From d89ea22b6653e4c56ac15432ce01c6e6b6573f3a Mon Sep 17 00:00:00 2001 From: parrt Date: Tue, 26 Jan 2021 16:30:21 -0800 Subject: [PATCH] Fixes #119. cleaned up the class names normalization as well. If you do not specify class names for classifier, "class i" is what you get. I reran all of the examples. --- dtreeviz/models/shadow_decision_tree.py | 22 +- .../dtreeviz_sklearn_visualisations.ipynb | 18813 +++++++------- notebooks/dtreeviz_spark_visualisations.ipynb | 20388 ++++++++-------- .../dtreeviz_xgboost_visualisations.ipynb | 10686 ++++---- notebooks/examples.ipynb | 15805 ++++++++---- notebooks/partitioning.ipynb | 70 +- 6 files changed, 36658 insertions(+), 29126 deletions(-) diff --git a/dtreeviz/models/shadow_decision_tree.py b/dtreeviz/models/shadow_decision_tree.py index 222dedaf..2a89fba4 100644 --- a/dtreeviz/models/shadow_decision_tree.py +++ b/dtreeviz/models/shadow_decision_tree.py @@ -55,14 +55,11 @@ def __init__(self, self.feature_names = feature_names self.target_name = target_name - self.class_names = class_names - # self.class_weight = self.get_class_weight() self.x_data = ShadowDecTree._get_x_data(x_data) self.y_data = ShadowDecTree._get_y_data(y_data) - # self.node_to_samples = self.get_node_samples() self.root, self.leaves, self.internal = self._get_tree_nodes() - if class_names: - self.class_names = self._get_class_names() + if self.is_classifier(): + self.class_names = self._normalize_class_names(class_names) @abstractmethod def is_fit(self) -> bool: @@ -391,14 +388,17 @@ def get_leaf_sample_counts_by_class(self): index, leaf_sample_0, leaf_samples_1 = zip(*leaf_samples) return index, leaf_sample_0, leaf_samples_1 - def _get_class_names(self): + def _normalize_class_names(self, class_names): if self.is_classifier(): - if isinstance(self.class_names, dict): - return self.class_names - elif isinstance(self.class_names, Sequence): - return {i: n for i, n in enumerate(self.class_names)} + if class_names is None: + return {i : f"class {i}" for i in range(self.nclasses())} + if isinstance(class_names, dict): + return class_names + elif isinstance(class_names, Sequence): + return {i: n for i, n in enumerate(class_names)} else: - raise Exception(f"class_names must be dict or sequence, not {self.class_names.__class__.__name__}") + raise Exception(f"class_names must be dict or sequence, not {class_names.__class__.__name__}") + return None def _get_tree_nodes(self): # use locals not args to walk() for recursion speed in python diff --git a/notebooks/dtreeviz_sklearn_visualisations.ipynb b/notebooks/dtreeviz_sklearn_visualisations.ipynb index 8bf2416d..51729c18 100644 --- a/notebooks/dtreeviz_sklearn_visualisations.ipynb +++ b/notebooks/dtreeviz_sklearn_visualisations.ipynb @@ -57,12 +57,7 @@ { "data": { "text/plain": [ - "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n", - " max_depth=4, max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort='deprecated',\n", - " random_state=1234, splitter='best')" + "DecisionTreeClassifier(max_depth=4, random_state=1234)" ] }, "execution_count": 4, @@ -122,7 +117,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -195,7 +190,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -389,29 +396,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -425,18 +432,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -444,7 +451,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -456,20 +463,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -479,141 +486,153 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:47.748843\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -622,19 +641,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -644,18 +663,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -666,13 +685,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -684,10 +703,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -700,16 +719,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -724,13 +743,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -741,25 +760,37 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf4\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.435564\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -767,21 +798,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -789,24 +820,36 @@ " \n", "\n", "\n", - "\n", + "\n", "node3->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf5\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.478007\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -814,12 +857,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -833,51 +876,63 @@ " \n", "\n", "\n", - "\n", + "\n", "node3->leaf5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.521316\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -885,50 +940,62 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf8\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.565569\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -936,141 +1003,153 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:47.927879\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1079,21 +1158,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1104,15 +1183,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1123,12 +1202,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1139,10 +1218,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1155,15 +1234,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1178,13 +1257,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1194,153 +1273,165 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->node3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:48.279844\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1349,19 +1440,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1371,18 +1462,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1393,13 +1484,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1411,10 +1502,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1427,15 +1518,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1451,13 +1542,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1468,142 +1559,154 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node10\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:48.042613\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1612,21 +1715,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1637,15 +1740,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1656,12 +1759,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1672,10 +1775,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1688,15 +1791,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1711,14 +1814,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1729,141 +1832,153 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node13\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:48.159936\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1872,19 +1987,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1893,34 +2008,34 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1942,18 +2057,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1961,15 +2076,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1984,13 +2099,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2000,42 +2115,54 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.610298\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2043,7 +2170,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2051,49 +2178,61 @@ " \n", "\n", "\n", - "\n", + "\n", "node10->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf12\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.655731\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2101,24 +2240,36 @@ " \n", "\n", "\n", - "\n", + "\n", "node10->leaf12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf14\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.690469\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2129,21 +2280,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2151,51 +2302,63 @@ " \n", "\n", "\n", - "\n", + "\n", "node13->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf15\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.734939\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2203,153 +2366,165 @@ " \n", "\n", "\n", - "\n", + "\n", "node13->leaf15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9->node10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9->node13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:48.395714\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2358,18 +2533,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2377,29 +2552,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2413,18 +2588,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2432,15 +2607,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2456,13 +2631,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2472,153 +2647,165 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.142575\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2627,19 +2814,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2648,16 +2835,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2666,17 +2853,17 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2695,18 +2882,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2714,15 +2901,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2738,13 +2925,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2754,142 +2941,154 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node18\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:48.510652\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2898,19 +3097,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2920,18 +3119,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2942,13 +3141,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2960,10 +3159,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2976,15 +3175,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2998,13 +3197,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3015,141 +3214,153 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node21\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:48.648064\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3158,21 +3369,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3183,15 +3394,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3202,12 +3413,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3218,10 +3429,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3234,16 +3445,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3259,13 +3470,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3276,48 +3487,60 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf19\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.779585\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3325,24 +3548,36 @@ " \n", "\n", "\n", - "\n", + "\n", "node18->leaf19\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf20\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.819891\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3350,73 +3585,85 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node18->leaf20\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf22\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.864637\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3424,41 +3671,53 @@ " \n", "\n", "\n", - "\n", + "\n", "node21->leaf22\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf23\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.964508\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3466,10 +3725,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3477,141 +3736,153 @@ " \n", "\n", "\n", - "\n", + "\n", "node21->leaf23\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node17\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:48.773045\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3620,21 +3891,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3645,15 +3916,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3664,12 +3935,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3680,10 +3951,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3696,16 +3967,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3721,13 +3992,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3737,153 +4008,165 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node17->node18\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node17->node21\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node24\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.018595\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3892,21 +4175,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3917,15 +4200,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3936,12 +4219,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3952,10 +4235,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3968,15 +4251,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3991,14 +4274,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4009,142 +4292,154 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node26\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:48.898788\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4153,19 +4448,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4175,18 +4470,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4197,13 +4492,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4215,10 +4510,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4231,15 +4526,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4254,13 +4549,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4270,24 +4565,36 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf27\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:50.132009\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4295,21 +4602,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4317,50 +4624,62 @@ " \n", "\n", "\n", - "\n", + "\n", "node26->leaf27\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf28\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:50.177630\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4368,30 +4687,42 @@ " \n", "\n", "\n", - "\n", + "\n", "node26->leaf28\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node24->node26\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf25\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:50.059290\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4399,21 +4730,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4421,153 +4752,165 @@ " \n", "\n", "\n", - "\n", + "\n", "node24->leaf25\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16->node17\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16->node24\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:49.350603\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4576,18 +4919,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4595,31 +4938,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4636,10 +4979,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4652,16 +4995,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4677,12 +5020,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4692,24 +5035,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node16\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -4720,18 +5063,30 @@ "\n", "\n", "\n", - "\n", + "\n", "legend\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:47.407868\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4740,16 +5095,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4765,10 +5120,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4777,10 +5132,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4792,7 +5147,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -4812,37 +5167,49 @@ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", "\n", - "\n", + "\n", "node3\n", - "Pclass@1.50\n", + "Pclass@1.50\n", "\n", "\n", - "\n", + "\n", "node6\n", - "Fare@28.86\n", + "Fare@28.86\n", "\n", "\n", "\n", - "\n", + "\n", "leaf4\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:50.903690\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4850,21 +5217,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4872,24 +5239,36 @@ " \n", "\n", "\n", - "\n", + "\n", "node3->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf5\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:50.946279\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4897,12 +5276,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4916,51 +5295,63 @@ " \n", "\n", "\n", - "\n", + "\n", "node3->leaf5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:50.994564\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4968,50 +5359,62 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf8\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.042827\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5019,75 +5422,87 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2\n", - "Age@2.50\n", + "Age@2.50\n", "\n", "\n", - "\n", + "\n", "node2->node3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9\n", - "Fare@23.35\n", + "Fare@23.35\n", "\n", "\n", "\n", - "\n", + "\n", "node10\n", - "Age@36.50\n", + "Age@36.50\n", "\n", "\n", - "\n", + "\n", "node13\n", - "Embarked_label@1.50\n", + "Embarked_label@1.50\n", "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.084699\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5095,7 +5510,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5103,49 +5518,61 @@ " \n", "\n", "\n", - "\n", + "\n", "node10->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf12\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.132657\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5153,24 +5580,36 @@ " \n", "\n", "\n", - "\n", + "\n", "node10->leaf12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf14\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.170610\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5181,21 +5620,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5203,51 +5642,63 @@ " \n", "\n", "\n", - "\n", + "\n", "node13->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf15\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.219604\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5255,93 +5706,105 @@ " \n", "\n", "\n", - "\n", + "\n", "node13->leaf15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9->node10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9->node13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "Pclass@2.50\n", + "Pclass@2.50\n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16\n", - "Cabin_label@3.50\n", + "Cabin_label@3.50\n", "\n", "\n", "\n", - "\n", + "\n", "node18\n", - "Fare@38.35\n", + "Fare@38.35\n", "\n", "\n", - "\n", + "\n", "node21\n", - "Age@13.00\n", + "Age@13.00\n", "\n", "\n", "\n", - "\n", + "\n", "leaf19\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.270082\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5349,24 +5812,36 @@ " \n", "\n", "\n", - "\n", + "\n", "node18->leaf19\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf20\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.315140\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5374,21 +5849,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5396,51 +5871,63 @@ " \n", "\n", "\n", - "\n", + "\n", "node18->leaf20\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf22\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.358094\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5448,41 +5935,53 @@ " \n", "\n", "\n", - "\n", + "\n", "node21->leaf22\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf23\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.409170\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5490,10 +5989,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5501,52 +6000,64 @@ " \n", "\n", "\n", - "\n", + "\n", "node21->leaf23\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node17\n", - "Age@3.50\n", + "Age@3.50\n", "\n", "\n", - "\n", + "\n", "node17->node18\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node17->node21\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node24\n", - "Age@17.50\n", + "Age@17.50\n", "\n", "\n", "\n", - "\n", + "\n", "node26\n", - "Fare@7.90\n", + "Fare@7.90\n", "\n", "\n", - "\n", + "\n", "leaf27\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.495907\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5554,21 +6065,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5576,50 +6087,62 @@ " \n", "\n", "\n", - "\n", + "\n", "node26->leaf27\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf28\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.534500\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5627,30 +6150,42 @@ " \n", "\n", "\n", - "\n", + "\n", "node26->leaf28\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node24->node26\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf25\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:51.451926\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5658,21 +6193,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5680,41 +6215,41 @@ " \n", "\n", "\n", - "\n", + "\n", "node24->leaf25\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16->node17\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16->node24\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "Sex_label@0.50\n", + "Sex_label@0.50\n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node16\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -5725,18 +6260,30 @@ "\n", "\n", "\n", - "\n", + "\n", "legend\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:50.681304\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5745,16 +6292,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5770,10 +6317,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5782,10 +6329,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5797,7 +6344,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -5818,153 +6365,165 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", - "\n", + "\n", "cluster_instance\n", "\n", "\n", - "\n", + "\n", "node10\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:52.122682\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5976,21 +6535,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6001,15 +6560,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6020,12 +6579,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6036,10 +6595,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6052,15 +6611,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6075,14 +6634,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6103,41 +6662,53 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:52.948924\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6145,7 +6716,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6153,142 +6724,154 @@ " \n", "\n", "\n", - "\n", + "\n", "node10->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:52.366967\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6300,19 +6883,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6322,18 +6905,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6344,13 +6927,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6362,10 +6945,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6378,15 +6961,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6402,13 +6985,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6429,148 +7012,160 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node9->node10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:52.492537\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6582,18 +7177,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6601,29 +7196,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6637,18 +7232,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6656,15 +7251,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6680,13 +7275,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6705,148 +7300,160 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:52.795426\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6858,18 +7465,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6877,31 +7484,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6918,10 +7525,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6934,16 +7541,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6959,12 +7566,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6983,67 +7590,79 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "X_y\n", - "\n", - "\n", - "Pclass\n", - "\n", - "Age\n", - "\n", - "Fare\n", - "\n", - "Sex_label\n", - "\n", - "Cabin_label\n", - "\n", - "Embarked_label\n", - "\n", - "3.00\n", - "\n", - "4.00\n", - "\n", - "16.70\n", - "\n", - "0.00\n", - "\n", - "145.00\n", - "\n", - "2.00\n", + "\n", + "\n", + "Pclass\n", + "\n", + "Age\n", + "\n", + "Fare\n", + "\n", + "Sex_label\n", + "\n", + "Cabin_label\n", + "\n", + "Embarked_label\n", + "\n", + "3.00\n", + "\n", + "4.00\n", + "\n", + "16.70\n", + "\n", + "0.00\n", + "\n", + "145.00\n", + "\n", + "2.00\n", "\n", "\n", - "\n", + "\n", "leaf11->X_y\n", - "\n", - "\n", - "  Prediction\n", - " 1\n", + "\n", + "\n", + "  Prediction\n", + " 1\n", "\n", "\n", - "\n", + "\n", "legend\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:52.015878\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7052,16 +7671,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7077,10 +7696,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7089,10 +7708,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7104,10 +7723,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -7125,12 +7744,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -7147,12 +7766,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -7176,7 +7795,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -7297,7 +7916,7 @@ "max 3.0 63.000000 23.250000 0.0 145.000000 2.000000" ] }, - "execution_count": 24, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -7308,7 +7927,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -7429,7 +8048,7 @@ "max 3.0 63.000000 23.250000 0.0 145.000000 2.000000" ] }, - "execution_count": 25, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -7447,7 +8066,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -7462,7 +8081,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 26, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -7474,7 +8093,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -7495,22 +8114,22 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 28, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] @@ -7534,21 +8153,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mae', max_depth=3,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort='deprecated',\n", - " random_state=1234, splitter='best')" + "DecisionTreeRegressor(criterion='mae', max_depth=3, random_state=1234)" ] }, - "execution_count": 29, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -7562,7 +8176,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -7585,28 +8199,40 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + 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- " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7774,19 +8400,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7796,18 +8422,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7819,16 +8445,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7838,13 +8464,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7856,10 +8482,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7873,7 +8499,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7887,15 +8513,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7903,13 +8529,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7920,23 +8546,35 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:55.159003\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7944,77 +8582,77 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8024,19 +8662,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8046,15 +8684,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8064,15 +8702,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8081,16 +8719,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8106,18 +8744,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8126,7 +8764,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8140,15 +8778,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8156,13 +8794,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8173,25 +8811,37 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf3\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:55.926136\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8199,85 +8849,85 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8285,19 +8935,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8306,15 +8956,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8323,15 +8973,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8339,25 +8989,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8369,11 +9019,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8383,30 +9033,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf4\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:56.000939\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8414,81 +9076,81 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8496,19 +9158,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8517,15 +9179,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8534,15 +9196,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8550,24 +9212,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8579,11 +9241,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8593,30 +9255,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf6\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:56.071784\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8624,29 +9298,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8654,19 +9328,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8675,15 +9349,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8692,15 +9366,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8708,25 +9382,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8738,12 +9412,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8753,30 +9427,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:56.155806\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8784,57 +9470,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8842,19 +9528,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8863,15 +9549,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8880,15 +9566,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8896,25 +9582,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8926,12 +9612,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8941,29 +9627,41 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:55.281777\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8971,225 +9669,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9199,21 +9897,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9224,16 +9922,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9245,17 +9943,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9265,17 +9963,17 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9294,18 +9992,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9314,7 +10012,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9328,7 +10026,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9341,13 +10039,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9358,35 +10056,47 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:55.686423\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9394,684 +10104,684 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", " \n", " \n", " \n", @@ -10081,19 +10791,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10103,18 +10813,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10125,13 +10835,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10143,10 +10853,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10160,7 +10870,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10174,15 +10884,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10190,13 +10900,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10207,24 +10917,36 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node9\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:55.469934\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10232,605 +10954,605 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10840,19 +11562,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10861,16 +11583,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10879,17 +11601,17 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10908,19 +11630,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10929,15 +11651,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10946,15 +11668,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10962,13 +11684,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10979,23 +11701,35 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:55.583633\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11003,88 +11737,88 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11094,19 +11828,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11116,18 +11850,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11139,15 +11873,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11157,13 +11891,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11175,10 +11909,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11192,7 +11926,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11206,7 +11940,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11219,13 +11953,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11236,25 +11970,37 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf10\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:56.239425\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11262,592 +12008,592 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11855,19 +12601,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11876,15 +12622,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11893,15 +12639,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11909,25 +12655,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11939,11 +12685,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11954,30 +12700,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node9->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:56.349229\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11985,22 +12743,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12008,19 +12766,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12029,15 +12787,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12046,15 +12804,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12062,24 +12820,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12091,12 +12849,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12106,30 +12864,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node9->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf13\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:56.426168\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12137,67 +12907,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12205,19 +12975,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12226,15 +12996,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12243,15 +13013,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12259,24 +13029,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12288,12 +13058,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12303,30 +13073,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12->leaf13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf14\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:56.501177\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12334,30 +13116,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12365,19 +13147,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12386,15 +13168,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12403,15 +13185,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12419,25 +13201,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12449,10 +13231,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12462,41 +13244,53 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8->node12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:55.798389\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12504,900 +13298,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13407,20 +14201,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13430,15 +14224,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13448,15 +14242,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13465,14 +14259,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13486,18 +14280,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13506,7 +14300,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13520,15 +14314,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13536,12 +14330,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13549,13 +14343,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13566,24 +14360,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node8\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -13593,10 +14387,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 31, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -13614,7 +14408,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -13629,7 +14423,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 32, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -13641,32 +14435,44 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_instance\n", "\n", "\n", - "\n", + "\n", "node9\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:57.434865\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13674,605 +14480,605 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14285,19 +15091,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14306,16 +15112,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14324,17 +15130,17 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14353,19 +15159,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14374,15 +15180,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14391,15 +15197,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14407,13 +15213,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14424,24 +15230,36 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:57.780310\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14449,22 +15267,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14472,19 +15290,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14493,15 +15311,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14510,15 +15328,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14526,24 +15344,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14555,12 +15373,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14570,30 +15388,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node9->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:57.555864\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14601,684 +15431,684 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15291,19 +16121,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15313,18 +16143,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15335,13 +16165,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15353,10 +16183,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15370,7 +16200,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15384,15 +16214,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15400,13 +16230,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15417,30 +16247,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node8->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", - "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:27:57.670018\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15448,900 +16290,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16354,20 +17196,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16377,15 +17219,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16395,15 +17237,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16412,14 +17254,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16433,18 +17275,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16453,7 +17295,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16467,15 +17309,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16483,12 +17325,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16496,13 +17338,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16513,62 +17355,62 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "X_y\n", - "\n", - "\n", - "Pclass\n", - "\n", - "Fare\n", - "\n", - "Sex_label\n", - "\n", - "Cabin_label\n", - "\n", - "Embarked_label\n", - "\n", - "Survived\n", - "\n", - "3.00\n", - "\n", - "16.70\n", - "\n", - "0.00\n", - "\n", - "145.00\n", - "\n", - "2.00\n", - "\n", - "1.00\n", + "\n", + "\n", + "Pclass\n", + "\n", + "Fare\n", + "\n", + "Sex_label\n", + "\n", + "Cabin_label\n", + "\n", + "Embarked_label\n", + "\n", + "Survived\n", + "\n", + "3.00\n", + "\n", + "16.70\n", + "\n", + "0.00\n", + "\n", + "145.00\n", + "\n", + "2.00\n", + "\n", + "1.00\n", "\n", "\n", - "\n", + "\n", "leaf11->X_y\n", - "\n", - "\n", - "  Prediction\n", - " 24.00\n", + "\n", + "\n", + "  Prediction\n", + " 24.00\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -16586,12 +17428,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -16608,12 +17450,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -16637,12 +17479,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -16659,12 +17501,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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76SSv7Q7RZJ3Hnqz3+Nd57Ml6j9/Y19c6jP/KrVzEfjjJ88YYD10un5skY4z/1BqsWVVdMsY4sztHh3Uee7Le41/nsSfrPX5jX8+xJ8a/Fa4R+59JTququ1fVCUkem+SNzZkAADZd+zViY4ybq+qXkvyPJMclefkY46PNsQAANl17EUuSMcZbkrylO8cWc0F3gEbrPPZkvce/zmNP1nv8xr6+1nr87deIAQCsq61wjRgAwFpSxLaYqrp9Vf1FVX24qj5aVb/WnWnVquq4qvrLqnpTd5ZVqqqTq+p1VfXxqrp8+Y3itVBVp1fVh/b5ubGqntGda7NU1cur6tqq+sg+6769quZV9cnl4ymdGTfTQcb/mOVn3teqatt+g+4gY/+PVfVXy7/9t1fVd3Vm3EwHGv8+r/27qhpVdceObF0Usa3ny0kePMa4b5L7JXlYVT2oN9LKPT3J5d0hGpyX5G1jjO9Nct+s0T+DMcYVY4z7jTHul+QHk9yU5PW9qTbVhUkett+6Zyd5xxjjtCTvWC5vVxfmm8f/kST/Msl7Vp5mtS7MN4/9BWOM+yz//t+U5D+sOtQKXZhvHn+q6m5JpiR7Vh2omyK2xYyFv1suHr/8WZsL+apqZ5JHJHlpd5ZVqqqTkvx4kpclyRjjK2OMG1pD9XlIkr8eY3y2O8hmGWO8J8n1+61+VJJXLp+/MslPrjLTKh1o/GOMy8cYVzRFWpmDjP3GfRbvkG38mX+Qv/0k+S9JnpVtPPaDUcS2oOWpuQ8luTbJfIzxgeZIq/Q7WfzL+LXmHKt2jyTXJXnF8rTsS6vqDt2hmjw22//OGQfyHWOMq5Nk+Xjn5jysUFX9RlVdleRx2d5HxL5JVZ2V5HNjjA93Z+mgiG1BY4z/uzxEvTPJA6rq3s2RVqKqHpnk2jHGB7uzNNiR5P5JXjLG+IEkX8r2PjV1QMtJnc9K8t+7s8AqjTGeM8a4W5JXJ/ml7jyrUlUnJnlO1qx87ksR28KWp6benQOcT9+mfjTJWVV1ZZLXJHlwVf1Bb6SV2Ztk7z5HP1+XRTFbNw9PcukY45ruIA2uqaq7JMny8drmPPT4wySP7g6xQvdMcvckH15+9u9McmlVfWdrqhVSxLaYqrpTVZ28fP4tSWZJPt4aakXGGOeOMXaOMU7N4vTUO8cYj2+OtRJjjM8nuaqqTl+uekiSjzVG6nJ21vO0ZLK4tduTls+flOQNjVlYoao6bZ/Fs7Imn/lJMsa4bIxx5zHGqcvP/r1J7r/8TFwLW2Jmfb7BXZK8sqqOy6Iov3aMsVbTOKyxpyV59fL03KeTPLk5z0otT1FMSX6hO8tmq6qLkvyzJHesqr1JfjXJ85O8tqqeksU3xx7Tl3BzHWT81yd5UZI7JXlzVX1ojPHQvpSb4yBj/4nl/4R9Lclnk/xiX8LNdaDxjzFe1puql5n1AQCaODUJANBEEQMAaKKIAQA0UcQAAJooYgAATUxfARxzpml2apLPJDltPt/41FF4v19Jcm6S6+fzje/e77ULk+yYzzcOOKfdNM32JnnufL5x4ZHmANaPIgastWmanZLkN7KYv+wtB9jk6atNBKwTRQxYdyctH981n2/s3f/F+Xzjb1ecB1gjihhwzJum2T9M8rtJfjLJ/8nidkHPnM83vrh8/ZFJfj3JvZJ8Ocnbkjw1i/t5vmv5Np+Yptmvzecbz9vvvS/MPqcmp2n2C0mem0WBe/5+256R5MVJfjDJF7O4gfMvz+cbNx/VAQPbhov1ge3g5UnumOTHkjwiyelJLkySaZrdPcnFSc5P8r1Z3DrowVncRua9SR6wfI8fTvLCW9rJNM0emuS8JL+S5EeSPCjJXffZ5A+yuE/gGUl+OskTkjzlCMcGbGOOiAHHtGma3TPJTyW543y+cf1y3ROTXDlNs7tl8Tn39Pl844Llr1w5TbONJN8/n298ZZpm1y3X/818vvF3h9jdzyd5zXy+8fvL/Twli5sUf92pSd6c5LPz+canp2n28CRfOPJRAtuVIgYc674vSSXZM02z/V/7nvl84x3TNPvyNM2ek+TeSb5/+XPRYezrXkle+vWF+Xzjb6ZpduU+r5+bxY2rz5mm2VuzKG0fPIz9AGvCqUngWLcjyZeS3G+/n9OSvH+aZvdN8rEsStifZnGq8DVHsL/ab/mrX38yn2/8tyR3T/JrSe6U5A3TNHveEewL2OYcEQOOdVckuUOS4+bzjSuSZJpm/zjJb2cxJcUTkvz5fL5x9td/YZpmpyX55GHs6yNJfmif9zkpyT2Wz2+f5LeSvHA+33hRkhdN0+y5SR6X5HmHsS9gDShiwDFtPt+4fJpmb0vy+9M0e1qSv0/ykiyK2dXTNPtCkntP0+yBSa7P4iL9H0qy5zB29+IkG8tvTv5JFke+br/M8ffTNPsnSb57mmbnZvH5+vAkTk0CB+XUJLAdPCGLI1xvz6IgfS7Jo5av/W6SP08yz+JbkqdmUaDud1t3Mp9vvCfJzyb55SSXLPdz2T6b7MqimL0/yZ9lMfv/027rfoD1UWOM7gwAAGvJETEAgCaKGABAE0UMAKCJIgYA0EQRAwBooogBADRRxAAAmihiAABNFDEAgCb/D2zk17A6gPKrAAAAAElFTkSuQmCC\n", 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" ] @@ -16688,12 +17530,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -16710,12 +17552,12 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -16739,7 +17581,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -16860,7 +17702,7 @@ "max 1.0 80.000000 512.329200 1.000000 146.000000 2.000000" ] }, - "execution_count": 40, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -16871,7 +17713,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -16992,7 +17834,7 @@ "max 1.0 512.329200 1.000000 146.000000 2.000000 1.000000" ] }, - "execution_count": 41, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -17010,7 +17852,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -17025,7 +17867,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 42, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -17037,7 +17879,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -17057,7 +17899,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -17077,22 +17919,22 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 45, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -17109,7 +17951,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -17121,7 +17963,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -17145,7 +17987,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 38, "metadata": { "scrolled": true }, @@ -17153,15 +17995,10 @@ { "data": { "text/plain": [ - "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mae', max_depth=2,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort='deprecated',\n", - " random_state=1234, splitter='best')" + "DecisionTreeRegressor(criterion='mae', max_depth=2, random_state=1234)" ] }, - "execution_count": 47, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -17175,7 +18012,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -17184,12 +18021,12 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -17206,12 +18043,12 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -17235,21 +18072,16 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mae', max_depth=3,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort='deprecated',\n", - " random_state=1234, splitter='best')" + "DecisionTreeRegressor(criterion='mae', max_depth=3, random_state=1234)" ] }, - "execution_count": 51, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -17263,7 +18095,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -17272,12 +18104,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -17294,12 +18126,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -17323,7 +18155,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 46, "metadata": { "scrolled": true }, @@ -17331,15 +18163,10 @@ { "data": { "text/plain": [ - "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mae', max_depth=3,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort='deprecated',\n", - " random_state=1234, splitter='best')" + "DecisionTreeRegressor(criterion='mae', max_depth=3, random_state=1234)" ] }, - "execution_count": 55, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -17353,7 +18180,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -17362,12 +18189,12 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -17384,14 +18211,14 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 49, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -17405,20 +18232,6 @@ "source": [ "trees.rtreeviz_bivar_heatmap(skdtree_bivar)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -17437,7 +18250,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.6" }, "toc": { "base_numbering": 1, @@ -17455,4 +18268,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/notebooks/dtreeviz_spark_visualisations.ipynb b/notebooks/dtreeviz_spark_visualisations.ipynb index c0fea438..71a6ce9f 100644 --- a/notebooks/dtreeviz_spark_visualisations.ipynb +++ b/notebooks/dtreeviz_spark_visualisations.ipynb @@ -208,7 +208,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_0d107b9a930d, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", + "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_d234d90a8024, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", " If (feature 1 in {0.0})\n", " If (feature 3 <= 3.5)\n", " If (feature 4 <= 2.5)\n", @@ -281,16 +281,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "df = spark.read.parquet(\"../../dtreeviz/testing/dtreeviz/models/fixtures/spark_3_0_decision_tree_classifier.model/data\")" + "df = spark.read.parquet(\"../../dtreeviz/testing/testlib/models/fixtures/spark_3_0_decision_tree_classifier.model/data\")" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -606,7 +606,7 @@ "16 -1 (-1, [], -1) " ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -617,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -629,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -645,13 +645,21 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/parrt/opt/anaconda3/lib/python3.8/site-packages/dtreeviz-1.1.4-py3.8.egg/dtreeviz/models/spark_decision_tree.py:87: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " self.thresholds = np.array(node_thresholds)\n" + ] + }, { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", "G\n", "\n", @@ -662,132 +670,144 @@ "\n", "node2\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:23.982896\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + 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@@ -815,28 +835,28 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -849,10 +869,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -865,7 +885,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -884,13 +904,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -900,7 +920,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -910,14 +930,26 @@ "leaf3\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:24.977428\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -925,13 +957,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -955,40 +987,52 @@ "\n", "leaf4\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:25.023970\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1005,32 +1049,44 @@ "\n", "leaf5\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:25.070584\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1038,10 +1094,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1053,132 +1109,144 @@ "\n", "node1\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:24.098943\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1187,21 +1255,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1212,15 +1280,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1231,16 +1299,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1256,10 +1324,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1272,15 +1340,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1296,13 +1364,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1312,7 +1380,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1327,132 +1395,144 @@ "\n", "node6\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:24.779860\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1461,18 +1541,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1480,29 +1560,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1516,18 +1596,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1535,15 +1615,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1559,13 +1639,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1575,7 +1655,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1591,132 +1671,144 @@ "\n", "node8\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:24.282945\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1725,18 +1817,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1744,29 +1836,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1780,18 +1872,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1799,15 +1891,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1821,13 +1913,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1837,7 +1929,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1847,14 +1939,26 @@ "leaf9\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:25.113538\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1862,21 +1966,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1894,14 +1998,26 @@ "leaf10\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:25.156598\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1909,21 +2025,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1940,33 +2056,45 @@ "\n", "leaf11\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:25.204162\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1974,7 +2102,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1986,132 +2114,144 @@ "\n", "node7\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:24.401753\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2120,21 +2260,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2145,15 +2285,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2164,16 +2304,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2189,10 +2329,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2205,15 +2345,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2228,13 +2368,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2244,7 +2384,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2259,132 +2399,144 @@ "\n", "node12\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:24.638799\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2393,19 +2545,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2415,18 +2567,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2437,13 +2589,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2455,10 +2607,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2471,15 +2623,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2495,14 +2647,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2513,7 +2665,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2529,149 +2681,161 @@ "\n", "node13\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:24.520738\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2679,34 +2843,34 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2728,10 +2892,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2744,15 +2908,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2767,17 +2931,17 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2793,7 +2957,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2802,41 +2966,53 @@ "\n", "leaf14\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:25.324104\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2853,41 +3029,53 @@ "\n", "leaf15\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:25.365069\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2904,42 +3092,54 @@ "\n", "leaf16\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:25.411036\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2975,149 +3175,161 @@ "\n", "node0\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:24.894325\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3125,31 +3337,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3166,10 +3378,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3182,16 +3394,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3207,13 +3419,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3224,7 +3436,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3251,14 +3463,26 @@ "legend\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:23.126351\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3267,16 +3491,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3292,10 +3516,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3304,10 +3528,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3319,10 +3543,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -3340,14 +3564,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -3371,12 +3595,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -3400,12 +3624,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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6p3OBGU2oS5IkqWPVG9DOpXa17Tz+Oifno8C3gPObUJckSVLHqncmgVXA54HPZ1llMLAyz6tPNbUySZKkDlXvFbSXOKSGJElSc9X7kIAkSZL6iAFNkiSpZOoKaFlWGdXsQiRJklRT7z1ov8myyn3AJcCleV69q3klSZIkdbZ6A9rWwAeAo4GzsqxyO7WwNifPq93NKk6SJKkT1TvMxgpqk6XPyrLK5sD7gfcB52ZZ5Wbgx8AlDr0hSZK0/l7PQwLDgN2AEcAqYAm1K2v3Z1nlmAbWJkmS1JHquoKWZZV9qIWwDwHbAf8JfAG4Ms+rzxbbfA74HnBpc0qVJEnqDPXeg3Yj8CtgMvDveV59ci3bXNaowiRJkjpVvQFthzyvLn21DfK8+itqIU6SJEnrod6HBJZmWeVg4JPU7j9bCSwEvp7n1T82sT5JkqSOU+9Atf8A/AJ4GvghMLN46zc+GCBJktRY9XZxTgJOzPPqrJ6NWVb5b+Ar1PFgQEQMAq4HNiqO++8ppS9ExJbAHGAn4D7g6JTS48VnzgSOB14ETkkpXVNnvZIkSf1WvcNsbAr8vpf2XwOD69zHc8DBKaWRwCjgkIjYDzgDmJdSGgbMK9aJiN2BY4A9gEOA70fEgDqPJUmS1G/VG9C+DZyfZZUtVjdkWWUT4BxqXZ6vKdU8XawOLH4ScCR/7TKdSW0QXIr2S1NKz6WU7gXuBvats15JkqR+a61dnFlWWUItQAEEsD3wUJZV7qU2QO1Qat2VdT8kUFwBuxnYBfheSum3EbFNSmkpQEppaURsXWy+PbWhO1brLtokSZLa2qvdgzax0QdLKb0IjIqIzYGfRcSIV9k8etvFKzaKOBE4Edhozpw5fP7zn29MsZIkSS2y1oCW59WZa3tvfaWUnoiI66jdW/ZIRAwprp4NAZYVm3UDO/T4WBfwUC/7mgpMjYjh48aNW9SsmiVJkvrK65mL83WJiLcUV86IiI2BCnAncCVwXLHZccAVxfKVwDERsVFEDKU2B+jv+qpeSZKkVql3mI1GGALMLO5DewMwN6X084j4DTA3Io4HHgA+DJBSWhARc4E7gBeAk4ouUkmSpLbWZwEtpXQbsHcv7cuBMWv5zGRq839KkiR1jHXq4syyyhuK122zrPLhLKsMa05ZkiRJnaveqZ72K4bdeFeWVbYBbgKmA/OzrPKBZhYoSZLUaeq9gvYNajfv/57a1EsvAFsD/wR8sTmlSZIkdaZ6A9rewNfyvPo0cARweZ5XnwWqwNuaVZwkSVInqjegLQfemmWVnYB3AlcV7fsAS5tQlyRJUseq9ynOi4DLgZXAbcC8LKv8E/A14Kwm1SZJktSR6rqClufVs4FPAOcD783z6irgPmBcnlf/rXnlSZIkdZ66h9nI8+rPgJ8AI7KssjFwc55Xf960yiRJkjpUXV2cWVZ5E/D/gA8Cq4DhwDezrPIW4P15Xl32ap+XJElS/eq9gvZ1YDAwFPhL0fbPQAK+3YS6JEmSOla9Ae0I4LQ8r96/uiHPq3dTGwftvc0oTJIkqVPVG9A2pvYE55o2AqJx5UiSJKnegHYF8JUsq2xerKdiHs7vAD4oIEmS1ED1BrSTgeepDVj7RuBW4M5i/dTmlCZJktSZ6nqKM8+rTwEfzLLKzsBuxecW5Xn1zmYWJ0mS1InWGtCKMNabhWtuk+fVexpclyRJUsd6tStod1MbRmO1NR8GSEVbAgY0uC5JkqSO9WoBbWifVSFJkqSXrDWg9RzzbLUsq+xF7R60VcCteV69q4m1SZIkdaR6p3oaAvwUGA08Rq1L881ZVpkHHJ3n1SeaV6IkSVJnqXeYjenUpnjaOc+rg/O8ugWwK7WBar/frOIkSZI6Ub0B7SDg03levW91QzHV08nA4U2oS5IkqWPVG9AWA6N6ad8JuK9RxUiSJKnOe9CAmcD3sqyyD/Ab4AVqge1kYEaWVf5h9YZ5Xr2o4VVKkiR1kHoD2qnAE8AHip/VnlyjLQEGNEmSpPVQ71RPjokmSZLUR+q9gkaWVfYA3k7tyc2eUp5XL2loVZIkSR2s3nHQJgNnUuvSfHaNtxNgQJMkSWqQeq+gfQr4VJ5Xf9jMYiRJklT/MBsrgF83sxBJkiTV1HsF7V+oDbNxNvAAtbk4X5Ln1QcaXZgkSVKnqjegbQD8DfDLNdqD2j1oAxpZlCRJUierN6B9ndr4ZtOozckpSZKkJqk3oA0CvpXn1XuaWYwkSZLqf0jga8DELKts0sxiJEmSVP8VtMOAdwJ/n2WVP1Obi/MleV59a6MLkyRJ6lT1BrQLix9JkiQ1Wb1zcc5c23tZVllz6qdeRcQOwCxgW2rDdExNKX0rIrYE5gA7AfcBR6eUHi8+cyZwPPAicEpK6Zp6jiVJktSf1TvV0xDgLGAP/jqkRlCbl3NXYLM6dvMC8M8ppT9ExKbAzRGRAxOAeSml8yLiDOAM4HMRsTtwTHHM7YBqRAxPKb1Y99lJkiT1Q/U+JHARUAF+A+wH3AAsBd5BLbi9ppTS0pTSH4rlFcBCYHvgSGD1FbqZwPuL5SOBS1NKz6WU7gXuBvats15JkqR+q96AdiDw8Tyv/itwK/DzPK8eTS2cHb6uB42InYC9gd8C26SUlkItxAFbF5ttDyzp8bHuok2SJKmt1RvQAniwWL6D2pUzgLnUnu6sW0S8CbgM+ExK6anXOOaaUi/7OzEibgIumzNnzrqUIkmSVEr1BrSbgfHF8i3A2GL5betysIgYSC2cXZxS+mnR/EhEDCneHwIsK9q7gR16fLwLeGjNfaaUpqaU9gE+OG7cuHUpR5IkqZTqDWifA/5vllVOo/Yk5juyrLIQ+ClwaT07iIgApgMLU0rf6PHWlcBxxfJxwBU92o+JiI0iYigwDPhdnfVKkiT1W3UFtDyv/gbYEbg4z6vLgX2A7wInAifXeawDgI8BB0fELcXPYcB5QBYRi4GsWCeltIBaF+odwNXAST7BKUmSOkG9A9VCLcw9WSxvCWwCLMzz6qp6PpxS+jW931cGMGYtn5kMTF6HGiVJkvq9uq6gZVnlfdTu//rbLKsMBf4b+ATwH1lW+ccm1idJktRx6r0HbTLwZWAetZH9HwbeDhwLnN6c0iRJkjpTvQFtV2B2nlcTcARwebH8R2qj/EuSJKlB6g1oDwEjs6yyFzAC+HnRPpba/JmSJElqkHofEvg6tfHLVgHz8rx6Q5ZVJgJn89chMiRJktQA9Q6z8X1gNPARal2cAFXgnXlevaRJtUmSJHWkuofZyPPqLdRmEVi9fmNTKpIkSepw9d6DJkmSpD5iQJMkSSoZA5okSVLJrMtUT5KkPjJt1nQef+bJ196wn9pik804YfzxrS5DKi0DmiSV0OPPPMm2Y3ZtdRlN8/C8Ra0uQSo1uzglSZJKxoAmSZJUMgY0SZKkkjGgSZIklYwBTZIkqWQMaJIkSSVjQJMkSSoZA5okSVLJGNAkSZJKxoAmSZJUMgY0SZKkkjGgSZIklYwBTZIkqWQMaJIkSSVjQJMkSSoZA5okSVLJGNAkSZJKxoAmSZJUMgY0SZKkkjGgSZIklYwBTZIkqWQMaJIkSSVjQJMkSSoZA5okSVLJGNAkSZJKxoAmSZJUMn0W0CLioohYFhHze7RtGRF5RCwuXrfo8d6ZEXF3RCyKiLF9VackSVKr9eUVtBnAIWu0nQHMSykNA+YV60TE7sAxwB7FZ74fEQP6rlRJkqTW6bOAllK6HnhsjeYjgZnF8kzg/T3aL00pPZdSuhe4G9i3TwqVJElqsVbfg7ZNSmkpQPG6ddG+PbCkx3bdRdsrRMSJEXETcNmcOXOaWaskSVKfaHVAW5vopS31tmFKaWpKaR/gg+PGjWtuVZIkSX2g1QHtkYgYAlC8Livau4EdemzXBTzUx7VJkiS1RKsD2pXAccXyccAVPdqPiYiNImIoMAz4XQvqkyRJ6nMb9NWBIuIS4N3A4IjoBr4AnAfMjYjjgQeADwOklBZExFzgDuAF4KSU0ot9VaskSVIr9VlASyl9ZC1vjVnL9pOByc2rSJIkqZxa3cUpSZKkNRjQJEmSSsaAJkmSVDIGNEmSpJIxoEmSJJWMAU2SJKlkDGiSJEklY0CTJEkqGQOaJElSyRjQJEmSSqbPpnqSJKlTTJs1ncefebLVZTTVFptsxgnjj291GW3LgCZJUoM9/syTbDtm11aX0VQPz1vU6hLaml2ckiRJJWNAkyRJKhkDmiRJUsl4D5okqc89cP8DfPWCb7S6jKZZ+sjDbEt734Om5jKgSZL6XAx8Q1vfRL9kdnerS1A/ZxenJElSyRjQJEmSSsaAJkmSVDIGNEmSpJIxoEmSJJWMAU2SJKlkDGiSJEklY0CTJEkqGQeqlSSpwZ5Z8TS3/Hheq8toqoGrjBDN5G9XkqQG22zzN/H1sw9rdRlNNWvW/FaX0Nbs4pQkSSoZA5okSVLJGNAkSZJKxnvQJEnSOpt/9/189YJvtLqMptlik804YfzxLTu+AU2SJK2zp1es4MVHultdRtMsX/VwS49vQJMkSets6Habc/rph7S6jKZp9VOqBjRJUp9r93HCVq7431aXoH7OgCZJ6nPtPk7Y1752datLUD9nQHsdps2azuPPPNnqMpqm1TdGSpLU6Qxor8PjzzzJtmN2bXUZTfPwvEWtLkGSpI5W+oAWEYcA3wIGABemlM5rcUmS1HTLlz7Mwz9u3yfkvEdLenWlDmgRMQD4HpAB3cDvI+LKlNIdra2svd1z12JOO+1TrS6jaTbaKIARrS5DelW777wd48e3799T79GSXl2pAxqwL3B3SukegIi4FDgSMKA10SabbezNu5IktVDZA9r2wJIe693A6LVsO/Dee+9tfkXAo488Cve+qU+O1QqP//lx7rqrfbtWHn30sbY+P4Bf/vJ/uO6637a6jKZ54Q0D2PatO7S6jKZ67JGl7Lff5q0uo2na/d9hu58ftP85PvzwMu66666mHmPXXXfdMKW0srf3IqXU1IOvj4j4MDA2pfSJYv1jwL4ppZN7bHMicCK1eUWvAC5pRa1NdjQwt9VFNFG7nx+0/zl6fv1fu5+j59f/teM53tdfA9r+wDkppbHF+pkAKaWvtLSwPhYRN6WU9ml1Hc3S7ucH7X+Onl//1+7n6Pn1f51wjj29odUFvIbfA8MiYmhEbAgcA1zZ4pokSZKaqtT3oKWUXoiITwPXUBtm46KU0oIWlyVJktRUpQ5oACmlXwC/aHUdLTa11QU0WbufH7T/OXp+/V+7n6Pn1/91wjm+pNT3oEmSJHWist+DJkmS1HEMaCUVEYMi4ncRcWtELIiIc1tdUzNExICI+GNE/LzVtTRDRNwXEbdHxC0RcVOr62m0iDg1IuYXf0c/0+p6GiEiLoqIZRExv0fblhGRR8Ti4nWLVta4PtZyfh8u/gxXRUS/f0qut3Ps8d6/RESKiMGtqK0R1vJneE5EPFh819wSEf16tPG1/RlGxMkRsaj4+/rVVtXXFwxo5fUccHBKaSQwCjgkIvZrcU3NcCqwsNVFNNl7Ukqj2u3x8IgYAZxAbcaPkcDhETGstVU1xAzgkDXazgDmpZSGAfOK9f5qBq88v/nAUcD1fV5Nc8zgledIROxAberAB/q6oAabQS/nB3yz+K4ZVdy/3Z/NYI1zjIj3UJtNaK+U0h7AlBbU1WcMaCWVap4uVgcWP211w2BEdAHvAy5sdS16XXYDbkwpPZNSegH4FfCBFte03lJK1wOPrdF8JDCzWJ4JvL9Pi2qg3s4vpbQwpbSoRSU13Fr+DAG+CXyWfv5d+irn1zbWco6fAs5LKT1XbLOszwvrQwa0Eiu6/24BlgF5Sqnd5u75N2pflqtaXUgTJeC/IuLmYtaLdjIfOCgitoqITYDDgHadf2mblNJSgOJ16xbXo3UUEUcAD6aUbm11LU306Yi4rege7Lfd8K9iOHBgRPw2In4VEe9sdUHNZEArsZTSiymlUUAXsG/RpdQWIuJwYFlK6eZW19JkB6SU3gEcCpwUEQe1uqBGSSktBM4HcuBq4FbghZYWJfWi+A/EWcDZra6liX4AvI3aLTFLga+3tpym2ADYAtgPOB2YGxHR2pKax4DWD6SUngCuo/d7DvoiP8YAAATHSURBVPqrA4AjIuI+4FLg4Ij4UWtLaryU0kPF6zLgZ9Tu12obKaXpKaV3pJQOotYdsbjVNTXJIxExBKB4beuulTb0NmAocGvxndMF/CEitm1pVQ2UUnqk+E/9KmAabfZdU+gGflrcAvQ7ar0v/fZhj9diQCupiHhLRGxeLG8MVIA7W1tV46SUzkwpdaWUdqI2hde1KaW/b3FZDRURb4yITVcvA++l1i3YNiJi6+L1rdRuMr+ktRU1zZXAccXyccAVLaxF6yildHtKaeuU0k7Fd0438I6U0sMtLq1hVv8HovAB2uy7pnA5cDBARAwHNgT+3NKKmqj0Mwl0sCHAzIgYQC1Iz00pteVQFG1sG+BnxRX4DYAfp5Subm1JDXdZRGwFPA+clFJ6vNUFra+IuAR4NzA4IrqBLwDnUetOOZ7aE4Afbl2F62ct5/cY8B3gLcBVEXFLSmls66pcP72dY0ppemurapy1/Bm+OyJGUbvv9T7gH1tWYAOs5RwvAi4qht5YCRyX2ni0fWcSkCRJKhm7OCVJkkrGgCZJklQyBjRJkqSSMaBJkiSVjAFNkiSpZBxmQ1LbyLLKTsC9wLA8r97dgP39K3Am8FieV3dc470ZwAZ5Xu11/L4sq3QDE/O8OmN965DUeQxoktSLLKtsAUymNp7UL3rZ5NS+rUhSJzGgSVLv3ly8/jLPq91rvpnn1Sf7uB5JHcSAJqltZVllM+DbwPuBv1Cbsumf87y6onj/cGASsDvwHLVJ308A3gH8stjNXVlWOTfPq+esse8Z9OjizLLKPwITqQW789bYdk/ge8DfACuAi4HP5XnVyeUl9cqHBCS1s4uoTaZ8IPA+YFdgBkCWVYYClwEXAG+nNn3TwcAngf/hr5NN7w9MebWDZFllLPAt4F+B/wPsB2zfY5MfUZtLd0/gaOBjwPHreW6S2phX0CS1pSyrvI3apNGD87z6WNE2Hrgvyyo7UPv+OzXPq1OLj9yXZZUqsEeeV1dmWeXRov3PeV59+jUO9wng0jyvzi6Oczy1CblX2wm4Crg/z6v3ZFnlUGD5+p+lpHZlQJPUrnYDAnggyyprvjc8z6vzsqzyXJZVzgJGAHsUP5e8jmPtDly4eiXPq3/Ossp9Pd4/k9pk5CdmWeU/qYW5m1/HcSR1CLs4JbWrDYD/BUat8TMMuDHLKiOBO6iFs/+m1uV46XocL9ZYf371Qp5Xvw8MBc4F3gJckWWVc9bjWJLanFfQJLWrRcAbgQF5Xl0EkGWVXYBvUBs642PADXle/cjqD2RZZRiw+HUcaz7wzh77eTOwc7E8CDgfmJLn1e8A38myykTgWOCc13EsSR3AgCapLeV5dWGWVa4GZmdZ5WTgWeAH1ALb0iyrLAdGZFllNPAYtYcD3gk88DoO9z2gWjzJ+StqV8oGFXU8m2WVvwV2zLLKmdS+dw8F7OKUtFZ2cUpqZx+jdkXsv6gFpweBI4v3vg3cAOTUntrciVqwGrWuB8nz6vXABOBzwE3FcW7vsck4aoHtRuDX1GY7OHldjyOpc0RKqdU1SJIkqQevoEmSJJWMAU2SJKlkDGiSJEklY0CTJEkqGQOaJElSyRjQJEmSSsaAJkmSVDIGNEmSpJIxoEmSJJXM/wcBkxoJ2mZ2qAAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -3429,14 +3653,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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" \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3811,18 +4047,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3830,28 +4066,28 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3864,10 +4100,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3880,7 +4116,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3899,13 +4135,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3915,7 +4151,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3925,14 +4161,26 @@ "leaf3\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.703426\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3940,13 +4188,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3970,40 +4218,52 @@ "\n", "leaf4\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.751047\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4020,32 +4280,44 @@ "\n", "leaf5\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.802941\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4053,10 +4325,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4068,132 +4340,144 @@ "\n", "node1\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:33.722465\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4202,21 +4486,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4227,15 +4511,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4246,16 +4530,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4271,10 +4555,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4287,15 +4571,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4311,13 +4595,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4327,7 +4611,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4343,132 +4627,144 @@ "node6\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.490348\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4480,18 +4776,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4499,29 +4795,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4535,18 +4831,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4554,15 +4850,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4578,13 +4874,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4603,7 +4899,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4619,132 +4915,144 @@ "\n", "node8\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:33.850874\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4753,18 +5061,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4772,29 +5080,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4808,18 +5116,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4827,15 +5135,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4849,13 +5157,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4865,7 +5173,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4875,14 +5183,26 @@ "leaf9\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.848398\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4890,21 +5210,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4922,14 +5242,26 @@ "leaf10\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.891123\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4937,21 +5269,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4968,33 +5300,45 @@ "\n", "leaf11\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.937836\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5002,7 +5346,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5014,132 +5358,144 @@ "\n", "node7\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:33.974959\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5148,21 +5504,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5173,15 +5529,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5192,16 +5548,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5217,10 +5573,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5233,15 +5589,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5256,13 +5612,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5272,7 +5628,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5288,132 +5644,144 @@ "node12\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.338800\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5425,19 +5793,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5447,18 +5815,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5469,13 +5837,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5487,10 +5855,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5503,15 +5871,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5527,14 +5895,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5555,7 +5923,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5572,149 +5940,161 @@ "node13\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.093437\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5722,34 +6102,34 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5771,10 +6151,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5787,15 +6167,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5810,17 +6190,17 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5851,7 +6231,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5860,41 +6240,53 @@ "\n", "leaf14\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.977651\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5911,41 +6303,53 @@ "\n", "leaf15\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:35.018269\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5962,42 +6366,54 @@ "\n", "leaf16\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:35.065346\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6034,149 +6450,161 @@ "node0\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:34.622257\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6184,31 +6612,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6225,10 +6653,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6241,16 +6669,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6266,13 +6694,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6293,7 +6721,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6361,14 +6789,26 @@ "legend\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:33.497933\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6377,16 +6817,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6402,10 +6842,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6414,10 +6854,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6429,10 +6869,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -6457,7 +6897,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -6466,7 +6906,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -6476,7 +6916,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -6488,7 +6928,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -6500,7 +6940,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -6691,7 +7131,7 @@ "[891 rows x 8 columns]" ] }, - "execution_count": 32, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -6709,7 +7149,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -6726,28 +7166,48 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [ { - "data": { - "image/svg+xml": [ - "\n", - "\n", + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/parrt/opt/anaconda3/lib/python3.8/site-packages/dtreeviz-1.1.4-py3.8.egg/dtreeviz/models/spark_decision_tree.py:87: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " self.thresholds = np.array(node_thresholds)\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", "G\n", "\n", "\n", "\n", "node2\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:39.934400\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6755,191 +7215,191 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6949,15 +7409,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6967,15 +7427,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6985,15 +7445,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7003,15 +7463,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7021,15 +7481,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7038,16 +7498,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7064,10 +7524,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7080,7 +7540,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7095,7 +7555,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7116,13 +7576,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7133,7 +7593,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7142,14 +7602,26 @@ "\n", "node5\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.044343\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7157,62 +7629,62 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7222,19 +7694,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7244,15 +7716,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7262,16 +7734,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7280,13 +7752,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7299,10 +7771,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7315,15 +7787,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7333,15 +7805,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7350,13 +7822,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7367,7 +7839,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7378,14 +7850,26 @@ "leaf3\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.725418\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7393,108 +7877,108 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7502,18 +7986,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7521,15 +8005,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7538,26 +8022,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7570,11 +8054,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7584,7 +8068,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7600,14 +8084,26 @@ "leaf4\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.784605\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7615,73 +8111,73 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7689,18 +8185,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7708,15 +8204,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7725,26 +8221,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7757,12 +8253,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7772,7 +8268,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7788,14 +8284,26 @@ "leaf6\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.843285\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7803,57 +8311,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7861,18 +8369,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7880,15 +8388,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7897,26 +8405,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7930,10 +8438,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7943,7 +8451,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7959,14 +8467,26 @@ "leaf7\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.901649\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7974,14 +8494,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7989,18 +8509,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8008,15 +8528,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8025,26 +8545,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8058,10 +8578,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8070,7 +8590,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8085,14 +8605,26 @@ "\n", "node1\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.241884\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8100,225 +8632,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8328,18 +8860,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8347,29 +8879,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8382,10 +8914,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8398,15 +8930,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8416,15 +8948,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8433,13 +8965,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8450,7 +8982,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8471,14 +9003,26 @@ "\n", "node8\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.524250\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8486,684 +9030,684 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9192,28 +9736,28 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9226,10 +9770,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9242,15 +9786,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9260,15 +9804,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9277,13 +9821,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9294,7 +9838,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9304,14 +9848,26 @@ "\n", "node9\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.334739\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9319,480 +9875,480 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9802,18 +10358,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9821,29 +10377,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9856,10 +10412,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9872,15 +10428,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9890,15 +10446,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9907,13 +10463,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9924,7 +10480,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9933,14 +10489,26 @@ "\n", "node12\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.434365\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9948,213 +10516,213 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10164,19 +10732,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10186,15 +10754,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10204,16 +10772,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10222,13 +10790,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10241,10 +10809,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10257,15 +10825,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10275,7 +10843,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10289,13 +10857,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10306,7 +10874,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10317,14 +10885,26 @@ "leaf10\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.959765\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10332,437 +10912,437 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10770,18 +11350,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10789,15 +11369,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10806,25 +11386,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10837,12 +11417,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10853,7 +11433,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10869,14 +11449,26 @@ "leaf11\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:41.029235\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10884,52 +11476,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10937,18 +11529,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10956,15 +11548,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10973,26 +11565,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11005,12 +11597,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11020,7 +11612,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11036,14 +11628,26 @@ "leaf13\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:41.090486\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11051,201 +11655,201 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11253,18 +11857,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11272,15 +11876,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11289,24 +11893,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11319,12 +11923,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11335,7 +11939,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11351,14 +11955,26 @@ "leaf14\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:41.155352\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11366,21 +11982,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11388,18 +12004,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11407,15 +12023,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11424,25 +12040,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11455,11 +12071,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11469,7 +12085,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11496,14 +12112,26 @@ "\n", "node0\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:40.627376\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11511,900 +12139,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12414,20 +13042,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12437,15 +13065,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12455,15 +13083,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12472,14 +13100,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12493,10 +13121,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12509,15 +13137,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12527,15 +13155,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12544,12 +13172,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12558,13 +13186,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12575,7 +13203,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12602,10 +13230,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -12623,12 +13251,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -12652,12 +13280,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -12681,12 +13309,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -12710,7 +13338,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -12726,7 +13354,7 @@ "Name: 3, dtype: float64" ] }, - "execution_count": 38, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -12738,7 +13366,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -12758,13 +13386,13 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", "G\n", "\n", @@ -12776,14 +13404,26 @@ "node2\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:46.421704\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12791,172 +13431,172 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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" \n", @@ -12966,19 +13606,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12988,15 +13628,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13006,15 +13646,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13024,15 +13664,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13042,15 +13682,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13060,15 +13700,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13077,16 +13717,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13103,10 +13743,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13119,7 +13759,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13134,7 +13774,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13155,13 +13795,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13172,7 +13812,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13181,14 +13821,26 @@ "\n", "node5\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:46.528527\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13196,62 +13848,62 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13261,19 +13913,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13283,15 +13935,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13301,16 +13953,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13319,13 +13971,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13338,10 +13990,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13354,15 +14006,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13372,15 +14024,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13389,13 +14041,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13406,7 +14058,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13417,14 +14069,26 @@ "leaf3\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:47.121345\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13432,108 +14096,108 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13541,18 +14205,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13560,15 +14224,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13577,26 +14241,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13609,11 +14273,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13623,7 +14287,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13639,14 +14303,26 @@ "leaf4\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:47.181399\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13654,73 +14330,73 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13728,18 +14404,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13747,15 +14423,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13764,26 +14440,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13796,12 +14472,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13811,7 +14487,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13827,14 +14503,26 @@ "leaf6\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:47.240777\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13842,57 +14530,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13900,18 +14588,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13919,15 +14607,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13936,26 +14624,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13969,10 +14657,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13982,7 +14670,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13998,14 +14686,26 @@ "leaf7\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:47.299539\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14013,14 +14713,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14028,18 +14728,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14047,15 +14747,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14064,26 +14764,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14097,10 +14797,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14109,7 +14809,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14125,14 +14825,26 @@ "node1\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:46.618871\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14140,225 +14852,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14371,18 +15083,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14390,29 +15102,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14425,10 +15137,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14441,15 +15153,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14459,15 +15171,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14476,13 +15188,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14493,7 +15205,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14514,14 +15226,26 @@ "\n", "node8\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:46.890578\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14529,684 +15253,684 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15216,18 +15940,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15235,28 +15959,28 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15269,10 +15993,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15285,15 +16009,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15303,15 +16027,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15320,13 +16044,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15337,7 +16061,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15347,14 +16071,26 @@ "\n", "node9\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:46.705555\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15845,18 +16581,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15864,29 +16600,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15899,10 +16635,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15915,15 +16651,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15933,15 +16669,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15950,13 +16686,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15967,7 +16703,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15976,14 +16712,26 @@ "\n", "node12\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:46.799829\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15991,213 +16739,213 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16207,19 +16955,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16229,15 +16977,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16247,16 +16995,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16265,13 +17013,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16284,10 +17032,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16300,15 +17048,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16318,7 +17066,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16332,13 +17080,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16349,7 +17097,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16360,14 +17108,26 @@ "leaf10\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:47.360227\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16375,437 +17135,437 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16813,18 +17573,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16832,15 +17592,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16849,25 +17609,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16880,12 +17640,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16896,7 +17656,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16912,14 +17672,26 @@ "leaf11\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:47.430033\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16927,52 +17699,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16980,18 +17752,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16999,15 +17771,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17016,26 +17788,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17048,12 +17820,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17063,7 +17835,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17079,14 +17851,26 @@ "leaf13\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:47.582373\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17094,201 +17878,201 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17296,18 +18080,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17315,15 +18099,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17332,24 +18116,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17362,12 +18146,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17378,7 +18162,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17394,14 +18178,26 @@ "leaf14\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:47.645672\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17409,21 +18205,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17431,18 +18227,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17450,15 +18246,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17467,25 +18263,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17498,11 +18294,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17512,7 +18308,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17540,14 +18336,26 @@ "node0\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:28:47.000395\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17555,900 +18363,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18461,20 +19269,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18484,15 +19292,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18502,15 +19310,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18519,14 +19327,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18540,10 +19348,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18556,15 +19364,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18574,15 +19382,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18591,12 +19399,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18605,13 +19413,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18622,7 +19430,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18690,10 +19498,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 40, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -18701,20 +19509,6 @@ "source": [ "trees.dtreeviz(spark_dtree_reg, X=X)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -18733,7 +19527,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.6" }, "toc": { "base_numbering": 1, diff --git a/notebooks/dtreeviz_xgboost_visualisations.ipynb b/notebooks/dtreeviz_xgboost_visualisations.ipynb index 81cb7c2a..ade793e2 100644 --- a/notebooks/dtreeviz_xgboost_visualisations.ipynb +++ b/notebooks/dtreeviz_xgboost_visualisations.ipynb @@ -384,7 +384,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -393,7 +393,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -473,7 +473,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -578,7 +578,7 @@ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", "G\n", "\n", @@ -589,132 +589,144 @@ "\n", "node4\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:50.766844\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " 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\n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -767,13 +779,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -785,10 +797,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -801,15 +813,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -825,13 +837,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -842,7 +854,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -851,32 +863,44 @@ "\n", "leaf7\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:51.822282\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -884,7 +908,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -901,42 +925,54 @@ "\n", "leaf8\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:51.865951\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -953,132 +989,144 @@ "\n", "node1\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:50.940697\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1087,18 +1135,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1106,29 +1154,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1142,18 +1190,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1161,15 +1209,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1185,12 +1233,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1200,7 +1248,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1215,132 +1263,144 @@ "\n", "node2\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:51.436153\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1349,19 +1409,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1370,16 +1430,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1388,17 +1448,17 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1417,18 +1477,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1436,15 +1496,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1460,12 +1520,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1475,7 +1535,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1485,33 +1545,45 @@ "\n", "leaf3\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:51.781764\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1519,7 +1591,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1536,132 +1608,144 @@ "\n", "node5\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:51.110022\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1670,21 +1754,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1695,15 +1779,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1714,12 +1798,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1730,10 +1814,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1746,16 +1830,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1771,13 +1855,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1787,7 +1871,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1796,132 +1880,144 @@ "\n", "node6\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:51.271520\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1930,21 +2026,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1955,15 +2051,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1974,12 +2070,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1990,10 +2086,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2006,15 +2102,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2029,14 +2125,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2047,7 +2143,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2058,14 +2154,26 @@ "leaf9\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:51.899804\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2076,23 +2184,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2109,32 +2217,44 @@ "\n", "leaf10\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:51.943473\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2142,10 +2262,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2163,14 +2283,26 @@ "leaf11\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:51.982772\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2178,21 +2310,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2209,42 +2341,54 @@ "\n", "leaf12\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:52.021045\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2273,132 +2417,144 @@ "\n", "node0\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:51.599150\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2407,18 +2563,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2426,31 +2582,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2467,10 +2623,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2483,16 +2639,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2508,12 +2664,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2523,7 +2679,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2551,14 +2707,26 @@ "legend\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:50.480618\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2567,16 +2735,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2592,10 +2760,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2604,10 +2772,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2619,7 +2787,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -2639,7 +2807,7 @@ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", "G\n", "\n", @@ -2650,132 +2818,144 @@ "\n", "node4\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:52.653679\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2784,19 +2964,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2806,18 +2986,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2828,13 +3008,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2846,10 +3026,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2862,15 +3042,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2886,13 +3066,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2903,7 +3083,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2912,32 +3092,44 @@ "\n", "leaf7\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:53.926815\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2945,7 +3137,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2962,42 +3154,54 @@ "\n", "leaf8\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:53.979892\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3014,132 +3218,144 @@ "\n", "node1\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:52.899948\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3148,18 +3364,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3167,29 +3383,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3203,18 +3419,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3222,15 +3438,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3246,12 +3462,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3261,7 +3477,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3276,132 +3492,144 @@ "\n", "node2\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:53.436992\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3410,19 +3638,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3431,16 +3659,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3449,17 +3677,17 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3478,18 +3706,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3497,15 +3725,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3521,12 +3749,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3536,7 +3764,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3546,33 +3774,45 @@ "\n", "leaf3\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:53.752889\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3580,7 +3820,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3597,132 +3837,144 @@ "\n", "node5\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:53.099647\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3731,21 +3983,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3756,15 +4008,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3775,12 +4027,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3791,10 +4043,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3807,16 +4059,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3832,13 +4084,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3848,7 +4100,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3857,132 +4109,144 @@ "\n", "node6\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:53.273778\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3991,21 +4255,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4016,15 +4280,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4035,12 +4299,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4051,10 +4315,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4067,15 +4331,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4090,14 +4354,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4108,7 +4372,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4119,14 +4383,26 @@ "leaf9\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:54.016492\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4137,23 +4413,23 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4170,32 +4446,44 @@ "\n", "leaf10\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:54.059528\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4203,10 +4491,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4224,14 +4512,26 @@ "leaf11\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:54.100996\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4239,21 +4539,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4270,42 +4570,54 @@ "\n", "leaf12\n", "\n", - "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:54.145639\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4334,132 +4646,144 @@ "\n", "node0\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:53.642044\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4468,18 +4792,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4487,31 +4811,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4528,10 +4852,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4544,16 +4868,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4569,12 +4893,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4584,7 +4908,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4612,14 +4936,26 @@ "legend\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:52.426672\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4628,16 +4964,16 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4653,10 +4989,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4665,10 +5001,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4680,7 +5016,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 18, @@ -5126,7 +5462,7 @@ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", "G\n", "\n", @@ -5134,14 +5470,26 @@ "\n", "node4\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:56.260685\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5149,83 +5497,83 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5235,19 +5583,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5257,18 +5605,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5280,15 +5628,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5298,13 +5646,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5316,10 +5664,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5333,7 +5681,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5347,15 +5695,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5363,13 +5711,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5380,7 +5728,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5390,14 +5738,26 @@ "leaf7\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:57.418049\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5405,10 +5765,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5416,19 +5776,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5437,15 +5797,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5454,15 +5814,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5470,23 +5830,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5497,11 +5857,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5510,7 +5870,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5526,14 +5886,26 @@ "leaf8\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:57.505538\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5541,82 +5913,82 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5624,19 +5996,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5645,15 +6017,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5662,15 +6034,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5678,25 +6050,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5708,11 +6080,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5722,7 +6094,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5737,14 +6109,26 @@ "\n", "node1\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:56.440866\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5752,225 +6136,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5980,19 +6364,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6002,18 +6386,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6025,16 +6409,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6044,13 +6428,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6062,10 +6446,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6079,7 +6463,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6093,15 +6477,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6109,13 +6493,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6126,7 +6510,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6141,14 +6525,26 @@ "\n", "node2\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:56.995009\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6156,684 +6552,684 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6843,19 +7239,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6865,18 +7261,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6887,13 +7283,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6905,10 +7301,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6922,7 +7318,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6936,15 +7332,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6952,13 +7348,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6969,7 +7365,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6980,14 +7376,26 @@ "leaf3\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:57.317962\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6995,151 +7403,151 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7147,19 +7555,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7168,15 +7576,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7185,15 +7593,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7201,25 +7609,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7231,12 +7639,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7247,7 +7655,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7262,14 +7670,26 @@ "\n", "node5\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:56.602103\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7277,605 +7697,605 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7885,18 +8305,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7904,31 +8324,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7944,19 +8364,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7965,7 +8385,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7979,15 +8399,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7995,13 +8415,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8012,7 +8432,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8021,14 +8441,26 @@ "\n", "node6\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:56.789662\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8036,88 +8468,88 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8127,19 +8559,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8149,18 +8581,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8172,15 +8604,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8190,13 +8622,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8208,10 +8640,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8225,7 +8657,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8239,7 +8671,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8252,13 +8684,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8269,7 +8701,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8280,14 +8712,26 @@ "leaf9\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:57.589123\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8295,423 +8739,423 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8719,19 +9163,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8740,15 +9184,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8757,15 +9201,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8773,24 +9217,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8802,12 +9246,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8818,7 +9262,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8834,14 +9278,26 @@ "leaf10\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:57.666231\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8849,191 +9305,191 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9041,19 +9497,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9062,15 +9518,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9079,15 +9535,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9095,25 +9551,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9125,12 +9581,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9141,7 +9597,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9157,14 +9613,26 @@ "leaf11\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:57.746043\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9172,67 +9640,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9240,19 +9708,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9261,15 +9729,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9278,15 +9746,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9294,24 +9762,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9323,12 +9791,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9338,7 +9806,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9354,14 +9822,26 @@ "leaf12\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:57.824263\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9369,30 +9849,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9400,19 +9880,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9421,15 +9901,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9438,15 +9918,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9454,25 +9934,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9484,10 +9964,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9497,7 +9977,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9524,14 +10004,26 @@ "\n", "node0\n", "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:26:57.191349\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9539,900 +10031,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10477,29 +10969,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10513,18 +11005,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10533,15 +11025,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10550,15 +11042,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10566,12 +11058,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10579,13 +11071,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10596,7 +11088,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10623,7 +11115,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 28, @@ -10649,7 +11141,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -11128,16 +11620,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "dataset_reg_test = pd.read_csv(\"../testing/dtreeviz/models/fixtures/dataset.csv\")" + "dataset_reg_test = pd.read_csv(\"../testing/testlib/models/fixtures/dataset.csv\")" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -11155,7 +11647,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -11164,7 +11656,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -11174,22 +11666,22 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 43, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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E6/WyaNEiFixYcMoBdktLC5WVlaxZs4ZAIEBFRcUpBdhtbW189NFHZGRkkJmZeV7qYPv7+zlw4AC7du3igw8+ICcnh8LCwtPaRnd3NzU1NbzwwgsEg0HS0tLOWoA9MDDAoUOH2LFjB2vWrCE5Ofm0gv+T8Xg8HD58mKqqKjZu3EhhYSGLFi06KsCORqN4PB7++Mc/0tbWRjgcpre3lwMHDpCQkMDMmTP51re+hVarJRqNEggEePrpp+ns7MTj8Rz39r/+9a9jtVrP2v05EyqVCq1Wi8ViYenSpaSkpJCdnc2GDRt47bXXaGlp4cEHH8Rmsyk7yYIgXFpEgC0IU4h8mLy5uZmtW7fy4YcfotFouPbaa6moqGD69OkXeoiTyONtampi7969fPDBB7S0tJCbm0tSUhJ5eXnExMTQ2dlJf38/7e3tVFZWYrFYjpvRPBa/38/Q0BD79+9Hr9fjdrtP6XoqlYqEhARiYmLO9C6etmAwSCAQwOFwUF9fj8PhIBQKnfY2xsfHlfs7NjZ21sYnd79wuVzU19czPj5OIBA4a9t3Op0MDg7S0tLCq6++yi233HLMHYxIJEJPTw+dnZ24XC5ycnJQq9Xs2LGDhoYGGhoa+Nu//VusViuRSITR0VFqa2sJBoNoNBq02r9+fQWDQVwuF21tbdxzzz2TzrtQ5DKQnJwcUlJSyMjIoLOzk4aGBkZHRykuLubaa6/FZDJNifEKgnB2iXe1IEwxwWCQ//mf/2HPnj2EQiGeeeYZZs2ahdFovNBDOyafz8cvfvELtm7disfj4aWXXmLOnDmTsojz5s3jmmuu4Y477uCOO+5ApVIRDAZP+Tbmz59PKBRi06ZNdHZ2ntJ1EhMTWbVqFXPnziUuLg6DwXC6d+20qVQqCgsLlYVGfve7353RdkpKSjAYDLz++ut0dXWd1THm5eVhNBqJjY3lV7/61RltY2I99JH1xKOjo5jNZq6++moef/xxIpHIMa8fCoX45JNP+MpXvkJWVhYmkwmAl156ibVr1/L+++9TV1fH7Nmz8fv91NbWUlFRwa233kppaSmJiYnK9oaGhqiqquKf/umfKCgoIDMz84zu17kSGxtLaWkpv/zlL3nqqadYs2YN//iP/8hbb71FSUkJZrN5ypR5CYJwdkydY8yCIDA2NsZPf/pTPvroI+bNm8dvfvMb5s+fT2xs7IUe2jH5/X5+/etfs2XLFmJjY/n6179ORUUFZrP5qMvGxcVRVFTEN7/5TdLT0/H7/ad1WyqV6owmdJ7vDDaAWq3+zPXxKpXqnC1WcuT4Tnf70WgUr9fL2rVrj8quFxcXU1ZWRn5+/nGvH4lEiEajrFixguzsbOLi4pTzrrrqKioqKgCU7K5WqyUhIYEf/vCHLF26FJvNhk6nU35GRkbYvHkzq1evJjU19bTuy/lksVh45JFH+I//+A9SUlL4xje+wQcffHBaO5uCIFwcRAZbEKaIsbExDh06xJo1a1i2bBmrVq1i+vTpymIxU00gEGBkZIQ1a9YwMjJCSUkJ1113HXq9/pj14SqVCr1ez8qVK6mrqyMhIUE5b2xsjLGxMfr7++nt7SUtLY2MjAwKCgqOe/tut5ve3l4aGxuxWq2kpqZOKjuRFwRpa2tDrVYTFxdHWloaIE2sGx0dpaWlhSuuuAK3283g4CCtra1YrVYKCwux2WyfKTg+EafTyfj4ON3d3fT19WG1WklPTz9hCVAgEKCzs5OWlhYMBgMpKSnMmDHjqNtyOBwMDAzQ19eH3W4nJyfnrEz6kx/P+vp66uvrqamp4eDBg0yfPn1SNll+vZ5oZ0itVqPVaklPTycmJmbS5ex2O2q1mpkzZ5KdnY3BYECn01FQUEB8fPxROwTRaJSRkRH27dvHV77ylUmvq6lEHrfZbGb69Ok88sgj/OIXv2Dnzp2kpKSwatWqCz1EQRDOIhFgC8IU0dvbS01NDR0dHTz66KMsWLBgSnca8Pl8DAwMUFNTQ0xMDGlpaZSXl58wuFSr1UybNo1gMIhWqyUSiTAwMEBbWxsOh4NIJEJnZyednZ1kZWURFxd3VKAbjUbp7u6mvr6etrY2duzYgcViIS8vD4DCwkKCwSBut5u+vj62bt1KWloa+fn5pKamMj4+TlNTE01NTVRXV5Obm8vIyIgyMU+n07F8+XJmzZp11rOh0WiUwcFBurq6GBoaAqCrq4v29nba29tJSEggOTl5Uk2ufJ2DBw/S1NTEnj170Gq1ZGRkoNPpyMvLQ6/XEw6HlTrkwcFBhoeH6e3t5fDhwxQVFVFWVkZ2dvYZZas9Hg92u53u7m62b99OXV0dDQ0NxMTEEA6HT/txkCcBxsfHK7cRjUYZHR3l0KFDeL1eli9fTlJSElqtFp1Od9wSH4/Hw/DwMF1dXZSVlR3z6MlUotVqSU5O5qabbuKll16ipaWF7du3c9VVV02plpuCIHw2IsAWhCkgGo1SU1PD2rVrmTNnDtdee+1Z7exwLjgcDlpaWgiFQmRkZJCSknLSOnGVSoVOp2POnDmAlJV9/fXX2bFjB9OmTeOHP/wh5eXlfO973+ODDz5gfHycBx98EJ1Op2wjEAjwwgsvYLFYiEQidHR00NXVRVxcHNdeey1PPfUUo6OjVFdX8/zzz7N27Vruu+8+br/9dmbNmsXu3bt5+umn2bJlCw6Hg+TkZEZHRxkcHGRsbIwPP/yQ+vp6br/9dr70pS+d9cdt7dq17N69m2g0yjPPPMPMmTN58skn+fOf/4zf7+e+++6bFCQGg0E++OAD9u3bRzQapaurS1nFc8+ePfznf/6nUnKzd+9e1q9fT1FREXPnzkWv1/PYY4+RkJDAqlWrePzxx0/aRxom11iHQiGam5v5+OOPefLJJxkbG6O0tJQbbriBhx9++DN37JBvKxAIsHnzZp5//nlyc3P513/9V9RqtVLnfTzt7e10dHSg0WiYMWPGlC2nmkiv15ORkcE999zD66+/zoYNG3j00UexWq0iwBaES4QIsAVhCnA6nTQ2NtLQ0MCPf/xjLBbLhR7SSdntdpqbm4lGo9hstjPKtodCIV544QUKCwvJyMhArVaTlZVFSUkJTqeTzZs386UvfUkJsKPRKAMDA/zd3/0dN954I+np6YTDYf73f/+XDz74gDfffJM777yT+fPns2zZMqxWK+vXr1duT6VSsXLlSgYHB9FqtbzzzjtMmzaNOXPmYLPZCIVC3HbbbXR3d7Njx45zEmC/9tprxMbGsnDhQlQqFenp6RQXF1NXV8dHH33EnXfeOSnAHhoaYvXq1Tz00EPk5OQQDod56623eOmll3jjjTe49tprWbx4MSqVim9/+9s888wzlJSUEBsby6xZs9i7dy+bN2/m5Zdf5vbbb6esrOykY4xEIjidTt555x1eeeUVDh8+TCAQ4Pbbb+dzn/sc+fn5JCQkEBsb+5kDQr/fT1VVFa+88gpvvvkmY2Nj6PV6pa2jPAnwePbs2UN3dzfXXXfdRdeN4/Of/zzV1dVs2bKFjz76iFtvvfWi2EEQBOHkLq5PI0G4RPX39zM6Oko0GmXWrFnnpePF2fBZVxrU6XQ89NBD2Gw28vPziUajDA0NMT4+zujoqPKYyFQqFTabjdzc3EmT4xYsWEBvby/79u2jpqaGoqIizGYzFovlqFUBY2JiiIuLU66bkZFBUlISZrOZcDhMVlYWjY2NjIyMfJaH5rjuv/9+DAYDWVlZAIyMjDA2Nobdbsfr9R7V0i85OZns7Gzy8vIwm81Eo1HKyspYunQpW7dupa6uDo1Go0z227ZtG01NTUod/PDwMGq1mpiYGHp7e4/be1zOVldXV1NfX09tbS319fWYTCauv/56SkpKmDdvHtOmTSM+Pn7SUYXPQqvVkpeXxx133KHsEBw4cID6+npeeuklHnnkkWMG2PLroqGhQdkJudiWIbdYLKSmpmK1WmltbT3tdo6CIExdIsAWhClgZGQEl8uFRqMhOzv7osjEabVapSTE7/efUS9lrVbL5z73OYLBIKFQiI6ODkZHR3G73fh8vqM6jch9ra1Wq9LWDaTOFaWlpUSjUVpbW3E6nUoXjmPRaDTKeRaLBb1er5xntVqVLhnnwk033UQgEFDurxxc+/1+xsfHj2prZ7FYSEhIUIJMlUpFVlYWs2fPBqCzsxO9Xq9MLh0eHp5UF52UlER5eTnhcBidTnfcAFReOXTr1q18/PHH7N27F5PJxL333svy5ctZtGiRMsnwbJLrydPT01mxYoWyc9TS0sKGDRv4/Oc/T0lJyTFv1+l00t3djc/nY+bMmVNq8aWTkculrFYrZrOZgYGBY7Y0FATh4jT1v8UF4TLg9XoJBoOoVCqMRuNFESjEx8crS58PDg4yMjIyKaN9qpKTk6mqqqKuro6amhqWLl2K0WgkKSmJnp6eU9qGnOFVq9WMjo6edgvAieQs6KnUKp8ulUpFYmIijY2NHDhwgO3btyvlHampqaecNbfZbEybNg21Ws3Y2Bg9PT3ExMSQlJTEQw89pDwvx3OsxycYDFJVVcXLL79MY2MjarWae+65hy9+8Yvk5eUpR1VOVhN9puSjICtXrkSn09HS0sK6deuUhXqOzJhHIhEOHDjA2NgYNpuN2bNnX1TZa1lMTAwxMTG43e5z8poTBOHCEAG2IEwBycnJmEwmwuEwPT095ObmTsqqTkUJCQmUl5cTFxfH8PCwMtkwOzv7lLchL6pTU1NDXl4e3/ve9zCZTLS1tdHQ0HDK2zEYDJhMJuLi4o7qqzyVRKNRfvOb31BTUwPAT37yE0wmE2+++Sb79+8/5e3o9XpMJhNGo5HMzEysVisul4vW1lY8Hg+RSOS0l+COiYlh5cqVvPPOO1RVVbF582Zef/111q1bx5w5c1i9ejV33303RqPxnC/vbbPZuOKKK/j000+JjY095hGdUCjE+++/T2ZmJuXl5RfFTumxyOVQs2fPvmjvgyAIRxMBtiBMAenp6SQlJaFWq6mpqSE1NXXKB9h6vZ6kpCRuuukmNm3aRHt7O2vXruWRRx45aQA2NDREMBgkEonw1ltvMW3aNAoLC0lISFDKN04nmyd3AQkEApSVlU3JSaKhUIjx8XHeffddzGYzCxYsIDExEY1Gg0ajOa3763Q66e/vx+/3M23aNCwWCz09PXg8Hvbu3UtsbOykHuJy28La2lrmz59/zG3K9elpaWksXLgQm81GSUkJ1dXVDA0N8dZbb3Hw4EGWLFnCjBkzKC4uRq/Xn5OssVwuM3PmzGOWpUSjUcLhMHv27GHhwoVMmzbtosteR6NRhoeH6evrw+VyUVJSclGUhgmCcGrEu1kQpoDExETS0tKwWCzs2rWLK664ApPJNKUzWhqNhvj4eG6++WZaW1sZHh7mgw8+4PrrrycjIwODwTBp/OFwmGAwSH9/Pw6HQyk12LdvH8XFxUqNscvlwul04na7CYVChMNhwuGwUp8aDoeVvslyUNXX10d/fz/x8fGUlZUprePkoFXehiwSiSjbOzKwlS8r3wac/mqME7cvr1oYDocZGxujpqaGuXPnkpCQgEqlwu1243Q6cblcx7y/8vUn3t+RkRE6OjqIj4+ntLQUq9Wq1GBv27ZNmcRpNpvx+XzY7XaGhobo6+sjGAwqi8YcOVaQdpwyMzPJyMhgzpw5ZGdns3PnTnbv3s3GjRux2+0MDAzgcrnIzc0lKSnpqJ1B+f7K4z7yMZbPHxwcxGQyYTAYlG3I/bwHBgZYtmzZMReOCYVCOJ1Ompubufnmm0+4auRU1tTUxMDAACqVitLSUhFgC8IlRLybBWEK0Gg0lJaWsnjxYl555RXuv/9+EhMTz/sS36dLp9Nx11130dzczPr169m0aRP//u//zqOPPsr06dMnBV5er5eenh5+/etfs3z5cqVOWKVSUVVVRXp6OitXruTgwYNUV1fT3NyMRqPB7XYTiUSUumG5Xj0SiSgB/IEDB+ju7mb16tUsWrQIg8GAy+VSJl56PB5cLpcS6Pl8Pnw+H4CyLfm88fFx5TbkoPZU647lbfj9fjwejzLeUChEJBJRJk42NTWxbds2Pv/5z9PQ0EB1dTUHDx4kEAjgdrtxu914vV5UKhU+n49AIDDp/ra1tbF//35Wr17N4sWLsdlsZGVlkZaWxiuvvEJnZyd9fX1cffXVNDc3U1tbS39/P//v//0/4uLi6O/vx+12K49FIBA46j7K8wFuvfVWbrjhBux2Oy+++CIvvvgi77//PjExMTz88MM88MADZGRkTHoMAoEATqdTeXxDodCkIDscDuN0OnnuueeoqKigqKiIrKwsVCoVLpeL/v5+urq6+Pd///djHo2w2+3U1dXh8XgoLCykqKjopM/NVCLvdPzpT39iYGCAwsJC5s2bN6V3qAVBOD0iwBaEKWLhwoUYDAbefvttfv3rX/P5z3+em2666UIP66Q0Gg3f+ta3uOWWW/j444955pln2L17N8nJyRQUFGC1WmlqaiIajZKbm8uDDz5IYWEh8fHxBAIB7rrrLrZv387zzz/Pp59+yn333ccdd9xBYWEhzzzzDA8//DCPPvoo2dnZfPnLX+bnP/85TzzxBGvWrKG4uFhpQ1dWVsYdd9xBTEwMhw8fpra2lrfeegu/38++ffvQaDSkp6djNBrZvHkzu3fvJhKJ8Lvf/Y4777yTvLw8amtr2bVrFwMDA4RCIX71q1/x0EMPERcXd8pZ7Pr6et577z0++ugjANatW4darSYYDDJv3jw+97nPsW3bNtatW0dTUxN33303CxcuJDs7m8cff5zvfOc73HvvvVx11VV89atf5de//jXvvPMOlZWVzJo1S5lMmpaWxr/8y78oZTVZWVn86U9/4l//9V85cOAAu3fvJiYmhqysLK677jq+/OUvYzKZaGxs5JNPPuHdd98F4MMPPyQajaLValm6dOkx76dWqyUxMZGHHnqIu+66i0OHDrF161ZeeuklbrzxxkkBdlVVFVVVVWzbto1oNMru3bvRarXExsZy1VVXKZn7QCDABx98wG9/+1uMRiNFRUVcccUVxMfHk52dze9+9zsSEhKOGXT29fXx4YcfsmLFCtLS0i66zK/dbufTTz9lzZo1XH/99dx5550iuBaES4zqJHV/YkqzIJwnwWCQgYEB/uu//osdO3awZMkS7rzzTq688sqLor5UXkp77969SnkHgNFoJBKJEBsbS2pqKvPnz8dsNqPT6QiFQmzbto2Wlhb8fj9Wq1XpAz40NMS+ffvIyspi/vz5WK1WxsbG2LNnDyBlz41GIzqdjpiYGBITE5V2bnJJRHNzM+3t7RiNRmw2G2VlZWi1Wg4ePEh3dzdut5uSkhKljnlwcJBPP/0Ur9eL0WikrKyMOXPmnLC93URyXW1bWxudnZ309/eTmJhIQUEBubm5pKen8+mnn9LW1sbY2JgyUdRoNOL1etm6dSsZGRnMmDGD3NxcRkdH2bdvH4FAAK1Wi8lkQqfTodPpiI+PZ9asWcpRgHA4jN1uZ8uWLXR3dzM6OopKpSI7O5uSkhJKSkpITExkZGSErq4u2tra6O3tJSEhgezsbAoLC8nMzDzp/QyHw4yMjNDZ2cn+/fu55ZZbsNlsyvl9fX0MDg7S3d1NW1ubUtddXFxMTk4OBoMBlUqF1+tl3bp19Pf3o1arSUpKIj8/n5iYGBISEsjKykKr1R5zPD09PVRWVhKNRlmyZAlpaWmn81K9oIaHh6mvr+dXv/oVo6Oj3Hvvvdx0001kZmZe6KEJgnB6GoA3gMeOdaYIsAVhCvF6vezdu5fvfve7xMbGsmjRIv7+7/8em8123GBjKnK73UqtrkajITU1lfj4+OMupS6Xa0xcYCcSiSjt2Y6838FgEL/fj9vtJiUl5Zxl/6LRKD09PafVn1gu7TnRQiyBQIBwODxp1b5oNEowGDzm/Q2FQgQCAWVp95NlbOU6dpA61JytRWEmkhemmdhT/EzIXU+0Wi0xMTGn9Br3er2MjY1hNBqJi4s7J/fvbItGo7hcLqqrq9m2bRtPPvkkDz74IHfccQcLFiy40MMTBOH0iQBbEC42r776Kq+88gpbtmzh61//Ot/4xjdITU094xUThTMTiUT47ne/i8vlOuXr3HfffRQXF5OSknIORyZcLOTv2Gg0yscff8yTTz7J7t27qaio4Omnn1a6BwmCcNE5YYB9cRWuCcJl4pZbbiErK4t58+bx61//mrq6Om677TZuvfVWbDabCLLPE5VKxcKFC09r4RqbzXbRLHUvnHuRSITe3l5effVVnnzySTIzM7nnnnv4p3/6JxITE8V7WRAuUSLAFoQpyGg0UlJSgl6vx+PxUFlZyfvvv09LSwu33XYbc+bMOW65hXB2zZs3b1KLv5NJSUmZ8t1fhHMvGo0yODiotDj89NNPmTNnDsuXL6eiooLk5GRl1VBBEC49IsAWhCkqOTkZi8WCxWLB6XSyf/9+Wlpa0Ov16PV6srKysFgsyqQx4exTqVQUFhZe6GEIF5FwOIzf72d0dJSqqiree+89qquriUaj3H///Vx11VUUFxeL96wgXOJEDbYgXAR8Ph8ff/wxr732Gs8//zwVFRVcf/313HrrrcyePVu5nPjSFoTzb+L3qMPhoLm5meeff54XX3yR1NRUlixZwg9+8ANyc3PP+TLzgiCcN2KSoyBc7KLRKD6fj/HxcXbs2MELL7xAS0sLHo+Ha665hgceeIBp06ZNapcmCML5Ia8s+fzzz7Nx40YaGhpQqVR88Ytf5Oqrr6asrEzpVy52ggXhkiECbEG4VIRCIfr7+9m3bx+HDh3i0KFDHD58mKysLAoKCpgxYwbz588nJyfnlFueCYJw+qLRKKOjozQ1NdHS0kJNTQ21tbXo9XpsNhvz589n8eLF5Ofnk5iYKDqFCMKlR3QREYRLhVarJSsrS+kwsn//fp577jmqq6tpamqiqakJp9PJvHnzSElJwWQyYbVaxWQqQfiM5GSU1+vF6/Vit9tpbGyksrKS2tpa9u/fT3JyMitWrOCKK65g1apVYidXEC5jIoMtCBexaDRKNBpl48aNfPLJJ2zcuJHKykpyc3NZtGgR11xzDV/4wheIj48/qvZTfPELwvEd67sxHA5TU1PDnj17ePvtt9myZQsWi4WioiJuv/12HnzwQaxWK3q9/gKMWBCE80yUiAjCpUp+//p8Pnw+Hy6Xi/r6ejZs2EBtbS3Nzc0YDAYqKiqYNWsW8+bNY8mSJcTExIhD1oJwAvJ76/Dhw7S0tLBjxw42btxIb28vKpWKadOmccsttzB37lyys7MxmUyYTCZxtEgQLh8iwBaEy0U4HGZkZITGxkY6Ojpoa2ujrq4Or9eLSqUiJiaGoqIiiouLycrKIjc3l4KCgotqGXZBOJdGRkYYHByktbWVgwcP0tnZyfDwMHa7nZiYGNLT08nMzKS0tJTy8nLS09OJj48XO6yCcPkRAbYgXI4CgQAOh4NNmzaxY8cOmpqa6OjoIBwOU1JSQmFhIaWlpSxatIj4+HhiY2OJiYkhLi4OvV4vMnHCJS0ajSo9q/1+P16vF7fbTXt7O21tbVRVVVFZWYnb7SYmJob8/HxWrlzJ4sWLKS4uFh17BEEQAbYgCNLkrNHRUTZt2sTGjRvZv38/Bw8eRKfTMWvWLEpLS5k1axbLly+npKQEk8l01DZEwC1cjI73PTc6OkpdXR0HDx6kqqqKjRs3Mjg4SCQSIT09neuvv55ly5Yxa9YsZs6ceZ5HLQjCFCcCbEEQIBKJEIlE8Pl8SheEvr4+duzYQV1dHYcPH6azs5NgMEhGRgZ5eXnMmDGDRYsWMX36dFJSUjCbzRf6bgjCaQuHw3i9Xurq6jh06BDNzc0cOHCA+vp6AoEAsbGx5OXlMXfuXGbMmEF+fj6FhYXKUR159VRBEIQJRIAtCMLRAoEAHo+Hw4cP09PTw9DQEAMDA7S3tzM6OkogECASiaDT6UhOTiYxMZHk5GSys7Ox2WwkJyeTkpJCSkoKKpVKZLeFKcHtduNwOBgYGKC7u5vBwUFGR0cZHBykt7cXt9tNKBRCq9ViMplIS0vDZrORkZFBbm4uaWlpJCYmkpiYeKHviiAIU5sIsAVBOLlIJEIgEKChoYH9+/fT3NxMY2MjBw8exOv1otVqMZvNFBUVkZOTQ2Zm5qRMn06nQ6/XKxk/rVaLTqcDRGmJcHZFIhFCoRB+v59AIEAwGCQYDOLz+RgaGqK/v5/W1laam5vp6upiaGiI4eFhdDodNpuNrKwsysvLWbx4MSUlJdhsNmJjYy/03RIE4eIiAmxBEM5MJBLB7XZTX19PS0sLDQ0NVFZWUl9fz8jICD6fj/j4eEpKSpSOJGVlZRQXF5OZmUlmZuZR/bePJIJv4Ugn+V7C4/EwODhIXV0dra2tdHZ20traSk1NDYODg/j9frRaLTNnzmT69OkUFRVRVlbGokWLSElJwWg0nqd7IgjCJUwE2IIgnBl5IZuJWUK/34/P52NsbIzu7m5qa2uVQ/F9fX10dXUBoNPpMBqNFBYWkpeXR3p6OtnZ2eTm5pKenk5iYiIJCQmACLKFyQKBAC6Xi87OTnp7exkYGKCnp4empiYOHz7M6OgoDoeDYDCI1WolKSlJmTdQUFBAVlYW+fn5WCwW5WiKfHRFo9GIlnqCIJwNIsAWBOHskidL2u12enp6GBkZwW63Mz4+rtS8OhwOnE4noVAI+GtWMjY2FrPZjMlkIj4+nqSkJMxmM/Hx8ZjNZuW00WjEZDJhNBpFAH4JkUs53G43Y2Nj2O12XC4XDoeD0dFR7HY7TqcTp9PJ2NgYLpdLeQ3Jtf46nQ6TyURqaioJCQlYLBYSEhKw2WykpKRgtVpJTExEp9OJ144gCOfKCQNs7fkdiyAIlwK1Wo3RaMRoNJKenq78PxqNEgqF6Ovro7+/n+7ubpqbm+no6KC/v5+enh5GR0cJhUJEo1E0Gg1paWkkJSWRkJCgZCJtNptyOikpCa1Wi0ajQaPRoNPplL+1Wq1ynlqtRqPRiAmX51k0GlU61IRCIcLhsPITDAYJhULKeaFQCI/Hg9PpZHR0VDnyMTY2xujoKH19fQwNDeF2uwkEAuh0OgwGA1arlfT0dKZNm0ZOTo5yNKSoqAij0YhWK77KBEGYWkQGWxCE8yYSiSiBlRyAt7S0KKe7uro4fPgwwWBQyXgbDAala0lqaiqZmZnKaZvNRnZ2NgkJCUrW8mzU115OAfrJ6p1PRg6a7XY7g4ODDAwMMDo6ytDQEJ2dnfT39zMyMsLQ0BDd3d3Y7XYlIw2QkpKCzWYjMzOTwsJCcnJySEtLIysri8LCQmUCoijrEARhihElIoIgTA1ytlPOZoZCISXLGQ6HCYVC+Hw+hoeHGR8fZ3R0lP7+fsbGxnA4HIyPjyvnOZ1O7HY7gJK9VqvVxMXFKSUncumA1WrFZDJhsViUv2NjY4mLiyM+Ph6j0YjBYMBkMmEymS6rOl23243X68Xr9eJ0OnG73Xg8HuW0/CM//qOjo5NKOORa6IkZbL1ej8lkIjk5mYSEBMxmMxaLRWn3KB+dSE9PJy4uDp1Op3SdkY9QyKfFUQlBEKYoEWALgnBxkCdVykGe3NNYPi3X6h55vtfrVZa7DgaDx9m2lkgkjkhkFhpNC1rtIHq9VIIgtxic2GZQp9MRExOjTIzTaDTo9fpjlqfIf6vVavR6/aRgMCYm5pgBolxLfKxAXt75ONbjIwez8me3HNTKbRblQFe+XDAYnFS2ofN48IZCOP7S5s7v9xMMBgkEApNOy0cRTpbhVqvVymMVFxdHXFwcsbGxmEwmpdZe3pmRTxuNRuLi4rBYLMrjJgiCcJERNdiCIFwc5EA0Pj6e+Pj4E15WzoYPDw8rE+PkiZZyhlueODc8bGdgIEBzc4TxcTMmUz8xMfsBKeicWEcsL66j1+uVOnM5gI6NjVXqwCf+xMTEKMGyPClTDkzj4uKOG2DLHS6OPE8Oco/F6/VOCrD9fr+S/ZfPCwQCBAIBQqGQ0gEmHAyi9nhI6u9nJBqlU6PB6fejUqlQq9XK74k/coZfDpatVisJCQnK85OQkEBiYqIyYVVeoEXeaREEQbhciQy2IAgXrVOtH25rg48/hu9/HzIy4F//Fa69Ngy4sNvtuN1u3G63EpS7XC48Hg8Oh0MpgQgEAjidTqVV4cSMudfrxefzEQqFcLlcSuY3HA4rp48UDodxOp3HPE/OpB9r8t7EhXzk0/IOgclkQqvVYjAYlMV/jEYjMTExxKtUzDl8mFU7djA4fTr1115LpLRU6dgiZ5jNZrOShT5ZD/MTESUdgiBc4kQGWxCES9PJgrhIBPbsgd/+FqqqYMYM+NnPoLAQ4uM1QDxxcXFKRjgSiRwzoy0HzJFIBGDSafnvIy/z1zFEjjEyjnnZiffreHXHR/5/4mXlUgs5Iz3pPMDg9WL87neJb24me9MmuPtu1KmpqPT6o7LX8m2IQFkQBOH0iQBbEIRLktMJTU3w3HPQ0wMFBXDbbVBSAnFxoNGoAM1nytJeVKJRaY/jzjvRbNhAzN698M47cO+9kJoKoqRDEAThrBEBtiAIlwy52sLthvZ2WL8e1q2DJUvgmmukWFKrhcsyKatSgUYDN90Efr/0AD3/PMyZA3o9pKT89XKCIAjCZyJqsAVBuGTIH2dvvglr18Irr8C3vw333AMzZ0rnXfbxYzQKDgccOgS33w6lpfDFL8JXviIF4Jf9AyQIgnBKTliDLXojCYJwSYhEwOeDH/wAfvELaGyEn/wEHn0Upk2T4kYROyI9CCaTVJD+9NNSsP366/CrX13okQmCIFwyRIAtCMJFLxSC7m547TWpW4jNJpWErF4NaWlgMFzoEU4xGg0YjbBwIaxcKZWIfPQRVFZK9TWCIAjCZyICbEEQLlryvL3hYaiuhj/8Qfp7+XKp6mHuXCmWFI5Bq4XMTPjc56C4WEr5r1sHAwMQCFzo0QmCIFzUxCRHQRAuag6HVN2wfj309cGf/yzVWyclXeiRXSSWLpUy2CYT/PSnYLXCtdf+tWhdEARBOG0iwBYE4aIUCMDICHzzm1LyNT1dig8XLIDYWFFvfcpUKigvh4QEaUWe55+Hri742tekCZDigRQEQThtokREEISLjtstBdUvvij1up41C269VQqu4+Kk6gfhNMTGSuUid90FycnSg/rGG9LhgXD4Qo9OEAThoiMCbEEQLhrRKASDUoJ1xw6p5tpslhaQuesuab0UtfhUOzMGg7SXcsUVUjuW11+XAm2vVyp0FwRBEE6Z+CoSBOGiEY1CSwv86EfSkudWK7zwgrR2SmLihR7dJUCthn/+Z2mGKMCDD0J9vbQwjSAIgnDKRIAtCMJFwemE/fvh61+Hw4fh6qulNs7p6dIcPVEq/BnJjcJjY+H666WCdpcLfvlLePttqVTkxAuTCYIgCH8hAmxBEKY8uQ3fCy9IMd/SpXDzzdIcPJ1OlIWcVWq1tGz6nDlSyUhnJ2zZAp98cqFHJgiCcNEQX0uCIExZ0ahUndDYCJs3w8svSw0vvvAFuPFGqWxYZK7PgZgYaYWehx6SFqTZv1+qyXY6xaRHQRCEUyDm2guCMGVFIrB3L/z4x1Lt9apV8OSTUqcQEVifY1qttDfzox9JezZ//rO0IM2990rBtyAIgnBcIsAWBGFKGh+Hhgapz3VcHNxxBzzyiJRQlcuFhXNIfoDnzpUOI6jV0oo+FgtceSWUlFzQ4QmCIExlIsAWBGHKGR6Wmle89ZbUlu+KK+CqqyAvT4rzRHB9HplMMH261K5v506pVicalVq42GziyRAEQTgGEWALgjBlRKPSCo1yzfWrr8Itt0jZ6zlzQKO50CO8TGVmSg3Hb7xRqsUOBqW9nZUrpSdFBNmCIAiTqKInbrskejIJgnDehMPSAjI/+pHUim/JEqkVnygLmQKiUekJ+s53pEy20wnr1kFGhjQpUhAE4fLSALwBPHasM0UXEUEQpoTRUdi+Xaq5jkTgzjulQDs2VgTXU4ZGA3/3d1IbF40GvvENOHhQWvlREARBUIgSEUEQLrihIaithTfflIJrueY6P1/UXE8Z8pOQmysdWhgYkBag2bBBmgS5cKF4sgRBEP5CBNiCIFww0ahUznvwIGzcKJX33nWXlCAtLxcLyExJer1UEJ+QAB9+CO+8IxXOFxVBUtKFHp0gCMKUIL6+BEG4YCIRaQ2Tn/xE6hhy663wX/8FM2eKROiUFhsrBdR/+IO0l/TWW9LS6mIpdUEQBEAE2IIgXCAul9Qt5B//USrhvfFG+N73/jpfTgTYU5hKJS1EU1oK3/qW1Ct77Vp48UUYHLzQoxMEQbjgRIAtCMJ55/FAc7NUcz04KJXvXnON1PlNTGi8SKhUUnuXhQth8WLIzpbKRfbvl+qzBUEQLmOiBlsQhPMmGpXKQvr6pHZ8f/iDtIbJHXfAokWiz/VFqaBAmpHq9cIPfiCt8KjRQGKilOUWe0uCIFyGRIAtCMJ5E43CyAg8/jhUVkoLAf7hD1IsptNd6NEJZ6y0VFqMpr1dWh2opQXi42HBggs9MkEQhAtClIgIgnBeBINSr+tvfxvq6mDWLPjFL0Si85KgUkFcnFRQv2qVVPfz/e9DZ6fUwk8QBOEyIwJsQRDOuXAYurqkhf9qaqSykFWrYPZsKXMt2vFdAtRqqUf29ddLT3B3t1ST3dcn7V0JgiBcRkSJiCAI54zctc1uh3374De/kZKdd94JV14pVREIlwh5durNN0t7TS0t0jr3hYVSdjs5+a+XEwRBuMSpoifuWyqamgqCcMaiUennJz+RFpLp6IA1a6SYy2QSsdYlKRqV2sS0t0vBdno63Hab1M5PpxNPuiAIl4oG4A3gsWOdKQ7MCoJwzgSD8LvfwQcfSLHVD34grU9iNIo465KlUoHBIPVc/K//kgrsP/oI/ud/xEI0giBcNkSALQjCOeFwQEODVHdtNMK8eVJZiNEo2vH5fD4GBgbYsmULo6OjBAKBs34bkUgEh8PB7t27aWxsxOPxcJIjlmePRiOt9rhkCVRUSKsHffSRVIDvcp2fMQiCIFxAIsAWBOGsC4Wgpwc+/BC2bpV6XN96K0ybJiY0AjidTpqamvjtb39LZ2cnXq/3rN9GOBymr6+Pl19+mU8++YTR0dGzfhsnpNFIrfv+5m9gzhxpAZo334TeXjHpURCES56Y5CgIwlnX0ABvvy214Xv4YXjwQSguvtCjmjr6+vqorKzkz3/+M9dddx1msxmLxXJWbyMQCHDgwAHefvtt+vv7KSkpITMz86zeximZP/+vkxz/5V+kwPvGG6XMtiAIwiVKBNiCIJw1kYi0SvZTT0F9PSxdCg89BFlZouZ6osLCQqxWKyUlJcyfPx+r1XrWb8NgMLBixQqee+45EhMTyc/PP+u3cUpUKql93+23w549sGGDtNqQ2Sy18xMvDEEQLkEiwBYE4ayIRKTmEe+9B4cOSSW4t9wC+fmi5vpIcXFxGAwGEhISiIuLQ30O6mY0Gg3JycksXrwYrVZLTEzMWb+NU2Y0SuUit90Gv/+91MLv3XchI0NqJ6MVX0WCIFxaxKeaIAifWTQKPp9Ud/2//ytVBCxfDg88IAXXKpU06S4UChGJRABQ/SVzqdfr/7KNKOFwmHA4jEqlQq1Wo/vL+umRSGTS9TUaDRqNBu2EwEyewBcKhQiHw8pp+XKfJYiNRqNEo1GCweCkMYVCIVQq1aTtT7wf8v1TqVTK/ZUvo1arMRgMyuXUarUy5kgkMmkbarV60uNxrPs68TFTqVTKZeTHNxQKodFolPMikQiBQEAJvOXTarUarVaL5i97RfK45e0FAgEikYhyHzQajTK+iZc/il4v1WO3tcHmzVJ7mauvhpISqSH6KWayJz42MTExymn5/snPxZGPt/z8TXz96XS6oy4rCIJwNogAWxCEzywSgU8/hf/8T+no/w9+AFddJWWxZUNDQxw4cIDe3t5JAeZNN91ETEwMdrud5uZmGhsbMRqN5OTksGjRIgDGx8fp6upi37599Pb2Mm3aNEpKSpg9e/YR44iwe/du2traGB8fR6PRUFRUxKxZs0hPTz/j+xcMBhkaGmL79u2sXr2aQCBAX18fO3fuJDU1ldmzZ5OXl4darWZkZITa2lra29tRq9XcdNNNWK3WSRnkaDSKy+Vi3759xMXFkZ2dPWl8HR0dNDc309DQoGSic3JyWLp0qXIZj8dDZWUlDQ0NSqBcUFDA0qVLiYuLU26nurqaYDBIfHw8M2fOBKRJln19fWzYsIG7774blUpFR0cHH374IWlpacycOZP58+cfFaR6PB4++OADOjo6cDgc5OfnM2PGDDIzM0lPTz/5ToxKJfXDLi2F//f/4Mtfltr3VVRIe2WnYGRkhNbWVqqrq/nKV75Cb28vzc3N7N27l6ysLK666iqysrImjT0cDuNyudi0aRMdHR2EQiFsNhsrVqzAZrNhNBpP6bYFQRBOlQiwBUH4zN58UyqtbW2Fxx+HhQvBYpmclDSbzRQVFfGrX/2K7u5uZs+ezbe//W30ej1qtZr4+HgKCgp4/PHHueeee0hOTiYSiVBbW8vevXsJBoPMmDEDrVbLO++8g9frZeXKlTz88MPo9Xq6u7tZv349sbGxlJeXYzKZ6Ojo4IUXXuDrX//6GQXY0WiU+vp61q1bx44dO2hpacHpdOLxeLDb7QQCAV588UUKCwspKyujrKyMmpoaxsbGGBwcpLKykurqar7whS+wfPlyotEo3d3d7Ny5kx07dvDxxx/z9a9/nbi4OGV8r776Kj6fD4vFwp133kl7ezs7duxg9+7dSoBdVVVFdXU1BoOBVatW4fF4aGtr49lnn2XGjBkEg0G6urp4/fXX2bx5MzNmzOCGG25g5syZVFdXs2nTJiVQBtBqtXR1ddHR0cEbb7xBeXk5t912G5/73OcAKWs9ODjId77zHW666SYqKiowGAz89re/5Y9//CNGo5GCggKmT5/O5z//+WM/1vKLISZGCqgfe0wKtn/3O2kFovvvl0pFjpNNDofD7Nmzh5deekl5jC0WCz09PfT19dHb28uLL75Ic3MzV199NStWrACkCaXV1dW89tpr3HDDDSxevBi/309NTQ1f/epXufnmm7niiitYsGDBab8+BEEQjkc0zBIE4YyFw9DcDB9/DJ2dUje25cshJUVaWGYivV5PQkICqampDA0N0dnZidvtVg7Rq1QqNBoNLpeL+Ph4rFYrPp+P9evX4/P5SE9PJzc3lxkzZhATE0NXVxfvvfce/f39+Hw+hoeH+eijj1CpVCQlJZGdnU1+fr5SHnCm5DKO0dFRGhoaGBsbIy4ujry8PMrLy9HpdBw6dIitW7cyNjZGamoqJSUllJSUEBsby44dO+js7FTGoNPp0Ov1hMNhamtrGRsbIxAIKOdv3bqVjo4ODAYDmZmZ5OfnY7FYlNKGaDRKa2sr27dvR6fTkZqaSm5uLtnZ2Uq5iEajQa/Xo9fraW5upqOjA4/HA0DsXw4r2O12WltbsdvtxMbGUlhYyKJFi/D7/TQ1NbFnzx7lMRgaGqK6upq9e/ditVopKCigoKCArKws2traaGlpweVykZSUNKls55jUaukFsnChVCLS3Q3btsGuXSddiMZoNBIMBhkZGaGtrQ2Px0NSUpIyWXR0dJSamhoOHjyolOrs3buXHTt2MD4+Tk5ODvn5+RQWFjJt2jRGRkbYunUrW7ZsYXx8/Pz1CRcE4ZInMtiCIJyRSAT8fmn9kO3bpflqX/uaNKnxWJUCGo2GuLg4/uZv/oa6ujocDge1tbUsXLgQlUpFIBBgZGSEvLw8MjMzMZlMDA8P88ILL/DjH/+Y5cuXExMTQ0pKCnPnzqWnp4etW7fS1NREbGwsY2NjfPrpp9x1111KfW1+fj6LFy8mOTn5jO6jSqWiqKiIiooKhoaG2LFjB8nJyVx11VUUFBQQjUZpb2/njTfeoKmpCYvFwl133YXBYKC3t5dgMMhTTz3F0NAQ4XAYrVZLWloaixcvJhwO88QTT0y6vWg0SmVlJQsWLCA2NpZgMEhmZiYzZ85Er9crAWBHRwdVVVV8/vOfJxKJEB8fr4wzNjaW+Ph48vPzue2223juuecm3UZpaSmDg4O0t7ezb98+0tPTufLKKykqKgJg7969NDQ0UF1drVzn8OHDbNy4kUAgQFZWFunp6YRCIW6//XbefPNNTCYTS5Ys4e677z61Wne9Hmw2KYP97W9LPbKffx7mzpXqio4xK1aj0TB79myWLl3K0NAQHR0dZGdnM3/+fJKTk/F6vaxdu5aenh6am5sBaUGfdevW0dTUxOrVq5k/f75Sk56RkcG7777Lpk2bGB4eZtWqVcycOVOpPRcEQfgsRAZbEIQzYrdDdTX8279JmesHH4TVq088V02j0bBq1Spmz56N1+vl2WefJRQKAdDT08O6deu4/fbbycjIoK+vjzVr1uB2u3nllVf44Q9/yHe/+12++93vsn37dvx+Pzk5OXR2duLxeDAajeTm5vKNb3yDZ555hsrKStRqNV/96lcp/oxNuA0GA0ajEZVKxbRp00hISFDOy8vLw2KxEBsbS1lZmZLBjYmJoaioiEgkwvj4OCMjI8p1tFqtEuhNpFKpyM3N5ZNPPuFb3/oW69evx+PxsGjRIu655x6lrjgxMRGDwcBDDz3E66+/TnNzM2azmb//+79XxqZSqZQJlkea2FUkJydn0g5Ibm4usbGx9Pf3KwG90+mko6OD4eFhfD6fsrNQVlZGYmIiwWCQ/v7+03tQ1WppguNjj8E118Brr8GvfgVdXSe8mnwEQK1WU1RUpNSbq9VqiouL8fl8jIyMEIlE2LJlC7W1tXg8HubOnTsp+Fer1SxfvpzExES6urr485//TFAsgCMIwlkiMtiCIJw2rxd27oSnn4bCQrjzTqms9lSSl2q1mquuugqXy8WGDRvYvXs3s2bNYmxsjP379/OFL3yBhIQEWltbOXz4MFarldtvv53Fixcr2wgEAgSDQQKBADk5OSQkJKDX63n00Uf5v//3//LGG2+wbds2Fi1axDe+8Q2Sk5NPXrpwEnKgemRHEL1ef1THDflvuetHMBjE7/efdPvRaJQHH3yQN998ky1btvBv//ZvrF+/nuuuu47ly5djMBhQqVRUVFTg9/v5+c9/zq9+9Svee+89FixYwCOPPKJ0xjhyPCe63YmXm9hpRJacnExZWRnr1q2jubmZjIwMsrKycLlcBAIBEhMTmT179ul145AvO2MGBALSz3PPScX7K1bAXyZkHmu8xzOxk0skEqGjowO3201cXJyygzRxO+Xl5VitVjo7Ozl8+LBShiMIgvBZiQBbEIRTJsdc+/dLJbMNDXDXXVIsZLOd/PpygDNz5kza29t5//33+fTTT0lISMDtdhONRklKSlJqlL1eLz6fD5vNRlFR0QnLD6xWK4sXL+bWW2+lsrKS/v5+NmzYQF5eHldffTWFhYVKwHs2najNm/x/uc3gqZgzZw52ux21Ws22bdvYsmULoVCIQCDArbfeil6vJzMzk2XLltHU1KSUyQwPD5Oens4tt9xCYmLiZ7pPRwbYqampzJ8/n9LSUmpqajAYDASDQdrb28nIyCA/P5/S0tIzuzGzWcpkX3edVMy/Y4dUImKzST+nEbQf+TyEQiGlPd/o6Oik+6RSqcjIyFA6iMhHUgRBEM4GEWALgnBafD5pGfQdO6T1Qx5+WIqDTqfN9PTp0zl8+DDJycl88MEHZGVlYbVaycvLQ6vVKj2dNRoN/f39OBwO/H6/MkEPJvc2lksesrKy+MEPfsAbb7zB+vXr+eCDD/jxj39MbGwsNpvtMwee55Ic/KWkpHD77bdTUVHBU089xZ///GfeeustDhw4wJVXXklycjImk4nS0lIef/xxnnjiCT766COqqqr4/ve/z4wZMzAajWd1YRm5Jv7GG29k165d2O12wuEwlZWVVFRUMH/+fGbMmHHmN5CaKmWtb79dKhUZGZEOjVx99Qk7i5yISqXCbDaj0+lwuVw0NTUdtZNjtVoxGAzo9XrMZrPohy0IwlkjAmxBEE6ZzyctJPPOO1BcDP/+79LkxtONS7RaLdOmTeOBBx7g8ccfx+/3s3LlSu6//36l3CI+Pp7p06fj8/n46KOPiI2N5eabb1a2EQqFcDgcvP3221xxxRVKx44777yT+++/nxtuuIEvfvGL3HfffezcuZO0tDRuuOGGs/honF3yzsL7779PcXExZWVl/OxnP+PGG2/kmWeeYcuWLbz33nvcdtttdHZ20t3dzS233MI///M/87nPfY5t27bxta99jU2bNqHT6Y7qEf5ZyOUuqampfPe73yUlJQWLxcItt9xCTEzM2VmJ0mCA739fmj378cfwyCOwbh3k5Ul7cqdJrVYzZ84crFYrPT097N69W1m4B6THu6mpibGxMUwmE/PnzxcTHAVBOGvEJEdBEE7JyAjs2wd/+APMnw933CGVhqhUpx9gy4fnb7rpJkwmE16vl2g0Sn5+vpJFTE5OZsWKFaSmpvLxxx/z+9//ntdff5329nYqKytZu3YtTzzxBEuXLsVmszEwMMC7775LKBRCrVaTkJDAokWLsFqtmM3mSdnv9evX87Of/Yzvf//79PX1nbQ+OhAI4PP5AKmeemKg5vP5lBUVnU6nkomORqP4fD6lHnhiCYK88iBIEwgDgYBynY0bNypdMNRqNXPnzqWgoACz2Ux8fDwqlYrGxkY2bdqkXCY7O5t58+ZhNpsxm83ExMQQjUaV9n9+vx+n06ncfjAYxOv1An9dDVLmcrlwu93K+KLRKIcPH+a9995j69atOJ1OIpGI0i7P4/GcvfIKrRbuuw/uvluqxX7sMWkmrds96fGWny+5BEQep8PhUJ4PgMLCQpYtW0ZhYSG1tbUMDAwoz6Pc41yj0VBaWsqNN974mev0BUEQZOLTRBCEk/L7pRWuN2yQ5qJVVMCiRWC1nvk25V7ShYWFpKamkpqaqnSEAJQ+0Ndddx2ffvopBw8e5J133qG1tRW/3084HFbqkfV6PW63m8bGRnbs2EFmZiYGgwGn00lJSQlFRUWTFj85dOgQmzdvZmhoiKuvvpry8nJsxygij0ajjI6O0tHRQVdXF9FolMbGRmXSpNfrpbGxkbGxMTweD1VVVaSlpaHX6+nv76exsZFQKERvby8NDQ3YbDaCwSCtra00NjYCUgu8w4cPk5ubi8Vioa2tjaqqKjIyMkhPT8fj8RAXF8f06dMpKChAq9UyPDzMwYMH+fTTT0lPTycajeJ0OikrK6OwsBCDwcDAwAC1tbX4fD7GxsZobW2lt7cXjUZDT08P3d3dALS0tJCRkYFer2d4eJienh5GR0cJBoMcOHCAoqIivF4vQ0NDNDU1sWXLFhoaGoiJiUGn02EymUhISCAtLY0FCxZgMBjOLKMt76UVFEgvsLY2WLsWtm4FrZbI/PkMj47S1dXF4OAgkUiE+vp6jEYjgUCAgYEBenp6cLlcjI6OUl9fz/Tp01myZAmBQICNGzeyceNGZs2aRVJSEuFwmIMHD1JcXExhYSHZ2dmiREQQhLNGBNiCIByXPCdsZESa1PjCC9IS6KtXS6tdfxZyX+xFixaRl5fHrFmzJp2vVquJjY3lO9/5Dmq1mi1btrB27VrWrl1LdnY2S5cu5Rvf+AYmk4lwOEwkEsHtdvPCCy+waNEikpKSlP7GV1xxBSUlJcq2R0ZGaG9vp7e3l127dpGamnrMABukntP19fVKK7ydO3eSkpKCTqejv7+fPXv2KDXJ69evZ+bMmeh0Otrb29mzZw8xMTEcPnyYbdu2MX36dOx2O3v27GHXrl2YzWaam5upq6sjIyODhQsX4vP5lDrnlStX0t/fj16v5/rrr2f+/PkEAgFCoRCDg4P84Q9/YOXKlUorwJtuuonFixcTDAY5ePAg69evB6Qs+YEDB6irqyM2Npampiba2towm83s3r2bhIQEEhIS2LdvHx0dHbhcLnQ6He+//z73338/Op2O+Ph4hoeHeemll4hEIpMyx2lpacydO5df/OIXpKenf7b6b50Oysvh//wf2LRJKvh3OIgUFdHS3ExjYyODg4MYjUY2btyo1Nbv37+fnp4eQqEQIyMjbNiwgaKiIlatWkVmZibBYJDf//73rF69msLCQkKhEI2Njdx7771UVFSI7LUgCGeV6iQrV4llrQThMhaNQigE3/wmVFZK/3v/feno/dloyOH1evnCF77AN7/5TRYvXkx8fPwRty99BIXDYdxuN4ODg6hUKlJSUoiLi5vUwSMSiSilAw6HA5AmDALKpEmZz+ejv7+fHTt2YDQamTdvHjk5Oce4/1GlxEOeIKdWq5XblVcLlMcpr0Ypk8+buFLlxG3K15u4TbmsJBQKMTo6Snp6uhL8ydcPhUJKz+aRkRHi4uKwWCzKKo7y2CeOe+LYjvy/fPvy/+VxaTQa1Go1XV1d1NfX09PTw6xZs9BoNNjtdqXLS2trK3V1dZSVlXHfffeRmZl5Wq+DYzzw0jKhBw/CP/0T9PQQXbKE6JNPElarj3rc5Ps7sdRFHvvE8xwOB93d3YRCIWw2G6mpqcqkWpG9FgThNDUAbwCPHetMscsuCMJx+f3w4ouwdy+kp8P990vB9dlI9rlcLrq7u7HZbFitVqVd2kRy0KPVajGZTMriLBN7T8vUajUGg0G5/MRg81i8Xi8tLS186UtfOu5Kj3LgdbyShxOdJ4/pePfpWGOLRqMYDAYlCNfpdJP6WsvX12q1yvVTUlLQaDRHbe9EYzve/4+1nc7OTjZs2MCHH37IL37xC6xWKyqVSqndjkQizJo1i4qKCg4cOHB2JjyqVFKrvoICeOgh2LQJ1caNqF55BfW118JxAvjjPd/y82ixWJQVMfV6PTqdTgTWgiCcEyLAFgThmLxe6O2F996DuDiYPRuWLj3jrmnKRLu6ujqMRiNOp5OWlhZmz55NUlLSSTs4aDSaSRMVjzQxC3msVRIncjqd+Hw+cnJysNlsJ9zu+TRxMRvguOOaeF/lnYpzRa4lr6mpwev1YjKZMBqNGI1GPB4PDoeDYDCIRqNR6r/PCpUKTCap2N/thgMHpK4iaWnS4ZNTabw+aXPSjsmRR0kEQRDOBRFgC4JwlGgUhoelzPWGDfCd78ANN0gt+c6UPAnxJz/5CRkZGQSDQXp6evjDH/5Aamrq2Rv8KRgeHiYSifA3f/M3yuqIwrENDw8zMjKC3W7nww8/ZN68eaSnp2OxWOjt7aWlpYXe3l6cTicPP/wwJpPp7A4gN1cq/A8E4FvfgqwsKbu9cqX0Wzx3giBMQSLAFgThKHY7rF8P//EfUmB9113SitafhVw7XFNTw759+5g/fz6PPvooeXl5573/sDzhUQTWJ3fdddeRkpJCQUEB77zzDj/96U/xer3ExMRQUlLCDTfcwJIlS6ioqDh32fS8PKl9X2ur1Fnk0CGpb/ayZSLAFgRhShKTHAVBUMgfB7/9rdTAoa4Onn4a5syRaq8/i0AggMvlYu/evYC0il5ubi42m00EulOY3AJwaGiIkZERxsfHiUQiSl18SkoKCQkJSm32OXsuIxFoboYnnoD6einAfvJJqWRkipT4CIJwWRGTHAVBODXhsNR+eNs2qTVfRQXMnSvVYH9Wer2ehIQEVq1apQRiIrCe+uQlx81mM4WFhYTD4ZNO7jxHA4Fp06TSkFAINm6EDz+Ea66BnJyz09ZGEAThLBEBtiAIgJS99vmkZdD37oVZs6RWxPHxZ+8o/JFt7ISLzwV7/uQX4U03SS/Khgb4+c+l1Y7MZpA7wYidNkEQpgCxVLogCACMjUFNDfz0p7B8uVTyOnfuhR6VIBxBr4crroBnn5X+/tWv4Ne/liZBCoIgTBEiwBYEgUBAWkjmf/9Xmk92yy2weDGo1SIhKEwxKhXExEiN2f/t36RWftu2wf/8DwSDf51IIAiCcAGJAFsQBBobpbKQ2lq4+mppperTbDMsCOePWi1NcrzqKpg3T8pkr18vrfzocl3o0QmCIIgAWxAuZ/JS6G+/DZs3S//7xjekhfLO9xw2QTgtarXUE/tLX5ImOlZVwZ/+BB0d0mxdQRCEC0hMchSEy5jfLy0k8+c/S7HKf/wHZGeL4Fq4iEyfLmWzjUb47nel/918s5TdFgRBuEBEgC0Ilym/H/r7pbbCmZmwYgUsWSLqroWLjFot1WPfcAPs3Al79sD4OCQmQlmZ2FsUBOGCEJ88gnCZGhyU6q7r66VuIYsWQWqqCK6Fi5DRKM3OvflmadGZ5mbp0IzdLtVACYIgnGciwBaEy1A4LE1ofP55qaXw7bdLq04LwkVLo4EvflHKZBuN8N//LQXaLpfoLCIIwnknAmxBuAx9+CG89Rbs3i21EJ4xQypjFYSL3kMPwT/8gzSZ4MtflmbvOhwXelSCIFxmRIAtCJeRSERaAv2NN6CzE1avlrqcxcWJ0hDhIqdSST8Gg7QM6T//s5S5fvVVePlladKByGQLgnCeiABbEC4hkYiUrDtWl7JoVFpQZs8eqV2wwSAF2CkpoNOd/7EKwjmhVksv6uXLpdWSurpg61aorpbeGCLIFgThPBABtiBcIqJRaSG7hgap7DQUkv4nxxPRqBR8/+pXUjJvwQK4664LOmRBODd0OqmLyPe+BxkZUFMjvfA9Hmkv9Fii0b8G4CIIFwThMxIBtiBcIiIR6OuD66+Hv/1bePFFGBv76/kHD0qTGrduhUcegXvukVacFoRLkkoFRUXScur33w8ffAD/+Z/SsqXHEo3Crl3Q0yMd6hEEQfgMRB9sQbhEjI9De7uUpd61C3p7Yft2eOAByM2VSkPWrJE6mc2fLyX2RN21cMmSa7ILC6Um77298M47YLGAzydNPpCNjMC+ffDLX0pdSK68cvL5giAIp0kE2IJwibDbpVWiIxFpAZnhYWhrk46UT5sm9bzu7YWvfhVycqSJjYJwyTObobgYbrwRPvkEKiulQzc5OdKbo79fagb/7ruwZYt0+aQkEWALgvCZiABbEC4RQ0NS/bVWK9ViBwLSYjL/9V9SHGEySTHFF74grcURjYoMtnCZSEmBa6+V6qJefVUqA5k2TZoIuWmTFFy//rp02aoqsNmky8pZcEEQhNMkAmxBuET09krJuWN1EHE4pImPHg88/bS0HkdamogdhMuIVgvf/rY0AXLDBvjKV6CiQuou0t3918u1tcH+/dIiNUVF0gI2giAIp0kE2IJwCYhEpJKQpqZjN0kIh6UfhwNeeAFaW6WVG6++WgTawmVEr4dVq6Q3SUODNDFhdFQ65CNTqaT/ffKJtPy6CLAFQTgDIsAWhEuAyyXFBENDxz5f7joWCMCBA9KEyNFRKc5YvVoqIRG9sIVLntcrTXAMBKQ9zqGhYx/yGR+XZgjfe68UlIs9UEEQTpMIsAXhEtDdDQMDUn/rk4lGpbU3+vqkln0Wi9Q0ISHh3I9TEC6IaFTam+zshKeekuquh4ePHVxHItLkhQ0bwO2WVmTSiq9KQRBOj/jUEIRLQG2tFDtoNMeOGY4UjcL06XDLLXDNNVIMIQiXrPFx6dDNl78sZa19PmklpuMJhaTrbNok7X1mZ5+ngQqCcKkQAbYgXAJaWqSM9IkWoJMbImi1cOedUtxw5ZVScC2OgAuXrJ4e2L1bmt3b1yfVWx9vNceJolEpwC4qEgG2IAinTQTYgnARkwPq9nbpqPaJAmytVmrVN2uWlLleuFCKHQThkhYKSe1zhodB/ZfFi09lKfRoVFp8pr9fqtnW68/tOAVBuKSIAFsQLnLRqLT6c0/P8eMGrVaqtS4qgiefhIICqRe2IFzycnOlwzSxsfDYY1I/S7f7xCUiINVa1dZKe6+jo1K7HUEQhFOkvtADEAThzIVCUnnIyIjUIOF4li+HH/xAWiq9tFTUXAuXmZQU6bDN9u3w29/Cl74kBdxa7YnrozQa2LlTKhURBEE4DSKDLQgXsWAQDh6UuoeoVH/NYMsxQ2ws3Hwz/M3fwMyZUjs+tVrUXAuXGbVa6kNptUoN4LOypKXQ16yRstSDg9Kb4sja7HAYamogNVVanUms7CgIwikSAbYgXMRCIWlxmUDgr/9TqaRYIiUFiovh9tthxQopuBaEy5YcHGdlSUuhFxdLbyCzWVp0pqcH7PbJdVZyT8vmZukwUVKSCLAFQTglokREEC5igYC0qrPP99fvfTlRd+ON8POfw913i+BaECbR66Ug+xvfgP/8T/jud6W+lTrd0Yd4PB4p+N6799R6YAqCICAy2IJwUfP7Yds2aQn0aFSqrZ43D37847+WhAiCcAK5uZCZKdVSrVsHL70ElZVSH+xIRAq4h4ak/y9fLpY8FQThlIgAWxA+g+hfDid7vV4CgQCBQAC/3084HCYQCBAMBiddPhKJ4D3GbMTY2FjU6r8eUFKpVOh0OnQ6HRqNBoPBgE6nQ6/XE/uX9h8+n4qREan7WCgEeXlS670vfUlqxWe1igXoBOGkNJq/1mgvXw7JyVBXB1u2wMaN0mGi8XHYswfGxkCnI6rTEQ6Hlfd6OBwmGAwSCoUIhUJEIhGi0SjBYJBwOEzkOH235cuHT5AZ12q1aLVaNBrNcYavQaPRoPtL4C+f1mq1xMTEoFar0Wg0yt8qUeIiCOeF+PoVLnvRaJRoNEooFCIYDOL3+yd9WQaDwUmnJ36JRiIRQqEQHo8Hn8+H3+/H5/MRCoWUgDs6oaYzHA7j8XiOGkNcXNxRAbZer0ev16PVaomNjSUmJoaYmBiMRiNarRavN47DhxPwerOJjR3AZvNQVBSiuFiHx6MjEJC+mOXt6HQ6tFotKpVKfMkKl7RoNKq8N+UAVv4t/0QikWP+HU5JIVJYSLi5GU1sLMmRCBa/H1NnJ51btzKYkoJLqyUcDuPz+ZRtyzvUclANEAgEThhAy58voRO0DJTft9rj7C3LwbT+L3265cvqdDpiYmKU4NxgMKDRaFCr1cpnzcTPAzkwly9zvNMT/54Y/Gu1WhHAC8IEIsAWLmnRIxpDy6eP/B0IBBgdHWVkZITe3l4GBwex2+04HA6Gh4cZHR1lbGwMu93O6OgoDocDn8+Hz+fD7Xaf1pfKsS575DhPdp/i4uLQaGYCq9Bo/ons7DcxGlvo6hrnqacSSUlJwWw2Y7FYsNlsZGRkkJSURGJiopLpksdx5O/jjVEQzqfjvXdPdh6gHEEaGRnBbrfjcrlwOBzY7XbcbjderxePx4PL5cLj8SinnU6nctrldBKfkMAtajVXeDzM9fv501e+wmuhEAf+kpE+G++TU9nG6Xw+nGwb8o66RqMhPj5eCZLj4+OJj48nNjaW2NhYTCYTJpNJOR0fH09cXByxsbEYjUbi4+OxWq3Ex8djNptJSEhQsuTHu38n+4wRnzvCpUR1kjfuZ39XC8IFJGene3p66Onpobe3l87OTjo7OxkaGmJ4eJi+vj76+/sJBAJKVlqr1RIXF0d8fDxJSUlYrVbMZjNms1n5UjEYDMTExBAbG4vZbCY2NhaDwYDRaESv1xMTE6NkoGVqtRqTyTTpiyQajeJyuSYdRpYzYn6/n0AggNvtxu/34/V6cTgceDwe2toSaG1NIjm5EziE1zuMwzHG+Pg4o6OjOJ1O3G43oVBIyVrp9XrS09NJT08nOTmZlJQUcnJyyM3NJT09nczMTDIzM8UXnXDBRaNRZcdX3rHt7+9XXtvy69zhcOByuXA6nUpALR9Rkt//E3/k92lsbKwSMB7rdFxcHGaTiVi1GmswSN7oKFl799K6YgXD06ej0WiIi4tTssVyGZecNQaUMg3dceq2VSoVsbGxxz0fUI6MHY98pEzOgk887fF4lCy+x+NRylrkzzqPx6Nk0OUdjImJA/lx9Pl8uFwuvF6vMh458z3xx2g0YjKZiI+PJzExEYvFMul0QkKC8hmalpZGcnIyFosFi8WCTqcTnzvCxaYBeAN47FhnigBbuOjJh4NHRkYYHh6mt7eX3t5eRkZGGBsbY2hoiJGREVwuF4FA4KiaZjlIlr8I5ABaLq0wGAwYDAbltPyFOfHQqBxITzxUeuThWPhrbbX8t/z+CwaDk7JUcqAvH76eeKhb/vJ0OHTY7XpiY+1Eow6CQZ8SlPt8PuWL1ul0MjY2pgQhXq9XKYOZWDMeExODyWQiKSmJlJQUEhISSEpKIiMjg8zMTCUDLg4DC5+Fx+PB7XZjt9sZHh5mfHwct9uNy+VifHxcySK7XC7sdrvyWpZf1/J7TKPRTHrfyf+X35sxMTFHBdByeZV8nYnlFxOvK5+n0+lQq9XoIhHi/H4sAwO4MzLwJySgmvBen/h+l38DSunFkVld2cTSjOM5WY22/Dkhf37Inxny58bEcploNKqUwsg14hPrxY8shZNPH1ljHgqF8Hq9uN3uSQG53+8/qpROLqGZuK1wOKw8J/JnqtVqxWQyERcXR1xcHAkJCcTHx2MymTCbzSQnJ2O1WjEajRjESlnC1HDCAFuUiAgXFfnLxOFw4PV6lUO7drtdyUR3dXUxNDTE2NgYDoeDsbExpabZaDSSkJBAWloaiYmJWK1WUlJSSE1NVU4nJycTHx9/3C/Fs0kOVOX6yTOTcdxzIpGIUuYyPj7O2NgY/f39DA0NKRlA+bTdbqe7uxuAxMREJdOUmppKTk4OqamppKenK4eK5Wx9fHz8CYMI4fIgB2k+n0/ZeZOD44k7c3Lp1cjICAMDA8p72ePx4HA4lBIO+X8Tg1/5NSeXL8gZUqPRSFxcnHJaDtLMZvOk8+Pi4s585zAahUgEM0ht/M7T6/1E9dcXSiQSwe12KztD8nPncrmUnSWHw6Hs0Ms7VPLz6/f7GRkZUZIFkUhEeY7k59hisWA0GpXPmNTUVFJSUpSgW056yImSI5Mgcn25IFwoIoMtTFlH1lzKX+Dj4+Ps3r2bhoYG2traOHjwILW1tXi9XlQqFWazmfLycnJycsjMzCQ/P5/p06eTnZ1NcnIycXFxF/BeTU0ul4vh4WG6uro4dOgQbW1t9PT00NnZSX19PU6nEwCj0cjMmTMpLy+noKCAadOmUVFRgdlsVg7xilruS9PxaqAnZk6HhoZob29nYGCAgYEBOjo66OnpYXh4WDktly3JO702m23S0ZLk5ORJp+XSArmkSaPRiNfVRUzOpg8NDTEwMKDs+Pf29k7a8ZdfN6Ojo4yOjipH+fR6PSaTiZycHDIyMkhJSSEtLY3c3FwyMjJIS0tTPuuP9VkkPp+Es0iUiAgXp0gkgtPppK2tjR07dlBXV0dTUxMHDhzA7/djtVqx2WyUlJRQUlJCcXExeXl5FBYWKu3tjpz9Lsobjk0+jHy8zgp9fX10dXXR0tJCQ0MDTU1NSk27Xq+noKCAgoICysvLWbJkCeXl5SQkJEy5zJtw5uSjIYcPH6a/v5/e3l5aWlro6Oigv7+fnp4ehoaGCIfDqFQqtFotycnJ2Gw2EhMTSU5OJj8/n+zsbGw2GykpKWRkZChlGBPfnxN/H/k/EIHRxUyOOeTSlIm/5b+PPB0Oh7Hb7XR2dtLf309fX58yGX10dFRJDvh8PiKRCBqNRjn6lpqaSmZmJuXl5WRnZytzTWw2m3gdCZ+VCLCFi0MwGMRut9PW1kZNTQ2HDx9mYGCAkZERgsGgcrjQbDZTWFioTJhJTExUyjvi4+OxWCyA+BI+mybWyModVcbGxhgcHKSlpYXR0VGlI8PEiZT5+fnMmTOH/Px8JcstTG1yWdXg4CDd3d3KfAZ5ToPD4SAQCBAOh5X6Znlyn8ViUUqs4uLilPes3InCYrFM6lQhl2yI96pwIvKEV7kkRf48kmvAvV6vMvFVnhA7NjaGx+PB7/crc060Wi0GgwGTyYTNZsNms5GUlDSpDM5isYijnMKpEjXYwtQUiUQIBoM4nU7lMGBfXx91dXXs37+fnp4e3G43er2e3NxcCgsLlXKPWbNmERcXJwK280SuX01NTVX+FwgEcLlc1NbWcujQIdrb25WMZk9PDyaTiebmZgYGBigrKyM9PZ2EhAQSExOVSaQisLow5KMT8k6RPIHQ7XYrh+57e3vp7u5mYGBA6eQRCASUyWkWi4X09HSlrCM1NZXs7GwyMjKUWmhBOBtUKpXSXvBE7HY7Y2NjDAwM0NnZSV9fH8PDw/T399PZ2YnD4WBwcJBAIIBer1fKkZKTk8nJyVFKS5KSkpQJshMnwYvyJOF0iAy2cF5N7D8dDAbp7e1l586drF27lr1799LX10ckEmHZsmVUVFQwe/ZsFixYQG5urphEdxEIh8McPnyYyspK9u/fz65du9i5cycajYbMzEwWLlzILbfcwpIlS0hLS5vUmkt8cZ0bx5rLEAgEcDgcVFVVUVdXR3t7O21tbRw4cIDh4WHC4TA6nY7CwkKmT59Obm4u+fn5lJSUkJ+fr7SuFISLyfDwMENDQ3R2dnLo0CElIdDc3ExrayuRSISYmBiSk5OZM2cOxcXF5ObmUlJSwsKFC5W2jGKuifAXokREmDrcbje9vb288cYbfPzxx7S0tGC32yktLWXevHnMmjWLpUuXkp6errTgkms0xYfY1CfXTsotuQKBAENDQ1RXV1NXV0dVVRWVlZXExcWRm5vLkiVLuP/++8nNzRUZz3PE4/HQ29tLa2sre/bs4cCBA7S3t9Pd3Y3X61UWI8rKymLatGkUFRWRl5dHfn6+Ukcvz2GQ/5ZrogXhYiLXdcsTLSeu7OlwOJSjb4cPH6axsZGOjg5lwq58JDUnJ4fS0lLmz59PeXk56enp4rPr8iUCbOHCkQOugwcPUl1dTXNzM83NzQwPDxMfH4/NZqO4uJjCwkLS0tKw2Wykp6cTGxsrAupLQDQaxe/3Mzg4qCzqc+jQIRoaGujv78dut5Oenk5paSklJSXMmTOH6dOni+DtDMjvtc7OTnp6eujq6lK6wYyNjeF0OolEIkrbuvj4eHJycpSaaXk+gzy3wWw2izIe4bIgH1F1Op1K/3V50SK73c7AwABtbW3KIl8ej4doNEpSUhLJyclkZmYq2W65bErMLbgsiABbOL/k15TcC3VwcJCPP/6YyspKOjs7GRsbo7i4mMWLFzNnzhwWL15MYmKi+DC6DMgLAu3atYuamhr27NlDa2srSUlJ5OXlsWDBAlasWKEs9S5PNhKvjaPJ3RXkFTvdbjfj4+PU19dz+PBhOjo66OjoYHh4GLVaTVxcHHl5ecyYMUMp+ZgxYwYWi0V0exGE4wgEAtjtdg4cOEBLSwudnZ20tLTQ2tqK1+tFo9GQlJREfn4+hYWF5OXlUVRUREJCgrIzazKZxFHYS5MIsIXzZ2JP3F27drFlyxZee+01Dh48yLx581i8eDE333wzK1asEEvjXubkWuDNmzezbt069uzZQ01NDTNnzuTuu+/myiuvZNGiRZNWxbucHVlLLbex3LZtGzU1NVRXV7Nt2zbsdjtWq5Xs7GyWL1/OggULKCkpoaCgQOzICsJZEI1GGR4epr6+nqamJmpra/n444/p7u7G5/NhsVhYtWoVCxcuZObMmSxcuJD4+HhRu33pEQG2cP4MDQ1RU1PDz372M1paWtDr9cyZM4evfvWrTJs2jcTERGJiYpSVC8UHzOVL/uyRV/gbGRmhoaGBZ599lv379xMOh5k2bRr/8i//wuzZs0lKSrrAI76wIpEIXq+XTz75hMrKSqqqqti3bx+BQACbzUZ+fj4LFy5k2bJlSjcEebnvibXT4j0nCJ+NPFl44pLwgUCA3t5eDh8+zN69e9m5c6dSVmIwGFi8eDFLlixh7ty5LFmyBIPBIN6LFz8RYAvnVjQaZXx8nE8//VSZxObxeJg+fTpFRUWUlZUxe/ZsrFbrZ1wSXLiU+f1+xsfHqampob6+XlnUxmQysXjxYubOncvSpUuxWCyXxReT/CV++PBh2traaG1t5cCBAxw+fJhIJIJerycpKYmioiJl/kJmZiaZmZnEx8djMBgu9F0QhMuK2+3G4XAoC3P19/fT399PR0cHQ0NDhEIhDAYDOTk5zJkzh5KSErKzs8nKygJEwukiJAJs4dzx+XyMjY2xf/9+1q5dS319PUNDQ9xwww3cdNNNzJgxg7S0tAs9TOEiI/dDX7duHR9++CFpaWmUl5dzyy23MGvWLBISEk7aE/diJJd+yPMXhoeHqa6u5tChQzQ1NdHY2IjJZKKgoIDp06crGTGr1XpJPh6CcDHzeDwMDQ1RV1fHtm3bqKuro7e3F6/XS3l5OWVlZUybNo2ysjJSUlKwWCzExsaKSd4XDxFgC2fXxNdMS0sLa9eu5fHHH8disbB48WK+9rWvsXLlSjQazQUcpXApCIfDfPjhh/zud79j7969eDwefvSjH3HTTTeRn5+vXO5iz/xMfE85nU527drFpk2b+POf/0xfXx+pqamUlpZy2223cfPNN2Oz2TAajRdwxIIgnC6Hw0FnZycbN27ktddeo7m5GZfLRXp6Ol/5yle49tprKSsrm/Tevtg/2y5xIsAWzq5oNIrb7ebZZ5/lo48+orKyknvvvZfPfe5zlJaWivZewlkjT4S02+0cOnSI119/nVdffZUlS5awevVq/vZv//aSaOnocrmU/vBvvPEGIyMjGAwGrrnmGmVnIjU1VVnNTqwoJwgXH3kF1UAggM/no6uri6amJjZt2sT69etRq9VkZ2dz8803c88995CcnCxKvaY2EWALZ488Ge2ZZ55h586dRCIRysvL+fznP09JSQlWq1VkroWzLhwOMzY2xqFDh3jjjTc4dOgQWq2WpUuX8tWvfpXExMSLrr4/Eong8/n45JNPqK2tpampifb2dqxWKwUFBRQXF1NeXk5RUZFy6FgQhEuHy+VidHSU9vZ2ampqaGhooKenB6fTybRp0ygvL6e8vJwlS5YQExMjdqqnnhMG2KL5qXDKAoEA/f39VFVV8cYbbxAfH8/ChQu55557mDdvnqgbE84ZjUZDcnIyy5YtQ6/X88ILL1BdXc0bb7zB7NmzmTt3Lqmpqeh0ugs91FPi8XgYHx+nra2NdevWUV9fz/DwMOnp6axcuZIlS5YwY8YMpQ+4IAiXHpPJhMlkIjs7m7lz51JZWcmePXv46KOP2LJlCx0dHXR1dRETE0NxcbGYvHyRERls4aTk10hvby/r1q3jpz/9KampqfzgBz9g2bJlJCYmnpfbP1Wnu5d/ou1fzBmDkz1uZ/NxOtNtnqmRkRG2bdvGj3/8Y8bGxvjBD37ADTfcQGpq6nkdx+mY+PhVVVXx8ccf84tf/IJoNMqqVau48cYb+dznPkdMTMwF31k93nN9vh7Xc/GePJvvh9Pd1pne9qm+5z7LfTvyuse67FQb/2d9LqfyZ344HMbr9fLyyy/z1ltvUV1djU6n40c/+hErVqygsLBQueyFHqsgSkSEzygSidDd3c0Pf/hDampqiIuL49lnnyUrKwuj0XjOg4FIJMK+ffsYGRnB5/NNOk+lUqHX60lNTSU1NRWr1XraWT+5n2l7ezstLS3s3r2biooKysvLyc7OPpt35bwKBAI4HA4++OAD+vv7MRgMZGRkcMMNN2AwGE77eevt7aW+vh632638LxKJ0N/fz/XXX09WVtZ5K9OIRCK43W46Ozv5yle+QiAQYP78+fzoRz8iPT39ggeoxyIfAfrJT35CZWUl4+PjrFixgkceeYScnBzMZrNSBnKhvzjD4TA+n4/q6mp2795NfHw806dP54orrjgvYxseHqapqYm9e/fS3d2N1Wpl0aJFXH311Wf83MrLYTc2NiqZwXnz5lFYWEhiYuJpbTcSiRAIBNi7dy+dnZ2Ew2GmT5/OzJkz0el0R21LDpo+/PBDBgYGiEQiTJ8+ncWLFx+3a0Q0GmX79u04HA6CwaDyf7vdjl6v5/bbb1d2xsLhMC6Xi3fffZfe3l4ikQhLlixhwYIFJ+1KIa8A2tHRwZw5c445eTYSieDxeNi0aRPd3d24XC6ys7O5+uqrSUpKOu6Roz179jAyMoLf7590e36/n7vvvlv5HJLnWtTX19PZ2cng4CBz585l2rRpxMfHHzV++fIHDx6ks7OT/v5+5fJms/mkz2UkEqGxsZFDhw5ht9uJiYlh+fLlU6LmWW7P6fV6GRkZobGxkeeee47NmzdTVFTEsmXLePTRR0lLSxPlmBeeKBERzlw0GsXn8/Hcc8/R3t5OXl4ed9xxBzk5Oect06ZSqUhISKClpYUDBw5QU1ODyWRizpw5pKWl4fF4qKysxOVyYTAYmDNnDtdff/1pBZEDAwMcPHiQPXv2sH79erKysigoKDjH9+zcGR0dZc+ePWzatImuri76+vqIRCLEx8dTW1vLvffeS05Ozil/mYyOjrJr1y5eeOGFSV+WGo2G/Pz88941Rq1WYzQaycvL4+tf/zpvv/02LS0tvPDCC3zzm9+ccos4DA4O0tTUxJ///GcOHTpESUkJpaWlXHnllZSUlBAXFzellit3u9309vaye/duPvjgA/Ly8khJSTkvtz04OMihQ4fYvn07V111FR6Ph7a2Nurq6liyZMkZtTELhUI4nU6eeuopbDYbKSkplJaWUltbS01NDVlZWdxwww2ntBCPz+ejvb2d1157jeLiYhISEgDYt28f+/btY/ny5ZSUlChjdDgcHD58mLfeeouSkhLy8vLQ6XSMjY3xy1/+knnz5jF79mylFzJAMBhkZGSE3//+9wwPDxMOh5XzkpOTmTFjBlqtFpVKRSAQoK+vj2effRav18vY2BgOh4PKykrmz5/P1VdfTUVFxaT7IAfMO3fu5MCBA3R2dlJQUEBpaelRAbb8Wvj9739Pb28vQ0ND2O12jEYj1dXVLF++nJUrVxIXF6c8duFwmOHhYf70pz/R1tZGKBRStifPMZDHHwwGGR0d5emnnyYnJ4ekpCQKCwupqqqiqqqKkpISli9frjw3wWAQu93OU089RVZWFklJSRQXF1NdXU11dTWFhYVcffXVx3wuvV4v3d3dVFZWcvjwYUpLS5k2bRoWiwWz2Twl3oPyuOPi4tDr9RgMBh544AEyMzNpaGigsrKSn//85zz44IPk5uZitVov9JCF45h6aR5hSvF4PHR2dvLOO+9gMBioqKjgjjvuIDY29rwGVDabjZiYGIaHh/nggw/YvXs3Ho+H+Ph4tFotQ0ND7Ny5kzVr1vD666/T3NyM0+k85e17vV4cDgf9/f1UV1djt9snZY0uNi0tLezdu5fdu3fj9Xrx+/309/dTWVnJH/7wB6qrqxkcHDzl7XV2dtLQ0MDBgwfp6upSfnp6eoiNjUWv15/3bIpGo8FoNHLnnXeyaNEitFotb7/9Nt3d3Xi93vM6luOJRqPY7Xbq6urYtGkT7733HomJiaxatYr77ruPVatWYbFYpsQX+0ShUAiPx8PY2Bj19fW0t7fj8XjOy223trZSW1tLS0sL8+fPp7y8HKvVqmSKT7dkLBqN4nQ6qaur44033mBgYIDExERmzZqF3+9n+/btfPTRR/T29p5w23Jmsbu7mz179vD6668DkJeXR35+Pm63m3feeYd9+/YxPDysXK+3t5e9e/eydu1arFYrM2bMYObMmRiNRt577z02b97MwYMHJ92Wz+dTMqytra2T3nM+nw+DwYBOp0OlUuF0Ounq6mLfvn2o1WrMZjM6nY5du3bx0ksvKQt/TRSJRBgfH6exsZENGzbw8ssv09LSQiAQOOp+Dw0NUVtby/bt2xkdHcXv9+N2u6mpqeHll19mw4YNtLe3T7pOKBSisbGRgwcP0tLSMmn8DoeD2NhY5TU/OjpKbW0tr7/+Og6HA5vNRllZGT6fj40bN7Jlyxb6+vqU52B8fFy5/NjYGCkpKZSVleH3+9m8eTMff/yxcvmJz10gEKC5uZmdO3eyadMment7SUlJYcaMGZSXl0+ZAHsinU6HzWbj2muv5YEHHmDx4sVotVreffddPvnkE1pbW3G5XBd6mMJxTK1XkzClRKNRWlpaeOmll2hpaeEf/uEfWL16NfHx8ed1HCqVCrPZTEVFBQ6Hg2effZbMzEyuvPJKrrvuOuVya9euZc2aNfzxj38kEonwd3/3d0rm42TbLy4uxmg0otPp+OMf/3iu79JZc7yA4MUXXyQtLY2nn36a6dOnMz4+zrZt23j11Vd5+eWX+dOf/kQgEOD+++8/pe1/+OGHqNVqnn32WfLy8pTHVKVSkZSUdME6eMivjS9/+cukpaXxj//4jzz//PPcfffdlJWVXdAstvzYbd++nWeeeYYdO3Zw/fXX8x//8R/YbLYpPSEzMTFR2Xl9++23z+ttv/vuu3R3d1NeXo5Op+Omm27i6quvxm63Ex8ff0ZzB+rr6/ne976nlDUsXbqUaDTKI488wsGDB9m1axe/+c1veOyxx07YrSESifDKK6+wefNmpk2bxi233EJ8fDzRaJSCggJeeOEF1qxZg9Pp5OGHHwbgo48+YuPGjcyfP59ly5ZhNpsBuO6661i/fj2NjY04HA5Wr16tjHd0dJQ//elP/PCHPyQ7O3tSljIuLk4pg4tGozQ3N9PZ2clLL72ExWJBo9EwNDTE008/zX//939TW1tLQ0MD8+bNU7YRCoXo6+vjyiuv5ODBg+zevfu4j92ePXtYs2YNTz75JCUlJUQiEXp6evjP//xP3nrrLT755BMMBgM///nPlet4vV5eeuklHn74YQoKCkhOTla2aTAYlOcxGo2yc+dOfvnLX1JYWMjq1auZPn060WiU//N//g/btm3jk08+AeD73/8+KpWKPXv28LOf/Yz8/HxWr17NzJkziUaj/P3f/z27du1i27ZtaLVaHnvsMWWnPxqN0tvby/e//33a29tZvHgxv/zlLy+azkMqlYqysjKKioq49dZbefzxx/nBD37Avffey913382VV16pXE6YOkQGWziu3t5etm/fzgsvvMC//Mu/cMUVVyiTyC4Eg8FwwlZly5Yt4/rrrwekgPDIQ5Mno1arlQ/kUzlUPFVEIhG2bNlCfX09oVCIAwcOUFFRwYoVKyguLgbAYrGwbNkyHnnkETQaDQ0NDXR2dp5029FolKGhIVpaWnA6neTl5ZGRkUF6ejrp6emkpaWdUT332ZaWlsaVV17Jt771LX7/+9/z6aefnlaG/lzweDzs2bOHf/7nf8Zut/O1r32N//7v/yYtLW3KZcqOZ+JRifP1fpCXmJ542waDAZvNdkbba2pqYvfu3VRXV3PnnXdOmlehVqtZsmSJEhw7HI5J5RgTRaNR9u3bx549exgdHeXuu++etHqmRqPh1ltvpa+vj3Xr1ilHURwOB729vTQ1NR31eaTT6ZTacJnL5aKvr49Dhw6RlpZGdna28n5LT0/HarWi1+uVuSO5ubksXrwYq9WqvA/NZjN33nmnUmZwZNlHTEwMs2fPpri4+IST1JubmzGbzdx8881Mnz6dmJgYZanvb3/729hsNgYHB6mtrVWu4/V66e/v59ChQyQmJh41/sTEROVxq6+vZ/fu3Rw6dIi77757UhmSWq1m5cqVWCwWXn75ZdxuN3V1dezZs4e6ujruvvvuSa8JtVrNVVddhc1m409/+hMul4tIJKIcwXj00Ufp6Ohg8eLF/N//+3+n9A7u8ej1ekpKSnjiiSe499572bt3L4899hjV1dWn9V0nnB8Xx6e8cEFUVVVx8OBBTCYTq1evxmazXdBJFScLeo1GozIhxuPxKB848uHB3t5eBgYGGB8fx2azHfVhfzIulwu73U5fXx+Dg4OYzWZSU1OVIFa+LZBKKkZHR3E6najVapKSkrDZbCQlJSmXGRsbY2hoiIGBAVQqFSaTCavVOilDfDzy5MLW1la2b9/OoUOHWLVqFaWlpZhMJubNm0dCQoLyJSJnerOyssjKyjrloDgYDLJt2zYOHjyo1EpmZWWxcOFCioqKlNUUL/TOiFarJS0tjeuvv57nn3+egwcPkpeXN+kIx/kUDAaVulWj0ciVV17JTTfdpAQzF/rxOlUnes9FIhGcTicdHR04HA78fj/Z2dlkZ2cftSPs9Xqx2+10d3czMjKiTEyePn26UubQ39/P0NAQnZ2djI2N0dHRwe7duzGZTEqgdiZaW1tpamoiGAxSUFCAyWRS7htAamoqycnJjIyMcOjQIcrLy0lKSjpqO9FolJqaGgYGBtBqtRQWFirvIflxysvLQ61WK3M6Zs+ereyQ7tu3j+rqambNmkVSUhKBQICGhgYSEhKYM2fOpPHu3LmT1tZWfv3rX5Oenk5mZiYVFRXMmjVLWWQoGo2iVquxWCyYTKZJ7+dwOIzdbqegoICCgoKjdk7kyeHACT/TjUYjBQUFqFQqpSRF/jsrK4vs7Gwikcik2+7p6eHTTz+ltbWVp59+muzsbNLS0li+fLnSelJ+7A8dOkR7ezvhcJjCwkJlToh8fmZmJhaLRXluGhoaaG1tJRQKUVhYeNSkYHkHRL78jBkzcDqdfPrpp9TU1LBgwQIWLFhwxjtrF5r82CcnJ3PXXXcRiUSora3l97//PT/60Y9ITEwUEx+nEBFgC0eRaw2rqqro6OiguLiYWbNmTfnDaYFAQJmAFxsbq2SH/H4/7e3t9PT0MDQ0xMjICJ2dncoXxLRp00543+RDtj09PQwMDOD1emlqakKj0ZCSkkJCQgIJCQloNBpCoRBDQ0O0tbUxNjaG2+0mEAig0+mYM2eO8sU9NDREb28vvb29jI6OEggElC/LvLy8445D/uIcGhqioaGBvXv3smHDBtRqNfPnz0etVpOYmIjJZDrqg1an0ynBitVqVQKNEwmHwzQ1NeF2u+no6ODgwYNYLBa6u7tZtmwZKpWK3Nxc4MIHjWazmTlz5pCfn097eztVVVXKYffzPbbx8XGam5t59913ueWWW1i5ciWLFi264I/R2RIMBnG73bS1tdHR0cHw8DDj4+O0t7dTUVFBZmamMvlvfHycwcFBurq68Hg8tLe3EwgEMJvNpKSkYLVa8fl8DAwM0NTUxPDwME6nk6GhIZqbm0lMTCQSiZCamnra8yKMRiOdnZ10dHSgUqmUIy4TWa1WrFYrwWCQpqYmZaLdkaLRKIcOHWJ8fJykpKRjdqtJTU1Fr9fT399PY2Mj5eXl5OfnU1paykcffcTmzZtRqVQUFRVht9txu93Mnz+fhQsXKtsYGhqio6ODUCjEW2+9hcFgIDMzk/HxcWJiYpSSEZVKpcxDkMcHUv328PAwBw4cYM6cOUyfPv2MW6laLBaSkpKOeszUarWSEHC5XJMer7GxMeXo4QcffIBOpyMpKUmpA8/Pz1dKRtra2ujt7UWtVpOenn5UVlkuVfL5fDQ0NFBfX09PT4/yXB6ZHElMTMRsNuP3+2loaCA3N5fe3l7ef/99+vr6SE9PJzk5mY6ODrxeL3q9nri4OOU+XgzvT3mMy5cvp7+/H4fDwZo1a3jwwQeJiYlRSpCEC08E2MIxhUIh9u7dy/j4OPfcc48y43uqmViDLHcZCYfDzJo1C5vNRigU4vDhw/zoRz/i2muvZcWKFeTm5vIP//APPPXUU5jNZp577jlycnJOeDtvvfWWUqf4u9/9jp6eHp544gleeeUVnE4n9913HyaTidHRUZ544gmys7NZsmQJJSUl1NfX88Mf/pBQKMTMmTMBqUY6EomQm5vLvffeS11dHW+//TY1NTXcfvvtx7x/kUgEh8PBW2+9xe9//3s6OjoA+NKXvsQDDzxATk4OarX6uLPKo9GokvletmzZpH6qx6PVapk/fz7RaJSmpiYaGxupqqrit7/9LWvWrGHRokW8+uqrU2LnS87K3Xbbbbz55ptUVlYSDofPe0ZHrlt99913CYVC/OM//iP5+flT8v1zJqLRKAMDA9TW1rJmzRruuOMOiouLaf3/7L13eFxnmb9/T9OMpFHXqPcu2bItufeSxE7vCekJkGVh4UcJnYWFZflmyQJZFpIAYdmEhBTSSLPjEpfE3ZYtS7bVe2+jMprezu+PwzlItuw4jmPL9ntf11z2aM7MvDNzznue87yf5/M0N/ONb3yDdevWcfPNN3PPPfeg0WjYvHmzmpF94403GBwc5K233uLxxx/H6/Vyyy23EBUVRUpKCn6/H5PJhMfjIT4+nuLiYsLDwzGZTAwMDNDW1nbGRY46nY6FCxcyNDTE0NCQWi9wYlBmNptVHXVHR8dpizm7urpwOBwkJSURHx8/6TfVaDRER0djNBrxeDy0t7cTDAYpLy8nGAzy1ltv8dhjj7Fv3z7y8/NpbW3l4YcfZtGiRZOOxfT0dJYtW0ZISAiHDh2isbGR48ePU1VVxebNm/nKV77C7bffPmm/Vr4TRZP94Ycf8tRTT/Hss89SVFSEJElntf+dqt5GeT+r1UpkZOSkC4TExEQWLVqEz+ejurqa5uZmWltb+fGPf8y7777Lvffey1e+8hU10z88PIxGoyE+Pv6kADsiIoLw8HCCwSDt7e10dHRgtVrRarWn3N5sNhMMBuno6MDtdtPb28uWLVvQaDT09vayfv16fvvb31JZWUl2djbLli3j4YcfZvbs2ervON1RVhKuu+46EhMTee+993jttde49dZbWbBgwYUenuDviABbcBLBYJC6ujqGhoaIiYlh+fLl027ScbvdDA4O0tzczODgIMePH+fNN9/k+PHjzJgxg8cee4yCggKampr45je/ydq1a5k/fz5ZWVnodDruv/9+BgcHaWhooLGxkdTU1NO+32uvvYbJZGLevHloNBqSk5PJy8vj+PHjbN26ldtvvx2z2czo6CgvvfQSjz76KFFRURiNRkpLS1m9erUqpwC5kGvWrFksWbIEgMLCQhYvXjxlVtnr9bJx40Z27NjBrl276Ovr44orruDhhx9m8eLFpKSkTJmxPpH+/n6OHDmCTqfjpptumnRSPBUGg4Hly5ezcOFC/H4/brebY8eO8etf/5qqqio+/PBDnn76aW699daP/A7PBxqNhuXLl7Nx40YGBgZobGykoKDgvAXZSuBx5MgRKisrue+++6bMtF3MuN1uPvjgA5599ln+9Kc/ERcXh06no6ioiG3btrFz5050Oh2lpaWUlpayceNGurq61KLThIQEcnJyyM3NZfv27axatYrk5GTS09OJjIzEbDbj9XpJTk6mtLQUvV5Pa2srb7zxBo899tgZaU21Wi3h4eFUV1djs9mw2WzodDpV5jCRkJAQVdM8NDR0kte+giRJWK1WvF4ver1+Sg2vyWRCr9fj8/kYGBggGAxiNptZsGABr776Kvfffz/V1dUcO3aM/Px8SktLT5Ir5ObmkpGRwRVXXIHX66W7u5vKykr+8z//k8rKSl5//XUkSeKuu+6aNDa3281vfvMbduzYQVVVFUNDQ9xzzz3ce++93H333RQVFZ3Jz3tGeL1ejhw5gsfjYe7cudx9993qY6mpqVgsFpYtW6Z+D7W1tfzgBz+grq6Ot99+Wy1MHh0dxeFwnPK3MRqNGAwGgsEgg4ODDA4O4nA40Gq1p91eqR3p6+ujr6+PoaEh8vPzueeee1i4cCEGg4GmpiZ+8Ytf8O6777Jjxw42bNhAYmLiBffB/jiEh4eTnZ3NPffcw9atWyktLRUB9jRCBNiCk1CynB6Ph7CwMFJSUqZVgC1JEjabjcOHD+N2u3G5XLhcLmbMmEFpaSmFhYUUFhYyPj5ObW0tx44d4+tf/7raSluSJPLz89UgOy8v7yMDMMWaUJFvjI6OYrPZ1KVs5aSv1WoxmUxs2LABj8fDsmXLyM3NZeXKlZMKRE0mE/X19bz55ptER0eTl5dHYWGhmn32+/10d3fT2NjInj17OHbsGG63m5ycHG6++WbKy8vJyclRvazPRLPd0tLCrl27ePDBB8nLyzujhjwajYbQ0FBV6xgMBjEYDHzuc59j06ZNvPnmm+zcuZM1a9ZMmwBbaYA0NDREf3//JI38+UDZJ+x2O+Xl5ad1pbgYqa+vp6GhgZ6eHvbu3as2VnG73fj9fvR6vdpUp7S0lKuuuorx8XEyMjLQaDSMjY2pgdXQ0BAejwetVquuQCjZOb1eT0hICHq9nvj4eObNm8eXv/zlM85gh4SEqGM7XfGy8nrKvn6quUAZ00Td9alQfNo1Gg1arRafz0dHRwdZWVlERERgt9tpa2vjz3/+M9dffz3z5s1Ts8UGgwGDwaBKP8LCwjCbzXg8Hh577DHa29s5fPjwpABbGdvixYtJTk5myZIlHDx4kD179rBr1y6ioqLOWYAtSRJOp5N33nmHVatWsWjRokkSFL1ej16vV8dvNpuJjIzkkUce4YknnmBgYIB9+/bx4IMPotPp1N/+dHp/jUZDWFgYISEhp91+4r4RGhqK1WplaGgISZKYPXs2WVlZJCUlYTAYMBqNrFq1Co/Hw549e9i9ezerVq0iJSXlnHxP5wNFqlNWVsa7776L1WrF5XKd1gxAcP4QAbbgJCRJYmRkhGAwiMlkUrWU0wmv16vqmPV6PRaLhTlz5pCRkaGeSFpaWlRNZ3Z2thq8Klm0G264AUmSzsjR4fbbb8fn8yFJEr29vQwODjI6OorL5WJ0dJRgMIhGo8FkMjFz5kx27dqlbq9k95RAWAnwDxw4wIYNG9RCocTERGbOnAnIQdrx48fZuHEjf/3rXzEajSxYsIArr7ySO+64g4iIiI+VlR0aGqKtrY2uri6+853vkJiYeFZOFlqtloSEBG688UYA9uzZQ3V19bTyYlVkAH6/n+Hh4Y/tm/xJsdvtqhtFbm7uJVd01NDQQGdnJ36/n4aGBjXICQaDxMXFMXv2bLKzs9Xvfd26dXi9XvXCfXh4WO3KOjIycka66piYGBYsWDDJau5M0Ov1mEwmjEajWltyolwiGAyqziExMTGnXW0ICwtDr9ercqsTg7xAIKBehMbGxqLRaHA4HHR3d7Nr1y5mzZoFyMfjO++8w8svv4zZbCY+Pl6Vj52IIpNIT0/n1Vdfpa+vj7q6uknbKBcnK1euZMWKFYyOjvLuu+9SV1dHU1MT27Zt4+tf//o5cUey2+10dXVRV1fHl7/8ZUpKSk7ryBEWFkZ6ejoPPfQQ27Zt49ixYxw/fhxA/W0cDseUv43yfWo0GrW2RFltONVvOXF7p9OJzWZDq9VSWFhITEyMKmeLjY1l6dKlDA4Osm3bNg4cOKAWpV5MGI1GcnNz1XnH6XSKAHuaIAJswUkoy40Tl+GmUwZOyVLed999p3WJ6OvrUxuhTJxYFc408FG0mw0NDVRXV7Nz504WLlwIyHrD4eFhddv4+Hh+8IMf8JWvfIXNmzfz7rvvsmjRIh5//HG1exnAl770JSIiInjhhRf42te+xqJFi7jvvvv4zGc+g8lk4tixY7z11lu8/PLL2Gw2HnjgAW666SbWrl07yQP3o34XJch5/vnnkSSJu+++W3Vu+CQoxVZLly7lxRdfPOWy+vlGKfyamFU93wG2w+HA5/OpTT8utIXhuaazsxO73U5GRgb/+q//+pHzQ0xMDN3d3Rw5coQtW7Ywa9YsnE4nqampkxqynA7ldz2bixWliFFpbx4MBie9jsfjwe12o9FoyM7OPm3xr9LwKhAI4PP5TppTFPcik8lETk4OOp2OiooKPvjgA7Zs2cJ7772nOpbMnTuX7373u/ztb3/DZrPx+OOPq5/1RJSVsaVLl7J58+aTGqlMRKORO9/ed9997N+/n+3bt9Pc3DzleD8ukiRx8OBBNm/ezP333095eTlRUVEf+TxldWD+/PkMDw/T3NyMJEnExcURERHB4OAgXq/3pNU4t9uNx+NBp9ORk5NDb28v3d3d9PT0qFKdqbbXarXk5ORMKl6Mjo4+6UIgOzubrKwsJEmivb39vDVTOpfodDp1nnG73TgcjimLdAXnHxFgC05Co9EQFRVFMBhUuwBejMvcik+s0kp9/vz5JCcnn9XrPP300xw5cgSNRsO//uu/EhkZidfr5ejRo5O2NRqNlJSU8Nxzz7FlyxY2btzI+++/z/33388//dM/8cADDxAZGUleXh5f+9rXuOGGG3jqqafYsmULv/jFL9i0aRPPPfcc5eXl5Obm8tnPfpZXX31VLRR7/PHHueeee7jxxhuxWCwfqe11uVy89dZbxMTEkJeXd0a66zMlIiKC3NxccnJypk3GRJIkHA6HGvArbgvnk/j4eMLCwvD5fLS3t5OXl3dReu6eCq1Wi91up729/YyCtr/+9a9UVFTQ1dXFL37xCyIjIzlw4MApm5tMxdjYGC0tLezfv/9jSUQefPBBkpOTSUlJQZIk+vr6MJlMk+RRdrsdu92ORqMhNzf3lIV9Go2GnJwcIiIi1Pbk6enpk5qZjIyM4PF4MJlM5Ofno9PpqKys5PDhw6xcuVIN9mJjY7nzzjuprKxk9+7dVFRUMD4+jtlsPu3+mpmZSU5OjuqW9FHMnz+frq4uent7z0kh8nvvvcfg4CCzZs3iqquu+th65bS0NHJycvD5fGqixGKx0NTURG9vL6mpqZNec3x8XNVc5+Xl0d/fT1tbG8ePH6e3t5e0tLRJc8/4+Dh2u13dPiYmhvr6eoLBIMePH+fKK6+cNJ7Y2Fji4uIwGAyEh4dfNP70E/F4PHR0dCBJkur6IpgeXHx7k+BTR6vVkpKSQkhIiLq8mZOTc6GH9bEzkZGRkarF16FDh8jIyJgUYHu9XrWpQ0FBwZSv4ff7GRsbY/369YSGhlJeXk5CQoJa5HSiy4fT6aShoYGCggJWrVpFXFwc8fHxrF+/npqaGurr6ykrK6OxsZGoqCiKi4u5//77iY+PZ9++fdTV1VFdXa16tZrNZm699Vays7PVjm3r16+npaWFwsJCZsyYwdy5c9WM7USsVqvqUTx79mwyMjImnYxsNhsjIyOqNvbjYrPZaGtro7S09Iz03OcDpZW1w+EgJCSElJSU855BDgsLIzExkcjISA4dOsTy5csvqsKpj0K5gBgbG2P//v3MmTNnkjWYx+NR5Vt5eXls3bqVgYEBiouL1Q6WyjL/meJ0Ounq6mL37t0Eg8GP3F7JmN5///0UFhZSXFyMVqultbUVi8UyaX+1Wq3YbDa1EcqpLhY1Gg0LFixg+/btaoF1amrqpGx4d3c3er1e9b7WarUMDAzQ1dWlHmdKNj4iIoK5c+dy/PhxxsbGGBsb+8jjqLW1FZPJpFpjfhTj4+NERUVN8p4+G5Q241arlbi4OPLz8wkNDVVf0+/309HRQWpq6mkv+pV29zNmzACgtLSU48ePs2/fPpqbm0+yBOzv78ftdpOSkkJKSgrl5eV0dHSwY8cOWlpaiIuLm/R7DQwM4HQ61e/fbDaTnJxMTk4OLS0tJ2WoHQ6HKk/JzMw8qSHPxYDT6eTQoUMkJydP6ZIjuHBcWmuXgnOCVqslNTWV8PBwXC6Xupx3oVH0dSBP6B91orVYLKSlpWE0Gjl48CANDQ0MDAxgt9sZHh6ms7OT1tZWenp61I5fymsq9wOBACMjIxw4cACn00liYiJarRaXy4XD4cDpdKo6QSUY37VrF4FAgMLCQq677joeeughDAYDAwMDdHR04Pf7OXjwIF1dXURFRbF27Vruu+8+SktLsdvtNDc3q8ufkZGRLF26lM9//vPcd999rFy5kpGREd577z3efPNNNm3axPHjx+nv78fhcKif3el00tbWxqFDh5Akifj4eEwmk6p/7evro6Ojg5aWlknP6e/vV5dfA4EAbrebvr4+7Hb7JK2s1+vFarXS0tLCwoULz2iZ+HwgSRJNTU24XC4iIiJITk4+rxlsjUaD0WgkLS2N5ORk9u/fj81m+9j+zdOFQCCgal2VYyM9PR2LxYLH42HTpk10dHQwNjaG2+1mbGxMPa5aW1sZHh7myJEjanZSq9Xi8XhwOp04HA71mA4EAqrsQnmviW4hyrHo8/nwer1nfAPZlaOkpIS4uDiOHTvG6OjopM/U29vL+Pg4c+bMIT4+Xs30SpLEwMAAvb29WK1WNBoNs2fPJjMzE71eT3V19UnfT0tLi3rhrGiwfT4fdrudoaEhdV5RSE5OVpf3/X4/Xq9XbUDlcrnUbYPBIB6Ph6amJiIjI5k9e7b6nna7XdW0T7TrCwQC9PT0EBMTw9y5c6f8fZXvX/kMU6GMac+ePTgcDsLDw7FYLIyMjGC1WhkYGFALshUdsNIvQNlnFNlha2srGo1GdWMqKiqisLCQqKgoqqurJ2mxg8EgXV1d+P1+Zs2aRWxsLCUlJaqWWqn9mLh9d3c3Ho9HbeYTERFBSkoK8+bNo7Ozk9HR0UnZ/6GhIYaHhwkJCaGkpOSUqxfTFWUe3r9/P/n5+WddWyP4dBC/hOAklAA7MzOTtrY2tm3bxpo1ay6oFluZoJ1Op1poqPxf4cSxFRUV4XK51CYPdrudI0eOsGbNGmpqaqitrSUsLIxHH32UkJAQ9fVBllb4fD41K600mfjwww+55ZZbqK2t5dChQxw9elRtuuFwOOjr6+Ovf/0rt912G5GRkYSGhrJkyRISExOJjo4mMjISSZJYv349fr+fhQsXIkkS5eXl5OfnT2paMxFFvzh37ly+8IUv8Morr7Bx40b+8Ic/8OSTT/Lggw9y7bXXsmbNGgCqq6vZvHkz27dv59Zbb+W9995TXysQCNDa2kpYWBi5ubmsWrVKfc5rr73G6Ogo3/ve97BYLLS1tfHss8+ybt06iouLVb9wRQcZDAb5whe+MC2yJsqJduvWrYCsr5zo3HI+mT9/PiMjI/zgBz+gsrKSefPmkZSUdNHIrJTvUmk37ff71dbfS5YsoaWlhXfeeYdf/vKXjI+Ps3TpUkpKSqitrWX79u2UlJRw//3309nZSSAQoLe3l82bN3PffffR2tqqdjVUVpGcTifBYJCBgQG1YZTValXHkpKSwi233MItt9zysT+L0WikuLiY22+/nZdeekk91gBV5uV0OvnJT34yKSsbCAR44oknGBoaIicnh29+85vExsayevVq/H4/zz77rNrcQwmAd+3axZ133skNN9ygrpwUFRVRV1fH1q1bcbvd6hwgSRI1NTXodDry8/PJyMigra2NHTt20N3dzQ033MCMGTMwGAx4PB6am5sZHx/nhhtu4I477lDHv3v3burr61m9ejWFhYUYDAYCgYDa7OjKK6/knnvuOen3BVkeowScHo9nUqCtFGT39PRw8OBB/vSnP3Hdddfh8/k4duwYIAfoVqsVq9VKYWEhcXFxapOnO+64g8LCQsLCwvD7/TQ3NzM2NsacOXN44IEHAFnCNWfOHK677jqeffZZ1qxZoxYZer1eDh06RFRUFP/yL/+CXq8nOjqaWbNmceONN/Lcc8+xYsUKtcOn1+ulsrISvV7PD3/4Q7V+qLi4mK9+9atcffXV1NbWkpmZqa5YKm44eXl53HTTTRdVBlvxbd+/fz9bt27lT3/60zm1YhR8cjQfkZm88GlLwQXjmWeeYcuWLRw6dIidO3dOar19PgkGg+zbt4+NGzeya9cutm/fjk6n4/rrr2fVqlWsWbOGkpKSk67clQChrq6OH/3oR9TX1zM2NkZoaCgzZ87ktttuY9WqVeTl5VFbW8uGDRt4//332bx5MwsXLuTmm2/myiuvpLS0lK9+9avs2bOH/v5+SkpKuO222zCZTHR1dfHYY49x9dVXc+edd5KZmcndd9/N2rVrVVeTgYEBGhoaWLFiBatXr0ar1bJ27VpCQ0OZO3cuK1asUDPHgUCARx55hNDQ0CmlDcoJ0OFwMD4+Tn9/P++88w7vv/8+N9xwA1//+tepr6/nG9/4huoBPNWys0aj4Y477uAHP/gBqampaDQaXn/9dX72s58xOjrKK6+8QlZWFjU1NTzwwAMEAgGSkpIoKSlhxowZmEwmMjIymDNnDpmZmdOiEFbJ5ixdupRly5axbt067r333gsyFo/HQ21tLV/96lfxeDw8/PDD3H777dPSkWcqBgcHqa+v58knn2TTpk2EhoYyZ84cvvWtbzF37lxsNhsVFRV861vfwmazqZKMtLQ0/umf/okFCxZQUFBAIBDgpz/9KVu3bqW2tpbZs2dz9dVXY7FYGB8f54c//CFLlixh8eLFLFmyhNdff50NGzYwPj5OdnY2P/zhD5k/fz6JiYmfSOfv9XoZHh7mF7/4BXa7naioKEpKShgcHESv15OVlcW1116r2gSC3K3yyiuvpK+vj7lz5/L888+r+vOGhgaeffZZ3G43+fn5xMbGMjw8rFq/FRcXq5nw4eFhjh8/zlNPPcXY2Bjl5eWkpqbS0dFBW1sbK1asYOnSpapc4qmnnuLtt98GYNGiReqFoslkYvXq1Wr7cJATAf/7v//LCy+8QG9vLzNnzqSwsFC1A5w/fz4pKSnExMRMmk+cTicHDhxg48aNbNu2jcrKStLS0njkkUdYsGABeXl5xMbG0tTUxIsvvsgzzzxDX18fYWFhU85LiYmJbNiwQW2E9cc//hGA8vJysrOzSUtLU4s009PT1U6OGo1G7eL5+OOP43K5SEhIIDc3l6GhIQwGA8XFxaxatUr9bZROlb/85S9xuVzExcVRUFDA4OAgBoOBgoICrrjiCnX7YDCI2+1m586dbNy4UU26hISEcPToUeLj47n11luZNWuWagE43VE825944gk2bNhAVFQUzzzzjGpBKDhv1AGvAT+a6kGRwRackvLycrq7u9m5cyfvvPMOV1999QXxOlYauyxbtozMzEyuuOIKQC74ycjIID4+fspJXznpFxYW8vDDD9Pe3q4uD+fl5aknOsUlZMGCBVgsFlasWIHFYqGwsFBtfXzHHXcwZ84crFYrsbGxlJeXEx4ejtPpxGAwkJ6eTnFxMXFxcXzjG98gOTlZbdubkJBAQUGBqvELBAJ89rOfVavoo6OjiYmJIT8/X/W/PZ0frEajUa27oqOj8fv9pKSkUFBQgFarJS4ujs985jNcccUVqv3Yieh0OmbNmqWe6AAKCgp46KGHcDqdJCUlERYWRmZmJv/yL//C8PAwoaGhaoMdk8mkSnCmQ3ANsv5y/fr1eL1eiouLmTNnzgUbS0hICGlpaTz44IP89re/Zfv27aqLS3h4+LR3FgkNDSU1NZUbb7yRuXPnotPpiIuLIz09nZCQEOLi4pg3bx7f+ta36OrqUp0esrOz1aBOcf249tprycvLo729HYvFQklJCTExMWg0GlV2lZaWRnZ2NldffTUlJSX4fD7MZjMFBQVERkZ+4mVvg8GAxWLhxhtvVF1/oqOjyczMJCIiYkrtqlar5e6778Zms6nzBMi+zjk5Odxyyy0MDg4SERFBaGgoubm5xMbGqt+RguJBfe+999LZ2UlMTIwqsygrK6OoqEhtQpWQkMC6detISUlheHiYrKwsUlJSiI+Px2g0kpWVpWbZJUnCYDCwYMECDAYDPT09JCUlqe3AFd/+qYJivV5PWloay5cvJycnh6GhIbXGZGI7+ejoaJYuXYrJZDplgx+lKN5isRAaGsry5csxGAwMDQ2Rnp5OUlKS+ppKkejE+cJoNJKcnMzNN9/M8PCwKo3LysoiKipKdW6ZuH1iYiI33XQTw8PD6HQ6oqKiyMzMJDIy8qTib8WBpaysDJALZsPCwjCZTOo8nZ+ff9EE136/H7vdznPPPUdFRQVGo5EHHnhALdYUTB9EBltwSlwuF5s3b+ZnP/sZiYmJfO9736O8vPyiWkabyETZx9nYp3k8HoLB4KRMmrJ8rixHKtpHJcvs9XpP6tSm6AWV546MjKiFk2eLYgv3STyXFX2r3+9XK+olScLn8zE0NEQgEMBoNGKxWKbdiUhxinn00UcZHR3lRz/6EVddddUFla74/X7Gx8f553/+Z1pbW4mLi+PHP/4xJSUlF61jwVQMDg6q+19iYuKU+4ayb028eFT2rYnNW84Hil2fw+FQddJTIUkSLpeLQCCgdoY88fFgMKjqhj+qDkGSJDwej6oDjo6OJiIi4qTP7vf71UJRk8mkXkyfjkAggMPhUIuvL2SgFQgE8Hq99Pf3YzQaCQ8Pn1QEezqUbLPb7VYvwj5qe0XTf7rfciJutxubzYbRaCQsLOyiCkq9Xi9jY2PU1tbyrW99C71ez5w5c/j5z3+O2Wye9hfulyCnzWCLAFtwWnp7e9m/fz933nknjzzyCNdffz1Lly6ddgGW4PJFkiR27NjBu+++yxNPPMHrr7+uap4vNJIk0d/fz5NPPslf//pXnE4nv/vd75g7d67qaCOOJYFAcDqUOK2pqYmdO3fyr//6r8TFxfHVr36V2267TVjzXThEgC04exRnjO9+97tUVlZSVFTEI488QllZmbhaFlxwgsEgFRUVPP744zQ2NrJw4UJ+9rOfnRNZwblAWdEYHBzk+PHj6jjLyspYvXo1999//yl1rQKBQKC4OT3zzDO89957NDU1kZWVxU9+8hO1MP5S6xZ7ESE02IKzR9HD3XHHHdjtdvr7+/m///s/vv71r5OamnrRykUEFz9Op5Pe3l6effZZBgYGyM/P59Zbb502wTXI2Wm9Xo/FYmHWrFk8+OCDrF+/nqGhITZs2MDw8DBr1qwhJyfngjmeCASC6UlfXx+tra3s2LGDnTt3otFoWLp0KWvXrqW4uBiz2SyC62nM9DgLCaY1er2eK6+8ktbWVjZv3szbb7/N/PnzWbZsGampqdOmi5/g8sHpdNLT08PevXtZv349c+fOZeXKlaxatWpannCUIPuOO+4gKiqKd999lz179vDss8/i8XhYtmwZM2fOJCoqSi1iE9IRgeDyQtH1u1wuRkdHqa6u5sCBA7z88suEhYWxdu1arrjiCrUjpZgjpjdCIiI4I5QDf8+ePfz3f/8377//Pl/+8pe59tprWb58+YUenuAyY8eOHaxfv57f//73XHfddXz1q19l4cKF094JQJlvHQ4HLS0t/OpXv+KNN97AYrEwZ84cHn74YVauXHlaJxmBQHBpIkkSw8PD7Nq1iyeeeIJjx44RDAa58cYb+eEPf0hiYqJauC3mh2mB0GALzg2SJKnL8o899hgHDhzAbDZzzTXX8I1vfAOTyTQts4eCS4NAIIDT6eRXv/oVmzZtwuVysXjxYr797W+TlJQ0qUnIdCcYDOLz+RgeHmb//v3s2rWLiooKmpubKSkpYdmyZSxdupQVK1ag0+kums8lEAg+Hoqb1JYtW9i2bRuHDh2iubmZ4uJili9fzrx58ygrKyMuLg6dTifqNaYXIsAWnDsUa63t27ezadMmmpqasNls3HLLLSxZsoTs7OxJ3soCwblgYGCA1tZWdu/ezVtvvUV0dDQFBQWsW7eOlStXotfrL8ogVOlK2tDQQH19PZWVlbS3t6PX64mNjWX27NnMmDGDjIwMkpOTiY6OvtBDFggEnxBJkhgZGaGjo4OOjg4aGho4dOgQNpsNrVZLeno6CxYsoKSkhMzMTFGfMX0RAbbg0+Hw4cNs3bqVP//5zxgMBu644w4WLVrEzJkziY2NFZk3wSciGAwSCAQYGRmhqqqKffv28dprrwHw0EMPsXr16gvaTOZc4/F4aGlp4aWXXuLAgQM0NTVhNBpZtmwZZWVlzJw5k6ysLMxmMyaTiZCQEJHNEgguAhSJpcfjwe1243Q61aD66NGjHDx4EJ/PR1lZGQsWLOCmm24iJydn2hRrC06JCLAFnw5KM5XOzk6+9a1vcfDgQbUQ48c//rHafUwgOBuUFso/+clP2Lp1Kx6Ph8WLF/Nf//VfqoPNpXYBp8zHIyMjtLa28vLLL/P222/T29tLIBCgrKyMW265hcWLF1NaWorZbL7kvgOB4FJDkiTGxsbYu3cv+/fvZ9OmTRw8eJDIyEiys7NZt24dDz74ICkpKZjNZkBorC8SRIAt+PRQtKQDAwPs2bOHvXv3smnTJsLCwrjyyitZsmQJq1evPqk9rkAwFcqJaNu2bezZs0cNrK+55hoWL17MwoULSUhIwGAwXNLZW6Ubns1mw2az0dLSQm1tLbt37+bo0aNq58B58+axcOFCiouL1Vbd4jgTCC4skiQxMDBATU2NKv06cOAAdrud0NBQiouLWbVqFTNmzCAtLQ2z2UxUVNQlP69dgogAW3B+aG9vp6GhgX379rF3714AYmNjKS4uZtGiRRQVFYmstmBK3G43g4OD1NXVsW/fPurq6hgZGQFg6dKlLF68mPz8fNLT0y/wSM8vkiSpzgK9vb3U1NRQW1tLV1cXQ0NDBINBQkJCiI2NJTExkezsbDIzM0lKSiIhIUGthxBBt0Dw6aAcowMDA/T29tLf309XVxfNzc309fVhs9nw+XzodDpSUlJITU0lPz+f0tJSkpOTiYyMFMfnxYsIsAXnD7/fj9Pp5IUXXuC9996jubkZt9vNunXruOqqqygoKCA+Pp6IiAhCQkKETvsyJhAI4PP5GB8fZ2BggNraWjZv3symTZswm80UFBRwzTXXcM899xAaGiocaviHLKuxsZHq6mp2797NoUOHGBkZIRAIkJ6ezuzZsyksLKSwsJCCggKMRiMGg4GQkBBMJhMGg0H4bAsEZ4lSG+JyufB6vXi9XtxuNzU1NRw7doyGhgaOHz9OZ2cnYWFhJCQkMHv2bFauXElZWRnp6emiQdulgwiwBeePifuT2+2mpaWF9957j9/85jeMjIwQGxvLkiVLeOCBBygvLychIUGc6C9TRkZGqK+v57nnnuP999+nr68Ps9nM5z73OW699VYKCwsnnYjEfiJz4pzt9Xppb2+ntraWrVu38v7779Pd3Y3dbicuLo7S0lIKCgooKipi6dKlFBcXExoaKpaiBYKzQGly9cEHH3DkyBFqamqoqqpieHiYiIgIUlNTmT9/PuvWrWPu3Lmkp6ef1IxNzGWXDCLAFlwYFH32+Pg4PT097Nu3j6qqKioqKujv7ycrK4vi4mJWrFjBFVdcQVRUlJCPXMJ4PB5GR0fZunUrO3bsoL6+nq6uLlJSUigvL2fWrFksWLCAxMREIiIiMBqNIgg8A5TjzOv14nA4sNvt9Pb20t7ezrFjx2hqaqK7u5uBgQEkScJisZCWlkZubi6zZs2isLCQ5ORkVdsuTv6Cyx1JkvB6vXR3d9PT00NLSwuVlZU0NDTQ39/P2NgYWq2WlJQUUlJSyMrKYvbs2WRmZhIXF0doaKjq9iN01Zc0IsAWXFgU7+yWlhba29upq6ujqqoKq9WK3+8nLCyMwsJC0tPTSU5OJiMjg9zcXEJDQ4VN0UWMz+fD5XLR1NREZ2cnvb29dHZ20tDQgMPhwGAwEB8fz5w5cygsLCQrK4vs7OyL1tN6uiBJEuPj4wwPD9PR0UFXVxeDg4MMDAzQ3d3N2NgYwWAQrVaLwWAgNjaW6OhoYmJiSExMJC4ujujoaKKjo0lISCA6OloECYJLEsU6b2xsjP7+foaHhxkbG2N4eJj+/n6GhoYYGxtTddTBYBC9Xk9UVBRpaWkkJSVhsVhITk4mKyuL2NjYi6rhleATIwJswfQiGAxy/PhxPvzwQyoqKti3bx+jo6Okp6eTnZ1NWVkZq1evxmKxYDabJ2lHp3sr7MsVxefV5/PhdrvxeDzY7Xb6+/vZvn07R44coa2tje7ubqKjo1m8eDHz589nxYoVFBUVieDtUyYYDOL1elXtdnNzM01NTVRVVTE6Oorf70er1ZKRkUFWVhZJSUmkpqaqFz7KcWgwGNDr9eh0OvR6PQaDQdRRCKY1imba7/dPuvl8PjW47uzspLa2ls7OTvr6+ujs7KSzsxMAo9FIdHQ0ZWVlFBUVkZubS1FREQUFBWLfF4gAWzC9mLjPKcUi77//Prt37+bw4cPs378fj8ej6kbnz5/PqlWryMvLIy4uTgRj05BAIIDVaqWhoYEdO3Zw8OBB6uvraWxsJCwsjEWLFlFeXs7SpUtZs2bNSRpgcZL6dDnVPC9JktpNrrm5merqampqamhra6Orqwun04nRaMRisZCRkUFRURE5OTmkpqaSnp5OQUEBCQkJQtolmLY4HA4GBgZoaGigtbWVnp4eOjo6qK2tpbW1ldHRUXw+H+Hh4eTm5pKVlUV+fj5z5syhoKCA1NRUkpOTTzlHibnrskYE2ILpi5L5dDgcOBwOnE4nIyMj7N27l4aGBtrb22ltbcXlchEVFUVSUhIlJSXMnTuXnJwcVQMnXBHOD8rvNVGbeOjQIY4fP87AwAA2m42wsDBycnLIyMigsLCQRYsWER0dTXh4OGFhYWpzFPF7XViUud/n86kabrfbjdvtxuv14nQ6aWtro6WlhYGBAQYHB1UbMofDgdfrxWg0EhERQWxsrLpMnpKSQnx8PBaLhZSUFBISEoTcS/Cp4PP5sNvtDA0N0d3drco6+vr61GB6bGwMu92Ox+MhLCyMiIgI4uPjSUlJITExkcTERDIyMsjOziY0NJSQkBBCQkLU/yurNiCCacFJiABbcPEQDAbx+/00NjZO0u22t7czOjqKx+NBr9djMpmIiYlRdaNJSUmqljQuLo74+HhCQ0MxGAwX+iNdtCga6sHBQaxWK6OjowwPD9PX10d/fz8jIyOMjIzg8Xjw+/0YjUZiYmLIysoiLS2N5ORk0tPTycvLQ6/Xi5WHiwhJkggEAgwNDU36rQcHBxkaGmJ8fBybzYbVasXtdiNJElqtVv2dDQaDuj9ERUURHh5OeHg4UVFRmM1mwsLCCA8PJzIyErPZTGhoqHoTF1+XN4qvtNPpxOl04nK5cDgc2Gw27HY7TqcTh8PB2NgY4+PjOBwOxsfHGRkZwe12EwgEVLlaMBhEp9MRGhqqnh8iIyOJiYkhISGBuLg4YmJiiI+PJz4+XkgQBR8XEWALLm4kSaKpqYmamhoaGhqoqqqisrKS8fFx/H4/BoOBgoICMjIyVHeEgoICYmNjCQ8PV7WiE7WjSiHd5Rz0BYNBJElSNYmKTtHn8+H3+3E4HIxYrfTX11PX1ERrVxeNXV00NDSo33tERATl5eXMnj2bgoICSkpKyMnJESepSxyPx4PNZqO5uVnVrSorGp2dnYyOjqrHZyAQwGQyERkZSXJyMvHx8cTExKhZb6WQMjY2lri4ODVI1+l06HQ6tFqtelP+phy7yv/F/jZ9UVa9TrwFAgE1GFbuT9RLDw0NMTQ0xMjIiJqVVu4rjZfGxsbU+gG9Xq8mWFJTU8nJyVELEVNTU8nNzcVsNouki+BcIgJswcXNVPuo3++ns7OTjo4Ojh07xqFDh2hoaFBdEwDMZjNxcXFqUUpmZqY60WZmZhIdHX2SP+nlhNPpZHR0lPb2dpqbm+nu7lb9lJuamhgZGUHjcPALwB0dzVB2Nn1z5lA+bx4zZ85UL2imagAjAp5Lm484b+Dz+XA4HLS3t9Pd3c3g4CB9fX20tbXR09Oj3u/q6iIQCKivN9HVRHFnUALyifejoqKIiorCYrEQHh4ugqZpjFLwbLVaGRkZYXR0FKvVOmklTAmY+/v7GR0dVQNnkOcSg8FAWloaqampWCwWkpKSVDnSxPthYWEf2ZBKzE2Cc4gIsAWXFhO1o4pu1OVy4fF41KCxqamJ/v5+rFarak82PDyMw+FAkiRVY2c2m0lISCA5OZnY2Fj1RK50mzSbzaoUJSQkZFrrSP1+Px6PR13Ot9vtqj5xcHCQ4eFh9UQ2MDCA3W5X9bYajYbw8HDi4uJUbWJ8XBzJUVFcceAA+vffJ6jV4p89m9Cf/ARTZiYhERFqYCNOWoKJKFlLr9erroj4/f5J9xXHmeHhYWw2G6OjowwODk5a9lcy4cr98fHxSRltvV5PSEgIRqMRs9lMVFQUkZGRhIeHq/cjIiJU7b8iRzEajYSGhhIeHo7JZMJoNKqyFbEvy6tbdrsdl8ul6vIVyYbH48HtdmO327HZbKpkw2azMTY2pnqxKxIORUKmrGYEg0EAIiIiJsmHYmJiJv12FotFlRhFRUWpTlLKCuREfbRyX0g8BOcZEWALLh8CgQBut5u+vj5GRkYYGxubpCNVTuQ2mw2v16tq9JSlaOX/BoMBk8mknoTNZjNGo1E9Eev1ZgwGDQaD5qSJXvm/sow9Mbum0WjUJXCNRqMGIn6/f1JWUNEPKhKOiUGKcmGh3Pd4POoFhnLiczgc6onR5/NNWo5VTnQ6nU4tUlNOboqmXckSxoSHk97YiO6DD9C2tIDVCitXwpIlUFAAWVmg0cg3geBjoOz7SiCn6GqVYmclaDvxvrKvK4WYyrEzMfBWjr0T7yvZUKV1vNFoVI/fiXagynGsBG3Kcaw8X8mSKu4pWq2WkJAQgEmPK48pc8tUTJx/TmTicTsVyuMT5w5lXgA5e6xomr1er6qtV74zZW448WJIufhRWoErlnYTg2VF2qG8/kT5x8T7yoVQWFiYOoeGhoYSERFBREQEoaGhhIWFERkZOen+RL2+4jokgmfBNEME2AIB/MMLeGBgQG2+oSxVdnd3MzQ0pGa8FW9g5UQRCAT+HnSHEhUViyTlEhHh//vNqGbFjEajmhFTTpwTZSgajQaj0agG4EoArZwIFVwul3ryVAJor9er/t9ut6v3HQ6HWuDj8/nUk7USzE8s6ImPjyc1NZWkpCTi4uKwWCykpqaqVmunPIG1t8OOHfDCC1BdDddeC6tXw1VXQVwc6HRwGevZBZ8+SnCoZEbHx8exWq0nZbwV1wjlYvrEDLhyXCvHtvJ/5b7JZFIvrhXJgcFgUAswlSBcWfVRjrOwsDD1+J54UT0xwzoVEy/OT0RxeAkEAlM+Vwl4Jz6uXHxIkoTD4VADXWX1Tgmag8EgLpdLDZgnZqg9Ho86hyi3iRcqer1eXSFQEhBKttlsNhMREUF0dPSkx+Pi4iY9LgJmwSWACLAFAvho3ehEvF6v6pQwODjI4OAgY2NjWK12mpu9PPfcvaSlVZKTs4uQkGOMj4+ry6lK9lg5ObrdbvV1lYydcmLXaDTodDrCw8MnZbCUk7iSSQ8PDyc0NBSTyURERASRkZGq64JiO6Wc0BSJi5IROtWJfSpOecKTJPnmcMDvfgd/+hOMj8OMGfA//yNnssPCzvh9BIKPy8c5fk+HkgFXMuJOp1Nd+VGCcSXYVDr4KZryYDCI0+lUA1SbzaZmhJXjWrkYVlACXiWrfKrxTPX5tFqtesE+FXq9Xs3CKyjba7VaIiMj1Qv9yMhI9QLBZDKp846SwVckNGFhYWpGWbHWVOYZpf33x5lTTocIsAUXOSLAFgg+DhOzWRM7gAUCATo7gzz2mMShQzE8+KCLO+6wo9f/wxpq4tLoxMzYxNdWlnuVABs4KZtzYuZo4lL3ic4KSkZJOZEq2bCJmexzciKTJAgGYWgIqqpg927YsAH8frj1VlixApYtkzPZ4sQpmKZM5WhxopPFiXKHidlu5V9gkjxDySJPfBwmH/OnGs+J84TCxDlgKibOExP/pmw/sdOgIlGZOCec+P+p5pYTXVwud/clgWACpw2wp2/FlkBwgZh4wlEyRCAnbB0OqK2FhQuhvDyC3NyIy0cZodHIUpDERCgvlzPWRiO8/z4cOCDrswcHZelIZCQIZwfBNEQJFgUCgeDTRATYAsEZ0tsLx4/LAfa3vgVFRZex7Dg+Xi50LCuDmBj4y1+goUHObMfFQWGh/HeTSd5eZLQFAoFAcBlxuYYHAsHHZutWeOklOaa84grIzLzQI7rAaDRyFvuLX4Tnn4dvfhPGxuC66+Df/x02b5YlJQKBQCAQXGaIDLZA8BFIElRUwL590N8PP/oRRERc6FFNAyZmpVNT4eabYe5cOZu9ezccOSJrtL/9bUhJEUWQAoFAILhsEAG2QHAaFPOMHTvk2r7UVFiwQJYeC9XDBIxGsFggKgquvx5CQmTJyLFj8Oc/y3KSwkLhmy0QCASCywIRYAsEpyEYBJcL3ntPjhkXLICcnAs9qmmKRiMH2ldcITeh2bkTnnkG/vAH6OyENWvk1H90tPDNFggEAsEljTjDCQSnYXQUXntN7q0yZw589rMXekQXCWlp8JnPwN/+JktEDh6E734X7rwTamrA6bzQIxQIBAKB4FNDZLAFglPg90NPDzz9NCxdCvPnyxIRwRmg0cgZ6tBQuO8+mD1bFrG/+658lbJ2rdxy/aqr5Gy2kIwIBAKB4BJCBNgCwSno6ZElxE1Nch+V/Px/uM4JzgDFNzs5WZaORETI93fskAsgx8bAZoNVq2TZyCm61QkEAoFAcLEhAmyB4ASUwsbjx2HXLlmHfeWVQnv9iYiNhUWL5AY1WVnwf/8HW7bA4cNyYeSMGbK3tvDNFggEAsElgNBgCwRTMD4uNyjctg3++Z8hOxvCwy/0qC4BQkLgnnvguefgP/5D7vZ4yy1y555XXwWf70KPUCAQCASCT4zIYAsEU/Dqq3LHxthYWUIcFiaSqp+YiV+gxSLrr4uK5CrSDz+EJ5+Ulwy+8x3hmy0QCASCixoRYAsEEwgEZFu+HTtkacisWbKiQS+OlHNLSIjcUj06GhwOOfg+fhyOHoUXX4TFi2XRu9IuU1zdCAQCgeAiQoQNAsEEfD7o7pYbES5dKmuvQ0Mv9KguUTQa+cpl2TJZ4L57t+yZ/cQT0NYm+2lHRso34TQiEAgEgosIocEWCCbQ1wf/9V9ygnXFCrkpoeA8kJwsW7W89Zbsm33okKzLvvNOuSOk8M0WCAQCwUWEyGALBH9neBjq62HjRtmqec4cuQZPcB5QfLNNJrkIcuZMOHAA3n4bvvhFuOkmeUlh0aILPVKBQCAQCD4SEWALBH+nsREqKmSZyJIlkJ4uunmfVxTf7NRU2RM7MlL+MbZtgz17YGhI9s1evFjW7QhhvEAgEAimKeIMJbjskSS5a+OBA7B9u1xbN2+ebHQhuEDEx0NMjNwBMixMbrl++LDcs95igYwMOQAXSwwCgUAgmIaI/JxAgBy77dgBnZ3wb/8m9z4RXGC0Wtl8/Hvfgz//WdZkNzTIbdYfe0wuihQIBAKBYBoiMtiCyx5JkuO30VEoK4MFC+TEqDCtuMAoP4BGI3sl3nQTFBfLTiPbt8t6nr175U5AEREimy0QCASCaYMIsAWXNR4P9PTIphXp6bK8NybmQo9KcBJhYbIuOzpa/sG2b4eODnnZwWKBuXNlz+zY2As9UoFAIBAIhEREcPkiSXJL9J07ZdvloiK45poLPSrBKdHpwGyWW2t+/etw442ypufRR2W3kZoauWlNMCj/uAKBQCAQXCA00ulPROIsJbhkCQblmrn77pOVBw8/LMt7hTTkIkCSwOuFkRFZo/3BB7Jm+5Zb4Ec/kgNxne5Cj1IgEAgEly51wGvAj6Z6UEhEBJctR4/KDnBdXfCTn0BJiQiuLxo0GllzHRsrB9iLF8P+/XImu68P7rgD5s+HlJQLPVKBQCAQXIaIAFtw2SFJEAjAsWNQWSk7vs2YAXFxF3pkgo+FViu33Cwqku9HRIDLBXV1sGkTDA7KgXdhoWi1LhAIBILzigiwBZcl4+Oy7/WRI3DddZCWJtfRCS5SiorkIsdFi+Cb34TNm2Ubv95euROk4pktOgcJBAKB4DwgzjaCyw7Flq+yUva7/trXRHB9SWAyQXY2vPQS/Nd/yRKR//xPufX6+vWyXlsgEAgEgvOAyGALLiv8fjl7/cYbsuPbwoWyNEQkNi8BNBr56slohOXL5Yz2nDnwzDPw29/C1q1w772y0blosy4QCASCTxFxlhFcVjgcctfGri65C/fixbKMV3CJoOisLRa5C2REhOzBWF0t2/i98YZsH5OfL7djF04jAoFAIPgUEHk7wWVDMCjXvb3yiizHLS+XA2zBJUpYmBxI/+QnsqtIdDT8/vfy7fBhGBuTq12FZ7ZAIBAIzjEiwBZcNgwPy84hf/kLfP7zskTXZLrQoxJ86oSHw+c+B08+KQfXH34IX/0qfOlLclY7ELjQIxQIBALBJYYIsAWXDbt3w8aNsnpg7VpZoiuc2y5xNBr5ptPJkpArroD/+R9YsULuAvn//X/w6quyjERksgUCgUBwjhAabMEljyTJaoDqaqivlwsbMzLkxOblitPpZGhoiMHBQYqLizEajejOsR7Z5/PhdDqpr68nKyuLqKgojEbjOX2Pj0VICCQlwcqV4PFAaKh81bVhAzidMHeu3NIzJERceQkEAoHgEyEy2ILLguZmqKqSNdj33y8H15ezc8jQ0BC7du3ij3/8IwMDA/j9/nP+Hm63m87OTp566imOHj3K+Pj4OX+Pj41GI3d/vPNO2S/7ttvkpjS//73s3djfDz6fLNgXCAQCgeAsuYxDDMHlgiTJtsj9/bJD29VXC+eQpqYmPvjgA1577TVqampwuVzn/D1GR0eprKzkpZdeYv/+/XR1dZ3z9zhrNBrIyoLvfleues3Lkx1GrrhCtvMbGLjQIxQIBALBRYxGOr3uUIgSBRc1drtcx3b33bB6NVx/Pdx0k/zY5awCsFqttLe309HRwZIlS4iJicFgMJzT93C5XFitVvbu3cusWbNITk4mMjLynL7HJ0aSYHQUGhrg0CE52PZ45J1lxQq46ip5qeNy3lkEAoFAMBV1wGvAj6Z6UGiwBZcsSuy0Y4ccHxUUyN7XIlaCuLg4IiIiyM/Px2w2o/kUvpTQ0FBSU1O5+uqrCQ0NRT8dm7toNBATI+8Y0dFgs8F778mCfYdD9nOcP1+2/JuO4xcIBALBtEScMQSXLIEA9PTInbNnzYLZs4Okpwfx+f6xMKPRaNTiPkmSCAaDSJKERqNBq9Wi1WqRJGnSY8FgEJ1Opz6uoKwGBQIBgn/X8Go0GvU9PkkQO/H9dTrdSWOdWKAoSRKBCdZzSmA78f0lSUKv16PVagkGg2i1WjQajfoZJn5WQH184nelPH6qz6q8VmhoKBqNRn2fiZ8nEAhgMBgmvdbE1zhxzAB+v1/dbuLvdOJn/FiYTFBYCN/5juyd/eyzsmSkqgoef1yWk0RFfWqNaab6PpTfceJnnOr7OHF/m+q7EwgEAsH5RUhEBJcs+/fLtnyPPirXsYWE1GOztTLwd32tXq8nPj6elStXEhISQkdHBx0dHbS2thIVFUVpaSk5OTlIksTAwAA1NTU0NTUxMDDA3LlzKSkpISMjQ30/SZJwOBwcOnSIuro6fD4fJpOJ7OxslixZQmho6Fl/FrfbTWNjI+3t7axZs4bh4WGamppoamoiISGB5cuXExUVhSRJDA0NsXPnTmw2G2FhYVx77bWEh4dPCsKDwSA9PT00NzcTHh5OUVERZrNZDdqOHDlCW1sbXV1d6HQ60tPTyc3NpaSkRP2s/f391NTUUFNTg0ajISoqivT0dJYvX65emPh8Pvbt24fRaMRisZCTkwPA4OAgLS0tHDlyhM9+9rP09vbS2NjIoUOHSE1NZeXKlaSmpk66gPH7/TgcDl5//XX6+vrQaDSkpqYyZ84c0tPTiYyM/OROKJIkFzh2dcG+ffCzn8HIiNxi/cYbYenST/b6p2B0dJSenh62bdvGfffdh9vtpq2tjR07dpCcnExZWRkzZ86c9H0Eg0EcDgc7d+6kvb2d0dFRkpKSWLx4McnJyURFRX0qYxUIBAIBICQigssNSZKz17t3w969sGaNnJQMCUmkrm6AV199le7ubu69917Ky8vVDK/FYsFqtfLKK6/w7W9/G7PZjNPppLGxkQ8++ICEhAQyMzPR6XT89re/JS8vjyVLlvCZz3wGgMOHD3P8+HH0ej0rVqzA4XDQ2trKs88+S2lp6VkF2F6vl/r6ep577jlqampwOp243W66urpwOBzYbDaeeeYZdu/eTX5+PomJiVRXVzM0NERbWxs9PT0cP36cL37xi6SmphIIBOju7ua9997j0KFDNDU18fnPf56srCzMZjMej4dXXnkFnU5HYmIi11xzDa2trWzcuJG+vj41wN60aRODg4MYjUauvfZa9X1efPFFli5dytDQEHV1dWzfvp1NmzZx4403snz5cjIzMzl06BCvvPIKR44cwWq1EhsbS3d3N729vXR1dfHyyy/T1tbGqlWrWLZsGSDruauqqvjTn/7EDTfcQElJCQMDA7z77rv83//9H5mZmaSnp5Odnc39999PyNlWsSqe2YmJsHw5/OpX8PzzcPCg3KXoppvgrrtkG5pzkM2WJImqqio2bdrEhx9+SFtbG2FhYXi9Xrq7u2lra+ONN95g0aJFXHPNNVxzzTUAjIyM0NLSwtNPP83SpUvJy8vDZDJx+PBh/u3f/o2ZM2dy5ZVXsnjxYpHJFggEgguAcBERXJK0tMjFjVarXKcWEwPR0eHExsYSFhZGTU2Nak+nLKkbDAa0Wi1DQ0PEx8djMpkYHR1ly5YtaDQaLBYLWVlZFBUVMTo6ysGDB3n//fexWq0EAgHq6+vZtWsXGo1GDfZSUlIYHx9Xl/DPBq1Wy+DgII2NjVRXVzM4OIjJZCIpKYmMjAzsdjs7duzg4MGDWK1WzGYzGRkZxMTEYLfb2bx5M2NjY2p2WqPR4PV66erqoqKigrGxMQKBAJIk4fF42LRpE2NjY0RGRpKdnU1OTg7BYBC3261KFw4cOMDRo0fR6XRkZWWRnZ2tvp/yHsFgEKfTyYEDB+jp6cHpdAKg0+mwWq20tbVRW1vL8PAwJpOJ5ORkCgsL1XEdP35c/Q46Ozs5cuQIu3fvJi0tjfz8fLKzs4mOjmbPnj3U1dUxNDR07go1TSbZM3vFCtl2JiNDNlPfsEF2GWlpkb2zzwFarRabzUZHRwc1NTUMDQ2h0WhISEigpKSEwcFBqqqqOHjwICBnrpubm/nggw9ob28nLi6OnJwcCgoKyM7Opqenh71797J582bGx8cnyYUEAoFAcH4QGWzBJYWywr9xo2wMERYmWx4bjaDTGUhJSeH+++9ny5YtdHZ2Ul9fz4wZMwCw2+24XC41MDYYDLS3t/O73/2ODRs2kJqaisFgIDMzk8WLF7Np0yZ27NhBfX095eXlNDU1UVFRwa233ookScTExFBYWMiSJUswnWVP9pCQEGbMmMGaNWsYGhriwIEDJCQkcOWVVxIVFcXo6Cjd3d386U9/IjExkaSkJB588EE0Gg179uwhKiqK3/3udzgcDlW/nZ6ezjXXXEN/fz+7du2a9H5er5dt27axaNEiQkNDkSSJnJwc5s+fT0xMjLrd4cOH0el0rFu3jkAgQEpKCjNmzKC3t1e9GJkzZw5Go5H//u//Vp+n0+mYO3cuK1asUMeemZnJvHnziI+Px+VysXnzZvW3Udi/fz+7d+/G7/dTWFhIWFgYer2e2267jSeffJKioiLWrl3Lrbfeelbf85RotXKgfe+9coXs++/Dr38td3387Gfh2mshO1vOZJ9lllij0TBr1iy6u7sZGBjg2LFjpKWlsWTJErKyspAkiX379tHV1cWRI0eQJAmv18vOnTt58cUXueGGG1i4cCHx8fEA3HjjjezevZutW7fy5z//mVtuuYXCwsJz3kRIIBAIBKdHZLAFlxSBgJxofOqpf/QTSU39R1OZ6Oho1q5dS3FxMVVVVbz++uvqc48cOUJlZSWf+cxnCA0Npaqqiq1bt+JyufjVr37Fv/7rv/Kd73yH73znOzQ2NmIymbBYLLS2tuL3+4mPj0en0/Hwww/z+uuv09zcTGxsLF/+8pc/sT2d2WzGZDJhMBgoKSlROyLq9Xry8vIICQkhPj6ewsJC9TkxMTFkZ2fj8XgYHBzEZrOpjxkMhimDLr1eT25uLr/+9a/52c9+xtatWwG45ZZbWLNmjbpdamoqTU1NfOlLX2L9+vVYrVYKCwt5+OGHVZ2wVqs9pVQjJCSEkJAQtFoteXl5hP+9raZWqyU/Px+3283Q0JC6fX9/P11dXVitVrxeL5IkERERwYwZMzCbzYyNjWG1Ws/26/1o5s6V26q//768Y/3P/8A998Cbb8q2fp8Qg8GgfldZWVnExsaqj+Xk5GAwGOjv7wdg7969VFRU0NfXx7x58066eFu0aBGZmZmMjIzwwgsvMDo6+onHJxAIBIKPh8hgCy4phobg3Xdl/+vyctnO+MTkol6v5/rrr+edd96hsbGRiooK5syZQ0dHB52dndx8880YDAasVit9fX3ExsZy7733Tsrgut1u3G43Wq2WrKwsTCYTixYtwuVy8dvf/pbf/va3bNmyhXnz5vHAAw+cdQb7RBR3CEVXq9FoMBqNU7poaLVaVV/u8Xjw+XwnvdaJ90NDQ/niF7/I008/TXV1NT/96U/ZuXMnn/nMZ8jOzlYD+5tuugmj0cibb77J//t//48FCxawZMkSrrnmGsLCws7oc5xqHBOdShQUjfX+/fupra2lpKSE8PBwnE4nfr+fjIwMtYDyU0GrlVurZ2fDT34CW7bA4cPw85/LSyVr18pWNZ+gg9HE33Tid6JImBSZUVdXFyMjIwCEhYVNKnwEVC2+3++nra3tpN9dIBAIBJ8+IsAWXDJ4vdDXB5s3Q2YmFBXJ7moTUQKXJUuWcPDgQY4dO8aePXvIyMjA5XIhSRKJiYlotVo8Hg9OpxOfz0dBQQFJSUmnLRjLyspi+fLl1NXVsWfPHg4fPkxfXx/JycmsW7fu3LhcTMHpXlMZ70Q7vdNhMBhYunQpzc3NHDhwgLq6Ot555x2ioqJYtmwZc+fOxWAwUFpaisvlYmRkhA8//JAPPviA0dFRTCYT11133dkXGTK11V5ubi6lpaUcOHCAnTt3otVqiYuLo7Ozk7y8PGbMmEFmZuZZv+cZodPJxY2LF8tXbWFh8NZbsG2bHIB7PLKfdljYP5ZMzgEnfh9er1e15rPZbCf9rklJSaqDiMfj4SOcogQCgUDwKSAkIoJLBpsNmpvlPiE33AAzZsixzlQsXrxYlVP87W9/o76+Hr1eT2pqqpox1Ol0BINBuru7cblcJxWLKd7FPp8PSZKIiopizpw5PP7449x5553ExsZy4MABHnnkEVpbW3G73Z/2V/CJkCQJrVZLeno63/rWt/j2t7/NVVddRXNzM7/4xS94/vnnVZlJQkICV199Nf/1X//FzTffjNvtZv369Xz/+99neHgYv99/Tsc2e/Zsrr76aq699lpefPFFNm/ezPbt23n77be56667WL16NXl5eef0PadEo5GbzyxfDl/8Inz/+9DZCc88IzuOtLXJV3qfoKj1o4iIiMBoNOL1eqmpqcFzgkQlOjoas9mMwWAgMjLypAy3QCAQCD59xMwruGTYsEHudG2xwP33y3Vpp8JgMLBy5Uquuuoqdu3axaOPPkowGGTdunXqNklJSWRlZeFwOPjrX/86ydUCZJlIZ2cnzzzzDMPDw1RWVrJlyxbMZjPf//73+cMf/sDPf/5z+vv7ef/992lsbPy0Pvo5IRgMMj4+zquvvorT6WT58uX88pe/5MUXXyQhIYFjx46xadMm3G4327Zt4/Dhw8THx/Of//mfPPvss3zuc5+jp6eH9evXT9JPnwu0Wi1hYWGkpqbyq1/9ihtuuIGrrrqK//iP/+ArX/nK+QmuTyQ6Gq67Dtavh9tvh9ZWuOIKeOEF+UrvU2LGjBkkJyfjdrvZu3fvSRduLS0t9Pf3o9VqmTdv3ifyXxcIBALB2SEkIoKLHkmSpSEHDkB7O9x9N0RGnrqztbLkXlpayujoKM8++ywjIyOEh4eTmpqqbpebm8uCBQuIi4vj5ZdfZmxsTPWC7u7upqGhAZvNxurVqzEajdTV1XH06FGuvfZa1W1kzpw5mM1mwsPDVQs5SZJ48cUX6e3txWg0cv/99xMREXFaqYeioVaatyjL/pIkqfZ5Pp8Ph8OhPicQCOD1egG5ScvEDHwgEFCt+RQrN8Wmb8OGDcydO5f4+HjCwsJYuHAhiYmJGI1GVV+9d+9eEhISWLRokVp4WVNTQ0REhNrURpIkNZPtdDpxuVyTPo+SefX7/ZM+z/j4OB6PRx0TQGVlJRUVFRw4cIDZs2erbhpjY2PqisN5z9QqntmpqfJON2OGfIX3xz9CZaVs8XfLLfKO+BEuIz6fTw2UlU6VCoq7jbL/ZGRkUF5eTk1NDUePHqW3t5fo6Gi1ULSpqQmn00lWVhZXX301ZrP5U/oCBAKBQHAqRIAtuOiRJDh0SA6udTq48krZXe2jnNMsFgu5ublkZWWRlJREbGysGqSA7MKRl5fH6tWr2blzJ7t372Z8fJzm5mZGRkbweDzExsaSnZ2NRqOhv7+fY8eOcejQIRITEwkGg/j9fgoKCsjMzJzUWW/37t3U1dVhMplYsmQJM2bMmDLADgQCjI2N0d7ezvDwMD6fT+3e6PF41I6OHo9Hbe6SkZGB3++nt7eX9vZ2ANrb28nJySEyMlJ9jb6+PgKBAK2trfT29hIaGorf76empobKykr8fj9RUVG4XC5SUlKIiYkhLS0NgLa2Nvr7+6msrCQpKQmPx0NISAj5+flkZmaq79/Q0IAkSfT19dHZ2Ul/fz86nY6enh6sVivBYJDGxkYiIiLw+/0MDQ3R29uL3W5nZGSExsZGcnNzGR4epqOjg9raWvbs2UNERAQhISHo9XqioqKwWCwkJSWRm5uL0Wg8f81VNBq5+LG4GOLi5OraF16QTdhdLvlv5eVgNsvSkhOQJInR0VF6e3vp6+sDoLW1lcTERPR6vfrY2NgYoaGh1NfXk52dTWlpKWvWrOHVV1/l4MGDeL1e0tPTkSSJ+vp6zGYzq1atIicnRy10FQgEAsH5Q8y8gosapWvjCy/A6Kgc56xceWa2xIq13fLly5k5cybp6emTHtfpdGRkZPDTn/6UL33pS9TX16vNPgoKCrj33nu55pprCA0Nxev1qu2tf/Ob33DVVVfh8/kYHR3lxhtvZOnSpZNcSFpbW6mpqUGr1fLBBx+QnZ09pdOIz+ejoaGBvXv30tXVhSRJqie3yWSira2Nbdu24fP5aGtr45133qGkpAS73U5lZSX79u3DZDLxwQcfEBoaqnpnv/feexw7dgydTsfBgwfVjpYWi4XR0VFeeukljh07xqxZs+jp6aG4uJjS0lLmzZuHx+PBbrdTU1OD2+3mmmuuYWhoiPHxcW644QYWLVpEY2MjBw8e5J133iEkJET1CM/NzSUsLIyqqio6OjrQ6/VqEWVsbCyVlZU0NzfjcDjo6enhb3/7G1/96lcxm83o9Xo6Ojp49NFHJxXuSZJESUkJa9as4Zvf/CbJycnnv3uhTgfJyfBP/yQ3pfnzn+Vq2yNHZEu/wkLZ3m+Ki6jGxkaOHj2qXnBt2bJF9fmuqKigoaGBkZERfD4fr7/+Ol/4wheYN28eGRkZjI2N8cILL1BdXc2iRYsIBoNUVlayZMkSbrrpJvR6vejkKBAIBBcAzUdUmIvyc8G0xmaD48dlKewDD8Add8CSJfJjZxJX1NXV8fWvf50nnniC1NTUk/SqSudCJVtss9nQaDSkpqZiNBonBTAejwev14tGo2FkZASz2awuzxsMBnU7RQZRW1tLY2MjZrOZK664goiIiJPGN7GQMhgMIkkSer1ezUoq0pBgMKjKJEJCQpAkSZWBBINBdDqdelOkG4qzyMQulhqNRu3GqLioJCcnA7IOWsmyO51OVcIxMjJCfHy86hxiMBgmvX8gEFDHpnxfU30ejUYzSdai0WjQ6/UYDAaqqqpoa2tjcHCQ+fPnMz4+jsPhwOPx4HK5qKiowG63U1hYyL/8y7+odoLnHeWKr69PXlb58Y9hYECWitx6q6zRnrS5pK50KN+nTqdDr9ej1WoJBoOq7zfI3+1EqZHP58Nms6k+50lJScTHx2M0GtHpdKLAUSAQCD496oDXgB9N9aDIYAsuavr65GRhRgaUlckZ7DNN2FmtVoaGhkhISCAqKmpKaznFkzg0NBSLxUJ0dDQajQaTyXRS8GI0GtXXUILGU7Xu1ul02Gw2rFYrK1euPKVPtuJmcjp99qkkAKd7zqkekyRJvSgwmUyqdvzELGhYWJh68WEwGNSAbuK4tVrtaT//VJz4nGAwSFtbG6+++irBYJAvfOELxMfH4/P51IsHv99PXl4evb29dHZ2XtiMrUYja67j42HBAnj0UfjLX6C2VnYZaWqCz3xGLhL4+wXN6X7fiV7mJ7+V7IEeHR2trqKYTKYp902BQCAQnF9EgC24aBkbk40bdu2ChQshP19ehT8VLpeLsbEx2traMJvNdHZ20tPTQ2lpKaGhoR/pUa10HzwVExuEfJRzw9jYGFqtFovFQkpKyrQJiCZ+htN93onbnUljmbNFkiQ6Ozs5fPiwqq3WaDRERESg0WjweDyqPjk2NlbNwl9wTCZITJQz1lar3AGytlbugpSaCiUlkJIib/cJmZjVFggEAsH0QATYgosORdVUXw/790NjI/z613KAfToGBgaoqKjgiSeeoKioiI6ODiIiInjsscfOu5VZe3s76enpLF26dPoEhdMQJcAeHh7G6/WyceNGli5dSmJiIhqNhoGBAaqrq+ns7CQuLo7bb799+hT1abVgNMqekTNnykH2z38OPT2ynummmyA9Xd5O/P4CgUBwSSE02IKLDkmS+3j8+Mewdasco/zxjxARcfoGem1tbXzwwQd87WtfQ6/Xc+edd3LjjTeydu3ak9pTf9oouuTz/b4XG4rO+K233mLfvn0cOXKEqqoqNBoNkZGRZGVlcdttt7Fy5Uqys7PVC6Vp950GAuB0ym3VH3kEurtlw/af/hSWLZOdSAQCgUBwMXFaDbYIsAUXHcEgfPCBbM7Q3w/f+x5ccw18VHduh8PBwMAAhw8fRqPRkJWVRWpqKomJiedn4IKPjTI/KbZ+im5eKeYMDw8nIyODxMREwsPDp43UZkoCAXA45GWXbdvkIki3G+65R+4MWVx8TlusCwQCgeBTRRQ5Ci4dJAn8ftixQ9Zgp6TA/PmnbiozkfDwcLKyssjIyBDZ44sE5fdJTU0lNTVVdSfRarXTO5ieCp1OXmZZuVLeYfV6+NvfZDu/YFC+FRbKftlivxQIBIKLGhFgCy4qlCTgq6/Kscjq1XKQfaYorg2CixPFuu+iRaORl1pWr4bZsyEzE/7932XpyKFD8P/+n9yc5gy6PwoEAoFg+nKRpYAElzvd3fCHP8DgoGzQcMcdF3pEAsFZEhMjFzu+9hqsWiXrnlavlosh/97VUSAQCAQXJyLAFlw0uFxyO/Q334Q1a2SnswnNEQWCiwuNRpaDFBbCvffC174GUVFyccEzz8C+fRd6hAKBQCA4Sy7itVbB5UZPD9TVyb06HnxQbi7zUYWNAsG0RqOB6Gi5S5LFIi/RbN4sB9c+H5jNkJsr2/1dbJpzgUAguIwRM7Zg2iNJ8m3XLtl8ISwMbrxRlq8KBJcEJpMcSP/sZ/DQQ2C3wxNP/MM32+v9hwG8QCAQCKY9IoMtuCjo6oKNG+VmeN/8ptyx8WKudRMIpkSng3/+Z9m2b+dOOeBuaoK77oLbb4e0tAs9QoFAIBCcASJEEUx7gkF44w0YGJCbyqxbJ0tDhMmC4JJC2aGNRjmbHRoqa7RfeQU2bJCdRj73Obkr5DlosS4QCASCTw8RYAumNX4/jI/L0hC9HmbMgLw8IUcVXOJERspaqOhoubL30CE4cACSkuQDISNDrvAVV5kCgUAwLRFhimBao3SX3rFDdg257jphESy4TNDrITFR9sn+4hflrPZ//Ac8+aQcbPv9/yhQEAgEAsG0QmSwBdOalhZ49FEoKJCt+RYvvtAjEgjOI0pjmhtugCVLZL/sX/1KDrAXLJAPjpgYUZAgEAgE0wyRwRZMW7q74dgxOHwYbr5ZTuCJOEJw2aEE2XFxsHYtfPWr8nLOkSPwox9BZSUMD1/oUQoEAoFgAiJcEVwwHA7Z6jcYnCwnVVa8GxrkANvrhRUrZPmpkIYILltCQiAnR85mGwyyld+OHZCVJRcqlJTIB4lAIBAILjgigy24YLS3y8m3AwfkIDoY/EdwHQzK/TY+/BCWLoXyctmaTyC4rNFoIDtb7rT085/Lyzq/+IUsG1m/HgIBocsWCASCaYAIsAUXjJoaeO45+Mxn4JZb4MUXZc11MAjvvAN798rbfec7snOZQCD4O6Ghcov1l16Cf/s3ueDxhz+Er3xFvnL1+y/0CAUCgeCyRkhEBBeMoSHo7ZVXt48c+YcdX1mZnLnW62XL34ICYcsnEExCo5Gb0kREwNVXyxqr7GzYvRv++7/liuBly+RlH6GrEggEgvOOCLAFFwyrVb5JEvT3Q1+fnNVuaZH11zNnyo1lzGYRIwgEJ6EcFIWFsmd2XJzc9XHPHlkqotPBokWyl/ZU1cGKjKS9XW5cEx8vP0ccbAKBQPCJEXlBwXlHkYj29spOIfAP/fXIiJyE6++Xa7gcDhgcFNJSgeC0pKfLDiP/939yE5pNm+DrX5eLIO32Ux88gQD86U/w3nvyEpJAIBAIzgkigy24IASDskRkaGjy3yXpH/LRQ4dkF5E//QkeeghuuknOaoeFnffhCgTTH70eUlPhD3+QK4TfeENurf7QQ7LzyJo1stZKyVDbbPDEE3IhRFISdHTIem6BQCAQfGJEgC24IAwNydnp09Vi+XzyzemUix6bmmQ3kTvvlJN0Ot35G69AMO1RdNkxMbL1TkwMREXJRvIjI/IBdM89suZqaAiOHoXXX4eBATnL/f778uMZGaKqWCAQCD4hIsAWnHckST6nu1ynl3wojwUCcPy43EtDkuRknEAgOAU6HWRmgsUi33/qKWhslAsecnJkv+zGRti6Va4uliRwu+UCiF275AMsJERosQUCgeATIAJswXlHkuTVaKfz4z2vpEROzBUViXO/QPCRhIXBNdfIwfbLL8MLL8DDD8M3vykH1q+88o9tNRo5i/2f/ynb+ERFyc1sBAKBQHBWiCJHwXlHkuQEmst1ZjIPrVaOC/7t3+Cf/kkE1wLBx6KgQG6v/sILcmOaJ5+UNVc+3z+WiSRJ7vbU1gZ/+YtcaSwQCASCs0ZksAXnHUmCnh45wP4okpPhiivg3nvlzLUocBQIPiZ6vZyRTk6WD6CxMTlbHQicvK3fLxdIxsTIS0YWi7iiFQgEgrNAZLAF5x0lwHY6T63B1mggJUUuarz9dli8WLbpFQgEZ4HLJWuwHQ5Zb+33T33wSRLU1soWPtXV//ibQCAQCD4WIoMtOK8odrzNzbJLWDB48jZarZx0u/NOuPlmWLnyvA9TILh0kCSoqoLXXpOLGKc66E7k4EH47W9h9WrRRlUgEAjOAhFgC847waAs9XQ4Jv9dcRkzmWSZ6NKlcv8MgUDwCdi7F158Ufa7PpPg2u+Xl5i8Xnj1VbmBTWzspz9OgUAguIQQAbbgvOL3w+ioLA+ZKAHVamVnsNxcuO02ObhOTpb/JhAIzoJgUL6KfeUVuWjx49j2BIPyEtNf/iJrscPDhTe2QCAQfAzE2p/gvOL1yj0uJhoYaDTyuTs3F5Yvlwsas7JEQaNA8IlQ2qIOD8uaq4wMucnMmUo+vF748EPZhH5g4NMdq0AgEFxiiAy24LzidEJ7++TgWq+HuDj42tfg2mvlbs8CgeATonR1/M1v5CYy+/bJko+jR8HjkQ8+n+/Uzw8G5eLI55+Xt7v/fvnvwlVEIBAIPhIRYAvOKw6H3LFZkuREmtksZ6v/7//kJnORkRd6hALBJUZkJMybB7Nnw4MPyvrqujqorIT33pP/7/XKB6TfP/m5fj9s2ya/RkEBLFx4YT6DQCAQXGSIAPsyQJIk3G43wWAQv9+P1+vF7/fj9/uRJEl9XJrCjkuSJAKBAF6vd8rX1mg06HQ6Qk4hltbpdBgMBrRaLVqtlo6OEFpaIgkGjVgsErNnS9x1F+TnawkL05xR4xmBQPAxUAocQkJkLbXJJGe2s7Plro319XJr1ZYW2Z5vfFzOXmu1cqGExyNb9r38sry9wXBWziKSJOFyuQgGgwQCAXw+H36/H5/Pp85DXq+XYDB4yrnoVPMUyHORyWRCM0WGXaPRoNVqMf5dR67VatHr9ej1egwGA3q9Ho1GQ2ho6JTPFwgEgo+LCLCnEZIkqScfJQAOBoPq3wKBwEfeV15DOUkp9x0Oh3pSc7vd+Hy+SSczp9NJcAqHASUod7vdU45Zq9Wi0+lOeWJTHlMC7O5uC3V1swgGjZhMvcTE2MnMlKiv16HTydsoJ8OJN51ON+n/U91X/qacOJXXEggEEwgPl29paXJmu7NTbq165AgYjQQ7O5HGx5EcDrTDw2iCQTQdHUhbtuD83OewR0fj1+lOOQ9NnH9OnIfsdjvBYHDKeQhQ56ETg2jlNex2+yk/lkajITw8HI1Gc9Jxr8wpYX8v7FAu/ENCQjAajRgMBnQ6HeHh4eq8oTxn4nz0UfPQif/q9fpJ2wkEgssHzamyAX9HdBj4BHzEd3vSNoFAAKfTic1mY3BwkOHhYRwOBw6HA5vNhs1mw+l04nQ6GR0dZXx8HKfTqT4+Pj6Ox+PB6/XidDrxeDz4fD58Pt+0CTQlaRHwKBrNMPBrJGkf4FNPjkajEb1eT1hYGGFhYYSGhmI2m4mOjiY8PJywsDDMZjNRUVHq/YiICCIjI4mIiCAiIoL4+Hji4+MxmUzoJqTEz/Q7mC7flUDwSTiT+Weq7TyHDuE7cIDAhx8Stnkz+vFxtIEAvpAQqlesYPecOQyGhOBwOhkbG8Nut0+ah+x2uzoPud1uXC4XPp+PQCAw7Y8tSZLUrLbJZCI0NBSDwYDRaJw074SGhqrzzsR5SbkfHh5OZGQk8fHxxMTEEBERgclkmvReYj4SCC566oDXgB9N9aAIsD9FrFYro6OjjIyM0NfXx8DAAGNjY+rfRkZG1MB5eHiY0dFRfD6fmgFSMjlarVadtJVJ32w2q/dNJhMRERGEhYVhMBjUk0NISIiazTWbzaqUY+JJQ6fTqUujyjLpRJTsjdlsnvIzKvKRU0lIfD4fzr/bgwUCAdraTGzZYuHaa9vRaEbw+ey4XC4CgYB6IlYy5m63G4/Hg9PpZHx8HJfLhdvtVk/mTqdTve/3+ydlnbRaLaGhoYSHhxMbG0tMTAxRUVFERkYSHR2t/i0mJoaEhASSk5OJiYkhPDwcvV4s7AgufpSM8cDAACMjI1itVvr7+xkeHmZ8fBybzabOUePj44yNjTEyMoJ7fJyAxwMeD3qPh1JJYrZGwwKtlkUGA79ITaUuKgpnRIQaXCrzknI/JCRkUoZYyeRGRESo2eMT5yEAk8mkSspORLkI151CRxYIBHA4HFNeWEzMmivbut1u/H4/LpcLr9dLIBDAbrerK4hKhl3JsjudTux2uzo3neq+svKozEMajQa9Xo/JZCIuLk5NBkRHR0+6HxsbS1JSErGxscTGxpKQkEBYWJjIfAsE0xcRYJ9LFA3z8PAwVqtVzdiMjY0xNjamZpxHR0ex2+2TgsKJAaAiYZi4pKjX69UAWAmojUYjISEh6k3JrijLmspNeXwqmYRWqyUkJET9v3ICmxhQn05SodFoCAkJmfKxicvEU6Gc2JT/j41paG3Vk5trQ6MJqCczRYqiLDErgbai0VSy8cr37/V61ZOfcuL0eDy43W4cDgd2u13d/sSla+W9lGVpk8mE2WwmNDRUzUxFR0erFzGRkZHExMQQGRlJZGQkcXFx4sQnuCBIkoTf71ezxcPDw4yMjKhZ5BPnIWWVy+Vy4fF4CAQC6nF/osxBCQSNRiNGo1E+FsxmMkwmUg0G0nU60gMBDgaDDIeH44uNPWleUgJrZT5RZBLK3GM0GtX3/rjzEKDOQyc+PlHDfarvTTnugUlzgpJdn6gBnzgfKTcl0J5qHjrxvpK9VxIDSkb/RCmfMg8pv6siqQsNDVVX5yZmxJWVvPDw8EkBukgMCAQXhNMG2OKIPAFlAnS73ZOCOK/XqwZwdrudnp4eenp6GB0dxWazMTIywtjYGOPj49jtdkZHR5EkST3BhISEqEuFSsAWFRWlLitGRUWpQZ0S4EVHRxMaGqoW5kxHJgbtZ0JMjOwaAlNnxM8GSZLUTLdycaP8LsrFz4n3ldUDh8Oh/tbBYBCNRkNERIT6WynLwkpWKS4ujtTUVGL/HlycmKVTLn5OFQgIBKdDCbSUeUcJzJQgzuPx4HA41Ix0T08PQ0ND2O12dd+fGHD7fL5JxXyRkZHExsYSHh6O2WwmNjZWlT0o+31kZKQ6D8XExGA2m+UgWqcDv5+Vw8NywWRMzIX+ulSUY+1EGcaFQPmd7HY7IyMj6jlhfHxcXT2YeIFkt9vVx8fHxycVfxqNRlV+Eh0dra6ymc1m4uPjSUlJIT4+Xg28J17gKPPQxESMQCA4f1yWGezTfWZlEmxoaKC1tVUNpNvb22lpaWFoaAibzYYkSRgMBqKjo7FYLCQnJ6uTXVxcHGlpaaSkpBAXF0dcXBzJyclqdkYwPVAyVlarle7ublX33tvbS3d3N0NDQwwNDdHT00NnZydOp1PNkEVHR5OYmEhSUhLZ2dnk5OSQnJxMcnIy+fn5ZGRknDLrryCC78uTj9JG9/b20tPTQ3d3N21tbbS2ttLX10dfXx9tbW309PSomVitVktycrI6z6Smpk66n5KSot6PjY0lLCxM7HfTFEmScDgc6uqoMg9ZrVaGh4fp6+ujq6uLoaEhrFYrfX196r4UEhJCcnIy2dnZJCUlkZycTFZWFtnZ2aSmppKSkkJCQsJp31/sFwLBx0ZIRE7E5/NhtVqpq6ujo6ODzs5OGhsbaW1tZWBggNHRUfx+P6GhoURFRak63bS0NBITE1XNbnZ2tpo1OJWzxcT7ICax6YSy73+US4vyf5fLxdjYGO3t7bS3t2O1WhkZGaGrq4vOzk5GRkZUKZBSbJmYmEhmZiZFRUWkp6eTnp5OQUEBRqNRXGxdhii2l0NDQ/T396vzjhJU19fXMzg4qFrXhYWFkZCQQExMDLGxsaSlpZGRkUF8fDwWi4XMzEzCwsImSTImzjsnOl+IVZXpy0S3lanmpIl/CwQCeDweBgYGsFqtDA0N0dnZSWdnp1rPMzAwQH9/v5oMioqKIjs7m7S0NNLS0sjOziYzM5P09HSSkpI+MiEgEAhO4vIMsBXPVavVyuDgoJqFtFqtqk5xeHhY1eHqdDpVe6ic1JRltxN1uRNlHErxjuDSR1myVyQmEx1dlGX58fFxBgcHsVqtapGUx+MhGAyqTgQWi4XExETi4uKwWCzqCU/RUgoufhSph3IBNjg4qK6GDAwMqPKksbExdUlfCZIVyZjyb0REhFqXoUjHFJcdZQ4SgdHlxUT71Ynz0OjoKA6HA5fLxfj4uJowUmRyig5c0X8rErjo6GiSkpJITU1VXZgUKZzQdgsEp+TSD7CVyWKintblck26qleWWgcHB7HZbACEhYURHx9PQkICWVlZ5OTkqEuqWVlZREZGiuBZcMb4/X6cTicdHR20tLTQ09NDb28vra2ttLW1YbPZcLlc+P1+NQOpSEyKiorUoFsptlQu+BRdqQiiph/K/KlopV0ul1rY7HA46Orqoq6ujp6eHvr7++no6GBwcBAAg8GA2WwmMzOTlJQUkpKSyMzMJCcnh6SkJOLj40/p3iMQfBSKb/jg4CC9vb20t7erqyV9fX20t7erridarRaLxUJGRoYqMSksLCQ9PZ2wsDC16FWZl5TGYmJOElzmXFoB9lTj9Xg89PT0sGfPHurq6mhsbKS6uprm5mb8fj8mk4msrCzmz59PXl4eOTk5FBYWUlhYqNrTCQSfFsrSr6Lvrquro7q6mqamJhobGzl27Bher1e94CsrK6O8vJyCggIKCwspLS09pW2Z4Pxz4hzk8/loaWmhqamJI0eOcOjQIRobG+nq6mJsbAyTyUR6ejrZ2dnMmjWLWbNmkZmZSVpaGpmZmaIhkuC8o0iVWltb6erqoqOjg6NHj3LkyBFaW1vp7u7G4/EQHR1NRkYGBQUFzJkzh/LycvLy8sjMzDxl0aTYlwWXEZdWgO31ehkbG6Ouro4DBw5QW1tLc3MzDQ0NBAIB4uLiSExMJD8/nxkzZpCfn096ejqxsbGq08PEqnqhSRR82pyo9Z7Yrl6xGGxqaqK9vZ22tjbq6+upra3FZrMRCATUoFsJuBctWkRCQoJwBbgASJJEd3c3DQ0NNDY2cuTIEfbu3cvIyAg+nw+z2Uxubi4ZGRlkZGRQVFRESUkJZrNZtdZUajYmysvEHCQ4nyhzkmJFqMxFimONshJXU1NDR0cHHR0d1NbWMjw8jE6nIyoqitLSUsrLy8nPz1fnJnE+FVxmXNwBtqIta2pqoqGhgd7eXrV4Q7GgCgkJUZdaY2NjJzl7xMfHExkZOS3smwSCE1Gy20rDj+HhYfr7+9XGREr9gKLjVlwjMjMzSU5OJjU1leLiYhITE9VlW8G5QZGeNTU10dnZqdZx9PT0MDY2htvtVvXTioVjYmIiFotFbWSUkJCAxWI5ZfMUgWC6odQPjI2N0d/fz8jIyCQXE6XGRJG7hYaGEh0dTXZ2NhkZGaSmppKWlkZubq6oDxBc6lxcPthKZ0ClScLAwABdXV0cPHiQo0eP0tPTg81mw2g0UlhYSE5ODnl5eZSWllJaWnpSe2yBYDqjZHwsFgsWi2XSY8PDw3R3d3Po0CGOHDlCc3MzHR0dVFdXqx642dnZWK1WiouL1eK3iIgI4VJyFpxYOKb4Su/bt0+17Wxubsbj8RAZGYnFYmHmzJnMnTuXgoICMjIyPtIKTSCY7mg0GgwGg1rsOBFFu93Q0MDhw4c5evQovb292O12Dh48SF5eHtnZ2RQUFOByudQ5Sek+LDLcgsuJC57BPvH9R0dH6ejoYPv27bz99tvU1tZitVoxm81ceeWVlJWVMXPmTBYsWEB8fLwIpgWXDT6fD7vdzoEDB9izZw9HjhyhqqqK9vZ2UlNTmTlzJosXL+b666+nuLiY0NDQSc8XJ7bJnDj3BINBnE4nW7duZe/evRw+fJi9e/fidrvJzc2lqKiIRYsWsXLlSnJzc7FYLOIiRnBZEwgE6Ovro6mpiZ07d7Jnzx7q6+tpb28nLCyMpUuXMnfuXBYuXMiaNWswmUwnHTNiXhJcxExviYjf72dwcJB9+/bxt7/9jZqaGgYHB9FoNMybN49Zs2ZRXFxMWVkZkZGRamteJUMnDk7B5YKSYVXaMCsrPcePH2fPnj3U1NTQ2NjI6OgomZmZasB94403qnZugn+gyHMOHDjA7t27OXz4MIcOHcJms6mrA+Xl5axcuZKEhAR1ZcBkMqmSDzH/CC5nlGJJv9+P2+1WbUz7+vr48MMPqaiooL29nf7+fqKioli4cCFz585lyZIllJWViYy24GJn+gXYPp+P8fFxDh8+TH19PW1tbTQ2NuL1ejGbzcTFxZGTk0N+fr7a3CU5ORmdTicORoFgAoFAgMHBQbXraHd3N/X19fT19eF2u9FqteTl5TFjxgxyc3PJy8sjLS3tsj2OJEnC6XSq1ok1NTVUV1czPj5OIBDAbDaTl5endkTMzMwkNzeX0NBQUVQqEJwByoV/S0sL7e3takfK5uZm7Ha72vSmtLSUGTNmkJWVRWZmJqGhoZftvCS4aJkeAbYkSXg8Hmw2G8PDw3R0dLB+/Xqqqqro7+9Hq9WyatUq5s+fz8yZMyktLRWdpQSCj4nP56Ouro5du3ZRWVnJkSNHGBgYoLi4WNULz5s3j5iYGMLCwi4bj20lw6ZI0A4dOkR1dTUVFRXY7XZyc3MpLi5m2bJlLF++nKioKIxG44UetkBw0eN2uxkZGWHnzp188MEHqoQkMjKSBQsWMGvWLMrLy0lLSyM6Olp1+xIILgIubICtvL4kSdTW1vLKK6+wbds29u7dS3x8PGvWrGHFihXccMMNJCcnC02jQHCO8Pv92Gw23nrrLV599VWOHTtGb28vJSUlPPzwwyxdupQ5c+aowfWlFmRPnNv6+/s5evQozz33HG+++SYAqamprFu3jgcffJDs7GxiYmIu0EgFgsuHoaEhGhsbef7559m4cSMDAwPo9XpuueUWPvvZz1JcXDypuPJSm5cElxQXNsAeGhqitraW1157jXfeeYfw8HCys7O5+uqrueKKK4iKilLb/goJiEBw7lA024ojhtLk5rXXXqOqqorw8HBKSkr44he/qNY4XEq43W76+/v53//9Xz788EO6u7sxm83cfvvtLFy4kJycHKKjozGbzZM8qQUCwadHIBBQC7ZHRkZobGzkwIEDvP7667hcLrKysli5ciWf/exnsVgsYiVJMJ05/wG2Uoi1fft2KisraWxspK2tjcTERGbOnElhYSEzZ84kLy9P+GQKBOcJh8PB0NAQhw8fpqKigra2Nvr6+khISGDevHnMnj2bsrIyYmNjL9pjUpIkvF4vR48e5fjx4xw6dIja2lpCQ0NJSkqirKyMBQsWkJGRQUxMjFiKFgguID6fD6vVSkdHBwcOHODw4cMMDg7i8XgoKSlh/vz5zJgxg5KSErUxnEAwjTi/Ptgej4fx8XE6Ozt5/fXXOXLkCA6Hg9zcXO6++27mzZtHSkqKOFAEgvNMeHg44eHhZGZmUlZWxoEDB9i4cSPbt29XC5IkSaK8vJzIyMiL7uJX0Vm3trayefNm9u7dS3V1Nfn5+SxbtowlS5awePFi4f4hEEwTDAYDSUlJJCYmUl5ezocffsju3bvZunUrf/vb3+jo6KC/vx+DwUBWVpbocyG4qDgnGeyJr1FfX8+2bdv45S9/ic1m46677uKaa65h3bp1F1QCMtXnPJ9j+bTff+Lrn4vX/Yj94ow51Vg+7nhPNR7luacb74mv/3E+28TnnsnzztXnPV94vV4qKir47//+byorKxkbG+MHP/gBd955JykpKcD0Gu9UKN/t8PAwFRUVfO1rX2N0dJTs7Gwefvhh7rrrLkJDQy9YfccnOZam+3f/UZzJfv9xjt2P+54f9bpn+t4f9zc823nj434Xn/S7m47zUiAQwOFw8NJLL/GHP/yB3t5eLBYLv/71rykvLycqKgqYPuMVXNZ8+hIRSZLw+Xw89dRTvP322/T29pKdnc33v/99tXgoLCxMfsMLcFAoLY+rqqqoqalheHiYsrIyFi1adF5aqHs8Hqqrq9VOlC6Xi1WrVrF48WLMZvMnfv1gMIjVaqW7u5uoqCiys7M/0esFAgEaGxupq6tjfHwco9HIsmXLiIuLw+Vy0dbWRldX1yknd61Wy8yZM0lISDip2QnI+8vY2Bi7du1Cq9Vy7bXXnnY8kiTR0NBAY2MjIyMjGAwGli5dSnx8PKGhoar1WmVlJU1NTQwODpKWlkZ5eTlZWVmTNHwOh4Pm5mY6OzsJBoOnfM8ZM2ZgsViIiIhQ/9bU1ER7eztOp1P9m8/nY2hoiFtvvfWUXtOSJGGz2aiqqqK7u5u77777tJ/3fKK4+1itVg4fPsx7773Hm2++ydy5c1m3bh133XUXcXFx0/ZkpkhC3njjDbZs2cKWLVsoLS3lzjvvZMGCBaSlpV3QDnKSJHHw4EFGR0fxeDxn/LycnByysrIIDw//FEf36aLMSz09PURERJCTkzPlduPj43R1dVFdXU1/fz9paWlkZWVRVFR0VtZtyuu43W71b263G5vNxp133qnW+0iSxPj4OHv37qWpqYmRkRGWLVtGcXHxSU2E7HY7zc3NdHV1nXbemDlzJhaLZdK8Xl9fT2dnJy6XS/2b1+vFarVy++23q6tFynm0vb2dtrY2WltbmTVrFllZWVgslpMyt8r2HR0dtLW10dLSQmlpKZmZmSQmJp5Rptdms1FdXU1HRwd33333tDjOFX/68fFxOjo62L17N6+//jp1dXVcf/31rF27luuvv/6iW2ETXJJ8uhIRv99Pd3c3O3bsYP369YSGhrJq1SpWrVpFaWkp4eHhF9w/1u/309/fT2VlJbt372Z4eJiUlJTTTpTnivHxcXp6enj//feZPXs2Pp+Po0eP8uyzz5KXl0doaOhZL3m5XC66u7upra1l9+7dqsb0bANst9tNd3c3VVVVtLa2kp2dTUZGBpGRkYSHh6PVamlra+Mvf/kLra2tpwywQ0JC+P73v09sbOyUjwcCAd5++2327NlDSkrKKQNsj8fDwMAAhw4doqWlhbS0NNLS0oiMjMRsNqPT6fD5fIyMjPDnP/+Zjo4OBgYG6OvrIyIigoqKCubMmcPNN9+snvCGhob4y1/+QltbG36/f8r31Wg0fOc731EzJZIkMTw8zPbt29m2bdukQCkkJITk5GSCweBpJ/vNmzdz4MABHA7HtAqwNRoNJpOJ5ORkysvLMRqNaDQaampq2L59O06nk4ceeoiYmJgLfhyfSCAQwOl08te//pXt27fT39/P0qVLufHGG5k/fz7p6enn5QL6o4iIiFA9t48ePUp4eDiFhYVkZGSoQZxS+DUyMkJVVRVXXHEFsbGxF2WA7XK56Onpoba2ll27dpGYmMicOXNOCrCDwSADAwNs2bIFl8tFcnIyxcXF9Pf3s3//fvbu3cu9996rFqF+FJIkYbVaeffdd6mursbr9aqPhYaGqtJEjUaj7jv/+7//i9VqxWazMTg4SH19PYWFhcyZM4frr79efd2+vj513ggEAlO+v1ar5Xvf+x7R0dHq84aHh9myZQu7du2aNB6j0UhycjKSJKnjcblcPPfccxiNRmJiYsjOzqauro66ujri4+O59tpr1fErxct/+ctf0Ov16vYNDQ3U19cTGxvL9ddf/5EXlsq8NDY2Nm3mJWXMUVFR5ObmotfrCQsL45133qGhoQG32834+Di33347RqNRSEYE05azDrCV4GpwcJDKykqef/55urq6+MxnPsOVV17J8uXLz9kgPymBQACbzUZvby81NTX09/dz0003nZcAe2BggKNHj1JZWcldd92FxWJheHiYrVu3YrfbCQaDZz1B2O122tra2LVrF7/5zW+46aabSE5O/tivI0kSfr+f1tZWDh8+zLZt29BqtSxcuJAZM2ao9mVK9nrz5s3Y7faTJD8ejwen04nH4+E73/nOlO/l8/kYHR3lzTffpKamhrlz555yPJ2dnRw9epR3330XrVZLSUkJs2bNIi4uTt12dHSU2tpa3nnnHbRaLZIkMTAwQHV1NYcOHeL48eOUlZVRVFREIBCgt7eXTZs2Ybfb0Wg0k7JUPp8Pl8vF8PAwX/3qVyeNSflu9uzZM+lkHxERQXJy8ildKAKBAOPj46xfv54jR46ckxWLTwOtVktqaiqJiYmkpqby61//mqNHj/LCCy8we/ZsysvLp1VRYDAYZHx8nMbGRp577jnVy/rBBx/kqquumjbjBEhOTqauro7u7m7eeOMNUlNTiY6OpqSkRN3//H4/TqcTv9/PgQMHSE9PZ8mSJWd1PF9oHA4HHR0d6rx0/fXXk5iYOGkbpQNgVVUV69evJzExkUWLFpGXl8e2bduorq5m586dzJ8/n6KiojNyuAkGgzQ1NXHw4EEqKysnHY+JiYmkpKSoHTidTif9/f28//77pKSkqImgrVu3sn//ftra2rjiiiswmUx4PB66u7vVee9EDb/X68XlcjE2NsYjjzwyaUwtLS0cPnyYvXv3Ttono6OjSU5OVsfjcrloaGjg+eefZ/Hixaxdu5a5c+fS0NBAZWUlAPPmzVMz6x6Ph8bGRl544QXKysq4+uqrmTt3Lk1NTVRWVuL1elmwYAHx8fFTHgvKvPTee+9x+PBhQkJCzuzHPc+EhYVRXFxMbm4uMTExPP3001RVVdHV1cXs2bPJyclRkz8CwbRDWY45xe2UBINByev1Sj/72c+kJUuWSElJSdK2bdukoaEhKRgMnu6pF4RgMCgdP35cuu+++6T09HTpj3/8ozQ+Pv6pv+9LL70k3XzzzdJPf/pTaWBgQP3euru7Ja/X+4m+q2PHjkkVFRXS1q1bJY1GI915553Syy+//LFfx+/3S62trdK6deuk0tJS6Qtf+ILk8/mkYDA4aXwVFRXSM888I33zm9+UGhsbpbGxMcntdqu3iooK6ec//7lUXFwsuVyuKd+rra1Neuqpp6SSkhIpNTVVuueee07aJhgMSm1tbdI999wjFRYWSg8++KDkcDhOGo8kSVJVVZV03XXXSXv37pWsVqvk8Xikvr4+6f/7//4/qbCwUEpISJA+//nPSzabTTp+/Lj017/+Vfra174mHT9+XBoeHp40/mPHjklPP/20lJ2dLfX3908az7e//W3pqaeeknbv3i21t7ert+7ubsnlcp3ydxweHpb+8Ic/SPPmzZPS09OlpUuXfuzf53wTDAalsbEx6aWXXpIWLlwopaSkSH/729+kgYGBCz00FbvdLm3cuFEqKyuTsrKypN///vdSe3v7tJ17Ojo6pBdeeEECpAULFkgvvfSSuj+feHv00Uel559/Xjp+/PiFHvpZUVNTI1VUVEg7duyQtFqtdNttt0kvvPDCpG2CwaA0MjIilZeXS5///OdP+j42btwoLViwQLryyiulgwcPntH7ut1u6V/+5V+k559/Xtq/f/+k47Svr2/ScdrW1ib94Q9/kPr6+iS32y0Fg0HJ5/NJP//5z6XS0lJp3rx50u7duyWfzydVV1dLL774ovT1r39dqq2tlUZGRibNG1VVVdITTzwh5ebmSsPDw+p4AoGA9I1vfEN6+umnpT179kwaT09PjzqeQCAgHT9+XFq2bJl0/fXXnYKrPQAAx/ZJREFUS2+//bb6Pfj9funf//3fpdmzZ0vf/e53pZGRESkQCEgNDQ3SsmXLpOuuu056/fXXJ22vfIbvfve70tDQ0JTf1ejoqPT0009LCxYskDIyMqQFCxZMy2NnIsFgUGpubpYef/xxKSMjQ1q4cKG0c+fOU55rBILzQK0kSf8hnSKGPuvLPr/fz29+8xvWr1+P0Wjk97//PfPnzyc6Onra6qJ0Op16pXu+xjg8PExbW9ukbIperychIeETa8jy8vIoLi4mMzPzrF8nGAxis9l45JFH6O3tZfHixXz/+99Xs9MTX9dkMlFWVsa3v/1tMjIyMJvNhISEqDdF+3jllVdOmVHo7++nubmZ+vp6li1bhsVimXI8LpeL7373u9TV1TFr1ix+/OMfYzKZThpPV1cX4+Pj3HDDDZSUlBAVFYXBYCAuLo4vfelLzJw5U9Vm+/1+QkJCyM3N5fvf/z65ublERkZOGn9jYyP19fWsXbtW1W37fD4GBwdpbGwEICMjg+TkZPWWkJBwyuzP8PAwLS0t1NbWMnfuXLKyss7qNzrfaDQawsPDWbt2Lb/5zW9ISEjgf/7nf3j22WfxeDznrAD2bFAmrueee47f/e53jI2N8fTTT3PjjTeSnJw8LecejUZDSEjISfUIyv488QZwyy23sGTJkpOyvtMJRaKwYcMGRkZGJj2mdMU83f4+PDxMVVUVdXV1JCQkkJqaOul7iI6Opri4mH379lFVVUVnZ+dpx+PxeFSJR1hYGOnp6ZOO07i4OPU49fv9REZGsnTpUmJjY9WOwVqtluuvv560tDT0er2q3TcajRQWFvK9732PnJwcIiIiJs0bdXV1tLS0cNVVV6kyKmXeaGhoQKfTnTRvWCwWdTwdHR1UVFRw6NAhrr76avLz89V9QavVMmvWLMrKyvjLX/6C1WqltbVV3X7t2rUUFhZO2n7GjBnMnz+f559/nqGhIXw+36TvamRkhJaWFmpqaigrK7uo5qX09HRuv/12nnzySXp7e/ntb3/LX//61ws9NIFgSs5qHdXlctHb28uGDRuIi4ujvLycefPmERYWNq2Xak538pUkSfUJtlqt2O12UlJSsFgsqqZOIRAIMDo6Sl9fH2NjY3g8HpKSksjJycFoNOLz+eju7lZN9AcHB+np6aGqqoqEhATMZjPZ2dmfOBgwGo0EAoFPtLzX29vL/v37qaiooKysjLKyMtU94kQSEhLQaDSqROPEyvempiasViurV6+etB9If294UllZSW9vL0uXLmX//v1TLl1arVZ1PPn5+cybN089+Z6IwWAgPj6exYsXq4VLIF9Ipaenk5aWNqkjWGxsLBEREVgslkkBjTL+jo4O2tvbufrqq9UTpcPhYO/evTQ2NuL3++no6CA5OZmFCxeSnZ2tvtZElM/b0NDAsWPHWLZsGRUVFRgMho9V6HYh0el0REVFUVBQwF133cUbb7xBZWUle/bsYeXKlRfUDai6upp9+/ZhtVq5+eabmTVr1rTUiE/kRDnSVLjdbhobG0lJSVHbRUuSxOjoKKOjo4yMjDA0NERycjJJSUmTLlB9Ph9Op5OGhgZmzpyJy+VicHCQoaEhZs+erRaZK/INu92O3+8nOTmZ1NTUM2rmoVzcNDU10dzcTG1tLZWVlRQUFEzqghkSEoJOpzutb7HT6aSnpwePx4NOpztpLjCbzWRmZuJyuejo6KCvr4/09PRTjm14eJh9+/bR0tLCq6++SmVlJYmJiSxZsoScnByioqImdSxVNNknJjncbjcWi4WYmBgSExPV+S46Olr9vk+cN9ra2ujp6eHKK69UP4fNZlOLJ99++22amppISkpi8eLFZGdnTyoc7uzspLa2FrfbTXZ2tnq+UR6Pj48nOTmZwcFBmpqacLvdHD9+HJfLRWZmplrromwfFxdHSkqKun1UVBRJSUmAfO5qamriyJEjLFu2jEOHDhESEjKpIHQ6YzAYSEhIYM6cOdx4442q/GbevHmUlJRMywtsweXLWQXYiiPCkSNH+MIXvsC6detITU0912M7bwSDQdxuN11dXXR0dNDT00Nvb69adJOfn69OYi6XC5vNRmNjI8PDw/T29mK1WomPjyciIoL4+HgkSaK7u5vm5mba2trUYLy2tpaRkREsFgsZGRn4/f6PlQ00Go3n/AKmo6ODd955h+7ubm699VZSUlIYGBjA4/FgMBgIDQ1VM71TZZwVnE4nbW1tuN1u5syZc9I4rVYrtbW1aDQabrnlFioqKqZ8nYGBAd566y06OztZsWIF2dnZ6nj0ej0mk4no6GhCQkIwm82EhYWdpGvWaDSYzWZiY2PVCySNRnPaVtgul4uuri76+/spKytTgzWXy0V1dTVjY2Ps2LGDHTt2EBMTw4MPPsiKFSuYNWsWCQkJ6vsqjIyMqK4jd911FzU1Naf/IaYhOp2OiIgI7r33Xvbu3UtXVxcbNmxg2bJlF8SVQ/q7bnfHjh00NjYSHR3NfffdR3x8/EVf6CRJEna7nR07dvDAAw9gNpsJBAKMjY2p7jnKBXt8fDyzZs3CZDIRERGB3W5naGiIzs5Otm3bRlxcHFarlerqaurq6khNTSUhIQG/3097ezsNDQ0MDAxgt9tJSkpSs+Wnqg+Q/u7U4nQ6GR4e5sMPP+TAgQNUVFSoxYIfF4/Hw/DwsPraJ2ZZw8PDycjIQKPR0NvbS39//2lfb2xsjKNHjzI+Ps7bb7+NXq8nLi6OsbEx1qxZQ35+vpoY0Ol06HQ6tQBW2a8cDge1tbXqxYuygjCx5uNEXC4XnZ2dDA0NUVZWpgbYTqeT6upqbDYb77//Ptu2bSMmJoaHHnqIVatWqS5FAN3d3TQ0NACQlJR00u8QGRlJXFwcXq+X+vp6bDabuv1Uv1tkZCTx8fH4fD4aGhrIyMggKSkJSZLUeam1tZW7776burq6036v0xGj0UhSUhL3338/1dXVajFtUVGR8LgXTCvOKsBua2vjD3/4AzNnzuTqq69m8eLF53pc5xWn08mBAwd4++23mTVrFiUlJYSFhfHd736X9PR0rrrqKn70I9mFpbKykq1bt7Jt2zaee+45li9fzvHjx7n//vsZHBzkqquuoqysTL3giIuLQ6vVEhcXR1FREQkJCZhMJvr7++nq6jrpxHIqNBoNs2fPPudFcl1dXWzatAmtVktXVxevv/46v/zlL6msrCQ3N5dVq1bx0EMPMWvWrFNOXEp2emxsjMTERGbPnj3pcb/fz+9//3tKS0spKioiPDz8lK81ODjIhg0b1Mr9jRs38uSTT1JZWUl6ejoLFy7ky1/+MrNmzVKzcjD16sTY2Bg+3//P3nmHR3Ve+f8zTdLMqPfeG0IUARIdjHHBmMTdsWPH3mSTteO03SQb/+IkuymbZNP72omDE8eOe7cx2JjehAAVVJAQaqhrpJE0RTOadn9/3NwbCUQ1GAHv53nmgdG9M/PeO3PPPe95v+ccLytWrDhjdPPw4cNYLBbMZjNz585V389sNlNWVqbe3I4dO0ZtbS3/8z//Q15eHitXruSJJ544aULx9NNPEx8fz+23337a453u6HQ6UlJSuOeee3j77bd54YUX+MY3vkFkZORHHjGW/lFS8IknnqCoqIjrr79+0nd1OeP1eunp6eH//u//uO2224iIiGBkZIS//e1vfPDBB3ziE5/gnnvuobu7m0996lPs2bOHtWvXcu+997J582aef/553nnnHbxeLz6fj56eHvbu3UtjYyOLFy8mJSWFgYEB3n77be6++26ioqI4evQojzzyCOvWrePOO+/kjjvumHJsfr9f7W/wu9/9DqvVSmFhIbfeeisPP/zwaSeup0JxcDUaDW1tbSdJQMxmM6mpqYA8OT9RhnIiUVFRLFq0iPHxcRoaGmhtbaWpqYlvf/vbzJ49m3Xr1vH973//lAEKi8XCtm3b+MUvfsFXv/pV1qxZc8ZjkCSJmpoahoaGCAsLo6SkRN0WGhrKwoULGR8fp7GxkZaWFurr6/n+97/PSy+9xOrVq/nNb36DVqvFarXS19cHyPeLE6VEZrNZrWjU1dWlBnaU/SfaQZArpihR8K6uLkZHR9VtzzzzDNHR0dx5552XdXKgXq+ntLSUdevWsWPHDp544gn+5V/+ZdomawquTs7ZwXY6nXR1dXHw4EH++7//m7S0tMv6BufxeGhvb+c73/kOv/rVr9Ss5KKiIrZv3055eTmvvvoqH/vYx5gxYwaHDx/m/fffp7CwkKCgIMLDw0lLS6OwsJCqqiry8vIoLS0lJSWF0NBQtX5pQkIChYWFxMXFMTY2xmuvvcYTTzyBxWI5q3HqdDrefPNNZs2adcGO3Wq10t/fz9DQELm5udx7773MnTsXnU7H0aNH+eUvf8nbb7/Nnj17eP3114mLi5tyKdnn87F582ZSUlKYOXPmpG3Dw8M0Njai0WjIzs4+ZS1ckCuC9Pf3Y7FYyM7O5vbbb+eaa67BYDDQ3NzMn/70J7Zt28aBAwfUagynqrNdXV3N0NAQSUlJPPDAA2cs17Zt2zZMJhNlZWWTfs9ms5lly5Yxb948PB4PdrudpqYmvve979HV1cW2bdtYv3499957L2FhYbhcLmpra/H7/aSkpDBjxowzfQ3THo1Gw7XXXqtWYNmxYwcrV6487YrGxWB0dJS6ujp6enr4zGc+w+rVqy9L29Pa2srjjz/Opk2bANm5VspLDgwMqGXgRkdHee6555gxYwbR0dEYDAZSU1PJz8/HarWyf/9+7r33Xq677jpsNptanSg6Opobb7yRRx99lGPHjjFnzhw2bNjAhg0b+NWvfqVWosjOzmbr1q3s2bOHsLAwCgsL1WV2v9+Pw+Hg3Xff5bXXXqOtrY2xsTHWrFnD2rVryc7OVleHzmf1IDo6mjlz5mAwGGhqaqK1tVVdzdNoNHi9XpxOJyBLJE5V8lMhJiaG5cuXM3/+fDweD1arlaNHj/Ltb3+bzs5O3n//fbKzs3nggQfUiaEkSbjdbl5//XW2bt3K1q1b6e3t5bvf/S7vv/8+jz76KDNmzDjl8UmSxJYtW4iIiKC4uHjStvDw8El2w2az0dTUxH//93/T09PDli1bWL9+Pffffz9Op5PR0VG0Wq1aVWQier1etbtDQ0MMDQ0xMjJyyv0NBoO6v9Vqxel0qnbJ6/WSnJx8RcgpNBoNN954Iw6Hg71793L48GEKCwsn9S4QCC4l5+xgDw8PY7FYcDqdFBcXn6RPvtxQSvd1dnbS0NCAxWJRDarH40Gj0agRofz8fAoKCli7di25ubmYzWa1gYHH42F0dJTR0VE0Gg0Gg4GQkBB1yVAxkoqjV1BQwK233ordbj+rcWq12vOKFJ0ORaMpSRLFxcVkZWWRlpamJvgsXboUl8tFTU0N+/fvZ/ny5aocQkHRGysa7tzcXNVwezwe+vr62LdvHwsXLiQpKUnVqE+F1WpVHYyioiJycnJIS0tT5SBLlixhdHSUXbt2UVFRwfLly0/SZSrLvVu2bFFrOyckJJwyUqPoSqurq0lNTT3pxqPT6dQW4yA7QxEREXz2s59VSw3u2rWL22+/nZCQEIaHh9m1a5d6PkNCQi5pUuCFIjIykvj4eGJiYmhpaWHhwoUf+RiUMpHBwcEkJyerutLLjbCwMGbMmEFZWRkgR4itVisdHR28/vrr6u8vLCyMdevWkZubS3Z2tpr7ocgrlGidIk1Trs3c3Fzy8vKIjY0lKiqKzs5O2traOH78OHV1dao2Wik5B3LgpL+/n/z8fHWlprGxkUOHDuF2uykqKiIrK4slS5Ywd+5cIiMjz0q3fSqMRiNJSUlcc801tLS0UFlZybvvvkt+fj42m422tjb27NlDIBA4KwdbsVnKCp/ymoceeojnnnuOkZER9uzZw3333Tdp5UWn05GTk4PP5yM5OZmKigqOHDnCoUOHePPNN8nNzZ3SwVbsRmVlJbm5uRQWFp7WbsTFxal247XXXqO1tZXdu3dz9913q/r8E5NdpyI4OFh1qs9mf+U3MjIyws6dO5k5cyY5OTnToj78hSAtLY3k5GR0Oh3t7e1kZGQIB1swbTgvB1vRzmVlZV2WjRAm0tXVRX19PZIkqU0gFLRardoMwuPxIEkSpaWlalTS5XJht9vp7e3F6XQyMjJyVnrE4OBgFi9eTGlp6TmN9UIvf1mtVkZHR9HpdBQXF6sZ9SDfoFatWsXw8DDl5eXs2bNH7c44EaWaQENDA2vWrCEnJ0fdNjQ0RGtrK7W1tXzqU58iJCQEp9OJ1+vF6/Xi9/vx+Xw4nU60Wq26FKzRaCgqKpoUMY+JiWHZsmWqprG8vJyCgoKTHGyv14vVaqWyspIbb7yRFStWnNYRkCQJl8tFY2MjBQUFZ4w4GwwGEhMT+cxnPsPw8DDHjx/n0KFDapSqo6ODqqoqbrvtNmJjY3E6nWrHNb/fr2o9lUnYdE7Mm0hQUBAREREkJCSoyWkfNUoDk/DwcKKjo8+qNvJ0JC4ujmuuuYZ77rlH/ZvL5aKpqYkDBw6oDl1UVBQPPfQQINuiwcFB+vr6sNvtjI6OTvpdK5NQkLtAKgnJcXFxfPDBB3R2duJ2uyflPvj9fsxmMzNmzCAhIYHx8XEcDgc7d+7kgw8+YO/eveh0Ou69915WrVrFsmXLCA8PvyCRz6CgIGJjY7njjjv4y1/+QmdnJ2+//TbXXXcdIyMjNDU1sWfPHjQaDQkJCZOSlc+GkJAQUlJSePjhh2lqamL79u1UVVVN6n2gVHdZuHAhZWVl+Hw+XnzxRdavX09DQwOvvvoqX/rSl9TmSxNRqh0pFYIKCgrOeLxJSUl87nOfo7+/n97eXg4dOoTX6yUoKEidiCuPiZ8XCATUcUdGRuJ2uyfpx6faX1kFiYyMVLtDVldXc+uttxIXF6euDng8Hvx+v2rHLze7FBUVpcpkuru7L5skcsHVwTk72G63G7fbrZbymk4NHc4HpcJHfHw8X//6188qKqbRaNizZw9bt24lLi6OmJgY0tPTsdvtZx2tVBJtLiUmk0ltRTxVE5G8vDwyMzMJBAK0tbVNavWrMDw8THV1NePj4+Tm5pKbm6tue+mllzh27BhFRUXs2LFjUlON1tZWRkdHVdlBREQEOp1OlXxERUWdNKHIyMggNzcXSZJob2+fMvrf2dnJ73//e+644w4WLFhAenr6ac+B0+mkqqqKsbEx0tLSTpK4TIVSumvGjBnMmzePN954A5/Px+7du9myZQuzZ8/mwIEDk5aijx49ytDQEF6vl/fee4+wsDByc3M/dFv7jxKDwYDZbMZut5+ym93FRJmcXAl250RCQkLIyMjg85//vDpxUKRlu3fvprq6mq6uLsrKyoiLi2N4eHiSrZkYyTxxtaajowOXy0VOTg7f+973TjmG8fFxtm/fzpNPPklTUxNarZZ/+7d/43Of+xwZGRlTOpofhuDgYP71X/+VBQsWcOTIEWpra6msrOSaa67BZDJx7Ngxmpubyc7OVvXY54LSpXTevHn09PSwe/fu0zYX0+v13HfffQwNDREIBNi/fz9utxuz2XySrXY4HFRXV+NyudS27mcznuDgYIqLi2lubuadd97B7/erSYyBQEB1eCf+vpVGNgCZmZkEBQXR39+P3+8/4/4ZGRkcPnyYjo4OZs2axcGDByfZ1aNHjzI4OIjL5VLtUnZ29qRAyXTHYDAQGhqK3W4/ZXdegeBScM53qYiICCIiItQkNKPROG27050NWq1W7WLodrvl4uCnSfzYt28fO3bsoKKigscee4y0tDRsNhvvvffeWd98vF4vTU1N1NTUnFXEW1lCvOWWW845knM6EhMTiY2Nxe/3qxHoiURHR6vaz6luMgB9fX1s376dFStWEB8fP+nc2e12Ghsb2bFjx0kRke7ubux2O8PDw/z4xz8mJyeH+++/n4SEBCRJ4siRI6xYsWLSa6KiotTOZFM5WQcOHKC5uZmioiJWrlx5VtG20dFRNm/erLbWPtukH6V8V1ZWFpmZmeh0OrWk2L59+046XiXyGAgE+OEPf0h2djb33HPPZeVgj42NMTAwoGpnP2qCgoKIiYlRHYJAIHDZJmmdiEajITw8nNtuu01NWhseHubZZ5+lvLycVatW8cUvfpGIiAh27tx5xt/1xO1arRabzcbAwAA+n++U9feDgoJYtmwZf/vb36iurmbXrl28+eab7Ny5k9mzZ7Nq1Spuu+02jEbjBQsOaDQaCgsLycrKYvXq1eqK0rFjxygvL+fzn/88+fn55y1HUSLgWVlZHD9+/JS/l4nnIy8vj9mzZ9PY2EhQUNCUr7FarXzwwQcsXryY5OTkc7IbsbGxk+xGbGysmhQ/MDBw0uqMUrkKZIfZaDSqSY4Wi4WEhAQ1CRJQZYvK/m63m87OztPaJb/fzw9/+EOysrK4++67LxsH2+fzqVV0JtYWFwimA+fsYCulz4KCgmhqaiI2NvaydrBDQ0OJiorC6XRy+PBhVd+poEgYjh07Rnp6OtXV1Wzfvp2srCySk5OJjY0955brfr+fnp4eKioqJmV4nw6dTsfq1asvqIMdFhZGQkIC6enptLa2nhShdrlc6t/S0tJO0u1JkoTVaqWqqooVK1ac1GRoyZIlarRtIopGurm5mZiYGO68805iY2PJzs5Gr9eTmZlJR0cHDodj0uvcbrc6IUlNTVXlPJIk0dLSQk9PDyC3FI6MjFQdcKVsYkRExCR9nlIa7cCBA6q+/FwidFarFavVysyZMwkKCqK4uJg777xzysTV7du309LSgtfr5a677iImJua0CZ/TjdHRUVWzn52dPWVy6cXGZDKRnZ3N2NiYWrrtcmwlfip0Op2aZ+Fyueju7uaNN94gOztbbVCiNIA6F2JiYggODmZkZITKykpmzZo1qfLE+Pg4g4ODWK1WiouLyc/PVxO0ExMTOXLkCIODg2o5z5KSErKzs8nIyDhtreuzJSQkRJVIBAIBtea6yWTihhtuUJMyz5eBgQFcLhczZ848q/fxeDzo9XoKCgqmnIxIkoTNZuPgwYMsX76c2NjYczoHSpLizJkzVXtXXFyMTqejs7OT5OTkSQ726OgoVquVhIQEtWmOIu3r6uoiLS1tkoM9OjrK0NAQ8fHxJCYmqkGxqezSzp071drail26XJxrQC2p6/V6ycnJuSR2SSA4FefsYEdFRZGYmEhMTAwHDx6ksLDwtElk04lAIKBq1hSnODY2ltTUVPx+Pzt27FATZUJCQvB6vdjtdgYGBqirqyMkJIT6+nqqqqq44YYbCAkJUWtou1wufD7fJL2c3+9Xl3EnLqkrUZqRkRGsVusZx63RaNDpdFMuf008FuXYToXNZlMTDKOjo9WOZyUlJdTU1KjblSjH4OAgw8PDGAwGioqKTppIeb1eBgcHaWho4Ctf+cokIw9w7bXXcu211540Dq/Xi8vlwuFwkJeXx6OPPjppW2lpKTU1NYyMjODxeNSohNVqZWhoCJ1OR2FhIZGRkfj9fsbHx6moqMDpdKo3CMURDwQC+Hw+mpubycvLm+Rg+3w+RkZGqK2t5aGHHjpJX+73+9VqBmazGYPBgE6nQ5IkNfG1p6eHlStXEhwczPz585k/f/6U35Hyfi6Xi//3//7fKb+j6Up3dzddXV04HA5mzJhxUmmwj4LQ0FDy8/PR6XS0traqzTvgo+vMer5MvE4n2oVToTRi2bZtG4sXL1Ybk4yPj+N2u/F6vWojmokP+KdmV7HJaWlpahBh06ZNxMbGkpiYiMFgUK/hjo4OBgYGKC4uJjg4mKysLDIyMli+fDkbNmxg586d7N69m0OHDnHdddexePFiJElSG2eduJqkHO/ZSuaU/QcGBvjggw9oamqiuLiYhQsXnjSxt9vteDweQLZjilRCkXMoSYBK7kN7ezs2m40lS5ZM+vvY2Bgmkwm9Xq/+PRAIMDg4iMfjYfHixVNG6r1eL8PDw9TV1fGFL3zhpKCHYjeU9z/Rbij19lesWEFQUBAZGRnMmTOHqKgompqayMnJmdRXwmKxYLFYmDVrFgkJCZhMJtxut1pqMTc3d1IuytDQEP39/cyaNYvExESSk5NZtmzZlOddq9WqybOPPvrotL+OFJTfVVNTE+3t7QQFBZGfn3/Z54QJrizO2cHWarWkpqby8Y9/nOeff57S0tLLZuaoJNhJksTY2BiSJFFUVIQkSSQlJfH444/T3NzM2rVrWblyJU1NTWp95x/84Ad0d3erTvfbb7/N2rVrGRwcpLq6moqKCjweDw6HQ00gGRgYUJffLBaL6iAbjUZuueUWbrnllg99TEpyndKw4XQatGeffZampiYAfvnLX6LT6Zg1axb/8R//wU033URdXR0JCQnk5+cD0NDQQF9fH/n5+dxxxx0n3egUjeT4+DiLFi26ICsZ2dnZPProo6xZs4aGhgby8/NVXXRzczNtbW3k5eVx2223ERkZyejoKBUVFTz55JPMnTuXnJwc/va3vwGyo+F0Omlvb1dLJE7Uch4/fpwjR44wNjbGggULToqGDg0N0dzczMsvv8xdd91FXl6e6oS3trbS39+P2Wzmi1/84mWTFHS+vPrqq1RUVJCbm0tZWdklWYo1Go1kZmayZMkSysvLCQQCLF269LJwCsbHx1W70Nvbe8bqQcokFGDLli1kZmaSk5NDU1MTFRUVauRSeU+n06l247PZbLjdbnUSdM0119DY2MjWrVv50Y9+hN1uZ/HixeTm5tLU1MTmzZspKyvj3nvvnTQGjUaDyWTirrvu4rbbbmNkZIRnnnmGZ555hjfeeIPg4GAeeeQRHnjggZO6vyp2EpiykcyJ+P1+hoeH+da3voXL5WLdunV8+tOfnrLaxUsvvaSWnPv1r3/NwMAAtbW1bN68mXvuuYf8/HxVxnjs2DGsVitJSUl8/vOfR6/X4/F41A6LN910E1lZWWpwYHR0lLa2NnQ6HY899tiUn9/R0UFjYyNjY2OUlZWd1IhGadn+xhtvcPfdd6sVXQBaWlqwWCxERkbyhS98QbUbubm5fOpTn+LVV18lLS1N7SWg1NHu7Ozkf//3f9WOpTk5OTzwwAO8+eabJCcnM2/ePPVcK2UPf/azn13QFc/pRiAQ4JVXXqGhoYGVK1eSkpJyyfOaBIKJnLODrdFoSEtL48EHH+SNN97g1VdfxeFw8OCDD16M8V0QXC4Xzc3NrF+/npqaGkZHR3nzzTfJyclh7ty5ZGZm8sc//pHvfOc7VFVVUVlZya9+9SsSExO59tprufvuuzGbzWRlZbFs2TLa29spLy/n4YcfVlu0fvOb3+SnP/0pb731FoODg9x+++1s3LiRffv24Xa72bFjByUlJZSWlpKXl4fJZPrQjkF1dTU1NTXs2bMHSZI4dOgQZrOZ8PBwFi9efFIzkOrqarVFud/vR6vVEh4eTnFxMU8//TTbtm2jsbGR/Px8goKCqK6uJjY2lt///vdTJjhVVlbS09PD8uXLL1gClMlkoqCggD//+c/s2bOHP/zhD8ycORODwcCRI0cwGAz88Y9/JCwsjN7eXvbu3ct//dd/0dnZSW1t7UmOn6Kpf+ihh05KeDxy5IhqnKdquqCUGXzjjTd45513yMnJURM5g4ODWbp0KQ8++OAV7Vx7PB527drFxo0bCQ0N5ZFHHrlkCYYajQa9Xs+XvvQlfv7zn7N7927++te/8qlPfWrafgeSJLF792527NjBrl27AHni/frrr+NyuVi7di2ZmZknjT8mJobi4mJuvfVW9u3bx29+8xs2b97M2rVr+dd//Vfee+89ysvL+fKXv8z8+fOprq5m7969ADzxxBOsWrWKxYsXk5+fj16v5+abbyY6OprvfOc7PPfcc7z00kuYzWYSExPVRMMTgyQTr2e9Xk9UVBQPPPAAa9asobGxkfLycl5++WVuvvnmSQ52TU0Nhw8fVu1SZWUlZrOZyMhIlixZotqlQCCAzWajqqqKmpoaqqqquOaaa5g3bx7JycmqXOZEu1JbW8v27dsJBAJ4vV7cbjdtbW28+uqrvPnmmxQXF5OZmakmZt57771kZmZOkoxZLBY2bdrEX//6VzIzM8nOzmbmzJmEhYVx4403kpaWdsqAQW1tLc3NzaxcuRKj0Til3ejp6eH111/n7bffJi8vj5ycHHJycggODua6664jOztb/c4VnfjXvvY19Ho95eXl1NXVMXv2bIaGhjCZTNx3330UFRWpkpzY2Fi+9rWvERQURGVlJY2NjcydO5ehoSH0ej3333//pP2vNDweD0899RQHDhwgNTWVRx55RHRxFEw7zutOaTQaycrK4oYbbqC1tZVt27ZRUFBAaWnptJxB6nQ61binp6fjcDjUaKbJZMJsNjN//nz+9V//lePHjzMyMoJGoyEzM5P58+eTl5enVv0oKSlR60YruuGsrCyMRiM2m03tfJeYmMiCBQuIj49n9erVhIWFkZ+fT1RU1AU7R5GRkaqONyoqCqPRSEZGBikpKVO2VV++fDmpqalotVp1m1KvtaysTG0uERYWRnBwMCaTiYSEBIqKiqY0XsnJycyfPx+tVntO2lCtVsvy5cunLL+lVBIpLS1Fq9VisVgIDw9X20KHh4er2kWTyURmZiZ33HHHKSNkilOWnp5+kqwhLi6O2bNnM3PmTLWr3ERCQ0PJyclRKwvExMSQnJxMdnY2ISEhZGVlkZqaelbHvXDhQmJjY9Wl7csBp9NJT08PL7zwAjqdjqKiIhYtWnRJb2RarZbi4mIWLFjAoUOHeP3119WVixMlStOFuLg45syZQ3h4OHPnzgUgPT2dnJwcwsLCppTXGQwG4uPj+eQnP8mMGTMwGAzExcVRWFioduqbPXs26enpagQ2IyMDh8NBfn6++t4gnzOlJfojjzxCZ2cnHo8Hg8FAVlYWJSUlqr77VCjXUWxsLJGRkURERBAVFUVkZORJNaojIyPJyspCkiQiIiIwGo2kp6eTmpo6aSIeCATo6OhAo9Go1+eCBQvIyso67WqYIpkJBALo9Xq10ct9992HxWIhJSWFpKQk0tLSMBqN5OfnT9JJ6/V6UlJSuO222+jo6CAmJoaEhAQyMjIwmUzk5OQQGxt7yvOhdKudPXs2QUFBJ10LYWFh5OXlcd999zE4OEh8fDxJSUlqXfzc3FySkpImvc5gMJCUlMSNN96IxWLB6/WqFapCQ0NP6vCo1+tJSkrihhtuYGBgAI/Ho+5vNpun7Ag5FQsXLiQmJmbKClHTFaWJ0JtvvklCQgJlZWXMmDFDONeCaYfmDBq5U270+/3s3LmTn//85/T09LBmzRr+4z/+Q41OXK4/9tHRUex2uxpVmCpap9QMNZlMk26OHo8HrVY7bUuIKTpxSZIICwub8jtSMtaDg4On1FZORLkRKDf8i/Gdj4+PMzIyojr8F1KaYLVacblcmEwmIiIipnR0lChZf38/Wq2WkJCQK3rZVWF8fJzOzk7Ky8v56le/ys0338y6detO2U77o2bLli1s3LiRv/71r3zjG9/gxhtvVFeGriSUfA29Xj/pt6/ke5yPre3v78fn86llAM/3ulW0zIqG+Vzxer1s27aNtLQ04uLizvq6UjTogUBArRSklLjr6+vDYDBgMpnO2JhLkiTsdjs6nW5St8QzMTQ0pEpwzmQ3+vr61KDBiVKSU6G81mazER0dfcaAjJJnMjo6elb7X844nU4aGhrYuHEjv/71r/niF7/IjTfeyNKlSy/10ARXJ43AK8B3ptp43g628rr333+ft99+mz/+8Y98+ctf5oEHHmD27NmXrYN94vmY6jhOrD974t+n67Gfatyn2ud0+53rvufLxfyMc/2uL8YYpivl5eW8+uqrrF+/nhUrVvCDH/yAoqKiaXPzliSJpqYm1q9fz+9+9zs+8YlPcMstt3D77bdf6qFdcKayKx/G1pyNHfgo3ud8r+2pXnc+1+mF/Pwz7XOh3//D7H858/e//5033niDDRs28LWvfY3PfOYzZGZmXtHHLJjWXBwHW8Fut3Ps2DHeeecd1q9fz6xZs1ixYgWf+tSnTqqLLBAIpi9KUujf//53nnvuOVwuF3l5eXz3u98lNTVVbUo0XXC73QwODvLEE0+wdetW3G43y5cv5+tf/zpxcXFXTDtogeBqxu12Y7FY+NWvfsXWrVsJDw9n1apVPPTQQ8TGxora14JLyWkd7A+tZVA6P9144410dXXR0dHB7t278Xg8rFmzhuzs7DMu1QkEgkvLyMgIra2tHDhwgHfeeUftOLd69Wqys7PPq/7yxSYkJITk5GRuuukmtFottbW1HDp0iKeeeoqysjIKCgouq1rjAoFgMsePH1cTaisqKsjKymLu3LnceOONJCYmigCeYFpzQcTCERERLFiwgIiICNavX8/OnTt54oknCAQC3HDDDWp9ystZmy0QXGko9ZIdDgdNTU1s2bKFV155hZGRER588EGuvfZatX7udL1utVotS5cuJTExkb179/LEE0/w5JNP0tHRwapVqwgLCyMsLEytRSwQCKY3Sh1xu91ORUWFmm+RlJTEHXfcwaJFi8jNzb3UwxQIzsiHloioO/7jfTweD0eOHOHJJ59k/fr15Ofns2zZMj7zmc8wd+7caZsAKBBcbSgdEX/3u9/x2muvqdVxfvvb35KamjpJYjFdHWwFSZLw+/3Y7XZ+8IMf8M4772C1WlmwYAHf+MY3mDVrFnFxcZd6mAKB4Az09fVRU1PDT3/6U6qqqkhJSWHdunU89thjamGB6W6PBFcNF1eDfSJKZ8PBwUG2bdvGli1baGpqwm63c/3117NmzRrmzJlzUmMCgUBw8VEi1hs2bGDv3r2Ul5czNjbGypUrKSsrY8mSJWoL7Mtt+VXpxNfb28vhw4eprKzkvffew2KxUFxczPz587nlllsoKCgQ0WyBYBrh8/loaGjg9ddfp7KykqamJhITE1m7di0lJSXMnDlT7RgtnGvBNOLiarBPRKvVqi24V6xYgclkIisri+rqaurq6hgbG6OmpoZZs2ZRVFREXFwcZrNZXDQCwUXEbrfT399Pe3s7NTU1HDp0iOHhYWJjY5k3bx5LlixRNcsajeayvB41Gg06nY7U1FQMBgMxMTGEhYWxe/du7HY7+/btY3BwkIULF5KVlUVSUhJJSUnipi0QfMQok+Guri56enpob29n//79HDt2DL/fT0lJCUuXLlV7V1wNpVEFVx4XPII9FUNDQxw4cIDf//73NDU1EQgEmDNnDrfddhuzZ88mOTkZo9GodsUSNzuB4MOhSCY8Hg8ul4u2tja16+f777+P2Wxm7ty53HTTTXziE5+YsjHRlUAgEGDv3r289dZblJeX09DQwJw5c1i8eDElJSUsXLhQbawkdNoCwcVF0Ve73W7sdjt79+7l0KFD7N+/n/r6eubMmcPSpUtZt24dpaWlwhcQTHc+WonIlG/yj88IBAJUVFSwd+9eXnvtNSoqKtTuZLfddht33303kZGRZ13wXyAQTI3f71ergvztb3+jvLwcSZJISUnh/vvv56677iIlJWVSt7cr8WY20b4NDQ2xf/9+1q9fz4EDBxgcHCQmJoZPfvKTXH/99cyaNYvExMRLOFqB4Mqmu7ub6upqNm/ezIsvvsjIyAhJSUmUlpbymc98hrKyMiIjI9X9r0SbJLiiuPQONvzzRjc2NobD4WB4eFgtq9XQ0EBTUxORkZHMmDGDGTNmUFpaSllZGUajUUSVBIIzIEkSg4ODtLe3s2vXLsrLy+ns7GR4eJjMzEwWL15MUVERhYWFxMXFERkZSVBQ0BUZtT4VPp8Pp9PJ4OAgPT09tLW1sWfPHioqKvD7/WrL7eXLlzNr1izS09Onbft1geByYHh4mPb2durq6ti9eze1tbU4HA4MBgMLFy5k+fLlZGRkkJiYSGxsLCaTSRRCEFxOTA8H+6Q3liT6+vpobGykubmZuro6mpub1XbU0dHRzJw5k+TkZBITE0lNTSU1NfW82/IKBFcaTqeTkZER2traOH78ON3d3XR1ddHZ2cnY2BhGo5HY2FhKSkooKSkhPT2dpKSky1ZjfSFxOBxYLBbq6uqoqKigo6MDq9UKQHh4OCkpKSQnJ5ORkUFmZiaJiYlERkZeca3YBYILidPpxGq1qvkeHR0d9PT00NPTg81mQ6vVEhMTQ2ZmJqWlpcyePZuYmBhxXQkuV6angz0Rn8/H2NgYGzZsoKKigrq6Ourr69FqteTl5VFYWMjChQtZunTpJL1kUFAQOp1O6LYFVzyBQEDVL3o8HrxeL729vRw7doytW7dSXl6OxWJhfHyc3NxcVqxYQWlpKQsWLCA1NVVcH6dAkiR8Ph/19fXU1taya9cudu3axejoKHq9Xj2Xc+fOJTc3l/j4eIKCgtDr9RgMBrW2vzi/gqsJxW/wer34fD7VLvX29nL06FGqq6vZuXMnLS0tSJJEVFQUK1as4JprrqG4uFit5COuG8FlzvR3sCeOQbnhjYyM8MEHH7Bv3z6qqqqoqKjAYDCQk5NDQUEBpaWlLF++nJycHOLi4sSFKriiUTot1tfXs2fPHnbv3k1PTw92u52UlBSuueYa5s+fT1lZ2UnJQeLaODUn2j9JkhgfH6e+vp7Dhw+zdetWNm/ezOjoKBqNhtTUVFVCMnPmTBYtWkRoaKhYVRNcVSjleKurq2lsbKS2tpbdu3fT2NiIz+cjIiKC5cuXs2zZMubPn8/cuXMxmUwn2SJhmwSXOdPfwT7pQ//hZNtsNhwOB6Ojo3R1dXHgwAF1yamjowOtVktsbCwpKSnMmjWLuXPnkpWVpS7nCgSXG4FAgPHxcZqbm2lvb6e1tZWqqiqam5ux2+0EAgFSU1PJzs4mIyODrKwsCgsLCQsLw2w2qw9x4zo/JElCkiRcLhdjY2PY7XYsFgstLS20tbXR3NxMc3Mzo6Oj+Hw+IiMjmTVrFrm5uep3kZubS3BwsMgdEVwR+P1+XC4XLS0tNDQ00NbWxrFjx2hoaMBmsxEcHExkZCS5ubnk5eWRmZlJZmYm0dHRhIWFYTKZRIMYwZXK5edgn4jP58PhcNDY2DipbqaiNQ0EAphMJqKiooiLiyM2NpaEhATi4+OJiIggIiKCuLg4IiIixLKUYNowPj6Oy+XCYrEwODjIyMgIIyMjDAwM0NXVhdVqxWazMTY2hiRJhIeHExsbS3Z2Njk5OapOWNFVCy48ymS/v7+f3t5e2tvbOXbsGL29vQwODjI0NITBYCAkJASTyUR8fDxpaWlERUURGRlJTEyMansUZ0MgmK44nU7sdjsjIyPq73tkZASr1UpXVxcDAwOMjY0xPj6O1+slNjaWuLg4kpKS1ElmYmIi8fHx4l4ruBq4/B3sE1FueopWu7GxkUOHDlFfX4/L5UKj0RAbG0txcTEZGRlkZGQwc+ZMcnNzCQkJQa/Xqw+tVotOp1OjTcIgCC4kSkMFn8+H3+9XH4oMamBggNraWo4cOUJHRwednZ20tbWh1WqJjIwkKSmJRYsWUVZWRkFBAVlZWSJCPQ2w2Wz09/dTWVnJvn371O9vcHAQjUZDQkICycnJ5OfnU1RURHZ2NqmpqSQkJKDX69XckYl2SGi5BRcbZYVmoh3y+/2qjert7eX48eO0tbVx5MgRjh49Snd3NxaLBY1GQ1xcHFlZWcyYMYOFCxcyb9484uPjCQsLu9SHJhBcCq5MB3uq5w6Hg9bWVlpaWjh8+DCHDh2iubmZnp4enE4nYWFhpKenk5mZSWFhITNmzCAjI4O0tDSys7PFjFtwwVGSERUHrKOjg8bGRurq6hgYGMBms6HT6SgsLCQnJ4e8vDy16kdCQoJaJk5oqqcXJ+aNALjdbjo7O6msrKS5uVm1Q01NTXg8HvR6PQkJCRQXF5OZmUlaWhp5eXkUFRWpsjbx3QouFopzbbVa1QpeTU1NHD9+nOPHj6s2ye/3ExISQkFBAXPmzCEnJ4fc3FzmzZtHWloaQUFBgLBJAgFXooM9FUqkcHx8HLfbzdjYGE6nU9VRHj9+nKamJiwWC1arFYvFwsDAABqNBoPBQGhoqFqPMzExkYyMDFJSUoiJiVEfIAyJYDJ+vx+Hw0FPTw+Dg4MMDg6qUeje3l719+Z2uwkODiY0NJTExEQSEhLUKGdubi5RUVEYjUZCQkIwm82YTCYMBoOoCXsZEQgE8Hg8OJ1OXC4X4+PjainF3t5eVdrW19fH8PAwo6OjjI6OEhwcjNlsJjw8nLS0NLUsYHx8vNrOPSIigtDQ0Et9iIJpzMRAk/Kb6+3tpb+/n/7+fjo6Ojh+/Dg2m039fUZGRhIZGUlUVBSJiYlkZmaqpXGVspQhISGqXbraaucLBGfg6nCwT0UgEMDr9WKxWGhvb8disTA0NKRqKpVESofDQVBQkPoICQkhPDxcfURHRxMeHq4mbISHh6uaSqPRiNlsvmLbTV+tKGXxlEna2NiY2ijJZrOpEzhFr6j8ze1243a78Xg8+Hw+QkJC1BtZdHQ0iYmJJCUlER8fT3x8PCkpKQQFBYnJ2xWIJEl4PB6sViuDg4NqDonVamV4eBiLxcLw8DAulwufz0dwcDAhISEEBwcTHBxMWFgYsbGxhIWFTXoodigsLIzQ0FCMRiPBwcFqZ07xW7qyUO7TLpcLt9uNy+XC4XBMaZfsdjs2m42hoSHsdjvj4+N4PB5cLhcejweDwYDRaCQyMpL4+Hiio6OJjo4mKSmJtLQ0YmNjiYqKUktQCgSCU3J1O9inQtFxWywW+vr6aG5u5ujRo3R2dtLV1UVLSwsjIyP4fD4kScJgMJCSkkJcXBxxcXGkpKSQlpamPk9MTCQ6OlrVU57qodFo1P+DuBF+lCi/9UAgQCAQUFc9TvVwu904nU4GBgY4fvw4AwMDWCwWent76ezsZGBgAKvVisPhQKfTERQUhNlsJjMzk/z8fNLS0khNTSUvL4+8vDxCQ0MJDg6+xGdBMF1QfmMtLS10dnbS3d3NsWPHVFmbIiHy+/3odDpCQkJITk4mJSWF2NhYYmNj1QZcMTExREVFERsbq+q7p7I9igzuRM23sEMfLYotUmQbij3y+/2ntEt+v18NEA0NDdHd3a3qowcHB+nt7aWrq4vx8XEkSUKr1RIREUFiYqK6UpaXl0dKSopaiUgEhQSCD4VwsE/FVDrKif8fHh6eVD2gra2Nvr4++vr6aG1tpbOzk/HxcQKBAAAhISHqTU6p7qDcCJOTk0lOTlYjmfHx8RiNRmHcPkJ8Ph/j4+OMjIzQ39/P0NCQqkfs7OyctLrR1dWFzWbD7XYD8m8iMjKS2NhYtTyesnyfmZlJVlYWsbGxhIeHn1SOSmgVBVNxqlwS5V+fz8fw8LAqN+rr61N1/Iod6ujowOv1IkmSKndLSEhQZW2K3Zn4PD4+ftLKnEiu/OiZqIceHR3FZrMxMDCgSs2Ghobo6+uju7ubwcFBtTuiEvDRaDQEBQWpciJF1piRkaHapYyMDKKiok5K4Be1qAWCC4ZwsM8HJZqgdM1zu93qUpvH42F8fJyxsTFsNpsqEVCimYru0m6343Q61dJHSsUSpQucIi0JDQ0lMjKS8PBwQkNDCQsLm1TWS9nPbDarerjQ0FBMJtNV1Tpe+Q6U5VBluVTR2yuSDUXbarfb1WXTkZERVb6hdB7z+XwAquRHOafh4eGq5lUpjRcfH4/ZbFaX4kNCQggKClJLtCndRUWirOBCoayyKXIjr9er5pgonfPcbjeDg4Pqb36iVEmxT8pzl8tFIBBQf6c6nY7g4GBV+hYaGkpUVJT63Gw2Ex0drdodxQ6dqMs1mUxXXc1vn8+nyjQUPbNihxS7pLQNn/hdDA8P43A4VNnZ+Pi4Ws3D6/Wi0+lU+U9ERATh4eHqOQ4LC1NLPkZERBATE4PRaFS7GiuyIkXmGBwcjF6vF/ZIILh4CAf7YqAs47lcLux2O6Ojo6pT7XA41CQmRSc3MjKi1g9V9HDKMt7EsoFKCa+JN0G9Xq/e4BTjaTKZVGOqGFiDwaAuByv/n1gObKKDr0S7Jhrf07V9PpUjr5R3mgqfz6cufQJqOShFF6+cQ6/Xqy6BKvsoN52JbXiVc6doECc+V/ad+LoTH4FAAI1GM0nnajQaCQ8PJyoqStWzRkREEBkZqT5XtNNBQUFXnSMhmL4oUdCJeSSK3XE6nWqy28jIiDrxt9vtJznsis1R7M6JzxWbodfr1URcxQ7JzyPR6w0YDBAUpJ1khyaWJDyVHTrRtkwsW3giynuduE2xJX6/f8rzdKKdUp6faHcm/u3E5z6fTz1vAZ8PjdtNlstF8/g4XR4Prn/YpYklOb1e76SSeCc+n2jDjUaj2jBKsTuRkZGEhoZiNpuJiIggKiqK0NBQNRAjVh8EgkuKcLAvNcqNUIkuKZGmkZER9bnValUbiyjPlRul0kxHeZ+JD8X5VgyzwWDAYDBgNpvVSLmStKnX61XnUqfTYTQa1SoVkiRhNpunjMBqNBqMRuOU25RIzlS/I8XxVSQ0ys3J5/PhdDonJREq8g3FmfZ4PIyNjakRapvNNmk8Jz6Um1JoaKiatKNEgKKiooiOjlZXCZQGINHR0YSGhqplpwSCKxm/3z8p+c1ms6l2SAkSDA0NqVFWm82GxWLB4XCoTvmJ9sfvl3C7E9HrgwgLk4iJCWA2/7MCjlINR3koNkexSVqtVl2JU1Bs1VQTesWxP3GbUsHF4/FMedyKfVHw+XyMjY3h9/vVY1M6Fk60U4pt8nq96qRlbGwMjcdDnCTx8NgYm7RaqvR6vCYTGq1WtbGhoaHExcWpCfERERFqzWhltUCRlYWFhRETE6NKzAQCwWWBcLAvNVOd47P520RNppIdrixDKhKIic8dDod6c7DZbGrERVm+VCJWSpRFcXJBvgkpN5OpxmWz2aYcsxLVmiqyOzG6fuJzZQIQFBREaGgoBoNBXeIMCQlRJwnK0nVERMSkpdKJz0NDQ9Ub9NnoC4UGUXA1ci4258TnSjRXsTOyTGuMjg43v/lNAcPDkJLSzw03vI/TaVcr6Ey0ScqkeeL//X4/TqdTtTuSJOFwOE65Kqbse+I4lej4qTplKqVYFfR6vRpQUFYDldcrci+DwUBYWJhqp5QostlsJlyjIWlwkDk/+hGB+Hg8S5bg+fa3CYuLQz9hZfBMtuZs7JNAIJi2nNbBFkV2PwI+rBGVJEnVBE9cujxRTqFkoCvazYkZ6hMrZ0zMVJ/oxE98fiKnuuFNrEhwpm0TnyvR8BMrG5xY8WDicvLE5yc+zvWcCgRXGx/GDimrZQaDgcjISFyuAL29AX74wwAOh5Fly+DBByPIzo4jEPCf0tacaIcAdR+FE59P5HTbFJtxNtsmPj+V/TnRNk2yOUCQ10tQXBy8/DLBBw4Q+NGP0H3/+2iio9GI+vUCwVWPsAKXAYpOUTQdEQgElwLF4ZTlCwb6+mDXLmhrg4UL4dproaQkiLAwM1fNPFeSYOVKGByE3bvRHTgA774LS5ZAVhYI6ZlAcFUjPDaBQCAQnBElcDw0JDvXf/wjGI3wL/8CJSUQHn5Jh/fRo9FAWhrccw+kp8O3vgW//S34fBAWBomJ8j5XzYxDIBBMRGiwBQKBQHBGAgFwOOBLX4LKStBq4ZVXZB8zOPgq9iMlCVwu6OyET3wCxsdh7lz45S8hIUE+UQKB4ErktBpsceULBAKB4LT4fHLk+rHH4PBhmDMHfvhDSE2VlRBXrXMN8sGHhMgzjR//GBYsgKYm+OpXoaNDdr4FAsFVh5CICAQCgeCU+P2yn7h/P+zeDQUFsHw5LFok+5VXtXOtoNXKepmFC6G/Xz4phw7BW2/BihWQnw9m86UepUAg+AgRDrZAIBAITkJRD9pssGcP/OlP8v/vu0/2I2NjL+34ph0aDURHw+23y0mOx47Jmmy3G0wmyM2VHXExIxEIrgqEBlsgEAgEJyFJ4PXCo4/KSY3Dw/D881BUJAdjhZ94CpQTNzwM998Px4/LSZC//rUc/hfVoASCKwWhwRYIBALB2RMIwMgIfO97sGMHZGbCd78LhYWyEkI416dBo5Gd6Kgo+aRdf70c+v/616G2FkZHL/UIBQLBR4CYSgsEAoFARZKguxuqquCDDyApCRYvlqXEYWHCuT4rtFowGGDePBgYkE/qli3w9tty0fCZM2UHXCAQXLEIB1sgEAgEquba5YKKCnj6abmRzBe/CMuWQUbGpR3fZYdGI4f7b74ZsrOhqwueeAKcTtn5nj8fdDoxYxEIrlCEBlsgEAgEqoP9wx/C5s3Q2gpPPQVlZXITGeEHnieSJGtuxsbgC1+AgwflpMef/ESeuQQHX+oRCgSC80NosAUCgUBwaiRJbiLz29/Cm2/KDvV//qccZBUJjR8SjUaWjJhM8LWvwa23QmgofPvbsG0b9PZe6hEKBIKLgJCICAQCwVWOxQL19bBhg+xcz58vS4WjooRzfUFQnOziYvlkSxK89hq8+65caHzBArnro0AguGIQDrZAIBBcxXg8UFMDL74I5eXwv/8Lq1bBjBmXemRXGBqNrLm+9lq5TrbVCs89J4vedTpYvVquPiJmNALBFYFwsAUCgeAq5k9/gnfegepq+MMf4Kab5H4pgouERiM72L/5jSwV2bIF9u6Vxe9r1sjtMQUCwWWPcLAFAoHgKsTphNdfl5vHhIfL+XerV0NEhKxmEFwklAh1cDB87nMQFwc7d8L//I/89/nzIS3t0o1PIBBcEISDLRAIBFcZVqvcyfvNN+XnM2fCDTfINa+FQuEjQKORddgFBbBypVxl5G9/g40b5b/r9fKXIRAILluEgy0QCARXEV4vNDbCW2/J0pBvfhNuvBEWLrzUI7vKUGYyixZBaqrcOvOpp+SlBUmCj31MaLIFgssY4WALBALBVcSrr8LLL8st0P/7v+HBB0UBi0tOaqqswQ4NlUu5fPOb4HbLJf1Mpks9OoFAcB4IB1sgEAiuAlwu2LUL/vxn+fn998Pdd8sJjUJzfQlRItR6Pdx3nyyI37EDfv5zWae9YIFooykQXIYIB1sgEAiucEZHoaNDloXY7VBSIlcLycoSCoRpg0YDOTmwfLmsyT56FN57T95mMEBy8qUdn0AgOCeEgy0QCARXMD6f3Pb8/ffl6PUXvgDr1sm1rgXTkPnzZWfaboff/x5sNtnhvvVWockWCC4jhIMtEAgEVzA7d8Kzz8oJjQ89BP/+7yIYOu1JTITHHoOgIHj7bXj0URgehk9+UtZpCwSCaY9wsAUCgeAKxO2Wm8f89rdyEHTtWvi3f5PLLut0l3p0gtOi0cjR6rvvBrMZtm2To9mhoXLVkezsSz1CgUBwBoSDLRAIBFcYTif09spR6+PHoahIbhJYVCQUBpcNiiZ72TJZItLcLOt8tFo5sp2aeqlHKBAIToNwsAUCgeAKwu+H7m7YvRt+/Wu5nPIdd8gSXsFlyLx5sjPt98NPfiJnrHo8cO+9QpMtEExjhIMtEAgEVxCHD8Nf/yrXu771Vvje9yA9/VKPSvChiIuDL39Zdqhfe00uYO50yrUWw8Iu9egEAsEUCAdbIBAIrgA8HrlayC9/KUewFyyAr3xF7rgtAp2XOYom++Mfl+Uh27fD44/LmuzFiyE391KPUCAQnIBwsAUCgeAyx+2GwUHYtAkaGuS+JNddJ6sLtFrhXF8RaDRy4fJly+RW6rW18MEHcsZqSIjQZAsE0wzhYAsEAsFlTCAgO9cHD8IPfiAHNO+8E+66SzjXVySzZsll/MbG4Gc/k8v3ud3wwAOysy2+cIFgWiAcbIFAILiMOXYM/vIXeOEFuUPjT38qV3ETspArmNhYWZMdHAyvvw7f/77cUeiee+RW6wKB4JIjHGyBQCC4DPH5YGBArhRy9Cjk58v9SNLSZJmucK6vYDQauX36unWTNdkREVBWJktJBALBJUU42AKBQHCZ4fHIyoAPPoADByAhQZbmLl0q+11a7aUe4UeLz+fD7XbT1tZGUlIS4eHhBAUFXdDPkCQJl8tFf38/Pp+P1NRUQkJC0FyqmYyiyV66VNZkHzwo/yD0erk5TXz8pRmXQCAA4CozwwKBQHB5I0kwMgI1NfDNb/4zkPmlL8nBzKvNuQZwu910dnby61//msrKSkZHRy/K5/T19fHqq6/y7LPPMjg4iCRJF+VzzoniYrj9drjvPnjrLXj5Zdi7V66bPR3GJxBcpYgItkAgEFwmSBL098NTT8Gzz8rlkX/zGygoAJPp6pWFjI6OUl1dzQsvvEBGRgaxsbHExcVd0M+QJIn6+no2btyI1Wpl6dKlJCYmop0OM5rYWHm2pdPBe+/BN74hO9hr1sjRbIFA8JGjOcMMXEx/BQKBYBrg98uR65/+FKqqwOWCr34VrrlGzmvT6S71CC8dLpeLgYEBtm3bRklJCenp6URFRV3Qz5AkiZ6eHg4fPsz4+DiLFi0iPj5+ejjYIM++Ghvh7bdlqYjLBd/5jlx1JCnpUo9OILgSaQReAb4z1UYRwRYIBIJpjs8HNhvs3Ak7dsj9RZYtk2tdm81XpyxkIkajkfT0dG6//XaMRiMGg+GCf4ZGoyElJYWoqCgkScI8HSPDhYXyLMztltt5fvCB/HejESIjL+HABIKrD+FgCwQCwTRGkuSu2EePwre+JTvTd9wBn/60hNksIUkSfv8/958YUZUkSdUJazQa9aH8LRAIIEnSpG1K0p6yj/Ieyn7Ke32YyO3Ez5/4PoFAYNJYz3QcJ75faGio+vxUx6Ew1fk48Vin2ickJEQd61Tnc+LfJj6feO5OHNeJxz3x2M46iVLZb9EiudNQXx/87W9yNqzJBEuWyPt8SB2Rcn4CgQC6fyybnMuxnur8CgRXGsLBFggEgmmKJIHdLvtJzz4LXi+sXw8zZ0JIiIudOyvo7+/H6/Wi0WgICgpi1apVREZG4vf7qa2tpa2tDY1GQ3R0NKtWrQLA6XRisVjYvXs3AwMDxMbGkp+fz6JFi04aQ01NDe3t7fT09KDT6UhPTycnJ4eCgoLzPi6/38/Y2BibN2+mrKwMs9nM8PAw27ZtIyoqiszMTObOnYtWq2V0dJT29naqq6vRaDQsWbKE1NRUjEbjpPf0+Xzs37+foKAgYmJiyMnJUbdZrVbq6upoaGgAZEc8LS2NZcuWodfLt8FAIMD+/ftpa2tjdHQUnU5HTk4OxcXFJCYm/uP7kDh69ChDQ0MYjUZmz56NXq/H4/EwMDDAe++9x+LFi8nIyKCjo4MPPvgAs9lMeno611133UmO5NjYGAcPHqS+vp6BgQEyMjLIysoiLS2NzMzM85/EJCbKTWg0Gti/Hyoq4P/+T27tecJ5O1ccDgd9fX1s3ryZu+66C41Gw/Hjx9myZQsJCQnMnDmTkpKSkyZATqeT8vJyjh8/jsViISkpidLSUpKSki64nEcgmA4IB1sgEAimIYGAvNL/+9/LRSE0GnjsMVkFIPcSMZCdnc2LL77IsWPHCAoK4rvf/S5GoxGtVotGoyErK4u//vWvZGRkkJ+fD8CxY8c4evQoHR0dqpNcV1fHm2++yc0338ytt95KTEwMbrebN998E4C4uDgKCgpUR8pqtZ63g93e3k5VVRWbN2+mpqaGe+65h+joaAYGBrDZbGzatAmj0cj8+fOZNWsWLS0t9PX1MTAwwMGDB6msrGTlypXcfvvtAAwODtLS0sLOnTt57733uOGGG1iyZInqYO/evZvu7m60Wi3XXHMNg4ODHDt2jBdeeIFFixah1+vp6+tjx44dAOTk5BASEkJPTw+vvvoqoaGhREZGMjAwwFtvvUVFRQV+v59bbrmF4uJi2tvbqamp4aWXXuLo0aP09PQwY8YMWlpaaG5upru7G7PZzNjYGGvWrCEkJESdYPz4xz8mNTWV7Oxs5s+fzyuvvMJ7772H1+uloKCAnJwcFi9eTFFR0dmfYMWxNRrh05+GmBi5TvaPfwzf/rZcMD06+ry+u4aGBnbt2sXWrVtpbm4mODgYvV5PZ2cnx44dY8OGDRQXF2OxWLjxxhsBsNlsdHV18ec//5nZs2eTnJxMbm4uNTU1/OIXvyA9PZ0bbriBRYsWiUi24IriKlfuCQQCwfQjEACHQw4+bt4sS0TmzYObbpJ9I4MBdDod8fHxaDQaurq6qKmpAWSJiPIIDQ1lYGAAn89HXFwcfr+fiooKjh49iiRJpKenk5ubi8Fg4MiRI7zxxht0dXXhcrlwu91s3LiR4eFhIiIiyM/PJzs7G4fDgc1mO+9j8/v9DA0NUVdXx759+2hpaWF4eBitVktKSgp2u53a2lreeustOjo6cDqdBAUFERcXh9VqZc+ePRw6dGiSVGFsbIze3l527NhBe3s7DodD3V5ZWcnBgwcJBALk5uaSk5NDVFQU/f396j4Wi4VNmzbh8XhITEwkLy+PzMxMLBYLbrdbHffAwACVlZUcOHCA4eFhAoEAfr8fm81GfX09VVVVtLS0MDQ0hMFgIDU1FbvdTk1NDTt27MDr9QJyWcGjR4+yefNmHA4HOTk5zJ49m9DQUI4ePcqWLVuor6/HbrerrzknFCnIvHlyFmxpKdTWwtatcOSI/OM6D7xeLwMDA9TV1VFVVcXx48dxOBwEBweTnp5OV1cXhw4dYu/evYC8KtDd3c2uXbs4fPgwJpOJjIwMZsyYQUpKCi0tLezatYv3338fh8OBf6LWSSC4zBERbIFAIJhGSBKMj0NHB/zXf8lS2k99Sg5G/kOpAMiOdEhICHfffTc2m43333+fyspKcnNz1SipshSfkZFBQkICdrudv/71ryxdupSvfvWrGI1GMjMzGR0d5dixY7z00kvceeedhIeHExISwsaNG1m6dCmhoaFotVry8vJYuHAhSR+iKkVOTg7j4+MMDAywZ88eoqKiKCsro6ysDJB1u2+99RabN2/m4Ycf5uabbyYhIYHx8XEkSeLFF1+ktbVV1W/Hx8cTHByM2Wzm//7v/076vMrKSvr6+li7di0ASUlJFBcXs2TJElVDbLFYeP/997njjjsIDg7GaDRSUFDAkiVLiIuLIyQkhMzMTO655x6qq6tpbGycdDxarZa6ujqampqIi4ujtLSU+fPnAxAcHMyrr77K7t27VWfZarXy8ssvY7fbSUhIIDc3F4Dbb7+dpqYmLBYL11xzDZ/97GdVXfk5ozjZK1bIDWmOHZOXQ3p75SzZ2bP/ud9ZMmfOHEZHR+nr66OhoYHU1FSuueYa8vLyAGhpaaGxsZGKigpAdsjLy8t5/PHHueWWW1i6dCkpKSkA3HHHHdTW1vL+++/z+OOPc+utt5Kbmzs9k0cFgvNARLAFAoFgmiBJsizk73+Hr3xF9ol+9Sv413+Ff/glJ7F06VLKysqIjo7m8ccfZ3h4GJB11q+99hpLlixh9uzZOJ1OXn/9dSwWCzt27OC///u/efTRR3n00Ud56aWXGB4eVqO2VqsVvV5Pbm4uP/vZz/jf//1ftm7dCsBdd93FypUrP9RxGgwGwsLCAMjIyFCdLoDk5GQSExPR6XQUFRWpDqZWqyU3N5egoCAcDgf9/f3qa7Ra7Sk7NyYlJWGxWHjooYd4++236evrIzMzk4ceekitNmI2m8nIyODLX/4yf/rTnzhw4ABarZbPfe5zqrQGICgoaEpdtDLZ0Wg0xMfHk5qaqm5LSEggMTGRrq4uNUI7Pj5Oe3s7FosFp9OJz+cDoKCgQD32np6eCxPR1ekgLU0unr5ihVyK5pFHoKlJFvWfI3q9nuDgYADS09OJjY1Vt2VkZGAymejt7UWSJA4cOEBFRQWdnZ3Mnz//JOd5/vz55ObmYrfbee655xgcHPxwxyoQTCNEBFsgEAimAZIk17r+y19gyxZ5Ff/RR2HuXFlGe6p8N71ez4wZM1i5ciUvvPAChw8fJiQkBIPBQFVVFV/4whdITU3F5/PR1tZGcHAwJSUl3HPPPep7eL1e3G43drudvLw8EhISCAkJ4dOf/jTr16/n0KFD9PT0UFFRwR133DHJgfywKHIWhaCgIDXxUKfTTdLlGgwGNBqN2hr9xMomE1Ger169mkAgwMsvv8yvf/1rduzYQVlZmRrRVsrvPfjgg/zyl7/k7bffpqamhoULF/Lggw+edcv1U1VY0Wq16HQ6tVIIyGUFCwsL2bRpEz09PXR0dKiRfa/Xi8FgoKioSD0PHwqNRnayIyLg4Yflbo+7d8OPfiQ3pMnMlCPa58GJ353yfSnH2tfXx9DQkFrW8MTJSXZ2NsnJyfj9ftrb21U5jkBwJSAcbIFAILjESJLcF6ShATZulCuHFBbCLbdAQoKsuT4VGo2GzMxMFi5cyNNPP01VVRUxMTGq/jc2Npbw8HBGRkYYHR3F7/eTkJDA/PnzT1sizefzsWrVKo4ePcqBAwdoampicHCQ6OhoFi1axMyZM9Hr9Rc8Me1Ep+3EbUoJPCXqeyaKi4vxer10dnayd+9ehoaG6O/vJyIigtWrVxMcHExMTAyrVq2ioqKC6upqqqur6erqIjs7m7KyMlJSUlQ5ybky1Tk2mUyUlJSQlpZGX18fFRUVhIWF0dnZiUajITc3l+Li4gtXz1txshcvlguqj4zIiY+LFslF1gsLITj4Q5fwO7Fsodvtxuv1qlVEJk4yQI7ux8TEAHJFlRO3CwSXM0IiIhAIBJcISZIfPh90dcF3vwuVlXJO2v/7f7J09mx8rOzsbJYvX058fDwffPABu3btore3l6ysLEJCQlSHVamYYbVaT0qem5g0qNQ4zs7O5rvf/S6PPfYY119/PQ0NDfzXf/0XTz/9NHa7/SKckQuHcjwJCQlcd911/OEPf+DOO+/E7/ezYcMGvvjFL2KxWPB6vRiNRnJzc/nNb37DI488wty5czl8+DBf/vKX2b59O06n84KOLTIykrVr13LnnXfS1dXF+vXr2blzJ88//zzR0dHcf//9lJaWnnX0/KzQaGQn+oYb4Otfl2duP/85vPIKKHKb03d2PmfMZjMhISH4fD7q6+txuVyTtkdFRREeHo7BYCA8PPy8JzECwXREONgCgUBwCQkE4LXX5BJ8+/bBL34hS2TPpQqeVqslJiaGhx56iNbWVp599ln+/Oc/c++99xIREQHI8oqZM2ciSRJVVVW88sork95DkiRsNhuvvfYa+/btw2az8fLLL+N2u1m1ahU/+9nPeO6554iMjKS2tpbNmzdfyNNwUfB4POzevZvy8nIiIyP5n//5H9avX89XvvIV+vr62LhxI8ePH6ezs5NXX32V4OBgPvWpT/Gb3/yGp59+GofDQXl5OeXl5Rd8bDqdjri4OB588EG+853vMHPmTB577DH+8z//k7vuuuuCf55KcDDk5cHLL8OMGfDOO7J0pK8PLnAVj8LCQtLS0tRkR8cJ1Us6Ojro6ekBZD32eSd0CgTTEOFgCwQCwSVAiV4/95wsiz1+HL74RSgrk4OL5xLM02g0mM1m1qxZQ2RkJG63G5vNRnZ2thoFDQkJYcmSJSQlJdHa2sozzzzDu+++S0tLC01NTezZs4f169eTmJhIWloaLpeLTZs2YbPZ0Ov1hIeHs3DhQuLi4jCZTGqiG0BjYyN//vOf+fnPf87Ro0dPilSeiN/vZ3x8HJClKBOT+TwezyRZwcRt4+PjavfJiRIRSZLUiLzL5VI/XynTV1lZiUajISQkhBkzZlBUVKRGV7VaLRaLhc2bN+Pz+dSSgAsXLiQ8PByj0aieQ5/Pp5bmm+gsBgIBXC6XugIwccxutxun06kekzKR2bp1KwcOHMBqtWIwGJAkCZfLpe530dBoQK+H+HjZsV60SI5g/+hH0N4uZ9meBp/Pp353fr9/0rGOjY3hcrnU7yYlJYXi4mLmzZtHfX09fX19k85be3s7o6OjpKSkcN111xEp2rkLriCEBlsgEAg+YpRSfG1tsGEDdHdDcjLcfrtcLWSC73rWGAwGZsyYQXp6OjabjaioKCIiIlT9r16vJysri7KyMvbs2UNNTY1aaxpgdHSUoaEhkpKSiIuLY3BwkMOHD1NfX68mqfn9frXKx8RSfZ2dnbz77rt0d3eTnZ2N0WgkLS1tynE6nU4GBwfp6uoCoL+/n/7+frW5TXd3N4ODgwQCAY4ePUpsbCwajQa3201nZyculwuHw0FbWxvp6elqHeu2tjb1/z09PVitVkwmE+3t7dhsNhoaGoiNjcXr9RISEkJWVpbaEbKzs5OamhoaGhpISEjAYDDg9/vJzMwkLS2NqKgoRkdHaWtrw+FwMD4+TmdnJ4ODg5hMJoaGhtTKGYODg3R3dxMVFYXNZmNgYIChoSE8Hg/Hjx8nODhYrSJy5MgR9bPMZjNBQUGYTCbCwsKIj48nLy+PkJCQCy+d0GggKAhWrpQF/xYLvPeerE1auBBycuQZ3gmabJvNhsViUSu4dHd3Y7FYMJlMjI6O0t/fz8jICB6Ph/b2dpKTk5kxYwarV6/mxRdfpLa2lqCgIPV7O3r0KBqNhqVLl1JQUHDhNOcCwTRAONgCgUDwEaEEJiVJDhr+8pfwwQdwxx1yres5c87/vbVaLSaTicWLFyNJ0qTycoDaSv1rX/saaWlpPPXUU6xfvx6AmJgYioqK+OUvf0lSUhIGg4FAIIDFYuHJJ59kzpw5FBcX093dzezZs5k3bx4LFy5U33toaIjDhw/T3t7O9u3b1VbkU9He3s7BgwfZvn07Op2OXbt2ERISQmRkJF1dXWzZsoWamhokSeL555/HbDaTmZnJ8PAw7733nqoff+mll8jOzsbtdtPQ0MDrr7+ORqPhyJEjhIWFkZOTw/Lly7HZbFRWVvKTn/yEm2++meHhYWw2G7feeivLli1Do9FQV1dHf38/P//5z1m0aBGxsbEMDAxwww03sHr1ajIyMmhoaOCFF16go6MDu93Ozp07Wb58OZGRkbS1tVFeXo4kSRw8eJDIyEiSk5PZt28fhw4dorW1FY/HwyuvvMLtt99OVlYW0dHRdHd3U11dfVI0PiwsjNLSUn7961+TkZFx8aQT4eGwbp0sGbnvPlmTfd998NnPyh2NJGmSk3306FF1RUCv17Np0yZ0Oh0hISGUl5dz+PBhenp6CAoK4tlnn+XTn/40c+bMITMzE5vNxnPPPUd5eTmrV6/G7/dTXl5OUVERX//61wkKChKdHAVXFJozLEVdxHUqgUAguLpQzO0778Drr8sy2P/9XzmQWFBwdgmNZ+Jzn/scJSUl3H333cTExJzktPj9fjweD2NjY3R1daHRaAgPDyc6OhqTyaRGSwOBgNphcXx8nPHxcRITE5EkCZ1ONykBz+VyMTQ0xObNmwkNDSUnJ4d58+ZNOT6/34/X62V8fBy/34/BYFAfgUCA8fFxVYphMBgIDg5Gq9WqEgq/349Go0Gv12M0GpEkSX1Pj8eDVqtVazXr9Xq1G6JGo2F0dJTIyEhCQkIA1H+9Xi82mw2tVsvY2BgajYaYmBgkScJgMKDT6fD7/bjdbnw+H5IkodVqMRqNalk6l8tFIBBAr9er41bkFMprQkJC1Dre+/fvp729ndTUVGJiYrBarbjdbsbHxxkaGmLPnj3MmTOH66+/Xm1ac1FQllNaWuDzn5frQ86YAU88ASbTJK2Sz+fD4/Hg8XgIBAIEBQVhMBjQ6/X4/X71HCjnNjg4WK0q4nK5sNlsDA8PMzIyQmJiIlFRUZP2Ew624DKjEXgF+M5UG0UEWyAQCD5CNmyAN9+Eujr4l3+RO1mnpX1459rr9dLb24vZbFarM0yFTqfDaDQSHBys6qgVh3AiWq2W6OhoVe/s8/lUR+hEFIe2r6+P6667jvT09FOOU6fTqVHPqbadTiZwqm0Tm5+ciBL9lSRJPeYT60sbDAb1WI1GI8BJ49Pr9aeNJE/1+UFBQSdVAhkaGqK+vp6nnnqKb3zjG6SkpBASEqJOOPx+P2NjY+Tm5tLW1nbKkoUXDEUukpEB//ZvsHkzHD78zw5HCQmyZhv5HOj1ekwm00lvc+Kka/JHaFQJTGhoqKrjvyjyF4FgmiAcbIFAIPgIGB+Hnh549105WBgRIWuuc3JgCl/zjAQCAZxOp6plDgQC1NXVkZKSQlJS0hlLvGm12tM6jBMjijqd7pQOLMiJiTabDbPZTFJSEtHR0ed+QBeJiQ7qqdpwTzxWxcG+WFitVpqbm9m2bRuPPfaY2po9PDwcj8eDy+VCq9WSnJyMx+NRO15eVLRaudnMddfJEeyeHjnzdu5cKCmREwOU1usfAmWl4iM5JoHgEiMcbIFAILjISBIMDsKf/gQvvgg33wz33gurVp3/e3o8HpqamvjZz36myjwOHDjA7373O2bOnHnhBn8WjIyM0NXVxWc/+1lV0iGYmsHBQY4fP87w8DCbNm1iwYIFZGdnk5SURFdXF+3t7XR0dNDS0sIjjzxCXFzcRze4xEQ5ISAvDz75STlJ4JOfhAcekDNvT9BkCwSCUyMcbIFAILjI7Nkjd2j87W/hP/8TbrpJDgx+WCRJoqGhgd7eXuLj43n00UcpKipSa19/VCQmJhIfH39hWntf4SxYsIDExET0ej27d+/mb3/7m1qqLycnhwULFlBWVsY3vvENwsLCPvrJSkyMXLrvlVfgK1+B9euhsRF+9jM50i0QCM4KkeQoEAgEFwlJgl274I034OBBWeb65S/LspAPq6Lw+/1YrVa2b9/O2NgYRqOR0tJSUlJSLmwHQMEFRUn4a2lpoaenh+HhYcbGxtDr9URERJCQkKDWIldaw3/k+P1yO/W//Q1274aODvjSl+DjH4eoqI9+PALB9EQkOQoEAsFHjdcLQ0Pw/vtyzlggALfdBjNnysUZPixKJ8Dbb78d+KeOWFRimN5oNBpMJhOzZs1i1qxZ+P1+JEmaXtF/nU6OZK9ZA06n7GC/8YZc6qagQDjZAsFZMI2uaIFAILgykCQ5APjWW/D447BsmVzn+h++8AVFVGG4vJnW39+MGfCJT0BSEjzyCMTFwa23yo63kIsIBKdFONgCgUBwgamqkhvI/PCHcim+W2+FxYsv9agEgvMgO1vWM3k88ItfyG1HLRY58VGslggEp0RMQQUCgeACIUmyHOSNN2RpyIoVctR65szzK8UnEFxydDq54+N118ldHwFeeEHWZjudl3ZsAsE0RjjYAoFAcAHw+cBqhW3b5ITG0VE5J2zBAoiPv9SjEwg+BAaDXLpv7Vo5U7etTS6L09kpnGyB4BQIiYhAIBB8SCQJbDbYvl2WhcyaJde5/tznLvXIBIILyA03yBm6QUFy2b6oKDmyPXeukIsIBCcgHGyBQCD4kBw7Bjt3wmOPwcqVcP/9st8hEFxxlJXJUWyXC/74R6ivl1usL14snGyBYALCwRYIBILzRJLktuevvy7Xu87Pl6uFlJRcmFJ8AsG0IyhI1jw98IBch7K7G558EhISIDVV7vgoEAiEBlsgEAjOB78fHA4510vpxXH99XDNNbKfIYJ5giuWoCBYsgRWr4awMNi7V55hDg3JBeAFAoFwsAUCgeBckSQYG4Pqaviv/4KBATn/61vfkv0N4VwLrmg0Gvnx2c/CffdBZib853/C/v0wOChfIKfvEi0QXPGIVukCgUBwjhw/Dvv2waOPyo3tPv1pufdGRIS8XTjYgqsCSZKriLS1wUMPybPOj30MHn4YkpPFhSC40jltq3QRwRYIBIKzRJKgtxfefhteeQUiI+HBB+W8r/Dwfwb2BIKrAo1GTjbIyIAvfEG+IA4elDXZNpusoxIIrlKEgy0QCARnQSAAbjdUVMgVQ5qaYPlyuXJZRoboHC24StFqITQUbrlFnmmOj8OGDXI7U7tdONmCqxZxSxAIBFctilT0TJJRSZKd67Y2+H//Ty7Lt3y5XAo4JkZudicQXLUoTvY3vwl33CFn/375y3DkiHzhnOoCO9sLUCC4DBEOtkAguKoZG5ODbj7fqfcZGoIdO+Duu2UpyCOPwHe+IyqSCQSTiIiAu+6CP/xBjl5/+9vwl7/ITvZUjIxAY6O8JCQQXGGIOtgCgeCqRZLkCmO9vXIA7mMfA73+nzpqSZJbnm/cCFu3yn/7zGfkCmUxMUJvLRBMQquVddhFRXIb07fektubms1ygXidTr5oAgF51vrss9DaKr9u2TL5X3FRCa4QhIMtEAiuSiRJvs9v2yY3o4uMlBvF5OeDwSBv93qhpga2bJFL8s2bJ0tNY2LkfQQCwQkEBUFsLHziE1BbK5fceeMNWLoU0tNlJ9vhkEv6vfwytLdDXJycFBkWJs9wBYIrAFGmTyAQXJUEArI8ZO5c6OqCkBDZgX72Wfl+HwhAXx/cdpssIZk7F373O9kRFwmNAsFZUF8vX1BPPilnA3/ve7LG6vBhuPde2dH2emXH+oUXYNEi+QITCC4PTlumT0wVBQLBVYnFAu+9J//r8cj3+YoKWQLywANyEO4HP5C12Q8/DB//uHzvFyvYAsFZkp8vX0xZWXLReI9Hdqrr6+WItZL44PXCU0/J+wkHW3CFIBxsgUBw1SFJsgT03Xfl6LRSxMDlkoNrL70k3+ctFrlZ3fLlkJgoItcCwTlhMEBKihyZLi2Fo0flTo+Dg7Jzraygezxy/eyeHnn/0NBLO26B4AIgHGyBQHDVMTYm38v37JlcPSQQkNueK3lZYWFy4mNamqgYIhCcFwaD7DCnpMgJDUNDJ5fl8/lkLXZrq9x2XTjYgisA4WALBIKrjsZGKC+Xtdcn4vfLq9iBAERF/bM7oyQJeYhAcE5IEjQ3wzvvwNNPn77WtU4nZxyHh8tOtrjYBJc5YsFTIBBcdXzwgVzY4FSSD78fnE45oHb//XKxg+Hhj3SIAsHlzyuvwM9/LndkOlMjGZ8PNm2Sl5WU5jQCwWWMcLAFAsFVgyTJVcOamuSujKe7hwcCsj772DF4/XV4/nm5L0Yg8JENVyC4PHG54M035Znp3r3yhXM22GzyBVpbe1GHJxB8FAgHWyAQXDVIknzvbmsDq/XMQbJAQG5It3MnvPaa/Dq//6MZq0Bw2eLzyRdaba3cxelcXtfdLeu3RARbcJkjHGyBQHBVoDSWefZZ2VHW6c7+dcPD0NBw+q7PAoHgH5hM8C//AnfcAbNny1osg+HMumpFs/3SSycnQgoElxmi0YxAILgqcLnk1eeVK+VCBhOrh5yI0rHZ74cFC+Dmm2HFCrnZjGg0IxCcAUn6Z7ZwX5/sND/+uCwXsdnkC+hUS0FaLURHw9tvw8yZcikfgWB6IhrNCAQCgdUKW7fKyYunu7cr1UPS0qCsDJYske/zGRnyfV8gEJwBjUZueR4ZKde3jIiAz31OjmbX1srF5qcq4QPyBeh2yxVFUlKEgy24bBEOtkAguOLx++WmMe+/f2rnWqeTV7ajoiAvT45W33MPzJkjr24LBILzwGiUH7fdBsXFcpUQk0luLDM0JDvTXu8/99do5OWlbdvgppsgOfns9VwCwTRCONgCgeCKx+GQS+69887J0hCtVl7RNpnk+/+3vgUlJfJ9XSAQXEDy8uTHvffCrl1yCb+aGrm7k6K5liTZ6d6yRU6WyMiQZ70CwWWGcLAFAsEVz44dsHHjP58rGupAAIqK5G6NS5fKjnVkJAQFXZJhCgRXB0FB8gVXWChnD2/fDk88Ieu3fL5/1sL84AM5+r1mzSUdrkBwPggHWyAQnBGPx4Pf78fv90/6v/8feguPx0MgEGCqpGm/34/P5yNwigLSOp0OvV6P9hSZg8HBwWg0GjQaDVqtlqCgIPR6PTqdDp1OR1BQEFqtFs0UFQokCTweqKqCAwf+ed+OiZE11gsWQGmpHLnOzITExPM7PwKB4BzQaGTHOTlZdrbDw2Wd9sGDcOSIXKje75cv2qwsuP76f2YeA5Ik4fV6VRvk9XoJBAKqHQLUv5+qkIPP58Pn851yu0ajUW3LVJxotwwGg2qTDAaD+nq9XrhZVyvimxcIrhAkSUKSJAKBgHrjCQQC5/xQ3mfiY2xsTL0hOZ1OvF4vPp8P7z+0ky6Xa8qblSRJ+Hw+xsfHVWd8IhqNBr1eT3BwMLpT6CzNZjNarRatVotOp8NkMmEwGNSHyWRCp9Op+wCqw63RaHE4IqiuNtPUZECj8REW5iQtLcC8eRJ33qll3jwdBoPsvI+NaVXnXXHoBQLB+THRBik2SbFRqr3RaAhkZhKIiyMQHIzk86Hp7ibMbsfU2IinuprRxkbsej0SqK93u934fD48Hg9utxu/38/Y2JhqZ7xeL+Pj41NO7CVJwuPxqE76VOh0OkJCQk7pIJ9ot4xGo+pcG41GtFqtaqtgok36Z7BA+Zti2yb+fartE+2g8rlTBRYE0wNRpk8gmKac4dqccn+v18vQ0BBWq5XR0VFsNhs2mw273Y7T6cTpdKp/m/jcbrczNjaG0+lkfHwcl8uFx+NhfHwcj8dzkY7wwmIwGAgNDcVgMBASEkJwcDAmUxgOx6fo71/L+Hgq0dFdLFmynczMYWJj/YSFhREREYHZbMZsNhMREUFCQgIRERGEhoYSdJ5aEXHTE1wpnKsdmojD4cBmszEyMqLapLGxMcbGxrDZbDgcDtXu2O12HA4HfoeD4OFhPtbczMqREVq0Wv5mNvOKJDHu8aiO8eWCRqNBp9OptkmxT6GhoZhMJkwmE6GhoYSHh2M2mzEajYSFhREWFobZbFa3R0ZGEh4eTlhYGFFRUURHR6PX68/L1gj7dME4bZk+4WALBNOUsbExRkdHGRgYYGBggJGREUZGRhgcHGRkZES9eVmtVvW50+lU5RgTI9Imk4ng4GBCQkJUox0cHIzRaFSNvLI9KChoUoRYr9djMpnUyG5oaCh6vR6DwaA6oMHBwej1+imjPRqNRo0yT4Xb7Z5yKVeJnjscDvX/Pp9Pdf69Xi9erxen06nKUJSolhy98jA25mfLlpsJChrAaOzAaDyM1zuA1+vA43GpN3slsjYxUqREx2NjYwkLCyM0NJSoqChiYmLUG2BsbCyJiYlER0cTGRn5oW56AsF0RLnurFbrJDtksVgYHh7G4XBgt9sZGRk56bmycqVEsAOBwKRry2g0Ehwc/I/JsPw8JDgYU3AwiSEhZHi9RDgcBNnt1C9ZguEfkgu9Xq9Gl4OCgggJCUGr1WI2mydFgo1G4ykj0EqE+lR26UwRcMXWKNsVO+L1ehkbGyMQCOByuVT5irLd7/czPj6O2+3G7XarAY2xsbFJz10ul/rc7XafFNk2GAyTAgNRUVGqXQoPDycqKorIyEgiIiKIi4sjPj6eyMhIwsLC1Ki64EMjHGyBYLqgRJmHh4fViM3IyAg2m001shMjzmNjYzgcDjmy8w8nEJgkXVCcOeWmokRBlCjuRKc5KChIvaEpDvSJz5UbmLIMOVHnrNVqCQ4OPmmZUtEiTiWp0Gg0qj5xqvPh8/kmHduJ28fHx9X/BwIBVZ6i3LQV3aVyc/unLtPH+LifY8dyMBisBAUNodf34Xa78Xg8qnRFidJ7vV7cbjcOh2PSPhPHooxx4liVCYrRaJwUiTKZTOrNTrkRKk640WgUNznBJUP5/Sp2xuFwqDZJmXQq9mlsbAyXy6U+d7lcjI+PI0nSJNujMPFvir0xGo2qTZpodyZO5pXnitMcHBxMqCQR6nZjcrsZzspC9w+7pDiYig1S7M9EO6PYnVPJvJT9p9ouSdIkmd1UnGi3FDukaMEn6sQn6sOVYIDX61VtjLJaqAQNTnzu9XrV8z7RIZ+Y9zLRJk20VcpEIzQ0VP0eFEdcCbAoz0NDQzGbzURFRREREXHa3BgBIBxsgeCjQzHMiuFUDOlEzbLT6aSrqwuLxcLg4CADAwNYrVbVsVYkHsoyqBItNpvNhIeHExMTQ1RUFKGhoYSFhRETE0NkZKTq3EVHR6sSB2WZ8WqMqCoVvzSaM3doVm5oDocDi8WiOhOjo6MMDg6qkxyr1crw8PCk70rRnys3u4iICMLDw9XvIiYmhoiICCIjI0lKSiI9PV2VpSjOhDKhUSZByo3tavzeBB8eZTKq2CFl8qk4fIpTNzAwwNDQEIODg/T29mK1WrHb7epvW4lIu1wugEm/04l2KDw8XI2WKs8jIiKIiIhQJ5mKjRKJf+eG4iwrkhpF1jc0NKTaKWUlU5kwjY6OqpIcJWCgTBg0Go0qM1Ec6+joaHUVLioqipSUFJKTkzEajeokSAm8KI+goKBJQZarFOFgCwQXmlNdN36/n8HBQRobG+nt7aW3t5eWlhba29vp7e2lr6+Pvr4+VY4QHBxMamoqSUlJxMbGEhsbS3JyMsnJyZOeJyYmqjIMwfRAufEpE6XBwUH6+vro7u7GYrEwNDREb28vXV1dDAwMMDw8rDoqRqORiIgIMjMzyc3NJSkpicTERLKyssjPzycxMZGoqKjTOtjC+b66Od29OxAIMDo6ytGjR1W7097eTkdHh2qDOjo61Ei0VqslIiKC5ORkoqOjJ9kdxflKSUlRf5dKdFP8Bqc/Xq9XtU1K8Ka3t5eenh51EjXxucPhAOTId2RkJLGxsWRmZpKRkUFiYiJJSUlkZGSQm5tLfHw8ERERp/38K/w3IhxsgeBCIkkSQ0NDdHd3093dzbFjx9Qb2cDAAN3d3Xi9XlXjpzjKMTExxMTEqA51fHw8sbGxqm5w4nLnRInGxKoWV7ixuqxQbOfEkoVKdHDi/ydGEO12O21tbZMih93d3QwODjI8PMzw8LD6uwkLCyM1NZWMjAxSU1NJTU0lNzeXvLy8SdUJBFcfykrZwMAA/f399Pb2cuzYMdWB7u/vp729XZUq6HQ61YFWbNHE/AHFLp1oh071XJENCHs0/VF+KxNldRNt1ERbpVRkGRwcpKOjQw0UKAGEkZERRkdHGRoaQq/Xq3KTlJQU0tPTSUlJUW1WTk4O4eHhmEymS30KLibCwRYIzgePx8PY2Bj9/f3q7N5qtWKxWFRDo5SsU/SISp3muLg4VZ6hSAaUpVJFwqFsV5xnwZVNIBBgfHycoaEhVWKiLO0qz4eHhxkcHMRut6vSE2VZ12AwEBkZSUJCgprAlJSURFJSkrpcHxERIX5LVwjKvVmRBAwMDKgyjuHhYYaGhtTkZiWZbmIZOJ1OR3R0tKqrNZvNREdHT7JFEyvohIeHi9/OVU4gEMDn86lyuIl2SqkAY7fbGRwcZGhoCKfTicvlUnXmStnV0NBQYmNjiYqKIioqioSEBHX1Izo6mri4uCtlBeS0DrZYbxZc9SiJdhOTS8bHx1VD0tjYSFNTE11dXfT09NDV1UUgEFAz4dPT08nNzVWlHdnZ2eTm5hIeHo7RaLzUhyeYJijJRqmpqVNuVzSzbW1tdHd309PTQ2trK8eOHaOnp4eBgQHsdjuBQEC9geXk5JCXl0dGRgZpaWmkp6cTFBSk6iaVBFex+jG9UeRGSkUdpYLF+Pg4FouF3t5empubOXbsGN3d3fT399PX16cmEprNZtLS0sjOziY5OZmkpCSysrLIzMxUnWyB4EwoASJFFjQVgUAAp9Opyh57e3tpbW2lra1NXT0ZHR3F5/MREhJCZGQk2dnZZGVlkZKSQlpaGvn5+ZjNZtVWKYn2V5qmW0SwBVc9kiTR1dXFkSNHaGxspLq6mkOHDtHV1cXw8DCA6sjk5eUxa9YsioqKSE9PJy4u7pS1koVDIzhXTmWPlcYYx48f58iRI7S1tdHS0kJVVRW1tbU4nU4AYmNjmTNnDvn5+RQUFFBaWkpxcbHa+EIwPfH7/bhcLioqKjh69CgtLS3U1tZSWVmJzWbD4/EQEhJCcXExubm5ZGdnU1RUREFBASkpKcTExJwyP0PYIcGF5nR+o8/no7u7m6NHj3L8+HE6Ojqorq7m8OHDatQ7KCiIgoICNedk7ty5LFiwgMTExDNquqcZQiIiECgoy/S1tbUcPXqUY8eOUVtbS319PW63G51OR2JiInl5eWpEsLi4mLi4OLWMlDLb1k8oGSUQXEwm1gGfWL7L7XYzNjamav8bGxtpaWmhu7ubvj65JGFsbCzp6enk5eUxb9485s+fT3x8POHh4Zf6sK5KlJrI7e3tHDx4UHWo6+vrsdvtasOk1NRUdXVC+f5CQ0PVkpoTy9oJmZlguqDYqYm9CiauCg8NDdHc3ExLSws9PT10d3fT0dGhJtomJSUxf/58Zs+erU4mw8LCpuvvWzjYgqsXpbV3c3Ozusze3d1NZ2cnY2Njqn46ODiY2NhYoqOjiY+PJyUlhbi4ODWbXkQABdOVQCCg1jHu6emhp6eHwcFBLBYLnZ2dDA4OqiUfDQYDKSkpqiYyIyOD7OxstZSg4MLjdrux2Wx0dHRMSnDt6+vDarWqtZQ1Gg3Jycmqvj4uLo6UlBQ1KfEK0q0KrlLGx8fVvKbe3l41gbKrq4vu7m7sdrvaVCckJISoqCgSExPVBEqlikl8fPx0uQ6Egy24elAyppWuWA6Hg97eXrZu3UpTUxPt7e20t7ej1+tJTk4mIyODuXPnsnDhQnJzc0lISBDVGQRXBIqe+9ChQzQ1NdHU1ER1dTUOhwOTyUR8fDyzZ89m8eLF5OTkkJaWpjbNUereCs4dpWKMsrowNDREV1cXBw4coKqqivb2dqxWK5IkqZVhcnNzmTNnDvPnzyc6OvpKr7wgEKgouQcNDQ1qRa6DBw9y7NgxHA4HgUCAjIwMZsyYQX5+PjNnzmTmzJmTuoAGBwcDl0QOJRxswdWDkhT0zjvvsGfPHmpqaqivryc0NJTZs2erDsWKFSuIjY2dMglxmsyMBYIPxVSt591uN4cOHaK+vp6amho2b95MZ2cnISEhpKamsm7dOm644Qby8/NJS0u7RCO/vBkYGKCtrY0dO3awYcMGmpqaGBwcxGQycc011zBnzhyKioooLS0lMzNzyqQuYYMEVwun8kGVVZ+6ujp2797N7t276erqYnR0lLS0NFatWsW8efMoLS2ltLT0UjXmEg624MpFqbywY8cO9u7dy+HDh2loaMDv96u1OEtKSli0aBERERFqS2uTyST004KriomVKsbHx3G73TidTjW6XV9fz8GDB3E6nYSHh5Oens5NN93E6tWrSUpKwmw2X+pDmJY4nU56e3vZtWsXmzZtorW1Va1nPnfuXAoKCsjPz2f27NlERUWplV2MRiMGg0E40wLBFCgrQUpreKfTycDAAK2trezbt4+6ujosFgsul4uUlBQWL15MSUkJZWVlZGdnf1TXlXCwBVcWkiRht9vVzmTK0rfdbsfn82EymdTkoJSUFHJzc8nKysJgMAiHWiA4AaW+8vHjx9UkSaXtclBQkHotZWdnM3v2bBISEk5ZOedqQJmk9Pb2Ul9fT2trK52dnbS0tDA+Po7JZCIiIoKMjAwKCgpIS0sjKSmJ1NRUoaEWCM4TSZJwOp0MDg5y9OhRWltb1ZyT3t5eAMLDw0lOTmb27Nnk5eWpLd8v4nUnHGzBlYFSQcFut6uz2MrKSvbv308gEKC4uJg5c+awYsUKFi9ejNFovKJqagoEHwVHjx6lrq6OQ4cOsWHDBoaHh4mNjaWoqIiPfexjzJ49m5iYGMxmsyqxutKdRiX673Q6cTgcWCwWqqur2bhxI42NjVitVsLDw1mzZg3z58+nqKiIoqIiUd1DILiIjI6O0tXVxdatW9m+fTutra1YLBaysrJYunQpc+fOZe7cucTFxWE2mzEYDBfaJxAOtuDK4Pjx41RVVfGXv/yF9957D6PRSFZWFh/72Mf41Kc+RWJi4knJQeLmJhCcG8o9QYnUvv/+++zYsYNt27ZRU1NDXl4ey5cv57bbbmPt2rVXRRMbpbzn3//+d958800OHTpEX18fpaWlXHvttSxdupRrrrlmShnNlX5uBIJLxYn+a09PDzU1NTz77LPs2LGD4eFhQkNDueeee/jkJz9JQUEBUVFRF3IIwsEWXL643W76+vp46qmn2LNnD729vURERHDrrbcyb948MjIyiIyMJDIyEr1eLyQgAsEFQonaOhwOnE4no6Oj1NTU8P7779PU1ERfXx/z5s3jE5/4BHPmzCE3N/dSD/mCIkkSXq+XiooKysvL2bBhAx0dHeTn51NcXMyKFSuYMWMG4eHhmEwmzGbzVTHZEAimI0r9bbfbzejoKP39/TQ3N1NRUcF7772HVqslOzubFStW8MlPfpKYmJgLIXUTDrbg8kK5sSmdFauqqqiqqkKv1xMfH09JSQnLli1TnWshAxEILj5+v5+enh6qqqo4cuQIDQ0NdHR0kJqaSnZ2NnPmzGHlypVERERc9qUuHQ4Hra2t7N69m9raWnp6enA4HMyYMYPi4mK1XFhcXJyY1AsE0xC3283AwADHjh1jz5491NXVYbPZ0Gq1zJs3j5KSEvLy8igoKPgwycbCwRZcPvj9fjweD93d3bzxxhvs3LmTAwcOkJ+fz7p161i2bBmLFi0CxNKrQHCpsFqttLa28vjjj7Nnzx4AsrKy+PrXv86MGTPUahlw+VynSg19h8NBe3s7mzdv5q9//Svj4+MkJCRw44038i//8i/ExcVNWd5TIBBMPyRJwuPxsHXrVnbt2sXmzZvp6upSy/WuW7eO5ORkgoODzydYJxxsweXD8PAwNTU1fOELX8BqtZKRkcHnPvc57rnnHoxG46Sb9eVy4xYIrjQm3jdqa2t57733eP7552lsbOThhx9m7dq1rF69Grh8rlNJkujs7OTJJ59k06ZNVFdX8+CDD3L33Xczb948YmJi1H0vl2MSCK52JtqqQCCA0+nk73//O0899RSdnZ1ER0fzi1/8grKysknX+FkiHGzB9EaSJAKBAJs2bWLLli288847pKWlcccdd7Bo0SIyMzOJjIy8ZPpGRdvV1dVFa2srhw4dYt68eRQWFpKamvqRjKGhoYG2tjba29vV2XdRUdEF072OjIwwPDzMyMgIc+fOPefz7PV6GRwcpKWlhebmZnJzc0lLSyMtLW3KBgAOh4Oenh7q6uro7+8nOTmZ9PR0CgoKJk2kAoEAjY2NDAwMMDY2dsrPz8jIICEhgejoaBobG7FYLDidzlPun56eTkJCAnFxcVNu7+vro7u7m6amJvR6Pfn5+aSnpxMdHT3l/kr7361bt3LvvfeeshOfJEkMDw8zODiIx+OhuLh4yv3Gx8cZHBzkgw8+wGKxkJqaSkFBAbNnz75UDRVOicvlwm63093dzVNPPcWuXbswGo2UlpbyrW99i+jo6GkvGenr62Pbtm389re/xeVykZ6ezic+8QmWLVtGdHQ0RqPxkna2VGRzdXV1atWSOXPmUFpaqq4UXEzGx8epq6ujoaGBvr4+xsbGWL58OWVlZYSGhp73+3q9XrW28bFjx8jPzyc1NZXU1NQP9TuXJInR0VF27dqF2Wzm2muvnXL74cOH6e7uZnR0lJycHGbMmEFcXJzaGXDi/jabjdraWrq6uhgeHiYnJ4fCwkLi4+NP+g4kSWJsbIzq6mra2toYHBwkJSWFkpIS0tLSTvn+R44coaOjA6vVSnZ2Nnl5eSQnJ5/xO5YkidbWVtra2rBYLOj1ehYuXKhW+1FK2x49elTdJzs7m5ycHFJTU09akZl4z+vo6KCtrY2ioiLS0tJISEg4q+/GbrfT0NBAe3s7d99997SwWYqvMTIyQnt7OwcOHODNN9/k6NGjrF69mmuvvZZbbrmFkJCQsx3vaR1s0QtXcEkJBAJ4vV42bdqkNmnIy8vj4x//OMuWLSMrK2tatA0eGhpSW7i+9957xMbGfiSd7vx+P4ODg+zbtw+DwUBycjKNjY288847uFwutRPc+Rgvn8+H0+mkurqa6upqfD4f8fHxzJkz56zfT5IkXC4X7733HiMjI4SHhxMfH09XVxfHjx8nNDSUm2++WR1jIBBgcHCQ3bt3Mzo6SnR0NOnp6QwPD3Po0CGqqqq44447MJvN6HQ6fD4fr732Gp2dnad1mG+77TbCwsKIjIzkjTfe4Pjx4zgcjlPu//GPf5ywsLBJf/P7/YyPj1NeXk5XVxcej4fk5GQ1ifZ0CTENDQ1UVFSwbds2br/99pN+sx6PB5vNRl1dHRUVFQQFBZGWlnaSgy1JEuPj4xw4cICDBw/S19en1lsuLy9n/vz53HLLLYSHh5/ua/lIMRqNhISEEB4ezsc//nGCg4Npbm6mqqqKZ555hrVr15Kenv6hHLGLgZLEWVtby8GDB9m4cSNBQUEsWLCAefPmsWzZMnWCeKnx+XxYLBZqamrYt28fFouF+Ph4/H7/Rf9sp9NJX18fO3bsICcnB7vdTkdHB8899xxZWVnnVQ5VsRvvvvsuDoeDsLAwEhIS6OjooKOjg7CwMNauXXveTrZyT9m1axe5ubmTHGy3243VauXtt98mMjISs9lMWFgYnZ2dtLe3k5KSwsqVKzGZTGg0GsbHx9X9lYTW0NBQ1cYlJiayatUqdX+v18vo6CgvvfQSra2t9PX10d/fT0REBHV1dcyZM4cbb7xR3d/j8TA8PMzbb7+tJsumpaXR09NDZ2cnMTExXH/99WoS7UQ8Hg8jIyNUVVXR2tpKZGQk0dHR6jj1ej1er5fh4WE2bNiAwWAgLCyMjIwM+vr66OrqIjw8nLVr12IymdBqtQQCAdxuN6+99hqBQACz2UxCQgLNzc20trYSHR3N9ddfD5x+JUeRdw4MDHD33Xef83d4MdBoNOh0OmJiYjAajWqzp3feeYfW1lY8Hg/j4+PceuutmM3mDz2pFg624JKhOBO9vb2sX7+etrY2kpKSePDBB7ntttumVdRrZGSE7u5uGhsb2bVrF+vWrWN8fPyif67H46G+vp79+/ezbNkyVqxYQUdHB88++yypqan4fL7zTvJUoqRbt27lmWeeITIykptuuumsX69EA5TxaLVa7r77bhYuXMiGDRuorKyko6ODRYsWER0djV6vJxAIUF9fz2uvvUZoaCgPP/wweXl5bN26lcOHD7N9+3bmzp1Lbm4uZrMZp9PJG2+8weDgIF6vd5Kzo3TxHBkZoaSkhLlz5zI2Nsabb75Jf3//afefOXMmCxYsULf5/X6cTift7e28+uqrOJ1O0tLSuOmmm0hMTDzlOVYiVXv37uX111+nsbERj8dz0n5KY5KtW7fyxBNPUFRUxJo1a6Z8z+HhYcrLy9m0aROzZs3C7/fT0NDA8ePH2b59O4sXL1ZvntMFjUZDSEgI119/PdnZ2WzevJlnnnmGxx9/nMjISLRaLbm5udOmLrSycjs4OMj777/Pli1bqKmp4ZFHHuGWW25hxowZ08r++Hw+rFYrbW1tVFZW0tfXx80330wgELjonz04OEhtbS179+7llltuISkpiaGhIZ566im++MUvEggEzskGKdHR9vZ2nn76acLCwrjjjjtYuHAhb7zxBlVVVfT29rJo0SIiIyPP+XtQHNwXXniBhoYGfD7fpM9WquH89a9/5aGHHmLevHkkJiby/PPPs2HDBmJiYsjMzKSwsBCQI7GHDx/m6aef5tOf/jTz5s0jOTmZ559/no0bNxIaGkpWVhaFhYXodDrGxsZoamri6aefRqPRIEkS/f39DA0NsXv3bkpLS5k5cya5ublotVqcTieHDx/mmWee4Y477qCkpISMjAxefPFFtmzZgtfrJT8/n4KCAvWaV/IFBgYGaGxs5KWXXsLn87Fu3TpKSkqIj49X93M4HNTW1vL3v/+d66+/nnXr1pGVlcWrr77Ktm3bGBwcpKioiPz8fAwGAx6Ph/b2dv70pz9RVFTE2rVrWbRoEc899xyHDx9mfHycBQsWnLLAQCAQYGxsjLfffpsDBw5MiwnqVJhMJoqKisjLyyM1NZUnnniCyspKmpqamDFjBvn5+YSHh3+48Suz+FM8BIKLhtfrlfbt2yfddNNNUlxcnPSTn/xEamhokAKBgBQIBC718CYRCASk7u5u6cUXX5QA6Sc/+YlUU1Nz0T+3t7dXuummm6RvfvOb0pYtW6RAICD5/X6pr69PGhkZ+VDnqb+/X6qoqJCqq6ulWbNmScXFxdJjjz0m+f3+s3p9IBCQRkZGpGuuuUa66aabpF/84hfqd+fz+aTXXntNys3Nlf7zP/9TampqkgKBgGSz2aTS0lLp05/+tPTMM8+o+wcCAWnr1q1SWVmZdN1110l79+6V+vv7pY0bN0r//u//Lr3//vtST0+P5Ha71Ud3d7f0zjvvSAUFBdLbb78tWSwWadOmTdJ//Md/SBs3bpS6u7sn7d/b2yu9++670owZM6RXX3110rmzWCzSq6++KsXFxUl33323tG3btkljO905eOaZZ6S7775bys7OlhITE6W+vr6T9uvo6JAOHDggVVdXSwkJCdKyZcukH//4x1O+37PPPivt2rVLGh4ennRuvvSlL0mA9MILL0gdHR1n9R1dCgKBgORyuaTW1lZp2bJlUnZ2tvS5z31Oam9vnzbXtTLGT3/601JeXp60ePFi6a233pI8Hs+0tT+BQECqr6+XHnjgASklJUV68sknJZvNdtE/+8UXX5RuvfVW6fvf/77U398vBQIByePxSF1dXer5OhcCgYBksVikFStWSGvXrpX+8Ic/TLIbf//736W8vDzpG9/4htTa2nrO421ubpZ+97vfSYWFhVJycrL0b//2b+o2v98vbdq0SZo3b570ta99bdL9JhAISF/96lelJUuWSOvWrZNcLpfk9/ulbdu2SSUlJdJXv/pV6fDhw5P2/+Y3vyktWbJEWrt2reR0OiVJkqTKykpp3bp1Unl5uWS1WiW32y319PRIX/ziF6Xc3FwpMTFR+uxnPys5HA7J7/dLBw4ckObOnSv9+7//u7R///5J7/+jH/1IWrx4sXTjjTdKVqt10jk8fvy49PnPf17KycmR7r//fslqtZ702/X7/VJDQ4M0Z84c6Utf+pK0Y8eOSe//29/+Vlq0aJF0/fXXSz09PZLf75daWlqkZcuWSTfffLP0wgsvTPpufvGLX0jFxcXSo48+Kg0MDEx5/kdHR6U//elP0qJFi6T09HRpwYIF0+56mohyfD09PdJf/vIXqaioSMrJyZFef/11aWho6EwvPyJJ0g+kU/jQ03NqIbgqePfdd3nmmWc4fPgwP/3pT7ntttvIysqalrVkNRoNWq1WnbF/VGP0er20tLTg8/nUz9NoNERHRxMaGvqhxhAVFUVRURFZWVknaQLPhqGhISorK6msrKSkpITS0lL1vGi1WtLS0li9ejWvvPIKHR0dDAwMUFtbS1NTE9HR0aSkpKj7azQawsPDKSws5MCBAxw+fJje3l7CwsL493//dxYuXEhsbCwGg0F92Gw29uzZw/Lly0lISECn0xEaGspXvvIVlixZQlxc3KT9HQ4Hu3btYunSpSQmJqrRJa/XywsvvMCTTz5JZGQkX//611Ud+um+5/HxcTo6OmhqaiImJoaSkpJTnqvExERmzJhBZmbmKSPP0j8ie/PmzSM3N5ewsDD18wsLC1m1ahUgSzKmc6tyjUZDUFAQycnJ/O53v6OkpIS6ujq++c1v4nK5PpKo65lob2/n//7v//jggw+46aabeOyxx1ixYoXaUnk62h/gkrRaHxkZ4fjx45OilUrJ1PMZj8ViUe1GaWkpJSUlk+xGZmYmq1at4qWXXlKlWmdLf38/ra2ttLa2snTp0pOairS3t1NfX09LSwv5+fmqDVUeGRkZhIWFUV5eTkNDA3V1ddTX13Ps2DHy8vImXZMajYa0tDQiIyPZt28f9fX1NDQ04HQ6uemmmygsLCQsLAyDwUBsbCyf/exnKSwsVCPWfr+frq4u6urqOHr0KNnZ2ZNyjTQaDSkpKcTFxanvPzw8rK7Eff/736eyspLc3Fy+9a1vTRqbQm9vr/r+GRkZREdHT3r/xMREUlJSKC8vp76+nsOHD1NZWUlVVRWrVq2isLBw0ndTWFjIggULeOGFF7BYLHi93knnd3R0lPb2dhobG5k1axZZWVnn9Nu4FCjHFxsby5o1a/jd735HSEgITzzxBH/5y1/weDwnNbQ5W6bPGqPgqkH6x5LZ3r17OXr0KGVlZSxZsuSskjkuNae7mYyPjzM2NkZ3dzdjY2OEhoYSFRVFUlLSSfuOjo5is9nU5ML4+Hji4+PVJLq+vj7sdjstLS1YLBb6+/tpa2tTS4SlpaV96PrfBoNB1eidzzKY1Wrl8OHDOBwOkpOTJx2nRqMhLCyM7Oxs+vr66OjoIDw8XD03Op3upKVfs9lMRkYGTqeTzs5OCgoKyMrKmjLhSfrHUm91dTVr165Vy8KlpKSccn+73U5VVRU33XSTmi0uSRL79+9n3759dHV1UVZWRnZ29kn67BNREmW2b99OYWEhx48fP61GPCgoSD3Xp/sNKTe9kJCQSd+vsgyflZVFYmLilB0DpxNarZagoCAKCwu58cYb1S6Q5eXlzJs3j8jIyEs2NqvVypEjR9i4cSMzZ85k6dKllJSUEBERccnGdC5MnGhPRPqHrtlqtWK1WhkbG1MTf088Nr/fj81mw2KxYLPZ8Hg8xMfHk56eTlBQED6fj76+PkZGRmhpaWFgYID+/n6OHDnC0NAQJpOJ9PT083L2FcmJ0+kkNTWVhISESccWERFBZmYmvb29tLe3k52dTUpKymnfU/qHXK2qqoqBgQFKSkqorq4+aTI7ODiIxWLB7XYTEhJykt1LSEggJiYGm81GS0sLRqORgYEB3G73lGXcEhISiI2NxW6309raSk5ODjExMSxcuHCShlej0ZCVlUVKSgpRUVH4fD414bm/v18dz4nvHxsbS2JiIna7nba2NlJTU9HpdBw8eJDy8nJiY2OZN28eWVlZU94PRkdH6evrw+VyERwcfNL5iI6OJjk5WX1/pW600+kkMzPzpMo5MTExpKam0tvbS1tbGzExMer3FwgEaGtro7q6mvnz51NXV0dHR8dp7eJ0wmAwEBMTw5w5c7jlllvYvn07Bw8epKKigiVLlpzXb1042IKPnEAgoCZ7jY2N8fnPf56srKxppXk8F6R/1Nm0Wq309fVRV1endpxMTU1l6dKlhIWFodPpVH1aa2srFosFi8XC0aNHSU1NZfbs2cyaNQuTyUR3dzednZ00NDQwPDxMT08PjY2N6HQ6YmNjiYmJUZMGzxadTndBdbuKgy1JEnFxcSdFi0wmE0lJSWoUPiQkBLvdDsia0onaSJAdbGUFo7e3l9HRUTIyMqb8bK/Xi9Vqpb6+nm9+85tqpYfMzMxT7j88PExdXR2PPvroJAf7zTffpLKyEr/fz/Lly3G73VgsFiRJQq/XEx4ejl6vn3QDc7lc9Pb2snPnTv7nf/6HnTt30tjYeL6nEpBvYHq9Xj2PStREyVPo7e1lyZIlpKenn3ECMB1QdNm33HILPp+P/fv388Ybb5CamvrhtY0fgvb2dg4dOsT+/fv5zW9+w6JFi87owE13lGTxnp4e2tvbOX78OH19fWRkZDBjxgxyc3PVxFiPx4PdbufYsWP09vbS3d2N1WolNTWVNWvWEBMTQyAQoL29Xa0KNDw8TFdXF9XV1SQmJhIXF0dqaur/b+/No9u8q/z/lyzJ8iLLm2RL3h3vcRI7tuMkzdqGEGhLQgNth1KgHUjnlIHpzADzLQeGw9KBwxlaCjPQDhwYKMzhtNBDmzI0aZJma/bEiRPHduLa8b5bki1bsvbfH/l9PiM7ztZmsdPP6xwf+7EfS48ePbrP/dz7vvcSDAavK7qn1+sZGhrizJkzhMNh0tLSZrQbVqsVv98vO4tci4M9OjpKXV0dJpOJT33qU5w+ffqS/URgQ5yH6fbTarWSnp5OOBymtbWVlJQURkdHgYs2ZPr+aWlpWK1WuX9hYSFZWVmXFPSKDF1aWhrp6eny+ne5XDidzssej9lsJiMjA4C2tjYWLVoEwJ/+9Cfa2tooLy+noqKCsbEx/H6/DFwYjUaZtXM4HPL4pxfFikwiXPxcALS2tspzMb2YWhSj+nw+mpubZVcRgLGxMc6fP09DQwPf/va36ezsvOT8z3b0ej3Jyck88cQTNDc3097ezmuvvcaSJUuIjo6+bidbOdiKW04gEOBnP/sZAKtXr+aBBx64zUf0/qmrq+Pw4cMMDAzw0EMPYTQaefnllzlw4AAbN27kO9/5DmazGafTyUsvvcTOnTv55Cc/ySOPPEJPTw+f+9znyM7O5t577+WRRx6RkgePxwNclHPk5eWxYMEC4uLicLlc9Pb2Yrfbr+n4NBoNNpuNefPm3bDXPD4+Lo1oUlLSJVGy6Oho6cj29fVNMdgXLlygo6Njyv5xcXGya8PIyAgjIyOXfW5x44+KiqKysvKqHSra2to4d+4cUVFRVFRUyAhqOBzmr3/9K93d3aSkpNDY2Mirr77Ku+++i9vtprS0lO9+97uyfZdg165dnD9/ns9//vNSunIzCIfDsnXlhQsX+OUvf/leerXeViwWCytXrmR4eJgf/ehHfPSjHyUzM/O2ReFff/11KRV6+OGH74ihMR6Ph6NHj/LGG29QXl5OeXk5RqORp59+mszMTNavX8+3vvUt4KKt2rlzJ7t27eK3v/0ta9asobGxkUcffZSenh7Wr19PVVWVlHCZzWb5vaysjPT0dGJiYhgYGLguCYdGo2HRokW4XC66urqAi3Zt+mIxJiZGXuO9vb0MDQ1d9bG9Xi8//elPqaqqoqSk5LKdp6Kjo9Hr9YRCIc6cOcNdd9015e8Wi4W0tDTC4TC9vb0kJiYSHR0tg0JiyJnAbDbL/fv6+tBoNFe0RQ6Hg2AwyNq1a9HpdOj1eqKjowmHw5w9e3ZK4TVcdICtVitwMas5MTFBKBRi69atsrPM22+/zX/9139x6tQp0tPTqaqq4itf+QoLFiyQjw/Q1NR0iYwtOTlZOvADAwN4vV56e3sBZIu/SOLi4qTtFO0KBS+99JJc3MzU8WSuoNFoyMnJ4dFHH+Uvf/kLv/3tb/mHf/gH0tPTr1tKqRxsxS1FVHgfO3aMjRs3snr16jn7QQRkS6Pnn3+eFStWsGXLFunI2u12nE4nr7zyChs3bmTx4sV4PB5eeeUVCgoKSE5ORqfTkZGRQWFhIXa7nWPHjvHII4+QlpZGQkKCvHmlpKSQl5dHWVkZUVFRvPnmm2zfvp3du3df03FqtVo+/elP841vfOOGvXav14vD4SAqKgq9Xn9JilKr1WIwGNBoNIyNjaHValm4cCHR0dG0trbS1tYmo18ajUa2DQyHw6SkpFy25zRAfX09nZ2drFmz5pqi8g0NDbS1tbF27Vp5w/H5fAwPDzM4OIjJZKK6uponnngCvV5PX18fdXV1/PSnP+Wpp57iqaeeYtOmTSQlJXH69GlGRkYwGo1UVFTclG4efr+fgYEBfvOb37B161a6urrweDw8/PDD/Nu//Rvl5eWyU8BsR6PRkJeXx/33388Pf/hD6uvrsVqtV9Ss3wzC4TBDQ0M0Njbi8Xh4/PHH38+I5FmDz+ejs7OT7373uzzzzDMUFBSQkJBASUkJu3bt4vjx47z++us88MADFBcXc/bsWXbu3ElBQQHR0dEkJCSQlZVFcXExZ86coaioiNraWjIyMjAajVgsFrRaLWlpaRQXF5OWlobH4+GNN97gV7/6FYODg9d0nFqtlpdffpnJyclrshtwMeIssl6XY2RkhJaWFjQaDQUFBZfNesHFaaP5+flERUVx8uRJnE4n4XBYXgOTk5NMTk7KgERpaamsvTl58iQOh+Oy+1ut1kuGoQnC4TCnT5/G6XRis9l45JFHZKvOoqIidDodp0+flpkz8Rher1cGWdLT0wmFQgwODjI4OEhubi4f/ehHuffee9HpdLS0tPDKK69w4MABtmzZIjtDlZaWotPpOHPmDAMDA5c8vpgvkJaWRm9vL06nU74307NMOp1OvjcOhwO32y17pPt8PllrMpcR52blypUMDQ3x+uuvs2fPHj70oQ/Jxci1ohxsxS3F6/UyMDDA6OgoWVlZNzSiejvwer00NTXR2tpKRkYGra2tMqo7PDyMXq/H7/dLTXVCQgKrV6+muLiY/Px82UZJSBiE8yeiGkKTrtfrMRgMMjKTkZFBZWXlNeuwo6KiKCkpuQln4CIzFYZF/k6n0xEfH09GRgbLly+nq6uL06dPyxv9xMQEHR0dHD58mFAodFkHWzjkQhe6ZMmSK7Z+E/u3tbXR19dHbW2t3N/r9dLT04PP55Op9Ly8PJmiNhgMHDp0SE71Kyoqorq6mqNHj2I0GmWR1M1AyCsKCwtZv369XJDU19ezbds29Hr9nHGw4WLkKyMjA7PZzMDAAAMDA7f8GERU0uFwoNfrWbx48axtIXY9DA4Ocu7cOdra2mhvb8fj8Ui7IK5/j8dDT08P8+bNIzc3l9WrV8tIr9/vZ2JigmAwSE9PD06nE41Gg8FgkH2CNRoNer2euLg42S85JyeHVatWSQnF1YiKisJkMk0psrtcQWmk3bjSAtbn89Hb28vx48epqamR0fXJyckZ909JSaGwsJDa2lo6Ojo4cOAAADabTbbHbGpqkg62zWYjEAiwdOlSOjo6OHjwoAyKOJ1Ojh49SmNjo9x/psh5+P9vqbdnzx6sViuVlZVkZmYSFRUl9ebLly+no6ODI0eOYDKZyM7Olv2thdRFyGYGBwdl676ioiLy8/OJiYkhJSVFSoP27t1LXV0dVVVV5OTksGLFCjo6Ojh69Cjp6enk5uYyOjrKmTNnOHXqFHDRgXe5XNf13ghpzoEDBygsLJw1cytuBMnJyVIy1NLSwvLly6/7MZSDrbilTE5O0tvbSyAQwGq1XveKcLbhdrs5cuQIHo+H1tbWKRHl0dFR4uLiKCsrIxAI4PP5SElJ4amnnpJ6aIfDISeJORyOa46G1tTUsHjx4usaNHGjI606nU6m10VboumFhUKjaTQaSUlJwWw28+CDD/Kb3/yG1tZW/vjHP7JhwwYcDgfnz59n9+7dhMNh0tPTL+tA+nw+WltbsdvtVFdXX3WR4fP5ZAFPTU2NPA/i5hwKhcjOzqa4uFimRE0mE4WFhTz44INs27aNpqYmjhw5QmZmJseOHWPTpk1UVlbK6JLQN4bDYRnVEoV+7/Xcms1mHn74YR566CFOnDjB9u3baWxs5I033iAvL48VK1bMmeirVqslJiaG7OxsxsbGrij/uVmEw2G6u7vxeDwkJibKPsRzHbFYDQaDHD58eMqiz+fzkZ6ejkajkdmhpUuXsmDBAjlUZHBwkN7eXlwuF6Ojo9dUlGYwGFi9ejV33XXXdWmwDQYDp06duia7ARc/h1eSEjkcDlpbWzl58iSf+cxn0Ov1eDwevF6vLCQMBoMyyhwXF0dpaSmbN2/mBz/4AXv27MHpdFJdXU1vby+7d++moaFBLiDElMZPfvKT/OAHP2Dv3r2MjY1RW1tLb28v+/bt4/Tp03L/mY41EAjgdDp55513uP/++1m1apWMAsfHxzNv3jwefPBBfvjDH3Lw4EE8Hg8rVqygr6+PQ4cOcfLkSdmVSchCAMrLy8nIyJDn0mw2c9dddzE+Ps5bb73FwYMHZeDgoYce4kc/+hHHjx+XEpW+vj6OHTvGiRMn0Gg0ZGVl4XQ6iYmJmdJq7nLvTWJiIqFQiJ6eHg4fPiylX8ImBgIBQqGQzPKKTMBs6t9/JXQ6HYmJieTm5soM4nU/xk04LoXisgSDQTlhLyYmZs7rH71eL+fPnycxMZHNmzfzuc99bsrfIyUQAqvVyoEDBzh16hRdXV3U1tZisVhk+vFaiIqKkmm820VsbCxWq1UWWAUCgSnHIyJjAFlZWVitVvR6PU888QTLly+nqamJ+vp6Tpw4wapVq0hISODdd9+ltbWVgoICcnJyLnnOcDhMXV0dw8PDGI1Gamtrr+gkif0HBweJj4+XEWy46PSJ1lZimlskRqOR6upqWZh15MgRGhoaKCwsZHBwkO3bt8t96+rq6O7uZnJykl27dmGz2TCbzSxZsuR9nWNx3dTU1JCRkUF9fT3bt2+nr68Pv98/pyQOorOM1+u9bITxZiKyReFwmOjo6PfUmnI2MjQ0RFdXF+np6fy///f/LikInMkGRUVFceDAAfbs2UNqaipms5nc3FwaGxuv2QZptdr31MkoLi6O9PR0gsHgjHZDSMWAS7qMTOc3v/kNfX19lJaWsnPnTvkafT4fbW1tjI6O0tnZyZtvvonJZKKyspJ58+bxpS99icWLF1NXV8fQ0BAnT57kgQceoLe3l+HhYUZHRykvLychIYHk5GT+/u//nsrKSk6ePMnAwAAnTpzggQceYGBggMHBQTmsZaZONO3t7bz44ov8zd/8DbW1tWRlZU35u81m44tf/CKVlZXU19fT09PD8ePH2bRpk+wC0tvbS1lZGR6Ph7a2NjQaDUlJSZdcw9nZ2VKi0dHRgdPpJDU1lSeffJLFixdz5swZ2tvbOXr0KB/72MdksXZHRwelpaW43W4sFotsBRgMBqc4xD6fT0pK8vLyaGho4I033qCiooITJ05w5swZuW9zczNDQ0O4XC7efPNNEhISyM/Pp7Cw8Jquk9mAmHzpcrkuKcq/FpSDrbiliMI3jUYjW9SJIo65SlRUlEytTjdI050fh8PByy+/zOHDh1m+fDlPPPEEycnJ7Nu377qiaSIdLIqFroZGo6GsrIza2tprfo6rIUbuajQa7HY7o6OjmM1m+Xeht4f/q84X50NU269YsQK4aLh3797NkSNHePzxxykuLp6xZaNItZrNZgoKCq56gw+Hw+zdu5ekpKRLWhvGxMSQk5NDVFQUfX19l1S9R0dHy0VBTEwMOp0Ou93On//85yk90eGiDtThcDAxMcELL7wgJTzv18GG/7uGYmNjWbp0KSdOnCA2Nva29ER+P4TDYfr7+8nKyrotXVBEmzExbW9kZGTOFYzOhEajIRgM0tHRgdfrvTjgIsKWTL9Gjh8/zr59+zhy5AhPPfUUOTk5uN1utm3bds3PGQgEaGlp4cyZM9cc2YuKiuLee+/FZDLJFn92ux2XyzVFDub1eqXdyMjImFJcPB2Xy8XZs2fZv3//FGczFArR1dWF0+nE4/Hw7LPPMm/ePCwWi7wGREGkiHR7vV6am5uZmJjgC1/4guweJHpAL168mJKSEtn7eXJykpaWFkZHR9myZQtJSUmXRGePHTtGW1sbxcXFrFq1Sva5jkRsV1RUUFxcLOtuJicnuXDhAn19fTzxxBOkpqbi9/tll5N33313SpEhXLTJZrNZZhfFAjwcDrNw4UIKCgrk43u9Xrq7u2lra+Pv/u7vZKtYsQAYHh4mIyNjyqJhcnJSauKzs7Npb2+np6eHY8eOyXobgcjMBgIBfvSjH5Gbm8umTZvmlIPt8Xjo7++ntLT0PWUjlYOtuKUYDAZsNhsGg4H+/n56enrmtIMtUvkej4fOzk5aWlqmFHmI6O6FCxeIj49nfHyc1157DavVKvvOihZw15NqHRgYoLGxUernroYoHLqRDnZKSoos8hscHGRkZGSKg+3xeBgYGJC9UiN7HwstZ3JyMqFQiO3bt1NfX49Wq2X9+vVYrdZLFhyi1+2JEyfIzc2lqKjoig5mZG/czMxMiouLp+yv0+lISUkhOzsbt9stq+cFoqViKBQiKSmJoqIi0tPT6evru0Sac/78ec6fP4/b7WbFihVkZGRcsdjqvSAG0eTm5pKamjqn5A2iIHZgYICUlJQrOk03C5EGNxqNjI+Pc/78eZYuXfq++8nfbuLj40lMTMTlctHc3Ex8fPwUmxoIBPB4PLS3t8ssyNtvv43VaiUnJwebzXZNnToiCQQC9PT0cPToUdlm7mrodDpWr16NxWJh0aJFaLVa+vv7GRkZmeJgu91uBgcHpd24Un/y2tpakpOTL+mmFAwG2bdvH8FgkIyMDO6+++4pjyWyKQkJCbLN6ksvvcTw8DDp6emsX79+Slu26fv7/X5eeuklBgYGMJvNfPjDH56yfzgcpr29XcohFy9eTEpKypRR5/39/VICM/3xg8Egv//97+np6SEpKYkNGzbIAVMWi4WcnBy6u7tly0GB3+9ncnKScDiMzWaTciHR3cRoNEq7+Ic//IH29naMRiMbNmzAaDSSlZXF/Pnz0Wq19Pb2kpubO+X8u1wuaeetVqscl97f33/Je3PkyBEuXLiA2+3m7rvvxmKxkJ2dfU3XymxALML7+vr4yEc+8p605crBVtxSxDAQs9lMR0cHzc3NUyZ5zWaEYRI/i1RzQUEBGo2GpqYm9u7dK4c1CMM9MjLCyZMnZVp0+/btfPOb38Rms6HVavH7/VIzGBUVJR9b6NfgorMX6YBPTEwwODh4zb1GtVrtJdEOgXhs8XU5hPEOhULEx8eTlpbG8uXLMZlMdHZ20tXVRXFxsdzf5XLR3t5OaWmp7H08/XyKYQvbtm3j+PHjlJaWsmrVqhmlQyLKVF9fT01NzZTnmonI/RcsWEBpaemUvwuNXXV1Nc3NzbLlmIj6iCLIcDgsizM3bNgw43O99tprbN26lcHBQb7+9a9fNq0def3MdLxwcWESFRU1pcBLXEudnZ0sXLhwTt2oADnIYnh4mKysrNvSd1oM+7BYLIyMjHDo0CGqq6svGUo0m4n8jIrvYvhHIBBgz549xMfHYzKZ5MCY8fFxhoaGOHbsGLW1tZw+fZqjR4/yjW98g7i4OOkwer1eaQtmsjuRP4fDYcbGxujp6bkmPb1Go0Gr1eLz+cjKymLp0qWYTCYZAY2Mao6NjdHZ2UlZWRmZmZlTsh1ut1um6hMSEti4ceOMzzc5Ocl3vvMdPB4PS5Ys4Xvf+95lz6fX62V4eJjf//73hMNhFi1axIoVK2ZcwEba9P/5n/+Rk1dXrlwpF2qhUAiv18vRo0dxuVyYzWYWLFiAz+eTGYZgMMi5c+em1H2IxxcF73/4wx+YmJhg4cKFrFq1Cq1WS3R0NGlpaSxZsoSmpibsdruUisHFup+hoSE0Gg3FxcWXZGjEIl10t7Lb7SxYsIDVq1ej1WrJzc2lpqaGhIQEWlpaKCgoIDs7W0bB7XY7fX19zJ8/H5vNRk5ODmvXrp3x3D777LPs3LmT4eFhnnnmmTnzGRMMDg7S1dVFf38/CxYseE9ZN+VgK24pojr9E5/4BHv27MHv97Np06ZZP5kOLkbhhDbQ7Xbj9/sxmUxs3LiRH//4xxw5coSzZ8/icrm455575DSwHTt28P3vfx+9Xi8jzjt27CArK4uCggKam5s5fPgwXV1dZGVl4Xa75Q1MRJbsdvuU4qMPfehDrFu37rqOfyYDFwqFGB0dJRgMyhvD5Th16hSvvPIKTqeTr371qxQXF5ORkcFjjz3GkSNH0Ol08pj8fj9dXV0cPHiQF198UY7cjURMk3v66aeZmJjgvvvu4wtf+MJlIwVOp5PTp0/jcrkoKiqivLz8iq93bGyM06dPMzY2RkFBAQsXLrxkn6ioKP7lX/6F7373u3IASU1NDXq9HpfLxaFDh8jOzuaee+657vM9nWAwyOjoKKFQiGAweEn/YFEg+etf/5rMzEwWLFhAUVERcNHpHhoaoqGhgRdffHFOjCCO5OzZs/ziF78gPz+f6upqCgoKbstxGAwGVq1ahdvt5oUXXuDRRx8lJSVlzgy5Gh8flwNJRNHi/PnziYqK4vnnn+dnP/sZzc3N3HfffaxZs4Zz587JdnTf+9736Onpwe/3y0zahz/8YUZGRuSUTTGERtiagYEBXC4XwWCQoaEhmbmJjY1l8+bN1z3DQNiA7OxsHn/8cfbv309MTAxr1qwBLkrF2tvbOXbsGD//+c9l8ELwu9/9jqamJsLhMM8999z7zj54vV5OnDjBL37xC6xWK1u2bLli4bRYsP/85z8nLS2Nxx57jGXLlk3Z3+FwcPz4cV544QWqq6vJz8/n17/+NYB834TmOS0tbUqhv8/n49y5czz33HMkJSXxxBNPsHbt2imPn5mZybe+9S02btxIU1MTTU1NcgBNW1sbzc3NFBUVsXnz5kuyw36/n/b2dr7//e8TGxvLk08+yYYNG+Tji6LLv/3bv2Xr1q2YzWaqq6vlwuLcuXOcO3eOZ5999ora+DuBP/7xj+zevZvc3FzWrl37nnwU5WArbikikvHII4/Q0tJCS0sLzz//PF/72tdmbdFROBympaWFnTt3smPHDgDefvttOZ1x0aJFfPvb3+aVV15h165d/Md//AcvvfQSiYmJZGdn85nPfIa0tDSio6OZP38+999/PydOnODFF1/knXfeYf369XzmM59h586dHD16lK997Wvcd9999Pb2cvDgQeBiEZ3FYiEhIYHa2lpiY2Pf982lv7+fpqYmWTTndrvZs2cPW7dupaKiAovFMqUjQX9/P/v378fhcPD5z3+ecDiMwWDgS1/6EsnJyfT09PD0009TVVXF6Ogobrebz372s8yfP1+mQcXC4cyZMzQ0NFBXV8fSpUtlVFakimdaDAwPD7N3715qa2tJT0+/ajW63W5nz549LFmyBJvNdtn98/Pz+fKXv8ypU6f4xS9+wcmTJ4mJiWFsbIy6ujqeeeYZmdJ+r7S3t9PU1MSePXtwuVy0trby9ttvU1FRQU1NDSkpKRgMBsLhMIcOHaKtrY1QKMT8+fNZvHixTCU/99xz5ObmztrPykycPHmSXbt2ceDAAb761a/KYUK3GnFNrVu3jmAwyKFDh3j22Wd59NFHWbhw4ayOsE1OTtLa2srvfvc7zpw5g8vl4n//938pLi5m4cKF5OTk8JOf/IRnnnmG06dP09DQwAsvvIDFYmHFihV8/OMfJz4+nry8PJYuXcr58+epq6vjH//xH6mqqqK4uJivfOUr/OQnP2Hbtm243W42btzIW2+9xZEjR5icnGT//v1s27aNmpoaCgoKZLu+90JcXBxf/vKXSUhIYGhoiK9//etUVVXhdDpxu908+uijlJWVERcXN+V9qa+v58iRI0RFRcmM3/W+b36/Xy6eDx48iN/v5+6772b58uVYrdZLhqSIBcmhQ4c4dOgQHo+HtWvXyv2NRqPcv7e3lwMHDvD9739fDrea/lkVnTm2bNlCZmamzDIcOnSIY8eOYbfbWb16NcuWLZPDuSKPR7TvfO6556TNElK9lpYW3G43//mf/yllZMFgEJfLxdGjRzlx4gS9vb2sWrWK2tpabDbbJbrwlJQUvvzlL6PT6Th79iz/+q//yqJFi7Db7YTDYT71qU9RVlb2niYbzgVEJmjbtm0Eg0G++MUvEhMTo0alK+YGIlVbW1uL3+9nx44d3HPPPZSVlV0yNne2EBsbS25uLnfddRdWqxWbzUZhYaE0rlVVVXg8HtLT02XhodlsZt68eVRUVMiiNIvFwkMPPUR+fj46nY60tDTy8/NlxXpBQYFMY4tCnNjYWEwmEyUlJVLzdiMMW3R0tNQWP/LII/h8PkwmE6mpqcTExFziUGZkZLBu3TrGx8elIxzZD7ezs5PR0VGMRiOxsbEYDAZSU1MxGo3yRhwKheju7iYQCJCcnExpaSlVVVUUFhZeUWsJF28soqtARkbGVc+BwWAgMzNTFu5crqdrbGysXASI44qOjsZoNGIwGKiuriY5OfmKz5eXlyd1hjPJW8R0uqKiIh5//HE5GS81NRWDwSDPj06nk9fYxMQEWVlZZGRkkJCQgMlkYsGCBURHR88J/bVoAffWW29x9uxZbDbbZQu9biVWq5VFixaxevVq9u/fT25uLlqtlvnz5wMzL+5uN1FRUcTFxckJjWNjY1itVsxms+yPv3z5cj796U/T3t4u5WD5+fnU1NRQWlqKVqslNjaWyspKvF4v8+bNw2w2U1xcTGFhIXFxcQwPD6PVasnOziY5OZni4mL0ej2VlZUkJiaSlZVFfHz8+17cC7uxZs0auru7cblcxMfHy97bwm5Mfy+qqqpk558rfQa0Wi1Lly6VhdiRuN1uuru70el0ZGdnExsby+LFiykqKprxMT0eD93d3fK86PV6qqqqKCoquuQ8iMJo4SDPhEajITo6mtzcXOLi4vB6vXR1daHVasnIyMBms8lFz0xBgaioKGJjY1m+fDnR0dF0dHRgMpkwGAyUlJRImyUcYJ/PR3d3txyGk5SURHV1NSUlJTMW7un1enJzc1m3bh19fX14vV5pC+Pj47FYLJcsfGaioqICg8Fw1UFBs4mJiQl6e3t59dVX8fv9lJWVSfnPe7ELmqsUVl171ZVCcZ3U19fz5ptv8u///u88/vjjPPzwwyxYsEB2j5iNN7qrIYbKaDQaTCbTJbpjuBjBcLvdU8bYwv/1DZ2trdciteJGo/ES4y+0hQ6HQ44Ynk4gEGDnzp3SUb6eYjchkxDFOler6hb7x8fHyxvE1QiHwwwODsqJZTdrkMzVmJycxOfzodPpiImJmRMOdSTBYBCPx8M777zDd77zHcLhMBs2bOBb3/rWrCgqFHURjz32GBUVFaxbt47Pf/7z0nmbjZ+/a2V0dJSxsTHpUM3kpIkC3ulRaJ/PJ/X/twohmXI6nSQlJV1RruPxeKSdnB7ZvVb6+vqor6+npKSE9PT0qxavDQ4OUldXR3FxMenp6Tdczmi32zly5Iicknm9Wl+/38/IyIhcaE23c2NjY7zzzjsUFxdjsViuGsiIJBQKSc226L5yp+L1euns7OSdd97hq1/9Kvfffz/33XcfDz300JX+rRn4E/CvM/1ROdiK20Y4HKajo4OtW7fy9NNP85GPfISPfexjPPbYY8DcdLCnf56uNmFwehP/K/3P7eZqr+1aXvu1np/38vw34rnez/HdSGbLcbxXuru7OXToEFu2bKGyspIHHniAJ598ctYsHsVi8LXXXuP555+ns7OTdevW8dxzz2Eymea0I3G9n8PbbYOu51q/EZ+LW2FHbubxXO//30qbO5f561//ytatW/n973/PJz7xCb7yla9QXl5+NVugHGzF7EVUcP/3f/83O3fuxO12s3LlSv7pn/4Ji8UyYy9khUIx+xBO61//+ld2797N22+/TVZWFk8++aQcDw2z5yYtuiLs37+f/fv38/rrr7No0SI2b97MsmXL5lS/XoVCcf2I9qG/+tWv2LZtG36/n4ULF/LP//zP5OTkXIsU5ooOttJgK24roi/2hg0b8Hq9nD59muPHj/OHP/yBmpoaCgsLZ5zop1AoZg9erxen08n+/ft566236Orqwmw2s3nzZqqqqrBarbPGsRaIwTM1NTUYDAZGR0dpaGhgx44d9Pb2ctddd1FdXT1FI69QKO4Ment7aWtrk4OXjEYjhYWFrF+/Xurr36/NUg624rYTFRXFkiVLsFqtHD58mJ/+9Kf8+Mc/5r777mP9+vUkJiYSFxeHVqtVNzqFYpYgejJPTk4yNDREU1MTP/zhDxkaGmLBggV89rOf5dFHH531faazsrKwWCwsXLiQb3/72xw8eJCDBw9y7tw5WScghnzA7InAKxSK60Noyj0eDydOnGDHjh38+c9/JjU1lS9+8YusXLlSFjvfCJRERDFrECnmiYkJnnnmGd544w3sdjtLlizh6aefZv78+XfEaGOF4k4gGAwyPDzMb3/7W3bt2sWhQ4dYuHAh3/zmN6mqqsJisUyZhDebiRymsm/fPrZv384vf/lLOcxk3bp1fOQjH7ljW5MpFB8E+vr6OHnyJM8++yz19fVYLBbuvfdevvGNb5CQkIBOp7vez7fSYCvmDmKKWGdnJydPnuTkyZPs3r0bt9stewbfe++95OTkzOlCJIVirjIxMcG5c+fYv38/r732GqOjo9hsNhYvXszmzZvJy8sjISFhzgxvmc7o6CgDAwM0Nzfz6quv0tLSgs/nIzc3lwcffJDa2lrZrk2hUMxuAoEAp06d4s0336S+vp6WlhZSU1NZu3YtFRUVso/8e5SEKA22Yu4gBtHk5+cTExODxWLBYDBw8OBBuru7GR8fx+l0UlNTQ05ODhaLRUa1VWRJobg5eL1exsfHeffddzl//jznzp2joaFBjoouLy9nyZIlVFRUzHpJyNUQkjSz2SxTyW1tbQwMDPCXv/yFzs5OCgsLycvLkwNXlLOtUMwOxDj43t5eBgYG6Orq4vDhwzQ3NzM5OUl+fj4rVqxg1apV5Ofn39SJlCqCrZj1hEIhdu/ezauvvsrhw4dpa2tj1apVrFmzhqqqKhYvXkxMTAx6vf6GDWFRKD7IiPuC6H1ut9tpb2/nj3/8I2+//TYul4vk5GS2bNnCpk2bsNlsd6yT6XA4aGlp4U9/+hMvv/wy4XAYm83G3XffzQMPPEBWVhaJiYlT+torG6RQ3FoCgQDBYBCfz4fT6WTv3r0cO3aMgwcP0tLSQk1NDStXruT++++nurr6Rn1GlUREMbcRxVShUIiBgQH27t3Lr3/9axoaGpiYmCA3N5fPfe5z3H333ZSWlt624SAKxZ1CKBTC4/Gwa9cu/vSnP1FXV0dLSwsZGRl8/OMfZ+3ataxZs2bKsI871amM1Gf39fWxY8cO9u3bx1tvvcXw8DBlZWUsXryYj33sY3z4wx8mNjZWydcUiltMY2MjdXV17Nq1i9dffx2fz0dOTg7Lly/n8ccfp7y8HJPJJBslKAdboYggHA7j9/txOp309/dz4cIF3n33XY4dO0ZzczNxcXGkp6dTWVnJypUr5eStq038Uyg+6IjPVktLC8ePH6exsZHm5mba2tpIS0sjLy+P8vJyli1bRnp6OklJSSQmJn6gMkbiHDkcDhwOB8PDwxw8eJDGxkY6OzsZGhoiOzub0tJSSkpKqKysZNGiRarNn0JxE+js7KS9vZ3GxkYOHTpEa2srXq+XuLg4qqqqWLJkCdnZ2aSnp2O1WomLi7sZE0qVg624MxkZGaGnp4e6ujoOHjxIb28vHo8Ho9FIdnY2eXl5ZGVlkZGRQXZ2NqmpqcTHx6vokkIBcqx9b28vQ0NDsrDvwoULOBwO3G43KSkpLFy4kNLSUsrLyykrK5vzGusbgeh4dPbsWc6ePcv58+dpbGxkaGiIuLg4kpKSsNlslJeXk5aWhtlsJj09XUpplMOtUFwbwkeNXNh2d3fT1tZGX18fvb29DA8Po9frsVgszJs3j7vuuosFCxaQmpp6yej4G4xysBV3NuFwmPHxcY4dO8aJEyfYt28fR48eJRwOk5ycTHV1NevXr2fRokVkZ2cTGxuLTqdDq9VO6a39QXcaFHcuQmYVCASkVtHj8XDmzBn27NlDfX09DQ0NDA8Pk5eXx/z581m6dCmbNm0iMzPzZt+k5jxer5fBwUHeeOMNjh07RmNjI+fPn8dgMFBaWkpZWRnLli1j1apVmEwmDAYDWq1W2iG1aFEoLiLkoIFAQH73+/00NDTQ1NREXV0de/bsYXh4WA6qW79+PWvXrqW0tJS8vLxbebjKwVbc2YhrWDgRYgRyfX099fX17Nmzh7179xIKhUhKSmLRokWsXLmShQsXUlxcTElJCaAcbMWdi8fjwW63c+jQIY4cOUJDQwNnz56lt7cXq9VKSUkJ1dXV3HvvvVRWVmI0GtFoNGrxeY1E6rTD4TBer5eBgQF2795NXV0dZ86c4dixYwBkZmaSn5/PkiVL5GCLzMzMO7ZIVKG4HgKBAJ2dnXKheubMGQ4ePIjT6SQ2NhabzcY999zDPffcQ1lZGYWFhXKBKr5uIcrBVnywEJG68fFxxsfHcTgctLa20traSkdHB52dnXR1dQEQFxdHVlYWlZWVFBQUkJOTQ2FhIcnJySqNq5iTeL1ehoaG6O7ulhKGrq4u+vr6cLlcWCwWzGYzNpuNsrIyioqKSE5OJiEhgdTUVBISEpSM6n0SCoXw+Xw4HA5cLhejo6P09PRw8uRJenp6GBgYoK+vj0AgQGJiIhaLhZKSEtmTNyMjg6ysrA+Uxl3xwWN8fJyRkRHZ+rO9vZ329nZ6enrwer3Ex8djNpvJycmhuLiYzMxMbDYbKSkpJCcnExcXR2xsLHDbggDKwVZ8sAmHw4yOjkrH+t1336W5uZmhoSHGxsYIBAKkpKSQlJRESkoKOTk52Gw2TCYTJpOJpKQkzGYz8fHxGAwGdcNTzApEpw+73c7Y2Bgulwu73c7w8DCDg4MMDAzQ29uL0+nE5/MRDodJS0ujoKCArKwscnJyKCsrIyMjQxUC32TEhNrGxka6urro6emhra2Nrq4uxsfHCQaDJCQkkJKSgtlsxmw2k5mZKRc8RqOR5ORkUlJSZEtShWIuENmcYHx8nLGxMRwOB06nU2qqOzo6sNvtuFwu3G43er2etLQ00tPTycrKoqSkhMLCQsxmM8nJybf7JUWiHGyFIhJRoNTZ2UlraytHjx7lwIEDtLa2Mjg4iN/vx2azkZeXR15eHqWlpVRVVZGVlYXZbJZFSuJL6bgVN5NQKDTlKxgMyghpf38/9fX1tLW1ceHCBRoaGmhra8Pn86HT6cjKymLZsmXMnz+f8vJyamtrVaHvLCEcDnPhwgWam5tpbm6mrq6Oo0eP4nQ68Xq96HQ6SkpKyM3NJTc3l9LSUioqKrBYLLLdmLA9kV+g7JDi1iPsk7i/iu1AIIDL5aKxsZH29nYuXLjA2bNnOXfuHE6nE4/Hg8FgoLy8nOLiYubPn8+yZcsoKyubC9k05WArFJFEXvORPbYnJydxOBw0NDTIvr+tra2cPXuWiYkJDAYDSUlJzJ8/n/nz55Obm0t2djYlJSUUFBSodlyKG044HKavr4/u7m46OzulM9be3k5nZyd9fX1otVrS09PJycmhsrKSiooKioqKyM/Px2azTdEm3uk9q+cSM9WOBINBBgcHpbzn1KlTtLa2SsckFAphMpkwm82yHWBOTg7Z2dkUFhaSn5+v7JDittDf309vby9dXV2cO3eOlpYWmTVua2sjGAxiNBpJS0ujoqKCBQsWkJ+fT0FBAeXl5cTExEhJ1ByyVcrBViiuhVAohN/vl2kst9uN2+1mbGyMjo4OOXp1ZGSE4eFhXC4Xk5OTGAwGEhMTSU1NxWKxkJOTQ25uLqmpqaSkpGC1WklNTVV6SsUlhMNhJiYmcDgc2O12+vv76e7uZmBggIGBATo7O7Hb7Xg8HgKBgBzhnZiYSFJSEjk5OcybN4+kpCQSEhLkl9AmRkdHq2tujiDuxT6fT46mFylzt9uN0+mktbVV2iC73c7IyAgTExP4fD4MBoPU0VssFrKyssjKyppihywWi5x4q1BcC+K6HBkZkbKOgYEBuru7GRoaYmhoiM7OThwOB5OTk/j9fmJiYkhJScFkMpGYmIjNZiM/P5/U1FRpq4xGI7GxscTGxmI0GudqJx3lYCsU7xUR3e7v76evr4/+/n56enro6elhaGgIh8PB6Oio1I3FxMRgNBqlAUlISMBisZCamiqdHqPRiNFoJCYmhtjYWOLj46UzdBMa4StuE6LY1uPxMDk5icfjYWJigomJCTweDx6Ph/HxcZxOJ6OjozidTkZGRhgfH8ftduPxeHC5XOj1enndCE2i2WyWA2AiW08q7kyEJKizs5OBgQEGBwfp7e2lr6+PkZERRkdHL7FD8fHxGI1G4uLiiI+Pl3ZI2Jv4+Hj5s9g/NjYWg8FAdHS0Cgh8ABBZE6/Xi9vtZnJyksnJSWmnIu2WuM6Ehnp8fFzatvHxcXQ6nbyu0tLSsNlspKamYjabZbbXZDLJosQ7BOVgKxQ3A9FLuL29nebmZjo7O+nu7ub8+fOcP39e3vDC4TDR0dGkpKSQlpZGdnY2OTk5pKeny22bzUZiYiLx8fGyPZpIk01vQaTap90eZkrpiwXYTNvBYBC3201vby+Dg4MMDg7S1dVFV1eXdJK6uroYHR0lHA6j1WoxGAwUFBSQnZ1Ndna2bCMpJpIZjcbbfBYUsw2xiLtw4QItLS10d3fT09MjJW4jIyO4XC5CoRAajUZGuUWE22KxYLFYyM7OJiMjQ0YZY2NjL7E9M9miOZTOv+O5XhsVCATwer0MDw/LgVNCohQZoe7p6cHn8wGg0+kwGo3MmzePzMxMsrKyKC4ulsXTVquVhISED8q1oBxsheJmMJMxi9yemJjA6XTS0dHBhQsX6O/vp7+/n87OTi5cuMDg4CAOh4NgMAggI5WiFVFqaiqpqamXbJvNZiwWi4x6K24NQkJkt9txOp04nU7ZqWNoaIjh4WGpQxweHpbZDXE9iMr4/Px8rFYr6enp5ObmSq10WloaVqv1Eh2icmAUV2K63Yn8ORwO4/P5mJiYoKOjg46ODtlhprOzU0bEh4eHsdvt8vrSarUkJiZitVplZxObzYbVaiU5OZnU1FQyMjKmyABEcEBx+/D7/Xi9XhwOh8ywOhwOaZfsdjt2u52BgQF6enpkZw/hcOv1euLj48nOziY3N5e0tDTS0tLIyckhJydHyoxSU1OvaKM+QNeBcrAVittBMBjE7/dLOYDX68Xr9crtyclJ3G43w8PDMsokeneL9JuQEUxMTKDRaNBqtURHR6PX6zEYDMTExEhNW3x8PHFxcbKncVxcHHFxcVLrFhMTMyUVHBMTIwuiPggGUWQcROpTpEXFtkiNijHhExMTjI2N4XQ6pQ7W5XLh9/vx+/0y+iN0hEIeFKktNJlMpKWlkZiYOEUfLdLwQjYktsXExA/C+6G4NYjOM8LmCDsk0v8+n4/JyUlGR0dly8exsTGZgROfDVGXInS2Op0OvV4vv8fExMjr22g0YjKZMBqNUopiMplkq9OYmJgp177YjouL+0Dqw8Ph8JT3xuPxyHMttiOlY2K+g7BLQmom9g8EAvh8Pvx+P6FQCIPBMMXuR9opcc9ITU2V71nk/SE6Olras+joaPmlAJSDrVDMTkQLI7vdLgdSiF7Goieo2BbtjCKNcDgcRqPRSCMobnYxMTFSz63X64mOjpbG0mAwzOjU6XS6KWObdTrdlDaEIqoaFRWFXq+XP0feDMX/zcSVxkH7/X5mskOR7Z7EdmTrJ7Et/l84EsFgUP6vGLMrvotFzfQbWeQ+Xq9XOtFiPzFiPBAIYDAY0Ov10qlITk7GZDKRkJBAUlKS7F0sts1ms3Qu1I1JMRuJ7KI03e6IBb8ochP6XFFcKT47oVBoit2J/JxEbgtbExcXN8UuCSdOFGGKfYXdmG53prcpjLRTws5Mb184HZ1Od9mI6+Xskjhfws5M3z+yleblfha9oQOBgLRTIggjFjzCTomi18iFvbBl4mexLe4J4nwL+56YmCgDL0ajUc58EIEZsS1qgxTXjHKwFYq5TigUYnx8nNHRUYaGhmTE2+VySamJiDqJ1KDoQuB0OmdMHwtEMVRklCIuLk7e4IQzLm6URqNRRtIjjbFerycuLm7Gm5X4/+nRqXA4jMvlmvFGFggE5E1EnIPIG5DP5yMYDOJyuaY4xyLC5vP55DmYmJjA7/cDXCK30Gg0suBURJyFwyzS4yICbTabZaRHTOBTw4cUHySEdlfYGWF3hEN+uW1hs8RnONIeie+iXkVEwePj46UdEj2RxcI20omMzOiJQAEwxdmfCeHQT//8ComfkO9FIhb4Ho9nit0aGxub4iwL+yVskVisCydZdIgRi3zxXNOlFhqNBp1OJ22QcJDT0tKmLORTU1PlPmJhn5SUhNFovOzrV7xvlIOtUMx1pvfuvp7vgEwvCtmDMO7Ttz0ej2xV6PP5pnTBEJESl8tFMBjE5/Ph8XiA/9Mni+3pTExMyKhzJBqNRg4+mX6TE068uDlEbosbo06nw2QyTUlTi79FR0fLVLWQzxiNRqkXjdyeaVjQtX6f/rNCcSdzLbboSn8TtmJsbExK4UQ71Eg7NDY2JhfGgUCA0dHRKbI74bT6fD75s3BshbMa6dDO9DpE9momhB2ZHv0WTq/JZJryOzH8R+iYdTqdXACIYIRYGAi7JbKJkRIa0fVFbIv/i3yuK32PPMbp/6O44VzRwVZ9nRSKOcD7NZZarRaj0SgjLOImJG5Y4meRwoxMZUbKLkT0KjIVKpi+Hcn0dOr0Y5vpNYl0cKTjG5kOFjcREYESGvVIOUuk9EWr1V52+wNWmKNQvGfery0Kh8My2pySkiLtS6RdEk54pB2KtDuX+4q0U4D8XyEzm86V7NLlpCXiNU+Xw4nx9ZH2KdIWRcpZRFRa2CFhi2ayU9OleIq5g3KwFYoPAMJgKxQKxe1ELGZVsZziTkfNU1UoFAqFQqFQKG4gysFWKBQKhUKhUChuIMrBVigUCoVCoVAobiBXE2V235KjUCgUCoVCoVAo5g4DwNjl/ng1B/vQjT0WhUKhUCgUCoViztMDdF3uj1frg61QKBQKhUKhUCiuA6XBVigUCoVCoVAobiDKwVYoFAqFQqFQKG4gysFWKBQKhUKhUChuIMrBVigUCoVCoVAobiDKwVYoFAqFQqFQKG4gysFWKBQKhUKhUChuIP8fHuY9Q2IMJ00AAAAASUVORK5CYII=\n", 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" ] @@ -11207,7 +11699,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -11282,7 +11774,7 @@ "16 3 2.0 29.125 1 -1 1 0" ] }, - "execution_count": 44, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -11294,7 +11786,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -11303,7 +11795,7 @@ "14.333333333333334" ] }, - "execution_count": 45, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -11329,7 +11821,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.6" }, "toc": { "base_numbering": 1, diff --git a/notebooks/examples.ipynb b/notebooks/examples.ipynb index 466e27d4..e980e944 100644 --- a/notebooks/examples.ipynb +++ b/notebooks/examples.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +124,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:34:59.378532\n", + " 2021-01-26T16:29:11.213243\n", " image/svg+xml\n", " \n", " \n", @@ -147,264 +147,264 @@ "
\n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -414,10 +414,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -438,7 +438,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -473,10 +473,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -493,7 +493,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -509,13 +509,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -526,7 +526,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -539,7 +539,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:34:59.460947\n", + " 2021-01-26T16:29:11.304450\n", " image/svg+xml\n", " \n", " \n", @@ -562,184 +562,184 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -749,10 +749,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -773,7 +773,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -811,10 +811,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -831,7 +831,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -847,13 +847,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -864,7 +864,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -879,7 +879,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:00.007652\n", + " 2021-01-26T16:29:11.834391\n", " image/svg+xml\n", " \n", " \n", @@ -902,14 +902,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -917,10 +917,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -938,7 +938,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -954,7 +954,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -999,7 +999,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1019,7 +1019,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:00.068085\n", + " 2021-01-26T16:29:11.893312\n", " image/svg+xml\n", " \n", " \n", @@ -1042,259 +1042,259 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1302,10 +1302,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1323,7 +1323,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1339,7 +1339,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1388,7 +1388,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1408,7 +1408,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:00.131931\n", + " 2021-01-26T16:29:11.960527\n", " image/svg+xml\n", " \n", " \n", @@ -1431,110 +1431,110 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1542,10 +1542,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1563,7 +1563,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1579,7 +1579,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1627,7 +1627,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1647,7 +1647,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:00.191371\n", + " 2021-01-26T16:29:12.024577\n", " image/svg+xml\n", " \n", " \n", @@ -1670,83 +1670,83 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1754,10 +1754,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1775,7 +1775,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1791,7 +1791,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1839,7 +1839,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1858,7 +1858,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:34:59.540430\n", + " 2021-01-26T16:29:11.389352\n", " image/svg+xml\n", " \n", " \n", @@ -1881,439 +1881,439 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2323,10 +2323,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2348,7 +2348,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2368,7 +2368,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2407,10 +2407,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2427,7 +2427,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2440,13 +2440,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2457,7 +2457,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2482,7 +2482,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:34:59.844429\n", + " 2021-01-26T16:29:11.658340\n", " image/svg+xml\n", " \n", " \n", @@ -2505,85 +2505,85 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2593,10 +2593,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2620,7 +2620,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2642,7 +2642,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2675,10 +2675,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2695,7 +2695,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2708,13 +2708,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2725,7 +2725,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2739,7 +2739,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:34:59.689848\n", + " 2021-01-26T16:29:11.486763\n", " image/svg+xml\n", " \n", " \n", @@ -2762,55 +2762,55 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2820,10 +2820,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2847,7 +2847,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2868,7 +2868,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2904,10 +2904,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2924,7 +2924,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2940,13 +2940,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2957,7 +2957,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2970,7 +2970,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:34:59.766686\n", + " 2021-01-26T16:29:11.572915\n", " image/svg+xml\n", " \n", " \n", @@ -2993,134 +2993,156 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3132,14 +3154,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3150,7 +3172,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3165,7 +3187,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:00.250810\n", + " 2021-01-26T16:29:12.158522\n", " image/svg+xml\n", " \n", " \n", @@ -3188,52 +3210,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3241,10 +3263,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3262,7 +3284,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3278,7 +3300,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3324,7 +3346,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3344,7 +3366,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:00.312197\n", + " 2021-01-26T16:29:12.220990\n", " image/svg+xml\n", " \n", " \n", @@ -3367,12 +3389,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3380,10 +3402,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3401,7 +3423,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3417,7 +3439,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3463,7 +3485,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3483,7 +3505,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:00.370681\n", + " 2021-01-26T16:29:12.284595\n", " image/svg+xml\n", " \n", " \n", @@ -3506,38 +3528,38 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3545,10 +3567,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3566,7 +3588,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3582,7 +3604,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3630,7 +3652,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3650,7 +3672,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:00.427729\n", + " 2021-01-26T16:29:12.361585\n", " image/svg+xml\n", " \n", " \n", @@ -3673,10 +3695,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3684,10 +3706,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3705,7 +3727,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3721,7 +3743,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3767,7 +3789,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3798,7 +3820,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:34:59.923120\n", + " 2021-01-26T16:29:11.748903\n", " image/svg+xml\n", " \n", " \n", @@ -3821,515 +3843,515 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4339,10 +4361,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4364,7 +4386,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4384,7 +4406,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4417,10 +4439,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4438,7 +4460,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4468,13 +4490,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4485,7 +4507,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4512,7 +4534,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -4589,7 +4611,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:00.926773\n", + " 2021-01-26T16:29:12.977105\n", " image/svg+xml\n", " \n", " \n", @@ -4612,264 +4634,264 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4879,10 +4901,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4903,7 +4925,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4938,10 +4960,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4958,7 +4980,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4974,13 +4996,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4991,7 +5013,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5004,7 +5026,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.025575\n", + " 2021-01-26T16:29:13.083974\n", " image/svg+xml\n", " \n", " \n", @@ -5027,184 +5049,184 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5214,10 +5236,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5238,7 +5260,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5276,10 +5298,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5296,7 +5318,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5312,13 +5334,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5329,7 +5351,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5344,7 +5366,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.624464\n", + " 2021-01-26T16:29:13.619382\n", " image/svg+xml\n", " \n", " \n", @@ -5367,14 +5389,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5382,10 +5404,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5403,7 +5425,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5419,7 +5441,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5464,7 +5486,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5484,7 +5506,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.680283\n", + " 2021-01-26T16:29:13.678451\n", " image/svg+xml\n", " \n", " \n", @@ -5507,259 +5529,259 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5767,10 +5789,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5788,7 +5810,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5804,7 +5826,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5853,7 +5875,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5873,7 +5895,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.744279\n", + " 2021-01-26T16:29:13.830091\n", " image/svg+xml\n", " \n", " \n", @@ -5896,110 +5918,110 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6007,10 +6029,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6028,7 +6050,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6044,7 +6066,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6092,7 +6114,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6112,7 +6134,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.804324\n", + " 2021-01-26T16:29:13.893044\n", " image/svg+xml\n", " \n", " \n", @@ -6135,83 +6157,83 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6219,10 +6241,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6240,7 +6262,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6256,7 +6278,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6304,7 +6326,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6323,7 +6345,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.212916\n", + " 2021-01-26T16:29:13.188272\n", " image/svg+xml\n", " \n", " \n", @@ -6346,439 +6368,439 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6788,10 +6810,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6813,7 +6835,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6833,7 +6855,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6872,10 +6894,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6892,7 +6914,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6905,13 +6927,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6922,7 +6944,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6947,7 +6969,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.451010\n", + " 2021-01-26T16:29:13.450018\n", " image/svg+xml\n", " \n", " \n", @@ -6970,85 +6992,85 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7058,10 +7080,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7085,7 +7107,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7107,7 +7129,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7140,10 +7162,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7160,7 +7182,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7173,13 +7195,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7190,7 +7212,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7204,7 +7226,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.302296\n", + " 2021-01-26T16:29:13.281985\n", " image/svg+xml\n", " \n", " \n", @@ -7227,55 +7249,55 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7285,10 +7307,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7312,7 +7334,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7333,7 +7355,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7369,10 +7391,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7389,7 +7411,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7405,13 +7427,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7422,7 +7444,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7435,7 +7457,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.377384\n", + " 2021-01-26T16:29:13.368303\n", " image/svg+xml\n", " \n", " \n", @@ -7458,134 +7480,156 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7597,14 +7641,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7615,7 +7659,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7630,7 +7674,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.863605\n", + " 2021-01-26T16:29:13.953696\n", " image/svg+xml\n", " \n", " \n", @@ -7653,52 +7697,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7706,10 +7750,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7727,7 +7771,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7743,7 +7787,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7789,7 +7833,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7809,7 +7853,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.921285\n", + " 2021-01-26T16:29:14.020034\n", " image/svg+xml\n", " \n", " \n", @@ -7832,12 +7876,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7845,10 +7889,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7866,7 +7910,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7882,7 +7926,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7928,7 +7972,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7948,7 +7992,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.976258\n", + " 2021-01-26T16:29:14.085704\n", " image/svg+xml\n", " \n", " \n", @@ -7971,38 +8015,38 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8010,10 +8054,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8031,7 +8075,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8047,7 +8091,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8095,7 +8139,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8115,7 +8159,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:02.036445\n", + " 2021-01-26T16:29:14.154919\n", " image/svg+xml\n", " \n", " \n", @@ -8138,10 +8182,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8149,10 +8193,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8170,7 +8214,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8186,7 +8230,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8232,7 +8276,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8263,7 +8307,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:01.531575\n", + " 2021-01-26T16:29:13.534002\n", " image/svg+xml\n", " \n", " \n", @@ -8286,515 +8330,515 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8804,10 +8848,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8829,7 +8873,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8849,7 +8893,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8882,10 +8926,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8903,7 +8947,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8933,13 +8977,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8950,7 +8994,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8977,7 +9021,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -9038,7 +9082,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:02.689548\n", + " 2021-01-26T16:29:14.776833\n", " image/svg+xml\n", " \n", " \n", @@ -9060,157 +9104,157 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9219,10 +9263,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9242,7 +9286,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9300,10 +9344,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9316,7 +9360,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9352,7 +9396,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9366,7 +9410,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:02.980418\n", + " 2021-01-26T16:29:15.160503\n", " image/svg+xml\n", " \n", " \n", @@ -9449,7 +9493,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:03.025792\n", + " 2021-01-26T16:29:15.207827\n", " image/svg+xml\n", " \n", " \n", @@ -9528,7 +9572,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:02.843954\n", + " 2021-01-26T16:29:14.933757\n", " image/svg+xml\n", " \n", " \n", @@ -9550,207 +9594,207 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9758,9 +9802,8 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9773,16 +9816,17 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9790,10 +9834,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9806,7 +9850,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9814,10 +9858,9 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9830,22 +9873,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9866,7 +9910,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:02.932845\n", + " 2021-01-26T16:29:15.027595\n", " image/svg+xml\n", " \n", " \n", @@ -9939,7 +9983,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:02.542559\n", + " 2021-01-26T16:29:14.628154\n", " image/svg+xml\n", " \n", " \n", @@ -10053,7 +10097,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -10109,22 +10153,22 @@ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "cluster_legend\n", "\n", "\n", "\n", "node1\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " 2020-11-25T13:35:03.497194\n", + " 2021-01-26T16:29:15.722823\n", " image/svg+xml\n", " \n", " \n", @@ -10139,142 +10183,142 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10290,12 +10334,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10311,7 +10355,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10354,15 +10398,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10370,7 +10414,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10387,14 +10431,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10410,20 +10454,20 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", "\n", "node4\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " 2020-11-25T13:35:03.605272\n", + " 2021-01-26T16:29:15.880164\n", " image/svg+xml\n", " \n", " \n", @@ -10438,205 +10482,203 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10644,15 +10686,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10660,17 +10705,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10683,23 +10725,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10708,13 +10750,13 @@ "\n", "\n", "leaf2\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " 2020-11-25T13:35:03.798591\n", + " 2021-01-26T16:29:16.080769\n", " image/svg+xml\n", " \n", " \n", @@ -10774,19 +10816,19 @@ "\n", "\n", "node1->leaf2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf3\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " 2020-11-25T13:35:03.836672\n", + " 2021-01-26T16:29:16.120180\n", " image/svg+xml\n", " \n", " \n", @@ -10851,8 +10893,8 @@ "\n", "\n", "node1->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -10863,7 +10905,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:03.872564\n", + " 2021-01-26T16:29:16.158944\n", " image/svg+xml\n", " \n", " \n", @@ -10935,7 +10977,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:03.916741\n", + " 2021-01-26T16:29:16.201902\n", " image/svg+xml\n", " \n", " \n", @@ -11013,7 +11055,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:03.716934\n", + " 2021-01-26T16:29:15.996610\n", " image/svg+xml\n", " \n", " \n", @@ -11035,118 +11077,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11155,10 +11197,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11180,7 +11222,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11233,10 +11275,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11249,7 +11291,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11292,7 +11334,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11300,9 +11342,9 @@ "\n", "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", "\n", @@ -11322,7 +11364,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:03.347553\n", + " 2021-01-26T16:29:15.568676\n", " image/svg+xml\n", " \n", " \n", @@ -11414,7 +11456,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -11483,7 +11525,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:04.812291\n", + " 2021-01-26T16:29:17.016441\n", " image/svg+xml\n", " \n", " \n", @@ -11505,124 +11547,124 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11631,10 +11673,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11650,7 +11692,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11697,10 +11739,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11713,7 +11755,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11754,7 +11796,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11767,7 +11809,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:04.924881\n", + " 2021-01-26T16:29:17.148508\n", " image/svg+xml\n", " \n", " \n", @@ -11789,124 +11831,124 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11915,10 +11957,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11934,7 +11976,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11981,10 +12023,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11997,7 +12039,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12036,7 +12078,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12051,7 +12093,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:05.146839\n", + " 2021-01-26T16:29:17.512122\n", " image/svg+xml\n", " \n", " \n", @@ -12140,7 +12182,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:05.213192\n", + " 2021-01-26T16:29:17.595685\n", " image/svg+xml\n", " \n", " \n", @@ -12228,7 +12270,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:05.272564\n", + " 2021-01-26T16:29:17.672115\n", " image/svg+xml\n", " \n", " \n", @@ -12314,7 +12356,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:05.331272\n", + " 2021-01-26T16:29:17.745415\n", " image/svg+xml\n", " \n", " \n", @@ -12405,7 +12447,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:05.043483\n", + " 2021-01-26T16:29:17.388187\n", " image/svg+xml\n", " \n", " \n", @@ -12427,124 +12469,124 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12553,10 +12595,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12572,7 +12614,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12618,10 +12660,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12634,7 +12676,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12674,7 +12716,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12704,7 +12746,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:04.378789\n", + " 2021-01-26T16:29:16.661399\n", " image/svg+xml\n", " \n", " \n", @@ -12873,7 +12915,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -12953,7 +12995,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:05.790598\n", + " 2021-01-26T16:29:18.303841\n", " image/svg+xml\n", " \n", " \n", @@ -12975,172 +13017,172 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13162,7 +13204,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13238,10 +13280,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13254,7 +13296,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13299,7 +13341,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13312,7 +13354,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:05.924993\n", + " 2021-01-26T16:29:18.450049\n", " image/svg+xml\n", " \n", " \n", @@ -13334,157 +13376,157 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13493,10 +13535,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13518,7 +13560,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13566,10 +13608,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13582,7 +13624,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13621,7 +13663,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13636,7 +13678,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:06.224217\n", + " 2021-01-26T16:29:18.673366\n", " image/svg+xml\n", " \n", " \n", @@ -13714,7 +13756,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:06.269473\n", + " 2021-01-26T16:29:18.804105\n", " image/svg+xml\n", " \n", " \n", @@ -13792,7 +13834,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:06.305802\n", + " 2021-01-26T16:29:18.841609\n", " image/svg+xml\n", " \n", " \n", @@ -13868,7 +13910,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:06.348890\n", + " 2021-01-26T16:29:18.887184\n", " image/svg+xml\n", " \n", " \n", @@ -13946,7 +13988,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:06.142037\n", + " 2021-01-26T16:29:18.584210\n", " image/svg+xml\n", " \n", " \n", @@ -13968,172 +14010,172 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14153,7 +14195,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14197,10 +14239,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14213,7 +14255,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14262,7 +14304,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14313,31 +14355,31 @@ "\n", "proline\n", "\n", - "13.40\n", + "13.84\n", "\n", - "4.60\n", + "4.12\n", "\n", - "2.86\n", + "2.38\n", "\n", - "25.00\n", + "19.50\n", "\n", - "112.00\n", + "89.00\n", "\n", - "1.98\n", + "1.80\n", "\n", - "0.96\n", + "0.83\n", "\n", - "0.27\n", + "0.48\n", "\n", - "1.11\n", + "1.56\n", "\n", - "8.50\n", + "9.01\n", "\n", - "0.67\n", + "0.57\n", "\n", - "1.92\n", + "1.64\n", "\n", - "630.00\n", + "480.00\n", "\n", "\n", "\n", @@ -14357,7 +14399,7 @@ " \n", " \n", " \n", - " 2020-11-25T13:35:05.664435\n", + " 2021-01-26T16:29:18.154160\n", " image/svg+xml\n", " \n", " \n", @@ -14461,7 +14503,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -14514,7 +14556,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -14524,29 +14566,4623 @@ "id": "RqsLDXZ16bOL", "outputId": "b58f399a-b25a-4048-a751-567cd31f845e" }, - "outputs": [], - "source": [ - "regr = tree.DecisionTreeRegressor(max_depth=3)\n", - "diabetes = load_diabetes()\n", - "\n", - "X_train = diabetes.data\n", - "y_train = diabetes.target\n", - "regr.fit(X_train, y_train)\n", - "\n", - "X = diabetes.data[np.random.randint(0, len(diabetes.data)),:]\n", - "\n", - "viz = dtreeviz(regr,\n", - " X_train,\n", - " y_train,\n", - " target_name='progr', # this name will be displayed at the leaf node\n", - " feature_names=diabetes.feature_names,\n", - " X=X, \n", - " show_node_labels = True\n", - " )\n", - "viz" - ] - }, - { + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "cluster_instance\n", + "\n", + "\n", + "\n", + "node2\n", + "Node 2\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2021-01-26T16:29:19.334471\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.3.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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a/notebooks/partitioning.ipynb +++ b/notebooks/partitioning.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -55,7 +55,7 @@ "DecisionTreeRegressor(criterion='mae', max_depth=3)" ] }, - "execution_count": 9, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -105,7 +105,7 @@ "DecisionTreeRegressor(criterion='mae', max_depth=3)" ] }, - "execution_count": 18, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -120,12 +120,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -151,12 +151,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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7u7/dlxDqqzM3GhuotywymQwTExMcO3Zs1Rvwe7/rIU49f4YrMyJerwfJ6cRsoU5juSkrCAJutxu32013d3dtuW3bqKpKLpejUCgwNTVFPp+vkYjX620gkmYkcj9mQ+51tMniNqC+OrOVAqvlqFoWxWKRS5cuceTIEZxO56qfd7vdvO//+Q+UP/nXzKQtQh6JN37XiTX3sZEApyAIuFwuXC5XUxKpWiLT09M1ElEUpYFA1jr+W4U7RRa2bbfJoo31sVxkdzNNYaIoYhgG586dY//+/bhcrhWfiSUSRL91BlUzOHFgO4ce3MV3v+YY16dmObhzkGOH96+5j63IhtSTSFdXV215MxLJ5XLk83meeeaZBlfmVhLJnSKLW43blcVqk8UthGmaFIvFhurMzZCFbdskEgn279+/qmLWP3/jBWazAAJffeoy4/OLXC47KEkevj0V54HZeYYH+lbdx618iJqRiKqqnD59msOHD9dIZHZ2lnw+j2EYyLLcYIlsBYm8VNyBZrgdJeNtsrhFWK06c6NkYds2Y2NjuN1uwuHwqp8r6SYgMjY9TzKbYzYxTd5yMJHUcIg2QzL81L9947r7up0QRRFFUVAUpeHcbNtG07QtJ5GXqmVxu9Ami1uAKlE0q87cKFlcu3YNt9vddEJYPY4/OMTnvvo8M6kifSEZ3enhhbPXoaMH0TB59uosP7XG+ndTi7ogCE1JBGhwZ+bm5sjn8+i6XiOR+uCqLMsr9tkmi82jTRZbjPrqzGZZgGr8oRVMT0+jqio7duxgbGxszc+ePLKfdC6P++ICWd0gk83jdoooLgduxYtmlddc/16p4FyNROotkfn5+RqJOJ3OGnlomvaSFBG6XWiTxRaiKrJrGMaq1Y+tWhbxeJxYLMbRo0dbvskfPXmEf372bxjPS8i2QZdPwKOYyIrBIw8Or7nuvUIWq0GWZTo7O+ns7GxYXk8i6XSaQqFALBZrIJHVLJGtwO0er3Ar0SaLLUK1lLpeZLcZWiGLbDbL2NhYrZai1XSrx+Nm765hfDkNyenkX18skEjMEnArbOteOxvyUkU9iTgcDjRNY8eOHWiaRqFQIJfLsbCwwLVr19A0DYfDgd/vX+HObPZhfKmkTaFNFluCampwuXZmM6xHFqVSiYsXL3L48OFa4K4VPYsq+kJesqKHb5+7yGImgxYeJCFJ/NFXn6Qz4OXovgebukf3umXRCuqnxMuyjCzLdHR0NHxG1/WaJbK4uMj169drJNLMElmPRG5FX0gztC2LewSapjXVzmyGtchC13XOnj3Lvn37cLvdteUbeZC//5Un0Uf/lf9z/TyqpwO7mMZUfEwZBv/n/HX+4tlz7Bsa4HBvJ686dGBT+9gq3I0VnE6nk46Ojg2TiNfrrVkk9d2ftyNde7sCt22yuEmspZ3ZDKuRhWVZnD17lp07dxIIBFpapxkcDgdfev4MqaF9mOUihujEm5nH4RA5NzGPEAriMaCQKrFjbp6h/krtxd2UDbmV+9zsQ7UWiRQKBfL5PLFYjLGxMVRVxeFw1IjDMAzK5fItbyG/1WiTxU2gWp3ZKlFA8wfftm0uXLhAd3d3Q+VjFa08yPV/L2g6TjULAji0MoptoIa3E7ckrHiG/d0hhICfkqZtaB/3Om5F/MDpdBIKhQiFQg3LDcMgn8+TSCTQNI2zZ8/W3NTl7sy9QiJtstgkNE0jHo/j9Xo39EU3iz9cv34dRVEYGhpadZ2NYFtfHxfmchiSjKwW8IXDFHUNU/FR0g0uXrjAHllg1yOHALg6PY1ZLq9by3Gv43bWWTgcDkKhEJIkUSwWOXz4MFAhkWpgNZFIMDExQblcrpFIfWC1qi9yt6BNFpuAZVkkk0nGxsY4cuTIhtZdntmYmZmhVCpx8ODBLTu+o3v3UPYlOTcXQ/eF6cktUA74yNsSfq8DpXsHusuDbducGRvna5kshbKKGo/xXFkj6HQSkh10yAoDHSE6gsGXhBDOnchMLA9wOhwOgsEgwWCjaHKVRPL5PMlkksnJyRqJLO/ibUYi7ZjFXYhqdeZmp4TVr5dIJFhYWGjQpdgMVFXlzJkzSJKE3+9nh1di3ivTsb2fbHyO7Q8eY3x6mgVdh75hAoJFSZAYm57h8WQKvD4kBMxAkKTPT3Jpu7aqY01MoqgaQaeDkNNJhyzTocgMdnYS8Ps3/fDdiWrK+mzI7dxnK9dooyQiimJDA95ytbRbgTZZbADV6kzLsnA6nTdFFrlcjuvXr3Ps2LGbemubpsmZM2d44IEHCAaD5PN5/P488UyaL52/yqISYGFmgYVUBjGTQLBtDu/fh5RY4LPZOJ6dO3EAArA8YiGIIpLbi+H2kgAS1etQ0rCujuE2KlZIh1MmKDvpVGSGwmF8Pt9dZT5XcacI6masmdVIpDpCIp/Pk8vlbvYwWzuW27KXlwDqqzOrrsRmycIwDC5cuMChQ4duqpOyKtg7ODhIR0cHkiQhOZ0kiwW+adpcsGySiowyv0A5GMbn9dEpqow99Q2SPcPo/gCh50+zb98eRFGCVvUsRBHJ60XDSwyIAdhgFcoQv4LHNAg6nIQUmZAs0+VS2N7Xd0sqJDeCe5EsVoMkSQQCAQKBwG0rYW+TRQtoVp252exB9Y1w/PjxFVqYG8W1a9fweDz09/cDcH1uls+PXWZ8epZTloTudaHE51E9ARzFLJokkhMMJpwKZjCIA4FF0UXXwiJun49yuczU1BROh6NStKQoNbm9ltLCkgQ+P2WgDCwAWOCZneOt3d3cWaq4c2TxUoj3QJss1sVa1ZkbJQvLsjh37hyyLK8wKzeK2dlZSqUShw4dqi17/PpVvqMWmfV70CfmsEsaDltHjE1TDPRiyl6k1AKBDjem04EJyCWNrt5hXC4XqXSa3p4edMNAU9VaX4Wu6wDITmcjiTgcsM7DJxXyvGHnTjx1RWZ3CndDgPNWoR3gvAuwkerMtWDbNhcvXqSrq4v5+fmbOqZUKsXc3BzHjh1rOKZkqcSCLFF2KDj0MkW3FxxOBEPAdjhxlgsQ6sTVKePW8pjFMj2SSC6VQe5xVtwQQcDpdOJ0OvEuO35d0yrTssplctksumEgcKN0ukoiDkkCQcDSVF4d9DPc23NT57tVuJsDnPcC2mSxBraKKADGxsZwOBwMDw9vmiyq7tCVK1c4evRog3lr2zYP+oNYs9eRCnkMUcIIhXDGk5g+P4JpYrkUxHIBxd+J3NWBdm2GctcAE6ZN7voEoa6OVfctCEKFDJapktuWhbZEIqVikUw6jWEYCILAA+US4cMHSSaTK7o67xfB3rYbch9guXZmM7R6483OzlIoFBpcho1CEAQ0TavpcC4fJZAvFlmURYbiCa44JQyfgi0JiKUiZmcYByJCsYS3lMDZc5RAOkva31Hbdt60CW3muEQRxeVCWaYLGkon+f5jhymXyywsLHD16tUGkRpZltE0DcMwbtsIgTtFFrdDnLjthtwh1E82X+9LWO8GTCaTK1yGzQRGBUHg3Llz7NixY4UOp2ma/N8rF0m4HOT9CjhljO4u3OevIadj6OEwliwhSi5kR5jj6RRH+4f450vXSYoiZcvGg91yNmQ9KLkc/2b/PsJN4jKappHL5Ugmk5RKJZ599lkMw6jNIfH7/Q1jFbcSbcvi5tAmi2UwTZN8Pt8gsrsaqjUTq90M+Xyeq1ev3nQtRdX9GBwcbJDer/4tevEcM24nF69dIN/bgTIbx8jmEBUJo7cTUdeQSiUMp4tipoDoEdg32Msrdu3i2YkpBMOgx+vi9LXrdJkaaU0nrevkBQk8nkqWo1WUSry+r6cpUUAlvhEOh3G5XBQKBY4dO9Ywh6TaT1GdiObxeBpIxOPx3FQh2Es1wHk70CaLOmxksjmsTRblcpnz589z6NChFfUF1bRrq2+56elpBEGopUjr8e0rl7jqVZhZnKLY7UNMphECLkLlIqRzZD1+BEtHDYQRNQ2xt4s5SeJbsSneevARXn/kYO141Xyek0tl59UCtNlEgnhZJa1VSCSla5QcMqLbjbDsIbAMg4dkBw8MN+9xqUe9dbXWHJLqbNaqSE2hUAColUBXScTtdt+0FXgrcKsDnLez+a9NFkuoFl1txGwURRHTNFf4pIZhcPbsWR544IGmtRTVZrJW9hOPx4nH46uOAHiguxdvJk1O05kr5hADLqR0no5cAd8DO7l4cQq9sxPL6UA0DbBtJEGolGwuO6blv3u9XvZ6veytW25ZFrl8nulEgpR6wwpJ6zp9ksSrTh5f95xW22ezv1dns/b03MioVAcu5XI5MplMrb+mfqhzlUiWa0u81MiiinbM4jahShQbNRklSVpRPVfVpdi2bduKtuUqWpXJy+fzNXm9S5cuNa3U6+rooKujg5nkAtcCXgaA7HgcqydEYn4WoysATge+xByCYSDYGt12H490VHoJTNPkysw0YosVqaIoEgwECC7T3DBN87aZ+fV9EcuPodrRWd9HURWoKRQKZDIZnEv1IrcDt4Msblsn7W3Zy12MVrUzm2F5M5lt21y6dInOzs6GN+F66zWDqqo1N6ZaQbkWwZzcsZczM+c5d22RRSGEsVAEyYHpd6N3dKAPhvFfusaAw0mv4GRP7wCWZfH3Z15gOujF0DRCC7M83PLZN+JuCOLVl0DXo6otkU6nSSQSzMzMoGnaihkkfr9/yzMz7QDnSwS2bROPx1FVlUAgsGGGliSpQQNifHwcURQZHl5bSXs9sjBNk7Nnz7Jnz56aG7PeOn3hLh64JHLZGcRdTlNwKBjlEoKlIqcSiIUSTlujsGuQf5EKdJ99jke37WFCkZCA2flFzmYyvHJykv11k9RfCqhqS7hcLvbu3Vsb/1itUM3lcg2DjJZPiPf5fJt+4NsBzpcIVFUlmUxiGMamyq/rH+C5uTlyuRyHDx++KR3OqmpWf39/g6x9K67Ly/Y8yNTVGURJIGlqxDLz2AURPaCgmCZClw+XW0HqCfLExCQjDx7GpelcW4wz6/Gjd4b5aiZDMBZjYFnW5aWA5W5Ss/EB9XNZc7kck5OTKzIz9a3h6xFB2w15CUDTtJo/q6rqprZRfeiTySSzs7Mryq/XW68ZxsbGUBRlhT5BKwrfOwYHeE02R3FhiszsFURRx+wawKEZSIEgnfkEdkAmWcrTa2goisL39gzy6bkYQaeO7FSw3W5iudwtJ4u7tYJzreHO9ZmZxcXFWmbG4/HUsjJ+v78hM9Mu977HUV+d2SxI2SpEUaRQKDA/P7+i/Hq99ZrtszpJqyrBVo+Wu1wdNpf7TVL9Q+izKUxZwJKcOAsGjpKKHE9AvoSqO3hm/DIntu3hRx7Yx+PFInN6Ancmw64HH2zpPDYD27Y5e/U6c/EUbktbf4UtxM1kQ9bLzOTzebLZbK3Br5pNKhaLJJNJQqHQPaO1uRruO7KoKg9VqzOXxx02Atu2mZ6e5ujRoyvKr9dCM7LIZDJMT0+vap20qsz1+OxlciEn1kQCze1EuTaLHgpipdPkdnbTNZnA80A/5VCQv50+xz+ce4FjO/bwGoeb85kMbzpxAv+yLMNW4snTF3hyPo9hWZQWZzlyOEcw0DwtvNW4FdZMfWamr+/GlPpqZiaVSpFOp5mZmWnQ2qy3RG42M9N2Q24B6su4q6bhZsnCMAzm5+fp7u7G6/Wuv0Idlj/4pVKJS5cuceTIkVWj8a1aFrIIcrZEWVEQixrqjj4stwtzMIRzbh69043qdSJcT3LZ40fu8KJmYuj+TvZ0dRFcpZ5jqzCZyiM6HaBqlJweJuYXOHIPk8VqqGZmnE4n+/btqy2vZmaWzx+pjlOskkir0+FvqwjxbdnLXYD5+XnGx8fZt29fg7uwGbKo6lJ0dnbWIusbQT1ZGIbBuXPn2Ldv35rbWs+yGBsbI5PJsM2SeSE5idchU0hnMSUJKVfECvuR0wX29A+jL2rEFB+m24VTdLJQ0skbOguZJPF0iq7Q6t2nN4sur8xMpjIY2qmV6e8Kr7PG1uFumKJezcwsr8Gpn8lan5lRFGUFidypVOx9Qxbnz5/nb//2b/noRz/asHyjZGHbNpcvXyYYDOJ2uymX155O3gzVYKVt25w7d47h4eEVtQHLsRZZVDMxR44cYVc+z/jzBTLJOTKD3YCAs5DCuZBkWNQ56u7lqH+Qx6auIpRs6HDjSOVIlEpc7XEwm7nCq3Jhjg7v3vB5tYLXnTiM+8xFFjM5PMMddHfeOmJqhjtNFqthvcxMPp9vyMy43W78fj8ej4fBwcHbcoz3DVlUK/iWo1qy3SomJycB2L59O/F4fFMuTDWoevXqVQKBAL29veuus5obUi13PnbsGIqioCgKP37iu/j21/4WSbAQbAuh28duTea1wT6mMwnOFWOkpxYIeTsJLKY56AswOexlbnIGzXDgchZvGVlIksSrjh0km80yMTFxS/bxUkGrmZm2G7LF8Hq95PP5Fcs3kg2Zn58nnU7Xaik2m0kRBIFEoqKVvWfPnpbXWU4WqXSaz3znn+jfNkRq6gydso+ApFBKZVnILmBu340tCLguTeHzutGDEpOKSjIep/DIEL1FkW2Wl15V5h8uT1LsCZMRRNRrk3xvz3TN9H2pVCC+VFCfmenq6rptqdn7hiz8fv+qlkUrgcNUKlXLVlS/nM3ODimVSmSzWR555JFNjz3UNI2/fiaK99gOdLebOWBRKhOfHeM7F57HGPLgWlzANi0Ep4ZH9DOan6M41EfG9OBIp3AHOpgXbQa6htgl2lwr5en0exkI96Pres3stW27ZvZWf1rp8mzj9qBtWWwxfD4fxWJxU+sWCoWalF19tmIzwdFCoUAsFmNgYGBDb+x6y8K2bf72ya9iPdDTMG0dIGEUMD0OsAS0XWEcMymEuMZZNYba34c2t4hh2VjJAhnRgS+ZYvg1j/JQJoth2gx19vGAW2Xnzp1AhdjyxQIuWaFQKNRqCaozXuv7KrYiDfhSwkttdux9RRbNLIv1UJWyO3DgwIpaio1aFtVtrTbTdC3U7+trzz7JfJ+DrtDKEnXJFnAgIfQFkCcW0Tv8cHSYcl7FTpWhww1YOLIZikaZlNfDbz3xf+j3d+KZXWRAFtgxsI1sLsd8Ps1odgbd5WAwa/JDBx5qqCWon9u5uLjItWvXatJ59VbIaqpXL3XL5KVUvQn3EVk4nU4Mw9jQOtVpX3v27FnRDg0bI4tq6/ru3btrIxA3guq+zly5wLP2In39O5p+bjDcR2YhSWwmg2bbSEUNBzaCYSNn0ghaDtGjYDw8jCiKlE4vcnooxETAwluEHQ8GeMpOYSTnuDIxi9rbgaI6WJRFwqef5ZUHbhSgrTYtq6p6lcvlmJiYqLky9WXR8NJ78y7HS0nLAu4jsqhitVz78uXVtObAwEBDOqserboh1TEAPT09hMNhEonEhmMdgiCQzKT4WuYKdtjF7MwkQV8I3zLrwu3zEujtJlwUSM7GKYUlzE4fpIq4Q05sRcAc9COqJqKpkxcEpKAL0yWSkSxMVcfhknEGPAhemZJHpIQFqspzcolrU0/htSQCokJIdOGXXAz4O+kIhmrWQzUrszyCXx23l81mSSaT5PN5nnrqqS2vaLxb0LYs7lGsJb67XB6vWkvh9/ubStktX289TE5OIklSzf3YTGC0XC7zLwsXKfc4UXsqqtr5xAI7nDKKtzFuYWBhyALOoQCaU0I8G8OWRGxBItAfhOkMVtlEsG10txdLAMs0QdcRpBvXaHtPF8biIrog0CU66Ng2gCAItYlji2jYtopRWkQaz/ODO07S3dlFM9SrWPX19ZHJZJiammLfvn0NzVlVV6a+GKlKJPfag/dS0rKA+4gs1kLVQqh+sVNTU1iWxY4dO9Zcr5UajcXFRVKpFEeOHGlYbyNkYVkW//fct7F2dmIZ+Rvalx0KxVRuBVmELCdOVaVcKiKmbMrbBnAYFsV4BuflBUIlA2dvCHtvL/FvXqOsqRQkB95EnpnJaTo6O/B3hPD4PBz2rX0NBEFAkh0c8PevShRroVlF4/I28fHx8Zo2atWV2Yj25p3CS0nLAu4zsnA4HGiatiJQWe9OLC4ukkwmOXLkSEu6FGuhWnhUn26trrcRsvj7b42S2xnAKTtwaQ5yhongkBBiZXxdfSs+7/a48V5J49oRIG0J2KaOmNOwOtwY0xb2ri6spErpxUmKwx2YvUGcRQNNMLmQnCUsa+zSdEzTxLJtunt7EB3N35C2bdMds3jZvs3PRFmO1YqR6js8q4131SFQ9VaI3++/LbM61kM7ZnEPw+v1UigUViWLTCbD5OTkiod7M1BVlYsXL3L48OEVN+5GyGJ+fp5uyUOf6WNqZgFv7xDXxyYpOAw6evtxuhvPJbUYY0rIIh4YIJ9MYswWsXwmlmXhShaxjwwiWCJFBMzJBEJPEMEpYvpljKREOSCRKua4PJvBcWgYBIHU+BgP7t7d9KZU4mVet+P4mtdrIbHIi4kLeB0efKIbv9OHW2y9S7eK1To8q81ZuVyO+fn52kAjRVEaMjK3O6Dajlncw/D5fOTz+RUBS0mSKBQKTExMrNn52SqqWZQHHnhgRR0EtE4W2WyWqakpXvHIyymXy4RsmQN7DmDvPkmpVGI2HSOVKpE1y2RtlaJoksimYchNyJZJX0uQ3zcEkgCajuPsPNaiC61kYUogySIuy0SLZSGn4soWcO7pRVMNivkyQalyo+e9IkZZW0FM5Mq8umM3Ps/qXbeapvFs/CxCn0IWjSwappEkF89SSueIXc3ildx4RRedng66QuENWwVruTLVWSTxeJxisciTTz7Z4Mr4/X5cLtcteTu3yeIexmqFWbZtc/36dQ4dOrThLtLlbyvbtjl//jyDg4Nrqnu3IthbtUwcDkdDUVa13HePZ/uK7UbPPsWZcg4NiwlDQJAEBFnEMkX0/hAIYNkGGAIC4Mrl8RkSvaZCds8Asi4gx3UC/gD60nadqokkN94qlm5w2AixrXv1ALBt2zw19iz0NmY3JIeEK+RGk3TK3TZliiQocrU8jzWh40bGK7jxSm58opueYA8Bv39DD97yWSRVF+aRRx6pjRFIp9MNrkx9LGQrXJmX0swQuM/IwuPxrCjMMk2TRCLBwMDAqrM5NoLr16/j8XjWzKKsJ5FXbYHfu3dvzTJZrZFM0zTOz17CxmJf/wN8z+FX4L1+hnOxawQWMyQdAtZAB8pcHFGxsHUDc28PkqpjmjaOc/P0Cy56HuiCmRR928N0Dw/gcDiYXYhjC9Df0dcwlcy2bXrjNg/vP7DmtTg/eZlMp4ZDaO02k10yuGRsII9BnhzzVpbzuXHEBfCIFQvEK7kJOgP0dna3TO5Vlax6V6b+OzIMo2aFzM/Pc+XKlRVjFavuTKsEcLuyIe2YxS1A1Q2pomoFBAKBpsOAWkH9dLG5uTmKxeK6A5DXE+yt1mR0dHSsus5iKsZiLsZ4bALfgyEEQeDb06cY2ftatvk6eXzqFLkjg0iCgfPqFB7FQFO8aLqFYJgIgFDUsRyg7/CTKWVwuy16bRcujwdJdrLb19y9cMXKvH7nyTVv0sVkjGtM45Q3rvdRD0EUUPwe8IMOpFFJozKpx3l+/gJO3YlXcC25Mm56/GG6mmRl1tOycDgcdHR0NFzzZq5MNStTPxFtNVfmdmRDbmcm6L4mi6tXr+L1epFledPSetWHuNoz0Ypo71rNa9PT04iiuEKjoH6d+fgCz5fPI4ddTOQX6I9bhLvDmH6bfD7P+MIUE4pO2e9CFxTsB2Q6z03h81qolknh6Qm0Djcuy8I97ENzW2gdHszTKTr8IrsTLhwehaypkrHKZC0VzSMheRSEvMZ3hfc2jcVUoWkaz8TP4uxdnShsbk6IRnI6kDoqt28JixIFYlae+FyS12yCLJphtbGK9RPR0uk0U1NTDcOMqgSi63o7ZnGvoj5mMTU1ha7r7Nmzh/n5+Q2XgldRFe2tNpq1Ynau5oYkk0lisVgD4WTyWR6feh6nJLG4MMvzT10laWVQvE72+nfi8XvJFfKECSPlBXx9PjxeL+JcGa3HjyKJkCzjCQVQSgbewQ58ggy2gRjyUOxxoaVKZJJJzH4PsT4HupHhzZ3H0A0dt+zC4/GQyWaYzSZQZJnBrtX1N2zb5jtjz0HvHUhdxnResfdlTf+0ldPS1nNlcrkcc3NzNb2TRCKxaVfmbsJ9RRbV1GksFiMej3P06NGaLsVmxwEIgsCFCxeaNpqttc5yFIvF2sT16o2k6zpPzJ7GHHJj2DaT8QyuPR2UchJxsUjm6lk6Qx0IU2WKhRxHBw8hiiI7u4Z5OLuT4vQcZaeAT3HidDsxuxXKs1nKoomVyiHJJsb5NGJGxwy4kYY7GItP4/K5OP9/nyaws48eT4jXdB5g39BuOlqQ27s0dYV0RxmHcHvJQs+WeaTz4KpBydshqbfclZmYmEAURbq7u2skEovFGoY71wdVN5OVabshtwh+v59UKsX4+PgKXYrNuCHVhrAdO3bcVHC0qsO5f//+Wl+Ebdt8c+x51F5nbYZx1XT3BHyQszGLZQSvQtfLdrCQzfHY9L8iXjSw8yoeUWG/LjIpFAkNhegOdTFHCXVQRMjm0UUfDs3C1eFB8FkoHhdpLLKyijGeorDPQzpkkStlcS9cYt/Q+spZ8XSCK9YUDuXm4hQbhWmYDGpd9A2vLFCr4k4NRZZleVVXptqxm0qlGuay1mdkbsVIxc3i7jiK24RCocAf/dEf8fjjjze8gTajeFXtH1EUZVPTzOq3c/78ebZt29ZAOC9MXCDeZSCJla9IEAT87gDaXAnLK+DOOti+/xBORa6URhfzbN+7k6sT19H3h1iMZ8hdm0XslEklUpS9PlKJEqWQjDHgQZK82BMZXJITSRDo7gojjM/j1Qw6fSFKQREbsDs9ZK6u39qv6zrPLJzG0Xd7iQJAidkc27t2UPlOiPWuFeAURbFGBvXQdX3VkYr1VojX612z3+lW4L4hi0QiwR/8wR/wile8YsXDvRkRm+npaWzbxu/3b3pIEVRSrV6vt0GHc2xhiqtKEsnZWJ8ge1xs6+3DKGs4OuWaRRRPxOnt6SWXTGP0yhiGTsbIYw+4CG3vQl3I45zR6HMFuF7IQlnB1MpYDht5KktAcqAKDvxpi5Pdu0i4ysQyRZwuCUdR5aGu/euex3euP4fVc8MKul3QUyov6z+2bqzoTpDFZuosnE5n06xMuVxuOg3txIkTt61L974hi1KpxLvf/W4uXbq04m8bJYt4PF6LeVy7dm3TOpwLCwsUCoWGCWTJbJrny+NInc3jH6IkInsrb2/btlmMLdLZ0YnD4UB2KeQXplG7RKyQE/1KBrNPx+GXsQUD/4lBeq4KJGwbl8eDq+ykY1ihXwmjGhpqr49ZMwtzGgOGjcNvsc3pZefBfmzbJpZKkC/m2dY3hMPhIFvI8czMGaYz82iyzqC4AVGfLagnMjSD3cIAncHmEgINu7tHyKIZBEHA7XbjdrsbXBnDMG5rD8x9QxZDQ0O85jWv4bnnnlvxt42QRT6fZ2xsrBbz2Gy8wzAMJicnOX78eO0mVlWVby6cgb7WAqXJVBKPx1NLY3oCPpQxC10wUEoazr4A5bEsXt2JJxAC26Z3Zx/q9UkU1aB37wBkdfoHhhEdErOzs/T19SEIApqmoWkaqXKZvxyPkvxmnFjAQArIhF60+b7+k7yQv0LOp1G2ygS8QYLpHL7Q7RsY5I1LHNjX2qjFrcyGtIpbXcEpimLbDblVWE1ar1Wy0DSN8+fPc/Dgjaj7ZuIdmqahqmpDH4plWTwx/jx6v9ySKZ/P5zFNk3DnjSE9tm0jGCaSz4niCyBrDroCHspimQJFMhMzBLq76Ba8yAEZIWPQrXQ0dJRWH6qqgI3f74fublKo2D4V2ykx51H532e+QudAEN3jAMOCfI40Llx+Nw7p1t9WVlzjZdtf1vLDcqcsi7aexT2KtRS+1yOLeom9+nGFm9GmOHv2LG63uyHV+szYWVI9NpKw/ptI0zSy2eyKkvLk/CLKziBaKoMpCCh5cIYd6F0ugoIbT0HFnCqxZ9+RWq+HXtaYnZzAFsAw1jiPgoHL70RzCchlEbkvgAHIihNLtuBSGXmPzMLCQqW13bQwVIOOjhC+kB/ZKW/Zw2oUNQ56duFt0sBWDSou39edyIa0KzhvMSKREQl4BpiJRkd/IBIZ6QQ+NzAwSF9fLx/84K9uOk251qChtZpybNvmwoUL9PX1rehY3YgbYts2ly5doru7m3g8XiOZy7PjTPiySI71A1WmaRKLx+jp7lkhA6jqKs5OhU5vD2qxhFkooZc1sASQBBweGXeHjENx1taZnhtDGqy4MamLcQbMoRVvQ1M1+IHeh3h86gXGlBR+nPhdAXJqFp8qI8ypHHnoIZxyZbvFfIFLsSmMHol4YpbujAd5qWNVluWG9PBGYVs24ZyXXQ/saHptvnnhCXCBIii4JTeK4KLT2wnW7RcIvh1dp/c1WQD/BbgAVOf5vQ/42p//+Z+PPPbYZ3nsscd417vetakNezyepl2n613w8fFxFEVpOiZOkqSWqz+np6eBSvwkmUxWApTpOKfNSST/+ilHG5tYLEZHqKMhsGXbNpZlEeoIk5+bouwxKBfzhLr9lLUyjIPtFXCYEj39NzpVk4tJpnJpnFNFujt92F4BUzdqDz1UHs6+tMLLD5zgZfuP86/nn2JMX2C+GGN3715cLgXvMT+CeOMaxtMpHP2+ys015EZYNBkeGsa27YoLpqkUCgVUVWVyarISnJVlFFlBVuS1rZBFjYd3Na/SPDN2Gtc2pfaAaqiodpl4KUY2lkXNqBTHCiiCC6/DQ3eop5aCvBVot6jfQkQiI0PA9wMfAd67tPhNwGuB34pEvodf/MVf2DRZOByODccX5ufnyeVyDRmLerRqWSwv5RZFkXw+z7dTFxDX6KGoh2mYeAPehqa3KlGIoojH72WncxdXL53FP9yB7HNhA8qik67BGy6LZVnoqsZ1NY3W7UfzOzDmsgQKNtL2xltCXtB4dM/DQIVUX73vEYqXHqd3R9eKh0wra6RTGYySjmWIiA4Jy7QoJPOk3SmCXaFaLMThcFAsFgmHw5iGiaqpaJpGIVVA1yvN8VUrRFEUFFnBKug8HG5epTkXmyPvzaEsE9URBAHFI+Pt9iD4BKywSYkCBTPHRGYC5sEluHCJblyiQsjTQXgTmhrN0CaLW4tPAr8M1PsZvdHo6BxAOBwmnU7f1A7qu0TXQ1W6ba3msFYCnKVSiatXr3L06NGGm+fJ2dMY2z0tBTQLhQI29ooakeq5VLfrUJzYqkVifB5P0I835MftDa1wWbSSRh4NURDRE2XsokAwFK79HcBOqzzaf6T24FiWxddffAKtH5Rl16NcLHNpYRK7x4VlGzjGNAS/Qm4hRs++XhZJkp/KM7RtuGE9AQGHw4HD4WiIQSy3QhLxBJ4xm5n+INlEpqHPwjAMrqQv4+5fo3HNbrQgRUnEG/TA0uXUUdFRSZTjXJg6j8Ny4BbduCQ3HslDd7Abn8+3ISukTRa3CJHIyA8Ai9Ho6LORyMhrb8U+1vui60mkVCpx6dKldZWz1gtwGobB2bNn2bdvX0NA82JyAn1/AFcLN5+maWQyGZyOxrdd9aGWJKl23DPXrqMNWjh8HnJzWeRJJ77jjQQjCAKGVkZKpTF3VrIhykwJ/7AfSZKwbRuzpHGYfkLeALqu8/z1s3x94tvk3CUCqRD9+TDdPTdy/vFkEvo9CDZInW7cpsxgoIupgI3DVbl+hUzrE+EEQahZIfhBmjN43RsebWjWqvZZTKQn8O31oKZdyLKCosgr4i42dkukLLvkiq4GYC91sxasHFOZSewFu2aFKKJC0BWku7N7VSukHbO4dXgU+MFIZOSNgAsIRCIjfwksRCIj/dHoKIlEYlX1qVZh23ZTy6LaCVqNQZw7d44HH3xwXXGV9bQpzp8/z/DwMIFAoLb8/PQVEr0WQXH9L9qyrFpAM56I1469eh7LI//ZUhp5KWApb3dhXNabblc1yuzY00s2lsXCRvDLdHZ0VrZt2fRn3Rx4YG8luDt1hcfzzzHRmUawLbLXMuS8CfK5DF7Fjzfgw+lwkIkvojoMyiUV93QZu1dFd2g4vRWSFK3N3djVKs2qBVLfZzE5N4EpGAjOSp1KoVgglUpiWhYOSUJWFBRZxtikBAFUvmNP6EZLftUKSZbjXJy+gGzKuAQXiqjgdnjoDnTj9/vvSG3HrcRdQxbR6Oj7gfcDLFkWvxiNjr41Ehn5OPC2yme+yitf+cqb2o/L5UJV1RV6DFV3QhRFzp07x/DwcEs9H2vVaIyNjeH1ehvEZWfj85wX5nG45ZayAbFYjFAwhNPpRFh6N9bHKURRxDJMbCpydQouimUVyeXEyKgEnSsnqQF43D5KxTiB3gDpWJrezt4a6SgLGq/cfeM6/9MLX+NyYAHbAcr2AOVECUVXmdMTqFKWQMFHR96Jmc9hKDYCBmKXjDXkpPRCCsoVCf+BzroAcYuJEFNfvUqzVCoxVhjD01f5LustN9sG0zRQVQ1tyZXRDYNCsbgUTJVXtUJaxXIrpEyJklVkJj+NHbOZLc7iGXOjiC4CrgBdoa6WO5PvRtw1ZLEGfhv4m7e97W309PTwq7/6qze1sWqbejOyME2T8fFxAoFAQ6/GWljNslhcXCSXyzXMC8kV8nwnewWxW0FIFdcli1Q6hSzLtbqOqvVTbSASRZHk/AJpPQki+OwAOw/u59qZc5Qp4RM9DB98oOm2/Z0d2AmbmSvT9IR68AWDTE9cwSprPOI5WHO9vv7iEyxsK+H0eUESKJ2NIzscOG0RrUtBMKBYKJFKx3B5ZDxhDzlHkcJMlvNnrmAF3IQ9PnZLLtx+d+WcWzQwKlWaDg48uLJK07ZtTk++iHugueUnCNyIhXg9OBwODNMkGAguVaeuboUoilIh502Y+IIo4Am4IQAuXUHv1tDRSGkJLs9eRDIrsRBFcOER3YSDXQQDwXvCArkrySIaHX0ceHzp/wng9WxJN8ENtaz6eRRQIYvZ2Vk0TWPPnj0tb69ZNqQ647O+lNswDJ6YeQG7v/JmWU1Ts4pisYiqqvT13rBKqmThcDhqb8O0lkLpq2RH8okcHVo3e482z9wshyFA79AQnR2dzExeQ+pxMGCGyIslFhIL9IZ7uZacQH7AjV4uYmgGkg5ew0nHzg7GxhMYho0zqOAb8mKVbQpTKcpSGUwDPexBcjhQ3SbzuRyDdh82NtigGxX3yLKt2rnVznOJTay4xiPbmldpXp+5hhE2kFvUzbCX9iGKAi6Xgsu1jhWi6yAIW2aFOGUnzu7qsdqolChbRWYLs5hxE0VQcAlu3KKLcKCLns6elrZ7v8YsbguqlsVyGIZBPB7n5Mm1tSWXY3k2RNM0Lly4wKFDh2pvZ9u2+db15ynVaVOsRRa6rpNKp+jvWyn6a9t2LaBp2yuDdq0ee42MllwkC4tAWaYr3IlpmJQKZbL5LIIiYcZVfD1eitMZBpx9HDh+iMtXruLtCVOwyphOcBRtOgbCWGkH8bJFslzGEDWcXQqCUwDtRgWlaVRmtPT39d/IvNRdC0EQMAo6h9w78Lg9K2JMuXyOGWMat2t1ab8mF25Vg2a5FVKFZdkrrRDTrNSFLAVfFVm+KSvE7XfVcn8mOmmtjJSXWiaL24k2WVBJS2azWR588MENvznq3ZD6Sen1tRCnJy4SC+uI4o3LvRpZWJZFLBaju6u7wTS1bRtFUYgn4pXejaUCJo/gIz+XRRAF/FKwVp25FgzDIJFMMNA/ULvJO/Newl4falHFlXQwsKufb4x9h+EjO5CnFGLXEuzs2MnA/kpnqcPrwusFl6WQNgsUSiUc00n8hojaI+H2hbGmcxiTWRyKxd7d+5ZOBGLxGJ2dnUjNppzZYJs2HVk32/YM1wre6rUbTk+/iGtwA0SxdP3Y4AO9nhWiaiqFfH5VK2QzsGIWex9o7jreadx3ZLFctFfTNM6dO0dXV9em/MaqG1IVw+nq6iIcvtHcNbE4w2U5jiSvLBZqRhbxeJxAMNCgUVANaAYCATo6OjBNs9YVKrhk5LIf27KxFQfpdLpWyNSM+GzbZnFxEZ/iZnFmAluw8Ro+3rz/tTglJyW1RHhvJ8+NnUbrFRCB3uF+eodvWDnJeIKckSOZ0imbOlasiMchUhZsCrEi3gf7QQD3ThfixSKPHn8IUzNJzsdJxOOYkolgWs3nlApATOfhHQ/Xvo/qdbIsi0uTF6HLJpcrcHpxAVWEflFh//YBqsGQZpxgA1sRFWjVCkmmkmiaxuzcXIVAWrBCyhmVfd37W7JSbvfMELhPyaJqWdRbArlcblO6FNW+kpmZGSzLYnj4RtFROpvm2dL15toUQqWUuh6ZTAbJIeHzNmYwlhdeSZJU0zeo/0yVQIqlIul0GtOqmMyKrNQIJJvN4va4KWRiuAZcWIaFe07D7/HhdDrx+3yMz0+y4MvikFZaKaZhcmniGqrPInd+DiHkxBYFdLeMs1dES5g4xrIw4IGsxnBHD2PXr7GYi2F2OtAllS5PiHKHzvzMLP1DjSX0elbloY4DDWRZfXhS2RRxRxy37ObCzAzZvsp1ul7W6FxI0t3dsfT5ygWu/rt0geCWtos3WiGWZTMzO0NPdzeqqqJq2ppWiCiKeEteuge719nTDbSVsm4xqm5IdT5Hd3c34XCYYrG46XEAhmGwsLDQUOmpaRpPzJ+B/uapMkEQasE9qKQBS6XSiixMlcDqC69W216trXzJCbZtG8MwapWQmUwGTdNwOp0UynlQBQIlBx3hIKqq4nQ6KZVKPDd/FmV782a9+bl5GHSRX4zjerQXu2hgFnWMrIalGjg7FAJZB/KCgN/djcPlIKbHce70YRoqTpdCuajR4RDRaawBMQ2TgXIn/YMrM1GmaXJh8TzugQpBGvUBUdmBWtKXrs+NGEjl5Vv53TBNZEnCshrjH7fqWbNtG1Goq06t61RuZoXkZ/Ls8ezlsnW5oTr1bpoQf9+RRdUNmZycRJIkhoYqPvhmpPWg8pBrmsbJkydrZr9t2zwx9jzaGtoUAjfcEMMwSKaS9PX2rSjLhs2LnAiCgNPpxOl0IssyhUKBbdu2ATB7vQgJFY/LQ+zcAmeCZ2ry9g+HD1PMlCihUbCKFKwyJVFD9DswsfAobhY1HVGTwbBwBhWKF5KInR4cqsiOPbuZnplmMjdFOp5C6XejLup07OvFVHUEG0zNxC00Eqkcszm2u7mW5oWJ8zh6btyu2z0+TidymF4nwUSZgZ07ale2cu431jUMnWKxSCgYrBWz3bhGS9+G0HzdzcK2bYRV9TcbrRC9rHPEc4SBrsFaderCwgLFYrGivbqkuxkIBO7ohPj7jiz8fj/T09OkUimOHDlSewglSao1MLWKaqWny+VqKLZ5dvwsqR4LUVg9WFpfhbkYW6Qr3NUQY6ivNL3ZHHx1H91d3bV9DHQOs08P43V72RHZjiAINbXp6gQuTdPwuFz0+jtxK27KRZUu28M/X/0WLllGz+tg2RiTaTyyjDVeZHh4L3Nzs5R2WKiLJq5tndhlE1lUyD6zSF9vHwGvH1/eQ3igTiIupfKy3uZamovJRdKuNC7HjZqKgZ4wHQUv5XKZ4K5+RGm1a2QTi8fpCocRxeXbbm6FNBLH5qwQ225dP0NIiuzau7smn9fTcyMTYppmw4T4+rGKe/bs2fBs3pvBfUcWCwsLfOYzn+Fd73pXw0O4UcuiqnExNDTE1NRUbfm12XHG3Jl1tSmqZBGPx/H7/MuqD28QxVYoLSUSCbxeb+3GMko6x+RtPLhzV8PnlqtN14/vy2azzM7PYmg6PVaALkeIfCpHSsvR4e4mtKsTs2DSLXRwWbuGpLgrVaUBGS1XwOV34wyI7Nm5B1+w0cUxNYNd9NMRXDmXxDAMLicv4WrSJOb2unB7135YsrkcTqcTV9M0azNLojGVu1krpNVmxVKqzOGeI6t+VpIkgsFgQzWxbduUSqXbbmHc/WVjW4iZmRk+9alP8epXv3rFbNONammOj4/jdrtrdQq2bRNPJ3jBmETyrp82EwQBVVUrKc9lYj7VOMVWVPXl83kM0yAYCJKNJZibuoZ+ZZbt4YGWjtHlchEOh5nWF0nu1snusnAYImWvhtjlxJUVGOwfxCu4CYc66NM7OK7swxmHgNtH4UISDCBr0Lm9g4XkbMMDWK3S3D/cWKWZK+Q5P3mRrz//dZTezZVIG4ZONpsl3LmyVHyNs6ZCCMt/bhyvZVWyUzd+KstsG6qn1gpZ2JZNUAu1NLyp4QiXLJDbTRb3lWXh9Xr5wAc+wLe//e0Vf9uIlubi4iLZbLZWyi2KIoVikW/FzyG0qE2h6zq6oTf0jQANFsXNBrd0XSedTtPf349aKFGyk/j8TrYPD/DC1Au8fO/LV6xzefoK8+oiHsmDR3AT9nZgGibpUImckccQTGJKln5XH5qlI/ZZBN1+fOEAxckcQz2DaKpGcazAeGKanXoYu1tg+MFtyG6ZbD5DR8xFSajEQyzd4jXbX9lwroVigW/PPU3ZrxPviVGYLTI8tFJ4aG1U3I9wZxihBanC9dG6FSIIFfdBqC1rboWUF1RO7Hxo80d0m4Of9xVZhEIhTpw4wejo6Iq/teqG5PN5JiYmGjIfgiDwjfHnMIaVltoeDMMgk8ngUhrH1a3WSboZWJbF4uIi3d2VOEW+UMQhCwx7eyrxGWllfCaejjNmTSP3KuQokbWLTJfnycWznM5cw+6TMGyTXCqG2C/i9Cq4Ot0UFvJst4bwiS7+df7bZIs5VLWIuUNk17YdxKYXUUtlDN1AWgDRNOnzh/B6B3EFXCiywvnJSyTMDFbOYKY0R9KTwcpphLd3kS+13tpeRdX9WGuA881jdQLJZDJLM2VslsdCBAG0ks52745NTxu7EwLE9xVZQMUvbyat1wpZVNW9Dx061GACXklPYR/uRG6xmCYWixHqCDUcx/JO0ptFIpnA5/fVYiGKy41vvowrrGCUDTrFRtNX13VeiJ1Frkv1CoKA0y3TuaOL8r+exlRETMNA6nKycGEO74CPbrEDLeghvrjImG8ee49IuWyRuJyBvM1zk1lCUoDOhQCvPPAI294w3BC0i8fjfOfFp5npzuLyuVkQE3hEiZJbxdQNAiUNl7mxuE3V/RgcWN/V2noIFIsFEAQ8DYLCjcFUMSkysHsAwzAaXjr1/7/bcN+RxWq9IeuRhWVZnDt3bkUp94XpayR6LcKrRuMbkUguBRsVV+04qkSxFZkPqDSyWaZFwB+oHfuOcpCTh1/JxOIEbtnN8PZGxapnx59HXGPyuSTauAe9mJZJaSaPrWv4O70EgiHy01meSs8Qz6YRZmwEt4hZ1pEHPChhD7pms5CPM9Q7iLBUexAKhWraJGWPidSZRdd14sk0+UwBSRPQiyrZmQQ9u3eSz+eQZQVZdpJKZimXVXp7u5pkQbba/dgYbNsimUrR37d87uoNK6SULHGs74Zl2sz9XR4zqS67k7jryCISGXEB3wAUKsf3t9Ho6K9ls1k+8pEPMz+/cFMq336/v6Hcu4r1ApxXrlwhHA43lHLPJ2KcYxbJ05o2RS6fw7ZsAoFArUQctjZOUVXVGhi40ffhXjR4+e5HkCSJB7etbPe+OnONbLCEU1ydLIKKn+xEEVO0sFM63qAbc7yE3lFGFJzY2yV8riCGbqDO5lHni8jdHoyMjmBZlGWLJ698h5ArSED20xfurVk9Q4F+ZtJx5A4Ff9lFVizQGexE1m2GDvYxF1sklcoQDAaYml8gHgTJJ3Ptwhwndu7F5XbVSDZ3W9yP1ZFKpQj4/UirzE6xTItOM4zff0MMafl3Xl/eXo964sjlcvh8vtsa5LwbsyEq8LpodPQocAz4vkhk5OWf+9xjHD9+nD//8z/n+PHjPPbYY5vauNvtplwur1i+VoBzZmYGwzAaSrkLxQJPZS4gBhVEYe1RAkAtBVltja+vs6juf0viFLFKnKL68OTHYuwUw6uuk8qkuG5M4vQ0v+nUksr1ies43U68howfNz19YXYe3MnOh3bh9/tweJyVEmbFieR0IBkS/oAXcVbDaQg4ygI+wY0+aBPvynDZPc7X5p8gevVxLk5cpqezi5cHDjOc6ECaKuGTFfSZAj2BMGOJaewhB+aQg0K5jBXyEOzrwO1yofYozMzOMTc/x/T0FPPzcySTSVwu11ID2u3tn9A0jXK53KCKtuIzMY19w/vW3E69Xkn1p94KMU2T973vffzzP//zlh7/erjryCIaHbWj0dHqq9+59GM/+eSTRCLfA0Ak8j08+eS3NrX91cz81Rq7UqkU8/Pz7Nu370aLtWnyjekXsLpda65bRXV4cf2sj+o61TjFVpiY8UScQCBQe2Mvjk3g9RjEuxJ8++pTK8jQNE2eXziNM7x6anJmcQax30nHg114On240g7C/g4Ur4JR0lGcCn6HF0V3Uk6WMWeKOHSB4V2DPHh0N+5Z2O3exr7Dh2rn6HA6cIcrrtyu/h0AdIe6uDh2nsT2PBk5T1YscHnqGlK4koaWHBJlUcVpCzWRX9mUGOjvZ3BgkMHBISzLwh8IoKkqsXiM6elp5uZmSSQT5PM5NE27hQ1YNvFEnHC4i9XUfdSCyi7/rk3VztQTyOnTpxkfH+fNb37zzR3yBnHXuSFQGzT0LLAH+B/R6Oh33vzmN9VcgJtV+V5Nh3M5SqUSV65c4ejRow2l3E9ef4FCr1RjWkEU1tThrB9eXA/LtGr6CDeLXC4HNvh9FdfMsixcqk7/noo1pHaWSaQSdIdvVE0+e/0F7D5pzQyOLd54uPzdAfxON6ZmoC/ouCQPnf0VS8maNnHOCRTTOeQeAadLxNagf1c/fQODDXNFAPScyqHgg7WGsYXYAtOOWUzbpCSbSEEBbaqEYz5PYHsI27ZxGBI7ega4Oj+HIdpsU4J4fUvCP/kcsqLQ2dFYU2GaBqqmoakqhWIRXa/0kNQ3csmyfNOxomwuV1PZanodbRtX1sXAno2mgBthWRbve9/7+IM/+IPbrq51V5JFNDpqAscikZEQ8HeRyMih+kacm0Grb3DTNGuivfU3wNmpy8x3lpHq/Pu1LItkKrmiQxQqN4/P7yOVTmEYBpIoISuVQTsblXXTNI1MNtOgT+FZ0OkJ3ZhqbpVsPOEbgdmxuXHSgfyacQoAr+Qlly/i8Dgw5lQ6hgdxOBtvG13XMbHYuXMHaStGYjGBgYlZLBCQOlYQhWVahAshBvZUshWWZXFh4TyBbj+z42PI+wOoeRV/bwfejBvHooVoCQwNbccpOznq292wPcMwyGSzDPSvzH5IkgOP24HHXT9rxVpqrtPI53OomoZtWZUemqVWcllWcDgkWtEANE2TbCbDwMDqRFBOlDkxcHLdba2Hv/mbv+HgwYMcP378pre1UdyVZFFFNDqajkRGHge+r6Ojg0QiQTgcvmmV79Vmh1R/r5ZyDw4ONpTZTsVmueSIIcly0+0tR76QxzQahxfDjcBVR6ijwbVR1cqgnVQ6ha7deANWSaTZG7BaT9HT03ND/yFR4tGB49iWxfm5C1iixW73zlrnYzaX5Yo6hrNr/crIvoE+xJkF0lNJ+poQRTUV3N3VjZovk4gl8e8OUM6XyU8W2P/KYys3umBybHeloG1qcYpT48+SJUMxXcAty1h5C5/iolQoYqoKQ9u3o7hXO1abWDxGuDPc8ptWEEQUxYWiuOom1Njouo66FHfIZLOVNHGdNqcsNyfxRDJBR2fnqvs3DZMeeprOZt0I8vk8v/d7v8fXv/71m9rOZrEuWSwpbf8L8JZodPRzdctPA89RmRY2CZhUYiAJ4G3R6GguEhnZBXwMGAKKQAn45Wh09Nwa++sG9CWicAMjwEdf8YpXEI1+lbe85cduWuXb4/FQKpVoZq1YlsXExASKojQMHs7kszxTuIoYXlmh2SzAqWka2Uy2aYUmrOwklSQJj8fTkJa1LKvWXp7L3fC5qwQiO2WyuSzBYLBmzpsljRPKDkL+Csk9Gmy8TqZp8uzsizgHWyuhzmfyZMni2udjMbkISZtA5w0CXVxYRM2V0D0apmXiDnvQizqSIBHsWWlVaCmVkz2HkSSJZCbJs5kXmQnOY3mhUCoQcLoxkza6U8Vdlujc5eXMhecJhjvxOfz0DzZKDeZyOZwOxxZkPwScThmnU4Y6PZGK0FBFj6KYTqHpOgJL09IUBWwb0zTxLmsfqIe5aLJ3982rX33iE5/gne98Z20Mwu1Gq5bFReDHgM8BRCIjh4H6J+17otHR8tLfPgr8x0hk5NPAF4F3RaOj31762yPA/6BCMKuhH/jzpbiFCPxNNDr65Ww2w4c+9GH+6Z++ctMq316vl3w+v4IsJElicXGRTCbToMoNcGVuHEkUKCWKiEGlQRKuKqRbRf2sj+XSeBspvBJFEZfL1dBZWC9yk06n0XQNXdcpFAvIDidDSTdDB/tWjcm8MHYaq09oObKdyadRupek9sMuMovZGllk01nmYtP0PNBDPBdDKcooLheK3wXYaPllehW6yaDZQ1eoEueIpWKkxDSWCxyKAxwCOVvF2+3DGEuz98B+Utk0/j1BFIdCuVQin8nVGtHWcj+2ChWhIQ/uJm5MWVVJpdM4HQ6mp6eXuTEyDocDNaeyt/PBm44vjI2N8fWvf71pq8LtQqtk8SLwQCQyEopGR9PAW4G/ArbVfygSGRGBEHAJ+DfA16tEARCNjj4diYx891o7ikZHTwMrHLJAIMjHP/7xFg93baxWmGXbNhMTE5w4cWLFl/vQ3gp5qKrKXHKBpJYnZ5bI2iWMUhHTc+Pzi7HFpsOLq6XcN9NJWhW5qW5z2/A2BEGotNeP59jp6+f8+fOoqloRwvH7azoI8WyChC+DU2pdH9KBg5KhIi3NLXUu3TKWaXHp7EU8gzKGZqAEZTILaaSSSG4uSzAUYqCv0Yd3xgQO7j1Q+31iYYLZzAwloUxoZyembREYCNET6iLtUMgXS9gCWKqJ5JJAAb1UJSCb+Abdj61C1Y0pFouV4rJgiIobY6BpKmVVJZutFJmZkxZd23swVKMmaLPR47Vtmw984AP81m/91h3TsoCNxSy+APxQJDLyZ8AjwEe5QRZfjURGTCqJ7aeBvwB+EbhaXTkSGfkHKpMl+yORkddHo6PTN3/4m0PVsqiHrusUCgUOHmw+eLcKRVHY0b+NHXXL5ubmGJ+ZxCP5uTo3gU8ERbQxRA1HdQhNnftxszBNk1gs1hCncOYsRh58tOZ+1LeX53I5xifGOVu6hKvfg1y+MWzYKa8dSO0Z6GVmahpN0HBaDvqGK27VlUuXcQ8rCCGB2MwC/s4AuqDSvWcQy7QwYjZyXZxBT6i8fOCh2r6uT17nAlfwPOhHMhUSLywQ6uzA7/IBAm7FixkzEWwJwSGhlw2MmEFwZwiouB+OLXE/Ngdd1ygWi3VBzRtCQ1WDtbhQ5NjLj6Prem08RD6fRxAEfD5fTdRmPUGbxx9/HFEUGRkZufUntgY2QhZ/DfwhcB14Ytnfam5IFZHIyBRQa6mLRkfftLT8qQ3ud8vh8/ka+jKqWpx+v79B+7FVKIpCdyhMwB0At8aRI0cqtRXpBLFkirRRIGtqlCQD3e/A6VE2XVdh25WAXigUqh2rUdQ4qWyvEQXcaC+vtphP6bPsOrAHy7RqE8vTmXRF9LeaSlSWtDplpRZrEARhxTDjfD6PLqi4PS4W52LghMlnpjj06orVIEoilqDVPm+UDXZKwwSW0rqapnF6/jR2p4jscSIjI++VGLL6UFM6hlgmKHjYfngIQRAo5otoqkZwZxBBFGruh0txc316Apco09/ftyW1Kq3BJp5IEA6vnCQPkFiMUyqV2CntqhVo1Vf+mqZZExpaXFzk2rVrGIaB2+2uaYr4/f5acdmv/dqv8bnPfe7eKfeORkevRyIjXuA9VMYM7lpnlX8A3heJjLw8Gh19CiASGdlDJdh5+6WJ67Bc4fvq1at0dnaiquqmpPUkSaJcLpNMJmuDhRwOB31dvfR0dmOaZu3Nkcqmmc8kyJpFcmaJnF1Gd4PT514RDGyGbDaLQ3Lg81WCcJZlMZTzsmfvjlXXOT1+Dr3XRqLyIK8Q+7XshkBqQkvcCKTKN9K5oiRi6AbpdBq/208ylyK4oxNTMwl6O0hOJOh9sA+9pCNLN1wlT1Jmz94b6c6zE2fo2dXN1YvjOENOLMOCEoR6AnR2r9Se8Pg8eHzVmEHF/XCKTmbNOM5uhaJWxJiZYdvQ0Ip1bwXy+Xxt5upyzM/MYQYMHE6RnJYjl8/Val+qkCSJQCDQUOlZFbSpCg3NzMzw2c9+lq9//ev4fD6eeOIJ3G43A3ekOa6Cjb7hPwf8h2h09PJSpmNVRKOj+Uhk5N8Avx2JjPQv7csA/lM0OjqxucPdGtTHLGZnZ9F1nb1793Lt2rVNkYVlWSSTSU6cONFgTlZLcyVJqsUpujrCdHXceMvYtk0mm2EuEyNrlshaJXJmEdUNDr+rwW0pl8vk8/mGG8azYPDyPY+semwzi7MsuuM4HatnPwRRQHEpKK5GtS5d01E1lWKpSCqdwjTNSuTf60X2+omNxbACJk5JxuV34SiIGIs2iuyho79yjuaixoltr6i9FafmpyiHyshumWPbDnHhwmWS8QQuxcmcUJkIZ9omkijR29e7gkBz+TySJFEsazi6ZUzDRHRKlGx1ze+okC+QSWeQZZmunq41P7sWLMsknU6v+tCqtoosSITdYTydHhYS8yvIohkEQahlw6qizUNDQ3zzm9/k13/917l06RJzc3N3lCyEOzF/YJPYsgP92Mc+RiAQ4Pu///u5du0ax45VtB/Hxsbw+XwbSk3Zts0LL7yAZVmcPNlYdFOtFnQ6nRuKVdi2TT6fZz4dI2UWyFll0lqO8eQs/buHUdyVN5qdKDPSdazB/ahHsVTiW7NP4dik0tRypFIVwnC73aiqSiqeJFVKISoiiq6wbcd2XIqrQpgCaAWN/exmW2/FjSmXy5yaexp3jxvLskjHUkxNzZDrLuLr8VNeKKHPFdh1cje2aWMsGOzYsbO2f8MwGLs+huiEVCpNTMoRGAphFky2Wb3s2LGt6XGXCkWmUzN4ez0VqyenrEjBtopYPIbb5cK3CgFMjk/gCbjoD/dTzpbZJT9Ad+fmUp3vec97+O7v/m5+4id+YlPrbxKrmrd3dVHWrYLP50NVVS5fvsyRI0dqb/3NKHxX5fU0TWtYXhX/3ShRADVF52pXrW3bvPjiizw8tAfDsEklc2StEtvdO1clCtu2eWbyOaSBzU3GWo5yuUypVKK/vx9BEPB6vXR2dlYsEFXHsCojB5KpZIUkEfAtKEh7RHK5HB6Ph6++GCXtz2BdMcklM4i9Iik7g9N24TZMxKCEmaqkfAWHgC4Z9WdEPB6jUMgjdAiUrBLd3X7UmEbQ7UFepcsTIJ3O4O2tuDFOtxM1u7KRsNVrYBhGzQVshk5nmA61A2vRYsA1vGmiePHFF7ly5Qqf+tSnNrX+rcB9SRayLPPYY4/xpje9qcHv3ChZxONx0uk0Bw4c4MKFC7Xl1W1sRc8BwMTEBG63uybj3wrOTZxH67aQ1lAYbxWWZRGPx1eMKoAKsckuGRm5oaBMny5zbP8hSqUSExMTXLh+gZn+BRSXC1VUKXpKDPT2oJo6umBSyBQQVAHZuJE9Eo0b1y6Xz6OWVFSfga/Pi9OloOV0enq6cHvcCInV4z1ej4dENoESUCp1LtbGr4lt2yQS8SXl7eb70lWd7d7t7BxcL5y3Nqr9H7/7u797V01Xv+/IwrZt/uzP/oyenp6GCDVUyGK5hbAaisUi169f59ixYw1aGNU4xWYsimZIp9PEYrEVLs5amI8vMONcRJa3xv2Ix+OEQqEVpd6rQcuoHOs5VBvum8vnGDfHsSXQZB1V11CLlYyMp9NF7GwCl1Omv7sH74CXuXMzYNkMD+0AbsgQOp0ygXCAUq6IJDrILeToctmoaY2dO1bvywh0BFEXNYoLRURbZGhwqBLrkFrv9s1kM3g8nkqF5yoQEyI79uxc9e+t4vOf/zx79+7d0Hd+O3DfkcX58+cZGhpqGpdo1bKozgvZt28fsizXKjMty6o0hUnSlnSSVieyHz9+vGXiKZfLnEmfR+7bGqKoZo2Wj1RcDaZh0qt20TtYIQrbtjk3dxYlKGMt6Dg7ZFxOhVI+T/pCmkIih08KsPfYLiSnxOzMNOHtQURRIrG4gEOWKJSKhDs7sS3IpHKEujvRyxo9w90MDwytMTOE2jGEQiG6HF1YlsX18evYLgtbheGeIdy+1Uu1oSLTl8/nGVyjUayUKnG4d3VJ/1ZRKBT4nd/5naY6sXca9x1ZHDx4kPe85z38yZ/8yYq/tTIOoDr2cGhoqJb6qm8GE0VxS4jCtm3Onj27oUEytm3zzMTWxSmqadKB/oFWmi8BkBZsjuw9WPv90uQlhG6QSzI9PV0U00UE28ajOPDILpSjCpZkc27yAkd3HyHYGSTg91XIN2iRTCSRnBLxeJxkIokm6qRnkgz1DTG0bXBd6TzTMLk+cR3BC3bJxqE7cG1XauS7OLfIdt+ONbawVFPRGV6VCCzLokPrIBQItXaR1sDv/u7v8lM/9VMNg4buFtx3ZAEri7KqaGUcwNTUFE6ns6HJDG5UaG6V+zE2Nobf799QZubi1CVKXQYOYQu+VrsS+e8KN9O5bA4tWeaR/hul8qlMipi0iNvpxut04M/4SeYSKG6Z8FAnc3OL+L0BJElE7zY4c/o0nf4Q7lClslTPmjgUhaHBIeZm57D8Nk6HE982H+mpNMKsgABYhoXH48EXqBTV1T/UC/MLeIZu1LDEzsfooj51ujYLFopFRFFcs1JUXVA5sePmXYaJiQm++tWv3tH+j7Vw35LFZkR7k8kk8XicY8eONSyvEsylS5cIBoO1fozN1vEnk8la3UariKXiTIrzyMrWWBXpTBpFUXC5W7NqTM1gGwN0LL1d6wcZW6bF4vwCggV9nT34e3xk0mmMgo5NZUCPXtYoqCr7H9rL4tU4Xd3dYFeKwK5cuspUahK524liuzDjJl0dXYRDYa5fvIK7VyFdLJFKJHH7Ky5FtStU1zVEW0SiEtQMBAKUpkvgAbtsM9C1umthWRapZJL+NRrVtJLGDt/Om7YmbdvmV37lV/jIRz6yqSri24E2WdRhLbIol8tcvXqVo0ePNlgO1TjFyZMn0fWKBH08HmdsbAxd1/F4PLVGrkAgsO6NoKoqly5d2lCcQtM0XoyfRe7fmptMLasUi8UV1tNqsG0bOSax74EbYsAXJs7j6HVg2zZj49dRhhSwIP5iAsUvo2cMnHkHiediyCEFSRDwhd3EZxP4u3yITgcL5TiukJuMlUEKOXD3eCjFSpiqgazIzE9P0bHLi+iQEHCgpnQGB4dqnbmqpuL2uJk6P4XgFsAQGAj34w8FkAQJRVHWrJpNpZIEgsE1G/+cKSfDe4dX/Xur+MY3voFpmnzv937vTW/rVuGuJItIZGSYSjNaH2ABn4pGR9lKhe+NkIVpmpw9e3aFalaVKERRrJVGe73e2kNm2zbFYpFcLkcymWRiYgJN02o9ANWS3/ou0rNnz/LAAw9sKE7x7PgLiH1b041oW5Xek97e3paDdXpc42XDj9Q+v5hcJONKo0gudE3H9lRqMUpaEcEvMPX8NMNHBtj3sgc4e+ocgV4vgkPA43Vh5SxM1eL65HW0XgNNzlHIFNATKq6QgpbVcOpuVKlEZ3+IrJrB5XNRzmu1sr1qZ66iKPh9fkLBDkRRwMKqiepqS6X9Doej0la+1FpesRAqoyVVTVuRMatHKVHkWH/r1t+q10/X+eAHP3hX9H+shbuSLKiUhf9CNDr6XCQy4geerfhz/8zx48d5y1t+jMce+yyPPfYY73rXuza8cVmWm6ZIm5GFbdtcunSJ/v7+BtUsuBHQXM3dqBYveb3ehpmo5XKZbDZLOp1mamoKVVVrTUOKouD1elueOHVl+ir5zjIOcWu+yngiTjAYbNmF0osae107G1SgSmoRt+qhtFBCEzSMgoHqLCOIkM5lkENOZsbnCPUFGD46hFouI1giuasFdu7ciSWAx+9B9eVx+B14nD7i8wUWzs4jajadAwFKZpFuZxAhK5GeyVHKqgwP72g4Nq2scXXyGoIPKAvs7NuBz+fnRl7HxjBuaHTmcjkMXUcURXTDIBgIoGk6suxkeWzDNEzCdndLpdzr4X/9r/9FJBJhz549N72tW4m7kiyi0dE5YG7p/7lIZORCPB7f++STT/KJT/wOUFH4/sVf/IVNkYUoNpfub5YNmZmZQRCEFTX51c9tNKBZHWrrdrtrPQC2bTM/P8/4+DihUIgLFy5QLpdRFKXBhXG5GscdJjJJxpnG6dqaNGmhUKgoZLf4ANi2jT/tYdcDOxqWb+/fwfalJn5N07isX+IbVx6nYJXoeqALzdDAgrKq4vV5CYe7KE6VOflde8kX85TLZQRRJKcW0UwVXdPw9XiRDRHF6SSbzOIPBMjFVZyiC0lU2H5gb4MgEcCZ8+dQO1Uog8/lYyGxwHbP9rpPCDgcThwOZwPZpdMpVE0DQahIHC6V7StLcRBFlrHiNg/uXjmDZaNIJBJ85jOfWTeoubi4yMc+9lGSyRSiKPDGN34///bf/ts1re3Pfvav+cpXvoIoirz73T/Lww8/DMDly5f5+Mc/hqZpPPLII7z73T/b0ovpriSLekQiIzuA4/v27SOVSm2ZwjesnBe5/KFPp9MsLCw0zDUFajUVDodjSzIfqqoyPj7OiRMnGtyccrlc60Kcm5ujVCohy3KNQC7PX8H0mVj5MrJ3823vUKkdSaVSG0qTmgsaJ7avPdhXlmUs0WKoa4A5fQFFkvHoPsp6ESOp4Q8EMYo6siVTLBRJZ9IMDgxiWzaJcwnSxSSCCxTZSdeOEImrKQzNwMhb9B1bPfBYyBbQvBqiW0TxucjP5wnKq8/zqJ2TaZAvFBgcGGhIy9ZLHMZm47jnPTyTegav19vQVr7RoPaHPvQhfumXfmnNEnKoWL0//dP/mb1791IsFnn3u3+GkydPrmptT0xM8Pjjj/Mnf/JpEokE/+2//TKf+cyfIUkSf/AHv8/P//x72b9/P7/yKx/g1KlTPPLI6s2IVdzVZBGJjPiAzwP/1ev1fn6rttvKQ9WsdwRuxCkcDseW1FNUtTSWx0OAmh5Fffq0KmiTzWYJCgEciwXUGRXTUULwSOAW0B0GeEVkX4sEYkMsFiMcDreeJs2pHA6uPObl0HWdojPLngd245p0c+XqdTp73QSyQfb07gbDxiN62X18D08+9STDHcM4404uzV7C6XIQLPrIJ3IE9nkpxooUZot0BTvZvW9tkz2TzmBYGmgCuWs5ZM1J/6HlIwVXIp5I0NnRsaJ+oypxqCgKwXyIh0YewrIsCoUC2Wy2pktRbbSrErrf71/1Gp05c4aLFy/yR3/0R+seV/00PI/Hw7Zt24jH46xmbT/55Ld47WtfiyzL9Pf3MzAwwKVLl+jt7aVYLHLgQEV7ZGQkwpNPfuveJotIZMRJhSj+Khod/QLAVip8V+MTzR746lzTvXv3rgg0Lm85v1lcu3aNcDhMZ+dKHYdmqAbuqpPNoGLqVwkkl8tRSFUIRJdKCB4RXAKGbGG7QfG7VmQAMtkMiqK0rDplWRZddVL+9bBtm3NjZ3A6ZDwOL53BTmyjMiCnf7CXxcQi28ODKEMy9oLFyZ0V03hubo7ucDcHDhxgdmGG4v4CDtnJzMIUYxevkz2XQ3I4GB4cJOTpwimv/QYvC2WC3SF0Qcclu/DHfciutbNFxVIRbHvZQONl242VeGio8mCJotjQ8Fc9/2pQO5VK1YLaLperQdTG6/Xy/ve/n9/5nd/ZsHU6Pz/P1atXWcvajscT7N+/v7ZOd3c38XgcSZIa7p3q8lZwV5JFJDIiAP8LuBCNjv5udflWK3wXi8Wmo+auXLlCV1cXHR0rJ40DW+Z+xGIx8vn8irqNjUKW5RVzWKtSblUCycfyaLqGJpYraUS3gOmwKDk1cuUsgxsRjpm/IeW/HFcmL2OFdXJGnqyVZC41SbFUIHZmnnJBQwm4ajqkRakE3HDDqj61uSRqnC/lwGMzvG2I3UO7yF4vcOKBhwCBxfQCJbNE2S5TskpYioU74L5hGdkQCgSxLBvLsPDZzQlA15a+U6dEMpmkr3d168M0DHqlvjVJdbWgdr1F+Pd///f8/u//Ppqm8Vd/9Vfkcjle97rXrXnJqyiVSvzmb/4GP/Mz726qTl9Fs5hcxchstrw1v/OuJAvgUeA/AGcikZEXAD784Y/wlre8ZcsVvpeTha7r6LrOAw80SrdvNqC5GkqlElevXuXkyZO3JF3mdDrp7OxssFgMw2ggkGw8i5lR6XaFsAoquAUs2cZ028h+palLoqXKnOw+0tSySmVSZB1J0rkUeCwsbDxOLwO7+4A+TNNkKjVJvLiAmTQhLXJBOsfUtWke3LOvts2hviHmr8wzmZ5Al1T8ko9UIkU43FMbKNzsbR5LxygYeUpWiQ6zg5krM4heCY/gpnvbykrY2elZCmKl98XK2fT1963pWpqLFnt2723tC6hDvcRhd3c3/f39fOYzn+Gxxx5jbm6u5SIswzD4jd/4dV73utfz6le/Gljd2u7u7iIWW6ytW3Ezu+jqarQkqu5nK7grySIaHf0mK8NsNrBlCt/NCrOqisx79+5tGtDcKvejGqfYv3//ba3WczgcdHR01CymCxcu1HpcqgSSS+RQNRVdUDHdAoJbwHTaGC4Lp8fJgNFLV8dKpSnTNLm6eAkxLGDbleCv5IBivkCYys0oSRJ9vn4y5QyCLBAeDJMrpZF3CcScs8yPT6MILhRBwYObLkcnUpeAJ+QiPpZid3fzgcLVt7nH48EwDARB4PTYi8iyk1KmRG+wDztmU7JKGJKOHKz0hhTI4+32VgSQMzH8azTLlbNlHgzfvKQ/wCc/+Ul+8id/kgMHDtRiB+vBtm1+53c+wbZt2/nhH/7h2vLVrO1XvOKV/NZv/b/8u3/3wyQSCWZmZnjwwQeXRhu4OX/+PPv372d0NMqb3vTmlo7hriSL24HlZKFpGhcvXsTn860givXqKTaKK1eu0NPTc1Mxl5tFLBZDVdXawOf6YzFNk3w+f4NAUjnKahlTsPAPeysanH5/A3FenryIu0/GtEzsslUZZw0IdiPnu1yuyjxTrUgyk6BUUBkaHGx4CEvlAsl8gkwphTMlU0gXMLI2eodOMpUkFAyteGizuSzPzT6Lrpgsji/g63bjdrno3d+NFJM4svMoUPme46kYyUIKoSBg5k0y+TSyJDMzNYPpMCqVnn2DtRiHbdt4C166+2++uWtycpJ//Md/5KmnntrQeufOnWV0dJSdO3fy0z/90wC8/e1vX9Xa3rFjB695zXfxzne+A0mS+Lmfe0/t+3rPe/4Ln/jEx1FVlYcffqSl4Cbcp7J6AD/zMz/DD/7gD/KqV70Ky7J48cUX2bZtG7Ozs+zZs6fml2qaViOKrXirLCwsMDc3x9GjR+9YtZ6qqjz33HOcPHmyZcvGsqwagWSzWfL5PJZl4fP5MGyDvC9NR08QURRJ5VLkjTyCLdDpCeNxuStZkXIRSZQomFkUt0wun0NQHQz1VcqlTdNkLj1HWk0hiuBwOinnS3T4w4SUDnweH+WCip7XcaKgiC5kZEK+TsaTY+gDJmW1zERqDKFo09kVRMo4GXJt4+iOlbNBr89c4+zCWVS1jN8KwKCFIINu6eSmi/QO9qCqGk7dwat3fde6mZ/1YNs2P/mTP8k73vEO3vjGN97Utm4h2rJ6y1FvWVy7do2Ojg7C4TALCwu1+IRpmpvS0FwNxWKRsbGxWxanaAW2bXP+/Hn27t27IRdIFMUVitSWZZHJZHhm7Du4HE7m5uawbRunLOOX/SguBdmpVCpW1RSKRyYWi+Hr9KLpGqIk4XDduAUTuQSiT0CUwBYtSvkissuJ3xHA763EKNw+F25fNUNlodslpktjzOTHMaehUCpRtArkZjMILhN93uBlRx9tek5D3cPMTyzw8MMPMzZ7nWJXrva9pBIZ9LyGwy9glnVK5eJNk8UTTzyBpmm84Q1vuKnt3Cnct2RR7Q9ZWFigXC7XSm2rKdVqnGKriKLaX7J///47OlVqamoKj8fTkD7bLERRZCY5xcCDvTeGMttLA4ZVlUKxSDKVolDO4Q97UDUbT8BNJpVFVipVk3a5rvDJtrAsE03VUQJOTFOlkNcoSUVChJoegyAIuDwKO3buYDY/h+Cy0PMSXceHKCaL6JrO1bnLJHNxfIqf7s6e2vW/cuUKu3fvxul0sq13O89OPo3SI6NlK5kkLVyGHIR8IWZSM4SCHU2PoRUYhsGv/uqv8ld/9Vd3df/HWrhvycLr9aJpGpOTk7VZH1AhC8MwaoVXW1VPcfny5ab9JbcT+Xyeubk5Hnpo7arLVjEfm0Pzl3CLN2pRapPfZbk2oDyZSYJiYNkWuqZTSmkYsoWetekIdlIsFnE4nJTVEgU1j21bpKez+AIe5KCTXC5Lt9GzZqZCURR2yDuIp2Pgr5B9IBQg5BVxdToxFZW4lmdqbgzJdGCUTJKLaQKd/lpvziM7X0EsuYivw8/XFr+Kt9+DIAgkphP0u2+us/Qzn/kMr33ta1dk2e4l3LdkIYoif/qnf8rnP//5hptQEIRanGKriGJ+fh5d1xm6TUNwmsE0Tc6dO8fBgwe35Lw0TWMyN46/f21JOoCQv4NkJo4lgFGy6enqpae7B8OoWCClcplkegYhaOMW3AgaWA4Lt8+NqVqEuoPkijk6Amu/2QUB3Iobr+jFEA1s20LQHChLGh9O2Ymz24lt28zMzDB0so+0EGM2NoWoi5VMjOhC1VX8Xj9mwcAQDCRdYtfuzYvwJpNJPv3pT/PNb35z09u4G3BfkoVpmvz5n/85hw8fXtHiLooiiUSiUtYbDN50arNQKDA+Ps5DDz10R83Pq1ev0t/fv24PQqu4MHkO30BrFZ+iKNDV0Y1hmMzl5+jq6UIQqM0G9QGSU0J1FkAQUBSZckbFLFpIooN0MoMfKEgFFEVBkhysdil9Hh9m3qBslBERCfetrK9Ip9N4fT6cSwLEgfCNa2KhUTBKaNkyxXgBS4cd7t0tSwY0w0c+8hHe+9733lGrcitwX5LFmTNnOHDgQK3CrgrTNOnv7yebzdZKdesFbKo/rcYcqnGKgwcPbkkfyWYRj8cpFotbZgJPz09jh3QEofWAn21X2t/DnZ1ITYq9gr4gi+kyuqAhWiL7th0kV85gCzayQ0FxKKiqSjaXq7iIUkW8Rl4a8GxaJiW1hM/tJegLsdpjqes6hWKRwTUme0kOiZ07d1SqLqeKPHygtdRiM5w7d44zZ87w3//7f9/0Nu4W3JdkcezYMd7xjnfwhS98obasWk+hKAr9/f0rBGyqCljXr1/HMAy8Xm8DgTQjg4sXLzI4OLgpgZ6tgqZpXLlyhRMnTmyJZVMul5ktT+LvXb3UuBnyhTyiKDbMFqmHIEBvR2/DMs+ysur68mbDMFE1FVVViSdilMUCskchHlug091NwOfH4XA2WCC2XSHOrvDq4rv1MMsWB4YObfq6Ved/fOITn9gyl/ZO4r4kC2hMna4l4V9f619PIM26DesJJJ/PY9s2g4OrazzealTTpHv27LnptF91exemzuIbXD9OYdvUHlTDMG+ohG8AyUwCw9YRbIEOf7jhu3E4JBwOD16PB0PQ8Hrd2LaF4TbJJbPoWqVsXxLFmhKWaZpIdQONTdNCEATEJtJ6tm0jF1yE+zafNfriF7/I0NAQL3vZyza9jbsJ9y1ZVHtDYOMS/oIg4PP58Pl8NVGc+nblqakp4vE4brebc+fO1QhkedXjrcbMzMyKFvebQbFYRLcMtAUV0SXiCbhWpJVVVa1oUAggWCLhUM+a7sdqyOVzCIqFy1GJGaXzSbpCzSsoBURs20QQRCQBQgEfIX8IqHy3qqZRLpXIZLM4HA5mZmcpGyUEt40kSISUToLLxkDmF4ocHdq8YnepVOKjH/0oX/nKV9a0TDRN473v/Xl0Xcc0TV796tfwtre97baK2rSK+5YsqnUWNzOTtB7VdmW328309DSPPPIIHo+HQqFAJpNhdna2Zm34fL4agfh8vltCIIVCgZmZmS1Lk0KFYB958OW1wc2x5CJls4SGimqVkNwSRSuHN1BxHyzLYjE2jyg6VnU/VoNhGY3KV2vc8+FAmMXMIrZg4MBJMBiq/U2SJDxuN4VCga5wGKfTSSqbAtnC6ZIxTZOZxBT5XKGmhCUi0e3suylr7Pd///f58R//8XVFj51OJx//+Cdwu90YhsHP//x/5eGHH+Zb3/rmbRO1aRV3NVlEIiN/CvwAsBiNViY0bZVor8/nq1kWW1V4Zds2Fy5cYHh4uJZ1WK53YFlWrfNzZmaGXC5X+1w9gdzM8VT1OA4cOHBLiGj54Ga4Mfn92YtPYZRNdFunbJZJJTLsP3Rwja01h8/tJ5FbxO1T0FUD0XaSyiZxSM5aNSdUip2K5SJhX3jVwHO5rKLrOl6vh4yeRPIJWGUDUVRwKV4clpP+YB+qpqGqKouX4xhOiM3GVxB7K9/L9PQ0X/rSl1rq/6jKLFbPpdoIdztFbVrFXU0WwJ8B/52K0jcAn/vcY1si2uv1ehkbG+Pxxx/n4YcfprOz86ZNtpmZGURRXKHXWQ9RFAkGgw1ptPrGrampKXK5XM1Sqd6oXq+3ZQK5du0avb29tzWwWiWQI3tOMJuZwOmSuH5+nJPbX4mYEeoskDKSW8TdxIWph9PpoMvfS6GUxyUqZO00LrdC2dDQszqdgU5UVSVRjCF7nBSKWYJKJx5XowVTHWjc3d1DrpRB8S7VXJRlCtkClgJBudKY5na5sEo2rzr6XXQEOzBNs0bsU1NTtZdLlSibWYa2bfPBD36Q3/zN32zZMjFNk3e/+93Mzs7wgz/4Jvbv339bRW1axV1NFtHo6DeWNDhr2CrRXqfTydve9jaeeOIJPvnJT5LJZNizZw8nTpzgxIkTHD9+HL/f3zKB5HK5TZv9kiQ1JZDqjToxMUE+n2/oz6gSyPLjSyaT5HI5jh9f2Th1O9DZESbgDzI1NclQ9y727G6Uv6taIIlUnJJZRLXLSwQiVMRr6gjE4ZAI+oOksilcvspDLjkkdFXFsmxypSwuX+WBVDwKhUJ+BVlkslncHg+y7MShOjGMMpJDIuDzYxVEwqEbmRHLsnCXvXQMVIq/JEkiFAqt2pFbbxn6fD4uXLhAPB4nnU7zAz/wAy1fM0mS+OM//mPy+Ty//uu/xtjY2KqfvRWiNq3iriaLZtgq0V5JkvjIRz5S+900TS5evMjTTz/Nl7/8ZT70oQ9RKpXYt28fx48f5+TJkxw9ehS3273iS6gOSj58+PCWmf3NbtR68ZqxsTEKhQKSJNXIw+121wYU3ckCMNM0mZ9faEqca7kw8WSMklmsWCC2iuQCd8CNIrvIqSWcihOwyeZzaLZOuVzCLbjweDxL4suNlkpVhNgbdBNLLxL0dFAom2hlHUmQGogCoLBQ4vjww2ueWzNir3bkPvPMM3zmM59BkiQeeughPvaxj/H617++5evm8/k4evQozzxz6raK2rSKe44sbhUkSeLgwYMcPHiQ//gf/yNQKeA5c+YMp06d4rHHHuP9738/lmVx8ODBGoEcOHCAT37yk7ztbW9bU+ZsK7BcvKZ6jLlcjkwmw5UrVxAEgbNnzza4MB6P57aRR3Vw9J49e1ouXluPQASzSDaTI2GksRw2nj4Fl1tBVCC5kMIsW7icbrpDjWnOWDyO6LZx+CokEs8uMhBemcq2bRu1rNHvHtpUk1/V4jNNk0gkwu/+7u+i6zqGYay7bjqdxuFw4PP5atIBP/qjb7mtojat4p4ji60U7V0PTqez5pb89E//dG1A0IsvvsipU6f4kz/5E775zW/i8/mYn5+vfXbfvn23rbO0Kp9XKpXo6uriwIEDtTGK1TqQYrFYGyFQ/Vk+g2SrsLCwgMPhuOl07XICOcAhbNtmYnqCS7EzpKwMmlhCL+pYTguPrzGmUygUsC0L2V/3PTgqc1Xr6yoy+Qx5PUtuMk9gYPNdpalUij/+4z/miSeeAG6Usq+HZDLJxz72USzLwrZtXvOa7+LlL385Bw7sv22iNq3irhe/WYpZfDkaHT0I8KlP/TGBQKAW4MzlcrzrXf/pjhzbxYsXefvb384Xv/hFzp07x6lTp3jmmWe4ePEiHo+HY8eO1Qhk7969t6zGolAocObMGR5++OFV96FpWo1AstlswwySrSIQTdN49tlneeihh24ZWdq2zZlrLzKjjiM4odPVidfjIzGeoburB5UyZaPEfHqenXu3E88t4grI2DZoOZ3+OsvCtmEuPYWpWfR6ejFKFof6TmwqZfrLv/zLHD9+nHe84x1bebp3AqveAHc1WUQiI58FXgt0dXV1OX7yJ9/Go4++kg996MMsLi7WGLeZQvftgGVZDTGUKmzbJp1O88wzz3Dq1CmeffZZrl69SigUqpHHiRMn2L59+02nbC3L4tlnn+WBBx7YcKOSqqoNBFI/Ba1+DmsrBGLbNqdPn2ZgYGDLisDWwsVr56FDx+VRKBc1fFpHTXHr0qVLIIDikcmpWWZT05iiQWdPGH+Ht057A6aTEzgKDrZv2046luVQ78bJ4vz587znPe/hiSeeeCmUdd+bZLEM98yBNoNt28RiMU6dOlWzQCYmJujr6+P48eOcOHGChx56iP7+/g293a9evYokSezcuXNLjnE5gVS1HpYTyHLMz88Tj8c5dOjQTR9Hq8d6ffoqmlXG4/CxfbBy/rlcjosXL67o8q1lYTJxSkYRdamQLB6L09XVicPhwE8nD2zf2EhCy7J485vfzG/8xm/w6KPNFbnuMbTJ4m5EVVfh1KlTPP300zz77LPMzc2xbdu2Bgukq6urKYGkUimuX7++ZU1iqx1jdZBz9ac6Cb7efTlz5swtdT9aPdZnnnmGffv2tVRjYts2pmli23alF2QTcgRf+tKX+PKXv8xf/MVf3LMKWMvQJot7BZZlMTY2VrNAnn32WZLJJLt3765lYI4fP46u63z+85/np37qp25Ka2EzsG2bUqnUUIlan8LdaCv/VmF6enpLW/HXQ7lc5ru/+7v5x3/8xzvaMLjFaJPFvQzTNLl8+TJPP/00zzzzDM899xxTU1McPXqURx99lBMnTnDs2LGmRVq3GgsLC8RiMQ4ePFhr5a/+LG/l38zg4FZRTTs+8sgjty1u8PGPfxyn08kHPvCB27K/24Q2WbyU8NnPfpZ/+qd/4hd+4Rdq7ssLL7yApmkcOHCg5r4cOXKk5QDlZrBe9qNeC6T6s7yV3+/3b4kw0JkzZ+jr67stwVWA2dlZfviHf5innnrqtlt2txhtsngpYW5urvbA1UNV1VoNyKlTpzh79iyCIHDkyJGaC7OVU9BOnz5Nf3//hh5Qy7JWEEh1/shmW/kTiQTT09McPXp0M6exYdi2zTvf+U5+9Ed/lDe/+c23ZZ9V/NIv/SLveMc72bdvH7qu88M//O9461vfyo/8yL8H4L3vfS8/+7M/y5kzZ/j6179WI+ITJ07y1re+lWg0yle+8k81seqqqv373/+Bam9JmyzuR1RFep5//nmeeeYZnnnmGc6fP4+iKBw7dqxGIA888MCG3+5V92Mrsh/1WiDVKWiWZa3oxG1GIKZpcurUKY4dO3bb3vBPPfUUH/vYx/jHf/zHLelW3ggee+yzSJKDH/mRH+G5557j//7fL5PJZPjEJ34HTdN4xzvezr//9/+eM2fO8Iu/+EvIsoxhGPzWb/2/vOENb6yV4M/Pz/ORj3yY/+//WyH31x4ydD+iKtLz6le/ujZI17ZtMpkMzz33HE8//TQf/ehHuXz5MoFAoJbCPXHiBLt27Vr1QdA0jevXr2+ZVka1w9bv99cChfUT0NZq5R8fH6e/v/+2EYVpmnzgAx/gT//0T287UUDFQvirv/pLfuRHfoSnn36aN7zhjXz6039CoZDnypWrHDlylC9+8Yt84hO/U7MgHQ4HH/zgr960O9omi/sM1bmmr3vd63jd614HVNu4EzX35Qtf+AJjY2P09PTUCOTkyZMMLs0kjUajPPLII7c029FsAtryVv5MJkO5XKa3t5fp6WmCweCGWvk3g//9v//3Ujl2awONTdPkZ3/23XR1dfHhD3/kphWwHn74YSYnJyuVrGdO8/a3v50TJ07w3HPPcf36GA8//BDPPPNMrUDvm9/8Jn/3d19A0zQOHTpcm5O6GbTJog0EQaCrq4s3vOENtdF6tm0zNzdXI5C/+Iu/qD2Qtm3z3ve+l5MnT9LT03PbMjD1HZ+2bfPcc8+xf/9+BEFY0cq/WS2QtZBOp/nDP/xDvvGNb7S8zt/93d+xbds2isUisLoey0YUsEKhEKdOnaKzsxNZlnn44Uf4znee4tq16/zQD/0QHo+bbDZLIBDgVa96Fa961as4depp/uVfHr+p87/9dlQb9wQEQWBgYIA3velNfPjDH+YrX/kKX/va10ilUrzzne/kO9/5Du94xzt4+ctfzlve8hY+9rGPMTo6SjKZbKq5sNWoBnk7OjoIhUJs27aNQ4cO8fKXv5wTJ07Q19eHYRiMj4/z9NNPc+rUKS5evNggb7hR/PZv/zY/93M/19D1uxZisRjf+c53eMMbbgxBfvLJJ4lEvgeo6LE8+eS3lpY3V8BKJBI1BSxBEBgZiSAIAp/97F/XLI9Dhw5x5coVAAKBAD/4g2/iD//wf6JpGlCxbs6cOdt2Q9q4fTAMg0996lO86lWvqi2zLIsrV65w6tQpvva1r/Hxj3+cbDbL3r17a/GPY8eObUhIaD1omsbExMSqMZO1Wvmz2SzXr1+nWCyuKCRbq5X/4sWLnDp1it/7vd9r+Tj/8A//J+9617solYq1ZVuhgCVJEi+88AL/7b+9D6h0uPp8vlpm44d+6If40pe+xPve998QRZFCocDRo0f5T//p5hou22TRRssYHBxcUakoiiIPPvgg///27jgmyvuO4/j7OHSSoMEeXVjaCmQ5gai0FjXpmtLW3ZMlXWyt2tQoDlttU07qYtWBdjVitVXW4pKuJkCXLFs4l2KW0oxZ+5zGpn/QHtRU5o1eo22jUUBPOON2KBz89gd3T7kqcEfh7pDvKzFyzz3o7xL98jzP5/f7/nJycigqKgIGi0pbWxsul4uGhgYqKiq4ceMGeXl5RgKTn59/20ZCkTh79izZ2dlRPTMJLeW/6667jGO3W8o/bdq0sAIS6o+5Y8cOKisrI450P/vsM9LS0pg7dy6nT3856vnRdMCaMWMGoZ60IRUVe8JeL1u2jGXLlg3792VkZNwuCRmRFAsx7pKTk1mwYAELFiwwlmz39vbS2tpKc3MzDoeDsrIylFLMnz/fuAKZN2/eqCs+u7u7jYeaP9a0adOwWCxhq4aHLuXv6Oigvr6eEydOEAgE6Ozs5OLFixFN7Xa7z9DU1ITL5aK3txe/38/+/W8mZAesSEmxEDExffp0Fi1aZNw6hNaXhOaAVFdX43a7SU5ODusDkpuba8wBCQQCeDwe8vPzJ+yh6vTp00lPTzcu/fPy8vj000/ZunUrbW1t+Hw+Nm7cOOqfs2HDRjZsGDzv9Okvqa+vp7x8BzU11QnXAStSMilriLfe+gOff/45aWlp1Na+B4y89cBwUZcYG6UU169fN+aAtLS04PF4SE1NZeHChVy9ehWr1cr27dtjtv6jqqoKpZTRqWosQsViMDq9Nmw/lrq6Oo4d+wiz2UxJid3odOXxeMI6YJWWlk5kAiUzOCPR2tpKSkoKlZUHjGJRW1vDzJkzh3Tm+q8Rdb3xxj7eeedPt0RdYvwopejq6qKxsZGdO3eyePFizp07h8ViCZtENmfOnHGfX9He3s7KlStpamoynl1MATKDMxL5+fl0dHSEHYt2s5dIJ+uIyJhMJiwWC319fTgcDgoLC1FK0dnZacwBcTgcnD9/nnvuuYcHHniAgoICCgoKyMjIGPNPYKUUu3btYteuXVOpUIxIisUooo26xMQY2tvSZDKRkZER9sR/YGCACxcuGF3Iampq6OzsJDMzM6yRkCXCHdRdLhdXrlyJ+UKxRCbFYoyGj7pEPCQlJZGZmUlmZiarVq0CBgvIuXPnaG5u5pNPPqGqqgqfz3fLZlKzZs0KKyCh9R+1tbVxWf+RqKRYjCLaqEskjqSkJKxWK1arlTVr1gDhm0k1Njayd+9e/H6/sZnUgw8+SFtbG4sWLYpZP9HJQorFKKLd7GUsLl++TGXlAbq6uklKMvHEE79mxYoVksRMgOE2kzpz5gwul4v6+noaGhpwu91xHmnikTRkiH379tHaeppr164xe/ZsRtt6YLioK1pXr16lq6sLq9WK3+/Hbi+homIPH398TJIYEWvD30yHOhtPgl9Txmuv/V61tLSo9euLldfrVUop5fV61fr1xUoppRyOOuVw1Bnnl5X9Trnd7ngMVdx5hv0/KE9vEkxHRwdnz54lNzd3xCTm7rt/anyPJDEiFqRYJJCenh727KmgpMQ+4ibLSpIYEQdSLBJEIBCgomI3S5f+0miBF0piAEliRNxJGpIAlFK8/fZbzJnz/RwBiE0SI6JXVLSWlJQUkpLMmM1mDh069KPb5S1ZsgS7fVNC72omaUgCOHPm32zZsoXs7GxMpsGLveeff568vNwJTWJ6e3t55ZUt9PX10d/fzyOPFFJcXCyR7SiKitby7ruHwjaiHssaotLSTdjtm4x2ecuXPz3mRG0cSRoibjUwMKD8fr9SSqm+vj5VWrpJud1uVVNTrQ4fdiillDp82KFqamqUUkp999136sUXX1A3b95Uly5dUuvWFalAIBC38cfL2rVrlM/nCzsWbXLl9XrVc8+tN44fP35cHTxYNfGDH52kIeJWJpPJWCQVCAQIBAKYTKao+0RONSaTifLyMuz2Ehob/wmMvIbodsmV1+u9pV1eoida8sxiiuvv78dut3Pp0kWefPIp8vLyZPHcKA4e/CPp6el0d3dTXl7GfffNGfZcFUW7vER+XgGShkx5ZrOZ6upqDh/+Ox7PV3z77bfDnjv8P/ypJXRFMHv2bB5++GE8nq+iTq4SqV1epKRYCABSU1O5//77aWlplsh2BD09PcYeID09PXzxxRdkZWUZyRVwS3J18uRJent7aW9vN5Iri8VitMtTSuF06jz00C/i9rkiIbchU5jP5yM5OZnU1FRu3rzJqVOnePbZ1TGNbMdjx65Y8vm62b17tzH2xx9fyuLFS8jJyeH11/dy9OhHRnIFkJWVRWHho2zcuAGz2czLL2821vBs3vzbsHZ5CZCEjEii0ynsm2++obLyAAMDAyilKCx8lHXr1o2pT+RYHTlyhK+/9uD3+9m7d5+0MYw/iU5F4rl8+bLatm2bOnXqlHr11Z1KqegjSDHuJDoViSe0Y1dS0vc/zGTxXOKSYiHiYuiOXZFQksTE3WR6ZiHuIJpmexNYBwSAGcAs4B/AYuAxXXe2a5rtZ8BJXXfmaJptB4CuO98Mfv8xYLeuO5vi8gGmILmyEHGh684duu68V9edWcBq4ISuO4uAD4Hi4GnFQEPw6w+B1Zpm+4mm2bIBK+CK8bCnNIlOJxlNsz0GvA/8Z8jhK8D/gFm67lwx5NwOXXdmBL9eCOwD0oAbQDewWdedF2Mz8ojtB97XNNsG4DzwDICuO92aZgt97gCwSded/fEb5tQjtyGTTLBYvKTrztU/OP4X4Algq647/xY81qHrzozg5fxx4Gldd3qC7y0HntF159oYDl9MYnIbcmcpByo0zXbvD47/BngvVCgAdN35AVAUw7GJSU5uQyanpZpmOznkdWPw90vAa8CfgV8NeT8b+BeAptlSgKPB4/cBP5/QkYo7hhSLyenEMLch6LqzTtNsT2uarWTI2xcYLBjourMHeCz4PeEbuwoxArkNuTO9BGwDZgZf/xV4QdNsxqQGTbMVAKlxGJuYpOTKYnL64W0IQGfoC113ejXN9grwQfD1BU2zrQXe1jTbTAbnNXQDWmyGK+4EkoYIISIityFCiIhIsRBCRESKhRAiIlIshBARkWIhhIiIFAshRESkWAghIvJ/Aa7pLxecfJcAAAAASUVORK5CYII=\n", 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9d19GqM/O3KxvoN6yyGQyTE5OcvLkyTUfwG//lkc5+83zXJ8V8Xo9SE4nZgt5GitNWUEQcLvduN1uuru7a8tt20ZVVXK5HIVCgenpafL5fI1EvF5vA5E0I5GHMRryoKNNFncB9dmZrSRYrUTVsigWi1y9epXjx4/jdDrXXN/tdvPe/+ffU/7YXzGbtgh5JL7zW06ve4zNODgFQcDlcuFyuZqSSNUSmZmZqZGIoigNBLLe+O8U7hVZ2LbdJos2NsZKkd2tFIWJoohhGFy8eJFDhw7hcrlWrRNLJIh+7TyqZnD68E6OHtjDt77+JGPTcxzZPcjJY4fWPcZ2REPqSaSrq6u2vBmJ5HI58vk8zz33XMNU5k4Syb0iizuNuxXFapPFHYRpmhSLxYbszK2QhW3bJBIJDh06tKZi1j995UXmsgACX3zmGhMLS1wrOyhJHr4+HeeRuQWGB/rWPMadfImakYiqqpw7d45jx47VSGRubo58Po9hGMiy3GCJbAeJvFymA81wN1LG22Rxh7BWduZmycK2bcbHx3G73YTD4TXXK+kmIDI+s0Aym2MuMUPecjCZ1HCINkMy/Oi/+c4Nj3U3IYoiiqKgKErDudm2jaZp204iL1fL4m6hTRZ3AFWiaJaduVmyuHnzJm63u2mHsHqcOjDEp774TWZTRfpCMrrTw4sXxqCjB9Ewef7GHD+6zvb3U4m6IAhNSQRomM7Mz8+Tz+fRdb1GIvXOVVmWVx2zTRZbR5ssthn12ZnNogBV/0MrmJmZQVVVdu3axfj4+Lrrnjl+iHQuj/vKIlndIJPN43aKKC4HbsWLZpXX3f5ByeBci0TqLZGFhYUaiTidzhp5aJr2shQRultok8U2oiqyaxjGmtmPrVoW8XicWCzGiRMnWn7IX3PmOP/0/F8zkZeQbYMun4BHMZEVg8cPDK+77YNCFmtBlmU6Ozvp7OxsWF5PIul0mkKhQCwWayCRtSyR7cDdbq9wJ9Emi21CNZW6XmS3GVohi2w2y/j4eC2XotVwq8fjZv+eYXw5Dcnp5F9eKpBIzBFwK+zoXj8a8nJFPYk4HA40TWPXrl1omkahUCCXy7G4uMjNmzfRNA2Hw4Hf7181ndnqy/hyCZtCmyy2BdXQ4ErtzGbYiCxKpRJXrlzh2LFjNcddK3oWVfSFvGRFD1+/eIWlTAYtPEhCkvjDLz5NZ8DLiYMHmk6PHnTLohXUd4mXZRlZluno6GhYR9f1miWytLTE2NhYjUSaWSIbkcidqAtphrZl8YBA07Sm2pnNsB5Z6LrOhQsXOHjwIG63u7Z8My/yd736DProv/B/xi6hejqwi2lMxce0YfB/Lo3xF89f5ODQAMd6O3nt0cNbOsZ24X7M4HQ6nXR0dGyaRLxeb80iqa/+vBvh2rvluG2TxW1iPe3MZliLLCzL4sKFC+zevZtAINDSNs3gcDj43DfPkxo6iFkuYohOvJkFHA6Ri5MLCKEgHgMKqRK75hcY6q/kXtxP0ZA7ecytvlTrkUihUCCfzxOLxRgfH0dVVRwOR404DMOgXC7f8RLyO402WdwGqtmZrRIFNH/xbdvm8uXLdHd3N2Q+VtHKi1z/94Km41SzIIBDK6PYBmp4J3FLwopnONQdQgj4KWnapo7xoONO+A+cTiehUIhQKNSw3DAM8vk8iUQCTdO4cOFCbZq6cjrzoJBImyy2CE3TiMfjeL3eTd3oZv6HsbExFEVhaGhozW02gx19fVyez2FIMrJawBcOU9Q1TMVHSTe4cvky+2SBPY8fBeDGzAxmubxhLseDjruZZ+FwOAiFQkiSRLFY5NixY0CFRKqO1UQiweTkJOVyuUYi9Y7Vqr7I/YI2WWwBlmWRTCYZHx/n+PHjm9p2ZWRjdnaWUqnEkSNHtm18J/bvo+xLcnE+hu4L05NbpBzwkbcl/F4HSvcudJcH27Y5Pz7BlzJZCmUVNR7jhbJG0OkkJDvokBUGOkJ0BIMvCyGcexGZWOngdDgcBINBgsFG0eQqieTzeZLJJFNTUzUSWVnF24xE2j6L+xDV7Mytdgmr3y6RSLC4uNigS7EVqKrK+fPnkSQJv9/PLq/EglemY2c/2fg8Ow+cZGJmhkVdh75hAoJFSZAYn5nlyWQKvD4kBMxAkKTPT3J5v7aqY01OoagaQaeDkNNJhyzTocgMdnYS8Pu3/PLdi2zK+mjI3TxmK9dosyQiimJDAd5KtbQ7gTZZbALV7EzLsnA6nbdFFrlcjrGxMU6ePHlbX23TNDl//jyPPPIIwWCQfD6P358nnknzuUs3WFICLM4uspjKIGYSCLbNsUMHkRKLfDIbx7N7Nw5AAFZ6LARRRHJ7MdxeEkCieh1KGtaNcdxGxQrpcMoEZSedisxQOIzP57uvzOcq7hVB3Y41sxaJVFtI5PN5crnc7Q6ztbHclaO8DFCfnVmdSmyVLAzD4PLlyxw9evS2Kimrgr2Dg4N0dHQgSRKS00myWOCrps1lyyapyCgLi5SDYXxeH52iyvgzXyHZM4zuDxD65jkOHtyHKErQqp6FKCJ5vWh4iQExABusQhni1/GYBkGHk5AiE5JlulwKO/v67kiG5GbwIJLFWpAkiUAgQCAQuGsp7G2yaAHNsjO3Gj2ofhFOnTq1Sgtzs7h58yYej4f+/n4Axubn+PT4NSZm5jhrSeheF0p8AdUTwFHMokkiOcFg0qlgBoM4EFgSXXQtLuH2+SiXy0xPT+N0OCpJS4pSk9trKSwsSeDzUwbKwCKABZ65ed7W3c29pYp7RxYvB38PtMliQ6yXnblZsrAsi4sXLyLL8iqzcrOYm5ujVCpx9OjR2rInx27wDbXInN+DPjmPXdJw2DpibIZioBdT9iKlFgl0uDGdDkxALml09Q7jcrlIpdP09vSgGwaaqtbqKnRdB0B2OhtJxOGADV4+qZDnTbt346lLMrtXuB8cnHcKbQfnfYDNZGeuB9u2uXLlCl1dXSwsLNzWmFKpFPPz85w8ebJhTMlSiUVZouxQcOhlim4vOJwIhoDtcOIsFyDUiatTxq3lMYtleiSRXCqD3OOsTEMEAafTidPpxLti/LqmVbpllcvksll0w0DgVup0lUQckgSCgKWpvC7oZ7i357bOd7twPzs4HwS0yWIdbBdRAIyPj+NwOBgeHt4yWVSnQ9evX+fEiRMN5q1t2xzwB7HmxpAKeQxRwgiFcMaTmD4/gmliuRTEcgHF34nc1YF2c5Zy1wCTpk1ubJJQV8eaxxYEoUIGK1TJbctCWyaRUrFIJp3GMAwEQeCRconwsSMkk8lVVZ0Pi2BvexryEGCldmYztPrgzc3NUSgUGqYMm4UgCGiaVtPhXNlKIF8ssiSLDMUTXHdKGD4FWxIQS0XMzjAORIRiCW8pgbPnBIF0lrS/o7bvvGkT2sq4RBHF5UJZoQsaSif5rpPHKJfLLC4ucuPGjQaRGlmW0TQNwzDuWguBe0UWd0OcuD0NuUeo72y+0U3Y6AFMJpOrpgxbcYwKgsDFixfZtWvXKh1O0zT5v9evkHA5yPsVcMoY3V24L91ETsfQw2EsWUKUXMiOMKfSKU70D/FPV8dIiiJly8aD3XI0ZCMouRz/+tBBwk38MpqmkcvlSCaTlEolnn/+eQzDqPUh8fv9DW0VtxNty+L20CaLFTBNk3w+3yCyuxaqORNrPQz5fJ4bN27cdi5FdfoxODjYIL1f/Vv0ykVm3U6u3LxMvrcDZS6Okc0hKhJGbyeiriGVShhOF8VMAdEjcHCwl1ft2cPzk9MIhkGP18W5m2N0mRppTSet6+QFCTyeSpSjVZRKfFtfT1OigIp/IxwO43K5KBQKnDx5sqEPSbWeotoRzePxNJCIx+O5rUSwl6uD826gTRZ12Exnc1ifLMrlMpcuXeLo0aOr8guqYddWv3IzMzMIglALkdbj69evcsOrMLs0TbHbh5hMIwRchMpFSOfIevwIlo4aCCNqGmJvF/OSxNdi07ztyON82/EjtfGq+TxnltPOqwloc4kE8bJKWquQSErXKDlkRLcbYcVLYBkGj8oOHhluXuNSj3rrar0+JNXerFWRmkKhAFBLga6SiNvtvm0r8E7gTjs472bxX5ssllFNutqM2SiKIqZprpqTGobBhQsXeOSRR5rmUlSLyVo5TjweJx6Pr9kC4JHuXryZNDlNZ76YQwy4kNJ5OnIFfI/s5sqVafTOTiynA9E0wLaRBKGSsrliTCt/93q97Pd62V+33LIscvk8M4kEKfWWFZLWdfokideeObXhOa11zGZ/r/Zm7em5FVGpNlzK5XJkMplafU19U+cqkazUlni5kUUVbZ/FXUKVKDZrMkqStCp7rqpLsWPHjlVly1W0KpOXz+dr8npXr15tmqnX1dFBV0cHs8lFbga8DADZiThWT4jEwhxGVwCcDnyJeQTDQLA1uu0+Hu+o1BKYpsn12RnEFjNSRVEkGAgQXKG5YZrmXTPz6+siVo6hWtFZX0dRFagpFApkMhmcy/kidwN3gyzuWiXtXTnKfYxWtTObYWUxmW3bXL16lc7OzoYv4UbbNYOqqrVpTDWDcj2CObNrP+dnL3Hx5hJLQghjsQiSA9PvRu/oQB8M4796kwGHk17Byb7eASzL4u/Ov8hM0IuhaYQW53is5bNvxP3gxKtPga5HVVsinU6TSCSYnZ1F07RVPUj8fv+2R2baDs6XCWzbJh6Po6oqgUBg0wwtSVKDBsTExASiKDI8vL6S9kZkYZomFy5cYN++fbVpzEbb9IW7eOSqyDVnEHc5TcGhYJRLCJaKnEogFko4bY3CnkH+WSrQfeEFXrNjH5OKhATMLSxxIZPh1VNTHKrrpP5yQFVbwuVysX///lr7x2qGai6Xa2hktLJDvM/n2/IL33ZwvkygqirJZBLDMLaUfl3/As/Pz5PL5Th27Nht6XBWVbP6+/sbZO1bmbq8Yt8Bpm/MIkoCSVMjllnALojoAQXFNBG6fLjcClJPkKcmpxg5cAyXpnNzKc6cx4/eGeaLmQzBWIyBFVGXlwNWTpOatQ+o78uay+WYmppaFZmpLw3fiAja05CXATRNq81nVVXd0j6qL30ymWRubm5V+vVG2zXD+Pg4iqKs0idoReF71+AAr8/mKC5Ok5m7jijqmF0DODQDKRCkM5/ADsgkS3l6DQ1FUfj2nkH+ZD5G0KkjOxVst5tYLnfHyeJ+zeBcr7lzfWRmaWmpFpnxeDy1qIzf72+IzLTTvR9w1GdnNnNStgpRFCkUCiwsLKxKv95ou2bHrHbSqkqw1aPlKleHzbV+k1T/EPpcClMWsCQnzoKBo6QixxOQL6HqDp6buMbpHfv4/kcO8mSxyLyewJ3JsOfAgZbOYyuwbZsLN8aYj6dwW9rGG2wjbicaslFkJp/Pk81mawV+1WhSsVgkmUwSCoUeGK3NtfDQkUVVeaianbnS77AZ2LbNzMwMJ06cWJV+vR6akUUmk2FmZmZN66RVZa4n566RCzmxJhNobifKzTn0UBArnSa3u5uuqQSeR/oph4L8zcxF/v7ii5zctY/XO9xcymR48+nT+FdEGbYTT5+7zNMLeQzLorQ0x/FjOYKB5mHh7cadsGbqIzN9fbe61FcjM6lUinQ6zezsbIPWZr0lcruRmfY05A6gPo27ahpulSwMw2BhYYHu7m68Xu/GG9Rh5YtfKpW4evUqx48fX9Mb36plIYsgZ0uUFQWxqKHu6sNyuzAHQzjnF9A73aheJ8JYkmseP3KHFzUTQ/d3sq+ri+Aa+RzbhalUHtHpAFWj5PQwubDI8QeYLNZCNTLjdDo5ePBgbXk1MrOy/0i1nWKVRFrtDn9XRYjvylHuAywsLDAxMcHBgwcbpgtbIYuqLkVnZ2fNs74Z1JOFYRhcvHiRgwcPrruvjSyL8fFxMpkMOyyZF5NTeB0yhXQWU5KQckWssB85XWBf/zD6kkZM8WG6XThFJ4slnbyhs5hJEk+n6AqtXX16u+jyysxmKo2hnVqZ/q7wBltsH+6HLurVyMzKHJz6nqz1kRlFUVaRyL0KxT40ZHHp0iX+5m/+hg996EMNyzdLFrZtc+3aNYLBIG63m3J5/e7kzVB1Vtq2zcWLFxkeHl6VG7AS65FFNRJz/Phx9uTzTHyzQCY5T2awGxBwFlI4F5MMizon3L2c8A/yxPQNhJINHW4cqRyJUokbPQ7mMtd5bS7MieG9mz6vVvDG08dwn7/CUiaHZ7iD7s47R0zNcK/JYi1sFJnJ5/MNkRm3243f78fj8TA4OHhXxvjQkEU1g28lqinbrWJqagqAnTt3Eo/HtzSFqTpVb9y4QSAQoLe3d8Nt1pqGVNOdT548iaIoKIrCD53+Fr7+pb9BEiwE20Lo9rFXk3lDsI+ZTIKLxRjp6UVC3k4CS2mO+AJMDXuZn5pFMxy4nMU7RhaSJPHak0fIZrNMTk7ekWO8XNBqZKY9DdlmeL1e8vn8quWbiYYsLCyQTqdruRRbjaQIgkAiUdHK3rdvX8vbrCSLVDrNJ77xj/TvGCI1fZ5O2UdAUiilsixmFzF37sUWBFxXp/F53ehBiSlFJRmPU3h8iN6iyA7LS68q8/fXpij2hMkIIurNKb69Z6Zm+r5cMhBfLqiPzHR1dd210OxDQxZ+v39Ny6IVx2EqlapFK6o3Z6u9Q0qlEtlslscff3zLbQ81TeOvnoviPbkL3e1mHliSysTnxvnG5W9iDHlwLS1imxaCU8Mj+hnNz1Mc6iNjenCkU7gDHSyINgNdQ+wRbW6W8nT6vQyE+9F1vWb22rZdM3urP61UebZxd9C2LLYZPp+PYrG4pW0LhUJNyq4+WrEV52ihUCAWizEwMLCpL3a9ZWHbNn/z9BexHulp6LYOkDAKmB4HWALanjCO2RRCXOOCGkPt70ObX8KwbKxkgYzowJdMMfz61/BoJoth2gx19vGIW2X37t1AhdjyxQIuWaFQKNRyCao9XuvrKrYjDPhywsutd+xDRRbNLIuNUJWyO3z48Kpcis1aFtV9rdXTdD3UH+tLzz/NQp+DrtDqFHXJFnAgIfQFkCeX0Dv8cGKYcl7FTpWhww1YOLIZikaZlNfDbz71f+j3d+KZW2JAFtg1sINsLsdCPs1odhbd5WAwa/K9hx9tyCWo79u5tLTEzZs3a9J59VbIWqpXL3fL5OWUvQkPEVk4nU4Mw9jUNtVuX/v27VtVDg2bI4tq6frevXtrLRA3g+qxzl+/wvP2En39u5quNxjuI7OYJDabQbNtpKKGAxvBsJEzaQQth+hRMB4bRhRFSueWODcUYjJg4S3CrgMBnrFTGMl5rk/OofZ2oKgOlmSR8LnnefXhWwloa3XLqqpe5XI5Jicna1OZ+rRoePl9eVfi5aRlAQ8RWVSxVqx95fJqWHNgYKAhnFWPVqch1TYAPT09hMNhEonEpn0dgiCQzKT4UuY6dtjF3OwUQV8I3wrrwu3zEujtJlwUSM7FKYUlzE4fpIq4Q05sRcAc9COqJqKpkxcEpKAL0yWSkSxMVcfhknEGPAhemZJHpIQFqsoLcomb08/gtSQCokJIdOGXXAz4O+kIhmrWQzUqs9KDX223l81mSSaT5PN5nnnmmW3PaLxf0LYsHlCsJ767Uh6vmkvh9/ubStmt3G4jTE1NIUlSbfqxFceoqqr88+IVyj1O1J6KqnY+scgup4zibfRbGFgYsoBzKIDmlBAvxLAlEVuQCPQHYSaDVTYRbBvd7cUSwDJN0HUE6dY12tnThbG0hC4IdIkOOnYMIAhCrePYEhq2rWKUlpAm8nzPrjN0d3bRDPUqVn19fWQyGaanpzl48GBDcVZ1KlOfjFQlkgftxXs5aVnAQ0QW66FqIVRv7PT0NJZlsWvXrnW3ayVHY2lpiVQqxfHjxxu22wxZWJbF5y88jbW7E8vI39K+7FAopnKryCJkOXGqKuVSETFlU94xgMOwKMYzOK8tEioZOHtD2Pt7iX/1JmVNpSA58CbyzE7N0NHZgb8jhMfn4Zhv/WsgCAKS7OCwv39NolgPzTIaV5aJT0xM1LRRq1OZzWhv3iu8nLQs4CEjC4fDgaZpqxyV9dOJpaUlkskkx48fb0mXYj1UE4/qw63V7TZDFp/9yhdJ9yk4nQ5cmoOcYSI4JIRYGV9X36r13R433utpXLsCpC0B29QRcxpWhxtjxsLe04WVVCm9NEVxuAOzN4izaKAJJpeTc4RljT2ajmmaWLZNd28PoqP5F9K2bbpiJq84uPWeKCuxVjJSfYVntfCu2gSq3grx+/13pVfHRmj7LB5geL1eCoXCmmSRyWSYmppa9XJvBaqqcuXKFY4dO7bqwd0MWSwsLLAr1MexUIgr02MEB/ZybWqStKniCnYgORtvYWopxrSQRTw8QD6ZxJgrYvlMLMvClSxiHx9EsESKCJhTCYSeIIJTxPTLGEmJckAiVcxxbS6D4+gwCAKpiXEO7N3b9KFU4mW+bdepda/XYmKJlxKX8To8+EQ3fqcPt9h6lW4Va1V4VouzcrkcCwsLtYZGiqI0RGTutkO17bN4gOHz+cjn86sclpIkUSgUmJycXLfys1VUoyiPPPLIqjwIaJ0sstks09PTnDp1qiLVXyhyeM9hXrunondRKpVYSMdJ5otkzTIFQWMinsAedhH0hUnfTJA/OASSAJqO48IC1pILrWRhSiDJIi7LRItlIafiyhZw7utFUw2K+TJBqfKg570iRlnD6V7xgufKvK5jLz7P2lW3mqbxfPwCQp9CFo0sGqaRJBfPUkrniN3I4pXceEUXnZ4OukLhTVsF601lqr1I4vE4xWKRp59+umEq4/f7cblcd+Tr3CaLBxhrJWbZts3Y2BhHjx7ddBXpyq+VbdtcunSJwcHBddW9WxHsrVomDoejabq32+1mt3uY3cu/O51OegU356Q0BUNl1hQRJRFbFrBMEb0/BAJYtgGGgAC4cnl8hkSvqZDdN4CsC8hxnYA/gF7dr2oiyY2PimWYHDNC7Ohe2wFs2zbPjD8PvY3RDckh4Qq50SSdcrdNmSIJitwoL2BN6riR8QpuvJIbn+imJ9hDwO/f1Iu3shdJdQrz+OOP19oIpNPphqlMvS9kO6YyL6eeIfCQkYXH41mVmGWaJolEgoGBgTV7c2wGY2NjeDyedaMoG0nkVUvg9+/fX7NM1iok0zSNS3NXsbE42P8Ir9xzDHPsPBdjN/EuphElMAc6UObjSIqFpRuY+3uQVB3TtJEuzNEveOg50AWzKfp2hukeHsDhcDC3GMcWoL+jr6ErmW3b9MUtHjt4eN1rcWnqGplODYfQ2mMmu2RwydhAHoM8ORasLJdyE4iL4BErFohXchN0Bujt7G6Z3KsqWfVTmfp7ZBhGzQpZWFjg+vXrq9oqVqczrRLA3YqGtH0WdwDVaUgVVSsgEAg0bQbUCuq7i83Pz1MsFjdsgLyRYG81J6Ojo2PNbZZSMZZyMSZik/gOhBAEga/PnGVk/xvY4evkyemz5I4PIgkGzhvTeBQDTfGi6RaCYSIAYlFHkEXMXQGK5TwhF+w1fZimhOV2snfnzqZjdMXKvHHXmXUf0qVkjJvM4JQ3r/dRD0EUUPwe8IMOpFFJozKlx/nmwmWcuhOv4Fqeyrjp8YfpahKV2UjLwuFw0NHR0XDNm01lqlGZ+o5oa01l7kY05G5Ggh5qsrhx4wZerxdZlrcsrVd9ias1E62I9q5XvDYzM4Moig0aBVVB4eo2C/FFvlm+hBx2MZlfpD9uEe4OY/pt8vk8E4vTTCo6Zb8LXVCwH5HpvDiNz2uhWiaFZyfROty4LAtlyEfZZeIIKZjn4ng6RB7z7EESJOKZLFmzTNpSyVplyi4Rhy3wLeH9TX0xVWiaxnPxCzh71yYKm9sTopGcDqSOyuNbwqJEgZiVJz6f5PVbIItmWKutYn1HtHQ6zfT0dEMzoyqB6Lre9lk8qKj3WUxPT6PrOvv27WNhYWHTqeBVVEV7q4VmrZida01DkskksVisgXAy+Syjc89jChaJ+CJf//oV0kYG2S1x0L0Pj99LrpAnTBgpL+Dr8+HxehHny2g9fhRJhGQZTyiAUjLwDnbgE2SwDcSQh2KPCy1VIpNMYvZ7mOuGfymN8Zbuk7idMm65Yobbtk0un6eolujrWruBkm3bfGP8Bei9B6HLmM6r9r+i6Z+2s1vaRlOZXC7H/Px8Te8kkUhseSpzP+GhIotq6DQWixGPxzlx4kRNl2Kr7QAEQeDy5ctNC83W22YlisVireN69UHSdZ2n5s4h9HuRbJtYbAzX/g70ooucWOa5yYuEAkGEqTLxXJzdoZ3k8jl2hgd5LLub4sw8ZaeAT3HidDsxuxXKc1nKoomVyiHJJsalNGJGxwy4kYY7GI/P4PK5uPR/nyWwu48eT4jXdx7m4NBeAn4/gQ38Olenr5PuKOMQ7i5Z6Nkyj3ceWdMpeTck9VZOZSYnJxFFke7u7hqJxGKxhubO9U7VrURl2tOQOwS/308qlWJiYmKVLsVWpiHVgrBdu3bdlnO0qsN56NChWl2Ebdt8dfybqL3OWg/jqunu8nqxchZCwULxeOl65T7y+TxfTV/nXy5dQF3K4RFk9mo2sxTw9/kZCHSygIo6KCJk8+iiD4dm4erwIPgsFI+LNBZZWcWYSFE46CEdssiVsrgXr3JwaGPlrHg6wXVrGodye36KzcI0TAa1LvqGVyeoVXGvmiLLsrzmVKZasZtKpRr6stZHZO5ES8Wt4v4YxV1CoVDgD//wD3nyyScbvkBbUbyq1o8oirKlbmb1+7l06RI7duxoIJwXJy8R7zKQxMotEgQBvzuANl/C8gq4sw52HjqKU5ErTrh8jsHBQW5MjmGcCTMfz5C7uYjSpVAozDHl8ZCeLVEKyZgDXkTJgz2ZwSU5kQSB7q4wwsQCXs2g0xeiFBSxAbvTQ+bGxqX9uq7z3OI5HH13lygAlJjNyf3rO5XvhVjveg5OURRrZFAPXdfXbKlYb4V4vd51653uBB4askgkEvz+7/8+r3rVq1a93FsRsZmZmcG2bfx+/5abFEEl1Or1eht0OMcXp7mhpJCcjfkJssfFjt4+jLKGo1OuWUTxRJzenl5yyTRGr4xh6GSMPPaAi9DOLtTFPM5ZjX5XkLFCFsomlqajOsAxk8YvOigKIp6UzuHAIHlZwJ0p43RJOIoqj3Yd2vA8vjH2AlbPLSvobkFPqbyi/+SGvqJ7QRZbybNwOp1NozLlcrlpN7TTp0/ftSrdh4YsSqUS73rXu7h69eqqv22WLOLxeM3ncfPmzS3rcC4uLlIoFBo6kCWzab5ZnkDqbO7/ECUR2Vv5etu2zVJsic6OThwOB7JLIb84g9olYoWc6NczmH06Dr+MLRj4Tw/Sc0MgYdu4PB5cZScdQ530K2FUQ8MY7iTrdKAuZeks6Th9EoMOF65hiVQ6hWroFMtFdvQN4XA4yBZyPDd7npnMApqsMyhuQtRnG/KJDM1grzBAZ7C5hEDD4R4QsmgGQRBwu9243e6GqYxhGHe1BuahIYuhoSFe//rX88ILL6z622bIIp/PMz4+XvN5bNXfYRgGU1NTnDp1qvYQq6rKVxfPQYumfDKVxOPx1MKYnoAPZdxCFwyUkoazL0B5PItXd+IJhMC26d3dhzo2haIa9O4fgKxO/8AwokNibm6Ovr4+hD0CmqahaRqqpvJk6QqLX/9nlpQyokem6yWB7+g/w4v56+R8GmWrTMAbJJjO4QvdvYZB3rjE4YOttVrczmhIq7jTGZyiKLanIXcKa0nrtUoWmqZx6dIljhy55XXfir9D0zRUVW2oQ7Esi6+Mv4A+oLRkyufzeUzTJNx5q0mPbdsIhonkc6L4Asiag66Ah7JYpkCRzOQsge4uugUvckBGyBh0Kx0NFaXVl6oqYOOn8vLHi1lEnwPJKTHvU/lf579A50AQ3eMAw4J8jjQuXH43DunOP1ZWXOMVO1/R8styryyLtp7FA4r1FL43Iot6ib36doVb0aa4cOECbre7IdT63PgF0r0gtfBAa5pGNptdlVKeXFhC2R1ES2UwBQElD86wA73LRVBw4ymomNMl9h08Xqv10Msac1OT2AIYxtrnIRQMXH4nmktALovIfQEMQFacWLIFV8vI+2QWFxcrpe2mhaEadHSE8IX8yE55215Wo6hxxLMHb5MCtqpTceWx7kU0pJ3BeYcRiYxIwHPAbDQ6+t2RyEgn8KmBgUH6+nr5pV/65S2HKddrNLReUY5t21y+fJm+vr5VFaubmYbYts3Vq1fp7u4mHo/XSOba3ASTviySY2NHlWmaxOIxerp7VskAqrqKs1Oh09uDWixhFkroZQ0sASQBh0fG3SHjUJy1bWbmx5EGK9OY1JU4A+bQqq+hqRp8d9+j/PP0i4wrKfw48bsC5NQsPlVGmFc5/uijOOXKfov5Aldj0xg9EvHEHN0ZD/Jyxaosyw3h4c3CtmzCOS97HtnV9Np89fJT4AJFUHBLbhTBRae3E6y7LxB8N6pOH2qyAP4zcBmo9vN7L/ClP//zPx954olP8sQTT/DOd75zSzv2eDxNq043uuATExMoitK0TZwkSS1nf87MzAAV/0kymcS2bRZTMc6ZU0j+jf0UNjaxWIyOUEeDY8u2bSzLItQRJj8/TdljUC7mCXX7KWtlmADbK+AwJXr6b9V7JJeSTOfSOKeLdHf6sL0Cpm7UXnqovJx9aYVXHDrN4wdP8S+XnmFcX2ShGGNv735cLgXvST+CeOsaxtMpHP2+ysM15EZYMhkeGsa27ZofpFAooKoqU9NTFeesLKPICrIir2+FLGk8tqd5lub58XO4dii1F1RDRbXLxEsxsrEsakalOF5AEVx4HR66Qz21EOSdQLtE/Q4iEhkZAr4L+CDwnuXFbwbeAPxmJPKv+Lmf+9ktk4XD4di0f2FhYYFcLtcQsahHq5bFylRuURQrgrWpK4jr1FDUwzRMvAFvQ9FblShEUcTj97LbuYcbVy/gH+5A9rmwAWXJSdfgrSmLZVnoqsaYmkbr9qP5HRjzWQIFG2ln4yOhLOm8Zu9jQIVUX3fwcYpXn6R3V9eql0wra6RTGYySjmWIiA4Jy7QoJPOk3SmCXaGaL8ThcFAsFgmHw5iGiaqpaJpGIVVA1yvF8VUrRFEUFFnBKug8Fm6epTkfmyfvzaGsENURBAHFI+Pt9iD4BKywSYkCBTPHZGYSFsAluHCJblyiQsjTQXgLmhrN0CaLO4uPAb8A1M8zeqPR0XmAcDhMOp2+rQPUV4luhKp023rFYa04OEulEjdu3ODEiRMND8/X5s5h7PS05NAsFArY2KtyRKrnUt2vQ3FiqxaJiQU8QT/ekB+3N7RqyqKVNPJoiIKInihjFwWCoXDt7wCkVV7dd7z24liWxZdfegqtH5QV16NcLHN1cQq7x4VlGzjGNQS/Qm4xRs/BXpZIkp/OM7RjuGE7AQGHw4HD4WjwQay0QhLxBJ5xm9n+INlEpqHOwjAMrqev4e5fp3DNbrQgRUnEG/TA8uXUUdFRSZTjXJ6+hMNy4BbduCQ3HslDd7Abn8+3KSukTRZ3CJHIyHcDS9Ho6PORyMgb7sQxNrrR9SRSKpW4evXqhspZGzk4DcPgwoULHDx4sMGheSU5iX4ogKtFh2Ymk8HpaPzaVV9qSZJq4569OYY2aOHwecjNZ5GnnPhONRKMIAgYWhkplcbcXYmGKLMl/MN+JEnCtm3MksYx+gl5A+i6zjfHLvDlya+Tc5cIpEL058N099yK+ceTSej3INggdbpxmzKDgS6mAzYOV+X6FTKtd4QTBKFmheAHad7gjW96TUOxVrXOYjI9iW+/BzXtQpYVFEVe5XexsVsiZdklV3Q1AHu5mrVg5ZjOTGEv2jUrRBEVgq4g3Z3da1ohbZ/FncNrgO+JREa+E3ABgUhk5H8Di5HISH80OkoikVhTfapV2Lbd1LKoVoJWfRAXL17kwIEDG4qrbKRNcenSJYaHhwkEArXlF6evkei1CIob32jLsmoOzXgiXht79TxWev6zpTTyssNS3unCuKY33a9qlNm1r5dsLIuFjeCX6ezorOzbsunPujn8yP6Kc3f6Ok/mX2CyM41gW2RvZsh5E+RzGbyKH2/Ah9PhIBNfQnUYlEsq7pkydq+K7tBweiskKVpbe7CrWZpVC6S+zmJqfhJTMBCclTyVQrFAKpXEtCwckoSsKCiyjLFFCQKo3GNP6FZJftUKSZbjXJm5jGzKuAQXiqjgdnjoDnTj9/vvSW7HncR9QxbR6Oj7gPcBLFsWPxeNjr4tEhn5CPD2yjpf5NWvfvVtHcflcqGq6io9hup0QhRFLl68yPDwcEs1H+vlaIyPj+P1ehvEZefiC1wWF3G45ZaiAbFYjFAwhNPpRFj+Ntb7KURRxDJMbCpydQouimUVyeXEyKgEnas7qQF43D5KxTiB3gDpWJrezt4a6ciLGq/ee+s6/+OLX+JaYBHbAcrOAOVECUVXmdcTqFKWQMFHR96Jmc9hKDYCBmKXjDXkpPRiCsoVCf+BzjoHcYuBEFNfO0uzVCoxXhjH01e5l/WWm22DaRqoqoa2PJXRDYNCsbjsTJXXtEJaxUorpEyJklVkNj+DHbOZK87hGXejiC4CrgBdoa6WK5PvR9w3ZLEOfgv467e//e309PTwy7/8y7e1s2qZejOyME2TiYkJAoFAQ63GeljLslhaWiKXyzX0C8kV8nwjex2xW0FIFTcki1Q6hSzLtbyOqvVTLSASRZHkwiJpPQki+OwAu48c4ub5i5Qp4RM9DB95pOm+/Z0d2Amb2esz9IR68AWDzExexyprPO45Upt6ffmlp1jcUcLp84IkULoQR3Y4cNoiWpeCYECxUCKVjuHyyHjCHnKOIoXZLJfOX8cKuAl7fOyVXLj97so5t2hgVLI0HRw+sDpL07Ztzk29hHugueUnCNzyhXg9OBwODNMkGAguZ6eubYUoilIh5y2Y+IIo4Am4IQAuXUHv1tDRSGkJrs1dQTIrvhBFcOER3YSDXQQDwQfCArkvySIaHX0SeHL53wng29iWaoJbaln1/SigQhZzc3Nomsa+ffta3l+zaEi1x2d9KrdhGDw1+yJ2f+XLspamZhXFYhFVVenrvWWVVMnC4XDUvoZpLYXSV4mO5BM5OrRu9p9oHrlZCUOA3qEhOjs6mZ26idTjYNDqIC+UWEws0hvu5WZyEvkRN3q5iKEZSDp4DScduzsYn0hgGDbOoIJvyItVtilMpyhLZTAN9LAHyeFAdZss5HIM2n3Y2GCDblSmR5Zt1c6tdp7LbGLFNR7f0TxLc2z2JkbYQG5RN8NePoYoCrhcCi7XBlaIroMgbJsV4pSdOLurY7VRKVG2iswV5jDjJoqg4BLcuEUX4UAXPZ1rCwzV42H1WdwVVC2LlTAMg3g8zpkz62tLrsTKaIimaVy+fJmjR4/Wvs62bfO1sW9SqtOmWI8sdF0nlU7R37da9Ne27ZpD07ZXO+1aHXuNjJanSBYWgbJMONyBaZiUCmWy+SyCImHGVXw9XoozGQacfRw+dZRr12/g7QlTsMqYTnAUbToGwlhpB/GyRbJcxhA1nF0KglMA7VYGpWlUerT09/XXrkH9tRAEAaOgc9S9C4/bs8rHlMvnmDVmcLvWlvZrcuHWNGhWWiFVWJa92goxzUpeyLLzVZHl27JC3H5XLfZnopPWykh5qWWyuJtokwWVsGQ2m+XAgQOb/nLUT0PqO6XX50Kcm7xCLKwjircu91pkYVkWsViM7q7uBtPUtm0URSGeiFdqN5YTmDyCj/x8FkEU8EvBWnbmejAMg0QywUD/QO0h78x7CXt9qEUVV9LBwJ5+vjL+DYaP70KeVojdTLC7YzcDhyqVpQ6vC68XXJZC2ixQKJVwzCTxGyJqj4TbF8aayWFMZXEoFvv3Hlw+EYjFY3R2diI163Jmg23adGTd7Ng3XEt4q9duODfzEq7BTRDF8vVjky/0RlaIqqkU8vk1rZCtwIpZ7H+k+dTxXuOhI4uVor2apnHx4kW6urq2NG+sTkOqYjhdXV2Ew7eKuyaXZrkmx5Hk1clCzcgiHo8TCAYaNAqqDs1AIEBHRwemadaqQgWXjFz2Y1s2tuIgnU7XEpmaEZ9t2ywtLeFT3CzNTmILNj7Tz5sPvQGn5KSklgjv7+SF8XNovQIi0DvcT+/wLSsnGU+QM3IkUzplU8eKFfE4RMqCTSFWxHugHwRw73YhXinymlOPYmomyYU4iXgcUzIRTKt5n1IBiOk8tuux2v2oXifLsrg6dQW6bHK5AueWFlFF6BcVDu0coOoMacYJNrAdXoFWrZBkKommaczNz1cIpAUrpJxROdh9qCUr5W73DIGHlCyqlkW9JZDL5bakS1GtK5mdncWyLIaHbyUdpbNpni+OIYWbeMCFSip1PTKZDJJDwudtjGCsTLySJKmmb1C/TpVAiqUi6XQa06qYzIqs1Agkm83i9rgpZGK4BlxYhoVrXsXv8eF0OvH7fEwsTLHoy+KQVlsppmFydfImqs8id2keIeTEFgV0t4yzV0RLmDjGszDggazGcEcP42M3WcrFMDsd6JJKlydEuUNnYXaO/qHGFHo9q/Jox+EGsqy+PKlsirgjjlt2c3l2lmxf5TqNlTU6F5N0d3csr1+5wNX/L18guKPl4o1WiGXZzM7N0tPdjaqqqJq2rhUiiiLekpfuwe4NjnQLbaWsO4zqNKTan6O7u5twOEyxWNxyOwDDMFhcXGzI9NQ0jacWzkN/81CZIAg15x5UwoClUmlVFKZKYPWJV2vtb2VZuW3bGIZRy4TMZDJomobT6aRQzoMqECg56AgHUVUVp9NJqVTihYULKDubF+stzC/AoIv8UhzXa3qxiwZmUcfIaliqgbNDIZB1IC8K+N3dOFwOYnoc524fpqHidCmUixodDhGdxhwQ0zAZKHfSP7g6EmWaJpeXLuEeqBCkUe8QlR2oJX35+tzygVQ+vpXfDdNEliQsq9H/cafeNdu2EYW67NS6SuVmVkh+Ns8+z36uWdcaslPvpw7xDx1ZVKchU1NTSJLE0FBlDr4VaT2ovOSapnHmzJma2W/bNk+NfxOtX17bqcataYhhGCRTSfp6+1alZcPWRU4EQcDpdOJ0OpFlmUKhwI4dOwCYGytCQsXj8hC7uMj54PmavP1j4WMUMyVKaBSsIgWrTEnUEP0OTCw8ipslTUfUZDAsnEGF4uUkYqcHhyqya99eZmZnmMpNk46nUPrdqEs6HQd7MVUdwQZTM3ELjUQqx2xO7m2upXl58hKOnluP606Pj3OJHKbXSTBRZmD3rtqVrZz7rW0NQ6dYLBIKBmvJbLeu0fLdEJpvu1XYto2wpv5moxWil3WOe44z0DVYy05dXFykWCxWtFeXdTcDgcA97RD/0JGF3+9nZmaGVCrF8ePHay+hJEm1AqZWUc30dLlcDck2z09cINVjIQprO0vrszCXYkt0hbsafAz1maa3G4OvHqO7q7t2jIHOYQ7qYbxuL7siOxEEoaY2Xe3ApWkaHpeLXn8nbsVNuajSZXv4pxtfwyXL6HkdLBtjKo1HlrEmigwP72d+fo7SLgt1ycS1oxO7bCKLCtnnlujr7SPg9ePLewgP1EnEpVRe0dtcS3MpuUTalcbluJVTMdATpqPgpVwuE9zTjyitdY1sYvE4XeEworhy382tkEbi2JoVYtut62cISZE9+/fW5PN6em5FQkzTbOgQX99Wcd++fZvuzXs7eOjIYnFxkU984hO8853vbHgJN2tZVDUuhoaGmJ6eri2/OTfBuDuzoTZFlSzi8Th+n39F9uEtotgOpaVEIoHX6609WEZJ55S8g0d272lYb6XadH37vmw2y9zCHIam02MF6HKEyKdypLQcHe5uQns6MQsm3UIH17SbSIq7klUakNFyBVx+N86AyL7d+/AFG6c4pmawh346gh2shGEYXEtexdWkSMztdeH2rv+yZHM5nE4nrqZh1maWRGMod6tWSKvFiqVUmWM9x9dcV5IkgsFgQzaxbduUSqW7bmHc/2lj24jZ2Vk+/vGP87rXvW5Vb9PNamlOTEzgdrtreQq2bRNPJ3jRmELybhw2EwQBVVUrIc8VYj5VP8V2ZPXl83kM0yAYCJKNJViYHsO4Mc+O8EBLY3S5XITDYWb0JZJ7dbJ7LByGSNmrIXY5cWUFBvsH8QpuwqEO+vQOTikHccYh4PZRuJwEA8gadO7sYDE51/ACVrM0Dw03ZmnmCnkuTV3hy9/8Mkrv1lKkDUMnm80S7lydKr7OWVMhhJU/t8ZrWZXo1K2fyjLbhuqptUIWtmUT1EJ0hFaT5LojXLZA7jZZPFSWhdfr5f3vfz9f//rXV/1tM1qaS0tLZLPZWiq3KIoUikW+Fr+I0KI2ha7r6IbeUDcCNFgUt+vc0nWddDpNf38/aqFEyU7i9TvZMdzPi9Mv8sr9r1y1zbWZ6yyoS3gkDx7BTdhbSdJKh0rkjDyGYBJTsvS7+tAsHbHPIuj24wsHKE7lGOoZRFM1iuMFJhIz7NbD2N0Cwwd2ILtlsvkMHTEXJaHiD7F0i9fvfHXDuRaKBb4+/yxlv068J0Zhrsjw0GrhofVRmX6EO8MIwrYETSv/bcEKEYTK9EGoLWtuhZQXVU7vfnTrI7rLzs+HiixCoRCnT59mdHR01d9anYbk83kmJycbIh+CIPCViRcwhlsT2zUMg0wmg0tpbFe3ViXpVmBZFktLS3R3V/wU+UIRhyww7Oup+Gek1f6ZeDrOuDWD3KuQo0TWLjJTXiAXz3IucxO7T8KwTXKpGGK/iNOr4Op0U1jMs9Mawie6+JeFr5Mt5lDVIuYukT07dhGbWUItlTF0A2kRRNOkzx/C6x3EFXChyAqXpq6SMDNYOYPZ0jxJTwYrpxHe2UW+1HppexXV6cd6DZxvH2sTSCaTWe4pY7PSFyIIoJV0dnp3bbnb2L0QIH6oyAIq8/Jm0nqtkEVV3fvo0aMNJuD19DT2sU7kFpNpYrEYoY5QwzhWVpLeLhLJBD6/r+YLUVxufAtlXGEFo2zQKTaavrqu82LsAnJdqFcQBJxumc5dXZT/5RymImIaBlKXk8XL83gHfHSLHWhBD/GlJcZ9C9j7RMpli8S1DORtXpjKEpICdC4GePXhx9nxpuEGp108HucbLz3LbHcWl8/NopjAI0qU3CqmbhAoabjMzfltqtOPwYGNp1rbD4FisQCCgKdBULjRmSomRQb2DmAYRsNHp/7f9xseOrJYqzZkI7KwLIuLFy+uSuW+PHOTRK9FeE1vfCMSyWVno+KqjaNKFNsR+YBKIZtlWgT8gdrYd5aDPHrs1UwuTeKW3QzvbFSsen7im4jrdD6XRBv3oBfTMinN5rF1DX+nl0AwRH4myzPpWeLZNMKsjeAWMcs68oAHJexB12wW83GGegcRlnMPQqFQTZuk7DGROrPouk48mSafKSBpAnpRJTuboGfvbvL5HLKsIMtOUsks5bJKb29XkyjIdk8/NgfbtkimUvSvmF7WWyGlZImTfbcs02bT35U+k+qye4n7jiwikREX8BVAoTK+v4lGR38lm83ywQ/+BgsLi7el8u33+xvSvavYyMF5/fp1wuFwQyr3fGKJi8wheVrTpsjlc9iWTSAQqKWIw/b6KaqqWgMDt+o+3EsGr9r7OJIkcWDH6nLvG7M3yQZLOMW1ySKo+MlOFjFFCzul4w26MSdK6B1lRMGJvVPC5wpi6AbqXB51oYjc7cHI6AiWRVm2ePr6Nwi5ggRkP33h3prVMxToZzYdR+5Q8JddZMUCncFOZN1m6Egf87ElUqkMwWCA6YVF4kGQfDI3L89zevd+XG5XjWRzd2X6sTZSqRQBvx9pjd4plmnRaYbx+2+JIa285/Xp7fWoJ45cLofP57urTs77MRqiAm+MRkdPACeB74hERl75qU89walTp/jzP/9zTp06xRNPPLGlnbvdbsrl8qrl6zk4Z2dnMQyjIZW7UCzwjcwVxKCCKKzfSgCohSCrpfH1eRbV42+LnyJW8VNUX578eIzdYnjNbVKZFGPGFE5P84dOLamMTY7hdDvxGjJ+3PT0hdl9ZDe7H92D3+/D4XFWUpgVJ5LTgWRI+ANexDkNpyHgKAv4BDf6oE28K8M19wRfWniK6I0nuTJ5jZ7OLl4ZOMZwogNpuoRPVtBnC/QEwownZrCHHJhDDgrlMlbIQ7CvA7fLhdqjMDs3z/zCPDMz0ywszJNMJnG5XMsFaHe3fkLTNMrlcoMq2qp1YhoHhw+uu596vZLqT70VYpom733ve/mnf/qnbR3/RrjvyCIaHbWj0dHqp9+5/GM//fTTRCL/CoBI5F/x9NNf29L+1zLz1yrsSqVSLCwscPDgwVsl1qbJV2ZexOp2rbttFdXmxfW9PqrbVP0U22FixhNxAoFA7Yu9ND6J12MQ70rw9RvPrCJD0zT55uI5nM1qV5YxuzSL2O+k40AXnk4frrSDsL8DxatglHQUp4Lf4UXRnZSTZczZIg5dYHjPIAdO7MU9B3vdOzh47GjtHB1OB+5wZSq3p38XAN2hLq6MXyKxM09GzpMVC1ybvokUroShJYdEWVRx2kJN5Fc2JQb6+xkcGGRwcAjLsvAHAmiqSiweY2Zmhvn5ORLJBPl8Dk3T7mABlk08EScc7mItdR+1oLLHv2dLuTP1BHLu3DkmJiZ4y1vecntD3iTuu2kI1BoNPQ/sA/57NDr6jbe85c21KcDtqnyvpcO5EqVSievXr3PixImGVO6nx16k0CvVmFYQhXV1OOubF9fDMq2aPsLtIpfLgQ1+X2VqZlkWLlWnf1/FGlI7yyRSCbrDt7Imnx97EbtPWjeCY4u3Xi5/dwC/042pGeiLOi7JQ2d/xVKyZkyc8wLFdA65R8DpErE16N/TT9/AYENfEQA9p3I0eKBWMLYYW2TGMYdpm5RkEykooE2XcCzkCewMYds2DkNiV88ANxbmMUSbHUoQr29Z+CefQ1YUOjsacypM00DVNDRVpVAsouuVGpL6Qi5Zlm/bV5TN5WoqW02vo23jyroY2LfZEHAjLMvive99L7//+79/19W17kuyiEZHTeBkJDISAv42Ehk5Wl+Iczto9QtummZNtLf+AbgwfY2FzjJS3fx+PcsimUquqhCFysPj8/tIpVMYhoEkSshKpdHOZmXdNE0jk8006FN4FnV6Qre6mlslG0/4lmN2fH6CdCC/rp8CwCt5yeWLODwOjHmVjuFBHM7Gx0bXdUwsdu/eRdqKkVhKYGBiFgsEpI5VRGGZFuFCiIF9lWiFZVlcXrxEoNvP3MQ48qEAal7F39uBN+PGsWQhWgJDQztxyk5O+PY27M8wDDLZLAP9q6MfkuTA43bgcdf3WrGWi+s08vkcqqZhW1alhma5lFyWFRwOiVY0AE3TJJvJMDCwNhGUE2VOD5zZcF8b4a//+q85cuQIp06duu19bRb3JVlUEY2OpiORkSeB7+jo6CCRSBAOh29b5Xut3iHV36up3IODgw1pttOxOa46Ykiy3HR/K5Ev5DGNxubFcMtx1RHqaJjaqGql0U4qnULXbn0BqyTS7AtYzafo6em5pf+QKPOagZPYlsWl+ctYosVe9+5a5WM2l+W6Oo6za+PMyL6BPsTZRdLTSfqaEEU1FNzd1Y2aL5OIJfHvDVDOl8lPFTj06pOrd7pocnJvJaFtemmasxPPkyVDMV3ALctYeQuf4qJUKGKqCkM7d6K41xqrTSweI9wZbvlLKwgiiuJCUVx1HWpsdF1HXfY7ZLLZSpi4TptTlpuTeCKZoKOzc83jm4ZJDz1Ne7NuBvl8nt/93d/ly1/+8m3tZ6vYkCyWlbb/GXhrNDr6qbrl54AXqHQLmwJMKj6QBPD2aHQ0F4mM7AE+DAwBRaAE/EI0OnpxneN1A/oyUbiBEeBDr3rVq4hGv8hb3/qDt63y7fF4KJVKNLNWLMticnISRVEaGg9n8lmeK9xADK/O0Gzm4NQ0jWwm2zRDE1ZXkkqShMfjaQjLWpZVKy/P5W7NuasEIjtlsrkswWCwZs6bJY3Tyk5C/grJvSbYeJ1M0+T5uZdwDraWQp3P5MmSxXXQx1JyCZI2gc5bBLq0uISaK6F7NEzLxB32oBd1JEEi2LPaqtBSKmd6jiFJEslMkuczLzEbXMDyQqFUIOB0YyZtdKeKuyzRucfL+cvfJBjuxOfw0z/YKDWYy+VwOhzbEP0QcDplnE4Z6vREKkJDFT2KYjqFpusILHdLUxSwbUzTxLuifKAe5pLJ/r23r3710Y9+lB//8R+vtUG422jVsrgC/CDwKYBIZOQYUP+m/atodLS8/LcPAf8hEhn5E+CzwDuj0dGvL//tceC/UyGYtdAP/Pmy30IE/joaHf18NpvhAx/4Df7xH79w2yrfXq+XfD6/iiwkSWJpaYlMJtOgyg1wfX4CSRQoJYqIQaVBEq4qpFtFfa+PldJ4m0m8EkURl8vVUFlYL3KTTqfRdA1d1ykUC8gOJ0NJN0NH+tb0ybw4fg6rT2jZs53Jp1G6l6X2wy4yS9kaWWTTWeZjM/Q80kM8F0MpyiguF4rfBdho+RV6FbrJoNlDV6ji54ilYqTENJYLHIoDHAI5W8Xb7cMYT7P/8CFS2TT+fUEUh0K5VCKfydUK0dabfmwXKkJDHtxNpjFlVSWVTuN0OJiZmVkxjZFxOByoOZX9nQdu278wPj7Ol7/85aalCncLrZLFS8AjkchIKBodTQNvA/4S2FG/UiQyIgIh4Crwr4EvV4kCIBodfTYSGfnW9Q4UjY6eA1ZNyAKBIB/5yEdaHO76WCsxy7ZtJicnOX369Kqb++j+Cnmoqsp8cpGklidnlsjaJYxSEdNza/2l2FLT5sXVVO7bqSStitxU97ljeAeCIFTK6ydy7Pb1c+nSJVRVrQjh+P01HYR4NkHCl8Epta4P6cBByVCRlvuWOpcfGcu0uHrhCp5BGUMzUIIymcU0UkkkN58lGAox0Nc4h3fGBI7sP1z7fXJxkrnMLCWhTGh3J6ZtERgI0RPqIu1QyBdL2AJYqonkkkABvVQlIJv4Jqcf24XqNKZYLFaSy4IhKtMYA01TKasq2Wwlycycsuja2YOhGjVBm82O17Zt3v/+9/Obv/mb90zLAjbns/gM8L2RyMifAY8DH+IWWXwxEhkxqQS2nwX+Avg54EZ140hk5O+pdJbsj0RGvi0aHZ25/eFvDVXLoh66rlMoFDhypHnj3SoURWFX/w521S2bn59nYnYKj+TnxvwkPhEU0cYQNRzVJjR104/bhWmaxGKxBj+FM2cxcuA1telHfXl5LpdjYnKCC6WruPo9yOVbzYad8vqO1J6BXmanZ9AEDafloG+4Mq26fvUa7mEFISQQm13E3xlAF1S69w1imRZGzEau8zPoCZVXDjxaO9bY1BiXuY7ngB/JVEi8uEioswO/ywcIuBUvZsxEsCUEh4ReNjBiBsHdIaAy/XBsy/Rja9B1jWKxWOfUvCU0VDVYi4tFTr7yFLqu19pD5PN5BEHA5/PVRG02ErR58sknEUWRkZGRO39i62AzZPFXwB8AY8BTK/5Wm4ZUEYmMTAO1krpodPTNy8uf2eRxtx0+n6+hLqOqxen3+xu0H1uFoih0h8IE3AFwaxw/frySW5FOEEumSBsFsqZGSTLQ/Q6cHmXLeRW2XXHohUKh2liNosaZOj8F3Covr5aYT+tz7Dm8D8u0ah3L05l0RfS3GkpUlrU6ZaXmaxAEYVUz43w+jy6ouD0uluZj4ISp56Y5+rqK1SBKIpag1dY3yga7pWECy2FdTdM4t3AOu1NE9jiRkZH3SwxZfagpHUMsExQ87Dw2hCAIFPNFNFUjuDuIIAq16YdLcTM2M4lLlOnv79uWXJXWYBNPJAiHV3eSB0gsxSmVSuyW9tQStOozf03TrAkNLS0tcfPmTQzDwO121zRF/H5/LbnsV37lV/jUpz714KR7R6OjY5HIiBd4N5U2g3s22OTvgfdGIiOvjEZHnwGIREb2UXF23n1p4jqsVPi+ceMGnZ2dqKq6JWk9SZIol8skk8laYyGHw0FfVy89nd2Ypln7cqSyaRYyCbJmkZxZImeX0d3g9LlXOQObIZvN4pAc+HwVJ5xlWQzlvOzbv2vNbc5NXETvtZGovMirxH4tu8GRmtAStxyp8q1wriiJGLpBOp3G7/aTzKUI7urE1EyC3g6Skwl6D/Shl3Rk6dZUyZOU2bf/VrjzwuR5evZ0c+PKBM6QE8uwoAShngCd3au1Jzw+Dx5f1WdQmX44RSdzZhxnt0JRK2LMzrJjaGjVtncC+Xy+1nN1JRZm5zEDBg6nSE7LkcvnarkvVUiSRCAQaMj0rAraVIWGZmdn+eQnP8mXv/xlfD4fTz31FG63m4F7UhxXwWa/8J8C/n00OnptOdKxJqLR0XwkMvKvgd+KREb6l49lAP8xGh2d3Npwtwf1Pou5uTl0XWf//v3cvHlzS2RhWRbJZJLTp083mJPV1FxJkmp+iq6OMF0dt74ytm2TyWaYz8TImiWyVomcWUR1g8Pvapi2lMtl8vl8wwPjWTR45b7H1xzb7NIcS+44Tsfa0Q9BFFBcCoqrUa1L13RUTaVYKpJKpzBNs+L593qRvX5i4zGsgIlTknH5XTgKIsaSjSJ76OivnKO5pHF6x6tqX8XphWnKoTKyW+bkjqNcvnyNZDyBS3EyL1Q6wpm2iSRK9Pb1riLQXD6PJEkUyxqObhnTMBGdEiVbXfceFfIFMukMsizT1dO17rrrwbJM0un0mi+taqvIgkTYHcbT6WExsbCKLJpBEIRaNKwq2jw0NMRXv/pVfvVXf5WrV68yPz9/T8lCuBf9B7aIbRvohz/8YQKBAN/1Xd/FzZs3OXmyov04Pj6Oz+fbVGjKtm1efPFFLMvizJnGpJtqtqDT6dyUr8K2bfL5PAvpGCmzQM4qk9ZyTCTn6N87jOKufNHsRJmRrpMN0496FEslvjb3DI4tKk2tRCpVIQy3242qqqTiSVKlFKIiougKO3btxKW4KoQpgFbQOMRedvRWpjHlcpmz88/i7nFjWRbpWIrp6Vly3UV8PX7KiyX0+QJ7zuzFNm2MRYNdu3bXjm8YBuNj44hOSKXSxKQcgaEQZsFkh9XLrl07mo67VCgyk5rF2+upWD05ZVUItlXE4jHcLhe+NQhgamIST8BFf7ifcrbMHvkRuju3Fup897vfzbd+67fywz/8w1vafotY07y9r5Oy7hR8Ph+qqnLt2jWOHz9e++pvReG7Kq+naVrD8qr472aJAqgpOleram3b5qWXXuKxoX0Yhk0qmSNrldjp3r0mUdi2zXNTLyANbK0z1kqUy2VKpRL9/f0IgoDX66Wzs7Nigag6hlVpOZBMJSskiYBvUUHaJ5LL5fB4PHzxpShpfwbrukkumUHsFUnZGZy2C7dhIgYlzFQl5Cs4BHTJqD8j4vEYhUIeoUOgZJXo7vajxjSCbg/yGlWeAOl0Bm9vZRrjdDtRs6sLCVu9BoZh1KaAzdDpDNOhdmAtWQy4hrdMFC+99BLXr1/n4x//+Ja2vxN4KMlClmWeeOIJ3vzmNzfMOzdLFvF4nHQ6zeHDh7l8+XJteXUf21FzADA5OYnb7a7J+LeCi5OX0LotpHUUxluFZVnE4/FVrQqgQmyyS0ZGbkgo02fKnDx0lFKpxOTkJJfHLjPbv4jicqGKKkVPiYHeHlRTRxdMCpkCgiogG7eiR6Jx69rl8nnUkorqM/D1eXG6FLScTk9PF26PGyGxtr/H6/GQyCZQAkolz8Xa/DWxbZtEIr6svN38WLqqs9O7k92DG7nz1ke1/uN3fud37qvu6g8dWdi2zZ/92Z/R09PT4KGGClmstBDWQrFYZGxsjJMnTzZoYVT9FFuxKJohnU4Ti8VWTXHWw0J8kVnnErK8PdOPeDxOKBRaleq9FrSMysmeo7Xmvrl8jglzAlsCTdZRdQ21WInIeDpdxC4kcDll+rt78A54mb84C5bN8NAu4JYModMpEwgHKOWKSKKD3GKOLpeNmtbYvWvtuoxARxB1SaO4WES0RYYGhyq+Dqn1at9MNoPH46lkeK4BMSGya9/uNf/eKj796U+zf//+Td3zu4GHjiwuXbrE0NBQU79Eq5ZFtV/IwYMHkWW5lplpWValKEyStqWStNqR/dSpUy0TT7lc5nz6EnLf9hBFNWq0sqXiWjANk161i97BClHYts3F+QsoQRlrUcfZIeNyKpTyedKX0xQSOXxSgP0n9yA5JeZmZwjvDCKKEomlRRyyRKFUJNzZiW1BJpUj1N2JXtboGe5meGBonZ4h1MYQCoXocnRhWRZjE2PYLgtbheGeIdy+tVO1oSLTl8/nGVynUKyUKnGsd21J/1ZRKBT47d/+7aY6sfcaDx1ZHDlyhHe/+9388R//8aq/tdIOoNr2cGhoqBb6qi8GE0VxW4jCtm0uXLiwqUYytm3z3OT2+SmqYdKB/oFWii8BkBZtju8/Uvv96tRVhG6QSzI9PV0U00UE28ajOPDILpQTCpZkc3HqMif2HifYGSTg91XIN2iRTCSRnBLxeJxkIokm6qRnkwz1DTG0Y3BD6TzTMBmbHEPwgl2ycegOXDuVGvkuzS+x07drnT0s51R0htckAsuy6NA6CAVCrV2kdfA7v/M7/OiP/mhDo6H7BQ8dWcDqpKwqWmkHMD09jdPpbCgyg1sZmts1/RgfH8fv928qMnNl+iqlLgOHsA231a54/rvCzXQum0NLlnm8/1aqfCqTIiYt4Xa68Tod+DN+krkEilsmPNTJ/PwSfm8ASRLRuw3OnztHpz+EO1TJLNWzJg5FYWhwiPm5eSy/jdPhxLfDR3o6jTAnIACWYeHxePAFKkl19S/14sIinqFbOSyxSzG6qA+drs+ChWIRURTXzRRVF1VO77r9KcPk5CRf/OIX72n9x3p4aMliK6K9yWSSeDzOyZMnG5ZXCebq1asEg8FaPcZW8/iTyWQtb6NVxFJxpsQFZGV7rIp0Jo2iKLjcrVk1pmawgwE6lr+u9Y2MLdNiaWERwYK+zh78PT4y6TRGQcem0qBHL2sUVJVDj+5n6Uacru5usCtJYNev3mA6NYXc7USxXZhxk66OLsKhMGNXruPuVUgXS6QSSdz+ypSiWhWq6xqiLSJRcWoGAgFKMyXwgF22Gehae2phWRapZJL+dQrVtJLGLt/u27YmbdvmF3/xF/ngBz+4pSziu4E2WdRhPbIol8vcuHGDEydONFgOVT/FmTNn0PWKBH08Hmd8fBxd1/F4PLVCrkAgsOGDoKoqV69e3ZSfQtM0XopfQO7fnodMLasUi8VV1tNasG0bOSZx8JFbYsCXJy/h6HVg2zbjE2MoQwpYEH8pgeKX0TMGzryDxAsx5JCCJAj4wm7icwn8XT5Ep4PFchxXyE3GyiCFHLh7PJRiJUzVQFZkFmam6djjRXRICDhQUzqDg0O1ylxVU3F73ExfmkZwC2AIDIT78YcCSIKEoijrZs2mUkkCweC6hX/OlJPh/cNr/r1VfOUrX8E0Tb7927/9tvd1p3BfkkUkMjJMpRitD7CAj0ejo2ynwvdmyMI0TS5cuLBKNatKFKIo1lKjvV5v7SWzbZtisUgulyOZTDI5OYmmabUagGrKb30V6YULF3jkkUc25ad4fuJFxL7tqUa0rUrtSW9vb8vOOj2u8Yrhx2vrLyWXyLjSKJILXdOxPZVcjJJWRPALTH9zhuHjAxx8xSNcOHuRQK8XwSHg8bqwchamajE2NYbWa6DJOQqZAnpCxRVS0LIaTt2NKpXo7A+RVTO4fC7Kea2WtletzFUUBb/PTyjYgSgKWFg1UV1tObXf4XBUysqXS8srFkKltaSqaasiZvUoJYqc7G/d+lvz+uk6v/RLv3Rf1H+sh/uSLKikhf9sNDr6QiQy4geer8zn/olTp07x1rf+IE888UmeeOIJ3vnOd25657IsNw2RNiML27a5evUq/f39DapZcMuhudZ0o5q85PV6G3qilstlstks6XSa6elpVFWtFQ0pioLX622549T1mRvkO8s4xO25lfFEnGAw2PIUSi9q7HftblCBKqlF3KqH0mIJTdAwCgaqs4wgQjqXQQ45mZ2YJ9QXYPjEEGq5jGCJ5G4U2L17N5YAHr8H1ZfH4XfgcfqILxRYvLCAqNl0DgQomUW6nUGErER6NkcpqzI8vKthbFpZ48bUTQQfUBbY3bcLn8/PrbiOjWHc0ujM5XIYuo4oiuiGQTAQQNN0ZNnJSt+GaZiE7e6WUrk3wv/8n/+TSCTCvn37bntfdxL3JVlEo6PzwPzyv3ORyMjleDy+/+mnn+ajH/1toKLw/XM/97NbIgtRbC7d3ywaMjs7iyAIq3Lyq+tt1qFZbWrrdrtrNQC2bbOwsMDExAShUIjLly9TLpdRFKVhCuNyNbY7TGSSTDCD07U9YdJCoVBRyG7xBbBtG3/aw55HdjUs39m/i53LRfyapnFNv8pXrj9JwSrR9UgXmqGBBWVVxevzEg53UZwuc+Zb9pMv5imXywiiSE4topkquqbh6/EiGyKK00k2mcUfCJCLqzhFF5KosPPw/gZBIoDzly6idqpQBp/Lx2JikZ2enXVrCDgcThwOZwPZpdMpVE0DQahIHC6n7SvLfhBFlrHiNgf2ru7BslkkEgk+8YlPbOjUXFpa4sMf/hDJZApRFPjO7/wu/s2/+TfrWtuf/ORf8YUvfAFRFHnXu36Kxx57DIBr167xkY98GE3TePzxx3nXu36qpQ/TfUkW9YhERnYBpw4ePEgqldo2hW9Y3S9y5UufTqdZXFxs6GsK1HIqHA7HtkQ+VFVlYmKC06dPN0xzyuVyrQpxfn6eUqmELMs1Arm2cB3TZ2Lly8jerZe9QyV3JJVKbSpMai5qnN65fmNfWZaxRIuhrgHm9UUUScaj+yjrRYykhj8QxCjqyJZMsVAknUkzODCIbdkkLiZIF5MILlBkJ127QiRupDA0AyNv0XdybcdjIVtA82qIbhHF5yK/kCcor93Po3ZOpkG+UGBwYKAhLFsvcRibi+Ne8PBc6jm8Xm9DWflmndof+MAH+Pmf//l1U8ihYvX+xE/8J/bv30+xWORd7/pJzpw5s6a1PTk5yZNPPskf//GfkEgk+K//9Rf4xCf+DEmS+P3f/z1+5mfew6FDh/jFX3w/Z8+e5fHH1y5GrOK+JotIZMQHfBr4L16v99Pbtd9WXqpmtSNwy0/hcDi2JZ+iqqWx0h8C1PQo6sOnVUGbbDZLUAjgWCqgzqqYjhKCRwK3gO4wwCsi+1okEBtisRjhcLj1MGlO5Vhw9ZhXQtd1is4s+x7Zi2vKzfUbY3T2uglkg+zr3QuGjUf0svfUPp5+5mmGO4Zxxp1cnbuK0+UgWPSRT+QIHPRSjBUpzBXpCnay9+D6JnsmncGwNNAEcjdzyJqT/qMrWwquRjyRoLOjY1X+RlXiUFEUgvkQj448imVZFAoFstlsTZeiWmhXJXS/37/mNTp//jxXrlzhD//wDzccV303PI/Hw44dO4jH46xlbT/99Nd4wxvegCzL9Pf3MzAwwNWrV+nt7aVYLHL4cEV7ZGQkwtNPf+3BJotIZMRJhSj+Mhod/QzAdip8V/0TzV74al/T/fv3r3I0riw5v13cvHmTcDhMZ+dqHYdmqDruqp3NoGLqVwkkl8tRSFUIRJdKCB4RXAKGbGG7QfG7VkUAMtkMiqK0rDplWRZddVL+9bBtm4vj53E6ZDwOL53BTmyj0iCnf7CXpcQSO8ODKEMy9qLFmd0V03h+fp7ucDeHDx9mbnGW4qECDtnJ7OI041fGyF7MITkcDA8OEvJ04ZTX/4KXhTLB7hC6oOOSXfjjPmTX+tGiYqkItr2iofGK/cZKPDpUebFEUWwo+Kuef9WpnUqlak5tl8vVIGrj9Xp53/vex2//9m9v2jpdWFjgxo0brGdtx+MJDh06VNumu7ubeDyOJEkNz051eSu4L8kiEhkRgP8JXI5GR3+nuny7Fb6LxWLTVnPXr1+nq6uLjo7VncaBbZt+xGIx8vn8qryNzUKW5VV9WKtSblUCycfyaLqGJpYrYUS3gOmwKDk1cuUsg5sRjlm4JeW/EtenrmGFdXJGnqyVZD41RbFUIHZ+gXJBQwm4ajqkRakE3JqGVefU5rKocb6UA4/N8I4h9g7tITtW4PQjjwICS+lFSmaJsl2mZJWwFAt3wH3LMrIhFAhiWTaWYeGzmxOAri3fU6dEMpmkr3dt68M0DHqlvnVJdS2ndr1F+Hd/93f83u/9Hpqm8Zd/+Zfkcjne+MY3rnvJqyiVSvz6r/8aP/mT72qqTl9FM59cxchstry1eed9SRbAa4B/D5yPREZeBPiN3/ggb33rW7dd4XslWei6jq7rPPJIo3T7Vh2aa6FUKnHjxg3OnDlzR8JlTqeTzs7OBovFMIwGAsnGs5gZlW5XCKugglvAkm1Mt43sV5pOSbRUmTPdx5taVqlMiqwjSTqXAo+FhY3H6WVgbx/Qh2maTKemiBcXMZMmpEUuSxeZvjnDgX0Ha/sc6hti4foCU+lJdEnFL/lIJVKEwz21hsLNvuaxdIyCkadklegwO5i9PovolfAIbrp3rM6EnZuZoyBWal+snE1ff9+6U0tzyWLf3v2t3YA61Escdnd309/fzyc+8QmeeOIJ5ufnW07CMgyDX/u1X+WNb/w2Xve61wFrW9vd3V3EYku1bSvTzC66uhotier0sxXcl2QRjY5+ldVuNhvYNoXvZolZVUXm/fv3N3Vobtf0o+qnOHTo0F3N1nM4HHR0dNQspsuXL9dqXKoEkkvkUDUVXVAx3QKCW8B02hguC6fHyYDRS1fHaqUp0zS5sXQVMSxg2xXnr+SAYr5AmMrDKEkSfb5+MuUMgiwQHgyTK6WR9wjEnHMsTMygCC4UQcGDmy5HJ1KXgCfkIj6eYm9384bC1a+5x+PBMAwEQeDc+EvIspNSpkRvsA87ZlOyShiSjhys1IYUyOPt9lYEkDMx/OsUy5WzZQ6Eb1/SH+BjH/sYP/IjP8Lhw4drvoONYNs2v/3bH2XHjp183/d9X235Wtb2q171an7zN/9f/u2//T4SiQSzs7McOHBgubWBm0uXLnHo0CFGR6O8+c1vaWkM9yVZ3A2sJAtN07hy5Qo+n28VUWyUT7FZXL9+nZ6entvyudwuYrEYqqrWGj7Xj8U0TfL5/C0CSeUoq2VMwcI/7K1ocPr9DcR5beoK7j4Z0zKxy1alnTUg2I2c73K5Kv1MtSLJTIJSQWVocLDhJSyVCyTzCTKlFM6UTCFdwMja6B06yVSSUDC06qXN5rK8MPc8umKyNLGIr9uN2+Wi91A3Ukzi+O4TQOU+x1MxkoUUQkHAzJtk8mlkSWZ2ehbTYVQyPfsGaz4O27bxFrx0999+cdfU1BT/8A//wDPPPLOp7S5evMDo6Ci7d+/mJ37iJwD4sR/7sTWt7V27dvH6138LP/7j70CSJH76p99du1/vfvd/5qMf/QiqqvLYY4+35NyEh1RWD+Anf/In+Z7v+R5e+9rXYlkWL730Ejt27GBubo59+/bV5qWaptWIYju+KouLi8zPz3PixIl7lq2nqiovvPACZ86cadmysSyrRiDZbJZ8Po9lWfh8PgzbIO9L09ETRBRFUrkUeSOPYAt0esJ4XO5KVKRcRBIlCmYWxS2Ty+cQVAdDfZV0adM0mU/Pk1ZTiCI4nE7K+RId/jAhpQOfx0e5oKLndZwoKKILGZmQr5OJ5Dj6gElZLTOZGkco2nR2BZEyToZcOzixa3Vv0LHZm1xYvICqlvFbARi0EGTQLZ3cTJHewR5UVcOpO3jdnm/ZMPKzEWzb5kd+5Ed4xzvewXd+53fe1r7uINqyeitRb1ncvHmTjo4OwuEwi4uLNf+EaZpb0tBcC8VikfHx8Tvmp2gFtm1z6dIl9u/fv6kpkCiKqxSpLcsik8nw3Pg3cDmczM/PY9s2TlnGL/tRXAqyU6lkrKopFI9MLBbD1+lF0zVEScLhuvUIJnIJRJ+AKIEtWpTyRWSXE78jgN9b8VG4fS7cvmqEykK3S8yUxpnNT2DOQKFUomgVyM1lEFwm+oLBK068puk5DXUPszC5yGOPPcb43BjFrlztvqQSGfS8hsMvYJZ1SuXibZPFU089haZpvOlNb7qt/dwrPLRkUa0PWVxcpFwu11JtqyHVqp9iu4iiWl9y6NChe9pVanp6Go/H0xA+2ypEUWQ2Oc3Agd5bTZnt5QbDqkqhWCSZSlEo5/CHPaiajSfgJpPKIiuVrEm7XJf4ZFtYlomm6igBJ6apUshrlKQiIUJNxyAIAi6Pwq7du5jLzyO4LPS8RNepIYrJIrqmc2P+GslcHJ/ip7uzp3b9r1+/zt69e3E6nezo3cnzU8+i9Mho2UokSQuXIQchX4jZ1CyhYEfTMbQCwzD45V/+Zf7yL//yvq7/WA8PLVl4vV40TWNqaqrW6wMqZGEYRi3xarvyKa5du9a0vuRuIp/PMz8/z6OPrp912SoWYvNo/hJu8VYuSq3zuyzXGpQnM0lQDCzbQtd0SikNQ7bQszYdwU6KxSIOh5OyWqKg5rFti/RMFl/Agxx0kstl6TZ61o1UKIrCLnkX8XQM/BWyD4QChLwirk4npqIS1/JMz48jmQ6MkklyKU2g01+rzXl896uIJZfwdfj50tIX8fZ7EASBxEyCfvftVZZ+4hOf4A1veMOqKNuDhIeWLERR5E//9E/59Kc/3fAQCoJQ81NsF1EsLCyg6zpDd6kJTjOYpsnFixc5cuTItpyXpmlM5Sbw968vSQcQ8neQzMSxBDBKNj1dvfR092AYFQukVC6TTM8iBG3cghtBA8th4fa5MVWLUHeQXDFHR2D9L7sggFtx4xW9GKKBbVsImgNlWePDKTtxdjuxbZvZ2VmGzvSRFmLMxaYRdbESiRFdqLqK3+vHLBgYgoGkS+zZu3UR3mQyyZ/8yZ/w1a9+dcv7uB/wUJKFaZr8+Z//OceOHVtV4i6KIolEopLWGwzedmizUCgwMTHBo48+ek/Nzxs3btDf379hDUKruDx1Ed9AaxmfoijQ1dGNYZjM5+fp6ulCEKj1BvUBklNCdRZAEFAUmXJGxSxaSKKDdDKDHyhIBRRFQZIcrHUpfR4fZt6gbJQREQn3rc6vSKfTeH0+nMsCxIHwrWtioVEwSmjZMsV4AUuHXe69LUsGNMMHP/hB3vOe99xTq3I78FCSxfnz5zl8+HAtw64K0zTp7+8nm83WUnXrBWyqP636HKp+iiNHjmxLHclWEY/HKRaL22YCzyzMYId0BKF1h59tV8rfw52dSE2SvYK+IEvpMrqgIVoiB3ccIVfOYAs2skNBcSioqko2l6tMEaWKeI283ODZtExKagmf20vQF2Kt11LXdQrFIoPrdPaSHBK7d++qZF1OF3nscGuhxWa4ePEi58+f57/9t/+25X3cL3goyeLkyZO84x3v4DOf+UxtWTWfQlEU+vv7VwnYVBWwxsbGMAwDr9fbQCDNyODKlSsMDg5uSaBnu6BpGtevX+f06dPbYtmUy2XmylP4e9dONW6GfCGPKIoNvUXqIQjQ29HbsMyzIq26Pr3ZMExUTUVVVeKJGGWxgOxRiMcW6XR3E/D5cTicDRaIbVeIsyu8tvhuPcyyxeGho1u+btX+Hx/96Ee3bUp7L/FQkgU0hk7Xk/Cvz/WvJ5Bm1Yb1BJLP57Ftm8HBtTUe7zSqYdJ9+/bddtivur/L0xfwDW7sp7Btai+qYZi3VMI3gWQmgWHrCLZAhz/ccG8cDgmHw4PX48EQNLxeN7ZtYbhNcsksulZJ25dEsaaEZZomUl1DY9O0EAQBsYm0nm3byAUX4b6tR40++9nPMjQ0xCte8Yot7+N+wkNLFtXaENi8hL8gCPh8Pnw+X00Up75ceXp6mng8jtvt5uLFizUCWZn1eKcxOzu7qsT9dlAsFtEtA21RRXSJeAKuVWFlVVUrGhQCCJZIONSz7vRjLeTyOQTFwuWo+IzS+SRdoeYZlAIitm0iCCKSAKGAj5A/BFTurapplEslMtksDoeD2bk5ykYJwW0jCRIhpZPgijaQ+cUiJ4a2rthdKpX40Ic+xBe+8IV1LRNN03jPe34GXdcxTZPXve71vP3tb7+rojat4qEli2qexe30JK1HtVzZ7XYzMzPD448/jsfjoVAokMlkmJubq1kbPp+vRiA+n++OEEihUGB2dnbbwqRQIdjHD7yy1rg5llyibJbQUFGtEpJbomjl8AYq0wfLsliKLSCKjjWnH2vBsIxG5at1nvlwIMxSZglbMHDgJBgM1f4mSRIet5tCoUBXOIzT6SSVTYFs4XTJmKbJbGKafK5QU8ISkeh29t2WNfZ7v/d7/NAP/dCGosdOp5OPfOSjuN1uDMPgZ37mv/DYY4/xta999a6J2rSK+5osIpGRPwW+G1iKRisdmrZLtNfn89Usi+1KvLJtm8uXLzM8PFyLOqzUO7Asq1b5OTs7Sy6Xq61XTyC3M56qHsfhw4fvCBGtbNwMtzq/P3/lGYyyiW7rlM0yqUSGQ0ePrLO35vC5/SRyS7h9CrpqINpOUtkkDslZy+aESrJTsVwk7Auv6Xgul1V0Xcfr9ZDRk0g+AatsIIoKLsWLw3LSH+xD1TRUVWXpWhzDCbG5+Cpib+W+zMzM8LnPfa6l+o+qzGL1XKqFcHdT1KZV3NdkAfwZ8N+oKH0D8KlPPbEtor1er5fx8XGefPJJHnvsMTo7O2/bZJudnUUUxVV6nfUQRZFgMNgQRqsv3JqeniaXy9UsleqD6vV6WyaQmzdv0tvbe1cdq1UCOb7vNHOZSZwuibFLE5zZ+WrEjFBngZSR3CLuJlOYejidDrr8vRRKeVyiQtZO43IrlA0NPavTGehEVVUSxRiyx0mhmCWodOJxNVow1YbG3d095EoZFO9yzkVZppAtYCkQlCuFaW6XC6tk89oT30JHsAPTNGvEPj09Xfu4VImymWVo2za/9Eu/xK//+q+3bJmYpsm73vUu5uZm+Z7veTOHDh26q6I2reK+JotodPQryxqcNWyXaK/T6eTtb387Tz31FB/72MfIZDLs27eP06dPc/r0aU6dOoXf72+ZQHK53JbNfkmSmhJI9UGdnJwkn8831GdUCWTl+JLJJLlcjlOnVhdO3Q10doQJ+INMT08x1L2HfXsb5e+qFkgiFadkFlHt8jKBCBXxmjoCcTgkgv4gqWwKl6/ykksOCV1VsSybXCmLy1d5IRWPQqGQX0UWmWwWt8eDLDtxqE4Mo4zkkAj4/FgFkXDoVmTEsizcZS8dA5XkL0mSCIVCa1bk1luGPp+Py5cvE4/HSafTfPd3f3fL10ySJP7oj/6IfD7Pr/7qrzA+Pr7mundC1KZV3Ndk0QzbJdorSRIf/OAHa7+bpsmVK1d49tln+fznP88HPvABSqUSBw8e5NSpU5w5c4YTJ07gdrtX3YRqo+Rjx45tm9nf7EGtF68ZHx+nUCggSVKNPNxud61B0b1MADNNk4WFxabEud4UJp6MUTKLFQvEVpFc4A64UWQXObWEU3ECNtl8Ds3WKZdLuAUXHo9nWXy50VKpihB7g25i6SWCng4KZROtrCMJUgNRABQWS5wafmzdc2tG7NWK3Oeee45PfOITSJLEo48+yoc//GG+7du+reXr5vP5OHHiBM89d/auitq0igeOLO4UJEniyJEjHDlyhP/wH/4DUEngOX/+PGfPnuWJJ57gfe97H5ZlceTIkRqBHD58mI997GO8/e1vX1fmbDuwUrymOsZcLkcmk+H69esIgsCFCxcapjAej+eukUe1cfS+fftaTl7biEAEs0g2kyNhpLEcNp4+BZdbQVQguZjCLFu4nG66Q41hzlg8jui2cfgqJBLPLjEQXh3Ktm0btazR7x7aUpFf1eIzTZNIJMLv/M7voOs6hmFsuG06ncbhcODz+WrSAT/wA2+9q6I2reKBI4vtFO3dCE6nszYt+Ymf+Ilag6CXXnqJs2fP8sd//Md89atfxefzsbCwUFv34MGDd62ytCqfVyqV6Orq4vDhw7U2itU8kGKxWGshUP1Z2YNku7C4uIjD4bjtcO1KAjnMUWzbZnJmkqux86SsDJpYQi/qWE4Lj6/Rp1MoFLAtC9lfdx8clb6q9XkVmXyGvJ4lN5UnMLD1qtJUKsUf/dEf8dRTTwG3Utk3QjKZ5MMf/hCWZWHbNq9//bfwyle+ksOHD901UZtWcd+L3yz7LD4fjY4eAfj4x/+IQCBQc3Dmcjne+c7/eE/GduXKFX7sx36Mz372s1y8eJGzZ8/y3HPPceXKFTweDydPnqwRyP79++9YjkWhUOD8+fM89thjax5D07QagWSz2YYeJNtFIJqm8fzzz/Poo4/eMbK0bZvzN19iVp1AcEKnqxOvx0diIkN3Vw8qZcpGiYX0Arv37ySeW8IVkLFt0HI6/XWWhW3DfHoaU7Po9fRilCyO9p3eUsj0F37hFzh16hTveMc7tvN07wXWfADua7KIREY+CbwB6Orq6nL8yI+8nde85tV84AO/wdLSUo1xmyl03w1YltXgQ6nCtm3S6TTPPfccZ8+e5fnnn+fGjRuEQqEaeZw+fZqdO3fedsjWsiyef/55HnnkkU0XKqmq2kAg9V3Q6vuwtkIgtm1z7tw5BgYGti0JbD1cuXkJOnRcHoVyUcOnddQUt65evQoCKB6ZnJplLjWDKRp09oTxd3jrtDdgJjmJo+Bg546dpGNZjvZuniwuXbrEu9/9bp566qmXQ1r3g0kWK/DADLQZbNsmFotx9uzZmgUyOTlJX18fp06d4vTp0zz66KP09/dv6ut+48YNJEli9+7d2zLGlQRS1XpYSSArsbCwQDwe5+jRo7c9jlbHOjZzA80q43H42DlYOf9cLseVK1dWVfnWojCZOCWjiLqcSBaPxenq6sThcOCnk0d2bq4loWVZvOUtb+HXfu3XeM1rmityPWBok8X9iKquwtmzZ3n22Wd5/vnnmZ+fZ8eOHQ0WSFdXV1MCSaVSjI2NbVuR2FpjrDZyrv5UO8HXT1/Onz9/R6cfrY71ueee4+DBgy3lmNi2jWma2LZdqQXZghzB5z73OT7/+c/zF3/xFw+sAtYKtMniQYFlWYyPj9cskOeff55kMsnevXtrEZhTp06h6zqf/vSn+dEf/dHb0lrYCmzbplQqNWSi1odwN1vKv12YmZnZ1lL8jVAul/nWb/1W/uEf/uGeFgxuM9pk8SDDNE2uXbvGs88+y3PPPccLL7zA9PQ0J06c4DWveQ2nT5/m5MmTTZO07jQWFxeJxWIcOXKkVspf/VlZyr+VxsGtohp2fPzxx++a3+AjH/kITqeT97///XfleHcJbbJ4OeGTn/wk//iP/8jP/uzP1qYvL774Ipqmcfjw4dr05fjx4y07KLeCjaIf9Vog1Z+Vpfx+v39bhIHOnz9PX1/fXXGuAszNzfF93/d9PPPMM3fdsrvDaJPFywnz8/O1F64eqqrWckDOnj3LhQsXEASB48eP16Yw29kF7dy5c/T392/qBbUsaxWBVPuPbLWUP5FIMDMzw4kTJ7ZyGpuGbdv8+I//OD/wAz/AW97ylrtyzCp+/ud/jne848c5ePAguq7zfd/3b3nb297G93//vwPgPe95Dz/1Uz/F+fPn+fKXv1Qj4tOnz/C2t72NaDTKF77wjzWx6qqq/fve9/5qbUmbLB5GVEV6vvnNb/Lcc8/x3HPPcenSJRRF4eTJkzUCeeSRRzb9da9OP7Yj+lGvBVLtgmZZ1qpK3GYEYpomZ8+e5eTJk3ftC//MM8/w4Q9/mH/4h3/YlmrlzeCJJz6JJDn4/u//fl544QX+7//9PJlMho9+9LfRNI13vOPH+Hf/7t9x/vx5fu7nfh5ZljEMg9/8zf+XN73pO2sp+AsLC3zwg7/B//f/rZL7azcZehhRFel53eteV2uka9s2mUyGF154gWeffZYPfehDXLt2jUAgUAvhnj59mj179qz5ImiaxtjY2LZpZVQrbP1+f81RWN8Bbb1S/omJCfr7++8aUZimyfvf/37+9E//9K4TBVQshL/8y//N93//9/Pss8/ypjd9J3/yJ39MoZDn+vUbHD9+gs9+9rN89KO/XbMgHQ4Hv/RLv3zb09E2WTxkqPY1feMb38gb3/hGoFrGnahNXz7zmc8wPj5OT09PjUDOnDnD4HJP0mg0yuOPP35Hox3NOqCtLOXPZDKUy2V6e3uZmZkhGAxuqpR/K/hf/+t/Ladjt9bQ2DRNfuqn3kVXVxe/8RsfvG0FrMcee4ypqalKJuv5c/zYj/0Yp0+f5oUXXmBsbJzHHnuU5557rpag99WvfpW//dvPoGkaR48eq/VJ3QraZNEGgiDQ1dXFm970plprPdu2mZ+frxHIX/zFX9ReSNu2ec973sOZM2fo6em5axGY+opP27Z54YUXOHToEIIgrCrl36oWyHpIp9P8wR/8AV/5ylda3uZv//Zv2bFjB8ViEVhbj2UzClihUIizZ8/S2dmJLMs89tjjfOMbz3Dz5hjf+73fi8fjJpvNEggEeO1rX8trX/tazp59ln/+5ydv6/zvvh3VxgMBQRAYGBjgzW9+M7/xG7/BF77wBb70pS+RSqX48R//cb7xjW/wjne8g1e+8pW89a1v5cMf/jCjo6Mkk8mmmgvbjaqTt6Ojg1AoxI4dOzh69CivfOUrOX36NH19fRiGwcTEBM8++yxnz57lypUrDfKGm8Vv/dZv8dM//dMNVb/rIRaL8Y1vfIM3velWE+Snn36aSORfARU9lqef/try8uYKWIlEoqaAJQgCIyMRBEHgk5/8q5rlcfToUa5fvw5AIBDge77nzfzBH/wPNE0DKtbN+fMX2tOQNu4eDMPg4x//OK997WtryyzL4vr165w9e5YvfelLfOQjHyGbzbJ///6a/+PkyZObEhLaCJqmMTk5uabPZL1S/mw2y9jYGMVicVUi2Xql/FeuXOHs2bP87u/+bsvj/IM/+B+8853vpFQq1pZthwKWJEm8+OKL/Nf/+l6gUuHq8/lqkY3v/d7v5XOf+xzvfe9/RRRFCoUCJ06c4D/+x9sruGyTRRstY3BwcFWmoiiKHDhwgAMHDvC2t70NqJDK5f9/e/cfE/V9x3H8eRw6SdCgZxcWW4EspxAr1qImXVPauvtmSRdbtW1qFIdV25STuvhroF2NWG2VKS7pagJ0ybKFcylmKc1Ya7+n0fgHelBTmTd6jbaNRgE94YzboXDw2R/efctVgTsqd4e8H4mR+94X/Vyib77f7+vzeX9aWnC5XNTV1VFWVsatW7fIyckxEpjc3Nx7NhKKxPnz58nKyorqmUloKf+UKVOMY/dayj9u3LiwAhLqj7l161bKy8sjjnRPnTpFWloaM2bM4OzZL4c8P5oOWBMmTCDUkzakrGxn2OtFixaxaNGiAf++9PT0eyUhg5JiIe675ORkZs+ezezZs40l293d3TQ3N9PY2IjD4aCkpASlFI8++qhxBTJr1qwhV3x2dnYaDzV/rHHjxmGxWMJWDfdfyt/W1kZtbS3Hjh0jEAjQ3t7O5cuXI5ra7Xafo6GhAZfLRXd3N36/nz173kvIDliRkmIhYmL8+PHMmzfPuHUIrS8JzQGprKzE7XaTnJwc1gckOzvbmAMSCATweDzk5uaO2EPV8ePHM3XqVOPSPycnh5MnT7Jp0yZaWlrw+XysXbt2yD9nzZq1rFlz57yzZ7+ktraW0tKtVFVVJlwHrEjJpKx+9u37A6dPnyYtLY3q6g+BwbceGCjqEsOjlOLmzZvGHJCmpiY8Hg+pqanMnTuX69evY7Va2bJlS8zWf1RUVKCUMjpVDUeoWNyJTm8M2I+lpqaGI0c+w2w2U1RkNzpdeTyesA5YxcXFI5lAyQzOSDQ3N5OSkkJ5+V6jWFRXVzFx4sR+nbn+a0Rd7767m/ff/9NdUZe4f5RSdHR0UF9fz7Zt25g/fz4XLlzAYrGETSKbPn36fZ9f0drayosvvkhDQ4Px7GIMkBmckcjNzaWtrS3sWLSbvUQ6WUdExmQyYbFY6OnpweFwkJ+fj1KK9vZ2Yw6Iw+Hg4sWLTJs2jccee4y8vDzy8vJIT08f9k9gpRTbt29n+/btY6lQDEqKxRCijbrEyOjf29JkMpGenh72xL+vr49Lly4ZXciqqqpob28nIyMjrJGQJcId1F0uF9euXYv5QrFEJsVimAaOukQ8JCUlkZGRQUZGBi+99BJwp4BcuHCBxsZGTpw4QUVFBT6f767NpCZNmhRWQELrP6qrq+Oy/iNRSbEYQrRRl0gcSUlJWK1WrFYry5cvB8I3k6qvr2fXrl34/X5jM6nHH3+clpYW5s2bF7N+oqOFFIshRLvZy3BcvXqV8vK9dHR0kpRk4rnnfs3SpUsliRkBA20mde7cOVwuF7W1tdTV1eF2u+M80sQjaUg/u3fvprn5LDdu3GDy5MkMtfXAQFFXtK5fv05HRwdWqxW/34/dXkRZ2U4+//yIJDEi1ga+mQ51Nh4Fv8aMt9/+vWpqalKrVhUqr9erlFLK6/WqVasKlVJKORw1yuGoMc4vKfmdcrvd8RiqePAM+H9Qnt4kmLa2Ns6fP092dvagScxDD/3U+B5JYkQsSLFIIF1dXezcWUZRkX3QTZaVJDEiDqRYJIhAIEBZ2Q4WLvyl0QIvlMQAksSIuJM0JAEopdi/fx/Tp38/RwBik8SI6BUUrCAlJYWkJDNms5mDBw/+6HZ5CxYswG5fl9C7mkkakgDOnfs3GzZsICsrC5PpzsXe6tWrycnJHtEkpru7m40bN9DT00Nvby9PPZVPYWGhRLZDKChYwQcfHAzbiHo4a4iKi9dht68z2uUtXrxk2InafSRpiLhbX1+f8vv9Simlenp6VHHxOuV2u1VVVaU6dMihlFLq0CGHqqqqUkop9d1336nXX39N3b59W125ckWtXFmgAoFA3MYfLytWLFc+ny/sWLTJldfrVa++uso4fvToUXXgQMXID35okoaIu5lMJmORVCAQIBAIYDKZou4TOdaYTCZKS0uw24uor/8nMPgaonslV16v9652eYmeaMkzizGut7cXu93OlSuXef75F8jJyZHFc0M4cOCPTJ06lc7OTkpLS3jkkekDnquiaJeXyM8rQNKQMc9sNlNZWcmhQ3/H4/mKb7/9dsBzB/6HP7aErggmT57Mk08+icfzVdTJVSK1y4uUFAsBQGpqKnPmzKGpqVEi20F0dXUZe4B0dXXxxRdfkJmZaSRXwF3J1fHjx+nu7qa1tdVIriwWi9EuTymF06nzxBO/iNvnioTchoxhPp+P5ORkUlNTuX37NmfOnOGVV5bFNLK9Hzt2xZLP18mOHTuMsT/77ELmz1/AzJkzeeedXXz66WdGcgWQmZlJfv7TrF27BrPZzJtvrjfW8Kxf/9uwdnkJkIQMSqLTMeybb76hvHwvfX19KKXIz3+alStXDqtP5HAdPnyYr7/24Pf72bVrt7QxjD+JTkXiuXr1qtq8ebM6c+aMeuutbUqp6CNIcd9JdCoST2jHrqSk73+YyeK5xCXFQsRF/x27IqEkiYm70fTMQjxANM32HrASCAATgEnAP4D5wDO67mzVNNvPgOO67pypabatALrufC/4/UeAHbrubIjLBxiD5MpCxIWuO7fquvNhXXdmAsuAY7ruLAA+AQqDpxUCdcGvPwGWaZrtJ5pmywKsgCvGwx7TJDodZTTN9gzwEfCffoevAf8DJum6c2m/c9t03Zke/HousBtIA24BncB6XXdejs3II7YH+EjTbGuAi8DLALrudGuaLfS5A8A6XXf2xm+YY4/chowywWLxhq47l/3g+F+A54BNuu78W/BYm64704OX80eBJbru9ATfWwy8rOvOFTEcvhjF5DbkwVIKlGma7eEfHP8N8GGoUADouvNjoCCGYxOjnNyGjE4LNc12vN/r+uDvV4C3gT8Dv+r3fhbwLwBNs6UAnwaPPwL8fERHKh4YUixGp2MD3Iag684aTbMt0TRbUb+3L3GnYKDrzi7gmeD3hG/sKsQg5DbkwfQGsBmYGHz9V+A1TbMZkxo0zZYHpMZhbGKUkiuL0emHtyEA7aEvdN3p1TTbRuDj4OtLmmZbAezXNNtE7sxr6AS02AxXPAgkDRFCRERuQ4QQEZFiIYSIiBQLIUREpFgIISIixUIIEREpFkKIiEixEEJE5P8d8+UEMw/r+wAAAABJRU5ErkJggg==\n", 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" ] @@ -180,12 +180,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -202,12 +202,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -260,12 +260,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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O/PfETmlI1lZt3YT1LsWmYP+zhvVWFCZmTQ51PIy9boeFCRIlZ4Sarwl17wkcXHboYFes51pa7r1hWUeH5zEZ+1zPKiuhP7YT9JtKNYYe6tmE0AzLvw84DzuMMgoY6H28pqYn3Y7qdEVdaU+umXPFsuZcdA2wrfdxn6xraWvORadh4by5T5w79Eni7Dy1F7CZaxXPq5Ts5XWIT6RdhGHQQ7DjFoXj7MTue7FeX29sL/zqT2I4heMupckra7BevuSYAko+0cK4/Q5Z19GGPo8dt9gCG9a6nMTw5ifMjtgw91LsXL1cTamWj9MQn4iI5FIuJ0mIiIikOcSnrpiIiMB6fCNQknpQIiKSSwooERHJJQWUiIjkkgJKRERySQElIiK5pIASEZFcUkCJiEguKaBERCSXFFAiIpJLCigREcklBVRB3H333VmXICKSKgVUQYwbNzbrEkREUqWAEhGRXFJAiYhILimgCmL48OFZlyAikqo0r6ir60GJiAjoelCS5FyUdQkiIqlSQImISC4poEREJJc2zLqA9TVmzGgaGxurtuvZsyeTJ1/Y9gVlpHfv3lmXICKSqg4fUL16bcekSX2qtpsz57F2qCY7EydOyroEEZFUaYivIMaOPTvrEkREUqWAKoglS5ZkXYKISKoUUCIikksKKBERySUFVEF4H2ddgohIqhRQBbFw4Q1ZlyAikioFVEFMnz496xJERFKlgBIRkVxSQImISC4poApiwoSJWZcgIpIqBVRB1NfXZ12CiEiqFFAFMWDAMVmXICKSKgWUiIjkkgJKRERySQFVEH379s26BBGRVCmgCmLEiJFZlyAikioFVEEMGTI46xJERFKlgCqIhoaGrEsQEUmVAkpERHJJAVUQ3btvkXUJIiKpUkAVxIIFC7IuQUQkVQqogpgz55qsSxARSZUCqiDmzp2bdQkiIqlSQImISC4poEREJJcUUAUxc+asrEsQEUmVAkpERHJJAVUQQ4cOyboEEZFUKaBERCSXFFAiIpJLCqiCGDhwYNYliIikSgFVEIMGHZd1CSIiqVJAFUT//v2zLkFEJFUKqIJYvvy1rEsQEUmVAkpERHJJAVUQ9fX1WZcgIpIqBVRBzJp1WdYliIikSgFVENOmXZx1CSIiqVJAFcSiRYuyLkFEJFUKKBERySUFlIiI5JICqiDmz78u6xJERFKlgCqIhoaGrEsQEUmVAqogxo0bm3UJIiKpUkCJiEguKaBERCSXFFAFMXz48KxLEBFJlQKqIPr1OzzrEkREUqWAKgjnoqxLEBFJlQJKRERySQElIiK5pIAqiN69e2ddgohIqhRQBTFx4qSsSxARSZUCqiDGjj076xJERFKlgCqIJUuWZF2CiEiqFFAiIpJLCigREcklBVRBeB9nXYKISKoUUAWxcOENWZcgIpIqBVRBTJ8+PesSRERSpYASEZFcUkCJiEguKaAKYsKEiVmXICKSKgVUQdTX12ddgohIqhRQBTFgwDFZlyAikqoNsy6gkjFjRtPY2Fi1XbduXRg0aI+2L0hERNpVbgOqV6/tmDSpT9V2U6bc2A7ViIhIe9MQX0H07ds36xJERFKlgCqIESNGZl2CiEiqFFAFMWTI4KxLEBFJlQKqIBoaGrIuQUQkVQooERHJJQVUQXTvvkXWJYiIpEoBVRALFizIugQRkVQpoApizpxrsi5BRCRVCqiCmDt3btYliIikSgElIiK5pIASEZFcUkAVxMyZs7IuQUQkVQooERHJJQVUQQwdOiTrEkREUqWAEhGRXFJAiYhILimgCmLgwIFZlyAikqrcXlFXWmfQoOOyLkFyZvacX/P66jerttu8S1dOGvTjdqhIpHUUUAXRv39/fR+ffMTrq99km0N3rdru5ZufaIdqRFpPQ3wFsXz5a1mXICKSKgWUiIjkkgKqIOrr67MuQUQkVQqogpg167KsSxARSZUCqiCmTbs46xJERFKlgCqIRYsWZV2CiEiqFFAiIpJLOg9KpKBeffEVXpz3ctV2nZs2aIdqPk4nEks1CqiCmD//uqxLkJzZeYd6Djn8mKrtFt+QzXtHJxJLNRriK4iGhoasSxARSZUCqiDGjRubdQkiIqlSQImISC4poEREJJcUUAUxfPjwrEsQEUmVAqog+vU7POsSRERSpYAqCOeirEsQEUmVAkpERHKp3U/UHTNmNI2NjVXbdevWhUGD9mj7gkSkJvrmB2lv7R5QvXptx6RJfaq2mzLlxnaopjh69+6ddQlScPrmB2lvGuIriIkTJ2VdgohIqhRQBTF27NlZlyAikioFVEEsWbIk6xJERFKlgBIRkVxSQImISC4poArC+zjrEkREUqWAKoiFC2/IugQRkVTpiroFMX36dH0fXzu7cOpFLH3++arteuywA6NHnd4OFYkUiwJKZB1t1nVzhv1wcNV2WV1SXaSj0xCfiIjkkgKqICZMmJh1CSIiqVJAFUR9fX3WJYiIpEoBVRADBhyTdQkiIqlSQImISC4poEREJJcUUAXRt2/frEsQEUmVAqogRowYmXUJIiKpUkAVxJAh1U8YFRHpSBRQBdHQ0JB1CSIiqVJAiYhILimgCqJ79y2yLkFEJFUKqIJYsGBB1iWIiKRKAVUQc+Zck3UJIiKpUkAVxNy5c7MuQUQkVQooERHJJQWUiIjkkgKqIGbOnJV1CSIiqdIl30Vy4sKpF7H0+eertuuxww6MHnV6O1RUTLPn/JrXV79Ztd2yF15m6+23qdpu8y5dOWnQj9MoTcoooApi6NAheB9nXYash826bs6wH1b/yqrFN1zXDtUU1+ur32SbQ3et2u75uUtravfyzU+kUZY0Q0N8IiKSSwooERHJJQVUQQwcODDrEkREUqWAKohBg47LugQRkVQpoAqif//+WZcgIpIqBVRBLF/+WtYliIikSgElIiK5pPOgCqK+vj7rEqSDevTRR/jpacOrtmvapBPbUP28oKzUegLuS8tezuR51FqfTvxdSwFVELNmXZZ1CdJBbbHlVhx70vCq7WZdPr7Na1kfrTkBNwu11qcTf9fSEF9BTJt2cdYliIikSgFVEIsWLcq6BBGRVCmgREQklxRQIiKSSwqogpg/X99wLSLFooAqiIaGhqxLEBFJlQKqIMaNG5t1CSIiqVJAiYhILn1iTtS94477+fOfj67armfPnkyefGE7VCSybmr95ocNNurMIYent95Vr6/kgXm3Vm238o23uPDy6uflZfWNDtJxfGICqr5+a8444z+rtpsz57F2qCZ9w4cPz7oEaSe1fvPDvNnTU13vtlv2qPkbJ/L8jQ7ScWiIryD69UtxV1lEJAcUUAXhXJR1CSIiqVJAiYhILimgREQklxRQBdG7d++sSxARSZUCqiAmTpyUdQkiIqlSQBXE2LFnZ12CiEiqPjHnQRXdkiVLsi5BKsjqxFqR1sjjJekVUCJtLKsTa0VaI4+XpNcQn4iI5JICqiC8j7MuQUQkVQqogli48IasSxARSZUCqiCmT5+edQkiIqlSQImISC4poEREJJcUUAUxYcLErEsQEUmVAqog6uvrsy5BRCRVqZ2oO2bMaBobG6u269atC4MG7ZHWalPXUS8NP2DAMYWcan7h1ItY+vzzVdutWr2az3Tpkkq7HjvswOhRp9dco7St5559LteXkM+qvjx+80PaUguoXr22Y9KkPlXbTZlyY1qrbBNFvzR8R7NZ180Z9sPBVdvNmz295m9rqNZu8Q3X1VidtIe6jTrl+hLyWdWXx29+SJuG+EREJJcUUAXRt2/frEsQEUlVakN8b721kqVLl1dtt2rVvwvRzp5vNkMK5T744AOOOuq7uaknTStWvMWyl16o2m7N6tWptVux4q2aXsssausI7d5e82/eWvZ61XbvrHlb7ZqxakVt25ZVK1bmdnk9evSoupxa1DU1NaWyoHvvvbJpm226pbIsaZ3HHnueudc+xOCR47IuRUQ+4V57ZRlHfvOAujSWlVoPapttutGjR/e0Fiet8PLLb/DKspfYetvtsy5FRCQ1OgYlIiK5pIAqiE027Zp1CSIiqVJAFcTIs8/PugQRkVQpoAriFq/rQYlIsSigCuK2eGHWJYiIpEoBJSIiuaSAEhGRXFJAFcSJp/486xJERFKlgBIRkVxSQBXEry7VNHMRKRYFlIiI5FIq38VXV1fX+frrJ/D005umsThppSeffJlVq1Zz35I7si5FRD7hXl/+Kgfu3atzU1PTO+u7rFS+zbyuru6LwPZA43ovTEREOrKewAtNTU1/W98FpfVt5u8CjU1NTU+mtDwREemA6upSudIGoGNQIiKSUwooERHJpdSuqCsiIpIm9aBERCSXFFAiIpJLCigREcklBZSIiORSTedBORcdCIwC9ga2A473Pr66ymP2BGYA+wHLgSuAid7HmpUhItJBORdtC5wP9AU2BZ4GBnsf39rCY44GzgJ6Aa8AM7yPp1RbV609qE2Ax4DTgDXVGjsXbQZ4YBmwL/BT4AxgZI3rExGRnHEu6gbcCdQB/YDdgVOBf7XwmMOA3wJXAnsAQ4ARzkXDqq2v1dPMnYtWAsNa6kE5Fw0GLgC29j5eE247GxgM9FAvSkSk43Eu+gVwkPfx/q14zG+Bjb2Pv5O47VRgNLBjS3mQ1lcdlfsacHspnIKbgInY9zQ900brFRGRtnMkcKNz0QLgYOBF4FfAzBaC5lPAv8tuWwP0AHaihe9wbauA2gZYWnbbssR9CigRkY5nZ2yIbhp2HGov4NJw34wKj7kJuMS5qA8QA58HTg/3bUsGAQVQnqZ1FW4XEZGOoRNwn/fxmeH3B52L6oGhVA6o2cAuwB+AjYC3gEuA8cD71VbWFl7GekpJW4X/lyEiIh3RS8Dfy257HNix0gO8j5u8j3+GTbbbCcuGv4a7G1taWVsF1N3A152LPp24zWHjlS0WJCIiuXUnsGvZbb2AZ6s90Pv4fe/jF7yP3wEGAHd7H1ec/Qe1nwe1CTZuCBZqOzoX7QUs9z5+zrnoPGA/7+NDQ5vfAucAVzsXTQpP4OfAuZrBJyLSYU0D7nIuGgMsAL6MnUZ0VqlBeR44F20JfB+4BZswcXz4/aBqK6u1B7UP8GD4tzFwbvh5Qrh/W2yMEQDv4zexHtN2wH3ATOAi4OIa1yciIjnjfXwvNpPvaOzc2MnAWGBWotlH8iAYBNyL9cC+CHzD+/ivVKHLbYiISC7pu/hERCSXFFAiIpJLCigREcklBZSIiOSSAkpERHJJASUiIrmkgJIOzbmos3PRya1o3+RcFLX2vjQ5F9U5F53iXNQp/H61c9G8Vi7jU85FDzoXbZ1ybec7F52U5jJF1pUCSjq6AdiJgh3JgcBlrN/n7wzgRu/jtL/b8nzgTOeiLVJerkirKaCko6ur3iR31qtm56Iu2NWpZ1Vr21rex29gl0cYkvayRVqrLS+3IVKVc1FP7Ppgx2JXYd4EmAeM8D5+N7Q5APuarD2Bp4HzvY/nOhd9A/hNaNMEfA54Dfu+sG8B3cKyz/I+/u9W1vWpUM8PsR25m4FTvY+XJWr+XmjzWeBW4ATv41fD4/tgX+/1eew7yJ4CNsUuMfCXsJp3nYsODj9v6lx0LfY1Mq8BY7yP51YobwDwtPfx84l6vwxMB/bFriYwyfv4qvAazcN6medh34U2CbgfuBzYHvgd8GPv4w/C4v4PmO1cNDlxm0i7Uw9K8mIctuE9MvybBOBctA2wCLgWC6gJwKXORUcAdwHDsUsAbAs8j4XT7kAf7Du/bsM2tp9qZT2/wK4MfTj2pZadgBuci5K9nzOxADsC+Co27IZz0c7YRv567IJu92LXyyHU+N3wc4/wHMAC9bHwHBcAv3Iu6l6htsMAX/olfBnnzdhlD74MjAEuC8EOsDUWpt/AhvAuAKYCx2HfkXYs0C+x/L9gl0TYs9KLI9IeFFCSFz/3Pr7d+/gWbG//xDCJYCjwF+/jS7yPn/I+XoCF0PDwtf1vAh94H7/sffw+cAdwivfxQ97HDdiGeHPsi4trEobQhoXl3ON9/BgwEAu8AxJNzw3334YF6L7h9hOBB7yPJ3gfP+F9PA5YAnbJAWB5aLcsPAeAe72Pz/M+fhoL585Y0DZnHyyMSvoDK4ChYX3zsSuWbhDu3xA4w/v4H9iw4AbYJbrv8T7+H+z6PruVFuZ9/G+sp7ovIhnSEJ/kxV2Jn+8DumN7/rsDhzkXrUzcvyHwSoXlzAGODDPRdgP2DrdvUKF9c3bGAuJ25z4yqe/T2KVjSkNr/0zc9xZ2tVCAL2G9pqQl2HOq5MNleR+/Gdb76QpttwJeTfz+BeChEH6lZcwACEN8YIEDsCb8n7x+zxps6C/pNdZeZFQkEwooyYv3Ej+XwuQD7D06H5hY1r7SpaLnAPsDc7GZci9hF9BsjdLn4iCsh5b0CtYjA3in7L7S8N97fHwiRLWJEc09n0qP+YCPjn6U19Gc98p+r3ZsaYMKNYm0GwWU5MVe2HEUsCGsZcC/gCeAA72Pnyo1dC4aig3ZjQGaErdvBvwA2N/7+O5wW99wd2tmzv0T2zhv6X18f1hOVyz0zsZ6Sy35G3a8J2lv1vZi1vcaN8uALRO/NwDfcS7qVJrU4Fx0FbAUWLyO69girEckMwooyYtpzkUnAF2xC2LO9D5uci6aBfw0XKXzKuA/gAuBUeFxK4GuzkW9sJl1q4CjnItewobjZoR2NU+S8D5e4Vw0G5gRTgJ+EZtc8CUsDKqdHHslMMq56Cxshtx3ga+zdhivNFz5FeeiR2qtK+GBUEvJtdjkkWnORTOxMPwBcAg2VNkqzkWbAj2xoVaRzGiShOTFdcAN4f+rsCt14n38LDaTLsJmuV0EnON9fFl43GLgH8Aj2Eb7WOA72CSC6WE5L2Cz21pjJHY+0ALseNLGQB/v4zUtPmptzd8DfgQ8ig05/i9rh+IeDcu+HZuR11p/wgKvtL43sVl4X8Veh3OxKe93Nf/wqvYHlobJISKZ0RV1JVOJc4rqk8N4HZlz0R7ARt7HDyZuW4jN1BufwvI3AZ4D9muL18y56BrgSe/jyWkvW6Q1NMQnkr5dgN84F/UHngQccCh23tR68z5e6Vx0KXAy4dyrtIRzqg4FRqS5XJF1oSE+kZR5H/8BG4r8NTbJ41Sgv/fxuhxvquR8oE84kTlNP8O+hWJ51ZYibUxDfCIikkvqQYmISC4poEREJJcUUCIikksKKBERySUFlIiI5NL/A+PzS8Sh0lEWAAAAAElFTkSuQmCC\n", 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" ] @@ -288,12 +288,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -315,14 +315,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -331,7 +324,7 @@ "(['proline', 'flavanoid'], 'wine', ['class_0', 'class_1', 'class_2'])" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -359,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -371,12 +364,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -398,12 +391,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -421,13 +414,6 @@ "plt.tight_layout()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {