From bb755e28aa4bd46dd844b9cbc77018c8be57215a Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Sun, 8 Mar 2026 20:44:02 +0000 Subject: [PATCH 01/16] Introduce Simulated Annealing - Working commit - Introduces new base classes, and simulated annealing itself - Temporary tester file --- databallpy/optimization/optimization.py | 39 ++++ .../optimization/simulated_annealing.py | 178 ++++++++++++++++++ databallpy/optimization/tester.py | 51 +++++ databallpy/optimization/xTArray.pkl | Bin 0 -> 57817 bytes 4 files changed, 268 insertions(+) create mode 100644 databallpy/optimization/optimization.py create mode 100644 databallpy/optimization/simulated_annealing.py create mode 100644 databallpy/optimization/tester.py create mode 100644 databallpy/optimization/xTArray.pkl diff --git a/databallpy/optimization/optimization.py b/databallpy/optimization/optimization.py new file mode 100644 index 0000000..8d1955c --- /dev/null +++ b/databallpy/optimization/optimization.py @@ -0,0 +1,39 @@ +from abc import ABC, abstractmethod +from databallpy.game import Game +from dataclasses import dataclass +import pandas as pd + +class ObjectiveFunction(ABC): + pass + +@dataclass +class OptimizationResult: + best_frame: pd.Series + best_result: float + +class OptimizationAlgorithm(ABC): + @abstractmethod + def __init__(self, game: Game, selected_frame_idx: int): + pass + @abstractmethod + def run(self) -> OptimizationResult: + pass + +class Constraints: + pass + +class Filters: + pass + + + +def optimize_tracking_frame( + game: Game, + selected_frame_idx: int, + objective: ObjectiveFunction, + algorithm: OptimizationAlgorithm, # --> in the beginning only SimulatedAnnealing + constraints: Constraints, # Within optimization algo + filters: Filters # The variables the optimization algo is allowed to change +) -> OptimizationResult: + optimizer = OptimizationAlgorithm(game, selected_frame_idx) + return optimizer.run() \ No newline at end of file diff --git a/databallpy/optimization/simulated_annealing.py b/databallpy/optimization/simulated_annealing.py new file mode 100644 index 0000000..55a94ab --- /dev/null +++ b/databallpy/optimization/simulated_annealing.py @@ -0,0 +1,178 @@ +# Annealer +import random +import math +import pandas as pd +import numpy as np +from databallpy.features.pitch_control import get_team_influence +from databallpy.schemas.tracking_data import TrackingData +from databallpy.optimization.optimization import OptimizationAlgorithm, OptimizationResult +from copy import deepcopy +from databallpy import Game +from databallpy.utils.utils import sigmoid + +import pickle + +from pathlib import Path + +BASE_DIR = Path(__file__).resolve().parent + + + +class SimulatedAnnealing(OptimizationAlgorithm): + def __init__(self, game: Game, selected_frame_idx = 210, distance_perturbation = 0.2, num_iterations = 1000, max_tti = 1, weighted_pitch_control_parameter = 1, pressing_parameter = 0, players_to_press = []): + + #data + self.game = game + self.frame = game.tracking_data[game.tracking_data['frame']==selected_frame_idx].iloc[0] + + self.attacking_team = self.frame['team_possession'] + self.defending_team = 'home' if self.frame['team_possession'] == 'away' else 'away' + + #annealing params + self.distance_perturbation = distance_perturbation #how much to move the player + self.p_0 = 0.5 + self.T_0 = -100/(math.log(self.p_0)) + self.T = self.T_0 + self.cooling_rate = 0.9 + self.num_iterations = num_iterations + self.max_tti = max_tti + + self.grid = np.meshgrid( + np.linspace(-self.game.pitch_dimensions[0] / 2, self.game.pitch_dimensions[0] / 2, 106), + np.linspace(-self.game.pitch_dimensions[1] / 2, self.game.pitch_dimensions[1] / 2, 68), + ) + + self.defending_player_ids = self.game.get_column_ids(team=self.defending_team) + self.attacking_player_ids = self.game.get_column_ids(team=self.attacking_team) + + self.player_to_starting_pos_and_vel_map = self.frame[[c + '_x' for c in game.get_column_ids()] + [c + '_y' for c in game.get_column_ids()] + [c + '_vx' for c in game.get_column_ids()] + [c + '_vy' for c in game.get_column_ids()]] + + # since we are only moving defenders we can precompute this + self.attacking_team_influence = get_team_influence( + self.frame, + col_ids=self.attacking_player_ids, + grid=self.grid, + player_ball_distances=None, + ) + + #define objective + self.weighted_pitch_control_parameter = weighted_pitch_control_parameter + self.pressing_parameter = pressing_parameter + if self.pressing_parameter > 0: + self.players_to_press = players_to_press if players_to_press else self.attacking_player_ids + + + #compute matrix of importance of controlling each square in grid system + with open(BASE_DIR / "xTArray.pkl", "rb") as f: + self.xt_array = pickle.load(f) + if self.attacking_team == 'away': + self.xt_array = np.fliplr(self.xt_array) + + #TTI Implementation from https://github.com/devinpleuler/analytics-handbook/blob/master/soccer_analytics_handbook.ipynb + def tti(self, origin, destination, velocity, reaction_time, max_velocity=5.0): + u = (origin + velocity) - origin + v = destination - origin + u_mag = np.sqrt(np.sum(u**2, axis=-1)) + v_mag = np.sqrt(np.sum(v**2, axis=-1)) + dot_product = np.sum(u * v, axis=-1) + angle = np.arccos(dot_product / (u_mag * v_mag)) + r_reaction = origin + velocity * reaction_time + d = destination - r_reaction + t = (u_mag * angle/np.pi + + reaction_time + + np.linalg.norm(d, axis=-1) / max_velocity) + + return t + + def perturbation(self, input_frame): + new_frame = deepcopy(input_frame) + #randomly choose 1 of the defenders + #TODO see if we can cache the unselected players to avoid recomputing every time + self.selected_players = random.sample(self.defending_player_ids, 1) + for c in self.selected_players: + #move each player up to a maximal distance from their starting positions + x_col = c + '_x' + y_col = c + '_y' + vx_col = c + '_vx' + vy_col = c + '_vy' + player_initial_x_pos = self.player_to_starting_pos_and_vel_map[x_col] + player_initial_y_pos = self.player_to_starting_pos_and_vel_map[y_col] + player_initial_vx = self.player_to_starting_pos_and_vel_map[vx_col] + player_initial_vy = self.player_to_starting_pos_and_vel_map[vy_col] + new_x_pos = new_frame[x_col] + random.uniform(-self.distance_perturbation,self.distance_perturbation) + new_y_pos = new_frame[y_col] + random.uniform(-self.distance_perturbation, self.distance_perturbation) + + #check if the new position is reachable within max time to intercept (tti) + if self.tti( + np.array([player_initial_x_pos, player_initial_y_pos]), + np.array([new_x_pos, new_y_pos]), + np.array([player_initial_vx, player_initial_vy]), + 0.1 + ) < self.max_tti: + new_frame[y_col] = new_y_pos + new_frame[x_col] = new_x_pos + + return new_frame + + def compute_objective(self, input_frame): + + team_influence_defending = get_team_influence( + input_frame, + col_ids=self.defending_player_ids, + grid=self.grid, + player_ball_distances=None, + ) + #+ve is defending team, -ve is attacking team + net_sigmoid_diff = sigmoid(team_influence_defending - self.attacking_team_influence, d=100) #this makes the sigmoid steeper and more binary + + defensive_matrix = self.xt_array * net_sigmoid_diff + + objective_matrix = defensive_matrix + + #pressure method only works on tracking data object, need to temporarily reconstruct it with the new data + pressure_score = 0 + temp_tracking_df = pd.DataFrame(input_frame).T + temp_tracking_df = TrackingData(temp_tracking_df.astype(self.game.tracking_data.dtypes)) + + # add terms for tight marking by multiplying the objective by pressing parameter for controlling pitch near attacking player + if self.pressing_parameter > 0: + for attacking_player in self.players_to_press: + pressure = temp_tracking_df.get_pressure_on_player(temp_tracking_df.index[0], attacking_player, [106,68], d_front = 9) + #compute the mean pressure on a player by dividing by number of players in p + pressure_score += pressure / len(self.players_to_press) + + #take the overall sum of geospatial terms + return sum(sum(objective_matrix)) * self.weighted_pitch_control_parameter + pressure_score * self.pressing_parameter + + def run(self): + best_score = 0 + best_solution = deepcopy(self.frame) + latest_frame = deepcopy(self.frame) + last_checkpoint_score = 0 + T = self.T + print('running annealer') + for i in range(1,self.num_iterations): + perturbed_frame = self.perturbation(latest_frame) + new_score = self.compute_objective(perturbed_frame) + #take the new result if it's better, or randomly take a worse result with decaying probability + if (new_score > best_score) or math.exp((new_score - best_score)/T) > random.random(): + latest_frame = perturbed_frame + if (new_score > best_score): + best_score = new_score + best_solution = perturbed_frame + latest_frame = perturbed_frame + T = T*self.cooling_rate + if i%200 == 0: + print(f'iteration {i} / {self.num_iterations} best_score {best_score}') + if last_checkpoint_score == best_score: + print('No improvement found in last 200 iterations... exiting.') + break + last_checkpoint_score = best_score + + + print(f'best score {best_score}') + return OptimizationResult(best_frame=best_solution, best_result=best_score) + + + + diff --git a/databallpy/optimization/tester.py b/databallpy/optimization/tester.py new file mode 100644 index 0000000..77c26bd --- /dev/null +++ b/databallpy/optimization/tester.py @@ -0,0 +1,51 @@ +#imports + +from databallpy.optimization.simulated_annealing import SimulatedAnnealing + +from kloppy import skillcorner +from databallpy import get_game_from_kloppy +from databallpy.visualize import plot_soccer_pitch, plot_tracking_data +from databallpy.features.pitch_control import get_pitch_control_single_frame + +import pandas as pd +# import accessible_space +import pickle + +import matplotlib.pyplot as plt + +match_id = 1886347 +tracking_data_github_url = f"https://media.githubusercontent.com/media/SkillCorner/opendata/master/data/matches/{match_id}/{match_id}_tracking_extrapolated.jsonl" +meta_data_github_url = f"https://raw.githubusercontent.com/SkillCorner/opendata/master/data/matches/{match_id}/{match_id}_match.json" + +dataset = skillcorner.load( + meta_data=meta_data_github_url, + raw_data=tracking_data_github_url, + # Optional Parameters + coordinates="skillcorner", + limit=500, +) + + +#actual script +game = get_game_from_kloppy(dataset) +game.tracking_data.add_velocity(game.get_column_ids() + ["ball"], allow_overwrite=True) +game.tracking_data.add_individual_player_possession() +selected_frame_idx = 210 + +pc = get_pitch_control_single_frame(game.tracking_data[game.tracking_data['frame']==selected_frame_idx].iloc[0], + game.pitch_dimensions, + n_x_bins=106, n_y_bins=68, + ) +print(f"total pitch control: {sum(sum(pc))}") + +annealer = SimulatedAnnealing( + game, + selected_frame_idx, + distance_perturbation=0.2, # how many yards to randomly move the player on each iteration + max_tti= 2, # the maximum time to intercept, i.e. don't move a player if they can't reach that space within 1s + num_iterations=1000, # the number of perturbations to do + weighted_pitch_control_parameter = 1 + ) + +result = annealer.run() +print(result) \ No newline at end of file diff --git a/databallpy/optimization/xTArray.pkl b/databallpy/optimization/xTArray.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7925ac9fe514c8df812fc6d8828271213d36a559 GIT binary patch literal 57817 zcmeI5U5Hgx7>50=2#GGjh$6ZO2_z#TN-9>rL{2GIgmjTQYD}WzFdbvYBqmE8>nQ5% z)T}5qO+`dgY>-L=3+cj(3Zx=JBtohSA&CB{+4HTpGqaD2^?mC&$La9SMK61uceQ_9 z&;Hi>_S$QAjd|_FsS*8uJF}@%r%w5&@2~$qO(RNK^QwFA?mL*jcAfp=k>**>s(-vN|HD#G zi?gcir_Jwg==Aw3_C9!ONy%AN&ZonlS6n;KOt|@yJxiTck!w&4~-i&apekU zRXMJ*KR&&F`+NUCu+!?~gU6ry`0dTksuNd^|N6vl+niPVFZ(sSomL+|z5R`2*X?sw zeY3Apd&_>ORVzpETRDQCJPCi~N%*sJ1izLe_^cekujL3n@L^T(VO8=(o(9G-@L^SO zqE+x=RdAwJD@X8KIf9=&ktgz0J-z}TRwYm5sgZfI@=&Y4(Rh;ciRTlFKFO23?DHjY zmFwQ^^CWuhy>|Ie(|gxAt4iKugU^4&b%rD&oKn`tgTCJ52Ykl&R{{wZYIRba&DbH_Kcq31eUnfuGsV}kqUmRn6N_*~vI7Xhx z6M3>fw*@~%B~RpuJdr24FI3N$koyDk5!O5e{Os@5cN~-&&#d!|;16*VDz6t$l0JLI z&*#yr9N$_$uSu_RzMFhLF|K!fKW+8K)~(K}Q}Q&!_+8Gb+UKkA$@%p9^UX9e%M4n1L z)x5>{DgI8L2EQjI*P}!I{uBFq?C%Zi@1^Dka6cbvO+CKUdiCN-`bEq9I6kha`8~OR zuJidRy~=eM`u?8Gi`(hv$LaOmw!1Dpxow}*s+>=C{E3yz`yEI9dx&vOZFfV>5&r+F z$Cp}wVrsuX6tSe8j5@e*AOS z*{#m1PtMJc@I7Z$>wAGRU+{>3PeiX@_PtFwWAZVl)q1r%v`_Ms_eZ=!tF+MRqRp%?L9KkyHdPtxB5Ct4*>gSpdk1Rr^lbyUIOa8+wuf?vxKeB_Bdo$Grsj8BYD z1N(*6ejfaYJ6dIYVthKaq%yu@d}4fJe5yQeLO&0Rew14r+UL0N)%$%yZC($)hi_YT&#WKbaaNV{+U?gt(`!Df?%?r5PODe;eFgEc z)1Nx4%Ju)o&ku`htbK)km;0*ydz#kqQ)vG~RUYCdR4&I6jzgi}!HHJMQ~o`}3U6AD z;IncBKY7Z>`|0s4#7(F?#7nGP%MpC!i9E5t7mi!tM62Y9Jdr2z6dO;;eWKEj({7_5 zr&aXdtSa}r__zta)NxR1y->d?R4)Cd^!}T*e&{$t<<{{a{Mx)8e2p5Po)E8>o&#Jx()>V+V=?IOXVW9UZ`D$%B?)q>JJ@9s2qG)6`W|5JmvGLE4+~>SvNA& zE<@$D@?otXI*w4el_U7I9KlDP$kVyTPoZ`hD!1}bt3Pxcp>ptHRdAwJ@n8h%>ueqX3x7b>^*^WaC^(W>mf z+q_xk=;AF9#=DB~Roj=Rw**y0X7F zZS}_1W#)b!_+x)h`h|>7j88d7h+p8ts^sba!IR9>w6Dif?|5U~pY^<~<0km!xWe!6 z7Js*L1b@D&`1Ia2&Z=@9GN)BpSMn{tp15AIKBZOVI_~lFjpKSt|K9;;Rmqcl;OAY% zRj#|eKW1%r@LSt=z5O^&t%47$7I}$ngAc1(If7r#pFEML!TCBXk6Mo4vvLG~jGyA~ zR*v8YA66w#c_cfaj!WQ9t~=usxC19z1s_%|@)FwyA6B(;1V4EqPv>JkhL$7vtQ^4~ zO5M>)35jW`nX~pbF1Pz?^cy|wA0@|;wtxvy*|&WS9!m9pI?_fuGaPq ze` Date: Mon, 9 Mar 2026 20:08:16 +0000 Subject: [PATCH 02/16] Define objective functions - Created a re-usable ObjectiveTerm function which takes in at least an input frame and computes a float from it - Used these reusable objectives and weights in the SimulatedAnnealing class to compute the objective --- databallpy/optimization/optimization.py | 140 +++++++++++-- .../optimization/simulated_annealing.py | 196 +++++++++--------- databallpy/optimization/tester.py | 114 ++++++++-- 3 files changed, 309 insertions(+), 141 deletions(-) diff --git a/databallpy/optimization/optimization.py b/databallpy/optimization/optimization.py index 8d1955c..2d8a22e 100644 --- a/databallpy/optimization/optimization.py +++ b/databallpy/optimization/optimization.py @@ -2,38 +2,146 @@ from databallpy.game import Game from dataclasses import dataclass import pandas as pd +from enum import Enum +import pickle +from pathlib import Path +from databallpy.features.pitch_control import get_team_influence +from databallpy.utils.utils import sigmoid +import numpy as np +from databallpy.schemas.tracking_data import TrackingData + +BASE_DIR = Path(__file__).resolve().parent + + +class ObjectiveType(str, Enum): + GRID = "grid" # computed for each grid cell + PLAYER = "player" # computed for each player + + +class ObjectiveTerm: + def __init__(self, computation_type: ObjectiveType): + self.computation_type = computation_type + + def compute(self, input_frame: pd.Series) -> float: + raise NotImplementedError + + +class WeightedPitchControlObjective(ObjectiveTerm): + # Computes the net pitch control for the defending team over the attacking team + def __init__( + self, + grid: np.meshgrid, + attacking_team: str, + defending_team: str, + defending_player_ids: list[str], + attacking_team_influence: np.meshgrid, + xt_array: np.ndarray | None = None, + ): + super().__init__(computation_type=ObjectiveType.GRID) + self.grid = grid + self.attacking_team = attacking_team + self.defending_team = defending_team + self.defending_player_ids = defending_player_ids + self.attacking_team_influence = attacking_team_influence + if not xt_array: + with open(BASE_DIR / "xTArray.pkl", "rb") as f: + self.xt_array = pickle.load(f) + else: + self.xt_array = xt_array + if self.attacking_team == "away": + self.xt_array = np.fliplr(self.xt_array) + + def compute( + self, + input_frame: pd.Series, + ) -> float: + team_influence_defending = get_team_influence( + input_frame, + col_ids=self.defending_player_ids, + grid=self.grid, + player_ball_distances=None, + ) + # +ve is defending team, -ve is attacking team + net_sigmoid_diff = sigmoid( + team_influence_defending - self.attacking_team_influence, d=100 + ) # this makes the sigmoid steeper and more binary + + return sum(sum(self.xt_array * net_sigmoid_diff)) + + +class PressureObjective(ObjectiveTerm): + def __init__( + self, + game: Game, + players_to_press: list[str] | None = None, + attacking_player_ids: list[str] | None = None, + ): + if not attacking_player_ids and not players_to_press: + raise ValueError( + "Either players_to_press or attacking_player_ids must be provided" + ) + + super().__init__(computation_type=ObjectiveType.PLAYER) + + self.game = game + self.players_to_press = ( + players_to_press if players_to_press else attacking_player_ids + ) + + def compute(self, input_frame: pd.Series) -> float: + # pressure method only works on tracking data object, need to temporarily reconstruct it with the new data + pressure_score = 0 + temp_tracking_df = pd.DataFrame(input_frame).T + temp_tracking_df = TrackingData( + temp_tracking_df.astype(self.game.tracking_data.dtypes) + ) + + for attacking_player in self.players_to_press: + pressure = temp_tracking_df.get_pressure_on_player( + temp_tracking_df.index[0], attacking_player, [106, 68], d_front=9 + ) + # compute the mean pressure on a player by dividing by number of players in p + pressure_score += pressure / len(self.players_to_press) + return pressure_score -class ObjectiveFunction(ABC): - pass @dataclass class OptimizationResult: best_frame: pd.Series best_result: float + class OptimizationAlgorithm(ABC): @abstractmethod - def __init__(self, game: Game, selected_frame_idx: int): - pass + def __init__( + self, + game: Game, + selected_frame_idx: int, + objective_terms: list[ObjectiveTerm], + weights: list[float], + ): + raise NotImplementedError + @abstractmethod def run(self) -> OptimizationResult: - pass + raise NotImplementedError + class Constraints: pass + class Filters: pass - -def optimize_tracking_frame( - game: Game, - selected_frame_idx: int, - objective: ObjectiveFunction, - algorithm: OptimizationAlgorithm, # --> in the beginning only SimulatedAnnealing - constraints: Constraints, # Within optimization algo - filters: Filters # The variables the optimization algo is allowed to change -) -> OptimizationResult: - optimizer = OptimizationAlgorithm(game, selected_frame_idx) - return optimizer.run() \ No newline at end of file +# def optimize_tracking_frame( +# game: Game, +# selected_frame_idx: int, +# objective: ObjectiveFunction, +# algorithm: OptimizationAlgorithm, # --> in the beginning only SimulatedAnnealing +# constraints: Constraints, # Within optimization algo +# filters: Filters # The variables the optimization algo is allowed to change +# ) -> OptimizationResult: +# optimizer = OptimizationAlgorithm(game, selected_frame_idx) +# return optimizer.run() diff --git a/databallpy/optimization/simulated_annealing.py b/databallpy/optimization/simulated_annealing.py index 55a94ab..c6c6b6b 100644 --- a/databallpy/optimization/simulated_annealing.py +++ b/databallpy/optimization/simulated_annealing.py @@ -3,9 +3,13 @@ import math import pandas as pd import numpy as np -from databallpy.features.pitch_control import get_team_influence +from databallpy.features.pitch_control import get_team_influence from databallpy.schemas.tracking_data import TrackingData -from databallpy.optimization.optimization import OptimizationAlgorithm, OptimizationResult +from databallpy.optimization.optimization import ( + OptimizationAlgorithm, + OptimizationResult, + ObjectiveTerm, +) from copy import deepcopy from databallpy import Game from databallpy.utils.utils import sigmoid @@ -14,61 +18,64 @@ from pathlib import Path -BASE_DIR = Path(__file__).resolve().parent - - class SimulatedAnnealing(OptimizationAlgorithm): - def __init__(self, game: Game, selected_frame_idx = 210, distance_perturbation = 0.2, num_iterations = 1000, max_tti = 1, weighted_pitch_control_parameter = 1, pressing_parameter = 0, players_to_press = []): - - #data + def __init__( + self, + game: Game, + selected_frame_idx: int, + objective_terms: list[ObjectiveTerm], + weights: list[float], + distance_perturbation=0.2, + num_iterations=1000, + max_tti=1, + ): + # data self.game = game - self.frame = game.tracking_data[game.tracking_data['frame']==selected_frame_idx].iloc[0] + self.frame = game.tracking_data[ + game.tracking_data["frame"] == selected_frame_idx + ].iloc[0] - self.attacking_team = self.frame['team_possession'] - self.defending_team = 'home' if self.frame['team_possession'] == 'away' else 'away' + self.attacking_team = self.frame["team_possession"] + self.defending_team = ( + "home" if self.frame["team_possession"] == "away" else "away" + ) - #annealing params - self.distance_perturbation = distance_perturbation #how much to move the player + # annealing params + self.distance_perturbation = distance_perturbation # how much to move the player self.p_0 = 0.5 - self.T_0 = -100/(math.log(self.p_0)) + self.T_0 = -100 / (math.log(self.p_0)) self.T = self.T_0 - self.cooling_rate = 0.9 + self.cooling_rate = 0.9 self.num_iterations = num_iterations self.max_tti = max_tti self.grid = np.meshgrid( - np.linspace(-self.game.pitch_dimensions[0] / 2, self.game.pitch_dimensions[0] / 2, 106), - np.linspace(-self.game.pitch_dimensions[1] / 2, self.game.pitch_dimensions[1] / 2, 68), + np.linspace( + -self.game.pitch_dimensions[0] / 2, + self.game.pitch_dimensions[0] / 2, + 106, + ), + np.linspace( + -self.game.pitch_dimensions[1] / 2, self.game.pitch_dimensions[1] / 2, 68 + ), ) self.defending_player_ids = self.game.get_column_ids(team=self.defending_team) self.attacking_player_ids = self.game.get_column_ids(team=self.attacking_team) - self.player_to_starting_pos_and_vel_map = self.frame[[c + '_x' for c in game.get_column_ids()] + [c + '_y' for c in game.get_column_ids()] + [c + '_vx' for c in game.get_column_ids()] + [c + '_vy' for c in game.get_column_ids()]] + self.player_to_starting_pos_and_vel_map = self.frame[ + [c + "_x" for c in game.get_column_ids()] + + [c + "_y" for c in game.get_column_ids()] + + [c + "_vx" for c in game.get_column_ids()] + + [c + "_vy" for c in game.get_column_ids()] + ] - # since we are only moving defenders we can precompute this - self.attacking_team_influence = get_team_influence( - self.frame, - col_ids=self.attacking_player_ids, - grid=self.grid, - player_ball_distances=None, - ) + self.objective_terms = objective_terms + self.weights = weights - #define objective - self.weighted_pitch_control_parameter = weighted_pitch_control_parameter - self.pressing_parameter = pressing_parameter - if self.pressing_parameter > 0: - self.players_to_press = players_to_press if players_to_press else self.attacking_player_ids - - #compute matrix of importance of controlling each square in grid system - with open(BASE_DIR / "xTArray.pkl", "rb") as f: - self.xt_array = pickle.load(f) - if self.attacking_team == 'away': - self.xt_array = np.fliplr(self.xt_array) - - #TTI Implementation from https://github.com/devinpleuler/analytics-handbook/blob/master/soccer_analytics_handbook.ipynb + # TTI Implementation from https://github.com/devinpleuler/analytics-handbook/blob/master/soccer_analytics_handbook.ipynb def tti(self, origin, destination, velocity, reaction_time, max_velocity=5.0): u = (origin + velocity) - origin v = destination - origin @@ -78,71 +85,59 @@ def tti(self, origin, destination, velocity, reaction_time, max_velocity=5.0): angle = np.arccos(dot_product / (u_mag * v_mag)) r_reaction = origin + velocity * reaction_time d = destination - r_reaction - t = (u_mag * angle/np.pi + - reaction_time + - np.linalg.norm(d, axis=-1) / max_velocity) + t = ( + u_mag * angle / np.pi + + reaction_time + + np.linalg.norm(d, axis=-1) / max_velocity + ) - return t + return t def perturbation(self, input_frame): new_frame = deepcopy(input_frame) - #randomly choose 1 of the defenders - #TODO see if we can cache the unselected players to avoid recomputing every time + # randomly choose 1 of the defenders + # TODO see if we can cache the unselected players to avoid recomputing every time self.selected_players = random.sample(self.defending_player_ids, 1) for c in self.selected_players: - #move each player up to a maximal distance from their starting positions - x_col = c + '_x' - y_col = c + '_y' - vx_col = c + '_vx' - vy_col = c + '_vy' + # move each player up to a maximal distance from their starting positions + x_col = c + "_x" + y_col = c + "_y" + vx_col = c + "_vx" + vy_col = c + "_vy" player_initial_x_pos = self.player_to_starting_pos_and_vel_map[x_col] - player_initial_y_pos = self.player_to_starting_pos_and_vel_map[y_col] + player_initial_y_pos = self.player_to_starting_pos_and_vel_map[y_col] player_initial_vx = self.player_to_starting_pos_and_vel_map[vx_col] player_initial_vy = self.player_to_starting_pos_and_vel_map[vy_col] - new_x_pos = new_frame[x_col] + random.uniform(-self.distance_perturbation,self.distance_perturbation) - new_y_pos = new_frame[y_col] + random.uniform(-self.distance_perturbation, self.distance_perturbation) - - #check if the new position is reachable within max time to intercept (tti) - if self.tti( - np.array([player_initial_x_pos, player_initial_y_pos]), - np.array([new_x_pos, new_y_pos]), - np.array([player_initial_vx, player_initial_vy]), - 0.1 - ) < self.max_tti: + new_x_pos = new_frame[x_col] + random.uniform( + -self.distance_perturbation, self.distance_perturbation + ) + new_y_pos = new_frame[y_col] + random.uniform( + -self.distance_perturbation, self.distance_perturbation + ) + + # check if the new position is reachable within max time to intercept (tti) + if ( + self.tti( + np.array([player_initial_x_pos, player_initial_y_pos]), + np.array([new_x_pos, new_y_pos]), + np.array([player_initial_vx, player_initial_vy]), + 0.1, + ) + < self.max_tti + ): new_frame[y_col] = new_y_pos new_frame[x_col] = new_x_pos return new_frame def compute_objective(self, input_frame): + objective_total = 0 + for i, objective_term in enumerate(self.objective_terms): + score = objective_term.compute(input_frame) + objective_total += score * self.weights[i] - team_influence_defending = get_team_influence( - input_frame, - col_ids=self.defending_player_ids, - grid=self.grid, - player_ball_distances=None, - ) - #+ve is defending team, -ve is attacking team - net_sigmoid_diff = sigmoid(team_influence_defending - self.attacking_team_influence, d=100) #this makes the sigmoid steeper and more binary - - defensive_matrix = self.xt_array * net_sigmoid_diff - - objective_matrix = defensive_matrix - - #pressure method only works on tracking data object, need to temporarily reconstruct it with the new data - pressure_score = 0 - temp_tracking_df = pd.DataFrame(input_frame).T - temp_tracking_df = TrackingData(temp_tracking_df.astype(self.game.tracking_data.dtypes)) - - # add terms for tight marking by multiplying the objective by pressing parameter for controlling pitch near attacking player - if self.pressing_parameter > 0: - for attacking_player in self.players_to_press: - pressure = temp_tracking_df.get_pressure_on_player(temp_tracking_df.index[0], attacking_player, [106,68], d_front = 9) - #compute the mean pressure on a player by dividing by number of players in p - pressure_score += pressure / len(self.players_to_press) - - #take the overall sum of geospatial terms - return sum(sum(objective_matrix)) * self.weighted_pitch_control_parameter + pressure_score * self.pressing_parameter + # take the overall sum of geospatial terms + return objective_total def run(self): best_score = 0 @@ -150,29 +145,26 @@ def run(self): latest_frame = deepcopy(self.frame) last_checkpoint_score = 0 T = self.T - print('running annealer') - for i in range(1,self.num_iterations): + print("running annealer") + for i in range(1, self.num_iterations): perturbed_frame = self.perturbation(latest_frame) new_score = self.compute_objective(perturbed_frame) - #take the new result if it's better, or randomly take a worse result with decaying probability - if (new_score > best_score) or math.exp((new_score - best_score)/T) > random.random(): + # take the new result if it's better, or randomly take a worse result with decaying probability + if (new_score > best_score) or math.exp( + (new_score - best_score) / T + ) > random.random(): latest_frame = perturbed_frame - if (new_score > best_score): + if new_score > best_score: best_score = new_score best_solution = perturbed_frame latest_frame = perturbed_frame - T = T*self.cooling_rate - if i%200 == 0: - print(f'iteration {i} / {self.num_iterations} best_score {best_score}') + T = T * self.cooling_rate + if i % 200 == 0: + print(f"iteration {i} / {self.num_iterations} best_score {best_score}") if last_checkpoint_score == best_score: - print('No improvement found in last 200 iterations... exiting.') + print("No improvement found in last 200 iterations... exiting.") break last_checkpoint_score = best_score - - print(f'best score {best_score}') + print(f"best score {best_score}") return OptimizationResult(best_frame=best_solution, best_result=best_score) - - - - diff --git a/databallpy/optimization/tester.py b/databallpy/optimization/tester.py index 77c26bd..7a2a663 100644 --- a/databallpy/optimization/tester.py +++ b/databallpy/optimization/tester.py @@ -1,17 +1,51 @@ -#imports - from databallpy.optimization.simulated_annealing import SimulatedAnnealing - from kloppy import skillcorner from databallpy import get_game_from_kloppy from databallpy.visualize import plot_soccer_pitch, plot_tracking_data -from databallpy.features.pitch_control import get_pitch_control_single_frame - +from databallpy.features.pitch_control import ( + get_pitch_control_single_frame, + get_team_influence, +) +from databallpy.optimization.optimization import ( + WeightedPitchControlObjective, + PressureObjective, +) import pandas as pd -# import accessible_space -import pickle +import numpy as np +from matplotlib.colors import LinearSegmentedColormap +from copy import deepcopy + + +def generate_fig(game, frame_idx, save_path=None): + frame = game.tracking_data[game.tracking_data["frame"] == frame_idx].iloc[0] + pitch_control = get_pitch_control_single_frame( + frame, + game.pitch_dimensions, + n_x_bins=106, + n_y_bins=68, + ) + cmap_red_green = LinearSegmentedColormap.from_list( + "reds", [(0, 1, 0, 1), (0.5, 0.5, 0, 0), (1, 0, 0, 1)] + ) + + fig, ax = plot_soccer_pitch(field_dimen=game.pitch_dimensions, pitch_color="white") + idx_relative_to_game = game.tracking_data[ + game.tracking_data["frame"] == frame_idx + ].index[0] + fig, ax = plot_tracking_data( + game, + idx_relative_to_game, + team_colors=["green", "red"], + ax=ax, + fig=fig, + heatmap_overlay=pitch_control, + overlay_cmap=cmap_red_green, + add_velocities=True, + ) + if save_path: + fig.savefig(save_path) + return fig, ax -import matplotlib.pyplot as plt match_id = 1886347 tracking_data_github_url = f"https://media.githubusercontent.com/media/SkillCorner/opendata/master/data/matches/{match_id}/{match_id}_tracking_extrapolated.jsonl" @@ -22,30 +56,64 @@ raw_data=tracking_data_github_url, # Optional Parameters coordinates="skillcorner", - limit=500, + limit=500, ) -#actual script +# actual script game = get_game_from_kloppy(dataset) game.tracking_data.add_velocity(game.get_column_ids() + ["ball"], allow_overwrite=True) game.tracking_data.add_individual_player_possession() -selected_frame_idx = 210 +selected_frame_idx = 210 -pc = get_pitch_control_single_frame(game.tracking_data[game.tracking_data['frame']==selected_frame_idx].iloc[0], - game.pitch_dimensions, - n_x_bins=106, n_y_bins=68, - ) +pc = get_pitch_control_single_frame( + game.tracking_data[game.tracking_data["frame"] == selected_frame_idx].iloc[0], + game.pitch_dimensions, + n_x_bins=106, + n_y_bins=68, +) print(f"total pitch control: {sum(sum(pc))}") +grid = np.meshgrid( + np.linspace( + -game.pitch_dimensions[0] / 2, + game.pitch_dimensions[0] / 2, + 106, + ), + np.linspace(-game.pitch_dimensions[1] / 2, game.pitch_dimensions[1] / 2, 68), +) +frame = game.tracking_data[game.tracking_data["frame"] == selected_frame_idx].iloc[0] +attacking_team = frame["team_possession"] +defending_team = "home" if frame["team_possession"] == "away" else "away" +attacking_team_influence = get_team_influence( + frame, + col_ids=game.get_column_ids(team=attacking_team), + grid=grid, + player_ball_distances=None, +) annealer = SimulatedAnnealing( - game, - selected_frame_idx, - distance_perturbation=0.2, # how many yards to randomly move the player on each iteration - max_tti= 2, # the maximum time to intercept, i.e. don't move a player if they can't reach that space within 1s - num_iterations=1000, # the number of perturbations to do - weighted_pitch_control_parameter = 1 - ) + game, + selected_frame_idx, + objective_terms=[ + WeightedPitchControlObjective( + grid=grid, + attacking_team=attacking_team, + defending_team=defending_team, + attacking_team_influence=attacking_team_influence, + defending_player_ids=game.get_column_ids(team=defending_team), + ), + PressureObjective( + attacking_player_ids=game.get_column_ids(team=defending_team), game=game + ), + ], + weights=[1, 100], + distance_perturbation=0.2, # how many yards to randomly move the player on each iteration + max_tti=2, # the maximum time to intercept, i.e. don't move a player if they can't reach that space within 1s + num_iterations=1000, # the number of perturbations to do +) result = annealer.run() -print(result) \ No newline at end of file +print(result.best_result) +new_game = deepcopy(game) +new_game.tracking_data = pd.DataFrame(result.best_frame).transpose().reset_index() +generate_fig(new_game, selected_frame_idx, save_path="test.png") From a09a00e6410101b58ccce819bc306e7de75f4cd9 Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Sun, 15 Mar 2026 20:50:40 +0000 Subject: [PATCH 03/16] Add a Constraints interface - Adds a class for generic constraints, which can be computed to check validity based on an input frame and player id - Implemented a specific constraint for simulated annealing that ensures the player is within a TimeToIntercept radius, i.e. the perturbation from simulated annealing is such that the player could get there within X seconds. --- databallpy/optimization/optimization.py | 50 +++++++++++++- .../optimization/simulated_annealing.py | 68 ++++--------------- databallpy/optimization/tester.py | 4 +- 3 files changed, 64 insertions(+), 58 deletions(-) diff --git a/databallpy/optimization/optimization.py b/databallpy/optimization/optimization.py index 2d8a22e..d4fa0dd 100644 --- a/databallpy/optimization/optimization.py +++ b/databallpy/optimization/optimization.py @@ -111,6 +111,13 @@ class OptimizationResult: best_result: float +class Constraint(ABC): + + def compute_prerequisites(self, game: Game = None, frame: pd.Series = None) -> None: + return None + @abstractmethod + def check(self, proposed_new_frame, player_id) -> bool: + raise NotImplementedError class OptimizationAlgorithm(ABC): @abstractmethod def __init__( @@ -126,10 +133,47 @@ def __init__( def run(self) -> OptimizationResult: raise NotImplementedError +class TTIConstraint(Constraint): + # TTI Implementation from https://github.com/devinpleuler/analytics-handbook/blob/master/soccer_analytics_handbook.ipynb + def __init__(self, max_time_to_intercept_seconds: float = 1, reaction_time: float = 0.1, max_velocity: float = 5.0): + self.max_time_to_intercept_seconds = max_time_to_intercept_seconds + self.reaction_time = reaction_time + self.max_velocity = max_velocity + + def compute_prerequisites(self, game: Game = None, frame: pd.Series = None) -> None: + self.player_to_starting_pos_and_vel_map = frame[ + [c + "_x" for c in game.get_column_ids()] + + [c + "_y" for c in game.get_column_ids()] + + [c + "_vx" for c in game.get_column_ids()] + + [c + "_vy" for c in game.get_column_ids()] + ] + + def tti(self, origin, destination, velocity): + u = (origin + velocity) - origin + v = destination - origin + u_mag = np.sqrt(np.sum(u**2, axis=-1)) + v_mag = np.sqrt(np.sum(v**2, axis=-1)) + dot_product = np.sum(u * v, axis=-1) + angle = np.arccos(dot_product / (u_mag * v_mag)) + r_reaction = origin + velocity * self.reaction_time + d = destination - r_reaction + t = ( + u_mag * angle / np.pi + + self.reaction_time + + np.linalg.norm(d, axis=-1) / self.max_velocity + ) -class Constraints: - pass - + return t + + def check(self, proposed_new_frame, player_id) -> bool: + if not self.player_to_starting_pos_and_vel_map: + raise ValueError("Player to starting pos and vel map not computed, try calling TTIConstraint.compute_prerequisites() first") + x_col, y_col, vx_col, vy_col = [player_id + suffix for suffix in ["_x", "_y", "_vx", "_vy"]] + origin = np.array([self.player_to_starting_pos_and_vel_map[x_col], self.player_to_starting_pos_and_vel_map[y_col]]) + velocity = np.array([self.player_to_starting_pos_and_vel_map[vx_col], self.player_to_starting_pos_and_vel_map[vy_col]]) + destination = np.array([proposed_new_frame[x_col], proposed_new_frame[y_col]]) + + return self.tti(origin, destination, velocity) < self.max_time_to_intercept_seconds class Filters: pass diff --git a/databallpy/optimization/simulated_annealing.py b/databallpy/optimization/simulated_annealing.py index c6c6b6b..585dcf9 100644 --- a/databallpy/optimization/simulated_annealing.py +++ b/databallpy/optimization/simulated_annealing.py @@ -9,14 +9,11 @@ OptimizationAlgorithm, OptimizationResult, ObjectiveTerm, + Constraint, ) from copy import deepcopy from databallpy import Game -from databallpy.utils.utils import sigmoid -import pickle - -from pathlib import Path class SimulatedAnnealing(OptimizationAlgorithm): @@ -26,6 +23,7 @@ def __init__( selected_frame_idx: int, objective_terms: list[ObjectiveTerm], weights: list[float], + constraints: list[Constraint] = None, distance_perturbation=0.2, num_iterations=1000, max_tti=1, @@ -63,38 +61,14 @@ def __init__( self.defending_player_ids = self.game.get_column_ids(team=self.defending_team) self.attacking_player_ids = self.game.get_column_ids(team=self.attacking_team) - - self.player_to_starting_pos_and_vel_map = self.frame[ - [c + "_x" for c in game.get_column_ids()] - + [c + "_y" for c in game.get_column_ids()] - + [c + "_vx" for c in game.get_column_ids()] - + [c + "_vy" for c in game.get_column_ids()] - ] - self.objective_terms = objective_terms self.weights = weights - - - # TTI Implementation from https://github.com/devinpleuler/analytics-handbook/blob/master/soccer_analytics_handbook.ipynb - def tti(self, origin, destination, velocity, reaction_time, max_velocity=5.0): - u = (origin + velocity) - origin - v = destination - origin - u_mag = np.sqrt(np.sum(u**2, axis=-1)) - v_mag = np.sqrt(np.sum(v**2, axis=-1)) - dot_product = np.sum(u * v, axis=-1) - angle = np.arccos(dot_product / (u_mag * v_mag)) - r_reaction = origin + velocity * reaction_time - d = destination - r_reaction - t = ( - u_mag * angle / np.pi - + reaction_time - + np.linalg.norm(d, axis=-1) / max_velocity - ) - - return t + self.constraints = constraints + for constraint in self.constraints: + constraint.compute_prerequisites(game=self.game, frame=self.frame) def perturbation(self, input_frame): - new_frame = deepcopy(input_frame) + proposed_new_frame = deepcopy(input_frame) # randomly choose 1 of the defenders # TODO see if we can cache the unselected players to avoid recomputing every time self.selected_players = random.sample(self.defending_player_ids, 1) @@ -102,33 +76,19 @@ def perturbation(self, input_frame): # move each player up to a maximal distance from their starting positions x_col = c + "_x" y_col = c + "_y" - vx_col = c + "_vx" - vy_col = c + "_vy" - player_initial_x_pos = self.player_to_starting_pos_and_vel_map[x_col] - player_initial_y_pos = self.player_to_starting_pos_and_vel_map[y_col] - player_initial_vx = self.player_to_starting_pos_and_vel_map[vx_col] - player_initial_vy = self.player_to_starting_pos_and_vel_map[vy_col] - new_x_pos = new_frame[x_col] + random.uniform( + + new_x_pos = proposed_new_frame[x_col] + random.uniform( -self.distance_perturbation, self.distance_perturbation ) - new_y_pos = new_frame[y_col] + random.uniform( + new_y_pos = proposed_new_frame[y_col] + random.uniform( -self.distance_perturbation, self.distance_perturbation ) - + proposed_new_frame[y_col] = new_y_pos + proposed_new_frame[x_col] = new_x_pos # check if the new position is reachable within max time to intercept (tti) - if ( - self.tti( - np.array([player_initial_x_pos, player_initial_y_pos]), - np.array([new_x_pos, new_y_pos]), - np.array([player_initial_vx, player_initial_vy]), - 0.1, - ) - < self.max_tti - ): - new_frame[y_col] = new_y_pos - new_frame[x_col] = new_x_pos - - return new_frame + if all(constraint.check(proposed_new_frame, c) for constraint in self.constraints): + return proposed_new_frame + return input_frame def compute_objective(self, input_frame): objective_total = 0 diff --git a/databallpy/optimization/tester.py b/databallpy/optimization/tester.py index 7a2a663..88a4fd6 100644 --- a/databallpy/optimization/tester.py +++ b/databallpy/optimization/tester.py @@ -9,6 +9,7 @@ from databallpy.optimization.optimization import ( WeightedPitchControlObjective, PressureObjective, + TTIConstraint ) import pandas as pd import numpy as np @@ -106,7 +107,8 @@ def generate_fig(game, frame_idx, save_path=None): attacking_player_ids=game.get_column_ids(team=defending_team), game=game ), ], - weights=[1, 100], + constraints=[TTIConstraint()], + weights=[1, 1], distance_perturbation=0.2, # how many yards to randomly move the player on each iteration max_tti=2, # the maximum time to intercept, i.e. don't move a player if they can't reach that space within 1s num_iterations=1000, # the number of perturbations to do From 0cfe6a22e0a5f8ddf71e63a2d46087b00bb8e8b6 Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Sun, 22 Mar 2026 11:36:56 +0000 Subject: [PATCH 04/16] Add visualization function and optimization runner - Makes a minor addition of an alpha parameter to visualize.py also --- databallpy/optimization/optimization.py | 26 ++- .../optimization/simulated_annealing.py | 25 +-- databallpy/optimization/tester.py | 149 ++++++++++++++++-- databallpy/visualize.py | 6 +- 4 files changed, 154 insertions(+), 52 deletions(-) diff --git a/databallpy/optimization/optimization.py b/databallpy/optimization/optimization.py index d4fa0dd..dc26d79 100644 --- a/databallpy/optimization/optimization.py +++ b/databallpy/optimization/optimization.py @@ -140,6 +140,7 @@ def __init__(self, max_time_to_intercept_seconds: float = 1, reaction_time: floa self.reaction_time = reaction_time self.max_velocity = max_velocity + #FixMe - this is a lazy implementation def compute_prerequisites(self, game: Game = None, frame: pd.Series = None) -> None: self.player_to_starting_pos_and_vel_map = frame[ [c + "_x" for c in game.get_column_ids()] @@ -166,8 +167,6 @@ def tti(self, origin, destination, velocity): return t def check(self, proposed_new_frame, player_id) -> bool: - if not self.player_to_starting_pos_and_vel_map: - raise ValueError("Player to starting pos and vel map not computed, try calling TTIConstraint.compute_prerequisites() first") x_col, y_col, vx_col, vy_col = [player_id + suffix for suffix in ["_x", "_y", "_vx", "_vy"]] origin = np.array([self.player_to_starting_pos_and_vel_map[x_col], self.player_to_starting_pos_and_vel_map[y_col]]) velocity = np.array([self.player_to_starting_pos_and_vel_map[vx_col], self.player_to_starting_pos_and_vel_map[vy_col]]) @@ -175,17 +174,14 @@ def check(self, proposed_new_frame, player_id) -> bool: return self.tti(origin, destination, velocity) < self.max_time_to_intercept_seconds -class Filters: - pass - -# def optimize_tracking_frame( -# game: Game, -# selected_frame_idx: int, -# objective: ObjectiveFunction, -# algorithm: OptimizationAlgorithm, # --> in the beginning only SimulatedAnnealing -# constraints: Constraints, # Within optimization algo -# filters: Filters # The variables the optimization algo is allowed to change -# ) -> OptimizationResult: -# optimizer = OptimizationAlgorithm(game, selected_frame_idx) -# return optimizer.run() +def optimize_tracking_frame( + game: Game, + selected_frame_idx: int, + objective_terms: list[ObjectiveTerm], + weights: list[float], + constraints: list[Constraint], + algorithm: OptimizationAlgorithm, +) -> OptimizationResult: + optimizer = algorithm(game, selected_frame_idx, objective_terms, weights, constraints) + return optimizer.run() diff --git a/databallpy/optimization/simulated_annealing.py b/databallpy/optimization/simulated_annealing.py index 585dcf9..2b6dd68 100644 --- a/databallpy/optimization/simulated_annealing.py +++ b/databallpy/optimization/simulated_annealing.py @@ -24,6 +24,7 @@ def __init__( objective_terms: list[ObjectiveTerm], weights: list[float], constraints: list[Constraint] = None, + defending_players_to_optimize: list[str] = None, distance_perturbation=0.2, num_iterations=1000, max_tti=1, @@ -34,13 +35,8 @@ def __init__( game.tracking_data["frame"] == selected_frame_idx ].iloc[0] - self.attacking_team = self.frame["team_possession"] - self.defending_team = ( - "home" if self.frame["team_possession"] == "away" else "away" - ) - - # annealing params - self.distance_perturbation = distance_perturbation # how much to move the player + # annealing params, per https://www.geeksforgeeks.org/artificial-intelligence/what-is-simulated-annealing/ + self.distance_perturbation = distance_perturbation self.p_0 = 0.5 self.T_0 = -100 / (math.log(self.p_0)) self.T = self.T_0 @@ -48,19 +44,8 @@ def __init__( self.num_iterations = num_iterations self.max_tti = max_tti - self.grid = np.meshgrid( - np.linspace( - -self.game.pitch_dimensions[0] / 2, - self.game.pitch_dimensions[0] / 2, - 106, - ), - np.linspace( - -self.game.pitch_dimensions[1] / 2, self.game.pitch_dimensions[1] / 2, 68 - ), - ) + self.defending_players_to_optimize = defending_players_to_optimize if defending_players_to_optimize else self.game.get_column_ids(team="home" if self.frame["team_possession"] == "away" else "away") - self.defending_player_ids = self.game.get_column_ids(team=self.defending_team) - self.attacking_player_ids = self.game.get_column_ids(team=self.attacking_team) self.objective_terms = objective_terms self.weights = weights self.constraints = constraints @@ -71,7 +56,7 @@ def perturbation(self, input_frame): proposed_new_frame = deepcopy(input_frame) # randomly choose 1 of the defenders # TODO see if we can cache the unselected players to avoid recomputing every time - self.selected_players = random.sample(self.defending_player_ids, 1) + self.selected_players = random.sample(self.defending_players_to_optimize, 1) for c in self.selected_players: # move each player up to a maximal distance from their starting positions x_col = c + "_x" diff --git a/databallpy/optimization/tester.py b/databallpy/optimization/tester.py index 88a4fd6..e319154 100644 --- a/databallpy/optimization/tester.py +++ b/databallpy/optimization/tester.py @@ -1,7 +1,17 @@ from databallpy.optimization.simulated_annealing import SimulatedAnnealing from kloppy import skillcorner from databallpy import get_game_from_kloppy -from databallpy.visualize import plot_soccer_pitch, plot_tracking_data +import matplotlib.patches as mpatches + +from databallpy.visualize import ( + _plot_heatmap_overlay, + _plot_single_frame, + _plot_velocities, + pick_bw_for_contrast, + plot_soccer_pitch, + plot_tracking_data, +) +from matplotlib.colors import to_rgb from databallpy.features.pitch_control import ( get_pitch_control_single_frame, get_team_influence, @@ -9,11 +19,14 @@ from databallpy.optimization.optimization import ( WeightedPitchControlObjective, PressureObjective, - TTIConstraint + TTIConstraint, + optimize_tracking_frame, ) import pandas as pd import numpy as np from matplotlib.colors import LinearSegmentedColormap +from matplotlib.legend_handler import HandlerTuple +from matplotlib.lines import Line2D from copy import deepcopy @@ -47,6 +60,114 @@ def generate_fig(game, frame_idx, save_path=None): fig.savefig(save_path) return fig, ax +def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2, save_path=None): + # frame_1 = game_1.tracking_data[game_1.tracking_data["frame"] == frame_idx_1].iloc[0] + # frame_2 = game_2.tracking_data[game_2.tracking_data["frame"] == frame_idx_2].iloc[0] + # pitch_control_1 = get_pitch_control_single_frame( + # frame_1, + # game_1.pitch_dimensions, + # n_x_bins=106, + # n_y_bins=68, + # ) + # pitch_control_2 = get_pitch_control_single_frame( + # frame_2, + # game_2.pitch_dimensions, + # n_x_bins=106, + # n_y_bins=68, + # ) + # cmap_red_green = LinearSegmentedColormap.from_list( + # "reds", [(0, 1, 0, 1), (0.5, 0.5, 0, 0), (1, 0, 0, 1)] + # ) + + fig, ax = plot_soccer_pitch( + field_dimen=game_1.pitch_dimensions, pitch_color="white" + ) + idx_relative_to_game_1 = game_1.tracking_data[ + game_1.tracking_data["frame"] == frame_idx_1 + ].index[0] + idx_relative_to_game_2 = game_2.tracking_data[ + game_2.tracking_data["frame"] == frame_idx_2 + ].index[0] + + before_alpha = 0.2 + + fig, ax = plot_tracking_data( + game_1, + idx_relative_to_game_1, + team_colors=["green", "red"], + ax=ax, + fig=fig, + alpha=before_alpha, + # heatmap_overlay=pitch_control_1, + # overlay_cmap=cmap_red_green, + # add_velocities=False, + ) + fig, ax = plot_tracking_data( + game_2, + idx_relative_to_game_2, + team_colors=["green", "red"], + ax=ax, + fig=fig, + # heatmap_overlay=pitch_control_2, + # overlay_cmap=cmap_red_green, + # add_velocities=False, + ) + + after_legend_markers = ( + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor=(0.0, 1.0, 0.0, 1.0), + markeredgecolor="green", + markersize=9, + linestyle="None", + ), + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor=(1.0, 0.0, 0.0, 1.0), + markeredgecolor="red", + markersize=9, + linestyle="None", + ), + ) + before_legend_markers = ( + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor=(0.0, 1.0, 0.0, before_alpha), + markeredgecolor=(0.0, 0.5, 0.0, 0.9), + markersize=9, + linestyle="None", + ), + Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor=(1.0, 0.0, 0.0, before_alpha), + markeredgecolor=(0.7, 0.0, 0.0, 0.9), + markersize=9, + linestyle="None", + ), + ) + ax.legend( + [before_legend_markers, after_legend_markers], + ["Before Optimization", "After Optimization"], + handler_map={tuple: HandlerTuple(ndivide=None)}, + loc="lower right", + framealpha=0.95, + ).set_zorder(20) + + if save_path: + fig.savefig(save_path) + return fig, ax match_id = 1886347 tracking_data_github_url = f"https://media.githubusercontent.com/media/SkillCorner/opendata/master/data/matches/{match_id}/{match_id}_tracking_extrapolated.jsonl" @@ -92,10 +213,11 @@ def generate_fig(game, frame_idx, save_path=None): grid=grid, player_ball_distances=None, ) -annealer = SimulatedAnnealing( - game, - selected_frame_idx, - objective_terms=[ + +result = optimize_tracking_frame( + game, + selected_frame_idx, + objective_terms = [ WeightedPitchControlObjective( grid=grid, attacking_team=attacking_team, @@ -106,16 +228,13 @@ def generate_fig(game, frame_idx, save_path=None): PressureObjective( attacking_player_ids=game.get_column_ids(team=defending_team), game=game ), - ], - constraints=[TTIConstraint()], - weights=[1, 1], - distance_perturbation=0.2, # how many yards to randomly move the player on each iteration - max_tti=2, # the maximum time to intercept, i.e. don't move a player if they can't reach that space within 1s - num_iterations=1000, # the number of perturbations to do + ], + weights = [1,1], + constraints = [TTIConstraint()], + algorithm = SimulatedAnnealing, ) - -result = annealer.run() print(result.best_result) new_game = deepcopy(game) new_game.tracking_data = pd.DataFrame(result.best_frame).transpose().reset_index() -generate_fig(new_game, selected_frame_idx, save_path="test.png") +# generate_fig(new_game, selected_frame_idx, save_path="test.png") +diff_frames(game, selected_frame_idx, new_game, selected_frame_idx, save_path="test_diff.png") diff --git a/databallpy/visualize.py b/databallpy/visualize.py index 99b897c..80baafc 100644 --- a/databallpy/visualize.py +++ b/databallpy/visualize.py @@ -402,6 +402,7 @@ def plot_tracking_data( add_velocities: bool = False, heatmap_overlay: np.ndarray | None = None, overlay_cmap: Colormap | str = "viridis", + alpha: float = 1, ) -> tuple[plt.figure, plt.axes]: """Function to plot the tracking data of a specific index in the game.tracking_data. @@ -491,7 +492,7 @@ def plot_tracking_data( _, ax = _plot_velocities(ax, td, idx, game, []) _, ax = _plot_single_frame( - ax, td_ht, td_at, idx, team_colors, [], td, game, pitch_color + ax, td_ht, td_at, idx, team_colors, [], td, game, pitch_color, alpha ) if variable_of_interest is not None: @@ -863,6 +864,7 @@ def _plot_single_frame( td: pd.DataFrame, game: Game, pitch_color, + alpha: float = 0.9, ) -> tuple[list, plt.axes]: """Helper function to plot the single frame of the current frame.""" # Scatter plot the teams @@ -873,7 +875,7 @@ def _plot_single_frame( td_team[x_cols], td_team[y_cols], c=c, - alpha=0.9, + alpha=alpha, s=90, zorder=2.5, ) From 1fb9a140616370248c363017d14b58141b7723e9 Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Wed, 15 Apr 2026 19:28:50 +0100 Subject: [PATCH 05/16] QoL Fixes: - Move optimization objects into separate classes - Add logging properly instead of print statements - Formatting --- databallpy/optimization/constraints.py | 67 +++++++ databallpy/optimization/objectives.py | 92 +++++++++ databallpy/optimization/optimization.py | 173 ++++------------- .../optimization/simulated_annealing.py | 53 ++++-- databallpy/optimization/tester.py | 176 ++++++------------ 5 files changed, 283 insertions(+), 278 deletions(-) create mode 100644 databallpy/optimization/constraints.py create mode 100644 databallpy/optimization/objectives.py diff --git a/databallpy/optimization/constraints.py b/databallpy/optimization/constraints.py new file mode 100644 index 0000000..6372270 --- /dev/null +++ b/databallpy/optimization/constraints.py @@ -0,0 +1,67 @@ +import numpy as np +import pandas as pd + +from databallpy.game import Game +from databallpy.optimization.optimization import Constraint + + +class TTIConstraint(Constraint): + + def __init__( + self, + max_time_to_intercept_seconds: float = 1, + reaction_time: float = 0.1, + max_velocity: float = 5.0, + ): + self.max_time_to_intercept_seconds = max_time_to_intercept_seconds + self.reaction_time = reaction_time + self.max_velocity = max_velocity + + # FixMe - this is a lazy implementation + def compute_prerequisites(self, game: Game, frame: pd.Series) -> None: + self.player_to_starting_pos_and_vel_map = frame[ + [c + "_x" for c in game.get_column_ids()] + + [c + "_y" for c in game.get_column_ids()] + + [c + "_vx" for c in game.get_column_ids()] + + [c + "_vy" for c in game.get_column_ids()] + ] + + # TTI Implementation from https://github.com/devinpleuler/analytics-handbook/blob/master/soccer_analytics_handbook.ipynb + def tti(self, origin, destination, velocity): + u = (origin + velocity) - origin + v = destination - origin + u_mag = np.sqrt(np.sum(u**2, axis=-1)) + v_mag = np.sqrt(np.sum(v**2, axis=-1)) + dot_product = np.sum(u * v, axis=-1) + angle = np.arccos(dot_product / (u_mag * v_mag)) + r_reaction = origin + velocity * self.reaction_time + d = destination - r_reaction + t = ( + u_mag * angle / np.pi + + self.reaction_time + + np.linalg.norm(d, axis=-1) / self.max_velocity + ) + + return t + + def check(self, proposed_new_frame, player_id) -> bool: + x_col, y_col, vx_col, vy_col = [ + player_id + suffix for suffix in ["_x", "_y", "_vx", "_vy"] + ] + origin = np.array( + [ + self.player_to_starting_pos_and_vel_map[x_col], + self.player_to_starting_pos_and_vel_map[y_col], + ] + ) + velocity = np.array( + [ + self.player_to_starting_pos_and_vel_map[vx_col], + self.player_to_starting_pos_and_vel_map[vy_col], + ] + ) + destination = np.array([proposed_new_frame[x_col], proposed_new_frame[y_col]]) + + return ( + self.tti(origin, destination, velocity) < self.max_time_to_intercept_seconds + ) diff --git a/databallpy/optimization/objectives.py b/databallpy/optimization/objectives.py new file mode 100644 index 0000000..fb5c033 --- /dev/null +++ b/databallpy/optimization/objectives.py @@ -0,0 +1,92 @@ +import pickle +from pathlib import Path + +import numpy as np +import pandas as pd + +from databallpy.features.pitch_control import get_team_influence +from databallpy.game import Game +from databallpy.optimization.optimization import ObjectiveTerm, ObjectiveType +from databallpy.schemas.tracking_data import TrackingData +from databallpy.utils.utils import sigmoid + +BASE_DIR = Path(__file__).resolve().parent + + +class WeightedPitchControlObjective(ObjectiveTerm): + # Computes the net pitch control for the defending team over the attacking team + def __init__( + self, + grid: np.meshgrid, + attacking_team: str, + defending_team: str, + defending_player_ids: list[str], + attacking_team_influence: np.meshgrid, + xt_array: np.ndarray | None = None, + ): + super().__init__(computation_type=ObjectiveType.GRID) + self.grid = grid + self.attacking_team = attacking_team + self.defending_team = defending_team + self.defending_player_ids = defending_player_ids + self.attacking_team_influence = attacking_team_influence + if xt_array is None: + with open(BASE_DIR / "xTArray.pkl", "rb") as f: + self.xt_array = pickle.load(f) + else: + self.xt_array = xt_array + if self.attacking_team == "away": + self.xt_array = np.fliplr(self.xt_array) + + def compute( + self, + input_frame: pd.Series, + ) -> float: + team_influence_defending = get_team_influence( + input_frame, + col_ids=self.defending_player_ids, + grid=self.grid, + player_ball_distances=None, + ) + # +ve is defending team, -ve is attacking team + net_sigmoid_diff = sigmoid( + team_influence_defending - self.attacking_team_influence, d=100 + ) # this makes the sigmoid steeper and more binary + + return sum(sum(self.xt_array * net_sigmoid_diff)) + + +class PressureObjective(ObjectiveTerm): + def __init__( + self, + game: Game, + players_to_press: list[str] | None = None, + attacking_player_ids: list[str] | None = None, + ): + if not attacking_player_ids and not players_to_press: + raise ValueError( + "Either players_to_press or attacking_player_ids must be provided" + ) + + super().__init__(computation_type=ObjectiveType.PLAYER) + + self.game = game + self.players_to_press = ( + players_to_press if players_to_press else attacking_player_ids + ) + + def compute(self, input_frame: pd.Series) -> float: + # pressure method only works on tracking data object, need to temporarily reconstruct it with the new data + pressure_score = 0 + temp_tracking_df = pd.DataFrame(input_frame).T + temp_tracking_df = TrackingData( + temp_tracking_df.astype(self.game.tracking_data.dtypes) + ) + + for attacking_player in self.players_to_press: + pressure = temp_tracking_df.get_pressure_on_player( + temp_tracking_df.index[0], attacking_player, [106, 68], d_front=9 + ) + # compute the mean pressure on a player by dividing by number of players in p + pressure_score += pressure / len(self.players_to_press) + return pressure_score diff --git a/databallpy/optimization/optimization.py b/databallpy/optimization/optimization.py index dc26d79..8948596 100644 --- a/databallpy/optimization/optimization.py +++ b/databallpy/optimization/optimization.py @@ -1,16 +1,14 @@ from abc import ABC, abstractmethod -from databallpy.game import Game from dataclasses import dataclass -import pandas as pd from enum import Enum -import pickle -from pathlib import Path -from databallpy.features.pitch_control import get_team_influence -from databallpy.utils.utils import sigmoid -import numpy as np -from databallpy.schemas.tracking_data import TrackingData +from typing import Any -BASE_DIR = Path(__file__).resolve().parent +import pandas as pd + +from databallpy.game import Game +from databallpy.utils.logging import create_logger + +LOGGER = create_logger(__name__) class ObjectiveType(str, Enum): @@ -26,85 +24,6 @@ def compute(self, input_frame: pd.Series) -> float: raise NotImplementedError -class WeightedPitchControlObjective(ObjectiveTerm): - # Computes the net pitch control for the defending team over the attacking team - def __init__( - self, - grid: np.meshgrid, - attacking_team: str, - defending_team: str, - defending_player_ids: list[str], - attacking_team_influence: np.meshgrid, - xt_array: np.ndarray | None = None, - ): - super().__init__(computation_type=ObjectiveType.GRID) - self.grid = grid - self.attacking_team = attacking_team - self.defending_team = defending_team - self.defending_player_ids = defending_player_ids - self.attacking_team_influence = attacking_team_influence - if not xt_array: - with open(BASE_DIR / "xTArray.pkl", "rb") as f: - self.xt_array = pickle.load(f) - else: - self.xt_array = xt_array - if self.attacking_team == "away": - self.xt_array = np.fliplr(self.xt_array) - - def compute( - self, - input_frame: pd.Series, - ) -> float: - team_influence_defending = get_team_influence( - input_frame, - col_ids=self.defending_player_ids, - grid=self.grid, - player_ball_distances=None, - ) - # +ve is defending team, -ve is attacking team - net_sigmoid_diff = sigmoid( - team_influence_defending - self.attacking_team_influence, d=100 - ) # this makes the sigmoid steeper and more binary - - return sum(sum(self.xt_array * net_sigmoid_diff)) - - -class PressureObjective(ObjectiveTerm): - def __init__( - self, - game: Game, - players_to_press: list[str] | None = None, - attacking_player_ids: list[str] | None = None, - ): - if not attacking_player_ids and not players_to_press: - raise ValueError( - "Either players_to_press or attacking_player_ids must be provided" - ) - - super().__init__(computation_type=ObjectiveType.PLAYER) - - self.game = game - self.players_to_press = ( - players_to_press if players_to_press else attacking_player_ids - ) - - def compute(self, input_frame: pd.Series) -> float: - # pressure method only works on tracking data object, need to temporarily reconstruct it with the new data - pressure_score = 0 - temp_tracking_df = pd.DataFrame(input_frame).T - temp_tracking_df = TrackingData( - temp_tracking_df.astype(self.game.tracking_data.dtypes) - ) - - for attacking_player in self.players_to_press: - pressure = temp_tracking_df.get_pressure_on_player( - temp_tracking_df.index[0], attacking_player, [106, 68], d_front=9 - ) - # compute the mean pressure on a player by dividing by number of players in p - pressure_score += pressure / len(self.players_to_press) - return pressure_score - - @dataclass class OptimizationResult: best_frame: pd.Series @@ -112,12 +31,14 @@ class OptimizationResult: class Constraint(ABC): - def compute_prerequisites(self, game: Game = None, frame: pd.Series = None) -> None: return None + @abstractmethod - def check(self, proposed_new_frame, player_id) -> bool: + def check(self, proposed_new_frame, player_id) -> bool: raise NotImplementedError + + class OptimizationAlgorithm(ABC): @abstractmethod def __init__( @@ -126,6 +47,8 @@ def __init__( selected_frame_idx: int, objective_terms: list[ObjectiveTerm], weights: list[float], + constraints: list[Constraint] | None = None, + **kwargs: Any, ): raise NotImplementedError @@ -133,55 +56,23 @@ def __init__( def run(self) -> OptimizationResult: raise NotImplementedError -class TTIConstraint(Constraint): - # TTI Implementation from https://github.com/devinpleuler/analytics-handbook/blob/master/soccer_analytics_handbook.ipynb - def __init__(self, max_time_to_intercept_seconds: float = 1, reaction_time: float = 0.1, max_velocity: float = 5.0): - self.max_time_to_intercept_seconds = max_time_to_intercept_seconds - self.reaction_time = reaction_time - self.max_velocity = max_velocity - #FixMe - this is a lazy implementation - def compute_prerequisites(self, game: Game = None, frame: pd.Series = None) -> None: - self.player_to_starting_pos_and_vel_map = frame[ - [c + "_x" for c in game.get_column_ids()] - + [c + "_y" for c in game.get_column_ids()] - + [c + "_vx" for c in game.get_column_ids()] - + [c + "_vy" for c in game.get_column_ids()] - ] - - def tti(self, origin, destination, velocity): - u = (origin + velocity) - origin - v = destination - origin - u_mag = np.sqrt(np.sum(u**2, axis=-1)) - v_mag = np.sqrt(np.sum(v**2, axis=-1)) - dot_product = np.sum(u * v, axis=-1) - angle = np.arccos(dot_product / (u_mag * v_mag)) - r_reaction = origin + velocity * self.reaction_time - d = destination - r_reaction - t = ( - u_mag * angle / np.pi - + self.reaction_time - + np.linalg.norm(d, axis=-1) / self.max_velocity - ) - - return t - - def check(self, proposed_new_frame, player_id) -> bool: - x_col, y_col, vx_col, vy_col = [player_id + suffix for suffix in ["_x", "_y", "_vx", "_vy"]] - origin = np.array([self.player_to_starting_pos_and_vel_map[x_col], self.player_to_starting_pos_and_vel_map[y_col]]) - velocity = np.array([self.player_to_starting_pos_and_vel_map[vx_col], self.player_to_starting_pos_and_vel_map[vy_col]]) - destination = np.array([proposed_new_frame[x_col], proposed_new_frame[y_col]]) - - return self.tti(origin, destination, velocity) < self.max_time_to_intercept_seconds - - -def optimize_tracking_frame( - game: Game, - selected_frame_idx: int, - objective_terms: list[ObjectiveTerm], - weights: list[float], - constraints: list[Constraint], - algorithm: OptimizationAlgorithm, -) -> OptimizationResult: - optimizer = algorithm(game, selected_frame_idx, objective_terms, weights, constraints) - return optimizer.run() +# def optimize_tracking_frame( +# game: Game, +# selected_frame_idx: int, +# objective_terms: list[ObjectiveTerm], +# weights: list[float], +# constraints: list[Constraint], +# algorithm: type[OptimizationAlgorithm], +# ) -> OptimizationResult: +# LOGGER.info("Running optimization with %s", algorithm.__name__) + +# optimizer = algorithm( +# game=game, +# selected_frame_idx=selected_frame_idx, +# objective_terms=objective_terms, +# weights=weights, +# constraints=constraints, +# ) +# return optimizer.run() + diff --git a/databallpy/optimization/simulated_annealing.py b/databallpy/optimization/simulated_annealing.py index 2b6dd68..3a9a2f2 100644 --- a/databallpy/optimization/simulated_annealing.py +++ b/databallpy/optimization/simulated_annealing.py @@ -1,19 +1,18 @@ # Annealer -import random import math -import pandas as pd -import numpy as np -from databallpy.features.pitch_control import get_team_influence -from databallpy.schemas.tracking_data import TrackingData +import random +from copy import deepcopy + +from databallpy import Game from databallpy.optimization.optimization import ( + Constraint, + ObjectiveTerm, OptimizationAlgorithm, OptimizationResult, - ObjectiveTerm, - Constraint, ) -from copy import deepcopy -from databallpy import Game +from databallpy.utils.logging import create_logger +LOGGER = create_logger(__name__) class SimulatedAnnealing(OptimizationAlgorithm): @@ -36,7 +35,7 @@ def __init__( ].iloc[0] # annealing params, per https://www.geeksforgeeks.org/artificial-intelligence/what-is-simulated-annealing/ - self.distance_perturbation = distance_perturbation + self.distance_perturbation = distance_perturbation self.p_0 = 0.5 self.T_0 = -100 / (math.log(self.p_0)) self.T = self.T_0 @@ -44,7 +43,13 @@ def __init__( self.num_iterations = num_iterations self.max_tti = max_tti - self.defending_players_to_optimize = defending_players_to_optimize if defending_players_to_optimize else self.game.get_column_ids(team="home" if self.frame["team_possession"] == "away" else "away") + self.defending_players_to_optimize = ( + defending_players_to_optimize + if defending_players_to_optimize + else self.game.get_column_ids( + team="home" if self.frame["team_possession"] == "away" else "away" + ) + ) self.objective_terms = objective_terms self.weights = weights @@ -57,10 +62,10 @@ def perturbation(self, input_frame): # randomly choose 1 of the defenders # TODO see if we can cache the unselected players to avoid recomputing every time self.selected_players = random.sample(self.defending_players_to_optimize, 1) - for c in self.selected_players: + for player_id in self.selected_players: # move each player up to a maximal distance from their starting positions - x_col = c + "_x" - y_col = c + "_y" + x_col = player_id + "_x" + y_col = player_id + "_y" new_x_pos = proposed_new_frame[x_col] + random.uniform( -self.distance_perturbation, self.distance_perturbation @@ -71,7 +76,10 @@ def perturbation(self, input_frame): proposed_new_frame[y_col] = new_y_pos proposed_new_frame[x_col] = new_x_pos # check if the new position is reachable within max time to intercept (tti) - if all(constraint.check(proposed_new_frame, c) for constraint in self.constraints): + if all( + constraint.check(proposed_new_frame, player_id) + for constraint in self.constraints + ): return proposed_new_frame return input_frame @@ -90,7 +98,7 @@ def run(self): latest_frame = deepcopy(self.frame) last_checkpoint_score = 0 T = self.T - print("running annealer") + for i in range(1, self.num_iterations): perturbed_frame = self.perturbation(latest_frame) new_score = self.compute_objective(perturbed_frame) @@ -105,11 +113,18 @@ def run(self): latest_frame = perturbed_frame T = T * self.cooling_rate if i % 200 == 0: - print(f"iteration {i} / {self.num_iterations} best_score {best_score}") + LOGGER.info( + "Iteration %s / %s, best_score=%s", + i, + self.num_iterations, + best_score, + ) if last_checkpoint_score == best_score: - print("No improvement found in last 200 iterations... exiting.") + LOGGER.warning( + "No improvement found in last 200 iterations. Exiting early." + ) break last_checkpoint_score = best_score - print(f"best score {best_score}") + LOGGER.info("Finished simulated annealing. Best score=%s", best_score) return OptimizationResult(best_frame=best_solution, best_result=best_score) diff --git a/databallpy/optimization/tester.py b/databallpy/optimization/tester.py index e319154..51f536d 100644 --- a/databallpy/optimization/tester.py +++ b/databallpy/optimization/tester.py @@ -1,33 +1,23 @@ -from databallpy.optimization.simulated_annealing import SimulatedAnnealing + +import numpy as np from kloppy import skillcorner -from databallpy import get_game_from_kloppy -import matplotlib.patches as mpatches +from matplotlib.colors import LinearSegmentedColormap -from databallpy.visualize import ( - _plot_heatmap_overlay, - _plot_single_frame, - _plot_velocities, - pick_bw_for_contrast, - plot_soccer_pitch, - plot_tracking_data, -) -from matplotlib.colors import to_rgb +from databallpy import get_game_from_kloppy from databallpy.features.pitch_control import ( get_pitch_control_single_frame, get_team_influence, ) -from databallpy.optimization.optimization import ( - WeightedPitchControlObjective, +from databallpy.optimization.constraints import TTIConstraint +from databallpy.optimization.objectives import ( PressureObjective, - TTIConstraint, - optimize_tracking_frame, + WeightedPitchControlObjective, +) +from databallpy.optimization.simulated_annealing import SimulatedAnnealing +from databallpy.visualize import ( + plot_soccer_pitch, + plot_tracking_data, ) -import pandas as pd -import numpy as np -from matplotlib.colors import LinearSegmentedColormap -from matplotlib.legend_handler import HandlerTuple -from matplotlib.lines import Line2D -from copy import deepcopy def generate_fig(game, frame_idx, save_path=None): @@ -60,28 +50,27 @@ def generate_fig(game, frame_idx, save_path=None): fig.savefig(save_path) return fig, ax + def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2, save_path=None): - # frame_1 = game_1.tracking_data[game_1.tracking_data["frame"] == frame_idx_1].iloc[0] - # frame_2 = game_2.tracking_data[game_2.tracking_data["frame"] == frame_idx_2].iloc[0] - # pitch_control_1 = get_pitch_control_single_frame( - # frame_1, - # game_1.pitch_dimensions, - # n_x_bins=106, - # n_y_bins=68, - # ) - # pitch_control_2 = get_pitch_control_single_frame( - # frame_2, - # game_2.pitch_dimensions, - # n_x_bins=106, - # n_y_bins=68, - # ) - # cmap_red_green = LinearSegmentedColormap.from_list( - # "reds", [(0, 1, 0, 1), (0.5, 0.5, 0, 0), (1, 0, 0, 1)] - # ) - - fig, ax = plot_soccer_pitch( - field_dimen=game_1.pitch_dimensions, pitch_color="white" + frame_1 = game_1.tracking_data[game_1.tracking_data["frame"] == frame_idx_1].iloc[0] + frame_2 = game_2.tracking_data[game_2.tracking_data["frame"] == frame_idx_2].iloc[0] + pitch_control_1 = get_pitch_control_single_frame( + frame_1, + game_1.pitch_dimensions, + n_x_bins=106, + n_y_bins=68, ) + pitch_control_2 = get_pitch_control_single_frame( + frame_2, + game_2.pitch_dimensions, + n_x_bins=106, + n_y_bins=68, + ) + cmap_red_green = LinearSegmentedColormap.from_list( + "reds", [(0, 1, 0, 1), (0.5, 0.5, 0, 0), (1, 0, 0, 1)] + ) + + fig, ax = plot_soccer_pitch(field_dimen=game.pitch_dimensions, pitch_color="white") idx_relative_to_game_1 = game_1.tracking_data[ game_1.tracking_data["frame"] == frame_idx_1 ].index[0] @@ -89,86 +78,32 @@ def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2, save_path=None): game_2.tracking_data["frame"] == frame_idx_2 ].index[0] - before_alpha = 0.2 - fig, ax = plot_tracking_data( game_1, idx_relative_to_game_1, team_colors=["green", "red"], ax=ax, fig=fig, - alpha=before_alpha, - # heatmap_overlay=pitch_control_1, - # overlay_cmap=cmap_red_green, - # add_velocities=False, + heatmap_overlay=pitch_control_1, + overlay_cmap=cmap_red_green, + add_velocities=True, ) fig, ax = plot_tracking_data( game_2, idx_relative_to_game_2, - team_colors=["green", "red"], + # RGBA must use 0–1 floats (not 0–255) for matplotlib + team_colors=[(0.0, 1.0, 0.0, 0.5), (1.0, 0.0, 0.0, 0.5)], ax=ax, fig=fig, - # heatmap_overlay=pitch_control_2, - # overlay_cmap=cmap_red_green, - # add_velocities=False, - ) - - after_legend_markers = ( - Line2D( - [0], - [0], - marker="o", - color="w", - markerfacecolor=(0.0, 1.0, 0.0, 1.0), - markeredgecolor="green", - markersize=9, - linestyle="None", - ), - Line2D( - [0], - [0], - marker="o", - color="w", - markerfacecolor=(1.0, 0.0, 0.0, 1.0), - markeredgecolor="red", - markersize=9, - linestyle="None", - ), - ) - before_legend_markers = ( - Line2D( - [0], - [0], - marker="o", - color="w", - markerfacecolor=(0.0, 1.0, 0.0, before_alpha), - markeredgecolor=(0.0, 0.5, 0.0, 0.9), - markersize=9, - linestyle="None", - ), - Line2D( - [0], - [0], - marker="o", - color="w", - markerfacecolor=(1.0, 0.0, 0.0, before_alpha), - markeredgecolor=(0.7, 0.0, 0.0, 0.9), - markersize=9, - linestyle="None", - ), + heatmap_overlay=pitch_control_2, + overlay_cmap=cmap_red_green, + add_velocities=True, ) - ax.legend( - [before_legend_markers, after_legend_markers], - ["Before Optimization", "After Optimization"], - handler_map={tuple: HandlerTuple(ndivide=None)}, - loc="lower right", - framealpha=0.95, - ).set_zorder(20) - if save_path: fig.savefig(save_path) return fig, ax + match_id = 1886347 tracking_data_github_url = f"https://media.githubusercontent.com/media/SkillCorner/opendata/master/data/matches/{match_id}/{match_id}_tracking_extrapolated.jsonl" meta_data_github_url = f"https://raw.githubusercontent.com/SkillCorner/opendata/master/data/matches/{match_id}/{match_id}_match.json" @@ -213,11 +148,10 @@ def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2, save_path=None): grid=grid, player_ball_distances=None, ) - -result = optimize_tracking_frame( - game, - selected_frame_idx, - objective_terms = [ +annealer = SimulatedAnnealing( + game, + selected_frame_idx, + objective_terms=[ WeightedPitchControlObjective( grid=grid, attacking_team=attacking_team, @@ -228,13 +162,19 @@ def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2, save_path=None): PressureObjective( attacking_player_ids=game.get_column_ids(team=defending_team), game=game ), - ], - weights = [1,1], - constraints = [TTIConstraint()], - algorithm = SimulatedAnnealing, + ], + constraints=[TTIConstraint()], + weights=[1, 1], + distance_perturbation=0.2, # how many yards to randomly move the player on each iteration + max_tti=2, # the maximum time to intercept, i.e. don't move a player if they can't reach that space within 1s + num_iterations=1000, # the number of perturbations to do ) + +result = annealer.run() print(result.best_result) -new_game = deepcopy(game) -new_game.tracking_data = pd.DataFrame(result.best_frame).transpose().reset_index() -# generate_fig(new_game, selected_frame_idx, save_path="test.png") -diff_frames(game, selected_frame_idx, new_game, selected_frame_idx, save_path="test_diff.png") +# new_game = deepcopy(game) +# new_game.tracking_data = pd.DataFrame(result.best_frame).transpose().reset_index() +# # generate_fig(new_game, selected_frame_idx, save_path="test.png") +# diff_frames( +# game, selected_frame_idx, new_game, selected_frame_idx, save_path="test_diff.png" +# ) From ce530c1c2d73cff9c251ebb9e09d30bc7ba32678 Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Thu, 23 Apr 2026 21:23:47 +0100 Subject: [PATCH 06/16] Implement optimize_tracking_frame Creates a wrapper function around the Algorithm.run() method that can easily be extended to hot swap optimization algorithms --- databallpy/optimization/optimization.py | 38 ++++++----- .../optimization/simulated_annealing.py | 10 ++- databallpy/optimization/tester.py | 65 ++++++++++++------- 3 files changed, 67 insertions(+), 46 deletions(-) diff --git a/databallpy/optimization/optimization.py b/databallpy/optimization/optimization.py index 8948596..4c158e0 100644 --- a/databallpy/optimization/optimization.py +++ b/databallpy/optimization/optimization.py @@ -57,22 +57,24 @@ def run(self) -> OptimizationResult: raise NotImplementedError -# def optimize_tracking_frame( -# game: Game, -# selected_frame_idx: int, -# objective_terms: list[ObjectiveTerm], -# weights: list[float], -# constraints: list[Constraint], -# algorithm: type[OptimizationAlgorithm], -# ) -> OptimizationResult: -# LOGGER.info("Running optimization with %s", algorithm.__name__) - -# optimizer = algorithm( -# game=game, -# selected_frame_idx=selected_frame_idx, -# objective_terms=objective_terms, -# weights=weights, -# constraints=constraints, -# ) -# return optimizer.run() +def optimize_tracking_frame( + game: Game, + selected_frame_idx: int, + objective_terms: list[ObjectiveTerm], + weights: list[float], + constraints: list[Constraint], + algorithm: type[OptimizationAlgorithm], + **algorithm_kwargs: Any, +) -> OptimizationResult: + LOGGER.info("Running optimization with %s", algorithm.__name__) + + optimizer = algorithm( + game=game, + selected_frame_idx=selected_frame_idx, + objective_terms=objective_terms, + weights=weights, + constraints=constraints, + **algorithm_kwargs, + ) + return optimizer.run() diff --git a/databallpy/optimization/simulated_annealing.py b/databallpy/optimization/simulated_annealing.py index 3a9a2f2..51d15c3 100644 --- a/databallpy/optimization/simulated_annealing.py +++ b/databallpy/optimization/simulated_annealing.py @@ -22,11 +22,10 @@ def __init__( selected_frame_idx: int, objective_terms: list[ObjectiveTerm], weights: list[float], - constraints: list[Constraint] = None, - defending_players_to_optimize: list[str] = None, - distance_perturbation=0.2, - num_iterations=1000, - max_tti=1, + constraints: list[Constraint] = [], + defending_players_to_optimize: list[str] | None = None, + distance_perturbation: float = 0.2, + num_iterations: int = 1000, ): # data self.game = game @@ -41,7 +40,6 @@ def __init__( self.T = self.T_0 self.cooling_rate = 0.9 self.num_iterations = num_iterations - self.max_tti = max_tti self.defending_players_to_optimize = ( defending_players_to_optimize diff --git a/databallpy/optimization/tester.py b/databallpy/optimization/tester.py index 51f536d..7536dc7 100644 --- a/databallpy/optimization/tester.py +++ b/databallpy/optimization/tester.py @@ -14,6 +14,7 @@ WeightedPitchControlObjective, ) from databallpy.optimization.simulated_annealing import SimulatedAnnealing +from databallpy.optimization.optimization import optimize_tracking_frame from databallpy.visualize import ( plot_soccer_pitch, plot_tracking_data, @@ -148,29 +149,49 @@ def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2, save_path=None): grid=grid, player_ball_distances=None, ) -annealer = SimulatedAnnealing( - game, - selected_frame_idx, - objective_terms=[ - WeightedPitchControlObjective( - grid=grid, - attacking_team=attacking_team, - defending_team=defending_team, - attacking_team_influence=attacking_team_influence, - defending_player_ids=game.get_column_ids(team=defending_team), - ), - PressureObjective( - attacking_player_ids=game.get_column_ids(team=defending_team), game=game - ), - ], - constraints=[TTIConstraint()], - weights=[1, 1], - distance_perturbation=0.2, # how many yards to randomly move the player on each iteration - max_tti=2, # the maximum time to intercept, i.e. don't move a player if they can't reach that space within 1s - num_iterations=1000, # the number of perturbations to do -) +# annealer = SimulatedAnnealing( +# game, +# selected_frame_idx, +# objective_terms=[ +# WeightedPitchControlObjective( +# grid=grid, +# attacking_team=attacking_team, +# defending_team=defending_team, +# attacking_team_influence=attacking_team_influence, +# defending_player_ids=game.get_column_ids(team=defending_team), +# ), +# PressureObjective( +# attacking_player_ids=game.get_column_ids(team=defending_team), game=game +# ), +# ], +# constraints=[TTIConstraint()], +# weights=[1, 1], +# distance_perturbation=0.2, # how many yards to randomly move the player on each iteration +# max_tti=2, # the maximum time to intercept, i.e. don't move a player if they can't reach that space within 1s +# num_iterations=1000, # the number of perturbations to do +# ) + +result = optimize_tracking_frame( + game = game, + selected_frame_idx = selected_frame_idx, + objective_terms = [ + WeightedPitchControlObjective( + grid=grid, + attacking_team=attacking_team, + defending_team=defending_team, + attacking_team_influence=attacking_team_influence, + defending_player_ids=game.get_column_ids(team=defending_team), + ), + PressureObjective( + attacking_player_ids=game.get_column_ids(team=defending_team), game=game + ), + ], + weights=[1, 1], + constraints=[TTIConstraint(max_time_to_intercept_seconds=2)], + algorithm=SimulatedAnnealing, + num_iterations = 3000, + ) -result = annealer.run() print(result.best_result) # new_game = deepcopy(game) # new_game.tracking_data = pd.DataFrame(result.best_frame).transpose().reset_index() From fad05221d9c3320fda07e8ff81d2d2a9baa0d51b Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Thu, 23 Apr 2026 21:31:56 +0100 Subject: [PATCH 07/16] Move constraint initialization to the base class of optimization algorithm --- databallpy/optimization/optimization.py | 15 +++++++++++++-- databallpy/optimization/simulated_annealing.py | 12 +----------- 2 files changed, 14 insertions(+), 13 deletions(-) diff --git a/databallpy/optimization/optimization.py b/databallpy/optimization/optimization.py index 4c158e0..2380e5d 100644 --- a/databallpy/optimization/optimization.py +++ b/databallpy/optimization/optimization.py @@ -48,9 +48,20 @@ def __init__( objective_terms: list[ObjectiveTerm], weights: list[float], constraints: list[Constraint] | None = None, - **kwargs: Any, ): - raise NotImplementedError + + self.game = game + self.frame = game.tracking_data[ + game.tracking_data["frame"] == selected_frame_idx + ].iloc[0] + self.constraints = constraints or [] + for constraint in self.constraints: + constraint.compute_prerequisites(game=self.game, frame=self.frame) + if len(self.objective_terms) != len(self.weights): + raise ValueError("objective_terms and weights must have equal length") + self.objective_terms = objective_terms + self.weights = weights + @abstractmethod def run(self) -> OptimizationResult: diff --git a/databallpy/optimization/simulated_annealing.py b/databallpy/optimization/simulated_annealing.py index 51d15c3..cc69adb 100644 --- a/databallpy/optimization/simulated_annealing.py +++ b/databallpy/optimization/simulated_annealing.py @@ -27,12 +27,8 @@ def __init__( distance_perturbation: float = 0.2, num_iterations: int = 1000, ): - # data - self.game = game - self.frame = game.tracking_data[ - game.tracking_data["frame"] == selected_frame_idx - ].iloc[0] + super().__init__(game=game, selected_frame_idx=selected_frame_idx, objective_terms=objective_terms, constraints=constraints, weights=weights) # annealing params, per https://www.geeksforgeeks.org/artificial-intelligence/what-is-simulated-annealing/ self.distance_perturbation = distance_perturbation self.p_0 = 0.5 @@ -49,12 +45,6 @@ def __init__( ) ) - self.objective_terms = objective_terms - self.weights = weights - self.constraints = constraints - for constraint in self.constraints: - constraint.compute_prerequisites(game=self.game, frame=self.frame) - def perturbation(self, input_frame): proposed_new_frame = deepcopy(input_frame) # randomly choose 1 of the defenders From ad6eb3c588fc54c5569c326f1f1600c454a327a8 Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Thu, 23 Apr 2026 21:53:57 +0100 Subject: [PATCH 08/16] formatting --- databallpy/optimization/constraints.py | 1 - databallpy/optimization/simulated_annealing.py | 9 +++++++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/databallpy/optimization/constraints.py b/databallpy/optimization/constraints.py index 6372270..fec9e17 100644 --- a/databallpy/optimization/constraints.py +++ b/databallpy/optimization/constraints.py @@ -6,7 +6,6 @@ class TTIConstraint(Constraint): - def __init__( self, max_time_to_intercept_seconds: float = 1, diff --git a/databallpy/optimization/simulated_annealing.py b/databallpy/optimization/simulated_annealing.py index cc69adb..0358964 100644 --- a/databallpy/optimization/simulated_annealing.py +++ b/databallpy/optimization/simulated_annealing.py @@ -27,8 +27,13 @@ def __init__( distance_perturbation: float = 0.2, num_iterations: int = 1000, ): - - super().__init__(game=game, selected_frame_idx=selected_frame_idx, objective_terms=objective_terms, constraints=constraints, weights=weights) + super().__init__( + game=game, + selected_frame_idx=selected_frame_idx, + objective_terms=objective_terms, + constraints=constraints, + weights=weights, + ) # annealing params, per https://www.geeksforgeeks.org/artificial-intelligence/what-is-simulated-annealing/ self.distance_perturbation = distance_perturbation self.p_0 = 0.5 From 9034eea8ed15a02ed8a98bf3917a8d87d74bca14 Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Thu, 23 Apr 2026 21:54:32 +0100 Subject: [PATCH 09/16] cleanup optimization.py --- databallpy/optimization/optimization.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/databallpy/optimization/optimization.py b/databallpy/optimization/optimization.py index 2380e5d..265c8b6 100644 --- a/databallpy/optimization/optimization.py +++ b/databallpy/optimization/optimization.py @@ -35,7 +35,7 @@ def compute_prerequisites(self, game: Game = None, frame: pd.Series = None) -> N return None @abstractmethod - def check(self, proposed_new_frame, player_id) -> bool: + def check(self, proposed_new_frame, player_id) -> bool: raise NotImplementedError @@ -49,7 +49,9 @@ def __init__( weights: list[float], constraints: list[Constraint] | None = None, ): - + if len(objective_terms) != len(weights): + raise ValueError("objective_terms and weights must have equal length") + self.game = game self.frame = game.tracking_data[ game.tracking_data["frame"] == selected_frame_idx @@ -57,12 +59,10 @@ def __init__( self.constraints = constraints or [] for constraint in self.constraints: constraint.compute_prerequisites(game=self.game, frame=self.frame) - if len(self.objective_terms) != len(self.weights): - raise ValueError("objective_terms and weights must have equal length") + self.objective_terms = objective_terms self.weights = weights - @abstractmethod def run(self) -> OptimizationResult: raise NotImplementedError @@ -88,4 +88,3 @@ def optimize_tracking_frame( **algorithm_kwargs, ) return optimizer.run() - From 00edf438f8cc1b5185be7662c0105c9f1e9b357d Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Thu, 23 Apr 2026 21:54:52 +0100 Subject: [PATCH 10/16] use existing xt_array --- databallpy/optimization/objectives.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/databallpy/optimization/objectives.py b/databallpy/optimization/objectives.py index fb5c033..b87b46e 100644 --- a/databallpy/optimization/objectives.py +++ b/databallpy/optimization/objectives.py @@ -9,8 +9,9 @@ from databallpy.optimization.optimization import ObjectiveTerm, ObjectiveType from databallpy.schemas.tracking_data import TrackingData from databallpy.utils.utils import sigmoid +from scipy.ndimage import zoom -BASE_DIR = Path(__file__).resolve().parent +XT_MODEL_PATH = Path(__file__).resolve().parents[1] / "models" / "open_play_xT.npy" class WeightedPitchControlObjective(ObjectiveTerm): @@ -31,8 +32,10 @@ def __init__( self.defending_player_ids = defending_player_ids self.attacking_team_influence = attacking_team_influence if xt_array is None: - with open(BASE_DIR / "xTArray.pkl", "rb") as f: - self.xt_array = pickle.load(f) + open_play_xt = np.load(XT_MODEL_PATH) + # we are using (y, x) orientation instead of (x, y) so that it matches + self.xt_array = zoom(open_play_xt, (106 / 264, 68 / 196), order=1).T + else: self.xt_array = xt_array if self.attacking_team == "away": From b53cc9f9ec6fdf4ff511f9b157a343c1a422f03e Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Thu, 7 May 2026 20:26:36 +0100 Subject: [PATCH 11/16] remove xTarray --- databallpy/optimization/xTArray.pkl | Bin 57817 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 databallpy/optimization/xTArray.pkl diff --git a/databallpy/optimization/xTArray.pkl b/databallpy/optimization/xTArray.pkl deleted file mode 100644 index 7925ac9fe514c8df812fc6d8828271213d36a559..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 57817 zcmeI5U5Hgx7>50=2#GGjh$6ZO2_z#TN-9>rL{2GIgmjTQYD}WzFdbvYBqmE8>nQ5% z)T}5qO+`dgY>-L=3+cj(3Zx=JBtohSA&CB{+4HTpGqaD2^?mC&$La9SMK61uceQ_9 z&;Hi>_S$QAjd|_FsS*8uJF}@%r%w5&@2~$qO(RNK^QwFA?mL*jcAfp=k>**>s(-vN|HD#G zi?gcir_Jwg==Aw3_C9!ONy%AN&ZonlS6n;KOt|@yJxiTck!w&4~-i&apekU zRXMJ*KR&&F`+NUCu+!?~gU6ry`0dTksuNd^|N6vl+niPVFZ(sSomL+|z5R`2*X?sw zeY3Apd&_>ORVzpETRDQCJPCi~N%*sJ1izLe_^cekujL3n@L^T(VO8=(o(9G-@L^SO zqE+x=RdAwJD@X8KIf9=&ktgz0J-z}TRwYm5sgZfI@=&Y4(Rh;ciRTlFKFO23?DHjY zmFwQ^^CWuhy>|Ie(|gxAt4iKugU^4&b%rD&oKn`tgTCJ52Ykl&R{{wZYIRba&DbH_Kcq31eUnfuGsV}kqUmRn6N_*~vI7Xhx z6M3>fw*@~%B~RpuJdr24FI3N$koyDk5!O5e{Os@5cN~-&&#d!|;16*VDz6t$l0JLI z&*#yr9N$_$uSu_RzMFhLF|K!fKW+8K)~(K}Q}Q&!_+8Gb+UKkA$@%p9^UX9e%M4n1L z)x5>{DgI8L2EQjI*P}!I{uBFq?C%Zi@1^Dka6cbvO+CKUdiCN-`bEq9I6kha`8~OR zuJidRy~=eM`u?8Gi`(hv$LaOmw!1Dpxow}*s+>=C{E3yz`yEI9dx&vOZFfV>5&r+F z$Cp}wVrsuX6tSe8j5@e*AOS z*{#m1PtMJc@I7Z$>wAGRU+{>3PeiX@_PtFwWAZVl)q1r%v`_Ms_eZ=!tF+MRqRp%?L9KkyHdPtxB5Ct4*>gSpdk1Rr^lbyUIOa8+wuf?vxKeB_Bdo$Grsj8BYD z1N(*6ejfaYJ6dIYVthKaq%yu@d}4fJe5yQeLO&0Rew14r+UL0N)%$%yZC($)hi_YT&#WKbaaNV{+U?gt(`!Df?%?r5PODe;eFgEc z)1Nx4%Ju)o&ku`htbK)km;0*ydz#kqQ)vG~RUYCdR4&I6jzgi}!HHJMQ~o`}3U6AD z;IncBKY7Z>`|0s4#7(F?#7nGP%MpC!i9E5t7mi!tM62Y9Jdr2z6dO;;eWKEj({7_5 zr&aXdtSa}r__zta)NxR1y->d?R4)Cd^!}T*e&{$t<<{{a{Mx)8e2p5Po)E8>o&#Jx()>V+V=?IOXVW9UZ`D$%B?)q>JJ@9s2qG)6`W|5JmvGLE4+~>SvNA& zE<@$D@?otXI*w4el_U7I9KlDP$kVyTPoZ`hD!1}bt3Pxcp>ptHRdAwJ@n8h%>ueqX3x7b>^*^WaC^(W>mf z+q_xk=;AF9#=DB~Roj=Rw**y0X7F zZS}_1W#)b!_+x)h`h|>7j88d7h+p8ts^sba!IR9>w6Dif?|5U~pY^<~<0km!xWe!6 z7Js*L1b@D&`1Ia2&Z=@9GN)BpSMn{tp15AIKBZOVI_~lFjpKSt|K9;;Rmqcl;OAY% zRj#|eKW1%r@LSt=z5O^&t%47$7I}$ngAc1(If7r#pFEML!TCBXk6Mo4vvLG~jGyA~ zR*v8YA66w#c_cfaj!WQ9t~=usxC19z1s_%|@)FwyA6B(;1V4EqPv>JkhL$7vtQ^4~ zO5M>)35jW`nX~pbF1Pz?^cy|wA0@|;wtxvy*|&WS9!m9pI?_fuGaPq ze` Date: Sat, 9 May 2026 11:29:03 +0100 Subject: [PATCH 12/16] normalize interfaces so everything takes in frame and game - Previously I was trying to save compute by only passing in what is necessary - It's much cleaner to use if all the constraints and objectives just take in game, frame and some optional extra arguments. - I will revisit this if we want to make the pre-made objectives a bit more flexible --- databallpy/optimization/constraints.py | 4 +-- databallpy/optimization/objectives.py | 40 ++++++++++++++----------- databallpy/optimization/optimization.py | 5 ---- 3 files changed, 24 insertions(+), 25 deletions(-) diff --git a/databallpy/optimization/constraints.py b/databallpy/optimization/constraints.py index fec9e17..caf49de 100644 --- a/databallpy/optimization/constraints.py +++ b/databallpy/optimization/constraints.py @@ -8,6 +8,8 @@ class TTIConstraint(Constraint): def __init__( self, + game: Game, + frame: pd.Series, max_time_to_intercept_seconds: float = 1, reaction_time: float = 0.1, max_velocity: float = 5.0, @@ -16,8 +18,6 @@ def __init__( self.reaction_time = reaction_time self.max_velocity = max_velocity - # FixMe - this is a lazy implementation - def compute_prerequisites(self, game: Game, frame: pd.Series) -> None: self.player_to_starting_pos_and_vel_map = frame[ [c + "_x" for c in game.get_column_ids()] + [c + "_y" for c in game.get_column_ids()] diff --git a/databallpy/optimization/objectives.py b/databallpy/optimization/objectives.py index b87b46e..150b23e 100644 --- a/databallpy/optimization/objectives.py +++ b/databallpy/optimization/objectives.py @@ -18,26 +18,34 @@ class WeightedPitchControlObjective(ObjectiveTerm): # Computes the net pitch control for the defending team over the attacking team def __init__( self, - grid: np.meshgrid, - attacking_team: str, - defending_team: str, - defending_player_ids: list[str], - attacking_team_influence: np.meshgrid, + game: Game, + frame: pd.Series, xt_array: np.ndarray | None = None, ): super().__init__(computation_type=ObjectiveType.GRID) - self.grid = grid - self.attacking_team = attacking_team - self.defending_team = defending_team - self.defending_player_ids = defending_player_ids - self.attacking_team_influence = attacking_team_influence + self.grid = np.meshgrid( + np.linspace(-game.pitch_dimensions[0] / 2, game.pitch_dimensions[0] / 2, 106), + np.linspace(-game.pitch_dimensions[1] / 2, game.pitch_dimensions[1] / 2, 68), + ) + + + self.attacking_team = frame["team_possession"] + self.defending_team = "home" if self.attacking_team == "away" else "away" + self.defending_player_ids = game.get_column_ids(team=self.defending_team) + + self.attacking_team_influence = get_team_influence( + frame, + col_ids=game.get_column_ids(team=self.attacking_team), + grid=self.grid, + player_ball_distances=None, + ) if xt_array is None: open_play_xt = np.load(XT_MODEL_PATH) # we are using (y, x) orientation instead of (x, y) so that it matches self.xt_array = zoom(open_play_xt, (106 / 264, 68 / 196), order=1).T - else: self.xt_array = xt_array + if self.attacking_team == "away": self.xt_array = np.fliplr(self.xt_array) @@ -63,19 +71,15 @@ class PressureObjective(ObjectiveTerm): def __init__( self, game: Game, + frame: pd.Series, players_to_press: list[str] | None = None, - attacking_player_ids: list[str] | None = None, ): - if not attacking_player_ids and not players_to_press: - raise ValueError( - "Either players_to_press or attacking_player_ids must be provided" - ) super().__init__(computation_type=ObjectiveType.PLAYER) self.game = game self.players_to_press = ( - players_to_press if players_to_press else attacking_player_ids + players_to_press if players_to_press else game.get_column_ids(team=frame["team_possession"]) ) def compute(self, input_frame: pd.Series) -> float: @@ -90,6 +94,6 @@ def compute(self, input_frame: pd.Series) -> float: pressure = temp_tracking_df.get_pressure_on_player( temp_tracking_df.index[0], attacking_player, [106, 68], d_front=9 ) - # compute the mean pressure on a player by dividing by number of players in p + # compute the mean pressure on a player by dividing by number of players being pressed pressure_score += pressure / len(self.players_to_press) return pressure_score diff --git a/databallpy/optimization/optimization.py b/databallpy/optimization/optimization.py index 265c8b6..16aa92c 100644 --- a/databallpy/optimization/optimization.py +++ b/databallpy/optimization/optimization.py @@ -31,9 +31,6 @@ class OptimizationResult: class Constraint(ABC): - def compute_prerequisites(self, game: Game = None, frame: pd.Series = None) -> None: - return None - @abstractmethod def check(self, proposed_new_frame, player_id) -> bool: raise NotImplementedError @@ -57,8 +54,6 @@ def __init__( game.tracking_data["frame"] == selected_frame_idx ].iloc[0] self.constraints = constraints or [] - for constraint in self.constraints: - constraint.compute_prerequisites(game=self.game, frame=self.frame) self.objective_terms = objective_terms self.weights = weights From 795e99027213927fe2b0c3864c11501242708f39 Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Sat, 9 May 2026 11:30:59 +0100 Subject: [PATCH 13/16] replace tester with draft of optimization docs --- databallpy/optimization/tester.py | 201 ---------------- docs/features/optimization.ipynb | 377 ++++++++++++++++++++++++++++++ 2 files changed, 377 insertions(+), 201 deletions(-) delete mode 100644 databallpy/optimization/tester.py create mode 100644 docs/features/optimization.ipynb diff --git a/databallpy/optimization/tester.py b/databallpy/optimization/tester.py deleted file mode 100644 index 7536dc7..0000000 --- a/databallpy/optimization/tester.py +++ /dev/null @@ -1,201 +0,0 @@ - -import numpy as np -from kloppy import skillcorner -from matplotlib.colors import LinearSegmentedColormap - -from databallpy import get_game_from_kloppy -from databallpy.features.pitch_control import ( - get_pitch_control_single_frame, - get_team_influence, -) -from databallpy.optimization.constraints import TTIConstraint -from databallpy.optimization.objectives import ( - PressureObjective, - WeightedPitchControlObjective, -) -from databallpy.optimization.simulated_annealing import SimulatedAnnealing -from databallpy.optimization.optimization import optimize_tracking_frame -from databallpy.visualize import ( - plot_soccer_pitch, - plot_tracking_data, -) - - -def generate_fig(game, frame_idx, save_path=None): - frame = game.tracking_data[game.tracking_data["frame"] == frame_idx].iloc[0] - pitch_control = get_pitch_control_single_frame( - frame, - game.pitch_dimensions, - n_x_bins=106, - n_y_bins=68, - ) - cmap_red_green = LinearSegmentedColormap.from_list( - "reds", [(0, 1, 0, 1), (0.5, 0.5, 0, 0), (1, 0, 0, 1)] - ) - - fig, ax = plot_soccer_pitch(field_dimen=game.pitch_dimensions, pitch_color="white") - idx_relative_to_game = game.tracking_data[ - game.tracking_data["frame"] == frame_idx - ].index[0] - fig, ax = plot_tracking_data( - game, - idx_relative_to_game, - team_colors=["green", "red"], - ax=ax, - fig=fig, - heatmap_overlay=pitch_control, - overlay_cmap=cmap_red_green, - add_velocities=True, - ) - if save_path: - fig.savefig(save_path) - return fig, ax - - -def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2, save_path=None): - frame_1 = game_1.tracking_data[game_1.tracking_data["frame"] == frame_idx_1].iloc[0] - frame_2 = game_2.tracking_data[game_2.tracking_data["frame"] == frame_idx_2].iloc[0] - pitch_control_1 = get_pitch_control_single_frame( - frame_1, - game_1.pitch_dimensions, - n_x_bins=106, - n_y_bins=68, - ) - pitch_control_2 = get_pitch_control_single_frame( - frame_2, - game_2.pitch_dimensions, - n_x_bins=106, - n_y_bins=68, - ) - cmap_red_green = LinearSegmentedColormap.from_list( - "reds", [(0, 1, 0, 1), (0.5, 0.5, 0, 0), (1, 0, 0, 1)] - ) - - fig, ax = plot_soccer_pitch(field_dimen=game.pitch_dimensions, pitch_color="white") - idx_relative_to_game_1 = game_1.tracking_data[ - game_1.tracking_data["frame"] == frame_idx_1 - ].index[0] - idx_relative_to_game_2 = game_2.tracking_data[ - game_2.tracking_data["frame"] == frame_idx_2 - ].index[0] - - fig, ax = plot_tracking_data( - game_1, - idx_relative_to_game_1, - team_colors=["green", "red"], - ax=ax, - fig=fig, - heatmap_overlay=pitch_control_1, - overlay_cmap=cmap_red_green, - add_velocities=True, - ) - fig, ax = plot_tracking_data( - game_2, - idx_relative_to_game_2, - # RGBA must use 0–1 floats (not 0–255) for matplotlib - team_colors=[(0.0, 1.0, 0.0, 0.5), (1.0, 0.0, 0.0, 0.5)], - ax=ax, - fig=fig, - heatmap_overlay=pitch_control_2, - overlay_cmap=cmap_red_green, - add_velocities=True, - ) - if save_path: - fig.savefig(save_path) - return fig, ax - - -match_id = 1886347 -tracking_data_github_url = f"https://media.githubusercontent.com/media/SkillCorner/opendata/master/data/matches/{match_id}/{match_id}_tracking_extrapolated.jsonl" -meta_data_github_url = f"https://raw.githubusercontent.com/SkillCorner/opendata/master/data/matches/{match_id}/{match_id}_match.json" - -dataset = skillcorner.load( - meta_data=meta_data_github_url, - raw_data=tracking_data_github_url, - # Optional Parameters - coordinates="skillcorner", - limit=500, -) - - -# actual script -game = get_game_from_kloppy(dataset) -game.tracking_data.add_velocity(game.get_column_ids() + ["ball"], allow_overwrite=True) -game.tracking_data.add_individual_player_possession() -selected_frame_idx = 210 - -pc = get_pitch_control_single_frame( - game.tracking_data[game.tracking_data["frame"] == selected_frame_idx].iloc[0], - game.pitch_dimensions, - n_x_bins=106, - n_y_bins=68, -) -print(f"total pitch control: {sum(sum(pc))}") - -grid = np.meshgrid( - np.linspace( - -game.pitch_dimensions[0] / 2, - game.pitch_dimensions[0] / 2, - 106, - ), - np.linspace(-game.pitch_dimensions[1] / 2, game.pitch_dimensions[1] / 2, 68), -) -frame = game.tracking_data[game.tracking_data["frame"] == selected_frame_idx].iloc[0] -attacking_team = frame["team_possession"] -defending_team = "home" if frame["team_possession"] == "away" else "away" -attacking_team_influence = get_team_influence( - frame, - col_ids=game.get_column_ids(team=attacking_team), - grid=grid, - player_ball_distances=None, -) -# annealer = SimulatedAnnealing( -# game, -# selected_frame_idx, -# objective_terms=[ -# WeightedPitchControlObjective( -# grid=grid, -# attacking_team=attacking_team, -# defending_team=defending_team, -# attacking_team_influence=attacking_team_influence, -# defending_player_ids=game.get_column_ids(team=defending_team), -# ), -# PressureObjective( -# attacking_player_ids=game.get_column_ids(team=defending_team), game=game -# ), -# ], -# constraints=[TTIConstraint()], -# weights=[1, 1], -# distance_perturbation=0.2, # how many yards to randomly move the player on each iteration -# max_tti=2, # the maximum time to intercept, i.e. don't move a player if they can't reach that space within 1s -# num_iterations=1000, # the number of perturbations to do -# ) - -result = optimize_tracking_frame( - game = game, - selected_frame_idx = selected_frame_idx, - objective_terms = [ - WeightedPitchControlObjective( - grid=grid, - attacking_team=attacking_team, - defending_team=defending_team, - attacking_team_influence=attacking_team_influence, - defending_player_ids=game.get_column_ids(team=defending_team), - ), - PressureObjective( - attacking_player_ids=game.get_column_ids(team=defending_team), game=game - ), - ], - weights=[1, 1], - constraints=[TTIConstraint(max_time_to_intercept_seconds=2)], - algorithm=SimulatedAnnealing, - num_iterations = 3000, - ) - -print(result.best_result) -# new_game = deepcopy(game) -# new_game.tracking_data = pd.DataFrame(result.best_frame).transpose().reset_index() -# # generate_fig(new_game, selected_frame_idx, save_path="test.png") -# diff_frames( -# game, selected_frame_idx, new_game, selected_frame_idx, save_path="test_diff.png" -# ) diff --git a/docs/features/optimization.ipynb b/docs/features/optimization.ipynb new file mode 100644 index 0000000..4a9d102 --- /dev/null +++ b/docs/features/optimization.ipynb @@ -0,0 +1,377 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b112328c", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "# Optimization with Simulated Annealing\n", + "\n", + "## Introduction\n", + "\n", + "This notebook documents an optimization module that can be used to optimize player locations in a single tracking frame. The optimization currently only supports the **Simulated Annealing** algorithm, and allows creation of any arbitrary objective or constraint.\n", + "\n", + "The approach is useful for use cases such as:\n", + "\n", + "- Finding defensive repositioning that maximizes xT-weighted pitch control.\n", + "- Evaluating trade-offs between pitch control and pressure.\n", + "- Measuring how far a team's position is from some theoretical optimal.\n", + "\n", + "\n", + "```{warning}\n", + "Although we try to keep everything up to date, notebook examples can evolve slightly differently from the package internals. If you spot inconsistencies, please open an issue.\n", + "```\n", + "\n", + "## Optimization setup\n", + "\n", + "The high-level API is `optimize_tracking_frame()`. You provide:\n", + "\n", + "1. a game and selected frame,\n", + "2. objective terms,\n", + "3. optional constraints,\n", + "4. an optimization algorithm (`SimulatedAnnealing` in this example),\n", + "5. algorithm specific hyperparameters like `num_iterations`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7a7bde49", + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from kloppy import skillcorner\n", + "from matplotlib.colors import LinearSegmentedColormap\n", + "\n", + "from databallpy import get_game_from_kloppy\n", + "from databallpy.features.pitch_control import (\n", + " get_pitch_control_single_frame,\n", + " get_team_influence,\n", + ")\n", + "from databallpy.optimization.constraints import TTIConstraint\n", + "from databallpy.optimization.objectives import (\n", + " PressureObjective,\n", + " WeightedPitchControlObjective,\n", + ")\n", + "from databallpy.optimization.optimization import optimize_tracking_frame\n", + "from databallpy.optimization.simulated_annealing import SimulatedAnnealing\n", + "from databallpy.visualize import plot_soccer_pitch, plot_tracking_data" + ] + }, + { + "cell_type": "markdown", + "id": "2ef8f31c", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## Load tracking frame\n", + "\n", + "We'll start by picking one frame from this match, where the Green team LCB is making a pass to the LB." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a57bedd0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/amar/Documents/databallpy/databallpy/utils/get_game.py:921: UserWarning: All frames in 'tracking_dataset' are 'ALIVE', databallpy expects 'DEAD' frames as well (e.g. for more accurate event synchronization). Set `only_alive=False` in your kloppy `.load_tracking()` call to include 'DEAD' frames.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "match_id = 1886347\n", + "tracking_data_github_url = (\n", + " \"https://media.githubusercontent.com/media/SkillCorner/opendata/master/data/\"\n", + " f\"matches/{match_id}/{match_id}_tracking_extrapolated.jsonl\"\n", + ")\n", + "meta_data_github_url = (\n", + " \"https://raw.githubusercontent.com/SkillCorner/opendata/master/data/\"\n", + " f\"matches/{match_id}/{match_id}_match.json\"\n", + ")\n", + "\n", + "dataset = skillcorner.load(\n", + " meta_data=meta_data_github_url,\n", + " raw_data=tracking_data_github_url,\n", + " coordinates=\"skillcorner\",\n", + " limit=500,\n", + ")\n", + "\n", + "game = get_game_from_kloppy(dataset)\n", + "game.tracking_data.add_velocity(game.get_column_ids() + [\"ball\"], allow_overwrite=True)\n", + "game.tracking_data.add_individual_player_possession()\n", + "\n", + "selected_frame_idx = 210\n", + "frame = game.tracking_data[game.tracking_data[\"frame\"] == selected_frame_idx].iloc[0]\n", + "#frame id is not the same as index\n", + "idx_relative_to_game = game.tracking_data[game.tracking_data[\"frame\"] == selected_frame_idx].index[0]\n", + "\n", + "fig, ax = plot_tracking_data(\n", + " game,\n", + " idx_relative_to_game,\n", + " team_colors=[\"green\", \"red\"],\n", + " add_velocities=True,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "1410aa6c", + "metadata": {}, + "source": [ + "## Build objective terms and constraints\n", + "\n", + "For this example, we will define an objective function containing two objective terms:\n", + "\n", + "- `WeightedPitchControlObjective`: rewards pitch-control effects from defensive movement (weight = 1)\n", + "- `PressureObjective`: rewards pressure on the attacking side (weight = 1)\n", + "\n", + "We constrain the algorithm with `TTIConstraint`, so players are only moved to physically reachable locations within a given time-to-intercept window, in this case 2 seconds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4acd2909", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "attacking_team = frame[\"team_possession\"]\n", + "defending_team = \"home\" if attacking_team == \"away\" else \"away\"\n", + "\n", + "objective_terms = [\n", + " WeightedPitchControlObjective(\n", + " game=game,\n", + " frame=frame,\n", + " ),\n", + " PressureObjective(\n", + " game=game,\n", + " frame=frame,\n", + " ),\n", + "]\n", + "weights = [1, 1]\n", + "constraints = [TTIConstraint(game, frame,max_time_to_intercept_seconds=2)]\n" + ] + }, + { + "cell_type": "markdown", + "id": "baa03eea", + "metadata": {}, + "source": [ + "## Run simulated annealing\n", + "\n", + "Now we call the optimization API with `SimulatedAnnealing` as the algorithm backend. Internally this performs repeated perturbations and probabilistic acceptance steps to search for a strong tactical configuration." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2154446d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best objective value: 23847.34865429709\n" + ] + } + ], + "source": [ + "result = optimize_tracking_frame(\n", + " game=game,\n", + " selected_frame_idx=selected_frame_idx,\n", + " objective_terms=objective_terms,\n", + " weights=weights,\n", + " constraints=constraints,\n", + " algorithm=SimulatedAnnealing,\n", + " num_iterations=1000,\n", + ")\n", + "\n", + "print(\"Best objective value:\", result.best_result)" + ] + }, + { + "cell_type": "markdown", + "id": "63259729", + "metadata": {}, + "source": [ + "## Visualize original vs optimized frame\n", + "\n", + "The helper functions below are copied from the prototype script and allow plotting a single frame and a transparent overlay difference between the original and optimized positions." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2c646b8d", + "metadata": {}, + "outputs": [], + "source": [ + "def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2):\n", + " frame_1 = game_1.tracking_data[game_1.tracking_data[\"frame\"] == frame_idx_1].iloc[0]\n", + " frame_2 = game_2.tracking_data[game_2.tracking_data[\"frame\"] == frame_idx_2].iloc[0]\n", + "\n", + " pitch_control_2 = get_pitch_control_single_frame(\n", + " frame_2,\n", + " game_2.pitch_dimensions,\n", + " n_x_bins=106,\n", + " n_y_bins=68,\n", + " )\n", + " cmap_red_green = LinearSegmentedColormap.from_list(\n", + " \"reds\", [(0, 1, 0, 1), (0.5, 0.5, 0, 0), (1, 0, 0, 1)]\n", + " )\n", + "\n", + " fig, ax = plot_soccer_pitch(field_dimen=game.pitch_dimensions, pitch_color=\"white\")\n", + " idx_relative_to_game_1 = game_1.tracking_data[\n", + " game_1.tracking_data[\"frame\"] == frame_idx_1\n", + " ].index[0]\n", + " idx_relative_to_game_2 = game_2.tracking_data[\n", + " game_2.tracking_data[\"frame\"] == frame_idx_2\n", + " ].index[0]\n", + "\n", + " fig, ax = plot_tracking_data(\n", + " game_1,\n", + " idx_relative_to_game_1,\n", + " team_colors=[\"green\", \"red\"],\n", + " ax=ax,\n", + " fig=fig,\n", + " add_velocities=True,\n", + " alpha=0.4\n", + " )\n", + " fig, ax = plot_tracking_data(\n", + " game_2,\n", + " idx_relative_to_game_2,\n", + " team_colors=[(0.0, 1.0, 0.0, 0.5), (1.0, 0.0, 0.0, 0.5)],\n", + " ax=ax,\n", + " fig=fig,\n", + " heatmap_overlay=pitch_control_2,\n", + " overlay_cmap=cmap_red_green,\n", + " add_velocities=True,\n", + " )\n", + " return fig, ax" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f8013a7f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/amar/Documents/databallpy/databallpy/visualize.py:874: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " fig_obj = ax.scatter(\n", + "/Users/amar/Documents/databallpy/databallpy/visualize.py:874: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " fig_obj = ax.scatter(\n" + ] + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "new_game = deepcopy(game)\n", + "new_game.tracking_data = pd.DataFrame(result.best_frame).transpose().reset_index()\n", + "\n", + "fig, ax = diff_frames(\n", + " game,\n", + " selected_frame_idx,\n", + " new_game,\n", + " selected_frame_idx,\n", + ")\n", + "fig, ax" + ] + }, + { + "cell_type": "markdown", + "id": "2d49a104", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "You now have a complete end-to-end example for frame-level tactical optimization with simulated annealing:\n", + "\n", + "- loading and preparing tracking data,\n", + "- defining objective terms and constraints,\n", + "- running the optimizer,\n", + "- and comparing baseline vs optimized outcomes visually.\n", + "\n", + "You can extend this setup by tuning objective weights, changing constraints, or plugging in additional optimization algorithms exposed by the optimization module." + ] + }, + { + "cell_type": "markdown", + "id": "ba71dae0", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (databallpy-local)", + "language": "python", + "name": "databallpy-local" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From aed180701c18cd9d814a4f6bd997c4db89b85dc5 Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Sat, 9 May 2026 11:37:01 +0100 Subject: [PATCH 14/16] Clean up optimization.ipynb --- docs/features/optimization.ipynb | 718 +++++++++++++++---------------- 1 file changed, 357 insertions(+), 361 deletions(-) diff --git a/docs/features/optimization.ipynb b/docs/features/optimization.ipynb index 4a9d102..4aa8891 100644 --- a/docs/features/optimization.ipynb +++ b/docs/features/optimization.ipynb @@ -1,377 +1,373 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "b112328c", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "source": [ - "# Optimization with Simulated Annealing\n", - "\n", - "## Introduction\n", - "\n", - "This notebook documents an optimization module that can be used to optimize player locations in a single tracking frame. The optimization currently only supports the **Simulated Annealing** algorithm, and allows creation of any arbitrary objective or constraint.\n", - "\n", - "The approach is useful for use cases such as:\n", - "\n", - "- Finding defensive repositioning that maximizes xT-weighted pitch control.\n", - "- Evaluating trade-offs between pitch control and pressure.\n", - "- Measuring how far a team's position is from some theoretical optimal.\n", - "\n", - "\n", - "```{warning}\n", - "Although we try to keep everything up to date, notebook examples can evolve slightly differently from the package internals. If you spot inconsistencies, please open an issue.\n", - "```\n", - "\n", - "## Optimization setup\n", - "\n", - "The high-level API is `optimize_tracking_frame()`. You provide:\n", - "\n", - "1. a game and selected frame,\n", - "2. objective terms,\n", - "3. optional constraints,\n", - "4. an optimization algorithm (`SimulatedAnnealing` in this example),\n", - "5. algorithm specific hyperparameters like `num_iterations`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7a7bde49", - "metadata": {}, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "from kloppy import skillcorner\n", - "from matplotlib.colors import LinearSegmentedColormap\n", - "\n", - "from databallpy import get_game_from_kloppy\n", - "from databallpy.features.pitch_control import (\n", - " get_pitch_control_single_frame,\n", - " get_team_influence,\n", - ")\n", - "from databallpy.optimization.constraints import TTIConstraint\n", - "from databallpy.optimization.objectives import (\n", - " PressureObjective,\n", - " WeightedPitchControlObjective,\n", - ")\n", - "from databallpy.optimization.optimization import optimize_tracking_frame\n", - "from databallpy.optimization.simulated_annealing import SimulatedAnnealing\n", - "from databallpy.visualize import plot_soccer_pitch, plot_tracking_data" - ] - }, - { - "cell_type": "markdown", - "id": "2ef8f31c", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "source": [ - "## Load tracking frame\n", - "\n", - "We'll start by picking one frame from this match, where the Green team LCB is making a pass to the LB." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a57bedd0", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/amar/Documents/databallpy/databallpy/utils/get_game.py:921: UserWarning: All frames in 'tracking_dataset' are 'ALIVE', databallpy expects 'DEAD' frames as well (e.g. for more accurate event synchronization). Set `only_alive=False` in your kloppy `.load_tracking()` call to include 'DEAD' frames.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "match_id = 1886347\n", - "tracking_data_github_url = (\n", - " \"https://media.githubusercontent.com/media/SkillCorner/opendata/master/data/\"\n", - " f\"matches/{match_id}/{match_id}_tracking_extrapolated.jsonl\"\n", - ")\n", - "meta_data_github_url = (\n", - " \"https://raw.githubusercontent.com/SkillCorner/opendata/master/data/\"\n", - " f\"matches/{match_id}/{match_id}_match.json\"\n", - ")\n", - "\n", - "dataset = skillcorner.load(\n", - " meta_data=meta_data_github_url,\n", - " raw_data=tracking_data_github_url,\n", - " coordinates=\"skillcorner\",\n", - " limit=500,\n", - ")\n", - "\n", - "game = get_game_from_kloppy(dataset)\n", - "game.tracking_data.add_velocity(game.get_column_ids() + [\"ball\"], allow_overwrite=True)\n", - "game.tracking_data.add_individual_player_possession()\n", - "\n", - "selected_frame_idx = 210\n", - "frame = game.tracking_data[game.tracking_data[\"frame\"] == selected_frame_idx].iloc[0]\n", - "#frame id is not the same as index\n", - "idx_relative_to_game = game.tracking_data[game.tracking_data[\"frame\"] == selected_frame_idx].index[0]\n", - "\n", - "fig, ax = plot_tracking_data(\n", - " game,\n", - " idx_relative_to_game,\n", - " team_colors=[\"green\", \"red\"],\n", - " add_velocities=True,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "1410aa6c", - "metadata": {}, - "source": [ - "## Build objective terms and constraints\n", - "\n", - "For this example, we will define an objective function containing two objective terms:\n", - "\n", - "- `WeightedPitchControlObjective`: rewards pitch-control effects from defensive movement (weight = 1)\n", - "- `PressureObjective`: rewards pressure on the attacking side (weight = 1)\n", - "\n", - "We constrain the algorithm with `TTIConstraint`, so players are only moved to physically reachable locations within a given time-to-intercept window, in this case 2 seconds)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4acd2909", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "attacking_team = frame[\"team_possession\"]\n", - "defending_team = \"home\" if attacking_team == \"away\" else \"away\"\n", - "\n", - "objective_terms = [\n", - " WeightedPitchControlObjective(\n", - " game=game,\n", - " frame=frame,\n", - " ),\n", - " PressureObjective(\n", - " game=game,\n", - " frame=frame,\n", - " ),\n", - "]\n", - "weights = [1, 1]\n", - "constraints = [TTIConstraint(game, frame,max_time_to_intercept_seconds=2)]\n" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "id": "b112328c", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "# Optimization with Simulated Annealing\n", + "\n", + "## Introduction\n", + "\n", + "This notebook documents an optimization module that can be used to optimize player locations in a single tracking frame. The optimization currently only supports the **Simulated Annealing** algorithm, and allows creation of any arbitrary objective or constraint.\n", + "\n", + "The approach is useful for use cases such as:\n", + "\n", + "- Finding defensive repositioning that maximizes xT-weighted pitch control.\n", + "- Evaluating trade-offs between pitch control and pressure.\n", + "- Measuring how far a team's position is from some theoretical optimal.\n", + "\n", + "\n", + "```{warning}\n", + "Although we try to keep everything up to date, notebook examples can evolve slightly differently from the package internals. If you spot inconsistencies, please open an issue.\n", + "```\n", + "\n", + "## Optimization setup\n", + "\n", + "The high-level API is `optimize_tracking_frame()`. You provide:\n", + "\n", + "1. a game and selected frame,\n", + "2. objective terms,\n", + "3. optional constraints,\n", + "4. an optimization algorithm (`SimulatedAnnealing` in this example),\n", + "5. algorithm specific hyperparameters like `num_iterations`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7a7bde49", + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from kloppy import skillcorner\n", + "from matplotlib.colors import LinearSegmentedColormap\n", + "\n", + "from databallpy import get_game_from_kloppy\n", + "from databallpy.features.pitch_control import get_pitch_control_single_frame\n", + "\n", + "from databallpy.optimization.constraints import TTIConstraint\n", + "from databallpy.optimization.objectives import (\n", + " PressureObjective,\n", + " WeightedPitchControlObjective,\n", + ")\n", + "from databallpy.optimization.optimization import optimize_tracking_frame\n", + "from databallpy.optimization.simulated_annealing import SimulatedAnnealing\n", + "from databallpy.visualize import plot_soccer_pitch, plot_tracking_data" + ] + }, + { + "cell_type": "markdown", + "id": "2ef8f31c", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## Load tracking frame\n", + "\n", + "We'll start by picking one frame from this match, where the Green team LCB is making a pass to the LB." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a57bedd0", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "baa03eea", - "metadata": {}, - "source": [ - "## Run simulated annealing\n", - "\n", - "Now we call the optimization API with `SimulatedAnnealing` as the algorithm backend. Internally this performs repeated perturbations and probabilistic acceptance steps to search for a strong tactical configuration." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/amar/Documents/databallpy/databallpy/utils/get_game.py:921: UserWarning: All frames in 'tracking_dataset' are 'ALIVE', databallpy expects 'DEAD' frames as well (e.g. for more accurate event synchronization). Set `only_alive=False` in your kloppy `.load_tracking()` call to include 'DEAD' frames.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "code", - "execution_count": 10, - "id": "2154446d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best objective value: 23847.34865429709\n" - ] - } - ], - "source": [ - "result = optimize_tracking_frame(\n", - " game=game,\n", - " selected_frame_idx=selected_frame_idx,\n", - " objective_terms=objective_terms,\n", - " weights=weights,\n", - " constraints=constraints,\n", - " algorithm=SimulatedAnnealing,\n", - " num_iterations=1000,\n", - ")\n", - "\n", - "print(\"Best objective value:\", result.best_result)" + "data": { + "image/png": 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", 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" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "match_id = 1886347\n", + "tracking_data_github_url = (\n", + " \"https://media.githubusercontent.com/media/SkillCorner/opendata/master/data/\"\n", + " f\"matches/{match_id}/{match_id}_tracking_extrapolated.jsonl\"\n", + ")\n", + "meta_data_github_url = (\n", + " \"https://raw.githubusercontent.com/SkillCorner/opendata/master/data/\"\n", + " f\"matches/{match_id}/{match_id}_match.json\"\n", + ")\n", + "\n", + "dataset = skillcorner.load(\n", + " meta_data=meta_data_github_url,\n", + " raw_data=tracking_data_github_url,\n", + " coordinates=\"skillcorner\",\n", + " limit=500,\n", + ")\n", + "\n", + "game = get_game_from_kloppy(dataset)\n", + "game.tracking_data.add_velocity(game.get_column_ids() + [\"ball\"], allow_overwrite=True)\n", + "game.tracking_data.add_individual_player_possession()\n", + "\n", + "selected_frame_idx = 210\n", + "frame = game.tracking_data[game.tracking_data[\"frame\"] == selected_frame_idx].iloc[0]\n", + "# frame id is not the same as index\n", + "idx_relative_to_game = game.tracking_data[\n", + " game.tracking_data[\"frame\"] == selected_frame_idx\n", + "].index[0]\n", + "\n", + "fig, ax = plot_tracking_data(\n", + " game,\n", + " idx_relative_to_game,\n", + " team_colors=[\"green\", \"red\"],\n", + " add_velocities=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "1410aa6c", + "metadata": {}, + "source": [ + "## Build objective terms and constraints\n", + "\n", + "For this example, we will define an objective function containing two objective terms:\n", + "\n", + "- `WeightedPitchControlObjective`: rewards pitch-control effects from defensive movement (weight = 1)\n", + "- `PressureObjective`: rewards pressure on the attacking side (weight = 1)\n", + "\n", + "We constrain the algorithm with `TTIConstraint`, so players are only moved to physically reachable locations within a given time-to-intercept window, in this case 2 seconds)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4acd2909", + "metadata": {}, + "outputs": [], + "source": [ + "objective_terms = [\n", + " WeightedPitchControlObjective(\n", + " game=game,\n", + " frame=frame,\n", + " ),\n", + " PressureObjective(\n", + " game=game,\n", + " frame=frame,\n", + " ),\n", + "]\n", + "weights = [1, 1]\n", + "constraints = [TTIConstraint(game, frame, max_time_to_intercept_seconds=2)]" + ] + }, + { + "cell_type": "markdown", + "id": "baa03eea", + "metadata": {}, + "source": [ + "## Run simulated annealing\n", + "\n", + "Now we call the optimization API with `SimulatedAnnealing` as the algorithm backend. Internally this performs repeated perturbations and probabilistic acceptance steps to search for a strong tactical configuration." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2154446d", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "63259729", - "metadata": {}, - "source": [ - "## Visualize original vs optimized frame\n", - "\n", - "The helper functions below are copied from the prototype script and allow plotting a single frame and a transparent overlay difference between the original and optimized positions." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Best objective value: 142.12329025559984\n" + ] + } + ], + "source": [ + "result = optimize_tracking_frame(\n", + " game=game,\n", + " selected_frame_idx=selected_frame_idx,\n", + " objective_terms=objective_terms,\n", + " weights=weights,\n", + " constraints=constraints,\n", + " algorithm=SimulatedAnnealing,\n", + " num_iterations=1000,\n", + ")\n", + "\n", + "print(\"Best objective value:\", result.best_result)" + ] + }, + { + "cell_type": "markdown", + "id": "63259729", + "metadata": {}, + "source": [ + "## Visualize original vs optimized frame\n", + "\n", + "The helper functions below are copied from the prototype script and allow plotting a single frame and a transparent overlay difference between the original and optimized positions." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2c646b8d", + "metadata": {}, + "outputs": [], + "source": [ + "def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2):\n", + " frame_1 = game_1.tracking_data[game_1.tracking_data[\"frame\"] == frame_idx_1].iloc[0]\n", + " frame_2 = game_2.tracking_data[game_2.tracking_data[\"frame\"] == frame_idx_2].iloc[0]\n", + "\n", + " pitch_control_2 = get_pitch_control_single_frame(\n", + " frame_2,\n", + " game_2.pitch_dimensions,\n", + " n_x_bins=106,\n", + " n_y_bins=68,\n", + " )\n", + " cmap_red_green = LinearSegmentedColormap.from_list(\n", + " \"reds\", [(0, 1, 0, 1), (0.5, 0.5, 0, 0), (1, 0, 0, 1)]\n", + " )\n", + "\n", + " fig, ax = plot_soccer_pitch(field_dimen=game.pitch_dimensions, pitch_color=\"white\")\n", + " idx_relative_to_game_1 = game_1.tracking_data[\n", + " game_1.tracking_data[\"frame\"] == frame_idx_1\n", + " ].index[0]\n", + " idx_relative_to_game_2 = game_2.tracking_data[\n", + " game_2.tracking_data[\"frame\"] == frame_idx_2\n", + " ].index[0]\n", + "\n", + " fig, ax = plot_tracking_data(\n", + " game_1,\n", + " idx_relative_to_game_1,\n", + " team_colors=[\"green\", \"red\"],\n", + " ax=ax,\n", + " fig=fig,\n", + " add_velocities=True,\n", + " alpha=0.4,\n", + " )\n", + " fig, ax = plot_tracking_data(\n", + " game_2,\n", + " idx_relative_to_game_2,\n", + " team_colors=[(0.0, 1.0, 0.0, 0.5), (1.0, 0.0, 0.0, 0.5)],\n", + " ax=ax,\n", + " fig=fig,\n", + " heatmap_overlay=pitch_control_2,\n", + " overlay_cmap=cmap_red_green,\n", + " add_velocities=True,\n", + " )\n", + " return fig, ax" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f8013a7f", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "id": "2c646b8d", - "metadata": {}, - "outputs": [], - "source": [ - "def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2):\n", - " frame_1 = game_1.tracking_data[game_1.tracking_data[\"frame\"] == frame_idx_1].iloc[0]\n", - " frame_2 = game_2.tracking_data[game_2.tracking_data[\"frame\"] == frame_idx_2].iloc[0]\n", - "\n", - " pitch_control_2 = get_pitch_control_single_frame(\n", - " frame_2,\n", - " game_2.pitch_dimensions,\n", - " n_x_bins=106,\n", - " n_y_bins=68,\n", - " )\n", - " cmap_red_green = LinearSegmentedColormap.from_list(\n", - " \"reds\", [(0, 1, 0, 1), (0.5, 0.5, 0, 0), (1, 0, 0, 1)]\n", - " )\n", - "\n", - " fig, ax = plot_soccer_pitch(field_dimen=game.pitch_dimensions, pitch_color=\"white\")\n", - " idx_relative_to_game_1 = game_1.tracking_data[\n", - " game_1.tracking_data[\"frame\"] == frame_idx_1\n", - " ].index[0]\n", - " idx_relative_to_game_2 = game_2.tracking_data[\n", - " game_2.tracking_data[\"frame\"] == frame_idx_2\n", - " ].index[0]\n", - "\n", - " fig, ax = plot_tracking_data(\n", - " game_1,\n", - " idx_relative_to_game_1,\n", - " team_colors=[\"green\", \"red\"],\n", - " ax=ax,\n", - " fig=fig,\n", - " add_velocities=True,\n", - " alpha=0.4\n", - " )\n", - " fig, ax = plot_tracking_data(\n", - " game_2,\n", - " idx_relative_to_game_2,\n", - " team_colors=[(0.0, 1.0, 0.0, 0.5), (1.0, 0.0, 0.0, 0.5)],\n", - " ax=ax,\n", - " fig=fig,\n", - " heatmap_overlay=pitch_control_2,\n", - " overlay_cmap=cmap_red_green,\n", - " add_velocities=True,\n", - " )\n", - " return fig, ax" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/amar/Documents/databallpy/databallpy/visualize.py:874: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " fig_obj = ax.scatter(\n", + "/Users/amar/Documents/databallpy/databallpy/visualize.py:874: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " fig_obj = ax.scatter(\n" + ] }, { - "cell_type": "code", - "execution_count": 13, - "id": "f8013a7f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/amar/Documents/databallpy/databallpy/visualize.py:874: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - " fig_obj = ax.scatter(\n", - "/Users/amar/Documents/databallpy/databallpy/visualize.py:874: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - " fig_obj = ax.scatter(\n" - ] - }, - { - "data": { - "text/plain": [ - "(
, )" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "new_game = deepcopy(game)\n", - "new_game.tracking_data = pd.DataFrame(result.best_frame).transpose().reset_index()\n", - "\n", - "fig, ax = diff_frames(\n", - " game,\n", - " selected_frame_idx,\n", - " new_game,\n", - " selected_frame_idx,\n", - ")\n", - "fig, ax" + "data": { + "text/plain": [ + "(
, )" ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "id": "2d49a104", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "You now have a complete end-to-end example for frame-level tactical optimization with simulated annealing:\n", - "\n", - "- loading and preparing tracking data,\n", - "- defining objective terms and constraints,\n", - "- running the optimizer,\n", - "- and comparing baseline vs optimized outcomes visually.\n", - "\n", - "You can extend this setup by tuning objective weights, changing constraints, or plugging in additional optimization algorithms exposed by the optimization module." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "markdown", - "id": "ba71dae0", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (databallpy-local)", - "language": "python", - "name": "databallpy-local" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "new_game = deepcopy(game)\n", + "new_game.tracking_data = pd.DataFrame(result.best_frame).transpose().reset_index()\n", + "\n", + "fig, ax = diff_frames(\n", + " game,\n", + " selected_frame_idx,\n", + " new_game,\n", + " selected_frame_idx,\n", + ")\n", + "fig, ax" + ] + }, + { + "cell_type": "markdown", + "id": "2d49a104", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "You now have a complete end-to-end example for frame-level tactical optimization with simulated annealing:\n", + "\n", + "- loading and preparing tracking data,\n", + "- defining objective terms and constraints,\n", + "- running the optimizer,\n", + "- and comparing baseline vs optimized outcomes visually.\n", + "\n", + "You can extend this setup by tuning objective weights, changing constraints, or plugging in additional optimization algorithms exposed by the optimization module." + ] + }, + { + "cell_type": "markdown", + "id": "ba71dae0", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (databallpy-local)", + "language": "python", + "name": "databallpy-local" }, - "nbformat": 4, - "nbformat_minor": 5 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } From 6f7228b3869a951e9c05b86b08adbd424252d526 Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Sat, 9 May 2026 11:40:09 +0100 Subject: [PATCH 15/16] ruff formatting --- databallpy/optimization/constraints.py | 2 +- databallpy/optimization/objectives.py | 18 ++++++++++-------- 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/databallpy/optimization/constraints.py b/databallpy/optimization/constraints.py index caf49de..e1a7626 100644 --- a/databallpy/optimization/constraints.py +++ b/databallpy/optimization/constraints.py @@ -8,7 +8,7 @@ class TTIConstraint(Constraint): def __init__( self, - game: Game, + game: Game, frame: pd.Series, max_time_to_intercept_seconds: float = 1, reaction_time: float = 0.1, diff --git a/databallpy/optimization/objectives.py b/databallpy/optimization/objectives.py index 150b23e..816fd52 100644 --- a/databallpy/optimization/objectives.py +++ b/databallpy/optimization/objectives.py @@ -24,15 +24,16 @@ def __init__( ): super().__init__(computation_type=ObjectiveType.GRID) self.grid = np.meshgrid( - np.linspace(-game.pitch_dimensions[0] / 2, game.pitch_dimensions[0] / 2, 106), - np.linspace(-game.pitch_dimensions[1] / 2, game.pitch_dimensions[1] / 2, 68), - ) - + np.linspace( + -game.pitch_dimensions[0] / 2, game.pitch_dimensions[0] / 2, 106 + ), + np.linspace(-game.pitch_dimensions[1] / 2, game.pitch_dimensions[1] / 2, 68), + ) - self.attacking_team = frame["team_possession"] + self.attacking_team = frame["team_possession"] self.defending_team = "home" if self.attacking_team == "away" else "away" self.defending_player_ids = game.get_column_ids(team=self.defending_team) - + self.attacking_team_influence = get_team_influence( frame, col_ids=game.get_column_ids(team=self.attacking_team), @@ -74,12 +75,13 @@ def __init__( frame: pd.Series, players_to_press: list[str] | None = None, ): - super().__init__(computation_type=ObjectiveType.PLAYER) self.game = game self.players_to_press = ( - players_to_press if players_to_press else game.get_column_ids(team=frame["team_possession"]) + players_to_press + if players_to_press + else game.get_column_ids(team=frame["team_possession"]) ) def compute(self, input_frame: pd.Series) -> float: From 028e232dc2ff915d07f69994450048c42e62018d Mon Sep 17 00:00:00 2001 From: Amar Shah Date: Sat, 13 Jun 2026 21:37:08 +0100 Subject: [PATCH 16/16] Customisation Fixes - added random state, verbosity, patience - replaced skillcorner data - made it explicit in the docs that you can specify which players to move --- databallpy/optimization/constraints.py | 8 +- databallpy/optimization/optimization.py | 4 +- .../optimization/simulated_annealing.py | 45 +++++-- docs/features/optimization.ipynb | 125 +++++++++++------- 4 files changed, 119 insertions(+), 63 deletions(-) diff --git a/databallpy/optimization/constraints.py b/databallpy/optimization/constraints.py index e1a7626..dd7ce6e 100644 --- a/databallpy/optimization/constraints.py +++ b/databallpy/optimization/constraints.py @@ -32,7 +32,13 @@ def tti(self, origin, destination, velocity): u_mag = np.sqrt(np.sum(u**2, axis=-1)) v_mag = np.sqrt(np.sum(v**2, axis=-1)) dot_product = np.sum(u * v, axis=-1) - angle = np.arccos(dot_product / (u_mag * v_mag)) + + denom = u_mag * v_mag + # stationary player edge case + if denom == 0: + angle = 0.0 + else: + angle = np.arccos(dot_product / denom) r_reaction = origin + velocity * self.reaction_time d = destination - r_reaction t = ( diff --git a/databallpy/optimization/optimization.py b/databallpy/optimization/optimization.py index 16aa92c..94f9a9d 100644 --- a/databallpy/optimization/optimization.py +++ b/databallpy/optimization/optimization.py @@ -50,9 +50,7 @@ def __init__( raise ValueError("objective_terms and weights must have equal length") self.game = game - self.frame = game.tracking_data[ - game.tracking_data["frame"] == selected_frame_idx - ].iloc[0] + self.frame = game.tracking_data.loc[[selected_frame_idx]].iloc[0] self.constraints = constraints or [] self.objective_terms = objective_terms diff --git a/databallpy/optimization/simulated_annealing.py b/databallpy/optimization/simulated_annealing.py index 0358964..b5ebc14 100644 --- a/databallpy/optimization/simulated_annealing.py +++ b/databallpy/optimization/simulated_annealing.py @@ -10,6 +10,8 @@ OptimizationAlgorithm, OptimizationResult, ) +from tqdm import tqdm + from databallpy.utils.logging import create_logger LOGGER = create_logger(__name__) @@ -26,6 +28,10 @@ def __init__( defending_players_to_optimize: list[str] | None = None, distance_perturbation: float = 0.2, num_iterations: int = 1000, + patience: int = 200, + log_interval: int | None = None, + random_state: int | None = None, + verbose: bool = True, ): super().__init__( game=game, @@ -41,7 +47,15 @@ def __init__( self.T = self.T_0 self.cooling_rate = 0.9 self.num_iterations = num_iterations - + self.patience = patience + # log the best score 5 times over the course of the run by default + self.log_interval = ( + log_interval if log_interval is not None else max(1, num_iterations // 5) + ) + self._rng = ( + random.Random(random_state) if random_state is not None else random.Random() + ) + self.verbose = verbose self.defending_players_to_optimize = ( defending_players_to_optimize if defending_players_to_optimize @@ -54,16 +68,16 @@ def perturbation(self, input_frame): proposed_new_frame = deepcopy(input_frame) # randomly choose 1 of the defenders # TODO see if we can cache the unselected players to avoid recomputing every time - self.selected_players = random.sample(self.defending_players_to_optimize, 1) + self.selected_players = self._rng.sample(self.defending_players_to_optimize, 1) for player_id in self.selected_players: # move each player up to a maximal distance from their starting positions x_col = player_id + "_x" y_col = player_id + "_y" - new_x_pos = proposed_new_frame[x_col] + random.uniform( + new_x_pos = proposed_new_frame[x_col] + self._rng.uniform( -self.distance_perturbation, self.distance_perturbation ) - new_y_pos = proposed_new_frame[y_col] + random.uniform( + new_y_pos = proposed_new_frame[y_col] + self._rng.uniform( -self.distance_perturbation, self.distance_perturbation ) proposed_new_frame[y_col] = new_y_pos @@ -82,7 +96,6 @@ def compute_objective(self, input_frame): score = objective_term.compute(input_frame) objective_total += score * self.weights[i] - # take the overall sum of geospatial terms return objective_total def run(self): @@ -92,29 +105,43 @@ def run(self): last_checkpoint_score = 0 T = self.T - for i in range(1, self.num_iterations): + progress_iter = tqdm( + range(1, self.num_iterations), + desc="Simulated Annealing Optimization", + leave=True, + disable=not self.verbose, + ) + + for i in progress_iter: perturbed_frame = self.perturbation(latest_frame) new_score = self.compute_objective(perturbed_frame) # take the new result if it's better, or randomly take a worse result with decaying probability if (new_score > best_score) or math.exp( (new_score - best_score) / T - ) > random.random(): + ) > self._rng.random(): latest_frame = perturbed_frame if new_score > best_score: best_score = new_score best_solution = perturbed_frame latest_frame = perturbed_frame T = T * self.cooling_rate - if i % 200 == 0: + + # logging + if self.verbose and i % self.log_interval == 0: + progress_iter.set_postfix(best_score=best_score) LOGGER.info( "Iteration %s / %s, best_score=%s", i, self.num_iterations, best_score, ) + + # early stopping + if i % self.patience == 0: if last_checkpoint_score == best_score: LOGGER.warning( - "No improvement found in last 200 iterations. Exiting early." + "No improvement found in last %s iterations. Exiting early.", + self.patience, ) break last_checkpoint_score = best_score diff --git a/docs/features/optimization.ipynb b/docs/features/optimization.ipynb index 4aa8891..72e435c 100644 --- a/docs/features/optimization.ipynb +++ b/docs/features/optimization.ipynb @@ -34,26 +34,24 @@ "2. objective terms,\n", "3. optional constraints,\n", "4. an optimization algorithm (`SimulatedAnnealing` in this example),\n", - "5. algorithm specific hyperparameters like `num_iterations`." + "5. algorithm specific hyperparameters like `num_iterations`,`verbose`, `random_state`" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "id": "7a7bde49", "metadata": {}, "outputs": [], "source": [ - "from copy import deepcopy\n", - "\n", "import numpy as np\n", "import pandas as pd\n", - "from kloppy import skillcorner\n", + "import os\n", + "from copy import deepcopy\n", "from matplotlib.colors import LinearSegmentedColormap\n", "\n", - "from databallpy import get_game_from_kloppy\n", + "from databallpy import get_saved_game\n", "from databallpy.features.pitch_control import get_pitch_control_single_frame\n", - "\n", "from databallpy.optimization.constraints import TTIConstraint\n", "from databallpy.optimization.objectives import (\n", " PressureObjective,\n", @@ -80,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "id": "a57bedd0", "metadata": {}, "outputs": [ @@ -88,13 +86,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/amar/Documents/databallpy/databallpy/utils/get_game.py:921: UserWarning: All frames in 'tracking_dataset' are 'ALIVE', databallpy expects 'DEAD' frames as well (e.g. for more accurate event synchronization). Set `only_alive=False` in your kloppy `.load_tracking()` call to include 'DEAD' frames.\n", - " warnings.warn(\n" + "2026-06-13 21:37:41 - /Users/amar/Documents/databallpy/databallpy/utils/get_game.py - INFO - Trying to run the function `get_saved_game`\n", + "2026-06-13 21:37:41 - /Users/amar/Documents/databallpy/databallpy/utils/get_game.py - INFO - Successfully ran the function `get_saved_game`\n", + "2026-06-13 21:37:41 - /Users/amar/Documents/databallpy/databallpy/features/differentiate.py - INFO - Trying to run the function `_differentiate`\n", + "2026-06-13 21:37:41 - /Users/amar/Documents/databallpy/databallpy/features/differentiate.py - INFO - Successfully ran the function `_differentiate`\n", + "2026-06-13 21:37:41 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Trying to run the function `plot_tracking_data`\n", + "2026-06-13 21:37:41 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Trying to run the function `_pre_check_plot_td_inputs`\n", + "2026-06-13 21:37:41 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Successfully ran the function `_pre_check_plot_td_inputs`\n", + "2026-06-13 21:37:41 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Trying to run the function `plot_soccer_pitch`\n", + "2026-06-13 21:37:41 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Successfully ran the function `plot_soccer_pitch`\n", + "2026-06-13 21:37:41 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Successfully ran the function `plot_tracking_data`\n" ] }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -104,28 +110,12 @@ } ], "source": [ - "match_id = 1886347\n", - "tracking_data_github_url = (\n", - " \"https://media.githubusercontent.com/media/SkillCorner/opendata/master/data/\"\n", - " f\"matches/{match_id}/{match_id}_tracking_extrapolated.jsonl\"\n", - ")\n", - "meta_data_github_url = (\n", - " \"https://raw.githubusercontent.com/SkillCorner/opendata/master/data/\"\n", - " f\"matches/{match_id}/{match_id}_match.json\"\n", - ")\n", - "\n", - "dataset = skillcorner.load(\n", - " meta_data=meta_data_github_url,\n", - " raw_data=tracking_data_github_url,\n", - " coordinates=\"skillcorner\",\n", - " limit=500,\n", - ")\n", + "game = get_saved_game(\"synced_game\", os.path.join(os.getcwd(), \"../saved_games\"))\n", "\n", - "game = get_game_from_kloppy(dataset)\n", "game.tracking_data.add_velocity(game.get_column_ids() + [\"ball\"], allow_overwrite=True)\n", "game.tracking_data.add_individual_player_possession()\n", "\n", - "selected_frame_idx = 210\n", + "selected_frame_idx = 1800\n", "frame = game.tracking_data[game.tracking_data[\"frame\"] == selected_frame_idx].iloc[0]\n", "# frame id is not the same as index\n", "idx_relative_to_game = game.tracking_data[\n", @@ -157,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 20, "id": "4acd2909", "metadata": {}, "outputs": [], @@ -183,20 +173,38 @@ "source": [ "## Run simulated annealing\n", "\n", - "Now we call the optimization API with `SimulatedAnnealing` as the algorithm backend. Internally this performs repeated perturbations and probabilistic acceptance steps to search for a strong tactical configuration." + "Now we call the optimization API with `SimulatedAnnealing` as the algorithm backend." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 21, "id": "2154446d", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-06-13 21:37:41 - databallpy.optimization.optimization - INFO - Running optimization with SimulatedAnnealing\n", + "Simulated Annealing Optimization: 10%|▉ | 98/999 [00:01<00:13, 68.25it/s, best_score=137]2026-06-13 21:37:42 - databallpy.optimization.simulated_annealing - INFO - Iteration 100 / 1000, best_score=136.71605449926656\n", + "Simulated Annealing Optimization: 19%|█▉ | 192/999 [00:03<00:11, 69.92it/s, best_score=139]2026-06-13 21:37:44 - databallpy.optimization.simulated_annealing - INFO - Iteration 200 / 1000, best_score=139.3815399502609\n", + "Simulated Annealing Optimization: 29%|██▉ | 292/999 [00:04<00:11, 63.47it/s, best_score=142]2026-06-13 21:37:45 - databallpy.optimization.simulated_annealing - INFO - Iteration 300 / 1000, best_score=141.71543917195638\n", + "Simulated Annealing Optimization: 39%|███▉ | 393/999 [00:06<00:09, 63.58it/s, best_score=144]2026-06-13 21:37:47 - databallpy.optimization.simulated_annealing - INFO - Iteration 400 / 1000, best_score=144.06175354704402\n", + "Simulated Annealing Optimization: 50%|████▉ | 497/999 [00:07<00:07, 69.73it/s, best_score=147]2026-06-13 21:37:48 - databallpy.optimization.simulated_annealing - INFO - Iteration 500 / 1000, best_score=146.96234229585576\n", + "Simulated Annealing Optimization: 59%|█████▉ | 593/999 [00:09<00:06, 65.80it/s, best_score=149]2026-06-13 21:37:50 - databallpy.optimization.simulated_annealing - INFO - Iteration 600 / 1000, best_score=149.410165922906\n", + "Simulated Annealing Optimization: 70%|██████▉ | 697/999 [00:10<00:04, 70.88it/s, best_score=152]2026-06-13 21:37:51 - databallpy.optimization.simulated_annealing - INFO - Iteration 700 / 1000, best_score=151.543930973152\n", + "Simulated Annealing Optimization: 79%|███████▉ | 793/999 [00:11<00:03, 68.10it/s, best_score=153]2026-06-13 21:37:53 - databallpy.optimization.simulated_annealing - INFO - Iteration 800 / 1000, best_score=153.490574284253\n", + "Simulated Annealing Optimization: 90%|████████▉ | 897/999 [00:13<00:01, 62.26it/s, best_score=155]2026-06-13 21:37:54 - databallpy.optimization.simulated_annealing - INFO - Iteration 900 / 1000, best_score=155.49460476140783\n", + "Simulated Annealing Optimization: 100%|██████████| 999/999 [00:14<00:00, 66.94it/s, best_score=155]\n", + "2026-06-13 21:37:56 - databallpy.optimization.simulated_annealing - INFO - Finished simulated annealing. Best score=157.83342375384015\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Best objective value: 142.12329025559984\n" + "Best objective value: 157.83342375384015\n" ] } ], @@ -209,6 +217,10 @@ " constraints=constraints,\n", " algorithm=SimulatedAnnealing,\n", " num_iterations=1000,\n", + " random_state=42,\n", + " verbose=True,\n", + " log_interval=100, # log best score every 100 iterations\n", + " patience=200, # stop if no improvement found in first 200 iterations\n", ")\n", "\n", "print(\"Best objective value:\", result.best_result)" @@ -226,15 +238,21 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 22, "id": "2c646b8d", "metadata": {}, "outputs": [], "source": [ "def diff_frames(game_1, frame_idx_1, game_2, frame_idx_2):\n", - " frame_1 = game_1.tracking_data[game_1.tracking_data[\"frame\"] == frame_idx_1].iloc[0]\n", - " frame_2 = game_2.tracking_data[game_2.tracking_data[\"frame\"] == frame_idx_2].iloc[0]\n", + " frame_1 = game_1.tracking_data.loc[[frame_idx_1]].iloc[0]\n", "\n", + " frame_2 = game_2.tracking_data.loc[[frame_idx_2]].iloc[0]\n", + " pitch_control_1 = get_pitch_control_single_frame(\n", + " frame_1,\n", + " game_1.pitch_dimensions,\n", + " n_x_bins=106,\n", + " n_y_bins=68,\n", + " )\n", " pitch_control_2 = get_pitch_control_single_frame(\n", " frame_2,\n", " game_2.pitch_dimensions,\n", @@ -246,16 +264,10 @@ " )\n", "\n", " fig, ax = plot_soccer_pitch(field_dimen=game.pitch_dimensions, pitch_color=\"white\")\n", - " idx_relative_to_game_1 = game_1.tracking_data[\n", - " game_1.tracking_data[\"frame\"] == frame_idx_1\n", - " ].index[0]\n", - " idx_relative_to_game_2 = game_2.tracking_data[\n", - " game_2.tracking_data[\"frame\"] == frame_idx_2\n", - " ].index[0]\n", "\n", " fig, ax = plot_tracking_data(\n", " game_1,\n", - " idx_relative_to_game_1,\n", + " frame_idx_1,\n", " team_colors=[\"green\", \"red\"],\n", " ax=ax,\n", " fig=fig,\n", @@ -264,11 +276,11 @@ " )\n", " fig, ax = plot_tracking_data(\n", " game_2,\n", - " idx_relative_to_game_2,\n", + " frame_idx_2,\n", " team_colors=[(0.0, 1.0, 0.0, 0.5), (1.0, 0.0, 0.0, 0.5)],\n", " ax=ax,\n", " fig=fig,\n", - " heatmap_overlay=pitch_control_2,\n", + " heatmap_overlay=pitch_control_2 - pitch_control_1,\n", " overlay_cmap=cmap_red_green,\n", " add_velocities=True,\n", " )\n", @@ -277,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 23, "id": "f8013a7f", "metadata": {}, "outputs": [ @@ -285,10 +297,20 @@ "name": "stderr", "output_type": "stream", "text": [ + "2026-06-13 21:37:56 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Trying to run the function `plot_soccer_pitch`\n", + "2026-06-13 21:37:56 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Successfully ran the function `plot_soccer_pitch`\n", + "2026-06-13 21:37:56 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Trying to run the function `plot_tracking_data`\n", + "2026-06-13 21:37:56 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Trying to run the function `_pre_check_plot_td_inputs`\n", + "2026-06-13 21:37:56 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Successfully ran the function `_pre_check_plot_td_inputs`\n", + "2026-06-13 21:37:56 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Successfully ran the function `plot_tracking_data`\n", + "2026-06-13 21:37:56 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Trying to run the function `plot_tracking_data`\n", + "2026-06-13 21:37:56 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Trying to run the function `_pre_check_plot_td_inputs`\n", + "2026-06-13 21:37:56 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Successfully ran the function `_pre_check_plot_td_inputs`\n", "/Users/amar/Documents/databallpy/databallpy/visualize.py:874: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", " fig_obj = ax.scatter(\n", "/Users/amar/Documents/databallpy/databallpy/visualize.py:874: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - " fig_obj = ax.scatter(\n" + " fig_obj = ax.scatter(\n", + "2026-06-13 21:37:56 - /Users/amar/Documents/databallpy/databallpy/visualize.py - INFO - Successfully ran the function `plot_tracking_data`\n" ] }, { @@ -297,13 +319,13 @@ "(
, )" ] }, - "execution_count": 12, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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7lEODdWIVjOd0zwmf7zh0R9SYLl8QVWA+ZVxlIup9Ru0DwwvDYU7PnDDXlS9ExV6z5azgVVzUaa/he8GRGyRJ7ixdYdOdIbwe0nkIdBYdE/tJJBKJRCKRSPkXyxeVFTBiADZ7NsCECQC77AKw++5qgGX5oP9PM00jK3rEcmVZnww0RceUhQIz7cQZQHvsERVcYlWMRfeWjYtpiy0qy972tgjWWZ6t2J7l3Ipi/V97bVT46YtfTK678cZKCLRumiFWwZjtS+YADx4sLygVj5+diz33jKo1s3DqZpLPSWfzqlEzRwVwj92kzMu6lgUPL39Yuu7bL3477G/czHGJ5bNXzw6Xv+ORd1Rtc9vi24K+Ul/V8l/N+1VQuKcQdN7TGSzetDix7u2PvD3s72vPf628jPVx8EMHh8t/POfHifa3LLqlqv/NfZuD9z/x/rD9DvfvULV+adfSqmU/n/vzcExb3rtluD2vs548K+zrlFmnJJaz12z5eU+fF2DE2o6fOR7VlkQikUgkEomUE40axTxVP339859RX8ceW71u/vwgmDo1Wv+FL+D6O+aYqP0ZZySXX3RRtHybbZLLr702Wj5pUhAsWlRZPm9eEAwfHgSFQhA88khl+Xe+E7Xfddcg2MxdI99zTxC0tETrzj67snzx4iDo6AiCtrZkP5s2BcH220ft2flUacqUqM1ttwXWesc7om1vuCFoJGG5k5xYhRZ2LYQDnj0AtixuGU4dM6l9Upif+nz38/BW31uhi/inbftj1BH67NzPwuKXFsOO7TvCpLZJ0Bf0wQvdL8C83nlhWPDFW1wMEwZNSGzzpxl/gn1n7Qs/XvpjuGf1PbDloC3h8Q2Pw6t9r8Ke7XvCl7f9cqL9cS8dB1NengJbd2wd7mNV7yp4quspWFZaBiMKI+Cqna+qGtcuj+4CY4tjw20GFQfB7E2zYU7vHBhTGAM37HZD1Ry0v9rpV3D3I3fD/639P3j+wedh586d4blNz8FzPc+F5+riGVSBmEQikUgkEomUUmxeVxaiy1xaFjbLVxaO9eMfA0ycWHnNcm5ZSDIrhsTCktncrWyqnSefjMJtWRViXiedFFUgZm7pjjsCHHhg5Hw++GBUAIrNycpXFWbzvbIiSs88A7DlltF8rKtWRU4rC2eOCznFYmNn+bvf+U5UdIkVeGIhxmw6IDZVThzirAqxZg40mzaHFaciJUQQq9C0zmnhlDiPbngUnux+EjZ0bQhhc2xhLJw09CT4wXY/gB2H40JzmU4eczLcuPJGeKn7JXii+wkIIICRhZFwVOdR8L1tvgcHjDmgaptth20Lj+31GHzihU/Ao5seDXNwWbjuR0Z9BH6z828SIcZM7xv2Ppi9cTY8uelJ2LhpYzje8cXxcMaIM+AnO/wEJndWJ5UfO/xYuGPtHXDrhluhBKUQXln7X8z4hbRoFAshnrXvLPj4cx+Hmetnwgs9L4Qhxmzff9j5D2FFZBKJRCKRSCQSKZXYlDHx1DmsSJRMLESXh1gGfAwkP/nJCBTnzo1CbhlAsil5WHivqP/8J6qszOaJveOOStgwy0H9zGeSbVtaomJVrJgSg0y2LSsQxXJ1GTCfdlp1/2y+WRbOy+Z6ZdWLWX4rg+W//CXaRqW4WrKsTxIUmB1rOg9r166FESNGwJo1a2B4nKBMIpFIJBKJRCKRSCSSJ2G5c0BPsUMikUgkEolEIpFIpMYSQSyJRCKRSCQSiUQikRpGBLEkEolEIpFIJBKJRGoYEcSSSCQSiUQikUgkEqlhRBBLIpFIJBKJRCKRSKSGUUNMsXP33XfDokWLoKWlBU466STo6Oio95BIJBKJRCKRSCQSqSk0b948ePzxx6Gvrw8OOOAA2HrrrSHPyg3EDhkyBLq6ukJQHT9+fGLd8uXLobu7O3w+adIkKBbJQCaRSCRSvrV06dLwYkD2u0YikUgkUp60YcMGWL16dfh85MiRIZupftcGDRoUtq+ncjNPLPuRL5VKmfRNIpFIJBKJRCKRSKT0YoYig9kshOXO3DixMcSykzJp0sTEukWLFofrCoUCTJ48qW5jJJFIpIEq8+3Oqi0gfwoyGKp648VLlpV/1yZMGOfQTRbnMPC0i6AGn6Mgrx9ukkyFQgbnpeB5l4UUzX0fn9CfoXvc3lOOUbl5Fu9tVirk72PcIOrq6oIVK1aGz5kLO3LkiKo2MZMxbqu3cgOxLNRqwYIFIcC++urL5eUPPfQwvOMdR4XPjzzyCPjf/66v4yhJJJKrioX6f+FlqVKQzR1JHwqCUu37QETWmPrUrXdZp9xGMlZZW+yyuL+ddj8EFi5aEgLs80/dK26YfFnqM/ZdtS9h3NKxiH1gtnFogxmfsi9FWxVcys6Vsg/MZ9f0WbX47Ivnt1FUwKRpFQxtFH0UZNtJ2haKLWaiELar6jvD9VXnqJBuX1WvxeMXjh1zHmVtsMtk/ZnaK/sxrMOst21XVsqUQ+v9Nci+MFq1ahVMmbJl6LCOHj0KXnzxWWhra0u0mT59O1iwYGEuUmTydfYEsZP4zW9+u/z61FM/UNfxkEgkEolEIpFIJFKzadSoUXDMMUeHz996az787nd/gDyrmGeA/fSnPxc6sUysQtYHPvD+eg+LRCI5qNld2Lwrb3d7cyXfzpmn/ny45zYOotX4bRxW1302mAvLHFgvLizrw/WRQqjxs3OhOx8274HsM6R6bzXbWUcnpDhPjeqyk0g2+vrXv1J+/s1vXgD//e9NkFfl7sqG1Zl68MGH4Mgjj4XLL/9LuKy1tRX+/OdLoL29vd7DI5FIlvA6UAB2IB1rsytVKDFuB9YX79YX67LxOgCAcxvHduG5kZyf+CHdXrEvJTzpwC8GNQWwxbAnPpSqIYj62A/q2DTnR9U/+nMivs+Sz4NPkK0al2HbxPkQzoHtOKr7Fj7f4t+BY+i/1XeX5c0g15QPzHrbdiQ/2nfffeDzn/9s+Ly3txdOPvlU+MIXvgTz5y+AvCkXObG33347rFixIny+ePESOPzwyMqOAfbyy/8EBx10YB1HSCKRbDSQYY4/9jznyTarXC62vAkDIbXIhTXkwTr16TMHFum8SuHexXFVvS+mi2wsVKaBz6w+l6boC9WYJfmE4nlI5Iby4+f3yW/DtY/fp4KsLd+u/70v54nGn484T1TYpqpfzHphXLpt+fGy81F1Dgryttr9SPvuS+bGsuPmcmOr+pdI1ga7zEW6fkz78DWGhIRzbqtMxpSDfdnohz+8EBYvXgz/+tfVobn4+9//IXxsu+224bSnTFlVJrZRLs7cwoULw4pYTPw0OyyE+Pbbb4ZTTqFcWBKpEURuZH7PR9ofyjz+0KYWEj5cXViXmrdZAyzWXa2VQ6tzXk3bKh1Xneto4bJKZeOeiq6u7JGVXPeNOD7lOVL1LelL+t4pnNnkAo+urKVjmxyX+u+u5o4s9uaRROibTHV2RJvZjc3jsbW0tMAVV1wKP/zhRdDZ2VlePnfuXOju3hw+ZzPG1Fu5cGInT55cfs6mIjjqqCPD/Ff2oBBiEqkxlBdYy+u5IVe2yS8oPLiwtQBY05hr5dCmcV5tQyFVwGZ0WlHvqdvnJeirzYVroaVoP3bxhpV4HhKOacnNodW5s6IT6sOVdXRd+W2NjiwnW0e2Fu5jlq4fubHNp2KxCOef/3k444zT4NJLL4ObbroFXnppDmzcuLG8vt4qBMwn9jTprKvYCZk+fTosWbIEpkyZnJhih0Qi5VsEr3aqN8ymuetrtS0CAFzzqWzX2UAPZntsbtpOexwGixYtgUmTJsDzT87MNow4JcDWCnBR8GrjBPkEV9Nn1uLzXytIzRRyy400bVRTssiWO04RU9VGNxWNxRQ6vtYljtVi2p3cT7nT6NPtNNBUO/XYn4sYMm611bbhXLFTpkyB+fPnQxbCcmcunNjBgweHua8kEqmxRADrds7qDbLNqrSQ7Ro2jCmuYlJqFzZDgHUaiyxs2AFeMwXXlNDqBKr1Ch1U5SxKjqEKbMUxY3JfZQ5t3I/GmVXmzBaReaMpnFXXdQlHlsuPlfbDqV75sVnlljabG0uqFgshzoMDG4vIkUQiOYkANv25I5gdmPlLWYcRp+rLt4ObNbz6BNcU0LqwuBz+OPZmeHz0S7C+tQuG9g6CfVduDx9d/k6Y3DcGnKWARGvpjk0s+GMCW9tiTv3Lq2CW3962UBMmxBgRXmxa5x1ksy705BhWbAOR9SxG1MxFl/Ja5CnPIoglkUhWInhtbJhlP5J5LCTRsOHTHlzYrPJg8wawWcOrNbjq4B/jsgYl2Fjogs9ucQnc9banoVSIQYqtA3h+/JtwZXAnHPH6HvDrNz8Bg0ttUItCZEqp4EbntErOhxXQ6mCW3961qrDJldXlwiLW5QFkrYXIv0WDrAKKVWMkN5ZUSxHyk0gk/BcGFW8a0Oe1me8S1wrsTS6sdtsaASxbV1XF1WK9WHFYWW1YGI90TKoKuIlFkkq5quq6muq8DNTiR/XK6uq+DGDfveN34Y63PQVTXpsKe71zLxgxegRMmDIBDv3poSHMlooB3LHlk3DijO/ARuiu7lfcDVe11+aBEn9OVOdHcayo8yQ7r5L9KCsbi9sJ56WqT93csqq2Yj+6fciWm9bpjknRh6kfp/ljxT4G0I1Ln6r1eaP3yU7Ne0VCIpGacqqYZlUtz3Ezw2iWjhaqcFGK/qz6NxRy0vVjdVGehfuqA2JJGxlMSad5sQRXHsaUQKYAXraPz067BOaOXQiFoADt726HoXsOhfVL10Pn3Z0w77fz4ICrDojaFgFeHrcQPrvlH/zAqESpYNcWbBXn0NTWCLPiNpibJ4khKkBWaJslyKaZekc5DheQRXw3yT4P6EgIDXBpgdwUsk9ATrLQAL+SIZFIJhG81lZNA7I5Kv7gIhegMF6gyaaQ8eLU4C7EvQKsrfuaBl6xrmuyoZuLaHIT+x8LW1bAXVs+HV5FbTVnK3h9zuvw4HcehL62Pnh9+9dh249sCxv/FE1FEaoAYftFLauMfad+aISGW12fhnMrbSvrOwXMqvsSQFZ0ZVV91AtkVfvNwJVL1V/KG3Z1leebjVmL3Fi8Gvsqg0QiZSoC2Pooz873gHdxPcnmglUXRmysRKxbZ3GBrrvot8199QKvJsCyBVfZeBRQyMb/x7G3lHNgC6UoB5M5spU+AN545o1kt4UA/jjuZjRwOssBcq2gNrGhxCXXubNin4mu1J9zq8+rzrXEwjAWZBO70YCsol2mYcU+3ViL7U19+IA0Aj0SE0EsiUSSKq8QNZCU9XtAQJoihM108WkCO4u+hY6c+sliXX8Dd/c1Dby6gJSibaJfBbSKgPfYmMp89sx5nbLlFDjw2wdCW3cbTH9+Orx8+cuwbu26qr4eG/1ydg4sFo4R7bVQ63ADQdnGxZUVxihdZ8qT5fpQCQWyOlgVj0MlLPAiQNbUdyporZEbmwmgkhvblCKIJZFI1V8MBLC5UZ5d2Typke7M+3A6wnUZQ6oWYCXhw86hyFEH7vDKL8KCqwbiMAC3obUrqkIMEIYQt97QCuueWgfDpgyD1jNaYbsPbQejxoxKblsA2NDWVRsYsIVcC6iVblcLmFW11TmbtiBr+tzaLNeArM3fna/vCxuRG0tqBNEUOyQSqSyCpXy/N1lMxeMy5U6jTNNjU6TEuJ3djoXXFvvSQSQyXLEmAJsYluC+8uuMTq7mol/WXraNqpqwKMU5077fim2G9HZE72s/yL6606sAt0fPV8JKmPTVSbDNYdvAcljO7QhgSE9HzRwtbW66bAx8W3G9MGVLrKp5XPm2cTt++pj+96o8TY/YhvUjTMtTNb+saSocyZyyiflkFXPJqqbASQixTWK5KHYMBXM77bqUc8fK+taOmZfu3HgUejw2qtHYc30Omkx0dkgkUvRlQG7fgH2PMvuhNFwwNMoPtHUosU1fmn6TG9qHPer6RwNsVcivIXxYNy5NmKjRedWEDNsWEorHZgyVlQF1/2Pf5dsmVm3zzDbQuaETWje3wr7X7gvPXf4cLP8WB7D9EreTKna5dQ+sbMKNsU5tYqiOBbpk75urK4sML+YaKrc3LheE2cY1P1bZThb5UIsUCoe2jXCzE6tmOpZmUWNcQZBIpExFANs4GijvVaMAbhq5Fj/RhhGr+nAF2GSHqPBhcyEojfuKCTXGTOkigS7rHE8N+H10ydFQ5Ao5Tbl6CgzaYhC0j2qHjRdvhEnXT4J5u85LbFMsFeBjC4/yA6gY0NX1ZwJbzLqUMIsKMeb3gQDZeCyZgizSSVeCLPJGllVYcUZFnrKUaX++QbsR1ezHl1YUTkwiDXANFChqxvfMZ3ixbYhwo4QU11w2LpmDC+sURuxjGwuAVa2zDh22ce9UfaguBHVupErcusmbR8IRr+4Kd0yfHdoB9150L8BF0brn4LnqbQOAI17dBSb1CXmy4XGl/zsutLTYfya5EFRlSLC4ThVyzIXSRl1L2inCgqUhxsrQYCG8WNVOFeqbIrTYdrlWWYQVZxCuKm2jOEZVf7kKi00ZUpyrYyGRE0siDWQRwDa2fL9/mfw4N1AOkouyKLri1M63O5vcqPLUUH1Ytc4pdDh+iZ2uxeS6YlxH1XIJ1P9q7odg22WTwul0tAoAtl0yCX455+wQWMWHD8n6Ne5D59iqjh3pziqdWexNCl20gM7VxLiqGTiyacOKVccgaqC5sbXqI89q9uNLo+a+uiCRSEoRwDaH6lm92Af01vuudqYXCDYFnZQNHS6Cebnk/CUgQQOw/DBVLq4k91W9L4dwUyw8uYCrAfgGBx1ww5NfhaNe2TUMFQ7f7/h09T9ny4+cuwtc/8SXwvbJPkt+HxqhwFYXgowMKza+H4gbFqp1WpC1hcuU0+/YCBNW7J5aUKfcWJfvIgcRwNF50InCiUmkASgC2OaTr+rFjRomXLNxZ3Hx5tCnL6dIOxYfAMu3Ex295EonV046BkUb9LKoQ/ny8vii89EJrfCnlz4Ki+atgj9NvgseGzcvnEZnSM8g2Hfp1vDRhYfDpN44hDjjz6fu8y8L8xRANhGSzB8/H3YsCznWhRqrwoxlIcbY8GJZaLGuHSa02KSswoo5YbfXhrRaVir2LZeQ4pqH6FJIcdOIIJZEGmAigG1e1QNkUW3ZRVON7tynlVUxleqN0f2i2ikcHJ+wrgZQM8Bi81+N7qtN3qsOoCXrrZbp3j9d2G9Qgkk9I+A7b7wX4A11M+2+fUkFKLLPjDjNCneMVkBrCbNKqJLkymYCsjJlnB/LL1fm9YpCjt93bqxsXza5sb7lnLvbZBoIx2grglgSaQCJALb5ldV8svVQo7rCJrlUOlUqgzy+zABW4756h9eU4GqCVqPqddNGt18ROMTjSMznagBaF5h1dGUxIBsNX99OOpYMCj1hpAJZLKjk2Y1ViSCM5FsEsSTSABEB7MCRD5D17sZmuH295DtXzdqFzQPA6sBUBbApQoe9wKstuOo+mzawWiuwVQGKuH8d1JqAVubOmmDW1pXFgCzfn8Hh9Fax2CAU9GLl6sYKIGurNG6sy3FSSHFOz1PORWeCRBoAIoAdePJR8Mnrj6XHO/+N9yPuOO1OLVRHgJUWbnItEKR6LR6fWMFVVehIVTCJ71cFpWIbXVuTXLbD7l+3XnH88nMlnFfMe4OZUzhtwSfF/qwKPcnk6eaRS5Enb5XRTTeHcibnAlRNqIFynBiRE0siNbkIYAe20rqyWJc0j25qHsfkLZTYlwvrYf+uAKvaXnuBbXJekSHD8uq8GihVCfseenqvM3ViZWHAklxV/vxVhRqLYcaKkGJxnTI0V+XIYuSj0JNtWHEWcnVjUyrNcbkUeCKRbEWfJBKpiUUAS6rl58B4caJxY1XbNswFj8m98R1KbKOs8mAN403tqulcOpl7KAsb5o9L5rpiHFfVcoy7mdaFddkeu3/MMfLCOLMYV1axzsmRtZlH1uZvSTHtjlF5cmNN88ba9KVrk+YGjeX+vSrluOt5gzRvN2frJXJiSaQmFQEsSfZ5cHFl8+hoklK6rWkAVnXh7QKwWbivkpDhKtlcjNsuT9s2zfZpXVhxncydVTizaFdW49BaO7KYqsU+Cj2VD6uJ3FjZucixqEqx/flodg3soyeRSKQBJtebG9gfyzRubNrx5OUH3aWoU2ZhyBZgmweAtXJfxdcK5zWxT5nza+NG2ribaZxY1+2x+8esMy0TziXKlVX1V0tH1rQfmRxvEOXNjbVVmpuXdOOTlLXIiSWRmlDkwpJMn488OrKq/pvdCcaEEhvlwYU1jq0GACvtR3xt67xiXVfsMsw6l3a1cmJtXVh+OcKZ1bqyGhc2C0fWxdXEVCs29pWxy+nqwpmm23F2Oy2Ot5kcxHofS9BE59JFA/fISaQmFQFsTsQufPjHAPqc5PpHVbjQshqrzxwq1758OEKVhUYX1ivAGvJfbR07tPMqHoesf4zzqGsvW+/iwqZ1cm1dWNvj1rUzubK6vjJyZDH5sdJ9G6IpbP6WvbmxyM9BFpEgtVKj5cWS6ityYkmkJhIBbI3kAqWYbVKGftUiT9aLK8pAki4e0EoLq6YLYatCThYAa+2+iv0g3Vej81qqoxPr63Oexol1qUqsc2d1ObO8Iyq6svF7JnM2s3Bk4zE4zh/LNdC7sTZ91cORy2DO2Fps69rfQHMngwF2vLwG5lGTSE0oAtgmcFXr5N7afnYwP5iuP6o2VYqzHEe9ZQoltgoZlneg3UYKvlkCLO/OWbqvif2o+lQtkzmSWBdTt87GQVW5r7VwYm2Wq17Hy2xyZWXb+XJkZX2YnFfZzR/D3LH1dmN13wE6N9bHnLFpqxQ3U3pIHo4lyMEY6qHG/HUnkUikgRAKXMNx1BxkM3Io0kJrPcZcVsoLERewNYYeSrcxQ4WpL234sGr/ptBh3bixIOYKfSbHNi2gpukDA7SY5bavsQWH0oCstG9DW4uwYqkcgZWEE4UUuykYgCBLEEsiNYHIhfWgPEBrnaE2LyDbqO5p5nK44DblwrqEETvlKdoCrAhRru6rK7zaupWy9bYuahph9+sCs7p2ste8BEfWG8hK+zf8LTj8rVjlxtYBMhJObk6rFJNqp2CAvU90pUAiNbgIYJscXGs8/ixA1peaMaTYOO2Ny0UJEnZlF+jm/FkLkLDJz3UNH+ZlE/Kqa2MDrjbAyk/x4+uhE3bcrudE9brqBoQGZHV96qToP1M3VgXdkmWueew+CzzpZAopzjNYOYc7DxB4DHI0lqyV3192EolkFAGsoxoZXGtwTOxzZfPZMkFird3YWs1VWy8ZizelcGGly5D5b5g8WGuAxYQPm9zAtG6sbOwmaLUFTldh94EBWswy19cqkFU5hBg31uYzWE83tlZuvMcqxbUAxYEEW7VWMEDObWP/kpNIA1gEsA5qRnjNGGbrCbLYfrLMg82zi+tb6GrEji5YuX8HgE30rXNzXV6blumW82NycUhtHyZhxqGDWdMyzDlNA7KYz4ZMFkWevLmxMtlAcgZwm2VIMUp1AvYBO+4BDrID59eZRCINXA0EeM0o1LieN0uaFiBNF7qugGGxTOvSSNsbAJefCzZLgHWBJ6yr6AquKrmCqE62ferGmcaRNp3vLEC23FT1uTGAqm831qEfTFvl3LaKfdcKVmoJRY0IYHkcc5DDMflUk14hkEjNLXJhkRqI8JrBecB+3urpxpra2ABxM8CzbSixUzEn7PQmpn5cAFbXlwmoMIBrC66usOrDfZX1I5MPmK0HyEqWGXPJXdxYmZBubC1Cil2AJOupdtDjaHKYyrOCJj73jf9LTSKRSDIRvHo9JzUB2RTtrcGzwfNi01yk+HZhdcukrpkvgNUBKybsFQNuWHBVKQtAtd2vKNUxpQVX8TXy+LTFnjB9+nJjXUDToZ+6hhTnbF9ZjrWZ4c1WQZOei+b5FSeRBojIhTWI3NfMzo8vkM3jlDuN4r5aX4yYQEe5Iz8uLHqZC8Cq+nIBLxW8ysasA0gXSE0jG6B1fZ8x7rbqtfKGhWo8jmHFnt1YlwJPPkOKM9tPk+TFNiuUZX3OgiY7b43xq00ikUIRwGpE8FqTnFls5WLnYklIhxRT4ClNSDFmfIn+MnD+vQKr5uJVtx9rF9YU8mlyqWwBNo0bKxuPTcituM7m4t3k0KZ1bHXrRTm8T8bzqFqnAFmjG2valwM4pco9zzikuFZT7ZDA6/lsBEgMGmCMWBHEkkgNIgJYjSh0uObnL1OQRbZPDaUaYOb79u7S5jWU2dK1lTpd8Tqbyq8ywMYCrKpPV/dVNk7V+DGAmQZKdf1g26vWuTqy2NdpgMCUG5toanmTpVZyGXcjpCLkZNykdGoWVzanv6QkEomEFAGsHzm6spmAbArIS+PG+oLVQrF+FZ3NzirC8dJO04FzYbVggwkj1m3vO5wYmx+qW17rcGLsfmyOwwfI6sYhe25zIW3rDtcqpDjnjmia+WJJza2gwUGWIJZEagCRCysRhQ/n4rxiwot9gWyt3dhcK+2FM2J73VQkaBdWtz9MHixGaQEWM1bf4cRM7ByKD1dhYBazXAX0mL7EdZbnQ1mp2LSfnKsWlYu9SRhLPSGnUQGr0cYd9LuyjTZuptZ6D4BEIulFANv47mvWRYMy+fGJzzGyEAj7nJaCPu05UI1Tt86lrdjGpn9Unwx883ThmQZUMedF149pnlTEMqc8WJvnaeFVJuz7bwumpvYthu+SeFyymzOqdWy5uIydH/57S9bGdp3qubgvm/77P8NV37GyPiXL2I2aAuZ8SM9RUPVbJB2LhdJu77sfn8rjmEjy34NGeZ8aY5QkEonUQADLfgD4R0Pvz9KV9erIpijy5GMcLvPR5lKYGxEx4CBCiXUhmi4urNh/zQFW5mK6hhMz+XBWMX3r+rfNicWE2LqGc/t0Y7MMKa6FPBR3UvVXrwrFeXbw8jy2PCtoEGe2AX59SaSBK3JhGyd8uJbQajMWL+OxOPdpQNZnWLFVZWJfIcV1+HxaTeFhkRunCyXmGqnX6UDJRx6sT4DFjl11TnyFBLvIFWZtXGdd35h1mOcuIbe+QdR3XqxhfK6Q0Ahw0bDH0wgRNnUKNQ5yCLb1v9oikUhSEcByyim85glcdfIKsxmCbBaFnmzHkKVy8zmxuVDDtHXJxRRlcnCx0KrbNybXE+vIMtlAK583a/vwBbMuyzDQj1lnIavpdvJepXigqgYwmDegapSxNZNy8otKIpFIjQGwjQKumTm0SFc2a5DN0o1VhRQntjeANbZCcdpqyTX7HCIuyrQXbqZcWE07tHRw61KsSDcWDLzagqipH4xsnWAfY6u1PO8TFXUwkIo7kUgNosa7CiORBoDIhc0fwDYquOqUCmgRMGuqXGwNso7tstpe2o/0nBTs5qjNccVkXVVi1AW5KRfW1J+LI2vjJmLapD0HrqoXcJKr1PDSpRKQa0hqVOX3l5JEIg1s5QRgmxFeZUoFswZ5A1lEfmxN3dh6ygPoprp4TVnV2Bgy6suR9d3eBLA+nNe0/afNy80jgNdajTZeEmkAKie/xiQSKRa5sPkA2IECr16OG+nK6vZZS5DFrjNKAb2VhZb7a8TPW1pX06ZdFi4sNow4TTXgLOQKslkXdMK0s9mf50q6qSQbt2R85GySSLVRA/5ikkjNKwLY+gPsQIVXL6HGdQRZG6V1Y+upTMbhAb6cL9xtCjphpKt4mwZgdfurl2uXR7cwSxecRGog0c2M7JWPX2USiUSqM8ASvHo6NwZXVpcnmwZka+nGGgs8SY4/9f5rkSfrI/fRNh/WpV8XR1bXn26ZbcVfG7HzET9clTZ3Fyvb6ZTqKWkRsZyNkUQipRZBLImUEw14F7bOAJvXCsJpHlmNDdlYu9oLyBra6F5n4cb6mAM3dZ95qdZqmw/rex5Q22JOWQIsD6sqcNWt8w2ymHNdDzB1uYmSN4D2IHLwSCSc8nPlRiKRBq7qBLD1dl+zBs+s+vfpyqbq32NYcSo3NsV+c12hOAaENA6tTb6qqb2v+WIxwOYDYNO4rD5c2maTb2BtQgAm5Ut0QyJb5eBXkkQiDWgXto4AW499ZumUuozBdRxW23sCWen+sgorNhVt0oyhfyN9/6p1OYgKKAN0/K+n4le5VV7HiJyTWasWTzeEkJEL1uK3dfk7Qy4ryM5DTt/3PEUGkUh5Fv2lkEh1FgFsbVVLgMwDtGLkC2hdL8hVebLo0OIUIJvGgVWBdvVCTYcOF9LlffRva3odn/dCscXQLr+fUaN8AwkW/nRyAdD478RmW9tjx7R3PZ/8dpjnDVCp3ufNqgGjnN4gIDWX6FNGIpHqozpcnNQaXhtRacAbDbMKqUAWBYuWIKtah3FjMYWmTPuyCSmugs1mUYP+jVhDGAZMXV1X3Vh8ubBp21uq0NKS3f5l3yVYhzeF6v17UL6Blcexpbh5ShrYok8AiVTPP8CBGkbcpADbyPCqkgvQpnVlsX2mAVn0OkNYMXqZ5iKyarsm+wx5VaODvAxqXb8PXQDWxYXNKpQYuw/M5tIIiAb6rDT655qkFOXFZif6qyGRSLVVEwJsM8Jr2uNM48rWC2RtlMaNreqjGS9gfYSj+lKez28WAGuzjU8X1jaU2GbfNXBLZXK9ITWQZPObQCL5FH2iSKQ6acC6sDVU1nA5UOA1jTtrbKdwZW3yZG1BVrWuVm6slauNzHvF5sVmrjwDo88x1vM4TftOk89bDxdWJU/h1eiiTjUuYFa3MNo85RmTSCnUSmePRCLVTDX88cwaXmuprPbnI8wpHpupL2M79tmQTCfCQLYU9FX1JfZTtYxdVHJTaPDrZdvbtlOuF/aLVv925T7Z+fLw/shAOij1Ve8vbb+e+rGS67luZLkCbC1dWFvxN4+yzIettXyOVfUbYFNFvcHk43jyck7Yd2NextJMojNKItXjD28gurBNALC1cl7TFFdKs5+04bVYZ1azUrrYlyOraos6bhc31lOBp5pIqFBcNc2OabtmlI8KxT6V9bm2cWGx/WQVSuwpH7bm8zE3898LiVRj0V8TiUTKXk0CsI0OrbUYC2Y7bRtNeLGsH+MyDXxiQolVYcXatpqpOLT5dGIosACU1iHEPhWPJQY7l89pfA5kbpvvnEfb8an2pQNZtk38yFKYfbBxqsJmxW1Vy3iZbgj5gFNBic+F6TfD5fOC/Uxg+85gep3E90mac+njBoThO5NEqrfo00gikbJVgwNsVmCZF2jNCmox2xhhFpEnK+ujFiBrdHJ1LrCQG5v5jRfsfLG+hXFzsecurbtngrZ4mWy5ChBl26YBW1kfpgrENvAaLzct8wWwmHZSF7WAfn9NLmyafFhdXruxP9MYLbZvuOl1LI6nbnnBdRBVKfavxvsUkEgNrgEZSlwDZQWaWfTZiD/AsdIArfX6FK5spiCrWa8NK9bc0FG6qq5ubBYhxSo31sEJdnJjVf1iAQnjSOr2o4NG1bhsHhiZxqA7Jszx686Z2B570wDx/hg/D6b9ygCXP0dSIHWEfFenuAnhDTuevI2b1ByiTxWJRGp4F9b3D6Rv0GwU1zXr48LArGJFvkBW04/2OKKVzpWKraQCS0xYcwbjEPefkKWTbVxvA7K6ZRgXtBbCwLMPeBUjDrAuq/g6DcBawKFTLqxvF9awjXHfiu0TgK3qV3EuMN8/eZeP78Vm+70lVYveYRKphhpQLmwDA6zPvmr9Q1ooFKwfeZp2x8aVVYUX+wZZqbChjRbr6uLGKmG3AdxYrCuIBdk0MGvr0vrqz3bcGIizBdYsANYmjDhLF9aknIcSW0kYy4DMh834/aCQYr+iKXZIJFLDKq8Am/WPvS/wxPQXSKa9sTl+0xQ1qjZsnXRbyVQ84jQ84rZVfXFTsyj3oxhLon1/P7L1Koc2mj4nOobydDdZSRxffNyK6Ywyl27/7Hzppivin5v6l7UX+1f1p1vOhJnSJ2uHFutCm5ZjHHCs46pbZwsGrmHEJmfWxYXNUSixi5oxH5ZEikWfIBKpRiIXFpreMc3Kec3COa3VvjHnRNVG68oK8uXI+siP5ZZKliWPQVnkKaUbi5GywJPgutqqqh/NmNBurGq9DWTJPgO27mu8rtYOmmm/tseCORc+AFazX69hxPwuDDcRXFxYl1BiIwxjxqf63rE4H1xD9Bhw3RUb+mZzvURurD819ieBRCLlTw0IsHnqp9JffaA1K6jFwix6W0V4cc1BVtKH9DhcQn1TyAfklvuyDCnWKk1urIvTZwJZ3fYYmM4CaPl+Tf27wCsmfNgXwNYqjNiTC6scu2msumWI/vP2O1RPUGx0SCXVTvRJIZFq8Yc2UHJhByDA+nRf8wquPqHWdL5M+bKShdo8WbE/nyAr60PbTlxXTzfWNN2Oqxur6kcDBM6VabFQK0KZCuR0MGgDtGkfNvtRrReFPWbTuROfY9elAVgTLCb61rc1ubBZF3TKbSixyYluMDc17yBMbqwf5ftdJpFIpIzkC2AHIrj6OJ4YLq3cV0+ubFUfjiAr60MbVqwDOU8gKz1mJaDKQTaVGyuTuG0CQASQ5WHLBV75faSBWRPQZuXC2u5Ltz4+PvFzKoNP1TLda8w6Yf9pAFb6WVcBrBRGDeuNzi3ehXUF24YOJdZ9B2W5X1/K6m+ZlJmosBOJlLHIhfWnvIRK+YLXLFUyFCuyUdHxePlj1BWIMhV4kq1jy6vaCwWDrAo+KYo9mZ7L+pCNrbxM16a/0FNV3+I27Jwo3l/lNorXmD7i/bEL7KAkWd5ShKCPG5fsGONtxH+5940BTtDX/37xfTGJ24jP4zamdbJl/OdbLP4kSlf0KWuZ9oO5kaBb5tJG55jawquiP0wIsRJg4z5UAGvYh9TNNDm3aYHTUUkYdndhjWkYA9hB9SlVAUASXnT2SCTSgAojrjfA+nZdGazKHj7lYx8Yh9bWmcW4slbhxRk7smp31e3zYD3lDsIJVru2RTtHVgcHGEeW70vcRuW22jiJqmUy97KWbqyqf+l7x41V5yjbuq6yZTbOrKv7KnlffQBsQqb1KoCV/I0anVtE24Howg70UGKSP5ETSyJlqAHjwmasZgBY3+CaB8nGgXFtTQ6tyZmVuZxV7S1c2SrHMDy4yl1y/o656L7qHFn5savc1eS0O1g3NnZHMU6vagxVU/6oXFsfjmz5DbF0ZMXzqnJbZa6s7LVpWbxvUaq/vazdWMx3j2oMGIcVu8z0GgOv4naK574AtgyHFjCoBFgT+FqAbT1c2CoZXNhayrTvZgwl5n9HSPais0YikXLtwuYBYHUOYS2c1yxdVt+yHavu/MTnHeXAyt5jhCur7NPgymIdWV0fKkc2dX4sL6w7KzqyjjI6sjJgUTiyiTxZcZusnFhb91PnhtrItl+TU2tarluGcWvF19w4E++dDALrAbDi8Zr2oxq7bDuZEPuoiQsryHY7VHsLACQXVi4q9OQmcmJJJFLTKy3Aum+bHl4bXfwx6Fza+FypcmexDqytKyv2K3NU5/ctgF/0/AYebHsY1hXXw7BgKBy4eT/4fOunYEpxUmK7xPbC249yR20dWZkUTqqPPkxubPKgFI6szFGVOLLh09iVjT87Yp4s30f8XFwne61axi+XrRPXi8rCUcFAgq5Nlk6seIMppfuaGcBKbqJ4yYP1ALamglOpXFjdb5CDC4v9PfQNqwPBqSRX1l4EsSRSRhoQocQN4MLWA2DzCq9hqKgHubp1GKDVhRqrwoxVMFsFslEjKcjy28fbbihtgHM2nws3D78NSgUOTAOA2fAs/DG4HN655mi4vPh7GFwcLAdZYTwqKHUWNqxYB65ZhRVL9pEKZLnjTWwXP2fCvlYt45fL1onrZbJ9P21DGU3tVeuxyzEwq4NXh+JN0Saadj4ANjE8BMCavsd9gK1GDeHCNgOk2v79ZSzZTdl67FslTY3Gmitf7xyJRCINUID1GS7MAET28CVV/zb7wByvKtQYG04sbSeEF6u23xhsgkNKR8BNI26FUrEEh372UJi0xSQYNnwYTJg6AQ79wqFQ7C3CTSNvg8OKx8HG0sZEH7rxmcOEM5o/lpeibWaFnnR98c/5cFPuvdIWfLIJe9WFyupCbVXrZZJto3vY9mVqo1pnWq5rF0sIb5aGDmOKNwnrqoCN/zz5AlgDnGPyYOsSRqyRtp2FC+u8L0k/qjFlDd/N6NYyqKzlo9HUfO84iZQDkQubTvX+MaolwPoA16xg1cdYMOMxAa0NzKKWIfJkz958Lrw0ZA4Ehei281uffAtWv7Qa1q1dB5tnb4a1s9fCQT85KFz/4tA58OHSJ6v6KMcTF6oBri4ga4LcDEA2hA4eRLi+ymOSASw/FlWerNivrC/Va3GZbjl2fRYPzD51bXTHil0mnOv4fVDCq6pPcSyyv00V1MWfJX48Yl/FYrKIk6wNv780AGsAW2uA1Um1vfg9p3GRTb9psu9Mm3GZpOuvrjesHWCelB9RODGJRGpK1SrkKq376qp6w2qaserCkXUhx6pQY1U4sWxZJV+1Orw42n8fLOhbCDcPu6UMsEyv7fhapZ+gAIViAXrn9vYvALh5xO2wcN1imNIyWT3/nxBSa8x3zaJiMRsWZv5YH6HF0Zsvr1ocnmyhv/5l5T74Zdz7xcNTVb5sfJ7j7fjXfP+8VLmx/DrVepNk+0srU3+q9TbLZWCmCxmW9aN57RQ6LI4rAaHy7VEQ6gtg07zPBTdYrtW8sFm5obZw7VUEsA0vugVBIpFylQtbz7uytQJYV/e1Fm5rLUKOsE6trTuLcWGlrqzgzP58868qObCcDvvxYTBk6BBYOX4lvDH7DVjxmRWVsRZK8Ive35b3IYy28pRzhGT/1syRZa5VBo5sQebi8TCic2X5PlVOnvB+WbmzmGU2DqjYNu2FMmZfmLGp1puWi+dP5rrG59/kuipeS51XmXsqAizGfY3b8X3XAWC9hRFrPju+ijlhRGHEpLyKnFgSybOaPpSYANYZYF2dV6/5rD4gFDV1TtHp+GSOAsadtXVmE65stKDsyj7Q+pB0nPd+7V6ArwFs9eJWsMU/toA5E+ck1j/U/mjCLU2Ok9t/HhzZ/teM1VM7suGbVH4ztQWfwiYaVzZcH5860Y2VObMmd7Z/TFL3Vee8mi720zq0GGH7dQVpjNsaLpR8z1m4rlEXmvXi34oq71XYLg28NhrA6sKIhYbK7eTNDe+TTBafdwojJmUpglgSidQ0yrMD6+q8plE9CzXoppLxAbRpYFacykAMMV5fXFc1PQ4vFlo8YbcJMOmcSbD4zsX927Lt1lftQ5QKKOsNsuF5iA0xAWylINvfrnz+RGDlQTZ840AeXty/DBViLD53AVpxfzahv7UAV5Uw+9O1UfzdSaG1FuCaBbxm5b7WGmAFecuDbbIwYueorVr/7ZIyE0EsiURqChe2VgCbd3jNe4VB1fQ4tkCrcmcxMGt6PbQ0LJxGRweyhZ4CLJ27lBss225oOUlH58S6gGy0i/7xZgCyJrCNAS5+H7B5slFbhCsbNaw+ZhXMiq/5qXmiDaOXItBGB5F8M8Vc2ryAq0yYMeiAKA20IpfVBF7FtlgATeG+ZgWwVdIdS0Z5sKg+JP3o+qrlTWXSwBR9Ukgkn39QzR5KnJHq9aOVN4B1zXfNskR+gPzPuX9kvq0uh1aWO4vJmZW9Zjqk7+AIYvs1eP1gOOiKg2D46uHh8m2e3QaWXLQEtjpmq0T/B/UcWJ3zJyhep8pHla4X2rCLb+/T7yBfW+XJCvuWrhOrzkqOuepc8P2Jz/lxCvmbfH5n1ZQ94iMW36/ukVYu+5GNm/88C8crrSYsmxJH3BdiWfw+VcGcCG6YisOSbasAEAuTngBWemyIbZTj54TqN00eLEKosGKLzzmFEZNqIYJYEolUdxc2rdzCgfMHsPUAV19w6gt0bYA2DcyqXrPnZ6//YCX8le2vEMDGqzZCy9YtMGTYEOg+sRumHj8VnvjlE+U2xaAIX2z/rHYfWlBVgKwaTiOQDR95BNkisugTB7PiMiXMivtQwW0CHKqBTQ95CkAUAUocj+tDlGn/VW6mBbCqoFV2/lzBVXyPhZsWNvAqnTZHaKuFzP6/k8S5kLVzAeOMANbq9yqDMGKsKIyYVE9RODGJRKqr6hVGnBeAdYFXV6VxTH1Itv+CJmbXFHqsCjeW5c2KYca6kOJ/XPwPgD0B4IzoVu+mIZvgqTueKve1ATbAW/AWf2DwznXHwZSOKfIw1P7+ZSHDiVBc1k54e7F5siBuF1/3x2HB0QlzCi1WFnziznv8zlblyYbnovyGSXJlK+HEfCBLwP9Z8AzA31wQc1t14ca8hPeG61AdbmsKSc5IpvF4qUorW6doj3LoqgBbAekuYcNiW20/GrcS2wfSJfUNsLIIkbrmwXoIIzb9Vmf6W27hIpMaRwSxJJInNXUocY5dWBfZ/FjmBWBd4bXe4OoTbGXVhnVAK8ubxcDs9dffCHAJAOwAAHsZYpYCgBkbdoS/tl+W7Etx0ScCbNXxCdDoteBT+fz0P5EUdLJ5bVX0idsff9jld18yt2z4VFUASgb8mD9rXfEmFdy6QqUvYb+DTBfqliCr/J5UOccpwLUe8FrVT0qwrXcI8UAAWCfQJYBtWhHEkkikAeXCZnW3N0/w6hNcfU7voytEohu/DGoxQKtzZ3Uwe+6558Jjjz0Gn978Kfjt+t/C/4beVJk3lm0WVEKI37XunXBlx+UwuDi4vH0ZILlxqwAWA6peQTZlwSdl9WDelcVUMI4aVoo/8W8gP/ORyp2VuLD850B1I0EpU2XiuE29buhhjyUFyKaBVim4IoDIBlxTwasjiDqBrQZgrfoggFW/L1gRwDa1CGJJJA8iF7b2qgXAYl3YPABsWnD1Casu+9ABrglqVUCrc2d1MPuNb3yj3OcRcAS82fMm/KzrZ/BAy4Ph9DusCvHBfQfDFzu+ANMGRyHEpX7C4gGy8vHpD72VOK9KuBXcz6pzIrqjXOXiaECm9vF5SenK6kKMZTDLnwIhzDh6Kg811gItk8hCkvOmBFsswNb7gtgTyGq/B1XbYqDVF7jKttFBpyu8Zu2+Rguc9k0AKz8XVqr33yspcxHEkkikuqgWuayNCrC1gNdaQGva8ajAlj9mW6C1gdm4vy3atoBftf0q0XfiPQqC8o0sBrPakDyFwyoeQ0O4sroQY6wzG25TOXZpqHG5X87B5QAqEXJcPt/Vb4HphkDuZXFRbvy+s4DVtNDqBVxl613DhoX1vtZlnv8aNVD3a+i7EUOICWBJOhHEkkikhsuFzRqAfQNsFu6rC7xmAa42wG37vonjlUEtBmgxMBsDrQ5mVc/5uUl5mK0cN2ihlO/TNrzY1pWVzz8bn9/+Jw4urDgWN5iVhxrzYxSPJ+xL+FhIoVbjwpo+l9ahyRnK6m/INGYVfMhgNS201gJcXfpzCCv25b5W9UMAq3/vsMrJ3yopexHEkkgp1dShxDmE0KzDiOsFsFnBa1pw9Tn3LKYv3XtlgloV0MrcWVPerC+YFY9N6mJqworLxyAAJH9sNq6s2J+zK6twYV1hNrFNuB1/kBqgRUBtuI3qz8D01YCA3JrL5iLd5IapgNUy5NgVWsNmqBDkOsGrL/fV1A8BrP7cYUTwOuBEEEsikWqqPIcRNxPAusCrT2B1lWwMqvdQB7UmoBVhlt+evb+6MGO+D9nzGB7FY6iCXoUry4/VxpX1HV4cnQw3F1YEXhPMJvqpKtqkAVrZtDoSF1YGa1LHVtgu1xfGyO82rbNq6kdx/FJorTW41hBexfWm8Gib/dgALOq8DcQQ4jz/nZIyE0EsiURqmFDi3DkiNQJYLLzmDVzFvl3fPyzYquaNlQGtyZ3VhRnjXVl5/loVRCJc2XoUfRJdWWlbjQsrdVklMJvYp7idDmglzCT9S1GcJ1MQjdK9zYmMcJponKJqsW3+rCO01gRcPax3dV/F9b7d1wEJsASvA1oEsSRSClEosZ0GiguLgUmf7qstvKYB11puqw0tNoCtC9CqQo1VYcYmmOVGpi38JHM1dcft4sr6qGCMglmLZfy8rBigrdpecgzS09cP4pIV2rlhC62RK51L+cyN1YGqbl8ql9YGbmXF21zAtYHgtRYFnDD7lPWjbGf4PnZdZ9OmSgSw2Ur53ZifuecJYkkkUlO6sI0OsL7dVxf4rHd4sWr/yvBii6l2+HOscmfF7WxhVhh0tC0kYVbmsGIqGJePAenKYnNlU8OsxF1WjRMDtNA/RjTUKsBU9XVg/ivr31ADu7mvVIxp6+jU2gBrGmiV7suhjW0fVmHDiP2R+1otcl9zoFLOvt+QIoglkUgDOhS4UQE2k+l6fBZxcrhbK1YXThNerApllgGtrTtrA7P8/nmALDuz3HhVIcb8MWUVYizmy8q3ic9b/2uLEOIqUEcCbQzb5ZeAmA+2aBNSjHRblW5u7YWC0uqNzG10IcVOebOKeG1XaK0h3NYcXqNGqfaBXmZ5Q8K3M2vTJiFyXv2qlI/vszQiiCWRSLlXli5sowGs92JRjuDqOj9t2j5VoGvKv9XlwMqA1uTOYmC2auxCWG8Ms1mHGNvAbGVoeJhNbCeBVAy4KoFW59IqnFQpyLdwhbdEIf8E0MCbpVy/2zAhxanyZi2AVdGXVRiyB3CtBbzK90G5rwSwdVKp8cGVF0EsieSops2HzSCUOA1UZungYlzYWgGsL/c1C3jNAlhdpRqLao5YaXgxAmhN7iwGZmX7TwCnIsQ4cRyKEGOdZCG+0nxZTzAbbRePUz2OxPEgl4vrxIuwQktrVSVo1adV5VJrAdcyjzZzObpRqO9RQ99KUK3sxKrfVNCaJbhGC+sPr477wfalbEfua+Ic1DutJrVKDT5+jQhiSSRSUwkLvdgwYh+qBcD6hte04JrFD7/uvZWNVzVXLN8XZg5ZEWhtYJbvWzWNjk2+rI+5ZRP7RhZ/Mo0lvujXhRq7OrGJ8cuAEwG2svzaxDY2cNpSn4tb5xt6SPA1Qmo0CKf9WOfOpoEwZDuT61oreMXsR9pvDeE1zTqbNrUMHcZfK6ijenKtUoOMM4UIYkkkUm4LOjVDGHFagPUWpuyxmJRtv76k25fsvRePRwW1KqA1hRubYFY1RjTMWuTL8v2YXNeqbQS31xVmo+3i88XtwKcTi1gfH4stqNl++q3AN61SXtSjABX7G+A5d1a5XQaA6+S65gxepftK62rnxX3NEF59XR/k3qEt5XRcGYgglkRyUNOGEmegLMOB8x5GnDXA1gNeff5wM8fR198YqtiTJERY3Fa8QDGFG/MwGx2UfHy6AlDSOWYlxZ9c8mVttrGBWel2vDvLH3sKoBXXYdbL2lT2r7kRonJwdeIKY2UlNID6uEGZde6sA+SmBVwMuHp1Q2sIr3me97We8FqL647yTcI8qZSz8WQsglgSidQUqnUYcd4B1lsVZMcfaR2gZrW9CL46sFW5tFVurCQMmXdddaHGyeOphBqbCkDJij+FfSCg0ySd46lrUwWzsgEZ3NloewTQCg6nEVolIcZKlxQbCmx5MRgCsExYKM4qCsYSDtAX/6b8WZ+OrGIbJXy5QqvFfoyua9TI3BfBKzTyDfPcgGwpB2MYyBC7dOnS8N9FixbD9Onb1Xs4pBxq6dJl0NfXB4MGdcCqVcvqPZzm0wAq6JTWhc0zwNYaXtPCqk/JxmICWx3UmhxatDvb/56KebPcKMt9V7vHFjCrAVOZTDmzsjbl8RjcWR4seaBUAS1rw09ho4NaNLRqCjYZQ4HrlPfqU07fodj82RRurHF72ylgkNCq7CMNSLq4rjl1XsdN2Aq6u7uhpaUFxo0bC9mp0HTZT7YBHB73DLXW4sVLEtxWT+UGYhmcMJVKJViwYGG9h0PKsbq6uus9BFITu7CYMOI0SgOwad1X474zBNdawUC1A6oHWwzUyoBWN/VOZdtKH2F7Puq4f5jJIlAImJXkzCb2Ych/9QWz/JgSYbgGdzcBj+J5VLi0/Pa20KqFVURV4nD7cDwNBrNZVjGuY9ixco7clNCaClyjhg0Lr/E6BrDsGpw9Fi5cpGxLIvHcVk/lBmLZnR/2h1MsFmHSpIn1Hg4ph2IuPfuMsM8KybPydEuzzi6sSalc0owAthbwagOu9XSvlM4b95nSga3UfbUAWjnEBUoX1xpmNQWgbMHUaRveYZUBrSHcmN9P1b64cynOySo6tWI/0v4kY3aG1H5nNu/urBR+Vq6Gwf+5CdofeQoK69ZDMGwobN5/T9j4vndCMHqkE/iivqPTuLEOwKrtMyXs5QpcLfrU9iGs46/DJ06cAP5UaJZLkxy5sfWb9o45sXm5Fs8NxI4fPx4WLFgQAuyrr75c7+GQcigWZs5c+vHjx9V1HFTUKV8gWksX1tc0N3kBWNO2WHB1Oe4s554V54zFFH4Sj5X9nUvzYZFAqysqBRqYjfYdv1B/tjHVjHVgqptvFhueLG0X1qTCA60ImCqoxYKtClgx4cPYz3Eu3VkZ8HV3w/Af/AY6/3MTQF8pvMpmnxj2SWx/eBYM/fVlsOn974K13/wMQHt7JoV4nEG10sC+b9twZCy0Ro1r4/bWodowCyFmDiwD2HkvP5vpDYxGvYltUk1ucNX5e2frbXcKPyeM2+qt3EAsiUQiNfIPWCqYzABgs4bXWs036yLdPnVzx/KfP/4cqFxaHdBWjUkWbixxcGV5s9giUL6dWZvtjEDLhRwXQD1fq7EysdBeBFsl3CLyXK3hlHNn6+HSar8ru7th1Ee+BG2znoFCiTtrgwZB4dlnAcaOBRg1CjqvvhFaX30DVl3xS4CO1mxuIKYAVeN+XEKSlaHKOLc1K3D10a/LctM6zPqahbEPdNUJYIOcRp8QxJJIA12NFK+TYxe2GQDWBK9ZgWspo+lJipKLUtnYdGDLPmMml1YFtHx4WXk6HQnMJgBYkTfL7T15PLzrKsJs0FftCKeEWcy2yqlzZEWhwoGCOjxbdGpljqqsirQEbo2Qq9injcognNUFJxIShv3oN9UAy/T97wO88UYEseH7E0Db47Nh2A9+Besv/KrVUIxwmmxclxxa3Tobt1XZjw/XN0NHOa/wStCab4ANcgqtoghiSSSSd7n+QNXrhy3LMOJaAmy94BULrVnBqsv+eMDVga0Jak1AG/UhOYf8S/5i2jJvViYRZsXdmaDUNG2EtEiToa0WaMX5WS0gWdZ3ZaEmF1YDueF67vxqgdfSqa2Jlq+Ejn/eAAUxSW/PPQGOPRbg/PMBrr66vJi1Y47sxvM/kcyRNcny+7omebQ6YPMBrQ0CrmnWYda7uq7NDq+m7848A2yjgCsvglgSidTU8jUv7EAE2DRFqlyglQebLMSDiWpsMdxi55HVAa0oVfgyNtTYOm9WU9GYv9jCuLOqizMboFVNu2OCWjH8WDoWRaiwNkS4HK6tH3cIvOWB5v9Cr/Pam6oBlhVh+fOfAT71KTl8lEow6OobYdMnzskOSHzl0RrzbDUFZzS/B3VzRHMCriiR41p7ZQywQQN8p6lEEEsiWYiKOjVeQad6hhHXG2DrAa9YcM0aWG33GcOObPwMbE3zyIbb8tuEebSVqXZQ+bMGd9Y21DgBqgh3VlZ5WAaMNkCrcya0c8Fy+bQYsDWNzUuF4rBxMfcXf+0PPVFd2uzLXwZ46imA++8HOOyw6o1KQbhd1yc/nAugSQWqUQfu/VtCZS3A1WkfdXJcs74GsOk/r3+j9QbYoNHPS78IYkmkgawc5cNm8cOXVxc2rwDrejwYcLWB1lr8wFZXDy6hwRYDtSXdMfH7Vh1rDJE1dGddcmDFisyiuytur2pvrFSMAFtnwOX2H47dtkpxzioVF9auT0Ls1lsDfPzjAHvsod6GPdat9z/NToptjKAadZivPFtLaG0GcPX52+37OkDWX7MAnIua7dgJYkkkUsMpDy6s7zDiWgBsLeEVA61pf1AxNwpk0+2Y9i0WSkr2VzRCrdSl5cOOEx0agLYW7myhAoPacGNMyLBkex2casFS4p7KHNtwjFWFndSAG7bXfHbL77/N57POlYoT+x0+LPw0lj/5Bx8MMGECwMv90xe2tQEMGwawbBnA8ccDPPZY2J5thy+W5/4djILTyo7qnmvrDIwe3dZU40C/X/G59nTOM9ze176z+Bv1lhfr+cZY0GQAy0QQSyKRvClPRRuydmFd5BKaXC+AzQJebX5EfU3NY9OPqphTsmhTCQW1VePg3Mf4vajKoXUBWg/uLDrc2CFkOC3UqvJfjXAbLVTnIjPIjVaqx8ydC5e/XdP3oc+LSn5fvYccAG0PPhqGCIdiRZzuvLPS+IADAC69FGD33QGWLo2WFQvQe/D+eIi1AdHqwWbzu4JwD1MVhqqxq5snxzXVTYscXRd4uWFVC3kE2CBvx+ZRBLEkElKUD5udfOZK5dmFte0vbwCrgte04Gp7znz9KFeHFMvHoatUrIfaSn+ykGAxhzYRZisLIeZV8ufOCgdQHkcZaAts7trq914WMqwNF1bJMI0OOi9XkfeqdZA1oCuF3qgx+BAPyT61+bT3Q+dPfgNQ6o0WbNoEsGBBpQFzYNkx8MtaWmDz6Sd7CeGtZ1EobN+pAdARWJsdWvMMrA0JsykUNNGxqEQQSyINVOXQqWx0+QwjzhJg6w2vGGitxQ8wxj3UTcFjgloeYeNzmHBpVUCb6NTenQ3Y/LAxnfWv74O+MqDEQFsIwTmwyp+1AVpROsA15sdKQNSm6JQp39WuuFO+LxSDcWOg+4z3Q8ffroaC7HjuvRdg1KhK+2IBuk9/PwRjR9cWVDKeosUHzGY+7U/aSsxewTX5bzNDa+ahwHV2YYN6H0ONRBBLIpG8KE93bk2hxFm5sHkCWB/uqy28pgFX7Hk1zWnrM+pCH1ZsrlYcK8w3hFJV6HEtgDZNuLEsf1YFtKpwYZ0Ligoddggj1vWTaXGnHBV46rrwW9Dy8ivQ+ugsOchyANu73z5h+ywq1ebhdyMv4ci1WJ+129ps0JpbkE2hoIHHbiuCWBKJ1DDK8w+otxzOGgOsjftqC6+uc9e6wKrrD7fsM6Xbt3IuWEz+I3dvxRvQOoQbB32l8nF7A1pFDq0Jam2B0ypHNhxUySl00Ln4i1DgqW4XlJ2DYMPVf4HOC34A7X+/GoB91kpB+BEM/yqLhfCzsfnMD8Cmi74J0NFRt+/emt0A9Qh3NQlZrjO0ZvGe+07LyuqmZt1ANuVNsCCDMecZigliSSRSUyltQad6u7D1Blgb99VXyDP2YqSeP9Bl6JOMUwa20nzbRBEn9bWLDdCGVWg1QMsDqwvQykKOExd4fA4tV+VYFXbsUtgpHKfBsVWCqcZZxYYQp6lmqgKBmlwYdnZC18UXQffXvgBtV/0bWu97CApr14VViHsPPRB6Tj8ZgrFjlLVoa3XTMPV+6jhfba3b1ApYXd6TetQNke3TF9g2miMbeB5rIxw7QSyJNBCLOg3gfFhTKLGLfBUmsgLbGgGsjfvqq+CUbhvsPn0JU7FYCramsYr9FRoLaFUXeAmX1hVqEVP5qPZvuzzeP/YzZ5U7q+tHAwneLx7HjYOez30yfFSNw3JsmSplqHLW4bHoys2+gDUjaE3z/jbCtRA/xrRA6wqytQbgwNO+GgFceRHEkkik5spragAX1qZ/GaSlhd00AJsWXl3B1RZW01y88BdBuv2q8mArYBtvG1S3Ez/PCpc2V0C7bBkUL78C4O67AdasARgxAuDww6Hw4Q+Hjl587PyxYqEWVdhJOCEq91UHsrL22PWqsGHfF35VebY2UsG151zWrOTje74WuZ4+gTaPIcGNAKs2x5Hm96BmQOp4YyzwMLZGg9dYBLEkEqlplLagk4uUAJdRGHHaKsS1ANisCk1h+vMhTN9hCLFqjPExVRi2qs+qS0tV0SbuI91XiopD1RxoN2yE4vlfhsLlV0DALrTCcfQP7667AC64AArnngvBL34R5lbqqhAroRYDtpbFncL9pYBZXRtVe+w2mUnIyW1E+bphWatcW6v2nqcKchpDvWE1/p32NFVVrWA2jwpSftc0KrzGIoglkUikDFxYG6Xt3xVg6wWvLuBquvio5Y+xKoRYB7fi+MTRSos2idPlhOuqgTbcXlKNOC3Qhm0390DLO0+AwoMPQYG5r/wwWTGgF18E2G03CP70Jyiw57fd1l8kqDI2HdTyObUYsJX24ThHbFYFnnIFtQ0q35E3afrLqoBUw8Kqr3Qkm348Aa8rzGbuxjq4sEGK8TTLdxJBLIk00NSA+bB5rUqcBxc2DwCbBbx6yZfN4IfadDHDrwl0x6iBWheXVhp2nBJomVrO/1IEsGwHw4YlV86eDfCvf0VDKZUguP9+gM9/DoLf/z7aT+LvVg61TmBrA7fhQRicV8sCTz6KOzXLRWSjfven6jfj+W1d2vsF1UL+rhNk40kBtuxc5Q5kLUQAG4kglkQaILkhDT89Qg5DibMq5pRXgEU5jGnCmi1DjrHrdfs0KeGCIly75Pqg/P+qcGLxe0UBnGHbGgNt7P6WliyF1kuvkM8zus8+ADNmAPzlL5UhMJC99DIIvvsdgLFjoQ/6oMCFJ/NQK1Y9tgVbHdyqPktGCEUWeErkrDrmsWUCtl1dUHh5LhQWLALYtCmsThxMmQTBdtsCDBoEja7MbmbWoaCU63ZerkeUv5U5A1eb43AAWheQzYNcvyeCnAC4TxHEkkikAS+XUGJbF9amb4yyBFhf7msaeHWt4Oy7WjG2P1WRp3IvpnBi/uIU69I6Ai0foqzqe8PGTXDTO0+EM1WQ9pGPANxyC8CiRcnlrP3ll0Pw5S9VhSeLUJsabLljRQFueRv9xWu9CjxJCzphILmvDwqPPAbFRx+DwqLF0S2J1laA3t6w12DSRCjtty8E++8L0NIkN2UzLlSVFpjrWgG4xi6q6VhrAk+Oeba2IJuJG5uy0vlABVgmglgSiUTKWFkVczK1wQCYD4DFQGgaeHUt/qSaHsi3GHwYx2J4D7FQ2xdwRZiCvnI7mZPaPzg00MZ9v/XWfDj5/WfBz1+cI6+TO3gwwKmnAnzwg5IDKUHh7nug9OXzpeNPFJFyBNto3MkzZgO40T5azRe9lvlzaeaTdSnUFO6jrw+Kt9wOxfvuh2DYUAi23Qagra3SqKcHYMkSaLnhv1BatRpKxx/bPCBbw3QTH/vI1kX1e2w+z6lNX6n/bhxgtpEcWdvzEzQpvMYiiCWRSLkWbs69bEKJrX8wUrqwaSv9mvoSXdgsANYFXn2AqwlYs7pIiQo5IW4ycOPWu7ZQ7ZAiQ49lLi0PqWycZVdUUen40YcfhVNOOQc29LXCuDHToLD8jerBnXwywMaNADfdVLWK7SlYvTrxtxACtqKactVBCHPUVs6BuFB+4a2qRqw83xrQreybu1TCXhx7nKtSuYtCEQoPPQqF+x8EmDIVCiOGh8Df1dcLnWecDYXOTlj7m1/C0KnTIFi7For3PwAwZgwEBx8Iza5agG0uiyXlvI5EzcLsLWG2EUC2HgAb5ByCCWJJpIGknPxQ5ukH1gY8swpZtZGz62kJsC75r6Y+XOFVOhYNOGIuRnzkSDOwwl74JMcr+fxrxpNwXzUurWvYcQy0V/3javjcZ78K7ZO2h3Enfh3W/fdiCJa/UZ0pd+65AFdeGbqA1ccJEIwYDqVSb9KZFKsec1ArVmAW3VqVYxudi2Q70bn1PkcsAnqrx9iaSaXV5fPnw9MX/hC6li+HVzb3wMpVq2HNmjVwwkUXwhZPPw1v22svuPmWW+HUU06BgM3pu3ZdGHJc2mfvpsiRzXV9i5z81jaDUkU4sPfBM8jmqcDTQC/8RBBLIpFIGSoVqDmGEfsG2HrAKxZcdRcdNqBqe2MiLHxk0X9ynJUw4IqEi1RkBeRwy4I6NFgXdszv45577oNPfeKLAC2tMHT63tC7dhk8uMUucNAbT0MLfxG43XYABx4I8KEPyQdXLELp7YeG57MKTrkxiaBqAlvZNjq4jc6LzK2Nz1WQCmbTXVj7uWh84/Y74MWZ98Ir7PjHbQXFwZNgy4OPh13f83644f/9Dj64114wY8cZlfFOmgSFV14BePkVgN134wfgtP+mUg3zSGsFpDWdVqhGkOT0d2fhytbckUXmw1qlJAUDp/ATQSyJRCLVuKCTq1zCiPMOsMb9W4CrCip9OOfxPiswiOkzbhMYgbdYdQ2thloeBsOxJfrBu7Q80O65165w/pc/Aw888Cg8+dBVsHrmX+DXHYPhS0EALWJBJzaVzrx58gNpaYGes88o7zsBzYbjMIGtDm6ZbAA3PA+aC3UT7PrMhXWdV3bvSZMh2G8/ePHRR2HI6MkweMvd4bSffAlu/MN/YPP61eFZ2nnnnSv9t3eEJ7WwcBHAHntwA2iOC9osVS8ntBkdWF8hw9ZTXiFdWQzI5tWNDRzGlMfjwIoglkTSiKbXae582KzBs9YubFpY8wmwvuFVdlHhAq1p77LbbF9BWBncm95f0a3FObWq0GOVSxu3HDpsCHz9W18Mn3d3d8NTs56Bhx96HG6+7O/wroWLKiD71a+qj7dYhJ4Png59o4dDgT9PKidYFkosAVsmE9zK+lJtXz5XEI1RB7oY4HWF39RQ29UF+x54ALwxZAjcdffdcOTHzoaFr8yH1555GSbAW9DS2gotLcJlXmsbFLq6kyHmacZA0p63Rh1brSsOp/m8WcGsRXhxXoSeQi4YWADLRBBLIpFqqjz/yLuqHi5sFmHEunW6/du4r1nAqwpaTcCZ5Q+4+Dk3zRPbJwEwLNTK4K28X9U++49dFnbc3t4G+x2wF+x/4D4An/kI9J14FhQfeQIKmou/oFiAvgP3g66ffC96R4QxJc40B7UYsMW4ti6Ay/dl+m6yAV5XADaBcRUcdw4G6O2Dk08+GVpGj4b933ME/OKcr0HPmnXQN7wbWltaqm/ysWl3OjuNEJva9SI19W8jdqyunxeX6Aa0O4oA2UYo9MRroFYtJoglkQaKajx3XCPLdUoXTB+YPp3uqHoMI/YFsDbuqwleZc6lCZ5V+xVVymAqnqLwtladU9k2Atjq3VqNU6vYzinsuL0N1l53JQz5xkUw6K9XRzlcYSGofqeZzddZLEL32afCph9/BwptrQDcZ082lU84egFKTWAr6yM+p1XtNICrA1HWf7heczMhBl7Zfk0ArNu3k6ZOgWKxCIW+PnjvJz4BpYkT4Wv//k34Xd/a1gZFVrzp7LOj+XyXLYum22HT60yb5vx7oARqEkn6eZH/jWTltOILtDWGI5sFbAZNArBMBLEkEqnp7xrnRa4/HtZ5qB4qEfsEWBv31QSvGHBVnWcTrPr8cS8VhJxYYd8i5Ebjc4daPqc2BECQTLHDgaNV2HFHK6z72Xdh4zc+D4P+fg203fsQFNauC6sQ9xx6IHSfdTLA2LH92yerEot1S/jpfGzBFgu38bEySWFWAbn8PjDAiYFe2b69fYduty2UJo4HWLgAoKMd+l5/Hf5+xV/g1ddehbPOPx92/uQnIbjm3wCbNkU3KhYtBJg0EWD77bSFw2rtNJPyJ1Oajs+QeRunFbsNypU1gKzJjU2dF4ss6mRSLYo+5VUEsSQSqWGVh3xY36HEWf/I2BRywgKsa/iwT3iVnTcVtJrOcdrcYtk8sFVOrOSjK4JtFdQJYMufI/ach4lkoSg7l5bfDw+IvaOHw4bPnQvAHsJYCvw5S3xOksfAz0/rBLYWcCvrr7LcHEJsgl1+3y4uawzBtiBcVkc7BPvvB8Vrr4dg+XJoHTEcznrve+CBBx6AbadNidqsXx/9u2YNwNq1EBx5ZOiu8255WpkKaNVLBNf1+/21AVyXfFiM44pyZVOCbJbyNTtBM1csJoglkUi5VCM7vWkKOmXtwtYLYLHuqw5eMa6rDFzThIe7XMDICsJV7UsG3Qiw1bm16ps2/dDX1Q2tc1+H1kXLADZ1A3R2QGnSBOjZdisIBnWkqnhsU7xJ5DWdWyvd3gJuZeMx9W0Lu5Xd49vKxhhuawuDB+wPpRUroHDvfRCsXQsdEyfAEUceEYYP911+aRhCXFi8BArr10Nw2KEQHLCfVxfWFvizkOqc+ZgLeqBKvCnmA3CxYGsbPpzalW2Q0GJRARV8IoglkUi1UyOAqY98WN/7du4vRSGnrAAW677aVDoWwdXmhgEWUrF5s0UolvuMr4vYv6bCTiqgEsEWC7WJQlF9fdDxxFPQ8fiz0LJkORQCgKC1FQq9Uehv34Sx0L3PbtCz7+5RzqRD6HHVWESg1FUkRri1qeC2fzz82BOr4iHqXFmL0GEb8E0dbsyuv995LBRGjYTCI48CzJ0HBbb/1tawiFPALuAnTYDSke+A4ID9odDC+szmu8x0Q8C3yrBTg8J69VC93W3MTQAb0BXB1gS1tnM2m1zZWruKafbpa6xBkxd8IieWRCKR6qBauLA+CjnVAmBV8KobS7iO6w8Drjpg9VXcSdVP1XLF218VJiuG1wrAp4PasK++Phh8+wPQ+eCTMOI3VyYL+rS0Qt+KpbD+/10Anf+7EwqrVkH3MW8vg2ya0GNV5WP+8LVT7QjHaQu3TDKgUkJu/xj545E26f8X5cymDBfmP9Pa/RULEBx0AMBee0BhzstQWLAwnH4HBg2CYMpkCFgOLCvyFB5iD65PR6mqQ/sUn+PdzDdjawXnad4vGejip6Pii7QFXmDWGWQ1bqwupLhe88X6DDVuRHiNRRBLIpEGnLLMh8WGEru0sZFNGHEeAdYVXjHg6pora69KYSdMTmxiE05VUMX1pYNaBmydjz0Lgx6cBT0Tx8Ky33w3AQKjzv4sdM95Bnonj4fC2vXQ8eAT0DdqGPQcsDe6QFR4LLrx8mO1rEgsLdhkAbfhWBTT7cTnQCYt6HJjNwFvctwVOTm0GCBmObK77gzAHlUdqL+XfENZVqHFMWxlHRVjuuGQlwt+X+dY+jsmLLMBXRFsMVAbA21amK0XVNayqBMBbEUEsSQSqSnlMx+qFqHEtXRhVet04IjZ3gSwtvBaNT4kvFaFKVvkyWYx3Y6sX1l1YhmUhgrcoLbQ1Q1z/vh/sOyVN+Ed574PCtwYWiZOg5Yx42HTc0+E70Vh2BDoW7se2h97BjbvPiPMkY32ldhzZZeOUKsNQXacaidqJwcnHeCGY0sx3U7YvwsIKnJ30Zv7hpgahwG7KIQTz86kcqqlOkMyVmlgzfazozr3GLjlv+tNQOsDZnUgm4Ub61tpITwYAA5sLIJYEkmhNBcZuVODzRGL+YHFTAFgq1p9oWd9kYR1YdGginBIZQDry31VwavJdcXkyqLDf1PKpr+qKsUCoIVFV8QKv4L72dPTC1d99ocw4Zb7YC4AzFi8DEaPGQXtbW1hk85d9oHNr82Bvg1r4h6gd/xoaH/9LSjOfRV6dt6+sq/yvtVz0+qgVpVTK25XOVeCUky1E7VVO4MmyC2P2fN0OzH8poJRSzdY21XasWQIxj7dtYTTXyMozvr7P22oehrZOrZYoMXCbC1BNg+qV65sXkUQSyKRmrJAU9bCXgBlEUrs6sL6DCOuBcC6OK8mcJXBZC3Ci9HhxApV5b6K23PrV69cC1/46EUw4eHZsOXUGdA7/wX43c//CpOmTICPf+5MKLS1w6AddoPVN/+rfMEWQB9AG3O7SlBcuBiCnber7Cvh1ttDrSjT1EFpqhHrptphUkGu6XsNC7tY6MWOrWZAnAEY88W8fE5XVYs5SbH98fIFxdZTNVlAMfYzpgNAX1CLAVoTzGYCsg2sIIvrjar2+YF8glgSiUTyJF8/ipg5UJVjQDp+mDDiWgBsGvdVBa8mcLUJL1a1x6hyPgJ0OHG4P8x1puDGxtu88vIb8OmzvwtLl2+Asw86FYqbN8GQ4eOge8GLsGjBEti4cSOM2WdvCHp6oPuVFxIXJGxMQUsRCpu6oK/Uo3RPjVP5VLZSF24yuLXiPmV9qLbTheummm4HAbvlcfXnvNmArwsA+wZiq7zgjMOmQ9BIccHsowCU/IaIP6j2BcN+XGDzuZYdu7aqN3csGKDVwawOZHVjswZZhRurCin2CcSuMwjUDmDzJYJYEok0oNzbek/H4DuUzIcLayNMDqwvgPUJr2lCi1XHl8o91+xLBrhGsA1K8OBds+Arn/wxBEPGw/izLoS+5W9C62tPwaBpu0DLoGGw9sn/wVtvLISpH9kXNj7/uCQ/NQDo7YO+QW3h+Phx8KPlwSQxlY8F1MZj1gkDttZT7aSAXN3+lG01ocxZALAvGPYREsyO2zWPkL0/rhfQZahx+JutyrVOOQaZbPtEVcJG/K7hHd4gce5sCl2lBVodzLq6ss3quvIaqMWeCGJJJFLTqdGLOrmOpxYurK8iTjbhw7JtVG11risGXLEh17bCzBMbqw8k8CDJg+XB9rkn58Bnz/luOC/oqP2OBij1weKhY0JXIVgxPwRYpkETJkH7lC1h1S1XhecjAW09vRAUIaxkzNYlwJnbvy7H1QZq+erHyoJNCMdWNg5Vf/WcascWCG2c3zzCcBoIdv0Gd4Vfn+DrG1SxfaVxeHWAKzsnqvfVFmh1MOvqytqAbKMAbhqH1rVQYyOIIJZEIpEy+vJ2+XGxCSV2cWF9hRHbAqxL/quN+4qFVwy0ZjGfLHae2FgygNKFJG8xfRKcdu674fGHnoN5My+DVXf/GdYNHQl7DhoBo9sGwUoAOOnUo2HGe94H3W+9Cr2rlnFjifptW7oCesaPgu5tpkVejOjUeoDa6otTPdjy4yu/VACoCkyVrqsBsrBT7dhOs2NdGTZliLCLG5wmHzgtALvkwcbHZ3OjKZmHjd9fVUExDzmpPkBV14ct4LLXWLBNA7Q6mHV1Zb0Aa5YFnhTT62QFkEEGYcZ5EUEsiUQiZTQ/bB5dWBuZ8mBV88BWti8p+3QJHzaBqS5kOLFtirlkK31Y5hZVQbZxgyqJsMQ7tkNGDIbPffvsECzXr90As2e9BE8/+gK8dftjMH7eyzAcANatXQ+rZ94Qtk/kp0IALes3QmH9Rlh/2B7Q19EidSNdoVbsxxwpYQZbZSViyb5N0+BoHVeEm2g9zY5j8aQ0FYTrBcIu02Hagq8t9NYKeKX5tEjgdYVddb63Oi80bZEmG6B1hVlbV9YGWG3a1nKqnbzmyeZJBLEkEqnhlMX0OrWQbaia64+LLxcWOyZZISddCDEWYGXuqwpMMc6r7Vyy4rHpqsKaCjuJbVsMH2FZz8apdfqPY/DwTjjgHXuEj+KXT4fBNz0EpZsfgWHTxkPQsxmgrbUChj290LZsFbRu2ATrDtgNNuy1Q2I+WXGf4lQ+iX0XNNWUhWPBhyCXW8krEcscQsu/G2wRIxN02obPOhdPSlEwCZPzq97Y4fvI8quavZ+6CBMZ8NqCoQuEYsClOpzdvB8l1GmOSVt8zBJWrfIkETmtJqA1ubMqB9jkyuqm4smlG1sDBU0OsEwEsSQSKVfKSwGoWsgUSqyTzYWei2SQi61E7BNgbdxXW3itDi0uOYAqMtdIaNdnvDYqQYvkb6Fk6diWWgqw/vj9YeiEUVCYNQfaX18EhSCIqhD3lcILtZ4Jo2HNYbvDpr1nRHQd9EorH5ugloc4cW5bMafX5NbKwFbuyODhVjvNDsJNtYFOdIixo0Nq7f56gGAnALa9SC5kA7xZwW7WoGvr5to4uNjQZBVsYoFWNyZd3zaurAxkbcKKGyU3th55siCGU+SI6wliSSQSKQP5/kHEhhL7dmFNebCyPsV2unBdW4CVua8meNW5rtVwqXeaXfNkq/JZjVvIQVd0cENYrMohTb4strTAuv12hPW7bQ2DX1kIbYtWQLFrM5QGtUPvpLHQtfVUCAa190c4lNDT+dhArbgdxq0V+0sLt+VxaYodGafZQYKsK2Rahxg7Fk+qlQtsDb42IaCIv7v4fcbAbhzGbIJdcaoo0zh56UBXXnm7lBpw0+bhpgVaHczqbh7pXNm0IJs3qcZou9xXiHGqfIAaiiCWRCINGNlOr5O3ysRZyqYacWUbcyEnXREnXQEnHaCa4NaUT8uPXQau0vxeU46s5YUDZi5Y2aV/L4MPYY0Itkq3tqMFNuy0JQB79KtyYZmcUqfKSdXBZyI3VmirgVqTWys9FlXRK2n+tTqXzhVy4+NgwsAjFnp9zM9aKwC2vawtep4XFpsD6xt0fUNuFoCbBmxj/mP/Kl3SFEDrArMqVzYtyKLdWElIsSwvttFhudHgNRZBLIlEIqUUKnTHYz5smlBiU7Vh0xhkYcSVvquhVOxLBqcuACsNSzaEDetc1+rCSyW397kQGJcXAtxFuAp0qzxHYZc6qNU5p7rqxwn3FYQ+dPCrgdpovQHuJWBrU4UYl28rE8ZxLGYCi7bgG8v1stM6tNiyiBI2TaJYY9CtTKujAU0+AkHzvcsXo1KNz2YOWgzg+gRbp/xVJNDawizWldXfpGp8+XZhA8zfbYPAayyCWBKJBAM9Z9W1uIXPfbpWPLQNJTZJ58LqwogxlYhtQogx4cOm0GEZvNqAqxTiFZCqFyvsVBlXCxYaFO9bVVhuCqjVQWbifOhCii2g1lScSgZ8MpgX3WDbOWSj9nq4NF8k46sMm9zdqvYO8I3J6/XirNpAr0V4sOk7EDMHLGa+V0zOqy/AxcAtPyYbGFWdL1PerUvVZCzQ+oBZnSuLAVnvbmyDKBhgAMtEEEsikZpK5uk7Gk9ZhDVjc2GxYcRiXyqA1bVVQaoufFgHr/xYVfBqAlcZtPJAqpf8fcNur4RdY2iyWERJ7FeTU4uEWl1IsQ5qpeM1uLUyoFOFO8vgNhqTG5RhnFy8I2Q3tU55Sh3LOVqzdn6zKHCFgV1TCyzk1hJwMXCLDfnFuqzi93JaqNWNowo0HXJc2b6wrqxvkK15lWIJKFrdRM8gRxYaEF5jEcSSSCRSzlSLUGLdemwxp8p2aqCs6kfR1gSwuvBhaSEoC3hNAD0SWjHFnALuX0x7ZQcmuJW54AU3qK3qKVBUD+bmqLWF2qpKxQi3Vty/LdzKwpJtoMwEYzZhy/ZhkPbVhGvh/GKE+dSH8InIMzVdjKeF3LSAy3+GZIDrw7n14diqoVaRCuGSy2qZ46pzZX2CrKi8Oq++xhQ0McAyEcSSSCSSZ9XqR9E1lNjkwmq3FVxYnfNtKuRkC7C68GFd6LAtvGKh1ccddG14peqyvApCW6zcWn34cXJqH2w+rQ3UyoRqb5hOyARkumJVOsj1OZdspT87aHTJ/ytaM6n/KsIYoeDTA1yawm9N25tyXc3bK9Yb4DYt2OqgVv55DeTwiHBnfcCsT5B1gVZMG1lxp4a73ijhtssj5MciiCWRSKQ6qxY/hlgXViZdMSexjSkPNi3AaisZa9xXW3jF5MeG2yAdVmy7WC0aA0EEPJlzqXNrRZjTQa1rPq0ItbqcWoxbKxu3jWtb7lYDj1rIVYypqg/L0FwbhzV78LVxfJH9ergAxkCuq4Ory8VNA7emCsVpXFtTKLJN+C8maka2nXU+q0WeaxqQFdXIbqwo2zEGuvYIgG2Ec0IQSyKRSBlLvMiwcvB4aLOdIsiwH9tc2PI4BBe21gCLCR/GwqsNuKpAVA33lYBizFQfvHpl879qANeUZ+oKteKUPq75tKZCUSiwVRV0soBb1djKwzKAohF0w06i4zQBb9ifQyXiLMHXP/AanDEEZKaB0DTuqQvcZgW2rlBrA7Q6KSHUAWazBFlMWLETtPrMi/WowCFHtlkAlokglkQikRpQmFBi6Xay/MmULqwPuQIsxn21gVcTuKrOgynn1TYnlgGQal99CLBNA7W66se+ikSF+xGPzxFsbeBWNbaq/jSAhs0fxcwFjHV4awW+vqHXHNKMuyEgkw5GsoBbXVhyFmDrA2p1Li3WoU1WD5dvYwOz0mWS8GIsyPoIvRc/S7VyZ03pPrplTjIAbKPAayyCWBKJREoh31/6WfyI6KoI2+47KxdWui9LgJXlvpb3LwkbVsGrCVxVQFrl5HL/2kAsAxpVexncsgtaccw2UMuANlHQyjmfFl8kSgVWurlqZeMxSVWt2Bw6bOgXM62OBRCiHN4U4GvzrYI5NnZcpkrwDCR0N8gimNRAavhZV8OmDmBt4bYMoRrQtAFbFfjx28j25xtqsS4tJvJGVhDKxpl1cWUxIOsSVlwLSK1X/8r9NhnAMhHEkkgkkkRSyPM0R2wtlOYHSQTP1GNJUYnYFmAx7isGXk3gqoJM1TmzOZf2rm21xE+qFmo9ubQ2RaIwc8ZKc2stXVeVc6vqx9SfbuwqoafUsc15tTFMLcZb8nRMaaY70wGuC9yW3VDFOl9g6+LWukBtNTyWnIEWO72NDGZtwoxVrmwWIOvixuZVXgoIlkpNBa+xCGJJJBIpp7KFyDShxLofMlVFYqwLm9ifRR5sWoDVhQ7L4FUHrrp5bjFiMIZRC+grsWIBtwouxf0EjqHHGpfWFmqjJfp9Y8E2bBfYwZ4OcHX92bqnNsCbB+g1HXfYF6YfQ8ElF8h1gVsX19YWbE1Qq3NrsVDr4tJigFZ5400BqLYwawRQR5CtGq8BZE1ubB4KPGH3bzXOUjqArfc50YkglkQikRqgMjF/AaLKh8UKM62OqwtrCiOWtdVNpeMLYLHwqnNcZecEB6dsD+r3KTlnap814GLAVge1qfJpU0GtofqxYv8qQFLBrc6F1IGdCXJNfTuHC1v+6aELPSG/KzCwawJdXU5qFoCrglsd2OpgEOuw1hJqXVxaF4dWlKpvLMxiQbZqOwPIYvJj06gKanNa3Mn1WqDRAZaJIJZEIpE8ylgRuMY/CroLSQxA27qwuv3oHFpVIacsAFYWNqxyXcULRxlkqiEyULZLTDeDgtCidN8ysA0hQNhXVa6pa+ixJJ82uS4d1KLcWmEMGDhSz7mrh1CrkF0PDqdbfqxn4MXkxlvO++ur6JIMblW5ty5gq3Jlaw21NsWU+P717ftBUSyeJkmTscltZeM25cuqwotNebK2IGvrxqaROFes0clFzsvqtfBTqdQQ1ymuIoglkUikJpNtdcMy/KX84VK5sLJ9iS5sYnsOkG0AFpP7KrqvGHgV4VEGRPJw48q/2JxaH2Jga7Mvm9DjNPm0Vduq8nkRYFtVCVkxHgzAKR1c7Nyw2LBijLNrsV/b/Uf91gZ0dZCrA1xVryq4zRJsdeCJDUF2hVrfQItxZ2UfDmxuq6srmwXI2qieIcWZ76fk1n+jACwTQSyJRCJlKJuw3FTFmJD7wbQTp9VxcWF1c7raFHJKC7AqeJWNQwevGAjVwWIaaNVVK3aBWtN4MsunDdeXjACGA9vqUORyfyr4cwDcqE9E7mkGYcU20It1eTHOMiaH1wi6usgMxRjCytuK7UoZgW0FMkupoFZcbuPUmlxUTNixCWjt3NkgdGdlebOuMJs1yCa2raEb61Ou4b2BxzDiRgJYJoJYEolEalCZfnDShhLbyKaYk20hJ98Aq3N/dfCKAVlV0RnXeXYLhul2XOBWDEFO49Smnp8W4dbKxhCOQ3EdKoNbE+D6ADcU7NpWCkaGM6OBF7lv434NNwRcpsaxhdu0YGvj1qZ1anUOK6ZfrOsptkVXCQ6/f0EaZoyBWVOIMQZkq8Zk4bCaoFcn05RM9QK7VPstlaz6bDR4jUUQSyKRSDlUAjIURZ287AcZSpzGhbVxMGWFnLIAWFd4rZ4LVnfeKgHFLrApmxPWBLci2GJAVxd+LOuPH1OafFqsW2sDtuGYNIClAtxwH+pV/ePyV2QJXWXawjBCFWRCfB50jmg5bFbXhyZf2WVqHNXnXAaKNuG8MqhVgyYHif2fPBv4NDmsCeBDOqmmkGMXmI3a4mAW48ragKxJLhWLm3HKHV6B7O/UM8BWr8+Pi00QSyKRrJR1RUBSOqUNN1KFEmfpwvL7TazXFGDyCbAxvKYBVxMY2EBs7JTpHFhVXh8GUEQo5eHdp0uLgdpw/8JnDOPWyvpSjckH4Ib7A4SQ13e+YRc9NY6HQlXGok3KIlqK/jTuKRZssVCr2h/WqVW5tDbw6QK02DBefntbmOUlCzPGhhibQDZxLkyFmiwcVpu2upBirftaxwrFqdKNAl8Amy8RxJJIJNIAkc4htZGtCysWc1It0+XB8m35/eoA1sZ91RWa4scjW6db5iLTdDxKZ0pycW3ryvp0aZ2cWqxbG7Yr2RcVcgTcaAz69eG+oU4VhZGfv6xBV9W/zlHFgqYKbG2gFhPSG0MVD7VpXFofQGuT5yqGG0v3q6g+bIJZF1fWlCebBmRtijw1mxsbIF3YZgVYJoJYEolEqpN85aVK5w7FTJ+j+nETXFgxlBjblzF0mINKUx6sbBqdNACrc19V8JqmuJMPwNUBrPQivmq8+u38u7T2UJsabMO26nG5Ai5TL7tANraqrqQsk7YAVYbublrQ1Tmtupskqj6V74ekvcpBTQO1cqfV3qXNC9DaVgzGTqXDjks2z2wMs66urCvIJsZqUeRJpXrmvjKJ+za9ruVY8iyCWBKJRBrgKoOhQyVlWWVfrAtrE0acGGdBsk0KgLWBVxtw5afY4eemtVWLJrwY665ioDbrqXyicSQlu9WCBVtdMSYXwMVUDsZgp8nRtXF2fbu7aUFXOz2OpjATNq9VB6FY4PQFtTYurSns2BVojVAqwKMOhDEwawJZ/lhUIcZZgWxibBZhxSphqxRnAbhp+8vKhQ0aCGCZCGJJJBKpiXNfff0o6UKRTS6sKVRXFUYsqybM58FmAbBYeBVfmyDVtbCTrm8Gt65gK0KtKfTY5NJioFbuGFcLC7Yu+ZdKuC1vV/JQPAmnusCuYZ+64kvRPtQurOq7xiY8WQmXGUJt/HcmAqCpf0zYsatDi3FnbXNnMdPfiJLn1Za8gmxif5ptXd1Yl5DiejuzPhQ0OcAyEcSSSCRSDoT5AUlTmdg2H1YMJZa2kbmSEhc2MQ6DC6vLgxULOdkALCZ8WDcFj+x4Rbg0FndCXiSUL4QNubEyuJWBLQ5qhbGiRgqpoBY7FhuwdQlT9Qm5dnPB4qa7wYUxe8rZdZw6xxZulWAquymDcOZdoRbj0qpCe20dWgzQ2rizOtfYBmZVtQ10fchyZV1Bttrx1YQH81CLdGOzDClmx+B7ujpRqDGVcON2Alhk3/UQQSyJRCLVSNgfSJuwXpls8mHFUGIdKGMKOvlwYaueKwo5uQJsGng1gWsSVLkpdiwujkxtw4tjhQMrjg8DtZgCTVlAbZhriAUZyT50n3IV4BoBE/GnZwTdsB/cGcO6lL7CmFNVYDYArrw/+ec0QE+jhAsFdoFarEubFmgxRaF8u7OuziwvTJ4r78rK8mR9gCw2rNh2yh6bkOJayrrgUkl/s7hZAZaJIJZEUkgX8kIi1Ur8HLH1krRwlBDK69OF5aFSV8gpbK91VtMBrGk/leM2/9CXCukuBooBf/FXQjm3GKjFzCtrG3rsCrWyY9D1rwI7Nj5dCLbuos0ElbIpgaT7wf52eITdtKDLClJpb2Ip92tXYRgLthi3VhkKLGmDAc8q0LMAWlUObdpwY13xJqPL6lBwSZSx6JImvNgqzxW5DpvzqnJj81alOE8FpRoJYJkIYkkkUq6E/YEigVPF4LShxCYXVtof0oWVhRHrCjmJ88Am1ksg2QSwsnHawKsJVG3yYsUwU1nfMdhioFaWX2sCWtkyE9CqtpEDdjXUSivDWoBttE4trXtrAFw8TOJBl/9sq1QI5POlovODuXHpINUFcFVzE9uArcqRx7ikmFBgUz6tyaEVgRaTQ6uCyDTurAlmtdPsGPqI2gfGwk8qd9QVZG0KPamU1o21lstcsTUEwsBHvYwGAFgmglgSidRUivKQGuMLuFbC5sPa5txiCzqJbUXAtAkjru6rAqCyIk62ACtzX5PgbQbXyj7iMdoXdjLlw8r2rYNak0vrK+wY49JioTYaFz5MVdW3bry8MDEPujzc8tiw19Dowk7pw5dNkKs7N6GzLenfB9i6Qi3WpcXAqC3Q6pxgrCNq485iYVZV0VgsAIV1RjFhvon9ewLZxP4VYcW1uNnN58jWq9CTuE9MKLEo63E3CMAyEcSSSANB7K4hu3tIagqlrUKsyofFArDsAlUVSqxzYcUwYqwLG7ZXwbIHgJWGKBvg1QSpthCrc2ZlF+9YqFW5tFmEHUf7cZcObG0LDOH2Z5Yv0PUNu1lWXS5aOreyvFulY+8AtS4urQmCRaBFO5imfaTInfUFs9hjqiiwKryUdE/Tg2zacGEl+CpCivm82HpXJU677wBbd0PVroEAlokglkQiWcvlLiiFCTeWXEKJbV3Y6LXahVVNp8OHEfMAKk6jkwZgVe4rD4pV4cWQYTixwtnSrZNBrcmlxYQdq8aLgVpM+LGrdHDr60YCJloBm8WOgV1smLApfFkHubpwZdX+VedC5trKHFtXqHVxaV2A1me4sa07Wy+YFf90RLBM9O0JZHX7k+2rGa4jXEHVlwsbWAJslRsM+RFBLIlEIjWR0pb7x26PqXSIyYXVubCYMGJxG76Qkw3AmtxXW3iNlnHVibMIJ0aCrcypVbm0mLBjWf/YkGEMzKi21y1PC7iq3NusQFdXfMp3+LIJclWAq4JbG7D1CbUuLm2WQGtyZ/MEszYhxvwptslZdQHZZoDSvMBuYAo99gCweRNBLIlEajix0B8WAjSQ5CNf1bV/m1Bi3ThscmF1YcSqPFjdXLCybTDuqw286iAoy3Bifj0WJl1dWpcpfHTLrHMqFefRtE4Wlpw8DnPeqQ/IrYwJJx/hy/pQYTXgKt1bxXeLfIoccxgyBmpdXFoboFUCp7CeB0ps7mwtYVYJlch9y7aVzSvrArKJ/hUga+vG+goprpdyAYmlxgRYJoJYEolEalBh5pMVc1pd8mEx61Wuq60LqyvmZMqD5ZcnARcPsDL3FXtsstei+pBT7bRwU+ro+tXBq0+oxbi04bg9hAurLiuzuqTCQi7m780n6DIVUzq6rlMKqeBWVVFZ5tqqii+JwGqCWheXVge0MkB1dWcxubPGcXiAWV+uLC9dWxEwxX3KQJZ3Y3X7qkdYcR7ni7UBz8DVhW1ggGUiiCWRSCSJwjvrucr+yE8+bCJkGFHQSQe80TrhteDC6sKIdYWcsgRYG3gNLOA1lqq9Dm5Vbq1pOSb02KXicTheBYCI+8UsV7m2sm34bU2h2SbwxECuDehiQ5dxY9PLZUohXbgwFmxdoVYMP5YBLZMtVPLA6tOddS0E5QqzvHOYgD1EiLHOlU0LsrrtVCDrazocjBtro7xUIsauw6gZAZaJIJZEIg0YsR839iNHkksWFmybY5sASLGir2FeWJULG74WADIZYiwv5CTmwGIAVhY+bAOvPHwGjuHbqhA8sX8RajFQKANaI+QicmlV26aB2nidDlB108Ko+sT0bdOmHqCrO2f8eu20OZJ1PsAWA7XK6r4pgNbGnUUBasYwaxNm7OLKitvqXFnWD/+O6VxPjFMqCgOytm6sjVyd23pXK8YoQLiwrn3lXQSxJJJGujuUpOxExR76P39I4K7VD48phNe0jU8XVjcG2XPZGNMArA5e0+Y02wAuFmq1rquNa+sAtLZQqxobZl283nS+VXm3Yj++HFPfoJsmbFlbWTgl2GKgFuPSugCtjTvrI9TYFWZtCkDZhBi75soq3dSUIJv17zj2OKq249zavEr7m245DU6AdGGV+8xxmDVBLIk0UERzxTZVFWEfii+MZBe0qPw/h1Bi2f6qgDClCyvuQxVG7AKwGHi1LcIlAzuVZAVwVONJC7TGtgagdYHacNyOBZyw65l8QC5mfy7t0ubn6lxcXdXntGCLgVqsS+sLaDHVhTGhxuLfQRqYxVQzdgkxts2VVQGgT0fWNqwY48aaZBNSbFPcqRHCjAPM+JoEYJkIYkkk0oBXnkKGjMWUdEVaUk6PE/Wh/4HTbosMJWYSp92QVSQWXVidE6wKI1YBbAJQNfmvKvdVhFcTFGGmU8G0FQFXB7VpgdamrawwlA3U8n2poEm1nWpsaUASO01OrUHXBLk6wHWZRkh2eS8DWwzUuri0NkDLYFZ0InXurE2oca1hNm2IMTa8OJkny32nCi5wLUHWJFe319S24Yo7IYS5rmlUgGUiiCWRSE5yCRWqVZhwWHQmJ1BaD6kqD9uCugqKfYYSY1xY3ZywmHBfVCEnR4CVwY4NsNrK5NrKcgbTAK1qubEtEmrTgG14LEh4NbVxaVdr0MW4zCrAtYVb3fsrnQvWALUuLq0OaMX3QOfO6nJndaHGWJjV5cyW22kg1DZfVtaHzJXFhBfrfpOxIMvLd/6qjRvbaGlITjfOLUOJnbdvAIBlIoglkUgNqXrMFVvPwlAL+hbCb/r+CA+3PQrrixtgaGkIHLB5X/hUy0dhcnGCdluMQ2tqo7t4tg0llrmw4r5ky/g+bcKIbQs5JdpZuK9qeI1wPa14hxQDtT6AVrXcBnrC5ZKwa1ew5fs23TDQhSaLfflsF7f1Bbppwqhd4FZ1Q8EFal1cWh3Q2rqzmFDjNDBrBb2aPFdTvqzMlU0TXixCdEV2jiymdkiasGJpfwZANuW9mtYbo7PYtYcD6Kn6xEY76foKTKHF2DDiBgFYJoJYEolEqrN0P1obShvgvNKn4bYRd1VAgP12BwDPwgtwafBXOHr14fDHwi9hUKHdft+Si1X+olb146qDXlkoscyFTbwuBNpcWHFKnfh1cl0FPHkINQEs3z7RDum+ysYZt/QpnVMpG4sKaJnii0hVYShrSEUuT6xTFMOSTenjArhMGMg19eUKsL5ClwspAdYWbmVgq3JrTVDr4tLqgNbVndWFGrvALLaasY8QY1tXFhterK0mrHJDU1QTxoCsTCZITgu8mSuFe5plmlPQBNFqBLEkEomUUzGAPbJ4AswZ/jIE4m9w/2sGAreNuhOOXn8S3NpzDQwudqL69jEHrircVvY62qed46oMORZcWJuQYhnAyvYvA1g8vILXEGNdoSeVeyruUxV2nKYwFHa5aV3Veo1rG65PCbj8/jDviy3o2rTFjNNUrdoVYFXvgwi2rlDr4tLKoDQGWld3NguY1YYOY9sZQoyxriy26BOm4FMtQVYmU1tTuLJLyLFNcae6SgPDgYMLq+ioepEYfZYjo5YglkQaSNPsNFCF4kbLb8lCHy19pgpgOzZ1wIRdJsDq5ath7eq14TK2fs7QufCJVV+EK+ES5/0lckQR+bBV21cBaGAs6BS2E6qVsvfexoWN9iWHV10lYhFYbQG2p6sXNqzogg0rN8H6FRthw0r2vAs2ruiCN59YDGuXbAzbblzdDTN/8SQMHjMIhoweBINHd8KQMezfQTB4VAcUW4teqheLF7uyPrA5tPyx8zDL7ycNuIpjdAHbsI04NgTgqvan2rdP0DXBvNhOB6GmeWC1LriFo+4CtTKg5cdbjgQQPpc6KOVhNmwrcWelRZIMMIvNmeWrGbvky6qgVAeI4jGZQLZqrAiQ1SkNyBr79ujGNupUOvVS0OBhxLmD2KVLl4b/Llq0GKZP3w4aRUuXLoO+vj4YNKgDVq1aVu/hkEikJtH8vgVw64g7qxzY/b69H6x727oQYhMqANw+8h5YuGoxTCqO9xpCpLvYxpT4R7mripBjny6sbEw6gO0J+mDl62tgwbPLYP5zS2HpvFWwfsWmEFw3rtwEmzf2Aka9XX1w76+elq8sAHSO6AiBloHt0PGdMHHGGJi08xiYvMvYcDkWSNPArIs7y+9Ltr80bqyqTVU/hvl5sQ6urZObVdgyxsk1OaxMqulyVOfdFWoZ0Eb7k1f6lfVvCjnm3VkZ+NnCrCnMWFYASuaW2oQYu7iy2FxZU55s3IeLI2uSFaR6cGNN/eqW5X32grHj3wbd3d3Q0tIC48aNVbTi3hc0YwYp1pobL1m6LMFt9VRuIJaBIFOpVIIFCxZCo6mrq7veQyCRGkL1dljrWZzJRr/pvaTqAn2HWTvAwlsXwvCfDQf4QPU2rP3vgkvhIvhGJlPzqNq7hBKrxoSBYB8urEybu3vgjacWwdwH58NrsxbBwueWQde6zZCpAoBNq7vDx4pX14SLXrjp9fLqEVOGwuRdx8Lb9psI0w+eDGO3HlEuaKYLF8bCrKs7awJa2X5d3FibdtK2FpBrC7qmsfBjwrq5ad1Z3Tos2GKh1jfQytxZXagxFmargBMBs1i3VAWpLq6sTXixS56sjSMrTr8j+832Mb+ryY11AV4p5MraSZzaWk6zwwCWMQ97LFy4CBpNff3cVk/lBmLZnQj2RhaLRZg0aSI0iphzzMbNxk8iDTTVG0ibeZqdh9oeTbwu9hYhOC+Awb8bDH0aCH+k/Ql2FegsTFEnHFhWXwioQolN0+rE/aV1YWVhxKsWrIVnbp8HL858HV59bGEYIqwT48fOUXFYcOSesued/eHB4fL+ZX8989YwtHjwmA448eJDYCMLNw5d3CjkOAw9Dp93h883r++p2t+aBevDx4u3RGA7bMJgmH7QZNjuqC1gm8OmQPvgtvI5M8GsT3fWBLTifmsGrUioxEJuWkcXMyaMm6sDXFQRLcV7IctZxUJtGqCVOpUKd1YXapwWZsvgHJSs8mWr8k81kJrWlU2bJ+sbZF3DitO6sU2V2iVwz8SJEzy5sPrGPsxc5sTmhXtyA7Hjx4+HBQsWhAD76qsvQ6OIhT4z53j8+HH1HgqJRPKssFpknaoYrC+uLxdvYjrkp4dA3x598MChD8BuM3eTb1QA2MC266tcPGqrCCNDJivtDWFKyKrEuvBecZmtCxuvl7mwcZtVC9fBEze+AE/fPBfmP6sOiRo+YTBM2WU8TNllLExijxljYOi4TnZVnZByDtO26CKtpa0Ftj18KpjU29ULq+evh0XProCFz64I/1303Aro4cKW1y3ZCLOvnRc+WjtaYOtDpsBOJ2wFOxz9NmjrbPUSaizbXlXduLytosqxbN+8ZOPIElqx26SFXV+hyybANcGtbfiwCWpVjqwN0OrcWalbmgJmGcjqtrHJl62FK5tFniwWZGWyBVljHzJH1JCfiw0fli6TuK15ufnOQoiZA8sA9uWXniqPTfp7yhVm0qXwBIaCTlXrMYWcBO281xGwaPGSkNvqrdxALImUZzXVHcAGKu6Ux7lia6WhpaHR3dACwBbztoB5f5gH659ar98oABjSN8TYt3ReSEVRJ90FNib0N9qfZFlQQhV0ko1X5sLKwojDpf1j3NzTA0/f8jI8dPUzMO/ht6Q1LIZPHALbHDQ1fEw/cBKMmDi0qj9e0rEiYUem1kGtMHabkeFjl5O2jvbZV4KlL62C1x5cBK8+sAjefGwJ9GyKoLa3uw/m3Plm+Ggf2gYzjtsS9jp9e5i6x3h0qLEL0JpCjsPtJecBC7a6cWUJreK2WcEuBnRNgKuDW13+LcZV5ftXzbGq2hYDtDp3Vhdq7AKzvCsbb2NT/ClLVxYDsqp9YPNkbUCWl3JqHJPbalHkSVop2FDgKSv5rlBsyqH1lWMb6PrxCLDJbfNTAIoglkQikTIs3uCqA3v2g2fgufD5tAemwRNLnoAh2w2B0TAaXut5DdatWwejx46GiTdNhBf2e6G83f6b9870m92UD2sTShwtr/7RVBV00rmw4lji56uXrIP7//k0PHjVbFi3PKoWzGvKTuNgp6Onwy7HTIex24+McqIUQJwcjzAWBLzYTrPDLtaLLUWYuNOY8HHAR3cOwfX1RxbDnNvegJdufxM2LOsK27JQ5Kf/PTd8TN5tLOx79gzY6fitQrcW6876AFpbsHUFXN14XaHVdVtb2MXk6ZoAVwe3NmBrA7UYlxYLtDp3Vhdq7ANmTcWfVCHGtq6sMQcW69wiw4trBbKZTI0jcWOxIcUuU+004jWBSUFGY83zOSCIJZFINRf2ByYvYT/10GdaPwF/Cq4IL3hnfWAWjDpyVHnddg9vBy+c+wK0P90OL49/OXHx+8nCR7z/GNnmw4bLkHAab69ydWUurPha5sK++dxiuP2Pj8BTt74Mpd7kNmO3HAl7nbQ97Hni9jD6bcOVU+mooLMWACtuE1+wMyhlubDscdyF+4fO7DPXvhrmzHavi3JqF85eDtd/8T6444ePwV6n7wD7fWgGDB41yAijpnBj2XGo+sGCbblfwzmUQS4/XozqAbw2zq4JcnUOrs651YGta/6ryaWVhQgnAFPhzppCjU0wa6pmjMmX9eXKonNgU4YX+wJZrGzCik1FnrBurI+KxKaw59wKO8erRq4ubJ4BlokglkQikXJ4t3ZqyxQ4ds2RcMvI26FrcBcsHry4vG7CuAlhmPHiqZVl7Nrp6NXvgMktdoXx+IsXVVEnjLChxapQYlVBp2h5tfurcmHfemEJ/PeX98HsO+Ym1hdbCrDr0dvAwWfvBtP3nVLeP9vOBLAySM6DmEu75QGTwsdxF+4Hz93wGjz+lxdh8Qsrw/UblnfBfb9+Gh657DnY95wZcOB5u0DnyA5U3yaYjWVyedOArQ/I5eUSHmy7fRZFqXRw6wK2slBkmVOLzX9lkk1To2ovfrZ4d1YXmouBWR0sMphV5cvWy5XNE8jKhAkrlm7nUlUY6cbqZA22lnPJ1sqdVe1Dm/+aAnwbEWCZCGJJJFIqDWS3NGv9ufhbOGL9u2DO0JcT88XOfvtsAH6a2ABg+3XbwCXw88zGIjqiNvmwNqHEcT8mx1Xc38K5y+C6n8+Ep26dk2gzdHQnHHjqrnDgGbvCiMlDEtthoMQEsCoX1jfssv50oNg2qBX2OGVb2P0D28D8x5fBY395EV649XUI+gLYvKEXHvjdM/DYlS/C/h/eCQ48b2foGNaOAk/+otg0Pl6Yvk3FxbCAG+7fIhfZBLxpYBfrtpramtq5gK0Mal2BVvVaB7PiaxnMYsOMpaDJ/Y2owI5AFlmMSZhDVhcqbOPG+hA2pNgVVLNSlnmygWZbkwvbCFMOqkQQSyIhRcWdmkdp5oqt5Tyzg4uD4c7Sf+G81Z+BW4bdzq7ioorF7BFULliPXn04XAI/g8HFTvTcqyp40BV1cs2HdQklVvUl9rF29Qa44Rcz4Z5/PAGlvkr7EROGwtGf2B8OOHVnaOmoXMyKAGsTRlwvgLURy+mdtu942GKfieHUPPf//hl46uqXodRTCvNmmTM766o5cMRX9oLd378ttCIu7rCurA+oda2enRXwpg1ldnVmdeBqC7YyqNW5tBigNb1WhRqbYBabM2vryqYNL24WR1YmlRtrUzXYVOTJdpnLnLG+VMu5Yhsm/DinIoglkUhNW6G4HnPF+g43GlIcAv8oXgpb7boLrP9kJxSPHgw9Xa/C0GAonD7l/fDJ4rkwpWWSVU6TqDTnSJUPqwsllkkXSiz2FQJxXwnu/MdjcN0v7oENqzeV140Yz+B1Pzjo1N2gZVA1vJqEDSPOI8CKGjFlKLzrBwfCwZ/YFR74/Wx46t9zQ5jdsHwT3PiVB+Dxv70Ix313f9hyr0mo/lxgFnteXCA3zXRRNgCcxuX14cyaCkip1uugFuvS6oDWxZ1NA7OqEONauLLYPNk8gaxMurBinbKY49VHSLFuLK7rmkmBgwvbKADLRBBLIpFIDaDe+QEM+uNOMOatz8ObP3k3HP3+w+GHl31H+4MjL64UeC3qJGtT2ZdieSGwCiUWl8+fswQu/dr18OrTC8rrOwa3wTGfOACOOHefsPiRrC+MC4tRIwAsr5FTh8K7fngQHPTxXeCOHz0RFoFiYvPQXv6+m2CfD+4IR39lH+gY2o7qTwVLaZTm3NUDgFXga5u7awJTVRvdehuoxbq0MqA15brawqyuAJSq+JMPV9ZnnmxiXHUC2Vi2+bGyqVNUYcW1cGNdI+OaClAV88Oic2MthY3kyosIYkkkUl1EFYpxuv32u+ClF+dAd3c3dLZ3QtDbHV5OjBgxXH1uU87j5pK7o7oYZxd6ytBlQyixWNCpd3Mv3HjJvXDj7+6Dvp7KugNO2gVO/MrbYcTE/pzXoJS4UPftwpq2zUqmvFgRtAsCMI3aYjh84JLD4bWHFsFt338Elry0Klz++F9fDOeaPfGHB8O2b59mPS4MiGWpLM696Ty7hjpjpxlycWWN8CrLcbUEWjGHVubOmuBWWeVXA7ZivqxPV1YXXpy24FMtQTaWbaEnmZLjlrijhiJPadzYLOeMbdgKxT5CiYPmyYWN1SS3KkgkUj2V5zt1NpLdvc3qBw9b6OKM08+D71zwQ+jd3A0tQ0dD0NcbLr/isr/DuFHbwH/+faPz+6CqTOySmxq3U4U164A1DiXWObCLXl0O33//n+C6X95TBthJW4+Fr179QTjn5yfAqInD+o9JDqImFzZNGHFeHViVtjpwEnz0phPh6G/tC22d0b3stQs3wN/OuQ3+9+2HoKcr+oylValG/2Uh9p7aPlRinzHdo7zPQkn50J1T1TrdcumyQnTzJ3EDiN0Q6n+I24nniV8v7lu2LkC0S+4r+i/aNvpP154/v/z7w38PJf7W+e/DfpgNK6lzxy4bO1+4Tjym6Bz2lb8X+e/SRH+G7y1dG1N1eVUfsu1sInmkNzNl21i6e7obRPrtNL9hCGCrdepR1tdTgYcZBvJ+bUcQS2oYMSeKqa+vfneP0uQdkkgu2m2XGQDFVph45k9h+L4nQbFjCIz/wPehfdJ2YT7wtttNR/VjDbjChYTss68CCB1Y6EKJZduyY7z337Pgmyf8Fl57dmG4rKW1CO/61MHw7ZvOhW332aJqOxcXFjf2fP+g24yXTc9zwLk7wyduPwmmHzy5vPyxv74Af3j39bDwpeXQKKoVLIsQJsoVdk1wG/ZtAbY2y1XLdEArbiMev2wdBjTjdabnfISGCgplwMmPTwWnMpCN28vGjgXZ6PxlA7KYadJkn1txu+7uzdFyiZvHH5MMFrFwrLuGcgbUnINWLsYb6F3YRgRYJoJYUkNo4cJFsHJlFPq2dOkyeOyxx+s9pMZXI1ff86A8lNzH6Ic//h5AqRc2L3kFCi1t4bLWkZNg8+J5cMgh+8Guu+3s7eaKqR/bfFjV1DrYUOLNXT3wx69cA3/6yn+ge2N0gTVx+hi44Lpz4b1fOhzaOlqTDqqjC6tSXhxWFgoZPzBiYcTxw6RR04bBmX87Bt554QHlXOJlL6+GS0/8L8y+LjnXLilSVrArSge1YT8SoBXHZ1qua1d+7QCzyr40oBkfowp4sa6sqn/Z+FxAlpctyFbWu4Ns2vm+E/sI+sIbhZf+/h+wauWacNmaNWvL1XlNvwlYN1bbhwZQWUixch1iPxgQw7i0zjm2yErBNsCozIct5eP3qpYiiCU1hJYuXVp+zu4SHnHEMXDppZfXdUyk/EhXDl83kXut5fJDuO9+e8GkyZNg7WPX9eceFWDVPZdDsaUF/nz5r53Ggc3nw96d1V1gqcKETaHES+evhG+f/Hu495pZ5WVvP3Uv+N5/PwZb7DxRuf80ubDY9o0OruJ+WgstsP9ZO8HH//cemLDD6HB5b3cf/OcL98JN330IejfnJwLFJczX58NGtoBr2p+NUysbg2xsunZpYFYcP8aVjY9RtQ3Wla0FyCr7VYCsCTYxIGtab3MTMu5jw4aN8LmPXgA//t5vuXUAGzdWKr7rjgcrnyHFA1E+QoObzYVlys/VHYmk0e677wbjx48rv968eTN86lOfDR+9vX7yt0jp5PKlh92mUb5QZfKRU/ulr3wGetcsga5XZ0HXm8/CprkPw9lnnwJjxkbAkUYueUAqZ8EUKoYJJWb9vPzUG/CNE38Drz0XVR9u72yDT/76ZPjwj06EjsFRBd2qi3JT6JzgwtqqFqHEtQBXfh/ifsZvOwo+ev27Ya/Tti8ve/QvL8Dfzr4VNq2J0jlqIV8QWeux2YwVC7c+oFa2T8yyNDArjl/Vr+y5L5DlQbneIKuCTRXIymQFuoiw4oXzF8Mpx38cbr7xrsS2o0YNh87Bg6TjlfUv208WIcW+2+RevpzVIN2MBHkWQSypYdTSEoW6DRkSVSBlYm7s+953Cqxfv76OIyORstXZ55wOnUOGwJrHroOVd/4xfP6jn3ynJndwlUU/FBdcEYiaL8ZU43rs9mfhu6dfAutWbghfT9hyDHz3uo/D/ifsUrVP8aJenA9TN05ZwRdd340KrjpolaltUCuc+KND4N0/Ohha2qP2rz28CC47+X+wZmE237P1AFV2UwL7cJUL5JrAFgu1aYA2Dcyq2tq6smJ4sWrbSv96MPQNsrIxqEBWOnZDUT3b/Fhsv7FefG4unHz8x+Dll14NXw8ZOhhGjIoq3sfzrstucMq+27MOKfbdphFhLSsFmHPAYJp/5EgEsaSG08iRI+Cyy/4EbW1RfuCtt94GRx11HCxevKQm+2+q4k4DPC+2UfJpi8UinHbqe6H7zWegZ9nr8IMffhNaW+1nSHOdekf1mdf9AOqm1lGFEt/8l/vhpx+/MsyFZZpxwHT43vUfh2nbT9BfWMuKnUgu/HQurMkF8a2swNUWWlXa+7Qd4EP/PB4Gj44cmaUvr4I/nXQjLH5hBfiQb2i1gVJbMLXtG7sfV7BV9WEDtLL+fcBsVq6syln1DbI2VYt14+VlKvTk6raqtjNVCL7vnkfg9Pd8GpYtif6Wt5w+Ff5z66UwaFBH/3gl/adwYzHrdNLlxabtu5EVKPJhbUKJtX02gAhiSQ2pM888HW666QYYMWJE+PrJJ5+Cww8/Gt56a369h0YagMpiYnXxh/u7F34dWlrbYNoWU+GD55xm3t6UH6W5Yy+7YNI5rxjXVeZilPcfBHDVL26Gy77H8n6jdge/Zw/46l/OgSEjOqU5s7V0YU0wgoVFV9fVd79YbbHXBDjv2hNg9JaRQ7NuyUa47AP/gzeeWJyq37Tg6tMtzVq2kIsBWxXUqrYzTecj9uv8WhFibOPKyp5Lp61RQG1akMVMv1Pe3gCyvgs9mdab+mVtb//fvfCJD34dNm6Icl732Hsn+Nd//wDTt4mqvFftK4UbKxtDpd++uuXFpp1HPUu5zNGeqVGRM+dVFEEsqWF12GGHwsyZd8AWW0wLX7/yyithwafXXnu93kMbsGq0u3i1AlIfGjp0KMx68m54bNbdNdmfTbEHUz4sfyEYQ22lzwD+cfHN8O9f315e9p5PvQM++fMPQGt70m0WL9JswNo1F9aXbAATA68+wLWI+G/MliPgvP+cAFP3GB9u072+B/529m3w+mOLawawPoA1r8WgXMBWlI1Li3Fn08CsKcSYH1takOWf1xJkVRWLpd87iDlkdWPllTas+H/X3gVf/Pj3oLc3Wnf0Ow+Fv1z9Sxg9ZqRzLqu2srBj1JrNdYTNPjBViPN4PeBt3tiSvqBTI16/1f/dIZFSaMaMGXD33XfA1ltvHb5+44034Mgjj4FXX32NzmuDyFdxJ12F4rRhwT6KM/n4oZz2tmnQ3h4VNvIt7MWAznkNARUJKSHOBn1w5f+7Af7z+zvKy8/59rvhlC8dE14oyvqrAlnktDo6F1Z1cW0jHiZd4BIbMuzStwpQsdsOGzMEPnzVu2CbQ6aGyzZvYCB7K7z2cDRvL1a259YFWvNYEMoWck1gq9veBLQYd1bsy+a1jSurCi9WuawYkK30nQ3I+px6x2d+rHQ91++N19wOX/3MD6GvL1p/0inHwi//9D0Y1Nlh/G3FurGy8VqtyyifNQ9wlocxWI8v5y4sE0EsqeE1bdpUuOuu22CHHaKKmvPnL4DjjjsBFiywu8CyEeXFNpbyNM1OnmSqTGxb1EnMhzW1+/vFN8F1f6w4yx/+/nvguA8dLN2fCJrKC2aNC+syL6wNRNkCJgZcsTmuOjfVh9o7W+GMPx8N2x4WRb70bOqFv3/oNqvQYptzY3PefcBqKaP/bMevOgasU6s6JrFtom8PMCt97smVtQHZ5HnRw6Gq3/IyyXh9VCxOmx8rk279HTffD9/4/E/KN3pPOesEuOjnX4FCi/7mrM/KwjpAtQLSHIcC100lxeckSHHOGwBgmejKjtQUmjRpItxxxy2w4447hK9ff/11eOc7T4Dly5fXe2ikAah6hiCZ7uTb3kHXFXXCXKTzU+vw7W+84h645veVEOKP/eD9cMxZB2ov/tO6sKaL5lrIBK4maHUFVbFf00NVufiMPx0N2x8e5c/1dPXBVR+5A5a9vNp6HCbZzndrKxfY9LEfzP5snFrVtrJxmNphwoxVfWohNwXIypaZ8kuT/erhUA6/enjUTROm6pOX6Waaa1ixbNkDMx+FL33iIij1Q8lpZ58I3/7x58Nigb6cQteQYhszAFPcqdHczzQQaT3rQCndtEZ5FUEsqWk0fvx4uPnm/8JWW20Vvn7ppTnw3vd+ADZtqp60m5SdGvkLMU8q1rgCsquw+bDl9kEJ7vnvY3Dp9/9TXnbu994LR51+QNWFmG1BJ93FY1ZFQrDCgqsoDKy6QqlOqn46OtrgtEuOKocWs/ljrzz7FljdP/0OFqx9FaGycV9rAa0uYzGNyQVodc6srp0qZ1bVhw6o0oCsqf/AAe5McGhTsThtWLF0zIbvUVs39oVn58LnP/I96O3pDV+f+IGj4Zs/+Ex5Ch1x/3kIKbbZR5ZuLX+OBty1VUkelRA/8iSCWFJTafLkSXDLLf8N/2V69NHH4EMfOrd8F5KUT+Xti3EgS3Vn35g31X/xa5paZ/bDc+CX5/+t/Pr9nzkKjv3gQaFjonNgq5wepAsrFnTynQvrKiy46rbDAGoBilYPjBjInnnJ0TBll3Hh67WLNsCVH7wZutZulh6H7hzUwo1NC65ZFnaSjVM3XgzQytrL9iO2S/SlAdm4D9VzXXgxFmQxoIydR9ZnWHHa+WNly1yA2wTsCxcsgU+ffQFs2tQdvj7yuIPh+xd/qezAomtR1Cik2KYfn9sMRAUp547PkwhiSU2nrbbaEm644dqwmivTddfdABdc8N16DyvfapD5YrMo7pTXioS1ks1cfzYgEIMvH0q8+K3l8ONPXQq9PdFF0FGnHgCnfuHYqu1iIBZl68KKubC1AlWM64oFVxOw+oBSTH+iOoa2wzlXvBPG9E+/s2zearj6c3dBqb9wjHhsWTiymPfTFl6zKArlCrhYoPUFsyZXVhdejA41RoCssQ8HqEsbVpx22p3KNob33IMby6bPYQC7bMnKcNnue8+An/yOzS0uj+6Rnk/LysK+clVd+mmkGiXeAbJkzoe1CiVuMMNn4FypkQaUdt11F/jHP/4KLS3Rl/bFF/8c/vOfa73uo5G+OEn+izthKxvnUZjPrq6ok8ud2U0buuDC8y6Btas2hK/3OGwHOO/C9zJqsrqIt3FhdcK4sLaVcdPAq6wtBlql+wqKTg/tsUn2O3RsJ3zoyuOhc2RH+Prle96Cuy6eJd3eFA6dhbAAW88qxlio1QGtansbmBXbJPoxhBf7AlnZ/s0hwni4jUHWJqy4vMyiWrFMtXJj2Y3c73zx5zD3xWiqwWlbToZfX/E96BjUnho2bUOKrX5vcuK8moodZjmePDqeQQ7HxIsgltS0OvbYo+GnP/1x+fV5530CXnjhhbqOaaAo7198zQKjNvO6+p7PL3JLcfmw7MLqF1/5K7z20oLw9eQtx8H5v/4gtPQ7A3Eoscl1SuzfcCGtc2HThJbKZKowLON0HbxWb6+GVlsY1ckGbuPxjHnbCDj9d0dBsb/S6b2XPAXP/+9VJZjWCmRN73FacJVNg6N6+IZaDNDKxqpr5+LK8tuq+rEBWdWNJRPApc2P9V3kyXXuWB9u7F//eC3c/r/7w+dDhw2G3115IYwaPUI+x2uKkGLUdoj+XX57BnqF4lRFuALNtg3mwjIRxJKaWp/85MfhjDNOC59v2LABTjnljPBfEslWprlimzEE2WbuWD4flg8lZrrp7/fCAzc9GT4fPGwQfPPSj8KQ4YOV/amm58G4sJix8sKGY9YaXmXgigFM3VQ7rtPw6PbHxrftQVvA8d86qLzs2q/dCyvfXGsNsjKpzq/JtdQJC66+wDQN5GKBVrWdbBy6djaurEvBJxXIytbLxuWaH2sbVpx1kSeZ0rqxTz3+PPzqh1eUX//g11+Gt20zRduntm/HysIYyHIBMZcKxVjpQm6bVUGKaZvyoua76iKRhApzv/vdr2G33XYNX7/88lw4//yveDtHTRVSXOe82Eb4wmwmiWFTPvJgZZr34hvwxwuvLr/+3MVnwuStx5WdXExBJ18urPEC3QJQMHO72sKrCVxl/fmaExbbn2o8B56zC+xx0nbh8+71PfDPz9wBvZv7tGHUVX1bjN/VucUArAuophEWanVA6wNmbVzZ8jrNeK1yWj1ULLZdljasOM2UO77d2LWr18PXP/2TchHL8z53Grz9aKHiewo3ttJHKZVD6lLcKcvK8vW67vCdGxs45MM2gwhiSU2vzs5O+Pvfr4QhQ4aEr6+44kq45prK9B6kxlKtijsNRKlgMp4T1hZou7s2w48+82fo2RxN8XD8Bw+F/Y/eVbnv2NF1cWF9AKwPyeGqgIJXHSiq4NLVecU6s7p1/DjZDcP3XHgojNlyRLhu/uxlcMfPHlceM9+37tzoHO+scmnrLRugtYFZ2X50bXR/75jQYoxM+bHY8YhjsnVjTXJxY7FjNPZpAD8G0T/42m9h8YJl4eu99tsZPv7F08GndGPwlReL2Zcv5TokOaOw3sAilLhRTIXm/AUgkQRtt9228Ktf/bz8+rOf/QIsWbKEzlOGapQvwTyEItdKmIsNzB1fPh9Wd2F55c+uhzfnLgqfb7XjVPjQN05Cj5V3YcUQRD60MOHaCACrUtoCPrriTUJLNLyK4CoDR1vYzApeZcvZuAcPGQRn/voYaGmLlt3/p6fhzVlLk4BuAbOY8451eXXtsxBz2uJHGmFgViZfIItehyj2ZBP+b+PGmrbXLcOG1ppkyo2Vb2M4BkmfsrHdfuP9cMf/Hgifjxg5DH74m6+Ui1qqtpFDnB3Y+S7YRNcMtYPdoEmuzwhiSQNGZ555OrzvfdFF9IoVK+Ezn/k8uXYDTDYVitPmuKq2r0XuLPaut+putO1dalk+7AtPvwL/ufT2cH1beyt86dfnQFtHC+e4VkKJTS5sdEwlbRixLKxRdGGxoZUukoGZar0MXvntVMAovsauw4Krqg/VOtmYp+4yHo754n7hcxYUcc1X7oaert5qt1kBs7pz6gtkdbIuxMTBqgpcVW1sANfVlZW1telbF8HgK8wzrRvrBJ2G3FjbkOLEesO8sdJtHI6BjWPFslXwk2/9sbzsGz/+FEyYPBbqnTJlA0m1SMsqhybXILy24QCxhHBh2bL4kSMRxJIGjFi4G3Njx44dE76+4Yb/wvXX35C6X8qLbfwvf9cKxWkrGxcL+a2MnOZzvbm7By7+0uVQKkUXc6d//niYts1EZxcWE0YcSxVGbHvxbiMTjNnCqw5OxddSqAyKqIe4bywcy7bjX7/93L1g2q7jw2XLXlkNd//yCek5iM8df/7EfajCi+stHy4r3w+mLxdXVvY3YopE8B1W7MONxa7HhhSbhL0B4NK3z5Dii7/9Z1izal34/Kh3HRw+yttIPlPYvFjdb3GaaWiw+xiwQOlRAQfsqc5Djs9h/X8JSKQaaty4cYmwYlbkad266AeA1DgayD9MtZRrUadrLr0d3pwXhRFvu+sW8J7zDi+7rC4urG0YsSoP1of7KgKUDcCWtzHAK/9c9TpcZoBTk2wBF7MsPL7WIpz606OgpT16fe+lT8Pyl1cnjlucLsjWlTW9D7I+fMkHvKYBWt3nFxtebMoJ14Fs2rBi6fgs5o6Vbu+pwBNWLtPt+PosxcfwyL1PwR3/jcKIR44eDl/7wSeg3vJd3CnLceRdtjcXQg2wmiAEsaQBp/e9771wzDFHh88XLFgIF130o3oPieRZWRV3yktuq43EHz3xTjymCmYZNCX5sGIo8ZIFy+Gq3/433LbYUoTP/ujM8nywaV3YeL3suQ5g42Wmi/+0ubKigyiTmPOKeV5+LQFNFiLv+6Hcl8Gl5V9P3G4MHPGJvcNlpd4SXP+9+8K/SxFkRVdWdT515xS7Ps8A6wK0qs8zNrzYF8jahsKmdWOzLPCEdXdN8hVSrPp+ZtEuP/nWn8qvP/etD8GoMSMyhfe0xZ2yVHyecnWDu0bzrgYeKx1XrcvT+ZSIIJY0IMOKf/nLn8GgQYPC17/73SUwd+68VH1SSLFcufpBaRA1Iijz+uMP/g+6Nm0On7/rrMNg+oypyraiC6vMh+13YWVOLHN+sACrEn9BbgOzYhisbHl5mVC0SdxOBbLhaw208v2k/a/clwFqxX3KXrN/j/j4vjB66vDw9bwH58MzN79SdS6YbEDWNqxYtr3z9Dx1+j7DOLPS7TIAWdU627BiX25srQs8uYwhizlj/++Km+Ct16Nolz32mwHvOvlw9L5tr1dc54tNu1+d4nNB1xiNAdpZiSCWNCA1ffpWcP75nw+f9/T0wDe/eUG9h0TqV9Y/SlkWd6pF0aYsFDusafXCk/Pg3puiaVVGjBkGZ3zh+PCiTxVKLJPowvIXtvF62cWyLcDq5tz0Lf62hApaq5bxTqgIlAoAjfNHbR/82ExQK45NHDf/un1QK7zngsPKy2/5yUNQ2ly5EHcBWZewYqxqOTesrTCubC1A1sf3hNiPLxD1NTasXOaM9XFzZNWKNXDZr68u35T/8oUfC/9Nc/xZ/u766DuG/lKTzntq5Y5awmfQxGZCY15xkUge9MUvfh4mTZpYLvL00EMP03ltIPn+Yk5bpKmRpCrMkfacXv7TyvzLZ33xBBg6fLBxGx5wdWArOrF8HqyuCnF8Qc5DKwZcbQBXBlliziffTguyEteVb1vUwKjrf1b96cKOBQBm/+561Daw7UHTwtcr3lwLj/37hSR4c65sovCVBLD5c5yo9uyQp4ytfJzYJgc3qXyBbBbyMXeseNPKx1jqFVIsk1ORKQGMr/jNNbBh3cbw+btPPRK2m7EVZKG0xZ28wGsTA1iWChSw30yhxEz1/0YmkeqkoUOHwne+U3Fgv/e9C1P1RyHFAyMvttkV57uaJObDPvHAc/DUQy+G6ya9bRwc/YGDEm1VBZ1EiS6sDcCK8MoDrOoYxP9k4/EhGcDG/4b/yUKGFaCZ2C7D/2SOLQZo+XbMITrhS5WKqXf+5jHo7YrOqS3M8u3jdTyA8hDqcy7agSDXz7ntdlm4pbV2YNPkxRq3MRzLkoXL4dq/3RY+H9TZAR8//zR5yHCGRa1qobyOq6lAvJTTcVloYH5bk0j9OuusM2CbbbYJn8+ceR/cc89MOjcD5Qu8Bg4uH15synXNWygyX8iJSSzqJN4wuOyn15Rff/CL74aW1kI5lBjrwurCiDEAK4NXvo0JWON2rhJd2AIWZBXwGveZNbi2QqsT1KqAVnRnt9p9Cuxy1NbhMaxZsgEe/NvsxDGKMGsKMca6sj7noi23b1A31jqP02Lu2FqqVq6yrVzOiYu7y1zYns294fMPfOh4GDthtHUfmJHVq/pws18vZKqgecAfq/p/G5NIdVRraytccME3yq9//OOf0vuRA+UpLzaLwkt5A9a0F2iP3/csvPT0q+HzLXeYAoecsFdVG4wLK9tvAnIRABtLhNc0x5soYOMY7ogBWH65S4hwIcXDBW5lQBsfi+jOvuuLB0Oh/0/n7j8+AX3d1W6sT1eWV15BNow0qFEeblr4ywIejbmwOQWaeoH0ssUr4X9X3x0+Hzy0E876+HugWdRUkWw1dEeDrObszenfnqjGupIikTLQySe/D7beOnIJZs68Fx5//AnnvuiLuLZqhru2xUJLQ4drMaC8+s+3lF+f8dl3QbEY/bQYqw5rXFhXgBXd11hiTqzqwY/NRWXgSkBQQQmwovsqhUIksKb5T9afat9SB1XjzjJN3X4C7PHO7cPn61dsgieue1EKsGld2UYDWVfVq1JyMwtbodip7xTzwcb616X/Lbuw7z/7OBg5arhxG9W+s7hWcZqKaAB9jtMeaz3PVVAqlR86p37AQuzSpUv7/11W76GQBphaWlrKlYqZfv7zX9Z1PLnRAAxNkYUG17NCcbF/PLIiQfVWnA/7yotvwqz7nw+XTdpiHBx4zB7hj62qKmolP7ZkfbGoA1hZ6DCTbQViFciq+uAhTicVwMr6UMGrDlixIcGq/2Tbu0CyNPy5H2aPPHff8vmYedksZkVKj1fnyvoEWVG1zJHN0oF1DSk2VSlWKc2csaqxuAqbC5q3PFrTTQlWyOm6v98ePm/vaINTP3x8TY/BN/Q2U85rnhVwnytXCI7AtaLly1cluK2eys2VUV9fX+JfEqmWOvPM02HChPHlSsVvvTWf3gCPcvny9FPZkH4ofV+YsG34i6d/X3pr+fl7PnwEiMYyX9BJ58JGfatdWBPA8n0yyYo8mR6+wgWTLmxFJoA1uaRx+1r9J4KtCWj58Ykwy8RyY7fZJ5o3ePG8lfDiva9pAZZfpgovFs+tDcjy28jWq/rIIqy40dxYDEC5VijG7i9vIJpmmh2T/nv1XbBxQ1f4/Lj3vR3GjB8FeRFFoPlXmuufADMNEQemtqHEff3b5oHXcgOxJFI91dHRAeee++HyH+af/3wpvSGk3KmeubQxjPJavWIt3HVDNDUVm07n6JMPLK+LQ4nFPlQurA+AZVJVKA7XGX6cVTm1WBUMgGQCWH5Z/K8KXsVlLR7+swFcHVjzy8QwY96NvfeKJxNT88iOP3GeJOHFWYBsM0g1NzLJ83n2ECZsbF8qwbV/jSoSM33gnHdCo4qAd2CFUGet5vz2JpEcdN5554aFnpj+8pe/Qk9Pj9N5pC/pxpDP4k7YCsWu/ddTIXQqfnRvv+4B6O2J7sYed+oh0DlkkDSUOC7oJPbLAyl2LCqAVU6vw8bT/xBfi+vibWX7jY7D9uKj8j6GmMbBlApgRUjU/ccDaEHznwxYZe2Y82oLtjp3Vgazux25PYydNjJc99IDr8PKBWsT0/KI5wETXixzfePlmCl4RJBVubEyR5a5sS6ObKKCcwYhywN1qqBm1NOPvghvvroofL7nATvBNju+rWbuNQFXgxR/CgZm1Bl9y5FI/Zo0aSK8613RHc4lS5bCrbdG+ScDWh6/GLP6MWykH1keTmVwiwVeGeT6dGljcNVd8PSW+uDWf99ffn3MBw6Wgirv3sYuLN8O68LGoBoDKl/nmF8erhPgNK4CK3vE4tub5pd1rVAs3jyRAawJXmO4ZMDJAyjfplX4T9aPap0IuTzYtkGbtB/T2Pnjayu2wEHv3y06jwHAY9c9n5yWR+HKiv3pwosT7QxT8JTfF24b2Xay7X3AbBrZ7s+UE2wjEfx9t8dsn7bPRomO+d//3VN+ftIZR0NelNfq0SR/EvNh86b6fwOQSDnSOeecXX7+t7/9va5jIfmRbV5s3oo7uewnTcVjTCQBA8uXn3sdXn3prfD1jD23hqnbTEisrwJVyeuyk2oBsEzxsvi5CVz7JI/yWASoVYGsyo0VwV2WD8tuOYgurAyg+PBcfrkMLlUgyfDWZmod1l6NzPG4ql1dGdBiYfbA9+1anm7n4WueDQs8Vc0xq9jWlyvLLze5siL8qVzZWsGsaR9YF9YEgdh+EueWP+cWbrAvIMXCbj0B2GbfG9ZvgntufiR8PnzEUDj0mEo4fiOpkW4250mZTaGjUoO9TwSxJBKno446AiZOjC7GmRO7Zs0aOj911ED54dNBZ1yh2FU2QI2pGBlD223X8C7sQYpKpcmCTioXNvFaA7BxyDAfPhwDrA5c+b7LMKqBWhPIuiqeSqf8WoA0cVnFTZTDqwiNEZTaP2JXVgW3SUiujMUUcqyC2XFTRsGOB24VHtvyN1fDK0+8VQFUbn7Z+Hyo/uVh1taVNeXJymDWdgqeLGDWtU+MC+vTqa13yoeL8pTGEev+2x+H7q7N4fMj330QdAxq974PqhQ8wFRqnuuqfH1jkUh1FsuJfd/73hs+7+7uDisVu4jyYhtDtcqL9SHdNDu1mms2/lyzQiP33fp4+LytvRUOO36fqpsOKidW58LKxANsvH0irDjAg2tJsTzcD7+dBmTFsckkgyBe4jywTDzsxX3w7isGXFsLreFNC9WjpdBafsjWs+15uDVVI04Dswe9d/fysT9968vJrQ2urHieXIs+6fJkxW1k62V9ZAmzmH6yyoXNAnBd3NBag7bTGD1+H99x44Pl50e/5xBv/ZKaXwHCBGh0o4AglkQS9IEPvL/8/Prrb6TzMwDzYn0DKe+GmvJiZe1UfdXrHL70zKuwfHE0V9yeB8+AIcMHV0Er5nnswurCiBPtxbDifoCNAbTqP0n+a9VriUMbSwTZ8nJuDK4ubHm5AhJV7qsOXGX/tRXayg92cR0/4mVV++X646GWd2pNMKsKNea33/PIHaClNTofT902Jwz7r5on18KV9RleLL4/vHS5shiYdXZSPQOsCc50fbmAHXZblyJXsj6znFu7ViC9fu1GePz+Z8Ln4yeNgV333r5pYWRgy+0aK8j4Pc97PiwTQSyJJGjfffcJizwx3XXX3bBhwwY6Rw0uH/PF1iovNi2kZhUSx4ec3XfrE+XnBx+zZ2JexHj6HD6UOC7oFD8XXdjyck0YsSz/NQbYeHsptCr+K+9faBvuh1vGVy4WwdpFol8ZQ12yTQSwTCp4lYErD6nlEOGgoH2EhZu47WLAlUGtCLRxyHEcbiyGGuuc2aHDh8COB0QhxSsWroH5zy+VTsmDAVnRnRVBNj7v/Hb8MkyeLMaVFfuxBVp+ufhIoyxDiVXb8c54Guj1Kdk42Oc0r3p05tPl6u+HHrMPFIv5OI82Iriu8fkupZi7tQFvhDTeXwSJlLHYD8U733lc+LyrqwvuvnumUz8UUtz8P4L1zKGq577vvy2C2GKxAPsfVQkLleWOimHEKheW70ME2JIi/zXhviqgtVfyENvF/fFQzYNs2orEGIkOrAh9KnjloTUGU9ZSF1YsPuLeVXCrA1qdO6sLM2ba+5idysf/1O1zkiDKQ6pj0SdsnmwsH+HFsn508gWqeZhSx9X9NLqzNS7MlBfofvCuJ8vPDzlq77qOhVRf5fk6qJ7Kx18qiZQzxRDLdOedd9V1LLlQA4QU56F4SJowZNe8Vm1Isqdj4yFzyaJl8OYrC8PnO+65NYwcM0z5vsbbxS4sk8mFjaUCWD7/NeG+KqA17ot/qIBWHFf8mndjXRWjosyFlQFsvE0MsEwyeC2DJ5f3qnJAGerGD+l6MVdWA7QYmJXlzMbHxf7b+8gZ5fPzwoOvJdbF5yte5hpejMmTxU7Dw2/Dr0/jyvqQNozZAQB1Ra98yQiuLnPvZgi7JkA3nlPL42F1Bx6//9nw+eAhg2D3/WZ47Z9Eaga11nsAJFIeddhhh4RFnnp7e+HOO++u93BICDGIKngGUjE0h/VvA+F8+8RzKJTDc2V9xsvidqxCcanUF15I8aCn2x+DYl00QAhLXJtoW3Mo0pMPvVB+vueB1RdWZSeTCyWO/y0/Z2MsJJeJYcTxNiLAVoUP97flgdVUfKmFM7F7g+QPIrsYDR1YtrwQgVD8mm3Xx7UXXzM09CUeYOPPdQyvTAwuw3+5deFrWcik9O8iWiYCegws4eesUKwAfX87BrRBgeWvtkRh5P1tWvrPQ/wJKsbnknuPY5iNPxtjJoyEyVuPg4WvLIPXnlkA3et7oHNoR7iObSMFg/i9C6IbDPE+ZP/G7cJxFUrlv5/4GPu49vGxx58XBnLxZ4lvz59r/maH2EYFhL4cfRMgq8BTdk51kKqDeMzUOnx7I7g6HJMLpMq2cYlsMf3exH+jGIl/o6+89BasWh7NjrDH/jOgoyP6uyCRamEcBA2QD8tEt25IJImGDRsG++0XVVydN28eLFy4yOk8UUhxftxbH3mxWQvrxsYXT/yFF2bb0Cljbpri4gtzIffkQ8+Xn+92wA5SxzUGdB5gY4nLYhdWNZ2OCLCJok2coxo6rIXe8nbl6XiEPNmwH2GKHt6hFfsXCz3pIER9g0F+XlUurA5g+XDh2HWN54YNCzFx4ansdVj4STNPbNxGzMEUp9pJuLT9Di3vzsbr42JQvDMb58zK8mV3OmDr6Nz2lmDuE28pKyHLXFmX6sW20/C45MrqoDDu0ybk2GY73f4xAJtVQSdVP1L3lPt+cnFXZTdy5Meu/860Bf5wvUVEjc7dferhyvfsPofsCi7Cvl9ZpqaQQ2whV3AMAqd95C0qzkUEsSSSQgcfXJn78uGHH6bzRCHFTlPtJCoTp6hSrJsvVty+akwhlPi5UImd2PaONpixRwQgWPGhxDL4FfNgw2WBOnxYhFcRVnskUCu2E4E2EWLMgSyfHys6xoljRDhtMaTKlvMAyy8vhw5zIcVl0JSApyxkuLXQlngkITHahodbHoZlQBvn0cpCjWUwy4cZx67sLgduWz7OFx96rXxcMhh1Kfrkq3qxKcRYBbMm6BHhVPXASLcvDGzbhBHnzYXFwq5xHFKX1m+EBUbPPD6n/Jw5sWn6SrsNiZRXUTgxiaTQgQceWH7+4IMPl+ePJeVXeQwpdt63Yj9xSKRyvbDcdrxRiHGp6sKHhYCuWLoalixYHi7bcY+toX1QG/QGvYm2otPKVyUO/43zWmMQFKFVcGTLECkJHy4Dr2QuVx46RYXhpvFFM8/2/SHDLMQ4Dg3mQ4vjNnzYcOQUJy8OlaGwkotI3oUVFQOrCLBlACs78pyjKHGCVDcwGIxGh1S5k89GEx5DGFrOhdqWuyiGNxYYVLD3tlSITg37jMWhxnF3bB0fZswfedzvzvttU172ytNvlY8jfvdUf81xKDD/vvQfbGIf4vmOQp+LifBiPpQ4bh+2E0KEdSHG8bj5YyufZ24MqrmF08gGXlXtTQCrBHwEFKlg0wUofbmwpr8PG9gz9m/xmxR+JoMAnn3i5fD1kGGdMH37LdDbk5pcWYX5Bvp+8+raEsSSSArtu2+lGuCTTz7lfJ7YxaBr0R6SX7GLg0LBf+gUn+Oqgt/yOkVubCxTLqtu3+K2tn2Z9NKzr5afb7/rlol1sotzWeiwyYWtmkqnWB0+zLeL3VQRWo2wIMAPuzDnc18jkE1eoDLwYfCrAleT6yYDWJULqwNYHbyKF+TYi3ERvOL+2GhUQMvOAQ+z0fksVUIqw7zV6CmDWT5fNtaI0cNg3JRRsGzBKnj9hYVsIOWpRNj+KmWu9GDKXFl2g4S9P3GurLgN/y8PstFQo/ePz3tV5cqG7R1glm8vbmMjTKEl1fvuE2B9TavDu7CyVAfbvyvM/l2m1knAe4b5sMuXrIIVS6N5uGfstg20tMgjeGQip5WUZ+j0LYorIJEUGj16NGy1VTSP4ezZz0Bfnz8YaFjVMa806y9lm2qYWcwZK2sj5r7GIcUY9wMbPiy7waK6EJrDQey2uyQhVlbUKbmsOi+WL9KkCiMO1wsAy4cF8//GDxZGLAstloURy17zocWiA8xXKxZDim2ghD/H/HuFAdiqEF3Wrv8/bbXh/jza+CFbXgkv5voM86hbyiHIcdgxH8YchxqLYcbxnLNiiDH/3/RdpobH3rVhMyx5baWwtuI8i+HF8fnKMrwYU8FYtl7cXiY+5NjmYfPZEveXBmCrtrUMI07rwprWu1QQTtQVSBlK7Csfds4z3M3CXaZDPUU5rbVX3gA0yNl4eBHEkkga7bHHbuG/GzduhLlz59G5aoAvRt/9uk6bo9tOlRvrWuBJ9zzRPsac/uJO8b6xFypznqtcXG0ngVhe0mJODEwFdzaGx3hZOYxYKOQkhg+LAKvKc+XhlM+R1cGsCWSZ+AJPthVneSiLHSEe1srtFADLw2sIo3xBKAmQsv9YqG9cYEm3jAfcSuGoaqBVwSw/Dr4AVGJ8Qq7s1jtNKx/z688ulM4tq5qPVjenbLxM9S8Ps/H7opuKR1wuy1fVwWzWLpluHz4A1jaMWDdOFxdWdmwusOtS0KmWbee9+Eb5+fY7b5U6kisrEPU5PR2J5CIKJyaRNNpppxlw7bXXh89feukl2GGH7Z3OF4UUD7yQ4qr1iNxUzHQ7tvsOQ4o9Af5rL88P/x00uAMmv218Of+R75cfB1+tVxVKLDq1sjBiEWDFPNh4Wdi3BCgTDqkqh5J/HefAcqHF7XHYaKEErV19MOmVjTByURcM2hRAqbMNNkwYDiu3HgXBINZOfTEuwhT/XHRh4zFVASwXOhxvG28n9h2v06k8rQ73PrI+wvcmnkInzNWrvH8McMP3Ohxjf5tCJWc27DGeokcSYtza3x/z7HmInT93af+Y+nNzhTzV+IhlYcX8lDnhBbYkT5aXLLw4Pt+y8GJdrmx4aNxnj9+WFz9mWbixi0xwhAFYk1zCiG2cV58urC/YdQ0lTozLAfRem7ug/ByTD9sIbmkjjNElKq0ZZ6AIGmR6HSaCWBJJox13jKYQYXrxxZfgPe85kc4X+/LOAALrUbhJVJxX56vAUxa5seKcseJctOEFfjz3qwGsE/2GOaHsHFfmjeWLPfT29MKityLAmLrlxDBvEQPFqgv1uKATU7mAk1i9uH+5DGD5tuKyxP5FqBXAJi7yxMYTXqhyABv/y4ArBKhSAFs+vhy2mbUGRi7uCn27oLUFWnoDKMAi2DB+CCzeaxIs2XMqlFpwFwJlN5x3+4Qw4lgygOWn4OGBlt+GF+/qVH2++HlhQ5dMPJfJ5REscnAqmT83/DxyxZ94OI8LP03ZckJ5H4tfX14+JyrHjO3HNU9Wlh/Lg6xuTllZriy/TpUvG0v8fJrAKfFegB/olMGrjQPrM4xY5sKqXG8smJrAmf9MmQo62YQn86HErvmw8f5enxvdLGxpbQm/a037dhHV6XCT6jcvXp7n0NtmFEEsiaTRtttWpn949dXX6Fw1CJSa+vXlxlZVApZAIw+ymMrB/LIYZEU3VgRZcd+x+5rop3zwEQIUY4hjF/mFlnKepyxHjF18L56/HPr6ojZTt5pQNWZddUMxH5a/OOfDiatc2P4w4nA7JMiq9lte1g+r0grFEoBl/7Jz3NcbwOnz3w6773UcDD1xe1j0yoPwyHVf6S99VIRBhU7Y84gvwzt2OgR6gh54sutBALg8MRbMhad4Ec67sIn8aM61lb0OlwlhiOJFe9lNTeyfq04suLCiOyvCbFwEip31VvaZhbgAVn+78JxWu7KTpo6DYksRSn0lWPzGcsH1TB5DBTqj90UGrwno4t5bFchWba8o+sQkVjAOzyPCmeXb8W118gWu/JhM7esJsKr9yvowwaYJZk25sL5dWCwcs9+mhW8sCZ9PnjYeOjo6pGM2KevQ9cS+LMKdGxWereG0Dm5moLhh3sxqMn+fRPKrLbd8W/n566+/nqqvZgw7aUap8nywubGmCw1dvqqsTdl50xR50uXGYi98xH0mLroKRXjr9UXl18w5010kydxX0eHmQ4mZZC5s2E5TyCnOc42LNumKO/UUe9FFnWQFo6bPWgEjnn4FnnvwjzBv9rVhNMKKji64afqrMG/R47D7rmdDZ3cRXjp6Oiz88HGwe/t+cOrJp6HOveigxi5s+fwLYcQ8sMb/lV/HOatcsac4l1Us8hRuI2krLeYk5Mnyy/i8WX6bGMDjeWZVubJt7W0wcdqY8NgWvb4sXFc+Ls6V1eXJxuelPDYxfLs/TzZ8CP8l2imKPpkKP4nrovdRPc+rS8Eml+11+7cBWP4cZAWwLs6qTRixTS5sLVxYXvH+Vq9YC5s2dofPJ28x3qoPE7imAdtGhc+0Suusptlety05vpHIiSWRNBo+fHhYpXjlypXwxhvRHIakxggprpcbi5lyRwwBloUV287vGruhsul2YrjQubGmkOLF85eV9zVpmt3FlQ5u41BiJpkLqyvkpJpiJ85tLL/mqiGHuYrsXxZiigglZv+2dPXA9CdWwRsb7oS1k4ZA55YzYOnbhkLpxefgj18BKHZ0QvEnp0LhoINgxqxlUCouA3jbT+C7HzgTrvjNn7kY5qT4i+oYGGS5sOF70w+aIsAmwon725TfO8XFq/SzxbWPck+TTn70ZvWvZ+eEy5Hlndn4vQy3kYQYS3NlA4CJ08bBwteXwaYN3bBhTRcMGzmk4uCWHU99niw//rThxfE2slzZ+P2S5cvy7yXvtvIgqSoEZguyKunyXdO6r7UAWNMYXWDXtSJxli6sOLUOUzwPNxOLULDZj9N66U3O+vy2kzJWiatdwX//N3AINEEsiWTQpEkTQ4hdvHhxZkWBBrKyznPNOje2FiBrG1YM/HbIu7a6kGJ2cb5q+ZrysrHjR5WfMwiRnSuVwtxXSf4qD7O8C4txSuNKxkxiv6acWH552EfQWgWyE+ZtghFLu2DplkNg7ZqN8JcHn4XJD6yFIz/2BMBvd4GWBx8EYGF/Tz8N64YOhS/stx8cu/e+cOxue8DyFSvgd3/+HZtARno+ZBfffFiw6DSKAFtxGvvdVCGkOO6vvI8YSPv/lUWIlAG1HzYr+axR+HE5bFiAWf61LMRYlisbg+yY8SPL+1+7fH0IsTI41eXJ8uHFiVxXIbxYBbLJ/tS5stHHRZ4vG28bn8dYtkBrK1OhJgy8+gJYjDAAa4JVmzBi2efGdfy+XVhxjthY4yaOcQp/rpdsKlbbhEbXQ+xaj5R/EcSSSAZNmDABnn/+Beju7obVq1fDqFGVC3hbUZXifIGv7qaELcjKVEuQ1fWnU9RvZZv4riy/nF00reQgdvS4EcZ+MZVXE9PghNAEVS5s2JfJiVXlxMoqFatc2H71QG8VyI5cvCmMPlj0zBq48phF8KnlJ8Le220Ps/7zH1j86qtw/MSJAOvXA/T1wWd22w3O/Pzn4e3jxsEbCxbAR847D2645hp4qngfvL7hufL5VF38hRfCBbkLKwNYvtBTeToeGbQqLhwZmDKJubHxmSvnvZZzSyufDx5mWbsY+uLXieJP/a6smCsbg+zIMcPL+167YgNssU0MiP3tElDaEs5FXA2s+jzZcj6rokI1xpWNP0OiK6uDWb4N3y56v/XfYzHk2lYTFvdpC69pABbjwsrWm/JgfYQRG9M9PLiwqnGoXFh++cpla8vPx3A3C1336Ts02Dd85vEGdqiuLjYlBcD8twA2bQIYNAhgymSA7beLnuu0bDm0XPFXKN53P8DqNQAjhkPfYQdD71mnA4wbW5Pj8KW8hy0TxJJIBo0dW7kbunLlqlQQS2oOl9e1WrEryMbSg2x0AS8NK2ZPOGeWDykOL/o5YGcXQPzFePx85fLV5XGM6geOcBwhZsglFnEyKZwXVuHCYgCWh1aVIxteSCtc2PiiNQp1rTixrZv6oLcVoGfrIiw/FuDjlw6DlkIBbnnoIRi+1VbsSwJg8GDYOGQI3PDcc3DF0UdD8fOfh21+8AN4V7EI/7zqKjjp3BPgM+//GiyatxK+cMnpsM2uWxjDD0UXNv6XB1g+fLi8jgPbcp8C2Opy9NlnoVz0qb+typ2NAVUs9iSGGJfb9QOyGF48emzlxsia5evL54NVL45BFuvIRucJNw2PKbw43l+8LBpTdeEnLMzy7fi2KnmdCqerF8a/shaGLt4AbV290DOoFdZPHAKrth4NfeHUUGZ49QGwLoWcZPvCrOelcjON26lcY4ULm+Y3Z+2qdeXnI0cNdxqXi2RjriVg5ibntq8P4KGHosfChVHqVGsrFHp7ovWTJkJwwP4A++8P0CKMuasLWr/0VWj5y9+i0N1S//ckO76Z90HbhT+G3rPPgM0//n4UuVMDBTmH0LQiiCWRDBo5shLmxpxYkv+82DyGFOvcWJUwYcUuU+9g549VhRVjFYcUR30n82LXrFpfbjdi9LD+C11c3/F8r/F/cVGnOIQ4/je+2Je5sBiAVc0VKzqyKhe2DK7QC23s57H/dddggGJvCR59x0b48F/ZBatwgMyF7emBOQcfDFstWQLF9naAoUMBnn0Wdl+3Dn5/771w3sfOhblPLITNG7vggpP+AAefsBt8+uJTw2k0ElPrlHNj5S6sCLCJ0ON+gC0X/5IVCBP65+fylUGuDmgr4cCVOWNlIcYxyCbaCeHFI0dXLtjXrdpQAcD+aXj6JOHCrC/sfLL8ehuQDc8HIlcWA7PxtiboxFQuNvXBq9BXgmmzlsHUWUtg2NKN4Xd3qbUYTg3FvsPXjx8MC/aaAAv2msjmdakZwNpAaS3CiFUurI1sc2FFR3nd2g3l1yyk3lcocS1DkPMeKqws3MgA9n83AcycCXDEEQAf+hAUxowBePNNCG67NfyOh0WLoHjt9RCsWAHB8cdX7q51dUHbu06CwkMPQ4EB7Kc+BXDOOQC77AKFW24BOOmkEGpbr/gbFOe8DF3X/avOR9wcIoglkQwaMYJzCNZUQipdRSHFjVXgKYv8WHH7eHw6R5YPK2YSHVk+PzacFqZ/OQPZMm4q3Ni4wFNff58MOiKHNgopZj10d0UVM5k6OzvDcM5wfP2VZENw4S72i/2vbS+eZNPv8Bf1OoCN4TXhyJpyDgUXNgbXYn94M3u9bFI7lIoBvLJFN3z2lx1QOKCVlYeOHuFO+gD+7/9g/YknwrjrrgPYsCG6iLngAhjZ1QWLFi+GYntnCLDD9joBSpu74IEb7oAn75kDX/n92bDnQTslhhRf2MpcWJV4gC3PuSmAa/Q8+VkvQIt0LuH4MxGr7NTHn7/+UGM+zDjhtvIhxZrwYgayTIM6K85ET3dvCPTs8xgCJAey0Xmo9JW24FP/Seg/BuG1ZDtVriwGZk1A67PAU/nGRl8Jtr1zPrztkYWwecRgmPTpH8CY6ftAe+cI6Fq/DF5/8CrovuMa2P6W16Bz1WZ45cgtIWipVGaWjVt8rV1nUcjJFmBlfxdZhhFjXNi02rh+U/n50GGDU/XlVtQpXRX/tKr1zezE/pj7ygB2ypTIKX3qqej5kOhmArS1AUybCsGatVCYeR8Eo8cAHLR/uKr1y1+vACwTc3EvugjgyCMBpk6t7K9UguJDj0L7175d0+NsVhHEDkBt3LgR/vGPf8Ls2c/AbrvtCmeccRoMHpzuy7KZ1dlZyX/o7t5c17GQspFrwS4dyEb9lqouGMQc2XCZIqc17oMH2XiZLLQ4Lcjyc8bGDmwMEZu7o3Cq9o62/gu4AALmRiHnuwynNkFeoMShxLyqKhBrwol1YcViheLKTqN17f0ObEtY5CkCmre27YAVE9rhY+O+BkfdxF18PPlkGEYc6tOfhqE//CEsX74cgsGDoXDFFQB/+xus2WMPmDhhApTWr4fh+78fhu56DLSNmgRDZhwGK275FXz3rD/A3m+fAd+65GPQ1tlWdS74eWHL51LmznIAK8KrrGJxch8g+bxGcMtcI96t5WE2hFCDK2sKL47zWAdx4XWbu3rKU/DE700FZJNwGsFqQeq4xSCrK/iEdWXFbWW5svHnSwWzOqAVx20rVV9TZi0JAXbdxKHQN3o4dG9cBY//84uwcfVCGDl5Buz9gZ/A0xtXwLpnHoItHlkAm0d2woJ9pyj7ds1/lQGsbQixjzxYUxhxGoBN68IydXdVrjE6BrUrx67al+mmoUvYrmuobxogxQI2u579v3/9B5555jnYbddd4bTTPwBDhgy175/lwD78MMCwYcy5AHjttWg5c2JjiI01YjgEa9dC4eFHAPbaHWDdOihecWUFYJnYzUym3XdPQGy4b+bIXvkPGDVmDCxEHSVJJYLYASb2B3/44UfDU089XV522WVXwN13304gq9CgQZ3l511dlbukpPqHFPtyYzFFnphUjmy4vYUrG7ZXFHxSubL8dlWuLAe7Isj2D84IsmHeTv9FfOzGhhDCLgaDysUVu7CKjqN/nk42dygr8MOAoX8Ozvi1LGDaxpnlQ4mZZLCagFlJhWLenS3DBue4iTAbb7sZestA2zWoCC/uMwRufutC2OHU78DRd/Sf2sMOq9ypX7cOtv/yl+HVjg4Ienqg8N//hv08PWwYHH3oobD+pXkw4oBTQkeWqXPL3WHyRy6BVTMvhyfuuRlO2fOrcPWjPw2n9YrPb/nzEc8fW8ABrApeMdNxyKdeiIpMVdz5vkSYcezK8lPnlCGVB1khvJgH2UGDkk5sObe7wEC6RQuyYb+Sgk+VdbiCTyqQjduqoDZ+ziSrZIwB2ixCP9u7SrDFrKWweUg7bB7WDtDTBXPvv7w8F/CahS/CijefglFTd4EVrz0BvWvbYfKTi2HxbhOgryOaDzhr9zUtwMrcWFuA9TEnrC8Hsbv/ZiFTe0f0XZtWps9TmrFnncta/u6SHAO7nj326PfA008/U152+eV/hTvvull5Pauc750VcVq4CGC77XADmzgBCvNegcKcl6Fwx52J6WtQKpXgtI0b4VuIps2e15pGtfXtSXUXc2B5gGVir6+6iuLzVWrhkvdLtl9UCumKqpDyK52TqPpxZD/C0qIZ/f/x2/N98NslnnNgUp4jlIFL/+sYZNmjDDP9y8vAE7dLbBetK4MQtz/2uqenN3zd1tZavnBpiUGKCylOHmNRuVyUKeyXh1FpbqwwFQ979BR7K+sKJdjc/5otTxSN4tcVon/jfsN+Cr3wwj5DYOz6oXDP4QCbW4vQ1dkJ0N4OhZYW6Gtthc1DhsDgvj549047wT133AE9P/oRvLLTTvDfUgk+eMYZcNvvfw1v/fp06Nu0Nrwo6XrrOVh1159gw3N3h8fV2tYCm7ujcxwrqgDMObDc+6kD2PizVX5v+x/R+6t/tBTbwkfYvtha+ez0z1Ibf05ai23hPsXPUTyueHn4/heifuL/yu24NvwFOzsP5X6CQtmVjZe1qKYf4hzZYtW6/rEL5zP+fEa99/8Xnqv+6IF+IJO9D6rn4d9GkAzJLd/0EUBO9nCRrJ8xr6yGoUs3woZx0QU9Oz9L5qyCF+58HVYvXA+FYjuMnLQjbFj6KhSDQthuyNKNMPaVNVXApwPNZgFYH9WI07iwobipXVq5aw9bFzYtVKt+s3z15Sr2uxaP5V//vCYBsExPPz0b/vnPq6v2bRzD/AXRuWchwxi1tUVwuWABFO+Z6QSxB22upOiQ3ERO7AATCyG2Wd6Mmt87H36++ZfwQOtDsL6wHoYGQ+Hg3oPgi+2fg6mtybAPUvMorRuLKfZkcmXjceicWVmIMT9+nSsbhxfH1Yn5qsXlfSkc2UJ/WDHzh2K4iMNEWZGn+GZOX1+/48tVLo722X9+FPNuYkKJGTDGRZ1kjxhkq9xYRU6sNKyYe3vLLqyQCxnnw5bzYlmpp9YiFLfcAi7fejW033YIFA97O3z3u98t99XCijvNnAm/PeEE+PwvfgErP/YxOO6RR+Dmnh7o+MUv4FN/+1vYbs1D/wcb5zwIfeuWQ7G1DXbccws447PvhL0O3gVa+3+S+Yvd+JyKnxf+dRXAChduYrViTCVt3uWvfO7iz2/cb//7JnFlTeHFoiPL39djN0rKhZnisPg4vJhzZKP3NVmJWO/Iqgs+ubqy/PtjcmYr71m1Q+s7J5aJVSFmRciCNvbZiLTopeXw2qOL4KW734CPXv1PWPbKPJgz8xYYMXEIBG1t4XsydNF6WDZjnNZtlb5uQoC1dWExAJsHeZ8qB/GZ9e3aPvtMNGWZqGeeedb++Ng0Oq2WSMTaszDk1WuszyZrP6JEc9GmFUHsABPLgbVZ3kzaUNoAH+z+ENw87FYodXIXtAHAbHgGLgn+CMevOw7+2nEFDC5WQlH6WNGWfhXjQi6khgsprhXIZgmz/Ph5mGVgGo6LK/hUns+zH2TjHNm4LxFkw/koE4WjKmHF7EK7tb+Kbl9vBAdBf9GnuEoxu8CLQ4rL5ymEAu68KS504jliTeIrEGNyYmXViuPKxEqYLXD5sMVegFJrGWjb29uhWBgJk3a4Fz564b0A3/te1RhZMPDld90FwB5h3wB/CgBW9K9fN+u/MGHL0XDsF0+A4848GDpaO6AdqsMGY2cxFu+i8y5s2FYAWB28lpdzBcKkkhRyKo+Fg1m+AFScU40NL+ZBtrff6Wdqa2V518XkXLL9Y+JBlo0hAk85yCbecw4uXUE2/iyJN2psYFYHtOVT75ATK+urrasvrEIci7mtU2eMCyH27H/8B8Ztuy387MjDYeOK5TBk9GDYYtdxsOeYwdDa74Tz0r3WTZ8zUAFWJ+VxcREw8Q1Dm35lEvs3jSy3U99ItMuuO0uX77rrLvadscia3mQkjFGsPZszduSISmF7pFj7NWL1PJK16Ip8gIkVcdpjj90Ty9jr008/FZodYA/qPQxuGn4LlIolOOR3h8CO++wYhrDte9K+4bcPW/6/4TeH7TaWNkrzYPn82LSikOL8ioGsSXHIoU5iiHBiHRfmiQ0zxoQYx2GhiXDg/tDiOMyzcgyV0GIxrDixvlAM3TGmns1RvmLsxvIhxZhwQ1kbTE6s6MLy0JoIKZaEFSfWcSHCcShx/AjDjyEKLZZO6QMl2L13a7jge4PggYMB+gyHwAD2fgD4PEuh2nYMHPepA+HXT34RLr7783DsOQdBK+LOPx+Gq6tEzL+H5c8AF1bMQoTjcGH+sxeHD8vCipP7qXyu+NB19iloKcTbV0KEy9tLwotlocV9vRVwa+13Ytlniw8BjsOL+dBiJvZKFlocrVOEZPPhw8J/fLXc8D/ub50PL5b1G7/mn6tCjcUKwLLQY5uHqN5BreE0Ogxe2YNp3PRRcOYfL4Etdp0Bf7nobzBoz5Nh+F4nQO+gLeDFmW/A8/+bC70dlZBK2bFhALbqmJsQYHWyDiPuFyucF2uzpJCk71Bil1BfbVoNAuV8ucCnnvZ+2H33pAGz+x67wWmnfcC+s6lTopvxbBqdcJDsbia72VeoPOdNDFb3gJ2HKVOg9I63J9cxsfasWB37jmfr2HM+VLlYhAfbazNXbDOLnNgBJpbszoo4sRzYuDoxA9hmr058dveH4aXhc8IiIUzdk7th2LeGwX537gfd8yt5CWz9C0NehA+u/RBc0/l/4bJNm7rK6zs8FVog5duNjdr1O6IIV5YJ48yG/VpWMy6HDXPOrBhi3L8i3lklvLiy04QjGxd6itv1ScKKyw5Uf1hxW/8PMMuN5SGfv4COAYAdoVjcSXbxqRIPrbx4Fzb8NwZW5DQ7VRWJJXPGxi6s6ObG/7a1tMJ+fbvAh/4+D778g1Vw7mUAxVL0iCe2LxWjx+WtBfhsVwAtwzvgyzecCUM7h4Tzz+qcNj7vtHpdspxRIoyYA1jxpkb8PO5Dul9uf3E0QLyn8sdGCBu2cWXj8GKVI9vbU/m7CB3v0O2szCUbO6dxlAHvyDLFU/Dw765uLlmx4JOscFP8vptcWbFvmRMbnU9+39UOLXpaKIXEvjZOHBZegBd7+qDUFp2DnY77ArQP2wYuPuJYaJnxrvB7pXXMNOhe+DKwv/DpB0yBFZOGmp1YRPXhcEwaaLMBWFXbWgKsKJ9hxPHfaVw4j4kV00vjwjbTPK4ysevW2+64Ef71r2vguWeeDx3Y004/xe16docdACZPAli8GGDaNIA994TC3nuXVxfO+ygECxdAcMP10YLFSyCYNBGC7beDYPIkaPnO95N5sd/6FgCXbhKGHbPpe97xjuh1Swv8k41z3TpIpWBghyQTxA5AsT/wc8/9MAykHNibht1SBlimx977WPjvYU8fBjBf2KAAYfsFXQtgSuuUxNyw/JyxpGxCimshO+jFTb/DX8RhgVaEWn5Mqul5dDDLT6kTjikejwZkmZhr1lfq0YYVDxsxtFzcbNP6bugc2l4OKY7HHuYiBlEoYnyRnrggFcJ4sYpBNXwu/CtWLpa14SsXayWBVzE/lv3bVmyFPdt3glu/3QN/+/RbcMw1y+Hg+3th5BqAzUM6YMkB02HOKbvAD995I2xevBFGDG6F9s4249Qp4mcy4VZy7noCVDnXka9cHL+O+zVNs5MYR/z5k32OwvPU//noz8OOl6UB2XWrN5S3GzZ8SNVcsjzIhmPkc2T74TYG2Rix48+hDGQr/6pBVgwvLp+c+Nj6c2XjffH9i8/j1/x7Ky6LpXJobbVy61GwfvxgGLKsC9ZPHgaDho+HaXudCH093XDhi89BochC5Qvw+L+vg7+d83t45zHTYcgeU+CVrccm+lE5r7UOH641wIqyzYO1CSOONYSbG3b9ukpEmIsLq9qHL/DV9Yn6nvGQJ8yuZz/84Q9W3ayzFgsLPuAAgGuvA2DXfLNmQcAeZUjkYJHNE7tuHZSOOvL/s3cecJMT5R//JVvedr0X6nH03jsooICIIFKVoiIKoqIidkRAQUUQ8a8ooCBVQAFBqqDSeznKUY47Do7rvb11y//zTDLZyexMMslm9933vTxH2GwymUyy++7Od39PcY5rbUXpC6fA/vO1lTI7FGqiCDdhPdk2CiedgOUP/DveWFPzLIXY1Aa9Xdb720oMrKHRxOTS3stxWfYSrFixwts+YsSIRMdGE8BmjjkZzApuVJAlM60lq3K3CksG5Z1LSujkG684kRAzGVNNTxdqxQQ8pK6xBE+CmmbZTs1ZKsNDk3cOKxxkPaBlv0vQkeRelcXIkZX3/uoVnRhCky0xEKhcUeqYsgsbOTjgRgmL6LHkuo7SJL3MXEMdxZfUilLZRsFSwy5NOCuFJyomKrLeNgW4qvbrTIZXrszKj9SuxW7BxqO3wHtfzmL+aXlky1nkylnky3mmuIrGYUV+jGoVQPWrrKpkTip3c5MELPS+EOOqdT+KsPeWouYwL8UTBWRXLV/jnX/4yKHeNShBVpXsSQJZDrziDypyLVkRZMXr8+9zQNZElXX60KuxKqCtZ63Yckse83eahE0feBf51T3oxiI8fNFBKBVLuPviJ5AZvw1KPZ3oXfA2PvqpLbDBqDa8u+NEZXmduLGvAxlga0nk5Bwffn2y0Q843Nas6KxLhl9Vn/VQfE2taeZAe+0FLFvuKKYEshMm+JM9kavx/Pmw1qxB+SP7AXvu4e0q/PoXyL35FvDU0/56sQqALe21O3p+cT6QQmzNlkJsaoPensg+WdNxS5bwlCzAqFEjExvXoLN+TvAU57jo7QU32ojXGhRHJAKuto6d/L0oQzE/TtGOYJYDrqzKsu3ulFkEWVYLtFRg8Y4EsiNHCRC7fA0mrz+OQSm5HzMwZf04rqYErc65+KRfjAx01UXX9ZhSabBkUAxmbGQtZ1uQaiu6GstxsXybTrkNMxleVW7FzN1Z+EftKKuy7R7jqNm+3+4TNV/MtKTCeutSrKyz7sKo7j3mmjcHE+sMSzDL30f8fDLIOtthDLIrl1fc6kaOHMbGXXFrto1BlkxM9uRdE0voZFVlLhZhle6Pqp4sb6dK+uRdnwJmo8CrDK1JQcXCnSejPH8hWp55CRPf70ZbZxlrO2zs8HEbLz35MqwlwCcOnsoAdu4e62HRzusFug4Hwaup+joYAVY2HcAGARxdC/8Bh2zF8lXqY2pQYaO4EqvgUvU9Fqg+1zk+N1GjONbDDwdGjwaeegp4913nw5BAttDnzG8mjEfp4wcBe+zhtOeeKC0t6PvXncic831krr3eOa7k/qBM+ykulhTYUz6H3l9cAOQNS/mEmcU+CLGuWgqxqQ16ozI6cfKfs+NoErBwIXtsaWlJXIlNrf9NzNgb7Tj1F0dUuCULSxDFXHpl8NBBrQSzXjkVV3FlZ+LleDxV1gEOEWTZBIZiO12QHSVA7Iplq5niKiuxzgTfmepz9ZWrsTxeltXeZIqZ+8gh130uKq8iyFJ7ExCVATYqxOrglcNtzrtOZ8m54MJ+CCg756M2KiWN/VhhcSCifoouVDnPw4zhv5SRmExUYcVkXb59dKxQY1FnvDRTGMx6IOtul0GWA6gzVjsUZFcIEDti5HABFCsuy06ypxCQlbIWi1AaFWR17sW+1zQAZsPgVRcvq9ofZqrjC92dmHzxLTjq5tVS3HYRe9hFnGIDDxycQcd2ozFrl42wgIA34/4AoXBp1sW9Jhn7GnZ8o2NgTQHWJJFT1TgVkDh6bOVzdsmi5aHnTcLC+jQF36B2UdTWfo3ZJTDdd1+A4mHfegv4cA7Q1Y1yawsweRKLgWUuxKrv/9ZWFK64DIVzf4jMdTfAfvQxVn4HI4ahuN8+KJz0WZTHjHLahpQ4S83MUohNbdAb1YGNk/+cHUd19eYvYI8TJkyIBShhlroU968aKx6XxK/BJpmNw0x+nyndk+VNnC/4PbDd+pzyPlfhkl2Mi6WCD2S9WEcXZCdNmuCdasHcxS6QOJN26o3iY2mSWKasq8K+CsA6UMvcYN1/LKusC6o08esTJlQqkNWZmNzJ2yat8+cchuVH+TidWzEDTleJhQpoaSxlcqGuGEv2JCi5Ub54K8q1mFW6ejLudxmudh92MlTr42K99794niCY5SArlHRSKbLsPSWV8eGlm0SQXfDhIm//hInjKm24a7KotErld/jYawFZbmEgy9vIsbImyqx4DnmbuD0JYJl583I8efNzeGNGD27ffW/8/ZRTgIMOAsaMwZLly3HFlVfi4ksuwZlde+O4J5/Ch1/YGZmM82OSbEm4DjdCfR3IAMuvaez4URWIXbgs8PuoFhW21u+5pnEBrpcRqO6wA8DLT3LoNIHPsWNQPPsstvDPJi/HRQqviVoKsakNetunsDerAyuaXbCRLZCLCH2mlJHvzrMSO4V8wXfcqu5VWLZsGXu+4YbrN3zsA86aJMFTXJDlx/a3a5NpiR8t1GqA1ksipXAxzthZD2QJdP0nAzbYYD3v6dw5C9kkRvwCoYk7bVGpsdXOxBV10CmA4oKrGxfLwTbIdMqqSok1UWM5zPI2OrdiXmqH9otuxdylOMvbKCYrcr1a2Zz45MrzsPeg6D4sPoYBrKhuBp5LqDXMxlyS4mW5si/VHFaBLGvD9ytq0344Z4EXEzhixDA3hrpSR1aMj+WJm7x6ry4MU/K+uCAblLkYhrGyQTAbBrTydtX+IJOPnTBzFn48ZCj+s/vuWEyfyfPnOxA7axae+/KX8ZNLLkF27VosnzYNuz9TwIe/uBFLfnp6pT/F+8EUXuXnQXGj9XYf7i+Alc0EYMkmTh7nrc+bs0jfRx0U2UZAahSVVQ6DSC012dJ3RmqD3r6dP6vq1+V9f7Yvett68ejPH8Xz9zzP1rf++Nbefmp/dv6bmD37fW/bRhtt1NBxp+bP0NvIY/nx4pJUP0ksTr9l38JrhjIX3UzOqy/rq+lpZ2BncrDtrK92J6/pSSDLy7X4aoZaWWy0ceX9P/eDBZ466KmqVtYBW3pkz51z0z+CWVF9Za7DHLBERVZyY5QnyrKJcbA69VWXqThs4bAq16TlpXzYuluPlo+FYmE5qLC4WA2M6NbDJns+lVXhSsyPUwGsWIKHbZfqwqoWfz1Yod6wUPvVV9pHeE/JtWTlOrJiDVmqEbtgrjNhn7R+RfHn1+W913h9WDcGm98H3g8HD10dWXbdvh9RHJDl2+VHuXyU/Ch6DXATt8u1pNn73l3k9uJxoql+ANL9KMStZ8lCfPf6x3HUs89hjG2jh8p7nHceA1iyw666CrmHH8YOO+7ojL8EfPrmVeheukhZ/1o+h+56q65f2mcZtKs+V23uw3HqwCYBsKaJnOS/85Gjh6OtvZWtz/tgoVkfNcTCRoFh03jYev0AHBbLn9q6ZynEpjbobb3sejhs9aG+L6ZHf/qo4+MnLNP+N83ZWQZrT+V1ZsyY4R0zZcrGdRujSpkYsNZESQZqBVm5r7jAWe9zVJYK1FYBrVBD1HsuwKwIsnxdBNz1158M2y3o/uEH89h2Ale+ODBagQyusVYwV1gXshRz43DLTbeuMlVSJx3QRoFZ3q+vHi1P7CQsPpgVygEpx1pjVmJ2P4SsxPw19YNjNcDy47w2BosKZqlPD2Yl1+W4ILtw/hIUi859WW8DCtuohrMgkPX2CyArbjMBWf7e5fvFRxXIym1kYPLDhK0F2rjQGvRv2B3/ZfGvWmtpAXbbDXNmzqyctwQMuaNS8kMFyOI1BsGrfP3ijwFR1FcOsPxHlLBjgtyHTQFWtHoAbBBYOu9XC+ttOMHzeOnr7asJPGtOtlRDfGrQeWoB3cFS53YgmNXfybZCrLlHl1pqCdn1Lddii7WbV31JVVkZ2Grtlqw92ZuUMt21LbfcIn09+sGSUFSbxeIAr2q7CdCyEjqCSivDLANYEWZdkGXQKkAtX29tacOGGzkuxe+8PYuV6RBhh0CWr4tqrLfuOQ77/4kqLBltoyNM1dggC4NUkzai0qoCW76vJD5342JF89yOpW0mJk58OQip4tvkeFYlwEb5J6m3SlVW7DMmyM548z1v3FOmbuDBp/fjh6wMSiArQiYHWQ4npiDr7A8GWfEHGbmNCmZVIBikzurANqpt8cziYIi95hpgxgy8+OijlXGUgM2fWVQ1XvmaxOtWPY+jvsp9BKmvuuNN4l+bCWBlE9tuvKkTulQsFDFn9vyaVdikYUXVd1ywrMVtudkBK7X6W/oOSG2dsHa7HU9mH8UnV30CNgUPcgWWzF2n7YevOoy1o/Zkb7wx3etjiy3qC7GDSo1N2JJyDW6UmUKqkZJLIEpAGrR44FrwFvE5h1oOs7Qwt2KCz0yeQSxbuPuw7ezL2nnYrpswbdt6my3ZuLu7ejD3/UXI2Xnk7BZkrZyz0D++zuA1g5y7nSamzmOW1VD1/lFtVWEby1rsTpqp5ipN6rlKyybT7kRffIxruvqy4qNP5RVUVp1LMcXFeq9vQJEdHtcZx2RXYlUWYn8iqExV27DF6cuvssqle0SQFS0qyL493XFzJdtiq6kVSJVA1gFXCTYkkPWur0aQ1QFTmCqrgll5n06dDQLbKMuQVU5pD6X94Q/A5psDRx7pLxlGSRBXVd6TfPw6IA+6R/w+qe6zeO+q71lFfRVfE9UxqvPwcesAzwRgg1TbqnPVALBByammbFbJvzHr7Tn+fgIAXWe6hE5RFN2wDPrBx9bXDVj8kS21dcvSVzy1dcY67A78ve1WzOiejjNXnoHt126HTdZOYY/0nLbTfg6wZC+/7LgYt7e3Y9NNp/bj6AeYNZFLcb1hNoqiqm1H90uzsHqtYYsAr/w4EXL9UFtUAi2DWTdeliuytGQzeQ9qt9l2K2/Ib73xLiu9Q5NBvp8BrQusBKt5K+9osORyLIIrh9ayBmZpPQhgJVfkqDAbFEerfJRchEVolbfxuFhxHzemxbr1c1X7TN2IffsDMo6KKipvazzxVYAsH4NvTLIiG2MiSe8lbptvtYmyjQyyHHS5cZD1TfI1IOtcVzDIivtUqqx8bFSY1QFtENyaGB23ZhhlV1bY738P7L478PGPA6v8NUip/ZphDsKbxrzGgVcd9Mrw6iUti6C+8nGHuQ+b1IANTRYlvZeiuBAHASyNYdOtKqFLb70muHwHTNl1KmxN7sAhxyYZD5skgKbuxuH31bd9AMN/mp04tXUyRvay7CX+jfnqdpSV+L33HDe37bffDhmqH1ZnS8vt1CfjsKov0cL6jQO+gccoIF+nxEc9d1F1beXKxIYnHPIpbjRpzuSIxFB2a8daJZtBL9tXdpS9bbfd1ut3+uvv4FOfPrRSLNRuQV+px6kf6w2eJpduDVVKfOTWDyVgo7zFzD0XJRTKlBW54lrM8MR9zgCVjcFVaFnNURt9lDmYssS6+ylyTAWyprVldRBM++jcrI3rMsxrwzpZcP3ldtQl7Mvs+KyCLFjW6BiqrA5uZRWWt41bXoP93bllmei4spiBWKoH67zXhPI7hhmLp7/u5B5obW3BRpus70EqzzLMMxTLFpaxmI3dBdkirzXLahQ7r5VYSkfMWkzXpMpcrKony8bhbgtaF8HQK98UVmIn5ufd23uMwx6Pz0fZzlICfpTKZRSuvBL2XnvB/uhH0bd6NYr5PIplqhgLdOfzQLmId/YYx0akgqUqMAtwsZX3Bx2rU16Tzj7cCPfhODGwqjFsuW3lB/M3X3s3sK1qn860CnONrsRJwmWSACp+v6UG+nIQ6qUNDkshNrXUNPbccy946zvt5GRxTK1/y+0kCbJyv3XvxwBcdS7HStN9GbnJl+TjRXiliUi5XKwCWlsoxUMwSzGzpWKfU3LHBnbZeSev3+effcUBVqqGw2qc9KLkPqe5OI8BJVh1BuCAhrPNKb2TcwGWXI9ZwqRyCTmCXTemlMWWooS+coEptAwYCSg53LrrZHK9V2667TK4qmrH8v0lYVvOBVdedodK7YhQy64jJLmTLka2FlOBq86NuOpYRdZPeg/42rrAqgJZAmdefkc8F4FRmC1auARz3p/L1rfadjP2Y6H4nmcg6IIsmQi1HKAdWCTAqPyIw4/jfXmld9wSQbWCrNPWvTVyiR3FOn+uA1o+ZtmivEf48V1HHYKfP/0uLvzfo8Cjj2KDDTZA9vTT0dvTg/z776No2yiUStj7xhtxxhln4P8AnLzP3tj9M9ugLQA+6wWvpq7DScS+RlFf+wNgycZOGIUx40exOrFvvPIOylQUWbiEoO/ApFRYf5/B37m6TOra9v0ElfxzLlVqB4+lEJtaahp76qmnvPW9994zvU9NYvUC2WYB16r+FLAaeE43y2uVufesbNs+eCVjUGtVEjWRizEBJQOZjDOhR6EH601eHxtP2QjvzZqNl16chr7eEnJ5B1yzjsDGZvbMpVaCNJrok0txpuyArQOoDsByN1s6roBClRpLrsUEiV5pHkGFZetcmXWhUwbIWkDWO96tC+rFwgoAK6uzKuMAzxNARf3y9VwqfUmeZBDQuI4JSZrYc4NSFdSGg6z3N8ffd+42sX8Osk5xYgcwfaCrUWNfeNbNCg9g1z138ODTO0YBwo53QKV+LG/jxMcSyArKrDtuXw3ZGkGWtxFV2TCYFV9DcZucuEyEWvkYU2sbMx7bbHA7CrZTPgcffMB+UOQOR04BF+B0dynawB1TXsea0Qc2DbzKz3XwmpT6GnQ+p58I1x8Q/6oaozwOGvMOu2yJh+99EmtWd+Ldt9/H5ltNCR1zkEoaVYWN40qcpDXiO17+PKwl7reexj+DG3Y+20Z5gCi2zfmKpZZaE9gTTzzpre+5Z+MgdlAleGrS2NhaLTD+VYxpdU2MX1Ud73te8i/VWYed+FZVMifd4juuWPCWUqGXbS+W+pz9bvxssdjnxcx6CaCyLSz2dZ999mZj7u3txbSX30A20+IkgKJYWhYj65TcyVl5ttA6T/REz+nRiYnN+ZI80cSKJkZibCxBqwe0PBaWLy58eolnxHI9EeNjdfVkxTbyoxwXKyd38gN8shYYBytlCQ5SI4L++dpqatGqziHHx4YleXr+6QrE7rbnjpW4V+EY/lyOieRjCYuP5YmenH1S24A4VzFGVkz4xNtVt1fHg1ZG7gcJFSz63uM1LMu+/yU8t0eWAWqQ0f5n98xh5fe/oh2jfH3yNaruRZS4V/F+Bt1T8fWqNfY1KOlT1PjXJAGW799h10r+gReffi2wrcrihg/U4kqsOk9gRmYD1+R6w7LKeMKzZqpskJraUohNLTWFrV69Gs8++zxbnzp1KiZNmpjepyay/vhyCa39KoErmQiuYh/K5xxcA7IWqwDWrNxOAOBqYLZY7PXDLE3+Mznsu88+3viffvI5L3MxqxXLkzvZArwKiZ4YtErZijnAMrB1AZbHxtJztlcAWTEzMV/3gDYmyAbCqpClmD+qtrH2CboHRzGdm7Cswpq404mQGQVkuRob1Z59+kX2SDWId9p1O207GWT5uqrsTuXaJTgJqCHL26tAVpXwSXyU1wMhRwN+8r9araVtCF7/8zdxx4nD0Zd1YFVMyE/PaTvtn371t5BrcxIaqsYQCm4G8BpU8zVq4qaomYejJG+S3YdlgNUdmyTAku2yVyX/wDOPv+xrF3buoL9v3/Vq4uqtGl2Ja2mnszifLUnZug6zzeT5JlvqTpxaagp79NHHUShQSgzgoIMOSO9Rk8XGOt36XWL79QtMAa+6PnTuwro2JuumyRrIlZhMjpNkjyy2Meu5G3NAoRhDHi9rIYMDDqj8PTz00H/w3e9/m7kQkyJLVij1ImvnnHRFJUqGxJ2H3WRO5YKj1jJXXIqKpUenDXMldtXYjFVgkCqus6ROLsxyl2JK6MQhl/qocgM2SOwkmiom1tsmQKoYF+uLk6VYTZ7sSezXuwv+f+SqShN89oODbZ7kSal6eG5xahWWHWeYQdjnPswnkKzik/N+8MWtKuJjuVtxUJKnuXPm4u03neyr22y/BYYNHeKluWLuwhHcik3jY8VET3w7mc61OKp7caW9sy4mfxL7023z7mkCIJttbcea88/C1V+fj/wd92OzZxZhyKoiy0L8zh7j0XfUoWgfMwktinOpFLAqcAp5noTrsNOP3v1VB6+1xL46fQXvryfAkm22xRSMGjMCy5aswAtPv4ZCXxG5vPo9YepG3CiLes4o7fsznrXZYdZyQ0DWJUshNrXUFHbfffd76wcd5MQJNdLSLMX9GyNr/GUVAq9yXz7VNWC//Fg1JgX4moyd1E3WRpg4sCuwHVWK3ctSpVZoyXbglYMHrW88ZSq22WYbvP7663jh+ZewdPEKjBk7EkUq78PnUjw+1huvMD7udkvgUXYgroS8GxfrPBIEFst5J3txmZI/VRI/OfGnWadUDYFwKYtihkOmX5HVxbgGWZiCy6HVt80gJrYeFpZQRY6FTeSccoxsDfFaDz/0mLd+4AteJJYAAQAASURBVMf31Z9Tka1YBj/eRvwbJBDh8bGBICvEp8og6499rQZZPo4wmFUBrTh+FbTGSfyl6qdjzGTgy1/Ce1+utKGfnFpigGszwmt/xb6awKvqmLBrYc9tYI99d8R9d/4XnWu78OIzr2GP/ZzEelHcl6OqsP5r0ym1ih89IpbuqhVQm1kdHJRxslZz3u/mHFVqqfWjkXp07733sfXW1lYccMBH0tejyWNjk6j/GuouXGmodR3W9VUV7yqNOdQluFioxMhSPKvgSqyKfxXjXlUxsE7cK7kLu89pu7vw7Wyfu61Q7PG20XV/6lOfcsddxr8f+i9ymVanniy5CrsxsrSes/OeezGPkc1ZLax+LP2j5+K/Fv68nGeg2kptqN5sOe/Vjm2lfe46LTTRzZeyDGbzbm1Zep4RHsMWbrW6IydpYumaJMw0mRNflLVipe3KPly3YpPYWBFiP3bw/mzSLO4XXX6155PcislUbsVieyX4BLgWi4+ya7G4L2g9yC1X50Jsx/insqA2cpyrfIyqjyhuw6p6ryZxr1Fch/nrqHIdjhv7mpT6KoOjEcC6tv/HdvPW//fvZ5Ttg84flPCqEVZLOZ5mOsc6kevDGpg4ODBHnVpqdbTnnnseCxYsZOsHHngAOjo6+uV+D6oETw0yYxCN014Brrq4V7l/p2F0eJWTO6mSNHlgKy5UIke3FPqcRQBb9ijEyIrPfTBb6GbLoYce7F3jv/51H0v6RKAqg2zWbkHebmULB1mKkyWQzUoAm0OexcXm3ccWF1wZ0JYceM2X/ABL22nxkj4RuEogq4NZ0YJgVvV8oJrOlVgE17gW5MKssjWr1+Kpx59j65MmT8BW22xmdh6DJE+V5/6YRh9QKBI9xQFZMU5WbCevy/AUBI5hUKq7LyZgK5476PzyNl0bfg9MYl7jwKuXQT0AXlnbiPAaFvsalrwpCffhIIAl2+eA3ZDNOQ6T/7n/KZ9HCx+H7vymJW50sbD68/g9PYL6DrKkXaCbVSnsL7NqvB+UoXggWOpOnFpqkt1229+99SOPdBSn1Jo3NlZ/ulLdf3kNgtdKo3B34Sr3YRdcdft8z3kmxVLwWLQxkuSWxONpLBdyyL1YcCum5wS05E4Mu4Sdd94eEyZMwIIFC/Dggw9h5co1GD58CFBwJieFEilxFqxSb/U9c8eft9xMzVYZxRI5FDuuxKyN+4+2UVwjK8ND8E41ZF2XYu5azN2KWb/sEgsMg3tR0CZ9kkvr8G1yO9XzRht7/er0ZxMErlUldlxXWjnuik1kY7i23fevh9Hb2+epsJZlKf/MaGIfFBvL9/PYWLZNKKUT5lZM7z+vH7f0Du0zcS0m4+7FZEEuxvwc3Pg2GSTlmOhaFLSwzK5Bym3YtiCXYVX7QAU0APiqzhsQ9xp2bFTlVXkdEeFVHpOyT80xQ4Z2YM99d8Tj/3keC+cvwSsvTMdOu22jHIfp+cwh0/x9F+otEfMDLE5m4kbGzA50ocFqsEtyPWxgoHZqqTXIKJnTP/5xB1tvaWnBEUccnt77dd1iqK+VhobuwhrlVd7HXYkZRJTLjmJa7GOL6Fqscinmaqpue7HQU2lT6PO5F8uuxigVcMwxR3mldm679Tbksq3IZltZpmJHleVqbJ5tI7ditnhuxXnmVkz5iOmRlNgWtHhld5gC62Yr5m7F3KVYtxBw8kdPmQ1xKeamU2iDzFeGRxG/KLoEsyROvH5ughOHOO50SSivYRmRw1yK/3HbPV77Tx/9CSeTMFO4/C7FQedXwUCVIhXBrTiqIsuv1esnwMVYNVYlvEhKaS2LbEEKbZAKq1Nd66m8yvdQpbyGqa/yfY3qOpyE+hoXYLkdcmQlnOm+O//jjSVoHGH9R81onBQsKr1AUgW1sWYPLuwbXFeTWmo12r///YjnSnzIIR/H8OHD+/WeDvRf+gZ0LIlByZzAxE2SYhr4aAivHFxZPVeC16AYWFYqRxMXKy28XRjQijB7/HGf8a77xhtvYOV4slRLllyKtSCbRz7T5gNZvrA6sgxlnX/0nMXJum7FDGBLzqMMsxxAaT8vv+MDWkUpHtW6CcCGgS0rwSPViVW2Yxl11e1MMxTXwwgiuQoTFhsb16V47ofz8eTjz7L1jTbeADvtoi+tw02Mp1XCqwwLopuwxq2Y9Sm4Fcv7wkBWV4ZH5WIsPtdt0wFmVDPpU7dPtU0HrmGlcmqBVw+cY8Brkq7DOmgMAnc+Ll8fmtdAPkY+7sCD90Zrq5OC6/67H0Vfj//7J64bcbBZxgmdkvpxLRDEY/zgNhjgOKlrsAbBvdDZ4L2y1FKLYddd91dv/aSTTkzv4boIsgGxr3EzD6sefeqrsI2DqQ5evTZiPCxTZwvM5dKDVXfRJXnygW1fRaV1wFUA1kIPin3dVcrsNltvhi233JyN/emnn8Hbb78ZDrJusicWHyuosjzRE4+JdVRZ5zmrH8uTO7m1ZJl+6wIsAStfp3hYnuBJBlpTmNVBbT0tThbaelgUF8I4ZXu4/eP2u1lSMLLPHPtJ5kqsHk8lwVNcNdav5lWAxQc6MUA2qiprAovydcT9Z9Kfbp84flPVVYbXSvuBD6+yWpmE+qo7Tt5P/zqGtONjhzmZu1etWI1HHnjCN56wPrT7alBhdedUKq2qbM0xkzElpf6mprEBeK8G3ohTS61ONm/efJakhmzChPG+5DX9aYNOjW1WkNXAK5lKfY0DsFWJpBTqq6jKBsKrC66B0Nqndx+uUmNFkHXXOdCKUMuV2XKxD8cf+2nvev945R9dd2MHZMm1mACWMhez7MUErpnWCsiyRw6ybqInm7sT5zyQJXiVsxVzJZYmxGKSJzHBU9VjCMwmYSqX4ijHUsmXJCzqxE1UX8VtQWqs6blkl+JSsYzrr73V2/+ZYw+v7JdcivXjNVdjxXEo1706ydFAVmwTpspGAdowGA0yU7DV7QsCV1OX4YEGr7wP33ND12FTEI1znGifPr4yF7ntxn8px2MCzlEhMkpCp6SsXtBZa/hEkA30uNKBnNyp+UeYWmoNsmuu+TOLiSU75ZSTkM2mec/WGQuA6iD3Yd9zTfZh5TYRVKUSPN46AawIr+J+Dq/8uS5LsQJcg9qIIMvXGdAKUOu5Gvf14HPHHem5uv31+huxcvkSN4NxD0sC5SmyLsxyRZaA1XmkTMXOoz8+lqGq88/KKrMVs3UpWzFXYNm6+xikxkZZOCCLVg/FVlRlxfdaFLW2UZOqoGRhPC5WtgfuewQffjiPrR/4sf2w4UbrG52LQ6pJHc4wNVbeHgdkVYquH6b84CfDIW8fRT0N+2fahzymKOAqg/tAgVfeRnVvwuDVxHU4rvpqctyue+6Ajaasx9aff2oaZrz5XlU/YefRWT2AVAXJScbDrtPqqgosrcYls2o2W4ffCamlVrGenh5cc81f2Homk8GXv3xaU92eVI1tPoDVlc+R9ykBVqXMatRXn2sxdxkW4NVkQbHoW5hCKy1sO5XeIWDtddyHxXW20HpvN3MvJpgdMawdRx/1SXY9q1atxg033sT2EcjSfppsZHNtTJXliixfWOkd97HFqjzyhf61oBVtbK21Uku2nGfbWkp5VnaHFlonYKVHGWRbhW15aTGtJSsqvByI6VGerDN9sIGuyJQ4KonPBlWiKb5NzERsCstBEH31nyohG1/6yslG44sS5xdVjdWBrO48PijTuBebwGwQ0IaBqWymYKuDVlNwVV+fP5GU+LfA75uqVE6j4TVOyZy4rsOqH1VMwLLqOHdctm3jhM8f4W2/6S93avsJO0+wG3F4LKx4TJgrcS1mEn8bK6ldA7MXDwSzBvgPAgN79KmllpDdeOPNWLhwEVunjMTrrTc5vbeD3a04xH3YKIETa2wQ9xoFYDXqK4dXZZyrAmgZlHKXYrc2LKmntPjcjd3F2ydu6+2uAlsRZmn9Sycf492KK6+8Bn0EuASyfV0MZul6eOZiGWSZC7EGZFmsrBAfS//ayq0eyNI2glZa5zDLQZbAVQmyQqysDLI6mFUpsGQcWJVJoSIASFx4TVpt5aWKtOfk72Mh43JUe+3V6XjqSSeh06abTsH+H92rpomqChZEC1NjgybKshobBrJyW9VY5Al0EFSK/cRRYOW+g6DVFFyTUl0HIryGwmEEeDU6VhrXkccdzOJjyf7594eweNGymgA2qXj4IDONh+0PsOxveOtXt2l78KBf01wJqV/iY2qpNcqKxSIuvfRy7/m3v/3Nprz5g1KN7Q+QDYDXSAmcWOOIGYgVcKsCWFl95TGvKngV3YirwJW3c+NaIYGrDLLOce4iwi3BrAC2IshuuelG2HuPndmlvDvzPfzj73dWyvG4qiy5F/M42TCQ5cmeVIosB1j2TwBZD14FmG3lMMtdi7kCKyaEkuBVhllfjC1f3OeiUTu+JDU5E9+HuvW4FqSuRi3/o1VjSSWWznPZJb/z1r/45RO1CZ24hcXFmgJtWIZVnRobF2RlVValzKquLZPgP9l00BoHXHWqazPAq6rEUNLwGsd1mB9r0k41tqFDh+DYEx2vl96ePtx4zR01AazpZ5NJLKzv9YqZtKmWzMT9DaTNalYcpVz+IUgBvBl3WzPwWtO88uPGjXMfx/b3UFJbx+z22/+BmTNnsvWPfGR/7LrrLv09pHXPGgGyIfAaF2B17WsGWEF99W0TH2V4FcGVbecQ6y7Mbdjd3tvrLBxe5ed86fEDrNO2UoKHtn3jK5VM3r+85Lfo7V7ruBxHAFkeLyuqsdyF2KfIErwSrLpJnnjip6wLsu1dFrZ4rRd7PrQaH71rNbb90WJs8mAXhnS5SaAEmBXVVBFe5SRRZLIrMQdaGVxVqm2jjKAxTKH13pfUVgOzsitxaJ8G6uzrr03HPXc/wNbHTxiH4z/r1BmOowyFJbXxTd4M1dgkQFanyvLnOnVWB7VxTe43CFqjgiuZDlzDXIb7I2FTveE1afU1CDZP+fLRyOVzbP3m6+7C8mUrvb6iWNX1xoDAKOBoGg9ba+xurOzFdUz4FNeaHcrHjBnp47b+tDRzTWrrtFEipwsvvMh7/v3vn4NmNoKspH/pbBojwKxHggJDQI4LsDK0Bm1LBGAFBZYfK+7nx/F1du5iZV01draN/+JaFL5A+Tb6Us0UgEzWAeBMFlbBBrI5lC0L++yyDXbdaVs8/9JreGfGLNxx5904+jNHONeUzfvuA4FsmDF3aqqjatN1AEXqx62qyuYpZSfREcsGbLn3tljE5BeXYuMXl2Hook7Wes3qPgy9dSkyf7Wx+6nD8cahbXhl9xb0ZQk+S8hQTVe3rivPLCy6D4sZjOXMxyLIymYpVNmoE02yBaUluDn3T7zcNh2dmS50lDqwe89OOL30eayXrYQ8sOsXytywzwn+unmvMQ0iUzXRo/eP6USOw2qQklt9jHP+X/3it962r3/zy2hra9XCL33G6RRnusYgqKb7rEqCJfZJsBOlFi/BF2WOFvvm4+CvP3//iH3z11wcj/g+kMdZL7fKoPeeUjHUuM9XtQtRJEVoVcUbmyia8ljCkjWp+jF1Ya1JPTU8Vnu8wRjHTRiDo44/FLdefzfWrunEX678G8750Rmh543rRhw1I3FSZXv6w+rhTl1vs0I+C9cFSyE2tXXabrjhJrz77rts/SMf2Q8f/ehH+ntI67Zx4EwKZhMCWH/j8DI6vuOlMjqxAFZ0HRYBlpRXDbx64Cq6PPOxy4/idcvuQ/w5AaxdIAqlX3/YI43LYtttnH3GiTj+tO+xppdcdiUO/8SBaGntcK7N9n/VUNbi0HhPGj7KDDLbLEatlZfAciCXK4bkar3Fw4ux8bOLsabDwpKNhqInX8KchxdjUZZyHrdh4rWr8NElRYxYWsbjh7YzkO2Fk42cmVXJNizDK9umcCP2rkfhVlmLdZa6cWHb7/HUmJdQomt3x0fM9yZm4Pry7Tho2X64snQZOux2oMYftjiUijAbBVRZ+4DJ1LRXXsW9/3qQrU+YOB4nnXKctm0QYKoAVdwm7w9rT0BEP5Dwc/J9fHJI0FV2738QyLL9sH0gy+6JBLNBQKvaH9VMfijRtUkKXJOAV+VYDDINy9Zf8BrpeMMx8v5OP+tE3Hnr/cyl+KY/34lTvnQMxo0fk7gbcZDFTehkfK0B72OT8zW7ktlswGrJfYs/frouxWxu0YQ2+F7p1FIztDVr1uD88y/0np933rkD4t4N2tjYpNyLudtwggCr+vLQxb/61sWSOd4JzQG2KvaVHkXXYRFyCSwF12HPldiNe2XgSUtfn9MHuQ67LsZV7sPctZiW7m52DHcrLvHH7u6Ki3FvN/beYQvsvtM27BJnzJyNv17/NxR7uyrZjHnCJ8pcXOxVuhZTKR4na3EbqyGbt1rQarf5Su/kpX9UfmeTF5Zj/7ZDsOP5d+MTl07HR475PZuAr5zWiezYjXHklTfilGem4fjrl+JLo3+M7Z/tcVyIhWRPcrZiHuPqAaqoyApqbJXiGpCd2KQkT1e5B18Zfi6eGPsiSjZ5J7gAS+aul+wSHhr9PxyaOxadpS4kZSpwjZOB2N+ujB//sPI5e9a3TvfKMkUxJRQEwLvKpVgVG2vqVhzkWiy6Fwe5GPNjVa6kqv1R/5n0JY9N5yoc5C4s9i3eG9FlmLsLJ+EyHOQ2bOKWa5rASnWd2nNo7rnp8XycUQCWbMKkcTjupE+x9a6ublzxq78o26n6j+JGnFRd2IEGlPR5tU6YHdEF3ba9Rcxi3d82sN5dqaWWoF122eWYP3+Bl5F4r732TO9vM5kIo0FfLKbtjE8bALBSIie5jay2VrUxBVge0yqpsQw+ufuwEPPqqa8SvHrgKkIrwW9vjxMbS3Dbq1j6/Eu5u8sBXALaXg663c6jFzPbgx+cKcTGXnYVFi9ayMCVJXqSQDYoRjZn5xnE8hqyYsInOWNxe7eNDV9cgq7u5Xjzmeswa9qdbJrbu7CIroU9aBk/FcsWrcSDf30Qr953P1bN6sPWD3Sig2JkObwK7sEqeJUTO8lZieXETtkavlovav0jZg+fi46hHegYUlmyuSw23W7TSkMLeGfouzgDZ0eOT636YUXcp1FgTVyJVT8I/fOue/HkE05G4o033jBEhVVPjkxCKGQ4Uq3rtsUFWRmAvDYamA0C2jC4DTLTfnTjiAquKnj1jtOAqy5eVhxXEsmaVPCqulem4FkveDWJzeX9yvbVs07B0GFD2Prfb7kXb02fWTPAmoYp6VTYKK7EpvGwQUmdon4mDHqzmgcsG2nr0CucWmoVmzXrPS8jcS6Xw89/XlEKBoKtE2psGKwmAK1BZXSqG+vV2KpjVYmcFADL21YpsIo4WF/8K3vuug+L6ivfJ8KrC64etPJ9HFjFddVzCW65UlulzvZ0Y6fNN8JRh+zPLmv5ylX45WV/dLIYCwDrA9m+buZaXKXI2i1oybQZ1ZCdMHMtRizqwdx5z2PJO0+h0LWaTaKWv7oaNrkSj9kALz38HGa8+h6KuRFYW7Bg/XklNny+11NiW4uVEjx5vgjqLE/0RPtFkGXPBQAQQaAKlMSMtZZ6cru4tAxPj32FAeraNWt9y8ZbboxJx0/yH2AB/x75P3xYmOu4YbuuqCymuOz+SOK6Z7OF3ncC5Fa5ufPtUkInHcDy/nV/E52dXTj3Rz/znl940Y+Qy+ujmMRrSPrzLqrbYlVbBciS6UCWtYsAkqLVqsDK5wlSW2sB1yRU1yjJmlRjaUSyprDja4FXPt6qbQH9jhozAmd88yRPObzwR5f7FMQkATap2NZBm8ujCc3SwH9g4ix53wD5AWBgjDK11BI0+rD/5jfPRjepSgDOPPMMbLrp1PQer0OmqgOrsthuxCFqV1V7KQa2KhOxCmA91dUFWVGVleGVg6sMr8y92FVcXWVXVmG9xVVh2ULQKqqzPW592Z5ufP8rx6K9rZVd1g233oPXXn0Dxe5On1txobfTWfq6UCz0VIEsuRMTyLZmO6oAVgbZkfO7HRU067gW2zSZhoVF01YxV2L6Mu5d9B7WvPZv9C1+j8VAZnIWJnxQ8urJ5nlJnqJbX9atMcsXmuizbMYSyIouxVyFZUmdNPGzVZNgabL79/xDXgymaFs+tyVmTZ+FNz//ZtU+ig/+ffmawPeZ7z23cAGsX/0K9qGHwd5rH/ZoXfJrlBct9EGtDmBV4MuhWS6v89vLf4+5H85j2w48aH98/JADKn0EKMb1/JHOdDItq7FRQFalyoYBbRjY6kzVhy6+VTWOuOCatOoqquHN5jJsAr+6PlRjlcccJ572pC9+BhtuvB5bf+GZaSxOlp9LPkdScBlVhQ1TQk1/WAq6BqMY2RgZiOsZf1qzi28EV2BrgMBoXEsTO6W2ztk//nEHHnzwIbY+efIk/PjHP8BAtEGdqbiOppsgR3EjVrWtUlajuBFL7apiYFUAyx95O3edJ3ziMOxt59fBsxbzddOEDfTFSeenRzeZE+ubkj6wfUWW7Gnc0DZ8/cRP4ZdX34ZSqYTv/OTXuPuGy0GpnMSkVLJlNFmL85liJeGNNFRK7NTSQ0l3nXI7xEU02Sv1lLB2UScyQ5ZixX+vQanQh9axLWibkEPrRkMx+YSxWN3W4tWL7bMLXrZi3yUryudwkOXgKqqx9DyriJUVp2A65Yzs9fYZyu1j/jwGQw4dgucnPa/c/0z+BS9DMcEjc8NjGZ7dX6ppItPView5P4R93fXe6+7mioL1n/8CP/kpSl/8AsqX/RpoafErtrIyKwBu1Q837nFvvvk2Lr/sD2w9m83iZxefy+rCii89V4tN6tUmPan0kji5SZ3kJE+6xCeqZE9if6pjuIkAyZNAyRYHZGULSjAWJWOunKBJlaRJ1afqx5rqvsPbqMC13omagrZHuXe68ZrWWw3rv6W1Fedd/G188fhvs+e/vOAP+OjH9sFot/yJ7jxRsvDWI8NwLVDVHyV4BrXZtvn3f5Pa4Eb01FKTbPHixTjrLOdDn+zSS3+FoUOHpvdpHbFaANbkODlzcZQ4WOiU2DCA5UorrXP1lcOruIiqqrfNXXr7wpceUmArSixbRFdj17WYVNkvHvFRTN3QcX2dNn0G/njtrY5bMVdjKeFTX4/gVlytyJIS6yR4ImXWcSn23Ivd2FhSY+2WdmSJn1n6JwLKDArdzuvc2tqFjfYbg12/uTl2OXtTdExsQ649i5Y+G6UWp66sl9hJUF5lBZYvYsysDK9iaR5PxRMyMoXVECXrzHRX5cxoW9uGaX+bBnxJ8yYk12N7DQKtuxu5w4+C/ZfrYBUKsCZMgHXnncCSJbAWL4Z1yy2wRoyAfc2fYX/ik0xRl2vJqgBWB6O9vT342hnfRi+9L8jb5eunYeqmGxv//SVltaoQOlfhIEXWRPVjx2nc0E1NdbxW8Q1RH00V16RV17guw6aqqzhO1XXLptveCOU1Sv+87T4f2Q2Hfsrxbli+bAV++oNf+85VPS5zN2LdOeV1kz7C2kSNh12XrJFKqjUAXYqbf4SppZagGzEB7JIlS9nzI4/8FI488ogBfX/XydjYfrpXOjdilWulyaQ8KA7WFwsrwq4KYAX34SrXYb5N3E7QymCUEjxxAO3RL11dzkLZiFl7AWg5zNJCbsXUprsL+VIRl3zrZNi2M5G67Kpb8Pab77CEUiqQ7euruBbnsq0MZPNZx6XYSfRUgVi2kEuxC7SFiWNZ8qdcH6mgpINaaB/WhkO/twcO+PaO2PxjG2DE+CEMcGlilylZLMPiGtrGY2LLFZiVF9FdOAheaRFdiWWlicw3MVd8/bYXWx1pVLCdbt8Jre2tePGwFzVvJKCj2OGuutV03dhX7ubLFNinnoHFf2T5/e+dxw03pGxLRPvAFVew/dYTT8L+9jlV7sO+9zx3MVa4EZP93+/+hJdemsbWN9t8Ks75/ln+vw+FK7HptjDTwYmJyQmeTI+VFcsgEAqalAaBqSnwiucJchNOAlzFPuPEutY7UVOYi3TQ9ijgL4+5nvAqtz/v52dj5KjhbP3+u/+D++5+JBbAykp4FLfgWo5J2ga7+2yt12sNsvszuK4mtdQC7MYbb8Y//nEnWx89ehSuuOI3zL1toFsKsrXdozjZiNUnieZGLLeTlVjmd+m5FBcctZbXf5UBllx5+XMZYGXVlQNvXx9T5cT2Vk+vf+nuhtXb6yzucw60DFi76JFAV4BZV5XdYeNJ+NKRB7Jr7Ontw9d+fCk6V62sAtliT6cPZCnZEwNZKreTbavEyboASxmKHTW2FVkrj+LUjdA3bhSGLO1GLtMO286xCdvwkSOQp/I85YxTrsduY5P0tm4LayeNwLJNRvoB1nAhgODuxCLMWoIrMRk9F02uO8vWpa/gbTo3rYLYVdeswpanbIlSVv8e3L13Z/17fPESZK+7oQKwZFOmALfdBqxdS7XGgFtvBbbd1hl3qQT7L9cylVb1Q40IsL73sGsvvfgyLvqZowjR5+sVv/8VWlpyVeNTlrYKqZFaSw1VVamdimKuisMLdkmV3WplCNSBCO/HBGqjgGoYEMljSQJck4p1TUJ1TTLWNY7qmgS88vOYtqdzjhk3CuddVMlQfu45v8K8uQtrAthGuAPXEg9baxhVlGuol5dIXcwS/m4FJbsmcG1y6E1jYlNbJ+ydd2b43IgJYMePH9+vY0qt+cw0EZNOhdVt823XuRGTqTIYM5fiUlUSpyqA5bGv/JG3IXDlUEw1Yklt4zGR1B+DZHVZIG87WcatEZex2RdbmT/nC/XvAjYKWfb4raMPwn+ffx0z5izA9Hffx88v/wsu/N7pbKpC8bIy+HtftpaFbK7NBztVbqw0dLuM7lZg9c6bYdP8Vhh1xOe9vg743v1Y/v40PHPzN7DDId/D+tt9wtu30X4nYdLaR3DNyl+jDwW2jcXGWs66ziiJkgiw4joDWP6vHDxBVSlptG3ErUOBb1R+Xt7o7Y3w+lOvY/1r19eOic71VetU7x5RXCyrJVtyXPLy199U/bpedhlwzDHAvfc6E58TTgDuuUe40BLsa/+K4ne+5YdVCWBFN2Lat3LlSpz6ha+ij95nAL72jS9jl113NIsl56c2gN16Go+NlV83DtByfCyZmIxLjJPlx4aBeFLqSJhqHDfGVdd3VakeVekUjet81FjXoB8ETMYatY+g9nHiXYPOE2tswvkPO/IgPHjf/5gSu3LFKpx95k9x4z/+D5lMpmaADRyzQUKnNG/HwIqLtRSx/M1sKcSmNuitq6sLJ554CtaS6gDgC184BUcf/RkMJkuTPAXfG50Fflir6rzqkjtJMObbxjML+zoTshELbeU4WDEJkwewXK0NA1imxFbaMdVVhlemzJZgiWMXwVW8BzwjYsZGmZQBglkRaqn/rAOvyFGSpRJaM1lccdbncOQPr2Bq7HV3PIS9d94ahxy0LyxFvdEin/zYNnKWzUDW94OB+6+SAbeEklVE1y5b471/P4NlX7wWPe1ZrBnbjr5cCQUUmIvx9LsuxsxrfoDcmh7M2GssXj1oPHqzReSsbCjAErhy4zGxMsByN2JeP5YpUBLI6iapFMNL9t+7nsNfz7kH1gZDUD5qDQPZDf+8IYbvOxzTNnVcc6usDBy0bD9Mykxw7pNiYp353xPVEPvkk8BppwHLlzvPn34auPhi4aJLsP77P5TPdtyA5bI8rInkRkzhGmd/6weYNWs227bjTtvjBz/+djW4GrgNy0mdGmHKhE7CNh3IysmeRFgUYTYK1AaZqWu0OA4TcDUFwnU1SVMtpWjqBa+VthZ+fsn3Me2l6Zj34QI89/TL+O0l1+A7PzjDePz95Ypq7KpsWFu23lmL+9MGGmA2wppbJ04ttRqNJlZnnvkNTJv2Knu+2WabsmROg9FSt+Jo98Q0mVOUL42geFiTbMRVbsRcreXrouux6EIcALCe2zCBSV+h4kLsrluiq3E3JX4qVFyIaX9vn7s426iN517sPicXYrbOnpNLcZ/nYrzF+JH48YmHeffh2z//I955ZybKbqInMeETj5Hl7sUE83INWe5aTC7CLbZTS5bcj1d+bDcsOXRXWEM6MPL9FRg9YxlGz1qJcTNWYMzs1Si3t+HtQzfBzAM3co4tUW3YPFrKeWTl2FiDRQRYKu7D4mMF12KtC6OQCIfb84+8jkvPvgH5cVMw6ZkrkZkxlinNj/7qUUx7VA+wm62egt+VLvbe50wZ5XVh3dhYa+VK//SalNd//9sB2SFDnIXWH3qo0oSWlav8PxoI721RHef7//iHq3H7bU64xtBhQ3H1X65APp9XJn5SZSWWAVIFzvJ6Ep95KohQKYpyW3mSLLvh6lx2VX1G+acz8Vyqc4a5CtfqLqxqM1DchYPur4m7cJDLcBAsR3Ed5mPRjWH4iGH4zZXnw3Z/bPz9b67Fvx94LORYcxW26p5FVGHDXIl9fQ8wuKzdzLwhEjubFVF9p+fi0kSWKrGpDWr7wx/+iJtuuoWtd3R04NZbb2KPg9VSRdbM4v6aqVRjVdmLVW7BXicumAptfX3KbsRchRVhlUCXn1tQWFUAy9sw9dUDXVd9FZXYkgsNdJxr7Bg+Rtt1IabnmQzNUCqKLBm5EBO0FF2VOZ/33JY/t+/2ePqNmbjv2dewurMLX/r+r3H31T/DiFEjfejClVj+SF+muVw7A1k5IRBBD22j+NgyqaVZoHP3bbFyu43QMnMO7PmLWJbd7pYylk9swcJNhqCrtYQ8CrBRQJ+bgKmPgNOiLTZzLc7xsfCRlf1qrAivnguxlNCJ15Gt5Cb2G5+80uMbz87ExV+5DpmRkzDu2AvRN/99FHdehuyfsygeU6wofLwejjuGA5btg9+VLkK77bhd071hTZgiS+7d9AiUhg9jZ/PGMGoUsNFGLJETi28m+93vgO9+l5IFAEuXstOUhw/TAqPoQkyg/Oj/nsCPfniBt/83v70IG228gZf4ydde6kfOciyqsGJbUbEMrDFbY9ysiUIbpIzIqiw3ESpVCm0UC4JicRzK7VFiMfvZXTjKWKMqmkHHxFVc454v9DhD1+Vddtse3zv3a7j4/CvY87PPPA93PngdNt10iuJYO7CvOOOpxWopLRWogjcJECeuokYsj2MFnX8Al9pJITa1QWsPPPAQvvOd73nPr776Smy11Vb9OqbUGmNRFZowFdYkVjY0K7Hsciy5E4sqrNOp4E4sqrAExgSaYp1XN2sxB9QqgOVgS2qsvE7wSu7GUlyslwiI9UGQ6MTAspqwBL/clZgsk2GgTX2UW/IuFNMx5FbcAitTwq++8EnMmr8Yb32wAO/NXYivnfdb/OXic5AfIpR/5S7LboIKrkoQxDKQlbLiKmvItgFdW09B31broRvd6EE3etGLDLqRL/ey/pha6gKoxSHUdp5zCCKYLbjrVW6mEsB6yiyN1wVaJ7GTpC65wMz6gI1Zr36I80+6GpkhozH++J+jsGIBFt1+Hoa0t+H2rS9H74dF3Jy9G9Pa32TldzqK7di1e3uchpMwyZ6ArJ2rZCHmkzlhgknb+/bfC1nRpXjpUmDGDODMM4Hzz3e20fqcOc4+93UofmTfQHh13hpFvDdrNj5/8ldQdH/8OOtbZ+DIoz6pzFzsU3D5e7+7B9kZs2DPW8AShxVb8yzjdN/UDYGWrPHEL4kJYhCwchPfI0Egy8akgFlTCI1qOmitFVyb2V24Ua7CtbgLNwJeK+1tnPbVz2Hay2+wLMWrV6/FqZ/9Fu68/zqpfmwwwEYZY9SMxFHa1yOpk8qi1M5tRrPEzyD67nSLcRPAV4UyxemziS2F2NQGpb366mv43OdOdpLHAPjOd76Nz3zmKKwLtq6rsWEAW+sHcyjYCiosb+dzh9TFwqpUWCEbsdaNmIMudwt221gywJKbsKy+0jYOtLwvMoJWNjEWkj7xGE/+he9CrbPL9uJgnfPnYeWcY9l1ZLPoyOdx1dePwxHnX43lazrx6POv48eX/RkXn3MaiwotiRBrO/BKiiyf6JAiK8ITv7ek3OVdVVZW4URVjyurXlZhy1Fh6ZFVRaXd7ul5P7arwPJ+CGpV6qsIsOoyKC4yk0rrHj9/5mKcd8xVsPJDMP6Ei1HqWoWFt/4ILXkbf334Fxg6fAgrHfSd0mnIrM0iY2XZc1r4PaH3Op2Xrp1NOlxQZHtpPLDQc+IxaL3gEv908IgjgN/8Bpg717nnL78MfOpTlf2ZDHpPPgGQ3H59qmmpiMWLl+Doo07EihUr2baPHXwAfnju2T6ArVLQ+fu9WETuuVeQf/4VZBY6mZBJ6UehyNi/b/wodO+yLbp22QqljAuFCpU2aeVVleBJhlsVyMrjM4HZegCrPKakwDWu6trf4Bp0TH+Ba9jxcZRg78cxy8KvLj8XM2e8j7fffBfvz/4Qp518Nm6+40q0trbUDLCmcwudK3HocTUop/V0wR0IQFcTpA5QNTaF2NQGnb333mwcccRRWEOlIwAcddSRuPDCn2JdsnUVZOMArKkKq/wSC/nQF38F5TGuShXWaVCtwrrrogqrdCMWIZeDK3chVgEsA+GiEyPrqblCgidqU/KvM1ClLz9K7ETrlAeJK7EETNztOe9mKS5kUc5mYdFjixMbuf6QNvzhzGNw8q9vRF+xiFvuewxjRw7D2acdz6ZWZTvjKIrMvdc197z0fs7lWiv3ki4/I7iqsvvi3jIRbOi2us8JIHvKvWzCx9RY5nYLtBI0uUDKwYW5ENOlcJBFiUGtDK+sXwFgWT1aRUws2S+PugHFniK+9odjcO7hV6Fkt2DCZ3/B3icLb/khslYR1zz4M4weN0L7nqJx0HkqCa5KVP3WS+zEVVkOshgzGouP/ATG/uOeyojefBM45BD1e9a20XfyZ1EePdJ578lJl9z39KrVq3Ds0Sd5iZyoHuwfr7qM1QZWAawvDrZYRMuD/0Prky+iOKQNhW23QschRyE3eSP2Hu99/12UbroaHf/6L+xlK7D643s4Luwxa8cmYWEgG6Re6KAzCG5NQFU+t86SBFdVf0m6C0dJ0NTI5ExB5zM5r9HxNcCraB1D2nHdLb/FEQefgkULl+DF56fhG6f/CFde8ytks+axqY2qCxsnQ3Lk+qg1Xmt/WlKqqDVA1NUoNrA19NRSk2zu3Hk45JBPYt68+ez57rvvhr/85Wov2cG6ZOtaoqdGXq+caMaXWViVMIrHvarK6fB1DrxhKqzsRizGwZbL/vI5QgInL5ETwSslauLbewuwKFkT7aNt3b2wKWmTtC5vY8/pOHpOiZ063Zqy3T2VxFB86epmCyV92mOjibj0tCO9+3PFTf/CX/9+H0rd3Sj1dHl1ZL1asn3dKBS6nRqyhR6vhmw2k68ke6I6sBknyROvIZu38qB/9Jz+5dizPNpon/uPkjqxf+XK0sLSNGXZOgFpruQu5ayXAIqec/WV2rLtrkYrqrHitKlzeQ9mvzIPc6YvwPc++n/oK9oYf8LPYWVbsPCWH8AqduP3//whJq4/1vj9xxViERS5cWikfbkrf43iXrt5P0Ro+yU34r12R8+vLvAldSJw5QuBXHdPF0458St45eXX2P5Jkyfitn9ci2HDh4YCLD2SAksA2zdxLN5asxZLNt8NxUIBS6/6JZZe/UtYuRzaP/clFCeORdtTL6Ht+TeUky8Okd79cJ/L21WWxI98URPzVLUVki3JS+BxIfVhdcmgVLVagxI0BSVpUiU8CkrSpBq7ru+g7Y1MzhR0PpPzyn3oTDc2cXzq4/QK86TJE/CXmy5He7sTM//gvf/FOWed73mnmVjSKmxS0Btm9ey77hbyGZ3ktVlyX9K5B8J9bP4RppaaoS1atAif+MThmD3bUQa23HIL3HHHbWhrcz7E10VbV0DW5DrjqLDyc117RcfKhE5V/Yuux0JG4lAVViq/44uDdWHUB7A8/pVlHnayFXvQ6j56kNqngNmePrZUwavwnK13dsPuchYGtS68MoDt6sa8hUtRWNOJw7ffFF86eA/vfvzk/27GjXc86GQs7umqZC3uq8AsA1m29LDSO6qMxVk77wPZFrsCrPQv5y4EqhxeW9wl58IobaOj+HP+zwNaZJXwymrFlrMVNdYHD8Cj173EXsMR+38e2ZGTMf7YC5HpGIWFf/shyl0rcMmt38TGW6wX+P4VYVDMSMz3c3AU27BtLXmsvvN69Hz+BKaOE6xyDZAlcaLn2Sx6v/A5dP7zFtZeBle+dHd3M4D9338fZ8ePGDEct/79L5g4eXzlfAEAW+7uRsvz05gCWx42BP9+4HG0j5+AB/9wNfq61rLXvPvtaciOmYDS0A4Uh7Sj7YXX2XtMBetJmwx4un0m7q1RgDZwTAHAGpqRN0FwTSK7sGrMYdfRX1mFkwDXoEzDtcCrCmDl/rbdfkv86bpLkM876er+cdu/8JPv/5JVbQhL5BQGsPUGUtN42KgKa9BY6b4MCuC1rMQSWzU7yKbuxKkNCiPl9ZBDDsPbb7/Dnm+00Ua47757MGbMGKzrNthdixsN6nHccVTZjKvciaOqsLIbMY+DFeCWJ1jyAJZcg7n6SnGU3L2YuxW78a9OP27CJ9fNmDIRs3FTLCwfr+viybLiZjMuTLtL1nFtvv/txbhx+mzMnr8cn+3txWfaWzB12BAcvWApNhnSjkvXdGIJgB9dcQNK5TJOOfYTKNGXMy29lZT+vvhYN9mTE0fpABO9D1ooq5NQQ5YeyR2YuW2WqjMDexl/3UcWB0k6Ht0bgjarxBI8idt4/VfeD89UzNd5siinXWUy8eK9byEzbByG7f4ZDN/jaJQLvZh//bdRXLUQP732dGyxY3UGUfaWYO7CJd+EWeVSTCOyxL93N07Wi5FtbUHnb36Orh99Gy033o7co0/CWrka5eFD0bf/3ug96ThgjKsC85hjMSNw2QHYkz/3FTzy8KNsW1tbK2669WpstsUm/ve5BmDZazdjNuyFi9E3ZQN8OGcu5nwwF4/ecAe2PuBA3HvZxTji6IPRusX26Jk53cHmsaOQn/0h8u9+gO5tNvHdA1MLa6tyDY6S/Im/Nro+6jERDAQoXU3iJsgubBrnGtVVuJ5uwkHnjdKPyfdw0FiDEhDpSujs/9G98X9X/QJnnPpdlnzt+mtvY5+NP//VDz0PtSRdc6PONXw/mjRJJuGBaJahq/BgcylOITa1AW9z5nyIgw8+DDNnzmTP11tvMu6//x5MmjSxv4fWNDbYQTbMonxoy67CQX0oXYkFFVZ2JQ4tqyOX4jFVYWU3Yg6roiuxAmCrIFYos8Ph1euPtrPEO+7waJ2OZ7F9tnNMqYTVvb34y3sLcdcHi/H26m5kiwVcbtk4tUwgBtidPbA6e/BxAAet6cTZAK4B8E0A5/7uRhRLZXzxhMNZnzw+li12xgPZglh6h/9Txiy7/kbuY7FUgUEqqcMuyY2LZRPEsvOlyMAXJRYfyyGSgNYZUTW4ylDL4mVF6CyVsWj2SgzZ8TCWeIVt6+lEYeVC5HJZjFtvlPK96CRuUn9N8xhY7lLMEzx5l+2CrHgb6AeB8phR6P7m6WyRja6z6hyure3sxCmf+wr++x9HgW3vaMdNt16FXXbbQQmv4vGiMmvPX+D8jWRtPPPEi8i2DcG8JcBeG26KU//3NHuf9c3/AKuffcQ5cS7D3lfZ+YuAbTap+tFK50pcjwRPYVmMw2C2FguNt6wBXJPMLjwQwLVR0JoEuDp9RI/xFV/jQw47AJf+7nx868xzmdp441//jp6eXlxy+XnIZnPKo02vITBzcQKuxM2uBNbTGgGclkGCp2YG33X33ZHaoLDp06fjgAM+5gHshhtuiIcffhBTpmzc30NrOhuMrsW1XJOxa7DyxBHba1yJ3Q1VfXsqrOq8GhW2Cm7JWD9lLwOxp84KiqkPYAlEyW2Y3IvJzZiAt7fAnjMIdp/LC3Ml7itg+tJV2PTu53DRq+/h7b4cRmxzEKZ/71x85blnkevuRubOO321UzMXXIDcq6/i9L4+zPzNb0Dpn376+5vwyytvQpHiY/t6USr0OrGxhR5vnR5LxT5kMnmn/I6dR8bOOY9WDjk7z7L45iznkeJjyfG3zW6rxMOixYuTbaH95ErsxsPy+FfPjZi7FgsLdxumyS5zMRZcirmrIb/Wvp4iysUC2qfu7r0H+pZ+gPyEqejt7sGvv35t9VtGzrTs/qtyFxbUWL7Pcz0WAF929VX9E9uIxy9esgRHfepzHsB2DOnALbddjT332sXXn1PnVTiP5FrMklB1dbMfQNau7cQrr0xHy/rb4ku/+Do+eHc+vrfDAfj6kCGY+9ILGHXMl73CuKVshrkTyxmJgyZWyh+dpJq04r1N0sJcTU2O1cW1qtx/2dLdi4433seoR17GuHufw5j/vIJh0z9g2+X2qnPV4i5sGueahKuwzg1XHGeYm7DJfVeZiatx0BijuAwHuQ2L51D3W33MZ475JH575c+Rcb1nbv/b3Tj91O+ii0I9Aswk27SubVyLo5wrt6XKbrz7oHBTTjI8IklLldjUBqz973+P4thjP4uVK53yDlOnTsUDD/wL66+vjytb122wKLJR4DU0FjbkuDA1NpLJrsTidu5KrNrOIVU2jQrrAapbysQroVPwgytfZNdhT33lWYrdddGskv/LjPZubANtGRtdyGLc8T/HL1+8BxvOeAPWz14FDjqI3CT843/3XeC734V12mmYDOByAF8F8Pub7sHchUtx6U/PQivVuiNV1i25w75gC+6XqWUxkCWgpbhYEZxKtot9dP8st/xOuaLMFlxJuQdg8EzzJuZWSse67QtlKqpDbrmOMisqsd59EGMNy+qJcG9nL6xcK+z2YVj+2PVY+9rDKK5ZBjuXwzZ7TMXp5x8X+LZxVFfbp86ybMnMFbqixtpUJpYVLHJjaN2JJYM39zlrF3Qu6Zf3mTPfw/FHfxGz3/uAPR8y1AHYXXffSQmFVVm9Hbz12pRa88zN/IXnprHfW0ZsuRtGjh+Np/75KOwxU1GaPR1Xf/07uGj2bFhtHSh1rYFVKKLYmtUCbJAKW68SPKY1ZZM+Z5UVSxj24jsY8dK7yC9c4arcGXbP6O+jd/worN55U6zeeXPmSVFPd+F6KK71UltNXxvTSbvp96pJDGcc5dXp2w4856c/8wm05PP42le+j76+Ah649xEcf9RpuPbGKzCKspGHKLBhcbRRVdgorsRJz1saBbhJuflGVkOtSr3YsL6aWWkNsxRiUxuQduONN+P0089EHyWyAbDTTjvizjv/jgkTxvf30Jre+GRzoMJsPRXluG7HJu2MS/S4bsU60K1SW8V9UiwsmQei7nYPXl1XYqbUUuImHbyymFrJzZTKNEhjp+nIUNvGQSM7cPfileh4+0kc/+pDyLzivl477FANsddf7zwedxyb5nyJXIoBLAVw18NPYdGylfjTJT/AqHHjnNqxmaz3a3DRzsIqOs/F+FjfOLkyySHW2cgGy9yB3e94D2RdV2L2j9yfmUuzH2bJeFxs5dqr1SZSZPmksFikCUUP5v/la4CdwaSNR+GT3/8MPnbCnsiTaix9FfMYV34NMsxyKKV1/sOUM94KsHpvDT4uVn6nyCaUoe9c92/s+edeximfPR1Lly5nzydMHMdciLfaZnNPefWNTREXKwIsWd/EMcijjBfIlXj0elj1/jtY9N772GnH0fjn3y9hbQ765g/Qs3wpA1hyWadkvYUJYwMhVdwnuxjL9zEJl2JTkK3VgsqP0NudlNfhT7+JQkcrujYaj3KOkoo57zv6G88tXonR9z2L/PK1WH7QLvQLTE01XeO6CycBroMVWp3+aounDQNYbp84/CBcN/T/8OXPf5t5Q1D5nSMOPRnX3fx/RuOMO77+tKjJnxruwlvL8XbluzhRGB0gdWNTiE1twNmKFStx6qnkaubYIYccjJtu+iuGDBnSr+MaaDbQVNk48Gqkwhp8UIe6HofEw6rOFxgPK7bl8bDiNjmhkwurvsRKLqBWyvFU72dqDR2vAFgnc3EFZoNUWHYdhRI+/8Ei3L1sDTq2+zi+aGdgR/wSpF7v3GNbfPylt9Dd24enXnoDh510Nq761fex7fZbV1yaMlmU7KwXI0vxsZlsi9JFVkz0VBmsO7HhiZ0EkOVuu31WwQezpNzawuugjdPjLsWi4mABQ0d1YO9Pb4dPf+0ADB02BDnkkBGIImySzZRXNwEROS5729ycVQ7I0t80v5MIhVmdUdzcX6/9G37yw4vQ2+v8ULjFlpvixlv/hMnrTaxSXU3glavFvVM3wILObrStXI2lWI3exe/jr589Fsdddhk+ungRMtkMCovmYeXd17H22UVLURg/Gl1TJ1edSwW1JpO4JH8I4/CQBMyG1cuUY1yHvfgWA9ieCSMx5vivYehWuzG3dW7z/vZb9ORmobS6E8Oeno7CyKFYu/s2/a66NhO4Jg2tSYJrkvDqbbco2dNeuONf1+Hk48/EwoWL8d6s93HYQccjm8vWVYU1sSixszq38dTUyrNYt36wqLEpxKY2YIwy65GtXbvW23baaafi8ssvRTabvpUHsyrbrBmIPVgNa6fLSBwUD8sX3l58rnAl5u28jMSuKzF3I/apsNyNuFj0x7tK6it/bvcWHPVVGKdsn/5gMf6ztgdDdzkCIw/4Ena/5nSmU0UxOsMuqztx87mn4bRLrsfSVWvwwbyFOOKL38XPvn8GPnf8kQxgmSrruhbbBYLZDHK5Vu8es7Iw5E5cLiLHQJTX4JUmf0JmYrI+uLDqAiOtc2WW6sIWrEL4ZJxcii1/TOyIse347dPnsHNWxQQGTLQ5sPJsxN6jkIG4ZFXcm0W3Yu+OSu9P8Znq775zbSfO+fZPccft93jb9t53d1xz/eUYNsypA6t0tQ+BV69Nawuw387Y/dU3se+UDTBp66kYNnIo8MSdWPrEHb6+rFWrYa/pxKr9d0S5Na8F2CAXY7E/3T1OwsTX0QRoTYA1MDlTdy+Gvvguih1tWNTZh+zSVVj5v3ux+KG/YejwIb7a6MWh7Sit6cKwF2egc/vN2L1MSnVtJLjWCq31ANaooGYnNIag94/J/dt62y1w94M34pQTvoa33pyBNWsq8yr5eykMYI1V6ZiuxEHnHpSWlApq6V2KGzqOOlo6809tQNgrr0zDokWLvef5fJ7B66mnfqFfxzVYrJlV2bgAG/VXxTDVNnZsrASuVSArw6l8uPvjjdc2yJWYTFBheUInsZyObz+BqgJgCVplmLV61WMkhfPAecvxfE8fhu/9WQzf+wSsfPpWdCybG/E3eIcn7bWd2HHDCbjroq/jzN/ejFdnvI+e3j6cc8EVeGHaW7jovG9j6MhRTIkVYZZ+aab4WP5rMynDWRe4crbzWLIU7yUJZLna6cCsA7L0nGDWhMk5wIpxs840UEqGE8fFzf21nMCVq7F8X0WNFdRijSrLjVxkxUnhu+/Mwmmf/xbeedtJlEd22ukn4YfnfRO5XK7qRxufq7MEr866X53l+4Z9cn9MzOfQ9tRLKHZ1oTCkFXBVINa2r8AUWALY1Xtth85dtgoFWFMwjft5onMprhVQg84X1H/HzAXIL1yG7o0m4MV/PYPS9gvQuWIFbvvV32BbFoYMH4IRI4dixKihGDl6OHbZbQsMm78U7TPnoXPrjWtSXZMG17hqa39Aa9S/W1NoNR1HrfAq2nrrT8bdD9yEs886F/fc9aC3ffnyFejs7EJ7e5tB5uRMYiqs6TmSBNsor89AMCtAUY2lxjY5yKYQm9qAsHEUE+ca/cL8yCMPYrfddu3XMQ02a0ZVNmkFttHuMpFibDmIBpXWIQtzJabnQkIn0Y2Yl9UR3Yg5rHLFtQpm3aRPKiuUSthr4UpM7yti5EdPxdBdj8SK/12LVc/dAdu2UCZQjnK/6PJa8kBvL9Yb3oHbzj8DP7/pPtxw72Ns/63/fAjPT5uO/7vkXOyy687sS7lk9zKg5WV3GMgSuGYqyqyY6EnJNwLI0sSLQIoDLE0ai646a3oxvF6saGLCJ+5GLGZ3lY2AKevGvPIyO9ylmK1LaqzfrVgGWedszrVKccPs/gDX/flvuOiCy9HtZisdMqQDv77ifBx2+EFeOx248vGEwav395DJYPXH92Dura3Pv4b87LnO+9lNSMRiYMeNYgosA9hMJhRgdX9rSaqwUUA2SWhVwUt+wVJQ+WPk8th1n21Bd3jPz38Be33hVKxavATP3HoXHvn9lZj7+hwUe97Gxpuuh+FlIDd/KbD1xqGqa9w411rBtd7QWk+VNQ4UmcfWRodXtk8zHr69Y0g7rrzm19hhp21x4U9+7e6zWA3oKP3Vsi+J9lXHK9TdRFTcWpTNGCaCpW4dTQ6a9bQUYlMbEEY1X0eNGoklS5Zi3LixKcDW0cTJaH8AbRLgagyPig/+wGOF9krlVhMPK6uvYjysscmKbYArMVdhmYIa5Ebsls8JAtgPR/Xiis+txZN7FrB6KDB0NbD7YzZGXGbhyoV9WE3ush87HUN3OBTLHvo91rzyAA7vaMF27S3A4lUMQEDu/rTQl21LizNGSspG22g/X1pb0LXZ+ih3dcOybbTYNi744pHYZaup+P7vbkRXdy9mzf4QnzzuDHzj9JNwztlfQ7uifiwHWQavGaHUDAca6aUjGMu5SaA4tPKJIQcdnpm46mXR1At1JtwB5SgiqHW+LMA8DtaFbh3IsgROQTDr2odz5uGcs87Hk48/523bfMupuOraS7HxJhtUQWAYuIrjVSV+8lx/bQudu2+Dru03Q+7d95Gbvxjo7mbZiymJE8XAkturGC9uArBBbsTNVmYsKrhyYz+EdJGLvzOFGz12JF559D784wc/RHarQ7HBtlvhsz/8AjIjJuC+b30BW243FRPXH4fyzLnIdDseBkmqrvUC10ZDaxzlMI6SlwS4JgGwlecWvvq1U/H7y/+MZcuWY/jwYWxbUm7E1WNWuxJHuf9JqbyD3qxoLsVaNbZJLYXY1AaMtdDkl/2I3zxK4WC3RgFtkpNLrSITU0ExPi7mL7TapE5BJiVa8h0v7zMwBq/COrkQr20p4QsXLMcDR/SB5XFiki+Ac4BpvyHYrRy/4uGrsObFf6Gw7EOc1NGCKyeMRF+pjDxB7I9/DPz0p5XG3d1UHwv46EeBq68GPv/5yrm//nW0vPQErMf+wVxKkS0AhQI+te9O2HrzjXH2pddi2puzmAvz5X/4K+598FFccvFP8JEDPoJSiSC8gGKxF9lsC3ueKeeQKRV88bGeybfJzVrcV+5j2Yd5CR4PyBR1W8k8ZVR6rlNiSYUVJ2B+t2N5SAxJvf6dkjr+9jpFlp3TyqAoJXHiMEuZ3f/8p1tw+SV/QldnpVbk5089Dj/4yVlobadEWaXAv1MVuIrtVHApuyOXW3Po2npjtojtxLZR4VUej3Lsmh8lwuJZa1Vjw8A1DF65ldryTLUm2CDbYoMOPLpyGXrffhpzcu34363/xvZ7bYGHczYOOWJfx6G96NzvOPBqFB9bZ3ANjYltQmCNp/42Bl7l8bWQF4zr7RYlkZNqf5QETaYWdQzNZo1KnmTV4FLcyHEmYSnEppZaav0GtM2ijMStJevLTFyriUmdFPvi9hnJXADuzBWw/01L8fZ2lCBJ2P9dAI7Hmd/KJQawO2Zt/Gl4B0qk8uayKAxrR/b882Gdf776fF/4grO4DLlmx02x7NP7A+TKRi7MhQKQKQB9vZg6cQzu+O2PcOXtD+Hyv/wdhWIRM2bOxpHHfhHHHv0p/PzCn2DSehsw1+JSKeupsdlMEaVywa2tSo8ZlKysV3aH/vFapgRlOYv4vOjFv3LYYdv4bXUTLDnjrsCr6O4rTkQtnaKl2CbWhPXOXyb48ddJ5Wos79erBeuCrNO2GmbpGl989lWc+71f4u03K7GvkyaPx69+ey723m+3UGANAlfvGgKAU6XUmsBpHIBVfcbEBdh6gqspvHIrTBzLvBUoPIDK6rS1t+Cg/XZA4cFnsfWaZRizWRZtu26IMzdfH62UNZ1+FLIs9E0a603247gM16K6mrofB7UPO29/uAT7j43+3WgaQ21yHXEBNqrFrt9roMKKY611rlGLYttokKsJHu1106U4hdjUUkttwMKnbJG+AGJ+4MdO8CSfN0i5FWJetcernosqrOr65IROXqkd182YvkgLJXzxFys8gN3783vjuZufYxnAu7q6Ai/t1UIJfYUCcnCAsXfMUOaynOnuDZxK0J3omTQaKz6yAywC10IB5Z4e1xXZfV7oQyaXw9c/dzg+tt9u+MElV+OlV99ix9/297tx3/2P4BvfOB3f+uZZGDFyLCu5Y2dyyJSLyGYq5XcYi3rhoW4dWYY1WZYH2Mkq7JTfoQkX1YjVlbpxrLIuqqpidmLuVsxVWDlDsWwiJFfuUdnvSiyALHcrFkGW73f6c2z2zA9x6UV/xIP3/q8yMsvCiV/4DM7+wems9E8YrAZBqzh+eZ8OLOsFr/J5dMdUji01FFjjwiuf1HdtMhl940Yit3gFCmNHYlznUGz8zJtOePfU9WB9+yyUf/97bPrKO8C0d1kyp2Wf2Avdm6zXUNU1CrgGxsPWAVhrTeqThDttveE1aF+crMKq45KMhY1qcfpXxctSWbGmMg2UauNiI7gUDyY1trm199RSSy21BpfMqeV45bFyVuKoFgTbpiAuJH0KMgLZD0cXcP+nen0K7J5f3RNdF3WFnwbAn9b2ODG1lFCqDPRMHMkUWfoK5QuZ99yy0LnZelj6id2cL1pSjGghmOVqrLgUC9hyk/Vx1zW/wK/O+yZGDB/K+luzdi0uuvhSbLXNTvjDlX9Ad3cnU2Mzdo5NurzFziJj5ZiymbVyyNktyFl59jxv5ZG1aH8W9I/VfKXnDHCdf1TflW13//F1MXGTClLFmrAmRlBFbqsOula/dp6KzDITO+7SrPyNG9vrbUcJC+Yvwrnn/AqH7vc5H8Bus93muOOBP+O8i8/GkGEdyjq7xXLB61tc+DnE84hj8hYhFpkfK46fXauQ9VjcrmvD91XulRDv3ACAlUGVnvMlCsjwRTbV+4e/f70xt+axZuctkF25FhOuvhtthxwJa/b7sFevhnXTTcAf/gDr0ktZ8if6e2x/bSZG3/OEr4+qck9ubLm8X2wjj8OJnPTHT3r1nEP60rWVzxVUikf+F5g1XFpMzff5ETKmsNc6iuoadk2snebehe2rF8CaqrC12ICIh00wjjQWqFu1J9JqdvdssuYfYWqppZaagQ2EXw1rtqBrFCFVVHBNY2Tddld8do0TAytbxfM00N6jOD2eHKqnj42ld+wwrN10InomOEDbN7QNfaOGYu0W62PxYbth1S6bwSKFmGVOriwgNZYlnnLV2L5elIsFlHt7WcKqzx11CB6/7wZ84cSjvVrRixcvwVlnnY3NNtscl//mcnR2diNr530LgW3WznmAS9DKQZaAlYMsbctZuSqQ5QArQq0aYkUt1jJSYXXGAZVDohJkXZjlUDd71hz85Jxf46A9jsOtN9zt1doeO24Uzv/l2bjtvj9iq+2moljqY4sMqrTNB6TuwsBWB60CuLLxin1qjhH/idelg1dvnwZeVfGvKoCluxlXgY0DrmRBIGMCr04fzr81u2yO1tkL0PLBIlj77w/cdRfVnwMmTwZ+8hNg1Spgjz3cY4C2N2dj8k+vMoZXeQwqeE0SXIMg0RRYVbBqAqw6UI0TzxoHWuVrDG0bAKh8v8p012SqokexQGU9oitxlLGolNZms7oAoh3Qpxs7HzV7c7ODbOpOnFpqqa17NghiR3h5HdlYhmJupIbKJpXW8TIWu67ET+7eW5VU99XrX0VrVyu6UUkApLNNaEoigLNNSY4p228ui97RQ1FabzSL4aMstMXWPMs0xGrYZmxYOQJVN6GTuzBgzdhOnCiBKmU1zmQdmO3rxejhQ3DxT76Jr3zpRFz06ytx97+ceocffjgXZ599Nn72s5/hjDO+jNO+/AWMGDUEGdtVKqnuK3lsEfQJ8bHOzXVm/TyBD8XJ8u1lyZ3Ycf11/vG4WZ3q6gOHqlqywcZjY1kJHdetmCYYYowsa2cBb0x7B3/5/W14+L4nWBIsblRO49SvHo9TvnIMqwHp9cvN4IcgZfx4QAxqUBmcoHqvUdyGVedVHefvo/GfAXHchv3H+/8w8ys6MezJ1/xb//AH4FvfUp+/XMaYWx7CwnNORnHU8PDkTgqQjOMqHEcNNAG5epWzCbP+UhRNgCKOS7axG3gTqLCDNelTf5gluRUPREshNrXUUhvUKmyYQmuc1CmkvI6681JyMB3UVqG2ihmH+XN5m6r9mqH+eqhLvrEE9iU2uod0A0MUWX0FoynOGRmLwbBoDGQzZadsT8ZGkRLSUHmfjI1SJlOBWFJiCV5zkguxkKmYYmNRzLFasmWC2UIfe9x4/Qn4yx8vwSuvfQmX/vZPuP+Bh9m5ly9fjosu+iUuueQyHPbJQ/DZzx2D/T66B1NhKZFWzs774mP5tRNiWujz/J85yDIl0o1Z9ZWbcWFWOdkPyFqsm1w5sbcE0qT6+SeBYnwsa2sBq1aswYP/fAz/vPUhTH/1XV9fBK/HnvhJfPHM4zBq9AinFm4MzwUTeAyCUXl/lLam51cd16zwagqw8vuJHzPm1kcC/6aVViph9N/+jcVnHpsYvCYBrmFAF9X9txarB3DFdYE1ha+48cTKcSk2RQVYUxV2MFij40dN42IteZ9BuR1dfGyzWgqxqaWWWmoaqykWNgiOa1WCdcdrtmsnum5SJw7B1G7Iavqiq0xk3t7p7Ur7swFcoh/Wt2wLLSy3UAluhRrPyLW4RPVd3Jq0JYJWqk1LX5C2xYCSQaxKjeXuxb29fjWW3IszWQeE6bHQix232xK33HgV3p4xG1f87ircetvfUSgUWEmZu+68hy0TJ03AcSd8Gkcffzg22GgiSlZFmbXKjvsjucx68zIBZElttTyXXqop63dJ9Wcolu6BInOxyli9Wmni4GCrP4lTX7EPLz89HXf/7WH894Fn0Evu24JR/dDPnXoEjjn5kxg2fIijIMfIMh6UyC0qiEZtH6Xea9LwSkp8nERN9VRfxeOGPjGNJWrz2cknO8v8+cBf/gL85jf+iWupjCFPvMIgVnYZrpfqGgdcTaG1UcmVGhmvGcl1toYMzqZjreePAialeMTtSZbuqTWxVz3MB5FCcidjULajZSlWqbEDJakTWQqxqaWW2oC2yB+2g8CVWBv7Imwvs1p/1es+y9goZ21YFARL2ZBtG3s/k8eru3Qpf43Hr4DJr0zG3H/PlfoBdtoD+NXTDox6JoAs9c0yI9N5aAO1yznuz2W+nba5mZPZOs+gTK8ZgW2e9hecrM3FgnMsPbJjaXsB5ayzvuXmU3HVn67AeT89F1dd9Rdcf/0NWLRoERvL/HkLcPmlV7Jl6223wMGf2B8fPWQvTNnMydxaJtijmLOyA6QEMuWy88VOJXocyHdceTP+hMeVlyIkA6zOlbhslWGVLSnBUyWZUHdPD155+k089uDzePTB57B8ycqqPrbcbiqO+uwh+OTRB6CttdXpR8hazE3MRKwCkiAo5H2GHaOE0oQU16AxxoVW3bZGZR02gVe2n+JxV3f6W11xBXDOOcCyZcCuuwK33eb87Vx+udAvkFm1tuIZUCfVNUrNWKd9fBhrFKjWK6FQPRL3xIJXAwW2USpsEq7fzZIAqj+hMI4aqzyuSS2F2NRSSy21OiSG0qatp8k1AR2BGHs0PC9rK/RF5WdInSSjmFFXhCsTfPFGWdsDSQaRBLN0HAGhZA7olnDWTUPwp692ecmd9rhtD7x+yOtYM3QNtnhxC6x+ZzX2uWgfPNH2hJPsaRMApwF3TQUD4jKNJeze8F+XCVZphUGpNJmme8eT+lB7+uJ1Sw8xN20CV1Jocy7A8kRBtJ6pZM9df/3JuPjin+Oiiy7G3ffcheuuvRb33/+Ql+TojdfeYstlv/wTxk0Ygz333Qm77b09dtlrW4yeNMK9Oc4Ej4CJ7i1zx6W0r26JGw6yPF428NoD9lMtVwbJAnQSu816czZeevJNvPTkG5j27Fvo6e6tOnbYiCH4xGc+gsOPPRCbbbWxN5EsoFAZkwvlynNHhD4dPIYprbpjG+kqrIJV02PqUfM1DsCyMbGM0gJ7vPxypdGzzwK/+IWjygoQS+2Lw4bUDK+1gmtS0DrQMt7Woh6aHhsHYI1fzxiJzKKOpRarNanTQAC3pIHTGsCxsSnEppZaagPW4sS7NtTYF34pOdWVud4Kz8V1gkfOpiKoCoDLjYEuqbBFpz8GsASUWRvrLc3g0H/mcd+nnTI7nf/XCfvLNjoKHeie3I1NvroJHvvOYxXpsQQccROw3iJSdd3+DUDWuyRXkfXidpkyW/KXBnJr5jL4Za7KFDfrbndjfQle2XN2fBGlEim0BdjFAkp2AblcO4484kh88rBD8OHcObjp5ltw5x1345WXX/XGsmjBEvzz9ofYQjZm/Chsue0m2Hy7Kdh0mw0wZev1MXLsMCfRlKDKiiArXZ33aDLZ7u3uw6IPF2LGa+9jxquz8c7rH+DdN95Hd2ePsn2+JYfd998eBx+5L/b7+O7sOZ1JjJdlL5EwVxTdneOaVv00BNZGqq1xgLVWMwXYMOgLA8c1e++AYSqX4iCvE9tC5z47Ks+nOkfYGJIC1zBorQVWG6nIJRk/GKWv8PsXkHSpBoBNSoXVZSU2sYGUuTgphTOwH9vvUmyixg5Ut+IUYlNLLbXUms0E1bRMMauy+zBBFC+jQ/v4lzhtLwkqr9ieuQwHxMtkKi7F1/54OPafshRvbV/Cq49VIG8t1mI2ZleOKQFbvwDc9JXq7pjyG2CeWzGt0zWSoiru4xNzF1w9gPXUWCcRk+WqsKJLcSlTgO2288q4ENTaGdiZHCZPmoxvfetr+PpZX8YHH7yPe+65Dw8/9Ciee+YldHVVMjAvWbgMj9Py8POVcVsWho7owIhRQzF89FAMHzUEw2gZPQTDR3Y4j7Rv1BAU+5zXqK+3gOf/8zpWLVuDlUvXYDU98mXpGmf7stXoWquGVdHGTBiJXfbZFvt8bCfsvv8OaGtv9UCDIJBPAOWswyKMcNU16oQ7bEJTT2BtFmgNi5ONC7Cm6qu4b9nxH8OkS24A+OTzmGOABx4AVq8Gdt4Z+P73gd//3j+QTAYrTvhEJHg1ylQbEVyThNZGQGqjkttEPY8J8EUFWFMLew8loZLXKx52wJhhXKwVFTgHCcg2DcR6sUrzF2DKlM0wUIzGS7Zo0eL+Hkpqqa1TFuuD1SAett8+sBm0OrGXShdjGQqlum9+hZan2LX97sWuMbdfOg8lVyq5cbG9DsCSjtfeY+N/x4/AqReuwn1HkZLpioq0lN3kQiXgk7cCN51ho13DXtSvbASsZUF59X2NcuWVrRed18uNi2XqbFFwJXaVWZYQilRYF2q5+7GsxpbtImw765SooXhCyk6MMtZbfz2c9pVT8IXTTkBn91q88PzLePzRZ/DSC69h+qvvYPWqtf5rKpexavkatmDmfJgYQeqFp/4RcWzCemOw6bYbYYc9tsAu+2yD9TeZ6E1cWayujH1l9QTXSUglvROk93qcuNjKafVxVvUA1v5QWONaPQCWrDR6JJaecDBG33Q/q7OMr30NuOoqJ/nZ3LlOuZ1LL/Xal20LK044FKXRI2LBq6nqqgLXYBdXuymTJtXbahlLfeE1vORO1NdCVmEbCaTN9JqLRjXNyRYsWIjNt9xZ2it8noaHsBo1NOrGoNHCRUt83Naf1jQQy+OTqKbd3LnzMNCMjz+11FKrv5mAZlJt6mYihJq6HUtxsQw6xWROArCSS68lxcN6Cq+ia9GlmOCwo9vGbd8ahnkX9OA3n+/Bk3sVsXoYMHRVGfs8nsHZf7SwgfdR7RxnbCKk0zp9E7nKq6PCCsmd2OCErM58cd2HWdwsi4+ljMUZNya2gFKxD1Y2V1FiywS9FTWWqbc0fbYyDGgpG3FrSyv23HsX7LbXjiiW+lAo9WHWe+/j9VffYkA7a8YcLF+6ki0rmHIaXjc3zBxlt91Rb0cPxaixwzFlq/Wx+XYbYbNtN8LwEUPdds79ZfG47nuFEj6RUQkebjqgNZn48jq3US0oe3F/wGqcpE6JqEaqeNcaADYIMPn6vJ9+Ba0zPkDHc2/A2n9/7dgIYDt33w4Lzz+zqu+oLsO1gqvJva5HDdVGWj3GYx4nbMUcm3m8bFibuH9PydXybY6kTqbcM2+e2Q+izWTFJuCepoHYTCbDXkjbtjFx4gQMFCMFll7I1taW/h5KaqmtE9bMri1B5k/qJMWrBE265QkEh1HBRGCtylAsJHfyzHVTFuNiSYHlLsW8DWUKnrw0h8sutjwV1ZeUKeM+ugAbKRZWVGQJNEVlltyL80Jyp6IiuRMD2WIlO7HrUszjZL0kT7YTE8sUWVJiUa3GEoRlrKxTZsfN2kuvC6kHU6ZsiA02noSPfWofBrq8PYHZ2q5OrFi2EssIapeuwoplqxjgkpvwGy/MwBvPz2TKbb41h2NO/zhzO3ZglR6HYMToYcw1OZPNKIGCYm7pfJStmL/veXyrk0XZD7POMZV1GqU4maTrqjUeNo5Sm6QbcL3qvIr91qv8imymABuY6bq1Be/ddBEm/vRPGH3LA+7fRtlzmmBeGbaNFZ/9hAOwra2+s0ZRXqvGmxC4mgJHI0C1mWA4cmyowX2MXL+3xjjYMBW2Vldi33rMeFf6jE7KorjftrS0oKenh/HP2LFj5FEpVxuixoY0XLhoMeM1Gnd/W9NA7Lhx4zB37lwGsLNmvdPfw0kttdRSYyZ+ITUUoMXkDKr4Up5pWJehWE7uxGNipSRP3LhLMVunvskdN2sjQ27GBMlFcZ9/m6+fCCDLy+w46/Srbq4C+O5knCd2qkruJLoUU5ZiqidblBbbLbnjuhiXXJDVqbEEjATMWTvHJE0Gwm4ipCyyrutuyZnwlYH2tla0TMpj3KTRbDtrT1mL3UzDJ+7xXSxZsIKB6ue+ebhREXkx8RLPVsz7VMEsmdiLCLQy1PriYRMAtXpkA64XpEaxckL3KEiFjQOwujZWaysW/OIsLD7nFIz620PoePJl2CvXojR8CDr33pG5EBdHj9D2Wyu8RgXX+ErhwILQRimQtcBrrQCrHo896F+vJGzJove184uquUbJrF054DhtGw3E6zIWb7PzgZi/YCHjtv62poHY1FJLLbUwM4VIZbt+qg9L7r6+2qlViRSkfQSgKnWWK7C89o28nYxlzRVAVaHaegmcpLhYntSJtc/yMjgZlG3XtVeE04KrmkrAyp9TP6I6qwVbzXauzDITlF9IcbHio5elmMfFlhVqbDnLQDabbWEaJau1Sa7E5GbMXLEtBrmUVMp29zEPIWScmrB0Y8oFD2RJ2SRIJHC0ufJJoGmVvVI5cX6ll0GW3SoNzAYBLVmBgbrqPvvHYVI6ppY41LhwahqTG9VMFGkas3m8ZoBaGgIptQCsuL08ZjSWfu0EtsjKlVUjvJqqrqr7lRS0DmTgScpNNmn1ulaADXMjjqLCJn2PgiysxFMyJwlIpBhipt8VltSu6jiTMWhqxw6E0jspxKaWWmoDwuqpgpr2XXOmPteVuApSSd0s2ygzQJW2k+uvfE7qR8gmbAnld3j5HAZxXIHNZR34E+NiZZdiasP8jrNMweVJnkiNpW2y+loGuTBLvwK7wCzWi2UQa9tOP7blwC1tE2JoaczMfZmM9okTjJAsx1HMgVtKAFV2JvbueWiiVSK3ZHpedgviWBkGkNxtlx7p65y5GbNyOs4+Vxf1vT84yPIYM920TfVe4mPi8CbDLJkIyEFAW7FwN2KC3XpbvYC01rFEdbEOy1AcZroyOrUArErlNU3aFKgQGyV3MgfXOPGVtVoj4aieFiXOMy68Rjl3ra9T0PE6V+KazteP5XXEuYPJumlfcUx5/AAF2RRiU0sttaa3KB/YzRgzq1VWeSkcMUaWZyguS7+gismZaJ+oshK8EmCyY0SYzXjwWs46ICqW2hHVWO8cTIW1UcpnYfcWYOWyKDG4tWG70KqDWRW8kjGAJWh2a9KKS+Xclr8sD7u+mJMXMS5WWNj9JE9gAk2a0BS5SuWosjSxo4ViYy0CXXc7m6SXBaB0dFxBjbVdN97a33vyr+oy+DGY1gAtO56yEJer7xsbcxMoWf319xl07fIPBnHV2KhuxHEBNgxeg/pLCl6TAtd6lJQZLBYX3voLYBuhwprEwyad+bgp5hQBiqoVQ42NCrL+DWgaSyE2tdRSWyct7IupXl9cPqCVMxRLQOt9+fB9fBF+GGWgSm1U9WIFNZYSPJXLfkXWq0ObzwK9sgrrlsgh12FXJfWm+iqY1cErG0+mWoV13YzZ4rVzj5UzHbuQ7qnOKhOTO0nmQaybSdcqZ2BZGfblzDIWc1B1J0COIks1V+m1IkW24AArC5QtVamxPM6UKbNsHuCosbWaPDFh45dBWRHOpHJlDgLc/jTuIh3XTK9HdS+D3LgbafUCWFPX4SC34TB4jQquplCRwmo0M7mvgWWOdK+XqdtyP/zdpFYH04Bss1oKsamlllpT20BWYX2/doqAGpahWBcXK+yvcinmaixXYVVqrMXdhiXr6XMUYPhV2IqLsRsja2dZsiUVzLJHFbwKKqtKheWuxD4VlsMw22cwOdIlr+AZjYVtfMlYOS8ulk/uuIuxEwPrgqub0Ze7FJPyLKqx7Dg6wqq45MZ19+IwzC0jxjtqXI+VLroGcxAd6A40C4NgFeQGAa0KZMPU2KB9JipsUgBbi/oaBK8mqmtcaK13QqPBbFFUxiQB1giYQ1TYRrsSD0QL+h4JU1+tuGrsAAPZFGJTSy21prXEoLTBSZ3YlwMLb3VVVDFmUloXkzsx8BTjYkXFlO6FxqXYU2N9KqxajfW2S27FJSo9g0LlUQZZN9ExK8WjgVlZeZXjZLUqLLXlKiyNNSgOVoZa+bVlMa8EHe7TUtHvUuw1U8fFOuBadPa5X+SiS7Gj0rquueVKpmJezsZ59arfb078bMkIXsO2s9tAQK35+zBxHSY4l8c3UC0IyEXI1QGtCciamu64MDfiOAAb5j6cNLwau5VGLOOisnUZXhoFr0kBbJgbcVgfcVyJB6sF/gBaQ6KowQqyKcSmllpqqSVkLHbELc1iZDwu1lsv+eNiueuvyqXYjX1lUCf2ISZ40qmxYj8cktmE3nUjLvrdidlxebe0jhsDWwWzgupaBa/0mMtqVVixfU0mfkG77sXediEe1gNdRVysRXVZ+TqDVL9LMd0orsaWBTB1JnLhLqthoBqUxVecLAYBrtMRZVM2v6dyf804YdQBohgjHAS2YnbneoJsLQBRC8DWor5GgdckwTWxpD0R3q/N5rETZHH/Dk3AMGrfJgBb74RUUdvFtUa+R+J67lhR1dhBCLIpxKaWWmpNaVE/1Pt9YhLwge8rsyPHvcrbRJgNcimWzu1zKS5HV2NZOR1SVGnuw2NkhYzFDsC6RpVmZJh1rQpebansjiIW1gNYF2wdyM0IoKtRZ8UvY/H159sF9VVM7lSy9XGxdD1BLsX8dZHVWDHBU7Uaa9UMsCb7ZSNdPXasmuLvKQoUJ2EqsNSZDJyBya8UMKsCWd8xNdxLUzfipAG23vCqhPAgt9U6wEwcS7LvWr93kr7OesCr7hjV30NUN+Ik3i+6pE6DAVxrzURsagMVZFOITS211JrO6v2hnXT/Jl80Kldi94nQyFXz6JGvR3UpjqPG5rKwKKFTS47FxzKQ5W67ojsxnYfXh6W+BJhl91UBpfw5y1wsq7BkriuxL6lTRFVWdh+2kFMWhJfdimkyL8bFii7FHGpkl2IGty7UcrVOTvAUx0RATbYUTXLJigiK65nMRYbkoL+pIMDVAa0KZoNAtia3YsXkPAxSGwmwpvBqorpqa40mVB+2ma0ZriGSm3ZCAKscR40lbEzjZOP0J4Ku+CMOhZU0yhIB0hCXYiumGhs4PnGO0mSWQmxqqaXWVBbnQz7wmDrEw1YlbDI9h6S4WuTZ6sbFMvdXtk5xsSW/S7Fz0mCXYjnBk06NJVVVzv3T56quPNNwkaCMZv8lT5Et2y6A09gIWoXr5vtEaGWPYi1Y7k6cz6KUzTr7aJuwcMXVUWozzphom3vdDMQFSPcAXjFZiTu5dNRVv3uq7ZXQcZ+7wFoLuIrHcYANgtdawLZeWXeT6NHURToMcGX4rIzRDoRZGWRrMSWQGsaRynGsQQBrGv8aVX0NgldT1bWWZEBhfdfTKA5+IFnkMjkh9z9ylukYbsRxY2EHtMWIY40Cu5bpD+i1gmyTWgqxqaWWWtNYf394Jn5+DqryL6PiZF2sFyvGwdIjz1IsfuFI8axGaixBouvUWiZJlZfcEb8CCE5R8NrR2SyL4IeYjuC6El8qAi171CmxshqrAlgxYzE9z2b9rsQu1DrXZPnr43pqrlBeyNBMVD4RaGnCRXGxSiipxYVNAbBBwFo2ST2ssGTV3dpMBLRaYoB12ZvD1FSCWR3IhrkVxzUTN+IgUDQBWJX6mjS8JgmuzQQxpmPpL9iNe6/iwmvQsSZuxLVanHE1i8UBwaBjovZnmbYfBCCbQmxqqaXWFBb3Q7M/P2x1H/b8S9bbJ2QollVY9wC1SzF3Jy5LdV3D1FgCRRdG2Tj6Cj63Yo5BBK3UN4NZrsZyQKWyOG62YitTElRaP9DSdkr6JGcO9tRYvi1jOwBLLsvUNwdYDrMEr6IKy6+Jb3dVZjkhlc/1mK/LkKsxOUNxGKCKyiuPh/W3lV5njfE+VAAnw6YptJJLc5BlmI94ckb3JSoYc+gyjW1V3R8d2KqAlr+u/HyyKptEmSHlhN6gnIkqDtY7nu+rM8CawqspuAbBRRwISwJWkv5+iHIdcYA3KbA3uXf1BNioKmxcV2LRTbg/raHgJ8GnFVeNTbB9f1kKsamlllq/W39+WCYeH8tK5hSDkztxU7kUk6hYFlyKKXmSqMYydRJqNZZiW+maCgW1WzEEuCX4JEB0YZaBrKfG8nWC0DJzLyZVlrL1OtmQFUDL76cAreJzlrBJA7AOrNp+FVaAWg9Yed+iGuvcXAFco02udW0cSIWgwlZeU57IqdKutvcQhyz+qALXMEiV+2qES3EUMA6D3iDA1YGtCmhlmA1SZUWQ1amxtdw3DyYDlFYt3MYA2CixrzqADYLXKOBarwRDUc30HPX4DuoPpdn0euN4j5jGwPdXeaSBVpYpSfXV5PgobsXa9k1mKcSmllpq/Wp1/ZBUfDgn6eYT1RwYJTdc6F2KXTdi58sF6gRPKjWWoK9QULsVe+7EjisxQ6Q8YPX2eSDLBlUQ1FghTpapsi7MOtfhApyg0HIToZU9CrVjy1kXUAWI9Z6TSiursDxGNpuFxV2JxXhYvk24v757Guc1YhO1igsxZSiOMkESMxQLd4XBkC/ZlASuKoCVwdVU+QwC2f6oC0uwG6Qo6wBXB7Z8uwpoZZhVqbL1iQ8271PnRlwvgE0aXiNnJa4RVnV/e3Fd65XnCPXaaN7JfJT7Gxf0dABr4kYcVYVNwpW42V2Oo1rVPMRAjbXWAZBNITa11FLrN6v1w7HpPlyjpKPXuBTzmFllgqcgNZa60bkVE5ByGGRnKVSBrKfAsgzIgrswB1rGj65SlXGukSu0oonQWrlWpwSQDmDFLMk+FdZ99NyG5XhYN6kT3SPmauy9DBqwNbSqib6gwjpf6i7kCnViRXU2yFSxnTLABsFrrSAr7pfBy+7pw7CZi9E+fyUyPQUUW7LonDgcqzYZixKp6DGMZ28OUnJlGOH3XwevKhj3Mn8HwKwKZHVqbFyToTIoYVPkvkMAVuU+rALYpOBVm5W4RldWU4vTR1zwDbqm/vgeivq5Vsv9jgKwSauhScBovVyOnc/8YlOrr8YWArK+52geSyE2tdRSSy0B0/7q6SmvZIovCbEuLHNFFhI7CWos78tTY+m7UwY7MckTAazoVkwf94WCl+iJ1cyhjMX5XAVk7ZIDzir3YjZ8d/ykzrJxknoqTQpd0HWGU3EtroJYAlS3nA5TYfM5R73l7sUZSXEVn+tU1qB9BiZP6OUJiug6LMbHBoFaVNAU+wmCV1OYDUqcRONntXGLJYx58QOMfekDtC1azX6IIUXcIuXdstA1bigW77QBluy8gae0iwCvs6A2unumUm1lqBVBUIZxuZ4rXWMUkPX6qVOCJ25RVdhaADZMfQ2CV+OsxEGu0k007Q0aSz0Al/UbEyySev/VUuqoVoBNUoVNAk7Fv5OBNqcIU2NVZqzGkvHvzjpUdKiXpRCbWmqp9YsNOBU2Yqp85ZeHC6WyS7EHqJJLscNMGjWWj8kWXIl5kicCQUrmxN2KmSJLmXWzlURP3MWYwDafc85N291yOM4A+Xn8iZx8Y5DjfBXJnbz4WAlgPcWVzk/wS/t5RmJVQifBldinztJ2RUyrseuZnYFVKuj3B9TRTKomLE2gOdTp4LU66VP4+zEMNK1iAes/MgPjn3kPhfYWrNpwFMo5IY6yr4i2xWuw/gOvI79iLeYeuDkD2bAzhwGsTmUVwVal1Mquxz41PECVDQPZepTcaTTABiVvMlFfg+A1Cria/H00wt0zcuiIYtxJuCz3h2ur6WdUUgAbte84NlB+KEmizE691VgrqF0Dx1urpRCbWmqpNdwaAqAJxMMGffj7vgTk+BRNcqdILsUMalXldhRqLB8DW3dVVtetGLkc0NdXAVme4EkAWS/u1V1nCqyrylZBbK7SxrePm1QrtkqJpYlIXnQpJhi13KROLoAL7sSkKFepsGI8rAi2BKNikidDE18rmqCVinqgrYeJE2UVtIrbTDIaB5kMgGNfnIPxz8zC2vFD0Te0FS0jJmDKwadj6KTNUSz0YP7zd2PeM3cgtzrH2nWPaMWi3TYMdIqlya9pjGvQtiCgFdVZuQ8RZlUgW6tR3DO/TvF8cVyJkwZYU/fhuPCqAolmLYeiO3ekLK11Att6WSQlM0GANTlv0rVtazXKTt/0amuY2fHU2MECsinEppZaag21JECy6WJh48bFarIUezDpqbEErdSt2879gmEgK4K07FbsKrJ+kHUTPAkgy2JkyXM057oHkypLYEsqLVdlWb8VcGX9iFCrU2Hd7QxeRfdh5jKccRI5ufDKQZadkwCWq61suwu3tCjiYdmExwVbtl0AW29bgDrLJ6YlzY8PtpuwKSmTIU8XB6sCWJPETSZm9fRizEsfoLc9h96hLez+bXnMj7H0nWfw5u0XoHXEBGx9woXoWbUES6Y/itzqbox9aQ6WbD9JGSOrSrjEzhOQoCkq0IowqwJZ+bnsXqxzFU6q5I5sVWprQKbipABWBaUq9dUUXqOAq1k8bH3gxMQzQTfGgQy2UVXIsNcoqqYZtdxSvV2JB1NSpziQa0VwIR7oIJtCbGqppTb4ALZJPnj5l6kYF8uTDXvbuOIqZikOUGPFTMVVSZ5Et2K+rgNZF0ydGFgXTF13YZ6NmJmryvK2zuCqobYKYvlEgid6cl2DRfdhMQaWAaxYL5a7DIuJn4R43ypXYr7PcPLUDBMdXZxrdcZiM/di3TadjZi5CG2LVmEluRCjjLbRk9A2ej188PjNKJcK6Fz2IRZMewgTdjyYQWzn2CEY/v5ylvxp2VYTjNyHabsJ1IYBrQizKlVWbqdyFVapsUlnK5ZVWGUbjRtxkgAbBK+qdso2BpARR82rl+nOZwK38nUk4YpcT7iNnWW4hs+9WhI51VpuqBk+r5slgZMJbFpJgmwTzalkSyE2tdRSa4g1tXpagyXxheQDVssyV2PpWJVbMQdYoeyO0rWYJ3ti6qZbB1aAWcpezFVZ8YtMCbUcVvkYZEWW13wV1Vce+yqu09hkN2KuwgruwxZtk1yJfefnoEr3U1Bu/fe99jgqMcFTeNImUrDluNZKLGwQwEZJ8qQaj8pa569kLnXFHN2XMsru/ShZ5crk27LQPm5j9rycc97rdFxpq3FGAKtSX1WlcVQQKj+Po8pGUWPjmApEw1RY1XG+EjkJA6yJ63AUeNXHwprdT9OYyiDTeUyorOq1bwDUVs4d/hmjAt3+yPJrJfSaJaHCRvnbDHRnNxx3f85Pos4haplzWHFAlqyGhIn1tBRiU0sttbpbUl8QtaiwqmOT/uISvwiM4mKFzMR+mNWosdSUK69sB9RuxfyRNnG3YrovVYqsC7IivHKYdcvw+FRZd1wy1FZ9k4gQy9VZMfZVjI/l2YiFhE4MYHlsLMGqkMBKTOjkcyXmEx/JdbhqMiR9Gftqtxq8H3TQGmZi32EKqg5gg0BWN6YgZTbT04dihmKhnde3c+kcdK9YiA33OwmzH7sB7SMnYfx2H0cm38bwm50nYzE3ZLGEjQnA6lyFZaANStoUpsrKIGuqxsaxIGALin0NcusNil1NEmBN4NUEKgLvQZ3Kmpj0Hwa4/Qm1jU5KZJzcLsZraarM16rCVp2jzu+txMOFGhEna5upsWHbB5rgkEJsaqmlNngAtsmNfXmILsU8QZSUrVhUY9l2OVMxq3xSSfLEQDabQ7nQV63C8kRPZIJrMavxyljYTeTkZSOuxMf6VFkXaMnKFA5ZksCWm1QflhsDVh77ytddMGWgq4BUT1UWY2H5frYuqLGKeFjxvusm7v1lBFiyAkPbwgBWhDwZXIOAVVXDtrfFKaPDz0Wv9Wt//ymmHvQV7Pn1G9C9agme/ut12Pm4k/HG47Ow1b4bs/aFlowHiPIYOCDKMJpbuhqb3fYyJj41G/nV3egd2ooFe0/BjGN2RN/oob5rFtVZlRIrwqwOZMV7wmE5CfdWntTJdFIfBLTiMarkTyYAGwWAVfuiwmvNGWtrzuwdDhCqsQSBbbNBbbPAa70ANqoKG0mVDWjbr+V1Eo4vVYKoXTvIhu1rNkshNrXUUkstCRO/QIRfa8W42NAvMZ7gSW4rqqoklopuxV4crBQfyz7hNSBLca9ssuYmZyKXYlqneZ4Ir4KLMbsG+p9UWoeDbVUsrBQj66mvghJL42UAy7eRSszjYGUVVoiFJVdirsLKyZt88Opu954rJvpszHX+wg6adhOE6Urn6AC2GORaHHItJR7PDGDNxA5QNRmrr4BSLsMmk51L3serf/shSsUyHr9lGvb4wnfw7nMv4eUH3kZhTTf2nToGqyd0KM+tgtp8Twm7X/AQNr/tFViUGatMoOnck0lPvocdL/0P3jluJzx/7iEotWR96qwVosRWQLoaZINiXbkay39EqjUuNmyiHqTCitvFdnEA1lTl9fYZwqsKdIKgta7KYszYU3m89YZadlyDQSCqe3ySAJtaNPgLaxtLjY1ogwFkm9PJObXUUhsU1nAVtoZfOuOMNTTLY9BET4AuGb58x4vusqLCKB/LEyGxLMB+F9yquFI3NpWgkMWs8lhUnliJu/O6z8WFb1fuz+cr+1or67TddzxvL6wrAZaPRSql4yVzUsS98nvDt/PXSTXRDwO+emdJ1amw/LnqkUNiVftyybseAlXdItryTUZh7bghaF/c6Y67jPaxG8HKtWDVsm6M22Jv7PXFU/HYvS+jY/O9sfyJ9/HUK/OwbONR3vl855X+ZXoK+PjJN2Hzv70Me4ONYN17L6xly4APP4R1zjmwymXYhRI2v+VFfOyUG2D3FKrU6LCY4Mp9qYaSoARZKqMMxTpTglzINp/iqlBag+Jg4wIstaGF9lepvO4+33FCO769so/OI1yPnfEW/73x/1PeP8nF33QxNXkMYaCluxZ13859iKrkx72Wevdvci1RATYpFTaqDRagjvP+sFTHqOYRQd4jIfvq8b5N0lIlNrXUUquLNdOveI0ci9EvrGxQGhcfMWsxywxMsbWVmFhfkieW0EmIj2Vf6u52T4UV1kvuOoN9Zx9TN8tlx72YtpdK1S7GQi3YqgzFtOpeS6AK647Dcx3mmYo5fIuZiEVgdQFcVmGr6sZqYmGV6qtBQqdSANDUYmHZg1WZiFUAWxmnsC5Batj5Si025u00AVMfmIHsaqfMztit9sN6Ox4OO5vD4plv4g+fOhxzP1iJcZvsijHda3DjK9PQ9f0HcPKlhyCTy8B233vyDwIEXntc+G9MeOFDNmXG3XcDd90FfOpTwJQpwL//zWAWt9wCq1TG+Oc+wC4X3o/nfna4tkyOrMpWx7s6iqzKrThJUwGANmGSJplTmBtwXIAVx2LqOhzmMhwlK23Sk96g/kJLjQhjDFJqxeuLEk8b9ceqsHujyiabtJmBuOa1rfE1N2kX5kocJR62X2Nn62DG6qht7lZs0m/169Y8PxykEJtaaqk1tdVLhe0PVy+VS7EDtRHcjIUkTwxkC5W4Wl+iJzFjsQyytJlBoZvsyT2GZQ7WwSwZ3TMOpyqwlWrDVi7ShRHBdZjDK8sQzNVhUTHmC4NWV5HmMbCsPmzWjX+lPgRVW1CrvS9fOfmTd98rWXgb+X4IA1ldZmMTgA2EVsW+eTtPQMuKTqz/zIfoWd2D9x6+BjMfvdabtI4buwy9T72FEWuX44X9TsaMHQ7B4nsvRdeqHnzpysPxzlMfYMYzc/DpH33EA1qy/JI12OLWabApdnrLLYDNNwfOP995D77zDvDnPwNf/jKDWHauchmb3/oyXv72R9E3aohRvdegBE5xTFUnNqxfERDDVFi5vajOqoBTBFjTBE46gNW5DuvchlVqa1wwiRODbvIjUhQX3mYDWmV/dVS9aoHXONYMeQeCjLKy19tqdck1Od5KyIV5oLgPy5ZCbGqppZa4DcQPwyhmFK8ixcVW768kaxIhVX70qbHicbROm9w4WK89T/REQMoOcNVYCCosK7vjbuOZiwmOxePYmASYZecTr1m4Hn5tYpypSonlgJnLVcOrDLCiizQtnhIrKbC8H9HNWnLFFmGWvx5BVq/ajuJEVy6tozOli7FGfY1TN5Zep/cOnILuEa2Y+NJ8jHh/BawyUMrYsIsljBzVio4jt8D/3fM2Xnrt3xh15A8w9qjz8PZdF+Kyo2/B/HeWolQoYtdPb4X1hLI7W97+GmyKgSXzXhthkkzbttvONxZSZCn502un7+00kQBVVGT18a5+NTbJOrBiUiedW3CYCsvbBwEs7TcFWJPkTaLrcK3wqvvbSRpagvoLAlxxfEkBrUk5n6SBNilLquxR0m7Eqj5rhfhmd32VFdI4ca+1qLGDEWSb/BVPLbXUBpol+SGYhArbjB/Ksb5sxfhXcQIqlKcR42NZnCgDQH9yJC9OlraL0MhjWd1YWQazQu1WX6Zg2k6xrrS4yZp4jCtffGoqz0LMY19FgBUUVzmWlzIui1mHVSqsyo3YaatPesONl0Dyldlp8ARUjCFVuRJXJXIyAFixT925xH/FDPDhbpPw0he2xxtHb4nZ+6yPeTuNZ49vHr0VFv7iIGx97ZHomfcqFt/+E7RM2gzjjv05Fsxai9aNdkJ2yHA8fftrvvNNfmp2pRTF228Ds2cDF1wAUNz0VlsBX/wiMGyYdDPKmPDULOU90m1TZW3W3WedqSBXB3k+4AtQYX1JmhRuxFXHJACw/L3vxcX64lsV8eFCbKQYHyrHleriLvl5goAzKN5V+XdrYOJ5Tc+tbWMQQxslfraWGNokLcr5kwTYJC3MlbheIQPNOGcIMkv3Omhquw70OFjRUiU2tdRSG9gAWweLfW5VLIpcL1b+NdZ1DVapsFVqLFOrXEWKQJI64G7FivhYv7LqKrCycfdieXyUwZirya6bsTdm8RrlLMqCsaRR/BiV67CYqEkGZQ6iDKgrKiyPjfVAXUxsJcTGsnMqHivqlNUwBTaKhSUwCgJYFbRGsWJLFou3GssW2Tbdd32ccdORuOrz92DR376PsUdfiElnXAcr14rl//0zXvjnQzjyh/sBVH4JQMvqnsoUk953RxwB/OY3wNy5TizstdcCX/mK7xzUPr+qR+kmnHSpnFrMRIUNcyPWZSKWY2CjAKx4nApew5TXMGVMB4xJTnrjxMDK41IptVEU2qTU2UYrtHH+LpIG2LgqbL2tX8vr1GAqddR0W5CZxMEOBJgfmK9qaqmllloNGYnr/eGs+4Kv2h4j0QUzF/y8+E+mTgrZeiV3W1/WYtUiqrIcJAVl1lNn3YWpqe7iz1xc2c5chsVjeOKmXMh5XYC1ZLdi3p6rK7x8jugyLLgSi3Gy8n30TWgNJ6ONtDAVNgxgo6iwpv/INtxxAk645ED0LHgPq1+8B3a+jf0YMGSbA9G5vBNv/K+ionYPbfGjwPTpwMEHA2PHAjvuCLS0AI8+6hsjte8d1qK9rko7tQoblKnY1CyqOaSAVbk+rIkKG+RGHBSnqkviJCqtcQA2THmVz8P7lBVPU/XUivgvzEzPa6rQho07yKKqs7JCm4RaW0s/JmOPCpq1uJQH/QAUq78GJXVK2o2+luu2dHOFmOcaCKpsqsSmllpqg1aFNe0nqF1NYwmIi1X+0qmIjWWQzuNcRTWW7XOzFYuKLLKwigWfIOtP9qQZK1dgvYzE7nM3uZMvUVNRvA4pzlE0OS5WVF4V2YVVAOtL5iTDuZTQyedKLMCr74tYGK94/+uVjTgJ8wGqAcCq1kUrKrIYy5bxvYGcvvp6CrjzgseZm3fb1N28fflxG6NlwsZ49u9vYpuPbcK2zdl7PWzw5PuVIrnbbgvMnAn09QGf/KTjTnzggf6T2hbm7rWR75yiIhs1vlWVpbgWJVc+TlZhg5I5yW7EukzEQQDL2/Nzx1FfdcprkOoaBnz9VQM2TF0VryFIna1FmY2jzlafp7GgYFZWSP+6RgGbpFTYMFfiZoGtUAUzJC7WtM9ISqmtjo+Ncv5mtRRiU0sttUEJsE1t4peKm6iJuRSrvqiC3IpjgCzgugZzOBUXcWwcXHk7oR8frMpfjuI+eV0Hr6Ki7EvqJEAt3y+psL54WcmV2PflK+xjw27y91mVkiqNNygmVJnd2ABcw9qXS2VM3Hw0Vi/6AAtuOBstY9dDy5Td0bbJrmjf8qOY/p+/YtWytRg2qgNvHLM19rz0aWQKbj/HHguccQbQ2gpMmwYceSTw2mv+cwJ469jtmfqcCclEbJLkKa7pXG/J7AjPGdZq3IhVmYjrAbBR4FVWleICSNxJr0kiJh1UmgJtHFfjqDDLztOEHh5RVGOd1cONuJkhqVaL45ZbiyuvpTs2BGQH6twrhdjUUktt4FoTuhKrTIyLFb9kwtRY7ZeOKchS31alVA5flKqsCK/iOk99TMYVWh3Q8n4qA/VvVy18n0aB9deHdZM9kXszf07t+OmEkju+bYrn4oQ06VhYBxxKiboSq8rohAGsEkSj1rYUXWo7Mvj8tYeiZ00vZjwxB289/AHe/M8DWPjsP9jrUi4U8fLd72Dfz2+PtaNb8fpxW2PbW153yuyce66zaIzejleXyrj3+hdw+Fn7srFbtJF+rzCA1CC11gR0VeV1KvuCY0NVrpBhbsSqOFjnufq9mwTAmsBr1bUkpMiZmKq/IKiMA7S1qLOmWY2bDWiTgNeoAFuP89TLBgK8RVFjrRggG3hcE1sKsamlltqgVGGb/sNYl+BJHLfOvThKjTsRirnJICuORbfubPCO97kLM1jWKLDic536ymJ2rSpllZfTkd2IZeOuxL5rFuJhgyZbUd4ndHxRcgn3ygu5ypsMnlGNg1g9LE5iGX6MCFctQ/LY5pBN2GIVLcx9bRGm/2c23np0DvLtlWnF/87dF6NnLMOkF+Y5IKsxehs+t/42+OmG22PRb29C18oe7HXMdrj8pL/hmB8egH0/s6PxeFWw6gdKtctvUDwsP04GU1UsrKkbsdeHz61Yr8Cq4l8bCa8m8XO1WFDZj6B2pkCrU2d1YQRJuRon4W4c16LF61qJAmySKmzSWYkbUSO2v5VXqwaQHRDzJ9dSiE0ttdRiW79+0MVQYRtyTTx+NUociwRDgW2jxMcWK+qpDmQr7sUhMOseo3wuTviD3IlV6qsIsJQEirsF80RVshuxMMGvxMCK8KueKCX9674zAS5pJ1bKsi01KLQmFqbC1mIqmGXbM2Wsv/14thz8rd2rsh3fef2nsd+Fj2KbW99gdWDZ4iZxohhYrsCeO2YDdOx1HEa1D8d//nolHr3xJRQLRTx87QuRIFZnviRNmsRL7PokVVSlwqpiYVVuxCJcquJgGwmwJvBqCq71UMrCYFXVTge0UWA2yM3YKItrDJitN9BGVV7jAmw9LM65qn6Aka6/6TITh4BkInGwdTh/M1oKsamlllpTWKQPzJAPYF1fqu11/6AWkjupdysSPomuxLIaG+h+HB1k2Tm5Ksv6iwmzQW7FshIrwquw3QNYXmJHjqMV1FrvGFmFVUGuxuK6ETuvmVBySPgRQgWqjS0mUZsbMVeS5ZjUIJil84mJoET33b4WG//92YF45lt7Yuvb38D6T32IllU96BnWgrl7bYDpx2yLR++bgaXn3Y/uvm6MPvQs2C0dWDv9v2ibugfmPPh/mPPOQqy/2fjAcat/NDC787IrcZgKy9vo3IjD4mB1tWDDAFblPlwrvJqAa9DfUBJZYFUZwk2gVgemYTAbN2Y2DGajfJ7IoFkL1NbiLlwLwDZChR0MVm8gtKKqsRFAlqyZYTaF2NRSSy2WrVOuv4360qHaryGZC0XIVdeg1YAs2y8ot157N06WwLFcVsOs/IWng9mwdT5xltyHfe6/orLK68MGqbAB8bD6+2+hVFTUzG2A+y8HGpMyMJkE3JN1ABvWrwnMmoIsrXeNbscLp++Kl06vqLV8/34n7oj2YS3469n3YUlfF8Yc8UN0bLU/ysU+rHr8r3jqH6/iuB98rOYkTmGuxKYqbJgbsXO8pawHK5fS4WPi7U0ANqr6GgavJuBaz5Ilct9hUKv6TIwCs2EuxkEwm5QqW3XeBpWESSpBVyNqBrP+Gnxf+stMldeowGqFgSyZIcw2ozXvyFJLLbXUElRh622hMWOqL2NdEiR5nybGk5WZcTP1+s6vqCHL3XM9KHQB0R+fajl1XVld2JxfBZXqtlZtk/eL6zxJk1tvltWMFbMQCwDrJXKq2letwnr3TXQlFhVdsQSPu7/WL2STiZ0MP6rjkixNEgVgCU6jgHFQ+6q+DVyYVcBPCau2/ugmGLPhSPTOnQ64PzBYmRzatvwInvrHayjyLMcGlqFsUK7J0BvkShykwqqSOQW5Ecs/sKhK6dQKsLw+KK/56VVcFerJ+o51j1e5Lat+HGI/CrlLrTVgo9SGFc+rPLfGw0K7XXOuIE+NIEgL/awPub7+tnpmmDY9X1TFP6ivZrN6Q1/U/q2w9vK8YgDZwB15aqml1m82EFXYZlF7w768ZffYqvYKoPWBrAB3Ish6iZIEMPXay3BKMEjAJ8IsXwhIZQCWgdXLLJxzFg6vsvrKswurANZXA7Zyff5JdrjLcFImq2hsWwgQRYGtWo3DYdKxsGEwGwSyYfVrxW3/+s0TWDRrKbJjpqD7/VdQ6uth2zu2ORCrl67BG0/MRD2MXInFhE46FdbZbhm7EZPxtqpSOmEAK4KoCKD8fKbqqym8eteigMcw+JQBWLXozARsdUCr6z8OzKpM/BFAdYwpdDULeCU1lmZSYRvpMZCoKcAxLthzC/w7G6Qgm7oTp5ZaauusCiu3D3yecCKpMNc0VTyqGBMruhWTG3B1oifHr7jiWuw+V7gXs2vVuQ9zV2PnhgiDq0wWKDGUkyTKd4H+6+CP7rpPfRVVYm8bB1rH9dhrL2Uw9vpzXYlNJs5RzYl3q96mMp6hWE6ApDPR5TaKm7IKBE1UWBMLcttVuRkHuRabugDvf5KTvGnav2di0d/Ph53Lo2XD7dE2ZVdkh47CE7dPw3Yf2RRxLCh5k7jNVIU1dSP2waEbB+tz7Q1QYL3nIfGvQfDKj1Ndtwyu8jWorJa/Kd2xUUvZ8LHKLsc610vVOVTxq7q2tWYxlq8p6XJeJpYkREcF2CRV2Ga1oPdHvUw3f7AC5hWhcw5D9+JmshRiU0sttUiWqrAJq7oiMIoJgvgXThC8iserQJZMjJHlz915iFgr1gezYr+6BBBi7C6HXH5cwLoIr+y5AcCK4CqrsF6fAVmJ406QODhUZTSVYlXDS+tYHvgV6pidOMh04yuFbA+CWVOQFfsUoV2s6zpmyggc85MDcfS5B2DRrOV47ZGZeO0/szDz4T+yOPFXHl6Fvp4CWlryRmBcpRYqEjOpj4umwga5EasSOfFzmLgQJwWwJvAaRaFUHV+vBE7auFYFzGqhVQW4AfGyUWNlg47rT5hNWgFOuh5sI66nWTITm74/kjjWqgVkBxjMphCbWmqpDQwbAB+otZj45SIneDJRZn1ZkKv2uYqsB6XuZkmV9cGsC9CRTQOx3mRVVF/dsYkA648TFmJchf48FVaeBCfkEuVAh78mrHcKYTv7BZ4V93VKKlkBGYpFhZaPUkzu5ABdJcanSpl1f4mg+NFKez8IBqmyuiRROiVY3BfHZIDV9e09uu0zdgYTp47B5KnjcMhX9kT3yh688fgsLJ2zEvl8rmpc8npYPKz4fuGuxGEqrMqN3HMVVrQR3Yh1pXR0LsTec00CJ9F9OGl4VSpkCbpnqvpSKarePk0JKxEATWG2mVTZoOvpL9gL+8EiCGDrocLGcSUecDViFT8QRwFWKyasGp/DIINxf1sKsamlltqAiytthCtxI8vsGIFt0LqgzIoZi337qkBWdC925VpTmFWV1OEmbZcBVAuwbhuu1PomMQqQ9Z1DnJTH/OWdJml0rQQKshuuA6hlBiPFctEFjEqZHREgef1YbXInaWLOoSsMIsX9QeBK8MjjUuVrCcp2LJ8rDGDlrMUm7tNxoLhjeBv2+OS21cClyBBc2ScBJYNbCXYDYmFVbsQ+aJUA1jtecCNOEmDD1Ncg1+GK50Lt4FqrqiXWVVadSwe0RuApZ2rXKbAaVTZqOZ4g99HIylkCQFuL6toogB00FvP7vJnchy3TH1xU3/NN9NKmEJtaaqkN+LqwiZ2nhuOMfn13J1r8y0X5JSOBDrOguq0BbsUsnlUBsmy87n4OsmxbAMz6rjNA8aya1EgZmLUAKytFsgrrm5DrgVZl/F5XjZUDRblUHfPKE+vAr8j6QcrBHQJDeiyz9aKnqoqW4XDL2ji3VwZTMlM11gfOEtQGgayJ1aLAmvRVVXPVVWE94HP3qzIJByXTklVYVSysbpsIsDo3YhlgxTa1AqycgVinvnpjDlFfo8CrClqjACtXt6h0VZDJfcpQqwNapbKqcMutRZWth3uxfA4TC4M/Gl+SgFgLwCZ9rpozKsfwGqjXj9eNdB+uxRp5rnpYCrGppZaakTXzB109gNNn/eBSo3Up5vs1yqzsVlwFuS4ks/5pO99GhwbALAQoDjVN6SAv/tVt4wNYXRkhaXvVJF01HgO4ZaBP11l1KP3QUPJUVn4OAl2usnLVlsGrwqWYt1O5oZZoE3dH5tAJtw95LBJQ6dqoVFkdyNZae1ZVN1YGP5UrsQnMqkzVJkyF9T+3I7kRe30KNWFFgPW1EyG0AQAbpL7q4DVIdRUn/TpojeJ+adJWBN0gqDUGUg3MJqnKBk30TVRZecy1WFIAWwtQhvURZYz1dmFvJlO+jyK47Catxg4GkE0hNrXUUmu4NbsK23AL+CJTwqqBMutN5MT4WB3ImsBslBhZebIsqq8hAKtTYb3rU/TP700tk20OGCK8clj1DcF1KebHyC7FtqDG+voXXFdVaqzfnbdaqQ1TY739wrYgkOUWBWhVAFvVRgDYOOCahArL96uSOZm6Ecsw7LWV4mBVAOudzxBgRSANch9Wqa8myqtOdVWBqw5EIyuKBlAsK7d8PHFh1kSV7Q+QDTu+0Wb6WRnHjTip9kn01yxJneJYVGC1EgBZsmZ5j5paCrGppZZaqA12FTZKfGw970Xgl01IEogqwOVuxQLIsvEzOpJAVoxxDYNZk+vg0Opt8Lv/ekmc3G3KX+NluJVdiWNOjFSuxTK8OuBRcTFmMGuVWFwsYZ8HD67aKroUO8mb1EoEARR1Kmc2jqPGkguuSZKnMNdiDotBMBsEr6alhHTXJbsSq9rEVWH52E3diAOhVZHISQRY37iFWrC1Aqx4HB+PqfqqAlv5XqugNYnPuLBYPPncKoXWBGajqrI69+J6JnwyOb4RFuXzMq4bcT1U2EEdX5ugWQnEwPb3ezSqpRCbWmqpNdQGyodjv16LDJjirgA1VuVWLIKsL0aWjDf1xNZgmI18DaKp4l+lhE2+kjqqLsU+5SzGMaFWdCuWlVedOhvmUkyP8iSQb+frGQazZNVxsSZqLJ/g69yJVSptkOlgNgrA6jISR42xVZ0zTIVVJXOq6jfEjZgdq3EjVgGs8riIACsncJLdh6Oor0HwGgSuus+xJD7f5Pukik0VxxQVZk1V2TD3YlOQ1bU1iZOVr7uR34VJKqFR3YiTVmGV5+hnF2TTWrFRXIrrobpapqWgmshzIMhSiE0ttdQCrV8/yNYFV2IDU4Jr0D7PbTgAZGX3Yok7fVO3OLdSzlIsuQ/zsfO2VW7EikfxmmNNXmiiLMyzREB1Jqzqffw5TwClcinmiZ1El2Lf7XBVWlmNrSR00t/kMDVWleRJtCiJnnjMrInrcJBFcSWWEzrpjglSYVXHiyosu++SBbkRiwAbdI2iG3E9AValvurgla27+3TwWg/vExWsqNx3VfsqYGcOs2GqbH+AbFSgaYR3TzP2V0s/cY9tdHmd/gJBK0GQbfa5VAqxqaWWWsMs6Q/DhrgSN2GdtEhQawqy7GBBLRRVYJUyqqtjK45FPi4IYI0vPtoEhpfNkbcRMHDQlN2y2T66R269WHGf7FJsl4lJhWMZWFIMbLWbY5gaK8d16tRYBlaWM6kPcyt2+nEyFpMRzFbdjwjqq4kCq1JNldsU8bP83CJAisqqDJZchZXbysmc2LgFFTbpOFgdwHogqgDYirpqK+Nfw9RXE3iNCq61fEabuRDrlNjK30UQzNYKsqzvoGMSBFmy/oLZeqifQX0mocKu067ECaqxJhbl+OrXsHlekxRiU0sttea0JlRhjfpVjbvOvwAHQa3SIoAsG74BsFaNSQWwEftQGo1NnPxSyaJM1h/vSuPN2L7nFNNKftFiTCwDAXIlpkcGu0SkLqC6iqUHIyxm1YFTPkEt822O1scUVlJjnX0SQLl4RIDLYJUlZyIArLTlsbEqkFUan0uU9fOKIJgVxxbHTOBVXNfFwcYBWOeOhwOsmMzJBGDF48LiYE0AVlcDlquvuvjXKqVW4Toc5DbMITAIXAM/z2r5zJISNpmCqwnMyqpsmHuxSU3ZeoFsFFVWvg/iNcQ5NqqFxcDGAdg4fSVlAzmpk6lZ62gyJ9FSiE0ttdS0luSHWzOqsEmdv9m+BELVWJXJIEtmArPyuZXF0SuqKx+fYtDxoFbhJqxuUmKTSdvKsvWyC1LcHZi7CzvxrZVSOzy+lam3QoIn21VC2WW5wMtjY0mdLQkQQkbQVCw7QEp9cbdiR7WtIJ8JyDpthC2sbE9lXQTXMJiVTYRbb+yatvzaI6mwAQBbBbsGACueq1kBVlVCR6e+yu285wFxryqXYd3nU9VnVQishgGYEn7kPgWoVbsQq2HW/7wcW5UdSCAr92uSmK5WS7IWbJIqbCOO7Q/Tvl8iqrFhFsVtuNnmMKaWQmxqqaW2zqqwjXYl1gJkf5gqGZT8JSqoss5TxaTKK+kTAK+68ysmICYTH52LMFNP3Ume+Mj6dfdx4OUuxSXyzC06sa4OPOjVWBF2GerQNkGNZcDr1oIVXWX5+TMWGMhyt2IOsmLJnaiKLM+KLLoXc7YLg1l5exiwcguLRQ11HY4JsJX2FfjzgLOJAVZ2CxYB1lR91SVt0imvUcA1DmCFHesDI/F8CqDVwayJKlsLyMrWjCCrOk+j4bW/VFhV3/VWcZOGuXoDorUOxb+qLIXY1FJLre7W3ypsf5wzqTGqMhTH60ivxlaBLJnCvVg3PsXG6m1BKqymbaC5LsMqk4FWbM/UWBdsKpNi28tSzONgw9RYvk3MVMzgVXIr9i6JxamSIkvjc0CXg6yjjCI2yCrdi4XnOnDl+/j4opoJvJq6D/NHvt5IgBXHXA+A1cW/RlFfa4JXA2hN4vNK/NuWz+EBEx9LRJjVqbK1gGygZ4ri+GYB2WYB2HocN5Ctnq9vLa7DVsT414EEsinEppZaakobSB9kYdaoBCb1TAIluwhHfe7rS0ry5Os/yL2YW9C9MwFYWYWVYuhMrcq1TgRbOS5WbF8ue+ss07BY+5VNUv1qLDETbeMKqteOuRU7k2gxyZNXcoffEheMmfuwFB8rqrdxQVZ0L2bA6o7JUz8VMKuDV5NatUH76Pny8io8kn8eM9vnotvuRWspj00718dBvbtjjDWiZoBNKgY2ai3YWgBWFf+qVGk16qspvPr+hgV4lSfXQZ9/cRO+VMGeAmqrYNZp6B2vcylWqbJBcbJxQVYJrQmDrHg/BjLAJpV0qb/cgftzjhPVpTh2fzGU1oGkyqYQm1pqqTWX9VNCp7iuxAPhg15WY31mCrK+fgx/ZY8b4xrhl3xHda1MglTQWuVS7CqnznYXEFi+KLUaK2Yqdo7IoMjOaymTPHEwFQN1RZAV42NViZ5MQJbBcpB6ysE1BGbjKrIqeO0p9+Kv7ffi9bGzULa44uYowx+OWIT/lV/Cdoum4sudn0bGzlfFufLXSAW2SQKsOOZGAawu+7DsPqxTX43hVQOuSf+QpztehlB5exXM8jELICu2DwZbfZxsM4Nsf8FslNjXWgBWd2w9XIlVAKxK6pR4eR3Vd2mdLQnF1YqRlbiZ5zjrnt6fWmqpNdTWRVfiRPqrxR1Lo3y6G6u//KUarL7JQhwQpWPkcjs6FbaW87jGJqCaUiK+e+0COZ848n180iNCiU8hc0FGhhUPUDzAcaI7RTdR7/Jc6OEAJIIVQVdGOJaBmaBC0nEEcaIy6UCUX6lkECermK7a7HPrlf6ptoX9k48hgP31yJvw2riZKJMkTW8zPg9112n7tPEz8PNR16KvVEgEYMX7Gwaw3FS1YBsBsL72QhvbznoA67zf3LZ2BplMzom9Zt4DjgeBamHvf/dvgN7ftPj2u+93eRv7m0hiEf7eqs4hbPf+FN0xCg0C/4Z1f9MynIgAE6TwyZBkEpuv3BZQWsYE2thnRp0SK8nnaYRFdSMeaEmZGuJaHfBdGPrjgmUn/xoJ34nN5ibeXKNJLbXU1m2rgwprokDUFYwb+Gtt4BeM4otRB7JVfXEoFRfdOVTnCRpXTFdilVVNdN33kzyRd1eEfU6ssKyEcdhwIMTWg4gLiz74YeuVsWWsbAVkJcBi/cQAWd6P/C8TALMEsuLiayPBnhHIuv1c33YfFgxf6ssSvevdu2KzHTZDe0c7xk8aj33/uC/bP2/4YlzVfqd3DhHIxfOaACy/3yYAKydyMgVY6jtr55Cxcs6Z6XWys8iwbdnK+yQEYFlfds7fhsOrBLpcfTWFVw6FMrz62mrgU9c+aKkyBdDKf3vGMBtwbC0gK38OxQHZpFTJRkFmPEi2EncjrlWFHYxWLyC0DEG22YA0jqXuxKmlllqVNav7SMPGFQDTYSpf1fYAC2zXpK9BoNE9sCOWheCuhNKxVW7BNF9n/rdurin6Iubb2LnJSzjr1IS11a5TXj+W4zxrCwGmYqZiO5NDqdjn4RwdJyd58ox5XBcY4AB97Gu1iAIyvq9XF8KsLFAusEfKa1wo97FETyhn2ZiKZerHzLWY+uMuwCxhlJtJWdwntvG5XArbWBvpvSaCbJhRf8vKq5gCK849d3pgJ8z56hyMv3E8uvbtQnZVFnMWzuG3A6+Mewcr3l+L0dbwQHgVt/O4Uu5CLMIr3bMgF+L+Ul/5+7CqjRT7qnMdDnMblr0K5HUVVMoW1b1Qd4y3XzwnL88VkrzJKX+lTv6kChEIcy32jUtwF5Y/E8KyFle117hjBvVj6sKZpItxPCi2GgLscVTYwQBckSwgNjZJt2FrgCVyki2F2NRSS61u1owfjv01prjldaJkJg68tjomnYpkLqzS/dBOYkSg1YCx0mjSTyVxGNW5SZ0E0K0AQAX+KFOxs88tmSMnb6JoVzuHYqnPe6wajQuylYmjA7J8UkhzawYyjLwdkCWQzFo5Z8JqFViMLKl6tJ1NiK2ym3DKUaOcUjqlyn53wsxB1flXgVkZZFVAqwNbU+PA+Uj+uUoMrGtd53Zh6k+m4omPPMGerxq5ii3eS2WVcX/+CZzcd3gVvDr3vaIxO8/N4l9ZH00CsOJ+fk0+9VWTuMkUXuP8oFbL55/uWBlMxW3eWAJgVgRZ1lSE2ZggK2ctlservZaYGYvDLMoxMoBGgdp6ug3HVUjrpjg2yBU56msX5fWKC5FJwqc1gEE2hdjUUkttnXIlDm0ToFxE/qBXuBJrXfFCu2qiL5mkgVhQY1mZU91k1USNpYmNWweWmwi1zvOKWsvBV5XkiT2yRFAuPPH6tG62YpurtG7ZHQdS1V+vDFhJV3WVWIJTUl55sifLKqFAz4WsxWGqbBZZLcwiBGh5H9y4ey7BsomJ8agz2l2F1bW2tW1468W3sN8n9sNGm22EtavWYpN9N8HMK2Zi8cTFXru32t9HdmVWCa9hrsPsuUECp1oBls4eBq98bDqADVJfq+G14jrM3/c61dUEXGtRXuOosjqlNQhmQ1XZBEC2PxM91WoimCoTYyVgtYJmUqBaqyuxKqlTM1gsUAzJVGwlXFqn6eYZBpZCbGqppeazZv0Qa7Zx1epKHNpnsyuqCbgU16zG0mRfAlmKHJUVWN7WNxZ3P5swU2ZhUlLdBDsgl04K76NVdvqCoMZmUWSqq+vGK6u0Asj6cxNXMhkzkGWbs85r7mbwzdJzBrcl1+XZr8rScTSFzUjIKsKsytXY2VZtSndjzbYg68n0+jwRhy8fjq5yF+bfNR+9/+7F2tFrYZ9uY70T18PiR1yItei4Hi28yuqrp9SK6qtQe3Wgqq81wasQ0xr1h7e4n1O6ya4OXqsAVwGzOlW23iDrG38DQDYJxaseSqsJgMZ1I9btS12JG2NWxPfcQIPZFGJTSy21QWlJKBBJfZCrJk61xsMOCNdhnQlQ6oGsaqIqKrN83afGKmDZ7Y+7FcvH8/7JfPGxLvzK8bEMaGODbGXiR67CnvLK9tDJC57KyibdzM2Y2sJXgsdTZd3jHPdiPcwS8Fa7ETuPYuwsu1aFKhspJhY2WostrizubOsc0skex31jHN7Z8B22Puf8Ofhw0w+ZStvV0eWoz6ss3HfdEzj88/v7zhvkOhwU+8raNgnAhqmvurhX0W3YFF7jQGzYvrjwyrfp1FmfMtvPIFsvN8owkOVj7W8zVU8HeqKlxMvrJGyB78Ma1di477lmep8GWQqxqaWWWl0s0offAKgNm4Qr8TpjcRI8ScfqJqdiX2FuxQxkeW1YoY3vtVTExzpDyABugicCWeZW7LoOO+BqALJihRmWcdiJeXXO6yixdrmEglXw4mUZYpaLzL2Yxk8uxrJ7MXNvFmCWLlvEWQ6zpHIGAa1oMtyaGI9B3aJzY3wwYoG3fdWIVZi8wWT1Me49putYcP0y/OWCu7DtbptiylbrGbsOm6ivZHEB1pdt2BBe2fkUAKtL3GQKrzK48u2qR3ld9bzKVJ95mr9d0zhYnRpb5TosqLJym7ggG9Xq4Vas6ivovAMZYJNUYaPA8kAszRP7NU8AZOOePyk38XpZc48utdRSa6g1669ug8WVWLk/CJxN4L5ZVdewcYn3jU8UOfiLx4qqE6mp7qPPhBIivom/ezzfJreRn5PaWtlXdsrmuFBCGYs5zOhK77DyKeTQ7JZRqRiVz8n6YMoruWPZDDp9JXh4qR3YzMU44+2ntayvFA/tp38c5mid98PL8qhK8zhHOW35P+qdl7sxWfi/w3r3rcCpa1O/PBWLfrcI4+eOR0tXCza4YAPscOAOnkrLvKP/kIWVbcHvvneLc128vJB7L7j6yuNSffckxH2Y31NaKB6Zl8Lh23h5HFX5nDjqK/XH3xtVdV+VNV/97zmxVI78/tS/R9Wlb1THh9V3rfrbjFEPVv7bUz1XrQcBOtsdUIJHNv85yv1WP1bVl+q4/oCEZgeTdXFOUetrYpn+KDHIXvvBdTWppZZaak3mSpzE+ZrtC9fYFJNk7eQ1yOValWyLT+w1E3HWH1e7JHCtmoCL8OuryVlm4KEDWQdY6DGrBFnRaBLN4YnDFgesiuroAJYMsiKwsbGULQZpOQZelbqyKpjlpWocSMwgh1wVzAY91/0T242zRmHnRVs6KrFrj33/MUw4cAL6tu9Dx/odKHYWMecGNwFUCcj+d2OM/8jPMGzXIzHrjbn4cOZCn/oqZx7mgM3vBcN5uv4mdB9maqvtQDN7zwjqa6kkQaoAr6Tge/uKhcD3rRZm3eNk+E1i8f4WA6BWXlftSxJkVX1FBVl5Yh9HfYwLskHH1sOinKsZVNgo4214UidNSae6ZIg2yNBvRQDZwQKzqTtxaqml1r82yF2JE42HjThG5Xl0YzOoJxnrtdLUjFTGxvH4WKFurNdWiIn1thtmKxbjY+XsxAQDDuBk2TbuEuxtt7MMZIvkVlyuriHL+nN9iuk83LWYuQT7YmIJuEoouP1bpLi5GYp55mIeJ8sTPlFMn+xe7E/6BH/iJyFmVsw0TM7Dzu2ouBt7r7PrdsyNux+bmHjcNztPwvdXXo4Phy90skNnynjs0seAS539z+AZ7zWxpw/HxEcvgb1eK3Kj18Oq5+7A5d+9AVfc+SN/3KsLjc79Sz7zcKNiXzm88ve+B69SqRxPOZVATgVogT/wBDyPY0Guw1GyDvO/ZW0bRZxsmGuxuF+3rr0uw/hYk7I7gecxyFpcb/fiqNAy0ONgm9UC32dh74EQt+Ko76P+dmlPwgYHiqeWWmpNZUl+MDbbh2yYemB0nOL4qvZxobpUO3wqAT+oDzdeNNQ0yo3YP1dQ2SKoSbxd1URfVGRVLsMKRZapWEW/kkXb+H4CWX6Mo3g5iixT1SRFlrsOU4wsj6fkS2UeyJVV7rrquLV6rrPuc+72KrrCMmdgvo+pj9mKi7HCzZjUWd63066i0DqtKv+4Usv/ccXW9J/ortxht+FXy7+N3RZuC4uCeGnOzuft7jrbfhOQPXYK7GIr25VpG4ahO30Sb748GwvnLPPuCb9Wgld+vfz+eOfl7sKS63AzqK9MwSd4kV2H3fcjV149RVVQXvk2WW0Vn9dDaa1SXaW/VZXaqvoblv++TZRYFZSHKrJB/fj2q9XYIEvSrdjU6qWO1aPfOCpsYucegPGwjTQrito+wFXZVIlNLbXUmhIW45qxIhmxj1hjMVRhg84XGVgTUGFNz1EFrkFjNVBuPPVVzlisyVJcqyILAodM5WuQqa+0M0NJm/o8JbaiyjrJnti9o+GVil7WYm/CTdmBKbGTc1Xe/8WETw4IOYmpSEXVqbKU9KlkkeJa8imz5a4uDJn5IXILlsDu6kWhNYeeCSOwZpPxKLY4UEf9sksUFFrxfpNKK9Z65fVk4xhXS4dYWfy4+ytY/P5y3JV/BG+0v4uuTA/aii3YtnMzPP3paVj0/FLYG5aw8unbkB+/CVuG7fpprHrxblx6zrX4zd9+yMCV9yuqquxto1Be+fb+gld9zVcJ9ISYV++1EH6kUT2qfjQLgraoP5ZVmfR36XudDRI3mZTQCVVPTRVZTaIn8V7UM1txJNXLsIaseA21fg81GxTX05V4oGYmrocaW+/3cjNZCrGppZbaOmdKpdG0bXjnMUeVrCuxyXGq/Tql2d8oxpcdPyYAZk1AlnXBS+dwNqgqveOKgFQih76cbYojdfvPZKtB1m1DwCrDLasfSxNmd2iqOrJMJRPciJ22wo8EBJ+8DxdiSlQXVnA7ZtDJuNapDevkCpZgttiHjuffRPuLbyG3aBlTn8vZDKxCAeSF2z1uOFbtPBUrd94UpYx/gqiDWuXrLhh3SZZNBGDnEivPJ1pjcWbhs8Aqt63rArrV56bi9s4HMG/Ou1jxwWvee8JuHwErk8crz76NFYtWYdz4Mc52Id6Vn6PZ4JWPSxf3GhVeq9THAHA1/RwzBi0RQmUTMgjztrrnMsyK20UolbfpQFa8jiqwEUBW1U55jKGFZSuO7C5qCLJiX3G+g/pTWeuvczc8HrY/rY4gSzaQYDaF2NRSSy1Ri/QB2E/xsIPClTjAbTiSChs2Tv/AAnaVlHVbo8CsbwIreKN6v+CLcbK+GNdqkKX/0XiqVFkCVAYgwja3TQkFP8i60MtEX7emLM88y0G2bFsO6LLxWr44WXFqLcZxcmAt88RFiljZKpgt9mH4v1/CkKenodDRiszHDsXwj3wKuZHjUOrpwvLH7kbxwbsw7v4X0bKiE4sP3JGomo3ZuRsOIHGV1hcHS+AtTALF8jvkdKwzWbnlsOrdAzdjMVdqP/7pfdji3N4SXnv2bTz18MuY/vJMzJm9AF2dWaxe1oUJE4LL5ZjCK7/v9VZfZXhl71037jUMXnUqrM+NXgeuJnHszg7ta1h5sZz40urN0t8pu6nqeFf+3AeOIaqqaptJuS1ffKwhvMZRY+OUykkSZHl/4jWqtjfK4roRJ+UCPFBciVkpNoXruwlcJqmKWjWU1RkIMJtCbGqppda0loQb8KB1JTZpE6XMTZCqEwKvqnWV+SBXBbPy5FilykogK0Mte2TZg+xA92JvDC6oiiDL3YiZUews9cX+y/gSPrEkT4IbMXvNXbXWuY6KOzGHIdruJY/yyqlYyLoqLYdcGWY7XnwDQ59+Db0TxyK37c4YeejxWHLPX9H94QzY+VbYHUNRmDwOpVVrMeLpt1EYOQyrd9vSSwbF6sAKQOt7XQTclgE3zHyldVxXaWU2WCFBE1kuZ2PXfbbH7vvs6AN8dlxArVenXz288vM2El6rXIcjwqtOdQ0DV9/2AFCVP4uUIKA6XgG2QSorH5NSlY0AsioIVkFpHLdiE0siyVPSICv2Ww/rryzK9XQlHtRmqMbWAsUDAWZTiE0ttdTWKYviShyj8xqODYfR2MBqoMIa9+X1E/2+8WO0MGuqyvJtOpdiEVoN4mQ5qIpZikVY5XBLEMjiXwkDSX1liXxsL05Wdi+uGCmDWXYcU2slYJVjZWWYRXcXOl54E+WhQ4BhwzB838Ow8okHMOeZZzB71oeYPXMuFi5YimNPORRjJ45CaU0XRrw4E53bbYpya55lNnbuofNIUOvdfn6DXLiNnJOUXY4iyY0Erfw5e3klGBW3+WBVyjbs9OOHV7Gf/nAdVsEru69SkjFTeNW5E/u2OU989zvo71qGuFCXWA654jkESDRxGRbXVW0jgax0HXHciqOosVHdinWQUC+QTdqSyETcCBV2XTIj8LTrD7L82Ga1FGJTSy21pv6lrT/Hq3Ln609XYqPjTe+N7hrkvqX+Yo9PAFexDw9oFTDrAalKlXXPEzVONizhk5jcyTmFA7NBCZ907sVCemKWvVhM+sQnFV4MraDKyjCbmzUPuUXLsWTkUEx/djo+dfZ6ePalmdj5axdgs2HD8O5Tz+BvZ57hJUIqjh2FltkLMGTWQnRuvTFzSWbXUqYI1xKyHBwcR2lvjCLcRjUOq97rqoFWZ18wuIrbkoZXsa8kXIc5vPL7GxVeg9yJfc8DoLVmjw3xdaPrUym3EtAGwWygq7CkrBqBbEB8bJhbsWqbCLJJxhNGdttsApA1BdhGqrBJWTMndUrMZdhuDMg2q6UQm1pqqSVmA/0DsildiWuMj41UszYAYLXwaqrIyu282rAS0AbBLL8kcWItqbLclViOky0X3UksO4ddnfCJ9VeqZCvWJXxyt7FLsDK+OFnZvbgyT7aqkj5xVdaZpFdiZVUwm5+/lLnt3nf/E5i7bC2OsG1sf9RxuOacS9FTbsOnTj0EX7r5ZrQ9e4fTT85mym7LguXo2WZTATioNI0zNu9lEeK2uGIbx2Q1VgQLFbSKz2X3YX48h0exTRS3YX6uesS9RoFX2a1Y3OdtDwFXHbQm+ZmrmuTKUOsDWhXMBiRyMgFZ8bpUIGvqViz3o7Ok1Ni44NCfIJsUwCatwur60/UVNalTovMUer9pYDkoLra/oNMaAC7CUSyF2NRSS61/LAR+on7IRoXExM30V1/VdfPJbFxglduo9ilK6tQEsCbwKo9HnGwooNbnbhwCs6qkT0qAZRmUHDAxUmUFUOXuxTxO1oMkdxsBrKfuift5GR4PpCoxsXY5gyL62MSVwywlgeIqrQpm7e4CrFweHz9kf1z713+yPp++/1ms6ckBKOBfP78Y5z3zHyyY9iBKvd3OObM55HqcRE40DFJgM0KCJzEZE3d+lgG3lsmwDKziNhXMyoor384hk++zfc/N3Ia916cf4DXUZVgDrsbQmmA4hJjojN8z8Xwi0KpgNsxt2ARkA6HTAH45yDaTGtuMINufCmxq/afGDjZVNoXY1FJLbZ2xKBPAurkS17gvrgpbtS9oWxDABkB4qKnaKbKfiqP2zaEkV2GxrexiXJMqK5Th8dyLBVVW3iaqstzVmEBWNNrGAIamjxnbAVYXXkWYZS6/QkwsW29vg10oYvyE8fjMpw/C0vffR++8dzx3udKaxeyRJWSy6Gu9jEyxDLu1HRn2vFJSh8r1cKjlxuHWu/8RJ+5BqmwgzEqKKd+ncheuPK9AJz8uLObVuf8JwKtzcyLDa5grsUpxrVJp3WNES1qF1WUg5vt9Cqvo1q8CRxNIDWljArYmbsX9rcaaWCNBNkk33rgqbDMmdGpGqDOGTXvdBNkUYlNLLbUBZc3+oWvqSiwcUFtCJ/kcii+ySGPSAWxEeCVQ1BlBnLYPWc3xuRvzsahdjMXETzLA8pqxRqqsW4aHZy/mqiyBa0lSXfk2D2CZsilPxiwHYt0MxmxSLQKrALNcrWVt3ZhZa/J6sAiMC2VsttlULHn6Mex74lF45xvnoTc/Bod877tY8dbrQIEm8jbQ2wfYWRQnjUfWzrN+PJWVALZc9PIP+1RYT/1Wv24i6CqTOUkTUB/MSvGwsvuwSnGVwZW3i+My7PWVsPKqcx1W7VeprirFVfW3rPx7TeqzUKG4BpXU8eBQVmUD3IuN1VbhepXHBLgVCwdXuRXL/TabGtsIkI0Kr82kwqbJoVA3kB0I8yqdpRCbWmrruA3UD6+mug+1uBLHSJhk4lqscksMdSM2BVjpXgUBq3JomvYMbn1jE3a6QOu5Gnvw6iZ9cdXVKph1AZaDq+xqXHahNihW1ldTVnY3pnhZYRvLYOxCrWjZbAuKxT6W+InDLAEwAasKZsWY2fLmm6M8YTyyi5aiOHkixi6YhQWZHL5zx5/Z+N5+5GF0PXAH8myibCGzeAnKE8ahvNmmjhLLwraccj3s3ogJp0QXYhdwdRYU3SarYEHA6tsmxbhWtklJmCTIFcHV1y7AZZifNyl4rVJYJZfhINVVq7iqwFUVKpGAG3FVUrUwoJWAlO/3lcByNmqhMwxKTQDXuwfSMc2ixtYKsmzcCcJsHOXV5DVIWoWNY1HjYZvNjN4TUX5IsaODLD/HQJwPphCbWmqpDXhTffDW8mGsPbaRrsQRvoiMjglzIzYB2CjwGuX+8y9QoT8d0AopiSqTcI2bMZtIFYvOpFpSY9mjqNSy45zETkE1ZUVVlp77lFl3W6HsJHvyjbZURMZN/MTUWAlmafGUVxlm29pR2mNX5P55P8qr17BSOxPmvI3rDrwQr746HaPHjMZ3f3AG3TRg9RpkOrvRdcDesNvaGQqyc7L6ry6kulArGgdc5hYd8W8nyKXY50YsZycWoNXZXg2bfH+gomroMsz6NYRXXZ1XHbwGAa0puPruO2+j8qxIaqJZ5QQh/c1p6r1GUWVrAdmw9oHWj2psEpYUzNYrA3A9VNok+2yWzMS1JndqJMgORJhNITa11FJLxCJ96CWc1MnEqvo0/JCPMpZEXYnl4yO4FitVWE1/iQFsTUp21WxaD7QlVSIoPl4pDtad7Mowq3Ix5lDruBKHZDAWsxYLNWZlF2PhAlkcLd17Hi8rw6xVcifpCphll7bHHigtX4Xc40+htLoTxfFjcMKJR6PrmpuxwQYTYdOwFy1mENuz967o221Hr+wNZThmfZAbMZtUOXGx4gTZA1x2Q4WRG6gEsikzEyth1q+iiv0FgStvJ6qufFvUOq9R4VW3T9zmrQt/h6HgqvgbVN77pBI5STGvTt+VVTm5mgeu4t9fmCqbIMh69yakvUqNrTq2ydVY+VxxYLYWeA1h+vDjE1ZhB7srcV1+JLHjg+xAgtkUYlNLLbXUGmEGrsRxfgiIosIq+w8D2LjwGvcLVFaD2Dkr6978lAOoLx6Wn1vKZCzCLI+XFWJjxSzGzoTdzMXYy1ostvEyGPvVAFY71sqwWqxySR4VzFbFxeZaUDrsUPSNGoXMsy8gN+sDZMtlfO1j+zsZld+bw8C294B9UNhtR0+ocl5zR12tAGHl/ooqgQe4oinmnGycmsmoClZlYPW2SWor3y+Dq7g9SHX1tVO4DPPxBcGr7AKsAliVKiu7DIuqaxi4in93vr9RU88PE2VSMJ+LMDdZYdVkCldBpEqVjQKyJhalbTNkKg46JlZfGpitd61V5VgGuAvvQLXI7xu7NpDl52xmSyE2tdRSGzBWr18Fa+pX4bZk5C6cFKBqxuMpA4HZi2sA2AjKtun91U2ufeNUAS0fLt8uqKxaZVaAWdm1WHQxZkItAx0hQzGCXYxZbKw4ZgZNQkked3LNAEyjzHL1zouLJZilx332RmGXnWC/8y7w4VyguwvlljyKkyaguNkmKLfkYJMaxe+7q7hyWOVQy9c5PPLJcYa1j/73wOEyKCZWXFclevK2GYAr7yOKyzD/QUALr0GZhlXxriaqawi4GkGryd+W+7dpMulUldKR4161MCuDrHBMFJCVr6EWt+JmVWProb7VE1pNFNiw91dcFTYOLMWJh63LHCKgVmySLsX9BbLNbCnEppZaaoPOavmiSiIe1uxE0fvQKjoGX1JBbcMANlB9TTBmTz5ODbVCe29eUHJcjjm4erDq7TaHWemRq7JiFmOmwGpcjEWYFWNixfqzYiZjUmY99ZYptJYbw8ohtgK0njrbnkNp++1R3m5bp286T4nA1blXvJ14T0WIldUccYIlQm7Q66SbdMrbZVgV24jQKgMvdxXmx8qQK7sgR1Fd+TXL8Cqqpqp93v0xVF1DwTUs0ZqJAiuZl+hMY6GldPiqAmZFVdb7wSgIPiWQla/LBExrNkM1tl4WBB11cSMdZIrbYHclbsh7wR68IJtCbGqppTborSnjYb3jFBPZuGaiwlZO7Du/rr2p+moC/zXH7ImTbNZGHGe1Osth1UsCJcMs7zMCzJrGyzqKqzc6FIu9DqSKaq0As3zdA9oAdZYuUnY3tjOOyzBPCOWc1WnLFVcd2HpqrKvcVkZtFoenUljESbGssIrH6KCV7zMBV/G5sepai8uwpLoqodQUXIP+hjR/O2GfFVJi7GAV1vf3pFdZnfEgWJUNAlkDxVV3rY1QY0WXYp0aK4OGqRrb7CBrCrC1qLCNtGZJ6lQvi/WesXkSxMEFsynEppZaagPa+nsCYGRRvjgiTHpNrepY1T1TJHHSAmwYvEbNkhzhl2NxEq4CWhXMsu3uEx/M8rhYWZlVJX2KAbMexZYdJVZUXSn2la+XKSOyBLPsGkLUWX7/ZeW1ZFWrrjqwdbaVE/mbkie5KvXV2V5xD+btwtRZ2VVYBa78OmVwDVNW5X2q9u7GYHdhFbgGqK2RFVjTH9/s6CqsuM+LJ9e5DIuqrGm5nBrcipOy/lJjw6w/QTYpgA09vglciZvdorwPYr9n7MGlyqYQm1pqqTXW+iEzcd0sbjxsAtdYDaZqFTZwPPzYJAA2KPY2zISJcuBx0hewb7Lu1oJlhEoT4AIHSZ2rsbOt7GUldoGDAWlGX5YnAswKzsQsJlaVAMpxP3bVIzExVIA6y3oUgNZ5iSoxtErV1W3vjMZf+kc0Drkqk99LWpdiQSLnoBrkUlxRYyV1VnAN9qmwIeCqchfm44+juga6C5uAqynICtuFm15Z7+lF9r05yCxaAqu7F+XWPIrjxqCw8fpAS17fh+oHIAloq8BVBbgxQNa5N5r42CC3Yt8tiFZyp0qNDXBnbgY11mR/PSxJeG8WFbZZLcm42JrNHjwg2zQQu2jRIvY4f/4CTJmyWX8PJ7UmNHpvkC2iMhKpJWJJfWkOKPCsceym7RvuShxmkgobGWCDlCL5CzHsedgYY5qYkZgZF4FkoPXFxQpJoPi6DLMx3YwFIZYpsWLMrLV0ObI33YrMY0/BWrUaGD4MpY/sj+LJJ6I8flwl8zF3qXUTQXmLqNDSpFlwJ/bVIw1xJyY3ZO/+Se/DWtyJ2XZJXRW3icfJSmvVPhdanTGqXYX59QTBqayg6rYFuQuLP0RFAlfVjz++vz3FD2IchopF5Ke9ifyrb8Fessxpm6mUjSqNGYXe7bZA7/Zbsu1KoCA40/wApARX728oQGkNAVl+rT63YhEogxTXGGqsSdtGKK9Rkzz1B8iq7gGfW9Fca5NNtkjybBj81j8uzOWaTxu9gwULFvq4rT+taSC2SB/G7HOrhLlz5/X3cFJrYuPvldTWLWvEl7v2HGHqcdCEpQ6uxJFVWJMxGAKsFl6jKLFxv3lN3Sl5chtPnZXdjSvqbLnkAiht8yBUglm3ziwpo1FgtkKxZZQKvc7ktqcX7T+5GPmb/86SMaFE2qnT0v7f48hecBEKp3wOhV9dBLS2VcfOwgEXEfxEl2N2OoXqqgNbsY23vwYzdilWQKu8TaW2emM0AFfeNkiR9bsDm7sLRwHXIGjVfnbwtsUiWh9/HvkXXkO5ow25r34T+a13gNXainJvD3pffRG9N/wZrQ8/CXvFKnTvuyvKOcXrIoKtCLQm4Cr/QGQCsioFNSA+VtU+khqrsSA1Vu7b1MLUWNPj4rap1XTXms7DUxtoc/GmgdhMJsMA1rZtTJw4ob+Hk1oTGv1KSH80ra0t/T2U1JrYQicAdUjqZHJMU6jVOhVWNbZaADbIJVI+TYQvQktQDZm5SpTvvHKcXRXQ+t2NGcfJrsYcTLkCK/RL6mwgzNLl8MROvltSgNXVhWEnno7scy/D4vfo8MOBCy6AtemmwMqVbD179dWw334H3Xf+DVZrmwesorsxmajQsrFJKi07r6TUVkGrAFEccKO8f7UuxSKwhrgUy0orP18VoKpiXJ2bq1RbteAa5i4cBK7ise661zYqtKoSqUn3OT9tOvIvvIri2NEoD2lHz3OPo+uhfwJ9vbDaO9Dx2dOQO/p49Nxzh9NuWAf6dtq2ul8d0CpiXrWxsbyNCcjKbYLiY3UAG0WNjZAsqlaXYhOL61bM2/DxJWlh94PmVt3dPWw+Pm7c2KTOinXD+jeZVDmR05eNlVjiNXqf9Lc1DcSOGzcOc+fOZQA7a9Y7/T2c1FJLbZBY3eAx7reGDJJxLaoKa3BeT4VV3DMlwAaBrHR/QmHVxP26aAC24tjEGFua4CqAlhRUOXaWq7O+uFk5CZQCZlkJHh/Y+kvsdPz0l36APfhg4A9/AE48EXj8cWDYMGD8eLbffuoZ5L73Y/T+5pcVcCVG4Ou2XeVyLKq07N4ISq3zvKLWOrfcgdtaXIl9r4U0YdWBrJiURXYP9o1FUlu9fRowjQKu/PrV+8PBNbJ3hOkPQqJ1dzOILbe1oNzRysZVWsQ91Rz1r1goACPHMJW2vKYV+WlvoXerTf0xsrLKyuM7uXuveB06cFWBrNteBbJenyHxsWI70UyU2riuwUFKb5JqbFy3Yt05aunHxJYtWxj5uLBY2LgJnYL6DUrqFJSZOOxe1vwjtMGcwDQuNukf0SNZyNxkk023xrx58xm39bc1DcSmllpqqTW7mX5JRPkyieqKmPgXlexGrJhcRwJYE3iNew0quA4CW1Gp5ZNhCWidybhanZVdjXVJoDyYZScX3YmFe7JoMVr/dmcFYMkuvJApr3j0Uef5ihXOwqCjjNz1t6Dn+99CecwYT931ANXNbuwoxvBUWrZPglq+TX4kxda7Z4KiLYJuVJMnmOKkkr+PwoBV3K6CUtV6zeDq7IwOrhK08nNUteOmiEFXGY0j996HyCxejp7J4/DWO+9h2YpVWL5yNTY57iTs+tUz0dIxBGuWLMGtZ30TR+0wBcXRw5GdswC59+agb7ONhRdF4zIswyx3Mw6Ig1WCqazIhkGoyq3Y0J1Yea9My+0kmOCpFosCp7WosrXE/tYbYNeZH7sb+AOGlZQr+gAqx5NCbGqppTZgrdm+eJrFQlXYIDfihAC2Cl5V50riS1Ix1xLB1qfUqtw3TdRZA1djlgSK+hPVWUGZab3tnw78cmtvB3beGbjvPuDttx0VltTYb3yD/LXc8RaRveEW9J51RpUKK7sQi/s51PL9VWDrbAiMVdWZuF/19ydDaFU74T2iBFbhdaoCVWmbCJZqV+DGgGscaNV6RAjbMwsXs+fvzlmA6+94iGV6zrYPw/OXXInbfncTJm69NbbfZ1u0F3tRLpW98k50XN+mGzqdiFmITWBWvA5NHGyQa7F3nZr4WJ+ZqrGKbXFV2KC+o1otamxU4IgKs/UG2FqtHipsakg2pnoAwGwKsamllto6a0l82CeW1CmKhUyio1qVChsVYIPUV/keR0kAFddEEdTLTCxALVdoNeqsB7Q+dVZOBIWKOsthlp27os5WbksZuaee99+nkSOdfo48EvjYx4ClS4E//hG48UbgoIPc+1JG9rEn0X3mqc7kxFYDaxDUchdnFdjK6z7IlU2TFCfIjU50ndNCrfRe0yqtbltVOz+UmpfEMQHXQDdhlXuw4geicqDbfcB7v7sH5YyNqZPHY9L4MVi8toghex7nKehLl83DnFdexgmXXo7CDb93uiNoouPc8/jm/CK4amBWq8pqQNa7Jt4n+3NRuxXHVmMVgBsbag0TPIlqbLOpbfU00/6bUYUNciUeiKV24gKplXRysKq/veZR2FOITS211JrG+lNZ1Z47AcASVZ9Yx9XiShyiwgbFwUYFWC28RgHXJIC2atIrTKIFqA0FWp5xmKuz7CBbqc4ymGUnECf4PEkRYK9a4//qX7PGebziCuCDD5z1884DZsxwVNrOTqf9ylVOZmO31EzZjXn1wasCapUuxKVgiOWQG2dSa/L3owJZ3aMKbpX7JLXV2x4xOZMWXCPGnPPzyNeudoUPfq+XczmgUGQwddwh++KKG/6JrnefQ/tme7H9vfPexpCtd0XHeutjpftesyhGNpetjNPtywNLNi53o0pdjQiyPCbWB6oqt2IdcEZQY42ANcylWHWfQwA4yKW41tjYxIGjBmuEAtvI84jWLPe4UWY10fuqnpZCbGqppZaaZKEf/qqajhG+MKrqw+omxlEsiT6C+kkCYKOqsCbXoFNp5O3icz5/NgBaUj+dxhLQesfZenW2ckNQGtLuqKJ8E2Uifv999TW5k3pWX3boEK/GLCjDsZeBOMPcjYOgVlRhZbBVrgu32wMHRDPV34FOhQ0CVu1+CSrD3IS940zWA8BVB61KlVW8RhWomnxWlEoojh7mtO3rw7jRI3DwgXth5eRN8e6cVeizOzB2bAsO+/GP0ff26447MQGsBRTHjqwooTqYVWUh5pAaBWQ1oOldpyZbMb+/QWqsfD9M3I0b7VI8WIAjyn2opwob1ve6ZrW8N6wmeF/V21KITS211Bpn/R1b0d/nT9LC6kyqLEyFrRVgTeE17Llu3LWapLa6Gytuw57Sagi0srsxuwVuzCqRgXCPenbZDi3PvuL/AeSqq4Cvfx144AFKDQr85CfAI48Aa9e6Y7HQu+dObo1ZF5x1LsQqqHXbqVRYVZsocbFhk6Oq/YaKbNV7UKe0ym0jqq3ecQFuwqHgGgatYd4IIT9+9a4/AS2jhiOzZDmK48dg7x22wKrDPofPbLMdcm1tWLVgPvremobiYw+y8WeWrkBh1Aj0Tp7guMSLPMCH6j71XmJd1mFNwifvWhSuxCKomroVB6mxKsgM2lalKusgtUaX4qTVWFUfAxVg62lpPGx0swY5yKYQm1pqqQ0aa4YP63qNIZLCGqQW1WJRAbZRIGuqxorbhBhWGWq9ey2qtCqgdSUsZ6Lgdzf2hT2Vy1h72H4YduWNDhxz+8UvgFGjgGnTnOf//S9w0kmV/ZkM1n76EICUWOFcDA6I2wLiYp2hOGDLanIqVFluHHC9a9HcZpU6G/R+l/f5nqtc5BUqq6+NpLQ6DwY1W5NSW3U/AgnbfO1098f07zGbQe+WU9D2+IuwVq+BTXVi//wbnHPTv1CyMhg9vB1nn3ykU1ZpbSfszi507botkM/6xsd+YBHVV1sqicPGpHAVVoCs6D6sciWO4lZsqsYagWuAJe1SHNeSym6cpCWtRIepsPVSvgdCPGycuNgkQNQaxCCbQmxqqaXW9NYMH8CRx5C0O2+UY+qhwpoArA5ak4RZXZsgkAUwv2UV/rTRY3h+/HtYm+9FR28Ldlu4Mb78wf6YWBylgVp/6R1ftmNxsq4DWuFeFYd2YO3hB6Djnw/DEmtzfuc7ziIZKaqdRx2C0vAhgBsT68TquhNBWw21fJtzHYXKcxluubmA641ddb81SXVU97nqOhSQ6j3VwarQn9Y9WG6j2FcNpZWkT2o11hBaTYA1zvtaYT1bTIG9cjVaXn0b5bWdGD9qBA7aY3s8+NTL2HmLjZmrcWb5Kljd3ejefkv0bLmJ+96s9MHnyx6/VamyLsyagKwhbKrcimtWYwMU1riuwXH7ClVWFWpss8FG1PtVbxV2wLsS0/u3TjCdgqzaUohNLbXUUotpJr+qq5SjwHZJnT+GChsKsGJbE4A1hVcdlBoPXFCTJOu0enDWjrfjP5u/jZJFE2U3MLUMTJ88D9fv8BQOeGdL/Pb1z6K9nK92Oa4BaCvjK6NcKGD5109E9r05DEg8kFVdDrkR77QNVn7nNKCv1+mbzkm3XFpXQa0DHhVluEqFleCWW9Vzr+RKsab3ZCRFVnTNDVNTDaDVG5NSjY0IrSpgjfq+9t+I6m2y+6oFpq5STHX+zVnIzl2IA8eMwKTtt8DUEcOQnbcIxVHD0bvzVujZZjMHXgW3YPZ689hU5mIcosqGgazGfVi1bqrG6m+PmRqr3KYB3qCaseoxqGvGxgVQE7difh0DDWDrqcKmrsS1mzUIFdkUYlNLLbXU+sEiKa3cFGqotk3QuQMm58EHlmsH2CiqrHgOo/GVqgD2yI9ehRkTFiPXl8NeX9sdsx+ejRVLVmDs5LGY9N1JePKLT+Lh/2/vTIAlOco7//WbQ6M5dA6DNEgIZIGxAROLWTDCsjls2ciAWbMrwkcYW2vABrxyeAnw7Q0IbEOEzbEYzBkOR5hlWcB7gNeWgDUIsZbAgICVzWWQQSM0EkiaezTzujeqXme9rKzvy/wyK6u6qvv/U7xQv66qzKzqnn716+/LL7/7Vnr22W+g/37DS/wiGym0NebFmQ6+5qV07p++i3Z/8KNVWuvcqTduqtfW6Oizf4Tu/fVfoslaUaXn9FxY5ynNReEoc0PIiGwlrEZ4XbE1l8iKujbShKvU0A3ZTcaXVivJqnNssrSK+1ji2lZafe9l4d9oY/1kgdr6xmsTOvnIy+jkwy6hbd+8s5wj+12Puoym27bR0X3n06mLLyDavm3jyxlL3sp2Kpk1/Zv2zbgpLLJO2q+UPuy+9zXRWLftoNwy/75ihbjNPjmisZq04i6FY2gR2K77iJ6/P8CU4lzviUlPX5L0BSQWALC0LMsHtYaYVGIvgTRidg6sdGzoOe73NnMIHa593HtLgS1u4Lee3kpbLtxCkw9N6MilR+jBNz2YvvD0L9BjL3osffrKT9OXLzhI1z76XfS2z/78Zp+uwBZUFY6t/0tCW4nMrKwwXEZot6zRPdf+PN33iz9Fu/73x2jHp2+ltaPHy0jbye9/FB195tNoeu5ZpcCWxxgZtfovb4KLDYXU+qKz1u+1m1dLAtwU4procrg3wKEbROe1k1JuOUn0Cez8iVbFmHzpwWJWgvvYt0ay23YE7vrGJVvW6NSlF5U/JW4k0f13wq3rykVlXZE1bblRViObmrRiRTS2qwJPsYTa0q4Zy0lGW5E1Y8lByrXRyuWi5sKObT7sUJgsSVQWEgsAaMUyfBAO/RqoBJU7zid/kiDE4ks9jonKuv23nFN4xxn30Uce8aUqAnV813H66Cs+Wm2/9QdupSc85Qm04+M7iK7ciFQVKcff+ty36YLT54aLQDWis9ZNux11Kp+gjeVPrJTj9T076dDVP06Hn3vV5rFGgouorSm2ZNahLTdspCoXclsTUhOptaK5nLjW5NZ6zlBFcFPmxJrrxdB4bwXmkbKiG7lea01atfPA7Xa4965mSSnmHNNhoo7rQrTWfY/ax5jMciOWQZGtVy2u2q+qc/NpxVHR2MwFnrpMKc5d4CmWtjLbpUC2XVJHA1KJu5HPyRKILCQWAACGildQw/Nh2U3cupVSv6EobIzAauRVI65KOXjrQz+xMQdWYPuJ7XTbzbfRJT9zyWbTkxm95aEfp9//p6dbgjjvz76pV0ZnNxN0N+bEVuJhHWcv2+OtdGzJaBHVqqTWRFtdsTXRHjtiOxdocmXWjMW5RlV7CQVVfeLa2M6IYu39nRBl7WQJKbst5pyk9+ZdR79JX//k+2j/Ld+iXYdP09E9W+nAYy6khzzu2fSAnRfpvyQoYAo2NdLZy/2a79dKOE07SpGtVSSuzZWWhdUXjXWPFaOxElLKfoA+UopzR2NTZTaHuOZK8dUUyQJx1zN3dH42UpmFxAIAVhLVhzZ3U+oryJPpD4G3ndT5sJ5okWZ+bnaB1cqANKaAjN+8/1825giyJ0P0uF96HB172DG66adu2nx+QnTzhV8nutUat7OebNWvFSkVo7ObDrsxt9WJ0tbWo7ULQ83F1J3bKklt6akhsa3tO5fb6rw3Jbfaz1LwZAL/fjhR3diFi4IqhTVGWn3CKr0/tRkC0ykdP3WEvvK/3kA//r576bHFW6J46cvruk7Tj3+dpmuvo795zjl02bN+jc7cujNezMzu5ftkfeP9ZFe+buzvphc7ImuOq75Y2XyvslFTO61YG41VRFfta6hNKTaP+0gpzhmNTVl2p+vIas404q6jsKFU4iHLWcq82K6YjFRmIbEAgJXH/uBe6Id4YgpidLqxZn/mBl+FJAqh6GtIDjhp9Vyvu+kIL7EzoitedAXd/cW76Zsf+mYtslXsXyy/Q6dOl/MPa300Iq8eqa3+b127eSR2M0prvW621NpRWrO/R2o3jguILSO31dPuTaLpL+LGNngT7ovChmQ1l7D6MguYdryPud+d5wqBPfymV9NVN5+iLVOiI29/O514xjPojJ076ciRI/Tpj32MHn/ttXTVf7uTPnXbq4he/Jt05vbdfJv2e4p9P7pFm+pru7rHySJrRV/N/GgrQtsYj/u7HbUVo7HW+9SV3cSUYo7GXGD3snpSivuMxnLHL5qcAosobDd0lQo8GVmKMSQWAADGGhX2tcnsz6USZ43CxgisRhbs8Qrjm66v0wve8n66+vLvoysf83C6897D9MK3/hUdfNRRogdviOlm30RXvPgKOnjTQbrjw3fQ4bMPOydKtOvkto2+3FRhM5YtUhqxESRzg25HYmd1iXX/75NaK1LrSm25jyC21Vjd5+yiTm4KsZti7Bxjy68X5RcnrKg6+0ZVDM4hrDHi6vk385UPvrES2ILfuO46uusv/oLe8alP0Z69e+ni972P/s8rXkFXv/CF9LibT9FfX/IGevSzXpaUJlvByawZpzNX1k4RDoosJ6ieIk+1Pk1/voJOLVOKY9KEYwVV2j/33NihiCxSe8fDpEORLRiDzEJiAQAgNwHh1IhjY19tP9z+sYVnfFHYnAKbIq/OmH733dfThz//Vfr4F79Bz3n8P9O7/+8XaEoT2v6lh9P9l3+pJrFXvOQKuvPGO+ngRw7SoXMPNc9tRvT4b1xUzl+tBMDIj101tcBEYV2prW6up3IkNvR/jdTO95XEtjzelttaO9Z26/kKV6RS760V7082qhqzvE3t/eWpoB16z7WJwDp93XX8QJlCbAS24GOf+Qz9xt69tPvYMaJjx2gfEX1t795yW7Ffsf8tT7ud9p55YeQcWS6NfdpMMWZS3jmRrc5lwkdPG6nDdtuM7FbXzD4nZYEn99iQgHaRUlzfLlcpbhuN5doYssD2EYVtm0qsIbuolcugzUaZUuzifsk0RCCxAADQE95qwVHtdPRHj43eepbUiUEjsOtxy5h86Y676L984vO0ff9308kDX6R3ffwWOvPhl9N5T/0lmt12mg5MX1BFp/bftp9ueNMNdMYZZ9DWS7bSLtpVPv/Yn3ss3fBnN5SP12YTeuH/+36azCOxVdEli5rcmnTN8mArtdhEQDdHXM2JrUVqzXGJUhsU23KczJI6VnXi+skFbjob5xVRMCzwRYpY6EmM1maIsPoEtfHlif6LoK/f8sFyDqzNr+/bR+s//dM0u+46muzeTWfeey99421vq7YX82W/dsv/pL0/8MJ6u0y1YfY5lyq66hHZRgS0WHbIep1t+XTTihnh1FQP9hV40kqlNr04JKU5U4rHTt8C2wdDFa8xpv9O7L853U6DjgISCwAAy0Bgjl70zbimgJT2/5kE1l2D8xff/Fc02baD9j3n9+j41z5NW3efT2c8+NF0/Mt/T99511uJLiOin924oT9wyYHSJU/O/zPcQBsCW9ygP/VzD6ULjhdyu9H/hLvBduS2IbVm/HZhJ7KW2LEjtUZ83crHWrmVorXueOz97TYEmRWlIEeV4tg0XZ+suvtqo61aWdX+m3LY//k7Sym1edI3vkG/8Ja30PN+9VfpEY94BL3s136Nnvd3f1dtL/bf/7k7iZ7gXguFzIr/D4jsvI3G3FU7rZgRz0Y01lyXwD5tUoq1UViW+dj6TilOjcaWffYUkV1U+jDSlld3HmtOILEAgFas8gdoiOq6pFyfTFFb1XzYUL+a9GetwHLHcAIryGsplkT0mg/cQHfcc4jOf8Z/pC07z6bdj3wKnT7yHTr4X3+XTtz2WTpn1076o09cRX/8w39PX77oO/UCTo1xED3sm+fR6z/6Y7JQ2eLqpE+WY3TFthzzzKpOPG1Gal0xNVHamEit81w5b5VJIa7GxQmu0w57i61NcdW8R2w8X1KoKgSnpgnHLJXjOyfm39Wuw+u1WFWxx4+ur9PV551H159xBtHXvkbvu/12+qc//3P615dfXu5T7L/zyHozClv9+7Ge840nJLLmfLhCTVxacS112BONtd9Pwpc/2pTiBlLkmOmnbQQ1Z0pxqsiatrqW2RSR7CsK20cq8VAYckrx0IHEAgDAkpGUtuxLJfbtH9uXe1xV3dcffTPyWvDNb99Hb/nIp2nHpY+jXd/75Or5E7fdUgrsU77nEnr7L/wEra2t0RXvvYiuffL19JHHfH1j3djiHmxjnZPyp0ghfuotD6HX/92VtHNa3NQJ5+PKrPN7NT5WMmb1ubVuW67U2o+l9GP7sfK5huAanBvGSnZsfO+FAOz7KLCObKu5qxpZDYmqLz068J4/uqdI6d4U2e+cey7ddvvt9B/W12nniRPlc1fddRcd2b+f7j73XNp7zz3l2/HY7i10rtS2eYlcmXX/rxFZ9z3IzGOtLbsTEY3l5sM22g6kFEtViiU0acxBElOKUws8aUS2y3myXQlsV32DwDWdrGYwARILAABjgfn2OekPVxvJ9UVUtWnEqQI7///z/uz9NJ1Oadf3XEHHvvQJmh4/TNMTR2j92L3lrdY/3HawFNjJ+pR2rW+lt1//dDpww2F66/d9lm6+5A46esb9tOvkdnr8bRfSC275Ptp/Ys+8p/m8Vd+N63zMM24JHvd3e53YU6c2HhrpraK1fLRXFFt7jqsRW26/mMeaKsQxxYZio7EpAuv+HjunlhPVQKRYy4FH7qPpjbdXhZ323n8/vfylL6V3/OM/0suvv54mj3wknX7Zy+jjN9xAP3vPPRtdFynvj3oAPYgbg1u0qTyA6ThWZM05N4TSE43ljomQwKiUYoccS+24KcXVvFhtO5misbEiW/abQWZTBVIrsH1EYTWsotBNVlBkIbEAADAgsv8RCrUXunGPrY7sHqcVWCat1I6+mv3uOnSU/vngxo3/tz/42trNWSGuO87YTheds7sUWHsc+4/vov9005OIbnLGWe7nnoeT9sswOV1Ph+Tl1rLY+ZxYV8gbYuucr0psy/4Vcqv5XXquLdr5pSmi6v5uPw6JKvfFUCDqXHtfOhRH7nzgE2i69v7N6sSzGf3OT/4kTX/7t+n01q109913000f/Sg98k/+ZPO4LUQPfcSV/FJOLkZmpbnUnMiW47Des6Gqwm6RpyqC6nzJUy3F4xR4csU3JaW4g6V2NMS202a5Ha3ItonKto185hTYHFHYXKnEQxK+XCnFkxUTWUgsAAAsC6E/XilCmxqN9W2LEVhHTh6w+0x60/OuoqMnTtL+s/fQxeftoQt2n0nbt87/nHHzkKXUUO352tRuzK3t85v9Um6rk7H+X7QliIZPbMs27bmM3DhCcuv+PokU1xSpjY3GhuZn+6Q1UlRdSXWldNIiY+GPP3AjvfnDn6S3/uiErvnwbENkjx2j3VdcUe2zm4geYh2zvkb0t88+ix69/YFxfdopxiGRNY8rOXXeU7YIugIqRGMbqcOmPUZegynFDotcaodrq/l8nmhsisiy43T7y5SymyuFuM8obG7GJoWTFRJZSCwAAIyRFAGzEJc04W70Y6U1Yh1crs+G1Dky8/RHXbqxn7TGqCZNlNtPg32MHamVBHPOxKwTW/5i3czbbdji6qZvaqK2XN9C+43f7Xtgd78cU/Kk10ATjc0tqTFj8cEI2+e/8S2itS30khum9PDHEP3gLRvrwEoUAvupx2+l77r85+rnIa1HzM2FtSVTI7Lza9RIK7a2R0VjpQJPMfNVOfn17R+RXhw63rfUjqadIaz52sU80xiB7SsKC3RMVkRkIbEAAADakSEKqxVYtbyGRGjOpM0f+tNTmnE3b1VfVij2lF1UaTNltIrcFu3YKclto7bSdfBJrm+/NvjkkHtdFHNTfaI6CUVuNeNy32cS69PaFxEH7ztK2857EO19zu/R89bvpd+85NV0zQfuKpfQKX5MTbEi47v4+Ztnn0WXXf6zdObajs1/A2Y5HGmMqSLLznl10opjorGe1GF1SnGLebHR2515sX2nFOeKxvZBboHNhSaVeKzylrtK8WQFRBYSCwBYSewP+GX/sOeW15F3DtwkaKNVmnTSkMAyx7ACGyGvorSmRGXLm71NIW1gpxNzS+zUbugTxdYTtWUF153v6Zt3mYvQe0YxL5VN+U0U1aCcRn4W2POjv330BG3ZdyltO+cCIrqAXrn1j+h39v0KXfOo++nHD63ROSc2qhAf+N699JBLn0yPPuOBjXM0V6OSWVNN2ERlJZE15ys9NseZ07SX3RH24SoV1/Zf0z/PimHmebHevnpMKe46rbhLukgh1kRhh5hKPHYmS35vA4kFAPSHc/Pd9QdxynHL/qHffVEoj0jEiqIjtW6xplqbHnltiGvqHFnNWAX5q41hLqllFJcT26odRmy5dGQpndiO3NptO8ykMXNL7STgK5Qkpfayz7eRVN97OfELjBrzS3Xk/nXadtbe6um1HXvo+Hk/RK+57kP0zt076JO/fw2dS0QPkj4P569Z8ZpUkqkWWeu9IcmsMhrL7hdIKa7a4sSOE80O58XmXGpH216bAk9DEdlYgR1aFFbfFv7GLwOQWAAA6IhBCrFCapKOi31Om0YcK7CSvGrmyeaQGVdKDUU6cYEdGbWjuJak1uS2HKs7r9U+R2ubc1xtvm3t+DqiSJh5vG2isoWoBLbHbFMLaot1XpOYC9b9p9dpx+7zyxvuY//4MfrOh99G0+P30eWXPoje/HM/Vo2fjaxbUmuuWS0qGxLZ8gAhrVgbjfUVbRJSiqtjuIib0FbsvFgNMWnImqV2dGnHeaOx5fb5q9+nzKZEX2PmB/cVhR3c39sFpxQP9j4kE5BYAMDSkP3DOjFy3KD4Az30PyKp56mVV49UZBFYjbwq5sm2miNrU0ipiRwU/zd9cYWhHLlopChzkVuzXYrCuoJbPcmkRXqWEqoRKxiK95Q3tTdGTmPn3XJj8bz27NxnhyMn76fZ9HS5ZvGdf/lyOnn7rfSAPbvoT//9M+jxD7lw4/UyUxis+9SGqlhi2ojK+kTWPFakErNRVimbgCnwFBRenwQmzIuVijipC0d5+tagLSKljcZq/lb1FZVdtMCC7plY791lAhILABgMo/jGsLiJDKU15ZLfBRGsTCweqBRaJwrL7p8gsCp51cyRnea/cZyYc12zbhhtOXXHxghqJbdmu51izImWHcG12mmMrQi4akQ2Nlqi/becEjVtU6grIeLeuPYMtx64u/z/4U/9D9q6dRu97GnfTy/64X/VPA9LZotrWrzHq8gsM9dVLbKmHymtOBSN5SoLC5FXb0ox12/X82IZkufFJvYXW6lYK7LlkDqQ2dS5r7lTiDVR2JypxKvMKO6xIoDEAgBATxRpm8XN32j/yEgpxaForDRvNVTMSVPAySewmjTjcj/mBskzjzMUUWQjm6Y906yVwjmx17AxkutLLy7H7PThSm55ICMRonRE3JhKkazUL258Ufo2c1m7+iJJELR9O88o1yv+wYdeQP/5OT9Eu7dv3yyq5n4pYZi/DUxklr1VD4msGdN8Xzb1VxuNlfbVpBTb81gzzYv1tVGTVE+EVSuzuVKKY+bGaj/vc0dl+xDYvqOw2r+bg/372mFK8WjuMSKBxAIAAMhHSBy4KGwojVjqwxVYj+TW9iu3OzeEbjXcFoJUibmVTtxoz/m9El/33mXLlrrkGtENRWHLMcbMf1VGY7ugrYxGRFZj08VF6Tdrt1o89Jw99OWX/wzbH3tPaq8BO49GslHZkMh6UtNFeXWf8xV4CqUUB6K2bebFNoo75ZgXO+/DnRcb03ZslNe3bmyMyJZ9J8ps26rDXQjs0KOwSbKnydZaMJMlEVlILAArzLJ8kKnpOM1XfTOyKtc9EK2VKtOyacShFGJP9FWUV424amRVwcQUSRKqAdfasrfbhYCq56wD5xHdUDRXjOhW7UT829BEzlL+nSlkVC2hmVLCJ8XirgIxwRIT4KteA+56F/soRbaR9hpKKzbP2fs0BHVLnJza7cZGQRc5L7aDlGJNNNYnsjlk1JXbnEvldFGFGEvqLJbJEtyHQGIBAKNlKB/CyeNoI9Ujn3fbwJdWrBVY5hiNwNbkNVK2xTF79gneWtqi4o7BlQWl5Iqia3CENxiFbEknMhqZAq5DiCitTWrn4C26ZUVci2MaSyvZ81slkXVTae2qwty8V03k1Zc2zLXT5bzYASOvE5vvPHL8Letifdey3chzzJlGrI3CDuE+YEwpxUO7h0oFEgsAoFX/IEw9x1zfrI9KUqXoak0Wp8nrhtZ3jDx/3zxZI0NKeW2Ia+i1YCSwCozM3O1Toq3+G0NRWq1x2KIbiuR6RdfgROIaacxdEyGhwXTvTLC30MV1suXaJ7SupPqisvZ8Vldky342HheqwkZjOXF0iiaJ+7nbTH8SoSq/scWRchR3UkR3U+bFprYXG40d4t/RrgR2UVHYoV3fITAZceViSCwAACwY9c3LCObaNLBlgxs7F/n0pfWG0ohjBdaOkGrElZNVc3zjNdy02Ma2U/MIavBmbn4cI72tRDcgvBLq5XhySueivswp0ne5ucsN2d7CCi2bOixFZe19uDmyjqSVacWlEFvRWHOsTzQ9gipVKa7vM92QcM/71juPtcviTh4a818182IV4lxPa26uGTtmkV1kBHaVo7CLYjKg954WSCwAoF8SI4xdfMDabUqPQTcSwQqVL5XYR4TAStHX2njccTjiGkyF9aQ3pyzdYksvL75WG47sNq6zR3htuHm7bHuZyNpu6JyVcLfQrtLUpNaO0NoyW1WkdlOHGZEtcIo9VefkSyt2o7Huefuiq1KV4sh9QvNn1VHMDos7pdKmqBNHSGRNP8sosJgLO1wmA3jvxQCJBQAsFa6Ajl1IQ+OvbS/+AJnH1pcFRUXRaqmPIcIJDHfOmrmwtXbDAivKq0ZcfUvDzNM+y8daSRMiqFHia8luUHgNvijv0PBEwmWcYwKp3HYRJZvyNXHSts17KiSzbOqwNE/WqVpcLL8jRmO5OdLue0JKKXbak7ZzlYVT2skxP7aP4k6hlGL/+HTRWA2L+NvV9VxlrcB2UZE49lou+r6hr3mxY5ZZSCwAYFAMTjq5yHFEWm/U+Qx1HmxbbDkLzHusiaZnfdlgGrEksFz0VZDXhrgy0uqVPXtObOSXCLXr4IvQcqJlt8NJa0B8OcJpz+nELnvTGk0q91ZFFJbZxspsjMgWONvK9ou04uIBE40Vl8cJRV+lbfY+gSh2sLhT5grFKaQsoZOaUiyJLIempkKfMpF6nfteD9ZmUPcKS8hkaPdjDpBYAADoAldI7ShpS1oVlOpSlH0puW3351KFpW0pAquUV1ZaRUG15sS2uebMsWK677pCWj1jEdvlZHjklOckrXu6uSLSZvTWjcLOf7cjtKzMzufMmuV6asEVV2SLbUKhJ04+G3NjueV2IuS1Sk9WCGybfTqrUOwZU2hebJcpxVI0VvtZ3qVMtHkdukgjXuS6sKDJkCuJQ2IBAKvFgteKLQqYFNGLNm0sPaFKx74IbU6BleQ1NiIrHNOGhqxGRGujJZgjscBT59cmclzS7bKbTVqTWltoOZnl0oylqOy8DXZOrPO4VuTJV7iJfW6eVe1dTicwLzaiuFPnFYoDxZ3aSnJsSnFqNDZWZE1fOcgxr3eR82BjrsNY/54uMqV4DEBiAVhxcgnTosRrKMIXO45qf61UdyHfdpu+9keQ5qxNSWVTiLk2THQ3Vl4D82S7oJSnlHTlNiKaEs3t4PzZOaEajBhK6dj2EjnWe6OUT/OlByOztsiy4slh78+lFXPzWjlZjZVGX1sSvvm1LSoUDxrhuuaKxsZm13DCriXburYdpRAvRRR2Gc5hJEBiAQD9MwIpGsVasYKE1qK9GdOYg2PqOsodWVzJW8TJMBeSWIGNkVex70wRyARt2YArDJSBvtZwLVNwI99vDUm12iqw26t9ObCFkczivWOKRJkILCOyG/15orGBCGyjWBM3l9SkAWcquNRqX0VKb+P52GV2lLQpHNW2vdgiT6l/S/pO94wV2EVHYYfYPsgDJBYAMBq00c6YqOhQltnpZa1YS2h9/RU3w6ECTIOGq0Zs46QRxwisT15FoVIWdkqJVKZEYWt9OpI2BkQRTYi8Vu0xhbRqacLWdWpEZQMiW0VjHZGNjsYKkV22SjFzbPT2XMWRUnDGZkd2YyoUS+PSzIttm1K8jHQpsF1FYccupEgploHEAgAGR26BbN1eaoXi+XFtKxRHLbPTZ3Q1c1Q2KoLnmw+bSozAaoo9zTKl00rnVUT0ckW+3QjkUEmNvnoKXonRV6c/TmbZ9GKNyNqy6ou6hlJ5JRENpYvHRFULWkZgfduzy19CJDiVcOQ2PhpbHtdVdk+P6cNdCezYpRTkAxILAFg9FlXcaR4JNem+beRTTD1ru1Zsm0hvTrildlLmw7qpxFIUViuwvrTimPmwbYU7VxsME/t95UYMF0h01Fk775XZt7aPtc19nk0vDqUWF7hpxRwhuZVkzSeTOaW0I7pYZqdN/+682JiIr0Ro7dhOp6kMTGABSAUSCwAAA4wgs23lku/UdoYwlzkkMaFU4oR+OIGNmi/rE86hp227w+PmXfZEzDI/vugrK7Xa6Kv7vCuyBldk7VRgKxrLHRNKKRafCxV3UhZlCraT0HbMWrEpdFGhWJtSnBKN1YpsefwCZbaPNWC7jMKm/D0eYqQXKcU8kFgAQDZWoUJxJ8Wd2qTlaoo7pfYdUxQqtYBUGylWimp0FDZRYJvyurlO7Ma+8ntkYi3zM2iYcc7MnNDOUd5QC2nWrNi6YloQkNaCSk5dkbXnyNq4acXlc9bcWG1RJ9+82Pl89tbzX6UiUdL+HRVl4jvjl9nxHxKQWc+82JQ2eaFOE9lFRWXbyOsQ5sGC1QASCwBYjHwmRvW6KO4kHbc0xZ20ghnzmgwhKhuKdHrGlyKwKnnlxlEWdlpXCat2qaAhMTnFCGPmdMJSlDVRa1M4SSG2UpRV2uZ9jhNZX1oxNzdWI2Yx82LNtqj04fnnhRaNSMbMhQ2tFZtBLt3/q9uJiE7HjFsrsmW7Hcts28hrl2nEfURhhwyisU0gsQAAWvXiTqq+Bl7cqfMKxW0Ed0hr0CojntriQV6BdQs7CX1rxLW3ZWsUBNc9bSnjnABL164RBbbfs3Y00bw21hxfbo4ru03znDYi68JVKjZt++bLgkGhkVYpGhtDV1HZHGnDsQKLKCxoCyQWALCajKC4U7AqsXVDU9s3pbhTjGjGSGo5B3CqWr6nkKMhyVoJNx7NGJ1z5CRMEr3ka5ByXMLSOrleI0mGfQLsSp17XWtSa14Dj8w2RLbcn08Zlp6LjUJy6cBV/yNa6kg1xzZ2bq23u/AyO23n2sYstZNzTVlNNFYSzlSpzTnftWuB7SuqumzR22UHEgsAAH1HddtIJLdfaHtsm74IcxuhDbXlkd9Fw0VmxShsIO2Vk7SgGHZ1Lbq+xh4p08hwY+6nc+18UlsJrSSznMhKMql9TkBcyzWGsUnuACsft8Yj5W2jsTEiK8moJLRdFmgaYiViyOhqAIkFAGSlj3mkbHGljioDq+fgZkrzyjkvtlbcyY7O+ubINoS2RXGnNnIsCa30uBCSjpacSamO3FpeI+RyUdFrTVpxypI4oXOz+7WvqSS0osz6RDYirVhMKe6DscqgpkBUhnNTR0uZvnRSGh+N7UJk+6wm3FZehxqFHQOYF1tnOT75AACt6XMdvoqOb77cc/Kdo+r8ufF6UuWqNs2yGeb3+f+9FT25G2purqB101Lbbj/29OO9Jm6FUredmNfPvUH0Hbuob/ZjxDFUzClFYIvfzQ/XxvqU/VkU0ng0Pyz2+XuuhdROcY3NjzeV2/6iwXrtYtcIDsLN46314fnSa0BZCGPBJztmm/t/LUVKsdVY0hjq+828cjjE6OZYBDZVerPIMqot9woisQCA1Y3qdjQvNnbc0enJLcbd27zYEVc69lYl9qGtQKycZ6sRVPXYFoT0ZYXv3BoRXmZZHK4dLkJrIqLeqKwUkfWk7zair750YUVEcRmLONlzWMdEjvVlNe2FCj21jcqOXWABCAGJBQCAoQh23+vF9jUvNqK4U7CvDtBLKhdZa16jKIF1o4keuYsW1j6ieYGUYs2YG+m6gRRiSWo5oeVktlH8SSGy6oJOXc1bXaK04dYiGFgrNqeEpqQU5+x/SCLbJjqcIrB9RmHHBFKKN1ndT0QAwKhTk9nU2oz92m1p280VdXDTkKVxRVf9lNKNXdx+u3w93RtQjwAMJVLFiplCymtyxqTKcvJW9GV+olJw+0pH9fWvTQ+2zlE6X286snAt7f3sFONCZGtfNJjXTkot5s45sA+bPm7tP7gq3JGI2RyZRCOLjMR+6RPYX5tSHHM+GrkbQnrxGAS2Dasgv8vIMO4IAACrOy+2Y0Y9L1Y5rqzzYt2oWOy82Elou0e83X19ES1r35rc2uNdQORKXAs2cNMvCSxLhKhKgtjFjxqlbPvaFWXWdy00N6qe+c3e12PFKKYljF0ifPNjY8fj7h/TplbyFiGybQW6zxRiiOjqsXx3rACAcRGaK9b1H+4WotNaiLn9tcWeuP2E7a4o125AfW26whkS2Mjfo4o72WNucwNtp5JqxhFJLVW1BW2EKUksM5Asti1klm3LPnYAKdXc+0xV2dlGeo+G3ruxxdeW8ItMjWAmNpynnUiR7UNmc/STKrCIwgIty/dpBQAAmUgR6LGmFDfONTbCGvp9iz7C27jBl6KubrvzOY1uG5yksqnJ82Oq/d3fPe2FiJYWjhEWeooSWoXMNp5rIam+ucscOb/saLTte1/7nsststy/TXHH4dxCdpmibB9XSylO6M9fQXkWLZk5hTZnm30L7CCisD1GnYt5sQASCwBYwXmxKRFUX8rtfIf+U4pr0da4lGIxGhubUlw8dn+fhLZbsslt7yqlmLvOMXLZRcGeTLSNvJp5opqftmMMjtO3zJBGZH0inONmV/E+6Hv+dlCwYwU89ssxromOKxN3Ii4t/g3pl9fJI7I55LMLGV5JgQULYbh/kQEAC2EV5sWOJaU4JK7WgXEpxVFFnTwpxR1GY0eXUqyNXjE01jtlC0flqVqcQ0xzCK5aZoVjo8bbUUqx+j0j/Ztx3zPOe5oV4Zh1nbljc0eTe5h3HiMpOYVG1VZA2NIq7KZH9Gwp1fzkZmzL6ECAx83y3a0CAMbHoufFZiZ7BCKUUhwekGqbL6W4vEH2RaPbFngKVSleVEpxCKu/RaARuhxRVPV4IqU2KLNKAY0t8tQZPUTrvanpmghqinwPaO1Z1X4tCzOlphRrU4jD/Y1LBtuOGVFYkMLwPqEAAKAlOVOKxaV2Okopjo7salKKpf21KcV9F3jyRXC1cpsrpTjjvNi+BbYvcdWMITSO4DJCzP4rQYoUa96bGUVWMx1iqSNxHaXPjkVki3EuQmBXPQq7hnmxkFgAwLhRr+G6oGhucr99phT7oqYLLPDE9udpK4rUKsKa+ZCZKhSnSlwbec2+nE7kuNqI7CjXXnUj+X1GSUcckVUjvJ+8AtPyC5K45XXCIjtkmW07tjYiOSgJHfBrtMyM+JMJANAVuYRvVGnAmefFalKKo66PMqW4VYGnRa8Zu+CUYrcq8RhJkdcUOW0jtSGZHWOUlY3MW19mRC2v43nfq543beSMyKbCZKdk/7ugeL/41oTVHNfoMqJKcWof9X2GJ0ljFthBCTBIZrx/qQEAy0XmebFtb5T6SCne3CCkFGvnwnLj0ERjlcfMO/bvG/O7c4NdyPHMt39xzlJKsdNWVEqxHTFl0obFpXbc3400W/1x0diavNhy7Rb0UVRoltKZi35jI8FFW+anj+PKYz1jZNtTXIMsyxgp5zj7vvAQKxMLRZ24/dkvaYR/+97rrxXZtp+/Y/rCMsO8WGZjcN/24jUMkc0RHYZEghyM/1MHAACUuDdiOSMCvqhoTqH2phuHoh5uOwlrxi60wJNPWIvnY6Kxvqiu6csnKwqR5cgtsjll1m5T85NCaFzJ7Qqvj7utFnk342Bes9AXGCzcNq9gTvyRU+41F/r3ViYu2tV8BrWt4j7mtOMEQtHYLqoqLzK9OFffi46iZhfoBX65sLbi82JX6xMHALAy68Um0edNWEqBJ+U8WTaV2SObYoGnRkR0wdFYe5snMluJSiEJTDS21o8vGhsRAa3BSZEtTxlFNjSm7PNzWxAai3gesa+DdJ04gWWkMvQai4Jr96FMJfamxZdjEv6tS1kgoahsKOJafknliUanfNYOXG5Zqekopd2/Ruw0Wij7ENqc/QxOQMGoGfYnCwBgYSxkPusSpxSbY6LHFCu1UoEneyySHA4xGltE/TzViRvRWO75QpDNcWuB/XOkFc/7HJLILlpmuxRYXxpxeP6pJ5KRMworpBJzxwbFlmvT3b+lzPq2u/Poc3zmLuLvjT76OU1KKZb2zyWym8fkl9kuJHkIAgsJXi4gsQCAlSKYUjySAk/Jy+1oCjwNKRprExGNjSryNBesmLRilcgm0oXIltsXILIagY5JIQ4KbMdRWLef5IJOoarE7nlMnC9zmPazp//6treVzrbRWaUwdk1qgafcIrtxXLp02tLaRXR3aeVxIPOUVxVILABgKeg7bTlc6KTbAk/qSLAghUOOxpY35tporJ0+7LSbrchTbMEgIyjWy2DESRONVYtsQrpzn1FZTT8pacHa/buIwvqKTknvQ3VBJ20qcUwKcLH/pEVUNmUbN8YeI61tCjS1lq1Mct2ucu8s+qdLhhJBXVqRXmEgsQCAzmm7lExyW8r9ffuo+rPlro8CT8qbxLFGY6W2SynxCWtqNNaTVmyjjsY6fTbaySWynnFqZbYroW3dbsY04kVFYdUFnWJTiSPkUbWtIPEz0FvtfMG0lhZrXqw6pTizbI9dvCCf3bO2wsWdhvvpAwBYOKNa5zVivMH9lDdmOaOxwQJPPnHloj5ji8aWESN/NLZGiyV3ZtY+obRitbj6xGbSFKiuRVbcl9svk8zGinG2Qk7CMdz4skdhfcvkSKnELimpxNIXFb75sIGoa7DC8ViZy2hXEdhGSnFENFa3Ruz4RDaXgPf1hUFio/nbBFGM+FMJALC0ZI7G9pJGnEt8xxiNLdpqG4212q3dTDv71qKxyiV3yue5JXBskXXSiu3IbrrINgs92ZLnFVmn2JMbQY5JL9Yui2NLaMpPiOBYmHPg9nWvh3StiutrrrEosG4UNvE1LzF92GP2pRJ7vrxonUrsE1bfNl+FY+mzK7EWAFhOkR20eIKlAhILAFj6qC4rd0Mq8DSmaKxzA+wTTvZ3qS1NkScjnVxasS2sdiViQWS91Yqzimx5NlW/BltkK9Hi5CxQuViUWU90Nsd6rxpU/QjjVcurcH3EFGLzGhTvhZDAWmNht9vvLUtgK+z3q9Wvdw63e37c55IkuNxr7n7BpGnLPQ+7OSmizGYg+ItXDTXTJ4tUdlB4auhSN9T05yGOCeRhmJ8gAIDBMNQbjUEWeNLcyFnHtF6OQim1oWhsKbKV+K75o7H2Yzcaa2/zRnHX1O1604oFYS0JzY91RLYROcsgsjXsSLAQldXIrNnGRWa9QuubP8qIre9He4yIZ0yp8hotsFZ/osAy7yFWYLnIvjW24FxYrjiZK+eMWLPpvz7JjY2wRu4/tr8VKsER5sXmqlKsHoe13xClLPeYBh/NRSrxIBjfJw4AYDUYcErxKKKxmxsbfdQe+9KKrXZFWefSf+3H2t9dCfWlFWurFds3//bzXKGnLkTW/f5CWEfWN1c2NF9Wis5603UDUhsiOnob6FcaM3d+vjnD9hcBtetqfXEQK7CqasTckjqaisTclw42miislP4bHWHl05LLf/cx7Qf2G6PoJhMZjY0RrqHIbBfjGMJ5jY21FS3utEKfJgCARdPXDYwmfbhtSnHb5XYGEY1NSSsWoqLeIk+atGKDL61YOz9WK7J2oSfTpiCyVVtKkRVTUq00Vk1U1oyvGiMjg1x01h6HKpW3rdxKbSnSmhvbJHmNiL7WBNaQKrA+oQ2lEXcUhW0Qk0qckEbMz3ttIbdsFxGfqS3pouiQNhqbs8DUomS2q35ztgkZXn4gsQAAoCTncjuLjsb2WuRJPM6JxIbSipl2gsvu+J53RdZNB7VE1l16xyuyjYja5jnUZFYRlRWLPwl9sQKonZ/qthvzEyDUvyTiGnkNpg8XWPNfWwlsbBpxMQbTtzb9vSDwhRobOY1NJXa/TBL62uxTEOU2n5stpDc3Q6lgm7qubNfSZvroqp/RCCxSiQfDcD49AACDJee34kNbMzZXNFbReefRWDY6zEVjpWiqNhprH2uOCaT/BiOz9mMhrThlfqy30JMhQmSr9gqZMkLERWA9MhYTlZXmy/rmiLr7aaQ2d5EnTbu+8YXk1Rt9DVUgziWwGdKIo6Kw3LV0/70kySk/t5b9XFK0m7sy8SJSkGsS5JkXmypLmjViU9vNLYN9CfIQ2wLDBhILAFhKurrxkdqV5ox2HY1lx8NGWyf5l9zJmVbsbrNv4LXzY1NE1ll6JySy1XFmjGY/TlI5OOH1RGWl+bJmrKzMctLqE8YWRZ5iZVglrgp59UZffRWIcwssM5farUbs9sE+lr5AstoNRmE1EVgpKiutaS2124YFRl+7Fhw2pViI3HUlsuZY7if2mK4ZytxeNYjCDoqtix4AAAC0oRCxmD+Cmv0b+xQ3Xda38ckUN4rOH0HTV+x5NJiPkW2HG7/9nHlcVCZeI5oVj8vnij/a01Jky3uzot0iQjO1brDsduzzKwXRimK4/RVUx5Wd8tus48r5scX/zfjMfoVIFM+vTzelwPfYHLs+LaVksjYt7j43xGN9vZQQc0tf66/4fyEvp+fHFWOet1lWi53Wf7cxv1f7mP7NcUZ2zDgMxXjmwjSZ92so+3fSjGvXwMY8z6UlW1THtyQkyxXSnFlOxF2JlyKvgfmv2uc0ArvZnyWwzhi8X6IIYquOwrq/a6WTi9aKgst9EeeItbuPRrIHgvn8HWLfucc2JGEc+pxaMHwgsQAAFa0lK7WtFgKp7aeNCEuPa/LFtF9ERGfTdZ2QziWvuHGc2c8rxLW2bS6Zdt9uWxtj2+inIZjW4/I4I6m2UM6jprP1+bk5slk9drfZ0mvf8BrpLJ4vxi6J7PzGf2Jk2wjBXBpNgKQs4iiJbIklsvP9zNcO5W25VmQ3j5g3W5dbe7+qfVtmXaG1ZFYrtF6pta9vinzGEooASzLrE1etvFr9J0dkBYG1U9zFebDV2ISouhOFrYswsy6zLYiu7M7b0AquuExPYz+F3IYkW2BsFYtDYllEYxvVYosv96Rod8v+xsgoBRZR2MEBiQUAgI6lfWHRWDbaavXPiq4TFTUR2i1EMyM8XLvO4w2R3YzeivIqiay9n0Zk5234RLYSVklky7EWN5rz1GIjgfO+S0l1/nrOTs8v2/yetSak1c6T+rEBma2eV0RnOaH1SW25zRbXFGktjm8pu1ppZcXVU3VYktLo6GtOgVVWzvattVy+f9yUeo/sRj3HLNOjng8bgaqiu7aPRCHsgraSqTl+mUR2lAI7AtYmW1qvVzw2luNfBACgF1apwFNoDNIN2cLnxnJRlNgiT2VkkjnGvtG2C7/YN8ZlmuJEnh9r78c95rZt4dtWVSw2oiClcZrnuXVkiz7MnExbXJTzZGdMdeJqH6kYlDRnlisC5RSC4opB2T9S0SftfNkK5X6afsQxMufknnftmmivZeC5xrG2wM7fR9WxnMBa51W1xchzTBpxVJquNgqrKfwkpRx7fnc/08YmXrYMScWdcs+NbfTVYp8hM7r5rzaIwg4SRGIBAEtNm+jmUKKx2rRi9pg+0opj5sdyUdnaYyZ6K0RkC6oIp9N2IyLLRV7tiKybdhyYJ1vuE4jK1uBSh525sb7n2ehs2a4coa2OOz2PynJfpMx6SicW+vdGWwvYdNfIyGtKRNYXfbV/dwV2bdL4QqPRLjM/lk0j7iIKm1Agyk0ljp4Pm4oyHVmFm7XRkkVFRu2pK2OiS3kdrRiD1kBiAQBLQY4CT8E2HFnsam6sT05V55QxrbgU2eJ3N63YElPV/FgpVZgp0sSKrFukyWo7KLLlsUIKse1IbtqxZ55slV4sFH2yKa6fO182Sma5bY7INVKOy2PWWUE0Ultui81isCNVbVIoJWlNEddYeW2TPhwrsG4GQHU+1mNPtkJtfG7aryuqUhTW/T1U/ZhrpyGqgX6kbcJjV4wlci7fk1NEubZi58bGjGcs6cVjWprH00n3fYAkILEAgCiWucBTzmPFCKUiGlsjtciTIjpbk2hWdJn5sfPITPz82EChJ0lk3WPsea5qkZ2fi28uLFPNV5onW55TICpbyKxb2IkT15DMqraV18gpCFX2qZNaSXA5YsTVK6ohadWIawt5rT3vpDdnFVi3XWEerC+NOFjMKRSFZaKyjYJOmpTjkCBrt606GUW2YKgyuxQCCwYNJBYAsDQMKRqr6pNJ6005F9++WdOKNcvuNGTUKsbEFXrypRdLacgxImsXaOJEttheBIq5tGN7WyFUbnpxKCprFXaKqlDsi8AK26qepCitTxbNFwwx4pmC1L/BHbdCXBv7SNtCEVlXXnMJrK+QkxmPkBrMphHbpERhIws6xaQS+6KkqqJOnu2popY1Ypk5JTnXmIcks32JZW8CiyjsoIHEAgCGM1d01aKx9r5zkUyJxor7RaQVl/0Xz/vSinPNj+X2kcS2rcjO/y+KrDUXtiGy3DzZEmVU1sKITJVdqK1QrInOzreXx4eitK7YlqcTmaJZSG/sMRzcuJTi2thPIbZiRFaIvmYXWLdvex6sEwltpBHbx7pRVk9VYy7lmI3C+n4PpRLXtm2OVSNVQxCvVKF0t6lTijd29s4dTpHvRcpsn/cDiMACAyQWALCaQjywaCy33dtWzrRiaX6skcfQsjtt5sdq0osjRLagFFb7hCWRtbe7a8m6uOnFgahs/YJTY76s3YcUha0JrTsWNyLGDFmaycVGa33YwpsYUZVghVUjrcw+0enELaOvtTZ8c2BDhZyEebC1NGL7WPcY6fdQFNa7PSKqmyuteIFCa0vjUOabpo6jT5nt+293r/0hCjt4ILEAgHERimhqUmsTaBzrGUeXRZ5aR4U9khuaH7vhslN+fuyiRdb8Lq0ja4lsOW53LVmLUHqxJirbeG8UbVh/catKxjNFFNYntL60Yq7SMLOvy6Qx9vRqs6G+SoSKyNGpxLHy6pv72qfAOv2Vu0jViJnIqiiUoSiss90t6BRKJa5v86Q9S2NTMASZbEtqNLatUHNfzuZgUV86Q2CBCyQWADCuAk9DicYq2og4uJciT96qx63nx9alt1OR5XCW3zEiW47Z3ZerXBwTlY2RWbO/SXM2fZj1Zecy6/alTStmz08QVVFsFce2JtBvroisd5sn8lo7Viuvpn0rfThJYKV5sFw1YvuxVO2Yi8ra+8ZEYT2pxD4pipoD28X7rSvc+faKlGIvHYus245NqM0hpe0OaSxgOEBiAQDjY0WisV2kFbPj0syPtaVVUeipEcnNKbJScShbZOfRLHXBp5iorJFBKcXYSKhzc1qlidpPWtWMq1TjWcsobIKcVtHXDteJLQgKskZamf2ioq4BeY2Ovlq/e4s4pQqs3ZYvbTiQUhyMwtb29URNpT4U68OqZWxMYtsmGqukixTnsYhh7+NEGvFogMQCAJaSXioVp/bpiYRmTSv27StFZ2PWj3UKPXkrFjMFpZJEtvzdU+XYFk2u4FPVBpNeXCAUfYqKyrprtjoRtdocWPM8k2ocEtpye5vUYs8xneMZT1Jhp67kNVZghW0agRULOTFjr6URh8RWG4X1RGTVVYl94+iwCvGQ6CIau6qMRbTBYoDEAgDGmVIciMZm7St3NFZqL3dasUtEmrE7P5Yr9FT2R0zxJyta6l16J0ZkDU4EOLivW/BJ2K+xnixDfIqxtW0uI1UBKCnCaqUah4Q2WLHYtLHg6GtnacTOfo01bEPyaj1fW+amTfS1hcCKhZxsSQ2lEduP7feGNgpbzpv1iHMOuNexR3lNWcKmc7nuMa14DCyeDQ2sAAALRUlEQVRMXhGFHRWQWADA0jKEaGzXacW55sdyMh0q9BSsWJxTZNnnHJE1BAo+ladotzNHE5WtpRAzhZ/KdupB9Hlb9Tmz1Rjm42BTjT1Cy8k0J6ttCjw12okgKrLborCTN+raUl4HI7AxwupGZd0orDZSKkVhFanEMfNhY/CtQzukebFdpRSbvgqWWWYhsEALJBYA0ApEY5vR2D7TitnXpE2hJ0suXZFtpAdzFYu7EFmDJLLluIVjQvNkXVKjsq7sulRiND9gvjRP2Ybdrv06uuduC5o1h7bcl5vT26bAkzknM67UdGNllDcqIuuLuCrEtZW8agXWLNeSIrDueG0xjUkjnlNr393mK+IkyLAqlZiBTR9WvK8ax83PpUuRaxv1TDo+Iq14WaOyEFgQAyQWAAASorEx8t53WnFq+rW30JNUsbgPkdWkGPuO8aQXl+ekjcrGphhbN6S1AlBOg5K4egs6OX+93SitOCZPenGS6Pax1A53s+4R12h59VUeToy+thJYqZCTK7DcuTv7N9KIA4/LVOIWUdjgvtUhnveDuTYZikANTfRyRGOXMSqLua8gBUgsAGCpl9tJqVScJK0R0diFpBW7+2mjs8GKxdzSOxlF1qCJzEpL8PjSi31R2TYya0Uxy2toFYjauF726xcntO426a85J7a1MQ5smR1RWDlp9UVcI8W10a8kp6kC64hnlMAauHmwMWnEzHVJmQsbXC4ntO/AhLPLiGbX0dhW/QyIhQss5sGOlkFI7NGjR+n0aeGvLQAA+FBEGLsiVORJE4H1NJ6eVpxrfqwjsvY5hpfeiRBZAyeykvBKkVl7nqwyvbhAXFOWSzEWluPxRmbdSCEntE5VY18kNii10l94Jw2ZI1OgiCXUNyurgvz4xDVaXiWByxF9bSOwxf8nAWllRLnqwz4na59Keuz2rfPYeBiIpmqqEpt9fa97JgFbpMjFiGQwGrsiIrtwed0YxKJHMCpmsxlNF3S/NViJvfHGG+nOO+8sH99xx7fomc/8N/Tc5/47uvrqf0vbt29f9PAAAD0z2mhsxDhU0dhAWrFm2Z1sImvLdG6RFdaRLaitPWsTqlxcXtfA/uZ5d66skJLciMoqZFa1pIxY1ZgX2nJftw13u3S+ir/6UgQ3G5KoBqJ1bHRYirh6xLWVvLYU2No6sG0Elhszl0asLOYUVZFYSCWOjrx2Ee1vgU8EG9ukf19KuhLZgjHILAR2PNxxx7foHe94J33gA39NX/zil+jYsWM0FAYhsQcOHKgeF4Z/3XXXlz9/8Aevpre97c30pCddvtDxAQCGJZ5jjcZ6jw2ILDsv1UYxP7a1yDIVi7sU2c2U30B01TdPlhNgA7OmbDAqGyOzVgPlnNgt/lTnWnS2Nn+2wDrYKgrFdFUbo2dIPG4Bqa4ICEBIWFXiqkkZDslr7uhrqsC6Y5WkNiGNmHvMCWZMQSef0MZIltl3MJWJI2kVIU1cP3bIUdlByOuSRmCnbFn8Fu1Np/Ta176BXvnKV9Hx48fZ7YtmEBK7f/9+2rFjB504cYLW1taqC/PVr36VrrzyKnrnO99Kz33u1YseJgBgxHQZje00rTjj/NgasSK70XB/ItuYu2rGrSje5KYXa44zx2pTjFUyO9n8nzUnlq0k7Lbt3I9Ic2jdsTg9B/dzib9lTic471YjrTHiGoqoRsyNzSKwvmNdgQ1FYq1jgtWOY6OwqQWdqt3CQlVLb/YdkyB1Q0VV5KmFyBoWLbSDEVegZn19na655vn07ne/p3puMpnQZZddRv/yL7fRyZP3l6nFi2YQX9VceeWVdP7555ePL7jggfSRj1xHl1/+xPL3Yq7sNde8gG688RMLHiUAIMRC/1gqUrt845O2sctCtDxPsfiJGC2xUvaYiIR7A1h7ropkyDeH4g2k/XuVqjipj4M7xumznuZYH1cpssXzXOXVsq1JLUpaVWjlburt59193OPsn2JMJhrm9FkKdCHk0rHFtkJQTVVX8+PKrNVPIUrmp+p7izAm7phC0uyfguL6WD9mTO6PeA3MufT4ExwLN37mXN3rUbtW5f6BazyX18b+9vvHft/O135NEli77zYC6z5nC6yB2Td7FJbZ3uifoepjoBHDZZK1YlyLGNui+g0yAPkaOr/1W79bCWwhry960S/TV77yT/SFL3yG9u7dWz6/xf3ycFUjsTbFxSrShz/0ob+hl7zkWnrnO/+8FNnnP/9X6LOf/STmyAKwQkRHLZ0I6DKmFUvHtSn0pKaKqraPyBa4acLl8fY9LVO5uHEchxRx9c2VNWm9ToqxW8W4vJzC5akVYlLOia2lECvvCfxR2qrD4BhdysJVii+DcsJGVm24GyVnfqs64sps086NtWUrS/pwToGdw86D5b7YqklvYhS2h1RiH4uOLuZK7+0yGsuNo8vrN0hhdYHABrn55k/S6173hvLx1q1b6d3v/kt65jN/gobIYD8FCsN/4xtfX0Vki9Ti97znvYseFgBgiW4u2pAzGht9rHJ+mKoPX/TV87vqpsqNyHr28Y3Ne5zvxpq5kQ/u6wqGiTz7vnXW9MPtb/fnm7PJHeMc15A4O0prY6KXDGzUMzPqPrhxMufEnnuswNb68LwGnMCmonmPap8L/TsPjTVUrZrbrkwlXuq/QX1+YZpZvnJHSSGwy8Mf/uFrqsevetUrByuwBcP4xPGI7Kte9Yrqdzs3GwAAAAAAAABAe+655x7627+9rnx88cUX0Ytf/Ms0ZAYtsQVPfOIP0IUXXlCFuIcwkRgAAAAAAAAAloV/+IfPlEWdCp71rGfStm3baMhMZgorPHToEJ199tl033330VlnndXJQIr1YE+dOlVWJzbSarjrrrvp/vvvLx8X24p9AACrQfz3Vov8omvWYij8zmITs9TznSkOE8bSeHrW7oVTvbgzzUUI78NuFw4KnaemrRnRwcPHaDqb0dpkQvv27JT3T33Ldv2lbhft91FdNqWP6pDAsRPFk5PY7cKTvjFNWhxb2y91+0SxW4txuQdoLmHUa5frfai5DrlYnsrMiwFBsBBHjx6je++9t3xceN/u3bvYdWOLVWQKwTVulhutdw6msJMx/+LC3H775rqx3MUDAAAAxkIhst86dHTRwwAAAABUFAJZ/IS8bZEMRmLNOrHFPNh9+/bVtp08ebK6WGeeeWZZwRgAAAAYMgcPHiz/dnF/1wAAAIAhcfr06Sq6WmTIFtWJpb9rhbctmsFI7NGj+JYaAAAAAAAAAIAfTC4FAAAAAAAAADAaILEAAAAAAAAAAEYDJBYAAAAAAAAAwGiAxAIAAAAAAAAAGA2QWAAAAAAAAAAAowESCwAAAAAAAABgNEBiAQAAAAAAAACMBkgsAAAAAAAAAIDRAIkFAAAAAAAAADAaILEAAAAAAAAAAEYDJBYAAAAAAAAAwGiAxAIAAAAAAAAAGA2QWAAAAAAAAAAAowESCwAAAAAAAABgNEBiAQAAAAAAAACMBkgsAAAAAAAAAIDRAIkFAAAAAAAAADAaILEAAAAAAAAAAEYDJBYAAAAAAAAAwGiAxAIAAAAAAAAAGA2QWAAAAAAAAAAAo2GrZqfZbFb+/9ChQ12PBwAAAAAAAADACnJo7pvGP1tJ7OHDh8v/X3zxxTnGBgAAAAAAAAAAiP559tln8xuJaDILaS4RTadTOnDgAO3Zs4cmk0lodwAAAAAAAAAAIIpCTQuB3b9/P62trbWTWAAAAAAAAAAAYAigsBMAAAAAAAAAgNEAiQUAAAAAAAAAMBogsQAAAAAAAAAARgMkFgAAAAAAAADAaIDEAgAAAAAAAAAYDZBYAAAAAAAAAACjARILAAAAAAAAAIDGwv8HUVamWiGoTREAAAAASUVORK5CYII=", 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" ] @@ -314,7 +336,10 @@ ], "source": [ "new_game = deepcopy(game)\n", - "new_game.tracking_data = pd.DataFrame(result.best_frame).transpose().reset_index()\n", + "\n", + "new_game.tracking_data.loc[[selected_frame_idx]] = (\n", + " pd.DataFrame(result.best_frame).transpose().astype(game.tracking_data.dtypes)\n", + ")\n", "\n", "fig, ax = diff_frames(\n", " game,\n",