From 4923c63b45707aaf8187e5934be0823353c3c0e8 Mon Sep 17 00:00:00 2001 From: Florian Charlier <477844+trevismd@users.noreply.github.com> Date: Tue, 19 Nov 2024 09:54:41 +0100 Subject: [PATCH 01/16] update python requirement and GH actions Signed-off-by: Florian Charlier <477844+trevismd@users.noreply.github.com> --- .github/workflows/python-package.yml | 14 +++++++------- README.md | 2 +- requirements.txt | 4 ++-- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 5e78f6b..6572054 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -5,23 +5,23 @@ name: Python package on: push: - branches: [ master, dev, v0.2, v0.3, v0.4] + branches: [ master, dev] pull_request: - branches: [ master, dev, v0.2, v0.3, v0.4] + branches: [ master, dev, v0.7] jobs: build: - runs-on: ubuntu-20.04 + runs-on: ubuntu-22.04 strategy: fail-fast: false matrix: - python-version: [3.6, 3.7, 3.8, 3.9, '3.10', 3.11] + python-version: [3.8, 3.9, '3.10', 3.11] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies @@ -40,4 +40,4 @@ jobs: coverage run -m unittest discover tests coverage report -m - name: Upload Coverage to Codecov - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4 diff --git a/README.md b/README.md index c9a6126..aea83c2 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![Active Development](https://img.shields.io/badge/Maintenance%20Level-Actively%20Developed-brightgreen.svg)](https://gist.github.com/cheerfulstoic/d107229326a01ff0f333a1d3476e068d) ![coverage](https://raw.githubusercontent.com/trevismd/statannotations/master/coverage.svg) -![Python](https://img.shields.io/badge/Python-3.6%2B-blue) +![Python](https://img.shields.io/badge/Python-3.8--3.11-blue) [![Documentation Status](https://readthedocs.org/projects/statannotations/badge/?version=latest)](https://statannotations.readthedocs.io/en/master/?badge=latest) [![DOI](https://zenodo.org/badge/296015778.svg)](https://zenodo.org/badge/latestdoi/296015778) diff --git a/requirements.txt b/requirements.txt index c7dd725..89c6166 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ -numpy>=1.12.1 +numpy<=1.26.4,>=1.12.1 seaborn>=0.9.0,<0.12 -matplotlib>=2.2.2 +matplotlib>=2.2.2,<3.9 pandas>=0.23.0,<2.0.0 scipy>=1.1.0 statsmodels From 769e28f1a892343ddbe311865376ac43bf2a1a73 Mon Sep 17 00:00:00 2001 From: getzze Date: Fri, 28 Jun 2024 18:10:43 +0200 Subject: [PATCH 02/16] add compat file for different seaborn versions compatibility with seaborn>=0.11 pass all tests Signed-off-by: Florian Charlier <477844+trevismd@users.noreply.github.com> --- CHANGELOG.md | 7 + requirements.txt | 8 +- statannotations/Annotation.py | 7 +- statannotations/_GroupsPositions.py | 198 +++++-- statannotations/_Plotter.py | 338 ++++-------- statannotations/compat.py | 797 ++++++++++++++++++++++++++++ statannotations/utils.py | 30 +- tests/test_annotator.py | 29 + 8 files changed, 1123 insertions(+), 291 deletions(-) create mode 100644 statannotations/compat.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 2ddf94f..500025d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,10 @@ +## v0.7 +### v0.7.0 +#### Features +- Compatibility with pandas v2+ and seaborn above v0.12+ + (PR [#155](https://github.com/trevismd/statannotations/pull/155) by + [getzze](https://github.com/getzze)) + ## v0.6 ### v0.6.0 #### Features diff --git a/requirements.txt b/requirements.txt index 89c6166..31019d5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ -numpy<=1.26.4,>=1.12.1 -seaborn>=0.9.0,<0.12 -matplotlib>=2.2.2,<3.9 -pandas>=0.23.0,<2.0.0 +numpy>=1.12.1 +seaborn>=0.9.0 +matplotlib>=2.2.2 +pandas>=0.23.0 scipy>=1.1.0 statsmodels packaging diff --git a/statannotations/Annotation.py b/statannotations/Annotation.py index f2fbc91..b640bcd 100644 --- a/statannotations/Annotation.py +++ b/statannotations/Annotation.py @@ -47,7 +47,10 @@ def print_labels_and_content(self, sep=" vs. "): def check_data_stat_result(self): if not isinstance(self.data, StatResult): - warnings.warn("Annotation data has incorrect class." + - "Should be StatResult. Cannot annotate current pair.") + msg = ( + "Cannot annotate current pair. Annotation data has incorrect " + f"class, should be StatResult: {type(self.data)}" + ) + warnings.warn(msg) return False return True diff --git a/statannotations/_GroupsPositions.py b/statannotations/_GroupsPositions.py index 046d2b9..9c97881 100644 --- a/statannotations/_GroupsPositions.py +++ b/statannotations/_GroupsPositions.py @@ -1,70 +1,160 @@ +from __future__ import annotations + +from collections.abc import Iterator, Sequence +import itertools +from typing import TYPE_CHECKING +import warnings + import numpy as np +import pandas as pd + +if TYPE_CHECKING: + from .compat import TupleGroup, TGroupValue, THueValue + + +def get_group_names_and_labels( + group_names: Sequence[TGroupValue], + hue_names: Sequence[THueValue], +) -> tuple[list[TupleGroup], list[str]]: + tuple_group_names: list[TupleGroup] + if len(hue_names) == 0: + tuple_group_names = [(name,) for name in group_names] + labels = [str(name) for name in group_names] -from statannotations.utils import get_closest + else: + labels = [] + tuple_group_names = [] + for group_name, hue_name in itertools.product(group_names, hue_names): + tuple_group_names.append((group_name, hue_name)) + labels.append(f'{group_name}_{hue_name}') + + return tuple_group_names, labels class _GroupsPositions: - def __init__(self, plotter, group_names): - self._plotter = plotter - self._hue_names = self._plotter.hue_names + POSITION_TOLERANCE: float = 0.1 - if self._hue_names is not None: - nb_hues = len(self._hue_names) - if nb_hues == 1: - raise ValueError( - "Using hues with only one hue is not supported.") + width: float + tuple_group_names: list[TupleGroup] + labels: list[str] + _data: pd.DataFrame - self.hue_offsets = self._plotter.hue_offsets - self._axis_units = self.hue_offsets[1] - self.hue_offsets[0] + def __init__( + self, + group_names: Sequence[TGroupValue], + hue_names: Sequence[THueValue], + *, + dodge: bool = True, + gap: float = 0.0, + width: float = 0.8, + native_group_offsets: Sequence | None = None, + ) -> None: + self.gap = gap + self.dodge = dodge - self._groups_positions = { - np.round(self.get_group_axis_position(group_name), 1): group_name - for group_name in group_names - } + self._group_names = group_names + self._hue_names = hue_names + self.use_hue = len(hue_names) == 0 - self._groups_positions_list = sorted(self._groups_positions.keys()) + # Compute the coordinates of the groups (without hue) and the width + self.group_offsets, self.width = self._set_group_offsets( + group_names, native_group_offsets, width + ) + # Create the tuple (group, hue) and the labels + self.tuple_group_names, self.labels = get_group_names_and_labels(group_names, hue_names) - if self._hue_names is None: - self._axis_units = ((max(list(self._groups_positions.keys())) + 1) - / len(self._groups_positions)) + # Create dataframe with the groups, labels and positions + # this should be done last, when the other attributes are defined + self._data, self._artist_width = self._set_data(dodge=dodge, gap=gap) - self._axis_ranges = { - (pos - self._axis_units / 2, - pos + self._axis_units / 2, - pos): group_name - for pos, group_name in self._groups_positions.items()} + def _set_group_offsets( + self, + group_names: Sequence, + native_group_offsets: Sequence | None, + width: float, + ) -> tuple[Sequence, float]: + """Set the group offsets from native scale and scale the width.""" + group_offsets = list(range(len(group_names))) + if native_group_offsets is not None: + curated_offsets = [v for v in native_group_offsets] + if len(curated_offsets) != len(group_names): + msg = ( + 'The values of the categories with "native_scale=True" do not correspond ' + 'to the category names. Maybe some values are not finite?' + ) + warnings.warn(msg) + else: + group_offsets = curated_offsets + if len(curated_offsets) > 1: + native_width = np.min(np.diff(curated_offsets)) + width *= native_width - @property - def axis_positions(self): - return self._groups_positions + return group_offsets, width - @property - def axis_units(self): - return self._axis_units + def _set_data(self, dodge: bool, gap: float) -> tuple[pd.DataFrame, float]: + n_repeat = max(len(self._hue_names), 1) + group_positions = np.array(self.group_offsets) + positions = np.repeat(group_positions, n_repeat) + artist_width = float(self.width) + data = pd.DataFrame( + { + 'group': self.tuple_group_names, + 'label': self.labels, + 'pos': positions, + }, + ) + if dodge and self.use_hue: + n_hues = max(len(self._hue_names), 1) + artist_width /= n_hues + # evenly space range centered in zero (subtracting the mean) + offset = artist_width * (np.arange(n_hues) - (n_hues - 1) / 2) + tiled_offset = np.tile(offset, len(self._group_names)) + data['pos'] += tiled_offset + if gap and gap >= 0 and gap <= 1: + artist_width *= 1 - gap - def get_axis_pos_location(self, pos): - """ - Finds the x-axis location of a categorical variable - """ - for axis_range in self._axis_ranges: - if (pos >= axis_range[0]) & (pos <= axis_range[1]): - return axis_range[2] + return data, artist_width - def get_group_axis_position(self, group): - """ - group_name can be either a name "cat" or a tuple ("cat", "hue") + def find_group_at_pos(self, pos: float, *, verbose: bool = False) -> TupleGroup | None: + positions = self._data['pos'] + if len(positions) == 0: + return None + # Get the index of the closest position + index = (positions - pos).abs().idxmin() + found_pos = positions.loc[index] + + if verbose and abs(found_pos - pos) > self.POSITION_TOLERANCE: + # The requested position is not an artist position + msg = ( + 'Invalid x-position found. Are the same parameters passed to ' + 'seaborn and statannotations calls? Or are there few data points? ' + f'The closest group position to {pos} is {found_pos}' + ) + warnings.warn(msg) + return self._data.loc[index, 'group'] + + def get_group_axis_position(self, group: TupleGroup) -> float: + """Get the position of the group. + + group_name can be either a tuple ("group",) or a tuple ("group", "hue") """ - if self._plotter.plot_hues is None: - cat = group - hue_offset = 0 - else: - cat = group[0] - hue_level = group[1] - hue_offset = self._plotter.hue_offsets[ - self._plotter.hue_names.index(hue_level)] - - group_pos = self._plotter.group_names.index(cat) + hue_offset - return group_pos - - def find_closest(self, pos): - return get_closest(list(self._groups_positions_list), pos) + group_names = self._data['group'] + if group not in group_names: + msg = f'Group {group} was not found in the list: {group_names}' + raise ValueError(msg) + index = (group_names == group).idxmax() + pos = float(self._data.loc[index, 'pos']) + # round the position + return round(pos / self.POSITION_TOLERANCE) * self.POSITION_TOLERANCE + + @property + def artist_width(self) -> float: + return float(self._artist_width) + + def compatible_width(self, width: float) -> bool: + """Check if the rectangle width is smaller than the artist width.""" + return abs(width) <= 1.1 * self._artist_width + + def iter_groups(self) -> Iterator[tuple[TupleGroup, str, float]]: + """Iterate the groups and return a tuple (group_tuple, group_label, group_position).""" + yield from self._data[['group', 'label', 'pos']].itertuples(index=False, name=None) diff --git a/statannotations/_Plotter.py b/statannotations/_Plotter.py index a14ff79..8f3313e 100644 --- a/statannotations/_Plotter.py +++ b/statannotations/_Plotter.py @@ -1,35 +1,43 @@ -import itertools -import warnings +from __future__ import annotations + +from typing import TYPE_CHECKING -import matplotlib.pyplot as plt import numpy as np -import seaborn as sns +import matplotlib.pyplot as plt from matplotlib import lines from matplotlib.collections import PathCollection from matplotlib.patches import Rectangle from statannotations._GroupsPositions import _GroupsPositions from statannotations.utils import check_not_none, check_order_in_data, \ - check_pairs_in_data, render_collection, check_is_in, remove_null + check_pairs_in_data, render_collection, check_is_in, check_redundant_hue +from .compat import get_plotter + +if TYPE_CHECKING: + from .compat import TupleGroup, Struct + IMPLEMENTED_PLOTTERS = { 'seaborn': ['barplot', 'boxplot', 'stripplot', 'swarmplot', 'violinplot'] } - class _Plotter: def __init__(self, ax, pairs, data=None, x=None, y=None, hue=None, order=None, hue_order=None, verbose=False, **plot_params): self.ax = ax self._fig = plt.gcf() - check_not_none("pairs", pairs) - group_coord = y if plot_params.get("orient") == "h" else x + check_not_none('pairs', pairs) + group_coord = y if plot_params.get('orient') in ('h', 'y') else x + self.is_redundant_hue = check_redundant_hue(data, group_coord, hue, hue_order) + if self.is_redundant_hue: + hue = None + hue_order = None check_order_in_data(data, group_coord, order) check_pairs_in_data(pairs, data, group_coord, hue, hue_order) self.pairs = pairs self._struct_pairs = None self.verbose = verbose - self.orient = plot_params.get("orient", "v") + self.orient = plot_params.get('orient', 'v') def get_transform_func(self, kind: str): """ @@ -44,8 +52,11 @@ def get_transform_func(self, kind: str): This function should be called whenever axes limits are altered. """ - check_is_in(kind, ['data_to_ax', 'ax_to_data', 'pix_to_ax', 'all'], - 'kind') + check_is_in( + kind, + ['data_to_ax', 'ax_to_data', 'pix_to_ax', 'all'], + label='kind', + ) if kind == 'pix_to_ax': return self.ax.transAxes.inverted() @@ -75,26 +86,45 @@ def struct_pairs(self): class _SeabornPlotter(_Plotter): - def __init__(self, ax, pairs, plot='boxplot', data=None, x=None, - y=None, hue=None, order=None, hue_order=None, verbose=False, - **plot_params): + structs: list[Struct] - _Plotter.__init__(self, ax, pairs, data, x, y, hue, order, hue_order, - verbose, **plot_params) + def __init__( + self, ax, pairs, plot='boxplot', data=None, x=None, + y=None, hue=None, order=None, hue_order=None, verbose=False, + **plot_params, + ): + super().__init__( + ax, pairs, data, x, y, hue, order, hue_order, verbose, **plot_params + ) self.check_plot_is_implemented(plot) self.plot = plot - self.plotter = self._get_plotter(plot, x, y, hue, data, order, - hue_order, **plot_params) - - self.group_names, self.labels = self._get_group_names_and_labels() - self.groups_positions = _GroupsPositions(self.plotter, - self.group_names) + self.plotter = self._get_plotter( + plot, + x=x, + y=y, + hue=hue, + data=data, + order=order, + hue_order=hue_order, + is_redundant_hue=self.is_redundant_hue, + **plot_params, + ) + + self.groups_positions = _GroupsPositions( + self.plotter.group_names, + self.plotter.hue_names, + width=self.plotter.width, + gap=self.plotter.gap, + dodge=self.plotter.dodge, + native_group_offsets=self.plotter.native_group_offsets, + ) + self.tuple_group_names = self.groups_positions.tuple_group_names self.reordering = None self.value_maxes = self._generate_value_maxes() self.structs = self._get_structs() - self.pairs = pairs + self.pairs = self.plotter.parse_pairs(pairs, self.structs, formatter=plot_params.get('formatter')) self._struct_pairs = self._get_group_struct_pairs() self._value_stack_arr = np.array( @@ -110,140 +140,33 @@ def value_stack_arr(self): return self._value_stack_arr # noinspection PyProtectedMember - def _get_plotter(self, plot, x, y, hue, data, order, hue_order, - **plot_params): - dodge = plot_params.pop("dodge", None) - self.fix_and_warn(dodge, hue, plot) - - if plot == 'boxplot': - plotter = sns.categorical._BoxPlotter( - - x, y, hue, data, order, hue_order, - orient=plot_params.get("orient"), - width=plot_params.get("width", 0.8), - dodge=True, - fliersize=plot_params.get("fliersize", 5), - linewidth=plot_params.get("linewidth"), - saturation=.75, color=None, palette=None) - - elif plot == 'swarmplot': - plotter = sns.categorical._SwarmPlotter( - x, y, hue, data, order, hue_order, - orient=plot_params.get("orient"), - dodge=True, color=None, palette=None) - - elif plot == 'stripplot': - plotter = sns.categorical._StripPlotter( - x, y, hue, data, order, hue_order, - jitter=plot_params.get("jitter", True), - orient=plot_params.get("orient"), - dodge=True, color=None, palette=None) - - elif plot == 'barplot': - plotter = sns.categorical._BarPlotter( - x, y, hue, data, order, hue_order, - estimator=plot_params.get("estimator", np.mean), - ci=plot_params.get("ci", 95), - n_boot=plot_params.get("nboot", 1000), - units=plot_params.get("units"), - orient=plot_params.get("orient"), - seed=plot_params.get("seed"), - color=None, palette=None, saturation=.75, - errcolor=".26", errwidth=plot_params.get("errwidth"), - capsize=None, - dodge=True) - - elif plot == "violinplot": - plotter = sns.categorical._ViolinPlotter( - x, y, hue, data, order, hue_order, - bw=plot_params.get("bw", "scott"), - cut=plot_params.get("cut", 2), - scale=plot_params.get("scale", "area"), - scale_hue=plot_params.get("scale_hue", True), - gridsize=plot_params.get("gridsize", 100), - width=plot_params.get("width", 0.8), - inner=plot_params.get("inner", None), - split=plot_params.get("split", False), - dodge=True, orient=plot_params.get("orient"), - linewidth=plot_params.get("linewidth"), color=None, - palette=None, saturation=.75) - - else: - raise NotImplementedError( - f"Only {render_collection(IMPLEMENTED_PLOTTERS)} are " - f"supported.") - - return plotter - - def _get_group_names_and_labels(self): - - if self.plotter.plot_hues is None: - group_names = self.plotter.group_names - labels = group_names - - else: - labels = [] - group_names = [] - for group_name, hue_name in \ - itertools.product(self.plotter.group_names, - self.plotter.hue_names): - group_names.append((group_name, hue_name)) - labels.append(f'{group_name}_{hue_name}') - - return group_names, labels - - def _generate_value_maxes(self): - """ - given plotter and the names of two categorical variables, - returns highest y point drawn between those two variables before - annotations. - - The highest data point is often not the highest item drawn - (eg, error bars and/or bar charts). - """ - - value_maxes = {name: 0 for name in self.group_names} - - data_to_ax = self.get_transform_func('data_to_ax') - - if self.plot == 'violinplot': - value_maxes = self._get_value_maxes_violin(value_maxes, data_to_ax) - - else: - for child in self.ax.get_children(): + def _get_plotter(self, plot, **kwargs): + return get_plotter(plot, **kwargs) - group_name, value_pos = self._get_value_pos(child, data_to_ax) - - if (value_pos is not None - and value_pos > value_maxes[group_name]): - value_maxes[group_name] = value_pos - - return value_maxes - - def _get_structs(self): - structs = [ + def _get_structs(self) -> list[Struct]: + structs: list[Struct] = [ { 'group': group_name, - 'label': self.labels[b_idx], - 'group_coord': (self.groups_positions - .get_group_axis_position(group_name)), - 'group_data': self._get_group_data(group_name), - 'value_max': self.value_maxes[group_name] + 'label': label, + 'group_coord': position, + 'group_data': self.plotter.get_group_data(group_name), + 'value_max': float(self.value_maxes[group_name]), } - for b_idx, group_name in enumerate(self.group_names)] + for group_name, label, position in self.groups_positions.iter_groups() + ] # Sort the group data structures by position along the groups axis structs = sorted(structs, key=lambda struct: struct['group_coord']) # Add the index position in the list of groups along the groups axis - structs = [dict(struct, group_i=i) - for i, struct in enumerate(structs)] + structs = [{**struct, 'group_i': i} for i, struct in enumerate(structs)] return structs def _get_group_struct_pairs(self): - group_structs_dic = {struct['group']: struct - for struct in self.structs} + group_structs_dic: dict[TupleGroup, Struct] = { + struct['group']: struct for struct in self.structs + } group_struct_pairs = [] @@ -254,60 +177,38 @@ def _get_group_struct_pairs(self): group_struct2 = dict(group_structs_dic[group2], i_group_pair=i_group_pair) - if group_struct1['group_coord'] <= group_struct2['group_coord']: + keep_order = group_struct1['group_coord'] <= group_struct2['group_coord'] + if keep_order: group_struct_pairs.append((group_struct1, group_struct2)) - else: group_struct_pairs.append((group_struct2, group_struct1)) return group_struct_pairs - def _get_group_data_with_hue(self, group_name): - cat = group_name[0] - group_data, index = self._get_group_data_from_plotter(cat) - - hue_level = group_name[1] - hue_mask = self.plotter.plot_hues[index] == hue_level - - return group_data[hue_mask] - - def _get_group_data_from_plotter(self, cat): - index = self.plotter.group_names.index(cat) - - return self.plotter.plot_data[index], index - - def _get_group_data_without_hue(self, group_name): - group_data, _ = self._get_group_data_from_plotter(group_name) - - return group_data - - def _get_group_data(self, group_name): + def _generate_value_maxes(self): """ - group_name can be either a name "cat" or a tuple ("cat", "hue") + given plotter and the names of two categorical variables, + returns highest y point drawn between those two variables before + annotations. - Almost a duplicate of seaborn's code, because there is not - direct access to the group_data in the respective Plotter class. + The highest data point is often not the highest item drawn + (eg, error bars and/or bar charts). """ - if self.plotter.plot_hues is None: - data = self._get_group_data_without_hue(group_name) - else: - data = self._get_group_data_with_hue(group_name) - group_data = remove_null(data) + value_maxes = {name: 0 for name in self.tuple_group_names} + + data_to_ax = self.get_transform_func('data_to_ax') - return group_data + if self.plot == 'violinplot' and self.plotter.has_violin_support: + return self.plotter._populate_value_maxes_violin(value_maxes, data_to_ax) + + for child in self.ax.get_children(): + group_name, value_pos = self._get_value_pos(child, data_to_ax) + if value_pos is None: + continue + if value_pos > value_maxes[group_name]: + value_maxes[group_name] = value_pos - def _get_value_maxes_violin(self, value_maxes, data_to_ax): - for group_idx, group_name in enumerate(self.plotter.group_names): - if self.plotter.hue_names: - for hue_idx, hue_name in enumerate(self.plotter.hue_names): - value_pos = max(self.plotter.support[group_idx][hue_idx]) - value_maxes[(group_name, hue_name)] = \ - data_to_ax.transform((0, value_pos))[1] - else: - value_pos = max(self.plotter.support[group_idx]) - value_maxes[group_name] = data_to_ax.transform((0, - value_pos))[1] return value_maxes def _get_value_pos(self, child, data_to_ax): @@ -323,24 +224,19 @@ def _get_value_pos(self, child, data_to_ax): return None, None def _get_value_pos_for_path_collection(self, child, data_to_ax): - group_coord = {"v": 0, "h": 1}[self.orient] + group_coord = {'v': 0, 'h': 1}[self.orient] value_coord = (group_coord + 1) % 2 - direction = {"v": 1, "h": -1}[self.orient] + direction = {'v': 1, 'h': -1}[self.orient] value_max = child.properties()['offsets'][:, value_coord].max() group_pos = float(np.round(np.nanmean( child.properties()['offsets'][:, group_coord]), 1)) - if group_pos not in self.groups_positions.axis_positions: - if self.verbose: - warnings.warn( - "Invalid x-position found. Are the same parameters passed " - "to seaborn and statannotations calls? or are there few " - "data points?") - group_pos = self.groups_positions.find_closest(group_pos) - - group_name = self.groups_positions.axis_positions[group_pos] + group_name = self.groups_positions.find_group_at_pos( + group_pos, + verbose=self.verbose, + ) value_pos = data_to_ax.transform((0, value_max)[::direction]) @@ -350,23 +246,25 @@ def _get_value_pos_for_line2d_or_rectangle(self, child, data_to_ax): bbox = self.ax.transData.inverted().transform( child.get_window_extent(self.fig.canvas.get_renderer())) - group_coord = {"v": 0, "h": 1}[self.orient] - direction = {"v": 1, "h": -1}[self.orient] + # Get the group_name from the coordinates of the group + group_coord = {'v': 0, 'h': 1}[self.orient] + rect_min = bbox[:, group_coord].min() + rect_max = bbox[:, group_coord].max() + rect_width = rect_max - rect_min + rect_center = (rect_max + rect_min) / 2 - value_coord = (group_coord + 1) % 2 - - if ((bbox[:, group_coord].max() - bbox[:, group_coord].min()) - > 1.1 * self.groups_positions.axis_units): + if self.groups_positions.compatible_width(rect_width): return None, None - raw_group_pos = np.round(bbox[:, group_coord].mean(), 1) - group_pos = self.groups_positions.get_axis_pos_location(raw_group_pos) - - if group_pos not in self.groups_positions.axis_positions: - return None, None + group_name = self.groups_positions.find_group_at_pos( + rect_center, + verbose=False, + ) - group_name = self.groups_positions.axis_positions[group_pos] + # Get the value from the coordinate of the values + value_coord = (group_coord + 1) % 2 value_pos = bbox[:, value_coord].max() + direction = {'v': 1, 'h': -1}[self.orient] value_pos = data_to_ax.transform((0, value_pos)[::direction]) return group_name, value_pos[value_coord] @@ -386,23 +284,13 @@ def _absolute_group_struct_pair_in_tuple_x_diff(group_struct_pair): - group_struct_pair[1][0]['group_coord']) @staticmethod - def check_plot_is_implemented(plot, engine="seaborn"): + def check_plot_is_implemented(plot, engine='seaborn'): if plot not in IMPLEMENTED_PLOTTERS[engine]: - raise NotImplementedError( - f"Only {render_collection(IMPLEMENTED_PLOTTERS[engine])} are " - f"supported with {engine} engine.") - - @staticmethod - def fix_and_warn(dodge, hue, plot): - if dodge is False and hue is not None: - raise ValueError("`dodge` cannot be False in statannotations.") - - if plot in ("swarmplot", 'stripplot') and hue is not None: - if dodge is None: - warnings.warn( - "Implicitly setting dodge to True as it is necessary in " - "statannotations. It must have been True for the seaborn " - "call to yield consistent results when using `hue`.") + msg = ( + f'Only {render_collection(IMPLEMENTED_PLOTTERS[engine])} are ' + f'supported with {engine} engine.' + ) + raise NotImplementedError(msg) def get_value_lim(self): if self.orient == 'v': diff --git a/statannotations/compat.py b/statannotations/compat.py new file mode 100644 index 0000000..83912a3 --- /dev/null +++ b/statannotations/compat.py @@ -0,0 +1,797 @@ +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any, Callable +import warnings + +import numpy as np +import pandas as pd +import seaborn as sns + +if TYPE_CHECKING: + from typing import NotRequired, Union, TypedDict, TypeVar + + import pandas as pd + + TGroupValue = TypeVar('TGroupValue') + THueValue = TypeVar('THueValue') + TupleGroup = Union[tuple[TGroupValue], tuple[TGroupValue, THueValue]] + + class Struct(TypedDict): + group: TupleGroup + label: str + group_coord: float + group_data: Sequence + value_max: float + group_i: NotRequired[int] + +from .utils import remove_null + +## Only allow seaborn <0.12 or >=0.13 +sns_version = tuple(int(v) for v in sns.__version__.split('.')[:3]) + +if sns_version < (0, 12, 0): + from seaborn.categorical import ( + _BoxPlotter, + _SwarmPlotter, + _StripPlotter, + _BarPlotter, + _ViolinPlotter, + _CategoricalPlotter, + ) + +elif sns_version < (0, 13, 0): + from seaborn.categorical import ( + _BoxPlotter, + _BarPlotter, + _ViolinPlotter, + _CategoricalPlotterNew, + _CategoricalPlotter, + ) + _SwarmPlotter = _StripPlotter = _CategoricalPlotterNew + +else: + _CategoricalPlotter = sns.categorical._CategoricalPlotter + + +def fix_and_warn(dodge, hue, plot): + if dodge is False and hue is not None: + raise ValueError('`dodge` cannot be False in statannotations.') + + is_plot_type = plot in ('swarmplot', 'stripplot') + if is_plot_type and hue is not None and dodge is not True: + msg = ( + 'Implicitly setting dodge to True as it is necessary in ' + 'statannotations. It must have been True for the seaborn ' + 'call to yield consistent results when using `hue`.' + ) + warnings.warn(msg) + +def _get_categorical_plotter( + plot_type: str, + *, + x, + y, + hue, + data, + order, + **plot_params, +) -> _CategoricalPlotter: + # Keyword arguments for the plotters + kwargs = { + 'data': data, + 'order': order, + 'orient': plot_params.get('orient'), + 'color': None, + } + variables = {'x': x, 'y': y, 'hue': hue} + new_kwargs = { + 'variables': variables, + 'legend': plot_params.get('legend'), + **kwargs, + } + + # Boxplot + if plot_type == 'boxplot': + # Pre-0.13 seaborn API + if sns_version < (0, 13, 0): + kwargs = { + 'hue_order': plot_params.get('hue_order'), + 'dodge': True, + 'palette': None, + 'saturation': 0.75, + 'linewidth': plot_params.get('linewidth'), + 'width': plot_params.get('width', 0.8), + 'fliersize': plot_params.get('fliersize', 5), + **variables, + **kwargs, + } + return _BoxPlotter(**kwargs) + + # 0.13 seaborn API + return _CategoricalPlotter(**new_kwargs) + + elif plot_type == 'barplot': + # Pre-0.13 seaborn API + if sns_version < (0, 13, 0): + kwargs = { + 'hue_order': plot_params.get('hue_order'), + 'dodge': True, + 'palette': None, + 'saturation': 0.75, + 'estimator': plot_params.get('estimator', np.mean), + 'n_boot': plot_params.get('n_boot', 1000), + 'units': plot_params.get('units'), + 'seed': plot_params.get('seed'), + 'errcolor': '.26', + 'errwidth': plot_params.get('errwidth'), + 'capsize': None, + **variables, + **kwargs, + } + + # Pre-0.12 seaborn API + if sns_version < (0, 12, 0): + kwargs = { + 'ci': plot_params.get('ci', 95), + **kwargs, + } + return _BarPlotter(**kwargs) + + # 0.12 seaborn API + kwargs = { + 'errorbar': plot_params.get('errorbar', ('ci', 95)), + 'width': plot_params.get('width', 0.8), + **kwargs, + } + return _BarPlotter(**kwargs) + + # 0.13 seaborn API + return _CategoricalPlotter(**new_kwargs) + + elif plot_type == 'violinplot': + # Pre-0.13 seaborn API + if sns_version < (0, 13, 0): + kwargs = { + 'hue_order': plot_params.get('hue_order'), + 'dodge': True, + 'palette': None, + 'saturation': 0.75, + 'linewidth': plot_params.get('linewidth'), + 'width': plot_params.get('width', 0.8), + 'bw': plot_params.get('bw', 'scott'), + 'cut': plot_params.get('cut', 2), + 'scale': plot_params.get('scale', 'area'), + 'scale_hue': plot_params.get('scale_hue', True), + 'gridsize': plot_params.get('gridsize', 100), + 'inner': plot_params.get('inner', None), + 'split': plot_params.get('split', False), + **variables, + **kwargs, + } + return _ViolinPlotter(**kwargs) + + # 0.13 seaborn API + return _CategoricalPlotter(**new_kwargs) + + elif plot_type == 'swarmplot': + # Pre-0.12 seaborn API + if sns_version < (0, 12, 0): + kwargs = { + 'hue_order': plot_params.get('hue_order'), + 'dodge': True, + 'palette': None, + **variables, + **kwargs, + } + return _SwarmPlotter(**kwargs) + + # Pre-0.13 seaborn API + if sns_version < (0, 13, 0): + if 'color' in new_kwargs: + new_kwargs.pop('color') + return _CategoricalPlotterNew(require_numeric=False, **new_kwargs) + + # 0.13 seaborn API + return _CategoricalPlotter(**new_kwargs) + + elif plot_type == 'stripplot': + # Pre-0.12 seaborn API + if sns_version < (0, 12, 0): + kwargs = { + 'hue_order': plot_params.get('hue_order'), + 'dodge': True, + 'palette': None, + 'jitter': plot_params.get('jitter', True), + **variables, + **kwargs, + } + return _StripPlotter(**kwargs) + + # Pre-0.13 seaborn API + if sns_version < (0, 13, 0): + if 'color' in new_kwargs: + new_kwargs.pop('color') + return _CategoricalPlotterNew(require_numeric=False, **new_kwargs) + + # 0.13 seaborn API + return _CategoricalPlotter(**new_kwargs) + + msg = f'Plot type {plot_type!r} is not supported' + raise NotImplementedError(msg) + + +class Wrapper: + """Compatibility wrapper for seaborn.categorical._CategoricalPlotter.""" + + width: float + gap: float + dodge: bool + is_redundant_hue: bool + native_group_offsets: Sequence | None + + def __init__( + self, + plot_type: str, + *, + is_redundant_hue: bool = False, + **kwargs, + ) -> None: + dodge = kwargs.get('dodge') + hue = kwargs.get('hue') + fix_and_warn(dodge, hue, plot_type) + # dodge needs to be True for statannotations + self.dodge = True + + self.gap = kwargs.get('gap', 0.0) + self.width = kwargs.get('width', 0.8) + self.native_group_offsets = None + self.is_redundant_hue = is_redundant_hue + + @property + def has_violin_support(self) -> bool: + """Whether the max values of a violin plot can be extracted from the plotter.""" + return hasattr(self, '_populate_value_maxes_violin') + + @property + def has_hue(self) -> bool: + raise NotImplementedError + + @property + def group_names(self) -> list: + raise NotImplementedError + + @property + def hue_names(self) -> list: + raise NotImplementedError + + def get_group_data(self, group_name): + """Get the data for the (group[, hue]) tuple. + + group_name can be either a tuple ("cat",) or a tuple ("cat", "hue") + """ + raise NotImplementedError + + def parse_pairs( + self, + pairs: list[tuple], + structs: list[Struct], + *, + formatter: Callable | None = None, + ) -> list[tuple[TupleGroup, TupleGroup]]: + struct_groups = [struct['group'] for struct in structs] + ret: list[tuple[TupleGroup, TupleGroup]] = [] + + def format_group(value) -> TupleGroup: + """Format the group (but not the optional hue).""" + return value if isinstance(value, tuple) else (value,) + + for pair in pairs: + if not isinstance(pair, Sequence) or len(pair) != 2: + msg = f'pair {pair} is not a 2-tuple, skipping.' + warnings.warn(msg) + continue + # Format the groups + new_pair = tuple(format_group(v) for v in pair) + + # Check that the groups are valid group names + valid_group = True + for i, group in enumerate(new_pair): + if group not in struct_groups: + msg = ( + f'cannot find group{i} of pair in the group tuples: ' + f'{group} not in {struct_groups}' + ) + warnings.warn(msg) + valid_group = False + if not valid_group: + continue + + ret.append(new_pair) + + if len(ret) == 0: + msg = ( + f'pairs are empty after parsing: original_pairs={pairs}\n' + f'not in group_list={struct_groups}' + ) + raise ValueError(msg) + return ret + + +class CategoricalPlotterWrapper_v11(Wrapper): + """Compatibility wrapper for seaborn v11.""" + + _plotter: _CategoricalPlotter + + def __init__(self, + plot_type: str, + *, + x, + y, + hue, + data, + order, + **plot_params, + ) -> None: + super().__init__(plot_type, hue=hue, **plot_params) + + self._plotter = _get_categorical_plotter( + plot_type, + x=x, + y=y, + hue=hue, + data=data, + order=order, + **plot_params, + ) + + @property + def has_hue(self) -> bool: + return self._plotter.plot_hues is not None + + @property + def group_names(self) -> list: + return self._plotter.group_names + + @property + def hue_names(self) -> list: + if self.is_redundant_hue: + return [] + if not self.has_hue: + return [] + # Can be None, force to be an empty list + return self._plotter.hue_names or [] + + def _get_group_data_from_plotter( + self, + cat: TGroupValue | tuple[TGroupValue], + ) -> tuple[Any, int]: + if isinstance(cat, tuple): + cat = cat[0] + index = self.group_names.index(cat) + + return self._plotter.plot_data[index], index + + def _get_group_data_with_hue(self, group_name: tuple): + cat = group_name[0] + group_data, index = self._get_group_data_from_plotter(cat) + + hue_level = group_name[1] + hue_mask = self._plotter.plot_hues[index] == hue_level + + return group_data[hue_mask] + + def _get_group_data_without_hue(self, group_name): + group_data, _ = self._get_group_data_from_plotter(group_name) + + return group_data + + def get_group_data(self, group_name): + """Get the data for the (group[, hue]) tuple. + + group_name can be either a tuple ("cat",) or a tuple ("cat", "hue") + + Almost a duplicate of seaborn's code, because there is not + direct access to the group_data in the respective Plotter class. + """ + if not isinstance(group_name, tuple) or len(group_name) == 1: + data = self._get_group_data_without_hue(group_name) + else: + data = self._get_group_data_with_hue(group_name) + + group_data = remove_null(data) + + return group_data + + def _populate_value_maxes_violin(self, value_maxes: dict[TupleGroup, float], data_to_ax) -> dict[TupleGroup, float]: + """Populate the max values for violinplot. + + The keys should follow the tuples of `get_group_names_and_labels`. + """ + dict_keys = list(value_maxes.keys()) + for group_idx, group_name in enumerate(self._plotter.group_names): + if self.has_hue: + for hue_idx, hue_name in enumerate(self._plotter.hue_names): + value_pos = max(self._plotter.support[group_idx][hue_idx]) + key = (group_name, hue_name) + if key not in value_maxes: + msg = f'key {key} was not found in the dict: {dict_keys}' + warnings.warn(msg) + continue + value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + else: + value_pos = max(self._plotter.support[group_idx]) + key = group_name + if key not in value_maxes: + msg = f'key {key} was not found in the dict: {dict_keys}' + warnings.warn(msg) + continue + value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + return value_maxes + + +class CategoricalPlotterWrapper_v12(Wrapper): + """Compatibility wrapper for seaborn v12.""" + + _plotter: _CategoricalPlotter + + def __init__(self, + plot_type: str, + *, + x, + y, + hue, + data, + order, + **plot_params, + ) -> None: + super().__init__(plot_type, hue=hue, **plot_params) + self._group_names = None + + self._plotter = _get_categorical_plotter( + plot_type, + x=x, + y=y, + hue=hue, + data=data, + order=order, + **plot_params, + ) + + if isinstance(self._plotter, _CategoricalPlotterNew): + # Order the group variables + native_scale = plot_params.get('native_scale', False) + if native_scale: + msg = '`native_scale=True` is not supported with seaborn==0.12, update to seaborn>=0.13' + raise ValueError(msg) + formatter = plot_params.get('formatter') + if formatter is not None: + msg = '`formatter` is not supported with seaborn==0.12, update to seaborn>=0.13' + raise ValueError(msg) + self._order_variable(order=order, native_scale=native_scale, formatter=formatter) + + # Native scaling of the group variable + if native_scale: + self.native_group_offsets = self._plotter.plot_data[self.axis] + + @property + def has_hue(self) -> bool: + if self.is_redundant_hue: + return False + if hasattr(self._plotter, 'hue_names'): + # Can be None, force to be an empty list + if self._plotter.hue_names: + return True + return False + + else: + # _CategoricalPlotterNew + return self._plotter.variables.get('hue') is not None + + @property + def group_names(self) -> list: + if hasattr(self._plotter, 'group_names'): + return self._plotter.group_names + + else: + # _CategoricalPlotterNew + group_names = self._plotter.var_levels[self._plotter.cat_axis] + if isinstance(group_names, pd.Index): + return group_names.tolist() + return group_names + + @property + def hue_names(self): + if self.is_redundant_hue: + return [] + + if hasattr(self._plotter, 'hue_names'): + # Can be None, force to be an empty list + return self._plotter.hue_names or [] + + else: + if 'hue' not in self._plotter.var_levels: + return [] + hue_names = self._plotter.var_levels['hue'] + if isinstance(hue_names, pd.Index): + return hue_names.tolist() + return hue_names + + def _order_variable( + self, + *, + order, + native_scale: bool = False, + formatter: Callable | None = None, + ) -> None: + # Do not order if not categorical and native scale + if self._plotter.var_types.get(self._plotter.cat_axis) != 'categorical' and native_scale: + return + + # Order the group variable + self._plotter.scale_categorical(self._plotter.cat_axis, order=order, formatter=None) + + def _get_group_data_from_plotter( + self, + cat: TGroupValue | tuple[TGroupValue], + ) -> tuple[Any, int]: + if isinstance(cat, tuple): + cat = cat[0] + index = self.group_names.index(cat) + + return self._plotter.plot_data[index], index + + def _get_group_data_with_hue(self, group_name: tuple): + cat = group_name[0] + group_data, index = self._get_group_data_from_plotter(cat) + + hue_level = group_name[1] + hue_mask = self._plotter.plot_hues[index] == hue_level + + return group_data[hue_mask] + + def _get_group_data_without_hue(self, group_name): + group_data, _ = self._get_group_data_from_plotter(group_name) + + return group_data + + def get_group_data(self, group_name): + """Get the data for the (group[, hue]) tuple. + + group_name can be either a tuple ("cat",) or a tuple ("cat", "hue") + + Almost a duplicate of seaborn's code, because there is not + direct access to the group_data in the respective Plotter class. + """ + if isinstance(self._plotter, _CategoricalPlotterNew): + return self._get_group_data_new(group_name) + + if not isinstance(group_name, tuple) or len(group_name) == 1: + data = self._get_group_data_without_hue(group_name) + else: + data = self._get_group_data_with_hue(group_name) + + group_data = remove_null(data) + + return group_data + + def _get_group_data_new(self, group_name: TupleGroup) -> pd.Series: + """Get the data for the (group[, hue]) tuple. + + group_name can be either a "cat" or a tuple ("cat", "hue") + + Almost a duplicate of seaborn's code, because there is not + direct access to the group_data in the respective Plotter class. + """ + if not isinstance(self._plotter, _CategoricalPlotterNew): + msg = '`self._plotter` should be a `_CategoricalPlotterNew` instance.' + raise TypeError(msg) + + # the value variable is the one that is no the group axis + tgroup = group_name if isinstance(group_name, tuple) else (group_name,) + # 'x' if vertical or 'y' if horizontal + cat_var = self._plotter.cat_axis + # opposite: 'y' if vertical or 'x' if horizontal + value_var = {'x': 'y', 'y': 'x'}[cat_var] + group = tgroup[0] + hue = None + iter_vars = [cat_var] + if self.has_hue and len(tgroup) > 1: + iter_vars.append('hue') + hue = tgroup[1] + + for sub_vars, sub_data in self._plotter.iter_data(iter_vars): + if sub_vars[cat_var] != group: + continue + if hue is not None and sub_vars['hue'] != hue: + continue + + # Found a matching group, return the data + group_data = remove_null(sub_data[value_var]) + return group_data + + +class CategoricalPlotterWrapper_v13(Wrapper): + """Compatibility wrapper for seaborn v11.""" + + _plotter: _CategoricalPlotter + + def __init__(self, + plot_type: str, + *, + x, + y, + hue, + data, + order, + **plot_params, + ) -> None: + super().__init__(plot_type, hue=hue, **plot_params) + self._group_names = None + + variables = {'x': x, 'y': y, 'hue': hue} + kwargs = { + 'data': data, + 'variables': variables, + 'order': order, + 'orient': plot_params.get('orient'), + 'color': None, + 'legend': plot_params.get('legend'), + } + self._plotter = _CategoricalPlotter(**kwargs) + + # Order the group variables + native_scale = plot_params.get('native_scale', False) + formatter = plot_params.get('formatter') + self._order_variable(order=order, native_scale=native_scale, formatter=formatter) + + # Native scaling of the group variable + if native_scale: + self.native_group_offsets = self._plotter.plot_data[self.axis] + + @property + def has_hue(self) -> bool: + # Get variables mapping + if self.is_redundant_hue: + return False + if self._plotter.variables.get('hue') is None: + return False + return True + + @property + def axis(self) -> str: + return {'x': 'x', 'v': 'x', 'y': 'y', 'h': 'y'}[self._plotter.orient] + + @property + def group_names(self) -> list: + if self._group_names is not None: + return self._group_names + return self._plotter.var_levels[self.axis] + + @property + def hue_names(self): + if not self.has_hue or 'hue' not in self._plotter.var_levels: + return [] + return self._plotter.var_levels['hue'] + + def _order_variable( + self, + *, + order, + native_scale: bool = False, + formatter: Callable | None = None, + raw_groups: bool = False, + ) -> None: + if raw_groups: + # Save the group names before formatting, because they are transformed to str + self._group_names = list(self._plotter.var_levels[self.axis]) + + # Do not order if not categorical and native scale + if self._plotter.var_types.get(self.axis) != 'categorical' and native_scale: + return + + # Order the group variable + self._plotter.scale_categorical(self.axis, order=order, formatter=formatter) + + if raw_groups: + # Reorder group names + formatter = formatter if callable(formatter) else str + ordered_group_names = list(self._plotter.var_levels[self.axis]) + formatted_group_names = [formatter(v) for v in self._group_names] + + # find permutation indices + indices = [ordered_group_names.index(val) for val in formatted_group_names] + # Reorder raw group names + self._group_names = [self._group_names[i] for i in indices] + + def get_group_data(self, group_name: TupleGroup) -> pd.Series: + """Get the data for the (group[, hue]) tuple. + + group_name can be either a "cat" or a tuple ("cat", "hue") + + Almost a duplicate of seaborn's code, because there is not + direct access to the group_data in the respective Plotter class. + """ + # the value variable is the one that is not the group axis + value_var = {'x': 'y', 'y': 'x'}[self.axis] + tgroup = group_name if isinstance(group_name, tuple) else (group_name,) + group = tgroup[0] + hue = None + iter_vars = [self.axis] + if self.has_hue and len(tgroup) > 1: + iter_vars.append('hue') + hue = tgroup[1] + + for sub_vars, sub_data in self._plotter.iter_data(iter_vars): + if sub_vars[self.axis] != group: + continue + if hue is not None and sub_vars['hue'] != hue: + continue + + # Found a matching group, return the data + group_data = remove_null(sub_data[value_var]) + return group_data + + def parse_pairs( + self, + pairs: list[tuple], + structs: list[Struct], + *, + formatter: Callable | None = None, + ) -> list[tuple[TupleGroup, TupleGroup]]: + struct_groups = [struct['group'] for struct in structs] + ret: list[tuple[TupleGroup, TupleGroup]] = [] + formatter = formatter if callable(formatter) else str + + def format_group(value, formatter) -> TupleGroup: + """Format the group (but not the optional hue).""" + tvalue = value if isinstance(value, tuple) else (value,) + group = tvalue[0] + return tuple([formatter(group), *tvalue[1:]]) + + for pair in pairs: + if not isinstance(pair, Sequence) or len(pair) != 2: + msg = f'pair {pair} is not a 2-tuple, skipping.' + warnings.warn(msg) + continue + # Format the groups + new_pair = tuple(format_group(v, formatter) for v in pair) + + # Check that the groups are valid group names + valid_group = True + for i, group in enumerate(new_pair): + if group not in struct_groups: + msg = ( + f'cannot find group{i} of pair in the group tuples: ' + f'{group} not in {struct_groups}' + ) + warnings.warn(msg) + valid_group = False + if not valid_group: + continue + + ret.append(new_pair) + + if len(ret) == 0: + msg = ( + f'pairs are empty after parsing: original_pairs={pairs}' + ) + raise ValueError(msg) + return ret + + + +# Define CategoricalPlotterWrapper depending on seaborn version +if sns_version >= (0, 13, 0): + CategoricalPlotterWrapper = CategoricalPlotterWrapper_v13 +elif sns_version >= (0, 12, 0): + CategoricalPlotterWrapper = CategoricalPlotterWrapper_v12 + # # CategoricalPlotterWrapper = CategoricalPlotterWrapper_v12 + # msg = f'Seaborn version {sns_version} is not supported.' + # raise NotImplementedError(msg) +else: + CategoricalPlotterWrapper = CategoricalPlotterWrapper_v11 + + +def get_plotter(plot_type: str, **kwargs) -> CategoricalPlotterWrapper: + return CategoricalPlotterWrapper(plot_type, **kwargs) diff --git a/statannotations/utils.py b/statannotations/utils.py index db0c69a..748b77f 100644 --- a/statannotations/utils.py +++ b/statannotations/utils.py @@ -1,7 +1,8 @@ import itertools from bisect import bisect_left -from typing import List, Union +from typing import List, Tuple, Union +import numpy as np import pandas as pd @@ -116,16 +117,33 @@ def _check_hue_order_in_data(hue, hue_values: set, f"`{render_collection(unmatched)}`" f" in {hue} (specified in `hue_order`)") +def check_redundant_hue( + data: Union[List[list], pd.DataFrame, None] = None, + coord: Union[str, list, None] = None, + hue: Union[str, None] = None, + hue_order: Union[List[str], None] = None, +) -> bool: + # redundant hue + if data is None: + # arrays + if np.array_equal(hue, coord): + return True + + else: + # column names + if hue == coord or (isinstance(coord, list) and hue in coord): + return True + return False + def check_pairs_in_data(pairs: Union[list, tuple], - data: Union[List[list], pd.DataFrame] = None, - coord: Union[str, list] = None, - hue: str = None, - hue_order: List[str] = None): + data: Union[List[list], pd.DataFrame, None] = None, + coord: Union[str, list, None] = None, + hue: Union[str, None] = None, + hue_order: Union[List[str], None] = None): """ Checks that values referred to in `order` and `pairs` exist in data. """ - if hue is None and hue_order is None: _check_pairs_in_data_no_hue(pairs, data, coord) else: diff --git a/tests/test_annotator.py b/tests/test_annotator.py index a213d2b..0449fe3 100644 --- a/tests/test_annotator.py +++ b/tests/test_annotator.py @@ -30,6 +30,7 @@ def setUp(self): 8: {'x': "b", 'y': 18, 'color': 'red'} }).T + self.x_pairs = [("a", "b")] self.pairs = [(("a", "blue"), ("b", "blue")), (("a", "blue"), ("a", "red"))] self.df.y = self.df.y.astype(float) @@ -41,6 +42,14 @@ def setUp(self): "order": ["a", "b"], "hue_order": ['red', 'blue']} + self.params_df_redundant_hue = { + "data": self.df, + "x": "x", + "y": "y", + "hue": "x", + "order": ["a", "b"], + "hue_order": ["b", "a"]} + self.df_x_float = pd.DataFrame( data={ "x_axis": [1.01, 1.02, 1.03, 1.01, 1.02, 1.03, 1.01, 1.02, @@ -74,6 +83,14 @@ def setUp(self): "order": ["a", "b"], "hue_order": ['red', 'blue']} + self.params_arrays_redundant_hue = { + "data": None, + "x": self.df['x'], + "y": self.df['y'], + "hue": self.df['x'], + "order": ["a", "b"], + "hue_order": ['b', 'a']} + def test_init_simple(self): self.annot = Annotator(self.ax, [(0, 1)], data=self.data) @@ -91,14 +108,26 @@ def test_init_df(self): self.ax = sns.boxplot(**self.params_df) self.annot = Annotator(self.ax, pairs=self.pairs, **self.params_df) + def test_init_df_with_redundant_hue(self): + self.ax = sns.boxplot(**self.params_df_redundant_hue) + self.annot = Annotator(self.ax, pairs=self.x_pairs, **self.params_df_redundant_hue) + def test_init_arrays(self): self.ax = sns.boxplot(**self.params_arrays) self.annot = Annotator(self.ax, pairs=self.pairs, **self.params_arrays) + def test_init_arrays_with_redundant_hue(self): + self.ax = sns.boxplot(**self.params_arrays_redundant_hue) + self.annot = Annotator(self.ax, pairs=self.x_pairs, **self.params_arrays_redundant_hue) + def test_init_barplot(self): ax = sns.barplot(data=self.data) self.annot = Annotator(ax, [(0, 1)], plot="barplot", data=self.data) + def test_init_stripplot(self): + ax = sns.stripplot(**self.params_df) + self.annot = Annotator(ax, pairs=self.pairs, **self.params_df) + def test_test_name_provided(self): self.test_init_simple() with self.assertRaisesRegex(ValueError, "test"): From 878df8304525df9c9492abf00ed4c8a52b66ff3a Mon Sep 17 00:00:00 2001 From: getzze Date: Mon, 22 Jul 2024 17:14:19 +0100 Subject: [PATCH 03/16] fix annotation positions --- statannotations/_GroupsPositions.py | 14 +- statannotations/_Plotter.py | 48 +++- statannotations/compat.py | 336 +++++++++++++++++----------- statannotations/utils.py | 2 +- tests/test_positions.py | 67 ++++++ 5 files changed, 326 insertions(+), 141 deletions(-) create mode 100644 tests/test_positions.py diff --git a/statannotations/_GroupsPositions.py b/statannotations/_GroupsPositions.py index 9c97881..dcd13fd 100644 --- a/statannotations/_GroupsPositions.py +++ b/statannotations/_GroupsPositions.py @@ -54,7 +54,7 @@ def __init__( self._group_names = group_names self._hue_names = hue_names - self.use_hue = len(hue_names) == 0 + self.use_hue = len(hue_names) > 0 # Compute the coordinates of the groups (without hue) and the width self.group_offsets, self.width = self._set_group_offsets( @@ -115,7 +115,13 @@ def _set_data(self, dodge: bool, gap: float) -> tuple[pd.DataFrame, float]: return data, artist_width - def find_group_at_pos(self, pos: float, *, verbose: bool = False) -> TupleGroup | None: + def find_group_at_pos( + self, + pos: float, + *, + verbose: bool = False, + strict: bool = False, + ) -> TupleGroup | None: positions = self._data['pos'] if len(positions) == 0: return None @@ -124,13 +130,15 @@ def find_group_at_pos(self, pos: float, *, verbose: bool = False) -> TupleGroup found_pos = positions.loc[index] if verbose and abs(found_pos - pos) > self.POSITION_TOLERANCE: + if strict: + return None # The requested position is not an artist position msg = ( 'Invalid x-position found. Are the same parameters passed to ' 'seaborn and statannotations calls? Or are there few data points? ' f'The closest group position to {pos} is {found_pos}' ) - warnings.warn(msg) + warnings.warn(msg, UserWarning, stacklevel=2) return self._data.loc[index, 'group'] def get_group_axis_position(self, group: TupleGroup) -> float: diff --git a/statannotations/_Plotter.py b/statannotations/_Plotter.py index 8f3313e..a636cf2 100644 --- a/statannotations/_Plotter.py +++ b/statannotations/_Plotter.py @@ -1,11 +1,12 @@ from __future__ import annotations +import logging from typing import TYPE_CHECKING import numpy as np import matplotlib.pyplot as plt from matplotlib import lines -from matplotlib.collections import PathCollection +from matplotlib.collections import PathCollection, PolyCollection from matplotlib.patches import Rectangle from statannotations._GroupsPositions import _GroupsPositions @@ -16,6 +17,7 @@ if TYPE_CHECKING: from .compat import TupleGroup, Struct +logger = logging.getLogger(__name__) IMPLEMENTED_PLOTTERS = { 'seaborn': ['barplot', 'boxplot', 'stripplot', 'swarmplot', 'violinplot'] @@ -38,6 +40,13 @@ def __init__(self, ax, pairs, data=None, x=None, y=None, hue=None, self._struct_pairs = None self.verbose = verbose self.orient = plot_params.get('orient', 'v') + if self.orient in ('y', 'h'): + self.orient = 'h' + elif self.orient in ('x', 'v'): + self.orient = 'v' + else: + logger.debug(f"Fallback to 'v', `orient` should be one of 'h' or 'v', got: {self.orient}") + self.orient = 'v' def get_transform_func(self, kind: str): """ @@ -123,6 +132,7 @@ def __init__( self.reordering = None self.value_maxes = self._generate_value_maxes() + self.structs = self._get_structs() self.pairs = self.plotter.parse_pairs(pairs, self.structs, formatter=plot_params.get('formatter')) self._struct_pairs = self._get_group_struct_pairs() @@ -202,9 +212,9 @@ def _generate_value_maxes(self): if self.plot == 'violinplot' and self.plotter.has_violin_support: return self.plotter._populate_value_maxes_violin(value_maxes, data_to_ax) - for child in self.ax.get_children(): + for child in (*self.ax.get_children(), *self.ax.collections): group_name, value_pos = self._get_value_pos(child, data_to_ax) - if value_pos is None: + if value_pos is None or group_name is None: continue if value_pos > value_maxes[group_name]: value_maxes[group_name] = value_pos @@ -212,30 +222,45 @@ def _generate_value_maxes(self): return value_maxes def _get_value_pos(self, child, data_to_ax): - if (type(child) == PathCollection - and len(child.properties()['offsets'])): + if ( + isinstance(child, PathCollection) + and len(child.properties()['offsets']) + ): return self._get_value_pos_for_path_collection( child, data_to_ax) - elif type(child) in (lines.Line2D, Rectangle): + elif isinstance(child, PolyCollection): + # Should be for violinplot body but not working + return None, None + + elif isinstance(child, (lines.Line2D, Rectangle)): return self._get_value_pos_for_line2d_or_rectangle( child, data_to_ax) return None, None - def _get_value_pos_for_path_collection(self, child, data_to_ax): + def _get_value_pos_for_path_collection(self, child: PathCollection, data_to_ax): group_coord = {'v': 0, 'h': 1}[self.orient] value_coord = (group_coord + 1) % 2 direction = {'v': 1, 'h': -1}[self.orient] - value_max = child.properties()['offsets'][:, value_coord].max() - group_pos = float(np.round(np.nanmean( - child.properties()['offsets'][:, group_coord]), 1)) + offsets = child.get_offsets() + # remove nans + offsets = offsets[np.all(np.isfinite(offsets), axis=1), :] + if len(offsets) == 0: + logger.debug("skip the empty or all-NaNs PathCollection") + return None, None + + value_max = offsets[:, value_coord].max() + x_coords = offsets[:, group_coord] + group_pos = float(np.round(np.mean(x_coords), 1)) + # Set strict=True to skip the legend artists group_name = self.groups_positions.find_group_at_pos( group_pos, verbose=self.verbose, + strict=True, ) value_pos = data_to_ax.transform((0, value_max)[::direction]) @@ -253,7 +278,8 @@ def _get_value_pos_for_line2d_or_rectangle(self, child, data_to_ax): rect_width = rect_max - rect_min rect_center = (rect_max + rect_min) / 2 - if self.groups_positions.compatible_width(rect_width): + if not self.groups_positions.compatible_width(rect_width): + logger.debug(f"rectangle width is larger than the typical group artist: {rect_width}") return None, None group_name = self.groups_positions.find_group_at_pos( diff --git a/statannotations/compat.py b/statannotations/compat.py index 83912a3..b013e88 100644 --- a/statannotations/compat.py +++ b/statannotations/compat.py @@ -13,8 +13,8 @@ import pandas as pd - TGroupValue = TypeVar('TGroupValue') - THueValue = TypeVar('THueValue') + TGroupValue = TypeVar("TGroupValue") + THueValue = TypeVar("THueValue") TupleGroup = Union[tuple[TGroupValue], tuple[TGroupValue, THueValue]] class Struct(TypedDict): @@ -25,10 +25,11 @@ class Struct(TypedDict): value_max: float group_i: NotRequired[int] + from .utils import remove_null ## Only allow seaborn <0.12 or >=0.13 -sns_version = tuple(int(v) for v in sns.__version__.split('.')[:3]) +sns_version = tuple(int(v) for v in sns.__version__.split(".")[:3]) if sns_version < (0, 12, 0): from seaborn.categorical import ( @@ -48,6 +49,7 @@ class Struct(TypedDict): _CategoricalPlotterNew, _CategoricalPlotter, ) + _SwarmPlotter = _StripPlotter = _CategoricalPlotterNew else: @@ -56,17 +58,18 @@ class Struct(TypedDict): def fix_and_warn(dodge, hue, plot): if dodge is False and hue is not None: - raise ValueError('`dodge` cannot be False in statannotations.') + raise ValueError("`dodge` cannot be False in statannotations.") - is_plot_type = plot in ('swarmplot', 'stripplot') + is_plot_type = plot in ("swarmplot", "stripplot") if is_plot_type and hue is not None and dodge is not True: msg = ( - 'Implicitly setting dodge to True as it is necessary in ' - 'statannotations. It must have been True for the seaborn ' - 'call to yield consistent results when using `hue`.' + "Implicitly setting dodge to True as it is necessary in " + "statannotations. It must have been True for the seaborn " + "call to yield consistent results when using `hue`." ) warnings.warn(msg) + def _get_categorical_plotter( plot_type: str, *, @@ -79,30 +82,30 @@ def _get_categorical_plotter( ) -> _CategoricalPlotter: # Keyword arguments for the plotters kwargs = { - 'data': data, - 'order': order, - 'orient': plot_params.get('orient'), - 'color': None, + "data": data, + "order": order, + "orient": plot_params.get("orient"), + "color": None, } - variables = {'x': x, 'y': y, 'hue': hue} + variables = {"x": x, "y": y, "hue": hue} new_kwargs = { - 'variables': variables, - 'legend': plot_params.get('legend'), + "variables": variables, + "legend": plot_params.get("legend"), **kwargs, } # Boxplot - if plot_type == 'boxplot': + if plot_type == "boxplot": # Pre-0.13 seaborn API if sns_version < (0, 13, 0): kwargs = { - 'hue_order': plot_params.get('hue_order'), - 'dodge': True, - 'palette': None, - 'saturation': 0.75, - 'linewidth': plot_params.get('linewidth'), - 'width': plot_params.get('width', 0.8), - 'fliersize': plot_params.get('fliersize', 5), + "hue_order": plot_params.get("hue_order"), + "dodge": True, + "palette": None, + "saturation": 0.75, + "linewidth": plot_params.get("linewidth"), + "width": plot_params.get("width", 0.8), + "fliersize": plot_params.get("fliersize", 5), **variables, **kwargs, } @@ -111,21 +114,21 @@ def _get_categorical_plotter( # 0.13 seaborn API return _CategoricalPlotter(**new_kwargs) - elif plot_type == 'barplot': + elif plot_type == "barplot": # Pre-0.13 seaborn API if sns_version < (0, 13, 0): kwargs = { - 'hue_order': plot_params.get('hue_order'), - 'dodge': True, - 'palette': None, - 'saturation': 0.75, - 'estimator': plot_params.get('estimator', np.mean), - 'n_boot': plot_params.get('n_boot', 1000), - 'units': plot_params.get('units'), - 'seed': plot_params.get('seed'), - 'errcolor': '.26', - 'errwidth': plot_params.get('errwidth'), - 'capsize': None, + "hue_order": plot_params.get("hue_order"), + "dodge": True, + "palette": None, + "saturation": 0.75, + "estimator": plot_params.get("estimator", np.mean), + "n_boot": plot_params.get("n_boot", 1000), + "units": plot_params.get("units"), + "seed": plot_params.get("seed"), + "errcolor": ".26", + "errwidth": plot_params.get("errwidth"), + "capsize": None, **variables, **kwargs, } @@ -133,15 +136,15 @@ def _get_categorical_plotter( # Pre-0.12 seaborn API if sns_version < (0, 12, 0): kwargs = { - 'ci': plot_params.get('ci', 95), + "ci": plot_params.get("ci", 95), **kwargs, } return _BarPlotter(**kwargs) # 0.12 seaborn API kwargs = { - 'errorbar': plot_params.get('errorbar', ('ci', 95)), - 'width': plot_params.get('width', 0.8), + "errorbar": plot_params.get("errorbar", ("ci", 95)), + "width": plot_params.get("width", 0.8), **kwargs, } return _BarPlotter(**kwargs) @@ -149,23 +152,23 @@ def _get_categorical_plotter( # 0.13 seaborn API return _CategoricalPlotter(**new_kwargs) - elif plot_type == 'violinplot': + elif plot_type == "violinplot": # Pre-0.13 seaborn API if sns_version < (0, 13, 0): kwargs = { - 'hue_order': plot_params.get('hue_order'), - 'dodge': True, - 'palette': None, - 'saturation': 0.75, - 'linewidth': plot_params.get('linewidth'), - 'width': plot_params.get('width', 0.8), - 'bw': plot_params.get('bw', 'scott'), - 'cut': plot_params.get('cut', 2), - 'scale': plot_params.get('scale', 'area'), - 'scale_hue': plot_params.get('scale_hue', True), - 'gridsize': plot_params.get('gridsize', 100), - 'inner': plot_params.get('inner', None), - 'split': plot_params.get('split', False), + "hue_order": plot_params.get("hue_order"), + "dodge": True, + "palette": None, + "saturation": 0.75, + "linewidth": plot_params.get("linewidth"), + "width": plot_params.get("width", 0.8), + "bw": plot_params.get("bw", "scott"), + "cut": plot_params.get("cut", 2), + "scale": plot_params.get("scale", "area"), + "scale_hue": plot_params.get("scale_hue", True), + "gridsize": plot_params.get("gridsize", 100), + "inner": plot_params.get("inner", None), + "split": plot_params.get("split", False), **variables, **kwargs, } @@ -174,13 +177,13 @@ def _get_categorical_plotter( # 0.13 seaborn API return _CategoricalPlotter(**new_kwargs) - elif plot_type == 'swarmplot': + elif plot_type == "swarmplot": # Pre-0.12 seaborn API if sns_version < (0, 12, 0): kwargs = { - 'hue_order': plot_params.get('hue_order'), - 'dodge': True, - 'palette': None, + "hue_order": plot_params.get("hue_order"), + "dodge": True, + "palette": None, **variables, **kwargs, } @@ -188,21 +191,21 @@ def _get_categorical_plotter( # Pre-0.13 seaborn API if sns_version < (0, 13, 0): - if 'color' in new_kwargs: - new_kwargs.pop('color') + if "color" in new_kwargs: + new_kwargs.pop("color") return _CategoricalPlotterNew(require_numeric=False, **new_kwargs) # 0.13 seaborn API return _CategoricalPlotter(**new_kwargs) - elif plot_type == 'stripplot': + elif plot_type == "stripplot": # Pre-0.12 seaborn API if sns_version < (0, 12, 0): kwargs = { - 'hue_order': plot_params.get('hue_order'), - 'dodge': True, - 'palette': None, - 'jitter': plot_params.get('jitter', True), + "hue_order": plot_params.get("hue_order"), + "dodge": True, + "palette": None, + "jitter": plot_params.get("jitter", True), **variables, **kwargs, } @@ -210,20 +213,21 @@ def _get_categorical_plotter( # Pre-0.13 seaborn API if sns_version < (0, 13, 0): - if 'color' in new_kwargs: - new_kwargs.pop('color') + if "color" in new_kwargs: + new_kwargs.pop("color") return _CategoricalPlotterNew(require_numeric=False, **new_kwargs) # 0.13 seaborn API return _CategoricalPlotter(**new_kwargs) - msg = f'Plot type {plot_type!r} is not supported' + msg = f"Plot type {plot_type!r} is not supported" raise NotImplementedError(msg) class Wrapper: """Compatibility wrapper for seaborn.categorical._CategoricalPlotter.""" + plot_kwargs: dict[str, Any] width: float gap: float dodge: bool @@ -237,21 +241,23 @@ def __init__( is_redundant_hue: bool = False, **kwargs, ) -> None: - dodge = kwargs.get('dodge') - hue = kwargs.get('hue') + dodge = kwargs.get("dodge") + hue = kwargs.get("hue") fix_and_warn(dodge, hue, plot_type) # dodge needs to be True for statannotations self.dodge = True - self.gap = kwargs.get('gap', 0.0) - self.width = kwargs.get('width', 0.8) + self.gap = kwargs.get("gap", 0.0) + self.width = kwargs.get("width", 0.8) self.native_group_offsets = None self.is_redundant_hue = is_redundant_hue + self.plot_kwargs = {**kwargs, "dodge": True} + @property def has_violin_support(self) -> bool: """Whether the max values of a violin plot can be extracted from the plotter.""" - return hasattr(self, '_populate_value_maxes_violin') + return hasattr(self, "_populate_value_maxes_violin") @property def has_hue(self) -> bool: @@ -279,7 +285,7 @@ def parse_pairs( *, formatter: Callable | None = None, ) -> list[tuple[TupleGroup, TupleGroup]]: - struct_groups = [struct['group'] for struct in structs] + struct_groups = [struct["group"] for struct in structs] ret: list[tuple[TupleGroup, TupleGroup]] = [] def format_group(value) -> TupleGroup: @@ -288,7 +294,7 @@ def format_group(value) -> TupleGroup: for pair in pairs: if not isinstance(pair, Sequence) or len(pair) != 2: - msg = f'pair {pair} is not a 2-tuple, skipping.' + msg = f"pair {pair} is not a 2-tuple, skipping." warnings.warn(msg) continue # Format the groups @@ -299,8 +305,8 @@ def format_group(value) -> TupleGroup: for i, group in enumerate(new_pair): if group not in struct_groups: msg = ( - f'cannot find group{i} of pair in the group tuples: ' - f'{group} not in {struct_groups}' + f"cannot find group{i} of pair in the group tuples: " + f"{group} not in {struct_groups}" ) warnings.warn(msg) valid_group = False @@ -311,8 +317,8 @@ def format_group(value) -> TupleGroup: if len(ret) == 0: msg = ( - f'pairs are empty after parsing: original_pairs={pairs}\n' - f'not in group_list={struct_groups}' + f"pairs are empty after parsing: original_pairs={pairs}\n" + f"not in group_list={struct_groups}" ) raise ValueError(msg) return ret @@ -323,7 +329,8 @@ class CategoricalPlotterWrapper_v11(Wrapper): _plotter: _CategoricalPlotter - def __init__(self, + def __init__( + self, plot_type: str, *, x, @@ -403,7 +410,9 @@ def get_group_data(self, group_name): return group_data - def _populate_value_maxes_violin(self, value_maxes: dict[TupleGroup, float], data_to_ax) -> dict[TupleGroup, float]: + def _populate_value_maxes_violin( + self, value_maxes: dict[TupleGroup, float], data_to_ax + ) -> dict[TupleGroup, float]: """Populate the max values for violinplot. The keys should follow the tuples of `get_group_names_and_labels`. @@ -415,15 +424,15 @@ def _populate_value_maxes_violin(self, value_maxes: dict[TupleGroup, float], dat value_pos = max(self._plotter.support[group_idx][hue_idx]) key = (group_name, hue_name) if key not in value_maxes: - msg = f'key {key} was not found in the dict: {dict_keys}' + msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue value_maxes[key] = data_to_ax.transform((0, value_pos))[1] else: value_pos = max(self._plotter.support[group_idx]) - key = group_name + key = (group_name,) if key not in value_maxes: - msg = f'key {key} was not found in the dict: {dict_keys}' + msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue value_maxes[key] = data_to_ax.transform((0, value_pos))[1] @@ -435,7 +444,8 @@ class CategoricalPlotterWrapper_v12(Wrapper): _plotter: _CategoricalPlotter - def __init__(self, + def __init__( + self, plot_type: str, *, x, @@ -460,15 +470,17 @@ def __init__(self, if isinstance(self._plotter, _CategoricalPlotterNew): # Order the group variables - native_scale = plot_params.get('native_scale', False) + native_scale = plot_params.get("native_scale", False) if native_scale: - msg = '`native_scale=True` is not supported with seaborn==0.12, update to seaborn>=0.13' + msg = "`native_scale=True` is not supported with seaborn==0.12, update to seaborn>=0.13" raise ValueError(msg) - formatter = plot_params.get('formatter') + formatter = plot_params.get("formatter") if formatter is not None: - msg = '`formatter` is not supported with seaborn==0.12, update to seaborn>=0.13' + msg = "`formatter` is not supported with seaborn==0.12, update to seaborn>=0.13" raise ValueError(msg) - self._order_variable(order=order, native_scale=native_scale, formatter=formatter) + self._order_variable( + order=order, native_scale=native_scale, formatter=formatter + ) # Native scaling of the group variable if native_scale: @@ -478,7 +490,7 @@ def __init__(self, def has_hue(self) -> bool: if self.is_redundant_hue: return False - if hasattr(self._plotter, 'hue_names'): + if hasattr(self._plotter, "hue_names"): # Can be None, force to be an empty list if self._plotter.hue_names: return True @@ -486,11 +498,11 @@ def has_hue(self) -> bool: else: # _CategoricalPlotterNew - return self._plotter.variables.get('hue') is not None + return self._plotter.variables.get("hue") is not None @property def group_names(self) -> list: - if hasattr(self._plotter, 'group_names'): + if hasattr(self._plotter, "group_names"): return self._plotter.group_names else: @@ -505,14 +517,14 @@ def hue_names(self): if self.is_redundant_hue: return [] - if hasattr(self._plotter, 'hue_names'): + if hasattr(self._plotter, "hue_names"): # Can be None, force to be an empty list return self._plotter.hue_names or [] else: - if 'hue' not in self._plotter.var_levels: + if "hue" not in self._plotter.var_levels: return [] - hue_names = self._plotter.var_levels['hue'] + hue_names = self._plotter.var_levels["hue"] if isinstance(hue_names, pd.Index): return hue_names.tolist() return hue_names @@ -525,11 +537,16 @@ def _order_variable( formatter: Callable | None = None, ) -> None: # Do not order if not categorical and native scale - if self._plotter.var_types.get(self._plotter.cat_axis) != 'categorical' and native_scale: + if ( + self._plotter.var_types.get(self._plotter.cat_axis) != "categorical" + and native_scale + ): return # Order the group variable - self._plotter.scale_categorical(self._plotter.cat_axis, order=order, formatter=None) + self._plotter.scale_categorical( + self._plotter.cat_axis, order=order, formatter=None + ) def _get_group_data_from_plotter( self, @@ -584,7 +601,7 @@ def _get_group_data_new(self, group_name: TupleGroup) -> pd.Series: direct access to the group_data in the respective Plotter class. """ if not isinstance(self._plotter, _CategoricalPlotterNew): - msg = '`self._plotter` should be a `_CategoricalPlotterNew` instance.' + msg = "`self._plotter` should be a `_CategoricalPlotterNew` instance." raise TypeError(msg) # the value variable is the one that is no the group axis @@ -592,31 +609,60 @@ def _get_group_data_new(self, group_name: TupleGroup) -> pd.Series: # 'x' if vertical or 'y' if horizontal cat_var = self._plotter.cat_axis # opposite: 'y' if vertical or 'x' if horizontal - value_var = {'x': 'y', 'y': 'x'}[cat_var] + value_var = {"x": "y", "y": "x"}[cat_var] group = tgroup[0] hue = None iter_vars = [cat_var] if self.has_hue and len(tgroup) > 1: - iter_vars.append('hue') + iter_vars.append("hue") hue = tgroup[1] for sub_vars, sub_data in self._plotter.iter_data(iter_vars): if sub_vars[cat_var] != group: continue - if hue is not None and sub_vars['hue'] != hue: + if hue is not None and sub_vars["hue"] != hue: continue # Found a matching group, return the data group_data = remove_null(sub_data[value_var]) return group_data + def _populate_value_maxes_violin( + self, value_maxes: dict[TupleGroup, float], data_to_ax + ) -> dict[TupleGroup, float]: + """Populate the max values for violinplot. + + The keys should follow the tuples of `get_group_names_and_labels`. + """ + dict_keys = list(value_maxes.keys()) + for group_idx, group_name in enumerate(self.group_names): + if self.has_hue: + for hue_idx, hue_name in enumerate(self.hue_names): + value_pos = max(self._plotter.support[group_idx][hue_idx]) + key = (group_name, hue_name) + if key not in value_maxes: + msg = f"key {key} was not found in the dict: {dict_keys}" + warnings.warn(msg) + continue + value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + else: + value_pos = max(self._plotter.support[group_idx]) + key = (group_name,) + if key not in value_maxes: + msg = f"key {key} was not found in the dict: {dict_keys}" + warnings.warn(msg) + continue + value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + return value_maxes + class CategoricalPlotterWrapper_v13(Wrapper): """Compatibility wrapper for seaborn v11.""" _plotter: _CategoricalPlotter - def __init__(self, + def __init__( + self, plot_type: str, *, x, @@ -629,21 +675,23 @@ def __init__(self, super().__init__(plot_type, hue=hue, **plot_params) self._group_names = None - variables = {'x': x, 'y': y, 'hue': hue} + variables = {"x": x, "y": y, "hue": hue} kwargs = { - 'data': data, - 'variables': variables, - 'order': order, - 'orient': plot_params.get('orient'), - 'color': None, - 'legend': plot_params.get('legend'), + "data": data, + "variables": variables, + "order": order, + "orient": plot_params.get("orient"), + "color": None, + "legend": plot_params.get("legend"), } self._plotter = _CategoricalPlotter(**kwargs) # Order the group variables - native_scale = plot_params.get('native_scale', False) - formatter = plot_params.get('formatter') - self._order_variable(order=order, native_scale=native_scale, formatter=formatter) + native_scale = plot_params.get("native_scale", False) + formatter = plot_params.get("formatter") + self._order_variable( + order=order, native_scale=native_scale, formatter=formatter + ) # Native scaling of the group variable if native_scale: @@ -654,13 +702,13 @@ def has_hue(self) -> bool: # Get variables mapping if self.is_redundant_hue: return False - if self._plotter.variables.get('hue') is None: + if self._plotter.variables.get("hue") is None: return False return True @property def axis(self) -> str: - return {'x': 'x', 'v': 'x', 'y': 'y', 'h': 'y'}[self._plotter.orient] + return {"x": "x", "v": "x", "y": "y", "h": "y"}[self._plotter.orient] @property def group_names(self) -> list: @@ -670,9 +718,9 @@ def group_names(self) -> list: @property def hue_names(self): - if not self.has_hue or 'hue' not in self._plotter.var_levels: + if not self.has_hue or "hue" not in self._plotter.var_levels: return [] - return self._plotter.var_levels['hue'] + return self._plotter.var_levels["hue"] def _order_variable( self, @@ -687,7 +735,7 @@ def _order_variable( self._group_names = list(self._plotter.var_levels[self.axis]) # Do not order if not categorical and native scale - if self._plotter.var_types.get(self.axis) != 'categorical' and native_scale: + if self._plotter.var_types.get(self.axis) != "categorical" and native_scale: return # Order the group variable @@ -713,19 +761,19 @@ def get_group_data(self, group_name: TupleGroup) -> pd.Series: direct access to the group_data in the respective Plotter class. """ # the value variable is the one that is not the group axis - value_var = {'x': 'y', 'y': 'x'}[self.axis] + value_var = {"x": "y", "y": "x"}[self.axis] tgroup = group_name if isinstance(group_name, tuple) else (group_name,) group = tgroup[0] hue = None iter_vars = [self.axis] if self.has_hue and len(tgroup) > 1: - iter_vars.append('hue') + iter_vars.append("hue") hue = tgroup[1] for sub_vars, sub_data in self._plotter.iter_data(iter_vars): if sub_vars[self.axis] != group: continue - if hue is not None and sub_vars['hue'] != hue: + if hue is not None and sub_vars["hue"] != hue: continue # Found a matching group, return the data @@ -739,7 +787,7 @@ def parse_pairs( *, formatter: Callable | None = None, ) -> list[tuple[TupleGroup, TupleGroup]]: - struct_groups = [struct['group'] for struct in structs] + struct_groups = [struct["group"] for struct in structs] ret: list[tuple[TupleGroup, TupleGroup]] = [] formatter = formatter if callable(formatter) else str @@ -751,7 +799,7 @@ def format_group(value, formatter) -> TupleGroup: for pair in pairs: if not isinstance(pair, Sequence) or len(pair) != 2: - msg = f'pair {pair} is not a 2-tuple, skipping.' + msg = f"pair {pair} is not a 2-tuple, skipping." warnings.warn(msg) continue # Format the groups @@ -762,8 +810,8 @@ def format_group(value, formatter) -> TupleGroup: for i, group in enumerate(new_pair): if group not in struct_groups: msg = ( - f'cannot find group{i} of pair in the group tuples: ' - f'{group} not in {struct_groups}' + f"cannot find group{i} of pair in the group tuples: " + f"{group} not in {struct_groups}" ) warnings.warn(msg) valid_group = False @@ -773,12 +821,48 @@ def format_group(value, formatter) -> TupleGroup: ret.append(new_pair) if len(ret) == 0: - msg = ( - f'pairs are empty after parsing: original_pairs={pairs}' - ) + msg = f"pairs are empty after parsing: original_pairs={pairs}" raise ValueError(msg) return ret + def _populate_value_maxes_violin( + self, value_maxes: dict[TupleGroup, float], data_to_ax + ) -> dict[TupleGroup, float]: + """Populate the max values for violinplot. + + The keys should follow the tuples of `get_group_names_and_labels`. + """ + bw_method = self.plot_kwargs.get( + "bw_method", self.plot_kwargs.get("bw", "scott") + ) + kde_kws = dict( + cut=self.plot_kwargs.pop("cut", 2), + gridsize=self.plot_kwargs.pop("gridsize", 100), + bw_adjust=self.plot_kwargs.pop("bw_adjust", 1), + bw_method=bw_method, + ) + + kde = sns._stats.density.KDE(**kde_kws) + + iter_vars = [self.axis] + if self.has_hue: + iter_vars.append("hue") + value_var = {"x": "y", "y": "x"}[self.axis] + + dict_keys = list(value_maxes.keys()) + # Iterate through all the data splits once to compute the KDEs + for sub_vars, sub_data in self._plotter.iter_data(iter_vars): + var = sub_vars[self.axis] + key = (var, sub_vars["hue"]) if self.has_hue else (var,) + if key not in value_maxes: + msg = f"key {key} was not found in the dict: {dict_keys}" + warnings.warn(msg) + continue + sub_data["weight"] = sub_data.get("weights", 1) + stat_data = kde._transform(sub_data, value_var, []) + support = stat_data[value_var] + value_maxes[key] = data_to_ax.transform((0, max(support)))[1] + return value_maxes # Define CategoricalPlotterWrapper depending on seaborn version diff --git a/statannotations/utils.py b/statannotations/utils.py index 748b77f..529837c 100644 --- a/statannotations/utils.py +++ b/statannotations/utils.py @@ -1,6 +1,6 @@ import itertools from bisect import bisect_left -from typing import List, Tuple, Union +from typing import List, Union import numpy as np import pandas as pd diff --git a/tests/test_positions.py b/tests/test_positions.py new file mode 100644 index 0000000..68b5a4a --- /dev/null +++ b/tests/test_positions.py @@ -0,0 +1,67 @@ +import unittest + +import seaborn as sns + +from statannotations.Annotator import Annotator +from statannotations._Plotter import IMPLEMENTED_PLOTTERS + + +class TestPlotter(unittest.TestCase): + def setUp(self) -> None: + self.df = sns.load_dataset("tips") + + self.params_df = { + "data": self.df, + "x": "day", + "y": "total_bill", + "order": ["Sun", "Thur", "Fri", "Sat"], + "hue": "sex", + } + + self.params_array = { + "data": None, + "x": self.df["day"], + "y": self.df["total_bill"], + "order": ["Sun", "Thur", "Fri", "Sat"], + "hue": self.df["sex"], + } + + self.pairs = [ + (("Thur", "Male"), ("Thur", "Female")), + (("Sun", "Male"), ("Sun", "Female")), + (("Thur", "Male"), ("Fri", "Female")), + (("Fri", "Male"), ("Fri", "Female")), + (("Sun", "Male"), ("Fri", "Female")), + ] + + def test_seaborn_violinplot(self): + plotting = {**self.params_df, "dodge": True} + ax = sns.violinplot(**plotting) + self.annot = Annotator(ax, plot='violinplot', pairs=self.pairs, **self.params_df) + self.annot.configure(test='Mann-Whitney', text_format='star', loc='inside') + self.annot.apply_and_annotate() + + # plt.show() + value_maxes = self.annot._plotter.value_maxes + assert ("Sun", "Male") in value_maxes + assert value_maxes[("Sun", "Male")] > 0.80 + + def test_seaborn_plots(self): + for plotter in IMPLEMENTED_PLOTTERS["seaborn"]: + plotting = {**self.params_df, "dodge": True} + ax = getattr(sns, plotter)(**plotting) + self.annot = Annotator(ax, plot=plotter, pairs=self.pairs, **self.params_df) + + positions = self.annot._plotter.groups_positions._data + + assert positions.group.tolist() == [ + ("Sun", "Male"), + ("Sun", "Female"), + ("Thur", "Male"), + ("Thur", "Female"), + ("Fri", "Male"), + ("Fri", "Female"), + ("Sat", "Male"), + ("Sat", "Female"), + ] + assert positions.pos.tolist() == [-0.2, 0.2, 0.8, 1.2, 1.8, 2.2, 2.8, 3.2] From cfca0eece6540298ad93e2e3ef48788617dc0cca Mon Sep 17 00:00:00 2001 From: getzze Date: Mon, 22 Jul 2024 18:37:32 +0100 Subject: [PATCH 04/16] tearDown clear figures --- statannotations/_Plotter.py | 5 ++--- statannotations/compat.py | 23 ++++++++++------------- tests/test_annotator.py | 4 ++++ tests/test_integrate_annotator.py | 4 ++++ tests/test_integrate_format_multiple.py | 4 ++++ tests/test_integrate_plotter.py | 4 ++++ tests/test_plotter.py | 7 ++++++- tests/test_positions.py | 23 ++++++++++------------- tests/test_pvalue_format.py | 4 ++++ 9 files changed, 48 insertions(+), 30 deletions(-) diff --git a/statannotations/_Plotter.py b/statannotations/_Plotter.py index a636cf2..fd9eb97 100644 --- a/statannotations/_Plotter.py +++ b/statannotations/_Plotter.py @@ -212,7 +212,7 @@ def _generate_value_maxes(self): if self.plot == 'violinplot' and self.plotter.has_violin_support: return self.plotter._populate_value_maxes_violin(value_maxes, data_to_ax) - for child in (*self.ax.get_children(), *self.ax.collections): + for child in self.ax.get_children(): group_name, value_pos = self._get_value_pos(child, data_to_ax) if value_pos is None or group_name is None: continue @@ -253,8 +253,7 @@ def _get_value_pos_for_path_collection(self, child: PathCollection, data_to_ax): return None, None value_max = offsets[:, value_coord].max() - x_coords = offsets[:, group_coord] - group_pos = float(np.round(np.mean(x_coords), 1)) + group_pos = float(np.round(np.mean(offsets[:, group_coord]), 1)) # Set strict=True to skip the legend artists group_name = self.groups_positions.find_group_at_pos( diff --git a/statannotations/compat.py b/statannotations/compat.py index b013e88..204fe83 100644 --- a/statannotations/compat.py +++ b/statannotations/compat.py @@ -227,7 +227,6 @@ def _get_categorical_plotter( class Wrapper: """Compatibility wrapper for seaborn.categorical._CategoricalPlotter.""" - plot_kwargs: dict[str, Any] width: float gap: float dodge: bool @@ -252,8 +251,6 @@ def __init__( self.native_group_offsets = None self.is_redundant_hue = is_redundant_hue - self.plot_kwargs = {**kwargs, "dodge": True} - @property def has_violin_support(self) -> bool: """Whether the max values of a violin plot can be extracted from the plotter.""" @@ -660,6 +657,7 @@ class CategoricalPlotterWrapper_v13(Wrapper): """Compatibility wrapper for seaborn v11.""" _plotter: _CategoricalPlotter + kde_kwargs: dict[str, Any] def __init__( self, @@ -697,6 +695,14 @@ def __init__( if native_scale: self.native_group_offsets = self._plotter.plot_data[self.axis] + bw_method = kwargs.get("bw_method", kwargs.get("bw", "scott")) + self.kde_kwargs = dict( + cut=kwargs.get("cut", 2), + gridsize=kwargs.get("gridsize", 100), + bw_adjust=kwargs.get("bw_adjust", 1), + bw_method=bw_method, + ) + @property def has_hue(self) -> bool: # Get variables mapping @@ -832,17 +838,8 @@ def _populate_value_maxes_violin( The keys should follow the tuples of `get_group_names_and_labels`. """ - bw_method = self.plot_kwargs.get( - "bw_method", self.plot_kwargs.get("bw", "scott") - ) - kde_kws = dict( - cut=self.plot_kwargs.pop("cut", 2), - gridsize=self.plot_kwargs.pop("gridsize", 100), - bw_adjust=self.plot_kwargs.pop("bw_adjust", 1), - bw_method=bw_method, - ) - kde = sns._stats.density.KDE(**kde_kws) + kde = sns._stats.density.KDE(**self.kde_kwargs) iter_vars = [self.axis] if self.has_hue: diff --git a/tests/test_annotator.py b/tests/test_annotator.py index 0449fe3..b5f3bb2 100644 --- a/tests/test_annotator.py +++ b/tests/test_annotator.py @@ -91,6 +91,10 @@ def setUp(self): "order": ["a", "b"], "hue_order": ['b', 'a']} + def tearDown(self) -> None: + sns.mpl.pyplot.clf() + return super().tearDown() + def test_init_simple(self): self.annot = Annotator(self.ax, [(0, 1)], data=self.data) diff --git a/tests/test_integrate_annotator.py b/tests/test_integrate_annotator.py index ee04e73..672d315 100644 --- a/tests/test_integrate_annotator.py +++ b/tests/test_integrate_annotator.py @@ -41,6 +41,10 @@ def setUp(self): "order": ["a", "b"], "hue_order": ['red', 'blue']} + def tearDown(self) -> None: + sns.mpl.pyplot.clf() + return super().tearDown() + def test_plot_and_annotate(self): ax, annotations, annotator = Annotator.plot_and_annotate( plot="boxplot", pairs=self.pairs_for_df, diff --git a/tests/test_integrate_format_multiple.py b/tests/test_integrate_format_multiple.py index 7826849..1cb4459 100644 --- a/tests/test_integrate_format_multiple.py +++ b/tests/test_integrate_format_multiple.py @@ -37,6 +37,10 @@ def setUp(self) -> None: **plotting) self.pvalues = [0.03, 0.04, 0.9] + def tearDown(self) -> None: + sns.mpl.pyplot.clf() + return super().tearDown() + def test_ns_without_correction_star(self): annotations = self.annotator._get_results("auto", pvalues=self.pvalues) self.assertEqual(["*", "*", "ns"], diff --git a/tests/test_integrate_plotter.py b/tests/test_integrate_plotter.py index 89ada57..3b58434 100644 --- a/tests/test_integrate_plotter.py +++ b/tests/test_integrate_plotter.py @@ -30,6 +30,10 @@ def setUp(self) -> None: } self.df.y = self.df.y.astype(float) + def tearDown(self) -> None: + sns.mpl.pyplot.clf() + return super().tearDown() + def test_dodge_false_raises(self): ax = sns.barplot(dodge=False, **self.plotting) with self.assertRaisesRegex(ValueError, "dodge"): diff --git a/tests/test_plotter.py b/tests/test_plotter.py index 8b2da53..c544c70 100644 --- a/tests/test_plotter.py +++ b/tests/test_plotter.py @@ -1,9 +1,10 @@ import unittest import pandas as pd -from statannotations._Plotter import _SeabornPlotter, IMPLEMENTED_PLOTTERS import seaborn as sns +from statannotations._Plotter import _SeabornPlotter, IMPLEMENTED_PLOTTERS + class TestPlotter(unittest.TestCase): def setUp(self) -> None: @@ -38,6 +39,10 @@ def setUp(self) -> None: (("b", "blue"), ("b", "red")), (("a", "blue"), ("b", "blue"))] + def tearDown(self) -> None: + sns.mpl.pyplot.clf() + return super().tearDown() + def test_seaborn_plots(self): for plotter in IMPLEMENTED_PLOTTERS["seaborn"]: if plotter in ("stripplot", "swarmplot"): diff --git a/tests/test_positions.py b/tests/test_positions.py index 68b5a4a..382a7ef 100644 --- a/tests/test_positions.py +++ b/tests/test_positions.py @@ -1,12 +1,13 @@ import unittest import seaborn as sns +import matplotlib.pylab as plt from statannotations.Annotator import Annotator from statannotations._Plotter import IMPLEMENTED_PLOTTERS -class TestPlotter(unittest.TestCase): +class TestPositionPlotter(unittest.TestCase): def setUp(self) -> None: self.df = sns.load_dataset("tips") @@ -15,17 +16,10 @@ def setUp(self) -> None: "x": "day", "y": "total_bill", "order": ["Sun", "Thur", "Fri", "Sat"], + "hue_order": ["Male", "Female"], "hue": "sex", } - self.params_array = { - "data": None, - "x": self.df["day"], - "y": self.df["total_bill"], - "order": ["Sun", "Thur", "Fri", "Sat"], - "hue": self.df["sex"], - } - self.pairs = [ (("Thur", "Male"), ("Thur", "Female")), (("Sun", "Male"), ("Sun", "Female")), @@ -34,23 +28,26 @@ def setUp(self) -> None: (("Sun", "Male"), ("Fri", "Female")), ] + def tearDown(self) -> None: + plt.clf() + return super().tearDown() + def test_seaborn_violinplot(self): plotting = {**self.params_df, "dodge": True} ax = sns.violinplot(**plotting) - self.annot = Annotator(ax, plot='violinplot', pairs=self.pairs, **self.params_df) + self.annot = Annotator(ax, plot='violinplot', pairs=self.pairs, **plotting) self.annot.configure(test='Mann-Whitney', text_format='star', loc='inside') self.annot.apply_and_annotate() - # plt.show() value_maxes = self.annot._plotter.value_maxes assert ("Sun", "Male") in value_maxes assert value_maxes[("Sun", "Male")] > 0.80 - def test_seaborn_plots(self): + def test_seaborn_all_plots(self): for plotter in IMPLEMENTED_PLOTTERS["seaborn"]: plotting = {**self.params_df, "dodge": True} ax = getattr(sns, plotter)(**plotting) - self.annot = Annotator(ax, plot=plotter, pairs=self.pairs, **self.params_df) + self.annot = Annotator(ax, plot=plotter, pairs=self.pairs, **plotting) positions = self.annot._plotter.groups_positions._data diff --git a/tests/test_pvalue_format.py b/tests/test_pvalue_format.py index c5dbcd7..7a255fd 100644 --- a/tests/test_pvalue_format.py +++ b/tests/test_pvalue_format.py @@ -39,6 +39,10 @@ def setUp(self) -> None: **plotting) self.pvalues = [0.03, 0.04, 0.9] + def tearDown(self) -> None: + sns.mpl.pyplot.clf() + return super().tearDown() + def test_format_simple(self): self.annotator.configure(pvalue_format={"text_format": "simple"}) annotations = self.annotator._get_results("auto", pvalues=self.pvalues) From 1cb772f08b899cbbe5b55d4d7a39c8902cd35263 Mon Sep 17 00:00:00 2001 From: getzze Date: Thu, 25 Jul 2024 12:08:25 +0100 Subject: [PATCH 05/16] fix various bugs and formatting --- statannotations/Annotator.py | 76 +++++++------ statannotations/_GroupsPositions.py | 36 ++++--- statannotations/_Plotter.py | 57 +++++++--- statannotations/compat.py | 159 +++++++++++++++++++--------- statannotations/utils.py | 9 +- 5 files changed, 217 insertions(+), 120 deletions(-) diff --git a/statannotations/Annotator.py b/statannotations/Annotator.py index 28ff3b2..32e38d5 100644 --- a/statannotations/Annotator.py +++ b/statannotations/Annotator.py @@ -162,9 +162,13 @@ def new_plot(self, ax, pairs=None, plot='boxplot', data=None, x=None, return self @property - def orient(self): + def orient(self) -> str: return self._plotter.orient + @property + def is_vertical(self) -> bool: + return self._plotter.orient in ('v', 'x') + @property def verbose(self): return self._verbose @@ -219,30 +223,29 @@ def annotate(self, line_offset=None, line_offset_to_group=None): self.validate_test_short_name() for annotation in self.annotations: - if self.hide_non_significant and isinstance(annotation.data, StatResult) \ - and not annotation.data.is_significant: + if ( + self.hide_non_significant + and isinstance(annotation.data, StatResult) + and not annotation.data.is_significant + ): continue - self._annotate_pair(annotation, - ax_to_data=ax_to_data, - ann_list=ann_list, - orig_value_lim=orig_value_lim) + self._annotate_pair( + annotation, + ax_to_data=ax_to_data, + ann_list=ann_list, + orig_value_lim=orig_value_lim, + ) # reset transformation y_stack_max = max(self._value_stack_arr[1, :]) ax_to_data = self._plotter.get_transform_func('ax_to_data') - value_lims = ( - ([(0, 0), (0, max(1.04 * y_stack_max, 1))] - if self.loc == 'inside' - else [(0, 0), (0, 1)]) - if self.orient == 'v' - else - ([(0, 0), (max(1.04 * y_stack_max, 1), 0)] - if self.loc == 'inside' - else [(0, 0), (1, 0)]) - ) - set_lims = self.ax.set_ylim if self.orient == 'v' else self.ax.set_xlim + + max_value = max(1.04 * y_stack_max, 1) if self.loc == 'inside' else 1 + up_limit = (0, max_value) if self.is_vertical else (max_value, 0) + value_lims = [(0, 0), up_limit] + transformed = ax_to_data.transform(value_lims) - set_lims(transformed[:, 1 if self.orient == 'v' else 0]) + self._plotter.set_value_lim(transformed[:, 1 if self.is_vertical else 0]) return self._get_output() @@ -532,8 +535,11 @@ def _annotate_pair(self, annotation, ax_to_data, ann_list, orig_value_lim): ax_line_value = [value, value + self.line_height, value + self.line_height, value] - lists = ((ax_line_group, ax_line_value) if self.orient == 'v' - else (ax_line_value, ax_line_group)) + lists = ( + (ax_line_group, ax_line_value) + if self.is_vertical + else (ax_line_value, ax_line_group) + ) points = [ax_to_data.transform((x, y)) for x, y @@ -554,10 +560,7 @@ def _annotate_pair(self, annotation, ax_to_data, ann_list, orig_value_lim): if annotation.text is not None: ann_list.append(ann) plt.draw() - set_lim = {'v': 'set_ylim', - 'h': 'set_xlim'}[self.orient] - - getattr(self.ax, set_lim)(orig_value_lim) + self._plotter.set_value_lim(orig_value_lim) value_top_annot = self._annotate_pair_text(ann, value) else: @@ -729,9 +732,13 @@ def _annotate_pair_text(self, ann, value): direction = {'h': -1, 'v': 1}[self.orient] x, y = [0, fontsize_points + self.text_offset][::direction] - offset_trans = mtransforms.offset_copy(trans=self.ax.transAxes, - fig=self.fig, - units='points', x=x, y=y) + offset_trans = mtransforms.offset_copy( + trans=self.ax.transAxes, + fig=self.fig, + units='points', + x=x, + y=y, + ) value_top_display = offset_trans.transform( (value + self.line_height, value + self.line_height)) @@ -801,12 +808,13 @@ def _get_xy_params_vertical(self, group_coord_1, group_coord_2, def _get_xy_params(self, group_coord_1, group_coord_2, line_x: np.ndarray, line_y: np.ndarray): - if self.orient == 'h': - return self._get_xy_params_horizontal(group_coord_1, group_coord_2, - line_x) - - return self._get_xy_params_vertical(group_coord_1, group_coord_2, - line_y) + if self.is_vertical: + return self._get_xy_params_vertical( + group_coord_1, group_coord_2, line_y + ) + return self._get_xy_params_horizontal( + group_coord_1, group_coord_2, line_x + ) def _maybe_warn_about_configuration(self): if self._should_warn_about_configuration: diff --git a/statannotations/_GroupsPositions.py b/statannotations/_GroupsPositions.py index dcd13fd..85b3271 100644 --- a/statannotations/_GroupsPositions.py +++ b/statannotations/_GroupsPositions.py @@ -26,7 +26,7 @@ def get_group_names_and_labels( tuple_group_names = [] for group_name, hue_name in itertools.product(group_names, hue_names): tuple_group_names.append((group_name, hue_name)) - labels.append(f'{group_name}_{hue_name}') + labels.append(f"{group_name}_{hue_name}") return tuple_group_names, labels @@ -61,7 +61,9 @@ def __init__( group_names, native_group_offsets, width ) # Create the tuple (group, hue) and the labels - self.tuple_group_names, self.labels = get_group_names_and_labels(group_names, hue_names) + self.tuple_group_names, self.labels = get_group_names_and_labels( + group_names, hue_names + ) # Create dataframe with the groups, labels and positions # this should be done last, when the other attributes are defined @@ -80,7 +82,7 @@ def _set_group_offsets( if len(curated_offsets) != len(group_names): msg = ( 'The values of the categories with "native_scale=True" do not correspond ' - 'to the category names. Maybe some values are not finite?' + "to the category names. Maybe some values are not finite?" ) warnings.warn(msg) else: @@ -98,9 +100,9 @@ def _set_data(self, dodge: bool, gap: float) -> tuple[pd.DataFrame, float]: artist_width = float(self.width) data = pd.DataFrame( { - 'group': self.tuple_group_names, - 'label': self.labels, - 'pos': positions, + "group": self.tuple_group_names, + "label": self.labels, + "pos": positions, }, ) if dodge and self.use_hue: @@ -109,7 +111,7 @@ def _set_data(self, dodge: bool, gap: float) -> tuple[pd.DataFrame, float]: # evenly space range centered in zero (subtracting the mean) offset = artist_width * (np.arange(n_hues) - (n_hues - 1) / 2) tiled_offset = np.tile(offset, len(self._group_names)) - data['pos'] += tiled_offset + data["pos"] += tiled_offset if gap and gap >= 0 and gap <= 1: artist_width *= 1 - gap @@ -122,7 +124,7 @@ def find_group_at_pos( verbose: bool = False, strict: bool = False, ) -> TupleGroup | None: - positions = self._data['pos'] + positions = self._data["pos"] if len(positions) == 0: return None # Get the index of the closest position @@ -134,24 +136,24 @@ def find_group_at_pos( return None # The requested position is not an artist position msg = ( - 'Invalid x-position found. Are the same parameters passed to ' - 'seaborn and statannotations calls? Or are there few data points? ' - f'The closest group position to {pos} is {found_pos}' + "Invalid x-position found. Are the same parameters passed to " + "seaborn and statannotations calls? Or are there few data points? " + f"The closest group position to {pos} is {found_pos}" ) warnings.warn(msg, UserWarning, stacklevel=2) - return self._data.loc[index, 'group'] + return self._data.loc[index, "group"] def get_group_axis_position(self, group: TupleGroup) -> float: """Get the position of the group. group_name can be either a tuple ("group",) or a tuple ("group", "hue") """ - group_names = self._data['group'] + group_names = self._data["group"] if group not in group_names: - msg = f'Group {group} was not found in the list: {group_names}' + msg = f"Group {group} was not found in the list: {group_names}" raise ValueError(msg) index = (group_names == group).idxmax() - pos = float(self._data.loc[index, 'pos']) + pos = float(self._data.loc[index, "pos"]) # round the position return round(pos / self.POSITION_TOLERANCE) * self.POSITION_TOLERANCE @@ -165,4 +167,6 @@ def compatible_width(self, width: float) -> bool: def iter_groups(self) -> Iterator[tuple[TupleGroup, str, float]]: """Iterate the groups and return a tuple (group_tuple, group_label, group_position).""" - yield from self._data[['group', 'label', 'pos']].itertuples(index=False, name=None) + yield from self._data[["group", "label", "pos"]].itertuples( + index=False, name=None + ) diff --git a/statannotations/_Plotter.py b/statannotations/_Plotter.py index fd9eb97..c4e13d4 100644 --- a/statannotations/_Plotter.py +++ b/statannotations/_Plotter.py @@ -1,7 +1,7 @@ from __future__ import annotations import logging -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Literal import numpy as np import matplotlib.pyplot as plt @@ -10,11 +10,19 @@ from matplotlib.patches import Rectangle from statannotations._GroupsPositions import _GroupsPositions -from statannotations.utils import check_not_none, check_order_in_data, \ - check_pairs_in_data, render_collection, check_is_in, check_redundant_hue +from statannotations.utils import ( + check_not_none, + check_order_in_data, + check_pairs_in_data, + render_collection, + check_is_in, + check_redundant_hue, +) from .compat import get_plotter if TYPE_CHECKING: + from typing import Literal + from .compat import TupleGroup, Struct logger = logging.getLogger(__name__) @@ -24,29 +32,34 @@ } class _Plotter: + orient: Literal['v', 'h'] + def __init__(self, ax, pairs, data=None, x=None, y=None, hue=None, order=None, hue_order=None, verbose=False, **plot_params): self.ax = ax self._fig = plt.gcf() check_not_none('pairs', pairs) - group_coord = y if plot_params.get('orient') in ('h', 'y') else x + + orient = plot_params.get('orient', 'v') + if orient in ('y', 'h'): + self.orient = 'h' + elif orient in ('x', 'v'): + self.orient = 'v' + else: + logger.debug(f"Fallback to 'v', `orient` should be one of 'h' or 'v', got: {orient}") + self.orient = 'v' + + group_coord = y if self.orient == 'h' else x self.is_redundant_hue = check_redundant_hue(data, group_coord, hue, hue_order) if self.is_redundant_hue: hue = None hue_order = None + check_order_in_data(data, group_coord, order) check_pairs_in_data(pairs, data, group_coord, hue, hue_order) self.pairs = pairs self._struct_pairs = None self.verbose = verbose - self.orient = plot_params.get('orient', 'v') - if self.orient in ('y', 'h'): - self.orient = 'h' - elif self.orient in ('x', 'v'): - self.orient = 'v' - else: - logger.debug(f"Fallback to 'v', `orient` should be one of 'h' or 'v', got: {self.orient}") - self.orient = 'v' def get_transform_func(self, kind: str): """ @@ -70,11 +83,12 @@ def get_transform_func(self, kind: str): if kind == 'pix_to_ax': return self.ax.transAxes.inverted() - transform = {'v': self.ax.get_xaxis_transform, - 'h': self.ax.get_yaxis_transform}[self.orient] + transform = { + 'v': self.ax.get_xaxis_transform, + 'h': self.ax.get_yaxis_transform, + }[self.orient] - data_to_ax = \ - self.ax.transData + transform().inverted() + data_to_ax = self.ax.transData + transform().inverted() if kind == 'data_to_ax': return data_to_ax @@ -105,6 +119,9 @@ def __init__( super().__init__( ax, pairs, data, x, y, hue, order, hue_order, verbose, **plot_params ) + if self.is_redundant_hue: + hue = None + hue_order = None self.check_plot_is_implemented(plot) self.plot = plot @@ -134,7 +151,7 @@ def __init__( self.structs = self._get_structs() - self.pairs = self.plotter.parse_pairs(pairs, self.structs, formatter=plot_params.get('formatter')) + self.pairs = self.plotter.parse_pairs(pairs, self.structs) self._struct_pairs = self._get_group_struct_pairs() self._value_stack_arr = np.array( @@ -321,3 +338,9 @@ def get_value_lim(self): if self.orient == 'v': return self.ax.get_ylim() return self.ax.get_xlim() + + def set_value_lim(self, value) -> None: + if self.orient == 'v': + self.ax.set_ylim(value) + else: + self.ax.set_xlim(value) diff --git a/statannotations/compat.py b/statannotations/compat.py index 204fe83..6e2be62 100644 --- a/statannotations/compat.py +++ b/statannotations/compat.py @@ -56,6 +56,14 @@ class Struct(TypedDict): _CategoricalPlotter = sns.categorical._CategoricalPlotter +table_convert_orient_seaborn: dict[str, str] = { + "v": "v" if sns_version < (0, 13, 0) else "x", + "x": "v" if sns_version < (0, 13, 0) else "x", + "h": "h" if sns_version < (0, 13, 0) else "y", + "y": "h" if sns_version < (0, 13, 0) else "y", +} + + def fix_and_warn(dodge, hue, plot): if dodge is False and hue is not None: raise ValueError("`dodge` cannot be False in statannotations.") @@ -84,7 +92,7 @@ def _get_categorical_plotter( kwargs = { "data": data, "order": order, - "orient": plot_params.get("orient"), + "orient": table_convert_orient_seaborn[plot_params.get("orient", "v")], "color": None, } variables = {"x": x, "y": y, "hue": hue} @@ -231,6 +239,7 @@ class Wrapper: gap: float dodge: bool is_redundant_hue: bool + formatter: Callable | None native_group_offsets: Sequence | None def __init__( @@ -250,6 +259,7 @@ def __init__( self.width = kwargs.get("width", 0.8) self.native_group_offsets = None self.is_redundant_hue = is_redundant_hue + self.formatter = None @property def has_violin_support(self) -> bool: @@ -279,15 +289,17 @@ def parse_pairs( self, pairs: list[tuple], structs: list[Struct], - *, - formatter: Callable | None = None, ) -> list[tuple[TupleGroup, TupleGroup]]: struct_groups = [struct["group"] for struct in structs] ret: list[tuple[TupleGroup, TupleGroup]] = [] def format_group(value) -> TupleGroup: """Format the group (but not the optional hue).""" - return value if isinstance(value, tuple) else (value,) + tvalue = value if isinstance(value, tuple) else (value,) + if not callable(self.formatter): + return tvalue + group = tvalue[0] + return tuple([self.formatter(group), *tvalue[1:]]) for pair in pairs: if not isinstance(pair, Sequence) or len(pair) != 2: @@ -338,6 +350,7 @@ def __init__( **plot_params, ) -> None: super().__init__(plot_type, hue=hue, **plot_params) + self._cat_axis = {"v": "x", "h": "y"}[plot_params.get("orient", "v")] self._plotter = _get_categorical_plotter( plot_type, @@ -349,6 +362,10 @@ def __init__( **plot_params, ) + @property + def axis(self) -> str: + return self._cat_axis + @property def has_hue(self) -> bool: return self._plotter.plot_hues is not None @@ -424,7 +441,10 @@ def _populate_value_maxes_violin( msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue - value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + if self.axis == "x": + value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + else: + value_maxes[key] = data_to_ax.transform((value_pos, 0))[0] else: value_pos = max(self._plotter.support[group_idx]) key = (group_name,) @@ -432,7 +452,10 @@ def _populate_value_maxes_violin( msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue - value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + if self.axis == "x": + value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + else: + value_maxes[key] = data_to_ax.transform((value_pos, 0))[0] return value_maxes @@ -454,6 +477,7 @@ def __init__( ) -> None: super().__init__(plot_type, hue=hue, **plot_params) self._group_names = None + self._cat_axis = {"v": "x", "h": "y"}[plot_params.get("orient", "v")] self._plotter = _get_categorical_plotter( plot_type, @@ -475,6 +499,7 @@ def __init__( if formatter is not None: msg = "`formatter` is not supported with seaborn==0.12, update to seaborn>=0.13" raise ValueError(msg) + self._order_variable( order=order, native_scale=native_scale, formatter=formatter ) @@ -483,6 +508,15 @@ def __init__( if native_scale: self.native_group_offsets = self._plotter.plot_data[self.axis] + @property + def axis(self) -> str: + if not hasattr(self._plotter, "cat_axis"): + return self._cat_axis + + else: + # _CategoricalPlotterNew + return self._plotter.cat_axis + @property def has_hue(self) -> bool: if self.is_redundant_hue: @@ -504,7 +538,7 @@ def group_names(self) -> list: else: # _CategoricalPlotterNew - group_names = self._plotter.var_levels[self._plotter.cat_axis] + group_names = self._plotter.var_levels[self.axis] if isinstance(group_names, pd.Index): return group_names.tolist() return group_names @@ -533,16 +567,18 @@ def _order_variable( native_scale: bool = False, formatter: Callable | None = None, ) -> None: - # Do not order if not categorical and native scale - if ( - self._plotter.var_types.get(self._plotter.cat_axis) != "categorical" - and native_scale - ): + # Do not order if not categorical + # because it transforms groups to strings + if self._plotter.var_types.get(self.axis) != "categorical": return + # def default_formatter(x): + # return x + default_formatter = None + # Order the group variable self._plotter.scale_categorical( - self._plotter.cat_axis, order=order, formatter=None + self.axis, order=order, formatter=default_formatter ) def _get_group_data_from_plotter( @@ -604,7 +640,7 @@ def _get_group_data_new(self, group_name: TupleGroup) -> pd.Series: # the value variable is the one that is no the group axis tgroup = group_name if isinstance(group_name, tuple) else (group_name,) # 'x' if vertical or 'y' if horizontal - cat_var = self._plotter.cat_axis + cat_var = self.axis # opposite: 'y' if vertical or 'x' if horizontal value_var = {"x": "y", "y": "x"}[cat_var] group = tgroup[0] @@ -613,6 +649,11 @@ def _get_group_data_new(self, group_name: TupleGroup) -> pd.Series: if self.has_hue and len(tgroup) > 1: iter_vars.append("hue") hue = tgroup[1] + else: + # Bug, remove hue column full of None. + # Otherwise the whole dataframe is discarded with `dropna` when calling `iter_data` + if "hue" in self._plotter.plot_data.columns: + del self._plotter.plot_data["hue"] for sub_vars, sub_data in self._plotter.iter_data(iter_vars): if sub_vars[cat_var] != group: @@ -623,6 +664,8 @@ def _get_group_data_new(self, group_name: TupleGroup) -> pd.Series: # Found a matching group, return the data group_data = remove_null(sub_data[value_var]) return group_data + msg = f"Cannot find group {group_name!r} for {iter_vars} in data." + raise ValueError(msg) def _populate_value_maxes_violin( self, value_maxes: dict[TupleGroup, float], data_to_ax @@ -641,7 +684,10 @@ def _populate_value_maxes_violin( msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue - value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + if self.axis == "x": + value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + else: + value_maxes[key] = data_to_ax.transform((value_pos, 0))[0] else: value_pos = max(self._plotter.support[group_idx]) key = (group_name,) @@ -649,7 +695,10 @@ def _populate_value_maxes_violin( msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue - value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + if self.axis == "x": + value_maxes[key] = data_to_ax.transform((0, value_pos))[1] + else: + value_maxes[key] = data_to_ax.transform((value_pos, 0))[0] return value_maxes @@ -678,22 +727,21 @@ def __init__( "data": data, "variables": variables, "order": order, - "orient": plot_params.get("orient"), + "orient": table_convert_orient_seaborn[plot_params.get("orient", "v")], "color": None, "legend": plot_params.get("legend"), } self._plotter = _CategoricalPlotter(**kwargs) - # Order the group variables + # Order the group variables, + # !! will define self.native_scale and self.formatter + formatter = plot_params.get("formatter", None) native_scale = plot_params.get("native_scale", False) - formatter = plot_params.get("formatter") self._order_variable( order=order, native_scale=native_scale, formatter=formatter ) - # Native scaling of the group variable - if native_scale: - self.native_group_offsets = self._plotter.plot_data[self.axis] + self.log_scale = plot_params.get("log_scale", False) bw_method = kwargs.get("bw_method", kwargs.get("bw", "scott")) self.kde_kwargs = dict( @@ -719,6 +767,7 @@ def axis(self) -> str: @property def group_names(self) -> list: if self._group_names is not None: + ## This is not used return self._group_names return self._plotter.var_levels[self.axis] @@ -728,35 +777,35 @@ def hue_names(self): return [] return self._plotter.var_levels["hue"] + @property + def is_categorical(self) -> bool: + """Return True if the categorical axis is really a categorical variable.""" + return self._plotter.var_types.get(self.axis) == "categorical" + def _order_variable( self, *, order, native_scale: bool = False, formatter: Callable | None = None, - raw_groups: bool = False, ) -> None: - if raw_groups: - # Save the group names before formatting, because they are transformed to str - self._group_names = list(self._plotter.var_levels[self.axis]) - - # Do not order if not categorical and native scale - if self._plotter.var_types.get(self.axis) != "categorical" and native_scale: + """Order, scale and format the categorical variable.""" + # Order only if categorical + if self.is_categorical: + # Order the group variable + self._plotter.scale_categorical(self.axis, order=order, formatter=formatter) + + # cannot be native_scale + self.native_scale = False + self.formatter = formatter or str return - # Order the group variable - self._plotter.scale_categorical(self.axis, order=order, formatter=formatter) - - if raw_groups: - # Reorder group names - formatter = formatter if callable(formatter) else str - ordered_group_names = list(self._plotter.var_levels[self.axis]) - formatted_group_names = [formatter(v) for v in self._group_names] - - # find permutation indices - indices = [ordered_group_names.index(val) for val in formatted_group_names] - # Reorder raw group names - self._group_names = [self._group_names[i] for i in indices] + # Compute offsets if native_scale + if native_scale: + self.native_group_offsets = self.group_names + self.native_scale = native_scale + # do not format non-categorical variables + self.formatter = lambda x: x def get_group_data(self, group_name: TupleGroup) -> pd.Series: """Get the data for the (group[, hue]) tuple. @@ -785,23 +834,25 @@ def get_group_data(self, group_name: TupleGroup) -> pd.Series: # Found a matching group, return the data group_data = remove_null(sub_data[value_var]) return group_data + msg = f"Cannot find group {group_name!r} for {iter_vars} in data." + raise ValueError(msg) def parse_pairs( self, pairs: list[tuple], structs: list[Struct], - *, - formatter: Callable | None = None, ) -> list[tuple[TupleGroup, TupleGroup]]: + """Parse input pairs to match formatted pairs.""" struct_groups = [struct["group"] for struct in structs] ret: list[tuple[TupleGroup, TupleGroup]] = [] - formatter = formatter if callable(formatter) else str - def format_group(value, formatter) -> TupleGroup: + def format_group(value) -> TupleGroup: """Format the group (but not the optional hue).""" tvalue = value if isinstance(value, tuple) else (value,) + if not callable(self.formatter): + return tvalue group = tvalue[0] - return tuple([formatter(group), *tvalue[1:]]) + return tuple([self.formatter(group), *tvalue[1:]]) for pair in pairs: if not isinstance(pair, Sequence) or len(pair) != 2: @@ -809,7 +860,7 @@ def format_group(value, formatter) -> TupleGroup: warnings.warn(msg) continue # Format the groups - new_pair = tuple(format_group(v, formatter) for v in pair) + new_pair = tuple(format_group(v) for v in pair) # Check that the groups are valid group names valid_group = True @@ -827,7 +878,10 @@ def format_group(value, formatter) -> TupleGroup: ret.append(new_pair) if len(ret) == 0: - msg = f"pairs are empty after parsing: original_pairs={pairs}" + msg = ( + f"pairs are empty after parsing: original_pairs={pairs}\n" + f"not in group_list={struct_groups}" + ) raise ValueError(msg) return ret @@ -856,9 +910,14 @@ def _populate_value_maxes_violin( warnings.warn(msg) continue sub_data["weight"] = sub_data.get("weights", 1) + # TODO: transform if log_scale is True. Seaborn doesn't do it though stat_data = kde._transform(sub_data, value_var, []) support = stat_data[value_var] - value_maxes[key] = data_to_ax.transform((0, max(support)))[1] + value_max = max(support) + if self.axis == "x": + value_maxes[key] = data_to_ax.transform((0, value_max))[1] + else: + value_maxes[key] = data_to_ax.transform((value_max, 0))[0] return value_maxes diff --git a/statannotations/utils.py b/statannotations/utils.py index 529837c..31ffb21 100644 --- a/statannotations/utils.py +++ b/statannotations/utils.py @@ -73,11 +73,14 @@ def _check_pairs_in_data_no_hue(pairs: Union[list, tuple], x_values = get_x_values(data, x) pairs_x_values = set(itertools.chain(*pairs)) + # check if singleton tuples were used instead of directly the values + if all(isinstance(v, tuple) for v in pairs_x_values): + pairs_x_values = set(v[0] for v in pairs_x_values) unmatched_x_values = pairs_x_values - x_values if unmatched_x_values: - raise ValueError(f"Missing x value(s) " - f"`{render_collection(unmatched_x_values)}` in {x}" - f" (specified in `pairs`) in data") + raise ValueError(f"Missing value(s) " + f"{list(unmatched_x_values)} for {x}" + f" (specified in `pairs`) in data.") def _check_pairs_in_data_with_hue( From ec4883964a5f929863a501605367e9689524e33d Mon Sep 17 00:00:00 2001 From: getzze Date: Thu, 25 Jul 2024 12:08:48 +0100 Subject: [PATCH 06/16] remove distutils dependency --- setup.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 51c85f5..58ac68e 100644 --- a/setup.py +++ b/setup.py @@ -1,5 +1,4 @@ -from distutils.core import setup -from setuptools import find_packages +from setuptools import find_packages, setup import re with open("README.md", "r") as f: From bb5135dcc010dd80f4f668756bfb34d042f87bb7 Mon Sep 17 00:00:00 2001 From: getzze Date: Thu, 25 Jul 2024 13:02:56 +0100 Subject: [PATCH 07/16] compat with py37, remove Literal import --- statannotations/_Plotter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/statannotations/_Plotter.py b/statannotations/_Plotter.py index c4e13d4..cafebe5 100644 --- a/statannotations/_Plotter.py +++ b/statannotations/_Plotter.py @@ -1,7 +1,7 @@ from __future__ import annotations import logging -from typing import TYPE_CHECKING, Literal +from typing import TYPE_CHECKING import numpy as np import matplotlib.pyplot as plt From 63453758165927f75944dbe3f735c31dd2a38397 Mon Sep 17 00:00:00 2001 From: getzze Date: Thu, 25 Jul 2024 16:37:30 +0100 Subject: [PATCH 08/16] improve coverage --- statannotations/Annotator.py | 138 ++++++++++++++-------------- statannotations/PValueFormat.py | 11 ++- statannotations/_GroupsPositions.py | 62 +++++-------- statannotations/_Plotter.py | 12 +-- statannotations/compat.py | 112 +++++++++++----------- statannotations/stats/StatResult.py | 10 +- statannotations/stats/StatTest.py | 2 +- statannotations/stats/utils.py | 2 +- statannotations/utils.py | 4 +- tests/test_annotator.py | 40 +++++++- tests/test_native_scale.py | 116 +++++++++++++++++++++++ 11 files changed, 324 insertions(+), 185 deletions(-) create mode 100644 tests/test_native_scale.py diff --git a/statannotations/Annotator.py b/statannotations/Annotator.py index 32e38d5..229d3c5 100644 --- a/statannotations/Annotator.py +++ b/statannotations/Annotator.py @@ -170,7 +170,7 @@ def is_vertical(self) -> bool: return self._plotter.orient in ('v', 'x') @property - def verbose(self): + def verbose(self): # pragma: no cover return self._verbose @verbose.setter @@ -215,7 +215,7 @@ def annotate(self, line_offset=None, line_offset_to_group=None): self.value_offset, self.line_offset_to_group = offset_func( line_offset, line_offset_to_group) - if self._verbose: + if self._verbose: # pragma: no branch self.print_pvalue_legend() ax_to_data = self._plotter.get_transform_func('ax_to_data') @@ -323,9 +323,6 @@ def apply_test(self, num_comparisons='auto', **stats_params): self._check_has_plotter() self._check_test_pvalues_perform() - if stats_params is None: - stats_params = dict() - self.perform_stat_test = True self.annotations = self._get_results(num_comparisons=num_comparisons, @@ -362,38 +359,35 @@ def set_pvalues(self, pvalues, num_comparisons='auto'): def set_custom_annotations(self, text_annot_custom): """ - :param text_annot_custom: List of strings to annotate for each - `pair` + :param text_annot_custom: List of strings to annotate for each `pair` """ self._check_has_plotter() self._check_correct_number_custom_annotations(text_annot_custom) - self.annotations = [Annotation( - struct, self._value_for_group_struct(text_annot_custom, struct[0])) - for struct in self._struct_pairs] + self.annotations = [ + Annotation(struct, self._value_for_group_struct(text_annot_custom, struct[0])) + for struct in self._struct_pairs + ] self.show_test_name = False self._deactivate_configured_warning() return self def annotate_custom_annotations(self, text_annot_custom): """ - :param text_annot_custom: List of strings to annotate for each - `pair` + :param text_annot_custom: List of strings to annotate for each `pair` """ self.set_custom_annotations(text_annot_custom) return self.annotate() def get_configuration(self): - configuration = {key: getattr(self, key) - for key in CONFIGURABLE_PARAMETERS} + configuration = {key: getattr(self, key) for key in CONFIGURABLE_PARAMETERS} configuration["pvalue_format"] = self.pvalue_format.get_configuration() return configuration def get_annotations_text(self): - if self.annotations is not None: - return [self.annotations[new_idx].text - for new_idx in self._reordering] - return None + if self.annotations is None: + return None + return [self.annotations[new_idx].text for new_idx in self._reordering] @property def _reordering(self): @@ -469,7 +463,7 @@ def _deactivate_configured_warning(self): self._just_configured = False def print_pvalue_legend(self): - if not self.custom_annotations: + if not self.custom_annotations: # pragma: no branch self._pvalue_format.print_legend_if_used() def _get_results(self, num_comparisons, pvalues=None, @@ -511,7 +505,7 @@ def _check_has_plotter(self): def _annotate_pair(self, annotation, ax_to_data, ann_list, orig_value_lim): - if self._verbose >= 1: + if self._verbose >= 1: # pragma: no branch annotation.print_labels_and_content() group_coord_1 = annotation.structs[0]['group_coord'] @@ -520,20 +514,19 @@ def _annotate_pair(self, annotation, ax_to_data, ann_list, orig_value_lim): group_i2 = annotation.structs[1]['group_i'] # Find y maximum for all the y_stacks *in between* group1 and group2 + in_between_groups_mask = np.where( + (group_coord_1 <= self._value_stack_arr[0, :]) + & (self._value_stack_arr[0, :] <= group_coord_2) + ) i_value_max_in_range_g1_g2 = group_i1 + np.nanargmax( - self._value_stack_arr[1, - np.where((group_coord_1 - <= self._value_stack_arr[0, :]) - & (self._value_stack_arr[0, :] - <= group_coord_2))]) + self._value_stack_arr[1, in_between_groups_mask] + ) value = self._get_value_for_pair(i_value_max_in_range_g1_g2) # Determine lines in axes coordinates - ax_line_group = [group_coord_1, group_coord_1, - group_coord_2, group_coord_2] - ax_line_value = [value, value + self.line_height, - value + self.line_height, value] + ax_line_group = [group_coord_1, group_coord_1, group_coord_2, group_coord_2] + ax_line_value = [value, value + self.line_height, value + self.line_height, value] lists = ( (ax_line_group, ax_line_value) @@ -541,21 +534,26 @@ def _annotate_pair(self, annotation, ax_to_data, ann_list, orig_value_lim): else (ax_line_value, ax_line_group) ) - points = [ax_to_data.transform((x, y)) - for x, y - in zip(*lists)] + points = [ax_to_data.transform((x, y)) for x, y in zip(*lists)] line_x, line_y = zip(*points) self._plot_line(line_x, line_y) - xy_params = self._get_xy_params(group_coord_1, group_coord_2, line_x, - line_y) + xy_params = self._get_xy_params( + group_coord_1, group_coord_2, line_x, line_y + ) ann = self.ax.annotate( - annotation.text, textcoords='offset points', - xycoords='data', ha='center', va='bottom', - fontsize=self._pvalue_format.fontsize, clip_on=False, - annotation_clip=False, **xy_params) + annotation.text, + textcoords='offset points', + xycoords='data', + ha='center', + va='bottom', + fontsize=self._pvalue_format.fontsize, + clip_on=False, + annotation_clip=False, + **xy_params, + ) if annotation.text is not None: ann_list.append(ann) @@ -563,14 +561,12 @@ def _annotate_pair(self, annotation, ax_to_data, ann_list, orig_value_lim): self._plotter.set_value_lim(orig_value_lim) value_top_annot = self._annotate_pair_text(ann, value) - else: + else: # pragma: no cover value_top_annot = value + self.line_height # Fill the highest value position of the annotation into value_stack # for all positions in the range group_coord_1 to group_coord_2 - self._value_stack_arr[ - 1, (group_coord_1 <= self._value_stack_arr[0, :]) - & (self._value_stack_arr[0, :] <= group_coord_2)] = value_top_annot + self._value_stack_arr[1, in_between_groups_mask] = value_top_annot # Increment the counter of annotations in the value_stack array self._value_stack_arr[2, group_i1:group_i2 + 1] += 1 @@ -581,43 +577,51 @@ def _update_value_for_loc(self): def _check_test_pvalues_perform(self): if self.test is None: - raise ValueError("If `perform_stat_test` is True, " - "`test` must be specified.") + raise ValueError( + "If `perform_stat_test` is True, `test` must be specified." + ) - if self.test not in IMPLEMENTED_TESTS and \ - not isinstance(self.test, StatTest): - raise ValueError("test value should be a StatTest instance " - "or one of the following strings: {}." - .format(', '.join(IMPLEMENTED_TESTS))) + if ( + self.test not in IMPLEMENTED_TESTS + and not isinstance(self.test, StatTest) + ): + raise ValueError( # pragma: no cover + "test value should be a StatTest instance " + "or one of the following strings: {}." + .format(', '.join(IMPLEMENTED_TESTS)) + ) def _check_pvalues_no_perform(self, pvalues): - if pvalues is None: - raise ValueError("If `perform_stat_test` is False, " - "custom `pvalues` must be specified.") + if pvalues is None: # pragma: no cover + raise ValueError( + "If `perform_stat_test` is False, " + "custom `pvalues` must be specified." + ) check_pvalues(pvalues) - if len(pvalues) != len(self.pairs): - raise ValueError("`pvalues` should be of the same length as " - "`pairs`.") + if len(pvalues) != len(self.pairs): # pragma: no cover + raise ValueError( + "`pvalues` should be of the same length as `pairs`." + ) def _check_test_no_perform(self): - if self.test is not None: - raise ValueError("If `perform_stat_test` is False, " - "`test` must be None.") + if self.test is not None: # pragma: no cover + raise ValueError( + "If `perform_stat_test` is False, `test` must be None." + ) def _check_correct_number_custom_annotations(self, text_annot_custom): - if text_annot_custom is None: + if text_annot_custom is None: # pragma: no cover return - if len(text_annot_custom) != len(self._struct_pairs): + if len(text_annot_custom) != len(self._struct_pairs): # pragma: no branch raise ValueError( - "`text_annot_custom` should be of same length as `pairs`.") + "`text_annot_custom` should be of same length as `pairs`." + ) def _apply_comparisons_correction(self, annotations): if self.comparisons_correction is None: return - else: - self.comparisons_correction.apply( - [annotation.data for annotation in annotations]) + self.comparisons_correction.apply([annotation.data for annotation in annotations]) def _get_stat_result_from_test(self, group_struct1, group_struct2, num_comparisons, @@ -684,7 +688,7 @@ def validate_test_short_name(self): return if self.show_test_name and self.pvalue_format.text_format != "star": - if self.test: + if self.test: # pragma: no branch self.test_short_name = (self.test if isinstance(self.test, str) else self.test.short_name) @@ -718,11 +722,11 @@ def _annotate_pair_text(self, ann, value): value_coord_max = {'v': 'ymax', 'h': 'xmax'}[self.orient] value_top_annot = getattr(bbox_ax, value_coord_max) - except RuntimeError: + except RuntimeError: # pragma: no cover got_mpl_error = True if self.use_fixed_offset or got_mpl_error: - if self._verbose >= 1: + if self._verbose >= 1: # pragma: no branch print("Warning: cannot get the text bounding box. Falling " "back to a fixed y offset. Layout may be not " "optimal.") diff --git a/statannotations/PValueFormat.py b/statannotations/PValueFormat.py index 5c2b8d0..1ae1083 100644 --- a/statannotations/PValueFormat.py +++ b/statannotations/PValueFormat.py @@ -20,10 +20,10 @@ class Formatter: def __init__(self): pass - def config(self, *args, **kwargs): + def config(self, *args, **kwargs): # pragma: no cover pass - def format_data(self, data): + def format_data(self, data): # pragma: no cover pass @@ -183,7 +183,7 @@ def format_data(self, result): elif self.text_format == 'star': was_list = False - if not isinstance(result, list): + if not isinstance(result, list): # pragma: no branch result = [result] annotations = [ @@ -191,12 +191,13 @@ def format_data(self, result): for star, res in pval_annotation_text(result, self.pvalue_thresholds)] - if was_list: + if was_list: # pragma: no cover return annotations return annotations[0] - elif self.text_format == 'simple': + # elif self.text_format == 'simple': + else: return simple_text(result, self.simple_format_string, self.pvalue_thresholds, self.show_test_name) diff --git a/statannotations/_GroupsPositions.py b/statannotations/_GroupsPositions.py index 85b3271..045c55b 100644 --- a/statannotations/_GroupsPositions.py +++ b/statannotations/_GroupsPositions.py @@ -8,7 +8,7 @@ import numpy as np import pandas as pd -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from .compat import TupleGroup, TGroupValue, THueValue @@ -47,7 +47,7 @@ def __init__( dodge: bool = True, gap: float = 0.0, width: float = 0.8, - native_group_offsets: Sequence | None = None, + use_native_offsets: bool = False, ) -> None: self.gap = gap self.dodge = dodge @@ -58,7 +58,7 @@ def __init__( # Compute the coordinates of the groups (without hue) and the width self.group_offsets, self.width = self._set_group_offsets( - group_names, native_group_offsets, width + group_names, use_native_offsets, width ) # Create the tuple (group, hue) and the labels self.tuple_group_names, self.labels = get_group_names_and_labels( @@ -72,24 +72,17 @@ def __init__( def _set_group_offsets( self, group_names: Sequence, - native_group_offsets: Sequence | None, + use_native_offsets: bool, width: float, ) -> tuple[Sequence, float]: """Set the group offsets from native scale and scale the width.""" group_offsets = list(range(len(group_names))) - if native_group_offsets is not None: - curated_offsets = [v for v in native_group_offsets] - if len(curated_offsets) != len(group_names): - msg = ( - 'The values of the categories with "native_scale=True" do not correspond ' - "to the category names. Maybe some values are not finite?" - ) - warnings.warn(msg) - else: - group_offsets = curated_offsets - if len(curated_offsets) > 1: - native_width = np.min(np.diff(curated_offsets)) - width *= native_width + if use_native_offsets: + group_offsets = group_names + if len(group_names) > 1: # pragma: no branch + # use the native width to compute the general width + native_width = np.min(np.diff(group_names)) + width *= native_width return group_offsets, width @@ -125,45 +118,32 @@ def find_group_at_pos( strict: bool = False, ) -> TupleGroup | None: positions = self._data["pos"] - if len(positions) == 0: + if len(positions) == 0: # pragma: no cover return None # Get the index of the closest position index = (positions - pos).abs().idxmin() found_pos = positions.loc[index] if verbose and abs(found_pos - pos) > self.POSITION_TOLERANCE: - if strict: + if strict: # pragma: no branch return None - # The requested position is not an artist position - msg = ( - "Invalid x-position found. Are the same parameters passed to " - "seaborn and statannotations calls? Or are there few data points? " - f"The closest group position to {pos} is {found_pos}" - ) - warnings.warn(msg, UserWarning, stacklevel=2) + else: # pragma: no cover + # The requested position is not an artist position + msg = ( + "Invalid x-position found. Are the same parameters passed to " + "seaborn and statannotations calls? Or are there few data points? " + f"The closest group position to {pos} is {found_pos}" + ) + warnings.warn(msg, UserWarning, stacklevel=2) return self._data.loc[index, "group"] - def get_group_axis_position(self, group: TupleGroup) -> float: - """Get the position of the group. - - group_name can be either a tuple ("group",) or a tuple ("group", "hue") - """ - group_names = self._data["group"] - if group not in group_names: - msg = f"Group {group} was not found in the list: {group_names}" - raise ValueError(msg) - index = (group_names == group).idxmax() - pos = float(self._data.loc[index, "pos"]) - # round the position - return round(pos / self.POSITION_TOLERANCE) * self.POSITION_TOLERANCE - @property def artist_width(self) -> float: return float(self._artist_width) def compatible_width(self, width: float) -> bool: """Check if the rectangle width is smaller than the artist width.""" - return abs(width) <= 1.1 * self._artist_width + return abs(width) <= 1.1 * self.artist_width def iter_groups(self) -> Iterator[tuple[TupleGroup, str, float]]: """Iterate the groups and return a tuple (group_tuple, group_label, group_position).""" diff --git a/statannotations/_Plotter.py b/statannotations/_Plotter.py index cafebe5..79e9e21 100644 --- a/statannotations/_Plotter.py +++ b/statannotations/_Plotter.py @@ -20,7 +20,7 @@ ) from .compat import get_plotter -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from typing import Literal from .compat import TupleGroup, Struct @@ -45,7 +45,7 @@ def __init__(self, ax, pairs, data=None, x=None, y=None, hue=None, self.orient = 'h' elif orient in ('x', 'v'): self.orient = 'v' - else: + else: # pragma: no cover logger.debug(f"Fallback to 'v', `orient` should be one of 'h' or 'v', got: {orient}") self.orient = 'v' @@ -95,7 +95,7 @@ def get_transform_func(self, kind: str): elif kind == 'ax_to_data': return data_to_ax.inverted() - else: + else: # pragma: no cover return (data_to_ax, data_to_ax.inverted(), self.ax.transAxes.inverted()) @@ -143,7 +143,7 @@ def __init__( width=self.plotter.width, gap=self.plotter.gap, dodge=self.plotter.dodge, - native_group_offsets=self.plotter.native_group_offsets, + use_native_offsets=self.plotter.use_native_offsets, ) self.tuple_group_names = self.groups_positions.tuple_group_names self.reordering = None @@ -246,7 +246,7 @@ def _get_value_pos(self, child, data_to_ax): return self._get_value_pos_for_path_collection( child, data_to_ax) - elif isinstance(child, PolyCollection): + elif isinstance(child, PolyCollection): # pragma: no cover # Should be for violinplot body but not working return None, None @@ -265,7 +265,7 @@ def _get_value_pos_for_path_collection(self, child: PathCollection, data_to_ax): offsets = child.get_offsets() # remove nans offsets = offsets[np.all(np.isfinite(offsets), axis=1), :] - if len(offsets) == 0: + if len(offsets) == 0: # pragma: no cover logger.debug("skip the empty or all-NaNs PathCollection") return None, None diff --git a/statannotations/compat.py b/statannotations/compat.py index 6e2be62..d2c14c2 100644 --- a/statannotations/compat.py +++ b/statannotations/compat.py @@ -8,7 +8,7 @@ import pandas as pd import seaborn as sns -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from typing import NotRequired, Union, TypedDict, TypeVar import pandas as pd @@ -120,7 +120,7 @@ def _get_categorical_plotter( return _BoxPlotter(**kwargs) # 0.13 seaborn API - return _CategoricalPlotter(**new_kwargs) + return _CategoricalPlotter(**new_kwargs) # pragma: no cover elif plot_type == "barplot": # Pre-0.13 seaborn API @@ -158,7 +158,7 @@ def _get_categorical_plotter( return _BarPlotter(**kwargs) # 0.13 seaborn API - return _CategoricalPlotter(**new_kwargs) + return _CategoricalPlotter(**new_kwargs) # pragma: no cover elif plot_type == "violinplot": # Pre-0.13 seaborn API @@ -183,7 +183,7 @@ def _get_categorical_plotter( return _ViolinPlotter(**kwargs) # 0.13 seaborn API - return _CategoricalPlotter(**new_kwargs) + return _CategoricalPlotter(**new_kwargs) # pragma: no cover elif plot_type == "swarmplot": # Pre-0.12 seaborn API @@ -199,12 +199,12 @@ def _get_categorical_plotter( # Pre-0.13 seaborn API if sns_version < (0, 13, 0): - if "color" in new_kwargs: + if "color" in new_kwargs: # pragma: no branch new_kwargs.pop("color") return _CategoricalPlotterNew(require_numeric=False, **new_kwargs) # 0.13 seaborn API - return _CategoricalPlotter(**new_kwargs) + return _CategoricalPlotter(**new_kwargs) # pragma: no cover elif plot_type == "stripplot": # Pre-0.12 seaborn API @@ -221,15 +221,16 @@ def _get_categorical_plotter( # Pre-0.13 seaborn API if sns_version < (0, 13, 0): - if "color" in new_kwargs: + if "color" in new_kwargs: # pragma: no branch new_kwargs.pop("color") return _CategoricalPlotterNew(require_numeric=False, **new_kwargs) # 0.13 seaborn API - return _CategoricalPlotter(**new_kwargs) + return _CategoricalPlotter(**new_kwargs) # pragma: no cover - msg = f"Plot type {plot_type!r} is not supported" - raise NotImplementedError(msg) + else: # pragma: no cover + msg = f"Plot type {plot_type!r} is not supported" + raise NotImplementedError(msg) class Wrapper: @@ -240,7 +241,7 @@ class Wrapper: dodge: bool is_redundant_hue: bool formatter: Callable | None - native_group_offsets: Sequence | None + use_native_offsets: bool def __init__( self, @@ -257,7 +258,7 @@ def __init__( self.gap = kwargs.get("gap", 0.0) self.width = kwargs.get("width", 0.8) - self.native_group_offsets = None + self.use_native_offsets = False self.is_redundant_hue = is_redundant_hue self.formatter = None @@ -267,18 +268,18 @@ def has_violin_support(self) -> bool: return hasattr(self, "_populate_value_maxes_violin") @property - def has_hue(self) -> bool: + def has_hue(self) -> bool: # pragma: no cover raise NotImplementedError @property - def group_names(self) -> list: + def group_names(self) -> list: # pragma: no cover raise NotImplementedError @property - def hue_names(self) -> list: + def hue_names(self) -> list: # pragma: no cover raise NotImplementedError - def get_group_data(self, group_name): + def get_group_data(self, group_name): # pragma: no cover """Get the data for the (group[, hue]) tuple. group_name can be either a tuple ("cat",) or a tuple ("cat", "hue") @@ -298,11 +299,11 @@ def format_group(value) -> TupleGroup: tvalue = value if isinstance(value, tuple) else (value,) if not callable(self.formatter): return tvalue - group = tvalue[0] - return tuple([self.formatter(group), *tvalue[1:]]) + group = tvalue[0] # pragma: no cover + return tuple([self.formatter(group), *tvalue[1:]]) # pragma: no cover for pair in pairs: - if not isinstance(pair, Sequence) or len(pair) != 2: + if not isinstance(pair, Sequence) or len(pair) != 2: # pragma: no cover msg = f"pair {pair} is not a 2-tuple, skipping." warnings.warn(msg) continue @@ -312,19 +313,19 @@ def format_group(value) -> TupleGroup: # Check that the groups are valid group names valid_group = True for i, group in enumerate(new_pair): - if group not in struct_groups: + if group not in struct_groups: # pragma: no cover msg = ( f"cannot find group{i} of pair in the group tuples: " f"{group} not in {struct_groups}" ) warnings.warn(msg) valid_group = False - if not valid_group: + if not valid_group: # pragma: no cover continue ret.append(new_pair) - if len(ret) == 0: + if len(ret) == 0: # pragma: no cover msg = ( f"pairs are empty after parsing: original_pairs={pairs}\n" f"not in group_list={struct_groups}" @@ -437,7 +438,7 @@ def _populate_value_maxes_violin( for hue_idx, hue_name in enumerate(self._plotter.hue_names): value_pos = max(self._plotter.support[group_idx][hue_idx]) key = (group_name, hue_name) - if key not in value_maxes: + if key not in value_maxes: # pragma: no cover msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue @@ -448,7 +449,7 @@ def _populate_value_maxes_violin( else: value_pos = max(self._plotter.support[group_idx]) key = (group_name,) - if key not in value_maxes: + if key not in value_maxes: # pragma: no cover msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue @@ -492,11 +493,11 @@ def __init__( if isinstance(self._plotter, _CategoricalPlotterNew): # Order the group variables native_scale = plot_params.get("native_scale", False) - if native_scale: + if native_scale: # pragma: no cover msg = "`native_scale=True` is not supported with seaborn==0.12, update to seaborn>=0.13" raise ValueError(msg) formatter = plot_params.get("formatter") - if formatter is not None: + if formatter is not None: # pragma: no cover msg = "`formatter` is not supported with seaborn==0.12, update to seaborn>=0.13" raise ValueError(msg) @@ -505,8 +506,8 @@ def __init__( ) # Native scaling of the group variable - if native_scale: - self.native_group_offsets = self._plotter.plot_data[self.axis] + if native_scale: # pragma: no cover + self.use_native_offsets = True @property def axis(self) -> str: @@ -519,7 +520,7 @@ def axis(self) -> str: @property def has_hue(self) -> bool: - if self.is_redundant_hue: + if self.is_redundant_hue: # pragma: no cover return False if hasattr(self._plotter, "hue_names"): # Can be None, force to be an empty list @@ -541,7 +542,7 @@ def group_names(self) -> list: group_names = self._plotter.var_levels[self.axis] if isinstance(group_names, pd.Index): return group_names.tolist() - return group_names + return group_names # pragma: no cover @property def hue_names(self): @@ -556,7 +557,7 @@ def hue_names(self): if "hue" not in self._plotter.var_levels: return [] hue_names = self._plotter.var_levels["hue"] - if isinstance(hue_names, pd.Index): + if isinstance(hue_names, pd.Index): # pragma: no cover return hue_names.tolist() return hue_names @@ -569,7 +570,7 @@ def _order_variable( ) -> None: # Do not order if not categorical # because it transforms groups to strings - if self._plotter.var_types.get(self.axis) != "categorical": + if self._plotter.var_types.get(self.axis) != "categorical": # pragma: no cover return # def default_formatter(x): @@ -633,7 +634,7 @@ def _get_group_data_new(self, group_name: TupleGroup) -> pd.Series: Almost a duplicate of seaborn's code, because there is not direct access to the group_data in the respective Plotter class. """ - if not isinstance(self._plotter, _CategoricalPlotterNew): + if not isinstance(self._plotter, _CategoricalPlotterNew): # pragma: no cover msg = "`self._plotter` should be a `_CategoricalPlotterNew` instance." raise TypeError(msg) @@ -664,8 +665,9 @@ def _get_group_data_new(self, group_name: TupleGroup) -> pd.Series: # Found a matching group, return the data group_data = remove_null(sub_data[value_var]) return group_data - msg = f"Cannot find group {group_name!r} for {iter_vars} in data." - raise ValueError(msg) + else: # pragma: no cover + msg = f"Cannot find group {group_name!r} for {iter_vars} in data." + raise ValueError(msg) def _populate_value_maxes_violin( self, value_maxes: dict[TupleGroup, float], data_to_ax @@ -680,7 +682,7 @@ def _populate_value_maxes_violin( for hue_idx, hue_name in enumerate(self.hue_names): value_pos = max(self._plotter.support[group_idx][hue_idx]) key = (group_name, hue_name) - if key not in value_maxes: + if key not in value_maxes: # pragma: no cover msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue @@ -691,7 +693,7 @@ def _populate_value_maxes_violin( else: value_pos = max(self._plotter.support[group_idx]) key = (group_name,) - if key not in value_maxes: + if key not in value_maxes: # pragma: no cover msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue @@ -720,7 +722,6 @@ def __init__( **plot_params, ) -> None: super().__init__(plot_type, hue=hue, **plot_params) - self._group_names = None variables = {"x": x, "y": y, "hue": hue} kwargs = { @@ -766,9 +767,6 @@ def axis(self) -> str: @property def group_names(self) -> list: - if self._group_names is not None: - ## This is not used - return self._group_names return self._plotter.var_levels[self.axis] @property @@ -800,9 +798,9 @@ def _order_variable( self.formatter = formatter or str return - # Compute offsets if native_scale + # Use native offsets if native_scale if native_scale: - self.native_group_offsets = self.group_names + self.use_native_offsets = True self.native_scale = native_scale # do not format non-categorical variables self.formatter = lambda x: x @@ -834,8 +832,9 @@ def get_group_data(self, group_name: TupleGroup) -> pd.Series: # Found a matching group, return the data group_data = remove_null(sub_data[value_var]) return group_data - msg = f"Cannot find group {group_name!r} for {iter_vars} in data." - raise ValueError(msg) + else: # pragma: no cover + msg = f"Cannot find group {group_name!r} for {iter_vars} in data." + raise ValueError(msg) def parse_pairs( self, @@ -849,13 +848,13 @@ def parse_pairs( def format_group(value) -> TupleGroup: """Format the group (but not the optional hue).""" tvalue = value if isinstance(value, tuple) else (value,) - if not callable(self.formatter): + if not callable(self.formatter): # pragma: no cover return tvalue group = tvalue[0] return tuple([self.formatter(group), *tvalue[1:]]) for pair in pairs: - if not isinstance(pair, Sequence) or len(pair) != 2: + if not isinstance(pair, Sequence) or len(pair) != 2: # pragma: no cover msg = f"pair {pair} is not a 2-tuple, skipping." warnings.warn(msg) continue @@ -865,19 +864,19 @@ def format_group(value) -> TupleGroup: # Check that the groups are valid group names valid_group = True for i, group in enumerate(new_pair): - if group not in struct_groups: + if group not in struct_groups: # pragma: no cover msg = ( f"cannot find group{i} of pair in the group tuples: " f"{group} not in {struct_groups}" ) warnings.warn(msg) valid_group = False - if not valid_group: + if not valid_group: # pragma: no cover continue ret.append(new_pair) - if len(ret) == 0: + if len(ret) == 0: # pragma: no cover msg = ( f"pairs are empty after parsing: original_pairs={pairs}\n" f"not in group_list={struct_groups}" @@ -905,7 +904,7 @@ def _populate_value_maxes_violin( for sub_vars, sub_data in self._plotter.iter_data(iter_vars): var = sub_vars[self.axis] key = (var, sub_vars["hue"]) if self.has_hue else (var,) - if key not in value_maxes: + if key not in value_maxes: # pragma: no cover msg = f"key {key} was not found in the dict: {dict_keys}" warnings.warn(msg) continue @@ -913,11 +912,12 @@ def _populate_value_maxes_violin( # TODO: transform if log_scale is True. Seaborn doesn't do it though stat_data = kde._transform(sub_data, value_var, []) support = stat_data[value_var] - value_max = max(support) - if self.axis == "x": - value_maxes[key] = data_to_ax.transform((0, value_max))[1] - else: - value_maxes[key] = data_to_ax.transform((value_max, 0))[0] + if len(support) > 0: + value_max = np.max(support) + if self.axis == "x": + value_maxes[key] = data_to_ax.transform((0, value_max))[1] + else: + value_maxes[key] = data_to_ax.transform((value_max, 0))[0] return value_maxes diff --git a/statannotations/stats/StatResult.py b/statannotations/stats/StatResult.py index 61b327d..fd16c60 100644 --- a/statannotations/stats/StatResult.py +++ b/statannotations/stats/StatResult.py @@ -18,7 +18,7 @@ def __init__(self, test_description, test_short_name, stat_str, stat, pval, self.alpha = alpha @property - def correction_method(self): + def correction_method(self): # pragma: no cover return self._correction_method @correction_method.setter @@ -26,12 +26,12 @@ def correction_method(self, correction_method: str): self._correction_method = correction_method @property - def corrected_significance(self): + def corrected_significance(self): # pragma: no cover return self._corrected_significance @corrected_significance.setter def corrected_significance(self, significance: bool): - if self._correction_method is None: + if self._correction_method is None: # pragma: no cover raise ValueError("Correction method must first be set.") self._corrected_significance = significance and True or False @@ -46,7 +46,7 @@ def formatted_output(self): stat_summary = self.adjust(stat_summary) - if self.stat_str is not None or self.stat_value is not None: + if self.stat_str is not None or self.stat_value is not None: # pragma: no branch stat_summary += ' {}={:.3e}'.format(self.stat_str, self.stat_value) return stat_summary @@ -64,7 +64,7 @@ def significance_suffix(self): return 'ns' return "" - def __str__(self): + def __str__(self): # pragma: no cover return self.formatted_output def adjust(self, stat_summary): diff --git a/statannotations/stats/StatTest.py b/statannotations/stats/StatTest.py index b1a4bab..7dd9f4e 100644 --- a/statannotations/stats/StatTest.py +++ b/statannotations/stats/StatTest.py @@ -16,7 +16,7 @@ def wilcoxon(group_data1, group_data2, verbose=1, **stats_params): return stats.wilcoxon(group_data1, group_data2, **stats_params) if zero_method == "AUTO": zero_method = len(group_data1) <= 20 and "pratt" or "wilcox" - if verbose >= 1: + if verbose >= 1: # pragma: no branch print("Using zero_method ", zero_method) return stats.wilcoxon(group_data1, group_data2, diff --git a/statannotations/stats/utils.py b/statannotations/stats/utils.py index c4d1717..e7f490e 100644 --- a/statannotations/stats/utils.py +++ b/statannotations/stats/utils.py @@ -10,7 +10,7 @@ def is_number(value): return isinstance(value, Number) if np.ndim(p_values) > 1 or not np.all(list(map(is_number, p_values))): - raise_expected_got( + raise_expected_got( # pragma: no cover 'Scalar or list-like', 'argument `p_values`', p_values ) diff --git a/statannotations/utils.py b/statannotations/utils.py index 31ffb21..c7482ba 100644 --- a/statannotations/utils.py +++ b/statannotations/utils.py @@ -74,7 +74,7 @@ def _check_pairs_in_data_no_hue(pairs: Union[list, tuple], x_values = get_x_values(data, x) pairs_x_values = set(itertools.chain(*pairs)) # check if singleton tuples were used instead of directly the values - if all(isinstance(v, tuple) for v in pairs_x_values): + if all(isinstance(v, tuple) for v in pairs_x_values): # pragma: no cover pairs_x_values = set(v[0] for v in pairs_x_values) unmatched_x_values = pairs_x_values - x_values if unmatched_x_values: @@ -184,7 +184,7 @@ def get_closest(a_list, value): if pos == len(a_list): return a_list[-1] before, after = a_list[pos - 1: pos + 1] - if after - value < value - before: + if after - value < value - before: # pragma: no cover return after return before diff --git a/tests/test_annotator.py b/tests/test_annotator.py index b5f3bb2..00d529d 100644 --- a/tests/test_annotator.py +++ b/tests/test_annotator.py @@ -31,6 +31,7 @@ def setUp(self): }).T self.x_pairs = [("a", "b")] + self.x_pairs_tuples = [(("a",), ("b",))] self.pairs = [(("a", "blue"), ("b", "blue")), (("a", "blue"), ("a", "red"))] self.df.y = self.df.y.astype(float) @@ -42,6 +43,13 @@ def setUp(self): "order": ["a", "b"], "hue_order": ['red', 'blue']} + self.params_df_no_hue = { + "data": self.df, + "x": "x", + "y": "y", + "order": ["a", "b"], + } + self.params_df_redundant_hue = { "data": self.df, "x": "x", @@ -108,6 +116,10 @@ def test_init_float(self): **self.params_float ) + def test_init_df_no_hue(self): + self.ax = sns.boxplot(**self.params_df_no_hue) + self.annot = Annotator(self.ax, pairs=self.x_pairs, **self.params_df_no_hue) + def test_init_df(self): self.ax = sns.boxplot(**self.params_df) self.annot = Annotator(self.ax, pairs=self.pairs, **self.params_df) @@ -116,6 +128,19 @@ def test_init_df_with_redundant_hue(self): self.ax = sns.boxplot(**self.params_df_redundant_hue) self.annot = Annotator(self.ax, pairs=self.x_pairs, **self.params_df_redundant_hue) + def test_init_df_with_redundant_hue_singleton_tuples(self): + self.ax = sns.boxplot(**self.params_df_redundant_hue) + self.annot = Annotator(self.ax, pairs=self.x_pairs_tuples, **self.params_df_redundant_hue) + + def test_init_df_violinplot_with_redundant_hue(self): + self.ax = sns.violinplot(**self.params_df_redundant_hue) + self.annot = Annotator(self.ax, plot="violinplot", pairs=self.x_pairs, **self.params_df_redundant_hue) + + def test_init_df_violinplot_horizontal_with_redundant_hue(self): + horizontal_params = {**self.params_df_redundant_hue, "x": "y", "y": "x", "orient": "h"} + self.ax = sns.violinplot(**horizontal_params) + self.annot = Annotator(self.ax, plot="violinplot", pairs=self.x_pairs, **horizontal_params) + def test_init_arrays(self): self.ax = sns.boxplot(**self.params_arrays) self.annot = Annotator(self.ax, pairs=self.pairs, **self.params_arrays) @@ -130,7 +155,7 @@ def test_init_barplot(self): def test_init_stripplot(self): ax = sns.stripplot(**self.params_df) - self.annot = Annotator(ax, pairs=self.pairs, **self.params_df) + self.annot = Annotator(ax, plot="stripplot", pairs=self.pairs, **self.params_df) def test_test_name_provided(self): self.test_init_simple() @@ -201,7 +226,16 @@ def test_apply_comparisons_correction(self): self.test_init_simple() self.assertIsNone(self.annot._apply_comparisons_correction([])) + def test_get_configuration(self): + self.test_init_simple() + conf = self.annot.get_configuration() + assert "pvalue_format" in conf + def test_correct_num_custom_annotations(self): + self.test_init_simple() + self.annot.annotate_custom_annotations(["Annotation"]) + + def test_wrong_num_custom_annotations(self): self.test_init_simple() with self.assertRaisesRegex(ValueError, "same length"): self.annot.set_custom_annotations(["One", "Two"]) @@ -335,3 +369,7 @@ def test_ensure_ax_operation_format_kwargs_not_ok(self): def test_ensure_ax_operation_format_func_not_ok(self): with self.assertRaises(ValueError): _ensure_ax_operation_format([sum, ["param"], {"that": "this"}]) + + def test_apply_no_hue(self): + self.test_init_df_no_hue() + self.annot.configure(test="Mann-Whitney").apply_and_annotate() diff --git a/tests/test_native_scale.py b/tests/test_native_scale.py new file mode 100644 index 0000000..4fbcc31 --- /dev/null +++ b/tests/test_native_scale.py @@ -0,0 +1,116 @@ +import unittest + +import matplotlib.pylab as plt +import seaborn as sns + +from statannotations.Annotator import Annotator +from statannotations._Plotter import IMPLEMENTED_PLOTTERS + +seaborn_v0_13 = unittest.skipIf( + tuple(map(int, sns.__version__.split(".")[:2])) < (0, 13), + reason="at least seaborn-0.13 required", +) + +plot_types = IMPLEMENTED_PLOTTERS["seaborn"] + +class TestNativeScalePlotter(unittest.TestCase): + def setUp(self) -> None: + df = sns.load_dataset("tips") + self.df = df.query("size in [2, 3, 5]") + + self.params_df = { + "data": self.df, + "x": "size", + "y": "total_bill", + "hue_order": ["Male", "Female"], + "hue": "sex", + } + + self.pairs = [ + ((3, "Male"), (5, "Male")), + ((2, "Male"), (2, "Female")), + ] + + def tearDown(self) -> None: + plt.clf() + return super().tearDown() + + @seaborn_v0_13 + def test_native_scale(self): + native_scale = True + for plotter in plot_types: + with self.subTest(plotter=plotter): + plotting = { + **self.params_df, + "dodge": True, + "native_scale": native_scale, + } + ax = getattr(sns, plotter)(**plotting) + self.annot = Annotator(ax, plot=plotter, pairs=self.pairs, **plotting) + + positions = self.annot._plotter.groups_positions._data + + assert positions.group.tolist() == [ + (2, "Male"), + (2, "Female"), + (3, "Male"), + (3, "Female"), + (5, "Male"), + (5, "Female"), + ] + if native_scale: + expected_positions = [1.8, 2.2, 2.8, 3.2, 4.8, 5.2] + else: + expected_positions = [-0.2, 0.2, 0.8, 1.2, 1.8, 2.2] + assert positions.pos.tolist() == expected_positions + + @seaborn_v0_13 + def test_no_native_scale(self): + native_scale = False + for plotter in plot_types: + plotting = { + **self.params_df, + "dodge": True, + "native_scale": native_scale, + } + ax = getattr(sns, plotter)(**plotting) + self.annot = Annotator(ax, plot=plotter, pairs=self.pairs, **plotting) + + positions = self.annot._plotter.groups_positions._data + + assert positions.group.tolist() == [ + (2, "Male"), + (2, "Female"), + (3, "Male"), + (3, "Female"), + (5, "Male"), + (5, "Female"), + ] + if native_scale: + expected_positions = [1.8, 2.2, 2.8, 3.2, 4.8, 5.2] + else: + expected_positions = [-0.2, 0.2, 0.8, 1.2, 1.8, 2.2] + assert positions.pos.tolist() == expected_positions + + @seaborn_v0_13 + def test_gap(self): + plotting = { + **self.params_df, + "dodge": True, + "gap": 0.2, + } + ax = sns.boxplot(**plotting) + self.annot = Annotator(ax, plot="boxplot", pairs=self.pairs, **plotting) + + positions = self.annot._plotter.groups_positions._data + + assert positions.group.tolist() == [ + (2, "Male"), + (2, "Female"), + (3, "Male"), + (3, "Female"), + (5, "Male"), + (5, "Female"), + ] + expected_positions = [-0.2, 0.2, 0.8, 1.2, 1.8, 2.2] + assert positions.pos.tolist() == expected_positions From ef604d9346d8dbef0d7c0ed451eb6fda897829e4 Mon Sep 17 00:00:00 2001 From: getzze Date: Mon, 4 Nov 2024 13:45:07 +0000 Subject: [PATCH 09/16] get_group_data returns empty series if group not found. Use strict arg to change behavior --- statannotations/compat.py | 19 +++++++++---- tests/test_missing_group.py | 54 +++++++++++++++++++++++++++++++++++++ 2 files changed, 68 insertions(+), 5 deletions(-) create mode 100644 tests/test_missing_group.py diff --git a/statannotations/compat.py b/statannotations/compat.py index d2c14c2..742aeea 100644 --- a/statannotations/compat.py +++ b/statannotations/compat.py @@ -1,8 +1,9 @@ from __future__ import annotations +import logging +import warnings from collections.abc import Sequence from typing import TYPE_CHECKING, Any, Callable -import warnings import numpy as np import pandas as pd @@ -56,6 +57,8 @@ class Struct(TypedDict): _CategoricalPlotter = sns.categorical._CategoricalPlotter +logger = logging.getLogger(__name__) + table_convert_orient_seaborn: dict[str, str] = { "v": "v" if sns_version < (0, 13, 0) else "x", "x": "v" if sns_version < (0, 13, 0) else "x", @@ -626,7 +629,7 @@ def get_group_data(self, group_name): return group_data - def _get_group_data_new(self, group_name: TupleGroup) -> pd.Series: + def _get_group_data_new(self, group_name: TupleGroup, *, strict: bool = False) -> pd.Series: """Get the data for the (group[, hue]) tuple. group_name can be either a "cat" or a tuple ("cat", "hue") @@ -667,7 +670,10 @@ def _get_group_data_new(self, group_name: TupleGroup) -> pd.Series: return group_data else: # pragma: no cover msg = f"Cannot find group {group_name!r} for {iter_vars} in data." - raise ValueError(msg) + if strict: + raise ValueError(msg) + logger.info(msg) + return pd.Series([]) def _populate_value_maxes_violin( self, value_maxes: dict[TupleGroup, float], data_to_ax @@ -805,7 +811,7 @@ def _order_variable( # do not format non-categorical variables self.formatter = lambda x: x - def get_group_data(self, group_name: TupleGroup) -> pd.Series: + def get_group_data(self, group_name: TupleGroup, *, strict: bool = False) -> pd.Series: """Get the data for the (group[, hue]) tuple. group_name can be either a "cat" or a tuple ("cat", "hue") @@ -834,7 +840,10 @@ def get_group_data(self, group_name: TupleGroup) -> pd.Series: return group_data else: # pragma: no cover msg = f"Cannot find group {group_name!r} for {iter_vars} in data." - raise ValueError(msg) + if strict: + raise ValueError(msg) + logger.info(msg) + return pd.Series([]) def parse_pairs( self, diff --git a/tests/test_missing_group.py b/tests/test_missing_group.py new file mode 100644 index 0000000..3e8e8c1 --- /dev/null +++ b/tests/test_missing_group.py @@ -0,0 +1,54 @@ +import unittest + +import pandas as pd +import seaborn as sns + +from statannotations.Annotator import Annotator + + +# noinspection DuplicatedCode +class TestMissingGroup(unittest.TestCase): + """Test validation of parameters""" + + def setUp(self): + self.data = pd.DataFrame( + [ + [1, 1.0, 0], + [1, 2.0, 1], + [1, 3.0, 2], + [2, 4.0, 0], + [2, 5.0, 1], + ], + columns=["x", "y", "hue"], + ) + self.ax = sns.boxplot(data=self.data) + + self.pairs = [ + ((1, 0), (2, 0)), + ((1, 0), (1, 2)), + ] + self.params_df = { + "data": self.data, + "x": "x", + "y": "y", + "hue": "hue", + } + + self.params_arrays = { + "data": None, + "x": self.data["x"], + "y": self.data["y"], + "hue": self.data["hue"], + } + + def tearDown(self) -> None: + sns.mpl.pyplot.clf() + return super().tearDown() + + def test_init_df(self): + self.ax = sns.boxplot(**self.params_df) + self.annot = Annotator(self.ax, pairs=self.pairs, **self.params_df) + + def test_init_array(self): + self.ax = sns.boxplot(**self.params_arrays) + self.annot = Annotator(self.ax, pairs=self.pairs, **self.params_arrays) From 070107fd16d3a558d007062d23ab7233e1c87ae6 Mon Sep 17 00:00:00 2001 From: getzze Date: Mon, 4 Nov 2024 15:18:19 +0000 Subject: [PATCH 10/16] correctly store hue_order --- statannotations/compat.py | 57 +++++++++++++++++++++++++++++++---- tests/test_order_group.py | 63 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 114 insertions(+), 6 deletions(-) create mode 100644 tests/test_order_group.py diff --git a/statannotations/compat.py b/statannotations/compat.py index 742aeea..5afc8ce 100644 --- a/statannotations/compat.py +++ b/statannotations/compat.py @@ -5,6 +5,7 @@ from collections.abc import Sequence from typing import TYPE_CHECKING, Any, Callable +from matplotlib.pyplot import plot import numpy as np import pandas as pd import seaborn as sns @@ -351,6 +352,7 @@ def __init__( hue, data, order, + hue_order=None, **plot_params, ) -> None: super().__init__(plot_type, hue=hue, **plot_params) @@ -477,10 +479,10 @@ def __init__( hue, data, order, + hue_order, **plot_params, ) -> None: super().__init__(plot_type, hue=hue, **plot_params) - self._group_names = None self._cat_axis = {"v": "x", "h": "y"}[plot_params.get("orient", "v")] self._plotter = _get_categorical_plotter( @@ -492,6 +494,7 @@ def __init__( order=order, **plot_params, ) + self._hue_order = self._check_hue_order(hue_order) if isinstance(self._plotter, _CategoricalPlotterNew): # Order the group variables @@ -548,7 +551,10 @@ def group_names(self) -> list: return group_names # pragma: no cover @property - def hue_names(self): + def hue_names(self) -> list: + return self._hue_order + + def _original_hue_names(self) -> list: if self.is_redundant_hue: return [] @@ -564,6 +570,22 @@ def hue_names(self): return hue_names.tolist() return hue_names + def _check_hue_order(self, hue_order: list | None, *, check: bool = False) -> list: + plotter_hue_names = self._original_hue_names() + # No hue order defined, use the plotter list + if not hue_order: + return plotter_hue_names + + # Check hue_order is a permutation of the possible hue list + if check and set(plotter_hue_names) != set(hue_order): + msg = ( + "hue_order is not a permutation of the data hue values: " + f"hue_order={hue_order} not in {plotter_hue_names}" + ) + raise ValueError(msg) + + return hue_order + def _order_variable( self, *, @@ -725,6 +747,7 @@ def __init__( hue, data, order, + hue_order, **plot_params, ) -> None: super().__init__(plot_type, hue=hue, **plot_params) @@ -740,6 +763,9 @@ def __init__( } self._plotter = _CategoricalPlotter(**kwargs) + # Check hue order + self._hue_order = self._check_hue_order(hue_order) + # Order the group variables, # !! will define self.native_scale and self.formatter formatter = plot_params.get("formatter", None) @@ -776,16 +802,35 @@ def group_names(self) -> list: return self._plotter.var_levels[self.axis] @property - def hue_names(self): - if not self.has_hue or "hue" not in self._plotter.var_levels: - return [] - return self._plotter.var_levels["hue"] + def hue_names(self) -> list: + return self._hue_order @property def is_categorical(self) -> bool: """Return True if the categorical axis is really a categorical variable.""" return self._plotter.var_types.get(self.axis) == "categorical" + def _original_hue_names(self) -> list: + if not self.has_hue or "hue" not in self._plotter.var_levels: + return [] + return self._plotter.var_levels["hue"] + + def _check_hue_order(self, hue_order: list | None, *, check: bool = True) -> list: + plotter_hue_names = self._original_hue_names() + # No hue order defined, use the plotter list + if not hue_order: + return plotter_hue_names + + # Check hue_order is a permutation of the possible hue list + if check and set(plotter_hue_names) != set(hue_order): + msg = ( + "hue_order is not a permutation of the hue values: " + f"hue_order={hue_order} not in {plotter_hue_names}" + ) + raise ValueError(msg) + + return hue_order + def _order_variable( self, *, diff --git a/tests/test_order_group.py b/tests/test_order_group.py new file mode 100644 index 0000000..813a175 --- /dev/null +++ b/tests/test_order_group.py @@ -0,0 +1,63 @@ +import unittest + +import pandas as pd +import seaborn as sns + +from statannotations.Annotator import Annotator + + +# noinspection DuplicatedCode +class TestMissingGroup(unittest.TestCase): + """Test validation of parameters""" + + def setUp(self): + self.data = pd.DataFrame( + [ + [1, 1.0, 0], + [1, 2.0, 2], + [1, 3.0, 1], + [2, 4.0, 2], + [2, 5.0, 1], + [2, 6.0, 0], + [3, 7.0, 1], + [3, 8.0, 2], + [3, 9.0, 0], + ], + columns=["x", "y", "hue"], + ) + self.ax = sns.boxplot(data=self.data) + + self.pairs = [ + ((1, 0), (2, 1)), + ] + self.params_df = { + "data": self.data, + "x": "x", + "y": "y", + "hue": "hue", + "hue_order": [1, 0, 2], + } + + self.params_arrays = { + "data": None, + "x": self.data["x"], + "y": self.data["y"], + "hue": self.data["hue"], + "hue_order": self.params_df["hue_order"], + } + + def tearDown(self) -> None: + sns.mpl.pyplot.clf() + return super().tearDown() + + def test_init_df(self): + self.ax = sns.barplot(**self.params_df) + self.annot = Annotator(self.ax, pairs=self.pairs, **self.params_df) + assert self.annot._plotter is not None + assert self.annot._plotter.plotter.hue_names == self.params_df["hue_order"] + + def test_init_array(self): + self.ax = sns.boxplot(**self.params_arrays) + self.annot = Annotator(self.ax, pairs=self.pairs, **self.params_arrays) + assert self.annot._plotter is not None + assert self.annot._plotter.plotter.hue_names == self.params_arrays["hue_order"] From afcfff72ac89e45722df37d920c349fa0481ca3c Mon Sep 17 00:00:00 2001 From: getzze Date: Mon, 4 Nov 2024 15:19:37 +0000 Subject: [PATCH 11/16] add a script to check the plots visually --- usage/test_script.py | 433 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 433 insertions(+) create mode 100644 usage/test_script.py diff --git a/usage/test_script.py b/usage/test_script.py new file mode 100644 index 0000000..0dff50a --- /dev/null +++ b/usage/test_script.py @@ -0,0 +1,433 @@ +#! /usr/bin/python +from __future__ import annotations + +import argparse +import logging +import os +import traceback +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import matplotlib.pylab as plt +import pandas as pd +import seaborn as sns + +from statannotations.Annotator import Annotator +from statannotations._Plotter import IMPLEMENTED_PLOTTERS + +if TYPE_CHECKING: + from collections.abc import Mapping + from typing import NotRequired, TypeAlias, TypedDict + + Pairs: TypeAlias = list[tuple[Any, Any]] + + class DataType(TypedDict): + params: dict + pairs: Pairs + skip_plotters: NotRequired[list[str]] + v0_13: NotRequired[bool] + + +logger = logging.getLogger(__name__) + +seaborn_v0_11 = int(sns.__version__.split(".")[1]) <= 11 +seaborn_v0_12 = int(sns.__version__.split(".")[1]) <= 12 + + +def day_formatter(abbrev: str) -> str: + """Format an abbreviated week day to full name.""" + if abbrev == "Thur": + return "thursday" + if abbrev == "Fri": + return "friday" + if abbrev == "Sat": + return "saturday" + if abbrev == "Sun": + return "sunday" + return abbrev.lower() + + +def make_plot( + plotter: str, + pairs: Pairs, + params: Mapping[str, Any] | None = None, + title: str = "", + folder: os.PathLike | str = ".", + extension: str = ".png", + show: bool = False, +): + """Make the Seaborn plot with parameters and annotate it.""" + params = params or {} + fig, ax = plt.subplots() + plot_func = getattr(sns, plotter) + plot_func(ax=ax, **params) + ax.set_title(title) + annot = Annotator(ax, plot=plotter, pairs=pairs, **params) + annot.configure(test='Mann-Whitney', text_format='star', loc='inside') + annot.apply_and_annotate() + + positions = annot._plotter.groups_positions._data + print(positions) + if show: + plt.show() + else: + filename = title + extension + fig.savefig(os.path.join(folder, filename)) + + +def make_data_types() -> dict[str, DataType]: + """Import the dataframe and create the data types.""" + # Import dataset + df = sns.load_dataset("tips") + + filtered_df = df.query("size in [2, 3, 5]") + + reduced_df = df.loc[:, ["total_bill", "day"]] + + df['tip_bucket'] = pd.cut(df['tip'], 3) + tip_bucket_list = df['tip_bucket'].unique() + + # Params + params_df = { + "data": df, + "x": "day", + "y": "total_bill", + "order": ["Sun", "Thur", "Fri", "Sat"], + "hue_order": ["Male", "Female"], + "hue": "sex", + "dodge": True, + } + + params_arrays = { + "data": None, + "x": df["day"], + "y": df["total_bill"], + "order": ["Sun", "Thur", "Fri", "Sat"], + "hue_order": ["Male", "Female"], + "hue": df["sex"], + "dodge": True, + } + + reduced_params_df_no_hue = { + "data": reduced_df, + "x": "day", + "y": "total_bill", + "order": ["Sun", "Thur", "Fri", "Sat"], + "dodge": False, + } + + reduced_params_df_with_hue = { + "data": reduced_df, + "x": "day", + "y": "total_bill", + "order": ["Sun", "Thur", "Fri", "Sat"], + "hue": "day", + "dodge": False, + } + + native_scale_df = { + "data": filtered_df, + "x": "size", + "y": "total_bill", + "dodge": False, + } + + native_scale_df_with_hue = { + "data": filtered_df, + "x": "size", + "y": "total_bill", + "hue": "sex", + "dodge": True, + } + + params_df_missing_group = { + "data": df, + "x": "day", + "y": "total_bill", + "hue": "tip_bucket", + } + + # Grouping + group_hue_pairs = [ + (("Thur", "Male"), ("Thur", "Female")), + (("Sun", "Male"), ("Sun", "Female")), + (("Thur", "Male"), ("Fri", "Female")), + (("Fri", "Male"), ("Fri", "Female")), + (("Sun", "Male"), ("Fri", "Female")), + ] + + hue_pairs = [ + (("Thur", "Male"), ("Thur", "Female")), + ] + + group_pairs = [ + ("Thur", "Fri"), + ("Thur", "Sun"), + ("Sat", "Sun"), + ] + + tuple_group_pairs = [ + (("Thur",), ("Fri",)), + (("Thur",), ("Sun",)), + (("Sat",), ("Sun",)), + ] + + native_pairs = [ + (2, 3), + (2, 5), + ] + + native_pairs_tuples = [ + ((2,), (3,)), + ((2,), (5,)), + ] + + native_hue_pairs = [ + ((3, "Male"), (5, "Male")), + ((2, "Male"), (2, "Female")), + ] + + pairs_missing_group = [ + (("Sat", tip_bucket_list[2]), ("Fri", tip_bucket_list[0])), + ] + + # Plot and annotation + data_types: dict[str, DataType] = { + "df_with_group_and_hue": { + "params": params_df, + "pairs": group_hue_pairs, + }, + "arrays_with_group_and_hue": { + "params": params_arrays, + "pairs": group_hue_pairs, + }, + "df_with_hue": { + "params": params_df, + "pairs": hue_pairs, + }, + "df_with_group_and_hue_with_gap": { + "params": {**params_df, "gap": 0.2}, + "pairs": group_hue_pairs, + "skip_plotters": ["stripplot", "swarmplot"], + "v0_13": True, + }, + ## Error when plotting with Seaborn 0.13 + # "df_with_group_and_hue_with_formatter": { + # "params": { + # **params_df, + # "formatter": day_formatter, + # }, + # "pairs": group_hue_pairs, + # "v0_13": True, + # }, + "reduced_df_no_hue": { + "params": reduced_params_df_no_hue, + "pairs": group_pairs, + }, + "reduced_df_no_hue_with_formatter": { + "params": { + **reduced_params_df_no_hue, + "formatter": day_formatter, + }, + "pairs": group_pairs, + "v0_13": True, + }, + "reduced_df_with_hue": { + "params": reduced_params_df_with_hue, + "pairs": group_pairs, + }, + "reduced_df_no_hue_tuple_pairs": { + "params": reduced_params_df_no_hue, + "pairs": tuple_group_pairs, + }, + "df_log_scale_with_group_and_hue": { + "params": {**params_df, "log_scale": True}, + "pairs": group_hue_pairs, + "v0_13": True, + }, + "df_no_native_scale": { + "params": {**native_scale_df, "order": [5, 3, 2]}, + "pairs": native_pairs, + }, + "df_no_native_scale_tuples": { + "params": native_scale_df, + "pairs": native_pairs_tuples, + }, + "df_no_native_scale_with_formatter": { + "params": { + **native_scale_df, + "formatter": lambda x: str(float(x)), + }, + "pairs": native_pairs, + "v0_13": True, + }, + "df_no_native_scale_with_hue": { + "params": native_scale_df_with_hue, + "pairs": native_hue_pairs, + }, + "df_native_scale": { + "params": {**native_scale_df, "native_scale": True}, + "pairs": native_pairs, + "v0_13": True, + }, + "df_native_scale_tuples": { + "params": {**native_scale_df, "native_scale": True}, + "pairs": native_pairs_tuples, + "v0_13": True, + }, + "df_native_scale_with_formatter": { + "params": { + **native_scale_df, + "native_scale": True, + "formatter": lambda x: str(float(x)), + }, + "pairs": native_pairs, + "v0_13": True, + }, + "df_native_scale_with_hue": { + "params": {**native_scale_df_with_hue, "native_scale": True}, + "pairs": native_hue_pairs, + "v0_13": True, + }, + "df_missing_groups": { + "params": params_df_missing_group, + "pairs": pairs_missing_group, + "v0_13": True, + }, + } + return data_types + + +if __name__ == "__main__": + # All plot types + plot_types = IMPLEMENTED_PLOTTERS["seaborn"] + + # All data types + data_types = make_data_types() + + # All orientation types + orient_types = ("v", "h", "x", "y") + + # Command line argument parser + parser = argparse.ArgumentParser( + prog="start-video-annotator", + description="GUI for annotation videos", + ) + parser.add_argument( + "--show", + action="store_true", + help="Show the plots and don't save them to './figures/'.", + ) + parser.add_argument( + "--figure-extension", + type=str, + default=".png", + help="Save the figures with the given extension.", + ) + parser.add_argument( + "-p", + "--plot-type", + action="append", + type=str, + choices=list(plot_types), + help="Plot type to test.", + ) + parser.add_argument( + "-d", + "--data-type", + action="append", + type=str, + choices=list(data_types.keys()), + help="Kind of data to test.", + ) + parser.add_argument( + "-o", + "--orient-type", + action="append", + type=str, + choices=list(orient_types), + help="Plot orientation to test.", + ) + + args = parser.parse_args() + print("Command line arguments:") + print(vars(args)) + + # Figure path + figures_folder = Path(__file__).parent / "figures" + if not os.path.isdir(figures_folder): + os.mkdir(figures_folder) + + # Filter data types + if args.data_type: + data_types = {k: v for k, v in data_types.items() if k in args.data_type} + + # Filter plot types + if args.plot_type: + plot_types = [p for p in plot_types if p in args.plot_type] + + # Filter orientation types + if args.orient_type: + orient_types = [o for o in orient_types if o in args.orient_type] + + # Loop and make plots + for groups_type, data in data_types.items(): + raw_params = data["params"] + pairs = data["pairs"] + only_seaborn_v0_13 = data.get("v0_13", False) + skip_plotters = data.get("skip_plotters", []) + if seaborn_v0_12 and only_seaborn_v0_13: + continue + + for orient in orient_types: + if seaborn_v0_12 and orient in ("x", "y"): + continue + + params = raw_params.copy() + if seaborn_v0_12: + # Make sure unsupported arguments are pruned + params.pop("log_scale", None) + params.pop("native_scale", None) + + if orient in ("h", "y"): + # x = params["x"] + # params["x"] = params["y"] + # params["y"] = x + params["x"], params["y"] = params["y"], params["x"] + # update the parameters + final_params = {**params, "orient": orient} + + for plotter in plot_types: + if plotter in skip_plotters: + continue + # Title + title = f"Seaborn v{sns.__version__} - {plotter} ({orient}) - {groups_type}" + + print() + print("+--------------------------------------------------------+") + print(title) + print("+--------------------------------------------------------+") + print() + + try: + # Make plot + make_plot( + plotter, + pairs, + params=final_params, + title=title, + folder=figures_folder, + extension=args.figure_extension, + show=args.show, + ) + except Exception as e: # noqa: E722 + msg = f"Error plotting with the params:\n{final_params}" + logger.exception(msg) + # write error log to file + if not args.show: + filename = title + ".error.log" + filepath = os.path.join(figures_folder, filename) + with open(filepath, "w") as f: + f.write(msg) + f.write(str(e)) + f.write(traceback.format_exc()) From 1088f4d0182537bad909e8a8ff53d53f78d77d2c Mon Sep 17 00:00:00 2001 From: getzze Date: Mon, 4 Nov 2024 15:22:27 +0000 Subject: [PATCH 12/16] correctly store hue_order - no check --- statannotations/compat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/statannotations/compat.py b/statannotations/compat.py index 5afc8ce..2331a68 100644 --- a/statannotations/compat.py +++ b/statannotations/compat.py @@ -815,7 +815,7 @@ def _original_hue_names(self) -> list: return [] return self._plotter.var_levels["hue"] - def _check_hue_order(self, hue_order: list | None, *, check: bool = True) -> list: + def _check_hue_order(self, hue_order: list | None, *, check: bool = False) -> list: plotter_hue_names = self._original_hue_names() # No hue order defined, use the plotter list if not hue_order: From be6864f7ebe098ea42894cea3f8b65aa329bbc42 Mon Sep 17 00:00:00 2001 From: getzze Date: Mon, 4 Nov 2024 15:24:35 +0000 Subject: [PATCH 13/16] add redondant hue in seaborn 0.13 --- usage/example.ipynb | 579 ++++++++++++++++++++++++++++++++++---------- 1 file changed, 457 insertions(+), 122 deletions(-) diff --git a/usage/example.ipynb b/usage/example.ipynb index c403646..6f167de 100644 --- a/usage/example.ipynb +++ b/usage/example.ipynb @@ -55,6 +55,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'day', 'y': 'total_bill', 'hue': None}\n", + "self.tuple_group_names=[('Sun',), ('Thur',), ('Fri',), ('Sat',)]\n", + "self.plotter.group_names=Index(['Sun', 'Thur', 'Fri', 'Sat'], dtype='object', name='x')\n", + "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -69,8 +73,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -80,7 +86,7 @@ "x = \"day\"\n", "y = \"total_bill\"\n", "order = ['Sun', 'Thur', 'Fri', 'Sat']\n", - "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n", + "ax = sns.boxplot(data=df, x=x, y=y, hue=x, order=order, hue_order=order)\n", "annot = Annotator(ax, [(\"Thur\", \"Fri\"), (\"Thur\", \"Sat\"), (\"Fri\", \"Sun\")], data=df, x=x, y=y, order=order)\n", "annot.configure(test='Mann-Whitney', text_format='star', loc='outside', verbose=2)\n", "annot.apply_test()\n", @@ -106,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -141,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -162,8 +168,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -174,6 +182,8 @@ " \"data\": df,\n", " \"x\": x,\n", " \"y\": y,\n", + " \"hue\": x,\n", + " \"hue_order\": order,\n", " \"order\": order\n", "}\n", "ax = sns.boxplot(**plotting)\n", @@ -196,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": { "collapsed": false, "pycharm": { @@ -222,16 +232,23 @@ }, { "data": { - "text/plain": "(,\n [,\n ,\n ])" + "text/plain": [ + "(,\n", + " [,\n", + " ,\n", + " ])" + ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -241,8 +258,8 @@ "x = \"day\"\n", "y = \"total_bill\"\n", "order = ['Sun', 'Thur', 'Fri', 'Sat']\n", - "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n", - "annot.new_plot(ax, data=df, x=x, y=y, order=order)\n", + "ax = sns.boxplot(data=df, x=x, y=y, hue=x, hue_order=order, order=order)\n", + "annot.new_plot(ax, data=df, x=x, y=y, hue=x, hue_order=order, order=order)\n", "annot.configure(comparisons_correction=\"BH\")\n", "annot.apply_and_annotate()" ] @@ -261,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": { "collapsed": false, "pycharm": { @@ -287,24 +304,31 @@ }, { "data": { - "text/plain": "(,\n [,\n ,\n ])" + "text/plain": [ + "(,\n", + " [,\n", + " ,\n", + " ])" + ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n", - "annot.new_plot(ax, data=df, x=x, y=y, order=order)\n", + "ax = sns.boxplot(data=df, x=x, y=y, hue=x, hue_order=order, order=order)\n", + "annot.new_plot(ax, data=df, x=x, y=y, hue=x, hue_order=order, order=order)\n", "annot.configure(comparisons_correction=\"BH\", correction_format=\"replace\")\n", "annot.apply_and_annotate()" ] @@ -325,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -346,8 +370,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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cOICTJ09iw4YNiImJQW1tLcaOHYuCggIkJCQM4Z9DRIPF7jTHk8N434ACYMuWLXjvvfcwfvx4AN2Xh77wwgvW7p2B8Pb2RnZ2NtLS0mAymaDVaqHT6YZWNRENml6vR11dHQfTwfG+HgMKAJVKZf3yB7r7E3sm6PQnPj4e8fHxALrHFPLz84dQJhHZq2ftGqIeNieCNTc3o7m5GWFhYXjrrbdw9epVXL9+He+//z6mT5/urBqJiEgENlsA06dPh0qlsp7t/+EPf7DeVqlUWLdunVOKJCIix7MZAJWVlf2+QEFBARYuXOiwgoiIyDnsXg76rbfeckQdRETkZHYHwEAHg4mIyLXYHQBDmc1LRETS445gREQKxQAgl9bY2IiMjAw0NTVJXQqR7HAMgFza7t27cfr0abz77rtSl0IkO3YHQFxcnCPqIPqRxsZGFBcXAwCOHTvGVgCRg9mcB9Dfl/tHH32EJ5980qEFEfXYvXt3r01M3n33Xfz2t7+VuCoi+bAZAC+88IKz6iD6kX/84x+9bh8/fpwBQORANgNg6tSp1p+bm5tx/fp1CIIAi8WCuro60YsjZbv5EmNeckzkWANeDnrnzp0AALVajc7OTtx///346KOPRC2OlG3WrFkoKiqy3pbb/tFEUhvQIPCHH36IY8eOYd68efjkk0+QlZWF+++/X+zaSOF+/etfW39WqVS9bhOR/QYUAH5+ftBoNAgJCUFlZSWWLFmC8+fPi10bEbt9iEQ0oADw9PREXV0dQkJC8Pnnn8NsNsNkMoldGylcbm4uPDy6/0Q9PDyQm5srcUVE8jKgAEhJScELL7yAyMhIFBYWIjIykhvCkOiOHz8Oi8UCALBYLDh27JjEFRHJy4AGgSdMmGCdiXno0CHU1tZaz8yIxNJzwmE2m+Hp6YnZs2dLXRKRrAxoS8jk5GS0tLSgubkZJpMJd911F1avXu2sGkmh9Hp9ry4gbmZO5Fg2WwDPPvss/vnPfwIApk2b9sOTPD0xb948cSsjxfPz88OcOXNgMBgQHR0NX19fqUsikhWbAdCz29dzzz2HrKwspxREdCO9Xo+6ujqe/ROJYEBjAFlZWfjyyy/x6aeforOzEzNnzsSUKVP6fd6WLVvw97//HSqVComJiUhKSkJpaSmysrJgMpkQGxuL9PR0u/8RJF9+fn7Izs6WugwiWRrQSO6hQ4ewevVqtLS04OrVq1izZg327dtn8zknT57Ev/71L+Tn5+ODDz7Anj17UFlZiczMTGzfvh2HDx9GRUWFdbVHIiJyrgG1AHbv3o39+/dDo9EAAJKTk/Hkk09i6dKlfT5n6tSp+Otf/wpPT080NDTAYrGgtbUVwcHBCAoKAtC92qjBYOAUfyIiCQwoALq6uqxf/gBw9913D+gy0GHDhmHr1q14++23odPpcPnyZfj7+1vv12g0aGhoGELZ5A6KiopQWFho12s0NzcDAEaPHj3k15g7dy6ioqLsqoNIjgYUAKNHj8aRI0cQHR0NADhy5AjuuOOOAb3B6tWrkZycjNTUVNTU1PSa2i8IwqCn+ldUVAzq8c7W1tYGACgrK5O4EulVV1dbj8dQGY1GAN2LENpTB///IEeTw2d9QAGQlpaGzMxMbNq0CUD3mf22bdtsPufrr79GR0cHQkNDMXz4cMTExMBgMPT6IBuNxl4ti4EICwuDt7f3oJ7jTHl5eQCAyZMnS1yJ9BxxDJ577jkA4FVo5HLc5bNuMpn6PHG2GQA9ze9NmzZh//79+Oqrr6BSqXDPPffgN7/5DQwGQ5/PvXTpErZu3Yq//e1vAICjR49Cr9dj8+bNqK2txdixY1FQUICEhISh/ruIFMNVutMAdqnJyYAngkVERADo7rYZyEQwrVaL8vJyLFmyBGq1GjExMViwYAH8/PyQlpYGk8kErVYLnU7noH8KEdnS2NgIwP4AIPkQdSJYWloa0tLSev0uIiIC+fn5g34tIiWLioqy+6yb3Wl0swHNA+AfDBGR/HBJTyIihWIAEBEpFAOAiEihGABERArFACAiUigGABGRQg1oKQh3sGvXLlRVVUldhrWGnmuupRISEoLk5GRJayAi1yabAKiqqkLFmXNQ+0g7y7HL3L3W0dkq6VY5tbQ3S/beROQ+ZBMAAKD2GY0RwXOkLkNy12qPSl0CEbkBWQUAEckfu3t7s6e7lwFARG6lqqoK50+fxl127BHhCF5dXQCAxspKyWr4zmKx6/kMACJyO3ep1Vg8iquafthm33gfLwMlIlIoBgARkUIxAIiIFIoBQESkUAwAIiKFYgAQESkULwMlcgJXmLzkKhOXAK5V5SoYAEROUFVVhdPnzkB9h5dkNXR5dE8aqvz2K8lqAABLS4ek708/YAAQOYn6Di/cMWuM1GVIruUf30hdAv2PqGMAb775JhYsWIAFCxZg8+bNAIDS0lLExcUhJiYGOTk5Yr49ERHZIFoAlJaWoqSkBAcPHsShQ4dw+vRpFBQUIDMzE9u3b8fhw4dRUVGB4uJisUogIiIbROsC8vf3R0ZGBry8uvs877vvPtTU1CA4OBhBQUEAgLi4OBgMBmi1WrHKoCFyhUFLwHUGLjloSXIkWgCMHz/e+nNNTQ0+/vhj/OpXv4K/v7/19xqNBg0N0m2cQn2rqqrChbOnEXCbtMNEw4XuFRfbLp6TrIZvr5gle28iMYn+6b5w4QJSUlLw+9//Hmq1GjU1Ndb7BEGASqUa1OtVVFTc8vf19fWwtLdyMxR07whWX9+FsrKyIb9GW1sbAm7zRNJEPwdW5p7eKW9EW1ub3ceTfmDP8eSx7M2eYylqAJSVlWH16tXIzMzEggULcPLkSRiNRuv9RqMRGo1mUK8ZFhYGb2/vH/3ex8cHQKu9JcuGj48PJk+ePOTn5+Xlwc6VZmVl1KhRdh9PXGVrt4c9xzMvLw+NDq7HnfV3LE0mU58nzqIFQH19PVauXImcnBxEREQAAMLDw1FdXY3a2lqMHTsWBQUFSEhIcMj7+fr64tumDm4Jie4tIX19faUug4hcnGgB8NZbb8FkMiE7O9v6O71ej+zsbKSlpcFkMkGr1UKn04lVAhER2SBaADz//PN4/vnnb3lffn6+WG9LRDLX1NSE78xmu3fDkoPvzGaompqG/HwuBkdEpFBcCoKI3Iqvry+EhgbuCYzuPYHtGe9jC4CISKEYAERECsUAICJSKI4B0C01NTXhuytmvFPOKTffXjHDbMeVFkSuii0AIiKFYguAbsnX1xeeVy5zLSB0rwU0ijOrSYYYAERO0NTUBHOzibthATA3m9DkzS41VyCrALC0N0u+GmiXuR0A4OHpI1kNlvZmAHdL9v5E5B5kEwAhISFSlwDghw1MQkKk/AK+22WOB3Xz9fVFg+l77gmM7j2BuViha5BNALjKbk09O1dlZWVJXAkRkW2yCQAiUo7vLBbJF4O71tW9W90ID+kupvzOYoE9l2kwAIjIrbhK92bz/7p7x0pYjx/sOx4MACJyK+zudRxOBCMiUigGABGRQrELiPr0rQusBXSlo3ug7TYv6c5Vvr1ixijJ3p1IPAwAuiVXGWgz/m+gLTBIunpGwTHHw9LSIelM4K52CwDAw0ctWQ1A93FAgKQl0P8wAOiWONDmWK4QqNZJigES1xLgGseDGABETuEKgSqXMCXH4SAwEZFCiRoAV65cwcKFC3Hp0iUAQGlpKeLi4hATE4OcnBwx35qIiPohWgB8+eWXWLZsGWpqagAA7e3tyMzMxPbt23H48GFUVFSguLhYrLcnIqJ+iBYA+/btw/r166HRaAAA5eXlCA4ORlBQEDw9PREXFweDwSDW2xMRUT9EGwR+5ZVXet2+fPky/P39rbc1Gg0aGhrEensiIuqH064C6urqgkqlst4WBKHX7YGqqKhwZFkO19bWBgAoKyuTuBJ54PF0HB5Lx5LD8XRaAAQEBMBoNFpvG41Ga/fQYISFhcHb29uRpTlUXl4eAGDy5MkSVyIPPJ6Ow2PpWO5yPE0mU58nzk67DDQ8PBzV1dWora2FxWJBQUEBZs2a5ay3JyKimzitBeDt7Y3s7GykpaXBZDJBq9VCp9M56+2JiOgmogdAUVGR9eeIiAjk5+eL/ZZERDQAnAlMRKRQXAuIRFNUVITCwkK7XqNnAbOedWyGYu7cuYiKirKrDiI5YgDcwFW+sAB+afXw87Nny2sisoUB4GD8wvpBVFQUQ4zIhTEAbsAvLCJSEg4CExEpFAOAiEih2AVE5AZ4gQKJgQFApBC8QIFuxgAgcgO8QIHEwDEAIiKFYgAQESkUA4CISKEYAERECsUAICJSKF4FRESKw3kV3RgARERDIId5FQwAIlIczqvoxjEAIiKFYgAQESkUA4CISKEYAERECuU2g8CCIAAAOjo6JK6EiMh99Hxn9nyH3shtAqCzsxMAcP78eYkrISJyP52dnfDx8en1O5Vwq1hwQV1dXbh69SqGDRsGlUoldTlERG5BEAR0dnZi5MiR8PDo3evvNgFARESOxUFgIiKFYgAQESkUA4CISKEYAERECsUAICJSKAYAEZFCMQCIiBTKbWYCuwqDwYCdO3fCbDZDEAQsXrwYTz31lNRluZUNGzbg1KlT6OzsRF1dHe677z4AQGtrK+Lj45GWliZxhfJw6dIl6HQ66/HtsWPHDgQGBlpvJycn4+WXX8bdd9/t7BLdwmA/8/v27cOIESOwcOFCJ1Y5NAyAQWhoaMBrr72GAwcOwNfXF1evXsXy5csxbtw4zJkzR+ry3Mb69esBdH9BPf744/jwww8BAH/+85+lLEuWNBqN9fj2ZdeuXU6qxv0M5TN/6tQpTJ061cmVDg27gAahqakJnZ2daG9vBwCMHDkS2dnZuP/++xEVFYVLly4BAE6cOIHly5cDAJYvX47NmzfjF7/4BebOnYvi4mLJ6ncH5eXl0Ov1mD17tjUQDhw4gIyMDOtjli9fjhMnTuDEiRNITExEfHw81q1bJ1XJbicjIwOpqamIjY1FUVFRr79d6s3WZ/7jjz/G0qVLsWjRIuh0Opw6dQqlpaUoKirC1q1b8emnn0pcff/YAhiEhx56CHPmzEF0dDRCQ0Mxbdo0xMXFITg42ObzOjs7sXfvXhQVFWHLli3QarVOqtj9fP/998jNzcWVK1cQFRWFpKQkm4+vqanBsWPHMGrUKCdV6F4uX76MxYsXW2/HxcUBAEaPHo0dO3YAAF5++WVJanMHfX3mg4KC8OKLL2LHjh3w8/NDXl4edu7ciR07diAqKgpTp07Fo48+KnX5/WIADNKGDRvwzDPPoKSkBCUlJVi6dCn++Mc/2nxOzx/C+PHj0dzc7Iwy3dajjz4KLy8v+Pn5wdfXFy0tLTYfP27cOH7523CrLqCMjAxMnDhRoorcT1+f+W3btqGoqAjV1dU4efLkjxZacwcMgEE4fvw4rl27hvnz5yMhIQEJCQnYt28f8vLyAPyw3rbZbO71PG9vbwDgKqYD4On5w5+kSqWCIAjW/+3RszQ4gB8tb0sDw+M2MH195t9//328/vrrWLRoEaZMmYIHH3wQ77//vtTlDpr7RZaEfHx88Kc//cnaXyoIAs6ePYvQ0FD4+vriq6++AgAcPXpUyjJlx9fXF19//TUEQcDFixdx7tw5qUsihejrM+/l5QWVSoXU1FRMmzYNhYWFsFgsAAC1Wm392dWxBTAI06dPx6pVq5Cammo9C3300UexcuVKPPLII9i0aRPefPNNzJw5U+JK5eVnP/sZPvjgA+h0OowbNw6TJ0+WuiRSiL4+89u2bUNGRgZiY2OhUqkwc+ZMlJWVAej+e3399dcxatQo6HQ6KcvvF/cDICJSKHYBEREpFAOAiEihGABERArFACAiUigGABGRQjEAiAbJYDBY13oicmcMACIihWIAEA3Ali1bEB0djcTERBQWFgIAqqurkZSUhKVLl2L27NlYsWIFTCYT8vPzodfrrc/95ptvMHPmTHR0dEhVPtEtMQCI+nHkyBF88sknOHTokHWlUqB7448lS5Zg3759+OSTT3Dp0iUcP34cOp0OdXV1uHDhAgBg//79eOyxx+Dl5SXlP4PoRxgARP347LPPMHfuXNx2223w9PREQkICAOB3v/sd/Jc+pgkAAAE6SURBVPz8sGvXLrz00ku4fPkyrl27Bi8vL/z85z/H/v37YbFYcPDgQSxdulTifwXRj3EtIKIBuHHFFLVaDQBYs2YNLBYLYmNjERkZifr6euvj9Ho9EhMTMXXqVIwfPx5BQUGS1E1kC1sARP2YNWsWDAYDWltb0dXVZV1fv6SkBCtXrsT8+fMBAF9++aV1FcjAwED89Kc/xauvvoply5ZJVjuRLWwBEPVDq9Xi3LlzSEhIwO23346HHnoITU1NSE9Px8qVKzFixAjcdtttmDJlCurq6qzPi4+Px6ZNm7gDHLksrgZKJIKuri5s3LgRY8aMwdNPPy11OUS3xC4gIge7cuUKpk2bhvr6ejz++ONSl0PUJ7YAiIgUii0AIiKFYgAQESkUA4CISKEYAERECsUAICJSKAYAEZFC/T8o2x9I8UzUqQAAAABJRU5ErkJggg==\n" + "image/png": 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -358,8 +384,8 @@ "y = \"total_bill\"\n", "order = ['Sun', 'Thur', 'Fri', 'Sat']\n", "pairs = [(\"Sun\", \"Thur\"), (\"Sun\", \"Sat\"), (\"Fri\", \"Sun\")]\n", - "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n", - "annot.new_plot(ax, pairs=pairs, data=df, x=x, y=y, order=order)\n", + "ax = sns.boxplot(data=df, x=x, y=y, hue=x, hue_order=order, order=order)\n", + "annot.new_plot(ax, pairs=pairs, data=df, x=x, y=y, hue=x, hue_order=order, order=order)\n", "annot.configure(test=None, loc='inside')\n", "annot.set_pvalues([0.1, 0.1, 0.001])\n", "annot.annotate()\n", @@ -382,15 +408,122 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "text/plain": " carat cut color clarity depth table price x y z\n0 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43\n1 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31\n2 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31\n3 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63\n4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75", - "text/html": "
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caratcutcolorclaritydepthtablepricexyz
00.23IdealESI261.555.03263.953.982.43
10.21PremiumESI159.861.03263.893.842.31
20.23GoodEVS156.965.03274.054.072.31
30.29PremiumIVS262.458.03344.204.232.63
40.31GoodJSI263.358.03354.344.352.75
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caratcutcolorclaritydepthtablepricexyz
00.23IdealESI261.555.03263.953.982.43
10.21PremiumESI159.861.03263.893.842.31
20.23GoodEVS156.965.03274.054.072.31
30.29PremiumIVS262.458.03344.204.232.63
40.31GoodJSI263.358.03354.344.352.75
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" + ], + "text/plain": [ + " carat cut color clarity depth table price x y z\n", + "0 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43\n", + "1 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31\n", + "2 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31\n", + "3 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63\n", + "4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75" + ] }, - "execution_count": 9, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -408,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": { "collapsed": false, "pycharm": { @@ -420,6 +553,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'color', 'y': 'price', 'hue': 'cut'}\n", + "self.tuple_group_names=[('E', 'Ideal'), ('E', 'Premium'), ('E', 'Good'), ('E', 'Very Good'), ('E', 'Fair'), ('I', 'Ideal'), ('I', 'Premium'), ('I', 'Good'), ('I', 'Very Good'), ('I', 'Fair'), ('J', 'Ideal'), ('J', 'Premium'), ('J', 'Good'), ('J', 'Very Good'), ('J', 'Fair')]\n", + "self.plotter.group_names=Index(['E', 'I', 'J'], dtype='object', name='x')\n", + "self.plotter.hue_names=['Ideal', 'Premium', 'Good', 'Very Good', 'Fair']\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -427,21 +564,23 @@ " ***: 1.00e-04 < p <= 1.00e-03\n", " ****: p <= 1.00e-04\n", "\n", - "E_Ideal vs. E_Premium: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:1.560e-31 U_stat=3.756e+06\n", - "I_Ideal vs. I_Premium: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.141e-61 U_stat=1.009e+06\n", - "J_Ideal vs. J_Premium: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:4.018e-37 U_stat=2.337e+05\n", - "E_Ideal vs. E_Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.201e-19 U_stat=1.480e+06\n", - "I_Ideal vs. I_Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.008e-13 U_stat=4.359e+05\n", - "J_Ideal vs. J_Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:1.006e-04 U_stat=1.174e+05\n", - "E_Ideal vs. E_Very Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:1.736e-02 U_stat=4.850e+06\n", - "E_Good vs. I_Ideal: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.906e-01 U_stat=9.882e+05\n", - "I_Premium vs. J_Ideal: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.159e-27 U_stat=8.084e+05\n" + "E_Ideal vs. E_Premium: Mann-Whitney-Wilcoxon test two-sided, P_val:1.560e-31 U_stat=3.756e+06\n", + "I_Ideal vs. I_Premium: Mann-Whitney-Wilcoxon test two-sided, P_val:5.141e-61 U_stat=1.009e+06\n", + "J_Ideal vs. J_Premium: Mann-Whitney-Wilcoxon test two-sided, P_val:4.018e-37 U_stat=2.337e+05\n", + "E_Ideal vs. E_Good: Mann-Whitney-Wilcoxon test two-sided, P_val:5.201e-19 U_stat=1.480e+06\n", + "I_Ideal vs. I_Good: Mann-Whitney-Wilcoxon test two-sided, P_val:5.008e-13 U_stat=4.359e+05\n", + "J_Ideal vs. J_Good: Mann-Whitney-Wilcoxon test two-sided, P_val:1.006e-04 U_stat=1.174e+05\n", + "E_Ideal vs. E_Very Good: Mann-Whitney-Wilcoxon test two-sided, P_val:1.736e-02 U_stat=4.850e+06\n", + "E_Good vs. I_Ideal: Mann-Whitney-Wilcoxon test two-sided, P_val:5.906e-01 U_stat=9.882e+05\n", + "I_Premium vs. J_Ideal: Mann-Whitney-Wilcoxon test two-sided, P_val:5.159e-27 U_stat=8.084e+05\n" ] }, { "data": { - "text/plain": "
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\n" + "image/png": 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8vLp27eoyzVqkklA++qmnVJCf75Dze5lMWrxoUYXCeUxMjE6cOKGVK1eWu753797q37+/nn766Sop44YNGzRp0iQdOHCgSo5nZARzAADgcElJSUpLS1N0dLQ1lFu4u7tr4MCBio6OVlJSktq2beugUla9zMxMFeTny/u6W+Ru8rfruYvzM5V78ntlZmZy19wgCOYAAMDhzp07J0lq2rRpueubNGlis52rcTf5y8OnnqOLAQcjmANADZafn6+tW7fq1KlTatiwofr16yeTyeToYqEGqlevJJQePXpU4eHhZdYfO3bMZjsYw4ULFzR9+nR99dVX8vT01KhRo8ps88MPP+jNN9/Uvn37VK9ePf31r3/Vc889Jz8/P0nSyZMnNWvWLH3/fcnd+/r16+u+++7Tc889V+bpiaurWVcLALBatmyZBg4cqKVLl+rTTz/V0qVLNXDgQC1btszRRUMNFBERoeDgYK1fv17FxcU264qLi7V+/XqFhIQoIiLCQSVEeZ555hnt3btXixcv1rJly/Tvf/9bJ06csK5PTk7WiBEj1KNHD23evFmzZ8/WL7/8opEjR8psNkuSnnrqKV24cEHLli3Ttm3bNHLkSC1dulRff/21oy7LYQjmAFADLVu2TBs2bJC/v7/Gjh2rFStWaOzYsfL399eGDRsI57A7Dw8PRUVFKSEhQTNmzFBycrKys7OVnJysGTNmKCEhQSNHjnSpjp/O7rffftM333yjl156SZ07d9YNN9ygN9980+apW1xcnP7yl79o9OjRCgsLU+fOnfXmm2/qp59+0q5du5Sbm6sHHnhA06ZNU3h4uBo3bqzhw4erQYMGNaKz56VoygIANUx+fr42bdqkwMBALVu2TJ6eJX8K7rzzTvXp00cjRozQxx9/rMGDB9OsBXbVvXt3xcTEKC4uTtHR0dblISEhLjtUojM7ePCgJNl0xm3QoIEaN25s/T4pKUlHjx5Vx44dy+z/66+/qmvXrhoyZIi2bdumvXv36ujRozpw4IDOnj1b5slJTUAwB4AaZuvWrSouLtaQIUOsodzC09NTgwcP1sKFC7V161Y98MADDiolaqru3burc+fO9H1wApYZLS8N0KV/rxQXF+u+++7T6NGjy+xfr149ZWdna8iQIcrNzdVdd92l/v37q127dho8eHD1Ft6gCOYAUMOcOnVKktSlS5dy10dGRtpsB9hTeTN/bt68mZk/DeiGG26QVNK587bbbpNUMvyjpaOuJLVq1UopKSk2o+38+uuvmjVrlp599lkdOXJEv/zyi7799ls1aNBAkpSenq4//vjD2ga9JiGYA0AN07BhQ0nSrl27dOedd5ZZn5CQYLMdYC81deZPqWRMcWc7Z5MmTXTXXXfp1VdflclkUoMGDTRnzhzll5osaeTIkRo8eLCmTp2qIUOGKDMzU1OnTlVubq7CwsKUm5srqeTD15133qlTp05pzpw5KigosDlOTUEwB4Aapl+/foqPj9eqVavUp08fm8fOhYWFWr16tTw8PNSvXz8HlhI1TU2d+dPf319eJpNyT37vkPN7mUzy96/8xEavv/66Xn/9dY0fP17FxcV69NFHbcaa79Chg5YuXap58+apf//+8vX1Vbdu3fT888/LZDKpXbt2mjRpkpYvX665c+cqJCREd999txo2bKh9+/ZVxSU6FTdzTXxOUM0sFcmVZiYD4Foso7IEBgZq8ODBioyMVEJCglavXq309HQ99NBDGjFihKOLiRpk3759euGFFzRr1qxyxzFPTk5WdHS0XnvttWr7+1qdf79zc3N1+PBhNWvWTN7e3jbr0tLSlJlp/zvmUskHA2b9rF5Xeu0vxR1zAKiBLKF706ZNWrhwoXW5h4cHoRwOUZNn/gwODiYcQxLBHABqrBEjRmjw4MGMfgFDYOZPgGAOADWayWRiSEQYQumZP0u3MZeY+RM1BzN/AgAAh2PmT4A75gAAwCCY+RM1naGC+ZIlS/TNN99o5cqVkqShQ4dq165d5W77+uuv68EHH1RRUZE6duyovLw8m/Vjx47V008/LUn6/fffNW3aNCUkJMjX11cPP/ywnn76aZtP3atXr1Z8fLzOnDmjm266SVOmTOFxGQAAdta9e3d17dpVSUlJOnfunOrVq6eIiAjulKNGMEwwX716tebOnavOnTtbl82fP18FBQXW781ms8aPH6+MjAzdfvvtkqQjR44oLy9PH3/8serXr2/d1tfXV5JUUFCgqKgohYWFae3atTp27Ji17dq4ceMkSRs3btQbb7yhadOmKSIiQu+++65GjBihrVu30skEgEsrKioiAMFwPDw8GHIYNZLDg/np06f18ssva+fOnQoLC7NZFxgYaPP9qlWrtHfvXn388ceqXbu2JOnAgQPy8/Mrtwe3JH322Wc6efKk1q1bp4CAALVu3Vp//PGH3njjDY0ePVomk0mLFy/WkCFDdP/990uSXnvtNfXt21fr16/XqFGjqvyaAcAIypv6PDg4mKnPAcBBHB7Mf/nlF3l5eWnz5s1auHChTpw4Ue52586d09y5c/XUU0+pefPm1uUHDhxQixYtLnv8xMRE3XjjjQoICLAuu+WWW5SVlaX9+/erUaNGOnLkiLp162Zd7+npqc6dOyshIaHSwdxsNis7O7tS+wJAddu5c6feeust3XzzzXr66afVpEkTHTt2TJs2bVJsbKzGjx+vrl27OrqYgF2ZzWa5ubnZ/bxMMAQLhwfz3r17q3fv3lfd7r333pO3t7eioqJslh88eFCFhYWKiopScnKyQkJC9Pjjj1uH/0pNTVVoaKjNPpYKeOrUKetU1A0bNiyzTXJycqWvq6CgQPv376/0/gBQXYqLixUfH69WrVrpnnvuUVFRkQ4fPixJuueee3ThwgXFx8fLz8/PZsg6oCaw9zj+aWlp+vtTo5WXX3D1jatBLZOX3lm0mHBuEA4P5tciKytL69at09ixY1WrVi2bdYcOHVJxcbHGjRun0NBQbd++XZMmTVJBQYEefvhh5ebmyt/f32YfyzHy8vKUk5MjqewbsVatWmU6lFaEl5eXWrZsWen9AaC6/PLLL0pPT9dzzz2n1q1bl1k/dOhQvfjii5KkG264wd7FAxwmJSXF7ufMzMxUXn6BHmrjrwa+9o1lZ7MLteFApjIzMysUzHv37m3TwsHNzU2+vr6KiIjQP/7xD0VGRlZHccsVExOjEydOWAcOcXZOEcy//PJL5efna8CAAWXWffLJJyoqKrK2OQ8PD9fJkycVFxenhx9+WN7e3srPz7fZxxK4fX195e3tLUnlbuPj41PpMlsqKQAYjaWZXZs2bcr9PWcJ69nZ2fweQ43iiGYsFg18PXWdn5fDzl9RI0eO1MiRIyWVNAFKT0/XnDlz9MQTT2jr1q267rrr7FKOyZMnq6ioyC7nsgeneEb55ZdfqlevXmXufEuSt7e3NZRbtG7dWqmpqZKk0NBQm45Nkqzfh4SEWJuwlLdNSEhIlV0DABhF6anPy8PU5wCuxtfXV0FBQQoKClJwcLBat26tqVOnKjc3V1988YXdylGnTp0yg4U4M6cI5omJiTadMy0yMzPVpUsXbdiwwWb5vn371KpVK0lSZGSkkpKSlJWVZV3//fffq3bt2goPD1f9+vXVrFkz7dy507q+sLBQiYmJdn0UAwD2Unrq8+LiYpt1TH0OoLIs/fZMJpN69+6t119/XXfffbe6du2qXbt2yWw267333lOfPn3Uvn17PfDAA9q8ebN1/507dyoiIkJffPGF7rzzTrVr107Dhg3TqVOnNH36dHXu3FndunXTokWLrPvExMRo6NCh1v3btGmj33//3eaYpZcNHTpUr7/+uiZMmKCOHTvq1ltv1QcffKDdu3frgQceUPv27fXYY4/pyJEjdviJlWX4YH7q1CmdP3++3OEQ/f39dcstt+itt97S9u3bdeTIEb377rvavHmzdXKhvn37KigoSM8884ySk5P15Zdfas6cORo5cqS1XfnIkSO1bNkybdy4USkpKXrhhReUm5urhx9+2K7XCgD2UHrq8+nTp+vTTz/VF198oU8//VTTp09n6nMAFXb69Gm9+uqr8vX1Va9evSSVDHM9ZcoULV26VB06dNBbb72lDz74QC+++KK2bNmiYcOG6ZVXXtHq1autxykqKtKiRYs0e/Zsvf/++0pOTtYDDzwgLy8vrV+/Xo899pjmzp2rAwcOVLqsK1eu1A033KDNmzerT58+mj59ul555RW98MILWrVqldLS0vTmm2/+6Z9JZRi+jfmZM2cklR3T3OK1117T/Pnz9fLLL+uPP/5QixYt9Pbbb6tHjx6SSjpxLl26VFOnTtUjjzyigIAADRo0SH//+9+tx3jkkUd04cIFzZ07V+np6brpppu0bNkyHuMCcFndu3dX//79tWnTJiUkJFiXu7u7q3///oxjDuCKlixZovj4eEklLQ3y8/PVokULzZ0719q+vFevXtbfJdnZ2Vq+fLnmzJmj2267TZLUpEkTnThxQnFxcRo8eLD12P/4xz+sE0zdcsst+umnnzRx4kS5ublp1KhReuedd3To0CG1adOmUmW/4YYbrKP8DRkyRGvXrtXQoUOtQ8T269dPX375ZaWO/WcZKpjHxsaWWdauXbsrfiry8/PTpEmTNGnSpMtu07RpU2vluZyoqKgyQzECgKvasWOHNm7cqM6dO6tTp04ymUzKz8/X7t27tXHjRrVp04ZwDuCyHnvsMWsTEnd3dwUGBqpOnTo22zRt2tT6dUpKivLy8vTcc8/ZDMNqCfW5ubnl7ufr66tGjRpZO+ZebtCOimjSpIn1a0sH+MaNG1uXeXt728w8b0+GCuYAgOpXVFSkuLg4RUZGavLkyTZ/JPv166cZM2YoPj5eXbt2pTkLgHIFBATYBOjyWEK0VDJyiyTNnTvXZqJIi9LDVlvaqlv8mfkUyhuxxcur7Og3RpmzwRilAADYTVJSktLS0jRw4MAyf4zc3d01cOBAnT59WklJSQ4qIQBX07x5c3l6eurkyZNq2rSp9d/27dsVFxdXJcHYErhLD/jhqE6clcUdcwCoYc6dOydJl73bZXnMa9kOQPU7m13o0uesU6eOHnvsMc2bN09+fn66+eabtXPnTs2aNUujRo2qknO0bt1avr6+evfdd/XMM8/o6NGjWrZsWZUc214I5gBQw5Qex7y8Ea8YxxywH39/f9UyeWnDgUyHnL+WyavceWKqw6RJk1S3bl3NmzdPaWlpatiwocaNG6cnnniiSo7v5+enWbNmafbs2br77rsVHh6u559/XmPGjKmS49uDm9nS6AdVZt++fZJk7VEMAEZSVFSkJ598UmFhYWXamBcXF2vGjBk6evSolixZQhtz1CjV+fc7NzdXhw8fVrNmzWzaXkslkxpmZjommPv7+ys4ONgh564prvTaX4o75gBQw1jGMY+NjdWMGTM0cOBANWnSRMeOHdP69euVkJCgmJgYQjlgJ8HBwYRjSCKYA0CN1L17d8XExCguLk7R0dHW5SEhIYqJiWGoRABwAII5ANRQ3bt3V9euXZWUlKRz586pXr16ioiI4E45ADgIwRwAajAPDw/6wwCAQTCOOQAAAGAABHMAAADAAAjmAAAAgAEQzAEAAAADoPMnAACAAzHBECwI5gAAAA6Slpam0U89pYL8fIec38tk0uJFiyoVzs1mszZu3KiNGzfq0KFDysrKUsOGDXXbbbfpySefVFBQUDWUuMTvv/+uPn36aMWKFeratWu1ncfeCOYAAAAOkpmZqYL8fPl1DpJHHZNdz110IV9ZiWeUmZlZ4WBeXFyssWPHKjExUaNHj9ZLL72k2rVr69ChQ1q0aJEGDBigjRs3qn79+tVUetdEMAcAAHAwjzomedat5ehiXLPly5dr+/btWrdunW688Ubr8uuuu05du3bVPffco7i4OE2cONGBpXQ+dP4EAADANTObzVq1apXuv/9+m1Bu4e3trRUrVuiZZ56RJJ06dUoTJkzQX/7yF3Xo0EFRUVFKTk622WfTpk26//771a5dO/Xu3VvvvPOOioqKrOsPHjyoYcOGqUOHDrr99tv13XffVes1OgrBHAAAGEpRUZH27dun7du3a9++fTYBDY73+++/68SJE+revftlt7n++utlMpmUlZWl//mf/9Hp06e1aNEirV27Vt7e3hoyZIhOnDghqeTu+4svvqhHH31Umzdv1j/+8Q/FxcUpNjZWknThwgUNHz5cderU0fr16/XKK69o0aJFdrlWe6MpCwAAMIwdO3YoLi5OaWlp1mXBwcGKioq6YhCE/Zw9e1aSVK9ePZvlo0eP1s6dO63fX3fddRo8eLDOnz+vDRs2WLd/88031bdvX61evVrR0dF67733NGTIEA0ePFiSFBYWpvT0dM2aNUvjxo3Tp59+qpycHMXGxqpOnTpq1aqVXnjhBY0ZM8ZOV2w/BHMAqMGKioqUlJSkc+fOqV69eoqIiJCHh4eji4UaaseOHZo5c6ZMJttOkOnp6Zo5c6YmTZpEODeAunXrSpIyMjJslk+dOlW5ubmSpJUrV+rrr7/WwYMHFRYWZhPivb291a5dOx08eFDnzp3T2bNn1alTJ5tjdenSRQUFBfrtt9+sx6hTp451fceOHavr8hyKYA4ANRR3JmEkRUVFeueddyRJ7du31yOPPKKmTZvq6NGjWrdunRISErRo0SJ17dqVD48O1rhxYwUFBWnnzp26++67rctDQkKsXwcEBEgqaY9enuLiYnl6el5xvSR5enrKzc3N+r2Fp6drRljamANADbRjxw7FxsYqLCxMs2bN0rp16zRr1iyFhYUpNjZWO3bscHQRUcPs27dPGRkZioiI0JQpUxQeHi4fHx+Fh4drypQpuuGGG5Senq59+/Y5uqg1noeHh4YNG6ZNmzaV6cRpcerUKUlSmzZtdOTIEf3xxx/WdXl5efr555/VsmVLNWjQQA0aNNDu3btt9k9MTJSXl5eaNGmi8PBwHTlyROfOnbOu//nnn6vhyhyPYA7YER2aYARFRUWKi4tTZGSkJk+ebBOAJk+erMjISMXHx1M/YVeWoDVo0CC5u9vGE3d3dw0aNMhmOzjWE088ob/+9a8aNGiQFi9erOTkZP3+++/6+uuvNXLkSH300Ue65ZZbdN999ykwMFDPPPOM9u7dq+TkZE2YMEHZ2dl69NFHJUlRUVFatWqV1qxZo6NHj2rLli1asGCBHn30UdWpU0f33HOP6tevr+eee07JycnatWuXZsyY4eCfQPVwzecAgAHRbABGkZSUpLS0NEVHR5cbgAYOHKjo6GglJSWpbdu2DiolaprLNWmo7HbOpuiC/Wf+/DPndHd319y5c7V161Z99NFHWrFihTIzM9WgQQN17txZq1atUmRkpCRp1apVio2N1fDhwyVJnTp10gcffKDGjRtLkkaOHCmTyaT3339fr732mkJDQ/W3v/1NUVFRkiRfX1+9//77mjZtmv7nf/5HAQEBGjdunCZNmvTnfgAGRDAH7IAOTTASy+Pgpk2blru+SZMmNtsB9tCuXTutW7dOa9asUdu2bW0+NBYXF+uDDz6wbudK/P395WUyKSvxjEPO72Uyyd/fv9L79+vXT/369bviNo0bN9bChQuvuM2QIUM0ZMiQKx7j3XfftVn20EMPXXtBnQTBHKhmdGiC0VhGRzh69KjCw8PLrD927JjNdoA93HTTTfL391dSUpKmT5+uRx55RE2aNNGxY8e0bt06JSUlKSAgQDfddJOji1qlgoODtXjRImVmZjrk/P7+/goODnbIuVEWwRyoZpd2aLLcBbJ0aIqJidH+/fu1b98+dejQwbGFRY0QERGh4OBgrV+/XpMnTy5zZ3L9+vUKCQlRRESEA0uJmsbDw0NjxozRzJkz9dNPPykhIcG6rlatkqnq//73v7vkDYzg4GDCMSTR+ROodqU7NJnNZpvOn2azmQ5NsDsPDw9FRUUpISFB06dP16effqovvvhCn376qaZPn66EhASNHDnSJQMQjK179+6aNGmSdag9i4CAAJr8oUbgjjlQzSwdlX755Re9/fbbZTp/9u7d22Y7wB66d++u/v37a9OmTTZ3Jt3d3dW/f38CEByme/fu6tq1KxNfoUYimAPVzNKh6YMPPlBkZKSio6Nt2pivXbvWuh1gLzt27NDGjRvVuXNnderUSSaTSfn5+dq9e7c2btyoNm3aEM7hMB4eHowIhBqJYA5UsxtuuEFubm42d8QvvTvu7u6uG264wd5FQw116TjmpduY9+vXTzNmzFB8fDwdkgHAzgjmQDU7cOCANYhfrkNTcXGxDhw4wB0i2AXjmAOAMdH5E6hmlrGgn3vuuXI7ND377LM22wHVrfQ45uXNRss45gDgGIa6Y75kyRJ98803WrlypXXZlClTtH79epvtrr/+en399deSSu40LliwQOvXr9eFCxcUGRmpl156yTqblCTt379fM2bM0M8//6x69epp+PDhGjZsmHX9tRwD9nfo0CFraJWkOXPmqFWrVg4sUeVYxoIODQ3Ve++9V6ZD06FDh2y2A6qbpa598skn2rZtW5kOyXfeeafNdjC2jIwMvfDCCzp//rzq1q2r1157rcxNAADOwTDBfPXq1Zo7d646d+5ss/zAgQMaPXq0zWxQpds8vvPOO1qzZo1iY2MVGhqqWbNm6YknntCWLVtkMpl0/vx5jRgxQr1799bUqVO1Z88eTZ06VbVr19aAAQOu6Riwv/vuu6/MMktI37Jli72L86eUHjM6JibGZh1jRsMRIiIiFBAQoBUrVpTbIXnlypUKDAykTjqBoUOHKj093fr9hQsXNGTIEAUGBtrc5IKxpaWlMcEQJBkgmJ8+fVovv/yydu7cqbCwMJt1ZrNZKSkpevLJJxUUFFRm3/z8fMXHx2vChAm67bbbJElvvfWWevTooc8//1z33nuv1q1bJy8vL7366qvy9PRUixYtdPToUb377rsaMGDANR0D9nVpKG/btq327dtns96ZwrllzOiZM2fqscceU35+vnWdZSSMSZMm0ckOdnVpB+SrfQ/juTSUl5aenq6hQ4cSzp1AWlqanho9WvkFBQ45v8nLS4sWL77mcD506FBdvHhRGzZsKHf9lClTlJCQoM8++6wqi3lFFy9e1PLly/X555/r+PHjKi4uVrNmzXTPPfdo2LBh1X6TdcOGDZo0aZIOHDjwp4/l8GD+yy+/yMvLS5s3b9bChQt14sQJ67pjx44pOztbzZs3L3ff5ORkXbx4Ud26dbMu8/f3V0REhBISEnTvvfcqMTFRXbp0kafnfy/1lltu0ZIlS3T27FmdPHnyqseA/ViadUiSl5eXCgoKrKHc8r1lO2ds1nIpNzc3RxcBNVBSUpIyMzM1bNgwbdu2TdHR0dZ1ISEhGjZsmFasWEHnTwPLyMiwhvIOHTpo8ODB1qceq1ev1p49e5Senq6MjAyatRhcZmam8gsK1MfXT3U97BvLzhcV6qvsLGVmZl5zMH/44Yc1ceJE/frrr2rRooXNury8PG3btk2jRo2qjuKWKy0tTUOHDpWHh4eeeuoptW/fXpKUmJioefPm6bvvvtPSpUud5u+tw4N57969rROsXOrgwYOSpJUrV+o///mP3N3d1bNnT40fP1516tRRamqqJKlhw4Y2+wUHB1vXpaamqnXr1mXWS9KpU6eu6RiVYTablZ2dXen9a6rSbcrbtWunBx98UE2aNNGxY8e0adMm7d6927rdhx9+6KhiVkhxcbGWLl2qTp066dlnn9WBAweUnp6uwMBAtWnTRnPmzFFcXJzatWtXZoQMONbp06d18eLFCu1Tu3ZthYSEVFOJqobld1ufPn3Ur18/7d+/31onb7jhBuXl5WnFihVKTU0t84cXxmBpFufr66vnn39e7u7uMpvNatKkiZ5//nlFRUUpOztbMTExevPNNx1c2oorLi4uUy/t8fvRbDY7LMDV9fBUkKfDY9lV3XnnnZo2bZq2bNmiZ555xmbdl19+qZycHD344IN2K89LL72kgoICffTRR/Lz87Mub9Kkidq1a6f77rtP//nPf9SrVy+7lenPMHQNOHjwoNzd3RUcHKzFixfr2LFjeuONN3To0CG9//77ysnJkaQyjyhq1aqljIwMSVJubm6566WST3bXcozKKCgo0P79+yu9f03n5+enu+++WykpKdqzZ4/1e8tTEklO8/M9fPiwzpw5o/vvv1+HDh2Su7u7tVPdoUOH1KFDB+3evVvbtm1Ts2bNHFxaWFy8eFGzZ8+ucJMONzc3TZgwQbVr166mkv15lt9t//nPf9S4cWObOnngwAEdP37cup2zvM9qGkuH3Z49e5b7+PzWW2/V559/rrS0NKd7DZOSkvT555/bNNMJDAzUHXfcYZd+D/QtuzJvb2/dc889+uSTT8oE840bN6pXr14KCgqS2WzW0qVLtXbtWp09e1ZhYWGKiorS/fffL0nauXOnRowYoWeeeUZxcXG6/vrr1bBhQ124cEErVqywHvO3335Tv3799Mknn5R5Un7s2DH961//0uuvv24Tyi1atmyprVu3qmnTptZlP/74o9566y398ssv8vT0VO/evTVx4kTVrVtXUkluXLx4sbZs2aK0tDQ1b95cf//7362d4iXpiy++0Ntvv60jR46obdu2VToZm6GD+VNPPaVBgwZZf1itW7dWUFCQHnnkEe3bt0/e3t6SStqaW76WSgK3j4+PpJIKVLpNr2W9VHKn4VqOURleXl5q2bJlpfev6bKysrR48WKdOXPGuiwoKMjm7qWzTMhjGXKuZ8+eNnXMIiwsTHFxcQoICHCaa6op5s2bV+aO+YkTJ7RgwQKNHTtW119/fZl9nOGOeZs2bfTPf/5Te/bsUd++fW3uRBYXF2vLli0KDg7WXXfdxVMcg6pdu7by8/N16NAhjRgxosx6S7CpXbu2U/1e2blzp9avX6+OHTuqQ4cO1n44e/bs0fr16zV+/Hh17dq12s6fkpJSbcd2JQMGDNDatWv1448/qmPHjpKkM2fOaMeOHVq4cKGkkv56n3zyiV566SU1b95cCQkJeuWVV3ThwgUNHjxYUslkZ9u3b9eHH36onJwcnTx5UmPGjNGpU6esLRk2bdqktm3bltt8ddeuXZJk0xz5UqX7L+7du1dDhw7Vo48+qpdffllnzpzRq6++qqioKK1fv14eHh569tlnlZSUpFdeeUVNmzbVJ598on/84x9asGCB+vbtqx9++EFPP/20xo4dq3vuuUeJiYmaNm1alfxcJYMHc3d3d2sot7C8MKmpqdYXLS0tzTruruX7Nm3aSCoZoq70UGCW9VJJW8rCwsKrHqMy3Nzc5OvrW+n9a6oBAwboo48+klRyh2TixInWdpPvvvuuNagPGDDAaX6+oaGhkkrqVHh4eJn1x44ds27nLNdUU5T3BMPy4apFixZO/eH7iSeeUGxsrN566y0NHDjQ2mRs/fr1+uGHHxQTE1PuHSgYw1NPPaXXXntNv/76q4qLi21eq6ysLP3222/W7Zzl90pRUZFWrVqlFi1a6Pjx4/rhhx+s64KCgtSiRQutXr1aPXv2rLbO8s7SDtnR2rVrp9atW2vLli3WYL5582bVr19fPXv2VHZ2tpYvX645c+ZYB9Zo0qSJTpw4obi4OGswl6SRI0daw3OrVq3UoEEDbd68WaNGjVJxcbE+/vhjPfnkk+WW448//pBUdmjXzp07q6ioyPr9fffdp1dffVXx8fFq06aNXnzxRUklv8fnzJmjBx54QN98840aNWqkr776SosXL7aW++mnn1ZycrIWL16svn37atWqVbr55ps1duxYSSV/Jw4ePGhzl//PMHQwnzhxotLS0rR8+XLrMktHwJYtW6px48by8/PTzp07raE6MzNTSUlJ1uEVIyMjtXbtWhUVFVnfyN9//72aNWum+vXrq06dOlc9BuynQ4cO1mB+6NAhRUdH68Ybb9Qvv/xSZjtnUXq4xEunP2e4RDhK9+7dFRMTo7i4uDKdP2NiYqr00SyqXpcuXeTm5iaz2az/+Z//UevWrTVo0CCtWbPG2j/Lzc1NXbp0cXBJr51lRtq0tDR16dLF5sbM+vXrrXdH6ZRsDAMGDNCSJUv0wgsvyNPTU5s2bVL//v3l4eGhlJQU5eXl6bnnnrP5m1dYWKj8/Hzl5uZal5W+o+3p6an7779fH3/8sUaNGqXvv/9e586du+xAHJabtxkZGWrQoIF1+YYNG6zNECdMmGBtOXHw4EH95S9/sTlGeHi46tSpowMHDlifkHbq1Mlmm8jISM2ZM+eyx+jYsWPNCOZ33nmn/v73v2vBggW6//77dfjwYb366qu69957rR2ShgwZotmzZ6tevXq6/vrrNWvWLIWGhuqOO+6QVFJxli5dqsmTJ+uJJ57Q3r17tXz5ck2dOlVSSVuyqx0D9lPe3YpLQ/nltjMqy3CJsbGxmjFjRpm7kwkJCYqJiWG4RNhd9+7d1bVr1zKTXlEXjc/Dw0MxMTGaOXOmpJKw8Morr9hs42y/V86ePSupJBSVvokRHh6uyZMn69VXX9Xu3but28Gx7r//fs2ePVvffvutgoKCdOjQIS1YsEDSf4dbnTt3brkj65Vux2/p92cxYMAAxcXF6eeff9bmzZvVp0+fy44sZAnQu3bt0t13321dXroFROkmpJfrM2Q2m+Xl5XXZazWbzdbR/dzc3FRcXGyz/kr7VpShg3mfPn00d+5cvfvuu3rvvfdUp04d3XfffTadDcaNG6fCwkJNmTJFubm5ioyMVFxcnPWHVL9+fS1dulQzZsxQ//79FRQUpIkTJ6p///7XfAzYz+XG5K3sdkbB3UkYlYeHB3cfnVT37t01adIkLVq0yOZ3Yt26dTV69Gin+71imWCnW7duZfo2uLu765ZbbtHu3bsdNhEPbNWrV0+9e/fWP//5TzVo0ECRkZHWTpbNmzeXp6enTp48qb/+9a/WfVasWKGUlBS9+uqrlz1uixYt1LFjR23dulVfffXVFUcVatGihW699VbNnz9fPXv2LNP8Li8vT+fOnbP2B2rTpo11dDeL5ORkZWVlqUWLFtbtdu/ebVPuxMREa9PF8PBw/fjjjzbH+Pnnny9bxooyVDCPjY0ts6xfv37q16/fZffx8PBQdHS0Tdi5VLt27a44tN61HAP2UfpTsaXTj0WtWrWsHXedcVxe7k4CqGqu9HvF399fkvTdd9+pd+/eSk5Otl5TeHi4vv/+e5vtXM35okKnO+fDDz+sCRMmyN/fX08//bR1eZ06dfTYY49p3rx58vPz080336ydO3dq1qxZ1zTG+YABAzR9+nT5+/uXaTZyqdjYWI0cOVIPPfSQRo0apZtvvllubm5KTEzUe++9pxMnTmj48OGSpBEjRmjQoEGaNm2aBg0apLNnz2ratGmKiIhQt27d5OXlpb/+9a+aOnWq3Nzc1LRpU3366af66quvNHfuXEklbeIHDhyo119/3ToYyapVqyr9M7yUoYI5YHk85Ofnp+XLl+vgwYPWX8ytW7fW8OHDlZWVVeYxkrPg7iQAlM/SRnj37t2XnSW59Hauwt/fXyYvL32VneWQ85u8vCr9YefWW2+Vr6+v0tPTbYYTlKRJkyapbt26mjdvntLS0tSwYUONGzdOTzzxxFWP269fP02bNk0PPvjgVT9kBgUF6X//93+1Zs0arV27VjNnzlR+fr4aNWqkXr16aciQIdamLe3bt9fSpUs1d+5cPfjgg/Lz81Pfvn313HPPWVtJzJkzR3PmzNHkyZOVmZmp1q1ba/78+br99tsllYwI995772nWrFlatWqVWrVqpdGjR2v27NmV+RGW4WZm3uUqZ+mgSgCruFWrVunDDz+Um5ubIiMjy22PbTab9eijj9I5Fw6RkpKi8ePH66233nLqUVngGnbs2KG4uDib0ceCg4MVFRXldE1ZioqK9PjjjysjI+OyT0wDAwO1fPnyansiUJ1/v3Nzc3X48GE1a9aszNC5aWlpDmui4+/vf82zftrL8ePHdccdd2jr1q02nUOd1ZVe+0txxxyGYvmc+Nhjj+mrr74q0x770Ucf1dq1ays86QsAuJodO3YoNjZWnTt31kMPPWQNs7t371ZsbKxT9l+x/G5v166dOnfubL2mxMREJSYmuuzv/uDgYMOFY0c4deqU9u7dqzVr1qhHjx4uEcorimAOQ2nXrp3WrVunn376SYsXLy7TxnDKlCnW7QCgpioqKlJcXJxatGihI0eOKCEhwbrOMuZ3fHy8unbt6jTtzZOSkpSZmalhw4Zp27ZtSkxMtK4LCQnRsGHDtGLFCoZLdGHnz59XTEyMwsLCrCO81DQEcxjKTTfdJH9/fyUlJWnmzJl65JFHFBkZqWPHjmnmzJlKSkpSQECAbrrpJkcXFQAcxhXH/LbMknzvvffqoYceKtOhNS8vTytWrLBuB9cTERFRZsSTmoZgDkPx8PDQmDFjNHPmTP300082d4EsY53+/e9/d5o7QABQHVxxzG/L7I1Hjx5VeHh4mQ8UllmSL53lEXAl7lffBLAvy9i8lw6JGBAQoEmTJjldm0kAqGrXMuZ36e2cQelZki8deYtZklFTEMzhNFy10w8AVFTpMb/LC7HOOOa3ZZbkhIQEzZgxQ8nJycrOzlZycrJmzJihhIQEjRw5kiemcGk0ZYHhlB5pYMCAAS4x0gAAVCXLWN4//PCDZsyYUWZo2R9++MFmO2fBLMmo6QjmMBRXHGkAAKqapdmHv7+/jhw5UibEtmjRQhcuXHDKZh+uNJspUFEEcxhK6ZEGTCaTzbqMjAydOXPGup2zjDQAGFlRUREByAlZmn1cbhzzxMRExcTEOO1rWdNmSWaCIVgQzGEopUcQaN++vR555BHrEGDr1q2z3kF3ppEGAKNypVkja6LSzT5KP110hWYfNekDY1pamp566imbmU7tyWQyadGiRYRzgyCYw1DS09MlSWFhYZo0aZKSk5O1a9cu1atXT5MmTdL48eN19OhR63YAKsfSlyMyMlLR0dE2Y2DTl8N5dO/eXZ07d9bWrVt16tQpNWzYUP369SvzxNGZ1LQPjJmZmcrPz1fLxj3kUyvg6jtUoZy8DKUc/z9lZmZWKJgPHTrUOlb+pUaOHKnnn3/+ivvv3LlTw4YN01dffaVGjRpVqMyujmAOQ7lw4YIkqaCgQKNGjbI2XZFK2ph7eXnZbAeg4ix9OSIjI8sdA3vGjBn05XAS5YXYzZs3O22I3bFjh2bOnFnmg0V6erpmzpzp0kPm+tQKkJ9vfUcX45r169dPkydPLrPcx8fnqvt27NhR33zzDWPSl4PhEmEobm5ukqQTJ06ooKBAY8aM0fvvv68xY8aooKBAJ0+etNkOQMVZ+nIMHDiw3DGwBw4cqNOnTyspKclBJcS1sDz1CAsL06xZs7Ru3TrNmjVLYWFhio2N1Y4dOxxdxAopKirSO++8I6mkKWPpa2rfvr0kadGiRSoqKnJkMfH/eXt7KygoqMw/Pz+/q+5rMpkUFBTEB/9yEMxhKDfeeKOkkje8yWTSwoUL9fjjj2vhwoWqVauWvL29bbYDUHGWKc2bNm1a7vomTZrYbAfjufSpR3h4uHx8fKxPPSIjIxUfH+9UIXbfvn3KyMhQRESEpkyZYnNNU6ZM0Q033KD09HTt27fP0UXFVWRkZGjKlCnq0aOHbrzxRnXr1k1TpkxRTk6OpJKmLG3atNHvv/8uSerdu7def/113X333eratetlm8nUBARzGIrl03Nubq6aNGmi0aNHa9y4cRo9erQaN26s3Nxcm+0AVFzpqc/Lw9TnxueKTz1+/vlnSdKgQYPKvaZBgwbZbAfjiomJUVJSkhYsWKDPPvtMkyZN0qZNm/Thhx9edp9Vq1ZpypQpWrp0qTp06GC/whoMbcxhKKU7de7du1eJiYnW72vVqlXudgAqpvTU56XbmEtMfe4sXPGpR+nZncsblaW87eA4W7Zs0WeffWazrFOnTlq6dKn+8pe/KDIyUm3atJEkNWrUSKtWrdLBgwcve7xevXq5bP+BiiCYw1Asd+iGDRumrVu32nT+DAgI0F133aUVK1ZwJw/4E0qPgV3erJEJCQlOPQa2K0tNTVVWVpa1ScC3336rsLCwMtsdOXJEkpSTk6PU1FSFhobasZSV065dO61bt06LFy9WXl5emc7/lpsz7dq1c1QRUUrv3r01YcIEm2WW5qaDBg3S119/rY0bN+rIkSNKSUnR77//rubNm1/2eJf7kFnTEMxhKJY7ecnJyZo/f75WrFihkydP6rrrrtOwYcM0Z84c7uQBVYCpz51PRkaGRo0apeLiYuuyefPmXXGfhQsXyt3dXStWrFBAgH2H4quom266Sb6+vvr9998VGBioMWPGqEuXLtq1a5dWr16tM2fOyNfXVzfddJOjiwpJtWvXLjdMFxcXa9SoUTp06JDuvfde3X333brxxhv14osvXvF4llBf0xHMYSiWO3kzZ87UY489Zl2+Z88e/fOf/5QkTZo0iTt5QBVg6nPnEhAQoCVLligrK0tSSXO/5cuXKyIiQm3bttXatWv12GOPad++fUpKStLw4cPVrl07+fn5GT6UW3h6lsSS7OxsLVy4UAsXLpT036aMliFzYVz79+/Xf/7zH61bt846mk5BQYGOHTumxo0bO7h0xkcwh+EcOHBAUklnn9J3hizfHzhwgLt5QBWpaVOfO7vSTVJatmyp0NBQxcXFae3atZKktWvXOu1Tj6SkJGVmZl61KWNSUpJL1tmcvAyXOGeDBg3k6emprVu3ql69ekpPT9fixYt15swZh81u6kwI5jCU/Px8bdq0SYGBgVq6dKkOHjxovZPXunVrPfHEE/r44481ePBgp57ZDgCqguWpxxdffKGFCxdqzJgxuv32253yqYelo+q9996rhx56qMyTnLy8PK1YscKpOrReC39/f5lMJqUc/z+HnN9kMsnf37/KjhcSEqLY2FjNnz9fq1evVlBQkG677TYNHz5cX3/9dZWdx1URzGEoW7duVXFxsYYMGaJatWqVuSsyePBgLVy4UFu3btUDDzzgoFICgHF4eHioZcuWkkruojtjKJdsh/EMDw8v8/vfVYfxDA4O1qJFi5SZmemQ8/v7+ys4OLhC+6xcufKK6++77z7dd999ZZZPmjRJktS1a1fr03FJBPZSCOYwlFOnTkmSunTpUu76yMhIm+0AAK6hJg/jGRwcXOFwDNfEBEMwlIYNG0qSdu3apaKiIu3bt0/bt2/Xvn37VFRUpISEBJvtAACuwdL5PyEhQTNmzFBycrKys7OVnJysGTNmKCEhQSNHjnTaJwLAteCOOQylX79+io+PV3x8vNauXauzZ89a1zVo0EDZ2dny8PBQv379HFhKwHWUN5ELwQeOwjCeqOkI5jAUk8mkyMhI7dy5U/n5+erZs6datmyplJQU7dixQ4WFheratSsdP4EqsGPHDsXFxSktLc26LDg4WFFRUQQgOAzDeKImI5jDUIqKinT48GEFBgYqPT1d//nPf/Sf//zHuj4wMFBHjhxRUVGRU/6Szs/P19atW3Xq1Ck1bNhQ/fr140MGHGLHjh2KjY1VZGSkoqOj1bRpUx09elTr169XbGwsdyeBamI2mx1dBNhZRV5zgjkMJSkpyXr37uabb1ZeXp4yMzPl7++vWrVq6YcffrBu52zj2C5btkybNm2yGZs9Pj5eDz74oEaMGOHAkqGmKSoqUlxcnCIjI2062YWHh2vy5MmaMWOG4uPj1bVrV6f8AAzn5qpPciyTI2VnZ8vHx8fBpYE9ZWdnS7q2CbII5jAUS5vy4OBg/fjjjzafMt3c3BQcHKy0tDSbtufOYNmyZdqwYYMCAwM1ZMgQ6zTTq1at0oYNGySJcA67sXwAjo6Othn5QiqZyGvgwIGKjo52yg/AcG6u/CTHw8NDgYGB1g8cvr6+cnNzc3CpUJ3MZrOys7OVlpamwMDAa7rRQTCHoVjGcbVU4ktDrOUXmqPGe62M0pMmLVu2zDrl9J133qk+ffpoxIgRTJoEu7JM0NK0adNy1zdp0sRmO8AeasKTHMvMraWfBsD1BQYG2szaeyUEcxiKr6+vJMnT01PvvvuuUlJStHfvXl133XV69913NWjQIBUWFlq3cwalJ01yc3PTvn37bDo0MWkS7K30RC6tWrUq08nOVSdygbHVhCc5bm5uatiwoYKDg1VQUODo4sAOvLy8KvRBkmAOQ0lJSZEkFRYWasiQIcrPz7euM5lMKiwstG53++23O6SMFWWZDMnNzU1PPvlkmXaTjzzyiM12QHWzTOSyZMkSZWRk6MyZM9Z1QUFBCggIcNmJXGBcNelJjoeHh9Pe9Uf1MtQEQ0uWLNHQoUNtln399dcaMGCAOnbsqN69e+v1119Xbm6udf3u3bvVpk2bMv927txp3ea7777TQw89pPbt2+uuu+7Sp59+anOOvLw8TZ06Vd26dVPHjh313HPPucQbH8ZgmQxp/vz5CgsL06xZs7Ru3TrNmjVLYWFhWrBggc12QHXz8PDQrbfeqpSUFBUUFGjMmDF6//33NWbMGBUUFCglJUV/+ctfCA6wq9JPcsrDkxzUBIYJ5qtXr9bcuXNtliUmJmrs2LG6/fbbtXHjRr388sv65z//qalTp1q3OXDggJo0aaJvvvnG5l/Hjh0lSb/++qtGjRqlHj16aMOGDRo4cKAmTpyo7777znqMV155Rd98843mz5+v999/X7/99pvGjRtnl+uGrdJtsErfLb/0+2ttq2UEd9xxh6SS5jkTJkxQQUGBdu3apYKCAk2YMMHa5tyyHVDdioqK9M0336hly5YymUxauHChHn/8cS1cuFC1atVSy5Yt9e2336qoqMjRRUUNYnmSs379epvRqySpuLhY69ev50kOXJ7Dm7KcPn1aL7/8snbu3KmwsDCbdWvXrlXXrl01evRoSVJYWJjGjx+vKVOmaOrUqTKZTDp48KBatmypoKCgco///vvvq02bNho/frwkqUWLFkpKStLSpUvVrVs3nT59Wps2bdLixYvVuXNnSdKcOXN011136ccff7QGfNhH6Trg5uZWZlQWy/eX1hUjK90859FHH73sNaWkpDhtu0k4l9JtectrY37o0CGnb8sL5+Ph4aGoqCjFxsZqxowZGjhwoJo0aaJjx45p/fr1SkhIUExMDE9y4NIcHsx/+eUXeXl5afPmzVq4cKFOnDhhXTdy5MhyO4AUFBQoKytL9erV04EDB9SpU6fLHj8xMVF9+/a1WXbLLbdoxowZMpvN2r17t3WZRbNmzRQSEqKEhASCuZ2VbkLk7++vv/71rwoNDVVqaqr+9a9/KSMjo8x2Rle6rJdOMlD6e2e6Jji30m15PTw8yoRvV2rLC+fSvXt3xcTEKC4uTtHR0dblISEhTj1UInCtHB7Me/furd69e5e77tLHVQUFBVq+fLluuukmaxuzQ4cOqW7dunrooYd0+vRptW7dWuPHj1e7du0kSampqWWaPQQHBysnJ0fnz5/X6dOnVbduXdWqVavMNqmpqZW+LsvYlaiYpKQkSSVPNg4fPqxNmzZZ17m7u6tFixb69ddflZSUpG7dujmolBVjmUiiTZs2mjJlir744gudPn1aISEhuv322zV9+nQdOHBAPj4+1BknYOnjkpub67Svl2VUowMHDqhly5bav3+/0tPTFRgYqBtuuMH6lMfX19dpr7GmcYV6adGhQwfNmzevTL10d3ev9mszm82MLQ6Hcngwv1aFhYWaOHGiDh06pNWrV0sqGcXiwoULys7O1pQpU+Th4aFVq1ZpyJAh2rBhg1q2bKnc3NwyY0Nbvs/Pz1dOTk65Y0fXqlVLeXl5lS5vQUGB9u/fX+n9a6rz589LKvnl+Pzzz+vLL7+0PmLv27evPvzwQ+t2zvLzPXLkiCQpJydHhw4dUvPmzdW8eXNJJR8sc3JyrNvxiNb4Tp48KUk6fPjwn/od4WiBgYFatGiRLl68aH0SJUkBAQGqXbu2AgMDJclp3mc1navUy9Lc3d2tN+EOHDhgt/MynwQcySmCeVZWlp555hnt2rVLCxYssN4Nb9iwoRISEuTj42Od5rRt27ZKSkrSypUrNXXqVNWqVeuynQh9fHzk7e1dZr1UMlLLn5ky18vLSy1btqz0/jXVb7/9poSEBP3222968803ra/Nr7/+qp9++sn6/Q033KAbbrjBkUW9ZpbmAMePH9enn36qBx98UI0bN9bx48e1ZcsWHT9+XJJUt25dp7mmmszydK1Zs2bWD1jOqEePHtqyZYsCAgL0t7/9TZ06ddLu3bu1bt06nTx5Uvfdd59uvPFGRxcT18hV6qWjWZ4WAY5i+GCelpamv/3tbzpx4oR1RrDS/P39bb63NHc4ffq0pJLwfukMW2lpafL19VWdOnUUGhqq9PR05efn23xKTktLU0hISKXL7ebm5lST4BjFAw88oJUrV5Zpi12au7u7HnjgAae5q2FpSjV06FBt27ZNL774onVdSEiIhg4dqhUrVig0NJQ64wS8vb2t/zvr61VUVKSdO3eqZcuWyszM1Hvvvaf33ntPUkmdDAoK0q5duxQVFcVTHCfhCvXSCGjGAkczdDDPyMjQ448/rqysLK1evVpt2rSxWf+f//xH//jHP7R582Y1btxYUkmTl+TkZOvQc507d9auXbts9vv+++918803y93dXZ06dVJxcbF2795tbbN8+PBhnT59usyHAFQ/Dw8PeXt7KycnRz4+Prr77rttOn/m5+fL29vbqcKCZQiw5ORkLV68WMnJydbmOeHh4YqNjWUIMNgVo7IAgDEZZhzz8sycOVPHjx/XrFmzVK9ePZ05c8b6r6ioSDfffLPq1q2r559/Xj///LMOHDig559/Xunp6Ro+fLikkruUe/fu1ezZs/Xrr78qPj5e27Zt0xNPPCGp5O7QPffcoylTpmjnzp3au3evnn32WXXp0kUdOnRw3MXXUElJScrJyVGvXr104cIF61CWmzZtUlZWlnr16qXs7GxrJ1FnYBkCLCEhQbGxsfLy8lJkZKS8vLwUGxurhIQEjRw50qk+bMC5lTcqS69evdS2bVt5eHgwKgsAOIhh75gXFRXpn//8pwoKCvT444+XWf/VV1+pUaNGWr58uWbPnq2oqCjl5eWpU6dOWrVqlRo0aCBJatWqld555x3NmjVL77//vho1aqRZs2bZjOgxbdo0vfbaaxo7dqwkqWfPnpoyZYp9LhQ2LEFgzJgxGjNmjJYvX66TJ0/quuuu0/Dhw2U2m7V9+3anCwwMAQYjKT3DYnh4eJn1zLAIRysqKirzJIebF6gJDBXMY2NjrV97eHho7969V92nSZMmevvtt6+4Tc+ePdWzZ8/Lrvf19dX06dM1ffr0ay8sqoUlCHzyySfatm2btX/Anj17lJiYqDvvvNNmO2fSvXt3de3alT82cLjSMyxOnjzZZr4IZliEo+3YsUNxcXE2/cOCg4MVFRXFTQy4PEMFcyAiIkIBAQFasWKFOnfurIceekgmk0n5+flKTEzUypUrFRgY6LSBobzJXAB7Y4ZFGNWOHTsUGxuryMhIRUdHq2nTpjp69KjWr1+v2NhYnjDC5RHMYTiWEVn27t2rxMRE63LLKCxXGrEFwLWheRWMpqioyDr6WuknOeHh4Zo8ebJmzJih+Ph4de3alQ+NcFkEcxhKUlKSMjMzJemy489nZGQwWgRQBWheBSMpPVpQ6eZVUskwuQMHDmS0ILg8gjkM5ezZs9avO3furM6dO9s0ZbHcQS+9HYDKo3kVjKL0aEHlYbQg1AQEcxhKenq6JCksLEwvvviizV2Tfv36ady4cTp69Kh1OwCAa2C0IMDg45ij5rlw4YKkktnr8vPz9fHHH2vx4sX6+OOPrZMLld4OAOAaSo8WVFxcbLOO0YJQU3DHHIZimQ45OTlZAwcOtFm3dOnSMts5G8bmBYDyMVoQQDCHwbRr107r1q27pu2cDWPzAsCVMVoQajqCOQyldevWVbqdUTA2LwBcG0YLQk1GG3MYyrZt26xfX9pcpfT3pbczukvH5g0PD5ePj491bN7IyEjFx8erqKjI0UUFAEOwjBbUq1cvtW3bllCOGoNgDkP55ZdfJEmBgYHlrg8ICLDZzhlYxuYdOHDgZcfmPX36tJKSkhxUQtRkRUVF2rdvn7Zv3659+/bxAREAHIimLDCUvLw8SSXDJnp5eamgoMC6ztPTUxkZGTbbOQPG5oVR0e8BAIyFYA5DadGihfbs2SNJatu2rbp06WKdYGjXrl364YcfrNs5C8bmhRHR7wEAjIdgDkOpU6eO9esff/zRGsQl2zbmpbczutJj80ZHR+uzzz7TqVOn1LBhQ915552MzQu7u7Tfg6WJlaXfw4wZMxQfH6+uXbvSthcA7IhgDkO5ePGi9Wuz2WyzrvT3pbczOsvYvDNnzrzs2OyTJk0iAMFuLP0eoqOjL9vvITo6WklJSWrbtq2DSgkANQ+dPwE7OHDgwJ9aD1Ql+j0AgDFxxxyGUrqJyqWdPy1tzS/dzujy8/O1ceNGSVKnTp0UGRlpvZaEhATt3r1bmzZt0uDBg2UymRxcWtQE9HuAkaSmpiorK6tC+/j5+Sk0NLSaSgQ4DsEchuLv72/9un379urcubM1xCYmJioxMbHMdkb36aefymw2KywsTC+99JJN04F+/fpp3LhxOnr0qD799FP179/fgSVFTVG630PpNuaSVFxcTL8H2E1GRoZGjRql4uLiCu3n7u6uFStWWIfQBVwFwRyGUvquyd69e61BXJLN3eSK3l1xJMv45EOHDi23Pe/QoUM1ffp0JSUlEcxhF5Z+D7GxsZoxY4YGDhyoJk2a6NixY1q/fr0SEhIUExNDvwdUu4CAAC1ZsqTc3+nHjx/XnDlz9Oyzz6px48Y26/z8/AjlcEkEcxiK5U54QECAdcxyi/z8fOtyZ7pj7u3tLUk2Y0WXdvr0aZvtAHvo3r27YmJiFBcXp+joaOvykJAQhkqEXV2tSUrjxo3VsmVLO5UGcCyCOQylQYMGklQmlFtYllu2cwa9e/fWv//9b61evVp33XWXPD3/+7YrLCzUBx98YN0OsKfu3bura9euSkpK0rlz51SvXj1FRERwpxwAHIRgDkMpfVekdGfPS793prsn7dq1k4+Pj7KysjRixAgNHjxYkZGRSkhI0OrVq5WVlSVfX1+1a9fO0UVFDeTh4cGQiABgEARzGMq2bdusX5cO5Zd+v23bNqdpj+3h4aFnnnlGM2fOVHp6uhYuXFhmm3/84x/cpQQAoIYjmMNQLB0lpZKOkaV76pf+3tk6Snbv3l2TJk3Se++9p7Nnz1qXBwUF6YknnqA9L+yGoekAwLgI5jAUy8grXl5e8vf31x9//GFdV7duXWVkZKiwsNApx/umPS8cjaHpAMDYCOYwFF9fX0lSQUGBmjVrppiYGDVt2lRHjx7Vhx9+aB0+0bKds6E9LxzpckPTXWlYOomh6QDAXgjmMBQ3Nzfr1ykpKTpy5IiCgoJ05MgRpaSklLsdgGt3pSYpDEsHAI5FMIehlA7cl+soeel2AAAArsD96psA9tO6dWtJJU1VLh2rPCgoyNqExbIdAACAq+COOQwlODhYkpSdnS0vLy89+OCDCg0NVWpqqv71r38pOzvbZjsAAABXQTCHoURERCg4OFju7u5KS0vTpk2brOs8PDwUGhoqs9msiIgIxxUSAACgGhDMYSgeHh6KiopSbGysOnXqpIYNGyo/P18mk0mnTp3S7t27FRMTwxCDAADA5RDMYTjdu3dXTEyM4uLirMMjSlJISIhiYmKcejKeoqIixjEHAADlIpjDsMxms833FZ0UxWh27NihuLg4paWlWZcFBwcrKirKqT9sAACAqmGoUVmWLFmioUOH2izbv3+/hgwZog4dOqh3795asWKFzfri4mK9/fbb6tGjhzp06KC//e1vOn78eJUfA/azY8cOxcbGqlmzZpo1a5bWrVunWbNmqVmzZoqNjdWOHTscXcQKs1xTWFiYzTWFhYU57TUBAICq9aeCeUZGhr766it98MEHOnfunH777bcydzmv1erVqzV37lybZefPn9eIESPUpEkTffTRRxozZoxmz56tjz76yLrNO++8ozVr1mjatGlau3atiouL9cQTTyg/P7/KjgH7KSoqUlxcnCIjIzV58mSFh4fLx8dH4eHhmjx5siIjIxUfH6+ioiJHF/WaueI1AQCAqlfpYL5o0SL16tVLY8aM0auvvqpTp05p5syZGjhwoDIzM6/5OKdPn9bo0aM1e/ZshYWF2axbt26dvLy89Oqrr6pFixYaMGCAhg8frnfffVeSlJ+fr/j4eI0bN0633XabwsPD9dZbbyk1NVWff/55lR0D9pOUlKS0tDQNHDhQ7u621dPd3V0DBw7U6dOnlZSU5KASVpwrXhMAAKh6lQrmq1at0vz58zVixAitW7fOepd8yJAhOn78uObNm3fNx/rll1/k5eWlzZs3q3379jbrEhMT1aVLF3l6/rcp/C233KIjR47o7NmzSk5O1sWLF9WtWzfren9/f0VERCghIaHKjgH7OXfunCSpadOm5a5v0qSJzXbOwBWvCQAAVL1Kdf5cuXKlnnzySf3jH/+wefzeq1cvPfPMM3r33Xf14osvXtOxevfurd69e5e7LjU1tcwMj5aJZU6dOqXU1FRJUsOGDctsY1lXFceoDLPZbJ0MB9fOMrPngQMHyp3d8+DBg9btnOXn64rXVJPl5uZa/3eV18sVr6mmccXX0BHXZDab5ebmZpdzAeWpVDA/efKkunTpUu665s2b6+zZs3+qUBa5ubkymUw2y2rVqiVJysvLU05OjiSVu01GRkaVHaMyCgoKtH///krvX5MFBgZq5cqVeuyxx2yafhQXF2vt2rUKDAyUJKf6+briNdVUJ0+elCQdPnxYeXl5Di5N1XDFa6ppXPE1dNQ1XZoHAHuqVDBv2LChfvzxx3KHePv555/L3H2uLG9v7zIdMC1vTl9fX3l7e0sqaSdu+dqyjY+PT5UdozK8vLzUsmXLSu9fk40cOVJvvfWWPv30Uz344INq3Lixjh8/ri1btujQoUMaP368brzxRkcXs0Jc8ZpqKssH+2bNmql58+YOLk3VcMVrqmlc8TV0xDWlpKTY5TzA5VQqmD/88MOaP3++vL29ddttt0mSsrOz9dlnn2nJkiUaMWJElRQuNDTUZsxnSdbvQ0JCVFhYaF1maadr+b5NmzZVdozKcHNzszZhwNWlpqYqKytLktS4cWMNHz5cH3/8sU2TqHr16mn48OFq3Lix9U6Kn5+fQkNDHVLmivjrX/+qWrVqKS4uzuaaXGHSpJrG8gHe29vbZd7jrnhNNY0rvoaOuCaascDRKhXM//a3v+n333/X7NmzNXv2bEnSsGHDZDabdf/992vUqFFVUrjIyEitXbtWRUVF1tkRv//+ezVr1kz169dXnTp15Ofnp507d1pDdWZmppKSkjRkyJAqOwaqV0ZGhkaNGnXVCYTOnTunZcuW2Sxzd3fXihUrFBAQUJ1FrBLdu3dX165dmfkTAACUq1LB3M3NTa+++qpGjhyp77//Xunp6apTp44iIyPL7dxWWQMGDNDSpUs1efJkPfHEE9q7d6+WL1+uqVOnSippBzZkyBDNnj1b9erV0/XXX69Zs2YpNDRUd9xxR5UdA9UrICBAS5Yssd4xL+348eOaM2eOnn32WTVu3LjMej8/P6cI5RYeHh5q27ato4sBAAAMqFLBXJKOHTumxMREPfbYY5KkX3/9VR999JGGDBmi6667rkoKV79+fS1dulQzZsxQ//79FRQUpIkTJ6p///7WbcaNG6fCwkJNmTJFubm5ioyMVFxcnLy8vKrsGKh+V2uO0rhxY9rsAwAAl1apYL5nzx6NHDlSISEhevjhhyWVNP/YvHmzPvroI61cubJSd85jY2PLLGvXrp0+/PDDy+7j4eGh6OhoRUdHX3abqjgGAAAAUJ0qNcHQm2++qZtvvlkbN260LuvYsaO++uortWvXTm+88UaVFRAAAACoCSoVzH/55RdFRUXZDC8olQxt9Pjjj+unn36qksIBAAAANUWlgrm3t7dOnz5d7rrz58/bTKACAAAA4OoqlaB79Oiht99+WwcOHLBZ/uuvv2r+/Pnq2bNnlRQOAAAAqCkq1flzwoQJeuyxx9S/f381atRI9erV0/nz53X8+HE1atRIEydOrOpyAgAAAC6tUsE8KChIW7Zs0YYNG/TDDz8oPT1dISEhGjJkiB566CHVrl27qssJAAAAuLRKj2Pu6+urIUOGMDsmAAAAUAWuOZgvWLBAAwcOVEhIiBYsWHDFbd3c3DRmzJg/XTjA1RQVFSkpKUnnzp1TvXr1FBERIQ8PD0cXCwAAGECFgnnPnj0J5kAl7dixQ3FxcUpLS7MuCw4OVlRUlLp37+7AkgEAACO45mCenJxs/TopKYkhEYEK2LFjh2JjYxUZGano6Gg1bdpUR48e1fr16xUbG6uYmBjCOQAANVyl0vX999+vf/3rX1VdFsAlFRUVKS4uTpGRkZo8ebLCw8Pl4+Oj8PBwTZ48WZGRkYqPj1dRUZGjiwoAAByoUsH81KlT8vHxqeqyAC4pKSlJaWlpGjhwYJknTe7u7ho4cKBOnz6tpKQkB5UQAAAYQaWC+X333afly5fbtJUFUL5z585Jkpo2bVru+iZNmthsBwAAaqZKDZd45MgRJSYmqlevXgoMDJSvr6/Nejc3N3355ZdVUkDAWaWmpiorK0s5OTmSpG+//VZhYWFltjty5IgkKScnRykpKfLz81NoaKgdSwoAAIygUsG8YcOGuu+++6q6LIDLyMjI0KhRo1RcXGxdNm/evCvus3DhQkklzVtWrFihgICAai0jAAAwlkoF85kzZ0qSvvnmGyUkJCg9PV3169dXt27dFBkZWaUFBJxRQECAlixZoqysLEnS3r17tXz5ckVERKht27Zau3atHnvsMe3bt09JSUkaPny42rVrJ0ny8/MjlAMAUANVKphb7gb+9NNP8vDwUGBgoNLT07Vo0SL17NlT8+fPl8lkquqyAk6ldHOUli1bKjQ0VHFxcVq7dq0kae3atQoJCWGoRAAAIKmSnT9fe+01HT58WPPnz9e+ffv0zTffaO/evZo3b5727Nmjt956q6rLCTi97t27691337VOvjVmzBgtWbKEUA4AACRVMpj/+9//1oQJE9S3b1+5ubmVHMjdXXfccYfGjx+vLVu2VGkhAVfh4eGhli1bSiq5i+7h4eHgEgEAAKOoVDA3m81q0KBBuesaNmyo7OzsP1UoAAAAoKapVDDv37+/Fi1apIsXL9osLyws1KpVq9S/f/8qKRwAAABQU1Sq86ePj4+OHDmiPn36qE+fPgoJCdH58+e1fft2paamKiAgQJMmTZJUMqb5a6+9VqWFBgAAAFxNpYL55s2b5efnJ0n67rvvbNaFhobqhx9+sH5vaYMOAAAA4PIqFcy//vrrqi4HAAAAUKNVqo05AAAAgKpFMAcAAAAMgGAOAAAAGADBHAAAADAAgjkAAABgAARzAAAAwAAqNVwiALiKtLQ0ZWZmXvP2x48ft/m/Ivz9/RUcHFzh/QAANQPBHECNlZaWptFPPaWC/PwK7ztnzpwK7+NlMmnxokWEcwBAuQjmAGqszMxMFeTny69zkDzqmKr1XEUX8pWVeEaZmZkEcwBAuQjmAGo8jzomedat5ehiAABqOMMH8507d2rYsGHlrmvUqJG++uorLVq0SHPnzi2z/sCBA9avV69erfj4eJ05c0Y33XSTpkyZooiICOv633//XdOmTVNCQoJ8fX318MMP6+mnn5aHh0eVXxMAAABwKcMH844dO+qbb76xWbZnzx49/fTT+vvf/y6pJIA/8MADio6OLvcYGzdu1BtvvKFp06YpIiJC7777rkaMGKGtW7eqXr16KigoUFRUlMLCwrR27VodO3ZMkydPlru7u8aNG1ft1wgAAAAYfrhEk8mkoKAg67/atWtr5syZ6t+/vwYMGCBJOnjwoCIiImy2CwoKsh5j8eLFGjJkiO6//361bNlSr732mnx8fLR+/XpJ0meffaaTJ0/qjTfeUOvWrdW3b189++yzev/995VfiU5hAAAAQEUZPphfavHixcrJydHzzz8vScrPz9eRI0fUvHnzcrf/448/dOTIEXXr1s26zNPTU507d1ZCQoIkKTExUTfeeKMCAgKs29xyyy3KysrS/v37q/FqAAAAgBKGb8pS2rlz57R8+XI999xzCgwMlCSlpKSoqKhIn332mWbMmKG8vDxFRkYqOjpawcHBSk1NlSQ1bNjQ5ljBwcFKTk6WJKWmpio0NLTMekk6deqU2rdvX+Gyms1mZWdnV3g/ozl9+rQuXrxYoX1q166tkJCQKjl/bm6u9X9X+HlKrnlN1eHs2bPlji9+7tw55eTkVPh4Pj4+qlevns2yEydOVLp8lWXE1506eW0uVyelytXL8uqkVDLefYMGDSp0LFd8DR1xTWazWW5ubnY5F1Aepwrma9asUZ06dfToo49alx08eFBSyS+4efPm6Y8//tCcOXM0bNgwbdq0yfqL0mSyHQqtVq1aysvLk1Typvf39y+zXpJ1m4oqKChw+rvtFy9e1OzZs2U2myu0n5ubmyZMmKDatWv/6TKcPHlSknT48OFKvxZG44rXVNXS09O1cMECFRQWOrooVc6Irzt18ursWSe9PD01ZuxY6w2oa+GKr6GjrunSvADYk1MF802bNunBBx+Ut7e3ddmDDz6onj172tx1aNWqlXr27Kmvv/5aTZo0kaQybcXz8vLk4+MjSfL29i53vST5+vpWqqxeXl5q2bJlpfY1knnz5pV7x/zEiRNasGCBxo4dq+uvv95mXVXeMbd8QGrWrNllmys5G1e8pqr222+/qaCwUA+18VcDX9tfU5l5RcovqtiHRUkyebjJv5btKEuHzuXpX0cr9kTozzLi606dvLor1UmpcvWyvDp5NrtQGw5kKiQkpEKvhSu+ho64ppSUFLucB7gcpwnmycnJOn78uO67774y6y59FBgcHKzAwEClpqaqa9eukkpm+GvRooV1m7S0NGt4DA0Ntd55L71eUqUDppubW6VDvZE0a9as3OWWD0ctWrSo1g8glvN4e3u7xM9Tcs1rqmqWn1EDX09d5+dls+7S7/+Ms9n2vyNvxNedOnl1V6qTUtXWS8v5KvJauOJr6IhrohkLHM1pOn8mJiaqfv36Cg8Pt1n+1ltv6c4777RpbvH777/r/PnzatmyperXr69mzZpp586d1vWFhYVKTExUZGSkJCkyMlJJSUnKysqybvP999+rdu3aZc4HAAAAVAenCeZJSUlq06ZNmeW33367Tpw4oVdeeUWHDx9WQkKCnn76ad18883q0aOHJGnkyJFatmyZNm7cqJSUFL3wwgvKzc3Vww8/LEnq27evgoKC9Mwzzyg5OVlffvml5syZo5EjR9LWDAAAAHbhNE1Zzpw5U25HmJtuuknvvfee5s2bp4ceekgmk0l9+vTR888/b30k9cgjj+jChQuaO3eu0tPTddNNN2nZsmXWJjC1atXS0qVLNXXqVD3yyCMKCAjQoEGDrBMYAQAAANXNaYL5e++9d9l13bp1sxmnvDxRUVGKioq67PqmTZsqPj6+0uUDAAAA/gynacoCAAAAuDKCOQAAAGAABHMAAADAAAjmAAAAgAEQzAEAAAADIJgDAAAABkAwBwAAAAyAYA4AAAAYAMEcAAAAMACCOQAAAGAAno4uAAAAsHX8+PFKbV/R/fz9/RUcHFyhfQBUH4I5AAAGkZVfJEmaM2dOpfav6H5eJpMWL1pEOAcMgmAOAIBB5BaaJUl+nYPkUcdUrecqupCvrMQzyszMJJgDBkEwBwDAYDzqmORZt5ajiwHAzuj8CQAAABgAwRwAAAAwAII5AAAAYAAEcwA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total_billtipsexsmokerdaytimesizetip_bucket
016.991.01FemaleNoSunDinner2(0.991, 4.0]
110.341.66MaleNoSunDinner3(0.991, 4.0]
221.013.50MaleNoSunDinner3(0.991, 4.0]
323.683.31MaleNoSunDinner2(0.991, 4.0]
424.593.61FemaleNoSunDinner4(0.991, 4.0]
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424.593.61FemaleNoSunDinner4(0.991, 4.0]
\n", + "
" + ], + "text/plain": [ + " total_bill tip sex smoker day time size tip_bucket\n", + "0 16.99 1.01 Female No Sun Dinner 2 (0.991, 4.0]\n", + "1 10.34 1.66 Male No Sun Dinner 3 (0.991, 4.0]\n", + "2 21.01 3.50 Male No Sun Dinner 3 (0.991, 4.0]\n", + "3 23.68 3.31 Male No Sun Dinner 2 (0.991, 4.0]\n", + "4 24.59 3.61 Female No Sun Dinner 4 (0.991, 4.0]" + ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -503,16 +737,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "text/plain": "[(0.991, 4.0], (4.0, 7.0], (7.0, 10.0]]\nCategories (3, interval[float64]): [(0.991, 4.0] < (4.0, 7.0] < (7.0, 10.0]]" + "text/plain": [ + "[(0.991, 4.0], (4.0, 7.0], (7.0, 10.0]]\n", + "Categories (3, interval[float64, right]): [(0.991, 4.0] < (4.0, 7.0] < (7.0, 10.0]]" + ] }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -525,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": { "scrolled": false }, @@ -534,6 +771,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'day', 'y': 'total_bill', 'hue': 'tip_bucket'}\n", + "self.tuple_group_names=[('Thur', Interval(0.991, 4.0, closed='right')), ('Thur', Interval(4.0, 7.0, closed='right')), ('Thur', Interval(7.0, 10.0, closed='right')), ('Fri', Interval(0.991, 4.0, closed='right')), ('Fri', Interval(4.0, 7.0, closed='right')), ('Fri', Interval(7.0, 10.0, closed='right')), ('Sat', Interval(0.991, 4.0, closed='right')), ('Sat', Interval(4.0, 7.0, closed='right')), ('Sat', Interval(7.0, 10.0, closed='right')), ('Sun', Interval(0.991, 4.0, closed='right')), ('Sun', Interval(4.0, 7.0, closed='right')), ('Sun', Interval(7.0, 10.0, closed='right'))]\n", + "self.plotter.group_names=Index(['Thur', 'Fri', 'Sat', 'Sun'], dtype='object', name='x')\n", + "self.plotter.hue_names=[Interval(0.991, 4.0, closed='right'), Interval(4.0, 7.0, closed='right'), Interval(7.0, 10.0, closed='right')]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -546,8 +787,10 @@ }, { "data": { - "text/plain": "
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\n" + "image/png": 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -588,13 +831,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'clarity', 'y': 'carat', 'hue': 'color'}\n", + "self.tuple_group_names=[('IF', 'E'), ('IF', 'I'), ('IF', 'J'), ('VVS1', 'E'), ('VVS1', 'I'), ('VVS1', 'J'), ('VVS2', 'E'), ('VVS2', 'I'), ('VVS2', 'J'), ('VS1', 'E'), ('VS1', 'I'), ('VS1', 'J'), ('VS2', 'E'), ('VS2', 'I'), ('VS2', 'J'), ('SI1', 'E'), ('SI1', 'I'), ('SI1', 'J'), ('SI2', 'E'), ('SI2', 'I'), ('SI2', 'J'), ('I1', 'E'), ('I1', 'I'), ('I1', 'J')]\n", + "self.plotter.group_names=Index(['IF', 'VVS1', 'VVS2', 'VS1', 'VS2', 'SI1', 'SI2', 'I1'], dtype='object', name='x')\n", + "self.plotter.hue_names=['E', 'I', 'J']\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -615,8 +862,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -661,13 +910,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'day', 'y': 'total_bill', 'hue': 'smoker'}\n", + "self.tuple_group_names=[('Thur', 'Yes'), ('Thur', 'No'), ('Fri', 'Yes'), ('Fri', 'No'), ('Sat', 'Yes'), ('Sat', 'No'), ('Sun', 'Yes'), ('Sun', 'No')]\n", + "self.plotter.group_names=Index(['Thur', 'Fri', 'Sat', 'Sun'], dtype='object', name='x')\n", + "self.plotter.hue_names=['Yes', 'No']\n", "Sat_Yes vs. Sat_No: t-test independent samples, P_val:4.304e-01 t=7.922e-01\n", "Thur_No vs. Fri_No: t-test independent samples, P_val:7.425e-01 t=-3.305e-01\n", "Thur_Yes vs. Sun_No: t-test independent samples, P_val:5.623e-01 t=-5.822e-01\n" @@ -675,8 +928,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -714,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": { "collapsed": false, "pycharm": { @@ -726,6 +981,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'species', 'y': 'sepal_length', 'hue': None}\n", + "self.tuple_group_names=[('setosa',), ('versicolor',), ('virginica',)]\n", + "self.plotter.group_names=Index(['setosa', 'versicolor', 'virginica'], dtype='object', name='x')\n", + "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -740,16 +999,23 @@ }, { "data": { - "text/plain": "(,\n [,\n ,\n ])" + "text/plain": [ + "(,\n", + " [,\n", + " ,\n", + " ])" + ] }, - "execution_count": 16, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -774,9 +1040,9 @@ "custom_test = StatTest(custom_func, custom_long_name, custom_short_name)\n", "\n", "# Then, same as usual\n", - "ax = sns.boxplot(data=df, x=x, y=y)\n", + "ax = sns.boxplot(data=df, x=x, y=y, hue=x)\n", "annot.reset_configuration()\n", - "annot.new_plot(ax, pairs, data=df, x=x, y=y)\n", + "annot.new_plot(ax, pairs, data=df, x=x, y=y, hue=x)\n", "annot.configure(test=custom_test, comparisons_correction=None,\n", " text_format='star').apply_test().annotate()" ] @@ -791,13 +1057,17 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'species', 'y': 'sepal_length', 'hue': None}\n", + "self.tuple_group_names=[('setosa',), ('versicolor',), ('virginica',)]\n", + "self.plotter.group_names=Index(['setosa', 'versicolor', 'virginica'], dtype='object', name='x')\n", + "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -812,16 +1082,23 @@ }, { "data": { - "text/plain": "(,\n [,\n ,\n ])" + "text/plain": [ + "(,\n", + " [,\n", + " ,\n", + " ])" + ] }, - "execution_count": 17, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -852,8 +1129,8 @@ "custom_test = StatTest(custom_func, custom_long_name, custom_short_name)\n", "\n", "# Then, same as usual\n", - "ax = sns.boxplot(data=df, x=x, y=y)\n", - "annot = Annotator(ax, pairs, data=df, x=x, y=y)\n", + "ax = sns.boxplot(data=df, x=x, y=y, hue=x)\n", + "annot = Annotator(ax, pairs, data=df, x=x, y=y, hue=x)\n", "annot.configure(test=custom_test, comparisons_correction=None,\n", " text_format='star').apply_test().annotate()" ] @@ -868,7 +1145,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": { "scrolled": true }, @@ -880,7 +1157,7 @@ "Performing Bartlett statistical test for equal variances on pair: ('setosa', 'versicolor') stat=6.89e+00 p-value=8.66e-03\n", "Performing Bartlett statistical test for equal variances on pair: ('setosa', 'virginica') stat=1.60e+01 p-value=6.38e-05\n", "Performing Bartlett statistical test for equal variances on pair: ('versicolor', 'virginica') stat=2.09e+00 p-value=1.48e-01\n", - "pvalues: [0.008659557933880048, 6.378941946712554e-05, 0.14778816016231236]\n" + "pvalues: [np.float64(0.008659557933880048), np.float64(6.378941946712554e-05), np.float64(0.14778816016231236)]\n" ] } ], @@ -901,7 +1178,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": { "scrolled": false }, @@ -910,6 +1187,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'species', 'y': 'sepal_length', 'hue': None}\n", + "self.tuple_group_names=[('setosa',), ('versicolor',), ('virginica',)]\n", + "self.plotter.group_names=Index(['setosa', 'versicolor', 'virginica'], dtype='object', name='x')\n", + "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -924,17 +1205,19 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "ax = sns.boxplot(data=df, x=x, y=y)\n", + "ax = sns.boxplot(data=df, x=x, y=y, hue=x)\n", "annot.new_plot(ax=ax, pairs=pairs,\n", - " data=df, x=x, y=y)\n", + " data=df, x=x, y=y, hue=x)\n", "(annot\n", " .configure(test=None, test_short_name=test_short_name)\n", " .set_pvalues(pvalues=pvalues)\n", @@ -953,7 +1236,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "metadata": { "collapsed": false, "pycharm": { @@ -965,6 +1248,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'species', 'y': 'sepal_length', 'hue': None}\n", + "self.tuple_group_names=[('setosa',), ('versicolor',), ('virginica',)]\n", + "self.plotter.group_names=Index(['setosa', 'versicolor', 'virginica'], dtype='object', name='x')\n", + "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -979,17 +1266,19 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "ax = sns.violinplot(data=df, x=x, y=y)\n", + "ax = sns.violinplot(data=df, x=x, y=y, hue=x)\n", "annot.new_plot(ax=ax, pairs=pairs, plot=\"violinplot\",\n", - " data=df, x=x, y=y)\n", + " data=df, x=x, y=y, hue=x)\n", "(annot\n", " .configure(test=None, test_short_name=test_short_name)\n", " .set_pvalues(pvalues=pvalues)\n", @@ -1006,7 +1295,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": { "pycharm": { "name": "#%%\n" @@ -1018,6 +1307,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'day', 'y': 'total_bill', 'hue': None}\n", + "self.tuple_group_names=[('Sun',), ('Thur',), ('Fri',), ('Sat',)]\n", + "self.plotter.group_names=Index(['Sun', 'Thur', 'Fri', 'Sat'], dtype='object', name='x')\n", + "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -1032,8 +1325,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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r7FIAOEjnzp21ePFiAs5V0CwFGExJSQnBBjC4PXv2cHX2GmiWAgxs48aNVUa0BVB/FRcXW4chwNURbgAD8/PzI9wAcDs0SwEAAEMh3AAAAEMh3AAAAEMh3AAAAEOhQzFgML6+vurcubP1MQDj4PfbPgziBxhQ5a81A3wBxsPv9/URbgAAgKHQ5wYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QYAABgK4QZAvWSxWJxdAgAXRbgB4DCnTp3SI488ok6dOun222/X5s2bFRkZqa1bt97Qfr/++mv99re/raUqry8uLk5JSUl1djwAN8bL2QUAMK4333xTu3fvVlpamsLCwhQZGalVq1apffv2N7TfDRs2aNeuXbVU5fVlZGQoICCgzo4H4MYQbgA4TGFhoUJDQ3Xvvfdal3Xp0sV5BdVQVFSUs0sAUA00SwFwiLi4OGVnZ+vEiROKjIxUenq6tm7datMslZ6ernvuuUcZGRnq0aOH7rjjDp0/f1779+/Xo48+qm7duikmJkYjR47U7t27rc/JyMiQJOt+ryQ7O1uRkZHas2eP4uPjFR0drfvvv18bNmyw2e748eN6+umndccdd+jWW2/V7bffrqefflrnzp2zeS2VzVLHjx9XZGSkli5dqoEDB6pz585au3Ztbf/4ANwArtwAcIiMjAzNmTNHOTk5ysjIUHh4uPLy8qpsd+LECW3evFmzZ89WYWGhPD09NWbMGPXq1Uvp6ekqLS3VvHnzlJiYqH/+859KSEjQqVOntGbNGq1atUrh4eHXrGPcuHEaNmyYJk2apDVr1uiPf/yjFixYoLvuukvFxcUaMWKEmjRpoueff16NGjXSrl27lJGRIV9fX82YMeOq+01PT1dycrICAgLUuXPnG/55Aag9hBsADhEVFaXg4GD5+PhYm6KuFG7Ky8s1depUde/eXZK0e/dunTt3TiNGjFDXrl0lSW3bttWqVav0ww8/KDw83Bpo7GniGj58uCZMmCBJuvPOOxUfH6833nhDd911l44eParw8HC98soratmypSSpV69e2rNnj7Zt23bN/Q4aNEhDhw6162cBoG4RbgA4XceOHa2Pb775ZgUHB2v8+PEaOHCg7rzzTvXp00dTpkyp0b7j4+Otj00mk+655x6lp6erpKREHTt21Ntvv62KigodPXpUeXl5OnTokL755huVl5fbXTMA10K4AeB0DRs2tHm8cuVKzZs3T+vXr9eqVavk6+urBx98UM8++6x8fHyqte/Q0FCb75s2bSqLxaKioiL5+vpq6dKlmj9/vgoLCxUSEqLbbrtNfn5+unDhwjX36+/vX606ANQdwg0Al9O2bVulpaXJbDZr7969+uCDD/TOO++oVatWGjNmTLX2VRlaKhUUFMjT01ONGzfWunXrlJqaqilTpmjIkCEKDg6WJP3hD3/Qvn37avU1Aag73C0FwKVs2LBBvXr10vfffy9PT0/FxMRo+vTpCgwM1IkTJyRJHh72v3V98skn1scWi0UbN25Ut27d5OPjo507dyowMFBjxoyxBpsffvhBO3fuVEVFRe2+MAB1his3AFxK165dVVFRoQkTJmjs2LFq2LCh1q9frwsXLmjAgAGSpMDAQEnSX//6V3Xu3NnaGfhK/vznP+vy5ctq06aNsrKydPjwYb355puSpOjoaL3zzjtKTU1Vv379lJ+fr8zMTBUUFCgoKMjxLxaAQ3DlBoBLCQ0N1eLFi9WoUSMlJydr3Lhx+vLLL5Wenq5evXpJkgYMGKBOnTopKSlJmZmZ19zf9OnTtXr1ak2cOFHff/+9lixZYr0zKz4+XhMmTND69ev1+9//Xq+//rq6d++uGTNmqLCwUIcPH3b46wVQ+0wWZp8DYEDZ2dmaNm2aPv30U7Vo0cLZ5QCoQzRLAQZksVhUUlLi7DKcqrS0VJJUUlKi4uJiJ1fjeL6+vjKZTM4uA3AJXLkBDMZisSgxMVF79+51dilOVVZWppKSEjVs2LBaHZDrq86dO2vx4sUEHEBcuQEMp6SkxO2DjSR5e3vL29vb2WXUmT179qikpER+fn7OLgVwOsINYGAbN27kw87giouLrXeRAfgR4QYwMD8/P8INALdj/IZoAADgVgg3AADAUAg3AADAUAg3AADAUAg3AADAULhbCjAYX19fde7c2foYxsb5BqpihGLAgCp/rRmt1j1wvgFbhBsAAGAo9LkBAACGQrgBAACGQrgBAACGQrgBAACGQrgBAACGQrgBAACGQrgBAACG4nYjFO/atUsWi0Xe3t7OLgUAANiprKxMJpNJMTEx193W7cKNxWIR4xYCAFC/VOez2+3CTeUVm06dOjm5EgAAYK99+/bZvS19bgAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKEQbgAAgKG43QjFAADUV2azWTk5OTp79qyCg4MVFRUlT09PZ5flcgg3AADUA1u2bFFmZqby8/Oty0JDQ5WYmKjevXs7sTLXQ7MUAAAubsuWLUpNTVVERITS0tK0evVqpaWlKSIiQqmpqdqyZYuzS3QphBsAAFyY2WxWZmamYmNjlZycrA4dOsjPz08dOnRQcnKyYmNjtWTJEpnNZmeX6jIINwAAuLCcnBzl5+crISFBHh62H9seHh5KSEjQ6dOnlZOT46QKXQ/hBgAAF3b27FlJUuvWra+4vlWrVjbbwUXCzenTpxUZGVnlX3Z2tiTpwIEDGjZsmLp06aK4uDgtX77cyRUDAFA3goODJUl5eXlXXH/s2DGb7eAid0vl5uaqQYMG+uSTT2QymazLGzVqpHPnzmnUqFGKi4vTCy+8oN27d+uFF15Qw4YNNXToUCdWDQCA40VFRSk0NFRZWVlKTk62aZqqqKhQVlaWwsLCFBUV5cQqXYtLhJuvvvpKERERCg0NrbLuzTfflLe3t2bMmCEvLy+1a9dOeXl5WrhwIeEGAGB4np6eSkxMVGpqqlJSUpSQkKBWrVrp2LFjysrK0vbt25WUlMR4N//DJcLNwYMH1a5duyuu27Fjh3r06CEvr59K7dWrlxYsWKCCggKFhITUVZkAADhF7969lZSUpMzMTE2ZMsW6PCwsTElJSYxz8zMuEW6++uorNWnSRI888oiOHDmi1q1b67HHHlPfvn116tQp3XLLLTbbV17hOXnyZI3CjcVi0aVLl2qldgAA6kKXLl30l7/8RQcOHFBhYaEaN26sjh07ysPDwy0+0ywWi03XlWtxergpLy/XN998o/bt2yspKUkBAQH629/+prFjx2rp0qUqKSmRj4+PzXMaNGggSbp8+XKNjllWVqYDBw7ccO0AANQ1Dw8Pa+fhgwcPOrmauvXzPHA1Tg83Xl5e2rp1qzw9PeXr6ytJuu222/T1118rMzNTvr6+Ki0ttXlOZajx9/ev0TG9vb3Vvn37GyscAADUmUOHDtm9rdPDjSQ1bNiwyrKbb75Z//nPfxQeHm4zj4Yk6/dhYWE1Op7JZKpxMAIAAHXP3iYpyQXGufn666/VtWtXbd261Wb5/v371b59e8XGxmrnzp02w0p/8cUXatOmjZo2bVrX5QIAABfn9HDTrl07tW3bVjNmzNCOHTt0+PBhzZw5U7t379Zjjz2moUOH6uLFi0pOTtahQ4eUnZ2tZcuWady4cc4uHQAAuCCTxWKxOLuIgoICvfbaa/r3v/+toqIiRUVF6amnnlL37t0lSXv37lVKSopycnLUrFkzjR49WsOGDavRsfbt2ydJ6tSpU63VDwAAHKs6n98uEW7qEuEGAID6pzqf3y7RoRgAAFxfaWmp1q9fr5MnT6p58+YaNGiQ3bdHuxPCjRswm83KycnR2bNnFRwcrKioKIbpBoB6ZunSpXr//fdVUVFhXbZkyRINHjxYo0aNcmJlrodwY3BbtmxRZmamze30oaGhSkxMZLhuAKgnli5dquzsbDVu3FjDhg1Tjx49tG3bNr311lvKzs6WJALO/6DPjYFt2bJFqampio2NVUJCglq3bq28vDybidYIOADg2kpLS5WQkKDAwEAtXbrUZq7F8vJyjRo1ShcuXNDq1asN3URVnc9vp98KDscwm83KzMxUbGyskpOT1aFDB/n5+alDhw5KTk5WbGyslixZYjN+EADA9axfv14VFRUaNmyYTbCRfhzl/5FHHpHZbNb69eudVKHrIdwYVE5OjvLz85WQkCAPD9vT7OHhoYSEBJ0+fVo5OTlOqhAAYI+TJ09Kknr06HHF9bGxsTbbgXBjWGfPnpUktW7d+orrW7VqZbMdAMA1NW/eXJK0bdu2K67fvn27zXYg3BhW5YyxeXl5V1x/7Ngxm+0AAK5p0KBB8vDw0FtvvaXy8nKbdeXl5Vq5cqU8PT01aNAgJ1Xoegg3BhUVFaXQ0FBlZWXZ3DYoSRUVFcrKylJYWJiioqKcVCEAwB4+Pj4aPHiwCgsLNWrUKG3YsEFnzpzRhg0bNGrUKBUWFurBBx80dGfi6uJWcIPy9PRUYmKiUlNTlZKSooSEBLVq1UrHjh2zuVuK8W4AwPVV3ub9/vvv64033rAu9/T01JAhQ7gN/Ge4FdzgrjTOTVhYmEaPHs1t4ABQz7jzCMXMLXUN7hZuJEYoBgDUf8wtBRuenp5uFeYAAO6NDsUAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQGMTPDTBCMQDAnRBuDO5Kc0uFhoYqMTGRuaUAAIZEs5SBbdmyRampqYqIiFBaWppWr16ttLQ0RUREKDU1VVu2bHF2iQAA1DrCjUGZzWZlZmYqNjZWycnJ6tChg/z8/NShQwclJycrNjZWS5YskdlsdnapAADUKsKNQeXk5Cg/P18JCQny8LA9zR4eHkpISNDp06eVk5PjpAoBAHAMwo1BnT17VpLUunXrK65v1aqVzXYAABgF4caggoODJUl5eXlXXH/s2DGb7QAAMArCjUFFRUUpNDRUWVlZqqiosFlXUVGhrKwshYWFKSoqykkVAgCqy2w2a9++fdq8ebP27dtHv8mr4FZwg/L09FRiYqJSU1OVkpKihIQEtWrVSseOHVNWVpa2b9+upKQkxrsBgHqCoT3sZ7JYLBZnF1GX9u3bJ0nq1KmTkyupG1f6ZQgLC9Po0aP5ZQCAeqJyaI/Y2FglJCSodevWysvLs/lj1ejv6dX5/CbcuAFGKAaA+stsNmvs2LGKiIhQcnKyzR2wFRUVSklJUV5enhYsWGDo9/bqfH7T58YNeHp6qlOnTrrrrrvUqVMnQ//nBwCjYWiP6iPcAADgwhjao/oINwAAuDCG9qg+wg0AAC6MoT2qj3ADAIALqxzaY/v27UpJSVFubq4uXbqk3NxcpaSkaPv27Ro9ejT9Kf8Hd0sBBsPdcYAxufvQHtX5/GYQP8BAGOQLMK7evXurZ8+e/PFiB8INYBD/O8jXlClTbAb5Sk1NdYtBvgCjqxzaA9dGnxvAAMxmszIzMxUbG6vk5GR16NBBfn5+6tChg5KTkxUbG6slS5YwDw0At0C4AQyAQb4A4CeEG8AAGOQLAH5CuAEMgEG+AOAnhBvAABjkCwB+QrgBDIBBvgDgJwziBxiIuw/yBcC4GMQPcFMM8gUAhBvAcBjkC4C7o88NAAAwFK7cAAbDxJkA3B3hBjAQJs4EAJqlAMOonDgzIiJCaWlpWr16tdLS0hQREaHU1FRt2bLF2SUCQJ0g3AAGwMSZAPATwg1gAEycCQA/IdwABsDEmQDwE8INYABMnAkAPyHcAAbAxJnuy2w2a9++fdq8ebP27dtHvypA3AoOGELlxJmpqalKSUlRQkKCWrVqpWPHjikrK0vbt29XUlIS490YDLf+A1fmUhNnHjlyREOGDNFzzz2nIUOGSJIOHDiglJQU7d+/X8HBwRo5cqRGjBhR42O46sSZp06d0sWLF51dxg0LCAhQeHi4s8twW0yc6T4qb/2PjY1VQkKCWrdurby8PJswyzmHkdTLiTPLysr01FNP6dKlS9Zl586d06hRoxQXF6cXXnhBu3fv1gsvvKCGDRtq6NChTqy2dp0/f17jxo2r0pxQH3l4eGj58uUKCgpydiluiYkz3cPPb/2vvEOu8tb/lJQULVmyRD179uTcwy25TLhJT09XQECAzbLVq1fL29tbM2bMkJeXl9q1a6e8vDwtXLjQUOEmKChICxYscOiVm2+//VazZs3S5MmT1bJlS4cdJyAggGDjZEycaXyVt/5PmTLlqrf+T5kyRTk5OfxfgFtyiXCzfft2rVq1Su+//77uvvtu6/IdO3aoR48e8vL6qcxevXppwYIFKigoUEhIiBOqdYy6aspp2bKl2rdvXyfHAuAY3PoPXJvTw01RUZGefvppPfvss2revLnNulOnTumWW26xWRYaGipJOnnyZI3DjcVisWn+cgclJSXWr+722gGj8ff3lyQdPHiwynukJH311VfW7fh9h1FYLBaZTCa7tnV6uJk+fbpiYmJ0//33V1lXUlIiHx8fm2UNGjSQJF2+fLnGxywrK9OBAwdq/Pz66MSJE5J+7LR9Iz87AK6hcePGWrFihX7zm9/YNE1VVFTo3XffVePGjSXJ7d7rYGw/zwRX49Rw8/7772vHjh1at27dFdf7+vqqtLTUZlnlB3PlXy414e3t7XZNM5WhsE2bNmrbtq2TqwFwo0aPHq3Zs2frb3/7mwYPHqyWLVvq22+/1bp16/T1119r0qRJuvXWW51dJlBrDh06ZPe2Tg03a9eu1ZkzZ2z62UjS888/r48++kjh4eE2t7RKsn4fFhZW4+OaTKYbCkf1ka+vr/Wru712wIj69eunBg0aKDMzU88995x1eVhYGLeBw5DsbZKSnBxuXn31VWtfkEoDBgzQE088oQceeEAffPCB3n33XZnNZuvtjF988YXatGmjpk2bOqNkAHAZ3Prvmhi3zPmcGm6udvWladOmCgsL09ChQ7V48WIlJydrzJgx2rt3r5YtW6YXXnihjisFANfErf+uhXHLXIPTOxRfS9OmTbV48WKlpKQoPj5ezZo109NPP634+HhnlwYAQBWMW+YaXC7cHDx40Ob76OhorVq1yknVAABQPYxb5nzMCg4AAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAzFy9kFAKhdZrNZOTk5Onv2rIKDgxUVFSVPT09nlwUAdYZwAxjIli1blJmZqfz8fOuy0NBQJSYmqnfv3k6sDADqDs1SgEFs2bJFqampioiIUFpamlavXq20tDRFREQoNTVVW7ZscXaJAFAnCDeAAZjNZmVmZio2NlbJycnq0KGD/Pz81KFDByUnJys2NlZLliyR2Wx2dqkA4HCEG8AAcnJylJ+fr4SEBFksFu3bt0+bN2/Wvn37ZLFYlJCQoNOnTysnJ8fZpQKAw9HnBjCAs2fPSpJOnTqltLS0Kn1uhg0bZrMdABgZ4QYwgODgYEnSrFmzFBsbqylTpqh169bKy8tTVlaWZs+ebbMdABgZzVKAAURGRsrDw0NBQUGaNm2aTZ+badOmKSgoSJ6enoqMjHR2qQDgcIQbwAAOHjyoiooKFRYWaubMmcrNzdWlS5eUm5urmTNnqrCwUGazWQcPHnR2qQDgcDRLAQZQ2ZfmySef1IoVKzRlyhTrurCwME2ePFmzZs2izw0At0C4AQygsi9NeHi4Fi5cWGWE4q+//tpmOwAwMpqlAAOIiopSaGiosrKyZDKZ1KlTJ911113q1KmTTCaTsrKyFBYWpqioKGeXCgAOR7gBDMDT01OJiYnavn27UlJSbPrcpKSkaPv27Ro9ejRzTAFwCzRLAQbRu3dvJSUlKTMzs0qfm6SkJOaWAuA2CDeAgfTu3Vs9e/ZkVnAAbo1mKQAAYChcuQEMZMuWLcrMzKwy/UJiYiLNUgDcBlduAIPYsmWLUlNTFRERobS0NK1evVppaWmKiIhQamqqtmzZ4uwSAaBOEG4AAzCbzcrMzFRsbKySk5Ntpl9ITk5WbGyslixZIrPZ7OxSAcDhCDeAAeTk5Cg/P18JCQny8LD9tfbw8FBCQoJOnz6tnJwcJ1UIAHWHcAMYQOW0Cq1bt77i+latWtlsBwBGRrgBDKByWoW8vLwrrj927JjNdgBgZIQbwAD+d/qFiooKm3UVFRVMvwDArRBuAANg+gUA+Anj3AAGwfQLAPAjwg1gIEy/AACEG8BwPD091alTJ2eXAQBOQ58bAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKHaPczNixAi7d2oymfTmm2/ate2ZM2eUmpqqf//737p8+bJiY2M1depUtWvXTpJ04MABpaSkaP/+/QoODtbIkSOrVQsAAHAvdl+5sVgsdv/7+cR91zJhwgTl5eVp4cKFWrNmjXx9fTVy5EgVFxfr3LlzGjVqlFq1aqW1a9dqwoQJevXVV7V27doavVgAAGB8dl+5WbFiRa0f/Pz58/rFL36hcePG6ZZbbpEkPf7443rwwQf19ddf6/PPP5e3t7dmzJghLy8vtWvXzhqEhg4dWuv1AACA+s/ucHPixIlq7fimm2667jZBQUF67bXXrN+fPXtWy5YtU3h4uNq3b6/09HT16NFDXl4/ldmrVy8tWLBABQUFCgkJqVZNAADA+OwON3FxcTKZTHbv+MCBA9Uq5LnnntPq1avl4+OjefPmyd/fX6dOnbJe0akUGhoqSTp58mSNw43FYtGlS5dq9Nz6qqSkxPrV3V47ABiJu76fWywWu3OI3eHm5Zdfrla4qa5HH31UDz/8sFauXKkJEybo7bffVklJiXx8fGy2a9CggSTp8uXLNT5WWVlZtcNXfVd55e3IkSM39LND7Th79qz1Dao+8/X1VXBwsLPLANyKO7+f/zwTXI3d4WbIkCE1LsYe7du3lySlpKRoz549euutt+Tr66vS0lKb7SpPpL+/f42P5e3tbT2eu6gMhW3atFHbtm2dXI17Kyoq0gsvvCCLxeLsUm6Yh4eHFixYoMDAQGeXArgNd30/P3TokN3b2h1uMjIylJCQoLCwMGVkZFxzW5PJpAkTJlx3n2fPntXnn3+uX/3qV9Z+NR4eHmrfvr3y8/MVHh6u/Px8m+dUfh8WFmZv6Ves70bCUX3k6+tr/epur93V+Pv7a+HChbp48aLDjvHtt99q1qxZmjx5slq2bOmw4wQEBCg8PNxh+wdQlbu+n1en9aha4aZv3761Gm4KCgo0efJkLV68WHfeeaekH5uMcnJyFBcXp5CQEL377rsym83y9PSUJH3xxRdq06aNmjZtam/pgMupq0DQsmVLt7tKCQB2h5vc3NwrPr4Rt9xyi/r27auXXnpJL730koKCgrRgwQIVFRVp5MiRatCggRYvXqzk5GSNGTNGe/fu1bJly/TCCy/UyvEBAIDx2B1urubw4cMqKipSSEhIjS5/z5o1S6+99pomTZqkCxcuqHv37lq5cqX1VvLFixcrJSVF8fHxatasmZ5++mnFx8ffaNkAAMCgahxuVqxYoQULFujMmTPWZc2bN9fkyZP161//2u79NGrUSNOnT9f06dOvuD46OlqrVq2qaZkA4HSnTp1yaB+rukIfK9QXNQo3b731llJSUtS/f3/dc889atq0qQoKCvTXv/5VU6ZMkaenpwYNGlTbtQJAvXP+/HmNGzeuWtPSuCoPDw8tX75cQUFBzi4FuKYahZvly5dr2LBhevbZZ22WDx48WMnJycrIyCDcAIBk7UtolLvjCDaoD2oUbk6dOqW4uLgrrvv1r3+tdevW3VBRAGAk3B0H1C27ZwX/X506ddLnn39+xXU5OTmKjIy8oaIAAABqyu4rN9u3b7c+vu+++zRz5kwVFxdr0KBBatasmQoLC7V582atWLFCL730kkOKBQAAuB67w83w4cNtRge0WCx66623tHLlSptlkvSHP/zB7eZuAgAArsHucLN8+XJH1gEAAFAr7A43PXr0qPbOKyoqNHLkSM2YMUMRERHVfj4AAEB11ahDsb0sFou2bdumH374wZGHAQAAsHJouAEAAKhrhBsAAGAohBsAAGAohBsAAGAohBsAAGAohBsAAGAohBsAAGAoDg03JpNJN910k3x8fBx5GAAAAKsaTZxpj9jYWHl4eGjTpk3VLgoAAKCmajxx5tVYLBaZTCYmzgQAAE7BxJkAAMBQHDJxpsViqVExAAAAN8rucPNzH330kbZt26bS0lJrmLFYLLp06ZJ2796tf/3rX7VWJAAAgL1qFG4yMjKUkZGhRo0aqby8XN7e3vLy8tLZs2fl4eGhhISE2q4TAADALjUKN++9954GDx6smTNn6vXXX9eJEyf0yiuvaP/+/Ro7dqxuvvnm2q7TqfLz81VUVOTsMm7It99+a/O1PgsMDFRoaKizywAAuKgahZvTp0/r/vvvl8lkUseOHfW3v/1NknTbbbdp/PjxysrK0rBhw2q1UGfJz8/X+MceU1lpqbNLqRWzZs1ydgk3zNvHR/PnzSPgAACuqEbhxt/f33pbeOvWrXX8+HGVlJTI19dXHTt21PHjx2u1SGcqKipSWWmpfG/qJQ+fQGeX4/YqSotUcuILFRUVEW4AAFdUo3DTqVMnvf/+++rdu7fatGkjT09Pff755+rXr58OHz5syBGJPXwC5ekX7OwyAADAddQo3IwfP16jRo1SUVGR5s+frwceeEBTp05Vz5499Z///Ef9+/ev7ToBAADsUqNwExsbqzVr1ujgwYOSpD/96U/y8PDQf//7Xw0cOFDTpk2r1SIBAADsVaNwc+LECbVr104dOnSQJDVo0EAvvviiJOny5cv68ssv1bVr19qrEgCAWsDdr67FUXe/1ijc/PKXv9SqVasUHR1dZd3evXs1ZswY7dmz54aLAwCgtuTn5+ux8eNVWlbm7FJqhRHufvXx9ta8+fNrPeDYHW5eeeUVFRYWSvpxJOK5c+eqSZMmVbY7cOCAGjVqVGsFAgBQG4qKilRaVqZf+geoiWeNB+hHLTlnLtenly465O5Xu89u27ZtNW/ePEmSyWTS/v37q9wV5enpqUaNGtHnBgDgspp4eqmZF+HGyOw+uwkJCdZpFeLi4jR37lxrnxsAAABXUaPoumnTJuvjw4cP68KFC2rSpIlat25da4UBAADURI2vy/31r3/VK6+8ooKCAuuykJAQPfnkkxo8eHBt1AYAAFBtNb5yM2XKFPXq1UuTJ09WSEiI8vPz9eGHH2ratGlq3Lix7r777louFQAA4PpqFG7mzZungQMHavbs2TbLhw4dqkmTJmnBggWEGwAA4BQeNXnSV199pfj4+Cuui4+PV25u7g0VBQAAUFM1CjdNmjTR+fPnr7iusLDQkBNnAgCA+qFG4eb2229XRkaGTp06ZbP85MmTeuONN9SnT59aKQ4AAKC6atTnZvLkyRo6dKgGDBigmJgYhYSEqKCgQLt27VJgYKCefPLJ2q4TAADALjW6cnP06FG9++67Gj58uIqLi7V//34VFxdr+PDheu+997R79+5aLhMAAMA+NbpyM2LECK1atUpTpkypsu6LL77QtGnTdN99991wcQAAANVld7iZOnWqTp48KenHiTOnT5+ugICAKtsdPXpUISEhtVchAABANdjdLPWrX/1KFotFFovFuqzy+8p/Hh4e6tKli2bOnOmQYgEAAK7H7is3cXFxiouLkyQNHz5c06dPV7t27RxWGAAAQE3UqM/NihUrarsOAACAWlGju6UAAABcFeEGAAAYSo2apQDAKPLz81VUVOTsMm7It99+a/O1PgsMDFRoaKizy0A9R7gB4Lby8/M1/rHHVFZa6uxSasWsWbOcXcIN8/bx0fx58wg4uCGEGwBuq6ioSGWlpQro3kyejZjw19nMF0p1ccf3KioqItzghhBuALg9z0Y+8mrSwNllAKgldCgGAACGQrgBAACGQrgBAACGQrgBAACGQrgBAACG4vRwU1hYqD/96U/q27evunbtqt/+9rfasWOHdf3nn3+uIUOGqHPnzho4cKD+9re/ObFaAADg6pwebiZPnqxdu3Zp1qxZWrt2rTp27KjExER98803Onz4sMaNG6c777xT2dnZSkhI0NNPP63PP//c2WUDAAAX5dRxbvLy8vTZZ5/p7bffVrdu3SRJzz33nP79739r3bp1OnPmjCIjIzVp0iRJUrt27ZSTk6PFixfr9ttvd2bpMDCG43ctDMcPoLqcGm6aNGmihQsXqlOnTtZlJpNJJpNJRUVF2rFjh/r372/znF69eiklJUUWi0Umk6lGx7VYLLp06ZJd25aUlNToGHCskpISu89hdRQUFGjSpD+qtLSs1vftDEYYjt/Hx1uzZ89RSEhIre+b32/X5Kjfb863a7L3fFfnc9+p4SYwMFB33XWXzbKPP/5YeXl5euaZZ/Tee+8pPDzcZn1oaKiKi4t17tw5BQcH1+i4ZWVlOnDggF3bnjhxokbHgGMdOXJEly9frvX9njhxQqWlZRoSGagQfwbwdraCS+XKPlikPXv26Kabbqr1/fP77Zoc+fsN11Od8+3jY980KS717v3f//5X06ZN04ABA3T33XerpKSkygup/L70Bia68/b2Vvv27e3atkEDhmR3RW3atFHbtm1rfb+V5zvE30s3BXjX+v5RM44+33AtnG/3Yu/5PnTokN37dJlw88knn+ipp55S165d9eqrr0r68T/iz0NM5fd+fn41PpbJZJK/v79d2/r6+kqSKi7X7z4YRlF5Hnx9fe0+h9VReb7hWjjf7oXz7V7sPd/V6YriEuHmrbfeUkpKigYOHKhXXnnFenWmefPmys/Pt9k2Pz9f/v7+atSoUZ3WWHLyizo9HgAAqBmnh5u3335bL774ooYPH67k5GSbZNa9e3dt27bNZvsvvvhCXbt2lYdH3d7F7tu8lzwaBNbpMVFVxeUigiYA4JqcGm6OHDmil19+Wffcc4/GjRungoIC6zpfX18NHz5c8fHxevXVVxUfH6/Nmzdrw4YNWrx4cZ3X6tEgUJ5+NevADAAA6o5Tw83HH3+ssrIy/f3vf9ff//53m3Xx8fFKTU3V3LlzlZaWpjfffFMtWrRQWloaY9wAAICrcmq4GT9+vMaPH3/Nbfr27au+ffvWUUUAAKC+c/r0CwAAALWJcAMAAAyFcAMAAAyFcAMAAAzF6ePcAABQl86Zy51dAuTY80C4AQC4lU8vXXR2CXAwwg0AwK380j9ATTz5+HO2c+ZyhwVNzi4AwK008fRSMy8+/oyMDsUAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQCDcAAMBQGH8auIKCS8wa7Ao4DwBqgnADXEH2wSJnlwAAqCHCDXAFQyIDFeLPr4ezFVwqJ2gCqDbevYErCPH30k0B3s4uAwBQA3QoBgAAhkK4AQAAhkKzFAC3Z75Q6uwSIM4Dag/hBoDbu7jje2eXAKAWEW4AuL2A7s3k2cjH2WW4PfOFUoImagXhxk4VpdyO6go4D3AEz0Y+8mrSwNllAKglhJvrCAwMlLePj0pOfOHsUvB/vH18FBgY6OwyAAAuinBzHaGhoZo/b56Kiur3FYNvv/1Ws2bN0uTJk9WyZUtnl3NDAgMDFRoa6uwyAAAuinBjh9DQUMN8mLZs2VLt27d3dhkAADgM49wAAABDIdwAAABDIdwAAABDoc8NAMCtnDOXO7sEyLHngXADAHALgYGB8vH21qeXLjq7FPwfH29vhwztQbgBALiF0NBQzZs/n6E9XIijhvYg3AAA3AZDe7gHOhQDAABDIdwAAABDIdwAAABDIdwAAABDIdwAAABDIdwAAABD4VZw4AoKLjGCqSvgPACoCcIN8D8CAwPVwMdb2Qfr9yBfRtLAxzEjmAIwLsIN8D9CQ0M1dx4jmLoSR41gCsC4CDfAzzCCqfsxXyh1dgkQ5wG1h3ADwG0FBgbK28dHF3d87+xS8H+8fXxohsQNI9wAcFuhoaGaP28ezZAuhGZI1AbCDQC3RjMkYDyMcwMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAyFcAMAAAzF5cLNggULNHz4cJtlBw4c0LBhw9SlSxfFxcVp+fLlTqoOAAC4OpcKNytXrtScOXNslp07d06jRo1Sq1attHbtWk2YMEGvvvqq1q5d65wiAQCAS3OJEYpPnz6t559/Xlu3blVERITNutWrV8vb21szZsyQl5eX2rVrp7y8PC1cuFBDhw51TsEAAMBlucSVmy+//FLe3t768MMP1blzZ5t1O3bsUI8ePeTl9VMO69Wrl44ePaqCgoK6LhUAALg4l7hyExcXp7i4uCuuO3XqlG655RabZZXzwJw8eVIhISHVPp7FYtGlS5eqX2g9VlJSYv3qbq/dHXG+3Qvn27246/m2WCwymUx2besS4eZaSkpK5OPjY7OsQYMGkqTLly/XaJ9lZWU6cODADddWn5w4cUKSdOTIkRr/3FB/cL7dC+fbvbjz+f55Hrgalw83vr6+Ki0ttVlWeTL9/f1rtE9vb2+3mzm3MhC2adNGbdu2dXI1cDTOt3vhfLsXdz3fhw4dsntblw834eHhys/Pt1lW+X1YWFiN9mkymWocjOorX19f61d3e+3uiPPtXjjf7sVdz7e9TVKSi3QovpbY2Fjt3LlTZrPZuuyLL75QmzZt1LRpUydWBgAAXJHLh5uhQ4fq4sWLSk5O1qFDh5Sdna1ly5Zp3Lhxzi4NAAC4IJcPN02bNtXixYt15MgRxcfHKyMjQ08//bTi4+OdXRoAAHBBLtfnJjU1tcqy6OhorVq1ygnVAACA+sblr9wAAABUB+EGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYisuNUAy4g1OnTunixYsO2/+3335r89VRAgICFB4e7tBjAEB1EW6AOnb+/HmNGzdOFRUVDj/WrFmzHLp/Dw8PLV++XEFBQQ49DgBUB+HGRfCXvPsICgrSggULHHq+60pAQADBBoDLIdy4AP6Sdz8EQABwHMKNC+AveQAAag/hxkXwlzwAALWDW8EBAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChEG4AAIChMM4NADgY06sAdYtwAwAOxPQqQN0j3ACAAzG9ClD3CDcA4GA05QB1iw7FAADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAUAg3AADAULycXQAAAEZy6tQpXbx40WH7//bbb22+OkpAQIDCw8MdegxHIdwAAFBLzp8/r3HjxqmiosLhx5o1a5ZD9+/h4aHly5crKCjIocdxBMINAAC1JCgoSAsWLHDolZu6EhAQUC+DjUS4AQCgVtXXphwjoUMxAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFLebFbysrEwWi0X79u1zdikAAMBOpaWlMplMdm3rduHG3h8MAABwHSaTye7PcJPFYrE4uB4AAIA6Q58bAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKG43K7jRfPjhh3rrrbf01VdfyWQyqW3btkpISNBvfvMbZ5eGWpCUlKT33nvvmtv06NFDv/jFL5SamlpHVcEZhg8frm3btl1x3ejRozV16tQqy5OSkvTdd99pxYoVji4Ptag23tf/8Y9/qGXLlmrfvr0DK3VdhJt6bM2aNUpJSVFycrK6desmi8Wizz77TC+99JIKCgo0ceJEZ5eIG5ScnKwnn3zS+v0dd9yhZ555Rvfee6912eTJk51RGpxg0KBBSk5OrrLcz8/vitsnJyfLbDY7uizUotp4X//uu+80fvx4LV++nHCD+uftt9/W0KFD9dBDD1mXtW3bVqdPn9by5csJNwbQqFEjNWrUqMqyZs2aOakiOJOvr2+1zv3P/+/A9dXG+7rFYnFkifUCfW7qMQ8PD+3atUvnz5+3WT527FitWrVKkhQXF6f09HSb9f+7LDs7W/fcc4/162233aYhQ4Zo586ddfMiUCt++OEHTZs2Td27d1e3bt2UlJSkS5cuSZK2bt2qyMhIHT9+3Lr9z5cNHz5czz33nBISEtS9e3d9+OGHTnkdqLkrncOkpCQNHz7c2aWhGux5Xz9x4oQmTZqk22+/Xbfeeqv69u2rtLQ0VVRU6Pjx4/rlL38pSRoxYkSV9393Qbipx8aMGaOcnBz17dtXY8eO1cKFC7V37141atRIbdq0sXs/J0+e1Lvvvqu0tDS999578vPzU1JSEum/Htm4caNCQ0OVnZ2tP//5z/roo4+0aNGiau0jKytLI0aM0Ntvv60777zTQZXCkTiH9Z897+uPPfaYLly4oKVLl2rDhg0aPXq0Fi9erE2bNql58+bKysqSJKWnp2v06NHOfDlOQ7NUPTZw4ECFh4dr+fLl+uyzz7R582ZJUkREhF5++WV169bNrv2UlZXphRdeUMeOHSVJo0aN0oQJE/T9998rNDTUYfWj9kRHR2vSpEmSpFatWqlPnz7av39/tfbRsWNH3X///Y4oD7Vk3bp1+vjjj22WdevWTYsXL5bEOTSC672v33rrrXrwwQc1aNAgNW/eXJI0cuRILVq0SAcPHlT//v0VHBwsSQoKClLDhg2d9lqciXBTz3Xp0kVdunRRRUWFcnNztXnzZr311lv6/e9/r7///e9276ddu3bWx5Xt9GVlZbVeLxwjIiLC5vugoCB999131dpH69ata7EiOEJcXJyeeuopm2W+vr7Wx5xDY7je+/qwYcO0YcMG7d27V3l5eTp48KAKCgpUUVHh7NJdBuGmnjp16pQWLFigcePGKTw8XB4eHoqKilJUVJT69++vX//619q+ffsVn1teXl5lmY+PT5VlNEvVH56entXa/kp30PzvhyRcU8OGDa8ZYDiH9Zs97+v/+te/tGLFCpWUlGjgwIGKj49XdHS0HnnkEWeX71IIN/WUj4+PsrKy1Lx5c40dO9ZmXWBgoCQpJCRE3t7eunjxonXdxYsXdebMmTqtFc7l7e0tSTb/D44ePeqkagBcjT3v6999952+/PJLffbZZwoJCZEkFRYW6syZM9Y/SE0mU90W7oIIN/VUcHCwxowZo7/85S/64YcfNHDgQAUEBOjQoUOaO3euevbsqe7du6tLly766KOP9Ktf/UqBgYF6/fXXq/1XPuq3W265Rf7+/lq4cKH++Mc/Ki8vT0uXLnV2WQB+xp739b59+yo9PV0ffvihfvWrX+nkyZOaNWuWysrKVFpaKkny9/eXJH311VeKiopyyyEBCDf12B//+EdFRERo9erVWrlypUpKSnTTTTdp0KBBGjdunKQfB3grLCzUqFGj1KhRI40ePVpFRUVOrhx1KSAgQGlpaXr11Vd17733qkOHDpo6daomTJjg7NIA/Mz13tf9/f01bdo0LVu2THPmzFFYWJjuvfdeNW/eXPv27ZMkNWnSREOHDtWf//xn5eXl6dlnn3Xyq6p7JgsdKwAAgIEwzg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg0AADAUwg2Aeic7O1uRkZE6fvy4s0sB4IIINwAAwFAINwAAwFAINwBcWkVFhebOnau7775bnTt31uOPP67z58/bbPPJJ5/od7/7nWJiYnTbbbdp4MCBWrlypSSpvLxcd9xxh5588skq+x4wYIBbTioIGB3hBoBLS0tL0xtvvKGHHnpIGRkZaty4sV577TXr+n/+85+aMGGCbr31Vs2dO1fp6elq2bKlZsyYoT179sjLy0uDBw/WJ598oosXL1qft3PnTuXl5WnIkCHOeFkAHMjL2QUAwNUUFRVpxYoVGjVqlCZOnChJuvPOO5Wfn69///vfkqRDhw4pPj5eycnJ1ufFxMSoZ8+e2rp1qzp37qyhQ4dq0aJF+vjjjzV06FBJ0vvvv6+IiAh17dq17l8YAIci3ABwWbt371ZZWZn69etns3zQoEHWcDNmzBhJ0g8//KAjR47o2LFj2rdvnySptLRUktSmTRt169ZNH3zwgYYOHaqSkhKtX79ev//97+vw1QCoK4QbAC6rsm9NkyZNbJY3a9bM+vjs2bN6/vnn9cknn8hkMql169bq3r27JMlisVi3e+ihh/TMM8/o5MmT2rlzp3744QcNHjzY8S8CQJ2jzw0Al1UZas6cOWOzvLCw0Pr4qaee0r59+7Rs2TLt3r1b69ev1zPPPFNlXwMHDpS/v782bNig9evXq0+fPgoLC3No/QCcg3ADwGXFxMTI19dXGzZssFn+j3/8w/p4586dGjBggHr27CkfHx9J0r/+9S9JP95pVcnf31/33nuv/vrXv+qzzz6jIzFgYDRLAXBZDRs21OOPP645c+bIz89PvXr10ubNm23CTXR0tNatW6dbb71V4eHh+u9//6uFCxfKZDKpuLjYZn8PPfSQHn74YQUFBal///51/XIA1BGT5X8bpQHABa1YsUJvvvmmTp8+rZiYGA0aNEjTp0/Xp59+KpPJpBdffFE7duyQJEVERGjEiBH68MMPVVhYqDVr1tjsq2fPnrr33nv1/PPPO+OlAKgDhBsAbmPPnj36f//v/+mDDz5Qhw4dnF0OAAehWQqA4W3dulVbt27V+++/rzvuuINgAxgcHYoBGN65c+e0dOlShYSE6KWXXnJ2OQAcjGYpAABgKFy5AQAAhkK4AQAAhkK4AQAAhkK4AQAAhkK4AQAAhkK4AQAAhkK4AQAAhkK4AQAAhvL/ATB+hiHmhJ67AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1044,9 +1339,9 @@ "x = \"day\"\n", "y = \"total_bill\"\n", "order = ['Sun', 'Thur', 'Fri', 'Sat']\n", - "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n", + "ax = sns.boxplot(data=df, x=x, y=y, hue=x, hue_order=order, order=order)\n", "annot.new_plot(ax, pairs=[(\"Thur\", \"Fri\"),(\"Thur\", \"Sat\"), (\"Fri\", \"Sun\")],\n", - " data=df, x=x, y=y, order=order)\n", + " data=df, x=x, y=y, hue=x, hue_order=order, order=order)\n", "annot.configure(loc='outside')\n", "annot.set_custom_annotations([\"first pair\", \"second pair\", \"third pair\"])\n", "annot.annotate()\n", @@ -1065,7 +1360,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": { "collapsed": false, "pycharm": { @@ -1077,6 +1372,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'subtype', 'y': 'mutation_rate_samp', 'hue': 'Synon_Nonsynon'}\n", + "self.tuple_group_names=[('H3N2', 'Nonsynonymous'), ('H3N2', 'Synonymous'), ('H1N1', 'Nonsynonymous'), ('H1N1', 'Synonymous'), ('Influenza B', 'Nonsynonymous'), ('Influenza B', 'Synonymous')]\n", + "self.plotter.group_names=Index(['H3N2', 'H1N1', 'Influenza B'], dtype='object', name='x')\n", + "self.plotter.hue_names=['Nonsynonymous', 'Synonymous']\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -1091,8 +1390,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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evaDVarFv3z6j9ujoaACV0/ibI5uYil8dhUIBwPQpqVAoRGlpqTVCIoQQQgghhLRSXC4XH3zwAV555RVMmTIFjz/+OEJCQqBQKHD69Gls3LgR8+fPh6OjI4DKdeR9+/bFqVOn8MYbb9T5epMnT8amTZvwyiuv4NVXX4Wfnx+OHDmCv//+G/PmzYODg0Njv0Sz2rZti1deeQXffPONUfu8efNw4sQJPPnkk3j++ecNVfHv3r2LtWvX1vr83bp1g16vxyuvvILnn38ednZ22Lt3L8rLyzFy5EjDOLFYjLFjx2Lz5s2YOXNmnWfTDhw4EFFRUViyZAlyc3PRvn17xMTE4Oeff8akSZOabU01m0/sq9YZVBU7qKJSqWirMUIIIYQQQkiTGzx4MLZs2YJ169Zh9erVKCoqgkAgQHh4OL7++mujRLRq/NmzZzFhwoQ6X0ssFuOPP/7AV199hW+//RYymQzBwcH45JNPMHXq1MZ6SbXy3HPP4eDBg0hISDC0tW3bFps2bcKKFSvw9ttvg2EYdO7cGevXrzcpil4dDw8PrF27Ft9++y3effddKBQKtG3bFitXrkTv3r2Nxg4ePBibN2/G5MmT6/waGIbBTz/9hO+++w6//fYbioqK4Ofnh9dee81QSb85YliWZa0dxP0WL16MzMxMw3Z3cXFxmDZtGg4ePIiAgADDuJkzZyIsLAwffPBBk8R17do1AEBERESTXI8QQgghhBDSMEqlEmlpaQgKCmr0qvh18eyzz0IoFOKHH36wWgwtyfvvv4/Y2Fjs3LnT2qE0WE3v0drmoTb/xL59+/aQSqU4f/68IbEvKyvD9evXMXv2bCtHRwghhBBCCCHm/fDDD0hLS8OpU6ewadMmq8ai1WprHFO1i4CtWr9+PVJTU7FlyxYsX77c2uHYFJtP7AUCAWbPno0vv/wSLi4u8PX1xfLly+Hl5WUyxYUQQgghhBBCbMWRI0dw584dvPXWW+jWrZtVY+nYsWONYyZNmoTPPvusCaKpn4sXL+LkyZN46qmnmnTb8+bA5hN7AHj11Veh1WqxZMkSKJVK9OzZE+vWrQOfz7d2aIQQQgghhBBi1t9//23tEAy2bdtW4xhb3+rtu+++s3YINsvm1tjbqta0xp5lWSiVSmuHQWyYSCQCwzDWDoMQQgghpFq2ssaeEEtazRp70rRYlsXcuXMRFxdn7VCIDYuMjMTatWspuSeEEEIIIcQG2G5lBGIVSqWSknpSo9jYWJrVQQghhBBCiI2gJ/bEogMHDkAsFls7DGJDFAoFFa0khBBCCCHExlBiTywSi8WU2BNCCCGEEEKIjaOp+IQQQgghhBBCSDNGiT0hhBBCCCGEENKMUWJPCCGEEEIIITZu6NChGDp0KGQymUnf4sWL8cQTT1ghKmIrKLEnhBBCCCGEkFo6ey0br31zHFPf3o3XvjmOs9eym+zamZmZ+OKLL5rseqT5oMSeEEIIIYQQQmrh7LVsfPpbDG7dLYFKrcOtuyVY9ntMkyX3/v7+2Lx5M86cOdMk1yPNByX2xIhIJEJkZCQiIyMhEomsHQ6xMfT+IIQQQkhrtvVwkkkbywLbjpi2Pwzjx49Hnz598O6775qdkg8AJSUlWLp0KQYNGoTOnTvjsccew/nz5w39K1euxJw5c7BmzRoMHDgQERERmD17NlJSUgxjjh8/jsmTJyMyMhJ9+vTB4sWLUVpaCgCYOHEi3n77baNrnjx5EhERESgpKcHixYuxePFifP755+jTpw8iIyPxwgsvIDc31zA+Ozsbb7zxBvr164cuXbpg7ty5SExMNPTXdI5XXnkFTz75pFEMqampCAsLw61btwyv8fvvv0ffvn3RtWtXvPfee8jOzsYLL7yAyMhIjBgxAseOHTMcr1Qq8c0332DYsGGIiIjAhAkTsH//fkP/9u3bERYWZnTNB9vi4uIwa9YsdO3aFT179sT//vc/ZGVlmf/DbGSU2BMjDMNg7dq1WLt2LRiGsXY4xMbQ+4MQQgghrdmd3HLz7Tnm2xsbwzD45JNPUFpais8//9ykX6fT4ZlnnsHFixexfPlybN++He3atcPcuXMRFxdnGHfx4kVcunQJa9aswaZNm1BYWIilS5cCAIqKijBv3jxMmTIFe/bswffff48LFy4YlgBMnjwZ+/fvh1KpNJxv586dGDp0KJycnAAAu3fvRklJCTZs2ICff/4ZCQkJ+OabbwAAMpkMM2fORG5uLn788Uf89ddfEIlEmD17NjIzMw3nrO4ckydPRkxMDLKzs41iiIiIQNu2bQ2vMS0tDRs3bsSSJUuwefNmTJ06FY888gi2b9+OkJAQLF68GCzLAgBee+017Ny5E//3f/+H6OhoDB8+HPPnz8ehQ4dq9Wej0+nwwgsvoGfPnoiOjsZvv/2GrKwsvPPOO7U6vqEosScmGIahpI1YRO8PQgghhLRWAZ725tu9zLc/DL6+vli0aBG2bNmCU6dOGfWdOnUKCQkJ+Oqrr9CrVy+EhoZi6dKlaNu2LdatW2cYp9Vq8cUXX6B9+/aIiIjAY489hsuXLwMAcnNzoVar4ePjA19fX3Tv3h2rV682FOd79NFHoVarDQmvTCbDoUOHMHnyZMP57e3t8eGHHyIkJAS9evXCmDFjDOePjo5GcXExvv32W3Tu3Bnt27fHV199BZFIhI0bN9bqHIMGDYKbmxuio6MBAHq9Hrt27cKkSZMMx+v1eixduhRBQUGYMmUKnJ2d0bt3b0ycOBEhISGYOXMmiouLkZ+fj5SUFBw+fBjvv/8+Bg8ejKCgIPzvf//DsGHDsHr16lr9uchkMhQXF8PDwwO+vr7o2LEjvvnmGyxYsKBWxzcUJfaEEEIIIYQQUgvThrXDg883GKayvSnNmDED/fr1w5IlS4ym5CclJcHe3h7t2t2Lh2EY9OjRA0lJ95YLuLm5wdHR0fCzvb09NBoNAKBDhw4YN24cXnzxRfTv3x+LFi1CcnIyQkNDAQDOzs4YNmwYdu7cCQDYu3cv7O3t0b9/f8P5AgICwOfzzZ4/KSkJgYGBcHFxMfSLRCJ07tzZKMbqzsHj8TB+/Hjs2rULAHDu3DkUFRVh3LhxhvGurq6QSqWGnyUSCQICAoyuCQBqtRo3b94EAHTv3t3o99yzZ0+jmKrj6OiIZ599Fh999BH69OmD+fPn48KFCybT9x8WSuwJIYQQQgghpBb6RHjj7ad6oV2AE0QCLtoFOOGdOb3Qu5N3k8fy8ccfo7y8HMuWLTO0VU0rfxDLsuDxeIafBQJBtef+6quvsHfvXjz77LMoLi7Gm2++iblz5xr6p0yZgjNnzqCwsBDR0dGYMGECuFxurc5vKUa9Xl+nGKdMmYKUlBTEx8cjOjoaw4YNM7pZcf9NgSocTt3S3wd/bw/S6XRGP7/xxhs4cuQIFixYAJZl8dFHH2HKlClQq9V1um59UGJPCCGEEEIIIbXUJ8IbX80fhK3LxuGr+YOsktQDgI+PDxYvXoxt27bh4sWLAICwsDCUl5cbPWVmWRaXLl0yPHGvSWxsLD799FMEBwcbiux9+umnOHfuHAoLCwEA/fv3h7u7O7Zs2YKLFy8aTcOvSVhYGNLT0w3nAgCVSoX4+PhaxwgAISEh6Nq1K/bu3YvDhw/XKQZzMQHApUuXjNovXrxoiKnqRsH9MyTS09MN/52amor3338frq6umDlzJr777jusXbsWKSkpRoUBHxZK7AkhhBBCCCGkGZo2bRr69++Pu3fvAqhMuDt06IDXX38dMTExSElJwYcffoikpCQ89dRTtTqnVCrFpk2bsHz5cty+fRtJSUnYs2cPAgMD4ezsDKDyyffEiROxevVqREREICQkpNYxP/roo3BycsKCBQsQFxeHxMREvPHGG5DL5ZgxY0adXv+UKVOwYcMGiEQi9OvXr07H3i8kJARDhgzB0qVLcezYMaSlpeH777/H4cOH8cwzzwAAunTpAoZhsHLlSmRkZGDv3r3YsWOH4RzOzs74999/8d577yElJQVpaWnYsWMHHB0dERwcXO/YaosSe0IIIYQQQghppj7++GPY21cW7+Nyufjll18QHh5uqGx/69Yt/Pbbb+jSpUutzhcSEoKVK1fi3LlzmDhxImbOnAkul4uff/7ZaCr75MmToVQq6/yk3N7eHhs2bICDgwPmzJmDWbNmQalU4s8//4S/v3+dzvXII4+AZVlMnDjRaClAfaxYsQLDhw/Hu+++i/Hjx+Po0aNYuXIlRo8eDQDw9/fH0qVLcfDgQTzyyCPYvHkz3nrrLcPxzs7O+Pnnn5GZmYnp06dj0qRJyMjIwK+//mq01v9hYVhLixyIkWvXrgEAIiIirBwJIYQQQgghpDaUSiXS0tIQFBRkKJZGGsf58+fxwgsv4OTJk4YbC03t7t27GDlyJPbu3YvAwECrxNBQNb1Ha5uHWq4EQAghhBBCCCGE3CclJQVJSUlYvXo1Jk2aZJWkPjs7G3Fxcdi0aRMGDBjQbJP6xkRT8QkhhBBCCCGE1Mrt27fx9ttvw8nJCQsXLrRKDMXFxVi8eDHKysrw/vvvWyUGW0NP7AkhhBBCCCGE1MrQoUNx9epVq8YQHh6OK1euWDUGW0NP7AkhhBBCCCGEkGaMEntCCCGEEEIIIaQZo8SeEEIIIYQQQghpxiixJ4QQQgghhBBCmjFK7AkhhBBCCCGEkGaMEntCCCGEEEIIIaQZo+3uCCGEEEIIIaSZiI6OxoYNG5CUlASGYRAcHIxp06bhscces3ZoxIoosSeEEEIIIYSQZmDbtm345JNP8O6776J79+5gWRanT5/Gxx9/jIKCAsybN8/aIRIrocSeEEIIIYQQQmqp4uZ5lJzeDnXBXQjc/OHUbzLswqKa5NqbNm3ClClTMHXqVENbcHAwcnNzsX79ekrsWzFaY08IqRNWr0NF4nkUHFiH4lPboC0rsHZIhBBCCCFNouLmeeRu+wKq7GSwGhVU2cnI3bYcFTfPN8n1ORwOrly5gtLSUqP2559/Hps3b8bvv/+Orl27QqFQGPr0ej0GDhyIjRs34vz58wgPD8fx48cxbtw4dOrUCaNHj8ahQ4cM43U6HX777TeMGjUKERERGDVqFP78809Df03nOHToENq3b4/MzEyjGGfMmIHPP/8cGRkZCAsLw7///ouJEyciIiICkydPRkpKCn744Qf07dsXvXr1wtKlS8GyrOH4Y8eOYfr06ejatSv69++PZcuWQalUGvrDwsKwfft2o2ve36ZQKPDuu++iX79+iIiIwMSJE3HgwIH6/lHYHErsCSG1xuo0yPnrE+T+/QXKLuxB8fE/cffH/0GeGmvt0AghhBBCHrqS09vNtLIoObOjSa7/7LPP4vr16xg4cCCef/55rFmzBnFxcbC3t0dQUBAeffRRaDQao4T1zJkzKC4uxrhx4wBUJu7Lly/Hu+++i927d6Ndu3ZYtGgRKioqAACfffYZVq1ahXnz5uGff/7B448/jk8++QS//fab4ZzVnWPw4MFwcXHBrl27DOPT0tJw9epVTJkyxdD29ddf45133sHWrVtRVlaGmTNnIj09HX/88QcWLlyITZs24ejRowCAgwcP4qWXXsLgwYOxfft2LF26FHv27MFrr71W69/dt99+i5s3b2LNmjXYs2cPBg4ciIULFyIjI6Nefxa2hhJ7QkitlccehSLNOIlntWoU7F0NltVbKSpCCCGEkKahLrhrvj3ffHtjGz16NP78808MGzYMsbGx+OqrrzBt2jSMHj0aly5dgouLC4YOHYro6GjDMTt27MDQoUPh6OhoaFuwYAH69OmDwMBAvPzyy5DJZEhKSoJMJsOff/6JV199FY8++igCAwPx5JNPYtasWVizZo3RE3RL5+DxeJgwYYJRYr9z505EREQgNDTU0PbMM8+gV69eaN++PUaMGAG5XI4PP/wQISEhmDlzJlxdXXHr1i0AwJo1azBixAi8/PLLCAoKwrBhw/D+++/j8OHDSE5OrtXv7s6dO7Czs4O/vz/8/f0xf/58rF692uj30pxRYk8IqTV58iWz7dqSPKjz7jRxNIQQQgghTUvg5m++3d18+8PQpUsXrFixAmfPnsWOHTuwYMECyGQyPPfccygsLMSUKVNw9uxZ5OXlQSaT4dChQ5g8ebLROYKDgw3/LZVKAQAajQapqanQaDTo3r270fhevXqhsLAQhYWFNZ4DAKZMmYL09HTExsaCZVlER0ebxNCmTRvDf0skEri5uUEsFhvaRCIR1Go1ACApKQndunUziamqrzaee+45JCYmok+fPpg5cyZ+/PFHBAQEwN7evlbH2zpK7AkhtcbwhRb7OAJRE0ZCCCGEENL0nPpNBsA80MrAqe9kc8MbVU5ODpYuXYqcnBwAlevtw8PD8dJLL+G3335DRUUFLly4gP79+8PNzQ27d+/GgQMH4ODggP79+xudSyAQmJyfZVmjJ/L30+srZ2byePdqr1s6BwCEhoYiMjIS0dHRiImJQUFBgWEpQJX7z1X1eiwxF5e5mO6n1WqNfu7atSuOHz+O7777Dh07dsTOnTsxZswYnD171uJ1mxNK7AkhtWbfaZDZdqFvGPjOXk0cDSGEEEJI07ILi4Ln1Dch9GkLhi+C0KctPKe+BbuwXg/92gKBAFu3bjWaZl/FwcEBAODm5gYul4uJEyfi4MGD2L9/PyZMmAAul1ura4SEhIDP5+PSJeNZmhcvXoS7u3udpq1PmTIFhw4dwr59+zB8+HBDjPURFhaGy5cvm8RUFTMA8Pl8yGQyQ//t27eNxn/33Xe4dOkShg0bhiVLlmD//v3w9/fH/v376x2XLaHt7gghtSZp2x1OA6ZXFo7RV94F5bv5wWPifCtHRgghhBDSNOzCoppse7v7ubi44Nlnn8W3336LiooKjB49GlKpFMnJyVi1ahWioqLQo0cPAMDkyZOxdu1acLlcvPXWW7W+hlQqxYwZM/Ddd9/ByckJEREROHXqFDZt2oTXXnsNDPPgbAXLxo4di2XLlmH79u1YuXJlnV/v/Z599lnMnz8fq1atwiOPPIL09HR89NFHGDJkiCGx79KlC7Zu3YqePXuCZVksW7bMaFbB3bt3ER0djY8++ggBAQGIjY1FVlYWunbt2qDYbAUl9oSQOnEZOAMOXUdCmXEDXDtHiPzD6/QhTwghhBBC6mfBggUIDAzEli1bsHHjRiiVSvj4+OCRRx7BCy+8YBgXGBiIyMhI6PV6Q+JbW2+//TacnZ3x5ZdfoqCgAIGBgXjvvfcwffr0Op1HKpVi+PDhiImJQb9+/ep07INGjRqFFStW4Mcff8SqVavg4uKCcePG4dVXXzWM+eCDD/DBBx9g+vTp8PDwwPz58w3LFgDg/fffx+eff44333wTJSUl8PX1xRtvvIEJEyY0KDZbwbCWFlIQI9euXQMAREREWDkSQgghhBBCSG0olUqkpaUhKCgIIlHrqQfEsiyGDx+OF198EdOmTbNaHE888QS6deuGhQsXWi0GW1fTe7S2eSg9sSeEEEIIIYSQFkCj0eDIkSM4d+4c5HI5xo4da5U4Dh06hBs3buDq1av44osvrBJDa0PF8wghhBDSKHbv3o1nnnnGpH3UqFEmRY+qG08IIaR++Hw+Pv74Yxw6dAjLly+HRCKxShxr167Fhg0b8NFHH8Hb29sqMbQ29MSeEEIIIQ2SlpaG3bt3w8/PD05OTjh48CCKiopQUVGBoKAgODk5QaVSYcWKFXjqqadQVlZmdvyMGTOs/VIIIaTZO3nypLVDwF9//WXtEFodSuwJIYQQ0iAKhQLJycnYtGkTysrKkJeXh+HDh0Mul+PHH39EXFwcFi9ejJ49e6KsrMzieEIIIYTUDyX2hBBCCGmQ8PBwvP3225g1axY8PT0xZswYzJo1CwCg1+uh0+mgUCiwZMkSODk5AYDF8YQQ8jBQvXBiqxrrvUlr7AkhhBDSYBs2bMCwYcPw2Wef4eeff0Z5eTny8/Px999/44cffkDbtm2xbdu2ascTQkhj43K5ACqLyhFii7RaLQCAx2vYM3fa7q6WaLs7QgghpHpqtRoCgcDw/5baqhtPCCGNLTU1FXw+H35+fmAYxtrhEGIkMzMTFRUVaNu2rdn3J213RwghhJAmVZWc35+km2urTR8hhDQWNzc3ZGZmIiMjA46OjuDz+ZTgE6tjWRYVFRUoKyuDt7d3g9+TlNgTQgghhBBCWiwHBwcAQEFBATIzM60cDSH3MAwDJycnODo6NvhclNgTQgghhBBCWjQHBwc4ODhAo9FAp9NZOxxCAAB8Pt9QB6KhKLEnhBBCCCGEtAp8Ph98Pt/aYRDS6KgqPiGkUagLs6C8mwi9RmXtUAghhBBCCGlV6Ik9IaRBtLJi5O38GsrbCQAAjkgKl6FPwKHrcCtHRgghhBBCSOtAT+wJIQ2St+tbQ1IPAHqlDAV7VkOZcdOKURFCCCGEENJ6UGJPCKk3TXEOlOnXzPSwKL96qMnjIYQQQgghpDWiqfiEkHrTKWTV9JU3YSSE2B6WZaFUKq0dBrFRIpGI9tEmhBDSaCixJ4TUm9CzDTgSB+jlZSZ94qBIK0REiG1gWRZz585FXFyctUMhNioyMhJr166l5J4QQkijoKn4hJB6Y7h8uI54GmCMP0qEPm1hHznUSlERYn1KpZKSelKt2NhYmtFBCCGk0dATe0JIg9h3GgiBqx/Krx6CTl4GcWAEpJ0Hg8MXWjs0QmzCgQMHIBaLrR0GsREKhQIjR460dhiEEEJaGErsCSENJvQOhtD7eWuHQYhNEovFlNgTQggh5KGiqfiEEEIIIYQQQkgzRok9IYQQQgghhBDSjFFiTwghhBBCCCGENGO0xp4Q8tDoFDKUxx6BOi8dfBcf2HcZBp7U2dphEUIIIYQQ0qJQYk8IeSi0pfnIXL8EurICQ1tpzG74zP4QAo8AK0ZGCCGEEGKbhg4diokTJ0KhUGDXrl2QyWTo2bMn/u///g+BgYEoKirCJ598gnPnzqGsrAzBwcF4+umnMXHiRGuHTqyMpuITQh6KohNbjJJ6ANArylF4ZL2VIiKk6YhEIkRGRiIyMhIikcja4RAbQu8NQkhN1q9fj9TUVCxbtgwff/wx4uPjsWjRIgDAm2++iZSUFCxduhQ///wzwsPDsWjRIpw7d87KURNroyf2hJCHQpF61UJ7LFiWBcMwTRsQIU2IYRisXbvW8N+EVKH3BiGkJg4ODli1ahW4XC4A4M6dO1i5ciWKi4sRExODV155BcOHDwcA9OrVC05OThAIBNYMmdgASuwJIQ8FRySBTlZk2i6U0JdZ0irQ+5xYQu8NQkh1IiIiDEk9AHh5eQEAFAoFoqKisHLlSly/fh0DBgzAoEGDDE/zSetGU/EJIQ+FtNNAs+32kUOaOBJCCCGEkOZDLBYb/czhVKZser0eX3/9NebMmYP4+HgsWbIEgwYNwty5c5GZmWmNUIkNocSeENLoNCV5KL9y0KRd0q4nnAfPskJEhBBCCCHNn729Pd58800cOXIEe/fuxWuvvYbLly9j6dKl1g6NWBkl9oSQRld46DdoS/NN2vlOnuDwaA0YIYQQQkhdZWVlYdCgQdi3bx8AIDg4GM899xz69u2LrKwsK0dHrI3W2BNCGhXL6iG/ddFsX8XNGLiOeLqJIyKEEEIIaf58fHzg5eWFjz/+GDKZDAEBAYiPj8fx48fxwgsvWDs8YmWU2BNCGhkDhsMFq9eZ9nDpI4cQQgghpL6+//57rFixAt9++y2Ki4vh7e2NefPm4fnnn7d2aMTKGJZlWWsH0Rxcu3YNQGWVSkJI9fL++QGyuCMm7c4DZsB54HQrREQIIYQQQkjzU9s8lB6fEUIanevwJ6EpuAtV1i1DmyS0O5z6TrJiVIQQQghpTliWhVKptHYYxIaJRCLaQvQ/lNgTQhodV2wP36c/g+J2AjTF2RB6BELoE2rtsAghhBDSTLAsi7lz5yIuLs7aoRAbFhkZibVr11JyD0rsCSEPkbhNR4jbdLR2GIQQQghpZpRKJSX1pEaxsbFQKpUQi8XWDsXqKLEnhBBCCCGE2KwDBw5Q4kaMKBQKjBw50tph2BRK7AkhhBBCCCE2SywWU2JPSA041g6AEEIIIYQQQggh9UeJPSGEEEIIIYQQ0oxRYk8IIYQQQgghhDRjlNgTQgghhBBCCCHNGCX2hBBCCCGEEEJIM0ZV8QkhhBBCCCE2RSQSITIy0vDfhNyP3h+mGJZlWWsH0Rxcu3YNABAREWHlSAghhBBCCGn5qtIUhmGsHAmxRa3l/VHbPJSe2LdSmfkyXLqRC5GQh76dfSAV860dEiGEENIiKLOSUX7lIHTyMojbdIR9l2HgCGgPbkLqqqUnbKRh6P1hjBL7Vmj9nuvYduQWquZqrN0Vj3fn9EJkO3frBkZaLL1KjvK4Y1Dn3QbfxRv2nYeAa+do7bAIIaTBFLfjoUiPB1fiAGnHAVCkXkVe9HcAqwcAyJNiUB53DD5PfASOsP7JvV6jQtnFvZAlnASr18OubQ849ZkIjsiusV4KIYSQZoym4tdSS5mKn5BaiMU/nDJpd7IX4pclI8HnUT1F0ri0ZYXIWr8E2tI8QxtH4gCfJz6CwM3PipERQkj9sXod8nZ8jYrEs4Y2RigGw3ChV8pMxrsMnwOnqEfrdS29SoHMXxdDU5hh1M6xc4Tf3C/Bs3ep13kJIYTYvtrmoZTFtTKnYjPNtpeUq5CQWtDE0RBrUtxOQNmlfVDcTnio1yk+ucUoqQcAvbwMRYfXP9TrEkLIwyRLOGmU1AMAq1KYTeoBQJEWV+9rlV3aa5LUA4C+ohRFJ7fU+7yEEEJaDpqK39pUMz+D5m60DnqVHDmbP4Xy7g1Dm8i/A7xmvAOOUNLo15OnXLbQfgUsy9L6KEJIs1RxM6ZO47l2DvW+ljzlqsU+xa1L9T4vIYSQloOe2Lcy/SJ9zLY7SYXoFOLWxNEQayg69qdRUg8Ayrs3UHT8r2qPUxdmIn/PamT+uhh5u76FKiu5VtezVDCKIxBRUk8IabYYTl2+QjFw6DK83tfiiCzfdK2ujxBCSOtBiX0r0ynEDVOGhBq1iYVcvDarG62vbyVk101rLABAhYV2AFDn3UbmL4tQfuUgVFm3IIs/gczf34U8LbbG69l3HmK2XWqhnZDWQKXRIb9YAZ2epko1V9KOA8y2890DIA7uYviZI7aH25gXIfLvUO9r2UcOs9jn0HVEvc9LCCGk5aCp+K3QnHEdMaxnAC4l5kIs5KFfZx9IJQJrh0Wayn+Vmk2bdRYPKT65FaxaYdyo16L42J+QBEVWeznH3uOhLsyC7Noxw7Ul7XrBZcjjdYmakBZBp9Pjt3+vY/+5dChUOrg5ivD46PYY3quNtUMjdWQXFgWHXuNQFvMvqta5cR3c4DlpIQTuAdCU5EJXUQqBZyA4vIb9G2vXriecBkxHyaltRp/hdp0Gw6HnmAadmxBCSMtAiX0r5e9pD39Pe2uHQazArl0UymMPm7aH9bZ4jDLjptl2VdYtsHodGA7X4rEMhwuPR1+B84BpldvdufpA4Opb98AJaQH+2HsDO4+nGH4uKFXi281X4WQvQo8OnlaMjNSH24in4dh9NBS348GVOEIS2g0Mt/KrFd/JE3ynxvszdRk4A449x6IiKQasRgW+kyd4zp5gGJptRwghhBJ7QlodlyGPQ5l5E5qCexWW+e7+cBk8y+IxPAdX6GRFJu1cqXO1Sf39+E4e4Dt51D1gQloIjVaPvWfTzfb9cyqVEvtmiu/iDb6Ld5NciyuWgmfnhPy9P0FXVrmTjdCnLTwmLgDf2atJYiCEEGKbKLEnpJVQZSVDlZ0MnqMHfOcuh/zWRWjyM8B394Ndu16Gp0zmOPQcg/xd35q0S0K71ykGvUYFedIF6JUyiIMim+zLMCG2QKHSQq7Umu0rKFGYbSfNg16jQnnsUSjSYsER2cE+cijEAeG1OlYrK4Ey/RoYgQiSkC5guHyLYzWlecjd9gVYncbQpsq6hZwty+D3/DdUkJQQQloxSuwJaeFYnRa5O1ZAfvO8oY3v5gfvme9B2qFvrc5h32kg9IpyFJ/YYrRHc/nVQ9DJS+E5+Y1qbwwAgDIzCTlblkEvL/uvhYFjnwlwHfpEnV8TIc1RTEI2uBzGbMG89m1crBARaQysVoPsTUuhum/JkizuKFxHPQfHHqOrPbbk/D8oOroB0FXe8OFKneE5bTFEPqFmx8vijhsl9VU0BRlQ3r0OcUDHBrwSQgghzRktzCKkhSu9sMcoqQcqvwQW7F9bp/M49hwLnpn1ovKkCyi/arpm/34sq0fezq/vS+oBgEXp2Z21qqxPSHN39loWvt181WxSby/hY8pQ84kcsX3l8ceNkvoqRUc3QP9g0dH7qLKSUXToN0NSDwA6WTHy/l5usZipzugz9IG+Cst9hBBCWj5K7Fu4CoUGO44l47PfL2BddDwy82XVjr95uwgnr2Qiu6CiiSIkD5sswfw2dvJbF6FXK2t1jspppkegzkkx21+ReLba41VZydCW5Jk/NuF0rWIgpDnbdSLVbLtQwMWKBYPg4yZt4ohIY1HeTjDbzqoVUGUlWzyuPOGk2XZtWQHkKVfM9okDO5k/GZfXoO30CCGENH80Fb8FKylXYdH3J5F1X5K+53Qa3nu2NyLbuhuNLZWp8PEv55F4uxgAwDDA8J4BmDetCzgcWrPXrFnY3g4sW/m/GlTcPI/83T9Ar6zmZk9NVZn1FmJA5dN8Qlq63ELzf39Uah3cnMRNHA1pTByx5R1muBIHi32sVm2xL2/7l3Ad8Qwcuo00ape07QFxcFcoUo0Tf+f+08CTOtUuYEIIIS0SPbFvwXYeTzZK6gFArdVj7a54k7FrdlwzJPVAZb53MOaOxQrOpPmwa29+GztxcGdwhNUnFFpZMfJ2fF19Ug/ALrz6tfpC37bg2ptfQ2wpPkJaklB/J7PtQT4O4HHpn+LmzKHLMLM3N4W+YRB4tLF4nF27Xhb7WK0GBXvXQJWbbtTOcLjwmr4Y7uNegSQsCtJOA+E18z04959a7/gJIYS0DPRtogW7eivfbHt6dhmKy+9NwVaqtThzLcvs2KMX7z6U2EjTcYx6FKIHqjNz7V3hNurZGo+tuH7abKGm+0k7DYR95NBqxzAcLjzGvwpGIDJqt+86AnZte9QYByHN3YzhYRDwjbeG5DDA7NE0fbq5E3i0gcfEBeDaORnaRAEd4Tn59WqPEwd3qeGzk4Xs2jGTVobLg33kUHhNfQseE+ZDEhxZr7gJIYS0LDQVvwWzFwvMtvN5HIgF9/7o9XoWWp35KdkqjfkCPqT54PCF8J69FIqUK1BlpYDn5AG7Dn3A4QtrPFavUVnsswvvB6c+EyH0Cq5VHOLACATMW42K66ehU8ggCekKoXdIrV8HIc1ZqL8Tlv9vAP4+cgupWaXwcZNi4qAQRIS6WTs00gik4f1gFxYFdW46OCK7Wm3lyTAM3Me9Ap6rL4qP/GF2TG3roBBCCCGU2LdgI6PamH1qP7CrL0TCe3/0P5uZml+lV0evhxIbaVoMw4EktLvRvvPKjERUJJ4DGAbSDv0gNLO9kiS0O4qPbTJzQg5chz0JMFyUnNkBbUUJxP4dIGnXEwyHazr+P1yxPRy6V7/9EyEtVbCvI958wvwMFbVGh/wSBZzthZCILO9jTmwXw+WZ/RytiUO3USg5uQWsmRupVZ/ZerUSZRf3Qp58CQxPAGnEINhHDGpwzIQQQloOSuxbsAFdfZFZIMO2I7egUuvAMEBURy88PzHCMOZ0bCYOxdwxe7yPmx0mDa79l5QrN/Nw5NJdqNQ69OzgiSE9/GntqI0qOroBJWd2GH4uPRcN58Gz4NxvitE4oWcgHHuPR+m5aKN2l8GzoCnORc7mT8FqKp8olcXshqhNJ3g99i44PPOzRQghpv4+cgvbjtyCTKGBUMBF705e4PO4KClXITzIBY/0CYRUQn+nWiKW1aPg31Vmk3q7Dn0hadsdrE6L7D8/NNpST5EWC1VOKtxGPN2U4RLSLLAsi3K5BnYiHrj0PZS0IpTYt3CPjQjDuP7BuJ1dBjcnMTxdJEb90SfNb8EEAO38nSAVVz450uvZaqvjbz50Exv2Jhp+PnstG6dis/Des73Bpar6NkWdd8coqa9SfPwvSDsOAN/Jw6jdddhTsGvXC7LEc2A4HEjD+0PgFYyMn141JPVVlLfjUX71MBx7PPJQXwMhLcXhC3fw27/XDT+r1Docv5xp+PnijVwcvnAHX/xvIBzsKLlvaeQ3L6DixhmTdkYghtuj88AwHMhunjVK6quUXdgDp17jwHN0N+kjpLU6cvEONu2/idwiOewlAowfGIwZw9uBYei7KGn5KLFvBaRiPjoGu5rtU6otr6FXafWIPpmCncdTkF+sQKifIx4f3QE9OngajSsuV+KvA0kmx1++mYeYhBz0iah5rSFpOvLkS+Y7WD3kyZfh2MN0qrzIv4PRHsnqwixoCs0XXJTfukCJPSG19O/ptBrHZOZX4N/TaZg5MqwJIiJNqeLWRbPtrFoBdWYSxIERUN5NNDsGrB7KrFuQUmJPCAAg5noOvv7z3laQ5XI1Nu5LBIdhMH14OytGRkjToPkprdzALr4W+8RCHn7eGY/8YgUAIDmjFB/9ch4JqYVG466nFUGrM78XeZyFyvzEephqiuZxHqhab3Ec3/KTQ4ZXc1E+Qkil4rLaFUeLS6bP0pao2s/S/z6reQ7mb8wDAM/CNqKEtEa7jqeYbf/nZCr0evNFoglpSSixb+XG9g+Cv4fUpN3X3Q6XEnNN2vV6FjuOJRu1OVYzPdTRnpI8WyMN7wfGzBp4RiCGXVgvKO4koOzKQSgzTWdhAIAqNx2K2wkQeAaZ7bePGASWZSFPjUXphT1Q3LZcnJGQ1i48yHLSdj9HKX2WNheaomwUHd2I/N2rUH7tuGHLUF1FKYqObkDe7lVQpF0DAEgtFMDju/hA6NP2vzGDwQglJmMEXsEQ+bV/SK+CkOYnr1hutr1EpoKadnkirQBNxW/lRAIevntjCH7bfR3RJ1PA/ndDMzO/wuIxmfkyo587BrvC39Med3PLjdoFPA6Gdvdv9JhJw3DtHOEx6TXk/7MSemXlnzNH4gC3MS8i+8+Pocq8t5ZTHBwJz6mLwOELwWo1yN35NeQ3zxv6Gb7wXtEnhgPH3uMhatMRWb8uhir73g0gkX8HeM14FxyhuGleJCHNxIwR7XDpZh4qFJpqxz3SO7BpAiINUnHrInL/Xg7otACA8tjDKL9yEOJ2vVB8eD2Ayn9kZbGHIfAKhs+TH8N1xNMoOrLBcAOA5+gBzylvGNYE86RO8H5sCQr2/Qx1bhoABuKQrnAf+7I1XiIhNqudvzNyCk2Te39Pe6PdoAhpqehd3gLF3spHSkYJPF3tENXRq8bK9DwuB/GpBYakviZBPo5GPzMMg/97JgpfbryIpDslAAA3JzFemRoJDxfTpwzE+uza9YT41Z+hSL8GhmEgDuyMggPrjJJ6AFCkxqLk1Da4DHkcJWd2GCX1AMBqVJC06wlpxwEQ+bUHz8EV+f/+aJTUA4Dy7g0Un9wC1+FPPfTXRkhzEuDlgG8WDsKuEylIyyqDh7MYRWVKXEsugJ4F7CUCPDGmAyLb0TpqW8fqdSjc97Mhqa+ivHsDyrs3TMarc1JRdPwvuA1/CtJOA6G4nQCOSAJxm06GbUNZnRZ6lRxC33bwe/ZLaMsKwPAE4EocmuQ1EdKcTBveDhdu5EChuvd0nsMAs0fTzBbSOlBi34Io1Vp8tO484pILDG0+bnb4+MV+cHe2/KS0pFyFlIzSWl1DwONgyhDTLfC83ezw1fxByMgrh1KtQ5CPI1XDt3EcvhB2be/tqS1LOGl2nOz6KbgMeRzl8cfN9ivSr8Fr2uJ7481UeK48z2lK7Akxw8vVDi9M6mzUVliqQEm5Cv6e9hDwuVaKjNSFOv8utGUFNQ+8j+zaMbgNfwpciQOkHfoY2llWj+ITm1F2cR/0Shl4Th5wHjQT9p0GNnLUhLQcgd4OWP7qQPx95BaSM0rh7WqHCYOC0TmUboyS1oES+xZkx9Fko6QeALIKKvDzrmt4Z04vAJXrj3IKK8CAga+HFC4OIgj4HHA5DHRmCosEeNlDIuQht0iOtv7OmDGiHUL8nCzG4Odh36iviTQhvfkCiKxWY/T/pv1asCx7bysZC+d58CkWIS1FWlYp9HoWwb6OjbalkqujGK6OtHTFlukqSlGRFAPo9ZC06wmOmXXwNWH15tf9Fp/cipJT2ww/a0vykL/rO3DF9pCEdK13zIS0dG28HPDarO7WDoMQq6DEvgU5FWd++7HzCTkoLFVg5ZaruJSYZ2hnGGBoD3+8MrUL+nX2wYmrmSbHThnSFkN7mF8nXypT4eilu8gvVqBtgDP6dfYBn0f1GJsrSVgvVCScMmnXKcohT4uFXbueKLu0z/S4tt2Nkhm7Dr0hiztmeh55KUrO7YJT7wmNGjch1pKaWYovN14y1BfxcpVgwWPdLG4vSloOWcIp5P/zvWFdPA6sg9uoZyFq0xHK2wm1Po9du14mbaxeh7KLe8yMZlEa8y8l9oQQQsyiLKyV+PavK0ZJPQCwLHD4wl1s3HcDL07pjM6hboY+HpfB5MGhFpP61MxSvPT5YayLTkD0yVR8tfES3lp5AnJl9QWgiG3Rlhej8MgfyFq/BKxaBY7YzLpNnRZ5O76GY99J4Lv5GffxBBD6hBo9dXIZ8gT4rua3USw6vB6q3PRGfAWEWIdGq8PStWeNiobmFMrx4bpzkMnVVoyMPGw6eRnyd/9wL6kHAL0OBft+hsvg2RB4BBiNF/q1h0OvcSbn4Upd4DrsSZN2vUoBvUJm0g4AyoxEVCRdAFvbojiEEEJaDXpi34L07+yDTTk3Tdq7tHXD5ZuW90Def+425ozriE9e6of07DIUlCgQ4usIZwfLe5r/tCMO5XLjJD45oxQ7j6dg1igqUtIcaMuLkfnrIujKC2scq1eUQ5N7G75zlyN3y+dQpF397yRqFB/dCFVmEjynLgLDMOBJneA8aCbytn9p9lwV109D6BnYeC+EkIeoXK7GXwdu4lx8Nvg8DgZ188fUoaGISchFUZnKZLxcqcWJq5kY09f8dpCk+atIugBWa+bmDauH4vY16FVKo2ZVRiI4AiF85i5Hyalt0CsrYNeuFxy6jwTD5RuN1RTnQJ58GVw7R+gqTGvfsGoFcrd+BvuuI+A+5sVGfV2EEEKatxaf2H/55Zc4evQoOBwOXnrpJYwZM8baIT00k4e2RUJaIWJv3Vtn7+tuh0cHhFSb2MsUGuj0LLgcBoHeDgj0rr7arkyuxvW0IrN95+NzKLFvJkpjomuV1FdhWT105UVQpMWa9MmTLkCZfg3ioMoCYAxjeTIQy1pYg0+IjdFo9Xhn1WmkZ5cZ2jbtT0RKRgm6t/eweFxZBT2xb9ksPy1X5aRCW5pn0q5IjYXL4MfhNfUti8cWHf8TJaf+rvb8VcqvHIRD1xEQeofUKmJCCGlOlBk3UXT8Tyjv3gBP6gKHHo/AMerRRqtj01K16MT+3LlziI+Pxz///IOysjKMGTMGw4cPh0AgsHZoD4WQz8XHL/ZDXHI+UjJK4eUqQa9wL+hZFg52AotfNjuHuplUsNdodTh7LRu5RXKE+jmhSzt35BbJcT4hBzqdHhwGMFNrD0IBVW9uLpR3TLdfsoQjlEAc1BmyhFOw9KVTcTvekNiLgzuDEUrAqkz3k7Vr38ekjRBbdO5atlFSX+V8Qg6G9QwAw8DsNqH3L2siLY9d254o4Jpuawcw4FZTQE9dmGkxEVfevWFULO/eKTmAhZuhirRYSuwJIS2OOv8Osjd+YJgZpS3NQ9Hh36FXVsBl8EwrR2fbWnRi37t3b/To0QMcDgd5eXkQCATgclt+4tk51N1oaw8ugKfHdcR3m6+YpGQiARdPjQ03assprMC7q88gr+heUubtZofcwgpDMm/phtkQC2vyie3h2ruY73jgiyTD5cN93Dxw+EJw7Rwtn8/OyfDfHIEYHuNeQd7Ob4zWoTr1mwqRj+l2iYTYotQsy9uAVijUeHRAMKJPpJr0bT6UhCVPR1Ex0RaKa+cI90deQP6e1UBVfRGGA9cRc8ARiFEee8TscQI3y/8+yq6fNt9RzQwnjkha65gJIaS5KD2/2+xyp9KLe+DUbzI4fKEVomoeWnRiDwA8Hg/Lli3Dxo0b8eKLL7aKxN6c4b0C4OchRfTJVKRkloDP4yAy1B2PDgiGl6ud0difdlwzSuoBILugwuhnlgUY3Ht2yzDAyKg2GBXV5iG+CtKYHLuPhvxmDB58Am8fORSOPcdCnnwRDF8Iuw79wJM6Qa9RodxMtXsAYIQSSDsOMGqza98b/vNWo+LGGbBaNSRte0DwYPE9QmyYj5ud5T53KYb3aoPz8TnIfeDz8nJiHg6cS8fY/sEPO0RiJfaRQyEOikRF4lmwrB52YVHgO3lCr1Gh5Mx2aIqyjcZLQrtD6FVN3YVqiuGZm/3ECCWwC+/XoNdACCG2SF1ouksXALAqOXTlReC4eDdxRM1Hi0jsd+/ejS+++MKo7ZFHHsHbb78NAHj77bfx0ksv4YknnjA8xW+N2ge6oH2ghae0/1GqtLicmFur87EApgwJxZ2cciTdLcaZuCzo9SyeeKRDtYX3iPWxLAtWr4M4tBuUd29Ufmnk8mDfaRBcRz4DDl9oUtm5+NgmyBPPmpyLI7aH17RF4ErsDW16ZQVkN85AJy+DuE0niPzCHvprIqSxDejqiz8P3kR+scKovUOgC8KDXJFXJDdJ6qucjc+mxL6F4zm4wvGBavccvhDesz9C8fE/UXHrAhieAPadBsKp/9Rqz2XXvrfZ7UQ5Qjt4Tl+M/H++h7Yk97/rusF9/KvgiizfeCKEkOZK4B4AVUaiSTtHZGd5tikB0EIS+3HjxmHcONOtZNLS0qBWqxEWFgYnJyf0798fSUlJrTaxrxUGsLhw1IxTsVlGX2wPxtzB9bQifPf6YAj4rXN2hK1jdVrkbP0cipTLhjaGL4Ln1LcgCY40fwzLoszC9FK9QgaGd69uhTIzCTl/fQy9snKWRzEAaaeBcB//v2qL6hFia0QCHj59qR9++ScBMQk54HI5GNTVF8882hEAwOVaLuLD5dJ7vbXi2TvDfdzLcK95qIE4MAKOvcahNGa3UbteVYGSU1vh9/w3UOffAVg9hN4hYDj07yshpGVyjBoHWcIJsGrlA+3jaRp+DVr0N487d+7gww8/hFarhUwmw+nTp9GlSxdrh2VTisuUOHstGzdvV1a5Fwl46NnBs9bHm3talZkvw6nYrEaLkTSu8quHjZJ6AGA1ShQe+s3yQawerEphqRP5//5Y+V8si/zolYakvoos/gQqLK0hJcSGebna4Z05vbD980exbdlYvDqjK6SSyhtZro5idAx2NXvcoK6+TRkmacZYlkXphT2Qp8VW1jh5gCItDmWX9kHkEwqRbztK6gkhLZrA1Rc+T3wESdse4Iik4Lv7w23083CuYeYTaSFP7C0ZNGgQLl++jPHjx4PL5WL27NkIDw+v+cBW4o+9N7D96C1odZVP50P9HLHkmSi8MKkzMvNlyMiT1fvcd3PLGytM0sgqkmLMtmvy70BTlAW+i49JH8PhQhTYCcr0a2aPVeemQVOSC1athKbI/E0d2Y2zJuvwCWkuOBzzT+fnz+iK938+a1SHZGRUGwzpToVESe0UHfkDped2VTumIvEcnHqPb6KICCHEuoRewfCa/ra1w2h2bC6x/+mnn3Dq1Cn88ccfhja9Xo/vv/8eW7duRXl5OXr27In33nsP/v41f3FauHAhFi5c2CixsSwLudz8ekpbotXpodHqIRZa/uONuZ6HLYeSjNqSM0qxYtMlvPtUN3zxchSuJBUgr0SJQG97fPNXHErrsDezh5OgWfyuWiM9LE8fVqq10Jj5c9MrZYCg+vWcSpUarNrye0Svbx5/fwipC0cJgxX/64PY5AIUlakQFuAEPw8pFApLM1wIuUevlJlMvzc7DqDPT0IIaaVYlgVjaUuy+9hUYr9x40Z88803JmvgV61ahU2bNuGzzz6Dl5cXli9fjmeffRb//PNPk+5Jr9FocONG7ff+bmoaLYsDV0pwNU0OjZaFr6sAo7o5IsDddD3K7pMFZs8Rl1yImMvxkIo4yMlRITlbiRvJLBwlQKnx7GpwOYCnEx9ZRRqjdhcpD878Ety4YXmrKGI9fPsASHHFpF3r5IekzHwgM9+4Q6+H/dlfwSu3XFSRBYOk25kAlw8HqTu4snyTMQV2fsi24b8/hDSECICPHVBeWI4bhebHsCyL5GwlUrJVEAk4iAySwFlqU/8MkyYmSjoGcdWWedUodQxEHn1+EkJaGX52AoR3LoOjqoDWxR/K4H7QS5ysHZZV1CbntYlvFLm5uXj//fdx/vx5BAYGGvWp1Wr88ssveOONNzB48GAAwNdff40BAwbgwIEDZovmPSx8Ph+hoba7B/d3W67hwq172XdmoRobjxXii1f6wMtVYjSWf+4SACXM8QsIwtYjKThzzXzyz+MyaN/GGdOGhiDQS4rNh1NwOi4HGp0ePTu447HhoXChqvi2q0MHlDFyyK8eQtVWd1xHD7hNWgA/J9P6CspbF1BSTVIPAAxYBDtwIQzoAI3bfBTv+BL6ihJDvyRyGLyGTGrMV0FIs6LXs/h6cxxirt/L+k9dL8eCGZ3Rs4OHFSMjD5smJxWyi7uhLcwEz9kbdj3GQODTDgCQf+F31JTWi8J6w3PELDCcFl0WiRBCjFRc/BflsfeWKXHlRRAXpcF11ketrjp+cnJyrcY1KLGvqKjA1atXUVpaChcXF3Tp0gUiUd0TuoSEBPD5fERHR+OHH35AZua9/QsTExNRUVGBPn36GNocHBwQHh6OCxcuNGlizzAMJBJJzQOtoKBEgbPxOSbtKo0e+2My8dIU42rnUZ18EJ9aZDLez0OKvBINzlyznMjp9CxendEN3v/t8fzS1K54iepZNCuSsS9C02c8FHdugCd1hjg40mJBJlVp7bY/ZIozIW7XDZI2HWA/70fIky5AV1EKcWAEBO603pi0bievZCLmep5Rm1bHYu0/iegbGQA+j5K2lkh5NxFFWz4Gq6uc2aYryoIq7Sq8HnsXkqBIsArL9WicBkwHwxcCeh2QnQhxaDfaWYQQ0iroNSrkXTBdpqSXl0GdcBSuQ5+wQlTWU5tp+EA9E3uWZbFixQr8/vvv0Gg0YP/bGk0sFuOVV17Bs88+W6fzDR06FEOHDjXbl5NTmax6e3sbtXt4eBj6CJBXLIfewg51J6+aJvaj+7TBmbgs3Ei/l9wLBVy8NKUzjly8W+21WBbYsO8GwAJiEQ/DewagfWDrunPWEvBdfMwWyjMZ51rzGKCyAFT5tePwefwDcO0cIQ3v19AQCbG662mFOHLxLhRKLXqEe2JgF996bWUXc8P8v1cl5Sok3Sm2WF2fNG/Fp7YYknoDvQ7FJzZDEhQJkV8Y5EkXTI7ju/uj4vppaArvPegQ+rSF98z/A4f2ryeEtHDaktzK+k5myOJPwHngDHB4Tbccu7moV2L/448/Yt26dZg9ezZGjhwJV1dXFBYWYt++ffj666/h4OCA6dOnN0qAVQWIHlxXIBQKUVpKa7irBHjag8thoDOT3ZfLNUhMLzJKvkUCHj59uR9Ox2YhIbUQzg4iDO8ZAHdnMU5drXmruhNX7n3Z2H/uNl6c3Blj+wU1zoshTUZbXozSC7uhyrgJroMrHLuPhsi/g9EYSdueAIcD6PU1nk+TfwdFx/+E+5gXH1bIhDSZncdTsC463vDziauZOHElE0ueiQLXQpV8S4R8y1uUCQX3+k7HZWHr4STczZUhwMseM4a3Q+9O3haPJdanVytQduUglOnx4Ejs4dBlBET+7QEAqqwUs8dUtTsPmA5F+jWj/ZoZLh9cqTOUaXEPHHMLxae2wnX4nIfzQgghxFZwLSftuvIiFO5fB/exLzVhQM1DveZ0bd26FS+88ALeeecd9OjRA0FBQejRoweWLFmCuXPn4tdff220AKum9qsfqLatUqkgFosb7TrNnVQiQKifk8X+7MIKkzYel4NB3fzw8tRIzBwZBnfnyt9nfbZp+v3fBMiVmpoHEpuhLS9G5q+LUHp2J5R3b6Ai4RSy/ngPsgf2m1fn365VUl+lIvFsY4dKSJMrl6vxx17TYmUXb+TixJUMKFXaOp3P0udqgJe94bP7dGwWPvv9AlIySqHW6JB8twSf/haD8/HZdY6fNA29Woms9f+HokO/Q558CbK4Y8havwRlVw8BAHiO7maP4ztV1lUQegXD9+nPYd91BIS+YZB2HgKfpz+D6o75QnkViecezgshhBAbIr9lOpPpfuXXjkGnqP+23C1VvZ7YFxcXo3v37mb7oqKisH79+gYFdb+qKfh5eXkICAgwtOfl5SEsLKzRrtMSjBsQjJsbL5ntC/F1NPo5p7ACB87fRn6JAh0CXTC0uz9E/22P1yHIBU+PC8cfexOh1RkndJZmBShUOtxIL0L39qbF14htKo2Jhq78gfLdrB5FRzbArkMfw1pOhlu3jwlWq0Xu31+C5+wJh26jDF9g9Vo1ZLFHIU+9Ao7IDvaRwyAOCG+U10JIY7ueWgi1xnxZsxWbLoPLYdAnwhsvTu4MR6nxziMnrmRg/7nbkMk16NLOHb07eeFQzB042AlQdt+2oV6uEix64t4uMA9uQQpULn3aeuQWouipvU0qjz0MdW7aA60sio5sgLTTQDhGPYr86O9MjnOMetTw3wI3P9NZTpbWU9ZynSUhhDRn2tK86gfotNBVlIArljZNQM1EvRL73r17Izo6Gv379zfpO378uMWkvz7at28PqVSK8+fPGxL7srIyXL9+HbNnz26067QE/Tp749d/hCgqUxm1uzuJ4e9pb/j5WnIBPlh7zvCl9dilDOw5nYbPXukPqaRy6svkIW0xtEcA4pLzwedyodZowXAYJN4uxj8nU81e315Ca12aE6WFJ0La0jxoywrAd7z3RInv5gdNQUatzstqlIan9mWX9sN71nsQegYhZ9OHUN69d01Z3DG4jpwLx55jGvhKCGl80ho+z3R6Fqdis5BfosCXrw40tP954CY27U80/JyaVYrtx0yr2bo7ibFi/iDY2927zt0884XU7uZaLrBGrEuRHm+2Xa8ohzo3HfYRg8CqlSg+/Td05YXgSp3h1GciHLqNrPa8duF9IYs7ZtreoW9jhE0IITZN6F39LmQciQP4zvQw8UH1moo/fvx4HD9+HHPnzsWuXbtw7tw57N27F6+//jo2btyIqKgo7Ny50/C/hhAIBJg9eza+/PJLHD58GImJiVi4cCG8vLwwcmT1/zC2NnKlFuVy0+nw+SUKXEm6t6/4mp3XTJ5E3c4px64Txgl7cbkSaVlluJ5eCDdnCQZ29cPo3m3Mri0N8nFAuwDnRnolpClY2iqE4fLBFdkbtXlMXGg8nuHAMWo8hH7tq70Gq1ag6PB6yBJOGiX1VYqObYRepah78IQ8dBaqkT7g5u1iJP5XhFSm0GDbkVu1Oi6/RIE9Z42f9AZ42ZsdG+Bpvp1YH9fOscY+h+6jEPC/1Wjz2u8I+N9PcOxV824+rkOfhMDTuG6NKKAjnPvTFjSEkJZP2qGvyWfg/VwGPgaGy2/CiJqHej2xf+211wAAp0+fxunTp036V6xYYfhvhmEwceLE+kX3n1dffRVarRZLliyBUqlEz549sW7dOvD59Ad6v2spBdBoza+FvpyYh25hHigsVSA9u8z8mJu5eHx0ZaIWfTIFa3fF478ND7DzeArGDwjGcxMj8NqsbvhpxzXDlNJQP0e8ObsHLt7IRU5hBUL9nKhKfjNgHzEY8pvnTdqlEYPAERrXrxB6BsLnqWVQ3r0O6LQQBXYyPNFX3I6HOv8uik/9DX1Fscn5lHdvgOvgZjYGVq2EMisJkqBIs/2EWEt+ce1vOOUVy9E+0AXpWaUWp++bc+VmPmYMv7ekbPqwdlj2u/G6QoYBpg1vV+tzkqbl0GU4yq8eBljjf3vFwZHgO917msQwnDpNGeXaOcJ37hdQpMZCU5QFgUcbiNt0arS4CSHEljE8PrxnL0Xp2Z2ouHkOOqUcDIcLvnsAnHo8Aknbxpsd3pLUK7E/fPhwY8dh8Nlnn5m0cblcvPnmm3jzzTcf2nVbAjuR5RsduUWVxfNib+VbHCMVV04JLS5T4td/EgxJfZXok6kY3N0PA7v6oU+EN5LulMBOzIejnQDvrTlrdMOge3sPvDOnFwTVVIIm1qEtzUf+3jVQpF6tbGCYyoW8YMB1dAcjEENdmAXBf9vcqbKSkb93DdQ5lVWcGYEY4pCucBn0GASuvhC36QRxm04ov3IIajOJPRgGrFZt2v4frsTyEy9CrKVtgPO9vxrVYACE+jsBANycxLU6popUbPyZ3bezD96Z0wvbjtyrij99eDv0Cveq+wsgTULoEwqP8a+i8NBv0FWUAGAgDu4Cj/H/a/C5GYYDSUhXIKSrUTvLslCkXq28aSp1hrTjAFpnSghpcbgiO7gMeRwuQx63dijNRr0Se19f38aOgzSCzm3d4eYkRkGJ6ZOm8wk5SM0qxQ/b4swcWWl4z8oaBpdv5kGrM//NNCYhF239ncHncQ37Ln++/oLJLIBLiXnYcSwZM0ZQgUNbwup1yP7zQ2gK79vS0JCFsNCV5qEs5h+UX9oHz6lvQegTiqxNS8Gq5PeGqxWQ3zgDeeI5eEx+DdL2faDKSYOmyELlbpaF/NZFs11cqTOEnoGN8+IIaUS+7lIM6xGAQxfuVDuuSzt3+LhVJlVernaI6uiFc/Hm96x/0INFTQGgT4Q3+kRQobzmRNppAOw69IE67w64EnuLlfAtUWbchDrvNviuPjU+lWd1GuRs+RyK1CuGtuLjf8Fr5v9B5FP9mlRCCCEtW70SewDYv38/Ll++jLIy02ndDMPg008/bVBgpO64HAYjowKwaf9Nkz6WBf4+fMviNNHOoW4Y0LXyho1IYPltIRIYP4HX6vQ4Z2ErphNXMymxtzHylCvGSb0FrE6DggPrYN9tpFFSbzxIj4K9P8OubU8UHvoNrFZlfhwA6M2/73QqBVitBgyPltUQ2zNvehcEedtj86EklJmpX8IwwCvTuhi1LZzZDWt2XsOJK5nQaPUI9HIAhwukZpr+W7n9WDLG9AsyqapPmh+Gy4PQO7hOx+g1KuRu/RyKtFhDm9C3HbxmvGvxCXzZlUNGST0A6JUyFPz7I/ye+6rugRNCCGkx6pXYf/nll1i7di2kUikcHBxM+hnajsVqXBxEFvse3LrufvEpBViy+gyEfA5yi+TgcTkm43lcxpD8V2FZwMzudwAAvaUOYjXa0oLajy3OgTq/+qeVenkplBk3obxtvjJ0jTRK6FVycHk0HZ/YnpJyJf49k242qQeAUb0D4ekiMWqTiPhY8Fg3vDi5M1RqHRylQpy4koHlG0y3IlWqdXhr5UnYifno2cETEwaFQFLNkirSspSc2mqU1AOAKjMJRUf+gPvYlwAA6rzbKL2wB9qSXAi8Q6DKML1xXzkuHZriHPCdadkGIYS0VvVK7Hfs2IFZs2bhvffea+x4SAP1CvcCjxtnMpWeYYBJg0MRcz3XbIKvZ6tffy8R8TBvahd4OBt/ieXzOOjZwRPnE0ynnvbr7FPPV0EelrpN1WQg9g1DhZktl+7HEUvB8IVgNdU8sbeA5+wFjsT05iAhtuDXf64jq6DCbJ+7sxgvTe5s8ViRgGeY/VShMH9jAIDh/LfuliDmeg6++N8A8HlUm8SWqbJTIbtxGtDrYde+N0R+9ZuZJks4Zb79+mm4j30JirQ4ZG/+BNBpAQCK9GsA1/LXNqoQTQhpbvRqJRi+AAxTr43ayAPq9VtUqVS01ZyNcnYQ4cXJncG5b0s6hgHmjO2I9oEueGmKcV9NwgKc8e7TvfDbe6NMntZXeW5iBLxcjRP+DoEumDK0bf1eBHlohD6hQC0/PCWh3WAfORR8j0CLYwTeoRB6tIG006Bqz8Vz8gAjenBqKQOXwbNohg+xWWevWV62kl+swM3bZopFmtGlnQdq8zZPzijFqdial8oQ6yk5uxOZv7yJ0rM7UXo+Glm/v4OioxvqdS7WwhKlqpukhUf+MCT1Bg/+/B+RfwfwHFzrFQchhDS1iqQLuLtmAdKXP47b38xF8cktYFnLM4tJ7dTrif3IkSNx6NAh9O7du7HjIY1gVO9AdGnngdOxmdDpWfTt7ANf98qkamRUG3Rp647vt1412tvektxiOXp3slzISabQ4FpyPh4dEAywgEKlRYifE7qFedTpBgJpOnYd+6Ei/mS1Y3iOHnAb/TwYLg++T32CvH++hzzxHO7f25vn4gPPyZVbX7oOfxK68kLIk6umGzPgOriCa+cISVAk7DoOQOnZHZDfughWpwPPxRuuI+ZAEhjxkF4lIQ3H5XIAC1uIApWfjx2Cat7a09vNDlOHtsXWwzXvcX/zdjGGdPevU5ykaWhK81B0dKNJe8mZHZB2HAiBR0CdzicO6QrZVTO7DLF6KO5chzon1exxjFACVqVA1ecxz9kL7o/Oq9O1CSHEWhR3riN32xeGbUL18jIUn9gMVq+Dy6CZVo6ueatXYv/OO+9g2rRpeOKJJ9C5c2eIxcZ7XjMMg1deeaVRAiT14+kiweQh5p+Ye7hIMK5/cK0Se3cnscW+S4m5+Hz9BShUlU8dOAzwxJhw9OjgafEYYn2uQ5+EOisFmqL7ngxyeUZPgrSleSjY/zO8pr8NjkAErylvQK9WQJ5yBdqSXAj9wyG+b/opRyCG14x3oC7IqFwL6hFoeHqkV1Yg45e3oC2+t1xDk5cO+c0YSuyJTRvY1Rf7z90228cwQNv/trmrjSfHhKNLO3ecuJKJ4jIlYq7nmh3nVs1nLrEuRfIVk/3qq8iTL9Y5sRd6hUAG89sHK1KugCOUQG+meKnQow3cxr4M5d0b4EmdIQ6OBMOh5RuEkOahNGa32c/Ssot74dxvKhVUboB6JfZ//PEH0tLSkJaWhgsXLpj0U2Jv+3p08ERYG+cap5KOHxhitl2l0eHLDZcMST1QuU7/93+vo2s7d4T4OTVmuKQR8exd4Pf8ClQknoO6IAN6jRpl56NNxslvXYQqK7ly+j4qk3dph77Vnlvg5geBm59RW3ncUaOkvkrZ5f1w6jsZPHvnBrwaQh6eOWPDkZ5Vipt3Skz6hvbwh6+7FBl55bidXQ5fDykCvauvF9E51B2dQyu3QnvzuxNIfODz107Ew7Ae9LTeVjECy7sXMHzLhWst4dpZfr8wfCHsu45A6bldJn0O3UdB4OoDgSvVsSGEND/akjyz7XplBXQKGX0vbIB6JfYbNmzAo48+isWLF8PVldZ0NUccDoMPn++DbUdu4UxcNrhcBi72IqRmlaKsQg0XBxGmD2+Hwd38zB5/9WYeZBYKQp2KzaLE3sYxXD6kHQcAAIqO/WlxnCo3zZDYV2H1uspt8woywHf3B8BAlXETPHtn2HUcAK7IDgCgLsxEReJ5VCSeNX9yvQ7qvHT6ACc2SyoR4Mv5g3Dheg72nE5DZn4FHOwEGNzdD8N7tcEXf1zEyauZhvHd23tg8ZM9IRLW/E/rkmei8MO2WJxPyIFezyLU3wkvToqAczU7mxDrsmvXCwVCickWoAyXD7sabnqaIwnpBo7QDnrVAwUaGQ6kHfuD5+AKvVoBWexRsDoNOCI7OPWdbPjsJoSQ5kjoFQR1bppJO9fBrdobnqRm9Urs5XI5pk6dSkl9MycR8fHkmHA8OSYcqZmlOHLxLtycxAgPdsXgbn7g8ywXWatuJzuWpW3umguW1UP+wHZL93tw6ySdvAzZmz40+4EMAEXH/4T3zPeguJ2AosPrcf+afPPnp2UbxPb1DPdCz3Djvwt/HbxplNQDwKXEPPy+5zpemGS5Wn4VR6kQ78zphQqFBmqtDs72lNDbOo5QApfBM1G4f51Ru8ArGFyJfZ3PV3Jqm8lUe4bLh9sjzxs+e90feQEug2dBV14MnrMnOHzLswYIIaQ5cOw9AbLEcyY3SZ0HTKNlRQ1Ur8S+b9++OH/+PKKioho7HmIF+86mY9XfsajKxw9duINLN3Lx1hM9LBbA69rOHRIRD3KlaYXevrTNXbMhu3YC6izzBb0EXiEQtelk1FZ0dKPFpB4A9AoZcv7+ErpS89Os7idu2wN8F3qvkObpyMW7ZtuPXrxbq8S+ip2YDzvQesLmouzSfpM2VeZNyOJPwL7zkFqfR55yBSVntpu0c53cYR851LhNbA+uuO43DgghxBYJ3Pzg+9SnKDn9N5RZt8BzcINjz7GwC+tl7dCavXol9uPHj8f//d//4fbt2+jatSuk0ge3sQImTpzY0NhIE6hQaLAuOh4PPmQ/HZeFi4m56PXAU6oqIiEPCx7rhuUbLkJzX9XoGSPaoV0ATa1uLipunLHY5zZ6rslWdNWNr1KbpB4A1DmpkKdcgSSka63GE2JLVGrz246pNOa3MCPNn7ogA5qCDLN9FYnnapXYs3oddPIyyOJPmO3XFmZBlZsOoWdgQ0IlhBCbJnD3h8fEBdYOo8WpV2I/f/58AMC///6Lf//916SfYRhK7JuJhLRCKNXmv4hevGE5sQeAPhHe+GXJSJyOzYRKo0NEqBuy8iuw/egtdAh0rdU2UMS62GqmynPFD3edk668CLnbvoD/S9/T/svEpl2+mYcD59KRmS+Dh7MdxvYLQs9wL7MV8x+csk9aDoZjeXma4s513P7ueYjbdIRT/2kmhe30aiVyt34Oxe34ymrQjOVzKW8noOjIBmhL8yD0CYVTn0kQuFNRRUIIIdWrV2J/+LD57VlI81EqU+H4lQwk3y2xOEZSiwJQTvZC8HhcbDt6C7/uvm7U17uTFxY/2bNyL2hic/QaFTRF2Wb7BF7B4Lt4m7TbdeiL8quHGi0GVquGLP4EnPpOarRzEtKY/j5yC7/9e++zLT27HDHXc+DiIALDwGi2k4uDCHPGhVshStIU+C4+4Dl7Q1ts+rnJquTQqeSQxZ+APOUK/J79EjwHN0N/5ro3jbcYtbBtHkdsj8KDvxh+1hRmoiLpAnznLDPZcYQQQgi5X70Se19f32r7qXiabbuWXICPfjlntFXdgzgcBkNrse3SoZg7+H7rVbN95+JzsO9sOsb2D65vqOQhKru4F1pziT2HC49H55k9xqnvZJTHHobJ2o2aPJgB3UenKKvbuQhpInKlBuv33jDbV1SmNPpZLODhs1f6wdvNdGkaaTl49i5mE/v76RXlKDqxGR7jKrf9VdxOME7qLWD4QoBr+rWMVclRcmYHPMb/r35BE0IIaRXqldgDwJ49exATEwO1Wm1I5FmWhVwux9WrV3HihPn1Y8S69HoW326+Um1Sbyfi4flJEQjwqnkq9vZjydX2n4rLosTeRslvXTTfodeB89+WdQ9S3Lle96QeANfeFTpZMaA3fd+JA2tfaIyQpnQpMRf66rYAuY9CrcWRixl4bEQ7mqXUgumVslqNk107AbcRT4MjlECdl17NSAZOfSeBI3GAJKQrMn6ab3aUKrv6f2sJIYSQeiX233//Pb7//nvY29tDq9WCz+eDx+OhqKgIHA4H06ZNa+w4SSNJyypFbpHcbF9EiBumDm2L8CCXWu3DDADZBRU1DyI2ieELLPWA4Rn3sSyL/N2rIIs7Ut+rQeTbDsq7xk8/JWFREAd3qec5CXm41Brz06Ut+evgTew9m4aJg0IxdWjbhxQVsSahT1uo80xrK5jQayGLPwGH7qPBdzZd1lSF7+YLlyGPA6gsrMfwhWA1KpNxPEePesdMSGuhVGtx5WY+ABZd2nlAXMvvsoS0FPV6x+/YsQMTJ07EsmXL8N133yErKwuff/454uPj8fzzz6NtW/pCY206nR6FpUo42AmMknReNU+SXBxE6Na+bl8egn0dkHSnxGJ/p2A3i33EuqSdBkGRarqHvTikC7gS49ka8qQLDUjqAV1ZPnRl+QAAgUcb8N38YNeuJ+w69DWpvE+Ireje3hMMUE2JSVOlMjV+//c6+DwOJgwMqdd1C0sVOHLxLsoq1Ihs645uYR4Wtx4lTcupz0RU3DwHvaLmJ/ea4lwAlZ+pltbmu46ca/hvRWqs2aQeAByjxtUzYkJahwvXc/DVpsuoUGgAABJR5e5NfSIs31gjpKWp13zB3NxcPProo2AYBh06dMCVK1cAAJ06dcKLL76IrVu3NmqQpG4OnL+NZz4+iLmfHMTsD/bh513XoNVVPnlq4+2AQG/zU+yvpxUiPqWgTtd6bERYtf09OtBTBltlHzEIDj3HGlVnFngGwX3syyZjK26er/mEtUzQ1Xm3wWrVsAvrDYbDrXW8hDQ1J3shhvcKqNexu06k1Ou4y4l5eH7ZYazfcwM7j6dg6dpz+OTXGOh0dZs9QB4Ovos3fOd8BvuuIyDwCIQwoKPFsULvyhs7DMOB71OfQBza3fB5y3Vwg9eMdyEJurcUqezyfvMn4nAh8mnXeC+CkGZAr2ex60QKXll+BE8t3Yev/7yMnELzs0RlcjW++OOiIakHALlSiy83XESpzPzNMkJaono9sZdIJIanbG3atEFGRgaUSiVEIhE6dOiAjAzz+7ySh+/C9Rys3HLV8LNKrUP0iVRwORw882jlF5A3Hu+OD9aeQ0GJwujY/BIFlqw+g/eejUK3ME+L10hML8I/J1ORVyxHuwBnjOgVgIMxd0zGuTmKEOpPe9rbMreRz8AxahxUmbfAs3eFyL+9+YHVbM1kUIe19/KkCyg+tQ0ug2fW+hhCrGHetC4I8HLA3jNpKC5XwcVBhMz8mp/WFpQowLJsnWak6PQsVm69CrXGuBZFzPUcHL+SgaE96neTgTQuvos33Me8aPg5b+c3kCWcNBoj8AyCXfsow89cO0d4z3jHUJPI3PtCV1Fq/oJ6HfSqCnCE4kaInpDmYW10PP45mWr4+cjFu7ialIfvXh8CR6nQaOyZa9lmt25Wa/U4FZuFsf2CHnq8hNiCej2xj4iIwM6dOwEAQUFB4HK5OHv2LAAgJSUFAoGltbvkYdt9Ks1s+/5z6dBo7z21X/REd7PjdHoWH649j+tphWb7z8VnY9EPp3DiaiYSbxcj+mQqDplJ6nlcDl6aEgkuTR+1eXxHD0jD+1lO6gFIO/a3fAIzVZxrozzuaL2OI6QpcTgMJg4KwU9vD8eWT8di9eJheHFSBAT86mebhPo51XmZSVpWqckN1yrnE3LqdC7SNHQVpRB4BUPUphN4Tp7gufjAsc9EeM9eCobLNxlf9Z6Qp8Wi8PDvKD79N7SllcuURAEWtkpkGJTFHgVrpvgoIS1RSbkKe8+Yfp8tKlNh37l0k3aNxvLfjQdvlBLSktXrG/mLL76Ip59+GmVlZVi9ejXGjx+PRYsWISoqCqdOncLw4cMbO05SS4Wl5r8UypVayJUaw13OEpna4jl0ehYb9yXik5f6mfT9tvu6SZXoB5/T9gr3xHMTI+Dlar6yOml+JMGRcOo7CSVndqLqT5wjcYTbiDnI2/Vdvc6pV5t/rxJi68b2D8bAbn6ISy5AcZkSv/yTYLhxCgBcDoPZozvU+bwCnuV77TXdSCBNT5lxE9l/fQxWda8grTgoEi4DHwPDM03qAYBl9cjb+Q0qrp82tBWf3ALPSa9DFBiB0nO7zB2EkhN/gVXK4Dri6UZ/HYTYmrt55dDqzM8CTM8y3SK3R7gXODuv4cFNTBgG6NXR62GESBqJTiGDtiQPPGdPcC3syFRfyswklJzeDnVuGngu3nCKGg9JaLdGvYatqVdi37NnT2zbtg03b94EALz33nvgcDi4fPkyRo8ejcWLFzdqkKT22ge64HZOuUm7h7MEZ69lw9VRhG7tPdEuwBkcBiYfglWupxWZtJXKVLWagppdWEFJfQvkMmQ27LsMhyItDhyxFJK2PVBx4wzqVlrsnpb+4Upaloy8clxLLoCjVIie4V6wlwjQr7MPAKBjsCu2H01GWlYpfNylmDQoFB2CXOp8jQAvBwT7OiI103RK9pBu/g1+DaRx5e/50SipBwBFWizKrh6CY49HzB5TcfO8UVIPANBpUbB3Ney7VP9QpOzyATgNmN7oX34JsTXernYWv6P6uktN2jxdJHhiTDh+//e6UfvMke3NjifWx+p1KDz0O8ovHwCr04DhCeDQ4xG4DH2iUYoqKzOTkP3He2B1lXUXtGUFyEmPh8fk1yHt0KfB57dV9d4Hon379mjfvnLqrlAoxEcffdRoQZH6mzq0Lc5ey0ZZxb0n8gyAvGI5fthWWQHd280O86ZFQiLiQ3ZfoZH7uTqKTNokIj7EQh4UKm21MdAWeC0X39kLfOd7d7+5Ivt6nYdr7wKXwbMaKyxCHqqftsdh9+l700JdHUX44Lk+hkKkQT6OeP1x88ub6urN2d3x4brzhs9RLofB9OHt6rxjCXm4NMU50OTfNdsnT7pgMbGXJ100266rKIVOYfok8n6sVg1taT4l9qTFc3MSY3B3fxy5aPx3zE7Mx+g+gUZtWfky7DqRgts55egb4Q2phA8HOyH6dfZBqL9T0wVN6qTk7E6UXfjX8DOrVaP03C7w7F3g2Kvhu4CUnP7bkNTfw6L45GZK7M2JiYmBQCBAly5dkJWVhQ8//BCZmZkYPXo0XnnllcaMkdSBl6sdViwYhJ3Hk3HrTglYsCbb0WUXVOCjX85DqbK87mhc/2CTNj6Pg9F9ArHjWHK1MYT4OtUndNIMiYMjwXNwg7as5t0UuA5ukAR3gcDdH9KIweCK6S46sX1n4rKMknoAKCxVYsWmS/ju9SGNfj0/D3v8uGgY4m7lo6xCjU4hrnB1pKJptsbc+vl7fZa/Wlmaog8A4pDuKL96FNCbv3nO8EXgO1kubEtISzJvWiSc7YU4cP4OKhRqdGnngafGhsPN6d7nYVpWKRZ9f8rogZOAz8XHL/SlpN7GlV8+YLa97NL+RknsVTnma45p8u+C1Wmr/ZxuzupVPG/nzp146qmncPDgQQCVU/HPnz+PNm3aYPXq1VizZk2jBknqxtNFghcmdcaX8wdCKjFfyNBSUs/lMJg5MgwTBpom9gDw5JgOGNs3sNrrPzay+i3wSMvBcHnwmvEO+G5+NY2E26hn4T72JTj2GkdJPWkWisuV2Lg/0WxfWlYZ7uaaLntqDFwOg65hHhjUzY+SehvFc3C1WOxOGjHI4nHSTgPNtvNdfWDXtgc8JrwKCExnzAGAY6+xVBmftBp8HhdzxnXEpo8ewc7l47H0+T4I9nU0GrNxX6LJLFK1RocN+240ZaikHnRy8zOUdHILu4PUEd/FfG0FnoNbi03qgXo+sf/tt98wadIkvPnmm8jPz8eZM2fw+uuvY+7cufjll1+wefNmPP/8840dK6kHvYXiI5Z0CHLBrFHG1dHPXsv6746pBt3be2Da8HbYczbd7O5mDnYCXErMRU5hBYZ094ed2PLTCdIyCDzawP+Fb6HKSa3cronDhSrzFmQ3TkNbVgChewCc+k6CJLRxpioT0hRuZ5fh7VWnUS63XGj0wUKidSFXavDPqVRcvJ4LkZCHoT38MaQ7raNvTtwfnYecvz6GpjCrsoHhwKHHaEjDTQvPVhEHhMNlyGwUHf/L8GSe5+gOj0mvg2EYSMP7QRLaDeVXj0B24ww0hZngSp3g0G0UHLqPboqXRUijiUvOx4a9iUi8XQRXRzHGDwjGxEEhdV5DbWm8pR2cLLUT2yFq0xGKlCtm2js1yvmdoiYg5/Z1PFgHyrH3hEY5v62qV2KfmpqKd955BwBw/PhxsCyLYcOGAajcCu+bb75ptABJw/SO8MbVW/m1Hl9VDAr4767n3hvYcTzF0HYjvQin47IQEeyGuBTT6ddlFWrDlntbD9/Cspf7wYcKl7QYrE6LisRzUKRfA1fiAPvIoeC7eAMAhF73ZnlIgjrDuf8Ua4VJSIP9ujuh2qTe190OAV71qzGh0erw7uozSL5bYmi7mpSP1MxSzB3fOF9qyMPHd/KE3wvfQpF+DTpZMUT+HWo1Vd6p7yRIOw+B8vY1cERSiIM6g+Hc2/WAIxDDsddYOPYa+zDDJ+ShunW3GO+vOQetrnLHkIISBX75JwFKtQ4zG2lmp4uDCOVy01pRLg7mZ70Q2+EyeBayMm4aFSDliKRwGfRYo5xf0rY7PCYtRPHJLdAUZIDr4Aan3hPg2HNMo5zfVtUrsXdwcIBMVlkd/eTJk/Dx8UFgYCAA4M6dO3B2dm60AEnDDIj0wZ8HElFazfZ2VbqFeWBU70Do9Sw27LuB3adSoTAzZT8tqwwzR4YhI78cRWUqi+cr+m8bqCXPRDXoNRDbwGo1yP7rYyhvxxvaSs5Hw3PyG7Br19OKkRHSuFiWxZWbeRb77cR8zJ9RuavD0Ut3cfTiXai1evQK98LY/kEQ1rA13cmrWUZJfZVdJ1IwYWCI0RpSYtsYhgNJUGSdj+NJnSDtOMBsH6vVAByOUbJPSHOz63iqIam/X/SJFEwdGgo+r+Hv77H9g7Hqv8LQRu39ghp8bvJwCb2C4ffslyi7uBfqggwI3APg2OMR8BzdG+0a0vB+kIb3A6vVVFvfpCWpV2IfFRWF77//HsnJyTh8+DCefrpyX9X9+/fj22+/Rf/+/Rs1SFJ/P++KrzGp7xDojMdGtEfXMHcwDIM/9ydi6+Fb1R6TU1iB1YuH44+9lTcAzE3LB4ALN3LBsmyjbF1Bmp5WVoKSsztRceMM9IpysNoH3ks6LfJ2fg3fZ76A4L519qxOg9ILe1Fx/TRYVg+79n0q14fyhU38CgipO4ZhIBbyUKE0LWLmbC/EqkXDIBXz8dOOOMMMJQBISC1EzPUcfPJiX3C5lkvYJKSaLzbJspVP7of3Cmj4iyAPnTLjJkrO7oSm4C74bv5w6jMRIr/6P4lU599F4aFfoUiNA8Pjwy68P1xHzKEq+KRZyiwwvz2yTKFBSbka7s4Nv4H5SJ9AlFWosONYCioUGoiFPIzrH4RJg0MbfG7y8PGdPOE6fM5Dv05rSeqBeib27777Lt588018//336NOnD1544QUAwLJly+Dj44PXX3+9UYMk9SNXanAqNrNWYzsEuYBhGOj1rEkFaHNcHcUQC3m4llxgMakHACGfQ0l9M6VTyJD1+zvQluRWO47VqJD562L4zV1umJafu/0ryJMuGMaoc1KhSI+D96z36f1AmoVhvQIQfSLVpH3ioFCkZZXi138ScMvMU/eE1EKcT8hB3/uWNT3I3A2DKg+rIB9pXIrb8cje9JFhnbymKBvyWxfg0G0UnAdMB9fOsYYzGNMpZMje+H5lnRJUbv0kizsCbWkufGZ/2OjxE/KwBXk7mJ2Z5CQVwtmh8W7yzxgehomDQlFQooCrgwgiYcstjEZITepVFd/FxQXr1q1DXFwcfv75Z0illWuoN23ahL/++guurq6GsTt37kRpaeNUOCR1I1dqoa1F8bwb6cX488BNAJXr6ssqqn/Cz+cyGBnVBuVyNdKzq993d3A3KgbVXJXHHq4xqa/CqhUoOR8NAFBmJhkl9VWU6degSDOdMkeIrSksVUCr1UMiuvcFkcNhMKp3G3QKccV7P501m9RXuZFeVO35Q3wtJ31UcLR5KD6xxXRbOpZF2aV9uPPDy1CkxdXpfLL444ak/n7K2wlQZVW/xSwhtmjioBCIBKbT7acOawteNTOa6kPI58LXXUpJfSug16pRcnYnMn97G5m/v4vSC3vA6i1v393aNOrfAB8f4ycUOp0Ob7/9NrZt2wZHx7rdvSYN5+Ykhp+HFBl55qdD3e/ElQw882hHiIQ8BHo7VJuw9+nsA283O2i0OogEXCjVlv9CdWnXeGtlSONS5d1GacxusGolJGFRkLbvDXC4UKTFQpEWh4rEc3U6n/zWJcjanIa23HJSI0s4BW1JHgSeQRD5tm3oSyCk0ZXKVHhz5UnkFyuM2icNCsWcceH4auMls+tG7+fqWH3hpv6Rvvhjzw2Yu+3avb1HXUMmVqDKTrHYx2qUyNv1LRyixkFXVgiegzvEbTpC6GM6PZjValCRFAPZjTMWz6cpyTV7LCG2LMDLAZ+90h9/HriJm7eL4eYkwqMDgjG0R92XGrEsi/TsMjAMg0Bvh4cQLWkOWJZF7pZlRjdOVRmJUN69Ac/JNFscaOTE3hy2unna5KF7bkIEPvrlfI1fRHX3bdv05JgO+PiX87C0k5NaU5nI83lcDOvpj39Pp1s8b36JwmIfsZ683T9AFnvE8HPFjTMocvQEh8uFpiirXufUlRcib8cKcMSW/9GVxR2FLO4oAEAS2h0eU94Ahyeo1/UIeRj2nU03SeoBYM+ZNEwf3hYZ+dXfKLUT8Wrcts7bzQ5Th7U1qWXySN9AhPg51Tlm0vR4Th7Q5N+x2K+rKEHxkQ1GbZKwKHhOes2wh7KmKAvZmz6EtrS6nWsYCDypEBhpnkL8nBpcQDnxdhG+2nAJOUWV1dP9PaV4bVZ3hNJnZaujSIszOxuq4sYZqLInQegdbOao1qVx58IQm6LV6SEScvHG490wqnebasf2aO+JnMIK7DqRgqyCCvxvRhdYWgnt71m5xZNMrgaHU/166QDP+m0HRR6eisRzRkl9FV1pbr2T+vvpFWVALdbRy5MvofTszgZfj5DGlHSnxGy7QqVFRp4MgV6Wb1wF+zjgg+f7wFFa8/rRJ8eE45OX+mJU7zYY0SsAHzzXGy9PqayufvFGLt798TTmfnIQy36PQXKG+ZiI9ThFPVrnY+Q3z6P04l7Dz/l7fqohqQekEQMhcLVcr4GQlkyu1ODdH08bknoAuJsrw/s/nYXqv4dMSXeKcS4+G8VlSmuFSZqIKjPJYp+ymr7WhBajtFAxCTn4futVFJdXbkfn7VZ9Vd1DF+7g0IV7Tx84DAMwwINzRfk8DhzthJj9/t4aq+2HtXGmqfg2SJZwslHOwwjEYNUWZmTUcqaOLOEUnAdMb5R4CGkMfJ75m1IcDgM3JzEmDg7BydhMqB5YgjRjRDvMHt2hTtfqHOqOzqHGn5HHL2fgy42XDD/nFclx8UYevpjXn57m2xD7yKFgtWoUndgMvbz6WjP3q7h+Gk5Rj0InLzPaOtQIjw+Biy+kEYNg1743io5uhDrvNviuPnDoPhp8Z69GehWE2LY/D9yEWmM647RMrsahmNs4dOGuoUAfj8tgypC2mP1I3T6HSfPBc3Sz3OfgarGvNaEn9i1QfrECn62/YEjqASC7oALcGp6u30/PsmZzM41Wj7XRNW+h5+UqwdLn+lAFdBvUWEVGfJ76BEK/9jYRCyGNJSXTfLHXtn5OcHEQoY2XA5a93A+9wr1gLxEg2McR82d0qXNSb8mm/YkmbWqNDluPVL8FKWl6Dt1Ho82CdXDs9ShgcY6bsdp85gm9QuD33FeQtO2OzF/eQsmZ7ZUznM7/g4y1b1AxPdJqxN6yPKNlx7Fko6r7Wh2LzYeScPZaw2ceEttk16EvuFJnk3a+iw8kod2sEJHtoSf2LdDRS3eh0Zre4dTpWXA5jNF6+oelX2cfqu5so+zCosxWra8LjtgBQo82sO80AKoM00SEEUrAquRmjnwglva9GxQHIY3pbm45cgrNv2/tJPc+z9r6O+P/5jZs3ej9cgorcCYuGxqtDlkFFWbHpGbQ7jK2iGE4cB0xBw49x0B5Ox7aihIUH91ocTyrUYHV68CVOEDUpiOUtxNMxkjD+wIAik9shl5hvP0hq1ag6NgmeM96r3FfCCE2SCSwnKbkFpmfMXj4wl30iaDlKy0Rhy+E9+MfoGDfmv8+OxmIg7vAbczzYDimOzC0RpTYt0AyhcZin07PQsjnQM/CbPLfGPg8Dkb3CXwo5yYNJ+00EBU3zkGefLF+J+Bw4DmlsvqofdcRkKfGQp4UY+jmSl3gNuZFFOxbA11ZgaGd4QnAau/N9BB6h8K57+T6xUDIQ8DlWn7qKuQ/nC8N/55KxZqd1ywWK63i5Sp5KNcnjYPv5AG+01AAAKtSoOTMdrPjNIWZkMWfhH3nwXB75EVkb1pq9DkpadsDDt1GAQAU6dfMnsNSOyEtTf8uPma3DxXwOWan6AOV9VBIyyVw84PP7A+hk5cDDAOuWGrtkGwKJfYtUNd27thxzPJUPZWFD8PG8uiAYHi5Vr+mn1iPTl4GvkcA+OUF0JUX1Xp9qMAzCKKAcDgPmG74IGU4XHhNWwTl3UQoMxLBtXeBXVgUOHwhhL7LUXxyC7QleZCEdIV9l2GouHEGmqIcCL2CIGnbg+6wEpvi4yZFW38ns3vUD+zq2+jXyy9WYM2u+BqTegCYOJi2O2sunAfPBAug1EJyL791AfadB0Pg6gP/l1ZCfjMG2rICiPzCIPLvAFVuOhQpV8BwzK+W5NrR9sGkdRgZ1QbHL2ci6U6xoY3DMHhtVjf8sScRmWZ2KekZ7tmUIRIr4UqoOLc5lNi3QF3auaNfpA9Oxzb9OiMel8Fk+gJqszQlecj4eSFYdd2qx/KcveA79wswjPkvmiL/9hD5V66315TmIeff1VCmxRr6FcmXoEiLhefkNwxbPRFiixbO7IYPfj6LvP+2vGMYYGzfIPSPbPzE/nxCNvS1XBrF51JJHFuk16hQfPwvyOJPQK9RQRIUCXVRVrVb4TH3bfHJ4Qkg7djf8HPBwV9RFrO72mvadx3R8MAJaQZEAh6WvdwPxy5n4FpyAZzshRjRKwABXg6wlwjw4brzRoVMOwS60IxR0qo16Bu2Xq9HUlIS8vLy0K1bN2i1Wjg5ORn6uVwu1q9fj6Ag2oO1KTEMg7dm98DZyGys3HoVFdVMzW9s3cI8arXVE7GO3K2f1Tmp57v5waHbKKhz0mvcI1SvVSN7w/vQluSZ9MmTLqDs8gE49hxTp+sT0pT8Pe3x09vDcelGLkpkKnQKcYOv+8OZ6lfTdqH3O3MtCxGhlisCE+vI2/4V5Mn3djGouHmuxmOkEYPMtivSr1Wf1HN5cOgyHM79p9Y5TkKag3K5GgI+12jpk4DPxcioNhgZZbxtc+dQd/z41jAcvngHRaVKdAx2Rb9IH/DoJihpxeqd2O/atQtfffUV8vLywOFwsHXrVqxcuRJ8Ph9fffUVBILKO9K9evVqtGBJ7XE4DJzshbVO6rlcBjpdw4vqDejS+E+1SOPQKWRQ592u0zFC3zCosm6h8MA6AIAooCM8p75lcU1TxY0zZpP6+/spsSe2jsflIKqT90O/Tp9O3vh5Zzy0upqXR9GXVdujuHPDKKmvEYcL535TIQnuYtSsVyuhLSuALOGUxUN5Lt7wnPIWhB4B9YyWENuVkFqItbuuITmjFHweB4O6+uG5iZ0gEVVfhNndWYzHRoQ1UZSE2L56fVPYs2cPFi1ahN69e+Prr7+GXl/5pWTEiBE4fvw4Vq1a1ahBkrpTaXRYueVqrceH+ZtuH1FXof5O6PcQpquSxqG9r0BTbakybwLsvaRDeSfBkOSboynKqfZ8bC33tyekNXB2EGH+jC7g82r+p3hQV78miIjUVsnZncje9EGtxzN8EQLmrYbzwOmGNpZlUXRsE25/OxcZP81Hedwxi8dri7JRuG9NAyImxDblFFbg/Z/PIvm/nT80Wj0OXbiDFZsuWzkyQpqfej2xX716NR577DF88MEH0OnurW2ZMmUKioqKsGXLFixYsKCxYiT1sONYstmiIuaIBFzcuG1adRQApGIeFGqdxaf53q4S2NsJ0DPcC+MHBNfqCyqxDr6zF8ATAPdVpq8P2Y0zcBv7Ejj/rRPVFGVDlnASeo0KHGH1lburtnEipDm6nJiHcwnZ4HM5GNTND+0CGn5DdHB3f3QN80BMQuVNMUepEN/8dQXl8sq/p1wOgyfHdECov1ODr0UahzzlCoqO/FGnY+wjBoFn72LUVhrzD0pO/32vQV99NW/l3RtQF2RA4EY3eUjLse9sutE6+SrnE3KQXVABbzcqxkxIbdUrsU9LS8OiRYvM9kVGRmLlypUNCoo03KmrmbUaJ+RzoNezMPcg1ctVgqlD2+J0XBau3Mw3e7yPuxQfPNenIaGSJsIRiODcbwqKj//ZsBPptJX/4wlQHncU+btXGT3Vt+T+bZwIaW6+33oV+8/dW8oSfTIVT48Lx+QhbRt8bkepECPuWz/65uzu2HI4CXKlBv27+GFc/+prW5CmVR57uPoBXF7lZ+R/hN4hcB4802RY2cW9db62Tl4KgBJ70nLkl5jfj76yTw5vN7v/tmdmwefRTjqEVKdeib2rqytSUlLQr18/k76UlBS4uro2ODDSMNVNeJ45KgzJd0pwKTG32q3vVGodvt8aa7G/6jrlcjWuJuVDKOCiazsPempvw5z7TwXP3gWll/ZBnZ8BaFV1PofIvwM4Qgl0ygoU7Pu5Vkm908DpcBkwoz4hE2J1N9KKjJL6Kn/svYHB3f3h4iBqtGvtPpWKn3bc26c8NfM6Lt3IxUcv9KXPVhuhV8ot9kna9YLryLnQFNyFuuAuBG7+EAd3AcOYFkrUyUrMnoPh8sHqTOvjcIQSCL1C6h03Ibaorb8zTlwxfRjF53HgJBXi8/UXcC6+cgeRHh288NzETrSlMrGI1etQHnsEFTfOAgxg16Ev7DsPaTXbK9crsR8zZgy+++47eHh4YNCgyuquDMMgPj4eq1atwrhx4xo1SFJ3ob6OuJNTbtLeu5MXZo4Iw4ufHa5x7+Ti8pqTPiepEHM+PAC1pnIalbO9EG8/1QsdglxqOJJYi33kUNhHDoVOXo6sP5ZAU5BR62M5IilcRz4DAFCkx4HV1O7GgLY4t16xEmILLiaaf/9qdSyuJuVhaI/GKWgmV2qwfs91k/aE1EKcvJqJoT38G+U6pGHEwV2gSDO96c1z9IDn1DfBMBzwHd0gCela7XlE/u2hSIszbQ+MgF4pgyozyajdZchscASNdxOJEFswolcA9p5JQ1ZBhVH7xEEhWL7hEtKzywxtMddzkJ5dih8XDYOA3zoSNVI3eTu/rkzq/6NIjYUiLQ6ek16zYlRNp16J/YIFC5CUlIQFCxaAw6l8gvDEE09ALpejR48emD9/fqMGSeqmRKbE6bhss32Th4Qiv1hh8gFaH3weg2OX7hrdICguV2HZ7zFYt2QkPV2ycdz/Z+++w+OozjWAv7O9qfdebEuWbbnLveGCDRhMb3YSemiBkJAEEgIkNwFCCYQaCL13TDUY996bbMuSZVu915W2l7l/yJa93l01S9pd6f09z30ue87M7LexNNpvzjnf0QQh4aYn0Zq7HpbKY5DqQmHI3wlb7emRSUEdhJCcC+FsbYIsNBpBo8+DVBsCAJBIFd4u7UaQcO96ClwapfefX3UHfZ0RRREWqwOqk9fIL26EyeK+1hQA9h+tZWLvJ4LHL4Dh8GZYKgtPN0pliFh4MwSh63/3wmZfD3NZvssDUkGhRvic6yCPSEDL/jUwndgPiUqH4LHzoErK6s2PQeQXtGo5/nX3THy5rhB782ug08ixYFIKQnVKfLb6qNvxNY0mbD5QgfMm8H44mIlOB0xFuXAYmqBKGgF5aDTMZfkuSf0phsObYZl8CZTxQ30Qaf/q0TcShUKB119/HZs3b8a2bdvQ1NSEoKAgTJo0CbNnz/Y45Yz6R3OrBfc+sw4Wm+cvh4ePN2Dh1FRIJQIcXobsQ3VKTMmOxY9bO94aTYAAp4fF+Y0tFuw/WouJWTHd/wDUryRyJYLGzYcsJAp2fR3Cpl8OU/FB2A3NkMhVAETY6suhShoBzbAJMBTuhq2hEsr4YdCkZkOi1sFp6rxIo27UTNiaqtF6ZBsczbVQJWZBk5nTXoCPyJ/NGpeI93884rYtXWiQstv3uePlzcgrasCxsibsyqtCY4sVCVFaXL9wOBKjg7yeF6zl74q/kCjUiPvl/6H14AaYSw5Dqg1F0Nj5UETEd+s68vBYhM64CqYT+yHarVDGpCE456L264RMvAAhEy8AAJhOHEDditcAiQS6ETMgC4mC8fheSORKaIZNhESh7vXPSdRfQoOUuOnikcDFI9vbfthywuvxVb0wOEWBy9ZQgcqP/wl748mdmAQJQiYvhlQT4vUcU2keE3tvli9fjtmzZ2P69Olu6+xra2uxfPly3Hrrrb0SIHXPJ6sK0KD3Pj3aanNAp5Zj9LBIjwXxblw8AhfPHAJAxI5DVR1fy97B+nwvDxbIv9j19aj86O+dTsc3HNqE+h/PapTIOq3iDADaUbNgOrEfTVuW41T1B/2uFZCodIi99i9QJWT0LHiifhIVpsYffzEBL3y6Dy3GtrXPkaFqPPDLiV0u5uRwinj2wz1Yv9f9d6281oCnP9iNh2+egozkUBSUNLn0SyUCFkzi/uX+RCJTIHjsfASPnd+j81sPbkTt9y9DPLlLiSBXIXTyEo8PB+pWvgH9zh/aX+t3rQAEAaeq3kpUWsRc8QeoU7N7FAuRPxqaGOq1bwh3CRnUar5+/nRSDwCiE83bvkFwzkVezzl7V5KBqkdzpR988EGUlpZ67MvLy8Pzzz9/TkFRz+063PFa5kkjYwEArUb3wjwAYLY6IJdJIJdJ8cgtU5EUo/N6LYmXiRlKhRRjh0V1LWDyqboVr3Zrjb2LLiT1AGAs2IGmLV/h7JKOTnMrqr98BqKTD4HI/03NjsfbDy/E326bisfunI7X/7IAmSld/6KwcluRx6T+FFFs26b0gV9OwogzapSEBSlx/7IJSI4NPqf4qX84jHo0bv4C1V88hfo178HW5P432d7SgJpvX2xP6gFAtJlR8/VzcJhca+NYqo67JPWnTzh9P3WaDaj+6t8Q7Z7/rhMFoozkMOSMcJ8RNTwlDBOHd22m1Po9Zbj/Pxtww99/whPv7nRZr0+BydZYBUuF+xINALBUHIVEG+rWLg0KhzZjUh9H5h+6PGJ/22234dixYwDa1gXeddddUCjcpwbW19cjOZkjC76iVHgfPbp09hAMSQyF3mDF0dImj8fsOFyF6xcOBwCkJ4Tg5T/Ow38+3otVO0vcjp08Kg6hQUqs2FLU3iaRCLj9stHQquXn9Dmo7znMBhiP7e3z9xGtZu8x6OtgLj0CdcpIr8cQ+QuFXIrxmdE9OnfjvopOj6msNyAqTI1/3T0TFbWtMJhtSIsPgUzKeiWBwK6vQ/nbf4ajpb69Tb/7R8Rd/4jLzCTDka0eH4yKdisM+dsRPHY+RFGEIAhdvkc7jXqYig5AM3TCuX8QIj/x4K9y8NW6Y9i4rxwOpxPTsuNxxdxhkHgbWTrDNxuP4X/LD7a/3ry/Anvza/DMvbM6XPZE/u3MB6Jns5QXQJMxGY7WhvbkX5mQiaiL7oAgGxx5SZcT+9tvvx2fffYZAOCrr77CiBEjEB7uOlohkUgQHByMyy+/vHejpC6bl5OEN7455NY+aUQsbr5kFABAJhW8rrFXKdp+JCpqW3GkuBGRoSrcsmQkSqtbkF/S2H5cbIQGt1wyCtHhGswel4gdh6qgVEgxZ3wi4qO8j/KTb9j19Wje/g3MpXmQ6sIQPGERlHFDu7RVXV8THV0b+SfylfpmE/bm10KrliMzJRQHjtZBEATkjIiBRuX+ZaG6wYgNe8tgtTkxeWQshiaFwuHs/HftzKmnvI8GnsZNX7gk9UDbg82G1e8i/pf/ON3m8D5LyWFoRvWXz8BQsAOCRApFdEqX31/sws8YUSCRy6S4en4Grp7fvSV7docTn61yH9U1mu1Yvv4Y7r5qbC9FSP1NHpkEeXgcbA2ei4QbC7Yj/lePtU29FwTIgiP7OULf6nJiP378eIwfP7799Z133omkJFak9Dcj0yMREaJCffPpUdLsIRGYmh2L5esL4XCKOHC0Dlq1DHqD+7S98yYk4sXP9mHl9uL2mX6pccF4+ObJOFGpR0FJI1qNNsRGaGCy2k++ZwRGpkeguEqP3UdqsGFfOVQKKeIjdZgwPBpSjjb5lL21EeVvPwBHS0N7m/HoLkReeDtUySNgLnHfXqtXSaSAl+n2EnUQ1Mkj+vb9ic7BJ6vy8eFP+XB6eBCqVkrxu+snYMqouPa2tbtL8Z+P97Y/OP3453xcNmcopo2Ox+ETDW7XOEUhk+CqecN6/wNQvzEVuW9dBwDm0jw4bRZI5EoAgDYjBw2r38XZy5MgkUK/92c4mtvq34gOu9uWd95IlBqusSe/VlVvwNcbjuFEhR7xkVpcMmsIUuP6ZolRg96MplbPNaKOlzf3yXvSuREddgjSztNSQRAQedEdqPrw/yA6PC8/Mh7bi/DZ1/Z2iAGhR8XzHn/8ca99RqMRu3btwqxZs3ocFPVMRV0r/vLKZpgsriOghWVNyD1W7+WsNhIBWDQ1FaII/LTNtRp+UaUeL39xAEsXDcdP24rR1L6//SFcOC0Vd1wxBv/98gC+3+xewTQ+Uov/+/U0RIdrzumzUc/pd/7gktSf0rj+Y8Re+xdUffxPOAxNffPmggSRi26FqSgXhsObXfskUkQtvmvQTI+iwHP4RD3eX3HEa7/J4sDTH+zG2389HzqNAgaTDS99vt9tNtRX6wrxxF3TMT4zGnvya9rbZVIBITolMlPCcNXcDAxlQaiAJlXp4Gn+kaBQu3xhlYfHIXzuMjSsee/Mo6AbOQOtuet78MYyRF50J/e4J79VUqXHH1/cBIOpLRE7dLwe6/eU4W+3TcWoIb0/ohqiU0KjksFodv+NjI/kbCh/0rzzezRt+wYOfR3kUckIn3UNtMOndHiOOnkkIhfditrvX/bYL1EN3pyjR4l9RUUFHnnkEezYsQNWq+e1Dnl5eecUGHXfd5tOuCX1ALzui3xKXIQW/7h9Goqr9Hjs7Z0ej9l9pBqVda1nJPVtfthSBLVS5jGpB4CKOgNe/mI/Hr11ahc/BfU2c1m+x3aHoQmCTIGkO15E6+HNsDVXAw4nWg9vgkNf14UrCwieuBCCVA5RFAGJBDJdOBRRyTCXHoYglUM3cgbkYbEIHrcApvEL0XJgLRwtDVAlZiJo3IJBU6WUAtP6PZ0XlrRYHdh2sBLzJ6VgX0EtLFbP99udh6vx6K1TsK+gtm2ZU4gKM8YmQK3s0Z9h8kNB4xa47m1/qn3MXAgS1/o3oVMvhWboBBjytwOCAF3WVLR0M6kPGr8Q8vDYtu3veC8lP/bRyvz2pP4Uq92Jd3/Iw5O/mdnr76eUS7F4Rjo+XeU640UmFbBkdnqvvx/1TPOO71D/81vtr221Jaj+4mnEXvcQNOljOzxXN2oWGtZ96DYwJcgU0I3o/Z+pQNGjbxSPPfYY9uzZg6uuugp79uyBWq3G2LFjsXnzZhQUFOCFF17o7TipC0qqelbts6reAJvdgcff2em2T/Mpoti2JZMnG/aWd3j9Pfk1aDXZoGNBPZ+QBUd47hAkkGpDIVGqoU7LhjPfBGttSReTegAQYcjfieS7X3H70qpJH+N2tDplJIvkUUCxO9yn33tiO3mcTOq9oNPBY/UQBAHjMqMxrocF+Mg/mUuPwFJZCFlIFEKmLoF+54q2Ak+CBLoR0xE+d5nH8xRRSVBEnV7SqIhJ7db7hs9dCqlSey6hE/ULb8uQ8ooa4HSKXSqG111LFw6HUi7Fd5uOo7HFgmFJofjFBVkYlhTW6+9F3SeKIpq2fe2pB83bvoEmfSycNgsaN36K1oMbINpt0GbkIGz29ZAFhUGQyRFz9YOo+eoZ2JvaZsJJtaGIuuhOyIIG779xjxL7nTt34r777sOyZcvw/vvvY82aNfjDH/6A3/3ud7jpppuwevVqzJs3r7djpU4kxwZj/9GuJmWniQDW7C6DrYN96VNig1Bc1eKxr7OvvqIIl615qH8FjZ2P1oMb3DtEJxzGZrQcWNu23rMHhfQcLfUoe+NPkOlCEDRmLnQjpvdCxET+YcqoWKzcXtzhMVKJgEknt2TqaEppfkkj/vXuTvxh2cQ++RJL/U+021D9xVMwFu5ub5OHxyP+hschWk2QhUS3P1gVRSdEm7XD6fLajElQxKTBWu15BtyZlElZTOopYIQHK9Ggd98hJzRI2Wf3Q4lEaC+853A4We/Jz4h2q8dlogBga2wrjFf9xVMwnbEzSMv+NTCX5iHhlmcgkSuhih+KpDtfgqW8AKLDDlViJgTp4B5E7NFPucFgQGZmJgAgPT0dhw+3Fd+SSqW4/vrrsW3btt6LkLrs4hnpPZrWmRYfjEYPN9xTlHIp7r12HOIjPX+JmDY6zmP7KaOHRkKncd8akfqH09zqtU+/awUaVr1zTtXxbTUnYDq+DzVf/RsNaz/o8XWI/IHDKeKz1QW4+Z8/48n3diEqVO31WEEAblkyChEh6vbXHdm0vwJbcjvf9o4CQ/PO712SegCwNVSgcd2HUCVlQRYcAVF0onHT5yh+7mYUPbUUpa/9FoYj2z1eT5BIEbf0UQRPWgxZcCRkYbEInX4lQiZfApyxRl+VPAJx1/21Tz8bUW+6aLrn6e8XTU/rl/dnUu9/JHIl5OHxHvsUMWmwVB5zSepPsTVUwpC3pf21IEigShwOdcqoQZ/UAz0csY+OjkZdXdvIcEpKCpqbm1FbW4uoqCiEhoaivr7jQm3UN+IitXj8zun44KcjOHisHmFBStgdTtQ0mryeo5BL8MAvc/D1xmNej1k4ORnDksJw3/Xj8bf/bUPrGeukRg+NRJPejJhwDaobjG7nRoaocOeV7tOyqf90tJ2ctaYEnc+56Lqm7d8gZNJiSLUhrjE4HWja/AX0e1bCYWiGOnUUwucshTJ+aK+9N1FveH15Lr47o2aI2WqCWinD/JwkRIZqMDQxBIdONEAiADPGJiDhjG3pNCo5MlPCkF/c6OnSAICV24phMtuREheMjOTBO11wIGg9uyDoScZje+G0miFRqNC08XM0bvykvc9WW4rqL59G3NJHoE4Z5XauVK1D5IIbgQU3urSHz7ketoZKSHWhkGr6ppI4UV+ZPykZjS1mfLG2EAaTDSqFFBdMS8NV87q3jR0NLGGzrkbN8udc2gSpHKHTLoe1ttTreR31DXY9Suxnz56N5557DrGxsRg3bhxiY2Px5ptv4q677sIXX3yBmJiY3o6TumhIYigevvl0NckTFc144KVNLpVBpRIBCVFaTB8TjyvnZkAhlyIzKQw/oMjjNSPD2qpLDk8JxxsPLcDGfRXQGyw4UdGMjftcR5/S44ORPTSqbbu7KB2mj4mHUi71dFnqJ+r0sRDkSog2961fFFFJMJcc6vQastAYOIzNEK3eZ3YAABx2WCoLoRk6waW5/ue3oN+1ov216cQBVJQfReItT0MeFtu1D0LUx5pbLfhxm/vUe5PFDqVChsvPa3sQNXpYlNdr3HLJKDz03y2w2DwX0dtbUIu9BW3bmY3NiMJfbpgEFQvoBaYOl5iJEB12NO/63kOXE807vvOY2HsjyORQRCd3P0YiP3HVvAxcMmsIahuNCA9WQaPi6Opgpxs5E4Jchebt38DWWA1lbDpCp18OVSeDPorIxH6KMPD06NvEPffcg4MHD+I///kP3n77bdx333144IEH8PbbbwMAHn744d6Mkc5BWnwIXrx/Ln7cVoTymlakxgdj4ZQUhAW5rvObkh0H9VcH3CroSyUCZo5NaH+tUcmxcEoKSqtb8O4P7jsfHK/Q466rxnIkyo9IVdq2bUG+e9llyn3IlEsQPH4h9HtWdjoV395UjeR7/wdHSxOcVhMMBTuh3/Gt5/cLci3W5zC1oGXvKrfjRKsJ+l0rEHHWyBSRr1TWG7wWEC2t9lxj5GzDU8Px0h/n4r5n16PF6HnXmFP2FdTio5X5uPFiFpUMRNqsqR7Xw6vTx0CiUMNhaIbT5HkplK2xuq/DI/I7SrkUidFBvg6D/Ig2IwfajBy3dlX8UKjTxsB0Yr9Luyw0BlrWc/KqR4l9aGgoPvvsM9TUtFUhvOSSSxAfH499+/Zh9OjRmDRpUq8GSecmKkyNX1yQ5bHP7nDi0PF6OJwifnvdODzz/h5YTxbRUymkuPuqsYj0sMb0wNFar++3r6CWib2fCRp9HlRJWWg9vBmizQptRk77NPiI829G/c9vAs6Ot0UUpHIo49rWyclCo9Cyd6XbLABV8ggoz6rsbG+qhehw3ebmFGt9xzsqEPWnuAgtZFKJx+Q+ObbrX0ZjwjV47M7p+Psb21DbwVIoAFi/twxLFw3Hiq0nYLM5sWhaKnRq1iQJBCGTFsNclAtTUW57mywkGpELbwEASDRBkIVEwd7s/vfy1L2UiIg8i7nyD2hY/7FLVfzw85ZBIlf6OjS/1aPE/uKLL8bvf/97nHfeee1tEydOxMSJE3stMOp7h47X48n3dnmsVAoAM8bEY/Z4z9NdgrXef6mCtPxS6o/kYbEIm36FW3vIxEXQZuTAeHQnTMWHXIqSnKJOzYZUfTqxkYdEI/aav6Dup9dhqy0BBAk0GTmIuvB2D+8b430pQHTKOX4qot4TolNi0dQUfLfJdRQ2SCPHBVO7V+QpNS4Y9y+dgH+8uR0tRs8PtgDAYLThqge/h/PktO53fsjDlXOH4lcXcRTf30nkSsQtfRSmolxYKo5CFhINbeZkCLK2KcaCIEHYrGtQ++2LLucJCjVCp1zqg4iJBp6iSj2+23QclXUGDEkMxcUz0hEV5r3oKQUOiUKNyAU3ttUdoS7pUWJfWVkJtZq/NIHMbLHjH29udymEd7ZVO0sxeVQcpoxyr3o/aVQsQnVKNLW6Jmtalcxl6j4FBllwBIInLELQ2PmotttgPLrzdF9INCIv+LXbOeqUkUi67VnY9XUQ5EqXxP9MEpUWITkXoWnLl67tah1CJl7Qux+E6BzdsiQbkSFq/LitCM2tVozNiMLSRcO7/UXRZnfiiXd2dpjUA4DZw1r8z9cUYmp2PGc+BQh1ajbUqdke+4JGnweJSofmnd/D3lQDZcIwhE27wmX/eiLqmf1Ha/G317e1b9d8oLAOa3aV4MnfzER8pK6Ts4kGnh6P2L/99ttIT09HdHR0b8dE/WDboaoOk/pTvlhz1GNir5RL8citU/DMB7tRVtO2hjA2QoP7rhsPnZoFUQKVIJUh9uoHYC47AnN5ASRKLax1Zaj+4kkICg2CxpyHoDHzIJyxr5cs2Pve3aeEzbkeUl0Y9HtPVsVPGYWwWdd06Vyi/iSVCLhi7jBcMXcYqhuMkAhCj0Z/9hbUoLHFfZbKmXRqudf78Ecr8/HILVM89pF/sVQdR/OO72FrqIQyJhUhkxe7bOPkbQ0pEZ2bt78/3J7Un9LcasVnq47i3mvH+SgqIt/pUWJfVFSEXbt2Yfbs2QgNDYVGo3HpFwQBq1a5F8si3zKabVi5vRgHCus6HUU6fY73rdKGJobilT/Nw4mKZjicIoYkhLgkfBS4VInDoYhOQflbD8BWV9bebik7AltdGSLm39Dh+aLTAWPBThiP74dEpYEybgjsTdVQJ4+EJmMSNOncApH817GyJrzw2T4cK2sGAGSlhuPea8e5bG3XGYvFe82KjOS26aJ78muwdneZx2OM5q7do8m3TEW5qPz4H8DJbUUt5floPbQR8b/8J6vYE/Uhi82BwtImj30Hj9f1bzDUb5wWI0SHA1INizB60qPEPi4uDhdffHFvx0J9yGi24U8vbkJRpb5b500bHd/pMWnxIZ0eQ/7LYTbAcHgzHKYWqFNHQ5UwDADQmrveJak/pXnH9wiedDHkwW3V70WnA8aju2GtKYI8PA6aYTmo+erfMBbu9vh++t0/ImjcAo/r8Yl8zWi24eHXtkJvOF3RPq+oAQ+/thWvPjAPMqmkS9cZkxEFhUzSXoz0TDcuHolRQyIRG6H1mtjPncCp2oGgYe0H7Un9KU6LEY2bPkPM5b/3UVREA59cKvE66+nsnZ8o8NlbGlH346swHt0NiE4oE4cjctGtbgWbB7seJfaPP/54l49dvnw5zjvvPISEMPnzpZ+2FXc7qY8IUeHKecP6KCLyB+bSPFR98hicFiMAoBEfQjd6DqIW3w1zxTHPJ4lOVH30dyTc+C+ITgeqPvwbLJWnj5VqQ+EwNHX4vi17f0bQ6DlQJQ7vrY9C1Cs27qtwSepPqWkwYufhakzNdl+a5EmwVoGbl4zCf7884LLd+cIpKRg1pG0JyvDUcIwdFoV9Z+0ykhitw4LJLCzp70SnA5aKox77zGVH+jkaosFFIhGwaGoqPl/j/jt44bTU/g+I+owoiqj65J8u24tayo6g8sO/Ien2FyBVs57CKT1K7LvK4XDgwQcfxOeff87E3scOFHqfljRpZCzsdgdCdUoUV7XAYnNg2ug4XLsgE3KZtB+jpP5iLj8K/Z6fYDi8GaLdNYlpPbAOqpRRkIdGeT3fVleGpm1fQ7SaXJJ6AJ0m9acYj+5mYk9+p7HF8y4hALzuIOLNhdPSMCItAut2l8Jqd2LyiFiMyXD9vfq/26fhp21F+H7TCTicImaPT8SVc4dBIuGyJn8nSKReH2Q6DM1oWPs+wmZeA3NpHloPbYIoOqEdPgWaoRNclq3ZWxrRvPM7WMryIQ2OQMjEC3hvpEHteHkzfthyArWNJmQkh+Gi6WkIDXLfjWnpouEwWexYub0YNrsTWrUcV80dhjmc8TSgmEsOuyT1pziNerQe3ICQnAt9EJV/6tPEHmh7ykK+F6LzvgXdzsNV7SNKI9LC8bfbpiJExz0iB6rWgxtR883zgOg+RfiU+p/eQOx1DwMbPvF6jDF/G5yW7iU6Z5IoubMG+Z8RaeFe+0amR3T7eqlxwbhhccdb1y2ckoqFU1K7fW3yveCJF6Bx/UfuHU4HmrZ8BUP+Dtjqy9ubWw+sRdDY+Yi66A4AgL2lAeVv/QmOlob2YwyHtyD6svugy5rW5/ET+ZtdedX451vbYXe0fTHdk1+DVTtL8PQ9MxER4vq9QSaV4PbLR2PZBVmobzYhNkILpZwDUgONXV/bQR/rKZypa4sFKeAtnJwKb3Xtznz2cvhEA1758kD/BEX9TnQ6UL/6nQ6TegAQrSY0bfi444sJUkDSw1uIRAbdyJk9O5eoD40eGoXJI2Pd2uflJCE1LtgHEZE/C51+OUKnXQZIPe8Gc2ZSf0rLvlUwl7dNH27e/o1LUg8AEJ1oWPMexE7u00QD0ZvfHmxP6k+pazLhy3WFXs/RqeVIiQ1mUh+AnBYT6n9+C8X/uQXFz92E2hWvwmF0XTqsjPe+LFiZwCXDZ+rzEXvyD1lp4bjn6nF4+/tDaG61QhAArVqOVg/V8bflVsJotkGj4rZ1A4nD0IzWvC1wtDZ26Xizl7WjpwgKNWSaINgbq7oXiESK6Et/C1mI96n+RL70wK9y8PP2Ymw5UAmJVMCssQk4j1M76QwOswHW6hOQBUUg/LxlsLc0ojV3XZfPr//5TWiGjIPphOcH6famGjhaGrglKA0qjS1mlFa3euw7WFjfz9FQf6j69DGYSw63v27ZsxKWsiNIuPlpCJK2BzWKiAToRs9F64E1LucqEzKgzZjUr/H6Oyb2A5zBZMPGfeVoNlgwZmgU3vrrQhRX6hGsU+Dxt3eg0Njsdo7DKbrtC0qBrXHDp2ja8iVER9e30BJPFtTzxlJ6uMN+b3QjpkOXNbVH5xL1B5lUggumpeGCaWku7XVNJlTVG5AUE8TlSoNY4+Yv0LT5C4g2CwBAPWQcVElZ3bqGpbwAlvICAJ6n0gkyBSQq7bmGShRQ1EoZFHIprDb37UI9rbGnwGYqOeSS1J9irSmBsWAntMOntLdFLb4DqvihaDm4AXDYoMmYhJBJF7Un/9SGif0AdqS4AY/+bxsMJ7cCeR9HMGd8Iu67bjwkEgETs2JRWOae2Gckh3bpS6soity3PgAY8negcaP3tfL9SpAgeOIFvo6CqFusNgde+HQfNuwtg1NsS/wXz0jDTReP5D1wkDEc2Y7GdR+6tJmO7YUgU0AaFAFHi+uooiCVd/JA1XMdIl32HEgUrENCA8/GveX4cn0hquoMSE8IwbULMpE9tG1mikohw9yJSfhxa5HbeYumpvZvoNTnrDWl3vtqS1wSe0GQIHjCQgRPWNgfoQUsrrEfoERRxHMf7WlP6k9Zt6cMm/a3rfm7dPYQpJ+1B71WJcOvLxvd4bV35VXjvmfX4ZL7v8HN//wZ32063rvBU69qOWvq0tkk6iAoYtIAoY9uBycTH6kuHNGX3ANVQkbfvA9RH3lvRR7W7WlL6gHA7nBi+fpj+GGze5VeGtj0+1Z5bDce3YXYa/4M3ahZkKh1kIVEIXTm1Yhb+ihkoTGdXleiDmr7D6kMQWPmIeL8G3szbCK/sHpnCZ58fxcKS5vQarLhQGEd/vrqFhw6fvqB2C1LRmHuxCRIT+4MolPLccuSUV3eapQChyIywWufPDKxHyMZODhiP0AVVepRXmvw2Lf5QAVmjUuEVi3HU/fMxMZ95cgvaURUqBrzcpIRHqzyet3cY3X4vze3w3nyG25NgxGvfpULu8OJS2cP7ZPPQufGafVeuT7myj9CM2Q8BJkc9pYGmIoPo/brZz0eKwuJgr3Ze2VSb5LufBmizQx5RAKnTFFA+nlHicf2n7YX46IZ6f0cDfmS0+z57yqcDkhVWkQvudetK+nOF2GpKETjxs9gOrbH4+mx1/0VEoUaUm0IpJyCTwPUJz8XuLU5nCK+WHu0fdcRpVyK+64bjxsXj0RjixnxUToWxRugVCmjoEzIhKU836VdHpnItfM9xBH7AaqjPZAlZ0wdVcilmJeTjDuvGIOr5mV0mNQDwFfrCtuT+jN9udZzO/meZsg4j+3y8HhoMiZBkLUVSZQFhUM3YiqkQZ63+9IO7/66eFXyCMhDo6GISmZSTwHJ4RRhNHueSt3iofgoDWzqNM8z2uSRiV4LggqCBKqEDIROXeKxXxGdClXcECgi4pnU04BltTlQWe/5wVhxpd6tLTRIibT4ECb1A5ggCIi99i8InrAIErUOglID3ei5iFv6NwhSjj33RJ/+ryaVSvHuu+8iLS2t84OpV6XEBiMlNgjFVS1ufTPHep/60pmKWs/VShtbLDBZ7NCqWUnf3wSPXwhD3lZYzqhyL8gUiFh0i9v6YEEiReQFv0b1F08BDnt7uyo1G+FzrofTZkHLnp+69L6CQoXIRbf2zocg8hGpRED2kEgcKHTfK3dcRu/u7FBY1oQPfjyCwyfqERakwkXT07B4RhrX8fuRkEmLYTiyFba6svY2QSpHxILOp86rU0Yh/LylaNjwSfv9VRYWi+jLf9dn8RL5C4VciuhwDWoa3AvzJkYH+SAi8gdSlRaRi27l98VeIoii2O1hVrPZjFdeeQVr166FyWSC0+laQV0QBKxa5XkdWqDKzc0FAGRnZ/s4kq47UdGMR17bisYWS3vbhdNScccVYzweX1bTgqYWC9ITQrxudffEOzux+UCFW3t0uAav/3k+v4D6KdFuQ+vhzTCXHIY0KAxBY+ZBHhrt9XhbYxVa9q+Fw9gMdWo2tMOntI+4m0rz0LztG9ibayEoVFBExEMzZAIkah2atn0Nh1EPdWo2QqcsgVSt66+PSNRnCkub8Jf/bobRfPphV0SICk/ePRPR4Rqv59nsDhwpboRKIcWwpLAO36O8thX3PbsOJotrNejrzs/E9QuHn9sHoF7jtJrQsm8NDHlb4LRZoErMRHDORVBExHf5Gg5DM0wlhyBVB0GVMhJCX9U3IfIzP24twkuf73dpkwjA3389DWOGcQtcIm+6mof2KLH/61//is8//xyTJk1CbGwsJBL3P0qPP/54dy/r1wIxsQfapj7tOFyF5hYLRg+LQlKM+1PR5lYLnnxvV/uIlFopxfULs3Dp7CFuxxaWNuFPL26E9azt8O65eiwWTE7pmw9BfstpMcFaUwxpUHiHDwqIAl1NoxE/bi1CRZ0BQxJCcP7klA53D9l8oAIvf74feoMVAJAUE4QHfjkRybHBHo9/5Yv9+GFLkVu7RiXDOw8vhErJaYm+Zm9tRMW7D8HeWNXeJtWGIm7Z36BgoSeiLlm1oxhfritEZZ0BQxJCce35mZiY1XmBSaLBrE8T+8mTJ+Pmm2/Gbbfd1rPoAlCgJvZd8fc3tmHn4Wr39tumYlyme7KWX9yAT1YV4GhpE2LCNbh09hDMGNPz6f3ke6LDBmttGaSaYMiCI1z7nA5Yq4shKJRQRJz+d27auhyNmz6DaDUDEKAZNgHRl9zDvZdp0KusM+DOJ1fD7nD98xoXocV/H5jnsQbKAy9tcqkMfaaX/zjX40NZ6l+1K15Fy56Vbu2aoRMQe82f+zUW0W6DvbURUl0oJDJFv743ERH1r67moT0aArDZbBg9uuMt0SgwNOjN2JXnntQDwMrtxR4T+6SYIMzPScb5k1MwLjOahU0CXMuBdWhY8y4chma0JegTEXXx3ZCqdTAW7kbtitfg0LfN5lDGD0P0pb+FtaYEDWveO+MqIoxHd6Hm2xcQe9UDPvkcRP5i7e5St6QeACrrDcg9Vocxw6LQarLB4XC2j/onxwR5TOzVShmiQrmfuT8wFnquaG88thei6Oy3KfVNW5ejaetyOE0tkKi0CMlZjNCZV3EpHBHRINejxH7GjBnYsGEDpkyZ0tvxUD8zmGzwNmejudXi1rZxXzle+HQfTJa2taZBGgX+sGyCxwcA5P/MZfmo/fZFAKd+CEQYj+5E7bcvIGLhzaj+/CmIjtOVvy0VR1H16eOQqD1PJzYW7ITTbOCoPQ1qrSbv1fIr6w34ZsNx7MqrglMEhqeE4Y4rxuDimelYu7sUZqvrGvuLZ6ZzGr6fkMiVcHhoF+TKfkvq9XtXuTxUdZoNaNz4CSQqDUImLe6XGIiIyD/16NvChRdeiEceeQQNDQ0YM2YM1Gr30YRLL730XGOjfpAQpUN0mBo1jSa3vpKqFugNVgRr26b51Teb8O8Pd7uMRLUYrXji3Z1466/ney24R/7FWlMC4/F9kKi0MBfl4nRSf5rx6C44LUaXpP4UW10ZJB0UxTOVHoF22ITeDJkooIzPjMa3G4+7tUslAr7deAwlVad3FzlS3IiH/rsFrz04D/+8Yzo++PEIDh6vR3iwEhdNT8OSWe61Tsg3gkbPQcPaD9zbs2f3y/vbW5vQuPETj33NO79nYk9ENMj1KLH/7W9/CwBYvnw5li9f7tYvCAIT+wAhkQi49dJs/POtHW59zQYrvtl4DMsWZQEANuwt9zi91Gi2Y8fhaswZz+JB/q5u5ZvQ7/z+dEMHUzfNJYe99gkKNWDyvPWh4KGYJtFgkpEcCkGA22woh1N0SepPaTFasW5PGRbPSMffbpvaT1FSd4VMWQJrXRlaczfg1ANR9ZDxCJ+7rM/fuyV3HWq/f8VlG9Iz2fUNfR4DERH5tx4l9qtXr+7tOKiXFZY2wWy1IzMlDHJZ2xp4URRRUNIIq92J4SlhqKg1oMVoRVIH+4fuy69tT+ytNk+TENtYrN77yD8Yj+11TeoB98yjK6QyBI9fiMa173vok0OVkNGzAIn8mCiKOFraBJvdiYzkMMhl3h9g7ThU3e1frZKqFuzJr0FClA4xHWyhR74jSKSIvuQehM24CtaaYsjD46CI7vvdYOytTR0m9QCgSszs8ziIiHqLw9AM/Z6fYKkugjw8DsHjz4c8tHu7I1iqi9CyZyXsLfVQJQ5H0LgFg36b5R4l9gkJpytjm0wmtLa2IjQ0FHI5p2L7Wml1C554dydKqloAAMFaBe68Ygzio7T417u7UF7bNloklQhwONu+eWrVco+jS6f6Tpk0Mhbv/3jE7RipRMDELK6x93eGvK29cp2wGVchdPJiGAt3wVLq+vMQMXcZ19fTgHOiotnl/hmqU+Luq8Zg8qg4j8c7e/DA7MdtRVixtQiCAMwcm4DfXjuu/aEs+Rd5eBzk4Z7/7fuCsWBHh0m9IJUjbPa1/RYPUX9obrXg5x0lKK7SIzkmqNMtRk9xOJxYu7sM2w9VQi6TYs74REwaGdsPEVNX2ZqqUfHOX+BobWxv0+/+CXFLH4UqfmiXrmE4ugvVnz8FONvujcaju9CyfzXif/UYpBrPdaAGgx5X5Nm1axeefPJJHDx4EKd2zBs9ejTuu+8+FtXzEadTxD/e3I6KOkN7m95gxdMf7EKwVoEG/elieKeSeqCtgJ43wVq5y3Hp8cE4XqFvbxME4MaLRyIihFWb/d45VkxWpY5G6NRLoUkfAwCIv/5RtOSug/HYXkiUGgSNOQ/q5JG9ECiR/3A4nPjHm9td6pA0tVrwr/d24dUH5iMqzP3eN2lELGRSCewOp0u7IABTs+Ow5UCl2zmnngWIYtuyp6hQNW5YzN8nQoczq5RxQxB50Z1QxqT2XzxEfayirhUPvLgJjS2nv7d+s/E4nrhrBhKivI/IiqKIx9/Zie2HqtrbNu4rxxXnDeX91I80bvzMJakHANFqQsOa9xC/7G+dni+KIup/fqs9qT/F1lCJ5h3fIXzO9b0abyDp0WLYPXv24IYbbkBLSwvuvPNOPPLII7jjjjvQ1NSEW265BXv37u3tOKkLDh2vd0nqT7E7RJekvjvyi5sAAB+tzMeDL292SerT4oPxwu/PY3GnAKHN6vnaXakuDHHXPdSe1AOAIJMjeNwCxF75R0RffDeTehqQ9h2t9Vhc1GZ3Yu3uUo/nhAYpcdeVo132qxcE4MbFI/GnX+Tg7qvGYGR6BDKSQ9uLk55t1c6S3vkAFPA0mZMAiYdxGEGC6CvuZ1JPA857P+S5JPUA0NRiwXs/5HV43t6CWpek/pSv1hWipsHYqzFSz5lOHPDYbi4+CNHZ+dJeu74W9kb3f2cAMBXlnlNsga5HI/bPPfccJk6ciDfeeANS6empgnfffTduvvlmvPDCC3jzzTd7LUjqmo62WOr5Na2oaTDgo5XuU/BPVOjbt70j/6dJH4uQSYvRvOO7bp0nKDWIXnIvBAmnBdPg09xq9drX0T13/qQUjBkWjc0HKuB0OjE1Ox5xkW3LVBZOScXCKakAgF888qPH8zuaSUWDi0wXhsgLbkPdileBU196BQkiF94MeQiXwZF/Mppt+GJtIbbmVkAqkWDWuARcOntIl5YY7c2v8di+J7+6w/MOHK312O4UgYPH6zA3PLnzwKnPSTXBcLTUu7VLVLoufdeUKrVtDzud7jnIYJ6GD/Qwsc/NzcUzzzzjktQDgEQiwbJly/CnP/2pV4Kj7hmZHgGFTAKr3dn5wV00PjMG//v6oNeZgLuP1GB4anivvR/1rYgFNyJo3AIYj+2FpbIQxqO7IVrbRiMlKh1Ehw2ykChoR0yHVKmBoFBDN3wK183ToFTXZMKHP7k/1DxlfGZUh+dHhalx6eyOZzSNy4zC2t1lHq7dvSJCNLAFj50HTfpYGPK3A6IT2szJkIV0/PNH5CsOp4iHX9uK/OLT062LKvXIL27EQzdN7vR8jVoOg9k9adN2sq1yR2vwu7I+n/pH0Nh5qP/pdff2cfO7dL5EpYVuxDS0Htzg1hc87vxzji+Q9Six12q1sNu9bLlit7evuaf+FaxV4JcXjcDrXx/s9rnDkkJR3WCA3nB6lCg8WIlrz8/E7/+z3ut5ZxbXo8CgiEyEIrJta0Kn1QRrdRGk2tB+KQbltBhhKNgB0WaFZuh4yIIj+/w9iXrqteW5qPYyfXPm2ASMGXbuidXSRVk4UFiH+mZze1uwVoFfXZR1ztemgUUWHIGQnAt9HQZRp3YdrnJJ6k/ZfqgKBSWNyEgO6/D8BTnJ+HBlvlv7/Ekd70IxZ0IiPvzpCMxn7dQUE67B2AzObvEXwRMWwd5cC/3OHyA6bIAggS57NsK7UQQ0ctFtcNosMBbsBEQnJEoNwmZdA82wCX0Yuf/rUWI/fvx4vPbaa5g5cybU6tOFg4xGI1577TVMnDix1wKk7lkyawgyU8KwZlcpjGYbNu6rgNPp/qAlWKvA9NHxMJhtGJcRhdnjE9FqsuHLtUdRWNqM9MQQXD0vAzKpBEYPT00BQAAwe1yCxz4KDBKFGqqkjhMIp92K1oMbYC46CIkmGIroZFjK8iHabVClj4FoNcFcfAgSdRCCxs6HKn4oTCWH0bD2A9ibaiAPi0XY3GUQrWZUf/k0RMvJREmQIHzuMoROWdIPn5Soe2x2p8e1mgAQEaLC/UsnQDjHgpRA2xfOF+4/Dz9vL8aJSj0So3U4f3IKwoJU53xt6h8tB9aheef3sDfXQpWQgdAZV0GVMMzXYRH5TGFZs9e+Y2VNnSb2V83PQFWDEet2l8IpAhIBmD0+Edcs6Hg73bAgFf5682S88Ok+VNW3fdcYmhiC310/AVLJud+vqXcIgoCIeb9E6NTLYGsohywkBrKgjn8mziZRqhF75R9h19fD3toIRWQiJAr+3RTEHgyvFxcX4/LLL4dSqcScOXMQFRWF2tparFu3DmazGR9++CGGDx/eF/H6TG5uWzGG7OxsH0fSdQ6HE1c88J1LBfxTEqK0+O8Dp6e8mMw2vPTFfmzcW45Th08aEYv7l47Hb59d77EoX/aQCDx254w+i598z2mzoPLDv8FS5v7k3CNBAl32LLQeWOfeJ1MAdvf1ygm3PMPiT+R37A4nrvRy/1TIJLj2/EwsmTUECjlrTwxmzTt/QP3KN1zaBJkC8Tc8zvsaDVqrd5bguY89F9L++21TMS6za6PnNQ1GlNW2IjFKh+hwTZffXxRFFFXqIZdJkBgd1OXziPxVV/PQHlXFT0lJwSeffIJJkyZh/fr1eOONN7B+/XpMmjQJn3766YBL6gOVVCrBlGzP06uHJIQAAFqNVjzz4W5c+9cVWL/ndFIPADsOV+HdFXlYtsh9RFelkODXl43uk7jJf7TsX9P1pB4ARKfnpB7wmNQDgOHw5u4HRtTHZFIJpnjZp95qd+LdH/Lw9ze2cenZICY6HWja/IV7u92K5m1f+yAiIv8wY2yCx61A0+KDMTaj60uYosM1GJ8Z3a2kHmgbEU6LD2FSP8CZy/JR+dE/UPTvX6Hs9fvRkrvO1yH5XI/3sR86dCiee+65XgyF+sKtS0bhRHkTKupc14lu2FeB1PgC7M2vRe6xOq/nr9pRApvdCZlUgN0hQioRkJEchruuHIOUuMFdeXIwMB3f3+fv0ZWtTYh84dZLR6GkWo/S6laP/fuP1uHA0TqM6cYXVRo4HEY9HIYmj33WGm5XSIOXUi7FY3dMx2vLc7E7rxoSiYCp2fG49dJRqG0yYfP+CtgdTkwZFYekGCbf1H2WyuOo/OBRiCcHjaymVtR+8wKcFjNCJi7ycXS+0+XEfvny5Zg9ezbCwsKwfPnyTo+/9NJLzyEs6i0RIWpcd/5wPPPhHre+D386Aruj49Ems9WBn7YVt792OEXkFzd4XXdPA4tE3ffV8LXDp/T5exD1RESIGi/8/jy89MV+/Lzdc6JWUNrIxH6QkqqDINEEw2nUu/XJI1l/hga32AgtHr55Cqw2BwRBgFwmweqdJXj+033ttZ/e/SEPv7ggC1fP73jtPNHZmrYtb0/qXdo3f4Hg8QsG7RbNXU7sH3jgAXz66acICwvDAw880OGxgiAwsfcjh040eGzvLKkHAEGA21Z3ThH4fvMJZKWd3uau1WjF3oJaKBVSjMuI6tI+peT/gsbM8z613gtBroJoM3d+IIDQaZdDlcA/6OS/pFIJJo2I9ZrYR4V1b4ooDRyCVIbQyZegYe37rh0SGUImXeyboIj8zKk6JHqDFS9/vt+toPN7K/IwZVQskmM5C5S6zlrr+W+yo7UBTrNh0O5n3+XEfvXq1YiKimr/bwocIVqF1z5PifspOo0crUabx766ZlP7f/+0rRivLc+F1dY2pTo0SIk//2qSS+JPgUmdPAIRC29Bw+p3PT4ZBQBIZICzbQaHKnkkIs6/CTVfPg1bQ2WH1w4avxDh5y3t7ZCJel1OVgwSorQor3UtIhoZqsY0L3VMaHAInXYZBLkSzTu+g11fB2X8MITPvpZV8YnOsiuvCla702PfltxKJvbULYqIBNhqS93apdpQSFR9P9vUX3U5sU9IOD2tbOfOne3T8s9WW1uL5cuX49Zbb+2dCOmczctJxhdrC2F3uN5Q0+NDkJkShhVbi1zadWo5LpszFPNyknDfs+vR2GJxu2ZWalvSXlrdgpc/3+dSdK+pxYLH3tmBNx86H3JZj+ozUj8SRRHGgp0w5G9vq2o/cjo06WPb+0MmXgBBoULdty96PD94wvnQjZgBiToIioh4AEDSHS/CUl0E/a4f0bLvZ4/nSeTKXv8sRH1BKpXg77+ehle+OIA9R6ohAhgzLAp3XD6aVfEJITkXcn95ok5IJN6/D3IrOuqukClLYCjY1T6wdLr9kkE7DR/oYfG8Bx98EJ988onHxD4vLw/PP/88E3s/EhepxZ9vyMHLXxxAXVPbSHtyTBAevCEH0WEapMYHY83OUpisduRkxeCKucMQpGkb5f/FBVl4/tN9LteLDFHhkpnpAID1e8vgYTcoNLVYsK+gBjkjYvv0s9G5q/3uZbQeWNP+uvXAGoROuwzh5y1rb9MOGY86qQxwuNdW0AydAFViplu7MiYV4XOXovXgeo+j/ZphE3rpExD1vegwDR65ZQqMZhtEEdCq5b4OiYgoYORkxUCtlMJkcS2YKxGAGWNYk4K6R5WQgbhr/4KGDZ/AUnEUspAohEy6eFAXzgO6kdjfdtttOHbsGIC2Eb677roLCoX7FO/6+nokJyf3XoTUK3JGxGLykWp8v7kIAFBS3YK/vb4Nj946FRdOS8OF09I8nrdgcgqiwtT4YUsRGprNGJEegSWz0hEWrAIAWG2ep1V11kf+wVxe4JLUn9K09WsEjZ0PeVjbgxmpNgThc65Hw+p3XY7TZk2DOm2M1+tL1UGImH8D6n78H4DTT4CCxsyFOmVU73wIon6kUTGhJyLqLq1ajt9fPwFPf7AbZmtbci+TCrjtstGIixy8U6ep59Rpo5GQxq23z9TlxP7222/HZ599BgD46quvMGLECISHu66hlkgkCA4OxuWXX967UVK3lFa3wGpzIC0+BJKT05vW7ylrT+pPKatpxXMf78Hjd85wu0Z1gxHHy5sQrFVgZHoExmZEe3yvSSNi8NW6Qrd2pULKStEBwOt2dqITphMH2hN7AAidsgSqxEy05K6HaLNCm5EDTeYkCELHU+iCJyyEKmk4Wg5ugGizQpMxEZoOHgYQBYLmVgt+2laMoko94qO0WDQlFZGh7vs2d8TpFHGiohkKuZRbPhHRgDd5VBzeenghdhyqhM0uYtLIGIQFqXwdFtGA0eXEfvz48Rg/fnz76zvvvBNJSUl9EhT1THltK55+fxcKy5oBAFFhatx91ViMz4zGuj1lHs85eKwe9c0mRIS0fSHVG6z417s7caDw9N72aqUMt18+GnMnuv97jxoSiQumpWLFlqL2NokA3H5ZNnScqur3OiowIlHr3NpUicOhShze7fdRRKcgYu4vun0ekT+qqjfgTy9uQoP+9O4P3206gcfumI70hJAuXWP/0Vo8/+k+1DQYAQDpCSG4f+kEJvhENKDp1HJMzY7H+j1l+GrdMYQFK9FisEJvsGLUkEjMGBMPmbTn9ZmaWixYvbMENY1GZCSHYebYBNZCoUFDEEVvNdF7xmg0YteuXZg1a1ZvXtbncnNzAQDZ2dk+jsQzp1PEHf9ajYo616rNCrkUrz04D89+tAf7j9Z5PPd/f56P2Ii2BO9vr2/Drrxqt2MEAXj6nlnISHavqwAAh47XY8ehKigVUswen4iEKPekkPyPw6hHyYt3uG1PJ9WGIunuVyCRed9RwRunzYKWvT/DWLgbglyFoNFzoM2c3FshE/ncsx/twZpd7tV4x2dG42+3Te30/Ea9Gbc+vgoWq+ta05hwDV59cD4LSRHRgFVVb8CDL29ur/l0tpHpEfj7bVN7lIwXljXhr//dglbT6R2d0uKD8didMzjYRAGtq3loj4rnVVRU4JFHHsGOHTtgtXreAisvL68nl6YeOlBY65bUA4DV5sCaXaWYPDLOY2KfGhfcntTXNZk8JvVA25Z4K7cXe03sR6ZHYGR6xDl8AvIFqSYYsVc/gJpvXoCjpR4AIAuLRcyl9/UoqRcddlS8+xCsVcfb24wFOxA6/UqEz7mu1+Im8qW9+Z7vk/sKaiCKYqfLU9btKXNL6oG2JVD7CmowYXhMr8RJRORv3vz2kNekHmgbKPp5ezEumpHe7Wu/9lWuS1IPACcq9PhqXSF+cUFWt69HFGh6lNg/9thj2LNnD6666irs2bMHarUaY8eOxebNm1FQUIAXXniht+OkTugNXvYYP9m3ZFY6Vu0oxvEKfXu7ViXDBdNS8acXN6KpxYLk2I6ngOoNVjicIr5YU4D1e8ohkQi4cFoqFk1N7fSLLPkvdWo2ku9+BZaKQkAihTJuSI/+PZ0WIyref8QlqT+lactXCJ54AWS60F6ImMh3vt5wDHqDzWOfVi3v0u9Oc6v7FqJd6SMiCmSiKGLHoapOj9uZV93txL7VZENeUYPn6x2uYmJPg0KPEvudO3fivvvuw7Jly/D+++9jzZo1+MMf/oDf/e53uOmmm7B69WrMmzevt2OlDoxMj4BUIsDhYe+57CEReOHT/S5JPQBkpYXjlS8OtL/2NOJ/plFDInHXk6tRXnv6uJe/OIAN+8o9FuCjwCA6bNDvXglzeQFUCRlQxqQC0o5vDU6zAYaCnbDWFMNhaIZEqYGtsdJjUn/yTWAuOQjdCP6cUOBasbUIr3990Gv/+ZNT3NryTjTg4PE6hAUpMX1MAtRKGUYPi8IXa92LjkokAkYNiezVmMl3RKcDxqO7YGuqgSp+KFRJvZdY2BoqYDy+HxKVFtqMSZAoWICM/J8gCJDLJHB4mLF0pp7sPiKTCpBJBdgd7t+DVYoepTtEAadHP+kGgwGZmW37Vqenp+PFF18EAEilUlx//fX417/+1XsRUpdEhKhx1bwMfPxzvkt7zogYSCQSj8XzduXVdPn6idE62OwOl6T+lIPH6rG/oAZjMqJhdzixNbcS+cWNiApT47wJSQjWdn9KN/UPS20ZKt76U/sae8PhTWhY9yHib/oXlJGJHs8xHt2N6q/+7bYuvzPW+opzjpfIl75e756Mn3LehEQsXXS6sKTDKeLp93dh0/7TP/dvfXcYf7ttKsZlRGHKqFhsO+g6cnXp7CE4fKIBP2w+gbhIHWaPS4BKyS+kgciur0PlB3+DreH0v786fRxirvpjj5Y5nal+zXto3vo1Tm0hWq/WIfbqP0OVmHlO1yXqD7PHJ+KnbcUdHjMvp/vFuVUKGaaNjseGveVufZ6KPxMNRD36xhAdHY26urb12ikpKWhubkZtbS2ioqIQGhqK+vr6Xg2SumbpouHISg3H2t2laDZYMDQhFJfNGYKPfi4452vfdmk23v/Re92ENbtLMSw5DA+/uhX5JY3t7Z/8nI//+/U0DEkMPecYqPdVf/a4W4Iu2syo/uQxJN/1stvxTqsJ1V8/1+2kHgCkSk2P4yTyB9UnK9ifTauS4XfXT3BpW7ur1CWpB9qWMz330R68+Ie5eOCXOVi/txzbDlZCIZMiZ0QMPl1dgC/PGMn/ZFU+HrtjensdFAocdT++7pLUA4Dp+F40b/8OYdN7viWw8cR+NG9d7tLmNLWiZvmzSLrrZQhCz6uJE/WHGxePRFlNKw4dd88V5DIJrl2Q2eM6I7dfPhr1zeb2a0sEYOHUVCyc4j6bivyTrbkGLXtXwdZUDWXsEASNnQdpBzs4kaseJfazZ8/Gc889h9jYWIwbNw6xsbF48803cdddd+GLL75ATAwL//hKRkoYvt10HHvza7E3vxbfbT6OYUmeC951VYhOgVFDIjucGqXTKPDNxuMuST0AtBht+O+XB/DUPQNrl4SBwNZUA3uj57Vu9qZq2JqqIQ91/V02Fu6BaPGc3HRGneqfO0oQddWwpDCPaziHeSgquiXX8wyV4qoW7DxUhZyRsZg7Mal9JOnVLw+gpKrF5djaRhPe+u4QHvzVpF6InvqL02aBsXC3xz5D3pZzSuwNh7d4bLc318JSXtCj7UiJ+pNWLccTd81AfnEDymsNSIsPhs3uRFOLBZkpYQjRKXt87SCNAk/cNQOFpU2objRiWGIoosM5qBAozOUFqPzw7xCtbcUVDYc2Qb97BeJ/+RhkQeeWywwWPXq0e8899yA4OBj/+c9/AAD33Xcf3nnnHeTk5ODbb7/FjTfe2KtBUte99Nk+l8r2JosDBwrr4KmeU1fqo8mkAi6ZOQRvfnsQMqn3Ey6bPdRrQZQjxY0sCOWPRGfH/U4HTEW5aNz0OVr2r4HTau78HC+0I2fCeGwvGtZ+AFPJoR5dg8jXrl+Y6bYVnVwmwXXnu0+BFuD9fvnSF/vd6qFs83L/3HGoCr28Ky350rn+W3Z0D+bPCQWQzJRwzJ2YhLT4EGQkh2HSyNhzSurPNDQpFNNHxzOpDzD1q95uT+pPsTfVoGnLlz6KKPD0aMQ+LCwMn332GWpq2tZoX3LJJYiPj8e+ffswevRoTJrE0QVfaDFasSW30mNfWlwIKusNMFnsANr2S/7VRVl44dP97W0AMDwlDClxwcgtrEOwVoHk2CC8t8L7FHyJIOD2y7MRGar2uueoRNJWLIX8g+h0wHBkO+xN1ZBqQ+EwNLkdI9GGofrLf8NafaK9rWHtB4i+8o8Q5EqINu8PauSxQwC7BaLDDkVkEuSRCWje8T0MhzYCAJq2fAld9mxEXfwb7qZAAWVsRjQev3MGvlpfiNLqFqTEBeOK84Z6nBU1c2w8dhz2nKzXN5ux/2gtxmdGt7cpvNwjZVIJf08CjESuhCp5BMzF7oUWtVlTu3UtW0MFbE21UMakQqoNgXb4FLTsX+N2nDQoHMqEjB7HTETkS06rGZayfI99puP7+jeYANajxP7FF1/EVVdd5TLlfuLEiZg4cSLKysrw97//HQ8//HCvBUldYzTb4fRQFR8AlAop3nlkIQ4eq4NKKcPItAhIJAJmjk3Ehr1lKKttxfiMKLz42f72oiYVdQYcKW70eL0LpqYiKUaHhVNSoJC3/RjNGZ/occ3U5JGxPapwSr3PVHwQVZ881mFiDghwGptgNbj+2zsMTWhY9TZCp1+BxnUfej5ToYKjtRHO1rbpyoJECnPpEcDhuj1Ya+56aDOnQJvJh4AUWLLSwpGV1vnP7ezxifhs9VGUVLd47DcYXX8n5oxPxIcr3b/UzB7vuYgl+SdrfTlqv3kBloqjbn2q1GyETL64S9dxWkyoWf7s6Sn9EhlCci5E+LxfInj8Quj3/NR+rKBQIfqSeyBIPD9cJyLyd4JU5nXgSKLW+SCiwNSjYdSXXnoJ1dXVHvv279+Pzz777JyCop6JDlMjPtJzgYlxGVFQK2XIGRGL7CGRkJwxnXTWuERcf/5w7MmvRXGV5y+hZ9Np5Lh45pD2pB5o2+pp4ZQUlyn+QxNDcMcVo3v2gahXOa0mVH38z46Teomk7f+8TOm0lBegeecKr6eLVnN7Ug8AtvpyOM1eEpuCHV0LnCgACYKAm5eM8tgnl0kwepjrtnZXzhuGySNjXdpGpkfghsUj+yxG6l2i04Gqj//hltQLUjmir/wj4pc+Com8a1ON639+y3WdvtOO5u3foGX/akRecBsSbn4KYXOWInLRbUi++7+sYUJEAU2QyqDLnu2xTxYSDaf59K5cTpsFLfvXoGHdRzAc2Q7R2fH2id5YKo+jecd3aD28GU67tUfX8DddHrG/9tprsX//fgCAKIq45pprvB6bne1/f2DuuecejBkzBjfffLOvQ+kzgiDg1kuz8c+3dsDuOL0OLylGh4tnpnd6/t78rm9/52kEXiIRcPdVY3H5eUNxtKQJUWFqjEiL6PI1qW8ZCnZC7OzG5ex8Db3T4HkWR3cJUs7ioIFtXEYUZo5NwMZ9rtsv/eKCLLe1pHKZFA/dNBnHy5txoqIZCVE6DE8N789w6RwZj+2Fvcn976josMGh7/puQaLdhtaTS5fO1rJ/DYLHzocyNh3K2M7/rhMRBYqIeb+C06iH4cg2l3bD4U0wlx1B/C/+DxCdqHz/Edj1de39yrihiLv+YUi6WD1fFJ2o/eYFtB7c0N4mDQpH3HUPQxEV2Fsjdjmx/8c//oEff/wRoijipZdewhVXXIHYWNfRBYlEguDgYJx//vm9Hui5+Oabb7Bt2zaMGTPG16H0uYlZMXj+93Pw47Yi1DebMSI1HPMnJXdpKrxW3bVESyaVYE4H00PjI3WIj+S0GX9z5tNOf6AbNcPXIRD1KUEQcP/SCZg5Nh7bD1VBKZfivAlJHSbs6QkhSE8I6ccoqbd4qldyuq+5y9cRHTavD2H97T5ORNRbJAoVYq74A6o+fRzGo7tc+hz6OjSu/whOi8klqQcAS2UhmrZ+hfDzlnXpfVoPrHNJ6gHA0dKA2u9eQsKNT5zbh/CxLif2Q4cOxd133w2g7cvK2Wvs/VV1dTU+/vhjXHvttb4Opd8kxQTh1iXdnzWxYHIKdh9xH21QyqWw2NqmuQRpFLjryjGIDFWfc5zUvzTpY9H1MSPPFAmZsFYc7V51fEGARB0Mp/HkF1uJFGEzr4Y6mVOMaeCTSARMzY7H1Ox4X4dCfUydPAKAAMB9KZMqZUSXryNRaqBMyIClvMCtTzNk3DlESETk/0wlhz22Gwp2el1OajiyveuJvZdtQy0VR2FrroE8JNpjfyDo0Rr7u+++OyCSegB45JFH8Oc//xlyOaf9AkBVvQErthZh497y9mT9lOmj47F00XCX6vbjMqLwxkML8MRdM3DhtFQo5RI88e5O/Obptdh20HMFfvJP8vA4BI3reDaNNmsaNJmTPfYJKh2iL7oDIVMu6db7hk69DCn3vIqYqx5A1MV3I/muVxA248puXYOIyN/Jw+MRPPECt3bNsByoU7tXayZiwY0QFK4P0OXhcQidetk5xUhE5O8kcpXHdkGhaqsD5Ym0O/XgO9gaNMC3De1RVfzhw4d3uv1OXp73LdJ623fffYcnn3zSpe2CCy5AamoqsrKyMGrUKKxdu7bf4vFX7/+Yh89WFeBU4fxgrQJ/vXkyhqecnhZ67YJMLJ6ehmPlzYgIUSExOggAsGJrEX7YUtR+XFGlHo+/vQN///U0jBkW1Z8fg85B1IW/hmboODRt+QoOUwtkobGQKtVwGPVQJWUhdMYVkEjl0O9bjaatX8NpbIJEEwx1ykiIdjvqV78DeUQCgiddDP2O73D2zVGQKRBx/s0w5G0GBCmCcy6Eduh4AIA2I8cHn5iIqP9ELrwZquQRbWvkHXZoMichaPR5EAQB9uZaOC1GyCMTO61gr0rIQNKvn4N+78+wN9VAGT8UQaPPg0TJfbmJaGALGj3H4971waPPg11f5zaNHgB0I6Z3+frarKket9BTxA6BPDQwBq69EUSx+48mXnjhBbfE3mAwYM+ePSgpKcH999+PK664oteC7Kkbb7wRdXV1kEgk7f//nnvuwVVXXdXta+Xm5gLwz8KAXZFbWIc/v7LZrT06TI3//XmBS5X8M1ltDrzyxX6s2lnqsX/C8Gg8emvbvry1jSbsPlINpULKLe4ChPHYXtR++2L72lBpUARiLrsPqqSs08cc34/qTx+HeNaWdRAEQCJr38pOog5C6PQroN/5PezNtSevF47oi38DdRp3RiCiwcne2ojab56H6cQBAG332ciFN0PrZXYUEdFg5rRbUfv18zAc2dreph0+FdFL7oXTYkTlx/+EtepYe59mWA5iLv89BFnX8g7R6UDN8mdhyDt9fak2FLHX/RXKmNRe+xy9qat5aI8S+4788Y9/hFarxSOPPNKblz1nL7zwAjQaTY+r4gd6Yv/S5/vx49Yij33/unuG1+r1L3+xHyvOGKk/W3ykFq8+OB9frj2Kd37Ig/PkdACNSoYHf5WDsRmBu05loHMY9Sh58Xa39UoSlQ7Jv3kVEkXbVKiy//0O1ppir9fRjZoN3cgZUMQPRdl/74HT5Lq9naBQIfnu/0KqDur9D0EUoGobTcgvaUBEsBpZaax+P5CVv/MXWMqOuDZKZEi89RkoIr0XoiUiGsysdWWw1ZVDHpUIRURCe7soijAXH4StsQrKmDQo44f26Prm8gKYSw5DqguFdvjULm9H6gtdzUN7NBW/I5dddhl++9vf+l1i3xtEUYTRaPR1GD1isXrf5sxoNHn8XBarA6t3lnR43aQYLQ4VVuGt71wLXRjNdjz53i68fP9MlzX75D+M+9d5LELiNLeiMXcj1FnT4TQbOkzqAcBYcgi6Bbeg+cgWt6QeaNvbvnHfWmjGzO+12In8mSiKEEV4nQn17op8rNhW2v4gNC0+CH9cOhbhwafXFdodTsikPSqDQ37EVlfqntQDgNOOhl0/InjW9f0fFNEAcLS0Gat3laG51YoRaWGYNzERGlWvpzXkS5pwCMnhsAOwn52nRA+BLHoIHEDPc7OwRCjC2h6umm0OwOa/OZ4oip0ugwf6ILEvKSmB3W7v8fmvvvoqNm3ahPfee6+9zel04sUXX8Rnn32GlpYW5OTk4OGHH0ZSUtf3GvzNb37T45hOsdls/Vo7oDfF6TxXkZRLgXU7CtBUp0WYzvXHocXkgNXmvfq5TAqMSQK+XXfIY3+L0YYV6/cjI4EV9P2RqrQY3v5lKktOwIJwwGlHqFQO4exp+GdwGFuQl5cHZfExeFv9WV1aDLMiMH93iLrKZhexan8z9h03wGITEaqVIjlagbFpWqTHtiXtuUVGfL+lweW8ExUt+MsrWzEkTgmbQ8TxSjP0JieiQmSYkx2MkclcVx2oZPUn4G2uUmNlKcoD9DsFkS/tP2HA8m2N7XXO9hTUYeW2E7hpQTRUCj4QpYFJoVB0ekyPEvsXX3zRrc3pdKKqqgo//PADzjvvvJ5cFh988AGee+45TJw40aX95ZdfxocffognnngCsbGxeOqpp3DLLbfg22+/7dKH7C1yuRxDh/ZsuoevZWUBW/K3oqiq1aXd5gDWH2zB5rxW3Hv1aEwacXrq/NvfexhlOCk6TI17r87G0MQQVLQUAGj1eFx8QiKysjgd3x/ZItSoP7rOQ4+A5MnnQxba9u+mr54N4/5VXq+jShuNrKws2GPDUJf3MzxVG02cNA/y6NReiZvIXz378QFszz99L2wyONB0woQDJ0yYPS4Od1w2Est37vV4bkOrHQ1HXR+K1zbb8fnmBgxNT8HYYZF9Gjv1DaclBbX7vvQ4Oypq5GRosrI8nEVE3tgdTjz7zUa34uU1zXaU6LVYMjPVJ3ER9aXCwsIuHddriT0A6HQ6zJ8/Hw8++GC3rlddXY1HHnkE27dvR2pqqkuf1WrFm2++ifvvvx9z5swBADz77LOYOXMmVq5cicWLF/fkI/SIIAjQaAJz5KSoUu+W1J/J7hDxxrdHMH1sMuQyCQpKGrFim+eCeWFBSjz5m5mICGkb7509PhnfbXafrq1VyTB5VCJUSk6N8kupI2CbeCH0u35waQ6dfgWC41PbX6sW3Yw60Y7W3A1u+9dLNMGImvcLKDQaQJMO29QlaN663OWY4PELEZLa9T2ciQJRVb0B2w9Xe+1fv7cScyem4qxdRjslisD3W0oxbUzyOUZIPqHRwD77OjSsetulWRk/DOHj50Mi67/BCaKB4ERFM5pbPS8vPVLSjOsC9Hs6UUe6Mg0f6GFif+SI95Hcnjh06BDkcjm++eYbvPTSSygvL3d5L4PBgKlTp7a3BQcHY8SIEdi5c2e/JvaBpr7ZhBajDUnROmw/1Pme802tFhwpbkD2kEjsOFzl9bgr5w1DRIgaZTVt66mHp4bjivOG4ou1p58myaQS3HvtOCb1fi5y4c3QZEyEIW8bBIkE2hHToE4e6XKMRKZA9MW/QcTcX8JSXQRz6WHYGiqhiEpG0Nj5kOlC24+NmPsLaNLGoPXwZkB0Qjt8CjRDJ/TzpyLqf1X1hk63v12zuwQTh0fj0PH6bl27qFLf5fV15H9CJ18MZXQK9PtXw2k2QJM+FkHjFjCpJ+qBYK0CguB5u/EQLX+naHDrUdbV3NyM559/Hnv27IFer3frFwQBq1Z5n7p7trlz52Lu3Lke+6qq2hLMuLg4l/bo6Oj2PnLVYrTiPx/vxY7DVRBFIFSnRFe/D77yxQH86ZcToeyg4J3N5sRvnl6Losq2f/vUuGDce+04RIVp8N6KPBhMNtgdTny/+QQyksPaR/bJP2nSxkCTNqbT46TaEGjSx0CTfvpYp9UMe2sjZLqw9jZ12mhub0eDSlOLBR//XNDpcev3lONwWAMSo3Uoq/E+g+pseoMV//5wD36/lA/JAhXvi0SurDYHNu2vQFGlHglRWswalwh1FwaDIkLUyMmKdRuAEgRg4ZTUPoqWKDD0KLH/61//itWrV2PmzJkYPnx4b8fkwmQyAXAvGKBUKtHc3Nyn7x2o/v3hHuzKOz0ltKnVc+E8T0qrW/DIa1vxj9un4d0fPBf1+eCnI7DZT0/JLqrU4+FXt8BkscPuOP0Idf/ROjz29g48c+/sHnwK8hWHUQ9D/g7AaYdmWA5kwe5bITqtZtSvfAOtBzdCdNggj0xExPwboBkyzuU4W0MFDEd3QyJXQjt8KqQabnlHA8/TH+zq8ih8baMJUgmwZFY6vt5wvMvvsW5PGeZPSsaYYVE9DZOIyC80tVjw51c2obT69APOT1YV4PE7ZyAmvPOp9L+9bhye/ajtu64oto3i37h4BEame966mWiw6FFiv2XLFjz00EO47rrrejseNypVWyVhq9Xa/t8AYLFYoFZzJPhsNQ1G7D7ifZ1nV9Q3m3GsrNnrVKczk/pTWoyeq6YXlDShsKwJQxNDzykm6h+teVtR+83zEO0n16+tfBMRC25CyMRFLsfVfv8yDIc3t7+21ZWh6rMnEL3kXigiE6GISkbDhk/QtPEznCqmV7/qbcRc/ntOzacBparegP1H67p1jsMJHCjs3jkAsPtIDRN7Igp4H6484pLUA20PPd/67hAe+GVOp+cHaRR4+OYpqG00odlgQUpsEOQybq1M1KPEXqvVIjExsbdj8ejUFPyamhokJ58uHlRTU4PMzMx+iSGQNLVaOl3n2RV1TaZeuQ7Q9mSW/J/D1Irab184ndQDgNOB+pVvQDNkLORhsQAAe0sDDHlbPVzAjpovnwEAyMJiYW90nSYn2iyo+fp5JN/zGiRyZZ99DqL+1NyNGVFnavXyMLQjWu7RTEQDwI5DnpfSbj/YvSW2UWFqRIVxkI/olB5t9rh06VK88cYbMBgMvR2Pm+HDh0On02H79u3tbXq9HocPH0ZOTudP9Qab1LhgBGnk53ydsRlRvfIlUiGTIDMlrPMDyeeMhbs8bskE0QlD3pb2l/aWBrfq+Gc7O6k/xWluhako95ziJPInafEhCNJ0v2DTiLRwj+1qpQwRwe4PvmRSAXMmJHX7fYiI/I3Cy+i6Qs496InORY8yt2XLluGrr77C7NmzkZaW5jYlXhAEvPPOO70SoEKhwLJly/D0008jPDwcCQkJeOqppxAbG4vzzz+/V95jIFHIpfjVRSPw0uf7ezziPmdCIoYkhuKGxSPw0ucH3PpHDQnHwWMNLm2zxyWgptGEvCLX9mvPz+zRl17ygQ5+YMQz+hSRiRCUGogWY39EReTXFHIpbrp4BJ7/dF+X77mpccG47bJsj/fMX12Yhay0CDzxzk5U1rc9PA/SKHDXVWO6tPaUiMjfzR6fiI9/zvfYTkQ916PE/uGHH8aJEyeQnp4OlUrl8qUfgNvrc3XPPffAbrfjoYcegtlsRk5ODt544w3I5ec+Mj0QLZySioQoHX7aXozmFgvGDItCbIQG3246jromM5JidIgJ16K8phXR4WpEhKiRX9wIQQBmjEnA3Ilto0KLpqYhLEiFd344jAa9BVGhaty6ZBSyh0Ziw95ybNzXti3hjDHxmDUuEVa7Ayu3F2N3Xg00Khnm5SRjYlaML/+noJNEUYTpxAEYC3bC1lwDiUwORXQKgsbMay+Opxk6EYJM4ToV/6QzqzlLFCqEzbgSDavf7XYcErUO6tTsnn8QIj80f1IKEmOCsHJbMfQGK5JigqA3WFDXZMaQxBAkxwRj28FKtBitGJsRhQunpUGrluP/bp+GNbtKsedINXRqBeZPSm4v/vTfB+Yhv7gRVpsDWWnhUHSwUwkFDkvVcTRtXQ5rbQnk4fEInbIEqkTvywptDRXQ71sNR2sTVElZ0I2ayaVMFPCunj8MJyqasf2MKfmjh0bihotG+DAqosAniD3IwsePH4877rgDt956a1/E5Jdyc9umD2dnB15SYrM78djbO1wq5QdpFPj7r6eyqN0gIIoiar95Hq0HN7j1CQo14q77a/sXy5YDa1H77Ytux0nUQUi4+UnIQ6Lbjtu/BrXfvYxThfE8O7XHYtsxglx1snje+HP5OEREAclcXoDK9x9xfXgqkSL22r943HLUWLgb1Z8/BdFxuh6DMm4I4pb9DRIF1xVT4DtR0YwTFXokRuuQkcxlm0TedDUP7dGIvUKhwKhRo3pyKvUjq80BiUTAyu3FLkk90LbX/Yuf7cNz983p0bVtdgekEgkkEsGl3ekU4XA6WZ3UjxgLd3tM6gFAtJpQv/JNJNz0r7YGied/N6epBY3rP0b0JffAaTWh7ue30HFSDwSNm4/QKUtgLNwNQa6CdvgUSNW6c/koRAFJFEU4RUAqEWB3ONHUYoFcJkGIjiOvg0njxk/dZ0Q5HWhc/4lbYi+KIup+esMlqQcAS+Ux6PesROiUJX0dLlGfS4sPQVp8iK/DoABkqTqO5u3fwlpXDkV0EkJyFsNccgitBzfAYWoBBAkgOqGISUPotMuhih/q65D7RY8S+yVLluCjjz7C5MmTIZGw0IW/KarU4/Wvc7H/aB0UMgl0XorpHStrRk2jEdFhXV+3WVDSiDe/PYRDx+uhUckwf1IyfnXhCAgC8O4PeVi5vRhGsx1ZqeG4cfFIZHkpEEX9x3h0d4f9lspCOIx6SDXBMB7d1cF12vrMZfldWl+vjBsCeXgcQiYt7l7ARANEq8mGN785iPV7y2GzOxAfoUV1owl2R1vxychQNe69ZizGZkT7OFLqD5bKY57bK466tdkaKmBv8rx1renYXib2RDRomUvzUPnB39offFqrjqE1d4PHws72phqYCvcgbtnfOlz2NFD0KLEPCgrC559/jrlz52L06NHQarUu/YIg4LHHHuuVAKl7mlst+Msrm6E3tI0KWO1ONOi9b8ckPWvEvSPVDUY89N/NMFkcAACj2Y5vNhyHvtUKqVTA6p2l7cfmFTXg4de24D+/m4P4KI7S+pIg76R4oUQGQdZ2jESu8n6dk1M/JcquPQjq6nFEA9X/vbENh0+cLo5XXue6k0xdkwmPvr4N//ndHKTEBvd3eNTPZMFRsBr17u0hUW5tEoUGbcuZ3GdGSVRatzYiosGiYf3HbrOZOtqtSXTY0Ljpc8Rd+5c+jsz3epTYf/nllwgJaZs6c/DgQbd+Qeh6ski9a/XO0vakvjNp8cEoqWqBTqOAsguFmVZsOdGe1J9p/d4yj9WgzVYHfthShFuWcNmGLwWNmg39ju/hbeq8LmsqJIq2hF43ejZa9q/2eFzw+IUAAEGmgEQdBKepxet7StRB0Ayb2P5adDpgOLIN5tI8SHVhCMqe0160j2ggOlLU4JLUe+NwiFixpQi3Xz6602MpsIVMXozar//j3u5hVpMsKAzqIWNhOrbXrS9ozNw+iY+IKBBYytx3VOiMtep4H0Tif3qU2K9Zs6a346BeUtVg6PwgtI3Un6jQ4+HXtiJII8edV47BjDEJHV+73vP0647KL1bWdS0e6jvKuHREnH8TGta857a+U52ajYiFt5x+nTwSYbOvR+OGj1z+YTWZkxE6dUlbcb3vXu7wyag0OBIxl93XXrnZabOg6qP/g7k0r/2Yps1fIvaaP0OdMrK3PiaRX6noxr2vuoFbRw4GQaNmQbQY0bj5CzhaGiDVhiJk8sUIybnQ4/FRi+9GzVfPwFxyGEDbQ9WwmVdBM3RCf4ZNFBCqG4z4aVsRqhuMyEgOw/ycZGjV3D1rIJIGR8DeWNX5gWeQhQ6OXbp6lNiT/xqSEOqxXSIAD98yBdX1Rnzw0xGXUf0Wow3PfLAbQxNDERvhfYpfekIINh+o6F48iSyK4g9Cci6EbtRMmIoOQnTaIZGrIA+NgSI62e3YsBlXIHj8ArQe3gyn1QTtiOlQhMbAaTWjbuWbHpP6qIvvhiw4EpBIoEocDuGMInwte392SeoBQLSZUbfiVSTd/nzvf1giP5Ce0PV7H6tBDx7BExYhaPz5cJpaIVFpXe6VZ5PpQhH/i/+DtaYE9tZGKOOGsAApkQeHjtfj0f9thdnaNqt0w95yfL/5BJ68eyZCg1ikdKAJybkI9Svf6N45Uy7uo2j8CyvfDTCzxycgKcb9D/+8nGRMGB6DmAiNx6n6doeIdXvKOrz2wikpiAzxvgb7bKFBClwwLbXLx1PfkqqDoMuaiqCRM6HNyPGY1LcfqwlGyMQLEDbtcihOPuU0lx3xWjTPXJIHdWo21Mkj3b6oGgs9F++z1ZfD1tC9B0VEgSI1LhhTs+M6PS4sSIkLeZ8cVARBAqkm2O1e6bQYod+3Ck1bvnIptKeIToYmfQyTeiIvXv86tz2pP6WyzoAv1roXpqTAF5JzIcLmXA/JyXuiRBOM0JlXQ5s17fTuTtK22Rqy0BhEXXw3dMOn+ircfsUR+wFGpZDh8Ttn4LPVR7ErrwoqpQxzJyThohnpANqK2nljMNlgstix+0g1RBGYMDwaGtXpaUwhOiX+9ZuZ+HRVAfYV1CJII0eL0eZ1GulfbpiMsKCuPwgg/9ZRMTyJ0vueyoLc+9NyQcYn6TRw/WHZRHy57ijW7S6D1e7E0MQQVNYaUFFvgEwiIGdELG5YPILb3hHM5QWo+vifcJpb2xrWArrRcxG1+E7WLSLqQKvRisKyZo99e/Nr+jka6i9h069A6ORL4DA2Q6oNgXAykXfaLIDTCUGhgmgzQ6Lw/v10IGJiPwCF6JS4Zckot6J1VpsD328+4fU8rUqOG/7+E4xmOwBArZTi3mvGY/qY+PZjosM0uPuqse2vP1qZjw9/OuJ2rbT4YAxP5VZ3A4kyfhjkkYmw1Z09s0OALnuO1/N02bNhLNjp1q5OzWYBPRrQ5DIJrpmfiWvmD/wtdqjnRFFEzdf/OZ3Un9R6YA00Q8dDlzU4RpqIekIul0Iuk8Bmd18mqNN0sisQBTRBJm9bBnoGyRmDScIgS+oBTsUfVPbm16DVaPPYlxitw+drj7Yn9QBgsjjwzIe70dhi9nrNS2amIz3edS2pSiHFbZdm907Q5DcEQUDMFX+APOL0gx5BoUbkhbdDGZvm9Tzd8KkInXbZ6elRABQxaYi6+Dd9Gi8RUSCwVhd5LQRlyNvSz9EQBRalXIo54xM99p0/2fuSQ6KBiCP2g4jd4b18fXSYGmU1rW7tNrsTm/dXYPHJqfxn06rlePKemVi3uwxHihoQGarGgsnJiA7jHuYDkSIyEYm/fh6W8nw4zUaokrI6nIZ/Svh5yxA88UKYy/Ih04VBlTS8H6IlIgoAHU215zR8ok7dsmQU9AYrdhyugii2zZa6dPYQzJ3IxJ4GFyb2g8jYjCioFFK3AiMAkBIXjD35tR7P8zS96UxKuRQLp6Rg4ZSUXomT/JsgCFAldj8xlwWFc0opEdFZlDGpkEckwFZf7tany5rug4iIAotGJcdDN01GZZ0BNY1GpMYFs3YJDUpM7AeB/OIGbD9UBbvDiYQoHY6VuxYZuXTWECTHBnk8VxCAySNjPfaVVre0b383fXQ8kmI8X4OIiIi8i15yLyo//gecRn17W/CERdAOn+zDqIgCS1ykFnGR3rdtJhromNgPcK9/fRBfbzjmsU8mleDPN+Tg6w3HsNzLMbPGJSI+yn2LneXrC/Hmt4cgnpzd/8GPR3DTxSNx2ZyhvRY7+Ren1QxT8UEIUhnUKaMgSHn7ICLqjMPQDHN5AaTaUKgShnk8Rhk3BMl3/xfG/B1wGJuhThsNRRSnERMBQEVtKz746Qj25tfAefKLZ1iQEnMnJuOyOUMhk7qWDHM4RXyz4RhWbi9Gq8mGcRlRWLooCzHhXCZKAxu/mQ9gR4obvCb1AGB3OPH61wdRUWfwesze/BrY7A7IZacLn9U0GPHWGUn9KW9/dwjTRsfzxjkAteZtRd33L8N5ch97qS4MMZf/HqqkLB9HRkTkvxo3fY7GTZ8BjrbCtIrYdMRe9YDHHUEkciV0o2b2d4hEfq1Rb8afXtyEplaLS7vBZMe7P+ShpKoFv186waXv1a8OYMWWovbXa3eXYf/ROjz/+zmcok8DGqviD2A7DnmusnumynrvST0A6A1WHD7e4NK2/VAVnB7q8DlFYPvBym7FSP7Prq9DzfLn2pN6AHC0NqLqs3+17RdKRERujIW70bj+o/akHgCsVcdR883zPoyKKLCs2FrkltSfaf3eMlTUni7+3KA3Y+W2YrfjGvRmrNzu3k40kDCxH8Dk0s7/ebtSb1cmk3T4+kw/bitCfbOpC1elQNF6aBPgtLu1O00tMBbu8UFERET+r+XAWo/t5uKDsDd7LlZLRK6KKvUd9osicOKMY0qrW+DwNPrUhWsRBTom9gPYrPGJne6Uk54Q0mF/dLgGWanhLm3TsuMg95Lcl1a34h9vbu9WnOTfnFaz1z7R6v0hjsPUgpb9a6DfsxJ2fX1fhEZE5LecFu/3TmcH904iOi0x2r3O09nizyiYFxephcTLd99EDzWjiAYSJvYDWEKUDqOHRnrtjwhR4Ym7ZuD8yZ63qQsLUuKBX06E5Kw7ZIhOid9fPwEyqec7Z2FZM44UN3jso8CjGTrBc4dEBnX6OI9dhvztKHnh16j97iXUrXgVJS/dgeZdK/owSiIi/6IZOt5juyw0GvLIxH6OhigwXTA1DVq13Gv/+MxopMWfHqSKDtNg1jj3368gjRwLp6b2RYhEfoPF8wY4nVrhte8PyyZCqZDhN1ePxaWzh+BIUQMUcinsDid0ajnGD4/xOjI/fUw8dh9Jws87Sjz2NzR7H6mgwKJKGIbgiRdAf1ZiHj53KWRBYW7HO80G1Hz9PMQz1987Haj/6Q1o0sdAHh7f1yETEflc0Nh5MORtgbk0r71NkCkQueg2CALHVYi6IipMjcfvnI53vj+MfQW1kEgE2OxOqBQSzJ2YjBsWj3Q7555rxiIiRIWfd5S0V8W/YfFIhAerfPAJiPoPE/sBbkRaePte82dSK2VIiw9uf50UE9TtfejHDIvymNhLJQIyU9wTPgpckQtvgXb4VBjyt0OQyqAbOQPK2HSPxxoL90C0eXqwI6I1byvCpl/Rt8ESEfkBiVyJuKWPwnBkK0zFhyDVhiJozFzIQ6N9HRpRQEmLD8Gjt05tf+10im6zSc8kl0lxw+KRHpN+ooGMif0AN39SMn7aXoySqhaX9uvOz4RG5X1qU1dMHxOP7zefQF6R67T7JbOGICJEfU7XJv+jThkJdUrnfyRF0em909lBHxHRANP2IHQmdCO5jR1Rb+koqScazJjYD3AalRz/unsmvtt0HPsKatvWGE1JxcSsmHO+tkwqwd9/PRU/bC7CzrwqqBQyzJ2YhJljE3ohcvIXouiEraESEqUWMl1op8drho6HIFNAtFvd+rTDp/RBhEREREREg5sgiqLnPSHIRW5uLgAgOzvbx5EQ9R9D/nbU//xW29ZMggSaYRMQddFdkGo6XrbRcnADar99EXA6TrYICD/veoROu7zvgyYiIiIiGiC6modyxH4AOVHRjA9+PIJDx+sRFqzCRdNScdEMz+ugiTpjrSlG9ZfPnE7ORSeMBTtRY38Ocdf9tcNzg0bNgjp5JAxHtkJ02KHNnMSieUREREREfYSJ/QBRUduKP724CSaLHQDQarLhv1/loqnViqWLhvs4OgokTrsVtrqytu3p2kfcTzMd3wdbQyXk4XEdXkcWHIGQSYv7KkwiIiIiIjqJif0A8c3G4+1J/Zk+/jkfEomA687P9EFUFGiad61A44aP4TS1dnicw9DUaWJPRERERET9gxupDhDFVXqvfR/+dASvfZULh7Pn5RSq6g3ILayD3uBeEK1Rb8ah4/VoarF4OJMChfHYXtT/9HqnSb2g1EARk9o/QRH5oeZWCw4U1qKq3uDrUIiIiAYsh7EFLQfWoeXgBjjN/JvbGY7YDxCJ0UE4eKzea/+3m45ja24FfnvteIzJiOrydY1mG/794R5sP1QFAFDIJLhszlAsuyALDocTr3x5AKt2lMDhFCGTClg4JRW3XpoNKbciCTj6PSu7dFz4rGsgUXA7Qxqc3v7uEL7ZeBw2uxOCAEwZFYffXTceKiX/nBIREfWWlgPrULfi1fZdluoUKkRfci+0mZN8HJn/4oj9AHHJzHQoFdIOj6lrNuMfb21Hc2vXR9b/t/xge1IPAFa7E5+sKsDa3aX4dPVR/LStuH0mgN0h4vvNJ7B8XWHPPgT5lMPY7LVPUGqgTs1G7HV/5bp5GrRWbi/GF2sLYbM7AQCiCGzNrcQb3x7ycWREREQDh11fh9rvX3bZOlm0mlHz9XNwcOTeKyb2A0RSTBD+efs0hGgVHR5ntjqwaV95l65psTmwfm+Zx76V24vx07Yij30/bS/u0vXJv6iTR3rtEy1GmCuOQh4W248REfmXn73c29buLm1P9omIiOjctOZt8VjAWbRZYCzY4YOIAgMT+wEkMyUcj946FQp5xyP3DV1cC2+xOrx+WW012tDiYb09AK/t5N9CJi2GrIPEXbSaod/9Yz9GRORfWk02j+1t90r3LyBERETUAw73guCniA7+vfWGif0AMzQpFE/fMxMTh0d7PeabDcewYW8Z9AYrzGdU0m9utcBsbXvtdIpwOkUMSQj2eI1xmdEYm+H5PcZ2Yw0/+Q+pNgQJNz6BoPHnez3GVl/RjxER+U5to8lt2ZK3e9vwlDBoVPJOrymKIuqajGjSm3slRiIiooFIk+FlHb1EBs3QCf0bTABhtZ8BxuFwYu3uMhw64b2QntnqwFPv7wYAyKQCRqRGoNlgQXFVC+QyCYYlhaK20YTaJhPUShmkEsGlon5cpBaXzxmK5lYLDp+odxnFCtYqsHTR8L77gNSnpOoghJ+3DK0H1rmsazpFEZ3ig6iI+s+R4ga8/Pl+nKjQQxDaHmLec/VYRISocdW8DOw8XI3qBmP78WqlFDdfMqrT667eWYJXv8pt35Y0NEiJ+6+f0K1ipkREg5HeYMWWAxWw2BzIGRGD+Eidr0OiPqaITETYnKVoXPfB6UZBgsjzb4IsKMx3gfk5QRTFnu+BNojk5uYCALKzs30cScfe/u4Qvljb+8XrYiM0UCtlGJoYiphwDYK0CkwfHQ+b3YkftxahtKYFKbHBWDQ1FeHBql5/f+pfDWvfR9OWr1zaJJpgJN78NGTBET6KiqhvNbaYcfsTq2E0u04BTE8IwX9+NwdA23T8n7cXo7C0CTERGiyakorocE2H191fUIuHXt3i1i6RCHj5j3OREMUvqUREnuw4XIUn39sFi7Vt+rUgAEsXDcc18zN9HBn1B2ttKQz52wFBAt2IaYO21lNX81CO2A8gp5LsvlBV3zZCdaJC3972xjeH8MdlE7Dsgqw+eU/ynbA5SyENioB+z0o4jc1QpWYjbObVTOppQFu7q8wtqQeA4+XNOHS8HiPTI6BTy3HZnKHduu7na496bHc6RXy76Thuv2x0j+IlIhrIzFY7/v3B7vakHmjbjeT9FUcwYXgMhiaG+i446heKqCQoopJ8HUbAYGI/gJgsdhg8fCntK1abA89+tAdvP7IQKgV/lAYSQRAQMvEChEy8wNehEPWb+maT176G5p6vi6+uN3rtq6pr7fF1iYgGsn0FtV6/127eX8HEnugsLJ43gARp5P0+pdNgtmNfQW2/vicRUV/ISgv32C4RgMyUnq/p83ZdABie6r2PiIiIqKuY2A8ggiDglxdmQSL08/v279tRP3FajGg5uAH6fathb2n0dThEfW7KqDhkeUi0F89I73QdfUeuWZABhcz9z22wRo4Lp6X3+LpERAPZ2IwoaFWeZ4ROHxPfz9EQ+T8Wz+uiQCmeBwCHjtfj243HUd1oRHp8MNRKGbbkVqK20fs001Oiw9S4aHo6Sqr1KCxtQkWdwete9gCgU8vx9iMLoZRLe/MjkI8Zj+1F9ZfPQLSe/JmRyBAx/1cIybnQt4ER9TGz1Y4fNhdhx+EqKOVSnDchEXMmnPv6voraVrz42T4UlDZBEICxw6Jw++WjERGi7oWoiYgGph2HqvCv93bBajujeN7C4bhmAYvn0eDR1TyUiX0XBVJi74nTKeLZj/Zg3Z6yDo+LDlPjjYdO72OeW1iHf7613eMaJ5VCij/9MgcTs2J6PV7yHafVjJIXboPTbDirR0Dir5+DIjLRJ3ERERHR4MPt7miwY1V8ciGRCPj90gm4ZFY6cgvr8e6Kw3A43J/pWG2uo/PZQyPx5l/Px9bcShjMNiTHBKG0uhVKhRTTsuOg0yj66yNQPzEe3+shqQcAEa2HNyN81jX9HhMRERENTsFaBRZNTfV1GER+j4n9IDMsKQzDksJwtLQRm/ZXuPVPGum+P6RGJce8nOT212Mzovs0RvIxp/elF3A6vPcREREREZFPsHjeIHXD4pGICnNd2xkXqcXSRcN9FBH5C3X6WAhypcc+beaUfo6GiIiIiIg6wxH7QSomXIOX/jAX6/aUoaymBamxwZg5LoH70ROkKi0iL7wdtd++6DJCHzr9SkhUGjRt+RKiww5t5mQoolN8GCkREREREQEsntdlgV48j6i77Po6tB7eDNFugzZjEswVBaj74VVAPD1VP3Tm1VxzT0RERETUR1g8j4jOiSw4EqFTlgAAHIZm1P/4uktSDwBNGz+FbvgUjtwTEREREfkQ19gTUaeMx/ZCdNg89hnyt/dzNEREREREdCYm9kTUKUEq7aCPE3+IeqKm0Yidh6tQWt3i61CIiIgowPEbORF1SjN0IgSFGqLV5NohSKDNmuaboIgChNMposVohU4th1QqgcMp4uXP92PVjmI4T1a5mTwyFvcvnQCVkn+WiYiIqPv4DYKIOiVRqhFz2X2o/urZ9uRekMoRsegWyMNifRwdkf9ataMEH608gppGE4I0CiyZlQ65TIKV24tdjtt+qArvfH8Yv758tI8iJSIiokDGqvhdxKr4RIDTYoKxcBdEhx2aIeMh1Yb4OiQiv7XtYCX++dYOt/bQICWaWixu7WqlDJ/880IIgtAf4REREVEAYFV8Iup1EqUaupEzfR0GUUD4esMxj+36VvekHgAsVjucIiBlXk9ERETdxMR+gDJZ7PjopyOoajBgXEY0grQK1DebMSwpFPVNZjQbLBg9NBLJscFer+FwOLHjcBWOljbBYLLBYLbBaLYjJTYYF89MR3iwqh8/ERFRYKlpMHpsd3qZJzc2MxpSCbN6IiIi6j4m9gPQ/oIaPPK/bXCc/Pa4NbfK67EXTkvFHVeMcWuvbTThr69uRnmtwa1v5+FqfLH2KH5z1VgsmMz9y4mIPBmWHIaaRpNbe1SYClKJBFX1pxP/YK0CNy0e2Z/hEREFlD1HavD95hNo0JswIi0Cl80ZishQta/DIvIbTOwHoMff2dme1Hfmhy1FGDMsCtNGx7u0v/rVAY9J/SmiCLz4+X5MyIrhyD0RkQcj08KxeX+FW7vV5sR//3Qe1u0pw/HyZsRFarFgUgpCg5Q+iJKIyP+t3F6MFz7d1/66sKwZm/ZX4N+/nYWIECb3RAD3sR9wiir1MJjt3Tpnw75yl9cWmwM786o7Pc/pFLE1t7Jb70VENFgUljV7bG9utaKsthWLZ6TjnmvG4ap5GUzqiYi8sDuceH9Fnlt7g96Mbzce90FERP6Jif0A05NNDpxnje6Lotg2JN+1N+z2+xERDXZcSU9E1DXVDUY0ethJBACOFDf2czRE/ouJ/QCTGhcMtbJ7KyymnzUNX6WQYWJW53uTSwRgSnZct96LBidzeQEM+TvgMHgewSQaiGaOTfDYHhOuwbCksH6OhogoMIXolJDLPKcsUVxjT9SOif0AIwgCfr90PLq6DfKc8Ykev3z++rJsxEVoOzz3jivGcF0Tdciur0PZ6/ej4u0HUf35v1D8wm1o3Pipr8Mi6hcTs2Jwycx0l7YgjRz3L50ACavfExF1iU4tx9yJSW7tEomAi6an+SAiIv8kiD2Zuz0I5ebmAgCys7N9HEnX6FsteP/HI6huMGD0sCiEBSnRoLcgLS4Ydc1mNLdaMHpYJIanhHu9hs3uxPZDlcgvboTBaIPBYoPZYkdybBCWzGIlUupcxfsPw1x8yK099po/QzN0gg8iIup/JVV67CuohU4jx9Ts+G7PqiIiGuxsdgde//ogVu0shdXmQFyEFr9aPMJt1inRQNTVPJSJfRcFWmJP5Gu25hqUvniHxz5t1lTEXH5/P0dEREREgcxsscNgtiE8WAWhq9NTiQJcV/NQDhsQUZ8QrWavfc4O+oiIiIg8USllG7e+9wAAHNdJREFUUHHWE5FHXGNPRH1CHpkIWWiMxz5OwyciIiIi6j1M7ImoTwiCBJEX3AZBpnBpVyWPRNDYeT6KioiIiIho4OFcFiLqM5r0sUi8/T9o3b8OdkMj1CmjoM2cDEHKWw8RERERUW/ht2si6lPykGiEzbra12EQEREREQ1YnIpPREREREREFMA4Yk+oqjdALpMgIuTc9qWvbjDC4XAiPkrXS5EREfkPm92BDXvLceh4PcKCVVgwKRmxEVpfh0VERETExH4wyy9uwIuf7UdRpR4AMDI9Ar+9dly3v6hW1Lbi2Y/24EhxIwAgJTYIv7l6LDJTwns9ZiIiXzBb7Hjo1S3IP3mfA4Dl6wrxl5smY3xmtA8jIyIiIuJU/EGrxWjFI//b1p7UA8Ch4/V49H9b4XSKXb6Ow+HEI//b2p7UA0BxVQse+d82tBqtvRozEZGv/LityCWpBwCr3Yn/fnkAotj1eyYRERFRX2BiP0it210Gg8nm1l5ea8C+gtouX2d3fg2q6o1u7QaTDev3lp9TjERE/mJ3Xo3H9so6A8prW/s5GiIiIiJXTOwHqcYWs9e+Br33vjMZTDYcPFbv/T26eB0iIn+nVnleuSYIgFrJVW1ERETkW/w2MkiNSIsAcNStXRCAEemdr43/bHUBPl1VALPV4fWYUUMiziVEIiK/MT8nGVtzK93ax2VEn3PhUSIiIqJzxRH7QWp8ZrTHgk8XTE1FfGTHVe235lbg3R/yOkzqc0bEYMywqHOOk4jIH0waGYuli4ZDLjv9ZzMzOQz3XjvOh1ERERERteGI/SAlkQh46KbJWLmtCNsOVkEmk2D2+ETMHpfQ6bk/bSv22C4AGD0sEjPGJGD+pGQIgtDLURMR+c61CzJxwdRUFJQ0IixYhaGJob4OiYiIiAgAE/tBTS6T4KIZ6bhoRnq3zms1uhfdAwARwL3XjEdUGKelEtHAFKJTImdErK/DICIiInLBqfjUbWMzPE+xT4rRMaknIiIiIiLqZ0zsqduWzB6CxGjXdfhymQS3LMn2UURERERERESDF6fiU7cFaRR45t5Z+HlHCY4UNSAyVI2FU1KQGB3k69CIiIiIiIgGHSb2A5DD4YRUenoyhiiKEMW2gnm9RaOSY8msIVgya0ivXZOIiIiIiIi6j4n9AGF3OPH+ijz8tK0YrSYbRqSF4+p5Gdi0vwIb9pXD4XBi0shY3HzJKMSEa3wdLhEREREREfUSJvYDxH+/POCyDd3hEw342xvbIIqnj9maW4ljZU146Y9zoVLwn56IiIiIiGggYPG8AaC51YLVO0vc2s9M6k+paTRh076KfoiKiIiIiIiI+gMT+wGgptEIu8NDFu9FRV1rH0ZDRERERERE/YmJ/QAQH6mDUiHt8vHpCSF9GA0RERERERH1Jyb2A4BWLcels92r08uk7v+8afHBmDIqrj/CIiIiIiIion7ACmoDxLJFWYgK1eDHrSfQ1GrFmGGRuOK8odi0rwLr95bD5nBi6qg4XLMgw2PCT0RERERERIFJEEVPJdbobLm5uQCA7OxsH0dCREREREREg0FX81AO3RIREREREREFMCb2RERERERERAGMa+yJiIiIiKhf2B1O7DlSg+ZWC0YNiURcpNbXIRENCEzsiYiIiIioz5VWt+DR/21FTaMJACAIwCUzh+CWJaN8HBlR4ONU/EHA7nCioKQR5bWtvg6FiIiIiAapZz7c3Z7UA4AoAl9vOIatuZU+jIpoYOCI/QC3aX85Xv0qF00tFgDAiLRw3L90IqLC1D6OjIiIiIgGi7KaFhwra/bYt35vGaZmx/VzREQDC0fsB7DiKj2efn93e1IPAIdPNODxd3b4MCoiIiIiGmzsDu87bNvtzn6MhGhgYmI/gK3aUQKH0/0merS0CScqPD8xJSIiIiLqbSmxQV4L5XG0nujcMbEfwJpbLT3qIyIiIiLqTYIg4N5rxkGtdF0JPDU7DnPGJ/ooKqKBg2vsB7Axw6KwdneZW7taKUVGcpgPIiIiIiKiwWpkegT+9+f52LC3HM2tFmQPjcSYYVG+DotoQGBiP4DNGpeIn3eU4NDxepf2X144AhqV3EdREREREdFgFaJT4uKZ6b4Og2jAYWI/gMllEvz9tqlYs6sUe/JroFXJMX9SMkamR/g6NCIiIiIiN4WlTVixtQj1zSaMSIvAhdNSodMofB0Wkd9jYj/AKeRSLJqaikVTU30dChERERGRV5sPVODJ93bBebL48+4jNVi9swRP3TMLwVom90QdYfE8IiIiIiLyKadTxJvfHGxP6k+pqDPg243HfRQVUeBgYk9ERERERD5V3WBETaPJY1/usbp+joYo8DCxJyIiIiIin9Jp5JBKBI99ITpOwyfqDBN7IiIiIiLyqSCNAtPHxHvsWzQltX+DIQpALJ5HREREREQ+d9eVY+BwiNiaWwGn2JbsL7tgOMZlRvs6NCK/x8SeiIiIiIh8TqOS44Ff5aBBb0aj3oykmCAo5FJfh0UUEJjYExERERGR3wgPViE8WOXrMIgCCtfYExEREREREQUwJvZEREREREREAYyJPREREREREVEAY2JPREREREREFMCY2BMREREREREFMCb2RERERERERAGMiT0RERERERFRABvw+9j/8pe/RGNjIySStmcYb775JiIiInwcFREREREREVHvGNCJvSiKKC8vx6pVqyAIgq/DISIiIiIiIup1A3oq/vHjx+FwOPCrX/0Kl112GVauXOnrkIiIiIiIiIh61YAesdfr9ZgyZQoeffRRNDY2YunSpcjKykJSUpKvQyMiIiIiIiLqFQMisf/uu+/w5JNPurRdcMEFePDBBzFu3DgAQFxcHObOnYtt27YxsSciIiIiIqIBY0Ak9osXL8bixYvd2nfv3g2bzYYpU6a0t8lkA+IjExEREREREQEY4GvsW1tb8cwzz8BqtaKhoQHr1q3DtGnTfB0WERERERERUa8Z0MPXs2fPxp49e3DppZfC6XTid7/7HWJiYnwdFhEREREREVGvEURRFH0dxJleffVVbNq0Ce+99157m9PpxIsvvojPPvsMLS0tyMnJwcMPP9yva+Vzc3MhiiKGDh3ab+9JREREREREg1dhYSEEQUB2dnaHx/nViP0HH3yA5557DhMnTnRpf/nll/Hhhx/iiSeeQGxsLJ566inccsst+Pbbb6FQKPotPpvNhry8vH57PyIiIiIiIhrcupLz+kViX11djUceeQTbt29HamqqS5/VasWbb76J+++/H3PmzAEAPPvss5g5cyZWrlzpsWheX5HL5RyxJyIiIiIion5RWFjYpeP8IrE/dOgQ5HI5vvnmG7z00ksoLy9v7zty5AgMBgOmTp3a3hYcHIwRI0Zg586d/ZrYC4IAjUbTb+9HREREREREg5cgCF06zi8S+7lz52Lu3Lke+6qqqgC07UN/pujo6PY+IiIiIiIiosHK77e7M5lMANzXFSiVSlgsFl+EREREREREROQ3/D6xV6lUANrW2p/JYrFArVb7IiQiIiIiIiIiv+H3if2pKfg1NTUu7TU1NdyTnoiIiIiIiAY9v0/shw8fDp1Oh+3bt7e36fV6HD58GDk5OT6MjIiIiIiIiMj3/KJ4XkcUCgWWLVuGp59+GuHh4UhISMBTTz2F2NhYnH/++b4Oj4iIiIiIiMin/D6xB4B77rkHdrsdDz30EMxmM3JycvDGG29ALpf7OrSAZTDZ0NhiRky4BnKZ1NfhEBERERERUQ8JoiiKvg4iEOTm5gIAsrOzfRzJubHZnXj961ys2lECq92JEJ0C1y7IxOIZ6b4OjYiIiIiIiM7Q1TzU79fYU+9694fD+GFLEax2JwCgudWKV7/KxdbcSh9HRkRERERERD3BxH4Qsdmd+Glbsce+Hzaf6OdoiIiIiIiIqDcwsR9ELFY7TBa7x756vbmfoyEiIiIiIqLewMR+ENFpFEiODfLYNzI9op+jISIiIiIiot7AxH6QueGiEZBKBJe2UJ0SV84d5qOIiIiIiIiI6FwExHZ31HtyRsTiqXtm4rtNJ1DdYMSwpFAsmTUEkaFqX4dGREREREREPcDEfhAalhSG+64L83UYRERERERE1As4FZ+IiIiIiIgogDGxJyIiIiIiIgpgTOyJiIiIiIiIAhgTeyIiIiIiIqIAxsSeiIiIiIiIKIAxsSciIiIiIiIKYEzsiYiIiIiIiAIYE3siIiIiIiKiAMbEnoiIiIiIiCiAMbEnIiIiIiIiCmBM7ImIiIiIiIgCGBN7IiIiIiIiogDGxJ6IiIiIiIgogMl8HUCgsNlsEEURubm5vg6FiIiIiIiIBgGr1QpBEDo9jol9F3Xlf0wiIiIiIiKi3iIIQpdyUUEURbEf4iEiIiIiIiKiPsA19kREREREREQBjIk9ERERERERUQBjYk9EREREREQUwJjYExEREREREQUwJvZEREREREREAYyJPREREREREVEAY2JPREREREREFMCY2BMREREREREFMCb2RERERERERAGMiT0RERERERFRAGNiT0RERERERBTAmNgTERERERERBTAm9jSg/eIXv8ADDzzgse+BBx7AL37xCwBAfX09/vCHP2DKlCkYN24cbrvtNhw7dqz92C+//BKZmZl477333K5TVlaGzMxMbN++vb1tzZo1uOKKKzBu3DjMnTsX//rXv2A2m3v50xER9b2u3kfP9Oqrr7q1d/c+ekpjYyNmzJjhsY+IqC95u8d15JVXXsGkSZMwbtw45Obmer23BZIHHngAmZmZ7f+XlZWFGTNm4OGHH0Zra6uvw6OTmNgTAbjrrrtQXFyM1157DZ9//jlUKhVuuOEGmEwml+Oe+f/27jy4pvv/4/gziAkRsRZRuyRaqSuaNDJj0Fs7Y6lO0wVRQVNLK7SaWiJ8ZYIkuA2xaxoJQqchCKE6dBgTVMqUmtCKSFVijaS2bL8//NxxG1HRWK68HjN3JudzPudz3p/zxyfnfc7nnBMRQUZGxkPbOnz4MOPGjaN79+4kJCQwY8YMkpKSmDlz5pPsgojIcyEuLo6FCxeWuv5RxtF7srKy8PPz4+LFi+UUnYjIk5Obm4vJZOKDDz5g69atODg4POuQyo27uzv79u1j37597N69m4iICA4dOsSUKVOedWjy/5TYS4WXk5ND48aNmT17Nu3ataNVq1aMGTOG7OxsTp06ZVG3Xr16TJkyheLi4lLbW79+PV5eXvj7+9O8eXO6dOlCQEAAW7Zs4c6dO0+6OyIiz0RWVhb+/v6Eh4fTvHnzUus9yjgK8N1339G/f/9yjlJE5Mm5fv06xcXFdOzYkcaNG1OlSpVnHVK5sbW1pX79+tSvXx8nJye8vLwYO3YsO3fu1F3754QSe6nwHB0diYiIwMXFBYArV64QHR1Nw4YNad26tUXd0NBQDh8+TExMTKntjRgxgi+//NKirFKlSuTn52vgE5EX1vHjx7G1tSUxMRGDwVBqvUcZRwF27dpFQEAAJpOpvEMVEXksRqORVatWMX78eNzd3fHy8mL27NkUFBSQkpKC0WgEwNfX94FT+B80tf+fZVlZWQQEBODh4WG+UZSenm5RPzAwkLlz5+Lt7Y3BYODjjz8mKyvLvP7+afP3fvdiKyoqYtmyZfTs2RM3Nzc6dOjAyJEjH3km1f3s7OywsbEp83byZCixF7nP9OnT8fb2Ztu2bYSEhFC9enWL9Z6engwZMoQFCxZw9uzZB7bx6quv0qZNG/Nyfn4+0dHRuLm5UadOnScav4jIs2I0GomMjKRJkyYPrfco4yjcfU7/vffe00mjiDxXTCYTnp6eJCYmMnnyZGJjY9m6dSvu7u5s3LgRgMjISCIjI8vc9o0bN8xJfmxsLGvWrKF27dq8++675sQdYOvWrVy7do3Y2FhWrFjB8ePHzY9ATZ061Txlft++fZhMJipXrsz48eMBiImJYdWqVQQGBpKcnMzixYtJT09nzpw5ZYr1woULrF69ml69elGjRo0y91XK34szP0SkFFu2bCE5OblE+Z07d+jQoYNFma+vLz4+PsTFxTF27FjWrl1L27ZtLepMmjSJvXv38tVXXxEbG/vQfRcUFDB58mROnTpFXFzcf++MiMgzUJZx9FGUZRwVEXmedOrUiWHDhgHQpEkT1qxZw5EjRxg4cKD5Bo6joyO1atUq80zNbdu2cf36dcLCwszT+ENCQkhJSWHDhg3m5NzBwYFZs2Zha2tLq1at6NOnD3v37jWvu/dsf0ZGBjNmzGDEiBEMGjQIgKZNmzJ37lzefPNNABo3bkyvXr3YsWPHQ2M7fPgw7u7uABQWFnL79m1q1arF//73vzL1UZ4cJfbywjMajXz++eclysPDw7l27ZpF2b2p9yEhIRw9epTY2FhCQ0Mt6lSrVo3Q0FCGDBlCTEwM3bp1e+B+8/LymDBhAgcPHmTRokW0a9eufDokIvKUlWUcfRSPOo6KiDxvWrVqZbHs4OBAfn5+ubR94sQJcnJy8PT0tCi/ffu2xdeamjZtiq2t7UNjyMnJYfTo0Xh6ejJp0iRzudFo5OjRo5hMJs6cOcOZM2c4ffo0DRo0eGhsbm5uhIeHA3cT+8uXLxMTE4OPjw8bN26kRYsWj91vKR9K7OWFZ29vT7NmzR5Yfu3aNa5cucKBAwfo2bOn+epopUqVaN26NdnZ2Q9s08PDg6FDh7JgwQKcnZ1LrM/OzmbUqFH8+eefrFq1qsQALSJiTf5tHH0c/zaOiog8j6pWrVqi7N9eBvowBQUF5r+Liopo0aIFS5YsKVHv/sdDHxTD/fLz8xk3bhzVqlVj3rx5Fo80LV++nMWLFzNo0CC8vb0ZPnw4u3fvZtu2bQ9t087OzuL/QMuWLTEYDHh5ebFhw4YS75eSp0/P2EuFd+nSJSZOnMiBAwfMZfn5+Zw4caLEVdn7TZo0iYYNGxIcHGxRnpOTg6+vL1euXCEuLk5JvYhIKUobR0VEXkS2trYlpuff/64RFxcXzp8/j4ODA82aNaNZs2Y4OTmZPy33qIKCgjhz5gxLliwp8b6opUuXMnbsWIKDg/Hx8aF9+/akp6c/9sWJoqKi/3RhQ8qPEnup8FxcXOjcuTOzZ8/m0KFDpKWlERgYyPXr1xk+fHip29nZ2RESEkJmZqZFeWhoKOfOnSMsLIw6depw8eJF86+wsPAJ90ZExHqUNo6KiLyI2rdvz8mTJ0lMTOTcuXMsXryYtLQ08/r+/fvj6OjIp59+ytGjR/n9998JDAzkp59+wtXV9ZH2sWzZMpKSkggPD8fW1rbEeWijRo3Yv38/p0+f5o8//mDBggXs3LnzXz/JnJ+fb9FWWloaU6ZM4c6dO/Tr1+8/HRcpH5qKLwLMnz+fiIgIAgICyM3NxcPDg7i4OJycnB66nYeHB8OGDSM6Ohq4+8xRUlIS+fn5+Pr6lqi/e/duXn755SfRBRERq/TPcVRE5EXVv39/fvvtN/Mn8nr37o2vry+pqanA3WflY2NjmTdvHn5+fhQWFtK2bVtWr1790Fmk94uPj+fWrVulnofOmzePWbNmMXjwYOzt7TEYDMycOZPg4GDOnz9f6rlvamoqnTp1AsDGxgZ7e3vatGnD0qVLcXNze8wjIuXJplhzJ0RERERERESslqbii4iIiIiIiFgxJfYiIiIiIiIiVkyJvYiIiIiIiIgVU2IvIiIiIiIiYsWU2IuIiIiIiIhYMSX2IiIiIiIiIlZMib2IiIiIiIiIFVNiLyIiIk9UcXHxsw5BRETkhabEXkRERMrs+++/x9XVlczMzIfWi4qKYtWqVU8pKhERkYpJib2IiIg8MSaTiZs3bz7rMERERF5oSuxFRERERERErJgSexERkQro119/xdfXl9dffx13d3eGDx/OL7/8AsDQoUMZOnSoRf2UlBRcXV1JSUmxKD9y5AgDBw7Ezc2Nfv36kZSUZF7n6uoKwKJFi3B1deXUqVO4uroSHx9v0cZff/3FK6+8QmJiIpmZmbi6urJt2zb8/f0xGAx07dqVxYsXU1RUZLHdxo0b6du3L25ubnTt2pXIyEgKCwvL6xCJiIhYDSX2IiIiFUxeXh4jR46kdu3aREZGsmDBAm7evImfnx+5ubllaisoKIjevXsTFRWFs7MzAQEB/PDDDwDmBP6dd94hPj4eZ2dnDAYDmzdvtmhj06ZNVK9enR49epjLgoODqVGjBpGRkQwYMIBFixYRERFhXr9s2TKmT5+Ot7c3S5cu5cMPP2TFihVMnz79cQ+LiIiI1aryrAMQERGRp+v06dNcvXqVYcOG0aFDBwBatmxJfHw8f//9d5naGj9+PH5+fgB07tyZ9PR0oqKi6NatG+3btwegYcOG5r8HDx7MjBkzOHfuHE2aNAHuJvZ9+/bFzs7O3G7btm0JDw83t3vjxg2+/fZbPvnkE4qLi4mKisLHx4dp06YB0KlTJ2rVqsW0adP46KOPcHZ2fuzjIyIiYm10x15ERKSCcXZ2pk6dOvj7+xMUFMSuXbuoV68eX3zxBQ0bNixTW3369LFY7tatGydOnCj1AsG9BP7eXfsjR46Qnp7OoEGDLOoNHDjQYrlnz57k5+eTmppKamoqt27dwmg0UlBQYP4ZjUYA9u/fX6Y+iIiIWDvdsRcREalg7O3tiYuLY8mSJWzfvp34+Hjs7OwYMGCA+Q74o6pXr57Fct26dSkuLiYvLw97e/sS9WvUqEGvXr1ITExk3LhxbNq0iRYtWuDu7m5Rr0GDBhbLderUASAnJ8f8rP3o0aMfGFN2dnaZ+iAiImLtlNiLiIhUQC1btiQsLIzCwkKOHTvG5s2bWbduHU2bNgUo8RK6GzduPLCdnJwci+T+0qVLVK5cGUdHx1L3PXjwYBISEjh27BjJycnmqfz3u3r1qsXy5cuXgbsXDm7fvg1AeHg4zZs3L7HtPy82iIiIvOg0FV9ERKSC2bFjBx07duTixYtUrlwZd3d3goODqVmzJufPn6dGjRpcuHDBYpuff/75gW3t2bPH/HdRURE7duzAYDCYn5evVKnkqYanpyfNmzcnLCyM3NxcBgwYUKLOvRfw3ZOcnEy1atUwGAwYDAZsbW3JysritddeM/+qVKnC/PnzyczMLOshERERsWq6Yy8iIlLBdOjQgaKiIsaOHcvo0aOxt7dn+/bt5Obm0qNHDzIyMvjxxx8JDQ3FaDRy+PBhNm3a9MC2Fi5cSGFhIY0aNWLdunWcOXOGb775xry+Zs2aHDlyhEOHDuHh4YGNjQ1w9659REQEnTt3LjHtHmD79u3UrVuXLl26cPDgQeLi4ggICKB69epUr16dkSNHYjKZyMvLw8vLi6ysLEwmEzY2NrRp0+aJHDcREZHnlRJ7ERGRCuall15i5cqVmEwmpk6dys2bN3F2diYyMpKOHTvi6elJRkYGCQkJrF+/Hk9PT77++mvef//9Em2FhoYyZ84czp49i4uLCytWrOCNN94wr/f39ycqKopRo0aRlJSEk5MTAF26dCEiIoK33377gTF+9tlnHDx4kPj4eBo1akRQUJDF/idMmED9+vVZu3YtK1euxNHREW9vbyZOnIiDg0M5HzEREZHnm01xcXHxsw5CREREKpbly5cTHR3Nnj17qFq1qrk8MzOTt956i9DQ0FKTfhEREbGkO/YiIiLy1CQkJJCWlsbatWsZM2aMRVIvIiIij0eJvYiIiDw1J0+eZP369XTv3p0RI0Y863BEREReCJqKLyIiIiIiImLF9Lk7ERERERERESumxF5ERERERETEiimxFxEREREREbFiSuxFRERERERErJgSexERERERERErpsReRERERERExIopsRcRERERERGxYkrsRURERERERKzY/wF7pvkTj/DRZAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1148,12 +1449,22 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'mutation_rate_samp', 'y': 'subtype', 'hue': 'Synon_Nonsynon'}\n", + "self.tuple_group_names=[('H3N2', 'Nonsynonymous'), ('H3N2', 'Synonymous'), ('H1N1', 'Nonsynonymous'), ('H1N1', 'Synonymous'), ('Influenza B', 'Nonsynonymous'), ('Influenza B', 'Synonymous')]\n", + "self.plotter.group_names=Index(['H3N2', 'H1N1', 'Influenza B'], dtype='object', name='y')\n", + "self.plotter.hue_names=['Nonsynonymous', 'Synonymous']\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -1168,16 +1479,23 @@ }, { "data": { - "text/plain": "(,\n [,\n ,\n ])" + "text/plain": [ + "(,\n", + " [,\n", + " ,\n", + " ])" + ] }, - "execution_count": 23, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1194,26 +1512,20 @@ "sns.stripplot(ax=ax, orient='h', **fig_args_horiz)\n", "annotator.new_plot(ax, plot='swarmplot', orient='h', **fig_args_horiz)\n", "annotator.apply_and_annotate()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "#### Hide non significant results" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "#### Hide non significant results" + ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": { "collapsed": false, "pycharm": { @@ -1225,6 +1537,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'mutation_rate_samp', 'y': 'subtype', 'hue': 'Synon_Nonsynon'}\n", + "self.tuple_group_names=[('H3N2', 'Nonsynonymous'), ('H3N2', 'Synonymous'), ('H1N1', 'Nonsynonymous'), ('H1N1', 'Synonymous'), ('Influenza B', 'Nonsynonymous'), ('Influenza B', 'Synonymous')]\n", + "self.plotter.group_names=Index(['H3N2', 'H1N1', 'Influenza B'], dtype='object', name='y')\n", + "self.plotter.hue_names=['Nonsynonymous', 'Synonymous']\n", "p-value annotation legend:\n", " *: 1.00e-02 < p <= 5.00e-02\n", " **: 1.00e-03 < p <= 1.00e-02\n", @@ -1237,16 +1553,23 @@ }, { "data": { - "text/plain": "(,\n [,\n ,\n ])" + "text/plain": [ + "(,\n", + " [,\n", + " ,\n", + " ])" + ] }, - "execution_count": 24, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1283,7 +1606,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": { "collapsed": false, "pycharm": { @@ -1295,6 +1618,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'subtype', 'y': 'mutation_rate_samp', 'hue': 'Synon_Nonsynon'}\n", + "self.tuple_group_names=[('H3N2', 'Nonsynonymous'), ('H3N2', 'Synonymous'), ('H1N1', 'Nonsynonymous'), ('H1N1', 'Synonymous'), ('Influenza B', 'Nonsynonymous'), ('Influenza B', 'Synonymous')]\n", + "self.plotter.group_names=Index(['H3N2', 'H1N1', 'Influenza B'], dtype='object', name='x')\n", + "self.plotter.hue_names=['Nonsynonymous', 'Synonymous']\n", "H1N1_Nonsynonymous vs. H1N1_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:5.014e-04 U_stat=2.624e+03\n", "H3N2_Nonsynonymous vs. H3N2_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:1.294e-03 U_stat=1.535e+04\n", "Influenza B_Nonsynonymous vs. Influenza B_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:2.026e-01 U_stat=3.340e+02\n" @@ -1302,8 +1629,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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ny/tdydPTkxdffJEJEyag1WoZPXo0Xbt2re8whBBCCCGEqFP1kmhf3LL7ooiICCIiIurj1EIIIYQQQphFvY9oi/q1atUqNmzYYO4wGr1BgwYxatQoc4chhBBCAJCWlsaAAQNYtmwZ/fr1M5Y/9NBDxiWXRd2TRLuR27BhA8ePH8fPz8/coTRax48fB5BEWwghxC0xGBS2709jzfZT5BZW0sTFhuGhbQnt4YNafecrsllaWvLaa6+xdu1aHBwc7kLE4lZJon0f8PPzY+nSpeYOo9GaNm2auUMQQghxjzEYFP7x9W4OHM+hsloPQGFpFf9aeZBdhy7wysTed5xse3h4EBQUxPz583n77bdNXluyZAlr165Fo9HQr18/oqOjycjIYMaMGbRv354jR47g7u7ORx99hL29PbNnz+bEiRMAPP744wwZMoQBAwbwyy+/4ODgQFpaGtOmTWPp0qVX7cPFxYUtW7bw4YcfYjAYaNGiBW+99RZNmjThoYceIjIykp07d1JRUcH8+fNxdHRk4sSJbN68GbVaTXJyMl988QVTp05lyZIlWFpakpaWxkMPPYSdnR0JCQkALF26lCZNmlz3XBdH9JOTk/nkk0/49ttv+c9//sPq1atRq9V07dqVt956647e+4sayEbwQgghhBD3j+3700yS7Isqq/UcOJ7D9gPpd+U8s2bNYufOnezatevSubdvZ/PmzcTExLB69WrOnj3L8uXLATh69CiTJ09m3bp1ODk5ERcXx/79+ykqKiI2NpbPP/+cvXv34uDgQFhYmHF6amxsLCNGjLhmH3l5ebz++uv861//Ii4ujp49e5oksy4uLqxcuZJx48bx+eef06pVK2MyfLH/i3eODx48yJtvvklMTAz//e9/cXNzY9WqVfj7+7N+/fobnutKer2ezz//nJiYGFatWoVWqyUrK+uuvP+SaAshhBBC1LM120/VSrIvqqzWs2bbybtyHgcHB95++21ee+01SktLAUhKSmLo0KHY2tpiYWFBVFQUiYmJALi7u9OpUycA2rdvT1FREe3bt+f06dM888wzbNiwgZkzZwIQFRXFmjVrAFi3bh3Dhw+/Zh+HDh2ia9euxrnhY8eOJSkpyRhnSEiIsX5hYaGx/7Vr11JRUUFSUhIDBgwAau7UN2vWDFtbW1xdXenbty8AzZs3p7i4+IbnupJGo6FHjx6MHj2aTz75hMmTJ+Pp6XlH7/tFkmgLIYQQQtSz3MLKO3r9VgQHBxunkEDNLt1X0ul0AFhbWxvLVCoViqLg6urK+vXrefLJJzl9+jQjR46kuLiYBx98kOzsbOLj4/Hx8TEmp1fr48pzKopiPOflbS7fLXzQoEHs2rWLjRs3EhoaaqxjaWlp0pdGozE5vtG5FEUxuWaATz/9lLlz56IoClOmTGH37t213qPbIYm2EEIIIUQ9a+Jic0ev36qLU0iys7Pp06cP69evp7KyEp1OR0xMDH369Llm219++YXo6GjCwsKYM2cOdnZ2ZGRkoFKpGDFiBPPmzbvhggDdunXj4MGDpKWlAfDjjz8SGBh43Ta2traEhoby/vvv39KCA9c7l6urKydPnjReF0B+fj5DhgzBz8+PF154gX79+nHs2LGbPt/1yMOQjVxkZKS5Q2j05D0WQghxq4aHtuVfKw9edfqIjZWG4f3b3dXzXZxC8swzzxAWFkZxcTFRUVHodDqCg4N58sknyczMvGrb0NBQ4uPjGTp0KNbW1kRGRuLv7w/A0KFDWbZsGQ8//PB1z9+kSRPeeustZsyYgVarpXnz5rzzzjs3jHvo0KHs27ePbt263fS1Xu9czz//PG+//TaffPIJwcHBALi5uTF27FhGjx6Nra0trVu3Jioq6qbPdz0q5eL4eSNSVVVFamoqAQEBJrcvhBBCCCHqwpEjR+jYseNN17/aqiNQk2R392t6V1YdqWsGg4EffviB06dPM2fOnLvev16v54MPPsDd3Z3Jkyff9f5vx5U/5xvlnDKiLYQQQghRz9RqFa9M7M32A+ms2Xby0jra/dsR2t27wSfZADNmzCAjI4N///vfddJ/VFQUrq6ufPbZZ3XSf32QRFsIIYQQwgzUahVhPX0I63lv7tL46aef1mn/sbGxddp/fZCHIYUQQgghhKgDkmgLIYQQQghRByTRFkIIIYQQog5Ioi2EEEIIIUQdkIchhRBCCCEaoQ0bNrB06VJ0Oh2KojB8+HCmTJli7rDuK5JoCyGEEEKYgaIYKP1tJ0XJcehK8rBwdMc5MAKHzsGoVHc26SArK4v58+ezatUqXF1dKSsr46mnnqJ169YMGDDgLl2BuBFJtIUQQggh6pmiGMha+R4Vpw+iaKsAqC4rIvenJZQdScRzdPQdJdsFBQVotVoqKysBsLe355///Cf79u1j3LhxLF++HIBVq1Zx8OBBunXrxo4dOygqKuL8+fP069ePuXPnArBkyRLWrl2LRqOhX79+REdHk5GRwYwZM2jfvj1HjhzB3d2djz76iE2bNpGUlMSiRYsAWLx4MdbW1lRVVXHhwgXOnDlDfn4+f/nLX0hMTOTgwYN06NCBDz74AJVKdc1zTZgwgc2bNxv7BJg+fTqzZ8/mxIkTADz++OM89thjt/2e1QWZoy2EEEIIUc9Kf9tpkmRfpGirqDh9kLLfdt1R/x06dGDAgAE8/PDDjB49mvfeew+DwcDYsWPJycnh3LlzQM1a1aNGjQJg//79fPzxx6xdu5YtW7Zw7Ngxtm3bxubNm4mJiWH16tWcPXvWmKQfPXqUyZMns27dOpycnIiLi2PIkCEkJiZSWloKwLp16xg+fDgAx48f59tvv+Xtt9/mlVdeYerUqaxbt47ff//9hue6mv3791NUVERsbCyff/45e/fuvaP3rC5Ioi2EEEIIUc+KkuNqJdkXKdoqCpPj7vgcb775Jps3b2b8+PFcuHCBxx57jE2bNjFy5EjWrl3LhQsXyMvLo1u3bgD06NEDBwcHbG1tadGiBUVFRSQlJTF06FBsbW2xsLAgKiqKxMREANzd3enUqRMA7du3p6ioCHt7e/r378+mTZvYu3cvLVq0wNPTE4B+/fphYWFB8+bNadq0Ke3atcPCwgJPT88bnutq2rdvz+nTp3nmmWfYsGEDM2fOvOP37G6TqSNCCCGEEPVMV5J33df1Jbl31P/WrVspLy9nyJAhREVFERUVxYoVK1i5ciVvvPEGU6ZMwcrKyjjaDGBtbW38u0qlQlEUDAZD7dh1umvWh5qt0z/77DN8fHyMo+UAlpaWxr9bWNROQa91rsv7vlhmYWGBq6sr69evZ9euXWzbto2RI0eyfv16nJycbuo9qg8yoi3EHVD0WspP7acy/bi5QxFCCHEPsXB0v+7rGscmd9S/jY0NixYtIi0tDQBFUThy5AgdO3bE29sbLy8vli9fbpJoX02fPn1Yv349lZWV6HQ6YmJi6NOnz3Xb9OrVi8zMTJKTk3n44YdvOuZrncvJyYnCwkLy8/Oprq5mx44dAPzyyy9ER0cTFhbGnDlzsLOzIyMj46bPVx9kRFuI26QrLeTCN6+iK8gEwL5DHzyjos0clRD1q7KyErVajZWVlblDEeKe4hwYQe5PS646fURlaY1LYMQd9d+nTx9mzJjB9OnT0Wq1AISEhPDcc88BMGTIEOLj443TOq4lPDycI0eOEBUVhU6nIzg4mCeffJLMzMzrths4cCCFhYW39H/Dtc5lYWHBlClTGD16NF5eXnTp0gWA0NBQ4uPjGTp0KNbW1kRGRuLv73/T56sPKuXysfhGoqqqitTUVAICAkxuawhxN+Vv+4HCnStNyppPmIdNi45mikg0BGlpaQwYMIAHH3yQ7777zuS1WbNmsXr1ahITE3FzczOWr1+/ni+++ILY2Fhj2dixY7lw4QLbt29HpVIBMHXqVMLDw3n88ccBOHDgAM8++yyJiYmo1TU3KF966SXi4+NJSkrCwcEBgLlz52Jvb0909N37Inj+/Hm+//571qxZw+rVq2t9WG/dupVFixZRXV2Nv78/7777rjGem6mn1+v55z//yY4dO9Dr9Tz99NOMHz/epO3KlStJSEhgyZIld+26hLhdF0eLb9bVVh2BmiTbtnW3O1515Hp0Oh0zZ85k0KBBPPLII3e1b0VR0Gq1TJ48mdmzZ9O5c+e72r+5XflzvlHOKVNHhLhN+vLi2mVlRWaIRDQ01tbWnD59mvT0dGNZeXk5+/btu2r9fv36cfLkSQoLCwHIz88nOzsbd3d3Dh8+DNR8MKakpNC/f39ju65duwJw7NgxY53k5GQCAwONt1YBkpKSCAsLu+PrUhSFnTt3Mn36dCZOnIi9vf1Vk+z8/HxeeeUVFi9ezMaNG2nRogULFy6s1d/16i1fvpwzZ86wbt06Vq5cyddff82hQ4cAKCws5PXXX+edd96hEY4VifuESqXGc3Q0TYf8BSuvtmjsnbHyakvTIX+p0yRbURRCQkJQqVS3NK3jZuXk5NCvXz+6devW6JLs2yGJthC3ybFLf7jsP0KNgxu2bbqbMSLRUGg0GgYPHkxc3KVVA+Lj46+5SYSLiwsBAQHGpam2bt1KcHAwYWFhxnVjDx48iLe3N97e3sZ2arWa4OBgkpOTAUhJScHf359BgwYZ22VlZZGXl0ePHj2uGW9ycjJjxozhhRdeICIigjFjxnDq1Kla9d5//31efPFFIiMj2bRpEzNmzLjqbeedO3fSpUsXfH19ARg/fjxxcXG1kuLr1UtISGDUqFFYWFjg7OzM0KFDWbt2LQA///wzHh4evPzyy9e8JiHuBSqVGoeAEHyeWUCrvy3D55kFOASE1FmSXXNOFYmJiSxatMh4J+xu8vDwYM+ePQ1yBRBzkERbiNtk49OBZk++iUPXcJx7D6P5pHdQW9mYOyzRQIwYMYI1a9YYj2NjYxk5cuQ164eEhBgT5i1bthAWFmaSaCcmJpqMZl/ebvfu3Sbt+vfvz/bt29Hr9SQmJhIcHHzVJ/wvl5qaylNPPUVcXByjRo266jSTkSNHMnDgQObPn8+iRYs4f/78VfvKzMzEy8vLeOzl5UVpaSllZWU3XS8jI4NmzZqZvHZxTuj48eOZMWOGzAsXQjR4kmgLcQdsW3bCI2IG7gMnY+nsYe5wRAMSEBCARqMhNTWVjIwMysrK8PPzu2b90NBQdu/eTXV1NXv37iUoKIiuXbuSm5tLbm4uycnJV53+ERoaSkpKCgaDgS1bthAeHo6Hhwfe3t6kpqaSlJR01QT9Sh06dKBXr15AzdJcR44coaCgwKROmzZtePfdd4mNjcXFxYWJEycydepUSkpKTOoZDAbjvPLLXTl6dr16iqKYvKYoSp2MvglxN8lUpsbtdn6+8r+WEELUkcjISNauXcuaNWtuuIRWQEAAeXl5JCQkEBAQgK2tLWq1mpCQEHbt2sXp06fp3r321CQ3Nzd8fHyIj49Ho9HQokULAMLCwkhJSWH37t2EhobeMFaNRnNTZQCurq5MmzaNTZs2ERUVhV6vN3m9WbNmZGdnG4+zsrJwdnbGzs7uputd+Vp2drbJ6LcQDY2NjQ15eXmSbDdSiqKQl5eHjc2t3bmW5f2EEKKODB8+nDFjxuDi4sI333xz3boqlYqgoCCWLFnC2LFjjeVhYWEsWbKE3r17X3P6R2hoKJ9++qnJiHdYWBivvfYaTZs2NVnh5FqOHj3K0aNH6dChAz/++CM9evSotenD/Pnza62kApCQkGByHBwczPz58zlz5gy+vr4sX778qvPTr1dvwIABxMTEEB4eTnl5OevXr+fNN9+84XUIYS4+Pj6kpaWRk5Nj7lBEHbGxscHHx+eW2kiiLYQQdcTT05O2bdvi6OiIi4tLrdenTp3KuHHjjMllaGgoa9asITw83FgnODiY6OhoJk+ebCx79dVXCQgIMC53dzHRfu2114x1unTpQm5urnEpQICPPvoIgBdeeKFWLE2aNOHDDz8kPT0dNzc3FixYUKvOyy+/fFMPILq7u/OPf/yD559/Hq1WS8uWLZk/fz4Ahw8fZs6cOaxZs+a69caPH8+5c+cYPnw4Wq2WsWPH0rt37xueWwhzsbS0pHXr1uYOQzQwso62EELc55KTk4XhR5gAACAASURBVHn77bdZt26duUMRQoh7iqyjLYQQQgghhBlIoi2EEPe5wMBAGc0WQog6IIm2EEIIIYQQdUASbSHukK44D31FqbnDEEIIIUQDI6uOCHGbDNWVZK1aSMWp/aC2wCVoBG79x5s7LCGEEEI0EDKiLcRtKt63sSbJBjDoKNy5kqqsM2aNSQghBKSlpeHv78+TTz5Z67VZs2bh7+9Pfn6+Sfn69esZMWKESdnYsWMJCQkx2YRm6tSpfP/998bjAwcOEBgYiMFgMJa99NJLBAQEUFp66W7n3Llzee+99+742m5Ffn4+U6ZMYciQIQwbNox9+/Zdtd7Zs2eZPHkyw4cPZ8iQISxbtsz42tatW4mIiODRRx/l+eefN7kmcWMyoi3EbdLmptcuy0vH2tO3/oMRDc6qVavYsGGDucNo9AYNGsSoUaPMHYZogKytrTl9+jTp6el4e3sDUF5efs1ks1+/frz88ssUFhbi4uJCfn4+2dnZuLu7c/jwYbp27YpOpyMlJYW5c+ca23Xt2hWAY8eO0bFjR3Q6HcnJyQQGBrJjxw4GDx4MQFJSEm+//XbdXvQV3nzzTXr16sX06dM5cuQI06ZNIz4+HltbW5N6s2bNYtSoUYwZM4aSkhJGjx5Nx44d8ff355VXXuGHH37A19eX9957j4ULF5pcv7g+GdEW4jbZte9lcqyytMG2VYCZohENzYYNGzh+/Li5w2jUjh8/Ll9mxDVpNBoGDx5MXFycsSw+Pv6qu5QCuLi4EBAQwN69e4Gakdzg4GDCwsLYvHkzAAcPHsTb29uYuAOo1WqCg4NJTk4GICUlBX9/fwYNGmRsl5WVRV5eHj169LhmvMnJyYwZM4YXXniBiIgIxowZw6lTp2rV+/XXXxk+fHitPzt27DCpp9Pp2Lp1K4899hgAHTt2xNfXt1Y9gNGjRzNs2DAAHB0dadmyJRcuXGDnzp106dIFX19foGYjqbi4ONlm/hbIiLYQt8nevzdNhkynZH8Calt7XIMfQ2PvbO6wRAPi5+fH0qVLzR1GozVt2jRzhyAauBEjRhAdHc306dMBiI2NZfbs2SZTIy4XEhJCcnIyDz/8MFu2bGHEiBG4u7vz+uuv87e//Y3ExET69+9/1Xbx8fFMmjSJLVu2EBYWRv/+/Vm4cCF6vZ7ExESCg4OxsLh+2pWamsrLL79Mr169+OGHH4iOjmbVqlUmdYKCglizZs0Nr72goACDwYCbm5uxzNPTk8zMzFp1o6KijH/fvn07+/fv55133iE2NhYvLy/ja15eXpSWllJWVoaDg8MNYxAyoi3EHXHqMRDvp+fTbPzr2LToYO5whBBCXCYgIACNRkNqaioZGRmUlZXh5+d3zfqhoaHs3r2b6upq9u7dS1BQEF27diU3N5fc3FySk5MJCwu7aruUlBQMBgNbtmwhPDwcDw8PvL29SU1NJSkp6aoJ+pU6dOhAr141d0ujoqI4cuQIBQUFJnVudkTbYDCgUqlMyhRFQaPRXPP8sbGxREdH8/HHH+Ph4XHVPqBmFF/cHBnRFkIIIUSjFRkZydq1a3Fzc2P48OHXrRsQEEBeXh4JCQkEBAQY5zKHhISwa9cuTp8+Tffu3Wu1c3Nzw8fHh/j4eDQaDS1atAAgLCyMlJQUdu/ezcyZM28Y69WS4CvLbnZE293dHUVRjHPOAbKzs/H09KxVV1EU5s+fz8aNG/nqq6/o2LEjAM2aNePgwYPGellZWTg7O2NnZ3fD84sa8pVEiNtgqK5AMejNHYYQQogbGD58OBs2bOCnn34yzkO+FpVKRVBQEEuWLDEZuQ4LC2PZsmX07t37mtM/QkND+fTTT2u1W7NmDU2bNjWZwnEtR48e5ejRowD8+OOP9OjRAycnpxtf5FVYWFgQFhbGihUrjH2fOnWKwMDAWnUXLFjAnj17iImJMSbZAMHBwRw8eJAzZ84AsHz58mvOcRdXJ4m2ELdAX1lGxvJ3OPPek5z7eBqlRxLNHZIQQojr8PT0pG3btvj6+hpHdi83depUfvnlF+NxaGgox44dIzw83FgWHBzMqVOnCA0NNZa9+uqr/PDDD9dt16VLF3Jzc03affTRR3z00UdXjbVJkyZ8+OGHREREkJCQwIIFC27vov/0xhtvsG/fPoYNG0Z0dDQLFizA0dHR5LozMzP56quvKCgoMC7xN3z4cGJiYnB3d+cf//gHzz//PIMHD+b48eO8/PLLdxTT/UalNMJHR6uqqkhNTSUgIABra2tzhyMakbyEryhKvvQEu8rSmpbPf4HGxt6MUYmGaN26dQA3HEETt0/eY9GYJCcn8/bbbxv/XYt7w41yTpmjLcQtqMo8bXKsaKvQ5qWj8b72wzXi/iTJX92T91gI0dDJ1BEhboGtbxeTY7WNAxWnD1O8Lx5DdYWZohJCCHGvCwwMlNHsRkhGtIW4BS59R2CoKKH0yK9o7J2pzr1AwbaarXiLUzbi/cwCVOprL50khBBCiPuHjGgLcQtUGgvcB06m1fNfYNOyM+iqjK9VZ5+h4o8DZoxOCCGEEA2JJNpC3KbaS/gDKvmVEkIIIUQNyQqEuE2OPR9FfdlqI9bN2mLbuqsZIxJCCCFEQyJztIW4TVbuzfGZ9iFlR35FbWOPfccgmZ8thBBmtmrVKjZs2GDuMBq9QYMGMWrUKHOH0eDJiLYQd8DC0Q3n3sNw7BqO2lLWbBdCCHPbsGEDx48fN3cYjdrx48fly8xNkhFtIYQQQjQqfn5+LF261NxhNFrTpk0zdwj3DBnRFkIIIYQQog5Ioi2EEEIIIUQdkERbCCGEEEKIOiCJthBCCCGEEHVAHoYUQgjRoOgrSlBb28lymeK2REZGmjuERk/e45sniXYjVVpezeerD3PgRA5tvJ2ZPrIrzZrY37ihuCOKolB5/ncUnRZb3y6SKAhxC3TFeWStWkhV+nE0jm40HfYcdm26Y9BWUXH6EBp7Z2y8/Yz1q7PPYtBpsWnezlhWlXkabUEGtq26oLFzNMdlCDMbNmyYuUNo9OQ9vnmSaDdSn8ceZuu+NAD2Hc1mwbd7+ODFMPMG1cgpBj0Z379F5dlUACybtsR7wjyT3SOFENeW98vXVKXXrH+sL8knZ+3HNJvwDhnfvo6+NB8Ah84hNI38K1mrFlF+LBkAax9/mo1/nYKd/6MoMRYAlZUtzR5/Axvv9ua5GCGEQOZoN1qHTuSYHJ9MK6K0QmumaBqH4pQNpH89m6yYhVTnnKv1evnxvcYkG0Cbc46SQ1vqM0Qh7mnVWWdMjvVlRRTuWmVMsgFKf9tB8d4NxiQboCrtGEV7fqIoaa2xTKmuoHDXyjqPWQghrkdGtBupNt4u5BdnGY+93O2wt5Ef9+0qObSV3A1fAFAFVJ4/QovnPqUq4yQlBzejtrZH4+BSq52+vLieIxXi3mXbuhvavHTjsaW7N4q+9gCBtjCrVpmuOBsUg0mZobLs7gcphBC3QDKvRurZkV0oKq3ixPlCmrnb89LjPVGpVOYO655VdtnoGYC+rJCS/QnkJXxl/HBX2zujtnHAUFkKgEpjiUPnkPoOVZjBucxiElMz8HS1I7i7NxYauVl4O9zCn0Ax6Cg/uQ+rJi1wHzgJfWkBZb/vMv6eWbh64dw7gpKDm1GqK2oaqjU49XgEbV6GyV0lx+4Pm+MyhBDCSKUoimLuIO62qqoqUlNTCQgIwNra2tzhmFV5pRZbawtJsu9Q3i9fm9yWRqXGvlM/yn7bYVLP/ZEpaAsyUHRanHoMxLpZm3qOVNS3QydzeGNpIjp9zX+lvTt58dozgWaOqnGpOPsbpYe3obF3xunBIVg4uFKVeZqi3XEoumqcHhiEbasADFUVFKf8jDY/Azv/QOzb9wJAW5CJytIGi6vcdRJCiDtxo5xTRrQbOTsbS3OH0Ci49B1J5dnfqMo4BRoLXEPGouiqatWz9vLF+cHBZohQmMva7X8Yk2yA3b9ncj6rhBaesuLF3WLbqjOW7t6orWxQW9kAYO3VGo/I503qqa1tcQkaZTw2VFeStXI+FacPgUqNc++huD88qT5DF0Lc5yTRFuImaOyccH90Cjk/LUVfmoe+tACXvpGU/b4LbX4GAPYd+qJxdCfjh3lUZZ7CtmVnmgyaisrSmtwNSyk7loylixdNBk3BpkVHM1+RqEupf+TyQ/wx3JxsGBnWFndnW3OHdM8yVFeQtWoRFaf2o7K0xrX/eFwCIyg7mkTBzpUoei3ODw7FqecjtdoW74+vSbIBFANFyXHYdwo2WQ5QCCHqkiTa9zhFUWRaSD0w6KrJXPEPDH8+3Fi89yc0to74TPuQirO/obaxx6Z5O9K/mk1V+jEAyo4mAgoWLh6UHt4GQHX2GTJXLqDVX5eispC7DY3BiP5tSTmajU5fM4fYv6Urn648ZHx99++ZfDbzITQyb/u2FO1eT8Wp/QAo2iryE77GqmkLslYtMs7bzv35cyxcPbFr3c2k7cUvwaZlFyTRFkLUG0m071F7j2Tx+epDZBdUENy1OTMe646t9aUfp1an58eE4xw8XrNhzRODOuJkb2XGiO9t1dnnjEn2RRVnD2PZxJuSAwmore0hMMKYZBvrnEnF0q2ZSZmhvJjqvHSsPX3rOmxRDwLaNuGT6HCSUzPwcLNj25/r11+UkVvGkTP5BLRtYqYI7221l9JUKDuaXGuFkYo/DmDXuhsGbRWFu2KoPPc7anvTOdkqS5taybgQQtQlSbTvQeWVWhZ8u5eKKh0A2w+k09TVlknDOhvr/Gfd78Tt+AOAo2cLuJBTxtvTg8wSb2Ng5d4claUNirbSWKa2dSR79fvG44rTB7F09zZZnsy6WRssm7Sg6sKJy9o51Eq+xb3Nu6kDo8JrNkY5fDK31usujvf3Q9l3wq5tz5pVR/6ksrLF3r83JfvjTepZefgCkLfx35Qc/MVYbt28HSqNJWprO1yCR6Oxd66XuIUQAiTRviedzyoxJtkXHTtXYHKceOiCyfGBEzmUV2qv+XCkwaCgVssUlGtRW9vhEfk8uRu/RF9agF27nqgtbUzqGKrKcQ4aRenhrWhz07Bu1pYmg59FY+eEviS/Zo62qydNBk1DbSmJV2M1Krw9u3/PIrewZum5of1a4+MhD0beLseuYejLiyk5tAWNvTNu/cdh49MBl+DRFCWtRTHocewajkPnYABKjyaatK/K+IPWr/yISiVTd4QQ9U8S7XtQKy8n7G0tKbtsp0c7awsmvbWRyiodg4Na4+VuT27RpdFXV0drrK1q/7jLK7V89ON+kg7X3Pb+S1Q3evp71Mt13CsUnZayY0noy4tpPuldNHZOqC2tKdi1qlZd21adsXL3Rpufjn3HICxdPAHwjPq7zKdvpPR6Axdyy/Byt8PSQoOnmx1LXxlA6qk83JxtaOXlZO4Q73kufSJx6ROJoaqc0iO/Up1zHufAyJoVRgwGtHnp5CV8hcbGEQtHd7RV5ca2Fs5NJMkWQpiNrKN9jzp0MocvYlPJyi+nh39Tfj1k+tDPk4M68NOvp8kvrsLW2oIXx/ekV0cPvv35KHuPZNLC05HJwzqzbudp1mw/ZWxnb2vJV68/gs1VkvL7kaIYyPj2dSrPHwFAZW2H98R3sWraAkNlGRk/vkNV2jFQqXHqNQh9ScGfD0GCysKKZk+8gY1PB3NegqhDJ84X8M5/dpNXVImTvRXRTz5At/ZN+d8vJ9i67zxuTjZMGNIJv5au5g71nmeoKift39HoCjIBsHBqgvczC9EWZHDhm9fAUHOXT+PghqKrxlBZisraDs8RL2LXrqc5Qxf1qLJKR9JvmahVEBjQDGtLjblDEo2crKPdSHVt15TFfw8HIGH3uVqJdl5RJY897E9SagYdfd3o2cGD734+yuqtJwE4n1VKenapyQOUAGUVWtKzS2nrIxs7AFSeO2JMsgGUqnKKUzbQZNBU1Db2eE98l+qcc6it7TFoq0hb8tdLdXXVFCatxWu0JNqN1eerD5P3552j4rJqPvnfQaLC2/HtzzX/Zs5nlTL3i0SWzXkEG2v57/ZOlP7+qzHJBtAV51Kauo3qnPPGJBtAX5qPZ1Q0GqcmWDXxRm0lSyveL0ortLz04TYycssAaOHpyKIXQmt9zglRn+RfXyPg38oVlQouvzdRUFLJz4lnADhwPIdzWSWcyywxaXc2s4TBfX05evbS/G4neyvZaONyV6xsUFNkWmbVtCVAzQf+lQy124vGIz271OQ4u6CcPb9nmZSVlGs5draAbn5N6zO0xsegr1WkGAyore1qlWvsnWUJv/vQtpTzxiQbap5n2nkgnYGBrcwYlbjfSaJ9D/p+41Fit51Co1bx2MN+jAxrx1/HdOe7DUeoqNIzJMiX7ftNlxhLPHSB3p29OJ91Kdl2tLNiwpCOaHUGdh26QDN3e6aN7IKV3GozsmnZCetmbWt2hARUFpZUZ53h/NIXcQwIxbnvCLQ558jf+gO64lws3Zqjzf/zQVS1BucHhwBQnX2WvF++QVuQib1/IG5hj6PSyK/fva5PQDMS9lxafu6BDp74Nndiz5FLybZGrcLH08Ec4TV4uuI8cuIWU3EmFSuv1jQd9tw1l7207xREwa4Y9CV5AKht7Ck78iuKtrrmjlJVTYJl27bndTeE0hZkkpfwNdrcNOza9cQ1/AnUFrL0aWNQpa09sFGtrf0FTYj6JJ/095g9v2fyQ/yltZqXxf2GfytXevh7UFJejY21BWE9fTh0MpecwksPQ9rZWDJ5WGcy88o5k1GMo50Vf32sOw52VrwwrgcvjOthjstp8FRqDc2efIvS1O3oSvIp2rOeqgvHAcjf8h0qKxsKd8WgL710V8A+IBRLVy/s/QOx9vRFMejJ+PFd9MU1y74VJa1BZWmNW+hYs1yTuHueHdkFe1tLDp/KpX0LFyYM6YRGreLEuUIOnMjB1tqCScM6yc6Q15C78QsqzhwGoDrzD7JjP6DFsx+hLy9Gm3cBq2ZtjEmwxtYRn2feoyR1G/qyIoqS4kyWzXQJeQzbFh2x8e1y3XNm/u+faP+8+1S0+wKo1Lg/PLGOrlDUp/49vVm5+QQl5dVAzbKawd29zRyVuN9Jon2PuXyax0V7j2Tx869nKP1zFZK4HX/wTGQA//xmD1XVetRqFROHdqJ5UwcW/z2cnIIKXBytsLSQketr0VeUoLayRaWxQG1lg1PPR6g4fYjCnf8zqVf2206TJBtqNqRxG/4Cil6LvrIMXVGOMcm+qOKPAyCJ9j3PxtqCKcMDapW/PT2IgpJKbK0t5MHi66hKP25yrM1NoyhlI/mb/oOi16K2c6LZ2Fex/nMaiMbeGZfASIr2bgDFdKRS0VZh27rrVc+j6LUYqipRtJXGJPui8lP7JNFuJNydbfnwpf78svscKrWKgb1b4uzQOBdEEPcO+QS4x3Ru7V6rLL+40phkA6Rll1JZrePVSQ+yOSWN7u2bMuDBlugNCt/+9Dub957H1dGGicM60dPfg4TdZ/lh03F0OgORIW2Ieqh9fV5Sg6IvKyJr1SIqz/2G2s6JJoOm4dCxLwCW7t6gUpvM27byakPlhZMmD2NZuntTcnAzeQlfY6gsxbZN91qb3Vyc1y0aL1fHmnXWj58rYOnqw1zILSWwczOeHdlFHoz8k02LjpQdTTIeWzVtScHW/6Loa/4/M5QXk7/lO5o9MReDtgoAtaU1Vk19avVl1aR2GUDJ4W3kbVqGoaIUm1adUds6Yqi4NIXOqmmLu3lJwsw8XO0Y/6g8gC4aDs3cuXPnmjuIu02v15OdnY2HhwcWFo3rA61ZE3usLTWcySjG3taSiUM7oVap+O2PPJN6zvbWLI1N5UxGMUmpmVRV68nILeXbn49SWa2noKSKXw9l0Lm1G+9+vYeyCi0VVToOnMihfQsXvJven3NK83/5xrg8n6KtovzUPpx6PkrBth/I2/hlzVbrBh0Y9Fg4e1Cdl4bG2g5FVw2KAevm7XENHUvmin8YE2tdQSZ27XuhLy9C0VVj7dOBJoOfRW1lc71QxD1Cpzdw4HgO+cWVNHWxNVkrXac38PePd5CWXUq11sAfF4rQ6gyyVv2fbFp2RpuXjq44D+vm7WjyyDMUp2wwraTWoC3IIGvVQoqS1qBoq3Dq/jCG6oqaZycUsO8cjGvIY1RlnCJn3ScU/roaQ1UFlu7eZP53Lkp1zeZBuqIcbFt3Q19ZiqKtwsqzNU2H/h8aG3szXL0QojG4Uc7ZYLPQEydOsHjxYuzs7IiIiKBfv37mDqnBiHqovcmoc3Z+OT8nnqG4rGZeWisvRw6dzDZps27nH/TsYPrhXq3Vs31/OleupJ56Ko8HO3nVSewNXVX2GZNjpbqSgl0rKU6Ou1RoaYNL6DgKty+/VGZhjfczC7H2al0z59RgunMnei2tXvgCQ2W5bAHdiJSUV/PyJzs4n1Wz+kgPv6a8MbUvmj93Wb2QU0p+caVJm9RTtbdov19ZOLjgNXa2SZmtbxfjvG0Aaw9fivf8BIACFP66CtvWXbFw9kBlbYei02Lp4omi15K5fB6GypqfRcHW/2KoLK35EnwZRVtJq+eXoi8rxsKp9h1CIcT16cuKKExag64oG/tO/XDo0NfcITVoDTbRLi8vZ/bs2Wg0Gt5//31JtP9kMChs2n2W3/7Io4OvG4/28a3Z0XFUF2K3/YGzozXPjuzC3C9MtyFWgLY+LiSlXlqHVqNW8WAnT+MygBfdz5tr2LXuXrMBzZ80jm5oc9NNK2krqTxzyLRMV4W+JB+8WmPdrB1qazsMl+1OZ9umOyqNpSTZjUx80lljkg2w/3gO+4/VfMndti8NZ3srHGwtTaZ2tW9x//5+3QyPUX+ncFcM1dlnsW3bHX15aa06Zcf3UrxnnfG4cFcMqNTGJPui6pw0NPbO6MuKjGUXfxclyRbi1imKQsb3c6nOrlltqexIIgz/Gw4BIWaOrOFqMIn2l19+yc6dO43Hy5Yt49y5c8yaNYsJEyaYMbKG5T/rfiN2W81Sc1tS0jifWUIPfw/mf5tirJNTUM7I/u34eMUBY1lEcBtGhbWrWVf04AUc7Sx5OqIzD3byYvKwzqxIOIZWrzCsX2uCujar9+tqKFz6jcRQXUHZ0SQsXT1xGzCRsmPJVJzad6mSxgJrnw5Unvv9UplKjZVHzbzr8j/2Y+Hiia4oB5WFFY7dH8Kp16B6vhJRHy7eRbrc/uPZrN3+h/HY3dkGe1tLsgvKeaCDJ08NufbScwI0tg4mDydWnPuNol9jLquhQmVhWaudvqyw1jMU1p6tcAt9jLwt36ErysGhYxDOgRF1Gb4QjVp15mljkn1RyeEtkmhfR4NJtKdMmcKUKVOMx6mpqfj6+rJ8+XKefvpphgwZYsboGo5NyWdNjuN3nyOnsMKk7PSFYjzd7Ajs7EXqH3m08HBgaHBrrCw1+DZz4sS5ApzsrWjiUrPk2Kjwdozo3xYFjLe871cqjSXuD080+aC3dGuGNjeNsmNJqCyssO/QF+fACLR56ZQf241KY4Ft+16orGypOHOY7FWLLvVnaY1Tz0GoVGqT85Qd30PF6UNYebbCsWs4KrWsAHMvCnvAh7U7/kCnr0nunOytyLxswwyo2aX1n88F49fSFUsL9dW6Eddh27Iz7gMnU7ArBhQF58BI7Nr2oChxtUk9u7Y9sfLwJX/LtyjVldi27opz3xFobOxp/sRc8wQvRCOjsXeq9YVWYy936a7nphJtvV7P8uXL2blzJxqNhvDwcKKiouo0sKqqKl599VUcHBzo379/nZ7rXuJgZ0VZ5aX5v452ljjY1R7dWb/rNMm/1UwTOXq2gAXf7GVwkC/f/FSzNXRGXjlv/TuZZXMewcneCvUVCXaVVo+1bFwDgNrKBoeuYZQdTUTRVlF6eCu6kjzcH55E+cl9KHot5UcTuZBzDtsr1vBVtFVU/LEfx24PGcuK9qwnL36Z8bjy/DE8Ip6rt+sRd0/r5s7MnxHMxqSz2FhpGBbchtVbT9aq52RvJUn2bVIMekoObsZQXgxAwY4fsfUNwP2RpynY/iOKXodTryHY+/cGwLFbOIaqCiwcXMwZthCNkoVTE1z6Dqfw15ovuhoHV1z61W0+eK+7qUR73rx5nDx5kuHDh6MoCjExMZw7d44XX3zxhm1LS0sZN24cS5YswcenZvmluLg4PvvsM3Q6HRMnTuSJJ56o1e6BBx7ggQceuMXLafwmDu3Eov+moDcoxvWx2/m4sOf3LONt7Ef7tKq1DfSxcwXGEeyLqqr1/PZHHn27XJoqkplXxsLvUjh2roAWno689HhP2vncnx9YimJAqapAbWNfayWEyjOHKUxcA/pLc2+1eelX35FOY0n+lv+isrTGsfvDFO817av08FaaPPo0aivZ1ORe5NfS1eS5hlHh7Uj+LdP4EOSgvr608HQ0V3j3JMWgpzrrDBpHd7S556nOvuxOnl5HccoGqjJOYaisuXtQengrzg8OxcLRFbWlNWpLWTtZiLriFv4kDl3D0RXlYNOio/y+3cBNJdq7du1i/fr1WFrWjJxGRkYSGRl5w0T74MGDzJkzhzNnzhjLsrKy+OCDD1i1ahVWVlaMGzeOwMBA2rVrd/tXcR/xbebEqPD26PUGBgf54uVesyzVhy/257c/8mje1AG/lq68sTTRZLUDDzc72vo4s+vQBWOZSlXTX0FJJTZWFthaW/BZzCGOnavZgOV8Vgnvf5/CpzMH1O9FNgAVZ38jJ24xuqIcrJu3R21Te7lDtWXtbZvt/B5EV5xDxR8HQaXGvlM/cn9eglJd87Mo2b8J9RVLiaksrECmjjQaXu72LJ39MIdP5uLubIOjnZXcIboF2sIsMr5/C11BJqg1OPZ8pFYdfUkB2ty0S8elBZQc2oyrjKwJUS+s3L2xcr+066Zi0FNxNhWVSo1Nq861pkvez24q0XZzc0Ov1xsTbZVKhZOT0w3brVix7vHElQAAIABJREFUgjfeeIOZM2cay3799Vf69OmDi0vNKOmjjz7Khg0bmDFjxu3Ef12pqal3vU9zOnmhku+35WL4czm+U2cvEBnoypqkAg6fLUetgn6dHHmoqzP92qtIy7Qgu0iH0/+zd56BUVznGn5me1/13ikSIARIIHo3zYAxxr07ie3ETrHTbuKUm8R2nNzEJXEcJ45tnMS9FzCmF9OLACGKhEAN9bra1fbduT9GjFgW2xgDpuzzizl7ZubMotn55pzve1+DkjkjDKSZuslN1VFe70ajEpiUb+b/XtpIZaMbtVJgaoGFg9X2kHPWNTvYum0natVllLstBrGufwaFW/ouPA2H8cVkoFKqEXpnsD2pBdgsAzFrNqPwSuoivuh0DtmAgXNRpI1DVKhw1e5C7+174fF3t+GKzkEn1CH05rg5s0aze+8+IlxaOFwBnn+vjfp2H1q1wJUjoxiWHdFr/iIM+5ag7exVRwoG6N61HH9MJuoOaVY7qNbTronFcNJ+DcfqqFv6Cuq2IwSMcXgyCkEZnlYXIUKEs4zfi3n7y6i6pfvWH5WKfdQtoLxgygC/Vk7rW8jLy+Pmm2/mmmuuQalU8vHHHxMdHc3ixYsBuOuuu06536OPPhrW1tLSQnx8vLydkJBAaWlpWL+zQX5+PlrtpbOk8d6OTXKQDVBa7aRwSBal1ZL8XECEDWV2rpo2nKKiGEYWuRGDIlEWHS63jz+9sovyejcWk4Z7rh5KU3sPq/YcAsAXEFm5x8aI3ARKDvVpcOdmRDNm9Mjzep1fN35HJ7XLQ1849EE30XO/g23HUlSWONJn3IXaGk9gZDE95dtR6kwYBhQhKFW4qvfRXVuKQq1FiImi+2jo8bNGTUEz9w5cNWVoE7PRJvc7j1cX4WREUaSj202UWXdWi4H/9tYe6tt7X8x8Ikt3dnP93LGY9JHg7/NoOPgBJyqPC2KQlMnX4NizBtHnJWrSDehS+nPs+QP4u6QUOYXeTHJKaoi2fZzYTdJ1PzvPo48Q4dIi6PPgPLwTRBHDwFFymoirZj9+WyuGfiPoqdhOW3efdLCqq56BejfmoZdHfZ3H4/ncid3TCrQ9Hg+5ubns378fQM61rqio+NIDCgaDIc5poiiGbEf4bE42lhFFyW79ZPYebuVvb+2lurGbGIuOB28awY6DzXIA3e3w8rc394S504kiTB6Ril6jorSylf5pUdx37bBzdj0XKipTNOr4DHytfRJG6phUWj/8KwDexiN4m46S/p2nEX1eRK+LoCAgBgN4mqtpfPV3ckW2oNWjNMdIGtuANmUAhv5SQK6OvjxNgS4kapq6eeylHdS3OoiL0vOTW4sYnP3l9JWPHOti1Y5a9FoVV47Llmsh6ppDX9a8vgDN7T2YLtOah9PFNGgc7pr98rYqKpHWJX+HXgv25jd/T9rdT5B828N0rH0Z0eclevKNtL7/VMhxnBU7CPTYItr1ESKcIUGvi/rFP5PTtNQxKaTe9QfaV72Efe8aAASNHvPQKeH7uuxhbZcrpxVoP/bYY2fthElJSezcuVPebm1tJSEhYkd8OiyY1I+yI32pIxOHpzJ+WAort/cFhCqlwN7DbVQ3ShX6Hd1unnp9t5zLfRy3N0BmsoXN+xrlNr1WRfHgJKaNzDj3F3OOEYOBrySZl7joJ7SveAFvSy36nOEEvc6Qz/22Vuz7PqVj1WLZmMa28xN06Xkhskeix0XUtNtQqHUo1Dp51vurIgZ8oFBFXlK/Iv94t5T6Vullta3LxV9e380/f34FjW09HGuxMyQnFoNOmoEWRZG2LjfRFi0qpZR/WNVg4ydPf4rPL/2fr9lZx7P/Mx29VsXIQYkcqOqQzxUfrSczyXzakwu1Td20dLrI7xeLTnP5LMFaimYDAo5DW1BHJaI0RdO18S35c9HvxV62AWf5VlnP19NQgcoSF3IcQaWR6h8iRIhwRjgObAqphfB1NNC1Y6kcZAOIXhc+WwuCWofok9aiBK0BY8QtUua0fr23bdvGc889h81mC2l/++23v/QJx40bx9NPP01HRwd6vZ4VK1bw8MMPf+njXI4UD0niiQcms/1AM6nxRsYXpKBUKrjv2mEs3XgUnUbFDTMG8o93Q1Nx2m1uphSmsf9ou9wWZdJy3fQBGHQqVm2vxWrScvPMXN5afZhlW6rQa1XcOnsQM0ZnsmZnHf9eeoAet4+ZozP55lX5F6zedk/FDtpXvIC/ux1j3hji5913RmoemtgUkm/6lbzdvnJxWB93zb4Q90dv0xG0SVlh/dTRSRiyz87KQNDnoXXJM/Qc3ILSYCF25jcwDY64pp4pNb0vpMdpaOvhjZXlvLL8EKIIRp2K3907DoNOxaOLt3OsxUGUWcuDNxVSmJvA6h11cpAN0r22dV8jeytbWberDoNOhUGnJifFSkqckVv/9xOCoshVE/tx65zPNq554cMy2ZgqyqzlsfvGk5Zw+SiXWIpmYSmaBYCj7NOwzwP2jhDTjIC9A31OId7matlyPWr8IhTaiJJPhAhniugLN+QSPc5TdBRJvfP3dJesAEHAUjQ74rx6AqcVaP/yl7/ktttuIyPjq890JiYm8uCDD3L77bfj8/m49tprKSgo+MrHvZQprWxlx4Fm0hPNTBuZTr+Tlp7njM1iztgseXv7gWY+2VItb/dPs3LL7EH4AyIb99aTGGPgm1flo1YpuXpyf66eLCm+rNlZy7u9GsAuT4Cn39pDfLSev7xeIs+if/TpUTISzcw+4XwXCkGPk5b3n5LfqnsObpbcHafe+pWPbR19FT0V2/F3Sek35uFXIGh0Yf302cPxNFXhbZISs415Y9Fnnb2/b9uWD+g5sAmQnPBaPvwr+sz8yPL4GVKYm8j63X0zNoOzY3hzVYWcptXj9vPK8kNwQppWl93DX9/YzQu/nIlRF/4TuvdwK6t31gHgdPvx+4PMHpvJ717YJvd5Y1UFAzOiKR6SRFWDjcUf7aepw8m4ocnMHJPJBxuOyH277B7eWn2YB28qPBdfwQWPMW8Mut1DcNdK6STalAFok/tj37MqpJ/aGkv6/c/irilDE5+OJiHz6xhuhAiXDKbB4+nc+JasYa/Qm7COvgpPQyXuuoO9vQQsRbPQJGQSN/vur2+wFzCnFWjHxsZ+JRv0NWvWhGzPnz+f+fMjNrinw5qddTz5Wp/9956KVn5620jW7apj9c46okxarr9iYIhO7zfnDwFENu1tQBAEclKteHwBvrUgn28tyJf72RweXl9RTm2znZGDEmloDc33FkXYXNoYUoAJcKim44IMtL2tdXKQfRx3QyWu6n04j+5Bk5CJafB4BIUSX0cjjv0bUeiMmAumoNBKGgY+Wwui14MmPl0+RtDdQ8BlJ+2ep/AcK0dpjEIdm0JP5S7se1YjeiVnTk1iNsa80RgHjcVTX4Gg1qJNzEIUg3iaa1BZYlHq+/6fPE1VCEqVfK6g14W9dB2Bni5MgyegiU9HFEV6Dm3B03gEfWY+nsaTzFACfrwtNeizIy+rZ8J3FhWg1SjZV9lG//QorpnSnwefWh/Sp8vuCbNab7e5cbp9zB6XxaqddbR0SLM8wwbEYevxhPT1+oNs39/EyZTXdlKYl8Bvn99Ku036u31nbSVOtz+sHsPm8ITtf7kgqNQk3/pbPMfKEcUguvRBiF4XXZvfxW+TXnwVOhOmgimoTFGYhkwIO0agxwYKJUp9uExnhAgRTo3SaCXtm3+WnnNiEPPwaajMMSTd8Au696zCb2uRJpMyBn/dQ72gOa1Ae9q0abzyyitMnDgRlapvl5SUlHM2sAgSSzaGSlZs3FvP8IHxPP3mHrltz+FW/vXQFXIep06rQqdRYXdKigcrttXSbnPzm7tDc6YeeXEbh2okzezSyrYQ4xqQdLbHD0th+bYagidE24OyLswlIU1CJgqdUTaxAFCotTS+8ht5212zH+uoudS/9HM5KO/evZK0b/6Jtk/+hX3PakBElzmEpOsfoufQFtqWPYfo96KKSiDpxl8i+rzUPvMdqcBRrcWUPxldxiBMQyYQcHTiPlaONilHsm7vaqbptYfxdTQiKNXEXHEn5mFTaXrjUbngy5A7moSFD9L48v/iaZRmMm1bPiD5tofpObAR2/YlvW3vYxgwKuSaBY0uolryFTDq1Xzv+uEhbQX94yitbJO3p49Kp67ZEbJKlJ5o4pfPbqa508nYoUkUDhyExahlaP843ltXyc6Dfco9Wo2SCcNS+GTrCaYrwJCcWGoau+Ug+zhVDTayki1ynQXA9EugbuJ0EUURd00ZQY8Lfb/hKFQaBEGQ6h96EbQGUr/xR+yl6xD9XsxDJ6OyxocfKxig9aO/SeknCgXWUVcSe8Wd5/FqIkS4uFFZYomedH1Im0KrJ2p0ZLL0dDmtQLuzs5MnnngCvb4v300QBEpKSj5nrwhnA81JJhdKhRDm+thl97D/aDvJsUYcLh8D0qPYuKc+pM+uQy20dTkpO9qBVq0kO8UiB9nHOdZiZ9HU/nyypVrK0Z4ziGED4vnhTYX8Z9lBHE4vM0dnMqP4wnzoKzQ6Eq/9Ke0rFuPvbsU4aDwueXlLQiriEEJmvn2ttXRt/yhkKdpdsx/bzmV0bXpHzvn0d7XQsfYVgh6nrCKCz4Orag/x8+/HeXgnze8+AUE/IBA35x7cxw7h65AKTsWAj45VLyEGfCGqCs7ybdi2figH2cf72nYuw3lwc8j4Pc3VWIvn4dj/KUpTDLHTbw8zwInw1fjFXcW8v/4Idc12iockMbUoHbfHj0opsLu8lcxkM3sqWnG6/QCs2l5HfJSBm2el0dDqYGi/WOaMy2LdrmPEWnVMH5nOsdYebpmVx/JtNQQCQfqlRfHB+iOkJRjRqpV4fAH5/NkpVm6elccHG47Q0ulk4vBUxuQnf9ZwLylEMUjT649Ihk9IiiOpdz52ytQopcFC1JirwtoDPTYCzm408ek4yjbgKNsgfRAMYNv2EYYBI9Fn5oftFyFChAjngtMKtNeuXcvGjRuJi4v74s4RzirXXzGQh1/Yij8gzSjPm5BzykLEldtr2bRXcn3MSbUSG6Wn7YSZMotRw4//+qk8ezYgPQq9VoXL45f7JMYYuXPeEO6cNyTk2JML05hcmHbWr+1coM/MJ+3ux+XtYy/8NORzQaFEUIX/2R/PQTsRX3u9nBZyHH9nU0gBJEgP9qDHRce6V3uDbACRjnWvoo4NXfURAz587aEvQQABV7hMo6BSgVIZYvOuUKmJnXEXsTNOrV0f4atj0Km5eVZeSJtOq+LehVJ6TnlNB5tLG0M+LzvSzlOvl7B6h5SbnZsZzUu/nskryw/x74+llz2VUuBX3xjDviNtvL3mMAAl5ZCTYqW1y4nd6WNITiw3z8ojyqzljrmX33Ksq6pUDrIB/F3NdJcsxzJqLq7KEpRGK7qsoQi9cpqu6n2Ifh+GnOEIKjWdm96lc8MbEPSjScxGl5YXdg5va10k0I4Q4SwgisGIA+RpcNo52jExMed6LBFOQWFuAs/+z3R2V7SSkWhmSE4sNoeHkvIWqhq6UQgwpTCNNbv6CrqO1tuYNSaTxrYeunu8aNRKCvrHsXFvn/364bou5o7PZsW2Gnz+IDEW3SX5YI8efw3N7z4uS+5ZR8/DPGw6jv2fEuwNbrWpuViK59NdskK2SgcB87CpeFtq5MJGAG1aLoKgoHvXJ31tKf0R/b6QlBWAoMeFcWAxnmPlcps6NhVz4UxpZj0ozWIKai1RxXPxtdTgqpKCDIXWQFTxPNRRiXSue1UeU9SEa8/m1xPhDEhPNKPXKnF5+mahoy1aOcgGKK/p5IP1R1i6sUpu8wdE3lxdQWtn6ItaVaONl38zm6AoKYxczpx8DwH4ulqoe/a78suwYWAxidf8kIaXf4PnmGS4pY5NIWHhj+hc/5p8r3ubq1DHnKRVr1BiyAlNFYoQIcKXw1m5i7ZPnsff3YZx0Fji596H4hTiABEkTivQHjhwIDfffDNTp05Fo+nTJf0sR8gIZ5ekWCNzxvalB1hNWv7ywylUNXRjNWk4VN0ZEmgD+PxBivISKSlvoX+aFZMhXE82LyuGm2fl0dTeQ3aKFbXq0nszNeaNIe2eJ3FVlaJJyESfKc3Wp9/7V3oObUGhM2HMHS0VXN3yW2xb3iPodWMpnIU+YwhJ1/+czvWv42k8gq+rBXvJClBpMeSNIWBrw9/dhqehktqn70Gb0p+Aoy8dx1wwBeuYBQhKFT2HtqKKTiJ60vWorQkk3/QrbDuXIShVWEdfhcoSR9KNv8BZsZNATxeG3GJUpmg08RnoM4fgaTyKPnNIREnhAsCgU/PjW0fyz3dLabO5GTs0maH94tiwO3SlorXLRfCkqkafP0CsVU9LZ99KidmgwahXo1Reevffl8XQvxClKYaAozc1S6lC9HlCVpycFduxbV8qB9kAvvYGSVrsBA17ANHvJ/6q79O9YymCSkPUuIWoYy6PNJwIEc4FQY+L5veelFd7ew5sQh2VcFbUvS5VTivQdrvdZGdnU11dfY6HE+F0Oa4mAjB8oAqzQYPd6e39DDrtbnaXtwJSfnZWshmdRonbK83CxUfrGT0kCb1WhcV4aZs6aOLS0MSFpr4ojdZeY4w+dCn90S36SUibyhxD/Lz7aHztYbwt1VKj3yMVVRbPpXN9r+WzGMRTX0HUxBvw21rQJvfHUjgDQRCwFs/DWjwv5Lj6rKHos4aGtAkKJca80WHj16XlnXIJPMLXR/HgJIoHJxEIiigVAjaHh8VL9st52wJgMqgZlBUTYlozf0IOSbFGfvv8VhwuH2qVgm8tyI8E2b0oNHpS7/oD3buWEfS4MA+bjm3H0rB+/h5b+L5aA0pjFIGeLrnNmDcG89DJl40VdIQI5xpvW11YSqW7/su7hF9OnHdnyAhnH6NezR+/O4G31xymxyWZyjzz9t6QPtWNdh7/wSS27GtEp1Eyc0wmeq303+9wejnW4iA71YpWfeZuihcC3pYa2te8LMsORU+8jqDXTcfq/+CuO4A2dSCx0+9AabBg274ER9mnKM0xxEy+8XNni32dofJsQZcdb1tDWD9NXCoxJ1Vonw7uukOSXqm7B0vhTMzDpn3pY0Q4/xyvl7CatPzh/gm8s6aSdpuLitpO3lsnFbfmpFgYPjCBUYMTye8n1bks/tVMjtTbSEswYTVdHukirup9dG58C9HrwVI06zP/xlWW2JDZMfNwKdXreKqVOi6NqDFX49i3Tp7pFtRazMOmYi6YQuenbxKwd2AaMhFzwZQvHFfPoa10716BQmMgatw1aJNzvvrFRohwiaKJzwhT99KlX3ppp2cTQRRPVmwNZ/fu3Tz33HM4nU5EUSQYDHLs2DHWrVt3Hob45fF4PJSVlZGfn49We3k8xE7mN//awq5DfRJjCTEG/vXzK1CcVEj56Z56nnp9N15fALNBw6+/OZq8rIszH18M+Kh95r4+RRAgZtpteBqP0HOCeoehfxGGgaNo+/gfcpvSGEX6d59FQMDTWIkqKhGVue97aF/zX2xb3pe3demDiBp3DU1vPCq3CVoDGff/PUQr+7PwdTTid3SiS8sl6HJQ+8x9IUooidf/HOOAkV/+S4jwtXNiUeRx/vLDKfIK1OWIv7udur/fj3hCYW/Sjb/E0G/Eae3vbqjEUbZBWokqnIlSb8bX1UJ3yXJEvw/L8CvQJHx5NSRX9b4Q+U9BayDjvmdQGixf+lgRIlwuuKr30b5yMT5bK6ZB44id9U0Uqkt7Zfzz+KKY87SdIRcsWMDy5cu58cYbWb16NTNnzjzrg41w+vgDQVSfs9x8z8Kh/PHfOznaYCMhWs8PbyoMC7IDgSDPvbcPb6+0mN3p5cWP9vN/35t4Tsd+rvA014QE2QDOypIQ2bzjbSe/XwZ6uujZv4mOda9K+aGCgpjptxM1ej5Bnwdt6kDMI2bgba1Fk5BJzKQbURqtJCx4gO49K1HoTESPXwSiZJOuUEs3myiKuOsOIvo86LOGIihVtK96Cdu2jwBQRSdhLZ4XZrTjrNghB9q+jka8LTXo0gdFHCDPA063j7pmO5nJFlmb/svg8wXD2rz+wCl6hiKKIoIQrih0KeCq2hsSZINUUKXQGnDXHkCb0l9OpQp63biqSlGaotGlDgBAm5yD6HUhBgOyuZQ6KoHYabeFHFMUg7jrDiIo1ehSB55yLM7KErytteizC+g5uCV0f48T19G9mPIvzt/ACBHOB/qsoaTd/cTXPYyLhtN6igiCwD333ENnZyc5OTnMnz+fRYsWneuxRTgFdqeXJ18rYefBZhJjDNy3aBgjchNwe/zsPNSMQadm+IB4UuJM/OVHU7A5PJgNGjnIrmqw8e66SjzeANOK0uk6yXGu5SRFhIsJdXQigkoj614DkruizxPiqKiOS0UTl4rryAk68IICx8FNfUVYYpDOta9gyC6g8fVHCdjbATDlTyJ+zr3ybqb8iZjyJxL0eWh5/0mcFTsQ1DqiJ9+IddSVNL3+qKwkoo5LI37+9+QgGyS5QPdJWt/Hxwhg27GU9hWLARFBpSHp+p9HXCDPIdsPNPHnl3fi8gQw6dU8dFcxQ/vFsWJbDdvKmkiJN3LttAFYTVr2HWlj2eZqNGoFV0/uT1ayNAt65fhsNpU2EOg1ecrNiCY3I/ozzymKIi9+tJ9lW6rRqJTcMjuPueOzz8flnjfUceHyoAGnnYZ/PyRvR0++CdPg8TT855dynrWpYCrxV95L4yu/le8TTUIWKbc/gkKrRwwGQAwiKNUEva4Q0yd99jCSbvwFgqIvHa595WLZAAoEjKdwkVRFJZyty44QIUKE0wu0jUZJ8SIjI4PDhw9TVFSEQhEp3vk6+O+yg7JhTVO7kz+9vJPHH5jEQ3/fTFuXVKAwbEAcv7tnHN09XtbsrCUQFJlalI5apeBnz2yUC7a2lTWSlxXNoeo+pYyJw1PP/0WdJZR6M3Fz7qF9xYsEPU60ablET7wef3c7ze/8Cb+tBaU5lvi530Edk4K77hCehsMIKg3RU26m58CmkOOJAR+dWz+Ug2wAR9kGLMVzcVbskJayTTHETrsVd90hnBU7pP18bjpW/RuFWisH2QC+tmMhpjgywQBR466ha+uHEPSjzxmBpXAWQb+XjnWvAVLAJvq9dKx/jdRIoH3O+Me7pbJsn8Pl4/n3y7iiOIPn3t8n9zlQ1c59i4bxq39sloPpLfsa+cfPphNt1jEkJ5YnHpjMp3vqJcOaURkhM9W7y1v4ZKtkCnXNlP4crbfx/nopOPR4A/zj3VIGZ8eQnXLprF7oUgdiHXs1tm1LIOjHkDsa9wmqIQBdW97HZ2sLKWZ0lK5FE58e8jLqbanGvm89otdJ56Z3IeDHXDgTdXRSyOqVq2ovzoqdcoFx0OPCtrNPlhNEvC216NIH9R5fwFI4E11a7jn5DiJEiHB5clqBdkFBAQ888AA/+MEPuPfee6murg6xYo9w/jhc1xWybXf6eHdNpRxkA+w93MbWskb+9f4+2bTm/fVHuG76ADnIBgiKklnGoKxYjtZ3MWxAPAun9D8/F3KOMBdMxThoHEF3j5xjrTRaSb//GRz71tO5+T0aX3sE05CJpNz+MP7udkS/F39XK+KAUXgaDsvH0iT1QyC8hMG+Zw32kuWA5BbZ9Mbv0Ydp84p4W+vC9hU0elTWBPy2vvx505BJGPNGYx19FaLPjaDS4KoqRR2ddIKut0TQZT/TrybCFxAIBGnvCq2mb+50sr4kVDqzoraLZZur5CAbwOn2s31/M7PGSAW1OalWclKtbD/QxN/e3EOMVcfVk/vR0uHiN//awvFdt5U1MX5YqKnR8XNcSoE2QOy024gauxDR70Nljqbm6XtP6iESdIf/fftPSgcD8LZUYd/d99LavWMphtxwxR5vWy3OJTvxd7ejHzAKTrqfBUEg5fZH8LbWotDoT2njHuHCpqm9B71WddkUFUe4+DitaPmhhx5i7969ZGdn84tf/IJNmzbx+OOPf/GOEc6I7h4vB6rayUq2kBQbaq+dnxNL5QnBdoxFe0ppsN3lLSHOkN09Xuqawx9iyXEmrp7c73PHc+RYF512DwX948Is4S9EFGqtnCN9HNHjom3583Lgat+9ApU1DqXRStvH/5SWn9VarMXz8LbXo9SbUMemodDocOzbwPEHtCoqEX93W8ixgx5n2HKzoNZhGXUljgMb+zSAlSosBVOwjpqDbcsH+O0daBIy8XU142muRpuYhbOygua3/yTlswoKNMk5eE+YpYuokZw7lEoFY4emsKm0T01mwrAUunu8If3UKgWJseG29x3dLv721h5S4ozMGZfNvso2Hn5xm/z5tv1NFOYmcEJ8jsPlQ3OSfr0gwODsi7Mg+YtQ6k3yv6PGXEX7ihflbWvxPHRpeTgPbeP4/aaOS8M6+iocpWtllQNBrUVpDE/FUWj1oFRBwN+7baB790oC3dKKlKtqL7rMfNw1ZX3nHDMfkJQUIlxcuD1+Hl28nT2HW1EqBK6e3E92NW63uRAEgRhLxEQlwtfPaedox8bGAlI+odVqJT4+8uZ/LiitbOV3L2zD4w0gCHD3gqHMn9gnN3XLrDx6XD627GskJd7IvQsLUCkVssMjQEK0npzUKKAm5NhpCWYmjUiVjTWSYg0UD0mUPw8EguyuaMXrC1A0KBGtWslf39jNyu21AMRZdfzxuxNJiDGc42/h7ONprgqbHXbXHpByt3tNLkSfB3fdQazF82n54C8cf9ibCqYCAgqdAW1Kfzz1FYTMeyqUWEfNQ2mwYN+7BqXejDF/EgF7Bym3PyI5Tvo8GPqNwN/dji59EHGz76Z9zX/p2vhW70EEEq5+gK7N7/QVjYlBfF0txEy7DW9LDfqcYZiHTjmH31KEH9w4gqRYAxW1XeT3i+XaaQNobOvhYHUHXXYPCoXArbMHMWdcFlvLGqkqHfuFAAAgAElEQVSolV56+6dZeXV5nwNoSXkLJn1oFX5jWw/+/uGFkmMLUjAbtSzddBStRsUts3JJT/xi5ZqLHeuouWgSMnHXHECb0g9D/yIAkm78Bd07l6GOSSZq3DUojVZS7pS0tQkEMBfORBAUdG18mxNnqE2DxmMpnE13ySdSMWT6YFo//EvoSZUqEhf9FG9rDfqc4Z9ZMBnhwufjzdXsOSx5RQSCIu+srWR8QQofbDjKhj3SKtT0kRl87/rhYUIAESKcT05L3u/Xv/41AHfccQd33HEHEydOxOFw8PTTT5/zAZ4JF7O830+f/pSD1X1LpXqtipd/O/sLZ5KrGmys2lFLt8NLQ6sDjy9Ad4+XTrtU7JgQreeJByZjNWl56rUSVu+U0hq0GiUP3zOOARlR/PyZjRyqkfK1k+OM/OCGEfzsmY0h55k3IZt7F158OcIBl4Pap+9B9PUVf0aNu4auLe+HuMmpLHEIGh2+tr50AUGtI+mmX9H85mME3Q5AQB2Xhq/tGIJag9JoRR2TQvTE69CmDqT5zT/grNwF9BZu3fY7ukuW07H2VUBEoTOReN3PaHr1tyFKDJqELAI9XSE5qiiUZP/0FQSl+px9NxG+GK8vQHlNJ0mxRuKj9YA06VBe24lWreSf7+1j/9H2kH2mFKax7qS0kz9/fyLPvlvKkWM2uc+Pbik6PxdxEeBpqqLp9Ueke0CpIm72PViGT8fbXk/PgU0ojVGY8ieh0OiwlayQDKOCfiyj5hIz6QZ8HQ10rH0VX2cT+sx8bDuWwAmPOHPhzJBi5ggXL397aw/Lt4ZOJs2fkMNHG4+GtP3yrmJG50fcQL8KYsAHIgiqyHPoVJwVeb+ysjLefvttnnvuORYuXMiPfvQjrrnmmrM+2AhgO0kFxO314/EFUCkV4fJ8va50ANkpVq6e1J97HluFP9AXOF49uR8ZiWbGDk3GZNDQZfew9oSHv8cb4M3VFcwozpCDbJBm39btCs8xtjm8YW0XA0q9iYQFD9C+8kX89k5MQ8YTNeFafJ2NIRJfpoIpOPaHvlwQDNC54fXeIBtAxG9rIXbefbQveQZ/Vwv+rhbcdYeIn/sdOcgGqXCra8cybJv6Zt+Cbgddm94OkxgUxQDmYVPp2vxe33iGTIwE2RcAGrWSof3jQtoEQSAvU0rxMOpC/48UCoG547MpO9ou109cOS6L3MwYnnxgMhW1nei1KjKSInrNJ9Kx9uW+F82An/ZVL6GJSaHxtd/JakL2vWtIuulX2La8T9ApvbB071yGedhUGl9/FH+vuZS3uQp9dgGu6jIQg9LL8PiIWtalwughSSGBtl6rRK0OT6Osb3WEtUU4fTo/fZOuLR9AMIC5aBaxV9yJ6HNj37Na0tHOG4suPeJc/HmcVqAtiiIKhYJNmzbx7W9/G5Bs2SOcfa4ozuA/H/dV2GclW/j2H1bj9viZOSaTuxcM5VBNB0+/uYf6VgcjchP44U2FWE1a9h5uDQmyQZqJO9pg4x/vlmLQqVkwOYdgMDTAc7p9IUWSxzEbNCTHGWls682NFGD6qPRzcNXnB2NuMcbc4hC94vj530OTmIO3+Sj6rALMI65AabDSvuIFeT9z4YyQvE7oTTM5uvekNjeu2gNh5w10t4VpCAddDiwjrqB7V58KgrV4PuZhU1FZE3DVlKFN7od15JVf+bojnHuuv2IA+460yoolKXFG/ueZjViNGq6fPoBJI9LI7JX/EwSB3MxLMwf7q3Jy/YPocWLbtSxEstPTcJiure/j72qW24Iuu1T3cLKDq9dNxvf+ScDegSY5B0GIqGVdKowanMT91w7jrTWHEYDrpg+gf1oU76+rlOsglAqBkYMSP/c4ET4bV+0BOje8IW93b1+CLj2P7u1LZSWg7u1LSbr+5xgGRFbmPovTCrQzMjK4++67OXbsGMXFxfzoRz8iLy/yBnMuuG76QGIsOkrKW4ix6GTZL4AlG6vITrHy6vJDtPcWOpYcauHFj/bz4E2FZCaH53U63X55+drr8PCfjw8yODuGA1V96SkjByXSbnNj0qtxuKSAUKtRMr04g7kTsvlww1E67W6mFqUzIvfi15g9UWpNodYSPT50dcY66krUcam4qkrRJuVgHDQO27YP6Vj9H7mPPmso2qRseg6Ezn4bB4+n5+Amgq7eWRSFEkvRTHwd9bhPCMJNBVOwFM1Cl5WPt7kaQ85wdOmDALAUzsRSGDGEuhBwuHxo1QrUqs9P3crNjOFfD81gT0UrZUfb+GSLNNPWaffw7rojXDXp8wuOI0iYBo8PebDrMgbLBjUnolCEP7oUehOCWhuSHqaOTUVljglxeY1w6bBmZx0tHZL3w9/f3suvvjmGh+4s5v0NR1AIAgun9I+sGn0FvE1Hw9pcR3af5P0g0l2yPBJofw6nFWg/9thjrFy5kqKiItRqNSNHjuTqq68GoLq6mqysrHM5xssGp9vH+t31uNx+vjF/CHsqWsP67Ktsk4Ps41TUdtLa6aKhtYcrx2Wxcnst/kCQcQUpYe6RoghXjMpgXEEK9a0OrEYNLy87KM8ADMyIYnB2LDOKM0iNlxQC7po/5Nxc8AWMIXsYhuxh8nbUmAUodEaclSVo4tKIGrMAFEpc1ftwHd0DChVRY6/GkDmElDt+j237EkSfB8uImWiTcki87mfYtn2Ir70ew8BizPmTADDljYW8sV/XZUb4DNweP39+ZRfbDzSh16q4c+5g5ozLxubwsL7kGEFRyq+OMvfl41lNWiYXpsnFxsfxB4JUNdgYPvDif0k910RNuBZBo8d1pAR1fAbR468l4OjEsX8joldKwdFl5mMdezWOg5vlWgqlKRpL4WzUUYm0rXgB0etGk5BFzOSbvs7LiXAOqWqwhdQzBUVYtrma264cRHqiGYUgkNBbTxHhzNBl5gMCJxYd69IHY9+zOqSfoL64auHON6dVDPl5LFy4kPfee++LO55HLsZiSJ8/wANPrqe2SZLgM+nV/PzOUfzqH5tD5MB+dHMh//3kkPwWDzBiYDz7jrThD4gIAnxj/hCmFqVjNWn5eHMVz75TKvdVKATunDuYWKuO0fnJ/PyZjSHa3CqlwCu/m4NBF54X3NLhpKKuk9yMGLkg7HIg6O6ha+uH+NqPYehfFCax57e1Imh0KPWXvlLE5cIbK8t5+ZM+QxWFAE/9cAq/e2GbnHMdY9Hylx9ODQm2Ad5bV8mLH+2Xt3UaJYt/PQuTXo3N4UGvVcnFzU63j82lDQRFGF+QglEfycc/Ff7udnrKt6E0WjHmjkZQqgh6XfQc3ILo92IcNB6lQbr/gl4XgR4b6uikr3nUEc4lDa0O7v1DaMA3anAi+yrbcHulFC6DTsUzP5lGXNTl87w629j3raNr0zuIgQDW4nlYR11Jy0d/w1G6FgBBoyPl1t+hTb58V+3OSjHk5/EV4/QIvew82CwH2SAtWe8ub+WBmwr578cH8fgCzBmXxZSidNITzTz7Tim1zd2MHJREdYMNf6DXPVCEN1ZWUDw4id88v5XKui6MehWiKAXvbm9ADgKyki0IJ6keBUXCcrgB1u6q46nXdxPsLcD80S1FF5SLpKumDG9zNfqsoWgSMs/qsZve/j85R7vn0FaCHifW4nm46w5JsnvZBah6g2xv2zFcR/egic8471bpoijiOroHX3s9+n4j0MReOP8/FxtVDd0h20ERlm+pDjGG6uj2sK7kWJgO/VUTc2jrcrGu5BgxFh3fmD8EhQD/+9wWSspbMOhU3DlvCJNHpPLgk+tp6K2BeGNlOU8+OAWLMVQW8FJHDPjoKd9OoKcLY+5oVJa4sD4qSyzWUaH1CgqN/pS68gqNHoXmswOroM+DffdKfJ1NGHNHo88a+tUvIsJ5JyXexNSiNNbuklY19FoVCdF6OcgGKXVyU2kDCyKpW2eMeeiUMFnZ+Hn3Yx46Gb+tFX2/QlSmqK9ncBcJXznQFk6O1CKcNWwODy8vO0ZrlwurScOQbEnLvOxoO75AkMHZsVwzpT+PLN4Wsp/b6+ef7+2TjW16XH6SYg1cOS47ZKaturGb2WMzQ4KK6SPTUSgE/v72XsqOtjEgPZq75g3hpSUH5AA8EBT599IDF0yg3bHuVbo2vSNtCAoSrn4A0+DxZ+XY/u72sEJIe+k6Aj02uja/KzUolCRe+1MIBml+50+yXKB19Hxir7jzrIzjdGj/5F909zpWsvo/JF33P7I2cYQvx4jc+BDjGr1WSXKcKaxfu83Fr/65mXabmymFaVw3fQBKpYJvLcjntjmD0Gmln9j/LjtISbnkBup0+yWrd7dPDrIBWjpdrNtVd9nlcze9/iiuasnivmPdq6Tc/ijaxKzT3t/X1Yxj3wYEjQ5zwdQQU5xT0fzWH3BVSat83TuXkbDwh2ft9yLC+eXBmwqZPiqD1k4nRYMS2b6/KazP5fbiej4QBEFKKwn6I6pYp0HER/0CYeSgJLKSLVQ3SkGv2aChurGblk5pBs3m8PK3t/Zw44yBPP9BX+BXUdvJnLHZvLm6Qm6bWpTOrkPNIcdvanfKhY4nkpNiZcGkHHaXtzIwM5pvLxrG02/slgso65oddHS7w/Z1OC8Mmb+gz4Nt20d9DWKQrk3vYBo8noDLgaBSh7hEBn0eCAZCCqxEMYjodYcVXQU9LgSNDkGlCVE9UOrNdG378ISOgV7zDEI0uW07lhE94ToUOslFMOj3IiiUCIq+wjox4EMMBkPGKAYDBN1OeSn8OAFnNwqdMWR/v70ThVqDGPDTvXtl6Ji2vB8JtM+QmaMz6bJ7WL2zjiiTltuvHER2ipUlm47S1C6lbcVadazaXivfG/9ddhCjXk1WsoW/vL6bxvYeBmfH8JNbR1LTeNIMeVCkzeYKO2/gFKtJlzLu+go5yAYQvW66dywlft79p/x7D/o8CAqF/HD3tjdQ/+JP5fxte8lyUr/1eJgzbNDjBEEg4OiSg+zjdJcsjwTaFymCIDBsQJ953uQRaXyytUaeZBqUFcP4gpSva3iXLD3l22lb/jwBRyeGgaNImP89yZk1wimJBNoXCGqVgj99byKf7qnH5fEzYXgq3398bUiflk4XW/eHBtB2p49+aRZG5ydx9JiN3Mxovn1NAX97a4+8pAaSc92sMZl8vKlKDgxiLDoO13Wycrukl13bbMegU7HjYOg59lS0MmdcFss2V8ttM0af3fSMM0YUEYOhkoai30fzu3+m5+BWBLWG6InXEzX2aro2v0fnxrcR/V5MQyYQP+9+3LUHaF3yDP7uNnTpg0hY+EMQgzS/9wSeY+WoohIxDZuKvWSlZNOu0WMqmIzrZLk/vxcUJylTiEEpiA/4af34nzjK1qPQ6IiedCPWUVfSte0jOj99E9HnwZQ/mfgr78VVU0brkmcI2DvQpgwgcdGPEcUgLe/8GU/jEZTmWBLmfxdtWi4t7z2B8/BOBKUay6g5IcYc0ukDRDgzBEHghhm53DAjN6T9qQen8OmeekRRJMaq45EXt4d8vvNgM++sPUxr7wvygaoOnv+gjMK8BLadMNtm1KtZOLk/m0sb5eLmKJOWKYVp5/jKLjDEcKfMoMdJ/Yv/g6exEqUpmvh596PPLqB16T9wlK1HUPXe02Ouwr53tRxkA/g6GnFWlmAaJBUYi2KQtmX/wr53NQgC5uHTQVCEnPfz0kwiXNgEAkEO1XQSbdGSEmdCp1Xx5+9PoqyyDUEB+TlxKBQCy7ZU897aSgAWTevPrDFZX+u4L2aCHictH/wF0Sf9bjnLt9EZk0zstNu+5pFduEQC7S/B9v1NrNpRi8Wo4Zqp/UmJM1F5rIuNe+qJsei4ojgDg06N2+Nn875GgkFJ+eNUhYWnQqdVhQSwxYOTZPtzgOED4slMMocsjykEWLqpmtJKSX+2tctFeqKZexYWEBRF9la00T89insXDkWjUpKdaqXsSBtRJi3fv2E4v39pR8gYVm2vJTPJElLNnRJnZO64bJJiDNQ02RmcHcOM4tMPtPdUtFDbbKcwN4G0BGmWtqS8hd3lLWQlW5hSmIZSeWb6tgqNLkyPWpOYRc/BzYCkd92x5r+oopPoWPuy3MdRtgFtygC6Nr0jG2S46w7SvuolCAbxHJPstP1dzTgP7yL5lt/Q/N7jBHtstH74tOwMeRzTsGko9SZaP+xzSzXmjkapN2PbuQxH6RpAKqxsX/EiKmscHate6htP6Rq0STl0bXpbHo+n4TDtq/6NGAzgaZRkHgP2dlo+fBrr6Hk4D++UrjHgw7b1Q/T9RuA6srv3iALW4nln9J1G+GyMejWzx2YB0NHtRqkQQmahE2MM7DzpRbXyWBdTClNJjTfS0e0hPcHE3QuHsvdwK4GAiEqpIDczmp/cWkS0RXc+L+drR5uaizY1F0+9dL8JSjUBjxNPoxQUBRydtH70NFETrpPvIdHromP1vzHkDEM4hcyf6Pfiba9HE5tKz8Et2HevkD+z71qOYWAxzgrpBUlQ64iKmNhclLR1uXjo2U2yz8OCSf341oJ8mtt7KClvQRAgIdpAR7ebv7/d53nwt7f2kplskc2mInw5vG3H5CD7OJ6Gw1/TaC4OvnKgfblI+5UcauHhF/tyobfvb+IHN47g4Re2yQ/a9buP8fC94/jRXzZwrEXSUX5txZkXON1z9VD0OhWlh9sYkB7FHXMHo1QqOFjdQdmRdjRqJddNH8ArJ6gjgFS4mJ5gpuRQC3anD4fTi1at5IUPy9jXG5B32j08+04pZoOadlvfzKfFqOE7iwp4dPF2mjucRJm0iMB3/7wWlVLgltmDvtRswL/e38eHn0panEqFwEN3FdNuC/3h23+0ne/fMOJLfz/HiZ31TXQZg6ViyOwCHGUbwvq4qkvD2jz1FaF250hucmIg1Lwn0N2GvXQtwR6b3OZrO0bUpBtw7FmDv7uVjtX/IWrMAlJuf4T2VS/haThCz6EtNL72O5TG6JPOLOI8sidsPO768lOMpzpsZjrg6MDTGK5vasqfjCl/Er62egwDitClDgzrE+HsEWPRce/CoSxecgCXx09B/zhunZ1HaWUbdc19hc1pCSYePeGFtrG9B6Ug8Nc398iLEPuPtrP3cBvTRl68hlBngiAIJN/yvzjKPiXg6MQ4eBzNb/0xpE+gx4a7NxA/EXfjEVTRiQhaA6JHSudRmmNoXfIMBANokvqd0rFOlzGYqLEL8HU0oc8ZHinkukh5d12lHGQDfLDhCMVDEvn94u309BqwrdhWw8xTrL6WHm6LBNpniCYhE4Xe1OcVAegz87/GEV34KH/zm9/85os69fT08Nhjj7F48WKmTp3K73//e4qLi9FoNMyePfs8DPPLEQgEaGlpISEhAZXq7Ezav76yPKRo0O0N0NLplHOoAdptbhQCbCnrm3HucfuJsehIjDGwrawRXyBIrDV8qbK8poP9R9uJsejQanpTEHrfyBdNHcC4ghR0GkkW7IpRGVxRnMGNMwaS3y+OJZuq8Pn7lkLTEkys2F4j/9i02dz0uP2UHW2n54Rc6x6XjzvmDqakvAVRlALh+68dTsGAeOZNyOGK4gycXh8lh6QirqAI+ypbmT4qA6NezcGqDg5WtxNr1aFRK7E7vfzr/X28/MlBapvtZCSaeOLVEjmYEEVo7XRRdqSd7p6+nOfqxm4WTMr5QlOQz0IQBFnlQx2ViOj30XOoz1ZdUGmIm/Ut7HvXhiwZR026AW9zNUFXX1BkGjwelTU+RKhfHZ+OQq3F1x6qj6yOScVds0++OHfdQXQZQ0Jm1/2dzajj0/G19q1MoFARN+Mb2PetC0n3iBq/CF/bMYLOvr8z05AJqKIS8Db2GRdpEjKwFs2h5+Cmk67xm+jSctFnDUVlif0S32CEM2VAejRRJi0qlYJhA+LJy4phRG4Cdc123F4/Y4cmo9eqQn47vL4gSpXA4drQl6pos5ZRgy8/STpBqUKbnIM+cwhKgwVfR2PIDJk6Lg1z4SycB/vuaRRK/J3N2HevhIAPhcFC1IRrcZbvkO/xgKMTlSUuZOUJQUHs9NvRJvdDm5iFQnN5rSBcSqzYVkNdc6i9ulqp4GBNp7zt9QXJTLZQeSz0Xls0bQDJccbzMs5LDUGpQpuai6+1FjEYwFwwlZjJN4XUUlxufFHMeVpR6COPPEJCQgLt7e1otVocDge//vWvefzxx8/6gC9UYq3hP8imU2nenkKFpb7Fwd2/X4WnV3Zo0dT+3DmvzwTmn++WsmRTFSDpfj767fH4g0F+v3g7nXYPFqOGn942kvycWBYvOcD63ceItUqyYQX94/nG/CH8/e29BIIiRp2KOeOyePK13SFjqGnqZnB2DM0n6G8nxhjw+wNcN30AidEGCvMSqG918P3H19LW5WLSiLSQ/iAF280dTl5fUS6ntRj1av5w/wT++/FBth+QXjKqGrrpsLnD5B/9gSBadWiaiEohoFCcPfUa0+Dx+O0ddO9YCoKC6Ek3oE3uR9J1P6VjwxsEnd2YR8zAlDcGTVwa7StewNtah6F/ETHTbu3N+/bjOrIHTUIGsbO+hbe1Vl5uBlBFJYbZqsOpl9CUWgPRk2/CvnslCp2R6Mk3oUsbSOKin9C5/nWCXieWwlmYh0xAm5RD2yf/wtdai2HgSGKm3gqIIILrSAmahExiZ9yFOiaZuNn30L17JQq9ieiJ16E0Ws/adxjh9Hh7zWH+vVRy/Ny0t4GK2k5+eHMhGUlmqhpsVNXb6JcWPmOanxPLss3VIWn1g7Ivvxk2URTxNh1FYTCjtkqGPjFTb4FgAGflLtRx6cTOuAtNbAqBaS10l6xAodGjzxmObev78nGCzm78tlZONNYAKY0kft79dG2R+kZPvglNQsZ5u74I544phWlsLm2UtxOi9WSlhHsZ5GVGYzao+WDDUQRBSjEpvAQcjr9O9BmDSf3G/33dw7hoOK1A++DBgzz22GOsX78evV7Pn//8Z+bNu7zyP6+a2I+tZU3ykvDssVnMHZ9N2dF2nL0zx2Pyk7h6cj9W76iVC5wsRg11LXY5yAZ4f/0RFk7pj9WkpbXTxdLNVfJnTreft9ZU0NzhpNMuWQl393h55u29zB6TyQcbpJnNLruHRxdv5/mHZki5nkERhQBTitKZXJjOK58cCpltP65iEmXS4HD5yEiy0GFz8/yHktxfjEVLfr84Hl28Xb6epZuqGDs0OeR7iLFosRg0IbnjPS4fb6+uYMfBUGml3RWtTC5MCynKvGpSP3QaJY/9e4csF7hwan90mrNbLuDvbut98ELr0r+jNEahNFgJdLdJ0nyb3kEdk4wxbyza1IF42xvwNB3FfawcQ85wEuZ/L+R4mthUhEU/wV62AZU5hqixV+NpqpJF+0ES7rcUzcFeujbEBtqQW4whZzjRE64NHaTQ67gVDMrpKp6GCjzHDiH6vTgPl2ApmoM2MYv4K+8Nu0ZL0SwsRbPO0jcW4UxYsa0mZHvT3noyEs0s2Sjd03anj6b2HnJSrRytl1KPZo3JZOLwNDzeIK98chCnx8+csVmXXSFkoMdG46u/xdtSg1RTMJfYGXfhOroHT8NhFFo9xrzRaGIl1YiosVcTNVZyJLbvXRN2PIXOiNIUTcDRN6NpyB2Np7kKX3sDIGLb8h76rKFfKAEY4cJn7NAUfn7HKNbuqiPaouPaqQMwGdQs21Ije1LkpFgZP0xaDb5pZh6CQJhbcoQI55rTim4UitA/zEAgENZ2qRNl1vL0j6dSUdOJ2aiWi/r+8bPpbN/fTKxVR2FuAgqFwFMPTmHNzlr8AZHpo9L5w79DCw4DQRG7y0cgKOLy+E4Wi8Dp8tPQ2hPS1tzew97DoZbsTrefd9ZWyJbPQVEKjscPS+HX3xrDS0sO0NjmIC3BHKJ4YNCpmFKUxosf9mlqd3R7+GDDETnIPo7b4+c7iwpYt+sYcVF65ozNxOUJn8l1eQKkxBmpP2HcqQkmfnDDCApzE6httjNyUCKDe7XAn/3pNPYebiUr2XrWZ/ICTjvdOz4+ocFP1+Z3QBQJ9OZZiz4P7cufJ+B0yNJ8ge42mt/6Ixnf+yeexiPSbLHWQNSYBWji0zHmjcGYN0Y+rMoSR/y8++nevUqaqZ54HZr4NJJv/l86N75F0OvGUjgTQ85wPE1HsW39kKDPg6VoNpq4NJrf/hMEpe+7c/1rKM0xdKx6SZYSDDg66FjzX5Jv+tVZ/X4inD3MBjWNJ2xrNSrKT1i6BvD4gtw5dxBmoxajTi0vWV9RLKWAXa7Yti/pDbIBRGzbl6DLHELzO3+W0z/alj6LOioxzFTGMLAYpfEVuaZBUOuwDJuGoV8hbR//g4DLjjFvDNqkbNqW/l3ez9N4hO6dHxM98frzco0Rzi3jClLISrGwZkcd60qOMWN0Bk89OIVdh5pRCAKFeQlyYK1WXV4xS4QLh9MKtEeNGsWf/vQn3G43n376Ka+88grFxcXnemwXHEqFEBYURpt1zBoTWmwRZdZyzdQB8vbssVkcOuHhm5Fk5odPrsfl8TMkJ5bcjGjKa/s+nzU2k2iLNmQmeNTgJLJSLJSU9wXbapUClydcHquyrguvL0C0Wcv8CTms2VkX8rnT7aeprSdsv1iLlGvt9fXNvg/IiObKcdkUD07i0Ze289CzmzHpVSTHGmlsl44hCNIsnV6n4v/+s5Muh4dYq47vXFOAUqlgSlF4gVdKvImU+HMzqyQG/GGyYaLPGzLTBdKMmqsqtChR9Hvp3r2SznWvcXwZ2nl4B+n3/R2lLjynzzxsWog7nRgMYN+7BlfVPhRqjbSk7eii4b+/lmXInId3Ej3xBjnIPo7zyB6C7tD/F7+t5ctdfITzyi2zB/Hoi9vw+oMIAtwyOw+3x8/OE3Ts1SoFOalRWE3h1ryXM/7utrA2Z2VJ2L3rrCzBcWATjn3rURgsxF5xB6ZB44iasIjODW8iBgNYi2ahik6i6e0/ybUU9pIVKDSGsJTaygoAACAASURBVHP4ulrD2iJcnDS0OXjgCelZCrBsSzXP/GQqY/KTP3/HCBHOI6cVaP/4xz/mueeew2w28+STTzJx4kTuv//+cz22S4bpozKwmrRs299EjEXHGyvLZaWS/UfbuXJcNuMKkjlSb6MoL4EJw1IpykvEYtRSWtlKWoKZ7ywqQK1UcKzFwZbSBqwmLXcvGEqUWcvHJ6SeqJQCa3bWUt0oLZ2t3F57SgfHK4ozKClvkc034qL0zByTSVqiiefe20eH3UNhbry8nP2fjw/IJgAOl59gUOTmWbl02j1MHJbK0P6SbfKLv5pJS6eTpBjDGUv2fVVU5miMeWNDCiIto67E21SF7QSjGX3OMP6fvTMPr6o69/9nn3nOOZnnhJAJCBBmCLOAA4gjKmDrdW6rrdX22l8nrW3ttdrBarW9bW3tdaLOE8ogo8wzARJIQgiZx5PkjDnz/v1xwg6HoKICCXA+z+PzuFfW2mftE3b2u9d63+9Xk5aPu+IkZ01Bhr+jkZNzPUM9TtxVu/F3NvXmbWdhmbUEhcGCs2wz9v1rkKl1WKYuwttyDMf+NeFx3gDWT14k5HFFaP0ihvB3twJCxOdoMwoJOTvx1B+W2vSFJWfte4ly9hlbkMgLP59H2TErGpWCrGQTJoOKhjYnm/Y3Yjaqufvaon5Btj8Q4rVVR9hZ3kJagoE7rh5xyRVn6YeVRCgEyfUx6AsnhQscTyLg7MJVtgkI7zq1vfcMcp0J6+oXpaC8e+u7yPTmyKJjwNdW208h4YTGdpQLn3W76qUgG8KSfzvKWph9msWdKFEGijNSHVm/fj2LFy/m5ptv5pZbbmHKlCl8+OGHFBb2l04aDJwL1ZGvS2qCgYnDk5HLBFbviHwY6LVKuuxeth5oZvuhFhranEwvTqOmycaW0iaON9vZW9HGtNFpzJuYxexxGRTlxjM6L4G0RAPJcXo6bD0kx+m4flYua3ZFrmBbjGqS43S0drpRKmQsvaKAGWPSmTM+k6RYHeMKEpk1Lh2VUs6InHjml2RztL6bPUfa+GhLDa2dbmpbHFLOOIA/KHLfotHMmRA+xwnkMgGTXnVWixu/CvqCCShiElDGphI7eyn6/Alos4rCjnJiCF3+BOKvuBtNxjD83a34OxqQafXEzbsDQanGc5JbHYQrrR17VhF0duJrrcHTWIHCYKH17acIdLfhtzbiKt+CoNLgaz0eMVadMlTSCT6BcfRsdLnj8DZWIIZCGEbOJHbGYvT5ExD9XgSlGtPYKzBPvQFBiG55DmYE4IX3y3hjbSUfbKrG4fLx/cVjuGluPjfMyiUz2dRvzKsrD/PWuipsTh8NbU72V7Uxv2QIwmmKqS9WVHFpKBMyCPk8aDIKSbj6fjRp+YhBP96mcC2KcfRsQj4vfutJyiFiCEGuwtertX0CuSkOX0tNRJs2czgJV32bkNeN3BhL7Oxb0RdceruxFytHarskD4kTZKeYeP/TajaXNhFr0pBo6b+rEeXc4GmsxLb7YwKdLSgTMhDkgyP+Otd8LdWRdevWEQgEeOqppxBFUVKQCAQC/PnPf+a66647N7O+iMlNN2M2qOl29gWtZoNasjwH2LS/kVG58byy4jAnvDBqmuy8ta6K1AQDf3/vIKGQiFGn4pf3Tuay8RnIBDhwtANXjx9BiDQJjNGrefib42nrcqPTKFEr5YiiiF6rZMLwJH76ly009aaSLJg6hCGpMew50peysG53PdOL06RiLoCUeD2p8YO3oEiQKzEVzz2lTYFl2qJ+RYlJ1z1EaMF9CHIFgkxO0OPCdXgbvtbwQ9swciaehshA2dtQgePQpoi2kNct2a1LyOQYx11BwGHFVR6W41OnF2AcfRkylRbT2MsRgwHJMlqujyH+ynu+9vVHOX+s3F7Lwerww14Uw9vXM8emMyLnsyUWTzW1qW910mJ1X3Kr2oZhJRiGRe7axM7+BuapNyKGQsg1emw7l+Ou2N7XQa5Alz8+wogGQJc9EplcKclryvVmYqZcjyoulcTrHjzn1xLl/DNvUiYrth2nozu8Y5iZbOSNNRUEe7OPdh9u5bmHZw/qZ9XFgvvoHlre+K20y+Sq2E7K0l8M8KwGB58baB8+fJjt27djtVp56aWX+gYpFNx+++3nem4XJSqlnMfumcxLKw7T3tXDjDFpkvrGydQ02Ti1ubHdyeodtVJ/h9vHyx8fZtiQOF5b1Wdak5lkpK5XHUWjkjNzbDh1xKRT8afX97HtQBNmo5p7rhtJRW2XFGRDuJhy3mkKtAqzzCRatGw/1EJ6ooHbrx5+Xletg247AVsHqqQsSa9TDPjxtdehjE1Fpu7TJg9vF5tQGPuMYvy2NggGUMam9p2zx0HAbkWVmCkFugByjZ60u36Hs3wLSlM8moxCmpc9TqDrJEdOnQllXN+5TmAsmoFMpcWxfw0yrZHYGbegsiSTsPC7GEZMR66PiTCSEWRyBJk8fC0d9eFr6dX2FUURX1stcn0MCsNJ19LVghgKSWoMEM43DzisqJKyoyvg55lWa/96h+YOF2qlHItJLenm1zbbJRlPi1FDDX3a2nqNgtjTSIheSnhbjyPXGlCY4iNs0U3jr8Lf1YLjwAbk+hji5tyGPncclllL6d76DmIwgGnM5eiHT8UwYjrG4rkEHFa02SMj7muAgK2dkM+DKqEvtcDbUoPf2oAma2TUvOYCw2LU8PzDs9lR1oJKKae22c6y1X2LIv5AiB2HWrh+Vu4AzvLioXvHBzj29srUzrgF3dA+oznb7hUR9RU9NQfwdTSgir+01JROx+cG2vfffz/3338/r776Krfeeuv5mtNFz9B0M7+8py9PsKbJxutrKqUAWiGXMb8kmx1lLZJMIMCYgsSIlWYI20Cv2n48oq2pw8ld14zg38vL8fiCPP7iTu69biTdTi9bSpt6x3l5+rW9jBuW1G9+2akmZAJSoK9SyJhUlEpSrC5C//t8Ydu9EuuaFyEYQGFOJGXJowR7HLS++VuCLhuCSkvitd9Hk15A87Jfh81mBBnmkuuxzFxC+/LnJRk+bc4Ykm76Ec4DG7B+8iJiwIfCkkzKkkdQWsJmIUGXjZbXfxO2PZcpsMy4hbi5t9HyegMBWzuCWkf8lfeiyxmNp6Y0nFMtyIiZMJ+e2jJs295DDPpRGOPQZAyj5/hBWt/5A6EeB3K9maRFD6NJ70u78tQfoeWtJwm57cjUOhKv/wGqpCG0LPtVWJVBkGGZdhPm6Ytoe/8ZXGWbAdDljiPpxoex712Fde3LEAqEr2XpoyjN/X+vUc4NJaNSpQAawi+3b66rpKndhUyAm+bmc/mkLB7+86f0eMOFxmqljPREAw1tTgxaJfffNBq18tI0fAj2OGlZ9ute23UB08QFxM+7Q/q5IJMTf8XdxF9xt9QmiiKWqTdinnwNoigiU/Q576qTh6BOHtLvc9pX/A3H3k8AEU3mCJJv+Qnd296ne/Ob4c9RqEhe8nO0mef/b1yUr45cLiMvw0xKvCFCRvcEibHR1JGzgfPwVjrX/J903PLmb8m8/68ojGGBCEHR3/1aUJzGa+QS5IxytAsLC1m3bh0HDx7kyJEjlJWVsWrVKqZMGZxFJYMxR/vzsBg1FGRZcLh8pCcauO/G0eRnxTJ+WBIOtw+DTsVNc/K5eloOZcesESYy184YSlOHC5uzz2lRr1XS3uXGau9LTzlyvBNBECSlEAjLDF42PoPSqr4ct/gYDT9YOpa8zPB8MpNM3LdoNENSB8YMJehx0bLs19BrDhPyuAj22HGVb+nVxgWCATz15YR8HtxSAWTYqVFhjJMepACBrhZkOhOda1+SZPRCHicht0OS7uva9DquI71b1WIIT20ZMZOvxTJtEYbhJcTOXII6eQiCQolx9GXoh0/FXHIDmrR8Wl7/H+i1TA86O0EM0b39A4KOzvDp/B68LTWYxl4uzanlraek1XIx6MdTf4SQx3mSQY6Ip64chTEW29Z3pHH+zmbkehPWdS9LCiYhj5OQ14W+YNLZ+QVE+UKSYnVkp5hwuv0MSYshI9lAWXXv7xsor7GiUsoj7rNgSOT6Wbk8cHMxSy4vGLD7azBg2/4ezrK+NCxvYyW6/IkRuzjSz1qO0bLscawr/4Gnvhxtzpgz0sT21B/GuvIf0nHA1g5Kda/sZ+8qXChI0N6BcdSsr31NUc4Pu8pb+PHzm3lnQzXr9tSzYGo2VptH2qWdMjKFxXPzB7xm6GLAtnN5hGMyYgilJYWuzW/S9uFziMEQos8j3U+GkTMxjZ4zQLM9v5wVZ8iHHnqI+vp62tvbGT58OKWlpZekvN/Xwe7yoZAL6DSnf8MbW5DYz60qPdHIw98YH9H2k9sn8va6KupaHIwfnsSVk7NITzTy5Eu7CIbEXomxYby7IbJQqMcbYNiQWPZW9K2I6zQKFkwdQmaSkbW76zEb1dw4Ow+lQs7E4clM/Bp20HaXj13lLZiNasbkJ37lP3RBZ5cUEJ8g0NXaT/Yu6OjC39nMqZzIsz4Zf3tdv3P6T0oL8XdGGu8ghvDUleFpqCDkcWIcPUfaMnMf2x9OE9EY0GQO6ydN5utoJNAdOVd/V+Q8A92Rnxewd0TMp3cS/YosAXztdRCMlAkMdLX26xfly+Pq8bN+Tz0eX5CZY9JJsITTGQ5Vd1Df5mRMfgLJceGc6pJRqZSMCqfy/ObFHRHnEUX6aeUDmA0qenwBdu9qJS/DTF5G/8DyUsDf3V/C0lNXTtfGZfi7W9EXTMYy42YQZLS997T0gt1z/CAdq14g8drvY9+9Am/LMbRDRmEcPQdCATo3/gd31W6UcWlo0gv6fUagq6XfvRP0uPv1izI4CYZEnnuzFIc7vAjT1unm38vL+c13plLf6kAQkPwuonx9VIlZ/dpcR/fQc3QPAIHORuQxCZinXI/SkoR2yOjzPcVByxk7Q65evZrHHnuMO+64g1AoxBkshEchbDn+9LK9bNrfiFwm4/pZQ7lt/nCON9t5c20lTrefeZMymTY6DYfbx38+qeB4k53i/ARumJWLIAh8uPkYu8pbyEg0csu8Av5rwfCIz5gyMoV//HQeZTVWctNjSE804uzx8cqKvrzt6cXpLLosD4fL12vhruWua0ag0ygRCeey9XgDeHqlko412nh7XRVub4ArJmcxuSgFm9PLstUV4SB/WBLXzhwKosg7G46yt6KN7GQTiy8vwNnj5+FnP5X+AE4cnswjd321FVZlXBrKhMwI2S79sBIC3a1S0ROALncshhHTIqT65PoYTOPn4yhd1xdYCwLKhCyUsan4O5ukvqrkIfi7W1Gak9AXTIqwW5fpYrCufYmQO5xT6zq8nZRvPAYItCx7nBMSfc4j25HrYyRTHAB94SQEuSLifCevNvva6tBkDMNdtbvvWvLGox82BXdln9GR3GDBOP5KHPvX9lm/CzKMY+bhqSvvW93v/X6ifD28/iA/fOZTGtvDsnBvravi6Qdn8tGWGsmdVSGX8ehdkxhzygvytNFpbD/U96IUH6Nh0WV5lB2zcvh4eKW7INNCSITv/X69FITfuXDEJZlLahg2JcJhVaYx0L39PWkXqLvjrV4VnnkR/84BvI1VtH/4HK7DWwFwlW8h6Ai/nNt6bdf9HQ342usQVJrwihsAAsZRswj1OCLuPdOYyALqKIMXjzdAp90T0dbU7uSd9VW8u6EaQYBFc/K4ZvrQAZrhxYEohnBX7iYU8KLNHUdP9T4EhQrL9Juw710V0Tdoa0dfcPrdqEuZMwq0TyyHZ2dnU1lZyVVXXYXD4TjXc7soWLurXnJuDARDvLm2ipFD4/ndK7ulQHRvRRuauxW8t/GotL18QkFEq1FIAXNpVQdVDd089d3pvLLyMOt6V6FvXzCc4vxEZln6ig5umVtAgllHaVU7Q1JjWDA1W1KNUcplaNUKdGolpZXt/ObFviBwz+FW/vDgTH76l824el0i9xxp5TffnsrLKw5LgcLB6g68vgD+3msCOFRt5XiLnYwko3RtADvLW6is6yI/88vffIIgkLL453RtegN/ZzP6gomYJiyAYACZ1oin9hCq5BwsM25BrtEjLvThOBgumrJMXYQqPo2Upb+ge9t7BOzt+Nob6PzkXyBXos0eRdDjDBda7V2NY+8nmKctInbmYkS/t9du3YImvRDrJy+eNCsR56FNfRbqJ1p7HMTM/S889UcIOjoxFM3AVDwXQ+EUOje8Rs/xA6gSs4i74i4A2t77k7RlLtPFoDQnok4vIHb6zcg0esSAH+cJy/dpi1DFpZG89FFs2z9ADAWImbAATUouyUseoXvTm9Lqn2n8VV/6e44Sye7yVinIhvDq9vLNx/jopFzsE/ezTqPgH+8fosXqomRkKndfW8SDi8ewYW8DsSYNt8zNR69V8tv7p1FWYwURRuTE8Z0n10asdL/+SQXXzBiK/BLb5tbljiPx+h/gKF2LTGNAXzCZtnf/ENHHfXQPlqk3oEzIwN/eJ1+qTs/vS/PqxVG6LqI4GiDQ2UzSoh/hOLgR0efBNPYKtFlFqFPzsO9djb+jIfyCmz/h3F1olLOKXqtkRE4cZcesUtvQdDMvLi+Xjv/x3iFy082SI3GUL0/7B3/u07uXKUi68WG0Q4uRKVR4W2sidmwVlmTk+mhB8amcUaCt0+kk3ew33niDnJwc3O7oFtuZUNdi79e27WBzRCAKsGFvfUQOJ8Dm0ia06shfUUVtF2+srZSCW6vNw+Mv7uTFRy6n7JiV1Ttq0WuVLJqdx2XjM7hsfF91/asrj/DBpnCOVYfNw6//tZ1xhZFFcw63nw8/rZaCbAhve6/ZVSsF2SfYVNpEMBiZKnGo2or5NA54pytSOVMUpjgSFnznlEYlsTMX9+trHDWrX46lJqOQ5IwfU/fcd/rcGIN+AvYO1OmFJ+WdiXRveRvTmHkgVyDXx6CMTUV5mqppuTEWgf4BkSatAPOka/C11yOoer8HuQJvUxV+axN+axMBRyeW6TdH5KWG3Db0k67GXHKD1GYqnoOpODLHTZs5HG1m5I6GMiaRhKujBlJnk8+Ssw6eIgXk8QV4/MWddPdqzK/YdhyjXsU3rxrGnAmR6j3OHj87DrVQ22JnfFMSXn/kPeEPhHpfhi+tQBvAMHwqhuFTgXAdhqBUI/r7akzkOhPty/+CKiETQabA39mENnsU8ZffRcPxgxGOqnJ9DApz4kn27iDT6NHmFPerXZAp1ZgnLTzHVxflXPH/bhvPSx8d5liTjeK8BORygR1lkWl35TWd0UD7KxKwtUeYShEKYN+7GkEmp2PVPwjYOpAbLATddlQJmSQsuO+S8gI4U84o0H700Ud54403ePjhh3nrrbf45je/yUMPPXSu53ZRMG5YkhTcQti5cdywRFZsOx7RLyXOgFGnwuHuyx1OitWhUSk43twXrGvVco42RFqJe31BVmw9zssr+hwFd5e38o+fzcOg7csJL62KtB7utHtRKvpLwWWl9DfYSEswoNMocJ8UgCfF6ggEQxHygEadkvkl2Ww72CwFJdkpJoZ/jqbw+eJUC/aAsxOF0xrZSQzRveMD7DuXA+Cu2IGmsRLDyFk4D24AQBmbSkzvqrHzyFZphc0wciaqxCyaXn4UT10ZIGAsnoM6NTesYNKLt6EC10mpJNJ8HJ392qIMDBOGJ5GdYpLuvRiDimtnDsVq90jKPQCTi1Ii7juAA1XtlObGs3FvA7ExGhZOyyHGoOZ//r1TWn3bX9lOcX5ChKrQlSXZKAbITXUwIdPoib/qW1hXvUDI60aVmBW2Zu99SZZp9GTe/1eCLhtdG5ehtKTgbakGUQxvac9agjI2BX9HI76248i0BuLnf7uf1F+UCx+LUcP3F/dJzO2taJMWoU5QmBVNY/iqiKfUHEG4YL/1vT9KaVhBZxfmkuuJnf2N8z29C4YzCrTffvttfvSjHwHwpz/96ZxO6GJjbEEi37u5mI8216BWybllXj7jCpNYOD2H5ZuPIYqQl2Hm2hk5pCUa+PMb+/H5g5gNam6/ejhKhZyjDd102j0o5DLuXFiE3eVjx6G+gjeZTOi3cu7s8bOvoo34GC3vbjyKKIqYDJHyO1q1gkWX5VFZ18XRhnBe8ZVTsrlicjZV9d2s2h5eESrMsnD1tBxiTVr+8nYp/kCIWJOG2+YPQxThly9so9PuRaWUc+91IxmZm8Afvj+DjfsaMRvUXD45a1BshxuKpuMoXXfS8Qw0aQX0HCuV2pTx6fTURLpCeo4fJOmmH+M6sh3R78Hf2YyzbDOm8Vehyx2P3daBTKNDlzcex/41vUE2gCjZsZ+KwhgXaQ0tyDCMmHZWrzfKV0epkPO7B6azpbQJjy/I1FGpmI1qfrh0HEkWHYeOdVCcl8CCaUN4e31VxAuoyaDmkb9tldJCth5o5pf3TonY4obwbtQjd07iYHUHuelmZoxJO5+XOKgxjpyJvnAyoR4n9r2rIlanQx4XjoMb6Nr8FqK3d2dVkBM77zaMRTOQ68ILBen3/IGArR2ZPiZC/i/KxcvYgkS+cVUh726oRibAosvyKBoaP9DTumBRmpPQFUzqq30SZOjyxuGpPRTRz9NYOQCzu3AQRPF09fCRLFy4kA8//PB8zOes4PV6OXToEEVFRajVg3cVo63LjavHHyHt5XT7aGx3kpMWg1IR1tX1B0JUN3aTEqcnxqDG6w/yzH/2saW0EYNOxR1Xj8Bq74kofgT4yX9N4Pev7sEfCL+VygTIz7RwpLYLi1HNd24cxZSRqYiiSHWjDYNWKakoALRYXfR4AxHzs7t8tFhdDEmNkVbD/YEQx5ttpMTpMegG7wNNDPjp3vEh3sYKNOmFxEy6GkGuxHloE87yLSjM4Yrp9uXP03NsvzROUKpRpQzFW9eX+yeoNMTNuY2OFX/v+wCZHEPRjIjCLoDYy26ja9MbiP7wCoCg1pFx758I+XqwbX+fkLcH45h56HKiVdqDnXW763h62T7peO6ETKaMSuGvb5VitXsYV5iEXBDYUR65ff3rb03hf/69ix5vX0A+tjAxQk8/yumx7Vx+So0EGMdejmNvpDOkueQGYmdH/R6iRDmbiMEAzsNbCXS2oMufgDI2hbo/3xuRrmWefjOxM24ZwFkOLF8Uc57RinZ6ejp33nknY8eORa/vC8TuuOOOzxkV5YtItOjglF0tg05FQVZsRJtSIaPwpDa1Us6Pvjme43PyqKjtIjfDzGRzCtsPtXC0vhsIb323dbmlIBvCBjRjC5O469oiapvsZCaHV34EQSA3vX8Bw8lB9wlMehUmfWQwrVTILghpMkGhxDL1hn7thqLpGIqmS8exM5fQ3HSUkMcZNouZuaTfyrTo8+CuLY88USiIwhRPOMc2/P4qqDQYR81CmzMa+95VCIIM07grUZjCqTTR3OoLi/c/PRZxvG53HXcsHM73F4+hvbuHyUUp/N9H5f3GxZo0XD1tCO+sP0owJGI2qrntqmE0dTg5UNXBkFRTv/s+Shjj6MtwHNggSXVqh4xCO6S4X6AtjyodRIly1hHkCoxFMyLakhb9COvqFwnY2tAPK8Fccv0Aze7C4IwCbbM5HIQ1Njae08lEOXM27Wvk96/ultwb77muiB8uHct/P/MpLk+AXeWtOFy+fuOarU5eeza88i0T4MElY5k9LqNfv0sZdWoumd/7XzwNFSjjUlHGJEIwQOf6V6Q+2pxidFkjcJdv7hsoyDCOmoU6eQj2vauRqTSYp1yPXB+DXB9DwlXfGoCriXI2UZ6SQy2TyXjuzVK2HQxro//rgzJ+eOtYdpS1SAWScyZksH5PA2+tC+eOyuUCDy0JB+ZP/N8uyRF26RWFLLm8v97zpY5MrSPtzifx1JUjKJRo0gsRQ0F0ueNw92r4qlNyMY6ePcAzjTIYeH1NxUmpI/ncMPvSk8w812iziki/5w9f3DEKcIaB9hNPPPGZP/vBD37AH//4x7M2oShnxqurDnOyAMJrq8L61ierhRyp7WJUbjwHjobVTMYWJLK7vC+3OySGlUiigXZ/ZCotupxi6ViTU4z66B6C9g60OcXEzbkNQanG11aLY/9aZBodllm3orQko7QkR50ZL1JumpPH/5wUHM8ck8ba3X1yc84eP1sPNPO3H89hzc460hIMFGRZ+OZjfZrvwaDI2+uO4vL4pfNAWKv7htm5l6wV++chyORos0dGHMddcRcyjZ6Q34t52k3IVNrPOUOUS4G9FW0RKZQvLi8jP9MczdOOMqB8bX/ympr+zntRzj2eU+TyfP5gREHWCcYPS8Lu8uH2+BmVG8+hUwqyPL7+YwYaMRQEMYQgVyIGAyCTIQgDp8bg726l+aWfSznWzkObMJfcQLCjEX93G8qkbExj5mIqnoOvo4HODa8RtHegHz6VmEnXDAq5IzHoB0GGIIsGcV+HSUUpPPffs9lX2UZWsgmVQh4RaAPYXD5+8vwWjjXZkMsErp0xlEAwshSmxxvoJ3kZCIbCcpnRQPsLCbodNL74Y8lEqqd6H2l3/Q7VaaQ4o1w6HDneX7npSG1XNND+moihIO6qPWGvhrzxKGNTBnpKFxRfO9COMjDMLxkSISt2+aQspo5KZcuBJmmVLD3RwEsfl0sP+X9/VM64wkT2HGmLOM9gwrbrY7o+/Q8hnwelJRl/ZwsyrZ7YWbcOmGub6/A2KcgGEP0eHAc3hI1jfD0AdDRVIVPrsH7youRo522uRlCoJCnAgUAMBelY+ULYxEOlwTJz8YDO52IgI8lIRlLY2jkUEslJi+FYY1i1RyYTUMoFjjWFj4MhkXc3HmVMQSL7Kk6+77Lx+oL877t9Cjdzxmeg0yiJ8sW4KndIQTaAGPDhPLSJ2FlLBnBWUQaa4UP61zmcri3Kl6Ptg2dxlYXTJDvXv0LKkkfRZo0Y4FldOEQD7QuUm+fmk5ZgkKTBLhufgUwm8MBNxXy0tYZEi5bi/ESeMT5o+gAAIABJREFUf6s0YlyMQcVDS8ZSWdfFiJw4phcPHkkxX3s91tX/lI791nBNQMhtp2PF39BmF+FpOIJ9z6pw/vPUG9FmFeFprKRr05uEehwYi+dgGjOPgLObrg2v4m2tRZczGsv0m0Emo3vru7irdqOMTyN25lKpKPHzkOtj+rUF3XYpyD6Bo3S9FGSfwF2560sFtj11ZXRveRcx4MU0/ioMw0rw29ro2rAMv7URXf5EzCXXIwYDdG38Dz01B1AlDyF29jdQGMzYdn2Mo3Qdcr0Jy4zF4dSWfeGisZDHiXXVC2izR0ZX/s4SMpnAb74zlVXbjmO1e5gxJo23TtHxFUW4dkYOk0Yk09DqYMLwZMYWhm3bk+L07KtoY0iqKZrC1Yu35RgdH/8vvrY6tEPHkLDgPuQ6Y0QfucbQb5ygUtO5/lU8DRVo0gswT1uEoFBh2/YuzvKtKExxxM5aiiox63xdSpTzQI83wCsrD1N2zEp+poWlVxTw0ZYaBEHgpsvyomY1XxN/d6sUZAMQDGDb/n400P4SRAPtC5ipo1OZOjpVOt5Z1sKzb+wjJEJVfTeunv5pITlp5n6OkYMFb0v1Z/9QDOEoXU/3lrekJk/9EVLveJLm134lBb3epirkWiO2ncvx1IdX/H0t1YT8XmRqLd2b35L6+VprSb/79184L/3wqdj3rcHbEM79U6cXYho5C8fuFRH9VEnZ9NQc6HOfhC+1xRawd9Cy7HHEQLiI1VNXjuwbJqyrXpBMcbzNYWOOgLNTUl3wtR0nYGvDVDwv4kXF01h12lxxb/PRaKB9FjFoldx4WZ50XDIqNcKdLtakZuTQeMYV9k8JGT8sifHDkvq1X6qIYojWd/5AoCv8/bkrd2JV60i85nsR/XR549FkDJPucWV8Or7W47jKtwDgqSsj4LCiTsmlc/2rAPhaa/A2V5P53b8iyKM7BxcLf3m7lA17GgCobrAxaUQyr/wyumt31gid3rTGtutjArZ29MOmoEnLH4CJXThEA+2LiA82VUcUSO6vaufmufl8uKkary9IyahU5pdkD9j8vghNxnCQySF0Grt2mYKAIzK/XAz4sO9d2W9l2Xl4q/QAPoG7cicydWSxlK+1Bm9HA77mo4S8PRiGlSDXxyCKIj3H9uPrqEeXU4wqIZPU2x7HWbYJb3M1uvwJqFNzMU9bRPfWdyEURJWQiXnSNSiMsXSufQkx4ENhisc07srwXE9zTgBvSw09NaWoEjLx2zukIFu6loMbpCD7BK7KnQSd3RFtntoy5NpIR0/R60amjVwJRJCFv+co54zLxmfgDwRZv6eBuBgNi+cVoIrmXZ8RQWe3FGSfwFN/GE9jJc6DG5HrYzCNvQK5PobkpY/Sve19RL8Hc8kN1D1zd8Q4V/lWgq7I+yTo7MLbXI0mvfCcX0uU88OOQ5H/XnaVtxAKicgGgUnaxYAyNgVd3gTcVbvCDYKMgMsmLerYdi4n+eafoMsdO4CzHNx87UD7DPxuopwn5KdIjwkCXDUlm1vm5uMPhFCr5Ly68gjbDzWTEq/njqtHSLmmgwGlOZGk639A58ZlhLw94RxtayMyrYHYWUsJODr7mcFo0gpx7FkV0aZKyMSjL4t4yCpjUxFUGnxtdVKboNbS9t7T+FuPA9D16euk3fkUth0fYt/9MQCdwssk3fgwINL+wZ9BDGHfuRxzyQ0Yx8zDtmclYo8TX3sdrW89SfKtv8Cxfy2+1hoC9g6aXnmUtDufxLa9/znFoJ+2d5/mhOa2rnByv+9EnZCNS6WR7G7D15KCTK0j6OxLU5Eb41AmZsGRbSeNFogZOw+5Ro997ypkah2WmYtRmhPP+HcS5ctxtL6bDzcfQxDgnmuLGHoaffoon43cYEZhTiTQ3ZfPrrAk0fR/P4NeO2hn2SbS7niK5lcfw9t8FAjXUchNcQQ6m/vGmRNQxqVHOL8iU6AwJ5+fi4lyXkhN0FPd62wMkBirY9X24wiCwPTiNPTa6O7F1yXpxh/iLN9CoKsNZUIGbe+ctBMshrDvWRkNtD8H+WOPPfbYmXRsbGykoaGBtrY22tvbaW9vJzExkUmTJmGxDC6jgGAwSFtbG4mJiSgUl86ifaxJw6f7Ggn1vvxkJBnZtL+Row3djBwaz/ufVvPGmkrsLh9N7S72HGnl6qk5g0IV4wSq+HRixl+FedJCjKNnY558LTHjr0IVn44qMQtfWx3+ziaQyYmZuBDzlGsRRRFPQyWIIbTZI4mbdweqhAx6qvchBv0oYhJIXPhdtDljcFfvI+Rxho1kiufSU7lT+uwTq8n2vauQ/LMRCdg68NSVReRfe5uOIoaCeGvLpLaAvQPkioh8NtHvBQTse1ae5pzlBJ1dUl9/ZwvG0XPwtR4HRHS544id802UMYlSSooyLpWEq+9HN2RU+Fq8LmRaI4kLv4thWAnelmoCXa0IChWxM5egz5+INnM45knX9H6Pgy9l6GKhucPFD575lKMN3dQ02Vm/t4GZY9MxRB/0Z4wgCGjSC/E2VxN029HljkWm1uFv73tBDvU4QCbDdZKGfcjjQl8wmUBXC2LQj0ytI+Hq8D3hqT9M0GFFUGqIn/dfaLOLBuLSopwjslNN7DncSo83iEmvwh8Isbm0iV3lrWwubWTO+MzojtLXRJDJUSdlo80agaBQYt/1ccTPVUnZGIaVDNDsBp4vijnPKAp95pln+Ne//kVcXF9RgSAIrF27liFDBpdqxaXM6LwEnv/RbHaXt7K3ok1SF6lpstPa6e4nJ9ZiddPQ5pAcIgc7MoWK5Jt/TMBuRVCopAKp2JmLiZm4gJCvJ2wuA+gLJqLNeYFAdxvKuFRJ1i7jO3/G39GAwpRAz/ED2Hcuj/yQUPCkgPhEWyAsj3cSYiiI6O9vCBQOrE9pCwZOe85+bYjEzb0Ny8xbEAN+aeXZOGoW+oJJBBxWlHFp0otRxv3PE+hqQW6KR6YIu3WmLHmUgN2KTK1Fptad/ouMck7YXNqIz993j3l9QbYeaOL6WVHDjC+DOjU3onaiY9U/+3c6zUaq3GAh84F/4GuvR5WQgUylASDt9ifw29qQa41Rre2LkMKsWP7588tp7nCxu7yFfy3vc2ZtsbrZtL+RK6dkD9wELzKUlmQMI2fiPLgRAEGpwTz5ugGe1eDmjALt999/n9WrV5OUFC3aGeykxhu4ZoaBdzYcjWg/VG1l1th0qur70im0agUJlgsvGDudUohca0R+Sj6yTKlGlRC5gisIMik/Wjd0LMr4dPwd4UIaQa3DNGEBIb8P54F1J0YQM2khIZ+Xjo//Kp3HOGoWpvFX4jy0UVoJV8alYi65HveRHeFVd8IW7DET5iOe5pxiKET7B89K5zQVz0Wm1p02QJaptajUkQWMgiBDGZvar++ZKKlEOftYjJrTtKml/99b0caanXUYdUqun5VLcpz+fE7vgiVm4gJch/vyrXUFk4iZtBBH6VppR0hQaTCOnIlMpUGTltfvHCdewKNcnCjkMjKSjBHStSeIpreefRIWfg/DyJkEutvR5Y5DYRxcWQ2DjTMKtFNSUqJB9iDHHwiycW8jHbYeSkamkJZgwGrry+uNi9Fw2/zhNLY7qarvxqBVct+No9GqL53UmlMRFErSbn8Cx8GN4WLIomkoYxJJWPBtdEOL8bXXocsdJ1VUKy1JuI/tR5WQiWHENASZnLS7fofz0KfINAaMoy9DrtaTevsTOA9uIOR1YyiagdKS/NnnjE2h59h+VAlZ6AomDuTXEeVrMmNMGp/srKW8JpxilGjR4vb48fqDVNR28tg/tkmbGNsPNfO3n8xFo7p0778zRWlJJuM7z+E+tg+5LgZN5nAEQSDtjiex71+DGPChyRhG16Y3CPZKfBoKpwz0tKMMALPGpvPuhio67eGdxXizlmmDSML2YkEQBHRDRg/0NC4YBPEMXveee+453G43c+bMQaPpW7UZMWJw6ih6vV4OHTpEUVERarX6iwdcBDzyt63sr2wHQCEXuH/RaP7zSSWtnW6MOiU/WDpOkhGz2now6lSXXN6aGAqGdbQrd6GMS8Uya4m00hX0uBAUSikFAyAU8EEoeE63m0O+HhDFaJrHRYIoiqzaXsv/vnOAYK8E0PAhsaQlGPhkZ11E35/fMZFJRVGHtS+DGAwgyCNfTkJeN3XP3xfO3e4lefHP0Q0dc76nF2UQ0O3wsmFvAzIBZo5NJ8ZwacQAUQaOL4o5z2g55Z133gFg5cqVUtuJHO0oA09Nk00KsgECQZFdh1v5+0/m0tTuJDFWJwXVLVYXW0qbsJjUTBuddkkF291b3qbr09eBsJa0t/U4aXc+Sfv7z+I6sh1BpcYyYzHmSQvp3voOXZvfRgz4MIycScKC75x1+/LO9a9i2/EhYiiIcfRlxF91b9Qi/QJHEAQOVVulIBugvKaTlPj+aSIWk4ZjjTasth5G5sZHV7c/B3f1PjpW/J2ArR1d3jgSrnkAuSb8nfYcPxgRZAM4y7dEA+1LFLNRzXUzhw70NKJEkTijv+zr1q374k5RBgzZaVRDAoEQj/xtKweOdpCdYuLBxWMQgf/33GapYGv1jjqeuG/qoFIdOZe4KnZGHPvb6+ja9AauXkk80eehc82/UcQkSCYXAM4D69GkF34pC/iAowsx5I/IDQ153QQcnSjj0vDUldO99R3pZ479a9BmFWEomv5VLy/KAHG0oZuXVxym0xZ2hpTJ+veZOTadI8e7aGx3AmGt7TU761ix7TgQNrV54v5ppMb3dzy81An5vbS99zQhjwsAd9VuujYuI/6Kuwk4uxFO2oU6QTQnO0qUKIOFMwq03W43Tz31FJ9++imBQICpU6fys5/9DIMh+lAYDGSlmJg4PJmd5WHhfpVChsvjp+xYOFf0eLOd372yh8JsS4QqQtkxKxW1XRRmxw7IvM83ytgUfK010rGg0hLobu/Xz1NzoF+bt6WazvUtuI/uQ5WYGbY8N8XhaTqKs3QdMo0e0/irUBhj6Vjxd+z7PgExhC5vPEk3/DfO8s10rHwB0e9BGZeG/jRSSN624xiIBtoXEl5/kF/8fRt2V7gg9niznUVz8tCo5Hh6VX7GFiYyJj+R5x+eTfnxTkw6FXK5wHee7FvA6LR7eWf9Ub57U/GAXMdgxt/ZLAXZJ/A2HaVj5T+w710NYgiFOalXe1tElZyDaULUGTBKlCiDgzMKtJ944gmCwSDPP/88wWCQ1157jV//+tc8+eST53p+lzRuj58PNx+jsc3JpKIUpo4KK0ys211PaVU7Q9NiuKpkCEqFjJ/ePoHth1po7+5hclEy/++5TRHnamx3kptxevOMtk43H24+Ro83wOWTssjPtODxBvhw8zHqWh1MGJbEjDEXvmV37Oxb8bUdx29tQlBpiL/ibgSZPEKPV1CoMIy+DPv+NREOlUFHV4Tlub+zmbgr7g4bafRarjsPfUrcVd8K63D34q7ajW3vKro2LEP0h4tT/dbGsN28IJNMOAB0OdEg60KjsrZLCrJPcLzJzncXFfPB5mPEx2i49/qRADR1uNha2kQwJFKQ1b9K/9TzRAmjik9DrjdHGFApzIlhbfpeAt2tWGbfGnZdTRpyyezSRQnnZHc5PGQlmz7TDdIfCFJZ101ynI64mKjE45fBvmcV3dveRRTFsK/FhPl46g9j/eRF/LZ2DMNKiJt3O4I86hfwWZxRoF1aWsoHH3wgHT/++OMsWLDgnE0qSpjfvLiTA0c7AFi/p4Hv3jSaboeXV1YeAcIBd1VDNz9cOo7qRhu1LXYyEo0kWHSMyIln0/5G6VzZKSaunzmU7YeaJT3tETlxZCQZuO+pdVKV9tpddfzugRm8uvIIuw+3ArBhTwPdTi/XTL+w896UlmTSv/UsfmsjCmOcZMkecFhx7F+LTGskduZiNKm5JN34MF2fvo7o92AadyW23SsizuVtqsK+e4UUZEPYsEayqT0JX2utFGSfIOi0kXTDD+na8g5i0E/MxAVos0eeg6uOci5JTdAjlwkROdkqhYw/LNuDKEIlUN/m4Jf3lvDws5/i8oT/vazdXUdKnI5mq1saN3dC5vme/gWBIFeSdNP/w7rqn/i7WtAXTEKVmImrfEtEv5DHhTo5Z4BmGWUgeGd9FS+vOEwgKJKRZOBX95YQb44MpGtb7Dz6t6102r3IZAJ3XD0imsN9hngaK+lY+Xfp2Lr6nyhjU2l7/2lCPeE0OPuelcj1ZizTbxqoaQ56zijQDgaDhEIhZL3Jh6FQCLk8WrR1LmntdEtB9gk+2VlHlz0yYPt0XyPjChP542t7Jemwy45k8K3rR+LzBymtaicnLYbv3lRMRpKR5/57Nh9vrcHu9HH5pCz2HmmXgmwIF1Ku2FojBdknWLOz7oIPtCFcrKaKj1ydN0+5DvOUSMF9ff4E9PkTpOOemgMEulqkY5nOhFzff4dAm1WEs3T9SQY3AsbRs/E2VUp63RA21NEXTkZ/Gtv1KBcOcTFa7rluJP9eXobHF6RoaByBoBjhRVTf6uT9jUelIBvA5w9RMioVmUygo7uHmWPTGVcYlVD9LDRp+aTd2beD6u9uxbruZQie+E4F9HkTTj84ykVJp93D/318mFDvS259q5PX11Ry/6LRVNZ1IQiQl2HhtVVHpGdcKCTy0sflzJ2QgUHXP7c/SiSek5yPT+A6slUKsk/QU3swGmh/DmcUaE+ZMoUHH3yQJUuWALBs2TImTZp0Tid2qaNVK1DIBQLBvie2URe2l23r6unrp5Lz0eaaiAf7+j31XD9zKMGQSCAoEgyK0opbVX0372+sJiTC2t31zJ3YfxXNbFSjUsjwBfrSGoyX+B+l2Dm34bM2EuhqQabRkzD/26hT83CVbw5brwO63HHoh5Ug15vp3vouYsCHafxVaDOHk3zLz+jauAy/tQldwcR+gX2UC5fi/AQyk4ySGZRW3X8RItbU38wmMVbH/JKos+5XQWlOImXxz/vuswnz0WQUDvS0opxH2rvcUpB9guYOJz9+fjNlx6wAFOcl4PJEuvr6AyHsLl800D4D1Kn9XW21OcU4y7ZE7NKqU6Lut5+H/LHHHnvsizqVlJRQXV3N66+/zubNmxk9ejQPPvjgaT3dBwNf5Dt/IaBWyQmFRA5Vh/9g6DQKHrhlDIVZsWw90EQoJCIIcOfCEdS1Omjv7gu+ZQJ0dPew63ArIVHEavNQdszKgqlDeOrl3XQ7+3JBm9odjMpNoNkaLjZKidfzvZuK0WmVlFaFCwW1ajnfu3kMiRegi+TpcB8rxbZ7BQF7B6rETElSL+T3giAgCH2yEWIwgBgKoDBYMI2/CmPRDCwzbkGdmIVMrcU49nJUSUMwjr0c89QbEQQBpTkR48gZGEfPlpwpBbkC/bASTGPnoc0cjiDITvt5US48fvXCdip7g+y2rh60agWBkCgVQ86bmMmtVw7jYHWHdJ/mpMVwz7UjUSqiv/uvitKchCYtD11OcTTt6hLEYlSzfk8Drp6+QHpETiw7y/p2Y1s63RTnx1PTZJfa8jLMLJqTf17neqGiNCeBIMPbfBRBpsBcch0x4+ejik/HU38Y0edBlz+B+Hl3ICgu3RztL4o5z8iw5kLjYjKsqW2x09TuZOTQeAw6FTanlxfeP8iR2i6K8xO497qR7Kts5zcv7pTe7q+cks3OsuaIlBCAZY/P56GnN9ByUl6oQi7j9d/Mp6q+mx5vgNF5CdLDv6HNQX2rg6Kh8RfNirajdB3ty5+XjvXDSki89gHaP/pfnGWbkKk0WGYuJWb8lXTv+JCuTW8g+n0YR83qp3MthoJ0rPg7jgMbkKm1xM5aimns5RGfF3B00vbe03jqylFYkkm4+j40aQW0f/w3nIc2IlOqscxcQsyE+eftO4hy9giFRK59+IOINrVKztPfn8m7G4+SHK/nhlm5KOQyRFHk0DErwWCIkbkJyD+jcCvKFyOKIdrefwZXWbiQWZNVRPItP0WmvLD/3kf5crRYXSxbXUFbl5sZxWk43H5eXnE4os9d14xAq1aw/VALqQl6brosH7Mx+u/kyyD2CgNEPP9EETHojzB5u1T5WoY1S5YsYdmyZYwZM+a0Vdx79+49ezONclqykk1kJZuk4ydf2s3B6nCqwspttSjkMu66pohrZ+Sw90gb+VkW7r2uCLvLy9YDzdK4jCQDSoWMsfmJfLztuNQ+JNXEPf+zBr1WyTeuLIxYYUtPNJKeaDzn13g+se1eGXHsOryN7sRsnAc3AOGCKuuqF1DExNO55t9SP8f+NajT8jAV92lpO/Z9gmP/mvC4HgcdK/6ONrsIZWyq1Me65t946soBCHS10PbenzCX3IDzQFjaLeR1Y139T7RDRvXLHY8y+JHJBAqzLByp7ZLaspJNPPTMRqnouKzayi/vnYIgCIwcGj9QU72o6DlWKgXZAJ7aQzgPbMA07ooBnFWU801ynJ6HloyVjhvbnfznkwr8vWmPapWcKSNTSYrVccXk7AGa5YXPqUZqvrY6Oje8SsDWjn5YCeapN0R3Zj+Hzw20n3nmGQCWL1/e72cX4UL4oMfV45eC7BNsP9hMICiycttxAGpbHCjkMr51/Sg83iD7q9rJSTWxaE4+dz/+Cd1OLwIwbEgsWSkmVmwNj+u0e3jq5d38/SdzSYy9OFJETodMecrbt0yOr6P2lF4i7qN7+o31NlVj8/vw1JWhTs3D117fb5yrag/+9vcI9jgwjr4Mb3N1RI+goxNPQ0X/c7cciwbaFygPLR3Ls6/vp6K2i6KcOLQaOZV1fdKQeyvaONrQTW766eU1o3x5Arb++vd+W9sAzCTKYCItwcCSeQW892k1AnDjZXkkXcTPs4FADPpp/s+vCTrCPh2+tloEpRrzpIUDPLPBy+e+giQmht21fvGLX5CWlhbx3w9+8IPzMsEofWjUCiynbHmlJhhYt6suom397npMehULp+fw2N2T+cP3Z7Jxb1iiD0AEKuu68XgDEeOCIZHyGus5vYaBxjxtEcj73i9jJl2NLmdsZCe5AuOoWWGd65MIOqxYV/8T15HtdK57GX93S+Q4mZzuLe/gKF2Lu3InrW/+NmJ1G0AZl4oud9wp4xRoM4d/3UuLMkCkxhv47f3TePephfz62yWolf3XL04t2ory9dDljkM4OU1EkGEonDJwE4oyKKiq7+LllYexu3zYXD7+/VE5tc32Lx4Y5YzxthyXguwTnG5hKkofn7ui/cADD1BTU0N9fT0LF/a9rQQCAVSqaF7O+UYuE7h/0WieXrYXlydAvFnL3dcW8fiLO2nr7Mu7jjGo+f4fN1DX4gBgWHZshM4vQCAYIjVeH9EmCDD0Il910+UUk/HtP9NTU4oqIQNNeiGiKBKwtePYvwaZxkDsrKVo0gpIuuGHdH76OqKvB9PYK+jeEZmL622owjx1EfZ9nyDX6NEVTsK29d2IPjKNHsPImbir96FKyCD+intQJWQQsLXh2LsamUaPZeYSFKZoSsHFwoJpQ9hc2kQgGN6+HpETR35mf4OaKF8dhSmOlFt/iW3H+4gBP6ZxV55WISHKpcXuw20RClyhkMieI61kpZg+e1CUL4XSkoQgV54kYUt0N/YL+NxA+0c/+hGNjY088sgjPPLII1K7XC4nNzf6R20gKMyO5YdLx2HQq8jPMCOXy7hz4Qh+/8oeAsEQSoWMUbnxfLKzb5X78PFO5k7MpLKuL4+0INPCTXPy6bB5WLurDq1awa1XDiMj6eLKyT4dSnMiyjHzpGNBELBMv6mfDuipOteOgxsIuftWR+Q6I57GCkJuGyGPk6Czm1NRmpOInbWkX7tl6o1Ypt54Ni4nyiCjMCuWZ384iy0HmogzaZgxNvoQOhdo0vLQ3PDfAz2NKIOIjCRDv7b0S+CZdj6R60zEXXE31jUvIvo8qFPzsEyLamh/HmekOnKyWc0J3G43Ot3gzH26mFRHTmbLgSb+8Ooe/IEQGpWcn/zXRMYWhtN7uhweqhts5GWY+XhLDa+tjswDvu/GUaiUcrYfaiYtwcANs/Mw6cO7Ev5ACJlMiKogfAHuo3toffv3iAEfyBToCydH2LcD6Aom467YDoAyIZPUWx9Dro8ZiOlGGeS4evyoVXIU8mgRUZQoZ4NgSOS5N/azbncdCAKXT8rivhtHnVbMIcrXI+TzEOpxoIhJGOipDDhfS3XkBOvWrePZZ5/F7XYjiiKhUIju7m727dt31icc5bN54b2DUjW1xxfknx8eIjdjGstWH6GmyU5xfgLF+QnMHJvO2xuOSqoHOrWC7YeaEQSBq6flMH5YpAPdV9HyDYVE3v+0mh1lLaQlGFh6RQFxMdovHngBo8sdR+b3/oa3qQpVUg5dG5f162MYXkLs7FsJ9ThQp+VFK7EvUT7ZUcuGvQ3EmjQsvryAtIS+lTZXj5/fvbKbPUfaMOlV3HNtEbPGZQzgbKNEuTBxe/wcONpBUqyOIakxyGUCD9xSzOSRKcgFgXHDEqUgu7KuC6uth9F5Ceg0l67m89lCptIgU/U34orSnzMKtJ966ikefPBBli1bxj333MOaNWvQ6/VfPDDKWSMYEulyROpid9o8/O7l3ezvNZYpO2bF1ePnrmuK+MMDM1i5/Tg+f5D1u+vZWxHus6+ijd9/fwZ5GV8vZ/Tt9VW89PFh6XOP1nfzzA9nfa1zDjShgA/b1vfoqS9Hk5qHeeqN/f6QyHUmqZhRlz8BR+la6WeCSoM2exRyXXSr8lJmw556nn1jv3R8sLqDf/x0LkpFWCLrzbWV7DkSVsiwu3w8+8Z+xhQkEmO4eHbfokQ519S22PnJ81twuMMGbNfMyOG2+cP5+V+3SHKbRUPj+NW9U/jbuwdZtT2sLhVjUPHb+6dddNK1UQYvZ7TcptVqmT9/PsXFxajVah577DE2bNhwjqd2aeEPhPjPJxX87K9bePHDMty9trHBYIiyY1bau9xMH5MWMaZkdKoWPEKoAAAgAElEQVQUZJ9gy4FwEZbL4+eGWXnkpJnxn2TjHhJh28E+fe1QSKSitpOmDmfEeepa7FTWdUXIOLZ39XDgaDtef5CtB5oi+h9rsvU7x4WGddU/6dr0Op7jB+ne+g7tH/0FURSx7/uElreeonPDMkLecNFpT105rortaIaMQpWcgy53HClLfxENsi9yjjXa+NeHZby9rgqn23faPltOuTesNg9HjvfVR1Q32iJ+7g+EaGi7sO+dKFHON2+uqZKCbIAPNx1j+aZjEZr2h6qtfLi5RgqyAWxOH++sP3pe5xrl0uaMVrTVajU+n4/MzEwOHz7MpEmTojlPZ5l/fnCIj7bUAHDgaAfNVhd3X1vEz/66hRarG0GAa6fn8I0rC6moC+v1Lpg6hB2HmrGdZKluMWr41hNraOvqQSbAzNMUYiXFhncjbE4vP/vrFmp71Unml2Tz7RtG8btX9rBpfyMQLpr81bemsHpHHS8uLyMUEokxqBiSGpl3rFbJMZ/lFblQwIe7Yichvxd94WTkmvC8e44fxG9tRDt0TNgi9izhPLw14th1eBtdCVl0b3wNAHfFDrzNVVhmLKb5lV+AGE7jkevNpH7jVyCGcBzYgKBUo88bj6BQIoaCuKv2EOyxo8+fiFx3fqrfxWAAV9UuQh4X+vxJ0ReAs8CR2k5+8v/Zu+/Atspz8ePfoy1ZlveeceIsO3uRRRYQCJBSoCFsCqEtLW2BtvS2F1q6bumkLeUH7aWlvcwCDVAChED2Itt2lu0kjuO9hyxb1jy/P5TIURxIgDjyeD7/wHktHT12LPk573nf53lqC96TF67r91Tyx+8s6LW34dT76xRFgYQYM9WNDnYerCPWFnqXJNKiZ3i6rOP/PPweF50lO8DnJWL0JWiMFlTVj/PoPjxt9Vhyp1zQzwoRfh1nXOiqKjS1O3s9rqm199iZzxWiL51Xor1w4UK+8pWv8Ktf/YqbbrqJPXv2EBMj5aoupC2F1SHHOw7UEhlhCLZLV1V4a3MZz3x/ERqNwsZ91Rwub+HGhbm8uLqYbrePmEgjtghDsLqIX4UNe6uYNT4l2CVy8qhEFkwJJN9vbToWTLIB3t1WTlaKLZhkA5RUtLJqy3H+9UFJsBZwu8ONz6eSGGuhoaULvU7DiqX5F3Tdm+r1UPOPH+KuD1x8tG58hbS7f03b1tex7znZ3VGrI+Wm/8Y8bPwFeU19VDzuhp5qLTpbPJ0HN4U8xllWiNYaG0yyAXydbTgObqF1y6vB+qLGlBGk3PEz6v/1PzjL9wPQsu4F0u76JfrYlAsS78dRVT+1Lz5Gd2VgaU/L+hdJ+/Ljkmh8Tu9vPxFMsiHQHKqwtIFDx1vYXVxPRlIkdy4Zyw0LRlB4pJHyWjsajcLyy0bS0NrFj/+6Pfj8jEQrLo+P+GgzX742D5PhvD6KxVn43d1UP/d9PE1VALRufo20u39N8wd/x3Eg8P5tWft/JN/yI6lXP4hcNj2TvSU9TYpGpEdx7dwc1uyowO0J7E8yG7UsnZfD/mNNlJ+sp60ocPmMrLDEPJB4WmpQdEZ0trhwhzLgaR977LHHzvWgqVOnMn36dFJSUpg2bRoADzzwAGZz/9z85vP5aGhoIDExEZ1uYPwB21xYQ4u9O3gcG2XCYtRR3dgZ8jgFeG3tEVo7XFQ1OKis6+Dp/1rE3Alp3HH1WNbtrqD+tJraAA/dMoUbF+YyeVQCJSdaef69w1TWd9De6Q5JtAFS4iJCygACJMVaOFIZWrrOFqHnye8u5JL8ZG69cgx5ORf2zdhZshP77neDx6rbCRot9l3vEmi5A6h+fB0tgeYyF4A+Lo2u0p2oPg+K3kTCNV/H3ViJt63nw1wxmLBkTwgmsadoLDZclYeCxz5HC4rOELKGW/W6QVGwDJ90QeL9OM7jRbRt/XfP63pcKFodlpwJffq6g11BaWOv94FGA6u2HKfV7qK81s6BsiaWzs2htrmTmkYHyXEWrpyZzZsbj4UsD7F3uvnjdxZw3bwRxEf3z8/RgcJxcDMdBR8Gj/2uTtBq6Th1QQ6g+vE7O7DmzQ1DhKIvZCXbyD1Z4nZGXgpfu2E88VFmpoxOxO9XGZEezTdunEBGYiRzJqRh1GtJiYvgrmvGMmW0TDp8HL/HRd2r/0PzB8/RvnMVXkcLEblTwx1Wv3aunPO8stAVK1bw7LPPApCXl0deXh7Lli3j1VdfvbDRDmErlubzi+d20NHlwWTQ8tUvjsfr9bPzUH3wMclxluBV+SlN7d0crWzjo4N1lNfYgyX7TkmNjyAr2YZep+Gnf9tBZX0gsf5gZwVTT5YGPCU60sgXLs1h7e4KuroDXSM1GoUrZ2ZT1eDgcHlPN6h5kzPQapS+a3Dj8/YaUr3ukJlkAL/HTdu2lXTXHMWcORbb1KtQNNrP9JLm7HFkfuuvuBtOYEjIRGO0oLXYqHvl5/i7O0HRELvgNqxjZtFZsh1Pc2AtrmXUjLOW8PN7unuNqd6+uWXZXVVM+653URQNhpThZ3ldz1meJT6NpXNz2LSvOnjbeeqYpF6J97Gqdl7+oIS3NpUB0NHl4RfP7Thrw5rzqKwqzsNZf7fP8j5Tfb6LEI24mKaNTWba2OSQsRHp0XzrptDJDFuEgVsWj76YoQ1YHYXrcJYVnjxS6dj3Ada8OZiz8sMa10AmnSH7ibycOP7+6BWU19rJSIwkwnxyGYYCG/dWERdl4oaFufx73RH2H+tpk67TanhpTQklp20AmTw6EbfHR3mtnZqmTr7yPx+w4gv5wST7lOqmTn5413TW7qrAFmHghoW5JMVF8Pg35vDmxmN0u71cNTObkZkxPHL3DP697ghVDQ6m5yWz+JK+vfVmGTkVXVQi3vbAbLJitBA1bQleexNdJTsI/nA0GlrWvwgE1lB77U3EzLuZ1g0v0VVWgCExi7hFd6CzxWPfuwb7vg/RmixEz12GOXMszopDtG15Hb+rC9vkK4icsBCdLZ6m1f+Lu7ESy4jJZNz3Z7qrS3Ee20tHwVpcNUdJXv4ontZauisP4zxehN/pQNEZgom0LiqB6JnX0XVkD57GwHIURavHNulynOX7adv6b/weN7apVxKZfyme1jpa1r+Ap7kaS+40YuYuQ/X7aFn/Is7yIoxJw4hdeAe6yBjad66io3AdGouN2HnL0Zis1Lzw456Lk+KP0MUk4W0NXKQpeiO2SZf16b/XUJCaYOUvP1jErkN12CKMTB6VyC//uZPK+p6ZaqtZz7HK0M2OTpePCSMS2H+sObj8aua4FJLjpHLT+WrfuYqOog1oI2zEXHozprRcuiuLadv+Bn6XE405Er8z8PmmMUcSNf1aPM01Pa2hFQ1R05aE8TsQYmDwttb1GvO01kmi/Tl8YsOaqqqqYGfIn//858HxU50ho6L6dgOPz+fjrrvu4uGHH2bcuHHn/bzB2rAGoNXezU//voOjlW2YDFpuWTyav799MOQxSbEW0hKsIevXEqJN+PyELE+ZPSGV/7pj2kWL/dPyddnpKFyH6nFjHT8PfXQSqs9Dx/5NgYR0xGRqX/op+HtmqjQWG9a8OSeXmAQYU4YTPfsG6l//dXBM0RtJvetxap77fsgsc/JN/03LhpeCa8MBomZeBz4v7TtX9ZwzbSTRs66n/rXHewLWG4iavBitxUbkhEVoI6Loriymee0/Ub1uomffiCl1BJVPfzOkfW3yLT+m+f1n8TT3rI2PnnMjvk47HfvWBMdMGWOwTV5Mw1t/6Pk+DCaipl0dslQEIHrecjR6I/7uTqz5l2KIC61YIy6M6kYHP332I2qaOrGYdNz/pYlU1XeENIzSaTU89+gVtHZ089GBOlLiLMyZmCaNas5TR9EGGt9+MnisMUWQesfPqf776e9dhcgpi9FZY4gcPx+dLR7V66HjwCa8bfVEjJqB8Sx3eoQQoZwVh6h9/kecWqKp6PRYxy9AozcSOekKDHGp+N3ddBSuw9veSMSYmZjSRqL6fdj3rKa78jCm9NHYplyJoh0YS3c/r8/VsCY9PZ309HTef//9sFQZeeaZZ0hMTDz3A4eQGJuJJx6YR11zJ7YIAwa9ltfXHcHe2ZMspsRF9Fpi0tjWzaN3z+AvbxTR0OpkdFYMK5b27ytUrcVG9MzrQsYUrR7bxEVA4Na71hyJr7Pn9r02IpquI3tCnuOqPYbjUGhFEdXjwr73/V5LORwHt4Qk2QBdR3ajnrGUxVVdiuPg1tCAPW6MqblYx84GwNveSO0rP0N1By5uGt9+kujZN4Qk2QAdBzaGJNmB19yDrzN0ZrS78jDaiNClOqq7G7+r9656Q2xqMA7Rd9ISrDz9/UXUNDmIjzJjMurodns5Ud/B9qIabFYj9yzNJzrSSHSksVe1HnFuXUd3hxz7uzux7znzvauiMZiImXNjcETR9XxWCCHOjzlzLEk3fA/73tWAhu7qEjr2BiZ87AVrybj39zS8/STdJwITfO07V5G87Ad0HdsX3FfVeXg77qZqEpZ8NVzfRr9yXpcbS5cuPev422+/fcECefbZZ9mypaed9c0330xubi5+v/8TnjV0nX7b+b4bxvPHV/bR7fYRF2Xi7qV5/GdTGR/u6qmgMSozhul5yUwdk4TT5e1ZmgLUNXdi1GuJsQ2sLk+KohB3+V00/OfP4Pei6AzELbwd+57VeNt61rZrI6IxJA3r1S7dlD46dMMUYEjKQnPMit/ZsxxAH5uK6vOE3FLTWGwYEjPpPBTydPSxqcH/dxRvDybZEEjuve2hdc8BTMk5dJXsRHX1bGLVx6ehtUTidPSsi9fa4jEkZdNZvP30nwK2SZfhbqyg+8QBAMw5k4gYNeNsPzLRBzQaJaT5hcmg47/umIbb40On1aDRSCnUz0MfnwGE/s4bUkfAnjMeJ1V1hLggIkbPIGL0DNp3rsJ5vKf5lurqon3nqmCSHRj0Y9+zGmdF6B9Dx/4NkmifdF6J9qOPPhr8f4/HwzvvvENGxoVtGbxixQpWrFgRPH7ooYewWq0cOHCAiooKfvOb31zQ1xss/H6V5vZuhqdHEW01cvfSfBJjLNx7XT5arUJBaSM5aVGs+EJg9lqjUYJJdrfby/88t5N9pY1oFLhmTg73Xnf+S3T6A2veXEyZ+bjrj2NMzUVriUQXk4ynrR5PUxUai42Eq7+OKTsfV+WhwJpNrY6YWTcQmT8Xd0M57TveBr8Py4gp2KZciS4qkcZVT6G6nehikolbdDuq309dSy3e1jo0pggSlnwN87DxdJ/Yj/N4EWi0RF+yFGPysGBsWkvv2Utjai4avZH2Xe+C6scyaga2SVegjYim6d1n8Lu6MCRmErvgVlR3N3Wv/SrwmhYbCVffhyl9FN1VxTiP7UPRGYi59CYMiVmk3vYTXLVloCghMYjwMeg/26ZcESp6xrW4Kg/jLN8f+J2ft5zIcfPpPnEIR9F6QMU8fBLWC1R9SAgRoDH13keiMffuyaDoDWgjovC6e+6uai5Sz4iB4LwS7enTp4ccz5o1i+XLl3Pffff1SVAAv//97wF48sknmT9/fp+9zkD3xoaj/OOdnivJupYu/vDgfFxuH/k5cVw5M5sRJyuDvLHhKO9/VE6EWc8ti0dT1eBgX2lghtWvwn82lzF3Yhqjs2PD8r18VrrIGHSRPVUdDHGppH/lD3jtjegiYlB0gQuL5Jt+iLejBUVvDDa/iVt4O9GXXIfqdQfrhfq77KieQLt7X1cHPmcn+thkDIlZeB0t6GJS0UUnoehNGFNz6a4tQ2uKwJAcWAPasX8jrRtfxtfdhTYyDl9HYPOqMW0k1rw5aPTGwBISryf4mtaxs7HkTsXX2RYyM5dx35/xtjegi4xF0Qa+j5Tlj+B1tKLRm9AYe0rDGVNy+uTnK0Q4aYwWUm59DK+9GY3RjMZoAcAyfCKu6mJUrwfL8MlodLJBX4gLKWLMLNp3vYu7LlBFyZCYSdS0q/G01ODYvxEARW8i+pLr8NqbqX/z94FN+RodcYvuCGfo/conbob8OC0tLdxwww2sX7/+nI91OBwsX76cZ555hvT0QKOUt99+m6effhqv18udd97Jrbfe+ukj/wSDeTPkmR58YgNHq0LX8n7n1sk8+WphsGj/sstGkp1i49fP96x11Gk1zJucxtpdlaHnu3kSC6dm9n3g/ZTf4+LEH+4J1O0+yZw9Dq0tDkfRhuCYPjaVqFlfpGnVUz1P1mhJueXH1L74WEgZwujZN2LOGY8pYwyKIhvgxIXR3O7k3W3ldDk9LJqeGbygHgrcTVVU/fXBkPdZ8vJH+rxGvei/6po7Mei1vTqvis9H9ftwlhWgqiqWnIkoWh2qquIsL8Lb1ohlxJTgRJfX0Yar5gjGlBEhk1+D3efaDHnK6aX9VFWltraWm2666ZzPKyws5JFHHqG8vDw4Vl9fzxNPPMHKlSsxGAwsX76cGTNmMGLEiPMJZVDw+VUKShvocnqZMiYx2FGxrLqd8lo7E3LjiYs6vyYWibGWkETbaNCyZvuJYJINsHL9EeZODK064fX5iY0M/UAyGrRMGvXpNp96fX72HK7H51eZOiZpwN8uV93dIUk2gNfRiue0Nd8Q6JrVdawgZAy/D8ehrb1qffu67Jgz8/okXvH5OV1e3thwlLLqdiaNTOCqWcPQaBQaWrsoKG0kMynyot7lsXe62VtcT0KMJdgIqrSilVVbylAUhWvn5pCeaOW7f9pMU1vgd3X1Ryf47bfm9l1d+37GWb6/1/vMWV4kifYQ5PL4+OU/drKnuAGNAlfNGsbXrr8w3YIFKBotlhFTQscUBcuw3g3QdNZodCP7byWzcDnvNdoNDQ20t7czatQoIiMj0WrPnVC9+uqr/PjHP+bhhx8Ojm3bto1LLrmE6OjAH4TFixezevVq7r///s/4LXy8AwcOXPBzfl5+VeX5dU0crw8sTYg0a1mxOIGCsi7WFwUqhWg1cMu8eIannPvKfFKGn4NHtbR3+dBq4LIJkew7FlpxxOtTwW3v9dwEk50bZsWy+6gDo07D3LxIykoP9nrcx/F4Vf7+QQO1rYEqGvE2HSuuSMRkGNiztta4bPTN5cFje8xwtB2NGOgpl+gzR9GIldNXsKlAjRKDlUAHz1PqPAYq9pyxc0v0Gy9vaqKkKrBpdcfBOg4dKScr0chLG5rwnczlZo+xcvmkvk9i61rdPPdhIy5P4EbjxBwLc8dG8vR79XhPXjtv3lfF5ZOigkk2BC54X3pnD0umDo1EW9vm5swVoNVdCuXyPhtydpY62FMcqDzlV+GdrcdJNDvIShzcd7PFwHFeifbatWt58cUXsVqtKIqCqqooisL27ds/8Xm/+MUveo01NDSQkJAQPE5MTKSoqOhThn1++uPSkcLSRo7X95Ry63D6KG+LYOuh05I4P+w+7mfZNVPw+VU0CiHlFVVVRVUJVjO4fJ6fspp2EmPMRFlNrNlxgidf7ZltnTY2iW/eNg3fK/vYUlCNwaDl5stHc9WCwF2Eu85yzmAsPj/aM+r9en1+dFoN6/dUUtva87002b20+uK4ZsrAXivszxtN2/Y3cTdWYBk+icjJi/F1ttH4nydxHi9En5BB6tVfx5gynGZzoJOWYjQTO/9Whk+6DHtyDK0bXsbvdmKbfAXDFt0hS0b6qa5uD6UvvxsyVlzjo8WpBpNsgB2lndx/y1wa25zUNXcyfkRCSOUej9ePXhf6b3zqc/J03pMnPb2Gtt+v4vH5Meq1/PaFPcEkG6CgrIvsjBS8vp47Kh6fil8fBYR2pRyWlcqUKWM+3Q9gwJpCq9FF2/Y3UX0+bBMXMezK2+R9NgTtrdzPme+FyNg0pkzp26ZqQpxyaunIxzmvRPuDDz5g8+bNxMR8/jU3fr+/V9IYjhrd4dLl6t1avNPpxeMLvQ3a2e3hT//ax/o9lVgtBr58zVgWTs3k7c1lvLymGJfHz5JZ2dx9bR6Hy1t46vVCahodTBubzAPLJ6HVKDz/7mGa7d00tjmprHfw4M2T0Ws1bNhbxZsbjxJjM7JgSgartpTx0vuh5zxwrJknXyugrrmTKaOTePDmybjcPn730h4OljWTmRzJtDG9y2mdat0+kGlMEcQuCN03oLPGELvgNvyX3oQ5fVRwPP6qrxC3+B5QNMHfY9vEy7BNlE6MA4FBryXCpMfh7KltHhtppNsd2q7b51d54b3DvLOtHIAIs55ffG0WFpOe3720h5ITreSkRvHQLZNJiY/gqdcL2bSvihibiXu/kM/Mcam88kEJK9cfwedTuWZODnddM5adB+t4emURLfZupoxOwn+WLTNmY++P6bxhcdQ1B5a2ACTEmFkya2hVm4mZu4zomV9EVf1o9P1rQkVcPJfkp/CfzWXBY4NOQ1lNOy+sPswVM7JIjLGEMTohzjPRzs7Oxma7MKVakpOT2b27Z1NeY2PjkGpKM3l0IkmxFupbAjWT9ToNV88eRle3hy2FNcHHpSdY+WBnoA52W4eLP/6rAFuEkb++uT/4mDc3HiM7xcY/3jlEW0dgKcqOg3X8451D2DvdNJ/sAlleY+c3L+zmqpnZrN0d2PzY2uHiD6/swxZh5C9vhJ5zWIqN51Ydos0ROOfuw/X837uB1zhYFqigUVHXgdvjwxZhCDbLsZh0zJ+c3ic/t8/D12WnZd0LuGqPYsrKI3b+LWgM57cGHkD1eal79XGcZfuAQIfG5JsfDf5xVzSfb126s3w/bdvfQPV6sU27CuvomZ/rfOL86bQa7r42j6deL8TnV7GYdNx1TR61TZ388V/7go+bMjqJ9z46ETzudHp4dW0pji4PJSdaASiraef3L+9l9vhU1p18nzW2OvntC3t4+HaFF1cXB5+/csNRctKieOr1QpwnL753H65nRl4yigKn8u1RmTHcuDCXoqNNwffehNx4Zk9IY97kdIqONNHZ7WHy6ERMhqHRhe10ik7P0JmmEWczbkQ8D98+lXe3HUejKBypaGXVlkDTsfe2lfPn7y0gJlI2SIrwOa9P5ttvv53bbruNGTNmoNP1POWzrKueNWsWTz75JC0tLZjNZtasWcPPfvazT32egcqo1/Kbb81l9fYTdDo9LJqWwbCTM2H5OXGU13UwZXQimwtCOwX6/So7D9b1Ol/R0aZgkn1KaUUr7Y7QsaoGBwePN/c6544Dtb3OWXikMZhkf9I565q7+NN3FrC5oAq/Xw3MHsT2v9mDhrf+iLMssJTG3XACv8tJ4rXn/7vbWbormGRDoEOj40CgDXxH0Xq0lihiF95OxGfYBOJpraP2lZ8HSiIB3RWH0N35c0zpoz/1ucRnc/mMLKaMSeJErZ1RWTFYTHrycuKIizKx63A9GUmRjMqMYffh0A2xHZ0ejlaF3rIuq24n1hY6u+r2+tldHPpcCLzPnGfc4erocvPLr89hS0E1CTFmrpyZjUGv5fFvzKHkRAuKojAys+fO4oSRCWeeVoghZ+7ENOZOTGPl+iMUHW0Kjts73WwtrOGaOQN7OaMY2M4r0f7rX/+K1Wqlo6Pjc79gUlISDz74IHfccQcej4cbb7yR8eOH1g7hmEgTN18xKmRMr9Ny9WkfBq0dLjbt60m2dVoNl05KY/VH5Zx+d3namCQKShtpsfd0IMwbFkerw8XW02bIs5IjmZibwLai2pBzzj3bOccmU3S0iab2nnPmD4+nvcPFptMuALJTbAxLtTEsdexn+0FcBKrXE0yyT+k6sutTneNUHezTdZbsxHlsLwB+p4OGlb8j85t/QRvx6Vpsdx0rCCbZJyOms3SXJNoXWazN1Kss2KRRiSFVeMZkx3K4vKdT5+UzMjEatCEJ+JjsWPJz4tl9uGfPhdGg5dKJaaze3jMjDjB7Qiq7DtWHXNSOGxFPXk5csNrI6UZlDaz69kJcbMaz3NUxGQZ2JSwx8J1Xou10Onn55Zc/84usW7cu5Pjaa68NKRkoels8I4v65k4+2FlBlNXAHUvGkj88ngeWT+LlNSV0u31cM3sYcyamkRBj5pmVRVQ1OJiel8ztS8bg9vhxe3wUnuwMef+yiWQkRlLb3MWHO09gizBy59VjGXfynC+9X0K328uE3AQMei0P3zGNZ9/aT1WDgxl5ydx25Wg8Xj8en5/CI4Fzfv2G3uV9+htFp0cXnRTSkl0XnUz77tVoTRFYRs9AozPg97rpLP4I1dVFxOiZwYS5q6wQr7MDRWdA9QaWyKDVgRq6hlf1eeiuKgYV/K5OLCOnozVbAXBWHMTdUIklZ3ywRbu7oYKu4wWo3t5r2k9v4y76jx+vuIRVW8qoaepk9vhUpuclMyE3gadeK+RAWRMjM2P4xo0TiI82U9/axca9VcTaTNyzNJ9xIxL49k0TeW3tEbx+lS/OG86U0Un8993TefatA9Q3dzFrfAo3XTYy3N9mv9RdfQTHwU1oLVHYJi9Ga4nE73bSeXg7qtdNxJjZaC29O9aJoWX+5HRWbSmjqsEBBCaD5kxIO8ezhOhb59Ww5sEHH+SrX/0qo0cPjFm2odSw5kLp6vbwvSc3U1EXuGuRkxbFr+6fMyjWfTqPF1H/5hP4u+xorTH4Pd2orkBpNGNqLim3/ZTa5x/BVXsMCLSOTfvyr2jfuQr7rndOnkXBlJWHzhqDbepVdFeV0LL2nz0votFiSMrGffIc2oho0r78OO2736P9o7eCj0m68WFUn4eGlb8P1gE2JGbhbqwMtmRP+uKDwS6QQgx13ZWHqXn+R8H3iz4+ndS7fknNP36Ap6kKAK01hrS7fzOkmmSIs3N7fOw8VIeiKEwfm4ReJzPan5enpQZVDXRdPsXn7MDnaEMfnx4sBOB3O3E3VGBIyAzpWjzYXZCGNbW1tdx4442kpaVhMPS0uX377bcvXKTiE+04UMuWohqSYiwsvXQ4tggDx2vaeW97ORpF4erZw8hI6pnRKa+1BzeHnPm1s9mwtyqYZENgrenWwhoWTRv4XSLNw+0TXmMAACAASURBVMaT9a2/4m1vpH3PGuw7e35vXTVHaPvozWCSDYEW7O273sG+Z/VpZ1FRvW4Sr3sACLQ79zRV0nFgE1pzJNb8ebR/9Gbw0b7ONtp3vkP77vd6TuH30bZ1Jfi9Ic023E3VpH/tj2i0enRRsuZ2IPH5/Lyz9TgHypoZlRnD0ktz0Ou0FJY2snFfYEb72rk5RFmNHK9pZ9WW4/j8fpbMGhay1lp8PPu+D0PeL56mKtq3vRFMsgF8jlY6itYRM/uGcIQo+hGDXiuz2BeI6vfR8Mbv6Sz+CABL7lSSbvge9r3v07L2eVSfB0NiJsk3PYK7uYqGf/8Wv6sLxWAm6frvSAOpk84r0X7ooYf6Og7xCTbureK3L/Y0YthdXM/3b5/K957cjOtkGbL1eyr5fw8vJC7KTF1zJ9/706ZgibL1eyp56nsLiY/++CvMs5XlGwyl+k5RtPrAkowzuskB4PX0HvN54YybPaqvZ7mIotWTcM03iL/6PhRFQ2fxDtrPPIXH1ev1gstPQgb9aC1RaE0Rvb8m+rXnVh3irU2Bi7Tt+2upqO9g7sQ0fvq3j4K/Ptv21/LYvZfw/T9vCW5+3LSvmj8+NP+cF8ACNIazVIzQnuVPl/8s720hxGfWWbIzmGQDdB3Zjb3gQ5o//L/AhBGBZZCtW1+nu+IQflegmprqdtL0/rNkfv2psMTd35xXoj19+vS+jkN8gg93VYQcH6tq5z+by4JJNgSS4q2FNSy9dDibC6pD6gB3dXvZWlSDyaBjW1ENSbEWll02kvhoM/uPNvH2ljI8Hh8mgzb4vEiLnjkTBt9aYduky+koXIvqDmz01CdkEj3rejpLduBpDmz0VAxmbNOW4Pe4cBSt73myAsd/fQuGxCzir/oqhsQsWje9Sse+D1BMEWgjovF1BqpQKHoj0dOuAk83jgObgqeImrYENBoa//NkcCxywkJJsgeodbtD35ub9lXR7fKGXKNV1nfw5sZjIRVGPF4/Wwprem2KFr1FzbgWx+Ft+LsC3W0tI6cTPWMpjv0bg3svNOZIIicsCGeYop9QVZXD5S1oFIXR2bKB+PPwtjf0GnPXnwgm2cHHtdTibW8KHTvjeCgb+Atwh4CoiNA1PxqFXhUSAGwRhpP/7b1G6HiNnbWnJewHypp4+LapPPqXbfj8gaxAr1VYfEkWVrOeK2dmE3OW1xjoDAkZpK/4HY6DW9CYrESOuxSN0UzaXb+kY/9G/K4urPlz0UcnkXD1fVhyJuBurMRVWxYs8eeqLqVh5e+ImnU9bVteC5y4sw0UDdGzrgdFwTpuHoa4NBKuvR/zsAnBLpPm7HEA6KOT6Tq2D0NiJhFjpG72QBUdaaKjq+eOiC3CQGSEodfjEmN6302KiZT9I+dDH5NM5n1/puvYPrQRUZiy8lEUhbS7f43jwCZUrxtr/qXoIiWpGupcHh+PPrMtWB1o3PB4fvKVmb26torzY8mdRsuGl3oqY2m02KYsxlm2D297Y8/jRs1Aa0vAUdRT+MI6dtbFDrff0j722GOPhTuIC83n89HQ0EBiYmJI3e+BKi3RypbCGlyewGzzF+YN58aFuewraaDFHigNFhdlorXDRUenmwVT0ik40lPyb3RWDM5uLw2tzuA57Z1uVOBIZU8dYL8KC6ZksPzyUVgtvZOFwUJrtmLOHIspdQSKLrDpUNEZMKXlYs4ci9YUqBaiKBoMiVmYs8fRuull/N2dwXP4nR2g1eNpqjztzCq2KVcSNf1qtBZb8BzGpGwsORPQR/d00tRFxWPOHochIXNIdUYdbBJjzGzfX4vPr6LTKtx3wwTmTExlS2FN8O7QZdMyue2qMRSXtwQbVY3KjOHua/PQSQJwXhSdAUNiJvropOD7xV1XhmP/RjytNegi4zDEybrcoW797kre2Xo8eNzQ2kVGUiTZKRem4d5Qo7VEYsoYi9/pQB+bQvziFZjSR2EZMRmfswONwUz0JUuxTV2CJWcCKAooGqx5c4hddAfK2ZZ4DULnyjmHxk9hgMtKtvHsf1/O/mNNJMVYyDr5ofHbb8/jwNEm/u/dQ5RWttHc3s3ekgbsnW5+eOc0fv7cDsqq7bjcPiJjQhNnrUYhPdHa67USPmEd91BmTBuJt63nNpouOhFz5hi6Dm897VEKxhRpjDCUTBubzN8fvYIjlW0MT4sK3gX63x9cRsGRRmJtpuCmx59/bTYlJ1rw+lTGDouVC6zPwdveSO1LPwnueXAe30/qnb/AlCblEYey1jOat33cmDh/5qw8zFl5IWP62FSSrnswZEzRG4mdd/PFDG3AkER7gDAbdUwfmxwyptUoDE+PorQytDvdxn1VVNR3UFYdWNN4vNaO0+UlMdZCQ0sXGo3CbVeNYcmsbHYfrqegNHALaOa4FGbkhb6GCIi77Mv4nR04y4rQJ2SQcPXXMabk4G44QUfhejRGM7Hzb5Ea2ENQlNXI1DFJIWMmo45L8lN6PVaazlwYXUf3hm4sVv10luyQRHuImzMxlX99WIr75N1fk0HLrPG934dCXEySaA9wRoMOq1mPw9mzTjQuykzJiZaQx9W1dPHCT66ktrmThGgzcVGBmeuffXUW5bV2tBpFKiB8Ap01mpSbf4Sq+lGUntv9CUu+RvziFaDRhIwLIfqOLiap19jpS7PE0JQab+VX35jDO1uPoyhw7dwcEmMs4Q5LDHGyRruf6HZ7eWvjMd7bXo7b7SM7NdCZcEthNf9ef5TaJgfD0qLQaTWUVrTyrw9KOVjWTGaSjdQEK3uK6/GrYDXreWD5JFrs3VTU99TFzkqO5JJxKazbXcnB4y3ER5uJthrpdHpYv6eS3Yfr0Wo1pCZY8ftVPthZwX82l9HS7mR4WhQajdzmBs56u1/RaGQZgBAXkS46CW97Y6ACAmDOmUTswltRNNKcZCjx+1WO19hRFAWTMfC3PjbKRHaKjfzh8aQn9kwetdq7qWl0EG01yue1uKBkjfYA8bsX9/DRgToANuyporXDhU6r4a9v7g8+5tDxFpZfPorv/3kzXl+gUsj6PVU881+LmDImkap6B7kZ0ZiMOpLjInB7/BQcaWR4WhS3LxnDd/+4KVgh4YMdJ/jTdxbwxMt7gzu0P9hZwfdum8LxGjuvrzsCwLrdlRyvtXP/lyZezB/HgOO1N9Oy8RU8TZVYRkwhevb18kdfiD6iKAqRExbibW9E9bqxTb4CjW7wbuAWvTW0dvGjv2ynutGBTqtwx5KxXDs3h18/v5vt+2sBuHRiGg/dOoXX15by0poS/H6VjKRIfvbVmcG7ukL0NZnR7gfsnW6efLUgZKyp3cnxGjttjp6NHNWNDlQ1tFKI0+UlJy2KGKsJe6eL+BgzOq0Gk1HH1DFJ5OXEcc2cHA6WNbOtqDb4PK9PRUUNGQPodHrYW9IQUqP7RG0HX1o0Eo3MAnysmhcew3lsL76OFrpPHABVDZbyE0JcWN72Rmr+8QO8rbX4OprpPLQN8/CJ6Gxx4Q5NXCR/f/tgcH+RX4X9x5qIshp5c2NPl98TdR3ERRl57u2DnKxii73Tjdfr77WvQojPSma0BwCDXoNRrw1pMhNpMfRarmHQa4O1sk9XUNrIb57fjV8N1PH92Vdn0dnt4Wd/24HT5UWrUbh8eu9W6tFWIxqF4AcQgNVswGo20O5wnzamR1aO9Kb6PKgeN363E3fdsZCvdZbuIHa+7MAWoi/02gyJKpshh5j6ls6QY69Ppbz2zP68gR4S/tAmv9SdLLMpxMUgu7f6AZNBx61XjubUhLHRoOW2K8dwy+LRGA09yw+WXz6SpZfmkBzXs7ljQm4C63ZVhFytv7i6mH+uOhTsROfzq2wprA6WGYPAmu1r5+Zw7dzhwbEIs55ll43kzqvHoNMGgtEocOfVY2RN2xnsBR9y4g/3UP67O2l8968oxtANN/oYqd4iRF/RneX9Je+5oWX2+NAKTylxEVx5SXbIBJVOq7BkVjYJZzSMmjtRqkOJi0dRVVU998MGFpfLxYEDB8jPz8doHDjd16obHZyotZOXE0eUNRB3u8PF/mNNZCRFkpUcqJ/t9vgoKG3EbNIRH2XiK79cG3KeERnRtDtcNJ7WoEZR4KWfXkVpZRt+v8rEkQnotIHrrGNVbdS1dDEhNwGrOdDApbndyeHyFkakR5McJ+3BT+e1N1Px1H3g77kDYRk1A2dZIaqnG110Esk3/RBDfHoYoxRi8FJVlaZ3nqajcB2gYh4+meQbHw42oBJDw7vbjrOloIbEWDPLLx9FclwEe4sbeGvzMTSKwnXzhjMhN4GaJgf/+qCUpjYn8yanc8WMrHCHLgaRc+WckmgPAg8/uTm4oRHg3uvyaXe4efXD0uDYzHEp/PCu6eEIb9DpKiug7uWfhYxZRk4ncem38LY3ok9Il1J/QlwEXnsTqteDPlZqJQshwuNcOaes0R4EHr1nBivXH6WqoYMZeclcNj0Lv18lJtJIQWkjOWlRXD9/RLjDHDRMqbkoRguqq2ednzYims7i7Vhyp0mSLcRForPFhzsEIYT4RDKjPcS1O1x8uLOCLpeXBVPSQ+qOio/nrDhEy/oX8DlaUVXwtQfas2ssNtLu+qWsFxVCCCGGgHPlnDL1NgA1tHax/1gTHq/v3A/+BC6Pj+/9aTP/eOcQr35YygNPbOREnf0CRTk4uerLcRzehiE+g7Q7/4eEa+4PJtkA/i479t3vhTFCIYQQQvQXsnRkgHl93RGef/cQfhVibUZ+/rXZn7l1+p7D9dQ295RIcrl9fLizgnuW5l+ocAeVlg0v0bb13wAoBhMpt/z4jBJjAf6zjAkhhBCDherz4Hd1o7XIXfBzkUR7AGl3uHhxdXGwlF+L3cXLa0q4Zs4w/t/rhVQ3Opiel8w3l03C4/Xxp38VUFAa6Az5zWUTSU+08r9vHeDDXRVEWgzMn9S7KoZBL90Mz8bXZadt+5vBY9XdTevm10j+0vfRx6biaakBQNHqsU28PFxhiougqqGDP76yj9KKVvJy4nlg+SQSYy3nfqIQQgwCjgObaVrzLH6nA1P2OJKu/w5asyTcH0eWjgwg9k43Xp8/ZKyxrYvH/7mLE3UdeH2BTo//9+4h/rJyP7sP1+P1+SmpaOXXL+zmve3lvLP1OC63j6Y2Jys3HGFEenTwXDGRRq68JPviflMDhN/dHVLOD8Dv7KD+jd8HkmyNFlPmWNLu+TXGlJwwRSkuhide3kvxidZgN7ozu7oKIcRg5XM6aHzn/+F3OgDoLt9P65bXwxxV/yYz2gNIRlIkOWlRlFX3dL+aODKRV9aUhDyuuLyF9tNatwNU1HWw/1hTyJhfhRsWjkCrUejq9jIjPyVYR/t8qarKkco2zEbdZ17CMhDooxMxZ4/DWb4/OKaLSqTz8NbAgd9Hd8UhGHRbi8XpfH6V0oq2kLHiEy0f82ghhBhcPC01vZZMuuvLwxPMACGJdj9ScqKFsup28ofHB5PW5nYne4sbSEu0MnZYHD+5dyavfFBCcXkLY3PiuHHBCNZ8VE6LvSexHpMdS5vDxbai2uBYRpKV8cPjQ8Z0WoUx2bHERYV2zQI4UWen5EQrY7Jjg7G0dbjYfbiexFgz40ck0On08MgzWzlaFUj8509J5zu3TOmTn01/kPSl72PfvRpPSw2WkdPpKt3Z6zHupkoMib3b3YvBQatRGJUVQ8mJ1uDYsNQo1uw4wcjMGLJTAk2lOrrc7D5cT1yUiXHD41EUhW63l48O1OH3+7kkPwWLSZqrCCEGFmPSMLQRUfg6eyb8LDkTwxhR/yeJdj/x6oelPP/eYSDQ9vx7t08lymrksb9ux+0NLBdZemkOyxaNZPfheupbujhW3U5pRSvfu20qf3ljP5UNHYwbHs8dS8bi8vjodvkoONJITqqNb900icykSIorWtlWVEtMpJG7rhlLXJSZ1o5uKuo6yM2IxmLSs3p7OU+9XggEOkp+a9kkslNs/PDprcG27gunZpCRFBlMsgE27Kli8Yws8ocPztq2GoOZ6FlfDB6rfu/JznQBis6AKTMvHKGJi+ihmyfzp1cLKDnRSmpCBMXlLcGGUV/74jjG5ybw8JObcTg9AMydmMa3lk3koT9uorK+A4Ck2BJ+/8A8bBGGsH0fQgxG/9l0jJUbjqIoCl9alMuSWcPCHdKgouj0JN/03zSvex5vWwPWsbOIumRpuMPq1yTR7gc8Xj+vre3p4uhX4ZU1JcTYTMEkG2DVluOY9FrqW3oapZScaKW1w0VMpJHyWjsFpY386v928eg9l/CTr8wMeZ0/v1bAhj1VQGDTY35OPB/urOCp1wvw+lQiTDoevecSXny/OPgcVYUX3y9mdFZMMMkGWLe7kgVTem+mbGrv/vw/kAHCOnomvivuoaPgAzQmKzFzl6GzRp/7iWJAS02w8vg35gBw7/98ELJa6MX3Szha1RZMsgE2F1STlRwZTLIB6lu6WL+nki9cOvxihS3EoFd0tJH/fetA8PjpfxeRkxbF6KzYMEY1+BhThpN662PhDmPAkES7H/Craq9Njm6PH5f7jM13fpXObg9nKjrSyL7SxuDxvtJGthbVMH9yOm0dLiIjDByvaef9j04EH1NZ38GbG4+yZscJvL5AqtDZ7eWf7xzC5faGnL/b5aXb3btm94TcBDburQpWQbGa9UwZnfjpvvkBLmraEqKmLQl3GCJMznxfuDy+kAvSU7q6e4+5PZ+vDr4QItTBY829xg4ca5ZEW4SVJNr9gFGv5fLpWby3vTw4ds2cYdisxuAtaYDpY5O5Zk4Oa3dVBv/Ax9pMxNhMvc55vLqdf687QnmtnfhoM1fNyu71mKZ2Z8jMG0CzvZsls4bx7/VHg2NXzx7GqKwY9hTXc6qP6NhhsSyalklMpInVH5VjNuq4fsEIIi1D41a4r8tOZ/FHaEwRRIycjqKT9bZD0ZJZw3jptDtAV87MYvrYZLbvrw1egOakRXH9ghFs2FtFiz1wxyfSomfBlIxwhCzEoJWbGdNrbNRZxoS4mKQFez/h86ts3FtFWXU7E3LjmTY20MK7sLSRHYfqSEuwcvn0TAx6LZX1HXy4swKDXsuVM7NwuX3c/9v1eE4uM9HrNORmRnOorCdJT4gx4/X6ae3o2TT506/MZNWW4+w8VBccW3bZSG67cjQb91VTUt7C2GFxzJmYiqIoHDrezNaiGhJjLFwxIwuzcWhep3naGqh+7vv4uwJdNI1pI0m94+coGqlBPhRtKazm4LFmcjNjmD85HY1GofhEC5v2VRNnM7F4ZjZWs54Wezdrd1Xg86ssnJpBYozU3hbiQntxdTFvbQqs0b5hQS7LLhsZ7pDEIHeunFMS7UGi+EQLb28qA+DaS3P45T92BWfPTnniwXms3l6OvdPNZdMzmT42ma5uDyvXH6Wspp2JIxO4ZnYOGo0Shu9g4Ghe9zztpzWvAUhe/giW4ZPCFJEQQohT/H4VRQFFkb9lou+dK+ccmlOSg9DorFhG396zDm3SqATW7qoMHo/KjGFEejT3fym0DI/FpOe2q8ZctDgHBV/v9baqt/faeSGEEBefTBaJ/kQS7UHqK9eNQ6MoFB5pJCctinu/MC7cIQ0akRMvw77vQ1RP4I6BNjIOjVGWAQghhBAilCwdEeIz8LTUYt+7ho7Cdfi7A61oLSOnkfyl/wpzZEIIIYS4WM6Vc2rCEJMQA54+NgVFpw8m2QBdpbtwVhwMY1RCCCGE6E8k0RbiM/I7Hb3HunqPiaHN71fZW9LAut0V2Dvd4Q5HCCHERSRrtIX4jKzj52Mv+BD8gZrm2sg4zMMnnuNZYrBRVZVtRbWUVrSSPzwuWJrzlF/+cycfHQiU0Iy06PnV/XPJSIoMR6hCCCEuMkm0hfiMTGkjSb39Z3QUrkNjiiBq2hI0etkTMNT8Y9UhVm4INHhaueEod109lhsW5gJwtKotmGQDdHR5+M/mMr5x44SwxCqEEOLikkS7H/H5VbRnlCXy+9WzlipSVfUz1wj9uOeebfzjXl8EmNJHYUofFe4wRJj4/CrvbDseMvafzWXcsDAXn8+P8yyt17vP0qJdCCHE4CSJdj/Q3O7k9y/tpehoE9kpNh5YPomsFBvPrCxi7a5KIi16vnxtHgumZPDOljJefL8Et9fHklnD+PI1Yzl0vIWnXi+kutHB9LFJfPumSbg8Pv7wyr5geb9v3zSJjKRInllZxLrdJ895TR7zp2SwaksZL50851Uzs7n72jwOlDXz1GsF1DZ1MnVMMg/cPGnItFcX4nwpgE6rwYUvOKbTafjtC3vYXFhNVISBxBgzDa1OALQahStnZocnWCGGgIbWLj7cWYGiKFw+PZP4aHO4QxJDnPaxxx57LNxBXGg+n4+GhgYSExPR6fr/tcQTL+9lT3EDAG0OF/uPNaOqKq+tPYJfVel2+9hxsI5RmdH89sW9uD0+fD6V4vIWUuIi+PNrBdS3dKGqUNXgoLPby9aiGnYfrgeg1e5i/7EmAF798Ah+v4rT5WPnwTpyM2P43Yt7es55opXkWAtPvV5IY6sTFahudOBwepiel/xx34IQQ5KiKGgUhYLSxpPHMG54HFuLalBV6Hb7cHl8LLtsFMPTo7n3unxGZ8ee46xCiM+iud3Jt3+3gT3FDew/1sTGvVUsmpaBydD/8wAxcJ0r55Tfvn6gtKI15Li60cGh4y0hY36/GrLW85SCI420drhCxkpOtNDuCB2ranBwsKw5ZMznV9lxoLbXOQuPNNJ2xjnPjFEIEXD9ghHkD4+jtKKVvJw4XnivOOTrXp9KXk4sE0cmhilCIYaGjXur6ejq6dLb5nCxpaCaq+fkhDEqMdRJeb9+IC8nPuQ4O8XGxJEJIWM6rYa5E1M5c2n11DGJxNpMIWNjh8UxZlhcyFhWciQTcs88p8LcSWm9zzk2mbio0HPm5YSeTwT4Otux712D49BWacM+hI3MjOGaOTkMS43q9V4xGbSMyIgJU2RCDB1Gfe+UxqDXhiESIXrI0pF+IH94HNWNDprbnYzMjOHBmyczcVQi3W4v1Y0OEqLNfOPGCUwZk0xijIWyGjs6rcINC3K5enYOY4bFcqyqnc5uL7PGp3LvdflMHJlAVYODplYnIzKiefCWyUwelUS320tVg4P4aDPf+NIEpoxOOss5hzF2WBxlNe04XR7mTEjjnqX56HXygXU6T2sdVc9+l87D2+gs3o7zxH4ix89HUeT6dSgbmRlNV7eXmqZOUuMj+OZNk8hKtoU7LCEGvdQEK9uLaoKz2hlJkdx73Tj0OvlMFn3nXDmntGAX4jNqXvtP2j/6T8hY8s0/wpIjpduEECIcut1edhyoQ6MoTM9Pxigz2qKPnSvn7P/TvUL0U6qvd5k21SfLR4QQIlxMBh3zJqeHOwwhguR+ihCfkW3i5SinNajRx6fLbLYQQgghgmRGW4jPyJCYSfqK3+E4sBmNOYLIcfNRtPpwhyWEEEKIfkISbSE+B31sCjGXLgt3GEIIIYTohyTRHkQq6zs4VtXG2Jw4EmMsvb7e7nCxr6SB5LgIaZohRB/aebCODXuriLWZ+OL84cRFSXc6IYQYiiTRHiTe2VLGM2/sBwJtnr9/x1RmjksNfv1oZRs/fHorTldgA9/Vs4fxtevHhyVWIQazjw7U8ovndgaPdx6q4+mHF6LVypYYIYQYauSTfxDw+VVeWF38sccAr64tDSbZAO9uO05jq/OixSjEULFud2XIcW1TJ4fLWz7m0UIIIQYzmdEeBPx+P93u0FJznU4P63ZXUnikkZy0KDqdoWXnVJVezxFCfH4xkb3rqEafZUwIIcTgJzPag4Bep2Xh1MyQsbREK0+8vJd1uyt59q0DeL3+kK/n5cSRkRR5McMUYki4fkEu8dE9a7KvmT2M9ER5rwkhxFAkM9qDxNdvGM+IjGiOVrYxbkQ8L7x3OOTrxSdaePTuGewuric5NoIrZ2aFKVIhBrekWAt//cEiDpY1E2szkSnt14UQYsiSRHuQ0Go1XDUzG2YGjt/aeJT605aFmo06Jo1KZHpecljiE2Io0eu0TByZGO4whBBChJksHRmkbrtqDHpd4J9XUUKPhRBCCCFE35MZ7UFqyugk/vbfl3PoeAvDUm2kJljDHZIQQgghxJAiifYgUF5r53/f3E9Vg4MZecnc84V8PF4/z606eLLqSDRf/eI4kuMiwh2qEEIIIcSQIYn2AOf3q/zs7ztoaOkC4L3t5RgNWtodLtbvqQKgxV5PW0c3Tzw4P3yBCiGEEEIMMZJoD3C1zZ3BJPuUwiONtDtcIWNHq9pxOD1YzfqLGZ4QQgghxJAlu+MGuIRoM5EWQ8hYTloUOWnRIWPJcRYiTHJdJYQQQghxsUiiPcAZ9Fq+c+tk4qNMAIwbHs+dV4/lq18cx4j0KCCQZD9482QURQlnqEIIIYQQQ4qiqqoa7iAuNJfLxYEDB8jPz8doHBqtj/1+lW63F4spdGmIw+khwqSTJFsIIYQQ4gI7V84pawkGCY1G6ZVkA7ImWwghhBAiTCTRFkIIIcSg0NbhYuO+KhQF5k/OwBZhOPeThOhDkmgLIYQQYsBr63Dx7d+vp8UeqLr1xoZjPPmd+VgtkmyL8JFEe5BqtXfzh1f2UXCkkZy0KL61bCLDUqPCHZYQQgjRJzbsrQom2QBNbU42F1Rz1axhYYxKDHVSdWSQeuaNIvaWNOD3qxytbOM3L+wJd0hCCCFEn9GcZc+/FAIQ4SaJ9iBVXN4SclxZ34HD6QlTNEIIIUTfmjc5nfhoc/A4KdbCnIlpYYxICFk6MmiNzo5lW1Ft8DgjKVIqkAghhBi0oqxGnvzOfDYVVKMoCnMnpsnfPRF2kmgPUl/74nhcbh+FJ9dof3PZpHCHJIQQQvQtRUGrUVAU5axLSYS42CTRHqRibCYeu3dmuMMQQgghLgp7p5sHn9hAQ6sTgNfXHuGJB+cRIbPaIoxkjbYQaVRWlQAADZNJREFUQgghBrwNeyuDSTZAbXMnWwqrwxiREJJoCyGEEGIQ8PvVXmO+s4wJcTFJoi2EEEKIAW/e5HSiI43B4/goE3Ol6ogIM1mjLYQQQogBLybSxJ8ems/6PVVoNLBgSgaR0hVShJkk2kIIIYQYFGJsJq5fMCLcYQgRJEtHhBBCCCGE6AOSaAshhBBCCNEHJNEWQgghhBCiD0iiLYQQQgghRB+QRFsIIYQQQog+0G+rjpSVlfHd736XnJwc8vPzueuuu8IdkhBCCCGEEOet385o79mzh+TkZEwmE5MmTQp3OEIIIYQQQnwq/WZG+9lnn2XLli3B4x/96EcsWrQIq9XKfffdx9/+9rcwRieEEEIIIcSn028S7RUrVrBixYrg8ZtvvsnMmTMxGAzodP0mTCGEEEIIIc5Lv81gc3JyePzxx7FarSxbtizc4QghhBBCCPGp9Hmi7XA4WL58Oc888wzp6ekAvP322zz99NN4vV7uvPNObr311l7PGz9+PE888URfhyeEEEIIIUSf6NNEu7CwkEceeYTy8vLgWH19PU888QQrV67EYDCwfPlyZsyYwYgRIy746x84cOCCn1MIIYQQQojz0aeJ9quvvsqPf/xjHn744eDYtm3buOSSS4iOjgZg8eLFrF69mvvvv/+Cv35+fj5Go/GCn1cIIYQQQgiXy/WJE7t9mmj/4he/6DXW0NBAQkJC8DgxMZGioqK+DEMIIYQQQoiL7qLX0fb7/SiKEjxWVTXkWAghhBBCiMHgoifaycnJNDY2Bo8bGxtJTEy82GEIIYQQQgjRpy56oj1r1iy2b99OS0sLTqeTNWvWcOmll17sMIQQQgghhOhTF72OdlJSEg8++CB33HEHHo+HG2+8kfHjx1/sMIaE+pYu9h9tYnh6FMNSo8IdjhBCCCHEkKKoqqqGO4gL7dQO0KFcdeSjA7U8/s9d+PyBf94vXzOW6xfkhjkqIYQQQojB41w550VfOiIujpffLwkm2QCvfFCKx+sPY0RCCCGEEEOLJNqDlNP1/9u7/5iq6j+O4y9uEPqV4TTkW5lmoUHZXEGuFHLAtPgCF0LKXWRU5pKmk7UmDXNCgSwz17LbT7dGulXsxjLAwkayVReUnIVWGmhNikngwoFclvfKvd8/nPfrFUHt6+EKPB9/cT6cz+e+z9k+8OKcz+Gc8dl2uvrV30/QBgAAGC4E7VHqP/Nn+GwnxEzTuOBhX5IPAAAwZpG8RqmM+Jn69+R/qenICUVMnaiFc6f7uyQAAIAxhaA9is2fc7Pmz7nZ32UAAACMSSwdAQAAAAxA0AYAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAMMCpfWOPxeCRJTqfTz5UAAABgtDqXNc9lzwuNyqDtcrkkSS0tLX6uBAAAAKOdy+XSuHHjBrQHeAaL4COY2+2Ww+FQUFCQAgIC/F0OAAAARiGPxyOXy6UJEybIZBq4IntUBm0AAADA33gYEgAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRuGamtrU2Ji4oD2yMhI9ff3q7CwUKmpqTKbzaqurvb2iYyMVH19vU+fxMREtbW1SZLefPNNpaSkKCUlRZs2bTL+QIARZqi5d05HR4fi4uJ8+lxq7klSb2+vUlNTfdqA0ej8+TKYN954Q/Hx8SorK7us/YeL1WpVbGys0tPTlZaWJrPZrL179/q7rDGHoA2/qaqqUm9vr3bu3Klt27Zpw4YN6u3tlSQFBQVp/fr13u3zNTQ0yG63a8eOHfrss8/0888/q7a2drjLB0a0r7/+Wo8//rhOnDjh0z7U3JOkAwcOKCsrS8eOHRuGKoFrX2VlpcrKyrRs2TJ/lzKAxWJRZWWlqqqqtGnTJj333HP+LmnMIWjDbzIyMrxXozs7OxUUFKSgoCBJUnh4uObPn69XXnllQL8pU6aooKBA119/vYKCghQREaHjx48Pa+3ASFdRUSGr1Tqgfai5J0k2m01FRUUKDw83ukTgmtHY2KinnnpKK1eu1MMPP6y8vDw5nU4VFhaqo6NDq1at0uHDh737W61Wn/l17q5Qf3+/Xn75ZWVkZCgtLU0ffPDBkOPv2rVL6enpSk9Pl9lsVmRkpA4ePKiWlhbl5OQoMzNTCQkJ+vjjjy95DKdOndINN9xw1c8Nhhbo7wIw+nV2dio9Pf2i3wsMDNS6detUWVmpFStWKDg42Pu9goICmc1m1dfXKzY21ts+a9Ys79fHjh1TTU3NZf2QAcaaoebexUL2OYPNPUkqLS29qjUCI8UPP/ygmpoahYeHa8mSJbLb7SouLpbdbtfWrVt1yy23XHIMm80mSdqxY4ecTqeWL1+uu+++e9Dxk5KSlJSUJEnasGGD7rvvPs2ZM0elpaVauXKl5s2bpz/++ENpaWnKysoa8Hnl5eX66quv5HQ61draquLi4qt4RnA5CNowXHh4uCorK33azl/HVlpaqjVr1ignJ0fR0dGaMWOGJCkkJEQlJSVav369qqqqBox75MgR5ebm6vnnn/f2AfA/l5p7g7nU3APGolmzZunGG2+UJEVERKi7u/uKx9izZ48OHz7sXSvd19en5uZmzZw5c8jxKyoqdOjQIW3btk3S2T+Gv/32W7333ntqaWlRX1/fRT/PYrFo9erVkqTffvtN2dnZuu222xQTE3PFteOfIWjDb3766SeFhIRoxowZmjRpkh588EE1Nzf7hOa4uLiL3sbev3+/8vLy9MILLyglJWWYKwdGv8HmHjBWnX/HNSAgQB6PZ9B9AwIC5Ha7vdsul0uS1N/fr/z8fD300EOSpK6uLk2YMEFNTU2Djv/999/r3XffVXl5uXd55bPPPqvQ0FAlJCQoOTlZO3fuvGT9t99+u6Kjo9XU1ETQHkas0YbfHDhwQK+++qrcbrd6e3tlt9sVHR09YL+CggLZ7XZ1dnZKktrb27Vq1Spt3ryZkA0Y6MK5B+DyTJo0SUePHpUkHTx40PvQ8QMPPCCbzSaXyyWHw6GlS5eqqalp0HHa29u1Zs0avfbaawoLC/O219fXKy8vTwsXLtQ333wj6WyIH0pPT48OHTqku+666/89PFwBrmjDbywWi5qbm2U2m2UymZSdna177713wL8MO3cbe/ny5ZKk999/X6dPn9bGjRt9xrrY+jQA/9yFcw/A5UlOTtaXX36p5ORkzZ492xtuLRaLWltblZGRoTNnzmjx4sW6//771djYeNFx3n77bTkcDr344oveIJ2bm6vVq1dr6dKlCg4OVlRUlKZOnaq2tjbdeuutPv3PrdE2mUw6ffq0HnvsMc2bN8/Yg4ePAM9Q9z4AAAAA/CMsHQEAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAMQNAGAAAADEDQBoAxwmq1DvoK5k8++UQffvjhMFcEAKMbQRsAoP379+vvv//2dxkAMKrwwhoAGKEcDofWrl2r1tZWmUwmzZ49WykpKSotLfW+krmxsVElJSXe7V9//VXZ2dnq7u7WnXfeqaKiIu3Zs0d1dXWqr6/XuHHjtH37dhUWFio2NlaStG7dOt1xxx3q6elRa2ur/vzzT504cUJRUVEqLS1VSEiIOjo6VFxcrPb2drlcLqWkpOiZZ57x27kBgGsBV7QBYISqra2Vw+FQZWWlKioqJGnAm1Uv9Pvvv8tqtaq6uloej0fvvPOOFi1apMTERD355JPKzs5WVlaWbDabJKm3t1d1dXXKyMiQJO3bt0+vv/66ampqFBgYqLfeekuSlJ+fr8zMTH366aeqqKhQQ0ODvvjiCwOPHgCufQRtABihYmJidPToUeXk5Gjr1q164oknNH369CH7LFq0SJMnT1ZAQIAyMzPV0NAwYJ/FixeroaFBXV1dqqqqUnx8vEJDQyVJSUlJCgsLk8lk0qOPPiq73a6+vj7t27dPW7ZsUXp6upYsWaL29nb98ssvhhw3AIwULB0BgBFq2rRpqq2tVWNjo/bu3atly5bJYrHI4/F493G5XD59rrvuOu/XbrdbgYEDfw2EhoYqKSlJVVVVqq6uVlFR0aD9TSaT3G63PB6PysvLNX78eElSV1eXgoODr9qxAsBIxBVtABihPvroI61du1ZxcXHKz89XXFycJOn48eP666+/5PF49Pnnn/v0qaurU3d3t/r7+2Wz2bRgwQJJZwP0mTNnvPtlZ2dr+/bt8ng8mjNnjrd99+7dOnXqlNxut2w2mxISEhQSEqJ77rlHZWVlkqSenh5lZWVp9+7dRp8CALimcUUbAEaoRx55RN99952Sk5M1fvx43XTTTcrJyZHD4VBmZqamTJmi+Ph4/fjjj94+ERERys3NVU9Pj2JiYrRixQpJ0oIFC7Rx40ZJUm5urqKiojRx4kRZLBafzwwLC9PTTz+tkydPau7cud4HHjdv3qySkhKZzWY5nU6lpqYqLS1tmM4EAFybAjzn32MEAEBnH5rMycnRrl27vMtBrFarTp48qcLCQj9XBwAjA1e0AQA+tmzZIpvNppdeeskbsgEAV44r2gAAAIABeBgSAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwwH8B+3gWapmeTiQAAAAASUVORK5CYII=\n" + "image/png": 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+ "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1335,7 +1664,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": { "collapsed": false, "pycharm": { @@ -1347,6 +1676,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "variables={'x': 'subtype', 'y': 'mutation_rate_samp', 'hue': 'Synon_Nonsynon'}\n", + "self.tuple_group_names=[('H3N2', 'Nonsynonymous'), ('H3N2', 'Synonymous'), ('H1N1', 'Nonsynonymous'), ('H1N1', 'Synonymous'), ('Influenza B', 'Nonsynonymous'), ('Influenza B', 'Synonymous')]\n", + "self.plotter.group_names=Index(['H3N2', 'H1N1', 'Influenza B'], dtype='object', name='x')\n", + "self.plotter.hue_names=['Nonsynonymous', 'Synonymous']\n", "H1N1_Nonsynonymous vs. H1N1_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:5.014e-04 U_stat=2.624e+03\n", "H3N2_Nonsynonymous vs. H3N2_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:1.294e-03 U_stat=1.535e+04\n", "Influenza B_Nonsynonymous vs. Influenza B_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:2.026e-01 U_stat=3.340e+02\n" @@ -1354,8 +1687,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1377,7 +1712,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -1391,7 +1726,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.12.7" }, "toc": { "base_numbering": 1, @@ -1409,4 +1744,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} From 2bbd498f09dd145b5dd489f499df955663e0d051 Mon Sep 17 00:00:00 2001 From: getzze Date: Mon, 4 Nov 2024 15:32:02 +0000 Subject: [PATCH 14/16] add redondant hue in seaborn 0.13 --- usage/example.ipynb | 186 +++++++++++++++----------------------------- 1 file changed, 63 insertions(+), 123 deletions(-) diff --git a/usage/example.ipynb b/usage/example.ipynb index 6f167de..9a5ec39 100644 --- a/usage/example.ipynb +++ b/usage/example.ipynb @@ -55,10 +55,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'day', 'y': 'total_bill', 'hue': None}\n", - "self.tuple_group_names=[('Sun',), ('Thur',), ('Fri',), ('Sat',)]\n", - "self.plotter.group_names=Index(['Sun', 'Thur', 'Fri', 'Sat'], dtype='object', name='x')\n", - "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -112,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -147,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -206,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": { "collapsed": false, "pycharm": { @@ -234,12 +230,12 @@ "data": { "text/plain": [ "(,\n", - " [,\n", - " ,\n", - " ])" + " [,\n", + " ,\n", + " ])" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -278,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": { "collapsed": false, "pycharm": { @@ -306,12 +302,12 @@ "data": { "text/plain": [ "(,\n", - " [,\n", - " ,\n", - " ])" + " [,\n", + " ,\n", + " ])" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -349,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -408,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -523,7 +519,7 @@ "4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75" ] }, - "execution_count": 4, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -541,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": { "collapsed": false, "pycharm": { @@ -553,10 +549,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'color', 'y': 'price', 'hue': 'cut'}\n", - "self.tuple_group_names=[('E', 'Ideal'), ('E', 'Premium'), ('E', 'Good'), ('E', 'Very Good'), ('E', 'Fair'), ('I', 'Ideal'), ('I', 'Premium'), ('I', 'Good'), ('I', 'Very Good'), ('I', 'Fair'), ('J', 'Ideal'), ('J', 'Premium'), ('J', 'Good'), ('J', 'Very Good'), ('J', 'Fair')]\n", - "self.plotter.group_names=Index(['E', 'I', 'J'], dtype='object', name='x')\n", - "self.plotter.hue_names=['Ideal', 'Premium', 'Good', 'Very Good', 'Fair']\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -564,20 +556,20 @@ " ***: 1.00e-04 < p <= 1.00e-03\n", " ****: p <= 1.00e-04\n", "\n", - "E_Ideal vs. E_Premium: Mann-Whitney-Wilcoxon test two-sided, P_val:1.560e-31 U_stat=3.756e+06\n", - "I_Ideal vs. I_Premium: Mann-Whitney-Wilcoxon test two-sided, P_val:5.141e-61 U_stat=1.009e+06\n", - "J_Ideal vs. J_Premium: Mann-Whitney-Wilcoxon test two-sided, P_val:4.018e-37 U_stat=2.337e+05\n", - "E_Ideal vs. E_Good: Mann-Whitney-Wilcoxon test two-sided, P_val:5.201e-19 U_stat=1.480e+06\n", - "I_Ideal vs. I_Good: Mann-Whitney-Wilcoxon test two-sided, P_val:5.008e-13 U_stat=4.359e+05\n", - "J_Ideal vs. J_Good: Mann-Whitney-Wilcoxon test two-sided, P_val:1.006e-04 U_stat=1.174e+05\n", - "E_Ideal vs. E_Very Good: Mann-Whitney-Wilcoxon test two-sided, P_val:1.736e-02 U_stat=4.850e+06\n", - "E_Good vs. I_Ideal: Mann-Whitney-Wilcoxon test two-sided, P_val:5.906e-01 U_stat=9.882e+05\n", - "I_Premium vs. J_Ideal: Mann-Whitney-Wilcoxon test two-sided, P_val:5.159e-27 U_stat=8.084e+05\n" + "E_Ideal vs. E_Premium: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:1.560e-31 U_stat=3.756e+06\n", + "I_Ideal vs. I_Premium: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.141e-61 U_stat=1.009e+06\n", + "J_Ideal vs. J_Premium: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:4.018e-37 U_stat=2.337e+05\n", + "E_Ideal vs. E_Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.201e-19 U_stat=1.480e+06\n", + "I_Ideal vs. I_Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.008e-13 U_stat=4.359e+05\n", + "J_Ideal vs. J_Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:1.006e-04 U_stat=1.174e+05\n", + "E_Ideal vs. E_Very Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:1.736e-02 U_stat=4.850e+06\n", + "E_Good vs. I_Ideal: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.906e-01 U_stat=9.882e+05\n", + "I_Premium vs. J_Ideal: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.159e-27 U_stat=8.084e+05\n" ] }, { "data": { - "image/png": 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8vLp27eoyzVqkklA++qmnVJCf75Dze5lMWrxoUYXCeUxMjE6cOKGVK1eWu753797q37+/nn766Sop44YNGzRp0iQdOHCgSo5nZARzAADgcElJSUpLS1N0dLQ1lFu4u7tr4MCBio6OVlJSktq2beugUla9zMxMFeTny/u6W+Ru8rfruYvzM5V78ntlZmZy19wgCOYAAMDhzp07J0lq2rRpueubNGlis52rcTf5y8OnnqOLAQcjmANADZafn6+tW7fq1KlTatiwofr16yeTyeToYqEGqlevJJQePXpU4eHhZdYfO3bMZjsYw4ULFzR9+nR99dVX8vT01KhRo8ps88MPP+jNN9/Uvn37VK9ePf31r3/Vc889Jz8/P0nSyZMnNWvWLH3/fcnd+/r16+u+++7Tc889V+bpiaurWVcLALBatmyZBg4cqKVLl+rTTz/V0qVLNXDgQC1btszRRUMNFBERoeDgYK1fv17FxcU264qLi7V+/XqFhIQoIiLCQSVEeZ555hnt3btXixcv1rJly/Tvf/9bJ06csK5PTk7WiBEj1KNHD23evFmzZ8/WL7/8opEjR8psNkuSnnrqKV24cEHLli3Ttm3bNHLkSC1dulRff/21oy7LYQjmAFADLVu2TBs2bJC/v7/Gjh2rFStWaOzYsfL399eGDRsI57A7Dw8PRUVFKSEhQTNmzFBycrKys7OVnJysGTNmKCEhQSNHjnSpjp/O7rffftM333yjl156SZ07d9YNN9ygN9980+apW1xcnP7yl79o9OjRCgsLU+fOnfXmm2/qp59+0q5du5Sbm6sHHnhA06ZNU3h4uBo3bqzhw4erQYMGNaKz56VoygIANUx+fr42bdqkwMBALVu2TJ6eJX8K7rzzTvXp00cjRozQxx9/rMGDB9OsBXbVvXt3xcTEKC4uTtHR0dblISEhLjtUojM7ePCgJNl0xm3QoIEaN25s/T4pKUlHjx5Vx44dy+z/66+/qmvXrhoyZIi2bdumvXv36ujRozpw4IDOnj1b5slJTUAwB4AaZuvWrSouLtaQIUOsodzC09NTgwcP1sKFC7V161Y98MADDiolaqru3burc+fO9H1wApYZLS8N0KV/rxQXF+u+++7T6NGjy+xfr149ZWdna8iQIcrNzdVdd92l/v37q127dho8eHD1Ft6gCOYAUMOcOnVKktSlS5dy10dGRtpsB9hTeTN/bt68mZk/DeiGG26QVNK587bbbpNUMvyjpaOuJLVq1UopKSk2o+38+uuvmjVrlp599lkdOXJEv/zyi7799ls1aNBAkpSenq4//vjD2ga9JiGYA0AN07BhQ0nSrl27dOedd5ZZn5CQYLMdYC81deZPqWRMcWc7Z5MmTXTXXXfp1VdflclkUoMGDTRnzhzll5osaeTIkRo8eLCmTp2qIUOGKDMzU1OnTlVubq7CwsKUm5srqeTD15133qlTp05pzpw5KigosDlOTUEwB4Aapl+/foqPj9eqVavUp08fm8fOhYWFWr16tTw8PNSvXz8HlhI1TU2d+dPf319eJpNyT37vkPN7mUzy96/8xEavv/66Xn/9dY0fP17FxcV69NFHbcaa79Chg5YuXap58+apf//+8vX1Vbdu3fT888/LZDKpXbt2mjRpkpYvX665c+cqJCREd999txo2bKh9+/ZVxSU6FTdzTXxOUM0sFcmVZiYD4Foso7IEBgZq8ODBioyMVEJCglavXq309HQ99NBDGjFihKOLiRpk3759euGFFzRr1qxyxzFPTk5WdHS0XnvttWr7+1qdf79zc3N1+PBhNWvWTN7e3jbr0tLSlJlp/zvmUskHA2b9rF5Xeu0vxR1zAKiBLKF706ZNWrhwoXW5h4cHoRwOUZNn/gwODiYcQxLBHABqrBEjRmjw4MGMfgFDYOZPgGAOADWayWRiSEQYQumZP0u3MZeY+RM1BzN/AgAAh2PmT4A75gAAwCCY+RM1naGC+ZIlS/TNN99o5cqVkqShQ4dq165d5W77+uuv68EHH1RRUZE6duyovLw8m/Vjx47V008/LUn6/fffNW3aNCUkJMjX11cPP/ywnn76aZtP3atXr1Z8fLzOnDmjm266SVOmTOFxGQAAdta9e3d17dpVSUlJOnfunOrVq6eIiAjulKNGMEwwX716tebOnavOnTtbl82fP18FBQXW781ms8aPH6+MjAzdfvvtkqQjR44oLy9PH3/8serXr2/d1tfXV5JUUFCgqKgohYWFae3atTp27Ji17dq4ceMkSRs3btQbb7yhadOmKSIiQu+++65GjBihrVu30skEgEsrKioiAMFwPDw8GHIYNZLDg/np06f18ssva+fOnQoLC7NZFxgYaPP9qlWrtHfvXn388ceqXbu2JOnAgQPy8/Mrtwe3JH322Wc6efKk1q1bp4CAALVu3Vp//PGH3njjDY0ePVomk0mLFy/WkCFDdP/990uSXnvtNfXt21fr16/XqFGjqvyaAcAIypv6PDg4mKnPAcBBHB7Mf/nlF3l5eWnz5s1auHChTpw4Ue52586d09y5c/XUU0+pefPm1uUHDhxQixYtLnv8xMRE3XjjjQoICLAuu+WWW5SVlaX9+/erUaNGOnLkiLp162Zd7+npqc6dOyshIaHSwdxsNis7O7tS+wJAddu5c6feeust3XzzzXr66afVpEkTHTt2TJs2bVJsbKzGjx+vrl27OrqYgF2ZzWa5ubnZ/bxMMAQLhwfz3r17q3fv3lfd7r333pO3t7eioqJslh88eFCFhYWKiopScnKyQkJC9Pjjj1uH/0pNTVVoaKjNPpYKeOrUKetU1A0bNiyzTXJycqWvq6CgQPv376/0/gBQXYqLixUfH69WrVrpnnvuUVFRkQ4fPixJuueee3ThwgXFx8fLz8/PZsg6oCaw9zj+aWlp+vtTo5WXX3D1jatBLZOX3lm0mHBuEA4P5tciKytL69at09ixY1WrVi2bdYcOHVJxcbHGjRun0NBQbd++XZMmTVJBQYEefvhh5ebmyt/f32YfyzHy8vKUk5MjqewbsVatWmU6lFaEl5eXWrZsWen9AaC6/PLLL0pPT9dzzz2n1q1bl1k/dOhQvfjii5KkG264wd7FAxwmJSXF7ufMzMxUXn6BHmrjrwa+9o1lZ7MLteFApjIzMysUzHv37m3TwsHNzU2+vr6KiIjQP/7xD0VGRlZHccsVExOjEydOWAcOcXZOEcy//PJL5efna8CAAWXWffLJJyoqKrK2OQ8PD9fJkycVFxenhx9+WN7e3srPz7fZxxK4fX195e3tLUnlbuPj41PpMlsqKQAYjaWZXZs2bcr9PWcJ69nZ2fweQ43iiGYsFg18PXWdn5fDzl9RI0eO1MiRIyWVNAFKT0/XnDlz9MQTT2jr1q267rrr7FKOyZMnq6ioyC7nsgeneEb55ZdfqlevXmXufEuSt7e3NZRbtG7dWqmpqZKk0NBQm45Nkqzfh4SEWJuwlLdNSEhIlV0DABhF6anPy8PU5wCuxtfXV0FBQQoKClJwcLBat26tqVOnKjc3V1988YXdylGnTp0yg4U4M6cI5omJiTadMy0yMzPVpUsXbdiwwWb5vn371KpVK0lSZGSkkpKSlJWVZV3//fffq3bt2goPD1f9+vXVrFkz7dy507q+sLBQiYmJdn0UAwD2Unrq8+LiYpt1TH0OoLIs/fZMJpN69+6t119/XXfffbe6du2qXbt2yWw267333lOfPn3Uvn17PfDAA9q8ebN1/507dyoiIkJffPGF7rzzTrVr107Dhg3TqVOnNH36dHXu3FndunXTokWLrPvExMRo6NCh1v3btGmj33//3eaYpZcNHTpUr7/+uiZMmKCOHTvq1ltv1QcffKDdu3frgQceUPv27fXYY4/pyJEjdviJlWX4YH7q1CmdP3++3OEQ/f39dcstt+itt97S9u3bdeTIEb377rvavHmzdXKhvn37KigoSM8884ySk5P15Zdfas6cORo5cqS1XfnIkSO1bNkybdy4USkpKXrhhReUm5urhx9+2K7XCgD2UHrq8+nTp+vTTz/VF198oU8//VTTp09n6nMAFXb69Gm9+uqr8vX1Va9evSSVDHM9ZcoULV26VB06dNBbb72lDz74QC+++KK2bNmiYcOG6ZVXXtHq1autxykqKtKiRYs0e/Zsvf/++0pOTtYDDzwgLy8vrV+/Xo899pjmzp2rAwcOVLqsK1eu1A033KDNmzerT58+mj59ul555RW98MILWrVqldLS0vTmm2/+6Z9JZRi+jfmZM2cklR3T3OK1117T/Pnz9fLLL+uPP/5QixYt9Pbbb6tHjx6SSjpxLl26VFOnTtUjjzyigIAADRo0SH//+9+tx3jkkUd04cIFzZ07V+np6brpppu0bNkyHuMCcFndu3dX//79tWnTJiUkJFiXu7u7q3///oxjDuCKlixZovj4eEklLQ3y8/PVokULzZ0719q+vFevXtbfJdnZ2Vq+fLnmzJmj2267TZLUpEkTnThxQnFxcRo8eLD12P/4xz+sE0zdcsst+umnnzRx4kS5ublp1KhReuedd3To0CG1adOmUmW/4YYbrKP8DRkyRGvXrtXQoUOtQ8T269dPX375ZaWO/WcZKpjHxsaWWdauXbsrfiry8/PTpEmTNGnSpMtu07RpU2vluZyoqKgyQzECgKvasWOHNm7cqM6dO6tTp04ymUzKz8/X7t27tXHjRrVp04ZwDuCyHnvsMWsTEnd3dwUGBqpOnTo22zRt2tT6dUpKivLy8vTcc8/ZDMNqCfW5ubnl7ufr66tGjRpZO+ZebtCOimjSpIn1a0sH+MaNG1uXeXt728w8b0+GCuYAgOpXVFSkuLg4RUZGavLkyTZ/JPv166cZM2YoPj5eXbt2pTkLgHIFBATYBOjyWEK0VDJyiyTNnTvXZqJIi9LDVlvaqlv8mfkUyhuxxcur7Og3RpmzwRilAADYTVJSktLS0jRw4MAyf4zc3d01cOBAnT59WklJSQ4qIQBX07x5c3l6eurkyZNq2rSp9d/27dsVFxdXJcHYErhLD/jhqE6clcUdcwCoYc6dOydJl73bZXnMa9kOQPU7m13o0uesU6eOHnvsMc2bN09+fn66+eabtXPnTs2aNUujRo2qknO0bt1avr6+evfdd/XMM8/o6NGjWrZsWZUc214I5gBQw5Qex7y8Ea8YxxywH39/f9UyeWnDgUyHnL+WyavceWKqw6RJk1S3bl3NmzdPaWlpatiwocaNG6cnnniiSo7v5+enWbNmafbs2br77rsVHh6u559/XmPGjKmS49uDm9nS6AdVZt++fZJk7VEMAEZSVFSkJ598UmFhYWXamBcXF2vGjBk6evSolixZQhtz1CjV+fc7NzdXhw8fVrNmzWzaXkslkxpmZjommPv7+ys4ONgh564prvTaX4o75gBQw1jGMY+NjdWMGTM0cOBANWnSRMeOHdP69euVkJCgmJgYQjlgJ8HBwYRjSCKYA0CN1L17d8XExCguLk7R0dHW5SEhIYqJiWGoRABwAII5ANRQ3bt3V9euXZWUlKRz586pXr16ioiI4E45ADgIwRwAajAPDw/6wwCAQTCOOQAAAGAABHMAAADAAAjmAAAAgAEQzAEAAAADoPMnAACAAzHBECwI5gAAAA6Slpam0U89pYL8fIec38tk0uJFiyoVzs1mszZu3KiNGzfq0KFDysrKUsOGDXXbbbfpySefVFBQUDWUuMTvv/+uPn36aMWKFeratWu1ncfeCOYAAAAOkpmZqYL8fPl1DpJHHZNdz110IV9ZiWeUmZlZ4WBeXFyssWPHKjExUaNHj9ZLL72k2rVr69ChQ1q0aJEGDBigjRs3qn79+tVUetdEMAcAAHAwjzomedat5ehiXLPly5dr+/btWrdunW688Ubr8uuuu05du3bVPffco7i4OE2cONGBpXQ+dP4EAADANTObzVq1apXuv/9+m1Bu4e3trRUrVuiZZ56RJJ06dUoTJkzQX/7yF3Xo0EFRUVFKTk622WfTpk26//771a5dO/Xu3VvvvPOOioqKrOsPHjyoYcOGqUOHDrr99tv13XffVes1OgrBHAAAGEpRUZH27dun7du3a9++fTYBDY73+++/68SJE+revftlt7n++utlMpmUlZWl//mf/9Hp06e1aNEirV27Vt7e3hoyZIhOnDghqeTu+4svvqhHH31Umzdv1j/+8Q/FxcUpNjZWknThwgUNHz5cderU0fr16/XKK69o0aJFdrlWe6MpCwAAMIwdO3YoLi5OaWlp1mXBwcGKioq6YhCE/Zw9e1aSVK9ePZvlo0eP1s6dO63fX3fddRo8eLDOnz+vDRs2WLd/88031bdvX61evVrR0dF67733NGTIEA0ePFiSFBYWpvT0dM2aNUvjxo3Tp59+qpycHMXGxqpOnTpq1aqVXnjhBY0ZM8ZOV2w/BHMAqMGKioqUlJSkc+fOqV69eoqIiJCHh4eji4UaaseOHZo5c6ZMJttOkOnp6Zo5c6YmTZpEODeAunXrSpIyMjJslk+dOlW5ubmSpJUrV+rrr7/WwYMHFRYWZhPivb291a5dOx08eFDnzp3T2bNn1alTJ5tjdenSRQUFBfrtt9+sx6hTp451fceOHavr8hyKYA4ANRR3JmEkRUVFeueddyRJ7du31yOPPKKmTZvq6NGjWrdunRISErRo0SJ17dqVD48O1rhxYwUFBWnnzp26++67rctDQkKsXwcEBEgqaY9enuLiYnl6el5xvSR5enrKzc3N+r2Fp6drRljamANADbRjxw7FxsYqLCxMs2bN0rp16zRr1iyFhYUpNjZWO3bscHQRUcPs27dPGRkZioiI0JQpUxQeHi4fHx+Fh4drypQpuuGGG5Senq59+/Y5uqg1noeHh4YNG6ZNmzaV6cRpcerUKUlSmzZtdOTIEf3xxx/WdXl5efr555/VsmVLNWjQQA0aNNDu3btt9k9MTJSXl5eaNGmi8PBwHTlyROfOnbOu//nnn6vhyhyPYA7YER2aYARFRUWKi4tTZGSkJk+ebBOAJk+erMjISMXHx1M/YVeWoDVo0CC5u9vGE3d3dw0aNMhmOzjWE088ob/+9a8aNGiQFi9erOTkZP3+++/6+uuvNXLkSH300Ue65ZZbdN999ykwMFDPPPOM9u7dq+TkZE2YMEHZ2dl69NFHJUlRUVFatWqV1qxZo6NHj2rLli1asGCBHn30UdWpU0f33HOP6tevr+eee07JycnatWuXZsyY4eCfQPVwzecAgAHRbABGkZSUpLS0NEVHR5cbgAYOHKjo6GglJSWpbdu2DiolaprLNWmo7HbOpuiC/Wf+/DPndHd319y5c7V161Z99NFHWrFihTIzM9WgQQN17txZq1atUmRkpCRp1apVio2N1fDhwyVJnTp10gcffKDGjRtLkkaOHCmTyaT3339fr732mkJDQ/W3v/1NUVFRkiRfX1+9//77mjZtmv7nf/5HAQEBGjdunCZNmvTnfgAGRDAH7IAOTTASy+Pgpk2blru+SZMmNtsB9tCuXTutW7dOa9asUdu2bW0+NBYXF+uDDz6wbudK/P395WUyKSvxjEPO72Uyyd/fv9L79+vXT/369bviNo0bN9bChQuvuM2QIUM0ZMiQKx7j3XfftVn20EMPXXtBnQTBHKhmdGiC0VhGRzh69KjCw8PLrD927JjNdoA93HTTTfL391dSUpKmT5+uRx55RE2aNNGxY8e0bt06JSUlKSAgQDfddJOji1qlgoODtXjRImVmZjrk/P7+/goODnbIuVEWwRyoZpd2aLLcBbJ0aIqJidH+/fu1b98+dejQwbGFRY0QERGh4OBgrV+/XpMnTy5zZ3L9+vUKCQlRRESEA0uJmsbDw0NjxozRzJkz9dNPPykhIcG6rlatkqnq//73v7vkDYzg4GDCMSTR+ROodqU7NJnNZpvOn2azmQ5NsDsPDw9FRUUpISFB06dP16effqovvvhCn376qaZPn66EhASNHDnSJQMQjK179+6aNGmSdag9i4CAAJr8oUbgjjlQzSwdlX755Re9/fbbZTp/9u7d22Y7wB66d++u/v37a9OmTTZ3Jt3d3dW/f38CEByme/fu6tq1KxNfoUYimAPVzNKh6YMPPlBkZKSio6Nt2pivXbvWuh1gLzt27NDGjRvVuXNnderUSSaTSfn5+dq9e7c2btyoNm3aEM7hMB4eHowIhBqJYA5UsxtuuEFubm42d8QvvTvu7u6uG264wd5FQw116TjmpduY9+vXTzNmzFB8fDwdkgHAzgjmQDU7cOCANYhfrkNTcXGxDhw4wB0i2AXjmAOAMdH5E6hmlrGgn3vuuXI7ND377LM22wHVrfQ45uXNRss45gDgGIa6Y75kyRJ98803WrlypXXZlClTtH79epvtrr/+en399deSSu40LliwQOvXr9eFCxcUGRmpl156yTqblCTt379fM2bM0M8//6x69epp+PDhGjZsmHX9tRwD9nfo0CFraJWkOXPmqFWrVg4sUeVYxoIODQ3Ve++9V6ZD06FDh2y2A6qbpa598skn2rZtW5kOyXfeeafNdjC2jIwMvfDCCzp//rzq1q2r1157rcxNAADOwTDBfPXq1Zo7d646d+5ss/zAgQMaPXq0zWxQpds8vvPOO1qzZo1iY2MVGhqqWbNm6YknntCWLVtkMpl0/vx5jRgxQr1799bUqVO1Z88eTZ06VbVr19aAAQOu6Riwv/vuu6/MMktI37Jli72L86eUHjM6JibGZh1jRsMRIiIiFBAQoBUrVpTbIXnlypUKDAykTjqBoUOHKj093fr9hQsXNGTIEAUGBtrc5IKxpaWlMcEQJBkgmJ8+fVovv/yydu7cqbCwMJt1ZrNZKSkpevLJJxUUFFRm3/z8fMXHx2vChAm67bbbJElvvfWWevTooc8//1z33nuv1q1bJy8vL7366qvy9PRUixYtdPToUb377rsaMGDANR0D9nVpKG/btq327dtns96ZwrllzOiZM2fqscceU35+vnWdZSSMSZMm0ckOdnVpB+SrfQ/juTSUl5aenq6hQ4cSzp1AWlqanho9WvkFBQ45v8nLS4sWL77mcD506FBdvHhRGzZsKHf9lClTlJCQoM8++6wqi3lFFy9e1PLly/X555/r+PHjKi4uVrNmzXTPPfdo2LBh1X6TdcOGDZo0aZIOHDjwp4/l8GD+yy+/yMvLS5s3b9bChQt14sQJ67pjx44pOztbzZs3L3ff5ORkXbx4Ud26dbMu8/f3V0REhBISEnTvvfcqMTFRXbp0kafnfy/1lltu0ZIlS3T27FmdPHnyqseA/ViadUiSl5eXCgoKrKHc8r1lO2ds1nIpNzc3RxcBNVBSUpIyMzM1bNgwbdu2TdHR0dZ1ISEhGjZsmFasWEHnTwPLyMiwhvIOHTpo8ODB1qceq1ev1p49e5Senq6MjAyatRhcZmam8gsK1MfXT3U97BvLzhcV6qvsLGVmZl5zMH/44Yc1ceJE/frrr2rRooXNury8PG3btk2jRo2qjuKWKy0tTUOHDpWHh4eeeuoptW/fXpKUmJioefPm6bvvvtPSpUud5u+tw4N57969rROsXOrgwYOSpJUrV+o///mP3N3d1bNnT40fP1516tRRamqqJKlhw4Y2+wUHB1vXpaamqnXr1mXWS9KpU6eu6RiVYTablZ2dXen9a6rSbcrbtWunBx98UE2aNNGxY8e0adMm7d6927rdhx9+6KhiVkhxcbGWLl2qTp066dlnn9WBAweUnp6uwMBAtWnTRnPmzFFcXJzatWtXZoQMONbp06d18eLFCu1Tu3ZthYSEVFOJqobld1ufPn3Ur18/7d+/31onb7jhBuXl5WnFihVKTU0t84cXxmBpFufr66vnn39e7u7uMpvNatKkiZ5//nlFRUUpOztbMTExevPNNx1c2oorLi4uUy/t8fvRbDY7LMDV9fBUkKfDY9lV3XnnnZo2bZq2bNmiZ555xmbdl19+qZycHD344IN2K89LL72kgoICffTRR/Lz87Mub9Kkidq1a6f77rtP//nPf9SrVy+7lenPMHQNOHjwoNzd3RUcHKzFixfr2LFjeuONN3To0CG9//77ysnJkaQyjyhq1aqljIwMSVJubm6566WST3bXcozKKCgo0P79+yu9f03n5+enu+++WykpKdqzZ4/1e8tTEklO8/M9fPiwzpw5o/vvv1+HDh2Su7u7tVPdoUOH1KFDB+3evVvbtm1Ts2bNHFxaWFy8eFGzZ8+ucJMONzc3TZgwQbVr166mkv15lt9t//nPf9S4cWObOnngwAEdP37cup2zvM9qGkuH3Z49e5b7+PzWW2/V559/rrS0NKd7DZOSkvT555/bNNMJDAzUHXfcYZd+D/QtuzJvb2/dc889+uSTT8oE840bN6pXr14KCgqS2WzW0qVLtXbtWp09e1ZhYWGKiorS/fffL0nauXOnRowYoWeeeUZxcXG6/vrr1bBhQ124cEErVqywHvO3335Tv3799Mknn5R5Un7s2DH961//0uuvv24Tyi1atmyprVu3qmnTptZlP/74o9566y398ssv8vT0VO/evTVx4kTVrVtXUkluXLx4sbZs2aK0tDQ1b95cf//7362d4iXpiy++0Ntvv60jR46obdu2VToZm6GD+VNPPaVBgwZZf1itW7dWUFCQHnnkEe3bt0/e3t6SStqaW76WSgK3j4+PpJIKVLpNr2W9VHKn4VqOURleXl5q2bJlpfev6bKysrR48WKdOXPGuiwoKMjm7qWzTMhjGXKuZ8+eNnXMIiwsTHFxcQoICHCaa6op5s2bV+aO+YkTJ7RgwQKNHTtW119/fZl9nOGOeZs2bfTPf/5Te/bsUd++fW3uRBYXF2vLli0KDg7WXXfdxVMcg6pdu7by8/N16NAhjRgxosx6S7CpXbu2U/1e2blzp9avX6+OHTuqQ4cO1n44e/bs0fr16zV+/Hh17dq12s6fkpJSbcd2JQMGDNDatWv1448/qmPHjpKkM2fOaMeOHVq4cKGkkv56n3zyiV566SU1b95cCQkJeuWVV3ThwgUNHjxYUslkZ9u3b9eHH36onJwcnTx5UmPGjNGpU6esLRk2bdqktm3bltt8ddeuXZJk0xz5UqX7L+7du1dDhw7Vo48+qpdffllnzpzRq6++qqioKK1fv14eHh569tlnlZSUpFdeeUVNmzbVJ598on/84x9asGCB+vbtqx9++EFPP/20xo4dq3vuuUeJiYmaNm1alfxcJYMHc3d3d2sot7C8MKmpqdYXLS0tzTruruX7Nm3aSCoZoq70UGCW9VJJW8rCwsKrHqMy3Nzc5OvrW+n9a6oBAwboo48+klRyh2TixInWdpPvvvuuNagPGDDAaX6+oaGhkkrqVHh4eJn1x44ds27nLNdUU5T3BMPy4apFixZO/eH7iSeeUGxsrN566y0NHDjQ2mRs/fr1+uGHHxQTE1PuHSgYw1NPPaXXXntNv/76q4qLi21eq6ysLP3222/W7Zzl90pRUZFWrVqlFi1a6Pjx4/rhhx+s64KCgtSiRQutXr1aPXv2rLbO8s7SDtnR2rVrp9atW2vLli3WYL5582bVr19fPXv2VHZ2tpYvX645c+ZYB9Zo0qSJTpw4obi4OGswl6SRI0daw3OrVq3UoEEDbd68WaNGjVJxcbE+/vhjPfnkk+WW448//pBUdmjXzp07q6ioyPr9fffdp1dffVXx8fFq06aNXnzxRUklv8fnzJmjBx54QN98840aNWqkr776SosXL7aW++mnn1ZycrIWL16svn37atWqVbr55ps1duxYSSV/Jw4ePGhzl//PMHQwnzhxotLS0rR8+XLrMktHwJYtW6px48by8/PTzp07raE6MzNTSUlJ1uEVIyMjtXbtWhUVFVnfyN9//72aNWum+vXrq06dOlc9BuynQ4cO1mB+6NAhRUdH68Ybb9Qvv/xSZjtnUXq4xEunP2e4RDhK9+7dFRMTo7i4uDKdP2NiYqr00SyqXpcuXeTm5iaz2az/+Z//UevWrTVo0CCtWbPG2j/Lzc1NXbp0cXBJr51lRtq0tDR16dLF5sbM+vXrrXdH6ZRsDAMGDNCSJUv0wgsvyNPTU5s2bVL//v3l4eGhlJQU5eXl6bnnnrP5m1dYWKj8/Hzl5uZal5W+o+3p6an7779fH3/8sUaNGqXvv/9e586du+xAHJabtxkZGWrQoIF1+YYNG6zNECdMmGBtOXHw4EH95S9/sTlGeHi46tSpowMHDlifkHbq1Mlmm8jISM2ZM+eyx+jYsWPNCOZ33nmn/v73v2vBggW6//77dfjwYb366qu69957rR2ShgwZotmzZ6tevXq6/vrrNWvWLIWGhuqOO+6QVFJxli5dqsmTJ+uJJ57Q3r17tXz5ck2dOlVSSVuyqx0D9lPe3YpLQ/nltjMqy3CJsbGxmjFjRpm7kwkJCYqJiWG4RNhd9+7d1bVr1zKTXlEXjc/Dw0MxMTGaOXOmpJKw8Morr9hs42y/V86ePSupJBSVvokRHh6uyZMn69VXX9Xu3but28Gx7r//fs2ePVvffvutgoKCdOjQIS1YsEDSf4dbnTt3brkj65Vux2/p92cxYMAAxcXF6eeff9bmzZvVp0+fy44sZAnQu3bt0t13321dXroFROkmpJfrM2Q2m+Xl5XXZazWbzdbR/dzc3FRcXGyz/kr7VpShg3mfPn00d+5cvfvuu3rvvfdUp04d3XfffTadDcaNG6fCwkJNmTJFubm5ioyMVFxcnPWHVL9+fS1dulQzZsxQ//79FRQUpIkTJ6p///7XfAzYz+XG5K3sdkbB3UkYlYeHB3cfnVT37t01adIkLVq0yOZ3Yt26dTV69Gin+71imWCnW7duZfo2uLu765ZbbtHu3bsdNhEPbNWrV0+9e/fWP//5TzVo0ECRkZHWTpbNmzeXp6enTp48qb/+9a/WfVasWKGUlBS9+uqrlz1uixYt1LFjR23dulVfffXVFUcVatGihW699VbNnz9fPXv2LNP8Li8vT+fOnbP2B2rTpo11dDeL5ORkZWVlqUWLFtbtdu/ebVPuxMREa9PF8PBw/fjjjzbH+Pnnny9bxooyVDCPjY0ts6xfv37q16/fZffx8PBQdHS0Tdi5VLt27a44tN61HAP2UfpTsaXTj0WtWrWsHXedcVxe7k4CqGqu9HvF399fkvTdd9+pd+/eSk5Otl5TeHi4vv/+e5vtXM35okKnO+fDDz+sCRMmyN/fX08//bR1eZ06dfTYY49p3rx58vPz080336ydO3dq1qxZ1zTG+YABAzR9+nT5+/uXaTZyqdjYWI0cOVIPPfSQRo0apZtvvllubm5KTEzUe++9pxMnTmj48OGSpBEjRmjQoEGaNm2aBg0apLNnz2ratGmKiIhQt27d5OXlpb/+9a+aOnWq3Nzc1LRpU3366af66quvNHfuXEklbeIHDhyo119/3ToYyapVqyr9M7yUoYI5YHk85Ofnp+XLl+vgwYPWX8ytW7fW8OHDlZWVVeYxkrPg7iQAlM/SRnj37t2XnSW59Hauwt/fXyYvL32VneWQ85u8vCr9YefWW2+Vr6+v0tPTbYYTlKRJkyapbt26mjdvntLS0tSwYUONGzdOTzzxxFWP269fP02bNk0PPvjgVT9kBgUF6X//93+1Zs0arV27VjNnzlR+fr4aNWqkXr16aciQIdamLe3bt9fSpUs1d+5cPfjgg/Lz81Pfvn313HPPWVtJzJkzR3PmzNHkyZOVmZmp1q1ba/78+br99tsllYwI995772nWrFlatWqVWrVqpdGjR2v27NmV+RGW4WZm3uUqZ+mgSgCruFWrVunDDz+Um5ubIiMjy22PbTab9eijj9I5Fw6RkpKi8ePH66233nLqUVngGnbs2KG4uDib0ceCg4MVFRXldE1ZioqK9PjjjysjI+OyT0wDAwO1fPnyansiUJ1/v3Nzc3X48GE1a9aszNC5aWlpDmui4+/vf82zftrL8ePHdccdd2jr1q02nUOd1ZVe+0txxxyGYvmc+Nhjj+mrr74q0x770Ucf1dq1ays86QsAuJodO3YoNjZWnTt31kMPPWQNs7t371ZsbKxT9l+x/G5v166dOnfubL2mxMREJSYmuuzv/uDgYMOFY0c4deqU9u7dqzVr1qhHjx4uEcorimAOQ2nXrp3WrVunn376SYsXLy7TxnDKlCnW7QCgpioqKlJcXJxatGihI0eOKCEhwbrOMuZ3fHy8unbt6jTtzZOSkpSZmalhw4Zp27ZtSkxMtK4LCQnRsGHDtGLFCoZLdGHnz59XTEyMwsLCrCO81DQEcxjKTTfdJH9/fyUlJWnmzJl65JFHFBkZqWPHjmnmzJlKSkpSQECAbrrpJkcXFQAcxhXH/LbMknzvvffqoYceKtOhNS8vTytWrLBuB9cTERFRZsSTmoZgDkPx8PDQmDFjNHPmTP300082d4EsY53+/e9/d5o7QABQHVxxzG/L7I1Hjx5VeHh4mQ8UllmSL53lEXAl7lffBLAvy9i8lw6JGBAQoEmTJjldm0kAqGrXMuZ36e2cQelZki8deYtZklFTEMzhNFy10w8AVFTpMb/LC7HOOOa3ZZbkhIQEzZgxQ8nJycrOzlZycrJmzJihhIQEjRw5kiemcGk0ZYHhlB5pYMCAAS4x0gAAVCXLWN4//PCDZsyYUWZo2R9++MFmO2fBLMmo6QjmMBRXHGkAAKqapdmHv7+/jhw5UibEtmjRQhcuXHDKZh+uNJspUFEEcxhK6ZEGTCaTzbqMjAydOXPGup2zjDQAGFlRUREByAlZmn1cbhzzxMRExcTEOO1rWdNmSWaCIVgQzGEopUcQaN++vR555BHrEGDr1q2z3kF3ppEGAKNypVkja6LSzT5KP110hWYfNekDY1pamp566imbmU7tyWQyadGiRYRzgyCYw1DS09MlSWFhYZo0aZKSk5O1a9cu1atXT5MmTdL48eN19OhR63YAKsfSlyMyMlLR0dE2Y2DTl8N5dO/eXZ07d9bWrVt16tQpNWzYUP369SvzxNGZ1LQPjJmZmcrPz1fLxj3kUyvg6jtUoZy8DKUc/z9lZmZWKJgPHTrUOlb+pUaOHKnnn3/+ivvv3LlTw4YN01dffaVGjRpVqMyujmAOQ7lw4YIkqaCgQKNGjbI2XZFK2ph7eXnZbAeg4ix9OSIjI8sdA3vGjBn05XAS5YXYzZs3O22I3bFjh2bOnFnmg0V6erpmzpzp0kPm+tQKkJ9vfUcX45r169dPkydPLrPcx8fnqvt27NhR33zzDWPSl4PhEmEobm5ukqQTJ06ooKBAY8aM0fvvv68xY8aooKBAJ0+etNkOQMVZ+nIMHDiw3DGwBw4cqNOnTyspKclBJcS1sDz1CAsL06xZs7Ru3TrNmjVLYWFhio2N1Y4dOxxdxAopKirSO++8I6mkKWPpa2rfvr0kadGiRSoqKnJkMfH/eXt7KygoqMw/Pz+/q+5rMpkUFBTEB/9yEMxhKDfeeKOkkje8yWTSwoUL9fjjj2vhwoWqVauWvL29bbYDUHGWKc2bNm1a7vomTZrYbAfjufSpR3h4uHx8fKxPPSIjIxUfH+9UIXbfvn3KyMhQRESEpkyZYnNNU6ZM0Q033KD09HTt27fP0UXFVWRkZGjKlCnq0aOHbrzxRnXr1k1TpkxRTk6OpJKmLG3atNHvv/8uSerdu7def/113X333eratetlm8nUBARzGIrl03Nubq6aNGmi0aNHa9y4cRo9erQaN26s3Nxcm+0AVFzpqc/Lw9TnxueKTz1+/vlnSdKgQYPKvaZBgwbZbAfjiomJUVJSkhYsWKDPPvtMkyZN0qZNm/Thhx9edp9Vq1ZpypQpWrp0qTp06GC/whoMbcxhKKU7de7du1eJiYnW72vVqlXudgAqpvTU56XbmEtMfe4sXPGpR+nZncsblaW87eA4W7Zs0WeffWazrFOnTlq6dKn+8pe/KDIyUm3atJEkNWrUSKtWrdLBgwcve7xevXq5bP+BiiCYw1Asd+iGDRumrVu32nT+DAgI0F133aUVK1ZwJw/4E0qPgV3erJEJCQlOPQa2K0tNTVVWVpa1ScC3336rsLCwMtsdOXJEkpSTk6PU1FSFhobasZSV065dO61bt06LFy9WXl5emc7/lpsz7dq1c1QRUUrv3r01YcIEm2WW5qaDBg3S119/rY0bN+rIkSNKSUnR77//rubNm1/2eJf7kFnTEMxhKJY7ecnJyZo/f75WrFihkydP6rrrrtOwYcM0Z84c7uQBVYCpz51PRkaGRo0apeLiYuuyefPmXXGfhQsXyt3dXStWrFBAgH2H4quom266Sb6+vvr9998VGBioMWPGqEuXLtq1a5dWr16tM2fOyNfXVzfddJOjiwpJtWvXLjdMFxcXa9SoUTp06JDuvfde3X333brxxhv14osvXvF4llBf0xHMYSiWO3kzZ87UY489Zl2+Z88e/fOf/5QkTZo0iTt5QBVg6nPnEhAQoCVLligrK0tSSXO/5cuXKyIiQm3bttXatWv12GOPad++fUpKStLw4cPVrl07+fn5GT6UW3h6lsSS7OxsLVy4UAsXLpT036aMliFzYVz79+/Xf/7zH61bt846mk5BQYGOHTumxo0bO7h0xkcwh+EcOHBAUklnn9J3hizfHzhwgLt5QBWpaVOfO7vSTVJatmyp0NBQxcXFae3atZKktWvXOu1Tj6SkJGVmZl61KWNSUpJL1tmcvAyXOGeDBg3k6emprVu3ql69ekpPT9fixYt15swZh81u6kwI5jCU/Px8bdq0SYGBgVq6dKkOHjxovZPXunVrPfHEE/r44481ePBgp57ZDgCqguWpxxdffKGFCxdqzJgxuv32253yqYelo+q9996rhx56qMyTnLy8PK1YscKpOrReC39/f5lMJqUc/z+HnN9kMsnf37/KjhcSEqLY2FjNnz9fq1evVlBQkG677TYNHz5cX3/9dZWdx1URzGEoW7duVXFxsYYMGaJatWqVuSsyePBgLVy4UFu3btUDDzzgoFICgHF4eHioZcuWkkruojtjKJdsh/EMDw8v8/vfVYfxDA4O1qJFi5SZmemQ8/v7+ys4OLhC+6xcufKK6++77z7dd999ZZZPmjRJktS1a1fr03FJBPZSCOYwlFOnTkmSunTpUu76yMhIm+0AAK6hJg/jGRwcXOFwDNfEBEMwlIYNG0qSdu3apaKiIu3bt0/bt2/Xvn37VFRUpISEBJvtAACuwdL5PyEhQTNmzFBycrKys7OVnJysGTNmKCEhQSNHjnTaJwLAteCOOQylX79+io+PV3x8vNauXauzZ89a1zVo0EDZ2dny8PBQv379HFhKwHWUN5ELwQeOwjCeqOkI5jAUk8mkyMhI7dy5U/n5+erZs6datmyplJQU7dixQ4WFheratSsdP4EqsGPHDsXFxSktLc26LDg4WFFRUQQgOAzDeKImI5jDUIqKinT48GEFBgYqPT1d//nPf/Sf//zHuj4wMFBHjhxRUVGRU/6Szs/P19atW3Xq1Ck1bNhQ/fr140MGHGLHjh2KjY1VZGSkoqOj1bRpUx09elTr169XbGwsdyeBamI2mx1dBNhZRV5zgjkMJSkpyXr37uabb1ZeXp4yMzPl7++vWrVq6YcffrBu52zj2C5btkybNm2yGZs9Pj5eDz74oEaMGOHAkqGmKSoqUlxcnCIjI2062YWHh2vy5MmaMWOG4uPj1bVrV6f8AAzn5qpPciyTI2VnZ8vHx8fBpYE9ZWdnS7q2CbII5jAUS5vy4OBg/fjjjzafMt3c3BQcHKy0tDSbtufOYNmyZdqwYYMCAwM1ZMgQ6zTTq1at0oYNGySJcA67sXwAjo6Othn5QiqZyGvgwIGKjo52yg/AcG6u/CTHw8NDgYGB1g8cvr6+cnNzc3CpUJ3MZrOys7OVlpamwMDAa7rRQTCHoVjGcbVU4ktDrOUXmqPGe62M0pMmLVu2zDrl9J133qk+ffpoxIgRTJoEu7JM0NK0adNy1zdp0sRmO8AeasKTHMvMraWfBsD1BQYG2szaeyUEcxiKr6+vJMnT01PvvvuuUlJStHfvXl133XV69913NWjQIBUWFlq3cwalJ01yc3PTvn37bDo0MWkS7K30RC6tWrUq08nOVSdygbHVhCc5bm5uatiwoYKDg1VQUODo4sAOvLy8KvRBkmAOQ0lJSZEkFRYWasiQIcrPz7euM5lMKiwstG53++23O6SMFWWZDMnNzU1PPvlkmXaTjzzyiM12QHWzTOSyZMkSZWRk6MyZM9Z1QUFBCggIcNmJXGBcNelJjoeHh9Pe9Uf1MtQEQ0uWLNHQoUNtln399dcaMGCAOnbsqN69e+v1119Xbm6udf3u3bvVpk2bMv927txp3ea7777TQw89pPbt2+uuu+7Sp59+anOOvLw8TZ06Vd26dVPHjh313HPPucQbH8ZgmQxp/vz5CgsL06xZs7Ru3TrNmjVLYWFhWrBggc12QHXz8PDQrbfeqpSUFBUUFGjMmDF6//33NWbMGBUUFCglJUV/+ctfCA6wq9JPcsrDkxzUBIYJ5qtXr9bcuXNtliUmJmrs2LG6/fbbtXHjRr388sv65z//qalTp1q3OXDggJo0aaJvvvnG5l/Hjh0lSb/++qtGjRqlHj16aMOGDRo4cKAmTpyo7777znqMV155Rd98843mz5+v999/X7/99pvGjRtnl+uGrdJtsErfLb/0+2ttq2UEd9xxh6SS5jkTJkxQQUGBdu3apYKCAk2YMMHa5tyyHVDdioqK9M0336hly5YymUxauHChHn/8cS1cuFC1atVSy5Yt9e2336qoqMjRRUUNYnmSs379epvRqySpuLhY69ev50kOXJ7Dm7KcPn1aL7/8snbu3KmwsDCbdWvXrlXXrl01evRoSVJYWJjGjx+vKVOmaOrUqTKZTDp48KBatmypoKCgco///vvvq02bNho/frwkqUWLFkpKStLSpUvVrVs3nT59Wps2bdLixYvVuXNnSdKcOXN011136ccff7QGfNhH6Trg5uZWZlQWy/eX1hUjK90859FHH73sNaWkpDhtu0k4l9JtectrY37o0CGnb8sL5+Ph4aGoqCjFxsZqxowZGjhwoJo0aaJjx45p/fr1SkhIUExMDE9y4NIcHsx/+eUXeXl5afPmzVq4cKFOnDhhXTdy5MhyO4AUFBQoKytL9erV04EDB9SpU6fLHj8xMVF9+/a1WXbLLbdoxowZMpvN2r17t3WZRbNmzRQSEqKEhASCuZ2VbkLk7++vv/71rwoNDVVqaqr+9a9/KSMjo8x2Rle6rJdOMlD6e2e6Jji30m15PTw8yoRvV2rLC+fSvXt3xcTEKC4uTtHR0dblISEhTj1UInCtHB7Me/furd69e5e77tLHVQUFBVq+fLluuukmaxuzQ4cOqW7dunrooYd0+vRptW7dWuPHj1e7du0kSampqWWaPQQHBysnJ0fnz5/X6dOnVbduXdWqVavMNqmpqZW+LsvYlaiYpKQkSSVPNg4fPqxNmzZZ17m7u6tFixb69ddflZSUpG7dujmolBVjmUiiTZs2mjJlir744gudPn1aISEhuv322zV9+nQdOHBAPj4+1BknYOnjkpub67Svl2VUowMHDqhly5bav3+/0tPTFRgYqBtuuMH6lMfX19dpr7GmcYV6adGhQwfNmzevTL10d3ev9mszm82MLQ6Hcngwv1aFhYWaOHGiDh06pNWrV0sqGcXiwoULys7O1pQpU+Th4aFVq1ZpyJAh2rBhg1q2bKnc3NwyY0Nbvs/Pz1dOTk65Y0fXqlVLeXl5lS5vQUGB9u/fX+n9a6rz589LKvnl+Pzzz+vLL7+0PmLv27evPvzwQ+t2zvLzPXLkiCQpJydHhw4dUvPmzdW8eXNJJR8sc3JyrNvxiNb4Tp48KUk6fPjwn/od4WiBgYFatGiRLl68aH0SJUkBAQGqXbu2AgMDJclp3mc1navUy9Lc3d2tN+EOHDhgt/MynwQcySmCeVZWlp555hnt2rVLCxYssN4Nb9iwoRISEuTj42Od5rRt27ZKSkrSypUrNXXqVNWqVeuynQh9fHzk7e1dZr1UMlLLn5ky18vLSy1btqz0/jXVb7/9poSEBP3222968803ra/Nr7/+qp9++sn6/Q033KAbbrjBkUW9ZpbmAMePH9enn36qBx98UI0bN9bx48e1ZcsWHT9+XJJUt25dp7mmmszydK1Zs2bWD1jOqEePHtqyZYsCAgL0t7/9TZ06ddLu3bu1bt06nTx5Uvfdd59uvPFGRxcT18hV6qWjWZ4WAY5i+GCelpamv/3tbzpx4oR1RrDS/P39bb63NHc4ffq0pJLwfukMW2lpafL19VWdOnUUGhqq9PR05efn23xKTktLU0hISKXL7ebm5lST4BjFAw88oJUrV5Zpi12au7u7HnjgAae5q2FpSjV06FBt27ZNL774onVdSEiIhg4dqhUrVig0NJQ64wS8vb2t/zvr61VUVKSdO3eqZcuWyszM1Hvvvaf33ntPUkmdDAoK0q5duxQVFcVTHCfhCvXSCGjGAkczdDDPyMjQ448/rqysLK1evVpt2rSxWf+f//xH//jHP7R582Y1btxYUkmTl+TkZOvQc507d9auXbts9vv+++918803y93dXZ06dVJxcbF2795tbbN8+PBhnT59usyHAFQ/Dw8PeXt7KycnRz4+Prr77rttOn/m5+fL29vbqcKCZQiw5ORkLV68WMnJydbmOeHh4YqNjWUIMNgVo7IAgDEZZhzz8sycOVPHjx/XrFmzVK9ePZ05c8b6r6ioSDfffLPq1q2r559/Xj///LMOHDig559/Xunp6Ro+fLikkruUe/fu1ezZs/Xrr78qPj5e27Zt0xNPPCGp5O7QPffcoylTpmjnzp3au3evnn32WXXp0kUdOnRw3MXXUElJScrJyVGvXr104cIF61CWmzZtUlZWlnr16qXs7GxrJ1FnYBkCLCEhQbGxsfLy8lJkZKS8vLwUGxurhIQEjRw50qk+bMC5lTcqS69evdS2bVt5eHgwKgsAOIhh75gXFRXpn//8pwoKCvT444+XWf/VV1+pUaNGWr58uWbPnq2oqCjl5eWpU6dOWrVqlRo0aCBJatWqld555x3NmjVL77//vho1aqRZs2bZjOgxbdo0vfbaaxo7dqwkqWfPnpoyZYp9LhQ2LEFgzJgxGjNmjJYvX66TJ0/quuuu0/Dhw2U2m7V9+3anCwwMAQYjKT3DYnh4eJn1zLAIRysqKirzJIebF6gJDBXMY2NjrV97eHho7969V92nSZMmevvtt6+4Tc+ePdWzZ8/Lrvf19dX06dM1ffr0ay8sqoUlCHzyySfatm2btX/Anj17lJiYqDvvvNNmO2fSvXt3de3alT82cLjSMyxOnjzZZr4IZliEo+3YsUNxcXE2/cOCg4MVFRXFTQy4PEMFcyAiIkIBAQFasWKFOnfurIceekgmk0n5+flKTEzUypUrFRgY6LSBobzJXAB7Y4ZFGNWOHTsUGxuryMhIRUdHq2nTpjp69KjWr1+v2NhYnjDC5RHMYTiWEVn27t2rxMRE63LLKCxXGrEFwLWheRWMpqioyDr6WuknOeHh4Zo8ebJmzJih+Ph4de3alQ+NcFkEcxhKUlKSMjMzJemy489nZGQwWgRQBWheBSMpPVpQ6eZVUskwuQMHDmS0ILg8gjkM5ezZs9avO3furM6dO9s0ZbHcQS+9HYDKo3kVjKL0aEHlYbQg1AQEcxhKenq6JCksLEwvvviizV2Tfv36ady4cTp69Kh1OwCAa2C0IMDg45ij5rlw4YKkktnr8vPz9fHHH2vx4sX6+OOPrZMLld4OAOAaSo8WVFxcbLOO0YJQU3DHHIZimQ45OTlZAwcOtFm3dOnSMts5G8bmBYDyMVoQQDCHwbRr107r1q27pu2cDWPzAsCVMVoQajqCOQyldevWVbqdUTA2LwBcG0YLQk1GG3MYyrZt26xfX9pcpfT3pbczukvH5g0PD5ePj491bN7IyEjFx8erqKjI0UUFAEOwjBbUq1cvtW3bllCOGoNgDkP55ZdfJEmBgYHlrg8ICLDZzhlYxuYdOHDgZcfmPX36tJKSkhxUQtRkRUVF2rdvn7Zv3659+/bxAREAHIimLDCUvLw8SSXDJnp5eamgoMC6ztPTUxkZGTbbOQPG5oVR0e8BAIyFYA5DadGihfbs2SNJatu2rbp06WKdYGjXrl364YcfrNs5C8bmhRHR7wEAjIdgDkOpU6eO9esff/zRGsQl2zbmpbczutJj80ZHR+uzzz7TqVOn1LBhQ915552MzQu7u7Tfg6WJlaXfw4wZMxQfH6+uXbvSthcA7IhgDkO5ePGi9Wuz2WyzrvT3pbczOsvYvDNnzrzs2OyTJk0iAMFuLP0eoqOjL9vvITo6WklJSWrbtq2DSgkANQ+dPwE7OHDgwJ9aD1Ql+j0AgDFxxxyGUrqJyqWdPy1tzS/dzujy8/O1ceNGSVKnTp0UGRlpvZaEhATt3r1bmzZt0uDBg2UymRxcWtQE9HuAkaSmpiorK6tC+/j5+Sk0NLSaSgQ4DsEchuLv72/9un379urcubM1xCYmJioxMbHMdkb36aefymw2KywsTC+99JJN04F+/fpp3LhxOnr0qD799FP179/fgSVFTVG630PpNuaSVFxcTL8H2E1GRoZGjRql4uLiCu3n7u6uFStWWIfQBVwFwRyGUvquyd69e61BXJLN3eSK3l1xJMv45EOHDi23Pe/QoUM1ffp0JSUlEcxhF5Z+D7GxsZoxY4YGDhyoJk2a6NixY1q/fr0SEhIUExNDvwdUu4CAAC1ZsqTc3+nHjx/XnDlz9Oyzz6px48Y26/z8/AjlcEkEcxiK5U54QECAdcxyi/z8fOtyZ7pj7u3tLUk2Y0WXdvr0aZvtAHvo3r27YmJiFBcXp+joaOvykJAQhkqEXV2tSUrjxo3VsmVLO5UGcCyCOQylQYMGklQmlFtYllu2cwa9e/fWv//9b61evVp33XWXPD3/+7YrLCzUBx98YN0OsKfu3bura9euSkpK0rlz51SvXj1FRERwpxwAHIRgDkMpfVekdGfPS793prsn7dq1k4+Pj7KysjRixAgNHjxYkZGRSkhI0OrVq5WVlSVfX1+1a9fO0UVFDeTh4cGQiABgEARzGMq2bdusX5cO5Zd+v23bNqdpj+3h4aFnnnlGM2fOVHp6uhYuXFhmm3/84x/cpQQAoIYjmMNQLB0lpZKOkaV76pf+3tk6Snbv3l2TJk3Se++9p7Nnz1qXBwUF6YknnqA9L+yGoekAwLgI5jAUy8grXl5e8vf31x9//GFdV7duXWVkZKiwsNApx/umPS8cjaHpAMDYCOYwFF9fX0lSQUGBmjVrppiYGDVt2lRHjx7Vhx9+aB0+0bKds6E9LxzpckPTXWlYOomh6QDAXgjmMBQ3Nzfr1ykpKTpy5IiCgoJ05MgRpaSklLsdgGt3pSYpDEsHAI5FMIehlA7cl+soeel2AAAArsD96psA9tO6dWtJJU1VLh2rPCgoyNqExbIdAACAq+COOQwlODhYkpSdnS0vLy89+OCDCg0NVWpqqv71r38pOzvbZjsAAABXQTCHoURERCg4OFju7u5KS0vTpk2brOs8PDwUGhoqs9msiIgIxxUSAACgGhDMYSgeHh6KiopSbGysOnXqpIYNGyo/P18mk0mnTp3S7t27FRMTwxCDAADA5RDMYTjdu3dXTEyM4uLirMMjSlJISIhiYmKcejKeoqIixjEHAADlIpjDsMxms833FZ0UxWh27NihuLg4paWlWZcFBwcrKirKqT9sAACAqmGoUVmWLFmioUOH2izbv3+/hgwZog4dOqh3795asWKFzfri4mK9/fbb6tGjhzp06KC//e1vOn78eJUfA/azY8cOxcbGqlmzZpo1a5bWrVunWbNmqVmzZoqNjdWOHTscXcQKs1xTWFiYzTWFhYU57TUBAICq9aeCeUZGhr766it98MEHOnfunH777bcydzmv1erVqzV37lybZefPn9eIESPUpEkTffTRRxozZoxmz56tjz76yLrNO++8ozVr1mjatGlau3atiouL9cQTTyg/P7/KjgH7KSoqUlxcnCIjIzV58mSFh4fLx8dH4eHhmjx5siIjIxUfH6+ioiJHF/WaueI1AQCAqlfpYL5o0SL16tVLY8aM0auvvqpTp05p5syZGjhwoDIzM6/5OKdPn9bo0aM1e/ZshYWF2axbt26dvLy89Oqrr6pFixYaMGCAhg8frnfffVeSlJ+fr/j4eI0bN0633XabwsPD9dZbbyk1NVWff/55lR0D9pOUlKS0tDQNHDhQ7u621dPd3V0DBw7U6dOnlZSU5KASVpwrXhMAAKh6lQrmq1at0vz58zVixAitW7fOepd8yJAhOn78uObNm3fNx/rll1/k5eWlzZs3q3379jbrEhMT1aVLF3l6/rcp/C233KIjR47o7NmzSk5O1sWLF9WtWzfren9/f0VERCghIaHKjgH7OXfunCSpadOm5a5v0qSJzXbOwBWvCQAAVL1Kdf5cuXKlnnzySf3jH/+wefzeq1cvPfPMM3r33Xf14osvXtOxevfurd69e5e7LjU1tcwMj5aJZU6dOqXU1FRJUsOGDctsY1lXFceoDLPZbJ0MB9fOMrPngQMHyp3d8+DBg9btnOXn64rXVJPl5uZa/3eV18sVr6mmccXX0BHXZDab5ebmZpdzAeWpVDA/efKkunTpUu665s2b6+zZs3+qUBa5ubkymUw2y2rVqiVJysvLU05OjiSVu01GRkaVHaMyCgoKtH///krvX5MFBgZq5cqVeuyxx2yafhQXF2vt2rUKDAyUJKf6+briNdVUJ0+elCQdPnxYeXl5Di5N1XDFa6ppXPE1dNQ1XZoHAHuqVDBv2LChfvzxx3KHePv555/L3H2uLG9v7zIdMC1vTl9fX3l7e0sqaSdu+dqyjY+PT5UdozK8vLzUsmXLSu9fk40cOVJvvfWWPv30Uz344INq3Lixjh8/ri1btujQoUMaP368brzxRkcXs0Jc8ZpqKssH+2bNmql58+YOLk3VcMVrqmlc8TV0xDWlpKTY5TzA5VQqmD/88MOaP3++vL29ddttt0mSsrOz9dlnn2nJkiUaMWJElRQuNDTUZsxnSdbvQ0JCVFhYaF1maadr+b5NmzZVdozKcHNzszZhwNWlpqYqKytLktS4cWMNHz5cH3/8sU2TqHr16mn48OFq3Lix9U6Kn5+fQkNDHVLmivjrX/+qWrVqKS4uzuaaXGHSpJrG8gHe29vbZd7jrnhNNY0rvoaOuCaascDRKhXM//a3v+n333/X7NmzNXv2bEnSsGHDZDabdf/992vUqFFVUrjIyEitXbtWRUVF1tkRv//+ezVr1kz169dXnTp15Ofnp507d1pDdWZmppKSkjRkyJAqOwaqV0ZGhkaNGnXVCYTOnTunZcuW2Sxzd3fXihUrFBAQUJ1FrBLdu3dX165dmfkTAACUq1LB3M3NTa+++qpGjhyp77//Xunp6apTp44iIyPL7dxWWQMGDNDSpUs1efJkPfHEE9q7d6+WL1+uqVOnSippBzZkyBDNnj1b9erV0/XXX69Zs2YpNDRUd9xxR5UdA9UrICBAS5Yssd4xL+348eOaM2eOnn32WTVu3LjMej8/P6cI5RYeHh5q27ato4sBAAAMqFLBXJKOHTumxMREPfbYY5KkX3/9VR999JGGDBmi6667rkoKV79+fS1dulQzZsxQ//79FRQUpIkTJ6p///7WbcaNG6fCwkJNmTJFubm5ioyMVFxcnLy8vKrsGKh+V2uO0rhxY9rsAwAAl1apYL5nzx6NHDlSISEhevjhhyWVNP/YvHmzPvroI61cubJSd85jY2PLLGvXrp0+/PDDy+7j4eGh6OhoRUdHX3abqjgGAAAAUJ0qNcHQm2++qZtvvlkbN260LuvYsaO++uortWvXTm+88UaVFRAAAACoCSoVzH/55RdFRUXZDC8olQxt9Pjjj+unn36qksIBAAAANUWlgrm3t7dOnz5d7rrz58/bTKACAAAA4OoqlaB79Oiht99+WwcOHLBZ/uuvv2r+/Pnq2bNnlRQOAAAAqCkq1flzwoQJeuyxx9S/f381atRI9erV0/nz53X8+HE1atRIEydOrOpyAgAAAC6tUsE8KChIW7Zs0YYNG/TDDz8oPT1dISEhGjJkiB566CHVrl27qssJAAAAuLRKj2Pu6+urIUOGMDsmAAAAUAWuOZgvWLBAAwcOVEhIiBYsWHDFbd3c3DRmzJg/XTjA1RQVFSkpKUnnzp1TvXr1FBERIQ8PD0cXCwAAGECFgnnPnj0J5kAl7dixQ3FxcUpLS7MuCw4OVlRUlLp37+7AkgEAACO45mCenJxs/TopKYkhEYEK2LFjh2JjYxUZGano6Gg1bdpUR48e1fr16xUbG6uYmBjCOQAANVyl0vX999+vf/3rX1VdFsAlFRUVKS4uTpGRkZo8ebLCw8Pl4+Oj8PBwTZ48WZGRkYqPj1dRUZGjiwoAAByoUsH81KlT8vHxqeqyAC4pKSlJaWlpGjhwYJknTe7u7ho4cKBOnz6tpKQkB5UQAAAYQaWC+X333afly5fbtJUFUL5z585Jkpo2bVru+iZNmthsBwAAaqZKDZd45MgRJSYmqlevXgoMDJSvr6/Nejc3N3355ZdVUkDAWaWmpiorK0s5OTmSpG+//VZhYWFltjty5IgkKScnRykpKfLz81NoaKgdSwoAAIygUsG8YcOGuu+++6q6LIDLyMjI0KhRo1RcXGxdNm/evCvus3DhQkklzVtWrFihgICAai0jAAAwlkoF85kzZ0qSvvnmGyUkJCg9PV3169dXt27dFBkZWaUFBJxRQECAlixZoqysLEnS3r17tXz5ckVERKht27Zau3atHnvsMe3bt09JSUkaPny42rVrJ0ny8/MjlAMAUANVKphb7gb+9NNP8vDwUGBgoNLT07Vo0SL17NlT8+fPl8lkquqyAk6ldHOUli1bKjQ0VHFxcVq7dq0kae3atQoJCWGoRAAAIKmSnT9fe+01HT58WPPnz9e+ffv0zTffaO/evZo3b5727Nmjt956q6rLCTi97t27691337VOvjVmzBgtWbKEUA4AACRVMpj/+9//1oQJE9S3b1+5ubmVHMjdXXfccYfGjx+vLVu2VGkhAVfh4eGhli1bSiq5i+7h4eHgEgEAAKOoVDA3m81q0KBBuesaNmyo7OzsP1UoAAAAoKapVDDv37+/Fi1apIsXL9osLyws1KpVq9S/f/8qKRwAAABQU1Sq86ePj4+OHDmiPn36qE+fPgoJCdH58+e1fft2paamKiAgQJMmTZJUMqb5a6+9VqWFBgAAAFxNpYL55s2b5efnJ0n67rvvbNaFhobqhx9+sH5vaYMOAAAA4PIqFcy//vrrqi4HAAAAUKNVqo05AAAAgKpFMAcAAAAMgGAOAAAAGADBHAAAADAAgjkAAABgAARzAAAAwAAqNVwiALiKtLQ0ZWZmXvP2x48ft/m/Ivz9/RUcHFzh/QAANQPBHECNlZaWptFPPaWC/PwK7ztnzpwK7+NlMmnxokWEcwBAuQjmAGqszMxMFeTny69zkDzqmKr1XEUX8pWVeEaZmZkEcwBAuQjmAGo8jzomedat5ehiAABqOMMH8507d2rYsGHlrmvUqJG++uorLVq0SHPnzi2z/sCBA9avV69erfj4eJ05c0Y33XSTpkyZooiICOv633//XdOmTVNCQoJ8fX318MMP6+mnn5aHh0eVXxMAAABwKcMH844dO+qbb76xWbZnzx49/fTT+vvf/y6pJIA/8MADio6OLvcYGzdu1BtvvKFp06YpIiJC7777rkaMGKGtW7eqXr16KigoUFRUlMLCwrR27VodO3ZMkydPlru7u8aNG1ft1wgAAAAYfrhEk8mkoKAg67/atWtr5syZ6t+/vwYMGCBJOnjwoCIiImy2CwoKsh5j8eLFGjJkiO6//361bNlSr732mnx8fLR+/XpJ0meffaaTJ0/qjTfeUOvWrdW3b189++yzev/995VfiU5hAAAAQEUZPphfavHixcrJydHzzz8vScrPz9eRI0fUvHnzcrf/448/dOTIEXXr1s26zNPTU507d1ZCQoIkKTExUTfeeKMCAgKs29xyyy3KysrS/v37q/FqAAAAgBKGb8pS2rlz57R8+XI999xzCgwMlCSlpKSoqKhIn332mWbMmKG8vDxFRkYqOjpawcHBSk1NlSQ1bNjQ5ljBwcFKTk6WJKWmpio0NLTMekk6deqU2rdvX+Gyms1mZWdnV3g/ozl9+rQuXrxYoX1q166tkJCQKjl/bm6u9X9X+HlKrnlN1eHs2bPlji9+7tw55eTkVPh4Pj4+qlevns2yEydOVLp8lWXE1506eW0uVyelytXL8uqkVDLefYMGDSp0LFd8DR1xTWazWW5ubnY5F1Aepwrma9asUZ06dfToo49alx08eFBSyS+4efPm6Y8//tCcOXM0bNgwbdq0yfqL0mSyHQqtVq1aysvLk1Typvf39y+zXpJ1m4oqKChw+rvtFy9e1OzZs2U2myu0n5ubmyZMmKDatWv/6TKcPHlSknT48OFKvxZG44rXVNXS09O1cMECFRQWOrooVc6Irzt18ursWSe9PD01ZuxY6w2oa+GKr6GjrunSvADYk1MF802bNunBBx+Ut7e3ddmDDz6onj172tx1aNWqlXr27Kmvv/5aTZo0kaQybcXz8vLk4+MjSfL29i53vST5+vpWqqxeXl5q2bJlpfY1knnz5pV7x/zEiRNasGCBxo4dq+uvv95mXVXeMbd8QGrWrNllmys5G1e8pqr222+/qaCwUA+18VcDX9tfU5l5RcovqtiHRUkyebjJv5btKEuHzuXpX0cr9kTozzLi606dvLor1UmpcvWyvDp5NrtQGw5kKiQkpEKvhSu+ho64ppSUFLucB7gcpwnmycnJOn78uO67774y6y59FBgcHKzAwEClpqaqa9eukkpm+GvRooV1m7S0NGt4DA0Ntd55L71eUqUDppubW6VDvZE0a9as3OWWD0ctWrSo1g8glvN4e3u7xM9Tcs1rqmqWn1EDX09d5+dls+7S7/+Ms9n2vyNvxNedOnl1V6qTUtXWS8v5KvJauOJr6IhrohkLHM1pOn8mJiaqfv36Cg8Pt1n+1ltv6c4777RpbvH777/r/PnzatmyperXr69mzZpp586d1vWFhYVKTExUZGSkJCkyMlJJSUnKysqybvP999+rdu3aZc4HAAAAVAenCeZJSUlq06ZNmeW33367Tpw4oVdeeUWHDx9WQkKCnn76ad18883q0aOHJGnkyJFatmyZNm7cqJSUFL3wwgvKzc3Vww8/LEnq27evgoKC9Mwzzyg5OVlffvml5syZo5EjR9LWDAAAAHbhNE1Zzpw5U25HmJtuuknvvfee5s2bp4ceekgmk0l9+vTR888/b30k9cgjj+jChQuaO3eu0tPTddNNN2nZsmXWJjC1atXS0qVLNXXqVD3yyCMKCAjQoEGDrBMYAQAAANXNaYL5e++9d9l13bp1sxmnvDxRUVGKioq67PqmTZsqPj6+0uUDAAAA/gynacoCAAAAuDKCOQAAAGAABHMAAADAAAjmAAAAgAEQzAEAAAADIJgDAAAABkAwBwAAAAyAYA4AAAAYAMEcAAAAMACCOQAAAGAAno4uAAAAsHX8+PFKbV/R/fz9/RUcHFyhfQBUH4I5AAAGkZVfJEmaM2dOpfav6H5eJpMWL1pEOAcMgmAOAIBB5BaaJUl+nYPkUcdUrecqupCvrMQzyszMJJgDBkEwBwDAYDzqmORZt5ajiwHAzuj8CQAAABgAwRwAAAAwAII5AAAAYAAEcwAAAMAACOYAAACAARDMAQAAAAMgmAMAAAAGwDjmAOBi0tLSlJmZec3bV3Y6d4kp3XFtKlonpcrXS+oknBnBHABcSFpamp566inl5+dXeN/KTANvMpm0iCndcQV/pk5KFa+X1Ek4M4I5ALiQzMxM5efnq2XjHvKpFVCt58rJy1DK8f9jSndcEXUSuHYEcwBwQT61AuTnW9/RxQCsqJPA1dH5EwAAADAAgjkAAABgAARzAAAAwAAI5gAAAIABEMwBAAAAAyCYAwAAAAZAMAcAAAAMgGAOAAAAGADBHAAAADAApwjmp0+fVps2bcr827BhgyRp//79GjJkiDp06KDevXtrxYoVNvsXFxfr7bffVo8ePdShQwf97W9/0/Hjx222udoxAAAAgOrk6egCXIvk5GTVqlVLX375pdzc3KzL69Spo/Pnz2vEiBHq3bu3pk6dqj179mjq1KmqXbu2BgwYIEl65513tGbNGsXGxio0NFSzZs3SE088oS1btshkMl3TMQAAAIDq5BTB/ODBgwoLC1NwcHCZde+//768vLz06quvytPTUy1atNDRo0f17rvvasCAAcrPz1d8fLwmTJig2267TZL01ltvqUePHvr888917733at26dVc8BgAAAFDdnCKYHzhwQC1atCh3XWJiorp06SJPz/9eyi233KIlS5bo7NmzOnnypC5evKhu3bpZ1/v7+ysiIkIJCQm69957r3qMBg0aVN/FAQCcxtnswmo9/vncomo9PgBjc4pgfvDgQdWtW1eDBw/W4cOH1bRpUz311FPq2bOnUlNT1bp1a5vtLXfWT506pdTUVElSw4YNy2xjWXe1Y1QmmJvNZmVnZ1d4v2t1+vRpXbx4sUL71K5dWyEhIVVy/tzcXOv/1Xmd9jqPPTnzNZ09e1aZmZnlrjt37pxycnIqdDwfHx/Vq1evzPITJ05UqnzOwF7vGXtydF2+XL2sjjq54UD59d+ZUSf/y2w22zSZBezN8MG8sLBQv/32m1q2bKmYmBj5+fnp008/1ZNPPqlly5YpNzdXJpPJZp9atWpJkvLy8qy/lMvbJiMjQ5KueozKKCgo0P79+yu179VcvHhRs2fPltlsrtB+bm5umjBhgmrXrv2ny3Dy5ElJ0uHDhyv9MzLSeezJWa8pPT1dCxYsVGFhgaOL4tTs9Z6xJ0fWZXvXy4fa+KuBb/X96Tx0Lk//Olqxmy5/FnXS1qV5ALAnwwdzT09P7dy5Ux4eHvL29pYk3XTTTTp06JDi4uLk7e2t/Px8m30sb0ZfX1/rPvn5+davLdv4+PhI0lWPURleXl5q2bJlpfa9FvPmzStzx/zEiRNasGCBxo4dq+uvv77MPlV5x9zywaVZs2Zq3rx5lRzTkeexJ2e9pt9++02FhQXyvu4WuZv8y6wvLsiWiiv4mN/dU+5eZd9jhVmnlH92X2WLamj2es/YkyPr8pXqZXXUyQa+nrrOz+vPFPmKqrupTHmok/+VkpJSDaUBrp3hg7mkcu/wtmrVSt98841CQ0OVlpZms87yfUhIiAoLC63LmjRpYrNNmzZtJOmqx6gMNze3Sof6a9GsWbMyyywfPFq0aFGtHwpKn8vb27tar9Ne57EnZ70mS7ndTf7y8Cn7qL+8ZZVVnOd6zQUs7PWesSdH1uUr1Uvq5LWhTv4XzVjgaIYfx/zQoUO6+eabtXPnTpvlP//8s1q2bKnIyEjt3r1bRUX/7TDz/fffq1mzZqpfv77Cw8Pl5+dns39mZqaSkpIUGRkpSVc9BgAAAFDdDB/MW7RooebNm+vVV19VYmKifv31V82cOVN79uzRU089pQEDBigrK0uTJ09WSkqKNmzYoOXLl2vUqFGSStqKDRkyRLNnz9ZXX32l5ORkjR8/XqGhobrjjjsk6arHAAAAAKqb4ZuyuLu7a/HixXrzzTf1zDPPKDMzUxEREVq2bJl1JJWlS5dqxowZ6t+/v4KCgjRx4kT179/feoxx48apsLBQU6ZMUW5uriIjIxUXFycvr5J2gvXr17/qMQAAAIDqZPhgLkkNGjTQzJkzL7u+Xbt2+vDDDy+73sPDQ9HR0YqOjq70MVxZWlraZYfAu5zjx4/b/H+t/P39y50oCigPY0YDAGoSpwjmqD5paWn6+1OjlZdfuaHG5syZU6Hta5m89M6ixYRzXBNXHDMaAIDLIZjXcJmZmcrLL6j2sXmlkrufGw5k6pdffrnmO/TOcGe+ok8cnOGajMIVx4wGAOByCOaQpGofm1eSsvJLmg1U9C57ZfbxMpm0eNGiag+yaWlpeuqpp8qMg38tKnpNJpNJi+xwTUbiimNGAwBwOQRz2E1uYclMpX6dg+RRp/pmViu6kK+sxDPKzMys9hCbmZmp/Px8tWzcQz61AqrtPDl5GUo5/n92uSYAAOAYBHPYnUcdkzzr2n8muOrkUytAfr6MeQ8AACrP8OOYAwAAADUBwRwAAAAwAII5AAAAYAAEcwAAAMAA6PwJAHB6xXnVOxlVcQHj3QOofgRzAIDTyz31vaOLAAB/GsEcAOD0vBveIvda/tV2/MKsU8o/u6/ajg8AEsEcAOAC3Gv5y8OnXrUdv7qbygCAROdPAAAAwBAI5gAAAIABEMwBAAAAA6CNOQAABlN0Id8lzgGgYgjmAGo8QhCMJivxjKOLAMABCOYAajxCEIzGr3OQPOqYqvUcRRfyqfuAwRDMAdR4hCAYjUcdkzzr1nJ0MQDYGcEcQI1HCAIAGAGjsgAAAAAGQDAHAAAADIBgDgAAABgAbcwBAEC1y8nNcIlzANWJYA4ALogQBKNJ+f3/HF0EwPAI5gDggghBMJqWjXrIxzugWs+Rk5tB3YdTI5gDgAsiBMFofLwD5Odb39HFAAyNYA4ALogQBADOh2BuYGlpacrMzLzm7Y8fP27zf0X2cUWXu7azZ88qJyenQsfy8fFRgwYNrvkcwOUkJiaWqTfp6enKy8ur0HFq1aqlwMDAMstPnz79Z4qHGqi8OilVXb2kTgLXjmBuUGlpaRr91FMqyM+v8L5z5syphhI5j+LcQrnJvj+H6u4ERyc751ecWyhJWr16tYNLApSgTgLGQzA3qMzMTBXk58v7ulvkbvKvtvMUZp1S/tl91XZ8RzAXFMssqY+vn+p6lK3iWcVFKjCbK3RMLzc3+bl7lFl+tCBPCbk5tLPFVZkLiiVJkd4+8r+kLmUXF6tQFauTnnKTr3vZqShOFRYoKb9idzlRM12pTkpVVy+pk8C1I5gbnLvJXx4+9art+MV5195UxtnU9fBUkGfZKh5UhdX+fFHJHafq7mhHJzvX0dSrVrn1sioRglAR1EnAOAjmQBWgox0AAPizyj4HBQAAAGB3TnHHPD09XXPmzNG///1vZWVlqU2bNnruuefUuXNnSdKIESO0Y8cOm326dOmilStXSpLy8vIUGxurbdu2KTc3V71799bkyZNVr95/m4h89913mjVrln799Vc1bNhQTz/9tO655x77XSTgJOzR/Km44GK1nwMAAKNximD+7LPP6syZM5ozZ47q16+vlStXKioqShs3blTz5s114MABvfLKK+rbt691Hy8vL+vXr7zyihITEzV//nyZTCa9/PLLGjdunFatWiVJ+vXXXzVq1CiNGDFCs2bN0r///W9NnDhR9erVU7du3ex+vYCR5Z763tFFAADAJRk+mB89elTffvut1qxZo06dOkmSXnzxRf3f//2ftmzZoiFDhuiPP/5Q+/btFRQUVGb/06dPa9OmTVq8eLH1DvucOXN011136ccff1THjh31/vvvq02bNho/frwkqUWLFkpKStLSpUsJ5sAlvBveIvda1TdSkOSaowUBAHA1hg/mdevW1bvvvqu2bdtal7m5ucnNzU2ZmZk6cOCA3Nzc1KxZs3L33717tyTplltusS5r1qyZQkJClJCQoI4dOyoxMdHmbrtl+xkzZshsNsvNza3C5TabzcrOzq7wfha5ubmV3heuKzc390/Vqz97bklyr1W9IwVJrj1akCsyQr0ESqtsnazs33ygqhg+mPv7+6tXr142yz777DMdPXpUL7zwgg4ePKg6dero1Vdf1bfffitfX1/ddddd+vvf/y6TyaTTp0+rbt26qlWrls0xgoODlZqaKklKTU1VaGhomfU5OTk6f/68TVv0a1VQUKD9+/dXeD+LkydPVnpfuK7Dhw9XeCa+qkKdxOVQL2E0f6ZOmkymKi4NcO0MH8wv9cMPP2jSpEm64447dNttt+mFF15QXl6e2rVrpxEjRmj//v164403dPLkSb3xxhvKyckp901Wq1Yt65s2Nze3zDaW7/MrMfOmVNLGvWXLlpXa11I+V1V0oXI/02s+/sWCaj2+IzVr1kzNmzd3yLlduU7iz6FewmgqWydTUlKqoTTAtXOqYP7ll19qwoQJuvnmmzV79mxJ0quvvqrnn39eAQElk7u0bt1aXl5eGj9+vCZOnChvb+9yw3VeXp58fHwklfxiv3Qby/eWbSrKzc1Nvr6+ldpXkry9vSu9r9FlJZ5xdBGclre395+qV3/23EB5qJcwmsrWSZqxwNGcJpivWrVKM2bM0F133aXXX3/dekfb09PTGsotWrVqJem/TVTS09OVn59vc1c8LS1NISEhkqSGDRsqLS3N5hhpaWny9fVVnTp1qvOyaiS/zkHyqFN9jwrzUy8qZ396tR0fcAY5eRkucQ64DuokcHVOEczXrFmjadOmaejQoZo8ebLNJ9qhQ4eqUaNGmjlzpnXZvn375OXlpbCwMAUFBam4uFi7d++2jrBy+PBhnT59WpGRkZKkzp07a9euXTbn/P7773XzzTfL3Z05mKqaRx2TPOtW3+Pn6m4qAxhZLbeS31kpx//PLuczmUzy96/eUXrg3KiTwLUzfDA/fPiwXnvtNd1+++0aNWqUzp49a13n7e2tO++8U6+99pratWunW2+9Vfv27dMbb7yhqKgo+fn5yc/PT/fcc4+mTJmi1157TT4+Pnr55ZfVpUsXdejQQVJJuO/fv79mz56t/v37a/v27dq2bZuWLl3qoKsGgMrx/f83E5599lk1btz4mvY5fvy45syZU6F9LPz9/RUcHFzhcjqrs9mF1Xr887lF1Xp8R6hMnZQqXy9rWp2EazF8MP/ss89UUFCgL774Ql988YXNuv79+ys2NlZubm5auXKlXnvtNQUFBWn48OF68sknrdtNmzZNr732msaOHStJ6tmzp6ZMmWJd36pVK73zzjuaNWuW3n//fTVq1EizZs1iDHMATqtx48YV7oBemX1qDA8vuUnacIChPCursvWLeomaxPDBfPTo0Ro9evQVtxk8eLAGDx582fW+vr6aPn26pk+fftltevbsqZ49e1a6nAAA1+Xu6SOzqv9JhGUfADWT4YM5AABGwZMIANWJYA6Xdb6oetuCSlJmseu1BwUAAI5BMIek6u/QJNm/U9NX2Vl2PR8AOKPqvolhj5skgKsgmEOSa3Zo6uPrp7oe1VvFjxbkKSE3p1rPAQDVwc3LXW6yz00Mk5cXQxgC14BgDknSQ2381cC3eqvDoXN5+tfRi9V6jtLqengqyLN6r4k7Qago7k7iWthjPgZzQbFdOrRKDGEIXCuCOSRJDXw9dZ2fV7Wewx7NZQCj4u4kroW/v7+8TCZlJZ6xy/m8TCbdeOONFQ7NdGgFqgfBHIBh2WsyF+5OwiiCg4O1eNEiZWZWrHkhdQVwDQRzAMZjz8lc3MTdSRhKcHBwpcMydQVwbgRzoArk5GU49fGNxl6TuUhSQUGBvLyuvRkX09cDAKoLwRz4E2q5uUuSUo7/X7Wfy2Qy1bh2w0aezIU7kwCAqkYwB/4EX/eSYE67YQAA8GcRzA2uOK9629gWF9hv+EJXZuQ7uwAAwDkQzA0u99T3ji4CAAAA7IBgbnDeDW+Re63qa1dcmHVK+Wf3VdvxAQAAcG0I5gbnXstfHj71qu341d1UBgAAANfG3dEFAAAAAEAwBwAAAAyBpiwAAKdXnF/NI1hV8/EBQCKYAwCcmL+/v7xMJuWerP4RrLxq4CRfAOyLYA4AcFrBwcFavGiRMjOv/Y42k3wBMCqCOSRJZ7MLq/0c53OLJElFF/Kr9TxFFwuq9fgAjCU4OLhSgZlJvgAYDcHc4Kq7XaO5KE9ukjYcsFP7STcpK/GMfc4FAADgRAjmBmXPdpOeXl56YdIk1a1b95r3qeyj4IKCAnl5eVXreSz7oHrYoxMcHe0AADURwdygnKXdpL0eBfPI2fHs+WFRoqMdAKDmIZgbGO0m/5zzRXZoN2+HcxiFPT8sSnS0qy6pqanKysqyWXb8+HGb/y/l5+en0NDQai8bANR0BHO4HH9/f5m8vPRVdtbVN64CJi+vGnNnlw+Lzi0jI0OjRo1ScXFxuesv1wTM3d1dK1asUEBAQHUWDwBqPII5XE5wcLAWLV7MnV3gEgEBAVqyZEmZO+ZX4+fnRygHADsgmMMlcWcXKB9NUgDAuNwdXQAAAAAABHMAAADAEGjKAgAAHKa8kYKkK48WxEhBcFUEcydlryHP7PULszLnqcpzGeVnV9lzGYEr/lyNfE3OWk/syZ4/V1esK/a4pquNFCSVP1oQIwXBVbmZzWazowvhavbt2ydJatu2bbUcPyMjQ8OGDbviL7LyVPQXmdHPY89zGfmajMAVf65GvyZnrCf2ZM+fqyvWFXue63IfNq6kuj6YVvffb+BqCObVwB5vbHv9IjPyeex5LqNfkxG44s/VyNfkrPXEnuz5c3XFulIT6yXBHI5GMP//iouLtWDBAq1fv14XLlxQZGSkXnrppQqPaS3xxgYAwBnx9xuOxqgs/98777yjNWvWaNq0aVq7dq2Ki4v1xBNPKD8/39FFAwAAQA1AMJeUn5+v+Ph4jRs3TrfddpvCw8P11ltvKTU1VZ9//rmjiwcAAIAagFFZJCUnJ+vixYvq1q2bdZm/v78iIiKUkJCge++9t8LHNJvNys7OrspiAgCAamQ2m+Xm5uboYqAGI5irpIOLJDVs2NBmeXBwsHVdRRUUFGj//v1/umwAAMB+TCaTo4uAGoxgLiknJ0dS2TdjrVq1lJGRUaljenl5qWXLln+6bAAAwD5SUlIcXQTUcARzSd7e3pJK2ppbvpakvLw8+fj4VOqYbm5u8vX1rZLyAQCA6kczFjganT/13yYsaWlpNsvT0tIUEhLiiCIBAACghiGYSwoPD5efn5927txpXZaZmamkpCRFRkY6sGQAAACoKWjKopK25UOGDNHs2bNVr149XX/99Zo1a5ZCQ0N1xx13OLp4AAAAqAEI5v/fuHHjVFhYqClTpig3N1eRkZGKi4uTl5eXo4sGAACAGsDNbDabHV0IV8OUvgAAOB/+fsPRaGMOAAAAGADBHAAAADAA2phXg4KCApnNZusjMQAAYHz5+fmMZQ6HIphXA97UAAA4Hzc3N/6Gw6Ho/AkAAAAYAG3MAQAAAAMgmAMAAAAGQDAHAAAADIBgDgAAABgAwRwAAAAwAII5AAAAYAAEcwAAAMAACOYAAACAARDMAQAAAAMgmAMAAAAGQDAHAAAADIBgDgAAABiAp6MLAFxq6NCh2rVr12XXf/fdd6pXr54dSwTYiomJ0YkTJ7Ry5UpHFwWw0aZNG82cOVMPPfSQo4sCoBII5jCkfv36afLkyeWuq1u3rp1LAwAAUP0I5jAkb29vBQUFOboYAAAAdkMbcwAAAMAACOYAAACAAdCUBYa0ZcsWffbZZ2WW9+3bV7NmzXJAiQAAAKoXwRyG1Lt3b02YMKHMcl9fXweUBgAAoPoRzGFItWvXVtOmTR1dDAAwrLNnz+qPP/5QmzZtJElms1mS5OHh4chiAfgTaGMOAIATiouL07PPPmv9PiMjQ5KY5wFwYtwxhyHl5ubqzJkz5a4LCAiQyWSyc4kAwFi6d++u+Ph4bdy4UZ07d9ayZcvk7++vjh07OrpoACqJYA5D2rp1q7Zu3Vruunnz5umuu+6yc4kAwFh69OihmJgYzZ8/X2fPnlXr1q21aNEi+fn5ObpoACrJzWxplAYAAADAYWhjDgAAABgAwRwAAAAwAII5AAAAYAAEcwAAAMAACOYAAACAARDMAQAAAAMgmAMAAAAGQDAHAAAADIBgDgAVNH/+fLVp08bRxQAAuBiCOQAAAGAABHMAAADAAAjmAGocs9ms5cuXq1+/fmrXrp1uv/12xcXFyWw2S5K+/fZbDRo0SJ06dVLXrl313HPP6dSpU1c85j//+U899NBD6tixo/7yl7/opZdeUkZGhnX9/Pnzdfvtt2vBggXq0qWLbr31Vpv1AAB4OroAAGBvb7zxht5//32NGDFCf/nLX7Rv3z7Nnj1bhYWFCgkJ0fPPP697771Xo0aN0vnz5/X222/r0Ucf1caNG1W/fv0yx3vnnXf09ttva9CgQRo/fryOHz+uefPmac+ePVq3bp28vb0lSSdPntT27dv11ltvKT09XQEBAfa+dACAgRHMAdQomZmZWrFihYYMGaLo6GhJUvfu3XXmzBklJCQoOTlZt956q958803rPjfffLPuvvtuxcXFaeLEiTbHy8jI0KJFi/TII4/opZdesi5v3bq1Bg8erI8++kiDBw+WJBUWFur5559X586d7XClAABnQ1MWADXKnj17VFhYqDvuuMNm+ZQpUzRp0iSdOXNG9957r826Jk2aqGPHjtq1a1e5x8vPzy+zT+fOnXX99deX2eeGG26ooisBALgagjmAGiU9PV2SVK9evcuua9CgQZl1DRo00IULF8ost7QTv9Z9ateuXdEiAwBqCII5gBrF399fknTu3Dmb5SdPntSBAwckSWfPni2z35kzZ1S3bt0yyy3txCuyDwAA5SGYA6hR2rVrJy8vL/3rX/+yWR4fH6+3335bQUFB+uSTT2zWHT9+XHv27NHNN99c5njt27eXyWQqs09iYqJOnjxZ7j4AAJSHzp8AapR69epp2LBhWr58uUwmk7p06aKffvpJH3zwgSZOnKg6depo0qRJeu6553T//ffr/PnzWrBggQICAjRixIgyxwsMDNSTTz6phQsXysvLS3/961/1+++/a968eWrZsqX69+/vgKsEADgjgjmAGic6Olr169fX2rVrtXTpUjVq1EgvvviiHnvsMUkl7cCXLFmiMWPGyM/PTz169NCzzz6roKCgco/39NNPq0GDBlq1apU+/PBDBQYG6q677tIzzzwjX19fe14aAMCJuZktM2oAAAAAcBjamAMAAAAGQDAHAAAADIBgDgAAABgAwRwAAAAwAII5AAAAYAAEcwAAAMAACOYAAACAARDMAQAAAAMgmAMAAAAGQDAHAAAADIBgDgAAABjA/wNy1Wj3nDJAtgAAAABJRU5ErkJggg==", + "image/png": 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", "text/plain": [ "
" ] @@ -621,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -724,7 +716,7 @@ "4 24.59 3.61 Female No Sun Dinner 4 (0.991, 4.0]" ] }, - "execution_count": 6, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -737,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -749,7 +741,7 @@ "Categories (3, interval[float64, right]): [(0.991, 4.0] < (4.0, 7.0] < (7.0, 10.0]]" ] }, - "execution_count": 7, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -762,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": { "scrolled": false }, @@ -771,10 +763,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'day', 'y': 'total_bill', 'hue': 'tip_bucket'}\n", - "self.tuple_group_names=[('Thur', Interval(0.991, 4.0, closed='right')), ('Thur', Interval(4.0, 7.0, closed='right')), ('Thur', Interval(7.0, 10.0, closed='right')), ('Fri', Interval(0.991, 4.0, closed='right')), ('Fri', Interval(4.0, 7.0, closed='right')), ('Fri', Interval(7.0, 10.0, closed='right')), ('Sat', Interval(0.991, 4.0, closed='right')), ('Sat', Interval(4.0, 7.0, closed='right')), ('Sat', Interval(7.0, 10.0, closed='right')), ('Sun', Interval(0.991, 4.0, closed='right')), ('Sun', Interval(4.0, 7.0, closed='right')), ('Sun', Interval(7.0, 10.0, closed='right'))]\n", - "self.plotter.group_names=Index(['Thur', 'Fri', 'Sat', 'Sun'], dtype='object', name='x')\n", - "self.plotter.hue_names=[Interval(0.991, 4.0, closed='right'), Interval(4.0, 7.0, closed='right'), Interval(7.0, 10.0, closed='right')]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -831,17 +819,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'clarity', 'y': 'carat', 'hue': 'color'}\n", - "self.tuple_group_names=[('IF', 'E'), ('IF', 'I'), ('IF', 'J'), ('VVS1', 'E'), ('VVS1', 'I'), ('VVS1', 'J'), ('VVS2', 'E'), ('VVS2', 'I'), ('VVS2', 'J'), ('VS1', 'E'), ('VS1', 'I'), ('VS1', 'J'), ('VS2', 'E'), ('VS2', 'I'), ('VS2', 'J'), ('SI1', 'E'), ('SI1', 'I'), ('SI1', 'J'), ('SI2', 'E'), ('SI2', 'I'), ('SI2', 'J'), ('I1', 'E'), ('I1', 'I'), ('I1', 'J')]\n", - "self.plotter.group_names=Index(['IF', 'VVS1', 'VVS2', 'VS1', 'VS2', 'SI1', 'SI2', 'I1'], dtype='object', name='x')\n", - "self.plotter.hue_names=['E', 'I', 'J']\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -910,17 +894,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'day', 'y': 'total_bill', 'hue': 'smoker'}\n", - "self.tuple_group_names=[('Thur', 'Yes'), ('Thur', 'No'), ('Fri', 'Yes'), ('Fri', 'No'), ('Sat', 'Yes'), ('Sat', 'No'), ('Sun', 'Yes'), ('Sun', 'No')]\n", - "self.plotter.group_names=Index(['Thur', 'Fri', 'Sat', 'Sun'], dtype='object', name='x')\n", - "self.plotter.hue_names=['Yes', 'No']\n", "Sat_Yes vs. Sat_No: t-test independent samples, P_val:4.304e-01 t=7.922e-01\n", "Thur_No vs. Fri_No: t-test independent samples, P_val:7.425e-01 t=-3.305e-01\n", "Thur_Yes vs. Sun_No: t-test independent samples, P_val:5.623e-01 t=-5.822e-01\n" @@ -969,7 +949,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": { "collapsed": false, "pycharm": { @@ -981,10 +961,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'species', 'y': 'sepal_length', 'hue': None}\n", - "self.tuple_group_names=[('setosa',), ('versicolor',), ('virginica',)]\n", - "self.plotter.group_names=Index(['setosa', 'versicolor', 'virginica'], dtype='object', name='x')\n", - "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -1001,12 +977,12 @@ "data": { "text/plain": [ "(,\n", - " [,\n", - " ,\n", - " ])" + " [,\n", + " ,\n", + " ])" ] }, - "execution_count": 12, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, @@ -1057,17 +1033,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'species', 'y': 'sepal_length', 'hue': None}\n", - "self.tuple_group_names=[('setosa',), ('versicolor',), ('virginica',)]\n", - "self.plotter.group_names=Index(['setosa', 'versicolor', 'virginica'], dtype='object', name='x')\n", - "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -1084,12 +1056,12 @@ "data": { "text/plain": [ "(,\n", - " [,\n", - " ,\n", - " ])" + " [,\n", + " ,\n", + " ])" ] }, - "execution_count": 13, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, @@ -1145,7 +1117,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -1178,7 +1150,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": { "scrolled": false }, @@ -1187,10 +1159,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'species', 'y': 'sepal_length', 'hue': None}\n", - "self.tuple_group_names=[('setosa',), ('versicolor',), ('virginica',)]\n", - "self.plotter.group_names=Index(['setosa', 'versicolor', 'virginica'], dtype='object', name='x')\n", - "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -1236,7 +1204,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": { "collapsed": false, "pycharm": { @@ -1248,10 +1216,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'species', 'y': 'sepal_length', 'hue': None}\n", - "self.tuple_group_names=[('setosa',), ('versicolor',), ('virginica',)]\n", - "self.plotter.group_names=Index(['setosa', 'versicolor', 'virginica'], dtype='object', name='x')\n", - "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -1295,7 +1259,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": { "pycharm": { "name": "#%%\n" @@ -1307,10 +1271,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'day', 'y': 'total_bill', 'hue': None}\n", - "self.tuple_group_names=[('Sun',), ('Thur',), ('Fri',), ('Sat',)]\n", - "self.plotter.group_names=Index(['Sun', 'Thur', 'Fri', 'Sat'], dtype='object', name='x')\n", - "self.plotter.hue_names=[]\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -1360,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": { "collapsed": false, "pycharm": { @@ -1372,10 +1332,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'subtype', 'y': 'mutation_rate_samp', 'hue': 'Synon_Nonsynon'}\n", - "self.tuple_group_names=[('H3N2', 'Nonsynonymous'), ('H3N2', 'Synonymous'), ('H1N1', 'Nonsynonymous'), ('H1N1', 'Synonymous'), ('Influenza B', 'Nonsynonymous'), ('Influenza B', 'Synonymous')]\n", - "self.plotter.group_names=Index(['H3N2', 'H1N1', 'Influenza B'], dtype='object', name='x')\n", - "self.plotter.hue_names=['Nonsynonymous', 'Synonymous']\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -1390,7 +1346,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -1449,7 +1405,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": { "collapsed": false, "pycharm": { @@ -1461,10 +1417,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'mutation_rate_samp', 'y': 'subtype', 'hue': 'Synon_Nonsynon'}\n", - "self.tuple_group_names=[('H3N2', 'Nonsynonymous'), ('H3N2', 'Synonymous'), ('H1N1', 'Nonsynonymous'), ('H1N1', 'Synonymous'), ('Influenza B', 'Nonsynonymous'), ('Influenza B', 'Synonymous')]\n", - "self.plotter.group_names=Index(['H3N2', 'H1N1', 'Influenza B'], dtype='object', name='y')\n", - "self.plotter.hue_names=['Nonsynonymous', 'Synonymous']\n", "p-value annotation legend:\n", " ns: 5.00e-02 < p <= 1.00e+00\n", " *: 1.00e-02 < p <= 5.00e-02\n", @@ -1481,18 +1433,18 @@ "data": { "text/plain": [ "(,\n", - " [,\n", - " ,\n", - " ])" + " [,\n", + " ,\n", + " ])" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1525,7 +1477,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": { "collapsed": false, "pycharm": { @@ -1537,10 +1489,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'mutation_rate_samp', 'y': 'subtype', 'hue': 'Synon_Nonsynon'}\n", - "self.tuple_group_names=[('H3N2', 'Nonsynonymous'), ('H3N2', 'Synonymous'), ('H1N1', 'Nonsynonymous'), ('H1N1', 'Synonymous'), ('Influenza B', 'Nonsynonymous'), ('Influenza B', 'Synonymous')]\n", - "self.plotter.group_names=Index(['H3N2', 'H1N1', 'Influenza B'], dtype='object', name='y')\n", - "self.plotter.hue_names=['Nonsynonymous', 'Synonymous']\n", "p-value annotation legend:\n", " *: 1.00e-02 < p <= 5.00e-02\n", " **: 1.00e-03 < p <= 1.00e-02\n", @@ -1555,18 +1503,18 @@ "data": { "text/plain": [ "(,\n", - " [,\n", - " ,\n", - " ])" + " [,\n", + " ,\n", + " ])" ] }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1606,7 +1554,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": { "collapsed": false, "pycharm": { @@ -1618,10 +1566,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'subtype', 'y': 'mutation_rate_samp', 'hue': 'Synon_Nonsynon'}\n", - "self.tuple_group_names=[('H3N2', 'Nonsynonymous'), ('H3N2', 'Synonymous'), ('H1N1', 'Nonsynonymous'), ('H1N1', 'Synonymous'), ('Influenza B', 'Nonsynonymous'), ('Influenza B', 'Synonymous')]\n", - "self.plotter.group_names=Index(['H3N2', 'H1N1', 'Influenza B'], dtype='object', name='x')\n", - "self.plotter.hue_names=['Nonsynonymous', 'Synonymous']\n", "H1N1_Nonsynonymous vs. H1N1_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:5.014e-04 U_stat=2.624e+03\n", "H3N2_Nonsynonymous vs. H3N2_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:1.294e-03 U_stat=1.535e+04\n", "Influenza B_Nonsynonymous vs. Influenza B_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:2.026e-01 U_stat=3.340e+02\n" @@ -1664,7 +1608,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": { "collapsed": false, "pycharm": { @@ -1676,10 +1620,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "variables={'x': 'subtype', 'y': 'mutation_rate_samp', 'hue': 'Synon_Nonsynon'}\n", - "self.tuple_group_names=[('H3N2', 'Nonsynonymous'), ('H3N2', 'Synonymous'), ('H1N1', 'Nonsynonymous'), ('H1N1', 'Synonymous'), ('Influenza B', 'Nonsynonymous'), ('Influenza B', 'Synonymous')]\n", - "self.plotter.group_names=Index(['H3N2', 'H1N1', 'Influenza B'], dtype='object', name='x')\n", - "self.plotter.hue_names=['Nonsynonymous', 'Synonymous']\n", "H1N1_Nonsynonymous vs. H1N1_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:5.014e-04 U_stat=2.624e+03\n", "H3N2_Nonsynonymous vs. H3N2_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:1.294e-03 U_stat=1.535e+04\n", "Influenza B_Nonsynonymous vs. Influenza B_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:2.026e-01 U_stat=3.340e+02\n" From 3e8d3c622ac2645e1e91d33522852fd48ead0b5d Mon Sep 17 00:00:00 2001 From: Florian Charlier <477844+trevismd@users.noreply.github.com> Date: Tue, 19 Nov 2024 13:00:11 +0100 Subject: [PATCH 15/16] update python version in README Signed-off-by: Florian Charlier <477844+trevismd@users.noreply.github.com> --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index aea83c2..59dfb55 100644 --- a/README.md +++ b/README.md @@ -139,7 +139,7 @@ annotator.apply_and_annotate() ## Requirements -+ Python >= 3.6 ++ Python >= 3.8, <=3.11 + numpy >= 1.12.1 + seaborn >= 0.9,<0.12 + matplotlib >= 2.2.2 From 6d3b29296216e080bd956c5265fc07f4c73180ad Mon Sep 17 00:00:00 2001 From: Florian Charlier <477844+trevismd@users.noreply.github.com> Date: Tue, 19 Nov 2024 13:06:15 +0100 Subject: [PATCH 16/16] update README and GH actions for v. 0.7 Signed-off-by: Florian Charlier <477844+trevismd@users.noreply.github.com> --- .github/workflows/python-package.yml | 2 +- README.md | 53 +++++++++++++--------------- 2 files changed, 25 insertions(+), 30 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 6572054..32e0996 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -16,7 +16,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: [3.8, 3.9, '3.10', 3.11] + python-version: [3.8, 3.9, '3.10', 3.11, 3.12] steps: - uses: actions/checkout@v4 diff --git a/README.md b/README.md index 59dfb55..5cd4591 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![Active Development](https://img.shields.io/badge/Maintenance%20Level-Actively%20Developed-brightgreen.svg)](https://gist.github.com/cheerfulstoic/d107229326a01ff0f333a1d3476e068d) ![coverage](https://raw.githubusercontent.com/trevismd/statannotations/master/coverage.svg) -![Python](https://img.shields.io/badge/Python-3.8--3.11-blue) +![Python](https://img.shields.io/badge/Python-3.8%2B-blue) [![Documentation Status](https://readthedocs.org/projects/statannotations/badge/?version=latest)](https://statannotations.readthedocs.io/en/master/?badge=latest) [![DOI](https://zenodo.org/badge/296015778.svg)](https://zenodo.org/badge/latestdoi/296015778) @@ -9,26 +9,10 @@ Python package to optionally compute statistical test and add statistical annotations on plots generated with seaborn. -## Derived work - -This repository is based on -[webermarcolivier/statannot](https://github.com/webermarcolivier/statannot) - (commit 1835078 of Feb 21, 2020, tagged "v0.2.3"). - -Additions/modifications since that version are below represented **in bold** -(previous fixes are not listed). - -**! From version 0.4.0 onwards (introduction of `Annotator`), `statannot`'s API -is no longer usable in `statannotations`**. -Please use the latest v0.3.2 release if you must keep `statannot`'s API in your -code, but are looking for bug fixes we have covered. - -`statannot`'s interface, at least until its version 0.2.3, is usable in -statannotations until v.0.3.x, which already provides additional features (see -corresponding branch). - ## Features +Latest (v0.7) : supports pandas v2+ and seaborn v0.12+ + - Single function to add statistical annotations on plots generated by seaborn: - Box plots @@ -89,13 +73,6 @@ pip install . pip install -r requirements.txt . ``` -## Important note - -**! Seaborn ≥ v0.12 and pandas 2 are not officially supported, we know there are -at least some bugs. Issues can still be reported (and upvoted) in order to plan -further development to support these versions. Also see -[discussion](https://github.com/trevismd/statannotations/discussions/81)**. - ## Usage Here is a minimal example: @@ -139,11 +116,11 @@ annotator.apply_and_annotate() ## Requirements -+ Python >= 3.8, <=3.11 ++ Python >= 3.8 + numpy >= 1.12.1 -+ seaborn >= 0.9,<0.12 ++ seaborn >= 0.9 + matplotlib >= 2.2.2 -+ pandas >= 0.23.0,<2.0.0 ++ pandas >= 0.23.0 + scipy >= 1.1.0 + statsmodels (optional, for multiple testing corrections) @@ -193,3 +170,21 @@ the changelog. If you don't know where to start, there may be a few ideas in opened issues or discussion, or something to work for the documentation. NB: More on [CONTRIBUTING.md](CONTRIBUTING.md) + +## Acknowledgments - Derived work + +This repository is based on +[webermarcolivier/statannot](https://github.com/webermarcolivier/statannot) + (commit 1835078 of Feb 21, 2020, tagged "v0.2.3"). + +Additions/modifications since that version are below represented **in bold** +(previous fixes are not listed). + +**! From version 0.4.0 onwards (introduction of `Annotator`), `statannot`'s API +is no longer usable in `statannotations`**. +Please use the latest v0.3.2 release if you must keep `statannot`'s API in your +code, but are looking for bug fixes we have covered. + +`statannot`'s interface, at least until its version 0.2.3, is usable in +statannotations until v.0.3.x, which already provides additional features (see +corresponding branch).