From f5f69b85fa2fc11b7af07c17c3fb536a663e8495 Mon Sep 17 00:00:00 2001 From: Ryan Abernathey Date: Tue, 5 Apr 2016 23:47:05 -0400 Subject: [PATCH 01/10] multidimensional groupby and binning --- doc/groupby.rst | 29 ++++++++++++++++--- xarray/core/common.py | 14 +++++++-- xarray/core/groupby.py | 53 +++++++++++++++++++++++++++-------- xarray/test/test_dataarray.py | 47 +++++++++++++++++++++++++++++++ xarray/test/test_dataset.py | 2 -- 5 files changed, 126 insertions(+), 19 deletions(-) diff --git a/doc/groupby.rst b/doc/groupby.rst index b5069b70cd1..e6fc83ae6a2 100644 --- a/doc/groupby.rst +++ b/doc/groupby.rst @@ -14,10 +14,11 @@ __ http://www.jstatsoft.org/v40/i01/paper - Combine your groups back into a single data object. Group by operations work on both :py:class:`~xarray.Dataset` and -:py:class:`~xarray.DataArray` objects. Currently, you can only group by a single -one-dimensional variable (eventually, we hope to remove this limitation). Also, -note that for one-dimensional data, it is usually faster to rely on pandas' -implementation of the same pipeline. +:py:class:`~xarray.DataArray` objects. Most of the examples focus on grouping by +a single one-dimensional variable, although experimental support for grouping +over a multi-dimensional variable has recently been implemented. Not that for +one-dimensional data, it is usually faster to rely on pandas' implementation of +the same pipeline. Split ~~~~~ @@ -149,3 +150,23 @@ guarantee that all original dimensions remain unchanged. You can always squeeze explicitly later with the Dataset or DataArray :py:meth:`~xarray.DataArray.squeeze` methods. + +Multidimensional Grouping +~~~~~~~~~~~~~~~~~~~~~~~~~ + +Many datasets have a multidimensional coordinate variable (e.g. longitude) +which is different from the logical grid dimensions (e.g. nx, ny). Such +variables are valid under the `CF conventions`__. Xarray supports groupby +operations over multidimensional coordinate variables: + +__ http://cfconventions.org/cf-conventions/cf-conventions.html#variables + +.. ipython:: python + + da = xr.DataArray([[0,1],[2,3]], + coords={'lon': (['ny','nx'], [[30,40],[40,50]] ), + 'lat': (['ny','nx'], [[10,10],[20,20]] ),}, + dims=['ny','nx']) + da + da.groupby('lon').sum() + da.groupby('lon').apply(lambda x: x - x.mean(), shortcut=False) diff --git a/xarray/core/common.py b/xarray/core/common.py index cac8dc7d176..6a288c35517 100644 --- a/xarray/core/common.py +++ b/xarray/core/common.py @@ -320,7 +320,7 @@ def pipe(self, func, *args, **kwargs): else: return func(self, *args, **kwargs) - def groupby(self, group, squeeze=True): + def groupby(self, group, squeeze=True, bins=None): """Returns a GroupBy object for performing grouped operations. Parameters @@ -332,16 +332,26 @@ def groupby(self, group, squeeze=True): If "group" is a dimension of any arrays in this dataset, `squeeze` controls whether the subarrays have a dimension of length 1 along that dimension or if the dimension is squeezed out. + bins : array-like, optional + If `bins` is specified, the groups will be discretized into the + specified bins determined by `pandas.cut` applied to the index of + `group`. Returns ------- grouped : GroupBy A `GroupBy` object patterned after `pandas.GroupBy` that can be iterated over in the form of `(unique_value, grouped_array)` pairs. + + See Also + -------- + pandas.cut """ + from .dataarray import DataArray + if isinstance(group, basestring): group = self[group] - return self.groupby_cls(self, group, squeeze=squeeze) + return self.groupby_cls(self, group, squeeze=squeeze, group_bins=bins) def rolling(self, min_periods=None, center=False, **windows): """ diff --git a/xarray/core/groupby.py b/xarray/core/groupby.py index 7cab6aaa376..b005e3d4171 100644 --- a/xarray/core/groupby.py +++ b/xarray/core/groupby.py @@ -130,7 +130,7 @@ class GroupBy(object): Dataset.groupby DataArray.groupby """ - def __init__(self, obj, group, squeeze=False, grouper=None): + def __init__(self, obj, group, squeeze=False, grouper=None, group_bins=None): """Create a GroupBy object Parameters @@ -145,14 +145,28 @@ def __init__(self, obj, group, squeeze=False, grouper=None): if the dimension is squeezed out. grouper : pd.Grouper, optional Used for grouping values along the `group` array. + group_bins : array-like, optional + If `bins` is specified, the groups will be discretized into the + specified bins by `pandas.cut`. """ from .dataset import as_dataset - if group.ndim != 1: - # TODO: remove this limitation? - raise ValueError('`group` must be 1 dimensional') if getattr(group, 'name', None) is None: raise ValueError('`group` must have a name') + self._stacked_dim = None + if group.ndim != 1: + # try to stack the dims of the group into a single dim + # TODO: figure out how to exclude dimensions from the stacking + # (e.g. group over space dims but leave time dim intact) + orig_dims = group.dims + stacked_dim_name = 'stacked_' + '_'.join(orig_dims) + # the copy is necessary here, otherwise read only array raises error + # in pandas: https://github.com/pydata/pandas/issues/12813 + # Is there a performance penalty for calling copy? + group = group.stack(**{stacked_dim_name: orig_dims}).copy() + obj = obj.stack(**{stacked_dim_name: orig_dims}) + self._stacked_dim = stacked_dim_name + self._unstacked_dims = orig_dims if not hasattr(group, 'dims'): raise ValueError("`group` must have a 'dims' attribute") group_dim, = group.dims @@ -167,14 +181,19 @@ def __init__(self, obj, group, squeeze=False, grouper=None): 'dimension') full_index = None - if grouper is not None: - # time-series resampling + if grouper is not None and group_bins is not None: + raise ValueError("Can't specify both `grouper` and `bins`.") + elif grouper is not None or group_bins is not None: index = safe_cast_to_index(group) if not index.is_monotonic: # TODO: sort instead of raising an error raise ValueError('index must be monotonic for resampling') s = pd.Series(np.arange(index.size), index) - first_items = s.groupby(grouper).first() + if grouper is not None: + # probably time-series resampling + first_items = s.groupby(grouper).first() + else: + first_items = s.groupby(pd.cut(index, group_bins)).first() if first_items.isnull().any(): full_index = first_items.index first_items = first_items.dropna() @@ -276,6 +295,13 @@ def _maybe_restore_empty_groups(self, combined): combined = combined.reindex(**indexers) return combined + def _maybe_unstack_array(self, arr): + """This gets called if we are applying on an array with a + multidimensional group.""" + if self._stacked_dim is not None and self._stacked_dim in arr.dims: + arr = arr.unstack(self._stacked_dim) + return arr + def fillna(self, value): """Fill missing values in this object by group. @@ -394,6 +420,11 @@ def lookup_order(dimension): new_order = sorted(stacked.dims, key=lookup_order) return stacked.transpose(*new_order) + def _restore_multiindex(self, combined): + if self._stacked_dim is not None and self._stacked_dim in combined.dims: + combined[self._stacked_dim] = self.group[self._stacked_dim] + return combined + def apply(self, func, shortcut=False, **kwargs): """Apply a function over each array in the group and concatenate them together into a new array. @@ -435,24 +466,24 @@ def apply(self, func, shortcut=False, **kwargs): grouped = self._iter_grouped_shortcut() else: grouped = self._iter_grouped() - applied = (maybe_wrap_array(arr, func(arr, **kwargs)) for arr in grouped) + applied = (maybe_wrap_array(arr,func(arr, **kwargs)) for arr in grouped) combined = self._concat(applied, shortcut=shortcut) - result = self._maybe_restore_empty_groups(combined) + result = self._maybe_restore_empty_groups( + self._maybe_unstack_array(combined)) return result def _concat(self, applied, shortcut=False): # peek at applied to determine which coordinate to stack over applied_example, applied = peek_at(applied) concat_dim, positions = self._infer_concat_args(applied_example) - if shortcut: combined = self._concat_shortcut(applied, concat_dim, positions) else: combined = concat(applied, concat_dim) combined = _maybe_reorder(combined, concat_dim, positions) - if isinstance(combined, type(self.obj)): combined = self._restore_dim_order(combined) + combined = self._restore_multiindex(combined) return combined def reduce(self, func, dim=None, axis=None, keep_attrs=False, diff --git a/xarray/test/test_dataarray.py b/xarray/test/test_dataarray.py index d53f9c54a9e..690dd467be4 100644 --- a/xarray/test/test_dataarray.py +++ b/xarray/test/test_dataarray.py @@ -1304,6 +1304,53 @@ def test_groupby_first_and_last(self): expected = array # should be a no-op self.assertDataArrayIdentical(expected, actual) + def make_groupby_multidim_example_array(self): + return DataArray([[0,1],[2,3]], + coords={'lon': (['ny','nx'], [[30,40],[40,50]] ), + 'lat': (['ny','nx'], [[10,10],[20,20]] ),}, + dims=['ny','nx']) + + def test_groupby_multidim(self): + array = self.make_groupby_multidim_example_array() + for dim, expected_sum in [ + ('lon', DataArray([0, 3, 3], coords={'lon': [30,40,50]})), + ('lat', DataArray([1,5], coords={'lat': [10,20]}))]: + actual_sum = array.groupby(dim).sum() + self.assertDataArrayIdentical(expected_sum, actual_sum) + + def test_groupby_multidim_apply(self): + array = self.make_groupby_multidim_example_array() + actual = array.groupby('lon').apply( + lambda x : x - x.mean(), shortcut=False) + expected = DataArray([[0.,-0.5],[0.5,0.]], + coords=array.coords, dims=array.dims) + self.assertDataArrayIdentical(expected, actual) + + def test_groupby_bins(self): + array = DataArray(np.arange(4), dims='dim_0') + # bins follow conventions for pandas.cut + # http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html + bins = [0,1.5,5] + bin_coords = ['(0, 1.5]', '(1.5, 5]'] + expected = DataArray([1,5], dims='dim_0', coords={'dim_0': bin_coords}) + # the problem with this is that it overwrites the dimensions of array! + #actual = array.groupby('dim_0', bins=bins).sum() + actual = array.groupby('dim_0', bins=bins).apply( + lambda x : x.sum(), shortcut=False) + self.assertDataArrayIdentical(expected, actual) + # make sure original array dims are unchanged + # (would fail with shortcut=True above) + self.assertEqual(len(array.dim_0), 4) + + def test_groupby_bins_multidim(self): + array = self.make_groupby_multidim_example_array() + bins = [0,15,20] + bin_coords = ['(0, 15]', '(15, 20]'] + expected = DataArray([1,5], dims='lat', coords={'lat': bin_coords}) + actual = array.groupby('lat', bins=bins).apply( + lambda x : x.sum(), shortcut=False) + self.assertDataArrayIdentical(expected, actual) + def make_rolling_example_array(self): times = pd.date_range('2000-01-01', freq='1D', periods=21) values = np.random.random((21, 4)) diff --git a/xarray/test/test_dataset.py b/xarray/test/test_dataset.py index 69e2a582b4c..5a3cde3deab 100644 --- a/xarray/test/test_dataset.py +++ b/xarray/test/test_dataset.py @@ -1545,8 +1545,6 @@ def test_groupby_iter(self): def test_groupby_errors(self): data = create_test_data() - with self.assertRaisesRegexp(ValueError, 'must be 1 dimensional'): - data.groupby('var1') with self.assertRaisesRegexp(ValueError, 'must have a name'): data.groupby(np.arange(10)) with self.assertRaisesRegexp(ValueError, 'length does not match'): From 4c65a7103ac0aa332cb25ec402a1c3e0ebc9a6ae Mon Sep 17 00:00:00 2001 From: Ryan Abernathey Date: Thu, 14 Apr 2016 22:04:34 -0400 Subject: [PATCH 02/10] added time dimension to multidim groupby tests --- xarray/core/common.py | 2 +- xarray/core/groupby.py | 18 +++++++++--------- xarray/test/test_dataarray.py | 17 +++++++++-------- 3 files changed, 19 insertions(+), 18 deletions(-) diff --git a/xarray/core/common.py b/xarray/core/common.py index 6a288c35517..c904ba0d0b4 100644 --- a/xarray/core/common.py +++ b/xarray/core/common.py @@ -351,7 +351,7 @@ def groupby(self, group, squeeze=True, bins=None): if isinstance(group, basestring): group = self[group] - return self.groupby_cls(self, group, squeeze=squeeze, group_bins=bins) + return self.groupby_cls(self, group, squeeze=squeeze, bins=bins) def rolling(self, min_periods=None, center=False, **windows): """ diff --git a/xarray/core/groupby.py b/xarray/core/groupby.py index b005e3d4171..6f26d8ab5d7 100644 --- a/xarray/core/groupby.py +++ b/xarray/core/groupby.py @@ -130,7 +130,7 @@ class GroupBy(object): Dataset.groupby DataArray.groupby """ - def __init__(self, obj, group, squeeze=False, grouper=None, group_bins=None): + def __init__(self, obj, group, squeeze=False, grouper=None, bins=None): """Create a GroupBy object Parameters @@ -145,7 +145,7 @@ def __init__(self, obj, group, squeeze=False, grouper=None, group_bins=None): if the dimension is squeezed out. grouper : pd.Grouper, optional Used for grouping values along the `group` array. - group_bins : array-like, optional + bins : array-like, optional If `bins` is specified, the groups will be discretized into the specified bins by `pandas.cut`. """ @@ -181,9 +181,9 @@ def __init__(self, obj, group, squeeze=False, grouper=None, group_bins=None): 'dimension') full_index = None - if grouper is not None and group_bins is not None: + if grouper is not None and bins is not None: raise ValueError("Can't specify both `grouper` and `bins`.") - elif grouper is not None or group_bins is not None: + elif grouper is not None or bins is not None: index = safe_cast_to_index(group) if not index.is_monotonic: # TODO: sort instead of raising an error @@ -193,13 +193,13 @@ def __init__(self, obj, group, squeeze=False, grouper=None, group_bins=None): # probably time-series resampling first_items = s.groupby(grouper).first() else: - first_items = s.groupby(pd.cut(index, group_bins)).first() + first_items = s.groupby(pd.cut(index, bins)).first() if first_items.isnull().any(): full_index = first_items.index first_items = first_items.dropna() - bins = first_items.values.astype(np.int64) - group_indices = ([slice(i, j) for i, j in zip(bins[:-1], bins[1:])] + - [slice(bins[-1], None)]) + sbins = first_items.values.astype(np.int64) + group_indices = ([slice(i, j) for i, j in zip(sbins[:-1], sbins[1:])] + + [slice(sbins[-1], None)]) unique_coord = Coordinate(group.name, first_items.index) elif group.name in obj.dims: # assume that group already has sorted, unique values @@ -466,7 +466,7 @@ def apply(self, func, shortcut=False, **kwargs): grouped = self._iter_grouped_shortcut() else: grouped = self._iter_grouped() - applied = (maybe_wrap_array(arr,func(arr, **kwargs)) for arr in grouped) + applied = (maybe_wrap_array(arr, func(arr, **kwargs)) for arr in grouped) combined = self._concat(applied, shortcut=shortcut) result = self._maybe_restore_empty_groups( self._maybe_unstack_array(combined)) diff --git a/xarray/test/test_dataarray.py b/xarray/test/test_dataarray.py index 690dd467be4..fb7c8e24fa1 100644 --- a/xarray/test/test_dataarray.py +++ b/xarray/test/test_dataarray.py @@ -1305,16 +1305,16 @@ def test_groupby_first_and_last(self): self.assertDataArrayIdentical(expected, actual) def make_groupby_multidim_example_array(self): - return DataArray([[0,1],[2,3]], - coords={'lon': (['ny','nx'], [[30,40],[40,50]] ), - 'lat': (['ny','nx'], [[10,10],[20,20]] ),}, - dims=['ny','nx']) + return DataArray([[[0,1],[2,3]],[[5,10],[15,20]]], + coords={'lon': (['ny', 'nx'], [[30., 40.], [40., 50.]] ), + 'lat': (['ny', 'nx'], [[10., 10.], [20., 20.]] ),}, + dims=['time', 'ny', 'nx']) def test_groupby_multidim(self): array = self.make_groupby_multidim_example_array() for dim, expected_sum in [ - ('lon', DataArray([0, 3, 3], coords={'lon': [30,40,50]})), - ('lat', DataArray([1,5], coords={'lat': [10,20]}))]: + ('lon', DataArray([5, 28, 23], coords={'lon': [30., 40., 50.]})), + ('lat', DataArray([16, 40], coords={'lat': [10., 20.]}))]: actual_sum = array.groupby(dim).sum() self.assertDataArrayIdentical(expected_sum, actual_sum) @@ -1322,7 +1322,8 @@ def test_groupby_multidim_apply(self): array = self.make_groupby_multidim_example_array() actual = array.groupby('lon').apply( lambda x : x - x.mean(), shortcut=False) - expected = DataArray([[0.,-0.5],[0.5,0.]], + expected = DataArray([[[-2.5, -6.], [-5., -8.5]], + [[ 2.5, 3.], [ 8., 8.5]]], coords=array.coords, dims=array.dims) self.assertDataArrayIdentical(expected, actual) @@ -1346,7 +1347,7 @@ def test_groupby_bins_multidim(self): array = self.make_groupby_multidim_example_array() bins = [0,15,20] bin_coords = ['(0, 15]', '(15, 20]'] - expected = DataArray([1,5], dims='lat', coords={'lat': bin_coords}) + expected = DataArray([16, 40], dims='lat', coords={'lat': bin_coords}) actual = array.groupby('lat', bins=bins).apply( lambda x : x.sum(), shortcut=False) self.assertDataArrayIdentical(expected, actual) From 016071c0f97bfff212443d43279216ae34989bb2 Mon Sep 17 00:00:00 2001 From: Ryan Abernathey Date: Tue, 10 May 2016 16:16:38 -0400 Subject: [PATCH 03/10] updated docs --- doc/groupby.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/groupby.rst b/doc/groupby.rst index e6fc83ae6a2..28f7312d2ad 100644 --- a/doc/groupby.rst +++ b/doc/groupby.rst @@ -15,8 +15,8 @@ __ http://www.jstatsoft.org/v40/i01/paper Group by operations work on both :py:class:`~xarray.Dataset` and :py:class:`~xarray.DataArray` objects. Most of the examples focus on grouping by -a single one-dimensional variable, although experimental support for grouping -over a multi-dimensional variable has recently been implemented. Not that for +a single one-dimensional variable, although support for grouping +over a multi-dimensional variable has recently been implemented. Note that for one-dimensional data, it is usually faster to rely on pandas' implementation of the same pipeline. @@ -159,7 +159,7 @@ which is different from the logical grid dimensions (e.g. nx, ny). Such variables are valid under the `CF conventions`__. Xarray supports groupby operations over multidimensional coordinate variables: -__ http://cfconventions.org/cf-conventions/cf-conventions.html#variables +__ http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#_two_dimensional_latitude_longitude_coordinate_variables .. ipython:: python From e879e8c887f0672113ddf0d6474f184c0f9381d6 Mon Sep 17 00:00:00 2001 From: Ryan Abernathey Date: Thu, 12 May 2016 09:16:15 -0400 Subject: [PATCH 04/10] fixed binning --- xarray/core/groupby.py | 16 ++++++++++------ xarray/test/test_dataarray.py | 2 ++ 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/xarray/core/groupby.py b/xarray/core/groupby.py index 6f26d8ab5d7..41c70d2c744 100644 --- a/xarray/core/groupby.py +++ b/xarray/core/groupby.py @@ -150,6 +150,7 @@ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None): specified bins by `pandas.cut`. """ from .dataset import as_dataset + from .dataarray import DataArray if getattr(group, 'name', None) is None: raise ValueError('`group` must have a name') @@ -182,18 +183,18 @@ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None): full_index = None if grouper is not None and bins is not None: - raise ValueError("Can't specify both `grouper` and `bins`.") - elif grouper is not None or bins is not None: + raise TypeError("Can't specify both `grouper` and `bins`.") + if bins is not None: + group = DataArray(pd.cut(group.values, bins), + group.coords, name=group.name) + if grouper is not None: index = safe_cast_to_index(group) if not index.is_monotonic: # TODO: sort instead of raising an error raise ValueError('index must be monotonic for resampling') s = pd.Series(np.arange(index.size), index) if grouper is not None: - # probably time-series resampling first_items = s.groupby(grouper).first() - else: - first_items = s.groupby(pd.cut(index, bins)).first() if first_items.isnull().any(): full_index = first_items.index first_items = first_items.dropna() @@ -201,8 +202,11 @@ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None): group_indices = ([slice(i, j) for i, j in zip(sbins[:-1], sbins[1:])] + [slice(sbins[-1], None)]) unique_coord = Coordinate(group.name, first_items.index) - elif group.name in obj.dims: + elif group.name in obj.dims and bins is None: # assume that group already has sorted, unique values + # (if using bins, the group will have the same name as a dimension + # but different values) + print('assume that group already has sorted, unique values') if group.dims != (group.name,): raise ValueError('`group` is required to be a coordinate if ' '`group.name` is a dimension in `obj`') diff --git a/xarray/test/test_dataarray.py b/xarray/test/test_dataarray.py index fb7c8e24fa1..fd00e2a9bef 100644 --- a/xarray/test/test_dataarray.py +++ b/xarray/test/test_dataarray.py @@ -1329,6 +1329,8 @@ def test_groupby_multidim_apply(self): def test_groupby_bins(self): array = DataArray(np.arange(4), dims='dim_0') + # the first value should not be part of any group ("right" binning) + array[0] = 99 # bins follow conventions for pandas.cut # http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html bins = [0,1.5,5] From 82e487883de59b6da12714bb94223d0f1a2efd24 Mon Sep 17 00:00:00 2001 From: Ryan Abernathey Date: Sat, 14 May 2016 22:42:59 -0400 Subject: [PATCH 05/10] add groupby_bins method --- xarray/core/common.py | 63 +++++++++++++++++++++++++++++------ xarray/core/groupby.py | 7 ++-- xarray/test/test_dataarray.py | 4 +-- 3 files changed, 59 insertions(+), 15 deletions(-) diff --git a/xarray/core/common.py b/xarray/core/common.py index c904ba0d0b4..5d9324cd35c 100644 --- a/xarray/core/common.py +++ b/xarray/core/common.py @@ -320,7 +320,7 @@ def pipe(self, func, *args, **kwargs): else: return func(self, *args, **kwargs) - def groupby(self, group, squeeze=True, bins=None): + def groupby(self, group, squeeze=True): """Returns a GroupBy object for performing grouped operations. Parameters @@ -332,26 +332,67 @@ def groupby(self, group, squeeze=True, bins=None): If "group" is a dimension of any arrays in this dataset, `squeeze` controls whether the subarrays have a dimension of length 1 along that dimension or if the dimension is squeezed out. - bins : array-like, optional - If `bins` is specified, the groups will be discretized into the - specified bins determined by `pandas.cut` applied to the index of - `group`. Returns ------- grouped : GroupBy A `GroupBy` object patterned after `pandas.GroupBy` that can be iterated over in the form of `(unique_value, grouped_array)` pairs. - - See Also - -------- - pandas.cut """ - from .dataarray import DataArray + if isinstance(group, basestring): + group = self[group] + return self.groupby_cls(self, group, squeeze=squeeze) + + def groupby_bins(self, group, bins, right=True, labels=None, precision=3, + include_lowest=False, squeeze=True): + """Returns a GroupBy object for performing grouped operations. Rather + than using all unique values of `group`, the values are discretized + first by applying `pandas.cut` [1]_ to `group`. + Parameters + ---------- + group : str, DataArray or Coordinate + Array whose binned values should be used to group this array. If a + string, must be the name of a variable contained in this dataset. + bins : int or array of scalars + If bins is an int, it defines the number of equal-width bins in the + range of x. However, in this case, the range of x is extended by .1% + on each side to include the min or max values of x. If bins is a + sequence it defines the bin edges allowing for non-uniform bin + width. No extension of the range of x is done in this case. + right : boolean, optional +I ndicates whether the bins include the rightmost edge or not. If + right == True (the default), then the bins [1,2,3,4] indicate + (1,2], (2,3], (3,4]. + labels : array or boolean, default None + Used as labels for the resulting bins. Must be of the same length as + the resulting bins. If False, string bin labels are assigned by + `pandas.cut`. + precision : int + The precision at which to store and display the bins labels. + include_lowest : bool + Whether the first interval should be left-inclusive or not. + squeeze : boolean, optional + If "group" is a dimension of any arrays in this dataset, `squeeze` + controls whether the subarrays have a dimension of length 1 along + that dimension or if the dimension is squeezed out. + + Returns + ------- + grouped : GroupBy + A `GroupBy` object patterned after `pandas.GroupBy` that can be + iterated over in the form of `(unique_value, grouped_array)` pairs. + + References + ---------- + .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html + """ if isinstance(group, basestring): group = self[group] - return self.groupby_cls(self, group, squeeze=squeeze, bins=bins) + return self.groupby_cls(self, group, squeeze=squeeze, bins=bins, + cut_kwargs={'right': right, 'labels': labels, + 'precision': precision, + 'include_lowest': include_lowest}) def rolling(self, min_periods=None, center=False, **windows): """ diff --git a/xarray/core/groupby.py b/xarray/core/groupby.py index 41c70d2c744..a81fe7b3e6e 100644 --- a/xarray/core/groupby.py +++ b/xarray/core/groupby.py @@ -130,7 +130,8 @@ class GroupBy(object): Dataset.groupby DataArray.groupby """ - def __init__(self, obj, group, squeeze=False, grouper=None, bins=None): + def __init__(self, obj, group, squeeze=False, grouper=None, bins=None, + cut_kwargs={}): """Create a GroupBy object Parameters @@ -148,6 +149,8 @@ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None): bins : array-like, optional If `bins` is specified, the groups will be discretized into the specified bins by `pandas.cut`. + cut_kwargs : dict, optional + Extra keyword arguments to pass to `pandas.cut` """ from .dataset import as_dataset from .dataarray import DataArray @@ -185,7 +188,7 @@ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None): if grouper is not None and bins is not None: raise TypeError("Can't specify both `grouper` and `bins`.") if bins is not None: - group = DataArray(pd.cut(group.values, bins), + group = DataArray(pd.cut(group.values, bins, **cut_kwargs), group.coords, name=group.name) if grouper is not None: index = safe_cast_to_index(group) diff --git a/xarray/test/test_dataarray.py b/xarray/test/test_dataarray.py index fd00e2a9bef..a2bfd98761e 100644 --- a/xarray/test/test_dataarray.py +++ b/xarray/test/test_dataarray.py @@ -1338,7 +1338,7 @@ def test_groupby_bins(self): expected = DataArray([1,5], dims='dim_0', coords={'dim_0': bin_coords}) # the problem with this is that it overwrites the dimensions of array! #actual = array.groupby('dim_0', bins=bins).sum() - actual = array.groupby('dim_0', bins=bins).apply( + actual = array.groupby_bins('dim_0', bins).apply( lambda x : x.sum(), shortcut=False) self.assertDataArrayIdentical(expected, actual) # make sure original array dims are unchanged @@ -1350,7 +1350,7 @@ def test_groupby_bins_multidim(self): bins = [0,15,20] bin_coords = ['(0, 15]', '(15, 20]'] expected = DataArray([16, 40], dims='lat', coords={'lat': bin_coords}) - actual = array.groupby('lat', bins=bins).apply( + actual = array.groupby_bins('lat', bins).apply( lambda x : x.sum(), shortcut=False) self.assertDataArrayIdentical(expected, actual) From b94f1b8c359496292b4671ad1b9ecd7bfb6a6e1a Mon Sep 17 00:00:00 2001 From: Ryan Abernathey Date: Mon, 16 May 2016 16:37:49 -0400 Subject: [PATCH 06/10] doc update --- doc/api.rst | 2 ++ doc/groupby.rst | 35 +++++++++++++++++++++++++++++++++++ xarray/core/common.py | 7 ++++--- 3 files changed, 41 insertions(+), 3 deletions(-) diff --git a/doc/api.rst b/doc/api.rst index a5f0f20428e..de051b42576 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -109,6 +109,7 @@ Computation Dataset.apply Dataset.reduce Dataset.groupby + Dataset.groupby_bins Dataset.resample Dataset.diff @@ -245,6 +246,7 @@ Computation DataArray.reduce DataArray.groupby + DataArray.groupby_bins DataArray.rolling DataArray.resample DataArray.get_axis_num diff --git a/doc/groupby.rst b/doc/groupby.rst index 28f7312d2ad..2bd61ff45e6 100644 --- a/doc/groupby.rst +++ b/doc/groupby.rst @@ -64,6 +64,33 @@ You can also iterate over over groups in ``(label, group)`` pairs: Just like in pandas, creating a GroupBy object is cheap: it does not actually split the data until you access particular values. +Binning +~~~~~~~ + +Sometimes you don't want to use all the unique values to determine the groups +but instead want to "bin" the data into coarser groups. You could always create +a customized coordinate, but xarray facilitates this via the +:py:meth:`~xarray.Dataset.groupby_bins` method. + +.. ipython:: python + + x_bins = [0,25,50] + ds.groupby_bins('x', x_bins).groups + +The binning is implemented via `pandas.cut`__, whose documentation details how +the bins are assigned. As seen in the example above, by default, the bins are +labeled with strings using set notation to precisely identify the bin limits. To +override this behavior, you can specify the bin labels explicitly. Here we +choose `float` labels which identify the bin centers: + +.. ipython:: python + + x_bin_labels = [12.5,37.5] + ds.groupby_bins('x', x_bins, labels=x_bin_labels).groups + +__ http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.cut.html + + Apply ~~~~~ @@ -170,3 +197,11 @@ __ http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#_two_dimen da da.groupby('lon').sum() da.groupby('lon').apply(lambda x: x - x.mean(), shortcut=False) + +Because multidimensional groups have the ability to generate a very large +number of bins, coarse-binning via :py:meth:`~xarray.Dataset.groupby_bins` +may be desirable: + +.. ipython:: python + + da.groupby_bins('lon', [0,45,50]).sum() diff --git a/xarray/core/common.py b/xarray/core/common.py index 5d9324cd35c..be6d52de52c 100644 --- a/xarray/core/common.py +++ b/xarray/core/common.py @@ -345,8 +345,9 @@ def groupby(self, group, squeeze=True): def groupby_bins(self, group, bins, right=True, labels=None, precision=3, include_lowest=False, squeeze=True): - """Returns a GroupBy object for performing grouped operations. Rather - than using all unique values of `group`, the values are discretized + """Returns a GroupBy object for performing grouped operations. + + Rather than using all unique values of `group`, the values are discretized first by applying `pandas.cut` [1]_ to `group`. Parameters @@ -361,7 +362,7 @@ def groupby_bins(self, group, bins, right=True, labels=None, precision=3, sequence it defines the bin edges allowing for non-uniform bin width. No extension of the range of x is done in this case. right : boolean, optional -I ndicates whether the bins include the rightmost edge or not. If + Indicates whether the bins include the rightmost edge or not. If right == True (the default), then the bins [1,2,3,4] indicate (1,2], (2,3], (3,4]. labels : array or boolean, default None From d6142465dbfd3778d4e819d22a494148223c87c8 Mon Sep 17 00:00:00 2001 From: Ryan Abernathey Date: Wed, 18 May 2016 11:43:03 -0400 Subject: [PATCH 07/10] test for non-monotonic 2d coordinates --- xarray/test/test_dataarray.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/xarray/test/test_dataarray.py b/xarray/test/test_dataarray.py index a2bfd98761e..d6e18508d6b 100644 --- a/xarray/test/test_dataarray.py +++ b/xarray/test/test_dataarray.py @@ -1353,6 +1353,12 @@ def test_groupby_bins_multidim(self): actual = array.groupby_bins('lat', bins).apply( lambda x : x.sum(), shortcut=False) self.assertDataArrayIdentical(expected, actual) + # modify the array coordinates to be non-monotonic after unstacking + array['lat'].data = np.array([[10., 20.], [20., 10.]]) + expected = DataArray([28, 28], dims='lat', coords={'lat': bin_coords}) + actual = array.groupby_bins('lat', bins).apply( + lambda x : x.sum(), shortcut=False) + self.assertDataArrayIdentical(expected, actual) def make_rolling_example_array(self): times = pd.date_range('2000-01-01', freq='1D', periods=21) From f957eb89f858b8fe13dfa9c4ff6a3bfebcdc229d Mon Sep 17 00:00:00 2001 From: Ryan Abernathey Date: Wed, 6 Jul 2016 15:55:33 +0200 Subject: [PATCH 08/10] bin coordinate name changed --- xarray/core/groupby.py | 7 ++++--- xarray/test/test_dataarray.py | 9 ++++++--- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/xarray/core/groupby.py b/xarray/core/groupby.py index a81fe7b3e6e..486d57c3b94 100644 --- a/xarray/core/groupby.py +++ b/xarray/core/groupby.py @@ -189,7 +189,8 @@ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None, raise TypeError("Can't specify both `grouper` and `bins`.") if bins is not None: group = DataArray(pd.cut(group.values, bins, **cut_kwargs), - group.coords, name=group.name) + group.coords, name=group.name + '_bins') + # RPA: we want to restore the original coordinates at some point! if grouper is not None: index = safe_cast_to_index(group) if not index.is_monotonic: @@ -209,7 +210,6 @@ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None, # assume that group already has sorted, unique values # (if using bins, the group will have the same name as a dimension # but different values) - print('assume that group already has sorted, unique values') if group.dims != (group.name,): raise ValueError('`group` is required to be a coordinate if ' '`group.name` is a dimension in `obj`') @@ -429,7 +429,8 @@ def lookup_order(dimension): def _restore_multiindex(self, combined): if self._stacked_dim is not None and self._stacked_dim in combined.dims: - combined[self._stacked_dim] = self.group[self._stacked_dim] + stacked_dim = self.group[self._stacked_dim] + combined[self._stacked_dim] = stacked_dim return combined def apply(self, func, shortcut=False, **kwargs): diff --git a/xarray/test/test_dataarray.py b/xarray/test/test_dataarray.py index d6e18508d6b..f5b88efd3fa 100644 --- a/xarray/test/test_dataarray.py +++ b/xarray/test/test_dataarray.py @@ -1335,7 +1335,8 @@ def test_groupby_bins(self): # http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html bins = [0,1.5,5] bin_coords = ['(0, 1.5]', '(1.5, 5]'] - expected = DataArray([1,5], dims='dim_0', coords={'dim_0': bin_coords}) + expected = DataArray([1,5], dims='dim_0_bins', + coords={'dim_0_bins': bin_coords}) # the problem with this is that it overwrites the dimensions of array! #actual = array.groupby('dim_0', bins=bins).sum() actual = array.groupby_bins('dim_0', bins).apply( @@ -1349,13 +1350,15 @@ def test_groupby_bins_multidim(self): array = self.make_groupby_multidim_example_array() bins = [0,15,20] bin_coords = ['(0, 15]', '(15, 20]'] - expected = DataArray([16, 40], dims='lat', coords={'lat': bin_coords}) + expected = DataArray([16, 40], dims='lat_bins', + coords={'lat_bins': bin_coords}) actual = array.groupby_bins('lat', bins).apply( lambda x : x.sum(), shortcut=False) self.assertDataArrayIdentical(expected, actual) # modify the array coordinates to be non-monotonic after unstacking array['lat'].data = np.array([[10., 20.], [20., 10.]]) - expected = DataArray([28, 28], dims='lat', coords={'lat': bin_coords}) + expected = DataArray([28, 28], dims='lat_bins', + coords={'lat_bins': bin_coords}) actual = array.groupby_bins('lat', bins).apply( lambda x : x.sum(), shortcut=False) self.assertDataArrayIdentical(expected, actual) From 237fc39ef8d7de464f85db66e586f03845b91dfb Mon Sep 17 00:00:00 2001 From: Ryan Abernathey Date: Wed, 6 Jul 2016 16:30:52 +0200 Subject: [PATCH 09/10] updated docs and example --- doc/examples.rst | 1 + doc/examples/multidimensional-coords.rst | 200 +++++++++++ .../xarray_multidimensional_coords_10_1.png | Bin 0 -> 55464 bytes .../xarray_multidimensional_coords_12_0.png | Bin 0 -> 106980 bytes .../xarray_multidimensional_coords_14_1.png | Bin 0 -> 9253 bytes .../xarray_multidimensional_coords_8_2.png | Bin 0 -> 58116 bytes examples/xarray_multidimensional_coords.ipynb | 337 ++++++++++++++++++ xarray/core/common.py | 2 + 8 files changed, 540 insertions(+) create mode 100644 doc/examples/multidimensional-coords.rst create mode 100644 doc/examples/multidimensional_coords_files/xarray_multidimensional_coords_10_1.png create mode 100644 doc/examples/multidimensional_coords_files/xarray_multidimensional_coords_12_0.png create mode 100644 doc/examples/multidimensional_coords_files/xarray_multidimensional_coords_14_1.png create mode 100644 doc/examples/multidimensional_coords_files/xarray_multidimensional_coords_8_2.png create mode 100644 examples/xarray_multidimensional_coords.ipynb diff --git a/doc/examples.rst b/doc/examples.rst index d8a8eff59a4..5848099a2ce 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -7,3 +7,4 @@ Examples examples/quick-overview examples/weather-data examples/monthly-means + examples/multidimensional-coords diff --git a/doc/examples/multidimensional-coords.rst b/doc/examples/multidimensional-coords.rst new file mode 100644 index 00000000000..bc0828ddb10 --- /dev/null +++ b/doc/examples/multidimensional-coords.rst @@ -0,0 +1,200 @@ + +Working with Multidimensional Coordinates +========================================= + +Author: `Ryan Abernathey `__ + +Many datasets have *physical coordinates* which differ from their +*logical coordinates*. Xarray provides several ways to plot and analyze +such datasets. + +.. code:: python + + %matplotlib inline + import numpy as np + import pandas as pd + import xarray as xr + import cartopy.crs as ccrs + from matplotlib import pyplot as plt + + print("numpy version : ", np.__version__) + print("pandas version : ", pd.__version__) + print("xarray version : ", xr.version.version) + + +.. parsed-literal:: + + ('numpy version : ', '1.11.0') + ('pandas version : ', u'0.18.0') + ('xarray version : ', '0.7.2-32-gf957eb8') + + +As an example, consider this dataset from the +`xarray-data `__ repository. + +.. code:: python + + ! curl -L -O https://github.com/pydata/xarray-data/raw/master/RASM_example_data.nc + +.. code:: python + + ds = xr.open_dataset('RASM_example_data.nc') + ds + + + + +.. parsed-literal:: + + + Dimensions: (time: 36, x: 275, y: 205) + Coordinates: + * time (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ... + yc (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ... + xc (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ... + * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... + * y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... + Data variables: + Tair (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ... + Attributes: + title: /workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc + institution: U.W. + source: RACM R1002RBRxaaa01a + output_frequency: daily + output_mode: averaged + convention: CF-1.4 + references: Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429. + comment: Output from the Variable Infiltration Capacity (VIC) model. + nco_openmp_thread_number: 1 + NCO: 4.3.7 + history: history deleted for brevity + + + +In this example, the *logical coordinates* are ``x`` and ``y``, while +the *physical coordinates* are ``xc`` and ``yc``, which represent the +latitudes and longitude of the data. + +.. code:: python + + print(ds.xc.attrs) + print(ds.yc.attrs) + + +.. parsed-literal:: + + OrderedDict([(u'long_name', u'longitude of grid cell center'), (u'units', u'degrees_east'), (u'bounds', u'xv')]) + OrderedDict([(u'long_name', u'latitude of grid cell center'), (u'units', u'degrees_north'), (u'bounds', u'yv')]) + + +Plotting +-------- + +Let's examine these coordinate variables by plotting them. + +.. code:: python + + fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14,4)) + ds.xc.plot(ax=ax1) + ds.yc.plot(ax=ax2) + + + + +.. parsed-literal:: + + + + + +.. parsed-literal:: + + /Users/rpa/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison + if self._edgecolors == str('face'): + + + +.. image:: multidimensional_coords_files/xarray_multidimensional_coords_8_2.png + + +Note that the variables ``xc`` (longitude) and ``yc`` (latitude) are +two-dimensional scalar fields. + +If we try to plot the data variable ``Tair``, by default we get the +logical coordinates. + +.. code:: python + + ds.Tair[0].plot() + + + + +.. parsed-literal:: + + + + + + +.. image:: multidimensional_coords_files/xarray_multidimensional_coords_10_1.png + + +In order to visualize the data on a conventional latitude-longitude +grid, we can take advantage of xarray's ability to apply +`cartopy `__ map projections. + +.. code:: python + + plt.figure(figsize=(14,6)) + ax = plt.axes(projection=ccrs.PlateCarree()) + ax.set_global() + ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='xc', y='yc', add_colorbar=False) + ax.coastlines() + ax.set_ylim([0,90]); + + + +.. image:: multidimensional_coords_files/xarray_multidimensional_coords_12_0.png + + +Multidimensional Groupby +------------------------ + +The above example allowed us to visualize the data on a regular +latitude-longitude grid. But what if we want to do a calculation that +involves grouping over one of these physical coordinates (rather than +the logical coordinates), for example, calculating the mean temperature +at each latitude. This can be achieved using xarray's ``groupby`` +function, which accepts multidimensional variables. By default, +``groupby`` will use every unique value in the variable, which is +probably not what we want. Instead, we can use the ``groupby_bins`` +function to specify the output coordinates of the group. + +.. code:: python + + # define two-degree wide latitude bins + lat_bins = np.arange(0,91,2) + # define a label for each bin corresponding to the central latitude + lat_center = np.arange(1,90,2) + # group according to those bins and take the mean + Tair_lat_mean = ds.Tair.groupby_bins('xc', lat_bins, labels=lat_center).mean() + # plot the result + Tair_lat_mean.plot() + + + + +.. parsed-literal:: + + [] + + + + +.. image:: multidimensional_coords_files/xarray_multidimensional_coords_14_1.png + + +Note that the resulting coordinate for the ``groupby_bins`` operation +got the ``_bins`` suffix appended: ``xc_bins``. 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/workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc\n", + " institution: U.W.\n", + " source: RACM R1002RBRxaaa01a\n", + " output_frequency: daily\n", + " output_mode: averaged\n", + " convention: CF-1.4\n", + " references: Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.\n", + " comment: Output from the Variable Infiltration Capacity (VIC) model.\n", + " nco_openmp_thread_number: 1\n", + " NCO: 4.3.7\n", + " history: history deleted for brevity" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = xr.open_dataset('RASM_example_data.nc')\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, the _logical coordinates_ are `x` and `y`, while the _physical coordinates_ are `xc` and `yc`, which represent the latitudes and longitude of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OrderedDict([(u'long_name', u'longitude of grid cell center'), (u'units', u'degrees_east'), (u'bounds', u'xv')])\n", + "OrderedDict([(u'long_name', u'latitude of grid cell center'), (u'units', u'degrees_north'), (u'bounds', u'yv')])\n" + ] + } + ], + "source": [ + "print(ds.xc.attrs)\n", + "print(ds.yc.attrs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting ##\n", + "\n", + "Let's examine these coordinate variables by plotting them." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rpa/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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pemzmljU8D6a9Ydtc3ujjYp4akdfqNXFDz2L9xgYBVS+N9dkcm/T6GGcHNDe/\neRvnRogZslviWTNLwEytj1abFbvTBpmoffWuSVXXAfglIjpBN5V4JTP/GhH9EbpNAm6hblD7bWZ+\nNjPfRkSvAnAbgMsAns181nGT90CN9c4sCDP5wvw4AaLVQ80sz4Y7yRuYIA7BTBrv9MTSaFun6+Oi\n7tQmq3O7bLM3Rnti9Ku00elW/dq+QUUdhncmn1dwP+6SsI/jsRyXg23y3sjxnGVRzusSVcnz5N1B\nGYYWbVm0X4ShxXmF6q7XXmHDaPLUwMw/ndCzpTVjnAI6prgPEe0B3AfAR9HtvvnkkP9LAH4TK0HN\nxQGaKPmrqtlU8qsT9oH63bJOOdPesh1b/hSgBvDzqqFn0tYIPevtjLIVOw0+6dxbRzNlU4F4N2Rs\nuTXmky2bB9S8M16oWUs9rdszV3c+GwAZw8Zt+5zDDADsp2/b/B4AjzfSH10p8yIAL5rU4KZ7nhaE\nGQkAq8KMhgun7QJSmspwYZedw7CTyuwHIMYBmNy7Y4yhDZI3791LnLExQGpbwEs/pgvAAWUv+Ww9\nh5vUlQrYRDgh9PWwsstAgsruS6hxu6bSULNpgBpd19pQs+aN3Rnj1O1E9DMA/gTA5wHczMy3ENG1\nzHxnMLsTwLXL9LTUxQOaKPnLmpBf9bjE8lPKGuVW89asDDW1PAtqavXItFYvTbOnaEBL1WPpVDcU\naPHOzAk1G2oXWA5mvLZbQ8ysomMeqrnC84sOR7x7zKYJOkPvTD9Jpx4oat6ZUe9rAGbU5H8tmNGe\nE9P7otK9vCGvTBPIkD43QIZUnsh3AUakm9FFjYOXG5lkeGjSZXFoU4AMbkQFxfjOpdemBjZ5beKc\nxf99nnxrEmp0vgsihv0YqIFVJqWNgJrWIZeQvDRLyxunfu+tn8Ztb/2MW46IrgbwXQBuBPApAP+G\niL5f2jAzE623OujiAs2mTZs2nSNNvfO1adOmTZs2nYa8ceornvhAfMUTH5jOX/MLH9MmTwHwAWb+\nUwAgol8F8E0A7iCihzDzHUR0HYCPr9Fv4EiBZtB7IqURf2T+oLclGY4o67TZEj7mplnlld1pe2ks\nmd6X2o5nU8LCpmqg76M0pZ4x62fGhJud1tqZNb0zpxlmtsJdLwC4xCer1Ltp06CWXDszoDyEa0Xv\nTNaOeDXKpn55ZYTnpBbSltlA1RXyZHrmnZH52itj7HKWeVSsCYjncRkrvR9ybFt4Y4BwubQ8Ntn4\nLz02VLpry/FcAAAgAElEQVQiQiZDlNOeGlEueTyy8/B/SNNeEvlWijU1qjuFfc2m4qWp1UvovTSD\n8wLC+LU0C2vGOPUhAN8YNq65Cx3g3ArgLwD8IICfCq+vW6Cbpo4SaKJ03GoVcByIaM1vCkFzyrsh\nTcYE2g0f03U77a0JNUXfRfvVMDEgAxMrfMzrz1DbGnx03el8hHv21J4907rQf0xdtfc4FG4W5cFM\nFVJWghkXOmbAzCmDTNQxP7Bs0wSdZbhZnA1TgIoGNcGMlW6BQU1WGZkGI82wc+tCee5BSp/eFmZW\nBRmVJ8vJNA9iXIDRn6n5GU+9dlF5mNFAf54gh8QlVK6TQZ+fhaPFcrENBTapB7EesRFArBKsuqc2\nC5B5JNqJ3WiBGk01Y6AmFRv4GsLHUw87wzDUrHmTd+o4xcy3EtGr0T0A+nJ4fQmA+wN4FRE9C2Hb\n5mV6WuqogUaryXOjf4Uj8wfXW6Q/2Ma+Oe2N8tao8mtBzeB7N+rzoEbbtnhpmttv6NsidU3twlnD\nUpy4e2tnrLTzDDNzvTIrg0xqpmm2t2nTdCWYSZNnavbOVOtthJkcIihPkwDgtQNlWyvv2PSfgzou\n+hfTB7wyer1MZiOgRPXNBJkWiNH15J+OkzdVXB6mejWEIEGK9uCYgMMCVpIrRdfJpW0cn1UZE1bE\n/ymL+uZSuUaoSbYTpYElb2fcBgGt7SytOeMUM78QwAtV8ifReWtW14UCmqi1weY0vTVNUGOkm1Aj\n+rQY1Hh5tTJeWoSa1r/UeGGLdU3dtcwrs0P9wZWWqrt/NXRnKNzsULE9qHOrX2NCzaz8wv6UYGbu\nwzLHemWGdn2boM1Dcw9T9LLU8lbYqrmtbxgVambJhxox0TVgYtDr0tL3Wj3RBvX2Ozv2+0EiX7Tt\ngoxMl7ZQ9hBflbcDWtd7Iw1w11SPnYc6N0e5d7coyOm/1zTzT13sG2dxFGEFabxvBBuWJj0emLDi\n5JugQo1QU8sPQCfTLNsxUGPOFVt3PFtYxzxOHSfQNE54Twts3DacsmO8Naat/iuS6S1lBdR0+Vzk\nxfJLQU1vS2ly2YOIZQcTVlo8K7res/TGNGv5+XMJQ1neiFAzL82DDG17SjCziFdmBZCJ2jYFuIfK\nCjdbUO4DNAHfO+P21YAbxztj9iXzDmEUzDB8+yE4sqCobl8JMQv5qQ75uSDkybaEffbepS1aIKYE\nGFKApDVrwyj5WYkfRapTzMg5kkKWTmJW3ucVoWnRaxPsB8FG2sXx2/PWpON8QuJCxTmAGvdGd7IZ\nCD1beS5zzOPUcQIN4MKCpbXBZrANcS3QZWZ5ayxbo581AOnyaRLUAH6eTs/T4pWu7FP1uTRzVVlH\nsyr0jH1OjGcz1TsTVQs1a103k+qqhH21wkxLiNlprpVZEWa6Zp1Z4KZ7vOZ4Z9rqj+XQlfO8M5a0\njQEIUzw5Zw4zooyGGdcrQ32aBpm8jAMysnzXcnbuAUwBLsZnPcZBB+SXxwxiRBsRdEgu9k/pnMNN\nfBEQEz033ffbADbKLgKA6a0RH4N0KumtnU8DalCzLeZyM7dxptxmaR3zOHW8QBOlP/va3FFPwmv1\n1cBmShtOvbO9NV6fVNoaUFPLswChNU330fLSAP2FabQnZ+A7PBVN3Z1sbP2toWZWWgvY1OzmwMwa\nIWZnBDJRx3zna9MEjdgMYKpc70z4qUmPiVmeZHnj3PvJGqCQeYKCjW57bZjxvC4ezGQgJPPlewR6\nGMns2baJp6G+VHd6dSAmK+uBTf55zrkLl9UlPSXoL582yERckGkCiEhUpNbcaLDpuxAKZmNzxVuT\ngVL+MUicOS2o0bbFHKmoozH0LPwNVjcIWFjHPE4dP9BoyV+Oo9lg09jGYmFoLRBg1avKnhnUmGmU\nrprablEvjfH5mf2u2E/d9cwqY9bTsgWzZeN5Z6rwcShtLO9ODWa8dTNrwswaXpkKyPAKGwVs2zZv\nGtTS3pk0aW/fCCCDGaN8cQOXFMxoINDpsq1gtxjM1Nr2YCbkpTqy8pzVCZXGoixIfCXmZgF1kCnP\n5WecX49qoWYtP4viMhnff/LIRMN+Nt5N3jnNvgu4AcCZsXgzMZfQgw30hgByEiH6JeFHQ02cM6iS\npw01xTul3M6uo5xX5OXK0LPTuBF7zOPUxQOaKPnrcbQY2Dj51fqdiXYrrGgPrVuvOi/6VOSPgBrR\npxrw+GnkTkD1BgFNIGR9drrvVtjZlM0E1taSXhor1CxqyHPjbQBQtRMD3Nz1Mt7vYyWvzBog03fl\neO98bZqgRu9MBi8jVPPODG7VnCbspXfGbMuBCjM6heplLMBZDGactodgJnsfKZ+FTczjPD/Yt4JM\nM8SIgYyyY2SauoZG15OFlkFcSolzyAkzbhdudJoEmzQHCHiShl3lial4ayhkx8+fEhFHKw0O60GN\n6U2p1GOWI98u9U+o+/hzL83SOuZx6iiBxuB4X/LX5dUnKqqGKdXqqeRXvS9GmTHemmhfQI2s12in\nDicKarI8oy0vz2pHg4cOEVvir3TEBgKexnplBj1KVn7LwntPQ96ZlhAxa93MOYeZYwSZqH3bFWvT\npvnemWxib3hnzDL+RgBSJaiQCSmyLyacoE9rsjfSZH8ywMrsG2EmK69gxvLKkAIQ+T7izDWO5Rn8\n1CHGA5ihDQJMO0esfgR6DU03hFLK04Bjwk0sH8rGhfwJbOQbEmtsWJwnaEmhbrKfAmpCW5m3pk9e\nD2r0HCr7TFFsFDAMMXnomXXT19sgYK2bsMc8Th0l0ER5fGAq/7vw6/RgQtdzSmAz29aBD7OMyK9t\n69wENZ59SK/CT7SpeWmAdhCy8oy0OQA0Sq2haGozgEnPr9HemVaIWhNmlgIZx3ZMeNlpgEzUMd/5\n2jRDUx6k2VTveO+M9sbUPC22B0fbUJ5nQQXUOeUw49Zv5Sm74fSRMFMJMTO9MtpeDioEF2Q8iDEB\nxkrLP6bRkvVkNaahlTJIMQFHw00o3zllepgpwIZkr1vC0OQHENpmKwStr3dxqMmrN+vpPjf0UGPA\nzxDsaLsIL9YGARqoltIxj1NHDTSbNm3adCw65v39N23atGnTxdcxj1MXCmgURPuSiOzVFW84DHli\nvHoqbbieAMdrYPbDqN+01XVSWWaqlyZrS+VVvTS67QXXsNTqHAyJW0qyvqH31RqKZuUNhZu1rJ2x\nvC2ed0b36yy9M2uGma2069mcJzBvOkLN3MGsFm5WnXNkXpI83MxS4YkJ4WalTcWbgjJ9yLbFAyPr\nafHOSI/LZO+MbF97Z+RXKu1TrFZ4GeGZIV3Gqkd9hPVNAYYHtOy5M0Y6kdgQmITHJr4VkcZ9cjCn\ndk9NdGfAXlfT9SZMJrK5i7GmBtIHs4yXRk8qPQ8OGsqYHh+EeZYIO7M8N7E+a8ezpXXM49SFApoo\nPT93JX+ZXl164j62HidvSvhZq70JKdJGnU+FmqKsyivrKkGnt+uuZM1hZ5UyUiawVJ5Hs6YGQ8am\n7oDWqrFbOM+FmZlrZk49zGzl7ZuP+c7XpuU1+OyZwfKxbF/XlHCzQgM2fT0kbGv15MfcYFMFGzj1\nKhCZBTO1NTNWmBmVQOKBjA4v8zYKkB9pvq4mv35NYeYs5Izz9H73MgNwSKSJCUW2mUAMRWsGGzG5\n4ThRBzJoGVpTI+cYC0BNXp8NMlmZrFew19IYc7K+ufCu9dwq2fRradbWMY9Txwk01oTYUUHPXn26\ngK5H/M0N1rMy2LR4d8Z6a+oel3ABG7Glcyvo1D03cKGmeG9WeaM9V9KjI213sL0ocyVGker6GU9j\nvTNjNwJYE2ameGUW2I75rEAmaup2mER0A4BfBvBgdL/MlzDzzxPR4wD8cwD3AnAZwLOZ+W2hzAsA\nPBPAHsBzmfkN89/BplEa2N1sTD2Fd2YQXEgcw/bOUG+beWdMGxtcql4Swy7NSbUdVepS7dbt2LUZ\nCzNZXvxIHa8MJeDRUBOPc5AZgpjeVoJM/pnunAFw9KYA4TA+UFHCSX9MCXA03Fiem45MhsGG+wzx\n7tP0HsDAmprMk4N+TjETaiJMyPIuoFBu1392pZ1Vlw8xDjDpzQEW1rZt81lIf48Df8NNYCMNzhPY\nOLaFvVF3zftinddApHn3s1BudNqU0LPajmZDYWfGZ1tva6T9FNU2DGj18NRCzbTtGJgpPCzLw8wa\nz5apLvw/xc0CZjyB+RKAH2HmdxHR/QC8g4huAfDTAH6cmW8mou8M599KRI8F8D0AHgvgoQDeSESP\n4SIWcNO5kbFBgHxAplemm3hT751xfmKmd0bbWM+dydqT9RhtSXgQbWZ2HoiYbZT2dbscVpaCmQQy\nol5or0w8juN4ascHmSGIkQAj4aX00Iy/VuUemq6hEwEvsWMSciLgeHATyxK4CWwStkRb6a2B2CyA\nu/MiBK1/B8l3Eb+aSVCj5kVlGR9kIPOBMkPXIfJ7iBGhZ2ZfTsdLM2OcOnMdL9Bo5b9vV9b8v1rf\nANgAxkS6pQ5ncux6Vpx6WrwRJtTI+mr2yrZ597OQPpim22wMPRt67zLP9WqtqOwzktPIlnQrP6UZ\ngDEadKwf0gowMzfE7Ay8Mqs+h2biE5iZ+Q4Ad4TjzxLRe9GBygHAA4LZAwHcHo6fDuAVzHwJwAeJ\n6H0AngDgLdN7v2myWnY3G6yjs82ePVNpL5vYN3pnfBuU7VnAEeRBBQ+V13UYeb4d53mq/tkwk+ou\nAWYOyHgQYwFMa6iZ57mRkpNW/ewZ6ZU5ESFoEXA8uInvOwJKDWwSTgjb3luTZvjop/ndcQ1qemuM\nhxqRKc+LMtpO9UD+RNJ0RtrpeZZTVrZd5AGZl2ZpTR2nzoMuDtBIVQBASs/Zp9Y16LXx6qjU7U7W\nDfsWCHJtXHDxbUdt6azTNHDIPqT6yIQaV61eGlmPWEdz1vBTyApH8yb3Y0PNhjYBMPqwOMxM8cqs\nvOD/VJ5Ds8CdLyK6EcDXooOTHwZwMxH9U3TT428KZtcjh5ePoAOgTaepGG42qgxBA099XQxl3hn3\nJ5Ym6gt5Z7I+5Plm2+G16KMCDxdYULGTMGPYLgozFpxI29gM8WiQ2ak0nR+lgWWKh+aE5DU8Tg66\nF88rE8txmIkfIqjIvsT6KmATJ/rR+1J6a9DTQNfDRqhR+S1QYwBElkcNdhAnUKDC8b3meRmsZJDj\nP5emK7u+l2aJceqsdDGBJkp+LzUgcYqMrWvQa6OgYSjdBSVngj/ZW+OCi2o/ywsXPQMWLBCpe2WQ\nQ46jzEuDShnj85kCLWMfrlnzlJADCF56s3fG7EflmTO6vBUO5vZJgUgLzCzhlRkBJ+cRZKI8V/5H\n3n4nPvL2jw+WD+FmrwbwQ8FT82wAP8zMryWivwngZQCe6hQ/a0zfJOVtBlBR5p1pCC/r2nEAhmRd\ndnnLO2M9dyapAA0fXop2rbys3T7NrEP3NdmvADPxNdQZbeWOZUBpS0AGMtobM8ZbAwA750+6xeGX\nXWZDnXF3q5pXRmqn8mW9BBtsgHxntCZvTVfz8lAjqCUDjFSLmvuIfAs4JOzo/MIbY0w6fYhx8uJ8\naGFNDTkjoi8H8H+KpEcC+F8APAzAXwdwN4D3A3gGM39qZjdNHSfQyM+7dZiWv6aK1O93cl1Vr41X\n3kmvemCU/SRvjW5XAYHlYenzaDzUVNL68+6CVuR7f8SGl6YJXOT7mbKGp6ZaXV642ZgNAjzvTNEW\n2/YTYWa19TIzvDKLgsxa2zY7u8dc/3XX4fqvuy6d3/qS3ytsiOhKAK8B8K+Z+XUh+QeY+bnh+NUA\nXhqObwdwgyj+MPThaJtOS7WHaQ6WRW/bzYRd0xwybLsqwCjvTFnOABDk6RpcsrLIyzZ5ZKw2dXsB\nJFi1l/qwJsxIO/SAYi32JwAeyAxBTEyX8NK6OUBVoo44gT2JuCBm3AdQ4ZXRnhsJQAfk8EPowYYh\nNxDoBt441FvemjWhJh5KnHFhhfIWNOhY06gsP7wFq5zV3ll7aaY+WJOZ/wBd9ACIaIduzPlVAF8B\n4HnMfCCinwTwAgDPX6a3uY4TaKTK33G7fW1OU2liTF2rg41j74KNByo6X9W5JtTo9l2o0dL2Rl53\nTIDRv1PfvnkkLJEFFN6OY62hZjNgZvLzZZbwylQgw4STcwQyUfuJQxAREYBfBHAbM/+syPooET2Z\nmd8E4NsA/GFIfz2AlxPRi9GFmj0awK2TO75pVZmbAbi20U6U0yKkUDc/DKxt7UzRzwqE1OobBS/q\n3M0zQs3619OBmeI5MuI4Na/sNMgMQUzr5gCWjSd5F762GcAJOAFODW5kXcxUgA2iLSSW+N6aaVAT\nOp/kQA1T6TFRUJMBjz6GOM9aq+Sr7lmwkkNOP6ca8tIsranjlNJTALyfmT8M4MMi/a0A/sYSDVg6\nfqDRaoCMsbbWn4pb1ymBTW2CX7U1QCXrl67DghGznulQ0xoCluzDH7HeJGDIS1Nrpwp3+v2NUWWO\n3BRu5tmYnpxGmMn6V4GZJR6WuYRXZgycnEOQibp8mLwd5pMAfD+AdxPRO0PajwH4uwB+joiuAPB5\nAH8PAJj5NiJ6FYDb0G/nfIrkvsmT9eyZqgLAVLdqTpPyymYBAmAKm1p5D1SG0uOxV8YAGxuievs8\nnMx5zowsl7W5HsxYWzZ3H2c7yHgQY62rWW8NTZd2YHszAA9uIrx0dfX1xzyA+3U4Q96aWVDTndeg\nJr4fbZlwJrxnN0RMlc3yrLpZnJORXoEVGPZ9mfW8NDPGKanvBfByI/2ZAF6xRAOWLh7QSE2Bm6XA\nxqlrKbCRA08NWNw2iwm7Dysu1CjbRaCmOFeQovpt9t95j4P2S2ioTs/Doo5N74xX11gvk97RbCmY\nqcDKkl6Z0wCZmz/6uwCAk+tck9Ga+gRmZn4z/BUWX++UeRGAF01qcNMyMsLNhjRpMwAzP/bBAJiY\nTxh+7oxszwMHD0ZCfSWMlGBT89Z4fTLrkhd1BSxNMLMLPdegEsfQYGfBDEFBTbBpBRnXU+N4ZTxP\njLe2Riq7FgmQAeTCfwNwKIebA6EacpbAhvvBOB51/3qEcaEmgA+nHxSykhFa+lwHauK8QuRnluEk\ng5WYjjwPKl1VUQALEJgMfRkTishoV9RTANQKmjpORRHRVQD+CwDPU+n/EMDdzGyBziK62ECzadOm\nTedEx7x7zKZNmzZtuvjyxqmPveNjuOMdd7RU8Z0A3sHMn4gJRPR3APw1AN++QBddrQo0RPQyAP85\ngI8z81eHtBcC+G8BxDf7Y8z86yGv6enWrnei2plYuNGuYus5UMa0ufraGsO2JfzM9eJQbpdMVH62\n+5kqs5iXJnbbeDZNtl5GyltHY5wv7b0pQtW0Y2BMuJkXojV27cwU70zrBgBzw8wWeDjmXM/MGpq6\n2HLTulprnGpouAxBq9pGu1o+zK2ic08KGXkzw80s74rV9oB9sXbGajOGm0l7ld6XW8Y7U1szI70z\n1uL+WuiZ9sxor4zlkdEemEkhZ5DXeMrqP6AbAC2PjfTWHJB7aqSt9tTsUHpvsvAz6a+IrhQOYzb3\n/hjhq4L20iClxHPDdeJ5aVR65iWh3kYeS68JpD2Enahfpw+Fl7mbAyTb/BNZSt44de3jH4prH9/v\n/v+7L3XHyu+DCCsjoqcB+AcAnszMdy3WUUNre2j+JYBfAPDLIo0BvJiZXywNpz7dWl/ABv+2G4Cl\nsB2wMzyPzfVMBpvWugxoMW28/OIvNm+7CFWTf4wNUKP74EKNtg39kLueeetlvGMA5cYAFhB1V+NF\nVUBOS7iZu57mSGBm4nqZMTuYnUeQiTrmJzBfcK0+TgFoh5doG0HD4eDqs2eyCb9hQEi7m+V1wq6z\nAh8FeMg+eBBj1WuBjbSX0CLtLZgpyqEOM3HG2AAz3nbMFrTo58yMARkZUtavrSkBR2vspgBFyBk6\nKNkR9+FHAWbiT/EAFGADoAhDk2toiClL6wpQ//lHlgnhaf2kHSjW1CRJqEEoZ0ANiXQNNcIswxkB\nLSlPzYUybCqrK+ZOxxB2NmecIqL7otsQ4O+K5F8AcBWAW7r9bfDbzPzsOX30tCrQMPN/CA+C07I+\nsUWebj3Ke9MKN412FkeY9SwBNpU+tawnMcFF1FWAimzHqGsq1Fh9tfvfgYbXVn4soMRL99pZQObm\nAS1pYzcDsCbvrTBj9WMIZmqL/1X+ql6ZhUDmNCBGam5s8qZ1tNo4NWb9jPbWuHYCjKzeOs+dAXq4\nKWFl6Hk0clAtwcYElaKOAXunXl2+AB2ZDnWeQKU/dmEm/BsDM93HWUILRLpOs0DGWzOjISYDnAaw\nqUmWiRPYnfDKJG8M8s0BSi9NuYZGem3ks2riQBy9NRF0OICO5a1xoYYh/k70XmcKahKVGFBTgZAM\nIET6EHC4dQyUt9IzL41pu/yYMmecYua/APAglfbouX1q1VmtoXkOEf0AgLcD+FFm/nOs8HTr8wA3\n5k9jAEaSmQUlVrtGeou3Jtot4a1ZBGpk/bDyKUGN7NdYL03xXrzPQvZ/rCZCTZN3RuZZD9FsgZki\nz4CZJXYyWwpkxm7bXCkzBDKHpd1xsd7NQ3NsWnWcagGY4mGaRR0i35IHK/LZM5l9HSzydsvyDGHn\nQYgu79nrtuPFmfI6i3SgAJW+XgdmwuR6Csxo7wtSnoCWAZAZghj9qo+lxm4KkELNgldGHh/CoCjh\nJnpuci9NJ+mZ2TF3ZUEZxGR2AnS8EDQLaroszqAGwi7BS0YqIj0WEQbxVHpvTBAxwAJAX2d8K32T\nwgCul8aEmwHgWUPHPE6dBdD87wD+13D8jwH8DIBnObbm1/bx3/qNlHvfG27Cfb/spsFGzwpuLJ4w\n67DmcBaU1NqdCDazvTXhPKtnJtT455RPkiWYSFsIOwUvBbjEsLOlLxLWvNhKa9z5rLRzQs1qdcu0\nhWFmrldmEZCZGFYWIeZNv/V5vOm3Pu/azdFC22FuOh3NHqf+6E/fDITJ1zX3fTiu+eIbkbw2QzuY\nOQADoN/dzMj2QAOADzdeGQL0LmpWeROCDNsiVAz9+bAHh8s2SKQLO1lHAp4BmInHc2DGe6ZMhBkd\nWqZBZghirBA0nd4qWT7z0EAcC6ix4GYHSuttKNShvTYH7sAm99b0Q4MGnVao6Sb3pKAGgCiZz/7V\npCn2V8CLNEvtBDLJaijNs2o1yLR6aXy78I7E1/ypO9+Hz9zx/lUiTI55nDp1oGHmj8djInopgH8b\nTpufbv3gJz3NqbyxD2cMN4uBjVduDNjUJvrqOpOVt8DGgpVgsyjU6P4S7A0C1HsqQMd6z1Kqrimy\nvDvEjHKyr4zcrZEd+Bl65oxeNzMFZlpDzJbwypwyyEQ9+Zu/CE/+5i9K5//4Z/7MLTtWW8jZ8WiJ\ncerRD/qWFG5W24IZwLC3Jq2X8fJhbgYQ8/xwMBtYpL0HIgWwoLSzypoARSJP25CwUWks8iwoykLN\n4ts1YCZ5V2bCTEt4mQQXDTIuzMCAGjVwjQUbCTLxPLaTIAYl1MTXA/r1NvH70F6bCCwnYDBsb80U\nqEEAkSrUkAo9E7SSuqzmGVVwceYtyVwBySwvTd9VNX/p0r74ITfhAdfelOxvf8+0fUksHfM4depA\nQ0TXMfPHwul/BeA94Xj+063199Dw9z0JbmbaNYGNUX4VsFE2o7w1Mb8GNSI9gxrEtHFQ09XXXxUy\nYNGhZxJeLDjxgIXCpbJlfGgJR/NsrHCzsd4ZYPwmAApm3MX/3rqbqV6ZGSCzZFjZUEjZfqUHbR6z\nK/+eplXHqagxu5sZikBShH0JUDG9LpXNAPI0KtIscDA9MTDSdBsKbHwbf1ezvA+syrKdZ8FMBJaZ\nMKMX/XvhZTWQ8SBmyTU0spy1hibBC9iEG2lzALDjvs4Yjhb30tFhaNFbMwdqZF6Cmu4DDuWRvuOW\n9TTZHdgMIHqqkYBiQosoa86bZFNI1ZY3aWU9yU6Fvhtll9Ixj1Nrb9v8CgBPBvAgIvowgB8H8FeI\n6HHovo8PAPjvAGCVp1tX4MBSM9y0gtNA+xZ7mOUdsAGMfo4AmwJMhM2Qt8Yt2wo1WVoj1BR1OsCi\nQKXsrw8sVY/NHDH74WZD3hkJIOx4QpaEmTV2MJsYXnYRQCa1f8QDxUXWqY1TItys6rGJoGIt/G8I\nN/PaLuHGCEEzAEWDh9UWC7vyvZTlCkAx3ocLOgpyujQubVLZCCUQF/cSZmK9c2CmXwNTemV0eFkN\nZMaGnem8FhUeGthraDTcgGz4SeXi2E1yZ7MeWmZDTeiDufMZBzvKt3T21tMUoWdMVXApIMKaaxjl\nUmWxjHEu6/bKme0vrGMep9be5ez7jOSXVeybnm5d9VR4WgtuWutuhJsxYANUPosGsDHLUiVflDc/\nn/SXnNstATXaVrY7xktTXBCiZqyjoRHzYDPcLKZLabuhUDOZdpYwc2QgszbEZP044oHiImutcQpA\nDy9DjyDaCVuzHowKN3NDsWammd4Y51x7cixIKsoVECOu3Qb8ZH3MgIYzuGne0QyYBTNeiJn0ymiI\n8UDGCzuTad2xfw2zdjPr82L6LtWv19MUa2iknQKb/jvpQQaEzFuzBNQwZWfI0EWRQHU9jQSOClzE\niQj3h5lNmo/0zeYJGlJYlNdzEmOOYvUxezsL65jHqbPa5WwRGX9HbdLf10C51eCmAja6qsFyY8FG\n/cFkZYfAxyhv5lkAMhFqWJVP7QZgMS9MXh9lGiqQM0Y8EoYssGGuP+9FT74lhEyFmaH1Mmsu+l9g\nhzMPZOZ6Yw4rjBTHPFBsmiBvTUvUkLdmwu5mnn1XH0qvjwsyVNqMABSZpj0tLd4Z7zxCTl6HhBcN\nQNynp7p6G3sTgDaYOdnlIGMt+o/H2iuzU3keyNhrZw5FGjC8u5m27zcA2HfnTOG+Xg441mYBMl9+\nP5m3BkjemggzEnaI0eEIEfaHXfpehqAmQYCCmvidFrufZeVlWnccvTfJS5MRwzBwDIKLdy7Sinma\nCWtJOM8AACAASURBVDbl5gBrDCnHPE4dNdBoLQI4pwk3A/n6t26WU2WbwcYAnRawKfJCfgEtsVx5\nXcqgpivDw1Cjy2fvhfrJdRi0XS+Nk7aUkgfmgNLrYsmys7wzQw/PzPJmwMyUHcyOBGTOAmKkLntP\nSNx0z1GLx8YLNwOWDzfL0vLxbdAjEs9r+SGtBj9jvTOZR0bWV9QpYCXZ97AS0/S6mTkw07JWZify\nWkDGApiWXc4sz418CrzcACDW03ll9mJSG+zZ3gUt1hPT9YLyuHHADoQDUXo4p/bWYHcAM2F/6Fs1\noYYpfJ9qk4D4dZLaKKB/QT5REZ205huSaRJZ+F6a1EVRZwEuus6YrN+myPfmaaTSltQxj1MXCmg2\nbdq06bzqmO98bdq0adOmi69jHqcuNNBM8thM9NYM1m+Qdmu+5d0cKut6kho9NUUZcQfBrNPz4iiP\nirU2Rm/pbHppdPlQf3/HJF/on7w0Y+9spLsovSObQaAdA/tKOU/aY1Pb8cwKN6s9D0Z6YKZ6ZwbW\nzFQX/st6OuO8ewt6ZtZaI7O2VyZr64gHik0z1Lybme19AeB7dQh2aJv2Zoj0Yc+M0Q/LM1M7bywj\nlXmEYlhZ4YXRXhsW+Vx4gkinR+9M9MIo70z0pkz1zgyFmen1MvXws9wzU+56ll/bThomOCfUD2L7\n5Jnpzg+8M3Y12+ehaBVPDYBsvE+7oDGZ4WfWmhoS7grLS5OFhQUvDZjS557vfBb7ElIzr4l0mTg7\nnlnzDOscffrosLOJ51nawjrmceo4gWYIDhyNCheT7aQKFqh/CJgG8jWHmGX1PNKDEKtCD1xG5Ftr\nZpKNBpZUpg41urxsuwhLU+8rAkoxIScJLQDFjQEcpRhWWf8U1cLSYp63dqb2vJklYGaBZ8tcBJAZ\nWoMzRcc8UGyaoDHPnzHzEEDIq3tMOgbXz7SEmBVrXKDOG+qQ5264Wa39lMdZ2cw2gY60ydfHxHLm\nrmYOzEQ7D2a8ncx0mFmxMYAKMRuCGAkv1tqZ2kYBaY1MqCPCyQntsRfw4sGNBTZZ24hbN+d5LVDD\nqc4SajiAi971rBvHw3cpYSbGm4XD+BtIO54B/W9Hg4LknVSekXCKK2VFmjsfkef9T7HIL+sr19Es\nrWMep44TaKKsz73xiz5N780acGOxSZFwVmCjQUOWsaAkpflQw8o21Q9hK6ElDODZdV0DzIoXhVGK\nnhgLGuTaGW/DAObpMDO0ZmaEV2YSyCy4a9l5hBgpPuKBYtPCqnhsuPqAzXLsqqe3r5/J+qDBZeT5\noM1YW4KAlBKQMoAJadbaGUCCS2+vNwLo0uwdzWow02/bXIcZ21tzKNKAEmJ6yOmvV2O2bN4JL01c\n09Id7xKkeHADQgE2IAFHwSOD8JHr73kIak7CmQU1RP2kIHliwrl8fGbHMWotDVV2PAuTgaleGm9e\n5dl753pSV7Nfcx3NMY9Txw00lvR3sRbgjGhnUbgx8ly4cepcHWxE3tgQtGao0X1Nx8YzZvTFYQnJ\njQhazKXnpVZuaPcz63kzC8LMYtsxTwg/A2yQmbNj2VlDTN7WtIGCiG4A8MsAHozup/wSZv55kf+j\nAP4JgAcx8ydD2gsAPBNdoORzmXm5R0lvmqahDQEIPsjE588U6cOL+6vpKk2Hm3kel7zPlXNdhwkj\n+twGl2bvDASsiPzcK9Pl1zYC8LZnrsHM0AYAdlofWqZBxoMYCTAnEz00QAdJ+/ChpPAy2HAD5GAD\nHBLwJIix5kUjoYbAZrhZ2sghQg3nGwR0zSvgSYN/6EwGRLp/PZ0MeWmKeZKoaxBcom08r0GKATrW\nXG5JTR2nzoMuHtBoLQA4o703lXZmw81AO9bfWZZwCmCjAWZUCFor1ECdq7x4cRj00sAJO5sJPxnA\nWGOLXFNT885Yu5hZ51NhZsgrMxAatgTILL1j2XmCmKzd6aPPJQA/wszvIqL7AXgHEd3CzO8NsPNU\nAB+KxkT0WADfA+Cx6J5m/0YiegzzgAtr0zpqXT9j5tkgY4JGTDfUul1z7dwMNxuqT8OK11dR3go7\nMyGHdB4b5URPC6ipr5uRNvJZM2OfMTMEM0MgoyEmAswaHpoIOBpuQMjABixDztKmzCbAjIEacHiA\nJ+XhZn1b/QDfjfH5pEI/dFO4bOQLzIkFFFRUvDQWfBRzH5k/ME86T2FnW8jZaUtT69iyUg31rBWe\nNgpuRsKPxShefUuDzaC3RtY1Fmp02+YfP/WTcuqhJoOhARX1jNHUaWMGOGrdjOWZATowMWBmzHqZ\nJq/MCh6ZJb0xS0HMfoWtvFPdh2nbYTLzHQDuCMefJaL3ArgewHsBvBjA/wTg/xJFng7gFcx8CcAH\nieh9AJ4A4C3Te79prIa2Z25aP7NIupEmpWBkUriZU5dOs2AlBxRxg0XYmt4Zo44+zIyzfGub5s6e\ns/yqNwZiDY0DMxJoroiw0uiVqYGMhpgEN8aAtmu41h3CD++EWGwOkENM7705mGAjQ876QW/Xr6EJ\n2zT3GwMQLosfvAU1cQ2NfPDmPnhnautpWEwKZGhZH3oW+2lsEJABVz+B8W+k9ompjlgPkM1PSxhR\nIIPp51nbC2rqOHUedJxAA/jf5Nj5yAKAAwxMlJeAm4Y6PPjRzOHZNoNNrYz+Y1bpprdmDNTArl/m\nRRgpQs/Se2pfS5O9hxbVdjGzdjw7cJ6ny+hdzjSYjIGZWohZA8i0LORfGmTmrItpgZg1AUZridhk\nIroRwNcCeCsRPR3AR5j53ZRPZq9HDi8fQeep2XQeNGP9TGHvAUsL9Djw0XxuwIm2LcLLrH5bdhbk\nmHYxr4ScbLDIoMbwwqDPl6FmMV+um0lpBszE/CGY8bwyFsh4ECPBZYyHpiubP0wT6LAlAk56Fk0F\nbCKGZM+hobjrmYSdXlfgUIUaCS7ezmcRcMrQMwDIdz4b2iDA9dIY8FL1skCYDcxVhm4WFzfsh84X\n1raG5jxpAqAsUb45RK2h/lFwY9XhwI/FKZbtINgMlRG2Td4akVf+jTtQA3VuXERivwioe2mKMDSd\nj6bfgfkgTWsns5q8ULOlYGZMiNkphJZZ0HGRIEbKc+V/+t0fwmfe/SeD5UO42asB/BC68f/H0IWb\nJZNK8bN505uSWh6oaaqyfqasw6tb96XM5wrsjAo3MwHL7pb2vFienGoom2hbel5SLzOQgYAYGOFn\nvQdmaBMAD2aukJ6XBpiRINOVO5ggoyGmDz8rr3ctcBOvRSfh/e+xww5ii+YENywehrlLYIO4xiZ5\neHTIWfC5GAOq3pVNQk3noRHhZixD2yJM9scyxCx8k2kfgJ5jxAYB6CYeVS+NnJcgVatgpS+csY+W\nKDcERNlHJcsBBiCtF3Y2J+SMiB4I4KUAvgpd757JzG8JecVaz6V18YBGy/puxvwIJgDOqBA1Bz6s\nutx6anVo+lenVLE12x64uzAIMLGMhB3ZKVL2BMR9Z+IfcFbOgZquaLyIheol1Ki8TMY6mmzL5lbV\nAMfyznihZkNbM49cLzMZZCbsWraEN2aJcLJWiFnzuTReF+7/1Q/H/b/64en8o7/y5sKGiK4E8BoA\n/5qZX0dEXw3gRgC/G7wzD0O3tuaJAG4HcIMo/rCQtuksNLCGxn3+jIaMZI/SfuR2zUV9lXzXs6LL\narCpldXA4gGRGPRYAkt2zHm94lWHlEXvTLTJNwKQHpgeYABjEwADZsztmBthRoJMV/5QgIyGmGxj\nAGcTAAkPcrF3Apm4hXOAmRPK4Qa0E2FpHcicEGMfPTGZt6araQhqtJcmlc5Cy0IIGiNbT8MCcNK6\nkwgsgACYvr7eSyP6kfpUTDZSfQW8xPJqHpOk6pZAIm2Hws7GeGfWcKbMvN/3cwB+jZn/ayK6AsB9\nAcBa67mGjhJoTA/CGE2AFLdsQ/lJ3psBuHHr8+pw0lvARrZdgE20q9190ADjwQ6Juox0fVei6p2J\nFwr03pcC6irwU7yHIQk78wGaQ7ubaVsNM3E3M+25qcHMWK/MGYDMWUPMqT5Y07tNPSDqiOUXAdzG\nzD8LAMz8HgDXCpsPAPg6Zv4kEb0ewMuJ6MXoQs0eDeDWmd3fNFa18LGh/BHp1rhgw00OPRZIVAGl\nBj7GcTXczEs3gCezUzZD3pkuL0JNb6s3AgDqoWbms2YcL0yWNxJmhkCmDzsLUCOuXx7UpHx1vudd\n2iCg280spAmYkcfd+9qXIJOOw3kD1OyyVfDhGwtpEWb2IZQshp5FQAHieptYUPhnwoTB99JEw35C\n0uSlcWBFR4yk+hpARpYp7Lz6jPOlNWOcegCAb2HmHwQAZr4M4FMh21rrubiOEmiiPDod/WVPgJRq\n+UrZZu9NY51VWDLApKibSxOq2VkwqS8C4bg5DE3Di2rWhBpRl4aa7I4HgCykrOKlsULWILdn3gHY\nY5RcuPG8M9auZ2NgZsgrI9Isr0wNVFrW0GiQWcobcyxemJpmxCY/CcD3A3g3Eb0zpP0YM/+6rL5v\nh28jolcBuA3AZQDPZj6jOLtNpvxdzSqeFic9rxdt0FOUI3Xu27M+r0BPASleG9oTM2CjF/177Wtv\nj/bOAHB3NRuzbsZ8YOYAzFwRYMKCmRrIRIiRAGNt02xt57wXH8iODmkL5xP0gNNNaEuw2YHSZgI5\nyITj9HkPQ82OuICcA7G7ngach55lWzYL0InQ4ntpetKRwNP1XU1OJPCYUMLZD9QFDQFAQzd+9W5n\nxQ3a7JzGhbM3asY49QgAnyCifwngPwPwDnTh0U+FvdZzcR010Hhq8mQMaQSkzCk7CXDGwk2trAE9\n+iaCZTcYjtYINjJNXiektyarnvvBV4agmVADdREwZEFQdw0nYD/hYnFwjuVmAEUZATNQZaSnJabF\nMggwMzbEbGR42VIgc1YQMwZg9ivCztTYZGZ+M+qrL8DMj1TnLwLwokkNblpPQxsCWOkjnj9jt6nr\nGyinAKHJozJQb1EHiXQq07vjHj7YsMlD0Ljod3XtTKxKeWe84wQqMLw0yOEm2lkwc+Vu3+yViSAj\nvTHpOD5sU1yvas+eSTbiuNvJrIOqbqvmADgVsAEf0IehMXbcQc4+9L1bb7PDpdiaATUHUB8uFwGG\nKQBLt54m2lihZ3LXs2yDgFhdi5cmmzD0IWbF/CRVKiYjap5TzFtUXpO3Rs/XLDvPdmF549Rn3/1B\nfPY9H6oVvQLA4wH8fWZ+GxH9LIB/BOBbAHyHsFuNai4k0GzatGnTedPhsO7dqU2bNm3atGmOvHHq\nPn/pEbjPX3pEOr/zFf9Bm3wEnSfmbeH81QBeCOBGlGs9n8DMH1+y38A9CGhme22s77i1/ESPzZxw\ntNHeGiPdzLY8OtIDo21qnppKmvbSFHdMgGxNjb+GJr42hJ2R2upZ98eT5SXQ4WHyuLZVcxGKJrwr\nahtn1zvTEmY21jOz8K5la3plzotHRuuYt8PcNE/VHc46V4GdZ5UxTD3vyJwNAXRe63qaUZ6alrp1\nuvTGSIdXNgDEvN6D0xpuBkRPTDgWXppaqFk8j2XGemekZybWEUPMpGfGerCmDi+reWtSmBntUwha\n/+yZffLUnITPIXpqwIewaQBn62qskLMrAddLo3c5i+FkUXvOvTNyLc0eOtSsP47f4eSwM+n90J4Q\nlOk170nzOhqZDt/O8/SsoanjFDPfQUQfDg9x/kMATwHwDmZ+SrSRaz2X6W2u4wQaY1I9RWcGOY2A\ns1Q42mS44TK7ABuROQZsmsPPsjci/vBTUr+mJgMY5SE2oWbf91vD0KTfltpGOVs/o8PNamtnpsDM\nhDCzpUBmTYhZel3MaQKM1jE/gXnTDA3tcGaWsfO8xf6DNla6gogpGwDosLNqOa8vYlDqQ8v0Fs49\njFjlslA1AEuGm8XjHFbszQGirHUzrTAjQQYI62YUyNhQM3wzKNYHxN3MOkWY2YMAeQwghaARdbui\nhbCz9NrVkGyHoGanBvEYbpbOqV9LY4WXuWFnAOIGAdWwszRRgRjz1SQkTCKy3c5M2ODsx1xbR2PO\nL/RcozFvzY0BZo5TzwHwK0R0FYD3A3iGyl91AD5OoImqfe4TP7YzgZyJgAMYfRuoaxTcWNBiNafs\nWsBGXg/cNAt+DDv5rJoCampK9gQwB7CRx6p97icZg4vx9PqZ+Fos+HdgRpadugGA45WZAjIt3pjT\nhJg1AGbtzQK2Zfn3MA1s17zKDmctD9RU5UZvCGCla7DR9QwdkzoeKJfGGAk76u93rnfG26YZsNfO\nFLucCZg5ofEwMwQy1vbNUTW42SPfqhnoIGePXQ4zymOTQhvgQQ2gNws4AUFvCqA3BJDnci0NCmip\ne2kApA0CMi+N9N6kgT00SOqZNDXosO6SxnwY9uq42TsjqtRQU117s5DmjFPM/LsAvqGS/0gvbwkd\nN9DU5I0jE76sxSFnIcABKoBi1bUw3JjZ4o97EGy0jU6z2lAANAg1xWsZepZ5fuSFLW4MIHc6cxQB\npwAdK93wuBSKoWPBrvqMmUaQ6Q4raSodyEFmzI5lFhzMgZhjBBitLeRsE4Bq+JntgSnTBwFClK2m\nVeClqNMAisl1iPc0J9xM50tw6c6FSaN3xtrZLOa7u5oJeNHnJ8J+CszUQEaCi7Vts/Ucmlhmz7vs\nwZpdSJkNNoh1j4SafnvmrkwHLPYGAdpTI7dxbvHSpO9YeGmYc2gBoZsBJLCJPw6IuYWYEHhzKsk2\nLcAylD7CLs9bfkw55nHq4gKNJ+u7WghygEbQGduHRsAZDFGbAzcDwNMCNqlu4/1oiCm8MxI6jLfR\nmYyHmvjeM8+MVQhA88M1VehZGnMyrwqjGmrmwUz2/BgHZmrhZQ0gs2RI2VSIWRpgzmqrZqljHig2\nLaSRHhtvh7OyrCrXAEzVEDR13uRtGajTBhgu8gtbBSlZOQ9wxLn2xFhp0jsT0/TOZgByiCHloaFD\nNiHXz5qpwUxcX6NBBoCAmnwrZ+tZNID/PBr5HJp8i2YAjPyhmujBpltwmntrdmAcKNLGrh8/1Lqa\nA+Uemz7c7IADn+Sfl9iz+BBhRUDPsJemHx4jzGRrbDyASWlqIsJ5WjXsLCX29Y0FnKl2S+uYx6nj\nBJrKxHx2fVILgc4kyBkDOI59FXAq7Zlw433mKr3IUkBUeGRUH6reGeR3RLLBEj3UdFk8CDVgLr00\n3a2dHm5i/fouiaUD0hWVBKDI4/7fRJhRC/2bw8sWAJm5EHPeAaZ1vc5UnT1SbTpT1dbSWAAydUMA\nt32/HtMD1FDOrd+zbanXBJiyb/nHmZeR4WbpXOeJY+2piXkaXoDe+5LSYR/rB2eOhRkJMqlOBTLZ\ns2jUdffEGLCiJ2NHBxywS5ATF/+f4NCtk6HeY7PDDgew6a25crfvoQaA9NYcOH6WHdTsozcmDMRW\nqJn22sQNAgbXz0gPDWAATGdTbA6AbmCvhZ25a2YcyJi9jsYpU/XoLKhjHqeOE2ikhi7mc76dhUBn\nEuQ0QotrPwA4rd6bQbiR9hpeZLIGnwawKbwz4byAJpFOqXz0w9hQk3mVd905hYsbh4rNuzFK5s2w\ng3GcLeAX8DEEM3rhv5h0m16ZyjqZFpA5TxCzZvjY2vBiibdtm++5GrnD2dQNAVKdLWU9ew8kanUa\nwFHkeTZDwGTlG2mkLtJyM4CiOElw6dL0zmZ9mgE1xXG+nqU/79fNAJgEMzWQ2eFQgEtt/YwMMTvB\nPgEOqH+4JkK7+/A4zQ5SAOmtkVDTgYjw0ASo0d4ZuZ5Ghp7JUDN9nLw0GcDkXhrm/nM/BDBJY32Y\nNKTNASLnUqvXppKmj1HOW7TNXK/Nmp6ZqGMep44faIa0EJQ01Tmi3tUhZwBwmr03Q3Bj2Wt40aYC\nfGpg43DSUHPC3ocaEFKuDU7hoOUBm4wMNLo07tMyaAkXf+2x8WDGWCsDBJhp9MqMBZm1IGYpgDkG\neLF0zK78TdPlxbnXdjhrq7ctbeqGAK0QVFsDM7SuxyrrhaRlYWa6LgeivM0A4qtOszw1Q96ZfEez\nECaWzvN1M3rNzBiY8UCm9+AoD40VKpw2BAhbM5OCGwU23aBZh5q0rTOQYAZ8wI4CwPCu87qAE9zI\n0LN4PMVLE70uSdELI17j9zsq7CzzxHD5Q1YwEs1qNmNAxpgOybfo31xeQMc8Tl18oPG0AJSMqreh\nzlUhZwTguLAiyrlA1Ag3S4BNTKuBjwc1vUenuzpoL012wSEIwAmVG3P5GFomt2umIqzMCDWrwYzw\nwhTPlZkAMmO8MRoclvbCtADMacGLf09zOZ0Trtp0WqrtcrZU+oDN4DqZCXmjPTNWHpANNCnfShN1\n2bBTQovM90LMogpPjQM6xdoZ2KDTnysvDXEGMyd0GA0zEWQ0xOSbA/gXmhN0zyrYM6UNAHK44QQ2\naISanfTYiM0C+mfjHLCL3hkVehbBZchLA9ahZd3XHkFHfpd7CTME9JsA9PX0k/YwIxB1W14WyThu\n5EYyKMt1+Qulq7yldczj1FECzeR1Kq1aA3Ymgs4ikDMScKasvamGphnnWZUTwQbi+hHzLZ6yoAZU\nHkfIAfX/3M+65SZGXFOTbQQwEmbkov9sQwAeBpkJ3pi5EDMHYE4DXk4DXDwd852vTWenIS+HaWOU\nqdq0wsjYY68PQ/ZWvhxjrHpQ5nvhZt2rvRkAUK6HiTYSarrXQ3YsYcIKNUvp4rk0FsxcSXsTZAAE\n2OnX1pyk/kmoqW3GssOOENbQ7DO4kfAS19ns0AHU3bjChJoUWgYg7oC259Af6kLKToJ35sCxv3JT\ngLqX5sCU1vjILZ13yMPO5Hcvw86ytGir74TKPJFme3TkREXkC41eR+PYVEPRVtIxj1NHCTSWhr6D\nxYBn6Lse245V3wTISdW1wIvVxhKA48GNRRniXF50dPPZnRJSedrWrt6Gmh2AQwSZkBNvNEVvjAg7\nY+b6Vx/hRUCM6Z0ZCTO13cuWApkWiDlLgDnPXpdROuKBYtOCGrll86DdhJ9V7adYCyEbXe8UcLHy\nB8LM9HbNWZo41q/yeGfklQBjQI3y1HR5BzfUrAgfszwzjldGg8wuHQuYUVe+CAMpP8BL3AAgwo3c\nBODEWD9zFV02oWaX0W+XdmCCDj3b80nnURrhpYmvMewswYzwxEwNOzMfsmlBw1CavqNqAcgQjDTC\nyqkAzhGPUxcGaDZt2rTpPOuYXfmbNm3atOni65jHqXsM0LRA5yJenFo7rfV7dTQSfFFdi9dmCY+N\nUabZW8PqhofjjRny1FDsH1s3TnIvTQox2wF06G59xLU0WdiZeN/mTURxBUjrZ2S4GTOw3/vnNe+M\nsVamO+Qmz8xpemWW8MicRRjZmN3UZumIB4pNy8jfCKBMb34GjS7XuN1z306bXVMY2tiytTqsgSt5\nYkqPjbV+xsofWj8DlLub6dciHM0IN4uveahYOBZrZ7R35iq6XPXORM9M9Mqk9TTiAqNDzopn0KB7\nBo0MNZOemt5jkXtpTnAo0g5gyO0+UygZH3DASf/+QxyYFXZ2YKRNA7R3Rq6jiZ9p3O2s6/fwOhp9\n7a3lTdkYQGrIizJ2/UzNE7OKM+WIx6l7DNC0aPWwtbmwM7F8E+RYdXMlfyzgDMGNNJVjkQc2Rles\na0AGI4ccaiDWPGbwQqHh8EbSxgDoJxnmAzbFrmZZuJm1dkbCTIQbL8xMgov1PJmBBf8eyJSbApTv\naWpI2dLwcjTQUtExb4e5aQF5u515Wzo3aPG1M44de+mVuobAham0s9JgpemeuZzIWXa2piZCDDiz\ntYCnBBhjq2aUD8Ps0uNzaOy1M3oTgFaYibaADjkT/Ye89u+C7R57UNrNbIe9uYbGgppDvAsY0g7Y\npTCy7kPsNwmQYLNHF25mhZ1ZWzgX62a43yVNhprJdTQACu9CeguUbwyQK8wURJ61MUCyluDRkG7m\neyBT9qqwXy3ULLZ7xOPUcQLNXDCYqFW9PF7drfWNLD8JcmqAI/JNwJkJNz1QIPvDzkJnRVq6FgV7\nYvTeFybQQUHNLhTubhX11zgBNumWkCGSXhntnYnel+hxCQDD+32TV4at43AOjPPGTIWYud6XVniZ\nAy7nAVpqmrrYkohuAPDLAB6M7gf4Emb+eSK6BsArATwcwAcBfDcz/3ko8wIAzwSwB/BcZn7D7Dew\nabpadimTavGyjK0T9TFssbu9TWBj/K2aaUYSNeSJY/18Gpk2dv0MkMNNDxQ2DBVbKxtrZ5JtBJUR\nMHMi6xjYFCD2oYOYsClAgJtsUWkFanbyLiB1O6TJLZz3fNK1LZ5PU/PSRM9MXONTQIxaRyOl19HE\n74+53Cigy8O4jQGQ51ngMXqnsyGtDCst2jYFOE8a812s8MNZ3Msz9H6G6hsBOoOQM1SXA0BD8KKv\nB0WsGGB6bVJZA2wymMmrCiBTQk3mmQnHHI5pR/mi/NSYAJdwnnln9ntgLxb/t8KMBzKVsLKpEDMW\nYM4aXpYCl6HHCy2u6e1dAvAjzPwuIrofgHcQ0S0AngHgFmb+aSJ6HoDnA3g+ET0WwPcAeCyAhwJ4\nIxE9htl4wNCm9VXzwMx5qGaLTQV63PA3XVeDR2Z6/0ammRDDRd44iPHrdKFGeGfk9sxR2jsDwAGZ\nACtis4DONoeZuOuZBzKWh2ZnfAYHJuGh6TYFgACbHXPvrck2AABAu24sCQCzTyFoHciktmPegJfm\nkL0y8hA0P+xMAkz/eXfvVW8UoDcG6L9gBIAx8pSNmZaFo4W8IdhRdY4NPZPpoyBprM73fcGqLh7Q\njFEr/Cz4BZ8q8NTqagSdWZ4cI33Ie9PETwbcaLDRMJMOOFyLDuiChhm9VyZrmbu8HUAswCZ5iqgI\nO6PMK8M5yBwOJczEcwUyAFJ4WUtYWQSZVogZAzBLwMvYWfTRAsugpt35YuY7ANwRjj9LRO9FByrf\nBeDJweyXAPwmOqh5OoBXMPMlAB8kovcBeAKAt8zp/aaFNeNG6KSbqLUyK3lnmm2qEGP8IVcA6Rcx\nKQAAIABJREFUx7JrgRg3vMwIN5Pp8liGe+m1M5Z3Rq6ryULNlGfGgpneW1MCjPVQza5PCM+eYQE3\nPdj04QoHgC5DQ80+xmWr9TQxnA0BWDTMRC9N3MI5PmhThp3F9zAUdhbP5Toa67u01tE0AUwNZIbS\naumt+WeuzUNzsXWKXp9Fw9qmwI5VpgFyAHVXwaqjBXCcZofgZhBsOL9W0a4r212/g/eFkP876WGG\nYpolGWJ2gAgvO3RQo2EmpnleGQUy0hMThynLG5OHnOVfmgYTCxqq62UWBpcloGVNYFnFlbFAf4no\nRgBfC+CtAK5l5jtD1p0Arg3H1yOHl4+gA6BNp6yaF8TUSC+LZzM4jjTCjbtGxjse097YeVNl4Ou9\nNFymVeStn2kJN4vyws3kq+edAZC8M139IbRMh5lVYCaGq6X+DHloEDw0AW66ATEuJs2hZq+g5SR5\nW3YhtAw4BM8LEELOgpdGr6U5MIpXK+ys+I6o3L7ZWkfTfY/5DU9TyoNjwwr3bpDWuwdjoMfKd46n\nbBYwS+catuo6SqAZc3dqNbec2+AI24l9WwR6xsBOo23Vm2N5cQYAJ4Mb8cebFRPOlFgnCXsNNvJf\num6zqEhUTMxpQwAmgHbBUyP7EIsEqKGDAJjooTFgpt8IoA1kgHEQMwdgloSXqeCyFqycaczVzPcU\nws1eA+CHmPkzJCaxzMxUn8Ed8TB18TQadKQmFK2OGQvdkJ20QYHXvhFGVuQ1/qSTM0d5a2rrZ1ql\nw82ysLO4bsaBGss7cyLgRK6ZGYKZeByVHugpPqMOYoJXBNErEoEFJtREaAF1+6LF4xh6tkf0hoh2\nDS9NfNDmUNiZhBvpuRkSiYFer6NJNzFr3pk0eagAjJXnQMVQuNm51ow+EtEHAXwa3drNS8z8hJD+\nHADPDun/DzM/b3Y/DR0l0IzR1PVNpwJCrX2b0JdZoW21v/mRti7kNLx3j6tSnXrMU3ATQcQFG/R5\nLCuIWUzASbgIMsAHRppE7no7iutm9gFQIsgIsEkAs993Xpn9oQoy0Rujw8laQ8laAaYGL2tCy9LA\ncgyLQ7zdY+76/ffjrj/442pZIroSHcz8K2Z+XUi+k4gewsx3ENF1AD4e0m8HcIMo/rCQtuk0Ndo7\nU9pPWpsyA1BaPDKujadGsJm8YUECFZE0hxdHrJ8BhsPNLBvPOxPzeqj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bjMNdA67u8pFm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lpnRNshslnpgsbm8VTJrTTe5cBwO+t5Wxd9pIUbgrWmJlhpcqtN6X4Gf3tiPRMtNS4/t9b0uKFJ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\nhSwnoEaZVmx9I82CGhT6E6DGF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aWpt1cPEDAnnHORNxFqav1eCjWAHQVtE6kMFO//s7fj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\nwMx8YFlj7UyPjLmbVdvcUE4RJc0Orzx9sb/T6Y92N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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14,4))\n", + "ds.xc.plot(ax=ax1)\n", + "ds.yc.plot(ax=ax2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the variables `xc` (longitude) and `yc` (latitude) are two-dimensional scalar fields.\n", + "\n", + "If we try to plot the data variable `Tair`, by default we get the logical coordinates." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1xWOCzPcKCtisC80drjrLE3JK3x0bPLbKGw2rDQvH+nSPLgOQLfbJFvsYHwHk\nq0tUwwKhVKzoDZi4yiY/Wuuhk+5giboYR0xfqQ4qTUj6eYSKkjyjs7JI0suptnephiOSXk73yArZ\nsRPIpVXXd2jpKFYmkSAox0Pq++/EFLvo9TNIo6mLXex4hFxcIV09juk5A2DSLqa3AvkAhAAhEUvH\nEeUITI3cXceWBRjtDEDeo9rYiFXP4ZqCRx7+qBzbyGgTlS2AzFVUndNJYyGb/jy09gk5hWBMgWgw\ng4TePuH12L9ImoloQnij0X5/ScMKcucKSWHidyIYcakkKnWORNvzD8e1zwPw6e99RYxMdanRlaFY\nH86MCvaT9930PAddxUpmF8mk/ZTxlnNSQm+gtgQmXWfQoSpqRusF5U5F71B3j9G4FAmFnO3qYSAa\nh4OSORw0F8DlDV63ch26bn7UdVGiS0PWc1CINRaVKcZb46jIG2aK55XLpuFYW8KPs2HjWGSmSPsp\nad9j/sbQXR1gtKFY26J3dDni5G0D0Maxta+SDQotKnsamCMb9CKd0lWNdkkX+o6lMxx5T1eRrh5G\nZDm2qrDjAjnagrIAqRBJCipFrR535ymG1A/c5V5PMuoH7qS69w5kf0B6/U3YQ1chTI0od7FCUvcP\nY/IV0iR3r8kEVRbY0RBZV4jMXWc9Gsd72oZ2giff1HL42gn/3GjdYmd5peshG1o575ALkamLrKCJ\nBGKlsj9eK4nUk3kDq1204XIBNVobpM8xhGhE5R1MWVEWu1HBht5D1lineLOmlYVENcnoFnUzNpij\nvX47kXQOYkodq5KnWVLT8sEXvTA+bkOWQgmSzCWFrbGUM+jUgeSQdBP6x/oMTw956IvrlLuzqdcX\nK6GdS4BqgreeyYP33OeRwFyiuDDT/ciEx1zTBVcsVhVNiByonBAaf4kJ5d8uMJrGe4MEozHeKhmd\nWXf9g9KEcst55kGhdY+uUKxtNcpfNXmA0CYhtI+IylPribyByx10SPo5Mssdvi8VdrjloaEO6WDg\nDEBZYItdbDGEtVPI/qJL+vYHzqPu9jFpBqYJ/fXaA1GJ22KI3TiN7C6BtQhdQtpF6TFSZQ4uEhKk\nhMVV0if3QFeY0RCMpltXlJvb1MXYwWoy3Mcmqd6WyATyyl2laaR1Jv5+xeSwcbBQMAp6Sum3DaxQ\nElklET4TSlIr5ai4rQgCwJh2AldN0Gzj5+1zBiLzyeDW5zVJ1TWR7RXgpekEsfW041Cn0C5Qm+WA\n7Cf5Sh7lMnYdAAAgAElEQVSNlPSGyX3nMwbdhN2zI3bPjmIeoBqWvmmimIiEAbJeysv/5pP7vtfF\nSOje2/XX16aFlubg8KB5JDCXKKEaOLAeQqO3alhSjTVpRzX4v6f9BQMQoKA2HTSE/EoKbIsXHvax\nWjM84/DbwRXQufxIhDi0Z/Xky4vR05dpggpFT8bVC4Tq2bZxqHeLCWWW+gZ1MsuRiw560utnYjWp\nWjmKSFKMh3YAMBrZW3TbOj1s0sFKCXWF1RrR7SP6A+fJlwV2XCA6OXL5KHJhGTt8CMZD7MIhbNZD\n1GMwGpt2EeMd1M5ZhKmdQUg6yIUUWxbo0JfIN4drJ2gVTNBCgwTlHpRyOFYmztgB2LqcKKSz2pCo\nyWR5G/IRUsX/opNPvN9EA7+0YW2FpLGjxKbx3A1jy8TILqw7flcCzKRMjELBR3Opi/ii8ffv4+pP\nJKHILfj/Ugm+5k17RnVE6Qw6vmLZRrYSSHRVOqJCnrD4pKN07nsQoQTVsHIst1TRGXQcwSH0GNIN\nzfUg5Uc2bucPj7hmcypTdAYZnB0xKnXMDRyUXMry95ur0tr+fwC/Chy21j50aSu9OJkbgbnM5Qko\nH//2mydooh95+Uvj4+D9z8XJJRqUCvjJ9lwVIcQ7rLW3eQNxM/Clg1jnxcrcCDwM+eH12/ntQ9eh\nBCQ6QcimnW5pLLqoyVLn/e8XdkcGyFRHSOdxTbYNCGH19gM7SCXoX37EefT9PPbd6RxaotweMioc\nBzvw5iP+nYbup1mkWNajMkIYoZ2EUBJbl5jNNaqdIeP1bWSWkK8uYYZbyG4fkaQO+04yZLePXFhG\n5Avo/ipWpaASxHhIcvRy7NgldQ0gChfNWK1dsldIbL6I1CVUY6xMQAhENUKUu8jxNsLU2NJHHXWJ\n1dp1J62rpjLW01q175EUuP/T0m7pMCGyhXcnGdRlbB/R1AbsLRQTSYZIU1cfnmTIvIetK1RVTUIy\nVYVMk5i7CFGKqGo0NUp14rpMVZH47qqh1mO6eC1ei9SoqeuZrmR2l9fMQXBr0pHQEOTj337znuMC\ni82dQ/jKYUXWz2K+S6YJqzc8GbiTh764znhrTNJNWLhsIR6b9nPK4TrVPq1YLlVG/h4s+epklSlU\npQ+8YvhSKKL7zFW5DLgN+HXg/wT+7ACWedEyNwIPU37wodt5/eHrSXcqTOba6RatH6SuDVa73itt\nEkf44YVCoIDPtlsPT/9Yg9Sjmp3TQ9Lb7qZ7eJFsse8ggWFBtTMk6eV0vKILhVNJN/MFXZ2IZeui\njIleU9Wk/Tw2WQOnROvdIu7jmtDVCFlg0wx15ChqadXlBlTmawAUNutikxyrUqSQ2OUTiHKEXT/l\nYJPFFex4hK0rqEvq++5AXXkdeuCSyEEZi6pAjrfBGGcYwB3n2UZmc81VHmcJkqRR2IHJ06qElq3H\nQkrHDtIOHgn5AVMWPifS1BRMFJ1NiZDKJcFTl+wWUiJ6A/fcw12J0VjjeiOJwtcxeENg/fwFaM0u\nyD0c5IvwwmcjPHxkjGnaXGsD0rXaCBXU08bNeiaRNWGY0eQcBLfdPf7kd35LPC5Am9LDklVRT1Qi\nlzsV/WN9Fk4MGG+NKB7aJBv06B5doXN6iFCCrJ/SW+3He1gNC8xBlu5OSWlwQ51GFQuVfsQSuAeF\nZrXnqggh/g5wr7X2M+IxTjzPjcBFSGksW5Um8z/A0LNklhhtXT/5GRWmQWJewHttSTeZiAikx103\nv7TF7tqIxRMLLFx+hM7yAsMH1ugsL5At9hhvuHYX2aDnBrN4zBqpqHd20FXVKKEW1TEyg0wofHOG\nIVQGiyRDLiyTHLkcvXAE2+ljVeYwe2uwIaEL4HF8m6Quv2AcK8eOhujNNczGGcq7T6I218ie8XxM\nbxm5c9YZks4CxmjkaBNRjdDb65jtDWwxxAy3qXaGE+sC1+4BJr1lNeOxY9k0OYTosbdw9+j9R6Og\n4/2L+6SpS3J7RpTs9l0S2xi3X5IiSFFJY5xEVcdhP4pk3yR2kmexG2yIXmSrjiPkFiaK2VIXYYXP\nL7xnyBVA2bCIPH8/5AhsxPsbJ8VoG3sR6cqRH6pQG3MaBlcsRUek9pXo/WN9OkVN2s98pNolW+xR\nbu9itGHpqsG+4y4vRX5k43Zeu3wdO7WlNM4ILCRyYsLfQci5jMut5ZBby/PXXXgo6I+BV+EKPX4W\nBwXFXS5tlRcvcyNwEfKDD93Of1m9Pj5vdy6UQjSMoBYzCJgIsYMIKTyf24AiFjK1GSEhKiiHbr6r\nLjULlx+he3SFrTsfQBelS+56KKSzvEiyfAg7LrB1Sb0zpB6NY5LYqr1UykgbzbPGO5YKpHRQUCd3\nSVoAXSOsAZk4fr+pEeUQTI0oHaQjdBUNgF4/E5V5ubbmaKjHeoi6QD50D2Z7HXnZNZiFI9ikgzA1\n9el7MMOtmKAWSpL0PB02rM1oZAqkk9x/YI9yb1f7Bmknhic3OKUu/PUL1TIEUrkoIEmRfc+Y8nCV\n+6yaKl7ngZfILIlQUKCqtqmlUqlYBCeURJR1LDJrGwtJEiucQ6QADZU1tA5vf3dkmmCo/Wgb0xQn\nxlbUfrlZm5Dgk7r49tX+9XJYsnXvJv1jfceu8lXo5bDClJp8uUdovVFu71INy4dVl3AxEqjbISqA\ng6/wPVckcGOnz42dpiXKf989u2ef1lyV/2qtfZMQ4pnA1cCnfRRwBS5X8Bxr7Zk9J3iEZW4ELlL+\n8dpt8fEbjj4dcBGBFMEANIwSmFT8IewOxUBSydgzPs1SRzE1Fq3bfevlROhfrG2iCzeZU1c1mZKx\nL7zq9rBVGRu4jde3Jyikbax5QmF4to3I8li1G4bDy7yPHW2jtJsvILIuVqXYrOfyAUYj6sIZBwD/\n3w63sOMCs7mGrUuy1VUXVVzxVZjtDUcf7Q+QvkBM1AVm40FXLVwWiCRDJRltCS0qHC3GKU+VGKeM\n1STDJ15nKxITLYUezuUguJaiDxTXAAGF470BEN0+sr/oqLRlEQvb4nmDgZESUTeYuGzRUIWvbAZi\nwzvjP4cQCajWuq1qD65vaK0iRA15x81XpomQlGq6wuqWsQ+N7ZpzNwWL3cM9rDaMt8ZYbSlxTCNT\nGsZ6TGeQkfZdTyqVuroBrQSd5UVUnlHtFmzf/SDrf7sR+2r9xWXPpPKG+btO3cpbrrzRrc/PURi2\nRk8uLGSxruBCWk8r4WYL7NSTXUUPSi4FZpo1V8Va+1masboIIe4Evm7ODvoKljAS780nnjFRFWy1\njV53m6NttevkGUZLGm1i8g2IibRpzzV0oVSZIul30WXlGsh5ZZ4u9BFdP4B9NHQ/5A03YCTMHzZa\nRyhEpekEF114rz/i3VnulF3PDSvR2xtR0cneADk45LxmaxBVAVK6XIE02DR3r2d9FHiF7iAikeXY\nkYd2llaRh6/AConYPBWNhcj9Ovz72dpFFjF/ICW2rhqF7vcTHpMP+4hpTz8agKY9BQH22bOPV+bh\nsU+Ki04eG+KFaCuurX1s5p/HqEBOtKYQSiKTFGuaz2RWC+sJQ+1fM63X2/2PFAl1yH202oQAJCpr\nZk6nziBN5xRUpjh841Mpt3Yp1jYZntoAoPK5Aqst462S7mHXwdaUFSpP6az0SQfOeFRbQ9b/doPh\n6aGbRVybOFwJmnbTByXahuph54gdeCvpS8PsZ81V+Vlr7Vtb+zym4+nnRuCAJWCr33b/pwF469XP\ncgo3kxgk1s/SVaWhu6I8ZCTJ+r6//7CK3l4wGtNTqKQUrpunUhglyQb9yZyDV4L1sIhtoWXogOmN\nQJJ3JubLCuVZL1kejYDzejPnYXuuPxB7BdlqjJuiMAJdOY4/YLIF1woCsGkXZWpU7Qq+rPeKA8NG\nDg65/EE5wgy3nAGY8Px9lOEL1doi/Lc3QD8iBaslwkwahrbHH8W/h5By5i+w8eQboxPOEwzD5PWk\nE0YqnkdKbMgVGOWMlJzKNRgdWU1N19EmigxYPzSKX4UJbeF9lIzef+IjAkMV1wBhdkHzk7faxJoC\nd5sk6aCPSlO6q0tuNrVS7Ny3hlCCcasB3nh9iFC7SN/dNRv0Y2Hi+p0PMVovkJniFSc/EZ0jM27u\n9JEbDrsoY6ek8t1OZabi72DWcJ3ziWshISmNjfO+D0Iupc7hPHNVwj5Pueg3OACZG4EDlFmh6yvu\n+vS++7/vpue5fIA2se1vgILaotJWdGFsM5jET+gKVat6PEZJha3LOFkMXIvrPZCIaoqtZJI6JZW2\njIBSjpY59O1/vYITaYbIew7vH25h/fuJLHffdGuw3RWErhDVboSHgpK048J50VJCfwUrFWK0Rb12\nCjsuUIvLEUbBmNiK2laVTzS712wLYhFSYb2DKwD82mm1gGbK04/3Qqq90UJ7n2AEWk3y2kZKJKl7\n7yRzyeFgCCbgJOOON7oxUlPw1LSijx6/blOGJw2Be17HkZrtViFCSYRuv8fk5w/4XEF4H3e+3pFl\nN2eiKMkGfQZXHydb7LH5N/dHB6czyJAexkm6Gdmg7xwKJRmdWWf37IjOoMOLP/YhwM0S6Aw66LOO\n8ts/2mPxxAJb926zde82ZaXJ84Tlq5boDDLXa8j/Ht584hkXBAllUpBKibHW0bUPEg7KZnw/Hkcy\nNwKPoYRW0dBAQO1EYOja6NoJu4E0KDfXuNoduWZqLZaPUNKNHiwaI5B0fdVri/eusmb0oghe6pQB\nQEoEjiHThjpElkclGGCQqERVik07oJKYEwh0T7m06qAq79WTdJwBGO+g1x9Er52KrBvZX3Retu9L\nZEfDJiox2q2r7YFDVLwBNtoD75gpz7IN27SGxID3vFtJY6H8PUpSF8F08uZ8RuNHivk/zzhqefnO\nmEhs3RipWKkcoqu2gq7CoXrvNr++SRprHSOC0I4isMCCtGchQFNBbFWrfsFXj+uqoljbotzepXtk\nmYUrXH2KvOsB/zE7x6UeuboIlWf0jq4gpGTjjvsmnBZ3ix1JorPk5mYvXrZAsV6w8SU3DvOV93wG\nCM3oFkjzhM6gw9Z9s2clv/6wI2UERR8cdaEEZajZOUA46JGoeP5ykrkReAwlzCoOHSqrosaUOk6F\naprKuTC8YQk1xVG946tNEZNUmLqamN8bk6NhhGTlkpKhmVn07rM89vfB5wMc7dF75CFx6hWaSJ0y\ntFXZGBAhHb/fGqxKIMmxCchqFyu6kHbBWge/CIEY71Dd+zeY3ebHbuvSGYIF177CbD2E9fCOLQvw\nSt+tr5UjMBqPezUKuSVtlRAU/ISn36aBBoMXE8Xu3ga2UISJdMsQ+PvoPsgmCrM1Td4hAWvUhFK3\nft84E6uF44fJZu26B9eNtKI9L0HoJpcQvH+pXQ1HOEbKySgCKcEbCyElFCVpr0u2vMDWXQ9Q7Y6w\n2ya22OgsL9A/vsrw1BrVcByn4k0TDXpHV+gdnlTer7jr07z7a26it9qjd7hLZ9DhoS+us3C0z/P/\n+v1xv2++9aO8/dqvZfmqJbJ+OhOL/8MjT9/zWogCykoz0oad2hxo76CH02/pK1Ge0EbgVxeuBeCn\ndk4+pusIieHQ5dFFCE1xWTmsyPppNAIqldSj0rVLHixhq0DH1HGK2AS0EPj+UiFUi1UipVdOju4Y\nJOYGAFG7xCUtPF6oJmHquoR2XGI3QD/VGJt2sFkXUY0d3bJuzWEQEuoxenPNJXcDno7z8M1wC9kf\nuDXVlTMSYZ+Q9FUK0jTmLGLXN6mg/Ty+VvnXwxJaynzyw2gMQAsymoRvfMQQn7sksE1Sdz3xfZ2x\ntFXpIwQVtzVdQQsPX0lEDUKZCRgHnDFoU2BVa9Sn0C3qa4s5BN5j98Vm7p7tZYiFRLLKMzorC4gk\ni1FktuiS38XaFqMz6774y83UMNrSGWTxu1aPxmS+JfmsucNCCXqHe+Qrk32WpuVlJz8RJ5qZfbz5\nMPhpesj8SFtfM2AP1Aicq8bn8SBPaCPw5SIqU7FDIzAxqendX3OTGyrjccl8JXfjJgd9VxAkFUin\nHE21t3AKQHU6TonWDesocORFx0cAIfEZaaEDkIlr3RCgGWM8Ju9bO6vUVw2nzsOXyh2Tdd1/4Yuo\nwLGGrGm8Yr822V90RqwlZncbWxZ+DW5cJYCQObauYrTgTiAbeMpoRAqkaXxf8PTPTLWUtoyJ4Wlx\njKG9EUI7kRsT0b5raEMFxf2iWsVm8ZzgIoc2jARNgpoSS4v941t/hM+rMRqT3UJlWANA5T/71u0M\nTeXi9QXKsq+2NtTILI01Bsims2w66JEt9ige2mTngc3opASiQjWs6Az8+NJ+HhsZmhlJXelboAT4\nMwxdmiXb97s2KbMgneCUZ9LV5OQeatLe+x9pExs9HpTMI4G5zGUuc7kIec/XPzcmhy9V3nj8BtLH\nyCOf5wQexxI+219duPYxgYRic67WAJFpSfIE6WsPsn5KZ9Chf3wVlWeuvYPRzjs3OrZGiK0UJthA\nasLTFJ08Nj8Tna7z7jsq1gbYTh+T9pBCIjyMYrUGzwQC790LEesDrHSRiasVUA72kRKb9WI7CcDX\nFCjn6fcHDi6pq+g5Y4yDhbY3YuuGCfgJJuCZiNmTxoSxbUNHoQK4v9hECFLujRaUmoiImFY6sa2E\nh2aSrMkLuA90sm7BSAhrCglio5rqYogwl00yhNTxMwqef7s1dpvq2cbhFSm1HiOVRE/VBgTiwL4F\ngqH1uFIYbRwV2Lgq8t1TaxEGksq1O7dTdOXx1siPOm0qtYUSbLSGyqtM0lvtITMZmW2zBtIECYni\nWbLQSfw5lftt+HVURY16aIS2fhTsASaGH+/soMc32HUeefX2yQMNGx+OvOP6r5t4PquXDHjudp6g\nUhmnjKk8Ixv0HMe/anj3MkkncwBKkvS6jpUjVVR60wYgtEZwuYFF8AYA6ZrDkWSTidKJBUpM2sPK\nBGEdE0iY2uUArAHfV8h0FjHdJUy+6GYNp13oDqA78AVqqVOgbcXsFbn1rJ8IryQtTDwq7KbNRXh9\nkhYqm//tx0kar3/aAIRzN20qWi0plOuoKtJsMoGcpvG8Iskmj2/tIzo5otOdTFLTZiJlE+0u2m0v\nprH9aRrp9AjM9jZggg0EOKpwgJmkYryxTTUsfMHYthuh6psbCiV8E0QRPeTR2qYfcyrpHlkmX+nS\n7qR7820fZ7Re+O+z8DToZj70hch3nboVlTmqaf9Yn8UTCwyuXGThsgXXu2jQibmCg2QGgWPpXejf\nV6I8oSOBtrxm8DRetfWFAzlXKIk/l0cDeK61Zbw1jlipyhTvf+7zAXjBhz4Qf2huZmuXtNeN3T+T\nXh4rhEMyVIIrRkocti06XdfquCwiEwVauHj0sLMGW8+6jdIS0lMj0z1FVTZJsYmLCmSx7SqE2+wg\nk4D2NFL0xPFWJQijXcvouor4uuz78ZW+/XRbeWIMNqyVBnPfk9wNChWfvG0Xcc0o6JqkizYGYGJ7\nK3IQSeoa6EGTZwnT1JLMFbHVDuOPxmTKkMXzak959Uljxx7SjnWVZO7znKrsbQ/SkVniisuqZi51\n3K/VIC8YjdjDqNVIUKZJbE1tjY77hMZzbqkiGoJm+Y7CrIuKYm2TzvICSbdDkmd7FKKuNFk/I1/u\nMd4cT7SzvlAJ753micuNLaSoVPkaB0H/7C7domaoD3ay2PT86cebPOGNwE/tnOQ1g6cB8OuL1/Lq\n7UcPFpK+ZUQwAC/55Id5303Pi9s/+KIXuqlJSx3Sfoek36WzsuBmAA8GIKWjUtYlZrdVHQtxRKTM\ne9FQhK9ypIaqRjGG5LA1xnnw1rouoQAycWMjfYWsLQvwE8jkaLOZAGZq1HDN9RRKc6wcIcc7YK2D\njfx/UY+x1dgVm0GczNWu6nWMoRZ0QysCiDDPXmaPK7wK/51na9v7to6xrfPsqzLasJCPGFzX1LRJ\ndMf9Go8+wDsY3bB/AIyMa4zXohRUzmiIACNFlhOxyhiY6Joamv/tJ21j0ZZZrwUFW+8WqDxzPYhS\n6WYfBAelNQPZXXKIJBSmqmMXW13WeyAU6SOIfHUJow07Z3YBxxy60LGToSGj0YY6UFSloDPoYo2l\nM+iQbToW2rxO4MLlCW8E4NK/MG+9+lmRMfHKez4Tw+BzfcFvvu3jvPOGZ8d9X/qZjwDNVKemcZzw\nfYckpqocPpsmsdhLLbnq2cjxNxo71pjR0Lc5bkElgYnS9oZbGLoIIw91jU0MVqVOYacd6K84Lvpw\n28MmiWv1sHkWG/rwgC/sSh3UYbxHq5TrBQQec9a+504LF5ZN5THQon46aXc1najMbT9v7wsTEUHz\nPl6hhy6hMOmd+20ThgMag9mOnqzx3v5kBXJ4z4naBP88tLQQYd+Qz4nPXcTgPiNnDGxVus/GGFcR\n7ruzus9Sxorx6V5B7l40hmCWwZgeqWnKOrYSt8ZglESoOraeJtxar+RVmsSWF/WwIMkzB1m2jMA7\nb3g2iS8AU3nmqoz7KVv3zi4G20/qkVP8ifF1NK2ammwxj2s66IrhuRF4gshBDaRwnOgLCx+/+daP\n7nlNKNdaOiTgnvO2d/G5f/hKVxOQOhy6Lkq27nwgDo4RSeYqckOSUWvMcIyQRWzVEAa6tCt/wRUr\niU5TBQw0FEnRKEyX5PWJ49R5wXa86yiexjSzh8EneVuFZgBl0SpGUw3vPzwPyryu9tAzp3n68bWw\ntpbsUejhes7xmpj1WrhPaavdtE/uGhmbFrl929fi93PnaPUlksrBRDFh3apj8NCRkBJb4fdzSeXQ\ntdQa4yeseSoqk9APeOqvkaCaeQWhJiQajXah2tQMY6sN1XBEvjog6edYbdzgot0RpqwnZlGEKKA9\n3CZszwb9PYqzd9gNoLHaMF4fYrVl8cTCRLHYtIS82c23fRxwDtZbrrzRwVJSsHjZAq7zqZufsHhi\ngYUvbQIH3EBuDgfN5WKl3VH0QuQD3/gChBJ0V/IYWXzq777MD6x3E8dCWwCAcmuX4X0P0llZJOvk\nkDd9zVXHtYswxS4iM7HVsUic0hJK+SKmxqMWSiHSDkZIp/Stado/QGzrEMUrMyFlq1/PFH7eyRsP\nt3DtH6jLPf18QkI0DnKBqfcyM9k6QrX6BBnTanXRUtDsNQ4TkMy08g/vPcvw+NoITKsqu64mqo8n\nrmv6fkkZK6AhRAQeIgqRUaiQrkp3Pb6bamyZ4a9Tpq1EsB9cowNE4xlEmhq5z898utOoNYa6KMkG\nPfrHV7HGGYXRmQ2q4Sh6/ELKZviNaYxNOCfAZTc9lSt+8f/1t0O4fkNSMnpwne0HdqiK/WGs9930\nPPpHm+/yW69+FuAqj195z2f4i8ueSaEE2UIWiyyFlPSP9Tl6tM9gtxVhPvw+dDPu0zwSeEKItvai\n8wGvuOvTvOXKG+OX3Wobi2HeecOzZ3r87/n658bHbS51aCmd5IJivXBsjFQ5jNYPJ9FVHSuDY48Y\nD0lYvEINicq6nEygBgik7bl7sTrAEok71k8NA1wC12YIIUFXEQ+3aRdhDUL5mbu2BdUECcqvJs4L\nBiKsEt+7k0NVNce0pe35t5k0rf/tCl7X18g0FcnT1zrDKEy8V2QMNffMdQR1iW+hXR5kZjWp2Mf4\ny6n3alFDYz+hFlwFLUMAk60yjIbKwUZhDYYa5SMMIzWGmkRlExFD2/ufZqQJ2Uw0U3mG7A9Q+QbV\nsHBRQTF2yfssjRXnwQirPKOzvIBQkuEDawB8+OaXcNM73k3aT0nyhN0H3SxioWbP4H7nDc9mcOVi\nfL501RK7Z0euZ9aU1KOa8daYfCVHSDeBDaC7knPo/tZnWe459GFLgGgfrzI3AvCwlX+YKtYeLAPO\nC3LcZfejDLS4d934nLjPSz/zkQkD0BaZuZbSMlOkfQedtFvqhgHk440dpJTUxZh00CP0/sezZpoT\n5hOsINHxWO8E5CJ9S4XKKWXv/QtTN60gRNReICS2s+Amh7XEygSbdpxyNNopSD9+0hPffeO3Bn6x\nWjfVtAFC8c3ZrJYRGhJp6gyHNwCzhr9EwxKTyFlDL52AahqYappOGttYhxxH+x6F9wsK3FpPgfX3\nbD/F35bpfZTEDXZJG2ZRnTp2VF162qx0NRxtCCkMsPH5AsqpVtTg+gn5+6QDjVg2ozVDA7npFHHY\nVu8WpFmOTFLy5UXXSqJwSVdTVsjWLIpAWw7fWYCNO9eby8wU1thYSZzmCdU5FOvhp62iK83O6ZL2\ngKYg33b/ZyMsVO2UKN+KIgywOZSpZrrY7r5vc8FyKRXDQojfAV4JnLHWPtO/9tXAbwEdoAZ+1Fq7\n11N8lGRuBPaR161cB8APr98OwG8sOQbRj202NNLXH76ef3TWGQKrLcJT5tKlFF1qsoV036KYzqDB\n4D/4ohfGkDPtpxHucQNjvAIP5f7B6/M4bxwP6TF311ahconYwPppMVFs3UAkExAGnuboFZVtK3H3\nhiCEV/a5axWdL2DSDuiGRWQ9jCR0y4gEDD3pILzSdB761BqCQg80SVoJWYjGIBZ2tZPBIRrQ+3v4\nNiRb3Y1stgdlHwyAajF9ptY2M7/QNpJtsdMqdsYxQSQR+kF7SCwhQkOYSaXpCtCMh9Faw3daLSVC\nPiDMjAjbSaH2Cr29CtHqTGrKGj3cic9VnpH6yFOlCfmq6zVVDQu6q0uxsV29Oznz4f3PfT5JNyHt\nZ6g0oSoc26jdNuID3/gCl/Pq+illlWa0XjA8vUvtYaNpgsUr7/kMb7/2a10B5VKHMLhpvDVmYSFj\nKUCxBzCr6xLhoN8F/hPw+63X/j3wC9batwshXuGfv+RS3uRSZG4ELkJmDZb/tvs/y9uv/VqEFKR5\nEvv9dAYOm2+HtJ1BRu9wD6kE2/c3P7R84Kigrhis7/rH6GakoFASE1oF7zo4x2qDLivHysl7PgHZ\nGnASksF74A850TY6YvHWuArhoLg9/BGUn01yB4kY7/F3lyJkNCFSekpoY1RidACItDPhQVuZIELG\nM0QNjVcAACAASURBVPUFZ2GQjNF7oJ/pa5pQ/u1kcdvIhX3bFNOpgrI9swfa5xEyGrk9Cn6WAdjP\nKITztNbk3oNmHkLIb0Bsod1OpAupXQ4lMKikcnUnbUZVq+mclSb2HgKQWsdpZGGfYCxkmrjooTU/\nXfo6A4VrEte/7KjrIusjNOMbDA7vO8vumR2yftoqEHNGqdblnmT2xO3Rhu0HdkjyhGLdVSmfiz76\nspOf4F03Psf10lrMo8culGDhxILb6fZ9D79guVCixyyx1r5fCHH11MsGWPKPl4H7LvoNDkDmRmCG\nvGbwNLr+C/W6lesmFP7rVq6bOXj6LVfeuKd74iz52Ld+04Gtcy5z+UqXT37ntzzWSzivPAIN5H4C\neLsQ4tdwwdhsfPhRkrkR2EdCxWHbAITHS6li5D2aPz52A5kUqMRT5nw30M6gA+uFG9DtB8MEj37h\nWD9WOi5etsBovaC7ktNddd6LzNJY+q/yLCa9glRDFwVMFAO1WyoEGMMnHtttoCMdMcsjQ8jh8MZH\nDb6tQmC/BKYQzls3+SKiHPmK2dJBOzLx+wnntQf+vJCgfZFYkLYH3WIhxZYTYUC970skfNQRKK4T\nfXfCKc8VBbR7BLU4/O3n00ng/YqsEBJU5qqgw/OwhuDZz4oSWsfbdnTQiiyaNRFzO7Zq1RPUrern\n6dN2WnTfdi1IWJvUkDKRD1BZivadQyegInAsI2PQVUW22HeMtDyL39+0n1PvjsgWl1FLq+7Y0RC5\nuYa870GSbsJ4czzRGVemCUk3Y7w1phxWJHkS52IcfvoRds/ssPbFdXRpYgTxspOfmH0fW/LSz3yE\n9z/3+XSWOn5tKUbbA+33c652EB89vcZHT6893FP+KPAT1to3CiG+B/gd4OaLX+GlydwIzJBXbX2B\n1wye5odXC5QQLKUSbZumc7PqCoLHoEs3LjLpJshMUe2UCJ8IC9GCylMUoIuKhaN9VJ6SLfYxxpD2\ncpJuRo2DP2Q2yW5xU6LaWLf/EZcFIm8Nmx+79s8RVpli7NjxaLJYqAq5BIMwziCYAPUkOTbpOOWc\nga0LqH0tgWrNA0g6UI8diwjXHgJABPpjUIJTxsD6OgRriMrbKUjr+/QkTqG3DNq+yr8trWTyxP5t\nhtFE2whfD+GfxzW31ysTEI4hZVtGsg33xDVOQ0LBWAbDKATgWV1hlwDztPMP0rGAbBn6KZlWm4rU\nffZxhOXemgX31g08FCbM6bLaA9Hoqor7JL0u1jj2z4RBKGvM9gZIRXLkcsTSKqLTZeW6gnx1wMbJ\ne9j80jp1Ubuq+KKid3QFlW1FskOYOGaqmmJrTP9Yj+e+672zP8dzSF3UjDfHJN2E7kqOVPKiZhTv\nJ+eqE7jpsiPcdNmR+Py1n7vjQk75v1prf9w//mPg/7uU9V2qzI3APhL6CL3+8PUMUkW+krO9tou2\nLiLQtqlKlMJ1VwxUstAkSypB2k9hwSnSNHdGQUhJZ8V1XrR9TTUsSPs5ST93BT8+AojUv1ZfGNPC\ndY3/AbsEYKvSFJB5vxk5SKBNtgaPB6/X5wwCmyb+CekSlKp2bRLwCj0whNKuU3q69APmp3riBIXo\nvfzYbRSadhRhfTKJ293xnvNvNUKXLhmtUnfcdH5jqrVEkFmJ4tjfp51XmIH/R97/VMI3rmOKGTUL\n/7ftY4PSnzB8UxFSW4Jy9zMG3GlUTAbHeoJgrKYpua2JZxPVxdJRWgP2b6hRnu7Z7kzq7tvebqTu\nO6l8/gHK9Q1S/1kkJ64mOXYlcmEZtXI3ab+Lyr/EQ184FZO7QklUqli95hBP//0/B/BtUrZ54Yc/\nOPteXICUw8pF0+SkCxlCScZb4/MfeIEiD76L6P1CiBdZa98LvBR4TKdazY3AeWSzMmRSc+hwF5VJ\n6qJ2Iev2GCXg752+Ne779mu/lo62VEVN5rnRnUE3/uiEkqS9bjPgPUscY2JYxMcTPzwl6awsYMra\ncbWVgbLGGIMpq6jo6qJ0xTvjApukLhJIUuTCMkZuI+oq1gXYlv4VaQqhb48fNj8hQjhYKOkAgVap\nsGQIdrGpG/YSZgq7zqH7QD9SRuU04T2H9wmHqASha6doQxI5sJTEDLbOrIZwMyTWQEzJBM+/DRe1\nEsGBJmuT1EFWvkOqnTISiFaFcHxNtP6HiKEVBbSMg/U0Whf5mBj5uCLBhuoaK8PDOuuy1VdJNpHC\nlIQCL3DfrdB2XPmeTe221eHeWOOGFemidFXExiCkY53JNKFY2ySrnHHOrr4OubiMLIakwy0WnzRC\nF2M2/nYNoYTvNOrux4dvdmSYS1H+QV528hO8/dqvRSpJ0k1QmTxgOOjiE8NCiD8CXgQcFkLcA/w8\n8L8BrxFCJMAI+OGDWOfFytwInEd+bPMLvHb5Ok4Utet9kil2z44iHPTG4zfwXaecIXjZyU/wjuu/\njlznqFSRr6Tknj7XLuyy2lBuDRFK0VleYPDkE46R4ZV5MBi6rCKjI+3//+y9e5RlWV3n+dl7n3Pu\nuY94ZVRmZVbxsHioIAzim0aknHYcRBuZ7h4a2xEfuHCVD1gMqNDNaOkw6iBKg7Jq6VKZxoXYtM4w\ntIoia3U1SPvi0Uy3xZsCqrKofETG48a999xzzt57/tiPc+6NiMzIjMjKR8V3Zay8z3PPvRH399v7\n9/v+vt+ccjh2q6m5RAFOt0UoJ1NsK5cggk+wGW+5VWM2V/rwNpJt4TZwQdeqDKsSpxIadgZ+WErg\nV8VZzwnIlRNMbwU52XBaQ54qGhVFAUyNsHMloYD2xG4IiKHPIFWTELyiqa3ZVaYhXk7SZgfQmgdo\nPAPmJnvbSURIF6sD3dXD+n6AMDVCl7OPh1Yim78+9x6NQejKJ8K514XZXUV4eb8DCMcRadYkNZ8M\nYh/D6xFRVZHx1dZhmg/0bSnqGTaR9xQQUqG6Wfy7BJwrXevx5dYImX6ZuttHLh5DLa0i8z5q9RS6\nKCnWR0y3pnSWNTJN2P5yoxnUVsw9CILtZejHtSnYB8VBZCOstd+3x13fcMUHPWQcJYF94K6NT/Ke\nU0+js+D+sCajMpaE5hF0Tj7wzf/I1Ty9zkrtfVudH2tFXdS+ceYawMnx25HFGL25Fr+o1WjSJISq\nRqZu5WZ14xsL7o/UaCcBLDNnYGImI2SSOe36YBrvHhxpocFAxjWTmwasSKSjKSaN5EMbRqXIfIFC\nZOSDFDlaAyEw+SJq+1wTyMPKFpomcCj7aN2aQRDM1r/DZV/SscnM0JrjhDtqYlwd+2dYo522zpzs\nwwy19FKYa+CGhrjNFxDFsCm3zJeAQv/Dml17FO7zMHF3YVE7S0ntxnK7vi/lzKxCMMrZVSspvo1m\nClkkWTSMCX9TpqybBCBnE4D7zCTV9oikl5P0cupxQdLzA2F1FcuUKksxVU390P0kSUpy/Hbor5D2\nF1i44zTrn36A6VbJ8PQ6WT9l6fErJP0um/efpdx234sPfetzAC6qJXQxfNcXPg64wUxn1HR4SeAg\nFNEbAUdJYJ84N61hWqOEYJBIFla7F6077meb+/EXfVezulpYcaWWyQgoMGUddxBB711Tz5iQBwOZ\nAFPV2LrEFiNsmmGzHNntu8lQHzBk3psJUMIrgoaVu5A6egPHQNWC2yWkTHAyEVqm2MFxjFAoUbn+\nQMtUPrJmZIJNfHAJPYI5T6NYevGr93hdKazoxNKJAOeGNp0wqwGkZks+83pDISjuUA3VsfwzW6Zy\nydBkA0zX0bplO0jPB/DQL7EGwU6pAxHmLax1u6PWHEU8XhtSuVX/nEpqfG/hMUGYDhyDajrBlAUy\neEeHp5R1XFjYyPTpxiAX2EJUfmfhBeWijESauJ1Fx3k+hL5VKBcJJdFnvgRGo5ZWsUmGXFhmcPtx\nTKkpRyXlqKKzPGD1a+6gszwg6z/gtIQu4jR2OShHFZ1JvadJ05XgKAkcAYCT/QxdGy6Umn43ObTt\nZjB9ty0hN+nlAlJjmJZDkm4HU9VII7GqWQ2HUlFbDsBUNbIsHPOnLLBp5pqMWd4ESN9EjlLFbcG4\nUAtXrXJIW55BJmhjEULQkSD0lEpmWGuRvkTEfOM0vlm/2gQEIbj796Nrd1tYkfpykA1eBRLXxHa1\nj5YwnJkN/LE23pp0bsk+CE+BnamZtxLAfEPXpD2sSpHFlpuSrotZtlAM3G4QzvohuxnKaBveryHS\naFufy47Pyv8+QrlOSIUZD/2All/Bp5n7HCuvlWQMphj7XSGRFWXrkmo0QUjJLS//NQDOvvEVsT8F\nNLuDskVEqOqYNLLlges3tSavhZII05Qkq60x6WiIWlgmvf2JqJUTHH/OP2L5Kx9g+KUzSCXp336c\n7Cu+mmTxNMWaYwtV+eGEoud97mP8xVd+3aHq/RykJ3Aj4OZ+d4eIbJCycLLPsUxFHnJnMaOzmPHn\nT3zmrs/5j8/8Zv7jM795z2M+413vRUqJqf3EL6BWTzmPXy/QFeh4YcUfykcyS2Z2AQFWG689U83p\nBuXux1sbyqVVt8pUXh9fCG8G020cw6Tyq+/QyJRYX+uWAl8fr5DC0WiD3lBccbdnDMJlmbidR5rv\nbBAHhC+dl58QddlMGoegKhOnhtruZ7R1heaPtQt2+3LH4wvhEpqQyOkQOd5ATEdQVzvYTeFc0XpG\naiPuato/4eHz7KHYQG7fLhvKauoa9yLvYcdbmO2NJiFALHMFAoBQPtlpjS0LquEYlaWs/uSvzpy2\nKWtMVc/U/OdhKreDqMcFerjhbqyr+NzwWYa/zcnZdcZfeoDppz8GUpI+4en0nvV8jj//ezn23H9M\n9xnPBqnQo6Gnqrp5AqEEf/3fP3fP39d+8T9++qMU6wXFenHpB+8DgZK9n58bEUc7gX1iulnSWeyQ\ndpQv07hEcLnGGPOoiym6KMnSzGv/OGOVSMkLDlJ+FScBwva0pRsPRGlfW5c+ATQNxUZDyAV6cEEq\nlH4Az8lXbiAqCKOBp5K6VW5tLEVtSSWYJEUIicS7kBndNE2FBMxsAgnG9H6gDJW1XmMuIPvVcqBj\nWrMLBVNlkFROeTTIR7e/iPNic63/9xr/sUJi045jPkmFqCaIatqUftoMoPg+WzCmucm03j80bKDW\nTiKU19plotBQj599QiO8l2RulsPbbzp57pYjm1KxlKNH2zGwL7/sl3a81xOvfjNn3vBT/m3IRip6\nTmIa3I5ruj6ks7KAkO61TVWjywqpVFS4TbyIXF1MGZ0+h5D/gD53GrlyIu7e9OYaev0cdVQnrakn\n9Yy89H9+7rdFTw24/F7B9zz0X5vP/IBQuyy2bibc3O/uEBEknY22SAzS09Au5hnw7R/720se11Q1\nxdom6cNfIjn5OMxwPTbwwPkCmLpCVSlSKbeln+FvG08vTWe/uEFGOgxXdftOryesvr0+jZOMpglM\nGlDGSRzXJS6Qm1jmADcwp6Sg1BYlJKkuCXaUot5Fu7cdKD3TqF0KiaWRMFMQAqkxjXRzuxbfWi2L\ntOPr4LNicjPicztkrffQB4KZBOASS+0Ti2qC+nzgD2W0diKbTwDWODaQTwQhAdtyAp3ZvkKYtm5K\nSS0arHT1dpNmmNFWY/c5z/ryfz8LL7l71/cZcOvP/Ea8/NDr70JljWtdG2Glb8oaMVDo6dTPtCh0\nVVEX/nrV0JyFkoweOotQ5+ksn/FsIxmTx3Rjm+nGkHpSx/mY6xFHPYEjHOEINzy23vZzsW8EO3cG\nYUdwPWC3klAoq+5nYXXYOEoCB8AeWtp3Az8KnPMP+1fW2vf6+14L/AhuPfpya+37rub5XQ56t3SR\nmYz65mGrGhzAQl9gv6bZAU/6zXfxuVe8mHpSsuIOiJ5O49yA1QbV6ZD0oVjbjFv2gKgK6W+XadJY\nR4ZVIkRaoc36viRjGp2fdgM4MHDq0u0GqqK5T5dkQtKxBp0OmNaGRNjW4FZLehr87kLPNExna96t\nYal201g2dMq2rMKO54bLUrl6eWsGYEb2YX7VPy8D0W4Kq8z5ItRlbAJHfR+/O5lBW6MnrN4D/TU8\nL3zG1vr3bLDFNiLLPZU3dQN4czIUlqZHEQ2DUje9LXsLUQ7ETEbY8ZZ7zrTw8wGS/ve9ju13/CK7\nISQFgKWXvh4AlTl9K4X7O4qT6l4qIsk7bjdQV3HYDBqZCZkmjM+uz6zoVZ6h0tQ1jUcTyuGYuihJ\nfK8reBBMt6ZIJSg84y7rp1FiZbpVUo52d4f5y6d8PWk/i14F2+ecgcALvvzfdn38leBmbwxf7Z3A\nblraFvh1a+2vtx8ohHgq8C+ApwK3A+8XQnyltRcTZX/kMD4/oS5qqqmms6Cw2lJtV1Ei+iATik98\n8x/y+Vf+S7a/eDpuxZNup2kGd/soQKy7/kPbLESlaazDBr/hAOsnha3WqMxPe4YadHhMKLeEQC1E\nE2xbom42ybBJ7gIjLp5lSiLLRgp7BrGG7jwIZo4f4PsG4RzCYFgoDe0mqiB03ZRZQrlG7tKUS2ZL\nGjPGMPPlnBDoZ+iadmeygJ0ln9bjha59bd99nu3PMQjkYV0fgE4POxmClJjRFnKwPFsaCwklzFy0\nab2h3AVx0ttKid5cc8nQKHr/88/MnLbKM2cMswvW73lN8/nM95i0ifeFv8224Fw1mlCNinh/ttDD\nGkOSd7DaMFnbxGpD0s0ot8boqnZyJ2lC79Qx9zGWNdONbb/IGWFKTfeWHqtfcwfTjW3WPvEgKpWM\n1yZ88FnPRlcmBv298N6veEacHTgojnYCB8AeWtqwe1/ue4F3Wmsr4AtCiM8C3wT8zdU7w/0hyEmr\nSYUSgl7q2AzTrSm9W7qHskV9wpv+gNO/8GPuC9RrgrqpauqNC35quBu/yE7bPW30hqKJe+sPti6x\npYwTo6L20hAycXXuOT2bmRW70c7XNu1gRYLpLFAYQZ5772JrSb0GRQjuO5RAw7CY1Tt3Au36vtNK\naNFFKxeYk9wFw7afr0xc89nPMTiWEI3efts/oB34mxvd/7oClc70OsJ5tdlI4XNpzy6AX6Hv1h8I\nibaaNvIOxrjLU9/MDcfNcuxkFN3VYiKaEZjz9FhoJoIheg1QV25WIsncLmHqAvL4j984+xkAyeIi\nphij+n4XYQzVuTNAE+B1a/U/3xwOU+kydX0DrUtUmqKW0+heBk1yCNBFSTl0K3SVJqh+N+4Q0sVe\ntEzteE+Dyfkh/ZOrLDzlKeRnH2L7tCsaqK1yB/8/7WcMTvQwxjI+P6G73IkLs4sx8y4HR0ng6uCn\nhBAvAT4MvMpauwHcxmzAfxC3I7gucHI5Z9tPN4Ztqi4Pd5Oii5Kk6wxlVLfnArEyVFvjyOcONLx0\n0Hfb8ihH0BZK8yt96Zu/ZYFNUxcoggNgYA216Y7WRFZQZPlIhe6vUiHR1rJRGnIlyJUA7QbLrEox\nQiFl4oap6tJTRSUWOSNr3GbZtBGCq1st2/jaNsnc8JVu2EdWZSCb5rWwLsg6R7LWcedX/WKP+2Ly\nKZ0nbVjFh/tDk3ee+dPe1VjbsJ60/71E8xp//CRtPJsrT+VMnQroblTZJom25CDCueMTQV3FBBiT\nTls0L5QGfVkwWT3pm8cp1BXp8VtJ65J6a9OtxJWkGhWoNNlRejRae1FBiZAq3q+LkrTvGGf1ZDpD\nNVVZglA9VFnPSKKk/RxdVQhfJqq2xpTDEXVRUheOLGHLAtXtkfZzth8a0rulS77iNIsmayOmWyUq\nlfRucRpWVlvS3JW06osY2V8urteG9WHhWiSBe4BQqPzfgV8DXrrHY3eVWbz77rvj5TvvvJM777zz\n8M5uFygBS49fJDs/ibXLclQdqlIhuKlemSaoTifKBASqKEAyGERlSNHJUf1FNzUb1DGl9wfI8otO\nxwpdxlKHraZNAJHKSR+0yhDt6d9MOUZQZdxlqdJ4v7YWhEIE2mdrWCsqgM6XV4yO5SL3QQetodTt\nKsK5xGAtmp1G61hWJs66siU65x4/N6cAsywjU0OSx2AeylZUxpVYVObnGmqn3xNE9OYxLwLXKre1\nB+dsp9+UvoSEwtOLVbrnzERMBC1DeaDxFgi+EWURvaMFLvgHi1FbFtBfjL/j6OscjlU7zSqZJiR5\nh2Jj6D8+GW+PWkJK+hLQhCTvxCnjJM2bmYGWVpYpa6z/u5YkVFWB9uWj/slVp/i5sU05HFFuTShH\npTeRn1CeO0t2/ARpr4uu3OfbWR4wuP04/eE4GtoHWZZ8Jecj+gIfPnchPv4wsNs8zs2ER/zdWWvP\nhstCiN8B/oO/ehp4bOuhj2EP27V2Engk8NILn+Q/P/fbZpzDivWCsrp4XfJyYcqKuiiptl3ZQKZJ\n3Hqbyq2E26t/kaTxJ9A94xc9SRtj+aggWnpzlsZo3k4nrsZsFGR9x72fb/AClbYoKegmAiWFax0Y\n09SuQ5BtlU5CKQPAMqv/E1a3VrnhMffkEOxrZlpB7YapTKIctainzjQnrtg1wYRmptnbOna7wdyW\nuHYfeNukx4Co3evJBNImSCe3PwX9wH+Np6ce+3T0F/7LTIkrNomTDvIJ34D53N+54wSNpiRF5AtQ\nDGdNeQKEcP2DXVpi4XcdJoHNaAhS0X2Bk6ifvOctiN6if0+q6RPN+yRLBfWIan09BvX5gbEwexIu\nA3GCeOpLk53lBXc4L29ijaEaFbF0abWZmUIOO5W6mFJvDN1jSzcn4Ly6nVrv9oPnOHb8BEk/p7OY\nxZ23UJLOsjNgKrdGsdTUWezw3FN38FzuiK/325/6/I7P73JxVA46ZAghTllrv+yv/k9A+Da9B/gD\nIcSv48pATwb+7pE+v70w/PI2aZ4gvFdAG+859bQDsxEeev1dyMxx/cdn1wHIlxdIF3tx+KceT9BF\n6YbIkgzRU3EAR3a62KrEbLnVkZUKEWrRSeYGx8AFzfbuQCrXHwir8rDyBJAJJu0irCFVTj00kQJR\nTxHTogm0UpFIgaynTtbY1GBlLAlFHSLV+nMLiSIE2dZqfyahgLuucGWqqnAlqBD8/Uo7MHlM2kFU\n02Z6OTy//X4hTtMGZdQI0QTy5PanUD/0qeZ9+lJOdfYLpI99+uwvUAiCKqh8/NdhPv/hvX/Z4Xz8\nYFdMcjArQmctwct5xvxnOonsLluMkd0+2XNePPMed51eDRISVRVLhrau3N9UmsY6vkpnZwVCYmgP\nkbWTRTWaUG6NYkkoeGPPP84Y42WxXVIo1rZcwB9N3I5DCQwglXDGTMMxZrRFttgjyROEcseqi5Js\nwZWJKj9w5pIH1JPSH//wFmgHlJLejSH5q8D34IqPnwN+2Fq7eQinekW42hTReS3tnwfuFEJ8LW5R\ndj/wYwDW2vuEEO8C7gNq4Met3ct145HHxkaBEoJcSXq3dGN5KFDSDgMLjz0Rt9R1UTJZ26QaTWLt\nVaVpvGzKAlEWyG4fqzVmtIUZbTXBwWhEVSI63bg7ADDbGwRVSUcb9fXlJMUo33QNpQ3laItMNumk\nuUsIvvHpKJTTmCRUOQJdz5RqBDRsE+Okj2Ni8AY1O1bBYfdg7e4G9uBsMAGbthrHKmuOpetGax9m\nmuXtxCBMa4cRyktzonDJbV8FQPXw52ZW6tWZ+wFIb71j11OUT/iGmUQgn/hN2C/8F2zWvJ4V0u2+\nwCXgsCuyBrTfjenKm8l47SY/HR1MhMIuL2D6/rdFi1GR7Cz7UJVeO8oFy3rjAkk/j0G8HbR1VcUg\nm/bzGVHDNkzlBNtE4UqD9S4sJNNOYtpQDkdR9yo0e3WpvXyERGjDdGPIxqcfcBIpfpgsJLdyOKYe\nFehiitUWXWkqP3m8F530SiEPJgexG0PyfcDPWmuNEOJXgNcCrznIixwEV5sdtJuW9u9d5PG/BOyc\nb79O0M0USTehs9ihu5KTL3biqPt7v+IZAFdMS8sWe2SL/cgImm4MKbfGlMMxad81w0jdlttq41Qb\n5XqUgwhlnqgh3zZfMTJ6CkfRuHaNOcmilESETJoGpDVYrxMkdOmCcGAChTr8jF2iR7t2H26XrSA4\nswL3j2spghJWw+E40quJhuQgBDbru6RUjpzOUDVxwVS1dhe7NaOFxCbMnoeQ0RZzHunJJ1Kef3CH\nblD18OdITz4R9fhn7HiOfMKsZLzQJZZuay4jgbTVowjvq6U9ZI1xO7zx0P1+9ewKV/Rd2af80Lti\n0FcrJ2JDOMIH/jZsMYrNWpU1VGNdOLE5U9aoNOHEa9/MQ6+/iyTPotR0O3jHz8Kzh/YDXQZqdeIn\niPXsJLySFOsTVLpGuthHKEH/5CoyS/z5FZRb7vyrosaUOpaTivViRnLioDhIOWg3hqS19i9bV/8W\n+GdX/AKHgJu743GIGHQSFm4bkPZTBrf26Z9cphoVdB4cHopQlUpT3xBWmHHRMIG0RJd1vF6PmtfS\nskQWI4J/AODpiK0VoNGQ9HeRNdBggq+AC0yirpx4XNZ3q+y2ho1fcYty7Lj87QZnMJ0JgT4MRbkT\nitfDSndH8G/vAto5xCeZwAiywtFbDYLKWKy1uHmyjE6eIEv3GQTmzXwSiDr+rf5CZN6E17TuObut\n8LNbHkN54SH3WdTl3mqpe8EaMDVCNANo8+Y1cSclJWhX+jFj16jNnvNi6o/9OcbrBu0Gdfz2ZmjO\n9w1EmjmBwiBNXbneUL1xwb3Xhd7MMVzwT6PaKMBtr7uHL//yT6BSx75hFzvK9v9tyCyJjnjzqIsS\nXWqssd5/Q/jb3aq+9uXPbLHnXPb8AshqjS4qylHpS0EiBv4kTxidPbwd+lXuCfwI8M6r+QKXwlES\n2CdUpuitdhnctkC+ukS20EMoRZonjEpNMJm/UgSetejkyHSMLGuSPIuq9NYY6mIaJzNl0P7BMUEk\nuBW9b/QCs/z4uuVX68krIkmR/cWGEiokVnaxWS96CIt62tTofQLwJ9Sq488lAGuIo79AZACFBNCi\nZ1rfUxDWRoVSEXSI/E+AEcp5OxtDZcBaSzd1xymMoJt2XU+iTTedM3W3tsVQCp4G7QYyLtjvhM5+\nFAAAIABJREFUCZXFJLJb0/ZikE/6Fvj8hzEZDYuqlZiETxLhc0EmrpxXzi4yktWTjcDfPPsrBH8/\nUyCkvz9J3dwHwHRCfe40QkmWXvJ6tt72c+6pXgQOYOWuX9lx/qde+1Yeev1dM8Yz4fFtb4v2FPtM\nUihrjL9fZQm6bJRLhRToymB9Lf9r//gvAOe5kS0aVN5xvbGyYTGl/ZLp1tSp+noVUuftLS+q6XW5\nuFrsICHEvwZKa+0fXJUX2CeOksARjnCDwHz6YBaMR7gyXGwn8MH77ueDn/jC5R9TiB8Cng/84ys9\nr8PCURLYBW9edM3AV2x9CoD/a/UpnFzOSboJ+eoSaS/3fsAVncWMW59+nHP3nT/Qa1ajAj2dkvQX\nUf1B4xuQJtTjIro4gdsVGIguY21OOHnPNQzrXUoVxpUGkMppzyjlewklorvgpoOTTsPwAYSuYzM3\nroBbkghCl76806ywZ7WC/C5AtVhAvg5vhaA2FoxbqSu8SlBrhR29ioVEmQpkisaiBOSpQlpXikqV\nQEwncW7BtnoaM/Ppfjq56RMwy1q6BMJxrTd4v2yYGlFNmwGYVj/Chp5Ee4o766KWGpqmXFhuGuah\nAT9Hp0WCSDrNcbRvLAeLybKIf0vFn91DsrgYh8kWf/gXuRhue909e973wOteivJ9g3j+vqGrsoRS\nz5ZoQj9AKomu3G766e/6i9mPq3S2qSrvoKs6lkNl5r6L060JxXqBLt2wmC41ujKk/UO0l7wIO+jb\nnvZEvu1pT4zXf/n/uffSxxPiecBPA8+11h6O6cEBcJQE9oms77XQi2Yism3aLTOFShXv/5pv5Dv+\n4e8v+/imdH/gqjtyg2CBPjgcR8aQVAoDUZJXKOlMY0IPwFv/Sakcf3xzzR/clQFkp+sShm8S26qC\naeFKBYBJe63g3pQlhKmh1q4E4Ru2cegqlJJajlrtBODOq9WU9TRUKwTaNM27EKfF/LCXbOr0WIMy\nFZ0kddYHetoMs3m2ErQCdYuiOo/oSdxmB+3RFA6YFAUi6VBbSG152eUgcCUh89m/cUE/6zalqNhA\n95e9hLUFREKjPdTuX6jEJWnDzucrNSMSCMThMrmwgsj7cQFwEDz0+rtmrs/oE1WQ9HOMNo7d48s4\ntWcRhdKmVQaZKb76d9694/jPfPf7+OSPvpCOlFRbY6rxJLKUsoU+i487gTVnGJ+fUG67aWOrLfIA\nWl7zEPLKj7UHQ/K1QAb8pXDfkb+21v74IZzqFeEoCeyCsAMI+KG1T/DnT3wmozNjylGFSlU0yxZq\nk3P3nUccsCdw+8//lmu85RlZfwGSPlIqlNdeB78t9WP7bVngKE/QyR0zKMvdANm0iCu8YKvohshy\nlzzC0FmaYZNOI17m5R4CN15MtxFmGgetoiBcm00zowU0y8KZuex7ACEBCJw3gbAWsLP+AfH5ZobF\nI4xGerZS0PoBmsnbgLDCV01gDTubuPr3tffoiHYJTI2bIBf1FJvmZMsnLvmcecgnfQv6/o968buk\nRZ1VsWcRP1fv+AZ+x9LeKbR7MoHWm3ac5hI+oYakmlQIcNalfsLc2VQazHgLfW7XuczLglMazaKg\nHBAlT2Q/J+k2q/OQCKSSoCRPvueP9jyuKXVsKgedIVsYdFnTP7VK78Qik/WCuqjRpWcvHfD7OIMD\nJIHLZUheCxwlgX2intRsPbiFzBRZP2VwaoHu8ZWoZzLdms4M9FwJTr32rZx/y6tIehvIpVWQCtUf\nkM6xMAy0BL5U3OoH6idlgch7yIVl9Po5t2vw08MiyAeEhqJSiHyASfOWfEOLqhgGs/ScrWI7UMFs\nQAr0TpgNyntAGxsTgRUCwewAVcOr941jtz0AL3PtKKGt7f98kLTGNbp1BRQ7EpVVjXZPp7+w6zlu\njiZYa9EWOomYlae4EgjpEtI8U8qNSzVJT7buCyWj1hCdTX1yC593krsdhjHg3chsxxvE6wpCkgi7\nOFOjNlP0+jmuBJG+3H5rSlJuudLP4375bTzwupfG3QC4Uk6CSwTz+kS7IdCw017OdH3Wya/cGjn7\nVekGzKQSaHPIul5HUtJHgEY0Tnoq2mRtRLbYJ+3nLJwaxFV5Z7FzoNepRgXV9ois20fkfUSSknqK\nnzWapNel3PTaLkrG4C/S1Ad34zTq8ayhhWV33l5PSCSp6xl46qDIB+jeymyZocVUEdCoas4Hvlb5\noaFdNvX2+SAeSkEAUghMsFPEJQIpRCOY5p839SwgJV3PoDKWVAoUuJV+m3I6z1AKrx0gZEwWbVnr\nzmDpor+TTT9Jqy10lHCzCElOZ/HYRZ93KZjOYMfn7pJD1dhntur+cUcQJD98GTDSVcMOqmr0noKl\npwlDaS2ZDlGOEeN1pzw6zzLaB8696ZXRXawtKaGyNJYwz7zhp0j6ebSSTPp5FEPUVY1Qijve+PsX\nfZ1nvOu9fP6V/xK5PEBlCXVRxsSjizIOpwWGXpInh5oEblTv4P3iKAnsE+W4orPgvnQyU+jSUKxt\nkuQZg1MLZIOU4UNOW//eb3jWDOf5OX+9f1bHqde+lTNv+ClUnpEOlkE2YnDCuMngtuFHrPP2FhEd\n7yPsJ4FlfyGWfqJapUyACtntQ9JB91cx3aWGnqhrJ8EQAsV0hKiLuPqcETrbbQirXSeXrSQQIKRT\nQ/CranDlFSlEE8OlwlhFZSzT2u18csAIlyystXSlcivaEBDndhw77CjDxK1MXPMbyLtzA3K7ICSA\nSW1IpXDy2Srdc8dwWWh9di7oewE96RcSxsSSlpmbnm73YahLl7CNE7pzXsQpJhu4XUGQ/9CV+916\nKW45Xsc8fD/1udNRgnq/WPvNnwaaKfbQowKXCLKFPuXQexG3aKSmqun0Bxgl9/Q32A3VqGAhz8gW\n+qi8g9XaTQ3770E6yEj7KXVRe+P6Q1y9J4fXZL4ecXPvcw4RL/jyf6Oe1NFJDPxAS1EilaS7OmDl\nCcu7msvsZpd3MWjfJLZl4YzDk9SxefJ+HASLXzi/ipOLx6C7CP0V1LGTbpI06SDygQv44Fyohhfc\ntGf/GHpwCzZfcIG0KhpZ5rps6u/VeFaHJyAE2HZzOE74trTw555nWzsAAOkTQLzfQqktRW2Y1CYm\nCm2JfQQlHePIqhQTfoSK/YbIPpobTLMqpeN3RvvBOV/SGE4109qSSEFBcigJQH3F1yIqz5aJU9KJ\nG9TrLPj/B5h8iWlniTLpolM/v+F3BGK6jZhsIsoRshjCaANG69it8zDaiL8Tq5wqqyy2kON15Hid\nZPOhVgKYgJQ7jGj2wsZv/yvACccl/dx5A+RZFDyUWULSz0l67uf2n/+tGTHEwHoLDLj9QBcVMk3I\nVxfpLA9iOdRqi0wT0jyhd0uPrJ/FxddhQXhXvv383Ig42glcBr77gf8vXv7gs54N9NClphxV5Mtd\n0n7O4mMgX8mptstYywT42//h20nyhGyQxj/Sp73jT3d9ndted4/fRj9MYjRyYQU5WHaTokaj6pJq\nOHYWlJOx01arpohMYseb2P4KdtB3pRbtSzqAXt/A1iVy4RimswBp7ko/xRBZjhopZV93Frpu3LIC\nWk3IsNpuD0/F5nEI/u2at7UIo1FSURuLFA1Lx1gL7h/WWmpjo+yQEq2+gX8vVgi0ldhWgxkhMP66\nKzNJpBDIVjLaHruV/aC39y5gbTim1C7obxSaUlsGmZzxgz8MyHKC8cNnQle+t+En+azFqhQrBKl1\nDB9RTmLSlcUQUU4cRXcyRE9GDS3YaGRduc+6O8L4cp+oJk5cry6oz51Grz0MQO+fvfqyz717/FhU\ntN2hUuoRBOXOv+VVdFYG3PLyX+Oh198Vm7uXA6MN043tKHMBbohL9T0rzRiSbkJapVTb1aFKSR+k\nMXwj4CgJXCFCiedD3/ocP7JuyFe6XgPIPaYaFZhSs312dNlaJrf+zG9w7k2vpCdlnAEQUiHyvtOG\nSTaotryvbFk4OqiUzogjySDrYtOu0wjSpTN7gWYnoStnyuJfz7b1+JXzFpbFZsM8mguAITnMUCzD\nA4PLGBA0hiL11BosKr6ukiJuHCwu2Bvrbje4ROAMvASB8GGB2j9Oh2QiBNonj3iO/tFKukSw0OvG\nJLAb1obj2HMAGJYabWCQSZY6jp7b6+Z7Pv9yIZ/wDfD5DzfMoGqMTXuxrxK9E4zzaRbTUTT7sdXU\nWYf6wJ9+4wviccsP/qGTmhgPEdkGcmELkQ/cMSdD6s01zOYaQkry59+167ntheHb73ar+U6O6uSx\nVBKF7KREGIXe3nYzA3Nkidtedw+nf+HH3EPThNt//rf29bpPe8ef8oWf/UFHjKgqZJqSSmeAE1RD\nrTaoVMGA64YddCPgKAkcEM/+qw/yoW99DuWoIskTEl+3dFvelOnGEJUqZC4YnFok6Tcr0Pte8k8A\neOrb/8Oux65GBZNz6/SkG+qS3b4r7fjyTgroiZPbFUnqvvhSYUZD1Mo68pbHYDt9VxPuLiEXxi6o\nt9UyTe0GucKLhpW79r4DAUH6GTx1NJkN/B6NlzBOCVMlLb0ePyNwkXzoWELNdYlFCJcAQnxXuMsh\n4GsLwvcYSh1KRq7WKYRAWncMYCZJ7IWido/pJpJMCbqJe3+HmQACgsic+ezfICqgnDQe0C0NJjHd\ndobyOEqveuqdex6zLSs9vfcdmMkoigdarzF1ucEfYPTO1wOQLC65hYlUbvhsMnLBv+N7NF6hFFwp\nRXnZ6vNveRW3vPzX9h3452HKKpaBtJ6iyxpdVBhtsMbGZnCaJ8hDLAndqGWe/eIoCRwCnv1XHwTg\nYy/8TvIVd1tdlJRbI+pJFXVMXGOsorO8QLm1twhYQJjOPP+WV9EDhDwZh70C68eUBXq03QRsqRyT\naLyF3FxDrZ5yU6Zp1yeILVRvKa5uIrMkyi47yqnQZRO82wiJI1zeDdYEaj9WtxzFxE6Td71HULbW\nTQWr1opOW5dbjHX3h/9DgNfWeiaR211UFpS0ZELG3sLFckBlLKV2O4GOf91jc+JqVwvySd8C+GRg\najdU9ukPxfKQNWaHI9h+0Lnz+w/1PJPlY80cSllghuvOXnJlpdEtmjomULHWSOQflD4NjgkkPTvI\nzllIPv2df7b7k8QhJIOjncAR9otnvvt9fPxF30WxPooqiNA0ccutCZ0VJxed9rvREelS0EVJuTmk\n0+0j/C7ATX4uI4feHyAoRwZIhSnG2DNfQo63nLF4cBULuwBdxoCPCUNg08g7n2EChdLPPE10R+N3\nlinkDNLrZiCq/dhwiNZtocRj/WXHHJoN3m2bCQNIazE01gVC4MtIllRK2ovCiwX1k0t9Ht4cXfJx\nVxMhGQDIr3z2NTmHvRCnzUMCGA2px35WY+p2BLpodgEqS1l+mVOGP/+WVx349UMfIfQE9gz8h42j\nJHCEq41P/ugLdx2ZP8IRriUm734Torfo2GTj4aWfcJMiuLDdrDhKAoeMZ7zrvXve97lXvJhqVCBT\nN08gWp62n/3JF8UdwxPf/Iczz7v1Z36Ds298BSq/0Ii/pRkkKXJptfEV9vaBwIwmjJ2MsFWF2d5o\n6pvWOKZIoCnGJmQdKZbO4NzMyhXMzwS0ee5BD8eaqNcjwuVg2xjKQh7zK3w9d720DY9ZSYH1/2tj\nvaw0aJrdgRCitSNwbKJKW0pjGW2OOLnU5/S6W+3fvtLf8Ts6ubTztpsd0/e/Lf4d5t/50j0fJ9Is\nmtW4ZmwRh7bq8WSmDzCPtjfBleKrf+fdfPJHX3jg41w2jnYCRzgMfPJHX0jazxncftytrCrvF1CU\n6GI689j7X/0DO6YoT7z6zZx70yvJATUZuUGw3iJq5bibEh6uQ2cRmeVOOkIpZx5SjGbtCZMMOxlC\nfzXSOsGVbIBZVUuZIKhnboto69X4BGB6K1TKDTol+CnX4EngXsRRHaVCeQXREPjnS0JteCEFtGnq\n/6W21NqVgaBp+AYZeeXZRPtpBD+aMb33HTPXiz+7Z6Z+L7t95GAZubDi7UsDW8zx4kNpRuWZEzZU\nckZY8bBxLXbMBxGQuxFwlAQeQXSWF+ifXHUOYcUUU9Z0vfFGNSqcZ6qve5570yvjVPCtP/MbABx/\n5Zs4+8ZXkPYLksmYZNkxhmztnKLs+lnkyokZi0ZbFm6gzE8QW2PQ6+eQK7dhuktOkrouZlf1YV7A\nX3YDR55aOhf43Y0Sq1Iq1WFtotHW0k2CuJykm2bktoxDZMJavAMxLSVp2qw+a5sAb62bFgZX/9eu\n79waJHO3KdkkjPl+YNvbZLcdwKMasqHszqTMQCdOU1cSkRIz3MBsrTmzeqlQHa9XhWMNIRVyWqAn\nboe5/Y5fZPD9P/fIvZergSN20BEOC9WoYPv0OTrLA3qnVgGYrg8xZe18Vn3Q752YnWpd+82fdrrr\nfhpTVxX12pSsqh1VD1zAL0aO/+237CLNUKunqM8+GB8jADNcJ5lsuiTQXUJOFaIczerwtCeCwyCY\nTOIwWFunx6oU019lszBsl4aiNowSJ7PgOP+alU7CIFNIgkhco/AcdgN2FyKHDUnCQjcRcSYALMZa\npjqwhCBDILBIKai1YwkFXG+Bvzr3JQDS44+7puexG3to8idvReY9RNdNqEdze7+jDCXH4G/syobN\noJhIU5J0CT1yfYTh2+9m4SV3X+23ctVws+8Ebu4Udx3hq3/n3dSjCdONIXVRRv+AYm2L7dPnKLdG\n3PHG3ydfXXSewkrSO7lK7+TqrsfrLDvpgvL0F6NvgBws7zQYT1JnUdjpkj/vZeTPv4v8+XdRn/kS\nanjO8fqTzKlPqhSbdRs5Ym8JOZMQ5iEENuuxURo2pxohYLGjyJQgUy5obxWaM+Oa85OaolX0b7N2\njOf4a+tW+POvpCSkSsTAXmrLdmXYKGq2pi55GlztP/5o93Nqee8EcP/5IV9c2+aLa9s77nvwwnb8\neTSh+z0/Ee0pwwCiGQ/dLqDVIBZSRrZQrJt7LSukQua9WC4KFpY3JPz72dfPLhBCLAsh/kgI8Qkh\nxH1CiG/Z9YHXCEc7gUcQT77nj/jcK16M1YbRw2vUo4LKC5Q94U2zNqOh3hood/nqEqrraIumLGLd\n1RpDvb1NQttk3Jd1qgqqctdGXfbsF1F/5E+RaY7pLrmhsqD+qTJs2ovG8v6FdlWaNPki2yJnbeTO\nZ5BJuokgTO1n0gX9C5OKsrZoo+inlkwJEh/QDU3tPgyCYV3gVwIS6RKKsbDU77I5muAKTQIlBHkq\nSZS7HqaMwfmqAHzm7JAgUfOEW65M9yc0k+FwdhXXegdwSQRTIunMjYIjWSQeADaI1c3BVqXTvFIK\n1e1Rb9/gSfTg5aA3A39mrf3nQogEuK62pY/aJHDP8lcDcNfGJx/R1w2BHYgqi202UOLt+XRRoouS\nE69+c7xv+Pa7Uf0BycrxuPIoT38RcFtwW4zAS0WH8hBA9wUv3/VcTDFCTUfQX3XKmrpyNf/QG0g6\nmNT7EJgaUQxdk9e6yrtNOxSqy/q4xmDJlSSRbrWuZBgEEwyQGJtSG8v5cc04VSx1JAPfxQ2qok5e\numH65ErOlHQWvN7PUr9LqccMgrY/uF0HOAN6XCKZ1CYmmoDPn3cr2ZAM7tglKXzm7BBtXaIKpjft\nNd4X17Z5/Opg18+0jWIyibunvHddfe8vifz5d1H8+W/7MlAZ5SmEUnRf+EoAxv/+Dc6cCOIgm9Ua\ngr1pkvoZlhu74HAlQ3rxuUIsAc+x1v4ggLW2BjYv/qxHFo/aJHAtEWr/u63Qj7/yTXs+b+Eld7P1\ntp8jwbE2RN4nPX6rO07bK6AYNXXbTpfJn7yV7vf8xI7jZc9+EfqTH3TmLN5YxUrlorJUlCKJZRol\nUrqDW1CTDYSXNrBZ39/nAnamnH4PuFW+wJV2MgUreZCGlpTaMqoNQghS6SZ1XeB25SAlmtp/aBov\n9WcF344v9nh4c0RiREwajzk24Itr22hrmdaWYVmzkCUUtaGbylieauOz51xSeNLxncnAvTfXf5AS\n5uWf5ncHF4bjmLTmz3e6vekE9Lz2z5U4kj3SyJ/3soYt5Bcd7b+jturo5N1vasohSYopxgijvcuX\nvLGbwwfrCdwBnBNCvA14BvAR4BXW2v1Nij4CeNQmgUd6BxBgtaEeTTBeg32/GL79bufJmiXR5MIW\noxj48at/vMgcxWhff7x2OkGN1jC68v0AJy+hZYoxlkltKLXFeq2eXmeJNO1iVcbUQG0MUggy5Uo3\nBhfUoZn2rYxr2KZSkHeEq9Ub6ymirjGV+eApWrSeUrvV+F64FKdfCVceUhJGpUanMuoAXQwGu4Nd\nZPBKp3vg9PqIbjL7pMo6JdPUHMzH92qh/sifuh2jnylpi9AF7FdjyFYVUMVjXY2g/6mX/VMAvuq3\n/+9DP/bFcLHv6b1/+1H+09997GJPT4CvA37SWvv3Qoh/A7wGuG6y4qM2CVwrPPmeP+L+V/8AVmtk\ndvmTiKaskVmFraod+u/jP35jIx/hdwaXSgTJM74Tfd+9SGuxpnbWlVnfSy94gTZjqY2jYk5qixAK\n5Ye/amOpvJInOG5+0N9pU/SVENRe56eXCjLP+Q9BftryDgBHPrLWXlRsbje0yzSfOTuMwXyz0GxM\natK++8w/e27Ik44v7LoD+KoTi3zq7BYSZ3SjretVKCl43LHm+O0BNYBxZaiMJVOSUo9RwjWzTdJp\n3keSXxUhumuN/XoR3JC4yHfozmd9I3c+6xvj9V/8zbfNP+RB4EFr7d/763+ESwLXDY6SwDXC5VrW\n7YdidyW68ADqqXei77vXnZPuetnqxE3m1q5UUxnLqDJUPlJ3EolquYFJ4co+IbCH4B4UOaXwvgC+\n9h+avalxBjZZRzGuTOwJhGEvgOXBlen4PPlEE+A/eWYLY90KX+1DVCwkAqy7vBse4xPCXuyh0jgK\nqxIugXQusqu5Fki+/rupP/KnCKlIvv67r/XpXL/Yh0/2XrDWPiyEeEAI8ZXW2k8D3wH8w6Gd2yHg\nxu7Y3CQI+upHOMIRrkO0PTMu9bM7fgp4hxDi48B/B/zSI3bu+4Cwl7vfvsYQQtgb7Zzn8cDrnD6L\nUI0lncwSTr32rdfytDCf/zC6v+p6A1mfLa3YLjVTb/d4ZrskVYJe6mQfUiVIpaNnhqEua+F4T0VW\nTlgJx9fAD3Z55k2eSJRxzBOj0liCCr/hhYs4gF0uPnPWNYHbO4TDxP3nh0xqVwrLlOt9COF8CfJE\nMJCuWX8o/sRH2BeEEFi72xjivp9v689/ZN+PT57w9Qd6vWuBo3LQNcBjX/+7AJx5w08hpLxsq72r\nhnrqTeadiXsqFakUlMYlAYClTsqg0xjDCOH4+RNtWJ9UdHzjVQjH8+8mEqGcqFsoCeGloR0DqHn5\nbn51a+VXK/gHpFJA4noIhTZMaxsZU5mSlCSHmtSO8AjhaGL4CFcLQRNIpQkqvQ7ysVRYlWHyRSrV\nQVvXC5hUhlRKHrOYs9JVZH6l21ECiWBrqjm9VbA5rdksaipjGJWas9sV26VhUpnYQBa+HxD8hYXR\nWCFvitXxY44NyJRjIAXKrLEuOaQC0htqfXiECCn3/3MD4jqIPI9uBHXQaw3z2b9x0hFJB53kjEvN\npLaxFKQCq0dJptpEwxdtLZUxLOXuT6mXKk4OUqa1Zbs00a+3KY00UtC7OQBujyeRJTTPtb8R0J4m\nfnhzRO1N6it7uKWtIzxy2OGud5PhKAlcR1i/5zWs3PUr1+z1beaClKoLEpkBNnL+pRCe22+aOQA/\n6JUnkkHmSkeJt3bsJAIlZOwBQEP3DM5hQQhuL5zbGkca5okbUOe/m8jgDsngkBLAeFLEEtrVLp8d\nweMmTwI397u7QSDThCR3Ovzr91wjCnHwD/Am9KlyFM1UukZwqgTT2jKqNKU2KOmCe+kpoM6Q3XkE\nKE/D7CTS+QDYsAsQUTG0MtbNHyCpkYwnBeNJgRSCpX6XSX1wT9prjaV+l0Gve2gJ4KCoT3+C+vQn\nrvVp3Hg4ODvousaNedY3GVZ/8lev9SnEP2IrnATFpDKUxvpgLljInNZPKt2PNjAqDetFFaUS1qea\n8+N6RqIhU07kLZWCxNOEauNE3qZeCXQe57bcRH1bVTR4/z7a0evmzlXtMs1yyvWH42V9/0fR93/0\nsE/t5sVNngSOykHXCa61yJb1HgHCGozKmEy1K/UoSebPrTIWJS2j0lAZw7jSsa4fGsBLufMs0Mbp\n/BuYEXFrh/ygFlobGyeEQ2zbLk2cPF7o3JhfrquFy9lZlOcfjOYwevEUsthClJOrdWo3JaLHxk2K\no2/XdYLll/2SmxtQkuHb737EX194qWib9RjVzrAlTNZWntkTVvDhvuP9jFv7HS8rYTnWVRzvJWhP\n/5TC/YEpCanEyz97Wmlr0lgIwbQ2zG8KHMVUxOcd4cphOguUqkM1OIFevg29fBvT4ca1Pq0bA0Ls\n/+cGxNF36zrC0ktfH427t9/xi4/Y65pPfyj6/+rOgKJ20g5O/6cRkTNe43+ho1jOE1LZcOCP5Ypj\nXXfuoVQRZJhTLy8thPsJf3RS4GimvmyUtUxjlHCickoKtL0xG8PXBaT3h/Bc9wuTmoftwJkG4QYE\nj3AJ3OTloBvzrG9iLLzk7mtWGrIqjWqh2tpYkx+kim4iY6DupZKer/t3E8lSR0VvgNrvCoKuf3tt\n1KaEhoVT6vsEy4MeS/0uS/2uTwqS3A+eXcwZ7Aj7gDVI7QxgnJy3xSYd0nOfvdZndkPA+l7Zfn5u\nRNyYZ/0oweidr7/Wp3CERwHMZ//mWp/C9Y2bfFjsxjzrmxxCNppC43/3y1f99Wxn4PSCkoxJbdDW\n1/69B0A/dT8S1+QN2jjLHcXJQcJiRwaNlqiYGVb92rryUGXsjLS0BHLlhsbaQ1RnN0do4zwAhDja\nBRwU2bHbopGNttBNJce6CqxB91bAXCeSJdczruNykBBCCSEONG16JCB3nWIm+Pt67tXDYpWKAAAg\nAElEQVTSbK8f/AdMbwXbWWC9lmyXxuv8uFmBbipRAia19TIIPhl4Ebjw22hbQ0LjxBV6BG2ryCAe\nB8zo65/dHFHopildakMYGbja2j+HjY88sEFlnOQGwNc/dvmSz7n//NDLaLv3fxh+xuWFhzD5kpvJ\nME6mOzMlcrKJGp4FQD7hGw78OtcjDkNAbrp+Zt+P76zc+ogLyAkh/t5a+42XfuQez7/RAuqjJQm0\nMf73b3AXfDC5Ut+AvaC/+HH0wnFQGdO0z4WJdvV62fj3KuH9e63F0FhABoQdALhk0Oaxt39bytf7\npXWKmvPeuw9vjlq2jjdXEhhX2imwSv8ZCE+7bZnrhM+x7XmQ+gXmlTbHq7NfwHYGmLRL5Tf/nWId\nNTznXvPxz7jSt3jd4zCSQLG5tu/H50ur1yIJvAlIgX8HxIEaa+2+hkFubgLszYK5WuPk3c6HOBh+\nHxhGIyebIBPSpS6JZ/2ERBB4/lJYEIIE4tSvtb7JK500aHDZaieIkA9S5VagaOgMlnY9lZNLfb68\n4f6Ow9TwjRb8AyrjlFXByW700kurUY4rSycBbZ3uUqdFEji9PiKVUGjLcGowWG7tJfH3c2xhd/Md\nKxPQpbfxTBB1gZyOEPX04G/y0YADlnmEEM8D/g3OoO53rLX/52GcVgvPxK215imF376fJx/tBG4Q\njP/4jfFydCVLsh0G8sX7nEx1MJ9vy+CG52XPefHMc/QnP+ger1L04knGveMMp26lHhy+Qs0/lIEq\n3TiOhZV7noi4ktWthGC9rWQqBapy08CdxWOXfM+fP+/0/59wy42VBN7/mXN0lERJOD+uOD8uMcbS\nSxWLeYqxltsXcvqZ5MKkYqHj1mLnRiXrk8rRc5VjYC11Uo73k2jXebEkYIHVXRLBdOsCAKIcI3SF\nqMaIukL4fsDNWgqCQ9oJXMY8Rb6wPPN6QggFfArnKHYa+Hvg+6y1141+xyWTgBDi5cDvW2vXL/vg\nQvwe8N3AWWvt0/1tx3DblscDXwBeZK3d8Pe9FvgRQAMvt9a+b5djPiqTQMDk3W+aCe4iSZ0dZJYj\nOl3MxK2iL5YEkJLs2S+Kt+v77m0ekw+ol29jmq+wNXVNYiUESjY9gjDgJQVU2jKuDEI47aCQNOZ/\nQ2FmQE2dFWO2dMuhfzbXGv/pc+fZnNb0UkVHSc6PS7amNRtFRel3NVki0cbSzxJu6aWsdNO4Qzi9\nVfDJs9usbZesDjJOLeWs5Cl3rHRJpcRgYxkuqLIudRSltqzkvm+Uyh3CcuX6w1iVuWnheoqoCuDm\nLgMFHEoS2N7a9+PzweJ8EngW8PPW2uf5668BsNYeWClSCPED1trfF0K8itmvnHAvYX99P8fZTzno\nVuDvhRAfBX4P+IvLiMJvA34DeHvrttcAf2mtfYMQ4mf99dcIIZ4K/AvgqcDtwPu9L+eNryR2iOi+\n8JVM3vMWAESWg9Ez96uV44hgNJ+mWK2hdiUJazS2dAGg+tt3A5B+8wtRT71z9hj3f5S8mpIunmRY\nOd6/taBbzH/ljWEsRE0gbQFjUS0rMYErhQgB4lGSvM9uT0mUZKuoOD8q0cZS1iYmgo1JhTaWEwud\nmBS0sYxLzblhwdp2yWOO9RjkCb1UsVm4xHJ+7HYKX7HS5XGLHRIl+MLGlLOjkqce73P7Qrrr+WQr\nJ6nO3O9W/kdsoMvGAfn/twMPtK4/CHzzgU6oQdj2LbBLEtjvQS6ZBKy1/1oI8b8B3wn8EPCbQoh3\nAb9rrf3cJZ77QSHEV8zd/ALguf7yvwXuxSWC7wXeaa2tgC8IIT4LfBNwRGKeQ/cFL6f4s3uQ/QWE\n14URSYpcWsV2+q4GHIZXrEFUBWK6jZmMXOKoK6xPDNXfvpv0m184c3x1x9ehAHH6E/SP3cF6EZRD\nBamEsM6xNA3g8DUJf3nt4A8+Adzk+XxYar48nLI+LskSyea4YlJqvrw5oawNWSJZ6mZsFxUb44qz\nWwVr6xOKcYWpDWknodNNMMbyReMmsTcXK85sT+mnijPDKVkiSZSMieHCpOL+9THaWLrJIgC31Js7\nei7prXdci4/k5sBFksAHPvABPvCBD1zs2Vdt5WOt/S3//90HOc6+GsPWWiOEeBg4gyvVrAB/JIR4\nv7X2py/zNW+11gbO1RncTgPgNmYD/oO4LHqEXZA//y6m738b8vgystsHlbrArzJs2vDubdbDqhQ5\nXkcl56GeugRQldjC1ed3SwQAcjpC6SmZStHW+QQUtUEKQSK9T7Bwrlmh+StwO4JUtlb/PvgLXd3U\nK9H1ScX6uGRzXMXbxqVmY1yxPa44dazLpNKsbZdMippqqjn/0BbFqMDUJWneY+FYlyRVFOOKj49K\nVle6rA46jtapJF9z+yKTSvPg5oTVXkY3VWyOKz54YY3HLXV52sqNqV9zPcNeRBPoOc99Ls957nPj\n9f/jl3Z4yJ8GHtu6/lhcbDs0CCG6wEtxVZQuPvFYa39kP8+/ZBIQQrwCeAmwBvwO8GprbSWEkMBn\ngMtNAhHWWiuEuFim3PW+u+++O16+8847ufPOO6/0FG5odL7jhyk/9C6EUlg9QqQZZF1X952OELrE\n5AuYwXE3ByAT1GgN6nXXH0jSuCOoP/KnJF//3YBrFNPpI0yNnGwyWDgBwLR2jmLhOxEsIsMvKVSB\nVFtLyxpkNZkJ/tnKyav8yVwbjCvN5rji5HIeaZ4PbxZcGE1jyefBC2O2t6aU05pqWjMZjtBTp+pZ\nas0kVSSpoq4024mknNRsLpZ084THHOuxPa3ZntZkiSSVgq2i4otrY85uFXzq/DZPXHEN9861/CCu\nIe69917uvffeQz3m5cp2z+HDwJN9ReQhXMn7+w5+VjP4feATwPOAXwD+F399X9jPTuAY8E+ttV9s\n3+h3B//kMk404IwQ4qS19mEhxCngrL99PmM+xt+2A+0k8GhHaPBO3/821OopRDWNPQAAsXmGZLKJ\nXrgVm3Ux3SWEShB1hcwLzGhr5vFt2LSLqCaoyQbdzhIgUX7yt5s4lpCxDZUzNC0TgetVhG20NSAk\n2fKJq/lRXHP0UoU2lmPdjM1pxWfObPPw5oQNvzM4u1Ew3ppSjCvqUrtEMNp0vRqj0XVJXWyjvMOb\nTDOqac14uyTvp0yKmrNbrqfzmJUenBzw8EbBF89uY63l4e0pm1P3u1geXJvP4FpjflH4C7/wCwc+\n5kFSgLW2FkL8JPAXOIro7x4WM0gIkVhra+BJ1tp/LoT4XmvtvxVC/AHwV/s9ziU7Htban59PAK37\n7tv/KUe8B/hBf/kHgXe3bn+xECITQtwBPBn4uys4/hGuEPXH30f98R2ErCPcgGibyBzhYDB2/z+7\nwVr7XmvtV1lrn2StPUwdmBAfS///phDi6cAycHy/B7mqw2JCiHfimsC3CCEeAH4O+BXgXUKIl+Ip\nouASim843wfUwI8/qrmgl4lYGioLRN5DHDuF7p1AZkOsMY4bXo1BZZjBcTA1cjqC7hLC1Ihygtl2\nfGiRpOjuErbTd43luiRVE2Tao6gN2rOCSu1KHNabzqfKDZaJeoqVCbUFiyS7yU05Aox1DJ9prSlr\nw4VRydp2STGpMMZSjCvGm6EUVKKnE6rCUWZNVVIXIyq5SdIdkGRdUrlEsblOOc4o+gPKSc26X7Y9\n3NvmE6c343F7g4wskVTG8rjkyIXtMHEdh6FQdP1tT71/HfD/AgNcrN0Xruq301q7V+3rO/Z4/C8B\nOzorR9gfTBhqkcr9Yk8O0IunwAd5NVoDMwQvKIbKMN0lkIlLCp0+YvsCtq5QozW0kNF8XpRjZNIh\nVRKr3YBY6cWBlIAkkVHqwCQdhHGE0kpbshtUYvdy0U0Vq4OMyliO9zs8+dYBZa15aFpTbJdMhiV1\npakrTTXapBxvUo02kZ7hZT3dV0iFTDJMXWKNxtTu92W0K/XoukYlCRudJDKKFk70WcgSN6n9KPm8\nHykcrCVwVXFcCPG/4pLBD/vb3ur/37fGyKNjifYoQf68l8XL03vfQTpYxpbbYJxrGKYGmSC2zrnr\nUpHkA0ynj83cDwOQ4w3QFWrzIUz/mEsSQjp2j+rMeASEBnDwIEiEYxFZoShr45rE+uZlBLWx1El4\n7EqXQhs2p857eZCnKCWxxmJ9NKkn2zEBmLqKSUAmKSrrIqTC1KWb98AlhXqyTT3ZjgnBPT6ju3KC\nvL/AqeUunUSyPtEs+YbAwC8KOguXFq47wt64fnMACjcjcCAcJYGbGPr8Q9i6dElAStTSKqK74KaI\njUZvriGGG4i8hxosY5MOViWOYjpxU5JydAFUiumtIExNkuZ+gtgCxrFeLAhLdB8D4i6hm0ioXBIo\nLzxEduy2a/FRPKIoa9M4pPn/hRQIKSinNdPtC5TDdcrRJll/iay/hDE6soRMXWIgJgFTuR2BLouZ\n5CCTFJlk1Ks9vrwx4aNf2uDM9pTtcpEnHesy8N/ucuPsTd+Uv5q4jncCD1trD9z5PkoCNyk6d37/\nzPXyQ+/CVhVm0jQMQzDBGMyGU5SUK7diOn1IHckwmGzHwTO/g+gIg1aCcesbUmrnGyBxhvJhVgAh\nb9r5gD/5xBnGlUYJGGQJo0qT+dLYw+OCC9uuB2CNRUgRSz7WaIRU9I8/lrS/RDm84Mo/WsegH34/\nIfiXnkkkpEJIRdodUGydY+OcGwxbW5+wutLl9IUJX3PbIt/y2CXuyIpr9tncLNDXb0/gUHCUBB4l\nyJ79ohmpiDbqj/15ozHkg7XtLGCFdDMHnuIpqgIhE0g6IJxAWidp6s+1sdR+B5D4JrHUFWI6wion\naVBunr8pdIP+4lNnGVeadM4KtJ8qRpVmUmumtWFaG6wBpSQqEagkIc0HqOMZ5WiTztJxpBSY3JVw\ndF1ST1yzWEgV6aOmFfzDfQB6OmG6eY5hIjHaMtkuOb82YW1UcnLQ4fhtvjR0k3zu1wLXcQ7Ytbd6\nuThKAo8i7DYVDJA883nxsr7vXkTaxaoM4QM31jh3Kg3SGjeFnHaRIqGbCKQQVKZpEk9qSwbk1Mix\n1x1Mc25GnN4q+P/Ze/dgWbKrvPO39s7Mep3HffRTLaklhMCAsQDbEh4Cu42BAIcDw2hkD+EwMVgx\nnsDGTDBj8zJhyQ7CYMCjMbaHsMeAcQxjrDFGgUMGS+BprAEkXpIQiAa66Var3/d5HlWVlZl77/lj\n7Z2Zde65t2/fvqfvOafzizj31MmqfFXdWmuvtb71rSuLirPTgrumBVvjHGOEhXGMMsPmOGO5WbAq\nDD4EjDXY0YR8ts1o+25GkxwxgrHbNJON1gEE77BZgY+Rg2lysmISp86pMzB50TqDuixZiDBqcvJR\nhvOBlfMsas89dogGXg6OazoohHDzgw5ugMEJnHKsHv6J60pIHwbJcnwsVOJqMLaXCmq0OLxSCmJe\nRAJCgNxYdKSAsGycDknxDj/aAGOpTUHuq+uc9WThFx+7yMp5rpQ1n3xmF+cDd82UFXTvTN+7jSJr\n2VKTIuPZq0tWywabGWwxoRhlZLmlmGSYWDdwzlPOc0xetPWB5AxcU2GzolcP0MdibcsmMkYYTTJm\nWyM+4+4ZG4Uls9Km9AbcGo4xRfS2YPjfMWAN5rO+BB77VfxkmxDTPiEvtOs3MlOCyVQOoprrdpNB\nrqyWxodWUyiYPL7eYgLHOq5+qVjUjkee3WNnWfPmezdwAZ7dW3HPrGDVeDYKyygznNsosEZYVg1X\nc8N4lpPllnxk1REUatSz3OCaQBZlI+rVFO8DTe3aOoDNTDujAcAYwfTSccUoYzwrOL9RMCkstQus\nmoDY0+F87xROt+zh4AROPdoZAi8B5k1vhcd+FTc7TyhUkC7YIjaOxRpBXepvACpM8PiRstWmuY5O\nXDjBBSisDqxPdYeTzlZZ1Jqi2RhnXF3qrIAri4qysLzhzIS7pjojoPaBRa2NY9uTgs3Nkeb/vRp7\n2zPgk8KyrBxiaLWDmtrRVFF8zwg260l0i7QRhBacAyYzmCjgtF82vDCvuH9zxH3b2uvxamFn3W6c\norXLoRicwClHcGqwDrKFbgT3yYeRUZQqj8qkiyZgRBjnE6Satw4g2PhfqKkwMqeIaqbe5NROewdW\nTcDkBiNGowfUEfhipseJx7reyMnjhJ99RNlAn766ZFk5Xnt20spGTwrLODOMs4K7phmXo4NYVo7N\nccZn3L3B8ox+HolBtKy7CW6X9ldc9YFipHLS9UodgRjBxgJ0mtLWRz9dYYywajx7ZcN+pY7g/GTK\n3WEHgPq5x7S+A2QPfM6Rv1+nAf6Ue4HBCQw4FKlTmOCpgzJ/JrlpDUjIx0r9DF47koMnxN++mNH0\nqmm1139sYfGjLaaTMdXVFw4568nB+VnBftmwPc0ZZ7btDSijmF5udETk1ijjUxcXjDLD+SjtMCks\ns0K/erX3rTP4wxfU0E9jimivbFhWSjlN21aNb1Utq8bTNB7nfNuIZqxhGYfTbI4zNuIEs8nWGWah\nRKrlK/o+nQa40+0DBicw4JVHdfkZrSMMGHACcMoDgcEJnHa8lDRQgv3ch/CPfhgDuGxEjq7irVsp\nOyj2DQTRyECaSovFJiPYHC8WHzuJK6cCc4U1mOAwpaYlijP3sJrvUUtGzsloJPsvj17AiDDNLdPc\nsj3KqH3gylIlIrZH3bxg0NrIvHbcH+cLLCvH9jTn3Djnvs0R09ziQ2BkLVfKmiJSSjfG+rV8YXcF\nwOY4ayOIqvHsLOo2IthfNeyVOsGsajQiaBrf7ls1nkXteP7MhDefn3Df9gNkSx08Xz//+DBx7Cbg\nj7NwxG3A4AQGHArzmV+sDz71caRZYbIR2KzVEUopILyHEHR+bbWArMDYAiPaaem8FoULK0hTrusI\nuZrccqLGTu6smnZ4zgNb2vuQHMNGYZkVmtLxqHjeuUnBPXcpffTTO0umueXN52c8uF2wUdh2FsPd\ndcZGYXnT2SmjzLCoPZfOVIwyE+mmcV5BCLwwr9hfNZTOt07h6qJmZ1mzrPT9bXzg6qLG+TmX5hVP\nXJzzh1emfOH9W/yR82eYzXW4X/2cTojN73vTK/guniwcZSQgIn8L+BvoxMb3hxC+/ejOdjgGJzDg\nhrAPvgV59MPKECqm+HyijV8mQ6pFN+jeO8AhoA5BlCc/zgxW4rQxVxGyop0sNto6d8fu61ZgRfAh\ncGXZcHaSs6gdi9qzPVIjXfvAU7sr3nxuggHu3Sh43faYcSa8MK/xIfD67TGv39JegulkTLZzEYCN\n0YitYkzpRhhgv/bM6zFja5jmhhACHp1y9ZrNEfNK+xRq56l94OKi4oW9Fftlw6JyXJ5XvLBbcnVR\nsxeH0Xzq0pzL8wofzvPZ5+9lI5RtM1/ztM45GYrF1+KomsVE5M+iM9f/WJzWeNMzAG4nBicw4EWR\nogL3+G9ifIO3mSqO+gZT7gFRW6jXWGaNMMsFFwIC5KFBmpPLV//QH15ilBm2RxkXFxWXFhXT3JIb\njQI2R5aPP7fHqvG8dkt1l77ggTPszJdcWDTMCsubiin3zDJcgLu3lH3Vl3KQnYtMxOCLKSLCZmHZ\nsg7wXFgJy8Zz1yRjZEWdz9iyqD21C2yPMrZHmc45XtZkRnDec3WhDCWAZeX4g+f3mRSWy8uG+zdG\nPLB1H+fCHJlr82nzzO+RveazX9k395jjCCOBbwK+N4RQ63nChSM70w0wOIEBNw37xi/CPf6bSDHT\nvoGE4JUVFFNG4mqMyZDgsUkjvym1A/kEw4fA9jjj9dsTLi4qntsr+YxzU3KrEc/2SL9OtkfhHInn\n3llG7QKND2wU19f67zsEU10GY3XYz3gbcGzklsIKRT0nz0ZkRp1Q5QIbI8PZSc4L84pRtqLIDBvj\njEv7VZsm0qH3FY88u8fl/Yrtac6DZ6d80f2bvO7Ma7E7zwAaFQwRQYcjFJB7M/CnReQfAiU6v/3X\nj+pk18PgBAa8JNg3fhF8+hPdhuCRugRjMLUhOP0vJbFIbPxK00bRAZy03PMvPnaxNQK1h+1RzhvP\nZpwd5zy7vyI3hkXt8CHwufdscHlZt4XEpy7v89pzG9yKalKXKtum2rnI5mjG1dJRNoFSpmz5inGW\nY43QeI22Vi6QGSE3oumqTcelzYrndkrtG1g17JcaGVyNfQ2N8zy7V7E9mrA92e60nga0uFGfwK//\n8v/Hr//K9cf5isgHgfsOeervovb3bAjhi0XkTwLvBT7j5V3tS4ecNF0MERmmTr4IVj//Y2137ujL\nvuG2H989/ps6kQwwKxU8sw++RZ/79Cfw+ZQwmumg+maFrOZdg9IJSTX84mMX28f9leA014Ywa4Sq\nCRSxi7dqArkVahewBu6aqDN87bnbM/G9uvoCIRspOwuV7gjZiDJOeMuiM7iycjivheFV49lZNbyw\nv+K5/RWX9ysWlQrbbU9zPvfeTb7gvg22R5aRAVPNkaWyt06as74eRIQQgrz4K6+7f/j1J2/eMf6J\n15+96fOJyM8C3xdC+MX496PA226XMNzNYogEBtwSTL2IDwz2dZ/fbrev+3z8c4+BaxBZdXMEThAD\n6EN/uP4d9EELssnI185xZpIxyTW1kxshDdgBKBvPPObh9xdLNqaTl31NB2U2qotP6bWhaTkX4HIZ\n5SwKQ24EH+D8VGsFWWxcA9gYZUxzyxvPTrhnYsmuPtVOnWtHjw5ocYQdw+8Dvgz4RRH5LKB4pR0A\nDE5gwC3AvvGLbvi8soBGSiOtV20UcBLwy0/od7C/+lfZI4kT1XRbWfu2J8AacHXg8rJme5Rz/0bB\nJBOm+dHN+i3uei31hSfZyMeEbMQ85GRGGFthozAU8UJ9UHmKUWbYKRtyK2yNMqyBrcJqpNao1LQd\n6gCH4ghrAj8K/KiIfAKogNsftt8EBidwChG88vfHX/nOO3N+W0SRuTrSRc2JiAR+rRf2+5BW+NoH\nkMfmACs6P8EH1UUqrBDnv3NuknP3NKewqt/jo/5ktbfg3Ob0tl9vfvfr28eyc5HZdATBga8Q5/G5\nUlXPTyznJhbntYaQGUFCQOolZrmrvR4DroujigQiK+ivHsnBXwIGJ3AKcaeMfwtjCTZDvGvnFps4\ng+C4w4hQB982uR3En3z9WT729FVWtcfVgVUjbI0t5yYZs7jyf26/4ZGL+zywNeazzr0yw3QOTg2r\nn3sMG+m7VqTr54BOtC81/B1I6Q1YhzvlPnJwAgMGsB4FvNjrUlQw4NWB+pRHSoMTGHD7IQZxDeIb\ngohKRRySDnKf+riOqhTTSlP70Qxs0U7DGm2eOdJL/a1ndqhdV9AFjQbGUes/zVBeSwN5yI1pX7u/\n8hSRr182nr2qYZQpiygFE09fmeNCYCNGC0eRHurjtLB7jgOO63jJ24XBCQy47ZBmBa5S41+osZP6\n1ubcLsuSxoeWeXNm42iMZ9l4fAhd7p9rV/u118HxRoTNkSU3BmtUJG+v8uzXmkYaZ4bPv2fGRmFw\nAQ4LHC7sLiisOo7kKAQtQtu4YfM2sIoGvHy4U+4FBidwClF+4Efax690fcA/9qtInEVgH3wL7lMf\nV4G5wxhCIRDEEEYzfCwmt/ITSajuiCFCa/ytmLXQ33nABHJjlB5ax2glFgo9Ae81Sqi9h6CRwUZh\n2B7ptVcusFN7dleOsvGcn+RMMmkjjBthZ75ke7buCMrlEo9gnaqEnoRBPCcdw1CZAQNeIqRath3C\n9sG34H//l/QJY3GPfAiJqRTJRnF7RsgneBHEOx1oH7TvVjAIkMXV8e3i3Sd8/v3b/NYz2iC1ajxl\no6v9UWbIjdHpaN4zyjI2RxkhdOMlQ1ADMc4No0zpomMrZFYoo4T2ldJpGqjQ4vGi9jy1VzMrLLPc\nsF/pUJmkNGEEzo1t6yR25kuWjadygVlumGVDPeKVxjBUZsCJRDK0rziCX2OiAPjlHLNx5vo00Vg7\nqFzAiiGzhc4pAAJgjW0NLsBiWTKd3D7WzR97zTa/8+xu+3duDT50U78SrAgIbdOVEfC95lAjUPlA\nGXWCRlbazt1Jpg1c6XjzyhECXFxUWBE2iqx1LiJwt5E2LdTHxaWjdIG7Jrlu2N8ZooEjxhAJDDjR\nWP38jzH68m+87cf1j34Y6M0diDCf+cW4Rz507Q7BE0YzyBy4ilCvtIDsGwJgXI2VjAB4RAXoYkQg\nwRNMpkb3kFz9reIPXthTlVOBzMIYg5Gc2vtW/mFkbVsn+Nz7ttp9P/HsDkYEI1A6TwidcJzEWQoj\nq5x8b4VVE3BBJR3SMBkfNNVUex38khrRVk1gZ+VofKo3NFxZ1pyd5IwzjU4KK9xbuGvuacDtx1AT\nGDDgpeIwyeiY3w/5KEYEq+65yB7KxMfXhchlT18+r01ngInHWb3MFfBTl/ev2WZFmOSCdZAbWqOc\nIoVPPqfRQuogdV5fE4IOkCEOFSuMtCmESSZYY6hcwHkdrlNYIQQ9jjXSGhlrNPnlCSxqdU6191xZ\n1ixqhwuwPcrYHFmcDy2DasDRoh6cwIABLw32j/65tb/FxkKv92B0iL1xlWpthmjg6yXYoisKp+0x\nCkACiLRUUoDV7uWXPZjGedp8vAsBg5Abwxc80K36+6miBBWMUxaRiDqDIkYMmRFqr4Z8kquqZzLu\nLgTGVgftiBiq2InU+C6CCKFLG1ljePDMpG1c8wQKI0xygxd7zXUNuP0Y0kEDThzGX/nONSXRo8DB\nNNCN4JdzDCDTbUKmgmchj8Xd4JFqjjQr/HgTP96mxuDIGRcFZrWn2jaRLZR6CkKMCKqrL1wjrnYz\nENGKg0gs8JJW4/r8oxf2dIUfV4EuhJY2amK1onaBURZX9kYY9bigyegva5jmqjkksX7gQyAzhlku\nTDKLC8n4R7G6aHRi+RwRITewObKId60MR9L8qXY0qjnYNTzg9sAPkcCAk47Vwz8B3NrQ+VtB9Ss/\nhRgLxhAaZQn5+R52PEPqVTuoHttzUlFsjomhalQeedkEzoy3sOWuppiMQSKF0yU6N7MAACAASURB\nVARPyLU4XF18iuKu19709V3eUwVUES3mIpCJrtblkJJDchDp9bXXeoHzmr+fZAbXG0ZujTDKIASd\nrNZnlxhRTSKVQ9emNB8CRkzrkOpYTHahi1I8sLdyTHNDBgRjCcZimi6tdjsiowHXYmAHDTiRSMXg\n5ADuNPK3fS31r/0MUowxG2fw481r+gDEVUi9pMgmCPrl21s5imyDcRFU8dJVhGyszWjV4pbz4iFo\nk5aI1gKKyOTp68R41r/9iaxTWKFBMBJaIy2iKZ1RpHBOMqMOQNsH1HGIUBjtEdBzq5PIYl3AIBiC\navvnhmXqS0ALxCHAvNbaSLqySZYxsjmzcGvNeANeHKc9HXSHeIQDTiuqX3rvodvrj7zvSM9bX3jy\nSI8/4NULFxVjb+bnpUBE3iEivyMiTkT+eG/7V4jIr4vIb8Xff/a231QPQyTwKoDEtEv1oZ+k+NL/\n/rYfv/7I+wh9lcpMOexiLDIuwDtNC3mPmW3hRzMdVO9qpClVXiJp8Sx3KKaG3OYghkUD+5VnaYRp\nPqGRMc4HxBRsTUdIudcWi6vLz1Cce82LXm9ahYMqhU4y7Qto6DqBQ+jUI62IUlfjdzyXLt0DGhmU\nLlA6j4vbNnLtC0gF3cIHah+ofMA3nrEVBLAtUyjSSk13Tr2WjiSVIov0WwSc98jIMEuf8S3WSAZc\nH0dYE/gE8HXAv4C1sPMC8BdCCM+JyOcB/xm4+XznS8TgBF4NMOZlacY3H/05ALIv/KoXf3GcZQD6\nv9pMZjow3Ttl/mRjMFk3zMRphzBO6aPiKp1zK0LIx0xHmwQMtQ8sG+XjVy7gg2dlLGem5ynKTgH0\nZuoDZzamuL0FjResKJtHG9UELwFigdaT2EKatunPGPjMuzd5/KJKNfvIKnLeUzU69B2UX+6B18cR\nk09d3mfZBGrvCcGwUahzcSH02D8wnYxZLEsdjej1vFYEawVnYsrKqkRFmikcTIapVK47RUX9eQMD\nbh1HRRENITwCaUGytv1jvT8/CUxEJI/zB247BidwytEvBle/9N42XVN8yV+6qf2TA7gRgnfaJXzA\n0Whx2GpkYCbK7PENUu4RbN4ucYPJwB6ghsbtEjyTLMf2kvWTTI22R4erMz5L3ix1oD1x9KIxWnMQ\n0x6nr0w6yUxL6UzCbWXTUTQxAXz35RTR3Gn6viYHAPDGuzb51KV9XDA9NlH32k9d2m/3B2gclHhG\nXqmk923PrnlPRTTCcCEQfEDSVDOvziD1G1iBTECaSgvuJ2B4z0nDEU4Wuxm8HfiNo3IAMDiBATeJ\nG0YBTff/U4qenIOxmgqqNAoQY3TqGcDIdIXh4AnZrDNgwSuF1GZIvcRaR6sWJLrqHWUWCQGPyjuH\nbKRD73vG/nqodi6SAdMepbLaWxCCRgNiAiHSQPv0UYhUUekmjgE8eXkfK1oMHoXQFoKNsCY74dvf\ngcYJ+5VnlhsuH5g8tliWkTHUXXMI2mwWIttI5Sj0ucwKk2zExDRrM4LrF55oH+f3vOGG78mLwf32\nLwDX9oC8GvByOoZF5IPAfYc89V0hhP/4Ivt+HvB9wFfc8gXcBAYn8CpC8SV/qY0Eql96701FAzeT\nAkp1hupDP0loKnUE3hOaSiOERBdF6xNSKLsnWO0ZkOChWhCs/ncMxUyjhtV8bTylRgw5frSBiAER\nbAjaqGssISuQaqmrfs8a7SFFIS09FXUGoPz6ND/A9fLvy9rT9NlC0ncGrBlpj67eJ5l2B7dMI6P7\nZJJmFAeM0xV+41nrLTiIygUMXROaoutGzoxGAuPMYOsFQQyjWBOpn3+cPt91tb+DlHuYat4pukaH\nbB98y9p5k+BfqK5lHDUf/bmbSwueItzICTz60Q/z2Ec/ct3nQwi3ZMBF5LXAfwD+agjh8Vs5xs1i\ncAIDbhuCcypc5z3Bu1YATozV4nRWIPlonRpqLCHEtigXl7auVvnpg2MQva50jXeEYkLIxmrQfEMo\nZvjRJiYOs0H0OiREwx8dTjoePanqauciNhthTUYV6w4+JF3/zgAEuvSRSjwoUvE2pXyS0e4bD+cD\nxmhkkNlAhjqF/dq3NYOEynlqr6v/zGjax/QjihAwItim1Hutrm0Uy+99I/WFJ/HTs50DvA7c47/Z\ndWJfL51kLGGlTqH+tZ9p+z+KP/X26x73tOBGTuCNb3kbb3zL29q/P/Cv/+mtnqb9gEXkDPB+4NtD\nCL9yqwe8WQxO4FWGtWjgV35Kt93GL3KoazAWU4yRyUwjgWT80yrcN+AdYnPt/O0t28VViKsJNidk\nhRaSg0fqRZxQFrrcdzyeuIYQAl4sJitgVbddxamzOP2WWCfwxQwvtjOk3mFiJOJD7CMAjJE2jWNF\n0zHp71TQs0BoG8qEXKBxXUMZ0K78s+gIbBweU1i5Jh2UXq9aRnrdk/G6amo/igFlRvVRnHtNdKae\nMlhcyJnM7iLLCmj0Pda+jGtX+2G0oZ+PsW1dheCjI1iuvXb1X/4Noy/7BgAWP/WD0NQtUyx436rZ\nzr7+u6/9z3JCcFQCciLydcAPAXcB7xeRj4YQvhr4ZuBNwLtE5F3x5V8RQrh4FNcxOIFXIVIaKDmB\nxOHP3/a1t3zM1JQmeU52/j6lgEKbegj1qpWYliyPqYayrRsAamTi8dragslaY27cqlup2qLVHgpi\nkKbE2FyNWvC6PLe9CEOqtkiMsXixlI2nsCPyOIi9CA3ZKKfx2rGcirypLlhYwYd1RwDqvvrqpgeN\nRmIW+dglnKQmDN2chD4yI8yi7HQIYEJHv13t7+DsCIoNTHCsdi9rveWQzyS/703UF55kvHE380bZ\nVaNik6zwSLPSGkp0ygEiS8tpWq2p8PmmvndRqkJylSj3aKooRQY3g71/824ANr/h3Te9z3HBUTmB\nEMJPAz99yPbvAb7nSE56CAYn8CpG8afe3joCuD3OAGPVAViLVEs1/gBNRViVyGgMdgqbGzFKyDtW\nkGuQegne4ee7SF5qMbnYiOmcZfvaYHNN6TSVylEbg1ntae9BcgxJYyh4VSq1kSUkeXu5lQuYbIQx\n0UF5RxE82CyKvKlctPOB2MDbGm4XIwZrBAnrLBKPxvexHaFNF2kPQpdKSnu8sDNvO4kBconP9gXz\n5h0j6SBCPu2ipR7U2O8xHW+1tQqMIctGhF6NROdBG0Ti+YxRB2yyNlrQ5w34vfazhi4CSNsE8E1N\niGFQ6DG7dn/s77H1jf/guvdxHFE110mRnRIMTuBVjpQK6nf0vqS+gIjRQ3+F1c//mBq1cg+aCu+i\nYbWWkB5PNmm2XwM2a3P1q3yG84EJtYrJuQZrnlFH4H3LCPLFBGlqQqapIhVRW6kxEoPUZZv7TnMK\nhJ7mfltjcBiTkVvBh85Qp/3E1RRSx5qBqC22BaCO4OC6MEUM/fZ7AyRzbEUQG9rHCR5YVh4RnUjW\nR0izCeLJ9hdLcqA2BYvKUXsiRXSMFWFk00pez1o//7i+PwC+wdQlI5sTrLKq9H01+j56T2Ck76XE\nonFTd6J9NuucS7PCl3NCXSOjMX7nUu+mlQAgeY4xltBU+LpBrCE4v+YMThKGeQJHBBF5AtgFHFCH\nEN4qIueAfwc8CDwB/KUQwtU7dY2vOrzMaWSjL/9Gyg/8CH7nkjKDYm0gAJIXyGxLlUJn52kCjPaf\nx+w8x2Q0I9hCi73FjMaOCMWUzP+eSkyPt3QgjQgS9toirzSVruyDaY27Foq9Gq64ym2jAjT1IQAi\nZHG8pcQVfKoN0BtkQ5RrlmaFtaN2td9X+0wBgBcwvWggmXVrusH1qR4gIhQCi+Db55wPNNDqGG1O\nJ6z2d9QBBSjJaCI1yIhGIclZBBGNjoop1GXr/EK6R1e1q33dEH+bTD1W8FpDENOm8KRe6fwH6eo1\noakJ8118ucDa88h4poV6vz5RTnJaJ2DITqwDgNPvBO6kdlAAHgohfGEI4a1x23cAHwwhfBbwC/Hv\nAXcI9Ufed+SaPwMGHHc4H2765yTiTqeDDtazvgb4M/HxjwMPMziCVwT5276W6ld+ClOMldPf44g3\nv/F+gJb1kf/Jr7nhsfzeFc0N5wUt7ydXpk+wBdKsyGyOWVyl+dTvInmOfeCztNjqGuwYqnwG5x7E\n7kdCRFNBCG2RWFw3cEa7kJUqSlNHOqkDqTV6MAZvtMgZxMRKrsPEDmP6LBhr8HH1HGLOW0JAXEUu\n4I2u0on35SKd9GB9N9USOt5Q0h6KSaoQGGeGSaYS0pPc0MtKtVEAcbUfQiCE0NJTkwh36hUIbcpK\nv1RpbsMakyp4zelDx/jpU0P7kVNK7fhG+wkiKyss54RKazV+7wpm+zxMt6Aq9f9MYga59dGXEov0\nJ7Ew3JxQ436zuJNOIAA/LyIO+BchhP8TuDeE8Hx8/nng3jt2da9CHKSK1h95XysG10fzG+9veeIh\njpJMjKPxV76T8gM/ooYo9g34qsRkuRZKm5Ls0hOwuIq79JwWdmeb+OkZwmgDWe5g9pbI9Cx+elY1\nhuoF0qiRTv0CvphoKsM34CxSa5MYWa7UxGbVFqUlHyHZmGD1GkJTIVRd4TgrNG2SKKViCBLlnUUI\nCMYWiG/ITR5nAXTSEIf1e5m4XSLDp98BnPoJlrVvewqsHD7LAMDUpQrqGQNRQiKdQyUvpCtAm6zN\nTx3aG5BougccgDKoTK+pLqwZfwm+XRikru+wnCuLKzK8JDYE4h00Nc2iJDiPcxVn/vo/PPzmTgBO\n6gr/ZnEnncCXhBCeFZG7gQ+KyCP9J0MIQURO97t/zJFYQmlIjMQooS8TkVB96CdboTrJcq0BjJX7\nHpqaUC4Qe4mwnNNceq5dNcp0CzOZ4YqpSj/4BlPuIdUCmWzrKMrVvkYnUQ5CqiViC/xkrFTRzCP5\nuG0Ck7pUEbrlnhYwTa8WAOo8oG0q07V51CoS00YGtj+ZLSR2jK7EvVNj3K8B9KHD66VTAPVpu2oA\nJYaR6gNpcXlkuoJwd17dUZoVeT7B2i6D284ySAa7vT+7lp8/eKzuAGZ9W5pqk7alKWZiWjmK0Pvs\np+/4Nhb/7nsxm2cg09Gg0lT41ZJmsTzRdYA+qlNyH9fDHXMCIYRn4+8LIvLTwFuB50Xkviihej/w\nwmH7vvvd724fP/TQQzz00ENHf8GvYhR/6u1Uv/Re7QJOhtFoykCyXI18L30kkxl280y7SnRXXsBd\nuYC79KymEsYz7Pn7kKxQhzHd1sJwMkrNCqLAnMQ0EM61XcAEj1nuKGuoiI7GZGszikM+xhRTzPyS\nnjOyhfoCdRBpnL5RumRWqINwgC3a4nBrmFOBlNgxHAIuCP2FYlrtW2L6JxZwrZU2Nabib1E6Oziy\nZPdDxwYCGG1st01hBA/1EptmMIvpckfJUfRX9yLqtNLzid0TG+IODvQB1Cn335t4vCBGNaBWZScW\nGDH9y9/J4v/5fmQy63o7mrqLFl5hA/rwww/z8MMP39ZjnvZIQMJ1VjJHelKRKWBDCHsiMgM+APx9\n4MuBSyGEfyQi3wGcCSF8x4F9w5245gEd+o1hZqxNYb4qu5GSVYndPo9MNgn1SmsEQPP0Y2As2f1v\nwJ69R+sD9RK3cwl77j7c1n1IvcQsd3Bb91HO7qZolmRXPt1pEKXPPnhCTAmFOHsg2CxKVauhFFdj\nyl3MpU8h+Uhz5iKqPRTnGASbaJBFqz+kN2fWjpXOiZhOtI6uuzjZiTU9oVgrSM+l/7VJL2hjOqGP\n1f5O+3i0sX3N+15dfqa7VruepluThkjO1Lue3EYvNWQz1lJC3rcdxK1DiY1j4hulhS7n+MUefu9q\nSwudvuPbANj5ke8m39rCzDbxyzn1VSX0bb/zFet3ui40HReuL9D04vuHv/Zvf/OmX/+jX/9FL+t8\ndwJ3KhK4F/jpmNvMgJ8IIXxARH4deK+IvJNIEb1D1zfgBmh7ApoKl7pGfZyya6x2k9Y1kl/SrlLv\n8GWc65vl+PkeoanbKMI9/yT1k7/P6LO/kHD+9fjxJsFm2rUrYzanZzGpQCzS8tbbXoA2fVFoeig1\ng4ngx1vIxjnYvQCTDIhy1lGCQqmjvb6Cpupy41IRQidDnV5rxCBxJKRtO8I4UACG3ERHsKZCmjSJ\nDnlfDzH8CdXFp9LRkZjCIoroEULX5BVCzG31tJdS5HCwRpAG1sf3L9ii1WIi9meA1gBCVaoD2LsC\nxjB9+99uD+OrhmZ/n8wYmt1dxJgT1xB2I5z2SOCOOIGoivcFh2y/jEYDA445Urg/jrOM+yg/8CP6\nIM4TaPPIWcH4z39T+7rFT/1g10jW1DQXnyObncVPtjGrOZvAcnQWP97CzC+rplA+6k7kPbQdrrH5\nzDfAqOP8g67o6woz9jqb2EVDn49adlFINYPYjdwWkK0nUGjeXaRj8IjVNE7LqIkGM4BEo983HZ3O\nUGj7Em4F2hfhe93QKYdvuqJv7HXoI5isSw0dJhIXpTP6TkGLww1hOVfHvdht60H9zt+z3/R9XPnh\n7wB2qefLa499wuFexkCmk4A7TREdcEIx/sp3XrOtNf7eaaqoGLc6M4ehv5qE6BRGY8yDf5SQT5Cm\nIhSBHZ9zdnoGqeZdoTJ4TVccnB+QtG6MJWSaEsJYZDRphevAQTbSNAi66tVDaqG4KxL7tmjc6Q7F\nv/t2IXjN5ceVeczEd4yg6DyMqFyENFFKg/V00I2wpvIZ6bGtWuraC7sZDe3fwV/DxcY168dMqaM+\nIiPIL3bx+1chK/DzPXx1Levo7Dd9303fy0nDEAkMGPBSYayuHJfzNmKY/IW/+aK7Td/+t1n+zA9h\nxjPkngdxW/dzpXTsVZ6Ns/eR7zwdGSjRiIpoNGDpDJ2roSm1VpBP8PkYWWWarmo0HdXPmXeqpAGx\nrBWP2ygBtBAePISsK7im/oKIFCGkOrJB1hk8walTQqebvRT0R0XWzz0WjxmVUrNRN0kNurRPio76\n24Lviu292Qpt7SDWCPAOVvOYArpKmO92HeBusXZtz3//39LT5doZ3JRVWxB+4F3/4iXd53HE4AQG\nDLhJHBYdvFRMvuZbWL7vPWRNhSlm5PYcADsrx10219VpNoqGuiLkY40a6qWmekDF6qRBTMYqGMYT\nzbWH5RwmM6WMGtumhQBd6Qevxe2+AJ3JogG363LUwMEpZm1ePh5PZS5CN8wG7ZN4uUgr9uz+NwPQ\nPP27XRF7LTKIht/30kcH0XMSbWNYZAn55Rw/38XPd+NrHME7TJ6daGnol4rVKReQu5OyEQMGHIrJ\n134r9ROPEB79Ne6tnufB7YLMiEocR5VRTBbnDYza32uDY6IOkhXwNm8VL/VpTXNoAXul0UM0guJq\n7Tpu6lZvR1yl6qTJmPc7cNv9qtZ4Stxu+vt4FwXaDMXZw6YN3jqyBz4HU+5qY11UUZV2RZ8eaxQi\nzWqtZpCutV8nEFcRlnv4co7fv6opoabGNzX13oJ6bz0S8M7jo0Dc6OwmxdYUsabtEj7pOCrZCBF5\nh4j8jog4Efmi3vZcRH5cRH5LRD4ZmZJHhtPxKQ04dZi+49tonnoM95sfYPP532G7uaqSxokKmfLh\nYjRHLgafT7qOWZMRshG5r7Cr/chEqnQoSlMRav2hqTrxswOGHedUmTQWk9WIVppOcfEa0g+JV3+g\n2SqmnCR4lb5oqhvc9U3ikOKuuFobug46qNDdW3sfyXHFa9fhMiskznwIVYnbuaQd3cYy+/rvxlfN\ndTn/SSH0nr/9T/BVo53DJ1g19CCOUDvoE8DXAf/1wPZ3AEUI4Y8Bfxz4n0Tk9Qd3vl0Y0kEDBpxQ\nuE99vO2bOEpi+uLffe9Nve6pd/2PzO47f4RXcmdwhENlHoGOOdaDB2YiYoEZUKGKy0eCIRIYcGwx\nfce34feu0jz7h2QXH0equUpR96aWSb3E1Etd/VqdGpZWuKaaY/YvYvYv6OuNVZnputaB9yNl54TV\nUruem1pTQ66GZqUNZc1K00P1SmsOjaaPtLmqaZk6eqCUWglt+qhLFTWarmmZQbeO7DWf3Q2LjzCf\n8Sd6aaBY8E4pqBgFHCYWB2jE4yq9Z6fDf8Kq1ML+cg5Acf9ryc6ca9M8L/zg/7x2/rTqX17aackA\npwV3QEX03wML4Fm0X+oHjlJSf4gEBhxrTP/yd7L6L/+GMNki5NPIBsq0sala6pCZfAQ211RNvdSU\nUVNBrYYck6mR965dMYeVg9ToFrV2Ql2BtesXkBVIFhuuGsB21MvECsX5rnkL1g0udF25h/HzbxH2\ndZ8fIwE9pn/0w5BPOpZU2zNB6xySON5BZ9BRTd06nTcWgrO7H0BGE1xV4qum7QV49ns7xlc2KWiW\nFdm4QMzpqQcAhBsY9yt/8FGuPPrR6z4vIh8EDisCfVcI4T9eZ7e3of/b7gfOAR8SkV+I/VW3HYMT\nGHDsIaMxbuNupC7Jdp/Djzf1iSgjkTp5cbXWDUxHlWyfO5CLVw2cWED2ThvDsgKZzPDLeSvIlprD\nxDgki4+p1Akl+qhIT6Y6nduv8/ChZee4Jz6GfcM1vZIvHQe5/aYnBRGu43T6212lmkzt/ha8Fswl\nLyDLodKeC7+cE1Ylrq7JZ5O1prBsXFDPS0yRMTq7eWpqAQn+Bk5g+01fwPabus/yiZ/712vPhxC+\n4hZO+fXAz4UQHHBBRH4J+BPA4AQGvDoR6pps5zko99RAFxOVh+iLoYlR9bXIgukMvFej7RxSjLsO\n5cq1kUESxgtNhd/TqDvNToCYby/GXf+ACEhsLEsdw7G5DNdpGx1U6RTfIJUaT/fEx9rn7BtbYshL\nQtrPPfIh7X9IzW+wrioK2ucQpTL0YqLcRIpa0nQwiBIRVzQNZiyhnLfHGJ3ZxNcNo2KT1ZU9ZVp5\nT7E1JZ9NonR4w+T89qlpIPOvjFPrFwaeBL4M+L+ittoXA+85qhMPTmDAscfoob9C+Z9+GACzfR5b\nr5TH3+jQmJCP13eI+XdsriJzTc8pAGKtpnmMbVUxW3VM41uF1NDU+pyLzsR0ufQkI5EkrqWfY084\nEAlo3cLq9RzG2b9J+Ec/3D42n/nF8UHW1QL65+zl/dcUVPtaQr3xkKGu2xqJb2rsTKOu0NTIaEKW\nF7i9q/iqYXy+0zpKDsTXDU358usexwk3igReDkTk64AfAu4C3i8iHw0hfDXwz4EfE5HfRp3Dj4YQ\nfvtILoLBCQw4IfDLuc4duKJFXvuazyQUk86oxU7cMN6EaoHUS6WLOu18TQi1GnYyHdIi5Iix+FJT\nQCGLKSDv1TE0cYmWF9pxbAwi3QAW4rSv0O/OhWuNcNwWxEDUPzIxKvCP/Wr72rYhLd6XedNb2939\nox/WNFTvfXGPfAj7R74U99u/gERFV0zWNYal9E/66c8DTpPTUkpsVWpabDSBxV48ltXXG6v3Pp7q\n69yC4DymyNZkJNJQ+dOE21jKWT9uCD8N/PQh2+e8guKZgxMYcCKQVu9h7wruwtNkdz+An52Daq4p\nliwn5FMdCZkVEIIOp3fK6w9NFaWudYUv/eaxKIqWhuaEQ4bmhLrSVJKxrWRE2znsDRh3bVEZOsOf\n7qNnUYLNkdX+NbLQfbQ00J7+T8gnraxDgv2jf47moz+HjMY6o+HgMV3dGf/+wBnv8E7rI6EqNa00\nnnYT5bzrZkbE/WQ0Rqqy7QWo9ubYPMcUKhtRbM1O9CSxgzjt0vWDExhwMhAnm0le6Eq0KjuaZojj\nEW1DsLlKKIjBj2bgJ9jZirBzSQ14lnfF4FgcDl7rBWa2Bd7hdi61OfVgosFsai0KZzkhTk5Lq+PW\niPeLrD3u91rmp5eWIgTIRnFITBIc0ueCv9ahhHzS1jn6B21+4/2tgQ5ViZltgqnb62spm021Pvu3\n7kaEtqmvnoMw46k63qZW5pT3+huuYQCZItP3IuLiD/2v3PUt//iQD/Lk4ajSQccFgxMYcCIw+Zpv\nWRtmE2rtkPXjrfVct3daCxBVCQ3jTbxvCCtlvLRfZ+M7gxfF5VI6pE0ZRaS+AoyJw9dtawzJtbbQ\nn8ilVMzeABznOiN/YHbvGow5/G/v9Xj97c4hxQS/d7nbHo2927mkw36yHPKiu8e44m+vs6k6Zxcd\nK2hEJMVYm4iM1UgnzYtI2kJZgQGaRUk+69RQxRiqvS5COQ24EUX0NGBwAgNODEI5b9MybucS+WwT\nv6lGLmkItVRNMbGxa0zIYwThYp9A1kuVpBX0qlwbkJN+6yq6hiKmWVIevZ/6OTDPNw19aYe19EdB\nAjpGstewleYB3KjJKjmUA68xsy1Nb3mnap9VSXCOkNXd7IPDjpvuO6aHxNr2Eu32eR0YdOm57t6y\nQllWvf2D9zRLLQJnk1E83umqB8DgBAYMODZIK9VQ14TFLn6+h8lG+DhmsuX295ugqgUhKzAbZ3Tl\nC21tgKYiVK5b+fdpoaOJrpzLea+AGvc7sGLXGkK9Vmjt5/7lYJPW2s6+M9Zt9xmdTDasRw2J4Zma\nwdJ1x5RWaDruf0rx9OsfesquR6Ktk8T9tWBserOke0OBjIU4Z1iMJTiPLdSh+qrR2gKcusKwO2X3\ncxCDExgwYMCAG2CIBAYMOCaYfM236PSxXNkqvpxjts7F1E+J1Av6A+LblFDwyGi8toJP6Zw2Dz7S\nXgOdjZzrqrip205jyTu2DHAtE6jtM7DXpGDagmlaXffomWtzfuNjZRNd20cgh1BQW36/66IByYou\n6sl6dZCU/++lu/QSuhRXAPxiTwvhiTUV2UFpX183iPVrctGniQ10EENheMCAYwQt0Eatn/luJ9S2\n3FWmTT4CVxGMjpOUeqUdxikFknoE+vAOjDoBydVw4n07T1dGkzZlQoPKR9RckzZZSymlnLuL1FFj\n6aeMtPlM9XraCWW98ZY3opb2Of+HFXz715PkMMKBdFcfayauqdTQL/bUAUZ2kBjT0WzpZKVNnrH5\nDe9+kU/tZGOgiA4YcAwR6hpfLnBXLmC8x6+WmAnaiHVAQz/4vF0ph6Zu0i+AZgAAGAJJREFUm8Fa\nxwDdqrhnMIPrrdibWnsIQKW9jIvOIhrW+LrgXEcVjXn3tW30opCYj9e+g/ik69UW4PAu5LUO36p1\nbiHe39o1RUfQIt1vv67Rd1hRJiIs57GZrtKoKCugqXGrVesATmMR+DAcVbPYccHgBAacGOz+2N9r\n+ejBe4wx2kk822xloWlqJAPqhTKFbKFKo9B1BSejmHjvUWIaOtpk4tNLXnSvcQ6xva7idJxI2UzH\nvN6KW5+PBtx0c4qD993MYmhX3or1Y/UbvoJzbbTSOoCmVhbUqCelccBYtyypNUeQnIOKxfmq1GvI\nYj9E+m0NrtT7Nvmrw3wM6aABA44RkkRBiOmUtfQQtNr9mKzLt/sGRhOkXHRGvq66nLdx2pwVV+W6\nuk65/8n6aMq6ijIRrnMe/VpDq0fkWxbNGpLxj86gdQSwFqGsmZ0+ZdWtRy/9cx3sBNbf0bk514nn\npfcqNabV6x3SYm37PmcbMdppKn2/0z7O41x1akTiboShMDxgwDGBKfS/a1qJKk1R+fEynmImMy30\nGqszAFIBNkofmNlmHJ6+16VNkjaO94hxavR7WOuwjavt0NSx89jGfL/pHIVZL772ow59Pq2uTZeC\niiWK0FTrxePeedtr6ck3rBn/NDAnGvu1pjDnYq7fHmiCW7+v9nTLOb5u2muSPO+cbjHG5BV11UlJ\nn3acdoro6Zn8MODUIzg1RNl0zPY7v6fNX/vlvCvexlV8aGrVwC/n6hjEIJNNTWuAGsWWR2/UqGdF\nGyG0k8ZaI+7WfjqF0fU+g1CVKnfd1NqFW6ffVSe9UJVx/75yZ2zyStefopG+A2gjAN9p+aTj9Ix4\ncK776UU1HcPHX3M/6dr7Q2VcVePKqose4nnEmlZC+sJ7vvWIP/U7j+DDTf+cRAyRwIATg7Q6TWj2\n98mAbLalRlyMOoM4SB5iY1lTYQCZbiOTGRIdgxBTIsYikxl4h59HQ5iM/3i2XjxOaRwTUyrpG+R7\nDsj7tXQLdOmrlsqJppTalNFa41avoNzP5/e1fXrOIaV55Dq01bU6AnTNYVkv/RQdXqjrNtIand3s\n0m/JgSWKqDHt59F3BHd/65HJ3t8xnFTjfrMYnMCAE4PgPCGmWy79s7/D+W/+AfZ/4h/oKrucx5V+\njl9pqqIvhRBWJTLZJGycI8tH+GIcV/vdSj8s1TmkubprKZnEBEp5eWt7sgw9I9pq6+T41MGc5Z0B\n7hndtZRSlkN2YGB8MvRrqaGugJvkHtK9csi+6fyh6SIB4rlD1WMZRckMt1zo7c7G2M0zmCTWl7qJ\n0+Hrhmp3QT4bn6pRkofhCOcJ/ADwF9BB8o8B3xhC2Ok9/3rgk8C7QghHpsZ3uj+9AacKZ/76P8TX\nzdpoQ181+P2r+PleS59sh8hnhT42amTDShlDfryJnH8gqm1GRk5dr9MrI9qh6/NddRJJedO5tXST\n37uitYblfH313VRt+ic0tb6+qbvUS0/Zs5+GCpGv30o89FNA7c2v5/3X6hcH+hdSyqst7MZroKna\na0737usGEx1UqgeEqsQ3dbv6F2swRUZTVqyu7uPr5ppI7bQghHDTPy8RHwA+L4TwFuD3ge888Pz/\nBrz/NtzCDTFEAgNOFA52ptbzJWIvY7bOY/pzAbIR+KYttspoguQjNaImw48LrKuQ5ZxQXjs/ACLt\ncs3QxpW9NZhCKZh+vtu+Rg1toV3H0EYPaZXdIrKaksloR1yOxvH67DXOKNUOdKZBv07gInXzEOnp\nuF+/ka2VhabXeJea52K+P927tCknTzMvseOinSEgxmCsoS4rfNVQucWh7+FpwFGlg0IIH+z9+RHg\n7ekPEfla4A+BI5dkHSKBAQMGDLgBvA83/fMy8NeA/wQgIhvAtwHvfvlX/+IYIoEBJxrnv/kHuPov\nv0uHoTcVMt0mVEvtDWg7Z42mPUaqsS9NGTWGRpjZFq4qYbXsVs3QFXjjyhd6HbIxcGi3O9/lxZvY\nYRsfp9W6b+r1SCLLdajLKObaE2vJWMT2isFpiEx7Tb2Vf78TOBWH87ylirZIzJ/IWGrvs1dPCE6v\n0VcqCeHrRqMk7zQSKFftOMmmXOGrBlc11PMSH1Nkb/onP/kyPsnjC99/nw+gfPaTlM/97nWfF5EP\nAvcd8tR3hRD+Y3zN3wWqEML/HZ97N/CeEMJCROSQfW8rBicw4MSjTWE4p1O+JjlhfiVKH3RduGbS\n6ND44FXzPx8Rzr2OrBhTP/n7HYMnTR6LxrBv7PtIzBmxBtP7KknM3wMxxePx1TIKrxmoIeQ+7get\nfEXU6lnr5E3y1RFJtnqtOSw1yx2YHdxnB4WqbJlSEFNQK9fb19HMS1ylYyhNnXX9GNZg8xzvHFT6\nPtTzktXVPao9TQP9kX/1vpf1GR5nhIOpuR5G9342o3s/u/1792P/YX3fEL7iRscWkf8B+PPAn+tt\nfivwdhH5fuAM4EVkGUL4P17qtd8MBicw4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p38Sd117MJdfdwgnfOI+KNBYXgFD48BEbAI4kweH3+7nk2xfR3dPHtT/6MSed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\n8ArZgoAI8f7FYZOCrQtCxwrm+QCzl6OyopwtW7aycdNGamvryM5uT9u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xcMfN19K9cwei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gHg\nriWr+Mitf+Gk7edw9h7bZbZNFw0EMigWJnY+p6vAhmnS6BdIoFsuUpT8eLVQjMOI0vQe7YHvztle\nN92KqiNagAqJyDsc7ifYsAqAoeeW4pTyVDcMZJrwgdBfRHLRrBajQSPAcsR3SLnhKXvugWeXATBh\np22JvQblmZNFwlL3Mgtp9b0P6r5e0AI0mwGuY+kFduiHmt6UOEUJ7dz6WoNrnlrKw739PDuY0Fp+\n+trXsLCrQz6bhJaUIAgRzVGJ0EjKU1APsAupHkMyqWoNP4q4Z+1Gbl25FtcyWTSlh4JhUg9Cdm1r\npzsnxwciwlAiJpKKlUkIwiRxaE0w0hGFMch7sBz5nDww3WThrzQc6VDIR2wK+qsuAKVF+L6PNyLE\nwGrsduT7GYei0WIof8+iRj7NRpNG/7Ae/+xSnqB/gIl776aPb3VPZaqk4BimRRw+yNALqwUKg5i3\ncl0VYCV2fjGlibNFQz4QgnKngOkUhGYjVyCqeHrhbuQKErmxJCJSI8yVMGuDxLk2jNDDGt1I2NYD\nhklY7oEowvLq2KUywca1IoGoV8VPKaqWHwrxDLwGESnkIgqJpABb9GgyNRVX9LRwNc1JOBimdDCB\nr/Uh4g1qcVD0Paz2CeIP2cNC/b/Zt1EjUoryqJLG5lCFHCSanaro8aSekxKhh9VKxmZaf778ADuf\n058L1S9FsSnsyTOJRocwi2WqQ4O89pzPc+QOc/nYYXvpRKP3q+cAsGbTMKcevDvHL9qJE879APFd\nN/Ku637LPxqqv1EanXu1U6hgi0D8VR3LP/Y29rzi5/zuXUcz0XXwwpCJc0Wzv3x3B6OrexlZvp5q\nn+BLjqwRP4vdBYoTxeDutgttRaGni0KqO7HiTKvX1KSuEhP1WnNoVFSnWmgOYoDJ1r2s/NiOxFEo\nenCoUImHsqCMZCUk8gI5eVk4pTxW3tXCVTUoKJqHSjDUdYwn9kwnM6pvB9IxSovm8iVUcyQzX0w4\npSmvcUWDAjKidL1tync8LVYP+9djFkqCA1woYXV0c/BZ5zN7ag9vOXhPDtl1O7EAU13Io0gPxMq5\nIw4jNgyOsOsnr6Amn/+79tmBGV3trBoY4fDtZrPrlG5O+snveXx1L8suOoucHA6mnX8lrbH2so/w\nqRtvZ0JbkbMO2QPPNDjp0utZN1ghjCJu/shb2XP+jC2Jxv+RWPrBk+mvNXjLL+7giDlbcdaO2+CU\n8tpVToUl6SdaTCytX1WSb+dd2qYLkWXo+7Rts43Yr2e6+FzaDkapXTQGBeKB9ULQCZiS8w0Q9K0R\njjYIbv3wClGsiMZpHqrcl9SiPx259hxuuUjH3OlU1wrdmV+rU5zSjVPKU5w8kdHVGwAYXrpWnFtr\nSCSdyY+o+D4fufcxXhiuUAtCbjj0NcwsFDAMQ1CRZPSO1vj5yrXcsnwNAw0P1zJ585wZ3PDiSoIo\n5oS5M9hvSg/7dk/AMrKUp3S0Ji6hH+GWVNHHyOjUQMg/frFsNY9tGuSRjQPMKBZ44/SpxAY8sHEA\nP47oqzfYs6OT920zJ3UeUwvKtU2ta2pkRUVanP5SwnB1LCEyTxlxbCbZMB2BbKiFqtJrpDuS+7UG\nYQu65pZLGhmPUhoOVSxKfzb8RkB5etK0LfIDJu4kPpf5rWZQX7mS3MQJjK4U739zaFTTb51SXs9T\nbrlIaf4C7B4xb0azdhI9LJqjWKOyZ4VbIFz8EH7vapyps0X1Pl/UfWRip4ARNAnberCq/UTDmzC6\npkAcEXTPxgg9jGYVq9ovBN2pBEMZhUT1qk4kFP1WO1SB+LteFciF2sZ2iAb7sk5PKQRDFcHSomzt\n1iidGo1cXnxH5fc19n38tUu1AYxCIvRzlvNl8v5bes512otYHd3aDjf2fSKvoZkEQbVB0GjqsUbt\nqzQ8drGAUWoXKD+S0ux7fOtHP+dPjzzH7556ccxns/er5/DWq3/NgtlT+d6fHmanGZNxTIN7X1wz\n3kd5szF45SfkM0oaYr4a57ktblRbYrOx29SJfHz/Xdhz+iQtulMLg9G1GwHY9PRqBpcN4TcCit0F\nerZP3BmCuk/QCOjYehJt04VtruoUbBdyqcZX/hhnqebgKCOr+ggaAZ5sUpVrz41Z3APYeVv+v6Bf\ni6Q4PC2kNExhjZsWhqvtAD2JqcRCVTgB7GJeb2fnc4k1bmpCbHVI0T79MtmIA8E5JfB15SnzWi4v\nJoFcXk4aalJPVSOl+0im0gNJJ3CECD+sVoQ1qLLXtLNOP3rSkMkGMtFRz0w953OuuZWbH3qG4Zb3\n5517bc+KoQqT2kusGqrgAF8+cn9mdInBfPpnrhrzPg1e+Qm63vtl/Xflus+ycaSKYxh0SFHf5jpZ\nb4n/vFjy3uPZMDzK6b+9l1Pmz+Kt28/JfAfsgqsXea3IZuQFRFFEsUc4SeW6yhQW7gqAPXkW/rKn\nAJF0R5Uh8jsvwl+3grB/Pf6Q5GlX6xod0QmL52vnqHrfYKbDNkijCUlpUhV39Vm3XIsojGmb2kFe\nOlbVNw5qF6uSHMNU9P1VWF8GDVUMEfNhGIRc+MjTWIbBBxbO4+oXlvPHtb2MBgFbl0vsP3ki79l2\nDoZh8M57HmarUoF3LJzLjFIRVy7mb1+9gQ/d8yi/OnRfZraVMtSpbB+O8edghX64JSdDbTIsk6AZ\n8KXHnmXZaI2jZ0xl14kTmGw6mf0Ny+CetX38dPUaLt95J/0ajE04XirZ2OUXfxj3+v6Z0fvVc14y\n2WjtsZF5fmGUMTQAkSRUNwzRMXuyfs/rG4coz5xMZVUvgy+sJd9Z1P1H1JxhurbWIoEoPOW7O/Rc\nkVuwh0AR6lWs7V4DcYTRrBIPrMdb8RxRo4YzdTbO7IVCsB2KxXhcHcHs7CFu1gjWLReISL2KO2d7\ngVRMmIE1upGgdxXRcD9mRzdRtYJZKgsth6JHufmkp4U0ajEcV6As0tHJLJaJGjVRAFN0J0XhTffO\nkMcTC38hBDfcvE5I1HykHKjC/g3EzYYwbxipadShtQFmmiKXzMsOuQkd+KPVjPhbaWlMxyaQ9sWB\n1Gapecx0bEHb7p4o7jdfEk5dlUFuuf0vvO+qX/LTtx3J4d/++ZjP1fLzTuPKp1/kktse5BvHHsxP\n/7qYjmKe3z299G/6XKokQ38OZbIxnvbx1RBb3Ki2xGZjZnuJlUOj7D5pAk2JRigB5PpHlgPgj4qJ\n2y05dM7u0DqNOIqko4mLWy7ijdTId4vqpGlZGYtBr1JlZPl6lM+6qkb6VR+v6mPnbZojTUIvpNCV\ndbAxLUM7pTSGajrxADHhimRCUSQSQXlzpD6GZxwiqiSiK24ucx6/2tCoRxxGxFY0Lt9SWVcqT+84\nijCKJlGzKV5reALmTdlumkqw2BQUEitQVAJLO1Fph6qmWFylaWgqYTFL7YSDfQLV8AIMUzT4w7KS\na1GIiDqG6huSEWJGemD85ulHcuprduTq2x/m5sdfwAtDOvN5Hlnbx66zpvKBw/biy7/6M398fgXL\nB4Z1sjFepBMNEEK7zW+9Jf7TY96VNxKecQzv2W4Od6/byCnzZgGCqqPGAcMyyXe3a8QyaDTxqw2N\nZtY2DjFhwSwKCwVVxcgXqd0vaAqr/vCwrnZ3LV9NZVWv/l6GjSYPPr6CHy9fxSP9g2xqevzyzDez\n38KtcUp5vJGaXoykI5K6DL+RdjpKaEcdW8teQnK/rgWzALBcJ9FS+D71viFdBEkoWTFhHHPRk8+y\nZrTG5Xvvyv6/u5PONx3Gh3eYz/deWMZ3Fi/j2aERTp07k7LjkDdNypbN2sFRnusb4pQbb+ah00/h\na48+x4cXzmerQjGDZCQWu+MLr7VexIswXZPQC/W2Tw4Nc+niF1k+OsqsYpFv7rITJcMibsaEllz8\nmbC6XieOYU29zrRCfsx5APb47e3jvv5KhDKoeKlYdu6pY15Tz6q+qYJpmXTvMJuuRfvLjvRQefZp\n2hYslIvexURSW9QcbtIcbuK0uZiWh+U0KU51ku7wnW1ST2hh5XJC52c71J96QLse5h0Xs62TGKEz\ncufsQLBxLVbXJGKvjuEWiEoTMIZ7MXJ5gg0rNA1J2bx6LzwGpoU53E9z41rMtk6iaiVreauouxLJ\njqoVYd/u5olGh8TPagUqQ8SyJwVRSKScrZoN8H1i08QsALaLWe4UhTKp10ga1Yaa4qQLYrKfBoh5\nSjdNrDX0nBqF2aJgHEWakqipVQPDKdTDEs14pTmFX60nDpZRpHU5WhTePVGLy5G0scuuu4lv/OFB\nfnvHXey1115jPhvLzzuNyPeZYph86oDdOG63BRyzy3w8czNOaggdI6CdzVSkXeterYnGy8UWZONV\nHM+/51g+8edH2WfGJI5fOIfmYBVXIgfVvgq1TbLxlmthORbtW3Uk0HZqYQGQ6xRLSqeU1zBievIe\nfGEVYcOjMKmLnByoASqr++h7YjUdMzs19aGyXoiVixOLGWEkiMk+8kLsgp2hFICYiNP8ZVV5y7Xn\nxri9KOG5crwCNMVKXLtNrrOcEZ+lG38F1QZRGArNRxhiuY626VQ+8EKDIrZJe9TrhCCKcNpkY6Rc\nUjFSvucAmBZhvaYbVll5F19WdQHcrk5hOWhamu8e10bERBGFifjddnl2+SrO/v4t1JseG4ZHafoB\nfhDhhyFXvvMojt97e3wvwHYdnlqxjjueXspdTy/jsTV9nLbHdpy1aGe62grjUqi2xH93fHTX7VhX\nrfOJ3RdqXYLlWth5V/dNmLDdrAxiFvo+jf4RCj2dWK5DcftdMGyXgbvFInb9gy9I/UOA5Yqfrdz/\no+65n2O3ns5+nRN46wOPsOvkCUzu6eK9B+/Ojq6rq9vq++1XG7p3hHJaggSRKHTlKc+cJK5fjmVx\nGJH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UeBLJ7wdBQGlupm5XHf9dupbjurfntFFm/1d9qIWHX32Pfy/+hNvHD2XKq+994fu3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\nSgQJ0WYD49CCDIB3Kgb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RfWE0WSMRShKtjyHHFE4qCPDP4wdaj7mxrHM20MjytcjJycHl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14,6))\n", + "ax = plt.axes(projection=ccrs.PlateCarree())\n", + "ax.set_global()\n", + "ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='xc', y='yc', add_colorbar=False)\n", + "ax.coastlines()\n", + "ax.set_ylim([0,90]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multidimensional Groupby ##\n", + "\n", + "The above example allowed us to visualize the data on a regular latitude-longitude grid. But what if we want to do a calculation that involves grouping over one of these physical coordinates (rather than the logical coordinates), for example, calculating the mean temperature at each latitude. This can be achieved using xarray's `groupby` function, which accepts multidimensional variables. By default, `groupby` will use every unique value in the variable, which is probably not what we want. Instead, we can use the `groupby_bins` function to specify the output coordinates of the group. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# define two-degree wide latitude bins\n", + "lat_bins = np.arange(0,91,2)\n", + "# define a label for each bin corresponding to the central latitude\n", + "lat_center = np.arange(1,90,2)\n", + "# group according to those bins and take the mean\n", + "Tair_lat_mean = ds.Tair.groupby_bins('xc', lat_bins, labels=lat_center).mean()\n", + "# plot the result\n", + "Tair_lat_mean.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the resulting coordinate for the `groupby_bins` operation got the `_bins` suffix appended: `xc_bins`. This help us distinguish it from the original multidimensional variable `xc`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/xarray/core/common.py b/xarray/core/common.py index be6d52de52c..6163cd65fc9 100644 --- a/xarray/core/common.py +++ b/xarray/core/common.py @@ -383,6 +383,8 @@ def groupby_bins(self, group, bins, right=True, labels=None, precision=3, grouped : GroupBy A `GroupBy` object patterned after `pandas.GroupBy` that can be iterated over in the form of `(unique_value, grouped_array)` pairs. + The name of the group has the added suffix `_bins` in order to + distinguish it from the original variable. References ---------- From dc50064728cceade436c65b958f1b06a60e2eec7 Mon Sep 17 00:00:00 2001 From: Ryan Abernathey Date: Wed, 6 Jul 2016 19:53:23 +0200 Subject: [PATCH 10/10] fixed style issues and whats-new --- doc/examples/multidimensional-coords.rst | 3 ++- doc/groupby.rst | 2 ++ doc/whats-new.rst | 6 ++++++ xarray/core/common.py | 8 ++++---- xarray/core/groupby.py | 7 +++---- 5 files changed, 17 insertions(+), 9 deletions(-) diff --git a/doc/examples/multidimensional-coords.rst b/doc/examples/multidimensional-coords.rst index bc0828ddb10..3c425e6b07f 100644 --- a/doc/examples/multidimensional-coords.rst +++ b/doc/examples/multidimensional-coords.rst @@ -1,3 +1,4 @@ +.. _examples.multidim: Working with Multidimensional Coordinates ========================================= @@ -16,7 +17,7 @@ such datasets. import xarray as xr import cartopy.crs as ccrs from matplotlib import pyplot as plt - + print("numpy version : ", np.__version__) print("pandas version : ", pd.__version__) print("xarray version : ", xr.version.version) diff --git a/doc/groupby.rst b/doc/groupby.rst index 2bd61ff45e6..9d4243c80ff 100644 --- a/doc/groupby.rst +++ b/doc/groupby.rst @@ -178,6 +178,8 @@ guarantee that all original dimensions remain unchanged. You can always squeeze explicitly later with the Dataset or DataArray :py:meth:`~xarray.DataArray.squeeze` methods. +.. _groupby.multidim: + Multidimensional Grouping ~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/whats-new.rst b/doc/whats-new.rst index d8f7b81dbdf..c0e74dd23cd 100644 --- a/doc/whats-new.rst +++ b/doc/whats-new.rst @@ -30,6 +30,12 @@ Breaking changes Enhancements ~~~~~~~~~~~~ +- Groupby operations now support grouping over multidimensional variables. A new + method called :py:meth:`~xarray.Dataset.groupby_bins` has also been added to + allow users to specify bins for grouping. The new features are described in + :ref:`groupby.multidim` and :ref:`examples.multidim`. + By `Ryan Abernathey `_. + - DataArray and Dataset method :py:meth:`where` now supports a ``drop=True`` option that clips coordinate elements that are fully masked. By `Phillip J. Wolfram `_. diff --git a/xarray/core/common.py b/xarray/core/common.py index 6163cd65fc9..c0d7a088626 100644 --- a/xarray/core/common.py +++ b/xarray/core/common.py @@ -344,7 +344,7 @@ def groupby(self, group, squeeze=True): return self.groupby_cls(self, group, squeeze=squeeze) def groupby_bins(self, group, bins, right=True, labels=None, precision=3, - include_lowest=False, squeeze=True): + include_lowest=False, squeeze=True): """Returns a GroupBy object for performing grouped operations. Rather than using all unique values of `group`, the values are discretized @@ -393,9 +393,9 @@ def groupby_bins(self, group, bins, right=True, labels=None, precision=3, if isinstance(group, basestring): group = self[group] return self.groupby_cls(self, group, squeeze=squeeze, bins=bins, - cut_kwargs={'right': right, 'labels': labels, - 'precision': precision, - 'include_lowest': include_lowest}) + cut_kwargs={'right': right, 'labels': labels, + 'precision': precision, + 'include_lowest': include_lowest}) def rolling(self, min_periods=None, center=False, **windows): """ diff --git a/xarray/core/groupby.py b/xarray/core/groupby.py index 486d57c3b94..3d5d61c747e 100644 --- a/xarray/core/groupby.py +++ b/xarray/core/groupby.py @@ -166,7 +166,6 @@ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None, stacked_dim_name = 'stacked_' + '_'.join(orig_dims) # the copy is necessary here, otherwise read only array raises error # in pandas: https://github.com/pydata/pandas/issues/12813 - # Is there a performance penalty for calling copy? group = group.stack(**{stacked_dim_name: orig_dims}).copy() obj = obj.stack(**{stacked_dim_name: orig_dims}) self._stacked_dim = stacked_dim_name @@ -188,9 +187,9 @@ def __init__(self, obj, group, squeeze=False, grouper=None, bins=None, if grouper is not None and bins is not None: raise TypeError("Can't specify both `grouper` and `bins`.") if bins is not None: - group = DataArray(pd.cut(group.values, bins, **cut_kwargs), - group.coords, name=group.name + '_bins') - # RPA: we want to restore the original coordinates at some point! + binned = pd.cut(group.values, bins, **cut_kwargs) + new_dim_name = group.name + '_bins' + group = DataArray(binned, group.coords, name=new_dim_name) if grouper is not None: index = safe_cast_to_index(group) if not index.is_monotonic: