From 136ffadb7d906cde5d33a177a86d862852c15b6c Mon Sep 17 00:00:00 2001 From: TieuLongPhan Date: Wed, 14 Aug 2024 15:39:19 +0200 Subject: [PATCH 1/6] fix bug --- .gitignore | 1 + Data/Temp/Backup/raw_results.json | 254 ++++++++++++++++++++++++++++++ syntemp/SynITS/its_extraction.py | 14 +- syntemp/SynITS/its_hadjuster.py | 23 +-- syntemp/SynRule/rule_writing.py | 16 +- syntemp/SynUtils/chemutils.py | 43 +++-- 6 files changed, 301 insertions(+), 50 deletions(-) create mode 100644 Data/Temp/Backup/raw_results.json diff --git a/.gitignore b/.gitignore index 928a95f..790df63 100644 --- a/.gitignore +++ b/.gitignore @@ -14,3 +14,4 @@ __pycache__ Data/DPO/* _test.py *pkl +Backup/* \ No newline at end of file diff --git a/Data/Temp/Backup/raw_results.json b/Data/Temp/Backup/raw_results.json new file mode 100644 index 0000000..ca512fc --- /dev/null +++ b/Data/Temp/Backup/raw_results.json @@ -0,0 +1,254 @@ +{ + "Valid": { + "fw": { + "Raw_0": [ + 4.04, + 7.86, + 94.87 + ], + "Raw_1": [ + 0.79, + 7.24, + 78.52 + ], + "Raw_2": [ + 0.33, + 6.5, + 69.74 + ], + "Raw_3": [ + 0.23, + 4.92, + 68.67 + ], + "Complete_0": [ + 46.88, + 90.62, + 92.56 + ], + "Complete_1": [ + 12.83, + 89.32, + 72.44 + ], + "Complete_2": [ + 3.26, + 85.25, + 51.6 + ], + "Complete_3": [ + 2.16, + 75.23, + 44.12 + ], + "Expand_0": [ + 64.93, + 90.82, + 93.25 + ], + "Expand_1": [ + 13.65, + 89.56, + 74.14 + ], + "Expand_2": [ + 3.61, + 85.75, + 53.33 + ], + "Expand_3": [ + 2.29, + 76.15, + 45.11 + ] + }, + "bw": { + "Raw_0": [ + 4.58, + 7.86, + 97.99 + ], + "Raw_1": [ + 0.61, + 7.24, + 84.84 + ], + "Raw_2": [ + 0.35, + 6.5, + 73.3 + ], + "Raw_3": [ + 0.2, + 4.92, + 64.07 + ], + "Complete_0": [ + 45.38, + 91.8, + 96.35 + ], + "Complete_1": [ + 15.88, + 90.04, + 89.78 + ], + "Complete_2": [ + 10.86, + 86.09, + 85.48 + ], + "Complete_3": [ + 7.93, + 76.09, + 82.03 + ], + "Expand_0": [ + 65.14, + 92.0, + 96.87 + ], + "Expand_1": [ + 16.23, + 90.28, + 89.87 + ], + "Expand_2": [ + 11.43, + 86.51, + 85.69 + ], + "Expand_3": [ + 8.34, + 76.93, + 82.24 + ] + } + }, + "Test": { + "fw": { + "Raw_0": [ + 4.04, + 7.92, + 95.21 + ], + "Raw_1": [ + 0.81, + 7.7, + 78.68 + ], + "Raw_2": [ + 0.35, + 6.62, + 71.35 + ], + "Raw_3": [ + 0.23, + 4.92, + 68.67 + ], + "Complete_0": [ + 45.68, + 90.38, + 92.54 + ], + "Complete_1": [ + 12.25, + 89.22, + 72.71 + ], + "Complete_2": [ + 3.22, + 84.45, + 51.74 + ], + "Complete_3": [ + 2.13, + 74.21, + 44.93 + ], + "Expand_0": [ + 63.16, + 90.58, + 93.19 + ], + "Expand_1": [ + 13.0, + 89.4, + 74.02 + ], + "Expand_2": [ + 3.55, + 84.95, + 53.56 + ], + "Expand_3": [ + 2.26, + 75.15, + 45.81 + ] + }, + "bw": { + "Raw_0": [ + 4.47, + 7.92, + 98.06 + ], + "Raw_1": [ + 0.59, + 7.7, + 84.28 + ], + "Raw_2": [ + 0.35, + 6.62, + 72.72 + ], + "Raw_3": [ + 0.2, + 4.92, + 64.07 + ], + "Complete_0": [ + 45.11, + 91.9, + 96.1 + ], + "Complete_1": [ + 15.83, + 90.24, + 89.07 + ], + "Complete_2": [ + 10.84, + 85.45, + 85.02 + ], + "Complete_3": [ + 7.89, + 75.15, + 81.74 + ], + "Expand_0": [ + 65.2, + 92.1, + 96.63 + ], + "Expand_1": [ + 16.17, + 90.42, + 89.15 + ], + "Expand_2": [ + 11.36, + 85.91, + 85.18 + ], + "Expand_3": [ + 8.29, + 76.07, + 81.79 + ] + } + } +} \ No newline at end of file diff --git a/syntemp/SynITS/its_extraction.py b/syntemp/SynITS/its_extraction.py index e5d3b35..01e9842 100644 --- a/syntemp/SynITS/its_extraction.py +++ b/syntemp/SynITS/its_extraction.py @@ -8,6 +8,7 @@ from syntemp.SynITS.its_construction import ITSConstruction from syntemp.SynChemistry.mol_to_graph import MolToGraph from syntemp.SynRule.rules_extraction import RuleExtraction +from syntemp.SynUtils.chemutils import remove_atom_mapping class ITSExtraction: @@ -156,13 +157,16 @@ def process_mapped_smiles( # Check if mapper_names is not empty to avoid IndexError if mapper_names: - # Update the target dictionary based on the determined conditions - if confident_mapper in mapper_names: - target_dict["ITSGraph"] = graphs_by_map.get(confident_mapper, None) - target_dict["GraphRules"] = rules_by_map.get(confident_mapper, None) - else: + if "[O]" in remove_atom_mapping(mapped_smiles[mapper_names[0]]): target_dict["ITSGraph"] = graphs_by_map.get(mapper_names[0], None) target_dict["GraphRules"] = rules_by_map.get(mapper_names[0], None) + else: + if confident_mapper in mapper_names: + target_dict["ITSGraph"] = graphs_by_map.get(confident_mapper, None) + target_dict["GraphRules"] = rules_by_map.get(confident_mapper, None) + else: + target_dict["ITSGraph"] = graphs_by_map.get(mapper_names[0], None) + target_dict["GraphRules"] = rules_by_map.get(mapper_names[0], None) return graphs_by_map_correct, graphs_by_map_incorrect diff --git a/syntemp/SynITS/its_hadjuster.py b/syntemp/SynITS/its_hadjuster.py index bbf1ba1..233cd18 100644 --- a/syntemp/SynITS/its_hadjuster.py +++ b/syntemp/SynITS/its_hadjuster.py @@ -301,7 +301,7 @@ def add_hydrogen_nodes_multiple( """ react_graph_copy = deepcopy(react_graph) prod_graph_copy = deepcopy(prod_graph) - react_explicit_h, _ = check_explicit_hydrogen(react_graph_copy) + react_explicit_h, hydrogen_nodes = check_explicit_hydrogen(react_graph_copy) prod_explicit_h, _ = check_explicit_hydrogen(prod_graph_copy) hydrogen_nodes_form, hydrogen_nodes_break = [], [] @@ -328,22 +328,13 @@ def add_hydrogen_nodes_multiple( max(react_graph_copy.nodes, default=0), max(prod_graph_copy.nodes, default=0), ) - permutations = list( - itertools.permutations( - range( - max_index + 1 - react_explicit_h, - max_index + 1 + len(hydrogen_nodes_form) - react_explicit_h, - ) - ) + range_implicit_h = range( + max_index + 1, + max_index + 1 + len(hydrogen_nodes_form) - react_explicit_h, ) - permutations_seed = list( - itertools.permutations( - range( - max_index + 1 - prod_explicit_h, - max_index + 1 + len(hydrogen_nodes_break) - prod_explicit_h, - ) - ) - )[0] + combined_indices = list(range_implicit_h) + hydrogen_nodes + permutations = list(itertools.permutations(combined_indices)) + permutations_seed = permutations[0] updated_graphs = [] for permutation in permutations: diff --git a/syntemp/SynRule/rule_writing.py b/syntemp/SynRule/rule_writing.py index 8fcc421..8cdd63e 100644 --- a/syntemp/SynRule/rule_writing.py +++ b/syntemp/SynRule/rule_writing.py @@ -18,12 +18,12 @@ def charge_to_string(charge): """ if charge > 0: return ( - "+" * charge - ) # Repeat the '+' symbol 'charge' times for positive charges + "+" if charge == 1 else f"{charge}+" + ) # '+' for +1, '2+', '3+', etc., for higher values elif charge < 0: - return "-" * abs( - charge - ) # Repeat the '-' symbol 'abs(charge)' times for negative charges + return ( + "-" if charge == -1 else f"{-charge}-" + ) # '-' for -1, '2-', '3-', etc., for lower values else: return "" # No charge symbol for neutral atoms @@ -51,7 +51,11 @@ def convert_graph_to_gml( for node in graph.nodes(data=True): if node[0] not in changed_node_ids: element = node[1].get("element", "X") - gml_str += f' node [ id {node[0]} label "{element}" ]\n' + charge = node[1].get("charge", 0) + charge_str = RuleWriting.charge_to_string(charge) + gml_str += ( + f' node [ id {node[0]} label "{element}{charge_str}" ]\n' + ) if section != "context": for edge in graph.edges(data=True): diff --git a/syntemp/SynUtils/chemutils.py b/syntemp/SynUtils/chemutils.py index 682b3cc..ea05efc 100644 --- a/syntemp/SynUtils/chemutils.py +++ b/syntemp/SynUtils/chemutils.py @@ -126,30 +126,27 @@ def remove_hydrogens_and_sanitize(mol: Chem.Mol) -> Chem.Mol: return mol -def remove_atom_mapping(smiles: str) -> str: - """ - Removes atom mapping numbers and simplifies atomic notation in a SMILES string. - - This function processes a SMILES string to: - 1. Remove any atom mapping numbers denoted by ':' - followed by one or more digits. - 2. Simplify the atomic notation by removing square - brackets around atoms that do not need them. - - Parameters: - - smiles (str): The SMILES string to be processed. +def remove_atom_mapping(reaction_smiles): + # Split the reaction SMILES into reactants and products + parts = reaction_smiles.split(">>") + if len(parts) != 2: + raise ValueError("Invalid reaction SMILES format.") - Returns: - - str: The processed SMILES string with atom mappings - removed and simplified atomic notations. - """ - # Remove atom mapping numbers - pattern = re.compile(r":\d+") - smiles = pattern.sub("", smiles) - # Simplify atomic notation by removing unnecessary square brackets - pattern = re.compile(r"\[(?P(B|C|N|O|P|S|F|Cl|Br|I){1,2})(?:H\d?)?\]") - smiles = pattern.sub(r"\g", smiles) - return smiles + # Function to remove atom mappings from a SMILES string + def clean_smiles(smiles): + mol = Chem.MolFromSmiles(smiles) # Convert SMILES to an RDKit mol object + if mol is None: + raise ValueError("Invalid SMILES string.") + for atom in mol.GetAtoms(): + atom.SetAtomMapNum(0) # Remove atom mapping + return Chem.MolToSmiles(mol, True) # Convert mol back to SMILES + + # Apply the cleaning function to both reactants and products + reactants_clean = clean_smiles(parts[0]) + products_clean = clean_smiles(parts[1]) + + # Combine the cleaned reactants and products back into a reaction SMILES + return f"{reactants_clean}>>{products_clean}" def mol_from_smiles(smiles: str) -> Optional[Chem.Mol]: From a48f7a1f81803d44f072bd217bf81d5d0b600671 Mon Sep 17 00:00:00 2001 From: TieuLongPhan Date: Wed, 14 Aug 2024 17:26:01 +0200 Subject: [PATCH 2/6] change rule writing format Mg++ to Mg2+ --- Test/SynRule/test_rule_writing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Test/SynRule/test_rule_writing.py b/Test/SynRule/test_rule_writing.py index 766f603..e4dcf2b 100644 --- a/Test/SynRule/test_rule_writing.py +++ b/Test/SynRule/test_rule_writing.py @@ -10,8 +10,8 @@ def setUp(self) -> None: self.data = load_from_pickle("Data/Testcase/templates.pkl.gz")[0] def test_charge_to_string(self): - self.assertEqual(RuleWriting.charge_to_string(3), "+++") - self.assertEqual(RuleWriting.charge_to_string(-2), "--") + self.assertEqual(RuleWriting.charge_to_string(3), "3+") + self.assertEqual(RuleWriting.charge_to_string(-2), "2-") self.assertEqual(RuleWriting.charge_to_string(0), "") def test_convert_graph_to_gml_context(self): From 98effe2ee996faa75837b485411fad1407acfe25 Mon Sep 17 00:00:00 2001 From: TieuLongPhan Date: Fri, 16 Aug 2024 09:50:37 +0200 Subject: [PATCH 3/6] update uncertain hydrogen handling --- Data/Temp/Benchmark/raw_results.json | 254 --------------------------- syntemp/SynITS/its_extraction.py | 11 +- syntemp/SynITS/its_hadjuster.py | 32 +++- syntemp/SynUtils/graph_utils.py | 93 +++++----- syntemp/__main__.py | 10 +- syntemp/auto_template.py | 3 + syntemp/pipeline.py | 8 +- 7 files changed, 92 insertions(+), 319 deletions(-) delete mode 100644 Data/Temp/Benchmark/raw_results.json diff --git a/Data/Temp/Benchmark/raw_results.json b/Data/Temp/Benchmark/raw_results.json deleted file mode 100644 index ca512fc..0000000 --- a/Data/Temp/Benchmark/raw_results.json +++ /dev/null @@ -1,254 +0,0 @@ -{ - "Valid": { - "fw": { - "Raw_0": [ - 4.04, - 7.86, - 94.87 - ], - "Raw_1": [ - 0.79, - 7.24, - 78.52 - ], - "Raw_2": [ - 0.33, - 6.5, - 69.74 - ], - "Raw_3": [ - 0.23, - 4.92, - 68.67 - ], - "Complete_0": [ - 46.88, - 90.62, - 92.56 - ], - "Complete_1": [ - 12.83, - 89.32, - 72.44 - ], - "Complete_2": [ - 3.26, - 85.25, - 51.6 - ], - "Complete_3": [ - 2.16, - 75.23, - 44.12 - ], - "Expand_0": [ - 64.93, - 90.82, - 93.25 - ], - "Expand_1": [ - 13.65, - 89.56, - 74.14 - ], - "Expand_2": [ - 3.61, - 85.75, - 53.33 - ], - "Expand_3": [ - 2.29, - 76.15, - 45.11 - ] - }, - "bw": { - "Raw_0": [ - 4.58, - 7.86, - 97.99 - ], - "Raw_1": [ - 0.61, - 7.24, - 84.84 - ], - "Raw_2": [ - 0.35, - 6.5, - 73.3 - ], - "Raw_3": [ - 0.2, - 4.92, - 64.07 - ], - "Complete_0": [ - 45.38, - 91.8, - 96.35 - ], - "Complete_1": [ - 15.88, - 90.04, - 89.78 - ], - "Complete_2": [ - 10.86, - 86.09, - 85.48 - ], - "Complete_3": [ - 7.93, - 76.09, - 82.03 - ], - "Expand_0": [ - 65.14, - 92.0, - 96.87 - ], - "Expand_1": [ - 16.23, - 90.28, - 89.87 - ], - "Expand_2": [ - 11.43, - 86.51, - 85.69 - ], - "Expand_3": [ - 8.34, - 76.93, - 82.24 - ] - } - }, - "Test": { - "fw": { - "Raw_0": [ - 4.04, - 7.92, - 95.21 - ], - "Raw_1": [ - 0.81, - 7.7, - 78.68 - ], - "Raw_2": [ - 0.35, - 6.62, - 71.35 - ], - "Raw_3": [ - 0.23, - 4.92, - 68.67 - ], - "Complete_0": [ - 45.68, - 90.38, - 92.54 - ], - "Complete_1": [ - 12.25, - 89.22, - 72.71 - ], - "Complete_2": [ - 3.22, - 84.45, - 51.74 - ], - "Complete_3": [ - 2.13, - 74.21, - 44.93 - ], - "Expand_0": [ - 63.16, - 90.58, - 93.19 - ], - "Expand_1": [ - 13.0, - 89.4, - 74.02 - ], - "Expand_2": [ - 3.55, - 84.95, - 53.56 - ], - "Expand_3": [ - 2.26, - 75.15, - 45.81 - ] - }, - "bw": { - "Raw_0": [ - 4.47, - 7.92, - 98.06 - ], - "Raw_1": [ - 0.59, - 7.7, - 84.28 - ], - "Raw_2": [ - 0.35, - 6.62, - 72.72 - ], - "Raw_3": [ - 0.2, - 4.92, - 64.07 - ], - "Complete_0": [ - 45.11, - 91.9, - 96.1 - ], - "Complete_1": [ - 15.83, - 90.24, - 89.07 - ], - "Complete_2": [ - 10.84, - 85.45, - 85.02 - ], - "Complete_3": [ - 7.89, - 75.15, - 81.74 - ], - "Expand_0": [ - 65.2, - 92.1, - 96.63 - ], - "Expand_1": [ - 16.17, - 90.42, - 89.15 - ], - "Expand_2": [ - 11.36, - 85.91, - 85.18 - ], - "Expand_3": [ - 8.29, - 76.07, - 81.79 - ] - } - } -} \ No newline at end of file diff --git a/syntemp/SynITS/its_extraction.py b/syntemp/SynITS/its_extraction.py index 01e9842..67350c0 100644 --- a/syntemp/SynITS/its_extraction.py +++ b/syntemp/SynITS/its_extraction.py @@ -137,10 +137,13 @@ def process_mapped_smiles( graphs_by_map[mapper] = (one_node_graph, one_node_graph, one_node_graph) rules_by_map[mapper] = (one_node_graph, one_node_graph, one_node_graph) rules_graphs.append(one_node_graph) - if check_method == "RC": - _, equivariant = ITSExtraction.check_equivariant_graph(rules_graphs) - elif check_method == "ITS": - _, equivariant = ITSExtraction.check_equivariant_graph(its_graphs) + if len(rules_graphs) > 1: + if check_method == "RC": + _, equivariant = ITSExtraction.check_equivariant_graph(rules_graphs) + elif check_method == "ITS": + _, equivariant = ITSExtraction.check_equivariant_graph(its_graphs) + else: + equivariant = 0 # graphs_by_map['check_equivariant'] = classified graphs_by_map["equivariant"] = equivariant diff --git a/syntemp/SynITS/its_hadjuster.py b/syntemp/SynITS/its_hadjuster.py index 233cd18..6a60a13 100644 --- a/syntemp/SynITS/its_hadjuster.py +++ b/syntemp/SynITS/its_hadjuster.py @@ -22,6 +22,7 @@ def process_single_graph_data( return_all: bool = False, ignore_aromaticity: bool = False, balance_its: bool = True, + get_random_results=False, ) -> Dict: """ Processes a single dictionary containing graph information by applying @@ -63,6 +64,7 @@ def process_single_graph_data( ignore_aromaticity, return_all, balance_its, + get_random_results, ) else: graph_data = ITSHAdjuster.process_high_hcount_change( @@ -73,6 +75,7 @@ def process_single_graph_data( ignore_aromaticity, return_all, balance_its, + get_random_results, ) return graph_data @@ -105,6 +108,7 @@ def process_multiple_hydrogens( ignore_aromaticity, return_all, balance_its, + get_random_results=False, ): """ Handles cases with hydrogen count changes between 2 and 4, inclusive. @@ -142,6 +146,7 @@ def process_multiple_hydrogens( ignore_aromaticity, return_all, balance_its, + get_random_results, ) return graph_data @@ -154,6 +159,7 @@ def process_high_hcount_change( ignore_aromaticity, return_all, balance_its: bool = True, + get_random_results=False, ): """ Handles cases with hydrogen count changes of 5 or more. @@ -179,7 +185,11 @@ def process_high_hcount_change( for i in its_list ] - its_list, rc_list = get_priority(its_list, reaction_centers) + priority_indices = get_priority(reaction_centers) + rc_list = [reaction_centers[i] for i in priority_indices] + its_list = [its_list[i] for i in priority_indices] + combinations_solution = [combinations_solution[i] for i in priority_indices] + _, equivariant = ITSExtraction.check_equivariant_graph(rc_list) pairwise_combinations = len(its_list) - 1 if equivariant == pairwise_combinations: @@ -187,12 +197,18 @@ def process_high_hcount_change( graph_data, *combinations_solution[0], its_list[0] ) else: - if return_all: + if get_random_results is True: graph_data = ITSHAdjuster.update_graph_data( - graph_data, react_graph, prod_graph, its + graph_data, *combinations_solution[0], its_list[0] ) + else: - graph_data["ITSGraph"], graph_data["GraphRules"] = None, None + if return_all: + graph_data = ITSHAdjuster.update_graph_data( + graph_data, react_graph, prod_graph, its + ) + else: + graph_data["ITSGraph"], graph_data["GraphRules"] = None, None return graph_data @staticmethod @@ -204,6 +220,7 @@ def process_graph_data_parallel( return_all: bool = False, ignore_aromaticity: bool = False, balance_its: bool = True, + get_random_results: bool = False, ) -> List[Dict]: """ Processes a list of dictionaries containing graph information in parallel. @@ -220,7 +237,12 @@ def process_graph_data_parallel( """ processed_data = Parallel(n_jobs=n_jobs, verbose=verbose)( delayed(ITSHAdjuster.process_single_graph_data)( - graph_data, column, return_all, ignore_aromaticity, balance_its + graph_data, + column, + return_all, + ignore_aromaticity, + balance_its, + get_random_results, ) for graph_data in graph_data_list ) diff --git a/syntemp/SynUtils/graph_utils.py b/syntemp/SynUtils/graph_utils.py index b3dce67..bd44951 100644 --- a/syntemp/SynUtils/graph_utils.py +++ b/syntemp/SynUtils/graph_utils.py @@ -1,6 +1,6 @@ import networkx as nx import copy -from typing import List, Dict, Any, Tuple +from typing import List, Dict, Any def is_acyclic_graph(G: nx.Graph) -> bool: @@ -313,63 +313,50 @@ def check_graph_connectivity(graph): return "Disconnected." -def get_priority( - its_list: List[Any], - reaction_centers: List[Any], - priority_ring: List[int] = [4, 5, 6], # Standard priority rings - priority_pair: List[int] = [3, 5], # Special priority requiring both rings - not_priority_ring: List[int] = [3], # Non-priority ring that disqualifies alone -) -> Tuple[List[Any], List[Any]]: +def get_priority(reaction_centers: List[Any]) -> List[int]: """ - Filters reaction centers based on their connectivity and specific ring sizes, - including those with both rings in the priority pair, and excluding those with non- - priority ring sizes, - unless a specific pair condition is met (e.g., 3 must appear with 5). + Evaluate reaction centers for specific graph characteristics, selecting indices based + on the shortest reaction paths and maximum ring sizes, and adjusting for certain + graph types by modifying the ring information. Parameters: - - its_list (List[Any]): List of identifiers for the reaction centers. - - reaction_centers (List[Any]): List of reaction centers to evaluate. - - priority_ring (List[int], optional): List of ring sizes given priority. - Defaults to [4, 6]. - - priority_pair (List[int], optional): List of two ring sizes that must both appear - together to qualify. Defaults to [3, 5]. - - not_priority_ring (List[int], optional): List of ring sizes that disqualify a center - unless paired appropriately. Defaults to [3]. + - reaction_centers: List[Any], a list of reaction centers where each center should be + capable of being analyzed for graph type and ring sizes. Returns: - - Tuple[List[Any], List[Any]]: Tuple containing two lists: - - The first list contains the identifiers from its_list that meet all criteria. - - The second list contains the corresponding reaction centers that meet the - criteria. + - List[int]: A list of indices from the original list of reaction centers that meet + the criteria of having the shortest reaction steps and/or the largest ring sizes. + Returns indices with minimum reaction steps if no indices meet both criteria. """ - priority_set = set(priority_ring) - not_priority_set = set(not_priority_ring) - priority_pair_set = set(priority_pair) - - # Filter to include only connected reaction centers - connected_centers = [] - connected_its_list = [] - for index, center in enumerate(reaction_centers): - if check_graph_connectivity(center) == "Connected": - connected_centers.append(center) - connected_its_list.append(its_list[index]) - - cyclic = [get_cycle_member_rings(center) for center in connected_centers] - # Filter indices based on priority and non-priority ring sizes - final_indices = [] - for i, rings in enumerate(cyclic): - ring_set = set(rings) - # Check for priority conditions and special conditions for non-priority rings - if ( - ring_set.intersection(priority_set) or (priority_pair_set <= ring_set) - ) and not ( - ring_set.intersection(not_priority_set) - and not (5 in ring_set and 3 in ring_set) - ): - final_indices.append(i) + # Extract topology types and ring sizes from reaction centers + topo_type = [check_graph_type(center) for center in reaction_centers] + cyclic = [get_cycle_member_rings(center) for center in reaction_centers] + + # Adjust ring information based on the graph type + for index, graph_type in enumerate(topo_type): + if graph_type in ["Acyclic", "Complex Cyclic"]: + cyclic[index] = [0] + cyclic[index] + + # Determine minimum reaction steps + reaction_steps = [len(rings) for rings in cyclic] + min_reaction_step = min(reaction_steps) + + # Filter indices with the minimum reaction steps + indices_shortest = [ + i for i, steps in enumerate(reaction_steps) if steps == min_reaction_step + ] + + # Filter indices with the maximum ring size + max_size = max( + max(rings) for rings in cyclic if rings + ) # Safeguard against empty sublists + prior_indices = [i for i, rings in enumerate(cyclic) if max(rings) == max_size] + + # Combine criteria for final indices + final_indices = [index for index in prior_indices if index in indices_shortest] - # Retrieve final lists based on filtered indices - final_its_list = [connected_its_list[i] for i in final_indices] - final_centers = [connected_centers[i] for i in final_indices] + # Fallback to shortest indices if no indices meet both criteria + if not final_indices: + return indices_shortest - return final_its_list, final_centers + return final_indices diff --git a/syntemp/__main__.py b/syntemp/__main__.py index e078d32..d16d668 100644 --- a/syntemp/__main__.py +++ b/syntemp/__main__.py @@ -16,7 +16,7 @@ def parse_arguments(): parser.add_argument( "--mapper_types", nargs="+", - default=["rxn_mapper", "graphormer", "local_mapper"], + default=["rxn_mapper", "rxn_mapper"], help="Types of atom map techniques used", ) parser.add_argument("--id", type=str, default="R-id", help="ID column") @@ -24,7 +24,7 @@ def parse_arguments(): "--rsmi", type=str, default="reactions", help="Reaction SMILES column" ) parser.add_argument( - "--n_jobs", type=int, default=4, help="Number of jobs to run in parallel" + "--n_jobs", type=int, default=2, help="Number of jobs to run in parallel" ) parser.add_argument("--verbose", type=int, default=2, help="Verbosity level") parser.add_argument( @@ -51,6 +51,11 @@ def parse_arguments(): parser.add_argument( "--log_level", type=str, default="INFO", help="File to log the process" ) + parser.add_argument( + "--get_random_hydrogen", + action="store_true", + help="Get random full ITS hydrogen", + ) return parser.parse_args() @@ -91,6 +96,7 @@ def main(): rerun_aam=args.rerun_aam, log_file=args.log_file, log_level=args.log_level, + get_random_hydrogen=args.get_random_hydrogen, ) auto.temp_extract(data, lib_path=args.lib_path) logging.info("Extraction successful.") diff --git a/syntemp/auto_template.py b/syntemp/auto_template.py index 02b1f7c..6a9ab4a 100644 --- a/syntemp/auto_template.py +++ b/syntemp/auto_template.py @@ -62,6 +62,7 @@ def __init__( log_file: str = None, log_level: str = "INFO", clean_data: bool = True, + get_random_hydrogen: bool = False, ): """ Initializes the AutoTemp class with specified settings for processing chemical @@ -120,6 +121,7 @@ def __init__( self.reindex = reindex self.rerun_aam = rerun_aam self.clean_data = clean_data + self.get_random_hydrogen = get_random_hydrogen log_level = getattr(logging, log_level.upper(), None) if not isinstance(log_level, int): @@ -181,6 +183,7 @@ def temp_extract( self.fix_hydrogen, self.refinement_its, self.save_dir, + get_random_results=self.get_random_hydrogen, ) # Step 4: Extract rules from the correct ITS graphs diff --git a/syntemp/pipeline.py b/syntemp/pipeline.py index b0eb0a3..03a267f 100644 --- a/syntemp/pipeline.py +++ b/syntemp/pipeline.py @@ -162,6 +162,7 @@ def extract_its( save_dir: Optional[str] = None, data_name: str = "", symbol: str = ">>", + get_random_results: bool = False, ) -> List[dict]: """ Executes the extraction of ITS graphs from reaction data in batches, @@ -216,8 +217,13 @@ def extract_its( if i == 1 or (i % 10 == 0 and i >= 10): logging.info(f"Fixing hydrogen for batch {i + 1}/{num_batches}.") batch_processed = ITSHAdjuster.process_graph_data_parallel( - batch_correct, "ITSGraph", n_jobs=n_jobs, verbose=verbose + batch_correct, + "ITSGraph", + n_jobs=n_jobs, + verbose=verbose, + get_random_results=get_random_results, ) + uncertain_hydrogen = [ value for value in batch_processed if value["ITSGraph"] is None ] From f64c1667c23263b9c2ca3e8f19f02fdb7d720905 Mon Sep 17 00:00:00 2001 From: TieuLongPhan Date: Fri, 16 Aug 2024 11:53:14 +0200 Subject: [PATCH 4/6] fix --- syntemp/__main__.py | 6 +++--- syntemp/pipeline.py | 18 +++++++++++------- 2 files changed, 14 insertions(+), 10 deletions(-) diff --git a/syntemp/__main__.py b/syntemp/__main__.py index d16d668..e480032 100644 --- a/syntemp/__main__.py +++ b/syntemp/__main__.py @@ -16,7 +16,7 @@ def parse_arguments(): parser.add_argument( "--mapper_types", nargs="+", - default=["rxn_mapper", "rxn_mapper"], + default=["local_mapper", "rxn_mapper", "graphormer"], help="Types of atom map techniques used", ) parser.add_argument("--id", type=str, default="R-id", help="ID column") @@ -24,11 +24,11 @@ def parse_arguments(): "--rsmi", type=str, default="reactions", help="Reaction SMILES column" ) parser.add_argument( - "--n_jobs", type=int, default=2, help="Number of jobs to run in parallel" + "--n_jobs", type=int, default=8, help="Number of jobs to run in parallel" ) parser.add_argument("--verbose", type=int, default=2, help="Verbosity level") parser.add_argument( - "--batch_size", type=int, default=200, help="Batch size for processing" + "--batch_size", type=int, default=1000, help="Batch size for processing" ) parser.add_argument("--safe_mode", action="store_true", help="Enable safe mode") parser.add_argument( diff --git a/syntemp/pipeline.py b/syntemp/pipeline.py index 03a267f..d1cb805 100644 --- a/syntemp/pipeline.py +++ b/syntemp/pipeline.py @@ -250,12 +250,15 @@ def extract_its( its_correct = collect_data(num_batches, temp_dir, "batch_correct_{}.pkl") logging.info("Processing unequivalent ITS correct") its_incorrect = collect_data(num_batches, temp_dir, "batch_incorrect_{}.pkl") - all_uncertain_hydrogen = [] - if fix_hydrogen: - logging.info("Processing ambiguous hydrogen-ITS") - all_uncertain_hydrogen = collect_data( - num_batches, temp_dir, "uncertain_hydrogen_{}.pkl" - ) + try: + all_uncertain_hydrogen = [] + if fix_hydrogen: + logging.info("Processing ambiguous hydrogen-ITS") + all_uncertain_hydrogen = collect_data( + num_batches, temp_dir, "uncertain_hydrogen_{}.pkl" + ) + except: + all_uncertain_hydrogen = [] # logging.info(f"Number of correct mappers before refinement: {len(its_correct)}") if refinement_its: @@ -273,7 +276,8 @@ def extract_its( logging.info(f"Number of correct mappers: {len(its_correct)}") logging.info(f"Number of incorrect mappers: {len(its_incorrect)}") - logging.info(f"Number of uncertain hydrogen:{len(all_uncertain_hydrogen)}") + logging.info(f"Number of uncertain hydrogen:"+ + f"{len(data)-len(its_correct)-len(its_incorrect)}") if save_dir: logging.info("Combining and saving data") From c4d1b676ca98dabc7556f2e6c982b09ddfcc6fca Mon Sep 17 00:00:00 2001 From: TieuLongPhan Date: Tue, 27 Aug 2024 10:43:31 +0200 Subject: [PATCH 5/6] update source --- Data/Temp/Benchmark/raw_results.json | 128 ++ Docs/Analysis/_4_templates_analysis.ipynb | 162 +- Docs/Analysis/_5_rule_application.ipynb | 1428 ++++++++++++++++- Docs/Analysis/_analysis/_plot_analysis.py | 14 +- Docs/Analysis/_analysis/_rule_app_analysis.py | 4 +- syntemp/pipeline.py | 9 +- syntemp/run_compose.py | 2 +- 7 files changed, 1637 insertions(+), 110 deletions(-) create mode 100644 Data/Temp/Benchmark/raw_results.json diff --git a/Data/Temp/Benchmark/raw_results.json b/Data/Temp/Benchmark/raw_results.json new file mode 100644 index 0000000..fdb48b6 --- /dev/null +++ b/Data/Temp/Benchmark/raw_results.json @@ -0,0 +1,128 @@ +{ + "Valid": { + "fw": { + "Raw_0": [ + 4.04, + 7.86, + 94.87 + ], + "Raw_1": [ + 0.79, + 7.24, + 78.52 + ], + "Raw_2": [ + 0.33, + 6.5, + 69.74 + ], + "Raw_3": [ + 0.23, + 4.92, + 68.67 + ], + "Complete_0": [ + 71.13, + 94.5, + 97.18 + ], + "Complete_1": [ + 22.88, + 92.92, + 89.08 + ], + "Complete_2": [ + 5.3, + 88.6, + 67.5 + ], + "Complete_3": [ + 3.22, + 78.03, + 58.88 + ], + "Refine_0": [ + 84.29, + 94.7, + 97.66 + ], + "Refine_1": [ + 23.78, + 93.16, + 89.58 + ], + "Refine_2": [ + 5.73, + 89.12, + 68.68 + ], + "Refine_3": [ + 3.3, + 78.93, + 58.97 + ] + }, + "bw": { + "Raw_0": [ + 4.58, + 7.86, + 97.99 + ], + "Raw_1": [ + 0.61, + 7.24, + 84.84 + ], + "Raw_2": [ + 0.35, + 6.5, + 73.3 + ], + "Raw_3": [ + 0.2, + 4.92, + 64.07 + ], + "Complete_0": [ + 73.26, + 93.46, + 97.86 + ], + "Complete_1": [ + 22.0, + 92.84, + 92.98 + ], + "Complete_2": [ + 13.92, + 88.5, + 89.29 + ], + "Complete_3": [ + 9.4, + 77.97, + 85.12 + ], + "Refine_0": [ + 93.4, + 93.66, + 98.11 + ], + "Refine_1": [ + 22.51, + 93.08, + 93.13 + ], + "Refine_2": [ + 14.55, + 89.02, + 89.43 + ], + "Refine_3": [ + 9.78, + 78.87, + 85.2 + ] + } + } +} \ No newline at end of file diff --git a/Docs/Analysis/_4_templates_analysis.ipynb b/Docs/Analysis/_4_templates_analysis.ipynb index d1e34f7..6d93188 100644 --- a/Docs/Analysis/_4_templates_analysis.ipynb +++ b/Docs/Analysis/_4_templates_analysis.ipynb @@ -25,22 +25,22 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "from syntemp.SynUtils.utils import train_val_test_split_df, save_database\n", + "# import pandas as pd\n", + "# from syntemp.SynUtils.utils import train_val_test_split_df, save_database\n", "\n", - "original_data = load_database(\"../../Data/Temp/data_aam.json.gz\")\n", - "original_data = pd.DataFrame(original_data)\n", + "# original_data = load_database(\"../../Data/Temp/data_aam.json.gz\")\n", + "# original_data = pd.DataFrame(original_data)\n", "\n", - "train, test, valid = train_val_test_split_df(original_data, target=\"class\")\n", - "train, test, valid = (\n", - " train.to_dict(\"records\"),\n", - " test.to_dict(\"records\"),\n", - " valid.to_dict(\"records\"),\n", - ")\n", + "# train, test, valid = train_val_test_split_df(original_data, target=\"class\")\n", + "# train, test, valid = (\n", + "# train.to_dict(\"records\"),\n", + "# test.to_dict(\"records\"),\n", + "# valid.to_dict(\"records\"),\n", + "# )\n", "\n", - "save_database(train, \"../../Data/Temp/Benchmark/train.json.gz\")\n", - "save_database(test, \"../../Data/Temp/Benchmark/test.json.gz\")\n", - "save_database(valid, \"../../Data/Temp/Benchmark/valid.json.gz\")" + "# save_database(train, \"../../Data/Temp/Benchmark/train.json.gz\")\n", + "# save_database(test, \"../../Data/Temp/Benchmark/test.json.gz\")\n", + "# save_database(valid, \"../../Data/Temp/Benchmark/valid.json.gz\")" ] }, { @@ -58,7 +58,9 @@ "source": [ "raw = load_from_pickle(\"../../Data/Temp/Benchmark/Raw/templates.pkl.gz\")\n", "complete = load_from_pickle(\"../../Data/Temp/Benchmark/Complete/templates.pkl.gz\")\n", - "expand = load_from_pickle(\"../../Data/Temp/Benchmark/Expand/templates.pkl.gz\")" + "complete_expand = load_from_pickle(\"../../Data/Temp/Benchmark/Complete_expand/templates.pkl.gz\")\n", + "refine = load_from_pickle(\"../../Data/Temp/Benchmark/Refine/templates.pkl.gz\")\n", + "refine_expand = load_from_pickle(\"../../Data/Temp/Benchmark/Refine_expand/templates.pkl.gz\")" ] }, { @@ -76,11 +78,15 @@ "\n", "raw_result = calculate(raw)\n", "complete_result = calculate(complete)\n", - "expand_result = calculate(expand)\n", + "complete_expand_result = calculate(complete_expand)\n", + "refine_result = calculate(refine)\n", + "refine_expand_result = calculate(refine_expand)\n", "\n", "print(raw_result)\n", "print(complete_result)\n", - "print(expand_result)" + "print(complete_expand_result)\n", + "print(refine_result)\n", + "print(refine_expand_result)" ] }, { @@ -109,7 +115,16 @@ "metadata": {}, "outputs": [], "source": [ - "len(temp_0)" + "len(data_cluster)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "34395 / 40012" ] }, { @@ -118,7 +133,7 @@ "metadata": {}, "outputs": [], "source": [ - "33690 / 40012" + "1-0.8596171148655404" ] }, { @@ -154,6 +169,13 @@ "- 37: C-I + OH" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -162,11 +184,13 @@ "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", - "from _analysis._plot_analysis import plot_top_rules_with_seaborn, load_and_title_png\n", + "#from _analysis._plot_analysis import plot_top_rules_with_seaborn, load_and_title_png\n", "\n", "# Set up the figure and GridSpec layout\n", "fig = plt.figure(figsize=(16, 10))\n", "gs = gridspec.GridSpec(2, 2, height_ratios=[1, 1], width_ratios=[1, 1], figure=fig)\n", + "plt.rc(\"text\", usetex=True)\n", + "plt.rc(\"text.latex\", preamble=r\"\\usepackage{amsmath}\")\n", "\n", "# Create a subplot that spans the first row across both columns\n", "ax1 = fig.add_subplot(\n", @@ -307,8 +331,82 @@ "metadata": {}, "outputs": [], "source": [ - "from _analysis._plot_analysis import create_pie_chart\n", + "#from _analysis._plot_analysis import create_pie_chart\n", "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "def create_pie_chart(data, column, ax=None, title=None, color_pallet=\"pastel\"):\n", + " \"\"\"\n", + " Generates a pie chart for the specified column from a list of dictionaries.\n", + " Displays percentage labels inside the slices only and category names in an external\n", + " legend without percentages. Allows customization of the plot title, supporting LaTeX\n", + " formatted strings.\n", + "\n", + " Parameters:\n", + " - data (list of dict): Data to plot.\n", + " - column (str): Column name to plot percentages for.\n", + " - ax (matplotlib.axes.Axes, optional): Matplotlib axis object to plot on.\n", + " - title (str, optional): Title for the pie chart, supports LaTeX formatted strings.\n", + "\n", + " Returns:\n", + " - matplotlib.axes.Axes: The axis with the pie chart.\n", + " \"\"\"\n", + " # Enable LaTeX formatting for better quality text rendering\n", + " plt.rc(\"text\", usetex=True)\n", + " plt.rc(\"font\", family=\"serif\")\n", + "\n", + " # Convert list of dictionaries to DataFrame\n", + " df = pd.DataFrame(data)\n", + "\n", + " # Calculate percentage\n", + " percentage = df[column].value_counts(normalize=True) * 100\n", + "\n", + " # Define a color palette using Seaborn\n", + " colors = sns.color_palette(color_pallet, len(percentage))\n", + "\n", + " # Create pie plot\n", + " if ax is None:\n", + " fig, ax = plt.subplots()\n", + "\n", + " wedges, texts, autotexts = ax.pie(\n", + " percentage,\n", + " startangle=90,\n", + " colors=colors,\n", + " autopct=\"%1.1f%%\",\n", + " pctdistance=0.85,\n", + " explode=[0.05] * len(percentage),\n", + " )\n", + "\n", + " # Draw a circle at the center of pie to make it look like a donut\n", + " centre_circle = plt.Circle((0, 0), 0.70, fc=\"white\")\n", + " ax.add_artist(centre_circle)\n", + "\n", + " # Equal aspect ratio ensures that pie is drawn as a circle.\n", + " ax.axis(\"equal\")\n", + "\n", + " # Add legend with category names only\n", + " ax.legend(\n", + " wedges,\n", + " [f\"{label}\" for label in percentage.index],\n", + " title=column,\n", + " loc=\"upper right\",\n", + " bbox_to_anchor=(0.6, 0.1, 0.6, 1),\n", + " prop={\"size\": 16},\n", + " title_fontsize=20,\n", + " ) # Set label font size\n", + "\n", + " # Set title using LaTeX if provided, else default to a generic title\n", + " if title:\n", + " ax.set_title(title, fontsize=32)\n", + " else:\n", + " ax.set_title(f\"Pie Chart of {column}\", fontsize=32)\n", + "\n", + " # Enhance the font size and color of the autotexts\n", + " for autotext in autotexts:\n", + " autotext.set_color(\"black\")\n", + " autotext.set_fontsize(18)\n", + "\n", + " return ax\n", "\n", "fig, axs = plt.subplots(2, 2, figsize=(16, 10)) # Adjust size as needed\n", "\n", @@ -407,7 +505,13 @@ "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "sns.set_theme(style=\"whitegrid\")\n", "\n", + "# Enable LaTeX rendering in matplotlib\n", + "plt.rc(\"text\", usetex=True)\n", + "plt.rc(\"text.latex\", preamble=r\"\\usepackage{amsmath}\") # Ensure amsmath is loaded\n", "fig, axs = plt.subplots(2, 2, figsize=(16, 12))\n", "\n", "\n", @@ -619,6 +723,15 @@ "triple = [value for value in temp_0 if value[\"Reaction Step\"] == 3]" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(single)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -647,6 +760,15 @@ "write_gml([double], double_path)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "write_gml([single], single_path)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/Docs/Analysis/_5_rule_application.ipynb b/Docs/Analysis/_5_rule_application.ipynb index 7f30030..14a98ad 100644 --- a/Docs/Analysis/_5_rule_application.ipynb +++ b/Docs/Analysis/_5_rule_application.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -21,23 +21,267 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import *\n", + "from _analysis._rule_app_analysis import load_database, coverage_rate\n", + "def automatic_results(\n", + " test_types: List[str],\n", + " temp_types: List[str],\n", + " predict_types: List[str],\n", + " radii: List[int],\n", + " base_path=\"../../Data/Temp/Benchmark\",\n", + ") -> Dict[str, Dict[str, Tuple[float, float, float]]]:\n", + " \"\"\"\n", + " Automatically computes coverage rates for combinations of test type, template type,\n", + " predict type, and radii. Iterates over the provided parameter lists, loads data,\n", + " and computes statistics.\n", + "\n", + " Parameters:\n", + " - test_types (List[str]): List of test types.\n", + " - temp_types (List[str]): List of template types.\n", + " - predict_types (List[str]): List of prediction types.\n", + " - radii (List[int]): List of radii values.\n", + " - base_path (str): path to data\n", + "\n", + " Returns:\n", + " - Dict[str, Dict[str, Tuple[float, float, float]]]: A dictionary where the key\n", + " is the test type and the value is another dictionary. The inner dictionary's keys are\n", + " combinations of parameters as strings, and its values are tuples with the results from\n", + " `coverage_rate` (average solutions, coverage rate, false positive rate).\n", + " \"\"\"\n", + " all_results = {}\n", + "\n", + " for test in test_types:\n", + " test_results = {}\n", + " for predict in predict_types:\n", + " predict_results = {}\n", + " for temp in temp_types:\n", + " for rad in radii:\n", + " path = f\"{base_path}/{temp}/Output/{test}/{predict}_{rad}.json.gz\"\n", + " name = f\"{temp}_{rad}\"\n", + " data = load_database(path)\n", + " if data:\n", + " predict_results[name] = coverage_rate(data)\n", + " else:\n", + " predict_results[name] = (0.0, 0.0, 0.0)\n", + " test_results[predict] = predict_results\n", + " all_results[test] = test_results\n", + "\n", + " return all_results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "base_path = \"../../Data/Temp/Benchmark/\"\n", - "test_types = [\"Valid\", \"Test\"]\n", - "temp_types = [\"Raw\", \"Complete\", \"Expand\"]\n", + "test_types = [\"Valid\"]\n", + "# temp_types = [\"Raw\", \"Complete\", \"Complete_expand\", \"Refine\", \"Refine_expand\"]\n", + "temp_types = [\"Raw\", \"Complete\", \"Refine\"]\n", "predict_types = [\"fw\", \"bw\"]\n", "radius = [0, 1, 2, 3]\n", + "# radius = [0, 1]\n", "results = automatic_results(test_types, temp_types, predict_types, radius, base_path)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 7, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Valid': {'fw': {'Raw_0': (4.04, 7.86, 94.87),\n", + " 'Raw_1': (0.79, 7.24, 78.52),\n", + " 'Raw_2': (0.33, 6.5, 69.74),\n", + " 'Raw_3': (0.23, 4.92, 68.67),\n", + " 'Complete_0': (71.13, 94.5, 97.18),\n", + " 'Complete_1': (22.88, 92.92, 89.08),\n", + " 'Complete_2': (5.3, 88.6, 67.5),\n", + " 'Complete_3': (3.22, 78.03, 58.88),\n", + " 'Refine_0': (84.29, 94.7, 97.66),\n", + " 'Refine_1': (23.78, 93.16, 89.58),\n", + " 'Refine_2': (5.73, 89.12, 68.68),\n", + " 'Refine_3': (3.3, 78.93, 58.97)},\n", + " 'bw': {'Raw_0': (4.58, 7.86, 97.99),\n", + " 'Raw_1': (0.61, 7.24, 84.84),\n", + " 'Raw_2': (0.35, 6.5, 73.3),\n", + " 'Raw_3': (0.2, 4.92, 64.07),\n", + " 'Complete_0': (73.26, 93.46, 97.86),\n", + " 'Complete_1': (22.0, 92.84, 92.98),\n", + " 'Complete_2': (13.92, 88.5, 89.29),\n", + " 'Complete_3': (9.4, 77.97, 85.12),\n", + " 'Refine_0': (93.4, 93.66, 98.11),\n", + " 'Refine_1': (22.51, 93.08, 93.13),\n", + " 'Refine_2': (14.55, 89.02, 89.43),\n", + " 'Refine_3': (9.78, 78.87, 85.2)}}}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## 2. Barplot" + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results successfully saved to ../../Data/Temp/Benchmark/raw_results.json\n" + ] + } + ], + "source": [ + "save_results_to_json(results, \"../../Data/Temp/Benchmark/raw_results.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2\n", + "Raw_0 4.04 7.86 94.87\n", + "Raw_1 0.79 7.24 78.52\n", + "Raw_2 0.33 6.50 69.74\n", + "Raw_3 0.23 4.92 68.67\n", + "Complete_0 71.13 94.50 97.18\n", + "Complete_1 22.88 92.92 89.08\n", + "Complete_2 5.30 88.60 67.50\n", + "Complete_3 3.22 78.03 58.88\n", + "Refine_0 84.29 94.70 97.66\n", + "Refine_1 23.78 93.16 89.58\n", + "Refine_2 5.73 89.12 68.68\n", + "Refine_3 3.30 78.93 58.97" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "results_df = pd.DataFrame(results['Valid']['fw'])\n", + "results_df.T" ] }, { @@ -45,6 +289,32 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "import pandas as pd\n", + "results_df = pd.DataFrame(results['Valid']['bw'])\n", + "results_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Barplot" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results successfully loaded from ../../Data/Temp/Benchmark/raw_results.json\n" + ] + } + ], "source": [ "import sys\n", "\n", @@ -56,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -72,7 +342,7 @@ " 0: \"average_solution\",\n", " # 1: r'\\mathcal(C)',\n", " 1: \"C\",\n", - " 2: \"FPR\",\n", + " 2: \"NR\",\n", " },\n", " inplace=True,\n", ")\n", @@ -81,7 +351,7 @@ " 0: \"average_solution\",\n", " # 1: r'\\mathcal(C)',\n", " 1: \"C\",\n", - " 2: \"FPR\",\n", + " 2: \"NR\",\n", " },\n", " inplace=True,\n", ")" @@ -89,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -98,9 +368,158 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Type'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", + "File \u001b[0;32mindex.pyx:153\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mindex.pyx:182\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Type'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/homes/biertank/tieu/Documents/Project/TACsy/SynEco/SynTemp/Docs/Analysis/_5_rule_application.ipynb Cell 13\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m fw[\u001b[39m'\u001b[39;49m\u001b[39mType\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39munique()\n", + "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/frame.py:4090\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4088\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 4089\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4090\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 4091\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 4092\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", + "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/indexes/base.py:3809\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(casted_key, \u001b[39mslice\u001b[39m) \u001b[39mor\u001b[39;00m (\n\u001b[1;32m 3805\u001b[0m \u001b[39misinstance\u001b[39m(casted_key, abc\u001b[39m.\u001b[39mIterable)\n\u001b[1;32m 3806\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39misinstance\u001b[39m(x, \u001b[39mslice\u001b[39m) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m casted_key)\n\u001b[1;32m 3807\u001b[0m ):\n\u001b[1;32m 3808\u001b[0m \u001b[39mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3809\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3810\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3811\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3812\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3814\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'Type'" + ] + } + ], + "source": [ + "fw['Type'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, "metadata": {}, "outputs": [], + "source": [ + "def plot_percentage(\n", + " df: pd.DataFrame,\n", + " ax: plt.Axes,\n", + " column: str,\n", + " title: str = \"A\",\n", + " color_map: Optional[List[str]] = None,\n", + " fontsettings: Optional[Dict[str, int]] = None,\n", + ") -> None:\n", + " \"\"\"\n", + " Plot a percentage bar chart for different categories and subcategories within the data.\n", + "\n", + " Parameters:\n", + " df (pd.DataFrame): DataFrame containing the data to plot. Index of the DataFrame\n", + " should be string labels in the format 'category_subcategory'.\n", + " ax (plt.Axes): Matplotlib Axes object where the chart will be drawn.\n", + " column (str): Column name in df that contains the percentage values to plot.\n", + " title (str, optional): Title of the plot. Default is 'A'.\n", + " color_map (List[str], optional): List of hex color strings for the bars. If None,\n", + " a default set of colors will be used.\n", + " fontsettings (Dict[str, int], optional): Dictionary containing font size settings\n", + " for various elements of the plot. If None,\n", + " default settings are applied.\n", + "\n", + " Returns:\n", + " None: This function does not return any value but modifies the ax object by drawing a bar chart.\n", + "\n", + " Example:\n", + " >>> fig, ax = plt.subplots()\n", + " >>> data = pd.DataFrame({'Value': [20, 30, 40, 50]}, index=['Type1_10', 'Type1_20', 'Type2_10', 'Type2_20'])\n", + " >>> plot_percentage(data, ax, 'Value')\n", + " >>> plt.show()\n", + " \"\"\"\n", + " if fontsettings is None:\n", + " fontsettings = {\n", + " \"title_size\": 18,\n", + " \"label_size\": 16,\n", + " \"ticks_size\": 16,\n", + " \"annotation_size\": 12,\n", + " }\n", + "\n", + " # Split the index into template type and radii\n", + " df[\"Type\"] = [i.split(\"_\")[0] for i in df.index]\n", + " df[\"Radii\"] = [int(i.split(\"_\")[1]) for i in df.index]\n", + "\n", + " # Sort data to group by type and then by radii\n", + " df = df.sort_values(by=[\"Radii\"])\n", + "\n", + " # Prepare color map for radii using coolwarm\n", + " if color_map is None:\n", + " color_map = [\"#3A8EBA\", \"#92C5DE\", \"#F4A582\", \"#D6604D\"]\n", + "\n", + " # Plotting logic with annotations\n", + " total_width = 3 # Total width for group\n", + " width = total_width / len(\n", + " df[\"Radii\"].unique()\n", + " ) # Width for each bar within each type group\n", + " type_positions = np.arange(len(df[\"Type\"].unique())) * (\n", + " len(df[\"Radii\"].unique()) + 1\n", + " )\n", + "\n", + " for i, t in enumerate(df[\"Type\"].unique()):\n", + " for j, r in enumerate(df[\"Radii\"].unique()):\n", + " #print(t)\n", + " bar_positions = type_positions[i] + j * width\n", + " heights = df[(df[\"Type\"] == t) & (df[\"Radii\"] == r)][column]\n", + " ax.bar(\n", + " bar_positions,\n", + " heights,\n", + " width=width,\n", + " label=f\"$R_{{{r}}}$\" if i == 0 else \"\",\n", + " color=color_map[j % len(color_map)],\n", + " )\n", + " # Adding annotations\n", + " for rect in ax.patches:\n", + " height = rect.get_height()\n", + " ax.annotate(\n", + " f\"{height:.1f}%\",\n", + " xy=(rect.get_x() + rect.get_width() / 2, height),\n", + " xytext=(0, 3), # 3 points vertical offset\n", + " textcoords=\"offset points\",\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=fontsettings[\"annotation_size\"],\n", + " )\n", + "\n", + " # Enhancements like axes labeling, ticks setting, and adding grid\n", + " ax.set_ylabel(rf\"$\\mathcal{{{column}}} (\\%)$\", fontsize=fontsettings[\"label_size\"])\n", + " ax.set_title(title, fontsize=fontsettings[\"title_size\"], weight=\"medium\")\n", + " ax.set_xticks(type_positions + total_width / 2 - width / 2)\n", + " ax.set_xticklabels(\n", + " [f\"$Q_{{\\\\text{{{t}}}}}$\" for t in df[\"Type\"].unique()],\n", + " fontsize=fontsettings[\"ticks_size\"],\n", + " )\n", + " ax.set_yticks(np.arange(0, 101, 20))\n", + " ax.set_yticklabels(\n", + " [f\"{i}%\" for i in range(0, 101, 20)], fontsize=fontsettings[\"ticks_size\"]\n", + " )\n", + " ax.grid(True, which=\"major\", linestyle=\"--\", linewidth=\"0.5\", color=\"grey\")\n", + " ax.set_axisbelow(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ta+Od73xn/PVf/3VERNx6663x4osvxne+852ICGNEC2ME9KK/+Zu/MUb0wXlExPUX89cEMql+fpvFMeJDH/rQ5f7P0RO6IYPL3/3xvtlO/v77v//7eO655+Lmm2+Wv0P+dm4tf8NmkiQxRvT4eYT8Ta968MEHMzlGtJK/B2q1Wq2T/1F6zczMTExPTzd+XlpaikKhsOn2g4ODjSC/vLzcuE/Z9PR0zMzMRETE7OxsTExMbPm65XI5RkdHI+JcQKmHr81sNGP99a9/fZw5cyZ27Nix5b70tgcffDDuu+++tMuAnvHeP19Mu4SWfey3/k3aJbTl/3r6v6ddQlueWTyx/UYZ8xv/9X+kXQKXgfHr8unG8asbx66IbI9fZ8+ejZ07d8pkLeqGDC5/sxUZHDqnG89fI5zDXk7OYckq49fl041jV4Txq9Nayd9XXKaautba5dRyudyWgTzi3AzRuvoM2YiI6667ru0ahoaGtt3m6quvjh07dqz7gghXqgAAAN2jGzK4/M1WZHAAAMgujfFtrA3E9ZnnW1kb4tfeG23t4+cvDbeRtds0cz802MzJkyfTLgEAAKApMjjdTgYHAIDs0hhvQTOhfO2s9OXl5cb3a8N9M/c6W1lZ2fCY0Kqf/OQnaZcAAADQMhmcbiSDAwBAdmmMb2Ptsm3NzDLfzGaz2DeztLS04b7QKsv6AQAA3UIGp9vJ4AAAkF0a49tYO0O9meWw1s5EXxumd+/e3fh+7X3PNrM2uK/dF1p15513pl0CAABAU2Rwup0MDgAA2aUx3oT6jPUkSbadab52RvuePXsa3+dyuUZIb2a2+toPANbOmIdWLSwspF0CAABA02RwupkMDgAA2aUx3oTJycnG99sFnKNHjza+LxaL657bt29fU8epVCqNcH/+MQAAAKCXyeAAAABcChrjTVgbpg8dOrTpfc7m5uYaz01MTFxwX7K14f7QoUObvt7a59buA+1429velnYJAAAATZPB6WYyOAAAZJfGeBNyuVxMTU1FxLll2sbHxy/YplwuNwJ0LpeLUql0wTaFQiHGxsYi4tyM9I0C99zcXGMm+9rtoV3XXntt2iUAAAA0TQanm8ngAACQXVemXUC3KJVKUS6Xo1KpRLlcjuHh4UaoPnHixLpl2ebn5y+YqV53+PDhKJfLkSRJzM3NxcmTJ2NycjKSJInFxcUol8vrjgMX62tf+1rk8/m0ywAAAGiaDE63ksEBACC7NMZbcPz48di7d29UKpWoVqsxPT19wTaLi4tb3pMsl8vF0tJSjI6ORrVa3XDWei6Xi+PHjwtSAAAA9C0ZHAAAgE6ylHoL6oF6dnY2CoVC4/F8Ph9TU1Oxurq6ZSBfu/3y8nLjOPWZ7YVCIUqlUpw6dWrd8eFifOADH0i7BAAAgJbJ4HQjGRwAALLLFeNtmJiYiImJicwcB7byne98J2677ba0ywAAAGiLDE43kcEBACC7XDEOPe7ZZ59NuwQAAADoCzI4AABkl8Y49LhXvvKVaZcAAAAAfUEGBwCA7NIYhx734Q9/OO0SAAAAoC/I4AAAkF0a49Djjhw5knYJAAAA0BdkcAAAyC6NcQAAAAAAAAB6msY49Lg3v/nNaZcAAAAAfUEGBwCA7NIYhx53/fXXp10CAAAA9AUZHAAAsktjHHrcl7/85bRLAAAAgL4ggwMAQHZpjAMAAAAAAADQ0zTGoccVi8W0SwAAAIC+IIMDAEB2aYxDj3vqqafSLgEAAAD6ggwOAADZpTEOPe6ZZ55JuwQAAADoCzI4AABkl8Y49Lirr7467RIAAACgL8jgAACQXRrj0OPuueeetEsAAACAviCDAwBAdmmMQ487cuRI2iUAAABAX5DBAQAguzTGoce9/PLLaZcAAAAAfUEGBwCA7NIYhx43PDycdgkAAADQF2RwAADILo1x6HE33nhj2iUAAABAX5DBAQAguzTGocc9/vjjaZcAAAAAfUEGBwCA7NIYBwAAAAAAAKCnaYxDj7v99tvTLgEAAAD6ggwOAADZpTEOPe6ZZ55JuwQAAADoCzI4AABkl8Y49LhqtZp2CQAAANAXZHAAAMiuKy/ni509ezZWVlYaPw8NDcWOHTsuZwnQd6688rL+MwcAADJA/oZ0yOAAAJBdl+Rs/emnn45yuRyLi4tRrVajWq1GkiSbbp/L5SKfz8fu3bvjzjvvjL179wrs0CHj4+NplwAAAFwi8jdkiwwOAADZ1bHG+NNPPx2lUimOHj0aSZJErVaLiIh8Ph8jIyORy+Ui4tws9VwuF0mSNGavJ0kSJ0+ejKWlpZidnY2BgYHI5/Px+7//+3HvvffGG9/4xk6VCX3n05/+dNx7771plwEAAHSI/A3ZJYMDAEB2XXRj/KGHHorp6elYXl6OXC4X+/bti9HR0SgUCrFr166Wj/f1r389Tpw4EeVyOf74j/84pqamolgsxszMTLzzne+82HKh7/zLv/xL2iUAAAAdIH9D9sngAACQXW03xr/xjW/E7/3e70W1Wo19+/bFsWPH2gri57vlllvilltuiYmJiYiIqFQqcejQobjllltifHw8ZmZm4g1veMNFvw70C/9eAACgu8nf0D38mwEAgOy6op2d/vAP/zDuuOOO2L9/f6ysrMQDDzzQkVC+kUKhEPPz87GyshJvfOMbo1AoxGc+85lL8lrQi4aHh9MuAQAAaJP8Dd1FBgcAgOxqqTF+9uzZuPPOO+PUqVNx6tSp+MQnPnGp6rpALpeLUqkUy8vL8V/+y3+J//Af/sNle23oZl/84hfTLgEAAGiR/A3dSQYHAIDsaroxfubMmbjjjjvi93//9+PIkSOxc+fOS1nXpnK5XBw7dizy+Xzs378/lRoAAADgUpG/AQAAoPOabowfOHAgDh8+HPfcc8+lrKdpn/jEJ2JiYiIOHjyYdimQae973/vSLgEAAGiB/A3dSwYHAIDsurLZDY8ePXop62jL3r17Y+/evWmXAZn205/+NF7/+tenXQYAANAk+Ru6lwwOAADZ1dI9xoHu89RTT6VdAgAAAPQFGRwAALJLYxwAAAAAAACAnqYxDj3uvvvuS7sEAAAA6AsyOAAAZFfT9xjvpE9/+tNx5MiROHPmTERE5HK5SJIkcrlcDA0NxeTkZLzrXe9KozToOZ/73Ofi7rvvTrsMAAAgBfI3XF4yOAAAZNdlbYx/+tOfjtnZ2di3b18cPXp0w21OnToVs7OzcejQoTh8+HDs2LHjcpYIPefFF19MuwQAAOAyk78hHTI4AABk12VrjB8+fDiq1WocO3Zsy+127doV999/fyRJEmNjYzE3NxdvfOMbL0+R0INe97rXpV0CAABwGcnfkB4ZHAAAsqvpe4z/m3/zb+LjH/94Wy9y5syZWFpaikOHDjW9Ty6Xi6NHj8b09HRbrwmc89a3vjXtEgAAgBbI39C9ZHAAAMiuphvjU1NT8cADD8T1118fX/rSl1p6kXK5HPv27Wu5uFwuF4ODgy3vB/zKdleJAAAA2SJ/Q/eSwQEAILuaboxPTEzEAw88ECsrK1EsFuNjH/tYvPDCC03tm8vlolKptFXgqVOn2toPAAAAupH8DQAAAJ3XdGM84lw4L5VKce+998axY8cil8vFX/7lX2673969e+NTn/pUfPOb32ypuD/90z+NW265paV9gPXe/e53p10CAADQIvkbupMMDgAA2dVSYzwi4hOf+EQMDAzEqVOn4vd+7/fiwIEDceutt8aPfvSjLfc7evRovP/974+Pf/zjWwb0s2fPxkMPPRR79uyJxcXFuP/++1stEVjjzJkzaZcAAAC0Qf6G7iODAwBAdrXcGI+I2L17d6yursbs7GwcO3YsTp8+Hfl8Pj75yU9uuk8+n49yuRxPPvlkFAqF+Ff/6l/Fm970ptizZ0/s2bMn3vSmN8V1110Xg4ODMTY2FiMjI/Hoo4+2/YsB53z3u99NuwQAAKBN8jd0FxkcAACyq63GeLFYjHK53Ph+eXk5/uAP/iDuv//+uPnmmzedkV4oFOLkyZPx6KOPxj333BM7d+6M5eXlWFpaitOnT8fIyEh84hOfiNXV1XjggQfa/60AAACgB8jfAAAA0BlXtrPTLbfcEkePHl33WKlUisnJyRgfH49CoRCTk5Nx//33x44dOy7Yv1gsRrFYbK9ioCXj4+NplwAAALRJ/obuIoMDAEB2tXXFeEREkiQXPJbP52NpaSn+y3/5L/HAAw/Erl274jOf+czF1AdcpC984QtplwAAAFwE+Ru6hwwOAADZ1VZj/NSpU5HL5TZ9fmJiIlZWVuKOO+6Ie++9Nz74wQ/GCy+80G6NwEXwbw8AALqX/A3dxb8/AADIrrYa49VqNfL5/Jbb5HK5mJ+fj2PHjsXXvva1yOVy8Zd/+ZdtFQm07zWveU3aJQAAAG2Sv6G7yOAAAJBdbTXGy+Vy7N+/v6lti8VirK6uxu/93u/FgQMH4tZbb40f/ehH7bws0IZCoZB2CQAAQJvkb+guMjgAAGRXW43x5eXl2LFjR0v7zM7OxsmTJ+N//+//Hfl8Pj75yU+289JAix555JG0SwAAANokf0N3kcEBACC7rmx1h7/4i7+IO++8M/70T/80nnzyyTh16lQMDQ1FxLnl2/L5fOzfvz/e9a53XbBvoVCIpaWlmJmZiT/8wz+MhYWFmJ2djfe///0X/YsAAABAL5G/AQAAoHNaumL81KlTMTExEfPz81EoFOLw4cNx4sSJePTRR+PRRx+NI0eOxMTERHzqU5+KD3zgA/HQQw9teJypqan44Q9/GG94wxuiWCzGxz72sTh79mxHfiFgvT179qRdAgAA0CL5G7qTDA4AANnVUmN8fHw8lpaW4tFHH4077rgjdu7cecE2u3btivvvvz8effTRePLJJ+Mv/uIvNjxWPp+PxcXFOHLkSBw7dix27doVn/nMZ9r7LYBN/fM//3PaJQAAAC2Sv6E7yeAAAJBdTTfGDx48GPPz83HLLbc0ffD7778/fvjDH8Y3vvGNTbcZGxuLU6dOxdjYWNx7773xwQ9+MH70ox81/RrA1r797W+nXQIAANAC+Ru6lwwOAADZ1XRjPEmS2LVrV8svcPDgwThy5MiW2+zcuTNmZ2fj5MmT8YMf/CDy+Xz82Z/9WcuvBQAAAN1O/gYAAIDOa7oxXq1W23qB1dXVqNVqTW1bKBRieXk5/uAP/iA+8YlPxM033xzf/OY323pd4JyPfvSjaZcAAAC0QP6G7iWDAwBAdjXdGG/3HmSTk5PxsY99rKV9SqVS/PCHP4xXvepVcccdd7T8msCvPPbYY2mXAAAAtED+hu4lgwMAQHY13RgvlUrxx3/8x/Hxj3+8qXuQPfTQQ/GmN70pRkdH413velfLheXz+VhaWmp7pjxwzurqatolAAAALZC/oXvJ4AAAkF1XNrvhzp07o1wux969eyOfz0cul4vdu3dHLpeLoaGhWFlZiYiISqXSCNP3339//MEf/MFFFbhz586L2h/63fXXX592CQAAQAvkb+heMjgAAGRX043xiIhcLhdLS0tRLpejVCrFiRMnIkmSddvk8/n4xCc+EQcPHhSqIQPe/e53p10CAADQIvkbupMMDgAA2dVSY7yuWCxGsViMiIgzZ85EtVqNfD4viEMGff7zn4/77rsv7TIAAIA2yN/QXWRwAADIrrYa42vt3Lkzbrnllk7UAgAAAGxC/gYAAID2XZF2AcCl5YMzAAAAuDxkcAAAyC6NcQAAAAAAAAB6WtON8YMHD17KOtpy9uzZ+NM//dO0y4BM+/rXv552CQAAQAvkb+heMjgAAGRX043x8fHx+MAHPhAvvPDCpaynaU8//XSMj4/H2NhY2qUAAABAx8jfAAAA0HlNN8YLhUJ84hOfiEKhEF/60pcuZU3b+vSnPx2jo6MxOzsbb3zjG1OtBbLud3/3d9MuAQAAaIH8Dd1LBgcAgOxq6R7jxWIxHn300Thw4EB8/OMfv+yz159++um48847Y25uLk6ePCmUQxO++tWvpl0CAADQIvkbupMMDgAA2dVSYzwiIp/Pxw9/+MP43//7f0cul4uPf/zj8c1vfvNS1NbwxS9+Mfbt2xfDw8Nx5513xqOPPho7d+68pK8JveL5559PuwQAAKAN8jd0HxkcAACyq+XGeN3s7Gz84Ac/iH/8x3+MW265Jfbs2ROf/OQnOxbSv/jFL8bBgwfjTW96UxSLxbjuuutiZWUl/uAP/qAjx4d+MTg4mHYJAADARZC/oXvI4AAAkF1XXszO+Xw+5ufno1qtxuzsbDzwwANRKpUi4tw90fL5fOzZsyfy+XzkcrmIiBgaGopcLhdJksTKykrjz+Xl5ahWq1GpVKJarUZExK5du2JycjImJibMUIc23X777WmXAAAAXCT5G7qDDA4AANl1UY3xunw+H6VSKUqlUpTL5VhcXIzjx4/H/Px8zM/PR0TEwMDApvvXarXG98ViMSYmJqJYLMYtt9zSifKgr33mM5+J++67L+0yAACADpC/IdtkcAAAyK6ONMbXKhaLUSwWGz+fOnUqqtVqVKvVSJIkIiJOnz4d1113XUREYzZ7Pp+PXbt2dbocAAAA6EnyNwAAADSv443x8+3atSt27doVe/fuvdQvBWzgN3/zN9MuAQAAuAzkb0ifDA4AANl1RdoFAJfWr//6r6ddAgAAAPQFGRwAALJLYxx63IkTJ9IuAQAAAPqCDA4AANmlMQ4AAAAAAABAT2v7HuN/8Rd/EUtLSzEyMhL79u2LHTt2dLIuoEPuuuuutEsAAAAugvwN3UMGBwCA7GrrivHf//3fj8nJyZibm4vJyckYHByMj3/843H27NlO1wdcpEqlknYJAABAm+Rv6C4yOAAAZFdbjfGjR49GrVaLiIharRa1Wi1mZ2djcHAw/uzP/qylY915553xyU9+sp0ygCY899xzaZcAAAC0Sf6G7iKDAwBAdrXVGE+SJAYGBqJWq0WxWIxCodAI6FNTU/GXf/mXTR9rbGws7r///vj4xz/eTinANl71qlelXQIAANAm+Ru6iwwOAADZ1VZjPJfLRUTEzMxMHDt2LE6ePBmrq6vxiU98Imq1Wtx///1NH2tiYiL27t0bs7OzZq7DJfDBD34w7RIAAIA2yd/QXWRwAADIrrYa40NDQxERUSwWG4/t3LkzSqVSrKysxN69e1s63uLiYtxzzz1RKpXiS1/6UjslAZuYn59PuwQAAKBN8jd0FxkcAACyq63GeKFQ2PS5XC4XDzzwQMvHnJ+fj3vuuSfGxsbaKQkAAAB6jvwNAAAAndFWY7w+U71arXa0mPn5+ajVavFnf/ZnHT0u9LO3vvWtaZcAAAC0Sf6G7iKDAwBAdrXVGJ+YmIhardbxYB4R8Yd/+IfxJ3/yJx0/LvSrnTt3pl0CAADQJvkbuosMDgAA2dVWYzwi4sCBA3HkyJFO1hIRESMjI7G6uhqf+cxnOn5s6Edf/epX0y4BAAC4CPI3dA8ZHAAAsqvtxvj09HQsLS3FN7/5zU7WE/l8PiLikoR+AAAA6DbyNwAAAFy8thvj+Xw+Dhw4EGNjY52sp7E83NLSUkePC/3qzjvvTLsEAADgIsjf0D1kcAAAyK62G+MREbOzs/H888/HBz/4wU7VE5VKJSIiVlZWOnZM6Gff/e530y4BAAC4SPI3dAcZHAAAsuuiGuMREfPz83Hs2LG49dZb44UXXrjogupLuCVJctHHAiL+/u//Pu0SAACADpC/IftkcAAAyK6LbowXi8V44IEH4uTJk7Fr1674y7/8y7aPdfjw4caM9Vwud7GlARFxzTXXpF0CAADQAfI3ZJ8MDgAA2XXRjfGIiImJiXjggQdiZWUlJiYmYs+ePS0H9OPHj8fk5GQMDAzEwMBAFIvFTpQGfe/uu+9OuwQAAKBD5G/INhkcAACyqyON8Yhz4fzYsWOxY8eOWFpaiomJibjuuuviP/yH/xAPPfRQnD17dsP9vvGNb8T+/fvjzjvvjIiIWq0WERGTk5OdKg362oMPPph2CQAAQAfJ35BdMjgAAGTXlZ08WLFYjKeffjrGxsbi+PHjsbq6GnNzczE3NxcR55ZnGxoailwuF0mSRLVabexbq9ViYGAgarVaFIvFuOOOOzpZGgAAAPQM+RsAAABa07Erxut27twZi4uLcezYsbjllluiVqs1vlZXV6NarUalUonl5eXG4xGxLpQfPXq002VB37r55pvTLgEAALgE5G/IHhkcAACyq+ON8bpisRhLS0uxtLQUBw4ciFwuFxGxLozX1Wq12LVrV8zPz8exY8di586dl6os6Ds33HBD2iUAAACXkPwN2SGDAwBAdnV0KfWN3HLLLTE7Oxuzs7Nx5syZKJfLUa1W4/Tp0xERMTw8HLt3745bbrnlUpcCfemJJ56I++67L+0yAACAS0z+hvTJ4AAAkF2XvDG+1s6dO+Pee++9nC8JAAAAfUf+BgAAgPUu2VLqQDbccccdaZcAAAAAfUEGBwCA7NIYhx63vLycdgkAAADQF2RwAADILo1x6HE/+tGP0i4BAAAA+oIMDgAA2aUxDj3u137t19IuAQAAAPqCDA4AANmlMQ497t577027BAAAAOgLMjgAAGSXxjj0uPn5+bRLAAAAgL4ggwMAQHZpjEOP++Uvf5l2CQAAANAXZHAAAMgujXHocfl8Pu0SAAAAoC/I4AAAkF0a49DjbrrpprRLAAAAgL4ggwMAQHZpjEOPe+yxx9IuAQAAAPqCDA4AANmlMQ4AAAAAAABAT9MYhx532223pV0CAAAA9AUZHAAAsktjHHrcs88+m3YJAAAA0BdkcAAAyC6Ncehxy8vLaZcAAAAAfUEGBwCA7NIYhx53xRX+mQMAAMDlIIMDAEB2OVuHHrd///60SwAAAIC+IIMDAEB2aYxDj3vooYfSLgEAAAD6ggwOAADZpTEOPe6ll15KuwQAAADoCzI4AABkl8Y49Libbrop7RIAAACgL8jgAACQXRrj0ONuvvnmtEsAAACAviCDAwBAdmmMQ48rl8tplwAAAAB9QQYHAIDs0hi/SEmSxMDAQONrcnKy6X3n5uZiZGQkBgcHY2BgIEZGRmJmZiaSJLl0BQMAAECXksEBAABo15VpF9DtpqenW94nSZIYGRmJarW67vFKpRKVSiVmZ2djfn4+CoVCp8qkj733ve9NuwQAAICOkMHJOhkcAACyS2P8IlSr1Zibm4tcLtf0DPMkSWLXrl2N7QuFQuzfvz8iIhYXF6NcLke1Wo2RkZFYXl6OfD5/iaqnXzz//PNx0003pV0GAADARZHB6QYyOAAAZJel1C9Cfcm2YrHY9D4HDhxoBPJSqRRLS0sxNTUVU1NTsbi4GLOzs41tx8fHO1ov/en73/9+2iUAAABcNBmcbiCDAwBAdmmMt6lSqUS5XI6IiNHR0ab2qVarsbCwEBHngvzU1NQF20xMTMTExMQFrwEAAAD9SgYHAADgYmmMt+nAgQMRETE2NhZDQ0NN7bN2JnqpVNp0u7XPrd0H2lFfJhAAAKBbyeB0CxkcAACyS2O8DQsLC1GpVCIi4uDBgy3tV1coFDbdLpfLNZ5fuw+04+GHH067BAAAgLbJ4HQTGRwAALJLY7wN09PTEXEuWG8VrtdKkiSq1Wpjv+3k8/nG9/UPAKAdP//5z9MuAQAAoG0yON1EBgcAgOzSGG/R3NxcI1y3MlP95MmTje9379697fZ79uzZcF9o1Y033ph2CQAAAG2Rwek2MjgAAGSXxniL6jPV8/l8jI2NNb1fkiSN74eHh7fdfu1s9eXl5eYLhPO8/e1vT7sEAACAtsjgdBsZHAAAsktjvAUzMzONcF0qlVratz7DvR1rAz206tFHH027BAAAgJbJ4HQjGRwAALLryrQL6BZJkrQ9U/18uVyupW1WVla23f6ll16Kl156qfHz2bNn2ykNAAAAUpflDC5/AwAAdCeN8SbVA3lE6zPVIyJOnz7dyXIucOjQofijP/qjCx6fn5+Pa66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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -112,15 +531,15 @@ " \"title_size\": 32,\n", " \"label_size\": 28,\n", " \"ticks_size\": 28,\n", - " \"annotation_size\": 18,\n", + " \"annotation_size\": 20,\n", "}\n", "\n", "# Plotting data\n", "plot_percentage(fw, ax1, \"C\", title=r\"A\", fontsettings=fontsettings)\n", "plot_percentage(bw, ax2, \"C\", title=r\"B\", fontsettings=fontsettings)\n", "\n", - "plot_percentage(fw, ax3, \"FPR\", title=r\"C\", fontsettings=fontsettings)\n", - "plot_percentage(bw, ax4, \"FPR\", title=r\"D\", fontsettings=fontsettings)\n", + "plot_percentage(fw, ax3, \"NR\", title=r\"C\", fontsettings=fontsettings)\n", + "plot_percentage(bw, ax4, \"NR\", title=r\"D\", fontsettings=fontsettings)\n", "\n", "\n", "fig.legend(\n", @@ -151,9 +570,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results successfully loaded from ../../Data/Temp/Benchmark/raw_results.json\n" + ] + } + ], "source": [ "import sys\n", "import pandas as pd\n", @@ -164,7 +591,7 @@ "\n", "results = load_results_from_json(\"../../Data/Temp/Benchmark/raw_results.json\")\n", "\n", - "valid = results[\"Test\"]\n", + "valid = results[\"Valid\"]\n", "\n", "valid_fw = valid[\"fw\"]\n", "valid_bw = valid[\"bw\"]\n", @@ -175,7 +602,7 @@ " 0: \"average_solution\",\n", " # 1: r'\\mathcal(C)',\n", " 1: \"C\",\n", - " 2: \"FPR\",\n", + " 2: \"NR\",\n", " },\n", " inplace=True,\n", ")\n", @@ -184,7 +611,7 @@ " 0: \"average_solution\",\n", " # 1: r'\\mathcal(C)',\n", " 1: \"C\",\n", - " 2: \"FPR\",\n", + " 2: \"NR\",\n", " },\n", " inplace=True,\n", ")\n", @@ -195,13 +622,146 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 175, "metadata": {}, "outputs": [], + "source": [ + "def plot_roc_curves(\n", + " df: pd.DataFrame,\n", + " ax: plt.Axes,\n", + " selected_types: Optional[List[str]] = None,\n", + " fontsettings: Optional[Dict[str, int]] = None,\n", + " title: str = \"A\",\n", + " add_legend: bool = False,\n", + ") -> List[Any]:\n", + " \"\"\"\n", + " Plot ROC curves for specified types from a DataFrame on a given matplotlib Axes.\n", + "\n", + " Parameters:\n", + " df (pd.DataFrame): DataFrame containing the data for plotting. Must include columns 'Type', 'C' for TPR,\n", + " and 'FPR' for FPR, where 'Type' differentiates data series.\n", + " ax (plt.Axes): The matplotlib Axes object where the ROC curves will be drawn.\n", + " selected_types (Optional[List[str]]): List of strings representing the types to be included in the plot.\n", + " If None, all types in the DataFrame will be plotted.\n", + " fontsettings (Optional[Dict[str, int]]): Dictionary containing font settings for titles, labels,\n", + " ticks, and annotations. If None, defaults will be applied.\n", + " title (str): Title of the plot.\n", + " add_legend (bool): If True, add a legend to the plot.\n", + "\n", + " Returns:\n", + " List[Any]: List containing matplotlib line handles for the legend, useful if further customization\n", + " or reference is needed.\n", + "\n", + " Raises:\n", + " ValueError: If selected_types is provided and contains non-string elements.\n", + "\n", + " Example:\n", + " >>> fig, ax = plt.subplots()\n", + " >>> data = pd.DataFrame({\n", + " ... 'Type': ['Type1', 'Type1', 'Type2', 'Type2'],\n", + " ... 'C': [90, 85, 88, 80],\n", + " ... 'FPR': [5, 10, 5, 10]\n", + " ... })\n", + " >>> plot_roc_curves(data, ax, ['Type1', 'Type2'])\n", + " >>> plt.show()\n", + " \"\"\"\n", + " if selected_types is not None:\n", + " if not all(isinstance(t, str) for t in selected_types):\n", + " raise ValueError(\"selected_types must be a list of strings.\")\n", + " original_types = [t for t in selected_types if t in df[\"Type\"].unique()]\n", + " else:\n", + " original_types = df[\"Type\"].unique()\n", + "\n", + " types = [f\"$Q_{{\\\\text{{{t}}}}}$\" for t in original_types]\n", + "\n", + " if fontsettings is None:\n", + " fontsettings = {\n", + " \"title_size\": 28,\n", + " \"label_size\": 24,\n", + " \"ticks_size\": 24,\n", + " \"annotation_size\": 18,\n", + " }\n", + "\n", + " markers = [\"o\", \"^\", \"s\", \"p\"]\n", + " markers.reverse()\n", + " marker_labels = [r\"$R_{0}$\", r\"$R_{1}$\", r\"$R_{2}$\", r\"$R_{3}$\"]\n", + " marker_labels.reverse()\n", + " marker_color = \"gray\"\n", + "\n", + " colors = plt.cm.coolwarm(np.linspace(0, 1, len(types)))\n", + " colors = [\"#3A8EBA\", \"#D6604D\"]\n", + " legend_handles = []\n", + "\n", + " for index, type_ in enumerate(original_types):\n", + " type_data = df[df[\"Type\"] == type_]\n", + " tpr = type_data[\"C\"].tolist()\n", + " fpr = type_data[\"NR\"].tolist()\n", + " tpr = [x / 100 for x in tpr]\n", + " fpr = [x / 100 for x in fpr]\n", + " tpr.reverse()\n", + " fpr.reverse()\n", + "\n", + " (line,) = ax.plot(\n", + " fpr, tpr, linestyle=\"-\", color=colors[index], label=f\"{types[index]}\"\n", + " )\n", + " legend_handles.append(line)\n", + "\n", + " for i, (f, t) in enumerate(zip(fpr, tpr)):\n", + " marker = ax.plot(\n", + " f, t, marker=markers[i % len(markers)], color=marker_color\n", + " )[0]\n", + " if index == 1:\n", + " marker_handle = plt.Line2D(\n", + " [0],\n", + " [0],\n", + " marker=markers[i % len(markers)],\n", + " color=\"none\",\n", + " markerfacecolor=marker_color,\n", + " markersize=10,\n", + " label=marker_labels[i],\n", + " )\n", + " legend_handles.append(marker_handle)\n", + "\n", + " ax.set_xlabel(r\"$\\mathcal{NR}\\ (\\%)$\", fontsize=fontsettings[\"label_size\"])\n", + " ax.set_ylabel(r\"$\\mathcal{C}\\ (\\%)$\", fontsize=fontsettings[\"label_size\"])\n", + " ax.set_title(rf\"{title}\", fontsize=fontsettings[\"title_size\"], weight=\"medium\")\n", + " ax.tick_params(axis=\"both\", which=\"major\", labelsize=fontsettings[\"ticks_size\"])\n", + " ax.grid(True)\n", + "\n", + " if add_legend:\n", + " ax.legend(\n", + " handles=legend_handles,\n", + " loc=\"lower right\",\n", + " fancybox=True,\n", + " title_fontsize=fontsettings[\"label_size\"],\n", + " fontsize=fontsettings[\"annotation_size\"],\n", + " ncol=3,\n", + " )\n", + "\n", + " ax.grid(True)\n", + " return legend_handles" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/yOHDh0MdmgxTWVkZ5eXl/eYABQUFbNiwYcDP/6WlpRQXF5Ofn9/vmN1uD/70crlc5OfnY7fb2bBhw7Bj602QDJUoEREREZFrT7jMQ8Jq+alLn3gaqEy6vLx8wLVgXS4XmzZtIi8vr9/k4UqeeFq7di2lpaX9Kjh6+ysvL++zdu2Vxi0iIiIi157Dhw/z4osv9mt3u928+OKLrFmzhgULFoQgMhkJm83Gvn37KCwsxGazkZKSQk1NDXl5eYMuC9W7r9+aNWsG7K+oqCiY8OitBC8sLBz2A1IFBQU4nc7g0rjr1q2jpKSE3NxcioqKruAuRURERGSyOHLkCJs3b+7XPhHnIWGV1ICeJ56WLFnC2rVr++1zcbknnkpLS/tVWAz2xNO6desGfeKpd+O/4uLiPsedTidLlixhw4YN/SYqVxK3iIiIiFxbAoEA27dvH/Kc7du3M2/ePMzmsCq6vibZbDZKSkqGfX5ubu6QFeGZmZkDbvw9XCOJRURERESuHYZhXPah+4k0Dwm7pMZEeeJpw4YNVFZWUlBQQFNTEy6XC5vNxhtvvDFgNcaVxC0iIiIi15aTJ0/idruHPMftdnPy5Emuv/768QlKREREREQmDcMwCHS243O34He34GluhGMf09ruHfK6iTQPCbukBkycJ54+X+FxOSONW0RERESuLa2XSWgEz2ttHeNIREREREQkXAS6Pfhb3fjcLvzuFnytLfjdrp7ERWsLvj7/3QJ+X5/rY+KSYFrmIL1fNFHmIWGZ1BARERERmUy8riZa3q7E/fYeiJl22fPj4+PHISoREREREQkFw+/H3+buk5S4mKy48PuFBIW/tYVAV+eIX8McHYMlIRHLlESMLt/lL2DizEOU1BARERERCQEj4Kd1/z5O7diG/5NDmIwANiAmw0anJWLQ6xISEpg9e/a4xSkiIiIiIlfn80s+BZMTrReSFZ9LXvjbW8EwRvYiFivWhEQs8YlYExKxJtiwxCdijk+gxeuny2Qhdtp00m6YR0SiDXNkFABer5f9r75KvNM5ZCXGRJqHKKkhIiIiIjKOvOfP4nqrkvNvvg6tLQCYgHNRcZyelsEtd32Bdz74YNDrly9fPiE25xMRERERuZZd7ZJPl2UyYYmLx3JJgsKakNjze7ztQvvF/zbHxGIymfp0cfjwYbZv337Jvn2HSHh/H8uXL2fBggWXvJSJvLw8Nm/ePGg4E2keoqSGiIiIiMgYM3w+Wj/6ANeenbQf/DD41JXHbOHElBSOT5mKOzKm5+QPPuDuu+/m4MGDfTYNT0hI6Df5EBERERGR0TH4kk9u/K2uS6osetqvZsmnS5MSlvhErPGJ/ZMXUxIwWSxXfD+HDx/mxRdf7Nfudrt58cUXWbNmTZ+5xfz581mzZs3nkiATcx6ipIaIiIiIyBjpPt2A660KXG+/gd/tCrY3TrHxSXQS9XE2Aqb+TzsdPHiQZ555hrq6OlpbW4mPj2f27NkT5skoEREREZGJrmfJp45gIuLzSYmxXPKpp6LC9rlkRUJwyaexFggE2L59+5DnbN++nXnz5vVpW7BgAfPmzePkyZMTeh6ipIaIiIiIyCgKeLtprXoX156ddBw5EGy3JNiw3fMQbVk38cbLrw3Zh9vtpq6ujuuvv36MoxURERERCR8Bb/fFjbKHXPKpp7rC8I3Nkk+97eaYuH5LPo03f8CgpbObxo5umto9NLZ3U1/7WZ9qi4G43W5OnjxJWlpan3az2Tzh5yFKaoiIiIiIjAJP/Umad+/A/c6unqe8AEwm4hblYlu6jPjFt3HS7eG3lb8fVn9DbdInIiIiIjIZXLrkk8fVRHxdDa43XsVobxubJZ/iE3qSFQljs+TTaDEMgzaPj6aObhrbPRf/bO+mscNDc0c3je09SYzmzm4CnyswmeE9R84wXidc5xxKaoiIiIiIXKGApwv3B2/j2r2TzpojwXZr8lRs99qx3ZuHkZjCrmNn2Lb5Qz6qd5Hkc3PbMPqOj48fu8BFRERERMbA1S75NBM4X7Vr6BeZoEs+DYfH5+9JRnRcSFBcmrDoTVRcONbtDwy7XxNgi40kJTaS5LgoknxWjMOfXva6cJ1zKKkhIiIiIjJCnSdqcO3Zgfu9PQQ6O3oazWbib74d29KHiVt0M3UtXZQcqOf1jw/h6vT2nGKChTdkEfXpCTwd7YP2n5CQwOzZs8fjVkREREREhjTgkk+tFxIUbteFJaB6ExZXseRTfAIur5/p188hIjGp75JPwUTFxFjy6VK+QABXh5emDk8wKdFbRRFcEqqjm6b2btq7RzY2U6KsJMdGkhIXRfKFhEVv4iIlLpLk2J4/E2MisF6y70UgEOCHP/xoyCWoeuccfr//iu89VJTUEBEREREZBn9HO+739+DavZOuz2qC7RHTZ2C7bxm2ex7CmJLIWzXn2Lb5I6pqm4LnTJsSxROL0nh8URqp8dEcPhzNiy++OOhrLV++fMJtxiciIiIik4MR8ONvax00KTG6Sz71VlRcfsknr9fLwddeI+fRR4mIiBiDOx8+wzBo9fj6LPnU1N7dp6qiN1nh6uhmJNuLR1rMPQmJCwmKpEuSFilxUSTHRZIS2/NnlPXKlsIym80sX758WHMOJTVERERERCYRwzDoPHYE156duD94G6PbA4DJaiV+yV3Y7ltG7PxsTrV6+PmBOl45VE1zR3fPOcAd16ewMiedu+ZM7fPk1IIFC1izZg3bt2/v8/RUQkICy5cvZ8GCBeN6nyIiIiISvoa75FOwuqLN3WfJp2EJ4yWfLtXp9dPUb8mn/omLpg4PXv/wx8hsgqSYvhUUlyYnLq2qiIu0jkulyWSecyipISIiIiLyOb42N+533sS1pwJP/clge+SsDGz3LSPx7gcgbgq/d55n29aP+OCzxuDTWSmxkTy+KI0nFqUxMzFm0NdYsGAB8+bN4+TJk7S2thIfH8/s2bNVoSEiIiIiF5d8ClZRuMZmyad+CYrwWPLpUj5/gObO7kGWfOpZCqq5oydp0dE9sqqE+Chrv+qJ3j+DS0HFRpIYE4nFPPHGZ7LOOZTUEBERERGh5wm3jiMHcO3eSeu+d4ITQ1NkJAm334vtvjxi5i7gTGsXvzxQzysHP+J8uyd4/W2zk1mRk869mdOwWoY3STCbzVx//fVjcTsiIiIiMoFM1CWfJqqAYeDu8l6onPj8XhWXVlV4gvvXDVeU1TzAHhWXVlVcOBYbRaQ1vL/8h8k551BSQ0RERESuab6WZlxv/w7Xnp14z54KtkfNziRp6TIS7lwK0bG8d+I827Z9xLsnzhO4UJZhi4ngsYVpPJmdRrotNkR3ICIiIiLjbcAln3oTFQNVV1zDSz5dqqPbN+CST40dHs63dnH8VBS/+OU7NHd68QeGP14Wk+nC3hR9N9NO7rNfRU9bbIRlwladyPAoqSEiIiIi1xwj4Kf94Ee49uyk9aMP4MLmeOboGBLuXIpt6TJirp/LubYuflVdzysHGzjT2hW8PjcjiRXZ6dyXNX1SPL0lIiIiIn2XfPI0NZJw8lOad2zFaG8bhyWfPlddMcGXfLqU1x+4uHF2n8qK7n77V3R6L7f8kwW6u4O/JUZHDFJF0Xf/isSYCMxhMFYyOpTUEBEREZFrhrfxHK63KnG9XYmv8VywPSZrHrb7lpFw+z0QFc0HnzWy7aWP+L3zPP4LT9QlREfw6E0zeTI7neuS40J1CyIiIiJhz+l08vrrr/PII4+QmZk5Zq8zrCWfWi9upB3o7Ohz/Qyg0TH0a5ijYy4mJ8J8yadLBQyDlk7vJUs+fX6viouJCnfXyJZ/iomw9Kmg6E1OJEZZcH5czbL77iY1MY6k2Egihrmsq1xblNQQERERkUnN8Ploq66iefdO2g84wAgAYI6bQuJdD2Bbuozo9Otoavfwf6obeOlAHafcF6sycmbZWJmTzv03TCfKGh6TUBEREZGJyjAM3njjDc6fP88bb7zBnDlzhl2NEFzy6ZJEhM/tGpsln6Yk0OTpZkbmXCISk8J6yadehmHQ0e2/uOTT56ooev9surCxtn8EY2cxm4JLPgWXfhpoc+3YSGIjB/5K2uv18tpnHzE/NYGIiIjRum2ZhJTUEBEREZFJqfvsKVx7KnC9/Qb+luZge+y8RdiWLiP+1rvBGoGjtomtr+xnT8254Lq9U6KsPLKgpyojc+qUUN2CiIiIyKRTU1NDQ0MDAA0NDRz75CjXT00ZOCkxlks+DVRdccmST16vlwOvvcbiRx+d8F+we3x+mju6g0s/9d2r4sISUBfaPL7AiPq2xUSQEhfVs19FcAmoniWfLv0zPlrLP8n4UVJDRERERCaNgNdLm+M9mvfspOPj/cF2S0Iiti88ROJ9eUTNSKO5o5vf7O+pyqhzdQbPWzgzkRXZ6Tx0YyrREarKEBERERlNhmHwu8pKTIABmAyD13/1C+ynjjCSr8Mn65JPl/IHDFo6LyYl+u5V0XfPilbPyBI9sZGW/hUUwQqLi21JMZFYtfyTTEBKaoiIiIhI2PM01OLavZOWd36Hv621p9FkIm7hzdiWPkz8zbeBxcpH9c1se+0Au46dwevvqcqIjbTw8PyZrMhJ54Zp8SG8CxEREZHJ7cgH73HqzJng74bJRHNUHGfikkiPNAcTEX030Q7vJZ8uZRgGbR5f/yWfLlRRBKst2j00d3YTGMHKWREWU7/qieCfn9tcO0YP70iYU1JDRERERMJSwOPBvfdtXHt20vnp4WC7NSkF2712Eu+1Ezk1FXeXlxerG9h2oJ7PmtqD581PTWBFdhr2eTMGXddXREREREaHe9+7VL60DVNENMYlyxSZTCaOLb6H+//iL4a9t8ZE4/H5+y751G+viovLQnX7h7/8kwmwxUZe3KOid3PtuMh++1fER1nDdvxERkqzNxEREREJK10nnT1VGe/uJtB5IUlhNjMl51ZsSx9mSnYumM0caGhh2/aD/O6TM8HJY0yEhbx5M1iRk8781IQQ3oWIiIjItcEIBDj/0n9zcMdrNM24of9xw6ChoYGamhrmzp0bgggH5gsEaPPBp+daafEELi751O65uCTUhU2127tHtvzTlCjrxQTF55ITl1ZYJMZEYDVr+SeRz1NSQ0REREQmPH9nB+739+DavZOuE8eC7RHTUrHdm0fiPQ8RkZRCm8fLbw/Us626HmdjW/C8udOmsDI7nWXzZxIXpY/AIiIiIuPB39lBw8//hVbHexyYOX/Ic998802ysrLGtNrAMAxaPb4+Sz41XUhM9FZV9CYrXB3dGMTCyX3D6jvSYg4u9ZQSG9mzsXa/6oqeP6OsWv5J5GpoRiciIiIiE5JhGHQ5P6F5907cH7yF4enqOWCxEp97J0lLlxG7IAdMJg6fcbNt5yEqjp7G4+upyoiymnnoxhmszEnnphkJKscXERERGUc1Ve/T9B//wpSudgxrBJ4pNvB6Bz3f7Xbj9/uxWkf+dWWn109TvyWfPH2WhGrq6FkCqndfteEwYZAUG0XKJRUUlyYnLq2qiIvU8k8i40VJDRERERGZUPxtrbS8uwvXnp146j4LtkfOSMO2dBmJdz+INSGR9m4f2w42sLW6jk/PtQbPm5MSx4rsdJYvmEl8dEQobkFERETkmtZavY+Ofy9mit+LJzKaG5/7NuunzaSjo2PQa+Li4vokNHz+AM2d3YMs+dSzFFRzR0/SoqPbP6L44qOs/aonev/srbSIjzTzzpuVPP7YA0RE6DOlyESipIaIiIiIhJxhGHQcPYRr9w5aq97B8PU8xWeKiCThti9gW7qMmBtuwmQycfSMm20ffMzOI6fp9PZMYCMtZh64IZUVOWnkzLLpKTkRERGREDAMg6bXN3O2/L+wGgbno+J4Z1oWU7AyNzGR+IQE3F3eC5UTnuAG2j2Ji8ZLqio8uDoHr+oYSJTVPMAeFZdWVVw4FhtFpPXy+1R4vV7M+kgpMiEpqSEiIiIiIeNzu2j5/e9w7amg+3R9sD0q43ps9y0j8a77scRNodPr55VDDWyrruPwGXfwvIykWFZmp/PITTNJjIkMxS2IiIiICBDweDj1yx/jfn8PAM4pU3GkZOA3mflF+ct8nJxLU6cXf2D4yz9ZTKYLe1P03Uw7uc9+FT1tsREWPdgico1QUkNERERExpURCND+8f6eqowPPwC/DwBTVDSJd9yHbekyoufcgMlkouZ8K1vfP8KOw6do7+45z2o2cf8N01mRnc4t6UmavIqIiIiEmPf8WWp//B08J4+D2cw+Wxo18dPAZMIERHncBFrO4LcmAZAYHTFIFcWF5MWFvSoSYyIw67OeiHyOkhoiIiIiMi68zY20vFWJ660KvOfPBtujM2/Edt8yEm6/B0tMLB6fn+2HT7G1uo6Dp1qC56UlxrAiO51HF84iKVZVGSIiIiITQfuRA9T/axH+NjfmKQnsSM6i0WymzwJPJhP2uCbW/tETJMdFEWG5/PJPIiKDUVJDRERERMaM4ffTVl2Fa/dO2qr3gREAwBwbR+Jd9/dUZWTMAeBEYxtb3z/K9o8baPX0VGVYzCbuzZzGypx0lsxO1pN6IiIiIhOEYRg0v/EqZ37zcwgEsKTP4d+n3kGi61P6pSwMg+ZzZ2g9W0/q3LmhCFdEJhElNURERERk1HWfO41rTyUtb1ficzUF22NuvImkpQ8Tf+vdmCOj6PYF2HnkFNuq6/io3hU8b0ZCNE9mp/PYTbOYOiUqBHcgIiIiIoMJeL2c/vVPaXmrsqdh8V18M+JWrm/ajwEM9hjKm2++SVZWlpYPFZGroqSGiIiIiIwKw+el9cP3ce3eSfvH+8Ho2QTSMiWBxC88iG3pMqJmpgNwsrmdl977jNcONdDS5QXAbIIvZE5jRXY6t1+XgsWsya6IiIjIRONtbqTuJ5vocn4CJjPtDz3NhsaZdHv8LDR5B01oALjdbvx+P1arvpIUkSunv0FERERE5Kp4TtXh2lNBy+9/h7/14h4YcTctxrb0YabccgfmiAi8/gBvHD3NtgP17Ku9WL0xfUoUTyxK4/FFaUyPjw7FLYiIiIjIMHTWHKHuJ9/D52rCHDeFGvuX+O5xMwYGd86ZxlfvLQCvZ9Dr4+LilNAQkaumv0VEREREZMQC3R5aq97BtWcnHUcPBduttmQS73kI2715RE6fAUC9q4OXDp7g1UMNNHd0Az1LEtw5Zyors9O5c04KVrM2ixQRERGZyFx7dnL61/+O4fMROWs2lXd8kf+q6QBgRXYazz44X5/pRGRcKKkhIiIiIsPWVXsc1+6dtLy7i0BHe0+jycyUnCXYli5jSs6tmCwWfP4Auz49w7YD9XzwWWPw+pTYSB7PTuPJRWnMSIgJ0V2IiIiIyHAZPh9n/vs/aH7jVQBib7mD0ll2dtW4AfjqvTfwh0uu0z4ZIjJulNQQERERkSEFujppef8tXHt29qydfEFEynQS77Nju8dORPJUAE67O3npYD2vHKynsb07eO7t16WwIjuNezKnYbXoCT4RERGRcOBzt1D/b0V0HD0IQNxja/nHrkyO1rqJtJj5u+WLePDG1BBHKSLXGiU1RERERKQfwzDoOn4M154duN9/i0BXZ88Bi4X4W+7Adt8y4hYuxmS24A8YvF1zjm0H6nj3+HmMC33YYiJ4fGEaT2SnkW6LDdm9iIiIiMjIdX1WQ+2Pv4uv8Rzm6BjMa7/CX9dYOO1uIzE6gqIVN5M9yxbqMEXkGqSkhoiIiIgE+TvaaHl3N67dO/HUHg+2R6bOwnZfHolfeBBrYhIA59q6ePngCV4+UM/ZtosbQuZmJLEyO5375k4nQlUZIiIiImGn5b3dnPrljzG6u4lMnYVr9Vf51geNtHm6yLDF8oNVt+ihFREJGSU1RERERK5xhmHQ+enHuHbvxL339xjeC5t5WyOIv/VubEsfJnbeQkwmE/6AwbvHz7PtQB3vOM/jN3rqMhKjI3h04SyezE5jdlJcKG9HRERERK6QEfBzrvzXNL6+GYC47FwOLf0jNr19An/AIGeWje89uZjEmMgQRyoi1zIlNURERESuUT53Cy3v/A7Xngq6T9UF26PSrsO2dBmJd92PZUo8AI3tHl451MDLB+o45e4Knrs4zcaK7HTuv2E6UVbLuN+DiIiIiIwOf3sb9f/+fdoPfghA8iNP8dKsu/nPPT3Vu/Z5qXxr2UJ95hORkFNSQ0REROQaYgQCdByupnn3Tlod74HfB4ApMoqEO+4laekyojPnYTKZCBgGez9rZNuBOvbUnMMf6KnKiI+ysnzBTFbkpDMnZUoob0dERERERoGn/iS1P/oO3rOnMEVGMv1Pv85PXMns2PsZAH9y+xzW3Z2F2WQKcaQiIkpqiIiIiFwTvK4mWt6uxLWnEu+508H26OvnYrtvGQl33oclpmdd5OaObl471MC2A3XUt3QGz100M5EV2ek8eGMq0RF6Qk9ERERkMmh1vEfDz/6ZQFcnESnTsRVs4O8+bOGj+lNYTCa+8dB8nsxOD3WYIiJBSmqIiIiITFJGwE9btQPX7h20VVdBIACAOSaWhDuX9lRlXJfVc65h4KhtYtuBOnYfO4vX31OVERtp6anKyE5n7rT4kN2LiIiIiIwuIxDg/Ev/zflt/w1A7PxszP/jGZ6prOFkcwexkRb+6fHF3HFdSogjFRHpS0kNERERkUnGe/4srrcqcL1Via+5Mdgec8OCnqqM2+7BHBUFgLvLy2sfN7Ctuo6TzR3Bc+enJrAyJx37vBnEqCpDREREZFLxd3bQ8PN/oc3xHgBJ9idoXPoUG145gKvTS2p8NN9feTNZU/VQi4hMPEpqiIiIiEwGgQBt+96l9fdv0H7oIzB6Ki0sU+JJvPtBbPflEZU2G+ipyqiud7G1uo43Pz1Dt7+ngiMmwsKy+TNYkZ3OvNSEUN2JiIiIiIyh7jMN1P7oO3Q31GKyWpnxJ1/lwxnZfHvzR3T7A9w4PZ7iFTczbUp0qEMVERmQkhoiIiIiYcDpdPL666/zyCOPkJmZGWzvPt1A467tZO7azmlPV7A9dkEOtqXLiM+9C3NEBACtXV62Hz7FtgN1HG9sD557w7R4VmSnsWz+TOKi9PFQREREZLJqO7CP+n//AYGOdqy2ZNL+6pu81BLDT16pxgDunjOV5x/NJjZSnwlFZOLS31AiIiIiE5xhGLzxxhucP3+eN954g+vS02jb9x6uPTvpOHIA6PlQZ0mwYbvnIWz35RGZOit47aFTLWw7UEfl0dN4fD1VGVFWM/Z5M1iZk86C1ARMJlOobk9ERERExphhGDS9vpmz5b8GI0BM1nxmfHUD//rROTbv/wSApxan8z/vn4fVbA5xtCIiQ1NSQ0RERGSCq6mpoaGhAYCGhgbeLvwa011neg6aTMQuvIVjsUnc+6friYyJAaC928fOw6fYeqCOY+fagn3NSYljZU46D8+fSXx0xLjfi4iIiIiMr4DHw6lf/hj3+3sAevZYW/Pn/F3FEd45fh4T8LX7bmRt7mw96CIiYUFJDREREZEJzN/VReVL2zBhYGDCZBjsj0liudlP0r152O61Q4KNj157DZPVypEzbrZV11Fx9DSdXj8AkRYzD96YyorsdLJnJWqyKjJOXC4XmzZtAiAlJYWamhry8vJYvXr1VfVZWFgIQFNTEwB5eXmsX79+0GscDgclJSU0NTXhcDiw2WwUFBQMeY2IiEwO3vNnqf3xd/CcPA4WC6l/uA7fbQ/wta37+eRsK5EWM//wyCLuvyE11KGKiAybkhoiIiIiE1BX7Qlcu3dwpGovZ5IygJ5EhGEy0RwVh+nLf8G0G24EwN3RxX63hW0v7uPo2dZgH7OTYlmRk84jC2aSGBMZitsQuWa5XC6WLFlCWVkZubm5wfaCggL27t1LUVHRiPt0OBzk5+dTUlKC3W4Ptufl5VFTUzNgn6WlpQCUlJQE2yorK8nPz6eoqIh9+/Zhs9lGHIuIiEx87UcOUv+v38Pf5sYSn0j6XxVyKuU6nnuhijOtXdhiIihacTOLZtpCHaqIyIgoqSEiIiIyQQQ8Htx738a1awedNUcwgOqZ8zEZBsYl1RUmk4ldu3ZD4gxeOljPjsOnaO+OAlqxmk3cf8N0Vuakc3NakqoyREIkPz+f1atX90loQE9yISkpiby8vD6JiZH0+fnrSkpKyMrK6ten0+nE5XKxYcOGPufb7XbeeOMNlixZQn5+PhUVFSO8OxERmcgMw6D5d69x5jc/B7+f6OuySH/mWzjazPztC3vp6PYzOymWH6y8hTRbbKjDFREZMSU1REREREKstyqj5Z1dBDrbexotFtwLb6PZ7e13vmEYNDQ08L9/tZ1GaxIANmuAP7h9Lo9nZ5AUq6oMkVByOp1UVlb2qY641Jo1aygqKhpRUqO4uBin08nGjRv7HcvMzMRut/frs6SkZMDzAXJzc7Hb7VRWVuJ0OsnMzBx2LCIiMnEFvF5O//qntLxVCUDCHfcx88vP8Oqn5/l+5WH8hsEt6Ul894nFJGh/NREJU+ZQByAiIiJyLQp4PLjefoMT//Qcx//+6zS/8SqBznYipqUy7ek/Zu4P/oMDU6YNer0B3OCp5YG50/jBihzWZXTxB7mzldAQmQB6kxmDJQqysrKorKzE5XINu8/eaorBlorKzc2lsrKyT1tlZSVz5swZ9HV6q0gcDsew4xARkYnL29zIyaJv9SQ0TGamr/kyM9Y/y8+qavlexcf4DYNl82fw/1uVq4SGiIQ1VWqIiIiIjKPBqjLMNyzC4bdy+9r/wdSsufh8PlwtLYP2YwJmRBn89SOLMAyD1w6MT/wicnm9m3EPpjfZUVVVNexqjaqqqiH7zMrKCr52b7IiOTkZh8OB0+nstwyWiIhMLp01R6j7yffwuZowx8aR9pfPEbFgMd/efpDKo2cA+NM75vAXd2VpeVIRCXtKaoiIiIiMsYt7ZWyns+ZosD1iWiq2+5aReM9D/GfZb2loaKDtd29iTZrBywcb+H30IrroBMBiMrE4zcb9N6SycGYiZpOJuLg4rFYrXm//JapEJHScTifJycmDHu9NTjidzmH3mZycTFNT02XPq6qqCiYwKioqhlxaqvf1lfAQEQlvrj0VnP71TzF8PqLSZpP+zLfoTJjKc791UN3gwmI2UWi/iccWzgp1qCIio0JJDREREZEx0lV7Ateu7bS8u7tPVUb8LXdgu/9h4hYsxmQ2c+zYMRoaGgBoaGjgmf947cJeGRamJ6bwRHYaTyxKY9qU6NDdjIgMW1NT05B7VPQmPEay/FRubi7l5eWDHq+pqRmwz6HiKC8vJzc3d9BzPB4PHo8n+Lvb7QbA6/UqmTqA3jHR2IwfjXloaNzH32Bjbvh8nC/7T1refB2AuFvuIPXLz1DfZfDN33xAXUsncZEWvv3IIpZkJOl/sxHSe338acxDYyKN+3BjUFJDREREZBQFPB7cH7yFa/eOAasybPfasSYmBdtPtXTw4kuvY9CzpFQAmNt1knmL5rIiJ4M756RgNWsbNJFwMtxkRWNj47D7LCgooLy8vM/yUpfq3U9juH0WFxcD8LOf/WzQczZt2sTzzz/fr33nzp3ExsYO63WuRb37n8j40ZiHhsZ9/F065hZPJzM/+B2xjacBOD8/l09mL6T+td1sPh1FZ8BEgjXA6umdnDnwrpYqvQp6r48/jXloTIRx7+joGNZ5SmqIiIiIjILhVmUABAyDvSeb2LK/lsNHPyG3o4nelY3NQGKgnf+RncDcrME3CheRa4vdbsdut7Np0ybKysr6HKusrMRut+NwOEhJSblsXw6Hg8LCQsrKyoZcemrjxo08++yzwd/dbjcZGRksW7aMhISEK7+ZScrr9VJRUUFeXh4REdqAdzxozEND4z7+Pj/mnpNOTv1bEb6m85iiokn98//J3JtvZ9exs7xYcRhvwGDe9Hi++9giUuKiQh1+2NJ7ffxpzENjIo17b2Xw5SipISIiInKFAp4u3B+8PUBVxgxsS5dhu+ehPlUZ7i4vrx1qYEt1LXWuTjAM7ug6GazS6GUymXjzzTfJytJGjiLhyGazDataYzgJiEtVVFRQUFBAYWEhGzduBC5WaPRuFD7UZuK98vPzKSkpYfXq1UOeFxUVRVRU/y/DIiIiQj7hncg0PuNPYx4aGvfxFxERQce+dzj1yx9jdHcTmTqL9Ge+ReSsDP5v1Wf829ufAnBP5jT+8dFsYiIsIY54ctB7ffxpzENjIoz7cF9fSQ0RERGREeqqPY5r1w5a3t1FoPNCeewgVRkAh0+3sKW6joojp+n2BwCIi7SSNwM6Drb3698wDBoaGqipqWHu3Lnjck8iMnqG2iQcCG74PZwExOeVlJTgcrmCyQy73Y7NZgsuJ3XrrbcOeX1+fj4FBQWsX79+xK8tIiIhZAQ4/9tf49qxFYC47FzS/vIbGNGxfP+Nw2w7UA9A/s0ZPLN0HhazHowRkclLSQ0RERGRYRhpVYbH56fy6JmeJabOXCyhnTttCk/lZGCfl8r//a//ZKgVQ1WtIRKeMjMzqaqqGvR4bxXHUJt4D8Vms/Wrsti7dy/AkMtJFRYWctttt7Fhw4Yrel0REQkNf3sbae/uxHW2J3GR8ujTTHv6j+jwGfz9to9470QjJuDrS+exJnd2aIMVERkHSmqIiIiIDGGkVRl1rg62VtfxysF6Wj0+ACIsJh64IZWnFmewaGYiJpMJn89HS0vLkK/tdrvx+/1YrfrIJhJOcnNzg5UUA3E6nUBPlcVocTgcQ1ZflJaWkpWVNeA5LpfriqpGRERk7HnqT1L3o38i7uxpTBGRzPrz/0nCHfdytrWL57Z9yLFzbURZzTz/aDb3Zk0PdbgiIuMiLGfILpeLTZs2AT3r0NbU1JCXl3fZNWEv12dhYSFwsRw8Ly9vyImBw+GgpKSEpqYmHA4HNptt0FLu0tJSysrKKCgoCJaIO51OHA4HL7zwAhs3bhzyqSoREREZP71VGc27ttPl/CTYPlhVhj9g8M7xc2zZX8f7nzUG22ckRLMyO53HF6WRFBvZ5zWsVivr1q2jo2PwWo24uDglNETC0Nq1aykuLsbhcAz4GX/v3r0jTmg4HA4eeughysrK+l3rcDhwOp3B+cznlZeXAww4T+mdk1zNXEpERMZGq+M9Gn72zwS6OvHGTCHzG//IlMwb+fRcK89t/ZBzbR6SYiP5/oqbWTAjMdThioiMm7CbJbtcLpYsWUJZWVmfCUJBQQF79+6lqKhoxH06HI7gZnmXThDy8vKoqakZsM/S0lKgZ03bXpWVleTn51NUVMS+ffv6PO3Uu+7t55/Ystls/e5FREREQmOkVRlN7R5ePtTAtuo6zrR2AT0bft9xfQpPLc7gzuunDrmecWJiIomJmoCKTDa5ubnY7XZeeOGFAT/nl5eXU1FR0a+99+GtvLy8fomL3uqOgaxbt46ioqIBl7NyOBw0NTUN+rBWZWXlqFaMiIjI1TMCAc6/9N+c3/bfAMTcuJCarMUsyJjDu8fP83evVtPp9XN9chw/WHkLMxNjQhyxiMj4CrukRn5+PqtXr+43OSgpKSEpKWnACcBw+/z8dSUlJWRlZfXr0+l04nK5+q1Fa7fbeeONN1iyZAn5+fn9JiolJSXU1NTgdDpJTk5myZIl2qBPREQkxIauyngY2z0P9qnKMAyD6gYXW/bX8eanZ/AFDAASoiN4fOEsVuSkk26LHff7EJGJpaysjCVLlrB27dp+D2Nt2LBhwDlLaWkpxcXFlJaW0tzc3OeY3W4P/vRyuVysW7cOu90+4D4ZTqeT/Px87HY7BQUF/Y43NTVRWVnZ77VERCR0/J0dNPz8X2hzvAdAkv0Jkp/6I/bv3MnLBxv4l92f4jcMcjOS+O7ji4mPjghxxCIi4y+skhpOp5PKyso+1RGXWrNmDUVFRSNKahQXF+N0Otm4cWO/Y5mZmdjt9n59lpSUDHg+XHwqq7KyEqfT2edpqTVr1mitWhERkQliqKqMpPuXE7sgp09VRnu3j52HT7Gluo6a823B9ptmJPLU4nQevDGVKKtlvG9DRCYom83Gvn37KCwsxGazDWvZ3N5latesWTNgf0VFReTn5wMXNxsvLCwcdP6Tl5eH0+kMVpkP5Eo3KxcRkdHXfaaB2h99h+6GWkxWKzP+5KvY7rXj6e5mV2MEH1x4AGf5gpl8M+8mIizmy/QoIjI5hVVSozeZMdgH76ysLEpLS0e00V1vNcVg5+fm5lJcXNynrbKyktLSUo4fPz7gdb0bAzocDk0SREREJpCApwv3+2/RvHvHsKoyAJzn29hSXcf2ww10dPsBiLKayZs/g1U5GcxPTRjXexCR8GGz2QZ9IGsgubm5Q1ZNZGZmUlZWNuz+ampqhn2uiIiEVtsBB/X//n0CHe1Ybcmkf+2bxGTNx+Pz8+0dH/NBS09Fxp/flcmX78jEZBp8iVMRkckurJIavZtxD6Y3gVBVVTXsao2qqqoh+8zKygq+dm/ZeHJycnAzPu2FISIiMvF1nTyOa/cAVRm5d5K09OF+VRlef4A9x86ypbqOD+sufsGYkRTLqpx0HrlpFgkq9RcRERGRq2QYBk3bt3C27L/ACBCTNY+0r20kwpaMq7Obb277iAOnWjBj8E37Ah7Lzgh1yCIiIRdWSY3evSgG05ucGGoTvc9LTk6mqanpsudVVVUFExgVFRX9lpb6fJzAoAkPh8NBVVUVt956q5IiIiIiY+RKqjLOtnax7UAdLx+op7GjGwCzCe7Jms7Ti9NZkpGsp+JEREREZFQEPB5O/fLHuN/fA0DivXnM+OO/xBwRQW1zO9/Y+iF1rk6mRFl5PLmNZfNnhDhiEZGJIaySGk1NTUMu59Sb8OhdX3Y4cnNzKS8vH/R4b8n25/scKo7y8nJyc3P7ndO7z4bdbmf9+vU4HA7y8vKGXAdXRERERmakVRkBw2DfySY2V9fx+5pz+I2ejb9TYiN5IjuNFdnpTI+PDsWtiIiIiMgk5T1/ltoffxfPSSdYLKT+4TqSHngEk8nE/vpmvvnSftxdXmYmRPO9x7M59N7uUIcsIjJhhFVSY7jJisbGxmH3WVBQQHl5eZ/lpS5VWVk5oj5799/42c9+1qe9N8GxYcOGYFtubi5lZWUkJSWxb9++Ias2PB4PHo8n+Lvb7QbA6/Xi9XqHFdtE0htzOMYu4UnvORlPer+Nv4Cni7a9v6dlTwWeE58G261TU0m8L4/4ux/AmmADwOf3g99Pa5eX7UdO89LBBmpdncFrFs9KZGV2GvdkTg1uvjjR/7fUe07Ggt5PIiIiY6P9yEHq//V7+NvcWOITSftqIXHzFwFQefQ0/7TjIF6/wU0zEih68mbiI80cCnHMIiITSVglNcaC3W7HbrezadOmfpvuVVZWYrfbcTgcpKSkXLYvh8NBYWEhZWVl/RIUq1evHvAam83G6tWryc/PH3Ijv02bNvH888/3a9+5cyexsbGXjW2i6t2oXWS86D0n40nvt7EX2dKI7cRR4muPYfH1fAFrmEy0zbyeluvn0TFtFgRM8PY7wWtOe0x86I7gcJsFn9GzlFSkyWBhvI9bEnxMjeyg89NTVHw64EtOaHrPyWjq6OgIdQgiIiKTimEYNP/uNc785ufg9xM1O5OMr/8NESnTMAyDX+89QcnvjwFw39zp/MPyRURHWPSggYjI54RVUsNmsw2rWmM4CYhLVVRUUFBQQGFhIRs3bgQuVmj0bhQ+1GbivfLz8ykpKRk0gTGY2267jfLy8iH36di4cSPPPvts8He3201GRgbLli0jISFhRK83EXi9XioqKsjLyyMiQhutytjTe07Gk95vY2uwqoyIaTNIuNfepyqjl8fnZ9exc2w9UM/hM63B9syUOFZmp2G/cTqxkWH1sagPvedkLPRWBouIiMjVC3i9nP71T2l5q+f7poQ77mPml5/BHBWFzx/g+787zCsHGwBYmzubv7r3Rixm7eUmIjKQsJq9D7VJOBDc8Hs4CYjPKykpweVyBZMZdrsdm80WXE7q1ltvHfL6/Px8CgoKWL9+/Yhfuzdeh8MxaFIjKiqKqKiofu0RERFh/eVFuMcv4UfvORlPer+Nrq6Tx2netR33e7v775Vx/3Ji52f32SsDoN7VwdbqOl491EBLV88TblaziQduSGXV4nRyZtkm1cbfes/JaNJ7SUREZHR4mxup/9fv0VlzFExmpud/ieTlKzGZTLR5vPztK9XsPdmE2QT/6/75PH1zRqhDFhGZ0MIqqZGZmUlVVdWgx3urOIbaxHsovUtBXWrv3r0AQ+53UVhYyG233dZnv4xLFRQUUFlZOeTyUiIiItJfwNOF+/09NO/eSZfzk2B7xPQZJC19mMQvPIg1ManPNf6AwXsnzrN5fy3vn2jEuNCeGh/Nipx0nlg4i+S4/g8KiIiIiIiMts6aI9T95Hv4XE2YY+NI+8vnmJLd8x3TmdYuntv6ITXn24i2mvn2Yzl8IXNaiCMWEZn4wiqpkZubG6ykGIjT6QR6qixGi8PhGLL6orS0lKysrAHPcblc2Gw2qqqqglUkA+lNxgyVOBEREbmWBKsy3t1FoOvCJt4W64WqjIcHrMpo7ujmlYP1bD1Qx2l3V7D9jutSWLU4nbvnTFMJv4iIiIiMG9eeCk7/+qcYPh9RabNJf+ZbRKbOAuDoGTfPbfuQxvZuUmIjKV55C/NTw295cRGRUAirpMbatWspLi7G4XAMmADYu3fviBMaDoeDhx56iLKysn7XOhwOnE4nhYWFA15bXl4OMGBCw+l04nA4WL16NXa7naKiokFj2Lt3Lzab7YorTERERCaDQFcn7g/eGrwq456H+u2VYRgGB0+1sHl/LW9+egavv6cuIz7KyuOL0liZk066LXY8b0NERERErnGGz8eZ//4Pmt94FYD43DuZ+Rf/C0tMz+fSd5zn+PvXDtDp9TMnJY4frLyFGQkxoQxZRCSshFVSIzc3F7vdzgsvvDBgUqO8vJyKiop+7S6Xi02bNpGXl9cvcdFb3TGQdevWUVRUNGCyweFw0NTUNGgVR2VlZfC11q5dS2lp6aDJj/LycsrKygaNQ0REZDLrOumkedeOEVVldHT7qDhyms3VtRw71xZsX5CawKrFGdjnpRJltYznbYiIiIiI4HO3UP9vRXQcPQjA1JV/yNQn1gQ/z27eX8s/v3mEgAG3zk7mO4/nMCVK+1iJiIxEWCU1AMrKyliyZAlr167tk9goKChgw4YNA1ZqlJaWUlxcTGlpKc3NzX2O2e324E8vl8vFunXrsNvtA+6T4XQ6yc/Px263U1BQ0O94U1MTlZWVwdfqXTaruLi4T39Op5MlS5awYcOGfnt5iIiITGZXUpUBcKKxjS3Vdbz+8Snau30ARFrM5M2fwaqcdBbMSByvWxARERER6aPrsxpqf/xdfI3nMEfHMGvdXxOfeycAAcPg3976lN/s+wyAxxfO4rmHFmC1mIfqUkREBhB2SQ2bzca+ffsoLCzEZrORkpJCTU0NeXl5gyYG7HY7NpuNNWvWDNhfUVER+fn5wMX9LQoLCwddyiovLw+n00lpaemgcX6+umPDhg1UVlZSUFBAU1NTcL+NN954Q3tpiIjINeNKqjJ8/gBv1Zxjc3UtjtqLDyek22JYtTiDR2+aRUK0nm4TERERkdBpeW83p375Y4zubiKmzyTj639DVNpsALq8fr69/SC7j50FYP3dWfzJ7XMwmbTfm4jIlQi7pAb0JCJKSkqGfX5ubm6/Co1LZWZmjmj5p5qammGfe6nPV4SIiIhcC4JVGbt20HX802D75aoyzrV18dKBerYdqKOxvRsAswm+kDmNVYszuG12MmZNBEVEREQkhIyAn3Plv6bx9c0AxGXnklbwDSxxUwBo7uhmw7aP+Ph0CxEWE99atpBl82eGMmQRkbAXlkkNERERmfh6qjK2435397CrMgzDwFHbzOb9tbxVcw6/0bPxd1JsJE8uSuPJ7DRtoigiIiIiE4K/vY36kh/QfsABQMqjTzPt6T/CZO7Z2+2zpna+seVDGtydxEdZ+d6TN3NzelIoQxYRmRSU1BAREZFRM3hVxkyS7n+YxC88OGBVRmuXl+2HT7Gluo7PmtqD7YvTbDy1OIOlc6cTofWGRURERGSC8NSfpPZH38F79hSmyEhm/tnXSbzjvuDxD+ua2PjSflo9PmYlxvCDlbdwXXJcCCMWEZk8lNQQERGRq3YlVRkAn5xtZcv+WnYeOUWXLwBATISFR26ayaqcDDKnThnP2xARERERuaxWx3s0/OyfCXR1Yk2ZRsYz3yL6uqzg8R2HT/HdnYfwBQwWzkyk6MmbSYqNDGHEIiKTi5IaIiIickUCXZ20vP8Wrt0jq8rw+Pzs+vQsm/fXcvBUS7B9TkocTy3O4OH5M4mL0kcUEREREZlYjECA8y+9wPltvwEgdn42aV/ZgDUhsee4YfCrD47zs3d69mJ94Ibp/N3yRURZLSGLWURkMtI3BiIiIjIiXZ/V0Lx7R7+qjIQld2FbumzQqoyGlk62VdfxyqF6XJ3ensvMJu6fO52nFmewOM2GSRt/i4iIiMgE5O/soOHn/0Kb4z0AkuyPk7r2zzBZe75a8/oDFFce5rWPGwD4wyXX8ZV7b8Csz7ciIqNOSQ0RERG5rCutyvAHDN7/7Dxb9tfx7vHzGBfap0+JYkVOOk8sSiMlLmp8bkJERERE5Ap0n2mg9kffobuhFpPVyow/+Sq2e+3B461dXv72lWqqapswm+DZB+azanFGCCMWEZnclNQQERGRQV1pVYars5tXDzawtbqOBndnsP222ck8fXMGd82ZinWA60REREREJpK2Aw7q//37BDrasdqSSf/aN4nJmh88ftrdyTe2fsjxxnZiIix8+7Fs7p4zLYQRi4hMfkpqiIiISB/Bqoxd2+k6cSzYfrEq46HgusGXMgyDQ6db2LK/jt99coZuf8/G3/FRVh5dOIuVOenMToobt/sQEREREblShmHQtH0LZ8v+C4wAMVnzSPvaRiJsycFzDp9uoXDbRzR2dDM1Lorvr7yZG6cnhDBqEZFrg5IaIiIiAlymKuP+h3uqMgZYE7jT66fiyCm2VNfxydnWYPu86fE8tTgD+7wZREdoc0QRERERCQ8Bj4dTv/wx7vf3AJB4bx4z/vgvMUdEBM95q+Ys//jaAbp8AeZOncL3V97C9PjoUIUsInJNUVJDRETkGnalVRkAnzW1s6W6jtc/bqDN4wMg0mLGPm8GTy1OZ8GMga8TEREREZmovOfPUvvj7+I56QSLhdQ/XEfSA4/0ebin7MOT/HDXUQzgjutS+H8eyyEuSl+xiYiMF/2NKyIicg3q+qyG5l07cL/3uaqMW+/CtnTwqgxfIMDbNefYsr+OqtqmYPusxBhW5aTz2MJZJMZEjtdtiIiIiIiMmvYjB6n/1+/hb3NjiU8k7auFxM1fFDzuDxj8ZM8nvPjhSQCeXJTG/35wPlaL9ooTERlPSmqIiIhcI3qqMvbg2rWjT1VGZOosbEsfJvELDw5alXG+zcNLB+t46UA959o8AJiAuzOn8tTiDG6/LgXzAEkQEREREZGJzjAMmn/3Gmd+83Pw+4manUnG1/+GiJSLG353ev08//oB3qo5B8BX7pnL/7j1+gEfBBIRkbGlpIaIiMgkd6VVGYZh8GFdM1v217G75iz+gAGALSaCJxalsSI7nZmJMeN5KyIiIiIioyrg9XL61z+l5a1KABLuuI+ZX34Gc1RU8Jymdg8btn3E4TNuIi1m/ubhhdjnzQhVyCIi1zwlNURERCYhf2cH7g/euqKqjDaPl+2HT7Flfx0nmtqD7TmzbDy1OJ2lc1OJtKrEXkRERETCm7e5kfp//R6dNUfBZGZ6/pdIXr6yzwM/xxvbeG7rh5xyd5EYHcH3nryZnDRb6IIWERElNURERCaTzhM1uHaPvCoD4NNzrWzZX8vOI6fp9PoBiImwsGz+DFYtzuCGafHjdRsiIiIiImOqs+YodT/ZhM/VhDk2jrS/fI4p2bl9ztl3solvvbKfNo+PdFsMP1h5CxlJcSGKWEREeimpISIiEuaupiqj2xdg17EzbNlfR3WDK9h+fXIcqxans3zBTKZERYz1LYiIiIiIjBvXW5Wc/q9/w/D5iJyVQcbX/4bI1Fl9znn94wa+V/ExvoBBziwbm55cjC0mMkQRi4jIpZTUEBERCVNXU5Vx2t3J1uo6Xj5Yj6vT23Op2cTSrOmsWpzOLelJ2vRQRERERCYVw+fjzAu/oLnyFQCm5N7JrL/4X1hiYi+eYxj84j0nv3jPCcBDN6byNw8vJMpqCUnMIiLSn5IaIiIiYWTQqowZadiWLiPx7sGrMgKGwQefNbJ5fy3vOM9jXGifNiWKFdnpPLEojalToga8VkREREQknPncLdT/tJiOIwcAmLrii0x9ci0m88W94rz+AN+r+Jjth08B8Ee3XU/BF+Zi1sM+IiITipIaIiIiYaDzRA2uXdtxv78nWJVhslqJX3IXtvuXEztv0aCVFS2d3bx6qIEt1XU0tHQG22/NSGbV4nTuyZqG1ayNv0VERERkcuo66aTuR9/F23gWc3QMs9b9NfG5d/Y5x93l5Vsv7+fDumYsJhPfeGg+T2anhyhiEREZipIaIiIiE9TVVGUYhsHhM24276/ljaNn6PYHAJgSZeXRm2axMied65K1yaGIiIiITG4t7+/h1C9+hNHdTcT0mWR8/W+ISpvd55yGlk6+sfVDPmtqJzbSwj89lsMd108NUcQiInI5SmqIiIhMMJ0njuHateOKqjK6vH4qj55m8/5ajp5tDbbfOD2epxZnYJ83g5gIrQcsIiIiIpObEfBzrvzXNL6+GYC47FzSCr6BJW5Kn/M+Pt3Chm0f0dzRzfQpUXx/5S3MnRYfipBFRGSYlNQQERGZAPydHbjf34Nr984RV2UAnGxuZ2t1Ha8daqDV4+u51mLmwRtTeWpxBjfNSNDG3yIiIiJyTfC3t1Ff8gPaDzgASHn0aaY9/UeYzH0f7tl97CzPv34Ajy/ADdPi+f7Km5k2JToUIYuIyAgoqSEiIhJCg1dl3I3t/oeHrMrwBQK84zzP5v217D3ZFGyflRDDypx0Hls0C1tM5Ljch4iIiIjIROCpP0ntj76D9+wpTJGRzPyzr5N4x319zjEMgxc/PMmPd3+CAdw1ZyrPP5pNXKS+JhMRCQf621pERGScXbYq4wsPYY1PGPT6xnYPLx+sZ1t1HWfbPACYgLvnTGXl4nTuuG4qFrOqMkRERETk2tLqeI+Gn/0zga5OrCnTyHjmW0Rfl9XnHH/A4Ie7j/Lbj2oBWJmTzl8/MA+r2RyKkEVE5AooqSEiIjJOrqYqwzAM9te72Ly/ll3HzuIPGADYYiJ4fFEaK7LTmZUYM273IiIiIiIyURiBAOdfeoHz234DQOz8bNK+sqHf8q0d3T7+8bUD/P74eQD+6t4b+OKS67RMq4hImFFSQ0REZAwFqzJ27aDrs5pg+3CrMto9PnYcOcXm/bUcb2wPti+amchTizN44IZUIq16qkxERERErk3+zg4afv4vtDneAyDJ/jipa/8Mk7XvV17n2zxs2PYhR8+2Emkx8/fLF/HAjamhCFlERK6SkhoiIiJjIMp1nrO//nfa9r49QFXGcmLnLRzyibCa861s2V/H9sOn6PT6AYi2mnl4wUxW5mRw4/T4cbkPEREREZGJqvtMA7U/+g7dDbWYrFZm/MlXsd1r73ee83wb39j6IWdau7DFRFD05M0smmUb/4BFRGRUKKkhIiIySnqrMprf3M51J524L7QPtyrD6w+w+9hZNu+vZX+9K9h+XXIcq3LSeeSmmUyJihjbmxARERERCQNtBxzU//v3CXS0Y7Ulk/61bxKTNb/feXs/a+RvXqmmvdtHRlIsP1h5C+m22BBELCIio0VJDRERkavUs1fGdlre24Ph6QIgYDaTcOsXSH7gkctWZZx2d/LSgXpeOlhPc0c3ABaTifvmTmPV4gxy05O0zq+IiIiICD17zTVt38LZsv8CI0BM1jzS/uqbRCSl9Dv3lYP1FL9xGH/AYHGaje89eTMJ0XpISEQk3CmpISIicgX8nR2439uDa3f/vTLi77XzfpuP5aueIiJi4ElTwDDYe7KJLftr+b3zHBf2/SYlLpIV2ek8mZ3GtCnR43ErIiIiIiJhIeDxcOqXP8b9/h4AEu+1M+OPv4L5c5+5DcPgZ+/W8Kv3jwOQN38G38pbqL3oREQmCSU1RERERmCgqgyT1Ur8rV/AtvRhYuctxOfzEXjttQGvd3d5ee1QA1uqa6lzdQbbl2QksyonnXuzpmG1aLIlIiIiInIpb+M5an/0HTwnnWCxkPrFvyDpwUf7VTR3+wJ8t+IQFUdOA/ClO+aw7q4sVT6LiEwiSmqIiIhcxlBVGbb7Hybx7geH3CsD4PDpFrZU11Fx5DTd/gAAcZFWHr1pJitz0rk+ZcqY3oOIiIiISLjqOHqQun8twt/agmVKAml/9U3i5i/qd567y8s3X/qI/fUuLGYTGx5awOOL0kIQsYiIjCUlNURERAbRefxTXLt3DFmVMdQTXx6fn52fnGPL/loOn3EH2+dOm8LTizOwz5tBbKT+KRYRERERGYhhGDT/7jXO/Obn4PcTNTuTjGe+RcTU6f3OrXN18NzWDznZ3EFcpJXvPJ7Dbdf132dDRETCn75JERERucRoVGXUuTp4szGCn/7yXVo9PgAiLCYeuCGVpxZnsGhmosrfRURERESGEPB6Of3rf6flrQoAEu64j5lffgZzVFS/cw82uCh86SNcnV5S46P5wcpbyJyqSmgRkclKSQ0RERGuvirDHzB45/g5tuyv4/3PGoEIwMeMhGhW5aTz2MI0kmIjx+dmRERERETCmNfVRP1PvkdnzREwmZme/yckL1814OfxNz89w7dfP0i3P8C86fEUr7iFqVP6Jz5ERGTyUFJDRESuWUNXZSwn8e4HLluV0dTu4eVDDWyrruNM64VkCDAnxs+6h27mC1mpWMyqyhARERERGY7OmqPU/WQTPlcT5tg40v7yOaZk5/Y7zzAMfrPvM/7trU8xgC/Mmco/Ppqt5V1FRK4B+pteRESuKYZh0HXiGK5dO2h5v39VRtL9DxNz49BVGYZhUN3gYsv+Ot789Ay+gAFAYnQEjy9K47EFqXz0+ze56/oUJTRERK5hLpeLTZs2AZCSkkJNTQ15eXmsXr36qvosLCwEoKmpCYC8vDzWr18/rnGIiIwF11uVnP6vf8Pw+YiclUHG1/+GyNRZ/c7zBQL8y5tH2VJdB8DTN2fwP5fO02dvEZFrhJIaIiJyTeitymjetR3PSWewPViV8YUHsE4ZuiqjvdvHzsOn2FJdR835tmD7wpmJPJWTzgM3phJlteD1evlorG5ERETCgsvlYsmSJZSVlZGbe/EJ44KCAvbu3UtRUdGI+3Q4HOTn51NSUoLdbg+25+XlUVNTM2CfYxGHiMhoM3w+zrzwC5orXwFgSu6dzPqL/4UlJrbfuR3dPv7+tQO8e/w8JuCZpTey5pbZ2rNOROQaoqSGiIhMWoZh0HX8WM9eGX2qMiKIv/XuYVVlADjPt7Gluo7thxvo6PYDEGU1s2z+TFblpDMvdehkiIiIXHvy8/NZvXp1n0QCQElJCUlJSeTl5fVJTIykz89fV1JSQlZW1oB9jkUcIiKjyeduof6nxXQcOQDA1BVfZOqTazGZzf3OPdfWxXNbP+LTc61EWc38wyPZLJ07fbxDFhGREFNSQ0REJp2eqozdNO/a0bcqY2Y6tqUPD6sqw+sPsOfYWbZU1/FhXXOwPSMplqdyMlh+00wSoiPG7B5ERCR8OZ1OKisrKSkpGfD4mjVrKCoqGlEyobi4GKfTycaNG/sdy8zMxG639+tzLOIQERlNXSed1P3ou3gbz2KOjmHWur8mPvfOAc89dq6V57Z+yNk2D0mxkRSvuJmbZiSOc8QiIjIRKKkhIiKTwmhVZZxt7WLbgTpePlBPY0c3AGYT3JM1nacXp7MkI1ml7SIiMqTeJEJmZuaAx7OysigtLcXlcmGz2YbVZ0VFBcCg5+fm5lJcXDzmcYiIjJaW9/dw6hc/wujuJmL6TDK+/jdEpc0e8Nz3T5znb1+tpqPbz3XJcfxg5S3MSowZ54hFRGSiUFJDRETC2mhUZQQMg30nm9hcXcfva87hN3o2/k6JjeTJ7HSezE5jenz0mN6HiIhMHg6HY8gkQW+SoaqqathVElVVVUP2mZWVFXzt3qWmxiIOEZGrZQT8nCv/NY2vbwYgLjuXtIJvYImbMuD5Lx2o4wdvHMFvGNySnsR3n1isimkRkWuckhoiIhJ2Rqsqw93l5fWPG9hSXUdtc0ew/Zb0JJ5anMF9WdOwWvqv5SsiIjIUp9NJcnLyoMd7Ew1Op3PQcz4vOTmZpqamy55XVVUVTGqMRRwiIlfD395GfckPaD/gACDl0aeZ9vQfYTJb+p0bMAxKfn+M/7P3BADLF8zkm3k3EaHP5yIi1zwlNUREJGxcrMrYjufk8WD7SKoyAI6ecbO5upaKI6fx+AIAxEZaeOSmWazMTidz6sBPiYmIiAxHU1PToEs+AcFEg8vlGnafubm5lJeXD3q8pqamX59XG4fH48Hj8QR/d7vdAHi9Xrxe73BDv2b0jonGZvxozEPjSse9u6GWU//2PbxnT2OKiGT6l75K/O334vMHwB/oc67H5+d7bxzhzU/PAfCl267jT2+/HgJ+vAH/qNxHONF7PTQ07uNPYx4aE2nchxuDkhoiIjKhXbYq44HlxNxw02WrMjw+P7/75Ayb99fx8emWYHvW1Ck8tTiDvPkziIvUP4siInL1hpusaGxsHHafBQUFlJeX91le6lKVlZX9+rzaODZt2sTzzz/fr33nzp3ExsYOq+9rUe/+JzJ+NOahMZJxjzv1GTP37cbs8+KNiaPhDjtHz7fCa6/1O7fDD1tOR1HvsWDGYPm0blIbD/P664dHM/ywpPd6aGjcx5/GPDQmwrh3dHRc/iSU1BARkQlqqKqMpPsfJuHu4VVl1Lk62FZdx6uHGmjp6sn4W80mHrgxlVU56eTMsmnjbxERmfDsdjt2u51NmzZRVlbW51hlZSV2ux2Hw0FKSsqovebGjRt59tlng7+73W4yMjJYtmwZCQmX/zf4WuP1eqmoqCAvL4+ICK33Px405qExknE3AgGaXyun6f2exGv0jQuZU/C/WRCfOOD5da4OCl8+QL2nk7hIC//Po4vITU8a9XsIN3qvh4bGffxpzENjIo17b2Xw5SipISIiE0ZvVUbzru2439+D0d2z5IXJGkH8bV/o2StjGFUZ/oDBeyfOs3l/Le+faMS40J4aH83KnHQeXziL5LioMb4bERG5VtlstmFVSYw0AVFRUUFBQQGFhYVs3LgRuFih0btR+KUbg19tHFFRUURF9f/3MiIiIuQT3olM4zP+NOahcblx93d2cOrn/0Kr4z0Akh56jNQ/+HNM1oG/iqqud/HNlz6ipcvLjIRofrDyFuakaFnYS+m9Hhoa9/GnMQ+NiTDuw319JTVERCTk/J0duN/dRfPuHVdVldHc0c0rB+vZeqCO0+6uYPud16ewKieDu+ZMxWJWVYaIiIytoTbnBoIbfl+agBiukpISXC5XMJlht9ux2WwUFxcDcOutt45LHCIiQ+k+00Ddj7+Lp/4kJquVGX/8FWz35Q16fuXR03xnxyG6/QEWpCZQtOJmUvQQkoiIDEJJDRERCYnRqsowDIODp1rYvL+WNz89g9ffU5eREB3BYwtnsTInnXSb1v0WEZHxk5mZSVVV1aDHe6snhtrEeyg2m43Vq1f3adu7dy9An/02xjoOEZGBtB38kPqfFhPoaMdqSyb9a98kJmv+gOcahsH/V3WCn759DIB7s6bxj49kEx1hGc+QRUQkzCipISIi48rf0d6zV8bnqzJmZZC0dNmwqzI6un1UHDnN5upajp1rC7bfNCOBVTkZPDQvlSirJkMiIjL+cnNzg5UUA3E6nUBPlcVocTgcrF+/PuRxiMi1yzAMmrZv5WzZr8AIEJM1j7S/+iYRSQMvcefzB/h/f3eElw7WA7Dmltl87b4bVVktIiKXpaSGiIiMuZ6qjE9p3rXjqqoyAE40trGluo7XPz5Fe7cPgEiLmWXzZ7AyJ50FMwbedFBERGS8rF27luLiYhwOR5/KiV579+4dcSLB4XDw0EMPUVZW1u9ah8OB0+mksLBwzOMQERlIwOPh1H/+BPd7uwFIvNfOjD/+CuZB1kZv9/j4u1eref+zRswm+PrSeeTfMns8QxYRkTCmpIaIiIyZy1VlJN79IJYp8Zftx+cP8FbNOTZX1+KobQ62p9tiWLU4g0dvmkVCtDYRExGRiSE3Nxe73c4LL7wwYDKhvLycioqKfu0ul4tNmzaRl5fXL9nQW1UxkHXr1lFUVNRvGakrjUNEZCS8jeeo/dF38Jx0gsVC6hf/gqQHHx30gaWzrV08t/VDjp1vI9pq5h8fzeberOnjHLWIiIQzJTVERGRUXb4qYzkxNywYVlXGubYuXjpQz7YDdTS2dwNgNsE9mdNYtTiDW2cnYx5GPyIiIuOtrKyMJUuWsHbt2j4JhYKCAjZs2DBghURpaSnFxcWUlpbS3Nzc55jdbg/+9HK5XKxbtw673c6GDRtGLQ4RkeHqOHqQun8twt/agmVKAml/9U3i5i8a9PxPzrby3NYPOd/uITk2kuIVN6vSWkRERkxJDRERGRVDV2U8TOLdDwyrKsMwDBy1zWzeX8tbNefwGz0bfyfHRvJEdhorstNJjY8es/sQEREZDTabjX379lFYWIjNZiMlJYWamhry8vL6bfLdy263Y7PZWLNmzYD9FRUVkZ+fD1zc5LuwsHDIxMSVxCEiclmGgevN1zn/4i/B7ydqdiYZz3yLiKmDV1y8e/w8f/dqNZ1eP3NS4vjByluYkRAzjkGLiMhkoaSGiIhcsYtVGdtxv//WVVVltHZ52X74FJv313KyuSPYfnOajVWLM1g6dzoRFvOY3YuIiMhos9lslJSUDPv83NzcfhUal8rMzKSsrGzM4xARGYrh9ZL60duc/+wTABLuuI+ZX34Gc1TUoNds2V/LP795FL9hsCQjme88nkO8lo8VEZErpKSGiIiMWLAqY9cOPLVXXpUBPSXoW/bXsvPIKbp8AQBiIiw8ctNMVuVkkDl1ypjcg4iIiIiIjIzX1UT9jzeR+NknYDIzPf9PSF6+atCHmAKGwU/f+pT/u+8zAB69aRYb7Av0sJKIiFwVJTVERGRYDMOgy/kJzbt39KvKSLj9HmxLHx52VYbH52fXp2fZvL+Wg6dagu1zUuJ4enEGyxbMJC5S/0SJiIiIiEwUnTVHqfvJJnyuJvwRkaR/dQO2m28f9HyPz8//s/0gb356FoB1d2fxpdvnDGu+ICIiMhR9YyQiIkPyd7TT8t5uXKNQldHQ0sm26jpeOVSPq9MLgMVs4oEbprMqJ4PFaTZNckREREREJhjXW5Wc/q9/w/D5iJyZztGFdzFv4S2Dnt/c0c03X/qIg6daiLCY2Ji3kIcXzBzHiEVEZDJTUkNERPoZzaoMf8Dg/c/Os2V/He8eP49xoX36lChW5qTz+KI0UuIGX39XRERERERCw/D5OPPCL2iufAWAKbl3Mv1Pv8bB37056DWfNbXzja0f0tDSSXyUlU1PLuaW9OTxCllERK4BSmqIiEjQaFZluDq7efVgA1ur62hwdwbbb78uhacWp3PXnKlYzVpLV0RERERkIvK5W6j/aTEdRw4AMHXFF5n65Fp8fv+g13xU18w3X/qIVo+PWQkx/GDVLVyXHDdeIYuIyDVCSQ0RkWvcZasy7n+YmLnDq8owDINDp1vYsr+O331yhm5/z8bf8VFWHls4i5U56WQkaVIjIiIiIjKRdZ10Uvej7+JtPIs5OoZZ6/6a+Nw7ew4OktTYeeQU3915CK/f4KYZiRSvuJmk2MhxjFpERK4VSmqIiFyjLlZlbMdTeyLYfiVVGZ1ePxVHTrGluo5PzrYG2+dNj+fpmzN46MYZREdYRvsWRERERERklLnff4uGX/wQo7ubiOkzyfj63xCVNnvQ8w3D4L8+OE7pOzUALJ07nX94ZBFRVn3+FxGRsRGWSQ2Xy8WmTZsASElJoaamhry8PFavXn1VfRYWFgLQ1NQEQF5eHuvXrx+1OMYibhGRkRjNqgzoWS93S3Udr3/cQJvHB0CkxYx93gyeWpzOghmJY3YvIiIiIiIyeoyAn3O//T80vvZbAOIW3ULaXz6HJW7KoNf4/AG+/8ZhXjnUAMAXl1zHV++9AfMw5xMiIiJXIuySGi6XiyVLllBWVkZubm6wvaCggL1791JUVDTiPh0OB/n5+ZSUlGC324PteXl51NTUDNjnSOMYi7hFRIZryKqM+5eTeNf9w67K8AUCvF1zji3766iqbQq2pyXGsGpxOo/eNIvEGJWZi4iIiIiEC39HG/X//v/SfmAfACmPPMW01X+MyTx4tUWbx8c/7qim6mQTZhP89QPzeWpxxniFLCIi17CwS2rk5+ezevXqPokBgJKSEpKSksjLy+uTmBhJn5+/rqSkhKysrAH7HGkcYxG3iMhQglUZu7bj/uAtjO5uAEwRkSTc9oURV2Wca+vi5YP1bKuu53x7T4WH2QR3z5nGU4vTue26FD2RJSIiIiISZjz1J6n78XfpPtOAKTKSmV9+hsQ7lw55jdtn4pnffsjxpnZiIix8+9Fs7s6cNk4Ri4jItS6skhpOp5PKykpKSkoGPL5mzRqKiopGlBwoLi7G6XSycePGfscyMzOx2+39+hxpHGMRt4jIYPwd7bS8uwvX7h1XXZVhGAYf1jWzZX8du2vO4g8YANhiIngyO50V2WnMSIgZi9sQEREREZEx1up4j4af/TOBrk6sKdPIeOZbRF+XNeQ1R8+28uv6aNr97aTERfL9FbcwLzVhnCIWEREJs6RGb1IgMzNzwONZWVmUlpbicrmw2WzD6rOiogJg0PNzc3MpLi6+qjjGIm4RkUuNdlVGm8fL9sOn2LK/jhNN7cH2nFk2nlqcztK5qURazWNyLyIiIiIiMraMQIDzL7/I+a3/F4DYeYtI+2oh1oSh98T7vfMcf/9qNV1+E5kpcfxgVS6p8dHjEbKIiEhQWCU1HA7HkF/69yYNqqqqhl31UFVVNWSfWVlZwdfuXTpqpHGMRdwiItCz9m3Lu7sHr8q4+4EhN/b7vE/PtbJlfy07j5ym0+sHICbCwsMLZrIqJ52504ZX4SEiIiIiIhOTv7ODUz//F1od7wGQ9NBjpP7Bn2OyDv0V0W8/quVfdh0hYMD1MX5+/PQt2OKU0BARkfEXVkkNp9NJcnLyoMd7EwdOp3PYfSYnJ9PU1HTZ86qqqoJJjZHGMRZxi8i1q6cq4yjNu3aMSlVGty/ArmNn2LK/juoGV7D9+uQ4nlqcwfIFM4mLCqt/LkREREREZADdZxqo+/F38dSfxGS1MuOPv4Ltvrwhr/EHDP71rU94wXESgMdumsn8zhriIjVHEBGR0Airf4GampoGXcIJCCYOXC7XsPvMzc2lvLx80OM1NTX9+hxpHKMRt8fjwePxBH93u90AeL1evF7voNdNVL0xh2PsEp4mw3vO39FO6/t7cL9VQXfdZ8H2yJnpJNy3jPg7lwarMnw+32X7O+3u4uVDDbz68SlcnT3jYjGbuC9zKiuy01g8K/FCYsQI63ELhcnwfpPwovecjAW9n0REJpe2gx9S/9NiAh3tWG3JpH/tm8RkzR/ymi6vn+e3H2TPsbMAFHxhLn9wcxqvv14zHiGLiIgMKKySGsNNVjQ2Ng67z4KCAsrLy/ssL3WpysrKfn2ONI7RiHvTpk08//zz/dp37txJbGzssPqfiHr3NBEZL2H3njMMopvPkXjiCPH1Tsz+niWhAmYLrWlzaLl+Pl3J08Fjgt17htMdxzvNfOi2UtNhAXqqOaZYAtyc4CMn3seUQDsN+z+jYf9Y3ti1IezebxL29J6T0dTR0RHqEEREZBQYhkHT9q2cLfsVGAFisuaR9lffJCIpZcjrmto9FL70ER+fdhNhMfG3Dy/CPm+Gkt4iIhJyYZXUGAt2ux273c6mTZsoKyvrc6yysjK4J0ZKytD/2I+1jRs38uyzzwZ/d7vdZGRksGzZMhISEkIY2ZXxer1UVFSQl5dHREREqMORa0C4vecuVmVU9q3KmJXRU5Vxx30j2iujpdPL64dP8dLBBhrcXcH2JRlJrFw0i7vmpGA1a+Pv0RJu7zcJf3rPyVjorQwWEZHwFfB4OPWfP8H93m4AEu+1M+OPv4L5Mp8XTjS28Y2tH3LK3UVCdASbnljMzelJ4xGyiIjIZYVVUsNmsw2r6mGkCYiKigoKCgooLCxk48aNwMUKjd6Nwi/d6HukcYxG3FFRUURFRfVrj4iICOsvL8I9fgk/E/k9d/m9MpYTM3f+sPfKMAyDw2fcbN5fyxtHz9DtDwAwJcrKozfNYmVOOtclx43Z/cjEfr/J5KT3nIwmvZdERMKbt/EctT/6Dp6TTrBYSP3iX5D04KOXnU84apv41sv7afX4SEuM4QerbmF2kuYNIiIycYRVUmOozbaB4IbflyYghqukpASXyxVMZtjtdmw2G8XFxQDceuutVxzHWMYtIuHP39FGy7u7ce3ajueSqoyotNnY7l9O4l33j6gqo8vrp/LoaTbvr+Xo2dZg+43T43lqcQb2eTOIibCM6j2IiIiIiMj4amlpGXSpwK4Tx3C98AuiWxqxTEkg7a8KiZuffdk+dxw+xXd3HsIXMMiemcimJ28mKTZytEMXERG5KmGV1MjMzKSqqmrQ473VEENtyj0Um83G6tWr+7Tt3bsXoM9+GyONY6zjFpHwYxgGnTVHce0eoCrj9nuwLX14RFUZACeb29laXcdrhxpo9fRsFB5pMfPgjak8tTiDm2YkjKg/ERERERGZmFpaWvjJT36Cz+cb9Bxz4mxWJtqY+8xGIqZOH7I/wzD45ftO/uNdJwAP3pDK3y5fSJRVD0OJiMjEE1ZJjdzc3GAlxUCczp5/fO12+6i9psPhYP369VcVRyjiFpGJyd/RRss7u3Dt3jEqVRm+QIB3nOf57f5aqk42BdtnJcSwcnE6jy2chS1GT1aJiIiIiEwmHR0dQyY0AAJmM8l/+r8um9Dw+gMUVX7M6x+fAuCPbr2egnvmYtYDUSIiMkGFVVJj7dq1FBcX43A4+lRO9Nq7d++IEwMOh4OHHnqIsrKyftc6HA6cTieFhYVXFcdYxC0i4WMsqjIa2z28fLCebdV1nG3z9PQH3D1nKqsWZ3DH9SmahIiIiIiIXOPMkUM/4NTa5eVbr+zHUduMxWTifz84nxU56eMUnYiIyJUJq6RGbm4udrudF154YcDkQHl5ORUVFf3aXS4XmzZtIi8vr1/yoLdKYiDr1q2jqKio37JQI43jSuMWkfA2eFXGddjuf3jEVRmGYbC/3sXm/bXsOnYWf8AAwBYTweOL0liRnc6sxJhRvw8REREREZl8TrV08o2tH3KiqZ2YCAv/9HgOd14/NdRhiYiIXFZYJTUAysrKWLJkCWvXru2TICgoKGDDhg0DVjyUlpZSXFxMaWkpzc3NfY7Z7fbgTy+Xy8W6deuw2+1s2LBhVOK4krhFJPxctirj/uXEZM0bUVVGu8fHjiOn2Ly/luON7cH27JmJrFqcwQM3pBJpNY/6vYiIiIiIyOR0+HQLG7Z9RFNHN9OmRPH9lbdww7T4UIclIiIyLGGX1LDZbOzbt4/CwkJsNhspKSnU1NSQl5fXb5PvXna7HZvNxpo1awbsr6ioiPz8fODipt2FhYVDJhpGGseVxC0i4WO0qzIAas63smV/HdsPn6LT6wcg2mrm4QUzWZmTwY3TNekQEREREZGReavmLP/w2gE8vgBzp03h+ytuYXp8dKjDEhERGbawS2pAT4KgpKRk2Ofn5ub2q9C4VGZmJmVlZWMex0jPF5GJbdCqjMhIEm6/t2evjBFWZXj9AXYfO8vm/bXsr3cF269LjmNVTjqP3DSTKVERo30rIiIiIiJyDXjRcZIf7T6KAdx5fQrffiyHuMiw/GpIRESuYfqXS0RkhC5blXH3/VhiR1aVcdrdyUsH6nnpYD3NHT3JEYvJxH1zp7FqcQa56UkjSo6IiIiIiIj08gcMfrz7KGUf1QKwIjuNZx+cj9WsZWxFRCT8KKkhIjIMwaqMXdtx7317VKoyAobB3pNNbNlfy++d57iw7zdT46JYkZ3GE9lpTJuiMnAREREREekrJiYGCwZ+Bp9/WK1WYmNj6fT6+cfXDvC28xwAX733Bv5wyXV6aEpERMKWkhoiIkMIVmXs2oGnfnSqMtxdXl471MCW6lrqXJ3B9iUZyaxanM69mdOwWvTElIiIiIiIDMzy2acsrz1Ad2Q06X/1TSKSp/Y7JzY2Fp81mmfLqjhyxk2kxczfLV/EgzemhiBiERGR0aOkhojI54xFVQbA4dMtbKmuo+LIabr9AQDiIq08etNMVuakc33KyJIjIiIiIiJy7TF8Ps7+9y+I83uZff8TTF+YPeB5zvNtPFf+AafdXSRGR1C04mayZ9nGN1gREZExoKSGiMgF/vY2Wt4d3aoMj89P5dHTbNlfx+Ez7mD73GlTeHpxBvZ5M4jVxnwiIiIiIjJMzbu20326Hkt8IimP5w94zr6TTXzrlf20eXxk2GL5wapbSLfFjnOkIiIiY0PfpInINc0wDDqPHcG1ewfuD97G8I5OVUadq4Mt+2t59VADrR4fABEWEw/eMIOnFqezcGai1rAVEREREZER8be3cX7rbwCYtuoPscTG9TvntUMNfK/yY/wBg5xZNr735GISYyLHO1QREZExo6SGiFyTBq3KSL8O2/3LSbxr6YirMvwBg3eOn2PL/jre/6wx2D4jIZpVOek8tjCNpFhNJkRERERE5Mqcf+m/8be3EpU2G9t9y/ocMwyDn79bw3++fxwA+7xUvrVsIVFWSyhCFRERGTNKaojItcMw6Kw5wrm33xiwKiPp/oeJzhx5VUZTu4eXDzWwrbqOM61dPX0Cd86ZylM56dxx/VQsZlVliIiIiIjIlfOcrqfpjVcBmP4Hf47JcjFZ0e0L8L2Kj9lx5BQAf3L7HNbdnYVZ1eEiIjIJKakhIpOev70N19tvcN2bW6jf1hxsv5qqDMMw2F/vYmt1HW9+egZfwAAgMTqCxxelsSI7jTStWSsiIiIiIqPk7Au/BL+fuJxbmbLolmC7u8vLt17ez4d1zVhMJp6zL+CJRWkhjFRERGRsKakhIpNScK+MXdtx7/09hrebKMAUEUnCHVdeldHe7WPn4VNsqa6j5nxbsH3hzESeyknngRtTVd4tIiIiIiKjqv3j/bR99AGYzaSu/XKwvd7VwTe2fsjJ5g5iIy380+OLueO6lBBGKiIiMvaU1BCRSWWwvTIi02ZTm5LGnX/2FaITbSPu13m+jS3VdWw/3EBHtx+AKKuZZfNnsionnXmpCaN1CyIiIiIiIkGu5mbO/LoEgMjb76PJZIVTp6g538YPdx2hqdtEamIi3195M1lT40McrYiIyNhTUkNEwl5PVcZhXLt2BKsyoO9eGZaMTA6+/jqW2Lhh9+v1B9h97Cxbq+v4sO7islUZSbE8lZPB8ptmkhAdMer3IyIiIiIiAtDS0sKrm/6R3HN1dJstbK1vpru0NHh8ERAwmfnSFwvIVEJDRESuEUpqiEjY8re30fLOm7h278BTfzLYPtBeGV6vd9j9nmntYtuBOl4+UE9TR0+CxGIycU/WNJ5anM6SjOQRL1slIiIiIiIyUu3NTSxorAXg48SZdFv6f41jNgLEmPzjHZqIiEjIKKkhIhNCS0sLHR0dgx6PjY0lMTFxWFUZV7JXRsAw2Heyic37a3nbeY4L+36TEhvJk9npPJmdxvT46Cu+PxERERERkZHq2vU6MQEfrdYojiVMC3U4IiIiE4KSGiISci0tLfzkJz/B5/MNeo7FYuGPb16A/703B6nKuH9ES0v1cnd5ef3jBrZU11HbfDGpckt6Ek8tzuC+rGlYLeYR9ysiIiIiInI1us+fwfP7SgD2J6cTMGleIiIiAkpqiMgE0NHRMWRCA8Dv91P/8oskdXdeUpWxnOjMG69oKagjZ9xsqa6l4shpPL4AALGRFh65aRYrs9PJnDrliu5FRERERERkNJwr+y/w+TgTHU9DTGKowxEREZkwlNQQkbARkTqL1PvzSLxz6RVVZXgDsP3waV461MDHp93B9qypU3hqcQbL5s8gNlJ/LYqIiIiISGh1HDuC+4O3wGRif1I6aE8/ERGRIH17JyJhI+0vnyN51qwRX1fn6mDLRyfZejKGrhNHALCaTTxwYypP5WSQPStRG3+LiIiIiMiEYAQCnPnNzwGIXHI3rvNdIY5IRERkYlFSQ0TCxkgSD/6AwXsnzrN5fy3vn2ikZ99vE6nxUazMyeDxhbNIjosaq1BFRERERESuiPv9t+hyfoI5OobovBXwmxdCHZKIiMiEoqSGiEwqzR3dvHKwnq0H6jjtvvhE0+2zk0n3nOKvnl5KdFRkCCMUEREREREZWMDj4Wz5rwBIeexpTpqn4MeE5cJjWgOxWq3ExsaOV4giIiIhp6SGiIQ9wzA40NDC5upa3vzkDL5Azwf+hOgIHls4i5U56aTGRfDaa3VYzFpmSkREREREJqamHVvxNZ3HmjKN5GUr+M4rh6iekst91yWw/gs3DHhNbGwsiYnaSFxERK4dSmqISNjq6PZRceQ0m6trOXauLdh+04wEVuVk8NC8VKKsFgC8Xm+owhQREREREbksb3Mj51/7LQDTV/8J+0618cFnjVit0azLu42ZiTEhjlBERGRiUFJDREIuNjYWi9mMPxAY9JxLS6qPN7axpbqO7R+for3bB0Ckxcyy+TNYmZPOghl6SklERERERMLLuc3/B8PTRUzWPKbcfi8//c0HAKzKSWeWEhoiIiJBSmqISMglJCSwIuCitaEO29KHSXrgkX7nREZFs+9MF5t3fsqHdc3B9nRbDKsWZ/DoTbNIiI4Yz7BFRERERERGReeJGlp+/zsAUr/45+z69CxHz7YSG2nhS3dkhjg6ERGRiUVJDREJuY4jB7Ce+JTkyEjmPv401oSLlRbn2rrYVl3PSwc/obG9GwCzCe7JnMaqxRncOjsZs0n7ZIiIiMjE43K52LRpEwApKSnU1NSQl5fH6tWrr6rPwsJCAJqamgC47bbb2LBhw6heIyLjxzAMzv73f4BhkHDHfURcfyMlv3oHgD9ccj1JsZEhjlBERGRiUVJDREKu8fXNANjusWNNSMQwDBy1zWzeX8tbNefwGz0bfyfHRvJEdhorstNJjY8OZcgiIiIiQ3K5XCxZsoSysjJyc3OD7QUFBezdu5eioqIR9+lwOCgpKaGoqAibzRZsLy8vZ8mSJezbt29UrhGR8dXqeI+OowcxRUQyPf9LvHSwnvqWTpJjI1mbOzvU4YmIiEw4SmqISEh11R6n/YADTGai7n+MFx0n2VJdy8nmjuA5N6fZWLU4g6VzpxNhMYcwWhEREZHhyc/PZ/Xq1X0SGgAlJSUkJSWRl5eH3W4fUZ+FhYVUVFT0a1+9ejVNTU0UFBRQUlJy1deIyPgJeL2cffGXACQ/vBJvfBK/eO/3AHz5zkxiI/W1jYiIyOfpX0cRCanG17cAUJexiK9t/YQuX89m4TERFh65aSarcjLInDollCGKiIiIjIjT6aSysnLQZMGaNWsoKioaUVLD4XCQmTn4uvq9fV7tNSIyvprfeAXv2dNYEpOY+tjT/MpxkuaObtJtMTy5KC3U4YmIiExISmqISEh4fH7eqjrMzPd2YwZ+GbmALl+AOSlxPL04g2ULZhKnp5JEREQkDPUmMwZLKGRlZVFaWorL5eqzJNRQehMlg2lqaurX15VcIyLjx+du4fxLLwIw/ak/oiVg4f+rOgHA+rvnYlWVuoiIyID0L6SIjKuGlk5++tanPPXzt/j4ty9iNgwOT8nghluy+df8W/n1H9/FqsUZSmiIiIhI2HI4HEMmC3qTHVVVVcPuMzc3F6fTSX5+/oDHS0pKWLt27VVfIyLj5/y23xDobCdq9hwS73mQX73vpNPrZ970eB64MTXU4YmIiExY+tZQRK5YS0sLHR0dgx6PjY0lMTERf8Dg/RPn2VJdx7vHz2MAcb4u7ms6BMCdX/oST92aM05Ri4iIiIwtp9NJcnLyoMd7Ex5Op3PYfWZmZrJ+/XpKS0vJysqipKQkuHxVZWUlLper31JSV3KNiIwPT/1JmndtByD1i3/BqdZutlTXAfDVe2/AbDKFMjwREZEJTUkNEbkiLS0t/OQnP8Hn8w16jsViIe2+Fbz6qYtT7q5g++3XpfA/Wj8i6mMvURlzmLnktvEIWURERGRcNDU1DbmXRW/Cw+VyjajfkpISsrKyKCwsJC8vj/Xr15OVlUVubu6g+3dcyTW9PB4PHo8n+Lvb7QbA6/Xi9XpHFPu1oHdMNDbjJ5zH/PRv/gMCAeJuvp3IrPmU7PwYX8DgtowkFs9MmND3FM7jHq405qGhcR9/GvPQmEjjPtwYlNQQkSvS0dExZEIDwO/388IHx2i1TCE+yspjC2exMiedtDgrx577Z/xAyiOrMOkpJBEREZlEhpusaGxsHHHfGzZswGazUVBQQGlpKTabjbKyslG/BmDTpk08//zz/dp37txJbGzsiGO/VlRUVIQ6hGtOuI157Jk60g99iGEyc3DqdWzf8jqV9TEA3BRo4LXX6kMc4fCE27hPBhrz0NC4jz+NeWhMhHEfakWYSympISJj6vqkOJ648yYeunEG0REWAJrf3I7f3YI1ZRoJt90T4ghFREREwkdhYSFZWVkYhkFhYSHFxcXBCozBKi+u5BqAjRs38uyzzwZ/d7vdZGRksGzZMhISEkb93sKd1+uloqKCvLw8IiIiQh3ONSEcx9zw+6n99rN0A0n2x7gh/w947qX9QDMP3TCdLz98U6hDvKxwHPdwpzEPDY37+NOYh8ZEGvfeyuDLUVJDRMbUPzyazcyZM4O/GwE/jdu3AJCybAUmq/4aEhERkcnFZrMNq1ojJSVlRP3m5eVRWFgY3BejqKiItWvXkp+fT2lpKU1NTf0qMK7kml5RUVFERUX1a4+IiAj5hHci0/iMv3Aa8+a3Kug+VYdlSjzTV36Rj061svdkM1aziYJ7bgib+4DwGvfJQmMeGhr38acxD42JMO7DfX3zGMchItJHq+N9vGdPYY6bgu2+vFCHIyIiIjLqhtokHHr23ICLG4YPR3FxMbm5ucHkRK/c3FxqampYv3495eXlVFZWXtU1IjJ2/B1tnNvyfwGYuvIPMcfE8W9vfwrQs0yvTcu6iYiIDIeSGiIybgzDoPG13wKQ9OCjmKNjQhyRiIiIyOjLzMwMJi4G0lvFMdRm4p9XUlLCxo0bhzyem5vbZy3kK7lGRMbO+ZfL8Le5iZyZTtLSh3nz07McOeMmJsLCn94x/L8PRERErnVKaojIuOk4eoiu459iskaQbH881OGIiIiIjInc3Nwhl59yOp0A/SoohuJ0Oi9b2VFQUNDnda/kGhEZG91nT9FU8TIAqWv/DL/JTMnve6o0/nDJdSTFRoYyPBERkbCipIaIjJvG1zcDkHjvQ1gTbKENRkRERGSMrF27FgCHwzHg8b17944ooQE9VR29yZDB1NTUsGTJkqu6RkTGxtkX/xP8PuIW3kJczhJePlhPnauTpNhI1i65LtThiYiIhBUlNUTkisTGxmK9zCbfVquV2NiedWG76j6jvboKTCZSHl45DhGKiIiIhEbvPhYvvPDCgMfLy8spLCzs1+5yuSgsLBxwj4vVq1cPeM2l1zocDtasWXNV14jI6Gs/cpDWfe+Cycz0P/gzOr1+fvFeT8LxT++YQ1zk0PMqERER6UtJDRG5IomJiRR85ascSlnCu3E53PbIatavX9/n52tf+xqJiYkANG3fAkD8kruITJ0VytBFRERExlxZWRnl5eX9qjUKCgrYsGHDgJUapaWlFBcXk5+f3+9YUVFR8PrPLxflcDj4/7P358FNnfm+//uWZxsPsgU2YwAZMhBIggwZSdIJFiEDJAEP6e69e0rHSvatW7du6mxrc07Vr27++RG79l+36u6OlKTnIcTO0JBAggWZIBO2QiYygAVhxtiybDzL1rp/OHbH8YANtmTZn1fVqbO9nkdLHxYireXvep5vYWEhZWVlA7abupTXiMj4MkIh6l54HgDzT9aRNH8hL35yHH9bF3MzknlwxfwIJxQREYk+ehxARC7Z5w1dnAomYklPY92qa4iLGbpOGvTX0/ThOwBY7t0czogiIiIiEWE2m6mpqcHpdGI2m7FYLNTW1mK32ykoKBjyNfn5+ZjN5mFXTlRUVODxeHjssccGHLdarcM2+76U14jI+Gl6/y06vqslJjmFWQ/9nEB7F3+rPgaA47YlxMfqWVMREZGxUlFDRC7Zjs9PAXDfsrnDFjQA/Lu3Q08PKVevINm6NFzxRERERCLKbDbjcrlGPd9ms9HY2DjinPz8/DH347iU14jI5Qt1tHP+pb8AMHNDEXHpGfzp7W9o6+rhquw07r4yJ8IJRUREopMeCRCRS3KmqZ2Pv2sA4IHl84ad19PWQuCdNwGw3LspLNlEREREREQirWHXy3QH/MTPmk1m/gZON7Xz8qcnAHh8zVJiTKYIJxQREYlOKmqIyCV57ctTGMCqBVnMN6cMO6/xrTcIdbSTOG8hM1bYwhdQREREREQkQoIN52n4vq9gdtGviImP57n3a+kOGay6IosbF1oinFBERCR6qaghImPWHQrx+penAdiwYvhVGqFgF/6qHQBY7tuESU8iiYiIiIjINFBX+WeMri5SrrqWtLxbOHz+Aru/PgPAE2u0Ja+IiMjlUFFDRMbso2MNnG/pJCMpnjtys4ed1/T+2/Q0NRKXNZP0G28PY0IREREREZHIaPd9S/OH74DJRPYjj2IymXhm32EMIP+qHK7OSY90RBERkaimooaIjNn27xuE37tsDglxQ/9nxAiF8H+/3Dpr3YOY4uLClk9ERERERCQSDMPg3D+eAyDj1rtIXrQE7wk/Hx5rIDbGxGO3LolwQhERkeinooaIjMn5lg4+OFoPwIYRGoS3HPyYrrOniEmegfkOe7jiiYiIiIiIRMyFA/tpP/I1poREZm3+dwzD4Hf7DgPw4Ir5I/YjFBERkdFRUUNExmTnl6fpMQyum2tmkSV1yDmGYdCw8yUAMu++l9hkfXEXEREREZGpLRTsoq7ijwBY7ttMfKaFt4/UcehsM8nxsfz6psWRDSgiIjJFqKghIqMWMgxe+6K3QfjGERqEtx/+ivbabzDFxZFl3xCueCIiIiIiIhHj372dYH0dcZkWLOsfprsnhGv/EQAesS0ka0ZihBOKiIhMDSpqiMio1Rz3c7q5ndTEOO5amjPsvIZdLwOQcdvdxGVkhiueiIiIiIhIRHQ3NdLwWgUA2QW/ICYxkde+PM2JxjbMyfH8NG9hhBOKiIhMHSpqiMiobf+it0H4uqvnkBQfO+SczlPHaTn4MZhMZN3zUBjTiYiIiIiIRMb5V/5GqKOdpEVLSL/5TtqDPfz+w1oAfnWTlRmJcRFOKCIiMnWoqCEio9LY1sW7R+qAkbeeanjjFQDSVt5E4pz5YckmIiIiIiISKR0njhJ41wNAzk9/iykmhhc/OU5Daxdz05N56DrdF4mIiIwnFTVEZFTe+Oo03SGDa3LSWTorbcg5wcYGmj54BwDLfZvCGU9ERERERCTsDMPg3Au/ByNE2urbSLlyGYH2Lv524BgAj92WS3ysfvUiIiIynvS/rCJyUYZhsP3z3q2nRlql4a/aDj3dJF+5jOTcq8MVT0REREREJCJaPj1A26FPMcXFkV34SwD+/PFRWru6WTorjfyrZkc4oYiIyNSjooaIXNSnpwIcb2wjOT6WtcN8Ke9payXw1hsAWO7dHM54IiIiIiIiYWd0B6nb9gcAstY9SMKs2ZxtbuflT08A8MTtS4kxmSIZUUREZEpSUUNELmrH9w3C116Vw4yEoRvcBd55k1BHOwlzF5B6XV4444mIiIiIiIRd495ddJ09RWx6BpYHCgF49v1agj0GeQuyuPGKrAgnFBERmZpU1BCRETV3BNn77TkANi4fusFdKBjEv3s7AJZ7N2GK0X9aRERERERk6uppucD57S8AMOvhnxObnMKR8xd486szADyxZgkmrdIQERGZEPrNo4iMaPfXZ+jqCZE7M5Vls9OHnNP84Tt0B/zEmbPIuPmOMCcUEREREREJr/P/fIFQawuJ8xdivsMOgGv/EQzg7itzuGZ2RmQDioiITGEqalyiysrKSEcQmXCGYbDj+wbhG5bPG/JJIyMUouGNV4DefWRNcfFhzSgiIiIiIhJOnWdO0vjWTgByHnkUU0wsn5z08/7RemJjTJTcmhvhhCIiIlObihqXIBAIUFhYiNvtJhAIRDqOyIT56lwzR+pbSIiN4Z5r5gw5p+WzarpOnyAmOQXzT+4Jc0IREREREZHwqtv2B+jpIfX61cy49gYMw+B/3jsCwMbl81iQOSPCCUVERKa2oTv+TnKBQICtW7cCYLFYqK2txW63U1BQcFnndDqdAPj9fgBWr15NaWnpoLk+nw8Ah8OBw+EY8bwVFRUUFBTgdrupqKjA4XCQn5+P2WzG5/Ph9XrZtm0bW7ZswWazXXJ+kYmw/ftVGnctzSE9aegVGA07XwYg8657iU1OCVs2ERERERGRcGv98iAtnx6A2Fiyi38NwDtH6jh0tomkuBh+fbM1wglFRESmvqgragQCAfLy8qioqBhQBHA4HBw4cICysrIxn9Pr9eJyuSgrK8NsNvcfr6ysJC8vj5qamgHzfT4fVqsVm81GVlbWkOf0+/34fL7+QksgEMDj8eDxeAbMM5vNg/4sIpNBa1c3nm/OArBxxbwh57Qd/or2w4cwxcWRmf9AOOOJiIiIiIiElRHq4dwLzwOQedd9JM6ZT3cohGt/7yqNR/IWYpmRGMmIIiIi00LUFTUKCwspKCgYVARwuVxkZmZit9vJz88f0zmdTidVVVWDjhcUFOD3+3E4HLhcrv7jBw4coKqqCqt1+CcwnE7noAKLy+WitrYWn89HVlYWeXl5lJSUjCmrSLjs+eYs7cEershM4fp55iHn9PXSSL/lJ8RnWsKYTkREREREJLwC73noPPkdMTNSmfXgIwDs/PI0xxvbMCfH87O8RZENKCIiMk1EVVHD5/Ph8XgGFBh+qKioiLKysjEVNbxe74jFib5z/thIr/F6veTm5g6aU1RUNGAliMhktv0iDcI7z5yk5ZOPALCsfzis2URERERERMKpp72N8y//FYBZGx8hNjWNjmAPz31QC8Avb7QyIzGqfsUiIiIStaKqUXhfMWO4gkJubi4ej2dMzbv7CiXD8fv9gwoRF+uj4XK5tAJDotrh8xf46lwzcTEm7l02d8g5/jdeBcMg9YYbSZy7ILwBRUREREREwqjhtQp6mptImD2PzLvvA6Dik+M0tHYxJz2Jh66bH+GEIiIi00dUFTW8Xu+IKx36ih3V1dWjPqfNZsPn81FYWDjkuMvlori4eMj3GcpQ206JRJsdX/Su0rgjN5vMlIRB48GAn6b39wJguW9TWLOJiIiIiIiEU1f9Ofy7twOQXfQrTHFxNLV38dfqYwA8dusSEuKi6tcrIiIiUS2q/le3rxfFcPoKHj6fb9TntFqtlJSUUFlZ2b/So0/fqo/S0tJRnatv26mLbTHl9Xpxu914vd5R5xQJl87uHnZ/dQaADcM0CG+s2oHR3U3ykqtJWbosnPFERERERETCqu7FP2F0B0m55jpSb7gRgL8cOEZLZzdLZqViv3p2hBOKiIhML1G14aPf7x9xlURfwWMs209B72qM3NxcnE4ndrudkpIScnNzsdlsw/bvGMrWrVupqKgYdtzj8eDz+cjPz6ekpASv14vdbsfpdF60D0hnZyednZ39Pzc3NwMQDAYJBoOjzjhZ9GWOxuxTnefrs1zo7GZOehLXz0kb9HcUam+j8a1dAGSsezBq/g71mZNw0udNwk2fOZkI+jyJiEDb4UNcOLAPTCZyHnkUk8nE2eZ2Kg8eB+CJNUuJGaIHoYiIiEycqCpqjLZY0dDQMOZzl5aWYjabcTgcuN1uzGbziAWKH6usrByx4NI39sNVHzabjYqKCjIzM6mpqcFmsw37+q1bt/LUU08NOr57925SUlJGnXOyqaqqinQE+ZG/n04EYlkS28wbu3YNGs88/Dmz2tvoSs3g3VPn4fTO8Ie8DPrMSTjp8ybhps+cjKe2trZIRxARiSgjFOLcP54HwHy7naQrFgPw/Ac+gj0GtgWZ3LTQEsmIIiIi01JUFTUmktPpJDc3F8MwcDqdlJeX96/aGM1qja1bt/Lss88OO15QUDDkcbPZTEFBAYWFhdTW1g77+i1btvDkk0/2/9zc3MyCBQtYt24d6enpF8032QSDQaqqqrDb7cTHx0c6jnzvu8ZWTv7tADEm+H89eCczUxMHjBvdQY69/Qo9wPxNP2PZmpFXGE0m+sxJOOnzJuGmz5xMhL6VwSIi01Xzh+/QcfQwMUnJzNr0cwBq6y+w69BpAP5jzVJMWqUhIiISdlFV1DCbzaNarWGxjO1JiR9vAVVWVkZxcTGFhYW43W78fv+IqzZ8Ph9er3fElRYjWb16NZWVlfh8vmFXeyQmJpKYmDjoeHx8fFT/8iLa8081b3xdB8Ati2cyJzN10Hjgo3fpCfiJzcgkc00+MVH4d6fPnISTPm8SbvrMyXjSZ0lEprNQZyd1lX8BwHJ/AXEZmQC49h3BAO5ams01szMimFBERGT6iqpG4SM1CYfenhvARRt1/1B5eTk2m21QTwubzUZtbW1/E/EfNhD/MZfLNeLWUxfTl1eNwyWSurpD/U8cbVw+f9C4YRj433gFgCz7xqgsaIiIiIiIiIxGwxuv0N1YT7wlm6x7HgTg4MlG9h+tJ9ZkouS2JRFOKCIiMn1FVVHDarX2Fy6G0reKYywFBpfLxZYtW0Yct9lsI+5R7fF4RnxPh8NBbm7uqDOJRMJ7tXUE2oPMnJHIzYsHr3Zq/ayGzlPHiUlKJvOueyKQUEREREREZOIFGxto2PkSANmFvyQmPgHDMPjdvsMAbFgxjysyZ0QyooiIyLQWVUUNm8024vZTPp8PYNCqi5H4fL6LruxwOBwjvq/X6x3xHNXV1aMqxlzq9lUi42H7F6cAuH/5XOJiBv+noWHXywCYf7Ke2JTBW1OJiIiIiIhMBedf+gtGVyfJS64m7cY1ALxXe54vzjSRFBfDr2+69J0aRERE5PJNWFHj2LFjHDx4kIMHD45bk8Hi4mJg+G2aDhw4MKaCBvSu6ugrhgyntraWvLy8Icf6soy0NVZ+fj6NjY3Djh84cACz2XxZW1iJXI5TgTaqj/sxARuunTdovL32G9q++QJi48iybwh/QBEREZEJMBH3LCIS3dqPHaFp/14Acn76W0wmE92hEM/sPwJAsW0hM1MH97sUERGR8BmXRuEHDx7E4/FQVVVFdXU1gUAAwzAGzDGZTJjNZrKyssjPz8dut7Np06YxvU9f74tt27YNuaqhsrJyyG2iAoEAW7duxW63Dyp6FBQU4HQ6h20EHggE8Hq9w25RNdIKjD7FxcW43W5KSkoGjfl8PiorK0dsRC4y0V77sreXxuqFFuZkJA8ab/i+l0bGLXcSnzUzrNlERERExkO47llEJHoZhsG5fzwHQPrNd5JsvRKAXYfO8J2/lYykeH62amEkI4qIiAiXuVLj2WefZcmSJRQWFnLkyBEKCgrweDz4/X4aGxsJhUL9/8/v91NdXc3TTz+N1WrlmWeewWKx8MQTT3Ds2LFRv2dFRQWVlZWDVms4HA5KS0uHXKnhdrspLy+nsLBw0FhZWVn/63+8xZTX66WwsJCysrJht5fqW+Ux0vZTfdtmlZeXD3ptXl4epaWlFBQUDPt6kYnUHQrx+pe9W09tXD54lUbX2dNcqPkAAMv6h8OaTURERORyReKeRUSi04WaD2j/9hCmhASyC34BQEewh+c/qAXglzctJjUxPpIRRUREhEtcqfHSSy/1r3yoqqpi8eLFF31NRkYGGRkZ/XP/8z//s/9cJSUl5ObmUlZWRnp6+ojnMZvN1NTU4HQ6MZvNWCwWamtrsdvtwxYG8vPzMZvNFBUVDTleUVGBx+PhscceG3DcarWO2CAcYNWqVZjNZlavXj3ivNLSUjweDw6HA7/fTyAQwGw2s2fPHvXSkIj64Gg9Da1dmJPjWZM7a9B4w5uvgmGQev1qEuddEf6AIiIiIpcgkvcsIhJ9QsEgdS/+Eeh9mCve0ntvVHnwOOdbOpmdnsTD1y2IYEIRERHpM+aixuOPP05mZibV1dXjEmDz5s1s3rwZj8fTvyrihhtuGPE1ZrMZl8s16vew2Wwj9rSA3sLHWPtxjPbcl/seIhNp++e9qzTuWzaX+NiBi7e6mxpp2rcHAMu9WqUhIiIi0WEy3LOISHRp9LxG8PxZ4sxZWO7t3XauuSPIXw4cA+CxW3JJiJuwtqQiIiIyBmMqajz++OM4HA5Wrlw57kH6fuH/X//1XwC6SRAJg7oLHXx4rB6ADSsGbz3l97yO0R0kyXolyVdeG+54IiIiImOmexYRGavu5ibqd2wDYNbmfycmqbfP4F8+PkpLZzdLZqZiv3pOJCOKiIjID4z6MYNPPvmEsrKyCbk5+KGnn36a2traCX0PEen1+penCRmwcn4mV2TOGDAW6mince/rAFju3YTJZIpERBEREZFR0z2LiFyK86/+nVB7G4lXWMm49S4Azl3ooPLgCQAeX7OU2BjdD4mIiEwWo16pMdE3Bj+0efPmsL2XyHQVMgxe+75B+IYhGoQH3t1NqK2VhJy5pNluCnc8ERERkTHTPYuIjFXnqeME3n4TgJyf/hZTTO+zn89/UEtXT4iV8zO5eZElkhFFRETkR7QhpMg0deC7Bs42d5CWGMdPlmYPGDO6u2l4858AZK1/CFNMbCQiioiIiIiITKhzLzwPRog0283MuHo5AL76FnYdOg3AE2uWatW6iIjIJDPmRuFj0dzcjMvlGvAFwDAMTCYTJSUlpKenT+Tbi8gI+hqEr79mDolxA4sWzR+/R7e/ntj0DDJuuzsS8URERETCQvcsItNXy2c1tH7xCcTGkV30q/7jrv1HCBnwkyXZXDsnI3IBRUREZEgTVtR49tlnMZlM/Od//uegsaamJlwuF7m5uWzatGmiIojIMPytnbznOw8MbhBuGAYNu14GIMu+gZj4hLDnExEREQkH3bOITF9Gdzfntv0egKz8B0jImQvAp6ca2ec7T6zJRMltSyIZUURERIYx6u2njh49ypYtW0Y19+DBg1itVn77298OOZ6RkcF//ud/0tDQwLFjx0YbQUTGya5DZ+gJGSybnUHuzLQBY61feOk8+R2mxCQy77ovQglFRERExk73LCIyWo3vvEnX6RPEpqYzc2MR0PuA1+/eOwzAA8vnsjBrRiQjioiIyDBGXdRYvHgx1dXVWCwW3nrrrRHnVldXs3bt2oues6ioCI/HM9oIIjIODMNgxxe9W09tXDG4QXjDzt5VGpl33kPsjNSwZhMRERG5HLpnEZHR6Gltof7VvwMw8+GfEZvSe9+zz3eez880kRgXw69vtkYyooiIiIxgTI3Cq6qqsNlslJSUUFxczIULF4act3jxYl5++eWLnu/ZZ58lPz9/LBFE5DJ9crKRE4E2kuNjWXtlzoCx9qOHafv6c4iNJWvdxgglFBEREbl0umcRkYup37GNnpYLJMxdQOad9wDQHQrxzL4jABTbFjIrNSmSEUVERGQEYypqALjdbkpLS1m1ahWLFi3ilVdeGTRn7dq1fPzxxzzxxBPs3buX5ubm/rFjx47x8ssvc889vV8cFi1adOnpRWTMtn+/SmPd1bNJSRjYVqdhV++/54yb7iDeMivs2URERETGg+5ZRGQ4XWdP4/e8DkDOI7/BFBsLwBuHznDM30p6Ujw/X7UwkhFFRETkIsbcKHzx4sX9zfQ2b95MUVERzzzzDJWVlaSl/Wtv/qeffpqXXnqJkpISfD4fJpMJ6N36Ji8vj6effnpUy71FZPw0dwR553AdABuWD9x6qqvuDBeq3wcga/3DYc8mIiIiMl50zyIiwzlX8Ufo6WbGChupK/IA6Ozu4bkPagH45Y2LSU2Mj2BCERERuZgxFzV+yGq1Ul1djdvtZtGiRZSXl/Poo4/2j2/evJnNmzcDvU37oPcGQ0Qi441DZ+jqCbF0VhpX56QPGPO/+U8wQsxYkUfSgkWRCSgiIiIyziJ5zxIIBNi6dSsAFouF2tpa7HY7BQUFl3VOp9MJgN/vB2D16tWUlpZe9LVut5uKigrMZjPQe23KysouOYtItGn96jNavB9CTAw5xb/pP1558ATnWzrJSUvi4evnRzChiIiIjMYlFTUCgcCAn0tKSigqKqKkpKT/CaiFCwcu11QxQySyDMNg+xcngd4G4X1PIgJ0NzcReK+3Aablvk0RySciIiIyniJ9zxIIBMjLy6OiogKbzdZ/3OFwcODAgUsqJni9XlwuF2VlZf2FCYDKykry8vKoqakZNsvatWvJz8+nqqqq/7jP58PpdKqwIdOCEerh3AvPA5D5k/UkzrsC6F3N/pePewuav70ll8S42IhlFBERkdEZc0+N4ZjNZl588UWcTicrV67kf//v/z1epxaRcfDlmSaONrSSGBfDuqtnDxhr3PM6RrCLpMVLSblqeYQSioiIiEyscN6zFBYWUlBQMKCgAeByuXC73Xg8njGf0+l04nK5BhQ0AAoKCnA4HDgcjiFf11fQ+HHxwuFw4Ha7x5xDJBo17X+LzuNHiUmewcyHftZ//K8HjnGhsxurJZV7rpkTwYQiIiIyWpdU1DAMY9ixgoICfD4fDQ0NLF26lE8//fSSw4nI+OlrEH73lTkD9ogNdXbQuKe3UZ7l3k0DVnCIiIiIRKtI3rP4fD48Hs+wRYaioqIxr47wer1YrdZhx4uKioYslJSXl+Pz+YZ8P7PZzKpVq8aUQyQahTraqXvpLwDM3FhEXFrvVrx1Fzqo+OQ4AE+sWUJsjO6FREREosGYt596+eWXsdvt7N27F5/PR1ZWFlarFavVSnp67xcDs9mMy+XC4/GwefNm1q1bx//8z/+Me3gRGZ3Wzm72fHMWgI3LB+4RG3jPQ0/rBeKzZ5OWd3Mk4omIiIiMq0jfs7hcLoBhixC5ubm43W4CgcCgVRfD6SuUDMfv9w95rq1bt1JSUjLkayoqKkb13iLRrn7nS/Q0NRKfPZvMtQ/0H3/+w1q6ekLcMM/MLYtnRjChiIiIjMWYVmo0NTXxf//f/zfbtm3DMAzWrl3L2rVrMQwDl8vFf//3f9Pc3Nw/Pz8/nyNHjpCWlobFYuGVV14Z9z+AiFzc7q/P0NEdYlHWDFbMzeg/bvT04H/jVQAs9zyEKUb7x4qIiEh0mwz3LF6vd8RiRV+xo7q6etTntNls+Hw+CgsLhxx3uVwUFxcPOFZZWUkgEBh0XGQ6CTac77/nyS76NTHxvavWjza0sPPL0wA8cftSrVgXERGJImNaqfH000+zZ88eMjIyBhxfuXIlK1euBGDLli1s3bp1wHhZWRkOh4OioiJeeOEFnnvuOdLS0i4zuoiM1o7vt57a8KMG4c0H9hFsqCM2LYOMNWsjFU9ERERk3EyGe5a+1SHD6St4+Hy+UZ/TarVSUlKC2+0mNzcXl8tFfn4+AB6Ph0AgMGiLqW3btgH09/Xwer1UV1ezatWqQb0+RKaquso/YwS7SLlqOWm2f61Md+0/QsiAO5Zks3yOOXIBRUREZMxGvVJj7969lJSUDLo5+LGSkhL27t076LjVaqW6upq8vDwWLVrE888/P/a0IjJm39Y1803dBeJjTaz/QeM7wzBo2PUyAJn59xOTkBipiCIiIiLjYrLcswy3FVSfvoJHIBAY03ldLhdlZWX4fD7sdjsOh4Py8vL+sR/zer39/3d5eTl+v79/Kyq73X5JzcpFokl77Tc0f/gOmExkP/Jo/wNen58O8F7teWJM8PhtSyKcUkRERMZq1Cs1DMMY1XJMk8k0YlO+0tJSCgoKePzxx3nmmWfYs2dP/762IjL+tn/eu0rjziXZmJMT+o+3HjpI5/GjmBISybz7vkjFExERERk3k+WeZbTFioaGhlGf84fZzGYzDocDt9uN2WwetjdGX3HF7XZTWlraf9xms1FRUcHixYupqKjoX/HxY52dnXR2dvb/3LdtVzAYJBgMjjn7VNd3TXRtwmeka24YBmf//hwAabf8hLh5VxAMBjEMg//fu98CcN81c5iblqC/szHSZz38dM0jQ9c9/HTNI2MyXffRZhh1UWPt2rUUFRXxf/7P/+H6668fcs7BgwdxOp28+eabI57LarWye/duXnrpJfx+v4oaIhOkPdjD7q+HbhDu39m7SsN8xzriUvVvUERERKLfdLhncTqd5ObmYhgGTqeT8vJy7HY7JSUlg1Zr9BVXhtoKy2w2k5+fj8PhoLa2dsj32rp1K0899dSg47t37yYlJeXy/zBTVFVVVaQjTDtDXfO0k7XM8X1DKDaOg2k59OzcCcCR1lg+P5dInMlgQYuPnTuH/vzLxemzHn665pGh6x5+uuaRMRmue1tb26jmjamnhtvtJj8/n6amJmw2W/+XY7/fT01NDSaTid27d4/6fJs3bx7L24vIGO399hytXd3MzUhm5YLM/uPtx2ppPfQpxMSQdc/GCCYUERERGV+T4Z7FbDaParWGxWIZ03ntdjtOp7N/ZUVZWRnFxcUUFhbidrvx+/0DVm305RhuJYbdbqeyshKv1ztkj40tW7bw5JNP9v/c3NzMggULWLdu3aQp8kwmwWCQqqoq7HY78d83o5aJNdw1D3V1cvz/+ifdwMz7C7jygUIAekIGL75wAGijaOVCim+1RiZ4lNNnPfx0zSND1z38dM0jYzJd976VwRczpqKG2Wymuroaj8eDx+PB5/MRCASw2WyUlJSwdq0aDYtMJjs+PwnAhuXziPnBVgz+N3pXaaTfeDsJM3Mikk1ERERkIkyGe5aRmoRDb4GlL+tolZeXY7PZBhUobDYbtbW1/dtReTye/jlZWVkEAoFh36cvZ3V19ZBFjcTERBITB/ddi4+Pj/gN72Sm6xN+P77m9W++Qre/nrjMmcy6bzMx34/t/vI0x/xtpCXG8YubrPp7ukz6rIefrnlk6LqHn655ZEyG6z7a9x9TUaNPfn7+sE/7iMjk4Ktv4fMzTcSaTNy3bG7/8a7zZ2n+eD8AlnsfjlQ8ERERkQkVyXuWvobjw+lbxWG1jv4pcZfLRU1NzYjj1dXVVFVV9f+5bTYbPp/voucea8Nykcmsu6mRhtdfAiC78BfEfF+Y6+zu4bn3jwDwixsXk5akX5aJiIhEq5hIBxCRibHji94G4bdZZzIz9V9P2Pnf/CcYIWZcu5KkK7TcWkRERGS82Wy2EQsFfYWGsRRdfD7fRVd2OByOAe+7evVqYPiiRd+KkaFWaYhEq7qX/kqoo50k65Wk33RH//GXDp6grqWTnLQkNt+wIIIJRURE5HKNuqhx7NixCYwRufcSmYo6u3t446szAGxYMa//ePeFZgLv9Tb9sdy3KSLZRERERCbKZLlnKS4uBsDr9Q45fuDAgTGvIrFarRdddVFbW0teXl7/zwUFBQB4PJ5h5wOsWrVqTFlEJquO4z6a9vV+3nMeeRRTTO+vPC50BPnzx0cBePSWXBLjYiOWUURERC7fqIsamZmZPPHEExOZBYC9e/cO+6VbREbn3SN1NHcEyUlL4qaFM/uPN+7didHVRdLCXFKuuS6CCUVERETG32S5Z+nrfbFt27YhxysrK3E6nYOOBwIBnE7nkOcuKCgY8jU/fK3X66WoqKj/mNVqpaCggK1btw6bo7S0dEy9PUQmK8MwOPeP58EwSL/xdlKWXtM/9tfqY1zo7GaxZQbrr5kTwZQiIiIyHkZd1MjIyKC0tJSioiIOHjw4IWH+67/+C6/Xy29/+9sJOb/IdLH9+62n7r92LrExvQ3CQ52dNHpeA8By7yZMP2gcLiIiIjIVTKZ7loqKCiorKwet1nA4HJSWlg65UsPtdlNeXk5hYeGgsbKysv7X/3g7Ka/XS2FhIWVlZYMKFM8++yyBQAC32z3geGFhIVartf+8ItGu5ZOPaPv6c0xx8WQX/rL/+PmWDl70Hgfg8duW9t8fiYiISPQaU6PwxYsX8+KLL1JUVITJZGLLli3ccMMNlxWgubkZl8vFiy++SFlZGXffffdlnU9kujsZaMN7ohETvUWNPoH9e+hpaSZ+Vg5pq26NXEARERGRCTRZ7lnMZjM1NTU4nU7MZjMWi4Xa2lrsdnv/tlA/lp+fj9lsHrDa4ocqKirweDw89thjA45brVaqqqpGzLF169b+YkkgEKCwsJCSkpKL/jlEooHRHaTuxT8CkHXPg8TPzO4fe/4DH109Ia6ba+Y268xhziAiIiLRZExFjT4vvvgin3zyCaWlpRw9epSCggLsdjurVq0iPT39oq8/duwYHo+HF198kaNHj+JwODhw4MClRBGRH9nxee8qjZsWWZidngyA0dOD/41XAci65yFMsdpDVkRERKa2yXDPYjabcblco55vs9lobGwccU5+fv6Y+3GYzWatyJAprentN+k6d5rYdDOW+/9VNPzO38rrX/beH/3H7Uu1Wl1ERGSKuKSiBsDKlSvZvXs3R48exeVyUVpaitfrJTMzE6vVCkBWVhZms5lAIIDf7ycQCODz+TCbzeTn5+N0Olm7du24/WFEprvunhA7D50GYOOK+f3HL9R8QPD8WWJT0zCvGdtNsIiIiEi00j2LyNQX09WBf/erAMza9G/EJqf0jz2z/wghA27PncWKuebIBBQREZFxd8lFjT6LFy/m6aef7v/5k08+we/34/P5CAQCNDQ0YLFYMJvNWK1WrFYrixcvvty3FZEh7POdx9/WRVZKArct7l1abRgGDbteBiBz7f3EJCZGMqKIiIhI2OmeRWTqsnz9CaG2VhIXLMJ8+78KkF+cDvDukTpiTOC4bUkEE4qIiMh4u+yixo+tXLkSQE8ziUTAju8bhN937VziYmMAaPvqMzqOHcGUkEDm3fdHMp6IiIjIpKB7FpGpoevMScxHvwIg55FHMcX0brNrGAb/s+8w0HtvtNiSGrGMIiIiMv5iIh1ARMbH2eZ2PjrWAMCG5fP6j/et0jCvyScuPSMi2URERERERMZbfeWfMRkGKdetYsay6/uPf3C0nk9PBUiIjeHRm3MjmFBEREQmgooaIlPE61+exgDyFmQx39y7j2zH8aO0fvEJmGLIuuehiOYTEREREREZLy1ffkLb5zUYJhMzC3/Zf7wnZPC7fUcAKFx5BdlpSZGKKCIiIhNERQ2RKaAnZPD6l30Nwgev0khffRsJ2bMjkk1ERERERGQ8GT091P3j9wAEFi8jIWdu/9ibX53B19BCWmIc/7Z6UYQSioiIyERSUUNkCvjou3rOXeggPSme23NnARCsr6P54/cAyLr34UjGExERERERGTeBd6voPPUdMSmpNFy9sv94Z3cPz31QC8C/37iY9KT4SEUUERGRCTTujcJFJPx2fN7bIHz9NXNIjOttjtew+58QCpGy7HqSFy2JZDwREREREZFx0dPWyvlX/gZA1oYiQp2m/rFXPj3JuQsdZKcmUnDDgkhFFBERkQmmlRoiUa6+pZP9vnrgX1tPdbc0E3hnNwCWezdFLJuIiIiIiMh4anitgp4LTSTMnkfGnff0H2/pDPKnj48C8Ogtuf0Pe4mIiMjUo6KGSJTbeeg0PYbBijkZLLakAhDYuwujq5PEBYuZce0NkQ0oIiIiIiIyDrrqzuKv2g5AdvFvMMX9a/OJv1V/R3NHkEVZM1i/bE6kIoqIiEgYqKghEsVChsGOL3q3ntqwYn7vsa5O/J7XgN5VGiaTadjXi4iIiIiIRIu6ij9idHczY9n1pF6/qv94fUsn27zfAfD4miXExehXHSIiIlOZ/pdeJIp5T/g53dTOjIQ47r4yB4Cm/W/Rc6GJeEs26atvi3BCERERERGRy9f27ZdcqH4fTDFkP/LogIe3/nTgGJ3dIa6ba2aNdVYEU4qIiEg4qFG4SBTb/n2D8HVXzyY5PhYj1EPDG68AkHXPgwOWY4uIiIiIiEQjIxTi3D+eB8B8h52kBYv6xxq6TLx+9AzQu0pDK9VFRESmPq3UEIlSgfYu3q2tA/7VIPyC9yOCdWeInZGG+Q57JOOJiIiIiIiMi6YP3qbj2BFikpKZ9fDPBoy91xhPyIA11llcPy8zQglFREQknMZc1Dh48CDFxcU88cQTfPrppxORSURG4Y1DZwj2GFyVncaV2ekYhkHDzpcAyFx7HzGJSRFOKCIiIhIZumcRmTpCnR2cf+kvAFgeKCIu41+Fiy/PNvFtaxwxJnDctiRSEUVERCTMxlzU+O1vf0tlZSVutxubzcaNN944qhuF//7v/76kgCIymGEYbP++QfjG7xuEt33zJR1HD2OKTyBz7f2RjCciIiISUbpnEZk6Gna9QndjA/Ezs8lat6H/uGEYuN/3AXDP1bOxzkyNVEQREREJszEXNbxeLwArV65k7dq1VFdXY7PZ+O6770Z83ccff8yNN954aSlFZIDPTgf4zt9KUlwM9qtmA9Cw62UAMtasJS7dHMF0IiIiIpGlexaRqSHY2NB/n5Nd9Cti4hP6xz44Vs+np5uINRn8+sZFEUooIiIikTDmoobVaiU/P5/q6mp2796N3+/nt7/9Lc8888yIr3v22Wfp6elh/fr1lxxWRHr1NQjPv2o2MxLj6DhxjNbPqsEUg+WeByOcTkRERCSydM8iMjWcr/wzRlcnyUuvIW3Vbf3He0IGz+w7AkBeejfZadp6V0REZDq5pKKG1Wrt/9lsNuNyudi6deuIr8vIyGDPnj3U19fzyiuvjD2piABwoSPIW4fPAbDh+wbh/jd6/02l5d1MQs7ciGUTERERmQx0zyIS/dqPHqbp/bcAyHnkt5hMpv6xqq/PUFvfQmpiHDeZg5GKKCIiIhEy5qKG3W7H7/df0puZzWY8Hg+lpaWX9HoRgapvztLZHcJqSeXa2RkEG87T9NG7AFju3RzhdCIiIiKRp3sWkehmGAbn/vE8AOm3/IRk69L+sa7uEM9+UAvAz2xXkBwbkYgiIiISQWMuapSUlPTvUXspzGYzK1eu5Lnnnrvkc4hMV4Zh9G89tXHFPEwmE/6q7dDTQ8rVKwZ82RcRERGZrnTPIhLdLlS/T/vhQ5gSEsgu+MWAsVc+O8HZ5g5mpSay+fp5EUooIiIikTTmokZGRgYrV668rOXYVquVioqKS369yHT19blmDp+/QEJsDPdcM4ee1hYC7+wGwHLvpginExEREZkcdM8iEr1CwS7qKv4IgGX9JuKzZvaPtXQG+dNHRwF49JZcEuO0TENERGQ6iruUF7nd7v59aq+//voxv97n81FdXX0pby0yre34oneVxp1Ls0lPiqf+tVcJdbSTOG8hM1bYIpxOREREZPLQPYtIdGqs2kHw/DnizFlY7hv44Nbfq7+jqSPIwqwZ3LtsDkZPT4RSioiISCSNeaUG9C7Hfvrpp7HZbHz66adjfr3H4yEQCFzKW4tMW21d3ez++iwAG5fPIxTswl+1AwDLfZsGNM4TERERme50zyISfbqbA9TveBGAWZv/nZjEpP6x+pZOXvB+B8Djty0hLuaSfp0hIiIiU8AlfwsoKSnhf/2v/4XNZuN//+//PerXPf744wQCAWw2PVUuMhZ7vj1He7CHBeYUVs7PpOn9t+hpDhCXNZP0G2+PdDwRERGRSUf3LCLR5fwrfyfU0U7SoiVk3HrXgLE/fOSjszvE8jkZ3J47K0IJRUREZDK4rEcbysrK2Lp1K08//TSxsbH8x3/8B3v37h1y7rFjx7jnnnt49tlnMZlMrFq16nLeWmTa6WsQvmH5PDBC+N94FYCsdQ9iirukneREREREpjzds4hEh44Tx/r7Beb89FFMP1iJcbyxlR3f3w89sWapVqmLiIhMc5f9m9DS0lLy8/MpLCzkmWeeweVyAWCz2cjKysLv9+Pz+fqXbhuGQWZmJmVlZZf71iLTRm39BQ6dbSI2xsS9y+Zw4ZOP6Tp7ipiUGZjvsEc6noiIiMikpnsWkcnNMAzqtv0ejBBpq24l5cprB4y79x+hxzC4bfFMbpifGaGUIiIiMlmMyyaUNpuN2tpadu/ezd13341hGNTU1FBVVUVNTQ2NjY0YhoFhGNhsNqqrq0lPTx+PtxaZFvpWadxunUVmSgL+XS8DkHn3fcQmp0QymoiIiEhU0D2LyOTV8lk1rV8exBQXR3bhrwaMHTrbxFuH6zABj69ZGpF8IiIiMrmM6541+fn55Ofn09TUhMfj4cCBA/h8PrKyssjNzcVms7F27drxfEuRKa+zu4c3vzoDwMYV82g//BXttd9giosnK/+BCKcTERERiS66ZxGZXIzubupe+D0AmfYNJGTP/teYYfA/7x0G4N5lc7HOTI1IRhEREZlcJmQj/oyMDDZv3szmzZsn4vQi08rbh+u40NnN7PQkVi+0cOr/+wwAGbfdTVyGll6LiIiIXArds4hMDo1vv0HX2VPEpmUw84GiAWMffdfAJycbSYiN4dFbrBFKKCIiIpPNuGw/JSITZ/vnJwF44Np5BE+foOXgx2AykbX+ocgGExERERERuQw9rS3Uv/oPAGY9/DNiU2b0j4UMg9/t612lsfmGBcxOT45IRhEREZl8VNQQmcS+87dy8FSAGBPcf+1cGt54BYA0280kzp4X4XQiIiIiIiKXrn77NnpaL5A47wrMd6wbMFb19VmOnG8hNTGOf1+9KDIBRUREZFKakO2nJlogEGDr1q0AWCwWamtrsdvtFBQUXNY5nU4nAH6/H4DVq1dTWlo65Hy3201FRQUOh4P8/HzMZjM+nw+v18u2bdvYsmULNpttwnPL1Lbji94G4bcsmklmdytHPngHAMu9myIZS0RERERE5LJ0nT2Nf8/rAGQ/8iim2Nh/jXWHePb9IwD826pFZCQnRCSjiIiITE5RV9QIBALk5eVRUVExoGjgcDg4cOAAZWVlYz6n1+vF5XJRVlaG2WzuP15ZWUleXh41NTVD5vB4PHg8ngHHzWbzoGwTlVumtmBPiF2HTgOwYcU8/FXboaeblKuuJTn3qginExERERERuXTnXvwD9HQzY0UeqctXDhh79bOTnGnuYOaMRApXXhGhhCIiIjJZRV1Ro7CwkIKCgkFFA5fLRWZmJna7nfz8/DGd0+l0UlVVNeh4QUEBfr8fh8OBy+UaNO5yuaitrcXn85GVlUVeXh4lJSVhyy1T277a8wTag8yckciNOckce+sNALLWa5WGiIiIiIhEr9avPqPlk48gJoacR34zcKyzmz997APg0VusJMXHDnUKERERmcaiqqjh8/nweDxDFhgAioqKKCsrG1NxwOv1YrVahx3vO+dwYz9c2TGcicgtU9/277eeuv/aubS8u5tQRzsJcxeQel1ehJOJiIiIiIhcGiPUw7l/PA9A5k/Wkzh3wYDxv9ccI9Ae5IrMFO67dm4kIoqIiMgkF1WNwvuKAsMVIXJzc/F4PAQCgVGfs6/gMBy/3z+qwsVIJiK3TG1nmto58F0DAPddNQt/1Q6gt5eGKSaq/tmKiIiIiIj0a9q3h84TR4lJnsHMh342YKyhtZMXar4D4PE1S4nTvY+IiIgMIaq+IXi93hELDH1Fg+rq6lGf02az4fP5KCwsHHLc5XJRXFw8ppw/NhG5ZWrb8eUpDGDVFVmkHvqY7oCfOHMWGTffEeloIiIiIiIil6SnvY26l/4KwMwHi4lLSx8w/ocPfXR0h7h2TgZ35M6KREQRERGJAlFV1OjrXTGcvsKBz+cb9TmtVislJSVUVlb2r5jo07d6orS0dMRzeL1e3G43Xq83bLll6uoOhXj9i94G4RuvnUPDG68AkLXuQUxx8ZGMJiIiIiIicskadr5ET3OA+Ow5ZK29f8DYicbW/i14n1izBJPJFImIIiIiEgWiqqeG3+8fsf9FX+FgrNs4uVwucnNzcTqd2O12SkpKyM3NxWazDdsHA3qLHj6fj/z8fEpKSvB6vdjtdpxO54D+GOORu7Ozk87Ozv6fm5ubAQgGgwSDwdH+USeNvszRmH2ivX+0nvrWTjKS4rmuyUf9mZPEJKWQetvdul6XQZ85CSd93iTc9JmTiaDPk4iMp2B9Hf43XgUgp/jXgx7Ycr9fS0/I4NbFM1k5f/iHAkVERESiqqgx2mJFQ0PDmM9dWlqK2WzG4XDgdrsxm81UVFQMO7+vSPHDVRw2m42KigoyMzOpqanBZrONW+6tW7fy1FNPDTq+e/duUlJSRnX+yaiqqirSESadl84mAHFcmdjGiRdfIRmoX7CEr996O8LJpgZ95iSc9HmTcNNnTsZTW1tbpCOIyBRSV/knjO4gKVevIHXlTQPGvjrbxN5vz2ECHl+zJDIBRUREJGpEVVFjIjmdTnJzczEMA6fTSXl5ef+qjaFWaxQUFAx5HrPZTEFBAYWFhdTW1o5bvi1btvDkk0/2/9zc3MyCBQtYt24d6enpI7xycgoGg1RVVWG324mP15ZKfc63dPLff/oAgP9YPpOe985BXBy2x/6fxJn1tNLl0GdOwkmfNwk3feZkIvStDBYRuVxtR76m+aP3wGQi56ePDthayjAMfrfvCAD3XDOH3JlpkYopIiIiUSKqihpms3lUqx4sFsuYzvvjLaPKysooLi6msLAQt9uN3+8fcdXGj61evZrKykp8Ph9Wq3VccicmJpKYmDjoeHx8fFT/8iLa84+33d+eIGTA9fPMJH+4mxYg45a7SJ6VE+loU4Y+cxJO+rxJuOkzJ+NJnyURGQ9GKMS5fzwHQMaatSRdMXBr5o+P+6k54Sc+1sRjt+ZGIqKIiIhEmahqFD5Ss23o7V0B/2q8PRrl5eXYbLYBPTCgdyup2tra/ibiP2wgfjF979/XOHwicsvUEzIMXvu+Md6mObG0HPwYAMu9D0cyloiIiIiIyCVr/ug9OnzfYkpMYtamfxswFjIMfvfeYQA2X7+A2enJkYgoIiIiUSaqihpWq7W/ADCUvtUQIzXl/jGXy8WWLVtGHLfZbAP2qHY4HOTmjv4JkonILVPPgeN+zjR3kJYYx9WH94FhkLryJhLnzI90NBERERERkTELdXZSV/knAGbeX0D8j7bU9XxzlsPnLzAjIY5f3Lg4EhFFREQkCkVVUcNms424jZPP5wMYtOpiJD6f76IrJBwOx4D3ra6uHlWRoq9R+ETklqlnx+cnAdiwcAYtH7wNgOXeTRFMJCIiIiIicun8u/9Jt7+eOMsssu55cMBYsCfEs/t7+1D+fPUiMpITIhFRREREolBUFTWKi4uBf23r9GMHDhwYc2HAarX2FxWGU1tbS15eXv/P+fn5NDY2Djv/wIEDmM3m/pUXE5FbppbGti7eqz0PQH7Dpxjd3SQvvYaUpddEOJmIiIiIiMjYBQN+6l+vBCC74BfEJAzsEfnqZyc53dyOZUYCRSuviEREERERiVJRVdTo632xbdu2IccrKytxOp2DjgcCAZxO55B9MQoKCoZ8zQ9f6/V6KSoq6j9WXFyM2+0ecr7P56OyspJnn332snPL9LHz0Gm6QwY3zEyED/cCWqUhIiIiIiLR6/zLf8Xo7CA59yrSb7pjwFhrZzd//Kj34cLf3JxLcnxsJCKKiIhIlIqqogZARUUFlZWVg1Y9OBwOSktLh1zx4Ha7KS8vp7CwcNBYWVlZ/+t/vEWU1+ulsLCQsrKyAVtU9W0nVV5ePmC+z+cjLy+P0tJSCgoKLju3TA+GYbDj+wbhxd0+Qu2tJMyZT+r1qyOcTEREREREZOw6vqulad8eALIfeRSTyTRg/B813xFoD7IgM4UHrp0biYgiIiISxeIiHWCszGYzNTU1OJ1OzGYzFouF2tpa7Hb7oEJCn/z8fMxm84DVFj9UUVGBx+PhscceG3DcarUOaBD+Q6WlpXg8HhwOB36/n0AggNlsZs+ePf29NC43t0wPn54KcKKxjdRYmP3Z2/QAlvUPY4qJupqjiIiIiIhMc4ZhcO6F34NhkH7T7aQsuXrAuL+1kxe83wHguG0JcbG67xEREZGxibqiBvQWCFwu16jn22y2EXtgQG/hY6yrJcb6mrHmlulh++e9qzR+mXiWnoCfOHMW6bf8JLKhREREROSyBQIBtm7dCjBuDzX1ba0L4Pf7AVi9ejWlpaVjOo/D4cDpdPb3ARQZLy2ffETb159jik8gu+CXg8b/8NFR2oM9LJudzk+WZEcgoYiIiES7qCxqiEwVzR1B3jp8DpNhcMPR9wHIzN9ATHx8hJOJiIiIyOUIBALk5eVRUVExYCW3w+HgwIED/dvgjoXX68Xlcg3aHreyspK8vDxqampGfR63243D4RhzBpGRGN1Bzm37AwBZ9zxI/MyBRYuTgTb++flJAJ5Ys3TQtlQiIiIio6GihkgEvfnVGbp6QtxjOofp3ClikpLJvOueSMcSERERkctUWFhIQUHBoK1pXS4XmZmZ2O32Ma8UdzqdQ26PW1BQgN/vx+FwjGpleN9KD5Hx5t/zOsG6M8RmZGK5b/Ogcff+I/SEDG5eZMG2ICsCCUVERGQq0OaVIhHywwbh9zV8AoD5J+uJTUmNZCwRERERuUw+n6+//95QioqKxrxSw+v1jrhVVFFRER6P56LncbvdFBYWjum9RUaj+0Iz9f/cBkD2pp8Tm5wyYPzrc83s+fYcJuDxNUsjkFBERESmChU1RCLk0NlmautbuLLjHDNOHYHYOLLsGyIdS0REREQuU99qieGKELm5uXg8HgKBwKjP2VcoGY7f7x+wJdVw5xgpl8jlqH/174TaW0m8YjEZa9YOGv/dvsMA3HPNHJbOSgt3PBEREZlCVNQQiZDtX/TuJfuzti8ByLjlTuKzZkYykoiIiIiMA6/XO2KBoa+oUF1dPepz2mw2fD7fsKssXC4XxcXFI57D5XJRUlIy6vcUGa3OU8dpfPsNAHIeeRRTTOyA8Y+/a6D6uJ/4WBO/vSU3EhFFRERkClFRQyQCWru62fPNOXI6G5l36hAAlvUPRziViIiIiIwHn89HVtbw/QL6Ch59KydGw2q1UlJSQmVlZf9Kjz59qz5KS0uHfX1lZaUag8uEObftDxAKkbryJmZcc92AsZBh8Mz3qzQevm4BczKSIxFRREREphAVNUQiwPP1WdqDPWxq/gKTYZB6/WoS510R6VgiIiIiMg4uthVUX8FjLNtPQe9Ki7KyMnw+H3a7HYfDQXl5ef/YcAKBAD6fT9tOyYRo+dxL6+c1EBtHTtGvB43v/fYc39RdICUhll/etDgCCUVERGSqiYt0AJHpaPsXp0gPtrKy7gsALPdtinAiERERERkvoy1WNDQ0jPncpaWlmM1mHA4Hbrcbs9lMRUXFiK/ZunXrmBuTA3R2dtLZ2dn/c3NzMwDBYJBgMDjm8011fddkOl0bo6eHc/94DoCMu9Zjsswa8OcP9oRwfb9K45GVC5gRZxrX6zMdr/lkoOsefrrmkaHrHn665pExma77aDOoqCESZt/WXeDrc81s9n9GTE83yblXkbx0WaRjiYiIiEgUcDqd5ObmYhgGTqeT8vJy7HY7JSUlQ67W8Hg82O32S3qvrVu38tRTTw06vnv3blJSUi7pnNNBVVVVpCOETcbRr8g5c5KehERqEsyEdu4cMO5tiuN0cwIzYg3Szx1i585DE5JjOl3zyUTXPfx0zSND1z38dM0jYzJc97a2tlHNU1FDJMx2fHGSxJ4u1vp7V2lk3bsJk8kU4VQiIiIiMl7MZvOoVmtYLJYxnddut+N0OsnPzwegrKyM4uJiCgsLcbvd+P3+Qas2qqqqLmmVBsCWLVt48skn+39ubm5mwYIFrFu3jvT09Es651QWDAapqqrCbrcTHx8f6TgTrqetle88FYSAnE3/xlV33zdgvK2rm2f/8hEQpOT2K3lw+bxxzzDdrvlkoesefrrmkaHrHn665pExma5738rgi1FRQySMOoI97P76LLf7D5EQ7CBh9jzSVt4Y6VgiIiIiMo5GahIOvT03gBH7bvxYeXk5Nputv6DRx2azUVtb278dlcfj6Z9TXl7Oli1bxhb+BxITE0lMTBx0PD4+PuI3vJPZdLk+/jdfIdTSTMKc+cxcez+muIG/Xnip+jiN7UEWmFN46LoriIuduJae0+WaTza67uGnax4Zuu7hp2seGZPhuo/2/dUoXCSM9n57jvaOTtb7DwKQtf4hTDGxkQ0lIiIiIuPKarX2Fy6G0reKYyyNu10u14gFCpfLhc1m6982wOfzYTabx1Q4ERmtrrqzNFbtACCn+DeDChqNbV38veY7AEpuWzKhBQ0RERGZfrRSQySMdnxxitWBw5g7LxCbbibj1rsiHUlERERExpnNZsPj8Qw77vP5AAatuhhJX5FiJA6Hg5qaGgC8Xi8VFRVDNhHve//HHnusf1XJZNhDWaJHXcUfMbq7mXHtSmZclzdo/I8f+WgP9nBNTjp3Lc2OQEIRERGZylTUEAmTYw0tfHaqkf/PeS8AWfYNxMQnRDiViIiIiIy34uJiysvL8Xq92Gy2QeMHDhwYU0EDeld1+Hy+EVd31NbWkpfX+wvmgoICCgoKhpxXWVlJYWEhzz777JD5REbS9s0XXKh+H0wxZD/ym0H9AU8G2nj1s5MAPHH7UvUPFBERkXGnNaAiYbLji1Nc23Kc+R0NxCQlk3nXvZGOJCIiIiIToK/3xbZt24Ycr6ysxOl0DjoeCARwOp1DrvIoKCgY8jU/fK3X66WoqOjSg4tchBEKce6F3wNgvnMdSfMXDprz3Pu1dIcMblpoIW/ByP1lRERERC6FihoiYdDVHWLXoTOsr+tdpWG+Yx2xM1IjnEpEREREJkpFRQWVlZV4vd4Bxx0OB6WlpUOu1HC73ZSXl1NYWDhorKysrP/1fT05+ni9XgoLCykrKxtVD42+7af6/n+R0Wp6/y06jh0hJjmFWQ//bND4N+eaqfrmLACPr1ka7ngiIiIyTWj7KZEweLe2jkz/Sa5pPQmxsWSt2xjpSCIiIiIygcxmMzU1NTidTsxmMxaLhdraWux2+7DbQuXn52M2m4ddbVFRUYHH4+Gxxx4bcNxqtY6qJ4bD4cDn81FdXQ309tToazDeVzQRGU6os4PzL/0FgJkPFBKXbh4055n9RwBYd/VsrsxOC2c8ERERmUZU1BAJgx2fn+Ke73tpZNx0B/GWWRFOJCIiIiITzWw243K5Rj3fZrPR2Ng44pz8/Pwx9+PoM5YsIj/WsPNlugN+4mflkGkf/JDWge8a+Pi7BuJiTDx265IIJBQREZHpQttPiUywk4E2jh/xkddUC0DW+ocjnEhERERERGT0gv56Gt54GYDswl8REx8/YDxkGPxu32EAHr5uPnMzksOeUURERKYPFTVEJthrX5zCXn+QGAxmrMgjacGiSEcSEREREREZtbrKP2N0dZF85TLSVt06aHzvt+f4pu4CKQmx/PImawQSioiIyHSioobIBOruCfHOwSPc5j8EgOW+TRFOJCIiIiIiMnrtvm9p/uBtAHIeeRSTyTRgvLsnhPv7Xho/y1tEZkpCuCOKiIjINKOihsgEev9oPStPVJNg9JC4aCkpVy2PdCQREREREZFRMQyDcy88D0DGrXeRvHjpoDnbvzjFqaZ2slISKLZdEe6IIiIiMg2pqCEygXYe9HF3w2cAzLxv06CnmkRERERERCarCwf20374K0wJicwq+MWg8baubn7/oQ+AX99sJSUhLtwRRUREZBpSUUNkgpy70EHiJ/uY0dOJyZJDWt7NkY4kIiIiIiIyKqFgF3UVfwTAcu8m4jMtg+Zs8x6nsa2L+eZkNi6fF+aEIiIiMl2pqCEyQV7/7Dj55z8BIOf+TZhiYiOcSEREREREZHT8u7cTrK8jLtOC5d6HB403tnXxt+pjAJTcuoS4WP16QURERMJD3zpEJkBPyOD4u28zM3iBnpQ0Mm67O9KRRERERERERqW7qZGG1yoAyC74BTGJSYPm/OkjH+3BHq7KTuOuK3PCHVFERESmMRU1RCbAx8fqueX4hwDMtG8gJiExwolERERERERG5/wrfyfU0U7SoiWk33znoPFTgTZe+ewkAP9x+1Ji1DtQREREwkhFDZEJcGDvu1zRUU93XDyz8u+PdBwREREREZFR6ThxjMC7VQDk/PS3mGIG/9rguQ9q6Q4Z3LjQwqorBvfaEBEREZlIKmqIjLOG1k7mfboXgMSb1xKbmhbhRCIiIiIiIhdnGAZ1LzwPRoi0VbeScuWyQXO+rWtm99dnAXhizZJwRxQRERFRUUNkvL39zkcsazlByGRi8YMFkY4jIiIiIiIyKi2fVtN66FNMcXFkF/5qyDnP7DsCgP2q2VyZnR7GdCIiIiK9VNQQGUeGYdDz9msAtF69mviZ2RFOJCIiIiIicnFGdzd1234PQJZ9IwnZswfNqTnu56PvGoiLMfHYrbnhjigiIiICqKghMq4++fxbrj3/DQBXFTwS4TQiIiIiIiKj0/jWLrrOniI2LQPLhqJB44Zh8D/7DgPw0HXzmWdOCXdEEREREUBFDZFxdXLHS8RgUDdnKRlW7S8rIiIiIiKTX0/LBc7/8x8AzHr458QmDy5YvHW4jq/PNZMcH8uvbrKGO6KIiIhIPxU1RMaJ/3w9C2urAci+f3OE04iIiIiIiIzO+e0vEGptIXHeQsx32AeNd/eEcO3vXaXxs7yFZKYkhDuiiIiISD8VNUTGyaeVFSQY3ZxNm83Vt94S6TgiIiIiIiIX1XnmJI17dwKQ/dPfYIqNHTRnxxenOBloJzMlgeK8heGOKCIiIjKAihoi46Cno4N079u9//ea+zCZTJENJCIiIiIiMgp1L/4RenpIvX4VqdeuHDTe1tXN7z/0AfDrm6zMSIgLc0IRERGRgVTUEBkHX+18jZTuduoT0rnpgfWRjiMiIiIiInJRrYc+peXgxxAbS3bxb4ac8+Inx/G3dTE3I5mNK+aFOaGIiIjIYCpqiFwmo6eHjr07ADi54iekpSRFOJGIiIiIiMjIjFAP5154HoDMu+4lcc78QXMa27r4W/UxABy3LSE+Vr9CEBERkcjTNxKRy3T+w/eY0drIhdgklj+wIdJxRERERERELirwnofOE8eImZHKzAcfGXLOnz8+SltXD1dlp3H3lTlhTigiIiIyNBU1RC6DYRic2l4BgHfBKpYvzI5wIhERERERkZH1tLdx/uW/AjBrYzFxqemD5pxuauflT08A8MSapcSob6CIiIhMEipqiFyGtq8+I6nuBJ2mOGbm368G4SIiIiIiMuk1vFZBT3MTCTlzybz7viHnPPd+Ld0hg1VXZLF6oSXMCUVERESGp6KGyGU4/s8XAXjfsgy7bWmE04iIiIiIiIysq/4c/t3bAcgu/jWmuPhBcw6fv8Dur88Avas0RERERCYTFTVELlHH8aPw7eeEMNF6o52M5IRIRxIRERERERnR+Yo/Y3QHSbnmOlJvuHHIOc/sO4wB5F+Vw9U5g7emEhEREYkkFTVELlHd6y8BcCBjCXffvCLCaUREREREREbWdvgrmj9+D0wmch55dMjtc2tO+PnwWAOxMSYeu3VJBFKKiIiIjExFDZFLEKyvo+XAPgAOWm/BNj8zwolERERERESGZ4RCnHvheQAy1uSTdMXiwXMMg2f2HQbgwRXzmW9OCWtGERERkdFQUUPkEjTs/icmI8Sh1AXk3ZynBuEiIiIiIjKpNX/0Lh2+b4lJSiZ7878NOeftI3UcOttMcnwsv75pcNFDREREZDJQUUNkjLpbmml8ezcAu3PyuO/auRFOJCIiIiIiMrxQZyd1FX8GwHL/ZuIyBq807+4J4dp/BICf5i0ka0ZiWDOKiIiIjJaKGiJjFNi7C4KdHE+aSdZ1Niz6si8iIiIiIpNYw5uv0N1YT5xlFlnrHhxyzmtfnuZEYxvm5HgeyVsY5oQiIiIio6eihsgYhLo6afDsAOCNWTY2rpgf4UQiIiIiIiLDCzY20PD6SwBkF/6SmITBD2W1B3v4/Ye1APzqJiszEuLCmlFERERkLFTUEBmDpv17CV1opj4+jRPzl3PjQkukI4mIiIiIiAzr/Mt/xejqJDn3atJvvH3IOS9+cpyG1i7mpifz0HV6cEtEREQmNxU1REbJCPXQ8MYrAFTNWsl9KxYQG6MG4SIiIiIiMjm1H6ulaf9eAHJ++igm0+D7l0B7F387cAyAx27LJT5WvyYQERGRyU3fVkRG6ULNhwTrztISm8T+rGXcv3xepCOJiIiIiIgMyTAM6l54DgyD9JvvJDn3qiHn/fnjo7R2dbN0Vhr5V80Oc0oRERGRsVNRQ2QUDMOgYdfLALxlWcHK3DnkpCVFOJWIiIiIiMjQLtR8QNs3X2KKTyC74BdDzjnb3M7Ln54A4InblxIzxEoOERERkclGRQ2RUWj75gs6jh4maIpj78zr2KhVGiIiIiIiMkmFgkHqXvwjAFnrHyLeMmvIec++X0uwx2DVgixuvCIrjAlFRERELp2KGiKj0LCzd5XGvqxrSEg3c+vimRFOJCIiIiIiMrRGz2sEz58lzpzFzPs2DznnyPkLvPnVGQAeX7NkyH4bIiIiIpORihoiF9Fx4iitn9cQwkTVzBu479q5xKl5noiIiIiITELdzU3U79gGwKxN/0ZMUvKQ857ZfwQDuPvKHK6ZnRHGhCIiIiKXR7+ZFbkI/xuvAuDNyKUu0cwD2npKREREREQmqfp//oNQexuJV1jJuO3uIed8ctLPB0friY0xUXJrbpgTioiIiFweFTVERhBsOE/TR+8C8MYsG6sWZDHfnBLhVCIiIiIiIoN1njpO49tvAJDz099iihl8y28YBv/z3hEAHlwxjwWZM8KaUURERORyqaghMgJ/1Xbo6aE2fQHHUnLYsEKrNEREREREZHI6t+33EAqRZruZGVcvH3LOO0fqOHS2iaS4GH51kzXMCUVEREQun4oaIsPoaW0h8M5uALZbVpKRFM8dudkRTiUiIiIiIjJYy+c1tH7uhdg4sot+NeSc7lAI1/7eVRqP5C3EMiMxjAlFRERExoeKGiLDaHxrF6GOdvzpOXyZegXrl80hIU7/ZEREREREZHIxeno498LvAcjKv5+EnLlDztv55WmON7ZhTo7nZ3mLwphQREREZPzoN7QiQwgFu/BX7QDgVfP1YDKxUQ3CRURERERkEmp85026Tp8gNjWNmRuLh5zTEezhuQ9qAfjljVZmJMaFM6KIiIjIuInKbzGBQICtW7cCYLFYqK2txW63U1BQcFnndDqdAPj9fgBWr15NaWnpsK/xer24XC78fj9erxez2YzD4aCkpGTQXLfbTUVFBQ6Hg/z8fMxmMz6fD6/Xy7Zt29iyZQs2m+2S88v4anr/LXqaA3SmmvkoYwnXzTWzyJIa6VgiIiIiIiID9LS1UP/K3wCY+dDPiE0Z+r7lxU+O09DaxZz0JB66bn44I4qIiIiMq6gragQCAfLy8qioqBhQBHA4HBw4cICysrIxn7OvOFFWVobZbO4/XllZSV5eHjU1NYNe43a7AXC5XP3HPB4PhYWFlJWVUVNTM+BcgUAAj8eDx+MZcB6z2TzozyKRZYR68L/xKgB7Z62kxxTLBq3SEBERERGRSah++4v0tFwgYe4CMn+yfsg5Te1d/K36GACP3bpE2+qKiIhIVIu6okZhYSEFBQWDigAul4vMzEzsdjv5+fljOqfT6aSqqmrQ8YKCAvx+Pw6HY0DxwufzEQgEBq3iyM/PZ8+ePeTl5VFYWDjonC6Xi9raWnw+H1lZWeTl5Q25qkMi68InH9N19hRGUgqvpVxJamIcd1+ZE+lYIiIiIiIiA3SdO43f8xoAOcW/wRQbO+S8vxw4RktnN0tmpWK/enY4I4qIiIiMu6gqavh8Pjwez4ACww8VFRVRVlY2pqKG1+vFarUOO953zh9yuVxs2bJlyPk2m438/Hw8Hg8+n2/AuYuKigas3pDJxzAMGna+BMCXi1bTGZvA/VfPJil+6JsDERERERGRSKl78Y/Q082M5StJvS5vyDlnm9upPHgcgCfWLCXGZApjQhEREZHxF1VrTvuKGcMVIXJzc/F4PAQCgVGfs69QMhy/3z+oEOHxeFi8ePGw79O3isTr9Y46h0wO7YcP0eH7FuLi+XPsEgA2LNd+syIiIiIiMrm0fv05F7wfQkwMOY88Ouy85z/wEewxsC3I5KaFljAmFBEREZkYUVXU6GvGPZy+Ykd1dfWoz2mz2fD5fBQWFg457nK5KC4uHnAsKyuLQCCAz+cb9ftIdGjY+TIA9Vetxh+bwtU56VyZnRbhVCIiIiIiIv9ihHo494/nATDfeQ+J864Ycl5t/QV2HToNwH+sWYpJqzRERERkCoi67aeysrKGHe8reIyl2GC1WikpKcHtdpObm4vL5erfvqpv1cePt5+qqqoatLXUj3MCwzb/9nq9VFdXs2rVKjUIn0Q6Tx2n5dMDYDLxQsq10Akb1SBcRERERC5RIBBg69atAFgsFmpra7Hb7RQUFFzWOZ1OJ9C7qhxg9erVg/r9/ZDX68XlcuH3+/sfFHM4HOrvF8Wa9r9F53EfMckzmPXQz4ad59p3BAO4a2k218zOCF9AERERkQkUVUUNv98/Yv+LvoLHWLafgt7VGLm5uTidTux2OyUlJeTm5mKz2Ybt3zFSjsrKSmw226A5fX028vPzKSkpwev1YrfbcTqdF+0D0tnZSWdnZ//Pzc3NAASDQYLB4Gj/qJNGX+bJlP389700uq9eycHOJJLiY/hJrmVSZZRLNxk/czJ16fMm4abPnEwEfZ4uTyAQIC8vj4qKigEPMjkcDg4cODDowanR6CtOlJWVDVjBXllZSV5eHjU1NYNe43a7AQbc13g8HgoLCykrK6OmpkZ9/6JMqKOdupf+AsDMDUXEpQ9drDh4spH9R+uJNZkouW1JOCOKiIiITKioKmqMtljR0NAw5nOXlpb2P7Hkdrsxm81UVFSM+Tzl5eUAPPvsswOO9xU4fvgElc1mo6KigszMTGpqakZctbF161aeeuqpQcd3795NSkrKmHNOFlVVVZGOAEBceyuLP3gHE1ARtwiApUldvO3ZHdFcMv4my2dOpgd93iTc9JmT8dTW1hbpCFGtsLCQgoKCQd/xXS4XmZmZ2O32iz7Y9GNOp3PIf+cFBQX4/X4cDseA4oXP5yMQCAxaxZGfn8+ePXvIy8ujsLBQ/+2IMvU7X6KnqZH47Nlk5j8w5BzDMPjdvsMAbFgxjysyZ4QzooiIiMiEiqqixkRyOp3k5uZiGAZOp5Py8vL+VRvDrdb4Ma/Xi9PpHPQ0FjDsEnOz2UxBQQGFhYXU1tYOe+4tW7bw5JNP9v/c3NzMggULWLduHenp6aPKN5kEg0Gqqqqw2+3Ex8dHOg71lX8iYISIz72G9+IWQE+IJ+65kWWzo+/aytAm22dOpjZ93iTc9JmTidC3MljGzufz4fF4hr2PKCoqoqysbExFDa/XO+Jq8b5z/pDL5WLLli1DzrfZbOTn5/evJh/p3DJ5BBvO43/jVQCyC39FzDD/zX+v9jxfnGkiKS6GX9+kv1sRERGZWqKqqGE2m0e1WsNisYzpvD/eAqqsrIzi4mIKCwtxu934/f5RrdooLCzE5XKNeY/c1atXU1lZOeLNRGJiIomJiYOOx8fHR/UvLyZD/p62Fprf7X067ci1d9J1LETuzFSum5+lRnpT0GT4zMn0oc+bhJs+czKe9Fm6dH3FjOG+2+fm5uJ2uwkEAqPe+qmvUDIcv98/6Fwejwe3283Ro0eHfB+bzYbH47lowUQmj7rKP2MEu0i5ajlpebcMOac7FOKZ/UcAKLYtZGbq4PtIERERkWgWE+kAYzFSk3D4V6O8sewJW15e3v+U0g/ZbDZqa2spKSmhsrJyxBsI6C1oXGqzvb68Xq93zK+Vy9f41puEOtpJmHcF/7hgBmDD8nkqaIiIiIjIJelrxj2cvgJCdXX1qM9ps9nw+XwUFhYOOe5yuSguLh5wLCsri0AggM/nG/X7yOTV7vuW5g/fAZOJ7EceHfZ+ZeeXp/nO30pGUjw/W7UwzClFREREJl5UFTWsVmt/4WIofas4xvKU0UhLsvvGbTbbiPvMOp1OVq9ePWiv2j4Oh4Pc3NxRZ5LwCQWDNHp2ANB5y3qONLSSEBvDPdfMiXAyEREREYlWPp9vxAey+goeYyk2WK3W/geucnNzBzx05fF4huydUVVVRW1t7bC9+/ref6TefjI5GIbBuX88B0DGbXeTvGjo+8uOYA+//7D37/WXNy0mNVErrkRERGTqiartp/qWRw+n70v5WPam9fl8F13Z4XA4qKmpGXLM7XaTm5s75AqNvuXk1dXVoyrG6GYi/Jo/eJvugJ+4TAuvxF4BnOMnS7NJT9KXfxERERG5NH6/f8QHrfoKHqPZWveHXC4Xubm5OJ3O/v5/ubm52Gy2Yft3jJSjsrISm8027JzOzk46Ozv7f+7rsxIMBgkGg2PKPh30XZOJuDYXDuyn/cjXmBISydz4yLDvsa3mOOdbOslJS+SBZbOn/N/TRF5zGZ6ue/jpmkeGrnv46ZpHxmS67qPNEFVFjeLiYsrLy/F6vUMWAA4cODCmggb0fsm/WGO82tpa8vLyBh2vrKwEGLKg4fP58Hq9FBQUkJ+fP6hp349zm81m7WMbZkYoRMMbrwCQtvYBdh+pB2DjinmRjCUiIiIiUW60xYqGhoYxn7u0tBSz2YzD4cDtdmM2m0fV/+/HysvLAXj22WeHnbN161aeeuqpQcd3795NSkrKmN9zuhhplf+lMPV0s8jzEvHAeeu1fPP+h0POa++BP51IBkysSmrG8+Yb45pjMhvvay6jo+sefrrmkaHrHn665pExGa57W1vbqOZFVVGjr/fFtm3bhixqVFZWDnnxA4EAW7duxW63Dyp6FBQU4HQ6h70RCAQCeL3eQVtUeb1e/H7/sD00PB5P/3sVFxfjdruHLX5UVlZe0o2IXJ6WTw/QdeYkMckzqM65jvZvj7EgM4Ub5mVGOpqIiIiIyJCcTie5ubkYhoHT6aS8vLx/1cZwqzV+zOv19t8DjbRafMuWLTz55JP9Pzc3N7NgwQLWrVtHenr6Zf9ZpppgMEhVVRV2u534+PFb+e3f9TL+9hbiMi3c+P/4X8QkDt34+5n9tXR+d4JcywyeLLyT2Jip3yNwoq65jEzXPfx0zSND1z38dM0jYzJd976VwRcTVUUNgIqKCvLy8iguLh7wBdzhcFBaWjrkSg232015eTlut5vGxsYBY2VlZf1NvsvKygZsRdX3Zf/Hx/sa9OXn5+NwOAa9n9/vx+Px9L9X37ZZ5eXlA/a59fl85OXlUVpaSkFBwaVeErlEDbteBiDzrvX883Dv9mBqEC4iIiIil8tsNo9qtYbFYhnTee12O06ns/+ep6ysjOLiYgoLC3G73fj9/lE9LFVYWIjL5broPUhiYiKJQ/wCPT4+PuI3vJPZeF6f7qZGAt/ft2QX/ILE1NQh55270MHLn50C4InbryQpMWFc3j9a6DMZGbru4adrHhm67uGnax4Zk+G6j/b9o66oYTabqampwel0YjabsVgs1NbWYrfbh/1Snp+fj9lspqioaMjxiooKPB4Pjz322IDjVqt1yJUfdrsdn8+H2+0eNuePt5IqLS3F4/HgcDjw+/39/Tb27NmjXhoR0Hb4EO2Hv8IUF0eT7S4Obf+GuBgT9y2bG+loIiIiIhLlRmoSDvT327tYb78fKi8v71+5/kM2m43a2tr+7ah+uGJ8KH0PdA234lwml/Mv/41QRztJi5eSfvOdw857/oNaunpCrJyfyc2LxlYsExEREYk2UVfUgN4v/6NdWg29X/R/vELjx/Lz80fdj6O2tnbU732p7yETq2FXby+N9Fvu4oXjvXu13Z47i8yU6fVEk4iIiIiMP6vVSnV19bDjfas4xtJTz+VyUVNTM+J4dXU1VVVVw95zOJ1OVq9ePWD1uExeHcePEniv9yG7nJ8+iikmZsh5vvoWdh06DcATa5Zq5bmIiIhMeUN/KxKZwjrPnKTlk48ASLNvZPdXZwDYsGJ+JGOJiIiIyBRhs9lG3H7K5/MBjOmBJ5/Pd9GVHQ6HY9j3dbvd5ObmDlnQGG1jcwkfwzA498LzYBikrV5DytJlw8517T9CyICfLMnm2jkZYUwpIiIiEhkqasi007dKI3XlTey/EM+Fzm7mpCex+oqRtwkQERERERmN4uJioLdH31AOHDgw5hXcVqu1vxgynNraWvLy8gYdr6ysBBhyyymfz4fH4xlTFpl4LQc/pu2rzzDFxZNd9Mth5316qpF9vvPEmkyU3LYkjAlFREREIkdFDZlWgo0NNH/wFgCWezex/YveZnoPLJ9HjJZpi4iIiMg46Ot9sW3btiHHKysrcTqdg44HAgGcTueQRYaCgoIhX/PD13q93kF9BL1eL36/f9geGh6PRz3+JhmjO0jdtj8AkLVuIwkzc4aeZxj87r3DADywfC4Ls2aELaOIiIhIJEVlTw2RS9XoeQ2ju5vkpddw3nIFn556nxgT3H+tGoSLiIiIyPipqKggLy+P4uLiAUUDh8NBaWnpkCs13G435eXluN3uQT0By8rK+pt8l5WVDdiKyuv14nQ6Bx33+XwUFhaSn5+Pw+EY9H5+vx+Px3PR/oMSXo17d9J17jSx6RlYHigcdt4+33k+P9NEYlwMv7559P1ZRERERKKdihoybfS0t9H41i6gd5XGn75fpXHL4pnMSk2KZDQRERERmWLMZjM1NTU4nU7MZjMWi4Xa2lrsdjsFBQVDviY/Px+z2TxotUWfiooKPB4Pjz322IDjVquVqqqqQfPtdjs+nw+32z1szrE0K5eJ193SzPl/vgDArE3/RmxyytDzQiGe2XcEgGLbQt3PiIiIyLSiooZMG4G33yTU3kbCnPkkXJvHrt/vA2DjcjUIFxEREZHxZzabcblco55vs9kuumoiPz9/1P04amtrR/3eMjnU//MFQm2tJC5YhPn24f+e3zh0hmP+VtKT4vn5qoVhTCgiIiISeeqpIdOC0R3Ev/ufAFjWP8y+o/UE2oPMnJHIzYstEU4nIiIiIiLTXeeZkzTu3QlAziOPYoqJHXpedw/PfdBbsPrljYtJTYwPW0YRERGRyUBFDZkWmj58l+6AnzhzFum3/KS/Qfj9y+cSF6N/BiIiIiIiEll12/4AoRCpN9zIjGXXDzuv8uAJzrd0kpOWxMPXa9W5iIiITD/6ba5MeUYoRMOulwHIsm/gTGuQ6uN+TMCGa+dFNpyIiIiIiEx7rV8epOXTAxAbS3bRr4ad19wR5C8fHwXgsVtzSYwbejWHiIiIyFSmooZMeS2f19B1+gQxScmYf7Ke1788DcCqK7KYk5Ec4XQiIiIiIjKdGaEezr3wPACZd91H4pzhV1/89cAxLnR2Y7Wksu7qOeGKKCIiIjKpqKghU57/+1Ua5rvWYyQl9xc1Nq7QUm0REREREYmswLtVdJ78jpgZqcx68JFh59Vd6KDik+MAPLFmCbExpnBFFBEREZlUVNSQKa299mvavvkSYuPIsm/kg6P11Ld2Yk6O5/bcWZGOJyIiIiIi01hPexvnX/kbALM2PkJsatqwc5//sJaunhA3zDNzy+KZ4YooIiIiMumoqCFTWsOuVwDIuOVO4jMt7Pi8t0H4vcvmEh+rj7+IiIiIiEROw2sV9DQ3kTB7Hpl33zfsvKMNLez8fsX5E7cvxWTSKg0RERGZvvRbXZmyOs+e4oL3QwAs6x+m7kIHHxyrB2DDcjUIFxERERGRyOk6fxb/7n8CkF38a0xxccPOde0/QsiAO5Zks3yOOUwJRURERCYnFTVkyvK/8SoYBqnXryZx3hW8/uVpQgbcMM/MwqwZkY4nIiIiIiLTWF3FnzC6u0lZdj2p168edt7npwO8V3ueGBM8ftuSMCYUERERmZxU1JApqbupkab9ewGw3LeJkGHw2pe9W0+pQbiIiIiIiERS27eHuHBgP5hiyHnkN8NuJ2UYBv/z3mEAHrh2nh7OEhEREUFFDZmi/J7XMLqDJOdeRfLSZRz4roGzzR2kJcbxk6XZkY4nIiIiIiLTlBEKce4fzwFgviOfpAWLh52731fPZ6cDJMTG8JtbrOGKKCIiIjKpqaghU05PexuNe3cCkHXvJkwmE9u/bxB+zzVzSIyLjWQ8ERERERGZxpo/fIeOY0eISUpm1sM/H3ZeT8jgmf29qzSKbFcwKzUpXBFFREREJjUVNWTKCbxbRaitlYTZ80hbeSP+1k7e850HYOMKNQgXEREREZHICHV2UFf5ZwAsDxQSl5E57Nw3vjrD0YZW0hLj+LdVi8KUUERERGTyU1FDphSjuxv/m/8EIGv9Q5hiYtl16Aw9IYNlszPInZkW4YQiIiIiIjJdNex6he7GBuJnZpO1buOw8zq7e3ju/SMA/PKmxaQlxYcrooiIiMikp6KGTCnNH79Hd2M9selmMm69C8Mw2PFFX4NwrdIQEREREZHICDY20LDrZQCyC39FTHzCsHNfOniCupZOctKS2HT9gnBFFBEREYkKKmrIlGEYBg07e28SsuwbiIlP4JOTjZwItJEcH8vaK3MinFBERERERKar85V/xujqJHnpNaStvm3YeRc6gvz546MAPHpLrnoCioiIiPyIihoyZbR+7qXz1HfEJCWTede9AGz/fpXGuqtnk5IQF8l4IiIiIiIyTbUfPUzT+28BkPPIbzGZTMPO/Wv1MS50drPYMoP118wJV0QRERGRqKGihkwZfUu5zXeuI3ZGKs0dQd45XAfAhuXaekpERERERMLPMAzOvfA8AOm3/IRk69Jh555v6eBF73EAHr9tKbExwxc/RERERKYrFTVkSmj3Habt688hNpYse2/DvTcOnaGrJ8SSWalcnZMe4YQiIiIiIjIdXaj5gPZvD2FKSCB787+POPf5D3x09YS4bq6Z26wzw5RQREREJLqoqCFTQsMbvas0Mm66g3jLrIENwpfPH3F5t4iIiIiIyEQIBYPUvfhHACzrHybeMmvYud/5W3n9y957mP+4fanuYURERESGoaKGRL2uc6e5UP0BAFnrHwbgy7NN+BpaSIyLYd3VsyMZT0REREREpqlGzw6C588SZ87Ccu+mEec+s/8IIQPuyJ3Firnm8AQUERERiUIqakjUa3jzn2CEmHHdKpIWLAJgx+e9TzjdtTSHtKT4CKYTEREREZHpqLs5QP2OFwGYtfnfiUlKHnbuF6cDvHukjhgTOG5bEq6IIiIiIlFJRQ2Jat3NAZr27QHof/KptbMbzzdnAdi4Qg3CRUREREQk/M6/+ndC7W0kLcwl49a7hp1nGAb/s+8wAPddO5dFltRwRRQRERGJSipqSFRr3PM6RrCLJOuVpFx1LQBV35ylozvEoqwZXKdl2yIiIiIiEmYdJ78j8PZuAHJ++iimmOFvvT84Ws+npwIkxMbw6M254YooIiIiErVU1JCoFepox7/ndaC36V5fI73tn58EYMPyeWquJyIiIiIiYWUYBnUv/B6MEGl5t5By1fJh5/aEDH637wgARSuvIDstKVwxRURERKKWihoStQLveQi1thCfPZu0vJsB+LaumW/qLhAfa2L9sjkRTigiIiIiItNN2xdeWr/8BFNcHNlFvxpx7ptfncHX0EJaYhw/X70oLPlEREREop2KGhKVjO5uGt58Ffh+lUZMLADbv28QfkduNubkhEjFExERERGR6SgUor7iTwBk5m8gIXv4B606u3t47oNaAP79xsWkJ8WHJaKIiIhItFNRQ6JS84H9dDecJzYtg4zb7gagPdjD7q/VIFxERERERCLDfOxrgmdPEZuazswNRSPOfeXTk5y70EF2aiIFNywIU0IRERGR6KeihkQdwzBo2PUyAFn5DxCTkAjA3m/P0drVzdyMZGwLsiIZUUREREREppme1hYsX3kBmPXwz4hNmTHs3AsdQf708VEAHr0ll8S42LBkFBEREZkKVNSQqNP65UE6TxzFlJBI5t339R/f8YMG4TFqEC4iIiIiImHkf72C2GAnCXMXYL7znhHn/q36GM0dQRZlzVAvQBEREZExUlFDok7fKg3zneuITU0DwFffwudnmog1mbhv2dxIxhMRERERkWmm6+xpmt56AwBL4S8xxQ6/8uJ8SwcvfnIcgMfXLCEuRrflIiIiImOhb08SVdqPHaHt0KcQE4Nl3YP9x3d80dsg/DbrTGamJkYqnoiIiIiITEPnKv4IPd205sxnxrUrR5z7hw99dHaHuG6umTXWWeEJKCIiIjKFqKghUcW/6xUA0m+8nfiZ2QB0dvfwxldnANigBuEiIiIiIhJGrV99Rov3Q4iJ4fzym0ac+52/lde+OA3AE2uWYtK2uSIiIiJjpqKGRI2uurM0H9gPgOXeTf3H3z1SR3NHkOzURG5aODNS8UREREREZJoxQj2ce+F5ADLuWEdXmnnE+e79R+gxDNZYZ3HdvJHnioiIiMjQVNSQqOF/81UwQsxYvpKkKxb3H9/x/ZNO9187j9gYPekkIiIiIiLh0bRvL53HjxKTPIOsDcUjzv3iTIC3j9QRYwLHbUvClFBERERk6lFRQ6JC94VmAvs8wMBVGicDbdSc8GMCHliuBuEiIiIiIhIePe1t1L38VwBmbiwiNi192LmGYfC79w4DcO+yuVhnpoYlo4iIiMhUpKKGRIXGPa9jdHWRtGgJKddc13+8r0H4TYsszE5PjlQ8ERERERGZZhp2vkxPUyPx2bPJXPvAiHM/OFbPwVMBEmJjePSW3DAlFBEREZmaVNSQSS/U2UnjnteA3lUafc30untC7Pyyd+upDcvVIFxERERERMIj2HC+d3tcILvo18TExw87tydk8My+IwAU3LCAnLSkcEQUERERmbJU1JBJL7DPQ0/LBeJnzSYt75b+4/uP1uNv6yIrJYE11lkRTCgiIiIiItNJXcWfMIJdpFy1nDTbzSPOrfr6DLX1LaQlxvHvNy4eca6IiIiIXJyKGjKpGT09/U9AZa1/CFNsbP/Y9s9PAnDfsrnExeqjLCIiIiIiE6+99muaP3oXTCZyfvpo/0ryoXR29+B+vxaAf1u9iPSk4Vd0iIiIiMjo6DfBMqldqH6f4PlzxKamY75tbf/xs83tfHSsAYANK7T1lIiIiIiITDzDMDj3j+cByLjtbpIWjtwf45XPTnLuQgezUhMpXHlFOCKKiIiITHkqasikZRgGDbteBiBz7f3EJCb2j73+5WkMwLYgk/nmlAglFBERERGR6aT5o3dpr/0GU2ISszb/+4hzWzqD/PmjowA8eksuiXGxI84XERERkdFRUUMmrbavPqPju1pMCQlkrr2//3hPyOD17xuEb1SDcBERERERCYNQVyd1FX8CYOZ9m4k3Z404/+/V39HUEWRR1gzuXTYnHBFFREREpgUVNWTSatjZu0rDfLuduLT0/uMffVfPuQsdpCfFc8eS7EjFExERERGRacT/5j/p9tcTlzWTrPUPjTi3vqWTF7zfAeC4bQlxMbr1FhERERkv+mYlk1LHcR+tX34Cphiy7nlwwNiOz08BsP6aOVrCLSIiIiIiEy4Y8FP/eiUA2QW/JCYhccT5f/jIR2d3iOVzMrg9d1Y4IoqIiIhMGypqyKTUsOsVANJvvI2EWbP7j9e3dLLfVw/ARjUIFxERERGRMDj/8l8xOjtIsl5J+s13jDj3RGNb/4NYT6xZislkCkdEERERkWlDRQ2ZdLrqz9H88XsAZK3fNGBs56HT9BgGK+ZksNiSGol4IiIiIiIyjXR8V0vTvj0A5Pz0txctUjz34VF6DIPbFs/khvmZ4YgoIiIiMq3ERTqAyI/539wOoRAzll1P8qLc/uMhw2DHF71PPG1YMT9S8UREREREZJowDINzL/weDIP0m24nZcnVI84/0xHDO6fPYwIeX7M0PCFFREREphmt1JBJpbulmcC7uwHIum/gKg3vCT+nm9qZkRDH3VfmRCKeiIiIiIhMIy2ffETb159jiosnu+CXI841DIO3/fEA3LtsLtaZWlkuIiIiMhFU1JBJpXHvToyuThKvWMyMZTcMGNv+/b609qtnkxyvBuEiIiIiIjJxjO4g57b9AYCsex4kfmb2iPM/Pu7nREcs8bEmHr3FGo6IIiIiItOStp+SSSPU1Ulj1WsAWO7dNGCv2kB7F+/W1gGwcbkahIuIiIjI5BcIBNi6dSsAFouF2tpa7HY7BQUFl3VOp9MJgN/vB2D16tWUlpaGNcd04N+zk2DdGWLTzVjuH/lahQwD9wc+AB5eMY/Z6cnhiCgiIiIyLamoIZNG07499LQ0E2/JJn31mgFjb3x1hmCPwVXZaVyVkx6hhCIiIiIioxMIBMjLy6OiogKbzdZ/3OFwcODAAcrKysZ8Tq/Xi8vloqysDLPZ3H+8srKSvLw8ampqwpJjOuhuaaZ++wsAzNr0b8Qmp4w4v+rrs9TWt5IYY/DzvIXhiCgiIiIybamoIZOCEeqh4c1XAcha/xCm2H9tL2UYBju+33pqg1ZpiIiIiEgUKCwspKCgYEAhAcDlcpGZmYndbic/P39M53Q6nVRVVQ06XlBQgN/vx+Fw4HK5JjzHdFD/6j8ItbWSuGAx5tvXjji3qzvEs+8fAeAmc5CM5PhwRBQRERGZttRTQyaFCzUfEqw7S+yMNMy3D7yp+vx0E8f8rSTFxbDu6jkRSigiIiIiMjo+nw+Px4PD4RhyvKioaMwrJLxeL1br8H0aioqK8Hg8E55jOug8fYLGt3YBkPPIbzDFjNzP79XPTnKmuYOZMxLIS+8OR0QRERGRaU1FDYk4wzBo2PUyAJlr7ycmMWnA+PYvTgKw9qrZzEjU4iIRERERmdz6VksMV4TIzc3F4/EQCARGfc6+AsVw/H7/gC2pJirHdHBu2x8gFCL1hhuZsez6Eee2dnbzp497e2n86sZFxOsOW0RERGTC6SuXRFzbN1/QcfQwpvgEMtfeP2DsQkeQvd+eA9QgXERERESig9frHVRg+KG+IkN1dfWoz2mz2fD5fBQWFg457nK5KC4unvAcU13LF5/Q+lk1xMaSXfzri87/e80xAu1BrshMYf01s8OQUERERESi8rH3QCDA1q1bAbBYLNTW1mK32ykoKLisczqdTqD3KSeA1atXU1paOm45JiL3VNCws3eVRsaatcSlZwwYq/rmLJ3dIRZbZnDtnIyhXi4iIiIiMqn4fD6ysrKGHe8rNPh8vlGf02q1UlJSgtvtJjc3F5fL1d8Lo2+1xY+3kpqIHFOZ0dND3QvPA5C19n4SZ4/8UFVDaycv1HwHwONrlhIXo2cGRURERMIh6ooagUCAvLw8KioqBjS7czgcHDhw4JL2hPV6vbhcLsrKygY8yVRZWUleXh41NTWXnWMick8FnSeP0fp5DZhisNzz4IAxwzDY/n2D8I3L52MymSIRUURERERkTPx+/4j9L/oKDWPd9snlcpGbm4vT6cRut1NSUkJubi42m21Qg/DxyNHZ2UlnZ2f/z83NzQAEg0GCweCYskeDpnfepPPUcWJmpJJx7+aL/hmff/8IHd0hluWkc8sV5v75U/HaTFa65pGh6x5+uuaRoesefrrmkTGZrvtoM0RdUaOwsJCCgoIBhQHo/YKfmZmJ3W7vf2JptJxOJ1VVVYOOFxQU4Pf7cTgcg24SxppjInJPBYHd2wFIW3ULCTlzB4x9fa6Zw+cvkBAbw/plahAuIiIiItFhtMWKhoaGMZ+7tLQUs9mMw+HA7XZjNpupqKiYkBxbt27lqaeeGnR89+7dpKSkjDpzNIgJdrGoqoI44Kx1OV+/8+6I8/1BE9tPJAEmroupY9euXf1jQ91bysTSNY8MXffw0zWPDF338NM1j4zJcN3b2tpGNS+qihp9zfGGegoJoKioiLKysjEVB7xe74hPL/Wd83JyTETuqSCurYULH+8DwLJ+06DxHV/0rtK4c2k26UnxYc0mIiIiIjIZOZ1OcnNzMQwDp9NJeXl5/6qN4e43LtWWLVt48skn+39ubm5mwYIFrFu3jvT09HF9r0irr/wzga4O4mfP45Yn/t+Y4ka+VX7qjS8xOM/NC7N4fMN1QO+ThVVVVdjtduLjdf8SDrrmkaHrHn665pGh6x5+uuaRMZmue9/K4IuJqqJG35f04YoQubm5uN1uAoHAiA3xfqiv4DAcv98/6FxjzTERuaeCzNovINRDytUrSLYuHTDW1tXN7q/PAmoQLiIiIiLRxWw2j2qVhMViGdN57XY7Tqez/2GosrIyiouLKSwsxO124/f7B6zauNwciYmJJCYmDjoeHx8f8Rve8dRVd5amva8DkPPIb0hITh5x/ldnm3jryHlMwH/cceWgazHVrk800DWPDF338NM1jwxd9/DTNY+MyXDdR/v+UdXJzOv1jvhL/76iQXV19ajPabPZ8Pl8FBYWDjnucrkoLi6+rBwTkTva9bS2kHHsGwAs9w1epbHn23O0B3tYYE5h5fzMcMcTEREREblkIzXnht4Hp4AxPdBUXl6OzWYbtLrbZrNRW1tLSUkJlZWVAx7YmogcU1FdxR8xuruZce0NpF63asS5hmHwu31HALjnmjnkzkwLR0QRERER+YGoKmr4fL4Rv5j3fRn3+XyjPqfVau2/AcjNzR1wE+DxeAgEApSWll5WjonIHe2a3nmTmJ5uEuZdwYzltkHjfQ3CNyyfpwbhIiIiIhJVrFZrf8FgKH2rJ0baBvfHXC4XW7ZsGXHcZrMN2At5InJMNW3ffsmF6vfBFEP2I49e9N7j4+N+ak74iY818dituWFKKSIiIiI/FFXbT/n9/hG/cPcVDkbbEK+Py+UiNzcXp9PZvx9tbm4uNpttyH1px5pjPHJ3dnbS2dnZ/3Pf/mLBYHBSdKYfi1Cwi8Ce3uXdafkb6O7uHjDuq2/h0NkmYmNM5F85K+r+fDI59X2O9HmScNDnTcJNnzmZCPo8XTqbzTbiFrd9DzONpaeez+e76IoKh8NBTU3NhOaYSoxQiHP/eB4A853rSJq/cMT5IcPgd+8dBmDz9QuYnT7yNlUiIiIiMjGiqqgx2mJFQ0PDmM9dWlqK2WzG4XDgdrsxm80D9qO9nBzjkXvr1q089dRTg47v3r2blJSUUZ1/ssg49jU5F5oIJs/gg8Y22LlzwLinPh6IJzcpyIdvD38TJnIpfvj0oshE0+dNwk2fORlPbW1tkY4QtYqLiykvL8fr9WKzDV6VfODAgTEXEqxWKz6fb8SHpWpra8nLy5vQHFNJ0wdv03HsCDFJycx6+GcXne/55iyHz19gRkIcv7hxcRgSioiIiMhQoqqoMZGcTie5ubkYhoHT6aS8vLx/1cZQqzXCbcuWLTz55JP9Pzc3N7NgwQLWrVtHenp6BJONjRHq4fj/tZMg0Ji7HPs99wxoANPZ3cP//OEDoJtH167kpoVja54oMpxgMEhVVRV2uz3iTY9k6tPnTcJNnzmZCH0rg2Xs+npfbNu2bchiQmVl5ZBFyEAgwNatW7Hb7YOKDQUFBTidzhEfvPJ6vQO2qLrUHNNBqLOD85V/BsDyQBFx6eYR5wd7Qjy7vxaAn69eREZywkRHFBEREZFhRFVRw2w2j2rVg8Uytl+E2+12nE5n/41DWVkZxcXFFBYW4na78fv9A24exppjPHInJiaSmJg46Phk6Eo/Fs3VBwjWnSEmJZWmRVcNyr/3SD0tnd3kpCVxizWH2Bj105DxFW3/ZiS66fMm4abPnIwnfZYuT0VFBXl5eRQXFw8oKDgcDkpLS4dcIeF2uykvL8ftdtPY2DhgrKysjMLCQhwOB2VlZQO2ovJ6vTidzkHHLzXHdNCw62W6A37iZ2aTtW7DRee/+tlJTje3///bu9fots77zvc/6H6xpU1SsS3Zsq0NO7brOJ6AcpKmdWJbgJ1k7JwmAahmJseJ20Ng5nRmXnTWIpbWrGknMy+4wBdd8yI9U0CdTJLTNKGByc1O0hjbk55OmromuRPbaeTYwVbii3yRCG5Jtm6UtM8LBghJACQAEsAG8f2sxbWkfcOfz3qIvZ/9fy4a2L5JQ++5vg0RAgAAoJauSmostdi2pPIieMvNNTvf2NhYuQfTfKFQSIVCoTwdlWVZ5WMajaMVcXcjz/M0/b2vS5J23vOAvA2VDeXHfjq3QPiD79pDQgMAAABdyzAMTU1NKZlMyjAMDQwMqFAoKBKJKBqNVj0nHA7LMAwNDQ1V3Z/NZmVZloaHhxdsN02z5oiLZuJY62aLJ8rtkquGPqt1G5cedfH2+Yv64j/OrT/yh+8PauvG9S2PEQAAALV1VVLDNE1NTk7W3F8aDbHUPLOLpdPpBYvpVds/OTmpfD5fTmo0Gkcr4u5GZ1/8mc45LyiwYaN23vdR6Yc/WrD/pZm39eNXZrQuID14+7UdihIAAABYHYZhNDSVbSgUqhihsVg4HG54dEWjcax1x//n/yvvwgVtvfm3dOX+31n2+K9O/Uru2Vnt7dumf/6uPW2IEAAAAEtZ1+kAGhEKhZacxslx5nrPNPKQ7zjOsiMkEonEgs9tNI5WxN2Npr/761Eav3ug6py1jz03N0rj/Tfu0lVXbmlnaAAAAAB6wFnnRZ380Q8kSVf//h8qEFh6dHjx7fP6mv0rSVLid27ShnVd1YQGAABYk7rqiezgwYOS5uaMrWZiYqLhxIBpmuWkQi2FQkGDg4NNx9GKuLvN+Vdf0lvPTEiBgAYe+L2K/bOXLuu7PzsmSfrYHYzSAAAAALC6PM/TG1/7S0nSzg/cq63mzcue8z/+8ajOzl7Sb12zQ/fcdFWrQwQAAEAduiqpUVr7Ynx8vOr+XC6nZDJZsd11XSWTSVmWVbEvGo1WPWf+ubZtL5jXttE4mo17LZn+m29Ikq4c/G1tuqZyyPYPC8flnp3VwPZN+u19u9odHgAAAIA17vTk3+vsi0cU2LRJ7/jk/7ns8a+4Z/St516RJP3fd9+87KgOAAAAtEdXJTWkucXxcrlcxaiHRCKhkZGRqiMeMpmMxsbGFIvFKvalUqny+YuniLJtW7FYTKlUqmKKqkbjaCbutWK2eEIn/+H/kyQNfPjjVY/59q8XCP/nt1/LkG4AAAAAq+ry7AW9+egXJUkDH/mENvYv35Eq8/e/0KXLnt5/44Dec11/iyMEAABAvbpqoXBpbpG7qakpJZNJGYahgYEBFQoFRSIRRaPRqueEw2EZhrFgtMV82WxWlmVpeHh4wXbTNJXP51cljmbiXiuK+cekSxe17ZbbtTV4S8X+106e1cSvpiVJD97OwnsAAAAAVlfxicc0e+JNbTD6NfCRTyx7/PNvnNKTL7yhgKR/9bvLT1MFAACA9um6pIY0lyBIp9N1Hx8KhTQzM7PkMeFwuOHREo3G0ejxa8GlM2/J/du/kST112g8PPZPr8qTtP/6fl1rbGtjdAAAAADWuosnZzT9+KOSpHdEH9a6zVuWPee//fBFSdIDt+3Wze+4sqXxAQAAoDHM84OWmvnB93X53FltvvZ6XXHHYMX+i5cv6zs//fUC4e9igXAAAAAAq+v4N/5al8+d1ZYbb9LO375n2eOf/tW0Jl8qauP6gP6v3w62PkAAAAA0hKQGWuby7KyK+W9Lkvo//HEFqqyV8fSvijrx9nkZWzfq7uBV7Q4RAAAAwBp27uVfyv27uSmFr/7UH1Ztk8x32fPKozQ+/u692r1za8tjBAAAQGNIaqBlTv3DD3Tp5Iw29A1o5/s/WPWYx//pNUnSR35rjzZtoDoCAAAAWB2e5+nNr/13ybusK/d/QNveefuy5/yvF97QC2+e1rZN6/WZ9+1rQ5QAAABoVFeuqQH/8y5f1vT3viFJ6r//Ywps2FhxzOmLAT318twC4Q8x9RQAAACAVfTWs5N6+2fPKLBhg66KfXbZ42cvXVb6738hSfr0/htlbN3U4ggBAADQDLrGoyXe+snTuvD6q1q3dbuMDz1Q9ZjnTq/XZU969x5DN/Rvb3OEAAAAANYq7+JFvfm1L0iS+iMf06arrln2nG8994qOnTyrgW2bNBS6odUhAgAAoEkkNdAS09/7uiSp794Pa/3WbRX7L3uenjs9N1DoY3cwSgMAAADA6pn527/Rhddf1ford2rgwdiyx7994aK++I9HJUmPvN/U1o3rWx0iAAAAmkRSA6vuzIs/09lfPK/Ahg3qizxU9Rj75RmdvLhO2zet1703X93mCAEAAACsVZfefksnvvlVSdI7Pv4vtH7b8qPCx6d+bXh54wAARUNJREFUpZkzF7TX2MbUuAAAAD5HUgOrbvq7c6M0dn7gPm00+qse8/jP5hYIj9xytbbQCwoAAADAKjnx7a/p0tuntfna62V88P5ljy++fV5/PfUrSVL8d27ShvU0kwEAAPyMpzWsqvPHXtZbP3laCgTU/+Hfq3rMzJkL+qFzQpL04O172hgdAAAAgLXs/OuvqvjkdyRJV/3+HyqwfvkOVF98+qjOzl7SbVfv0L03X9XqEAEAALBCJDWwqqb/5puSpCve8z5t3n1d1WO+97NjunjZ0zWbL+mmXVe0MToAAAAAa9mb4/9DunRJ29+9X1e86z3LHv+Ke0bfevYVSdK/vvtmBQKBVocIAACAFSKpgVUzOzOtU//wA0nSwEc+UfUYz/P02E9flSTdeeXFtsUGAAAAYG17+2fPzI0aX7dOVx98pK5z/vJHBV287On9Nw5ocG/1qXMBAADgLyQ1sGpmrMflXbyorTf/lrbddGvVY5551dVLM2e0ZeM63XrFpTZHCAAAAGAt8i5f0htf+++SpL57P6LNe/Yue87P3zil/M9flyQlfufmlsYHAACA1UNSA6vi0tkzmvnB9yRJAx+tPkpDkr793NwojQM3X63N1D4AAAAAq8D930/q/Mu/1Lpt27Xr9z5V1zl/8fe/kCTdf+s1eudVV7YyPAAAAKwiXitjVbh/+31dPntGm3Zfpyvevb/qMafOzeoHL74hSXrw9t3tDA8AAADAGnXp7Bkd//pfSZJ2fez3teGKHcueM/GraT39q2ltWBfQ8AduanWIAAAAWEUkNbBi3sVZFZ/4liRp4MMfV2Bd9Wr1/SOv6cKly7pp1xW6lZ5QAAAAAFbB9HdyunTK1aar96j/wEeXPf6y5+m//fBFSdLH79yrPTu3tjpEAAAArCKSGlixk0/9nS66RW0w+rXjt++pesz8BcIfuuNaBQKBNkYIAAAAYC26cOINFb8/18HqqqHPKrBh47Ln/K8X3tDP3zytbZvW6zPv3dfqEAEAALDKSGpgRbzLlzX9va9LkvojD2ndxuqNiJ+9fkqFE29p0/p1uv9Wpp4CAAAAsHLHs1+Wd3FW2269Q1e8533LHn/x0mVlfr2Wxr8YvFF92za1OkQAAACssg2dDgDd7a1np3Th2Mtat2WrjHs+XPO4b//0FUnSfe+8Wju2bNTs7Gy7QgQAAACwxrz++uvacXpGp57+31IgoKs/9Yd1jQb/9k9f1asnz6p/2yYdDF3fhkgBAACw2khqYEWmv/c/JUnGvR/W+m3bqx7z9oWLevLncwuEP/Sua9sWGwAAAIC15cKFC/rud7+rZ555RkHN6s5AQAO/G9aW681lzz1z4aK+8JQjSXrk/aa2baI5DAAA0I14ikPTfjnxDzr7ws+k9RvUH/lYzeOs51/X2dlLur5vm+681mhfgAAAAADWjOPHj2t8fFzFYlGSVPA26M1rb9en7vlIXed/zf6VZs5c0HXGVn2MzlYAAABdi6QGGja/d9SNAzfo3luC2tg3UPP4b5cWCH8XC4QDAAAAaNxPfvITPf7447p8+bI8z5vbGAjorQ2b9YWvjuvBBx/UnXfeWfP8mTMX9NeTv5IkxT9wkzasZ3lJAACAbsWTHBpy/PhxZTIZPfvss5KkX14xoG+97en48eNVj3/hzdN6/o1T2rAuoI/81p52hgoAAABgDTh27Ji+9a1v6dKlS79JaPyaJ+nixYv65je/qWPHjtW8xpf+0dHZ2Uu69eoduvedV7c4YgAAALQSSQ3U7Sc/+YnS6bSKxeKC3lEzp04rk8nomWeeqTjnsV+P0vhg8Cr1bdvUznABAAAArAG7d+/Wnj17ao76DgQC2rNnj3bv3l11/6vuGX3j2VckSf/6d2/SOkaPAwAAdDWSGqjLkr2jPK9q76hzs5f0xPOvSZIeuoM5awEAAAA0LhAI6N57761oh5R4nqf77ruvZtLjL/+hoIuXPb33hgHtv772tLkAAADoDiQ1UJdmekf94MU39Nb5i9qzY6v2X9/frlABAAAArDHBYLBqe6TUDjFNs+p5L7x5Sk88/7qkuVEaAAAA6H4kNVCXZnpHffu5uamnHnzXHoZ4AwAAAGharfbIcqM0/uKHv5AkRW65Ru+8akfL4wQAAEDrbeh0AOgepd5Rr7322oLGRCAQ0O7duxf0jvrl9Ft69pir9YGAPno7C4QDAAAAWJlgMKh4PF6x/Zprrql6/NRLRf3jr6a1YV1Awx8Itjo8AAAAtAlJDdSt1DvqK1/5yoLt1XpHlRYI/+19u/SOK7a0NU4AAAAAa0+pM1U9PM/T//PDFyVJv/fu63Stsa2VoQEAAKCNSGqgIfX0jrpw8bK+97O5BcI/xgLhAAAAANrsBy++qeffOKWtG9frs++rvt4GAAAAuhNJDTSknt5Rf1d4UyfPzeodV2zW+24caFN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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", - "from _analysis._rule_app_analysis import plot_roc_curves\n", + "#from _analysis._rule_app_analysis import plot_roc_curves\n", "\n", "plt.rc(\"text\", usetex=True)\n", "plt.rc(\"text.latex\", preamble=r\"\\usepackage{amsmath}\") # Ensure amsmath is loaded\n", @@ -215,14 +775,14 @@ "plot_roc_curves(\n", " fw,\n", " axs[0],\n", - " selected_types=[\"Complete\", \"Expand\"],\n", + " selected_types=[\"Complete\", \"Refine\"],\n", " fontsettings=fontsettings,\n", " title=\"A\",\n", ")\n", "legend_handles = plot_roc_curves(\n", " bw,\n", " axs[1],\n", - " selected_types=[\"Complete\", \"Expand\"],\n", + " selected_types=[\"Complete\", \"Refine\"],\n", " fontsettings=fontsettings,\n", " title=\"B\",\n", ")\n", @@ -239,7 +799,7 @@ "\n", "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0.15, wspace=0.2, bottom=0.2)\n", - "fig.savefig(\"./fig/ROC_test.pdf\", dpi=600, bbox_inches=\"tight\", pad_inches=0)" + "#fig.savefig(\"./fig/ROC_test.pdf\", dpi=600, bbox_inches=\"tight\", pad_inches=0)" ] }, { @@ -251,9 +811,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 155, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results successfully loaded from ../../Data/Temp/Benchmark/raw_results.json\n" + ] + } + ], "source": [ "import sys\n", "import pandas as pd\n", @@ -272,19 +840,19 @@ "bw = pd.DataFrame(valid_bw).T\n", "fw.rename(\n", " columns={\n", - " 0: \"average_solution\",\n", + " 0: \"AG\",\n", " # 1: r'\\mathcal(C)',\n", " 1: \"C\",\n", - " 2: \"FPR\",\n", + " 2: \"NR\",\n", " },\n", " inplace=True,\n", ")\n", "bw.rename(\n", " columns={\n", - " 0: \"average_solution\",\n", + " 0: \"AG\",\n", " # 1: r'\\mathcal(C)',\n", " 1: \"C\",\n", - " 2: \"FPR\",\n", + " 2: \"NR\",\n", " },\n", " inplace=True,\n", ")\n", @@ -295,39 +863,397 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 198, "metadata": {}, "outputs": [], "source": [ "def gmean(tpr, fpr):\n", " tnr = 1 - fpr # True Negative Rate\n", - " g_mean = np.sqrt(tpr * tnr)\n", + " g_mean = (tpr+tnr)/2\n", " return g_mean\n", "\n", "\n", "# Calculate G-mean for each row and add it as a new column\n", - "fw[\"G-mean-forward\"] = fw.apply(\n", - " lambda row: gmean(row[\"C\"] / 100, row[\"FPR\"] / 100), axis=1\n", + "fw[\"Sc-forward\"] = fw.apply(\n", + " lambda row: gmean(row[\"C\"] / 100, row[\"NR\"] / 100), axis=1\n", ")\n", - "bw[\"G-mean-backward\"] = bw.apply(\n", - " lambda row: gmean(row[\"C\"] / 100, row[\"FPR\"] / 100), axis=1\n", + "bw[\"Sc-backward\"] = bw.apply(\n", + " lambda row: gmean(row[\"C\"] / 100, row[\"NR\"] / 100), axis=1\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AGCNRTypeRadiiG-mean-forwardG-meanSc-forward
Raw_04.047.8694.87Raw00.064950.0563510.06495
Raw_10.797.2478.52Raw10.143600.0239160.14360
Raw_20.336.5069.74Raw20.183800.0146460.18380
Raw_30.234.9268.67Raw30.181250.0106380.18125
Complete_071.1394.5097.18Complete00.486600.8198650.48660
Complete_122.8892.9289.08Complete10.519200.4610870.51920
Complete_25.3088.6067.50Complete20.605500.2166980.60550
Complete_33.2278.0358.88Complete30.595750.1585110.59575
Refine_084.2994.7097.66Refine00.485200.8934350.48520
Refine_123.7893.1689.58Refine10.517900.4706740.51790
Refine_25.7389.1268.68Refine20.602200.2259770.60220
Refine_33.3078.9358.97Refine30.599800.1613910.59980
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" + ], + "text/plain": [ + " AG C NR Type Radii G-mean-forward G-mean \\\n", + "Raw_0 4.04 7.86 94.87 Raw 0 0.06495 0.056351 \n", + "Raw_1 0.79 7.24 78.52 Raw 1 0.14360 0.023916 \n", + "Raw_2 0.33 6.50 69.74 Raw 2 0.18380 0.014646 \n", + "Raw_3 0.23 4.92 68.67 Raw 3 0.18125 0.010638 \n", + "Complete_0 71.13 94.50 97.18 Complete 0 0.48660 0.819865 \n", + "Complete_1 22.88 92.92 89.08 Complete 1 0.51920 0.461087 \n", + "Complete_2 5.30 88.60 67.50 Complete 2 0.60550 0.216698 \n", + "Complete_3 3.22 78.03 58.88 Complete 3 0.59575 0.158511 \n", + "Refine_0 84.29 94.70 97.66 Refine 0 0.48520 0.893435 \n", + "Refine_1 23.78 93.16 89.58 Refine 1 0.51790 0.470674 \n", + "Refine_2 5.73 89.12 68.68 Refine 2 0.60220 0.225977 \n", + "Refine_3 3.30 78.93 58.97 Refine 3 0.59980 0.161391 \n", + "\n", + " Sc-forward \n", + "Raw_0 0.06495 \n", + "Raw_1 0.14360 \n", + "Raw_2 0.18380 \n", + "Raw_3 0.18125 \n", + "Complete_0 0.48660 \n", + "Complete_1 0.51920 \n", + "Complete_2 0.60550 \n", + "Complete_3 0.59575 \n", + "Refine_0 0.48520 \n", + "Refine_1 0.51790 \n", + "Refine_2 0.60220 \n", + "Refine_3 0.59980 " + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fw" + ] + }, + { + "cell_type": "code", + "execution_count": 200, "metadata": {}, "outputs": [], "source": [ "valid_result = pd.concat(\n", - " [fw[\"G-mean-forward\"], bw[[\"G-mean-backward\", \"Type\", \"Radii\"]]], axis=1\n", + " [fw[\"Sc-forward\"], bw[[\"Sc-backward\", \"Type\", \"Radii\"]]], axis=1\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 201, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Sc-forwardSc-backwardTypeRadii
Raw_00.064950.04935Raw0
Raw_10.143600.11200Raw1
Raw_20.183800.16600Raw2
Raw_30.181250.20425Raw3
Complete_00.486600.47800Complete0
Complete_10.519200.49930Complete1
Complete_20.605500.49605Complete2
Complete_30.595750.46425Complete3
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Refine_10.517900.49975Refine1
Refine_20.602200.49795Refine2
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" + ], + "text/plain": [ + " Sc-forward Sc-backward Type Radii\n", + "Raw_0 0.06495 0.04935 Raw 0\n", + "Raw_1 0.14360 0.11200 Raw 1\n", + "Raw_2 0.18380 0.16600 Raw 2\n", + "Raw_3 0.18125 0.20425 Raw 3\n", + "Complete_0 0.48660 0.47800 Complete 0\n", + "Complete_1 0.51920 0.49930 Complete 1\n", + "Complete_2 0.60550 0.49605 Complete 2\n", + "Complete_3 0.59575 0.46425 Complete 3\n", + "Refine_0 0.48520 0.47775 Refine 0\n", + "Refine_1 0.51790 0.49975 Refine 1\n", + "Refine_2 0.60220 0.49795 Refine 2\n", + "Refine_3 0.59980 0.46835 Refine 3" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "valid_result" + ] + }, + { + "cell_type": "code", + "execution_count": 182, "metadata": {}, "outputs": [], "source": [ @@ -338,44 +1264,235 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [ "def gmean(tpr, fpr):\n", + " \n", " tnr = 1 - fpr # True Negative Rate\n", - " g_mean = np.sqrt(tpr * tnr)\n", + "\n", + " g_mean = np.sqrt(tpr * fpr)\n", " return g_mean\n", "\n", "\n", "# Calculate G-mean for each row and add it as a new column\n", - "fw[\"G-mean\"] = fw.apply(lambda row: gmean(row[\"C\"] / 100, row[\"FPR\"] / 100), axis=1)\n", - "bw[\"G-mean\"] = bw.apply(lambda row: gmean(row[\"C\"] / 100, row[\"FPR\"] / 100), axis=1)" + "fw[\"G-mean\"] = fw.apply(lambda row: gmean(row[\"C\"] / 100, row[\"AG\"] / 100), axis=1)\n", + "bw[\"G-mean\"] = bw.apply(lambda row: gmean(row[\"C\"] / 100, row[\"AG\"] / 100), axis=1)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 178, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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AGCNRTypeRadiiG-mean-forwardG-mean
Raw_04.047.8694.87Raw00.0634990.056351
Raw_10.797.2478.52Raw10.1247060.023916
Raw_20.336.5069.74Raw20.1402460.014646
Raw_30.234.9268.67Raw30.1241550.010638
Complete_071.1394.5097.18Complete00.1632450.819865
Complete_122.8892.9289.08Complete10.3185410.461087
Complete_25.3088.6067.50Complete20.5366100.216698
Complete_33.2278.0358.88Complete30.5664440.158511
Refine_084.2994.7097.66Refine00.1488620.893435
Refine_123.7893.1689.58Refine10.3115650.470674
Refine_25.7389.1268.68Refine20.5283220.225977
Refine_33.3078.9358.97Refine30.5690780.161391
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" + ], + "text/plain": [ + " AG C NR Type Radii G-mean-forward G-mean\n", + "Raw_0 4.04 7.86 94.87 Raw 0 0.063499 0.056351\n", + "Raw_1 0.79 7.24 78.52 Raw 1 0.124706 0.023916\n", + "Raw_2 0.33 6.50 69.74 Raw 2 0.140246 0.014646\n", + "Raw_3 0.23 4.92 68.67 Raw 3 0.124155 0.010638\n", + "Complete_0 71.13 94.50 97.18 Complete 0 0.163245 0.819865\n", + "Complete_1 22.88 92.92 89.08 Complete 1 0.318541 0.461087\n", + "Complete_2 5.30 88.60 67.50 Complete 2 0.536610 0.216698\n", + "Complete_3 3.22 78.03 58.88 Complete 3 0.566444 0.158511\n", + "Refine_0 84.29 94.70 97.66 Refine 0 0.148862 0.893435\n", + "Refine_1 23.78 93.16 89.58 Refine 1 0.311565 0.470674\n", + "Refine_2 5.73 89.12 68.68 Refine 2 0.528322 0.225977\n", + "Refine_3 3.30 78.93 58.97 Refine 3 0.569078 0.161391" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fw" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bw" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 179, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "'FPR'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", + "File \u001b[0;32mindex.pyx:153\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mindex.pyx:182\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'FPR'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/homes/biertank/tieu/Documents/Project/TACsy/SynEco/SynTemp/Docs/Analysis/_5_rule_application.ipynb Cell 27\u001b[0m line \u001b[0;36m7\n\u001b[1;32m 3\u001b[0m \u001b[39mfor\u001b[39;00m type_, group \u001b[39min\u001b[39;00m fw\u001b[39m.\u001b[39mgroupby(\u001b[39m\"\u001b[39m\u001b[39mType\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m 4\u001b[0m tpr \u001b[39m=\u001b[39m (\n\u001b[1;32m 5\u001b[0m group[\u001b[39m\"\u001b[39m\u001b[39mC\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m/\u001b[39m \u001b[39m100\u001b[39m\n\u001b[1;32m 6\u001b[0m ) \u001b[39m# True Positive Rate (C is already in percentage, so divide by 100)\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m fpr \u001b[39m=\u001b[39m group[\u001b[39m\"\u001b[39;49m\u001b[39mFPR\u001b[39;49m\u001b[39m\"\u001b[39;49m] \u001b[39m/\u001b[39m \u001b[39m100\u001b[39m \u001b[39m# False Positive Rate\u001b[39;00m\n\u001b[1;32m 8\u001b[0m tnr \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m \u001b[39m-\u001b[39m fpr \u001b[39m# True Negative Rate\u001b[39;00m\n\u001b[1;32m 9\u001b[0m g_mean \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msqrt(tpr \u001b[39m*\u001b[39m tnr)\u001b[39m.\u001b[39mmean() \u001b[39m# Geometric Mean\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/frame.py:4090\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4088\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 4089\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4090\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 4091\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 4092\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", + "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/indexes/base.py:3809\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(casted_key, \u001b[39mslice\u001b[39m) \u001b[39mor\u001b[39;00m (\n\u001b[1;32m 3805\u001b[0m \u001b[39misinstance\u001b[39m(casted_key, abc\u001b[39m.\u001b[39mIterable)\n\u001b[1;32m 3806\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39misinstance\u001b[39m(x, \u001b[39mslice\u001b[39m) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m casted_key)\n\u001b[1;32m 3807\u001b[0m ):\n\u001b[1;32m 3808\u001b[0m \u001b[39mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3809\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3810\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3811\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3812\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3814\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'FPR'" + ] + } + ], "source": [ "g_mean_results = {}\n", "\n", @@ -401,7 +1518,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 135, "metadata": {}, "outputs": [], "source": [ @@ -443,12 +1560,12 @@ "\n", "\n", "# Example usage\n", - "log_file_path = \"../../Data/Temp/Benchmark/Raw/Log/Test\"" + "log_file_path = \"../../Data/Temp/Benchmark/Raw/Log/Valid\"" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 136, "metadata": {}, "outputs": [], "source": [ @@ -466,23 +1583,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 137, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[10067.556, 36444.666, 213271.507, 458275.105]" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "result" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 153, "metadata": {}, "outputs": [], "source": [ "valid_times_compare = {\n", - " r\"$Q_{\\text{raw}}$\": [9169.663, 34687.0292, 212203.163, 450822.167],\n", - " r\"$Q_{\\text{complete}}$\": [10067.556, 36444.666, 213271.507, 458275.105],\n", - " r\"$Q_{\\text{expand}}$\": [10882.231, 36850.825, 215493.644, 465514.313],\n", + " r\"$Q_{\\text{raw}}$\": [7248.640, 19696.1595, 153555.485, 329958.076],\n", + " r\"$Q_{\\text{complete}}$\": [7488.947, 20696.089, 160297.79, 384013.142],\n", + " r\"$Q_{\\text{refine}}$\": [8560.03, 21858.446, 166933.66, 400155.498],\n", "}" ] }, @@ -501,21 +1629,138 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ - "from _analysis._rule_app_analysis import plot_roc_curves, plot_processing_times" + "from _analysis._rule_app_analysis import plot_processing_times" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", + "def plot_processing_times(\n", + " times: Dict[str, List[float]], ax: Optional[plt.Axes] = None, title: str = \"A\"\n", + ") -> None:\n", + " \"\"\"\n", + " Plot processing times for various methods across different stages.\n", + "\n", + " This function takes a dictionary of processing times, converts them into hours,\n", + " and plots them using a bar chart.\n", + "\n", + " Parameters:\n", + " times (Dict[str, List[float]]): A dictionary where keys are method names and values\n", + " are lists of processing times in seconds for each stage.\n", + " ax (Optional[plt.Axes]): Matplotlib Axes object where the plot will be drawn. If None,\n", + " the current active Axes will be used.\n", + " title (str): The title of the plot.\n", + "\n", + " Returns:\n", + " None: The function creates a plot but does not return any value.\n", "\n", + " Example:\n", + " >>> times = {\n", + " ... \"Method1\": [3600, 7200, 1800, 5400],\n", + " ... \"Method2\": [1800, 3600, 900, 2700],\n", + " ... }\n", + " >>> fig, ax = plt.subplots()\n", + " >>> plot_processing_times(times, ax=ax, title=\"Processing Times Analysis\")\n", + " >>> plt.show()\n", + " \"\"\"\n", + " plt.rc(\"text\", usetex=True)\n", + " plt.rc(\"text.latex\", preamble=r\"\\usepackage{amsmath}\") # Ensure amsmath is loaded\n", + " # Convert to hours\n", + " for key in times:\n", + " times[key] = np.array(times[key]) / 3600\n", + "\n", + " # Stages\n", + " stages = [r\"$R_{0}$\", r\"$R_{1}$\", r\"$R_{2}$\", r\"$R_{3}$\"]\n", + "\n", + " # Create a DataFrame\n", + " df = (\n", + " pd.DataFrame(times, index=stages)\n", + " .reset_index()\n", + " .melt(id_vars=\"index\", var_name=\"Method\", value_name=\"Time (hours)\")\n", + " )\n", + " df.rename(columns={\"index\": \"Stage\"}, inplace=True)\n", + "\n", + " # Create the plot on the provided ax\n", + " if ax is None:\n", + " ax = plt.gca() # Get current axis if not provided\n", + "\n", + " custom_colors = [\"#5e4fa2\", \"#3A8EBA\", \"#D6604D\"]\n", + " palette = sns.color_palette(custom_colors[: len(times.keys())])\n", + " bar_plot = sns.barplot(\n", + " x=\"Stage\", y=\"Time (hours)\", hue=\"Method\", data=df, palette=palette, ax=ax\n", + " )\n", + "\n", + " ax.set_title(rf\"{title}\", fontsize=24, weight=\"bold\")\n", + " ax.set_xlabel(None)\n", + " ax.set_ylabel(rf\"Time (Hours)\", fontsize=20)\n", + " ax.set_xticklabels(ax.get_xticklabels(), fontsize=20)\n", + " ax.set_yticklabels([rf\"{y:.0f}\" for y in ax.get_yticks()], fontsize=20)\n", + " ax.legend(\n", + " title=\"Template Type\",\n", + " title_fontsize=\"24\",\n", + " fontsize=\"20\",\n", + " loc=\"upper left\",\n", + " bbox_to_anchor=(0.01, 1),\n", + " )\n", + "\n", + " # Add text annotations on the bars\n", + " for p in bar_plot.patches:\n", + " bar_height = p.get_height()\n", + " if bar_height > 0.01: # Adjust this threshold as needed\n", + " annotation = format(\n", + " p.get_height(), \".1f\" if p.get_height() < 100 else \".0f\"\n", + " )\n", + " ax.annotate(\n", + " rf\"{annotation}\",\n", + " (p.get_x() + p.get_width() / 2, p.get_height()),\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " xytext=(0, 9),\n", + " textcoords=\"offset points\",\n", + " fontsize=20,\n", + " )\n", + "\n", + " ax.grid(True, linestyle=\"--\", alpha=0.6)\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1881606/50864145.py:59: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_xticklabels(ax.get_xticklabels(), fontsize=20)\n", + "/tmp/ipykernel_1881606/50864145.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels([rf\"{y:.0f}\" for y in ax.get_yticks()], fontsize=20)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.rc(\"text\", usetex=True)\n", + "plt.rc(\"text.latex\", preamble=r\"\\usepackage{amsmath}\") # Ensure amsmath is loaded\n", "fig, axs = plt.subplots(1, 1, figsize=(16, 8))\n", "plot_processing_times(valid_times_compare, ax=axs, title=\"A. Time benchmarking\")\n", "# plot_processing_times(test_times_compare, ax=axs[1], title = 'B')\n", @@ -524,9 +1769,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results successfully loaded from ../../Data/Temp/Benchmark/raw_results.json\n" + ] + } + ], "source": [ "import sys\n", "import pandas as pd\n", @@ -548,7 +1801,7 @@ " 0: \"average_solution\",\n", " # 1: r'\\mathcal(C)',\n", " 1: \"C\",\n", - " 2: \"FPR\",\n", + " 2: \"NR\",\n", " },\n", " inplace=True,\n", ")\n", @@ -557,7 +1810,7 @@ " 0: \"average_solution\",\n", " # 1: r'\\mathcal(C)',\n", " 1: \"C\",\n", - " 2: \"FPR\",\n", + " 2: \"NR\",\n", " },\n", " inplace=True,\n", ")\n", @@ -568,9 +1821,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 154, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1881606/50864145.py:59: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_xticklabels(ax.get_xticklabels(), fontsize=20)\n", + "/tmp/ipykernel_1881606/50864145.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels([rf\"{y:.0f}\" for y in ax.get_yticks()], fontsize=20)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -602,14 +1876,14 @@ "legend_handles_fw = plot_roc_curves(\n", " fw,\n", " axs[1, 0],\n", - " selected_types=[\"Complete\", \"Expand\"],\n", + " selected_types=[\"Complete\", \"Refine\"],\n", " fontsettings=fontsettings,\n", " title=r\"B. ROC Curves Validation\",\n", ")\n", "legend_handles_bw = plot_roc_curves(\n", " bw,\n", " axs[1, 1],\n", - " selected_types=[\"Complete\", \"Expand\"],\n", + " selected_types=[\"Complete\", \"Refine\"],\n", " fontsettings=fontsettings,\n", " title=r\"C. ROC Curves Test\",\n", ")\n", diff --git a/Docs/Analysis/_analysis/_plot_analysis.py b/Docs/Analysis/_analysis/_plot_analysis.py index f09bdc1..8c9073d 100644 --- a/Docs/Analysis/_analysis/_plot_analysis.py +++ b/Docs/Analysis/_analysis/_plot_analysis.py @@ -65,20 +65,20 @@ def plot_top_rules_with_seaborn( # Add labels on top of each bar for p in barplot.patches: ax.annotate( - f"{p.get_height():.1f}%", + rf"{p.get_height():.1f}%", (p.get_x() + p.get_width() / 2.0, p.get_height()), ha="center", va="center", xytext=(0, 9), textcoords="offset points", - fontsize=16, + fontsize=20, ) # Setting plot labels and titles - ax.set_xlabel("Rule ID", fontsize=18) - ax.set_ylabel("Percentage (%)", fontsize=18) - ax.set_title(f"Top {top_n} Popular Rules", fontsize=24, weight="medium") - ax.tick_params(axis="both", labelsize=18) + ax.set_xlabel(r"Rule ID", fontsize=24) + ax.set_ylabel(r"Percentage (\%)", fontsize=24) + ax.set_title(rf"Top {top_n} Popular Rules", fontsize=32, weight="medium") + ax.tick_params(axis="both", labelsize=24) plt.xticks(rotation=45) # Rotate x-axis labels for better readability # Show the plot if ax was not provided @@ -131,7 +131,7 @@ def load_and_title_png( # Display the image on the specified axis ax.imshow(img) ax.set_title( - title, fontsize=24, weight="medium" + rf"{title}", fontsize=32, weight="medium" ) # Set the title with a specified fontsize ax.axis("off") # Hide the axes diff --git a/Docs/Analysis/_analysis/_rule_app_analysis.py b/Docs/Analysis/_analysis/_rule_app_analysis.py index c872306..2be6606 100644 --- a/Docs/Analysis/_analysis/_rule_app_analysis.py +++ b/Docs/Analysis/_analysis/_rule_app_analysis.py @@ -357,7 +357,7 @@ def plot_roc_curves( for index, type_ in enumerate(original_types): type_data = df[df["Type"] == type_] tpr = type_data["C"].tolist() - fpr = type_data["FPR"].tolist() + fpr = type_data["NR"].tolist() tpr = [x / 100 for x in tpr] fpr = [x / 100 for x in fpr] tpr.reverse() @@ -384,7 +384,7 @@ def plot_roc_curves( ) legend_handles.append(marker_handle) - ax.set_xlabel(r"$\mathcal{FPR}\ (\%)$", fontsize=fontsettings["label_size"]) + ax.set_xlabel(r"$\mathcal{NR}\ (\%)$", fontsize=fontsettings["label_size"]) ax.set_ylabel(r"$\mathcal{C}\ (\%)$", fontsize=fontsettings["label_size"]) ax.set_title(rf"{title}", fontsize=fontsettings["title_size"], weight="medium") ax.tick_params(axis="both", which="major", labelsize=fontsettings["ticks_size"]) diff --git a/syntemp/pipeline.py b/syntemp/pipeline.py index d1cb805..4387c1c 100644 --- a/syntemp/pipeline.py +++ b/syntemp/pipeline.py @@ -257,7 +257,8 @@ def extract_its( all_uncertain_hydrogen = collect_data( num_batches, temp_dir, "uncertain_hydrogen_{}.pkl" ) - except: + except Exception as e: + logging.error(f"{e}") all_uncertain_hydrogen = [] # logging.info(f"Number of correct mappers before refinement: {len(its_correct)}") @@ -276,8 +277,10 @@ def extract_its( logging.info(f"Number of correct mappers: {len(its_correct)}") logging.info(f"Number of incorrect mappers: {len(its_incorrect)}") - logging.info(f"Number of uncertain hydrogen:"+ - f"{len(data)-len(its_correct)-len(its_incorrect)}") + logging.info( + "Number of uncertain hydrogen:" + + f"{len(data)-len(its_correct)-len(its_incorrect)}" + ) if save_dir: logging.info("Combining and saving data") diff --git a/syntemp/run_compose.py b/syntemp/run_compose.py index 15b944a..9931477 100644 --- a/syntemp/run_compose.py +++ b/syntemp/run_compose.py @@ -91,4 +91,4 @@ def main(args): # python run_compose.py -s Data/Temp/RuleComp/Single/R0 -# -c Data/Temp/RuleComp/Compose_expand -d Data/Temp/RuleComp/Double/R0/ +# -c Data/Temp/RuleComp/Compose -d Data/Temp/RuleComp/Double/R0/ From d7fa8d8004fb28b892c6071a39d19177f53d3587 Mon Sep 17 00:00:00 2001 From: TieuLongPhan Date: Tue, 27 Aug 2024 10:43:35 +0200 Subject: [PATCH 6/6] update source --- Docs/Analysis/_5_rule_application.ipynb | 933 ++---------------------- 1 file changed, 50 insertions(+), 883 deletions(-) diff --git a/Docs/Analysis/_5_rule_application.ipynb b/Docs/Analysis/_5_rule_application.ipynb index 14a98ad..139e360 100644 --- a/Docs/Analysis/_5_rule_application.ipynb +++ b/Docs/Analysis/_5_rule_application.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -91,193 +91,27 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Valid': {'fw': {'Raw_0': (4.04, 7.86, 94.87),\n", - " 'Raw_1': (0.79, 7.24, 78.52),\n", - " 'Raw_2': (0.33, 6.5, 69.74),\n", - " 'Raw_3': (0.23, 4.92, 68.67),\n", - " 'Complete_0': (71.13, 94.5, 97.18),\n", - " 'Complete_1': (22.88, 92.92, 89.08),\n", - " 'Complete_2': (5.3, 88.6, 67.5),\n", - " 'Complete_3': (3.22, 78.03, 58.88),\n", - " 'Refine_0': (84.29, 94.7, 97.66),\n", - " 'Refine_1': (23.78, 93.16, 89.58),\n", - " 'Refine_2': (5.73, 89.12, 68.68),\n", - " 'Refine_3': (3.3, 78.93, 58.97)},\n", - " 'bw': {'Raw_0': (4.58, 7.86, 97.99),\n", - " 'Raw_1': (0.61, 7.24, 84.84),\n", - " 'Raw_2': (0.35, 6.5, 73.3),\n", - " 'Raw_3': (0.2, 4.92, 64.07),\n", - " 'Complete_0': (73.26, 93.46, 97.86),\n", - " 'Complete_1': (22.0, 92.84, 92.98),\n", - " 'Complete_2': (13.92, 88.5, 89.29),\n", - " 'Complete_3': (9.4, 77.97, 85.12),\n", - " 'Refine_0': (93.4, 93.66, 98.11),\n", - " 'Refine_1': (22.51, 93.08, 93.13),\n", - " 'Refine_2': (14.55, 89.02, 89.43),\n", - " 'Refine_3': (9.78, 78.87, 85.2)}}}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "results" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results successfully saved to ../../Data/Temp/Benchmark/raw_results.json\n" - ] - } - ], + "outputs": [], "source": [ "save_results_to_json(results, \"../../Data/Temp/Benchmark/raw_results.json\")" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " 0 1 2\n", - "Raw_0 4.04 7.86 94.87\n", - "Raw_1 0.79 7.24 78.52\n", - "Raw_2 0.33 6.50 69.74\n", - "Raw_3 0.23 4.92 68.67\n", - "Complete_0 71.13 94.50 97.18\n", - "Complete_1 22.88 92.92 89.08\n", - "Complete_2 5.30 88.60 67.50\n", - "Complete_3 3.22 78.03 58.88\n", - "Refine_0 84.29 94.70 97.66\n", - "Refine_1 23.78 93.16 89.58\n", - "Refine_2 5.73 89.12 68.68\n", - "Refine_3 3.30 78.93 58.97" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "results_df = pd.DataFrame(results['Valid']['fw'])\n", @@ -304,17 +138,9 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results successfully loaded from ../../Data/Temp/Benchmark/raw_results.json\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "\n", @@ -326,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -359,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -368,38 +194,16 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'Type'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", - "File \u001b[0;32mindex.pyx:153\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mindex.pyx:182\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'Type'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/homes/biertank/tieu/Documents/Project/TACsy/SynEco/SynTemp/Docs/Analysis/_5_rule_application.ipynb Cell 13\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m fw[\u001b[39m'\u001b[39;49m\u001b[39mType\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39munique()\n", - "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/frame.py:4090\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4088\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 4089\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4090\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 4091\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 4092\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", - "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/indexes/base.py:3809\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(casted_key, \u001b[39mslice\u001b[39m) \u001b[39mor\u001b[39;00m (\n\u001b[1;32m 3805\u001b[0m \u001b[39misinstance\u001b[39m(casted_key, abc\u001b[39m.\u001b[39mIterable)\n\u001b[1;32m 3806\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39misinstance\u001b[39m(x, \u001b[39mslice\u001b[39m) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m casted_key)\n\u001b[1;32m 3807\u001b[0m ):\n\u001b[1;32m 3808\u001b[0m \u001b[39mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3809\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3810\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3811\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3812\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3814\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'Type'" - ] - } - ], + "outputs": [], "source": [ "fw['Type'].unique()" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -506,20 +310,9 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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KiIhTp07F5z//+fjVr34Vr3rVq+Itb3lL/Kf/9J8iIuJd73pXPPvss/HDH/4wIi4u3fflL385nn766XjFK14Rx48fjy984QsRcXEQ8Pzzz8f3v//9iIj4+Mc/Hl/72tdifX09brzxxrjjjjvigQceiIiId7zjHXHVVVcN3uz5yEc+Et/5znfiySefjBtuuCHuuuuuuP/++yMi4i1veUtce+218Z3vfCciIv7sz/4svve978XPf/7zePnLXx5333133HfffRER8cY3vjGOHTsW3/zmNyMiYn5+Ph555JH46U9/Gi996Uvj4x//ePz1X/919Hq9eP3rXx+33HJL/P3f/31ERLz//e+Pn/zkJ/Hoo4/Gi170oiiVSlGv1+P555+P2267LV73utfF3/3d30VExHvf+9544okn4p//+Z9jZmYmPvWpT8VnP/vZ+PWvfx233nprvPGNbxzsw/cnf/Insb6+Ho888khERJw8eTK++MUvxrPPPhuvfOUr4+1vf3t86UtfioiLM+mee+65+MEPfhARIUcMkSNe/OIXR0TEww8/HP/m3/ybbTmi/3f1y1/+cvA3t1OOOH36dERczAHPPvvsSHNE///LO++8cxDDbjniZz/7WVx77bVjmSN+97vfDWKSI+SISckR4zyOeN3rXhedTife+ta3xr//9/8+/t2/+3fxP/1P/1OcO3currvuunjmmWcGf3c75Yj/9X/9XyMi4u1vf3v89Kc/veIc8b/9b/9bRETccsstcc8992RqHPH4449Ht9uVI6Y8R3z4wx8O9s92ZodncXExms1mrKysxNLS0pbn9npjtlKpRLFYjFqtFvl8PlqtVpw+fToKhULUarWhJ3gCDKM/MafVakWxWNz2/FGsejSKGIDpUyqVBvljfn4+qtVqJEkSzWYzTp8+HblcLrrd7pYbb5rNZlQqlcjn84M6LeJiTbW2thazs7NRLpej3W5f9oYZYA+9KRIRg4+9LC0tDY5dWVnZ8/hGozE4vlQqjfw8e/nNb37Tu3DhwuDjpz/9aS8iehcuXNj3OcimRx55JO0QIFOKxWIvInrVavWyz+fz+V3PsbGxse/r0bCq1erQ15BxJX/BaK2trW0ZD28eoy4sLPQiore0tLTja0edtw4zD44D+Yter9e7cOHC1Ndkw9Tg/THM5cZZm9VqtcHxCwsLIz/PMCbh3zpJkl5E9IrFYm9tba23sbHRq9VqvVwu18vlcoPn+vrvTew2pu2/pt1u7+v773V8//ttjmFSuQbA6PRzdJIk257bPLa9XP3dvy4cNMePIoZJIX/BaA1bg/ffU9ytj9TvNR00n+01vptUche93nA1maXUd3Ely89unuUzqvPs5Zprronrr79+ywdERLzwwgtphwCZMo7LEff1Z5YmSTLUno/jSv6C0crn89Fut2NhYSFKpVJUq9XB1/3lgzcvKbxZP7ftZ7/e/Rj1+caN/AVXZty3M5vErcz6Y8NmsxmFQmFwx1G1Wo2TJ09GxNacXK1WI+IPY99L9cex/eP20n9/Y7elhvv7wu/n32zcuQbA6Fx6x2Wr1Yputxv1ej3uuuuuQc643Huo/RVkyuVyajFMCvkLRmvYGrz/2G53g/dXQ8rq9j8HJXcxLEup72JzMbafPWI2H7P5taM6DxzUd7/73fjjP/7jtMOAzDjIUkiXutLCfCetVivm5+cHexBlgfwFo5ckybaJPf2lIZMk2XVJ3P4ybjstH3kQoz7fuJG/YHiTtJ3ZpG5l9pd/+Zfxuc99LmZmZuL222+Pm2++Oa677rr4v//v/zsiLjb877///vjEJz4x2H7nd7/7XTz++OPbthfo/57Pnz+/r+0Frr322oiI+N//9/893vve927bgmTzUsT33HPPWGxBcpCtzH72s5/FE088EX/8x388dtsUjfMWJBG2KRqHHJH2FiS7bVP07//9v4//4X/4HwYTe/r++//+v49/+qd/iq985Svx5je/ecetzH71q18N/n/8r//1v0aj0ThQjtgthv/4H/9jfPe7342vfOUr8Y53vGOitzJ7y1veEl/4whfiu9/9rhwhR0xUjogYz63MNo8jPvrRjw7GEd/+9rfjn/7pn6LVasXNN98czz//fDz11FPx8MMPD95T7Ha7O+aI/t/+DTfcEPfcc8/QOaJ/E+f6+np84xvfyMw44vHHH4//5r/5b+SIKc8RQ21ldti3r4+TGGIZt83Lqe22pORmKysrOy7XNqrzDGsSlnLjaPzVX/1V2iFA5ozTcsS9Xq/Xbrd7uVyulyRJb2NjY2TnTZv8BUejVCr1IqJXq9V2PSafz+97a6D9GPX5xo38Ra+nJuv1srudWZa2Mmu32zsuEdzP07tdG4bdvmevpYz7S4de7lo0KVwDYPTa7XZvYWGhVyqVetVqdbAtw165qv++7CiWDT5oDJNE/oKjsVsNvtdS6VdaR2d1KXW5i15vuPrbHeO72Hy3dn9Jr8vZfHfebneMX8l54KA+9rGPpR0CZE5/KaRqtRrr6+tx4sSJwZ3k/dnjR7UccafTiUKhEEmSxIMPPpipa4f8BaPVbDYjSZIty7ItLi5GvV6PfD6/693iEX9Y1Wg/S0R2u91YX1+PY8eO7ZqThjnfJJK/YHiTtJ3ZNddcE9dcc82Bv884qVQqEbF9SfRisRitVisajcaO14f+HSHz8/P7+j6lUikqlUrcd999O65c0h8jX+5aNClcA2D0xmHVo4PGMEnkLxitYWvwM2fOxOrqaqyurkahUNiyBWOlUhnkm0u3ZtxPDZ5lchfDssf4Lo4fPz74vL90w+Vsbnpvfu2ozgMH1V+KCRitflFcq9ViaWlpsLz6URbmWW6KR8hfMGrlcjnm5uZibm4uCoVCzMzMxOrq6iCHXE5/jLrbXmebnT17Nubm5uL06dMjOd8kkr9geLYzO1zNZnPbZP293pjN5XKDN2c32+uN2U6ns+133x8fd7vdbdsJ9fcxX1paupIfcWy4BsDR2G1iz2b9fXj7+/KmEcMkkb9gtIatwXO53OD9vcXFxZidnY35+fmYnZ2N5eXlSJJk8L7iZperwev1eiwuLsbi4uIgV3U6ncFj/XHYJJO7GJbG+C5yudygKN7Pnd79gVbE1rsAR3UeOKij2GMPuOgoC/Nut5vppniE/AWjtra2Nmh89Bsa1Wo12u32vnPIqHNNFnNXhPwFB7H5bu39/A2tr68PPt98l/iozpM14/DG7Llz5yJJkqjX61vO12w2I5/PZ6ax5BoAozXsxJ7Nhl31aKeJPVcawySRv2C0DlKD5/P5ePTRR2NhYSGSJBncdb60tBTtdnvoyeUPPfTQYKJjf4Webrc7eOzSCZCTSO5iWBrjl3Hy5MnB5/1N5nfSarUGg6ad7gAc1XngILK6RCmkKe3CfHNTfG1tLbONJfkLRitJkqjVatFut6PX60W73d733Xm9Xi96vd6+ivBqtRq9Xi9qtdpIzjeJ5C8Ynu3MDtc4vDGby+W2XHuazWYcO3YsqtXqln+HSecaAKM1DqseXUkMk0T+gtE6aA2ey+ViZWUl1tbWotfrxdra2mUnEF6uBu8/d7mPSSd3MayZXhb+z9+nmZmZwef7+bFbrdZgr9h8Pr9roVYulwcN71qttq0hMqrzDOPpp5+OG264IS5cuBDXX3/9gc/D5Pv1r38dL33pS9MOAzJldnY2ut1uJEkSuVxusFXGfhrV/WvR2traniuDVCqVWF5ejlKptGVwOz8/P3hj8nJKpdJE33kjfwGTSv4iQk0WMVwN3u12Y3Z2NiIujqna7fZlj++PhyK2jqtGdZ5h+LdmM9cAGK1OpzPYwqHT6USSJLG4uLiv5lL/OrSfyTy71d9XGsMkkb+ASSR3ETFcTeaO8cvYfNdfq9Xacb+F1dXVQTN7t7sER3WerKpUKjE3NxczMzNRKBRieXl536/tdrsxMzMTMzMz2/YpO6oYxt1nP/vZtEOAzBmX5Yg7nc5lP/azr+Y4k7+ASSV/wfBsZ0ZWuAbAaI3DqkdXEsMkkb+ASSR3Mayr0w7gsOzVEOjf3deXy+V2HCSdO3cums3mYN+F8+fPx+LiYnS73Wg0GoOZ5RFx2eUiR3WerNk8O79YLMb58+ejUqlEo9HYcb+yS/X3800zBmD69IvigxhmoZZqtbrjHd9yEwCQRSdPnhzsc1iv13edML6f7cxGcR4AAACyJbN3jK+srEShUNjysdmlz62srOx4nlwuF2tra4Omef+O70qlMmimXnrMYZ4nS/oN6YWFhej1etFoNGJjYyPy+Xw0m83BGxm7abVasbq6ekVvYFxpDJPgbW97W9ohAByI/AVMKvkLDmbz6mpnz57d9bjNz+20ItuozgMH4RoATCr5C5hEchfDymxjfJT6+5KtrKxEPp8fLMuWz+ejWq3Go48+uq8l10Z1niyo1+uD/XEvnZTQvwtzr7vBT58+HblcLubn51OLYRK86EUvSjsEgAORv4BJJX/BwdjOjCxwDQAmlfwFTCK5i2Fldin13ZafvRILCwuxsLAwNueZZP1G9E5vUCRJEvl8PlqtVjSbzR3vCK/X69FqtaJarR54n94rjWFSrK2txRve8Ia0wwAYmvwFTCr5i2lkOzO4yDUAmFTyFzCJ5C6G5Y5xUtHpdCIidr1D/vjx4xGx/c2TvtOnT0dEXNEEgyuNAQAAgItsZwYAAMC40xgnFevr6xERcezYsR2fn5ubi4iIhx56aNtzlUolut1uLC0tHfhu8SuNYZJ8+MMfTjsEgAORv4BJJX/BlbGdGZPMNQCYVPIXMInkLoaV2aXUGW/Hjh2Lbrc7aE5f6qmnnoqI2LYUX7fbjeXl5cjlcle8VP5BY5g058+fjw984ANphwEwNPkLRus9f9lIO4ShfeM/zKcdwoHIX0wj25nBRa4BwKSSv2B0JrH+jpjMGlzuYlga46QiSZLodDrRarV23L+7v3z5pU3r/hLqZ86cSS2GSfOLX/wi7RAADkT+AiaV/AWMm0l8c3YS35iNcA0AJpf8BUwiuYthaYyTisXFxWg2m7GyshJLS0tbnmu1WoM93y59vF6vR5Ik215zVDFMouuvvz7tEAAORP4CJpX8BTC9XANgdCZxUk/E5E7skb+ASSR3MSyNcVJRKpUGd2zPz89HtVqNJEmi2WzG6dOnI5fLRbfb3bL/d/9u8ZWVldRimEQf/OAH0w4BMmUSC/NJLcrlL2BSyV8A08s1AJhU8hcwieQuhnVV2gEwvWq1WkRENJvNKBQKMTs7G+VyOarVapw8eTIiIvL5/OCY/tLmKysrUS6XBx/9Rnmz2Rw8tt99wYeJYVLV6/W0QwA4EPkLmFTyF8D0cg0AJpX8BUwiuYthuWOc1OTz+Wi321GtVmN9fT1OnDgxuIu7UChERMSJEye2vW63RNftdgfPVavVyOVyhxYDAAAAAAAAMDk0xklVkiTblkbvdDrRarUiSZIolUoREVEsFqPX6+14juXl5ahUKlEqlQZ3gB9GDJPqzW9+c9ohAByI/AVMKvkLYHq5BgCTSv4CJpHcxbAspc7YqVQqEXHxru9pjmFUXv7yl6cdAsCByF/ApJK/AKaXawAwqeQvYBLJXQxLY5zUNJvN6HQ6Wx5bXFyMer0e+Xx+ZHdqd7vd6HQ6O+47flQxpOnb3/522iEAHIj8BUwq+QtgerkGAJNK/gImkdzFsDTGSU25XI65ubmYm5uLQqEQMzMzsbq6GkmSxIMPPjiy73P27NmYm5uL06dPpxYDAAAAAADAuKtUKjE3NxczMzNRKBRieXn5ssd3Op1Br6X/mv6qvFeqXq9HoVAYybkgQmOcFK2trQ3uyO7v512tVqPdbkcul5uaGA7bhz70obRDADgQ+QuYVPIXwPRyDQA2G4fm0uYY5ubmdo1B/gIiIubn52N5eTk6nU4Ui8XodDpRqVRifn5+x+NXV1djbm4u6vV6RMTgNcvLyzE3N7fjSr576XQ6Ua/Xo1wuR7lcvuyxchfD0hgnNUmSRK1Wi3a7Hb1eL9rtdiwtLQ19nqWlpej1elGr1XZ8vlqt7vr8qGIYZz/4wQ/SDgHgQOQvYFLJXwDTyzUA6Eu7udTtdgeN8PX19S0x7HT3pfwFzM/PR7PZjIWFhej1etFoNGJjYyPy+Xw0m81YXV3dcnyz2YzFxcUoFovRbrej3W4PXlMqlQaTfYaNYW5uLsrl8iAfXo7cxbA0xiHjHn/88bRDADgQ+QuYVPIXwPRyDQAixqO5dPr06eh0OrGwsBAbGxvRaDSi1+tFsViMVqu17U50+Qum2y+/97VoNpuRJEmsrKxsea5/0+GleSNJksjn89FoNCJJki3PVavViLiY34ZRLpdjaWkpGo3GrjdDbiZ3MSyNcci4l73sZWmHAHAg8hcwqeQvgOnlGgDU6/XUm0vdbjfq9fquMeRyuW1LqstfMN1+8a0vRETE4uLituf6Oarb7W7JRUmSxNra2kjjWFhYiGq1GsVicV/b3cpdDEtjHDLuIx/5SNohAByI/AVMKvkLYHq5BgD9RnSazaX77rsvIiJKpdK253K5XBSLxYiILcsUy18w3X7z1BMREZHP53d8/vjx4xER0Wq19nW+fi7s55vDIncxLI1xyLh777037RAADkT+AiaV/AUwvVwDgE6nExHpNpf6d6afOHFix+f7jzcajcFj8hdMt98/90xERBw7dmzH5+fm5iIi4qGHHtrzXKurq4NVKfqrXhwWuYthXZ12AAAAAAAAkAXr6+sRkW5zaa/mfH+59v5xAFdfe13866+fHeSwSz311FMRcXGrhp0sLy/HvffeG51OJ7rdbuRyuajVarvmIUiLO8Yh4/7oj/4o7RAADkT+AiaV/AUwvVwDgH5D/EqaS4VCIWZnZ2NxcTFyuVw0Go2hmku7fe++/r69m4+Tv2C6veTGWyJi99Us+o/vll8eeuihaLVag9x2/PjxwSScwyR3MSyNcci4m266Ke0QAA5E/gImlfwFML1cA4B+IyjN5tJuTffLHSd/wXR7xbs/HBF/2L5hs1arFc1m87Kvr9Vq0ev1YmNjI2q1Wpw/fz7m5uaiXq8fSrx9chfD0hiHjPvGN76RdggAByJ/AZNK/gKYXq4BwOLiYkRMXnNJ/oLpdtPb3xdJkkSn04n5+fnBBJ16vR533XXXYKWJ3baJ6MvlclEqlWJtbS0iIsrl8qFu2yB3MSx7jJOK9/xlI+0QDuQb/2E+7RAAAAAAgDFVKpW2NJeq1WokSRLNZjNOnz4duVwuut3uvptL+Xw+5ubmolwuR7vd3tfd4/3vsZejWOYYmBy1Wi0KhUI0m80oFAqDx1dWVmJtbS1WV1f3va1DkiRRKpWiXq9HtVrdcbIQpMEd45BxxWIx7RAADkT+AiaV/AUwvVwDgIiLzaWIGDSXZmdno1wuR7VajZMnT0ZEDN1cioioVqv7ek2/6b5bc7x/92b/DtAI+Qu4mJfa7XYsLCxEqVSKarU6+Pr8+fMREXHixIl9n68/+ab/2sMgdzEsd4xDxv34xz+Of/tv/23aYQAMTf4CJpX8BTC9XAOAiD80l6rVaqyvr8eJEycGd5L378I8zOZS/4718+fP79iAb7fbEbF1SWT5C4i4mD8uvbu70+lEq9XaMlEn4uLkm80TbC7Vn4RzmKtTyF0Myx3jkHGPPfZY2iEAHIj8BUwq+QtgerkGAH395lKtVoulpaVBs3q35tLlDNtcmp+/uB1kf4/fS7VarS3HRchfwO4qlUpEbF+14q677rrs/uHNZjMituaaUZO7GJbGOGTcNddck3YIAAcifwGTSv4CmF6uAcDlHFVzqd90v++++7Y91+l0Bufb3JyXv4Bms7ktFy0uLka9Xo98Pr8lZ0RcnKwzNzcXy8vL2yb4lMvl6Ha7kSRJLCwsbHmu2+1Gp9PZc1LQfshdDEtjHDLuE5/4RNohAByI/AVMKvkLYHq5BgAR6TeX+nekd7vdKJfL2+KIiFhaWtryuPwFlMvlmJubi7m5uSgUCjEzMxOrq6uRJEk8+OCD246v1WpRKpWiUqnE7OxsFAqFwevq9XokSRKNRmPb686ePRtzc3Nx+vTpbc/V6/VYXFyMxcXFwSSiTqczeKyfw/rkLoalMQ4Zd++996YdAsCByF/ApJK/AKaXawAQMR7NpXPnzkWSJFGv12N2djbm5+djdnY2ms1m5PP5bXety1/A2traYOJOf9uHarUa7XZ7173Ea7VaNBqNKJVKg+0i8vl8LC0tRbvdHnp/8YceeihWV1djdXV1sLpFt9sdPLa6urrleLmLYWmMQ8a98MILaYcAcCDyFzCp5C+A6eUaAESMR3Mpl8tFu90e3BnebDbj2LFjUa1Wd9x7XP4CkiSJWq0W7XY7er3elhxyOcViMWq1WmxsbESv14u1tbVtk282q1ar0ev1olar7frc5T42k7sY1tVpBwAcrrm5ubRDADgQ+QuYVPIXwPRyDQAi/tBcGlaxWIxisbjv46vV6mWbT/s9JkL+AiaT3MWw3DEOGfeqV70q7RAADkT+AiaV/AUwvVwDgEklfwGTSO5iWBrjkHFf+9rX0g4B4EDkL2BSyV8A08s1AJhU8hcwieQuhqUxDgAAAAAAAECmaYxDxt15551phwBwIPIX0NftdmNxcTHm5uZiZmYmCoVCVCqVPV9XqVS2vGZ5efmKY1ldXY1CoRAzMzOD866urm45Rv4CmF6uAcCkkr+ASSR3MSyNcci4xx57LO0QAA5E/gIiIlqtVtx2222D5nOxWIxutxvLy8sxOzsbnU5nx9fNz8/H8vJydDqdKBaL0el0olKpxPz8/IHi6Ha7USgUYnFxMVqtVuTz+UEstVpty7HyF8D0cg0AJpX8BUwiuYthaYxDxu32ZjHAuJO/gG63G3fddVd0u91oNBrRbrcH/11ZWYlutxvlcnnb6+bn56PZbMbCwkL0er1oNBqxsbER+Xw+ms3mtju896NQKESr1YpSqRS9Xi/W1ta2xLSZ/AUwvVwDgEklfwGTSO5iWBrjkHFXX3112iEAY2KcliKOiKjX61EoFHZ9Xv4Cms1mdLvdWFhYiGKxuOW5/mOtVitardbg8Xq9Hs1mM5IkiZWVlS2v6d/ZvZ/ct9nq6mp0Op0olUrb7g7fifwFML1cA4BJJX8Bk0juYlj+j4GM2+kuKmD6tFqtwV2XSZIMlhVeXl6O1dXVWFtbiyRJtr2uf9dlxMXli8+fPx+VSiUajca2OyT3o9PpRKvVinvvvTfq9Xrk8/ldj5W/gIceeigiYtdJNP07wM+fPz/IJ/1m+OLi4rbjkySJfD4frVYrms3mtmb7bvqN9HPnzu3rePkLYHq5BgCTSv4C/pdvPpp2CEP7//4/uavb7UalUolmsxmdTmew/Vm1Wr3s6yuVStTr9cFrTp06FUtLS1ccV7fbjdnZ2YiIfU+y52hojEPGfeYzn4lPfvKTaYcBpOjSpYg3N4JWV1djcXExyuVyrK2tbXnd5qWIN991WSgUBksRLyws7DuOzU32/ZC/gBMnTkREbMtPff07xY8fPz54rL+M2m4Tb44fPz64y3w/jfHV1dXodrtRKpUil8tFp9OJlZWV6HQ6kSRJnDlzJnK53JbXyF8A08s1AJjExlJExCv/y8UJ9Wk3ljqdTlQqlWi1WkPFAEynz3zmM3HbbbeNxQ1Bmw27Uh1Hx1LqkHG/+93v0g4BSNm4LEVcLpdjaWkpGo3GvmZJyl9AqVSKfD4fq6ur2ybW9B/rH9O3vr4eERHHjh3b8Zxzc3MR8Ye70ffSL4ZPnDgx2FpieXk56vV6LC8vR6FQiG63u+U18hfA9HINACbVj370o7jttttidXU1Ii42ibrdbiwvL8fs7Oyu+/jOz8/H8vJydDqdQTOqUqnE/Pz80DGsrq7G3Nxc1Ov1QQz95tbc3Ny2cTfAxsbGlhuC2u324L8rKyvR7XZ3XBFj8w1BvV4vGo1GbGxsDFam6+fCg2i1WrG6urrvVeo4WhrjkHGvfe1r0w4BSNl+liKOiDh//vzgsf0sRdztdoe6A3xhYSGq1WoUi8Vtd1fuRP4CIi7eLV4qlWJ+fj7m5uYG/11cXIylpaVtE236DfF+g/xSTz31VETEvt9U608aOnv2bNTr9UGx3Gg0Ip/PR6fTibvuumvLa+QvgOnlGgBMoueeeTr+5//5f061sdRsNmNxcTGKxWK02+1BDBsbG1EqlaLT6VjuHdjmF7/4xVjcELTZ6dOnI5fLHWiCEIdPYxwyrn9XFDC9Dmsp4s2vPQzyF9B35syZSJIkOp3OlmUdT506te3Y/vJou+Wn/uO7Nc4v1T+u2+3G2traYHJPsViMBx98cHDOzd9P/gKYXq4BwCT60fmvxzPPPJNqY6k/Cb/RaGxb8ri/jPowk/OB6fBf/st/iYj0bwjqq9fr0Wq1dtx2jfGgMQ4Z95WvfCXtEICUjcNSxAchfwERMViuPEmSWFtbi16vN5joUygUBsss9vUL20vfnIu42MA+6JtpCwsL24raXC4XpVIpIiLuvffewePyF8D0cg0AJtFj//T9iEi3sdQf7wMM4+qrr46I8bkh6PTp0xFx8T0ExpPGOABMgbSXIgY4iGazGZVKZXDnSL9ozefzsba2FrlcLsrl8pb9Dkul0uDu8vn5+Wi1WtHtdqNer8ddd901aG7vNvHnUv3jdnuTsL802m57LgIAwLh7zR+/LSLGp7F0qX4T3n69wKXe9a53jc0NQZVKJbrdbiwtLblbfIxpjEPG3XHHHWmHAIyJNJciPgj5C+gvmbjTXSgRf5iB3T+urz/Zp9lsRqFQiNnZ2SiXy1GtVuPkyZMRsfsbeJfaq5Hef3xzY1z+AphergHAJHrn+++Ot7zlLWPRWLrU6upqLC8vR8T2cT/AHXfcMRY3BHW73VheXo5cLidXjTmNcci4J598Mu0QgDEwLksR///Z+//gyM/6TvR9C0wcfsTTGnsJGyAwrTWwCUns1gwp8quAaZkkkITY6hlI1d2qPYdRn3D/2nOwlElq657UOXvGrU1qq+65tbF6SO25p7bWjCUMLCzBVg9wCIELIzWwEJYY1GPDZosQPNMeCAlh17p/zOlGmpFmpJZmWt16vapU1nR/+9sf2ePPV0+/v8/zbIf+BXSWarxyj8GOzodta5d0TC6H3isrK5mamsrk5GRqtVr3z51jjxw5sqUaOjNdNhswb/S4/gWwf7kGAIPq//g//o++B0sdnc8wRkdHU61WUygU1q0gBdDR+d2r3xOCOkuonzx5clv1c/MJxmHIPf744/0uAeizvbAUcS/0L6AzWN3sw7TO4xsF58ViMXNzc5mfn8/09HS3pzWbzRSLxe7e4NfTWUJ9cXFxw+dXVlaSrF9WUv8C2L9cA4BB9fjjj/c9WOo4d+5c93OI5PLv2pvdLAvsb48//njfJwQ1m80sLCykWCxmenq6x5+Em0UwDgBDbi8sRQzQi84egpuF0mfOnEnyg32+r2dmZibJ9pZg7PS7hYWFDQP6er2eJKlUKls+JwAA7DUf+MAH9sxKc/Pz81ldXc3FixczPz+fpaWljI2NXVUDwBe+8IW+TwjqzBbfqB+y9wjGYci97W1v63cJQJ/thaWIe6F/ASdPnkyhUEi9Xu8G0B0zMzPd2d+dG3w6OrNb1qpWq1lYWEipVNpwtni73U6r1boq/C4UCt07vq8MvyuVStrtdiYnJ7shfqJ/AexnrgHAIPqLc5/IQw89tOdWmisUCpmcnOwG9FfWAHDu3Lkk/ZsQ1Gg0uitkzM3NpVKpdL86QXmj0eg+1sv2EuwuwTgMufe///39LgHos72wFHEv9C+gUCjk7NmzKRQKqVarGR0dzcTEREZHRzM7O5tisbjhbPJKpZKxsbGMjY1lfHw8IyMjqdfrKRaLOXv27IbvderUqYyNjXXv9F6rVqulVCql0WhkdHS0e85O0H7lfov6F8D+5RoADKLGv70c3uzVlebWfv6wndWfgOH3qU99KsnemBC0sLCw7qsTmHduGlpYWOhpewl2l2Achtx3v/vdfpcA9NleWIq4F/oXkFwerJ4/fz5TU1MpFotpNBrdfbtWVlY2HPwuLy93Pzjr3MjTGeR2Zq5s1/Lycmq1WorFYprNZkqlUqanp7uzV9bSvwD2L9cAYBB97S++kGRvBEub6dR2ZQ3A/vYP/sE/SNK/CUHlcjmrq6sbfnU+O52cnOw+tlmf5ea5pd8FADfWS17ykn6XAPTZyZMnu8sQj4+Pr1ty+HpLEReLxXW/sG1lKeILFy7k4MGDPYdPHfoX0FEoFLa1V1exWLxqFvf11Gq1697wMz093V1W/Vr0L4D9yzUAGES3/9iP5z//xRd3FCyt1ctKc+12+5qfI3SWUBcqAWu99rWvzRNPPJHFxcUN+81enRBE/5gxDkPuJ37iJ/pdAtBne2Up4oWFhVSr1VSr1e4vl61Wq/vYlUu26V/AoNK/APYv1wBgEL3y8M8l6e9Kc0ePHr3m/uGNRmNbNQD7w//6v/6vKRQK3UlBa11vQtCVPWcrE4JarZZ9wgecYByG3GOPPdbvEoA9YC8sRXzu3LnuL6mdAW273e4+duUvr/oXMKj0L4D9yzUAGEQT/4935HnPe15fg6VisZixsbHMzs5e9VylUkm73d6wBmB/+/SnP70nJgQxOATjALBPdJYiXl5ezurqane/3M10liJeWVnJ6upqVlZWrruEcK1Wy+rq6oZLGHeeu9YXAAAAcHM970duy+/93u/1NVian5/P5ORkZmZmMjo6mvHx8e45FxYWNq0BYC9MCGJw2GMchtxrX/vafpcA0BP9CxhU+hfA/uUaAAyq3/qt38o73vGOzMzMZGlpKY1GI6VSKeVyedOb6peXl7szyjvBUrVave5N9ZuZn59Po9HI3NxcGo1G2u32dWsA9rfO716dCUFb1ZkQtB21Wm1bvWh6errnfsiNIxiHIff000/3uwSAnuhfwKDSvwD2L9cAYFA9/fTTefnLX973YKlcLqdcLm/rnMD+5XcvtstS6jDkvvSlL/W7BICe6F/AoNK/APYv1wBgUOlfwCDSu9guwTgAAAAAAAAAQ00wDkOuUqn0uwSAnuhfwKDSvwD2L9cAYFDpX8Ag0rvYLnuMw5D78Ic/nDe/+c39LgNg2/Qv4H//5Pl+l9CTQxf+XP8C2Kf8DgsMKv0LGER6F9tlxjgMuW9/+9v9LgGgJ/oXMKj0L4D9yzUAGFT6FzCI9C62SzAOQ+5FL3pRv0sA6In+BQwq/Qtg/3INAAaV/gUMIr2L7bKUOgy5UqnU7xKAPhvUpYj/if4FDCi/fwHsX64BwKDSv4BBpHexXYJxGHIf+tCH8ra3va3fZQBsm/4FDCr9C2D/cg0ABpX+BQyiW/7D/zt/0+8ievD83/rn/S5h3xKMAwAAAADAPvbrq1/N3/y7/6XfZWyLYAmA7bLHOAy5I0eO9LsEgJ7oX8Cg0r8A9i/XAAAA2LsE4zDk/u7v/q7fJQD0RP8CBpX+BbB/uQYAALBV7XY71Wo1Y2NjGRkZyfj4eGZmZjY9fmRk5JpfExMT266h2WxmYmIio6OjGR0dzcTEROr1+k5+rD1NMA5D7gtf+EK/SwDoif4FDCr9C2D/cg0AuPn2QrC01sLCQsbHx3d0DmD4NZvNHDp0qBtCl8vltNvtzM7OZnR0NK1Wa9PXlkqlTb+2o16vZ3x8PI1GIwcPHkySNBqNVKvVTExMpN1u9/zz7VX2GAcAAAAAAAZOs9nM0aNH0263UywWUy6X02q1Mjs7m3q9nuXl5RSLxQ1fu1mAtN1gKUlarVaazWbOnDmThYWFns4B7B/tdrvbuxYXF1Mul7vP1ev1VKvVVCqVLC8vb/j6zR7fjlarlWq1mnK5nPn5+RQKhSSXg/FKpZJGo5GZmZnMzc3t+L32EjPGYcj95m/+Zr9LAOiJ/gUMKv0LYPdsdxbgRq/vzACsVCo3vAbXAICb58pgaWVlpfvPubm5tNvta/b+5eXlDb9qtdq26piYmMjY2FgqlUoWFhZ2+mMB+0Cj0Ui73c7U1NS6UDxJ97Fms5lms3nDaqjVaikUCutC8eTyzPWzZ88muRzSX2vm+iASjMOQ+9jHPtbvEgB6on8Bg0r/AtgdO1lesmM7Ifpu1OAaAHDz7IVgKUkqlUqmp6ezuLiY+fn5G/pewHA4d+5ckmy67UJn1YmlpaUbVkOr1crU1NS6UHzt+3dqaDQaN6yGfhCMw5C7ePFiv0sA6In+BQwq/Qtg53Y6CzC5HGrX6/WrwpIbWYNrAMDNsxeCpeRyCF+r1VIulzcMmACudOTIkSSbL4neuaHn8OHDN6yG+fn5VKvVTZ/vbEOxsrJyw2roB8E4DLk77rij3yUA9ET/AgaV/gWwc7sxC/DEiRMpFAqZmJi4aTW4BgDcPHshWALoxeTkZEqlUur1+lUzsjuPdY7ZzOzsbKrVamZnZ3taGaNQKHTD74202+0kye23377tc+9lt/S7AODGeu1rX9vvEgB6on8Bg0r/Ati5rcwCbDQaWVpa2vADw4WFhTSbze7eiTerBtcAgJtnbbBUqVTW3cS0nWBpZWUlY2NjKZfL1zwWYDctLy+nUqlkYmIixWIxxWIxrVYrrVYr09PTqdVqm752dHS0G1x31Gq1TE9P71p9ndU2el19aa8yYxyG3Ac+8IF+lwDQE/0LGFT6F8DO7XQW4IkTJ5Jcntl9M2twDQC4uZaXlzM5OZmJiYmMjY11/1mtVjM9PX3NPb9HR0czMzOTer2emZmZjI+PZ3Z29iZWD+x3J0+e7AbijUYjrVYrpVIpx48fv+5rFxcXs7q6muXl5RQKhW4/2w2zs7Npt9vXvbloEAnGAQAAANhTdrK85MzMTNrtdqanp3e01+tuLHEJwI23V4MlgGuZnZ3N+Ph4isVilpeXu70oubxi0cLCwlWvKRQKKRQKOXv2bHcmd6lU6t4ENDMzs+O6Go1GZmZmUiwWr3lz0aASjMOQu/vuu/tdAkBP9C9gUOlfALujl1mA7XY7s7OzKRQK11x+8kbV4BoAcHPt1WAJ4Fo64XOpVMri4mL3RstSqdS9UadSqaTVaq173cWLF3Px4sWrbswsl8spFAppt9tX3dC5Hc1ms7u0+2arJg06wTgAAAAAe9J2ZwF2llA/efJk32oA4ObYq8ESwPV0buCsVqsbPt/ZDmg7N3oWi8UkP9juZ7tarVaOHj3aDcV3svLSXiYYhyH32c9+tt8lAPRE/wIGlf4FsDu2Owuw2WxmYWEhxWIx09PTfanBNQDg5tmLwRLAViwtLSX5Qc+50tjY2LrjtuOpp57a9mtarda633mHNRRPBOMAAAAA7DG9zALszBafm5vrWw0A3Dx7LVgC2KpO32q32xs+33l8s/62kc4NPUeOHNlWLWtD8bNnzw51KJ4IxmHo/dqv/Vq/SwDoif4FDCr9C2DntjsLsNFodD8MnJubS6VS6X51gvJGo9F9bLMPIXdSQ+IaAHAz7aVgCWA7yuVykmRxcXHD58+cOZMkmZiY6D5WrVY33eZh7eOdc29Fu93eV6F4IhiHofepT32q3yUA9ET/AgaV/gWwczuZBbiwsLDuqxNytNvt7mMXLly4ITW4BgDcPHslWALYrpMnT6ZQKKRer6der697bmZmJs1mM8VisXsjZnK5R01MTFx1fLPZTKVSSXL5hs0rw+12u51Wq3XVTURrQ/FhXz59LcE4DLlvfetb/S4BoCf6FzCo9C+AndvuLMByuZzV1dUNvzozuicnJ7uPbWX2YC8zEV0DAG6evRAsAfSiUCh0Z2hXq9WMjo5mYmIio6OjmZ2dTbFYvOqmn8XFxRSLxe7x4+Pj3a92u52pqalMT09f9V6nTp3K2NhYd9uhjs6WQO12O2NjY5t+zczM3NB/FzebYByG3OjoaL9LAOiJ/gUMKv0LYOd6mQW4F2pwDQC4efZCsJRcXqmkWq2mWq12b8ZqtVrdxzbbkgPY30qlUs6fP5+pqakUi8U0Go0Ui8VMT09nZWXlqhs5i8ViVlZWUqvVUiwW02w202q1Mjk5mcXFxe72QdvVarWu+TVsNwTd0u8CgBvrda97Xb9LAOiJ/gUMKv0LYOdOnjzZnQE4Pj6+brbfZrMAe9Vut3PhwoUcPHhw3QzBXmpwDQC4uTrB0szMTJaWltJoNFIqlVIul7sh9VqdYGl2djZnzpxJs9lMoVDI5ORkqtVqT0uonzt37qoZ6O12e91jvQZWwHArFArb7g/T09Mb3sCzmVqttmE/3Ozmz2FnxjgMufe+9739LgGgJ/oXMKj0L4Cd62UWYK82mwXYSw2uAQA3XydYWl5ezurqapaXlzcMgdaanp7uHn/x4sXMz89fMxSv1WpZXV3N/Pz8ps9d6wuAvUEwDgAAAMCes93lJYe1BgAAYHdYSh2G3E/91E/1uwSAnuhfwKDSvwB2Ty/LS17pestNbra8ZC81uAYAAMDeZcY4DLkf/uEf7ncJAD3Rv4BBpX8B7F+uAQAAsHcJxmHInTt3rt8lAPRE/wIGlf4FsH+5BgAAwN4lGAcAAAAAAABgqAnGYcj96q/+ar9LAOiJ/gUMKv0LYP9yDQAAgL1LMA5Drtls9rsEgJ7oX8Cg0r8A9i/XAAAA2Ltu6XcBwI31jW98o98lAPRE/wIGlf4FsH+5BgAAcD3/6Z/+er9L6Mk//jf/vt8l7JhgHIbcj/zIj/S7BICe6F/AoNK/APYv1wCAm0ewBMB2WUodhtwv//Iv97sEgJ7oX8Cg0r8A9i/XAAAA2LvMGIchNz8/n7e97W39LgNg2/QvYFDpXwA7979/8ny/S+jJHU/+/1wDAABgjzJjHAAAAAAAAIChJhiHIfcTP/ET/S4BoCf6FzCo9C+A/cs1AAAA9i7BOAy5AwcO9LsEgJ7oX8Cg0r8A9i/XAAAA2LsE4zDkPvWpT/W7BICe6F/AoNK/APYv1wAAANi7BOMAAAAAAAAADDXBOAy5e+65p98lAPRE/wIGlf4FsH+5BgAAwN4lGIch96UvfanfJQD0RP8CBpX+BbB/uQYAAMDeJRiHIfef//N/7ncJAD3Rv4BBpX8B7F+uAQAAsHcJxmHIPe95z+t3CQA90b+AQaV/AexfrgEAALB3CcZhyP3Gb/xGv0sA6In+BQwq/Qtg/3INAACAvUswDkPuoYce6ncJAD3Rv4BBpX8B7F+uAQAAsHcJxgEAAAAAAAAYaoJxGHKveMUr+l0CQE/0L2BQ6V8A+5drAAAA7F2CcRhyL3zhC/tdAkBP9C9gUOlfAPuXawAAAOxdgnEYcp/4xCf6XQJAT/QvYFDpXwD7l2sAAADsXYJx2KJP/969GRkZue7X+Pj4Va9tt9upVqsZGxvrHjMzM9NTHdd7/4mJiZ3+qAAAAAAAADBUbul3ATAobnnej+Rl//DaS6K1Wq2rHms2mzl69Gja7XaKxWLK5XJarVZmZ2dTr9ezvLycYrG47XpKpdKWHn/DG96w7XMD7AX6FzCo9C+A/cs1AAAA9i7BOGzR+O/9f/Nn/2zz2dgLCwupVCo5efJk97F2u90N+BzpoAAAkw9JREFUxRcXF1Mul7vP1ev1VKvVVCqVLC8vb7uerb5mZWUlP/qjP7rt8wP0m/4FDCr9C2D/cg0AAIC9y1LqsAtarVYqlUomJyczOTnZfbzRaKTdbmdqampdKJ6k+1iz2Uyz2bxhtT355JM37NwAN5L+BQwq/Qtg/3INAACAvUsw3oOZmZmMj49393QeGxvLzMxM2u32ts5Tr9czPj6e0dHR7r7Ts7Oz2z4P/VepVFIoFHL69Ol1j587dy5JNtx3PPnBsudLS0s3rLYf+qEfumHnBriR9C9gUOlfAPuXawAAAOxdgvFtaDQaGR0dzezs7LoZvp39og8dOpR6vX7d87Tb7YyNjaVarabZbHaD8Gaz2Q3db+QMYnbXzMxMms1mTp8+nUKhsO65I0eOJNl82fPOf+fDhw/fsPruu+++G3ZugBtJ/wIGlf4FsH+5BgAAwN4lGN+ihYWFTExMdEPsycnJ1Gq11Gq17tLZ7XY71Wr1muF4u93OoUOH0mq1klyeMdw5T2ep7VarlfHx8e4x7F3tdjuzs7MpFovrllDvmJycTKlUSr1eT6PRWPdc57HOMb2YnZ1NtVq96maNtebn53s6N0C/6V/AoNK/APYv1wAAANi7bul3AYOg3W7nxIkTSZJCoZCzZ89eFWS2Wq1MTEyk1WqlWq2mXC6nWCxeda4TJ050w/VarZbp6enuc9PT06nX66lWq0kuL8+92Uxj9oZTp04luTxrfDPLy8upVCqZmJhIsVhMsVhMq9VKq9XK9PR0arVaT+89Ojp61bL7V/6dSpL/+l//a0/nB+g3/QsYVPoX7K6ZmZk0Go3uzcCdG5NPnjx51apd11Kv1zM3N5dWq5V2u51SqZTjx49nampqW+eBa3ENAACAvcuM8S1Yu3/46dOnN5zdWywW190V3Am312q1WllYWEiSlMvlqwLMJJmamsrU1FSSy8tsXznLmL2jM1s8Sfe/2WZOnjzZDcQbjUZarVb3Q5idWFxczOrqapaXl1MoFDIzM3PVigUb3aABMAj0L2BQ6V+wO2xnxiByDQAAgL1LML4Fa8PpjZbL7iiVSt0B0EaB9tzcXPf7a80SXvvc2tewt3Q+gLleKD47O5vx8fEUi8UsLy93g+wkGR8f794ssVWFQqG7ckFn+f1SqdS9MePK2es//uM/vq3zA+wV+hcwqPQv2DnbmTGoXAMAAGDvEoxvwdoB9PWsPebKZa7XBqDXOlehUOg+v93QlJvnzJkzSS4veb+ZRqORmZmZlEqlLC4udv+7lkql7izvSqWyrQ9gLl68mIsXL171d6hcLqdQKKTdbq+7MeNjH/vYNn4qgL1D/wIGlf4FO3PldmbLy8uZn5/P9PR0pqenMz8/n5WVle6N6dVqddMx1ZXbmS0vL3fPs7i4uO5m9GuN7WCrXAMAAGDvEoxvQWewvZXwcu0xa/coa7fb2wrY1y69ZUm3vafdbnf/u3RmGWykM/t/o6X1kx/MNu91n/Erdf7e+DsDAAAMKtuZAQAAcCMIxregE3yuDbc3s1lYurS01P3+8OHD133PI0eObPha9obOhyXXu8mh899usz3GxsbG1h23W5566qnu97/0S7+0q+cGuFn0L2BQ6V+wM7YzY5C5BgAAwN4lGN+CtYPkzWb+XvnclYPutcuqd8LQa1kbpK6srGylTG6ixcXFJNeeLZ784L/jlcvqd3Qe3yw4367OjRlrb6z4y7/8y105N8DNpn8Bg0r/gp2xnRmDzDUAAAD2LsH4FhQKhW4Q2mg0Mj4+nkajkXa73V1Se2JiIvV6PcnlO8yvHHRvZw/pK20WqtI/nRne17vJoROcd/7+XKmzT/nExMSW3rdarW66tN/ax9cG9m6sAAaV/gUMKv0LdsZ2Zgwy1wAAANi7BONbVC6Xs7KyknK53A3CR0dHMzo62g3KS6VSFhcXu/uTbWbtYH0rx1y4cOG6x3/ve9/LpUuX1n1x43RuVjh48OA1jzt58mQKhULq9Xr3xomOmZmZNJvNFIvFq/7OdD7EufKmiEajse4mjI5ms5lKpZLk8moFa//+POtZ/jcHBpP+BQwq/Qt2xnZmDDLXAAAA2Ltu6XcBg6RYLGZmZiZLS0sbzuKuVqubLq29ds/nG+HUqVP5/d///asen5+fz/Oe97zce++9OXv2bJ5++um88IUvzGte85p88IMfTHL57vlnnnkmn/vc55Ikv/Ebv5FPfOITeeqpp3Lw4MH80i/9Ut73vvclSX76p386z3nOc7K8vJwkedOb3pSlpaX81V/9VW677bbcc8893aXnfvInfzIveMEL8ulPfzpJ8sY3vjFf/OIX/+9lxe64of8+bpQPfvCD+fa3v939cObxxx/PQw89lCNHjuTv/u7v8oUvfCFJ8pu/+Zv52Mc+losXL+Zf/It/kd/7vd9LtVrNP/tn/yw/8zM/kz//8z/PpUuX8sIXvjDve9/7sri4mG9961sZHR3N6173uvyTf/JP8oEPfCATExP51//6X+fcuXNJkkceeSS/8iu/kmq1mv/xf/wf84pXvCIXL17ME088kSR529velpe+9KV56KGHcs899+RLX/pSnnnmmbz//e/Pb/zGb+Shhx5KkrziFa/IC1/4wnziE59IkrzhDW/IyspKnnzyyfzQD/1Q7rvvvszPz+e//tf/mmKxmB//8R/Pxz72sSSX90v7y7/8y6ysrORZz3pWjh8/nkceeSTf+9738uM//uN5xSte0Z29/vM///P51re+lb/4i79Ikhw/fjwf/OAH8zd/8zd58YtfnFe/+tV59NFHkyQ/+7M/m+985zv58z//8ySX9xJ87LHHcunSpfzoj/5oDh8+nP/wH/5DkmR8fDzf//738x//439MkrzlLW/Jxz/+8Vy4cCG33357fuEXfiHvf//7kyR33XVXnvWsZ3U/MHvzm9+cz3zmM/nmN7+ZAwcO5OjRo3nkkUeSJK9+9avzvOc9L5/5zGeSJL/yK7+Sz3/+8/kv/+W/5AUveEF+9Vd/NQ8//HCS5FWvelUOHjyYT37yk0kuz/r/8pe/nK9//et57nOfm7e85S1597vfndXV1dx555150YtelD/90z9Nkrz+9a/PE088kfPnz+c5z3lOJicns7CwkO9///s5dOhQXv7yl+ejH/1okuQXf/EX841vfCNf+cpXMjIykre+9a153/vel7/927/NS1/60rzqVa/qrkjwcz/3c7lw4UK+/OUvJ0mOHTuWD33oQ/nOd76TH/uxH8vP/MzP5E/+5E+SJK95zWvy3e9+N1/84heTZM/1iOc///l585vf3F1V4ZWvfGXuuOOO/Nmf/VmSyx98Pv744/na176WW2+9Nffee2/OnDmTZ555JmNjY3nxi1+cj3/840mS173udfna176WVquVW265JZVKJe95z3vy93//93nZy16WsbGxfOQjH0mS/MIv/EK++c1v5vHHH+/+P/X+978/3/3ud/OSl7wkP/ETP3F1Y+CGmZiY6PYtPUKPGJQe8dhjjyVJXvva1+bpp5/Ol770pSRJpVLJhz/84Xz729/Oi170opRKpXzoQx9Kkmv+HnHHHXfkta99bT7wgQ8kSe6+++4kyWc/+9kkya/92q/lU5/61LrfI9773vcmSX7qp34qP/zDP9z9PWJQf/8aRM8880za7bYekf3dI970pjft0t+o/adWq3VvBq5Wq5uuwGU7M/ai48eP97sEAABgEyOrq6ur/S5iELTb7VQqlW7gVy6Xu8tfnzt3rvsBTbFYzOLi4lV7Rs/MzGR2djbJ5aXWrzervDMzOLn84f/8/Pw1j//e976X733ve90/X7p0KS996Uvz9NNP57bbbtvGT3pz/Py/2viDjb3uz/7Z5f8mIyMjSS5/YLKV/cHb7Xb3popms5lSqZRyuXzVhzcdnb8vm/23n52dzZkzZ9JsNlMoFFIulze9MeORRx7Jvffeu50fE7iGQexfb/3Zf9TvEnry4m98Vv+CXaR/3Tz6F8nlMdmBAwf27Jhsr1s7Ji6VSqnVat2Z361WKzMzM93x+UZj7NnZ2czMzCS5HJpPT09f8/0WFha6q3BNTU1lbm5uy7UOwn9r14CbxzUAds8g9q5kcPvXf/fEv+13Cdv2tcVz1z9oD/rH/+bf97sEbjD96+YZxN6V6F+7bTtjMjPGt6Ddbmd8fLw7S3hxcfGqALLVamViYiKtVivj4+M5f/78uuWsb7/99p7f/3rLdSfJrbfemltvvbXn92B7tns/SaFQ2NYHK7VabdPQPEmmp6ev+8FOx9obJgAGif4FDCr9C3aus51ZtVpdF5Kv1QnMN1u5rWO3tzPb6MZ06HANAACAvUswvgUnTpzohuLz8/MbDrqLxWLm5+czPj7enV2+drm3tYPsjZZhv9LaY7YyiIfN/PiP/3i/SwDoif4FDCr9C3bHXt3ObNC2Mnv+85+f5Lk37N8H6337299OEluZxTZFtiCxlRlspt1u6xG2MoOB9NBDD+3JHrGdrcwspX4d7XY7o6OjSS4H1BcvXrzm8RMTE91ByfLyckqlUpL1y7JNT09fczZwktTr9e5+aVtZ9u1Ke30pt0FdSqSzlPog+eu//uv8g3/wD/pdBgyNQexfg7gMUpK89c4X6F+wi/Svm0f/Itn7Y7K9bi9vZzZoW5klrgE3k2sA7J5B7F3J4PavQVyO2FLE7FX6180ziL0r0b9223bG38+6STUNrM4gPEl3P7NrWTsYX/vatbO+O7PPr6VzJ9eVr4XtWvv3EGCQ6F/AoNK/YGc625l1/l9aXFzM4uJid0up+fn5rKyspFgsdrczu3JG+Y3czuzWW2/Nbbfdtu4LOlwDAABg7xKMX8faELsz+/taxsfHu9+vrKx0v18bqneWlNrq+24lkAcAAIBhsJ3tzJIfzC5fy3ZmAAAAXEkwfh27NdO7UCh0/7yV8ywtLXW/30ogD5v5+Z//+X6XANAT/QsYVPoX9K7dbneXSS8UCpmcnNz02FKp1A3NG43GupvQ18763sp+4xcuXOh+v5PZ5uAaAAAAe5dg/Dq2O9N7baDd2Z+s49ixY93vOwP9jTSbze7d6hvdGQ/b8a1vfavfJQD0RP8CBpX+Bb2znRmDzjUAAAD2LsH4dZRKpe6M7Vardd1AuxOeFwqFq0LtarXa/f7UqVObnmftc2tfA734i7/4i36XANAT/QsYVPoX9M52Zgw61wAAANi7BONb0Nm3LLm819lGg+pWq5WjR49u+JqOUqnUXQau2WxuGHrX6/Vu+L72eAAAABh2tjMDAADgRrml3wUMgmKxmFqtlpmZmbTb7YyPj6dcLneXSj937ty6meRTU1ObLoF++vTpNBqNtNvt1Ov1LC0tpVqtpt1uZ3Fxcd3SbxuF6/TX//7J8/0uYdv+n8eP97sEgJ4c17+AAaV/Qe92ezuzer2e5PJ2ZpvdeG47M3aTawAAAOxdZoxv0fT0dBYXF7t3nDcajczMzGRmZmZdKD43N5e5ublNz1MoFLK8vNzdB60zc3xmZqYbil95DOzEBz/4wX6XANAT/QsYVPoX9M52Zgw61wAAANi7BOPbUC6Xc/78+UxPT69bWq1YLGZ6ejoXL17M1NTUdc9TLBazsrKSubm5lEqlbtheKpVSq9Vy/vx5S7exa/7mb/6m3yUA9ET/AgaV/gU7YzszBplrAAAA7F2WUt+mQqGQWq22K+eampraUpAOO/HiF7+43yUA9ET/AgaV/gU7YzszBplrAAAA7F2CcRhyr371q/tdAkBP9C9gUOlfsHOdldoqlUra7XYajca6ELtjbm7umjecd7Yqm5iYSKvV2nDmeKFQyNmzZ21nxq5wDQAAgL3LUuow5B599NF+lwDQE/0LGFT6F+wO25kxiFwDAABg7zJjHAAAANiTbGcGAADAbjFjHIbcz/7sz/a7BICe6F/AoNK/APYv1wAAANi7BOMw5L7zne/0uwSAnuhfwKDSvwD2L9cAAADYuwTjMOT+/M//vN8lAPRE/wIGlf4FsH+5BgAAwN4lGAcAAAAAAABgqAnGYchNTk72uwSAnuhfwKDSvwD2L9cAAADYuwTjMOQee+yxfpcA0BP9CxhU+hfA/uUaAAAAe5dgHIbcpUuX+l0CQE/0L2BQ6V8A+9PML9+VN7/5zRkZGbnm1/j4+FWvbbVaqVQqGRsb6x4zMzPTcy27fT4AABgGgnEYcj/6oz/a7xIAeqJ/AYNK/wLYn573I4X8w3/4D1MsFjf92ki9Xs/Y2FgWFhaSJOVyOa1WK7OzsxkbG0u73d5WHbt9PgAAGBaCcRhyhw8f7ncJAD3Rv4BBpX8B7E//r/mP5ctf/nJWVlY2/KrVakmSkydPdl/TaDRSrVZTLpe7xy0uLubixYuZnJzszvzeqt0+HwAADBPBOAy5//Af/kO/SwDoif4FDCr9C2D/2uwa0AmkJycnMzk52X28WCymVCplcXHxqhnlnSC90Whs+f13+3wAADBMBOMAAAAAcANVKpUUCoWcPn163ePFYjHLy8u79j67fT4AABgmt/S7AODGGh8f73cJAD3Rv4BBpX8B7F8bXQNmZmbSbDYzPz+fQqGw5XPNzc0lubxH+G7Y7fMBAMCgMWMchtz3v//9fpcA0BP9CxhU+hfA/nXlNaDdbmd2djbFYnHdEurXU6/XMzs7m+QHS6DvxG6fDwAABpFgHIbcf/yP/7HfJQD0RP8CBpX+BbB/XXkNOHXqVJLLs8avZ3Z2NuPj4xkdHU21Wk2hUMji4mJKpVJPtez2+QAAYNBZSh0AAAAAdllntniSTE1NXff4c+fOpdlsdv98+PDhFIvFnt9/t88HAACDzoxxGHJvectb+l0CQE/0L2BQ6V8A+9faa0C9Xk+ytVA8Sebn57O6upqLFy9mfn4+S0tLGRsby8LCQk+17Pb5AABg0AnGYch9/OMf73cJAD3Rv4BBpX8B7F9rrwFnzpxJklQqlW2do1AoZHJyMsvLy93Xt1qtnmva7fMBAMCgEozDkLtw4UK/SwDoif4FDCr9C2D/6lwD2u12dxnzcrnc07mKxWImJyeTJLVabce17fb5AABg0AjGYcjdfvvt/S4BoCf6FzCo9C+A/atzDWg0GkmSUqm0o/N19gRfWlraWWE36HwAADBIBOMw5H7hF36h3yUA9ET/AgaV/gWwf3WuAYuLi0muP1u83W5f8/nOkuedQPt6dvt8AAAwTATjMOTe//7397sEgJ7oX8Cg0r8A9q/ONaAzI3tsbOyaxx89evSa+313Zp5PTExs6f13+3wAADBMBOMAAAAAsIs6M7cPHjx4zeOKxWLGxsYyOzt71WzvSqWSdrudYrGYqampq87farWuek2v5wMAgP1AMA5D7q677up3CQA90b+AQaV/AexfnWvAVpcsn5+fz+TkZGZmZjI6Oprx8fGMj49nZGQkCwsLKRaL3WXZ1zp16lTGxsZy4sSJXTkfAADsB4JxGHLPepb/zYHBpH8Bg0r/Ati/rrwGFAqF675mfn4+i4uLmZycTKvVSrPZTKlUyvT0dFZWVra9H/hunw8AAIbFLf0uALixms1mXvnKV/a7DIBt07+AQaV/AexfnWvA6urqtl5XLpdTLpe3fHytVkutVtu18wEAwH5gKgMAAAAAAAAAQ00wDkPuzW9+c79LAOiJ/gUMKv0LYP9yDQAAgL1LMA5D7jOf+Uy/SwDoif4FDCr9C2D/cg0AAIC9SzAOQ+6b3/xmv0sA6In+BQwq/Qtg/3INAACAvUswDkPuwIED/S4BoCf6FzCo9C+A/cs1AAAA9i7BOAy5o0ePrvtzvV7P+Ph4RkZGMjIykvHx8dTr9U1fPzMzk7Gxse6xs7Oz265hdHS0+37X+hofH9/2uYHhdWX/AhgU+hfA/uUaAAAAe5dgHIbcI488kiRpt9sZHx9PtVpNs9lMqVRKuVxOu93O/Pz8hq+dmJjI7OxsWq1WyuVyWq1WZmZmMjExsa0aDh48mGKxeM0vgCt1+hfAoNG/APYv1wAAANi7bul3AcDNMT4+nlarlcnJyU2D8LUmJibSaDQyNTWVubm5dedpNBqp1+uZmpra0nuvrKxc8/mFhYVUKpWcPHlyS+cDAAAAAACA7RCMw5B79atfnXq9vq1QfGFhIY1GI8VicV0oniTz8/MZGxvLzMzMloPxa2m1WqlUKpmcnMzk5OSOzwcMj1e/+tX9LgGgJ/oXwP7166tfzd/8u/+l32Vsy/N/65/3uwQAALgpLKUOQ+55z3teZmZmkiSnT5/e0ms6YXi1Wr3quWKxmFKplHa7nUajseP6KpVKCoXClmsD9o/nPe95/S4BoCf6FwAAAMDeIxiHIXfq1Km02+1MTk6mUCh09wmvVCqZmZlJu92+6jWtVitJUiqVNjzn4cOHkyTNZnNHtc3MzKTZbOb06dMpFAo7OhcwfD7zmc/0uwSAnuhfAAAAAHuPpdRhyH3hC19Ikhw5ciQzMzOZnZ1d9/zCwkKWl5fXBdMXLlxIkhw8eHDDc46NjSVJzp0713Nd7XY7s7OzKRaLllAHAAAAAADghjJjHIbcN7/5zSSXZ44vLCxkcXExFy9ezOLiYkqlUlqtVo4ePbruNZ1AvBOQX+mpp55Kkg1nm2/VqVOnkqS7zDvAlX7lV36l3yUA9ET/AgAAANh7BOMw5L71rW8luRxiLy8vp1wup1AopFwu5+zZs0kuL4m+dln0YrHYfXwjncc3C86vpzNbPEmmpqaue3y9Xs/4+HhGRkYyMjKS8fHx1Ov1Tc9drVYzNjbWPbaX8H10dLT7ftf6Gh8f3/a5ga35/Oc/3+8SAHpyZf/yuwwAAABA/wnGYcg988wzSS4H0Ffu410oFLrLmJ85c6b7eLVaTZLMzc1ddb5ms5lGo7GjmjofBF8vFG+32xkfH0+1Wk2z2UypVEq5XE673c78/PyGtR06dKh7/s6xs7OzGR0d7e6dvhUHDx5MsVi85hdwY/2X//Jf+l0CQE86/cvvMgAAAAB7hz3GYcjddttt+e53v7vpbKCJiYksLCys+6B1cnIyxWIxrVYrExMTqdVqKRaLaTQaOXHiRAqFQtrt9qZ7kF9PJ4SvVCrXPG58fDytViuTk5Mbfni8VrvdztGjR9Nut7O4uJhyudx9rl6vp1qtplKpZHl5eUs1rqysXPP5hYWFVCqVnDx5ckvnA7bvBS94Qb9LAOhJp3/5XQYAAABg7zBjHIbcj/3YjyXJpiF25/ErZyB1PrxtNBoZHx/P6OhoKpVKarVajh07liQplUrbrqfdbneXYl/7ge+V6vX6lj9I7tTZbrczNTV11Xk7j125ZHyvWq1WKpVKJicnuzPugd33q7/6q/0uAaAnv/qrv+p3GQAAAIA9RjAOQ66zfPpm+4Fv9nipVMrKykqmpqYyOTmZWq3W/fPS0lKS5MiRI9uup7MM+/VC9c5emqdPn97Sec+dO5ckm86M77xfp/adqFQqKRQKW64N6M3DDz/c7xIAevLwww/7XQYAAABgj7GUOgy5Q4cOJUkWFxc33NO7s8zm4cOHr3quWCxetc94q9VKs9lMsVjsaYbR4uJikuvPFm+325mcnEyhUEir1crc3FxarVaKxWJOnjx51X7pnZB+s+VFO7OrNvo5t2NmZibNZjPz8/NX1QAAkCRnz571uwwAAADAHiMYhyH3T/7JP8kf//EfZ2FhIe12+6oPQOv1epLr7/fd0Zn9VKvVeqqnM8tpbGxs02M64fmRI0cyMzOT2dnZdc8vLCxkeXl53c8yOTmZUqmUer2eSqVy1b6cjUaje0yv2u12Zmdne74pANieV73qVf0uAaAnTz75ZBK/ywAAAADsJZZShyH3spe9LNPT00muDr8rlUp3NtOVM7gbjcZV+45Xq9UsLCykVCpt+GFqu91Oq9VKu93etJ7Oc5vteZ78YEbUqVOnsrCwkMXFxVy8eDGLi4splUpptVo5evToVa9bXl7O5ORkJiYmMjY21v1ntVrN9PT0lvb3vJZTp04l+cHNAcCNda0+AbCXPf7440n8LgMAAACwlwjGYch98pOfTK1WS6lUSqPRyOjoaMbHxzMyMtINuTf6kLVSqWRsbCxjY2Pd4+v1eorFYs6ePbvhe506dSpjY2M5ceLEpvV0wvZisbjpMZ19z9vtdpaXl1Mul1MoFFIul7vv3Ww2uwH6WidPnkyxWEyr1eqG+6VSKcePH9/8X9IWdGZYJdlwSXpg933yk59c9+d6vd7tRyMjIxkfH++uetExOjraff5aX5vt4buR651rYmJiV35eYHh861vfSuJ3GQAAAIC9RDAO+8Ty8nJqtVqKxWKazWZKpVKmp6c33ceyM2MpSXdP8VqtlpWVlV3Zj3Ir55iamrrquEKh0K3rzJkz656bnZ3N+Ph4isVilpeXs7q62v35xsfHs7Cw0HO9nfDNB8lw87Xb7YyPj6darXb7V7lcTrvdvurGnoMHD6ZYLF7zq1elUmnTL4CN+F0GAAAAYO+wxzgMubUzGaenp7vLql9PsVjc9nKdtVrtunuPr66uXvc8Bw8e7AZhG5mYmMjCwsK6pd4bjUZmZmZSKpW6e5Qnl4Os5eXljI6OplKpZGVlpadgrPPB9Vb3Ygd2rtO/xsfH02q1Mjk5ed2+tLKycs3nFxYWUqlUcvLkyW3Xs9mNRABXeuELX5gnnnjC7zIAAAAAe4gZ4zDkvvzlL/e7hG3rzKzabH/hzuNrP0zuBPLVanXD13RmR10vuN9Iu93uLnV65V7swI3z5S9/OfV6fcuh+PW0Wq1UKpVMTk52Z2sC3Ag/9EM/lMTvMgAAAAB7iWAchtzXv/71fpewbYcPH07yg73Gr7TR40tLS0k237t8bGxs3XHb0Wg0ksRyyXCTff3rX8/MzEyS5PTp0zs+X6VSSaFQ2JVzAVzLS17ykiR+lwEAAADYSwTjMOSe+9zn9ruEbessO7p2GdG1OksldwL05AcfIrfb7Q1f03m8l6VHO3WYYQU315/+6Z+m3W5ncnIyhUIhrVYrMzMzqVQqmZmZ2fT/943MzMyk2Wzm9OnTV+33C7DbXvnKVybxuwwAAADAXiIYhyH3lre8pd8lbNuxY8eSXN4LeKMPh+v1epL1e2R2Pujd7APozr6aa/dc36rOzKzOTC3g5vjrv/7rJMmRI0cyMzOTsbGxzM7OZmFhIbOzsxkfH99SON5utzM7O5tisbjjJdRnZ2dTrVYzOzvbXZYY4Er/2//2vyXxuwwAAADAXiIYhyH37ne/u98lbFuhUMj09HSS9R8Yd/7cmUG6dtbTyZMnUygUUq/Xux82d3RmihaLxe7+nB3tdjutVuua4Vrnuc32CQVujI9//ONJklOnTmVhYSGLi4u5ePFiFhcXUyqV0mq1cvTo0eue59SpU0nSXZa9V6Ojo5mZmUm9Xs/MzEzGx8czOzu7o3MCw+nDH/6w32UAAAAA9hjBOAy51dXVfpfQk1qtllKplEajkdHR0YyPj2dkZCQLCwsplUqZn59fd3yhUMjZs2dTKBRSrVYzOjqaiYmJjI6OdmeKbjQD69SpUxkbG8uJEyc2raXVaiXpbelSoHff+c53klwOdJaXl1Mul1MoFFIul3P27NkkSbPZvObM7c5s8SRXhUm9WFxczOrqapaXl1MoFLpBOcBaq6urfpcBAAAA2GME4zDk7rzzzn6X0LPl5eXUarUUi8U0m82USqVMT09neXl5w+NLpVLOnz+fqampFIvFNBqNFIvFTE9PZ2VlZccfBtuXGG6uZz/72UkuB9pX/v9XKBS6y6J3lhfeSCe03kkoXigUuoFVZ3bn2lBrpzPRgeHT+f3L7zIAAAAAe8ct/S4AuLFe9KIX9buEHZmenu4uRboVhUIhc3NzWz6+VqulVqtd85hBnXUPg250dDTf/va3Mz4+vuHzExMTWVhY6M6E3EgnNL9yKePtuHjx4oaPd2awt9vtNBqNdUsiA/vb2t+//C4DAAAAsDcIxmHIjX783+Rv+l1ED57/W/+83yUAffasZ11e2GazPXE7j28WjLfb7e4y6zcqtO7MAm02m4JxoOtP//RP87a3va3fZQAAAACwhqXUAYA9qbNk8IULFzZ8frPHOxqNRpLLSxPfaE899dQNfw8AAAAAAHonGAcA9qRf/uVfTpIsLi5u+PzKykqS5PDhwxs+33ndjZzJ3ZmRfuTIkRv2HsDgef3rX9/vEgAAAAC4gmAcANiT7rrrriTJwsJC2u32Vc/X6/Ukm+8fvrS0lCQZGxvruYZqtdqdeX6ltY9bRh1Y64knnuh3CQAAAABcQTAOAOxJ3/rWtzI9PZ3k6vC7Uqmk3W5ncnJy01C6E6Zvtkf5lce2Wq2rAvhGo5GJiYluCN/RbDa7NdVqtRQKhS38RMB+cf78+X6XAAAAAMAVBOMAwJ70nOc8J7VaLaVSKY1GI6OjoxkfH8/IyEgWFhZSKpU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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -570,17 +363,9 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results successfully loaded from ../../Data/Temp/Benchmark/raw_results.json\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import pandas as pd\n", @@ -622,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -744,20 +529,9 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", @@ -811,17 +585,9 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results successfully loaded from ../../Data/Temp/Benchmark/raw_results.json\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import pandas as pd\n", @@ -863,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -884,219 +650,16 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AGCNRTypeRadiiG-mean-forwardG-meanSc-forward
Raw_04.047.8694.87Raw00.064950.0563510.06495
Raw_10.797.2478.52Raw10.143600.0239160.14360
Raw_20.336.5069.74Raw20.183800.0146460.18380
Raw_30.234.9268.67Raw30.181250.0106380.18125
Complete_071.1394.5097.18Complete00.486600.8198650.48660
Complete_122.8892.9289.08Complete10.519200.4610870.51920
Complete_25.3088.6067.50Complete20.605500.2166980.60550
Complete_33.2278.0358.88Complete30.595750.1585110.59575
Refine_084.2994.7097.66Refine00.485200.8934350.48520
Refine_123.7893.1689.58Refine10.517900.4706740.51790
Refine_25.7389.1268.68Refine20.602200.2259770.60220
Refine_33.3078.9358.97Refine30.599800.1613910.59980
\n", - "
" - ], - "text/plain": [ - " AG C NR Type Radii G-mean-forward G-mean \\\n", - "Raw_0 4.04 7.86 94.87 Raw 0 0.06495 0.056351 \n", - "Raw_1 0.79 7.24 78.52 Raw 1 0.14360 0.023916 \n", - "Raw_2 0.33 6.50 69.74 Raw 2 0.18380 0.014646 \n", - "Raw_3 0.23 4.92 68.67 Raw 3 0.18125 0.010638 \n", - "Complete_0 71.13 94.50 97.18 Complete 0 0.48660 0.819865 \n", - "Complete_1 22.88 92.92 89.08 Complete 1 0.51920 0.461087 \n", - "Complete_2 5.30 88.60 67.50 Complete 2 0.60550 0.216698 \n", - "Complete_3 3.22 78.03 58.88 Complete 3 0.59575 0.158511 \n", - "Refine_0 84.29 94.70 97.66 Refine 0 0.48520 0.893435 \n", - "Refine_1 23.78 93.16 89.58 Refine 1 0.51790 0.470674 \n", - "Refine_2 5.73 89.12 68.68 Refine 2 0.60220 0.225977 \n", - "Refine_3 3.30 78.93 58.97 Refine 3 0.59980 0.161391 \n", - "\n", - " Sc-forward \n", - "Raw_0 0.06495 \n", - "Raw_1 0.14360 \n", - "Raw_2 0.18380 \n", - "Raw_3 0.18125 \n", - "Complete_0 0.48660 \n", - "Complete_1 0.51920 \n", - "Complete_2 0.60550 \n", - "Complete_3 0.59575 \n", - "Refine_0 0.48520 \n", - "Refine_1 0.51790 \n", - "Refine_2 0.60220 \n", - "Refine_3 0.59980 " - ] - }, - "execution_count": 199, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "fw" ] }, { "cell_type": "code", - "execution_count": 200, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1107,153 +670,16 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Sc-forwardSc-backwardTypeRadii
Raw_00.064950.04935Raw0
Raw_10.143600.11200Raw1
Raw_20.183800.16600Raw2
Raw_30.181250.20425Raw3
Complete_00.486600.47800Complete0
Complete_10.519200.49930Complete1
Complete_20.605500.49605Complete2
Complete_30.595750.46425Complete3
Refine_00.485200.47775Refine0
Refine_10.517900.49975Refine1
Refine_20.602200.49795Refine2
Refine_30.599800.46835Refine3
\n", - "
" - ], - "text/plain": [ - " Sc-forward Sc-backward Type Radii\n", - "Raw_0 0.06495 0.04935 Raw 0\n", - "Raw_1 0.14360 0.11200 Raw 1\n", - "Raw_2 0.18380 0.16600 Raw 2\n", - "Raw_3 0.18125 0.20425 Raw 3\n", - "Complete_0 0.48660 0.47800 Complete 0\n", - "Complete_1 0.51920 0.49930 Complete 1\n", - "Complete_2 0.60550 0.49605 Complete 2\n", - "Complete_3 0.59575 0.46425 Complete 3\n", - "Refine_0 0.48520 0.47775 Refine 0\n", - "Refine_1 0.51790 0.49975 Refine 1\n", - "Refine_2 0.60220 0.49795 Refine 2\n", - "Refine_3 0.59980 0.46835 Refine 3" - ] - }, - "execution_count": 201, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "valid_result" ] }, { "cell_type": "code", - "execution_count": 182, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1264,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1283,216 +709,18 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AGCNRTypeRadiiG-mean-forwardG-mean
Raw_04.047.8694.87Raw00.0634990.056351
Raw_10.797.2478.52Raw10.1247060.023916
Raw_20.336.5069.74Raw20.1402460.014646
Raw_30.234.9268.67Raw30.1241550.010638
Complete_071.1394.5097.18Complete00.1632450.819865
Complete_122.8892.9289.08Complete10.3185410.461087
Complete_25.3088.6067.50Complete20.5366100.216698
Complete_33.2278.0358.88Complete30.5664440.158511
Refine_084.2994.7097.66Refine00.1488620.893435
Refine_123.7893.1689.58Refine10.3115650.470674
Refine_25.7389.1268.68Refine20.5283220.225977
Refine_33.3078.9358.97Refine30.5690780.161391
\n", - "
" - ], - "text/plain": [ - " AG C NR Type Radii G-mean-forward G-mean\n", - "Raw_0 4.04 7.86 94.87 Raw 0 0.063499 0.056351\n", - "Raw_1 0.79 7.24 78.52 Raw 1 0.124706 0.023916\n", - "Raw_2 0.33 6.50 69.74 Raw 2 0.140246 0.014646\n", - "Raw_3 0.23 4.92 68.67 Raw 3 0.124155 0.010638\n", - "Complete_0 71.13 94.50 97.18 Complete 0 0.163245 0.819865\n", - "Complete_1 22.88 92.92 89.08 Complete 1 0.318541 0.461087\n", - "Complete_2 5.30 88.60 67.50 Complete 2 0.536610 0.216698\n", - "Complete_3 3.22 78.03 58.88 Complete 3 0.566444 0.158511\n", - "Refine_0 84.29 94.70 97.66 Refine 0 0.148862 0.893435\n", - "Refine_1 23.78 93.16 89.58 Refine 1 0.311565 0.470674\n", - "Refine_2 5.73 89.12 68.68 Refine 2 0.528322 0.225977\n", - "Refine_3 3.30 78.93 58.97 Refine 3 0.569078 0.161391" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "fw" ] }, { "cell_type": "code", - "execution_count": 179, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'FPR'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", - "File \u001b[0;32mindex.pyx:153\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mindex.pyx:182\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'FPR'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/homes/biertank/tieu/Documents/Project/TACsy/SynEco/SynTemp/Docs/Analysis/_5_rule_application.ipynb Cell 27\u001b[0m line \u001b[0;36m7\n\u001b[1;32m 3\u001b[0m \u001b[39mfor\u001b[39;00m type_, group \u001b[39min\u001b[39;00m fw\u001b[39m.\u001b[39mgroupby(\u001b[39m\"\u001b[39m\u001b[39mType\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m 4\u001b[0m tpr \u001b[39m=\u001b[39m (\n\u001b[1;32m 5\u001b[0m group[\u001b[39m\"\u001b[39m\u001b[39mC\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m/\u001b[39m \u001b[39m100\u001b[39m\n\u001b[1;32m 6\u001b[0m ) \u001b[39m# True Positive Rate (C is already in percentage, so divide by 100)\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m fpr \u001b[39m=\u001b[39m group[\u001b[39m\"\u001b[39;49m\u001b[39mFPR\u001b[39;49m\u001b[39m\"\u001b[39;49m] \u001b[39m/\u001b[39m \u001b[39m100\u001b[39m \u001b[39m# False Positive Rate\u001b[39;00m\n\u001b[1;32m 8\u001b[0m tnr \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m \u001b[39m-\u001b[39m fpr \u001b[39m# True Negative Rate\u001b[39;00m\n\u001b[1;32m 9\u001b[0m g_mean \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msqrt(tpr \u001b[39m*\u001b[39m tnr)\u001b[39m.\u001b[39mmean() \u001b[39m# Geometric Mean\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/frame.py:4090\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4088\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 4089\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4090\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 4091\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 4092\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", - "File \u001b[0;32m~/miniconda3/envs/SynITSG/lib/python3.11/site-packages/pandas/core/indexes/base.py:3809\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(casted_key, \u001b[39mslice\u001b[39m) \u001b[39mor\u001b[39;00m (\n\u001b[1;32m 3805\u001b[0m \u001b[39misinstance\u001b[39m(casted_key, abc\u001b[39m.\u001b[39mIterable)\n\u001b[1;32m 3806\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39misinstance\u001b[39m(x, \u001b[39mslice\u001b[39m) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m casted_key)\n\u001b[1;32m 3807\u001b[0m ):\n\u001b[1;32m 3808\u001b[0m \u001b[39mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3809\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3810\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3811\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3812\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3814\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'FPR'" - ] - } - ], + "outputs": [], "source": [ "g_mean_results = {}\n", "\n", @@ -1518,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1565,7 +793,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1583,27 +811,16 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[10067.556, 36444.666, 213271.507, 458275.105]" - ] - }, - "execution_count": 137, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result" ] }, { "cell_type": "code", - "execution_count": 153, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1629,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1638,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1733,30 +950,9 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1881606/50864145.py:59: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", - " ax.set_xticklabels(ax.get_xticklabels(), fontsize=20)\n", - "/tmp/ipykernel_1881606/50864145.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", - " ax.set_yticklabels([rf\"{y:.0f}\" for y in ax.get_yticks()], fontsize=20)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.rc(\"text\", usetex=True)\n", @@ -1769,17 +965,9 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results successfully loaded from ../../Data/Temp/Benchmark/raw_results.json\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import pandas as pd\n", @@ -1821,30 +1009,9 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1881606/50864145.py:59: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", - " ax.set_xticklabels(ax.get_xticklabels(), fontsize=20)\n", - "/tmp/ipykernel_1881606/50864145.py:60: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", - " ax.set_yticklabels([rf\"{y:.0f}\" for y in ax.get_yticks()], fontsize=20)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n",