRDxTyMDuI4tVlr0kDBQNIpk93ST7R5kySfOYaStq/Ke8UCSDq/ncewYHh4eL811H/oqMAcWzCHz7dP52Gmfe1WOu/O732fRgjo6ntl7UAvEWNEAOKBoUKMpGI5Le858xX3sHh6HyszHNPRT38H9xeWVFKfulb3MGRmg84Fn6F7ZwwlfeTuj2zuomjsDHJvQtGmiFTIzItIGojFK7VuIHXMsd138dd5y89sYXN9B9eyJ+CJhAlNnIulBnNEkSz/3Zjbe8QiFZIEZYY1TP3IyXU8JA2RJVggdvZiee/5MqKEa13Ewc0WqTj5LjMVGESefESpBTcfJpFDitcLIOBCChW/g+ZTKkzt72NGXxbGEJ1l/tJW+jEFfDobyVcxQbW65cgl7MyZ3X3sSlpTBlRV+vbPEF797N0YuTbRpJu+9aC5XrHqM3mcGmfXWYzDzBYx0HlmVYZy6Ew/X48CrHBw6XuHA41Xhti99u/L9rqfuZcaZ96LH6tn15lnEf7/1oFWBf5Ybrj+Vb9z4BN/8yWWE69eQ3pvmF8uF0ZEMB/koTAn6GDZsdudN5lf5OWp2Da3nzqP2DecI2XUwypNWMzfeJYwP86NiMuIVDfYxuGUFrnE6H/7IVwHGrWiwP6J48PIY2RF61jzEHWseQpJlbnYctHCc2lnHs+zUqSx68xySBYuJVj+SURAGi4AcqxFGh5KMo0fKN/4qruITngWOhWQbYrui4WgBoUSwDaE+kFUkq4S7f5sC4LoOriYLzwUCyK0LOfM3n+ejJ36Co6J+NqVL3L09ybFbNxCojaOFNEbWb2bX3zYy9zKdVNtzTLjwXCH9L9+My/E6ITXMp6meFsc//WiCdVsZWLubujPPwC3mkKoS5Dv3UvvmdxCdPQtt6lxcy8S1DBJnKRi7NuMaRYydGwi1ThH+DfFaCquewNy9DUmWxf6OjTMygBKvqxgiSqoPNS4m3WMxmqg+3HxWyCZNs+JrsH/RYOz9TiaF5NdxCzlhMgk4+TR2NksxlUFSZJKbOsorIj7g7zuje3h4/OsTufYxrnqREvCV8ImPHM9Ntz7H1265mEt1H4om86O/vLwyaUxhOGTYzAhrfOH2d1H9rT2HdxEeHodAMdVP06mTad04QHvO5P7eLCe0bSdQGyNUN4oxMsLuhzcxr6WR0vAQ/lbhQSDJCm6piBKvFTf1uQwzFtbjb51LoGazGOtPP1UoAWUFpW4iavMM5n0oVBnrcWzqzlMw9+7EKeawd22mduEM1PpmlEQDo089TGnbalGoKObEGJ5NoSQakMsx10pNI0q8DiczQGt8KoblsGNDH2ZeqDLTJYcFoSJZLcaWgSxH14U4vuM+lKNOhj3bAZj/S4uTlzYTilfz6Lcu5vcb+/hI5y8ZzRWwckVG27uZccP1Qm35x+WQKxyx35fHq4PnceAxbhRT/cQuupx3v+so4r5X/k9tUkDFMSzecWwjux9cTes7z0OP63z4nXP54tfPfUnzxVOWTCBtOVy6oJ758+s45jPvoOb0M0BWkCbN5n976rjypuWkBtJ0rV97kOmfhyBy7WPjuip8OIxdl5EdoXvVX7nzpu9Ru+wjnPSxu3jD73r5XleI9mArhYaj6KyaQ2dkBg/larjq8TTHfWcrR315FR95qIeH+mW6pThJvZGsXoOhV4GsYrmI9gdJFioDLSAMEsfaFyRZFBdktaxQ0HC1IMw4nu8OPs1gyeKE6gAAWixK9fHHseR/PkzipBM59msfZN0PHkPWVOzBbiRNp9Dbj1rfjNnVhlvMMbxyNdXTE/Te9xd2/rWNoz/7IVIrn0MORjE724gedxKuaYhUinwGZBk5VofZ2YZrGYxu72D7nY9R7OnByYxQ2vQc/ta5KPFa4QXhF9FQlCchmIYwQCz7J0iaLh5jRQLHxh4ZwMmmKv2bTi4t/AyyKdxCTsQ2lgsISiQuzJZkGSefx8wVic+YRO3i2Uw4/42o1bUwjj2PcPh9jx4eHofOV9JbDmus90ng2g4XzUzQ9eQmjrr6QsyCxdWXzuFLN57/ku950/FC9v3eM6Zw/bSTvKKBx2vKrCd0rn3meyyKiXEv0DSBxjedzaIvf5DA1Jks+vIH2Xn30zi2g9nZBqqP7I521IZmCju34poGfY89Q2RCmL1/vIcdf2njqE9eQX77ZiTVR/+Ms3BNAzefQZs2rxJ9LEcTWD0dAIxu382WXz5JKZXBLRXIr3mK8JyjkcMxZD0ofJ8cRxT+S0WwTJSaRiQ9hKOFGIlNRZGhOuynUFZ3PvSHb9ISVZB2ryWS62F+Q5TG7uex+jqhdwdO6xKu3NZArDbEs6u7uemDxzFFyfKx3jsxc0Uar/gIM264nvjNol0kv30zVmpk3H4Pnp/Ra4enOPAYV4IXfIvuy1qZHdnBkGHTV7RIW//cjeixzVU8+pNVLDplEsntSZKf+xmhuhD+qESm62Avgq/dcjGfu+aPnFoTxBfyseiz7xE3QP4ASlWCb+1U+fHvNhCO6Wx76P5X66N6/AvgOg6DW1YwuGUFT/8UPvkP9v/Z0/Cz8vfNx5/H9645kcaITmNYJaiyT1kgq0i2geS6QqVglfYVD+T9/ow6lig2yApf3PRrnn//p1nxxB6MVBolXosSieHaNlZ/J7VzaigMiBUAe2SAyIJjcXJpuh97gbrFMwGYeIZoE5lZNDC72rByBfofeghZkak5JYYNQq3gD4Dj4GZT+KbOpefOX/LUD57l6DNb8FfHRGGBsuFlIITV14kciSGZIoIRQPIHxOuKUikaOPk0kqohqRp2JiWkjfFa4WfgKxsnGkWhSpAVJFlGDkZxxlY4cmkco4gSqyZUNwGnmMdIDtF21y/Rq/xYhfGNaT2cXGfw1IseHv8Mzed+iR9V6bRljYPaCQ+FN02Jcc/P13LKiZNI702z8it3ogZUXNshubnjoP3HxvrZEY1lyamvxkfw8PinCb73t2TbHmJN3TKKfX0EQxGURCNuqYA12E3dwlYynf3UnX4qzsgA0UWLcUtF+ldupW7xTPzxCDXzWpEUGatoYHW3o2gqciSO+oNP4x5/HJRbDKRgRIy9xVxlrF/xf89x1Nkt6HW12Ol9bbtyJIad7CsbRSvCc6FsHI2s4OTSOC3HkC06NAZg8cQqbmxbRe2cE9AVGa1nI1bLYhwtwKzHfwlT56ImGnBGBvlRIcuWHUk+/ua5TIkFWKwN4WxciRKvI7b0jZjbX8AtFWm77ON0hHyM7kmPszni4Y33XlvioeMpDjzGHcdxaJlRzUknTER5Bf83+wbzKBIoPgV/VOPu9f2E6oPIiszAxj5mhDV85ePWaApfvvaPAExd1MDcy04WNz56CKUqwb3Fydz+m/UEIhpDnXtfxU/p8e/O4I41XP6V+3h+b4rBvIXhuAemKYBoYdgf90VFMFktJz+4WNVTuH/FXk6oDtD5yFpybdtB9Ylqf7yO+sUz6F3Ti5PL4BZySIEQxa1ryPaOoje30Pv8DrJdfeS7Bygk0xR6BnBsBz0Woea000ShIJ/B3LONF667EXtkgJUf/hIfPup93P7NJzAcl97VfSDL2NkMauMUpPJkQa0T5khSOQ5S0nTcsorANQ3RDqEowp/AsXEtA9co4hrFSkuFJCvgOMIXIRhF8vlAUUCWhRLBGPNHEMkNw6vX0fP48+x9fB3VMxtJ7hjGGecIt/3FIa/kMY5eTh4erztK6SHqVjzJJR2rX9H7tw7mCSgyiib+Lv12TR+J6dVIisye5V3UaEplX12W+Nw1Yqz/0FO3Hf7Fe3gcBs6qv7IoptPxwGoym4Svl5PPoETihJtqcB2nkjYkqRq5zWvJD+Xw1yToX7OLbPcQub5hCkNZYY541jvJrFxO9QlLcUsF7NEkpT07eOa/b8Qe2MsL13ydDx/1Pm7+xuO0ZQ06n9krjI3zefwtM3EKOTHXqEqg1jWJgoGslBcEhDJBUhSU7U/TEFJZPWDw7QfbAKiqr2NRpIg9MoChV6HkkhinXQ6WySOTLyR9zMUYtkNqMMdFE11m15Q9j2QFJdFA36/uYOsPfs+2n95H3dH1FEeKGDkDqzR+KWuS9CqM9x6HhPej8hh3vtpyOa3nzqNu/kTe+94FnFMfOiAy6R+xYrjA+tES0y46iUxPjvddMJ1QQzX5oTy/emYvbVkD04UZYY1hw6ZBV3nLtGoc2yHQ0ooUjCAHQqz1z+ST33sWPeQj1TtYcfj38ADhbzG4ZQVf/PbfeKJjhF0jJUq2W/FGsBS9UhRAEavt+xcOXEnCHatal7d/seOvnPTuhax/bA9/+vx9DPz5T/Te/wC5zetIbu5Aj+nihtxxMHZuwMwVqZ7RiBxNMLRtmEjrFNofWMvwjhFW3/Y4iuajMDxKz733I2k6u375B5758p/4xfIuPnbKDSxf1cc59SEWxXQmNYY56f9diqTpKGERNek6Nm4xh2sL40J8WiUmamwlwslncG274uaM6hMmh6EokqxgDXaLSVBZMikFQkg+nygQyApONoWs6diD3Qw88zyuUcRKDeOPhUUkFLDjz5sY3ZPGcrxgZw+P1xNvfNunWXDOtfxkwemcXRei3q/84zeVacsabEqXmH35uZTSBle9ZSZ6dZRcf44/7xmtGB9P0FWKjktUlbn87KkE3/vb8fo4Hh6HRPQzz7Phx3fx7PIufvOl+xl+/CF67n+Y3PbNdC9fj2NY+KYtwHVsjN1bcR2HqskJlEQDPS/0Ept/FD0rtpHaM8rA2nYGfvZdRnd2s/s3d4NPo+1n9/L4DXdz17p+rnnTN/jbs3s5uy7EzIjGcROjnPqty8CxUXS/UBj4fNiZkUohn7KS0C3mKwlKTiEnUpDSvfgUidmTqggkJrDz8Xtg+wqkhlb0judQMgP4n7sL17E5ZkKErOHwzqMb2HRpEbl3O/6nfsnAj27CGU2SWbWCYEOC+LR6rKLF3ue6aNuVYu1IEcV36H8LPP518QoHHuPOD7/2HRatn8vDl3yV6JRGTvvGRYf0Phn4rxMncvM913LpsmY+/s4fc+x1Z6H4FJJbeyjtF9UU98lEVZlqTWF2xI8W9jH3slMqOYZ4rwABAABJREFU/dp283ze852nkWSJYs6kf9NT4/RpPf7dGdr2PD+4ZzNretN0ZUwKtovtgotoWXACVcLrYIxykUByXVFUgLI3goTrD1M9twVNlpgzr5bOJ7aT2tnH7gfX0P7QLiRFEoO4UeSFb95LtnuQh360kk+deB1dQ3m2/fwBRvekKYwUWduTYdffNmLlivSt6WJ05bNkerKs35smVJbyDJYsXhgpUjspSusbZwnTQ1lBrkoIM0SjiKSLXGnXtlHidcLg0LHF17IZUwXLxM1nsJN9mHt34lqGUCr4fBVVgT3YjdnbRW77ZvI7tpF8dqVQFzy9gVhrEwAjbV1YRYNQYwIjZxCdGMWxHXyvRIJ0qEjlZIXDeHiSAw+PV87bJxxH/yGuMr716FpuvvtjvHvZJK676Dss+u9TcUybdOcQ+WS+sl9IkQirMj4JTmkIc9LAlHG6eg+Pf45Hn9rNW647hbMvnsWa7z9JzwudtP3xeTqf6aLhzGUi1llWWPXtv5LrTfLgT1dz7WmfoyNdYu8Dj7O3nM7wzN/a2fHndVhFg4GN/aRWryE3kOO54X3GgoMlm+eGC7Q2hJl69jQxrjs2cjSBW8yLdkIoRy1rKLViLEb1Iekh4T+k+pADIVxZZaRgsqM/y//d9N90PPZdlMYW8tVTkWQFe2QAddbxWHvbia28k8R930T9+Rfof+hh+u++i52/+jOxGc1Iqo9s9yBGJk+kuR7bsNFCGjWaQmvIN66tf4c71kveWH/IeIUDj9eE4Z1r+NBVX+aK+LvIdPbz3o8vO2gfGSotByCSEn71zF7W3fwHZr7teL52y8W8cPPDVM+eyPyvfaqSqqDLEgXbZciwMRyXUNTPcZ97K766CciRGD8y5jL/i89RKliEYzp7V3q+Bh5/n20P/ZG7VnaxbShHb9akYDk4LjgulUKBq/gqrQnAPrXBfriyQvAtH+LNd36S6ESRMCApMrbhsHooj5kzye3qYHDleuZ/4DQALvl/F3Ll2+cwbWqMH967AyNnMPXs6Sw7poFIY5iRnQPM//D5BJvqmPqmhVx9x+UokoQMHBsPcNl75nP8/1xFfPZk7JEBCnu7cEtFjI5tuCVxo49jVwwOAeRIXFyvY2NnRrCTfbilAk4ujT3Ui5MRKgIQqQlOLoMcCCFHYhjpHOndvWQ6++l9djOxGc24to2kyPQ8vRHXKFIzbxqR1in4q2PUzptMfFoNjuMiH4aR2j9CQvQ8Hs7Da3v08Dg8PnXNCYe03+83DrL+u/fQev4ivvqtt7D+juUkZk9gzqeu5vcbByv75WyXUdPGdOEtNceM12V7ePzTtD95Ly33WCj/8yvC9SEAzJzJ47tG2Hzrbylteo6RTW3M/8Bp1L/xjbztaxfw/rfMZO7UGN/6/koATvrd91mwsIHE9GryA2nmf+B0IlMmMPn02dzwf+9ChvJYr3P5u+dx8reupHbhdJx8muyeHlzLoLRnB3ZqUCwQFHKVFklUH0q8Tmw3DZzRJGZPB0p2kIxh88mzpjOpSkdTJKw9W/E//3thiFzIUXj0twy9sI69f3mE/lXb6XhwHZFJ9Zi5IrZhs/fxNTj5DIm5LQQSUQKN9Uw4firhxjCBkI+jTps8robD3lj/2uGZI3q8pjzywx8T+WgTQxvaX/L1/aOcPnXNCeQHRok015LrTWIXDeJTY3Sv2Mmex74CCMlirV9hsCSiF996dC3Hfeo8oTQIRclMO4VvXvNnZNVHtDrA5gd+/1p8TI/XAamBHCMFk4GcSW1QrfhzuJIsatOqBmZZ9eI6SPvXYfd/LsnIC85ixrdP46P1p3JhS4zE9GpOnpUgPjXGI1/7K0OGzaZ0iflVfjaOlnjbogZ+t6aP6z+5jIkf+jgfnnIhcZ/MGdOrCdWFGFy1iUznIOGmBHoiygf+ewkrfr2eZR87harWJtT6ZvylIpLPRzAcQwqEkLMpnGwKX1MrrmUIj4RSQRgnOjbYwmRRidcJpUE5ccG1TADs1CByNCE8DUJR7NEkODYDq7eLHk7bwR8L0718HRPPPJ5oNkN+MCVkk2Un6eCkBhIL54Is03TmUtTLvwiFcQp29vDwOOI0X/dZuOXcv7vPp645gWxvinBjjHxvEoDoxCgD6/ey9Q9fAIR/kV1WdPWXbG7JbeYnJ35kfC/ew+MVcNQbruFBLGrn1KAnIpyRLqHHdTZ9/142rOyhLWswv+oetmYMTp0W596tQ3x38GnCZ32By1veQo2mcMacGgKJAMNb95DtThJuSqAEg1x79RJ+/uPVnH7V8VTPmoxvYisAkiwTW9QoEqDyGcxcgcC0KeKCyuO4axSxRgZE20I4hhyvA8fmNyN1NEV8/HVbPxcfPYHQ4z9GPu5c3J1rcIt53FIBWfMx2t5LqCFOMZOmanKc5OYOGk9eSLSlETNXBFlBbWhmcPkz1C6dSN2yE6g5NsfRX52Hm8+gPrMe8n1H7hfj8argKQ48XnMKF11fMWXbnxdnLfSv62ZwyxCdT+xAVmTCTbUMbU8Srg/xq2eEsWFP0aIzb2K7Luc2hgnVCRNEORRB0nSuf2A7jmUQjGhs+dsfXoNP5/F6ob+jh0zJYiBborBfRctxwVXLiQKyIloSxr5KklAkvFiFUE5fuLXvMc74zRf5yUO7uGtdP4WRIk8M5dmULuGTYP1oCQdhCuYAN/7vcpzeXbSGfNT6VbbtzfCbJ/fQv6YTx3bxhXR2/eV5FJ/KpX/6ArVvOAff5FkYbWvF/4NgFHOgh/z2zfQ/txElWo3ZLYp2Tj4t/AwKOWHQaJmiJ7K8MgFCgeBaJsiy8AqJxITvwdj/X1kh0ixWHdKdQ6i6Rs28VoyREZRoDFXXkEMRAIJNdZUigiQrongxznhxjB4eR5bgBd/6h/v0vNDJ4JZBOh7dAUCgLs76Z7tRNJl7O1IADBk2I6ZD1nL4+NVLiHpFA49/Yd7AXBZtqWfVnRtIDxWYcu6J/OShXaxKFWkJ+XgqWWDIsPnDliFMFz5aexIAx8V1GnSVVTuG+fVjuykMpqie3YysqXQ99DyOafG5+z9H47lvQps8nVLbWtF2EIpi9XcyvOJZ9j6+Br2pGat3N65RxMmMYI8MVPyJkBXcYh4lXofS2MIPH93Jd59o59w5DSyo9eEW8xhP/RF7ZAApGMEpmyKHGuLYpoUki/l4rLUJYzhFsHU6siyjVCUAqDluMSgKcjSBcso7KW14RsRElw2Tx4PDHuu98f6Q8QoHHq85k04VA/4HLpz+d/f7+RN7uHt9P9n+HLm+YfRElDN+9klSe0Yr+8jAiOlwTHWAxPQ4R7//DSJmTtO5W1/CPXc+TlV9NW2P/mk8P5LH6xAjl2Z9Z4qS7ZA2bOFxUH4IDwN5n1kiHOBvgGMdnLggybiyWok//PL/XliRM8KBapsxvrft13z8hGv50M+v4oL3LeKCj5xIrV9l77p+8kN50rv7qGqpQ9ZUksuXIwUjKPE6tv/6YazBbszeDlLt3aR399Jw6nHIkThOsYjV30WubTuZDRsqhQDXMvE1z6hcn5tLC38EPYhbzCNrOm4xB4CTy2ClhnGLOcJNNVTPnkzdwlZqF84g0DwFLVEDiExrt1TEdWyUeG3FP8G1TEa378YqjW/x4PCznb3JhIfH4fKeUyf/3dd/9cxefr9xkOxAHiOTJ9xUw3/d9QnSezMH7fvOUyYzd0V4vC7Vw+NVpX5ihK2/+RP+E99c2bZ+9GCV3U8WnM7l6x5j+y/v5qrt93Pd+l9R61fperabPY9uIbWzj2hLI/5YmMHlzyCFxFi//ra/YPZ0kN+6gf6VW7CKJSaedRyOUcRI57AGuhlevY7sjnaURANyQCQlDSy4iHXBObjZFLe8fQG3XnwUz3WO4D72c5REA2qisXJtTiaFaxRJHNVC/eKZJOa2EJ8/l/Dco4ieeh6SqhE5ai6uZeDk0pWFO0lR6PrCR+h6ZBUbbv8LRjo3bj9nSTrcsX7cLu11h1c48Dgi2KZFMBF6ydfGfA5koCWk0T1SJLM3hWs7ZDetJ7lzpLJvU0B028QmV1E3vwk5WP6D5dP46+Y+9Koauta9MN4fx+N1ymjexHZc3DF/gzHGigJjcYb7Fw/2UxwcsG/5e0kPElVlup/ewnd+tuFlz33RzASdP7wd04XVN/2J7uf2cPM3HmewZNFVMMn2ZqlfMhstEmL7nzaSWLYM2R/A6t1N8+nzRIRiqYiZyROsjSNrOsbureIyTEOkHMTFBFwqGyQ6o0lce5/3gWsZoCgo0Wqc8mqBHIqCYyP7VOxiSUgUAV9IFwZNlgHlxAXGFAtlgyZJ9eHkM+Q79zK8tXN8V/UlCfkwHx4eHodP9fTEP9wnqsr0l2yS2wcxc0WyW7fS05WuvK6X/1YsueOmcbtOD49Xm9lvX8KnpxX48KRzXnafb/3mSrIPi/bbd/zsOoZ/dSsfmnYpfUWL9XvTpPaMMvGUo1F0jb0rdlF3+qlImo49mmTqmxYgB0KouoZjO2hRMa92MikAzFwRfyyM4zhlE2QZbJvG3heYW6Pj5NJoisyOZIELZtfhmzIbEGO/kmjAyaaQ/HrlWCDUg9rRJ6LMXorV21FRD7qmiH+UVE0YKne2ISsy/Rv7iUwIl1ddxgfpMMd6zxzx0PE8DjyOCI5pISn7bq7GXOFztltZeXUQ8UyzIxqz3n0WhZ4BCslR/tovqpZTgj52501OqA5Qv6CRCacdhxyJIUfi3C/P4ZEH7qc40k9+sOu1/ngerwNGu7aQTM2jZDskCyZ1IRVNkfDtd7PrSjKS64jiwFjBQJIr30uOvW97+asTruXre/6G69P57g/Ftv+dfDof+d47CJ12EZ+bcTFnzkoIQ7DtTwPw67Lb8hideZP//tH1SIEQUVnhjIvfibFzA4P33EmkdYroPYwEsU2L6rktBGfNxymrBWTNhxxNYI8MUBrJokarcFXETX8hJ+SEsgI+DRwbO9mHFIyI9oJSsSI3lCMxZMtEjVXjGkXUaqEykGQFNAUnnyG5cQelkSySIlNMZvDHxKRmwrKFdD3bjVmwxv336OHhcWSZ8l9vhR+t+bv7pC2HzrzJO99/psizHxjhqaRwkY+qMmnLYVFMJ3Dx7a/FJXt4vCo0/zYHv/0OLDgdgEf17ay56Zd88sNf4//2rOCSb1/Kx9/5Y+DHANz+h23wh22AmAO350w++pNPiAUxf4Djz3kzmacfIvPIY9QsnEOqrYtgXYzSSJbq2ZMJzT4a1yhidO9BUmRCi0/C3LWZkU1tuKYp4pcBq3c39O6GRedAEZZpPZirV2CHxFjvlIrYI4Mi4jFeh1vIEYwmcC0DWdMprV8uDJMVBTvZR8/T+xZBhrb0o2gKNXMaiM2YxOa7NrB9xwgl68UNyR7/jniFA48jghYJYmT2RSzZLhRfJtN9a8bg6c/cxak/uo5fnP8V5lf5WT9awijvX1vlp+nUxcihCHIkjhwI8cnvPI+iamR6X9qE0cPjH6GFq8mmivgVGcdxUSQq6oOiK7yNdVXGpzlgi9aESruC64C9nwx/TIEwlsjgExV8ZBXJKvLJPY/x0bplwC8BDnARfymaAj6K29cTOuUCcUxFQ5t7PImJrdijSSa/42Kx4g8Vc0NnNImkiFaB4t5OrFwRXzQIqo/k6o0A1C49Ftc0cYupSlSjkmgQKwgjA+WPYKOEY8JgSdVwLYNicoj8jk5c20aLhEi1d6PqGhPOfyPDK55l86+fZ/oFRzG0aS/hxiiFviEmLW1C3rvlYHOTV5HDVTR4ogMPj8PHXXwecMM/3G/IsFn+ubs5/far+fWXv1VZHBgzRdz723vg+m+O78V6eIwjZxRnctt5b+JzRYvnixbPv/fnf3f/SQGVzPq1xM48D0lWkHwa0WXnEBoZwB4ZpOWyS5A0HX/ZxBBZwcmksA0Lx7QY+POfkH0+/DHhNTT48EOYuSKNbzwTK9mL8+SdzEg0wOQ5KPFa4YmQS4t5g2PjmzoXGqfj7t6IHImRffYRRtu7MXNFVF3DyOSRfSrNb72Anvv+xpbfbWDi8RMojBTJ7B0m1FTLzAtnwb3b0AsqmUOMZ/1nOVxfIs/T6NDxWhU8jgiu7eAL6sTLcWxh9e//Uzzzz7eA49CWNSr9YYoEZ9QGAZA0XaySyjK/y06ikBqhMNI/rp/B4/VNMdVPIWPgU2Typo0sSYR8MkHJJOIWidhZtHwSqZRDMgtCXWAb+woG+0U1So6F5By8ui5ZRSSz9JKvvRRHRf3Mr/Lzlg8swcwVKb7wSPk4JSSrhJNLi3aA0SQ4jigWqD6hLhhKiv8nqg99YjPRRYvRauswBgfQYxFU3U+2rQ3JryNXJSreB5Km4xRyIqlEDyEHozj5DHJYeIkUOzuwiwZmTqwOOo4jjJNmTKLjl39g98ObKIwU2fSrNYzsSiEpMmtufwI9EXlJk9RXk8M2TPIqBx4eh80vNw8f8mTzDX+9FTs9TFvWYHe+nOjiwtsXNXCjVzTweB3gk4Sx99/jhOoAJ1QH+K+PnYQa0imuXY7r2DhlVaCTTSFHYiLdCNFaIAVC2IPdZDq6cEwL2adS1dpE1dwZ6AlhlDzG8HPPI2k6am0TkurDHdiDFAjhWqbwQtCDSJqOuWsz1gt/w8mmyK98DCtXxC4aVE1pxBfSiUyqJ9SQoOOXf2DPEztRdZW2JztJbh9GC2usvPlRZJ9KYnp8XJsBhNjzcMb6cby41xlHtHBgWRaf+9znaGlpIRAIMHXqVL7yla+IXpy/w5NPPsnixYvRdZ2pU6fygx/84IDXN2/ezMUXX8yUKVOQJIlbbrnlJY/z/e9/n5aWFnRdZ/HixTz11FOv1kfz+AcE6kRufFNArIoOGS9fhTyhOkD/H+/kJxd9vbLt5EQAAF2VmfeeJbjFXGWF9VPffpTCSD+jXVvH6/I9/kNID/aRN23CfpWerIntAoqGq5TFWnL565jCwHX2baNcMBhrY4CKQWKlqGBb2JE6PtJw+stewzuObeTa94ts8/ff+3ne/+StNF/5AWJnvRkcm9Km59j6la9hdraRfuFZ7KSIO7IGuyl2d+IW80iBEIHmKbiOTbF/iKHnV2P17ibXvptUWxdW0SB2tOhtlPUQbqkoWhMKOeRQFDkQqrQoSH5dTCi627GSvWjxOHptNdWzJqNFQthFg55nt9P33GaMnEFqzyiKppAqmPT05QjUxphxwVwaT5yPa4+f3EDiVch2Hrer+8/CG+v/s/nQVV9mZkT7h/tN0FV67vwFt17+k8q2Y2I6YVXmbGfOeF6ih8drwuXrHuP5kZdOF3jHsY185LKj+OLXz+Vtv7qGd/ztf5jwtksJH3sKAEbbOnbc/B1KbWvJt+/AHhnALeYxu9spdLTjWiZyMEKkdQqyT6WUyjKwejulvj5G2roY3roH27SItjTimMIM2cmMiHjlod5KIQFAqZuI5NdREg0o8TrU2iZ8NfUEJzaihnSMTI707j72PLaZXF8SRdcwcibFTIkhw6Y7axCojTHvPcdQv3gmI7tGkTVl3H6uhzvWe55Gh84RLRz8z//8Dz/4wQ+47bbb2Lp1K9/85jf53//9X2699daXfU9HRwdvetObOPnkk1m7di2f+cxn+OhHP8of//jHyj75fJ6pU6dy44030tDQ8JLHueuuu7jmmmv47Gc/y9q1azn55JM555xz6OzsfNU/p8fB1LzjA9imxdS4fsD2qCpToylMCqhM0MVja6bErV95sNKaoMsSPUWLk46qpb9gEZvRLPq/NB1JVrCMAunutiPxsTxeZzTNbKE1HiSiKcR1hZzpULJdCvhw/WFcnx9X8VWKBZJjiUQFx0KyD0wMcCWZdYWw2Mc2xOuKyp9nnPKy5z+7LkTDgkZqF05HT0RJrXqBwfvvpbD6cTp/eDtDG3YCMPXSc7H6O8l09rPp9j/hOjZq/ST05hbkSAy1cQqOUUQORgnPW0Ri8dEgKwSbRCJDqLGagRWrCE4UTspKVUKoDQKhsmzRECsTsiyMD/3i/xqWido4BSVey9bfPMWq2x5n1982MrRtmPxQnnBjjLq5NZhFi6imMO2oGgKJKkqpLKXhFIrf65b7T8Ab6z2mVR041ocUiZAiMUFXqdEUajSFwZLFLf/vUQrlgqIMtOcM3n35Qvqfuu0IXLWHx6vH0EebXva1kxMBGhc3MfnsY4m2TiTb1sboiicpbV5J1y9+Qc8zG7HTKSadsZhCzwD9K7ey4dY/4ZoGSjhGoKUVORxDndCCaxTRoiGq5sykbvFMXMchkKhC1lSCtXGGt+6hetE8MX5PnCZaEusm4hoiAUnyaSDLKPE60aY4msTYvRV1YitWepSOh7by11uWk9o9yuCWIXK9KVRdI9IYJms5hBSZmdPj+GMRCgMpbNNi+nkzXsOftMd4ckRnbc8++ywXXngh5557LgBTpkzhzjvvZNWqVS/7nh/84Ac0NzdXVhZmz57NqlWruOmmm7j44osBOPbYYzn22GMBuP7661/yODfffDNXXHEFV155JQC33HILDz74ILfffjvf+MY3Xq2P6PEy3JWMcxwwedkk+OW+eMWAImG7YDguiiRhOC4jpkO9X+Gquz6Oa5lce8FNtOdMFuZM3vLV8zFzBfzhGJJP44f5GZi59Ufug3m8rmhojLCwMYRV/vcoScK91yeBZJfAtipJCpLrCDUBooDgyqpQG1gGO5RGWjWDBVIWt/xndyzO8dzbL+fRS156UvzQQI7jm+sJTp+F1ScKA4pPpWpmC1o0SH4ghZNJIfk0Mp39xGdPpm7JXOzUMJKs4JsyG0nTcS0Dbdo8zF2bkTQdJRLHBiRVo/70U3EdG7mjF7kqgVPMiSKcHgLLBFlGDkXFBKWmkdz650VBopzZbCf7kAIhpl90HL4HhQFaIK4z652nIkcTNJyUY/aVPtx8hlIqgz8WYWJDTSWZYdwo/64O9xgeh4831nvMuGAWfH9l5blW7iku2A6aLMZ60wXFhev/+Alco8h1F32HEdPhzPwF9J384SN16R4erwrnJc9gTtnH6MU8lSxw+qR6fBNbMfbsINeXFF4EdXFcx8ExLKyigRYNYWTy1C6YzqQzolhFA7mYR22cIqIWVR/+o47HaFsn1IKhKFJ/J1IwQvTYpdgjg2Q6+8tqgxRSMILVsgQbmS892s7/m9iFEq/DaJiNdc8taFPnos1ZAq6D3bcHffYipr6xF390J76ASu2cGlredCxqtIqqaU3MuNhBT1SR700SmtxE1exp4NjUzGtF+u34rVVLHGZ8sjfWHzJHVHFw0kkn8eijj9LWJlaH169fz9NPP82b3vSml33Ps88+y9lnn33Atje84Q2sWrUK0zQP6byGYbB69eqDjnP22WezYsWKl3xPqVQinU5XHq47jrki/wF84sY//8N9bNetmCL1l2yk8h/Ber/ClKCPnr4csk8VCQ2BEK5tc+fTHV6LgserRsG0cV0xyXXK/xYPaIdzHaEwgIOKBmOtCPcOR5kmjyAZ5QzjseQFAMdCPeXtf/cacr1JZD1EYSCFL6RjFQ2cQg49UUWkuR59/kn4Jk2n+sSTMdN5nGKRoQ3t5PfswU724mRFBjOWiT2aFK0Mqg+1tkkYH2piJbD2pOMrjsuS6kNSFORIDNcycUtlaaXqIzBtNk4hJ6IWSyKBwU72EV60lBlvPZlZV7yZxOwJ4liOjW/KbBHRpIfQJ0xASTSgTZsnPBPG3ePg8B4erw7eWO9Rf8zMA56PLQwokkTBdiuKwqLjCol0uTAZ98l89uplr/n1eni82jz361/83dczXf1ihb+cOmYXjXKxPUygLkZ40VKUWC2x2a1IioyZK7L38XUYySGckQGcTAo3nxH+Rrk0bqmApOn4Js/CN6YsSDQw8e1vq3yPZfKZh3dx3BceIZk1sGadylr/TK6+rx3nLZ9icPYbMXesxR7Yi1I/CSedpOHCtzDj4uOY9V9nUHf0BLRJU0FWiJ5wBuF5iwAItU7Bv+BktGPfiGsUCU6fhaSM30LB4Y71XqfCoXNEFQef/vSnGR0dZdasWSiKgm3bfP3rX+cd73jHy76nr6+P+vr6A7bV19djWRZDQ0M0Njb+w/MODQ1h2/ZLHqevr+8l3/ONb3yDL3/5y4fwqTwOhWTbKpSJKlokWNlWoylkLYec7RL3yRXlwRgvfOZ7BGsC9JdsfJLNm98+FytXRAmI3klJUWh7dsNB5/LweCXIqo9SwcR0XCRFIlW0qQv5UCVEsWBMbSDJleLBWOyiVC4oPJiKcmFdHlfa70/tftGMSPJBLQ0vxh8P0/PnvyApMtWzJpPrG2Zg9XbyA6M0LZuHPTIgbvQDIYKNCYrJUWrmtaKEI1j9XRjbN6NPbEbyiWKAkmgQ6QqygmsaIl5JD1WymuVwrBLJOGauKEfiOKNJzF2bRQyjpovVCj2E5POhJBowO9uwiyWUWp26ExfjZFMiBzozIpQMuQxyRBgquoUc/lmLKykP44V82KkK3mzi1cAb6z3uW3AVY6kxUVXez9fIwXapLBIAPHPd94lPjQFiDvCRD37ltb1YD48jgBYJ0vPnv6BFQ8Ram1B8KsNb95DrTdFw3EyRbOTTUBunIA0MUkymqV04DQA7NUhm6zZCk5uQgyJBQalpFOOtY4PjCJVfJI4UDIMks0WfwU9f6OQbp07gvcc2s6ZnlGX/8xQnL2zkNu0xis4stgzmObGQQw7HeEGZzqKFjdir/opeV4uSaGDCObVIqg/fxFaobqKt9jhmao8JX6RiHvJZ/CdeiGzkxnW8l8peBa/4/d5Yf8gc0cLBXXfdxa9+9St+85vfMHfuXNatW8c111zDhAkTeM973vOy73vxL3hsReCf/cW/1HFe7hg33HAD1113XeX57Nmz6enpecl9PQ4NfzyC1ZtERiSy7W+QOGIebJq1buMgZ1wkVi2aAj6qWpuQNRVfUNzwfLqnBTO/7jW4co//BBzLZNe6du5va+VN0xM0RXxCaTAWqagFoZRFQgZFG3uTaEFQNFzVz9m1OdGasL+J4tjX8pK2nH3p6MVbHriBnoeepHbpsfhnLmTgj78m1zdMsC6G4lOpntvCyNY9uI6DPmECcrm9QK/3IakaSrwWJ5chPHUuVl8nZm8fSjSGuWcbcjiGHAhV1AauZZQvS/wflFSfaFMAJJ9WNmHKIUdiletT4nXidT0oJiWAqvpEQUH14ao+XNPEyWUw9uxADgYZWbuBqplTKuaKrn1oaRIe/954Y73H+y7/ApeXv0/vl+eesw9WdLzQnuKcmUJx0Bz0vRaX5+FxxPj2nz9B3xPPUX/yEtQJLfT84fcUkmm0SAiAWGsT/au3AxBIRFGCQbSIeLi2g1pdi+TXiTZOwR5Nkm1rwx8LU9yyGl/jJKH+kxWRxpAZgVwaORRlTmk93zj9aJRdLzArk2LWjCW842PzkUsZnG0NdGdMTh19DmnqUeRalrJowwMw43ikQAglFK0sMABCedixgdbBv2IUhHFiuKkG27BwHQdV13BKpSP0E/Z4NTmiYsxPfvKTXH/99bz97W/n6KOP5rLLLuPaa6/9u32HDQ0NB60UDAwMoKoqiUTikM5bU1ODoigveZwXr0yM4ff7iUajlYdXnTp8pvyxRLAuxnmTqw5pf9t1mXrZJYBQJ6R39yIpMpIi/hn/8o6/ke3fPV6X6/EfSLZ/NzfduY7BvEXRdjGcsjfBWPFAj+IqPtyxwVOScQNVdFjhfa0J+xcNxvTv++ng22/635c89zVv+gYTzj2LvQ88zuA9d4oI05BOqr2bqiVLsXJFJEUm3dFLauM2zIEeXKOImmgUMl/HIbu9DXuoF2QFqyiKAz3L12IN9VHaswN7VEQ0yhGRcjKWTiL5A/u8DWwbHBtkRUQz5jLIkbjwRYjXilWFMkokLuSPUDYrlbHTKWRdp3/FenY/vAlZD2Gnhul/buO4Shfh1YhjHNfL+4/BG+s9AM5tDB/SfgXbYcZ7LwQONlX08Ph35dYffOElt197wU3ULzuW7oefpu/eP6FFQri2Q7Z7kMSSxRiZPFokyPDmDlLt3WS7+iilsqKlJ1aN69gkn12JNdiNaxSRNRXbtOh9bjOFjnZGVjwltoeiYhz365WbfmX3GpAV5HgtzrpHcFc/gLNlBUq8jtZnf4RcXQ9VdYT2PC/MErs2ok6ajhyKoiQasAe7sUeTmJ1tZNauwkqP0rdyK21/Wosaq0bRVIY2tCPJ++bq48HhjvXe3/lD54gWDvL5PPKLelwVRfm7EU1Lly7l4YcfPmDbQw89xDHHHIPPd2iVaU3TWLx48UHHefjhhznhhBMO8eo9Xg1mPCRaFd5z6mQAPnLZUdxw/alcfemB0UvXffAYPnjP59j5s98BsCYleq6z3YNC/nTi2wnEX3oi6OHxSikke9jz/GNc8ePnGcxbuK6L7YIla6KAsF+bgav6abeiuKpOi5I+sFFekg+Iadzf42DSm0496Lw3/uhdfPVbb8EeGcAfDzPa3s1oezeyT6V26bE4mRGCU5qpPXYuZq6Ia9tku4coDg7jFHLI8VokTafq+BNxLRPJrxOavwQl0UDT6Uvw1U3AP3m6UBA4Dm5BFDkkTcct5nFLhcr2sQmHSFpQUKoSOJkRJFnByaQqJoljfglW726KPd1Yw0O4joOs6xT6htj2p80URorsuecR1v/oUayiUVE4jAuSmAwc1mP8ru4/Cm+s9wB45Hu/4QtfPQcA57d/Yvu3f8HHr15ywD7Xvn8Rn3ngi2z63h8AuG9v+jW/Tg+P8eCT1//fQdu+futb+dKN5+PmhZdBYTBFcvNuVF2j8Q2n4hhFIpPqqD9mJoWRAgCFgRGMdB4nn0cuqwYTJy5FUjUkVSM0cy7++kbqFs8k0DyF2DHHChWgXVYUlqOWza428queEH4IRhEl0YgcjCDpQYydG7DSo6QfuQdr2/NYvbvFXEJWwBDKgcK6FRjpHIWuveDYBJsnUhrJsvUPmyilDdZ/9x42/2I5xZG88GwoHZo3zSthzBzxlT68wf7QOaKtCueffz5f//rXaW5uZu7cuaxdu5abb76Zyy+/vLLPDTfcQHd3N7/4hTAV+eAHP8htt93Gddddx/vf/36effZZ7rjjDu68887KewzDYMuWLZXvu7u7WbduHeFwmGnTRD/Qddddx2WXXcYxxxzD0qVL+eEPf0hnZycf/OAHX8OfgIeiBbhy6dWsmPQM7w/7RBZ8VMivrv/kMgY3dXPHX9u5+Qer4AeruPkPH+bzrRO47euP8NTvt3D8m1pJHD2dv+wcoX/T8pc9z09/8hXed/lLV3s9PP4eZj7Nuj/9lnc58MMrl1AXUqnyK2KckWSQZSxflHf9ej2/u3gK5Ms3Q/sXClznwO/LhQPJdfAtOA246YBzXv/+XwNihe6kL55PzeKjeeEbv6Nh6VycbEocxjRI7+6j6ewTyezYhRYJEpw9T9z85zLi+D4NuSohbui725H0EEq8TigNHGefOkBRRMGhrDyQVB+oouUBEK0KRrHSL+k6DkokhmsaOKNJsW+5QOGaBj3PbMS1XaZeVAWOTXJzB7m8yVDGYP4VrdQdM5OBVdtxjPGbSHj86+CN9R4At3/lFm4HWHA63CiiOOcSJvmz97DnoVXc9pvNfPtHa+BHa/jW7/6bT81czzdvWcGssy9m20N//LvHHuNvv/sf3vi2T4/fh/DweIUUU/0HbfvsR34PiDjGsz/zRhLzZ7HiC7+lduE0Ch3tGJk8ik+lkByl5ZxjyHT1C2PkKRMqXgayppeNisW8wurvBFnBVxXFyWeQfVrlfJKqibG+KoHa0Iy84CzkXBI72kC/E6Jx95PC1Pj489CGOkWxQC0Xah1bRDaW05Zsw6LnmY1YBYvp/yXiHIc2ttNfsDDzJhe+5zgkWSa9u5fhbXuwjXFcKPB4zTiiioNbb72VSy65hKuvvprZs2fziU98gquuuoqvfvWrlX16e3sPyFtuaWnhgQce4IknnmDBggV89atf5bvf/W4lngmgp6eHhQsXsnDhQnp7e7nppptYuHBhJY4J4NJLL+WWW27hK1/5CgsWLGD58uU88MADTJ48+bX58B6A+EM6tO15qr8oKrHmVf9D1SlvRNZUZE2lftGBvw/XMkm193DRmS0oEvz13jaGN7czkDVw/87q1Rta4+P6OTxe/2RTBbKGRcF0MMb6cl0HV9G49r5t/Ppd83El6UA7/v1SFyrPX/zVffl/t3vzJlosKgwKFRm9qZnUtg6Kg8MMbWjHF9QZfG4toYZqJEUWhkT5DGZXG65j4+TSIk0h2YdS24Qky7iWUW5PiEH5Zh8QngaOLXwPxvohs6mKodFY0oJrmUg+sXoh+TRheCgruKUibqmIUpUg3FRD/eIZuJaJkc6JooZfRZMl1t+xnFxvEiNnwDga1ksIc8TDeXhWy68O3ljv8ffoesN1NC2bd8A2t1RkZOcA/3XiRO669mTSK753SMc6runQ2iE8PP6V6C/ZQsovK/ijfkIN1WS6+rGLBv2rt+OYFn3PbSbUkEDxqSjRagAKnbtxjCKuZYib+jHfAVmujOVjCSVKbZNQ+ZX9i1zTQMn0I+VHUbJDpA0bmoSPmNu1VcwfoFIscC1TGCqbBjiidTI6pYGG42ZiDQ9hjqaRfWolVn3dj1dQTI6SH8ph5Yowjgk1hzvWe60Kh47kellDr4iJEyfS3d0NviC+uZce6ct5XdJ2lsitLSYz/PSuLVx52dHc+rMNnFYbZMFbj+LBX63nhNOakWSZGW89GeWCjxE96WOV93/4S9dy25e+TfqZW2m65BYyve1H8NN4vB6QVR+qHkZWNYqpfs78wJXcc+lUUTAAJNvEVXxItrlPWfBSxohjOBZKfoQPtbzlJc936YJ6Zr31GHJ9SWRFJlAbJ1AXQ9U15FCUodUbSe3sI5AICxfmgIbsUzHTeUKtU0T6QT4j/AYCIXBs4VGgB4WPgeqrFBFcoyiioEYGDtg21sYwtvJQkSsizBRdoyiel1c7nFwGc0CYyfknTxcTGtOk7Wf30v1CL6d++72s/+491C9o4sQ77qUvk6epqYm9e/e+Kr+j2tpahoaGCNQ0ccIXf3tYx9p4x+cZWPcEAF1dXUycOPFVuEKPfye8sf61Y+3RA9z6y0184MLp/PDeHUwKqLz5klk8ck8by85sYfav7yF6wocOet8vfvpV3v2+z5NZ/i0iyz5+BK7cw+PQuGHrk/SXDl55P6M2yJIrlpDpHESLBgg31aInoviCOrKu0/3EarK9o4TqIgQbqlF1P7KmYuUKxGa1CAVANosarULSQzjZFPlu0eroq6lHnSD2katqcAtZoTzIp8VY7w+IIoGsCKNkxyEz4zSq9q7Crp2Ku+Wp/VQHzj5lw2A3ufbdSIpM5Oj5IMs4mRTbfnofO5/u4owvn8/Oe14gNrUaPRHl+Ft/R28696qN9+9617v4zW9+A8AJX7yLQM2EV3ys7mf+zLbfCr+pO+644wA1nMeBHNFWBQ+Pv0fDl/+Pwi++QnJzB4tiOv/3i41cdcks6o+ZjuJTmRTYTOOSGcQXzuPZz/yECY+v4aT3XQvA0z/9Kbd96dsARE/8yJH8GB6vIxzLxMiO8JWbb+CaJQ24sooNSLaBZFu4qga2daC/ARxYLNjPXBFJFl4JL8Nd6/qR193Ptx//GsMrniXT1Y+qa2iJGuRQhPqzzyI+fZswSlJkZJ9K+LjThVogl8bo2IYSjuAUcyK95IAigiMmA46Nk0mJzzeaRNJDQqVQNlqSdOHsjGXu8yRQffvMlaoSYgUCwHFQ4rWoDc3YyV7cUkHIGgE9HkSSJTr/8iSldIlUx6AwIBxHlMOOY3yVLsTDw+Mf8pFjPs1F917JD+/dwfvPm0bTSbMBmPC3XdTNn4z72M/I3/dJegKTOPvLjyLJ0P7kvbz7fZ8H8IoGHv/yVGvKSxYOHh3M8+iNT3DLg59jeOVqMp39KLqGPxYBWab5LW8k17ZdeAPZDv5YmND8Jbj5DFayl0LPAIG6OMbICL5QESOdI1AXE4pCWcHJjKA2z8TNpSFaA8UszmgSO9mHlR7F3zpHjOmOjaRqRId3YOzcADs3lNOUfEg+bb8UBRnfhBZiE1pwMqly5KONpPoI1UcI+lV6nt5EpjeDP6pR1do0rj4Csnx44/3hRjf/J3FEWxU8PP4e0RM+RP0P+rFyRZZdfSJFx2Xm5WJl1jYtNqVLPPytx5BCUWItMYojeR4+TyebKhzhK/d4vTLr7Ivpf+o2rlnSgC2p2C4Ytosta7iKWokldGVFJC28qEhwQGvCWDKDL/B3z+kAP7pQSLqbTj+W6DFL6H96NfbIIFbvbrLdQwQnNqLX15DrHmTwvrtJPvI3ksuXM7ShHbkqgayHRNGgvGogZIdlaWMxj5vPHBCt5BrCfNQtFcltXodbLCdEjH2+Yl4UIFQfTtlAcaxA4FomdrIXbFuoEwIhUWDQNRZedTKR5jpmvGUhgXgASfVq1x4eHoKnf/pTrtp+PwCz3nkqjmkRnTGVRwfz/PKbjyNHYqz9wIep8svseupe2p+89whfsYfHP8foS0SN7883L7wRM1egadl8qhYsYO/jaygNDZPdupVs9yCBRBR/LMzItj0MPfIQQ089TWpLO6n2buRwDF8kjF0soeqaSF0Ix4Q3gWVS2riC0qZnMdY8RmnbKuxkH6g+1Ooa8ls3MPzcSozuPRhd7Vg71qDE60QroqqJ+GTTEHfosixaE21b+B+VCkiyIhSJjoNjWCy48njCTQlmXbwQgPDkCePamujx2uHN2jz+5Zl46530Xf8+vvDVc7jlkpvozJs4wJSgj0TQhzJ1AdMuPB4jkydw8e1H+nI9XqdUT1vEhy6aS8lyKbgSiuzgkyVUWdo3HpYLA9L+aQEvVhS8+Lks872dd/GhaQfLoD9w4XR+e3879X4FI5PDyedxRpMEGxOotU3YmREANt/xV5pOmEHHw1tY9Mm3Yg0P4Z88nfyOrYBIPFAntIg0BFUTg7/jYCV7RcSSrGANdle2u5Yhtvt1XNtBDkZxy6sJIEwXQfRIyoEQTi4tUhbKcY7WQPe+SYtpIPl8NL3hFFxL7C+pGvGFObjvqcP9tbwskvRqKA68VQgPj9eS0Dn/j9Q91/Lpt3wb0wX4K3GfzLzaIErLPE7snQwnf/hIX6aHxyvia7OWce/oGu7tSB2w/YpzWrnjr+20hnw4hkUxmSYYSqEnogQmTsIeTZLp7GfTz56g9qhG9jyxi+NvOJ9SKkugsR61o0vEMddPwqeHcEYGkEJRoRr0aSIBKRSBQAh7NFlRHNqpYWzTwl9XR74viX/y9Mo8wB5NosRrkXwavqZWQCwmSD5NHDOXJt++A19QR62uRY7EkCMxpr7vnRVVoqT6qLsghDOaRPb7gdy4/FwVSTo8xYE31h8ynuLA41+e6hM/hBYNYuYKTAoIgzWArOWwPVXkoZPeTimVJXjFVwAY+dTUI3m5Hq9TJFnmxh+/QGe6hOG46IooGvhkocCTbGtsxwMeY/4HwkjRV/l+zAPBVTTsyEtHia5f3kXacnh8MM/A+i6y3UPs+PUDgOgvdPMZJEVm4kmz2P7HNSSmVzP0wnr8rSLONHT0YjE5SPZh9XVW+hrtZJ+4idf0ilHSmIGSrAfxNbaQ2bCBYmcH4TlHCy8EWSmbLpVVCaaB5A+Ir3oI17ErLs8AxnBqnwrBFIaKQNlEsawKGuexWpGlw3p4UwkPj9ee2JfWMzPirzy3XVjVn+PPx7ztCF6Vh8erw4uLBgBPLBfGsE8lC3Q/v5tCcpRtP38APVGFPZoU6QoBjSlnHUXHo+1UTa5iYPV2gpMn4zo2sUWLQfVhjwxidrUhBUJYvbuFqsCxhUrANEFWkIPCcBlZwdc4iWIyTb5zL7EZzZT27KDUsR253HIoBUSrouQPYCf7ROvD2MKIZYr3DqYOmBsgy7jFnFhMyGew+zrFgsQ4myMe1ljvDfaHjFc48Pi3YNr9YOaKnPqRkwko4n+4IsHSYxoJ1QVJtXdTVTZNin9z15G8VI/XKaXRIe798tkkAj76MkL1AqJVQcERrQovKha4koTkuriShCsrwjTRsQ5sV5BVkFW+t/Oug8556vuOAeAdxzfh2i69K9tQNJlc9yDZrj6QFarmzESLBln80bOpam3CSOfFoK3pONkUVn8XUiCEHIqIYkOpKNQDmk6xu1O0LJQTGNREo4hklGWqliwlOGs+UjBS7m0UkwEss9KeMJb/bPV34hZzOKNJ3EIOyaehT2gqf0RHSCVNE2NwoDK5cPLpcU9VOOzCgTeZ8PA4Ilz+k/dXvrddl+MnVxGO61zxmY8ewavy8Dh8frLg9IO2nX/pXPF1YpRAXKf3+V1IssxoezfD2/agRYIkjp6O7FNZ9N+nUjd/Eo7t4Boiycjs6aDQtRfXKCIHo5WCgWsaSKpGoWuvuOE3RYuiXJVADkWQ9BBVc2YSnNiIFIygz15EYMEJSLIifIoKOeTaSSKBobYJa287UuN0ka7g2ISbaohMnyrMlHPCbNHJZ8huWo812I2k6ZgDPVjJXuH/NE4c7ljvKQ4OHa9w4PFvw5QrLsexHd64oJ5FMR1NlnhyZQ8Tjp1EqCFxpC/P43WOJCvYjoumSDRXaZQsl7zpoCkSOJZQHIy1KpQr65JjC6+D/ZFVULQDPQ8c6yVbFf7n5qcB+PVz3aT3plF1H76QjhrSkWUZ3+RZyFUJAo31DKzdQduf1pIfSGOPJnGKOZAVJD0Ijk2pY7u4+S+vFpR2bMRXFUXSQ2TWrsIc6q+4JSMrorjg10Uco2MfYIwkl1chQPghaFNmI0cTOMU8TjHP8OZ2Bp9fs+9n59eRfD6sXFFEOToOsh76u1GUHh4e/7ksfmo6H7hwOhN0lZzt8szuFC1ntHLH//vukb40D4/D4rbdKw7adstP1gFw3940A5uHUAMKgXgAX1DEJfsmtqLWTyLSXE//6u1s+8MGRjuGRAG/kEPy6+i11Uiqj0LnbqH2kxUkn0Z2y0Z8IR2nmCO1YROF7h7cQg4nk6LUvoViTze+qXNRqhKiDUHVygrCKFIghN23WxQhZp2Eb97J5GKTRSuE6mOkrYueR5+t+CA56SQ4NkZa+CC5uTRKOIKdzR7cpunxb4nnceDxb0Pwvb9l8wlZpr5xLlPOsEhu7WbOVZfQv/xZlnWfCdx3pC/R43WKqoe4+D1vwqdI+GQJx3Up2sLjoGPUYGpUFTfB9oE3wq7iO8DvwFX9+6Ia9zNKlFyHm++5luve/O2Dzj1BV+kpWvxhyxCXagoTTwhTPWuyGOT9Okq8DqUqwaREAzXztpLe3cvwxh3ADsJNtQRnzwPLxFc3AdcoYqdTKLFq1OpapEAIs7ON8IwZ4mT7FTkkTcfJpZFDUeyh3koSA3rZ50APiT5Hy6zkPfsmtuJkUvhCASJTJmAND4rzloTZYmBCHXayDzmaEMcbz4nEYfY8AnjNCh4eR4b2J+/leCbBrEk839LNnGveS+RjDx/py/LwOGxuv/rbvPM7H6U9Zx6wXUaYId/bkeICx2X6uTOJzZiEFhWFeiVehxyKMvXSBPEZGzDSOXqe3oAvpBNqSBCZNw+3kMNfU42dzSLJMpIio+gaaqwaHJvorGngODjZFJKmo02aihyOIckKSrxOGCcXc0iN04HdDE45GRdoGN6M074SV5YJDvcgTZyGuXsrWiREuKkWs7cLbVKrMEr0B4g01+Gkk6j1zajxOuRIUixCjBNjyoFXiheqcOh4hQOPfyvmrggDDj3vTeCPReh56Ekmfv5m9pzxmSN9aR6vY6xijmdXd3PDGdPIGjYRv0JUlQmoEkGfTMkBw9WI+HUkI487dj/sOkJxUFYhVIoIzoGSPVfR8M87CTi4cNBT3LfvXev6YV0/fH8l86v8XH73Z3FLRVE8aDmaUCSOFt9Kams7fat2Em6qxervRAnHKPXuxVcVRdZ8wqBQ08Fx8M8+RpgbjuU0ywqSomCPJoXKYjSJ5A9UTBOdpCgmIMuV9AUnmxKTjrJ8MX7ccfQ9+AjVsyZXkhXcguh5lBMJUXTQdCR5/AoHEqB6cYweHv/2HNfRBF7RwON1wuYHfk97ziSkSOTsff16+y87/HnPKHx/JbCS2RGNq/7wKVF0r0rgmzKLmqoEZlcbQxt20vHQNua+a6loFXRs0js6CdTFkAElGsMfF+0C2qSp2IPdKLVNKM0zhJIwGKGr/hiaszvBsXBHh0CWcfZuw8mmqCk9jOTTsDIjyJE4zmhSjPXldoequTOEr1IsLOYRfh23VMAxLfwTWyupTpJfH9eFAkWSDmu89+IYDx2vcODxb8mEnwk3eT02mdB/332Er8bjP4GBne3YzjL8qozluIR9MhIQkGxwHXTHIGeFQA7guOXXrSLYVqV1oaIykMsKBUWrmCRasYl8d2gFm9//PlY/vodVqeJB1yADDbpKwXY4anYNTi6NNnkmIFQLUnUD/lgNtc0zqGrdQLZ7EH9dHXY2hb9xYuU4+0ySIhWjQtcoCn8DQFJ9yOFYxZkZRKuGU8ihJBrEQRxHpDT4AyjxOgannU6Nm4Xn78V1bBJzW7CKBiqIyUaiAbeQA9tGkhURATmOZkkeHh4eHh7/ivxkwekUfvNeNn/5Rn70uy3Y7oGFg7hPplpTGCzZzGuNYw10o88/SbzoOMjxWvzxWhoapxBuqhXeRo4Nlkl0erMwM0aM5a5loE8/GikUEWlJmg6yLBYGaqbQJGVwJRk3lIDRIaTaKci5YSSfJgyPAxHsOacj7VzBptZzOeOKW5hz2jIeW7AXJV5HrLUJORASHgj9nShVCfyTp1cWJ8ZiH73WxNcHXuHA49+aYqqfYqr/SF+Gx38AZn6U9pECixrDmLaL5bi4soQiq0iuAxiE7HLUkOuApAlpvyTjKmU/A8kn/BAcu9zaYFT2V/rb+fC8K/7uNVT5ZD5666XIiowky3Q//DT2fY8x8bRFICso0WrkSFw4Ije1QvcgmfbdhJtqUaoSlRt917HLqwDlVARNKfc27lMduPkM+LSyM3IaVJELLRIZTJHCoCjIkRhOLk39yFac0SEkf6C8LUOpbzeO2UVwylScjEhZkCMx4b8AMM6Fg8OPY3yVLsTDw8PDw6PM1Rse50NzHnvZ1w3H5Yr/vQjXdlB0jd5nN+M+s5EJJx4tohqbJyKHIiiJRkIzZpJ/+jlSW9qJNNejNTUI3wNVq5gnqg3NuLYtvAjK5siuZSAPtItFgLLRsRyJQbJL+BY7Nk4+A1UN7EmbtDRMZ97e57j1xit5Z6gTJztRmCpX15DZsQu1t5/QrDm45fZFRfXh5DOi7TGZFEbJ48Thtyp4g/2h4hUOPDw8PA6BbP9u3n3Db7n6v9/I5YubMJ3yTa8sUbAgqIVRJITKwDLAMvYpDeClq+1l6Z5k5Nl2480Hvfyu45v49XPdzI5onPveRTimxWh7N6VUlolnHo+syDSedRzmaBoznSfo2DjFPHb7ZlzHIZCIUkimMXMF6G5HDkZwjCJqolGkKeRsnEwKJV4r5ImZFK5VLmbIMjg29siAaDfQdCS/jtrQjDXQXfE1sAa6xXt2b8W1DNTGFuxkbyWyyRfU6fjd/TQcPxdJkdH1IK7jjGubwhiex4GHh4eHx78SG/92C99umHfAtnMbw9zfm2VSQOXCt8zEF/IzurObTPcwrW85GStfZOKpC8n1DVNMjqLoGv46MHu7KKWyoi0xV24dLBfpXbmIWtuEW8hh9nSUExciyKEokqLgZPNlRYLwWpADIZxMSnw/bTF2VSNa72bMYJxmvw9p0zqsvk7e3mDiZBWhKMilKfb0UEpl8E9ppOtPfyU+YxJmrkj8mJBQKao+UZAYx+FUlg9vvPc6FQ4dr3Dg4eHhcQgEaycRiNfzvkUiZjBrOoR9Mg6gyRKKI264XUlGklXRL7hfHKP04hCb8j64DrJZID9UOOicx3zuv/jjBd/khJMmsfeZ3dTOrcEuGkw6YzHICvVL5iDrIcyuPpSARvfydUiyTKgxgaJrGOkciuYj09mPHssTbLRBVrAGu8vZzmXFQDYFo0mUhmZkVROrD/kMIKSOkl+n2NlBYOpMUTTIppDDMVzLQInEhLJClsWxezuEG7Meomre0SArtLxjEq5pIsmyUB2EoiLmUQsAqXH5fUkSKIdZnPAWITw8PDw8Xk3qQgffep35fx/k/gtu4qSjahnaliQ+NUZuIMPkMxdgZrLULZ6JrOtYuQK+UIC+5zZjZNeihTX8sTBmrogvpJPtHqRqWhNaJIhjWtjZ7cg+FZJ9KPE6HNNAaZgMji1UiOUFAElRytGNPorb16PLCvIMjeLGZ/Ev9INtYM06GZWnkOpbsHetRw5FUeJ1hGbORZ8wghKJM6mpWaQmldUQciCEWTudX7flkMbT40CWD2u89xQHh45XOPDw8PA4BArJbqafcCIOLpr0Erm/cvnPqSTjIjwHXlJlsP82SRb7qzp71x3ccrPtjnv46EeX0vNCJ2pAJT5jEkY6RymVxegcINRYjRLzIWvi3HWLZ1JMpolOm4xa34zZ3Q6OjZkrMLJ1D6GWZozkEJnOdlTdT9XMFlEAKOaE+WEuI649FAHLRAJcWRE9ks0tYpIhK8jhGHIkRnrVSkKTTZR4nTBVVMG1wCnm9ikKHBvXFC0KbjEnWiI0HSebwjUP9nHw8PDw8PB4vRJcc+9B2zbedjfXffAYOpfvRtEUos01lFJZ0h292KZFuKkWLVJE9ol2wtoF0zEyeQK1Mfx1dYxu3YkWDQIwtKGdhiWzsQ2LTFc/ik8lNqNZ+AwVc1h7dyKpPpREo2hZcGycsvmhW8jhnzwd17FxNzyOEq/Fijdj3PNd1Jo1yBOmwEgv8vRjkKwS7tBe3FIBbfHZOLs3otQ24RpF3EIOORDCHk2iFHK8W7P5gndz/rrAKxx4eHh4HAKu4+A6LobtoisyWcPGdmSCPnAlSSgNHEt4GJQND5Fk3HIBwS0PmhLyvuLBfl+HDfugc1bPbGS0Y4C924dZeuUScr1Jwk21yD4VX0jkO0v+AHptNU6xiJkvouoaclUCp5jDN2k6yArpxx4h2ChWF2RFJtbaRCmVFROGdHJfDKNlIGk6dl8n+DQR0wRIerDipgyiMODm0kSPWYI9MoAcrxXvL/dK4tjI5X3dQk6cZzRZ8TgYfPQR/LHIv4HHgTfR8fDw8PB49Rh6/PGDtsWmVpPrzzA0kGfplUvI9w0Tmz4JM1/AH48gKTJqdS3+fBEjncfMFXFtGy0WxbUMqma2gKyQXLuZQG2M0kgWSZGJTKqnlMpiF0vIyT7RVmgJc2Szu114Fdk2+DRKW1ehzz8Jc/dW1IZmYdZomSh7NxBccobwJnIc0HTk4igrpFaObwrhC8cgN4wcr8XNZzB7OlCqEhi7t9L/6JPoiSpCDdXYxfFbKJAPM37ZUxwcOl7hwMPDw+MQ2Xj/78l94DgCqowkSSjlvjoZMB3QKCsNoOwRcKDiQHrxjbIki3YFSX7JFIVv3PgEX/z6uTxx1xbmdAyQ3J6kdk6OlvOWYioyRjqPkuwVN/hqjmIyTTGVITiaFIkJxTxOLk2osbo80XBQdD9KogFJ6UZSNRG3qOkoVQkR9xSMgE8DU0ww3HIxAZ8mLkr1IckKUlWirB5oxhlNIukh7GQfkl8XioLMCE4uw9Dzq1F1P8Pb9tCwZDbBFp34vNnIocir/wva/0fL4U0kxDE8PDw8PDxePbb9ce1B227+wSpuuP5UnvjTdmZs7WZwc5JMT5bW8xdTSmVxDAtzqB8tEsQX1BnZ0UVpJEuwIY0vpOMCRiaPqvtRdQ0loIkFhkgYI5NH9qlYQ30oYTHuusUcRjqH5jjY+TySLOOrmyBUgQHhTeAWc2Ihob8LOzOCW8yjLzkbu7cDyShyXG4PrixjOw6l9s2k2rsBGNrUxZQ3LMJfk6Bm3jTUplas/k5kdfxuOQ/bHNEzOThkvMKBh4eHxyHiOg5X/3wVv/vv4wlrMq4LluOK/GDXBUXFLXsXSI4l3iTtUxiMeR7svw3A0QIsiumseVHx4Oy6EK7j8N4zpmAbNg0L6qk5uhUpGCHYMpfS9nXY2Sx2Nsva2x9h6hvnomg+SkNJVD2DEqtGmzIbc6gfSZExMnlKqSxK9yDhSQ3CcTlajRQIIVclcI2iUB84tpAtlo0Sx5yR3bLpI+X0Bde2kRQFVE30TI5FNVomqD7ckUFqjl2ApPqIL5yH5A8gBSPCP0GWkZTxHYK8VAUPDw8Pj38lTvj6e/j9BTcdsG1SQIyF7zi2kUxPlurpcSadMgctEiQ0YybZrVsxc0XMXJFtv3uB5mVT0aJB7KJBMSmKB5Gj5yPt2EoxmcYqGljFEoo2jJ6IYmTyQuUHSJqOMTKCvyaBpIeEt4FPI797F0FFwS0VKe7eid7cgqSHsPo78TXPQJIVzG2rkEJRnGQfar1YNECW0ecdT/10oUioPxuQZZREI3ZNC3JhFGXyHCTft8btZ3q4hQNvrD90xt/W2sPDw+N1giTLZEYKGOU4xrFkhQOEBGNtCvs9l1y3ojZwXzxClVsc3nvfVw4630MDOR759hNEJoRpPG4GalBnw8+epvuhZ8isWoHrOGiTpgJw/E0fJZCowkiLqEPbtDAGB8i+8CSK7sfMFcl09hOfP5fo7FlIqg8jlcYxirhGUbgp2yKmSQ5GAbD6u3CNojBI1HQkPSQ+QzEnPArymYoLs1vM44wMCG8Ev8hvVusnVVyb7dSgKDg4NnIkJlQMHh4eHh4e/0FUfWENp9YED9jWVbD40c1PEZtcxfQLFhBpjLD+x8/Qt3IrqbXrsYoGgUkT0aJBlnzuUvyxMAClVAbHNMl2DzL01NOUUlnMfIFMVz+x1iaqWptwbYdiMo1jmKJtMJ9BS9SIlKV0EtcWqoNg63QkTUed0EJw1nzkaAK3mEOtb8Yt5ETEYkOzUBxqumhLVH0gKziZFPbIoPA3cGzkUBQnn0YZ7cbt3YFsFsa9NdHjtcFTHHh4eHgcIq7jsHfDKvLmMnyygl+VcFxQZTEmunCgc7DrCBXCWPHAsfcVFfZXIshqZRX/xTQ1hele2ctPfrOZS05u5oRvXE6pdy+Da3egRYNovcPUnPUGjF2bK5nPgeYpOPkMxsgIue5B4gvmEp0Zw0ivo7C3C72uFknTCS9aWnFVxrGF6sAyoZx+oE2dWzFEFNGMgyJWCSqTCtcysQe7UWubcAo50aIwmEepSuA6Nvndu3AMq5zysIHQ3IUYuzaLVgrbGqffFCCBcpjLCF4co4eHh4fHq01EP/j268SmCINbhrj9D9t4x7GNLLv5ckr9vex5cDV6PEgxOUrjeedgdrZhmxaSIhOb04qdzZLrHSbXl2TSm9+EWyrQ89CT5PqG8cfC6IkqwnOOxuzpEPGMRhF7ZBAiMXwtc5EDISibILsFsfAg+XVhnBwI4TZMw92xSpgeG0Xs0aQ4TjGHk8tU2g5T6zeJxYxIEEmRCbVOY/jRvxGePAG5rxMxQxofZEk6rPFe9sb6Q8ZTHHh4eHj8ExiZYQqWjVL+6ylLULLcitTNhQOTE/a7OXbHTAj3p1xIUGon0Bo6uHjw+42DlNIlcrbLz5/Ywx3vvAUtHmfyOy5BUmTixyzE3NuOf84S1JBOMZmm7/FnSK7fRnp3L0Y6T7Gnh9Et2/GFAhjpPK5RxBzqx9i5oXIe0ZpgitWEQAg5ICSM7HfNal0TSpWQN0qKUr58BSXRUDZOsoUBoh7ESvaCaaA3NBBsnog+oQlfSMceGUCtn1TJjh4vJPbJF1/pw5Mvenh4eHi82pzwuXMP2nZvR4qu3iwAd77Qy+3v/C6+SJiZ778ERddoOHMZZmcb/rlLCFRXke8dZu/DzzOwdgfZ7kFKqSwjL7xA//JnsQ0Lu1hCq45RTI6S37FVFP0dB6W2Cf9Rx6M2TkEORStKQAAlXodSlUBONCL5NJxJ87BDCZixBDkYwR5NImu6MDs2ikg+H+bedkaefx5fSCd29CwCdXH8sTBOPkOkZRIArmXiWuO3UKBIhzfWH2Zy838U3o/Kw8PD45/AKua4/p7NGLaL7bjYjlAcgLhZlUAUDpz9khMkeZ+/AeV2hf1bGiQZO1LPR+75zEue8/7yZAJgU7rEdZfcxqdO+TRGOk+ho51iXx9OLk3kpLNp+cCV1J+8hMT8WcRam0h1DDK0oR0ALRpE1TUKAyPYhoVS2yRaE0LR8mUowpugVMQp5JBDUTE5KO9T2PQC1mA3SrwOe2SgIl+UZAVMA8mv4xRz2Olhuv72LHY2haT6RGuC6iPbPVSRNaq1TfsiG8cJr3Dg4eHh4fGvxrR7w1w0M3HQ9udH9vkctWUNPv72/+OaN3wNM5On2NlBYSCFMzJI1QnLaP3g5Uw69xRq5rVSu3A6xZEiI9v2oOoaiaNakH0+UlvE2O9vaEBNNApjZFkRxsdFsYggyQrWjJMBSE86hu3hOWQeu0e0Jgy2I294GNpWIvk01EQjkj8gkpsKOZxMirY/PE102mTCs2cjh6LI0YQY6wErPSrOJ8uMq+JAPrzx3ktVOHS8VgUPDw+Pf5IVv/wVxbfNx3HBdR3CmgK4FICAXDY/lGWwnZc0R5Qc+0BVAoAkI88/g49f/Ve+9f2VL3vuuE9mxHSYGdHoeqoDu2jgmBbDW/cw4YylqIlGoRKQFbSmycz+4GSQFdTGKZWUBdcogiP8DNxiHiefEc/1EJIjbuYl1YdTyIFp4BZz2PkM+uxF5ZaFAeRoAiebQonEcTIjQkFgmaj1zRgd26hqbaLzgWcINdVQd+rJuI5DdNY00lu3EWqo5pGrfkAplRmn35CHh4eHh8e/JpnedgbvvJcJJ5yK7br0lw6MY5aBWr9Cf8lmStBH17PdOLaYM6Tau2k6c2mlRcBXFUXSdGZfeT6SqgmTYscmmBfjq5NLC5VAMYdrFJGrEsJjSLZFhHIxh9r2FE4hR6T9acKOjXTc6aIV0bGR/IGKCaLZ3Y6RSqPqGtrk6WTWr6XmqEns+O0jxGdOoO7E43CLOYJ1MZLrtxGsi3PPJ/5AfXUAxzw4ctrj3w+vcODh4eHxTyLJCr/b2Mu75jfiAIbtYthQ5S9XrR0LV9GE8WE5mUCYINr7H2Rf8WC/76d85ivc+gUNZ/vzrP3K//GzR3dX3nLRzAT3bE+iyxITdJXFHz2bbPcQd//vY9T7FTb/biOzLphJ84VniUlFeXXfLeaw+joZ3raHxNypDG3YSe3xC8lt2kygLoZa14QcjlXaEiSfJnwOCjkco4gaiYvtioJTViBgmUhlXwY5XidWMBwbq3c3clD0OFbPbcFI54Q6oVREDoSQZRk5EOLUb1yM+pGbwByflgUJRNrFYR3DW4Xw8PDw8Hj1ed/lX4BZyzjpfe/jb4t6ufkNn2V3XoyH5zSGK0rDGk1h8YdPJ9s9yJ3ffYYaTYHfbGD+0ibmvv88saLvOFipYYa37iE2YxL9K7cSbqqhkEwTakhg5gvEWptQgkG0sgJQUn24ji1SkADJHxALAI4t5gKqD0nVRCyjHkQKhECWiZxwJlI4RnHVIwQnNmJk8kxYOpvC8ChOPkNpKIkWi1Y+57lfPo+Rti7cO7aO289SlaXDGu89xcGh4xUOPDw8PP5JbKPII+t6eM+iCZVtqgx+RRKDrqJhOKDJKpamoRpiAuDKikhZ4MA4RlwHyjGOrhZEHmjnY6fccNB5796e5OpL56CFNOqXzGbH3c9SPaOB869eSt0Jx6DUNjH85GMU93bir0nQ9eAKJEXGF9RJdw5RSovopsikevK7OymmMgSb6vZdRlHEKbmqT5gdAr6J07CTvWKHso/BWJEA00CKaDgjA0iBENZgN77Js3CyKWLzYriOg+QrKxcASQ8RPWYJT37gJkrp0viuQEheHKOHh4eHx782sz52GR990bb7e7NccU4rWsjHhKWz2fjTp2g6vplLrlhM9ezJhGbOpfOPfyHX0Yk/Hmbnvc8hyTLhxio23vEEju0QTxfRQhpmvoBdMLANi9DxJ1aSD8YKDq5lIicm4ATjSHs2IAUjYJk4o0kkVRPti5aJBOjHnIm5Yy32yCBOsYg2qZXolBxyJEaoVSyqaNUxJFWj7tRlPHblzQA0LKjHHcdUhcONYzzMqcJ/FF7hwMPDw+MV8Pxvfkn3JfOYFPUjSWC7ULBdFEkFRwxEriSjGvkDigSS64LzEiZB5W2SbfDE2z550MvXXL6AP965iY2PdDD16DpK6bXMuOREdvxpBeHGKlzHZnTFcgoDIyQ3dxCfMQktEqTh9BNx0knU0G7yfcPIPhUzXyA6vRl/LIyRSuMzDeSyp4FUVimY5XYGp5DDGhb9imq0SrQzaLpQG2i6WJUIRcGxRWxTMYccjpHftIotv1yOY7ss/Nj5KOEYdjZFbudOTvzyJWK1452fHZ9fjoeHh4eHx784me+cxcdOeeyAbR9+51we+vMO1j7bzexZCezlm1j80Tew8SePEUgEaTxxPv2PPYGVL9L5+EbqFk5BC2m0vHkZTj6PoqnkB0bxx0JY+SK+YAB/LEIxmca4/49UzTsaSQ9h9Xfin7MEq6+T0toVaIkach2dyJqK7FPx1yRQG6eINkZZwc1lsEtFlEQjan0zVn8nhV3byXUPsvnOP6NoMku/eClYJlY2S2pzG8d/+hy0WJSNP3qQYnEcU5Q8XjM8c0QPDw+PV8h3nmjHdl1s10UppyuUbIesYQujRMfCVTVcn47r0/e9ccwU0XUOfJRfq5pcddC5bvnJOmaENU79yMns2DDA7X/YRt/KrUSa4kiyjFKVIP6GC2g49XgmnHg0wYZqtGiQXXfdz97H11BKZalbPJP8QBq7YDC6fTfFZBotFsUxLeRyMcB1bCSfT7grJxrBceh5eiOp9u6ySWIEeax1QVZw0kmcTKpSdMBxMPdso+33z1I9LUHd0Q10P/YCKAqSrBCc2IikB7HSo+O6AuGlKnh4eHh4/CtTXHThQdtu+81mJgVU3vihE9i6LckP791B38qtJGbWEogHUGubaLz4rTSfvYTm044m1Jgg3JRg5+8eZ8efVlBIponPaCLdOYwvEsQqljDSOUKN1QRrYzi5DFZ/pzA57u8CIDBtNrmOTnbcu4rBtTtwnf0WOwIhJL+O2dVGafs6AIxdm7FHRJLDrr9tpmlJIxOPn8Suux9HrW9G0f2Em2rQoiGKg8OM7knj971EqtSrhHzYqQreYH+oeIoDDw8Pj1fI88t34Jw3G58kYTmgyC6mBa4LJdtFU1QUCWSrCJaB5LoiUUFSkPY3TjzAQFFmzmWnwV/bDzrfo4N5Hv3SQ5Xn3/r+Sur9CqfNraVvze0s/tQl+OedgG/qXNx8Bi3RTqS5ntGOXlTdT3p3L/6oTqAujqprBCbUUewfQq+vEXJFTReeCJaJEi+3MMgyk998JqX+XlEc0PR97QoquKaJEq9F0nScXJrevz1C7bFzkRSJfFK0KCRmN2En+1BitZUYxtGOXnxBP4yOVyzj4UkXxRG8yYSHh4eHx/gw/b9+wBVlw+P9eXQwz6Nf36dEGDNMPqM2SN+673PsJ87DP2cJ2gwTJzOCvzpGtKWRoQ3t+GNhCsk0/qgffyyMrMhEpzRSTKbxx8PCLDESw+jahWM76M0tItI5HmbOZcsw03n0hgZhsgjCz0gTCx+BRcvAsSn29TG8dQ9VUxrR4zq5/jxGLsXEE6aS3bKRYMtUJL2IW8xRTI6y5BNvwPzvjeP2c/RaFV47vMKBh4eHx2GgyMLfoGS7jBYcpsU1JMRz1bVELGPZINGVFSTHFsUDOFB5IMmVh3b6O/n+jjO576Qr+Gt/7iXPKwPTwhqDJYtgTZCGY6dgJIcY/PHt2EWD6rktBKbORFJ9BHNFFF0jOr0ZJV6HM5qk0NuPmmgkXNskDJAso1IwcI1yHGMkhq95BkbbOkILT8A1DXBs3FJRtChoOmpdE8burWjT5iGpGk1vvQRzbzstb1jApl88Q+3cGiRFFtLIZB9KVYK+J57jiZ+uOmBV49VG8jwOPDw8PDz+hUnt2cS35p5K17si3P21v7F+tHTQPjLQHPSxO2/SNDtB4zGTyXT2s/fxHyApMvHpk4gcNRe/LFM9S7QDqCEdLR7HdWwyu/bia5qCr0moBCU9KBYKAiFc0wTbRp9/Ejg2meceJ3ba2eUIxbJCoGzqrDa1knrsAcKzZxNd9kYiS/KUtq5i4kmzWHP7UzQsqEPWVPT6Gop7O/GFdAbX7mDFz1dzwnsWo0iMWyKjfJiFA8kb7A8Zr3Dg4eHh8Qoxi1k+8odN3HLRUZRsB02RyJkOAVVGd0tgWfuKA/upCiTX3fd8rGiw3z6uJOP4I2xKHzyJGMMBjmqKMPttC+l9fheJ+TORNJ2IabH+h08wsLGH1jflAYgcNVfc7APZzZvQokECjfW4llGJVZTDsYppkshcBknVcHJpfJNn4ZYKSP5AJY7RKXsiKPE6YaQ4Moik6Zh720GWMTJ5IhPCTDptIVrDxMpEBVkhPnsyJ73DQP5t7/j8Yjw8PDw8PP5N2PjTZwgpL9097gBHVwd457Uns+uvW6hdOF3c9NsO6378DOF1vUw3Lcxcgdqlx4p4ZSDT0YUvpBOZOrGSauTk0siyjFrfDJr/gMULt5gnvGQZUvUEyA4jBcLgOpht65DjdciBEOEZM1Drm3FGk9jJPuRQFDO/h/jUKiaeMpfozOliAUJTUaoSxGYUOeE9EG1pxF8VgBEvgvnfHa9w4OHh4fEKGd65hrd98k1YjktAlQlrMoosYTguKuCqY5GM5Te4Dq4kiZYFWUFy5QP9DVwHyTaQ+nby4QUfOOh811y+gJbLLuEPl3ydE96zGNu0GG3vRY/rbPvFw8y+8nz8NdUkZiaYefVl7P7V7wnUxTBXrsa1bdK7+6hbPLOSxhCoi+OYlvBDSNQgqcLbAFnBtQxRIMikkCMxYXg02I1vQgtyMComIsEI9sgAsh7CKeaQQHgghGP0Pn8fqT2jbP/9ClRdZfYH34rr2MihCHZxX0TleOIpDjw8PDw8/pW5fN1j/OFF2659/yKmvPMifvLmr3LWuxcC0P1MO8GaIM9/8y8c96nz8MfDTFjcwMz3X8LGW+4kOrGK5MrVGOk8ub4RGpbMou2Pz1M/fyLRlkas8rgbaZkEjiPSE0CM745TURaQ6heLCEYRORhFaWiGxulgW6h6CCkcg1wauSoBjk33M+2M7Bpl653P44+uY/41FyH7VJxsCrtoYGTyBBrrKwsS44EiHW6rgjfYHyqeOaKHh4fHK0RWNX73wl4imkxAlZGAvOmQNx1Kso4tawfeHJdbEcaKBwcUDUAUFmSVXT/44UHnag352PVoB5858/O0zK3h4Z+sYs8T7Uw8bQF1C1tJzJ5Arn03A8+JPsLk8uVI5d7GuvPfgmM7TDpjMfmBEZpPOxozXyQ6axpmrkAplRVJCYGQaFMo5pCrErhGsdLnKEdiKFWJfdGKgRA4jmh9yIyI94ciqE2tyJE4My89mcmnthKI65g5k8Gnn0OtbcK1TPT6GiYsW8i46RbLHLY5oudx4OHh4eExTmx58JaDttVoCm1/aeNLZ3+B6U0RVvx6PTv+so3JZ8whNrWaaefOJbm5g+Gte3Bsl8GnnyNcH0JPREmcuBSraNC4dA6Zzn4mnz6XTPcwWiTI0IZ2islRCr39lPbsED5FhlAiIstiDB9rUZCFmbGTT4vXe3cgDXXi5NI4w31CaVjfjKSHWPipdzLjgllUT6/GdRxG1m3GP3MhcjiGGtJpXDoXKz0K4zieKvLhjfeex8Gh4xUOPDw8PA6Dz58zE4CAKuECfkVCkSR8Mig4ImZRlsVD2U/ktb+vwdhzRBJD9azJB53nw3ddx97REvOrdLq3Jpk5Pc7kU1sZ3tzBaHs3gbo44emt6IkqgnVR/LEw8emThPFRZxuhxgS5vmGqj55OsDFB8wVnkdmxi1BDQpy+mMPJpUH1iXaEZB+Yhlh5KOYwezqQqxLCJEn1iSKAYwuVQt1EsV8hh7l7G04ujTZlNlOuuorpV/0XC792DfVnny2kjXoIX1MrvqZWpHFUHBzJVIXvf//7tLS0oOs6ixcv5qmnnnrZfe+++27OOussamtriUajLF26lAcffPCVndjDw8PD49+G7z+756Btn/nthxkp2TToKrt7skxoCNG0ZALdK9rI9o5SGEgRndKI4hPzCbtoEGyoJjqlkdF16wg1Jsh09lM1rQktGqT1LSeT3NxBpLkOSZExc0XUWDVuue3Q6m7H7u8Skc5PPYyxcwOSHgREu6KIX9ZEFLNfpC9JPg17ZADJr6M0NDPlyvfRfOZCjvnGx2h4238BoE5oIXzsKQQXnczgeZ9CGkfFgXyYY/3hpCr8p433XuHAw8PD4xUSaZyGUr67LFgupu1iOaApEs7YYvr+N8fOvmKBKysHRDACleebfvHMQee6/e3f5i2fP4ewKlM7KUpvxyh3/XgV3c+JiYdjWGR3tCNrKqVUlh33vICRyaHUNqHUNhFonkJ4UgOSplNKZSh07iY6exaSImOkc1jpUXLtuzE723AyqfIxi7i5NGZvF8bgAG4ug+TTkPw6pW2rkSMxrMFukGVkXagVJL+OpCg4xRxW724kTcce6hWtDH4dJ5/GyaZwHXtc4xiPFHfddRfXXHMNn/3sZ1m7di0nn3wy55xzDp2dnS+5//LlyznrrLN44IEHWL16Naeddhrnn38+a9eufY2v3MPDw8PjteTGEw6OXv7eZd/nrI+fzgRdpdavsLsnyz33tpHem0bxKaghneTmDiRFppQusXfFLiRFxhfS8YV0tEgQRddQdY1icpTRHZ3UzGtFi4SwckVc26H/6dWMPLsCa3gIp5DDKeQwd23GSOcZ3boTZ2RQLCIAo48/ALLM4D2/FeaK5YQFSRHxy7Iu1Ifh405H8mk42RSSHsItFcRcQvXRlzXA538tf7SvCf+J473nceDh4eHxCknt2YhPkUmVbDRZIqy9RE6x6xz8vFwocGX2tSyMFQ8sg8mnToM1fQe87a3XLuPzH/8TZ9eF6N4zysSpMWZOjJLcnmRk1zCTzjmZTPtuHMOiYclsmnSN0OKTcEYGye/YimNa6IkqlEicQLO2T0mQK1I9ewqlVIb84Aip9m5UXSPSXA9AYEIdQxvaCdTF8Y8MIBtReu5/iKaL30z6mceoOvksSttWozZOQY7EMTvbUOubkUuizUHyabi1E4TXg2PjFHJY/Z0i4mkcVyA4QqkKN998M1dccQVXXnklALfccgsPPvggt99+O9/4xjcO2v+WW2454Pn/+3//j3vvvZf77ruPhQsXvpLL9vDw8PD4N6DTjR207eL3H8uXP3s/syMamiwxOaYzd0E9A1uHkBWZCUtnkx8cEYkKU6uJzZhEbNFiMhvXU0oJ80EtGkKNVaONZJE1lWIyTb4vSaA2jpHJISky2e5BCgMjqKFeZEVGi4RI7eiiqrWJ4o6NaE2TafvJ3Uw97zh67vwltUuPZeC+P6HoGqGGaiRFwX/UUrGYoPqEYaOmg6Yj2cIM2RrqxS3mWZJ/ppIuNR4cqTjG/8Tx3isceHh4eLxCJFnhvq39vHvhBHyyxJgpsiSNDURldUFlf8B2Di4msN8Osly5ad+fr3/1ERbFdLoKJrPqwySmV9N85iK08FrqF8/ESA4xsnUPUy49j2JnB0Mb2imlshjpHFUtjUSXnUNx3XLMng4KAymsooEvpBOsi2EXS/hjEZKbOtATUeyiQaq9m/rFMxneuAM9UUW+L0lkag5zqJ+R9n7Uv/2N2qXH0v69H5AfyiErErmBHDMuWkL3I8/imBb5oRyTTjmKzsc3UDtvMormo2p6M4W+IYITG3GM8ZtIjLUqHN4x9r0/k8mQTqcrz/1+P37/gSsohmGwevVqrr/++gO2n3322axYseKQzuk4DplMhurq6sO4cg8PDw+Pf3Ueah8+aNs3b1lBa8hHwXaZGtZoWFBH82nzgFU0LJqEbVr0PNfBvKvfRKFngL6V2xgtF/wDtXESp5/J8JOPkWnfjZEWnkSOaRGZVI/jOPhCAUbautAiQSzbEH5HUxrJD46gV0dJd/Tij4XJPLuS4kiRrkdXU9XaRNvP7iW9VxQmrKLF5FNbMR9fg6TIjO4ZYeJJs9j98CYajxWtltGWRox0nkhzPQOrtuGO43gvv4rmiIVC4R+O9fCfO957rQoeHh4erxDHMlm+bRAQ5jwS++x/TKds/SfJSK6D5Dr7WhUAybH3qQ3GeLmCAnByIsBZlx/DsGGzomuUjsf38NOP3UUpXWLdD5/EMS1aLrsEbButto74jEmouoaVK9L1+Fq6fvpjHMNEmzaPUOsUHNMkUBfHVzcBJRhE0nTCTbXYRQPHtFB1DbW6ltqlx1JMjmLmivQ8sZptv32GdFeGgbW7sZJ99K3rJdubpWd1H1WTqygkR3Edh2yvGHi7n96MVbDJ9w3jj4UxUmL73keew7HsV/k3sj+H1/P4Yo+DOXPmUFVVVXm81GrC0NAQtm1TX39g4ae+vp6+vr6D9n8pvvWtb5HL5Xjb2952WJ/ew8PDw+Nfm5G8waKYfsC2Y2I6b37PQroLJs8nC7Q9082tn76HfDLP879YQymVYcEn3o6TzyNrKom5Uwg1VFNKZRnc0MGO798BQGT6VKpaGoUHQm0cXzSIL6Tjj4XLigGZUiqHFglWFIlmvoiRM9j75EZ2/HkDQ9uT9K3rJ9s9yND2JIWRIpneLKG6ELZhYeZKJLcN4touvSvbMHIGIzv7CdTFMXNFjEyO3X99juJwGscev/H+8Mf6fYP9hz/84X841sN/7njvFQ48PDw8DoP2Tf0AOK4oFFgOuK5QHEj7FQLcspoAqCQrHFQoKPsfxM87eBA58fJjqV92LNWaQliV8Uf9TA1rjOxKkZiZIDh9Fk4mRWrDJtREI52PbUCvryHTPUwgEaV69mTU6lqs/k6USJxM5wCZzn7yHbsoDg4jByO4jkPtwhlMOPsUGs5chlPM0X7nfRSH04zsGmbCSfMwcgbTL5hHZGKMvY+vpfnkVqyiyJs0sia7H95K/7peCiNFzJxJem8GSZEojuSRFJn+lVvpfnozux/vwDasgz7nvypbtmxhdHS08rjhhhtedl/pRT0OrusetO2luPPOO/nSl77EXXfdRV1d3WFfs4eHh4fHvy5f/sSNrEkVD9h29odOoOnMpZyYCKJIEFAkmgI++nqyTD6qlsTcFpxsiuFte9Draul4cAOR5npSu0cBqJnXSqS5Hjs1jFpdQ65vGCOTI9MpCgD+WBjXdvDHwkSaa1F0DUXX6F+1A7tokO3NMO2SU3Adlzlvm0+4MUxxJE/tnFpc28UsWlhFk+T2AfYs7yK1Z5RSukSuPw+AmTMpJkfpe347I9t72PlQB7uf7MTMjZ/i4NXktttuO+SxHv7zxnuvVcHDw8PjMDjxpMnYrothl4sFiExh23GRZBlFAldWRZHAEW0KkvOiyvt+5oiuJGNHGzmhOsCK4UJlF8e0uO7N3+ZLN57P/2fvvOPkuMv7/56ys7P1dm9vr/dTPzVLspplWzYuuGFsA6ZDTJwYEzBxqAmE6gAhyc+EEgghdEwztmnGvUu2rN6l0+l627u93ds6Ozvl98ecThK2ic35DNjzfr3upZ3Zabvz0j7f+Xyf5/N861N30z2ao8Yrk0sUnJTBoV7Gd3c5zsrbtpI8kqTnzkepPqOD8LwWCoMjTB7aRvW65Rhj/XgjQcLzWkCU0EZH0UcH8UaCSJE4tlHGLmQxcznqz1qGUhlhbItTP3nmp/9mpn8zgF3WaXyjgjmVZNct36F6WQ0jO4YxyyaRlgomjiTJjeWpWR7HLOpEFjRRTKQ4+kg/whz2QDpxH2Z7jBOEQiHC4fAf3L6qqgpJkp4x25BIJJ4xK/H7/OQnP+Fd73oXP/vZz7jgggv+2Et2cXFxcfkL4vPffAsfuf6HM8t6psDNV3+JT33xSp788C/ZmdZo8nkwbQjVB9GzBca27SC6qIXknsP0HJjA+4utNG9eSKi5hmz/GGPbDhJfOZ98dy+iJBJoiAOQHxon259AVGQiHQ2Iqko5myPTO0JscSP+2himvpf04R42/c8/A9DwesebSAxFWDiVxMpnOfj12/EEFDKDWcr5MsG6IINPDmFNu0LLqkKosRJBFBkr9bJkXhSzd+6+Q0kQZhXvTx2K+Hy+/zPWwys33rvCgYuLi8ss6OpPUyzblC0Lz3Q/YI8koExHIpvpEobTsg8Ep0xBEB1BwT7F90CUEQyNN/3uC2xZ+76Zff71Vqdm7u4v3McZ1QEOJ4tE64Msev0agg1VKE0dVFkWvqVnYoz2s/gNefy1MYaf2EdiVzembuKL+ihNJBEkEVGR0VMprLKBIIqo85fhNXQQJURRwjZ05JrmmZZN9Redi+D1YRs6VjaFGIpiJgaR69sY/tmP0ZIZ8ok8WlqjYX0TlYta6Lt/D+d+8c0c/Pa9RNri+Oti2KZJ/4MH8HtlrPJzl2bMGuH0usU/6hAvcH9FUVi9ejX33XcfV1111cz6++67jyuvvPI597vtttu47rrruO2227jsssv+6Ot1cXFxcfnLovqrI1x3yvK/fflJAH77L/ewvtLHsGbQWh+k8y3r8FVHUBuaqcxrRNauxxwf4uwbLPzxKKPbDpPY00duJEe4MURkgYZlWch+Fdu0MPIagYY4sqogBsLYho4UrUaZV41/qY6t5bGLeRa8+wynU1JlLeZID3J1I2LcC8Usfd/+LoVEhsGnRwjXBalfXUt0QQPHf3eADR++mK47t+Ov8iEHfJhlg/6HjxL1iOzbP+54QM1RtYIgCLOK9wIvfN9Xarx3hQMXFxeXWdC3vwfNWI1l24DIWK5E3B+kbNknzXpE2REQrOlUPUEE2zxdMJhZb4FpIMbqnvV8T6U0wlmdBUGFew+M44sdoGpJLaHxNKG2JpA9eBevoXbBGdiGTnt9A3JtM7mdWzHyGnq2gJacIlAbQ4lXg1Em3zdEeagbOd4AloUtA6KErWtYxTxSKIptmSCKCIqKlc+CZSE3zgPLpOqMJUjRauCXVC5qIdi5lPJQL3qujF3SkBSRYjLjiBJqwLne+iDCDw7DHGoHfwpuvvlm3va2t7FmzRo2bNjAf//3f9Pf388NN9wAwEc/+lGGhob43ve+BziDiLe//e186UtfYv369TOzFz6fj4qKZ7bqcnFxcXF5+fNUSkMEGnwyD3RNov52DzUr6gmnc45HgexBWbSa+kWrsXUNX301cqyO9M4daMkpSqkc2YExPH4VJRxACQfI9Iw4JorVIPoCyPEGzFQCLAtB9SOGotilImIgjKUEECti2GUdyjooXoINcWpWL0RSRCoXteCrjlBMpJFVGUPTkX2y86cq+Oc3YRZ1qpfVsv8XhxDyc5dh+KfilRjvXeHAxcXFZRaMH9xC2boanyzhlQXaoz7Kls14wSCoiMR9MpLATMcELKZLEgSEEzYz1im1/raF7VGxJJl/uHEt//61bc84Z8aw2D5dF/mdB3pp2jJIq9/Da/79DRi7txBYfyGWlscY7HZmFkyTwIq1mMlRbF0jNF9EmbccayqJMdJLeKUzwyCoAWxdQ5A9GCO9iP4QglfFLhVBkhi66z6a3vzGmWwDLBNBDTjtF0MR2m/4G2zLxBjuRWlbRPtlafZ/5yG8YS+1axcDYGbS1GxcgT6ZRv7Fg5Apz9m9kWY5Tvljdr/22mtJJpN8+tOfZmRkhKVLl/Lb3/6WlhbHaXpkZOS0Hs/f+MY3MAyD97znPbznPe+ZWf+Od7yD73znO7P7AC4uLi4uf/Z8eHwfX4gve8Z6CxgoOuODHzwxSPipYWpVmavfvZ5QOkd4w2asfAZjqBspEgdRJLJ2PcZYP4KsEG6rQ1m0Gis1Tu7gPqJnLEfwBbCyaZR5y50JAXDGJqLk/DtvDRPf/ndib3svRvt65ONPInp92IZB1eVXA7Bo/UVO9mFqnMAZAQAO/XgbgeoAVcs7ADA0neo1i8gNjbPuxkrEL3TDHNkaScLs4v0fWzX5Soz3rnDg4uLiMkse6ZnkdZ1OTZskCJQMm9qATFA0KFnTTomIyNbJsoSZjgr2yU4LJ8wRAWxZpeUz/8mXbpEY/cIH2f+DnfRkSgwVy6TKFmFZJGNYqKLAgqDCur9ZT7mgseu/HiSX+AVr37eZUjpLcTxNy+XnONcWq0WoiDnHLzhZA2IogplKoKdS+NoXgihiGzgzDSUNShq2oSPICvUXnYtd0rAtE6mqDiufgWIe29BnZi0QRbTBfgRpkOimzUQ3nu1kGgScmkFzfAhBUYGeF1wK8EIQAHG2Hgp/5O433ngjN95447O+9/uDg4cffviPO4mLi4uLy8uChRfeBCvPJ/vQF0j/z2fZ9+0n6BrKciynU+GRGNYMVFFgadjL5ndvwFdZwf7vPU7yM79l3Y1nUUikyY1MsfBtr8bW8kjxBijrCNE41lQSu1REjVeSPXQYLZ0lvuFM9OMHkKsbEGTlpHGzUUbo20vluefD0GE82XEsXcMsZBEU1fE/KuZB9oBRxpxKYidHqLrwYjZdeDG2aSL6AiBKlHsPYRWyBGoryfYnkL0KFEtz8v2JgjCreD+bscgrLd67woGLi4vLLGg/+0oCikxOt/B7RMBGEgSyuoXiU/AKFtONGR0kGUwDG8vJODhRqiCc0uRmOgOh/zMfmql3PJWrF8b4xZEkV82rZMVfn0U5X8QT8CGIIme8+3x8za14WhfNZBiIwQhdX/sWbVe9itTeQyihAPnRJFbZwNQNGi89D0hRPH4EtaEZqboRyygjBELYxTyCoiJGq7FSCaf20Rdwsg2mZy6wLKx8BjEUQYo34CvmEQJhZ3sASXLMFC0LwR8Co4wUrUb0eoHcHNyV6dO+iOaILi4uLi4uc8Geu2/lS3XLuSl65mnrL6oOcG8iz5VtEZa+dQ3lbAFfZQVm2WDJG9fjb6hGae/EnBjB0gpI0Ti7PnsX865YzcTebuSASimdwzZtZNVDZEET/uookzt2o4T8hKNxxGgcWytgF/MYyVHk2mYwTcRgBFvLI6pORoFVyFIePIYxOYG6cAViNI5wYiygqM5YABxxoZxHbuzAzmeRGjrwdiYQvvSTOfv+RGF28X4OfZpfdrjCgYuLi8ss6HvqHjJXLcErO5FHkRx3X5/sCAEGIvIJbwPbAtMRCgTbni5X4KS3wYltbAvBtp4hGkQ9IpeuriNQ46epf4rqZXEGHjmIN+zF1E1q18yjXCgytu0Qpv5rLNMm1FBJbGkbdRs6AciPTlJ19iaEfXuwTQtRkRF9Abw1dWjDQ+ijg+R37SU8vxk5VgeShBiM0Pf922i89DwErw/96C7kulbEUBRBlLBFk+4f302oOU50QROBjc6sh6FraL3HUJvbELw+rHwGyRdxMhYCIdxHcxcXFxeXVzrfaFhx2rIqCrx2ZQ2SIlGfKRGqD3LsV/vwxXyM7hyg5fzFaMkp+h/ah577HaVMibrV9VQuaqHuzFYESUSQRKqWd5DpHUHyyMgBH8GGOLZpomcKFMZT5O78JbHONjwNrQiihBSr5eDnv8y8N5yHGFrL0I9/RN0Vl09nIBYRZIX++3fi39uNvzZG/HVvxZocI/vkQ9iWheiRCZ55LmIwAh4FIRTFyqWdLENR+tN8uS4vKuL/vcncYRgGH/vYx2hra8Pn89He3s6nP/1pLOsPu2U98sgjrF69GlVVaW9v5+tf//oztrn99ttZsmQJXq+XJUuWcMcdd5z2/ic/+UkEQTjtr7a29kX9fC4uLi9/Iq1LeehgAkUSUGUBZbrQLl+2ZtoznpZNMM2Jzgq25Dn9DVF2/p6FT9z1YZa9azPdW4ZYVRvk2OMDBGsC+KvDlDIlvNEgsupFEEUKE0Wnk0J1FMu08M9fhDY6SnR+E4WuQ3gjQXzVEbTkFGYqwdTBI6SPDjCy9QBacoqeOx9l+Df30n/H7xj97W9JHknw5Me+g13WkWK1PHz9fzDwgx9iphLs+MQ3aHvtOVQuamHyUB+Hb/kcA9/7HomHHyM/NM6Rb/6c0bvuoHBoL+ZUEiubxs5nT9ZXzhHitNPyH/v3xzgtuzwTN9a7uLi4PDcLQ8ppy5+78x9Y+vazSPdN0eST6TucJL6kCm/YS7ovAzgtmgEEScBf5cdXHUHP5gk115AfTeKvrSR1qA/JI+OrjlJKZynnixQTaXJD4+SHJrBNi+O/foqeH97B3n/7Lgf+9evkE3m2fvJn2FqBykUt3Hftpzn8L/9GeaCLp/75u9SuXUSgIe60Vf7sLQz++KdkekeYOjbEwe8/ysD3vsf4nT/GGDzG1AN3YY4PYQx1Yxv6nH1/bqx/6fiTZhx84Qtf4Otf/zrf/e536ezsZPv27fzVX/0VFRUV3HTTTc+6T09PD5deeinXX389P/jBD3jiiSe48cYbicfjXHPNNQBs3bqVa6+9ls985jNcddVV3HHHHbzhDW/g8ccfZ926dTPH6uzs5P77759ZliRXDXNxcXlhJI9ux3fBKoplG0O00QxhJm0uX7ZQJAGPKCOJ0y0ZrZOZBbYgIJx4eD61NaNlwO8FWVUUMDOTiJKIacNQSmPJ8jgTh5P0d3VRNG180T1Ypk3lwjrkgIo3EiS8aB4Dv3mExlAQLZlhbOdxIm1x6i55FVY+gxJKYxXzRFavwnpyG5JPIX1sFDXqZ+ipXurPbGZ8Tz9W2cQyLR5622fIZ0p0XrmQqb4kO971NQJ+D2Yuh6eqBj17AKNosP37O2nsjGNbFtH2KLLqRW1sxkwlkEJRjNTQnN4XgRfBHNEdS7wouLHexcXF5bkZL50U0VVRoDwxhqQqaIaFZtk0VAcY2DpEd7JI0bSo29OHVTaRVRlPQEEJKPjjUXru2U3TuUswizoTh0YIN1YQW9pGKZXDNi20ZAZ/dQRZVZBVhfxoCjXq5/j9x5l/+SLG940wfmQSURS4/dUfRBKgtiNKdjjHbTd8l7qgQimdJdRcw1T3ENpUiSfv2k77vCiljE5sfhQAbyRIdtd2JFWh0HUIq2xgl+fIGZE/nTniK5E/qXCwdetWrrzyypk+lq2trdx2221s3779Off5+te/TnNzM7feeisAixcvZvv27fzbv/3bzGDi1ltv5cILL+SjH/0o4LTDeOSRR7j11lu57bbbZo4ly7I78+Di4jJrhkcymLaNhAC2DQgokhOMbNt5iLVsEATRST4wfy+AnlqqcGJZPn0GQrNs9HQG/+Ll1Dbfh54vU5gocu/RSVZUeDnnjUtJHh6fSRcM1MbIDoyR7TpOKeN0YDC0EpIiUb1+GYLsZDqosTBiKILWe4xQUzWi4iHbn0AJ+ak/sxl/bYzkkTFyYwVEScAyLUzbZvjpIVo2d9BeNhneMYqeKSBHyqixMFN9KRRRQM/riJJIuneKys42x+fAKGMV84ihiLM8VwizN0d0xxIvDm6sd3FxcXluloa9DGvOuECzbEzdwL+wk9qOR5GOpzF1k9/2pmnwebjowlaSR5J4Ah7mXbaM3HTmQH40iW3aCJJIKZ3DX+WjankHnup6TL0fbzmEEvYz1TNCsCGOns2THZpEUhXmX74IySNjlk2Kpo1pWBRNG82yiWZKSIrIos4qEl0pCok0oeYaBFGklClRqUhoqRKyTyI7nKNygfOZDE1HCfuxTAs9WeA0r6cXGUGcrTnii3gxL3P+pMLBpk2b+PrXv87Ro0dZsGABe/bs4fHHH58ZKDwbW7du5aKLLjpt3cUXX8y3vvUtyuUyHo+HrVu38vd///fP2Ob3j9vV1UV9fT1er5d169bxL//yL7S3tz/reUulEqXSSTdQ2567/wAuLi5/WWQnizxwPMl5bTEi3pOzmZY9rYRzwtfglIyDEwi/Z5B4Iuvg98UFQEtmSN/1G/R8mUd70jT7PVy6vBpDMxh4oo/l79yE5FMYfGQfhlbGNm1Ej/MznxsYxSw6WQx7vvJrPAEFb9hLsC6CNxJEzxRI94wjKRKSIjJ5dAQ9V0bpGUdWZUJ1QbS0hr/KR/HwJBNDWVI/2oMkQNvmFvof2svCa6NUbz6bykUtxBYeYWy3k1WQHcmROtRHoLaS3NAEaiyMbVrY5tyWKrj8eeDGehcXF5fnpnFlDdx7fGa5MJpkfPed5MbyPJ0q4suUuLyjEj2v071liNVvWYkvHmVk21FKU7rzAD9fR42qWLqBqMjkEzkO/XgrsBV/lY9oRw2mVqIwOsnQlmP4oip6vkxmcJDCRBFvWEFSJOJ1AYyiSaDGz+EDE0wkCuSGc/gkgUUbGpg4OIYaC1O3oZP4GfOpeGIfY3sTSIpEMaWR7hmnYl4DWnIK27QoF4pOvLfc39KXA39S4eDDH/4wU1NTLFq0CEmSME2TW265hTe96U3Puc/o6Cg1NTWnraupqcEwDCYmJqirq3vObUZHR2eW161bx/e+9z0WLFjA2NgYn/3sZ9m4cSMHDhwgFos947yf+9zn+NSnPjXLT+zi4vJyJJ+aYk19BX5ZoGzZSCJ4RRHTslGfLX9Okh0BwTKmvQ54ZsaBJPPOV7XynQd6Z1Z/95Z7UUSBM5fGmThi8nefvpTErm7aLjmT4Sf20XXXdjquWMXiD7+X0TtuxyobTB4ZYfH1V1PoPY5tWUTm1eKLBdGzBULN1VSdsQSxIoYcb6BuuIfc0aNUbDzXOaEoUTq0na5fbMG2LNb+49UMP7wDPVemmNIo5stM6ibyE4NUzoty7BePs+g9b0WO1WJo+ygkC/hjfiItFaixMHq2gFU2SB8doO/hLoxieQ7vilO7OMtDuLwIuLHexcXF5blZfudvwL94Zvn7//oQkgArGsOkyhZ/908XMLr9OIvevJmD33+Inge6aNncwdIPXE//T3+BKImM7hxg5fuvJt/TT25kimBdmEB1kFJGo6KtmsiCJoy8RvzCi2hOjpLYsp2aV52LmRxFUP0Uevs59qsd5McKbPqXaznyw/tpiKqMT5Uo2zYpzaT4+CDNNQEmDw1S2dmB4gugTZUwNINQfRBv2EukLY6eKQCgJafofbALy7Sw/w9Pm9lwwqtgNvu7PD/+pMLBT37yE37wgx/wox/9iM7OTnbv3s373/9+6uvrecc73vGc+/1+v80TMwKnrn+2bU5dd8kll8y8XrZsGRs2bKCjo4Pvfve73Hzzzc8450c/+tHT1i9evJjh4eHn+UldXFxezmhTExTKJrKkIOKIB6Zl45EcM7YTeXC2IOKYHYgI1u8ZBYmy421gGSCI2IJIw8Z5cIpw0FtwHrSVgxOsiaj8/F/uZdnCSg5+4Gds/sCrqOhoIHbJa7ELWequeQO2ZVJvWVjZFOFzXk2gc5TCob0ooQCJXd0MP3kcI69h6gayqhA/Yz7eSJDJRx4gPzSOpCqM7exn5cdvYPKJxxjbsofkkQRVi2KMH5wAwKMJqFEv+USeujNbGPjFr6k7awUNm1dTzmuM7BilXDTwhhWsskGgIY5PVWh91UKEfbvm7J68KB4HL8qVuLix3sXFxeW5+dmhiZnXqiicjPXDWToCHr7/rw+xbkElt//dDzn3b9YRX2rR8PrXYRXzNL/5WgAartKwsmki511EaPlyit1HSew4glk2GdxyHC2ZQQn7KYz/lkBtDFlVGHvgEbL941imTep4ivWffzfHvvNTdn3pVxhFg2h7BPtYCn2qRFAWqfCIGJqB7JMZengH1asXUrduAR7fcYZ3jFJMa8g+CT1bINhQBUDr+fOZODRC+cCfr8eBG+ufP39S4eCDH/wgH/nIR3jjG98IOEG9r6+Pz33uc885mKitrT1tNgEgkUggy/LM7MFzbfP7MxOnEggEWLZsGV1dXc/6vtfrxev1ziz//mDFxcXllUu6bz+FsolnpsZOQJ1uxygJTmWfYJ9SrnBKacKMOaI1HVRF2REORIma93+WL52/me9e9Vl2prWZ8x3MOEF8faWP6s447a/uxDYtDK3ExK9/QcWShYihCJmdOxh4dD+y6iG2uJFyXiNQG6MwmqSirRp/tYakKngjQdRYBaPbDiF5ZCaPjmKZzkOaqZts/6cv4wkoJPaN4wl4GD84Qdm08SoSlmWz+A1r8EaCFBIpun93iFBzDaH57UwcTgLgr/JRyuhEAyrpowOO6VMyi+A6Er0icGO9i4uLy3Pzrnd9grp33sIj8Xv4/BcfnVl/KOtMMKyLqsSXVNF24ULs6ZbOfd//EbVrFyNIEiNb9zPV44gP0XnVlNI51MowpYyGL+ojUB3CM22YLKleUkcHMPIaE0cmpr2LbCRF4rH3/ifesJfx42lyhoVvIMNYyaTGKyEJAqvesRqrbGCZFt13HyVQW4m/NsbE4ST+Kh+RlgpM3UJSFfKjk9PjEp2KliiyR4LS3IkHLi8Nf9J2jIVCAVE8/RIkSfqDLZo2bNjAfffdd9q6e++9lzVr1uDxeP7gNhs3bnzO45ZKJQ4dOkRdXd0L/RguLi6vcDz+MCXDomzZeCWnJeNk0aBs2eiWPdOSUTjhXfBsv3Gntmy0DIRyEWlqiJvO+9hpogHA61fW8FdvXUq0PkjqeIpM7yjHfrWb8b19FEaTTO7cS//PfsljX7iXhk1LqFkzn8iCZqqWdzCxr5uhJ/uY6kkQ6WhAjVWghAIzgwqAUGMEJeD8noabQug5xy8hvqSKUqaEv8qHJIDoEZE9EuN7etj+pQeY6h7BH/Nj6Qb9v3yI5rM7iC+pov7MJgRJwBsJUcpoSB6ZUqY0545EboumPw/cWO/i4uLyh/nKPZ87TTQAuKIxzHvftpTqgMLA1iHG9w9x9JcHGNziZAtmekc5/KOHue97u6hd007lgloiC5qoWbuYdM84ySOOeB9qrsET8iOpjigqeWQESaRqYRWy6sEoGvirfGQmiuh5nYoaP6myiSQIKKJAqmzhVSRyQ0mGnuxj4uAoss/xc5rY203L5nkEqgNULXFMaH2xMJNdEwiSSHbY6eggyHMXTwVmF+vdUoXnz5804+CKK67glltuobm5mc7OTnbt2sV//Md/cN11181s89GPfpShoSG+973vAXDDDTfwla98hZtvvpnrr7+erVu38q1vfes0B+WbbrqJc845hy984QtceeWV3HXXXdx///08/vjjM9t84AMf4IorrqC5uZlEIsFnP/tZMpnMH0ybdHFxcXk2yoUMJdPCtkE3bWwgokpIooBh2ZjTM+uSpJz+KGpaJ80QwREPpjMPBEMH/XTBAOC9b1uK5JG5/+eHuOLGDTz+zaeIjxXYOZAhZ1i8ZrOJpDhuxx3nNCGrCp6AyuBDO6lc1ELt+k4KE09TzutM7O0m1FxNunuYpvPOQFa9TOwfQFIkAnURjJ4JtJRGMaUhSAJKQGHZW1dRe8E5CIrKjk98g2JKozBRpGpRJaZuoqU1sgNjM1kMoiJjajq2aaMlpwDQJjNULqhFeHju7gmANNuuCu5Y4kXBjfUuLi4uf5iWc5rhJwdnlv/myvlIHomf/vQQ73j3Oh757k44nOTpkRzjR5NcC6T70iSPTLJ6XQPlvIYcUBl4cA+RebVULW1CS2lMDWQQPTIVHQ2kjw4Q62xDDqhMHh1FjaqEG8MYmtOlSbdsJ2NAEbngtQtovewsMt2DdP92D4WJIsWUhjfsxRPwkBvJkTw0iBr1I0giFS0xynkNUzcp552xS3EiR/Wy+mmT5rkLqJIozCreu8mPz58/qXDw5S9/mY9//OPceOONJBIJ6uvr+du//Vv++Z//eWabkZER+vv7Z5bb2tr47W9/y9///d/z1a9+lfr6ev7zP/9zpj0TwMaNG/nxj3/Mxz72MT7+8Y/T0dHBT37yk9P6Og8ODvKmN72JiYkJ4vE469ev58knn6SlpeWl+fAuLi4vKz7z7R389uPnY0sC0ikKtk8Wndo7UwdJwRZEBNPAlpXpf70I5rRJ4AkBwbacEgb59J/o1y2pou3KzUjxBhY//R/s/vbTFE2bQ8NZ3v7RC4gs7uBHf/0/vOq6NdiWRepYYqb8oGp5B6V0FkPTsUwbX1SloqOB3vv2E24KMbL1AOV8CY9PxjJtpnomCDVGaH3HW7ALWcxsCk99G1Y2jeAPgVHmzP/4CIii02IxnwFAitWh7XmcTO8osqpgaCX2fnc7+bJF7Zmt2KaFv7bScVmeQ5NlgdkbHrljiRcHN9a7uLi4/GF+cPvhmddvOrOORe+4FESR9TtG2fr9XQwUDbrzWa67cR3+2hg/ueUeNm5qom51DYWJIsNP9eAJKDRsXEBuaBxLNzDLJpH6CsKtdfQ/eABJEbGt7mnfIS96royWyhKoDrDove9gTT6DVcjiae/ESo0jRuPEF60mfsXVTqy3LKx8BkGSEIMRxm6/bWZCwDIttvz8ID5JpGZFI96wF391GNEjUxxP/1mbI7olac+fP6lwEAqFuPXWW/9gS6bvfOc7z1h37rnnsnPnzj947Ne97nW87nWve873f/zjHz/fy3RxcXH5gyx41VWsXd0wE3w006JsCaiycNL3QFKcEoQTXgfTJogz3gYwIxzM+CDopdPOMzyaR1BUrKkk4cYwTx5LsTyiEm4Mcf//e5ig/CgrNjQwsr2PMz70Zmo2ZvA0LSD54P2ku4eoP28dhf5BWi9YTu/9e8kNjTPvilVo6SyS4sE2TSb29zPvDechRauRotXYZR0xGgdRxMqmnX9TCWcQMJVEqohhGzpWPotc3YAx0oMybzkRX4C9//VrMoNZGtbWsf+xQfoeOEL1shqO3LGPBa/pnPP74vLngRvrXVxcXP4wS8NetkwWARg8nmZdKIKVzxBtj/DQI/2siqjkDItffWs7NV6ZFfMryY3kWPuhKyilswQWLyPx4MNMdQ9RvXohmZ4Rms6O0HPvYSRFovn8TkrpLKJHxrYsxnb20fmuSxAUFSnegK3lkarqEIph7HwWZI8T8wFBlBACIex8FtsyEWO1FHc8RKRzAVOHjtF7334KyQKLVtSwbccIh+/YT/OmRg7fcZBVf7sRPatgm247xpcDf1LhwMXFxeXlwNEH7uADb/wkozmdClXGtiGoiOgmmJaF3yMiCiCLMrZtOXYGpnHSGPHUcgXAFmXweMFfQY1XYqzkbPd0qsiF+w5RHE/ResFyVh1OksvpNDaGObMzTnRRCwOPHGT59RfQf9d9JPaN4Ql4qFnRiBL2kzl6HKtskDo6wIp//Ft6vvdjDE2frn8MIigqVeedB5YJooRdzIMoYuezCIqKMdKLWBEjvXc/kkem8pKrGP3pD+h78BDLr78AQRSxtAK5Q4cItLdRzpeJzY8yujtBQ0OQcGMI0SOz7O3r6b1vP1bZnLubMkuXZXBLFVxcXFxcXho2vf0Mtty6BYAnJ4u8asdOMr0jzH/tmSzYMYpp23SurCFQEyC6oJ7jvzvAyne/iu67tpA6nsLQHqRxfRO+eAQtOYVZNsiPplj3ybdy/Ge/c7oa1TrGsr7aKuLrVgEg+AIIXhWMMrZpIvoC6Mf24l2ylu6vfh1vJEjteWcxcNvPGd05RKDGT7g5RnRBE7kDPdimRSlTIlAdYHjfOAuqnBbMoiSy6m83MvxUN8M7R7GMucw4mF28/5Ma/v2F4QoHLi4uLi8Ct96+n9tu2kRtUMawbMqmzaRmElREZBFMG8KKiHBCPLAswHyGaHACW5RBDXHjZy/nEx+8C4CyDZ/6p98AcGVbhHmXLODfv7aN6BEnAyHdu5eGszqQa5uZ95FLae0/gl3MU04MkxuaILJ8KQDh1lr03kN0vOcGBEXFNsoUtj9MvnsQPbuH6k3rsA2dgd88QjnvZD1kh3NMHEnSdl4LwztGUQIesv92H+HGEFWLYgw+tIuxfWPUn9lE6tgE4cZuBEkgdTyNIAoYRZN8Is/EkSRG8ZjzGa25nYGYveGRqxy4uLi4uMw9XzrnI3DrawAn1n/yI78C4OzYAdZf2sFXfnSA8JEkhmYw1TdF01ltqA3NdH7iYspD3djFPLmubvRsnujyxUSBbHcvpbEROt50BXLjPKxsityep8l0D5IfTVLRWoeoyPQ/6GRmiZJAPpHn4N5x5rX/msnBLKpPZud3d1BR46d+dR16Tqf3gWOU0jmm+qaItkcJN4WwTRtJAENzjBZ7H+lHfHwQQzMwbeY0nIrC7OK9O0nw/HGFAxcXF5cXgZEjXZStszAtm5TmtGZsDisAeETHNNG0wVPWZjwMbI8XoTxdjvD74oEggm1R9bp3svjTd8+0ZTrBXT1p+No2AH5+cAIOOq2YWrcNs/nJ45zxz3+LMdILsgdPXRPR6npG7n+Mhqteg699KVYmiTHWz9TefY7jsSTijQbxRoPkjx0jdXQAX3WESCSEnsnTcE4cU9MZ23GEYI0fy7SJtkeQPBJ6roxaIRJtj5I6NkHDxnmkjg5RzpeRFMd52TJt9FwZURKRfXN2G05+fbjmiC4uLi4ufxn816dvZeTLr+ef3vuz09Y/lizy2I8OAPCbkRyM5ACI7hjh4kePs/5zf43W34OsKvibagn6wwz+9iEaX30Olee+CrukYSRHSN7zS/RMHk/AhyegEulowNB0kgd6CNSECNRWUkikadjUSfulOold3RiagazK+CwbWZWZGsjgr/ITX1JFdjhH7aom0j3jpI6nkTwSoijgDXvJjuSwLRs5eCL+W4iSCHOUZCgJszVHdIP988UVDlxcXFxeBCqbW5nSylT5JAIeEWk6ENm2jWCZeLEQysaMEaItCE5bxhNeB9NCwUxbRkHEFmXMoMK7jz8IgshTl78eT8DDvU8MMqw9sx+yCPQWyuzfMcqCA7sRPTI7/vMums9uA6D1nW9FUAOYqQTGUDe24VyLGgtTGJ1EFEUK4ylkVaF+03KKiRTlvMb43j5Gt/egpTRKmRLesJfCRJHMlEbA76EuWoupm5i6ieyTSOzpAyBY57R3LEwUKGV0ZJ8TcmLzKxFEEbFrH7htnV1cXFxcXKj7VhJWnk/6n5chyB7uuu5rPDxReMZ2IpAqW+w/lqJz/wEkn8KT/+8eGtc3kk/kWPbhv0HwqljFPOX+o5TzGnomjxIOOF2OPDLFySlk1Uv8jPmOmaJpke5JkjySIDeSo5jSCDeG6OtOo1s2lYpE64Z6bNOZ5PD4ZFLHEpi6hT/mR/bJ+Kv8JLsmsU2bcGOI+JJqBEkkfXwS+ale0PVnfBaXvyxc4cDFxcVllgiiSLK3GzgLURBQJAHLtrFtG0USAPl0E0TLAMnjCAWi8zNsCwKCNXPAUw/umCoaGj98cugPXseJ3c9+/RICi5chyAotm7uRPDLlgobgD2EmR7F1DdsyUdo7sXuHyfSOIIii066pewhvJIQUieMTJdSyTmTZYkYe3EK4tY6jv9hGYaKIoRlUVPqYmnRe50ZyCJKArMp4w15yIzkMzSDSFsEX9aEEFPS8TrAmQGR+E9mBMQRpbisLZ9tiyZ2DcHFxcXF5qbn56i/9wfdPxPpXXb2Q0OJFCF4fTWd1IXpkAtVBkD2YqQRWPks5r6FURjC7h8hnC0iqQqipmsnDffhrK1FjFQDomQKNmxYx1T1E3ZmtHP/dITKDWSo8IrJHYiSnU5vSKBcNREnEE/Dg8ckUJgpIioS/ykcxpeGv8mGZFoqqEGquIXV0gEh7JTw5d9+XIMwu3rsJB88fVzhwcXFxmSWyGqSQHCKrmzMdFQIe56HYsqdNewTRMRrE44gG1rQ54nTGgWBZJzMOTqwzHXU+/cMv8/F/uOO0c76mpYJf9k1x7coaqpfFabnoTASPghSuRPD6yO7ZxeThPqqWdyCIIsFVG7ALWex8hqkDBwk21WJOjBC76FKMwW7EihhSRYzWecsdY8RSEbHCMVISPArN71yArWus3XwxtlHGmkpiWyZyTTPZLfdz7K5tTPVNUcroCJLAyr/eSOX6tRz8yo9Y9MazkIJB5HgDVj6LVBEj0LUP6Wf3Q744Z/dFctsxuri4uLj8hfDFr3yMQ5vOP23diVj/+mVxGs5soH7TMkSPjBiKIPoC5I8eIbHjCOHWWiTVS3TDRmwtjz7Ux1TPCBVtdWBZ1L/2NRgjvQiKilzfRmvbIqdbgldFXRnBNsrYhk6VUUaKVtP4ThNb18Ay0Y8fwLt8I6lf/5See3Yz2ZUi050C4Lx/uoRg51J6fngH867ehBSJI1bEsKaSSLXNRJYdRVRUhJ/cO2ffmyQIs4r3ohvtnzeucODi4uIyS8qFjPOvaSEiMFVyTBG9ok3OsAnKgG1hSCqSZDmCgC1iYzlmiadmG5wwSzzFNHHvdx4/7XxLw15azmniqq0Spm6S2DfO0LZfEmmpIFQfZOJwkuxIjuGpEsuPT7Lio9eR3/OUU47gkYmcsYLJbTuIndNKfsfjSB4ZWZSwppIIvgB2MY/gC0BZRwxFMUb7MSbHkSKVSKEogj+EoPoRPQq2rhFctYHO6mrkulYEj+PrYJeKIEqs+MInMcb6EUQJsSKGoKgIvgDetoUwh96IArPr6wxub2cXFxcXl5eO9zRm+btTlutVmbbzWnjj3gRm2aTv0X4O/rqLeHuEQI2fqb4MxZTG4FSJ9vphNn76jaS2bkFLTiEqMlWrl5E+cJSKha1kdz6F6JFR6xvQj+1FUP1Y2TRydQNWMY9UEcMcH8JMjSP6+5EbOxBECdsyUZdtwMxMEl63iWWdS5HjDSBKTgcmQFBUFv7TP2KVilipcaRoNYLXh6gGkGuasQtZBGnuHjlFYXbx3g31zx9XOHBxcXF5ERBlD8Wy86B/whRxKG+iygKSKCIJMtjPbBlkCwKCbZ/0ODgVQQTT4Gh/5rTVOcNCz5W549gkl9UFyegmtfWOn0C6d4rCRJGJrE5dUGH5P7wZY2yAib3deAIq8TVLwTTRM3l6f/AzvJGgU6Lw1A6qzlyJ6A+BGnCyI3QNZA9yfRtyYwdYFnYxj63lEUNRAMzEIJM7drPvu08Rqg+QHc5jmRa2ZbPq3efgCagMPX6AjqvORsqmkKobMQa7AbDcekcXFxcXFxcA0nd+77TlnGFRmCjw452jnB3zkTdtGiu8CJJAdjhHYaLIsSkNSRBY+vYNpA91M9U9hOiRqV69ECufITc0zlT3EIIoEmquJnW4j/rLLkSuacbKZ5BqWzFHe0GUkOva8DQtwCpkEcOVAIi2jVXMI6oBhu+4nf0/2E5FU4jscB5DMxA9Imf87dl4I0HGth2i5bUXONmI8QaM8SEEUSK5Zasb718muMKBi4uLyywRZQ9rXnctmuGo7zndpFC2qA16kASmSw5Ex8/ANLAFEWFaJBCmFfvT2jKe8DiY9kVY3BFlz87RmfP1Fso8+WAv779uJb/96UHe/LGLAZjYe5xwWw36vYfoHczQ1Bxm9P5HscoGQ0/2Ube6gZ67HmZo2wjxJVVULqxjYv8goiITbq1j5MEtZAcnKaY0bNPGW6FgFE30vI6W0gjWBfFFVQzNoPXiMwDw1dUQbq1l6VvXcPTOPRiawfzLF6ClCiQP9JAdzlHKlDj280dmyhjaLl7JgR9uwTLnrq8zAszWQsGdhHBxcXFxeamo2HgucPvMcsaw2ProAP9w41p++u2dvOsTlyBIIqNPHUFSJGzTZnI8z5pKH6V0lqmeBAcf6KVlaZypvqcY2TNG/apaqlc0MfJ0L77qCN5IkOHf3MdUX3LG8FiNqoDTSjE7nENSJDw+GdEj0v7qTsrZAtWrF+KLVbD4dct58tvbCXhEFl65iNzIFKnDfc6kRbJAPnEnpm5RtaSWyIImBh/ZR7i5CsuYo5YKgCjOLt7P1g/plYQrHLi4uLjMEkGUOLp1N8FLF2HaNiGPhEcS0AwLjyigSE4GgqgXHDFAPPnTa0uek+IBnCYg2JKCYGmc/dMv8eN51552zj1TJeY/OUir38MnP/Ir/u7NnaSOp6nbsIR1n30Xnlu+y9RAlr6HuyhldFrOaSFQW4meKVC70kKN+ikm0qhRFUEUOf5rx/QwNr+SirZqJFVBVr2M7+0BQFaday5ldAI1fp78wj2EG0MUUxrR9gi920dQRIGcYZEbmQJgZEcSALNsIkoi3rCXYE2A/GiS1lcthH275uaGTDPrFkvuYMLFxcXF5SUi8Dd3ct3vrdufKdH+myMzsf7dr1tE6niaRW84k443X47wqe+RTxQ4cNsu+tIa6zY3E2mLkxtJ07yxkWBdBbmhCXxRFVPTmTwyQmYwQ9WiGJG2OIIkooQDjO/poTBRJNJSgVk2UQIKalTl6W9spbIxxOE7DhBpCbN/7zhF06LKlpjsco57/P5eAtV+9FyZzGAWNaJSSEzhCfkJNVQy8Fg3gizN2fc2+1IFN9g/X1zhwMXFxWWWePyOK3FNwBEIPKJAybCp8IoIluH4GQgitiQ7yyc6LIjyTHtGYNpAUT4t88AWZUzFz63ZPVj3fJP9//1b/vfe4wD8/ODEzK5fme7zzJM/40sPfJozb/1Hph69n0f/5XfMO6+F5JFx8oks0Y4aRElgeNsgDeub8VVHkBQPtWvmocbC5Eech/3cQIKKjgYkRSTdNzVzHkkRKWVKBOsCVLRUEG2PALD0snlMHE7SVONndHeCxvUNeMMKpYyObdpkp7sspPumCNakKKY0rPIcZhy4uLi4uLj8hfG/K8/n6//9Sd5SfIKbzv0oAL88JQb/188POy92/or/9+slbLj1Hxj73e949GtPsLgjSnY4h1E0ic6rIjuUYnBLH40bWwg2+PEEfKixCqLz8hTH04iKTDGRRlKdsUuqJw2AIAp4fDKlTIlA2Evl/Eoq54MSUDhvaR39j/VQtTBG/xOD1K+uJdwYIjuSA6BcNDD1PJnBLMM7nExJQzOc9tMuf/G4woGLi4vLi8C8dSuIqB5My2a8YDhmOyUIKTKyqSFYFrYkgyg7AoKhnxQITggJggjTnRSwLaecwTQQzDI/XHABT6W0mfM1+WQGigYiJ1sznWD0vgdpeOs7iF7+Bi5dsAAxWk257zDDj+4iP5oiN5ankCxiFDSO3X2UtvNasS2LUioLwNieQaLtlRQSKUqZEh6fjJ4rowQ9aFMltFQJNeplqm/Kab9YoVDoK+IJeNBzZSItYVLHU85gARCmjR1M3cmsOFEKMZcz+gJuVwUXFxcXl78sjl5o8Pm157BtevmSmgD7MyUUUaA7Xz5t25H7H6Px9VdTd+1buHrdagRRRB8dpPtX2ygmczM+CKamc/Duo8y7ZAFW2UCNhfHFI3T9cj8Na+spjDolikrAg6lbSIrIRKKANF5AlUWSR5KoEZU8ecyySaA6QDGlEaoLku6bQgkoyKqMqZvYpo3kk8lMFAlX+dDzZUzdRC+Un/lhXyRm3VXBDfbPG1c4cHFxcZklhpYjPZ5HEkGWBKJeiVTJxCsLSAJONwXHGxHB0BFMY8bHwJamGwGd6nFwyr+CWWbfTTefJhoADBSdh/LfFw1qvBK3/ecTbH66l4Wv34BSGUHw+vAuWk3bkrVYU0msbIpC73HGd3XRem4zxVSRUGOUYEOcVNcArRcuI3mgh8muJLFFcbSURsdlnQQb4oxtO0QxVcQomsg+CT1XZvxAEj3vCB71q2sJtldSmMgzfnAC2SfhDSsYmoFl2tim7XgdiAL2HHZVgNmXKgiudODi4uLi8hKRffiL3BRZjUeA8nR8vHss/6zbVikSt3/zac49OErHFauRVAVvTR3qwhV0Ll6FrWsYY/0UBkZJ7O6iYW09g1v6iC2MEWquQUtOsfCaleSHJkgeGSe2ME5mMEvHJQuQVYXaoSS5MccAUVZlrLJJsitFvmwR8IhULYxRv7aR9PFJxg9OoEa9KAEPk4PZmUmDiUQBRRQw5zjYz7aLkhvrnz+ucODi4uIySwwtjygKpIoGQY+XXWMFVtT4kQSBgmGjSALmdNwUZCclUDCN08WCU5kWFQSjzMc7riB1Skr/JTUB7h7L847NLXz34T4ArppXib/Kx9CxFJs/8CqqLn2ts7EoYo4NIPhDlA7vAMvE07QAqbaZQCBMeOOrADBTCcdhWcsTXpPGTI4SWb2KySe3Eb/kctosE2O0n5EHt+CNBFErw6R7xmm9eBV6tsDQ4weZ6svQcVknxUQaQRIJNUbp3zKI4vcA5RkjJ6d20oOeL2PP4WBC4EUwR3THEi4uLi4uLwGHN2vcFFkNOKLBq+J+HhgvnBbrr2gMI6syw8kC5//teuqvvhrbNBE8CsZwD4IvgHZkD8VkhlBHK56mBQT9YSo2noMgSTOx3pxKOpMIWp7KZfOJ9g1Ted5FNL5qL+VsjskDPfirKwjURhnfP0zz+Z3YpoU3fISJw5PMv3wRxWQG0SMTX1rPxJEkpm4xOZpHEgRK09mFVdV+MhNFcoY1pxMFkmuO+JLhCgcuLi4us0QQRUzDwpqOjGfU+MmVLfJlC0kQqA86P7WCZTiCATg2wKd2FTi1HeOJjgulzGmiQUASeDqlcVldENknc9M7l5MbyxOsCbDzV12svnIhgiSSe+pBxrYdwjYtJFWh6cqL8C5d77RT1DWnN7OuYYwPYWXT2LqGlc8iVzcgVcScvsuGTvVVrdi6hugP4WlWab5uMbaWxy7r1IwPoQ0PM7a9i2BdBXquzMT+AWKLGyiMTtJ0+avofeAYer7sZBdMKydOOqOFrMquIZGLi4uLiwtQSudmXlcpEk+nNBaHnGy9d2xumSkROPLYAMvPakRWFXI7t5LYcYTcSIZAdZDmy89FXbgCn9eHXZ4ue7RMrFSCYn8vAMpUEjlWh1zbjOAPAaCuDmJNTaCuPAcllyZ45rlYxTxWKoG/7iBGvkj/w0eItFYQbiwz1ZekoiVGdnCSeVdv4vh9RyhldFRZxBdVKRcNDM1Az5dRfTKmbTtC/BxnGbrMPa5w4OLi4jJLbMti4vhRyuZGcmUT07ap8EpUeCXKlo1pg3TiGVk80WrxOTINbGvmX6PvyGlv16oyr7qglZoz2hjdfpz+xwcRJIHJrhT1TWGGnh6i/4kBjo7kqFIkxkoGl7xlOSP3PEy58DtMTad2fSfDT+xD8siYZYNIRz3Vl14OogSWia1rmKkEYiDsXJKiYuXSIErYxTRSrBZjrB85Vkekcx2h9Zs5+v++ihL0IIgiA48dwxv2MvrgE1Qvq6YwUSCfyOOLqhQmiuj58kyHhjmVDYQXo1TBxcXFxcVl7vn1zw7NvK7wiJy9voHmzYs5escuho6lkAQoHkoS9UpMHJ5kbN84XZNFJEFgqmxyxWsWMHT/VqzyY5QyGg2bOknsOEIxVUQJKITbaqg+ax22UQbZg13SsLLpmXOK0ThmKgFGGRuQa5sp6xpVl10Dipfqyyc48h9fw7YsrLJJ38Pd+GI+hh7dTeW8SkqZktNRIapi6gXKpo1o2giSQLwpjDA5dxHV7arw0jHLRE4XFxcXF4BgTSseSSCkSIQUEd200QwL07KRmTY6PNFmURBPCgjTZQmndlI48dqcSp52jvXLqmnYtISjd+7BMm36JgoMJfIMTBQwdZNwY5hISwVhWaTjzDpWL69G9Mj4qqPUrl1MZWcbAw/tITucQwn7UUJ+9EyB3u/8gN7//S5WPoN+/MBMOQWihG2UERQVpltGloe6keINSLFa7LKOXcyz8IN/T93aBST2jVO7qpFl738TfQ8fIzeSJVgTQEuViC+tQ42qNJxZR7Q9woaPXo6kzq12LQiz+3OVAxcXFxeXuWbdm9+Gbp2cjt+wsoYF16zj0E+3AzCsGfQWyuxMa5RNm2h7BH/Mx1DRYMWSKlbG/GgpjexQimBDFdEF9fTes4vU8RSRtjhK2EcplWPg1w/Q/eO7MceHyO5+GjEUQW7oQG5ZCIDoCyAEQiB7sPIZpFitM6kAiP4Qiz50E7Wrmhh8eoTGjS0sf/flDG8bJHkkSaQlwtRkkaqFVXjDCm1nNVI5L8q6D1xEtD2C5Jm7R0431r90uBkHLi4uLi8CXp8y42MgCgLe6V9X2+a5i+VPLU94lvVSrPa01RUtFWR6RpgYylK7smZm/aLOKqb6MpTzOrmxAk3NYURJwNRNZFVBjYWRgkH8okh+LI8gCcgBFVPPYWg6oeYaTK3kzESIJ4O7XdYRJAlbN53Bgyg6IgJg5dLOwCLegCBKVG7cwJnRIGp9PYKs0LC+GaOgMbY3wZqbzqP/wf00b15AqMm5bkESEea4sFCc5WjAHUu4uLi4uMw1T/3o+6wumTPL/iofffftZnwkz8KzmzCPJDFtuKQ9yvHxPNFEnuGBDGfFfBRTGkbZRFJEKhfUIQd8mJpOum8KX1SdjvUGtmXhi0fwRoPYlokaq8BMJRD8IexMcmaiAHCyD40yUiCMLQhgGKB4sbU89Zecj6QqVC2fh22ZxDurKGVKHH+gh9VvWUnyyBj1axtRY2Es3cATUKloq0b0SKA9xxcwSwRmF+/dWfTnjyscuLi4uMwSjz+MrIgnyxFw2gMpImBb2AhYNoiSgmBoM20WZ8oSTmBbYBkz7x+49TunnedrPzkIQKvfw7ypEgvaI1QtihHrbGV0+zEAfDE/U31TjO0bJ1QXJNM/QaZ/gnBzFVbZQM/reHwyhZFJREWmoqMBXyxMMZlh4I678VdH0J/cR7pnHID5V29E8KrI8QbEihiirGAmR5HjDRiJIaRoGcvSkGJ1hNZGsUtFBI9Cy5teB0D7dPnD8jVnOtkL0wIEgOhVIFuYm5vi4uLi4uLyF0Dqia/ywcDimeVv/7KLsg0dAQ/zyyZntFQQqA5Qv74N/8NdWKZFY3uEfMLJOmyqCwKQ2DNIoGaS/FjB6YRg2hQTaSRVIVAXwxePoGcLDD+8AyXspzC6jXLRoJzX6bhyHdn+MarWrUaKxhGDEWxRQrBMbI8CpSKCrCDXNFN35ZVOSYNlMu+v34Kt5VmCY/5cf5kJlulMMogSYihCbUML0rd/7cb7lwGucODi4uIyS2RfkOaOGOBkGJQtm5xuUemTkAURwdSRBNHxNbBO76QgWCa2ICA8i2nQyM7R05brVZmrr17I/nt7aLlwJY996tfIPpknf/sAl3/kQmzTojie4okHelFEgRrNMSjyhhXKeR09VyY3kie+pAp72mNB8sgUkxk8AZX6zauRotXYxTy150tMPLWD5IEeiok0aiyMIIl4IyEqzz4X2zJR2juxtDyZJx5k8JF9hJud4zZcdQV2PoMYrYbCtBnjtGhgl4rgUU58+jm5HzPfrZsy4OLi4uLyZ479i389rbVyhUfi9a+Zz1P399J83jIObPkdvvECW7eP8LqbNmEWdQrjafYdmWRSN8kMZGhNFok0hzF1E0MzSPZlaFrtZC2amo6kKhRGk6ixCuJnzMdTEcYuaQiSxMTeY0we6CE7OEmmdxQl5McTUKk5ey3KkjMRLJPi7keZPNTL0Tv30Hx2G0ZBY9773o2dzyIEIwBYhaxzTNEZ79hGGTM5iqAG5tQXcabkwGXOeV7CQWVl5Qs6qCAI7Ny5k5aWlj/qolxcXFz+kqhoWEChWCbq81C2bBRbwO8RSGkmAY+IX5LBPt3nwFm2p0WDU0KqIGKLMggiY5nSaecZ1gxyY3nidQH67tuNL6KSGcwyf36UgUcO0rt9hJWvX8o7v/pmBh7YwbF7jiOrMo1nzyfUVENxPE18WQpPQMXUdAxNJ909hDcSJFBbycTOQwQbJvDV1aCnUpiaTm4oiax6mOpJEGqsJHW4j57ffZn4sjoqF7dgFnUESSRQFyHVPYapWwx84D8J1QcZP5BEkARqV9aw6H3XYSZHHWHC0KdTIue2HeNsKyFeaeMQN9a7uLi4vPQYmn7a8oRuUkxp1Plk+h/aR7zCy1RWZ35Iofs3B+jqneKsK+ZxzYcvYOCRQwzuHiNcF6RqUYzqlfNJHuihelkNgiRimxb5RBZBGsLjV/FVRxl6dA9VyztQQn5K6Rx6poA+nQ2gpTRkVSHTP8Gxj3yHutW/I9RcjeSRMfIazWe3Mb5/BFn18Ph1n0QQBfKJAoWSQcPiGGf+x0coHdqOp75tZrJAH+p1siznCFGYXbx3RYfnz/MSDtLpNLfeeisVFRX/57a2bXPjjTdimub/ua2Li4vLXzoVTYsxDZ03bGghkdOpCigEFRFVFgnKFmDPlCOUJRVZ0BHKJwv9BNt2xIRTRQXLCbBv/sXHeOq8j512voe3DrGqKYxtWbSc00Lfo32U82XkRpk1b1uFHFCRotW0Xn0R0UVHsE2L7MAYSihAbmgcJeRHVhVK6RzR+U0EWhqQa5qxsilinaBnC5QmkvTfv4u2S8+cGXj46yrJ9ieoXNRCy+XnYGSmGHhoF5JHxrYstKkSgigSrPGx8PUb6frFVipawmgpjdHdY/h//HOygynq1i1gbHsX8994gdua6c8MN9a7uLi4vPSs3LqM14oC2ikGiY89OcSisJdy0aBpQwOe3WNYpo0aUTnn6jp81RFCzdUs/Zs2mo70kOkdYfxggkBtElFxHu9kVaGc16g5o41wax1SOOK0Wl45n3JBQweGHj9E47md5IfGZ7wQihM5ogvrabvkTErpHOO7uygXDZSAgqGV8QQUgjUB5l+9jmN3bUP2yQSKBtnhPAM/+CETB0dpOjdB7337WXDNWnwN9Vjms/g5ufzF8bxLFd74xjdSXV39vLZ973vf+0dfkIuLi8tfElMDh6ict4q7945wwVWdNIc8jBdNwKJCER3PAlMHy8IDjjggK2DoJ7MNTvU5mBYNEESEpZv5yu7/Zu8nvsh/39UFQG+hTLE3jdI/xUWbW2g5p4XJriQ9Twzy6rdcSHFkjF+84z+pqQlgTbs1enwygijS9eujBKr9VM6vRFJEckPjaOkswYYkSiRM8kAPlYtaMMsGC//2WsRghLr5y7B1DSufIdNzN7KqkNp/lIHHjmKbNvOuWEV2YIxiqoggigiiSHJ/D5ZpkxvJYZk2hmaQ2DNEbqxAYt84ofoAh79/D5Z++izLi82sZxFegbMQbqx3cXFxeWkZ3nkvX1t+HvnffYyb686nbEN3vsxU2UJ6fICL19VTvayaqb4p+vaPs/Sdm0ke6OFH772NhqBCulhGEgR8kkCoLsHRR/qpqPQRaalAUkTUmBPfA7Ux5IBK8kAPNWsXI0ginTe+HjEUJWKZ5A/sBqD3np3Iqpfx3V30P3ocQRJofdVCpnoSlDIllICH3Fie5IEep5vDSA7JI6HndXoe6MIomiS7nsDjkzl6+zaqltRgFOcu3gvMLt6/AkP9H83zEg6sZ+s3/gfIZrN/1MW4uLi4/CVSmBiivfocSoZNuuT8XgY9IomCiSAIxFUFAf2kIaJpnJ5pIJ5owXDSLFEwnSD7L+tvZFg7PcXvsnNbKGVKHHpqmIvPX0r16oXED/TQc9fDhFtrKZo2lmnTftF8/NVRAMr5ItXL4tSuaeP47w5Qu7LOyTpY3IJtWkx19aOEApTSOSRVYeyBR/AEfPhrKzHyGnJApfmCVaS7h4ivW0VkSQe5vmECbc34m2qp7Mxw9GdbkRSJfGIUUzcxdQuzbCJ5JLLD+ZkZh8JEkamBLGZp7lIX4cXoqvDKGk64sd7FxcXlT8P+9RneW3v+aQ7/11w2j2KywNChJGdfupKm84LU7O6i797tBBscXyWjbLLknGai82oopXMAxJvC1K2uZ2THMDXLq8n2j1O/aSmllNNJyRsJYpUNtNEpJg/04ItHkVWF4uQUkkem5YKVTOztpm5DJ9FFLUx1DxGojVG1ciHjOw6R2DOIIAmM7xuhmNLwhhWmxgqYto2YKmGZFhndpL7Cy8jBCfKJ/MxExlwgIMwq3r/SYv1scDtQuLi4uMwSy9AZSWsIAuimTVSVEAWo9ktUeCUmSzaa4MX2Bk8KBqdi6s9YZ8sqtigTlJ/5M/2/9x6n9cLFbJks8okP3sXwE/s48NN9tF52FjWXXsarrluDoRkUEhnUWJip7iEAvGEv43v7mP+aleRGMuh5ncGHdlMcTzvZApKIv7UZtb6B+NoVFBIpEjuOkO0fY3xXFyNbDyB5ZAZ/9yja2ARGvkjPHQ+w92u/ZfCh3TRsnIfHJxOsC592vYZmYJkWVnn6z7QRJRHbmttahVn3dnZxcXFxcXkJUEJ+gNNMEr9xxxGqVzTwwHiBT37kV/TcvZ2nf3aAtss3UPfqC7j4hvWIokApU0JUZMr5EqJHRvbJaKkCC646g6mBDKZu0nffbkrpLIIkooT9qDVVVHQ0EF85n+SBXtLdQ6SPjZLpHWV8VxdqLMzY9iOU0jmsssHw1kMc+N/7GN3ZT82qZnxRlUBNAG9YQUuVkASnm1RJNymbNj5JJDeWRxLAMm3sOQz3s431brx//vxRwsH3v/99zjrrLOrr6+nr6wPg1ltv5a677npRL87FxcXlzx1R9gAwkS0hCQLS9K+qZTsl/IoIlV4Br2hj2mBLzvYzQoEgnl6qIIhOBsJ0ycL79v+MjoDnGef92f97dOb1l7+/n7vH8tx2w3cRQxHqL7uI8758PfNefz7Z/jG0qRLlvIYa9ZMdziGIIoWJIpF5tVQtb0eNhSkXingCKvp4AmNyHFH103T15bS86RrkgErDBRtovvJCKjoaqFregZacopjMEGyoIr6sDln1kO4epjBRYPjpIUqZEsZ0poTTY1oCQFZlSpkSWrY0pwMJl9njxnoXFxeXl4YF98m8Y7NjNKue4vT3y2/vnHn933d18ViyyK3v+g5iKELNeZvY/LlrWPj6DeiZAsWUhp4tEGmpIDuSw9TLlPNl4ivaqF0zj0BtDD2bR41VYBUcQ0QpHKHjjZfQeOl5hJuraL5oLU2v3kC41TFALueLlPMa4eYqKloihOqDJPYMoOd1hrYNY2gGJd1Et2xM28Y7HetN26ZsOuOeqcmi+3D+MuEFt2P8r//6L/75n/+Z97///dxyyy0zxkiRSIRbb72VK6+88kW/SBcXF5c/VyTFhygrzKsJosoCXslJeiuZNj5ZcDwOLANbEJFPlCqIMrasIJRP6ZpwokzhRPnCtJhgBWK8b+hJDv7Vm4ktacAs6pQLGsfu7uJo7vSawaM5ncQdP6WQSNHyV+/AmkoSXwOh5hp8za1YhQwVHQ0EaitRoypjO/uoXdOG5JGJdDRQTGaY6h5CUhWq/H4kSQLTpLKzg7HHd1AxrwHbtPDV1eAJqKixCtRYBWPbDs6YJ9atm4+eO4gaVSllnOsTRAFTN7FMCy1bQhQF5jBrcQa3q8IfjxvrXVxcXF5azk13kH3kBqwzX4s8NYSQHKD/f77JV7+8lfwpQXNSNxm5606KiTRt73wznqkk9dXVhJp7CbU1URwZI9yWpKKtDss8QP/Dh6hf3w6APx6lnNfQklOYZQPRI+MJBUEUiZ+xgMT2w/iqo5haCX9tjGBDHFn14o0GSR8dwFcdwdRHiS6opzBRxBdVMYpOfD8hIpi2IxgUp8sTFVGYUy9kt6vCS8cLFg6+/OUv881vfpPXvva1fP7zn59Zv2bNGj7wgQ+8qBfn4uLi8ueOpPhQAhXsPJDAPK8DGyfTwC9PB8oTngbTr23F77RiLGunCQSntmkEHHPEsobdu5ebzpr+bb3jyB+8lhUVXnJD43j8KtreJxEVD8VE6qTDcryBsD+MXN3A0vdUI4gitmUhBkJY+SxKbSPhhWWkWC2IEoJXBaOMWBGjsb3T+bzRamxRwpqawJMaR4zGaVl1HsZYPzWWSeqppzDLJuV8GXDKFEzdRJREREmkbJrohkXA72EuUw4EXtkP/rPFjfUuLi4uLx31qy7i73/2z9x07oPAR//gtkvDXrL9CZSQn3LfYcr5IoXRyZlyh+DSFfiaknga57H24zVYWh6MMoKioqdS+Ka9j8RwDEEUkarqsMs6ViFL/ZUdiL4AdllHqmvDHOtH6TuMd+l6whvLGMM9VG9WmHxym9PRSZWxTAu9UD5tQsApXXBEg+Jclyowu3jvjhWePy9YOOjp6eGMM854xnqv10s+n39RLsrFxcXlLwVftBZ/rJ58RqNsQaZkoZu243AsCwiCgleysKdLEgTd6ZWMIGJLIoI13c7uRHnCdNaBYBmY+x/l/Rd/9hnnPDvmY2daO20G4t2vW8SSj7wXK59BqohhDPdQTgwTPnMDUk0T5tgAYiiKFG/A1gpM7T9IZPlSjIlRxEwSSy+jLlkNooSVzyD6FDDK2KaJ4AvMnMdMJWC6N7OgqAiKijHWz+Tjj6Nn85TzTqtJNariy5Qct2VFQvJIaFmnDhIEJI80tzK/ICDO8viv5FkIN9a7uLi4vDSEGxZw6f9+hEO/t35VRGV3WjvN9+D6y+ex7KM3YOXSiMEIxtgAdiZP5fq1yPEGjPEhpGg1ghrATCUYf2onVWeuINs3jBLyY2o6vnmLwbKwDR3RH8acSiKIEpR1ZwyQTSMoKtbEEFgmcl0rWBbl/qMktmzH1HRM3UBWZZSgx8k60AywbNSQl4lUEWU6s1ARBVRZdGL/HIkH4izj/Ss41L9gXrBw0NbWxu7du2lpaTlt/d13382SJUtetAtzcXFx+UtA8qrUtkbZtLwWzbAoWwIeUcLvFZBPSckXbAsM/bQMg9OMEk+0YTwl8+DZRIMr2yKs+bvzeOwf7uAf/+l8qs5cgRgIY5c0zMQgB7/xc/JjBWpW1lG9eiG2VgDdKYko9x5CkD3ku48RqK2k2N9LYJkjFti6BqIElokYCINlOstlHUGUsIp5BI8yIyxgmYihKHYhixiKULlxA8bYAMl9XVQvq2WqL0XNyjoCNVPYpo2syqT7prDKFoIoULO8GnHgAJhzeXdc/ljcWO/i4uLy0vC633yd3+9pc0VjmHUfuJCd77+dj35kM/G1KxADIczUOMbYAIf+9zfkxvLULKsh1tmGXcw7cRzQ9m1F8CjkBkYJ1MaYOnCUyvXrZo4tqM5kgGA4mYFYJnZZR4xWY2t5BEXFNsrYRUckFkQJMzmCIHuIr1mKkZkidXSA+JI42ZEsjWe1MtUzgalbqFEV+eA4pYxOhSrTtKGBdO8U1uicf40uLwEvWDj44Ac/yHve8x40TcO2bbZt28Ztt93G5z73Of7nf/5nLq7RxcXF5c+WQGU1akAh4lcoGRZtUS+6aVMybBRFwGs5gRzr9FIE4dS8vd/vsgCIxalnrLt6YYzFbzgDs2xw9cIYxUSa9L7D5EeSGAWN6KIWZNVD64WLyQ9NUEykGdt2Jw3nrCCx4wiRBU2UUk67pkIiReyMTrQje5DDFSB7EH0BxHDMGTioEfAoM+UUIpwUDZgeeFgmgqJiTiUpHDmApMhUbz4bOd5A+uF76LpzO6VMCX+VHzDwRX1Iisiit19Iz52PIszxlP4rOWNgtrix3sXFxeVPw2taKlj29jOxTYvNVX70TIH0gaPkR5Lo2QKB2koESaBhfQveSJBU1wCDjx9kwevPnvEoKOeLAOiZPBVtdWT37cEbCSIqHqRYLVKsDkvLI8UbkGubsUpFrGwaLGtmksDWNYTpskUxFKGcPUpxZIxyXqPmnA3IzQsYvu37DD7WxfiRSeILK8mPmfiiKuHGME3nLmFi73GUoAdJFqA8N9/XbDsjuGOF588LFg7+6q/+CsMw+NCHPkShUODNb34zDQ0NfOlLX+KNb3zjXFyji4uLy58tgihgWzYVPg9PDaUJKDHaIgqZkuMyLImy42dwcodn+hmcWG8ZTmYCOCmCv8cvjiThM/fzofdvZP5li+n+3WFaVYXhpwdoOquNu79wHxUeicCOUaLtEeJnzCeyoInBh3bhr6tkfFcXpYxGw6ZObMsi292LbVpUVNVgFbIYiSFIDCF4VaRYLWK4EsG2sUXRSVvUnNkHcbp0wTZNBFFCjjcQDISRKmLYpolt6ETOv5Tl9dVglJEbOkjc/wCpo8MYWhkzl6OY0p7x+V5sZttv+JU8lnBjvYuLi8tLw3UXtfO/9x6fWf5l3xS//Mz93HzDGla9bgl9Dx+nYa3G2N4Edavreeh/tk2XAgyyeF09jZsWIasKQ4/uBkA73IdlWtSsXoieLVBIpJE8MqLicVoyZvIoU0kERUUMRbEsC0FRHQ8jn1PiIMjKjGiAKIHsQVlyJsqCM0BRsXNpbF2j/vVvJNT0EIssC3/HfIbvfpDUsQSFiSKSR6YwUUSQBITZuhX/AQRmF+9fybH+hfKChAPDMPjhD3/IFVdcwfXXX8/ExASWZVFdXT1X1+fi4uLyZ4s/3gSAZdnopsWNa+pJaia/7Zrk/LYIxbKFIYmEPKpTmmAa0+KA/Uzx4BQRQSjl2fWJLz3neaf6kpTzZaqXVXPo5/sJN4bQMwXqY35qV1ZTtbQZbyTE5KE+Khe3UL9pGYVEmlBTDanDfWR6Rsj0TzDvdeciqH7yx3vwRoLI8QCpp58mvKAdK5umlEig1jfgXbwGU8sjyMqMwZJtmUjByIyYgGVilYpgnJxSUJdvms5eCFC1bjXVm89B8IfAKLP8I0uQt+0ArfQcn3L2zHVGw8sVN9a7uLi4vDRkH/13bgo/008GYLJrglKmRPWyao7f3wtAOV+iSpWpWhgjtiiONxIkOzBGqKkGAD2bp6KjgfTRAXJD40weHWHhtediaiUKI0nMskGouYbs8UFCrfXox/czdaQXXzxCaNNF2LrmZCPkMzMGyXZZd+K+VsCaSiKGok7GoawAENp0EaLXB0BkQRd1529Embcc2ygTWtpJobsL8dEtMEfhXhCEWcZ7d6zwfHlBwoEsy7z73e/m0CHHvqOqqmpOLsrFxcXlLwEtNYokCVimhSQK5MoWMa/Aa+ZHOJo2qA/KKJLIqGahiBIxwfE4sLFAkE4aI56CLYgIgoikPLd+3vPEIAsvbqP/8UFazmlGjfqxTYtwY4hwcxWixwNApKOBcl6jnNcINddQTKSmWyiGiSxowi7rYJkE2tvIdXUD3aixMILsYeLp3VS01TF18AgR2eO4LuNkGZT2bUUMRfA0znOuWdccQcCysPIZbK2AMT5EpnuQcqFIzfmbKSeGsUwLX/tCAApdh7AN1+DgzxE31ru4uLi8NBzPS1xUHeDexDNNZ4/uGmPBGTU88btuVqyoJlAdoFw08EVVlKAH0eM8xvniEfKjSbyRINEFTU6GgaqghAM01cYoJlLYpoUaCzN5uI9Qaz2egArAxM5DBBvipI8OoISfQIrEQZIQZIXSkd0AqEtWox/fjxxvcDyQgHL/UcypJNn+BJOH+5BVharlHZTSOUqpQ3gHB/A1NpE50kVhJOkK+S8TXnCpwrp169i1a9czDJNcXFxcXmlE21cgySKBoBePKBLwiJg2SJbOgjAgiYwXTWpVEKwythx0Mg6mWzHaooRgWs/wOLAlD7HFDXBX12nrV1R42TNVYmdao/p4mtj8SsYPjhNpqeCRh/vprA2STxym/aIFiEocI1/E0HRK6RxT3UNIqkJ0QRN6pkB40TywLIz0JLkh5zx6tkBwyTJG73uQ+PozGH5g68y+aixM1fJ5HP7Rw7RcsIJyf4Lx2+6h48qNDNy/nfa/ejNmKkHmSBeldBYtmUEQRSYOjiF5ZAINcbL9Y3hr6px0SMua04GEwOz6OsMru+7RjfUuLi4uc8/dXeMs8p3+OLYgqHA0p9Od12lKFqlSJLLDjrDw0NMjLAgqpMYLLAwoBOtEJ95KIpneUSYPDeINq4Tb6tAzeULNNUgeeWYsIKsKpck03kiQTPcgFR0NJHYcoTiRQ5vcRri1llBzDUd+toXm85eiZ/JM/vopGjZ1MvrTe5n3jqswk6MkDxzHLOqUCxqegEpi3yhW2aCys42p7iE8ARWxIgZ04Y2GsIxnejm9WIjC7OL9HFZRvOx4wcLBjTfeyD/8wz8wODjI6tWrCQQCp72/fPnyF+3iXFxcXP6csS0Lj1emwu9BEmE4Z1AbkDFEBdnSEUo54ooCouJkEmgZEGVsWUEsTc8unNJlAZjxOKh/7z/i+cLDlE/xUNwz5eT5TegmP3hikJtvWIOkiAiSwJlL44QbQ2QGsyT2DdMQ8qOEA/hrY46fQf8YnoAPbyRIOa9x7LZ76LjmXAxNdwYS6Ry+WBgrnyHUXMPxn96Lv7oCWXVSES3dILH9MPUbFpLpHUFSFeLL2xi4fzuSqtD1398j0lFPbmgCNRbG41eRfArzrmjALBtkekeIr1uFVchi6xqBxcvmqjPTDK/kB//Z4sZ6FxcXl7nnI++7hQOXLYSvbZtZdzSnAzBWMvnJ7jH+9qqF5BN5vGEvGxfG8AQ8mLrJ+MFxws1VeCNB1FgFkkcmeaCHcFsdtulk9A09uoe6DZ3omQK2aSFIEh6/k22ghP0c//U2bNPGMm3MsollDlNK52jYuID80DiSqlC5uJGxHUcQJIEj3/w5lYsbSR8bJdxchawqSKpC7apGtGSWib3d1J+1DFPTSW97kspzX8XEfffM6Xc4W3NEt1Lh+fOChYNrr70WgPe9730z6wRBwLZtBEHANN3UUxcXl5c/Hn8Yo5jD65NpqfLjEUXCXpGiYaGbNpWq05FAKGsIpbyTYSB5EMwyWMbJ17Y107kAptscywqilj1NNPh93nRmHfGV8+m5/x52DWd524fOY+Cxo4QbQ0QX1mNbFqH57SSe2EHy0DCegEKooZKB7iFEj0zjucsYfngHtWsXoyUzVMxvpjSZZuLBLXhCfuo2LKGc1ygmUjMzFb7qCHo2T3ZwEl9VEEESqehoIHmgF1n1kO4enhnApI8OoABTvSOU0k4nh/zQvbRcfg5DD27DGw3OlFS4/PnhxnoXFxeXl4ZHfrT3Od+7al4lNava2fG1x3gkrfGWv9vIoZ/vxV/lo+msFsp5jfpNyxl6dDdDTw4QaYsgSGPkE1l8UR+xzjaS+3uoWt5BYTRJZFEbuYFRCiNJlLCfpnOWYmg6yQO9lDIlbNNG9MjomTxTfUkArLJJoDpIKaMh+2Qm9g8QaoyixiqcrglhH7ZpUUxp+KIqx3/9NIvevJnBh3ZSGPkp9Ve8GkH69kv1dbrMIS9YOOjp6ZmL63BxcXH5i8ITqABAlESqwyrVAQXdtKnwSqiyIwAIJ1owTosCTocCx9vAPlXhFmWwjJnFE1kHNV6JsdKzP6Ad6UqxQhLJZ0pc884zqOhoYGJ/PxVt1SihALmhcY7ddg9q1E+yK0XVokr0bIFiqog37GX48f00X7AKPVsgsbsL/9A4AMNP97PkLWeR6Rlh5Ok+8ok8FS0VxJc2UErnCLfWoUYz5MeyyKpCYWQSNRbCyGuIkkgpncMbCSKpCuWC468geWTUWBhDcwyW4mfMB8DIF17EO/JM3K4KfzxurHdxcXF5abjg+rVs/+Kjz/re8fE8a+MRxooGr33dIioXt1DR1EO0PYovHiE3kODozx7D45MppjSql3koZTQEUSQ3lscye6hc1IKR15jY34+eKWBoOoNb+ph3xTImD/cx2ZVk4kiSmmWOAa5VNvDFo4hSAj1fRgl4KKaK+KI+DK2MxydTSueQPDJK2DFFzI04rZrVWAgAUysRbq1DVhX2fO5/kQN+mG4R+WLjdlV46XjBwkFfXx8bN25Elk/f1TAMtmzZ4tZDuri4vCKwyjqlUhF/yEttyMt5rRVIAhRNG1USEGwL26NiIiKXC9NdFcqOpwEgGI5QMJN5cEqbRlsQQfLw8SO/4O9ar3zW87dUOSmGZ7z1DDL9E0x1DzG8Y5TCRJGWVy0BoOOac8keHyS+JAvAwBP9tGxuxyhoeGNBrOlriXW24QmoeCNB6q+8DCubxltVSfUGx+k5sXUX4bY6RrYe4OkvPUT7Ba0E6xzhxCwbCJqOGgtTSufw14bJ9I6ihPwIooisKjRdvJGJXQfxqgqC7CE/NE5+dHKO7sxJZu+h8PIZTtx888185jOfIRAIcPPNN//Bbf/jP/7DjfUuLi4uLxEL7pP5a+DZXAAa/R7679/F5retZKpnguT+HrLDefRcmUBDFaZu0nz+crL9Y9QsK2EUDVLH09SvbZwuTRARJJFyQaP6jA5kVcHfUE3D+Wsxc1nUSIi6DZ1IqpfkgeMEamOM7+pi2w92s+ySdsyyM3khKRKGVsYbCWBqOv66SrL9CWRVQZBEAtVBQs3VGJqO5JExdYNSOktyKEmgxk85k5uz709gdl0VhFdwrH+hvGCB5rzzzmNy8pkDvqmpKc4777wXfAEuLi4uf4lYho5aEae2QqU26KUwbfwjApppOyaIpo6s52ayCSxv4KRAcEoWwow5oig7f4KI7VGx1RD//tN3P+v57zg2yVMP96NNZmi5eDW+6iiTmRKpnjT9Dztu+FNHelFjYdouXeOkFUbV6ZmECIGGOKamo9ZUkekdwRNQkWO16D2HKfb3MrZlD1gmtq5R9+oL6L1nJ6HmatZ/4ELCrbVULe/AE/JTypRounijk1mgKogeGX88gqwqpI5NYJsWT3/hdsKtdYiSSO7oUbyREMWJHPYcmhycMEeczd/LySNh165dlMvlmdfP9bd7927AjfUuLi4uLyW7Pvs/tPqfWb73m5EcD99zHNu0aLt0NeG2OsYmi0wNZel74AhK2Ee2fwxfdZSOK9fhqwoi+2Rs00IJ+QnUVgLgjQRJ7OpGVBwxON83RHYgQWE8heiRMbUSlYtaGHr8AP66Si742CVEFzRRv2ERFW3V5MfytF2+AduykFQF27TwxcJ4AirJw+PYlsX+H2zH1HTkgIqWzBCojZEZzGCbNsIcOhC6sf4kLzTWv1BecMbBifrG3yeZTD7DPMnFxcXl5YooK0heH42VflRZxC+L2IBXEhwTRABZOSkU2BZiKe9kGMBJ8cAyThoknihXsC0EQ8f2qMgX/TV/f/1T/L9v7nzGNeyZKtH6SD/h1lpiZ21gxcX7aXvtORSHE+z51qPMu6yT8V1dqLEwoiRiFA3K+RI1axc7swQeBUFRaThnJVIkjlgRQypkKRc0qpZ3IPpD6KOD2MM9LH7XlVhaHtEfJuwLkHr6aSqXzafm1a8Gy6L1nQswRvspT4zRf/9OSpkSkfZKTE2nujOOpyJMJBRk5Ik9+GJhGs9dhv3Lh16KW+UCPPTQQ8/6+rlwY72Li4vLS8eOn/+IR4/+in+dfzkDReO09w5lddrvOMhiSaTxkvNYfVkHLRediZbMsOdbj9KyuYPJAz0z5scAhQmno4IaC6OEnJbNTeevQK2tRQyEkNPOOCXUVI1tWk6GQCrHvGsvxEhPIkcqCccbyO5+mmBDFfWvfQ1mapyWN12DOT5Evm+IwccPYmgG4aYQqeMpGtbWzUxKjGw7StXSJtovXUnXnTux53KmwGWGFxrrXyjPWzi4+uqrASf1853vfCder3fmPdM02bt3Lxs3bnzRL9DFxcXlzxHRo9C2chHNlX46435Kpo0sCig4QoBgmaBP1/MJIrYogWA76y3DySw4JfPgVAFBsAxuX/FaHp74wx4AIqAXyogeD6IaoPG8lZSnMvibG1nw2hUYeY26DZ146ttIPf00q/6uAzlcgRRvwNY1it1H8TVXYgFWIYvg9WHmsvhb2zFzaQRfAN/y9dgl53OYxw9QzuWY6hlxshViYTwNYBUyCLrG2GPbnB7OmRJqVJ3xNyhlNPZ9424aNs6j8fILMJOj5IbGkVUP6Poc3aGXU6HBi8t9993Hpk2b8Pl8z3jPjfUuLi4uLx3J9zfxwXd+F4D3Nj74rNvUeCV8UZVQcw14FGpWL3SyB+urmXdZJ4XRSRrOWYG3bSHJx5+gankH/ngET3U9Zi5NpqufcEcjsmVh6xqm7ogLgY5WbF1DilbjDUbAMrFyaazkBJmjx8nc+wQVHQ2QzqEAtpbH1PIkntpLcTxNKVMiVB+kMFFEEEXKRYOuO3dQf2Yzne97G6W+LtLdQ7S/uhN71445+w4FZhfvX65jhT8U6/9YnrdwUFHh1LPatk0oFDrtIhRFYf369Vx//fUv2oW5uLi4/DmjZyepqQ7SGvUhiwKiAJIAmBZIsmOOaFoz2QaCbZ00RpQ805kIwslShWkhQTB1bqo9/1lrHU9wosZMEQV0y1HxBa+KJxTEu2QtxuAxKjs7UJefRengNgTVT+W552OM9GJreYbv+g2RBU14q2KOYKCoiLEYxvgQSst8xEAYqbqR8uAx0tufJtTWhG2ZlNI5lJCfUHMNiR1H8AR8WLk0he4uAp0rqT3vLPp/eR/esBfbtMkO5wjUBJy0yQmbgUeO0PfgYZa/6zwia9cjeL47dzdoln2dpw/xsuSaa66hVCqxevVqzj33XDZv3sxZZ51FMBh0Y72Li4vLS8TWxn7+893Pbop4KlNli9R4AXPaYFiNhVHaOykPdBFqrqH+DdeiH92NbZSpXL8WrfcYtmXR+/O7qVreQbitDkH2YGg6SlM7Wu8xfM2tiFHHDLGwfzuZ3hFiy+ZjaRpGXnMmBgIqI1sP0nDOcoyxAbI9A1QsX0b1hjMYvPcJfFEVLaUx1TdFbGEMURKwTZu+h7s59PN/o/NNK6l79QWYqQSSIkFpbr5HcZbx/pUY6/9Ynrdw8O1vO200Wltb+cAHPvCipCoahsEnP/lJfvjDHzI6OkpdXR3vfOc7+djHPoYoPrf9wiOPPMLNN9/MgQMHqK+v50Mf+hA33HDDadvcfvvtfPzjH6e7u5uOjg5uueUWrrrqqtO2+drXvsYXv/hFRkZG6Ozs5NZbb+Xss8+e9edycXF5+RNtX0FiIs9vD4xSE1BYGFMd1du2wDQcw8NTsS2EU9QAwfq9bgnT2QZSNsGZUZWnUtqznveNq2oZ7c8woZtc950bEBQV78IzsAUBNRzD1vLgUVDXnA+ijKe9EzFSDVoe0ef8bjfOW46ZSiDICrahg2kiKCpK+1LM5Aj6+BCiP4w2PEz66AD+eARR8aDWVGGXNLLdYzS/5kLEUBRLyxPoXEmp+yDJ/T14/CqmppM6niJY57grSx4Zb4Uy/TFFhh/fRz3MeWHh7M0RX56kUim2bdvGI488wsMPP8xXv/pVNE1j1apVbN68mc9//vNurHdxcXGZYxZfdxnf/PWXnvW9y+qCpEomedPi7f/1NgR/CKV1MYgSUoUj9Auqn/CmtSCKyDVNSPEGMMr4FBVB9tDWsQRrymmpiCjhrXTGJWrrPApdh9F27MY37UlUGJkk0uFkOZ4wO84NjdN+9XmIFTEo60RWrSZ/5ADpowOO+bFPJnU8TfUyp9xBCSp4Ah4CNX6CdUG0ZIbU009TsWThnH6PgjBLc8SX6VDh+cT6F8oLNkf8xCc+gdfr5f777+cb3/gG2azj1j08PEwu98IcM7/whS/w9a9/na985SscOnSIf/3Xf+WLX/wiX/7yl59zn56eHi699FLOPvtsdu3axT/+4z/yvve9j9tvv31mm61bt3Lttdfytre9jT179vC2t72NN7zhDTz11FMz2/zkJz/h/e9/P//0T//Erl27OPvss7nkkkvo7+9/gd+Ii4vLK5FwPI7skYiHVYKKdNK3wLJOmh3ap7RjPKVrgtOOUTj5PjilCpbBng99gnkLY8953qePJFnzpuVc/NYV9P/6EURfACufwdYKaHsfJ3nfb/A0dGBlJsEysKaSGH2HKA8cxSrmyT39CFY2jegPk965A7tcxjbKWIUsxmg/UqwO0R9mfOvTeCsjtFx1MZ66JgR/CLukocxbTuyMTgSvMxMtyB7MVAI9UyC6uAWzbCB6ZKLtUcp5HVM3McsGpm6RHytQTBbIJ3Ic+dkWsNyaxz8FkiSxYcMGPvKRj/C73/2OLVu28OY3v5kdO3bwxS9+EXBjvYuLi8tcMrXlq3g2XPGc7+9Na6x97ULOf0Mn/fduc2J9MY9t6JQObiO1bTue+jasbAosCzM1jtF/FL33EBhlsjufcmJ9IMzkzr2UE8PYZR1jtB9jtB9/x3xCTdWkDvchKh6aLz8Xye9HUr0Ymk5w+Sqqls9DilYj+kMIvgD6QDeWbhBqqsGebjetRlUmuybR82VM3USURPJjBQoTBdJ9aY79ahdWIftSfa0up/B8Yv0LRbBfoFtFX18fr371q+nv76dUKnH06FHa29t5//vfj6ZpfP3rX3/ex7r88supqanhW9/61sy6a665Br/fz/e///1n3efDH/4wv/zlLzl06NDMuhtuuIE9e/awdetWAK699loymQx33333zDavfvWriUaj3HbbbQCsW7eOVatW8V//9V8z2yxevJjXvva1fO5zn/s/r72xsZGhoSHw+PF0Xvu8P7OLi8tfPr5YPWddcyk+ReKvNrSwtCZAjV9GNDQwdCebwH6WYoNTOypM+x6cEBEEs4ygF3hvw0XPed7L6oLEl1QhKRKyKuPxyTScs5zEjiOO+ZFl4Y0E8UZCKGE/xWQGJeSnMJqkoqOB/Ogklm7gjQZRa2uxtTyFkSShxYuwCllsywTLwi7ryLFabKOMWBHDzmfQRwfxrdyIMdiNIHuQquoQFBVb1ygd2cXQo3tQY2Fs00KNVZDpdXwQTN2kXDQoJgtYpk1mMItt2gSq/bx+704mjDINDQ0MDg6+KPcmHo8zMTFBW3s7e/btn9Wx3v7Wt3DnHXcAMDAwQGNj44txiX9yDh06NDMD8cgjj2CaJps2bWLz5s2ce+65rFixwo3107ix3sXFZS64VzzI/mMp9meemb//mpYKQnVOOnlFSwWiJBBuq0FLZvDFIwxtOUb9+nYEUcQXq0DPFvDFI9PeQV5ERcbIF/FGQjNxWc8WUGuqyB4fRFYV5IAKgBKNgighhiJY2TSlRILAirVoh3biqapBrm3GNp0MydyuJ+m7bzfReTUYmk4hkSE3ksUynUdJf5WP7HAOq2yRGs4hCaD4PQykND4lDDOcyr5o8f4tb3kLP/rRjwDYs/8AbW1tf/SxvvPt/+V9f/d3AHzrW9/iuuuum/X1/TnwfGL9C+UFZxzcdNNNrFmzhlQqdVrt41VXXcUDDzzwgo61adMmHnjgAY4ePQrAnj17ePzxx7n00kufc5+tW7dy0UWnD6wvvvhitm/fPtN+4rm22bJlCwC6rrNjx45nbHPRRRfNbPP7lEolMpnMzJ/rDuri8sol0rSY2ohKXUQl6JWJeiWnRME6xezwVATxlDaLPmzJg+3xOsuSZ0ZosGWFpWHvM/ef5jcjOQb2JvBX+VCCTtumY3dtwyobFMbTlPMa+dFJSuks6sIVhBfOR5BEIguaGXxoF5ZuIKsKuaFxMkePM3mol1TXAPnuYyB7GHlsF4Ki4mlegFgRQwyEGfj5XY5osHw9+rG9CF5nsGFl05hTSfq+/yOGHt1D3YZO4uvPoGp5h9O7+fA45aJBoDaKVTYpZXRK0wOkQslgqG8Kaw4zDoQX4e+P5Wtf+xptbW2oqsrq1at57LHH/uD2jzzyCKtXr0ZVVdrb21/QQ/kfQ2dnJ5/4xCdYsWIF999/P4lEgl/84he8733vmxlIuLHejfUuLi5zx8JrVjNeMp71vV/2TdFzdJLgtHhgmTZDTziz/YOPdeGLqmR6xrAti/CZGwi31mKVDSIdDaS7BrBNZ+JCzxaY6h6i644tTOztJnt8kEBtJbmhcTwVYbw1dUixWrBM+m//Dam9hwisWEvpyG6UpnYQJcxUAruQpf9HP2HyUB+1a+YRP2MBStiPvzqMqVuUMiUkj4Rt2pQyOsWUhiQ43gyHxguIgKk/+2d9MfhTxXr48473zyfWv1BecDvGxx9/nCeeeAJFUU5b39LS4qjyL4APf/jDTE1NsWjRIiRJwjRNbrnlFt70pjc95z6jo6PU1NSctq6mpgbDMJiYmKCuru45txkdHQVgYmIC0zT/4Da/z+c+9zk+9alPvaDP5+Li8vKlqJtUh1X8HhFZFAB7xgjRFiUEW3TKEUQZe7oto1DWTmYiGKd0E5jeTyo8++zDCS6qDuCRBPbdfZzKmI9cSqOixs+urUOc+/olSKqCrCoooQDJhx/CE1AxywbZ/jFCzdUkD/SihH2kj0+y6I1nIYiVRDoaEBUPUihKw4WbECtiZLZvI7R8OXYxT+u7rsNMjsJ0NgKiBOB4JCgqdWef4fge9CcQEmm05BS9Dxyl+Zx2Jg6OEl9egRpN4wkoSIrI6O4xAIKigJCeo5szjTjLwsU/Zu8TqfFf+9rXOOuss/jGN77BJZdcwsGDB2lubn7G9idS8q+//np+8IMf8MQTT3DjjTcSj8e55pprZnX9z8X73vc+Hn30UT75yU9y5513snnzZjZv3szZZ589Y5rkxnoXFxeXuWP3/zzBWMl81vfWRFRSZZOnH+qjIaiQLpZRRIHHnx5mw8oaZJ9MoLYSSzcYv+9eRI8zkZAfTRJurSV5oAcl5Cd5JEHrhctovXgVlm6ghP146pqoDkcQFJWp/QeJrF6FbZRpu+7tmKkEWE55oVzSsP8/e+cd5kZ57u17ikajutJqe1+ve8c2bvQeWggBDqSQkATSTnJILyc9J/V8CeGkHQ5JgBASkhDSSKgGQrONce9t7e1dK63qaOr3x6zXOJTYGFPM3Nely9Lo1cw7Wl96Zp73eX4/U3e3AYlZrTi2zfCGPSS3dWDpFgMbh2g9exKDG/tpOn0aud4RbMvB0i2Gt48QlkXifgnDciiOHTsHJVEQjireiy8xffBaj/eHE+uPlCOuOLBtG8t67n/0np4eIpHIEe3rd7/7HXfccQe/+c1vWL9+Pb/85S/53ve+xy9/+eJK2/8sgHFgReDZ259vzD9vO5wxB/j85z/P2NjYxKOuru7FT87Dw+O4JRSPUNQP/g72ZA03YeBTYTxJ4IjSRNJAMDQEveDqGDg2gm25D8dxXRUOaCAUX7wPsDi+ihCPqfQO5amcFCMQVznz6vms/MN2RncNYWo6tm1TNmMyocmTCSSiyKqfUjpHqCaOliqgxlWkYHC8OiFHKZ1l182/xTENrNQQ0UWLXVumQhZECce2sLNp5OrGibkIaghzdJiOe55A8ClE2xoopbP4QirBiiCd/2in8bQZ+EIqoiRSObd1ouIAQJTE41K88IYbbuB973sf1157LTNmzODGG2+ksbHxkFL5Z3PTTTfR1NTEjTfeyIwZM7j22mt573vfy/e+971jNscbb7yR9evXMzg4yBe/+EUsy+LLX/4yFRUVLF26FPBivRfrPTw8jiWi9MK3YB0Fg2q/TGNcpStboq4xSrRM5eIrZ/HQ2n4y3VnyA6NIqkJseivh+goc20YQRUxNxx8Lk+sfwx/1E2lyWxzyA0lKqRx7f/knrEwax9SJnTAPO58BUcQpadjFPHYujb+6FkfXcLQCgiShZwt0PLAegIq5bRhFE0ESiTZE6Hh0P+VT4hh5jUzPGDWLWrENC0mR8EnCxHlKx6F3wWs93h9OrD9Sjrji4JxzzuHGG2/k5ptvBtyAnMvl+MpXvvKiZYfPx6c//Wk+97nPcdVVVwEwZ84cOjs7+fa3v8273/3u5/1MTU3Nc1YKhoaGkGWZRCLxomMOrDpUVFQgSdKLjvln/H7/IX7Wx+MFr4eHx+GRT2UZy8cYzZXoGtOYWRlCx4ckgCSJICng2Iil/HPdFZ7Ns/QOEGWsiha+9dMr+c8P/+55h6cMm3hYIdoQoWJ6OQMbh7Atm9S+MWadWEfFzBqMvIZj2dj5DIKWB0CQRGzLxh8JEptcg+STcQydSFM1Rr6IpelMumgJcmU9QiCEUyoiRBMIioo13IsYjrluDYAYjIxXHoiIkRhTPjjHFVfMuq0SvSvbaThlCoX+UbTkGEahCEByWwfh2jIKI0Us3cbIv8j38jLxcv5MZ7NZMpnMxOt/jglwsDT+c5/73CHbX6w0/oXK7X/xi19gGAa+8ZWkY4Ft25imia7rlEolDMOgo6MD8GL9AbxY7+HhcSy4+xu/oOpdlzOiPzdBW+mXsByHWHMZJ0yKkdw1iiAJ7Ht4Pxee1kx8cgWWpqPGIhjZHJamE6iMkd7djVHQ8MfClE+rRVYVjHwRJRJEUhVMTWfS5WcjxStxdM3VNghFcUQJc7ALMRjBGksihWNuC4Mo4egavmYfM2fNR1BUzN52QtURRrYPUjm7llhLGb5IEAB/1M9Yez9qXCWi2wy0p5AsB78ivajF9NEiCEcZ75/12WKx+C9jPby+4v2Lxfoj5YgrDn7wgx/w2GOPMXPmTDRN4+1vfzstLS309vby3e9+94j2VSgUnmPFJEkStv3C/72WLVvGQw89dMi2Bx98kEWLFk184S80Zvny5YDrRb1w4cLnjHnooYcmxnh4eHi8EOnuHexd186e/iy7hnI81ZXm6d4sYyWLnMnBCgPHnnBLwDbB0sHUJioPcGz3taUjmG4bQ/iyD/GT9rv4zy+c+Zzjbs2USOV0oo0RRJ/EtEtnMuX8ycQnlVHKlBja0oeRL7Hz98+w/VeP0f6XlcixciytRKytnvIZLQiiSHzuDAS/ijJ5LoFJ04gsWIJUlnBXGAwdfd82ClvXYg73uvZOAKJEbusmjM6dWGNJ7FwaKzWE2d+B0bWbkafX4Vg2C7/2AWre9CZa3/FW1EQZ8SmN1J40h/jUegKVMarm1DAwXKBQMuEYt48fqOh4qY9nM3PmTMrKyiYezyes91JK4/9VSf6x4Prrr2fevHlUVVXxgQ98gL6+Pt7//vezadOmiXl6sd7Dw8Pj2HHbd37Ef888jct2rXnOezuybll/rKWMYEWQ+dcuZfqls4g2RBjcMkRy1xB6XmfzbU+y87dP0fPYFrdNMRqkrK2e8unNAERbaimlc4RnzCAyZRLxRSdg5zPYebe6Mbt5M/md2zFHh1GmnoAwbtk8tm07+S3rsJIDOFoec7Abo2s32bUrGV67FceyWfCl99JyzTtpfcelBBJRIk3VNJw2Z7xVwcYX8jFYshgz3MTIsUzBHm2sf3a8/8hHPvIvYz28PuL94cT6I+WIKw7q6urYuHEjd955J+vXr8e2bd73vvfxjne84xABpcPh4osv5pvf/CZNTU3MmjWLDRs2cMMNNxyiZvn5z3+e3t5ebr/9dsBVVf7xj3/MJz7xCa677jpWrVrFL37xiwkFZXC/qFNPPZXvfve7XHLJJfzlL39hxYoVPPnkkxNjPvGJT3D11VezaNEili1bxs0330xXV9dzPKI9PDw8/hnHsihlUzi2Q1iV8YkCA7kSEUWmNe5HkwIErMxBocRnOyw821nhn20bAcGxcWSVUur5Le+KloOW0hB9ErKq4Aup1ESDyCGVka3dpDvHUOMqU69YRnEo7X4mmUGJhCj0JwnVJhBDEezieDVCMOLqF4gSYrwSp5h3kwqRGOL4RYQgSdhantD0maTXradsZgwsEVFRyXf3kNnfT7A2gZEt4JiuvaOgqASqYvjqW+i89Q+EauL44xH0TBFFFChary/Rue3bt1NfXz/x+vlWIA5wJKXxLzT++ba/XPT29nLddddx+umnM3v27Ocd48V6Dw8Pj2PPQO75dY3yloM2ViIQDyBIEsGaBE2nq1TOTGNqOqN7UvijfmZdfRqpPd1I0Ri53i1UzG0jtacbXyiAXF6Jouk4hoEUiQMg+BSksgRmsh+joFE+Yz4AjqkjqG7MDzfXkdnThVKeQVRDCLKPXO8Iud5hglVxtGRmPAGRQa6sR1L9hKZOY9fP/oASVjDyOlreICQJ5C2H0vNUVbxW+fGPf8zVV1898frFYj28tuP94cT6I+WIEwcAgUCA9773vUdtV/GjH/2IL33pS3z4wx9maGiIuro6PvCBD/DlL395Ykx/f/8hfsutra3ce++9fPzjH+cnP/kJdXV1/PCHPzxEVGL58uX89re/5Ytf/CJf+tKXaGtr43e/+x1LliyZGHPllVeSTCb5+te/Tn9/P7Nnz+bee++lubn5qM7Jw8Pj+EYQReItsymmBomEFHyiyPKmGFFFIugTJ4KGFSgjo9tEFRHR1HBEGcHQKMghFNENEEVzXPnYcgj5RAKlNI5tgj9Ern9s4pgtQR8dBbe0/+zrFjO6ux9Jkej8xx6UkIKkSOSH8pRPjhOpDdP1VA+NvcNsvWMtNfPbyfbnGN01RFlzjIq5bei9nQTmLiX92AqiCxYiliWQfQpOPoudTSEo6kTbgj2WdG2a8llENUj5aWdiDnZhZ7KIwQhqZTmiT8axbUL1lW5bQyGLoKgEZ57AyCMPM/Xf34OdS/PMV26l9sRG4jEV27Ih89zv92Xl+Swxj2wHE88ikQjRaPRFR7+U0vjDKcl/ufnDH/5wWOO8WO/h4eFxbFlcX8ZT48+nhhV259xqg3OvW4yWzFDKaLT/fTO+kG/CoSgxLUGoOsjup3qZ1D1E+9+3IYgiQ1sGMfIlLN2i8cx55PftJ7rkZIYfuI/yRScg+FU3Rusa2DZlbfVIVQ0TbYlSZT1OSUPwqyROP8OtNBgZRC6vmLB1FBWZSFM1UrwKO5vGLmSJLVxA/30PM+Pj12IO9/LMN39LsCJITUxFLxhYDhzQkD4mHKjgfOk7mHgWCAT+ZayH10e8P9xYfyS8pMRBb28vTz31FENDQ88pNfyP//iPw95PJBLhxhtv5MYbb3zBMbfddttztp122mmsX7/+Rfd9+eWXc/nll7/omA9/+MN8+MMfPpypenh4eADgj1YiiBKhykYWTSrn1JY4iiQQ84tgmwjOeAm+DTHHxLFVHEFEMHUEo0hId1f6EWV8z6o4EHT3ue2PkBGD3Ht/+8QxDyQNAB79+RpaW2PULW5g6ltaXVHC0TQAmf39aMkMs982n1I6x7xrT6Js9kwEv4pTzGOMDJLrHSF60plgGpQtPYmBe+8l1lbvWi8BYiSOYOqYg91IZQkKe3YQnDZrwrLJTg2BbVMaTaOqQQRFxV8TBNOAoWHArWIQJAkk14Fhz023MeWD1zDjqqXs+fNaBElAPPJOuSPEcS0yjwLhCC9ynl0af+mll05sf+ihh7jkkkue9zPLli3jnnvuOWTbP5fkvxz89a9/Peyxb37zmwEv1nt4eHgca4SvHkzMHkgaANx389NMqY/QfPokKue1UtZWj691FvrcNyE9ehsjT6+jZtFkTK3EnGtOJlBXRcWShThannxnL6M7Omn6t7ciSBKV55xLx69+S83iGfgqql0dI0XF0fIYPXsRZIXC/n0E6pPINU3u9ULnTiytRCmVQ1L9iJJItLUWWzfIdrvuC2IgBKI44ba084e3MOOTH2TqWxbQfu8m9IJBzrQJSC/Vt+Awceyji/cvwXb3tRrvX0qsPxKOOHFw66238sEPfhBFUUgkEs9RNz6SiwkPDw+P1xu+QBhZUVi6vImgItGfdVsUVEnEL0tkdYGwIhKUBQS7gGCbCHrxYNvCgX9t85B2BUcQERwbMZ/kiw0nAvD+S6YQqoogqQq1p5yA0jIDAG37OpTW6dz/b98g1hSl5axp1F5yCYGGXuxcGj2TJ7z4VMyednwt03G0AlJLAj/g79qNoKgIwSiOlqf20ssmWhWcYh4z2Q+mgRSvorBnB/6aGsSyBNg2ubVP4q+qQk+OYOQ1/FoBKV6JnU2DKGHkNYzu3RT7hghPnYpU1cDoji4azpjPjh/8nClvP4/5n2il655HSO4aho5j/Md6GSsODpd/VRr/UkryXw7e8pa3HPJaEISJEskDrw9gWZYX6z08PDyOMe/d+Ag7+kPc8OeP4zvxfBw1yueeGOaOWx/ils6t3H7rf/Ge7z/ITX/9AeHaEK3n7qMGME2d8hktFIdSVJ53PsZ4rLfHkoh1LcQnzyUy3Ove1Ms+nGKepssuRFBUsC1Xz2i8MlCQFexMkkB9HVK8EkENkd25HbWynFIqh1HQcCwLubzCjfWAkS+id+4h2z1EbNZU5Mp6Ml0j1CyZxtZv/4SpV5zCgk9PZe+dD9CzuhdDM19eteJ/xnGOLt6/hMQBvDbj/ZHG+iPliBMHX/7yl/nyl7/M5z//+eeIHXl4eHgc72T725l2ylI2bRmgqFtMToTwSSKG7RATbIKyDqaJoFsHRRAlBUcQ3Iy7pLiCiIBgm8B40mD8+Ucb3jRxrJv/sufggX+0+pB5zIgozGqMYhs2W+9YT7AyTvmFl2MXMqhqCDuXxhwdRgx1IygqtqJiJfvxNU1F27IKubYFwaeAKCKoQazBbgDkynrXNQEIzVlIfss6fHWtOIZOaNYJOLZFoCyBP5/B0TVyO3aQ7RokXF+J5JPpuvcpJn/4WrTt6zCTAwSryigOpalZMo1CVw+Zjn4ESUSNq4cEs+OFf1Ua/1JK8l8Onl0xsGLFCj772c/yrW99i2XLliEIAitXruSLX/wi3/rWtwAv1nt4eHgcS9678RHmlfm5bzDPfW/5AfCDiffeOv7vP054hM9G/Ux5+xx6V3ez8w+b8MciVFx4GXYhi3+2iqNr6MkRBKUDwecD28bo68DXNJXS9jVu22EwAqaBIEoY/R0IiooYjODYNlJZAl9DG6XtaxAjU13HpUXLwbaIVta7WgaFLOktOymlsji2jT8WpvPBtUz90LsZfeoJlIEB1HiQ0W378QVk2v+yEl9ARlJE4q0xNm4Y8OI9r0y8P9JYf6QcceKgUChw1VVXeRcSHh4ebygkRcU2deoWnEchU6KyoYxJVSGmV4SIq26ZnilIyJKNANg+FVHLulUFlo5ouqKHWDrIKpgajvCs39FniyUeBlMrgkiKRPnkOH3dGR7+9gO8ZfYspHgVgk9BTNTiq6pzSxLVIPq+rciV9WhbViElahAkt8JACIRwClm3qkAUsZIDyJX1EwKH4cWnuhccPgXHshBMMAbbXREl2Yep6YiKjCCJOJZN7UlzyDz9BOHZ88isX0f14plkuwZpv2c9iWmVlE9vxtR0iiM519/5WGkmObzkVYSj5cVK419qSf7Lycc+9jFuuukmTj755Ilt5513HsFgkPe///3s2LHDi/UeHh4ex5BFMXVC7+jFmFIVYnTPCEu+eAV//eAtrPjOg7x1xnS3OkBREdQg/toGV9zQr6JtXYNcUYO+e8N4BUEQKzngWjBqBeTqRrfCUMsjVzW4B5F9KNMXgm27bYaihJ3PYPTtRwyE3GuG9h4EScQyTApDacpnNDH44INUnnoSqTVrqZjbRtcjmxnYOEhiSjlKKEogFia1L01bTRjrWGoaOc5RxvuX/tnXcrw/nFh/pBzxFcH73vc+7rrrriM+kIeHh8frmbKmmTQtvRB/QCEY9dNSGWJGTRRJhLAiYjsO9oHYI4qIpfxzWxFMDUeNTFQcHOBAb55gPL+6MsA7ltZToUjEfSJXzKmkfnEd2/alKWutYs7ZrVz846vReztBlDCHesCx8U2ajVRRi+APYGfTlPZsQVSDmINdmEO9OLaFY+iYQ70kV9yPPZZEStTgmAZ2NjUhoOQYOk5Jw9HyIIpIlfVIZQlSG7dNzE9U3Dy0bZgMrNlBfscWAlUxRjbvRfLJ6DmDzsc72XnXGoY2tJPpyb6Mf50X4IBg0kt+HH8rJADt7e2UlZU9Z3tZWdmEt7MX6z08PDyOHYMlk5Wjxedsv2JOJXGfSFR2Y33N/Co2rO1HOfE8pp3SyFt+9A7MkQEQJazhXgB8TW67gBiMoCUz5PfuRVBUtK79GL3tiONCx1ZqiPTaZ3C0vKtxYOhYQz3PifX2WBIAKV6FVFnP4MOP4YyvZEeaXOE/x7LIdAyQ3bwZWVVI7e4mWBHCsR1GdiXZ/2gHI9sHXBcoSTjmGgderH8uhxPrj5Qjrjj49re/zUUXXcT999/PnDlzniPmcMMNN7ykiXh4eHi8VvFHK4jVNnLqSU3s7s0wo7GMSVVhljW6bgqDeZMyv+iqBiOCZR5sU4BDRHvEQgrHF3SFFC3drTpwbLBM9DX3veAcbMvm2utPIlAVp2zuHARZoWL2Kox8kclvO4913/k9S/7f9XTf8WsarryCkT/9huiM6fTc+yjDW/s54TNvRypLIARCWJlRBFFEDIQw+vZjjo5QNnOaW3UA2NkUYiiKM27ZiOz+zguKimOOCzWKIlVvvhSjYyfawACZ/f2Uz2hGjpVTPj1Hvj9JaMYc2Laf4ugY0cYIo3tS6HmdkV1JIrURHPtYBuujF0c8dhLQry4nnngiH/vYx7jjjjuora0FXH/pT37ykyxevBjwYr2Hh4fHsaJ5+cUoe5543vcCcZVP/r9LkUMqgUnTQBSpnPcUg7f+iN+85/tc+r0PsfBrH6D3rrupv+Iyhv/yB8qmtdL9wEr6null0cfehK+qDiEUxae5MdyxLexCFj2VIjJlklsxaBqYg12IgRDWQBdiWcKtNJR9CGoQRyu4+kemQcO73ou2+Sly3QOMbG6ndtksZFXBMkyKo2NUnX4KWjpLpmMAf1ShlNERJZHknlHC1SEyPVmkY+iqIHB04ojCGzjWHylHnDj41re+xQMPPMC0adMAniOY5OHh4XG8UcqM8JbzpgAwtT7K/KYYs6silAckwj6RsCKSKVmEJfNgBltS3LYEDsZK4cB7pjYhkig4tqsp0Lcb/+xlwI+fdw53PtPPuZ0ZTvnsuTglDSyLirPPxUoO0PvAYyz8zOUMPvgg5bNa6f/LX+hbvZ/aoRT1Zy6m+eo2HC2PY1sIooivvg1H17CLeaRIfEIcifGkgCAr6Pu2ISVqENWQuxoBCKKEIEo4ho5dzGPns8iV9aiiSCmVo/fxTYg+GX8sTNWyE3jmizex6AtXs/eOvxJMhBjdk8LSbXwBmfxQ/tiKJXm8ILfccguXXnopzc3NNDU1AdDZ2cm0adP405/+BHix3sPDw+NY0bnyHsaM57/Rvf3xbpZvHeHN332r234gK26sH+7lE1v+l8pf30zmntsom1zP8AP3sf+h7dQNp6g/dR6t77oKW8tP3PCLasitLCxpOKaBLxRA6+sjVJbAGR9j9Hfjq23EMQ1XNFHXxgWUI9jDvTimgZVN4atvIxqOYWk6Q2t3IUgiok+m7tQTeOxDP+Kkr1xCancf0YYow9tH0LIllKCPbH+OdNHw4v2rwOHE+iPliBMHN9xwA7fccgvXXHPNSzqgh4eHx+uNmrmn89jGPtqaYkyqClMfUakM+ciWLFRJQJEEEkIRUwgj4bolOCIgBhDMZ7UfODaMuydg6jj+MIKpgSRD/XS0J+5+0XkULRs5pGKMDCIFg0hVDZT6e6g/52Qc26L+XdfSc9vNVC6aTc05Z7Luaz9zHRmmnoCZSeIY49UC4/oF1liSwv59hNomY2kljK7dIEqIkRi+5uloO9YTmL8ce7gXMRh1LzRwLRsd23KTEaaOnKilbJpB/IS5DD31NLZuUuzto+2CefSveILWN5/M6LZ2yqfkGdw8jKSMd8kd6/LAo644OD6ZPHkymzdvZsWKFezYsQPHcZg5cyZnn332RFLAi/UeHh4ex4a20y5B2vHYi45xLBsrNYygBvGVJcjv20/58mXoz9xP2ekXYvZ3oNalqDjjDNZ89sfIIZWaSbOw8xl3MaCYB9tGVINY6WH0TB5/VRVWMUlx7w5kVUHwq/jbZpHd+AyhqdOwi3kEUQJDB9tCmTofp6Rh9rZjjSWRyhLE580iNmsqvStWATC4egvNpzax9bZHSUyrQlJEgokAvZ1jWGMlKiIKKeMYtwMcWJQ5ms8fhxxOrD9SjljjwO/3c9JJJ72kg3l4eHi83vAFowxs/gc+v0wirDC3Lsr0iiCWDTbu6qsAOEoQWc+5HzogKCfJOJJvorrAEUQcUT7YnvCsNgUcm1z3wIvOJR5WyPUOY1s2hd4h9H1b8YUCri5BIUvPbTdTfebpdD+wksKeHbScM5twfSWlravwtc1x+xyzaZxS0bVdtC1XqLCrA19FNXYxT8+DTyGGYzhaHn/rNMyedldIydQRZJ/7mWQ/Zn8HgqzgFPPY2RRSvIqhp54m1lZPqCZB/6rt5AeSjO4eIL1zP8ObOwnXliEpEmbxWCki/hNe3+ML8sgjj/Doo4+yefNmNm3axJ133sn73vc+3vte11Pci/UeHh4ex4bzz5jEvDL1Bd+vLPNTHE5jaxpaXx9G127URHRCtLD317fh2BbJdVswB7uYcdVSgpVxcpuewVfXiuBXYbyCwMql0ZJjaMkMmd37CDbWYGk6PY9uQE7UYib7UasrcEoaUlkCMRQFn4Kdz6JtXo22Yz1SZT12IYOZ7EeMxBhZt4XE7FbURJRcfxo9r2NqJsNb+xnrHEONq8QCPgKSQGdao9ovYR3TzkTHi/UvwL+K9UfKEScOrr/+en70ox+9pIN5eHh4vN4wChmUcBytoFMWVIgoEpVBmfKARFyVMJ/Vp+/4wxO2ioJtuWKHggiiPFFpINhuRQKCCKaGHYi7iQZJZnTb/ucc/4BI0uUzKzjxP86k4sT5ZDr66Vu1A300Td+Tmxl+6EHk+jaiLbVYY0kaLzyNkc3taMkxAq1tKNMXYo8OguxD8Ls6BVpfH8bIIGXz5+OvqgJRYnRnJ7KqTJQq6p17kCrrJ7QNrOQATjFPoaMLKVFDftc20pu3Yusaenc71eeeQymVo3/NLupPnYtj2YSqwgxt6kQJKYx1plBCPuSAhOSTXrG/ocehfO1rX+Pcc8/l4YcfZmRkhFQqdcgDvFjv4eHhcaz40llt7M7ph2yrUCRUUeCt0xIs+fR5xOfNIts9xPCGPViZNJmOAYZWPIyvaSqxtnqcYp7ECbMYWr2FdHsvocmTCc870V0cKObdJIOmkd7ejq2bxObNJjq5GVENkdnfjz8WxrEtpHgV+mja1S+ybex8BivpLmJku4fwVdWRWbuG4We2gW1T2r/L1TRIZhhr76d28TSUkEKsuYxiSkPPGWgpDV/IhyqLNAR9h+Ue4fHyczix/kg54laFNWvW8Mgjj/C3v/2NWbNmPUcw6Y9//ONLmoiHh4fHawlfMIpRyFA9+xQapjcyZ3KC86ZW0hj1o1kOUUV0xQ0lBd0GEFEYL3ezbRxBQLAtYPwGWRBdO0bbdJMHoohgaIh6ETtQhmAU6FvX/5x5pAyb9547icbTZhBqrsccHSZcX4ms+hnZ3E60tZatt6+kvmuI2NRG0ru70bMFmi67kF3/9ztG16yjbMrwhJezIEoYyQHCcxZia3kKe3biL48hhiIIokj9xW9CildhDnbhnzLHtWZSxldGDB2pqoGIP4CgBgnNmIOaGgbTQIrG2PXTXzH5ynOo1HS6VmxAzxtEG8oIVoRI7RslWBFkrHMMWZWxjFeg6sATR3xebrrpJm677TauvvrqFxzjxXoPDw+PY0NwzR/oKhiHbBvRLd6xtJ4ZVy1FrarEzqVRE1EEUWRkcztqooz2v28l3ztCfEYz2a5BiskMDeefQeefHmDk6XXEpzYiV9UjxatwTJ1CVw8Vp52KlRwgv3cvgZoKBEVFTZRRNq0VUQ2h7dlCcNosV2tJFBECIaRACDEYoXzhfPApRBcsJNjf4WojWTY7fvpbpl55OqZWom/VDrL9OeKTYlRMSzCwcYiy5ij5oQL+qJ/RZJGAJLji0ccqpB5tq8IbONYfKUdccRCLxXjrW9/KaaedRkVFBWVlZYc8PDw8PI4HJCUAgKHlEUSBgCJhOw6aZeM4rqihYOqIegG/4bYoCMa4zeKBADZeXeAIwiHWjBOMtzAMi1EAmk5pfd65/GbFfkythGMaiKqKY9mIPhlBcj+v5wz8sTCxubMx8hq+oIpUliBYFUXP5BHUIGJZAjESA0AMxyYcFIJTpiOGYwiy4h5MFLGS/Ri9He4YUUIMhMC2xm0dUzimDqLkCigpKvnOXoZWbSBYFSW1eQfRKU3oeYNAXEVLFbANEy2lISkikiKhhH3HvuLAcdwLoaN5HJ/XEui6zvLly190jBfrPTw8PI4N4qyTKVeeGwPvXtPnxnVRcisERRFfyE3c++Nhiin3GqNs1kz0TAHJJ0/EciNbQJAkBDWEEAghqCHkkOq2Hsg+gg21SNFyBL+Kns27CwTJfizddMeIovuvoSOqIczBbpDcOA+u/WKue4BMRz/h2jKG1mwjPqURy7AIVgTIDxawDGtCw8gfVZAUkYxpI4rH2I7xqGP98RnsDyfWHylHXHFw6623vqwT8PDw8HgtooTKcGwLq1REkkU+dkoruuXQ7Dfo0QVi4wuwglkC20TVXdsjO1yJWMq77Qmi7L5/IGEwnhV3RBkcG8HS+HjT+Xz0A4sYm95M89duYMFvzmZ9WjtkLprt8PhPn+LMT/hRokHUynJglNTeYbRkljNuvp4N37wNbfTvxKc3E66vYOjev6FEg9RecgnZtSsZemodDe94J4giUqIGs3+/uyph6KCGSD7+GDWXXIqdz2Bn0wQWnIo13IuUqAXbOmjDaOpoXfvxlSUp9g0RbGogWJtAS2epmNvG8IY9dN+/ilhrglz/GADFVBE1rjK0ZZhAIkCsOcb2v+05pn8/ATw7xhfg2muv5Te/+Q1f+tKXXnCMF+s9PDw8Xj62nJim4cZfs2J/mtCbvkDnV97E177w90PGaLbDX798D2d/5GSCVXHUynKsQoHBTT2k9w9zxk0f4Zmv3Ip15z3E2uoI1VfS9+e/Eqwpp3LZiWR37CT95GZarnknVnIAtWUyRp8b6wVRwrEtRp98kppzzsQc7kVPZwi0TcXo2o1c24IQjODYvZjDvUiJGnKb1+OPhcl0DBCur8AfCzO8YS91J89maO0u9t/3DKGqCPmhLIIkkOnOYuk2yV2jBCsCVM6sYKAvh2E5xzSaCs7R2S8Lx2ni4HBi/ZFyxIkDD4/XCuHqFuaeexpr7voD5rhXrYfHy0WmdzcA0YapyD6JwZzB5HI/3Ro0hEWSJYeEorrtCAeqCUQZQS+AKOOIEjjCIe8f0D/ANnF8QR5c+GYmhxVuuGktsBY+5roq/M/DX+f6s758yHy2ZkqcUihScd4FOKUijrGNcHUIgM7f30Ptic04lk18yRKKe3eQ6x0hMasVa7iXQGMDkfknknr4PmKnne2uLARdoSVsG2ssSfnyZdj5DAC+lunYY0mk1jkIWg47l0YcX8UwB7oopXMMrdtF40VnMfDIUxgFjUBlzO3FNEwKyTyCWMTIG0iKiJ4zUMI+7KiCltIYzg8Tjqsw+sr8LT0ORdM0br75ZlasWMHcuXOf04Zwww03vEoz8/Dw8Dj+yPy/U5DKEnw0NAOA9wJf2whrv/R/LPqvDxwytqNg4Ng20SWnuM4I/R3EmmM4tk3nH+6l+axpWEWdxOKF6AM9FIfThOorsHWNYGMNkblzST7yEOWnnIZjGghF157R1vJYyQGibQ2utkGihvCkWZhDvajzTsYc6cdKDiD4fMg1TRgdOygMpRjd0Un9uSfRc/8TCKJItLWaobW70PM6WkpDS7ntCwCSIk1UGmhjJXrX9BMPKyghH1JaBB2PV5BjEesPq1VhwYIFRySicPLJJ9Pb23vEk/HwOBKu+dClNFSGCMRrXu2peBzHjO5dj2XaDOZLFAyHRqXE3jGThM8ad0gYL8CT3FJ/wSwd3HZABNE23Qe4CQbbRiykuKcnw47scyPp9Wd9mUUxlf4znBIAAQAASURBVLbQwR95RRSof8c1484GNlJZgqYLTsIomgiSSLi+kurl87FSw6gtk2k4YwHhqVOxNddeydE14medj1MqIigqdiHjPg+EkMoSrn3TWBIxFMVODSOWJSitvAdHDbsVB7aF0bEDADNfpGJuG0NPPk3V0jkYeQ0tmSE/kMLIFgAw8gaCJKDG3TJH0Sfhj/oRJGGi3FI41oJJR+uqcJxWHGzevJn58+cjiiJbt25lw4YNbNiwgZ/97GesXbv2sPfjxXoPDw+Pf81Tn/gpfc+jC7Povz7AO5bWc+X86oltuu1Qe9kVbquC7EOKltN00WkYRfcaIlgZJz6jGVsrIIXDVC2cRmLWJMzREQDssSTlJ52CoxUQRAlzdBg7n0WQFaTKeuTaFrSOva7eUddupLIEmX/cgxSvgnHLZX3vZndfhnt90ffwKsra6ikmcxT6RymmivgCMrZlY+nuZ/xR/0QCAcCxHUzNRPJJE3M/Zhy1g9LRVie+NnmhWL9hwwY2btz4kvZ5WBUHGzduZNOmTZSXlx/WTjdu3EipVPrXAz08XiIV05fw6Joe5s6sItvf/mpPx+M4xBeM4o+U4wuWUczpSIJAumRRHRKZKo9hSZWImrtC77Yd6K74oSAi2BbOeFrWEQQE0f2pdQAsHSSRTzeex/LyACtHi897/LXj7QoHqg8GS+NigrIPsSyBY+rk9+2n6ewTGNncjpHXQPYhlSVwdA1l6gnY+Qy+sgRjq58i2FiDY1vIlfXYqWGkRC367g3I9W1Yw734WmYgh9qwkgP4GqeibX4SuboJq30TYlkCubIes78Dvb+HaEst+YFRZFXBGMtQvXAaqd3d+EJ+BEnE0vPYlo1PkckPFlDjKlpKw9RMMj1ZIrVh8kMFnGNdHni0+z8+8wY8+uijz7tdFEW+/vWvs3nz5sPajxfrPTw8PF6c7ndESFzxc/6+5O3P+/6vV7vJ1yc/+SNO/v5HSRk2gii5OkIRBWybYlcHky5aQv+qbRh5DSUaBEBQVCILlmAlB5AqQ4yuXkO0pQajf78b6wsZlOYpZDdtIDR5MqX+HgJTZ6MkKrDGkiiT55Jd/SiSIqNtfBxf4xSkRC3mUC9GNkeoJkFx3MrRsWzCtTEs3SRYIWIbJo7lJgcs3UJLaahxlUxPllKmhGE5qAGZVFqjLKJQOqaCyM5xq1NwNLxQrD8aDrtV4ayzzjrsizxB8Gw3PI4d8y+9imDYjyAKPPF4x6s9HY/jFFPLYWo5HNumcfZ0NMvGsGwcSUaQFKTsII4ScsUPfSJYxkExRNnvtig8W9tgvPLggB1ja8j3gkmDZ/PZc77C/zz8dX552bco7VyHr2mqWyFQ3UT83CZsLc/+e9cSrIrT9/AqqhdOQ88WCM9UkKobsceShFqb3DYEQ6e0fQ1SogbB1FEmz8UccpMG5kAXAFKiBkfX8C+5AGPLkwAIwQjFdY8SOPFsfKkh9L2bGdrQTtUJbdiGSdejm1FCCrblkB/MIykSoiQiSAKSIjLWOUYhWUTySVRMSxz8jnuPqbGz56rwEvBivYeHh8fLR3zpSfxu4VU8kXzxeH/Zjz/GI/99K5/6/kfQ927G1zLDFTaOVxJZegaObZH740pCtQlGNrcTn26S6x0mMc/A1zQVKzVEWVs9pXQWv0+htH+XK6LsU4icsAijazf+2gbM3nZs3UBpnY7esYPoqeeTX/2QW21g24w9dj9lp70JO5sit+kZhjZ0UDmvCdswGdrSR7g6hKVbFFPahBiipVv4o35y/TmK44se0YoAvpCPgGGzuT2FdUzD/VFWDRynFQfHgsNKHOzf/1xv8X9FQ0PDEX/Gw+Nw6N/TS3VrHacurGPvOq/awOPY4Ng21bNPIRCLowRkTMtBM20QXP0CARC1MfRoPbKluYmD8dYEwbaevaNDEgh2II7ct40B7fBK9zTb4a9XfYflF7TR9+jTtLxvFnapiCArOFqescdXICkSeiZPIBFFrqgBBshs3EDZ8sjBaVg2A48+ReUJUxnbvIX4yadj9O3HzqaR61qQq+oBdwWjtH0N/ukL8U87AW3LKjJPP0lo8mRG//4HUnu6qZjbRmJGA/1P7yZUHaFidiMjW7sRRBHLsDA1EzWuYhYtLN2asF70RxVMzSQ3mKd6biXyFgGM5ztrj1cDL9Z7eHh4vLzkt22geBh3zTnT5jM/+Chvuufb9Pz29zS0zADTcLWIgMxTjyBKAqVUFjVRhhINETRMUlt3k4jEcEoahf4kvmiQ4XU7SMxqJds1RNm0VvLbNiKrCv6qepx4JYJPwRpLovX1ARBaeg6pB/9CKb2OQG01g3/8HckdPcSn1hFtKqfniT2UT05QNaeOoS19+AIyxWQRS7cm2hEd26aU0bEcCIy3Waa7MjQtb2DySBFfTngj5uKPOw4rcdDc3Hys5+HhcdgUkn1o1RWs3j6EGo292tPxOE4JVjYiKQGCYYVYLMBo0WB2dXi8okBx2xEsHSXTSzFSjyqU3DYEUT6oZwATlosHxBPF3DB/O+NDpIwXz3C/+/Rm6pa28tBNq2lbXs/uFR3MvGgK6z/zHVL70pz8rSvBNIideQHRhUvo//u9bPz5ak6f2UZy234q5rbhFLLuFGQfouKjlM6R3LaPioVz0PduRpk0C1tRXW2DsoSrfZBN45+5GDubwuzbjzJ1PsrU+WQev59QaxOh5nrynb2UtdVjGSZWUaeUyhGIB9DGSoiSgA0T9osAlm4jqzLBiiDxyRUkdw0BIEriMU0cHK2rwhttPd2L9R4eHh4vH8U/fZT1H/7UROvh8/Ghy6dTdUIbD33/EeoX13L/Jf/J+Q/9iLXXf41MT5aTv/k2SiNJostOZ/rMOQw89AibblnFSV+6mMz+fsL1la6GgaISrE1QHEqhZwuM7uwkMauV0kgSf0U5cqIWu5h32xltC0H2EVl6OkbHTrSNj1O2/DQcyyL5yAric2cQaaom3z9Kxdw2JMW9XTQ1nUhdGD1nIIgCkiJhFk30vIFjOxMaB8GKABXTE4zuSVHKlEhMK8fuOXZZA8GxjzLeexmNw+WwxBE9PF4rTD/3MspbZ6IEfKz9/a8Z6z3yFTIPj8PBLOYACEb9BBSJk5vjjBTcu9xDApQgEhjrZkyOuAKJtnlo2duzxHcES8fp3Ipuv3iQumpBDbGWMirmT+OUK2Yy6xPvpbwhwpo/76J2ySTUuJ+xXR2oi89BCMewxpLUXnwRi64/g657n6LyxFkIkoQ1lsRKDeHoGmI4hj8WxhcMkNq8AzlRg9G5k577HsXRNeyxJI5WQAxFKe1c6/o5SxJWcgB7LEl0+VlgW+Q7exnZ0k62a4hs1xCF4TSFoQzaWAkjb+ALKYeciyCKVM6soGZ+Nf6oQnp/klhzDFn1YR/T2kVeBsEk72LCw8PDw+MlYpt0rO1/wbffeVIDFbObKJsxmRPfPIVZH3s30YYIvzn1wyz6/n+ihHyktu4meur5iLEK7GKemnPOZNFHzmDPH1eRmN0KgKPl0Xq6QJSQQyqWpiP6ZHK9IyixKJk9XXTe9VcwDczBLldgOVFLbs3jyJX1aMkx9H3bsDNJEqefgZVJU0rlSG7rYKy9l7HOJKV0jlz/GIWRIpmeDP6oH9EnYmomvoCMrMpUzqygcWENgXiAkZ1JwrVhlLCPTE8WxScdu+/ZcbxY/wrh2TF6vK7IpTVKuRSdK1cCkO7c+irPyON4RQ6E8al+TMPiykUN1IZlpsQVHNyVaEcQEBwbRxARRJF4pgsrUoVoajjPrjIQxIlEg+ML4ugaZ3zhfB4et158PkZ7stTMryG9cz/Vi2fgaHmWfPWdLB5vgch0DFBKZTF2bwSfwvCqZxBEkZGt3dQsmoygqDimAaKIo+VJ7+6i4pSTqVoyl00/vofpVy2nf8UTlE2up+6sZVipYXzN0zE6duCfuRh17sk4uoaVGkaZWu+WQK5/gmIyQ7ZrkNiURkZ3dpIfdB0UTM1AEEX0vI4vIKOEFARJwMjrCJKAbVgUkwUCiSCBuMrdd25lbkzFNI9hX6HjaRx4eHh4eLx6nPHHPBfkX7isLrUvTfVQGrm9g8azFoJts/gr7+JEU8fo2k3TqZMQJAl93zYEn4/0pq1oyTHS+4epP3kGkupHDpk4loUgiQyu3kLN6UuonD+Fvfeso2ZBE8WhFIGqOFULp5HftY3QrPkUN68mMH/5hHaCpCr46ttwdI3MM6swNZ3Uzk4qT5jMwNO70FKaK3g4fi4H/nXGk/+WbiGrMkbeoJQpEakLE4/Huel32zm7KkR7Xkc7luKIR61x4MX6w8VLHHi8rqhqjJIekhl+tSficdwjKwF8folg2E9txI9uOQg+KFoOAUEEn4rgOGCbbqJAEBFzw9jBOIJRPETXYCKRYJuIM5YTAa45ax23PdzxnONePrOCplNbyPWPkR/aR1kyQySdY3jDXia/53IE2UfL+UsRFBUrl0aKV7Hvvu1E6sJEGmKk93RTNmsqcnUTmY0biJ1xLonKejeJoGtMvngBal091VVVaAMDyIlaAIz92/C1zsJKDYEoIYai+GcuxhzsQuvYS2DqbGxrK6kdnQxtaKeU0ZEUkdxgfiJRoIQUSpkSlm5TypSQVRlBMhElEUu32fiPTtanNSRBoDNvvOFaATw8PDw83jis/vXt3DO4ic9GZj7nvQtrwyz4wHIGN+wnuWuI2hNbiGQLpHd3U3/JBTimQfXyeUhlCYz+bpTmKey9ZyPBigBlrRWkdnaiJqKoDU2kNmwmcfJJ1CQqJvZfv3wyFSfOp9DZiZ4poFRWAZDdsJbIvBOwU8MTTkvhE09D37uZQk8/kRMWkVq1EkEU6Vu5EyNvICkS+aE8AGpMxdRcRwXHdiimNGRVRs8bKCEfsirzxCOdE1bTO7IlynySl4Y/TvBaFTxeV7ztjDbKa8NIivpqT8XjOEcQJURRYEZjGWFFJuGz0G0IiA6CZSKW3CCKKLu6B+OWi8huqf6BnjvBsRFsE8HUESwdHBuzv4P7nux+3uPu68tRHMmR2pcmVBUm358m3ztM3cmz6bjzL/gaJtP35GbMzJirSTCWZPGX38bsj7+LQGWMUL174SAEI+jZPGZPOxg6Rk873SvWUhhOueWMlfWEps3C6G1H8KsIwQjG/m1u0iAQwh5LYmdT2GNJlMoqko8/TnD6PBKz3NJIOeB6M/sCMo5tj2+TUcI+LN0iVBVCCfsQJZFgRYD9u0fpLppI40r8qiigH3M7Rs/X2cPDw8Pj1aFm7unYv/4vzq0K0Rg4dK22M2+QHxgl050l1hxjdFc/xaEU5bNa6f3Lvci1LfT9Yx3F/e1IsXKs1DALP3Ups65/B8GqOMGacmzDBNvC0g2Ke3eAKFLs7qHr0c3ue0BkwRISp56GU8giyArBhlp3LK4YstG1G3OwCzMzRrCliZFHHyW+ZMnEtQS4VYWy6rYjmJqJL+SbsGGMNkTwBWQkn0RiWoId7amJpAFAtV9ma+YY2/Yedaz30hqHyxEnDq655hoef/zxYzEXD49/yeM7hzlvUQP1C85+taficZwj+QP4/DLNFSFCittuoJbGSOvgyIoreujYYGoTLQmOT0VK9yHqRRyf67OMZbqPA89tG+XUy/nS3r/xvV+95znHHTNcm6Oa+dWk9qWwLYdtv9tM+z1ryfZnuf/sD9B4ybkMrt2FVJZgcOVG5Mp69vzid1SdeTpVpy5n6Kl1ZNeuZPefN1Ea7MfWCviaptL67iupPus0BL+KNZakuG8XcmU9iBJSJI5c3+ZOMzWEY+qY/R1k93eTb+8gUBlj8MEHKQynXOeEojXe4yghiCKO5aClNPScuzqhpd3n/qif4e0j6LZDzrTRbAfNdl5ULOrl4pDkzUt4vJHLF71Y7+Hh4XF0DGz+B9U3DfLFd3z7Oc4KkgD5wSzVc6sY3DKILyCz/v9WsuePq8n1p3nksk/ReMm5DK1zY/3wmk2IkRi7f34XiVNPpfqMkxnb20t6yw7a/74VI1PASo8SbG5m2jvOpnLRbATZR2H7BsbWrEKubsLRNQQ1RGCya/VoJQdwdA1zqJds1yBj23YTqIozuOIRjKzbimjpFoGEez1zQPzwQGWhL+RDS7nPQ9VBhrePAIfeXL4ysd45+njvcVgcceIgm81y7rnnMmXKFL71rW/R29t7LObl4fG8TKoKUVem4vMfQ5EVDw9AlH34/BJhvzxx/+iIEobtHHRKsE0QZQRLRzC1iTHYJpq/7NAdPlsk0XFwBIGeRzc857jnnt9GfFI5NYtnEKkLk5hZz/xrl070GCohH4gSjm2jd+6h5vSlAFTMnYSja8j1bQSq4qiJMmzLRktmkGtbsIZ7sceSYOg4JY3eBx5DiccRyxLY+QzgVllYyQEArLEkcnUTpXQWOaRiGyZmXsOxbARRnNAuAJAUCUESJrycD7gpVM+t4pEnu9HGSkyfVUFA8poTXi94sd7Dw8Pj5eHHH1rGiH5oj//SM1uomtdM1cJpVM2qJFhVxtJPnUNhpICW0vBHFQRFxdTcisHKpScgBqNUndCGlRpCStSiJsoI1ZZjaiampuOrqsMcdZt5HdvCTA6Q3t1NsLFmwjnJSg2BbbtJA1PHLubx1bVSSucQJBGrqGPr7mKHpEhIioRtWAiSgC8gI0gCZtEiWBHAsRwsw6JqTiUPre2nmNaYObWcsOwVtB+vHPFf9u6776a3t5ePfOQj3HXXXbS0tHD++efzhz/8AcPwDLk9ji1Xza/nzdMq+PPnz3i1p+JxHBOsbCRUXkVLY4yZlWGCvvFWBEGk2k5jIuIIwkTSwPZHABBsE2QFR/YTTO4Fy6TXX4vjU90WhnEtBFsJIOaS3HjLxuceuyqKmohSGEpR1lZLoNxNQPhCPob2pNjXl+PJ639C05vPIds9hK3l2X/bb4hOn4yVHKCw5hHiS09CCEZY8pmLsLTShBd0atM2xEgcO5Ok4c3nIaghtK1rSK1aCbIPO5uisH8fRk87A/94GqN7N+XTm/FXJEhu20/Z5PqJ8kcl5EONqwQrguh5nVx/DjWuIgdk1LhK4ymtPPPXPZQrErmczh1P9TBYOobiSM+HbR/d4w1cvujFeg8PD4+Xh/BHrnzOtqp5zcihALZuUtZWT6SxmsJwCn/UT+eGATrb06z65I9pe+dbyPWO4BSy7L/9t5TNnIY5OoK2dQ2JU09DLq9g0cfPw9RK4FMQJJHRLXuQ4lWYmk7VkrkIskL6sRUMr3oGU3OTBWPbd6ENjpDcth+9u53Y1EbURJTUnm7URBmmpqOlNOSAhC+kEIirlDI6mZ4satyPIAmocZXJ509h73jr5ahucecz/WSeR/j4mC4bOEcZ69/A1YVHyktKCSUSCa6//no2bNjAmjVrmDx5MldffTV1dXV8/OMfZ8+ePS/3PD08+Mr3PseYZiIJ0BxyeNun/v3VnpLHcYqsBAiEFabUuAkBVXaTBAf63v3Zfhw16gofSgqCbeFIh9oQOpIPwSxRr/W6CQXHxopW4/jDblmc/fw30bv/uhM1UUbFkoXke0cojo5haTp1Sycx+60zWHJ2C7Iq8+Snfkaudxg5Ucvkj38MKz3qqifPX46ja/Tc/wS+hjZiU5twbAvH1AlUxjD695PtGnSrC1JD6NkCxaEUYihKsasDtboCQZKou/Ac5Oom9GyBQk8/iVmtGHkNOaQSiAdQ40GClTGqF01h5jtOouWsyUiKRLShjFhrJb2rO0kEfaxPazw8XHjecz3m9QeOc5SPYz3B1zZerPfw8PA4eg6U8D+brXesRfLJRGZMR0uOYRkmgiRSt3QS86+YzfxzWnFsh1Wf/RmZjn7EaIK2f/8gxsggeqaAOnMhdjZF38Or8Nc2EGurRxAlBElCiYYwejvcWK+G0AYGKAynsHWTwKRpZHbsRJBElEiQ6lOXIZdXku9PYuY1Kua24Vg2/liYUHUQJaQQSISpXTKF6VcsYtJZrYiSSMW0CmLNMXpXd6OIAoMli6dTz9+W4BOOcTj1Yv0rxlHVkvT39/Pggw/y4IMPIkkSF1xwAdu2bWPmzJn84Ac/eLnm6OEBwJSKED94ZC9jJZukIfGfZ095tafkcZwSTNRRVhFkUWMMvywiCQKmw0TFgCPKiNlBd7Bjuy0Ljn1QIFEc/2kVZQTbQjAN1zc5nwTLFQ2yEk38ZPvtLIodKvR5T0+GTT9/EruQoXrxDOquuIqas08l0liNrZs0nX0CDSe1MO2y+YzuGXH7FFNDGAWN0KRW9N0bESMx2v7jPzD7O5Dr2xAUFSlehVIeQ1RDyKqCWJZgrL0XPVNw2x1EEUvTEWSFobU7ST61CnOwC395DDURBUDyySiREIlZreSHspRNdv2fHctmbP8IsdYEPau7+eEPV3HnM/3c05N5we9YFYVjHKuP1tf5jV1x8Gy8WO/h4eHx0rm0YhFf/c7Fh2z7095R1v/vP7ByaeLTmyk/7UyqTlpCtKUWs6BRf/Is2i6cRes500juHEbw+bDzGYrJDIGqOKVdGxACIZquuhxzZABfbSOCoiKGY4RqyhFVFV8o4No47u5GkESqT1nstitoOr6QyuC6XfSveAJzdJiy1lrkkIpj2YiKjOiTScxqJTeYJ9bmxnpBEhndm6RyZiVdT3Vz4y0b+d3GQe4bzL/guYuAcaxD6VHHek/j4HA54sSBYRjcfffdXHTRRTQ3N3PXXXfx8Y9/nP7+fn75y1/y4IMP8qtf/Yqvf/3rx2K+Hm9Qlrz9av777q2MjOR5zy/X8dm/7eTXG/uYfu5lr/bUPI4zAok6yusqOGlmNfGAj9mVAYKihU/PAeAoAVenwDZdl4TxFgbBNicqCwA3wSD7JvQQBMsAU0cs5cn+8f9cQR6fMiEctCim8v3ffgCASedOQRAl+p7agt6xA8cwkFW3BHHjTY+w78E9OLZN3dJJjDzyMI5pEGhqwbEteh5+BnOgy9124tmkVq2ksHUt2BZiMIpv0izCU6ciBiNYholj224vZF8H0YVLsDJp4jOaic+d4VYihGMUh9I4lo0SDSJIInrWvUjofmQTxaE0eqZAqCrMxru2cveWYTT7X18lHM4Yj1cPL9Z7eHh4HB0Xf+QDE8+/+rl7ALhqQQ1jt90FwNQ3z0IQJYY37sFKDiD4A/giYeSgyuZfPM6ev25BUhUq59SSWrsBeyxJuLEGx7LoeXQDeuceECXUuUtJb9iE0b0bW8sj+FWUqfOJTp2EVFkPgBIJUezpJrdlHbE5090WiZZaqpfPR0tm8FVUY+Y1TE1HiQSRVT+WVsI2LDpWbCbbM4ptmJQ1l7H9b3v4867kYX0H3i358YX8r4ccSm1tLbZt87a3vY01a9Ywf/7854w577zziMViL8P0PDxcnv7NryaeL7jsbYwVDPYMZHG8viSPlxk1Wkl5bYRlLXFOqA7ilwR0W8YviAhG0a0s8AXALLkJBFPDkVUcUXYTB+PWhDg2WDaOICA4rqCiMP7/9T8//Dt+cvqFfHjmuyaOuzatsfaq/+OKOZV0P7GfPX/bSay5DHN0BH/bTNS6FpoqanCsB1j1p520pnJUnjAF2zCxkgP4mqYihmNMmbUEfd9WV/DQtqi44C0U1z9Oau0GIk1VroBidSPph++h5qKLKe3aSHLdFipPXoo53IsUjeFTg+BTCE1WwbYIVMWwDZNs1yCxmW203/0Ykk9CzxsEq6LkB5JYhvmCLQmvCg6eUvJR4MV6Dw8Pj6Pj9uBDxIHPbH+MA80Kv10/QMV7ruA/v3khACu//FuiDVFqhvrwt81EmTyXxmg5vtBTPHXLMzQMjJKY1epqExSy+BqnIPgDTJ53MqWda8E0sHWN8tPOJL9pDdnuQcL1lWBvRErUkHnqEeouPIfUmrXkeodJzJqEU8gSqKlAUIOIgRChZrALWSRVQYkGGdvfT/mMFnbf9QT+qB89ZxBtKCOzvx+AB4deuMLg1eConRG8e4nD5ogTBz/4wQ+44oorUFX1BcfE43H2799/VBPz8Hgh1t9956s9BY/jFDVWTTBRx2kzqpAEAUUSsBxQBBuhlAVAMMf9iSUFwSiAqSM4tmu/aJtum4JlTtg1HnBQOED69z/nkx9ezL/PfBeXTi7nT3tHAVgQUxnQzIlx9Yvr6FndS2p3N77eESpOPwO5pomRXSOUKxL7H26n/uxlCLKP0mA/YijC1m/+kGBFiLaPuvofjpZn9LGHCTfWkB9IIkgi6d0bCNVXkOseouyUc9j3t6eZes0l5HbsQIkGkcsrcEwDwedqNthann1/e5pIfTnaaIbCUBoAOSDjj6oUkznu+etuuosH5/6a4WgTB2/giwkv1nt4eHi8dCadcgnX6nW8d+MVfOnRb/DZs76EZjucVRmkTzPZeddabMtm2VevYPV//YF0ey/KUJr4suVIFbUMb+1FEQU6Hu2g7qQ5yIZJvrOXoGmw846HCddGmfSh63BKGk4mw+iTT7oOC3mN4lCKobW7CNaWU+gfJTxzDv1rdjPtytPIdg2iRIOoVZWucDK47g2ZMbpWbCBUGyPbkyLf66Y6/FE//qifwkiee+9vf43Geufo4v0bONYfKUfcqnD11Ve/6IWEh4eHx+sRQRRRQmUEIn6a4wFa4gFsByRbR9SybiuCqbvJAEsH23QFEWXFTRQ4tpsssO2D7QnjwUh4VlBKvPlKfvi/awAmkgYAluPQGJDxR/1UzqygMFLg9B9cw56/bqV35W4cXQNRpPGUyRQth9az2iZsFCWfjGMYOLaNkS9h9uzFSg3hlDT6Vu5EkH00/dtbUaJBWq++nHB9JZKqoO/bRnxaHVpPF3q2gBwrpzTg+jobvR3kdu1GGxxhytUXu5ZQRZNsb4pAIkyoppxQfQWje0ZfmxcSnsbBUeHFeg8PD4+Xzr4n/sLts4cA+N4FX51oz7McqPbL+EI+6k+sZ9vPH8D+w9/peWIP/U/vdm/mZR8tZ88l6JdpPXsSpXQOX1kUUZFBFJFVGceyMXvasYZ7cUoaozs7sS2b2jOXI6l+Gt+0DH8sghINMrr6aaJNCbTkGHo2jy+oUhpJ4ugaxf3tpLfsoDCUZso7z8c2TEqZEunONMGKEEo0QKSpkvT+9Gs01uPF+leQI6448PDw8DgeCVe3EqltI1EboaksQFvsoEuCI0oItu26JwiC25YwLnJ4IMstalkcnx8AwbbclgbJ535G9oFlYq5fgSX7DhEKagn66CgYbBorcVZlkAUffwtWLkt+YJSuvz1G8+mTqD97Gb333E/DlVdQd/kVnJDM0LOyk2BVjEiTSb5/lMr5JxGpjzOwvhd8CkbXbqR4FaIksPeuR8gP5qlZ0Ei+P0li4RwaL2pi6MmnCdUksA2TQGUMbAt/VRWOlifT0U+kqZr99z6DvGYHoZpyiskcNYsmY+kGu+7eSCmjUyiZqKLgaRZ4eHh4eHgAouzDNg20re4iwYBmTlQYPj5S4KREgAUffwtmZoxI1yCV//cJpFOm4P/0j+j96nU0XPFWYmecS/O63a5rQSQIQCmdJbJoOWr8aQY29tP8VgmtpwulPEYpU6L38U1kup+k/qQ2kpt2UT69mXBjDSMbdxGur8SxbEI1CUTFhxIM4pS0idaGzoc20rdqJ7HWSiRFompeI6JPZvud6zE1k0LpNZo08HhF8RIHHh4eb3hkNUR560wqG6Jc/6ZpLG8IYzkg6zkEs3SwBcG2XJcEvegmA2wbJNl9GCU3oeDYOIBgu4KIjuRDyo2w9/vfIz+YZdcTrt/xFXMquWvLMB0Fg4sbotzTk2Hx+xbj6BqObZM48xxCNeswChpdf3sMfyxMdu1K1EQZLW9/KxVzN/HkN+4l1hQlWBEkNm82zVe/jcaLhiZcFAr796GEFZretAwxHKP9N38j0lTJ2LbdmFqJUE0CgFI6h2NZjLX3YhsmtWefQrZrLYWhNJZhIUiiK5gUUigMpTCyBf64feS1L3r0ApaXh41Xvujh4eHhcQT86KYvs6wxzgynn482vImrFtTw2/UD/GnvKF/55oX81xf+zsnXLcEu5hF9MpXnnIu+bxumpjPypffR/J53M/b4CgJ1VUz5wDtp3r+Tx7/4J0JVQTfWz9xNy1VvofG8IbdSsjxGdl8PgXiApnMXg+yj56Gn8cfCjO7sHLdWjCBIIqV0FtuySe3uxsxrNL/1PFK7VlHoH8XIG0iKiJHXUEI+tGQGI186pDLyNYtjHV289/SQDhsvceDh4fGGRpR9KJFy1JCP958zhTK/jOmAJOC2HByoLrBMHFECR3DbExz7oJ4B7lhRy7quCxwQRQQcm6G7f81Tv9t6iMfxn7cOc0lrjHv2p7mnJ0NbyEdyRy+hmgSBuiqMvv0UhtMU+pO0XHkRw0+uJtc7jJYcQ+lPEj/nIvjGvTSeNC6alEsDIMUrEXwKcnUTvoY2Mvv76f3HOgRRpGbxdIrJMbTkGLXnnzUuoGiTefwZqhZOo/PBZ8j25Sil7wNAz7pih7HJNSS39xKsCNF+3+5DLiREXruqyY79Wp2Zh4eHh8fxxgUffj/XRrv47dzz+WmyCMA9mwa5pDXGA51jfO0Lf+eKOZUUh9IYLRpKeQxzsBs9W0DPFGg4/wx677yT2ovOJ71mNcpomvCCZZjGH2g9dzqldA5tcARpLIPaMhkAZfJcEi0zyPX+huENu7ENk0hTNUokSH4gSeXSEwCws2mG1u2ifFYrI1u7Gd07SqbnLuSARCHpih1WzmtieFMXwYogPau7uWvL8KvzRR4hju0cXbz3KiYPmyPWOPDw8PA4nghVNlHRNo83ndLClESIxXVhNHNcx+BAFvqAfsGz/nVEeeI94UCm2zZxhGf9rDo2gmVQddk7ePea2w45ruGAWubnA5dPB6Ax4ENLaaTbexFkBTubZnjDXkZ3D7D5+78hccIsIk3V7Pj9eizDxBru5cKHfkhy1yANbzoFbdi9mbdzaayRfozu3ey58cfUnHkS/liYutMXEpo8mfI5U1ATZdj5jNvKEIkRn95Manc3lu6e71h3BkEU8cfC1C6eSnrvAKZmctttGw9JGqii8JpNGnh4eHh4eLyS3PvTmwm8/TbKo/6JG6y85VDWGOHD7184MS7dmSa1sxNsGzufYWRzO4Nr97DlR7+naslciru30rdyJ6V0DruY58IHfsDg+i4a3nQKgiTiCwWwx5JYqSH0vZvZ/aObqTtjCb6QSuX8KcTmTCdQU4GaKMPRNaR4FXKihrK2ekY2t2PkjXHBwwKCKBKIB6hfPpn03gEAfv2bba+bpIHHK4tXceDh4fGGJVLbRuXk2dRNLueMKZUsrFYRrBKaoyAY2rjwoYEjqwh6AQQRR1YQTB2Bg/1+E5aLooxYyrtaB8+6o7aVEFbH1onXp1cEqagKIvpEglVRPvnhxfzy5+s4/YL5RKdPRlRUelesQs/rrHmim6gsMvwft3LWrR/n5B9MQZAVMhs3oCbaWfjDb7L729+hevEMCvv3Eaivw9c0lf0/v5XYlEYEn4/KRbNBlHC0PFJZgvjiKtLr1qMmhrA0HceyiTRVU0rnsHSLitmNSIoPJRJk0y8ef94LCJ/Aa1vXwHG8VgUPDw8Pj1ec3pRGWBY5rSZM+eQ4giQg+mQ+87Hl3Pl/a/jA+04lNKkVQfbRv+IJ9EyRlQ/tJySJDG25nTNv+hAzrmtCCEUp7NiMEg2x4HtfYN9P/5eKuW2MtfdS1laPXN/GvlvvJNZWh6ColLXVI0ZiYNuIwQhls8pJrlmH0j2AbdvYuknF3DYKQxks3aLp9KkA+GMRtt6+8vWbLLC9VoVXCq/iwMPD4w2LIEqIskA04CPok9AdEUeUsWw3CYDpuiccqD4QbAvBNnFkxdU3OLCfZ99g2uaEq8KEu0I+Rf/f7psYUttahqRI7vNlbiVBfUBGVhUcrYCZHMAsaOQHC6iiQMa06c/pFNt34xgGdjFPqLUJX1UdjqHTePYilGgItbpi4hhNbz4HNRFFUFz15JF1W0CUwLJwShp61i1NDNTXEairwsxrSD6Z+ORqbMPEF1TJdPSzem/qeb876VkWk69ZDlxMvNSHlzjw8PDw8DgCLvvYhwDQbYdgRYBSpoSkSCRmtRJtqaXMJyGpCo6uofV2YRQ0RnYmAchbNr05nVJ/DwD2WBK1rh65ugmAmsUzUCJBwvVurBcDIZrfchZqogzB5+oSjDyzCcfU3XgPmJqOIImE6yuJNFVNLBDULGjEKur4ggEK/ckXjPWvixvFo471XuLgcPEqDjw8PN5wSIpKfNI8lGAZFXVRPnbmZOZWBVFwb/oT5BD00rjtoj0RVBzZB4KIYI9XG4xbLzoCB9sVAEcQEQ7Y/Agi95/7EWZcPnvi/ce2DDOqW1y2uI72v6yicl4rl37+XARJxDF0xFCU3jV99A3kCcsiy98ynT/9eguB1jZuv/J7/Nv/u4zhDXtovOgsiuseRT3hVLBtHC0PdVMRRnsxhvoIzTqBHf9zC5XzWknMmoSZHkUKagiKSmLeNPr+sY5M19P4Qgrh2hi2ZSNLCqJP5kuf/NMh39nsqJ+tmdLE69d0tQEADo7lVRx4eHh4eLwyZB/5Nj/YmGFrpoRmO2zYm2JUt7gorjK8cQ9lbfVc8aU3YRsmdjGPUllF91MP0tWTJe6TmP/mqdxxxxbU+iZ++m/f59ofXsnI5nZqTj2R4r5dhOYtmTiWPWUZtK8hs3MvsYUL6Pz9PQRrygnXV1IaGUWJaIihKOXTm+l9fBOje5OIPolIXZhAPIAguSmBr33h74ecQ1vIR3veOHicV+arOyoc2z6qeO/pIR0+r4tEkoeHh8fLSfnkBUyaP4WK+jgff9M0IuOr/wgigqEhOM5EckCwLddZAcC2EYzSwWoD8eBPqPOsFfgD+3C1ECQWfuQ01v16IwDLywO8/bpFXDClnHBtmMp5raR2d2Pki+R6hxF8CmIoQmakyII3TeKtT/yMaEsti1rKsPNZZs+tZMsv/kHDBWeAT8FX18qGT3+D4vrH2fDFG9n60esRQlGMgsb6r/4UJawQmzMdIRhxpxyOoQ0MkNnTRa5/jEhDjHBtjGBNAllVUKIhfnvDYyyKqYd8Z89OGnh4eHh4eHgcyoW/3sMnfBvQbIfTK4K85dpFnDWzgkhdmPLpzYy19wKQ63VbAkRFJTdUYMFZLVy25a9EW2tZVhXCGOpj8bwq1v3wQaqXz0MMRfDXNrD56z9kbOVjbP32T9j1wXcjxqsAWP9ft+ALqa6GUZVruyiVJci2d5Bu7yW1L0V8UpzEtCpibXVIqoIvpHL7j1ZyekXwkHN4dtLAw+Of8SoOPDw83lCIso/y+hqmNsVorggxvybMaNHEsB2QRbfV4ECFgSiDjGuxqEbB0t32BTiYNBDFf2pbsA/22gkiguOw58/reXi4wLlVIaae3cK232+h7awWjKKJnnFbBhzbZnB9F7KqEKhKc+Gdn2XVp39K+40/oOn7v2TPfy9g9u59+EIKvTv6MUcGCMw/iezqR5l2xXIyHf00njaDvlW7sMdG8FeUAyAHVYZXbwDAyGuM7V81MddwbRTHsln3641sGiuxIKbik4TxC4fX+cWDwyF/l5e+Ew8PDw8Pj3/NXdcs4K6W67i4Icqks1vY8YetNJ/aiKXbZLsGAdCSYxOxXk3nOO/W/+Cpz/6cvZ//BI++/4cI3zuPTEc/+cECfckCJ4xl8NW2knz8MSZddCKFgSTVCyYxvGk/Vt9+glUxZNVHKZ1j+JltgGuxPHD7kxMtkZHaMJZuselvW1mf1jgpEUC3Hfo0kz7NfMHzed1g20cX773qwsPGSxx4eHi8oYjWTyMU9dNcEeKqubXUBkVqgwrg3iYK4CYOHNtNGIgyTkAFx0bUXXslR/Yd3KH9LFcFQcSRlEOOZ+5cza9Xu6sMi65bQmpXH03LGzCKJqN7R+lfP0AwEUDPGdQvn0x+YBRJ9fOHy77Fu37zcZ767M8ZOf9NvPsX1zH09GZ2PN3H7FMa8LfNwjENHNtGqWlATmZQIkFCVWEG/nQ3lqYjKRJKNIil6aT3D1MYKRKsCBBMhBAkEdswSe1LkTNtFFHg0eE8xvEUPz1xRA8PDw+PV4hvxGaRtxy+8KWTSO3uY9LZLRRGChRTGv3rBghVB4k1l1G/fDIjW7uoOqGNP739B7z959fy8EdvY9a2t7Hof6+m78nN9CULTJtTSWDqbJB9hOsrkKIxisNpfEEVf1Sl5+4/A2BbNr5IEFPTGdrUS7YvT3xSDDWuooQUTM0guWeUomVTrkisGDreYr19dPHe0zg4bLxWBQ8PjzcMgUQdM09dyLvOmsJnTm6iRTUm7BPFQgopNzyRBHBkFWwbOTOAUMoh6AUEU5vQOfhnHEHA8fndh+TDESWMLU9yz9v/38QYM6+hxoOEqiMYeYPCSJE9o0XKp5Sz98luIk3VjHWm+MXX7qOtMcID7/kh7d1ZKmfXMrhyIzv+sBnddmhf2YvRvx+jYwfp3d3Yusamnz+Jpenkh3J0P7aL1N5BwtUhRrb2kO0ZZXRPClmVibdVA/DEXdt5+I+72Lt1hFPeOZ/ZUf/xdSGBg2NbR/fwEgceHh4eHodB4Y6rKVckqv0SlqYTro0RGhcxzA8W2Dfs3szvf7STaEst+aE8N339fqZMifO7637O5rRGpC5M35Ob2fmn7eQth11bhjG6dlPatZH+1dvAttj485UUhlNkejL0ru6kMDRGpC7M8JZ+hjb1ktyTQo37KZ/mtjHc87tt/O3Pu+lsT3PSlbNpCfqOs1gPjnN0sd5bJDh8vIoDDw+PNwS+YJTqGYs4dVY1p7bEyRs2UctAANdq0bHdf2XFbVEYd1Gw1TKQZBxRxpbGnRYk2c1wi6KrY3DAjtEo4YgS2b/8go4H1iOrPh4eLlDtl1hSE6YwNIY/FqJ60TQsbRO2ZaNtHqJ3TR/xyiBytIytq3uZHfXzTHuasCzy3luu46Zrbubk5Q3EmsuI1IWpWdCAGIwiBkKoiTI2/+hPxJrLGFy3C1mViTa5bQqldJ5SpoQvpNCwtAF/LEwpnePuO7fyzutPRktmcCyb7/1oNQCqKLwORA89PDw8PDxefU569zU88M5J7Lei/HXuMqr9Mk3VIUrpHKJPpmphM6VUDtEnoa0s0fl4N/6oH4BHn+mnPuDjya3DSILAJ+76JP971Q84eblFpDbM/IBM5cwqpLIEAEokyNab76V6TiXJHb0IkkD5lDi+kIqWKpAfyhOsCNCwtJ5ghTuHP/9lN1dePRcj72oU3XDT2lftu/I4PnhVKw5aWloQBOE5j3//939/wc/85Cc/YcaMGQQCAaZNm8btt99+yPuGYfD1r3+dtrY2VFVl3rx53H///YeM+epXv/qcY9bU1ByTc/Tw8HhtEG2YSlVDlIUNZdRHfIRF073hNzUAVwxRkg+2KZg6juzDkX0IpTxiKe8KIz47aQDua1nBESW3GsGx+c8P/46b/7KH7qdcS6W3v2seNfOr8MdCBKtc9wJ/LAxA0C+jlvlZ2zFG9wMrKfNJLP6P0xnV3cqHv37wFqYnAqQ73VWFTE8WX8gVLnRsC1MrEa4O0XrePABik2vwhVRsw0RNRDCKbv9iKaMhKjLr/rKL7qKJ6JMZ2jLIjbdsnPiOjrukwYG+x5f68DQOXha8WO/h4XE88o6vvZ8nzrwU5b8/zKhucdrb51Izv9ptG6xNuNbGIRUtpaEEffijCk91pOlfvY22kMKFHzuV4ZKFJMAf3/1DpsVUkntGCVWH6N89ij8WxtE1BL8b80PVQWqXTAGgal4j/lgYI6/hj6roeQM1rrr2j6rCur+306e5x093jvHj32x7Nb+qY8vRxnrPVeGweVUrDp555hmsZ9lnbN26lXPOOYcrrrjiecf/7//+L5///Of52c9+xoknnsiaNWu47rrriMfjXHzxxQB88Ytf5I477uBnP/sZ06dP54EHHuDSSy9l5cqVnHDCCRP7mjVrFitWrJh4LUnSMTpLDw+PVxtR9lHR3MRblzeztCFKAMMVMRREhOKY+9zUcXwqgqkjYOLICoJtIpTyOP6QuyNBBOtZVozis15LMlgmH21408Rx7+nJUO2XCFTFCNaUY2QLqIkyzLyGpCo0nzmLjk2PUujNYjkOpYzGpjGN4g2P8MGPLGXVrzaw8N9mk+sfwzYsjKJJqCqILxRgeNUzOJYb7GoWT2ffvRuITSqnlM4hiCJjnWlMzSAxpZxSpkSkIcadP3yKKz+4hK0/WknHQztQ44c6JxxPuKYWnsbBawEv1nt4eBxv/M/+p1g7VmJtWoMtw5ySCOCPh1ETURzbnrip94VUWs6ZzcafP8XoQJ6caZPtSfNUssDIDY/x7x88kcd+s5kTr5jFWGcKURIoZUqU14SQVIXhtVsx8hqyqpCY1cq+ezdSPjkxUTE4sjNJKaNTM7+KwkiRxJRy/nrrei5822w23bKBvme6ECXhX5/Q6xjHto8q3juexsFh86omDiorKw95/Z3vfIe2tjZOO+205x3/q1/9ig984ANceeWVAEyaNInVq1fz3e9+d+Ji4le/+hVf+MIXuOCCCwD40Ic+xAMPPMD3v/997rjjjol9ybLsrTx4eLxBmH72JZy5tJEPL6hCMApohPALBqJRBFkFozCRNADcZICkIBglHDUy/tpNDDiycnCcbYOsuBUKpTwfabkEgIsbotzTkwFgsGSx7TfrAZh2yUwESWSsvRdfJEi4voLF75zPxt9s4swl9Tx6714+8q03k+sdRlRkLv5/l6PUNKAP9KBnCgSq4ozu6KB84Xx23fInQrUxLE1ncH0XgiQgiK7gYXLnMJmeLNGGCOUzmkjt7kZLFfjwj6/iM+/9FcCEYOPxi+OJI75G8GK9h4fH8cRPO1ayKVPirdMS/HFXEoAnkkWWZAr0ru6k6ZQ2AExNR/T5iDRVMePyuWy7cyOXn9LEHx7Yx39+40LG2ntxbJvLvnMpal09ic5ObN1ECiiM7e2lctmJbPz+nQTHLRMH1nch+iQc28YyTIa2DNPdlaGppWxcv2iQwkiBd33jYr74sbsBuGO88vG4xraOUhzRi/WHy2tG40DXde644w4+8YlPIAjPnxkrlUqo6qErZIFAgDVr1mAYBj6f7wXHPPnkk4ds27NnD3V1dfj9fpYsWcK3vvUtJk2a9ILzK5VKlEoHfcw90SwPj9cH/mgFpmHxqVNbAQfBKBIsZXF8biAWtAxI8kQyQDCKOL4AgqUjWDoOAQTbwhlvTRBM3a00OFB94NhoD/yKRz77BwBCksA9PRniPpGU4WaxV3SNcVZDFNEn449FqF48k1I6iyCJBGsSSALkh/KM6hY//s+/UqPKtJQHyOXWsPSDyxnZ2o0oCVTOa0VW/Qyveob6k2fR9ehmwrVlxCdXIEgi/liYwsAoSthHxfRyZFUm2zWIrCqEasITSQNw+9QUT9PA4xXGi/UeHh6vV+Kt8+j+VD2fOeVRyhWJP+9KUu2XGCy5N63/+9OnufKsVkRFRq0sB1HEGMsgSBKhmgSCJJDtz2E58LUv/J0KRWJZVYjVv9/Gye86gdTeIQojRZrPmoYcUum571FazplD75M78IUUYs0xAJRokGzPKIFEgFZVxh/1k+0dxR/1E0hEJ5IGHh4vN6+ZxMGf//xn0uk011xzzQuOOe+88/j5z3/OW97yFhYsWMC6deu45ZZbMAyDkZERamtrOe+887jhhhs49dRTaWtr4+GHH+Yvf/nLIWWSS5Ys4fbbb2fq1KkMDg7yjW98g+XLl7Nt2zYSicTzHvvb3/42X/va117u0/bw8DhG+IJR/JFyEm1z+OgVc5BEYSIpIGaHsJXQhPWiMC5qCLiVBpaBIwVwZBUxn8QOxsedFJ5VzmaZIIqY6x5i7Y33c99gnrMqgxPJgrOvWUC2ZxRBEhnePkz1nCoe/8UznPuZMhJnnoOcTWPnM8Rqmjjzu+Vs+tnDTA0r1DeXsX9/mnB1CLXMT657iK6VPbSe3sy++7cAIEoCiWmVBOIBtFSBcG2MXH8aX0gl158h3TlGxbQEajxIfihL75p+/t6fcz/LwYTBcZ808PoWX3N4sd7Dw+P1St/3lvPNWW/jkrlVtHeM0RZSWP6OeeT70wBkerKEqoNsvWM9ixJlxJeehBhMYhcyhKdN5ZSvRVn3wweYHfVTVxNibccY0YYI/kyJYjLDuid7WH5BG/sf3IGkSJQyJeoWFgCwdAsxLlMcyaEmopQyOqN7U8RbYxPaBu3P9POPkcLEfH0Cx52DwvPiHKVOgdeqcNi8ZhIHv/jFLzj//POpq6t7wTFf+tKXGBgYYOnSpTiOQ3V1Nddccw3//d//PdG3+D//8z9cd911TJ8+HUEQaGtr4z3veQ+33nrrxH7OP//8iedz5sxh2bJltLW18ctf/pJPfOITz3vsz3/+84e8N2PGDPr6+o72tD08PF5mfMEo4epW2k6cTTZVpKIuyuyqCGGfiKCN31QIImIpi2Aah3543CEB0TfeluDDVipd4cRnBxbbBknG3rGSj1/0XQCmhhUeHi6wIKZy/kdPJt87QjGlkZhWxfz3z0LPFDhnVgs7fruGk89+Eys/+RNqF9S6OgSzWihlSsy/oI3eNf3MWFBDpidL5cwKWi4/n2jLBvY/tB0lpCAHJEpjOj2ru6mYlkDPGzjWKABj+4coZUpUTEtQGCmS7c89p0yxXJEY0Y+yhP/1gOPgWF6rwmsNL9Z7eHi8Hvl51yo+NvkRlpYH+NuWIRbEVM742Olk9vdjaiblU2uoP3kWpqYTn9rI1ttXcsryU3j6y78kUhdGkAQq5zbj2A5zTmtidO8oZ57cSGpfmspZCVouPIlwfQV7/roVX8iH5JOwdJvuVb0kpsTJ9ecw8ga2ZVMY6STbn6N8cpxSpoTZaT6n/TAkCeStN0YMcyz7qOK9c7wvoryMvKquCgfo7OxkxYoVXHvttS86LhAIcMstt1AoFOjo6KCrq4uWlhYikQgVFa5XamVlJX/+85/J5/N0dnayc+dOwuEwra2tL7jfUCjEnDlz2LNnzwuO8fv9RKPRiccLlVh6eHi8eviCUdSySupnTUMNKXzh7SfwsXOmsLBaRTYKCHoOoZjGViMIRglbCbhOCIBgGa6lovCsn0XbRnBs157xgAgigCQjZYco7HJViq+cX83SBdW8Y2k9l/3XxTi2TSGZR0tp7P7rTrbe/iTt926i85FtLPnq23ny2q+z7FvvpWd1D9HWanqe3Mnca07mpju28kzXGGOdrj5Cen8ac2SA5I4eZFVGkARS+9JYhkUwEUSQRERJINOTpZjSUEIKRtGkMFLEsW1WbRx8znf0hkgaeLwm8WK9h4fH65Hq2aewI1vi6nMm0Tw9wWUnNXD+f70ZX1DFMkxKmRI7/7SVbb9eyf77N9P1jx0s+8qVPP3JH3DiZy5hZOco5VNr6X96HzPfsZT/+9MuHtiRZHj7CLZlM7JzlOLACKO7+lHjKsGKIOnOMZSQj2BFANtykBSR1L40pUxpQthYS7muUDt2Jp8z5zdK0sDjleU1kTi49dZbqaqq4sILLzys8T6fj4aGBiRJ4re//S0XXXQRonjoqaiqSn19PaZpcvfdd3PJJZe84P5KpRI7duygtrb2qM7Dw8Pj1UNWQ4Qqm1Dj1dTURfnseVNZVBehIqggFlIgijhKGMFxEHS3lE8wtEN34rhOCYJtuToGoui2Mtimm1A4UHXg2KQf+hOf/+CdXDm/mvqljYx1Z6meX0ugtprB9Z3oObeaoTenM9Qxxr4dSXZvGOShD9xM7YJa7EKWUkanfHozm1b18rdv3MdHrl3AgpowwYoAlTMrkFWZ1J5ujLyOKIk44xcCsipTPiWBP6qS6ckiqzJK2EcpoxGsCCIHZAZ2jjJmvMHL7w4IJr3Uh1dx8LLixXoPD4/XG83LL+au/l/ymR9dSVlzjFKmROXsWtSaGoY27qEwUsTSbXpTGj370mzZOMjTa/r40wd+QXxSDESRvmSBSFM1657u5d5vPcD118zl9PoIALHmMpSQj1zvMLZhYRZNtJSGpEgoYR/xSTFESSC1bww54FZcaSmNQFxFDsgMdYyxPVN6kTN4A3DUsf4Nfq10BLzqrQq2bXPrrbfy7ne/G1k+dDqf//zn6e3tnfBv3r17N2vWrGHJkiWkUiluuOEGtm7dyi9/+cuJzzz99NP09vYyf/58ent7+epXv4pt23zmM5+ZGPOpT32Kiy++mKamJoaGhvjGN75BJpPh3e9+9ytz0h4eHi8rshoiUjuZthNn01AT4V1LmqiLqDQJaZoiIo7sR8wM4vhUHEFA1POuxoFt4VgmjiAgMJ4k0Is4ku+g3SIc7J2TZBBE0r/+Ec/88BEAJl88h+HNnZzy9UuxDZNCTz8Ao3tTjGR1prSU8ZfNQ2i2Q50qM6CZzIm6woxn3fpxOn9/D5rtMKCZtDzTS+sZLfQ+00u4tgzHtklu7yXbl6OsuYxSpoQoiQTiKtGWWoY376dmfjVG8WA1RK4/h6VbdBQObcN4tljjGwbPVeE1gxfrPTw8Xm8s+rd38PjVdXyksYP/XDqJsc40iz95AaLiQ0+OYBZNUvvSdA3maW2McNeWYcDVFtjhwEn1CTANrvrlR9h31/3kTJsu3aJxTR8NS+vZ/nAHtWUKkiLRs3IfpUwJWZWRFLeaUFZdR4b8YAe1C6txLAdBEnAsh+y4blF73kB/o5faO0frqvAGuzY6Cl71xMGKFSvo6urive9973Pe6+/vp6ura+K1ZVl8//vfZ9euXfh8Ps444wxWrlxJS0vLxBhN0/jiF7/Ivn37CIfDXHDBBfzqV78iFotNjOnp6eFtb3sbIyMjVFZWsnTpUlavXk1zc/OxPFUPD49jgBKOUz5pHtWtNbznrMlcNqOScHEI0BBLRaxQOYBrtzgeHBxhvKoA3PYExwbbBHH8J3H8fccZt1vEdl8Dgm3wzU//Gc12+Nh75+PYNtPffQGC7MMeGWRw3S4AMnmDRNDHXRsHCUgCcZ9I0bJZEFMx8hojG7YTaaxGTUQB0G2Hh7ePcErOoOnkBkrpPLHWSgbWd1O7sI5cfwYl7EMJ+4g0lNO3ahelTImqefXouTQ9q/toPKmBcG2YX9zXfsh3JMIbL2ngODhHKY7o8Aa/GHsZ8WK9h4fH6407hn7BRxpX8s0fXUFmfz+zP3wJgihhjg4zvMFteRoYLlDmEyeSBuAKErYEfYztH8KxbCJN1QBYDuRMm/t2JTkzozPzrBaMvI4/qlAYKVAzv5rRPSlEn4QaVylrjtG3eh+5/hwNSxvID+XY/VQvU5bWEW2IcNvDHa/G1/Lawz7KeP9GT7wcAa964uDcc899Qbuj22677ZDXM2bMYMOGDS+6v9NOO43t27e/6Jjf/va3RzRHDw+P1x6yGkJSApRPmsfNnzubxjKVSWUK8sg+1yFBkieyyIJtua0GhoZgWwi2iQM4st9NCEg+hAOtCM9KKgATlou2EkBwbHZ97hNotsOimEoxVSQxqwxBUcG22P7rJ9ixfoCqkELKsIiGfEwOK0yLqYg+kVxOZ95lM6mcP4Unv3YPc9+9CCOvEfeJzJpd6foxn9xAMVUEoLhrkEAiiJEvUcqUKJ+cID+Uw7ZsylorMLIF8v1pMj1Zypqj3PS77QSkQ3uy3zCqyh6vabxY7+Hh8Xrippu/yk3LT+dtJ9bS89gWGs+ch6ioOLbN3r+sZtcT3URUmfa8zuyoH58Ap1YEKY63FC68sI36U+fx98/9kWVXn4BZNKlQJOaUB+jKlqg7sZbkriT+qEIpUyJSF0ZLaZiaSVlzGYWRArZhEmuO4Y/6yQ/lyPRkqZ8c5xd/28sbbCnA4zXCq5448PDw8HgpyGqYujlL+cXHT0YSBCaLKYQxEztQBoKIrUaQUz2IhRSOP/xcPYNxHFE6WKYmHNo/7VYjjCcRbJO+//kGa/62F4B5y+opJguM7uwk1FzP8NqtzLv+cpo2beP/vvEg5YpEIK5Slzcoa45SObOK+lPnMbK5ndCsEzj5K7D3L2vYvaaPRW+dwd779tI6tZzCSAFLtxEkwbVfkgQKeQPRJ2FqBv6oHy2ZBWDTA/vQbYdZJ9bx0BNdKOJzVZQDkohhvkEvMbxWBQ8PDw+PI+Ts91/LVT138wnTxheQKaY0+ldto6WinLFdHUx79wXEJ6/jO//vcar9EpWNUWbqFtEylZnL6qg7aQ6Zjn7UplbO+vTZDK7dw9MrOlhydgvtj3czoy1OMVkgkAhgG26csi0HLaUhqzKmZqCEfeg5HYDHV+wnJIlMn1XB/c/0e0mDf8b2WhVeKbzEgYeHx+uS8knzeP8Vs2mN+QkrEo4t4Ng2YjGFYJRcQUQAQZr4zAHXBEdSDrYj+PwIpnEwgXCg4mD8+cS2wX18+zv/AOCsyiDBigBZ3aJibhva0DBqogytr5dNt6zi3/5tBnrOYGjLMNGQj+p5dVQvmoYYilL/treReeoR1v7Pw/QlC0iCQN1Jc+h4tIP6pc1ke0YRfQ6mZiJKbiJDz+uIkoBtOfgC7s+2UTSZuti1tHviiW56i+bExURUFsmZNgFJIPNGTRqAlzjw8PDw8Dhi3vXla/nESIFLJ5cTnxRnrDtD7bJZ6KNp/LEwxe4e1v9yHR+4dBoAw9tHqFNlWs5ooe6kOYiKj9qLLyL5j0dZ/79P0DueAJh00RK6nuqhfmkTyV1DiJKIpEjj+kUCpmYiiK6GgS+kIEoCel7nxBOqEUSRNev6D3FGisriGzvGH8C2vcTBK4SXOPDw8HhNE0jUUUz2IYgijm3jj1Yg+QOc/6bpVIf9JIQigmYglHIgya51og+wTNdi0TTHdQpwRRD/2XJxfDuiCNazgocgHhRNBErb10y8tfAd8ykmM8z7+JWkN21Fz+bxxyJ0P7qJRR85g8iM6VjpYcqat7PnbzupXTYLqSyBo2v0330XT9+8mpxpYzhQXxVk623/YMGHTmNo4x4ESQTLwtItJEWimNKQfK66smPZGEXTdVnwSWgpjT8/00eZTzpkBSJj2lQo0hvcevHoNQ48PDw8PN5YzL34SlI/fAqAaZfOJtebZO6/X0KhqwfHshEkka5HN7Ps+tOJtjVgFQrEJ3ey7c6NVC2chhSNYeczDNx7Lw//dOVEFWCdKrPxphWceP0Z7L9/s7vQIYmYRRNZlckN5pFVGVmVsS0HUzPQc8Z4YkHnvq3DzxFBzJi2144IOI59VPH+hdroPJ6Llzjw8PB4TaMEyxBECatURM+NMvvcc6mribCktZwTaiOI2rCbLBBFN1kw/q9bOWC675m6+/qAEOKBxIFjT4zFPlhhcCDBIDgOjiDgdO/gk2//OQDvPKmBbb/bzLRLpmMM9eGPh3Fsm1BNOZa+Gy2dhR07kUMqNScvoHL+FPwzFk2cT1Uwwoz9g5QyJdY92YOpmZiaiW3bNJ97IltvexRfQMYf9WNqJo7lEKwNYGomet6glCkRrAjQs7qXJ4YL5C2HvHXQVeFAwuCNnTTw8PDw8PA4cv7jJx9jU07nmrNa2HT7Omb92xysTBpRkXEsG0lVEEQRUyuRae/BHw9TMbeNk6Y2Epy9CEfXkKsbqYxXsXDvAFpKY9WqHiQBTM3E0krMetepbP7Fo9iWPW6lXHJjfXUAPefGebNoEqwI0rd+gMdHCs9pQxQBGy9p4PHK4iUOPDw8XnMkpi4i07MbSQlwwZVn0lwR4o8P7SEcU7nu3Km8fUYZRXyEiiM4joxYyuMoAQTbwJFkN0FglXBkFWxzvCVBdjXyHeFZLQgH2xhwbDdRIEoIluEmGowCZJJ8+4xPo4oC59RFECWBbWMlFjVWk+8fRVRkypcuJr9zO5GGOIkTZrHxxj9Ss6AR0ddLzblnYHTtxtfQBoA+0IMv5EeQBNoaI1TOrMQfVRndtp+x9l6CFUFMzRivrvATrg4x1p1BUiTEcd0DS7f5+7gV0wEaAzLDJS9hMIGD16rg4eHh4XHYpD43hU0/3cPsuIqW0tiaKXFSWz16toAgiQRbJ5HasJlAPEAgUcbOu56mYloF/liY6lNPJL9pDYHWNpAVjKE+REkgWBFgRl2EmvnVmJrB4Ib9pNv7CMRVwG07VOMq0YYoqX1pBElACbmCiZZucd9g/pA5hiRXy8irp3sWnsbBK4aXOPB4zeGPVlDKjLza0/B4FTjp3dcQDvhQZBHLXsD6J3fz/uUtfOTna5g8rZL/unAGzWUKcqqDQHkTguX2DR7QJ3AEAcEouTsTRDcJcCAgWKbrtGBzMEhIsqtvwLOSBgdcFiwDfesqcnva6dPcFf17ejJcf3YL77l4AeHGGncX8UrGNm6kb+VOREngr+//GTPPamH/w3uI1EaItu5ErasD2cf/Z++8w+Moz719T93Zvqvei+XeG9iY3iF0EgKBQBJOSEjPST05kN7OSS8kOal8SUhIhVBCDx2DsXHvTZZkWV2r7btTvz9mtZLBlGA6c1/XXpKmvFN2te+8z/s8v58gq1i6SWx6M3v/uZ5wfbj0gFDAF9HwxULEpzXT8/BmBNH1cR7bl8QXVbF0Gz1jEKgKcMvTLJiCkkBP3sRjMo4XOPDw8PDweEEsv+wKfv7e9zFUNEkYNh+/ciHvO28p/roqBFlBUDUS6zbSu3InoiTw+Jf/yYLjmtl15258ER+B+kr3uUCUEAJh8iMpqhdNZc9ta4lPiVFIFCimivgiPuIdMarmV9J51wZ8ER+KXyZ9IIMW1zCyOrZlE6gM8OeHuw86RxGekXngweFrHHhljS8YL3Dg8ZqidcU5JHs731CBAzUUR88kXu3TeF3wf+9YiE8W2NCfwbAd6mMahu3whXcsZGXnCDPFIWy7EkeUELMj5cwBwbbcbAJBBNvNFkAQEcxiOQgA0kRZwnipgm0f5Kog2BaOICAm++n6xf/x6O/WsXI0z7GVfiwHqqM+tIoI4Y42BFnBMQ0cwyBYV0Ht4ils+u0qWudWYxYMaufVEO2op+ve9cy6ehqCqmF270StiBE57kysgs7Qhk5yI1ks3XYfFgyTYF0l0y5YQWJXD4WRFDFVIjecA6AwVmDX+uQz7ltIFslaXqbB03EO9554gQMPDw+PNzQf+MLH+e7UIf54wgeYc0ILe1cdoKIuiBoJEGxtRFA17MwYiBJqOEDNgmY237CWlqYwlm5TO6+G+mVTOLByO9Pf0YAYCGP07CRQHUObvxyroNO7cid6VkdSRfSszujOfsIttcy5/HiG1u/C0k1syyFbyi7IDmbZvC+JKgoUJukaRBWRhOENcp+OY1uH19/bXl//QvECBx6vKbpW3sbUE89nrGvzq30qLxle0ODZCVQ3kxvqAUDWgkR9ErIIb6nK4wgiZ0/pwBRkfKle3tJcj1NMIxTSOFoEMZfA9gURdHdQPV5eAIBt4sg+hFKZQjlYMDkdbbw0QSiVLlhFEGUE26L7V7/gWz9YSYMmc/HCWmrmVaMENZrPPhkA0R/EzmfJdXfib6hh4y/vo5jS2TWY5eh51eWSAse2aT5+LlJ1I04ujRivYd/f7kRbvYVs/yjhpgqSXSOIkoOl22QGsrTUVZHasx8zWyA+rRk5qJHY3sVY5xDbO8eQBOGgcoQqVWKg6AUNPDw8PDw8/h2Oec97aL3ibdwZVmluiRCoCjD//Jm0XnA6SBKIEnZ6jFzfCFqlwfa/riI7kGNrIs+xMyowsjpaXMM2TCpnNyJXN2Ln0kjRSvb8/SFC63eR6RujZkEzY7v7yQ3nEXGzB/01cYbW7URP5YhNbyY+vZnEzh7SvQl2bBpCEg52RVIEvKCBx6uO+PybeHi8csw+422Y3hfjm4Zicgh/pWspGK6fylDOZCRfGgRLKmJ2BF+6DwDByLtZBHoOQS/V9zt22TGhHDQYzzawLRBlt4RBUiaCBuLBX3uC45SDC0J2lOQt/4///d6jvOOIei5590LaT52BnjGY8uEP4Zg6gqKAKOKYOoG2Fu77yO/IDOQQRIHFC2twbBvZLzPt0tNpuOgSAm0tONk0djZFes1K2i+7kHBLLbVLZpAfSaGGFADMgoE/rrH/X6sRVRk5qCEHNTdDoa0ex3LYkCyydqxw0Plb3qz4oXEopS8exouX994mEgkuv/xyotEo0WiUyy+/nLGxsWfd3jAMPvvZzzJv3jyCwSANDQ1cccUVHDhw4GU9Tw8PD483GrPPeBvnXfsfnHLWVOZcPJ8pZ8zCyJt0fORDOKYBooRTLODoBYLtLfzzU38l2ZUCYPGUmGuZrEjMuPh4Gt5+MZG2egCcfJbkxk1Mv/JCAnWVNBw1i1z/aFkI2cibWIZF5+2Po0aC+GIhBEl0MxzqK7F0m1UlfYXJSILwit+j1w2H29e/zBoHb6S+3gsceLymEGWRzPDAq30aHq8Qll7gnHedD0CqdwcH0gUypdl0wciBpIJl4khqObMAmKhHK60v87SMAkeUygEFwXEmggyTgwdOqdOwTfSd67jmI38FoHF5K1plhEh7PfO++EnM7p04egHHMNC7diGqGp1/v5dgpR9JFbEMC1lTqD9yOvVHzXGtm2wLMRjhsY98l9XX/h9GtsCWH/0ef2MDoVmzANAzBkZJn6CYKlJI5Oh9dCvFRIZc/wijm3ZRHEtj6c/s2LwZiOfCmRBMerGvlzkoc+mll7J+/Xruuusu7rrrLtavX8/ll1/+rNvncjnWrl3L5z//edauXctNN93Ezp07Offcc1/W8/Tw8PB4o/Hen/4nQUkkVB9DCQcINVYz65NXYw50YxeyYOgYgwcQtAA9dz5CJKggazKCJOCL+Gg+diqNK6YytG4nTjaNGo9z3+Xf4smv/oHiWJr137qBcEstwSntGHk3yGBbNpIq4Yv4MPImfat2oadzpLsHGN64h8JIslya+HQKXjr9szOuceD19S87XqmCx2sK27QZ3r7q1T4Nj5cRWQtiFrIIoohj25w+p5a/4AYRcoZNzrBAsRFMA4TS7PrkgMBky0Sz4M7+OyUnhKdtM75MMPI4ih/BKLrBhNI2Dq6zguA4OINd/Oe53wHg41cuJNk5zMJvfQG0IE5mDCQJK5NBbZvF8KqnqKqopn/9AKH6EKIkku5Lo2d1imNpknt6aT5tGdmnHkXWVCpnVGJkdcZ29hBujNN587+YcvFZmKWAgS/iw7FtHMvBthwEUcQ2THb+YwPNx7YTaqzmN/fsfca9rPbJZeFGj9cX27Zt46677uKJJ55g2bJlAPzyl7/kqKOOYseOHcyYMeMZ+0SjUe69996Dlv34xz/myCOPpLu7m5aWllfk3D08PDxez6xq7+U3G4r858dWkOwaYd4X340YjGBnUwiihJ3L4Zu+iP4HHqP6iBD7HthHrDWCbTkUU0VS+9MY2QKp7hFmXHYK6fWrkTSVpuUNFFM6qc4BYq0x9tyykqkXnYQoCTiSQKAqgCgJ6BkDURIQFQmzoLP7zt1MO3s6wboKblzd94zzHXdS8Hj98Ubr672MA4/XDOH6DnY9fM+rfRoeLyPLLr2cr33r48haEMe2qZt/Aj+/dxf/94svEaptI6CILKoL4qghbNWPUyo5KGsViLLrhDAeECikcUq6BIhuHNQRhAndgpKzAqI8kWUwnmHg2K7LQmnZR478KADvO28a1YumsfCH3wSf39VL0Av03/cwUihE71//Rs0pJ7Pzd7fjj2uu33KpzMCxHHf2oC/Njhvvx1dbjzp1Plo8QOWsBmLTmwk1VtNxxVsZXvUUkuoGMbSoD1lTCDfFANj6cDe779zNzIuWUhhJs/eO9QDMjfjK91IEL2jwPDi2dXivSbMQ6XSaVCpVfhWLxec48vPz+OOPE41Gyw8SAMuXLycajbJy5coX3E4ymUQQBGKx2GGdj4eHh8ebge0nFLjh1p28960zic9sZf43v4CgajiGjqMXSKxejRQK0/O731F3ynHs+MN9hBuCGHkTI2sgazKSIpHen6CQKLDp53fir6/FV9+EFg9QNbuOyjlthFtqmHb5OQyv24pdGvQHqvzImkz1vHpERWL1A11svWsv899zJPnhDJ33bgVgcUw76Jy9oMFzc/h9/cTkVD6f9/r658ALHHi8Zkj37cHIpV7t0/B4Gbn6lGl8aF6E23/3Bf7rfz7DrCM7OGZuHZdN9ZEZ2MfxzSHU7BBYujvoV4MAbqaArCAUUqXSBVcXAFFG1PM4olQOEAiOc1BAYFw0UShmS/aMzsTLNBCGu/lwy1kAfPYTx9B+5lIiR52Ak89gJwYxe/dijfRRe+yRDJVEDQVRIj6jAX9lANuwCdW76srFVJEDT+6nmCoiawrZvZ04uTTt7/sPGi++mPxggvjSRTj5LEY6R6S9lnBTnNi0ZrR4gPT+MW67cw8NzRHmvGMh2/+6hsTeMX73sCsgObnmMSR7X9/PiYObxXEYr8kaB7Nnzy7XJ0ajUb75zW8e1un19/dTU1PzjOU1NTX09/e/oDYKhQL/9V//xaWXXkokEjms8/Hw8PB4ozP80UZu/PmTfOQTx9J6+hKCC47E0QvYuTRWYhArMUh4ShP773uCbH8CQVaJd9QiawrFlE6oPkQx5dol9q3tJ9mTppgqku3qRVAU2i45n+YrLifTO0TF4vnYqRGKiQzR9hq0eICKma2oIZWhTX3ccdceGkMqiy6Zz7a/rGN0d4IbHtsPcJCWkeJJGzwvjnN4ff3kwMGHP/xhr69/DrxSBQ8Pj5cVX6QKQZTQM6P4ZDcL4Jh4nilLG7l//QHOmV2L6Qsw9+y3U7QF/IIItjuTLhaSOIoPoZh1SxdKbglQ0iwYd0oQJiwVXb2C0u+TrRclV99g3H5RsAysvRv42InXAvBfnz6OulOOQ+mYhzV0AGukD6dYQPBpSPEaun7/R8IttejpHFZyhKEN3UTbq0h2JRnZMYKlW8iajGVYqCEFURIIL1qKIEpY/d0gSUTa68GyGNu0jcTuQSqm11GzaBpmQS/VNuap02Ri7TH6VndxoCfFIyP5Q95X3at3fE4cHBzrMPUfJt3irVu30tjYWP7b5/MdYgf40pe+xJe//OXnbHb16tUACIcQu3Ic55DLn45hGFxyySXYts1Pf/rT593ew8PD483MRzY9wGeudPjva06i+qgjUKfOd4MFQ704xTyCz48YiNBzyz2EGqtJ6X1YyRH61nRRPbee1P4U2YEsoiSAKrk/gVhrlPD8+QBYyRGsdIJgfSWObZHc00ti9zDR1hi1R87CSOUQRBGzYCIJAjXzqulfu5/ewSyrEoVDnrfhdfXPi2MdZn8/6XnquuuuO0h/wOvrD8YLHHh4eLwshOs7APjTD65i10iOgCIymNHBdtPz61WTS4+bwpIaFWyd2z5xDLrt4BdlV7vAMkFWcQQRZB+CWXSDAqKIYE6yH5wkfugIAgLiQdaMjiC4QQZKzgySCrbJwPU/5qtfuoeILHLJWR0Ikohc24I9NgymgRiIQKQSOzXC5m/+hKZjZvPg/9yNKrpf9JmBLLIm49gOvohKMaUDIGsySlBl2vvfiZUYBGDvX+4hWBdHDmqYha30r9lNfGoNNcvnkd67nwOPb0dSJHp3J1jxlg5CjZXsuWvbIYMGWsnX2RNKemUJh8MvKNL/4Q9/mEsuueQ5t2lra2Pjxo0MDDxTCHZoaIja2trn3N8wDN7+9rfT2dnJ/fff/6rPQHh4eHi8Vjn6Xe/mLdf+B/Pb49QvqQNAaerAzoyBabjPFMEI1kg/G39yC+1nLODub95DyrQ5BcgN5xnrHKGY0glU+XFsB3QLQRKItUaZ+YFLMYd6QRTp/MfDBGqiyEGNsU3b6frXVsINIZpOXEyqs4/9j27HsRy2bR/h1JNaibZXsefOnYcMGoiAJ338yuP3+72+/jnwAgcez0u4voMT3noKt13381f7VDxeY1ROd2fUFS2EnkuSTwwQbZzOsuOmcfT0KgCOq5U4trESALGYxVYDCNkRMHX+Y1bQzS6wbWpyffxpIMgFM6vw6RkEy8ABhMk2i44rmjiuYSDYlrt/qXRhPEBQzkQABIeypoHg2NhD+3jgHddy044Rrvn8KVQumsPAo09Rd+pJrjCSoiLXtZBffR/d9zxJqLmGlpPms+n6R6hujuCL+LB1k/iUOFVzW0nsHUPWFGRNwdItgrVBOi44lrEnnyDc3owUr6HxuPmk9vUxuG4fkZYKoq2V5AaTWJkMkelTGN64lwNP9bM6kWdG3mT3P7fyt63Dz7jfiuApK/87HG7GgfMilJarqqqoqqp63u2OOuookskkTz75JEceeSQAq1atIplMsmLFimfdb/xBYteuXTzwwANUVlb+2+fo4eHh8WYg/fB3+Ub1Ui793MnE505ncNVGak87FTufxdELSPEa8msfZvctTxCfWsvUc5aw9mcPUlvhpyWi4tg2FdPixDtqyQ3nsC13oiA3nKdiagXtb1nKyMrHiXY0IkUqqJzTRmEkycDaLqKtceoWNzPWOURhJEmko4nA9i56n+xj7ViBhbrNnjt3cvPu0WectyJ4mQb/Do5lH1Z/77yI56o3a1/vFcl6PC8zjl1OSPNiTB7PJFbfSKy+nprWatoXzqCqYwHLjpvGOQvqyetW2XdY0HOuE4KkuEKFk0sITN2N+DsOu4ay5M2JMgPBcQ7trzspMHBQW7gZBgcxKWjgCCJjD9zDTTtGAMj1j6JOmUPtioVY6UR5F7O/G7m+jdi0ZiRVoe/xrUiqVBYzFCQRS7dI7OxBUiXMgoFZMLAMCy0eILmrG4B83wB2NkVhJIUS9CMqEsUx11Giav4UMr3DCLJCMVXEF1FpD6pkB7MMDGQPeb+9B4l/g5dC4+BltGiaNWsWZ5xxBldddRVPPPEETzzxBFdddRVnn332QSrLM2fO5OabbwbANE3e9ra3sWbNGv7whz9gWRb9/f309/ej6/rLdq4eHh4erzdmnHohu65+J7PCPnKDYygt06leMgs7OQK25ZYRDvUiV9VRNbcZJajR89BmHMtBkATyiQKSplJM6uSGxsrtCqKIpEqoIYWxnT04lluSYOfSOLaNIImoQYVCwrVVjE+tpTCSRJAVsgM5FL/MrLBKPpHnwMihrRe9vv7f5DA1Dry+/oXjjQY9npXll13BZ94yk1Na/CCpXLToG7zjA9+jmHrmTKjHmw9RVvifq44kb9iIArTF/dQEFaI+Cb/oYCIiYyMmehBsC0d17RDtQBzBNhHTg9jBSle3QJRxBIF7Vvdw0bx6YpKIIymlkgNXk8BR/Yj5JLasgG0fVJowbs8Ik7IOYMI9QRDL5eqf/6T7xfzlb59HqLEaZIXkjk588RCh6kb6bvkHVt79Yt74h3X4S+rGodoA+UQBPau7DweiSH4khy/iI5/IE24IUUgUGNzUT6ixmnT3AE3nng62haypSH6VWHs1Rq6A5FcpjqWpXr6I3nsfBSDSFGFZVYBf37nnlXkD3/C8tBoHLwd/+MMf+OhHP8ppp50GwLnnnst111130DY7duwgmUwCsH//fm699VYAFi5ceNB2DzzwACeccMLLe8IeHh4erxNu6LyOv962i/dfexqRtjqQFdL7DhBuqUWK13Dg1tuxDRMjW2DNTduI+RUESSBYEyC1P40oifQ91YsoiQxuGsLIm5gFk+rZVQgpgWRXEllT0dM5Ws49FVt3Aw3hWAizoGMbJqIqY2QL1CybT8/droJ+xdQ4R9UED2mx7PHieDUyDv4d3kh9vRc48HhW7n3/AneApudxFJsT2qJ8+Wvv5we/eoyR3Ws9B4Q3OZLqJ2/YXNgsYJWCAYJtImaHwDIRZdWd5feFEAppN2ig+kuZByqIOmJmCCtc4+oXAItmVPPjR/byk+PD5QG/YFtupoKpu/tZphsIeFpmwXggAducyEBwbJBU9n798+y+cxe3drlfyp/6yHK67t3E3A+eR/rRe9yos2Wz99c3EKivoHfVPgCClX7A1S0Y1zBwLAdZU9CzesmayS2T8Mf9pHrSWIZFYSRJ3ZGz6L/nAaoXTcfIufWLlmGiBDRkzUe4uYaefz5ExcxW+lb3UDWzkt5SJoTHm4OKigpuuOGG59xmcrlEW1vbiyqf8PDw8HgzcM23Pst/z1fY89+f4NGb9vHez5xI36pdxObNRN/+FLKmkh9M0HPfH/HFQnQ/vBfLsKiK+7EtG0mVyA1PaAs5loNe0NEzBopfJlgTIFDlZ3RXgnyiQOWMauqOnMXQ46sJt9RiZvM4lo1V0FHDAdRwkIqZrfTcvZJIWz1DWweJNEUY2DT0Kt4lj1eaN1Jf7wUOPJ5Bx/HnsXBRA4KeRzByOKIMgoAsa/zHonpubK8j1Rf1AgdvcnzhCurCPrAySNkR7EDcLTuA8oDdVv3usvEggFOyvXFsHDWAM26XWOJ/zpzOKd95FE6Ius0IAoKDq3cgSuXyBedZhGjHSxsE28QRRPS19/OTy3/KnuzEMT548Wysgs6sS49FECWSe3qpnNPO3ttX0f3YfhqW1JE+kEHxywiSgGM5WIaFP65h5E38cTeYYOZN/JXuwwZA75MHECSB+JQY4eZaxGCE2mOPpOeOB2k++2ScQhYjVyDV2ec6M+gGSlBDT2cJ1gb410076M0bfPjSOeSG83St6+dfQ4dOY/R4YbwaGgceHh4eHq8sldOX0vWxGm467wJ+XDDJmDYXXTKb4liGKWcfAbZNunsArTJC5x1r2PpwN1Pm1ZBPFMhnDRRJwB/XGBjI0jQlRiFRRFJFZL+MnjUIN4QopooUUzr7Hz8AQO38aipmtiIFAlTMamP/A2tpPut4HL2AVSgyvLmb3NAYRi6PqMjo6Sy+iI+/37iZpGHz4UvnMLJrlP17x57VPcnjheHYh5dh6NieDOULxQsceBzE6VdfhV+VuP5tMxHSAziijODYCHoWG9Bklb98+CjOyRTpWW+RHznwap+yx6tA5fSlTF06i4AiIpg6iCaCnkMwiziCiCDJYOkIRRuhmCmXKWCZCI4NlomjBtzMAUlBzLn6AsGRXTz4mWNxCkk3SGDqOFLJNcG2JvQKeJpbwrgWwiRLxqE//oovX/NPt11J4OSmCF1jBQI1Edquugpz/26szBjhllrW/OAuhnvTTFnWwIGn+gnVBhFEAUmVKKaKiJKIkTdd/2bDJlQfQg0pZAddLYJiSscXUdEzBnrGQA5qJLftJtRYRbi5lg3f/7Nb1/juM4i01SH4NOxcDj2dQ0+55Q4NmszmVJHr/riFFRV+Vo56DxKHhQO2ZT3/ds/Zhhc48PDw8HgtM/O0t/LV336G6+4wSBoWi2MaScNGCQdofdflWIP7sXNpAJ78zl30D+WYs6KRA0/1E64P4Y9rCJJAdjBHXUMIAMuwsAwLJehaKxtZA0u3SY7miUQ1bMsmsXeM9jM1xrZ3EqirJNxSy6ov/ZFQfYhZlx5HsLHafXbIFvDFwti6iRpU6AiqPDic47o/bmFB1MeGZPHVvH1vCGzLOqz+/uUuVXgj4YkjehyEKou844hmxFwCR3Zrux1RdgMIRh7BKNKg6NxzzYmcePHZL/nxq2evoGrmspe8XY/DQ5SVg/4+58KjmNsWpzGsguh+jYjFNIKlI9hmeTvB0ic0B2zTHfyXxAzHtxMcB2QVJDdI5Rvdh2Dk3PXSRGzTESU360CUJ/Yrr5wULRZEPjLlreWgQa1PIms5HP+/l7AhWSS9P4E11OvaL2UyiIrMkf99IR0rGtHiGpXT4ihBt9bR0i1C9SEs3cI2bCzdQhAFMn0ZEnuTiJKIP67hj2tYuo0W9RFrjTH41A4ESWTgqR0YuTzFVBFBEsjt70PwaQw+sYnCSApJU9HTWXbc3ck9pSDEJYvrmDa/5qV54zw8PDw8PN6gpL9/Eh/+xScZKFrkLZuBosVJ//s2Vo7mSXWPYCUGEfxB7HwWSZE5+osXMGdFI7ImUzktjmVYSKqEYzkEawIUU24JoluS6JYoJoZyWLqFGlSIVwcwCyaSKtG0vJnehzciKgoHHttEMeEGJ0RJILmnFyXop+/xLRRGkti6SaZ3iG0PdvPgsJtJeMHUCubOen5Vfg+P1xJe4MDjGfxjwwGsULX7h6xNDMpEGcwCgp6lysnwX6dORxBf2o9QtLaGqpaml7RNj8NDlBVqZh9N64pzUAIRAtXNTKsLk9MtbMcNLAHu58RyrRUZT/uyzJJwYelzYptu5kFpW7GYBcstbxBMw11eKjMo7w/lAMLkrIPx9sqv8RKFkf1l7+PFMY3pIZUPXzqH5K5uLppXTbA2zPaf/RFx6hL6Ht9Cck8vmEZZVVnPGASq/IRqg4QbQkiqhCAJ+CIqWtRHuCGEElQIVPlRggoNy9qZfv4C/HENJaiQT+QJNVYRnT2D5gvPLls3FpM6qX19ZLoOULtiIYH6ypIis40/OBGYyScK/PbBrpfr7XzT4OActquC83KrI3p4eHh4vGh+ed5XGdUtRKDWJ/Oxd88n2z/KxQtrCdaE2XrdH5E6FrH/gXUk9/VhFYpUTK/DF9HIJwpEmlyL5UBVAH/cjy+iEqjyI2sysdYoalAhEnX79tYTpjLt3Ln4Iiq+iEqmL0WkvZbItBY63nEOjm1TPbsKPWOQ7R8luaeXxuMWEG6pJd0zgKTKBJWJZ2bLsPjDE72v3s17A3H4DkpeqcILxStV8DiInTuGmDunlk/fsZPPnNBBnZMCSXVXCqIbPLB0MAssDhRIPvpjTvjuo6z9+42HfWxfpIqxvj5k1X/YbY2jBCKeFsNhIKkadfNP5NNXHoHtODR/cDk//Ndu0gWTS5c2UeukcBQ/gp4tixa6+gYyglFwswJKoonjlEUNS0ECsZDGUXxu6UFpf8GZFHwYD0iUdBLKX/CW7n4ex9vXC6Bq9N/8d+KKyCXnTWdo6zAn/PEbCBHXvUEJaMhBDTUSJH3Lr2m/4hIcU0dQNWqqG8nt2o6sqey4ZSsNS+oASPdlkBTJ1Teo9JNPFAhU+RkezuOv9BOsq0RPZ916SE3ByBr0rtyNVhlFq81SHMu4Fo1dSdL7RzGyBRzLJtM7hG2Y6Kk8Q5NSFW/pHCv/LgJed/YicQ5f48CLG3h4eHi8NrnmW5+l9V1vpfW4FvrXD3DSn7+JEI6DoaMENdRwAFFVGLvpN7S/62J3J9uirqqWzK49iKrM5pu2Mf2UNvKJAoVEAVESyQ3nUUMKueEc4YYQuZEhREnEFw+jp7LImowadEsTex/bg6TIRNrqKY5lUCN+ittHyA6k0VN59FQWI1tAT+XJ9CUZyE88C42LNXscPoevceB19i8UL3DgcRA77r2JHffC0e96N1+7bxd+VeLakzuImml3YGdbE6niRg4xPcDD75tGz/t/yKzTPnZYx1aDUaYsnEY85qd/44MvwdXgBQ1eBKKsYJsGodo2ph9zFOcf3cbsmhBF0+LEiiJnXD6dzRmVeb4kjhR0hQvHdQ1MtzTBmTzoH2c8i0CS3aDAeCDANhGKJo7iBowE28QWRATcDINyoEEQwTbK7QqOjWObCGaB9R+/BoBf3r4bgMuWNyJIIm0nT0WIVoNjY3RuRZBEbMNEqm7EHtzMyMMPMbarh5bTjkQMxRh4chvRjkamnDIFUZEJNVaT2r8ey7AQbRHbsEjsHaOYKlI7r5rE3jFGtnQC0Liig+JYhkxfkmBNmOGNe8j0raNu6RS0yigVM1vpum8DZkFneOPekmZCEcdyynoGinCwf7MXNDg8Dj9w4D1MeHh4eLxW2HHvDzmQ1nlwzjKscx8mtrAWNajScnQzQjiOYFsYA92o4QDFsQzBKe2Y3QOMPvooyT29tJx9PIgiPQ9vpnZRO3POm4EaDqBVmuz7124kVQJAEAV6u5JUJArULaxhdFeC0W3dADQub8E2TDJ9KZSgSrp7kO4Hd9CwvJ1AdZxwcy37H92OpEokOwfRswbFVBFLt1gzVng1b98blsO2Y/T6+heMFzjwOCSP/fb/8Riw4LxL+NBYgV9eNBfNzLpCdY4Nouxa5OEq3jf6bHoe/DEfuWkL//jR/72oY6b79iArxxANKPgiVRRTw4d1DXPechFb7vjrYbXxYhBlBVFWMQvZV/zYh4svUsXVn7qC6772U6YfcxRXnDyNd86vxZ/swQ5XIyZHsQWReVqRjFZDQBYQk70TQQFcLQLBtlw9AqyJdZOzBQ6RFjZuyVjOOHBsN2ggySVRxYMFEI2dT/Hx0792UBsRWSSqiDy8YQBr/QDXrP0NWDpG51akeA16OkfFkoXs+PmfCdREGNkxiGM5PPiB3zNvbjVTzpiHpRsIkki4pZa9d6wnUOUv2TNZZcHE5ECO3HAe23ZoOroNgEB1HMeyaZ3Tjr++lv33PUHrKQvIjySRNBVJkbF0uxwsyA3n6d+TQJMn7p3h9V0eHh4eHh4H8Y/EU9zaleR/q+cB0Ox3hy9rtwyR2TDAZ1f/Eop5jIFuBC1Ial8fFUsXsfP/3YIWDzC0dRCAO/7yMxa0R5n51kWYBbdM0l8TZ9ufniTaGmVwkk1izK8wmiqSfKwXy3FoDvoopgpolREKIymajp+DGg4w8OQ2Wk+ejZHNIwc1bNvGsRxS+92JKzNv0tuVJCR71eEer3+8T7HHc7Lhlj+xaUMf9+9LUpCDmGoIxxfCVgMYaoicHET3xxEcm6hk8ruL5xCobn7Rx1tz860cSOQ5+Z0XHva5H7244bDbeDFMO/EcTrjiHa/KsV8sM069kEjjdC66+hKOaInziS99hM9dMJcr58XxZ/rcwbxRLIsTioU04fR+sHRsv2vDKBhFBMdxs1Kg/NPNFLDLdorY49aMTlko8SDGAwyTlpeFEEvLRSP/jKABQMq06cmb9OZNQrKIoxfQt69FkBWM7p1svXE1di7Ntge7qT9qDqIkcv+qXs583zJqFzSQ6uxDUhXG9o6SH0wQaYowvH0ULe4j3BAkWHJbqJtZQaQpTPWMCopjGVLdI3TevYFs/yiDT+3A0QtEOxrZ9Y81dN2/nd6HN9D9r6eomFFD83FzCVQFADhQMPlnX4ag9Cz+ks+D9wX+3By2xoE3C+Hh4eHxqnLl+vufkdbfkzc5UDDpzBr4JbevL+5cB4DRs5MtNz6FnRph9X37Sn29wL1r+njL22fTsKSeA6v24Fg2Q1sHyfQOUTE1ztDWYQJVfiJNYUL1IWzLpmlKjKqaALW1QTJ9KXLDOfY/soviWJaBNbtQghrxWa3svWsT3Q/uovfhjQxv3EOkKcLUcxYRaYoAbl9/50AW5cV19R7Px+FqHHilCi8YL+PA4znRYrVUNUb4yYN7mPG2edQFFXQLJNFGEgSyhk3UJ5F2VPwl27x1f/wUx3zszwxtXflvHSvSOJ18op+eHcPMP8xB/7evu5ZPf/iZA8tXgl0P3IY/9NZX5dgvBFFWUAJRJJ+fsy8/h7XrDnDW8VO4P6QyqyHC+Q0W57VUsCGtIGVHDhIiFGwLxyzgKD6wbaTUAACOGsDBLTPALIkdlhwUylkDgDMp02CynaK7oJRlIB7ia6mkhyA4Nj+b/3YatGf/6jqrPsSxXzmP/se3YOez2LkU+/52JxVz2tl3IEP1n+6jdW41e255nPsf7eHoWVXc9LMnWLGsgdoFTXQ/uI3Wk+bQv2Y3mb4M4YYgxZSObrlWi3rOIJ8oEGuNomcM1HCA7EAOSRVRghrZgTRbf/8Ac95/LsHaXbSdvgQ5VgG2Rf+jaymMpSkki9y9tp9h3SKuiCSMF5diN77X00scPADHwfY0Djw8PDxelyRX/gS1bzMf6rj/kOtPqwly0tfPZ+DJbThZ19Fg1y9vpGJWC090JYn//gHmLKxl+58e444nDnB0Y5gb/7CJE+dUU7+kgf0r99J+6mwOrNpDIVHAH3edxPKJAsVUkYJpoyaKxKfEyA5mCdaEGN094moZ5U0c22br7x9i9uXHowZVpl24DDUSpDCSJLmnFz2dJTec477NQ4fd14/j6R4dGtu2D6u/9yYJXjhe4MDjOdGi1dRXBcnrFl+6cwdNFX7qY35W7x1lTlOUq5Y2MlowUUSBjGFS7ZepD8ps+8VldLwjQap3x0S9+3MQqG6mfelizj+2jfcsbqBzrMht1z379rHWuYx1baZl+dl0P3H7QevmvOWily1oMOXY89j7yC3PuY1tGlx28lT2PNFBum/Py3IeL5TJrheyFqJy6mKmLmmnNqqxfGoliigypSbEFYsauPKIJtoYRSxmcYwCCwMBHDGAkB9zdQtKGQCCqbsWjJLsZiEIgruPrLjZBCUNA0eREUwDR3E747K9om2XLBxFHMsESUGwjHI2gWCb2LKGoOeekZFgJ/qJK2LZuvDp/Oix72AO9eLoBarmd+DoBfoeWUd8Zis/vfZ2PvqdC9l0/SPMe8+xPPTVO9iXMxjaMMBxVQGMrEFxLEN2MEtyTy/+uJ9wU5zRHYMk9iYJ1QcpJIpUTo3ji/jQM67eQqyjkeJYFiXoIzuQRvHLOLaNIKuucvNIiv47VhNuqiA3mITBJJse6ubsY5sxssZhqSqPP0R4QYNDc7gaB97DhIeHh8erQ8Fy+M+Oiw657ocPfRMrMQimQfWiaQD0/PMhwi3VfO3zd/KF/zmHbX96ktmXLeeOb9zNsG5xS+cYp9UEcWyH3HCWxN4xfKv24I9rRFvj9D7Rw3Bvmoq6IGbeoq4jjqzJ6FkdWZMJt9SQHczgi/jIDGQJVAVwLAdBcZ0Y8kNj9DywAV9EIzvoBjK2re3nrBVNFMYK/Hn9wGHfEy9ocGgc63DFEb07+0LxMl09npOxrs0kcwaZvMH+/jT3PLKPuzf1M5wu0jWcpWg5aJJIqmgjIrgTdI6NJMC0oxbTuuLsF3QcLVpNMOLjhCmVRHwSK7sTz7qtIIoES+UQyd5nDszPP2HKi7nUF0RdW+yQy0VZOejvTMFElNWX7TxeKI5tI2shtHgd/ngdDdPqaasOosoiiigS9klUhlQawwrVAdkVHBwXwYSyVaKrV1BCFF2Xg3GrxPGsgpLzQdlWsbS87KgwuSRhsu7BIdY5vuDTLwTBsRm+83YeGckf8lqb/TJG13awbUQtiKjKdP/5ZhpPPYZ9925iaYUfUZGpnlOFEtTIWzaaKBBVJERRwNItxrrGECURrTJCoL6C+qPmIvsloq0R9wFBElCDKo7lIKkSakjByBXQszpqOIAaUlCCPmoXteOYOsVUgVCrmz2jVUYRJAFBEtiWLrJvwyCp/el/+z2djCp6eY8eHh4eHm8sZpx6IauXHnvIdW0BhfzOzRPOS0DXjX+j7ZLz2XXrZhbHNGRNJdYeQ1LkcjZsVUn4MDecZ2jrMIblUDmjFkmVqJrfgaiIVDWGMfPu84+suZMAkiK5VssjKfSMQaixknBDCEu3qJrr2ofrWZ1oRyNGVifUWIWkuMfaltbp2jxEPnF4oojeYM3jtYKXceDxvDzwq18f9PdOIN6+AOe4RVz1pw2cPq+OC2bVIImQKlpUaBIScM/HVnDitx55zrZ9kSoEUWJs3ya2FPN8YCDDnNk17NozCrjlC6neneXtRVkh1jaPaFWQXiDZs+2g9o685J18/TP/+1Jc9jPQYrV07zi0YKMaqqCYGipHLW+8cwfOeK2/KL5q0cyKqYuZdfR85rfGGEwVWdwW55SOKjK6yfJgCj1ci5IbQUj2EgIcSUV0XC0DwdKxfeGJTAAjh62FEcxCSfwQd0Bv8wxNAqAsaOhYpiumWXJKQJoULChNmTsi7jqzgCPKDOgK9WJJD8EyXSFOx2ZoY+czrlER4N1nTaXt9EUA9D+8mpolM9h391pSPWmKY3ei+OXyQ0B2IMeOv64kazmc2hAmVB/EyBroWQPLsKhbWMuBVZ3Mvvw4xnZ2u9ZMQdW1YCpZNFmGhaRIWIaFIO3GzFsIkkjV/A6sgs7otm7koB9REth6/T344xrp7gEs3Wb3A12oosCDw7nDfn8LtuOlLj4Lzktix+hlHHh4eHi80nzx8sX849sjtAUU9uXc7D5NFHjXedNpOWk+SjjE4OPrqJjVys6/ryK1P01+5E8EqvxUDOdxLBsja7Dzpic5UDA5rSZIvCFEPlEglTUIA+1HN9H98F7mXnE06e4BbMNGi2n4IipG3iQ34vbRbv8ukB3MEqgM4Fg2geoYwTqRkW29iIo7lNr4i3sJVPlJdw9iGRb7Hj8w0dcfnta318c/D4ftquBpHLxgvMCBR5nll13Bvf8xC8Fx6DYDzD7948+6baJzA491buCY97yHXf1pwgvryeg2VX4JbLNs2fjFt8/nczmdXQ/chm0az2jHF67gU598K/ds6sexHUzD4oSZ1Vy+rIXzb/vzQUEDAF+0GjOfoXvdmkMOyJ/80w2HfyMOQbx9Ae2LZ9O1addBy9VQHNkfQpJVzEKm7KSw818344tUMeXY8+hefd8r4rBQNXMZejpBITmEGowSbZ7FBWfPRBIFljTHyRkWb59TjW+sByesgWWjZIdw1CCCnsORVRw1iDOuU+DYCIY7uy9Y+oRooW27GgeljANHVtwsA9sslxYIluFmDpQcFgQ975YyPB3h4GHvuL5BnT3qtj9eHmFbiEaeqRefSsftu1jQFMHSLeJTYrSeNAeAbX96nLnvPp7qhW7aYrIrReWMSiRVYsrZR1C/bIxff/omZkZ8iJLI6VcvZ+uf1zPSlUKRBGzboaI9RrIrSf3SVvof30LzKUtRV+4mNqWCzvv2IqkigiQQqAwg+2Us3SLcGMcXD1EYSVEcy5QfInof3UqwJoylW9iWQzFVQFJFIlV+Bp6l1OLF4D1QPDuHHbDzniU8PDw8XnEuveJa+n58EXvvWMvw9hFirVGajp2GFguz6fpHWPLR06ma34GezpHYO0bDkjocy2belSfRsKWTH37uVuZGfPglgTPfNpO99+3jQE8KVRSQBFCCCqn9KarnVNH9wEbaTl+MFuvEF1XpfbIPNaiWMgwVRElE1mTiU2IE6+IUxzLAeEanwtDGLvxxfzlTIZ/IIykS1VNiDB9GKaLHC8dx7MPr771JgheMl/3iAUDt3GP5/sULcLQISTlMs98ie9e1pB/4X97ywfcB8Otff5nNd//goP0evf56zp1Xz/6UjiRC3nTQkcE2kQQ4oS3KBSdPpW7+iUiqRt38E5h64vnMPuNtAMSap3Pq1Co+d8YMfvPORXzunNmcPb2Ku7cduhYs1jyLVO9O8ol+tHjdy3pPxvFXNiD7g/R3jjCycw2+SBUAldOX4o/X4Y/XMu+UY4i1zS3vI4gigcpGli1rYulb/z2hRFkLPus6SdWesUwQRZRAhPe++3hmHb+ME9/5Ns5/19lceuFc/KrMFUuauaAjwDvmVqMUU+MNTeyvZ3EUDcEouIGCcetDy0QsZhH1UmlAqRQBKKcIOqLkZgQIgpsVUAoWjAeOxkseHEFwgwulEgdhPIg03t74foKI4NiIxeyE/aLoZjs4goA6ZQ4Xf/xYjv32ewBoWD4FNRKgOJZm5kXLGN64h+JYBkFRWfaFS6ic1UTVvA723b2W679xLwD39GdY8uHjsQo6tmETjPiQVAl/TMO2bERFQihlRaS7B4g0RVACGnULa5BUierZ1Ti2zfD2EeJTKiiOZcn1jWJkC4iKjFYZoenEhYTqoziWTc2iDtSgiqSKOJbDxu7Uv/V5eCE8/YvcU24GcMqzEC/65T1MeHh4eLwq/O2ID9H4h1uwDZuWE2YQaqwm2z/C/Pcez8BTO8j0DqMENI77+tsI1seoWdTBzr89xs9/8CiqKPDEaJ6lHzgaJRygN6OTtRw0WSQYUJBUt9dUwwH8cT+Z3iHiU+KoQZXq2e4zXvXsKizDIjecJz4lhp7VyQ2NUUwVECQRXyxEzaIO/HE/Rt6kem4DgiQilUointg0+LLfI6+vdznsvt7LOHjBeBkHHgC8551HIQoCX31wH6v2jBD1K/zhktng2Nxw2QLUt36DPXqA992wjuTKnyDlk4RO/m8AvnbTJm68enm53loUoGhL+Bwb2xF579ImYgGF06aeyVW/WY0/7OOdR7XyM59MY2OE9piPKXEfOcPmpOYAP17Tz8++8oNDnmfL7AYyAx1kBjqxioeudT9cRFk5KDuismMRbz17Bj/60g8IVDejp0epnr2C089eTKZgktctJFHgmNMW8Y/tqwC3dKF57kx+eUY9un821X/9y6T23UG7Y1vPyETwRaoIVDaS7NmKrIXQM4lJ+ylIqh9LLyBrQUK17cSap1BZF2LHIytZ1BilMqSiySJtMT/1YY3pERDzSYSciSCpCJbuDub1DI6kIhgFHFlFKDkmoOdcAUPLQCjZJjqi5IocitKEXoFjlx0RyvoHpSCAYFslbQPbDSA4dtlhoaydAG6bgvCM5Y4ggm3i+IKu+GIpYOHkMuj7tmHldQR/kPnvOpLOe7cy65KjUCNB9HSO2iUzKIyk6L7rcTIDWfxxjaGtw6zcMUJv3qQloBCSRdb+7GEWve8YFr53OcWxDGZBJ9mVoJAo4Iv42L9yL7HWKD0PbUVSRYZ3DJfXpfanMPMmLcc0MbJjiFB9GD2rI6kimb4kvniIoXW7cCwbPaszsmUfRt4kO5glN5xnZ0Y/3I/oQRyqVMFwPKcFDw8PD4/XJ2ooTl3IR9WWO1j6sRPpuncT0y6sxF8TJz+YoGJWK0a2QO/DGxjrGiNQFWDnrdtY1Z8hadjkLYeC7XDPDx/mtI8dxxkfWkF+cAwjW2SsK4mRNZBUiYENBwhU+RnZ5Wa3GlmDQrKIP66R2p9ClETqFta4QooRH1BE1hSyg2niHQFGtnRi6TaWbjG6sx9wNRRyw3n2ZJ+ZZftS4/X1Hq80XuDAA4D71vby+79tonfNnYCrFXDh7zZh2Q51MY0fnTeLKUqRu65eCpaOrYV56p/fZ8lZ/8nav9/IURt2cP+PL6cmILsWNpIIjoUkiMgiXDCrBoCfv+cILvzqvzjpsoWELpjDR792M+plC9Ath0rV4adrB/nOD/8JwNyz387m2/9y0HkKgoBl6ji2jZE72Nf3xfL0kodo82ySPVuJNs8m3bebxqkVnDmzlr/MPxHb1Bkr5nnb244kpCl0VAXpGcuTzE0MBgPVzciqn91PrMT5z4X40n1EGmeU19slu0JLz2MFo+jpUSy9gCgrTD3mRARRINmzFV+4gqrpRzC8czVqMEpF+1yGdq0lXt3C9KPms3hqJSFNYf9oDsV3HCe1RfFJMbBNxFwCKIKuugP6ks6AYBRxZAVH1iZm88dn/UsDfMexS8ECs5QBUJr5dwR3hCqK7k/bcEsWyhfmLnuG1sHTKdkuMq55MG6/6EwEGoBSYKPolj1IqnuuokTzlf+B2bcPOehn/n+cSG5wDCXgZmIMrd/FgdXd7No6TGNNkMZ3Hsl1f9xCRBaJKiK67eCXRLYM50h9/wFqm8JoMQ2zYLqDf0Wif/0AldMqSO13hZBkv4w/rlFIuKUGesbALJiYeZOKqZVYhknFzFYOPL6D+mXTGNrQib8qhBLUECSR0V3DrrVTosgj3Yf+zL4YnYKILJK37Gd9YHjTP0h4GgceHh4er0v0TIKL3nkN+b+9H3iM6RcdhZktICkytqIwvHEPPY918diuBEtrg7ScMIuf/nkrAEFJKJckbE4V4YcP09Yaxcib+CIqI10pghEfiQMZ6mZWkOxyswBlTSZYE6SQLCKpEnpWR88YxNpjxKfEyA3nqVs6lYG1e6mc1cjINtd9Sa0MUBzLMtyVLGkh5FjZl3nJ7sXzBQbe9H09gH14rgp4GQcvGC9w4AHA5nvuozA2UR4QDPnQLZv+fQmMlhgZw8anyghGgV05mYjqsDcxIe42unstUd+7sRxQJYG8aaMqIg4QVERCqsCGgSx96SJ6LkNPssi2wQwVzS0UTJuwoOMgo0oiajBCuL6D/t1d5fbnn3MxG2/7M8MHUuSGeoCXzj7FF6nGNnXMQgbbNEj37aZm9tEccdwMHrnNorUuTMgn0bGohdmNEf7+lzzRgEpjTOP8mVUkChaposUvHt+Hv7KBaSuOxjQsutetxdEikE9y0WUn8Ogat9YtM1bAth3yYwMY2RSy6qeYHnXPwbA46/gp7N80g6WnL6epws8jYT+z59WxfGolf30oxMIZ1aiySDSgsqQpCoBlO2hOESGTLgUATBxZQ8yOlIUFBdstKcC2cWSfO8tvFEqj1ongAaJykNihIwgIT/9OLQUXgEn2ikyUHIzv79gH7VMOKowHDyYzeXvHxg5WImZHuPmId3DqNy4gdMTxyE0djN13O5neIURVxhcLEZ07G0EUye7ZzeCmAziWgyoKbO5NM8dyMxkqVIkKVaI5rnF3VxLddrAcCA5k2deVZFWiwPvOm4aeMfBFfChBhXRfmkBlgOxgFjNvUjOvhqGtQziWg6zJ5BMF0n0ZIk0R+p7cSeWsBrJ9IwTrYxjpHIIoImsqoiQyuC9J3nIY1i0Oxb/7SVYESJkTe3kCiYfGPszvCO9RwsPDw+OVIfm1Izh18wye/NMNxNsX0Hm5Tc/Pf4IaCSIpMtr06TimQWbXHvqe6i1rCjxyIM2shOtQVKVKhGSR6SG1bNu8J2vg359moGiybYfOO49uwtJdwUNZU8gM5AjVBkjsTaIECzQe2VDu67Wor/wMEGkO0/3gNuoWt5DrHyVUH6WQyCGIOmrEjy/icx0ULPtZ+/p/FxEvMPBCsG37sPp7++nPox7Pihc48AA4KGgAsH3NPvrW/4tY61zuu/b9pHSLgCLjl2Sm+XUEy6A+rCFrwXK6/b6xIm0xH0XTQZUEhgoO1T4TW5BxHAdREPjFI50cfdIs9ozmuGxBPVcf2cRjPSnm1oSIaxIhVWL28uk88KtHSPe5Votv/fgHuPMPtzPl2PPo37r+JXUp8EWqCNY0o2ghMgP7KCSHAHjiBxeyaTBLa1WAvYNZcobN8bNqCGsyn/jgaQA0hjVC+UGCapCcP4hflZhx3HFccco0MkWTW0MqD3WnOClk89Fj25lSEyKvW3QNZzFth0dXSohCI2N9PUQap1PMjJIcGCUWmMEXPnMBALpls/DiBYR9EjnD4qyjWlnYEGFmVZDWkIBgFDiioZntw3nE9FDZKlFwHATTwA7EEfNJrGAFYj6JHal1Z/H1LIJju7P59tNsEiU3M+Egq0TBKWUeWDjipO2f9j6MiyGOlyCUAwTjmQTjpQ7jiDKCWXAdFxzbLVMYP6Zt8uG28wD411V/AP4AwDuOqKf56HaUoGu55BSyOKLEvrvXUUgUECSBkz99ChuvX8nfvvsgtT6JjGmTMW0UASQBTqwOEAqpmAWTbWmdM2uDJPaOlT4TPjJ9GRqW1DOwcZBgTZB0X4bccA5Zk127xrhGpi9DpCnM6K5RAlUBkp2DZAezVEytRM/oJHtSSKpEbjhH3nLYk33pShQmP0h4QQMPDw8Pj9czm5en+OTZ/8siAeY6wPr7WdVZS/Ox7QiSiGWYiGPu89m++zaSHXT74//4r5PYcP0qfn3d4wAM6xZ5y6ZCldBEgRlhlaaAgmE57EjrnFAVIDfslrmqQYXsYJbmoxrZ91A30dYIg7sSZAeySIqEoLn2y+lSXz+8fZRYa5TE7gGSXUmirVHMgkkhUcC2bDdz0XbKLhAvBV7f7vFawwsceBySvvX/AmD3Hz7A/rROh6a7g0ZZA1PHUf0sJM9TN3+dBWd+HIC/rO/lvctaCSgiyaJFfUgGQShP23XENbq2DfHTL56MJolsHMiwojnCiW0xJAFG8hbX3bqNbMr1u1UCEQKVjeR1i2XnncZbFtbzv9f1kxnYd1jXFqhuxsgmMXIp1GCUtvnTOX5hA13Ds3js/m2k+vYQ8Ukc3xRgRlUbf908gCIKXH1kE0FF5EDGQJNEauUiFAsIlsHDvUn8qsypRzRz3sxqrr1zB+cub2HPaJ6TQtBuD7KsKU5dSGUkb9CdLHDijGqSBZPf3uPnHSd3sHrvKFu2DjKlIkB92EdfushgVuets6sRBdg5UmBuTZjplRqx/ABOVgRJRVUCzK4OIKT63Vl6M1meuRfzSXcwb+Sxg5UIen4ik0CUJ8oUSm4I4wP9slPCoaKw9qR9oFS6MJF14EzSPyhvZ5tuxoMol2wYJ5UkyJobaLCMgzIShNW3HPL9u3F1H/992iwibfWkuweJLZhLYt1G6pdNo7pQxLFsBtbsYunHTuGmD/7ZPUXg7NYo+5NFFsc0qhpdtwOzYHL+ikYyA7myirKlW6hBlQNP9VFVcmVI92UopoqA69csazJKUMHSXVEds2BgW3apBpLyw0R2MMf9fWmy1ks7ZTC5TOHZHiz+nYDCGzP4cJipi+CVKnh4eHi8zEQap5MdeMCtdpz0lfvn9QN85oQOwi215AfHUKZFGFq9hWhrnJoFzVgFnQOr9rD4A8dx63/dVt7viLifAwWTBVEfjTVBLN2ikNG59OgmcsN5fBFXZ8o2bJSgQvej+6mZU4VtOWh+mWKqiGVY6AlXB8EXUbENC1ESKKaKmAWDQFWAYqqIYzlkB7MMD+Z4YjRP4SVOeQ9KAnnLeQP2zy8th2vH6JUqvHC8wIHHc/L9ld28a3EDO3IqDSEZvwOoISTHBNlHBxky936F0KlfYEtviqGszswqP0FFJlm0UUQJnwQ5Ez512zZ+88ljUSUBVRJY1hQmUbBoDIBYyGLYQTbe5g70Io3TqZoyC1kVWT61kvcuaWDpf97GcEl88FAIoogoq2W9gEPZPwIEq5tRmmeRHeqhfekCPnnubM7siNOZ1LmxJkR99FgWf/ZuNn9xKfW+IB9dEAXHBAoIuTy1gQo0M4tgWTiKH8Escnp7Jae3R8jbApf9fj03XzqD/7c9zbvm10Kih06xhojPoTEAzUKOBfEg2DZ78jLRc2fhk0RmLmthXVOU41pjxJ0Mdn0FWcMmJLlfhovrAojFDAg6jhLA0KLIRg6/LGI5YAfiIIjYfrd8Adt0bRa1CFj6hF1i6adglWbAxzUNHErr3SCAIytu5sE447oEllFyTnCzG7ApCSqa428E492cgBuMECyd7IP/IHjihdha2G13UumCYFs4qn/ieIKIOfTsNka2brL/wXWM7Uti3rae0c4xFr7nCDrv2U77aTOpXTqNW665hQZNpiPoCiKmsgaNIZVgTYBiSkeLu4N823ImFJaDKqZkYBkWgcoAB57qJ1QbJNIURg0p7mdMEsozFmbBIFQfIrU/RWYwx56swSlnTWXXEwd4eDh3yM5eE4XDfrgYL1N4rtrHf6cLfTHd7eEEG16JQIWbIHOY6aJe4MDDw8Pj32LKseex95FDB/4PRdviRYTH1h9ynS8Wou/xLfQ91Y8gPUlyIMeidy5i37920HbyDBqWdXDr/9xLRBZpCShUqRIJw6IjqOCPaVi6heKXiUgiZt59RrENG19ExZJsHMshWOv29bHWKLHWCLI24bqQG85jGza26hCsCTLWlSSZ1tmZ0TnzxFa2rTrAE6P5l60/e6knHd6oOLZ9WP39S5XF/GbACxx4PCuRxuls2Z9EWtLIjLCNI7qp5pNTyt3ZbJ2Hb/4O7/zfB/l4T5IHrz2RrGET8YlkDRtZlLAchz3dY8w6z0/edPDLAoIgEPUBAhxwglz+qydpO+ZcapoifPrsWfzXL5+kqiHCxfPq+P5j3YQrQwedX938E+jf+OCkv13xQsvU0dMJbFMnN9KLrIXwhSsw8hlkn59LL1nO41sHGQ76OWZBPfNr3UDI1KjMtSe0oVsOD2yrdwfWujsT7whCeaCrSgKYlJwFcAfTeg5HVgk4Njdf0sHNnQVWNMeRUn04aoA6v4Ju2YhZN9XOFS+EqWqAQGuMuE8iWbQxLBtBABwwbFBEgbwj4Rcd93wkGUdSwTLd4I1tIhdTKI6DYBawfWF3sG6b7vbjWQSi7JYlTNIQYJLrATBpW8UNAsjqM3QKHEFAwBVMdEquC+OCioLjTOgegJt1YJuIepYPTruktPT3fPDi2cz67vcQrEk6B7aJYHDQsca27nnWz6Zj2UiamyFQNbOSxZ84l+G127AMC0ESuedb91ETVAnJriBixrSpivsJ1gYY2Z2gcmqc1P40giggqQaO5aBndQRJwMgaNBzRyOCmAapmVgK42Ql5C9kPesFGkATMlImkSuzefICdGZ2kYSEJArsf6GLtWOEZDxLjg/yC7bxkKsivZu2jJLhB+hcTBHiluujDfRjw7Bg9PDw8XjgDV9fyuas/xq+ObuLRr17PFz7xzefd56qzZjD4mUNPFJgFHVES8UV8VM2s5MgTFjG8cQ9G1sCxbG79/kNIgsCssJtFkDFtWsI+tLiP3t4MjY0hhvqyhDUZ2e8+n9iWjZ4xKKZ0Go+sZ2DTIPEpMWRNxsga2JaOiopt2UiqSDFlIYgCW7cMsyNdJGs5iMCalfvZndG9bIDXAI5tH1Z/7/X1L5znkT/3eDOT6t3JOQvqqVYtN63d1BFKjgCCZSJYhjsYFWUWRQ2MQp5gKVVbEV1V2wpNQrcdZFHgp+9aiigIBBV3hlwrJAgpIrfuGuP/nuihsiLARW+ZwVcunMfpU2I0Tq3kZ5cu4tYdQ+imVW47UN0MTNgaghtE8IdDVDbVUje1lYY581ACUaLNs6mafgSN8xYSbZrOnJOP5aIFDVx+whSOO7qF+pifNjGFoOcQ80lEPYdmZplSE3QbtnR3MDt+LEFE1HPl33Fsdwbdttz7U8xywAqwZyTL9IAOpjt4L1o20UwvglFAMAqltl2bwQbS+PPD5E2bRXVhYhQQjDyqYCOJAoooTKTw23Y5gCDouYMcCRwlMHHOtokjqTiTznt8u8k4gmuhKThO+XfsSSKGk/fFzQw4SNhw3ILRcSbu1XjbooRg6Txy6X8edMyf/nkr9O9xt00Pu+KN4wGM0vWIepZ09yBf/PpZPJ0rT5tCYs8AkbZ6Zlx0FBVz2hlYuYH84BgL33cCf/vhoyQMC9uySRq2K9gpCliGRXYghxpQKKaKBGsCxKfEMPImWlzDsRyKqSJ2KeVtvOwAXIsmPauXMw0KiQKWYdG9a5SVo3ky5oS7gSRA3rJZHNNYHNMm2pjUL70RxI6MFxE08DocDw8Pjzcmf73h63zu6hsBuOGx/ew76dQXtN+75tcy9FPXQWtyH3HV2VPpW72P+MxW5r77OKIdjQyt30W2P8HC9x3Pz3/wKP0Fk1qfhOGA5YDhOBR1i9xwnpAsUkzpVFT6ibZGKKZ01KBCIVEkX9IlsHQLWZORFAnHcrB0C0u3SPelsQ2LfKKApdvs3jvG2rFCOQNAEtzswazlsCyusSDqO8SVvXnx+vo3Ll7GgcezIqkaRzZG3dR0WUUopkFSD1Yan5Ruvutbx3L0T7azZSjHcM7gmJYIPtFBFV13hbhfQhDALwlIqT72UoUxpjOrOsTi+jCrGiJcOLMSywFBz3HTe5agCjYXz6ll8QduKJcpfPmL7+LTH/4aTdNrKKQWI6kaWiTCl65cSmPER86wWLM/yfWpIm87eyYza0P893fv5BefP4/hnM6CiEXUV8GMqhDLaxV6CyKNVtIdwJau6erlrdiqjphP4ACC7g6ExzMuJl+/KaooQgEcmwNCFFUU+OSKZjALOIqGUEgRkX04agDB0hH0PM54Wn+pXMBRg7Q5eQSziCOGS9aJBWQ1UBqoQxEFWVGRddfmx53hFxEsx3VJMPJAacZflN3XeNaBbT5jsD9eXuCMf8NLMtjWxDbjzgmH0jkYt24cz8QoITiOW6YgiOiP/B25upEbV/c9Y/ePLP1Q+ffv/P49+JecCP4wTunzVNy2hh/8Zj2wnnOaIty237VL+szHVxCb3szYzh6Se3ppOnUZI1s62fKXTWhhH/3r+6lSJSQBCqarbDwn6iuLGlqGRaz0AKH4ZWzDwhdRyQ7k3CyCrEG4PkT/+n4sw0KURIRS0CFcH0ZSRWzLwbEdLN1tv0qVGNatcgmCKApU+2TWjhWeed9eJcYH+IdbIvD0/V+rmQYAOC+FxsFLcyoeHh4eb3TWLjnmGcuuXH8/igDnTK/kbfWnPUOIG2Dw6x/izq/fz3f/8gE++fafAfCJq5dSvXAayT29jGzppP28ExjZ0smaGzcSDav0rx+gI6jilwSShk1nVmdxTEOTRQqmjW441DaFyxaMAL6ISjHlPnPZhk2wJsD+Jw6gBBWMrFHSOLKJtUbLekfjTO7jwQ2ch2SRiCyyKvHa6etfK7zSWRiexsErhxcU8nhWoi2zaQ1LOIqGWEi7JQqi9Jw2ex8/bzZRTeHG1T0UTPcf0XLGU5oFRKBoOTiyxqbBDJokMCWmIokCIdUd9EqliW+fmUMspJFFGN6+iqYjzyLWOrd82NSoO/uraCECIRVZEgipMooo8q8tA1imxVFtcWwHBFEi7lcI+WQEPUtdUGZRbQDBscka9sQ1lFT/dcuhKAfcGf7SIFqwD1E/5dhYtus4INgWN20dxC+LCGYBoZjFUfwAbtDFsd0MhEn7CraJEap1MxbGU6XMSSUGT3MikK2CO8Afv//WuMWi6QYKxkUOx9+XyT+ffuripLih8CxfBeJzfEU83SFhvCnHcbNRVI2t1/3h2fcv8diXb8Ec6sUeOYBgFhD1LP0PPlFe33BEffl3f3XctThUZAqJHJgGsqYSa4kQbY2wbU+CkCyiiu6HSBLAth1sw6ZiWhw16AaHbMummNLJDExYigaq/EiKhKzJBwUN9KyBKIml/Ryc0oyDP67RXzCp09z72Oh3fxZMmwMFE610Dq8Fxj8B0mGe0ng7L7bjEJ/28+Vm/GHixb48jQMPDw+P5+cHP/0Co89iQWg4sLYnxfCf3/+MdVNPPJ+x3f0AaItPKC+PdjQiqTKCJJIdyGGmkkiKTDymEaoNsn1/mgpVQhIEJMHNKsxbDoblUFEXRBUFREXEsRzMgklqfxpREtGzOqH6IHKp3xYkAUkVkVSp3Le7WYdOud/X4j725YxyXx9X3OUJwz7IFtnj1cPVODiMlxc4eMG8qoGDtrY2BEF4xutDH/rQs+7zk5/8hFmzZuH3+5kxYwa/+93vDlpvGAZf+cpX6OjoQNM0FixYwF133fWMdn7605/S3t6OpmksWbKERx555CW/vtc7X/7U2UhjvW5qvGNPKOLDwcEDx0YsZSO8dXqU2qDMJ06cyuX/7yksRMzSP6QqCWhOEZ9g8ecui7M7oliOG0io9Muc0Bp1Awx6DhwbQwnw291FjvvSv1jzz+9TWR9m+++v5r7NbsR60fw6InVNVDdX8OV3LmJeTYg5gQK3b+3nqGlV/ODjx3BmvcC+0RxzV8yiJerjvOocgm3i11NoVh5sm/aoiqDnEIw8YjGNYOSJ+ETSuuUO6PX8xAB+ck2+WUCwTXyi4wYBzAIXzKohIDmuJaJZcLUMJBnHH50QIwS3xAN38C5bhYlB+CTrQvfYObe8oZghY7jaEoLuBkwcxeeWIowLFgqCG9wYdy0Yf5/GhREdNwgiTA4oSOMuB67QoVs2MFEWcSgEI8fg735K5p4/Ye9ac3Apg5GDZD/d3/s6e/92Hz+/ecfzfs5u3j3Kx47/HF9e8VH0raspblvD/37v0fL6yW1EZ7QjhULUHnskgZoIW3//AEa24Noz+mU2JIv4gwqRqEYkqtERVJEVCSWokOxKAtB07DSk0rJAlZ9CoogaUigkCuUayEBlAH+l68scaQojazL5RJ7ccA7bsgmWlJoXRDVagwqLYxpDRYuILPLwsBuMeKnUlV/KL+ln01l6rmMcap36IoMi9tN+vtzYln1YL6/u8aXB6+s9PF6fzD377VTNXPas6+e85SLe8akPsXHFCc9ZgrcvZ/DR2hO4cv39dF3kLy///m1fcssXgQ9Nvbi8PDxtClIgQM2KpURbY2z+fw9SHMsw5bSpaHGNzakiuu1gOQ4xv0JbQCn3S3rGQBKgcXkrst+dDAjWuDaM/rjmCh6WyhFdMUQZWZPxRXz4Iir5kTz5RB6zYKAG3SyFYyv9zAqrLIj60Es6Ra+lrMI3O47lHGZf7wWAXiivauBg9erV9PX1lV/33nsvABdddNEht//Zz37G5z73Ob70pS+xZcsWvvzlL/OhD32I226bsGG59tpr+fnPf86Pf/xjtm7dytVXX80FF1zAunXrytv8+c9/5uMf/zjXXHMN69at49hjj+XMM8+ku7v75b3g1wHLL7sCNRQH4MbHutxa+nzJ3k9SS+ns5sFCe1AeWAt6jqgq0hBW+dt7lzJWtBgvnffLAo4gkrFELpxZRcaEuqA7wPXZBfqzJpJAuTRix0iBm5/qZd0XjuLRrgSFnE5AcsgW3UF2LKBw5YVzmDejisX1YdoVN+AQ0hTOmVVLS9QPjs07FjQwpylCb0qfUP63zfJgXbYKpVnykn6DbVLpE6hULIzKNrdWf9yRYLym3zbB1LH9cYRixm3LMrFsBzGfdO+PZZYEAm33HlqmG0QYL3koBQ+w7YPORzCLEwP9ko6EYBmEFNd+sVxSAW4goGRjWH5PnvY6KFhQeq+E5/qStCZd69ODB47N3Se9l0BdBWM7e9j6o98jGDmsXU8x/Kdfsfkz19D505+x45at2JZTjsy/77xpnNsa5T/O7KBKlQ5xUDcV8Kn//RPffvsPDrn+ms+fglzbAsDQqrUMbx1waxILOsNb+0kfyLCiwo+kSu7Dgl8iUuVHVCayBQRJYPMNT+GL+LB0i2KqiCAJ+Cv9GHmTQGUAQRIQJAHbsEqzERJaXMMfdwMJRtYoz1BEggqyJpM0LHTbITVJ6+Cl4qXszg7V1vOVLxxq3UttOeXxxsbr6z08Xn9suusHbL79L1z70dO55lufLS9vXXEOP/6/L7Di8nfx6DUnMLcpytySJtBVZ0/lzNog7zy6iWb/oauhf/v1e8j85C08GNnFzbtHn7H+C189EynqChMPr17PyI4hZE3BsW2GNveR2p9iaUwjrMn4JRHbsqkoPVdofrcsUQv72HnrtpJdYo7ccB4lqLiZBbZDtDmMY9sIpYmScXtmf9xfzkZwrRt9iJKAXxIRRbcsIms5bwidIg+PF4PgvIamVD7+8Y9z++23s2vXLgThmTNaK1as4Oijj+bb3/72QfusWbOGRx91ZygbGhq45pprDprJOP/88wmFQtxwww0ALFu2jMWLF/Ozn/2svM2sWbM4//zz+eY3n18BFqCpqYne3l5QAihzLn7+HV6HfPobn+aLR5as/SzdTbuX1bKon+A4YBbKqfwIIo6kktcqsIGcYSMJAj5ZwC86OIJIwXIwbYewItKbMYlrEkE7z7Dto1J1P4ppSyQsw3/fu5ezZ9extCHEpsEcS6plPnX3Pu66bw+mrrPuh+eydShPT7LA4oYwbYziKH62Zn1ossgUXx7BMjggRJEEgdt2DPO+5rw7Uy+I7vWUXAGwTTcAUBITtNVgeSZdSh4AScXWwu69KNkLYpnYoWp3YG/kcASRbKCGYGG05BJQcHUAJAVHVhHHMwUEYSLjQFJcHQSjUJ79dwTRLQuR1PLxEGXsQBzTAcXIlbQWXEFDwSy6tpCWcXD5QMly0RFEN9BRWjbefrlt23Y1LCaVUZT1C8bvTykwdOCXP8TWTf7n2w+Xt/3cf51A3+p9OJbDlvUDpEybk8+YwoGn+qmaWemqEifdtusWN2PkCmy7eRuKJnNrKQtABI6Ia0iCW0P49Ej+Zz6+gtxgkqnvugDHMNh/xwNYBR2zYDDWlSxrFEiqyOZ9SaZXBXBsB0lxHyZmv2Mxe+/aUlJMdssNZL/kpi5mDHwR16FhPDURYMoZc9j+93WIkkikKUxqf7o8MyGU/ZxNJEXiT2v7/51/rTccL0Q7YfzOTt7uBvaTxaKxsZH9+/e/JOdSXV3N8PAwLeEgd7/9lMNq6+P/Ws3d+w4A0NPTQ1NT00txim96vL7ew+O1TeuKcwhX+Ln6uo+zdqzAOU0R7vzB75ndEKU+7GP5rV8jP5RAvvYXBG/8MnvvWIusyWT6MkiqSP3SVvR0ji237MAfVLilcwyAk6sDzD5jSrnP/vWdB7snffYTx5DuHWXa5eeAbdF3/0qKYxkyA1lyw6UAgF9GzxpsGc7REVTRbQdVFFAkgRnnzaT3iR4KyeJB7UqqhJE1UIIKoiSgBBVsw0bWZJqP7WD3P7ciSAKRpgiju1zXK0ESUIMKZsFEkAQcy+FvW4dfkfv/RuSl7u8vu+wy/vjHPwJwz0Wn0BwJvui2/rJ9H198bAMAv/71r7nyyisP+/zeqLxmNA50XeeGG27gyiuvPOSDBECxWETTtIOW+f1+nnzySQzDeM5txh82dF3nqaee4rTTTjtom9NOO42VK1c+6/kVi0VSqVT59RqKt7xsfPKYVmx/1FXo94XdWXmj9GUsyhN6B1AekCKI+AQLs+Sk4JddXQNBz5E3HRRRIFmwMB032yAgWuQkPxWaVK7RD4vuIP6DR7XSGPGhWzZL4zZpW+b3P/srdW0xtv/oLAJOkSXVMke3RMvlEIgyTRGFlog7Ky9YOhWaTI1U5MoF1WwTG9zlpu7O7I+XIAiiGzQwdRwl4JYJ2BaCnsUOVpZLEyY7Crhih6XBuqnj+MKubaJZKA/Cx8UFxwfutj+KaLilD46klO5pwT22bSPo+YlADJSPZ/uCYOnITsnNwiy6QRvLQLBNVxjRNt3fx0sqxrMWJgcTTN09njVpmWO7y8rZFFbpHhURjDxCMUVx5a2k7vgjhZHUQUEDgG/+z4PsWT/A+nX9rBkrsKA9imVYLP3widQubkVSJCRVQg0pFMdcYcdZF8yi9bhm/vOqxVSpEotjbuphS1v0oKDBRy6fy7K4Rt0Jy5j+ofeQ3LgJQVFovezttJx7IsG6eNn9wLEcCokiScOm4/SpVM2oxDIsoq0REjsPEKgKoGfd74lUssBof9ZVTDYsgrVBppwxh2BtgIppcVpPnkF+JEXF1Api7bGy20Ix5ZY1iJJIuCGEGlTpLD0QvRk5VDDg2ZCEV1YwycE5bI2DN8P3/CuN19d7eLx2ibcvIN6+gKqGMN+66dpyf3zb/hSxSy/k7U/+mIXXf5qvXvNPeh7rZOxDb8fWTSpn1GIbFpIqIioSRs7db/bZ02hYUseHL51DXBE5UDD57Y1bKaaK/O6uiaDBx949nxUVfmqOnMP0915EdvduAJreej4tpx1J1ax61KCKYzvYloNRMOnNm0w9sZXGqXEkAapmVJIdSKPFNPJZA8d22DeaZyhZLE0auNoFrSdMIT4lTv3ieppWtGMWdCpnVFIxtYLccA5JFd1MxLiGFteItkYRJZG9BzKv/Bvi8YI4XI0DT8/ohfOaCRz84x//YGxsjHe/+93Pus3pp5/Or371K5566ikcx2HNmjX85je/wTAMhoeHy9t873vfY9euXdi2zb333sstt9xCX5+r7D48PIxlWdTW1h7Udm1tLf39zz5r+M1vfpNoNFp+HThw4PAv+jXOQ11JxFwCI1jtivuVauUFs1h+4bh1946kgqyBIGIJMj5JdG0EAR9u6UFGtymYNo1hBUmAZNECUSbgFN0Br1mYcC0QRKKaREARiRhJHEXjXX/YwOUfuIi/vO/IgwIWVX6JqE/C8YXBsfFJInIxhaNFcCQVn+nO0Jf1FsyJkoRy8MCxcWTNHcCX9AqwdBBlrKCbMieYxoROgeCWDZRLNByHguRHzI7gSOrE4LskFGhrbuaGo/rdAIs4nl0glKwMJ5V/mLpbIlC2OrRd+8vxQf94ScN4ecN4ScL4uY2XJ4wHDCzTDUqYRvncBdtyg0Dj5RQlxrMvxt0eCo/8A33raqxcDl8szPf+bw0AVarEmbVBVlT4afbLqKJAXJH40v+cQ+MRjciagiCJFEZSCJKIFtcQSsp8vliIcHMtgeoYgboKTl9cxzEXzuS4qgB/WtvPhTPc+31mbZBiqsjqRIHCgQMgSvSv2sHQo0+Q27oOpWU68VmtzP/MezjyU6cTqPITqg9y2tFNRNrqCdYGWPLhE5h6zhKq5rbii/gQRAFREggG3MDSeEri8PYRMj2D1C2dSqixinT3IL1PdJHanyLTl2Foxyh3bxshUOUnN5xH9kuM7kpQTB08q/FmQBHcF7zwQIDIhH3jZF5u+UinlF3yYl+eq8JLj9fXe3i8dkl0biDRuYEzv3Y1f9s6TFwROacpwrK4RrVP4snv38fWv2zki98+j3BDiExfmsJYGks3S2V/AURJpJDIoVVGiLTXE2mpIlgX54yFtXxg9z0siPr4wxO9nDPd7etPqwlSSBbZkCxQHB1DUDV6HtrM0Kq1FHZtQmmfQ6S9nkXXvJuF712B4pcJ1QR465ENVM1tJVgbYPH7jqLt1LlEWytdoWMBsjmDplJfbxoWhuWQTRU5sLoHS7eQNJVsf4L9j+1jrHOMTF+GwX1Jbt8+ghpUsHQLNaQwuivh2jR6g8sXzcstFn24fb0njvjCec3YMf7617/mzDPPpKGh4Vm3+fznP09/fz/Lly/HcRxqa2t597vfzbe+9S0kyU1J/uEPf8hVV13FzJkzEQSBjo4O3vOe93D99dcf1NbTZzocx3nW2Q+Az33uc3ziE58o/z1r1qw39AOFpGr8Y8MBzuiYjVxIlQfJ4yn67kYlOz9ZdVPiHRtHDVK0HIZyJi0RBd1yyNgypg1hn4BpO1iOqzy/qjfN8a1RgpQGwZaJILkfyS7dR11QJKObWME4Mz94E2t+eD6G5SAKgnsetg2iO7C2HXAk18JQdSYG0I7iL2cUSI5JR9yHbdcipgYO1mgoCRI6k3+WghNiIYVZ2YaUHsD2hRGLaXd9WZNAx6xoQdOzpaCDXirdkMrbiMU0jqwgJfaXj1Uu97BNUHxglgahk10Rxn+3zYltnYODCpN/lrHMg66vHMiwLXCMsoPE+CdeLF2zYJtQzGElBtF3b0RQVOzMGJ+58vecXB2gLaDw4Z+/E7Ogk+sfJTqthS2/uZeOc5aQ6uyj56GthGqDSKqILxai6Yxj6fnnQ6T2J/FFfPhrYmiVUdRwACWo0fvoFlpOmMaWG9eRtxyCkkB8SowzxwpsS+t0BFWuumAGRraA0bmFumUziB25HCsxiDXiPvw7+Sz+ljZmXlRA8ruOC7n+Uarmd1AcS+OviNK/ZjejuxLlzkENKmwZzjFTFhElETWo0PlAF/71A7SdNA0zb7paCSUbR1mROL4uxNCOUSraYwxvd+syRUko+zovjWmseROIJT29tvPfsXicbAmpCF6Q/82I19d7eLy2iLcvING5ASUQ4fbc4xz5X+fz+Fc2cuSnzsTIFiiOZQg317Dr5pXULZ1KbjDB3n+uJ9IcRlIkQo3VVC6aQ+fN/yJ9IIMv4iM+vQFJVfDFQmiVEbruXU/j8lb2fPaj+CURRYDKaXHOLZhsHMoxI6RwxQUz0FM5lM7tNB0zm9CM6Th6AWukH0ESsbMpojPamHmR+1ykRgIUxzJUzmrENkwM22Zw0wFGdycQRYFgqbRwZ+cYs8IqlgNRRSxrHjRIAqO73bIEX0RFEEUkQeDk2iBDPSkq6oIMbBxyb5JO2TWi2S/Tkz+0q5THMxHxdJHeSLwmMg66urq47777eO973/uc2/n9fn7zm9+Qy+XYt28f3d3dtLW1EQ6HqaqqAtz61n/84x9ks1m6urrYvn07oVCI9vZ2AKqqqpAk6RkzDoODg8+YmZiMz+cjEomUX8/14PFGQNZC6KY7IDd9kYkVk4X3SmJ/k9cJZpGgnXdFDh0bQRAQBQFZBEkQsB3X6hDgmJZSu+MCfIKII8rYskZjSMFxHMYKFp+4fQc/+fTJSIJAhU9wo76T6/GNgpvBYMml9HpXW6BoC2URQsEycEQZ1dERitmylkFZ9HCccV2ByUKIloFYzOKIMmIuMRFUmGyZ6NhuuQBuCUM5KDDelqlPDMzLg32nfA7jTgllTYHxa7PN0n7Wwfd+HMssWTKKBwsZlo5ftnmc7AhRCgAJZsH96dhQzCHkU2DpWIlBcpvdzIKRTbtId7suFsGAwumnT0H0B5EUmdic6WBbxKdWMbRuF4IkEm2Nk0/kSfWkEVUFSlkOsiYTbqxADQfRU1n0dI78SJKBTUPs/ucWZl4wh6qaAFnL4a/3drL0A0dz4RULuO6PW6iYUY9V0LF1gwMrt/P4f/6Q5JadbsBg6iyMwQNk9+wm0FyHryKGmS0wuq2b5J5ehjf3sPpH95dUlB1SWQNZk5FUibaAwnCpdtHIm8iaTKw9xr77d1FM6RRTRQqJAsM7RpA1mablDTQdUU+kKYxjOcSnxJA1maGiSVASWDNWeG18ob7CvBBtg8nbjP/+sotLOZ6rwmsNr6/38HhtocVqSXRuYM5bLmL6CaezryvJmu/eSs28GqRAADUcIDqtBce2ibVXk+kdQtZU4lPiJLuSFFM6giTiFLKIkisaHG6Ku1kHI0kcy6aYyNC3th+roOOLhahsjaCKAvc/0MXcdy7hwisW8OPfb6ZiVpObNg50P7CJBz70f4xt2Yk53E+gYxrm6DCZrgP44iFCjVUUxzIMbegk1z/KgSc6WfPLVWQHsxgFk5Rukc0ZGFmDuREfCcMmqohYDgiiQM2cKoa2DiNrMoVkkcTeJInOMYIRHw1L6miaU+3qHaSKbl/vl8iXnl29oMG/xytRomjbh+mq4AU2XjCviefc66+/npqaGs4666wXtL2iKDQ1NSFJEn/60584++yzEZ/mN69pGo2NjZimyd///nfOO+88AFRVZcmSJWVV53HuvfdeVqxY8dJc0BuAYmoYy3YYyVv0ZoxJM98lpX/HKafVj9fMj5cbiPkErc4wYiGNKIDluNoGjuNg2Q5G6R9UEgR8koAjye7AF3eg/Iu1fchWgaf6slzxo0f54NHtnNIeJSALrB8sUu1kJo5felWKxbJHvWAWEQopfIJVrv3HNpFyibJ4ox6unxisj6f8l0oHykGB8df4NqIMsnqQ0KBgm1jReqRkn5sFIYiu68HT7BvHgwHOZBtLcAfqgXj5T0dS3HN42ueZSWUJgqlPZBiIpbKD8SyHUpnDuHCi26grrlgOFhQyOOlR7LFhzO4dFB7/J8XNj2OnRjB7dpFZ9wRKOMTIFrcjHt64lw9ePJsF71nGtAtXlJq02fu3+zjw6Ea0ygjBxiocy0YNBzDzFnWLm5GqGxEDYcyC7taf2TZaZYTKBTOxdZPHfraSWW9fRG44j6WbRFujfOzd87ngmGYsw0SrjHDRvGpy/aPlYxpZg2RvmnW/eJS++1ey8j9/TLpnEK26AkyDvsc2MLKlk/71A2T6khSTuquqPJBzB/sxDbNgYukW8eoANUGVSJMreimpIkNbh8sdScXUCiRVopA38UVU+tcPEp9ag541qJgWRw0qCJLrHV2nyWiiQLXv0I4Rb1TEp/08FK+myZFjO4f18nhp8fp6D49XD0k9WBNEi9VSGBugcemZPLZ4C3dG7mDW4jqmnDGH9jOPANvCLOjs/ttDdN+3Fq0yilbpTvjIQQ3bcohNqUBrbEEIhN30f1VEUmT8NXFqlsygOJbmoe8/wLwrjmD3nbuxdJNQbZCr3rWAY45ws460ygiXLW8k0zOIIIlYhomeMUhlDR770UP0PbGFB66+jmzfKL5YCFlTOfDYJhI7exjaOkyyJ4Vj22RMm/4DGfKWg18SUUWBXNEkElRo9svIioSv5L5w4Kl+9KxBMVUk1uqWkhbyJpIqsn91H/EpcYqpIlPm1SCV9rEch7ZS6cO4Y5THa4TD7Ou9OYIXzqteqmDbNtdffz3vete7kOWDT+dzn/scvb29Zf/mnTt38uSTT7Js2TISiQTf+9732Lx5M7/97W/L+6xatYre3l4WLlxIb28vX/rSl7Btm8985jPlbT7xiU9w+eWXs3TpUo466ih+8Ytf0N3dzdVXX/3KXPTrhEzBpMYvIBTzCHrODRI4zoSo39MHp4gHpdcLegbVLKCWLAQdWcWngCWqSAKYpRJiwdRdHQB/lKIt8P7F9fxu0yB7hrJ85z+OYEpMZaRgUUWWtlgIzJx7TElBzCddRwE9ixYMIGQm3AkEPXfQ4F8wC0i5BJoogaxiVrjWfnJivys+KIhueYEgTogalgIUjqSUNQgcSS1rDdhqECkzNDHQF92sBztci5gppbiNBwtEGUfxI+pZt3S6dK8muxk4cqntcaxSwKK0vWCb7rEss+x84EiKG0AwJllkCiJCSSdhPGuBfJriznU4Bff+FYZGCc1dgBiKITd1MPqvu1CCGkPrdlF/1Bx23bqZGae3o4YD1C6dQc8D6+h5ZCc71w0wZVYliz50BvnBBP6GGuTqRhKrVjG2swdJFamc6876pbdtx7FcdwNBFPFVVbLl13fSsKyDEz97Kv2rduCL+PjuT5/kq9+9ALOgE5/Zim2YBOoqmH6+SLC+kus+8Xc+9O0L8EVURFFg3WAW6fbtFNJF1v9qJdWzdzK0ddjt+AVoO7YZy7DIDmbxRVQs3S5rLIy7KuhZHX9cI7U/jVCqv7N0C8dySHal3HuULqJIAo7tbj+wvresczAuxpi3bPZkbeZGfHRmJ97LNwP2034eismlCZ6F1ZsXr6/38Hh1iLXOZaxrM5buPltUzVxGITHAw9d/jPYHf8z9n/wy24fqibRUMvOSo+m8Yw1d929n/aYhZrVGWH7NhYxt7yTcXINS08DAw4+T7RvDF/FRNb8DFJXUxs1YuoUgiji2O4mw7Y8PU7u4lRM/eyoDa3YhKiLf/emTfPHrZ2EbBpH2egRRxF8To+UEm0hbPT/42F/46HcuxBdVsRyHx0byhO7ejW3YrP3ZI4Tqg+SG84ymikRUiVhrFMdyyA3nUUUBdVItvRb2oRhu5kEwoLiTAKpUDgSMpopIWYPsiDuJpEgCetYgbznsf2I/8SkxMgNZHNtGzxgkDJuEYdMRVNhTElr2eCb/Tvmix+uPVz1wcN9999Hd3X1I64u+vr6D/JYty+K73/0uO3bsQFEUTjzxRFauXElbW1t5m0KhwLXXXsvevXsJhUK85S1v4fe//z2xWKy8zcUXX8zIyAhf+cpX6OvrY+7cudxxxx20tra+nJf6ukKUFW66Yh6CnpsQQVS0srWfYz0tVWt84Dw+SB5Pm1fcoAG4pQuOICJbbuclyxoZw0a1LfK+GIIj4BNt9iYNLp1bw09X95IsmKR1m0qfAEXwyQK2r9IdlNumW1ogyaDbqBk3pb6su+A47qDb0kFScUTZPZeSNsO4nkIq1k441e1e2/g1lIINDjaCKLvtmEVXQHHyZRfT7nayVp7RH9dJKN+PUjaDUMzg+GNQTCMAzvj5iyLYYjmDYZyDLBMn/S2YxkRgAxGsIo4vWNKfcLMeEOWSSKOJ2bUNxzTI79nJwJPb0Coj1B57JIHWMI5pIFXW4eSz+GIh1Moq1HAvPQ+so+OMmTi2TbCxmse/8U8aj6zHH9d463WXkersA1HCLOjYuRzZbZuILVpAoCbGyOZOiokMemozo9u70LM6ofooVfM7cGyLWZcex66bVmIbFvGpNURaqvjEiQX0dJZAXSVKUCPdPUi2d4jKJfPYfePdfPh7b2XL7x5l0YdOo2puL/u//1DZQlFSRZJdSWrnVSMqEqO7RxnY5AZtHGtipCpKAoIkEK4Pk+5LI/v9ABgFEy3sQ88YmAUTNaiihhTyiQJqwLVsEkSBQJWf4R0jzFk4n+7HNjL19Cm0nDQH++ePsG0ox1DRLKcyekzwipUmPB3HDRIdVhPeNMRLhtfXe3i8MkSbZ9G6cD6mYbH1rr8x1rX5oPXD21eR/NoRdH/xPWzpGmP62dMRJFeX6LEv30bT8gb8cY33/N/lJPf04hg6lmFiFnSMfXupOWYZ8ekHGFy3CyNbILl+PSNbOtGzBpGmKPGZrQiKyrQLl9N191OYBZP41BpmX1zDTN3EyOUJNVbji4XJ9A5RGElRvXwRO357Bx/9zoVs/cNKFl59CpWzutjzg0cJN4QwsgZG3qSQKFI1s5JQokBqf5rccJ5CukjGtFFFt5Q1FFLRcwbB2gB9O0fRZJFszkASBNIFk6BlY9sOIVlE0WQSGR1JEPADqiJRXa/S25uh5bg2BjYN0XJ0E60nzYLfPsnmwSy9XqnCc/JqBA1syzms/t7LMHzhCI73ZPSieKN7Oz9w07dZWsFE0EBSJursx5lcMz95kGtNpPJDqeZfEHFkH7YaQNRzruaA4sMRZXRkVNzB9UjRodIn4AgiR3z+Pm7/3AlYNjQLybLQ4UQgwx34uVkH7oB+XGSRUvmDHap2gwySOlFGAK7FpBpwgyClddJol7uNGnC3myxSKKuuC4FZAElFMHIIpoGthREsHVsNupkEwkQAQMwnsUvnKNiW27biQyhmJ8oaRBlH9SMYxbK9pWBb7u9iSbdAksvBmMn6B5PfB9sXRCyVXjiywqarP0DjMbNJdw8wvLWfmZccjW2YbP3DYwRrgsy66kJsvYBv+iLsXBprcD9Dj6/GNkyKYxlGtg9RNbsWSZURFRmzoGMVdCTN1YComt+BrKn0P7kNSZHR0zl8sRDZ/lFEScTIm4QbK0jsGWDK2UfSfd86FL9M6/knk9vXjZEtMLhuD7VLp3HdF+7gcze8D0HV0IcG2fCL+1n6yXMZ3bIH2zDZdetmVnzlEhzbIn9gkOGNe1h/607qWiKYBVfEUBAFhnpStC2tZ3j7SLkTkDWZhiV16Fm97M2cHckjigKSKhFvjyFIAsmuFLJfIjmQQ5EECqZNJKqRGCsQVEQSRYvaCj91C2tI7U8TrA2U2+s4Yyabb1xPX95kqGjSXzCfd5CsicIbTizo2WYZlKeViE++Ny+1rzO4te/Dw8M0B/3cevqxh9XWp1dt4L5eNyDZ09NDU1PTS3GKHq8j3uh9vcfrm3d+5sP87Rd/pWbmEZx60hR+/Y0fAfA/P7qG9niAiy+/5qDtl116Ob/r/zWCJJLtHyWxd4z4lBi+WAhRErF0Ez2dQ9ZUHMumetE0JE2l77FNKOEAZraAqMpkB9KoIQUzbxKsj5HYPUT76QvpeWgzkiox9aKTyHb1Yhkm3fdvoXHFVP78nQe5+v+9D0SJ4uAg2/74KPOuPInR7V0IksiG3zzJSd+/DMfQyfaPMrqlk7W37aKxNYqeNVD8MoIo0N+donVuNX1bh8mYthsskEU6VjRi6TY7nzxASBbJWw667VAZcMsK1aCCXtI5yiQK6La7PqqIJA2bkCwyqlvU+mXqFtaS2p8iWBNk3Zo+2ir8zL54IU/9ZjUZ02ZnRmdYP8TzmMez8lL395dddhl//OMfAbjt9GNoCgZedFs3de7nq+u2Aq6A76EC3B4ur3rGgcdrj69873MsqQsg5BI4qjvwdWfxSx+XyYKCpXT+cur9+Cz6eC2+OWFXZ/ujCI7NsO2jUtMQihnwqaiOyZghokoCYVXkPX/dzH+fOp2nPr8CnAKCYAHyhNChUcAOVpaDD1akFjGffMZ1OGrQDSr4wuVgg2DkcRQ/ji/kDtwdGwsREbAqWtFtCIzsBlHGVv2uxSTgTConYDz1X5isO1DKRrD0gy0qJdkNOIzfL1PHDsQnMjnG763iKzshlMs/xi0Zy/aRVqm0oWTJaJSWSxKCnmHVlZ+iYVk7u/+5lQP9WaZdsIIDK7cjSiLb/vAo9Ue0UjmtgkBdBY5pIMgq+aceYGRLJ1pllIpZbQw8uZXms0+mZsl+HMtm9ffvIT4lxuiuBL6Iii/iY8pZCxl4cltZxEipieGviQFQMbMVPZ0l2TmIWdARRJHEti5ql05D1lQEUSI/kkSNBGk6fh4jWzr52DfP5clv3MTcy1eQ2NVD+2mzADDSuXId5ZPf+Bvtp82kevkiWtpaqDtqDoWRFE9d9xDBmgDhhhDVc6poPnERzp8eI7U/Rc28GsKNFey5a3vZdtG2HLSwD7NgYhRM1JBCYu8YlmFhGRY+VaKoW8w4qY3BTYNESpZMUUUklSyQeLDbfWB5aoD4lChKUGV4Wx+L378CUVHYfdt6lKDCI0/0Pmcqo/oGDBw83yzDK51x4HBwxsmLa+SN9R55eHi8/vn2ddfyq1u2ctbxU/jSCc3cfXsHP/jICo5uifD9s37Mj1f38f7+vzF67y6SXzsCKV6NvncLg0/twJe7kfDcdnoeWEfbOcdSNzCMrZts/H+PEqgMMNaVxLEd4u0xWk6YQd/jW9GzOmpQRbZs1EgAQRSpmBHCKugkdg9SHMsgSgLDG/dQs6AVoeR8kh9NogT8tJ++gNHtXVx67ek89Z2bmXb+YjK9QzQsa8cyTIxsATUSoGDaPPbFvzHzbQuoWjSbUGsD1QunURxLs+H6VYiSgBbXWHD2VGqWzCA38iDBvEWkKUz1vHq2/GUT/phGPKSSyhrloEC64OocJIZyblbCJGvFucc2sfOxXkKyiCRASHYnClJPHqB1ZiUDm4eYO6uKQJWfff/awZIrj0CQRFrv2YaRNdjYnWJn5tlLFN+IkwTPxqtZolC2UH6x+79J3qOXAk/dw+MZTKkIuAPuUtDAEWV3Bl+S3VlwYVJZwrig4Li2wWTxv8kiVqKIiUjOEggoIlbpoyeUBtqJgoUggCjAnq4EqiSUhQ8dQZiwIyy175ZNCBPnARMDdjXgliIY+QmhwvH1k/8u7SsJuHaOuMd3fMEJPYFJTD6e8LQ2BEs/KEtBmGylWDo2lAb/4zoFh0J4nn9J23SDE4aOoxfAtrCzKayhA7SeNItwSy1KUCHuk9j+50fRoj7yiTyWYZHpHcG2bKrmT0WuayGzYyeZXtcTPdzRRnEsTbCxGkcvkNjWRWpfH4pfRpQEV1E4USDROYaeypYFjKxShgKArPkwC0WMbAFREgg1VuPYNpH2erK9Q+ipHIUDvRjpHIWRJNn+EdSwGyGOtcfQ01l23b6TNb9chdI6E391HFs3ESUBy7DovGc7mAbq1Pko4RDR2TNoProJQRJoOn4OSlBjZHMngSo/9UvqiE2tY6xzCEESKKb0chrbuPghgKhIWLqNP67hWA6m4c4gGFkdPfv/2XvvOEnu8s7/XbGrc/f05Jmd2Zy1Wq2ykEQQQVhEk8FywsZg8zOHffad7+w7n42N4xkbY2wDBmPLhAOEiQoggXJcrTbn3cmpezpWV3XF3x/f6t5ZSUiCRbCC/rxe85qZnurqmu6Zfr7f5/kEl9APCaIIUYBMUsNcNEkNJTvny47n8WyH2uk5obNMaaTUp38da17wU/HmKyNMULveBl100UUXPxz88Qc/z5Hbv8RQzkBbOs7NH/xZGo4PN/0x4bc/yfu2JyjuPkTPtnXI6RxLt99G7fQ8kiyT27wGq1Qjt24YgoDi3hM0ZpY6Uj3BOvBZPl7GKtVQdFEjQRgUK4aOrKsEjpAuSIpEaqQX34lq/fwyoe9jzS0IyWLdpDGzJOIUXY/UUAq7VOWxz+7l3n95mPjaTcQLGUI/ELXCdDnwH7sJAx9t1UaMQob89o2MXjGCY7oMXzqGYugsPnqEvq199G0rMLBzhOqpIrIs4dkeoR8SVyQUCfxQmBq6tic8EBIaQcQ0AKhNCbmpH4rb/DBEkSCuyDSLFpmhFJ7t0aq16N/Wh2vaNOeWqU3XyY5nz/JUeCr8tDQNuvjpQZdx0MVZKN7zEQy7TBDP4q14v2ubWYVhAIqOFQgTGiWINPVOUyQYyCqhngRFxZJiLDQ9/AB6EwrpwEG3I8mBohJqhjACNDIMpsLOG/Dd77+UE2aI5JjCmyDakAtdv4QkycjNcrSJdgmTBeFBEMUMCvp/Asl38GMp1NqcuE2NiSm/JEeNEL1jPNhOZFAlCIwskqJD4CE3loQcoR3BCJ0GhaSo4voiD4WVUo5QkkHVIAiEPMFtdRgZkmsLlkb7eqBzTe1EBPzWGXnISp8Et0lQWxYxh7KCX15EkhXCwOfU7QdZ/+qLGNw5xI53vojY4CCSrDD+Oh91YAwCn+oD9+KaFhzfi2c7xPtzJPpzVA4cZea+o7imQ2roGNWJKkOXriIzmqZn4yDJ/hRW2cI1XeYePEmiVzSV9EycxlwVWVMpH5nlxF1TTFkuu7b1kd/oEs/HWT40wcLjs+x45zrm7j9A30UbOH3rbla/YhdHPv8AvVttxl6ygz3//B1e/KGfJ7BMvvOLH+DCX7kap95k57teyNEvPUT5ZJXqkVMUhlYjGQmUwiBbfvvd+OVF5EyB7I4S3uIMfcDDf/0N5h6dx3cC1LiCpIgFhaIpOE1hfpRJakw/MEMsE6OxYNK3tZcTexZQJInph+cAqLoBcUUi35dg3fWbue9fH+Xqt13E1N3HCf0QWZFY3DdPbjxL+WRFNCmqDhddMsTowSLfWTQxv0cX/CfNPMiQJZwgJLJJJYg+fmwLpx+Kx8EP6Vq66KKLLn5IOP3596He91kWv/kBmhtWsT3XwwWqTjg4iJLvJ3jsVrRkHKdSI7API2saeiZBaqSXpUcPMb97Etd0ST52iupEjcGdA+TGs2RWpcmOZ7HLwoeqfHwJPamhGiqxXJLKqRLxmoVreRy8a5KFls9F6/P0bBwi0RunfHSK+T0LXPjOcYp7T1DYtoZTt+5h9Ut3cOIbjxH6AcNXbebxj9/DDR/9ZfxGna+89c94wW++ELtU48r3vYgDNz3IwoLJ8r5j9OX7keNJ5HSOjb/8s6x/Sx0lW8CvlvBrFZx6k8c+eifLx8rUikKqaTdd/FA0+eOW15EytEvBiZLFeFLDCURzoTpTR5Gg4QXEFZmeTIxNr93KPZ9+jO0vXsPivnlatRaxTIzSsWXi+Sa16TqyIuNZHjsv6GP8ZIXb5hs/9Q3yH+ea5tw9Dn6IF/MTjm7joIsOLn7j2zGsktio+w6qIvTs7QbCsu2jKxKyJBFXJWw/RJF0dElM14NEXiQESDKhJBOTfAaTGp2GbCDkCqGiIyFSDlB1vBA0WXSHH54zubRfY53hgCs2zBII9kNb4x9p/qUwEBKJNjOg/dl3zjQE6guiQbDCGwFFj85ld7wRVk76QzU6XpIJI++AUNWEFKFjlCgKq+Q2O5KHle88bcaBFLjiZ23IavQ8iWuRHfPM/Z9oOCnJgN/5vSSnQdhsiGZEJFOQ40lC10EChi8dQ1JkRq69EH31Fqa/+GXUpEEslyZlN7Hmi3i2g6yryIqMauhYixXCIODE1/eiJTUkWaZ0pERuTY7mYo1kfwrfFY+fW9PH0v5ZatM1Yhmd5eNlDpyssOz46PJpliO931hCo1VrUTkxSyyXIjlYINGfw7MdVr/1dRz55892FibrX72LxKpB9LXbUT99D4FlArDtbRczecd+Vr1wK0Yhw8afvYz9n76X2//6Dq5zXZIjfSze+yhD178UOZVDihlIqo42tpG73/PXHddkAM/ySfTGWT5VQTVUZFmibHv4DTFhSHkB+b4ERx9bQJGEjGDK8liTiaF4AU4QMv6itcw8eJrefJzlI3PUZxvE8wbTD8+RG8vQqrVQDZX82lwn23q66bIlHeNQvfU9mwfPNX6YSQZPd642HbP9X3S+1OC2nOYHP8FP+Uqwiy66OO9glE7itmz6rrwUJVvg9E2fJ5ZLEculSa9u0piaBxBswFwKSZFpLpZFrb/lEIlCAt/xKR5eJtEbpzolmAVOwyVwfYy8QbPYpHysQu/mAqcfmWPKOkXV9fFDqLo+uiwxaKiUZxv0zZTQkjGMQoa1fTmcepPx17+CQx/7Es2iSHFa/bILSI30oo1tJPb5h/BqVTzb4Yp3X8Whzz/C1rdeRnKoh+0/fyXSTQ/wtb+7mxuCgFguTe3UQ4z+zItRsgUk3YDAR+3p474P/AuxjI5jusSTGpbp0rcqw8xEFdvycIKQmheQ12Sqro8iSWRUmaLtEVdEtZqyPAZiCm4IShCy+oVjnLztKFlNpnJymYnDJfp6E0w/PEd2JI2fDFANlex4hvKpCgAnGw4XZA32VOzzpvY9VzhfExPCIDyneh92OwfPGt3GQRcdvO+GLYSagSnHIQTFDym3fFJRXu3Ds3V2DKT449uPsX00y5fumyCTMbjv5lsZ2rqLxaP72XTNFTzy+Zt43x/9Fr99zRpSOmhOoyMvIHL6l1ybIJYCVvwRhgHjWQPJswj1BHKzLDbxIHT+svA5CFfIISTPRvLikfFgHKlVP2vqL7miORBq8TNsA98BSTpbVhE9fieRQDOQ7DqhahBqCSS71olUFA8si9vd5pn7PoVxZChJgkHQRpstEX3dvp/kuWeObbML2kaIYSDSGuwmoefgzU+iFAbBcwlbNkGzRmCZ5DePE1+zDnvyFFOf+HcSgz2oRozmXAlFU4nlUtROzxEGAa5pYy2W0TMJ1GQ8ihwKyYymyY5nCFwfzxYNA9/1iOVSNJcqeLbL6dmGMBRcaDIQU1ibN1iotYgrEvO2x7Ljs3eyxoOnKvTFVF7x7ivQ00n0dIL6vsfRkzqu5XHiq48gKxKFLSV6WzYX/NK1lI9OoegaU3cfZfiKtdROz9H7qp9FnTnBpb/bi/4P3+A//vJOXnDxIDOHSuyoNBh77ctYvv02Bt74NpoP3UG2E8/UpP+CHpYOioSF9t5dT+oY0e+WUmWSCQ1FV0ipsjBZyhtklppULBddljBUmYNfPICqKXiuz+K+JXLjWRzT4cKfv5jaZBFzwSQ5kOTgt0+zdtcglYkqG/Mx5mcbXNub4GjD+bHEN/0wJyBPd6427fO8Kr0hBOfIduj2DbrooovzDa0HvoaS78cvLzLxpVtJj/UD0JhZIvQDkkM9FPeeQEsn8O0WVqlGLJcS64GihdNwSQ+nBBPP8vAsjzAIyY5nSI/msIoNzEWTQ0tNNtgeZTdgVVxlQ1pnwfIoOsJEcMbyqLoBD/+/Q/TFVF73K5egJg3ifTnME8cxsjHmdjc5/tXdAAzsHCFv2qy/4QIqJ2YAmH3gJBtfdyFLe0/Rc9WVaL0Vdr7LQP7EXfztn93Ja3YNcvDoMi9q2oxceyGl/acYfu0NmAf2kBlNd56TZNS0D6O0BFVTiAGWH1DzAjKqTEZXiOcNiovNDsM1qUhUXSEdzGoyx289iemKhIaFfYuMby7QqrXY+oZtlI4sEvgBid44B+6bYf32XpaOLLN5IMVEscmL+0Stn/oJTl04r2r8CoRBeE71vts3ePboNg66AKBn/S5evy5BqOgkCJlrBvzyvz+G7wWouoKmyHz4TRfwh7ce5TN/9RHe/Fu/zkdvvBhVluD122m6PoOp60jrCsr7rqLhBkzXHHriKv2JFFqzBIAn6yihSAqQo6l9GG3WAQbDCqEUjzbp0e2SfMaAccXGPYiiFaWWGfkMRAkJiIZC2Kb/KzpuLEOsNSNYA9GkP9QSSC2TIPI0EBcjZAiyf8YAMpQkiKQPYSSZkKLNfCcaUTM6P+/4G7TPEfpPMJOEUFYI0n3C7NF3CAGpLUloIww6jQnBNPAJzTpSzMCdOIzaN4KczqHEk0jVEoas4C3OEBsaZeRaGXNuGVlXSY30UZ9coLlURksnaC6WiRcySIpMq9LAqTUZuWwYzxYb21bVQY2rZEazmIt1Ql/h5Lf2U24Jk8AeXSH0Q7IDCVRDxVxsMjaaJvRDdugKq1+yASWuU59c5BtfOsLcw6dZdc1GrMUK1dNzSFGnf3HfEv0X9NF/5UUc+NjXaBabXPa/f56gVqLxmYc59Lk90XF/wKV/9C5C1+HCd7+C/G0PseYdryd/083MPjxD30WnyW/fSPOhO5BkmdUv3YHveiQHezj5tQcZuXRE+Dw4PsvTdSw/oD+pi1imhEbNdKlFm3onCMki6PWr8gaNyPRoqeUzpAkWQ+/mHkZfuI3p7x5gfvckqYEkalylVWsxOJahd+sgo9ds4thX9vDAsvUTTV9cKUlYiR8m06GLLrro4qcNsqrR+Pgb8GZPUX/Bz/GaD99PJhfn69faOMf3Yp8+jt7Xz9hrXkx5zwHUZJyUEaMxs4S1VEYxdKylCkYhg6KpHS+i4YsH8Z2AVq3ViThcdeUItekaoR/y+BcPcqjusCquktdk4nmDtXnwnYBa1WY4G2PID5E1mXWvWE/oBzTmanz9W6c5edtRVl+3PrqGCoEv/IOKR0r0biowePUuHv/7r2Iumlz1R2/Fb9TZ/++PMrX3PnRZojb9j1z8396KZztsv/EqsnftZ8s7X0vhs9/k9Hcn6b1gHX0XbaCxVzQiVr9sO2EQoKcTTH13PyOXDVOdquE0XGpVm6oboEigSUJaW275TM/USSoyDS9gKKVjtzzGcwbFukPVFU2GYUPF8gNGdw4xeOlqFnZPML97Bi2pEbg+LddneDBJsj/J8OVrOHX7EXYft3+cfy5ddPEjQ7dx0AUA3/q7GwkVlUPLLr/w4XsJw5CBsRyf/vldZGMK03WHB6erNKJJ7Rf+7mPcd8/LGRjLsjBZJdubYGg0y4fesJ3euEpWDcgnLIJYEieEqpKjN2yguk1CVRcNBElEKUq+IxIXolSBzrQ+khh02AUgdP9RJCJhgOREvgqSJDbs7U17OyIy2ozrDRGpJnk2oaKLc3c8B5SOJ0IYRSl2EiIkGSRNPIasgu+IJoYWR1qR5ND2VpCe2LZc2ZCQFSTa1xaKaw8DAi0uJAsgkiIUIYuQfIfQbop0BcAvL1J55OGOCWB95g7GXryDxNgoM9+6n8yaIfR0AmvxKG7TRksYeLZDAOQ2rkJLGtQnF/E0G6fWJAwCWpVGlI0cSS8i12I9pWGXmyLa6N4ZTpgO87bHpfk4IyMp4RVgujgNi8xoukPVV+Mqc49MoCV1ercM8ao3bUFLJ8htXsPSo4cAKJ9cRk/qXPK+y+i57nqC8hKL+5bY+uYdBI0KSt8Iyf4kcweLXP/P7+Lk52/DOnmExPZLqN53F/G+HF9+21+y/eVryK3J8dhH76BZtLjkvS8kcF3UpPBfqJ2ewyrbqHEVq9REUiTiSQ2l6eK5PpmkRiyjkwH0pEataJHUZOaXmoxmYyzXWmR0pZP3DDB04QBOw+XEV3fTLFrk1+a47xsnqLo+u9bmAFjYM8Phx3ez+cIBxhIaJ0yXS3IGq4dTfOFg8YfzD/tjwFNRFL9Xw+D8aBqcm8tydIouuuiiix8pfubX38UnWl+kevftVE/MUPrw5/nC5evID2/h+Ie+S3bdCLF8itrh4wSuh6xp+LZD6PsMXXMReC6Vo5O4mopnnqn3vuN3mga+IyQJ+bUGzaKF7wQcuG+G002XWdtj2FAZG0gCdIyCewaTtGoOqqEiKRJzj84gKzJDl67iDW8y0JIGvTvWUT40QeB4VCeqqIbKpe97MT0vuIagWWNi/xIXv3k7Xq2K1jtAPG9gNl2u+6s3c+I/76dx7AS5y65g5uavklvTx6du/DCXXDXKwI4+dn/0u/iuz4W/dHmUxJDEqZk4NZPSkRKJ3jiu5aHogknoNJwO+6Dl+Ji+HzUFhL/BoukwmtCYrbZIqTLxyOxKUyT61vbgOz4nvn5AsAwKCR58aJZ52+O6DT0Efkhlosrjd0wwMpJidULjdNNlS1pnfdbgq9O1H+ef0E8dzjlVoUsvfNboNg66AGDXz7z/rO/3fPNv6Euo6LLEdN3h1/9jD0EQkk3HkFWNi173JuZOFvHcgGuuXc1fvXozphvwnVNl3rq1gOR7BLEkcsskJtvomkEjzACgIgkpROgRygqtWB+xwMbVEuhts8CIWSC14wiVaOMOSF60SQ8QkgFFQ/Lds6j/oaJ3EhsAYWIYpT50bpfk6Dw+YeAJ3wOZM82LjoyATnMCLR6xBDwhvQg8wSwIAiQpONN4kM40CNroGB22f9YSEg7ZtQj0JLJdjQwgA+RWvZPiEHoOfmme+dvuFFrFuRJa0mDgojW4pk310HEUXcW3HQJDx1quohoxnLpJdt0I9clF0f13Uzh1s+Nu7DsepWPLIrUgck3u2ZBH0VQc06F4pMQX9y2xNROj6gbsyhk4QYisyLRqwk1ZVmT0pE51okaiN45qqCKJQVPov2QzM3ftIb9xFbUT03i2w+LjMzgNl53vuQ5j9XrckweQE2lWvWCUwzfvp1VpMPaqF7LlHVcT+9pDLNz1MFbZ5tQ3HmGoVKPvNW8idXwvu+pNKieXya/vpVVrkV+bIzXSy+6P3Eboh4xcMcbMA5OEQcjycREl2SxayIqMkY7huz6u7XWYBvGoIRbLxChEi6RcXMPIxwj9ELUmmAetWov6XIPeTQVcq05losrFV49y+P4ZFE1hfrLGoKawZk2Oz901yet2DrAuYjmYJesZ/w/P10l9UomMD8On1zieT9ceQic94wc+R3cx0UUXXfwI8eF//F9c/+U/pBalFuiZBMOXr0NSFOonTqMYOmEQEPoBTj0aOMgyhQs3Uz1yiuV9x9CScVqVBp5poxiChVk+WcZ3ziQKJQeSxPOiaVA8UuIrE1XWJTWWHZ/tmRiztscY0KqdiRpUNAXX9pBkiUQ+TjwfR9FlCtvWMHvvPtKrBqiemMFerlE6tkyzaLHjl64is3EtrRMHUHI9bL1uNce+fgSn3mT1K69g6zuuYPKO/czc9TitmsPpb+2lb77E8A0vw5k6yTU3VKnPNkgP5VF0Ucvj/XkO/f23UQ2Vvm29zO9ZwLU8lo4sY6RjlCtiENKbj2PWWjgtTxgiqzJOENLwAvpiCookWAgDcRXVUNFTGmq51XmOlo+X6Vmf73gZXHnlKHsemkVSJGbmGozpMsODSf7fviV+dlOB3gXxepSaP3pZ4g8LK02On08IguCc6n03jvHZ46chEayLZ4F4YZjL334jX/3sB6nd/TeMpDSCECZrLp98eIpa2SLwA0rLTQLP5dj9j7B88nHmT8yw3HBQZYnBuMybtvZxx6Qw8JNc+0w8oeeQkFziqiTMFEFsxmOpTp6uInGW70Coxc/4GfhnWACdDfvKRsHKaEZJFowESRbT+nbzoO1f8MSYxDY7od0EWIEOgyCKg2z7Dkiu3WlAhKp+tsdB+7pWRlW2H2vl5+j8oSQj21XcE/vOmC+2zxP5GHz17X+JnkmysHuyE4kE0KrU0TMJFEPHd1wxWfADfMfFdz3skuh6e3aL5mKZwPFQDB0tnRCGhZaHa3mCLWCoNIsW9bk61Ykqtx4q0RdTcIKQQUNcU1yRUA2VwA8I/RDHdDj26Bw96/O4lkc8b9CqtVB0heNfugctYRAfGsCzW/i2g+8ErLthO7XTcxD4ICug6YxcvY2dv/ICZh6e4cDHvsbygVMomkJu4yrm9yzQmGuQWT9O6+hjWNNTgllRtmhVTLS4SnWihu965MazJAeSTHznpNC8+QHpoVSnMRL4QadpIMsS+ZxBTyGO5YsIptAPkRSJwA+IZXTkSFYhKRKyJqMlNZL9IkJSTwm36emH59hfa9GqOfT2J4hlYjRLFnYQUp2pY8RVhldlKKzPk3mGmEal/Xf8I8CzffPXJLD8EDcUi4rn24Kiiy666OJ8gpEbeMrb82suZOxtr0NLxjl9xzHCIBCNfj/ANS0UTUXRVVzTolVp4FsOgePh2w7W3IJoKAQBzXkhDdUzCZx6EzVp4FoevuOjxVX0pI5neRQPl5h4bJ6vTFQZNlT8EAYNFT88k3LV3oxVLJdDJ8oY6RiBG5DoTVCZqOI7AYc/ex+KppIe6ydwIm8kx2fDDVuwS1WQZWRdQ5IVCltG2frmC5m+f5ZH/ubrVI5OAYIVOb1nAXPBpGfLOH5pnuZiBaOQpllq4pgOyf4kjunSKgufJT2lMXHXFK2agxZXO0OBNhzToRp9n9Vk+noTLLTE934o1jMpVQw/tLiK7wTImoyiiySmWEass/SkBsDEY/McrLVoFi0GcwZ6Uqe8JOSkrVqLgbhKX0yhLxv74f2x/AghI2SaAaLud9HFU6HbOOgCAKs0SyEXZywbpxGoKLKELMEnH57kzodnKE2X2Hf7HcyfrgBQmzmKXVlgw6Xr+cKb1xKXfEJJRgk9LuhP8pljZsfoT3ItpMBHdizkVgMCr9MsIPDQZKlD8w9lRRzblgEoOkGyIPwOoBO7SCQbIBRJC1IYnmkaRAgVnVAVZoz4TufxUHTwPeF3gPAbwHPOxCm2v+6cKHhyYwDEOWQV2bGefHywYnu1wt+AwCPU44R6vPN4UhjgTR3jv7ziA9z3tvd2mBVhvUzo2Dz4/r+i7AY4NZPMaAbf8ejZtg7FiJEeO7MACaL0gw4t0RIJCk7dRJJlVCOGEhfTisD1qE8ukh5OEbiCutiqtWgWmzTmGiydrLAmqRFXZCabLi+6bpysppBPicm903TJjmfIjWfJxUWKQqI3ztLBIk7DRU/qZMZ6AZi/ZzeeaVObrjKwc4jeS3eSWzeCX15iz4e+BIFPc7FMZs0Ql77/erb+wkspHVmgWRSLo21v2UGzaDFzx0PIyQyuabP4+DSBG7B0sEhjwSQ1lOKxj3wLPaVTPFzqNAqaRSuiYfqYboCsyLi2mD4oukLgi989rkhYfkj/BX0MXNBH76YCAOXZBrWiJTwdVqVFpKOuUJuuUV1oitxrWeKGy4bRUxpG3sB3/Q6T4ZsLJpIscXqiyuKx8pm/+++BlckEzzWeqQEgRx9tFoGM8IB43iwowjP0xR/0oytV6KKLLn7Y+Lp9F83/PJvl+TO//i6+XP4S8xUbpybigQHyG1ahGjGSgwUkRUaSZdEsiIYDbQahljRolev4loOWTiBrYh2hGjqeaaMndcIgxGm4tGotzEUTc7GJ5YdsSes4Qchk0+WFlw0zbKisS2r4jo/rh6SGkvRkYvTqCp7tiVSlY2XkSNrYd8EQkiIze+8+fNejfLLMwAUD9F+2jVgujbO0yAMf/E8AXNMmPTbAzl+7hgvf9RJKR5bwLI9Wpc72n91Cfa7BwiNHkFSN0A84edsxABb3LVKZqCIrEvs+/QC9mwqUT1YJ/ZBYRmzg200Oyw86ngZuKBrysiyxXLIwZAk3DBna2svotj4K4xkkRaJWtGiUbQI3INmfwIsMDs1Fk+JiU6xrZInX7RTyitRAEsd0sCJ6/NfnGpRbPkstv+ON9HzBU6UhnU/swWeDc671XcbBs0ZXqtBFB7NzNR6ZrXLhYIaBpMqmN/0FF7zsRaxdk+fwnbfRs/ZCph/6euf4eGGYW966ClxLSAAkGWSZgdDibevjzHgGo/6SmMp7tpjeR5v5uCIm8a1QISa1p/MyEqyYzIsdihT4orHgiOjDIFkQjQVFFw2BwDvTUFi5SQcRf6gnkVt18B0hYXCbnUhEKfCQHJNAFwaJkn/GFwFJFqaF7SSHdsyjJBOqOrJjgWuLZocvkhGQzzQYOo2ByPAwVHT8dD+yKaYB7cZH2KwRemKjedMDM1x015cIfTFp0Ndu48sPz+IEIaOff5xYRmfsmjUsPLAP3/FQdBWjkEVLxnFNi8bMEnomQX1ykdRIL06tiZ5OYi2VcetNkiN9yLKMUzexyhaNBRNZk7HLQiIiKRJH5xoMGyrjOQPb8tiS1jl9/yypvIEWP/OW4Zoutek6qqESBiHmQpO1L19PGATE+3Ic/Mxu0sNJ6rMmO37pSuL5Mtl1Izzyp5+hVWsxdu1qdvzqS7nrNz8qphOvauDbDs2iydLBIr7jk1s9QSyXwqy1mN89w/DVM9ilKlbEgNGTOoous3SwSLI/gdMQkwctKZIS4nmDkcuGOfmt06QNlZbjY8RVNOgsimK6QhD4rN01yPF7pkilxPShZ0M+iqWyOpROEV1VxzJdNEViac4krkiUjpfRExqnJ6rEI+Ol6/oSfHtJGE8WyjZaUoN7p7lt0Xza/8PzaaLfvhZFEosvO5IrPF9wrh4HXalCF1108cPGrbvnuWn81Wz6nb9HUWR8P+Ctl67iK78xheWHDN+8H8/2GL92FQuPHunEzBmFDHomQavSwFqqoKcTLOyepP/CURozRWL5NPXJBezlGvG+HJIsE8ulWD46h2M6SLJEsyQa4YEfsLdisy6pszal06uL4cfE/iWyaR1FV5AVicANMBeb1EyXpCYjazJW2Wb82jE82yUz1svjn36U7EiaZsli29suIjMqrvOBP/kSdrnF6hePceE7r+U77/s4YRCy7uXr8GyHZtGifLJKy/HJrc6iGDquHzK/e4aeLQu0KvWOzFBPaSR645iLzQ47UtZkZEXGdwIyWYPBnf2cvnuKrKZg+QH5mPAocoIQxxWswh5doW9tjmN7F8lqMvGcQWFDHo6V8R0/Yk06KLpMrWjhBCFJTWapKgYMp05V6E/qTM0Js0k3hGsKce4uWVz4knHqcyKquXHvNA+Wnx+GiU9cc7TjlZ9PCINzq/fdVIVnj27joIsO9tz8WT6Z+yUMTcEPQppLU8TiGkM5A7dZZX7vd846fumz70ZympGxoCuM/ow0bjyPblcZ1hy85BBSGKCUBR2NKCIR3yPUE6gySH6LIDIDbPsABEYa2a6fkSLEs0hmSej/7fqZZkEU0djBE1IJkOTIL0DIFCTf6TQX5JYZxUTGRFTjyvu1N/8rpAxh+7GewI7AbxHEs8gt8wxrIWo+tO8faglCLYZslgQ7IvI2AJBicYJ6pfPw5vwyt37wNq77zWuI15u8ZtcgzaLFwI4+YpkYTr2JljTQEgZhIJgIiqHj2w6J/hyyppEekwWtUVNpVsrImopr2tROzZFbN4JnO1ilKLEherP1XZ/ZYou8JnO66bI9bzB+QR8L+5aQHbGocC0Pq2KjJzSssk2iVxgROg3BQFjaP0dlosba69bguz7lk1ViGZ2JOw7g2R712T2khlJ4tsfItTu5639+Aafp4oewtG8OzxZ0yvY1ze9ZIPDniCc1MU2QFVIjfaSHZwj9kOLhZXzXR5Ilxq5Zx8nbj5AaSqHoCqqh4js+J791mrrtiWjFuIodTRLaCwJZk1GBhX2LWH7I2m291GcbVCeEzCOeNzh+pMSmC/pQDY1q3UGXJYq2hx2ExBUVVVM4vWyx1PKZt72zCvHHPn+QC7MGw9nYk5oG7QXH+YiVv4MiSc/IlliJ8yLrOTx3j4Mu46CLLrr4YeOqoRTfnKzyyP+4nJPvfydr/+/H+L27FnnNBf14tkdhUwEjGxPyAtvBKAimXavSQImYBPG+HAA9m/qRdRVZU8XgIJ3AbdrYpRqZNUPYpSrNiDXn2R5mVG+Ljs+6pM6heotLeuKs2dbL/OFl/DDs6L3bvjy+A9m0jmW6YAtJ4NLBJaoLzc7vtDRVQ5clZh84hV2x4WCRRCGB7wQMXbmNe//46xRtj5Qqs3Rwkdp0XVyT6+OHITMPzeJaHk4Qshx5CiiGkAvmxrPUpuvY5RaSItG7uYfi4WVSkXmjltSoTdeZfmAWyxcyi4yuYHvBmRjmqP67tkdlooodhKxZk6NVa1E8vIye0tCTGsdPVli/NkcsE2NmpkFcEcyFmhdg+jAQU3m02MTyA8ru2fXln75wmAuyMQZi6pOaBudzrV+Jtlzh+YbQ73oc/KjQlSp0cRbu/dd/5fCeORzXJ14YJq4pjPYkCIOz/yGPf+vvREKBqhOqMYJ4FmQV2aqiSoiIQ1kYBUqujZ8b6TQNZKsaJSI0kSXA95CINvKBMEx0UKO4Q1s0BzyHIJbupCyEWrxzLHAmfaHtVRB93/Yh6HgV+F4UmRgSqpq4Js8943UQeB2GQCddwfeQPFf4GrTPGckZQkkCScZO9CE5jc7zI7UZFm2EAZLbEvGQvgO1RQLLxK+XCSwTfe023vPGzfzpP7wFu1TldX/1Rnp27SD0A9Zev42tb9lJft0APZvHyawepP/iTfTuWMfAVReS27iK5vwyrmkTy6Xx7BaxXJrAD7CWqxiFLOmxARL9OXzbYfnwBLMPnOqYHp06VeHB6Rq25TFluQwMJNlciOPZHrXpOmtePM7gzgGqyxbpoRTxnIGii+fdroo0hcxoGtd0qU7VieeNTrSTpEhYZZvqRA3P8hjcJTwLnIbL8S/dw7pXrEczVIy4ytobdlKZqFKdqRP4Qlaw9R1XkBnNkOiNM3LFOPbSstB2RlIE3/VRNIX0cIqjXzlIZjTD2Iu2oOiy0BxeMCBMj2QJTZGQIuOfeFJj1xu3EgRiMdVsecQyMdZs7CHZnyIMBP1RixoWY6Ppjhv1+PY+LD+g6PiMpWMsOz4Vy+V4w2HYUJ+0YXZDMP2AB+cbvHZN7iyq//m6kHhiYVi5kDBk6RnlCj/2pkEXXXTRxXmKK3/vZ/g/H3kLaukkYy+/DB67lb/c4XDJ+1/JC/7inQxdvpHsuhEKW0bpv3gTmdVDDF1zET1bxrGXa7QqDbRkHM+0SQ4WcGpNmvMlEv15UiN9qIaO73pUT8wwv3sKz/LwXZ/D03UeWLZoeAGTTcEg2J6JMXLZEE7DZfyyIcYvGsRsuuhJDScIUTUFRQLP9kjljY6PUbNoEU9qOA2X5Wiw4AQhcxFbcPjSEZaPl7EqNqdv3c36V6wlrsjossTYizZiWx6lposThFh+yLZ3XE5uPEtPIc7m12zCqTU78kvP9nBMh1bECJh7dIHUQIK+rX0ouoxrugxfPAhEXlmAoiv4oWh4bHnRGIoEtiVkirIiM96bIDueBUCNK9hVMTxaPZyiOlOnfKrCmo09NLyAE6ZDUpHwQ/BDYa7YE62BViJA+AHduWTy6tHMWT87X2v9E7HS3+B5I0vs4keKLuOgi7MQBgHTD32d6YfE97f+48e49QnHFO/5CPH6jNDxq2pkTihTU9OkpUaH0h+qhpAHqDphZISIJBNqMSE9cG3U2pyIHmyWozQEjdDIIId0UhAAsdGXFbEZ9x1CJdr0t5MKzkoyiNINOqkIkcOtJzbKsmOJFIXoWiTHEiwIRe2YKcorPBg67ANJFoZ+iPhFiCIiVYN4VTAqJM8+cx+nJcz/dEM0OCQZubqAe3I/Sv8ooesgx5OEtgmywtb//v+x+LX/pHpqEaOQ5fh/PsDcowtkRtOsftkWGjNF7OUaoy+6iFalgT42CoAxtoYRQ6cxU8SzHTzTxjNtUiN9JEb6ac4siuMKWRHHaEfPgybzuYfneMVAkinLY6Hl8cqrV1GdqCHJEr2bC1QnqlSnalQnavStylA8UiKeF94Qiq4Q+iH5tTlmHppDTwkDocAPKB1bRlIkITcYz+LZHsmBJE69iZ4Si436XINYxiDZnyBwAwLH66QyJHrjZEYz7Pvkvay7fjOxfJrQ9zn6xYeQFIlEb4JEf4Zm0cIq23iWR+/mHlzLY+7BY4xcvQVJkVl67Dh6SqNWtekbz1GbrpPPCR+C3V84yOimHuaOLtMzmKRZtKguW1QmqqSHUpHZY5zACAjcAC2p4dkeE/uXMP2QU6bLjOVxbW+C3RUbMzJYXBVXmbLONtlcdnwWWj53TtWeUTuYVCTMc40RPEc81ca/fd1+GD7t73D+JEOE5zxF6EoVuuiiix82Bv5xgdlfzHPgf/4fVn/ii7Q+/Yc8/GsfIjOaZvSaDTSmFlETItowDAKM/j4A4mOrGTF0qqfmOptqa6lMcrBAaqSX2uk53KZNYrCAVarhNBxcy6NVc/jP48tc25vgeEM0sl+xpUCr5mAA5kKTZslCUiSmT1YYHE6xPF3HUOWojotmcn5tjqWDRWxL+FQlg5DSkeWIkQYDo2mchouRNwj9ANVQqVguxcPLDF88SDat4zRdFE3F8oOOVGD91l72fvJ+1r58PcNXiLSo41/dA0CiN06yPyUaB9Gwo7CpB3PRZPl4icFdq5A1laX9Mxj5GPU5j/7VGUoTNTJJjcANOPSdSXr7E5SXmh3mRMvx2XP7KYYHk/hOQDxvEMvE8GyP3JjY9B85XML0AxZaon5fnjd4uGxT8wLWJDV6dYWi45/12i47wlfhlplnjmM8XyUB50f9/v4QBudW77u1/tmj2zjo4vvCrje8DaNVjTb0K/X8HikFCFURVYig+QeqgRQGyK2GiE10LWS3SaAnkcJQfPZaHY8CyXcJfQfN90TsYjtBIQxAjhNqCSSncSZ+sc0sWGle2P4+ajyEqibSElQdyYnkAdHnUI1FrIMWrLiOznnaj91OSmgnHrSTHyL5guQ7hPUyUjrfOdZ84HbiO6/qmMyFsopfXqRx9Cje3v0UXnq98DaIWBN+aQ7XtGnM1dEOnKJvxziqIYwH937yIS76tRfg2S2cejNKU1jGdzwSGwqohUFSQPXEDHomQfnoDImhApXDp6idnkeSZWRdZengElpSnPPLexZ45VAKWZF5wYY8iUKCeN4gN55jbvcc2fE86aE0k/dOISkSXhRZ6FqCqrh8vCxiiR6aw3d9GgsO8byBXW4hazKhH5LojWNEU4og2gw7DdHIMReaKFq5Qz8050tREkLI6pdt5/F/uZ8wCMmsGaJ6YgarVGP5eDlKPIix5c3D1OcanUxp3wnQ4iqBH7LwyDHqcw0KG3rIjKaxqy1q03VkTcZ3hRQik9RYPFYW2v2yYE7oTZdYJkYsE8NcNAXFMxDmOa7l0SjbJDWZE6ZDX0xhY0o/S37wYNlmeyaG5YedBcXqhMawoTJoBDxeXSGJ+R4w/ZB1SY0T5vkX6ZRRZawVdEAZQQFtL35k4Mfc8+hAxDGe48WcJ79LF110cf4iu2oL1alDT7p9eNfLeeFLNvCNz92BVZ5HicXRjBRf+Ntfwfz4f8VcMJn/rzcS78sxeuUw5kKTg5/ZzYW/8gJalTphENCYWULWRNxyYnwctaeXnCxTPjaFmjQoH50llktR3HuC2mQRRVfQkjHmHp0nlonhmi43H1/mRb0JdFniZzb0dHyA1ly3geLB+U76kFW2yad0PMvH8gM0RfgE+Y5JEIQsHSzi2qJpoEgSNVMYBsYViVRKJ5bRyYymAZFo0Ik1LFnIexaYKdvoskR9chE/hIYXsOOGLdz+L4+QUWWSQwVqp4Qfw/KpCk4QYkxUufCXL6OxeAwjHaNZtNCSWmdNUT6+KLyaogSkbFqnNFHryBI1RSKpnfErsEyXeFLDMl0GeuJoSQ273KJZsqjPNZAVGcd0KTccenSF/UVRs7dnYmfJDx6vtjqmkjVP/J7DhkpWk+nRFY4+C4NEOwgZNlRmbe8Zj/1R4/wZADw7BNHa8QdFV6rw7NFtHHTxfeH3f/aCSCKgdowMJd+FwMNTDNTQPiv20A9BQkaNNtOhGgM3ahCosQ5jQPJsQkVE30iuLW6XZbEh1xKd9IQO2h4HK7FSGhAxFTrMg/YhbV+CTgSjSiUzRr54UJxWjYEaP5PqsOLxREpEJE9Q9LOaJqEkQ26gI5MI7SaxdVuRMz3gecx/+p/IbV3H0c/cQWHrCAu7J8hfeRVKVrj3u5NHOXbTN+i/aB1Dl4zTf9k2kGXMmSJH7jjN2suHkWQZp9Yk3iOYA42ZIvH+HMboGPb0JIHroRgx7FKVVq3VMVTKrB5k6u6jaHGxwTYXTRRN4YqeOKYb0LA81uRjIm3BD2nVhJHf1L0T2OUWRj6G03CJD4goJKCTSKAo4nOyP0FpokbMD2m2PKxmSCEh4grbE4pMb5yhiwfRUxq+IxYqy8fLHS8CEH4Lruly8pZ9hEGIntRoVRoUD85jRhv0Nivh9O2HkGSpE5vYqrWIZWL0bR/GbdroKb0zcWmzIZpFq3OOeN4gXGyS6I1zeqLKQCaGntA615IaSlGbrlPYkCfwQ2rTNeJJDS2ucklSp1i2OP0Uec2WH6BIovBuzcR4vNoiq8lPGbVoyBKKxJMYBlX3R0/2fza+BO0FUhsBgp7avu95JVEI6fwP/MCn6E4huuiii2fAyqZBbnw7q3dt50/eupOvHZjn969bx3LD4Y5Pf47UwGr2fPRtLLznzcR2rGXo0nH6Lt4CQKvSYH7PAn3bCoR+IFiF6SSV4/M055aJ9+eIrxqlfuwkAIqm4tSaNObqZNc4hEFAbv0gcw+fRlYsVEOlsSDiii/JGTS8ANMP2NYbx2m4pIZSLO6bJZaJsbhvCc/2MPIxPBsUXUgK3KhhHkRyAqfpku9LYC02ScQUGk0Xyw+IKzJhELJ0ZBk/BCOu0rupgBxJE3pH0kxN1lAkwViTFDmKfJQ5efsRZMSGv1WuUzxSFE1+WUIHZEXm6M170QyVWEZHT2m4pkssoxPLxJAUidx4FisyIJYVC0VXaESb/DYzEt8nF9eoWC5K0yWmK/iu3zF8bhYtejcV8GwPq2yjmC7JTIxrgVnbY/4pNvfLjo8uS8jAWELjdNMlpeoducRKaCsMhldiqXX+NQ3g+dU0AFHrz6Xed2v9s0e3cdDFs8Ylb34HLxvRCEOl0ywARENAVpElhBwhut0PwQ9C3CDE0BJobvOM0aHbiu6ngKx0qP9Ap0EQqsaZx2izCxDpBB0vAzjbSyAIRHRjmyUgq4L90E5tUHWCFZGOsl0npRI1EkLk2iJhMn/GBNF3RaPAEywKv7xI2KwjZwvIPYOR/4JNaKSRqwviehSF0HORZAV/YQopFic50odaGOKfbj4CNx8B4O/+h8LS1/+T41/dQ3IgIRICNJXUSB+SkcBZWmTsZ64mu26E5cMTqIZOcrBAc6lMq2aTHMoBsHT/w+TWjWAtVSjunyBeSJHsT1E+PEHgB1QnRBPE8UPssk1qKMV37p/hym293PzoPLtyBqEfEi8kMLIx0RDQxKRh7cs2UTm1RHo0z9K+OXzHJzOaZnrPAkZcRdEUHFO8dlpULZ0gZKAnjhpXqEzWMNIx8nmD8myDxESVWCbGwIXDlI8XabY8UppYdPi2g6yJhk9tug7A6uvW8/i/3I9nCxlDYVMPSweLNF1hhDNwQT/FIyUUTcHIC5aAdGgO1VAxsjHivamoGdIicANcX/gbhEFIdjzL2LVrOfXtY+x4wSizj87j+iHZgQT1uUbH+NEq29hlm+Jik0xSY2quQVZTqHlBJ4qpDU2CrKYwY3m4oZhKvHIgyaztMbmiyZDXZMpu8JQ0RRkxiflRGww+28eSOaMj1eWzZRVPFevURRdddPHTgsrEfsojazh+2TUMuQGfAG4++AUaSpJ9n/gkUz//OWKZGIomEpEkI0Hz9CTDL76c9NgA1RMzqIZOaqQPp27iOz5qvyGkdw88Rmqkj/rkAkv7Z4jnDbLjWeqTS/iOT21a+AOZi018x6ewIc+X75vhutU5bj6+zK6cSAwobOoBxKa6EaUDrHv5WioTFQZ3DrB8rAxLTVJ5g/mlJllNJm2o1G0Px3RRpDPGs/1JMfAp1h3iikQma7CwbJEoNolldLZeOczysTKmH3RMdu1yEz8UjWg9Mlgcu3qU/Z/Zg+uHaIpEeijFzEQVq+UTt1zGNxc6hsWJ3jiViRqJgmA0KnpAbjxHZaLSafz7YYihiorUf0GfGBDcOcnGy4aZ37OA5/rEkwbVqTpGNoaRjWFXbJyGS7FskVJlJopNNEkMA6yn2JT2xVSO1FsEwOmmy3V9CY42nLMa/23poRuC+xSb0/OFofd0aNf851szoYvnBt3GQRfPCj3rd3H7+64iJEBqiUITtif+kozcLBOqsTM+BpKMH4g3RccPgQDdd8F3CPQEEq1IqmAIU8QIbemA8CZoPcm/QBx0JvEgbMcvPhFtb4TIyBBAXsE0kFZIDWSrSmBkkR2TINEDM4eR8/0E9TK+WSN0bPxqiaBeQY4nseYWaMwsMfzGNxGOXYDkx5CrC/jlReRkRlCkLROvNAeeizowxiN/cyuK/i0A3nHFCKqhMvn5Lwlzo4EER++d4Zr3Xo1nO8T7c8jxJAsPHaLvog1Ujk2hJQ2qp0UiQv6yS3Bq3yT0A7REnMbMEq5ps/T4KeK9KbymLaiNrkfoh7RqDr7jIykSruXx4EOzXDScZvF0lbGExvBgEkVXaNVaWKUmqiGiDM1Fm7lHJqjPmpSOlHAtj95NBYy80Ylh9B0f1VBp1Rwyo2kRYyRJBH7A4M5hiodLuKaLrMkYkYygdLxMfm2O8skKuizhNF0RZXjnaQI3QFIkYcxkupSPi0lI76YCy8fLzO5bEouWwMdIxygeKSHJEoneOPW5Bvk1ORRdJp6P45gOsVyKwPXJjGaI5+PAHM2iiHKsTdeY2z3P0K5BKqcqJHrj6EkxxZjes4CsSMjRDjk7nkVLajTmTHZXbAYN4WPQ/utraxXdEHZXbC7JGTxSEROPeyNTpPZmO6PKXD2Q4kjFfko6Y3vJMfhjpDA+XdMigA6F/4mNk87PzxOcaxxjV6rQRRc/XciMbGTLCy/nP955Kf95eIk/+dtvUjr6yFnHVO/7CNmrfuMp72/e8vtIxQn+47MyF17Yj2pozH3iwyIOeUWtd5u2iExUdcpHhUdS9cQMkizTXCqT6MvTc9nFtCq34NsO2mCB6okZEn15lo8KKaFdboIf4poOqqFSm65HKQWi3tz76DwvHExRLFv06gojKZ3ADzrmhm0/ocAPWNi3QG26TmOuIWr9SBpZk9FLFpoh0ggUSbynZrLCJ0iJjIHX7RwgNtcQhoTRxt2zPRYWTHo29LB8qkJeU0RDXJI4+t1J8VyrMslMDKtsUT5ZIQhCekbTLE/XWZqqEVdk/FAwGk4eKlFIaMQyOuXZBn1rcwCRGXM9kknaJPuT6CkN+aREcbFJPAgpn6wwv2eB0StGKB0pEcvE0JMasUyMuYPFDrtRNVSMfIyRjE59rsHuiv2UPgZt7K+12JjSO3X820tNZBBx4xF25QwO1Z3veQ44P3yNng4BnPcxzG1J6Q+M82nhcp6j2zjo4lnhF3/xJTS9gIQqoxgZcJpIHmcSDEBsvGWVUFGRFF0kJgBJTUaRJUJfAjWG5Dlnmxm6TdEAaMse4ExaQhvR8R1ZgCR3fBTOkijI0VaubYroO8iuRej7QmYQ+OC5wvfAc0HVCLPDHdmFbFeR8v0ERhZn3/2EnkPYslEHx7CmptH9gEOfvZ9WrcW/fugefrf4GBPv/wWRdrDrYqR8P5Ki4Bx9DDmRJgDsY/sw8gb9Fwzy91/+Z75wydu46PWbqU2WGLhoDbFck1e/5YU89pFbuOR//SKSkWThtttxzRbG4CCpVSX0TBItaZDYsBl/aYaeLeOY8yU8u9XJdc5vGgbAnCnSqgmvhOpUHc/20JM6j83W0WWJHWMZpuYa7K+1GIkLCn87HaEyUaWwIY9recQyOvFCgsxoBsd0mN+zSPFIidRAElmRO7GMsiITy+hiom+69A0ladUcJu+ZJog671bZRlZkatN1Uv0JJu6aIpbRUW0lerlCHNtBT+odqmA8b+CaDrIiC6fmSFepGWqnsSDJIme6sSDkF5WJKmEQRkZHOnpSJ7umF992MBeFx4FqqGz/uUtYeOwUid5EZ6HUXjhkx3Ms7FuksKlAejTP9L2nKR0vs9Ty2bqjD3e61jE/bP/l2UHIQExhoSUWB49UbF6/vodjiyb7a62zdIwvXZdHi6tkGg4v708+KaJx2FBJqfKz0kj+sPDERsFT1dCVx+hROgWcaZrIK447L6YT4bl7HHTZi1108fxFLNNLq1Z8xuN6N1/OqV+N4y3NoPYNE1iHCb79EDv/9qt8LW+QuaqHbfelqPzhhcjJDC//yIOd+w7vejmzu2/rfB/E0rhrrsC75Vbqb38d1/wD/bFBAADF0klEQVTZ25m54yFy60YAeNUbr2bPP97OJf/zRqRYnMXv3EXlVImRl1yGOV8ilksTy6WIj63GmZkgs1pEKjp1Ez2TwK7U6dk4FJkjiml9s2ixfKrCsuOT1RTuLTXJagoXZmOcqrU43hBsAEmRKDd9YjWHEyWLjZGvQaI3TqI30aH8Lx8vU5troCc04orUiTBWJAktKeR8ZtMlpQo56uy+pc6Ef8ES0cez8yaFhMbxOyei+4oNddtIMa5IQtZXtemJvBicIBQsg+h9O6XK5GMKNcdHkaDUdEk5PkZcrFXaEsXAD8n6IQM7+nEtD3PBRNEVkprMmheP01gwgTS16RpW2SY1IIYlmVVpDj++wKqLByPJxiK1osWy47N+bQ7mGk+54c+ocke2d7Th8KLeBFOWywlTPCftn13XlyCTNRh0Ay7Mxvj2UvOs8yQVCV2WnhTreD6h7XXwxLreXg+cF9HLQOifW73vehw8e3QbB108K3z9uyf5vReuptLyycUUFCUyBWwnF6xgA8gtk1C20VQdVZJFowAijwBB+W8bF8qOdUaSIMlRckIoGghPZBi0mwYrEQZCntCGLHcaBlLgETYqBLJMaDeRVI0w8JFUHYIASTeEpMC1RLRivUxQr+BOHcXY9WK0y14Ji6c48ld/T2rkBIHrMXnXSY6erPDyX72MtS8L8L/yt/TuWMe+T97NZ3/7Zj5y/HOETgt9/Q5Cx0b2fdS+EQobHqf/4k0s/ts/0lOIM/bal1Hdf5DUSC/WYoWFhw5y0W9cj6TqNPbuJjlYwJwpomQL9F15KX5lCXVoNaHnog6tRq4dQJJlwiBA1kSGczxhUDkxg6yphA3hQBwGIaqhct9UFUWSWBVXeWyi2jHeUyRo1Rw818ezPUYuGyJRSOLZLnomjlVssLBvEUVTkGSJeN7AaUQMAkMl8AVDIPRDnIaLaqjIiowkCzNFWZE7lMbseIbS8TKSLGE2XRIFIQUI/AC7FWCocud8+BDLCG8FzxWLBiOuIckSYRBilqyOlhIgcAPhu2AH5NdmcS2PzGgGc7GBpJikR/Ik+9Pk1w2wfHSehcdOsbB3CUVXSPTG6dvaS+VUBbtic/ybx+ndVGD24TkSpypCK+oJB+gj+5a+5/9Iu2nQhmM6rIqrrIqrWL6IcBo0VHLjWSoTVUw/oNYM2J6Jsb8mDJg0SWzKn0pP+Vzi2RT+lce0JRaadLbHgRE1FBRJekpa5o8aT4yR/QHO8EO5ji666OK5xbt+/33oqsyfvmwt2tx+8Dzcke0ojSWG33XzU5oXtlE8/CDp34YDVzVQjQP4rsfEd05waqrODb9zHaVf+lM2/vVdPLbjKpRffQMvuGuSRy99DW6zxjveeCGFX7yUj998gON3fhl17hDOQ9/mdU2b42tz/C4v4w83zhHvz+PZDouPHmHnu18Gmk5l96NoyTjpoTRyIk3fJdsJHRulbwRcB61/GLXW7Gi31aSBootmfXH/JGpc7bAH2u+7dxWbxBWJQUNlf63VaXKnVJlyw6Hs+hRMlyuvXYVqqHi2R6I3QX22wfyeRXzXx3d8NEPUVlVTkP0A0w2IR0lJTtPFD8XPcH1kWUJDotwStbovG2M+YtwtOz7D2RhmdB8xABB1Q4+mSyuZCk4QEgADMQXLDzHdAF2WMFSZYsR68B2fIAhJDYg1RCKjYy6YeLZHz4Y8Rt6gd+sAlVMlatM1qlP1Tq0vbMhTnaojN1yWT1VYuz7P6UfmyGQN1LhgReiyxOPHlr/n38tKr5/2hloYIypMNl3ymkxKlekdSVOebbDs+FRd/yzT43atP5+bBnCmUdBmHbRrvSKdXyyEMAjOqd53PQ6ePb4Hz7uLLs5GYTDNsu2jytKZpXQnKjEQqQW+h+RaEHjIrTpSy0RumUiBL26HjpdA++uVPgmddIO2d8HKKMQnfD7r6zbrQJbFdQQekt0grJWQ0j3guUiqBrKCN3uahS99jup3b2Hmpk/hHN+LbNch8PDHLkQZ3UBsxwtERKKiEo5uY+2bX07PlnGsUo2RK8ZYPZBkaf8MN//zQ+jrd7D02DEuef8r+cM/ezXTH/8oQRSvCBDUSiArxHIp9EIv2XUjXPK+l1J88FF6r3816uAY9akFnFoTpW8Ed/YU6UuuIrFmLWtvfCP7/+bTzN72XdSh1QRmHUnVcCaOYZdqhEGAbwlTJN9u4bsevu3g2aJpYEXmQIeWmuiyxMW9CU433bPc+g/VHZotj2QhLjSSU3WmH5hm5qE5SoeXkBS5I0UY2NGHY7odTwPHdFB0BVmRaNXOOCi3UxMAPNfH9gKCIGTgwmGxAV+VZu3lw1Rn6p3jEjEV1VAJgxBJFmZHsUyMVs1BM1QyQ6kOu8B3BBUzO5BAkiXUyDRJjiYqVtnGXGxGrs4h5kKT2uQyrtkS0VZ5g1bVITeeJYx+vrhvidmpGnZVJEI0S0081yfwA2aOlznddJmyPAYyMXblRBzlE3OaQSQovHFrL2+6QMRnLbR8LD/k8aqIcDracNj70CxfOlJiIKaSUWUORnRHEJpJPTJMfD7ADTnrWu1oMdheEHbRRRddPNewvvge3nLRCH+xvoh66iH8zBAfLQ3zxWN1fuO7dV76hhcRy/Q+43neteG95LeM06o0GLl0hLGBJNUTMyz9zMs5/t2vcbk6T89/fIXf/svXU/7Lq/mj//t7/NEWm1/cOcTtf/ASUgOrmbnpUyiZHHo6wfrXXcp/e/zDZN/4qyh9I4I5UGui9o9gnThK/ooXkFk/zuo3vpI7f+OjLD2yH6VvhKBeAU3Hnp6kuVRGUmRCP8Au1fDsFoEfoMZVfCfAabiYUeP5aMNBkWBjSudIvXVWLHDR8al5AWtTOi3HZ2HfEvvvnODAfTNUTlfRkhpqXEHRFPq29uI7fsdbKAhC0lEjoZ2upMsSnis2+7YXUG75+GGIH8LgzgFAmBBv2lygZrookqgLKVWOkhiEYXC2J056KEWr5qDLEv1JfUUDWtSXVEpHNVQGMjH8MBoqBFHS0YKJJIvYyPpcg+VjZcFeqDc7yQu58Sy+41OfbVCfNZmv2JQjxmRtuo4ThPiuz9KcyQnTYdb2WJ3Q2J6JAfCa8eyT/lZ6dYUX9SZ41XiWqhtEtT6g4QWU3YApy+ORY8vctmjSF1OIKzInTJdV0bAjqynPuzqpSWcGCG7YNnz8sV5SFz8GdBsHXTwrrB1Kk9bFn0vnjeKJG/c2+yDwBAsg8JC8M/E1HblBZJLY/ggVrcNAeGJSgtRmNax8vJVf+96ZRoTvIQU+smMS2iZh4BOaFULHRtINArPG/B33ohgxFENHTyfR1+8A36H61X9n6n+/TzAVEhnBRlB0JNfGPHGayokZhq7chp5JkB5KkV/fx7Lj88jv/jWDV25j+fAE6bEBUiN9+KV5ArNGYNb4+Jv+kmP/8AkW982K3yeRRk8nyK0bYfqmfycwawxevQtZV4UkojCIc3wvoWMz+/XbMPIGatIQ7IV0jtapI8iGgWe3zpgryWJS75oWRiFDq9aiNl3Dd3wUTWGp5bEqrlG3vbMWEm2cMF3KS00c0yXRmyC/NkdyIIFjOjTm6sTzwujQdwLya3Ii9ibqkre9E9pNAynavGtxsdjwQ6GDr7oB9ZmyoLg3XFrRQqfNUADQ4kJH6dkerVqLicfm8V2fWEbHrrawTJfAF02ImK7gNFycpouRj+FaHpIikRlN4zsBsYzO3KMLLO5bQlIkFvYtUT5ZQZJl6rMNYlkd3xXXDuC7PoPDKZE1vSrNzEwDPxSOzluvW01KlVl2fK76/VexaUOe167JdWKnVuKiYXFbLBPjwZLFvO2dNVXQJBhK6SQVqTNZ6dHFlAPEVMgJwvN6CvHEouGGZxYTIo4xfFL6wo8DISKe6Vw+ukOILro4v9G49X9By0KTZeRYnNnBS/m5r83z1u39pHSFuYrNfNnqSBZ6N1/Om3/r15/yXN96pYK1WGHViy/CKGTIr82hJg32nihT/+rv4BVWk/zofyX7xl/F3PcovzNegVaT3MJeblt3CY9sOUxx/xQEPnIyg5YwyK4Zwtst/I36L9smar3rEh9bjX1sH2HgM3/nvQxfPEjgeOA6KNkC9unjKLqKook1kayr6OkEANZShVguRavWYnbfEpYvGHuWH7AmqeOGT07qAZi3PQ5UWzS8gNx4hrENPYyvy9EsNWnMNYjnxdQ9cH3hfRCE2HVRqwM/IB41zf1QmCE7QSikg1EkY9tboV3fAz/oNBpA/MzyxUTfj95cfcfn2N5Fmi0PIx2jFSUVONH6IRETrIhqXfg4+KEwO873JbAqNqqhsnioyNLBIpIssXSyQvlkBVmRqURmzHbF7lDRPdtjpD9Jb3+C7Ko0c5G0QtEUNlwx3KnHu951JRv6k7x2Ta7DjlyJXTmDpCYTzxscqrdYanlPSk1YndA69VKRhCnyShnGUyUtnc94ovxQGD7+eK7liWjHMf7Atf58ok+c5+g2Drp4Rqx/8ev401duQokmoRIg+VExiJoEUuALiUEYCDPCdqoBdBoFku8geTaS20K2qkiO0HvJdrXjdYDvnGkoSPIZA8Y2osaEFHhnmgZRVKNgNjQJzBrICko6j3vyAObjD7Lwxc9Qve8u9EwCPZMgNjRKboegBYaxJOlLrqL/og3U7/wK9739N/EWJvHuuxkUlcqJGbFJHx7Gtx3UuMrnPv4I/+u7f8XO//pWDt10Dz2bx6kcFYY/1omjBI0K//GzH+A1/98LUA2d7KoMoeegr95CbNNF6KvW0X/5DgCUfL+QHXgOcr6fqW8/ij0/T/nEAqfvnCS9SnTvg3oFfWSc2olpmvPLUVFu4duCAeDbDq2KMK50TJea47N3rs6apE7Z9bmreLa+ro3JpovlC0mDVbaQNQXXdMmvzdOzvoBnu3iWT9/2ESE/kIXOMTOaxrM9PMvvbP5BFOa21swPQ+KKRD4mNvrJhIaRN/CdAD2hkegVOcrCaFHc5vohviMcmG3Lw3cCqnWHZCaGaqj0rMkBotHgh0KmoMVFA8I1XbS4itMQDQXVUIWZ02iaZtFi8u4THQlE21AJ6PghnL57ivLJKilV5oFlC6tsM/3ADE4Q8tLtfTz459/EXGyi6AoP71/qsA9W/u6u5bF4rEzR8VlqeSyv0EiuT+l8dbrGpfk49y1b5DX5LBPEjCp3FlTnK75XS6A9jWjrIX/sCIWR17l8dDsHXXRx/sL64nuQl04RpgrsPPT/cKeP03vPJ/i7xX/D++h/57rH/pnP3LiTE4+d7tynePhBbv7Hfwdg/60fItG3itz4dgCmP/UxAtdF7elFUmQUXeYLn3iUDQ/dzb9MqOx+5fVkVg9R/NcPE7gezbu/hjd3in+97Bd56XuvJvADYpkYoeeijW0ktuEC9FVrkZMZ5AuvQ+0bwSo2CGollN4hZu/Zh71QZH73JKfvniIxVBAMwvIisaFRGjNF6pOLnWv3bAfPFObH9enymefBD7mv2GRNUqfhBeyurBjYrMBCJCXIajKNhSaxyFsgvzZHdjzbYTDk1+bxnUBEIiY0MkMpEc1ousiKMDqWFbmTCGD5IU4QklIFRd8u22KintKwqy3BGsgbGKpMXBGb5/amOYyYBZYvmhRV1yceaf/7hpI0W4L10B5YKZLUYRLEcwZhEJJcsbHPDiTwbI/Je6aj38+hWbRIFOIEbtBhJ1plm9KxMpmo1tv1FtMPz+EEIS/e1MOejz+AWWvhNF3uPLDElrR+1nO57PjUHJ+pyRqm/+Rm/0BM4dtLTa7oibOv2iKvKfSsMFr0w7DjE9TFueNca3344591PG/Q9Tjogl1veBu7v/iZJ92eHlpHZmQjV1++inumqly/Lo8MgkUQeNFm3TubeeB7oKhRM0F4EkieIxINfOdMmoEki3SDdnKCHMkTlDNJDU/yMwjOZh60mwYdaUOzSujYICuEtomzMIk1PcXyoQkUTWX50CTFwyUGdvQz1ttDc2qeXP8owdGHkZMZlESC1swia6/fxrGPfYaBizeSv7iFoqvs/deHUD7zKKuuHKFv+wgvB+77jT/ns7vnAfjA62piQbH3BPO7p9j8VnjdB19Pc75EZvWQkCMsV9AsUzQKEmnKDz9MemyAQNVZ9arr0NZuJ7RN1r3z53CnjlKfbTB86RBhELD80KMUrn4BrVNH0DMJ4v05oenyAwLXI/QD8lvGac4vs7hvga9NiM3v+pT+PRcRnacVQXHsy8YIXOG43Le1l+LhEtnxLIEf0retgKyrkSwg1slITg0kcS0PWZHwTT966YQOUk9oqG5AsyVSEUpHlkVzomQhazKNhjA+9GyPwZ39FA8LTWEnX1mR0KOop2xauEFX6w6rXzjG0skKMUQX32mITOfqTJ2+TT20ak4nVjKWERTH5EAS3/GJ5+NYpSapoTSB63dMINupEn4Imd44392/xPqUzsmyTcMLhMQjeq2fDhXLpS+ZwKq12JUzOGU69OhnjD57dYWNKV1EVkYLC4AbhlIcqLYw/eCsRsPzCe2GQcD5YZbURRdd/OTC+uw7Ca2GSD06dD/W5GmKe08gKTLNxRrlk2WGLh5hbPCz3PS/X80r3vEwsqrjNMpUPv9uDklDpDSZw9ctU3jJxbx17wsIj3yExz/5IPH84wzuHCC/boCX+SHNX/pZHntghseAP3lTk+RQD8uHJlg+OseaVzq86vdfiV2pkxwsEPoBrajWy/k+5HSe8u1fIzZxjMSua9jw9legb9xJaDdZ/bbXYp04irloMnrpEKqhM3/HvQy98jrMwwdRDJ14fw6g43MQ+AGFbWtQDZ3T3z7KbYsmMrAmqXW8cp4OM5bHsKGiRrLD/NocMw/N0bdVSDly41nUhIGsSLh+iN0QXkmpvCEm/JqCbXkdn4Ka6ZLUZIJAbIT7VmWoztSJJ4Xxsp7S8OsOrukSBCGF8QyliRp+KGRtZrMtYxAxxP1JHc/1aXgBm7b2UV04M/CwKjbJTAyzZBHPGZ1UJ8EyjHWuv+SUSfTGMRdMkgNJMqPpSLoYoCU1WjUH1w9JZmJ851SZTekzPkNTlseJp/AyeqKH0aztcWE2xpTlsTGlc7zhnCU96BhPRw2V+5aFXHdjSqfq+iiSRNX98aQmddHFueC8GAx18ePFUzUNAOpzJ5h55Jt86s8+zJ9/cT9/cfeE8DhQdCEpaLMKIlND0QCQO0wDwgDZsZC8FoEex88OY/VvxsuNEqT6zsgTAg/Ja4kPtyXMDT3nDLOgLWtQ1KdsGogGhE3oucJbIPBB1SDwcWpNYrkUiaEChW2r2faOK1ETYkqc2XkR7ulDqH0j+KU55FQOc75EY6bI/ttOEbzzAxx4//vR0gl2/MJlbHnzRaRW9QMweMka1rx8c0cD15hZQk4kyK4fYcs7rqZVaSApMumxAbJb1pNZPYQxOkbtwXsImjVCu0l26yaUXB9KYRBt7XbQdELH5va3fYDAcdGTOoHrs++Td5EY7KF16gha7wB2qUroC3+D7/7zg7imjVHI4tsOoe/z+MEiYwmNQUPl+LN05r80b+A0hekhCHdaq2zTqrVQNIWFvUtUjs+TWZVm9YvXEE9qNMp253jfEa9L2xDRsz3CIKTl+JHpkEp+bRY1rnS8CrI9cXzXZ/zaVSKKyRfMAS1acCi60omOan/fNmdKZmL0bMgTzxk4pkN1po4WeTE4DRfXEkwIPaXhuz6lIyUArLIo3svHlrHKNoqudHwbpuYaTFkue0+UsXwxtdlfa3G66fJs8Pr1PeTiGssli8HhFA0vwPJDLD/gur4EG1M6ZTcQJlXRwuGaQhxDlvjOoknV9TlUd87rWKZnwvnWMAj94Jw+nmtvxHK5zI033kg2myWbzXLjjTdSqVSe9f1/7dd+DUmS+NCHPvScXWMXXZxvsL74HlHnvei9WVbwTJtYPkVqpI/e7eNsfvNl6OkEkm5w6VCSV73rRpxGGeuz78Q/dD99n/oftD7wbr7wp7eRfNeX+cv9f0ssl+aCn7+UtddvIbN6kDAI6L9oNWuv38Z1fUIm0JhZQkkkyKwZYs0rL8EzbZS4TnKoQGbDGKmRPozhYcoP3EvYrBOYNZLrVqP1DxN6LrGtlyHH4oQti2//0t8gKYLu7loeez9xF+mxARoH9hMfGaY5J+pW4Hh88u/upVVpEC9k8G0HPZPk4SOlDmttpXfR02FXzmDK8tCSGp4lpvmmGxBEnj7Lx8sUDy7QsyHPumtXkYipmE0XWRGJRoEfdJoGhYSGH4YEQYjli1QFgGQhLhgJTkBjsUkmqdFyfIZ3DVKZrFF1fQxVRk9oHeZBm8LvuX6U5iALk0ZFIr82h6HKqJpCuWJTdQNCX0gmQz/sDAl8x2fxQBEtrlKfbeC7PsXDJZpFC9VQO6lNpxeEl8GB+QZOELK7YjNlPbWU86lwVU+cHl1h1vZYV4jT8AICxHPSlifMWKIhsq9qo8sSG1M6MmJIY/khp5vueUPz/0nAOdf6czZSfnr8JNX6buOgi2eFfV//Iodna8yb3tkNgo6vgU8oSeKfL5IqSK4wOmwW1hPG0oSxlIg9VFSCWJIgkcfPDBIaaWg3I6CTrNA2XOw0GNpfw5mmQeAJeYPTigwQZSQjiSQrqENrhE5x4ypSqwZFUd+wjlWvuo5WcZnF79yFvvlikayg6kiahmLopMcGeGDZ4uj1L8cq26THBtDTSZJDBYxcWmgUo2n/zgv62JLWKWxbA1HCgZaMU9x7kvjaTaiZLHIyTe/1r+bwJ75MfFg0HiQjIXwLsgX8pRlQVYLyIqHv85J/eBelA6eiKbnQCE7fuYe5+w9Q2XeI7LoRAsejuVThaMNBSxpYS2Xqkwvc95F7WGp5pFSZpZb3rLXmfgiZyKCoVWvRqrVID6eoTQsDQy0y9Jl7dJ5T3zpJdlWaTK+g/nm2h2M6HaaB7/oo0ZQ9lTcI/ID0UJrUUJr0UBojus13hHvzxF1TuFHB9p2gQx1rmyXGMjFkRaZ3cw+ZoRST904zuLOfuUcXaNVaQu5gqEhRt9+zvY6xk9MQC57AF6kLqYFkR0bRu6lAPG+gJzU8y6fXUHGCkGXHZyyhPe3zlVQkXj2a4cJsrHPb3ZNVjldt+oaSNBabLDs+dhCy0PJJJjR6dWGGlNdkkoqQJNxdstiVMxhLaFSjyKaM+ty/Lf+o3vi1H6eEM4pjPDePg+d2Zff2t7+dPXv2cMstt3DLLbewZ88ebrzxxmd13y9/+cs8+OCDDA8PP6fX2EUX5xOaX34fUhCx21JZ5ERGNN8zCRGLvH6c3JZ1ZHdcwKr3vI/mob0s//Vv8a9vvYD6HR/EmzmBJCtIioyeSeCHIQvvHmBx3yzx/hxGLk1ysICajAt/oSDAdz1Gt/WxLqmR37hKRDv7AaqhM3v/IRLrNhDryaEWhuh92Ss48E9fIbVqkND3kdM5QruJnM4R1MtIioJXnAPgxR/6eSpHp0gNpTs1dvb+Q8w/dJjF+x8jt3EVgeNRObWE5Yd4lkdjpkjt9By3/t87WWr5DBrq98VS88OQC3riVJctHNOlfLJCPmdQnzU7dVeNq8zvWeTEXVMkeuNksqKx4Tu+kBKGQjLQjNYafihYgaqh0retl54NedLDScH40xTcSIo3v2cBWRaRjIqu4DRFBGPaUMlqQsYAYGRjZNM6k/dNM7hzgKWDRWwvQNZkMkmN3kg24LliveHZHoEbCF8GV8gtEr1xPEusRXo396AndfSUYBsMxUWtX2p59MWennitSaLZ0jY1BNhfEz4RAzGV+Yrd8ShygpCNKZ31KR0/hL6YwqChUnV9weqMKQwbKjUvQObHXB9/whAG51jvn+Opx09Sre82Drp4Vgg8ly//3T9y9Xv/DdksITlWJFmI/tvafgaBj+S7SIGPXVjLidg4moxIWrCqqL4tJAiKTpDII/mu8DEIA0JZEU2DwH9y+kK7GeG7yK4l/BJ8BxwbnIieJyvgufjlSBMY+BgjY8S2XAKyjL1QpHHsBM78NJIskxwq0Lj/WzhHH2P267ehjqyjdnoez27xlmvHuOmBGT717dMAnPjGYyw9dkyYD5YbKJqKXarhmi43/OIukXLgOuiFXpS+Edb/wus5ddOXQNWQVJ3q3bcTz8dR8v34pXm8mROc/NKdAKjDawgqReREGiVbQFu9heRgAUWXscqiU76wbxEtaeA2LZYPTyDrKrl1w/zWh96I73gYhSz/8Df3cMJ06YuJGKbvx2CvrauP5w0SvQlCP0TRFPSkhhqxAEpHSjimi11vYZVtXMujWbRExz8IcaLJvKKdoeavf+Um0sMp4Z0QbeD1lBa9XHLHG6FtTKNEBpyJXqFZDP2QgQv6kBSJ5WNlZE2kPFQmqgxfOkQQhNFkw+/4C7SZC6qhIikSzaLFyKUjqIZKsyiuIwxCZh6eozZdx7M9yhWbR5YtZiyPhZb/jNMb0w+5fbbO8YbDuoghUXR81qZ0Dk3UOu7WSUVi2fF5cL7B6abLjOXyeLXF0YbTWew9XrU5VHfYmTOw/B+NseD5wAwwfgSO0ufucfDcXduhQ4e45ZZb+PjHP86VV17JlVdeycc+9jG+9rWvceTIkae978zMDO9973u56aab0LSnb3J10cVPCpr/+X5k18LrWY2k6QS1ZYJGBYIAY2RMxCAHPtbcAubRI+DYZK97LbFcCu/zf4Z/x78xd8u3KO96A/XJBQqXXcw7/vjV/N67P8NNd5wmvnE7B266n4VHjxAvZLBLNVQjRnNumcpEld+46b04tSaB42L09aD2DrLh517J6c99DWSZwDJZ/u63iecN5FQOvzSPe/owRz5zh2hWGEm8+UnkeBI5mUEdWUdyUPgIVSaqAMw8NIueiSPrKrXTc4RBQG5NH7/1gRuI96ZIDPbwFx+6jynLI6vJHKo7ZxnyPRPcUJgS9wwmieeNTkyhossYeUPI/qJr0aKa7bs+jYZD0faEIaIioSkSiZhK1Q2w/IDVLxwjNZAUk34nIPBDEr0JtKSGtsKjQFZkspqMGTUNdFmiFSUlDW7uQTNUpiZrhJHxYvlkhZHLhkRcsxvg2h6u7eFHiQ56UuuwE13TZdULRklGPgd6SsN3fKajWu80XGqmy4MlUevLbtDxF3q656vNSBiOkiVqXkCPrnC66bK/1kKXJTRJmE8+sGwx2XQpOj6PV0WyRZtZsNDymbU9VsVVUqr8U8M4+FE0SM5nP6OftFrfbRx08YxoGwcBaEaKUIufkQ8gmgUEHpLvEsSzPOgPc48zgASsTvjInt1pAkiOhWxVz45hVHSRthAZLIJgHXRMDz23k5bQNlCUvBa4DqHniIaBrBA0RZIBgU9gmSIZwXPxF6dRsgVc06Z8bAo1k2VpzzFCP6A+uUAY+CSGCize/Hk+/G/7+dM/uYN//c4EHznx//i/X34/8fVbGLlqPfH+HPUpsYFfeOwUG/7gf3P5H76d+d3TVE7MIMUMWouLBGYNb3GGVS+/HG+5yNwt3yK19QIUQ0fSDR74b/+EdeoEG9/zC0JeAciJNKFjEzTruJNHieVTmAtNWjWHWCaG7wRMfOcEtVML+JZDc36ZhcdOYZdqGIUMJ2/Zx/qUjiLxrHSOT0TDC7CrLTFRMB0820XRFfJrczTmGgSu6PZLssTqa1adiU5URCRT20QpCM44JetJjeLBeUI/pLB1BIBEb4LUUFYYJQ4k6Fmfp257JPsTGNkYyf4kyYFEJCOQiWV0Zh6eE4aHptuZKmhxldp0jYYXsOqaNaSHUuhJHSvyc7DKNrGMHtEYQ+Z2z6GnNLSkhu8EHW1kcabOt48tU4wWLt/PAuxnNvSwPRNDkaTOguLrcw3Gkxq6LFF1A0w/7DRlFEnoHlOqTMMT7tdwxv16IKbQqys/kg31c412YXkq1YXWMbkSRqvPF9TrdWq1Wuej1fr+/89W4v777yebzXL55Zd3brviiivIZrPcd9993/N+QRBw44038ju/8zts27btnK6hiy6eD1j3wtdifu13+fNjMfxUL/LUXvx6mdAyCV0HyUgQ2E28hUmUbAHPdmjMLBHUSjz+u/+bwBGT+tBzSQ4WyNz5z/z1PzzEf/mZD/L7/+WLvODRu/nQyZtRsgU2v/lScuuGqU8uoCUMZu47yn3v/jvG7/suez98M/XJBZRMDmu+SGibtObnWfXSS3CrIrUpt+tiEVMcT3L3b3+cyuP72fZffoGgXobAR0qK5J2gXhEmzLZDqyoilI2c2MjPPDBF5fg8rmlTnSixtH+WVqUufA1uP9Qx5X221PonYmHZwrPExrstBcyvzWEumjRLFpIiUgYGdw5gl1uEfihYc1Gdazf867ZHXJHo601QOrZMs2jRs6GX0BdMwdRAEtd0iecNsiNpiraQSMRzBsmERjJi9qXyBooElYkaUmTCXTNdJFkiltEpHl6maHsMXzpEZiiFZogEJj8UtT5REOwC1/JYOriEaqiRgaJYo+oJjYVli9tOlaNaH35fzfOreuJc1SOGGflInrm7YndkIlXXxw3pmB0qEqxLaiSjbknvCp8jgIGYyprk+bEJ/FHh+cSusCyrW+ufBt3GQRfPCCUmilRqYLXomjtmJx2hTRkMUn0c1cdYVrJcPJjgigENza4KpkAg2ATiQA9kFbllColBhDbbIJQkQknqnFf8UCQytA0Z8TxC3xdFWFYIPUeYIkabcEnTRaNg5gTVg0eQ4kkW77oP17QobFtD/dQUPZvHOfrFh9CScebufgzPFNr3G4ZSDMTEte553+/xW6/7G6zjh6idnscu1fAdF9e0aVUdQtUgqFe4/G9/D69p05yaJwwCJFlBG12HZCRQcz0Utq3BnT1Fbt0Ij/7BP3D5H/0C8XUbaZ04QGibYkEhK0i6eJ7V4dUkdl2DpEjkxrO4pouiyzgNl6WDRWqTRconhaOyG113/wWDNLzgWescn4gpyzuzOfdD8mvzpIdTmIsmsYxOaigFCCaAkU9Qm653WAlGWtD1PdtD1RQ820PRFYy8QX1OTPQ90ybwQ3zHJ3DEz+N5g9p0nUxSwy6LpoVdsTt6REVTUHSF0A+FM7Mm/BEUTUHWFAI3oDcfxy7V0VNaJ/YpM5pGVmRimRiSIiErErImU52odXKpY5kYsYzOyJYCDS9gf63FSPz784q993SFuCJztOF0vC5ANA/iK1ImLF/IH5xA+B0MGyo9usKOscxZ57tzqcmheutJzYvn85v0Uy3O2lMW66m6Cj9EhEAQhuf0sXIIsXXr1o4+MZvN8sEPfvCcrm9+fp7+/v4n3d7f38/8/Pc24vzzP/9zVFXlN3/zN8/p8bvo4vmC665ZzYV/fZTf3SYjmyVhgGyZom7GDLy501QOHEVJ55m79Ts4NZPM6iEau+9n3asv48iXHiYMAiqHT+E2bYqP7uNtlw6xMSWat5f94/v4jTWvx6+WWHjkGM1F4bnjNm1c0+G9v/mXbLJP8viffIpWpYF5ahJZF/UiPrYaVA0tGSe7foTWxDFyG1bx3V//W675618hu2kNlUceFkOM8pIYaAQ+yDJqYYjCi14MiLrluz6yIiKLF/ctUp8u05hriPpfa6IYOj0b8sxY7jNOyr8X9lVtetM6Zk3U3NRQiux4Fqtsoyd1cuMZPMtHjQtpnxd5H5QbThSxHNJseZhugB8KXwM9qWFGJoauaSMpEqEfUjldRTVUUkMprLJNaoUML/TDjoRBViRUTRHrAzcgF9fIR02U0A+RFYmBTAxFVzpeBiASFFw/RE9pnWhlWZExF01cy+vIE4xsjLVbCph+yP5ai0Hj+6v1j1dtyq5gC6yUMT5YtpmPGhggBg81L8AJRAT1pmhtdFW0fmrjkYrNkfqz85/6SUC75j+XvYNzr/Vniv173/vebq1/Gjyf16Rd/IiwfHw3//6pP+bVN96AnkhQjRUAsdn3U73UcmsIJYkNUolCcwZleQKltoDkNpF8FwKPUDMItZhoAng2eDaSa0feBU5HmtCOVTzLy6D9ETUMQk+84YaeS2Cb4LmEthk1EwK8xRmc43vxy4s4dRNn4hi1UwtoyTjLhyaI5VL4rsfgrlW4pkXgevS++MU0F8tc/OvX8Pafv5APfOgNJPtT/Ok/vIUT//mg8BEoNpi++xjJoQKDu1bx8I3vZvrOx6h891skR0R8k55O4C0v4VdLqIUh1L4RtKFVBK6HtVxl1Qs3MX/HvdinjzN77z7syVMo+X6co4+JxZDrEJSX8MuLjL1kOwO7xsRU3/LFJliTKZ+soCV1VEMTXgfzyyzum+foszRCfCrM2h6SIqEnNTKjGWRNxWk4VCZqaEk98hIQEoaT3zrZ2ZQ7poNjCodi0w2EPjKaBpgLTWRFJtmfZOreU4R+gKxILB1cYuCCPmrTdQJfeBq0WQZGLnJzjhogVtlGS2oddoOii2aCVbI6JoqNBZPAD4lldLa+YRuliRqpoSR6SkNWZDH9UeSOB0Kz1KRVa+GaLpIs89otvSQViZlnOb3ZktbJazJb0rFI56hw26LJrpzBrpxBRpXZXbE7aQp2EOKGgqY4Y3nsr7UoOj5fOlg8yyMBeEp5yfkgK3ginml68HTXnFSkziTmuYVY5J7LR7hCq3Dw4EGq1Wrn4/d+7/ee8lH/8A//EEmSnvbjkUceAUB6ihzvMAyf8naARx99lL/927/lU5/61Pc8posuftLwa1euZt+vFZDLs0KeYNYgCPCXZnAOP0pQLeHZLRoH91E+sYCia8w/cADPtLFKNVZds57Q92kuVvB//S8J/YANr9nJq35uBx/40BsA+LOPvYODH/oUyaEczcUaE3ccJjnYw9Al43zTf4QD//1/8nOTnyM91t9pGni1Kn5tGbVvBKVvhPjoKpxak1alzpY3X8TMrd+ldnyC5QOnME+eQu0foXXwITFMcWyCehm/vMSa63cwfOkYQETzF+7/i/uWBNU/YqdZixWKh0tPcvj/fuCG4NoeqbxBciAJQGOuTnWihqLLNIsWRj5G6Iccv/UkerRRViQJJxD+BpYvfAnyKR3XD6nPiRjozGiauUfnkRVJNDtMh75tBWrTNQBB6Y+rtGotjHws8jsQ2vSW4wvzwkiCoOgyWlzFKttRcoJPY66O7wTE8warr1lFdaFJfjiFamhikKErnTrfTohqR0zKisQbt35/tb7NJByJa/ihqHuPV1v06kpHomj6IVnt7K2UG4q4xnai1Vcmqmd5JMD3x258vsKQJQxZQka89s9lxTrXWr/y5fj7v//7bq1/GnTjGLt4SqiRwWDgOSh6nHtPlvjrV29Gee0WHpszmanHSOlJXpRNEgSh8CqQZCRZBj8ySwwDkXjgO2e6eYoOblO8gbgWoRZH9p0zKQlRygJwJmaxHbkY+KJTHwSEtpg2rIQ7cVhM+ftGOP1vn6WwbQ2B47HvX+4AIDVSZfbhKbymzdf+3yF2rc0Rzxvs+oNfRskWSA4WqBybwq62mP3UPQR+wMi1FzL24guonJhh/BUXUzs9x/KhSbLrhrjwva/GmlsgsWYt05/4MsN/fCPu6cOo/SN485N4pTnKew7w+CcfZGjXIOnRPKmRPtx6k/KhCXIbVxEbGgVAG9tI0KyhZAtYx/cixQxUQ0c1dPSkjpty2fSGXTz+L/eTGkhSm64Ry8QobBnFqTf593unz/k1VzQFSZGY37MgpABzDfSERiwTwy7bmAuCxhjL6DSLFq4fEk+KSb8iCXqeXW+hagqyLOG7Pn1bh0RjYdollhEJCIO7Rjj+zaMY2RjxvGBZtOmS5qKJY7pkRtOoUUpCe7rQdk9uOygDZMcztGot7HJLeBUcL5LMxGjMmcQyMYzI/LB4pNRJftCTOiNXjDPzwATLx8t8fbpGVpM7+crPhEN1h15d4TvFJrtyRscVenfFJqlIrE/p9OoKiiQ9aSq00r8gQFAYL8kZPFKxsYOQpCI9L1IVnk6b2Y5k/F4w/RAt+nuRnsNfNeSppRLf7znaSKfTZDKZ73lsG+9973t561vf+rTHrF69mr1797KwsPCkny0tLTEwMPCU97v77rtZXFxkbGysc5vv+/z2b/82H/rQhzh9+vQzXl8XXTyfYH3xPVB9nNBzCR2b0HVwTx1A0g20sY3s/+BXWffqyygfmeXkNw+ipzSai2WKh0v4js+3v3qMHevyxAtxLvvHP2M6hFguzcIjxwj8kOPf/Baz1RY/9/pXsP61KnMPHGD8FRdTOTpFcf8kufWDXPrf3oDfqGNc8lJmP3kbO/7js/jf/Q/hWbQ0g7c0w9Qt9/PQl4+wfnsvfduH0JIGgetRn1wgt2EVidEhAPTVWwhdBzmdx544hqxrIiXJEbHGalxh/Q072P/vu0n2J6hO1LBKFutuGMScKfKZh+fO+TmN5ww82+PoQ7PEFSGdKyQ0VENDS3p4todZstATGsuRhj+TFJtzWsLnIPADArftTaTQf0FfZEwsohHrsw2GLh7h2NeOoiXF1F9WBHPS9gKUmhMxA7UOi8AxHWRZQjVUAj9E02SMbAzf9UlHrAXfEV4G5ZMVFEkMDmRNJjmQIJaJsXSwiCRL6CmxdhnYOcTMA1MsHVnmmwsmSUV61j5Cs7aHJokkhI0pvVP3io5P0fHJqMKvIasplN2z6exPfARFkrgwG+Px6rnR3p9PWNkc0WWJ8DlMmg44t3q/8vWKx+PdWv806DYOunhKeLbZ+dp3bD75d5/hC19Yh1MvY1eX6N14Kaqu4tot9ITBB37lUl43qosNvqIS+p5oBniukB54DqFmRNGN4s9ORC2GohGgxcAXsYzttAbpCWwDoGPGGD4himni3/6DmQcm0OIqkiyz/tW7OPmN3Si6jKyJCfXEd45RmayRX5vnlOny9jdcRG7zGkr33k9isMDuj97NptdvJd6XI9GbJLN6CLdpEwQBg5dtIQwC8htWoaeTpMcG8Bt1YrkUxQcfZWDXWpoP3YGSSBBmC0zfchdGQbzxrH/lBmK5FIHjEbjCyBCguPcEAFlZRk7n8UpzOEuLVI5O0f+Ci0lu3op97/2ocUHP2/Px+5Bkia8+MocfhlzREyczOs1nvnT4h/Ka+64P0VM6uHOA3JocrumixVUyo2lmHp5j4II+5vcI80ktYgAI34GYiF0aSmFE+svyqQrlk5VoumCgGDrmyTKhH0RmTAp22Wb8RWuRFJkjNx8kNZQicH1aNcGeqFVtMlkDSZciyqLckRtYJYvCpgKxTAxzcZ7seIbc2h4SvXGy64Zw602cWpPyyTKyIpMeT5PojdN30XoOf/4hEr0JHtgnfpfvx0hyIKZgRZvffVX7rGJl+iGPV1sd86MtaZ1DT0NJ/PZS86zvnw9Ng2fCymdyZSOkPZNRJIgrstCDPv9/3Seht7eX3t7eZzzuyiuvpFqt8tBDD3HZZZcB8OCDD1KtVrnqqque8j433ngjL33pS8+67RWveAU33ngjv/RLv3TuF99FF+cJLn7j27nnrWloWQS28CySVI1Df/kRFvctdtz/115/AQc+fVeHrt5YaBLsno6mzSLi9l1/JNzLl778GXKFDF/889u5+m070JIGid4kV122Bb9RwS7VGLxsCwA9W8bRM0my60ZwymUUTWX6nz/MwK5xvNv/Raxtkhmmbr2PWC6FYuhc9rpNxPtz2KUagR+QHOwBYPqew7QqdfpVDSWdx509RavSoFVpkN0wRv6iHXj2w8QywifgwGceQzVUPr97nrgisStnkHz4NP/xrVM/lOfWsz1CPyQX1xi9YriTQBTL6EiKxNLBIgPb+ygdWUaPUhBUQ6W6bJFMaOi2F3kYJPCdgNp0ndKREoEfosaVzmNUTi5j5AWrzq62GLlsWEgx7pxANdSOvBBgvmIzXEh0fAlkRaJVc9DiKr4TkOwXZo6T906TGU2TGkhi5I3IUyHAXKzTqjroSY1kv/jZ4CXrOfrl3ST7kzxybBmZZ19jZSCuiN/d9YKnZHTWPBFNOWN5z9j0f7axzs9XaNJTDxQ0SWzonSD8iSv3P621vts46OJZwa4sYFfOdMxmHvlm5+v00Dr+y58tcPP1u/iLV29h0JmPNv/tdARxnORYwsdAM4RnQdQgCFUd2bFEgyBKWCAMBIMBBJuh7XkQ+MJQMPCRjCSBWSNsWez93D7iSQ0trrLlzbtw6iaZ0SzLx0oMX7EGa7HC0sElnCCkWWzylkuH0NNJlh49xMR3jmEuNtn1nmtwmxbxnix9l25DTmQwjx9HSxro4xvwS/NUDp0g9H0qRyeF10GlwaobXoiSLeCX5oXXgiwz+/AUa6/fhjlTpFkyqZxcFlPyySKO6ZIeSpPfuIrQD5i4+Vb6L95EYtM2ksNrMEbGOPSxL2GXbdS4yvKxMkY+hmqo3HmyjOUHDBoq1/7uy/j4//o6cUVmY0rnwbJ9Tq9xOwrRrLU4/d1JejcXaBabOKZD6bjwVCgdK5MZTdO3tY+DXzuG6vi0HJ+YH5LqT9C3tZeFfYv0be2jWWqi6DLrb9jKiVsOISnCtRkgNZBENVRKx8ukRvrw7JaYMrg+gR8iazKViSr9q7PC9TYIscq2mC4oMvm1OVRD5dR3Jtj8mk2dxITlY0VatRZT98+w+fXbKR4pRn82AaVjy5iLMWrTNb67bwldlr7vYp7XZBZaPhtT+tNKQ6YsD0OWeEEh/rSNg590rFxItdUJfijMOOOK9NzKMMIzaSE/8Cmew5XOli1buP766/nVX/1V/umf/gmAd73rXbzqVa9i06ZNneM2b97MBz/4QV7/+tdTKBQoFApnnUfTNAYHB8+6TxddPN9xz9uywgDZsZETGWF+XC3xwK0n8cOQ8ZzBhhs2IckymVVp5vcsMP6idSwfWaR8soLv+piLJm/ZOUBzYoLGzBIHPvMYtuVx/X+9Ds9uYeTSDFxzGUq+n+V77xYMv1VrCaolqkdO4Tsuy4dO05xfxrMd1rz2RSiFQULLxJ6ZRB0xmH5gio2vuxBzXmz627T8xlwNp+GSWZVm9OrNyLrK9C1307dzA7HBQVLDa4hXSxz6l693/AXKJyukh5O4lsdtp8ooEqRUmZf81kv48z/4Bk4Qdhhq5wKz6aJIEgstj8Vvn2bzhQPUpuu0ai0mT4tEBf9gkURvnHw+y/zh5Y58UE9qSLLEwAV9LB0skl+boz7XQDVURl+wmpO3Het4HInjdWKZGNN7Fkj0Z1A0FUWfFkMdRTR7ShM1Vo9n8R0fBRnHdPEi+WQ8byDJElN7F7nwDVujgUOLwBXsw/LJCqtfOEZtqh7JJ11qB4tkBxJUJx7l1n2LpFT5+zaSVCRRv4YN5WkZCu2hw5Z07Jxfl+czvhcL0Q35vho2PyjakoMfFMFzWOx/0mp91+Ogi3NGfe4EeiLLFz/0US7/jZv40BEI9QSh+gTX2HZKgmt3Ihg7zYKV8oRIsiC1v27f3XOjBAUZVE2YCnou3/nlD5KMjHNUQ0VNxjn0+d3EcilatRZOrYmkyNTnGqRSeieCKLluNemxAS5810vY9Z5raVXq9F56Icl1q6kcPEHoOSTXrqFVblB55GFKjx/GnCtRPjpD5cQsrmnjmi1Cx8ZbmiF0bLRVGyEIuOR3XoddqmEuNqhN1SkdW2by3mkqE1WaxSYL+xY48fW9nPzGHhRDJzY4SPPIAe54+x/i15YZe8kO1t6wEz2pkeiNU5qocWdkiJjVFKYsj/2fvo+ffctWLh1KddyOf1C0NeeBHxBbEc/Ujk7UDJVkIS50hgvm/8/emYfJUZZ7+661q9eZnn3NZN9XkkBIWGXfBQVERDm4oqJ4BAXFz+MRREXF5YgrqAiCgCICIsgSwUDIRkL2ZTKT2bee3reqrqrvj+ruTEICCVlIoO/rmmump6urq6u763nf532e34/tz7WhSEJRXXnqFfNxBVx0vtKNnjDoXtZD7YwaamY1MrAm78W8thd/UxBvfTmxrjjD28L4ajyE1rez4YFVjDl9bH7Vw3kvfbVeEr3JYsJAEAUn4eCWkFTREY8cFUDxaky8cBqVE4KoPoWqyZXUzqxGkEQkRWLUKROdvlHDoqczRmRHjHFeZb+8r72SwESfWvRr3hc9iYxl8+JQ6i23O1QciRd3SRDyK1gUWzwOJaZ9YD+HeoXk/vvvZ8aMGZx55pmceeaZzJw5kz/+8Y+7bLN582ai0eghPpISJY4c/vvWG/jiayq25kNQNaxYCCsa4u+XfYd6t0yTR0H1KgQnNrPhwWV46ypIDaVJDcTQghpD4TSCKKB4VVwBlbJTz0XWVI6/+VwW3Xg6ejxJ5ZxpeMeMIrxmPYgigYljyUbihJetYHDFOqKt3Qy+voPwlh6Egm1wziA32E2qfTue6fMQVI1jv3wu6cEIsc44id4EgxuG6FrdT7wnQTaWpXtZL5seXsGmh5aR6I0iqTLpzi6eu+aH6KEhmk6exoT3zy2K+y1d1c/TeTtEZ3JvsuHBZXzyymlcMKa8qJ1zIOiWTSJnkTZtagMuZLeMt9aDaZiUKSINdd68roBFeHsU03Y0CHTLZvx5U/FUuela2oOpW3Qt76Vxfj0NxzbRu7yT6qlV9KzsxV/vp3xMJelwhnhvnGCDj2wkwaZH1zH+nAlIioS3xosoibi9CqmhNNmYTjLkvHeeKjeugFq0aK5qdNwoJl88DX+DF1dAJdAUcBYRPE6SonFBC6pXIWqYdHTFifcmaHQrDO6HJoSIE+8LE+F9FaF8LycN9sbIKsNDjXWAsf5Qa0m9m2J9qeKgxEHh658+Hk1exH//z4P88R+b+fsyD+fNa+LLk5SiQGIBwXIqDZBVRyhRELElpahzUEgYCKaBbZpYBTFE3bkw24ZerDx47X9/xahFzfSt7iMdzpDK5PA1VjPpA7PpW+GU9W1/ZhuWaVE3u4bqmS0ku4dQfSqhlWuJtvZSO3ci8Y5+/KNqkfxBnrzie4w+voHgzClI1Y24gp3EO/oRJBFvfSVaZQAzo+Otq0T2amT6h9BjKbTKAKF//pOq+bMQNS9aZYAdi7cXHQQkVSqWBAJ4az30bRrG1C1kTcVdE2Tmfx1PvL2H8KYdeT9kk46tw6yLZYuZ7UsmVRZLBOM9iaJg4KyU8bb7544p19jSm2DGpAomvv8Y+lZsw863hWRjWVSvgmXauIMapmGSGkqTNCyMWJYxi5pY/duXmf2JhWRCUfzNtay//xVM3aRvVRflLWX4GqvoeqWH4NggldPGYGZ0hreGiHXFGdoUwlvrASDQ7Mdb46d3ZQ9AcfCQGkqjJw0EScBT6cFI54oDj/C2AXz1ASqnNOBrrEZUZSJbOhne4vSCdr20FTWvxVBd5WFLb2K/+hwDskijW6Y778dcpUoM6SbNbvktVzHeSZ/mI1FUUbdspBGli+91KioquO+++950G/stVkJKugYl3m0cPzrI+eJWrOGkI4KcTvLsVd9n6tljaX+hg6xukhxIETjuBCYMhtnx3GZkTWbz8+0kchYzFzVRMbHOaQeIZej98/2Et/Sg+r0Mr2+jfEIzor+cxz98B+MWNhKcNQ25fjSuji5i7b2Iioy7phx3TTmWnsMV9CNpKonOPrKROL7GavqeeJyqOVORK6pxlfsY3rYFV0DFU+VGThgMd8WRBOc6p4oCiZxFVThLcFwbWmUZ0z8yl1TfMOEtnQxtCiFKIp1DKbrTRrE3/MKWMgJNfqeSoTOOZVi4vQrjvMrbdlCaHnDRmc4xb3QZp112DN0vbwMo+tnLioSeNFDz1sU5wyRt2rglmHJCE2vvW8X0K2aT7AtTOW00m/+yEkES6V/di7fWQ1lLJQNrB7EtC19jNc3HZwhtHS7GendQw0hm8FR50IIamUgGSZWKYsapoTTDsSyjar2Os0U+xvrqfUS2D+efwxlTyJpKNpJgaEMfslum86U2BEmgSpMRJZHVoRTVLnm/hAglYafjT0GvJyCL+zxeKPFG3slx0JHCuynWH4mLUiWOABTPWwuDjGRjbwyXJHLzjZcwc3ottmWzpS+O5a3c6ZAwAmHkFyTvqODYLeZ2tiqYTpZYECVHz0DM7ycvkAjkLfYM6mbXAVAxPkho/XbUgJeqKfVkYzqW6ajw+huDKF43ppFD8XvIRhIoXhdKwIN/VC3+UbVEVq3E7VWonNKIXNuMFQ2h1dUhSCK5ZIZcJotl5MhldOKd/cTae8lldPR4EkmVyYScMkVECSOeKq4imHlLo0L/Xi6Tc3r/3U6fXy6jo8eSWIZBtLWbTDRLaiiF6lMY0s3iSvdoj4Kv1gm4q9YPkss4fYmiJLzBK3h/KJTWq16VZF8I27KQNWcVIJc2i0kPb62zQmCYTlmYbtkYScOxVkqmcZX7SHQP4q31kg5nsE0bX2MlitfxQM7GHM0I5xzmigJGgSbn85YaShPdEUHOqynrCaMoiOQMZHYei2mYxR5J27Rwlfux8+fX21iNp9JbrJiwLRtZk9nYE6cjZbAutvcEy+6fVlUU6EgZxTK4goji/qxiHEm8Uxd9Vdy57FAQ0zyUOOKIB+qqUKJEicPN2aO0nULIeetly3RU8seePhrTtqkbFaDnr4/iqQ5SP7+ZRL4KrK5cw99UjhrwkMsYeGoCxNr7UNwySsBDcHIL3voKwstWIAkCgVGVKKOnkOvvwN1Qg6Sp6PEUtmlh6Tn0eIpkb4hoazcAlp5DEEXSAxFHsNEy0WMpFK/iaP6YlqNLlI/HZl5h3y05FXyZ4RiZUBRZcxHv6Ccbc4R9taBGTyZXLOlWBPBUulG9Ki+2RUiFUniq3ChumWa3sucTtw8MZnOoeQHCSKuToC84IBQm6aZuFqsORXGnm0JqKI1l2eQyOr7GSmLtvWhBLe92YFI+phrF7+gUJPodnSzLtMilc6heBdWr4q31IikyyYEk0XxlRTqSIRvT0RPO4kBV0F0URS7E+uyImO2udJyfchkdd7VjHa16VQRRKLortMWyuzgb7IndY6HIrpPcQqqglDTYP7SCs0X+tsihH3ccTFeFEm9OKXFQYo8YKWcC7K8fx98euJ1V/7iTeZddudft/+9/7uTOJzYyJuihazBJzjA5YXwVETReNWowyxqwZddO4cNcBiGbRMgZxSRAMWlQ/O1YLxaCs53NOEmDfLvCK1/6CWPPHE/Pyj7GfOxyKidUMP3qU2l7ZiMb7l/KhodeB0Bxy1RNrkSQRBLdg6h+D2WjHfG8wOh6Yu29VJx4Mh3PLCPZPYgoiVTNnoRYVkmydRuCqiFrLtzVQSLb+tj25HrMjI5WWYar3I+7MoCsqcQ7+nGV+9j+0DNkujoIbxsoTnwlRSLSESOXNglHMkTSBomBVF492ELxaiheNwNrdjC4YYDeVX0s2TjEH//RymDWZH7QTbNbpiNlYOVXBo4/vglBFBl9mtPj7/Uob2mTtzcKPefeWi+5ZAZXwMVDj27GSOfw1nro7IghKRKpoZTzv4CLhuYA088cQy5jkDNMQhu7ESQJye0oJLsCLnKZHIrXKSP0N3iR3TKDr20j3pOgaeEYpxzSMB0BpIxOoCmApIq4Ai6qp1ahBV1kYzrBseUAeKo8RcHF1FAaI6mTDmcIjKknsrWTSGs3O/61msHXttGxpBNTt1izZgB3pRsjaTCtOcD8oLbHfrtCu4YqCkXLQEWAqGHik8U3PGZfVjHE3X4fLt7sc7AvQ6C3+zl6Mworb4WB2eEQgjzgVoXSYKJEicPKN39wE3LXWuxU3In3wE/P/xZTPjid9ld6GPfJj1I/sYITH/k5Gx5aw5Lb/8lrD76OaTvX7ubjG7H0HInuIQRJIDCmHllTKZ/QTGRLJ8HjF9L+9EoS3Y7mUd2xU0AUSXd2IagaoiLjKvcxuK6bTY+uA8BbX4m7uhzV70HSVKKt3ShejU33PUeytZ1IW4hEOEM6nEGURKL9Tmwf1s3ipFMSHHthSVORvRodizfSu7KH9ld6eHZTiLufaqUnk6PZLROQRQzbSXhLqsh5C5vw1ngZ/b4JTgWjJLztmDKsm0gC+Bt86AkD2S1x/xPbyKVNR3wwnnUsj8MZYkNpZE2m1i0z6cRmcukcPZkckbYQlpFDVBxdIsWroicNREVGUhXKmv24Ai76Vmwj2Z+ieloVguQ4LbmDGulwmvIx5bgCLlwBldrp1ahehUw8S3lLANktIbsd3SI9YZCN6YiS4DxuXAN6LEWye4iBNZ30rdhGx38czYQ1W4cJNAUwdZMWr8IxeaHm3SlMbHdPXls4sa+UJjgwMpa9y+fT4tCf01KsP3yUWhVKvCnx3lY+9pV7+dS15zN9bAXPvfxz1vSnOeniG96w7cpH/sTlj8CN37mRybU+opkcH/jlq8ydWEXguBYmeisRU2HHqtHUEawcNux0TxiBYNtYlokgSk7JfCG5oGqOTWQyxtJNIaZ0x5n6gWks/9rPaT5xPGvvfr5o4deb0GlURARTQE/qKF6dwQ0DNMwfhae+EmF9G7lMlspZk3j6g//D65EMc2q8LLzlfOT60VjhQac8cfMWyiaM4vkv3c+0y2fiqy8vrqxrlQHUgAc9lmJ4Sx8VE+sIb4+gVbZRNb0JxasS64ph6iZKXkVYFQV0yymHk5MGoNCxeCuWaVEzrRrLMHnklW50y8YnOzaBQ8NpArLIqdUecukcVsAmOL6K8LYhMqEYnioPtmVzeibHU/3JN7w3b8WQbrImmqVhc4jqqVVkY1kq8uWD2ViWKr9arJ5Q8ysrif4kLWfOQ4+nUP6xiu5lvXQu7WHetYsc+8YBxxJxy9/WYJkWgaaAI/ioOf2UvSt2oAU1R625M4Y7qKEFPXhqAsS7HD0HX60XKb9y4wq46FvtCHRKqkagyY9t2rgDCl0vbcZb62HbU1uwDIvmRU0obhlPlZu0adP5+gCqKPD4hqG9noMyRaJMcVZkJCGvppyPJm/XN9va7ffh4kDsEt/q8QdCIdlyOMwjChUHB7aP0miiRInDyY0zNewBE2QFQVax42H6Mjle/dMaTrvxdBZfczvzv3QmL17yWYxMDtMmP+FWkBWJbCyLrCn0LO+mYX4jqt9DLqNjpNJUz53C3z/4bdZEs0wPuDj3+x9AaRiDFR50lPlb26mYMpoHPnsfp1w1m+B4pw0hvKUTb10FejxFNpIgvG2Qykm1RHZE8a5vo2pKPWUtQbqXdmKkc7hUCT2p45Yce2rIT1DSOdqe3Q5A47H1ZGNZnhlw4nVBlb7Q/laY9OoJg6qptQyu6yU1GMEVUPHqbk63KT52fzBsx1J4/OoBmhY0kAxlcEsisttJFtS6ZCRFQpREfHk74+GuOC1nzMZIZpD+soKeVX30rOpj5pVznBbRIcemufWfG50WwlpvscVRUkUG1w/hr/cjqY7osb/ehxb0YJXlSIfT2KZNWUsZroALWVNQvRbRHTGyuok34KJsbAAjaaB6Vbb/cz2BJj/tL3UiigJNCxqLY5NEzqJ/7QCZnPUGx6KRqKKAWxKI5ts/RzoClMrqD5x9GWMcbKwDFUcsxfp9plRxUOItiXZu5I6v3cHqzYO8/55VPPBaFzd+58ZdtlF9QQB8taO542t38OzGAT45xcP5xzazvjtGpVti2bCAWd7oiCaKsuOaYOV2bVGwHBtH2ywkDcziqgOAlU46TgqZJLNqvYiiwJjPXkvVpCoS3Y6ivqSKWIZFrVumrNlPy0kt1M0bz44XdzDqlIlImorg8dN06hzW3beC3378bkRJ5KM3nMIx156EGgyS3rIOMxoi1TfM+vtfYWj1ZqZ8cDq5jE5ocz/ZSAJ/cy1mWue26x5iaGMvLafPYnBdD4pXIROKo/q9eGoCJPtTpIbSmLpJKGWgWzZp06I1qdMRzzIcSvPsa338e+0gqx7fyutLuihTRBZVukkXrImAiT6VYLWnGCRty8LUTTJhJ0CauonqUVhY4d7n97ZwAWh2y0VvZle5l1hXnAl+taiI/NpAko7+JJ4qD8N50SHFLSP5/LgbG4h1xZA1GdWjoPq9JAdSqF6VWFe86IQAYJk2lmkT70mgeJ3KhEhbBCPpVGYAmBmd4Phaama1MOrUGQSa8isQmkztjBoyOcvxmPYp2JZF1fQmvLUedrzYiTuosSGcZvW/2hBEgVhXjPnTqwkb1i4JlYY9iEn2ZJzVFNN2VsPfjeWJb/eC/3ZKDTVx1+WcgroylFZ0SpQosWcynmoEUXLifyaJrWdYUOGmPWXQe+W3qZtdy+aHX3GcCDyOEJ43P8kdd+ZYguNraH1mO6PfNwHFq+GqqWHUaXNZ/ItX+PF/3U3atPn0V0/lpC+ejKjIxFa/Rm6wm3Qoyor/e4Gh17dx4qVTMeIphjb0keobpnxcA0Yyw61feYyhDX2MPfcYepZ3IEoi6XAa2auRHnI0iWzTJpI2GMrkihOZ1qRBe8r5ea49wjNtYZb+o5XnXx9AEWCcV9llwlqlSjT6VEfLR5UwMzqmbpEpxEjdQnPLzNvLivqbEZCdq7AroOIKaEQ748wqc5EJZxElkaXDabYPJtHKNaLDabZsj6BbNq6qCnyN1Qxuj2DaICuSs2iScKogQztizjjHMBElgVw+AWKkHetGUzeJdcWLiwrFtsIaPzVzRjPmrNkoXgXZ7bR7lLUEiBpOhWZh/FA/v4VAk5+upT14K92sGE7z+FOtuAKOI8W8xgDro9ldkgbN7jfG+ljOImxYxThUShYcGLuPDUrx/d1NqeKgxD6z+tEHcVc28NE7PgPAK3//Icdf+GUCjROJ9zoCO4n+dgAe+MHPefyPo+l9+Dq+NKecfhN++Nw2PrJgFMc11lHjziImBrEB0UjvrDgQZSfvJ0oIlgmC4FQfpJMIsoKdMxBUDbwBFnz1HMKbdpDrbce2LHpX9gHQn58gVld5cFd6sIwcQ+t2MPrUMeSSGUw9xy8/8nOihsklF0zg9JuuQ5AVoi/9C3dzE//69K8Z1k2ueuGnPHbljznxvPFFIcTwxh2OgJBXY/Vvl7CqL8GZY4JIqsiWvy5DVCQSvQkAGk8KsPbepZiGiSAKTolhziJqWIQNkzJFoiNl0IFB2LDQRAGfLLIloaMIzkR2asDFYNZRM3ZLAorXERASJYGhDf0Y6Z2WRYGmAP1rB2hN7rv9X+ECn85rFnR3J1BXdeOt8aInDAa3RwhUual1yYQNk8UvdjhtDQmRsohI8nO/Y/SJzaxrj6KKAlPGBdn81+V4qtwk+pMEx5RjGk7ix0jqyJqCO+jGMkyMtOMlHetN4KvxIKkiXUs78VR6iHW1I0gC6UiGSedNcDQMylRinXECZRrr+xKwtIepH5hGqm+Y4a1OhcKSjUN0pp2qjupwhlQozdbhNA2azDm1XtbFsnSmc1z6oan89c8b9ihu6HsXCyG93Vf1dh63p1aOQimoJAj7JVj1djnQyobSeLJEicNL1QmfA2DUgvMRZZHwjl76X76Hvgf/gORXiAY9tC3egaLJbMm71jS7FSonBAlvD+OpyjD61FEYKWeS/Z0P/R9Rw+Tq88bzoa87+x7+9/P4xzbx0Kd/R9K0+dTT3+W3V/wfl102BdXvxddYTbS1G2NTCFe5j8U/e4mNcZ3Ta5wKuA33v4xlWugJg9DmEA0LJ9O1tMfRMcq3ZKVNm850DkmAMkWkLR+XCwnU9pRR1MtpSxpUqRJp0yJp2lSoElrQhW3amLrJ0OYh9KTutF40BRAVic7XB9gY338x5EJs296TQFrVi6/Wi+KW6elLUiUJNLpl0qbN869205nOUeuS6BMEnv/iH2mYW8eqSAafLDKtzMWKXyxBt2w0WaRmQhAjaWCkcxjpHLJbRnbLSEmJTDiDntQZ7E0S8CooXoXNT26lcnyQ0LYwiiazI5JhwftGO60PeY2jhkoPK7pjuNf0M/GccaQHI4S3RwB4qTVMf9ZktEch0hHLn2+D0R6FiT6V1qROa9Lgggsn8vjft+wx1nsl4bC0zL3bebPxwchqjkNJoeXg7VLSONh3SomDEvtFOtTDuRMq0E2bb/xzC6d/6hO4VYnu3rm89uifi0r8AIqnjOe6dU6rE6kTEjz44Wn0ZUARBXKKB0ULgKljSTJCNrmrLaMgFi0bbQCtDFtxYWllSPEBAOwPf4Pmbf/GzmYoG9eI7NHY+uRmXgqluXRGNZlw1ilXr6souh5IqsLTP3qBWpfEWRdPZNKXv4DRsQUrFaf3lQ0s/vrf+fifrkf0+sn1tDHK7yK8PUK8ZwXVM+qRFJnaY0ax7r6VbA9nmJnP+KeG0lROqGBoU4jmRaPwj6pl+Z3PMPHCaWz5+3r89T5SoRSvD6aodUk0aDIvhdIEFZFEzkLJ+zUXbP7mB91UehQ64lnm1TrWQ9H+FMGxQZL9SURFompqLfGuCN4aX/Gci5KIae9bqZgm7py8DekmigCtSZ3q/iSyJiOpIs1z6+hb3U9jUGPdjigZyyYgi0SNHO0pmzXRLMpfNhXdBvrWD5I2bapdErUuGX3jENWTKpxjUxwbxXQ4TaI/yepNIQazJuO8CmrC0Xx4YTBFxrKpdUlMD7jwuGRW/30LY4+pw9RNJl82n1h7LzOba1n1i5fQ4yn+8MAGTqz2sD6aZaJPpTOdY2NcZ2Nc5/LZtUzzKjySb1G4sKWMzh1Rfvb71/d6ft6tSYMjBcOm2AJyqDngxEFpMFGixDtCx9Inin9rl24EoP96AXd1OeNOG82WZ9vZGNe5eHwF2weTtAANC8aQ6hvG11yDmdF57JdLGeVRWHDWJKZ8+TPo29djxBP0vrqFu7/1FF964DokfxBzsJtjq72Et0cY2hSifm4jAKNOmcCyu5fRnzWZVeZC9SikhlKUt5TRvbyXMae24G2sYtVd/2bOpxex8q6XipPhzrRBnSZj2rAult0l3sJOkd0GTWZWmYun+pOcU+sFHD2Y4NhyYp1xVJ+C4vUS64pRNamKXMZwXJoO0M52VSRDba9ExZhyLNNm8rx6ulb306DJvDCYwsKJ6YU2vZ7eBDyRXyDKWQznRZtHeRSqVInYphCNLWWIklBMeMQ642QiGV7aFGJYN5kacOHO5NjRGuHl4TT0J6l1SUz0qQRkkeUv7GDqtCpM3WTsGZOIdw9zxfmTWf/n19ETOo89sY3pARcb41mm+F30Z1PFSo7LZ9cSTBg8um0YgAuaArQmDe7684a9noNS0uDQc7iqOSwOMHFw0I7k3U+pVaHEftMZMzj7+y/y++/+jGd//Vu2bhniilPHoQUdZwNZc4JfuG0N33xwDTHRgy0ICDmdelnHLQv8z3OtIIgIOR3ByDpJg7z7gi27sGUXlurGlhRsUcIWBGxJRUyHiy0N/mQP1thjsfUMlScsomd5J9vzpXyDvUk8VW5n1XogQvm4RtyVZdx7+7OMn1TJhLn1jL/6MmIvP4eVcSoU+tcOcM3vP0O6ox1B1XjxS79m3JljAchEMvQu70RUZSw9x+LuOLUuKa8wnMNf7+OxhzZSPbWawQ0DbP37agB6Xm2j+fhGGhaMoa0zzjivQtiwWBPN4pUETNu5sKqiUFSd90qOz30kbTDK7wKchECgyk02lnVeUyiFpedIDaVJh9P0re4rOgwM6yZ1eyjF352Rg5iCGFPUsBiMZln9Wh/9/UnWvNLNUCZHeUtZ0T86lrP44PGNxccWAsOQbtKfdcSgWpMGSdNpD7j3xU4GNw8jSiKvL+li2386iXTE2BjXGdJNXg1neKwtwlP9yeIx9WdNnhtM8XhXjMVDKVYv6yEby2JmsmiVAQRJ5LgbzkBSZGI5iyd7E04Z6G59ja3tUccrOn97cXecoCKWgsRb8G4IDLZdclUoUeLdxJV/XM1jZ3+dwQ1DLA+nAegJpYqOQqm+YconNKMGvPzsRy8xc1yQ2cc2MOmzVxF79SUQRSwjR9fSbr7466uIbdoGosjDV/+c5kVNmLrj1NOzrAvFo2FmdJaEHH2htGmTSOi4g27++vhW6o+po+M/XWz882osw2LjQytoPLae2lkNxZXwvkyuWGlQEOIrLBKAc511SwIb43qx7UAUBYLVHmKd8aKWEEAmnCHWFWNoU6gY65Omvd9uSiOv7YNZkxfX9DMcSrPy1W56MjnqxgVx5w92SDe5Yn79G/aRsZxWPguncqIzbfBSKM2Dq/rY2h13FklW9rF97QCt7VF6Mjkyls2qSIan+pNO0iBPf9bkpVCal0JpVkUyrF476AgfpzJoQQ9aZRmzP7EAQRLpyeR4ZiBJZzrH4qFdY/2mtgj+Bm/x9r964ntsSSzx7uTAXRVK0X5fEey3Mo4ssUeampro7u4GxYMy7fJ3+nDeEU79xMd56GNzkEUBydLJiSoCcMnvVrH88eeKbQsAK5+8k0muJLakgG2RUvys6ElwcmUOIeM4OAi27egf5J0XbFHGdvmLbQyG6sMV73USDaYOkopgpLA8QcxV/0Isq+SO02+iM53jwpYytg6nmT+nlppZjShejYd+toREzuIjH59L/fHTECQJpXkiz330O4w6oYnmU+cgiCJKTQMPfeTHnH/rhfS+sp7B9UO4Ai6aTpxArL2Pvzy8iWa3jFsSnfYBTWb9UIqJPhVwev/kvBCiFnQ5PYCGRShlIAmwPJyhTHHCd3/WLA4kCmKI/VmTKlViVpmrqGmQS5vUz61zJs+6SXBsBamhpONGoJtkYzrZWJZnX+ujTJHoyw8s9mX1PKiIhHcTCRrtUWhP7fSJPq/ex5P5Foy9EZCd6olDNSmvUiVUUWBOUGPUoiYkVaJ6zngiWzr598Mb9mq7NPK1XNhSxt/zFlAl3oiWt946mO/hyFLF3TUO7qOLJCaNjY10dXUdlOerrq5maGiIKkHhm/4xB7Svu1M9rM45n/vOzk6ampoOxiGWOIooxfoji/id72P23/yc+pMvYuGICHanDRY2lzHqhCYUv4c//no5pg0f+cgMGk6YgeTxoDSO4+H3f5OpJ42i6YSpiKrjnvDra+/nym+eQ9+rm4nsiCJKIs0njiPaNsBfH99KtUtGFQUCsogkONUDdZqMN993nzQtgopEwKuQTBkkck67gSoKbI5ni5UBhVhcaNVyS0KxRXGUR2GCX0XWZGzLxlPpRs735pe3lBHvSaAFNTLhDJLqCED+Y2UvbklkMOvo8uzLNXtPpfkTfWqx0hHgxEo3L4XSuz90F3avnjjYNLudSo1ql8Rxp4wilzapmlqHHkvx7CMb92qn3KDJ9OTHPmfWeN+WeOR7hXdCwPBgx/srr7ySP/3pTwB80zeaKlF92/taokd4MONUM999991cc801B3x871be0YWl0aNHIwjCG34+97nP7fUxP//5z5kyZQput5tJkyZx7733vmGbH//4x0yaNAm3201zczNf+tKXyGR2Tir+53/+5w3PWVdXd0he47uVaedeyg8unk7GtMlZNpGcjGHZiALcf9Vs/n3PF4vbTjrjEj7845f41rIogmUiWCYeM83ceh+bsl5sRcNWPViav9ieUEDMRBGzcQQ9iWw5wc0WBCe5kG9rEPQ0gjeAnUlxwz+/zZk1Xl7tSzChwo3iVXn5/jWsu28Vg1mTqz41n8CYepRRE7FNk2++7yZe6Iwx+pwFiIqMNnUu6+96mFOuPR5BFAlObKZiQgWNi8bR/fI2/vDABib6VGoDLqdXT5N5fTDFOK9zwRJFAUESsEyLeCbHYG8Sd1BjIKnjlgReCqWpdkm4JSdR0OyWGe9TqXY5fYXDusmJlW4WNfoJlDmOA/56P43HOll/1avgq/UyvDXkiCbpJnrSIBvLsmbDEKM9Ct1p4w02Q3ujIIpYoDDBG5k0AHj1LQYSQHEF4lAxpJv0ZHI82ZvgF49sontZL+vufZk7f7Nql6TB6N1sKUe+llLS4M3JHOSkAexaPng4bJl2f+4Dsmg6jMf6bqYU60scLPxfep5119VR9c9/cky5xqpIhlllGv4GL0sf28JrD77OYNbko9fMwdtYhdrYApbJV07+KkuH04w+6xhkr4Z34iS2PPIfPvD5hYiiSMWUJspbyqif30zXy9v53V830+hWaHbLxaT1xrhOnSYXJ86xnIVXcpyPeqJZ1Pz/A7LIqkimqN6vWzaK4ExsyxSJjGUTNSym+FUWVLiZUu1B1mQUt0zTgkY8VY57kivgtEpqQY1cxtEzSocdMeVql0xfpuAAtG/nbk+l+SOTBgBrom+tm3Co9Wk687aPa6JZfv3YVgY3DLH5sQ38+J7VuyQNpvjVXSYxhaQBvD3HifcS77aqy5Id4+HjHa3jWb58Oaa5UzF/3bp1nHHGGVx66aV73P4Xv/gFN998M7/5zW+YP38+y5Yt45Of/CTBYJALLrgAgPvvv5+bbrqJe+65h4ULF7JlyxauvvpqAO68887ivqZNm8azzz5bvC1J+1fu9V5nwz//wsXDaW799HHMa/BTrklIgoBgW/gUkYl+i/t+/23u/k87Xz1zIv/7+AbSeo5fbEyR1k3+eyb4BINJfhe/eN3inAkVtMhphII4om0hGClsUUawchj+eqJZE5+vnn9sHWZWnZ9xSgIxMYhg6ojj5yKEuzH7O1EkgVOmVGGkcwxuGGJNNIMZyXDuhAr8o2oJLjyRX5/zdaaNLeezN72PmlNOQtA8ZDavIb1uOVOuPhtkhe5nljC0oZ8xZ88ktL6Np/7TiWnbSILjYCCpEkYmxzivgiIJqF4V03AGEBbQkXKEeqLdcdrzbgoAbkmkI2UwxqvQnRfsKYgQNboVqoJuvLXOQEKURLL5QOmt9ZAJ67irfEw7/Rhev/sFXAEXqaEUmXCWwWyOtvxqx7707omwR8GgPVHoxzySWLwjUqyUGMnuSY/dGbkqUWD3VZcChaz84RL4OZopnKPdVzKsEffD4TuPB8WOsfSeHxRKsb7EwcT9wV+RuvdK/ig4veyWabF57SCvhjNoosA59T78zbVUX/VZvjPxIo5rCnD9l0+k7tRF2JZFYvMW0m2tjL/IWTDofWU9g+t6GX3GdAbXtPHksh5M2yaoOC0KkuAICNdpcrHyYEg3iRpOn3/UMGl0K6RNm+70TjcFVRSKFYRDuknadNT8g/mKw7E+FcO0UfLVBelwhuGtw3hrvehJg6qgRtMJk9nyt1WOboBhkglnSZs2/VknXh3sSfyRqPHzr574Hl/nxvibC0GPrKYsUHgvSry7sOwDi/dH3qf+yOUdTRxUV1fvcvu73/0u48aN4+STT97j9n/84x/59Kc/zeWXO+WCY8eOZenSpXzve98rDiZeeeUVFi1axIc//GHAWem44oorWLZs2S77kmW5tPJwANiWRcfSJ/iaZXPZhVP47IJR2FjogCqBJGlcOE5k61A17//Uj1hy/008uKaHWXUBbnnkda6ecxwVZhQhm+QzU70gGtiSRgYF0wYvWfoI4JIFKjL9yHqCKtvEzim8f2I596wZ5OxfL+GJ285nMv0gigiaB2SF8WeNJbw9TDqc4bWBJDd9//0Ikkhg7nEYvW18+YQb+OzHZjLq3EWYiQQ/OP9/mVmu8XokQ7Nb4ZRrj0f1e1EDHkadMon0QJg/PLCBBXmbQ0USkDUZy7Bwl2uQtwsaCqfpz5rFkv1xXgW3JPJUf7LomAAQNcyioFBAFlkTdYST+rMm8yrcSKroJCYUiUhXlKrJFWTCGWRNRVQMojvC9K9+HlfA0T9Q3DKJ3iRuSSwKGe0LR/uFck9JgzfjgqYAj3fF3pA0gDeuuhQYadd0JCUPjqRjKVA4nr29K/taTnswKbkqHBmUYn2Jg03Vl1/kn8fUYSQNhtsibEnofP0bp+Otr8S/4BQEUeLzo87j2g9OZsy58wG49f3fZ1aZizVRp93grI/MwlNTjuLVaD55EvGOfu59dDNT/K5ivFZFgWHdpNrlVPiBkzTYEMtSpkikTZNJfhciFPvuC0nSRM5JEpTlf1qTBiJO7FpY4UaURDRVQAtqRHfE8NV6ycZ0gmPLKW8pIzWUpv/BZeTSJlrQhSzJRNKJoh7Se4X9TY4UWhL3NEYoJQ3enZRcFQ4fR4wGlq7r3HfffVxzzTUIe1GLzWazaNquvrVut5tly5ZhGM4q4wknnMDKlSuLg4ft27fzj3/8g/POO2+Xx23dupWGhgbGjBnDhz70IbZv3/6mx5fNZonFYsWfkjSEQ+/q59g+kODvmwcdgT9JpBDTsihct6CZ8SeczNzzvsQdX7uDD37xt2helfnXPcQFD7YRVYMIpoGQyyLqaVRJwCPZYOaocTsleKa3sliBIOSyYFtcNq2aDT+/hLNvfJhtYh2v6RWQyyGIEqM/eA6+ej+eSjefvfNS+lduQasMsPgT36Pzn6/wrZ9fTsW0MbQ//hI/+9wDDOsmcl79+ILvvB9BFEkPhFH9XkwjR7x7mPPm1GLaNrplI0oiespwtAcyObS8gKFpO0JHGctZqVBFgTXRDLUuifE+FUlwyujLFAm3JFDuVgCY5FeZGnDRkK8w8NZ4sfNXwECTH0mRcAVcdC7pQMzXJMZ7Ewh59eJEf4pQXln43YRXEvZb3OjNhKIe74rt9T7lTcZhWr6/teBWcSRwoIra7wSScOScvxLvHKVYX+JgkBrsZO73bkQLaqgeheu/cwHDG7tQ/R6e/cDNbP3x//GtOy6i4fgptD+9kts/fT+JnIUqCiRyFpfc+D4Ur0Y2kkDxatiWRWooyblTq5xxR/59T5sWZYrTWugkCiwkQWBSPrkwPq9ttCLfLlelOtWXQUUkadqYNozzqqiiQLNbptHtVC24JQFPlRvVq5DsT+EKqLgr3WhBF/1rBxHysT41lEZSRVwBF8mBFLGc9a6b/IpQrMLYV95s+wNpSXyzsUCJEiWOIDvGv/3tb0QikWKp4Z4466yz+O1vf8v73/9+jjnmGFauXMk999yDYRgMDQ1RX1/Phz70IQYHBznhhBOwbZtcLse1117LTTfdVNzPcccdx7333svEiRPp7+/n1ltvZeHChaxfv57Kyso9Pvftt9/Ot771rYP9so96TD3D4sf+g/vSk/nkjHIMUUPGAkFEB9y2zks3n0z0v09AlQT6kjkqNIlnFoziQ1MrCWVt1qa9bA2l+OlT69n26hoevfNjtEeynDPeiySCLqhoZpwBtQZZFHi5Lcb56eWkp57BKz/9EB5FYE1fnHk+CcHtpeuBR8jGsmzbHmF+eYDxH7uYxMaN+Ot9jLnqg3zjjP/HKI/CeZ+Yj08WufwT85AUmYaOIcKbdqDHUmRjGSS3iqy5uOfhjZw7oYKyCjeiJKAnDVTFmfSnkwaF4a1bElg6nGVRpTu/+p9jjFelqcxFTzTL9IALVRQQRQHLsrFMi6r8xHhH0uCkqVW4Ai6ysSyyW0b1KcR7EtimTfWMekJbwwxvC6MnDJIpA2NVH6pHYXlvYo+r6EcLeyonBPItF/v3uhJvo8xSZM+r94VhiWnbxHJH1uThUPeYHgpGnmOtkF08hCUIB6VVoVRzcNApxfoSB4sdf/gjuXSOjYMpTqguZ/J1HyOybCllzX4m3vJ1rp9wOZP8Ls76yCxUUeDjnzkWgKa2IVIDYcyMTqI3RuUUGVGV+cNjWzipyoNpQ0XAhZ4ykAQBlyoRyWsHuSURM2exIZZlflAjYznaRKM9CtPKXOxIGmh5l6RCkqInk8MriXglkc60wenjgtimjZ5wkmDBseWkw2ls06Z2Ri1tL7TTt7ofURKJ6SbJgRTpcIZXQ+mjOmmwN0E+i/2vINzf7feVI62S72hnd0HkQ4WVd0d4248vxfp95ohZALr77rs555xzaGho2Os23/jGNzjnnHNYsGABiqJw0UUXFQcfhb7FxYsXc9ttt3HXXXexatUq/vrXv/LEE0/w7W9/u7ifc845hw984APMmDGD008/nSeffBKAP/zhD3t97ptvvploNFr8ebPjfC8ha15kt5e+SAZbUneK9Fg5htMmQk5HEiCoSfQmcowvV1nVm+BDU4II2QRVskGlR2ZBc4D/Pn8KP/x/l9IeSTO/McBQOkd33EBL9oMoY1rwnedbOT+9HNvQ0TJh/C6R4YzJg8s7necVRWIdw2TCGXyyiJXJIGhechmdphMnYEZDmLbNMbNrkNwqsyZUYJuWU1nQmyDRGyEVSuKtL8fSc6h+D5P8KqIkYCQNPFVOu4Kdn7hJAmTSOaz8KoRPFpGEghiSgCYKmLq5i91i0rCKegeSKpHJWdS6JGS3jCAJyG7Z0TdQJOrnNqAFNbx1lbiDGoIoOE4Nlk3atNk4mCJqHL0DCXAm+wfrQvR2JtR7K/ssCPkdyQOJo3F1RISiVsihpiSYdORRivUlDhbdS3eg54WHs5EE8qjJAE6sH+zBsGHGpAoESeSUKVVYRg7LyBHvSRDvChPePkxglDMGUP1exngV3F6FId1E9Sn564BNKF/Nlzad2G3YdtEZqZAkcEsCsXyslwTyMdrCyGslVKgSSdOi2a1gmzaugIrqU1B9CoIkUDmhAkES8DZWYVs2mZyFFnSRNm3Chsn6aJbhozhpAO98e+QRM+F5D7G7xtGh4kBj/VG4FvOOcUR8j3bs2MGzzz7LJz7xiTfdzu12c88995BKpWhvb6ejo4PRo0fj9/upqqoCnAHHVVddxSc+8QlmzJjBxRdfzHe+8x1uv/12LGvPly2v18uMGTPYunXrXp/b5XIRCASKP3srsXyvUNY8BUEUCTRNYvLxU/mvRaNBEEkYFrYggijT6BVBksHUEWyLie4MQibGmbUWOjJCLouQidNghQmoEheMdnP5KIHL6jOMFmOMsQaYZPcjZuKIiUEaul7me/6V2KaJ6A0gtL/GR+9fQ4vbxKfJ2JKC5A8y4ZLj2bY5RNq06FmyluVf+zlGMk3V/Nmk2ju44X/PxTJtVL+X0adNxMzoZCMJYl1xkv1JXAEXtul8VpJ9IU65eBJ60kBURPo2DaN6lWIrQWFwIOVL5CfmWxJUUaBClQi6JCzLGWSYtmO/WOZX8bhk+tM5JEXC45JpmVNHaihNNpalYf4opl11MpIqEeuK0re6n7anX8dX7yMVShPpiKFbNq1JnS0JfZ+EEA82I4NAQBYJyG/vUqKJAsZuve/eA5xR7muAas4LUh2Nq/dHAm83eBzpyZgSh45SrC9xMJl25UJeWT8EwODqrbz0gWsBcH/lZ8RXvMz/+/Y55DI5PDVBxpwxCTOjkwmnaGuLMLxtGH+DDzOjY1sW2UicE04ehWVYzKhw09eTwO1V8LhkfPlWtYzlrGpqokBjvtXQtB3B3UJbXVDZua1PFmnQZGI5C0USqHXJTJtdi+yW0JMGDfMbmfO501G9Cn2r+xncMMTae5dTMT7ouCz1JYnlLPoyOdpTxjs+8T4aE9Wws63hnT5/71WkI1CTqcTb54hIHPzud7+jpqbmDb2Je0NRFJqampAkiQcffJDzzz8fUXReSiqVKv5dQJIkbNvea69iNptl48aN1NfXH9gLeQ9RO3Eycy6+nMs/fBLfOn8Kc+r9RLLOZVkAJ1lg6gh6GjEdRTAyIKvEJB/YFu54Nz1i0LFetC18mSGEdBQhm0Awsgh6Cj3QiF7eDKIMgohtmcjVjQiKim2ZCJqHn35gOtuTEt8/fzKCaWAlYyhVtSwPZxgzppwdL+7AW+Ml1TcMgG/adDr/vZHxF8zBP7aJbCRBvDeOrKnUzqh2VgD8HvyjatHjKR746RIUv4e62TVoZS58QQ096egb6CmDQJlGVdBNLGmgSAKTJlcSKNNw5RMJScNCFAVUj7OS0VLlcXQRgi6qXRLBseW4Aio7Xutj+keOJZfJYSSdXklXQCPRm8Ad1DB1i77V/dimTU8mRyxn7bMjwqFgZBCI5ayiErO2n6JNe5q0H2giZF8DVGc6R5Uq7fNg6EgaNBV6Qnd/rYfzGA90EHboE17OIP9AfkpjnYNLKdaXOJj4Jk6kPWUwttpL5yvdeKo8pAfCuGUR/+z5bP37aqZduQB/cw2ZUIxoZwzFLTN5Vi3lLWUoXo3AmHqykQS//OGLBEZVEBxbhuJVCPpUEnnRXNO2CbocMWPDdmLMxHofdeVaUf9At2zcklisPig4Mmh+F15JpLylDK8isn3tAFMum4dt2tim5QgkBj2YuoUr4ELWZPrXDRJ0SfRkHIeG/RE9PpSMjDdHUjx8K8KG9bYXN44EjqZzvScOR9LgQGN9af1o33nHNQ4sy+J3v/sdH/vYx5DlXQ/n5ptvpru7u+jfvGXLFpYtW8Zxxx1HOBzmRz/6EevWrdul7PCCCy7gRz/6EXPmzOG4445j27ZtfOMb3+DCCy8sljjecMMNXHDBBYwaNYqBgQFuvfVWYrEYH/vYxw7fCz8KcVc28MUbP8Ivf/EP9KzJcdNqmdoQoLnMRZ1bxBZEJ1mQs8DMIVgm2BaWJ4iYCiNYOcpkjaRWgcdM0xRtB8CWlOJzFB4j6klcg1tAEDGDTdiyRjY4Bk+0A1vRnGSElaPJHMR7zq0MvvR/WOkkds5AqqzjqlNasC2LCRfOxFXuLzoqkIoz9aqTyUYSGNEYFZNbCG9/DV9jFaIio8dTKF6NsqmTSHQPctZZY0n2RkgNpdETBt5aD/GeBL5aL/HeBHpSd2wYbRvLsuneFqZ+TBmxLh3TtknkLNKmxbQZNfSvHUD1qkiqhDuokRhIUTGphrr5owlv7iG8pZOGufX0re6l5eKzaDxJpGd5N4IkEOuK053QEXGE8TbGd/VaPtwq+3vTJTiSVu/35ZwM6Y4LhnEEWlC9GaookN7DxPtwfgYOV+/i28XmyD229yKlWF/iYCP6g1w6oxp/vZ9jfvB1BFUj1jgH4fEfk5MVjvncmWQjCfR4isppY4h1rcHbWIXs0TAzOqrfQ3DWNBLdg1x00iji3Y6GUCKcQXPLeD0Knko32a444axJmSKSNk0soHsgSWONl0TOaVMMG04CffKYcuhNII5IorslgYoJQZoWjiHc2k/fim1UT6ukY0knoy48lcaTZrHtqW24AirpcIbOdK5YedeaPDJFj4+kFWRNdESp3yzmx3LWEelGtC8cjcd8uDnQeF86xfvOO544ePbZZ+no6OCaa655w329vb10dHQUb5umyQ9/+EM2b96MoiiceuqpvPzyy4wePbq4zS233IIgCNxyyy10d3dTXV3NBRdcwG233VbcpquriyuuuIKhoSGqq6tZsGABS5cupaWl5ZC+1qMJV6CKbMwpATz9U59AEgXOmVnPMfUBzE+fQ1rPcd2i0VS6ZVy5FGRziJaJkHNWy21BxFbcWN5KxyLFW41s6SCpuE0dRBFbdYNlYaue4vMK6ahTXSCpTjWCICImQ859/kbalAaq3DLedS8iVTciiDI/+vk3kEUBRJFcXwfKqIkce/cP+N3cq5h9/SWkOrrY8cAjVM+ZgNY4iv4XX8HXWI1n8ix6/v4EU688kWhbL/5Rtc6xmxbRDZv5029W8JHPHEu8a5g5nzubzX/+N+6gG1M3CTT5sUyL3rYomWiGoE9lKK4TdEn0tkWpbfLT2x6lSpVwqRKhrcMYpo0RyzLz8hnYpkV4e5Ttz2xFccvUH1NPYEw9/uYaWs73Y2VSdL+4Bj1h4G/wMpB0kga1bpln+hJveL8Od2A5WMJEhzKQm29ioTjRpxYtGN2SQCL31kFHEoRib/47HcgPVPTvQFGEN7c+OiIGaAfo65zfRYmDRCnWlziY1E4/kb/5F7H021OYevWllNfOZ1yqlcg3P0Xl9DEoVbUMvb4aX2M1/llz6Hv6WSZcchyJ7kH8o2qxjBy2aRJZu5H7f/caH/n4XIY29DP72tPY9OASfLVe+tcO4KlyxifRtgiJnEWt22kz9Mki3QNJagMutg6ncUsiQUWkvysOgJmzmHH6GFSvyvbn2mh/oQNXoI+aGTUEJzZQNqae8RcBOYOeJWvJ6iYuYCiTI2qYVKkqr4Yz79wJPorIWPZeRY5rXVKxYsMtHX2LBCX2DZOSOOLh4h1PHJx55pl7LSv8/e9/v8vtKVOm8Nprr73p/mRZ5pvf/Cbf/OY397rNgw8+uN/H+V4jGxtC9QW584dfYHS5m5ZyDRGo8Sp8/p4O/nPzSQh6ytEzsHKOTSKwnSr8qogmi6iSgAzIlo5QUMe38xoIOR1blBEECzETByvntCSMwFbdIOwsL1NfeZCGvg6nVSFQgZ1JYo5bwJcv+SKffvIm8FWw5fePUTV9FKt+9TIXf/sChlauxVXup/74aST7hjEz26g99QSsZJzosleoPfl4bD1DzSkTyA12o46dhr7tdfRY0ik5rA4iaSpbHn6J6pkt9L66nWxMxxXQEKU4VTUewoMphuJOhUFB+DDWm6DRp9Kb0GkOuMjEs3gDLvwNXrKRBDWzJyBImzGSBpIq4qmvoHzGZGw9Q99/VuGuDjK8NYTqU3AFXDTX+9jek+CZvsQ7PyE7iBzK12Kxd8GbQtIA2OPK/Z4oDE7eAUmJPXIwj2N/JvqFcyDlkwe7K2UXvrG777NQbvlu+vyW2HdKsb7E/hD+2iTUsdPxfuIve7z/+cBTNLbHcF/7G85d+UeszpewLJP6009keOVqXMkMdacswM4ZhF99lep507Etk5opMzEHu5Ebx2G0rScTijKYNfHUlFPv1dj+xDJqZzXRt6qTbEyneqpGciBJQ6WHcCRDbzqHiKNXpIkC4YROrcvRMSjYPUqCQGNLGQCV08aw/bk2AGRNxlMToHLuDMzIMD1L1uKuKadnWReBKjeBpgC+wRRlisTiodThOtXvCvaWDhjZ5vFOJ9zfS+zNQaPE0c/R2/RT4qDTcMyZ+GpHAzDu5IsINE0kkjbI5iyCmkSlR0YU4OqzJ/I/z7fxyPY08RwIRhpEmS25Muq8MgGXhCwKGJbjYWxLKjnFA/l+VCHfxiDYFuR0p7JAlLELIlSCCIKIkDMQjKxTvdC6CjPUx/CmHQCoE2Zhj51HKGtjWxZiKozZtpbFT2+n6sQTeLI3weaHl+JrrCaypZPhTTvwVJfjaWnBjIYQNQ+mkSPT1UGqo4vIqpUkOvuIvPQC8Y5+1ICXcV4FPZ5Ej6Xw1vpJdA5Qf9xYqiZX0L+2n3hvguDYcqT8cUuC09uoisKIDLdATyiF6nGUk0Nbw3Qv62Xb46sQRAHFq1DeUoanOoggq077xLQxbP7LSmzLwkjnWP+y066QNkuX4YNJUBGpdUn7vL3TJnJkBMPdBSUPxv72JRgUzoE6QtRy9+MoaFuatuNp7pUElPxjVFHAKwnF/x1KbEquCiVKHI3Ef3IGvS+9hlA9aq/b/OeB1/llxfk81Zvg5SuvR9A8xFcsIdW+nbJxjbgbG7BScQTVMUxO9/aTHRomsmwpyR3dxF79D/GOfhSvxqJKN0Yyg21auINuUoMRGheNo3ZGNaHNIVJDacpaArs8f6GVoDOdwy0JebcF8s5KEO2O07e6n62PrQBwdBPGllM2uh5BlDCNHFUzx7Hl0dexLRvLtHl9WQ9A0XWpxMFB5MBFl99N7OvET2Tf9RU0cWdMVwRnHDDyf4eaUqw/fJQSByUA0MpriXRuJJdNM/eDH+b808bx3J1XcP6kGk4cFcCviGiSgGJm+MTMCr75vjF8YHIlblmkX67C8gQZU6biEkxkO4ckgJK3JRKsHJKlg2Xlf3LYooQtyo7rgpVDyMYQM1Gn1cF2tgEQjBTWyqdJrFmOEU8QnNCMXN2IXj+NW1fEeGTDAAC5yrF88dRbCMgiA4tf5Oeb7ifaGSfRPUjN3EnImotMKEZs81asVIr2R54CINUXwt1Qg6ypSIpMsi9ENhInPRDG45IRFRlfYzVGMkNqKEXfijbqj53oCCxdPA1Td6ZNZYpY9Gw2bZt6t0wqm0MVBWoDLhIJnehwGtWrMmpRM/4GHyd88yKCY8vpWz2AqMpYsRDa6PHEO/qdYxtKMziUQhUFBnuTbIzrpdXag0BBwLFMcUoYYzlrnybhBTeAd0KoaE8X6oN98ZYEdgn8e6JwDnbXshg5QDDtnYkNR+PDRhWFYoCWBGdFbm82mAeTAxdHLH3hSpQ4XNz03a/Q+/FKUhtfp3buJDwX3bnH7a5Z/TzN9T6+2Bzj9wsuQFJFBh59CFe5n0woRnogTKqjCyuToeOxfwGQCUXxjJsAgKjKxNp7yYSiZMMJkqaNIIlIbhVBFAlvj9C/ageVUxoQFZFJF091qgMFZwJauHZZwDivQtSwHOtl22ZYN0mbNrIi0XhsPd4aLyfdegm+Wg8dS7pQAh6MoX60xlHEO/oxdQtTN4uxPpGzdqmIK/H2KcT6RrdM0rTfEQeqI5V9GT/sjwNSJp84Y8RjChUehffhUEZ860DFEUuxfp95x1sVShwZZCLOZNVfP45QX4L55wep9kgoosBwxqTBlUMoWFxJjgiiKaoABDVn1VbCQrBy2LKGBE4CwMw5lQUFbKsogFjQMLBlF4JtOa0LeTcGK9SDFQ2R6+/ASKZxVVUg+cpBUVn29V8x/9mLGYxluOGEFs5/5if8Zv0Q//2ZeQRG13PbzY9zzbpOpn34GOIdA1TNn0X/so2M/a8PY1smyfWraThhBsMbd1A9dwpDqzaix1LImoq7Joh/tOPbHUkbeGqCAPgaq/BU56icPoZE9xCTP7SQ4Y07GNoconpsOa6Ai67V/cXJUSZfslhg4smj6Fragyug0r+2n+DYID1L1tJ88lSmfbyFbQ8/T9mYGqrnaVSfsIDgxGZevOWveQvHXGkgcRDRLRuvJNCRMqhSJYb20xv7nUjeHI4qh5Gva/fX+FZlh7plFysNRm5XSDDo+eojx9bMzm8vHNKBBBx4O0dpKFGixKEn8dx3sFY8QbrtD6i1VYieAMqoiSSemYDvzP95w/Y/63uerXYlt4yaw5LzJ1A5dQKxtl6Cs6YRa1vB6J/8AWVoO8aWlTSeJDL0eiv1559D5yOPYRk5ZE3F21iNt64C0e0IHAZG15NLZjDTOrWzVAJj6kn1hph82bHE2nqJ9yaobg7gCrjYvnbASX5C3k1ByAvWWkyb30DX6n5cAZWuV3oIji2n64XXGH3GdGZ/YRzbHn6ewKgqqjUXNYvmUjaukZdvfRLdshnSzSNWDPFoxLRtArLIYNY8MnR3jhAOx3iiUIk70sLUPoRPbNpwIN4jpSKffaeUOChRxF3ZQLBlCppHYetQkgsmViCYOvUapHChKAJqJupUCNg2ImkAp8VAlB39AkXbmRSAndoG1k6XhcJt57EiuHxYkowQ6iQ32M26H/6e1FAaQRKonFBB82lzsVIpvnzZLwD4/j1XsX44x7r2MKIAXkXk0y1ZrvvlCq6YX8/pNV62/acT1aeQjWWRT7iU0addjfnvP7H2pw9zzPdvIvSvJxFVGXXibIbue46aOaMRJZHA5PFE1m6ibMIoPvDH68l0d2CbFv5RtWTDCYZeb6Vm7iQ6n1uJpKkEmvwEmgKYuklFkx9PlYfBDUPImkxPKIVbEjHTObSgI7DkrfVS1hKkctoYQuvb2PTQcjxV62k60XFxWP3Tx+jYECJtWpQpEi8Ppw/jJ+C9gcVOK8D9TRq8U+xp4n6wYvC+9CK+2f1OWaIzSDD2UO9XcAKRBKcCwYLi6lwpVpco8d4k8dx32Pjpqwlvj/DyGe+nrKWMMefOw0wkirH+9e/cvcfHXlf3Pj598STOnl1LdEcUI2mgBTXe9/pk+uV6Xs7JdF93I9PvvR/x+T8iaSqiN0B42yBVU+oRJBH/hLGEVq7FW1/Jp+/5OPHtXbjKfbiCfjRRJLKlk/KJzbQ+voqK8ZWoXqfd0FWmUlvrxRVwEdkRRZREuhN68TqoBTWGdZOgKtF4bAOB0XWEt3Sz+S+rCDRtpW7eWCKtPXT+9Ck2bgqhCI5oX0kI8eBj2JTEEA8yb+WmVBhPFBYNdr9d4uin1KpQokiwZTrTj2ngA6eP51PzG7Fs0AUVJBVvZhg1HS4mAGxBQNATjpWirGJLMrbsVCAUkwaFdgPTcDQNcpliRYFgW8UfMR3F2rKcjt/+hrY/PkJgVAVVkyupmVHHr+5dS2h9G0pNQ/HD6jnmRO5d0UlNhQfZzmHZNtc1nQ3AQHuU6kkVXLzyYSZ89uPYps2r51/KaxdcyPCK12g6cRKbvv8T/ONGIyky5mA3U64+G09NEK2yjExPD4IoEt3aAYqKWl2Dq6YGV7nfeS2iyIo7n0LSVGRNpXZWA6ZuMrxtmNqZNYS2DhNJG4QjGdySiDuvTdC1tJPg2DJiXTHKxjWy5S/LSPbHkd0yqaEURjJNzdxJzLnhcqpqnCRDe6q08nCwqVL3XdPgSOJQDn32tfVzb/2OhdYFSYCALO6xl7RQDggFR4ZDP4iwOQitCqWxTokSB5X0Xz/H2vkRtlz3cbSgh6rJldTNruMvT24j2T2IUlXLKVUexnkVVjx0/xsef93aFwBoW9LF9I+ewJnWVKI/exAjqfP7HXdx36YfU7bkXiZ/5DQ2fepjqPPOwD+qFrG8ilm3XEv5xGaqZo4j29eHKImkB8IIsoqvsQo14EWrDJAJRXHXlLPxgVdRvCpqwEPNjBokVSK8PULFhCCJ/iThrEkoZRCQnVbFpGnRuaSLqTOrMdI5qmaMY/s/15PoT+IKqAysH8IyLeqOncKcz51JS96xoTNdivUHG+0wtMIdbezPhO/N2hXVfGvinvb3TqVpDrhVoRTr95lSxUEJABZc+VHa1rRx7UljEAWBtkiWRr+KX5UQ9BQIIpbLW6wgEEwDy+V3KgdsEUGSneoBQdxZcWBZCKYTEAUj5egb2BbI6s77B9tZ+60fsu0/nTROqUTWFObcej3JNa8C8Jmrc/zhx/+h57uLi8caH3cCZ7ni/P6VHYjpKKoaLN733GCKn677B+hpktPPYvRZL+Oft5BfnPlV/A0+6o6djH9UNdGN2/CPqkWuH4PlC+NRNSJrNzK8aQeKV6PpzEXODnMGgyvWkeobRvZoVM0cR9OHLuPFj3+bmVefQGh9G1qZi2xMp3tZD5IqMWFuPf1rB8jkLDS/CzOWpWFuPdue3o47qKHHkvgbfLQ+186pt1+Cq2UCuf4OBlZsIvb0SpKhNGWKVFqBOAS4D1AgaV9W5w9FSeShVCje12O1gD2lXQqvN23aSMKuqwwjH1d4nkKA1kQB4RAH61KrQokSRwZ33/0tpn7zv7h/3kdoGVeO4paZd9t1xFcvxzYtPvYJlcU/e4mXQs8A7FW4tlAt9sxAkg/d7Vg1n3f5TcRe+gu6oPKvhtlUPfEcFZNbCE5qIPTwPXiqy0nXz0Sum4rP5WbgiccYWteBFvTQeNIswLFhHnxtK5nhGO7qclS/lwW3/hfPX3sXDceNw0hmqJ6qse2pbfSu7EfxKsw4eRS9q/oIJ3TK3Qpu3aRifJAd6wapqHST7Avhq/fRtqSLRTeejn/caOxMkq4XVpGNZUjGsvjkUrXBocAtCaVV7t04WGOIkdUE+/KcIk6y4VBmFQ64VeGgHcm7n1LFQQkAetvCfPXaU3h60wBTqz3MCgr4FBGXkXDaD8i3HeStFwU9gWDbTiWBZSIYWcctwcgg6CnEbNJxWzB1yGXAzDn6BraNmI6SW/5PVlz5SV75zLdI9CaYcsYYsjGd15f18PLnbqd3yVoAqudMIDGi1Oz6a2bTHtFZ0OjnN5dORzB1/FaKn/Yv5mfdz/DT/sUIVo7PN5+DWxJY9qN/se67d/G5l/6PNU9vx9PklCn6GquwTIv23/yG9j8+yOCrq0gPhikf10DDohlYyRhWMkZmYBCAUWceS9+qbhLdgwiqxqyPn0S8sx9BEqmZO4lRi5rw1ng59oaz6V87QPXUKqePWxWpaPLTvayHhrl1eKrctD65jlhXjOpJFbjGTMLOpom2diOqMq0vdjKYNXl2IHn4PwTvATrzVloBWSQg79/l7520FyoE33eawuS/cCwjvbMtdhVG3B2RnVULBYGxQ7kmdFAqDg7h8ZUo8V4gdseJPGWupeHqSxjaNMzkefVkwlk2vD7Icx/9DqH1jlVh7bxJbIzv1PK5cXjdHvd3z+z3FX/AEUoEeGHmybRdfTEnbVzGxkc3ErzgQwiiiKe6HNuyGPj6x2m99gqSr71MNpIgOKmB+uOnkcvoIIrE2nsRVZn6RTNo+9dmMqEogjfAgq+ejR5zrBHLxjUy/pzxVIwPsuibF9O2pIvylgC6ZSMqIsEGH5EdUSad2IwroLL50XVE2iIEG3wEJk3AzukMb2xH0lTWPtvGkG7y3GDJdvFQEDacKHQkxM2jkX1ZUNhbnN/9d8FR6VBy4NWFpWi/r5S+UyXQymupG13Okq1DjK32Ue6SyEkaLtHGVjQEI4Mtjsj+Wzksb6WTFABH20AQipUGgm0zUgWl0LKA4HzcrFAPQ8tXI0gC2zY7KwapoTRaucb4seUkepMk+pPEO/rRYyk+etmU4r4m3nwTSn7VWDPixf0KtsU/wn7Snpritjkbtgyl6FzaQ2bCibglkR1PvIhtWkjlFdimhRrwIIgiVXNn4KmrJBtJkB6IkMvopDu7GN64Az2WItE9SKDJT++r20GUKFt4MjVzJxEYXU+0tRs96ZwLSZHxVLmxDJOAKiGpEqNOGkvFhCB188biq/cj5I/fFXA5h+8NkBoI462rRJEE+rO5UvbzEFKnOZ7bh8ry6lAJMB1Jn4k3S2QU/j+y1NEckUwwihZIpYl5iRLvZu74v1voe+YFJEWiuzuBp8qN4lVwBVRGN/jQUwaR7cOkQ1HSoRhXXL4z1lcs+tx+PVd3OEPf6gHmnfclfDUeBh78PZKqIHo8WKaFpKkoXg3fce/DXR0kl8yQGohgGTlim7YR7xgg3jFIJhSjemoVg+u6EWQFz4TJBKe04K4pJ9UXItEbIxVKYZsmTbNrMXWLKs0p3m2Y30jNtCrKWirx1XqR3RKiIuKr9wEgqBrpwQjuygBuSaQvkzt4J7vEGwjI4hGTdH83M/L87m2ccjjaE0scHkqtCu9xZM1LJtLPthUb+fmdV2Lm1c9dVr50zswVJ/y2JCPmsiCIiKlwPmHgtCcIlgmCnW9d2OmYUMBWPQjtq1l2wx1kYzqZaJZEQmf6gkZESUBUJPSkzujTJuIfVcvL33mK6ddejOgvxxX0wZ/Wc+WCRj6xXOUzi0x8ehJL8yMkh5GMASzVzVPTj+cp4FMXOZZLN3in8MPkRlyh7eTsHGf/+lOsu+sxFL+H3PAQilfDW1dJYHQ98dZ2AOpPP5G+55egxj0Ep08k2Rei7uyzsfUM5ccuILXxdaxkjBVf/RGjz5iOp64Cd0051bMnoMdT2JZF04JmGhbNILS+jWTfMNEdIWRNQVQUtKAHxR1HEEUqJtaRWLeGRPcg0bYhupfuYEfSoDNdGkwcSnoyOQKySGw/RJMcgb9dS+6PBDTx4JZj7k34aGS1hVcSSJr2HgcI1ojtYWcbQ2HwNnIQp4oCwiHUpnQqDg5sH0dSsqZEiaOFNU/9mP7zziT3qctYH9MJpQwmz6vHW+tBTxjYls3YMyfibaxmx7/Wcsuka3n+n6/TdtsnOO6JS7notou459eDe91/ocrgs5dP5a7Vzu3vffZOUpEQbHiZOUv+zfDXr0HWVOxsBsXrJjC6HtuyCP/rCRSvRtU5V9F57x/JRuJUz5tOJhSl/vxznFg/czqJLVuw9QxL/vsuplw2D0mRCUwbQ3ByC3o8RS6j42/wMeXDJ9K/cjNGPEUmnMIybVzlPsrGWMS64oiSiK/WS3jNevRYkuiOMG3Pbqcnk6OnlDg4pMRy1jtaKXgkUxjTvJmD0r60Xe4tKbNLiwJOK+OhxOTA4v3RIZN9ZFBKHLzHyWWSlLdMp278GLaEksxr8KOKkLFdaEbcqTjI6Qimga1oZLQgLj3muChYI4Je4e+84CHk+4MF0dEy6F5L98OPsHntIOMnVfLY6n4WVrgZ3hrm0W3Dxd1cGssy/0stjDm1BblpHKLmRZp3CRuS53Dcx8ZynCCCFUNMxrE0P7asAHD/5LNpdst0pnMMbw3zf+2PgSjzee8ULh5fwfz/PoOe/6zDW+uhdu4k9HgKrboCd2MDdiaJq74JQRQR/UFHVbkygNw4jtrqRszwAMntbbQ9vRpREpioqdQd04wec9oJqk45ldCL/yY4bw6ZjjZ8jVUMb9qBqMpF94T2FzrY8Fw7x330GARRJLIjytjzZmPpOYbWdRHvSdDRHmX4KFH5P9qJ5ay3TB6MnOju3qd/pHCwezj3djYKTgiZfGLxzZSVC5UGBScFIx/NR2oe6JaNTxYRBA66kIDP52NoaIg4OQbIUoPrbe0njUl33jlGFEXcbvfBPMwSJd6VXPD5T+P5yRdoa4swflIlj2wYYmGFm/61Azz5TKK43bXjgzTc+itmzvwzD3z15wB4LnoJWhby6TdJGjwWXYU4voInWocJbQ7tbFm460tc2FLGcTeciXXPN8iE4ow6/Rj0eArV78FVVYmVTiI2OHbLVngQV7kP1e9Fqm6k/vxGct2txNp66fz3RhSvQosq07yohdRAGFe5D/f4KYT+s4SKubNJd7Tja66hb9lGXEEfilfD6h5i8dPbkZZ0ce41c3FXugltHmbUKRMwdYPOl1rJZXL0hFJ0lwQRDwsWh0Z36GjHYs8WhLvH+jfjrRwWCm4KmihQp8lwkKU8fD5f8e9WUhxD2dvaj41NKzvbg0fut8QbEexSY8fboqmpie7ublA8KNMuf6cP54BpnHcOd3/lFEaVudAtm0q3TJni2CUKVg4xE8cWpZ2WiiNFEPfSlmCLMoKpQ+cGXrjqW7gCKlWTKxl/9WV88bT/V7w4NWjyHjPvE30qWxI6P3nhVqRxc7BVN3FPHf54F2Imip2M8fmZH+enQy9jLn2ML53/PRo0mZvbn+G6Omcw8eN/3EznE89RMaWFZF8IPZai4eR5mIk4oseDoGr0v7gcT30l3roKlIYx2JaJFY8g55MGiBJSWSVWJok52E16IExg/vFgWRi9bYTWbCa8uYeG46dgpDLYpkk2ksAyLaJtQ2x6pZvWpMH8eh8Nc+sITmrAXVFGxwtrGXPuXOId/bz+hxXEdJOXQiX7xSOV0srFWzOymkC3bFRRwC0JRA1rlyRMs1umO53jXrpIYtLY2EhXV9dBOYa77rqLz33OKXOuw8WF1PJ21BSWMMw6nHaoT37yk/z6178+KMdX4uji3RbrDyUvPvoDeo47kbJaDxXjK5j6+Sv54mn/rzhxmx5wsS6WfcO1dFaZizXRLNHfP8xffvyLPe77y+sX88Npp/Dkn7/LXyYdS5Uq8f2pJxerD67avpKq2z9J5bQxpAbDSIpMcPpE9HAYxetG8PgZWLISV7kfd005amUVUlklRk8b6thpGN2tCLKKqHmwcwapHTvIhKJUn3UO5mA3ZjREaH0bw5t7qZhUj6ypCJJErK0XNeAhtLmfV5d0sS6W5cwaLy3H1lMxsQ7/qFranl7NmLNmkxoMs+I3r9KfzbEmmj30b0iJEoeRPSVogopY1Ju47yDH+02bNjF9+nRM00RB4Aoace9RwvnN2UGKf+IkK8ePH8/69etRVfWAj+/dSqni4D3OqAXnM39BMz+/ZBouSUDWEyDJZEURQU+A6kHI6Y7GgaxiA0I2iWCksCXniyXsoTUBQMwmaf3frwNQOSFIrCvOC49tBR7i0hnVzP3CmXzn2gfoyeT44UPXYqVS3Hj1H4qP35JwdAPudp3Ap+w4SxIBTtDbnKoGyySzaQUAyUd+Rmh9G//X8SS5ytFc552GCLx/UiWtDzxOaijNA/es4tIPTSU4uYXo5jYCY+oRVA0zMkztwtnYOQMrkyEX6iW2tYPgnJnkQr2YkWFc46aRbV2PkcogKjLumiCtv7kXwPGHVmQESaR32WZs06Jq+iiGNvTx6AvtmDZUqBLT83oGetKgctpYel9Zh7+pnKHXW0n0Rp3XcYhLuUq8PYoVBwKowqFTan43JCYKyQHTtqnTZHTLRrdsKlSJoXw1TSFpcKhe6yc/+Ul+8pOfsGXLFvrIsoM0o/Hs1z6iGGzIJw08Hg/f+ta3DsWhlijxruHf5a14bvoITbNr0ZM6Lz7bhup7iI+e1MyUD5/Ab7/8COtiWe5MbSRpWNxSNg2A44Ja0VVgb0mDSWdcQnj189z3+29zmrid80YII2qiwDljg8xe9mv0uZP48Rcf4oorZxAYU8/QaxsoH9eIoHnRBweonjMRQdXIDoWwM0mGXt9Ixbw5GJ1b0SMxPJOmkVi3xtEcqq9Eqyyj9Ve/QxBFBElE8WqIkkBoYzcA1TNbGNwwyEOvdhcnTCdWutEtm2wsS828yXQ8s8xpVdjSSaI3iiIJB9xCVaLEkURAFkmbFqa9a/vk3hYFDxaTJ0/mk5/8JL/85S8xsFlBhBOp3K99WNgsJVK8/d3vfreUNHgLSpoh73HCOzYSTRn8aW0/g+kccdFDEheqrWO7fAh6CkFPIpgGYqwfMT6IkMtgi7LTrjDCepGc7mgiWBaYOXIblgCQCafoWz3An1f3sy6WpfGkWUy5bB7R1m5iOYvTqj18+bJfcOPVf+CiMeXArlZMnxyTI1U5nhPUfrByRMrHojfOIrRmMxe2lHHzZx7gX/e+xudHncf13mkoedeXk+78FON/9gfufqqVhWPL8dRVsOa3S0j2heh4ZhnbHnia4U07sHMGiCLhrZ3Et3dhGQZisAa5sh5R00iufw0pUE56IEx6IAKyQiaaRVRkojvCyJrK+CvOonNJF+te2MGqX73M3xfvQLecyRNA/dQqTrr9ctxBjTW/fBpZU2k6fQGiIuOrL8Oy7FKbwhFKYYKrisIhFfjZF5vHowXD3qlyXKg+ADin3kdf5tCKfyqKwve+973i7VcJY+5nP8QyIsVjvOGGG6ivrz+IR1iixLuL9COfpmpqLUZSp3/tAPct6WJjXKfxpFmMOWsmsfZeWpMG15w5li95pnBrcDpf+uQxANR433qQftdnFnD9NbN5fvYiXv3kV7lm9fNcs/p5ArLolFQ//hTB727l5s88wMLmMrTKAKt/+zKWniO0ro2uZ5YQ7+gHwEzEibX3Em3tJpfRkSrrkOtHo1aUE1mxHK2yDFESSefbE1JDjutBdEcEgHEXHU/ry928uriDJb94mfuXdu+yytoyuZKzb78YLajx2s//iaSptJx/EgCKWyaTs4gapVh/JHM0xdojgbRpFVsTC0mDEyvdh0XD45vf/CZerxeAjSSIsH8tQJtGPOb444/nkksuOejH+G6jlDh4D1M38xTGHjuf06fX8skZ5TSYIQLZEP5UH1K0FynajZiNO+0HtoXtLsfyVzuVBoLo6BoUqgwKbgqCiGAa0LuVv3z4h6z460a6lvaQ1U0CssgV8+vZ8vArGMk0t393MVfMr+eFEXZEj7VFALjp4S8V/2erPtyRHSCIWO4ybimbxvWeKdQsOo6ufLnftnx1womVbgwbbrhuAdefezur3n8JN914EoGmAI/f9QqZeBZXuQ9JU6ma3oIgidiGTqZ/iNr3nUJgfAtGMkP/P56k46G/kujsI9razdDKtbjK/diWRXZomKrpzQiSSKCpjEwoxj8/9Ws64lleCqVZMZzGLQlMD7g488IJHD+titrZjeiRGGMuPIHw9ihLf7eCrmeX0reqC0EUaU0ah0zlv8SBUbhImvaBi+0dCIZ9cAY0e9vHgQYDTRSKgkuFfemWTSJnkTYtLmwp48nexGHpNb3oootYtGgRABFybCbxFo/YSR9ZtuNck2pra7nxxhsPyTGWKPFuYO0/f8yPZn2Ep+9bw9alPSQNC68k8NGTmtn+xDIyoSi3f3cxt/3sUn7/zHbAcba58zerADjrlT/vdd+FBMG1d71CcEoLtS6JP7/YAcDCCjexnMVdWx/k71OOY9XUfn7y3P9SMSHIC79dRjLlTAZyGR1fYzWiJBbbCGtPPYGyCaMwMzp9TzxO96OPE9nQSqo3RP+yDah+L3osRTaSoHrWKCRNJdAUIBtJ8NcvPkhr0mBNNMvifFJhVpmL666aznn1Phrmj8KIpZhw8UK2bhhi8YPr2PHEi/Sv6UENuGlPGaWKgyOcd8v7c6gTILu3JhZi+6Uzqg9b221dXR1f+cpXAEcu6VXC+/xYA4uVRIu3f/CDHyAIpazRW1FKHLyHWfWzD6C4JC6aXIOgp50kgCBiCyK26nFEDfU05HSn8iATQ4z179xBvtrAFuW8poEEoog91MGL//U/pE2bycc3Uje7BkUS+MgHJjPpA/PYtKKX27+7mOOCGg8s70USnMCrjfB5/dKFP9j5NILgiDPKCl+oPaX4f2XyPFZFnBLHpGlzYqWbLQmdm248ie3PbOXmm07hD4t3UHf6SVimxScf/x/mfOI4LCOHu7ocUZHJhhNYRg5Lz5HevpnYth0YyQyx9j5kTSU9EEZUZWzTIhtxSpej27rZ8rc1rPnLBtpfaKd6juPisCaapdktIwkCM8s1mut9bPxXGzXTqonuCNG3dD3r7/kXqWyOxvFBNj+2CTsfoQazB09hWTvEfrnvBfZkL5Sx9uwkcDiO4e3cv7fHFAZFirDrPqQD/Nhk8poGBQazJknTJpMfUPx9R/RNHn1wEQSBH/xg5zVkBRH0fXj3bGyWjhh4fOtb3yoJJZUosRf++9YbaD3uRIKKxDGzaxg3v560aXHl+eNped8U1rzYyfd+9B+uv2Y2X7/uYUZ5FC5oCuxynfh88zl73HdBuwBgy3OP8s0bH6M/a5KxnFi/Lpblrq0Psvm27/D1b5zOlmfbaZ16EbXHtLBo0zIWfGYh7pogvsZqJE0lHYqRy+jo8RSp1q1Et3agx1IMretEVGT0WApRlZ1th6NIbpVYWy9r/7CCf//pdbY+tY3g5BZ8skh7PimhCHBatYex1V42P93G2NNHM7Shj/6VW1j9y2cpUyRG13pZ/vet2KaNpefoTpfcFI50jvaWwQJ7S9If7AWI3V2WHl67d3HTQ8GXv/zlYlVgO2l691GFcQ0xUnk/hUsuuYSFCxcesmN8N1FKHLyH6U3k+Ou1C2jS8kEs75RgeYKOqKEoYrm82C4ftqI52+QrDAQrh5B3UBBGuCuI0X5+fOIX2NIRQxLA31SOrz7Ak70Jpnz1OoxkmpeHnUzk+ElOL1KFKtGWNPbaOy7kstiqG7t97S7//9zYDxQvXscFNSRBwCeLvHj3chbeciHpgQjn1Hqx0knq54/m9e/+lr7l7TRccgmy5iITilIxxakwiLb3kuobJrxpB6mBWLEqwddYjac6iCCJpAYiJLoH8dRXMvH9sxg9r57g2HJC69tYdOPpXH3aaM4/bzzHNQVwBzXCgyk8Lpm+1f20LelizRPb6F87QNq06dg6zPaEzvJ1g/Qs7zyo1QYFFVtwvuClL/n+c7AHDm8nUO/pGEYOBN6OP7UkgFsSio+rUHcVEtrbce5LMkqEYpLAYtfj9x5oVuJtsGDBAi699FIA0lisIfaWj2kjTT9OFdPkyZP5+Mc/fkiPsUSJoxVZ8xI5/1y2DqcxbBtfvR9PlZtXwxlmfP06gKJ2QSrkKJZLAqwOpYr6RSMZee0RxJ1XNhEniVDQCTqx0o1bEhnjVfjLwmtoPm0uye4hTNtmYq4Hd0UZkavez8Br7Ty88HoC45pID4QJTmzGzOikBsIkugcJb+kkFUpSPqYaUZXRKstQ/V6MZIZsOEEmFEOrLGP2JxYwbXYt1VOriLZ2c9LXzubq00bz6YsncVKVB49LpnMohW7ZdL3Sw/rlPWz8Vxt9rWGihkl7f5KwYfLC6n66lnYd9Ha3Unw/sjhS3g+Rnd8pRXBECgsY9t6Pc1/HKoU4v6fnPdx4vV7+93//t3h7KWHst2hPTGEWxwSyLPPd7373kB7ju4mSOOJ7kHEnX8RPP3s8PfEMk1QDS/YjGilsWXOSB6KMGahHzMQh76hQcE6wRQnByGfz8u0JtiCAJCNG+7l/4ScIKhIVqkTDjGrant2O7JaY4lfpeejP3P7dxZxX7+PJ3gT3L+1mnFehNbnnnqQfpzby744YgpXEVjxcf/yX0ESBC6ZV8cLmYYZ0E1UUMEyb2oCL57pifGBRE6PPmI5ryjzGzzgeNfB7RLeXqtmTqJw2BoDISy9gpNJImsrwxh00nDCTTGgDDSfPo+ulzfgbfJSNrqfrPxvY/NLLLA+ndxEu1MSVxcl5xrIJKu1MD6zHJzuXTFEUUFHxehRiSQNXXrvAtG2svFhc0rRIm04P+APLew/6ezwyCVO4uL8bxPeOVg5mef7I99Fi/95XwwYpH/DrXDJp0ykrlgSBWM5itEdhWDffYFNZ+LwXBr17ej0jP2duySldPLXayzMDyXdM+PP222/nb3/7G4Zh8DoxpuLDu5ewZ2KzbES1wfe+9z1kuRQiS5TYnfKW6Xxr8a8wA06l4NSZ1ex4sYNMzmKcV6H74Uf47h0vcumMah5eO8ivH9tadE7YEwVLxQKfX/Mc04Map/zXPP70i6V0pnPFdsTZ547jnj+t54MnjuLYu3+AnYzRc9xnWTTm/2G89hye+kqO+f5NZLe8xpTevxBa34ri1Qitb6N+wTTiXWEmXjGHjsVb8FS50SrL6H55Cy//p5OOlPGm16qqlzoZ592ATxZJ5CzKFCfx6pYEhnQTdzyLaTvCzoVtgKJ+0SMbhg743O9OKaYfWRwp74cFRavjwthUERyb5IxlU+2SSOSsN3zeRyYV9uW1FMbBl0yq5K+bQ+/Y67/66qu588472bBhAwPobCfFOLx73X4FEXL5E/SZz3yGCRMmHK5DPeop2TG+TY5mi6bKifNomTGBf39xHuR0bNVDxHAuFeWKhRxqL+oamL5qbMXxLxf05E4HBXBaGUwDW1JAFNn85S/S9sIOfLVeTN3EMm1cAZUHlvdy0Zjyon4B7Kq2OtKuZSQ/feVOrljfwJ/PCmArrqLF4lf/+wS+96P/AI6a6/SAi5eH08wqczF7Th2eKjejTp+DGvAyvLEdb30leiyFp6aceEc/sfY+vI1VuCvKePTbT2HaNrPn1FE5udppTwjFcFcGuPMnLxcvsvuLVxLyVnQiumVTpojFv7cl9qVo+sDZ/eJfShzsP4rglPYf6edtpJJxlSoVxYp0y96nz28heWDaNj5ZLP4NkDadFoSoYe7x+1DQNSicJxGn57HRLe81KVjgYNsz7Ynrr7+en/zkJwBMxsfJe1FdXkecJQwDcNJJJ7F48eJSv2OJozrWHyqWtnTS+nI3wWoP2ZiOrMlIqsgjG4a4eHwFj24bLm470qKt1iXRn32jMOD565dyyZVfK94utCn8vPVhPjfOqRoKyCLHlGssHkox0adywsImFK/CtBs+iZ1JEV6+HMWrOboEZ53D8L+fY+j1VgKj65A1Fw/c/i8SOYtTZ9dSMSGIq9xHNpLAVe7jBz9bukcruX2hMBkrU0TSpo07fy2NGmbJKeko4mgZH408zoAs5kWIHT2hfZ3og7O9E+ud2C0JTqwHZ6Fgb+djT/8f7VGK7TtvxqGO908++STnn38+AAFkLqMBaQ9WzGF0HqYXG/D7/bS2tlJdXX3Qj+fdypFSVVPiMFJe38ivrzmWHWmJ16IKpiBTFd1GVXQbcqgdwNE5EGXEbBIxG0dMhZ1kQt45QcgZjggigG0hRvvZ8mw7/ekcqaE0lmkTHU6zfVuYiT6VF7tizCpzSg1nlbkoG1E2VUgaNGi7ru61/+F+fnzxNGxB4PbRZxb/X0gafPSkZk6u81HlVxnnVYgaFqlQiuFtYdb/8SVe/PojVMyYgGf0WMrnHkPnC69hmRbNp83F1+hcJCaOLefca48HwN9ci2Va1B8/jdV/WoNhs19Jg5FOEEnTJmxY9GRyDOkmrUmDdbEsWw5T0gDeeHE/GoLikYZh7yr2d6SSsexiieGQvnPAWqaIVKnSW7YaJE2bWH71oT9r0pPJ0Z816c+a6JZd1D9wSwKaKBCQxWIppFsSKFMkVFEgqIhYwPyg9pZJg8PFLbfcQiAQAGAzCYZ5Y5m0jsXKEZZMJZGkEiX2TMMxZ7LmxU7aUwaxoTSCJNATStHek2C0R+Gp7U7MF4F55RqNbqX42ELSYLRH2WWfZ8ZeKf79cO+rgHPNLSQNPn7OOE6u8xEs1xjtUUjkLGJdMYa3DbPsKz/m0cu/i39sE54xY6mYO5vWX96DHkvSfOocvPWViKrMzFEBPnrdQgRJIDixGdu0qJ07iX/d+xqwf0mDkfGgME7ozzqVWoXrZylpcHTxdlr/3glGjuMKMVsSnLbDoCK+ZatBJr+YYOUfHzas4u/CwoEIxd8jz0mhmlDESVoATA+49ilpcDg499xzOfXUUwGIkStaKu/OUiLFRoabbrqplDTYT46G70mJg4wgCkwIqoyxh5irDeMKbXcqCZw7HaFDxZ3/7cJy+bFdvp3379wRCCKikabn/t/Rn80VEwKm7kw4PvDDS/nA5xcSNizWRLN4JYFtCZ2NcZ2fb/8LH5xaxc+6n6FKld6QOOi54ZfOhEVW6cnkdtEzUAS498VOHu+K8fcdUaZVeTimOcDGHTFe3hwitC3M9I/MQ1A1zMggZqiPymljyEYSPHb9g9imxXe/8jfa2iJEtvWh+hTann4dNeBhza8X88xAcr/Pa2FQ9E5Y+RQmcZoo7PL8I4NhyWJo/wUHRfbey3ewj+ftiloqAm9YLRNxVhQGsyZRw8Qni2/4fhV4K/2BwqDYsJ0kn1M9I1DtclqSCuW6FaqEaTtiYcvDGYKKiDefaPBKwjv2+auqquJrX3NWM23YRfywwGqiZPLv8oc+9CHmz59/OA+xRImjhhfrnyFsWDS7FdKmhSAKJE2by350KR+9+XQyll0s1V8TzdCeMvjTKVdwyaRK7pn9PkZ7lF2S7AC+ax8v/v1Uf5KJPhUL+MiiJqb4Vf76bBuPd8V4dNswcxr8LJoQZH1njKXbwmzdMMSJnz8BQZQwwwPkQn3UzJuEmdF5+qt/wUzrfOvGx9jek2B4Sy/uoMa6+5aheDWW3fnsXtsn3oxSEv7dydH6vuqWzZBuEjUs3JJIs3vPsf6tYrBhOwsPhaRCmSJSNiKOF2J8hSqh50VKN8SyaKKwy887NbEUBIE77rijeHsVUbK7vas9ZOjA0VlrbGzk+uuvP5yH+K6glDh4D/K+RS1O8Y5tIeSMojsCBZtFwFY0R5BQEBGMNEJmp7CYkC9htgXBeVw2RXowgmk7JcqKV0FSJcrdCpLmIj0QKQqzJE2bpGlzw3ULCP/9TzyyYQjLE2RoD33VY8s1KlQbMRmi2S1j2DDRp7IloWPYcEy5VtzWU+nGHdSYPj4IOKVXoiRixSMAGPEE2/+xGsvIMe+SKYTWt6FbNj2ZHBsXd7D91R6svOrxUPees5T7yuGwm9sdK/+8pm2/4fkLyYM3E8R5rzDyE7anIDpyDq0Ih34gMbKN5O1QeA3SiNXxwmpBYVXBLTm9tn17UfLe34WxQhXCsL6zGkHN6x/ols2SkGNHWrgeFO4vJBgKg4vDyRe+8AWam5sB6CRDFzutohLkeD2/MqEoCrfddtthPbYSJY4WqqcuJNU7XPzeVwRc2JZNlSoheTzosWTxu53IWRg2/HzT/YTu+xh/3RwCoD1lMKS/sV2hwAVNAbYkdKYHXKxeO8jGuM7CKk/xfk+VE+unNTtVRIV4l4s5ri2WkaP18RVkQjFmvn8yw5t2YOFYNre/0sNr/9lZIh3eg0hjiXcfR9q452AeT0AWi98Bt+RoFXWm9xzrpf2sogsbFrplF9sVYaf1YiYf6wuxvzB2cmJ9Xu+Lw3/u586dy4c//GEAMlisHmG3uLtr0q233orH43nDPkq8OSXlp/cY/vpx3HbWBMc9wR1EMA0EPQFG3k5RkrFUL3K0B0txI5g5BNMoOi0IluHYI9q2k0CwcoT/+VeS/XEm1vuIDqchkkH1KCheBSOZJhvLEN1Nw+AHP1ta/NtWPfxs2U/Jbn6N0Po2qudNRz3uPFKahJiNFssVgaIa8zivwqpIhv/+zDw6l+xgY2uY2XPqKGsp48xaL9XTG3nmJy8yflIlvnofoiRgmTYVk1tIDYbpWbodw4budI7OdI6PnzMOWZO592cvH9VWSXtLWli7/X6v4ZV2+gwXevIN+40r9YW/C9sfakbqA7wdRg4YCm01haAdkEViOacM0Ss5wbwg1jXyNb8dDY+wYRHM9/SmTSeBMKPMRX82iyJAOP8EWj5xAM6AokGTkQRnhUTKWbD3+cNBxe12c9ttt/HRj34UgFeJ0IiGgMAKIpj5wsXPf/7zjB079vAcVIkSRxlrjt3I9n/EmFHhpjehEw1nqFAlvIqIbThWh4XrZiHWfG7ylbvs47HLvsLPbnk/XwoM8ky6jvdfcXPxvmtWP8+yfDXCuphTCdCgyTzZm+D6a2bTu6qXti3DTJzjuBycWuWmfl4Lf/7BYmaOChBoCiC7JTLhDOMvOpZUX4ieV1sBZwX1mYEkHzulBVeZyu9+umSPegsljm72pFXxZuOet6ttcSQw0jXBsJ2FuYLG0J4Wkd5OrE+aNgFZJG1apE3neab4VWJxfdd95u2YTdvGLTktkoVYX0giHi5uu+02HnnkEXRdZy0xpuLHj0wrKQbzrYozZ87kqquuOnwH9S7iSEvElTjE/PB/ryJpWMRNEVvREFNhbFHOtyVokNOREkOYvupiZYHlCRadFWzZ6U20BcH5Ge5hcPVWBjcMkY1laU8ZaH4XuUyOSRfPwFXfxC8e2cQdD36ar3/jdETguqumc1xQK/Y5Xu+dht0yE+2Uy7D0HK65p2GLEgPJvPBini9ePZNL8haOC2bUoIkClp6jemo18xY2kYk4bg+2abP89yucsird5MVn27j/b1uoO6aJwHEnUHv6+5hzyyc4pcpDnSazsMJNoKmM4PhaTNs+Kkv6S1/kN2dkEsAni1SoElWqhFsSi+euUI6nCE6w3L1C41CcY4udwkRvN1GhCOxSrTNS6bvwWU7mVwwqVIm6vbQs7C9hwyr2S57fUlYs+R05QHAcRHbqfQxmcyRyO1cxDidXXnkls2fPBmAIna0kCaGzGactqby8nFtuueWwHlOJEkcLq/5xJz2vbKR/7QDxTI6eTI5qlyPEOumiycjvv54f/XIFd4aX8/Ntf6ZKlfja19/HcUGtWHF4zernaZ42ng9/9Bas7q27JA0K9GdNrr9mdjHWnzyjmqAiYlsWFeODTFvYSHRHjFwmh+JVWfy7FQQVET1p8PfFO7j38W2MOmUC3lnHUnXqqcy84WPMK3faG5vdMoFmPxUT6w9LYrjE4Wdk3Nbyyerde/8L7X2F7Q8nB1OEsdAyMHIc41QZOtbkIy0YD4RYfuJv2HDRmHI25pMGI19HIXFR0EmKGmbeOezwn+PRo0fzhS98AXDWJgqLAyNdk+644w4kSdrLHkq8GSVXhbfJ0aq0/PB9t3FWi4etMfAoIgGXiF+yEPQUQi6LrXqxVQ+YOlK0F7O8EXm4A1txIWSTu2oc2Bahh+9h7e+WIEgCqaE0kiphmzaRtMHFv/kU913zK9qSelEA8cdP34JU3ch1x1y712P8Wdc/sV0+vlB9whvum+hTMW2bU04ahTvotCrImsKzj2zkg187k+3/WM3Tr/ZwTLnGopvPxl0TRNC8xLZsJ3j8QuxkDCSJdFsr8c5+/vzjlzh2ahWjThxHNpLg579bfdRmnzVRwC0Ju1R3vFcrDAoUsu+wU0Cw2iUXJ6+FktuRlQhHg4vCSEYORDTRcfNIm45VWNq0igPkapdMXyaHxU4XhrdabQkqjsPCyNJiJd96UBD/anbLbyiN3BdFZhG49zC4Kozk2Wef5YwzzgDAh0QZCt04Ccc77riDG2644ZAfQ4mji6M11h9suq8qY8X/vQDAYDRbvK6mTYsP/eHz/PrKn5IYUSb9+Z41KKLAnXUz97rPgg1jwUWhgCYKnFbrZVMsywnzG/A3+BAkAUmR+duD67niC4voWLyVf6zsZZxX5bwbT8NbX4EcKCOysZWqU04lF+p1dA+iIYZeb+WuO//DyXU+pl42Ez2e4ie/WXVUXedHUohrb2cF+d2MVxJIm06LnpIXDEzkrOL/RrI/loNHKoV4GpDF4njGwnFW2r0d6K1i/Uhb5pH7H/k525sD2p6OC954bg+Hi1KBcDjMuHHjCIedZMFEvGzJLxKcccYZPPPMM4f0+d/NlBYq30M0HHMmdy3ezrY4VHtkmsU45fFOpEg3UnwAMR11WhcyMYScjpDLFl0WxJTz5ROsnOOuAAi5DG3/XIMgCWTCWSzLRhAFREVk3Oxahlau5cKbzuCbT3yNWpfEOK/C9WfdukvSYOQH8LjgTs2CwvPtjm7ZXHD5NGzTZmhTiNRQmhf+spGJ9T5e/tHzLH+tn7OOa6ByfJC2p1dj6QaIIopXw+jYgjHQQ26gG1lTCW3opkyR8Df4yGV0/vHQhqMuaTDy/Jm2PcIOiuIEcfft3su4JZHBbI606ayWFwa/hdWHoy1pALseb8Zy3BHckli0V1JFoegpLgnOIKNClRBxBlbjvAoNmrxHkcSwYb1hAFJYWQC4sKVsj/2UezuH6ghtg3fiPJ9++umcffbZACQwi0mDlpYWPv/5z78DR1SixNHBhged9sJ00iheOyUBxo4P0vv8y/zXTy7nqxv/QrNbZrRH4a6GWbskDQoq7LBTn2jcyRfRufhnb3iujGWzeCDpxHrLIrw9QrwnwUN/WsecBj/P/2opS14f4JSWciZNrmTb46sRRBHbMpE0lVx/B2ZkmNzwILZpMbylF0mAutk1GKkMf/7j60fddX4kFm8sOz/cujFHIrv34g/r5h6TBuCcw6P5MwC7il8XNI0UAYb1nULdDZqMiFOJ2OyWqXVJe4z1BWel3fdf+Jxd2FK2T0mDwuPe6XMbDAZ3qSAsJA12F1Assf+U5hPvIXpWPUN32zB1XpkylwimjuWrBlkFUQbbQh5uRw53IMX6sDxBLG8llubHDNRju7zYkuqIKpo6xpbVpENpVK+KIAmIokAukyOdNJj1pct59Kf/YfNfVnDf5T9gWHc84Bs0mQ9OrXrDsYnAq2FnEP9SqpLPt1xQvG/kgGNOg59UKMmYs2aRHEghuyXOvfZ4lu6IsjGeJZGzqJ/XgpE0SA2liLR2E1q5Ftu0kII1ZEIxYu19pAYjTLz8FM657gSC42oBGMwefdoGu5eKmbZTKmbm+/czu/WbFjhaBxn7e8Eq9Pvr+dX1Qsld4fwAxVI6tyS8Y8HuYIsIpU3HFszxaXZWEnyyiC+ve9CXyeUrE+ziIMMtiYzzOq1Buw8sdr9dUE7++44oe2Jv7T5HwgrZ97//fURx17N92223oWnaXh5RosR7m9Tfv0wmnEVxy0QNk4AsEjWc68b8my/jgV8tY+VPn+HR+VcwrJuookCdJhfbDWBnO9VEn8qqfFth678f45vl0wB2sXgDeF+9E+snXnIsw20RjKTBRz5zLM+0hWlPGfRlcow7ZyKRHVGysSzhrZ2E1mx2HixKJLqHiHcOkI0kmHTl6XzquoVUTKxD1tTiNe/dxN6ure+VQX7hs1M4D4V2wz0JEB+N7ah7o+CAUIjJbslxQtBEAcOGnnyVoW7ZRS2lkbF+93MxMtaPdOTaW6w/kvnc5z7HmDFjdvnfRz/6/9u7/5io6z8O4M/PcdxBFxy/5BDpm8wMnOcvZMPaFClTN02tNcXQbBGbM1P/sMzVJlbT/rBmrtkWKZaZ4JZbm7YSMrEEEfmRuLL8Af4ADXWApvw47l7fP5TLgzvl+HWf+/h8bDj48P58Pu+nr+PzYm/uPvcKxo0b56MZaQNfqtBLBoMBNpsNgAIEBvt6Oj2mCwhEVJgJgToBOkvf+VaMIs63WHRuu/MNAEqXrwFHyy20371ZoXL3H7tdAOXOoTpEYAzQQR+kx61b7dDhzuuN7nczuJChFtivX8Ptexq7cveMwYE6BBj00AUGoP1mKwyPBsHRYQdE0HLbBsF/K7BGvQ6BJgOUgIA7k9EFAMrdiTnsgAD2dhsUfQAUBehosbmc0191/l/1dCy8GE8Dx5u69ebYncdX7tnW+fPSdQ6d4z0+a0BR0H732uFp3j19bN2+e1vCwMBAtLcP3h3OMzMzsX37dgBAUlISysrKui0mEAH+2+v709BgBW3/3rmHiXL3h94u/7VUuwgCdQoMjwTi1r//vf45AJ7vf3or0IhovR233DxjKVivQ4Dxbq+/0QpDiBH2djsAQcvd8Q7cuc4YA3QIfMQAnV5354IToIPzCuSwQ0Rgb7NBF6jXVK8nckfx8Lm7Xt/18670ioKOfur1gG/6fV5eHhYuXAgACAoKwunTpxEXFzco59YqLhz0UkBAABwOXz8Zh4jI/+l0Otjtg/fLfF1dHUaOHImWlhYcPHgQaWlpg3Zu8i/s9URE/Wcw+72IICUlBWVlZVi7di02bNgwKOfVMi4c9JLJZEJraysCAgIQHR3tkzmICOrr6xEbGwvFy/dnVRstZQG0lYdZ1Mvf8zQ0NMButyMoKAi3bt0a1HO/9957qKqqwr59+wb1vORf2Ov7n5byMIt6aSmPFrL4qt8XFRXhpZdewpkzZ2A2mwftvFrFhQM/duPGDZjNZjQ3NyM0NNTX0+kTLWUBtJWHWdRLa3kG082bN1FfX4+EhARfT4XovrT2c66lPMyiXlrKo6UsvnD8+HEkJyf7ehqawBd1EhHRQyckJISLBkRERBrHRYP+w4UDIiIiIiIiIvKICwd+zGg0Yt26dTAajb6eSp9pKQugrTzMol5ay0NE3Wnt51xLeZhFvbSUR0tZyL/xHgdERERERERE5BGfcUBEREREREREHnHhgIiIiIiIiIg84sIBEREREREREXnEhQMiIiIiIiIi8ogLB4Nk48aNUBQFq1atcm7bu3cvZsyYgaioKCiKgqqqqm77tbW14c0330RUVBRMJhPmzJmDS5cuuYxpbGzE4sWLYTabYTabsXjxYjQ1NbmMuXDhAp5//nmYTCZERUVhxYoVaG9vdxlTXV2N1NRUBAcHY9iwYXj//ffh7t6Zvc0ydepUKIri8pGenu7TLO7y2Gw2rFmzBmPGjIHJZEJsbCxeeeUV1NfXu+znD7XpaRY11sbd4yw7OxuJiYkwmUwIDw/HtGnTUFpa6rKfGuvSlzxqrA0RuaelXt+XPGq8brHXq7Mu7vIA/tvv2etJ04QG3LFjx2T48OEyduxYWblypXP7119/LevXr5ecnBwBIJWVld32Xbp0qQwbNkwKCgqkoqJC0tLSZNy4cdLR0eEcM3PmTLFarVJcXCzFxcVitVpl9uzZzu93dHSI1WqVtLQ0qaiokIKCAomNjZXly5c7xzQ3N4vFYpH09HSprq6W7777TkJCQmTTpk39liU1NVWysrLk8uXLzo+mpiaXMYOZxVOepqYmmTZtmuTn58upU6ekpKREUlJSZOLEiX5Xm55mUVttPD3Odu3aJQUFBXL27Fk5efKkZGZmSmhoqDQ0NKi2Ln3No7baEJF7Wur1fc2jtusWe7066+Ipj4h/9nv2etI6LhwMsJs3b8rIkSOloKBAUlNTXS4knWpqatw236amJgkMDJS8vDzntrq6OtHpdPLjjz+KiMgff/whAOTo0aPOMSUlJQJATp06JSIiP/zwg+h0Oqmrq3OO2b17txiNRmlubhYRka1bt4rZbJbW1lbnmI0bN0psbKw4HI4+ZxERj/t0GswsPc3T6dixYwJAzp8/LyL+WRtPWUTUVRtvsjQ3NwsAKSwsFBH11aWveUTUVRsick9Lvb6veUTUdd1ir1dnr/c2j9r7PXs9PQz4UoUB9sYbb2DWrFmYNm2a1/uWl5fDZrNh+vTpzm2xsbGwWq0oLi4GAJSUlMBsNiMlJcU5ZtKkSTCbzS5jrFYrYmNjnWNmzJiBtrY2lJeXO8ekpqbCaDS6jKmvr0dtbW2fs3TatWsXoqKiMHr0aKxevRo3b950fm8ws3ibp7m5GYqiICwsDIB/16Zrlk5qqU1Ps7S3t+OLL76A2WzGuHHjAKivLn3N00kttSEi97TU6/uap5Narlvs9WEu29VSF2/y+EO/Z6+nh4He1xPQsry8PFRUVKCsrKxX+1+5cgUGgwHh4eEu2y0WC65cueIcEx0d3W3f6OholzEWi8Xl++Hh4TAYDC5jhg8f3u08nd8rLS3tUxYAyMjIQHx8PGJiYnDy5EmsXbsWv//+OwoKCgY1S3x8vFe1aW1txTvvvIOXX34ZoaGhzuP4Y23cZQHUU5ueZNm3bx/S09Nx+/ZtDB06FAUFBYiKinIeQy116enj7H55APXUJj4+3mMGooeZlnq9t/3RE7Vct/raH9VUGy31+v7oj/5WG/Z60gIuHAyQixcvYuXKlThw4ACCgoL69dgiAkVRnF/f+3l/jpG7N0ppaGjolyxZWVnOz61WK0aOHInk5GRUVFQgKSlpULIoiuJVbWw2G9LT0+FwOLB169YHZlRzbe6XRQ216WmWtLQ0VFVV4dq1a8jJycH8+fNRWlrqtqH25zy9yeLN4+xBedRQG3f7EpG2er23/fF+1HDd6q/+6A57vTr6oztqrQ17PWkBX6owQMrLy9HQ0ICJEydCr9dDr9ejqKgIW7ZsgV6vh91uf+AxYmJi0N7ejsbGRpftDQ0NztXBmJgY/PPPP932vXr1qsuYzpXGTo2NjbDZbPcd09DQAODOCmRfs7iTlJSEwMBAnD59etCyWCyWHtfGZrNh/vz5qKmpQUFBgcuqvb/V5n5Z3PFFbXqaxWQy4YknnsCkSZOwbds26PV6bNu2zXl8NdTFm8fZ/fK446ufGyLqTku93pvrlrfU3FPY69X7e5g/9Pv++N3FHfZ6UqWBunnCw+7GjRtSXV3t8pGcnCyLFi2S6upql7EPumFSfn6+c1t9fb3bG7+UlpY6xxw9etTtzVLq6+udY/Ly8rrdLCUsLEza2tqcYz766COJjY2V5ubmPmdxp7q6WgBIUVHRoGVxOBw9qk17e7vMmzdPRo8e7XLXW3+szYOyqKU23jzO7jVixAhZt26diKinLj19nD0ojzu++rkhou601Ou9vW6pvd/3R39US2201Ov7qz/6U20elMUd9npSIy4cDKKud0y9fv26VFZWyv79+wWA5OXlSWVlpVy+fNk5ZunSpRIXFyeFhYVSUVEhzzzzjNu3mhk7dqyUlJRISUmJjBkzxu3bszz77LNSUVEhhYWFEhcX5/L2LE1NTWKxWGThwoVSXV0te/fuldDQUI9vz+JtljNnzsj69eulrKxMampqZP/+/ZKYmCgTJkzweZaueWw2m8yZM0fi4uKkqqrK5a1x7r3Q+kNtepJFzbW5N8u///4ra9eulZKSEqmtrZXy8nLJzMwUo9EoJ0+eVH1depNHzbUhIve01Ot7k0fN1y32enXWpWsef+/37PWkVVw4GERdm29ubq4A6PZx7wpkS0uLLF++XCIiIiQ4OFhmz54tFy5ccDnu9evXJSMjQ0JCQiQkJEQyMjKksbHRZcz58+dl1qxZEhwcLBEREbJ8+XKXt2IRETlx4oRMnjxZjEajxMTESHZ2tsfVR2+zXLhwQaZMmSIRERFiMBhkxIgRsmLFCrl+/brPs3TN0/lXFHcfv/zyi3Mff6hNT7KouTb3ZmlpaZEXXnhBYmNjxWAwyNChQ2XOnDly7Ngxl33UWpfe5FFzbYjIPS31+t7kUfN1i71enXXpmsff+z17PWmVInL3jhhERERERERERF3w5ohERERERERE5BEXDoiIiIiIiIjIIy4cEBEREREREZFHXDggIiIiIiIiIo+4cEBEREREREREHnHhgIiIiIiIiIg84sIBEREREREREXnEhQMiIiIiIiIi8ogLB0QqU1tbC0VRoCgKxo8f7+vpeK1z7mFhYb6eChERkSqx1xORv+HCAZFKFRYW4ueffx7w87z66quYN29evx3v8uXL2Lx5c78dj4iISKvY64nIX3DhgEilIiMjERkZ6etpONlsth6Ni4mJgdlsHuDZEBER+T/2eiLyF1w4IBpAV69eRUxMDDZs2ODcVlpaCoPBgAMHDnh1rM6/FmzYsAEWiwVhYWFYv349Ojo68NZbbyEiIgJxcXHYvn27y351dXVYsGABwsPDERkZiblz56K2thYAkJ2dja+++grff/+982mHhw4dcj6Fcs+ePZg6dSqCgoLwzTffAAByc3MxatQoBAUFITExEVu3bu3bfxIREZEfY68nooeB3tcTINKyIUOGYPv27Zg3bx6mT5+OxMRELFq0CMuWLcP06dO9Pt7BgwcRFxeHw4cP48iRI8jMzERJSQmmTJmC0tJS5OfnY+nSpXjuuefw2GOP4fbt20hLS8PkyZNx+PBh6PV6fPjhh5g5cyZOnDiB1atX488//8SNGzeQm5sLAIiIiEB9fT0AYM2aNfj444+Rm5sLo9GInJwcrFu3Dp999hkmTJiAyspKZGVlwWQyYcmSJf36f0dEROQP2OuJ6KEgRDTgli1bJk8++aRkZGSI1WqVlpYWj2NramoEgFRWVrpsX7JkiTz++ONit9ud2xISEmTy5MnOrzs6OsRkMsnu3btFRGTbtm2SkJAgDofDOaatrU2Cg4Plp59+ch537ty5buewefNml+2PPfaYfPvtty7bPvjgA3nqqadctuXm5orZbPaYkYiISGvY64lIy/iMA6JBsGnTJlitVuzZswfHjx9HUFBQr44zevRo6HT/vcLIYrHAarU6vw4ICEBkZCQaGhoAAOXl5Thz5gxCQkJcjtPa2oqzZ88+8HzJycnOz69evYqLFy8iMzMTWVlZzu0dHR18nSMRET302OuJSMu4cEA0CM6dO4f6+no4HA6cP38eY8eO7dVxAgMDXb5WFMXtNofDAQBwOByYOHEidu3a1e1YQ4YMeeD5TCaT8/POY+bk5CAlJcVlXEBAQM8CEBERaRR7PRFpGRcOiAZYe3s7MjIysGDBAiQmJiIzMxPV1dWwWCwDfu6kpCTk5+cjOjoaoaGhbscYDAbY7fYHHstisWDYsGE4d+4cMjIy+nuqREREfou9noi0ju+qQDTA3n33XTQ3N2PLli14++23MWrUKGRmZg7KuTMyMhAVFYW5c+fi119/RU1NDYqKirBy5UpcunQJADB8+HCcOHECf/31F65du3bft2LKzs7Gxo0b8emnn+Lvv/9GdXU1cnNz8cknnwxKHiIiIjVirycirePCAdEAOnToEDZv3oydO3ciNDQUOp0OO3fuxG+//YbPP/98wM//yCOP4PDhw/jf//6HF198EaNGjcJrr72GlpYW518lsrKykJCQgOTkZAwZMgRHjhzxeLzXX38dX375JXbs2IExY8YgNTUVO3bsQHx8/IBnISIiUiP2eiJ6GCgiIr6eBBH9p7a2FvHx8aisrMT48eN9PZ1e2bFjB1atWoWmpiZfT4WIiEh12OuJyN/wHgdEKvX0009j/PjxKC4u9vVUvPLoo4+io6Oj13eTJiIieliw1xORv+DCAZHKxMXF4fTp0wAAo9Ho49l4r6qqCgDvvkxEROQJez0R+Ru+VIGIiIiIiIiIPOLNEYmIiIiIiIjIIy4cEBEREREREZFHXDggIiIiIiIiIo+4cEBEREREREREHnHhgIiIiIiIiIg84sIBEREREREREXnEhQMiIiIiIiIi8ogLB0RERERERETk0f8Bg2kY0EqXbRIAAAAASUVORK5CYII=", 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", 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" ] @@ -884,12 +965,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -921,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -954,7 +1035,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_90f579ed4e37217d3b234d37ed767569 {\n", + " #map_f745297838fd1315d0dd11719999ca40 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -981,14 +1062,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_90f579ed4e37217d3b234d37ed767569" ></div>\n", + " <div class="folium-map" id="map_f745297838fd1315d0dd11719999ca40" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_90f579ed4e37217d3b234d37ed767569 = L.map(\n", - " "map_90f579ed4e37217d3b234d37ed767569",\n", + " var map_f745297838fd1315d0dd11719999ca40 = L.map(\n", + " "map_f745297838fd1315d0dd11719999ca40",\n", " {\n", " center: [0.0, 0.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -1002,54 +1083,54 @@ "\n", " \n", " \n", - " var tile_layer_2f27f7299ad10114d07e16903b64163b = L.tileLayer(\n", + " var tile_layer_027e35b5b23eae24ff6fa9b6145ab526 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_2f27f7299ad10114d07e16903b64163b.addTo(map_90f579ed4e37217d3b234d37ed767569);\n", + " tile_layer_027e35b5b23eae24ff6fa9b6145ab526.addTo(map_f745297838fd1315d0dd11719999ca40);\n", " \n", " \n", - " var image_overlay_36eba469b3e3422e5db43bde63db0b36 = L.imageOverlay(\n", - " "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArsAAAL3CAYAAACK+Y2VAAAgAElEQVR4nO3d7bGcuNYG0D63JgpH 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layer_control_29692a57fc9b00b71f2e895c5ffa0d57_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_2f27f7299ad10114d07e16903b64163b,\n", + " "openstreetmap" : tile_layer_027e35b5b23eae24ff6fa9b6145ab526,\n", " },\n", " overlays : {\n", - " "ndwi" : image_overlay_36eba469b3e3422e5db43bde63db0b36,\n", + " "ndwi" : image_overlay_9966b3813cbc0b3874c7b2b6aaefb827,\n", " },\n", " };\n", - " let layer_control_927c57b8cb5880ebbee42315eae832e1 = L.control.layers(\n", - " layer_control_927c57b8cb5880ebbee42315eae832e1_layers.base_layers,\n", - " layer_control_927c57b8cb5880ebbee42315eae832e1_layers.overlays,\n", + " let layer_control_29692a57fc9b00b71f2e895c5ffa0d57 = L.control.layers(\n", + " layer_control_29692a57fc9b00b71f2e895c5ffa0d57_layers.base_layers,\n", + " layer_control_29692a57fc9b00b71f2e895c5ffa0d57_layers.overlays,\n", " {"autoZIndex": true, "collapsed": true, "position": "topright"}\n", - " ).addTo(map_90f579ed4e37217d3b234d37ed767569);\n", + " ).addTo(map_f745297838fd1315d0dd11719999ca40);\n", "\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1069,25 +1150,34 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:rasterio._env:CPLE_NotSupported in driver GTiff does not support creation option WIDTH\n", - "WARNING:rasterio._env:CPLE_NotSupported in driver GTiff does not support creation option HEIGHT\n", - "WARNING:rasterio._env:CPLE_NotSupported in driver GTiff does not support creation option COUNT\n", - "WARNING:rasterio._env:CPLE_NotSupported in driver GTiff does not support creation option DTYPE\n", - "WARNING:rasterio._env:CPLE_NotSupported in driver GTiff does not support creation option CRS\n", - "WARNING:rasterio._env:CPLE_NotSupported in driver GTiff does not support creation option TRANSFORM\n" - ] - } - ], + "outputs": [], "source": [ "intertidal.astype(\"int16\").odc.write_cog(\"intertidal_map.tif\", overwrite=True);" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "Now that you have completed running this example, here's some possible next steps:\n", + "\n", + "* Download the exported `intertidal_map.tif` and load it into a GIS software (QGIS, ArcGIS Pro) to inspect the output classification.\n", + "* Return to [Analysis parameters](#Analysis-parameters), and re-run the analysis for a different location (`bbox`) or time period (`start_date`, `end_date`).\n", + "* Try modifying [Analysis parameters](#Analysis-parameters) to select a different tide model (e.g. `tide_model=\"GOT5.5\"` or `tide_model=\"HAMTIDE11\"`).\n", + "* Return to [Extracting low and high tide images](#Extracting-low-and-high-tide-images), and change the percentage thresholds (`lowtide_cutoff`, `hightide_cutoff`) used to identify low and high tide images.\n", + "* **Advanced:** In this simple analysis, we filtered to cloud-free images by discarding entire images with more than 10% cloud. Rather than filtering cloud-free images, consider masking clouds at the pixel-level using cloud masking bands that are packaged with the data (e.g. `qa_pixel`).\n", + "* **Advanced:** NDWI is just one of many possible remote sensing water indices that can be used for coastal analysis. Update the workflow to use a different water index (e.g. MNDWI), ensuring that you load any new bands required for the index calculation.\n", + "\n", + "In addition, consider the following questions:\n", + "\n", + "* What implications do the tide biases calculated in [Exploring tide biases](#Exploring-tide-biases) have for the outputs of this analysis?\n", + "* Are our outputs likely to fully map the entire intertidal zone? What areas of the intertidal zone are likely to be better or poorly mapped?\n", + "* If you experimented with running the analysis using a different tide model, how does this influence our results?\n" + ] } ], "metadata": { From 4bcb73516a3318b7f005867e6cbdaa2e25d8c215 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 21 Oct 2024 07:23:48 +0000 Subject: [PATCH 04/13] Update readme --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index b346b75..d990c5c 100644 --- a/README.md +++ b/README.md @@ -47,6 +47,10 @@ For instructions on how to set up these models for use in `eo-tides`, refer to [ To get started with `eo-tides`, follow the [Installation](https://geoscienceaustralia.github.io/eo-tides/install/) and [Setting up tide models](https://geoscienceaustralia.github.io/eo-tides/setup/) guides. +## Usage examples and case studies + +Interactive Jupyter Notebook usage examples and more complex coastal EO case studies can be found in the [`docs/notebooks/`](https://github.com/GeoscienceAustralia/eo-tides/tree/main/docs/notebooks) directory, or [rendered in the documentation here](https://geoscienceaustralia.github.io/eo-tides/notebooks/Model_tides/). + ## Citing `eo-tides` To cite `eo-tides` in your work, please use the following citation: From 3aa49178cbfa67ad7f979ee1f0636793ff14b469 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 21 Oct 2024 07:24:42 +0000 Subject: [PATCH 05/13] Update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d990c5c..d111363 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,7 @@ For instructions on how to set up these models for use in `eo-tides`, refer to [ To get started with `eo-tides`, follow the [Installation](https://geoscienceaustralia.github.io/eo-tides/install/) and [Setting up tide models](https://geoscienceaustralia.github.io/eo-tides/setup/) guides. -## Usage examples and case studies +## Jupyter Notebooks code examples Interactive Jupyter Notebook usage examples and more complex coastal EO case studies can be found in the [`docs/notebooks/`](https://github.com/GeoscienceAustralia/eo-tides/tree/main/docs/notebooks) directory, or [rendered in the documentation here](https://geoscienceaustralia.github.io/eo-tides/notebooks/Model_tides/). From 7acef1212c3a71e9e5a1fe34bfbde36224be69a1 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 21 Oct 2024 13:32:19 +0000 Subject: [PATCH 06/13] Fix plotting order bug --- docs/notebooks/Tide_statistics.ipynb | 40 ++++++++++++++-------------- eo_tides/stats.py | 6 ++--- 2 files changed, 23 insertions(+), 23 deletions(-) diff --git a/docs/notebooks/Tide_statistics.ipynb b/docs/notebooks/Tide_statistics.ipynb index 574ec47..45adea1 100644 --- a/docs/notebooks/Tide_statistics.ipynb +++ b/docs/notebooks/Tide_statistics.ipynb @@ -447,7 +447,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -510,7 +510,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5/5 [00:00<00:00, 14.01it/s]\n" + "100%|██████████| 5/5 [00:00<00:00, 39.37it/s]\n" ] }, { @@ -527,7 +527,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5/5 [00:07<00:00, 1.49s/it]\n" + "100%|██████████| 5/5 [00:02<00:00, 1.98it/s]\n" ] }, { @@ -608,7 +608,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_3e148216559840c72ca9f84d1f98ad0a {\n", + " #map_de6fd6dc676e994f44d648c75f3908d6 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -635,14 +635,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_3e148216559840c72ca9f84d1f98ad0a" ></div>\n", + " <div class="folium-map" id="map_de6fd6dc676e994f44d648c75f3908d6" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_3e148216559840c72ca9f84d1f98ad0a = L.map(\n", - " "map_3e148216559840c72ca9f84d1f98ad0a",\n", + " var map_de6fd6dc676e994f44d648c75f3908d6 = L.map(\n", + " "map_de6fd6dc676e994f44d648c75f3908d6",\n", " {\n", " center: [0.0, 0.0],\n", " crs: L.CRS.EPSG3857,\n", @@ -656,51 +656,51 @@ "\n", " \n", " \n", - " var tile_layer_f8b82cd52662d601449b07620ba7bbfe = L.tileLayer(\n", + " var tile_layer_494fc2e2a73cd023d6a3adc3894b5831 = L.tileLayer(\n", " "https://tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "\\u0026copy; \\u003ca href=\\"https://www.openstreetmap.org/copyright\\"\\u003eOpenStreetMap\\u003c/a\\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", " );\n", " \n", " \n", - " tile_layer_f8b82cd52662d601449b07620ba7bbfe.addTo(map_3e148216559840c72ca9f84d1f98ad0a);\n", + " tile_layer_494fc2e2a73cd023d6a3adc3894b5831.addTo(map_de6fd6dc676e994f44d648c75f3908d6);\n", " \n", " \n", - " var image_overlay_d029fe0c44bb2400357d3f51c2c2e454 = L.imageOverlay(\n", + " var image_overlay_9c270895249960ab066c6c4b656cf1ac = L.imageOverlay(\n", " "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAsAAAALCAYAAACprHcmAAAAwUlEQVQYla2Qvw7BUBjFf71u6s8g QiRXTBKD0eIZvItnMXsKHsArMFgESQeCpBhaiuJStXWQikic8eSck9/3wQ8yAAK7Gg5ud3r7BgDt eteICwuA9eOEpdXXZQEw1gWGp0pkNlUrbKpW+B6WAJNrmeleYc3KANSYR6X+phMhCYDpucTKzSEu BvmRIKjEI0kA61Dk4qTJOALz+ET4mmC7i2e23SzSkSSdEPMYIDz/84HaTWF6BgkNZyV5LJbfHvNn vQB1QUKDARyVYAAAAABJRU5ErkJggg== ",\n", " [[-17.774369088074852, 121.96465246015089], [-18.24423443292681, 122.45872586641661]],\n", " {}\n", " );\n", " \n", " \n", - " image_overlay_d029fe0c44bb2400357d3f51c2c2e454.addTo(map_3e148216559840c72ca9f84d1f98ad0a);\n", + " image_overlay_9c270895249960ab066c6c4b656cf1ac.addTo(map_de6fd6dc676e994f44d648c75f3908d6);\n", " \n", " \n", - " map_3e148216559840c72ca9f84d1f98ad0a.fitBounds(\n", + " map_de6fd6dc676e994f44d648c75f3908d6.fitBounds(\n", " [[-17.815037709843487, 122.00921674324115], [-18.178111016594787, 122.38538624620925]],\n", " {}\n", " );\n", " \n", " \n", - " var layer_control_d3334e0749b44838f3e6e6172f85bb0e_layers = {\n", + " var layer_control_14aad14024c22f1728b36440adc243e8_layers = {\n", " base_layers : {\n", - " "openstreetmap" : tile_layer_f8b82cd52662d601449b07620ba7bbfe,\n", + " "openstreetmap" : tile_layer_494fc2e2a73cd023d6a3adc3894b5831,\n", " },\n", " overlays : {\n", - " "spread" : image_overlay_d029fe0c44bb2400357d3f51c2c2e454,\n", + " "spread" : image_overlay_9c270895249960ab066c6c4b656cf1ac,\n", " },\n", " };\n", - " let layer_control_d3334e0749b44838f3e6e6172f85bb0e = L.control.layers(\n", - " layer_control_d3334e0749b44838f3e6e6172f85bb0e_layers.base_layers,\n", - " layer_control_d3334e0749b44838f3e6e6172f85bb0e_layers.overlays,\n", + " let layer_control_14aad14024c22f1728b36440adc243e8 = L.control.layers(\n", + " layer_control_14aad14024c22f1728b36440adc243e8_layers.base_layers,\n", + " layer_control_14aad14024c22f1728b36440adc243e8_layers.overlays,\n", " {"autoZIndex": true, "collapsed": true, "position": "topright"}\n", - " ).addTo(map_3e148216559840c72ca9f84d1f98ad0a);\n", + " ).addTo(map_de6fd6dc676e994f44d648c75f3908d6);\n", "\n", " \n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, "execution_count": 10, diff --git a/eo_tides/stats.py b/eo_tides/stats.py index 682ec8e..1931fd2 100644 --- a/eo_tides/stats.py +++ b/eo_tides/stats.py @@ -136,7 +136,7 @@ def _plot_biases( def tide_stats( - ds: xr.Dataset, + ds: xr.Dataset | xr.DataArray, model: str = "EOT20", directory: str | os.PathLike | None = None, tidepost_lat: float | None = None, @@ -167,7 +167,7 @@ def tide_stats( Parameters ---------- - ds : xarray.Dataset + ds : xarray.Dataset or xarray.DataArray A multi-dimensional dataset (e.g. "x", "y", "time") used to calculate tide statistics. This dataset must contain a "time" dimension. @@ -266,7 +266,7 @@ def tide_stats( return_tideposts=True, **model_tides_kwargs, ) - obs_tides_da = obs_tides_da.sortby("time") + obs_tides_da = obs_tides_da.reindex_like(ds) # Generate range of times covering entire period of satellite record all_timerange = pd.date_range( From bfccbe0023f05630231876c2923b4705b7be3f98 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Fri, 25 Oct 2024 10:30:55 +0000 Subject: [PATCH 07/13] Add phase tides --- eo_tides/eo.py | 175 +++-- eo_tides/model.py | 124 ++++ tests/test_eo.py | 14 +- tests/test_model.py | 84 ++- tests/testing.ipynb | 1652 ++++++++++++++++++++++++++++++++++--------- 5 files changed, 1610 insertions(+), 439 deletions(-) diff --git a/eo_tides/eo.py b/eo_tides/eo.py index 0c749dd..bef93d9 100644 --- a/eo_tides/eo.py +++ b/eo_tides/eo.py @@ -18,6 +18,34 @@ from .model import _standardise_time, model_tides +def _resample_chunks( + ds: xr.DataArray | xr.Dataset | GeoBox, + dask_chunks: tuple | None = None, +) -> tuple: + """ + Automatically return optimised dask chunks + for reprojection with `_pixel_tides_resample`. + Use entire image if GeoBox or if no default + chunks; use existing chunks if they exist. + """ + + # If dask_chunks is provided, return directly + if dask_chunks is not None: + return dask_chunks + + # If ds is a GeoBox, return its shape + if isinstance(ds, GeoBox): + return ds.shape + + # if ds has chunks, then return just spatial chunks + if ds.chunks is not None: + y_dim, x_dim = ds.odc.spatial_dims + return ds.chunks[y_dim], ds.chunks[x_dim] + + # if ds has no chunks, then return entire image shape + return ds.odc.geobox.shape + + def _standardise_inputs( ds: xr.DataArray | xr.Dataset | GeoBox, time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None, @@ -45,10 +73,10 @@ def _standardise_inputs( # Use custom time by default if provided; otherwise try and extract from `ds` if time is not None: time = _standardise_time(time) - elif "time" in ds.coords: + elif "time" in ds.dims: time = ds.coords["time"].values else: - raise ValueError("`ds` does not have a time dimension, and no custom times were provided via `time`.") + raise ValueError("`ds` does not have a 'time' dimension, and no custom times were provided via `time`.") # If `ds` is a GeoBox, use it directly; raise an error if no time was provided elif isinstance(ds, GeoBox): @@ -56,7 +84,7 @@ def _standardise_inputs( if time is not None: time = _standardise_time(time) else: - raise ValueError("If `ds` is a GeoBox, `time` must be provided.") + raise ValueError("If `ds` is a GeoBox, custom times must be provided via `time`.") # Raise error if no valid inputs were provided else: @@ -69,7 +97,7 @@ def _pixel_tides_resample( tides_lowres, gbox, resample_method="bilinear", - dask_chunks="auto", + dask_chunks=None, dask_compute=True, ): """Resamples low resolution tides modelled by `pixel_tides` into the @@ -87,11 +115,11 @@ def _pixel_tides_resample( resample_method : string, optional The resampling method to use. Defaults to "bilinear"; valid options include "nearest", "cubic", "min", "max", "average" etc. - dask_chunks : str or tuple, optional + dask_chunks : tuple of float, optional Can be used to configure custom Dask chunking for the final - resampling step. The default of "auto" will automatically set - x/y chunks to match those in `ds` if they exist, otherwise will - set x/y chunks that cover the entire extent of the dataset. + resampling step. By default, chunks will be automatically set + to match y/x chunks from `ds` if they exist; otherwise chunks + will be chosen to cover the entire y/x extent of the dataset. For custom chunks, provide a tuple in the form `(y, x)`, e.g. `(2048, 2048)`. dask_compute : bool, optional @@ -113,20 +141,6 @@ def _pixel_tides_resample( # and a single chunk for each timestep/quantile and tide model tides_lowres_dask = tides_lowres.chunk({d: None if d in [y_dim, x_dim] else 1 for d in tides_lowres.dims}) - # Automatically set Dask chunks for reprojection if set to "auto". - # This will either use x/y chunks if they exist in `ds`, else - # will cover the entire x and y dims) so we don't end up with - # hundreds of tiny x and y chunks due to the small size of - # `tides_lowres` (possible odc.geo bug?) - if dask_chunks == "auto": - if ds.chunks is not None: - if (y_dim in ds.chunks) & (x_dim in ds.chunks): - dask_chunks = (ds.chunks[y_dim], ds.chunks[x_dim]) - else: - dask_chunks = gbox.shape - else: - dask_chunks = gbox.shape - # Reproject into the GeoBox of `ds` using odc.geo and Dask tides_highres = tides_lowres_dask.odc.reproject( how=gbox, @@ -142,7 +156,8 @@ def _pixel_tides_resample( def tag_tides( - ds: xr.Dataset | xr.DataArray, + ds: xr.Dataset | xr.DataArray | GeoBox, + time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None = None, model: str | list[str] = "EOT20", directory: str | os.PathLike | None = None, tidepost_lat: float | None = None, @@ -152,7 +167,7 @@ def tag_tides( """ Model tide heights for every timestep in a multi-dimensional dataset, and return a new `tide_height` array that can - be used to "tag" each observation with tide data. + be used to "tag" each observation with tide heights. The function models tides at the centroid of the dataset by default, but a custom tidal modelling location can @@ -170,15 +185,23 @@ def tag_tides( Parameters ---------- - ds : xarray.Dataset or xarray.DataArray - A multi-dimensional dataset (e.g. "x", "y", "time") to - tag with tide heights. This dataset must contain a "time" - dimension. + ds : xarray.Dataset or xarray.DataArray or odc.geo.geobox.GeoBox + A multi-dimensional dataset or GeoBox pixel grid that will + be used to define the tide modelling location. If `ds` + is an xarray object, it should include a "time" dimension. + If no "time" dimension exists or if `ds` is a GeoBox, + then times must be passed using the `time` parameter. + time : pd.DatetimeIndex or list of pd.Timestamp, optional + By default, the function will model tides using the times + contained in the "time" dimension of `ds`. Alternatively, this + param can be used to model tides for a custom set of times + instead. For example: + `times=pd.date_range(start="2000", end="2001", freq="5h")` model : str or list of str, optional - The tide model (or models) to use to model tides. If a list is - provided, a new "tide_model" dimension will be added to `ds`. - Defaults to "EOT20"; for a full list of available/supported - models, run `eo_tides.model.list_models`. + The tide model (or models) used to model tides. If a list is + provided, a new "tide_model" dimension will be added to the + `xarray.DataArray` outputs. Defaults to "EOT20"; for a full + list of available/supported models, run `eo_tides.model.list_models`. directory : str, optional The directory containing tide model data files. If no path is provided, this will default to the environment variable @@ -199,23 +222,18 @@ def tag_tides( Returns ------- - ds : xr.Dataset - The original `xarray.Dataset` with a new `tide_height` variable - giving the height of the tide (and optionally, its ebb-flow phase) - for each timestep in the data. - + tides_da : xr.DataArray + A one-dimensional tide height array. This will contain either + tide heights for every timestep in `ds`, or for every time in + `times` if provided. """ - # Only datasets are supported - if not isinstance(ds, xr.Dataset): - raise TypeError("Input must be an xarray.Dataset, not an xarray.DataArray or other data type.") - - # Standardise model into a list for easy handling. and verify only one + # Standardise data inputs, time and models + gbox, time_coords = _standardise_inputs(ds, time) model = [model] if isinstance(model, str) else model - # If custom tide modelling locations are not provided, use the - # dataset centroid + # If custom tide posts are not provided, use dataset centroid if tidepost_lat is None or tidepost_lon is None: - lon, lat = ds.odc.geobox.geographic_extent.centroid.coords[0] + lon, lat = gbox.geographic_extent.centroid.coords[0] print(f"Setting tide modelling location from dataset centroid: {lon:.2f}, {lat:.2f}") else: lon, lat = tidepost_lon, tidepost_lat @@ -225,7 +243,7 @@ def tag_tides( tide_df = model_tides( x=lon, # type: ignore y=lat, # type: ignore - time=ds.time, + time=time_coords, model=model, directory=directory, crs="EPSG:4326", @@ -268,13 +286,13 @@ def tag_tides( # }) # Convert to xarray format - tide_xr = tide_df.reset_index().set_index(["time", "tide_model"]).drop(["x", "y"], axis=1).tide_height.to_xarray() + tides_da = tide_df.reset_index().set_index(["time", "tide_model"]).drop(["x", "y"], axis=1).tide_height.to_xarray() # If only one tidal model exists, squeeze out "tide_model" dim - if len(tide_xr.tide_model) == 1: - tide_xr = tide_xr.squeeze("tide_model") + if len(tides_da.tide_model) == 1: + tides_da = tides_da.squeeze("tide_model") - return tide_xr + return tides_da def pixel_tides( @@ -287,7 +305,7 @@ def pixel_tides( resolution: float | None = None, buffer: float | None = None, resample_method: str = "bilinear", - dask_chunks: str | tuple[float, float] = "auto", + dask_chunks: tuple[float, float] = None, dask_compute: bool = True, **model_tides_kwargs, ) -> xr.DataArray: @@ -298,10 +316,9 @@ def pixel_tides( This function models tides into a low-resolution tide modelling grid covering the spatial extent of the input data (buffered to reduce potential edge effects). These - modelled tides are then (optionally) resampled back into - the original higher resolution dataset's extent and - resolution - resulting in a modelled tide height for every - pixel through time. + modelled tides can then be resampled back into the original + higher resolution dataset's extent and resolution to + produce a modelled tide height for every pixel through time. This function uses the parallelised `model_tides` function under the hood. It supports all tidal models supported by @@ -321,12 +338,15 @@ def pixel_tides( Parameters ---------- - ds : xarray.Dataset or xarray.DataArray - A multi-dimensional dataset (e.g. "x", "y", "time") that will - be used to define the tide modelling grid. + ds : xarray.Dataset or xarray.DataArray or odc.geo.geobox.GeoBox + A multi-dimensional dataset or GeoBox pixel grid that will + be used to define the spatial tide modelling grid. If `ds` + is an xarray object, it should include a "time" dimension. + If no "time" dimension exists or if `ds` is a GeoBox, + then times must be passed using the `time` parameter. time : pd.DatetimeIndex or list of pd.Timestamp, optional By default, the function will model tides using the times - contained in the `time` dimension of `ds`. Alternatively, this + contained in the "time" dimension of `ds`. Alternatively, this param can be used to model tides for a custom set of times instead. For example: `times=pd.date_range(start="2000", end="2001", freq="5h")` @@ -363,12 +383,12 @@ def pixel_tides( degree / ~5 km resolution grid for FES2014). buffer : float, optional The amount by which to buffer the higher resolution grid extent - when creating the new low resolution grid. This buffering is - important as it ensures that ensure pixel-based tides are seamless - across dataset boundaries. This buffer will eventually be clipped - away when the low-resolution data is re-projected back to the - resolution and extent of the higher resolution dataset. To - ensure that at least two pixels occur outside of the dataset + when creating the new low resolution grid. This buffering + ensures that modelled tides are seamless across analysis + boundaries. This buffer is eventually be clipped away when + the low-resolution modelled tides are re-projected back to the + original resolution and extent of `ds`. To ensure that at least + two low-resolution grid pixels occur outside of the dataset bounds, the default None applies a 12000 m buffer if `ds` has a projected CRS (i.e. metre units), or a 0.12 degree buffer if `ds` has a geographic CRS (e.g. degree units). @@ -377,16 +397,16 @@ def pixel_tides( resampling method when converting from low resolution to high resolution pixels. Defaults to "bilinear"; valid options include "nearest", "cubic", "min", "max", "average" etc. - dask_chunks : str or tuple of float, optional + dask_chunks : tuple of float, optional Can be used to configure custom Dask chunking for the final - resampling step. The default of "auto" will automatically set - x/y chunks to match those in `ds` if they exist, otherwise will - set x/y chunks that cover the entire extent of the dataset. + resampling step. By default, chunks will be automatically set + to match y/x chunks from `ds` if they exist; otherwise chunks + will be chosen to cover the entire y/x extent of the dataset. For custom chunks, provide a tuple in the form `(y, x)`, e.g. `(2048, 2048)`. dask_compute : bool, optional Whether to compute results of the resampling step using Dask. - If False, this will return `tides_highres` as a Dask array. + If False, `tides_highres` will be returned as a Dask array. **model_tides_kwargs : Optional parameters passed to the `eo_tides.model.model_tides` function. Important parameters include `cutoff` (used to @@ -396,19 +416,18 @@ def pixel_tides( Returns ------- tides_da : xr.DataArray - If `resample=True` (default), a high-resolution array - of tide heights matching the exact spatial resolution and - extents of `ds`. This will contain either tide heights every - timestep in `ds` (if `times` is None), tide heights at every - time in `times` (if `times` is not None), or tide height + A three-dimensional tide height array. + If `resample=True` (default), a high-resolution array of tide + heights will be returned that matches the exact spatial resolution + and extents of `ds`. This will contain either tide heights for + every timestep in `ds` (or in `times` if provided), or tide height quantiles for every quantile provided by `calculate_quantiles`. If `resample=False`, results for the intermediate low-resolution tide modelling grid will be returned instead. """ - # Standardise data inputs and time + # Standardise data inputs, time and models gbox, time_coords = _standardise_inputs(ds, time) - - # Standardise model into a list for easy handling + dask_chunks = _resample_chunks(ds, dask_chunks) model = [model] if isinstance(model, str) else model # Determine spatial dimensions diff --git a/eo_tides/model.py b/eo_tides/model.py index 64e058e..f3527a9 100644 --- a/eo_tides/model.py +++ b/eo_tides/model.py @@ -865,3 +865,127 @@ def model_tides( tide_df = tide_df.reindex(output_indices) return tide_df + + +def phase_tides( + x, + y, + time, + model="EOT20", + directory=None, + time_offset="15 min", + return_tides=False, + **model_tides_kwargs, +): + """ + Model tide phases (low-flow, high-flow, high-ebb, low-ebb) + at multiple coordinates and/or timesteps using using one + or more ocean tide models. + + Ebb and low phases are calculated by running the + `eo_tides.model.model_tides` function twice, once for + the requested timesteps, and again after subtracting a + small time offset (by default, 15 minutes). If tides + increased over this period, they are assigned as "flow"; + if they decreased, they are assigned as "ebb". + Tides are considered "high" if equal or greater than 0 + metres tide height, otherwise "low". + + This function supports all parameters that are supported + by `model_tides`. + + Parameters + ---------- + x, y : float or list of float + One or more x and y coordinates used to define + the location at which to model tide phases. By default + these coordinates should be lat/lon; use "crs" if they + are in a custom coordinate reference system. + time : Numpy datetime array or pandas.DatetimeIndex + An array containing `datetime64[ns]` values or a + `pandas.DatetimeIndex` providing the times at which to + model tide phases in UTC time. + model : str or list of str, optional + The tide model (or models) to use to compute tide phases. + Defaults to "EOT20"; for a full list of available/supported + models, run `eo_tides.model.list_models`. + directory : str, optional + The directory containing tide model data files. If no path is + provided, this will default to the environment variable + `EO_TIDES_TIDE_MODELS` if set, or raise an error if not. + Tide modelling files should be stored in sub-folders for each + model that match the structure required by `pyTMD` + (). + **model_tides_kwargs : + Optional parameters passed to the `eo_tides.model.model_tides` + function. Important parameters include `output_format` (e.g. + whether to return results in wide or long format), `crop` + (whether to crop tide model constituent files on-the-fly to + improve performance) etc. + + Returns + ------- + pandas.DataFrame + A dataframe containing modelled tide phases. + + """ + + # Pop output format and mode for special handling + output_format = model_tides_kwargs.pop("output_format", "long") + mode = model_tides_kwargs.pop("mode", "one-to-many") + + # Model tides + tide_df = model_tides( + x=x, + y=y, + time=time, + model=model, + directory=directory, + **model_tides_kwargs, + ) + + # Model tides for a time 15 minutes prior to each previously + # modelled satellite acquisition time. This allows us to compare + # tide heights to see if they are rising or falling. + pre_df = model_tides( + x=x, + y=y, + time=time - pd.Timedelta(time_offset), + model=model, + directory=directory, + **model_tides_kwargs, + ) + + # Compare tides computed for each timestep. If the previous tide + # was higher than the current tide, the tide is 'ebbing'. If the + # previous tide was lower, the tide is 'flowing' + ebb_flow = (tide_df.tide_height < pre_df.tide_height.values).replace({True: "ebb", False: "flow"}) + + # If tides are greater than 0, then "high", otherwise "low" + high_low = (tide_df.tide_height >= 0).replace({True: "high", False: "low"}) + + # Combine into one string and add to data + tide_df["tide_phase"] = high_low.astype(str) + "-" + ebb_flow.astype(str) + + # Optionally convert to a wide format dataframe with a tide model in + # each dataframe column + if output_format == "wide": + # Pivot into wide format with each time model as a column + print("Converting to a wide format dataframe") + tide_df = tide_df.pivot(columns="tide_model") + + # If in 'one-to-one' mode, reindex using our input time/x/y + # values to ensure the output is sorted the same as our inputs + if mode == "one-to-one": + output_indices = pd.MultiIndex.from_arrays([time, x, y], names=["time", "x", "y"]) + tide_df = tide_df.reindex(output_indices) + + # Optionally drop tides + if not return_tides: + return tide_df.drop("tide_height", axis=1)["tide_phase"] + + # Optionally drop tide heights + if not return_tides: + return tide_df.drop("tide_height", axis=1) + + return tide_df diff --git a/tests/test_eo.py b/tests/test_eo.py index 1b1cb89..a0c8702 100644 --- a/tests/test_eo.py +++ b/tests/test_eo.py @@ -148,20 +148,20 @@ def test_pixel_tides_times(satellite_ds, measured_tides_ds): custom_times = pd.date_range( start="2022-01-01", end="2022-01-05", - freq="6H", + freq="6h", ) # Verify that correct times are included on output - measured_tides_ds = pixel_tides(satellite_ds, times=custom_times) + measured_tides_ds = pixel_tides(satellite_ds, time=custom_times) assert all(measured_tides_ds.time.values == custom_times) assert len(measured_tides_ds.time) == len(custom_times) # Verify passing a list - measured_tides_ds = pixel_tides(satellite_ds, times=custom_times.tolist()) + measured_tides_ds = pixel_tides(satellite_ds, time=custom_times.tolist()) assert len(measured_tides_ds.time) == len(custom_times) # Verify passing a single timestamp - measured_tides_ds = pixel_tides(satellite_ds, times=custom_times.tolist()[0]) + measured_tides_ds = pixel_tides(satellite_ds, time=custom_times.tolist()[0]) assert len(measured_tides_ds) == 1 # Verify that passing a dataset without time leads to error @@ -170,7 +170,7 @@ def test_pixel_tides_times(satellite_ds, measured_tides_ds): pixel_tides(satellite_ds_notime) # Verify passing a dataset without time and custom times - measured_tides_ds = pixel_tides(satellite_ds_notime, times=custom_times) + measured_tides_ds = pixel_tides(satellite_ds_notime, time=custom_times) assert len(measured_tides_ds.time) == len(custom_times) @@ -258,7 +258,7 @@ def test_pixel_tides_multiplemodels(satellite_ds, quantiles): # Run test for different combinations of Dask chunking @pytest.mark.parametrize( "dask_chunks", - ["auto", (300, 300), (200, 300)], + [None, (300, 300), (200, 300)], ) def test_pixel_tides_dask(satellite_ds, dask_chunks): # Model tides with Dask compute turned off to return Dask arrays @@ -268,7 +268,7 @@ def test_pixel_tides_dask(satellite_ds, dask_chunks): assert dask.is_dask_collection(modelled_tides_ds) # If chunks set to "auto", check output matches `satellite_ds` chunks - if dask_chunks == "auto": + if dask_chunks is None: assert modelled_tides_ds.chunks == satellite_ds.nbart_red.chunks # Otherwise, check output chunks match requested chunks diff --git a/tests/test_model.py b/tests/test_model.py index 12194e9..3493a11 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -5,7 +5,7 @@ import pytest from pyTMD.compute import tide_elevations -from eo_tides.model import _set_directory, list_models, model_tides +from eo_tides.model import _set_directory, list_models, model_tides, phase_tides from eo_tides.validation import eval_metrics GAUGE_X = 122.2183 @@ -13,6 +13,88 @@ ENSEMBLE_MODELS = ["EOT20", "HAMTIDE11"] # simplified for tests +@pytest.mark.parametrize("time_offset", ["15 min", "20 min"]) +def test_phase_tides(time_offset): + phase_df = phase_tides( + x=[122.14], + y=[-17.91], + time=pd.date_range("2020-01-01", "2020-01-02", freq="h"), + model=["EOT20"], + time_offset=time_offset, + ) + + assert phase_df.tide_phase.tolist() == [ + "low-flow", + "low-flow", + "low-flow", + "low-flow", + "high-flow", + "high-flow", + "high-flow", + "high-ebb", + "high-ebb", + "high-ebb", + "low-ebb", + "low-ebb", + "low-ebb", + "low-flow", + "low-flow", + "high-flow", + "high-flow", + "high-flow", + "high-flow", + "high-ebb", + "high-ebb", + "high-ebb", + "low-ebb", + "low-ebb", + "low-ebb", + ] + + +@pytest.mark.parametrize( + "models,output_format,return_tides,expected_cols", + [ + (["EOT20"], "long", False, ["tide_model", "tide_phase"]), + (["EOT20"], "long", True, ["tide_model", "tide_height", "tide_phase"]), + (["EOT20", "GOT5.5"], "long", False, ["tide_model", "tide_phase"]), + ( + ["EOT20", "GOT5.5"], + "long", + True, + ["tide_model", "tide_height", "tide_phase"], + ), + (["EOT20"], "wide", False, ["EOT20"]), + (["EOT20"], "wide", True, [("tide_height", "EOT20"), ("tide_phase", "EOT20")]), + (["EOT20", "GOT5.5"], "wide", False, ["EOT20", "GOT5.5"]), + ( + ["EOT20", "GOT5.5"], + "wide", + True, + [ + ("tide_height", "EOT20"), + ("tide_height", "GOT5.5"), + ("tide_phase", "EOT20"), + ("tide_phase", "GOT5.5"), + ], + ), + ], +) +def test_phase_tides_format(models, output_format, return_tides, expected_cols): + phase_df = phase_tides( + x=[122.14], + y=[-17.91], + time=pd.date_range("2020", "2021", periods=2), + model=models, + output_format=output_format, + return_tides=return_tides, + ) + + # Assert expected indexes and columns + assert phase_df.index.names == ["time", "x", "y"] + assert phase_df.columns.tolist() == expected_cols + + # Test available tide models def test_list_models(): # Using env var diff --git a/tests/testing.ipynb b/tests/testing.ipynb index 84c2164..48136b9 100644 --- a/tests/testing.ipynb +++ b/tests/testing.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -27,92 +27,1304 @@ "import numpy as np\n", "\n", "\n", - "GAUGE_X = 122.2183\n", - "GAUGE_Y = -18.0008\n", - "ENSEMBLE_MODELS = [\"EOT20\", \"HAMTIDE11\"] # simplified for tests" + "GAUGE_X = 122.2183\n", + "GAUGE_Y = -18.0008\n", + "ENSEMBLE_MODELS = [\"EOT20\", \"HAMTIDE11\"] # simplified for tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load fixtures" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load_satellite_ds():\n", + " \"\"\"\n", + " Load a sample timeseries of Landsat 8 data using odc-stac\n", + " \"\"\"\n", + " # Connect to stac catalogue\n", + " catalog = pystac_client.Client.open(\"https://explorer.dea.ga.gov.au/stac\")\n", + "\n", + " # Set cloud defaults\n", + " odc.stac.configure_rio(\n", + " cloud_defaults=True,\n", + " aws={\"aws_unsigned\": True},\n", + " )\n", + "\n", + " # Build a query with the parameters above\n", + " buffer = 0.08\n", + " # buffer = 0.5\n", + " bbox = [GAUGE_X - buffer, GAUGE_Y - buffer, GAUGE_X + buffer, GAUGE_Y + buffer]\n", + " query = catalog.search(\n", + " bbox=bbox,\n", + " collections=[\"ga_ls8c_ard_3\"],\n", + " datetime=\"2020-01/2020-02\",\n", + " )\n", + "\n", + " # Search the STAC catalog for all items matching the query\n", + " ds = odc.stac.load(\n", + " list(query.items()),\n", + " bands=[\"nbart_red\"],\n", + " crs=\"epsg:3577\",\n", + " resolution=30,\n", + " groupby=\"solar_day\",\n", + " bbox=bbox,\n", + " fail_on_error=False,\n", + " chunks={\"x\": 100, \"y\": 200},\n", + " )\n", + "\n", + " return ds\n", + "\n", + "satellite_ds = load_satellite_ds()\n", + "\n", + "def load_measured_tides_ds():\n", + " \"\"\"\n", + " Load measured sea level data from the Broome ABSLMP tidal station:\n", + " http://www.bom.gov.au/oceanography/projects/abslmp/data/data.shtml\n", + " \"\"\"\n", + " # Metadata for Broome ABSLMP tidal station:\n", + " # http://www.bom.gov.au/oceanography/projects/abslmp/data/data.shtml\n", + " ahd_offset = -5.322\n", + "\n", + " # Load measured tides from ABSLMP tide gauge data\n", + " measured_tides_df = pd.read_csv(\n", + " \"../tests/data/IDO71013_2020.csv\",\n", + " index_col=0,\n", + " parse_dates=True,\n", + " na_values=-9999,\n", + " )[[\"Sea Level\"]]\n", + "\n", + " # Update index and column names\n", + " measured_tides_df.index.name = \"time\"\n", + " measured_tides_df.columns = [\"tide_height\"]\n", + "\n", + " # Apply station AHD offset\n", + " measured_tides_df += ahd_offset\n", + "\n", + " # Return as xarray dataset\n", + " return measured_tides_df.to_xarray()\n", + "\n", + "satellite_ds = load_satellite_ds()\n", + "measured_tides_ds = load_measured_tides_ds()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = satellite_ds.copy(deep=True)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from odc.geo.geobox import GeoBox\n", + "import xarray as xr\n", + "\n", + "\n", + "def _resample_chunks(\n", + " ds: xr.DataArray | xr.Dataset | GeoBox,\n", + " dask_chunks: tuple | None = None,\n", + ") -> tuple:\n", + " \"\"\"\n", + " Automatically return optimised dask chunks\n", + " for reprojection with _pixel_tides_resample.\n", + " Use entire image if GeoBox or if no default\n", + " chunks; use existing chunks if they exist.\n", + " \"\"\"\n", + "\n", + " # If dask_chunks is provided, return directly\n", + " if dask_chunks is not None:\n", + " return dask_chunks\n", + "\n", + " # If ds is a GeoBox, return its shape\n", + " if isinstance(ds, GeoBox):\n", + " return ds.shape\n", + "\n", + " # if ds has chunks, then return just spatial chunks\n", + " if ds.chunks is not None:\n", + " y_dim, x_dim = ds.odc.spatial_dims\n", + " return ds.chunks[y_dim], ds.chunks[x_dim]\n", + "\n", + " # if ds has no chunks, then return entire image shape\n", + " return ds.odc.geobox.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "_resample_chunks(ds, None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cd .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "satellite_ds.isel(time=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [], + "source": [ + "from eo_tides import model_tides\n", + "\n", + "\n", + "def tide_phase(\n", + " x,\n", + " y,\n", + " time,\n", + " model=\"EOT20\",\n", + " directory=None,\n", + " delta=\"15 min\",\n", + " return_tides=False,\n", + " **model_tides_kwargs,\n", + "):\n", + "\n", + " # Pop output format and mode for special handling\n", + " output_format = model_tides_kwargs.pop(\"output_format\", \"long\")\n", + " mode = model_tides_kwargs.pop(\"mode\", \"one-to-many\")\n", + "\n", + " # Model tides\n", + " tide_df = model_tides(\n", + " x=x,\n", + " y=y,\n", + " time=time,\n", + " model=model,\n", + " directory=directory,\n", + " **model_tides_kwargs,\n", + " )\n", + "\n", + " # Model tides for a time 15 minutes prior to each previously\n", + " # modelled satellite acquisition time. This allows us to compare\n", + " # tide heights to see if they are rising or falling.\n", + " pre_df = model_tides(\n", + " x=x,\n", + " y=y,\n", + " time=time - pd.Timedelta(delta),\n", + " model=model,\n", + " directory=directory,\n", + " **model_tides_kwargs,\n", + " )\n", + "\n", + " # Compare tides computed for each timestep. If the previous tide\n", + " # was higher than the current tide, the tide is 'ebbing'. If the\n", + " # previous tide was lower, the tide is 'flowing'\n", + " ebb_flow = (tide_df.tide_height < pre_df.tide_height.values).replace({True: \"ebb\", False: \"flow\"})\n", + "\n", + " # If tides are greater than 0, then \"high\", otherwise \"low\"\n", + " high_low = (tide_df.tide_height >= 0).replace({True: \"high\", False: \"low\"})\n", + "\n", + " # Combine into one string and add to data\n", + " tide_df[\"tide_phase\"] = high_low.astype(str) + \"-\" + ebb_flow.astype(str)\n", + "\n", + " # Optionally convert to a wide format dataframe with a tide model in\n", + " # each dataframe column\n", + " if output_format == \"wide\":\n", + " # Pivot into wide format with each time model as a column\n", + " print(\"Converting to a wide format dataframe\")\n", + " tide_df = tide_df.pivot(\n", + " columns=\"tide_model\"\n", + " )\n", + "\n", + " # If in 'one-to-one' mode, reindex using our input time/x/y\n", + " # values to ensure the output is sorted the same as our inputs\n", + " if mode == \"one-to-one\":\n", + " output_indices = pd.MultiIndex.from_arrays(\n", + " [time, x, y], names=[\"time\", \"x\", \"y\"]\n", + " )\n", + " tide_df = tide_df.reindex(output_indices)\n", + "\n", + " # Optionally drop tides\n", + " if not return_tides:\n", + " return tide_df.drop(\"tide_height\", axis=1)[\"tide_phase\"]\n", + "\n", + " # Optionally drop tide heights\n", + " if not return_tides:\n", + " return tide_df.drop(\"tide_height\", axis=1)\n", + "\n", + " return tide_df" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "@pytest.mark.parametrize(\n", + " \"models,output_format,return_tides,expected_cols\",\n", + " [\n", + " (\n", + " [\"EOT20\"],\n", + " \"long\",\n", + " False,\n", + " [\"tide_model\", \"tide_phase\"]\n", + " ),\n", + " (\n", + " [\"EOT20\"],\n", + " \"long\",\n", + " True,\n", + " [\"tide_model\", \"tide_height\", \"tide_phase\"]\n", + " ),\n", + " (\n", + " [\"EOT20\", \"GOT5.5\"],\n", + " \"long\",\n", + " False,\n", + " [\"tide_model\", \"tide_phase\"]\n", + " ),\n", + " (\n", + " [\"EOT20\", \"GOT5.5\"],\n", + " \"long\",\n", + " True,\n", + " [\"tide_model\", \"tide_height\", \"tide_phase\"]\n", + " ),\n", + " (\n", + " [\"EOT20\"],\n", + " \"wide\",\n", + " False,\n", + " [\"EOT20\"]\n", + " ),\n", + " (\n", + " [\"EOT20\"],\n", + " \"wide\",\n", + " True,\n", + " [(\"tide_phase\", \"EOT20\"), (\"tide_phase\", \"EOT20\")]\n", + " ),\n", + " (\n", + " [\"EOT20\", \"GOT5.5\"],\n", + " \"wide\",\n", + " False,\n", + " [\"EOT20\", \"GOT5.5\"]\n", + " ),\n", + " (\n", + " [\"EOT20\", \"GOT5.5\"],\n", + " \"wide\",\n", + " True,\n", + " [\n", + " (\"tide_height\", \"EOT20\"),\n", + " (\"tide_height\", \"GOT5.5\"),\n", + " (\"tide_phase\", \"EOT20\"),\n", + " (\"tide_phase\", \"GOT5.5\"),\n", + " ]\n", + " ),\n", + " ]\n", + ")\n", + "def test_tide_phase_format(models, output_format, return_tides, expected_cols):\n", + "\n", + " phase_df = phase_tides(\n", + " x=[122.14, 122.30, 122.12],\n", + " y=[-17.91, -17.92, -18.07],\n", + " time=pd.date_range(\"2020\", \"2021\", periods=3),\n", + " directory=\"/var/share/tide_models/\",\n", + " model=models,\n", + " output_format=output_format,\n", + " return_tides=return_tides,\n", + " delta = \"15 min\",\n", + " )\n", + "\n", + " # Assert expected indexes and columns\n", + " assert phase_df.index.names == [\"time\", \"x\", \"y\"]\n", + " assert phase_df.columns.tolist() == expected_cols\n", + "\n", + "\n", + "\n", + "\n", + "# !pytest -q -k test_ebb_flow --verbose" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modelling tides using EOT20 in parallel\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:01<00:00, 2.72it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modelling tides using EOT20 in parallel\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:01<00:00, 2.64it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['tide_model', 'tide_height', 'ebb_flow']\n" + ] + } + ], + "source": [ + "test_ebb_flow(models=[\"EOT20\"], output_format=\"long\", return_tides=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modelling tides using EOT20, GOT5.5 in parallel\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:01<00:00, 4.01it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modelling tides using EOT20, GOT5.5 in parallel\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6/6 [00:01<00:00, 4.57it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converting to a wide format dataframe\n" + ] + }, + { + "data": { + "text/html": [ + "
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tide_heighttide_phase
tide_modelEOT20GOT5.5EOT20GOT5.5
timexy
2020-01-01122.12-18.07-2.800434-2.870334low-ebblow-ebb
122.14-17.91-2.664830-2.765702low-flowlow-ebb
122.30-17.92-2.855278-2.815728low-ebblow-ebb
2020-07-02122.12-18.072.2004032.171265high-ebbhigh-ebb
122.14-17.912.1107532.117294high-ebbhigh-ebb
122.30-17.922.2211572.128876high-ebbhigh-ebb
2021-01-01122.12-18.07-2.070869-2.066286low-flowlow-flow
122.14-17.91-1.889267-1.910610low-flowlow-flow
122.30-17.92-2.168566-1.993354low-flowlow-flow
\n", + "
" + ], + "text/plain": [ + " tide_height tide_phase \n", + "tide_model EOT20 GOT5.5 EOT20 GOT5.5\n", + "time x y \n", + "2020-01-01 122.12 -18.07 -2.800434 -2.870334 low-ebb low-ebb\n", + " 122.14 -17.91 -2.664830 -2.765702 low-flow low-ebb\n", + " 122.30 -17.92 -2.855278 -2.815728 low-ebb low-ebb\n", + "2020-07-02 122.12 -18.07 2.200403 2.171265 high-ebb high-ebb\n", + " 122.14 -17.91 2.110753 2.117294 high-ebb high-ebb\n", + " 122.30 -17.92 2.221157 2.128876 high-ebb high-ebb\n", + "2021-01-01 122.12 -18.07 -2.070869 -2.066286 low-flow low-flow\n", + " 122.14 -17.91 -1.889267 -1.910610 low-flow low-flow\n", + " 122.30 -17.92 -2.168566 -1.993354 low-flow low-flow" + ] + }, + "execution_count": 256, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# models = [\"EOT20\"]\n", + "# expected_cols = [\"tide_model\", \"ebb_flow\"]\n", + "# output_format = \"long\"\n", + "# return_tides = False\n", + "\n", + "# models = [\"EOT20\"]\n", + "# expected_cols = [\"tide_model\", \"tide_height\", \"ebb_flow\"]\n", + "# output_format = \"long\"\n", + "# return_tides = True\n", + "\n", + "# models = [\"EOT20\", \"GOT5.5\"]\n", + "# expected_cols = [\"tide_model\", \"ebb_flow\"]\n", + "# output_format = \"long\"\n", + "# return_tides = False\n", + "\n", + "models = [\"EOT20\", \"GOT5.5\"]\n", + "expected_cols = [\"tide_model\", \"tide_height\", \"ebb_flow\"]\n", + "output_format = \"long\"\n", + "return_tides = True\n", + "\n", + "\n", + "# models = [\"EOT20\"]\n", + "# expected_cols = [\"EOT20\"]\n", + "# output_format = \"wide\"\n", + "# return_tides = False\n", + "\n", + "# models = [\"EOT20\"]\n", + "# expected_cols = [(\"tide_height\", \"EOT20\"), (\"ebb_flow\", \"EOT20\")]\n", + "# output_format = \"wide\"\n", + "# return_tides = True\n", + "\n", + "# models = [\"EOT20\", \"GOT5.5\"]\n", + "# expected_cols = [\"EOT20\", \"GOT5.5\"]\n", + "# output_format = \"wide\"\n", + "# return_tides = False\n", + "\n", + "models = [\"EOT20\", \"GOT5.5\"]\n", + "expected_cols = [\n", + " (\"tide_height\", \"EOT20\"),\n", + " (\"tide_height\", \"GOT5.5\"),\n", + " (\"ebb_flow\", \"EOT20\"),\n", + " (\"ebb_flow\", \"GOT5.5\"),\n", + "]\n", + "output_format = \"wide\"\n", + "return_tides = True\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "from eo_tides.model import phase_tides\n", + "\n", + "phase_df = phase_tides(\n", + " x=[122.14, 122.30, 122.12],\n", + " y=[-17.91, -17.92, -18.07],\n", + " time=pd.date_range(\"2020\", \"2021\", periods=3),\n", + " directory=\"/var/share/tide_models/\",\n", + " model=models,\n", + " output_format=output_format,\n", + " # delta = \"15 min\",\n", + " return_tides=return_tides,\n", + ")\n", + "\n", + "\n", + "phase_df" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 261, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "phase_df.columns.tolist() == [\n", + " ('tide_height', 'EOT20'),\n", + " ('tide_height', 'GOT5.5'),\n", + " ('tide_phase', 'EOT20'),\n", + " ('tide_phase', 'GOT5.5'),\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('tide_height', 'EOT20'),\n", + " ('tide_height', 'GOT5.5'),\n", + " ('tide_phase', 'EOT20'),\n", + " ('tide_phase', 'GOT5.5')]" + ] + }, + "execution_count": 259, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " [\n", + " ('tide_height', 'EOT20'),\n", + " ('tide_height', 'GOT5.5'),\n", + " ('tide_phase', 'EOT20'),\n", + " ('tide_phase', 'GOT5.5'),\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['tide_model', 'ebb_flow']" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ebb_flow_df.columns.tolist() " + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tide_modelebb_flow
timexy
2020-01-01122.14-17.91EOT20Flow
2020-07-02122.14-17.91EOT20Ebb
2021-01-01122.14-17.91EOT20Flow
2020-01-01122.30-17.92EOT20Ebb
2020-07-02122.30-17.92EOT20Ebb
2021-01-01122.30-17.92EOT20Flow
2020-01-01122.12-18.07EOT20Ebb
2020-07-02122.12-18.07EOT20Ebb
2021-01-01122.12-18.07EOT20Flow
2020-01-01122.14-17.91GOT5.5Ebb
2020-07-02122.14-17.91GOT5.5Ebb
2021-01-01122.14-17.91GOT5.5Flow
2020-01-01122.30-17.92GOT5.5Ebb
2020-07-02122.30-17.92GOT5.5Ebb
2021-01-01122.30-17.92GOT5.5Flow
2020-01-01122.12-18.07GOT5.5Ebb
2020-07-02122.12-18.07GOT5.5Ebb
2021-01-01122.12-18.07GOT5.5Flow
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" + ], + "text/plain": [ + " tide_model ebb_flow\n", + "time x y \n", + "2020-01-01 122.14 -17.91 EOT20 Flow\n", + "2020-07-02 122.14 -17.91 EOT20 Ebb\n", + "2021-01-01 122.14 -17.91 EOT20 Flow\n", + "2020-01-01 122.30 -17.92 EOT20 Ebb\n", + "2020-07-02 122.30 -17.92 EOT20 Ebb\n", + "2021-01-01 122.30 -17.92 EOT20 Flow\n", + "2020-01-01 122.12 -18.07 EOT20 Ebb\n", + "2020-07-02 122.12 -18.07 EOT20 Ebb\n", + "2021-01-01 122.12 -18.07 EOT20 Flow\n", + "2020-01-01 122.14 -17.91 GOT5.5 Ebb\n", + "2020-07-02 122.14 -17.91 GOT5.5 Ebb\n", + "2021-01-01 122.14 -17.91 GOT5.5 Flow\n", + "2020-01-01 122.30 -17.92 GOT5.5 Ebb\n", + "2020-07-02 122.30 -17.92 GOT5.5 Ebb\n", + "2021-01-01 122.30 -17.92 GOT5.5 Flow\n", + "2020-01-01 122.12 -18.07 GOT5.5 Ebb\n", + "2020-07-02 122.12 -18.07 GOT5.5 Ebb\n", + "2021-01-01 122.12 -18.07 GOT5.5 Flow" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out #.columns.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['EOT20', 'GOT5.5', 'EOT20', 'GOT5.5'], dtype='object', name='tide_model')" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out.columns.get_level_values(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['EOT20', 'GOT5.5', 'EOT20', 'GOT5.5']" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['EOT20', 'EOT20']" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[\"EOT20\"] * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=============================== warnings summary ===============================\n", + ":241\n", + " :241: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility. Expected 16 from C header, got 96 from PyObject\n", + "\n", + "-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n", + "67 deselected, 1 warning in 1.48s\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import pytest\n", + "\n", + "# Define your ebb_flow function here or ensure it's imported\n", + "\n", + "def ebb_flow(x, y, time, directory, model, output_format):\n", + " # Dummy implementation for the sake of example\n", + " return pd.DataFrame({\"x\": x, \"y\": y, \"time\": time, \"model\": model})\n", + "\n", + "@pytest.mark.parametrize(\n", + " \"models\",\n", + " [\n", + " \"EOT20\", \n", + " [\"EOT20\", \"GOT5.5\"], \n", + " ],\n", + ")\n", + "def test_ebb_flow(models):\n", + " ebb_flow_df = ebb_flow(\n", + " x=[122.14, 122.30, 122.12],\n", + " y=[-17.91, -17.92, -18.07],\n", + " time=pd.date_range(\"2020\", \"2021\", periods=3),\n", + " directory=\"/var/share/tide_models/\",\n", + " model=models,\n", + " output_format=\"wide\",\n", + " )\n", + " assert ebb_flow_df is not None # Example assertion\n", + "\n", + "# Now, run the test\n", + "# pytest.main([\"-q\", \"-k\", \"test_ebb_flow\"])\n", + "\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 282, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modelling tides using EOT20\n", + "Modelling tides using EOT20\n" + ] + } + ], "source": [ - "## Load fixtures" + "phase_df = phase_tides(\n", + " x=[122.14],\n", + " y=[-17.91],\n", + " time=pd.date_range(\"2020-01-01\", \"2020-01-02\", freq=\"h\"),\n", + " model=[\"EOT20\"],\n", + " time_offset=\"15 min\",\n", + " directory=\"/var/share/tide_models/\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 289, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "phase_df.tide_phase.tolist() == [\n", + " \"low-flow\",\n", + " \"low-flow\",\n", + " \"low-flow\",\n", + " \"low-flow\",\n", + " \"high-flow\",\n", + " \"high-flow\",\n", + " \"high-flow\",\n", + " \"high-ebb\",\n", + " \"high-ebb\",\n", + " \"high-ebb\",\n", + " \"low-ebb\",\n", + " \"low-ebb\",\n", + " \"low-ebb\",\n", + " \"low-flow\",\n", + " \"low-flow\",\n", + " \"high-flow\",\n", + " \"high-flow\",\n", + " \"high-flow\",\n", + " \"high-flow\",\n", + " \"high-ebb\",\n", + " \"high-ebb\",\n", + " \"high-ebb\",\n", + " \"low-ebb\",\n", + " \"low-ebb\",\n", + " \"low-ebb\",\n", + " ]\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def load_satellite_ds():\n", - " \"\"\"\n", - " Load a sample timeseries of Landsat 8 data using odc-stac\n", - " \"\"\"\n", - " # Connect to stac catalogue\n", - " catalog = pystac_client.Client.open(\"https://explorer.dea.ga.gov.au/stac\")\n", - "\n", - " # Set cloud defaults\n", - " odc.stac.configure_rio(\n", - " cloud_defaults=True,\n", - " aws={\"aws_unsigned\": True},\n", - " )\n", - "\n", - " # Build a query with the parameters above\n", - " buffer = 0.08\n", - " # buffer = 0.5\n", - " bbox = [GAUGE_X - buffer, GAUGE_Y - buffer, GAUGE_X + buffer, GAUGE_Y + buffer]\n", - " query = catalog.search(\n", - " bbox=bbox,\n", - " collections=[\"ga_ls8c_ard_3\"],\n", - " datetime=\"2020-01/2020-02\",\n", - " )\n", - "\n", - " # Search the STAC catalog for all items matching the query\n", - " ds = odc.stac.load(\n", - " list(query.items()),\n", - " bands=[\"nbart_red\"],\n", - " crs=\"epsg:3577\",\n", - " resolution=30,\n", - " groupby=\"solar_day\",\n", - " bbox=bbox,\n", - " fail_on_error=False,\n", - " chunks={},\n", - " )\n", - "\n", - " return ds\n", - "\n", - "satellite_ds = load_satellite_ds()\n", - "\n", - "def load_measured_tides_ds():\n", - " \"\"\"\n", - " Load measured sea level data from the Broome ABSLMP tidal station:\n", - " http://www.bom.gov.au/oceanography/projects/abslmp/data/data.shtml\n", - " \"\"\"\n", - " # Metadata for Broome ABSLMP tidal station:\n", - " # http://www.bom.gov.au/oceanography/projects/abslmp/data/data.shtml\n", - " ahd_offset = -5.322\n", - "\n", - " # Load measured tides from ABSLMP tide gauge data\n", - " measured_tides_df = pd.read_csv(\n", - " \"../tests/data/IDO71013_2020.csv\",\n", - " index_col=0,\n", - " parse_dates=True,\n", - " na_values=-9999,\n", - " )[[\"Sea Level\"]]\n", - "\n", - " # Update index and column names\n", - " measured_tides_df.index.name = \"time\"\n", - " measured_tides_df.columns = [\"tide_height\"]\n", - "\n", - " # Apply station AHD offset\n", - " measured_tides_df += ahd_offset\n", - "\n", - " # Return as xarray dataset\n", - " return measured_tides_df.to_xarray()\n", - "\n", - "satellite_ds = load_satellite_ds()\n", - "measured_tides_ds = load_measured_tides_ds()" + "modelled_tides_df[\"ebb_flow\"] = pre_tides_df.drop(\n", + " \"tide_model\", axis=1, errors=\"ignore\"\n", + ").values < modelled_tides_df.drop(\"tide_model\", axis=1, errors=\"ignore\").values\n", + "modelled_tides_df[\"ebb_flow\"] = modelled_tides_df[\"ebb_flow\"].replace({\n", + " True: \"Ebb\",\n", + " False: \"Flow\",\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "modelled_tides_df" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jovyan/Robbi/eo-tides\n" + ] + } + ], + "source": [ + "cd .." + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================= test session starts ==============================\n", + "platform linux -- Python 3.10.15, pytest-8.3.3, pluggy-1.5.0 -- /env/bin/python3.10\n", + "cachedir: .pytest_cache\n", + "rootdir: /home/jovyan/Robbi/eo-tides\n", + "configfile: pyproject.toml\n", + "plugins: anyio-4.6.2.post1, nbval-0.11.0\n", + "collected 32 items / 22 deselected / 10 selected \n", + "\n", + "tests/test_model.py::test_phase_tides[15 min] PASSED [ 10%]\n", + "tests/test_model.py::test_phase_tides[20 min] PASSED [ 20%]\n", + "tests/test_model.py::test_phase_tides_format[models0-long-False-expected_cols0] PASSED [ 30%]\n", + "tests/test_model.py::test_phase_tides_format[models1-long-True-expected_cols1] PASSED [ 40%]\n", + "tests/test_model.py::test_phase_tides_format[models2-long-False-expected_cols2] PASSED [ 50%]\n", + "tests/test_model.py::test_phase_tides_format[models3-long-True-expected_cols3] PASSED [ 60%]\n", + "tests/test_model.py::test_phase_tides_format[models4-wide-False-expected_cols4] PASSED [ 70%]\n", + "tests/test_model.py::test_phase_tides_format[models5-wide-True-expected_cols5] PASSED [ 80%]\n", + "tests/test_model.py::test_phase_tides_format[models6-wide-False-expected_cols6] PASSED [ 90%]\n", + "tests/test_model.py::test_phase_tides_format[models7-wide-True-expected_cols7] PASSED [100%]\n", + "\n", + "=============================== warnings summary ===============================\n", + ":241\n", + " :241: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility. Expected 16 from C header, got 96 from PyObject\n", + "\n", + "tests/test_model.py: 24 warnings\n", + " /env/lib/python3.10/site-packages/pyproj/transformer.py:817: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " return self._transformer._transform_point(\n", + "\n", + "-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n", + "================ 10 passed, 22 deselected, 25 warnings in 6.13s ================\n" + ] + } + ], + "source": [ + "!export EO_TIDES_TIDE_MODELS=./tests/data/tide_models && pytest tests/test_model.py --verbose -k test_phase_tides" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modelling tides using EOT20\n", + "Modelling tides using EOT20\n" + ] + } + ], + "source": [ + "phase_df = phase_tides(\n", + " x=[122.14],\n", + " y=[-17.91],\n", + " time=pd.date_range(\"2020-01-01\", \"2020-01-02\", freq=\"h\"),\n", + " directory=\"/var/share/tide_models/\",\n", + " model=[\"EOT20\"],\n", + " delta = \"15 min\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['low-flow',\n", + " 'low-flow',\n", + " 'low-flow',\n", + " 'low-flow',\n", + " 'high-flow',\n", + " 'high-flow',\n", + " 'high-flow',\n", + " 'high-ebb',\n", + " 'high-ebb',\n", + " 'high-ebb',\n", + " 'low-ebb',\n", + " 'low-ebb',\n", + " 'low-ebb',\n", + " 'low-flow',\n", + " 'low-flow',\n", + " 'high-flow',\n", + " 'high-flow',\n", + " 'high-flow',\n", + " 'high-flow',\n", + " 'high-ebb',\n", + " 'high-ebb',\n", + " 'high-ebb',\n", + " 'low-ebb',\n", + " 'low-ebb',\n", + " 'low-ebb']" + ] + }, + "execution_count": 279, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "phase_df.tide_phase.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def check_sequence(arr):\n", + " pattern = ['low-flow', 'high-flow', 'high-ebb', 'low-ebb']\n", + " # Check if length is multiple of 4\n", + " if len(arr) % 4 != 0:\n", + " return False\n", + " \n", + " # Check each group of 4 elements\n", + " for i in range(0, len(arr), 4):\n", + " if arr[i:i+4].tolist() != pattern:\n", + " return False\n", + " return True\n", + "\n", + "check_sequence(phase_df.query(\"tide_model == 'EOT20'\").tide_phase.values)" ] }, { @@ -176,45 +1388,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Modelling tides using EOT20, GOT5.5 in parallel\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:00\n return [fn(*args) for args in chunk]\n File \"/home/jovyan/Robbi/eo-tides/eo_tides/model.py\", line 299, in _model_tides\n raise Exception(textwrap.dedent(error_msg).strip()) from None\nException: The EOT20 tide model constituent files do not cover the requested analysis extent.\nThis can occur if you are using clipped model files to improve run times.\nConsider using model files that cover your entire analysis area, or set `crop=False`\nto reduce the extent of tide model constituent files that is loaded.\n\"\"\"", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[12], line 7\u001b[0m\n\u001b[1;32m 3\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m180\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m50\u001b[39m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Run EOT20 tidal model for locations and timesteps in tide gauge data\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m modelled_tides_df \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_tides\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43my\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mEOT20\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGOT5.5\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmeasured_tides_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mdirectory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../tests/data/tide_models\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Robbi/eo-tides/eo_tides/model.py:807\u001b[0m, in \u001b[0;36mmodel_tides\u001b[0;34m(x, y, time, model, directory, crs, crop, method, extrapolate, cutoff, mode, parallel, parallel_splits, output_units, output_format, ensemble_models, **ensemble_kwargs)\u001b[0m\n\u001b[1;32m 805\u001b[0m \u001b[38;5;66;03m# Apply func in parallel, iterating through each input param\u001b[39;00m\n\u001b[1;32m 806\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 807\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 808\u001b[0m \u001b[43m \u001b[49m\u001b[43mtqdm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 809\u001b[0m \u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43miter_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_iters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 810\u001b[0m \u001b[43m \u001b[49m\u001b[43mtotal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmodel_iters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 811\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 812\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m BrokenProcessPool:\n\u001b[1;32m 814\u001b[0m error_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 815\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mParallelised tide modelling failed, likely to to an out-of-memory error. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 816\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry reducing the size of your analysis, or set `parallel=False`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 817\u001b[0m )\n", - "File \u001b[0;32m/env/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m 1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m 1182\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m 1183\u001b[0m \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m 1184\u001b[0m \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n", - "File \u001b[0;32m/env/lib/python3.10/concurrent/futures/process.py:575\u001b[0m, in \u001b[0;36m_chain_from_iterable_of_lists\u001b[0;34m(iterable)\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_chain_from_iterable_of_lists\u001b[39m(iterable):\n\u001b[1;32m 570\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 571\u001b[0m \u001b[38;5;124;03m Specialized implementation of itertools.chain.from_iterable.\u001b[39;00m\n\u001b[1;32m 572\u001b[0m \u001b[38;5;124;03m Each item in *iterable* should be a list. This function is\u001b[39;00m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;124;03m careful not to keep references to yielded objects.\u001b[39;00m\n\u001b[1;32m 574\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 575\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m element \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m 576\u001b[0m element\u001b[38;5;241m.\u001b[39mreverse()\n\u001b[1;32m 577\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m element:\n", - "File \u001b[0;32m/env/lib/python3.10/concurrent/futures/_base.py:621\u001b[0m, in \u001b[0;36mExecutor.map..result_iterator\u001b[0;34m()\u001b[0m\n\u001b[1;32m 618\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m fs:\n\u001b[1;32m 619\u001b[0m \u001b[38;5;66;03m# Careful not to keep a reference to the popped future\u001b[39;00m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 621\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[43m_result_or_cancel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 622\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m _result_or_cancel(fs\u001b[38;5;241m.\u001b[39mpop(), end_time \u001b[38;5;241m-\u001b[39m time\u001b[38;5;241m.\u001b[39mmonotonic())\n", - "File \u001b[0;32m/env/lib/python3.10/concurrent/futures/_base.py:319\u001b[0m, in \u001b[0;36m_result_or_cancel\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 319\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfut\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 320\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 321\u001b[0m fut\u001b[38;5;241m.\u001b[39mcancel()\n", - "File \u001b[0;32m/env/lib/python3.10/concurrent/futures/_base.py:458\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 457\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 459\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 460\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n", - "File \u001b[0;32m/env/lib/python3.10/concurrent/futures/_base.py:403\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 403\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 405\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[0;31mException\u001b[0m: The EOT20 tide model constituent files do not cover the requested analysis extent.\nThis can occur if you are using clipped model files to improve run times.\nConsider using model files that cover your entire analysis area, or set `crop=False`\nto reduce the extent of tide model constituent files that is loaded." - ] - } - ], + "outputs": [], "source": [ "from eo_tides import model_tides\n", "\n", @@ -255,7 +1431,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -268,22 +1444,9 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(GeoBox((3200, 3200), Affine(10.0, 0.0, 1248000.0,\n", - " 0.0, -10.0, -1184000.0), CRS('PROJCS[\"GDA94 / Australian Albers\",GEOGCS[\"GDA94\",DATUM[\"Geocentric_Datum_of_Australia_1994\",SPHEROID[\"GRS 1980\",6378137,298.257222101,AUTHORITY[\"EPSG\",\"7019\"]],AUTHORITY[\"EPSG\",\"6283\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4283\"]],PROJECTION[\"Albers_Conic_Equal_Area\"],PARAMETER[\"latitude_of_center\",0],PARAMETER[\"longitude_of_center\",132],PARAMETER[\"standard_parallel_1\",-18],PARAMETER[\"standard_parallel_2\",-36],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"3577\"]]')),\n", - " array(['2022-02-01T00:00:00.000000000'], dtype='datetime64[ns]'))" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from odc.geo.geobox import GeoBox\n", "import xarray as xr\n", @@ -375,223 +1538,18 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(609,)" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "satellite_ds.chunks[\"x\"]" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['2021-01-01T00:00:00.000000000', '2021-01-02T00:00:00.000000000',\n", - " '2021-01-03T00:00:00.000000000', '2021-01-04T00:00:00.000000000',\n", - " '2021-01-05T00:00:00.000000000', '2021-01-06T00:00:00.000000000',\n", - " '2021-01-07T00:00:00.000000000', '2021-01-08T00:00:00.000000000',\n", - 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"execution_count": 67, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -638,21 +1596,9 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['2022-02-01T00:00:00.000000000', '2022-02-01T00:00:00.000000000'],\n", - " dtype='datetime64[ns]')" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "test" ] From 5713739a351b3170436639574bf44c6556d75e56 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Sun, 27 Oct 2024 23:05:27 +0000 Subject: [PATCH 08/13] Major refactor to use latest pyTMD, standarise inputs --- Makefile | 2 + docs/migration.md | 21 +- docs/notebooks/Case_study_intertidal.ipynb | 4 +- docs/notebooks/Satellite_data.ipynb | 10 +- docs/notebooks/Tide_statistics.ipynb | 8 +- eo_tides/__init__.py | 2 + eo_tides/eo.py | 166 ++- eo_tides/model.py | 164 +-- eo_tides/stats.py | 108 +- pyproject.toml | 2 +- tests/test_stats.py | 2 +- tests/testing.ipynb | 1280 ++------------------ uv.lock | 14 +- 13 files changed, 348 insertions(+), 1435 deletions(-) diff --git a/Makefile b/Makefile index b8c05e8..6f57e37 100644 --- a/Makefile +++ b/Makefile @@ -85,6 +85,8 @@ build-and-publish: build publish ## Build and publish. docs-test: ## Test if documentation can be built without warnings or errors @uv run mkdocs build -s +# On Sandbox: uv run mkdocs serve -a localhost:8000 +# https://app.sandbox.dea.ga.gov.au/user/robbi.bishoptaylor@ga.gov.au/proxy/8000/eo-tides/ .PHONY: docs docs: ## Build and serve the documentation @uv run mkdocs serve diff --git a/docs/migration.md b/docs/migration.md index cb81496..9e18ed1 100644 --- a/docs/migration.md +++ b/docs/migration.md @@ -34,6 +34,21 @@ Renamed for consistency with `model_tides` and `pixel_tides`. Update references to `tidal_tag` to `tag_tides`. +## `ds` param renamed to `data`, now accepts `GeoBox` + +The `ds` param in all satellite data functions (`tag_tides`, `pixel_tides`, `tide_stats`, `pixel_tides`) has been updated to accept either `xarray.Dataset`, `xarray.DataArray` or a `odc.geo.geobox.GeoBox`. To account for this change, the `ds` param has been renamed to a more generic name `data`. + +!!! tip "Action required" + + Update: + ``` + tag_tides(ds=your_data) + ``` + To: + ``` + tag_tides(data=your_data) + ``` + ### `tag_tides` now returns an array instead of updating data in-place The `tag_tides` function now returns an `xarray.DataArray` output containing tide heights, rather than appending tide height data to the original input dataset in-place. This change provides better consistency with `pixel_tides`, which also returns an array of tide heights. @@ -42,16 +57,16 @@ The `tag_tides` function now returns an `xarray.DataArray` output containing tid Update: ``` - ds = tag_tides(ds, ...) + data = tag_tides(data, ...) ``` To: ``` - ds["tide_height"] = tag_tides(ds, ...) + data["tide_height"] = tag_tides(data, ...) ``` ### `pixel_tides` only returns a single array -The `pixel_tides` function has been updated to only ever return a single array as an output: a high-resolution tide height array matching the resolution of the input `ds` by default, and a low-resolution tide height array if `resample=False`. +The `pixel_tides` function has been updated to only ever return a single array as an output: a high-resolution tide height array matching the resolution of the input `data` by default, and a low-resolution tide height array if `resample=False`. !!! tip "Action required" diff --git a/docs/notebooks/Case_study_intertidal.ipynb b/docs/notebooks/Case_study_intertidal.ipynb index 0d0d47c..ca77c2c 100644 --- a/docs/notebooks/Case_study_intertidal.ipynb +++ b/docs/notebooks/Case_study_intertidal.ipynb @@ -410,7 +410,7 @@ ], "source": [ "ds[\"tide_height\"] = tag_tides(\n", - " ds=ds,\n", + " data=ds,\n", " model=tide_model,\n", " directory=directory,\n", ")" @@ -502,7 +502,7 @@ ], "source": [ "tide_stats(\n", - " ds=ds,\n", + " data=ds,\n", " model=tide_model,\n", " directory=directory,\n", ");" diff --git a/docs/notebooks/Satellite_data.ipynb b/docs/notebooks/Satellite_data.ipynb index 118d244..8dd49db 100644 --- a/docs/notebooks/Satellite_data.ipynb +++ b/docs/notebooks/Satellite_data.ipynb @@ -292,7 +292,7 @@ "from eo_tides.eo import tag_tides\n", "\n", "tides_da = tag_tides(\n", - " ds=ds,\n", + " data=ds,\n", " directory=directory,\n", ")\n", "\n", @@ -554,7 +554,7 @@ "\n", "# Model tides spatially\n", "tides_lowres = pixel_tides(\n", - " ds=ds,\n", + " data=ds,\n", " resample=False,\n", " directory=directory,\n", ")\n", @@ -665,7 +665,7 @@ "source": [ "# Model tides spatially\n", "tides_highres = pixel_tides(\n", - " ds=ds,\n", + " data=ds,\n", " resample=True,\n", " directory=directory,\n", ")\n", @@ -820,7 +820,7 @@ "source": [ "# Model tides spatially\n", "tides_highres_quantiles = pixel_tides(\n", - " ds=ds,\n", + " data=ds,\n", " calculate_quantiles=(0, 0.5, 1),\n", " directory=directory,\n", ")\n", @@ -897,7 +897,7 @@ "\n", "# Model tides spatially\n", "tides_highres = pixel_tides(\n", - " ds, \n", + " data=ds, \n", " times=custom_times,\n", " directory=directory,\n", ")\n", diff --git a/docs/notebooks/Tide_statistics.ipynb b/docs/notebooks/Tide_statistics.ipynb index 45adea1..0ce64c8 100644 --- a/docs/notebooks/Tide_statistics.ipynb +++ b/docs/notebooks/Tide_statistics.ipynb @@ -189,7 +189,7 @@ "from eo_tides.stats import tide_stats\n", "\n", "statistics_df = tide_stats(\n", - " ds_s2,\n", + " data=ds_s2,\n", " directory=directory,\n", ")\n" ] @@ -358,7 +358,7 @@ ], "source": [ "statistics_df = tide_stats(\n", - " ds_s1,\n", + " data=ds_s1,\n", " directory=directory,\n", ")\n" ] @@ -458,7 +458,7 @@ ], "source": [ "statistics_df = tide_stats(\n", - " ds_all,\n", + " data=ds_all,\n", " plot_col=\"satellite_name\",\n", " directory=directory,\n", ")\n" @@ -560,7 +560,7 @@ "from eo_tides.stats import pixel_stats\n", "\n", "stats_ds = pixel_stats(\n", - " ds=ds_s2,\n", + " data=ds_s2,\n", " directory=directory,\n", ")\n", "print(stats_ds)" diff --git a/eo_tides/__init__.py b/eo_tides/__init__.py index 6049ddc..ff1830c 100644 --- a/eo_tides/__init__.py +++ b/eo_tides/__init__.py @@ -37,9 +37,11 @@ __all__ = [ "list_models", "model_tides", + "phase_tides", "tag_tides", "pixel_tides", "tide_stats", + "pixel_stats", "idw", "eval_metrics", "load_gauge_gesla", diff --git a/eo_tides/eo.py b/eo_tides/eo.py index bef93d9..536a3a5 100644 --- a/eo_tides/eo.py +++ b/eo_tides/eo.py @@ -6,6 +6,7 @@ import warnings from typing import TYPE_CHECKING +import numpy as np import odc.geo.xr import pandas as pd import xarray as xr @@ -13,15 +14,15 @@ # Only import if running type checking if TYPE_CHECKING: - import numpy as np + from odc.geo import Shape2d from .model import _standardise_time, model_tides def _resample_chunks( - ds: xr.DataArray | xr.Dataset | GeoBox, + data: xr.DataArray | xr.Dataset | GeoBox, dask_chunks: tuple | None = None, -) -> tuple: +) -> tuple | Shape2d: """ Automatically return optimised dask chunks for reprojection with `_pixel_tides_resample`. @@ -33,62 +34,63 @@ def _resample_chunks( if dask_chunks is not None: return dask_chunks - # If ds is a GeoBox, return its shape - if isinstance(ds, GeoBox): - return ds.shape + # If data is a GeoBox, return its shape + if isinstance(data, GeoBox): + return data.shape - # if ds has chunks, then return just spatial chunks - if ds.chunks is not None: - y_dim, x_dim = ds.odc.spatial_dims - return ds.chunks[y_dim], ds.chunks[x_dim] + # if data has chunks, then return just spatial chunks + if data.chunks is not None: + y_dim, x_dim = data.odc.spatial_dims + return data.chunks[y_dim], data.chunks[x_dim] - # if ds has no chunks, then return entire image shape - return ds.odc.geobox.shape + # if data has no chunks, then return entire image shape + return data.odc.geobox.shape def _standardise_inputs( - ds: xr.DataArray | xr.Dataset | GeoBox, + data: xr.DataArray | xr.Dataset | GeoBox, time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None, -) -> (GeoBox, np.ndarray): +) -> tuple[GeoBox, np.ndarray | None]: """ Takes an xarray or GeoBox input and an optional custom times, - and returns a standardised GeoBox and + and returns a standardised GeoBox and times (usually an + array, but possibly None). """ - # If `ds` is an xarray object, extract its GeoBox and time - if isinstance(ds, (xr.DataArray, xr.Dataset)): + # If `data` is an xarray object, extract its GeoBox and time + if isinstance(data, (xr.DataArray, xr.Dataset)): # Try to extract GeoBox try: - gbox = ds.odc.geobox + gbox: GeoBox = data.odc.geobox except AttributeError: error_msg = """ - Cannot extract a valid GeoBox for `ds`. This is required for - extracting details about `ds`'s CRS and spatial location. + Cannot extract a valid GeoBox for `data`. This is required for + extracting details about `data`'s CRS and spatial location. - Import `odc.geo.xr` then run `ds = ds.odc.assign_crs(crs=...)` + Import `odc.geo.xr` then run `data = data.odc.assign_crs(crs=...)` to prepare your data before passing it to this function. """ raise Exception(textwrap.dedent(error_msg).strip()) - # Use custom time by default if provided; otherwise try and extract from `ds` + # Use custom time by default if provided; otherwise try and extract from `data` if time is not None: time = _standardise_time(time) - elif "time" in ds.dims: - time = ds.coords["time"].values + elif "time" in data.dims: + time = np.asarray(data.coords["time"].values) else: - raise ValueError("`ds` does not have a 'time' dimension, and no custom times were provided via `time`.") + raise ValueError("`data` does not have a 'time' dimension, and no custom times were provided via `time`.") - # If `ds` is a GeoBox, use it directly; raise an error if no time was provided - elif isinstance(ds, GeoBox): - gbox = ds + # If `data` is a GeoBox, use it directly; raise an error if no time was provided + elif isinstance(data, GeoBox): + gbox = data if time is not None: time = _standardise_time(time) else: - raise ValueError("If `ds` is a GeoBox, custom times must be provided via `time`.") + raise ValueError("If `data` is a GeoBox, custom times must be provided via `time`.") # Raise error if no valid inputs were provided else: - raise TypeError("`ds` must be an xarray.DataArray, xarray.Dataset, or odc.geo.geobox.GeoBox.") + raise TypeError("`data` must be an xarray.DataArray, xarray.Dataset, or odc.geo.geobox.GeoBox.") return gbox, time @@ -111,27 +113,24 @@ def _pixel_tides_resample( gbox : GeoBox The GeoBox to use as the template for the resampling operation. This is typically comes from the same satellite dataset originally - passed to `pixel_tides` (e.g. `ds.odc.geobox`). + passed to `pixel_tides` (e.g. `data.odc.geobox`). resample_method : string, optional The resampling method to use. Defaults to "bilinear"; valid options include "nearest", "cubic", "min", "max", "average" etc. dask_chunks : tuple of float, optional Can be used to configure custom Dask chunking for the final - resampling step. By default, chunks will be automatically set - to match y/x chunks from `ds` if they exist; otherwise chunks - will be chosen to cover the entire y/x extent of the dataset. - For custom chunks, provide a tuple in the form `(y, x)`, e.g. - `(2048, 2048)`. + resampling step. For custom chunks, provide a tuple in the form + (y, x), e.g. (2048, 2048). dask_compute : bool, optional Whether to compute results of the resampling step using Dask. - If False, this will return `tides_highres` as a Dask array. + If False, this will return `tides_highres` as a lazy loaded + Dask-enabled array. Returns ------- - tides_highres, tides_lowres : tuple of xr.DataArrays - In addition to `tides_lowres` (see above), a high resolution - array of tide heights will be generated matching the - exact spatial resolution and extent of `ds`. + tides_highres : xr.DataArray + A high resolution array of tide heights matching the exact + spatial resolution and extent of `gbox`. """ # Determine spatial dimensions @@ -141,7 +140,7 @@ def _pixel_tides_resample( # and a single chunk for each timestep/quantile and tide model tides_lowres_dask = tides_lowres.chunk({d: None if d in [y_dim, x_dim] else 1 for d in tides_lowres.dims}) - # Reproject into the GeoBox of `ds` using odc.geo and Dask + # Reproject into the pixel grid of `gbox` using odc.geo and Dask tides_highres = tides_lowres_dask.odc.reproject( how=gbox, chunks=dask_chunks, @@ -156,7 +155,7 @@ def _pixel_tides_resample( def tag_tides( - ds: xr.Dataset | xr.DataArray | GeoBox, + data: xr.Dataset | xr.DataArray | GeoBox, time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None = None, model: str | list[str] = "EOT20", directory: str | os.PathLike | None = None, @@ -185,18 +184,18 @@ def tag_tides( Parameters ---------- - ds : xarray.Dataset or xarray.DataArray or odc.geo.geobox.GeoBox + data : xarray.Dataset or xarray.DataArray or odc.geo.geobox.GeoBox A multi-dimensional dataset or GeoBox pixel grid that will - be used to define the tide modelling location. If `ds` + be used to define the tide modelling location. If `data` is an xarray object, it should include a "time" dimension. - If no "time" dimension exists or if `ds` is a GeoBox, + If no "time" dimension exists or if `data` is a GeoBox, then times must be passed using the `time` parameter. time : pd.DatetimeIndex or list of pd.Timestamp, optional By default, the function will model tides using the times - contained in the "time" dimension of `ds`. Alternatively, this + contained in the "time" dimension of `data`. Alternatively, this param can be used to model tides for a custom set of times instead. For example: - `times=pd.date_range(start="2000", end="2001", freq="5h")` + `time=pd.date_range(start="2000", end="2001", freq="5h")` model : str or list of str, optional The tide model (or models) used to model tides. If a list is provided, a new "tide_model" dimension will be added to the @@ -224,11 +223,11 @@ def tag_tides( ------- tides_da : xr.DataArray A one-dimensional tide height array. This will contain either - tide heights for every timestep in `ds`, or for every time in + tide heights for every timestep in `data`, or for every time in `times` if provided. """ # Standardise data inputs, time and models - gbox, time_coords = _standardise_inputs(ds, time) + gbox, time_coords = _standardise_inputs(data, time) model = [model] if isinstance(model, str) else model # If custom tide posts are not provided, use dataset centroid @@ -261,30 +260,6 @@ def tag_tides( f"`tidepost_lat` and `tidepost_lon` parameters." ) - # # Optionally calculate the tide phase for each observation - # if ebb_flow: - # # Model tides for a time 15 minutes prior to each previously - # # modelled satellite acquisition time. This allows us to compare - # # tide heights to see if they are rising or falling. - # print("Modelling tidal phase (e.g. ebb or flow)") - # tide_pre_df = model_tides( - # x=lon, # type: ignore - # y=lat, # type: ignore - # time=(ds.time - pd.Timedelta("15 min")), - # model=model, - # directory=directory, - # crs="EPSG:4326", - # **model_tides_kwargs, - # ) - - # # Compare tides computed for each timestep. If the previous tide - # # was higher than the current tide, the tide is 'ebbing'. If the - # # previous tide was lower, the tide is 'flowing' - # tide_df["ebb_flow"] = (tide_df.tide_height < tide_pre_df.tide_height.values).replace({ - # True: "Ebb", - # False: "Flow", - # }) - # Convert to xarray format tides_da = tide_df.reset_index().set_index(["time", "tide_model"]).drop(["x", "y"], axis=1).tide_height.to_xarray() @@ -296,7 +271,7 @@ def tag_tides( def pixel_tides( - ds: xr.Dataset | xr.DataArray | GeoBox, + data: xr.Dataset | xr.DataArray | GeoBox, time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None = None, model: str | list[str] = "EOT20", directory: str | os.PathLike | None = None, @@ -305,7 +280,7 @@ def pixel_tides( resolution: float | None = None, buffer: float | None = None, resample_method: str = "bilinear", - dask_chunks: tuple[float, float] = None, + dask_chunks: tuple[float, float] | None = None, dask_compute: bool = True, **model_tides_kwargs, ) -> xr.DataArray: @@ -338,18 +313,18 @@ def pixel_tides( Parameters ---------- - ds : xarray.Dataset or xarray.DataArray or odc.geo.geobox.GeoBox + data : xarray.Dataset or xarray.DataArray or odc.geo.geobox.GeoBox A multi-dimensional dataset or GeoBox pixel grid that will - be used to define the spatial tide modelling grid. If `ds` + be used to define the spatial tide modelling grid. If `data` is an xarray object, it should include a "time" dimension. - If no "time" dimension exists or if `ds` is a GeoBox, + If no "time" dimension exists or if `data` is a GeoBox, then times must be passed using the `time` parameter. time : pd.DatetimeIndex or list of pd.Timestamp, optional By default, the function will model tides using the times - contained in the "time" dimension of `ds`. Alternatively, this + contained in the "time" dimension of `data`. Alternatively, this param can be used to model tides for a custom set of times instead. For example: - `times=pd.date_range(start="2000", end="2001", freq="5h")` + `time=pd.date_range(start="2000", end="2001", freq="5h")` model : str or list of str, optional The tide model (or models) used to model tides. If a list is provided, a new "tide_model" dimension will be added to the @@ -363,7 +338,7 @@ def pixel_tides( model that match the structure required by `pyTMD` (). resample : bool, optional - Whether to resample low resolution tides back into `ds`'s original + Whether to resample low resolution tides back into `data`'s original higher resolution grid. Set this to `False` if you do not want low resolution tides to be re-projected back to higher resolution. calculate_quantiles : tuple of float or numpy.ndarray, optional @@ -375,8 +350,8 @@ def pixel_tides( resolution : float, optional The desired resolution of the low-resolution grid used for tide modelling. The default None will create a 5000 m resolution grid - if `ds` has a projected CRS (i.e. metre units), or a 0.05 degree - resolution grid if `ds` has a geographic CRS (e.g. degree units). + if `data` has a projected CRS (i.e. metre units), or a 0.05 degree + resolution grid if `data` has a geographic CRS (e.g. degree units). Note: higher resolutions do not necessarily provide better tide modelling performance, as results will be limited by the resolution of the underlying global tide model (e.g. 1/16th @@ -387,11 +362,11 @@ def pixel_tides( ensures that modelled tides are seamless across analysis boundaries. This buffer is eventually be clipped away when the low-resolution modelled tides are re-projected back to the - original resolution and extent of `ds`. To ensure that at least + original resolution and extent of `data`. To ensure that at least two low-resolution grid pixels occur outside of the dataset - bounds, the default None applies a 12000 m buffer if `ds` has a + bounds, the default None applies a 12000 m buffer if `data` has a projected CRS (i.e. metre units), or a 0.12 degree buffer if - `ds` has a geographic CRS (e.g. degree units). + `data` has a geographic CRS (e.g. degree units). resample_method : str, optional If resampling is requested (see `resample` above), use this resampling method when converting from low resolution to high @@ -400,7 +375,7 @@ def pixel_tides( dask_chunks : tuple of float, optional Can be used to configure custom Dask chunking for the final resampling step. By default, chunks will be automatically set - to match y/x chunks from `ds` if they exist; otherwise chunks + to match y/x chunks from `data` if they exist; otherwise chunks will be chosen to cover the entire y/x extent of the dataset. For custom chunks, provide a tuple in the form `(y, x)`, e.g. `(2048, 2048)`. @@ -419,15 +394,15 @@ def pixel_tides( A three-dimensional tide height array. If `resample=True` (default), a high-resolution array of tide heights will be returned that matches the exact spatial resolution - and extents of `ds`. This will contain either tide heights for - every timestep in `ds` (or in `times` if provided), or tide height + and extents of `data`. This will contain either tide heights for + every timestep in `data` (or in `times` if provided), or tide height quantiles for every quantile provided by `calculate_quantiles`. If `resample=False`, results for the intermediate low-resolution tide modelling grid will be returned instead. """ # Standardise data inputs, time and models - gbox, time_coords = _standardise_inputs(ds, time) - dask_chunks = _resample_chunks(ds, dask_chunks) + gbox, time_coords = _standardise_inputs(data, time) + dask_chunks = _resample_chunks(data, dask_chunks) model = [model] if isinstance(model, str) else model # Determine spatial dimensions @@ -435,6 +410,7 @@ def pixel_tides( # Determine resolution and buffer, using different defaults for # geographic (i.e. degrees) and projected (i.e. metres) CRSs: + assert gbox.crs is not None crs_units = gbox.crs.units[0][0:6] if gbox.crs.geographic: if resolution is None: @@ -442,7 +418,7 @@ def pixel_tides( elif resolution > 360: raise ValueError( f"A resolution of greater than 360 was " - f"provided, but `ds` has a geographic CRS " + f"provided, but `data` has a geographic CRS " f"in {crs_units} units. Did you accidently " f"provide a resolution in projected " f"(i.e. metre) units?", @@ -455,7 +431,7 @@ def pixel_tides( elif resolution < 1: raise ValueError( f"A resolution of less than 1 was provided, " - f"but `ds` has a projected CRS in " + f"but `data` has a projected CRS in " f"{crs_units} units. Did you accidently " f"provide a resolution in geographic " f"(degree) units?", @@ -469,7 +445,7 @@ def pixel_tides( raise ValueError( f"The resolution of the low-resolution tide " f"modelling grid ({resolution:.2f}) is less " - f"than `ds`'s pixel resolution ({dataset_res:.2f}). " + f"than `data`'s pixel resolution ({dataset_res:.2f}). " f"This can cause extremely slow tide modelling " f"performance. Please select provide a resolution " f"greater than {dataset_res:.2f} using " diff --git a/eo_tides/model.py b/eo_tides/model.py index f3527a9..dca82b5 100644 --- a/eo_tides/model.py +++ b/eo_tides/model.py @@ -26,7 +26,9 @@ from .utils import idw -def _set_directory(directory): +def _set_directory( + directory: str | os.PathLike | None = None, +) -> os.PathLike: """ Set tide modelling files directory. If no custom path is provided, try global environmental variable @@ -72,7 +74,7 @@ def list_models( show_available: bool = True, show_supported: bool = True, raise_error: bool = False, -) -> (list[str], list[str]): +) -> tuple[list[str], list[str]]: """ List all tide models available for tide modelling, and all models supported by `eo-tides` and `pyTMD`. @@ -205,105 +207,31 @@ def _model_tides( # Obtain model details pytmd_model = pyTMD.io.model(directory).elevation(model) - # Convert x, y to latitude/longitude + # Reproject x, y to latitude/longitude transformer = pyproj.Transformer.from_crs(crs, "EPSG:4326", always_xy=True) lon, lat = transformer.transform(x.flatten(), y.flatten()) # Convert datetime timescale = pyTMD.time.timescale().from_datetime(time.flatten()) - # Calculate bounds for cropping - buffer = 1 # one degree on either side - bounds = [ - lon.min() - buffer, - lon.max() + buffer, - lat.min() - buffer, - lat.max() + buffer, - ] - try: # Read tidal constants and interpolate to grid points - if pytmd_model.format in ("OTIS", "ATLAS-compact", "TMD3"): - amp, ph, D, c = pyTMD.io.OTIS.extract_constants( - lon, - lat, - pytmd_model.grid_file, - pytmd_model.model_file, - pytmd_model.projection, - type=pytmd_model.type, - grid=pytmd_model.file_format, - crop=crop, - bounds=bounds, - method=method, - extrapolate=extrapolate, - cutoff=cutoff, - ) - - # Use delta time at 2000.0 to match TMD outputs - deltat = np.zeros((len(timescale)), dtype=np.float64) - - elif pytmd_model.format in ("ATLAS-netcdf",): - amp, ph, D, c = pyTMD.io.ATLAS.extract_constants( - lon, - lat, - pytmd_model.grid_file, - pytmd_model.model_file, - type=pytmd_model.type, - crop=crop, - bounds=bounds, - method=method, - extrapolate=extrapolate, - cutoff=cutoff, - scale=pytmd_model.scale, - compressed=pytmd_model.compressed, - ) - - # Use delta time at 2000.0 to match TMD outputs - deltat = np.zeros((len(timescale)), dtype=np.float64) - - elif pytmd_model.format in ("GOT-ascii", "GOT-netcdf"): - amp, ph, c = pyTMD.io.GOT.extract_constants( - lon, - lat, - pytmd_model.model_file, - grid=pytmd_model.file_format, - crop=crop, - bounds=bounds, - method=method, - extrapolate=extrapolate, - cutoff=cutoff, - scale=pytmd_model.scale, - compressed=pytmd_model.compressed, - ) - - # Delta time (TT - UT1) - deltat = timescale.tt_ut1 - - elif pytmd_model.format in ("FES-ascii", "FES-netcdf"): - amp, ph = pyTMD.io.FES.extract_constants( - lon, - lat, - pytmd_model.model_file, - type=pytmd_model.type, - version=pytmd_model.version, - crop=crop, - bounds=bounds, - method=method, - extrapolate=extrapolate, - cutoff=cutoff, - scale=pytmd_model.scale, - compressed=pytmd_model.compressed, - ) - - # Available model constituents - c = pytmd_model.constituents + amp, ph, c = pytmd_model.extract_constants( + lon, + lat, + type=pytmd_model.type, + crop=crop, + bounds=None, + method=method, + extrapolate=extrapolate, + cutoff=cutoff, + append_node=False, + # append_node=True, + ) - # Delta time (TT - UT1) - deltat = timescale.tt_ut1 - else: - raise Exception( - f"Unsupported model format ({pytmd_model.format}). This may be due to an incompatible version of `pyTMD`." - ) + # TODO: Return constituents + # print(amp.shape, ph.shape, c) + # print(pd.DataFrame({"amplitude": amp})) # Raise error if constituent files no not cover analysis extent except IndexError as e: @@ -321,30 +249,42 @@ def _model_tides( # Calculate constituent oscillation hc = amp * np.exp(cph) + # Compute deltat based on model + if pytmd_model.corrections in ("OTIS", "ATLAS", "TMD3", "netcdf"): + # Use delta time at 2000.0 to match TMD outputs + deltat = np.zeros_like(timescale.tt_ut1) + else: + # Use interpolated delta times + deltat = timescale.tt_ut1 + # Determine the number of points and times to process. If in # "one-to-many" mode, these counts are used to repeat our extracted # constituents and timesteps so we can extract tides for all # combinations of our input times and tide modelling points. + # If in "one-to-many" mode, repeat constituents to length of time + # and number of input coords before passing to `predict_tide_drift` # If in "one-to-one" mode, we avoid this step by setting counts to 1 # (e.g. "repeat 1 times") points_repeat = len(x) if mode == "one-to-many" else 1 time_repeat = len(time) if mode == "one-to-many" else 1 - - # If in "one-to-many" mode, repeat constituents to length of time - # and number of input coords before passing to `predict_tide_drift` t, hc, deltat = ( np.tile(timescale.tide, points_repeat), hc.repeat(time_repeat, axis=0), np.tile(deltat, points_repeat), ) - # Predict tidal elevations at time and infer minor corrections - npts = len(t) - tide = np.ma.zeros((npts), fill_value=np.nan) + # Create arrays to hold outputs + tide = np.ma.zeros((len(t)), fill_value=np.nan) tide.mask = np.any(hc.mask, axis=1) - # Predict tides - tide.data[:] = pyTMD.predict.drift(t, hc, c, deltat=deltat, corrections=pytmd_model.corrections) + # Predict tidal elevations at time and infer minor corrections + tide.data[:] = pyTMD.predict.drift( + t, + hc, + c, + deltat=deltat, + corrections=pytmd_model.corrections, + ) minor = pyTMD.predict.infer_minor( t, hc, @@ -692,6 +632,7 @@ def model_tides( time = _standardise_time(time) # Validate input arguments + assert time is not None, "Times for modelling tides muyst be provided via `time`." assert method in ("bilinear", "spline", "linear", "nearest") assert output_units in ( "m", @@ -868,15 +809,15 @@ def model_tides( def phase_tides( - x, - y, - time, - model="EOT20", - directory=None, - time_offset="15 min", - return_tides=False, + x: float | list[float] | xr.DataArray, + y: float | list[float] | xr.DataArray, + time: np.ndarray | pd.DatetimeIndex, + model: str | list[str] = "EOT20", + directory: str | os.PathLike | None = None, + time_offset: str = "15 min", + return_tides: bool = False, **model_tides_kwargs, -): +) -> pd.DataFrame: """ Model tide phases (low-flow, high-flow, high-ebb, low-ebb) at multiple coordinates and/or timesteps using using one @@ -916,6 +857,13 @@ def phase_tides( Tide modelling files should be stored in sub-folders for each model that match the structure required by `pyTMD` (). + time_offset: str, optional + The time offset/delta used to generate a time series of + offset tide heights required for phase calculation. Defeaults + to "15 min"; can be any string passed to `pandas.Timedelta`. + return_tides: bool, optional + Whether to return intermediate modelled tide heights as a + "tide_height" column in the output dataframe. Defaults to False. **model_tides_kwargs : Optional parameters passed to the `eo_tides.model.model_tides` function. Important parameters include `output_format` (e.g. diff --git a/eo_tides/stats.py b/eo_tides/stats.py index 1931fd2..9f8e3fc 100644 --- a/eo_tides/stats.py +++ b/eo_tides/stats.py @@ -14,8 +14,9 @@ # Only import if running type checking if TYPE_CHECKING: import xarray as xr + from odc.geo.geobox import GeoBox -from .eo import pixel_tides, tag_tides +from .eo import _standardise_inputs, pixel_tides, tag_tides from .model import model_tides @@ -136,7 +137,8 @@ def _plot_biases( def tide_stats( - ds: xr.Dataset | xr.DataArray, + data: xr.Dataset | xr.DataArray | GeoBox, + time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None = None, model: str = "EOT20", directory: str | os.PathLike | None = None, tidepost_lat: float | None = None, @@ -167,15 +169,23 @@ def tide_stats( Parameters ---------- - ds : xarray.Dataset or xarray.DataArray - A multi-dimensional dataset (e.g. "x", "y", "time") used - to calculate tide statistics. This dataset must contain - a "time" dimension. - model : string, optional + data : xarray.Dataset or xarray.DataArray or odc.geo.geobox.GeoBox + A multi-dimensional dataset or GeoBox pixel grid that will + be used to calculate tide statistics. If `data` is an + xarray object, it should include a "time" dimension. + If no "time" dimension exists or if `data` is a GeoBox, + then times must be passed using the `time` parameter. + time : pd.DatetimeIndex or list of pd.Timestamp, optional + By default, the function will model tides using the times + contained in the "time" dimension of `data`. Alternatively, this + param can be used to model tides for a custom set of times + instead. For example: + `time=pd.date_range(start="2000", end="2001", freq="5h")` + model : str, optional The tide model to use to model tides. Defaults to "EOT20"; for a full list of available/supported models, run `eo_tides.model.list_models`. - directory : string, optional + directory : str, optional The directory containing tide model data files. If no path is provided, this will default to the environment variable `EO_TIDES_TIDE_MODELS` if set, or raise an error if not. @@ -201,7 +211,7 @@ def tide_stats( An optional string giving the frequency at which to model tides when computing the full modelled tidal range. Defaults to '3h', which computes a tide height for every three hours across the - temporal extent of `ds`. + temporal extent of `data`. linear_reg: bool, optional Whether to return linear regression statistics that assess whether satellite-observed tides show any decreasing or @@ -247,6 +257,9 @@ def tide_stats( - `observed_slope`: slope of any relationship between observed tide heights and time - `observed_pval`: significance/p-value of any relationship between observed tide heights and time """ + # Standardise data inputs, time and models + gbox, time_coords = _standardise_inputs(data, time) + # Verify that only one tide model is provided if isinstance(model, list): raise Exception("Only single tide models are supported by `tide_stats`.") @@ -254,11 +267,13 @@ def tide_stats( # If custom tide modelling locations are not provided, use the # dataset centroid if not tidepost_lat or not tidepost_lon: - tidepost_lon, tidepost_lat = ds.odc.geobox.geographic_extent.centroid.coords[0] + tidepost_lon, tidepost_lat = gbox.geographic_extent.centroid.coords[0] # Model tides for each observation in the supplied xarray object + assert time_coords is not None obs_tides_da = tag_tides( - ds, + gbox, + time=time_coords, model=model, directory=directory, tidepost_lat=tidepost_lat, # type: ignore @@ -266,12 +281,13 @@ def tide_stats( return_tideposts=True, **model_tides_kwargs, ) - obs_tides_da = obs_tides_da.reindex_like(ds) + if isinstance(data, (xr.Dataset, xr.DataArray)): + obs_tides_da = obs_tides_da.reindex_like(data) # Generate range of times covering entire period of satellite record all_timerange = pd.date_range( - start=obs_tides_da.time.min().item(), - end=obs_tides_da.time.max().item(), + start=time_coords.min().item(), + end=time_coords.max().item(), freq=modelled_freq, ) @@ -355,7 +371,7 @@ def tide_stats( offset_low=low_tide_offset, offset_high=high_tide_offset, spread=spread, - plot_col=ds[plot_col] if plot_col else None, + plot_col=data[plot_col] if plot_col else None, obs_linreg=obs_linreg if linear_reg else None, obs_x=obs_x, all_timerange=all_timerange, @@ -390,12 +406,13 @@ def tide_stats( def pixel_stats( - ds: xr.Dataset | xr.DataArray, + data: xr.Dataset | xr.DataArray | GeoBox, + time: np.ndarray | pd.DatetimeIndex | pd.Timestamp | None = None, model: str | list[str] = "EOT20", directory: str | os.PathLike | None = None, resample: bool = False, - modelled_freq="3h", - min_max_q=(0.0, 1.0), + modelled_freq: str = "3h", + min_max_q: tuple[float, float] = (0.0, 1.0), extrapolate: bool = True, cutoff: float = 10, **pixel_tides_kwargs, @@ -420,13 +437,21 @@ def pixel_stats( Parameters ---------- - ds : xarray.Dataset or xarray.DataArray - A multi-dimensional dataset (e.g. "x", "y", "time") used - to calculate 2D tide statistics. This dataset must contain - a "time" dimension. + data : xarray.Dataset or xarray.DataArray or odc.geo.geobox.GeoBox + A multi-dimensional dataset or GeoBox pixel grid that will + be used to calculate 2D tide statistics. If `data` + is an xarray object, it should include a "time" dimension. + If no "time" dimension exists or if `data` is a GeoBox, + then times must be passed using the `time` parameter. + time : pd.DatetimeIndex or list of pd.Timestamp, optional + By default, the function will model tides using the times + contained in the "time" dimension of `data`. Alternatively, this + param can be used to model tides for a custom set of times + instead. For example: + `time=pd.date_range(start="2000", end="2001", freq="5h")` model : str or list of str, optional The tide model (or models) to use to model tides. If a list is - provided, a new "tide_model" dimension will be added to `ds`. + provided, a new "tide_model" dimension will be added to `data`. Defaults to "EOT20"; for a full list of available/supported models, run `eo_tides.model.list_models`. directory : str, optional @@ -437,7 +462,7 @@ def pixel_stats( model that match the structure required by `pyTMD` (). resample : bool, optional - Whether to resample tide statistics back into `ds`'s original + Whether to resample tide statistics back into `data`'s original higher resolution grid. Defaults to False, which will return lower-resolution statistics that are typically sufficient for most purposes. @@ -445,7 +470,7 @@ def pixel_stats( An optional string giving the frequency at which to model tides when computing the full modelled tidal range. Defaults to '3h', which computes a tide height for every three hours across the - temporal extent of `ds`. + temporal extent of `data`. min_max_q : tuple, optional Quantiles used to calculate max and min observed and modelled astronomical tides. By default `(0.0, 1.0)` which is equivalent @@ -478,9 +503,15 @@ def pixel_stats( - `offset_high`: proportion of the highest tides never observed by the satellite """ + # Standardise data inputs, time and models + gbox, time_coords = _standardise_inputs(data, time) + model = [model] if isinstance(model, str) else model + # Model observed tides + assert time_coords is not None obs_tides = pixel_tides( - ds, + gbox, + time=time_coords, resample=False, model=model, directory=directory, @@ -492,15 +523,15 @@ def pixel_stats( # Generate times covering entire period of satellite record all_timerange = pd.date_range( - start=ds.time.min().item(), - end=ds.time.max().item(), + start=time_coords.min().item(), + end=time_coords.max().item(), freq=modelled_freq, ) # Model all tides all_tides = pixel_tides( - ds, - times=all_timerange, + gbox, + time=all_timerange, model=model, directory=directory, calculate_quantiles=min_max_q, @@ -510,6 +541,11 @@ def pixel_stats( **pixel_tides_kwargs, ) + # # Calculate means + # TODO: Find way to make this work with `calculate_quantiles` + # mot = obs_tides.mean(dim="time") + # mat = all_tides.mean(dim="time") + # Calculate min and max tides lot = obs_tides.isel(quantile=0) hot = obs_tides.isel(quantile=-1) @@ -531,10 +567,12 @@ def pixel_stats( stats_ds = ( xr.merge( [ - hat.rename("hat"), + # mot.rename("mot"), + # mat.rename("mat"), hot.rename("hot"), - lat.rename("lat"), + hat.rename("hat"), lot.rename("lot"), + lat.rename("lat"), otr.rename("otr"), tr.rename("tr"), spread.rename("spread"), @@ -544,11 +582,11 @@ def pixel_stats( compat="override", ) .drop_vars("quantile") - .odc.assign_crs(crs=ds.odc.crs) + .odc.assign_crs(crs=gbox.crs) ) - # Optionally resample into the original pixel grid of `ds` + # Optionally resample into the original pixel grid of `data` if resample: - stats_ds = stats_ds.odc.reproject(how=ds.odc.geobox, resample_method="bilinear") + stats_ds = stats_ds.odc.reproject(how=gbox, resample_method="bilinear") return stats_ds diff --git a/pyproject.toml b/pyproject.toml index 559130d..962c000 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,7 +37,7 @@ dependencies = [ "odc-geo>=0.4.7", "pandas>=2.2.0", "pyproj>=3.6.1", - "pyTMD==2.1.6", + "pyTMD==2.1.7", "scikit-learn>=1.4.0", "scipy>=1.11.2", "shapely>=2.0.6", diff --git a/tests/test_stats.py b/tests/test_stats.py index e41bb20..06b2033 100644 --- a/tests/test_stats.py +++ b/tests/test_stats.py @@ -80,7 +80,7 @@ def test_tidal_stats(satellite_ds, modelled_freq): ) def test_pixel_stats(satellite_ds, models, resample): stats_ds = pixel_stats( - ds=satellite_ds, + satellite_ds, model=models, resample=resample, ) diff --git a/tests/testing.ipynb b/tests/testing.ipynb index 48136b9..39d8b7b 100644 --- a/tests/testing.ipynb +++ b/tests/testing.ipynb @@ -6,12 +6,13 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -e .. --quiet" + "!pip install uv\n", + "!pip install -e .. --quiet\n" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -121,46 +122,14 @@ "metadata": {}, "outputs": [], "source": [ - "ds = satellite_ds.copy(deep=True)\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from odc.geo.geobox import GeoBox\n", - "import xarray as xr\n", - "\n", - "\n", - "def _resample_chunks(\n", - " ds: xr.DataArray | xr.Dataset | GeoBox,\n", - " dask_chunks: tuple | None = None,\n", - ") -> tuple:\n", - " \"\"\"\n", - " Automatically return optimised dask chunks\n", - " for reprojection with _pixel_tides_resample.\n", - " Use entire image if GeoBox or if no default\n", - " chunks; use existing chunks if they exist.\n", - " \"\"\"\n", - "\n", - " # If dask_chunks is provided, return directly\n", - " if dask_chunks is not None:\n", - " return dask_chunks\n", - "\n", - " # If ds is a GeoBox, return its shape\n", - " if isinstance(ds, GeoBox):\n", - " return ds.shape\n", - "\n", - " # if ds has chunks, then return just spatial chunks\n", - " if ds.chunks is not None:\n", - " y_dim, x_dim = ds.odc.spatial_dims\n", - " return ds.chunks[y_dim], ds.chunks[x_dim]\n", - "\n", - " # if ds has no chunks, then return entire image shape\n", - " return ds.odc.geobox.shape" + "phase_df = phase_tides(\n", + " x=[122.14],\n", + " y=[-17.91],\n", + " time=pd.date_range(\"2020-01-01\", \"2020-01-02\", freq=\"h\"),\n", + " directory=\"/var/share/tide_models/\",\n", + " model=[\"EOT20\"],\n", + " delta = \"15 min\",\n", + ")" ] }, { @@ -169,16 +138,14 @@ "metadata": {}, "outputs": [], "source": [ - "_resample_chunks(ds, None)" + "!export EO_TIDES_TIDE_MODELS=./tests/data/tide_models && pytest tests/test_model.py --verbose -k test_phase_tides" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "cd .." + "## Testing pyTMD" ] }, { @@ -186,958 +153,104 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "satellite_ds.isel(time=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 239, - "metadata": {}, - "outputs": [], "source": [ "from eo_tides import model_tides\n", "\n", + "x, y, crs, method, model = GAUGE_X, GAUGE_Y, \"EPSG:4326\", \"spline\", \"EOT20\"\n", + "x, y, crs, method, model = GAUGE_X, GAUGE_Y, \"EPSG:4326\", \"bilinear\", \"EOT20\"\n", + "x, y, crs, method, model = -1034913, -1961916, \"EPSG:3577\", \"bilinear\", \"EOT20\"\n", "\n", - "def tide_phase(\n", - " x,\n", - " y,\n", - " time,\n", - " model=\"EOT20\",\n", - " directory=None,\n", - " delta=\"15 min\",\n", - " return_tides=False,\n", - " **model_tides_kwargs,\n", - "):\n", - "\n", - " # Pop output format and mode for special handling\n", - " output_format = model_tides_kwargs.pop(\"output_format\", \"long\")\n", - " mode = model_tides_kwargs.pop(\"mode\", \"one-to-many\")\n", - "\n", - " # Model tides\n", - " tide_df = model_tides(\n", - " x=x,\n", - " y=y,\n", - " time=time,\n", - " model=model,\n", - " directory=directory,\n", - " **model_tides_kwargs,\n", - " )\n", - "\n", - " # Model tides for a time 15 minutes prior to each previously\n", - " # modelled satellite acquisition time. This allows us to compare\n", - " # tide heights to see if they are rising or falling.\n", - " pre_df = model_tides(\n", - " x=x,\n", - " y=y,\n", - " time=time - pd.Timedelta(delta),\n", - " model=model,\n", - " directory=directory,\n", - " **model_tides_kwargs,\n", - " )\n", - "\n", - " # Compare tides computed for each timestep. If the previous tide\n", - " # was higher than the current tide, the tide is 'ebbing'. If the\n", - " # previous tide was lower, the tide is 'flowing'\n", - " ebb_flow = (tide_df.tide_height < pre_df.tide_height.values).replace({True: \"ebb\", False: \"flow\"})\n", - "\n", - " # If tides are greater than 0, then \"high\", otherwise \"low\"\n", - " high_low = (tide_df.tide_height >= 0).replace({True: \"high\", False: \"low\"})\n", "\n", - " # Combine into one string and add to data\n", - " tide_df[\"tide_phase\"] = high_low.astype(str) + \"-\" + ebb_flow.astype(str)\n", + "# # Run EOT20 tidal model for locations and timesteps in tide gauge data\n", + "modelled_tides_df = model_tides(\n", + " x=[x],\n", + " y=[y],\n", + " time=measured_tides_ds.time,\n", + " crs=crs,\n", + " method=method,\n", + " directory=\"../tests/data/tide_models\",\n", + ")\n", "\n", - " # Optionally convert to a wide format dataframe with a tide model in\n", - " # each dataframe column\n", - " if output_format == \"wide\":\n", - " # Pivot into wide format with each time model as a column\n", - " print(\"Converting to a wide format dataframe\")\n", - " tide_df = tide_df.pivot(\n", - " columns=\"tide_model\"\n", + "# Run equivalent pyTMD code to verify same results\n", + "pytmd_tides = tide_elevations(\n", + " x=x, \n", + " y=y, \n", + " delta_time=measured_tides_ds.time,\n", + " DIRECTORY=\"../tests/data/tide_models\",\n", + " MODEL=model,\n", + " EPSG=int(crs[-4:]),\n", + " TIME=\"datetime\",\n", + " EXTRAPOLATE=True,\n", + " CUTOFF=np.inf,\n", + " METHOD=method,\n", + " # CORRECTIONS: str | None = None,\n", + " # INFER_MINOR: bool = True,\n", + " # MINOR_CONSTITUENTS: list | None = None,\n", + " # APPLY_FLEXURE: bool = False,\n", + " # FILL_VALUE: float = np.nan\n", + " # APPEND_NODE=True,\n", " )\n", "\n", - " # If in 'one-to-one' mode, reindex using our input time/x/y\n", - " # values to ensure the output is sorted the same as our inputs\n", - " if mode == \"one-to-one\":\n", - " output_indices = pd.MultiIndex.from_arrays(\n", - " [time, x, y], names=[\"time\", \"x\", \"y\"]\n", - " )\n", - " tide_df = tide_df.reindex(output_indices)\n", - "\n", - " # Optionally drop tides\n", - " if not return_tides:\n", - " return tide_df.drop(\"tide_height\", axis=1)[\"tide_phase\"]\n", - "\n", - " # Optionally drop tide heights\n", - " if not return_tides:\n", - " return tide_df.drop(\"tide_height\", axis=1)\n", - "\n", - " return tide_df" + "np.allclose(modelled_tides_df.tide_height.values, pytmd_tides.data)" ] }, { "cell_type": "code", - "execution_count": 240, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import pytest\n", - "\n", - "@pytest.mark.parametrize(\n", - " \"models,output_format,return_tides,expected_cols\",\n", - " [\n", - " (\n", - " [\"EOT20\"],\n", - " \"long\",\n", - " False,\n", - " [\"tide_model\", \"tide_phase\"]\n", - " ),\n", - " (\n", - " [\"EOT20\"],\n", - " \"long\",\n", - " True,\n", - " [\"tide_model\", \"tide_height\", \"tide_phase\"]\n", - " ),\n", - " (\n", - " [\"EOT20\", \"GOT5.5\"],\n", - " \"long\",\n", - " False,\n", - " [\"tide_model\", \"tide_phase\"]\n", - " ),\n", - " (\n", - " [\"EOT20\", \"GOT5.5\"],\n", - " \"long\",\n", - " True,\n", - " [\"tide_model\", \"tide_height\", \"tide_phase\"]\n", - " ),\n", - " (\n", - " [\"EOT20\"],\n", - " \"wide\",\n", - " False,\n", - " [\"EOT20\"]\n", - " ),\n", - " (\n", - " [\"EOT20\"],\n", - " \"wide\",\n", - " True,\n", - " [(\"tide_phase\", \"EOT20\"), (\"tide_phase\", \"EOT20\")]\n", - " ),\n", - " (\n", - " [\"EOT20\", \"GOT5.5\"],\n", - " \"wide\",\n", - " False,\n", - " [\"EOT20\", \"GOT5.5\"]\n", - " ),\n", - " (\n", - " [\"EOT20\", \"GOT5.5\"],\n", - " \"wide\",\n", - " True,\n", - " [\n", - " (\"tide_height\", \"EOT20\"),\n", - " (\"tide_height\", \"GOT5.5\"),\n", - " (\"tide_phase\", \"EOT20\"),\n", - " (\"tide_phase\", \"GOT5.5\"),\n", - " ]\n", - " ),\n", - " ]\n", - ")\n", - "def test_tide_phase_format(models, output_format, return_tides, expected_cols):\n", - "\n", - " phase_df = phase_tides(\n", - " x=[122.14, 122.30, 122.12],\n", - " y=[-17.91, -17.92, -18.07],\n", - " time=pd.date_range(\"2020\", \"2021\", periods=3),\n", - " directory=\"/var/share/tide_models/\",\n", - " model=models,\n", - " output_format=output_format,\n", - " return_tides=return_tides,\n", - " delta = \"15 min\",\n", - " )\n", - "\n", - " # Assert expected indexes and columns\n", - " assert phase_df.index.names == [\"time\", \"x\", \"y\"]\n", - " assert phase_df.columns.tolist() == expected_cols\n", - "\n", - "\n", - "\n", - "\n", - "# !pytest -q -k test_ebb_flow --verbose" + "pytmd_tides" ] }, { - "cell_type": "code", - "execution_count": 200, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Modelling tides using EOT20 in parallel\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 3/3 [00:01<00:00, 2.72it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Modelling tides using EOT20 in parallel\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 3/3 [00:01<00:00, 2.64it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['tide_model', 'tide_height', 'ebb_flow']\n" - ] - } - ], "source": [ - "test_ebb_flow(models=[\"EOT20\"], output_format=\"long\", return_tides=True)" + "## Appending node" ] }, { "cell_type": "code", - "execution_count": 256, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Modelling tides using EOT20, GOT5.5 in parallel\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 6/6 [00:01<00:00, 4.01it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Modelling tides using EOT20, GOT5.5 in parallel\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 6/6 [00:01<00:00, 4.57it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Converting to a wide format dataframe\n" - ] - }, - { - "data": { - "text/html": [ - 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" - ], - "text/plain": [ - " tide_height tide_phase \n", - "tide_model EOT20 GOT5.5 EOT20 GOT5.5\n", - "time x y \n", - "2020-01-01 122.12 -18.07 -2.800434 -2.870334 low-ebb low-ebb\n", - " 122.14 -17.91 -2.664830 -2.765702 low-flow low-ebb\n", - " 122.30 -17.92 -2.855278 -2.815728 low-ebb low-ebb\n", - "2020-07-02 122.12 -18.07 2.200403 2.171265 high-ebb high-ebb\n", - " 122.14 -17.91 2.110753 2.117294 high-ebb high-ebb\n", - " 122.30 -17.92 2.221157 2.128876 high-ebb high-ebb\n", - "2021-01-01 122.12 -18.07 -2.070869 -2.066286 low-flow low-flow\n", - " 122.14 -17.91 -1.889267 -1.910610 low-flow low-flow\n", - " 122.30 -17.92 -2.168566 -1.993354 low-flow low-flow" - ] - }, - "execution_count": 256, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# models = [\"EOT20\"]\n", - "# expected_cols = [\"tide_model\", \"ebb_flow\"]\n", - "# output_format = \"long\"\n", - "# return_tides = False\n", - "\n", - "# models = [\"EOT20\"]\n", - "# expected_cols = [\"tide_model\", \"tide_height\", \"ebb_flow\"]\n", - "# output_format = \"long\"\n", - "# return_tides = True\n", - "\n", - "# models = [\"EOT20\", \"GOT5.5\"]\n", - "# expected_cols = [\"tide_model\", \"ebb_flow\"]\n", - "# output_format = \"long\"\n", - "# return_tides = False\n", - "\n", - "models = [\"EOT20\", \"GOT5.5\"]\n", - "expected_cols = [\"tide_model\", \"tide_height\", \"ebb_flow\"]\n", - "output_format = \"long\"\n", - "return_tides = True\n", - "\n", - "\n", - "# models = [\"EOT20\"]\n", - "# expected_cols = [\"EOT20\"]\n", - "# output_format = \"wide\"\n", - "# return_tides = False\n", - "\n", - "# models = [\"EOT20\"]\n", - "# expected_cols = [(\"tide_height\", \"EOT20\"), (\"ebb_flow\", \"EOT20\")]\n", - "# output_format = \"wide\"\n", - "# return_tides = True\n", - "\n", - "# models = [\"EOT20\", \"GOT5.5\"]\n", - "# expected_cols = [\"EOT20\", \"GOT5.5\"]\n", - "# output_format = \"wide\"\n", - "# return_tides = False\n", - "\n", - "models = [\"EOT20\", \"GOT5.5\"]\n", - "expected_cols = [\n", - " (\"tide_height\", \"EOT20\"),\n", - " (\"tide_height\", \"GOT5.5\"),\n", - " (\"ebb_flow\", \"EOT20\"),\n", - " (\"ebb_flow\", \"GOT5.5\"),\n", - "]\n", - "output_format = \"wide\"\n", - "return_tides = True\n", - "\n", - "\n", - "\n", + "from eo_tides import model_tides\n", "\n", + "x, y, crs, method, model = GAUGE_X, GAUGE_Y, \"EPSG:4326\", \"spline\", \"EOT20\"\n", + "x, y, crs, method, model = GAUGE_X, GAUGE_Y, \"EPSG:4326\", \"bilinear\", \"EOT20\"\n", + "x, y, crs, method, model = -1034913, -1961916, \"EPSG:3577\", \"bilinear\", \"EOT20\"\n", "\n", - "from eo_tides.model import phase_tides\n", "\n", - "phase_df = phase_tides(\n", - " x=[122.14, 122.30, 122.12],\n", - " y=[-17.91, -17.92, -18.07],\n", - " time=pd.date_range(\"2020\", \"2021\", periods=3),\n", + "# Run EOT20 tidal model for locations and timesteps in tide gauge data\n", + "modelled_tides_df = model_tides(\n", + " x=[x],\n", + " y=[y],\n", + " model=\"FES2014\",\n", + " time=pd.date_range(\"1980\", \"2020\", freq=\"9h\"),\n", + " crs=crs,\n", + " method=method,\n", " directory=\"/var/share/tide_models/\",\n", - " model=models,\n", - " output_format=output_format,\n", - " # delta = \"15 min\",\n", - " return_tides=return_tides,\n", - ")\n", - "\n", - "\n", - "phase_df" - ] - }, - { - "cell_type": "code", - "execution_count": 261, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 261, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "phase_df.columns.tolist() == [\n", - " ('tide_height', 'EOT20'),\n", - " ('tide_height', 'GOT5.5'),\n", - " ('tide_phase', 'EOT20'),\n", - " ('tide_phase', 'GOT5.5'),\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 259, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('tide_height', 'EOT20'),\n", - " ('tide_height', 'GOT5.5'),\n", - " ('tide_phase', 'EOT20'),\n", - " ('tide_phase', 'GOT5.5')]" - ] - }, - "execution_count": 259, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " [\n", - " ('tide_height', 'EOT20'),\n", - " ('tide_height', 'GOT5.5'),\n", - " ('tide_phase', 'EOT20'),\n", - " ('tide_phase', 'GOT5.5'),\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['tide_model', 'ebb_flow']" - ] - }, - "execution_count": 222, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ebb_flow_df.columns.tolist() " - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tide_modelebb_flow
timexy
2020-01-01122.14-17.91EOT20Flow
2020-07-02122.14-17.91EOT20Ebb
2021-01-01122.14-17.91EOT20Flow
2020-01-01122.30-17.92EOT20Ebb
2020-07-02122.30-17.92EOT20Ebb
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2020-01-01122.12-18.07EOT20Ebb
2020-07-02122.12-18.07EOT20Ebb
2021-01-01122.12-18.07EOT20Flow
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2020-07-02122.14-17.91GOT5.5Ebb
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2020-07-02122.30-17.92GOT5.5Ebb
2021-01-01122.30-17.92GOT5.5Flow
2020-01-01122.12-18.07GOT5.5Ebb
2020-07-02122.12-18.07GOT5.5Ebb
2021-01-01122.12-18.07GOT5.5Flow
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" - ], - "text/plain": [ - " tide_model ebb_flow\n", - "time x y \n", - "2020-01-01 122.14 -17.91 EOT20 Flow\n", - "2020-07-02 122.14 -17.91 EOT20 Ebb\n", - "2021-01-01 122.14 -17.91 EOT20 Flow\n", - "2020-01-01 122.30 -17.92 EOT20 Ebb\n", - "2020-07-02 122.30 -17.92 EOT20 Ebb\n", - "2021-01-01 122.30 -17.92 EOT20 Flow\n", - "2020-01-01 122.12 -18.07 EOT20 Ebb\n", - "2020-07-02 122.12 -18.07 EOT20 Ebb\n", - "2021-01-01 122.12 -18.07 EOT20 Flow\n", - "2020-01-01 122.14 -17.91 GOT5.5 Ebb\n", - "2020-07-02 122.14 -17.91 GOT5.5 Ebb\n", - "2021-01-01 122.14 -17.91 GOT5.5 Flow\n", - "2020-01-01 122.30 -17.92 GOT5.5 Ebb\n", - "2020-07-02 122.30 -17.92 GOT5.5 Ebb\n", - "2021-01-01 122.30 -17.92 GOT5.5 Flow\n", - "2020-01-01 122.12 -18.07 GOT5.5 Ebb\n", - "2020-07-02 122.12 -18.07 GOT5.5 Ebb\n", - "2021-01-01 122.12 -18.07 GOT5.5 Flow" - ] - }, - "execution_count": 183, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out #.columns.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['EOT20', 'GOT5.5', 'EOT20', 'GOT5.5'], dtype='object', name='tide_model')" - ] - }, - "execution_count": 165, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out.columns.get_level_values(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['EOT20', 'GOT5.5', 'EOT20', 'GOT5.5']" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['EOT20', 'EOT20']" - ] - }, - "execution_count": 168, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[\"EOT20\"] * 2" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=============================== warnings summary ===============================\n", - ":241\n", - " :241: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility. Expected 16 from C header, got 96 from PyObject\n", - "\n", - "-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n", - "67 deselected, 1 warning in 1.48s\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import pytest\n", - "\n", - "# Define your ebb_flow function here or ensure it's imported\n", - "\n", - "def ebb_flow(x, y, time, directory, model, output_format):\n", - " # Dummy implementation for the sake of example\n", - " return pd.DataFrame({\"x\": x, \"y\": y, \"time\": time, \"model\": model})\n", - "\n", - "@pytest.mark.parametrize(\n", - " \"models\",\n", - " [\n", - " \"EOT20\", \n", - " [\"EOT20\", \"GOT5.5\"], \n", - " ],\n", - ")\n", - "def test_ebb_flow(models):\n", - " ebb_flow_df = ebb_flow(\n", - " x=[122.14, 122.30, 122.12],\n", - " y=[-17.91, -17.92, -18.07],\n", - " time=pd.date_range(\"2020\", \"2021\", periods=3),\n", - " directory=\"/var/share/tide_models/\",\n", - " model=models,\n", - " output_format=\"wide\",\n", - " )\n", - " assert ebb_flow_df is not None # Example assertion\n", - "\n", - "# Now, run the test\n", - "# pytest.main([\"-q\", \"-k\", \"test_ebb_flow\"])\n", - "\n" + ")" ] }, { "cell_type": "code", - "execution_count": 282, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Modelling tides using EOT20\n", - "Modelling tides using EOT20\n" - ] - } - ], + "outputs": [], "source": [ - "phase_df = phase_tides(\n", - " x=[122.14],\n", - " y=[-17.91],\n", - " time=pd.date_range(\"2020-01-01\", \"2020-01-02\", freq=\"h\"),\n", - " model=[\"EOT20\"],\n", - " time_offset=\"15 min\",\n", + "# Run EOT20 tidal model for locations and timesteps in tide gauge data\n", + "modelled_tides_df2 = model_tides(\n", + " x=[x],\n", + " y=[y],\n", + " model=\"FES2014\",\n", + " time=pd.date_range(\"1980\", \"2020\", freq=\"9h\"),\n", + " crs=crs,\n", + " method=method,\n", " directory=\"/var/share/tide_models/\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 289, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 289, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "phase_df.tide_phase.tolist() == [\n", - " \"low-flow\",\n", - " \"low-flow\",\n", - " \"low-flow\",\n", - " \"low-flow\",\n", - " \"high-flow\",\n", - " \"high-flow\",\n", - " \"high-flow\",\n", - " \"high-ebb\",\n", - " \"high-ebb\",\n", - " \"high-ebb\",\n", - " \"low-ebb\",\n", - " \"low-ebb\",\n", - " \"low-ebb\",\n", - " \"low-flow\",\n", - " \"low-flow\",\n", - " \"high-flow\",\n", - " \"high-flow\",\n", - " \"high-flow\",\n", - " \"high-flow\",\n", - " \"high-ebb\",\n", - " \"high-ebb\",\n", - " \"high-ebb\",\n", - " \"low-ebb\",\n", - " \"low-ebb\",\n", - " \"low-ebb\",\n", - " ]\n" + ")" ] }, { @@ -1146,13 +259,7 @@ "metadata": {}, "outputs": [], "source": [ - "modelled_tides_df[\"ebb_flow\"] = pre_tides_df.drop(\n", - " \"tide_model\", axis=1, errors=\"ignore\"\n", - ").values < modelled_tides_df.drop(\"tide_model\", axis=1, errors=\"ignore\").values\n", - "modelled_tides_df[\"ebb_flow\"] = modelled_tides_df[\"ebb_flow\"].replace({\n", - " True: \"Ebb\",\n", - " False: \"Flow\",\n", - " })" + "modelled_tides_df.tide_height.plot(alpha=0.5)" ] }, { @@ -1161,177 +268,52 @@ "metadata": {}, "outputs": [], "source": [ - "modelled_tides_df" + "modelled_tides_df2.tide_height - modelled_tides_df.tide_height" ] }, { - "cell_type": "code", - "execution_count": 248, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jovyan/Robbi/eo-tides\n" - ] - } - ], - "source": [ - "cd .." - ] - }, - { - "cell_type": "code", - "execution_count": 291, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================= test session starts ==============================\n", - "platform linux -- Python 3.10.15, pytest-8.3.3, pluggy-1.5.0 -- /env/bin/python3.10\n", - "cachedir: .pytest_cache\n", - "rootdir: /home/jovyan/Robbi/eo-tides\n", - "configfile: pyproject.toml\n", - "plugins: anyio-4.6.2.post1, nbval-0.11.0\n", - "collected 32 items / 22 deselected / 10 selected \n", - "\n", - "tests/test_model.py::test_phase_tides[15 min] PASSED [ 10%]\n", - "tests/test_model.py::test_phase_tides[20 min] PASSED [ 20%]\n", - "tests/test_model.py::test_phase_tides_format[models0-long-False-expected_cols0] PASSED [ 30%]\n", - "tests/test_model.py::test_phase_tides_format[models1-long-True-expected_cols1] PASSED [ 40%]\n", - "tests/test_model.py::test_phase_tides_format[models2-long-False-expected_cols2] PASSED [ 50%]\n", - "tests/test_model.py::test_phase_tides_format[models3-long-True-expected_cols3] PASSED [ 60%]\n", - "tests/test_model.py::test_phase_tides_format[models4-wide-False-expected_cols4] PASSED [ 70%]\n", - "tests/test_model.py::test_phase_tides_format[models5-wide-True-expected_cols5] PASSED [ 80%]\n", - "tests/test_model.py::test_phase_tides_format[models6-wide-False-expected_cols6] PASSED [ 90%]\n", - "tests/test_model.py::test_phase_tides_format[models7-wide-True-expected_cols7] PASSED [100%]\n", - "\n", - "=============================== warnings summary ===============================\n", - ":241\n", - " :241: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility. Expected 16 from C header, got 96 from PyObject\n", - "\n", - "tests/test_model.py: 24 warnings\n", - " /env/lib/python3.10/site-packages/pyproj/transformer.py:817: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " return self._transformer._transform_point(\n", - "\n", - "-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n", - "================ 10 passed, 22 deselected, 25 warnings in 6.13s ================\n" - ] - } - ], "source": [ - "!export EO_TIDES_TIDE_MODELS=./tests/data/tide_models && pytest tests/test_model.py --verbose -k test_phase_tides" + "### Error for out of bounds" ] }, { "cell_type": "code", - "execution_count": 278, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Modelling tides using EOT20\n", - "Modelling tides using EOT20\n" - ] - } - ], + "outputs": [], "source": [ - "phase_df = phase_tides(\n", - " x=[122.14],\n", - " y=[-17.91],\n", - " time=pd.date_range(\"2020-01-01\", \"2020-01-02\", freq=\"h\"),\n", - " directory=\"/var/share/tide_models/\",\n", - " model=[\"EOT20\"],\n", - " delta = \"15 min\",\n", + "from eo_tides import model_tides\n", + "\n", + "x, y = 180, -50\n", + "\n", + "\n", + "# Run EOT20 tidal model for locations and timesteps in tide gauge data\n", + "modelled_tides_df = model_tides(\n", + " x=[x],\n", + " y=[y],\n", + " model=[\"EOT20\", \"GOT5.5\"],\n", + " time=measured_tides_ds.time,\n", + " directory=\"../tests/data/tide_models\",\n", ")" ] }, { "cell_type": "code", - "execution_count": 279, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['low-flow',\n", - " 'low-flow',\n", - " 'low-flow',\n", - " 'low-flow',\n", - " 'high-flow',\n", - " 'high-flow',\n", - " 'high-flow',\n", - " 'high-ebb',\n", - " 'high-ebb',\n", - " 'high-ebb',\n", - " 'low-ebb',\n", - " 'low-ebb',\n", - " 'low-ebb',\n", - " 'low-flow',\n", - " 'low-flow',\n", - " 'high-flow',\n", - " 'high-flow',\n", - " 'high-flow',\n", - " 'high-flow',\n", - " 'high-ebb',\n", - " 'high-ebb',\n", - " 'high-ebb',\n", - " 'low-ebb',\n", - " 'low-ebb',\n", - " 'low-ebb']" - ] - }, - "execution_count": 279, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "phase_df.tide_phase.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 276, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 276, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "def check_sequence(arr):\n", - " pattern = ['low-flow', 'high-flow', 'high-ebb', 'low-ebb']\n", - " # Check if length is multiple of 4\n", - " if len(arr) % 4 != 0:\n", - " return False\n", - " \n", - " # Check each group of 4 elements\n", - " for i in range(0, len(arr), 4):\n", - " if arr[i:i+4].tolist() != pattern:\n", - " return False\n", - " return True\n", - "\n", - "check_sequence(phase_df.query(\"tide_model == 'EOT20'\").tide_phase.values)" + "from eo_tides import list_models\n", + "list_models(directory=\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Testing pyTMD" + "## Stats bug" ] }, { @@ -1340,50 +322,7 @@ "metadata": {}, "outputs": [], "source": [ - "from eo_tides import model_tides\n", - "\n", - "x, y, crs, method, model = GAUGE_X, GAUGE_Y, \"EPSG:4326\", \"spline\", \"EOT20\"\n", - "x, y, crs, method, model = GAUGE_X, GAUGE_Y, \"EPSG:4326\", \"bilinear\", \"EOT20\"\n", - "x, y, crs, method, model = -1034913, -1961916, \"EPSG:3577\", \"bilinear\", \"EOT20\"\n", - "\n", - "\n", - "# Run EOT20 tidal model for locations and timesteps in tide gauge data\n", - "modelled_tides_df = model_tides(\n", - " x=[x],\n", - " y=[y],\n", - " time=measured_tides_ds.time,\n", - " crs=crs,\n", - " method=method,\n", - " directory=\"../tests/data/tide_models\",\n", - ")\n", - "\n", - "# Run equivalent pyTMD code to verify same results\n", - "pytmd_tides = tide_elevations(\n", - " x=x, \n", - " y=y, \n", - " delta_time=measured_tides_ds.time,\n", - " DIRECTORY=\"../tests/data/tide_models\",\n", - " MODEL=\"EOT20\",\n", - " EPSG=int(crs[-4:]),\n", - " TIME=\"datetime\",\n", - " EXTRAPOLATE=True,\n", - " CUTOFF=np.inf,\n", - " METHOD=method,\n", - " # CORRECTIONS: str | None = None,\n", - " # INFER_MINOR: bool = True,\n", - " # MINOR_CONSTITUENTS: list | None = None,\n", - " # APPLY_FLEXURE: bool = False,\n", - " # FILL_VALUE: float = np.nan\n", - " )\n", - "\n", - "np.allclose(modelled_tides_df.tide_height.values, pytmd_tides.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Error for out of bounds" + "from eo_tides.stats import pixel_stats" ] }, { @@ -1392,17 +331,13 @@ "metadata": {}, "outputs": [], "source": [ - "from eo_tides import model_tides\n", - "\n", - "x, y = 180, -50\n", - "\n", + "models = [\"EOT20\"]\n", + "resample = False\n", "\n", - "# Run EOT20 tidal model for locations and timesteps in tide gauge data\n", - "modelled_tides_df = model_tides(\n", - " x=[x],\n", - " y=[y],\n", - " model=[\"EOT20\", \"GOT5.5\"],\n", - " time=measured_tides_ds.time,\n", + "stats_ds = pixel_stats(\n", + " ds=satellite_ds,\n", + " model=models,\n", + " resample=resample,\n", " directory=\"../tests/data/tide_models\",\n", ")" ] @@ -1413,8 +348,7 @@ "metadata": {}, "outputs": [], "source": [ - "from eo_tides import list_models\n", - "list_models(directory=\"\")" + "stats_ds" ] }, { diff --git a/uv.lock b/uv.lock index b017693..37e645b 100644 --- a/uv.lock +++ b/uv.lock @@ -740,7 +740,7 @@ notebooks = [ { name = "pystac-client" }, ] -[package.dev-dependencies] +[package.dependency-groups] dev = [ { name = "black" }, { name = "deptry" }, @@ -773,7 +773,7 @@ requires-dist = [ { name = "planetary-computer", marker = "extra == 'notebooks'", specifier = ">=1.0.0" }, { name = "pyproj", specifier = ">=3.6.1" }, { name = "pystac-client", marker = "extra == 'notebooks'", specifier = ">=0.8.3" }, - { name = "pytmd", specifier = "==2.1.6" }, + { name = "pytmd", specifier = "==2.1.7" }, { name = "scikit-learn", specifier = ">=1.4.0" }, { name = "scipy", specifier = ">=1.11.2" }, { name = "shapely", 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"sha256:0895b8414afafc526712c498bd9de2b063deaac4021a3b3c34566283464aff8e", size = 248777 }, { url = "https://files.pythonhosted.org/packages/65/8e/bcbe2025c587b5d703369b6a75b65d41d1367553da6e3f788aff91eaf5bd/psutil-6.1.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9dcbfce5d89f1d1f2546a2090f4fcf87c7f669d1d90aacb7d7582addece9fb38", size = 284259 }, From c9d56bf8999942b592944cf0017e359612031cc5 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 28 Oct 2024 10:57:57 +1100 Subject: [PATCH 12/13] Update action.yml --- .github/actions/setup-python-env/action.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/actions/setup-python-env/action.yml b/.github/actions/setup-python-env/action.yml index e437ecb..608cc24 100644 --- a/.github/actions/setup-python-env/action.yml +++ b/.github/actions/setup-python-env/action.yml @@ -13,7 +13,7 @@ inputs: uv-version: description: "uv version to use" required: true - default: "0.4.18" + default: "0.4.27" runs: using: "composite" From 653117538992174c86171d27ca35925bfeb45c61 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 28 Oct 2024 11:09:42 +1100 Subject: [PATCH 13/13] Update main.yml --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index dbf5263..e621f1c 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -5,7 +5,7 @@ on: branches: - main pull_request: - types: [opened, synchronize, reopened, ready_for_review] + types: [opened, synchronize, reopened] env: EO_TIDES_TIDE_MODELS: ./tests/data/tide_models