From ea7fe20096e2f8ca9c56cf69757c41e95c2bc9d2 Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Wed, 28 Jul 2021 18:25:36 -0400 Subject: [PATCH 01/28] Create SingleMetricOptimization class; add implementations for short bursts and variants as well as tilted runs. --- gerrychain/optimization/__init__.py | 3 + gerrychain/optimization/optimization.py | 131 ++++++++++++++++++++++++ 2 files changed, 134 insertions(+) create mode 100644 gerrychain/optimization/__init__.py create mode 100644 gerrychain/optimization/optimization.py diff --git a/gerrychain/optimization/__init__.py b/gerrychain/optimization/__init__.py new file mode 100644 index 00000000..9c72c317 --- /dev/null +++ b/gerrychain/optimization/__init__.py @@ -0,0 +1,3 @@ +from .optimization import SingleMetricOptimizer + +__all__ = ['SingleMetricOptimizer'] \ No newline at end of file diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py new file mode 100644 index 00000000..0e930cf2 --- /dev/null +++ b/gerrychain/optimization/optimization.py @@ -0,0 +1,131 @@ +from ..chain import MarkovChain +from ..accept import always_accept +from ..random import random + +import numpy as np + +class SingleMetricOptimizer: + """ + SingleMetricOptimizer represents the class of algorithms / chains that optimize plans with + respect to a single metric. This class implements some common methods of optimization. + """ + + def __init__(self, proposal, constraints, initial_state, optimization_metric, minmax="max", + tracking_funct=None): + """ + :param proposal: Function proposing the next state from the current state. + :param constraints: A function with signature ``Partition -> bool`` determining whether + the proposed next state is valid (passes all binary constraints). Usually this is a + :class:`~gerrychain.constraints.Validator` class instance. + :param initial_state: Initial :class:`gerrychain.partition.Partition` class. + :param optimization_metric: The score function with which to optimize over. + :param minmax: Whether to minimize or maximize the function? + :param tracking_funct: A function with the signiture ``Partition -> None`` to be run at + every step of the chain. If you'd like to externaly track stats beyond those reflected + in the optimation_metric here is where to implement that. + """ + self.part = initial_state + self.proposal = proposal + self.constraints = constraints + self.score = optimization_metric + self.maximize = minmax == "max" or minmax == "maximize" + self.tracking_funct = tracking_funct if tracking_funct is not None else lambda p: None + + + def short_bursts(self, burst_length, num_bursts, accept=always_accept): + """ + Preforms a short burst run using the instance's score function. Each burst starts at the + best preforming plan of the previsdous burst. If there's a tie, the later observed one is + selected. + + :param (int) burst_length: How many steps to run within each burst? + :param (int) num_bursts: How many bursts to preform? + :param accept: Function accepting or rejecting the proposed state. In the most basic + use case, this always returns ``True``. + + :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 2D + numpy array of observed scores over each of the bursts. + """ + max_part = (self.part, self.score(self.part)) + observed_scores = np.zeros((num_bursts, burst_length)) + + for i in range(num_bursts): + chain = MarkovChain(self.proposal, self.constraints, accept, max_part[0], burst_length) + + for j, part in enumerate(chain): + part_score = self.score(part) + observed_scores[i][j] = part_score + + if self._is_improvement(part_score, max_part[1]): + max_part = (part, part_score) + + self.tracking_fun(part) + + return (max_part[0], observed_scores) + + + def _titled_acceptance_function(self, p): + def tilted_acceptance_function(part): + if part.parent == None: return True + part_score = self.score(part) + prev_score = self.score(part.parent) + if self.maximize and part_score >= prev_score: return True + elif not self.maximize and part_score <= prev_score: return True + else: return random.random() < p + + return tilted_acceptance_function + + + def _is_improvement(self, part_score, max_score): + if self.maximize: + return part_score >= max_score + else: + return part_score <= max_score + + + def tilted_short_bursts(self, burst_length, num_bursts, p): + return self.short_bursts(burst_length, num_bursts, + accept=self._titled_acceptance_function(p)) + + + def variable_lenght_short_bursts(self, num_steps, stuck_buffer, accept=always_accept): + max_part = (self.part, self.score(self.part)) + observed_scores = np.zeros(num_steps) + time_stuck = 0 + burst_length = 2 + i = 0 + + while(i < num_steps): + chain = MarkovChain(self.proposal, self.constraints, accept, max_part[0], burst_length) + for j, part in enumerate(chain): + part_score = self.score(part) + + if self._is_improvement(part_score, max_part[1]): + max_part = (part, part_score) + time_stuck = 0 + else: + time_stuck += 1 + + i += 1 + if i >= num_steps: break + if time_stuck >= stuck_buffer*burst_length: burst_length *= 2 + + return (max_part[0], observed_scores) + + + def tilted_run(self, num_steps, p): + chain = MarkovChain(self.proposal, self.constraints, self._titled_acceptance_function(p), + self.part, num_steps) + max_part = (self.part, self.score(self.part)) + observed_scores = np.zeros(num_steps) + + for i, part in enumerate(chain): + part_score = self.score(part) + observed_scores[i] = part_score + + if self._is_improvement(part_score, max_part[1]): + max_part = (part, part_score) + + self.tracking_fun(part) + + return (max_part[0], observed_scores) \ No newline at end of file From 1b256306b338350598b4fd06cb57cc3c2f3652c3 Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Thu, 29 Jul 2021 17:23:41 -0400 Subject: [PATCH 02/28] Add Gingleator child class of SingleMetricOptimizer for finding increased number of Gingles' districts; add further documentation SingleMetricOptimizer class methods; delint optimization.py --- gerrychain/optimization/__init__.py | 3 +- gerrychain/optimization/gingleator.py | 110 +++++++++++++++++++ gerrychain/optimization/optimization.py | 137 ++++++++++++++++-------- 3 files changed, 204 insertions(+), 46 deletions(-) create mode 100755 gerrychain/optimization/gingleator.py diff --git a/gerrychain/optimization/__init__.py b/gerrychain/optimization/__init__.py index 9c72c317..831dd950 100644 --- a/gerrychain/optimization/__init__.py +++ b/gerrychain/optimization/__init__.py @@ -1,3 +1,4 @@ from .optimization import SingleMetricOptimizer +from .gingleator import Gingleator -__all__ = ['SingleMetricOptimizer'] \ No newline at end of file +__all__ = ['SingleMetricOptimizer', 'Gingleator'] \ No newline at end of file diff --git a/gerrychain/optimization/gingleator.py b/gerrychain/optimization/gingleator.py new file mode 100755 index 00000000..7375b7de --- /dev/null +++ b/gerrychain/optimization/gingleator.py @@ -0,0 +1,110 @@ +from .optimization import SingleMetricOptimizer +from functools import partial + +import numpy as np +import warnings + + +class Gingleator(SingleMetricOptimizer): + """ + """ + + def __init__(self, proposal, constraints, initial_state, tracking_funct=None, + minority_perc_col=None, threshold=0.5, score_function=None, + minority_pop_col=None, total_pop_col="TOTPOP", + min_perc_column_name="_gingleator_auxiallary_helper_updater"): + + if minority_perc_col is None and minority_pop_col is None: + raise ValueError("`minority_perc_col` and `minority_pop_col` cannot both be `None`. \ + Unclear how to compute gingles district.") + elif minority_perc_col is not None and minority_pop_col is not None: + warnings.warn("`minority_perc_col` and `minority_pop_col` are both specified. By \ + default `minority_perc_col` will be used.") + score_function = self.num_opportunity_dists if score_function is None else score_function + + if minority_perc_col is None: + perc_up = {min_perc_column_name: + lambda part: {k: part[minority_pop_col][k] / part[total_pop_col][k] + for k in part.parts.keys()}} + initial_state.updaters.update(perc_up) + + score = partial(score_function, minority_perc_col=minority_perc_col, threshold=threshold) + + super().__init__(proposal, constraints, initial_state, score, minmax="max", + tracking_funct=tracking_funct) + + """ + Score Functions + """ + + @classmethod + def num_opportunity_dists(cls, part, minority_perc_col, threshold): + """ + num_opportunity_dists: given a partition, name of the minority percent updater, and a + threshold, returns the number of opportunity districts. + """ + dist_percs = part[minority_perc_col].values() + return sum(list(map(lambda v: v >= threshold, dist_percs))) + + @classmethod + def reward_partial_dist(cls, part, minority_perc_col, threshold): + """ + reward_partial_dist: given a partition, name of the minority percent updater, and a + threshold, returns the number of opportunity districts + the + percentage of the next highest district. + """ + dist_percs = part[minority_perc_col].values() + num_opport_dists = sum(list(map(lambda v: v >= threshold, dist_percs))) + next_dist = max(i for i in dist_percs if i < threshold) + return num_opport_dists + next_dist + + @classmethod + def reward_next_highest_close(cls, part, minority_perc_col, threshold): + """ + reward_next_highest_close: given a partition, name of the minority percent updater, and a + threshold, returns the number of opportunity districts, if no + additional district is within 10% of reaching the threshold. + If one is, + the distance that district is from the threshold is scaled + between 0 + and 1 and added to the count of opportunity districts. + """ + dist_percs = part[minority_perc_col].values() + num_opport_dists = sum(list(map(lambda v: v >= threshold, dist_percs))) + next_dist = max(i for i in dist_percs if i < threshold) + + if next_dist < threshold - 0.1: + return num_opport_dists + else: + return num_opport_dists + (next_dist - threshold + 0.1) * 10 + + @classmethod + def penalize_maximum_over(cls, part, minority_perc_col, threshold): + """ + penalize_maximum_over: given a partition, name of the minority percent updater, and a + threshold, returns the number of opportunity districts + + (1 - the maximum excess) scaled to between 0 and 1. + """ + dist_percs = part[minority_perc_col].values() + num_opportunity_dists = sum(list(map(lambda v: v >= threshold, dist_percs))) + if num_opportunity_dists == 0: + return 0 + else: + max_dist = max(dist_percs) + return num_opportunity_dists + (1 - max_dist) / (1 - threshold) + + @classmethod + def penalize_avg_over(cls, part, minority_perc_col, threshold): + """ + penalize_maximum_over: given a partition, name of the minority percent updater, and a + threshold, returns the number of opportunity districts + + (1 - the average excess) scaled to between 0 and 1. + """ + dist_percs = part[minority_perc_col].values() + opport_dists = list(filter(lambda v: v >= threshold, dist_percs)) + if opport_dists == []: + return 0 + else: + num_opportunity_dists = len(opport_dists) + avg_opportunity_dist = np.mean(opport_dists) + return num_opportunity_dists + (1 - avg_opportunity_dist) / (1 - threshold) diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 0e930cf2..82307fb9 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -4,23 +4,25 @@ import numpy as np + class SingleMetricOptimizer: """ SingleMetricOptimizer represents the class of algorithms / chains that optimize plans with respect to a single metric. This class implements some common methods of optimization. """ - + def __init__(self, proposal, constraints, initial_state, optimization_metric, minmax="max", tracking_funct=None): """ - :param proposal: Function proposing the next state from the current state. - :param constraints: A function with signature ``Partition -> bool`` determining whether - the proposed next state is valid (passes all binary constraints). Usually this is a + :param `proposal`: Function proposing the next state from the current state. + :param `constraints`: A function with signature ``Partition -> bool`` determining whether + the proposed next state is valid (passes all binary constraints). Usually this is a :class:`~gerrychain.constraints.Validator` class instance. - :param initial_state: Initial :class:`gerrychain.partition.Partition` class. - :param optimization_metric: The score function with which to optimize over. - :param minmax: Whether to minimize or maximize the function? - :param tracking_funct: A function with the signiture ``Partition -> None`` to be run at + :param `initial_state`: Initial :class:`gerrychain.partition.Partition` class. + :param `optimization_metric`: The score function with which to optimize over. This should + have the signiture: ``Partition -> 'a where 'a is Comparable`` + :param `minmax`: Whether to minimize or maximize the function? + :param `tracking_funct`: A function with the signiture ``Partition -> None`` to be run at every step of the chain. If you'd like to externaly track stats beyond those reflected in the optimation_metric here is where to implement that. """ @@ -31,22 +33,27 @@ def __init__(self, proposal, constraints, initial_state, optimization_metric, mi self.maximize = minmax == "max" or minmax == "maximize" self.tracking_funct = tracking_funct if tracking_funct is not None else lambda p: None + def _is_improvement(self, part_score, max_score): + if self.maximize: + return part_score >= max_score + else: + return part_score <= max_score def short_bursts(self, burst_length, num_bursts, accept=always_accept): """ - Preforms a short burst run using the instance's score function. Each burst starts at the + Preforms a short burst run using the instance's score function. Each burst starts at the best preforming plan of the previsdous burst. If there's a tie, the later observed one is selected. - - :param (int) burst_length: How many steps to run within each burst? - :param (int) num_bursts: How many bursts to preform? - :param accept: Function accepting or rejecting the proposed state. In the most basic + + :param `burst_length` (int): How many steps to run within each burst? + :param `num_bursts` (int): How many bursts to preform? + :param `accept`: Function accepting or rejecting the proposed state. In the most basic use case, this always returns ``True``. - + :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 2D numpy array of observed scores over each of the bursts. """ - max_part = (self.part, self.score(self.part)) + max_part = (self.part, self.score(self.part)) observed_scores = np.zeros((num_bursts, burst_length)) for i in range(num_bursts): @@ -55,7 +62,7 @@ def short_bursts(self, burst_length, num_bursts, accept=always_accept): for j, part in enumerate(chain): part_score = self.score(part) observed_scores[i][j] = part_score - + if self._is_improvement(part_score, max_part[1]): max_part = (part, part_score) @@ -63,32 +70,60 @@ def short_bursts(self, burst_length, num_bursts, accept=always_accept): return (max_part[0], observed_scores) - def _titled_acceptance_function(self, p): + """ + Function factory that binds and returns a tilted acceptance function. + + :rtype (``Partition -> Bool``) + """ def tilted_acceptance_function(part): - if part.parent == None: return True + if part.parent is None: + return True + part_score = self.score(part) prev_score = self.score(part.parent) - if self.maximize and part_score >= prev_score: return True - elif not self.maximize and part_score <= prev_score: return True - else: return random.random() < p - - return tilted_acceptance_function + if self.maximize and part_score >= prev_score: + return True + elif not self.maximize and part_score <= prev_score: + return True + else: + return random.random() < p - def _is_improvement(self, part_score, max_score): - if self.maximize: - return part_score >= max_score - else: - return part_score <= max_score - + return tilted_acceptance_function def tilted_short_bursts(self, burst_length, num_bursts, p): + """ + Preforms a short burst run using the instance's score function. Each burst starts at the + best preforming plan of the previsdous burst. If there's a tie, the later observed one is + selected. Within each burst a tilted acceptance function is used where better scoring plans + are always accepted and worse scoring plans are accepted with probability `p`. + + :param `burst_length` (int): How many steps to run within each burst? + :param `num_bursts` (int): How many bursts to preform? + :param `p` (float): The probability with which to accept worse scoring plans. + + :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 2D + numpy array of observed scores over each of the bursts. + """ return self.short_bursts(burst_length, num_bursts, accept=self._titled_acceptance_function(p)) - - + def variable_lenght_short_bursts(self, num_steps, stuck_buffer, accept=always_accept): + """ + Preforms a short burst where the burst length is alowed to increase as it gets harder to + find high scoring plans. The initial burst length is set to 2, and it is doubled each time + there is no improvement over the passed number (`stuck_buffer`) of runs. + + :param `num_steps` (int): Total number of steps to take overall. + :param `stuck_buffer` (int): How many bursts of a given length with no improvement to allow + before imcreasing the burst length. + :param accept: Function accepting or rejecting the proposed state. In the most basic + use case, this always returns ``True``. + + :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 1D + numpy array of observed scores over each of the bursts. + """ max_part = (self.part, self.score(self.part)) observed_scores = np.zeros(num_steps) time_stuck = 0 @@ -97,35 +132,47 @@ def variable_lenght_short_bursts(self, num_steps, stuck_buffer, accept=always_ac while(i < num_steps): chain = MarkovChain(self.proposal, self.constraints, accept, max_part[0], burst_length) - for j, part in enumerate(chain): + for part in chain: part_score = self.score(part) - + if self._is_improvement(part_score, max_part[1]): max_part = (part, part_score) time_stuck = 0 - else: - time_stuck += 1 + else: + time_stuck += 1 i += 1 - if i >= num_steps: break - if time_stuck >= stuck_buffer*burst_length: burst_length *= 2 - - return (max_part[0], observed_scores) + if i >= num_steps: + break + + if time_stuck >= stuck_buffer * burst_length: + burst_length *= 2 + return (max_part[0], observed_scores) def tilted_run(self, num_steps, p): + """ + Preforms a tilted run. A chain where the acceptance function always accepts better plans + and accepts worse plans with some probabilty. + + :param `num_steps` (int): Total number of steps to take overall. + :param `p` (float): The probability with which to accept worse scoring plans. + + :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 1D + numpy array of observed scores over each of the bursts. + """ chain = MarkovChain(self.proposal, self.constraints, self._titled_acceptance_function(p), self.part, num_steps) - max_part = (self.part, self.score(self.part)) + max_part = (self.part, self.score(self.part)) observed_scores = np.zeros(num_steps) - + for i, part in enumerate(chain): part_score = self.score(part) observed_scores[i] = part_score - + if self._is_improvement(part_score, max_part[1]): max_part = (part, part_score) - + self.tracking_fun(part) - - return (max_part[0], observed_scores) \ No newline at end of file + + return (max_part[0], observed_scores) From bdb2b01fbf30602db0e2de938aa64fbf4d10b883 Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Fri, 30 Jul 2021 14:46:46 -0400 Subject: [PATCH 03/28] Add more docs. --- gerrychain/optimization/gingleator.py | 99 +++++++++++++++++++------ gerrychain/optimization/optimization.py | 2 +- 2 files changed, 79 insertions(+), 22 deletions(-) diff --git a/gerrychain/optimization/gingleator.py b/gerrychain/optimization/gingleator.py index 7375b7de..169b1d37 100755 --- a/gerrychain/optimization/gingleator.py +++ b/gerrychain/optimization/gingleator.py @@ -1,19 +1,44 @@ from .optimization import SingleMetricOptimizer -from functools import partial +from functools import partial import numpy as np import warnings class Gingleator(SingleMetricOptimizer): """ + `Gingleator` is a child class of `SingleMetricOptimizer` which can be used to search for plans + with increased numbers of Gingles' districts. """ def __init__(self, proposal, constraints, initial_state, tracking_funct=None, minority_perc_col=None, threshold=0.5, score_function=None, minority_pop_col=None, total_pop_col="TOTPOP", - min_perc_column_name="_gingleator_auxiallary_helper_updater"): - + min_perc_column_name="_gingleator_auxiliary_helper_updater_min_perc_col"): + """ + :param `proposal`: Function proposing the next state from the current state. + :param `constraints`: A function with signature ``Partition -> bool`` determining whether + the proposed next state is valid (passes all binary constraints). Usually this is a + :class:`~gerrychain.constraints.Validator` class instance. + :param `initial_state`: Initial :class:`gerrychain.partition.Partition` class. + :param `tracking_funct`: A function with the signiture ``Partition -> None`` to be run at + every step of the chain. If you'd like to externaly track stats beyond those reflected + in the optimation_metric here is where to implement that. + :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + minority popultion within that district. + :param `threshold`: Beyond which fraction to consider something a "Gingles" + (or opportunity) district. + :param `score_function`: The function to using doing optimization. Should have the + signature ``Partition * str (minority_perc_col) * float (threhold) -> + 'a where 'a is Comparable``. This class implement a few potnetial choices as class + methods. + :param `minority_pop_col`: If minority_perc_col is defined, the minority population column + with which to compute percentage. + :param `total_pop_col`: If minority_perc_col is defined, the total population column with + which to compute percentage. + :param `min_perc_column_name`: If minority_perc_col is defined, the name to give the created + percentage updater. + """ if minority_perc_col is None and minority_pop_col is None: raise ValueError("`minority_perc_col` and `minority_pop_col` cannot both be `None`. \ Unclear how to compute gingles district.") @@ -40,8 +65,15 @@ def __init__(self, proposal, constraints, initial_state, tracking_funct=None, @classmethod def num_opportunity_dists(cls, part, minority_perc_col, threshold): """ - num_opportunity_dists: given a partition, name of the minority percent updater, and a - threshold, returns the number of opportunity districts. + Given a partition, returns the number of opportunity districts. + + :param `part`: Partition to score. + :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + minority popultion within that district. + :param `threshold`: Beyond which fraction to consider something a "Gingles" + (or opportunity) district. + + :rtype int """ dist_percs = part[minority_perc_col].values() return sum(list(map(lambda v: v >= threshold, dist_percs))) @@ -49,9 +81,16 @@ def num_opportunity_dists(cls, part, minority_perc_col, threshold): @classmethod def reward_partial_dist(cls, part, minority_perc_col, threshold): """ - reward_partial_dist: given a partition, name of the minority percent updater, and a - threshold, returns the number of opportunity districts + the - percentage of the next highest district. + Given a partition, returns the number of opportunity districts + the percentage of the next + highest district. + + :param `part`: Partition to score. + :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + minority popultion within that district. + :param `threshold`: Beyond which fraction to consider something a "Gingles" + (or opportunity) district. + + :rtype float """ dist_percs = part[minority_perc_col].values() num_opport_dists = sum(list(map(lambda v: v >= threshold, dist_percs))) @@ -61,13 +100,17 @@ def reward_partial_dist(cls, part, minority_perc_col, threshold): @classmethod def reward_next_highest_close(cls, part, minority_perc_col, threshold): """ - reward_next_highest_close: given a partition, name of the minority percent updater, and a - threshold, returns the number of opportunity districts, if no - additional district is within 10% of reaching the threshold. - If one is, - the distance that district is from the threshold is scaled - between 0 - and 1 and added to the count of opportunity districts. + Given a partition, returns the number of opportunity districts, if no additional district + is within 10% of reaching the threshold. If one is, the distance that district is from the + threshold is scaled between 0 and 1 and added to the count of opportunity districts. + + :param `part`: Partition to score. + :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + minority popultion within that district. + :param `threshold`: Beyond which fraction to consider something a "Gingles" + (or opportunity) district. + + :rtype float """ dist_percs = part[minority_perc_col].values() num_opport_dists = sum(list(map(lambda v: v >= threshold, dist_percs))) @@ -81,9 +124,16 @@ def reward_next_highest_close(cls, part, minority_perc_col, threshold): @classmethod def penalize_maximum_over(cls, part, minority_perc_col, threshold): """ - penalize_maximum_over: given a partition, name of the minority percent updater, and a - threshold, returns the number of opportunity districts + - (1 - the maximum excess) scaled to between 0 and 1. + Given a partition, returns the number of opportunity districts + (1 - the maximum excess) + scaled to between 0 and 1. + + :param `part`: Partition to score. + :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + minority popultion within that district. + :param `threshold`: Beyond which fraction to consider something a "Gingles" + (or opportunity) district. + + :rtype float """ dist_percs = part[minority_perc_col].values() num_opportunity_dists = sum(list(map(lambda v: v >= threshold, dist_percs))) @@ -96,9 +146,16 @@ def penalize_maximum_over(cls, part, minority_perc_col, threshold): @classmethod def penalize_avg_over(cls, part, minority_perc_col, threshold): """ - penalize_maximum_over: given a partition, name of the minority percent updater, and a - threshold, returns the number of opportunity districts + - (1 - the average excess) scaled to between 0 and 1. + Given a partition, returns the number of opportunity districts + (1 - the average excess) + scaled to between 0 and 1. + + :param `part`: Partition to score. + :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + minority popultion within that district. + :param `threshold`: Beyond which fraction to consider something a "Gingles" + (or opportunity) district. + + :rtype float """ dist_percs = part[minority_perc_col].values() opport_dists = list(filter(lambda v: v >= threshold, dist_percs)) diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 82307fb9..3840f885 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -21,7 +21,7 @@ def __init__(self, proposal, constraints, initial_state, optimization_metric, mi :param `initial_state`: Initial :class:`gerrychain.partition.Partition` class. :param `optimization_metric`: The score function with which to optimize over. This should have the signiture: ``Partition -> 'a where 'a is Comparable`` - :param `minmax`: Whether to minimize or maximize the function? + :param `minmax` (str): Whether to minimize or maximize the function? :param `tracking_funct`: A function with the signiture ``Partition -> None`` to be run at every step of the chain. If you'd like to externaly track stats beyond those reflected in the optimation_metric here is where to implement that. From 282559ae733e8c2dacab5b67786dc903d4c67ecf Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Wed, 23 Feb 2022 17:28:20 -0500 Subject: [PATCH 04/28] Refactor SingleMetricOptimizer class to have generator function over partitions and to store rolling best partition/score as instance variables. --- gerrychain/optimization/optimization.py | 88 ++++++++++++------------- 1 file changed, 42 insertions(+), 46 deletions(-) diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 3840f885..5a5e19e6 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -1,8 +1,9 @@ from ..chain import MarkovChain +from .. partition import Partition from ..accept import always_accept from ..random import random -import numpy as np +from typing import Union, Callable, List, Any class SingleMetricOptimizer: @@ -11,8 +12,8 @@ class SingleMetricOptimizer: respect to a single metric. This class implements some common methods of optimization. """ - def __init__(self, proposal, constraints, initial_state, optimization_metric, minmax="max", - tracking_funct=None): + def __init__(self, proposal: Callable[[Partition], Partition], constraints: Union[Callable[[Partition], bool], List[Callable[[Partition], bool]]], + initial_state: Partition, optimization_metric: Callable[[Partition], Any], maximize: bool = True): """ :param `proposal`: Function proposing the next state from the current state. :param `constraints`: A function with signature ``Partition -> bool`` determining whether @@ -20,29 +21,28 @@ def __init__(self, proposal, constraints, initial_state, optimization_metric, mi :class:`~gerrychain.constraints.Validator` class instance. :param `initial_state`: Initial :class:`gerrychain.partition.Partition` class. :param `optimization_metric`: The score function with which to optimize over. This should - have the signiture: ``Partition -> 'a where 'a is Comparable`` - :param `minmax` (str): Whether to minimize or maximize the function? - :param `tracking_funct`: A function with the signiture ``Partition -> None`` to be run at - every step of the chain. If you'd like to externaly track stats beyond those reflected - in the optimation_metric here is where to implement that. + have the signature: ``Partition -> 'a where 'a is Comparable`` + :param `minmax` (bool): Whether to minimize or maximize the function? """ - self.part = initial_state + self.initial_part = initial_state self.proposal = proposal self.constraints = constraints self.score = optimization_metric - self.maximize = minmax == "max" or minmax == "maximize" - self.tracking_funct = tracking_funct if tracking_funct is not None else lambda p: None + self.maximize = maximize + self.best_part = None + self.best_score = None - def _is_improvement(self, part_score, max_score): + def _is_improvement(self, part_score, max_score) -> bool: if self.maximize: return part_score >= max_score else: return part_score <= max_score - def short_bursts(self, burst_length, num_bursts, accept=always_accept): + def short_bursts(self, burst_length: int, num_bursts: int, + accept: Callable[[Partition], bool] = always_accept): """ Preforms a short burst run using the instance's score function. Each burst starts at the - best preforming plan of the previsdous burst. If there's a tie, the later observed one is + best preforming plan of the previous burst. If there's a tie, the later observed one is selected. :param `burst_length` (int): How many steps to run within each burst? @@ -53,24 +53,22 @@ def short_bursts(self, burst_length, num_bursts, accept=always_accept): :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 2D numpy array of observed scores over each of the bursts. """ - max_part = (self.part, self.score(self.part)) - observed_scores = np.zeros((num_bursts, burst_length)) + self.best_part = self.initial_part + self.best_score = self.score(self.best_part) for i in range(num_bursts): - chain = MarkovChain(self.proposal, self.constraints, accept, max_part[0], burst_length) + chain = MarkovChain(self.proposal, self.constraints, accept, self.best_part, burst_length) - for j, part in enumerate(chain): + for part in chain: + yield part part_score = self.score(part) - observed_scores[i][j] = part_score - - if self._is_improvement(part_score, max_part[1]): - max_part = (part, part_score) - self.tracking_fun(part) + if self._is_improvement(part_score, self.best_score): + self.best_part = part + self.best_score = part_score - return (max_part[0], observed_scores) - def _titled_acceptance_function(self, p): + def _titled_acceptance_function(self, p: float) -> Callable[[Partition], bool]: """ Function factory that binds and returns a tilted acceptance function. @@ -92,10 +90,10 @@ def tilted_acceptance_function(part): return tilted_acceptance_function - def tilted_short_bursts(self, burst_length, num_bursts, p): + def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float): """ Preforms a short burst run using the instance's score function. Each burst starts at the - best preforming plan of the previsdous burst. If there's a tie, the later observed one is + best preforming plan of the previous burst. If there's a tie, the later observed one is selected. Within each burst a tilted acceptance function is used where better scoring plans are always accepted and worse scoring plans are accepted with probability `p`. @@ -109,7 +107,8 @@ def tilted_short_bursts(self, burst_length, num_bursts, p): return self.short_bursts(burst_length, num_bursts, accept=self._titled_acceptance_function(p)) - def variable_lenght_short_bursts(self, num_steps, stuck_buffer, accept=always_accept): + def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, + accept: Callable[[Partition], bool] = always_accept): """ Preforms a short burst where the burst length is alowed to increase as it gets harder to find high scoring plans. The initial burst length is set to 2, and it is doubled each time @@ -124,19 +123,20 @@ def variable_lenght_short_bursts(self, num_steps, stuck_buffer, accept=always_ac :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 1D numpy array of observed scores over each of the bursts. """ - max_part = (self.part, self.score(self.part)) - observed_scores = np.zeros(num_steps) + self.best_part = self.initial_part + self.best_score = self.score(self.best_part) time_stuck = 0 burst_length = 2 i = 0 while(i < num_steps): - chain = MarkovChain(self.proposal, self.constraints, accept, max_part[0], burst_length) + chain = MarkovChain(self.proposal, self.constraints, accept, self.best_part, burst_length) for part in chain: + yield part part_score = self.score(part) - - if self._is_improvement(part_score, max_part[1]): - max_part = (part, part_score) + if self._is_improvement(part_score, self.best_score): + self.best_part = part + self.best_score = part_score time_stuck = 0 else: time_stuck += 1 @@ -148,9 +148,7 @@ def variable_lenght_short_bursts(self, num_steps, stuck_buffer, accept=always_ac if time_stuck >= stuck_buffer * burst_length: burst_length *= 2 - return (max_part[0], observed_scores) - - def tilted_run(self, num_steps, p): + def tilted_run(self, num_steps: int, p: float): """ Preforms a tilted run. A chain where the acceptance function always accepts better plans and accepts worse plans with some probabilty. @@ -162,17 +160,15 @@ def tilted_run(self, num_steps, p): numpy array of observed scores over each of the bursts. """ chain = MarkovChain(self.proposal, self.constraints, self._titled_acceptance_function(p), - self.part, num_steps) - max_part = (self.part, self.score(self.part)) - observed_scores = np.zeros(num_steps) + self.initial_part, num_steps) + self.best_part = self.initial_part + self.best_score = self.score(self.best_part) for i, part in enumerate(chain): + yield part part_score = self.score(part) - observed_scores[i] = part_score - - if self._is_improvement(part_score, max_part[1]): - max_part = (part, part_score) - self.tracking_fun(part) + if self._is_improvement(part_score, self.best_score): + self.best_part = part + self.best_score = part_score - return (max_part[0], observed_scores) From 49c37111269b38e99581fa6777ccaa62b5f382e1 Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Thu, 24 Feb 2022 11:36:14 -0500 Subject: [PATCH 05/28] Add progressbar options to SingleMetricOptimizer class. --- gerrychain/optimization/gingleator.py | 8 ++---- gerrychain/optimization/optimization.py | 34 +++++++++++++++++-------- 2 files changed, 26 insertions(+), 16 deletions(-) diff --git a/gerrychain/optimization/gingleator.py b/gerrychain/optimization/gingleator.py index 169b1d37..c2532b92 100755 --- a/gerrychain/optimization/gingleator.py +++ b/gerrychain/optimization/gingleator.py @@ -11,7 +11,7 @@ class Gingleator(SingleMetricOptimizer): with increased numbers of Gingles' districts. """ - def __init__(self, proposal, constraints, initial_state, tracking_funct=None, + def __init__(self, proposal, constraints, initial_state, minority_perc_col=None, threshold=0.5, score_function=None, minority_pop_col=None, total_pop_col="TOTPOP", min_perc_column_name="_gingleator_auxiliary_helper_updater_min_perc_col"): @@ -21,9 +21,6 @@ def __init__(self, proposal, constraints, initial_state, tracking_funct=None, the proposed next state is valid (passes all binary constraints). Usually this is a :class:`~gerrychain.constraints.Validator` class instance. :param `initial_state`: Initial :class:`gerrychain.partition.Partition` class. - :param `tracking_funct`: A function with the signiture ``Partition -> None`` to be run at - every step of the chain. If you'd like to externaly track stats beyond those reflected - in the optimation_metric here is where to implement that. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of minority popultion within that district. :param `threshold`: Beyond which fraction to consider something a "Gingles" @@ -55,8 +52,7 @@ def __init__(self, proposal, constraints, initial_state, tracking_funct=None, score = partial(score_function, minority_perc_col=minority_perc_col, threshold=threshold) - super().__init__(proposal, constraints, initial_state, score, minmax="max", - tracking_funct=tracking_funct) + super().__init__(proposal, constraints, initial_state, score, maximize=True) """ Score Functions diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 5a5e19e6..2291e73e 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -4,7 +4,7 @@ from ..random import random from typing import Union, Callable, List, Any - +from tqdm import tqdm class SingleMetricOptimizer: """ @@ -22,7 +22,7 @@ def __init__(self, proposal: Callable[[Partition], Partition], constraints: Unio :param `initial_state`: Initial :class:`gerrychain.partition.Partition` class. :param `optimization_metric`: The score function with which to optimize over. This should have the signature: ``Partition -> 'a where 'a is Comparable`` - :param `minmax` (bool): Whether to minimize or maximize the function? + :param `maximize` (bool): Whether to minimize or maximize the function? """ self.initial_part = initial_state self.proposal = proposal @@ -38,8 +38,8 @@ def _is_improvement(self, part_score, max_score) -> bool: else: return part_score <= max_score - def short_bursts(self, burst_length: int, num_bursts: int, - accept: Callable[[Partition], bool] = always_accept): + def short_bursts(self, burst_length: int, num_bursts: int, + accept: Callable[[Partition], bool] = always_accept, with_progress_bar: bool = False): """ Preforms a short burst run using the instance's score function. Each burst starts at the best preforming plan of the previous burst. If there's a tie, the later observed one is @@ -53,6 +53,11 @@ def short_bursts(self, burst_length: int, num_bursts: int, :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 2D numpy array of observed scores over each of the bursts. """ + if with_progress_bar: + tqdm(self.short_bursts(burst_length, num_bursts, accept, with_progress_bar=False), + total=burst_length*num_bursts) + return + self.best_part = self.initial_part self.best_score = self.score(self.best_part) @@ -90,7 +95,7 @@ def tilted_acceptance_function(part): return tilted_acceptance_function - def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float): + def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, with_progress_bar: bool = False): """ Preforms a short burst run using the instance's score function. Each burst starts at the best preforming plan of the previous burst. If there's a tie, the later observed one is @@ -104,11 +109,12 @@ def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float): :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 2D numpy array of observed scores over each of the bursts. """ - return self.short_bursts(burst_length, num_bursts, - accept=self._titled_acceptance_function(p)) + return self.short_bursts(burst_length, num_bursts, accept=self._titled_acceptance_function(p), + with_progress_bar=with_progress_bar) def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, - accept: Callable[[Partition], bool] = always_accept): + accept: Callable[[Partition], bool] = always_accept, + with_progress_bar: bool = False): """ Preforms a short burst where the burst length is alowed to increase as it gets harder to find high scoring plans. The initial burst length is set to 2, and it is doubled each time @@ -123,6 +129,11 @@ def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 1D numpy array of observed scores over each of the bursts. """ + if with_progress_bar: + tqdm(self.variable_lenght_short_bursts(num_steps, stuck_buffer, accept, with_progress_bar=False), + total=num_steps) + return + self.best_part = self.initial_part self.best_score = self.score(self.best_part) time_stuck = 0 @@ -148,7 +159,7 @@ def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, if time_stuck >= stuck_buffer * burst_length: burst_length *= 2 - def tilted_run(self, num_steps: int, p: float): + def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): """ Preforms a tilted run. A chain where the acceptance function always accepts better plans and accepts worse plans with some probabilty. @@ -161,10 +172,13 @@ def tilted_run(self, num_steps: int, p: float): """ chain = MarkovChain(self.proposal, self.constraints, self._titled_acceptance_function(p), self.initial_part, num_steps) + self.best_part = self.initial_part self.best_score = self.score(self.best_part) - for i, part in enumerate(chain): + chain_enumerator = tqdm(enumerate(chain)) if with_progress_bar else enumerate(chain) + + for i, part in chain_enumerator: yield part part_score = self.score(part) From 0043af44d3b0f8d068f74c2ba5cfab45d759ae4c Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Thu, 24 Feb 2022 16:08:18 -0500 Subject: [PATCH 06/28] add example notebook; patch progress bar --- docs/notebooks/Optimization.ipynb | 183 ++++++++++++++++++++++++ gerrychain/optimization/gingleator.py | 1 + gerrychain/optimization/optimization.py | 14 +- 3 files changed, 192 insertions(+), 6 deletions(-) create mode 100644 docs/notebooks/Optimization.ipynb diff --git a/docs/notebooks/Optimization.ipynb b/docs/notebooks/Optimization.ipynb new file mode 100644 index 00000000..5a2459d3 --- /dev/null +++ b/docs/notebooks/Optimization.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from gerrychain import (GeographicPartition, Partition, Graph, MarkovChain,\n", + " proposals, updaters, constraints, accept, Election)\n", + "# from gerrychain.proposals import ReCom\n", + "from gerrychain.optimization import SingleMetricOptimizer, Gingleator\n", + "from gerrychain.tree import recursive_seed_part\n", + "# from gerrychain.updaters import Tally\n", + "from functools import partial\n", + "import pandas as pd\n", + "import json\n", + "import requests\n", + "from networkx.readwrite import json_graph\n", + "import matplotlib.pyplot as plt\n", + "from tqdm import tqdm\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "response = json.loads(requests.get(\"https://people.csail.mit.edu/ddeford/BG/BG_05.json\").text)\n", + "graph = Graph(json_graph.adjacency_graph(response))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "POPCOL = \"TOTPOP\"\n", + "SEN_DISTS = 35\n", + "EPS = 0.02\n", + "TOTPOP = sum(graph.nodes()[n][POPCOL] for n in graph.nodes())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "sddict = recursive_seed_part(graph, range(SEN_DISTS), TOTPOP/SEN_DISTS, POPCOL, EPS)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "chain_updaters = {\n", + " \"population\": updaters.Tally(POPCOL, alias=\"population\"),\n", + " \"VAP\": updaters.Tally(\"VAP\"),\n", + " \"BVAP\": updaters.Tally(\"BVAP\")\n", + " }\n", + "part = Partition(graph, sddict, chain_updaters)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "proposal = partial(proposals.recom,\n", + " pop_col=POPCOL,\n", + " pop_target=TOTPOP/SEN_DISTS,\n", + " epsilon=EPS,\n", + " node_repeats=1)\n", + "cons = constraints.within_percent_of_ideal_population(part,EPS)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "gingles = Gingleator(proposal, cons, part, \n", + " minority_pop_col=\"BVAP\", total_pop_col=\"VAP\",\n", + " score_function=Gingleator.reward_partial_dist)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.4793650793650794" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gingles.score(part)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1000/1000 [00:51<00:00, 19.60it/s]\n" + ] + } + ], + "source": [ + "max_scores = np.zeros(10*100)\n", + "for i, part in enumerate(gingles.short_bursts(10, 100, with_progress_bar=True)):\n", + " max_scores[i] = gingles.best_score" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(max_scores)\n", + "plt.show()" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "de0b2e8f3822c864e297dc34a9420a5549c338b59d7a373e92a79db0315964f9" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('gerrydev')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gerrychain/optimization/gingleator.py b/gerrychain/optimization/gingleator.py index c2532b92..233ddc04 100755 --- a/gerrychain/optimization/gingleator.py +++ b/gerrychain/optimization/gingleator.py @@ -49,6 +49,7 @@ def __init__(self, proposal, constraints, initial_state, lambda part: {k: part[minority_pop_col][k] / part[total_pop_col][k] for k in part.parts.keys()}} initial_state.updaters.update(perc_up) + minority_perc_col = min_perc_column_name score = partial(score_function, minority_perc_col=minority_perc_col, threshold=threshold) diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 2291e73e..5d7b1403 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -54,8 +54,9 @@ def short_bursts(self, burst_length: int, num_bursts: int, numpy array of observed scores over each of the bursts. """ if with_progress_bar: - tqdm(self.short_bursts(burst_length, num_bursts, accept, with_progress_bar=False), - total=burst_length*num_bursts) + for part in tqdm(self.short_bursts(burst_length, num_bursts, accept, with_progress_bar=False), + total=burst_length*num_bursts): + yield part return self.best_part = self.initial_part @@ -130,8 +131,9 @@ def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, numpy array of observed scores over each of the bursts. """ if with_progress_bar: - tqdm(self.variable_lenght_short_bursts(num_steps, stuck_buffer, accept, with_progress_bar=False), - total=num_steps) + for part in tqdm(self.variable_lenght_short_bursts(num_steps, stuck_buffer, accept, + with_progress_bar=False), total=num_steps): + yield part return self.best_part = self.initial_part @@ -176,9 +178,9 @@ def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): self.best_part = self.initial_part self.best_score = self.score(self.best_part) - chain_enumerator = tqdm(enumerate(chain)) if with_progress_bar else enumerate(chain) + chain_generator = tqdm(chain) if with_progress_bar else chain - for i, part in chain_enumerator: + for part in chain_generator: yield part part_score = self.score(part) From f4142147fa6b0b5c76201469d9fe3bf2874829b0 Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Thu, 24 Feb 2022 17:26:57 -0500 Subject: [PATCH 07/28] Add simulated annealing functionality --- docs/notebooks/Optimization.ipynb | 60 +++++++++++++++--- gerrychain/optimization/optimization.py | 81 +++++++++++++++++++------ 2 files changed, 113 insertions(+), 28 deletions(-) diff --git a/docs/notebooks/Optimization.ipynb b/docs/notebooks/Optimization.ipynb index 5a2459d3..f97d31ea 100644 --- a/docs/notebooks/Optimization.ipynb +++ b/docs/notebooks/Optimization.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -114,31 +114,70 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1000/1000 [00:51<00:00, 19.60it/s]\n" + "100%|██████████| 1000/1000 [00:43<00:00, 23.08it/s]\n" ] } ], "source": [ - "max_scores = np.zeros(10*100)\n", + "max_scores_sb = np.zeros(1000)\n", "for i, part in enumerate(gingles.short_bursts(10, 100, with_progress_bar=True)):\n", - " max_scores[i] = gingles.best_score" + " max_scores_sb[i] = gingles.best_score" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1000/1000 [00:38<00:00, 25.76it/s]\n" + ] + } + ], + "source": [ + "max_scores_anneal = np.zeros(1000)\n", + "for i, part in enumerate(gingles.simulated_annealing(1000, gingles.hot_cold_beta_function_factory(100, 900),\n", + " beta_magnitude=500, with_progress_bar=True)):\n", + " max_scores_anneal[i] = gingles.best_score" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1000/1000 [00:53<00:00, 18.57it/s]\n" + ] + } + ], + "source": [ + "max_scores_tilt = np.zeros(1000)\n", + "for i, part in enumerate(gingles.tilted_run(1000, 0.125, with_progress_bar=True)):\n", + " max_scores_tilt[i] = gingles.best_score" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -150,7 +189,12 @@ } ], "source": [ - "plt.plot(max_scores)\n", + "plt.plot(max_scores_anneal, label=\"Simulated Annealing\")\n", + "plt.plot(max_scores_sb, label=\"Short Bursts\")\n", + "plt.plot(max_scores_tilt, label=\"Tilted Run\")\n", + "plt.xlabel(\"Steps\")\n", + "plt.ylabel(\"Max Score Observered\")\n", + "plt.legend()\n", "plt.show()" ] } diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 5d7b1403..6718ba47 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -5,6 +5,7 @@ from typing import Union, Callable, List, Any from tqdm import tqdm +import math class SingleMetricOptimizer: """ @@ -13,7 +14,7 @@ class SingleMetricOptimizer: """ def __init__(self, proposal: Callable[[Partition], Partition], constraints: Union[Callable[[Partition], bool], List[Callable[[Partition], bool]]], - initial_state: Partition, optimization_metric: Callable[[Partition], Any], maximize: bool = True): + initial_state: Partition, optimization_metric: Callable[[Partition], Any], maximize: bool = True, step_indexer="step"): """ :param `proposal`: Function proposing the next state from the current state. :param `constraints`: A function with signature ``Partition -> bool`` determining whether @@ -31,6 +32,10 @@ def __init__(self, proposal: Callable[[Partition], Partition], constraints: Unio self.maximize = maximize self.best_part = None self.best_score = None + self.step_indexer = step_indexer + + if self.step_indexer not in self.initial_part.updaters: + self.initial_part.updaters[self.step_indexer] = lambda p: 0 if p.parent is None else p.parent[self.step_indexer] + 1 def _is_improvement(self, part_score, max_score) -> bool: if self.maximize: @@ -38,6 +43,39 @@ def _is_improvement(self, part_score, max_score) -> bool: else: return part_score <= max_score + def _titled_acceptance_function(self, p: float) -> Callable[[Partition], bool]: + """ + Function factory that binds and returns a tilted acceptance function. + + :rtype (``Partition -> Bool``) + """ + def tilted_acceptance_function(part): + if part.parent is None: + return True + + part_score = self.score(part) + prev_score = self.score(part.parent) + + if self._is_improvement(part_score, prev_score): + return True + else: + return random.random() < p + + return tilted_acceptance_function + + def _simulated_anealing_acceptance_function(self, beta_function: Callable[[int], float], beta_magnitude: float): + + def simulated_anealing_acceptance_function(part): + if part.parent is None: + return True + score_delta = self.score(part) - self.score(part.parent) + beta = beta_function(part[self.step_indexer]) + if self.maximize: + score_delta *= -1 + return random.random() < math.exp(beta * beta_magnitude * score_delta) + + return simulated_anealing_acceptance_function + def short_bursts(self, burst_length: int, num_bursts: int, accept: Callable[[Partition], bool] = always_accept, with_progress_bar: bool = False): """ @@ -73,28 +111,31 @@ def short_bursts(self, burst_length: int, num_bursts: int, self.best_part = part self.best_score = part_score + @classmethod + def hot_cold_beta_function_factory(cls, duration_hot: int, duration_cold: int) -> float: + cycle_length = duration_hot + duration_cold + def hot_cold_beta_function(step: int): + time_in_cycle = step % cycle_length + return float(time_in_cycle >= duration_hot) + return hot_cold_beta_function + + + def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], float], beta_magnitude: float = 1, + with_progress_bar: bool = False): + chain = MarkovChain(self.proposal, self.constraints, self._simulated_anealing_acceptance_function(beta_function, beta_magnitude), + self.initial_part, num_steps) + + self.best_part = self.initial_part + self.best_score = self.score(self.best_part) - def _titled_acceptance_function(self, p: float) -> Callable[[Partition], bool]: - """ - Function factory that binds and returns a tilted acceptance function. - - :rtype (``Partition -> Bool``) - """ - def tilted_acceptance_function(part): - if part.parent is None: - return True + chain_generator = tqdm(chain) if with_progress_bar else chain + for part in chain_generator: + yield part part_score = self.score(part) - prev_score = self.score(part.parent) - - if self.maximize and part_score >= prev_score: - return True - elif not self.maximize and part_score <= prev_score: - return True - else: - return random.random() < p - - return tilted_acceptance_function + if self._is_improvement(part_score, self.best_score): + self.best_part = part + self.best_score = part_score def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, with_progress_bar: bool = False): """ From 568b157a11080bfdd34427f887993890f4034a82 Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Mon, 28 Feb 2022 16:52:36 -0500 Subject: [PATCH 08/28] Update docs to bring inline with partition generator refactoring effort. --- docs/notebooks/Optimization.ipynb | 85 ++++++---- gerrychain/optimization/gingleator.py | 8 +- gerrychain/optimization/optimization.py | 204 +++++++++++++++++------- 3 files changed, 210 insertions(+), 87 deletions(-) diff --git a/docs/notebooks/Optimization.ipynb b/docs/notebooks/Optimization.ipynb index f97d31ea..960056e9 100644 --- a/docs/notebooks/Optimization.ipynb +++ b/docs/notebooks/Optimization.ipynb @@ -96,88 +96,83 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.4793650793650794" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "gingles.score(part)" + "total_steps = 10000" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1000/1000 [00:43<00:00, 23.08it/s]\n" + "100%|██████████| 10000/10000 [05:13<00:00, 31.89it/s]\n" ] } ], "source": [ - "max_scores_sb = np.zeros(1000)\n", - "for i, part in enumerate(gingles.short_bursts(10, 100, with_progress_bar=True)):\n", - " max_scores_sb[i] = gingles.best_score" + "max_scores_sb = np.zeros(total_steps)\n", + "scores_sb = np.zeros(total_steps)\n", + "for i, part in enumerate(gingles.short_bursts(10, 1000, with_progress_bar=True)):\n", + " max_scores_sb[i] = gingles.best_score\n", + " scores_sb[i] = gingles.score(part)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1000/1000 [00:38<00:00, 25.76it/s]\n" + "100%|██████████| 10000/10000 [06:06<00:00, 27.31it/s]\n" ] } ], "source": [ - "max_scores_anneal = np.zeros(1000)\n", - "for i, part in enumerate(gingles.simulated_annealing(1000, gingles.hot_cold_beta_function_factory(100, 900),\n", + "max_scores_anneal = np.zeros(total_steps)\n", + "scores_anneal = np.zeros(total_steps)\n", + "for i, part in enumerate(gingles.simulated_annealing(total_steps, gingles.hot_cold_cycle_beta_function_factory(1000, 4000),\n", " beta_magnitude=500, with_progress_bar=True)):\n", - " max_scores_anneal[i] = gingles.best_score" + " max_scores_anneal[i] = gingles.best_score\n", + " scores_anneal[i] = gingles.score(part)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1000/1000 [00:53<00:00, 18.57it/s]\n" + "100%|██████████| 10000/10000 [05:55<00:00, 28.14it/s]\n" ] } ], "source": [ - "max_scores_tilt = np.zeros(1000)\n", - "for i, part in enumerate(gingles.tilted_run(1000, 0.125, with_progress_bar=True)):\n", - " max_scores_tilt[i] = gingles.best_score" + "max_scores_tilt = np.zeros(total_steps)\n", + "scores_tilt = np.zeros(total_steps)\n", + "for i, part in enumerate(gingles.tilted_run(total_steps, 0.125, with_progress_bar=True)):\n", + " max_scores_tilt[i] = gingles.best_score\n", + " scores_tilt[i] = gingles.score(part)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -189,14 +184,42 @@ } ], "source": [ - "plt.plot(max_scores_anneal, label=\"Simulated Annealing\")\n", "plt.plot(max_scores_sb, label=\"Short Bursts\")\n", + "plt.plot(max_scores_anneal, label=\"Simulated Annealing\")\n", "plt.plot(max_scores_tilt, label=\"Tilted Run\")\n", "plt.xlabel(\"Steps\")\n", "plt.ylabel(\"Max Score Observered\")\n", "plt.legend()\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(scores_sb, label=\"Short Bursts\")\n", + "plt.plot(scores_anneal, label=\"Simulated Annealing\")\n", + "plt.plot(scores_tilt, label=\"Tilted Run\")\n", + "plt.xlabel(\"Steps\")\n", + "plt.ylabel(\"Score Observered\")\n", + "plt.legend()\n", + "plt.show()" + ] } ], "metadata": { diff --git a/gerrychain/optimization/gingleator.py b/gerrychain/optimization/gingleator.py index 233ddc04..df917072 100755 --- a/gerrychain/optimization/gingleator.py +++ b/gerrychain/optimization/gingleator.py @@ -9,6 +9,10 @@ class Gingleator(SingleMetricOptimizer): """ `Gingleator` is a child class of `SingleMetricOptimizer` which can be used to search for plans with increased numbers of Gingles' districts. + + A gingles district (named for the Supreme Court case Thornburg v. Gingles) is a district that is + majority-minority. aka 50% + 1 of some population subgroup. Demonstrating additional Gingles + districts is one of the litmus test used in bringing forth a VRA case. """ def __init__(self, proposal, constraints, initial_state, @@ -26,8 +30,8 @@ def __init__(self, proposal, constraints, initial_state, :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. :param `score_function`: The function to using doing optimization. Should have the - signature ``Partition * str (minority_perc_col) * float (threhold) -> - 'a where 'a is Comparable``. This class implement a few potnetial choices as class + signature ``Partition * str (minority_perc_col) * float (threshold) -> + 'a where 'a is Comparable``. This class implement a few potential choices as class methods. :param `minority_pop_col`: If minority_perc_col is defined, the minority population column with which to compute percentage. diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 6718ba47..06663076 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -10,20 +10,42 @@ class SingleMetricOptimizer: """ SingleMetricOptimizer represents the class of algorithms / chains that optimize plans with - respect to a single metric. This class implements some common methods of optimization. + respect to a single metric. This class + + The SingleMetricOptimizer class implements the following common methods of optimization: + * Short Bursts + * Simulated Annealing + * Titled Runs + + Both during and after a optimization run, the instance variables `best_part` and `best_score` + represent the optimal partition / corresponding score value observed. Note that these are reset + everytime a optimization run is invoked and do not persist. If necessary the values should be + "snapshot-ed" externally to perserve them for comparision. """ - def __init__(self, proposal: Callable[[Partition], Partition], constraints: Union[Callable[[Partition], bool], List[Callable[[Partition], bool]]], - initial_state: Partition, optimization_metric: Callable[[Partition], Any], maximize: bool = True, step_indexer="step"): + def __init__(self, proposal: Callable[[Partition], Partition], + constraints: Union[Callable[[Partition], bool], List[Callable[[Partition], bool]]], + initial_state: Partition, optimization_metric: Callable[[Partition], Any], + maximize: bool = True, step_indexer: str = "step"): """ - :param `proposal`: Function proposing the next state from the current state. - :param `constraints`: A function with signature ``Partition -> bool`` determining whether - the proposed next state is valid (passes all binary constraints). Usually this is a - :class:`~gerrychain.constraints.Validator` class instance. - :param `initial_state`: Initial :class:`gerrychain.partition.Partition` class. - :param `optimization_metric`: The score function with which to optimize over. This should - have the signature: ``Partition -> 'a where 'a is Comparable`` - :param `maximize` (bool): Whether to minimize or maximize the function? + Args: + proposal (Callable[[Partition], Partition]): Function proposing the next state from the + current state. + constraints (Union[Callable[[Partition], bool], List[Callable[[Partition], bool]]]): + A function, or lists of functions, determining whether the proposed next state is + valid (aka passes all binary constraints). Usually this is a + `~gerrychain.constraints.Validator` class instance. + initial_state (Partition): Initial state of the optimizer. + optimization_metric (Callable[[Partition], Any]): The score function with which to + optimize over. This should have the signature: `Partition -> 'a where 'a is Comparable` + maximize (bool, optional): Whether to minimize or maximize the function? Defaults to + maximize. + step_indexer (str, optional): Name of the updater tracking the partitions step in the + chain. If not implemented on the partition the constructor creates and adds it. + Defaults to `"step"`. + + Returns: + A SingleMetricOptimizer object """ self.initial_part = initial_state self.proposal = proposal @@ -37,17 +59,35 @@ def __init__(self, proposal: Callable[[Partition], Partition], constraints: Unio if self.step_indexer not in self.initial_part.updaters: self.initial_part.updaters[self.step_indexer] = lambda p: 0 if p.parent is None else p.parent[self.step_indexer] + 1 - def _is_improvement(self, part_score, max_score) -> bool: + + def _is_improvement(self, new_score, old_score) -> bool: + """ + Helper function definiting improvement comparision between scores. Scores can be any + comparable type. + + Args: + new_score (Any): Score of proprosed partition. + old_score (Any): Score of previous partition. + + Returns: + Whether the new score is an improvement over the old score. + + """ if self.maximize: - return part_score >= max_score + return new_score >= old_score else: - return part_score <= max_score + return new_score <= old_score + def _titled_acceptance_function(self, p: float) -> Callable[[Partition], bool]: """ Function factory that binds and returns a tilted acceptance function. - :rtype (``Partition -> Bool``) + Args: + p (float): The probability of accepting a worse score. + + Returns: + A acceptance function for tilted chains. """ def tilted_acceptance_function(part): if part.parent is None: @@ -63,18 +103,51 @@ def tilted_acceptance_function(part): return tilted_acceptance_function - def _simulated_anealing_acceptance_function(self, beta_function: Callable[[int], float], beta_magnitude: float): - - def simulated_anealing_acceptance_function(part): + def _simulated_annealing_acceptance_function(self, beta_function: Callable[[int], float], + beta_magnitude: float): + """ + Function factory that binds and returns a simulated annealing acceptance function. + + Args: + beta_functions (Callable[[int], float]): Function (f: t -> beta, where beta is in [0,1]) + defining temperature over time. f(t) = 0 the chain is hot and every proposal is + accepted. At f(t) = 1 the chain is cold and worse proposal have a low probability + of being excepted relative to the magnitude of change in score. + beta_magnitude (float): Scaling parameter for how much to weight changes in score. + + Returns: + A acceptance function for simulated annealing runs. + """ + def simulated_annealing_acceptance_function(part): if part.parent is None: return True score_delta = self.score(part) - self.score(part.parent) beta = beta_function(part[self.step_indexer]) if self.maximize: score_delta *= -1 - return random.random() < math.exp(beta * beta_magnitude * score_delta) + return random.random() < math.exp(-beta * beta_magnitude * score_delta) + + return simulated_annealing_acceptance_function + + + @classmethod + def hot_cold_cycle_beta_function_factory(cls, duration_hot: int, duration_cold: int) -> float: + """ + Class method binding and return simple hot-cold cycle beta temperature function. + + Args: + duration_hot: Number of steps to run chain hot. + duration_cold: Number of steps to run chain cold. + + Returns: + Beta function defining hot-cold cycle. + """ + cycle_length = duration_hot + duration_cold + def hot_cold_beta_function(step: int): + time_in_cycle = step % cycle_length + return float(time_in_cycle >= duration_hot) + return hot_cold_beta_function - return simulated_anealing_acceptance_function def short_bursts(self, burst_length: int, num_bursts: int, accept: Callable[[Partition], bool] = always_accept, with_progress_bar: bool = False): @@ -83,13 +156,16 @@ def short_bursts(self, burst_length: int, num_bursts: int, best preforming plan of the previous burst. If there's a tie, the later observed one is selected. - :param `burst_length` (int): How many steps to run within each burst? - :param `num_bursts` (int): How many bursts to preform? - :param `accept`: Function accepting or rejecting the proposed state. In the most basic - use case, this always returns ``True``. + Args + burst_length (int): How many steps to run within each burst? + num_bursts (int): How many bursts to preform? + accept (Callable[[Partition], bool], optional): Function accepting or rejecting the + proposed state. In the most basic use case, this always returns True. + with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults + to False. - :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 2D - numpy array of observed scores over each of the bursts. + Returns: + Partition generator. """ if with_progress_bar: for part in tqdm(self.short_bursts(burst_length, num_bursts, accept, with_progress_bar=False), @@ -111,18 +187,26 @@ def short_bursts(self, burst_length: int, num_bursts: int, self.best_part = part self.best_score = part_score - @classmethod - def hot_cold_beta_function_factory(cls, duration_hot: int, duration_cold: int) -> float: - cycle_length = duration_hot + duration_cold - def hot_cold_beta_function(step: int): - time_in_cycle = step % cycle_length - return float(time_in_cycle >= duration_hot) - return hot_cold_beta_function - - + def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], float], beta_magnitude: float = 1, with_progress_bar: bool = False): - chain = MarkovChain(self.proposal, self.constraints, self._simulated_anealing_acceptance_function(beta_function, beta_magnitude), + """ + Preforms simulated annealing with respect to the class instance's score function. + + Args: + num_steps (int): How many steps to run for. + beta_function (Callable[[int], float]): Function (f: t -> beta, where beta is in [0,1]) + defining temperature over time. f(t) = 0 the chain is hot and every proposal is + accepted. At f(t) = 1 the chain is cold and worse proposal have a low probability + of being excepted relative to the magnitude of change in score. + beta_magnitude (float): Scaling parameter for how much to weight changes in score. + with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults + to False. + + Returns: + Partition generator. + """ + chain = MarkovChain(self.proposal, self.constraints, self._simulated_annealing_acceptance_function(beta_function, beta_magnitude), self.initial_part, num_steps) self.best_part = self.initial_part @@ -137,6 +221,7 @@ def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], flo self.best_part = part self.best_score = part_score + def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, with_progress_bar: bool = False): """ Preforms a short burst run using the instance's score function. Each burst starts at the @@ -144,16 +229,20 @@ def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, with selected. Within each burst a tilted acceptance function is used where better scoring plans are always accepted and worse scoring plans are accepted with probability `p`. - :param `burst_length` (int): How many steps to run within each burst? - :param `num_bursts` (int): How many bursts to preform? - :param `p` (float): The probability with which to accept worse scoring plans. + Args + burst_length (int): How many steps to run within each burst? + num_bursts (int): How many bursts to preform? + p (float): The probability of accepting a plan with a worse score. + with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults + to False. - :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 2D - numpy array of observed scores over each of the bursts. + Returns: + Partition generator. """ return self.short_bursts(burst_length, num_bursts, accept=self._titled_acceptance_function(p), with_progress_bar=with_progress_bar) + def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, accept: Callable[[Partition], bool] = always_accept, with_progress_bar: bool = False): @@ -162,14 +251,17 @@ def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, find high scoring plans. The initial burst length is set to 2, and it is doubled each time there is no improvement over the passed number (`stuck_buffer`) of runs. - :param `num_steps` (int): Total number of steps to take overall. - :param `stuck_buffer` (int): How many bursts of a given length with no improvement to allow - before imcreasing the burst length. - :param accept: Function accepting or rejecting the proposed state. In the most basic - use case, this always returns ``True``. - - :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 1D - numpy array of observed scores over each of the bursts. + Args: + num_steps (int): How many steps to run for. + stuck_buffer (int): How many bursts of a given length with no improvement to allow + before increasing the burst length. + accept (Callable[[Partition], bool], optional): Function accepting or rejecting the + proposed state. In the most basic use case, this always returns True. + with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults + to False. + + Returns: + Partition generator. """ if with_progress_bar: for part in tqdm(self.variable_lenght_short_bursts(num_steps, stuck_buffer, accept, @@ -202,16 +294,20 @@ def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, if time_stuck >= stuck_buffer * burst_length: burst_length *= 2 + def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): """ Preforms a tilted run. A chain where the acceptance function always accepts better plans - and accepts worse plans with some probabilty. + and accepts worse plans with some probability. - :param `num_steps` (int): Total number of steps to take overall. - :param `p` (float): The probability with which to accept worse scoring plans. + Args: + num_steps (int): How many steps to run for. + p (float): The probability of accepting a plan with a worse score. + with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults + to False. - :rtype (Partition * np.array): Tuple of maximal (or minimal) observed partition and a 1D - numpy array of observed scores over each of the bursts. + Returns: + Partition generator. """ chain = MarkovChain(self.proposal, self.constraints, self._titled_acceptance_function(p), self.initial_part, num_steps) From 1cebba5b85fb9f9b9dd6c5657ccc1aac326a65e7 Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Mon, 28 Feb 2022 17:39:34 -0500 Subject: [PATCH 09/28] Delint --- gerrychain/optimization/gingleator.py | 18 +++--- gerrychain/optimization/optimization.py | 80 +++++++++++++------------ 2 files changed, 52 insertions(+), 46 deletions(-) diff --git a/gerrychain/optimization/gingleator.py b/gerrychain/optimization/gingleator.py index df917072..91584501 100755 --- a/gerrychain/optimization/gingleator.py +++ b/gerrychain/optimization/gingleator.py @@ -30,13 +30,13 @@ def __init__(self, proposal, constraints, initial_state, :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. :param `score_function`: The function to using doing optimization. Should have the - signature ``Partition * str (minority_perc_col) * float (threshold) -> + signature ``Partition * str (minority_perc_col) * float (threshold) -> 'a where 'a is Comparable``. This class implement a few potential choices as class methods. :param `minority_pop_col`: If minority_perc_col is defined, the minority population column - with which to compute percentage. + with which to compute percentage. :param `total_pop_col`: If minority_perc_col is defined, the total population column with - which to compute percentage. + which to compute percentage. :param `min_perc_column_name`: If minority_perc_col is defined, the name to give the created percentage updater. """ @@ -71,7 +71,7 @@ def num_opportunity_dists(cls, part, minority_perc_col, threshold): :param `part`: Partition to score. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of minority popultion within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" + :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. :rtype int @@ -88,7 +88,7 @@ def reward_partial_dist(cls, part, minority_perc_col, threshold): :param `part`: Partition to score. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of minority popultion within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" + :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. :rtype float @@ -108,7 +108,7 @@ def reward_next_highest_close(cls, part, minority_perc_col, threshold): :param `part`: Partition to score. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of minority popultion within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" + :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. :rtype float @@ -127,11 +127,11 @@ def penalize_maximum_over(cls, part, minority_perc_col, threshold): """ Given a partition, returns the number of opportunity districts + (1 - the maximum excess) scaled to between 0 and 1. - + :param `part`: Partition to score. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of minority popultion within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" + :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. :rtype float @@ -153,7 +153,7 @@ def penalize_avg_over(cls, part, minority_perc_col, threshold): :param `part`: Partition to score. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of minority popultion within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" + :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. :rtype float diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 06663076..62e3872e 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -7,11 +7,14 @@ from tqdm import tqdm import math + class SingleMetricOptimizer: """ SingleMetricOptimizer represents the class of algorithms / chains that optimize plans with - respect to a single metric. This class - + respect to a single metric. In instance of this class encapsulates the dualgraph and updaters + via the initial partition, the constraints new proposals are subject to, the metric over which + to optimize and whether or not to seek maximal or minimal values of the metric. + The SingleMetricOptimizer class implements the following common methods of optimization: * Short Bursts * Simulated Annealing @@ -33,12 +36,13 @@ def __init__(self, proposal: Callable[[Partition], Partition], current state. constraints (Union[Callable[[Partition], bool], List[Callable[[Partition], bool]]]): A function, or lists of functions, determining whether the proposed next state is - valid (aka passes all binary constraints). Usually this is a + valid (aka passes all binary constraints). Usually this is a `~gerrychain.constraints.Validator` class instance. initial_state (Partition): Initial state of the optimizer. optimization_metric (Callable[[Partition], Any]): The score function with which to - optimize over. This should have the signature: `Partition -> 'a where 'a is Comparable` - maximize (bool, optional): Whether to minimize or maximize the function? Defaults to + optimize over. This should have the signature: + `Partition -> 'a where 'a is Comparable` + maximize (bool, optional): Whether to minimize or maximize the function? Defaults to maximize. step_indexer (str, optional): Name of the updater tracking the partitions step in the chain. If not implemented on the partition the constructor creates and adds it. @@ -57,8 +61,8 @@ def __init__(self, proposal: Callable[[Partition], Partition], self.step_indexer = step_indexer if self.step_indexer not in self.initial_part.updaters: - self.initial_part.updaters[self.step_indexer] = lambda p: 0 if p.parent is None else p.parent[self.step_indexer] + 1 - + step_updater = lambda p: 0 if p.parent is None else p.parent[self.step_indexer] + 1 + self.initial_part.updaters[self.step_indexer] = step_updater def _is_improvement(self, new_score, old_score) -> bool: """ @@ -67,7 +71,7 @@ def _is_improvement(self, new_score, old_score) -> bool: Args: new_score (Any): Score of proprosed partition. - old_score (Any): Score of previous partition. + old_score (Any): Score of previous partition. Returns: Whether the new score is an improvement over the old score. @@ -78,7 +82,6 @@ def _is_improvement(self, new_score, old_score) -> bool: else: return new_score <= old_score - def _titled_acceptance_function(self, p: float) -> Callable[[Partition], bool]: """ Function factory that binds and returns a tilted acceptance function. @@ -109,7 +112,7 @@ def _simulated_annealing_acceptance_function(self, beta_function: Callable[[int] Function factory that binds and returns a simulated annealing acceptance function. Args: - beta_functions (Callable[[int], float]): Function (f: t -> beta, where beta is in [0,1]) + beta_functions (Callable[[int], float]): Function (f: t -> beta, where beta is in [0,1]) defining temperature over time. f(t) = 0 the chain is hot and every proposal is accepted. At f(t) = 1 the chain is cold and worse proposal have a low probability of being excepted relative to the magnitude of change in score. @@ -129,7 +132,6 @@ def simulated_annealing_acceptance_function(part): return simulated_annealing_acceptance_function - @classmethod def hot_cold_cycle_beta_function_factory(cls, duration_hot: int, duration_cold: int) -> float: """ @@ -143,14 +145,16 @@ def hot_cold_cycle_beta_function_factory(cls, duration_hot: int, duration_cold: Beta function defining hot-cold cycle. """ cycle_length = duration_hot + duration_cold + def hot_cold_beta_function(step: int): time_in_cycle = step % cycle_length return float(time_in_cycle >= duration_hot) - return hot_cold_beta_function + return hot_cold_beta_function - def short_bursts(self, burst_length: int, num_bursts: int, - accept: Callable[[Partition], bool] = always_accept, with_progress_bar: bool = False): + def short_bursts(self, burst_length: int, num_bursts: int, + accept: Callable[[Partition], bool] = always_accept, + with_progress_bar: bool = False): """ Preforms a short burst run using the instance's score function. Each burst starts at the best preforming plan of the previous burst. If there's a tie, the later observed one is @@ -168,16 +172,18 @@ def short_bursts(self, burst_length: int, num_bursts: int, Partition generator. """ if with_progress_bar: - for part in tqdm(self.short_bursts(burst_length, num_bursts, accept, with_progress_bar=False), - total=burst_length*num_bursts): - yield part + for part in tqdm(self.short_bursts(burst_length, num_bursts, accept, + with_progress_bar=False), + total=burst_length * num_bursts): + yield part return self.best_part = self.initial_part self.best_score = self.score(self.best_part) for i in range(num_bursts): - chain = MarkovChain(self.proposal, self.constraints, accept, self.best_part, burst_length) + chain = MarkovChain(self.proposal, self.constraints, accept, self.best_part, + burst_length) for part in chain: yield part @@ -187,9 +193,8 @@ def short_bursts(self, burst_length: int, num_bursts: int, self.best_part = part self.best_score = part_score - - def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], float], beta_magnitude: float = 1, - with_progress_bar: bool = False): + def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], float], + beta_magnitude: float = 1, with_progress_bar: bool = False): """ Preforms simulated annealing with respect to the class instance's score function. @@ -206,9 +211,11 @@ def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], flo Returns: Partition generator. """ - chain = MarkovChain(self.proposal, self.constraints, self._simulated_annealing_acceptance_function(beta_function, beta_magnitude), + chain = MarkovChain(self.proposal, self.constraints, + self._simulated_annealing_acceptance_function(beta_function, + beta_magnitude), self.initial_part, num_steps) - + self.best_part = self.initial_part self.best_score = self.score(self.best_part) @@ -218,11 +225,11 @@ def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], flo yield part part_score = self.score(part) if self._is_improvement(part_score, self.best_score): - self.best_part = part - self.best_score = part_score - + self.best_part = part + self.best_score = part_score - def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, with_progress_bar: bool = False): + def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, + with_progress_bar: bool = False): """ Preforms a short burst run using the instance's score function. Each burst starts at the best preforming plan of the previous burst. If there's a tie, the later observed one is @@ -239,11 +246,11 @@ def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, with Returns: Partition generator. """ - return self.short_bursts(burst_length, num_bursts, accept=self._titled_acceptance_function(p), + return self.short_bursts(burst_length, num_bursts, + accept=self._titled_acceptance_function(p), with_progress_bar=with_progress_bar) - - def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, + def variable_lenght_short_bursts(self, num_steps: int, stuck_buffer: int, accept: Callable[[Partition], bool] = always_accept, with_progress_bar: bool = False): """ @@ -268,7 +275,7 @@ def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, with_progress_bar=False), total=num_steps): yield part return - + self.best_part = self.initial_part self.best_score = self.score(self.best_part) time_stuck = 0 @@ -276,7 +283,8 @@ def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, i = 0 while(i < num_steps): - chain = MarkovChain(self.proposal, self.constraints, accept, self.best_part, burst_length) + chain = MarkovChain(self.proposal, self.constraints, accept, self.best_part, + burst_length) for part in chain: yield part part_score = self.score(part) @@ -294,7 +302,6 @@ def variable_lenght_short_bursts(self, num_steps: int , stuck_buffer: int, if time_stuck >= stuck_buffer * burst_length: burst_length *= 2 - def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): """ Preforms a tilted run. A chain where the acceptance function always accepts better plans @@ -311,7 +318,7 @@ def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): """ chain = MarkovChain(self.proposal, self.constraints, self._titled_acceptance_function(p), self.initial_part, num_steps) - + self.best_part = self.initial_part self.best_score = self.score(self.best_part) @@ -322,6 +329,5 @@ def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): part_score = self.score(part) if self._is_improvement(part_score, self.best_score): - self.best_part = part - self.best_score = part_score - + self.best_part = part + self.best_score = part_score From 24260a21f7a0077670ce218271fed3898aab7170 Mon Sep 17 00:00:00 2001 From: Gabe Schoenbach Date: Tue, 1 Mar 2022 11:35:48 -0600 Subject: [PATCH 10/28] minor typo fixes --- gerrychain/optimization/gingleator.py | 16 +++++------ gerrychain/optimization/optimization.py | 35 ++++++++++++------------- 2 files changed, 25 insertions(+), 26 deletions(-) diff --git a/gerrychain/optimization/gingleator.py b/gerrychain/optimization/gingleator.py index 91584501..6b461321 100755 --- a/gerrychain/optimization/gingleator.py +++ b/gerrychain/optimization/gingleator.py @@ -12,7 +12,7 @@ class Gingleator(SingleMetricOptimizer): A gingles district (named for the Supreme Court case Thornburg v. Gingles) is a district that is majority-minority. aka 50% + 1 of some population subgroup. Demonstrating additional Gingles - districts is one of the litmus test used in bringing forth a VRA case. + districts is one of the litmus tests used in bringing forth a VRA case. """ def __init__(self, proposal, constraints, initial_state, @@ -26,12 +26,12 @@ def __init__(self, proposal, constraints, initial_state, :class:`~gerrychain.constraints.Validator` class instance. :param `initial_state`: Initial :class:`gerrychain.partition.Partition` class. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of - minority popultion within that district. + minority population within that district. :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. :param `score_function`: The function to using doing optimization. Should have the signature ``Partition * str (minority_perc_col) * float (threshold) -> - 'a where 'a is Comparable``. This class implement a few potential choices as class + 'a where 'a is Comparable``. This class implements a few potential choices as class methods. :param `minority_pop_col`: If minority_perc_col is defined, the minority population column with which to compute percentage. @@ -70,7 +70,7 @@ def num_opportunity_dists(cls, part, minority_perc_col, threshold): :param `part`: Partition to score. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of - minority popultion within that district. + minority population within that district. :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. @@ -87,7 +87,7 @@ def reward_partial_dist(cls, part, minority_perc_col, threshold): :param `part`: Partition to score. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of - minority popultion within that district. + minority population within that district. :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. @@ -107,7 +107,7 @@ def reward_next_highest_close(cls, part, minority_perc_col, threshold): :param `part`: Partition to score. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of - minority popultion within that district. + minority population within that district. :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. @@ -130,7 +130,7 @@ def penalize_maximum_over(cls, part, minority_perc_col, threshold): :param `part`: Partition to score. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of - minority popultion within that district. + minority population within that district. :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. @@ -152,7 +152,7 @@ def penalize_avg_over(cls, part, minority_perc_col, threshold): :param `part`: Partition to score. :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of - minority popultion within that district. + minority population within that district. :param `threshold`: Beyond which fraction to consider something a "Gingles" (or opportunity) district. diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 62e3872e..aa5e6b49 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -2,7 +2,6 @@ from .. partition import Partition from ..accept import always_accept from ..random import random - from typing import Union, Callable, List, Any from tqdm import tqdm import math @@ -36,7 +35,7 @@ def __init__(self, proposal: Callable[[Partition], Partition], current state. constraints (Union[Callable[[Partition], bool], List[Callable[[Partition], bool]]]): A function, or lists of functions, determining whether the proposed next state is - valid (aka passes all binary constraints). Usually this is a + valid (passes all binary constraints). Usually this is a `~gerrychain.constraints.Validator` class instance. initial_state (Partition): Initial state of the optimizer. optimization_metric (Callable[[Partition], Any]): The score function with which to @@ -66,11 +65,11 @@ def __init__(self, proposal: Callable[[Partition], Partition], def _is_improvement(self, new_score, old_score) -> bool: """ - Helper function definiting improvement comparision between scores. Scores can be any + Helper function defining improvement comparison between scores. Scores can be any comparable type. Args: - new_score (Any): Score of proprosed partition. + new_score (Any): Score of proposed partition. old_score (Any): Score of previous partition. Returns: @@ -82,7 +81,7 @@ def _is_improvement(self, new_score, old_score) -> bool: else: return new_score <= old_score - def _titled_acceptance_function(self, p: float) -> Callable[[Partition], bool]: + def _tilted_acceptance_function(self, p: float) -> Callable[[Partition], bool]: """ Function factory that binds and returns a tilted acceptance function. @@ -115,7 +114,7 @@ def _simulated_annealing_acceptance_function(self, beta_function: Callable[[int] beta_functions (Callable[[int], float]): Function (f: t -> beta, where beta is in [0,1]) defining temperature over time. f(t) = 0 the chain is hot and every proposal is accepted. At f(t) = 1 the chain is cold and worse proposal have a low probability - of being excepted relative to the magnitude of change in score. + of being accepted relative to the magnitude of change in score. beta_magnitude (float): Scaling parameter for how much to weight changes in score. Returns: @@ -156,8 +155,8 @@ def short_bursts(self, burst_length: int, num_bursts: int, accept: Callable[[Partition], bool] = always_accept, with_progress_bar: bool = False): """ - Preforms a short burst run using the instance's score function. Each burst starts at the - best preforming plan of the previous burst. If there's a tie, the later observed one is + Performs a short burst run using the instance's score function. Each burst starts at the + best performing plan of the previous burst. If there's a tie, the later observed one is selected. Args @@ -181,7 +180,7 @@ def short_bursts(self, burst_length: int, num_bursts: int, self.best_part = self.initial_part self.best_score = self.score(self.best_part) - for i in range(num_bursts): + for _ in range(num_bursts): chain = MarkovChain(self.proposal, self.constraints, accept, self.best_part, burst_length) @@ -196,14 +195,14 @@ def short_bursts(self, burst_length: int, num_bursts: int, def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], float], beta_magnitude: float = 1, with_progress_bar: bool = False): """ - Preforms simulated annealing with respect to the class instance's score function. + Performs simulated annealing with respect to the class instance's score function. Args: num_steps (int): How many steps to run for. beta_function (Callable[[int], float]): Function (f: t -> beta, where beta is in [0,1]) defining temperature over time. f(t) = 0 the chain is hot and every proposal is accepted. At f(t) = 1 the chain is cold and worse proposal have a low probability - of being excepted relative to the magnitude of change in score. + of being accepted relative to the magnitude of change in score. beta_magnitude (float): Scaling parameter for how much to weight changes in score. with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults to False. @@ -231,8 +230,8 @@ def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], flo def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, with_progress_bar: bool = False): """ - Preforms a short burst run using the instance's score function. Each burst starts at the - best preforming plan of the previous burst. If there's a tie, the later observed one is + Performs a short burst run using the instance's score function. Each burst starts at the + best performing plan of the previous burst. If there's a tie, the later observed one is selected. Within each burst a tilted acceptance function is used where better scoring plans are always accepted and worse scoring plans are accepted with probability `p`. @@ -247,14 +246,14 @@ def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, Partition generator. """ return self.short_bursts(burst_length, num_bursts, - accept=self._titled_acceptance_function(p), + accept=self._tilted_acceptance_function(p), with_progress_bar=with_progress_bar) - def variable_lenght_short_bursts(self, num_steps: int, stuck_buffer: int, + def variable_length_short_bursts(self, num_steps: int, stuck_buffer: int, accept: Callable[[Partition], bool] = always_accept, with_progress_bar: bool = False): """ - Preforms a short burst where the burst length is alowed to increase as it gets harder to + Performs a short burst where the burst length is alowed to increase as it gets harder to find high scoring plans. The initial burst length is set to 2, and it is doubled each time there is no improvement over the passed number (`stuck_buffer`) of runs. @@ -271,7 +270,7 @@ def variable_lenght_short_bursts(self, num_steps: int, stuck_buffer: int, Partition generator. """ if with_progress_bar: - for part in tqdm(self.variable_lenght_short_bursts(num_steps, stuck_buffer, accept, + for part in tqdm(self.variable_length_short_bursts(num_steps, stuck_buffer, accept, with_progress_bar=False), total=num_steps): yield part return @@ -316,7 +315,7 @@ def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): Returns: Partition generator. """ - chain = MarkovChain(self.proposal, self.constraints, self._titled_acceptance_function(p), + chain = MarkovChain(self.proposal, self.constraints, self._tilted_acceptance_function(p), self.initial_part, num_steps) self.best_part = self.initial_part From 0e17f4f94df86ffd6346425b36123927730f3d9e Mon Sep 17 00:00:00 2001 From: Gabe Schoenbach Date: Tue, 1 Mar 2022 11:50:55 -0600 Subject: [PATCH 11/28] last few typo fixes --- gerrychain/optimization/optimization.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index aa5e6b49..720cca65 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -17,7 +17,7 @@ class SingleMetricOptimizer: The SingleMetricOptimizer class implements the following common methods of optimization: * Short Bursts * Simulated Annealing - * Titled Runs + * Tilted Runs Both during and after a optimization run, the instance variables `best_part` and `best_score` represent the optimal partition / corresponding score value observed. Note that these are reset @@ -161,7 +161,7 @@ def short_bursts(self, burst_length: int, num_bursts: int, Args burst_length (int): How many steps to run within each burst? - num_bursts (int): How many bursts to preform? + num_bursts (int): How many bursts to perform? accept (Callable[[Partition], bool], optional): Function accepting or rejecting the proposed state. In the most basic use case, this always returns True. with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults @@ -237,7 +237,7 @@ def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, Args burst_length (int): How many steps to run within each burst? - num_bursts (int): How many bursts to preform? + num_bursts (int): How many bursts to perform? p (float): The probability of accepting a plan with a worse score. with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults to False. @@ -303,7 +303,7 @@ def variable_length_short_bursts(self, num_steps: int, stuck_buffer: int, def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): """ - Preforms a tilted run. A chain where the acceptance function always accepts better plans + Performs a tilted run. A chain where the acceptance function always accepts better plans and accepts worse plans with some probability. Args: From ec935fea212a07d4791db491895f9fa6292b769d Mon Sep 17 00:00:00 2001 From: Gabe Schoenbach Date: Tue, 1 Mar 2022 12:15:10 -0600 Subject: [PATCH 12/28] one more typo --- gerrychain/optimization/optimization.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 720cca65..9f4cbb54 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -21,7 +21,7 @@ class SingleMetricOptimizer: Both during and after a optimization run, the instance variables `best_part` and `best_score` represent the optimal partition / corresponding score value observed. Note that these are reset - everytime a optimization run is invoked and do not persist. If necessary the values should be + everytime an optimization run is invoked and do not persist. If necessary the values should be "snapshot-ed" externally to perserve them for comparision. """ @@ -304,7 +304,7 @@ def variable_length_short_bursts(self, num_steps: int, stuck_buffer: int, def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): """ Performs a tilted run. A chain where the acceptance function always accepts better plans - and accepts worse plans with some probability. + and accepts worse plans with some probability `p`. Args: num_steps (int): How many steps to run for. From 665d0ae37e46f0483c3f9d03ba7ce3185a7626bf Mon Sep 17 00:00:00 2001 From: Gabe Schoenbach Date: Tue, 1 Mar 2022 12:16:03 -0600 Subject: [PATCH 13/28] increase plt size --- docs/notebooks/Optimization.ipynb | 37 ++++++++++++++++--------------- 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/docs/notebooks/Optimization.ipynb b/docs/notebooks/Optimization.ipynb index 960056e9..6f9c5987 100644 --- a/docs/notebooks/Optimization.ipynb +++ b/docs/notebooks/Optimization.ipynb @@ -110,7 +110,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10000/10000 [05:13<00:00, 31.89it/s]\n" + "100%|█████████████████████████████████████| 10000/10000 [06:00<00:00, 27.72it/s]\n" ] } ], @@ -131,7 +131,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10000/10000 [06:06<00:00, 27.31it/s]\n" + "100%|█████████████████████████████████████| 10000/10000 [07:02<00:00, 23.66it/s]\n" ] } ], @@ -153,7 +153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10000/10000 [05:55<00:00, 28.14it/s]\n" + "100%|█████████████████████████████████████| 10000/10000 [07:04<00:00, 23.57it/s]\n" ] } ], @@ -167,14 +167,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" + "
" ] }, "metadata": { @@ -184,25 +184,26 @@ } ], "source": [ + "fig, ax = plt.subplots(figsize=(12,6))\n", "plt.plot(max_scores_sb, label=\"Short Bursts\")\n", "plt.plot(max_scores_anneal, label=\"Simulated Annealing\")\n", "plt.plot(max_scores_tilt, label=\"Tilted Run\")\n", - "plt.xlabel(\"Steps\")\n", - "plt.ylabel(\"Max Score Observered\")\n", + "plt.xlabel(\"Steps\", fontsize=20)\n", + "plt.ylabel(\"Max Score Observered\", fontsize=20)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] }, "metadata": { @@ -212,11 +213,12 @@ } ], "source": [ + "fig, ax = plt.subplots(figsize=(12,6))\n", "plt.plot(scores_sb, label=\"Short Bursts\")\n", "plt.plot(scores_anneal, label=\"Simulated Annealing\")\n", "plt.plot(scores_tilt, label=\"Tilted Run\")\n", - "plt.xlabel(\"Steps\")\n", - "plt.ylabel(\"Score Observered\")\n", + "plt.xlabel(\"Steps\", fontsize=20)\n", + "plt.ylabel(\"Score Observered\", fontsize=20)\n", "plt.legend()\n", "plt.show()" ] @@ -227,7 +229,7 @@ "hash": "de0b2e8f3822c864e297dc34a9420a5549c338b59d7a373e92a79db0315964f9" }, "kernelspec": { - "display_name": "Python 3.9.7 ('gerrydev')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -241,10 +243,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" - }, - "orig_nbformat": 4 + "version": "3.9.6" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 3c2f40b0305ae6dc03ff1459c17667b1b3a95a3f Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Tue, 1 Mar 2022 14:36:59 -0500 Subject: [PATCH 14/28] clear up class summary docs; "privatize" all instance variables and expose `best_part`, `best_score`, and `score` as readonly properties. Add stubs for new cycling beta functions. --- gerrychain/optimization/optimization.py | 155 ++++++++++++++++-------- 1 file changed, 102 insertions(+), 53 deletions(-) diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 9f4cbb54..2551e455 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -10,19 +10,22 @@ class SingleMetricOptimizer: """ SingleMetricOptimizer represents the class of algorithms / chains that optimize plans with - respect to a single metric. In instance of this class encapsulates the dualgraph and updaters - via the initial partition, the constraints new proposals are subject to, the metric over which - to optimize and whether or not to seek maximal or minimal values of the metric. + respect to a single metric. An instance of this class encapsulates the following state + information: + * the dualgraph and updaters via the initial partition, + * the constraints new proposals are subject to, + * the metric over which to optimize, + * and whether or not to seek maximal or minimal values of the metric. The SingleMetricOptimizer class implements the following common methods of optimization: * Short Bursts * Simulated Annealing * Tilted Runs - Both during and after a optimization run, the instance variables `best_part` and `best_score` - represent the optimal partition / corresponding score value observed. Note that these are reset - everytime an optimization run is invoked and do not persist. If necessary the values should be - "snapshot-ed" externally to perserve them for comparision. + Both during and after a optimization run, the class properties `best_part` and `best_score` + represent the optimal partition / corresponding score value observed. Note that these + properties do NOT persist across multiple optimization runs, as they are reset each time an + optimization run is invoked. """ def __init__(self, proposal: Callable[[Partition], Partition], @@ -50,18 +53,41 @@ def __init__(self, proposal: Callable[[Partition], Partition], Returns: A SingleMetricOptimizer object """ - self.initial_part = initial_state - self.proposal = proposal - self.constraints = constraints - self.score = optimization_metric - self.maximize = maximize - self.best_part = None - self.best_score = None - self.step_indexer = step_indexer - - if self.step_indexer not in self.initial_part.updaters: - step_updater = lambda p: 0 if p.parent is None else p.parent[self.step_indexer] + 1 - self.initial_part.updaters[self.step_indexer] = step_updater + self._initial_part = initial_state + self._proposal = proposal + self._constraints = constraints + self._score = optimization_metric + self._maximize = maximize + self._best_part = None + self._best_score = None + self._step_indexer = step_indexer + + if self._step_indexer not in self._initial_part.updaters: + step_updater = lambda p: 0 if p.parent is None else p.parent[self._step_indexer] + 1 + self._initial_part.updaters[self._step_indexer] = step_updater + + @property + def best_part(self) -> Partition: + """ + Partition object corresponding to best scoring plan observed over the current (or most + recent) optimization run. + """ + return self._best_part + + @property + def best_score(self) -> Any: + """ + Value of score metric corresponding to best scoring plan observed over the current (or most + recent) optimization run. + """ + return self._best_score + + @property + def score(self) -> Callable[[Partition], Any]: + """ + The score function which is being optimized over. + """ + return self._score def _is_improvement(self, new_score, old_score) -> bool: """ @@ -76,7 +102,7 @@ def _is_improvement(self, new_score, old_score) -> bool: Whether the new score is an improvement over the old score. """ - if self.maximize: + if self._maximize: return new_score >= old_score else: return new_score <= old_score @@ -124,17 +150,20 @@ def simulated_annealing_acceptance_function(part): if part.parent is None: return True score_delta = self.score(part) - self.score(part.parent) - beta = beta_function(part[self.step_indexer]) - if self.maximize: + beta = beta_function(part[self._step_indexer]) + if self._maximize: score_delta *= -1 return random.random() < math.exp(-beta * beta_magnitude * score_delta) return simulated_annealing_acceptance_function @classmethod - def hot_cold_cycle_beta_function_factory(cls, duration_hot: int, duration_cold: int) -> float: + def jumpcycle_beta_function(cls, duration_hot: int, + duration_cold: int) -> Callable[[int], float]: """ - Class method binding and return simple hot-cold cycle beta temperature function. + Class method that binds and return simple hot-cold cycle beta temperature function, where + the chain runs hot for some given duration and then cold for some duration, and repeats that + cycle. Args: duration_hot: Number of steps to run chain hot. @@ -145,11 +174,31 @@ def hot_cold_cycle_beta_function_factory(cls, duration_hot: int, duration_cold: """ cycle_length = duration_hot + duration_cold - def hot_cold_beta_function(step: int): + def beta_function(step: int): time_in_cycle = step % cycle_length return float(time_in_cycle >= duration_hot) - return hot_cold_beta_function + return beta_function + + @classmethod + def linearcycle_beta_function(cls, duration_hot: int, duration_cooldown: int, + duration_cold: int) -> Callable[[int], float]: + cycle_length = duration_hot + duration_cooldown + duration_cold + + def beta_function(step: int): + pass + + return beta_function + + @classmethod + def logitcycle_beta_function(cls, duration_hot: int, duration_cooldown: int, + duration_cold: int) -> Callable[[int], float]: + cycle_length = duration_hot + duration_cooldown + duration_cold + + def beta_function(step: int): + pass + + return beta_function def short_bursts(self, burst_length: int, num_bursts: int, accept: Callable[[Partition], bool] = always_accept, @@ -177,20 +226,20 @@ def short_bursts(self, burst_length: int, num_bursts: int, yield part return - self.best_part = self.initial_part - self.best_score = self.score(self.best_part) + self._best_part = self._initial_part + self._best_score = self.score(self._best_part) for _ in range(num_bursts): - chain = MarkovChain(self.proposal, self.constraints, accept, self.best_part, + chain = MarkovChain(self._proposal, self._constraints, accept, self._best_part, burst_length) for part in chain: yield part part_score = self.score(part) - if self._is_improvement(part_score, self.best_score): - self.best_part = part - self.best_score = part_score + if self._is_improvement(part_score, self._best_score): + self._best_part = part + self._best_score = part_score def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], float], beta_magnitude: float = 1, with_progress_bar: bool = False): @@ -210,22 +259,22 @@ def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], flo Returns: Partition generator. """ - chain = MarkovChain(self.proposal, self.constraints, + chain = MarkovChain(self._proposal, self._constraints, self._simulated_annealing_acceptance_function(beta_function, beta_magnitude), - self.initial_part, num_steps) + self._initial_part, num_steps) - self.best_part = self.initial_part - self.best_score = self.score(self.best_part) + self._best_part = self._initial_part + self._best_score = self.score(self._best_part) chain_generator = tqdm(chain) if with_progress_bar else chain for part in chain_generator: yield part part_score = self.score(part) - if self._is_improvement(part_score, self.best_score): - self.best_part = part - self.best_score = part_score + if self._is_improvement(part_score, self._best_score): + self._best_part = part + self._best_score = part_score def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, with_progress_bar: bool = False): @@ -253,7 +302,7 @@ def variable_length_short_bursts(self, num_steps: int, stuck_buffer: int, accept: Callable[[Partition], bool] = always_accept, with_progress_bar: bool = False): """ - Performs a short burst where the burst length is alowed to increase as it gets harder to + Performs a short burst where the burst length is allowed to increase as it gets harder to find high scoring plans. The initial burst length is set to 2, and it is doubled each time there is no improvement over the passed number (`stuck_buffer`) of runs. @@ -275,21 +324,21 @@ def variable_length_short_bursts(self, num_steps: int, stuck_buffer: int, yield part return - self.best_part = self.initial_part - self.best_score = self.score(self.best_part) + self._best_part = self._initial_part + self._best_score = self.score(self._best_part) time_stuck = 0 burst_length = 2 i = 0 while(i < num_steps): - chain = MarkovChain(self.proposal, self.constraints, accept, self.best_part, + chain = MarkovChain(self._proposal, self._constraints, accept, self._best_part, burst_length) for part in chain: yield part part_score = self.score(part) - if self._is_improvement(part_score, self.best_score): - self.best_part = part - self.best_score = part_score + if self._is_improvement(part_score, self._best_score): + self._best_part = part + self._best_score = part_score time_stuck = 0 else: time_stuck += 1 @@ -315,11 +364,11 @@ def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): Returns: Partition generator. """ - chain = MarkovChain(self.proposal, self.constraints, self._tilted_acceptance_function(p), - self.initial_part, num_steps) + chain = MarkovChain(self._proposal, self._constraints, self._tilted_acceptance_function(p), + self._initial_part, num_steps) - self.best_part = self.initial_part - self.best_score = self.score(self.best_part) + self._best_part = self._initial_part + self._best_score = self.score(self._best_part) chain_generator = tqdm(chain) if with_progress_bar else chain @@ -327,6 +376,6 @@ def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): yield part part_score = self.score(part) - if self._is_improvement(part_score, self.best_score): - self.best_part = part - self.best_score = part_score + if self._is_improvement(part_score, self._best_score): + self._best_part = part + self._best_score = part_score From e0b188f983afda16eeac701b0a54b03c9e14c25c Mon Sep 17 00:00:00 2001 From: jenni-niels Date: Wed, 2 Mar 2022 11:43:02 -0500 Subject: [PATCH 15/28] Add minimizing compactness example to notebook and add markdown cells with context. --- docs/notebooks/Optimization.ipynb | 140 +++++++++++++++++++++++++++++- 1 file changed, 137 insertions(+), 3 deletions(-) diff --git a/docs/notebooks/Optimization.ipynb b/docs/notebooks/Optimization.ipynb index 6f9c5987..05242f07 100644 --- a/docs/notebooks/Optimization.ipynb +++ b/docs/notebooks/Optimization.ipynb @@ -1,5 +1,23 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to Optimization in GerryChain\n", + "\n", + "This notebook walks through how to use the SingleMetricOptimizer class (as well as its Gingleator subclass) in order to prefrom heuristic optimization runs in GerryChain.\n", + "\n", + "There are 3 heuristic optimization methods who's use is shown in this notebook:\n", + "* **Short Bursts**: chaining together small neutral explorers ([More reading about short bursts here](https://arxiv.org/abs/2011.02288))\n", + "* **Simulated Annealing**: vary the propability of accepting worse plans over time wrt a temperature parameter $\\beta$.\n", + "* **Tilted runs**: accept worst plans with a fixed probability $p$\n", + "\n", + "## When do we want to use Heuristic Optimization?\n", + "\n", + "While sampling naively with GerryChain can give us and understanding of the neutral baseline for a state, there are often cases where we want to find plans with properties that are rare to encounter in a neutral run (aka they would take a long time to observe). Many states have laws/guidelines that state that plans should be as compact as feasible, maximize preservation of political boundaries and/or comunities of intrest, some even look to minimize double bunking of incumbents or seek proportionality/competitiveness in contests. Heuristic Optimization methods can be used to find example plans with these properties and to explore the trade-offs between them." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -78,7 +96,16 @@ " pop_target=TOTPOP/SEN_DISTS,\n", " epsilon=EPS,\n", " node_repeats=1)\n", - "cons = constraints.within_percent_of_ideal_population(part,EPS)" + "cons = constraints.within_percent_of_ideal_population(part, EPS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing for Gingles Districts\n", + "\n", + "Named for the Supreme Court case Thornburg v. Gingles which created their precedent as one of the litmus tests in bringing forth a VRA court case, *Gingles' Districts* are districts that are 50% + 1 of a minority population subgroup (more colloquially they are also called majority-minority districts). Because these are used to demonstrate liability it is common to seek plans with greater/maximal numbers of gingles districts to understand the landscape of the state space." ] }, { @@ -172,7 +199,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] @@ -222,6 +249,113 @@ "plt.legend()\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing for Compactness\n", + "\n", + "Another metric we may seek to optimize over is compactness. Below we look at minimizing the number of cut edges in a plan." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "num_cut_edges = lambda p: len(p[\"cut_edges\"])\n", + "optimizer = SingleMetricOptimizer(proposal, cons, part, num_cut_edges, maximize=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10000/10000 [05:03<00:00, 32.93it/s]\n" + ] + } + ], + "source": [ + "total_steps = 10000\n", + "min_scores_sb = np.zeros(total_steps)\n", + "for i, part in enumerate(optimizer.short_bursts(5, 2000, with_progress_bar=True)):\n", + " min_scores_sb[i] = optimizer.best_score" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10000/10000 [04:54<00:00, 33.99it/s]\n" + ] + } + ], + "source": [ + "min_scores_anneal = np.zeros(total_steps)\n", + "for i, part in enumerate(optimizer.simulated_annealing(total_steps, optimizer.hot_cold_cycle_beta_function_factory(200, 800),\n", + " beta_magnitude=1, with_progress_bar=True)):\n", + " min_scores_anneal[i] = optimizer.best_score" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10000/10000 [04:52<00:00, 34.13it/s]\n" + ] + } + ], + "source": [ + "min_scores_tilt = np.zeros(total_steps)\n", + "for i, part in enumerate(optimizer.tilted_run(total_steps, p=0.125, with_progress_bar=True)):\n", + " min_scores_tilt[i] = optimizer.best_score" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AUOAg4PiURVsPRSJGSYkTqWlJiRJuERGRjDZ79mzmzJmz7fH06dPZZZddqnWs7t27M336dEpKSli4cCGTJ0/eaczatWtp2bIljRs3ZtasWXzwwQfb9sViMQoLCwE49NBDGTduHN9++y0Aq1evZsGCBey99968/fbbrFq1isLCQp566qlyYxk7diyvvvoq8+fPZ/78+UydOnW7Ou5E9O7dmxUrVmxLuAsLC/niiy8AWL9+PR07dqSwsJDHHnusWscH2G+//XjyyScB+N///sd3331X7WOVSqRLyQXAk2HyvRN3/9rMngrH/aPGkWWIqBnF7mBohltEREQqtGHDBi655BLWrFlDVlYWu++++7YLGRO133770aNHD/r27UufPn0YPHjwTmOOOuoo7rnnHvr06UPv3r23a9l3wQUXMHDgQAYPHsxjjz3GH//4R4444ghKSkqIxWLceeedDB8+nOuvv5599tmHFi1akJ+fv9M55s+fz4IFC7Y7do8ePcjLy9vW8jAR2dnZjBs3jksvvZS1a9dSVFTEz3/+c/r168cf/vAH9t57b9q2bcvee+/N+vXrEz4+wHXXXcdpp53Gv//9b/bZZx86dOhAs2bNqnWsUhZvXbGZFQD/cPerKxlzM3CpuzeuUVRJNHToUC+9qjcdbnp5Jo+8P5+ue97K8I7D+cN+f6jegWa8AE+eCT+dBB36JzdIERERYebMmfTp0yfdYUiabdmyhWg0SlZWFu+//z4/+9nPtl1AWlZ5Py9mNtXdh+44NpEZ7pXAkUC5CbcFxURHAqsSOGbGi5hRUoJKSkRERETqgW+++YaTTz6ZkpISsrOzue+++2p8zEQS7qeAn5vZk8Cv3f3r0h1m1gP4MzCQ4MJKCUUjUJyMLiVKuEVERERSrmfPnnz88cdJPWYiCfe1wP7AKGCkmS0GlgPtgc5AFPgIuD6pEdZzpV1K1hUU8c6C6Vyy8UZ2b9c08QOtmgct82DOE7Ds7eQHugPDOGH3E+jWvFvKzyUiIiKSyeJOuN19g5ntD1wOnAPsBpRmY3OBh4C/ufvWpEdZj/Xq0IycrAhr1rQl0nQmE5Y9xcQV5bUxr4I75DWHBa+AVeP5CSoqKaKwpJBfDf1Vys8lIiIikskSmeEmTKZvAm4ys6ZAHrDW3TekIrhMcMzAThwzsBNwNH98aQb/mfwNn91wVOIHmjseHj0Jzn0dug5Lepw72nfsvmwt1t9OIiIiIjWVUMJdVphkK9FOQCwrQmFxNWuwa7mGOxaJUVhSWCvnEhEREclkFS58UxEza2tmPzWz28zs/h22DzOzRskNMXPEohEKi716F0+mIeEuKkndiksiIiKyvVWrVpGfn09+fj4dOnSgc+fO5Ofn07RpUy688EIAxowZw8UXXwwEKz3OmDEj4fM0bVr+tWTRaJT8/Hz69+/Psccey5o1a6r9vcj2Ekq4zexcYD5wJ8FS7ueU2d0eeB84PVnBZZrsaFB7vWpjNUo10pBwbyjcwOrNq3e6rd2ytlZiEBERaUhat27N9OnTmT59Oj/96U/5xS9+wfTp09mwYQN33XXXTuOrm3BXpFGjRkyfPp3PP/+cVq1aceeddybt2A1d3Am3mR0O3At8CYwE7i67390/B74gWAJeytEoO6jgGfrH8bzwyZLEnlyacJcUJzmq8jWKNeL1Ba9z0BMH7XTb//H9Gb9gfK3EISIi0tBNmDCBY445Zrtt7733Hi+88AJXXHEF+fn5fPXVV3z11VccddRRDBkyhAMOOIBZs2YB8PXXX7PPPvswYMAArrnmmrjOuc8++7B48WIARowYQekigitXrqR79+5AMNt+4oknctRRR9GzZ0+uvPLKJH3HmSeRGu5fA0uBg9x9nZkNKmfMp8A+SYksA40a3IWsiHHdC1+wZE1BYk+2aHBfSzPcv9/n93y+6vOdtm8u2sytU29l2cZltRKHiIhIOvx58p+ZtXpWUo+5R6s9+PWwXyflWPvuuy/HHXccxxxzDKNGjQLg0EMP5Z577qFnz558+OGHXHjhhbz55ptcdtll/OxnP+Oss86Ka9a6uLiYN954g3PPPbfKsdOnT+fjjz8mJyeH3r17c8kll9C1a9caf3+ZJpGEeyjwuLuvq2TMIqBDzULKXHmNY5w6rCvXvfAFxSUJ1nHXcknJgLYDGNB2wE7b129dz61Tb6XYa2emXURERKq2YcMG3nvvPX70ox9t27ZlyxYAJk2axNNPPw3AmWeeya9/XX7SX1BQQH5+PosXL6ZPnz4cfvjhVZ730EMPJS8vD4C+ffuyYMECJdzlSCThzgY2VjGmBaBMrBJZkSBxrn7CXYPVKpMgGs6012iZehERkTouWTPRtaWkpIQWLVowffr0cvdbHGt4lNZwb9q0iSOPPJI777yTSy+9lKysLEpKgt/7mzdv3u45OTk5276ORqMUFanhQnkSuWhyPjCkijF7A7OrHU0DEAl/3ovq+Ax3RaKRIOHWDLeIiEh6NWvWjPXr1wPQvHlzevTowVNPPQWAu/PJJ58AsN9++/H4448D8Nhjj1V53MaNG3P77bfzt7/9jaKiIrp3787UqVMBGDduXCq+lYyXSML9PHCAmf2ovJ1mdg4wEHg6GYFlKjMjK2IUlySYOJf+ZZruhDuc4S6upYs3RUREpHynnnoqf/3rXxk0aBBfffUVjz32GA888AB77rkn/fr14/nnnwfgtttu484772TAgAHbLoSsyqBBgxg4cCBjx47l8ssv5+6772bQoEGsXLkyld9SxrJ4e0KbWUtgGtCVIKnOAw4HLgMOAE4EvgKGuHtVpSe1ZujQoV56ZW1d0fuaVxi9X3euPrpP/E9aMh3uPQhO/Q/s8cOUxVYVd2fgIwP52Z4/48L8C9MWh4iISLLNnDmTPn0S+N0sDVp5Py9mNtXdh+44Nu4abnf/zsxGAA8DZWe5bw/v3wVOr0vJdl0VjRgliZaURGq3S0lFzIyIRVRSIiIiIhKnhJZ2d/cFwAgzG0jQ/q81sBb4wN2npiC+jJQVMR55fwFtm+VwwYG7xfek0raA//sd9Dk2dcHFIWIRXTQpIiIiEqe4E24zexOY5O6/c/dPCXpuSzVc88O+3PTKTD7+Zk38T2rTK7gvTLB/dwpkWZZquEVERETilMhFk8OBaKoCaUhO3qsrHfMaJdapJJoFQ38CdaCUQyUlIiKSqeK9tk0atkR/ThJJuOcQXDCZNGbW28yml7mtM7Ofm9lfzWyWmX1qZs+aWYsyz7nazOaa2WwzOzKZ8dSmoFNJgm/qaDYUb01NQImEEYmqpERERDJObm4uq1atUtItlXJ3Vq1aRW5ubtzPSaSG+37g92bWzd2/STi6crj7bCAfwMyiwGLgWaA3cLW7F5nZn4GrgV+bWV/gVKAf0AkYb2a93OvfdGs0Yon34o7GoLgwNQElEoZFKSpRY3sREcksXbp0YdGiRaxYsSLdoUgdl5ubS5cuXeIen0jC/SJBG8BJYRL8EbAM2ClrrGZCfijwVXhh5oIy2z8ARoVfH0+wvPwW4GszmwsMA96vxvnSqlq9uKPZULQZ3vkr9DwCOu6ZmuCqYBifrfwsLecWERFJlVgsRo8ePdIdhmSgRBLueQTJtQG3VTLOEzxuqVOBseVs/wnwRPh1Z4IEvNSicNt2zOwC4AKAbt26VSOU1ItGjKLiBGe42/QKlnZ/84+w8CM448nUBFeFNVvWkB3NTsu5RUREROqbRBLjRyhnNjsZzCwbOI6gdKTs9t8CRUDV65CW4e73AvdCsPBNksJMqqyosaUwwRnuPU+F/qPgkeNhy/rUBBaHE3ueyISFE9J2fhEREZH6JJGFb0anMI6jgWnuvrx0g5mNBo4BDvXvr15YzPYXbnYJt9U70UiEKQtWUVRcQlY0gWtXo1mQ3QTmv5u64KrQKKsRa7as4exXzqZny55cM/yatMUiIiIiUtcl0qUklU6jTDmJmR0FXAkc5+6byox7ATjVzHLMrAfQE5hcq5EmSeNY0GFx5YZqdB3ZvBZymiU5ovgd2u1Q9uqwF8s2LuPpL59OWxwiIiIi9UG1Em4z28PMRprZmTUNwMyaEFyM+UyZzXcAzYDXw3aB9wC4+xfAk8AM4FXgovrYoQTg0D7tACgsrkZ7vXZ7BLXcaTK0w1DuO+I+jtntGPXjFhEREalCQhc3mlk+QXvAQWU2/zvcdxDwCnCKu78Y7zHdfSPBEvFlt+1eyfgbgRvjj7puys4K/tapVsIdyaoTC+BELYrjuDtmlu5wREREROqkuGe4zawXMIGgR/ZtBMl1We8Aq/m+hZ9UIisS/NMn3IsbwKJQB/pgRyz4HjTLLSIiIlKxRGa4rwOygaHuPsPMriO42BEAd3czex/YK8kxZqRYNJgRfnv2Cuat2FDp2M4tGjOgS973GyJRSLSHdwpELahD16qTIiIiIhVLJOE+FHjG3WdUMmYhQT22VKF10xwAbnx5ZpVjs7MizPj9kd93M4lE60RJiWa4RURERKqWSMLdkmChmcoYwSy4VGHILi15+4oRbNpaebL61JRFPDjpawqLnaxouLGOlJRohltERESkaokk3MuBCi9mDPUjmOWWOOzSukmVYzq1yAWgsKSERoQZdyQLStI/q6wZbhEREZGqJdIW8E3gWDPrXd5OM9uLoOzktWQEJoFt3UyKyswil5aUpLE1IHyfcHua4xARERGpyxJJuP9EsMz6O2b2M6ATgJn1Cx+/CKwHbkl6lA1YLFraPrBMUhuWcpDmUg7NcIuIiIhULZGl3Web2UkEK0LeEW424NPwfg1wort/k+wgG7KsSNDNZLt+3ZEw4S4p/v7rNFANt4iIiEjVElr4xt1fDZdUPxsYTrBgzVrgA+Ahd1+d/BAbtnIXyNmWcBeRzmtUI2Ev8eI6UE8uIiIiUlcllHADuPsagoVvbkt6NLKT0pKSr1ZspMSdlo2zaW1lE+700Qy3iIiISNXiTrjN7AfAq+7KrmpTk5zgJTr/kSkANIpF+fToGDGAL56BIaPTFptquEVERESqlshFky8BC83sL2bWP1UByfb226019581lH+eNojThnWloLCYNT1+GOzcvC6tsWmGW0RERKRqiSTc/wJygcuBT8zsIzO72MxapyY0AciKRjisb3uO3bMTe/cI/qkfnb4WgI0FBekMbdsM9+bizWmNQ0RERKQuizvhdvefAR2BU4BXgD0J6rgXm9kzZnacmSVcEy7x69a6MRGD29+eD8Dn36xMb0ChxesXpzsEERERkTor0S4lW4GngKfMrB3wY4KOJScAxwOrzOw/7v7zJMcpwOBuLfn890dSWOwU3hylpHhrWuPpkdcDAEcL34iIiIhUJJGSku24+7fufqu77wkMAm4H8oBLkhWc7KxxdhZ5jWIUEaXPitfg3ycGtykP1XossUgMgMKSwlo/t4iIiEh9Ue2Eu5SZ9QJOBk6EoHmGpN5zdjAbYq1g81pYNAWmPVzrMUTDfuDqwy0iIiJSsWol3GbWwsx+ambvAzOBqwlmtx8ADkxifFKBW6Lnc0/Pf8H5b0D3/aC49ntyl3YpUVtAERERkYol0oc7AhxNULN9LMEShw68AYwBnnF3tauoJTlZER794Bse/eAbxnfdwu5e+/XcpV1K1BZQREREpGKJXDS5BGgLGPAl8DDwiLurRUUa/HFkfz5ZuJYx781nzRaDaO0n3OrDLSIiIlK1RBLuXOA+YIy7f5CieCROh+zRnkP2aM9Lny6h0LPgu69rPQatNCkiIiJStUQS7r2Bte6+LFXBSOJi0QixwnABnLWLIa9zrZ1bM9wiIiIiVUvkoskZwI2pCkSqJzsrwrTcvYMHm9fW6rnNDFDCLSIiIlKZRBLuNcCqFMUh1ZQVMT5fFS48U8v9sNWlRERERKRqiSTcHxAscCN1yNqCQgqKw5exuHYTbnUpEREREalaIgn39cABZnZeimKRahjRux0lFpbi1/JS76rhFhEREalaIhdNHg1MAP5lZj8DJgPLCHpxl+Xu/ofkhCdVyYoYWzx8GT9+DHbZt9bOXTrD/c6idzi739m1dl4RERGR+iSRhPv6Ml8PouLyEgeUcNeSaMT4rKQ7RIGNK2r13I2yGgEQi8Rq9bwiIiIi9UkiCffBKYtCqi0rYqwryYFOg6GWL140Mwa3G0xhLV+sKSIiIlKfxJ1wu/vbqQxEqicaieAObhEsDbXUsWiMwlq+WFNERESkPknkokmpg6LhK+gWgZLab8+XFcnSDLeIiIhIJRIpKQHAzAYCpwN9gCbufli4vTswDHjd3b9LZpBSsWgkyLhXbCiCyCbe+PCb7fY3yYlyzMBORCOWkvPHIjE+W/kZazavoUVui5ScQ0RERKQ+SyjhNrMbgN/w/cx42Q4lEWAs8HPgn8kITqrWMS8XgHmrNhOxEn7z7Gc7jenWqjGDurVMzfmbdATgyS+f5IKBF6TkHCIiIiL1WdwJt5mdClwDvAb8GjgFuKp0v7vPM7MpwHEo4a41JwzqzP4929Dsybuxos18eOqh2/ZNXfAdFz42jc2FqavtvmrYVYydNZaCooKUnUNERESkPktkhvtSYC5wvLtvNbOR5YyZCYxIRmASvzZNcyAWg+IC2jfP3ba9bbMcAIpLdmyVnjwRi9Aoq5EunBQRERGpQCIXTQ4AXnP3ypYzXAK0r1lIUi0W3aktYMSCuu2iktR2L9GFkyIiIiIVSyThNqCqzK09sLn64Ui1RaI7dSnJCi+ULPHUzXBDcOFkUUlRSs8hIiIiUl8lknDPASpcN9zMIsD+wBc1DUqqwaKwQ2Jd2pmkqDi1CTcEF02KiIiIyM4SSbifBAab2a8q2P8bYHfgPzWOShJntlNJSVY0SLhTWcMN0Cq3FU1iTVJ6DhEREZH6KpGLJv8B/Aj4i5mdTNgS0MxuAQ4AhgIfAPcmOUaJRyUlJbOXr6dNeAFlInZp3Zh2zXKrHDeswzBenPdiwscXERERaQgSWdq9wMwOBm4DzgCi4a5fEtR2Pwpc7O4q5k2Hci6abJoTA+Af4+fwj/FzEj7kgM55vHjJ/lWf2mz7juwiIiIisk1CC9+4+1pgtJn9EtgLaA2sBSa7+4oUxCfxKmeGu0NeLv+9dH++25h4B5H7J85j9rL1cY01UrOKpYiIiEgmSHhpdwB3X02wAI7UFRaF776Goi2Q9X35SL9OedU63PiZy3n/q1Xc8trsKsdOXfcdm4uKueW12Rzetz17dm1RrXOKiIiIZKJqJdxlmdkewNHAJuDxcBZcalssrLVeOBl6HFDjw/Xr1JwSd+5++6uqT912LVl5xdzx1ly+XL6ee88aWuPzi4iIiGSKRJZ2vxb4GdAvnOHGzA4DXgSyw2FXmtkwd1+V9EilcoPPhqljYOuGpBzuR0O78qOhXeMa+5ePPufpL6exa5c8CotTu8iOiIiISH2TSFvAo4FZpcl26E8El8tdB9wN9AAui/eAZtbbzKaXua0zs5+bWSsze93M5oT3LcPxZma3m9lcM/vUzAYnEH9my24a3E++r9ZPbRiOE40YRSluQSgiIiJS3ySScHcHZpY+MLPOwBDgLnf/o7tfDLwJnBDvAd19trvnu3t+eKxNwLPAVcAb7t4TeCN8DEHS3zO8XUCQ5AtAqx7BfcHqyselQOlFk1kRS3nPbxEREZH6JpGEuyVQNpvbj2B2+6Uy26YC3aoZy6HAV+6+ADgeeDjc/jDfJ/HHA4944AOghZl1rOb5Mks0Bj2P3Gm1ydpgZrhrhltERESkPIkk3CuAzmUeHwwUAh+W2Zad4DHLOhUYG37d3t2Xhl8vA9qHX3cGFpZ5zqIdYmrYzMBrv4a6bEmJZrhFREREtpdIcjwdOM7M+pvZ7sApwER3LygzpjuwtJznVsrMsoHjgKd23OfuToLLqpjZBWY2xcymrFjRkNqDG2lZgcYIZ7gjSrhFREREdpBIW8C/AG8Bn5TZ9rfSL8wsSlBm8no14jgamObuy8PHy82so7svDUtGvg23LwbKts7oEm7bjrvfS7jE/NChQxtOBmiRNOXbwQx3VsRYuWELz32800uyk2E9WtGpRaNaiE5EREQkvRJZ2v1dMzsGOJ8grXvM3V8pM2RfguT32WrEcRrfl5MAvACcDdwc3j9fZvvFZvY4sDewtkzpiaS5pKRt0xzenPUtP39iepXPOWZgR+44XU1mREREJPMlurT7q8CrFex7FxiUaABm1gQ4HPi/MptvBp40s3OBBcDJ4faXgR8Acwk6mpyT6PkymqWnpMTMwOGPI/vz0xG7VTn+osemsbYg8eXmRUREROqjGq80WVPuvhFovcO2VQRdS3Yc68BFtRRaPZTeGe5YNEKPNk2qHN+ySYwZS9bxy3AmvH1eLlce2TtI3EVEREQyTMIdRczsDDN7w8xWm1lReD/ezM5IRYCSAIukpS0ggCcwsz6iVzsa50T5aMFq3pr9LXdP+IqVG7amMDoRERGR9ElkafcYMA44hqAdRjFBq8A2wCHAwWZ2MjDK3VUvkA7pquEO+3DH6/wDd+X8A3cFYOzkb7j6mc/U3UREREQyViIz3FcDxxL03T4YyHX3jkAuQcI9mSAZ/3Wyg5Q4WYS01HCHJSXVEY0EZSRFJbX/h4KIiIhIbUgk4T6L4GLFEe7+trsXA7h7sbtPAEYA84DRSY5R4pa+Ge7qiobPVb4tIiIimSqRhLsL8Ly7l1ts6+5bCNr3aeXHdElTDbdR/YQ7K6oZbhEREclsiSTcS4BYFWNi4ThJhzT24QYSquMuVVpSohpuERERyVSJJNz/AUaZWfPydppZC2AU8FgS4pLqSFMNd+kEd3XquLO21XAr4RYREZHMlEgf7huA/sBkM7sBeAdYDrQHDgJ+R3Dh5B+SHaTEy9JaUuLuJFpdEglruB+f/A3tmudut29Yj1bs1b1VUmIUERERSZcKE24zK6H86VID/l3B9p5AQWXHlRRKYx9uqN4Md5eWjcnOivDw+wt22tevU3P+e+kByQhNREREJG0qS4zfIS31CVJtRnpruKvx49K3U3O++P2RlOzwh8LlT33Kp4vWJCM8ERERkbSqMOF29xG1GIckhZGWPty2rYi7WmLRnS8laJqTxdI1mzn5X+9X8Bzjmh/2pU/Hci8pEBEREakzEl7aXeowi9S7Ge6KHN2/A0N2aUnE2Onm7kyau4pJc1cm7XwiIiIiqZJwrbWZdQI6hQ+XuLvaANYVpW0BS4ohEq3F0yY/4T6wV1sO7NW23H1bi0rodc0rrNtcxObC4qSdE4LZ9tJWhSIiIiLJEFfCbWa5wC+AC4BuO+z7BvgX8A9335z0CCV+0WzYuAJuz4dLP4FI7X6AUZ0+3NWRnRUhNxbh9jfmcPsbc5J67E55ubz760OUdIuIiEjSVJlwm1ln4L/AAIIi4SJgVbi7NbALcCNwqpn9QDPeaTT8QljzDXz5KhQVQHaTWjltKkpKqvLP0wYz99sNST3m1AWrGT/zWzZsKSKvUVVrPImIiIjEp9KE28yiwAvAQOA9gsT6rdKZ7HDm+2DgN8B+wAtmtre7J/dzfolPqx6w+2FBwj1vAuzxw1o5bWlJyYSFE8iOZif8/JY5LRncfnBCzzm8b3sO79s+4XNVZuzkGONnfstrny8jr/H2CXffjs3p2qpxUs8nIiIiDUNVM9yjgUHAGOBc36FmIEy8XzGzV4H7w/FnAw8mO1CJU7OOwf3jp8MV86BJ65SfMi87D4Ar37my2sd4fdTrdGjSIVkhVUv75jkAXPn0pzvtG9StBc9euF9thyQiIiIZoKqE+xRgKXDhjsl2We7uZnYRcBRwGkq402ePH8K+l8B7/4TCjQRVP6k1sudIBrYdSHE1PtiYuHgit027jU1Fm1IQWWIO7t2O8b88iC1F238ff3p5FsvW6fIEERERqZ6qEu49gRfjuRjS3Teb2cvAcUmJTKrHDNr1C74uqZ3KnohF6NmyZ7WeO3/dfABKSmq/neGOzIzd2zXdaXvrptks/C79fxCIiIhI/VRVG4sWwLIEjrc8fI6kUyT8O6qWEu6aiFrQvrA6s+O1JRaNUFSsRVdFRESkeqpKuNcAHRM4XofwOZJOpT24S4rSG0ccIhb8CJakYcGeeMWixtbiuhufiIiI1G1VlZRMB442s9yqykrCjiU/AD5JUmxSXdtmuOtBwh3+zVdC3U1oY9EIKzdsYf8/v5nU47ZpmsPY84fTKLv2FikSERGR2ldVwv0kcDhwJ3BuFWPvANoDv0tCXFIT9WiGOxrGWhdquCsyakgXNm4pTmqf8UWrC5g8fzVL1xawa9ud68ZFREQkc1SVcI8BLgJGm1lP4I/A2+6+BcDMcoARBH249yeY3R6TolglXqUz3HW4LrpUaUlJXa7hHtilBX87uUVSj/naF8uYPH81Xy7fEFcanx2N0KVlo209z0VERKT+qDThdvdiMzsWeJUgoX4FKDKzsitNZhGsQDkTOE6L3tQh386EzkPSHUWl6kMNdyq0bBwsEPTTR6fG/Zx/nTmEI/ult1e5iIiIJK7Kpd3dfbGZDQN+BZwHdCO4OLLUQoJFb251940piVIS02q34N7qfm1wfehSkgpDdmnJ/WcNZePWqst+Nm4p5jfPfsbSNQW1EJmIiIgkW5UJN4C7FxCUk/zRzLrwfeeSZe6+MFXBSTXFcoP74q3pjSMOpTPcry94nb067JXmaGpPNGIcFufS9AVbg4R74tyVlCRYRh7LijByUGea5sT1VhcREZEUSPi3sLsvAhalIBZJlmhQrkBJYXrjiEO7xu0AGDtrLJcNvowmsSZpjqjuyY1F6JSXy/iZ3zJ+5rcJP79RLMqoIV1SEJmIiIjEQ9Nemaj0osniup9w79J8F3455JfcOvVWthRvUcJdDjNjwhUHU7A1sbKblRu3cOjf3qagsGGV64iIiNQ1SrgzUekM90cPQG4e5J+e3niq0Cy7GQCF9eAPhHTJzoqQnVXVOlXlK9KiPSIiImlVvd/gUrdlN4H+o2D9Mpj273RHU6WscEa+sB6UwNQnWdGghWChEm4REZG0UsKdicxg1APQda96Uccdi8QAJdzJFosGb+/C4uQt2CMiIiKJU0lJJotm14tOJaUz3FOXT6VHXo80R5M5YlEjYvDX12Zzy/9mJ/G4Ecacsxf77tYmaccUERHJZEq4M1k0Vi8unBzSPlicZ/3W9WmOJLOYGbeenM+8FRuSdszNRSXc+8485izfoIRbREQkTjVOuM0sBvQHNrl78qbRpOai2bB5LXz1JjRuAx0HpjuicrXKbQXAkg1L0hxJ5jlhUOekHm9zYTH3vjOPmUvX8e6cFTvt361tUzq1aJTUc4qIiNR3cSfcZnYyMAr4qbuvDrftRrDc+27h4+eBk9296uXzJPUatYJ1i+HfI4PHl8+Fpm3TG1M5She/mbFqRpojkarkZEVolpPF4x8t5PGPdl7zql+n5vz30gPSEJmIiEjdlcgM90+ATqXJduhvwO7Am0Br4HjgHOC+pEUo1Xf472HAqGCG++0/w5Z1dTLhBujevDuNYpoZrevMjFd+fgDL1m7ead9dE75i5tJ1aYhKRESkbksk4e4LvF76wMyaAz8AnnT3U8PSkuko4a47sptAt+GwfmnwuA7Xc7fKbYW7umnUB11aNqZLy8Y7be/WaimT5q7kjy8l9klFv87NGTlIK2GKiEjmSiThbgssLfN4n/D5jwO4e6GZvQ6clrzwJCnCtnt1uWNJxCIUu1ZErM/27JrH01MjjJ38TdzP2VpcQm5WVAm3iIhktEQS7vVAXpnHBwEOTCyzbTPQLAlxSTKVrjxZh2e4oxZVH+56buSgLgknzre8Npu7JszF3TGzFEUmIiKSXokk3HOAo80shyDRPhn41N1XlhmzC/BtEuOTZIiGM9zjRkOsTCnA/r+APU9NS0g7MjPNcDdAjbKjlDgc/vd3qE/pdsSMq47eg4P3aJfuUEREpB5IJOG+F3iIIPEuBLoDv9hhzBDgi6REJsnTaRDknwFby/RjnvsmzPlfnUm4IxZRDXcDdGS/9sxatp7ikvq1/Pz/vljOxLkrlXCLiEhc4k643f1hM+sNXBBuugP4Z+l+M9uXoGPJvUmNUGquUQs44a7tt91zAGyoOx9GRCxCidevpEtqbvd2zfjnaYPSHUbC9rpxPKs2bGHp2gIAGsWitGicneaoRESkrkpo4Rt3/w3wmwp2TwFaAhtrGpTUgkgU5r8LRVsgKyfd0eiiSalX8hrFeG76Ep6bHizWZAb/+/mB9GyvS1hERGRnSVva3d23AnW3DYZsr+twWPJxsBJl0/R/LB6xCI5KSqR++PvJ+XyxZC0A36zexF0TvmLxmgIl3CIiUq6EE24zGwicDvQBmrj7YeH27sAw4HV3/y6B47UA7idYHt4JFtgpAO4BcoEi4EJ3n2xBG4PbCPp/bwJGu/u0RL8HATruGdx//Cg0bh10MulzLOQ0TUs4EVRSIvXHgC55DOgSNG2auXQdd034ivEzl++0INB+u7eha6ude5aLiEjDklDCbWY3EJSURMJNZackI8BY4OeUqe2Ow23Aq+4+ysyygcbAk8Dv3f0VM/sB8BdgBHA00DO87Q3cHd5Lolp0C+7f+P3320qKYPCZaQlHNdxSX7VrlkN2NMKjH+zcf/y4PTtxez2sURcRkeSKO+E2s1OBa4DXgF8DpwBXle5393lmNgU4jjgTbjPLAw4ERofH2ApsNTMHmofD8oAl4dfHA4940M7iAzNrYWYd3X0pkpju+8EV86BoM2xeA3fvC4Wb0haOEm6pr1o3zWHK7w5j45ai7bb/ZMwU1m1Wb3kREUlshvtSYC5wvLtvNbOR5YyZSTATHa8ewArgITPbE5gKXEYwS/6amd1CMHO+bzi+M7CwzPMXhduUcFdHk9bBfU5Yd5rGlSgjFmHe2nl8teYrdmuxW9riEKmO5rkxmufGdtiWxaeL1nLhY1MrfW4sGuFXh/emW2uVnoiIZKpI1UO2GQC8Fs5CV2QJ0D6BY2YBg4G73X0QQYeTq4CfAb9w964Evb4fSOCYmNkFZjbFzKasWLEikac2TNtWokxfwj24/WAA3l70dtpiEEmmI/p1oHWTbOYs31Dhbday9Tw/fQkTvqw7LTpFRCT5EpnhNqCqz/zbEyzvHq9FwCJ3/zB8PI4g4d6fYKYb4CmCiyoBFgNdyzy/S7htO+5+L2E/8KFDh6r1RVVKV6IsLqp8XAqN3H0kN314kxa/kYxx7v49OHf/HpWOKdhaTJ9rX2XTVrXEFBHJZInMcM/h+9KOnZhZhCBRjnulSXdfBiwMF9QBOBSYQTBTflC47ZDw3AAvAGdZYDiwVvXbSRCJBvcTbkpbCEEDGtQaUBqU3FiEaMT42/9mc+Jdk9IdjoiIpEgiCfeTwGAz+1UF+39DsNLkfxKM4RLgMTP7FMgHbgLOB/5mZp+Ej0tXt3wZmEdQS34fcGGC55KKtOkV3KdphjkS/ihqhlsaEjPjppH96d85j2nfrNHPv4hIhkqkpOQfwI+Av5jZyYQtAcMLGw8AhgIfkODS7u4+PXxuWROBIeWMdeCiRI4vcRp4Mrz5R5g/MVgKvn3/YPm8WlI6w61OJdLQnLJXN1as38LH36yhqMSJRWvvfSciIrUj7hludy8ADgb+TXCh4zCCuu5fEiTHjwJHuXv6CoGl+pq0De4fPgbu2R8WV95ZIdmMMOGu8jIBkcwTiwb/FRcW6+dfRCQTJbTwjbuvBUab2S+BvYDWwFpgsrurHUh9tufp0Go3WDkb/vsr2FC7XRMiFv7tp0/UpQHKKk24ixyy0xyMiIgkXcJLuwO4+2qCBXAkU2RlQ48DoFmH4PGqOZWPT7JtJSWa4ZYGKDssI/nnm3NonB1cxByJGCcP7UqnFo3SGZqIiCRBtRJuyWClCffmtbV+asN00Zg0SLu1bUp2VoQHJn29bZs7RM245NCeaYxMRESSIZGl3R+MY1gJsI5gxckXw7Z/Up/kNAOLpuXUWt5dGqp9d2/Dl388erttu179X7YU6f0gIpIJEpnhHs33FbblXUbvO2y/w8yucfe/VjM2SReLQBoSXzNTH26RUCwa0UWUIiIZIpE+3LsBzwOrgGuAEUCf8P534fZngb2B/wOWAzeb2fHJC1dqRSSaloQ7QkQlJSKh7GiEwmK9H0REMkEiM9wnEPTbznf3ssupzwbeMbNHgI+Bd939H2b2GsGqkRcTJOpSX6RxhlsXTYoEIhHju01b0x2GiIgkQSIz3BcAT+2QbG/j7guBp8JxuPs3wEsEPbulPrEIlKRhhts0wy1SKhaN8OzHi3lz1vJ0hyIiIjWUSMLdnaDndmXWAD3KPJ4PNE0oIkk/S09JibqUiHzvD8f3A2DRdwVpjkRERGoqkYR7JXB4FWOOIKjlLtWCqpN0qWvMVFIikmb79WwDwFZ1KhERqfcSSbifBgab2aNm1q3sDjPrZmaPAfnAuDK7hgC1u4KK1Fyaarh10aTI92KR0uXe9Z4QEanvErlo8lqCiyZPB04xs8UEnUjaA52BKDA9HIeZdQQKgX8nMV6pDRYBL67906otoMg2sXD1SbUGFBGp/+JOuN19nZntC1wJnA3sCpTOdM8DHgH+4u6bw/FLgX2TG67UijS1BTQzJi2exG8n/jbh50Yswtl9z2b3lrunIDKR2heNGGbw8mdLmb9q4077s6MRLj5kd7q0bJyG6EREJBEJLe3u7luAPwB/MLNmQHNgnbuvT0VwkiZpKik5oPMBfPztx0xdPjXh5y7esJgOTToo4ZaMYWYcukd7Zi1bx+SvV2+3zx0WrymgT8fmnL1v9/QEKCIicUso4S4rTLKVaGeiNLUF/NMBf6r2cwf9exCFxYVJjEYk/e4/e2i52wu2FtPn2lfZtLX2S79ERCRx1U64JcNNfxSGnQedBqU7krjEIjGKSorSHYZIrciNRYgY/PPNOTww8et0h5MSxwzsyPXH9Ut3GCIiSVFhwm1m86p5THf33ar5XKkL9joX3rgBln5abxLurEgWhSWa4ZaGwcy47th+zF6emR8yTpyzkklzV6Y7DBGRpKlshjsCO7WMyAY6hl8XE/TmbkPQoQRgKaC1iOu7IecECfequUHSXSoShbZ9IJJIN8naEbUoC9YvSHcYIrUmk2u3f/XkJzz78SLcHTNLdzgiIjVWYcLt7t3LPjaz5sB4YAFwNTDR3YvNLErQLvBPBEn6YSmLVmpHdlOIZsN7twe3sn5wCww7Pz1xVaLES5i0eBLLNi6jQ5MO6Q5HRGqgcXaUEocXPlnC8fmd0x2OiEiNJTJVeSPBypEj3P1t96BRs7sXu/sE4GCgVThO6rOsbDj3f3DKY9vfLALrl6U7unJdMPACAFYW6GNokfru/AN2BWD1Rn1gKiKZIZGLJkcCY9293P8B3X2zmT0PnApcmozgJI06Ddq5fjsrF2a+CIf+Lj0xVaJv674APD3naT5Y+gGxSIyRPUfSPLt5miMTkUS1bpoNaNEfEckciSTcrYFYFWNi4TjJRIWbYOVsKC6EaFU/CrWrc9PO5EZzGffluG3bmmc3Z2TPkWmMSkSqIxbVsvYiklkSKSn5ChhlZnnl7TSzlsAoglUnJRMdem1wXwfb73Vq2on3Tn+PKT+ewuujXgdgc/HmNEclItWhZe1FJNMkMsN9D3A7MNnMbgTeAZYD7YGDgN8CHVANd+aKBh/z1sWEG4Je3ABNYk0AtBCOSD1lZmRFjKemLOKDeatq5Zxtm+Vy68l7bptdFxFJprgTbne/w8x6ApcAD5UzxIB/uvtdyQpO6phI+ONSRxPuUqWJt/pyi9RfPx6+CzOWrqOkFqpKVm7YwgfzVnPFEb3p1rpx6k8oIg1OQitNuvtlZvY48BNgEJAHrAWmAWPc/b3khyh1hoXt1kvq9nLSWeEfBluKt6Q5EhGprtpcZfKVz5bys8emsbagkC1F3///FjUjSzPeIpIECS/t7u7vA++nIBap6yL1I+GOhn8Y3P3J3fyo149o27htmiMSkbqsaW7wq/DYOyZutz07GuH5i/ejT0d1OxKRmkk44ZYGrJ6UlJgZJ/U8iafnPM2SjUuUcItIpYb1aMX1x/Zl49bvJxNWbtjCQ5Pms2DVRiXcIlJjlSbcZvYg8Jy7v1BmWy9gj7Lbyuw7Gzjb3Q9JeqSSfqUJ99LpkFe3V387Ztdjgp7cSz5gdcHqdIeTsEHtBtEit0W6wxBpEHKyoozer8d22xas2shDk+bz0fzviEaSV1bSODvK8F1bE41oyXqRhqSqGe7RwHygbHJ9GnAtEC1nfHeCjiWSiRqHLdYfPx2uX5veWKrQrnE7AO6YfkeaI6meUb1Gcd0+16U7DJEGq2WTbGJR44GJX/PAxK+Teuyx5w9nn920ZIVIQ6KSEolfryOh30j44lkoKYEkzvokW7fm3Xh55MusL1yf7lAS9ou3fsGGrRvSHYZIg9Y8N8aEKw7muyQuL//l8vX88slPKCis22V5IpJ8SrglfmbQrl+QcHsxia2bVPu6Nu+a7hCqpUl2E4rqeJ28SEPQuUUjOrdolLTjlXjQ47BE6/mINDh1O2OSuqd0VruOdyqpz2KRmHqIi2SgiAV126WJt4g0HEq4JTGlvbhdCXeqZEWylHCLZKAw30bptkjDo4RbEmPhj4zrM9FUybIsZq2ele4wRCTJjCDjds1wizQ48dRw55vZWWUfA5jZmcCOfY3ykxOW1Fn1ZPGb+mztlrW0ym2V7jBEJMm2VeQp3xZpcOJJuI8Pb2UZMKacsYY+Lcts20pKNMOdKru22JWv1ya3DZmIpF9pDbcmuEUanqoS7odrJQqpPzTDXStK9AeNSMYp/UhYF02KNDyVJtzufk5tBSL1xLYabiXcqRKxiBJukQxk6lIi0mDpoklJzLYZbvWJTpWI3pYiGUmruYs0XHH/ZjezeWZ2aRVjLjKzeTUPS+qsrZuC+02r0htHBjMzzXCLZCDNcIs0XIlMpXUHWlQxpgWwSzVjkfqgVY/gXjXcKaOSEpHMVDrDrXxbpOFJ9mfXzYCtST6m1CXRWHCvkpKUiVgEV7MfkYxT2odbbQFFGp5KL5o0s247bGpRzjaAKNANOAlQSUkmi2YH98X6uypVDJWUiGSi0pUmVVIi0vBU1RZwPtv31b4svFXEgF/WMCapy5Rwp5xKSkQyUySitd1FGqqqEu5HCP5rMOAs4FNgejnjioFVwBvu/r9EAjCzFsD9QP/wXD9x9/fN7BLgovDY/3X3K8PxVwPnhtsvdffXEjmf1FBWTnA/6TbY7ZD0xpKhIhbR0s8iGUh9uEUarqr6cI8u/Tpc3v1Zd78hyTHcBrzq7qPMLBtobGYHE6xuuae7bzGzdmEMfYFTgX5AJ2C8mfVyV1PoWtN+QHBfpBnuVDEzStAMt0imiZhquEUaqniWdgfA3ZPeHNjM8oADgdHhObYCW83sZ8DN7r4l3P5t+JTjgcfD7V+b2VxgGPB+smOTCkSzoNdRsHIOfPXW9vuatof2fdMTVwZRDbdIZiqtKJnz7XomzllZ5fiBXfNonhtLcVQiUhviTrhTpAewAnjIzPYEphLUiPcCDjCzG4HNwOXu/hHQGfigzPMXhdu2Y2YXABcAdOtW3jWeUiNN2sKXr8K/T9h+u0Xh1/Mht3k6osoYKikRyUyNsqNEI8ZDk+bz0KT5VY4/bVg3/nTigNQHJiIpF3fCbWYPxjnU3f3cBM4/GLjE3T80s9uAq8LtrYDhwF7Ak2a2a7yxuvu9wL0AQ4cOVeaSbEf9CfLP2H7b7Jfhvdth6wYl3DVkqKREJBM1y43x+i8OZNXGqkvyLn/qE1Zt2FILUYlIbUhkhnt0FftLL650gosa47EIWOTuH4aPxxEk3IuAZzyY5ptsZiVAG2Ax0LXM87uE26Q25TSDXfbZftvqsBtkcWHtx5NhIhZhU+Em/jblbzU+1lHdj6Jfm35JiEpEkmHXtk3ZtW3V41o3yWbG0nXc9PLMuI47sEsexwzsVMPoRCRVEkm4e1SwvQXBLPTvgPcIEua4uPsyM1toZr3dfTZwKDAD+Ao4GHjLzHoB2cBK4AXgP2Z2K8FFkz2ByQl8D5IqpQviKOGusT1a7UFWJIsnZj9Ro+MUFBWwbOMy/nrQX5MUmYjUliG7tOTRD77h3+8vqHLs1uISWjaOKeEWqcMSuWiyonf9AuATM3uNoG3geOCBBGK4BHgs7FAyDzgH2Ag8aGafE6xceXY42/2FmT1JkJQXARepQ0kdsS3hVveSmhrZcyQje46s8XFGvTCKzcWbkxCRiNS23/6wL7/9YXwXod/43xn8+4OqE3MRSZ+kXTTp7gvN7EWCix7jTrjdfTowtJxdP65g/I3AjdWJUVKodEGcx0+HWKP0xdH/JDjw8vSdvw7JieZQqE8cRDJe4+wsNheWcOTf36l0XNPcLO47ayitmmTXUmQiUirZXUqWE5R5SEPTdW8YeAoUbkpfDN98ALNfUcIdyo5ms6VYF12JZLqj+ndg7rcbKK6kwffqjVuZPH81c5avZ+9dW9didCICSUy4zSwKHAKsTdYxpR5p0gZOvDe9MTx6Emxand4Y6pCcaA7rt65PdxgikmJ9OjbnzjMGVzrm42++Y+Rd77FkbQHL1tas1CyvUYxG2dEaHUOkoUmkLeCBlRyjK0HtdT7BMu0itc+ioJL+bWLRGFtLVFMvIkGSDPCLJz6p8bHaNcth8m8Pq/FxRBqSRGa4JxC0/KuIAe8AV9QkIJFqi0ShRP2rS+VEc1RSIiIA9GjThLvPGMyagppd1/HGzG8ZP3M5xSVOtHTpTBGpUiIJ9w2Un3CXAN8Bk91dLfokfSyiGe4ycqI5rNm8hmfnPJuyc2RFsjik2yE0iTVJ2TlEpObMjKMHdKzxcb7btJXxM5dTWFxCNKKyEpF4JdIW8PoUxiFSc5EolCjhLtW+cXu+2/Id1753bUrPc83e13DKHqek9BwiUjfEIhEACotLyI0p4RaJV7K7lIikj2q4t3PxoIs5uffJBC3sk6+guIDjnzueTUVp7EwjIrUqFg3KSAqLU/P/ikimqjLhNrO/AI2AX7p7ucVf4aI1fwM2unvcK02KJJVmuLcTsQgdmnRI2fFLe3wXlqjXt0hDEcsKZriveOqTtMxwd23VmF8f1Rsz1Y9L/VJpwm1mRwG/An5eUbIN4O5bzWwWcLuZveHuryc5TpGqWQRcF03WlqxI8N+HEm6RhiO/awv6dmzOgtW1/8nWmk2F/PezpVx48G40z43V+vlFaqKqGe7TgdXAPXEc617gWuBsQAm31D6LKuGuRWZGViSLopKidIciIrWkX6c8Xr7sgLSc+z8ffsNvnv2Mgq3FSril3qkq4d4HGF/Z7HYpdy80szeB4UmJTCRR0SxYuxD+1DV4nN0Ezv0ftOiW3rgyWHYkmzFfjOHxWY+nO5QGJSuSxV8O/Av7dNon3aGI1JomOUEJyyG3TCBSpqSkaW4Wz120H+2b56YrNJEqVZVwdwaeSuB484Hjqx2NSE3sdT5kNwMc1i+DL56BlXOUcKfQ1XtfzezVs9MdRoNS7MWMnTWWmatnKuGWBuWgXm25cMRubC78/pPMZesKePmzZXy9cqMSbqnTqkq4HUjkc5solS+OI5I6HQcGN4BlnwUJ95KPYfdD0xtXBjth9xPSHUKDU+IljJ01ljWb16Q7FJFa1aJxNlcetcd22z7+5jte/mwZny1aSywaSVNkkBUx+nVqTlYaY5C6raqEezmwRxVjyuoDLKt+OCJJ0rh1cP/mH2DPUyGvS3rjEUmSiAW/0L9Z/02aIxFJv9ZNcgC48eWZaY4Ebho5gNP31ieqUr6qEu73gRPMrL27L69soJl1AA4FnklWcCLV1rwTHHQVvH0zbFyhhFsySrNYMxpnNU53GCJp1611Y56/aL8aL1lfE8UlJfxkzBS+27Q1bTFI3VdVwj0GOA141MyOc/eC8gaZWS7wCJATPkck/XbZN7hfrw9dJLO0btRa7RhFQnt2bZHW85cuLlZYrC5ZUrFKi43CftrPE8xcf2xm55nZrmaWHd56mNl5wPRwzPPuPj7lUYvEI7tpcF+wJq1hiCRbViRLCbdIHRG0SDUl3FKpeJZ2PxMYBxwB/KuCMQa8Fo4VqRuahassFm9JbxwiSRaLxNT/XKQOiUUjWu5eKlXl5bTuvgE4miCZnggUEiTYFn79LvBj4AfuvjF1oYokKLtJcD/hz+mNQyTJYpGYZrhF6pCIwb3vzOPVz5emOxSpo+LqX+OBx9z9IKAJ0D68NXH3Ee7+Hy8tYhKpK3LzIJoD6/UfoGQWlZSI1C3nHrArAK/P+DbNkUhdFU9JyXbcvRhYkYJYRJLLDA68HN66ETauhEjCP+4iFTML/qhLg1g0RkFhAVuLt5IdzU5LDCLyvV8e3osXpi9WHbdUKO4MJOxEMgz4wt1XpS4kkSQqTYj+ult645DMdMSNsO/FtX7aRtFGfLj0Qw556hDGjxpPbpZW2BNJt1g0QlGJEm4pXyJTfp2Bt4AfoV7bUl8MPBksAsX6+F2S7K2bYPVXaTn1ZYMvIzcrl1fnv8raLWuVcIvUAVnRCFuLVF0r5as04TaziLuX/XPNdth/HfA7d9dn9VI3NWoJw85PdxSSiT64Cwo3p+XUu7fcnRFdR/Dq/Fd5a+FbnLD7CUq6RdIsO2osXlPAi58sifs5Azrn0b1NkxRGJXVFVYnyd2Y2AXgTmF/BGKtgu4hI5srKhaJy1wKrFR2aBG0vb/zwRnKiOYzsOTJtsYgItG2Ww/iZ33LJ2I/jfs5e3Vvy1E/3TWFUUldUlXA/ARwCHAt4eLvQzNoA76Q4NhGRuisrF4rS1+N9SPshjDt2HKNeHMWmok1pi0NEAnecPphF38X/XrzhpZksWZO+P9qldlWacLv7BQBmtgtwCnAzMJwgCXegONx/PvC2u3+Z0mhFROqKWC4s+Rieu7DiMdEYHHgl5HVOSQidmnYCoLikOCXHF5H45cai7N6uWdzj2zXLYer81Vz+1CcVjjlmYEdG9G6XjPAkzeKqvXb3BWb2NEHCfRbwGTACOA/Yi2AFSjezZcAEdz8jNeGKiNQRux0C0/8DX1fwYV9JMaxfAh3zYeg5KQkhalEAil0Jt0h9s8+urXn/q1W8/1X5jd9WrN/Ct+u3KOHOEFVdNHkL8AbBapLbuPscYI6ZdQKGAn2BgwmS8INTEqmISF1y8G+CW0U2rgzaUaawQ05W2FteCbdI/XPSkC6cNKRLhftPu/cDCrYW1WJEkkpVzXBfBPyCoHRkJkEZyR5m1sjdtxUeuftsYDZwT6oCFRGpV6Kx4D6FK0JGLFgsWCUlIpmnSU6UCbNXs9eN46scm5MV4Z4fD6F/5/QsxiVVqyrhbgnsT1CzfThBR5I/ANea2UdADMDMYu6uRsciIqUiYcJdvDVlp1BJiUjmOnf/XWnbrOp2nwVbi3hu+hK+WLJWCXcdVtVFk5uB8cB4M3sQ+BL4B8FM9yHAnuHQtWb2ATCBoIZbHUxEpGErXXJ948qUncLMiFqUbzd9y6qCVbRu1Dpl5xKR2rXPbq3ZZ7eq39PfbdzKc9OX8PXKTcxcug6AHm2akBuLpjpESUAkgbGlyydNcvfL3X0wcGO47U6gOXAtwWqUIiINWyT8ZTf5vpSepnGsMU/PeZrjnjsupecRkbqpSU4Wsahxz9tfcfRt73L0be/y66c/TXdYsoOarhBZDODuVwCYWR5wUE2DEhGp98yg277w7YyUnuahIx/ivs/u47X5r+HumGktMpGGJDsrwtM/23dbT++//e9Llq9Lzyq4UrFEEu7lwDnARxUNcPe1wAs1DUpEJCN0GQrfvAervoLWu6XkFL1b9Wa3FsGxHce0+K9IgzOwSwsGdmkBwOMfLeTLZeu55+2vqnxe5xaNOHbPTimOTiCBhNvdNwAP77B5QlKjERHJJG33CO4n3QbH3Z6y00TC6sASL9nWuUREGqZe7ZsxYfYKbn5lVlzjR/RuS7PcWIqjkhqVlLj728DbSYpFRCSzDDoD3vwDFKX2493SJNvdqxgpIpnu6qP34BeH9apy3FNTF3Lt81+wcUuxEu5aoKkQEZFUym6a0sVvgG112yWUpPQ8IlL3mRmNsqNV3vIaBUn2r56arj/Wa4ESbhGRVIpmp7QXN7CtbrvElXCLSHwGd2sJwKS5q9i4Vb38U00Jt4hIKkWzoGhLSk+hkhIRSVTXVo35wwn9AVhXUEhhcQmFxfqjPVVq2hZQREQqYxGY+zps3QjZTVJyim0JN0q4RSR+zXKCNHDfm9/ctu2KI3tz0cG7pyukjKWEW0QklToPgSUfw8YVKUu4VVIiItVxaJ92/PYHfdhSFJSUPDhpPrOWrU9zVJlJCbeISCp1PwA+uh/mvA55XVJyisiK2QCUzPkfZDWueGBuC9hln5TEICL1T7PcGOcfuOu2x698voxvVm9i/IzlaYwqOfbv2aZOLW+vhFtEJJWadQzuX748Zaew5k2hdSv86fOgpIpZ7ounQht9XCwiO2vfPJc3Z33LeY9MSXcoNfbeVYfQqUWjdIexjRJuEZFU6joMLvoICjem7BSRheNh1iOUnPUcZDcvf9D8SfC/38LWDSmLQ0Tqt9tOzWf+yk3pDiMp2jTNSXcI20ko4TaznsBlwDCgJVDeXL27e9xrGJtZC+B+oD/gwE/c/f1w36+AW4C27r7SgmaztwE/ADYBo919WiLfg4hIrTKDtlUvQlETkXVfAlDSvh80alP+oPXhR8Sq8xaRCjTLjTGgS166w8hIcSfcZrYPMB5oBBQBy8P7nYYmGMNtwKvuPsrMsoHG4fm6AkcA35QZezTQM7ztDdwd3ouINFilC99U2hYwEs6PKOEWEal1icxw/wnIAX4KPOju5SXbCTGzPOBAYDSAu28FSleI+DtwJfB8maccDzziwW+VD8yshZl1dPelNY1FRKS+iqstYJiUK+EWEal9iSTcewHj3P3eJJ6/B7ACeMjM9gSmEpSsHAYsdvdPSmduQp2BhWUeLwq3KeEWkQarNOE+8+UziUYquCq/sAC6dIT3r4KPcmsxOhGR2vfwUQ/TtnHbdIexTSIJ91a2L+9I1vkHA5e4+4dmdhtwPcGs9xHVPaiZXQBcANCtW7ckhCkiUnft22lfRu4+kq0llSwhv345rFwAzXpA07rzS0hEJBVikVi6Q9hOIgn3e8CgJJ9/EbDI3T8MH48jSLh7AKWz212AaWY2DFgMdC3z/C7htu2Es/D3AgwdOlRLr4lIRuvQpAM37HdD5YO+fhcmPwM/GA09DqiVuEREJBBJYOxvgH3N7MxkndzdlwELzax3uOlQYJq7t3P37u7enSApHxyOfQE4ywLDgbWq3xYRiUNYdqIabhGR2pfIDPfxwJvAGDM7j6Deek0549zd/5DAcS8BHgs7lMwDzqlk7MsELQHnErQFrGysiIiUUsItIpI2iSTc15f5+oDwVh4H4k643X06MLSS/d3LfO3ARfEeW0REQtsS7uL0xiEi0gAlknAfnLIoREQktbb14dZlLSIitS3uhNvd305lICIikkLqwy0ikjaJXDQpIiL1VWlJyeqv0xuHiEgDpIRbRKQhaNYxuC+upFe3iIikRIUlJWZWApQAfd39y/BxPMV/7u6J1IaLiEiqNQkXuynclN44REQaoMoS43cIEuxNOzwWEZH6JhKFWBOYcDO8c8v220f+C/qdkLbQREQyXYUJt7uPqOyxiIjUM8fdDsu/2H7bxFvh25lKuEVEUkilHyIiDcWAUcGtrPduV123iEiK6aJJEZGGLJoNJYXpjkJEJKNVOsNtZmdV56Du/kj1whERkVoViUGxEm4RkVSqqqRkDIldKGnheCXcIiL1QTQGCybB/36X7kikMpEoDD0XWnRNdyQiUg3x1HAXAS8CM1Mci4iI1LYuQ2He27BybrojkQo5FG2GnGZwwK/SHYyIVENVCffbwEHASKA9cB/wpLtvTnVgIiJSC05/It0RSFXc4fctoEgXt4rUV5VeNOnuBwO9gFuAnsBDwFIz+6eZDayF+ERERBo2s7DWXgm3SH1VZZcSd5/r7r8GugAnAx8CPwM+NrPJZnaumTVJcZwiIiINl7rJiNRrcbcFdPcid3/a3Y8CdgNuAjoC9wJLzGyfFMUoIiLSsEWisG5puqMQkWqqVh9ud1/g7r8D/g9YDDQF2iYzMBEREQnFGsHn4+DTJ9MdiYhUQ8IJt5l1MrNrzGweQfeS1sCjwLRkByciIiLAqAeD++/mpzUMEameuJZ2N7MIcAxwHnBU+LzPgMuAf7v72pRFKCIi0tB13z+o4140BT5+bOf9jVtD76NqPy4RiUtVK032AM4FziGo194IPAzc5+6TUx+eiIiIANCiG8x5LbiV57JPoGX3Wg1JROJT1Qx36UoIU4DrgLHuvjG1IYmIiMhO/u8d2Lhy5+3z3oIXL4OCNdCy1qMSkThUlXAbUEgwu30tcK2ZVXVMd/ddkhCbiIiIlMpuEtx21CL8lfv6tUFpSXYTOPImyG1eu/GJSIXiqeGOEfTgFhERkbqmXV/oNAjWLYHV82DtQuh/Iux2SLojE5FQpQm3u1erbaCIiIjUkmbt4YIJwddLP4F/HQhbN6U1JBHZnhJqERGRTJHdNLh/+jy4pRcs/yK98YgIoIRbREQkc7TsAYf8DvqNhA3LYfmMdEckIsTZh1tERETqgUgEDrwc1i6GT/4D384IendXJKc5tO1Ve/GJNFBKuEVERDJNbh5EsmDircGtMhd9pKRbJMWUcIuIiGSanKbBhZTrl1U8ZvkXMP66oPRECbdISinhFhERyUQdBgS3ijRuFdxPewQWTAq+zmkGwy6AaCz18Yk0IEq4RUREGqIWuwSlJ589uf32zkOg2/D0xCSSoZRwi4iINERN2sCvF3z/eOFkePAI2LIhfTGJZCgl3CIiIg2V2fdfly4b/+qv4e2W6YlHJFlO/Q80bZfuKLZRwi0iIiLQpif0HwUFq9MdiUjNWd1aakYJt4iIiEBWDox6IN1RiGSkupX+i4iIiIhkGCXcIiIiIiIppIRbRERERCSFlHCLiIiIiKSQEm4RERERkRRSwi0iIiIikkJKuEVEREREUkgJt4iIiIhICinhFhERERFJISXcIiIiIiIppIRbRERERCSFlHCLiIiIiKSQEm4RERERkRQyd093DCllZiuABWk6fRtgZZrOLbVDr3HDoNe5YdDr3DDodc586XyNd3H3tjtuzPiEO53MbIq7D013HJI6eo0bBr3ODYNe54ZBr3Pmq4uvsUpKRERERERSSAm3iIiIiEgKKeFOrXvTHYCknF7jhkGvc8Og17lh0Ouc+erca6wabhERERGRFNIMt4iIiIhICinhTgEzO8rMZpvZXDO7Kt3xSGLMrKuZvWVmM8zsCzO7LNzeysxeN7M54X3LcLuZ2e3h6/2pmQ0uc6yzw/FzzOzsdH1PUj4zi5rZx2b2Uvi4h5l9GL6WT5hZdrg9J3w8N9zfvcwxrg63zzazI9P0rUgFzKyFmY0zs1lmNtPM9tF7OfOY2S/C/68/N7OxZpar93P9Z2YPmtm3ZvZ5mW1Je/+a2RAz+yx8zu1mZqn6XpRwJ5mZRYE7gaOBvsBpZtY3vVFJgoqAX7l7X2A4cFH4Gl4FvOHuPYE3wscQvNY9w9sFwN0Q/KcAXAfsDQwDriv9j0HqjMuAmWUe/xn4u7vvDnwHnBtuPxf4Ltz+93Ac4c/FqUA/4CjgrvD/AKk7bgNedfc9gD0JXm+9lzOImXUGLgWGunt/IErwvtT7uf4bQ/BalJXM9+/dwPllnrfjuZJGCXfyDQPmuvs8d98KPA4cn+aYJAHuvtTdp4Vfryf4Bd2Z4HV8OBz2MHBC+PXxwCMe+ABoYWYdgSOB1919tbt/B7xOCt/Mkhgz6wL8ELg/fGzAIcC4cMiOr3Hpaz8OODQcfzzwuLtvcfevgbkE/wdIHWBmecCBwAMA7r7V3deg93ImygIamVkW0BhYit7P9Z67vwOs3mFzUt6/4b7m7v6BBxc0PlLmWEmnhDv5OgMLyzxeFG6Teij8qHEQ8CHQ3t2XhruWAe3Dryt6zfWzULf9A7gSKAkftwbWuHtR+Ljs67XttQz3rw3H6zWu23oAK4CHwtKh+82sCXovZxR3XwzcAnxDkGivBaai93OmStb7t3P49Y7bU0IJt0gFzKwp8DTwc3dfV3Zf+NewWvzUU2Z2DPCtu09NdyySUlnAYOBudx8EbOT7j58BvZczQVgecDzBH1idgCboE4gGoT69f5VwJ99ioGuZx13CbVKPmFmMINl+zN2fCTcvDz+CIrz/Ntxe0Wuun4W6az/gODObT1D2dQhBrW+L8CNp2P712vZahvvzgFXoNa7rFgGL3P3D8PE4ggRc7+XMchjwtbuvcPdC4BmC97jez5kpWe/fxeHXO25PCSXcyfcR0DO8Ojqb4AKMF9IckyQgrOV7AJjp7reW2fUCUHp189nA82W2nxVeIT0cWBt+3PUacISZtQxnYI4It0maufvV7t7F3bsTvEffdPczgLeAUeGwHV/j0td+VDjew+2nhl0PehBcdDO5lr4NqYK7LwMWmlnvcNOhwAz0Xs403wDDzaxx+P936eus93NmSsr7N9y3zsyGhz83Z5U5VvK5u25JvgE/AL4EvgJ+m+54dEv49duf4COqT4Hp4e0HBDV+bwBzgPFAq3C8EXSm+Qr4jOBK+dJj/YTgwpu5wDnp/t50K/f1HgG8FH69K8Ev2LnAU0BOuD03fDw33L9rmef/NnztZwNHp/v70W2n1zcfmBK+n58DWuq9nHk34PfALOBz4N9Ajt7P9f8GjCWoyy8k+MTq3GS+f4Gh4c/MV8AdhAtCpuKmlSZFRERERFJIJSUiIiIiIimkhFtEREREJIWUcIuIiIiIpJASbhERERGRFFLCLSIiIiKSQkq4RURERERSSAm3iEg9YmZRMzvfzN42s9VmVmhm35rZp2Z2v5kdV2bsaDNzMxudxpBFRBq8rKqHiIhIXWBmUeAl4ChgDfBfgsUgsoF+wOnAHmh1WxGROkUJt4hI/XEaQbL9CXCQu68tu9PMGgN7pyMwERGpmEpKRETqj33D+zE7JtsA7r7J3d8CMLMJwEPhrofC0pLSW/fS55hZlpldaGYfmNk6M9tkZh+b2cVmtt3vCDPrHj5/jJntYWbPhWUtG81sopkdsWNMZpZtZpea2TQz+y48/nwze97MDkvSv4uISJ2mGW4RkfpjVXjfK46xYwjKTo4Hngeml9m3BsDMYsCLwJHAbOA/wGbgYOCfBLPlZ5Zz7B7A+8BnwL+AjsApwCtmdrq7P7FDHKcBnwOPAAVAJ2B/gtn68XF8LyIi9Zq5e7pjEBGROJjZIOBDgsmSx4BnganuvqCC8aMJZrnPcfcx5ey/HrgOuAP4ubsXh9ujwL3AT4AT3P35cHt34Ovw6be4+xVljjWUIAnfAOzi7uvMLA/4DpgG7F16/DLPae3uqxARyXAqKRERqSfc/WPgx8Dy8P5pYL6ZrTKzZ83s2HiPFZaLXAIsA35RNhkOv/4V4MAZ5Tx9LXDDDrFNIfgjoAUwsnQzYMAWoKSc70fJtog0CCopERGpR9z9STN7lqDsY39gUHh/AnCCmT0CjPaqP77sBbQC5gDXmFl5YwqAPuVsn+bu68vZPgE4O4zp4XCW+0XgWGC6mT0NvAt86O6bqohPRCRjKOEWEaln3L0Q+F94Ky0BOQl4EDiLoNTkuSoO0zq870lQVlKRpuVsW17B2GXhfV6ZbacAvyZoWfj7cNtmMxsHXO7uFR1LRCRjqKRERKSec/did38S+Hu46ZA4nlba5eRZd7dKbj3KeW77Co7ZYYdj4+4F7n69u/cCuhGUwkwM78fFEaeISL2nhFtEJHOUlnmU1oeU1mVHyxk7i6BbyfCwW0kiBptZs3K2jwjvPy7vSe6+0N0fI+iKMhfY38xalzdWRCSTKOEWEaknzOw0Mzt8x/7Y4b4OwPnhw3fC+9KLErvtON7diwha/3UEbjezRuUcs6OZ9S0nlDzg2h3GDiW4wHItQUkLZtbWzAaU8/wmBKUqRcDWcvaLiGQU1XCLiNQfewOXAcvMbCLft+jrAfwQaETQc7u0VON9YBPw83AmubTG+p/hwjl/APYEfgoca2ZvAouBdgS13fsBvwVm7BDHO8B5ZrY3MInv+3BHgP9z93XhuM7Ax2b2GfApsBBoDhxDUH5yewUXX4qIZBT14RYRqSfMrCtwHHAY0Jcg0c0lmMn+mGDhmv+4e0mZ5xxFcFHkAIKZZYAe7j4/3G8E9dSjCbqLNAVWECTzLwP/dveF4dju4faHgT8DNwMHAjnh+W9w99fKnLsFcClBqUlvoA2wmmCRnX8Bj8fRTUVEpN5Twi0iInEpm3C7++j0RiMiUn+ohltEREREJIWUcIuIiIiIpJASbhERERGRFFINt4iIiIhICmmGW0REREQkhZRwi4iIiIikkBJuEREREZEUUsItIiIiIpJCSrhFRERERFJICbeIiIiISAr9P84/fSlGdDitAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12,6))\n", + "plt.plot(min_scores_sb, label=\"Short Bursts\")\n", + "plt.plot(min_scores_anneal, label=\"Simulated Annealing\")\n", + "plt.plot(min_scores_tilt, label=\"Tilted Run\")\n", + "plt.xlabel(\"Steps\", fontsize=20)\n", + "plt.ylabel(\"Min #CutEdges Observered\", fontsize=20)\n", + "plt.legend()\n", + "plt.show()" + ] } ], "metadata": { From f1a233f8a4f721ca181c28a224476b1613dc2bc4 Mon Sep 17 00:00:00 2001 From: Gabe Schoenbach Date: Tue, 8 Mar 2022 16:54:12 -0600 Subject: [PATCH 16/28] fix some typos and jumpcycle call --- docs/notebooks/Optimization.ipynb | 59 +++++++++++++++++-------------- 1 file changed, 33 insertions(+), 26 deletions(-) diff --git a/docs/notebooks/Optimization.ipynb b/docs/notebooks/Optimization.ipynb index 05242f07..75960782 100644 --- a/docs/notebooks/Optimization.ipynb +++ b/docs/notebooks/Optimization.ipynb @@ -6,16 +6,16 @@ "source": [ "# Introduction to Optimization in GerryChain\n", "\n", - "This notebook walks through how to use the SingleMetricOptimizer class (as well as its Gingleator subclass) in order to prefrom heuristic optimization runs in GerryChain.\n", + "This notebook walks through how to use the SingleMetricOptimizer class (as well as its Gingleator subclass) in order to perfrom heuristic optimization runs in GerryChain.\n", "\n", - "There are 3 heuristic optimization methods who's use is shown in this notebook:\n", + "There are 3 heuristic optimization methods whose use is shown in this notebook:\n", "* **Short Bursts**: chaining together small neutral explorers ([More reading about short bursts here](https://arxiv.org/abs/2011.02288))\n", - "* **Simulated Annealing**: vary the propability of accepting worse plans over time wrt a temperature parameter $\\beta$.\n", - "* **Tilted runs**: accept worst plans with a fixed probability $p$\n", + "* **Simulated Annealing**: vary the probability of accepting worse plans over time wrt a temperature parameter $\\beta$.\n", + "* **Tilted runs**: accept worse plans with a fixed probability $p$\n", "\n", "## When do we want to use Heuristic Optimization?\n", "\n", - "While sampling naively with GerryChain can give us and understanding of the neutral baseline for a state, there are often cases where we want to find plans with properties that are rare to encounter in a neutral run (aka they would take a long time to observe). Many states have laws/guidelines that state that plans should be as compact as feasible, maximize preservation of political boundaries and/or comunities of intrest, some even look to minimize double bunking of incumbents or seek proportionality/competitiveness in contests. Heuristic Optimization methods can be used to find example plans with these properties and to explore the trade-offs between them." + "While sampling naively with GerryChain can give us an understanding of the neutral baseline for a state, there are often cases where we want to find plans with properties that are rare to encounter in a neutral run. Many states have laws/guidelines that state that plans should be as compact as feasibly possible, maximize preservation of political boundaries and/or communities of interest, some even look to minimize double bunking of incumbents or seek proportionality/competitiveness in contests. Heuristic optimization methods can be used to find example plans with these properties and to explore the trade-offs between them." ] }, { @@ -105,7 +105,7 @@ "source": [ "## Optimizing for Gingles Districts\n", "\n", - "Named for the Supreme Court case Thornburg v. Gingles which created their precedent as one of the litmus tests in bringing forth a VRA court case, *Gingles' Districts* are districts that are 50% + 1 of a minority population subgroup (more colloquially they are also called majority-minority districts). Because these are used to demonstrate liability it is common to seek plans with greater/maximal numbers of gingles districts to understand the landscape of the state space." + "Named for the Supreme Court case _Thornburg v. Gingles_, which created their precedent as one of the litmus tests in bringing forth a VRA court case, *Gingles' Districts* are districts that are 50% + 1 of a minority population subgroup (more colloquially called majority-minority districts). It is common to seek plans with greater/maximal numbers of gingles districts to understand the landscape of the state space." ] }, { @@ -137,7 +137,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████| 10000/10000 [06:00<00:00, 27.72it/s]\n" + "100%|█████████████████████████████████████| 10000/10000 [06:16<00:00, 26.56it/s]\n" ] } ], @@ -151,21 +151,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████| 10000/10000 [07:02<00:00, 23.66it/s]\n" + "100%|█████████████████████████████████████| 10000/10000 [07:48<00:00, 21.33it/s]\n" ] } ], "source": [ "max_scores_anneal = np.zeros(total_steps)\n", "scores_anneal = np.zeros(total_steps)\n", - "for i, part in enumerate(gingles.simulated_annealing(total_steps, gingles.hot_cold_cycle_beta_function_factory(1000, 4000),\n", + "for i, part in enumerate(gingles.simulated_annealing(total_steps, gingles.jumpcycle_beta_function(1000, 4000),\n", " beta_magnitude=500, with_progress_bar=True)):\n", " max_scores_anneal[i] = gingles.best_score\n", " scores_anneal[i] = gingles.score(part)" @@ -173,14 +173,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████| 10000/10000 [07:04<00:00, 23.57it/s]\n" + "100%|█████████████████████████████████████| 10000/10000 [07:41<00:00, 21.65it/s]\n" ] } ], @@ -194,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -223,12 +223,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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7/9OcGo4d24OWQJBxfSoY3r0T429+Q3ec7qJVCJfS1wlvepOibBC2+88lHCHbwcHBQYMKUYtftPB463SWyn4J98NmJ/HskqW8FNwfqwbUZBw1qrumkG0VVx01zBLzgs7FPnaGBFWZpGyEp037HmxWN0Q5ZVJvLp45OC0hGzLxhTTSTSF4nntgfx74wB5Nd6bf2Wx61aV+epYX0rNcMTmYPrSLISH7yJHdeHXZZpOpJ1LTu5wrjhgKRFsP60MYfiI5fm+ixvbAwVWhvQZupg6qYne9sUmhAIJNVbj82zH+hulx1pS+uvc8aJF7IrYjZDs4ODhoEh4Y3w2O4eXg/gn3W6t689iqdQnXC71uCOo1gMx9+leZ82aUCi3Bw+XbYUt6ZklDEWuaU/brbZuQnSmsLrZsC1FW1AOrRFe1rGS7fBwScU58dHBw6NBkQ4BySCVs6LNnz2dSVbtBXcx5ltCVdqbqvMhwejmM1ZrsVCshSppGC15GVpjy8ZPlYj1zhGwHBwcHDcIDo7ZpQ/Z79UwMLHYloSl4SBfBVnu052bI/le2ivx+E6N13eq2kUulp6rJzkUps4PjCNkODg4dlvCBHA6ZJ5lNtnLL+TJ2kSlhTOSxVjQVRt8pHD5pvTcSX3ss1HaII2RnkFtvvZWRI0cyZswYampq+OSTTwA499xz+eqrryxJo1+/fmzfntzn72233WY43ocffpiLL75Y8/7xxx8fOYY9k9xwww3ceeedAFx33XW8+eabGc+DQ/vF7MDY3gZAu4Sy5LGGbQ3s3YCl59WEsGEbWDurI2pYX206QKHpRJdNtqniyky7S0jVgsqSi5p8Z+Njhpg/fz4vvfQSixYtwu/3s337dpqblZ2/4cNhMsVtt93GNddcY1l8u3fvZuHChZSUlLBq1aqsnfZ40003ZSVdh/wmnY45B/v0doLMtHMDBxuInMzXXhpKWu+RyvTMaFZsKNMMC9YdAUeTnSE2bdpEVVUVfr8fgKqqKnr06AHA9OnTCR+eU1JSwi9+8QtGjhzJoYceyqeffsr06dMZMGAAL7zwApCoVZ41axbz5s1LSPP4449nwoQJjBw5MnK65FVXXUVDQwM1NTWcfvrpAPz73/9mv/32o6amhvPPP59AQPGK8I9//IMhQ4aw33778eGHH2q+2zPPPMOxxx7LKaecwn//+9/I9blz53LppZdywAEHMGDAAJ566ikA5s2bx/Tp0zn55JMZNmwYp59+OuFDkRYuXMjBBx/MhAkTOOKII9i0aRMAf//735k0aRJjx47lpJNOor6+PiEfc+fOjaTRr18/rr/+esaPH8/o0aNZvlxx+7Rt2zYOO+wwRo4cybnnnkvfvn1Tav4dHDr60GOHiCQRKTaD5ZJgZn1ejG9KsyDNLBWpVYfeZdsmO5ps9wnqNtkWRCxzqd3lPx1Tk/3KVbD5S2vj7DYajrpd8/bhhx/OTTfdxJAhQzj00EOZM2cOBx98cEK4ffv2MXPmTH73u99xwgkncO211/LGG2/w1VdfcdZZZzF79mzdWXrooYfo3LkzDQ0NTJo0iZNOOonbb7+de+65h8WLFwPKyYaPP/44H374IV6vl4suuohHH32Uww47jOuvv56FCxdSVlbGjBkzGDdunGo6jz32GNdddx1du3blpJNOitGSb9q0iQ8++IDly5cze/ZsTj75ZAA+//xzli1bRo8ePZg6dSoffvghkydP5pJLLuH555+nurqaxx9/nF/96lc89NBDnHjiifzkJz8B4Nprr+XBBx/kkksuSfr+VVVVLFq0iL/85S/ceeedPPDAA9x4443MnDmTq6++mldffZUHH3xQd3k6dDys9gjg4NCRCAt97UWTnc5btG2itgidZk4O2aVjCtlZoKSkhIULF/L+++/zzjvvMGfOHG6//Xbmzp0bE87n83HkkUcCMHr0aPx+P16vl9GjR7NmzRpDad511108++yzAKxbt44VK1ZQWRl7sMNbb73FwoULmTRpEgANDQ106dKFTz75hOnTp1NdXQ3AnDlz+PbbxEMftmzZwooVKzjwwAMRQuD1elm6dCmjRo0CFG26y+VixIgRbNnSdmDGfvvtR69evQCoqalhzZo1lJeXs3TpUg47TDmlLhAI0L27cgTz0qVLufbaa9m9ezd1dXUcccQRKd//xBNPBGDChAk888wzAHzwwQeRMjnyyCOpqKjQU5QO7Rh945B6qNz1OWItdgzWyqZTLZEjekuqzTbZOr6WEDb4fG5vlSQD5FKR5VJerCUz7a6j0DGF7CQaZztxu91Mnz6d6dOnM3r0aB555JEEIdvr9UZm/S6XK2Je4nK5aG1tBcDj8RAMtvmRbWxsTEhr3rx5vPnmm8yfP5+ioiKmT5+uGk5KyVlnncVvfvObmOvPPfecrnd64okn2LVrF/379wdg7969PPbYY9x6660AkfyH0woTfd3tdtPa2oqUkpEjRzJ//vyEdObOnctzzz3H2LFjefjhh1XNY+IJpxGO36HjYNnytDPQODiYpj17FzGK5d5FVOJJ3wTJ6e+sxrHJzhDffPMNK1asiPxevHgxffv2NRVXv379WLx4McFgkHXr1vHpp58mhNmzZw8VFRUUFRWxfPlyPv7448g9r9dLS0sLAIcccghPPfUUW7duBWDnzp2sXbuWyZMn8+6777Jjxw5aWlp48sknVfPy2GOP8eqrr7JmzRrWrFnDwoULY+yyjTB06FC2bdsWEbJbWlpYtmwZALW1tXTv3p2WlhYeffRRU/EDTJ06lSeeeAKA119/nV27Eo/EduhY6NEoag09HUUbac97JhMJZE7ZhtqRk9x5O/vIdvuw2u492+8TjS6vOOZiNvWUgzodU5OdBerq6rjkkkvYvXs3Ho+HQYMGRTYjGmXq1Kn079+fESNGMHz4cMaPH58Q5sgjj+S+++5j+PDhDB06NMa93nnnnceYMWMYP348jz76KLfccguHH344wWAQr9fLvffey/77788NN9zAlClTKC8vp6amJiGNNWvWsHbt2pi4+/fvT1lZWcQ9oRF8Ph9PPfUUl156KXv27KG1tZXLLruMkSNHcvPNNzN58mSqq6uZPHkytbW1huMHuP766zn11FP517/+xZQpU+jWrRulpfadrOaQ3+TDcNNe7F0Taa/vlT0yXaJt3kUsii+H6rpZna91mmy9Fx2yiSNkZ4gJEybw0Ucfqd6LNn2oq6uL/H3DDTfEhAvfE0JoanOj7bZfeeUV1TB33HEHd9xxR+T3nDlzmDNnTkK4s88+m7PPPls1DlA06hs2bEi4vmjRIgAmT56smv+wyUyYe+65J/J3TU0N7733XkKcF154IRdeeGHC9egyevjhhyN/R5fDxIkTI2VcVlbGa6+9hsfjYf78+Xz22WcxpisODmpoDYxamrJcEgZyFYlAJHUZlju2oXZ8T6eK5B/paMatP1bd0uiiYw79m/121x5whGyHDsX333/PD3/4Q4LBID6fj7///e/ZzpJDlkk+cDoDDVi37G6VrbyV6BVWci/neYAI22TbqL3NM6yqR6o22SL+t4kSy5KZVnuddDpCtkOHYvDgwXz++efZzoZDnqGpyW6nA0MmSCpvizbvIrlQxPbYZOfCm9mLSPjDxrREijplURqmn80LTbbU+NvBLM7GRwcHhw5NssEqH7yLZERUa//yYIcg382Ycin76RiOWJK+SjTOekHu4QjZDg4ODibpKH6y7SDliY/hZescOOrZlhOsO0AliRxGk4m0MpBGOthxZqit5HqB5gmOkO3g4OCggdktQNkXC62lPY+37fnd2huGTWws/ri5VFfamwu/9mo+5QjZDg4ODibJ9+X3bJJcky0tc3WWq7Tvt1OIHEaTEZvszJao0Yl027Hq9hl1pF8G2bPJlu1ONaHgCNkZYseOHdTU1FBTU0O3bt3o2bMnNTU1lJSUcNFFFwGKC7qLL74YUE5c/OqrrwynU1JSonrd7XZTU1PDqFGjOPbYY9m9e7fpd3Fw6CiYHRjbmwBllQCTr5OS9qplsxurP3e2q0+204/Gtrzk0CFQ7YGcELKFEG4hxOdCiJdU7vmFEI8LIb4TQnwihOiXhSymTWVlJYsXL2bx4sVccMEFXH755SxevJi6ujr+8pe/JIQ3K2RrUVhYyOLFi1m6dCmdO3fm3nvvtSxuB4d8JpcGTjPka/7b/IckEutXpH1quLLx4bJ2GE0GUs7Wu+kPH56wW5W+2rHqcb+NZ9LBYnJCyAZ+Cnytce8cYJeUchDwR+AOjXB5ybx585g1a1bMtY8++ogXXniBX/ziF9TU1LBy5UpWrlzJkUceyYQJE5g2bRrLly8HYPXq1UyZMoXRo0dz7bXX6kpzypQpkUNkpk+fzoIFCwDYvn07/fr1AxSt+oknnsiRRx7J4MGDufLKKy16YweH/MHqJd58pWO/vSKsdPQyyEfa8zfL18l1RyPrfrKFEL2AY4BbgZ+pBDkOuCH091PAPUIIIdM41eCOT+9g+c7lZh9XZVjnYfxyv19aEtcBBxzA7NmzmTVrFieffDIAhxxyCPfddx+DBw/mk08+4aKLLuLtt9/mpz/9KRdeeCE/+tGPdGmnA4EAb731Fuecc07KsIsXL+bzzz/H7/czdOhQLrnkEnr37p32+zk45BJpneLmDHSmSWWTnbFl6yx9w45QdTLZPvLF7ttem+x0Y022vuRghlzQZP8JuBIIatzvCawDkFK2AnuAyvhAQojzhBALhBALtm3bZlNWs0NdXR0fffQRP/jBD6ipqeH8889n06ZNAHz44YeceuqpAJx55pmacTQ0NETswbds2cJhhx2WMt1DDjmEsrIyCgoKGDFiBGvXrrXmhRwcbMYyG+LQv1qioKaA7oxTDg6RdpityaidewCybcSk593MKBCkyl8O5smqJlsIMQvYKqVcKISYnk5cUsr7gfsBJk6cmLR2WKVxzhTBYJDy8nIWL16sel9PYwvbZNfX13PEEUdw7733cumll+LxeAgGlflNY2NjzDN+vz/yt9vtprW11fxLODg42EJG7F1tSELRmSU99jEqZPaxOhfZEDzzfeUl+cFRgvivlMaCt+VkwvQs/b4ge5rsHPpUlpJtTfZUYLYQYg3wX2CmEOLfcWE2AL0BhBAeoAzYkclMZoPS0lJqa2sB6NSpE/379+fJJ58ElI7jiy++AGDq1Kn897//BeDRRx9NGW9RURF33XUXv//972ltbaVfv34sXLgQgKeeesqOV3FwyGn0nfjoHKvekXG+s0Ou4dTJ/CCrQraU8mopZS8pZT/gFOBtKeUZccFeAM4K/X1yKEw7nfO0ccopp/C73/2OcePGsXLlSh599FEefPBBxo4dy8iRI3n++ecB+POf/8y9997L6NGjI5sZUzFu3DjGjBnDY489xhVXXMFf//pXxo0bx/bt2+18JQcHhzzFHm15klhFtEbN3u4+W+75OoJbwMiJjxa9atJVW7Vjxi2WRNOJLhPeRRLC5FEVy6e8GiHrGx/VEELcBCyQUr4APAj8SwjxHbATRRjPa2644YbI39OnT2f69OkAzJ07l7lz5wKKhjrehd+rr76aEFf//v2ZP39+5Pctt9yimmZdXV3M7xdffDHy95IlSxKej84LwEsvJXhXdHBoF+jp3LWWeDvqsepCtN/lXTU6gkBsJ+2x/LL9Rmr9liWCqszM5DYh2Xban+SMkC2lnAfMC/19XdT1RuAH2cmVg4NDPmLVYldym+GOQ/zgnWj9apzk3kXCqZB9acbBNFYL18liy5dqYqd3kfQJadudA2ksI9s22Q4ODg5ZRY8gkMuidvtcZs2gCz8d2FHGWdn4mGFR1GpzkWyTlrtPC/MBGSjTdvLNso0jZDs4ODiYpL0ID6mw4zX1W6jm8hTHIZMYbW92Nk+ztdIyTXZH6XzynA4lZHeA/ZIOFuDUE4d4jA6M7X0AzMz7ZaYM9byKLZps66PMOTL5jpk5jCaNZy2eLNpzGE10zM44aAUdRsguKChgx44djgDlkBQpJTt27KCgoCDbWXHIEPpc+Gk92xFEJRJGdEv2V7XL7XAO0bTn5pH1V7NjciiSuyx1ME7ObHy0m169erF+/Xra22mQDtZTUFBAr169sp0NhxzCzgMk0iUbObNfeMqt453tmA50hMNowuVm3Qms2vHk+pTNcpvsHH9fB4UOI2R7vV769++f7Ww4ODjkGMk9FqTQZFublbxB7XQ9PUSvJKb2LtIW0sHBDFZPKnKpvau68MupHDpABzIXcXBwcDCLUTGvvS2TZ2XwzpB3ET2p2ONdpJ1VEhUi3kUsjs/oPTsw3ifYf6x6Qpqm0nJssq3EEbIdHBw6NAYPkXMAywrG8UPeMWgvXzmX5kX2bHx0bLKtxhGyHRwcHFKQyzbZmUDtMJp0SS545ZZY1rG/vnksP9bc5D07MLyn0OKDXuwT+J3abiWOkO3g4OCgQT5oWvNpA1280KUvmtz/Bg4OmUbNFCT+Si75Fe+oOEK2g4NDB0fPiY+5PfzYLWjbcxhNKjsdkTPHO7eXEx8zjeWvmNQmO7XQmX7y6cdo1ZTRtvojcea1FuII2Q4ODg4amB3HOoD8ZAm6VgrsnkB0BGk3yzglnJkySL8uS42/HcziCNkODg4dGkfGSk2imYcVGr1kccgcG+Jt8JPdAURP613oJfOTbT/pvI8La72L6PKKYzrm9l83M4UjZDs4ODhoIEQ+bM/LvLhmlfCUXJOdGVdiHWmSlfnDaNovudH22w/t9TRuR8h2cHBwSEHu22Tbm7/MC6Lt35VYRxLuM4La4Sw5WMiWiZIZsEF3SB9HyHZwcOjQpHPio+ZzGR7cc919md3x2E0Oymp5geUu/HLoO5h14ZdJDJd/1MpdqlU8q8nFCZEVOEK2g4ODg4MhrBgQc311wG469ttbT86XZ6jN2GmTbY2c6thkW4kjZDs4OHRo9AiMueJKTotMu/CzLrkk2rJImWffVjO3v37uYv3Gx+ySzuQyH3zuZxPHJtvBwcHBIefIyiqrBWmm8i5iWUI5SnYOEWq/ZkyZ8JMdjVmR0DJNtg4b9PbbevIHR8h2cHDo0OixydYaULWezbyNdH5ufMwXIaC92ovajuU22dn9Dumknh81KHt+srP9be3CEbIdHBwcHDKOriE8A+N8tob2juAnO5PkunvCXDx9UivmbLQKx1zEwcHBoYPS0TfpxQ+61nkXyb6fbD109K9vFusFy/wl0946lDTTeTr77a494AjZDg4OHZpkA1E+bFYSkf/LYJp2excRmbPJztYqdVZssjOdXijBTLSiXD/x0WpszUvud3t5gyNkOzg4OKTA6JiT60vXhuMXyX+bjjdPRvNcEq46Moa/Qw59t7b9HTmUqQTyoz3mE46Q7eDg0KFJZtuYy8NhNPko1Oe2sOFgJZnRMmdg1SOtZy3eBGppbFHkuLvSfMMRsh0cHBxS0NEFQju3WKkjI3ezYcsaj7NJ0SFdsqHJNj/xcOq7VThCtoODg4MGeWPOkOFB0RqbbF0ppZ2OQ/shryc7eZf1zPZ9+dHTGsejdUMI8ZDJOKWU8hyTzzo4ODhkFD3yonHtUz5oq9JI07J41IdWQW6tHthRxFn5bhk3K8pcgmou4Cw30UjrxEcFq4RJW75lDqwatTc0hWxgrsb1tnU89esS0CVkCyEKgPcAfygvT0kpr48LMxf4HbAhdOkeKeUDeuJ3cHBwSIe80WTbfay6DQnkkhDtkCc4VSYDiJh/MpxquyOZkN0/7rcL+CMwDbgLmAdsBroBM4BLUATmnxlIvwmYKaWsE0J4gQ+EEK9IKT+OC/e4lPJiA/E6ODg4ONiE/d5FZJTKz+6JTnaG9/YqVGSLjGx8TCMJq22y7XnfzLnO1Eq5vaEpZEsp10b/FkJcjiJgj4+79w3wrhDiEWAhcBzwJz2JS2V9py700xv6r72WtYODQ57hCEIKieVgRcnoicP5Ag5t5JIrxfYqqEiVvxzMk0yTHc95wBPxwncYKeVqIcSToXB/0hupEMKNIpwPAu6VUn6iEuwkIcRBwLfA5VLKdQby7eDgkIe0BII8/tk6ttY20dgSwCUEPSsKcQlobAmyeU8Dzy3eyLbaJgBcAoImxgV7bLIzR31za9YOGUmHenczL3by0bP1VbbK8tj4PXXI5koggK/yfZq2HoVd+/T1vIs9NtnWxxlNYZ+/4y7YSKCpK27/RgCOe+mmSF0pGdKaPH/uZgCkFASbq6hfdTlGv0G67ziyR6e0nre6jNOJzutWyi6X+xIH6zEiZPcDdqcIsysUTjdSygBQI4QoB54VQoySUi6NCvIi8JiUskkIcT7wCDAzPh4hxHkoAj59+vQxkgUHhw6NlJKm1iBul6AlEEQgKPS52dfUyu6GFgo8Lppag3hcgvrmAFc+vYRPV+8EYEB1Mau27QPgtMl9ePnLTUzoU8G2uiaqS/x8vm43O/cpg/UxY7qzt6EFgE9W7aSi2Eu3TgWM7lXGup0NLN2wh54VhSxZv8fUe+gRsCf0rWDh2l2640xlk923slj9uahxdL9+nfl0zc7I79lje7BgzU427mnUnY9krNq2z/alciPR33zcSH79/LKYa50KPOxtjBXq3ipr5fvKSmAeBSrxBOr7I1vLEb4d4GqBoD/mvlbZ6+XWE0bpDuuyoXzLi7y6wxZ63TS0BDTvezu/h7twLUgPTVtmIwPFeIpXAuApWkOwuTOttSM4er8+FPuVYf+lJZsA2LynQSPOjxAiCMEC3P5t2K3Z7FLqZ1vDFjylywBJgdfN5LGD2N4wgKrCKiC5kNunc1Gkr7EKb/nH+Krm0bxzKi07p8Xc08rLILGeB7y/p0A0h8Ip5VbuCUKrdaXYrZNaq1FHePYiPLtV7wWbu0CwAJC4fDsItlQY7k/i+7h4jhjZldeWbTEUZ3vBiJC9HTgCuFrtplC+yhHADjMZkVLuFkK8AxwJLI26Hh3fA8BvNZ6/H7gfYOLEic46Rw7y4soXeXPtm1wz+Rq6Fne1Pb3GlgAbdjdQ4HXT0hokKCUlfg8BKWlplQgBgaBsO/pXKsJEid/DviZlQAv/bgkG+XLrMv605CaCUnLF+GsYWDqGyhIfu+qbEUIQCCjVLiAVXYXbJXC7RCQvxX4PjVEDpUsICrxuXAL2NQWQSPweNwVeRah1CUFtYwsel4sHP1jFI/OVRaSa3uUsXrebskIve0JCK8AJ43ry7rfbIgNNz/JCttY20hLK14CqYlZt38fssT14e/lW6pqSa7L0EBawAf7zyfcAvLV8q2rY/4UG9TBb9jaxZW8TX2+upbk1CMAOiwfJeJ66YAqvLt3Mht0N3PK/r3UIOsntKH84sRfXPPtlwvXbTxzNOY8sAOCJC6awevs+Ztw5D4CfHjqYS/7zuWVCts/jisndieN78syiDQnhbjpuJNfFCb/R9KooZP0udYEL4D/nTua0B5SFRp9bXaN58JBqzpzSL0HInveLGYy/+Q1+eeRQmlqDXPf8MraLYgqDQcasOprlZQeyIZT2384cz/n/WoQMFOOt+IiCbi8BiQLm3AP6aeZVD6dP7qsr3IGDquhe1ibQzD2gHw9/tCattAGOr+nJ5Y9/oSvs1zcfyb8/XssNLyyjNW42+a8f78dF869CBn0IVzMte8YT2Dc0JkywqQtNW2dxwehD6BZ6l59PUPq+/le/HBP21cumceSf3sddtBp34QZkazHC3cDDZ09i054Wrn7mS44a1Y1Xlm6Oee7SmYN46ctNMX1CuP+rLPFxwcEDue/dlZrveNrkPvz1y8fxdW7bkvWXL0G66rmo5qKUZdSncxGL1+0GYFi3Ukb2KOOXRw7jqYXrUz4b5uQJvWLCu4tX4fLuxlO0kkDdEOZt+4zV811s3reZlsJesGdsJOyModW88802hol19HNt4aXAZPbKosj9E8f24oWV9Xzb2DsmzZ7lhWzY3dbuDh3elTe/Ti6Q/nhqf4Z2K024/s737/D3L/8e42mlqN9u3IXaZdCydySNG87EXbga4WpGiFaKfR709sS3nTCaUyb1Zth1r0b68WjuOGk0cyb1YfG63QSl5MS/fKQz5jZ+e9IYBnYpprzIxytfbuLO17+N3Pu/GQO59x3tepVtjAjZTwKXCSGeAH4ppVwdviGE6A/cAYxB2RypCyFENdASErALgcNC8USH6S6lDI/Os4GvDeTZIYe4c8Gd7GzcybEDj7VdyN66t5H9bnvL0ji95R9T0H0VAOc98QwtO03NJ9MmPJBEC9gAb329JUZbGN1xA6zargx+L3yx0d4MGuTEcT3572fpWYCVF3nZXa+UhyDI0a5PKRYNCGCA2EgRikmJ+N9bHLVvG8F1n3BqmaTgezc8CPP9TarxdheKdqYFd+Ta2F5lfBHStnvcLn48tT8Pfbg65rlDhmvXbwE0BxIHo2uPGc4t/0u/e/v9D8Zy7oEDOPqu92Ou969q0/wuuPZQJt7yZsz90yb34bevfsOI7p34atPemHu761s4aEh15LcrTsY+aEg17327jelDq1HD4xasuf0YgMgqCATxSPAHCvDITsiAMhwdPmwIMrAiFEYpdyECCRrAQp8bNQ4f0ZXXv0oUUsKT01TcesIofvWsoue5eMYgrjgiVmA9dmx300L2XaeOY0ddEy4hEELw2E/259S/x+/zh/vOGA/A9ceOoNinlMsZ+/flsBFdmfO3+azZUc+9p41nxrBq/r70XgBa64bg7bQ0IS4tXC71iWNrILaky4t87A1An8oiNu1R6r2aovPAwdW8+XXsBHtSv85ceeRQjqvpSc/yQp77fAOb97ZNLl2+rRT2/RvC1cwjG114K5oJNHWhfs0FnD21H8/u+AmtwbY+rcSvT2Q5YVxPzj94YMpwNx8/ih5lBVz62Ofsa9ZeKQDwVb3Np7u+4Iu9fpoCTYjSatgyAqSXymIfx47twTvfbMOH0g99NeJn/OWLtjiPn3UEs30eJu5u4IDb3wZgzsTe3HLCKIZc+wrRHgj/MXcSG/c0ROqhGgXexInuexveY/nO5UzuPjlyzV2oxOGrn8qIsil8tLJt3PJ3/R/CFer73Mp3ad55IO7OTwGSAwZWctNxIzn0D+9Fnrn0kMHc9daKyO9pg6twuQRfXHc4z3y+PibP4TYPSvuL5t7TxvPLp5ckVfYU+dzUNwcY16ecwV2VScXFMwfHCNnxk85cw4iQfR1wIHAycIIQYgOwBegK9ETpDT8DbjAQZ3fgkZBdtgvF5vslIcRNwAIp5QvApUKI2UArsBNt14IOGSQog9S31Gvebw22smT7EoKyTZjY2ai9nGQ1RgTsP/xwLB+t3JFa4yHaOkwhEoWkdLn80CH88c1vUwfU4CfTBvD7N1I/X13qj9gxZ4KwBl0LFfe2EQRBSlHq2WTXcg53LVAN5wu6GDmwjM/X7mKUazXDXbFC+z7px+0vgq8WA+BCUjzscHB52Ncc4N2oiUe4Yw/znezJV7JN6xm/lNqrojDmt5qGNfoJIQR9Ohfx3da6mDDRQnAqEgTyqASEEJTpMEW48sihLFm3h1eXKRrJiiIfoJgAxQvZ9SkEkKbQCo1XQ8OthhQSj8bieWQSIEOCtLsR4WrmnSsO4pA//Q/h286zKxRdW79R/2Vb43oQAYQIsER2Bc4nbD/87EUH0NASYHTPMk5/4JOU5ki9K9q0j5ccMkglhHnTkdlje8T8njKwUj0PnZU8nD011slX104FDO/eiTU76hECinwe1tcq/VbLrv1DQrZamWrn+aVLDmTW3R8AcOzYHgzqUhJzv3Oxj717lXL5hJCQrbYVVsCu+lj9Z4HXxUXT1cpQweXbhsuzj4rgFIZX9mDeN9to3TcQgkX4XaUIBDLqfYQQ3HbCaNXVozC3HD+K0ye3mYxO6lfBZ2vUTcT6VxZz4OAqRvYo49M1O+lRXhgXQklbePfiKdhIla8v75z6Eme/ejYLtiygdNivqf36NwD0CtUbn1CExiuOGs1fvlickGZ0GnecPAZQ2k1YC9wSCDJjWBcAVSG7W6cC9h/QOdLWqkr87GtqpaElQHOgmcrCSv566F/b3vFX/8Vd+D0njj2UG48dT7+r/tf2doG3E6qGDBQhhOCkCb24er8JbNkbu+K2X7/OiQWJMuk9fXJfTp/cl/vfW4k7fiYe4v9mDOSv81YydVAlJ43vGVmhVRsH3BoTwWgCAcm/z5nM13F9Vq6gW8gOudk7ELgCOBsYCIRr8nfAP4DfSyl1r/dKKZcA41SuXxf199VomKjkOh9u+JDX177Od7u+SyueJduXMKpyFC7hYs6wOcweONuiHJrnp+/8lHnr5pl6VuqwSmtoDjD8uldjlr6rSvzs3NdEUEK/yiKGd+9Et7ICPl61k017GijwuBnevZRvt9SliD2WE8f3ojUoDQnZpBCyv7jucAp9boZc+0rMda9b0K2sgHU7E5flDxvRNS0hW0uzl21mjenOXW9/Rzd2UCwSTSSqm4IMFJtUnoTbvA8y2bU85tp6WZUQzg2U7fRS4G5BIFkd7MoFLZezVxbTiJdddGL+z2bSvSx+EIU9uxu4auHbkd+/mT2aq5/RHsRTMTM0QGrRp3MRN84eyZheZby+bEuCQJuMCX0ruP/MCVSW+Nmxr5m/zlNfJi3wxA5wT194APXNbRojARHhJzzoHl/Tk9ZAkIHVJby0ZBMu3xbcRWuRrcVMGzKTF1e+iLd8AcHWUgQTYuJvCgkIWmYksZOM8F9B3FKxWRVCxGxcrSrxc8nMQfx14UIAigf8ESGCHPvc7yjqp4S57qOn2iKVlbTWDcft38S+orVKW5VKXiqL/fSpVASgFy4+MEbIiM9bdWmb3bfP7cLvaWtT4/tU8ObXW/BHle1+/Tvz62NG8PLSTZrfwkpagi0sD95LUb/N/PlrHw+v8dEYaKRfp34sC/pSPq+mgR7Vs4xXL5tGdYmfypIou/e4g0kkkhlDu+DzuDhzSl/+92Vim50ysDLUXwdx+beyfOcyvt27j037NvHd7u9AtGlYEc3gUsSFrsGjOKL7JF57v818RmucOHW/3hEh+x9nT+Lsf3wWc7+0wIP49jWo2wxScmLwewa7w5MDGfNvt29XwK5ijti3imHufZzY8i3jhtcx79ttjHGt4imxhaWAu0CZhO9fXwtPng31beZQB/T7JUVC8NLrLt70NVMmFKWAy6vfZtod+jA9ywu5cfbIyPWF1x7KhPCKk6sBd+Fa/nruTMZ16cbG0EplSyDIa5cdxBfrd/P+nnfxuWLrgQyU0lo3Eq/Qmx9BIBjgxdVPcvTAmXTz1uh+jzDnHaS9ivCLI4bxiyOGGYovmYl4QEoOHFzFgYMTx4VcwIgmm5AAfRtwmxCiBCgD9kgpjUk1HYC7Ft3F37/8e+T31B5TTcWzoU4RMFftWYVE8s737yQI2e+vf5/PNn9Gv7J+zBowC587dWebLhvqNjCofBDHDzpeM4zP7WNM9ZjI7xW7VvDrD38dEyYQlPzutW/435cbKfS62X9AJXVNrRHBOtq2dHtdm/Z1zY561uyI16S3xCxFet0iYo8cg2glWtvz1tq3mLf1U3xVsUvMrbWjCTZ1a3ssWsgmuZDt87jweVwJtouq+YnKr1GOGd09MtiVF9n/3dWY7vqcyzzP4CaAG4kgiAuJmyACSe+lPs7076JaaAiTK+AKv/otgEbp5Y7WU5g2uJo7v6niK9kvIUznYh83HjmSSx773PR7FHhd3HXKOA4aUp1UyB7evZMuswOtNNwuQe/ORVx26BAuO3QIR//5/YigfeTIbhHNshp+jysiCEXXloY4TXO8tn1C3wreX7Etad4KfW7OnNKPL0Lv5u/2HJ5ixQzm6RXd+N2C31HQXQkb3DosJgfH1fRg8brd7D9AXTOrhhQSd6gdFvvdvPzTaXz4XZwJlgybiwSpcA/kygPO5bL/LiHY1IUPfnE0AGu2N3PafYrQ46uch7vo+6Tp3nXquMgGXFC0xn6Pi0OHd+WSQwaxda/6Ks9vTx7Dso17IgI7wBPnTwFg4Vr1VbqRPTqxt7GFn0wboDrB00IgeOv7t3jn+3ci17xuLyMqR7BdfgaiO0WeKr7eqdT3fp36kc4hIsO6aXvxiK5L3coK+PaWo9jb2KIadknz3ZQMXYRwKRO6S9+LvV/g6Q1U4SlZRmHvf7XdkB51QUrlWnR+ZgxNnNAWNGyF5+ZEfp8KnBq3sPNxgZ+3iosY+Jmi4T4HFAfCnyqeG2aEwr8ruqKclafwk927oP5LRFFzRHraXlAALvhENnOpextVspV66afIp39lyhPS2P71jPH0i1rRKitsy7i/+jV8nT/mR688zIvHv0iFvyegmGr1qSyic2mQPz2/kE4+Ix5Z1CvLzyf+nBvn38javWvpVlkTub7w2kP5elOtgfhTpB71Lc3uLQ60I3ORGEKCtSNcqxAIBiIC9r+O+hejq0bjdpnTMv5z2T/53YLfMa3XNFbtWZVw/+lvn+aG+TdEfpf5yjik7yGm0jKClJJ+nfpx1sizdD/jdXkjz+5tbGHMDa8nhDGihT5qVDc+W7MrRviOZsWtRydorTydFlHY84mYa5fNU/71x5mTury7aNz0Q1y+rXgrPsFd1LYi4ev8PjJQFPsAkmBLZ2RrGSv3LMflErTK2IlAz4TlyDaSLbVXlfjYXqeySBTVMem1V7TaT8JhrkUMF2t5PzgaiYsALoIhUTuIoKpLV97btR0BzA+OoFHGTgamDKxk/kpt+/aPg8PZRgXDR43hq+VLTOdT64jlcOde7PNw+MhuqmHC/HHOWMb2KuexT5MLclpppBtGizG9ylj0/W7+NKfGfCTx+XG3rbbsa1XMfZp3TcZX8UloVaetvk3uXxljf5k03sgfik02wGWHDGFYt04xwl6x30OgfgAtu8eBCPLnH/yacd2HcvFeJYYeJT1C/wJob+iMJ95ko6rEz1c3HRlZmt5Wq0xG4jWpnYt9TBtcTa2KgNk3SjCqLPbhcgm21TZx8/GjGNOzDI8BM5owDy99mK92fEVlYSWBYICtDVvpU6osHjduOIXTJ+/HjV+cCqQ+mMSUGCKVOMPtJsZsQyX4G+ufYkvgU8BD846DCLaUcefxh9CnojMfb/yYv3zxF1qL36e485dtdsAhXGibN6kdl66GP1jPfP/FdHttt3Lh+L/CgOn86tkveePrbZHcnzyhNy+0XM3ellouP/NdijxF3P/eKu57bxVPXTiFHuVF/PHNFTz22Qaag8/hpW3C/frkf3LBgTXw2o9hs6JBP2vsvVCwkts/vZ39mv9Cl0ATTXj53FvA+QcP4G/vJo7b8WjZx3vcLtbcfgz9rn42ZkNoQ2sD/cq8PH3hFAZ1UWyVf/Ppb9hav5VeJb10lVdbkrHafYDD+h7GjfNvTHimssSPxDqzjOhvq/MzJ9CebLKByGbFk4DhQLGU8tyo6/2BL6WU2lvUOxD/V/N/1HSpsSSucGWM7/i/r1UG+9un3c5V719FUyBztrZGCXfWm/Y0MOa+RAHbCH/44VhOHN+Libe8oXr/vIMGRP52+TfgLV8ISNxFa5BBL83b2yYiVx01nMDeGm59oW35s2jA70Mab8WVla/iY4ItnWjZOxpvpy8R7mYKur2omb9TX74bgDJGAD9qi9en7YrLk0STffuJYzj3n4n2yOrL8OkzWXxNb5e6l5B4hrm+Z5Os5NyWX6jef+3Qg/j50vdU7wEUl/fmxaD9ru+NlI+n9EsKez1K7fIbQfpxFaynqM/fuWUpuJa58HcdS9OW4w3nQW0gUctXvN3pv8+ZzBkPxh4hoDa0FIVMhtSVgvoLwF20CndBlEY9nFjEjV5s6kGNEbLE79Hc2BTw7lFMRZAcOiJxo+iPQ/bIt7+iCN59O/VPCBNmTK8y064fQZ/tZzJmDO1C786FrNvZwOPnT+HnT34R2fdgRsAGaAw0ckCPA7j7kLvZWr+VQ548JNL/X3nIFCb1LwN9zknSQl+9CfDYSqXP27fyF8jWMgAmd5tKt7ICKvwVPLT0IRqLP8GF4tECEcRbquwrcJPoKUOE/hdDMAh719OT0KrM7u/pyTYGuDZx0K7tdBc72djjcHoM3x9GHA++IvZ4N7OVtjo4fcIo/v1+SBtbXA3eIn58RCUzJ45kQMge/bwjyvjbZ7sp97pi/NqEq0l0vly0rWsWD7mF2tZSGtafAcDVRw3XJWR7ktS/l1a9ROkwxYI22FyBy7crUg8m9G2zj97RqCgqfnfw71TjiRfk/3fpNH718X/4elPcfpmopiylTNlvZuaAIO1Ect3ruCEhWwhxDsqR6gUo7yaBc0O3uwLzUXxVP2hhHvMOPTbHVqblc/kYUTnC9rRfX/M6L616ibHVitsis755P/puB0p10U/0IOrybWFD/bfsakxcjvNVv467YAMf1RUz+dHllAxrQIRsC4OtRYCgtXYEzTumR5758ahjeOKzdUCUjaF0R2ywhWgm2FzOvpVXAeDtpPxb+811RFM69CYA7ppxFwB/W/I31u5o68CGdi3l8sMGa3qQqCzWtpnQ2ENiC15a+bfvNrwi+Wa3aD4IjEwdSAO9GoxktU2g3dn/34yBlBV66ZrCr6z0bOXEF05ESklhL2XVwlv2OcHmSor6Kl3apC6HsGLPMvYVJrrIS553Y20l+l3+duYE1evRBEU9nrLP+HjbFsY3zMZN4pKx3r5BCPCUKJrhlr0j8XZalvJZrW+YTAsZ8Nay2629wufzuLjg4IHc/spyzTCRPCem3HbP5Chs9JuprUSlIwA0BZoSTP/C5Tl3an92N+2OSic6Je2Nj2byE93Pr9i1gh4lPRAitq8SbmXFrj9D8AU2IUJ7LHwbimCXj35IPj7gTi5+bBG79jXxY/frFBWs4cqiIk7Z3cJp+86i6DU38/3NkYlX8SIXT/QsRn5yH7x1l1LJGhSznA/DTflPUX9vD+Vx6Hn0mHaYav618LhdMRs+K0v8PPd/U/nrV6/xUdRcU+0bN7QE8YfyIAN+XJ5a3L5EBUWy+tSi4m0ozPPfPQ9Aa91QWuuGUdDtedVwe5v2MqnbJLoUqe8JuWRm7AbU4d07UeB1k2qdI95HfPS+hXSJ/zbx7gxH9ujEx6t2appSHjOmu25XnNlCt5AthDgMxQ/1EuB6FJ/YF4TvSymXCiGWAcfTwYXsMEY7adU4om2WEKqDlhAqM34NpJR8sOED9rXso1txN92a9trmWn7+7s8BeG/9e/Qv09YqpWJfs7bLHjUOHd6Vo0d342dPfIG/6wv4On/E/Svh7R2DgP+LCevr/AEy6KMh0J2B5QNZtEoQbKoiUD+QQH1ql04RpLvNBtvVhJSxy5mBpmoIxpuLKMzoMwOAp1c8zZqQRdVosYoXjxsDfMv/Al/S29WkDCUyvBQLhRvLuXncXl5YvBFJm29miaBsu5vx4tvI7/C9fo17GCu2IRF02uljtFgV6TLbfDuLmGudpY/OQlnynuD6lpEi1v2cX7TiFQHubPkBzwUP1FVc22RZyjBCmF8SNMOwbqUs31zLESO7MaZXuXa+QuUU6PQmK3atoF+nfshAIcLdQEH35yLhWvaO4oqjbubmBT9j005jQnY6HDGyGx+s2J40zB7PBxT2eIYn10JJyW7OHn5x0vB6BA8Z8NNaOypGyA7X1/jHAyk+rJrtpQh6GNvSpLuXTBrOQnWamnlEbFIaaakE11fdJb7Kt3EXrcHl3waimblvQ0OgntFVo+NCykgerBhf1LMj6ck2pGsftQC1isAs/3k8J7rWMzro5l9NnXjJpyg9dstihHcnF+Dl7G3zOcEf5RoyyjLPDfwVIDxvaIGfrx6BRPBVaQF9q4r5eNUuJBBEMLyiDMTnUDkI+gwJfWMBnXrwi1eVPP3u5LG89fUWSgs89B46gYe+CnLxflNiXmdo1xK01xy1qeldTvHKWBHpsBGJ5mRju/fim7o1AAQae0T2MajRGmxlX8s+cNWHDoBRCNeT+G/aEmjh400fE2wtpWHd2bhLvtKM+8vtXzKh6wTN+6UFiSY56lW57aJE0quikLG9yjg05JZ0WLdOMR5prDwI69/nTuabzXu54N+LAPj9D2tYsGYnfTqrj7X3njbesrTtwogm+5coqr6DpZR7hRAJXkFQBPApKtcdMkQqjdNXO7/iorcUx/4e4eHT0z/F607u7uut79/ipvmKltYt3KobK7bXNbFhVwOtQYnHJahrakVKZTOVSyiz4e/rlCW6bzYb2zjxwFkTeXWp0qm6CxThpl/xGGqb1R32t+4ZzymDf85PDhqQYJOthQi20ke0xbdXNBEo3UB11fM0dlqGu7ELVaH7gZVnUxqAae6XYuL4b/iPZc+Byw11W/AGd3Nr4Z1M8Szhm8cEA1tauNzr4YxB3fjlzl0cVxe1VPcwnAmcqaIo2Pu24B9x5tzzCwt5tKWUbn2C3L5tBy9/VkSfPoXcuXU7hcmEnlai9/IAsCXuWOv1sor3gmNYL9X9HpshvPRl+vkknbkQiQPU4SO78dSFB6S0VQ+vopYVufEWdeHFE16k31Uv4SrYgAhNRiQugg36bB218pcyDIIW9iK829nU8C2eTouBIN/sHAQoGwqjP2tANlHQ/Un+t1Wy3bcIGfRS4PHSEmxRTc+QUBYXNH5y741betYyF0mVSEkwqPvQC12nLkrrBc/PNn8WozVubAngKf0SGdA+7VNvLupb6vF0Woy/i2L21rpvAMHmLswY0IeunYo5pr9i567v24XDpDmT/fJJPiz4KT+kG1/jg6Za8Lhg9zroLPjSFeDZIj9flRbipZUi93YeqFQ6lI87n87TOysjSoJ7TxtHRVGosxGCi/7zOTv3NSOlYLnszR4U7fGBnas4eVwvfvnt4kg2zu8/ALH9DOSgmTDhZzFZfPJ/Sr/+u3HHcEiUJPIrlQW1Cw4eyKR+nbn91eV8/v3umHupV2hi7xd6w+ZYSlmfOOiHTOjdh28ii5PKdZd/Cz965Uf0L+uPp9RPa62y+nvOa+ewaOsiSodCy56xwLGActplbeiMg292fsOavWtobG3k2g+vBaBl1yRd+e5WnHxPSSJa+1TarnvdLp6/OFbZMqpnaqWKGfpXFcds9uxZXkjPmp62pJUpjAjZE4H/SimTWb2vB4x+5XaHlSYb4cYc1lwkxB36qXc22dSq2AmOrR7LF9u+ICADeJNsOlmweQFPfvskdc11/GT0T9hQt4GPNn4U0/m0BIIJB1uo4fJtoXhgoi9VQ7haaK0dTueKbqxvUvPCYK7sp312IT/wz4/8Hl0Q2mBUrVz7Vd3X/MB/eVQ+EuP4b9ij5ZPKZtBA12p2FxVyez8AZbPVmT0P4V8bFB/et5QO4r87Z0Wef+zc/fjPJ9/zvy83xuigt1YsZ2OXRUnz/xNxBt9UzgPgx/IcipvLIs+3ua1SqCjyKqdUhu4vDg5iE/q9QhjFCkXHb08akzqQCno2g1aX+rnthNF8XPcOK/eEtUuCYKOaUJ0o7ozvW2Eqb/Gsql3Kp8GfUjII/rEGCkNjyxXvfspVo/+REH5P63q85QvZ09qdwsBgdm0bTmHPt9Luf0RU7UschJXfF0wfyJWPrwXgzP37MlZjpeDXs0Zw7XNL41wKiri/kuf30XMns2r7Pl2+v41S11zH+rr1BGSAQDBAUAZZsWcH7qLvcBdu42fz3uSNtYn7PgpDVWPzvh+YEGzauH/J/RT2fByAfWsuhMa+BCXMnTON4d1VTH5kuC2LhFVOy9gXsncu6wWNWxHVQ2HXCuQln8Gj+wFwo9wMEasERYieVD2d5rqT+SDYturS3OcgiDLT+tzVwqZgeiedKvuOgqh2wip43C4mD6jEm8Lm7pud31DbXMuOxh1s3reZ/brtlzLusgJlohU//npKlvP51u18vvVzCntB/To/3+7qx6o9qxjfZTyffb8BT+lXfLb5MyoLK2Oev+iti9ha32ZuMrP3TJ7/JvWKoku46FHcI2U4TURiO9Sz6TQbNtF/OX18UicCuYQRIdsHaJ8ooVCO2vm3HRQrl1GSpmOgmocH4CJPUcxvNVqCLfzk9Z/QKlsZ3nk4l46/lFs/vjUhnJ6DTe4+dRxvfreEt037oxFAAHfBRlqaov1hJua/lH2MX/032FXHbz1tG+p2U0In9lEh4jLx0N10264I05c3XwiAK/gKQVeQ/dYcSFFLER8FvcQfBruDTiwItp0G99oxIylsbSLc+bcuuA12LKVw70Bq947H2+spNkaZoO6tH878YJTqZcB01i7/mg+DsRtl/J61FAsftZsOT3jXET39rAo8zedyCEXMA+CT4EiCQW2b926uAjanOdAZISKyadiL6GkmBwyq5ONVyQ4zMt/WhBCcNrkPi99z6WyzsTr5mt7l3HfGBC7490LTeQCobVHciTVtPYLTxk/gnx/swtf5PVpKdqua2YQvTa34MavW9mHrrl3Q861QDvWVx/Kdy/FVvwrAnxd9R5GniPFlxxHfruL7iYOHVAOKkH3z8aM04z9lvz6csl8fjbv6JgNTB1UxdVByH7hmdbgXvHkBX2xL3D1YFDLzfGMt+N1+7jjoDnqXKsdhNzQH+ME/H8Lf5VUaWxPbkRGl/r6WfciAn/q15xFs6oHW/rdMjSUxFJQrQrZKXerbqT/LFp4C0oWUbpAe/m/6wfz3U3MbmLVWXuLfuyXQwgH/OYBRE4dw+cg/mUor+vOEhcj6lnpOfvHkmHAH9zo44hGrLZ+RnY/J0wiZF47vMp5FWxdR1Pthznj1YQDGdhnLp6v2Igq28OPXfoxHeKiIOui6KdDEUf2P4rzR5+F3++lV2ovnX39Z+yXU8qcTrdC2mSIlQcYpg5Jx9Oju9mbGQowI2WsAbYMfhcnAN6Zz016w0O401pZRbXtP3GCos4fX0xiDMkirbOWsEWdxUc1FMWlKZJSWXbk+bXAV72vYjh47tgcj+jbwtvqeDV24fOG43YDQLOcasZIJq58FTwFT3YqmwUOAqtBpZRtlFXVEzYKbOrGxy8H8eN1RLJeKQFDC6wiCvN1yILK1XFf+elfFOdj3KUuhuxuG0Fw7AZ94Ovb7qC1tq34Wicflo2VXojajrPduCED3soLQ26GqkYhJIsP9Z5u9YXqkej7+vQynlyVPUOF8h9tya90wRnQ6kGDDEgj6Y+pM9DvGnoQX+ldj30ZCmqF//7nsn/ir5iGlm38sfZ+ADPCrcYl7F/T2K3qIfgerqmJindaX3z1NexjfZTxnjzobt3Djdrn5emMdt/3vG9wuN59deTpl/til8X1NrQRbykOpRKVj4mUkEik9BJv0LYnHutFLPjbEPqhPOFTCRpZHE9IJU+QpjHgQSYb6OohBooq4KdBEc7CZtfuWcshwY5vnkxHvlUsgaJWteFKISJExMGb1p40JXSewaOsiAg29cRcqE5D6lnqCzV2A7xhaMZRvdn0Dos0tpJSScn85gyq0T8pUw3wb1X4uk04c2itGhOzngSuFED+QUj4Zf1MIcTYwBviVVZnLd6ycDWq58APjGx+j85asYYbvlReUU+QtiqSVPuppDqguZtW2JIslIeGxtXYEka3kKriQNHo6UXDNWqZe84pmuDBrLjyGDz5bx/Lvzftg1ovVnVY2NA5myYYyzih6ylMthN53U/v66cuveidriQRkgGBTFftWXcGzP+vBj175EcGkBy0l3xRohDa5z8o2YbySdS3qyvTe0yO/Zf12Ag0tuNwiQcBOiqXKFa0kYvvvuKesy0BMXlKPFXrRikF9pUbFfMEmoS8+Xo/Lo5rZ1P2DjPtXIdjcOSJk9y7tTdOWsTRvn8GJZwT5zae/aYs/5SdM3v4MezGyoB5Z1a/HK+3aC0aE7N8CpwCPCSFORjntESHExcA04ERgBXC31ZnMNzLqws9sx5dGRY4I6oaXptJZ0o//rRWXYoPc6i6yyO9d+i0+eud4Ks2X5oEpSRb2YlPJPVJVUT1VWAiRtPNV22xmtLPW3W5NVAlde/ZUdf6xdUbK2DvpZi75Owud4XKQWIsew4+Cdr3UWk1QvW9xselbpcjPibyWyYhldU8l/lTCqlZ5x5eJliZbGxcyUApt648xecqY8iQmGfVVkkyRd32MTnQL2VLKXUKI6cAjwA+ibt0V+vd94DQpZSq77Q6DlXZ0EW21VsevV5OdVBOSOqyae6u21UWd76tld6jv6bj8qT9l2MlApvoUi/uRfJj150Me9aLmxcQQatoxk8okLTMcbddzyeNT97UcL2yk/zEtMSGwKA6JtLztpyNYa+1biMQd2elu7fgSn0rsv/pJXccMxKUS2pIVFDWtedxFIdSFe/1tXzufamY+IslYFv+0dorSIpvszCnPMhFftjF0GI2Uci0wXQgxBsVVXyXKVOxjKWV6u34cTJFgk62zE7KjIutc6Uojbn0z7bQFIcvRsm3UvwkwZecZY4edwiY7eUyW06bjUVcv6p6bJdNkG85VIsYG8LhBOfpvNY1cGgKRmu21FpGyVtPaqdqqqKeo/KOiyc5RZVNi+eovM+04k96NT0Z9ZSpF2opwlzqDQi29hMhSRpNWv5i6fVi3Utl23d7eyqzZReqVxdAvM/mXqZ+zbo+EQG0PT8acNuTSMG0TRg6jeRv4UEr5aynlEhSf2A4q2OHCL1ncApW18lTxhu3skm160DALUTbrpLOkle4GDR15N5mC3ZirGzqeMRBtpj0VRLJmc7KJGx+NJ6jfu4hJbCmD2D0ZukwKDCrk1E1ZzBHrei67qPZjYXnWaFNV1ZCaylaSJNRXIlPXWzMlLWLiNiXYWfCBo9O1QrhUnYDGT5qFsoE4VX8t4sqo7Ub8c4na6+jndb9Vhgc2Kzc8d1SMGK3uj+LWwSEL6K3sur2LmOz9Uh0IYhfJbLDjf5vKhmaxWfhSwo6Nj2pNODc7xmwLVKlIZ0DRPXFJmoSaBjmJtxCN019Bvay1hIv4FhM53VEla21JW1HHwgKjBYKTyl92EW22p2WTrb866LT5tXvyF0286YQOpYbFSdqaXoyDJ4P1ONK+dJ76aWac1WOTbdnGR433MBKP5eN+rg8UBjEiZK8AetuVkfaEXbM/NTsxIzbWMXEl2FqmDhuVaGwckXAG40mT5HnPzZaaqryN5jrSweXm6wKptXm6Nj6S2kQocak2dbyJ6aRaJjaHVZ9Hs6yiEjArnMQOuPpXjdIh29XWjB1rNtElXKm68EzDlCPLX8mqumfkLcIbLo2nbcD0J+bv9N7Ryklv7JXcVNjkE0aE7AeAY4QQWicLOMRhReekZwCIDqP7mFizdopJBR37OuOYmGN2Naos9ZrpFxKybmXnEtYuxk+S9Keh7XXEeFyZJuLDOcdlGd37GYS6HWPa6WsMlFr5MjvBTh23ymArrRO643NriSbb0gUn/VpjtW9mRN6J37ytbekbm56IMxGyB2P7fdLZC6PlWcQO9NTd9McydVNL7fT0xptKCWBU2aaCQa8BVisQcn2cMIqRjY8vAocBHwoh7gA+Azaj0iNLKb+3JnsOqTA7gzXrXST6utHWlX6nGb2ZKYVrpxxtqXoGKrWnjIXPLVKucOh8h+QbH60pCNNCQqr7Fn0nzQ1icX+lGtzV3jPxWiZML+witq/QDGVib4lmdBloi+lqFvXVw0T7ZDA31qTbLuMVE3atEid4F0lQiCTmC3SYj0Q9EfnL4AFzaph1eKCer+wpaLK9SpIJjAjZqyAy7f5zknDSYLztDjs2PoZd+Kl1MmrHz6bKW7p2duobMPU/rf9qKG7dkZu0ydaMzuJOwPL+LPc7qTYdqL15TfvER73pWK45jkScmIIUOuqMeV2wmncLNbd28Rsf09Jkt72c6TgS4gxn2Or2moKYcjDxOlJKU88pfuPVzA6spG0FTv1O+0XLm4tuzXSaq0ymNemWD1c6bfksILLiaU10OYMRYfif5PKadA6SDVs/K49VT+ZdRD1c+mkmjyD6z+RxWSxqW4KegxVU30tol516OZjRltuPvRtjrYpHb0QGN0wZrI/m9VJhwcj8s+r5iez0sxSRKM9bhL6Mqm0yE+rylW5iq5BZ+/i430nMU6JD2UK4PGz0NCGlettLNfYYQd2tpVSPN5WC1/KibhtD9ZRzfBjTK9pC48zHrMguGU8yIxg5jGaujfloV1jZGcVvktBy4Wf2MBqjnVb00mE2lpchtQCdK+J1m2DSliOrByo7tIJWk+Y2AOVZHQ8n1EYbKkK8BtGKtNQ2P0W3+lQ22bHhteuY+nRMpT+R0XmJF+6sK9TcrbF60O4/jTdxI2uA6jbZ6ptWzaWh5V3EdHzRT5loKJYL97qUs8bGx0Q3kHGa7BTRaJ8gmRjSUD4MkWwvhoNZrDh32kEDK5fwjG6KSoUuTXYODYNKdqPzkyiYxD1hLIEMvGqiHbneRBOX9JMnlCof+qOygnywG4f2NaCou+tL9Yz+SXNa7g7jphDCik2kSepYquqnvUpkRR6MxZLqUJbkfXLqtNJZ4Ui9Cme+rafr8lBXGipvH3FXqXLio9444kMYyUdbOlF25ybe0bQcoKqyN7ap1vITH/NlwNCJKdtpIcQwYDhQIqX8l7VZctBCc7Of0F/RrVhmCjvpN36Eq7WNR1tjZ6WwZHGeLTblaA8dkt4qmNRHu0V5sQs9nylcDlp+q1WeCMUtYuI3bEcp49NTS8l6TXaufrRU0/doYvog1QeSx2JUODI9uTH1WMj0SJeArxFD2haCVq6c6M+/7o2Pcf9Gpxb/hBHMb8BOa0ktAbsVbe1g+EqJIU22EKJGCLEAWAY8BTwcde9gIUS9EOJYa7PYsUm1ezlBaLawUah5F1HVBoTNAXQ3GBOddcJyWuJfMXEbllYNZ8kw0YdYGE3TmHiZmxpZuycE8fEbTU/vxDEscsSmpTMNPfse0yCpi02dzyazXY38ttA2NpuTYlMeM3SsFKX7LfVo31P1y+aRcf+Gftlsk60vnLV5aDt4Sd1ERuu6blSCJ1cW5IbUaaTvtLpbz40SsA7dQrYQYggwDxiK4l3klbgg7wE7gZOtyly+Y0uD0ehjzHoXMZcF9WPV9Q7i6SMgiQs/a1KxZ+DXs+RqNmb9z2SnG9O0MtSZnXzufNOylEwpWCRqvs2ubIWfTxCw7NBk2zkZtMGPeVwClsRiVGjUN7mxeN+HTnOR9NPRTtua+I0Ijvo02dHhw3cUTNpyJ8SnjuahdEZXl/O5U80TjGiyrwd8wGQp5c9Q/GRHkEpvMR+YZF328hP7TkdLbSdmt3eR1Kfu6UncRPkIyFUNrV7SscnOFQ2HGazYM6fn/dMtIT0+k4VGXqz4Pur9hr54I+mb0N7HJxW7yVHEXYs8aAnZrtXq3kXSy5WdGx9jPpehfJp5p8x9nWSvEuMn24KKZyQGvbbaWqno7Sui9yVk7yTj7I6vkV4n252CxRgRsg8BnpFSfpUkzDqgR3pZaj9YuTxu2cbHsDWF1uCZRQzoGQB1jZuxeKLQLAZrW3zK8k592kjcZWOak2RJ5Do5k+9U5gKqa8QG4pexHkxSn/iokpwOkwZTmcuwH2q9xOcq+rf1y9n6V6ZSodccQZddtI5vo2tM0hAsU/Vd8XsDwILe08Jvp8cMqi1Z9TMpNOPWsY5r5LmULmo1Jr3GT3x0JSi9lGmn0dHYQQsjQnYFsD5FGIGi7XawA6HeyZoR5s3uHo7u7PW7HYoEME3iIKo1GIVt7Npb09d63wxnwwZyZZ6n18zCtlUFi0wcjAgH0WHbtOGRHZht4SK2q+0LO1Yd7WyTWnXUzv0OquYiGex3MqEI0tJYa5okppzFJruVeDN2fVNtjFcJmAHsLnuh8Xd7woiQvQUYlCLMSBRtdofGUj/ZFm98jD8S3ZS7IK3Zf6ZaSVgGsNK7iIYqzJovaZ3mK1ncbVhr920VdqeboEWzNcG4MtaZlm4DIR19SFs912fOZQyt9HVoVFMQ/V0UgxQ79j8YWdmJNxcJxZDsG+jU6Kf6jHr70uysPObexkfL09Xc16N9JkXyCGPrX0rzM4Mrl5FkbLbJNmq/bgVtvVn7EreNCNlvA8cKIYaq3RRCTEIxKXlNb4RCiAIhxKdCiC+EEMuEEDeqhPELIR4XQnwnhPhECNHPQJ7bFVpLWEYOo4l+JhWpbLK1BiezaSa1RBMiRYiEB/SHzRB6liC1cm12WTGX0PQ9q/MV7H5XPd5FFM8R1q4ctWmporTKcRupVA89UYsjeSZU4lDRZMcEyIZwlznS9S6iunHURpvszGx8NKbVtQq1OpzKlZ5VGPUiYvwEV+1xMyGs6hifHXLprIx8xYiQ/RugFXhPCHEhIdtrIcTI0O8XgVrgTgNxNgEzpZRjgRrgSCHE/nFhzgF2SSkHAX8E7jAQf1awsmKmFkzNabIz1XFq5SI9RFTnq7J0mmbsMVhig6oVh/6Nj5ox54GM3VbnzMeh68RHDe1frpM4psbmXHs/Rlv4xL+SPpCQXuKl2LZltJ9JhtCbX5OxJ0svVX5irlvgBtSq9qmm3NC7IT0cwniiQjNu9Wsm00F/OVkx2dMVhSByHoTJVJRoUk3aNVZejJ74aP4wmthYUiSjI470yYcxzQhGjlX/RghxEvAYcE/osgCWhP7dDZwopfzeQJwSqAv99Ib+i68txwE3hP5+CrhHCCFkHqhW7BjkNY9Vt+FgmGQbrrQ06knTtL31hO1IlEXoXCOVWygljMZ1TU128ue08pGPZKr6mCGliGMw7zLuDyN9iaKRT5GfqLCRa212Eqpxxj5pBdobNzOFen8auqdjiIl5XiV4SnMRZNySRMokk5D6YTPRGzNVUdeCmyVTfZVZTXYqc04z+dAjMFsn/qisEEcplvJAzMp5DJ34KKV8VQjRHzgL2B+oBPYAHwP/kFLuNJoBIYQbWIhi732vlPKTuCA9Cdl5SylbhRB7Quluj4vnPOA8gD59+hjNRs5i9MAB3S78DHWcMQ+qpKl9zypU9SaaWc/djsFUp5V0Q5zad8zN98+0MGVm3NO18TGNAVV1cpoiupQnPsbEkSSyND6AFSfFqpEL0z3jCorY5f/E+KwXDqM9fKidMhhTb9NNOv7bqppxZI7oumb1yqu2TXx4wqlxPXXMSni9JSWMCfkJqeWpMJyn+h5DGD5WXUq5G+Uwmj9bkQEpZQCoEUKUA88KIUZJKZeaiOd+4H6AiRMnZrXG2WGCkdqWV19tVbOz1gyr0VFobzi0maiOqC37icJlOsuW8XFZTerDaLSWrjXCm/Iskx1s9YAQdwKeGQy1W4OeQPTkzKifbK07evx9RyUaFZ9IlaQl5lN2nPiYWLV0xplm0tb09Wplaq1ZQDoYO4zGXP3IhIyY1Bd3vHcRg+/RFj51GzLVZwsRU0jJNmwawaW1T8ZAPB1BUE4HIyc+Hi2EMHQMuxFCwvs7wJFxtzYAvUN58ABlwA678mEldmgzEmwjpb6joC3LQ1SHa8dmS81nYx7NZKu2IC3ZpnkydxhNanK5o0vtYUFfPEllvwxpcrQ2GVvbBuMmtRpLyNEmHPHL1+mkF31doi5gWSXs5UK1TTyMJr347KyK+iZQxiZrqcjUiY9aacccRmOFTbaO90jpXSRlccYpszQmp2rmJno2YCemlh2lkKVp5acyPiVGhOaXgHVCiN8KIUZZkbgQojqkwUYIUQgcBiyPC/YCinkKKEe2v53r9tiZdOFnlHjtdLJGpKX1Nu07NCoGK0i28VHmqtRpcc3N0bdUJZ286no2QUNqjFzaSW/3d0228VkR5LTKwkpNtg3lrSLM6D1NUC8x/Vy6mnAZ24dpT3cSv1cyT09a6AumsRcn5WE0aqsU+shEd63un1rG/NuWH30Ti/jJbbo1OrXokLygzO3NCieqXrf0xWEduTp0m8WIkP03oAC4AvhCCPGZEOJiIURlGul3B94RQixBOab9DSnlS0KIm4QQs0NhHgQqhRDfAT8DrkojvYxieeVDaLZivadyxYc3lQdiZ9vJTp5TezaddK2Iy2jK1sUUpx1RMTlQd2OVRHulutKcO8KiHnS78MvEQKwjEdPLvWmgKiBobIrUs/Ex9oHUgW3Ta0hrzEUyTxIznjQ+daqSSKoUsalPNOKJKp1qoseLSToTYaPPJqvzqU0o439HT6TU/o4KL42Pb9a2z1iteqZob8J1GCPeRS4UQvwUxdvHWcDhKHbZdwohXgYeBl6WUrYaiHMJME7l+nVRfzcCP9AbZ3vDqAs/q+KNjltvR6ff37E54gfjxHxYaZNtD1YLK67QPNmYPbGlWdCfbIbTNezRw9C3ibfhNPOUnrvGjnc2iqp3kRSmKfHPmU7bzvqgc6KZzA5eTwypXaaaez5p3Q1bLBhx4Wfqc7WZuZmPQx9ZO4wmnHBc+lqOAVJPlm2a6OgOl0b6Qq0vsJcYMxkNpUG+Y8jGWkrZLKV8Uko5C+gF/AL4BjgeeBbYKIT4k9WZdGhr7KZOoVLBtCZbZZd7Joi3yU56uEfOacba7PhSLz8av2MyN1kg3ZRTLJWmGbsSR+YHSfMpai/rqy5wGE5JgIawkQ6xmjxr9K/p7fcwusTe9v9WkzJW3Z8hje+loYTVZcuc4gUMux63UNgzeophehNJdQVV/LWIOWjMk3pWDKxqjxZMztqXTGw5pjcySim3Sin/EDpIZhxwF8qmxEusyly+k4nZoOHDaGRs49d3fLNOe79U9y0sD0nmNrtZidXLb/mwxGbJxscMvKdeIUJVgBXqf+tLNy59KWJPFSS179xw+GRtVcv+OhJHjJZaq1+xX6uZHmpCjTbJTQJSk+rEx1RxaO0rSchLlMlGrm581OdiVuPZJI9a3c9HR6dpk63xLmruE9XD2UWqdPNgQOhgpO0tRAgxBPghcCLKYTIdHjuEv2R2cVa4LzOEjM2H4dc1YTMc83Yy1mosadi0sc7AUp8PZvWI8vUAmWjsfAW18rH6+PN00HVipeFYw+urieZcqU+a0/HLJkE6ZkJC9leetLyLWJErw11jSsWixua0hOc0zFAM1bK4iZYuhUzcb4ubUzpjq9EDu/RNbjUiDY1xbfcTtdfK1UQTLV2abIvajBBCZTw2Jk9Y/Y3bwVAXg2E/2QAhjyCnoNhm74dSg/aibFJ82KK8OcSh1qATDolIaSOoP7yad5FkO9ozdqJj5JdW3oUFLdWegT9Wc5h+GmE/pwL98WZLYNdK1YqNj5l0ZWm5a87Qv+qnKgpDG6GSm1Gp9B8qQpuatXmb7ao+rWa+YM67CJFCUld66J8wJR7ykzq8Zv2zzMhdn1Y39ZOJJGs6mq+VgbpmWJOd8hukl2ezLvys7pvsLvt2Jk+rolvIDvnIPgpFsD4W8KHUpLdQBOtnQpsUOzxWVkxd3g4wcax6Go1R2xF+phAaf4dNYSzsGCyJKkXHrCsGTREVyOHV+yxi3GxDv6AeX8f0HwZlLE/68pJ5s7R0SLYKZR36487FJfYEjXAKkw173iG237JT4NLbLjI9uUs3PT0eU+LTSXoqrMWvHzt6mNv4mE7Ns9awKTcxosneCFSjlMG3wCPAP6WUG+zIWHvADhd+qZbL9N7XY5OdtIMxoF2zBBFKNPZCqgf0k6G+2/TyowaRE7vyoGdKV8li8RdPA5vMUDSqhvZhNGoJhVc29KHtXSRkK5ygsbVei2/PiY9pxGUglLqdu/60Ev0z6382Uxg5jCad7KuZmtgxxqi9haoXkbSqpbGHE/2LJ39vtUPpzJBqvMnspKZ9qoqMCNkFwN+Bh6WUH9uUH4c47PN2YJ0mO/IrRZS2C+W2yJvWxaZnkqT5bMqyNZKPzKLlCtIIGZnQ6Zw42pYTtYh12vUbX8lKlXBcNhLMGnJ1QFR5l1RtJ4OrgKroMPOIMdlTOaAstl4k0Q/qybrWEeMpPnk6xaIm08qECZ71Qp9WPU51LoFx8y39K13J3zEHZ2FpoPY27WH/UTRGhOzJwB4p5Wa7MtNesHVZTcUuOfqoZ7022XqWAFVtspNtwDSgPzOKINbQUdWJf1T0hk98zFC7Nnesup5wZuLNLGlrspNEkO2O2Qr3uVraYm0hQ60Nhu12U6cXH0WMTbYI/198e0+/nOM3PmaTdCcLSZf2dUWQVvLxKdsTcUTGNtlv5wFqNtkSmXpioWLYY4S2CZP6dS209mIZLW/V8FGTPl2bXS3re/OjrhjFiHeRr4Bb7cpIe8TyDVI5MsPTnP1brDFKEpNGPnJTuIzptEznUUOD2U47JjOkW72kosq2PyFDhM0/kkwwYu5lJm/WKBJys722nQWQflxm40jYVB71XVO2eYtPfDXi7jW9dFSuidjVP8vd+WnZuGu08ZQbH6Ni1gqnx4GBVrrtThCNeZ3c7A/SxYiQvRvYYVM+2hWWHtwQ755LLW4DtmtGZrxqYY3Y51mJUgwy/gJqnY7QuJ5t9JiLqPbdyV5F9YH22VkZxa5JqaLktVJTmNimYvxka9YZ9etGThGMCSvaribEbZvG1S70ZdjqSa9R0ulHk3l6Sg9j9Soaqw+jySZSpnbhl4iGWY5GNKm8mGjmTWVFG8yajSVufNSTh7ZwaaAuzrQrjAjZH6NyBLqDNnZsfIzHrg0PZuJIvWJubXlkRtBPP89W5FK7M7YyFXtJtySTPW/FCaR64tDajJWy7ifPfOq8GbTTVNeWaUWuf8Jthws/Szc+qrxL6oNDNNqWBd2Vnjiiz7zU50s/s2KIlYoVI8OVWluzJg+J5leJG1DTXTnUzqdq/xE2ydE8rUdnspZgxcqrQxgjQvYNwDQhxLk25cVBBX0bsUy48DOwBJiqo2uz3dabuhnbPiNIw08kpGfxkqsSZ7wm20qb7Nwl/MppuTBsb+oNNTR2EGsdQBJdK0TUfX1eIBLbf6IwlTjYWl0Ts/1Z1feWhO7peNnUJ+ym93xbnpJPpFJrPsNmDrqS00zH+MPmH7XaOsWYWzpj7UhPjHqeN2tbHe81TD9C5a/Q70x0uipJtLe+3sjGx6OAecDfhBAXAp8Cm0nsd6WU8mZrspef2DH709KyGXblk6bA02avmJifzNkHR+chGhl1N5fQrxkwvnFF78X8J/lBFsa1y/HoO7Jao+2kWiZPHmFiXnQI3EYx1+TjhXErVsDiU8juJNIOYSI7gkJ0ommUqYZ3ESvMII3vR497wK6qEhdv+tp7baFXvb4ZTMeickg5OdO18dGavLRXjAjZN0T9PQ5t0xEJdGghO0ymhE4zg4SeTkTNu0g6pKfJFET3LKk3/+RmyzfbaVvlui0XsUpjldmisGO0N7bEHNMeom4bEYZUteQqz1vllzceqxeMpMpfScObfg9rBE9tF3KpLiSN1WRu1MncSarq1+09BEd746P6aZ7WlkXCnquIFkw9fLxayRrCaZqzybaa9jCmRWNEyJ5hWy4cUqLlOi/dY9UN5yNKOM+aZwtJxJ+omhbM4m7Q0thivpcFS2Xqrgzz27xEC8NlYzC8kRMfjWI+3tST4fS9ixjRrFmgyY78kZv1NG0vNYZfK2rJ3kDame5/rTWfSE3KjboWonWsuvFJVGx/rL7CZkYpFotRryia8VqwFGrVN89Z1/tpolvIllK+a2dG2iOWD9gWRmfUPi0ZkYXkjPX5IrnpQNT/5xLmOyOp+Tr5NOvXyqpVr5CJkhBCqH7HWA88aRK3eU/NbhqsMR1RO/ExcsWgv1xT+cCa75bg4kxEv5f2c6aVBeF9oGkKfWZssnXlV6ut6UpNPU+mPCOli7C+7sWqI1Kv5MZmJ8VLalpVaaxExpuDmbatdshVjGx8dMgCqVz4RQ6jibKVTkb8xoqknYyqltiCZVITGqwEF36E82eRHaIWlkSp1WFaqIXPgz45S/un9CPzdXCL1YQaFfziQ2dSg9geydS815ALPx0eZFKlY+owmjTKQm2ISc/lYbK04jTZejOuGcxYPs2susX8NutlLEmp6N78mVa/bm4VJ58wYi4CgBBiDHAaMBwollIeGrreD9gPeENKucvKTOYb9ml9LFyKSzMuO5fWU5Nc/6Wva8g8WnZ+up7V1ITkD+nWuWTPq2ud7MGK5V41Yk52lBrXra7ZMdZLahPouJxb4MIv1isGqiZfGUMmClT6vq9OpYYNY4FmvxsjSKebbqIAb3+vao1phSE0XiksYOp17Wea8IpSeGWkA2uyO7y5CIAQ4ibgGto04NHF4gIeAy4D7rYicw4qy0ka2mXdx6rHbWZMNgioxqWyzKyXdDoOEZV2hAQVXNSflvVR1nZ2Vg+6bZ1+7ttk2z4nS9eWVm+5qayqxGw8TC8bMUgZNn5K/nJtxhIiieCQ6umYlOPyobkO7pA2KsKliA+hUu6GtKgGvpuGdxErKraZMSB28plZm2wttO+r9cfR17UEdZ2mOBorEmZd/ykPhdOOy4PT1C1Bt7mIEOIU4FrgDaAG+E30fSnlKmABMNvC/OUlualHjcVIY0w1e8+1GajArl3Y6aFn+U29qLO4yTSXSFIEeo8vtiILZmLVl5U2QTbeW4h6vUncXJWsdqnHoG7vHf+Qld5F4nNrtyY7qZlAmm0rdXtO1XfG9lRmcyJM10y9cet417h6q4aRsdG2SbnOLEhkBo6SD02i4+YxeuqNJemnOGLY7vfvCBixyb4U+A44Tkq5BGhWCfM1MNiKjLUHLHf3oyGkaW3GUkPrOFbVsMm03GrL8ymykP5hJCkEghxDrWzNHUajjbp3kY5JZk2g7C1v7YPfNMyGRJvG25JJvoidqKodUGMF1gvY6bcHK7ttfUJK6gSNHDyT7K5+W+PoZUHlHzMCV7pFGX+Al9V+4o2e+JgQZ9L1IS0TP21Nttm3M3usejIyMca2VzvsaIwI2aOB16SUasJ1mI1A1/Sy5BCNFc7iNSI2lQdrXPilN7DKqDyoxSui/j8nkPoFIM1lYa1jfnPoNbWInPiYRhzKRDJXULMdTfWEHmMN9bqhVWeMtn31oV2qBFCxybZQFs6lOmteaDNrTmBV+nH9sqWFaqweGsFIK7ZaiWroSHfiT+c1ht4Tg0XCaKXP7MOq1XIr0sml9pyLGBGyBRBMEaYr0Gg+O+0D2zY+JqnNejvZBNutJFlN2sAyrjg1YlOYpXSzQC6Jnqmwc6OsmZUVtTh0nfiYcuJrLF3VOKJSi72eKnITZaxq55l8xctKkzgrakV7G+iTnaab+iAuiZWdYPqnH+pMR1W/YLT+GyOiyY5vtCnyoqcvs0errGGT3YE3TOY6RoTsFcABWjeFEC7gQGBZuplqL1hR4RMaqoz/ac5W0qxNtnqHazRNazpKTYf8yBwbddO3cUv9vaLv56bpiN1fJKOfPC4tKwZUs0epxxuLGRFE1H1wq5g52WIukv16KmV6XpLU2rORJp6ebbsBwdvwd7NLS24gBxE9kFX2xwbCmq7n6U3S7TD7SJqXmJOUEzd/ZnJ/WXs1/zYiZD8BjBdC/Fzj/jXAIOA/aecqz7GrYpqx/0ogvHSvxy1TCi13Ykdgd8egsoytQi6J1zEIPRul1K5qP6PuXaR9YnQlx0w9SDW4CWFOmNfzjK4wGa3dKpNDjY2QxmKNFg5Dk2KrSMMfdC6gS7jSDKJHwNOBhncRPYfRWF36CfXdklWiKBtvzWPtE134GT5NUc/30OkeUe+GUkv6B4NtyNGeJ8eIC78/AT8AfiuE+CGh6i6EuBOYBkwEPgbutziPeYsdlU9t2dbMKXCGNNkqWg1zm2DMl0dMJyO18qBPCM8mZlYAQPttVK/n5qvbTrqvbWyfQZJNwSYlgbZNWHqTStQuC/S1TakiLCcb8O3a+Ggf+r6B+b0l2mVlRBZLPFBLI7Usrsyl12/nTn0xkhMrNj4mj1u9fgaD6ha52dDyZuLbRaeQQ1XFUowcq94ghJgB/Bk4HXCHbv0MxVb738DFUspWy3PZgbFaUDfkXSTFvWzOYDPnVcNauzrr7fVzX5MdqUdpFKVI73ED6ehJRUVr3q4GCKHxdxRpVDcVxxUWklsfws5WacTjiInIEy5l2jWt1Sc+qqYRmdiqm0oY76+NfZNIGIMdiKYG3mptu47Xt6rva6/mIoYOo5FS7gHmCiF+BkwCKoE9wKdSym025M8hGpHYuOJtCnVrsk22jGxpNdSeTOpdJKekHo3vY4GNoFHzkmyS8S9iZuCyyKzDDJGvFrNcG2sXqfV3W5607SjV+gmlPxGhJ6MnbFqTcQtfXusQjAyTsMlQzytalGXtb6X9TOo+ON2Nj3ECZxqrl/Ekfy+18HYoJsxhzAZeXzx6w0S3b6vRMtdyzECswfCx6gBSyp3Aaxbnpd2RS0tl8eixs9N70ILxPtBsp6n2nN1lbKEm2/SAoS385XIdiyfdvNrphz0Sh4kBUFe86ZjaWoThuhct7Nsg5+gzlMhtUm9KtY+YepgyofRzkq7dMBgbK1T9WlsgcKtqyOPt0FP01bqUWbravLmVSFtc+Km4HdTlws+SnLRfjGx8VEUIMUwIcbkQ4nwhRJnBZ3sLId4RQnwlhFgmhPipSpjpQog9QojFof+uSzfPdmObCz8tbSZCt7Yh/lh1XemqLR1a4DLNCLF5SJ6QyOXhW2r+cLAYO2qAdhtMH60NWalOZTSjYUsWj5J2ck12rrnwS4w0uvy0Me9dRN8zKUtJEjOZSRZr2+mAWqFSC92mNu3qUMi0xR+ngTWenG2ojmMptLgp214K718akWsSaWUWmY+YI5e+Wv6jW5MdEm4vBEaGNNkIIQ4FXgR8oWBXCiH2k1Lu0BltK/BzKeUiIUQpsFAI8YaU8qu4cO9LKWfpzWuukKmNj2bQ4/tUXZC2fpDVjdD8kReYd4mkPWnIhyU9Kw6j0UNmlfoWCpmW5FuHBl6nWZlQaWe2KQ5ycJ5p7MAUjRcwYgpm8vsbEsSMlLOM7W8s9fdsIiotEykrMXrio3YdMTjhFeF/1G3DjWL4WyUz38mAqU4eLcSaxoi5yFHA8rCAHeI3KM33eqAbcBHwU0CXtllKuQnYFPq7VgjxNdATiBeyHUiiRROCT1Yqn+XD77YT3LWKimIfTYF9vLHpP7hcLQBUeHsQCBQB8OlqJfxry7ZQV9tMSyBIUEoGVpfgcbtoCQRZvmNFKM4dbN+0mtag5MW1GwFoCjSxZP0efrrqc5Zu2GPdO7rrwNUUc23d3nVs37uJwsLvACikCRFQzjxyF35EH/dOJru+ptXVyptAtdgLlFqWp/TRJ9yYijkStf54XVn2e2v2WaMTCj3p/Wnhn1i5eyUA3+3+juGVw1PHmyIfauNSsmd87vgFxdhVm6AMsnTXx7iLv2Wv6MQrq3fid/vZ1rI8MXySrC3a/h4F3Z8F4Mb5H+Bxu1izdw1QoTwaKrC317+Cu2ATMuiNPLt279qYuN5Z9w6+qtUA3PfFuph7B/U6COHZi7dsYcy94Z2HM7HbRJ5e+SgF3T9ChPqlg11faGfaMCZMeTSeSfwu6aUTRkrJzkal/20ONqPXZVpY6Em1mgHgLVtoOn9hCjwFMb9v+fgWzXu5xOZ9m9lWn7hFrI5V+Co/5R8rXm67GGqrq/asigkrEDS2NuJz+6IvWoIpm+y4+9sbtvPiyhdZV7uOlmALi7YsMpWX5kAzLv92vBXzNcPsadrD+xveTzSp8e5EtlTFrKJ/uPFD6lvqVeMp95ezX/f9TOUznzEiZPcDng3/EEL0BCYAf5BS3hK6Ngw4Hp1CdjRCiH7AOOATldtThBBfoBzbfoWUMuHAGyHEecB5AH369DGavKVYJUi98MVG7vnkG/DDS0s2IVxNuAvXMuzu0yNhCkrW0dLiZu7Dn1E6DD7Z8Twfb30TkLgLtrRFFnSDK8CcbT6oho9W7sBfDb99bQnBlgo8xSuQ0kugbhhhKyJ38TcU9YEnF66ldY8y7/FV1uLvokS5cM1emrZujCRR4k9enVLNso+u8fHwuluJP5L26Gd/pzxfpfx+0XsTt63vDIUF1Fc9ST3walT4qkALO7vW0Dlpagoje3RSvV6/9tykA5XP46K5NfkBqLMGzOKLzd+xr6layT+pNQO1jerOeTwuF2N7l/PFut2q92OLNnka/zdzEJc+9nnSMHbg87g4ZFgX3lq+1fTzWhw5qlvCtYVrd3H65L6azwSCAR5c+iBVhVVUF1bTs6QnM3rPSAhX6HXT0BKI/A6XdaHXnXAtmktmDuK9b5XB3u1SAsyZ2BtQBskr3r2C+pZ6vi/ZQKfh9Ty5Svlu5YU+RvdSLO9kQBFm/vL1NRT1gdXAle9FJSJdzB49kLs3tQkVWnXszQ1P4em0DNlayufbNuMSgkJPIYF9A5V0/eUMqRjC2trV4GomsG8wwZZyij1l7GjcwZCKISzdoLTx+5fcj1+p1ty7+I2YdJbtWIa3rAB/l9e4d3Hb1p2qwip+NflXPPjV3XjLIdjcmSmNWxnk2QTfvQWDDlHNdzzVpX46FcT2NfF9S0n/P9O7cDwbtpZwyDPXaMYVlEFcIrZe9e5cyOT+nTmupmfKvFz1/lUUuJVvtKezoLiskXPf/gMNxa0UD2rhyk99iM9kJC2JpK65joBsq08+dz8AZg7rwmerdyakoZcDB1WxGOjRYzU7W5RJUd9OfaksqOSD74cBxibYl4y7hMeWP8Ypw05hd+NumgJNlBeUs6NhB5eOv5R35m2MCa9HeBzWrZS1O+oT+s/qUn/CNzz/oAHMe0Wwo2EHn23+jB2NO1i9e3Xk/s3zb6YxEHvA9LSe07j+o+upb1UR9AT4u8AXu9ouvb/hfW755BY+2/xZ5FplQSVF3iLW162HupSvFENVqZ9olVOvkl4ABJu6qoYPl9mWgr9T1N/LLYuKUqSghL9x/o0JdyZ1m8S46nEp83jxjEE89un3gFI/AAq6Pc/hXc/n9S1w5RHDImGDBPnnV//k/iWJnplLBoGULib8+1cAMXVai5dPfJnepb0jv8+e2p9731GUHK60jZdzEyNCdgUQ3QNMRRnNX4q6thA432gmhBAlwNPAZVLKvXG3FwF9pZR1QoijgeeAwfFxSCnvJ+Sje+LEiTm4CKmPvY0tnHDvh+yub2HHvmY8pfsoVNoprXVDEN6duPybIuGbWzwE6oaC9NC8Yyou304kgoMHd2Htjn6s3uLm2J4X0LP0ER7a+hY93FuACmRQmaEXD7g7Jn3/1st59pxTmPbbdwg36L+feiQ11TX8++O13L1oXlvaOw+MefaHE3sxqLqE5ZtreejDts6wb2URfPYgbFIEu4N6fIjX+xEApQUe6hpbAcnqLS6EkOy3q4yuTX68opUJrhUgXEhfMf+rG0NpcyH/aZ7Gpt5fgtzNoVtH49/nZ43sxifB4SDd/KK1nKfnaB5OGsOj505WvR6oH0SgfpDmc9/cfCRjb3ydvSGheL/+iSL9CYNP4I1P+/By02YANu7bGBcisZoO766ugRfA3380gQN+8zatwbbnmlqUv/cUPqOZ13hmj+2RESF7QHUxW/Y00qO8EIDja3py7rQB/P71b7j77e8i4QZ1KeHQ4V158+stWlEBML5POb+eNYKV2+r4zyfKIDGyRyeuP3YkE/pW8Pn3u2LCD+2afDWjKaCsmJw54kx+POrHMfce+8n+3P32Cj5auYPPrzuMYb9WpnFCgNftpqzIy33n7580/p8fPpSfHz4UUITs934xgy6d/AB8veNrFm5ZyPgu49m/xyS6F3fH5/bRo7gHc86aHYmjZedUnjvnRwSDQVZv38f21q/546I/UOor5d9H/ZvygnI6F3Rm1fY1oScSBZ0/zakBoMjvppMYxKyeN3HN0W0a+/1ufZOtNFHgKeDp2U+zbmd9qP3D2VP7cf2xH0TCvjvufS5++2GljI58js4F3eha2qbVPO3l0wjKID8/fBD3LIaFZyzELdzc+smtvPX9W7SGPLyOCN5IVXE/BnZ5ji6f/xmeOQ+uXJm0PMN89qtDE64N6VLCe99u46cHHEVtoWD+xvkE5CYOHNmfZdsrOGnISapxuYSL2QNnx1wrLfDy+PlTkuYh0NiD5p0HcOAkZea/cMtCmt0bcLlhUrejKPAU0NgSpNjvRSAUQX7HSlwyiKgeRnVRNSXeEgB6Fg5j6aBSLjh4AGNueF1XGcQjENx/xoEc9XQlOxoXgwfOHnkOP5t4GQDvDdzGpj0NFERNDLWRIASTuk1iUrdJANx9yN0Jof5xdk/+++n3vLZMabcF3kQpKTy5DHPTcaOYOawLR4zsxmvLNjNjWBdeX7aF42p6sGCt0n67lxXwzx/vR3mRjyJvEfM3zWf+pkRN6xPfPkGXoi54XcqKy/aG7azYtYL61nqOHXAsR/Y/Mib8n95cwRfrdvP72bMoLFvDFe9ewZ0L7gRgYteJHNX/KI4fdDwel4eL3rwIgMP6HkaXoi58uOFDhlQMicQV7suDUpkoVBcqM86fTJ7KnQuUvAoEB/c+mH2rLybY2IOC7s9Ert96wihqG1vxupR4WlzbcPlddPbvz+DOfTmwZ+zYGp6A3HVKDVfHqSHPGnEW5489n1KfvtXbK44YyhVHKP3SZeMvo8Rbyl2f/5nXt/wNgLMPCPVZws2/vvoXAF0Ku/DwkQ9H4vh217d8vPELtu1tYkB1ceS6S7g4rO9hCRPXz7d+zs0f38yuxl2UeEvY27yX7sXdqSrx8+4vpnPtc0uZMym7ylG7MCJkb0Mx5QgzA2ghVvPsw+BmSiGEF0XAflRKmSApRAvdUsqXhRB/EUJUSSm3G0knk6Sjyf5+Rz0rt+1j+tBqelUU8t9lbUupLbum0rJrakz4m44bSUWRj0se+5ymrcdGrv/t4mO4+60V/H7Jt5QN9lPsUz61N5S11tqRXDVrLPWtDfz21W9w+zfjLf+cIr+kd+ciCrwuWkJxVRT5qCj2xXTQUrqQrYoWeLhYy6WeZ+j+yiP80OehoSXAUeV7GNmjE60Bic8VhP+9SSHQs08vPmWX4vwxTLiNhhI8rnETU5qCVIctk468A/a/gH7rdnPcvR8CcGbPSWzb/Ffeaz6FHY2xA8f8q2fSvaxQtXz7VhaxdkeblqO8SJlsGFkJPGtK3xity/tXzqB3Z3UNREUo/vvOmMAvQorxouAA6oKK5vHqo4bxm1eWR8LvbdmJu3gF/apdzBrTg7+/twq8e0EU0KW0gG9uOYqrnl7CkwvXM/eAfuzXu4zHlh9IYWEDfbttT1jWBzhhXE9e+GIjgSjh/LYTRnPNs19GfleX+tlW28TonmV8GWX+c+H0gXy0ciujeruoLPFx99vfMqFvGQcMquTud74N2RJKcDfiL17LeQcNxOMWeFwCr9tFQML/1j7GtadJ3K6tPLz0PUq6NjFh/Dcs3bSLYd1K+d71Cd0HwhkDlfwd2e8ohnQaj8slmHjLm5G8eNwuzjmwP1v2NkaE7DtOGsOonorWtyhUx4+v6cG1s0bQuShqqVeF5kAzAH63P+HelIGVTBlYmXA9TIHHxZhe5VFXok2C4MttX3L+G+dHBPmEtINK2tdNuY6B5QNVw1QUeZkysBsjK0cCMLoa3vm+AYBSbykDygckPOP3uPhy+5f8bN7PcBf3AumiR7dqmgJVCGBE9zKuOSLWJOaNyw9mb2NLQlwA5x0Um8aUHpO5bPxl7N9jf0ZWJuY7vFoT7gPdwo3b5cYlXIqGPVQFf3NCTSj/46FQwkd3KXY2Jm2KfnroYMb1qeCoUd1wucZz1ftXsWTbEgSCLkVduGTcJabi1UT6aNoym1sPPAaAq9+/mg11GwC4ZvI1lBeUK+GCAVj9LrQ0wKvKihw3JJrXTQsvuCSzkQ1vfNQIVOgp5O0fvh1ZyXC72vrFg4ZU63wx/cwY2oUZQ7vw8IereWnJJrp2apts/fywIcwY1iVBqO/aqSAiTIX/PWN/5eWnDKjk0kMGM2dSb3qGJuZ3zbyLtXvX4hEefG4fXYu68pcv/sJz3z0HwOOzHqeqUJnoXPDGBREznH5l/Tio10ExaT9CIYF9WynxljOp+/4c0e8ICtwFHNX/KKb2jB1Xw+YwM3rP4NiBxxLPD4f8kKbWJk4arEzepvSYwovHv0ifTn14f/37fLf7O4ZVDsMlXAQbeyXEHV5haw705uapN3PXW8tZtXYwl82YyYS+FQnp/fjA/tz11gp6V7QJtD1LerKhbgMVBRW6Bex4hBCcOPgEhICWYAsDywZG3v3mqTezYrdiMjq6ajS9O7VpoHt36s0hffWtPAHUtShLApv3bebsV8+mOdjM4X0P5/fTf0/fymL+dY66sqs9YETIXgzMFkKMAhqBOcAHUsqGqDD9CNlY60EoksqDwNdSyj9ohOkGbJFSSiHEfihCvN6NlVlF0zxCSvj6RZrrdvB+bXeWBAcgBLiFYGutMij/ZNoApg6q4rXVr9KoHgugCHHHju3BJSqaSU+MTaGM+n8g6Of0EXNoag1w639eJVj0Hd7ytjgKvG7Uh91wdErVudnzEGd6FEGoae8I8LgpBCaVAfui1tq61+A9+k5e7a1oRi7890KKfG7u/MFYTvzrB/g8LjaXXcPOxp1c3nIRRw2ewT0/GA5fPgUjjwdgbO9y/jSnhsseX8yJg4/npplzmXTrW0CbIPPHOWNVBexnLzqAPQ0t9Kss5qmF65k5vAtb97aV7GEjujJtcBUL1uyioSXApTMHUVHs48YXFTOZ8w8ewMCqEl5cspHjx6VeRg5z8cxBVBb7OGhIlbLOA/z76Ic5+43TqSgp4awD+vGbV5bj8m1lweYF3LzkbIr6wFbgoRXg7q48U+odDyiaod+ePIZbTxiNz+Piza+20LR1FgcO68JPD/Rz+sunU9jzP0zscgBFPhd7GloRZX6OOwyWb9lLgcfFz+a9BcBxh0teW6Y012Z3IaLhUG7/wQReW7aFu95SOtcLZ/Zgve9eXlg3D3ZByWD4BvjmeyhRkQ0f+lpfubhwUVpeRq3w8VGUgn9Hww72Nu/hrpmxKxHRgoXHJfBWfIjLu4dHV3xB1SY/a/euZW/zXibu38o2r+C51QfTuaAzT337FN/Xfq+ah7AWSk3ITpc3vn+D2pZaTht2WoL96rradWyt30pFQUXM0mk8868+JIVdcBuNIXOWSV2nMG/Dq7yx9g2KQoqhc15/kCsmXqH5bFmRl7Iib8L1Qq87oS353D7OGX2OrjzpJmz3unkJdB9rKorSAi/HjOkecy1a2M8aq9+Ff52gO/jA6hIWr9uNy5U4dvjcPhpaG2LthEOMrh4NKJrETO+Fnju1P3On9geUVSmA4d07RSa/einwuvnZYUNirvUs6UnPktj+9qfjf0pNdQ1VhVURATtMsu/duVgpN7dbUOYv486D79QM+6vJv2L2wNkJwneY4wYdx3GDjov8FkLQr6wfAA8c8YBmvAAH9Gjr23xuH8cPOp5HXv8Qgrs1n7n80MGcc2B/NjW0rfaEzZTSpbKwknNHn5tw/dC+h3Jo38RVIzOEV22W7VgWUTDsatqV7JF2gxEh+7fAO0D0LpXfh/8QQrhRTEjeQD9TgTOBL4UQi0PXrgH6AEgp7wNOBi4UQrQCDcApMle802uQNHtv3gAf/BFQ1P79gt05p/n3MUH8Hhe9KxTNqDs0yPYsL2Dlhtio9h/QmYMGt2ko7vHeRSFNPB84AH5zIee0tlLrnkUTlyVxPaa+waLQ66a2NfE9WuuG4i78nkCDMoKf6XmTTbIzrwT2Y8apD9G/qjjhGTX+esaEyN/PXKgsjc14Ipx66F9vIYw/M+a548f15Jgx3fGGymXWmO48/NEaAFwCThgXqzUoLfBQ29jKuD5t2oHwUlk05UU+/nXOZI6790O+WLebGcO6MK5PRUTIvvooRfv3w0ltQpGendzdywr5Wchk4MpJV7K9YTuDu1ZQXepnfe1iZj9/FMWD9+Ly7OPskPlqoKE3wwuP4zezZzDnb/PZ09DKbbPbBmohBD5P2MtLG0Uepc4IdxMb6ldS2KpokWp3K9/ZWwBBBKt2K3EEpcTlq0O4Wgn6dlAyeAGnvHa7Um6hIpr6mGL7N6Z6DOPKj+C+d1czuV8lM4Z35faXvwVEaOOWwCNLWfTLs1OWCYDX7Y0s80Zz8gsnp3y2IVhHQbcXkdLNG+u8uFwCt3DTu7Q3Ho+LJduXsWT74phyiR4QY/Lh8jK993RdeY5mW8M2zn29bWCqbWilsI+iobz7qyf4Zo+yEekXk36Bx2XqOALVpf1wnYuve2N6lbFpTyOXT/gZPxr1Q2Y/N5uJXfbnJ2N+wvlvnkN9a70igOhxeJDOBtX4/kQrsujL/Q6E9++ExnhLQWvyYYcv+SsOH8LUQVWpA7aEdFAnPQgLH4aNi5MGv/MHY3ln+VZ6lCUKUH8//O+sq13HgLLEFYxThp6iI9c6ifMuYoTTJ/dhRI9OjI/qb62mqrBK0/wnjNp4d9lhQyj2ezggyQpVmOqiamb2mWk6j8lQ6w8OHFTF59/vpkJlsgtKHS4r9FLkH8jZo86mNdhKY2sjK/esTFm/Hz13MuUa8WaKcn85AA8tfUhX+AHVxRw6XN2OPd8wcqz6+0KIWcBPUMb2R6WUr0QFOQDYQNTmSB1xfkCK1iylvAe4R2+cuYSqYBsSsAF2FA3EW9c2sNx6wihOGNcTj8sV2eTVVF9FsKgTMwcdzqYVbuqb2zYX/OKIoTEaqFnujwFYIXtCcx1BdwmjXKtDCtQ4TXYKrjh8KFe+rMynohtxsKk7lfvOZ/2utgWMJwLT+WPrySRuGdNHfCfRs6KAE5Joi71R2r2rjhoWEbL/d+m0hLB/O2OC6Y12qZjUrzNvfr0l6Ya8aM4c0TZhOGf0OXy8UfleT21YTwDJXcedzneb4LfPN9J5RA+GVAzh1mNKeeD9VXQrLU8Z/8DygVTsuorvN5fy8IUHMqFv8q2fjS0Bhv36VYS7jpJBd9Da2JMLJx7Pso17eXv5VqYOqqKw7Dt2N+7m7JFnM6P3IRQ2reQHE3tR29jKLXtjTWQGdSulyJtq405qUmkfw8XdtOUY/nvGlQkasz1Ne2hobauf1YXVMUvnZjh6dDde/nIzfo+LIRVDEAg21W2islAZsFtkKwhl7acl2MzErhM5ecjJpgVso/xxTg2rt++jU4GXTgX9+ez0zyjwFES09WYUumZlU4m6BlnrOr7QxPyRWXDtNvAkN/MxlA+b9DEXz4zdFqQp1IfTrxoCXUfCpiVJ4x3UpSSiDY5nbPVYxlbHavqn9pxKXXMdFQX2CbVGEELYKmAnTzy5gqtneSE3zB6ZwQzFcuHYi5jaU32v0MUzB3H4iG4MqFb/9mG8Li8/m/AzAP6wUHXxPwFdk0GbqS6q5q4Zd7G9cTuFnkL+u/y/Sb/VWz87OK8OWkuG0WPVXyXWkUP0vfdRvIN0eLY36DMXr/dVIWgTsmeP7RGxKw3TtaA/y7+7hpETajh32r7IMr5Ckkro9tFa1AWaYET3TuzarYT9uFDRkDxwlmK2kViPlYp/0oRedO22Hxe++Y+21EJhDxvRlWPH9uDEv3wU86RVTeLW40czrZe+WWyB183A6mJWbtuXsMkG4IBBVRxgUydzx0mjWbqxb4wtol5mDZjFrAGK6/erJrUSlJJOBV7kvs1E7EqAo0d35+jR3TViiUUIgSfQE9hnKC8yUIJ/423U1gY4/oczcNdt5LVd3zCsaCBXHTosJuz/zVA2g9Y2xm67/8fZkxhtcHnYLP4Uk5oyfxllfmvz8tuTx3LhwYMoL/Jx2vDTOG34aTH3F32/K9IeLp85ielDu1iafiqKfB5G9mh7Zy0Xa3b7VU88jETdjjjmd7T5Q3MdePT4BcpHrC/7+w69z/I485msmwcl4fwxF6iOUQB+jzviUai9MqNPmxru6W+fThq2vQjYYMGJjw6x/HHhHznzFUVjmWqgj3ePqlaxinzu0D39XXTYsX2J38MRI7tw0oQ2E4oPihQby0RtQ6L5QfxMM/zTZbO2wmwDs7JZ6umqK0v8HGzBhqISv4dOBbHLeUYVcIlFZrA0ZNvkzkzxzxjahaqS9G2bhRA5eRBmid+TE4OgkdP3osMbeSZdJFL1G2pqlgusL9fow7YycWBTyj4rr4QG8xtQs0n0YV+5JKSdd9AASv0e22phjlvPapLLEyIrMSxkCyFOF0K8JYTYKYRoDf37phDi9NRPt3/eXKtsAjxmwDEc3OvgFKFt7AiEACFwRzobvRU6MZz2IBVrgpJD/Zpp8vUVrOpnc/YbRq/Axx3xnQvkTk7UMTKgZVxA6dwfxp6a2TQzRny5dwzBIlvkosB59VHDWPjrw1Q3szq0f3QL2UIIrxDieeCfKO77SlHc+pUCM4F/CiGeD7nk67CUF5RzQI8DuH3a7SkHK4lIOHjFCmJSleoCSdsyrjbZmmka1TzlXreaPrkg7Gbj++fDMfHZRGvjY6rwYFyra+ZLCJSViHiNYvi0wsj1+Nh7WGtpGP3ema5T6t9G5Eajdsg4ykZ1G4wGZFv8+UYmjmzPFYx8+auBY1H8Ys8ACqSU3YECFCH7U2AW8EurM5lXGKo36p49Yq5Z1oDMV2htzyQhTbbOY4Htxsq+JtvtP11zESs9RGSajrKMmEnMlGlWvoKFDS8nXPhluyMxQxreRbJJtvstBwc1jAjZPwK+A6ZLKd+VUjlDU0oZkFLOA6YDq4C5Fucx77CysZuf7QmiO0ot11pGhPhwUK0nrHrvjtxZ5subZzOfuagByRdtkp58pvUmmh77Urn2y4/yS5scrLvtiXDf0CHGkDx+xQ7xfUIYEbJ7Ac9LKZvVbkopm4DniT0V0iEZ8Tvxk9Q7Q8KwqvYmVeeeOv7w+JBoZZgrDca6fGRfZrJ/MI59x9wZ/HVrH3NkBSWeTJSk0YmGGa2u2dI17MIvLpSlyMxMgISmQiOypk9eS0V5gBAi+ysXWSAXlQ4ObRgRsjcCqeytvaFwHRYjjVxZlLOpgYhYG0D9M0eVjY8ag1SCLsoZQ3IGs58iV79hjOvhDjiQWoXtLvySrJ4lf9D6fDn1xCz56V0EnG+eLziabHX+A5wshOikdlMIUY5yOuOjFuQrv9Fdf+zZhBQjuKeY5Sbd+Khzhmx5t5YD7a+jKQdU3zcLZZCvG2IyVWUjG5YNrWyFwmaoWKVMdNUXds2Ycjnfom9vWtg3m15KhYZRL08ORrH7lE8Ha+koEyIjQvZNwALgUyHEaUKIXiGPI71C7vs+Rtn8eLMdGW2PxJtZqHXO6XUW0eq/FBVawxREiSX5xsf2RLb75mwPDkKIrJdB3pODzcKYCz8bM5JB9Jmn2J2JqPTbS8HmMPk4QXdo32ie+CiECKIlc8G/NK4PBhqSxesQix2CaqxzPnXn/G0bH9WeDT+pnje7hwrDmqcO3K+mM6jEan5U4jYds3nydRkxU/KTmUlYzDM251NLm5j0xMcYLNJkZ/Cd2x156l0kmnztRwyRxy78OsLnCZNMGH6PDi2+mCMtoSfpxkczEUb/SGPjo6bHgPBhNLnRYqzta7Jb9Y1Xo1TeG/Snl+2vmUr72NbGsp3T7JGJjY9mSbbxURObBIVMaTa1TRWiNz462En0iY8OuU9HWXXQFLKllNMzmI92hd5ZtBQ2zrfDnXrEBjLutpZtZ5LDcXL15OCO0VQzQ4x4kIVOUNs0KX/I5YFeT4+TTq+UzUNg4snJQTwX89SOyMlvbhf51CnGke2+IZPYcAyRg35MbF7SFdZMR6PEH/1kfIeVKRd+2Vz+6jhNX5tsK91yWUjVIlODhpl0ws8YFUDMtkNJ4sbHyPWIZjchtVAgazc+Gj3l0nR6WpOLfF11yXYnYAYRddJovpV3GuTrxCIf+3kzGLadFkL0AHqEfm6UUnZol33xGHPhJ2zcPBjnl1V3Miou/HRqF7MlHLen7tSqd0nHhZ+ePjubE6GO0jk7pIdTTzoe+SpwOrRfdGmyhRAFQoirhRCrgXUoR6t/AqwTQqwWQlwlhCiwM6P5hP5ZtD2CSqzgHt74GB8mteCsd+Oj1d2aUS2EHd1qR+irc1FZFXb15qCOWRd+YS2yLnORNOqFoO37xdspR7v2S8hHJFHrNz5mRJOtOw2ncmeCvNwMaJQ83vjYkeznUwrZQoieKO75bgH6AgFga+i/QOjarcDHIS23g07iq5j1Gx+taXz54sLPirfNlQ4r0yUbnV6OFIEmubjxMdfLrEMic0GbHbXxMV8qSZ56F+lIgptD/pBUyBZCuIEXgDHAfOAYoJOUsruUsjvQKXTto1CYF0LPdFiMH11s14mPoX81VLJ6hMlMHcOcGI9Rzxgdt2PVenOz43l02WejWDUndPkioNhNml6GMlqOMV70dKZrQ6Vz6o6DQ27RkdpkKk32XGAc8DAwTUr5ipSyMXxTStkopXwFOAj4RyjsWfZkNX/QX4H0+o41K8Dqfyq8eVHVMXrc+7TpEXNPo+iQPtneNJTShZ+jrcpbF36RjWkJfaTFm6cjGyktjVZf2tHvFr/q0oEVAnYjyM/TYtMlb985T7NtlFRC9hxgE3CRTPIlQ/f+D9gMnGpd9to3trrwa1NlK79k/F2tlO3x8+2gD6NlaHWRO98wffJ1zAuTThVI3xbausLLvclYvjQumbcdQe59cxvJz08EZF+Rk0lSCdljgRjttRahMC+jmI10WIzMKu06xCWiYTZzOlz0D52vkisu/DrSElQYTXMRXf6Qo+JRdbmWBYSBNiQ73vdOa+OjNObOzmzpqrnwi3cjqL3x0Vqy7sKvLYDteejo5JKP9oyQxxsfoeNMiFIJ2eUo2mm9bAk906Ex4l0k2ibb8o2PFqH3fTpEx9bOETjygBmcMrMIC5cBcnMQz8U8tR9y85s7dGRSCdm7ge4G4usWesZBFzZ2CPEnPpqQArLlwi8XaI/vZJRsbXxUq3dqtTdXv5Gd5WamHUeXqa6JcLou/NT+Tpnv3FgNs5VczJMaeepdBKJWSvKlrDsqHejzpBKyFwNH6fGBHQpzNPCFBfnqEMSbWSSvd0aWeVU2JKYa+SNL7/olhFxz4deeMH46X/LfVpPNPjJvN/pYSC6XgdYmy+g8Z2rVK1OaTc0JRQ66m2yvOC788otc7sOsJJWQ/QSKdvpeHXHdA3QFHk83U/mOscNoklc0811z3MbHeIHeQhd+4clCtpQHVjbVbA+FGfWwppZYlgsgX02OcjnfMd/Z7smXUBc2U5aPxYfRQI4O4rmYp/aEU7x5QS73l1aTSsh+GEWbPVcI8Z4Q4nAhhD98UwjhF0IcIYR4FzgbRYv9sE15bXcY3TCoVwCLBDNlIqKWbioXftZitgG2p2abzcNoso1+F365+cXtLEszGx+BjB7MYs6Fn7VEb7TMhOmAlplMzGE0OVpfE5H5k9UoOtzGxxA5OZnUQUdZdfAkuymlDAghjgVeBQ4EXgFahRA7QkEqQ3EI4GtgtpQyYGN+cx4jFSfe8i3ZYGB4nIizyY4f+u3ohNpTt5an/Vba5Org5JhYmsfoN81OHYjvr9KnowziDm10qG/u9Il5Qcpj1aWUG4D9gOuAdYAXxYSkW+jv9cD1wH5SyvX2ZTWP0G0tosdkw0zyiZo+/R5CotLWOi0y7rfV3Vo2N63kijCnNxt2TgayMWAJIXJLra6TTNUbswKwGRd+ZpKKHAgi4/Iacs2YKa2btmbZpvS0tKgJ75uHlTuPyNRKSU6Qxy78OpL9fEohG0BK2SClvEVK2Q/oA0wO/ddXStlXSnmzlHKf0cSFEL2FEO8IIb4SQiwTQvxUJYwQQtwlhPhOCLFECDHeaDq5TIwLv6Th0hWoUj0cWl5V9eygnrP8a9r5Qza7Hz2ddjb79VzvnPN1+TaBrLyGtYnmVF3JF2GoHXgXcXDIFZKai6gR0lZbpbFuBX4upVwkhCgFFgoh3pBSfhUV5ihgcOi/ycBfQ//mJFYfRmPVxsf4mOzY+GhVv2xU82RHv5qtzjqTS/VqKak6RlDBruIx9v75KQikgykXfqLtuGldhxRZ5cLP1MZHa9DagOnQvsmpSZWDJh2pTerSZNuFlHKTlHJR6O9aFLvunnHBjgP+KRU+BsqFEEZ8d2cc0xv3LBqH0jnxUT2+zGx8TLfhtad2a1SIjX91w/viotLLdjGm3PiYg9qqGAceGaiIZsrAsACSxmuk3PioFbnF3zYrdSXm1aztix20ydX9JHaTi/2hHvI130bJqpAdjRCiHzAO+CTuVk8UW/Aw60kUxPMSidAtqBoeuOMPo9G98VFG/WXQhV+anZzZRmel9iJfO2qrSiBX3z6fvktHGTxUMf2ZrP++OfkdcjFPCci8nRTk5Dd3SCCf+vN0yQkhWwhRAjwNXCal3GsyjvOEEAuEEAu2bdtmbQYNYMy7iD0VTaj8ZeSphDtxHa6I/GuTC7887eCtpCMXgX4XfrlDpgcNo20krEXWZW9vNlPRacnESb2Waz+7ycjGR62NljGH0XTgRp0BOqoLP2e8zG2yLmQLIbwoAvajUspnVIJsAHpH/e4VuhaDlPJ+KeVEKeXE6upqezKrEyOH0eh14WceLQ8h7adhtqd3CZO2uYjNZeL06/lDLraPhL7OhsNoHDoeuTgBd+jYZFXIFkpP+yDwtZTyDxrBXgB+FPIysj+wR0q5KWOZtBEjmmwT251iJCFTw6zO/iqiq8nWxsf21LEaLkPz76564GOWpefoTXqaRFy/557waDfpuPAz+ryZlMIu/CSx7gIzXa+iXYTl1iQjD/qqfPYu0pFc+OUxudUm7cWwdxGLmQqcCXwphFgcunYNiptApJT3AS8DRwPfAfUoJ0vmLEYFPjtMLtKLM39d+LWnhpvxEx+jBFtnfDJOjFeWDKRnauOjwWcsfw9JEi8nsXtIrEsyQ365NU0VojY+Og3LViI+2h3ygnalHEuCaSFbCFEBlEgp16UMrIGU8gNSyGpSaTX/ZzaNbKB3Fm2kipk78VFEbXyMv586Cv2NQET9f+bp2P2qxgTI5MfIthigOaFTvZzt3OYHMS78dPlAN1+upl342UBHGcQd2nC+eZ7QgbpuQ+YiQogSIcTvhRCbge3A6qh7k4UQL7e3w2JsxbZDP8ydE6n3Vq668LOSbHfV+kvCvpwm0wrZKTTl48bHTBFuI5loK2ZT0NrgmPS7WWyTHS4fKWVGBnRN05iYjY/kiUYgP72LZPqUT4f06CirDrqFbCFEGTAfuBzYiOLTOromfwlMA061MoP5htHDaFIJqubrYdygJePvpu6E9L5LxIVfmh2z2UZn6XiQI31zJrofte8lRHaLIJcmWkaIMRDIsbEjFwWOfP3O6dER3znD5Fjbc3Awosn+FTASmCulHA88GX1TSlkPvAscYl328hM7BjVTY5JNh9G0/coNF365JtSkg1U1x4pPn6xY7dQmp5psdWhNdkbc0aXxrBARF36Jh1gluvZLSNWixhxtn5uRMktlk+1gP8LcBl+HzBO9Mbm9Y0TIPhF4TUr5zyRh1tJODorJBInDkDXExBkZtFIcRiMT/sg7rFSO5Zvgnu67R79ux9Qytl/Cbb2jLM9GkzMDeT65KGwH3kUcHHIFI0J2L2BJijB1QJn57HQsjLnwM9jphTc+poH+Ex9DSaaVmkMuEF3PsnMitREzptypcbHzEvsKzuwEyJQLPxNpadnFZtyFn8hBF37O5NV2jGzwdcgeOdMmM4ARIbsW6JIiTH+UDZEdGmMNPPmAbKavMLchUWjmJlMu/CKburLYAHOl6aerdbT9MJqcKamOiSkXfgb7BdN1UOOxsP9sVWwSijLmwk/vxse8ID+1wR21T8rXFaqOsupgRMj+DJglhChVuymE6I7iz/oDKzLWEbDrWHWF5H5ZNScC0eODbk12eOOj7sxZSp72MapkXQMTlXw2spK/A2UO51sYG4jT+u5C4++EYDlcXnaSL51Vtvshk3QUwS3fyfo4l0GMCNl/BiqBl4UQw6NvhH4/CRQAd1mXvfzD2KzSQEWzJ6iuZ7UahB0H6SjxZr8BOp119uQBx4WfNiIyoTXeRiTS0HPpmKbEf6PwRiftb2f9xkckIW902exPog6jyYF+rT3TkQS3aPL1vfNVA28U3YfRSClfE0LcCFwPLAVaAIQQ24EKlB7kl1LKj+zIaHvE/u0lQv/Gxwht4Yy68EuXXGh0edpfJWD4PaKKPqtlYCjtdvKxbCYXd/Lnq2DQIciBftgsuTCGODhEY+gwGinljSgu+l4AdgEBlOH5ZeBQKeXvLM9he8aIVsnGuFOnrU+TbZUG2hmA9WPnmJItwUxtoIw9tjz3BtJ8qbKZsNWXMtGFX8o2bYP3jZzY+JhQl3Ov7qqTJxU6jsg3z5cG2UHJhdXqTKFbky2EOAjYK6V8B3jHvizlN0YEAOUwMus73dg4jR2NbmYQyJdhQw/5qgixstPSE5MzhmmTiTpkzMY6d1z4ZToPGdv4mHLTdvI9Mg7pE+0b3cEhVzCiyX4HOM+ujLQndAs8tna6cZ16XOdjxUw/IQZnDEkbs0VolTCRbTlAl2mDugWUQxIyJXxoaa8TD7TSqGhW2WRHufBz+iWj5G/DysVVLodEOtJKgxEhezvQYFdGOiKKTba+TsHQhiWVOFOnksQTSdw9u7qxdF34WdFu20vbt+I1ksk7jsIolkxVm0gbMVlRM7FMm2yDY6aFoExPLhK/S/TGxzwi3/JL3ATPmVnlPB1l1cGIkD0POMCmfLQbjA0iNnYE4U5SY+OjHoy68EsXs42uozTWZFg6qGRxgNV1GI1z/JEhjG58zERzSvjO+XQiokPO4miyHXINI0L2tcBQIcTNQgivXRlqD+jVMuWIOG44cRH5N94ExZ70soLTVyctAjvlcLWBMp80U3ZWnbTLwYgrUBNJhc00EjY+klm78MgGTGRG6k5Ekx2fVsz75kkdzlOlhRCibeEgX8q6A9NRJkS6Nz4CV6O47rsGOEcI8QWwmcQxRUopz7Eof+0au7r/yOAoBGpDfvJUzWi8s9uhWWnflSudc7rjnNEiyZXuLl9t9XI53xEBNwe+cko/2e2WfHu/fMuvQi7UcQeHaIwI2XOj/u4W+k8NCXRYIdu2w2gME7/RyDip3iU+zmx1y+3JXCTbwrHQ+DujpNr32I6+t1HSnQRmwoWfaloJ1iE2b3yMMpHJ7sRZ5X2kzEub53zAceGXH+SKMisTGBGy+9uWi3aG3gokQ0OBrjiF/njb4jRQkWVY26UWX2YaRPobHztOw7UaNcG144qy6ZFrc4BMu/ALm2lo3cskOePCT+STC78cq8AO7ZKOsupg5MTHtXZmpCNitIoZqpSRfURq5iLWpJdok50vg0hq8qX5J/MJY32c9qO1SU/9MJrcqW+5kxN1su3CL5IPrZblbHzMLfK0L+/Iq1wOuYmhEx8drEe3JjvNYTydzifTwnM2l5LybWyxakhRFYic8SrnSKctSmTWJsKZbtPR2vtMvHPk/RI8+KlMCJ2GZQu5YyLkkAohOs7BQUbMRQAQQuwPnAuMA8qBPcBC4B9Syo8szV2eYsRcxKq44mNWHk5v46PeRpBtF37tkWz6E87qRENH2rm4zJjrkzNDLvzszIdmG493OdoeyfFKEqZdfwMHh8xiSMgWQtyC4mUkvreoAX4shLhDSnmNRXnrAGRu42MsxjpRbaE8zlzEUKxJ0jMosdgxJGRrnMm2Bia27LNTCM5kS5tMbnw0lZIg4sIvWbp2a5ejj9jOuY2PeUGeTAgc8pJsj3OZRLe5iBDiByju+75H0WQPAApD/54buv5LIcQPbchn3mBMY6S/qhkZk6L396vbZKtFZt4m0ipNdroDb3tqtkblzHRlllhvvlk01zGUdnv64sYwMhEJL6PnwuSl3dpkR6xFkmx8jJCn75jjtKc9QUbIhXbtoI0Rm+xLgC3AJCnlQ1LKNVLKptC/DwGTgG3A/9mR0bxCZ1u3tWloHe9rZRKpksxD2sM75Dvqh9FE3c/BQSVauMpFc5ZMoeXCL2IjnSmb2RjvgVls1NF1NW86l45bfx0yg6PJVmcs8JSUcrvazdD1J1FMRxx0o3fjowkXfhZ16qnStd5Xc/YaYK7Ib5kejztOl5fnhDWmBipItICbibZlzoWf9TbZEu18WI3miY9RIfKKvJkQtJHKs017JV/ftaMoI4wI2R6gPkWYekxspmxPGNKyZaRxhPITo1HR+6QxF37ZIlcEYyvJ9DvlShFqufBzSI9c0f4735b22WE5ODioYkTIXgnMEkKoPhO6fnQoXIdGt3cRaaeganTzYBKPyykmAzKlFiczWDFnybZSwPCJjxZXn1izDGvjbs/E+PG2sdzMtLFMavgi9t9x7gITNj7Gv4fF+QpvfJRKJ2s7EU120vfIE41jnrb7ZPXNIccQuTPxtxsjQvZ/gOHA80KIwdE3hBADgaeAEaFwDrowtptRrxYo5sRH3Rsfwxh34WcVHaXR5TrZnGjoEQIj7UA6A6kd2NkOU8fdTvuAbM/eDZNv+XVwyE2MmHb8ATgSOAY4SgixEdgEdAN6ogjsH4TCdVhyZjk0IwcwxJ/4aHuSDjmCnd9aTRDLV7tDq8mkhs5MmWtpE+PjSozbek222t8ZR3VSkSNjRDvG6S9ym4600qBbky2lbAYOA34FrAZ6oXgU6R36/SvgkFA4XQghHhJCbBVCLNW4P10IsUcIsTj033V6484XdLvwC/3PUJyah9FYi9Uu/HKhg8yZyZJO7CiybCwsdKTON1OYdeFnVqOdLK2U7SpfNz6G+65kGx/zpmrnV98Xxuk78ot8G2PNYmiTopSyBfj/9u47TI7qzPf4952knAMSCkgCBYRJ0iARZZDJmTV7DfaabMyaNTZOgL17wesNtnevvWDvYnMdAF8s7MUEGWMw0RgvcC1ASxQgjFAiCOWENJp594+qGfX0dKjuqU7Vv8/zzNPd1aeqTk9VnXr79An/DPyzmQ0EhgAb3X1Lkfu/Gfg+cGuONH9w91OL3H5FRA0SS9ca24mjRM/f8bE6xHmxVktBXWis0dvYpIh+sRVT7c2Kqjt3pZWvBrk6Jogppxo9G6qgokMkCYoeCSQMrIsNrju38biZTerNNmrK1rXdXra7l6TjY7c22RE1ekePZUvWLQm3UhtD+CXhxl3pT9CtA1+VBwhx/YISh3LFJEX9ylPkmNHF/qK0bvs6Xl33asb3lm9enm1nwePi22DVIph1PjQ0FrX/YHNGh3ewZeeWspQLeTs+FnuC7NoJ7Tth44ro66x6BnZuLW5/EByDGpeEe0GSmVnVV5bEJXKQbWazCdpj/9Dd383w/hjgUmChuy+OLYdwmJn9N7Aa+JK7v5Qlf5eG+2fixIkx7r4wWU+cP14PD3Zv7fLi6s0clXof6eiAx78NW9fA4HFw5JUM6NjMV5tuY/LT9zGmrZ1hTe8z3DYz0jYy7beDoU8zTJgDtHZtZndzkQZY9Sz803jGNu6CMaMA2NWxCxb/HAaNgb3nA/CV5tv5HrBz8G85+c5HWLl5ZVeQ1ffV+2HIRD6y4U/cYqOZtdcsAA4f8j7s2B3sNDb0rmAb038M729/v+ACsiFtoos4VOr6H9S3GYD99hxc0Hq9DfLm7zua3zz/No0NFmlbwwe0dD0fObAlR8rCGEZbRxtrt6+l3ds5Y3Y/Fj6/kqXrX+fhFQ+xrW0bb2zcPYBRc2MhfbdrX3NDcH7sM3SfyOs0WAML31hIe0d7pMB5QJ/gtnDmQeMKzt+EQRN4aPlDrP1gLbNGz+paPqh5EAAL31hIn8Y+NDWk3XoGj4OGZnj6B8HrcbNh7IE9d7DpbVj3Z5h0RM58DG4ZTLu3s3rrag4Zc0jBn6Nga4Nzsm3XB7BlDQwclT1t1MLl9Yfg538JGSpApKeOlP9TQ+ZB0BJl/KDxAOzRf48K56R3Nu/czPJNy8Fg2rBpXWVcJk+9/RSXPXgZZsblB13OJftfwsYdG7l76d0sencRr69/nVtPupXR/UeX8RPkVkhN9heBI4FvZHn/XeBiYB/gvF7mq9OzwF7uvsXMTgbuBqZmSujuNwE3AbS2tlb0K1LGIPHBns3JG+igyTrow0520AIbl8Nj/wzWCN4Osy/ggPbnubTpN7QtG0xTUzODG3fiGO/6MPo1D4O1S2HNElKD7KOnjYQN78ORn4cRwc346A82ctamxdzlmxjS0QF3/3VX+iWjJ9G4aRlLt4xgwz5TaGpspnWPVkb2HcZxD/wjQ968AoApwCOHXULLqNnwb8fzf3YEtVIfO24el8yY1+uA57rDr+PBtx5k0pBJBa33s4vn8J+LVjJmcN9e7R96BqsjBrSwdmvkbga99qFxg/nphYcwZ9LwSOn7twTf0sYO6QfAuYdM5B/ve4Vh/bMXVJl8538dyDUnzaBPU7Taw/4tTSz75inc+/xqpowcWNC+cmlqaGLphqUc/cuju5YN3AfOvnd3muF9h7PPkGkcM+lIpo8ZFNu+e6Olafe5f/CEoSXbz5QhU/jmUd+kdY/W/IlDX5j9BZ5f8zwAp+6dv+Vd3+ZGXvz6CfRvLrwm+crZV/LXBwVlS5/GPl3LPzrto8wcMZN2b2dEvxG0NKZ9MdvzIPjqKnjjEVhwDrR9kHkH930JltwLjX3g07+H0fsGy7evh7f+C7a8Bzs2c177Tg6f+w06Rk1n8pDJBX+OQs3681NMbmhj5s6d8MOjoPViOOTitIA6LFwe+QZsWh3U1Dc0Q2MTrF/Ws/b57eeDAHv+38GAUdA36hdvgwlzoalP/qSZrHoGdmwqbt0Kmjd+HkvWLWHWHrM4avxRlc5OyZ21z1mMGziOuWPnVjorBUuNkb742Bd58u0nAfjMgZ/pKj8y+d2y39Hu7TRZEw+/9TDjBo7jydVPctfSu7q229IQX6VPHAoJsg8DHvUsVbXu7mb2CDAvlpwF29yU8vw+M/sPMxuZbdbJavXm+1tZ1b4fRza+xFfbLuafmn/c7f0rm+7gm7s+vrtAHnsgrH4WgJawdnjR0bdx2OHzuObWRfzu5XdZ9s1TgrT3fgFevqfb9ga+EUYkex0e/IWubtvGKSv+wNgl98O8U+H//QUAfSe2wovL+PaatXDJd6FPGDS1fQC/vg5G7weHXQ73fIaW9q2w5D7YsBymnQgHfYLWmafF8n+aPnw604dPL3i9fUYP4pqT940lD+meuGo+uzrKV5NkZhwzPfq38KOmjuSrJ8/g7NkTAPjUvCl84tCJ9G+Jdmlff85BjBrYhz5NjYwf1h+A42eO4Ud/eJPLPrx33vVPPWDPyHmN4rMHf5bWMa00WiONDY3BY/h8eJ/hHD7u8PwbqYDRg/py60VzmD5mEHvE8GUvGzPjlCmnFLTOSZNP4qTJJxW0zsA+xbUkNDP6NfXrsbypoYn9R+2fe+WmPtAyIHjenuWLbdv28P0d8N4ru4Psx74FT9/YlawRmA7w2WehqXTHo9OR7Q0cOWQ2bFsL656BR/8h+Mvkv24IHodOhI52aG8Lvt0P3Wt32QtBbf3Ew2Hel0qe/26mHlfe/cXk2L2O5di9jq10NsqmubGZI8bl/kWnWjVYA6u2rOKIBUewpW0Lc8fOZfF7i9nctrkrzYIlC3hsxWPd1ntt/WtMGjyJvYfuzcPLH+Yrj38FgD0H7Mk9Z95DS2NL1f2KUUhJOgZYmSfNamBs8dnpLmyC8m4YwM8hGA1lbZ7VKipTk4X/eHQphzKMFR2jeLxj941m35kHwGsLuazp3iDITl/XnebGIMjuDPRuOi9TDVa0ivv+zf2ZO+UEmHJC9zfO/jG7xh1C0wNXwa4dKQV9uN39z4aDPxE0ZUn96fKcBdBQXSd03Pq1NBLcsqtTU2MDl87rHgxHDbABzsjQJGDYgBYe/MKHe523YkwZOoUpQ6dUZN+9NW9ajiYCEk1nDXdHW+b3B2T5H7dtg/4j4MLfBk1Pfnw8vPcSLHsCRuT/sthr7kGgfNH9wevHvgXvvwYv3hG87tY8xuCald0DapE6cu6McxnYHJz/Zsbpe5/ORQ9c1K257Z2v38mqLau6/RK158A9OWbCMXxy5idZtWVV1/KR/UbStwxfpotRSJC9Dch3FxkF7Ii6QTNbABwNjDSzlcC1QDOAu/8AOBv4azPbBWwHzslWk14VnroRPtjYo7nIs8vXcyhByLraR3Ytnz99NLyWYTspbRYmjxwA78DgvoX9/F+opqbwVNi4AgaMyJqfQPUegrgk/xOKVKHO9pg7ovSp9+7PG1tgVPhL2KnfgZ+cAIPj/aUlsqOvCh7nfTloCtJ38O5ydOgEBdhS1z408kN8aOSH8qZr3aOVG+bfkPG9KUNqozKmkCB7MXCGmX0h05B9ZjYYOCNMF4m7n5vn/e8TDPFXG+6/Gsb3LNTfWLMVC+8dHTRwX/scZg7rYFJK8PrA5+cBa8JXncudY6aPghfhgGztPC2Y1fGxLx3d/T+1T4E/+fUL2wC3bcuRKG0GSQ3zJCJxagxvSb/8JPzFj+CAvyxuO501x2Wtk8lQHo6eEfzlSydS59IrJ6u5PrUQhfzWfxNBTfWDZnZA6htmdiDwO2BkmE4ycIzLj9mbw67+DZO+8HC396aPGbT7hpASvDY15JvkIDBp5ABoGQSHXg7XbYS/uqOwzPXP0NEu20mekJNfRKrMiKkw9fjg+YZlGRKklD09yiHL8rwcCikTVX6K1ItCZnz8BcGkMXOB58xstZn9KZxe/VlgDvAzd19QmqzWjmzDZJkZXz5hBsO6hj/LMTtYz5UjpI2h8M4YQGfZh2qyRSROLf3hnJ8HzwuKW7MlLmNAm7c8VHkpEpXjiRjvvKBea+5+AXAZ8DJBR8jZ4eNLwKXh+5JBUZPOuEesNY4j8M20Xtq+FVSLSMnlKGdylYep5VPZK7ILKd9VjoqkM6zqJ0ArRsHjNHWOR21m/YGhwAZ3z9WQt67EcooUEszGHvhm+ATdpgH0zGlERCpGTdtEkqbYWWerSW+mVd9GMOKIRJCxJjv9BOq6Iezu+Lj75hGhdqc3N5RMJ3PONo8iIqWUqTyLWsZVok12nn0mIGAQKZn0cCghlXlFB9kAZnY6MJ/g3/O4u/8qllwllEct+CtZGEcJ1OugdigpPZtFak7U8i/1GnVQJYBIbUu/7ya+TbaZnWZmj5tZj5kpzOynwF3AFcBngV+aWd0H2Z7lnMjSFTLLVort+FgqlpKHCDXrNSwJP0+JJEIsX3SrqeOjiGRTr0P4nQ7MAp5OXWhmpwLnEzQX+QfgKuDPwJlmlnPs63qQ7dtX/prstCH8Cu742JuTMkLHx7zLRUR6K2rHxxwds8sd8EYqpxWEi+SSlCYiqfI1F5kD/MHdP0hbfhFBCXehu98BYGY/A94APgHU/TB+kRRUO52titxyvy5YlI6PcexHRCSXGMaervRkNBmTqewUiSIJvyznq8nuHJ4v3TxgA9DVPMTd3wF+AxwcV+ZqUbYiPdIQfj1uCBFvEHHcR9TxsZvkfZ8WqRE5b6zV3PExalKVLiLpkhBQZ5IvyB4G7ExdYGYTgeHAE96z0cybwIj4slebsp0skYvWYk+2OArvKJPRJPgmkczLXCRJUprTdfJso3tUUVmV0CBCJC5JaYedKl+QvRkYn7Zsdvj4XJZ10puWCNlqsvMUuqltsnM2LfG018WIsF63POiGISIllOmG6x4tWK1EQBt1nwq2RXqo146PLwCnmNnAlGVnEUR1T2RIPxl4O6a81aRcp0XBHR8rro47PtbBRxSpSpGnJ0/rBFnJYjMhAYFIJSWx42O+IPs2giYjvzezK8zs+wQdG98BHk1NaEEbiSMJplyva5HHdoy942OJhrzq2oclvuNjQj+WSA3KUp4VcpFWY8dHEcnL8USMk51vdJEfA38BnAAcRFCKtAGfc/f2tLQfIego+VDMeUyESKdKxo6PBXaYLDZKjNTxMc/yBEjwRxNJgFru+Fj7AYNIqSQhoM4kZ5Dt7h1mdgpwLnA4sBa4090XZ0g+ErgeWBh3JmtJ7uYiERUUKMd8Yubq+NhtMpqES+b1LlI7sn7jrdGOj11UuIjk43giRhzJO626u3cQNBu5LU+624HbY8pXTct8YhTS8THDZDRRhrXqVTVsoSdz7Z/8OVXjvVmkbmQpX6q142PUfAWJS5oVkVqUhIA6k3xtsiVG5en4GOdkNFl69yf4JpHQ61ykBuWpye7R8bGAJm+VoMJFJKf0EUWS0IREQXZVKuDEKkfBndrxsZz7FZH6lLV8iVpjXInyKeo+VXaK5FMvQ/hJETJ9+ypoxkdLrakppONjL05KdXzsJolDCYkkQyFtsstF5YVIbyXxvqsgu6x6UdNRrprjyB0fVRsjIiUUy5f5Mt60I4/vLSJRqLmI9JCvFWFBa6d2fIy6XtHBeCHrJe/bZqfav6RFkqAGOz6KSNGSEFBnoiC7jEozhF/qDkpd82O6mYhImSSs46OI5JTaXMQrPYtrTBRkl0mkNtkpqQOpbbKjzPhYIpn2kfCOj7o3i1RQEjs+JrzMFOkNDeEnkeQapi/vEH7FTlneLSLUjI+9kdQLXaTm1NRkNMktE0XKJSkjiqRSkF0CvQ/UCqk5Tl0ewwmacROpHR8z7VdEJE411ia7UvsUSSh3T0Q7bQXZZVK2jo9FK6QWKHnfNkWk2hTSjbzCZVKk3dd+wCBSKkkIqDNRkB2zXGVt/hkfQ4XUiKSn7XVtSoZPkDoZTbFNWmpMAn+1EqkdkcqXtIu0Vjo+JrzsFImD44lovlnRINvMfmJm75nZi1neNzO7wcyWmtnzZjar3HksRtGT0aSkDkTo+Ai7byS9uaEk4GQWkToQqagqd3lW6clwRGqbarJL42bgxBzvnwRMDf8uBW4sQ55KJm8IXA21xD06E6UwgKhNWGqbvnOIVFhNdXyMQIWKSE7dhvBTm+zec/fHgXU5kpwB3OqBp4ChZja2PLkrjpfqnIjU8THT61Kp/ZM/lzr4HiFSxdTxUaSuJPTyqXRNdj7jgBUpr1eGy6pa8c1F0mqyC+742JvIMEotUPIno0nodS5Sg2poMpqC9qNSRiQTDeFXxczsUjNbZGaL1qxZU+nsZBS542PGdOWajCZXx8dS7ldEJJTEyWi6JC+QEImbo+Yi5bAKmJDyeny4rAd3v8ndW929ddSoUWXJXCEinSo9vsWldnyMuF6xwW/Giuz6m4xGRKpEvjbZVSVKmViN+RapDkkIqDOp9iB7IXBeOMrIocBGd3+70pnKJ9uwMyUZwi9uuQJoM+qlFqY+PqVItYpQBkbq+FiNaiWfIuXlaXfeJAzh11TJnZvZAuBoYKSZrQSuBZoB3P0HwH3AycBSYBtwYWVyWm5FzvhYshrmApqv1LgEXNMiCZGhPKvqjo/l36VIUiQhoM6kokG2u5+b530HLi9TdmKRPcwtR8dHKL6kL6Tjo+p5RaSEct5wa7TjY0KDCJG4pHZ8TEonyGpvLlKn1PGx0pJygYskSxI6PopIvVCQXSaRhvDLGNgVsl7MMz72mIwmbJOtAFRESq2Qjo9Z05arrFKZKNIb6vgokeQqakvT8TEtbW9rmCMH0Mm8IJL7uURqSYEdH9PXqcpf2qoxTyLVo9uMjxrCT7LJPBlNYVsAurfJruhNI33fqrURkQqI2vGx3Ko1XyI1IgkBdSYKsssof012sQF1HEFvls5E6WnqpKlIfXxKkSpVax0fRSRWjidixBEF2TGLp0jvTcfHEpyUGTs+xr8bEZEu2fqoVO2NN0++qjbfIpWXhIA6EwXZJZDpZInW8bFrAykLiu0wWSB1fOwmmZe7SK2IUJMdaTKaKuz4mNBgQiROSRnhS0F2GUU/ZYqYjCauEzLjdupnMhoRqRa1NhmNykSR3kgPrJPQTltBdsyyDzpVRBBczGQ0RRf0mowmVfI/oUgVi9Qmu4okpNZNROKlILsEsn37Kl3HxzhFvFkktNYmoR9LJPnU8VGkpvUYwi8BN2QF2VWpiI6PpbyZdO6jHPsSEYEcHR+jrKwZH0VqSRKahmSiILtMIp0+PcbELmPTjMi1QPURYOt7hEglJbDjYwJq5URKKbUmOymhhoLsmOVqEhJ5xsdiOj7mWrcQdd7xMZmfSqQWqeOjSD1LQu22guwSKHoIv/Q0kTs+Zli3YOr4KCJVIue9tYAa63L9JFXQfmo/cBCJW3rc5AmJMxRkl1HkU6aQMjj2dtL13fFRRKpE3slo0sfxz/pCRGpAUsbGTqUguypluolEvGmUIvjt1vGxPiajUW29SCUVOIRfxcukbG3CUynwF8kmCU1DMlGQXQKZTpZoMz72Zgi/Xt5kNPwVoAp6keqRp/zpUT5VsuOjiPSWhvCTXiphx8eyzPiYWsNe+ye/iFSpbOWdOj6KJFISAupMFGTHzHt1nqTVZBfV8bGUMz5Sl7XbIlJNMjWnyxJ8V1PHx4QGESKlkJT22Qqyyyj6KVPJyWjU8VFEqkDejo+5qHwSqTk9+jLX/nWsILtMog3hl65KJqOpy46PIlI5cXV8LOdkNLUfEIhUSnpArSH8JKNcp0XeyWji6PjY23I+cgCtG4qIlFKeyWhydXysyl/aqjFPItUjPbBWTbZk1PsG/BXs+JhrH3U0GY0q60UqKGcZWqWjiEQt96vyC4CIlIKC7DIpaMbHXnV8LFaV3rjKLAnfnEUSIW/Zl96As8o7PopI3VGQHbNeNRfpUkCg1yNpr9uLZNiHdX+MOoyWiEhRspUv1dzxMc8+VWaK5NSjuUgCrhkF2SWQeTKaYhTQNKO3NSmajEZEqkq28qdaOz6KSLHSA2oN4ScFy3vKVHLGx/Q8ZGRZnidPMi5vkRoVZTKamuv4KCK5JCWwTqUgO2bxnCIZ2mTn6/jY9bIUN5eUbSa846PuzSLVroD+I2Vrk02EwkOFi0g2Se0PpSC7BDI3Fymi42Mhen0zSeYJLiI1qlcdH1WeidSa1DbZjici8K54kG1mJ5rZq2a21MyuzvD+BWa2xswWh3+XVCKfcehdx8c8Mz6WsnY502Q0Ca/yTeCvViI1JEf5krEiuxou2GSXiSKllISAOpOmSu7czBqBfweOA1YCfzKzhe7+clrSX7j735Q9gzGKdPr0aBpSTMfHIk9UdXwEEv/dQaSG5On4mKtNdt5txC3CflS4iESWlBkfKxpkA3OApe7+ZwAzux04A0gPsmtK9mFnelGT3ettRuQdsOoZ2LUTWgbkSKgbhoiUisPObRkWR/wVrTPN9g27l21bB1vXBGVc5x/AyOnQ1NJzG7t2wMpFMGAkNLbAhuUweV4MwbLKTpF8OrwjEUP4VTrIHgesSHm9EpibId1HzWwe8BpwpbuvSE9gZpcClwJMnDixBFnNrw3Y3tAA29axesN2rljwHNvb2gGY3rCcpUzIvvK/TIWt73Vf9ur98P5r0Xb+rb2CR+tlC6D7r4Ft7wfPJ384XBie6A2N8Oc/wFt/hCE5PouISG94B7zwSzjkYph4aNqbEWqsm/sHj/+9ACbMgQ0r4O7Lsu9v5PSey95/NXhsaIY+g2D7OrjsjzBqRpiNBlj/Jix/Ctp3qqZapBfavZ2Vm1eyYvMKNu/czOadmyudpVhUOsiO4tfAAnffYWafBm4B5qcncvebgJsAWltby/47w2tvPc/HJgWB59bF93L4fYcD8J1R9zF14GL6te3EvD37BmacDM/cAjiMCgv8x/4peGwZCE19M683akbwXstAmH0+7H92kZ8gvEF0BthD94I3fx88b+oTPB7zNRg/J3g+blaR+6luR00dxX0vvMPeo3PV4otISX3kWvj1FbD+re5BtrfvDmafvRV+/23oMxA62mFoSuVK38HQ0BRUUtx8yu7lx30Dhk4IAmRrgKUPda/tTtUZZHe0BQE2wA+OyJ7nfsNyf6bxh8DEw3pRRosk19a2rSzbtIyT7zy5a9mM4TMqmKN4VDrIXgXdqnfHh8u6uPvalJc/Ar5dhnwVrKmhkcO2f0D/jg6+um4tk5p+yrW7zues9vvxfs08tWMfnhz7VxySutLko2DSUXD8N2DPg+GU78D29cHPk9NOgLbtQbp+w3YHuulmnAx/+25xmT7kkiCYTnf4FTDrPFj9XLDfaScEyyfMCf4S7JxDJnDCfmMYPiDDz8ciUh77HBs8Ln0INq2EF+6AA8+F5U/CvqfBsEmwfhms3QydNV4jp3bfxoevghfvhDWvBK9bL4YjruieZt/TsufhZ2fBG4/0XH70NfDBxqApScsAmHlGUI4Pn5z7M43eFy66P3cakTp1/THXs3TDUhobGgHo19SP+RN61KfWHKvk4N9m1kTQBOQjBMH1n4CPu/tLKWnGuvvb4fOzgKvcPf33w25aW1t90aJFpct4Nq8/CLedDS2Ddhf8APO+zBv7f54xg/syoE+lv9dksfYN+F5YO/3hq+GYayqbHxGpX1vXwr9MyfzemTfCQR8Pnm9ZAz+cB43NcO4C2GO/numvPxD6DoVP/76wPHS0w4a34MYjoG0bfPJumDAXWvoXth0RSTwze8bdW9OXVzTic/ddZvY3wANAI/ATd3/JzP4eWOTuC4ErzOx0YBewDrigYhnOZ+pxcN1G6OiA/7ohqIluaISDPs7eQwZWOnfRZas1FxEphz4p5eWFv4UdW2DQHjBgFAzec/d7A0fBF1/Jva3PPAXWWHgeGhph+BS4ZlXQTKWxufBtiEhdq2hNdqlUrCa7lu3aCfdcDh9sgPl/B2MPqHSORKSebVodtKseOLrSORERyakqa7KlijS1wEf/b6VzISISSK2xFhGpQRWf8VFEREREJGkUZIuIiIiIxExBtoiIiIhIzBRki4iIiIjETEG2iIiIiEjMFGSLiIiIiMRMQbaIiIiISMwUZIuIiIiIxExBtoiIiIhIzBRki4iIiIjETEG2iIiIiEjMFGSLiIiIiMRMQbaIiIiISMzM3Sudh9iZ2RrgrQrtfiTwfoX2LeWhY1wfdJzrg45zfdBxTr5KHuO93H1U+sJEBtmVZGaL3L210vmQ0tExrg86zvVBx7k+6DgnXzUeYzUXERERERGJmYJsEREREZGYKciO302VzoCUnI5xfdBxrg86zvVBxzn5qu4Yq022iIiIiEjMVJMtIiIiIhIzBdkxMbMTzexVM1tqZldXOj9SGDObYGaPmtnLZvaSmX0uXD7czB40s9fDx2HhcjOzG8Lj/byZzUrZ1vlh+tfN7PxKfSbJzMwazew5M7s3fD3ZzJ4Oj+UvzKwlXN4nfL00fH9SyjauCZe/amYnVOijSBZmNtTM7jCzJWb2ipkdpms5eczsyrC8ftHMFphZX13Ptc/MfmJm75nZiynLYrt+zWy2mb0QrnODmVmpPouC7BiYWSPw78BJwEzgXDObWdlcSYF2AV9095nAocDl4TG8GnjY3acCD4evITjWU8O/S4EbISgIgGuBucAc4NrOwkCqxueAV1Jefwv4rrvvA6wHLg6XXwysD5d/N0xHeF6cA+wHnAj8R1gGSPW4Hrjf3WcABxIcb13LCWJm44ArgFZ3/xDQSHBd6nqufTcTHItUcV6/NwKfSlkvfV+xUZAdjznAUnf/s7vvBG4HzqhwnqQA7v62uz8bPt9McFMeR3AcbwmT3QKcGT4/A7jVA08BQ81sLHAC8KC7r3P39cCDlPAClsKY2XjgFOBH4WsD5gN3hEnSj3Hnsb8D+EiY/gzgdnff4e5vAksJygCpAmY2BJgH/BjA3Xe6+wZ0LSdRE9DPzJqA/sDb6Hquee7+OLAubXEs12/43mB3f8qDTom3pmwrdgqy4zEOWJHyemW4TGpQ+DPiwcDTwB7u/nb41jvAHuHzbMdc50J1+zfgK0BH+HoEsMHdd4WvU49X17EM398Yptcxrm6TgTXAT8NmQT8yswHoWk4Ud18F/CuwnCC43gg8g67npIrr+h0XPk9fXhIKskVSmNlA4FfA5919U+p74bdeDcdTo8zsVOA9d3+m0nmRkmoCZgE3uvvBwFZ2/7QM6FpOgvCn/zMIvlTtCQxAvzTUhVq6fhVkx2MVMCHl9fhwmdQQM2smCLBvc/c7w8Xvhj8vET6+Fy7Pdsx1LlSvI4DTzWwZQZOu+QRtd4eGPzdD9+PVdSzD94cAa9ExrnYrgZXu/nT4+g6CoFvXcrIcC7zp7mvcvQ24k+Aa1/WcTHFdv6vC5+nLS0JBdjz+BEwNezW3EHSiWFjhPEkBwrZ5PwZecffvpLy1EOjslXw+cE/K8vPCns2HAhvDn7IeAI43s2FhTcvx4TKpMHe/xt3Hu/skgmv0EXf/BPAocHaYLP0Ydx77s8P0Hi4/JxytYDJBx5n/X6aPIXm4+zvACjObHi76CPAyupaTZjlwqJn1D8vvzuOs6zmZYrl+w/c2mdmh4XlzXsq24ufu+ovhDzgZeA14A/hapfOjv4KP35EEPz89DywO/04maLP3MPA68BAwPExvBCPKvAG8QNDDvXNbFxF0nlkKXFjpz6a/jMf7aODe8PkUgpvqUuA/gT7h8r7h66Xh+1NS1v9aeOxfBU6q9OfRX4/jexCwKLye7waG6VpO3h/wdWAJ8CLwM6CPrufa/wMWELSzbyP4ZeriOK9foDU8Z94Avk84MWMp/jTjo4iIiIhIzNRcREREREQkZgqyRURERERipiBbRERERCRmCrJFRERERGKmIFtEREREJGYKskVEREREYqYgW0SkyplZo5l9ysx+b2brzKzNzN4zs+fN7EdmdnpK2gvMzM3sggpmWUSk7jXlTyIiIpViZo3AvcCJwAbgNwQTNLQA+wEfB2agWWZFRKqKgmwRkep2LkGA/d/Ah919Y+qbZtYfmFuJjImISHZqLiIiUt0ODx9vTg+wAdx9m7s/CmBmjwE/Dd/6adhspPNvUuc6ZtZkZp8xs6fMbJOZbTOz58zsb8ys233BzCaF699sZjPM7O6wycpWM3vCzI5Pz5OZtZjZFWb2rJmtD7e/zMzuMbNjY/q/iIhUNdVki4hUt7Xh47QIaW8maFJyBnAPsDjlvQ0AZtYM/Bo4AXgV+DnwAXAM8D2CWvFPZtj2ZOBJ4AXgh8BY4GPAb83s4+7+i7R8nAu8CNwKbAf2BI4kqJV/KMJnERGpaebulc6DiIhkYWYHA08TVIrcBtwFPOPub2VJfwFBbfaF7n5zhvevA64Fvg983t3bw+WNwE3ARcCZ7n5PuHwS8Ga4+r+6+5dTttVKEHhvAfZy901mNgRYDzwLzO3cfso6I9x9LSIiCafmIiIiVczdnwP+Cng3fPwVsMzM1prZXWZ2WtRthU1BPgu8A1yZGgCHz78IOPCJDKtvBP4+LW+LCAL/ocBZnYsBA3YAHRk+jwJsEakLai4iIlLl3P2XZnYXQZOOI4GDw8czgTPN7FbgAs//0+Q0YDjwOvC3ZpYpzXZg3wzLn3X3zRmWPwacH+bplrA2+9fAacBiM/sV8AfgaXfflid/IiKJoSBbRKQGuHsb8Lvwr7N5x0eBnwDnETQjuTvPZkaEj1MJmoxkMzDDsnezpH0nfBySsuxjwFUEwwt+PVz2gZndAXzJ3bNtS0QkMdRcRESkBrl7u7v/EvhuuGh+hNU6Rye5y90tx9/kDOvukWWbY9K2jbtvd/fr3H0aMJGgmcsT4eMdEfIpIlLzFGSLiNS2ziYcnW0/OttZN2ZIu4RglJFDw1FGCjHLzAZlWH50+PhcppXcfYW730YwmslS4EgzG5EprYhIkijIFhGpYmZ2rpkdlz5+dfjeGOBT4cvHw8fOjoUT09O7+y6CYfrGAjeYWb8M2xxrZjMzZGUI8L/T0rYSdJLcSNBcBTMbZWb7Z1h/AEEzlF3Azgzvi4gkitpki4hUt7nA54B3zOwJdg+nNxk4BehHMCZ2ZzOMJ4FtwOfDGuPONtPfCyez+QZwIHAZcJqZPQKsAkYTtNU+Avga8HJaPh4HLjGzucAf2T1OdgPwaXffFKYbBzxnZi8AzwMrgMHAqQRNS27I0oFSRCRRNE62iEgVM7MJwOnAscBMguC2L0GN9XMEk8n83N07UtY5kaBj4/4ENcgAk919Wfi+EbSPvoBgVJCBwBqCAP4+4GfuviJMOylcfgvwLeCbwDygT7j/v3f3B1L2PRS4gqAZyXRgJLCOYOKbHwK3RxgFRUSk5inIFhGRrFKDbHe/oLK5ERGpHWqTLSIiIiISMwXZIiIiIiIxU5AtIiIiIhIztckWEREREYmZarJFRERERGKmIFtEREREJGYKskVEREREYqYgW0REREQkZgqyRURERERipiBbRERERCRm/wMcndh6YUh6TwAAAABJRU5ErkJggg==\n", 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" ] @@ -261,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -271,14 +271,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10000/10000 [05:03<00:00, 32.93it/s]\n" + "100%|█████████████████████████████████████| 10000/10000 [05:20<00:00, 31.20it/s]\n" ] } ], @@ -291,34 +291,34 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10000/10000 [04:54<00:00, 33.99it/s]\n" + "100%|█████████████████████████████████████| 10000/10000 [06:31<00:00, 25.57it/s]\n" ] } ], "source": [ "min_scores_anneal = np.zeros(total_steps)\n", - "for i, part in enumerate(optimizer.simulated_annealing(total_steps, optimizer.hot_cold_cycle_beta_function_factory(200, 800),\n", + "for i, part in enumerate(optimizer.simulated_annealing(total_steps, optimizer.jumpcycle_beta_function(200, 800),\n", " beta_magnitude=1, with_progress_bar=True)):\n", " min_scores_anneal[i] = optimizer.best_score" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10000/10000 [04:52<00:00, 34.13it/s]\n" + "100%|█████████████████████████████████████| 10000/10000 [06:05<00:00, 27.34it/s]\n" ] } ], @@ -330,12 +330,12 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -356,6 +356,13 @@ "plt.legend()\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From f4725de01b28e4372006a5d9e7bf662ea1665795 Mon Sep 17 00:00:00 2001 From: Chris Donnay Date: Mon, 1 Apr 2024 10:43:18 -0400 Subject: [PATCH 17/28] fix random module --- gerrychain/optimization/optimization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 2551e455..611d585c 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -1,7 +1,7 @@ from ..chain import MarkovChain from .. partition import Partition from ..accept import always_accept -from ..random import random +import random from typing import Union, Callable, List, Any from tqdm import tqdm import math From b67ce877f29ae8ef34aa0b4ba4603563dfb4e335 Mon Sep 17 00:00:00 2001 From: peterrrock2 Date: Fri, 12 Apr 2024 14:24:31 -0600 Subject: [PATCH 18/28] Formatting, type hints, and making consistent doc strings --- gerrychain/chain.py | 7 +- gerrychain/optimization/__init__.py | 2 +- gerrychain/optimization/gingleator.py | 175 ++++++++---- gerrychain/optimization/optimization.py | 360 +++++++++++++++--------- 4 files changed, 345 insertions(+), 199 deletions(-) diff --git a/gerrychain/chain.py b/gerrychain/chain.py index bacef507..9b63f8a7 100644 --- a/gerrychain/chain.py +++ b/gerrychain/chain.py @@ -24,10 +24,9 @@ Last Updated: 11 Jan 2024 """ -from .constraints import Validator from typing import Union, Iterable, Callable, Optional -from gerrychain.constraints import Bounds +from gerrychain.constraints import Validator, Bounds from gerrychain.partition import Partition @@ -54,7 +53,7 @@ def __init__( proposal: Callable, constraints: Union[Iterable[Callable], Validator, Iterable[Bounds], Callable], accept: Callable, - initial_state: Optional[Partition], + initial_state: Partition, total_steps: int, ) -> None: """ @@ -69,7 +68,7 @@ def __init__( Metropolis-Hastings acceptance rule, this is where you would implement it. :type accept: Callable :param initial_state: Initial :class:`gerrychain.partition.Partition` class. - :type initial_state: Optional[Partition] + :type initial_state: Partition :param total_steps: Number of steps to run. :type total_steps: int diff --git a/gerrychain/optimization/__init__.py b/gerrychain/optimization/__init__.py index 831dd950..4192e674 100644 --- a/gerrychain/optimization/__init__.py +++ b/gerrychain/optimization/__init__.py @@ -1,4 +1,4 @@ from .optimization import SingleMetricOptimizer from .gingleator import Gingleator -__all__ = ['SingleMetricOptimizer', 'Gingleator'] \ No newline at end of file +__all__ = ["SingleMetricOptimizer", "Gingleator"] diff --git a/gerrychain/optimization/gingleator.py b/gerrychain/optimization/gingleator.py index 6b461321..ed07920e 100755 --- a/gerrychain/optimization/gingleator.py +++ b/gerrychain/optimization/gingleator.py @@ -3,6 +3,9 @@ from functools import partial import numpy as np import warnings +from typing import Callable, Iterable, Optional, Union +from gerrychain.partition import Partition +from gerrychain.constraints import Validator, Bounds class Gingleator(SingleMetricOptimizer): @@ -15,83 +18,125 @@ class Gingleator(SingleMetricOptimizer): districts is one of the litmus tests used in bringing forth a VRA case. """ - def __init__(self, proposal, constraints, initial_state, - minority_perc_col=None, threshold=0.5, score_function=None, - minority_pop_col=None, total_pop_col="TOTPOP", - min_perc_column_name="_gingleator_auxiliary_helper_updater_min_perc_col"): + def __init__( + self, + proposal: Callable, + constraints: Union[Iterable[Callable], Validator, Iterable[Bounds], Callable], + initial_state: Partition, + minority_perc_col: Optional[str] = None, + threshold: float = 0.5, + score_function: Optional[Callable] = None, + minority_pop_col: Optional[str] = None, + total_pop_col: str = "TOTPOP", + min_perc_column_name: str = "_gingleator_auxiliary_helper_updater_min_perc_col", + ): """ - :param `proposal`: Function proposing the next state from the current state. - :param `constraints`: A function with signature ``Partition -> bool`` determining whether + :param proposal: Function proposing the next state from the current state. + :type proposal: Callable + :param constraints: A function with signature ``Partition -> bool`` determining whether the proposed next state is valid (passes all binary constraints). Usually this is a :class:`~gerrychain.constraints.Validator` class instance. - :param `initial_state`: Initial :class:`gerrychain.partition.Partition` class. - :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of - minority population within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" - (or opportunity) district. - :param `score_function`: The function to using doing optimization. Should have the + :type constraints: Union[Iterable[Callable], Validator, Iterable[Bounds], Callable] + :param initial_state: Initial :class:`gerrychain.partition.Partition` class. + :type initial_state: Partition + :param minority_perc_col: Which updater is a mapping of district ids to the fraction of + minority population within that district. Defaults to none. + :type minority_perc_col: str + :param threshold: Beyond which fraction to consider something a "Gingles" + (or opportunity) district. Defaults to 0.5. + :type threshold: float + :param score_function: The function to using doing optimization. Should have the signature ``Partition * str (minority_perc_col) * float (threshold) -> 'a where 'a is Comparable``. This class implements a few potential choices as class - methods. - :param `minority_pop_col`: If minority_perc_col is defined, the minority population column - with which to compute percentage. - :param `total_pop_col`: If minority_perc_col is defined, the total population column with - which to compute percentage. - :param `min_perc_column_name`: If minority_perc_col is defined, the name to give the created - percentage updater. + methods. Defaults to None. + :type score_function: Callable + :param minority_pop_col: If minority_perc_col is defined, the minority population column + with which to compute percentage. Defaults to None. + :type minority_pop_col: str + :param total_pop_col: If minority_perc_col is defined, the total population column with + which to compute percentage. Defaults to "TOTPOP". + :type total_pop_col: str + :param min_perc_column_name: If minority_perc_col is defined, the name to give the created + percentage updater. Defaults to "_gingleator_auxiliary_helper_updater_min_perc_col". + :type min_perc_column_name: str """ if minority_perc_col is None and minority_pop_col is None: - raise ValueError("`minority_perc_col` and `minority_pop_col` cannot both be `None`. \ - Unclear how to compute gingles district.") + raise ValueError( + "`minority_perc_col` and `minority_pop_col` cannot both be `None`. \ + Unclear how to compute gingles district." + ) elif minority_perc_col is not None and minority_pop_col is not None: - warnings.warn("`minority_perc_col` and `minority_pop_col` are both specified. By \ - default `minority_perc_col` will be used.") - score_function = self.num_opportunity_dists if score_function is None else score_function + warnings.warn( + "`minority_perc_col` and `minority_pop_col` are both specified. By \ + default `minority_perc_col` will be used." + ) + score_function = ( + self.num_opportunity_dists if score_function is None else score_function + ) if minority_perc_col is None: - perc_up = {min_perc_column_name: - lambda part: {k: part[minority_pop_col][k] / part[total_pop_col][k] - for k in part.parts.keys()}} + perc_up = { + min_perc_column_name: lambda part: { + k: part[minority_pop_col][k] / part[total_pop_col][k] + for k in part.parts.keys() + } + } initial_state.updaters.update(perc_up) minority_perc_col = min_perc_column_name - score = partial(score_function, minority_perc_col=minority_perc_col, threshold=threshold) + print(f"Perc_up keys: {perc_up.keys()}") + print(f"Minority_co: {perc_up.keys()}") + score = partial( + score_function, minority_perc_col=minority_perc_col, threshold=threshold + ) super().__init__(proposal, constraints, initial_state, score, maximize=True) - """ - Score Functions - """ + # --------------------- + # Score functions + # --------------------- @classmethod - def num_opportunity_dists(cls, part, minority_perc_col, threshold): + def num_opportunity_dists( + cls, part: Partition, minority_perc_col: str, threshold: float + ) -> int: """ Given a partition, returns the number of opportunity districts. - :param `part`: Partition to score. - :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + :param part: Partition to score. + :type part: Partition + :param minority_perc_col: Which updater is a mapping of district ids to the fraction of minority population within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" + :type minority_perc_col: str + :param threshold: Beyond which fraction to consider something a "Gingles" (or opportunity) district. + :type threshold: float - :rtype int + :returns: Number of opportunity districts. + :rtype: int """ dist_percs = part[minority_perc_col].values() return sum(list(map(lambda v: v >= threshold, dist_percs))) @classmethod - def reward_partial_dist(cls, part, minority_perc_col, threshold): + def reward_partial_dist( + cls, part: Partition, minority_perc_col: str, threshold: float + ) -> float: """ Given a partition, returns the number of opportunity districts + the percentage of the next highest district. - :param `part`: Partition to score. - :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + :param part: Partition to score. + :type part: Partition + :param minority_perc_col: Which updater is a mapping of district ids to the fraction of minority population within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" + :type minority_perc_col: str + :param threshold: Beyond which fraction to consider something a "Gingles" (or opportunity) district. + :type threshold: float - :rtype float + :returns: Number of opportunity districts + the percentage of the next highest district. + :rtype: float """ dist_percs = part[minority_perc_col].values() num_opport_dists = sum(list(map(lambda v: v >= threshold, dist_percs))) @@ -99,19 +144,25 @@ def reward_partial_dist(cls, part, minority_perc_col, threshold): return num_opport_dists + next_dist @classmethod - def reward_next_highest_close(cls, part, minority_perc_col, threshold): + def reward_next_highest_close( + cls, part: Partition, minority_perc_col: str, threshold: float + ): """ Given a partition, returns the number of opportunity districts, if no additional district is within 10% of reaching the threshold. If one is, the distance that district is from the threshold is scaled between 0 and 1 and added to the count of opportunity districts. - :param `part`: Partition to score. - :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + :param part: Partition to score. + :type part: Partition + :param minority_perc_col: Which updater is a mapping of district ids to the fraction of minority population within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" + :type minority_perc_col: str + :param threshold: Beyond which fraction to consider something a "Gingles" (or opportunity) district. + :type threshold: float - :rtype float + :returns: Number of opportunity districts + (1 - the next highest district's distance from + :rtype: float """ dist_percs = part[minority_perc_col].values() num_opport_dists = sum(list(map(lambda v: v >= threshold, dist_percs))) @@ -123,18 +174,24 @@ def reward_next_highest_close(cls, part, minority_perc_col, threshold): return num_opport_dists + (next_dist - threshold + 0.1) * 10 @classmethod - def penalize_maximum_over(cls, part, minority_perc_col, threshold): + def penalize_maximum_over( + cls, part: Partition, minority_perc_col: str, threshold: float + ): """ Given a partition, returns the number of opportunity districts + (1 - the maximum excess) scaled to between 0 and 1. - :param `part`: Partition to score. - :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + :param part: Partition to score. + :type part: Partition + :param minority_perc_col: Which updater is a mapping of district ids to the fraction of minority population within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" + :type minority_perc_col: str + :param threshold: Beyond which fraction to consider something a "Gingles" (or opportunity) district. + :type threshold: float - :rtype float + :returns: Number of opportunity districts + (1 - the maximum excess) + :rtype: float """ dist_percs = part[minority_perc_col].values() num_opportunity_dists = sum(list(map(lambda v: v >= threshold, dist_percs))) @@ -145,18 +202,24 @@ def penalize_maximum_over(cls, part, minority_perc_col, threshold): return num_opportunity_dists + (1 - max_dist) / (1 - threshold) @classmethod - def penalize_avg_over(cls, part, minority_perc_col, threshold): + def penalize_avg_over( + cls, part: Partition, minority_perc_col: str, threshold: float + ): """ Given a partition, returns the number of opportunity districts + (1 - the average excess) scaled to between 0 and 1. - :param `part`: Partition to score. - :param `minority_perc_col`: Which updater is a mapping of district ids to the fraction of + :param part: Partition to score. + :type part: Partition + :param minority_perc_col: Which updater is a mapping of district ids to the fraction of minority population within that district. - :param `threshold`: Beyond which fraction to consider something a "Gingles" + :type minority_perc_col: str + :param threshold: Beyond which fraction to consider something a "Gingles" (or opportunity) district. + :type threshold: float - :rtype float + :returns: Number of opportunity districts + (1 - the average excess) + :rtype: float """ dist_percs = part[minority_perc_col].values() opport_dists = list(filter(lambda v: v >= threshold, dist_percs)) diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index 611d585c..a411f644 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -1,5 +1,5 @@ from ..chain import MarkovChain -from .. partition import Partition +from ..partition import Partition from ..accept import always_accept import random from typing import Union, Callable, List, Any @@ -28,27 +28,36 @@ class SingleMetricOptimizer: optimization run is invoked. """ - def __init__(self, proposal: Callable[[Partition], Partition], - constraints: Union[Callable[[Partition], bool], List[Callable[[Partition], bool]]], - initial_state: Partition, optimization_metric: Callable[[Partition], Any], - maximize: bool = True, step_indexer: str = "step"): + def __init__( + self, + proposal: Callable[[Partition], Partition], + constraints: Union[ + Callable[[Partition], bool], List[Callable[[Partition], bool]] + ], + initial_state: Partition, + optimization_metric: Callable[[Partition], Any], + maximize: bool = True, + step_indexer: str = "step", + ): """ - Args: - proposal (Callable[[Partition], Partition]): Function proposing the next state from the - current state. - constraints (Union[Callable[[Partition], bool], List[Callable[[Partition], bool]]]): - A function, or lists of functions, determining whether the proposed next state is - valid (passes all binary constraints). Usually this is a - `~gerrychain.constraints.Validator` class instance. - initial_state (Partition): Initial state of the optimizer. - optimization_metric (Callable[[Partition], Any]): The score function with which to - optimize over. This should have the signature: - `Partition -> 'a where 'a is Comparable` - maximize (bool, optional): Whether to minimize or maximize the function? Defaults to - maximize. - step_indexer (str, optional): Name of the updater tracking the partitions step in the - chain. If not implemented on the partition the constructor creates and adds it. - Defaults to `"step"`. + + :param proposal: Function proposing the next state from the current state. + :type proposal: Callable + :param constraints: A function, or lists of functions, determining whether the proposed next + state is valid (passes all binary constraints). Usually this is a + :class:`~gerrychain.constraints.Validator` class instance. + :type constraints: Union[Callable[[Partition], bool], List[Callable[[Partition], bool]]] + :param initial_state: Initial state of the optimizer. + :type initial_state: Partition + :param optimization_metric: The score function with which to optimize over. This should have + the signature: ``Partition -> 'a`` where 'a is Comparable + :type optimization_metric: Callable[[Partition], Any] + :param maximize: Boolean indicating whether to maximize or minimize the function. Defaults to True for maximize. + :type maximize: bool, optional + :param step_indexer: Name of the updater tracking the partitions step in the chain. If not + implemented on the partition the constructor creates and adds it. Defaults to "step". + :type step_indexer: str, optional + Returns: A SingleMetricOptimizer object @@ -63,7 +72,9 @@ def __init__(self, proposal: Callable[[Partition], Partition], self._step_indexer = step_indexer if self._step_indexer not in self._initial_part.updaters: - step_updater = lambda p: 0 if p.parent is None else p.parent[self._step_indexer] + 1 + step_updater = lambda p: ( + 0 if p.parent is None else p.parent[self._step_indexer] + 1 + ) self._initial_part.updaters[self._step_indexer] = step_updater @property @@ -71,6 +82,9 @@ def best_part(self) -> Partition: """ Partition object corresponding to best scoring plan observed over the current (or most recent) optimization run. + + :return: Partition object with the best score. + :rtype: Partition """ return self._best_part @@ -79,6 +93,9 @@ def best_score(self) -> Any: """ Value of score metric corresponding to best scoring plan observed over the current (or most recent) optimization run. + + :return: Value of the best score. + :rtype: Any """ return self._best_score @@ -86,21 +103,24 @@ def best_score(self) -> Any: def score(self) -> Callable[[Partition], Any]: """ The score function which is being optimized over. + + :return: The score function. + :rtype: Callable[[Partition], Any] """ return self._score - def _is_improvement(self, new_score, old_score) -> bool: + def _is_improvement(self, new_score: float, old_score: float) -> bool: """ Helper function defining improvement comparison between scores. Scores can be any comparable type. - Args: - new_score (Any): Score of proposed partition. - old_score (Any): Score of previous partition. - - Returns: - Whether the new score is an improvement over the old score. + :param new_score: Score of proposed partition. + :type new_score: float + :param old_score: Score of previous partition. + :type old_score: float + :return: Whether the new score is an improvement over the old score. + :rtype: bool """ if self._maximize: return new_score >= old_score @@ -111,12 +131,13 @@ def _tilted_acceptance_function(self, p: float) -> Callable[[Partition], bool]: """ Function factory that binds and returns a tilted acceptance function. - Args: - p (float): The probability of accepting a worse score. + :param p: The probability of accepting a worse score. + :type p: float - Returns: - A acceptance function for tilted chains. + :return: A acceptance function for tilted chains. + :rtype: Callable[[Partition], bool] """ + def tilted_acceptance_function(part): if part.parent is None: return True @@ -131,21 +152,25 @@ def tilted_acceptance_function(part): return tilted_acceptance_function - def _simulated_annealing_acceptance_function(self, beta_function: Callable[[int], float], - beta_magnitude: float): + def _simulated_annealing_acceptance_function( + self, beta_function: Callable[[int], float], beta_magnitude: float + ): """ Function factory that binds and returns a simulated annealing acceptance function. - Args: - beta_functions (Callable[[int], float]): Function (f: t -> beta, where beta is in [0,1]) - defining temperature over time. f(t) = 0 the chain is hot and every proposal is - accepted. At f(t) = 1 the chain is cold and worse proposal have a low probability - of being accepted relative to the magnitude of change in score. - beta_magnitude (float): Scaling parameter for how much to weight changes in score. - Returns: - A acceptance function for simulated annealing runs. + :param beta_function: Function (f: t -> beta, where beta is in [0,1]) defining temperature + over time. f(t) = 0 the chain is hot and every proposal is accepted. At f(t) = 1 the + chain is cold and worse proposal have a low probability of being accepted relative to + the magnitude of change in score. + :type beta_function: Callable[[int], float] + :param beta_magnitude: Scaling parameter for how much to weight changes in score. + :type beta_magnitude: float + + :return: A acceptance function for simulated annealing runs. + :rtype: Callable[[Partition], bool] """ + def simulated_annealing_acceptance_function(part): if part.parent is None: return True @@ -158,19 +183,21 @@ def simulated_annealing_acceptance_function(part): return simulated_annealing_acceptance_function @classmethod - def jumpcycle_beta_function(cls, duration_hot: int, - duration_cold: int) -> Callable[[int], float]: + def jumpcycle_beta_function( + cls, duration_hot: int, duration_cold: int + ) -> Callable[[int], float]: """ Class method that binds and return simple hot-cold cycle beta temperature function, where the chain runs hot for some given duration and then cold for some duration, and repeats that cycle. - Args: - duration_hot: Number of steps to run chain hot. - duration_cold: Number of steps to run chain cold. + :param duration_hot: Number of steps to run chain hot. + :type duration_hot: int + :param duration_cold: Number of steps to run chain cold. + :type duration_cold: int - Returns: - Beta function defining hot-cold cycle. + :return: Beta function defining hot-cold cycle. + :rtype: Callable[[int], float] """ cycle_length = duration_hot + duration_cold @@ -181,8 +208,12 @@ def beta_function(step: int): return beta_function @classmethod - def linearcycle_beta_function(cls, duration_hot: int, duration_cooldown: int, - duration_cold: int) -> Callable[[int], float]: + def linearcycle_beta_function( + cls, duration_hot: int, duration_cooldown: int, duration_cold: int + ) -> Callable[[int], float]: + """ + IMPLEMENT THIS!!! + """ cycle_length = duration_hot + duration_cooldown + duration_cold def beta_function(step: int): @@ -191,8 +222,12 @@ def beta_function(step: int): return beta_function @classmethod - def logitcycle_beta_function(cls, duration_hot: int, duration_cooldown: int, - duration_cold: int) -> Callable[[int], float]: + def logitcycle_beta_function( + cls, duration_hot: int, duration_cooldown: int, duration_cold: int + ) -> Callable[[int], float]: + """ + IMPLEMENT THIS!!! + """ cycle_length = duration_hot + duration_cooldown + duration_cold def beta_function(step: int): @@ -200,29 +235,36 @@ def beta_function(step: int): return beta_function - def short_bursts(self, burst_length: int, num_bursts: int, - accept: Callable[[Partition], bool] = always_accept, - with_progress_bar: bool = False): + def short_bursts( + self, + burst_length: int, + num_bursts: int, + accept: Callable[[Partition], bool] = always_accept, + with_progress_bar: bool = False, + ): """ - Performs a short burst run using the instance's score function. Each burst starts at the - best performing plan of the previous burst. If there's a tie, the later observed one is + Performs a short burst run using the instance's score function. Each burst starts at the + best performing plan of the previous burst. If there's a tie, the later observed one is selected. - Args - burst_length (int): How many steps to run within each burst? - num_bursts (int): How many bursts to perform? - accept (Callable[[Partition], bool], optional): Function accepting or rejecting the - proposed state. In the most basic use case, this always returns True. - with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults - to False. + :param burst_length: Number of steps to run within each burst. + :type burst_length: int + :param num_bursts: Number of bursts to perform. + :type num_bursts: int + :param accept: Function accepting or rejecting the proposed state. Defaults to + :func:`~gerrychain.accept.always_accept`. + :type accept: Callable[[Partition], bool], optional - Returns: - Partition generator. + :return: Partition generator. + :rtype: Generator[Partition] """ if with_progress_bar: - for part in tqdm(self.short_bursts(burst_length, num_bursts, accept, - with_progress_bar=False), - total=burst_length * num_bursts): + for part in tqdm( + self.short_bursts( + burst_length, num_bursts, accept, with_progress_bar=False + ), + total=burst_length * num_bursts, + ): yield part return @@ -230,8 +272,9 @@ def short_bursts(self, burst_length: int, num_bursts: int, self._best_score = self.score(self._best_part) for _ in range(num_bursts): - chain = MarkovChain(self._proposal, self._constraints, accept, self._best_part, - burst_length) + chain = MarkovChain( + self._proposal, self._constraints, accept, self._best_part, burst_length + ) for part in chain: yield part @@ -241,28 +284,40 @@ def short_bursts(self, burst_length: int, num_bursts: int, self._best_part = part self._best_score = part_score - def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], float], - beta_magnitude: float = 1, with_progress_bar: bool = False): + def simulated_annealing( + self, + num_steps: int, + beta_function: Callable[[int], float], + beta_magnitude: float = 1, + with_progress_bar: bool = False, + ): """ Performs simulated annealing with respect to the class instance's score function. - Args: - num_steps (int): How many steps to run for. - beta_function (Callable[[int], float]): Function (f: t -> beta, where beta is in [0,1]) - defining temperature over time. f(t) = 0 the chain is hot and every proposal is - accepted. At f(t) = 1 the chain is cold and worse proposal have a low probability - of being accepted relative to the magnitude of change in score. - beta_magnitude (float): Scaling parameter for how much to weight changes in score. - with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults - to False. - - Returns: - Partition generator. + :param num_steps: Number of steps to run for. + :type num_steps: int + :param beta_function: Function (f: t -> beta, where beta is in [0,1]) defining temperature + over time. f(t) = 0 the chain is hot and every proposal is accepted. At f(t) = 1 the + chain is cold and worse proposal have a low probability of being accepted relative to + the magnitude of change in score. + :type beta_function: Callable[[int], float] + :param beta_magnitude: Scaling parameter for how much to weight changes in score. + :type beta_magnitude: float + :param with_progress_bar: Whether or not to draw tqdm progress bar. Defaults to False. + :type with_progress_bar: bool, optional + + :return: Partition generator. + :rtype: Generator[Partition] """ - chain = MarkovChain(self._proposal, self._constraints, - self._simulated_annealing_acceptance_function(beta_function, - beta_magnitude), - self._initial_part, num_steps) + chain = MarkovChain( + self._proposal, + self._constraints, + self._simulated_annealing_acceptance_function( + beta_function, beta_magnitude + ), + self._initial_part, + num_steps, + ) self._best_part = self._initial_part self._best_score = self.score(self._best_part) @@ -276,51 +331,72 @@ def simulated_annealing(self, num_steps: int, beta_function: Callable[[int], flo self._best_part = part self._best_score = part_score - def tilted_short_bursts(self, burst_length: int, num_bursts: int, p: float, - with_progress_bar: bool = False): + def tilted_short_bursts( + self, + burst_length: int, + num_bursts: int, + p: float, + with_progress_bar: bool = False, + ): """ - Performs a short burst run using the instance's score function. Each burst starts at the - best performing plan of the previous burst. If there's a tie, the later observed one is - selected. Within each burst a tilted acceptance function is used where better scoring plans + Performs a short burst run using the instance's score function. Each burst starts at the + best performing plan of the previous burst. If there's a tie, the later observed one is + selected. Within each burst a tilted acceptance function is used where better scoring plans are always accepted and worse scoring plans are accepted with probability `p`. - Args - burst_length (int): How many steps to run within each burst? - num_bursts (int): How many bursts to perform? - p (float): The probability of accepting a plan with a worse score. - with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults - to False. + :param burst_length: Number of steps to run within each burst. + :type burst_length: int + :param num_bursts: Number of bursts to perform. + :type num_bursts: int + :param p: The probability of accepting a plan with a worse score. + :type p: float + :param with_progress_bar: Whether or not to draw tqdm progress bar. Defaults to False. + :type with_progress_bar: bool, optional - Returns: - Partition generator. - """ - return self.short_bursts(burst_length, num_bursts, - accept=self._tilted_acceptance_function(p), - with_progress_bar=with_progress_bar) - def variable_length_short_bursts(self, num_steps: int, stuck_buffer: int, - accept: Callable[[Partition], bool] = always_accept, - with_progress_bar: bool = False): + :return: Partition generator. + :rtype: Generator[Partition] + """ + return self.short_bursts( + burst_length, + num_bursts, + accept=self._tilted_acceptance_function(p), + with_progress_bar=with_progress_bar, + ) + + # TODO: Maybe add a max_time variable so we don't run forever. + def variable_length_short_bursts( + self, + num_steps: int, + stuck_buffer: int, + accept: Callable[[Partition], bool] = always_accept, + with_progress_bar: bool = False, + ): """ Performs a short burst where the burst length is allowed to increase as it gets harder to - find high scoring plans. The initial burst length is set to 2, and it is doubled each time + find high scoring plans. The initial burst length is set to 2, and it is doubled each time there is no improvement over the passed number (`stuck_buffer`) of runs. - Args: - num_steps (int): How many steps to run for. - stuck_buffer (int): How many bursts of a given length with no improvement to allow - before increasing the burst length. - accept (Callable[[Partition], bool], optional): Function accepting or rejecting the - proposed state. In the most basic use case, this always returns True. - with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults - to False. - - Returns: - Partition generator. + :param num_steps: Number of steps to run for. + :type num_steps: int + :param stuck_buffer: How many bursts of a given length with no improvement to allow before + increasing the burst length. + :type stuck_buffer: int + :param accept: Function accepting or rejecting the proposed state. Defaults to :func:`~gerrychain.accept.always_accept`. + :type accept: Callable[[Partition], bool], optional + :param with_progress_bar: Whether or not to draw tqdm progress bar. Defaults to False. + :type with_progress_bar: bool, optional + + :return: Partition generator. + :rtype: Generator[Partition] """ if with_progress_bar: - for part in tqdm(self.variable_length_short_bursts(num_steps, stuck_buffer, accept, - with_progress_bar=False), total=num_steps): + for part in tqdm( + self.variable_length_short_bursts( + num_steps, stuck_buffer, accept, with_progress_bar=False + ), + total=num_steps, + ): yield part return @@ -330,9 +406,10 @@ def variable_length_short_bursts(self, num_steps: int, stuck_buffer: int, burst_length = 2 i = 0 - while(i < num_steps): - chain = MarkovChain(self._proposal, self._constraints, accept, self._best_part, - burst_length) + while i < num_steps: + chain = MarkovChain( + self._proposal, self._constraints, accept, self._best_part, burst_length + ) for part in chain: yield part part_score = self.score(part) @@ -352,20 +429,27 @@ def variable_length_short_bursts(self, num_steps: int, stuck_buffer: int, def tilted_run(self, num_steps: int, p: float, with_progress_bar: bool = False): """ - Performs a tilted run. A chain where the acceptance function always accepts better plans + Performs a tilted run. A chain where the acceptance function always accepts better plans and accepts worse plans with some probability `p`. - Args: - num_steps (int): How many steps to run for. - p (float): The probability of accepting a plan with a worse score. - with_progress_bar (bool, optional): Whether or not to draw tqdm progress bar. Defaults - to False. - Returns: - Partition generator. + :param num_steps: Number of steps to run for. + :type num_steps: int + :param p: The probability of accepting a plan with a worse score. + :type p: float + :param with_progress_bar: Whether or not to draw tqdm progress bar. Defaults to False. + :type with_progress_bar: bool, optional + + :return: Partition generator. + :rtype: Generator[Partition] """ - chain = MarkovChain(self._proposal, self._constraints, self._tilted_acceptance_function(p), - self._initial_part, num_steps) + chain = MarkovChain( + self._proposal, + self._constraints, + self._tilted_acceptance_function(p), + self._initial_part, + num_steps, + ) self._best_part = self._initial_part self._best_score = self.score(self._best_part) From 3a6efc465ec81a80aab1b6045bda12129f4e56e9 Mon Sep 17 00:00:00 2001 From: peterrrock2 Date: Fri, 12 Apr 2024 14:25:09 -0600 Subject: [PATCH 19/28] Double-checking that the example notebook runs and grabbing the json for local storage --- docs/_static/BG_05.json | 1 + docs/notebooks/Optimization.ipynb | 97 ++++++++++++++++++------------- 2 files changed, 58 insertions(+), 40 deletions(-) create mode 100644 docs/_static/BG_05.json diff --git a/docs/_static/BG_05.json b/docs/_static/BG_05.json new file mode 100644 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a/docs/notebooks/Optimization.ipynb +++ b/docs/notebooks/Optimization.ipynb @@ -78,10 +78,10 @@ "outputs": [], "source": [ "chain_updaters = {\n", - " \"population\": updaters.Tally(POPCOL, alias=\"population\"),\n", - " \"VAP\": updaters.Tally(\"VAP\"),\n", - " \"BVAP\": updaters.Tally(\"BVAP\")\n", - " }\n", + " \"population\": updaters.Tally(POPCOL, alias=\"population\"),\n", + " \"VAP\": updaters.Tally(\"VAP\"),\n", + " \"BVAP\": updaters.Tally(\"BVAP\")\n", + "}\n", "part = Partition(graph, sddict, chain_updaters)" ] }, @@ -91,11 +91,13 @@ "metadata": {}, "outputs": [], "source": [ - "proposal = partial(proposals.recom,\n", - " pop_col=POPCOL,\n", - " pop_target=TOTPOP/SEN_DISTS,\n", - " epsilon=EPS,\n", - " node_repeats=1)\n", + "proposal = partial(\n", + " proposals.recom,\n", + " pop_col=POPCOL,\n", + " pop_target=TOTPOP/SEN_DISTS,\n", + " epsilon=EPS,\n", + " node_repeats=1\n", + ")\n", "cons = constraints.within_percent_of_ideal_population(part, EPS)" ] }, @@ -137,7 +139,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████| 10000/10000 [06:16<00:00, 26.56it/s]\n" + " 0%| | 0/10000 [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -223,19 +230,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -261,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -271,14 +276,21 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████| 10000/10000 [05:20<00:00, 31.20it/s]\n" + " 0%| | 0/10000 [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -384,7 +401,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.11.8" } }, "nbformat": 4, From 5b90ed42be4a57e4f5edb0ea3d3d063dbfedd45f Mon Sep 17 00:00:00 2001 From: peterrrock2 Date: Wed, 24 Apr 2024 14:28:55 -0600 Subject: [PATCH 20/28] Fix the epsilon balance bug --- gerrychain/proposals/tree_proposals.py | 4 +- gerrychain/tree.py | 291 +++++++++++++++++++++---- tests/test_tree.py | 239 ++++++++++++++++---- 3 files changed, 453 insertions(+), 81 deletions(-) diff --git a/gerrychain/proposals/tree_proposals.py b/gerrychain/proposals/tree_proposals.py index f711026e..e66a718b 100644 --- a/gerrychain/proposals/tree_proposals.py +++ b/gerrychain/proposals/tree_proposals.py @@ -4,7 +4,7 @@ from gerrychain.partition import Partition from ..tree import ( - recursive_tree_part, + epsilon_tree_bipartition, bipartition_tree, bipartition_tree_random, _bipartition_tree_random_all, @@ -119,7 +119,7 @@ def recom( partition.parts[parts_to_merge[0]] | partition.parts[parts_to_merge[1]] ) - flips = recursive_tree_part( + flips = epsilon_tree_bipartition( subgraph.graph, parts_to_merge, pop_col=pop_col, diff --git a/gerrychain/tree.py b/gerrychain/tree.py index 0f61583f..e85e85e3 100644 --- a/gerrychain/tree.py +++ b/gerrychain/tree.py @@ -23,7 +23,7 @@ - random: Provides random number generation for probabilistic approaches. - typing: Used for type hints. -Last Updated: 30 Jan 2024 +Last Updated: 25 April 2024 """ import networkx as nx @@ -186,9 +186,32 @@ def contract_node(self, node, parent) -> None: self.subsets[parent] |= self.subsets[node] self._degrees[parent] -= 1 - def has_ideal_population(self, node) -> bool: + def has_ideal_population(self, node, one_sided_cut: bool = False) -> bool: + """ + Checks if a node has an ideal population within the graph up to epsilon. + + :param node: The node to check. + :type node: Any + :param one_sided_cut: Whether or not we are cutting off a single district. When + set to False, we check if the node we are cutting and the remaining graph + are both within epsilon of the ideal population. When set to True, we only + check if the node we are cutting is within epsilon of the ideal population. + Defaults to False. + :type one_sided_cut: bool, optional + + :returns: True if the node has an ideal population within the graph up to epsilon. + :rtype: bool + """ + if one_sided_cut: + return ( + abs(self.population[node] - self.ideal_pop) + < self.epsilon * self.ideal_pop + ) + return ( - abs(self.population[node] - self.ideal_pop) < self.epsilon * self.ideal_pop + abs(self.population[node] - self.ideal_pop) <= self.epsilon * self.ideal_pop + and abs((self.tot_pop - self.population[node]) - self.ideal_pop) + <= self.epsilon * self.ideal_pop ) def __repr__(self) -> str: @@ -210,17 +233,25 @@ def __repr__(self) -> str: Cut.__doc__ = "Represents a cut in a graph." Cut.edge.__doc__ = "The edge where the cut is made. Defaults to None." Cut.weight.__doc__ = "The weight assigned to the edge (if any). Defaults to None." -Cut.subset.__doc__ = "The (frozen) subset of nodes on one side of the cut. Defaults to None." +Cut.subset.__doc__ = ( + "The (frozen) subset of nodes on one side of the cut. Defaults to None." +) def find_balanced_edge_cuts_contraction( - h: PopulatedGraph, choice: Callable = random.choice + h: PopulatedGraph, one_sided_cut: bool = False, choice: Callable = random.choice ) -> List[Cut]: """ Find balanced edge cuts using contraction. :param h: The populated graph. :type h: PopulatedGraph + :param one_sided_cut: Whether or not we are cutting off a single district. When + set to False, we check if the node we are cutting and the remaining graph + are both within epsilon of the ideal population. When set to True, we only + check if the node we are cutting is within epsilon of the ideal population. + Defaults to False. + :type one_sided_cut: bool, optional :param choice: The function used to make random choices. :type choice: Callable, optional @@ -236,13 +267,13 @@ def find_balanced_edge_cuts_contraction( leaves = deque(x for x in h if h.degree(x) == 1) while len(leaves) > 0: leaf = leaves.popleft() - if h.has_ideal_population(leaf): + if h.has_ideal_population(leaf, one_sided_cut=one_sided_cut): e = (leaf, pred[leaf]) cuts.append( Cut( edge=e, weight=h.graph.edges[e].get("random_weight", random.random()), - subset=frozenset(h.subsets[leaf].copy()) + subset=frozenset(h.subsets[leaf].copy()), ) ) # Contract the leaf: @@ -316,7 +347,7 @@ def _part_nodes(start, succ): def find_balanced_edge_cuts_memoization( - h: PopulatedGraph, choice: Callable = random.choice + h: PopulatedGraph, one_sided_cut: bool = False, choice: Callable = random.choice ) -> List[Cut]: """ Find balanced edge cuts using memoization. @@ -328,6 +359,12 @@ def find_balanced_edge_cuts_memoization( :param h: The PopulatedGraph object representing the graph. :type h: PopulatedGraph + :param one_sided_cut: Whether or not we are cutting off a single district. When + set to False, we check if the node we are cutting and the remaining graph + are both within epsilon of the ideal population. When set to True, we only + check if the node we are cutting is within epsilon of the ideal population. + Defaults to False. + :type one_sided_cut: bool, optional :param choice: The choice function used to select the root node. :type choice: Callable, optional @@ -343,18 +380,36 @@ def find_balanced_edge_cuts_memoization( subtree_pops = _calc_pops(succ, root, h) cuts = [] - for node, tree_pop in subtree_pops.items(): - if abs(tree_pop - h.ideal_pop) <= h.ideal_pop * h.epsilon: - e = (node, pred[node]) - wt = random.random() - cuts.append( - Cut( - edge=e, - weight=h.graph.edges[e].get("random_weight", wt), - subset=frozenset(_part_nodes(node, succ)) + + if one_sided_cut: + for node, tree_pop in subtree_pops.items(): + if abs(tree_pop - h.ideal_pop) <= h.ideal_pop * h.epsilon: + e = (node, pred[node]) + wt = random.random() + cuts.append( + Cut( + edge=e, + weight=h.graph.edges[e].get("random_weight", wt), + subset=frozenset(_part_nodes(node, succ)), + ) ) - ) - elif abs((total_pop - tree_pop) - h.ideal_pop) <= h.ideal_pop * h.epsilon: + elif abs((total_pop - tree_pop) - h.ideal_pop) <= h.ideal_pop * h.epsilon: + e = (node, pred[node]) + wt = random.random() + cuts.append( + Cut( + edge=e, + weight=h.graph.edges[e].get("random_weight", wt), + subset=frozenset(set(h.graph.nodes) - _part_nodes(node, succ)), + ) + ) + + return cuts + + for node, tree_pop in subtree_pops.items(): + if (abs(tree_pop - h.ideal_pop) <= h.ideal_pop * h.epsilon) and ( + abs((total_pop - tree_pop) - h.ideal_pop) <= h.ideal_pop * h.epsilon + ): e = (node, pred[node]) wt = random.random() cuts.append( @@ -386,9 +441,7 @@ class ReselectException(Exception): pass -def _max_weight_choice( - cut_edge_list: List[Cut] -) -> Cut: +def _max_weight_choice(cut_edge_list: List[Cut]) -> Cut: """ Each Cut object in the list is assigned a random weight. This random weight is either assigned during the call to @@ -427,17 +480,13 @@ def _max_weight_choice( def _power_set_sorted_by_size_then_sum(d): power_set = [ - s - for i in range(1, len(d) + 1) - for s in itertools.combinations(d.keys(), i) + s for i in range(1, len(d) + 1) for s in itertools.combinations(d.keys(), i) ] # Sort the subsets in descending order based on # the sum of their corresponding values in the dictionary sorted_power_set = sorted( - power_set, - key=lambda s: (len(s), sum(d[i] for i in s)), - reverse=True + power_set, key=lambda s: (len(s), sum(d[i] for i in s)), reverse=True ) return sorted_power_set @@ -448,9 +497,7 @@ def _power_set_sorted_by_size_then_sum(d): # are not modifying the object in the function, and the speed of # this randomized selection will not suffer for it. def _region_preferred_max_weight_choice( - populated_graph: PopulatedGraph, - region_surcharge: Dict, - cut_edge_list: List[Cut] + populated_graph: PopulatedGraph, region_surcharge: Dict, cut_edge_list: List[Cut] ) -> Cut: """ This function is used in the case of a region-aware chain. It @@ -502,8 +549,10 @@ def _region_preferred_max_weight_choice( # Prepare data for efficient access edge_region_info = { cut: { - key: (populated_graph.graph.nodes[cut.edge[0]].get(key), - populated_graph.graph.nodes[cut.edge[1]].get(key)) + key: ( + populated_graph.graph.nodes[cut.edge[0]].get(key), + populated_graph.graph.nodes[cut.edge[1]].get(key), + ) for key in region_surcharge } for cut in cut_edge_list @@ -514,7 +563,8 @@ def _region_preferred_max_weight_choice( power_set = _power_set_sorted_by_size_then_sum(region_surcharge) for region_combination in power_set: suitable_cuts = [ - cut for cut in cut_edge_list + cut + for cut in cut_edge_list if all( edge_region_info[cut][key][0] != edge_region_info[cut][key][1] for key in region_combination @@ -536,11 +586,12 @@ def bipartition_tree( spanning_tree_fn: Callable = random_spanning_tree, region_surcharge: Optional[Dict] = None, balance_edge_fn: Callable = find_balanced_edge_cuts_memoization, + one_sided_cut: bool = False, choice: Callable = random.choice, max_attempts: Optional[int] = 100000, warn_attempts: int = 1000, allow_pair_reselection: bool = False, - cut_choice: Callable = _region_preferred_max_weight_choice + cut_choice: Callable = _region_preferred_max_weight_choice, ) -> Set: """ This function finds a balanced 2 partition of a graph by drawing a @@ -575,6 +626,13 @@ def bipartition_tree( :param balance_edge_fn: The function to find balanced edge cuts. Defaults to :func:`find_balanced_edge_cuts_memoization`. :type balance_edge_fn: Callable, optional + :param one_sided_cut: Passed to the ``balance_edge_fn``. Determines whether or not we are + cutting off a single district when partitioning the tree. When + set to False, we check if the node we are cutting and the remaining graph + are both within epsilon of the ideal population. When set to True, we only + check if the node we are cutting is within epsilon of the ideal population. + Defaults to False. + :type one_sided_cut: bool, optional :param choice: The function to make a random choice of root node for the population tree. Passed to ``balance_edge_fn``. Can be substituted for testing. Defaults to :func:`random.random()`. @@ -604,6 +662,9 @@ def bipartition_tree( if "region_surcharge" in signature(spanning_tree_fn).parameters: spanning_tree_fn = partial(spanning_tree_fn, region_surcharge=region_surcharge) + if "one_sided_cut" in signature(balance_edge_fn).parameters: + balance_edge_fn = partial(balance_edge_fn, one_sided_cut=one_sided_cut) + populations = {node: graph.nodes[node][pop_col] for node in graph.node_indices} possible_cuts: List[Cut] = [] @@ -742,6 +803,7 @@ def bipartition_tree_random( spanning_tree: Optional[nx.Graph] = None, spanning_tree_fn: Callable = random_spanning_tree, balance_edge_fn: Callable = find_balanced_edge_cuts_memoization, + one_sided_cut: bool = False, choice: Callable = random.choice, max_attempts: Optional[int] = 100000, ) -> Union[Set[Any], None]: @@ -783,6 +845,13 @@ def bipartition_tree_random( :param balance_edge_fn: The algorithm used to find balanced cut edges. Defaults to :func:`find_balanced_edge_cuts_memoization`. :type balance_edge_fn: Callable, optional + :param one_sided_cut: Passed to the ``balance_edge_fn``. Determines whether or not we are + cutting off a single district when partitioning the tree. When + set to False, we check if the node we are cutting and the remaining graph + are both within epsilon of the ideal population. When set to True, we only + check if the node we are cutting is within epsilon of the ideal population. + Defaults to False. + :type one_sided_cut: bool, optional :param choice: The random choice function. Can be substituted for testing. Defaults to :func:`random.choice`. :type choice: Callable, optional @@ -794,6 +863,9 @@ def bipartition_tree_random( valid spanning tree is not found. :rtype: Union[Set[Any], None] """ + if "one_sided_cut" in signature(balance_edge_fn).parameters: + balance_edge_fn = partial(balance_edge_fn, one_sided_cut=True) + possible_cuts = _bipartition_tree_random_all( graph=graph, pop_col=pop_col, @@ -811,6 +883,89 @@ def bipartition_tree_random( return choice(possible_cuts).subset +def epsilon_tree_bipartition( + graph: nx.Graph, + parts: Sequence, + pop_target: Union[float, int], + pop_col: str, + epsilon: float, + node_repeats: int = 1, + method: Callable = partial(bipartition_tree, max_attempts=10000), +) -> Dict: + """ + Uses :func:`~gerrychain.tree.bipartition_tree` to partition a tree into + two parts of population ``pop_target`` (within ``epsilon``). + + :param graph: The graph + :type graph: nx.Graph + :param parts: Iterable of part labels (like ``[0,1,2]`` or ``range(4)``) + :type parts: Sequence + :param pop_target: Target population for each part of the partition + :type pop_target: Union[float, int] + :param pop_col: Node attribute key holding population data + :type pop_col: str + :param epsilon: How far (as a percentage of ``pop_target``) from ``pop_target`` the parts + of the partition can be + :type epsilon: float + :param node_repeats: Parameter for :func:`~gerrychain.tree_methods.bipartition_tree` to use. + Defaluts to 1. + :type node_repeats: int, optional + :param method: The partition method to use. Defaults to + `partial(bipartition_tree, max_attempts=10000)`. + :type method: Callable, optional + + :returns: New assignments for the nodes of ``graph``. + :rtype: dict + """ + if len(parts) != 2: + raise ValueError( + "This function only supports bipartitioning. Please ensure that there" + + " are exactly 2 parts in the parts list." + ) + + flips = {} + remaining_nodes = graph.node_indices + + lb_pop = pop_target * (1 - epsilon) + ub_pop = pop_target * (1 + epsilon) + check_pop = lambda x: lb_pop <= x <= ub_pop + + nodes = method( + graph.subgraph(remaining_nodes), + pop_col=pop_col, + pop_target=pop_target, + epsilon=epsilon, + node_repeats=node_repeats, + one_sided_cut=False, + ) + + if nodes is None: + raise BalanceError() + + part_pop = 0 + for node in nodes: + flips[node] = parts[-2] + part_pop += graph.nodes[node][pop_col] + + if not check_pop(part_pop): + raise PopulationBalanceError() + + remaining_nodes -= nodes + + # All of the remaining nodes go in the last part + part_pop = 0 + for node in remaining_nodes: + flips[node] = parts[-1] + part_pop += graph.nodes[node][pop_col] + + if not check_pop(part_pop): + raise PopulationBalanceError() + + return flips + + +# TODO: Move these recursive partition functions to their own module. They are not +# central to the operation of the recom function despite being tree methods. def recursive_tree_part( graph: nx.Graph, parts: Sequence, @@ -857,7 +1012,11 @@ def recursive_tree_part( # 98% of the target population and the target population. debt: Union[int, float] = 0 - for part in parts[:-1]: + lb_pop = pop_target * (1 - epsilon) + ub_pop = pop_target * (1 + epsilon) + check_pop = lambda x: lb_pop <= x <= ub_pop + + for part in parts[:-2]: min_pop = max(pop_target * (1 - epsilon), pop_target * (1 - epsilon) - debt) max_pop = min(pop_target * (1 + epsilon), pop_target * (1 + epsilon) - debt) new_pop_target = (min_pop + max_pop) / 2 @@ -869,6 +1028,7 @@ def recursive_tree_part( pop_target=new_pop_target, epsilon=(max_pop - min_pop) / (2 * new_pop_target), node_repeats=node_repeats, + one_sided_cut=True, ) except Exception: raise @@ -881,12 +1041,44 @@ def recursive_tree_part( flips[node] = part part_pop += graph.nodes[node][pop_col] + if not check_pop(part_pop): + raise PopulationBalanceError() + debt += part_pop - pop_target remaining_nodes -= nodes + # After making n-2 districts, we need to make sure that the last + # two districts are both balanced. + nodes = method( + graph.subgraph(remaining_nodes), + pop_col=pop_col, + pop_target=pop_target, + epsilon=epsilon, + node_repeats=node_repeats, + one_sided_cut=False, + ) + + if nodes is None: + raise BalanceError() + + part_pop = 0 + for node in nodes: + flips[node] = parts[-2] + part_pop += graph.nodes[node][pop_col] + + if not check_pop(part_pop): + raise PopulationBalanceError() + + remaining_nodes -= nodes + # All of the remaining nodes go in the last part + part_pop = 0 for node in remaining_nodes: flips[node] = parts[-1] + part_pop += graph.nodes[node][pop_col] + + if not check_pop(part_pop): + raise PopulationBalanceError() return flips @@ -1003,7 +1195,7 @@ def get_seed_chunks( def get_max_prime_factor_less_than(n: int, ceil: int) -> Optional[int]: """ Helper function for recursive_seed_part_inner. Returns the largest prime factor of ``n`` - less than ``ceil``, or None if all are greater than ceil. + less than ``ceil``, or None if all are greater than ceil. :param n: The number to find the largest prime factor for. :type n: int @@ -1115,6 +1307,18 @@ def recursive_seed_part_inner( if num_dists == 1: return [set(graph.nodes)] + if num_dists == 2: + nodes = method( + graph, + pop_col=pop_col, + pop_target=pop_target, + epsilon=epsilon, + node_repeats=node_repeats, + one_sided_cut=False, + ) + + return [set(nodes), set(graph.nodes) - set(nodes)] + # bite off a district and recurse into the remaining subgraph elif num_chunks is None or num_dists % num_chunks != 0: remaining_nodes = set(graph.nodes) @@ -1124,6 +1328,7 @@ def recursive_seed_part_inner( pop_target=pop_target, epsilon=epsilon, node_repeats=node_repeats, + one_sided_cut=True, ) remaining_nodes -= nodes assignment = [nodes] + recursive_seed_part_inner( @@ -1140,7 +1345,13 @@ def recursive_seed_part_inner( # split graph into num_chunks chunks, and recurse into each chunk elif num_dists % num_chunks == 0: chunks = get_seed_chunks( - graph, num_chunks, num_dists, pop_target, pop_col, epsilon, method=method + graph, + num_chunks, + num_dists, + pop_target, + pop_col, + epsilon, + method=partial(method, one_sided_cut=True), ) assignment = [] @@ -1227,3 +1438,7 @@ def recursive_seed_part( class BalanceError(Exception): """Raised when a balanced cut cannot be found.""" + + +class PopulationBalanceError(Exception): + """Raised when the population of a district is outside the acceptable epsilon range.""" diff --git a/tests/test_tree.py b/tests/test_tree.py index e419a3ca..1805b8ca 100644 --- a/tests/test_tree.py +++ b/tests/test_tree.py @@ -12,15 +12,18 @@ bipartition_tree, random_spanning_tree, find_balanced_edge_cuts_contraction, + find_balanced_edge_cuts_memoization, recursive_tree_part, recursive_seed_part, PopulatedGraph, uniform_spanning_tree, get_max_prime_factor_less_than, - bipartition_tree_random + bipartition_tree_random, ) from gerrychain.updaters import Tally, cut_edges +from functools import partial import random + random.seed(2018) @@ -66,35 +69,132 @@ def test_bipartition_tree_returns_within_epsilon_of_target_pop(graph_with_pop): assert abs(part_pop - ideal_pop) / ideal_pop < epsilon -def test_recursive_tree_part_returns_within_epsilon_of_target_pop(twelve_by_twelve_with_pop): +def test_recursive_tree_part_returns_within_epsilon_of_target_pop( + twelve_by_twelve_with_pop, +): + n_districts = 7 # 144/7 ≈ 20.5 nodes/subgraph (1 person/node) + ideal_pop = ( + sum( + twelve_by_twelve_with_pop.nodes[node]["pop"] + for node in twelve_by_twelve_with_pop + ) + ) / n_districts + epsilon = 0.05 + result = recursive_tree_part( + twelve_by_twelve_with_pop, + range(n_districts), + ideal_pop, + "pop", + epsilon, + ) + partition = Partition( + twelve_by_twelve_with_pop, result, updaters={"pop": Tally("pop")} + ) + return all( + abs(part_pop - ideal_pop) / ideal_pop < epsilon + for part_pop in partition["pop"].values() + ) + + +def test_recursive_tree_part_returns_within_epsilon_of_target_pop_using_contraction( + twelve_by_twelve_with_pop, +): n_districts = 7 # 144/7 ≈ 20.5 nodes/subgraph (1 person/node) - ideal_pop = (sum(twelve_by_twelve_with_pop.nodes[node]["pop"] - for node in twelve_by_twelve_with_pop)) / n_districts + ideal_pop = ( + sum( + twelve_by_twelve_with_pop.nodes[node]["pop"] + for node in twelve_by_twelve_with_pop + ) + ) / n_districts epsilon = 0.05 - result = recursive_tree_part(twelve_by_twelve_with_pop, range(n_districts), - ideal_pop, "pop", epsilon) - partition = Partition(twelve_by_twelve_with_pop, result, - updaters={"pop": Tally("pop")}) - return all(abs(part_pop - ideal_pop) / ideal_pop < epsilon - for part_pop in partition['pop'].values()) - -def test_recursive_seed_part_returns_within_epsilon_of_target_pop(twelve_by_twelve_with_pop): + result = recursive_tree_part( + twelve_by_twelve_with_pop, + range(n_districts), + ideal_pop, + "pop", + epsilon, + method=partial( + bipartition_tree, + max_attempts=10000, + balance_edge_fn=find_balanced_edge_cuts_contraction, + ), + ) + partition = Partition( + twelve_by_twelve_with_pop, result, updaters={"pop": Tally("pop")} + ) + return all( + abs(part_pop - ideal_pop) / ideal_pop < epsilon + for part_pop in partition["pop"].values() + ) + + +def test_recursive_seed_part_returns_within_epsilon_of_target_pop( + twelve_by_twelve_with_pop, +): n_districts = 7 # 144/7 ≈ 20.5 nodes/subgraph (1 person/node) - ideal_pop = (sum(twelve_by_twelve_with_pop.nodes[node]["pop"] - for node in twelve_by_twelve_with_pop)) / n_districts + ideal_pop = ( + sum( + twelve_by_twelve_with_pop.nodes[node]["pop"] + for node in twelve_by_twelve_with_pop + ) + ) / n_districts epsilon = 0.1 - result = recursive_seed_part(twelve_by_twelve_with_pop, range(n_districts), - ideal_pop, "pop", epsilon, n=5, ceil=None) - partition = Partition(twelve_by_twelve_with_pop, result, - updaters={"pop": Tally("pop")}) - return all(abs(part_pop - ideal_pop) / ideal_pop < epsilon - for part_pop in partition['pop'].values()) + result = recursive_seed_part( + twelve_by_twelve_with_pop, + range(n_districts), + ideal_pop, + "pop", + epsilon, + n=5, + ceil=None, + ) + partition = Partition( + twelve_by_twelve_with_pop, result, updaters={"pop": Tally("pop")} + ) + return all( + abs(part_pop - ideal_pop) / ideal_pop < epsilon + for part_pop in partition["pop"].values() + ) + + +def test_recursive_seed_part_returns_within_epsilon_of_target_pop_using_contraction( + twelve_by_twelve_with_pop, +): + n_districts = 7 # 144/7 ≈ 20.5 nodes/subgraph (1 person/node) + ideal_pop = ( + sum( + twelve_by_twelve_with_pop.nodes[node]["pop"] + for node in twelve_by_twelve_with_pop + ) + ) / n_districts + epsilon = 0.1 + result = recursive_seed_part( + twelve_by_twelve_with_pop, + range(n_districts), + ideal_pop, + "pop", + epsilon, + n=5, + ceil=None, + method=partial( + bipartition_tree, + max_attempts=10000, + balance_edge_fn=find_balanced_edge_cuts_contraction, + ), + ) + partition = Partition( + twelve_by_twelve_with_pop, result, updaters={"pop": Tally("pop")} + ) + return all( + abs(part_pop - ideal_pop) / ideal_pop < epsilon + for part_pop in partition["pop"].values() + ) def test_recursive_seed_part_uses_method(twelve_by_twelve_with_pop): calls = 0 - def dummy_method(graph, pop_col, pop_target, epsilon, node_repeats): + def dummy_method(graph, pop_col, pop_target, epsilon, node_repeats, one_sided_cut): nonlocal calls calls += 1 return bipartition_tree( @@ -104,6 +204,7 @@ def dummy_method(graph, pop_col, pop_target, epsilon, node_repeats): epsilon=epsilon, node_repeats=node_repeats, max_attempts=10000, + one_sided_cut=one_sided_cut, ) n_districts = 7 # 144/7 ≈ 20.5 nodes/subgraph (1 person/node) @@ -130,8 +231,11 @@ def dummy_method(graph, pop_col, pop_target, epsilon, node_repeats): # implementation detail) assert calls >= n_districts - 1 -def test_recursive_seed_part_with_n_unspecified_within_epsilon(twelve_by_twelve_with_pop): - n_districts = 6 # This should set n=3 + +def test_recursive_seed_part_with_n_unspecified_within_epsilon( + twelve_by_twelve_with_pop, +): + n_districts = 6 # This should set n=3 ideal_pop = ( sum( twelve_by_twelve_with_pop.nodes[node]["pop"] @@ -147,10 +251,14 @@ def test_recursive_seed_part_with_n_unspecified_within_epsilon(twelve_by_twelve_ epsilon, ceil=None, ) - partition = Partition(twelve_by_twelve_with_pop, result, - updaters={"pop": Tally("pop")}) - return all(abs(part_pop - ideal_pop) / ideal_pop < epsilon - for part_pop in partition['pop'].values()) + partition = Partition( + twelve_by_twelve_with_pop, result, updaters={"pop": Tally("pop")} + ) + return all( + abs(part_pop - ideal_pop) / ideal_pop < epsilon + for part_pop in partition["pop"].values() + ) + def test_random_spanning_tree_returns_tree_with_pop_attribute(graph_with_pop): tree = random_spanning_tree(graph_with_pop) @@ -164,9 +272,9 @@ def test_uniform_spanning_tree_returns_tree_with_pop_attribute(graph_with_pop): def test_bipartition_tree_returns_a_tree(graph_with_pop): ideal_pop = sum(graph_with_pop.nodes[node]["pop"] for node in graph_with_pop) / 2 - tree = Graph.from_networkx(networkx.Graph( - [(0, 1), (1, 2), (1, 4), (3, 4), (4, 5), (3, 6), (6, 7), (6, 8)] - )) + tree = Graph.from_networkx( + networkx.Graph([(0, 1), (1, 2), (1, 4), (3, 4), (4, 5), (3, 6), (6, 7), (6, 8)]) + ) for node in tree: tree.nodes[node]["pop"] = graph_with_pop.nodes[node]["pop"] @@ -186,13 +294,14 @@ def test_recom_works_as_a_proposal(partition_with_pop): proposal = functools.partial( recom, pop_col="pop", pop_target=ideal_pop, epsilon=0.25, node_repeats=5 ) - constraints = [within_percent_of_ideal_population(partition_with_pop, 0.25, "pop")] + constraints = [contiguous] chain = MarkovChain(proposal, constraints, lambda x: True, partition_with_pop, 100) for state in chain: assert contiguous(state) + def test_reversible_recom_works_as_a_proposal(partition_with_pop): random.seed(2018) graph = partition_with_pop.graph @@ -208,11 +317,10 @@ def test_reversible_recom_works_as_a_proposal(partition_with_pop): assert contiguous(state) - def test_find_balanced_cuts_contraction(): - tree = Graph.from_networkx(networkx.Graph( - [(0, 1), (1, 2), (1, 4), (3, 4), (4, 5), (3, 6), (6, 7), (6, 8)] - )) + tree = Graph.from_networkx( + networkx.Graph([(0, 1), (1, 2), (1, 4), (3, 4), (4, 5), (3, 6), (6, 7), (6, 8)]) + ) # 0 - 1 - 2 # || @@ -230,13 +338,62 @@ def test_find_balanced_cuts_contraction(): assert edges == {(1, 4), (3, 4), (3, 6)} +def test_no_balanced_cuts_contraction_when_one_side_okay(): + tree = Graph.from_networkx(networkx.Graph([(0, 1), (1, 2), (2, 3), (3, 4)])) + + populations = {0: 4, 1: 4, 2: 3, 3: 3, 4: 3} + + populated_tree = PopulatedGraph( + graph=tree, populations=populations, ideal_pop=10, epsilon=0.1 + ) + + cuts = find_balanced_edge_cuts_contraction(populated_tree, one_sided_cut=False) + assert cuts == [] + + +def test_find_balanced_cuts_memo(): + tree = Graph.from_networkx( + networkx.Graph([(0, 1), (1, 2), (1, 4), (3, 4), (4, 5), (3, 6), (6, 7), (6, 8)]) + ) + + # 0 - 1 - 2 + # || + # 3= 4 - 5 + # || + # 6- 7 + # | + # 8 + + populated_tree = PopulatedGraph( + tree, {node: 1 for node in tree}, len(tree) / 2, 0.5 + ) + cuts = find_balanced_edge_cuts_memoization(populated_tree) + edges = set(tuple(sorted(cut.edge)) for cut in cuts) + assert edges == {(1, 4), (3, 4), (3, 6)} + + +def test_no_balanced_cuts_memo_when_one_side_okay(): + tree = Graph.from_networkx(networkx.Graph([(0, 1), (1, 2), (2, 3), (3, 4)])) + + populations = {0: 4, 1: 4, 2: 3, 3: 3, 4: 3} + + populated_tree = PopulatedGraph( + graph=tree, populations=populations, ideal_pop=10, epsilon=0.1 + ) + + cuts = find_balanced_edge_cuts_memoization(populated_tree) + assert cuts == [] + + def test_prime_bound(): - assert get_max_prime_factor_less_than(2024, 20) == 11 \ - and get_max_prime_factor_less_than(2024, 1) == None \ - and get_max_prime_factor_less_than(2024, 2000) == 23 \ + assert ( + get_max_prime_factor_less_than(2024, 20) == 11 + and get_max_prime_factor_less_than(2024, 1) == None + and get_max_prime_factor_less_than(2024, 2000) == 23 and get_max_prime_factor_less_than(2024, -1) == None - - + ) + + def test_bipartition_tree_random_returns_a_subset_of_nodes(graph_with_pop): ideal_pop = sum(graph_with_pop.nodes[node]["pop"] for node in graph_with_pop) / 2 result = bipartition_tree_random(graph_with_pop, "pop", ideal_pop, 0.25, 10) @@ -250,4 +407,4 @@ def test_bipartition_tree_random_returns_within_epsilon_of_target_pop(graph_with result = bipartition_tree_random(graph_with_pop, "pop", ideal_pop, epsilon, 10) part_pop = sum(graph_with_pop.nodes[node]["pop"] for node in result) - assert abs(part_pop - ideal_pop) / ideal_pop < epsilon \ No newline at end of file + assert abs(part_pop - ideal_pop) / ideal_pop < epsilon From f0b2dc08ac40b5e7071245289d5d1bdbac66badb Mon Sep 17 00:00:00 2001 From: peterrrock2 Date: Thu, 25 Apr 2024 15:58:12 -0600 Subject: [PATCH 21/28] Update the way that the region aware works. --- gerrychain/tree.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/gerrychain/tree.py b/gerrychain/tree.py index e85e85e3..1d05f2b3 100644 --- a/gerrychain/tree.py +++ b/gerrychain/tree.py @@ -78,9 +78,11 @@ def random_spanning_tree( for edge in graph.edges(): weight = random.random() for key, value in region_surcharge.items(): + # We surcharge edges that cross regions and those that are not in any region if ( graph.nodes[edge[0]][key] != graph.nodes[edge[1]][key] - and graph.nodes[edge[0]][key] is not None + or graph.nodes[edge[0]][key] is None + or graph.nodes[edge[1]][key] is None ): weight += value @@ -1287,7 +1289,7 @@ def recursive_seed_part_inner( :type ceil: Optional[int], optional :returns: New assignments for the nodes of ``graph``. - :rtype: List of lists, each list is a district + :rtype: List of sets, each set is a district """ # Chooses num_chunks From 12724a58ac5259cd6cb9ed83993e948da9359be6 Mon Sep 17 00:00:00 2001 From: peterrrock2 Date: Thu, 25 Apr 2024 15:58:47 -0600 Subject: [PATCH 22/28] Add `from_random_assignment` method to the partition class --- gerrychain/partition/partition.py | 56 +++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) diff --git a/gerrychain/partition/partition.py b/gerrychain/partition/partition.py index a285e755..5025a2a8 100644 --- a/gerrychain/partition/partition.py +++ b/gerrychain/partition/partition.py @@ -5,6 +5,7 @@ from ..updaters import compute_edge_flows, flows_from_changes, cut_edges from .assignment import get_assignment from .subgraphs import SubgraphView +from ..tree import recursive_tree_part from typing import Any, Callable, Dict, Optional, Tuple @@ -63,6 +64,61 @@ def __init__( self._cache = dict() self.subgraphs = SubgraphView(self.graph, self.parts) + @classmethod + def from_random_assignment( + cls, + graph: Graph, + n_parts: int, + epsilon: float, + pop_col: str, + updaters: Optional[Dict[str, Callable]] = None, + use_default_updaters: bool = True, + flips: Optional[Dict] = None, + method: Callable = recursive_tree_part, + ) -> "Partition": + """ + Create a Partition with a random assignment of nodes to districts. + + :param graph: The graph to create the Partition from + :type graph: :class:`~gerrychain.Graph` + :param n_parts: The number of districts to divide the nodes into + :type n_parts: int + :param epsilon: The maximum relative population deviation from the ideal + population. Should be in [0,1] + :type epsilon: float + :param pop_col: The column of the graph's node data that holds the population data + :type pop_col: str + :param updaters: dictionary of updaters + :type updaters: Optional[Dict[str, Callable]], optional + :param use_default_updaters: If `False`, do not include default updaters. + :type use_default_updaters: bool, optional + :param flips: dictionary assigning nodes of the graph to their new districts + :type flips: Optional[Dict], optional + :param method: The function to use to partition the graph into ``n_parts``. Defaults to + :func:`~gerrychain.tree.recursive_tree_part`. + :type method: Callable, optional + + :returns: The partition created with a random assignment + :rtype: Partition + """ + total_pop = sum(graph.nodes[n][pop_col] for n in graph) + ideal_pop = total_pop / n_parts + + assignment = method( + graph=graph, + parts=range(n_parts), + pop_target=ideal_pop, + pop_col=pop_col, + epsilon=epsilon, + ) + + return cls( + graph, + assignment, + updaters, + use_default_updaters=use_default_updaters, + ) + def _first_time(self, graph, assignment, updaters, use_default_updaters): if isinstance(graph, Graph): self.graph = FrozenGraph(graph) From 30955ee39c092805998e310d4f7e265d0ba112a3 Mon Sep 17 00:00:00 2001 From: peterrrock2 Date: Thu, 25 Apr 2024 17:44:45 -0600 Subject: [PATCH 23/28] Update the optimizer docs --- docs/index.rst | 1 + docs/notebooks/Optimization.ipynb | 409 ------------------ docs/user/images/gingleator_all.png | Bin 0 -> 87304 bytes docs/user/images/gingleator_maxes.png | Bin 0 -> 35365 bytes .../images/single_metric_opt_comparison.png | Bin 0 -> 47870 bytes docs/user/optimizers.rst | 250 +++++++++++ gerrychain/optimization/gingleator.py | 2 - gerrychain/optimization/optimization.py | 135 +++++- 8 files changed, 379 insertions(+), 418 deletions(-) delete mode 100644 docs/notebooks/Optimization.ipynb create mode 100644 docs/user/images/gingleator_all.png create mode 100644 docs/user/images/gingleator_maxes.png create mode 100644 docs/user/images/single_metric_opt_comparison.png create mode 100644 docs/user/optimizers.rst diff --git a/docs/index.rst b/docs/index.rst index 0a222281..57635f2c 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -51,6 +51,7 @@ repository, where `bug reports and feature requests`_, as well as user/updaters user/data user/geometries + user/optimizers We also highly recommend the resources prepared by Daryl R. DeFord of MGGG for the 2019 MIT IAP course `Computational Approaches for Political Redistricting`_. diff --git a/docs/notebooks/Optimization.ipynb b/docs/notebooks/Optimization.ipynb deleted file mode 100644 index d85bad57..00000000 --- a/docs/notebooks/Optimization.ipynb +++ /dev/null @@ -1,409 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to Optimization in GerryChain\n", - "\n", - "This notebook walks through how to use the SingleMetricOptimizer class (as well as its Gingleator subclass) in order to perfrom heuristic optimization runs in GerryChain.\n", - "\n", - "There are 3 heuristic optimization methods whose use is shown in this notebook:\n", - "* **Short Bursts**: chaining together small neutral explorers ([More reading about short bursts here](https://arxiv.org/abs/2011.02288))\n", - "* **Simulated Annealing**: vary the probability of accepting worse plans over time wrt a temperature parameter $\\beta$.\n", - "* **Tilted runs**: accept worse plans with a fixed probability $p$\n", - "\n", - "## When do we want to use Heuristic Optimization?\n", - "\n", - "While sampling naively with GerryChain can give us an understanding of the neutral baseline for a state, there are often cases where we want to find plans with properties that are rare to encounter in a neutral run. Many states have laws/guidelines that state that plans should be as compact as feasibly possible, maximize preservation of political boundaries and/or communities of interest, some even look to minimize double bunking of incumbents or seek proportionality/competitiveness in contests. Heuristic optimization methods can be used to find example plans with these properties and to explore the trade-offs between them." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from gerrychain import (GeographicPartition, Partition, Graph, MarkovChain,\n", - " proposals, updaters, constraints, accept, Election)\n", - "# from gerrychain.proposals import ReCom\n", - "from gerrychain.optimization import SingleMetricOptimizer, Gingleator\n", - "from gerrychain.tree import recursive_seed_part\n", - "# from gerrychain.updaters import Tally\n", - "from functools import partial\n", - "import pandas as pd\n", - "import json\n", - "import requests\n", - "from networkx.readwrite import json_graph\n", - "import matplotlib.pyplot as plt\n", - "from tqdm import tqdm\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "response = json.loads(requests.get(\"https://people.csail.mit.edu/ddeford/BG/BG_05.json\").text)\n", - "graph = Graph(json_graph.adjacency_graph(response))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "POPCOL = \"TOTPOP\"\n", - "SEN_DISTS = 35\n", - "EPS = 0.02\n", - "TOTPOP = sum(graph.nodes()[n][POPCOL] for n in graph.nodes())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "sddict = recursive_seed_part(graph, range(SEN_DISTS), TOTPOP/SEN_DISTS, POPCOL, EPS)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "chain_updaters = {\n", - " \"population\": updaters.Tally(POPCOL, alias=\"population\"),\n", - " \"VAP\": updaters.Tally(\"VAP\"),\n", - " \"BVAP\": updaters.Tally(\"BVAP\")\n", - "}\n", - "part = Partition(graph, sddict, chain_updaters)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "proposal = partial(\n", - " proposals.recom,\n", - " pop_col=POPCOL,\n", - " pop_target=TOTPOP/SEN_DISTS,\n", - " epsilon=EPS,\n", - " node_repeats=1\n", - ")\n", - "cons = constraints.within_percent_of_ideal_population(part, EPS)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing for Gingles Districts\n", - "\n", - "Named for the Supreme Court case _Thornburg v. Gingles_, which created their precedent as one of the litmus tests in bringing forth a VRA court case, *Gingles' Districts* are districts that are 50% + 1 of a minority population subgroup (more colloquially called majority-minority districts). It is common to seek plans with greater/maximal numbers of gingles districts to understand the landscape of the state space." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "gingles = Gingleator(proposal, cons, part, \n", - " minority_pop_col=\"BVAP\", total_pop_col=\"VAP\",\n", - " score_function=Gingleator.reward_partial_dist)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "total_steps = 10000" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(12,6))\n", - "plt.plot(max_scores_sb, label=\"Short Bursts\")\n", - "plt.plot(max_scores_anneal, label=\"Simulated Annealing\")\n", - "plt.plot(max_scores_tilt, label=\"Tilted Run\")\n", - "plt.xlabel(\"Steps\", fontsize=20)\n", - "plt.ylabel(\"Max Score Observered\", fontsize=20)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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+//eGf1sWp89DXVOM2JKyG4dcgiCikFm+u+DRYyaWaKb1wFoK080nD8Ly7Qfx5fYq9XAEAb1Lc7DzUIPqc4kc069EU7nvVZKDXVXsYWYSbjz37nbuOX8UOheQOSKgfaMrQRCEKrx27sVMs4siCIIg0hL1KxUzQ/HS2lVm3XV2Y264RSan64rTlgO8sDodvOaxboGUe4Ig1HFicMisftYk7ZmRKaM0QWQotHPvLnh3mZnWBTubHu3Izcin9353txUtnbk3htOLKW6BlHuCIAwR7URpx50giI4MnbnPXOTKNp2VHz0wJ9PIdWsdJQ8dT6d+AdyoIDufj+kFKfcEQahiV0efaBaVaSZSpqAz9wSRFlALdRt8S8SNSo85tNNDa/fOQMqsMYzmW6bVc1LuCYIwRNTsjRRxgiA6MqJIM/FMpSNbZTitYJqJvuOWmhQjZeh0ucvBsrDmRrmdgpR7giBUcWK3ghYMEqEz9wSRDtCZe3dh6sx2ByhLlvxhesaQ6TdL3DaOeRZFRcO2vVB2RyDlniAIQ/BW+kmhJwiCINyE5Cq8DNMctJJjpXLNtmhgJgIzLzuPk0dA0jXrbF0Mcjmk3BMEoYoTHSY56UuA8oIgXE3cKwa1VTdhzqxb5Sq8DNEheKXDkOm3xSqke4rI4avwDL3kntyL4UKR3Awp9wRBGIK85dsNjW4EQRD2Qv2uY3TgrHejfu12BNDufRRS7gmCUMU28zAx8U9aMCAIIj3oyE7XMhW1Es0Ur/m80iFAv4GZ1TqYW5Q8p8VwSz6YxepUZNomFSn3BEEYo723JUXcasihHkGkA9RC3QUPxUZ65j6zSlgrOVYml8mhnpnwdb7stgUbJ6VxV06wIwjpKztvSLknCIIgCIIwCC1vZh56ztxnmM6vG6vSnwmLKU6nwOn4eZEJdcFOSLknCMIQvM/ckwWAAhlmLkYQmQu1VTfBQx1IPHKRaeqFo7vDDMpapuW3HpzUZdNVjxYgGJY906ZZpNwTBKGKUyummXYGiiCIzITO3GceHcFbvhZ6TNXdlicuE8cx3HiTAWE9pNwTBGEIMpOyGcpvgnA1dBWeu+DRZWbywg2vMdwqZTAThrxMSIMboGzUByn3BEGoojVw8zKnTw6HzPQJgkgHMlkB7Kjo8ZafqaVvqUM9l3nLd58S7jqBXI8gGF+wyrTZJin3BEEYgky3bCJ2PIHymyDcDLVQd8FjjGKZ9KerFRsvsd1o+u2WEnF6nmQkfjdWZzfK5GZIuScIQhWnByeCIAiCsBM6c++8gtyR5x6OOtRzLmpTpKvcVkDKPUEQhuA98CY70COHelHonnuCSAfozL27oC5THV5juKGde4Z3zJSfW8reaTmcjp8XVicj06abpNwTBKGKlskhKeEEQRBEZiG2/3/q+Jch+pKm4mfpmXvrgjYUg9vK1G3ypAWUaTFIuScIwhWQQz0F6Mw9QbiaqAJIO/cdk3TtmXnKrXeNP1N2lLVIx2R2lLLJZEi5JwhCFSXTveiOPinhBEEQRCYRHfVkd+4F9c+ZArvZvlVX4WVoxjLgZNrdmO8sMpk5ZpJp81hS7gmCcCWZ1tkah87cE4Sbie/cEwThBtwyXDqtKLslHwh7IeWeIAh1NAYHbvfc09l9giAIghM8FBumq/DSdFmH5cw9S9qMOdRj2YkliAgsdYEWMuKQck8QhDshXT8CnbknCFcTa6ECdVqZgrr/hMzoix01/XZZ+E7vsCfDS5p0XXgizEHKPUEQqiieuW//nnbcCYIgCLfBR2FjubONQzQOwE+BtOalTLgKLx1xZd5ZLFOmTWNJuScIwpXQmfsodOaeINyMnNM1Ir1R27lPcahnsSwdlY485DmZ9nTd7U9Pqa0h7ZX7e+65B4Ig4KabblJ97rXXXsOQIUMQDAYxcuRIvP/++/YISBBpDnnLJwiC0IauwnMXPCb7Gb1wwylphs7cs5zlz4C8d3qBwun4CWdIa+V+xYoVmD9/PkaNGqX63LJly3DJJZfgqquuwrfffovzzjsP5513HtatW2eTpARB6IUWDdqhM/cE4WoyWgHsoKiVaPJv6TpSaSnPVp5Dt1rpTPeFgXSXnzdsjh3NXIWXWaStcl9XV4dZs2bhySefRHFxseqzDz30EE477TTceuutGDp0KO666y6MHTsWjzzyiE3SEkT6otRh8h58SJknCIIgeGGft/z0xFnTb4vD1xmBVfI4raQbu8mAvxyEvaStcj9nzhyceeaZOPnkkzWfXb58ecpzp556KpYvX674TnNzM2pqaiT/EQRhH+SoLwqduScIN0M9VeahfuZeSPpstTTOwJosAYIleZCp+cpCR067HCz5QVkWx+e0AEZ4+eWXsWrVKqxYsYLp+YqKCpSXl0u+Ky8vR0VFheI78+bNwx133GFKToLIBDTP3JMSThAEQWfuMxCWIxdO784ahafUeqcB1pvluwOnlXQjddMteWcrGTaPTbud+927d+PGG2/Eiy++iGAwaFk8c+fORXV1dey/3bt3WxYXQRCpiwRkpt8OnbknCILQjRmlW8+Z+0yFVTG1zqEe4QROL0jIwSKSG+V2irTbuV+5ciUqKysxduzY2HehUAhLly7FI488gubmZni9Xsk7Xbp0wf79+yXf7d+/H126dFGMJxAIIBAI8BWeINIQrXvuCYIgOjLR3V3qETOPTHaW6HplyISAep2ruT4vDJKp6SLUSbud+2nTpmHt2rVYvXp17L+jjz4as2bNwurVq1MUewCYNGkSPv74Y8l3ixYtwqRJk+wSmyAyFqt22MncPwqduScIgtCLmS5Tzz336Yqmt3zmcAzE3UHOUGdCGtIFMxtOmTbbTLud+/z8fIwYMULyXW5uLkpLS2Pfz549G927d8e8efMAADfeeCNOOOEE3HfffTjzzDPx8ssv45tvvsETTzxhu/wEkXbYNDqRGT5BEOlJdOee+rBMg0qUcAJnbzJw35JEpiyo2UXa7dyzsGvXLuzbty/2efLkyXjppZfwxBNPYPTo0Xj99dfx1ltvpSwSEAShH9phtxg6c08QBKEbcz2mys59hvTFWgoTq2m7kfvFO8oZaqfTkAFZSBgg7Xbu5ViyZInqZwCYOXMmZs6caY9ABJFBaHnLJwiC6MjEl99ooZNIHxwdwWn+oArNr6QwLaiZyLJM26PKyJ17giDsg5c5PZnlK0Fn7gmCIHRj6sx9BDmHehnTFWskxMp0Mu3cWxe9TFzWxOa0kp4xdZXQBSn3BEGoojQ4WW2aSMo+QRDpAHnLJwj+OK0YE+6ByQEjVZcYpNwTBGEKUsIths7cEwRB2Er0iEVGX4XntAAauF0+FoymgV/aDfhDyISM10mmzWNJuScIQhXb7rlP6lvJUR9BEOlAfOee+iw3kSmO75yC+So8A9nM8g61JkIP1NrjkHJPEIQ5aAS2GDpzT8hDTY8grKEjLNTQdWvuhVfZZMq0IVPSYRek3BMEoYpT3vIzzUyKIAiCsA+rhqhMUTQ0FWxBYDvr7ELTb93hWyVPhtSVdMDMnDTTDEVJuScIwhTkLd9i6Mw9oQQ1GVdAV+FlHmre8gnCasiyIRnKDz2Qck8QhCpOeawlZZ8gCIIwimWbsRmyda+VDCtTaXUOumUn1mklPTNqKhsdKa1akHJPEIQpSAm3muiZe2elINwHtTx3QLu7mYsoZm7Z8kqZVQ71zMiX7usv6S4/b6zOj0wbS0m5JwjCFSR7xydv+QRBEIRRzOywqx2xyBS9S3PnPlMSykCm+mfIFCsTQh+k3BMEYYiouRkp4RYTy14apAkp1PbcAZ25z1yoRLUxMjKR0knwhqpUHFLuCYJQxekzYwRBEAShF1Nm3Wo79xkyJGop2DT2m8doDnI7MmHkHRdW8I7io4EXpNwTBGGI6ABA3vKthu65J+ShFuMOomfuaec+8yB/CtrQ0ES4AVqMikPKPUEQqjjmLT/TllIJgiCItKejKBFWDv1MDvU6RjbLwyntmZKHmZIOuyDlniAIQ3SUCY7j0D33hAK0/uUWhIT/J9wCD4WAmpg2RjYArJ4/6BXJumsTLQqYSMFMXmea5Sgp9wRBqKI1CPPaYU/uXDOtsyUIgiDSg45x5t5pCQglaPNECuWHPki5JwjCEG50upKZ0Jl7Qh5aAHMH5C3fnZBCYA4rc8/ye8td0hSdroNG4qdWk/6Qck8QhCqOnbmniTJBEAThANGFGjmHepmi/PBSPA15ZOcSs0r4aV5I6S4/4Syk3BMEYQjeK9LkQE8BOnNPKEBNxl3Qzr3LoC7TFKRgmsfpPHQ6fl5Y7oAxw7puUu4JgjCFVTvspOwTBJEO0HVpmUe0RKlsGciALKJjhkQmQco9QRCEq6Ez94Q8tPzlLqiFugvLuswMKWit/LHyvHhHGc6MJrODZA8zlB/6IOWeIAhV7PKWnxIuqS4EQaQBtLubedARC3aMOW2jNuNW0nXhxUydyrTWTso9QRCGIDM2m6Az94QSmTYjIQiOWLdxnxl9sWYqLEwm0xnqDMlnI/CaX2XKNI3mm/og5Z4gCFW0OlXaYScIoiNDV+FlHmre8jsS6apTuWZhIF0zMA2hrI5Dyj1BEIbg7i2fJsYK0Jl7Qh5qMwRhP5nSFWufuecTjmFszGe3Fanb5Ml0Ms2BMyn3BEGoonnmnhQMgiA6MPHdXeoL3YQZU17ylm8tVptZ07wkgpF8Ttcan65yWwEp9wRBGMJqs7dMW0k1DJ25JxSgJkIQ9pMpPbHWGM6qGBrJj0zJQ6vIFOsQwhlIuScIQhVNJZ6TgkHKPEEQ6Uh0d5fm4+7CTHkIQvTMPZGOuObMvcNkSi4wOWA0sSKSadNPUu4JgjAEmdXZBZ25J+ShFkIQ9pMpnrt5JcNIOG7LQrfJQxBmIOWeIAhVyFs+QRCEMuQt353wUNg68pl7K1POFHYHbk68LA8yZdGCJT8yJKlcIOWeIAhXkLxIQGb67dCZe0IBaiMEYT/UE0uxzASeMtoRMsUyRQ+ZNpKSck8QhCGiAzopGARBELRz7zZ4KJ0deufewqR3RAVSF7yOTHTg+tuRIeWeIAhXQub+UejMPSEPtRC3QG0z01BbqKGu2P1QGWUWbA71rJcjXSDlniAIVRRX2Dl3pKTMEwSRztDc0l3QZF8drd1z1vyzyqGencXnth1uqrv2kmkGqKTcEwRhClLKLYbO3BMKZNqEJF3pyKbbmUp0516uiblNEUxHrM5B6hsjZMoiAUsy6KhHHFLuCYJQRWkiQxMcgiCIOHTmnkgntEZw1jHe0EyAFDFVnMwdKpr0h5R7giBMwWvnPtkxHznqi0Jn7gnCzVBXlXlEe1tZqwzqil0PDZcRMiUbrC7PTLNAJeWeIAhVaOeeIAhCG+oRiXRCS2FiVqhIkyYIV0HKPUEQprBqhz3TVlINQ2fuCcLV0Jl7d2JG54wfsaCytQK2M9SWi+FauJ0f78B52JHxsTy0dOlSSyKfMmWKJeESBMEPpUGGnJcQBEEQHY1MGfp4JcNIOG7LQ9pMiOOyogFg/Xwz045WMSn3U6dO5Z6xgiCgra2Na5gEQWQONNgSBJEORHfuyaGeuzBzdEzNWz5hHjrWpw6/hRfK544Ik3IPkHMrguio0Jl7pyGHegRBEG6ho/TEZJ1HuAWqifpgUu4XL16s+FtLSwv++Mc/YsWKFSgrK8OFF16ICRMmoLy8HACwf/9+rFixAq+++ioqKysxfvx43H333fD7/YaFfuyxx/DYY49hx44dAIDhw4fj//7v/3D66afLPv/ss8/iiiuukHwXCATQ1NRkWAaCICKQt3yCIDoyosxfhPOYO3MfIZP9KfBS3o0Ew/KOmSlAupcarasQZmBS7k844QTZ70VRxBlnnIFvvvkGV111FR588EHk5uamPPfTn/4U99xzD2666SY89dRTuP/++/H+++8bFrpHjx645557MHDgQIiiiOeeew7nnnsuvv32WwwfPlz2nYKCAmzatCn2mVYkCYJIC2ITHOqzCIIgnKYjzR+tstDrODnoLBlTVTMlHTbBbJYvx9NPP40FCxbglFNOwZNPPqn6bE5ODp544gns3LkTCxYswBNPPIFf/OIXhuI9++yzJZ/vvvtuPPbYY/jyyy8VlXtBENClSxdD8REEoQztsBtAFIFwGyB42v+jkYsg0h1qxZlD/Mx95paq5lV4rOFYtQBgIli9sxK3TWMcPfZI85G0x5Ry/+yzz0IQBFx33XXM78yZMweLFi3Cc889Z1i5TyQUCuG1115DfX09Jk2apPhcXV0devfujXA4jLFjx+Kvf/2r4kIAADQ3N6O5uTn2uaamxrSsBMGTI01H4PP4UNNSg5JgCYK+oCXx2OUtP9m831aHeq1NwDvXA9U/yvyYlE7ZdLM8k8COz2SC8Mj/11InGwUhRRRF/Hi4EQ0tIWT5PMjyedDY0obmtjDyAj7sq27Cra+vwe6qRkvl6F2ag52HGnS/16c0B7uqGhBmqPZBvwdNrWED0hFWEFUAT/augggBS8MjsU7s57BUhFVdJnXF5mGZPpBfH/NkSg5SXdCHKeV+48aNAIBevXoxv9OzZ0/Ju0ZZu3YtJk2ahKamJuTl5eHNN9/EsGHDZJ8dPHgwnnnmGYwaNQrV1dX4+9//jsmTJ2P9+vXo0aOH7Dvz5s3DHXfcYUpGgrCK7Ue24/x3zkdIDAEAuud1x3sz3oPX4zUd9tf7vo79nefPQ8AbUH3eciW8rRlY/xbQVA0ACIkhhNvjFETAJ3ggWaeXLMFrfB/9bvMCYOfnvCXXhxiO/CdHVj5Q3NdeeRhoaQtjw74ahEURVXUtKMj2IxQW0RKKpOOtb/dE/l29B4XZfpTmZiE7y4t1eyKLpeP7FGPtnmqcPqIrvt9Xg40VtbGwZ4zpDgD4fl8NSvOy0Dk/iAO1zfh860EcN6ATyvIj9bK5LYRvdx3Bvmp3+FAxotgDwA4d75Fir87I7oVYu6fa9njHeLZijGcrLhU/xnHN/7A9fsIIIjwJ40QeGtFdOAgfQu2/ppIpG5tayWBNpyAA3YuysauKvQ/rCMraTScPhNdgZQn4PFxkCPjNzwl5EPB50Nzm3nEr0yxQTSn3UYd0u3fvxpgxY5je2b17NwBIdsWNMHjwYKxevRrV1dV4/fXX8bOf/QyffvqprII/adIkya7+5MmTMXToUMyfPx933XWXbPhz587FLbfcEvtcU1MTW5ggCKfZWLUxptgDwJ66PWhsa0ReVp7mu/Wt9TjQcABhMRxRlJP+fWvrW7FnF1+4GB5BfpCJDs4toRZsrNqIfoX9sOHQBjSH5Nt274LeKMsuw9qDa9E9rzvKcso0BD0E+AuANS8DH/wWALA8GMCN5WVo9ERk8ooibq06jFk1dZrpZsKbBVzwlPQ72U5fYSDQ82zZECC/a+SdqGKv9F9eZyCQrycltvC7N77Dm+0KvBZHGlpxpKFV8t2KHYcBQDYMtXA/33pQh5RER2FweT6uOr4vBpXn47xHv7At3uQWXoB63WFM6FOCYwd0wgMfbeYjVAYT8Hlw8tByvLd2n+pzWtZlhajDe4Hfo4eg3J/ImeX7PGyK19G9i/HNzsNMzzqBluKXm8WmIrSGwvjDmUNx1sPqC+Rl+QEcqI3MD/w+baW3U776xoIb+fUpgzDnxAE4WN+MsrwAdh5qwBurftS1ePv8lRPg8Qj48KbjcdqDMlZ+KpTmZuFQfUvsc17Ah4cuPgo3vrw65VmPAPQoztG1KAMAJw4uw+JNB3S9c+HRPfHvL3fqekcPxw/sZFnY6Ygp5X7AgAFYu3YtHn/8cZxzzjlM7zz++OMAgP79+5uJGllZWRgwYAAAYNy4cVixYgUeeughzJ8/X/Ndv9+PMWPGYOvWrYrPBAIBBALp17EQHYP6Nv2TRwBYuX8lLv/wcqZnbxx7o6qpf7h9l/mNLW/gjS1vaIYX9AZx7oBz8cqmVxDwZuGzGR8iOyF8MWGxAgDw1DSgrS3+uWwovijKQ2Pb/thXIUHA5537YlavQe3fJEwYYhM7ue8Svo9+580CJl4HdB6imZaOwOb9tZj+wFIAEaUpOhmL7rqzMKg8D5v3G194OaZvCb76oQoAcMHYHnhjVfzYxKnDyzGiWyHuW0TKkBpZXk/MkkKJJ346DhP7l2LU7Qttkso8xTl+XH/SQHTKy4pNXOddMBJjexVjze4jjspmZK/uX1eMR27Ap6rcZ/u96F2aI7FwOaZvCX4yrgduff07xffuOX8kbvvvWl3yLLx5Sqz9u4k/nz0Msyf1wQfr9mkr9xphDfPslFXsG8Us1CIH+b1G4sie3kCDtP34vdolfNVxffGHM4ai3++NO49m5aGLj0JFdRPmfaDPIvbU4V2wuaIW3+4+gpUyixDHD+yETzcf0FxMbWwJYUT3Qtx13gj86a11is99/rsTMfiPHwIAsrzyCwsDO+dhS2VkzCjK9uPuGSPwhzeVw1SCZSM2erzpquP6yi7BH9WzCJP6l+KxJduY4+2UH4DHI6BzfmRu06dTLpbceiJ+OFiPE/++JOV5v1dAa0gae3ZWJG+GdCnATScPxIMfbZH8LgiRvDmctFgOAFcc2wd/XyjtQ849qjs++r4S/1uzV/L9TScPwr7qJuz6ehdz+gDgX1dMQJ/b3kv5/ifjeuD1lXJHG82jZQDxl/NGWBJvumJKub/wwgvx3XffYcGCBbjuuutw//33IxiUVwaam5vx61//Gh9++CEEQcDFF19sJuoUwuEwszVAKBTC2rVrccYZZ3CVgSCsZmfNTty29Db8WJfagYoQ0dDagH31+9A1tyty/DloaG3Akt1L0BSKWNn8edmfY88XBgrhFbzwCB54BI/k7+JgMab3nq4qi88j332U+PNRkiXdZd5Wvw9NoSZ8tz2iPDSHWlB9/2BkhxIU+u5dgaz4FZl/KCtBlgiEAYQED8TO5djZcgRoA64dfS1Kg6W4+6u7gS4jgZMfU5WVkPLhugrM++B77E46411eEMD+mtR+dNP+2pTvtOjbKRcLbz4Bd/5vA5754ofY90f3LsbA8jz85+vdmmGM7lkUU+7PGNlFotxfNL4nJvQt7fDKfV7Ah7rmNu0HVehRnIOCoB/di7Kx54i2T4Ipg8owolsB/qlj0suTd64/FiO7F0IQBFQ3pk5weXHzyYMYd9LlZ56/PW0w/r5gk6YfhQvG9kBuQHs6tv6OU/HPJVtjyv3r107C0X1KAEBVuVebGF8+uQ+eXbYj5fvS3CxNeexiQt8SfB1d5BvXA14P2/KJh/G5beGuOL8lcgxThIAaRG59+uKCk/BZXlZMIY3C4m8mJ8vLHL9RAj4PWkNhDOich1E9inQr94XZfvzxrGHYe6QRk+/5RPLbkC758Hk9+PX0Qfh29xHVBbOG1sg4/tOJvTFjTHeM+PMC2eeyvHGLB6UFkntnjpZY3cw6pjdmHdNbVpk0ywtXHYMhXQtk+9Cg34NrpvRDwO8Bj9lFNkfz+O/+PB13/G+DoiI9ZVAZlm5m21nvX5Z6w5lare2UJ98v7LjnTCzbdtAy5Z7XMQUlMsso36Ryf8stt+CFF17Axo0bMX/+fLz11lu48MILMX78eHTu3BmCIMTuuX/ttddQUVEBIGJSn2jyrpe5c+fi9NNPR69evVBbW4uXXnoJS5YswYIFkQ5l9uzZ6N69O+bNmwcAuPPOOzFx4kQMGDAAR44cwb333oudO3fi6quvNpN8grCNr/d9jWfWP4Mv9iibmraEWjDzfzNxoPEASoOl+PCCD/HMumcw/7tUa5b/m/R/mDlopimZ+hXJO4yavX8XrqqWKoOTe/VArdcD1O0HAvKDQ3Ln+m3yQmHN9tifQ0uGorZFv8KZqeyuasA7a/Yi2+/Fih1V6FqYjfygD2+v3iMxB4wOkEpn3+QU+yj/umI8lm09iCc/+0Hxmf5ludh2IGJVEj3D1rlAagElCECJAcUheT59wqDOaNXYkbaL3502BP/vQ3N+ZJx0khfN29+eNljWfDOZe84fiQ/WVRiKqzhHfsdJDwIEWQWLtyrFely2GX7th1T4w5lDmZ7zeASM7FEU+ywn35/PHoY7/reBOe4rjpVX7t3ETyf2jin3UXic2S7JyQLagDZ4UQ3pkbbuRdnoWhA0raB3LQxa5g/k6z+cjFBYREluFnYcTLXmG9a1ABv2aVtadSvKxttzjkVhth9T23eXo+0rP+jH/501FBc8tlzx/cHl8cX8PI1Fqr6dclFV34J+ZfJHCJVy+/4LR+OWV9eohp3IXoZFSkFQlvf7O0+DIAj4ZON+2d/NkOgTJFKPlVVLuXqeH/Sr1v7ERZR4OPLMntQHhxtasO9IE/7LcMTuzeuOVfzt6N4lmu8b5cKj48eifzapN55bHjfx71eWiy6F1jiUTldMKffBYBCLFy/GmWeeiVWrVqGiogIPP/yw7LPRid6YMWPw7rvvmjJ5r6ysxOzZs7Fv3z4UFhZi1KhRsSv5AGDXrl3wJJyJOnz4MH7+85+joqICxcXFGDduHJYtW6bogI8gnGR3zW5UNUsnMg99+xC+OxDfmTmz35l4b7t0JfuDHz7AgcbIau2hpkM42HgQFTWRDrBfSyt6eSJtrmdYwPkL/wYsvFdBAsY1TG8bkCPzfW4Z4O8a/9wSVy4lId+wCshNuJ7y3Z8ANTvxl0m3Ax6frEWBR/CgJFiC0WWj8c62d9rDzLQ1V/387F9fY/sB7aMaRh3aPDn7aJw4uDMOJ5zlS2Zy/1L8+6pj0D/JDPX8sd1xj84dpSjSQxTS6Ulk987a1XwWbjt9iOxkipVxvYtx4dE90CkvgKue+4ajZBHc1j7ygr6Ycr/jnjO57shFU8rL4dnsSb1xP4NlyAuhk1Eq1CAXTZjm/TZ2jVoLY3vTs9gl3VFnS6iaIqz2G8uRjkSum9qf2ZqjIOhDTZNxixO9C4RZPg/8HgFejyAb78lDO+Oj7ysBREzDF948JWUBicUcP5mXfzER//piB849qhtm/HOZ7vejyJ3fL8xWX1S6aHxP/Pmd9Uzhj+5ZZEiu+T8dhxMHd2Z6VhAELLp5CsIiUCGz4HFUzyLFcj1/bA8IAnDzK2wKfpuCuczvzxiCv76fOh7l+L2xhZg3fjkpVvZ6F5FYnu5VmhNT7j0eACH15+VQ69WzGPwZxJ/14NZTh+Clr3YxKfelMjv3vvYFsCyLdtcHdM5Dt6Ls2Of/O3s4WsMiWtrC+M30wejcfhQiyuDyfEVrwy4FQbSGwhK/BJmIKeUeAMrLy/HVV1/h8ccfx2OPPYYNG+RXjIcOHYpf/vKX+OUvfwmvwlkbVp5++mnV35csWSL5/MADD+CBBx4wFSdB2IHWmfhfjv4lRnQagUndJmHV/lXYVx8/c7inTtoxt4Rb0HQkcpbqwtpazKpRP5+oFyEnG5BzijfpemDkVdLv/jMZaKmF2HkIUN2+A+8PRv6LhwgA6F7QG0d3OZqrrOlAbVMrfvH8SizffggAkJuV2k/WtxiYBSQwsnshHrtsLICIV/dZT31lOKw/nDEUm/fX4rV2M7zuRdmy5rKd84OYe/oQ3SajLLCa5yYzpEs+Zozpblqmm08ehGtP6I9/faFszaDG6B6FeOOXkwEAG/Yau26VJQd+ddIA/OMTZR8zsbAYtWK5CR5rPNOGlHPdKbbSc3lRThbOHt0t5axqMtvE7vhV6w3oKezHNO+3se/1+KfgyaOXjsWNL38bU3C08uiFq47BZU+n9gX/u+E43Pjyt5Iz/qcMK8eiDam7mdl+L44d0IlZuS/OzdJU7s8c1RVXHttXdhf2mL4leHL20fj58/ILYhP6RnYR/3LeCDzyyVb8+6oJ6F2aC79XQN+5kQXI6CJM/875ePyycRjwhw8AAPlBn6QtzJ7UG19tr8IZI7tCL71Lc3H7OZFrl7+7fTqOu+cT3Ysapw3vgmHdCkw75+tTmqPp2C3qJ+WEQRoOb9s5dXgX7YcS8LUvhCbXydmTeuOOc4ZLjgUlPzNjTA9UN7Tidh2WKckc1bNY9nuPR8Bnvz0RIgC/icVaJRLTUpjtx43TBqKhpQ2LNuxPKROzXRqrw0fJ4jljpB6ZB30GFr30UJS0iOX1CPjrjJGKz7/3q+NQ3xLC6DtSfcicNqILbj9neMqicoY5yzev3AOA1+vFnDlzMGfOHFRUVGDt2rWoqorsPBYXF2PkyJHo2lV/p0gQHYVQOKK0bTgUGbSyfdkoCUpNnPoU9MHVI69Glpdtx6I11Irm9jvSg2ERuPglIFDAJhBLT39gJbDhqZSv5c1lI9+JCef0la4eYVUwWJ9zM6Io4o7/bcALX+5M2Wkwq8jLkZPlRY/iiLnFwTr9K9eJWV6Q7cP4PiUx5V6tOE4eVh5TpIX2/zmN26qPVfIIEHDL9MFMyn0yVx/XF099Hl+4+O726RDDQMDnlS1BtXgevXQsThvRxfTxBcDesjMSld8roDQ3C9ed2B+LN+3HM76/4RjP91gr9sNPW+ailc/US5EzR3VFYbY/prBr9ZXHDeyEK47tg399sSP2nSAIGNwlH9OHlceU+9vPHoYZY3vITpq/u326RCnqlJel2sdk+71Y8pup8AgCpty7WPaZRy+NLETKKfcej4BThpUrhv/UzyILxJdN7I1Zx/RSzQOfRwBUFLo7z9XvrEtueCsI+lMUxyyvB+WFAeyvbpa1knj8snE4fmAnfNm+6Bvll1OlTqnlkpc8xv77qmNw/N/k8zrKv66YgBU/VOHco7olhq76Di8EQf64TfIzVuGTqwMWRCcAuPmUiBNguYUyybMK8auJZaWyLScP62ICzzjV8Hk9KMx23qrPSbiPMF26dEGXLvpW8giiIzP3s7l4d/u7ku/O6X8O/jjxj6bC/cn/foLscBjweBDofSww5ExT4SUjQP993paYCLtkxbW5LYSWtjDCIlDX3Ia8LB/8PgGHG1ohIHKtUG1za2xyUhD0Ye2easVdzGlDOuPPZw+Pff54435dZ2nlsHqFPREukzCZyw14wGuCGA1GKzSPAE2nalbB0ubksuPen4zCzKN7SpT7gqDxs+Vnjoos8CdHdfzATvhsi9Qbd37Ahw9uOh7H/T91RSSZ+P0Y5ss36p9CLveUnBhGr00LeD1Y+YfIMcG8cB1OyloNAJgofI+jPZuwPDw85V0r4dV0pg0tVzQFT1ZaS3MDmguIfTqlOvOKMudEYzcqFQR96FeWh/yEs9Ta7V1/DiXugh8/sBMWbtgv6/lcjWFdC/D6Lychy+vBUXcuklXuJw8oTXG2+NLPj8Hk/tZc/dW9KBvdx3S3JOxECnPM+akwilOLunZGa+aYGAB8c/htePOaEapLPbost3PPYo4fNrE1znNX3W2L+lZh7fIxQXBm6Y9L8U1FxAxvas+pGFs+1mGJzCGKYopi7xN8OKbrMZrvJk9g5Sbx0fvg+/aealxInchNrKOTq8SdBLedBTZDc1sIR//lI9SaOEMKQGK+nhvwoVdp3KlBpzzzV3MmrrCbHeO0FKjEspY8KTg/wPYplXMWYR1njuqGoV3z8bcPN9kabyI8nP5Zwb8uH4+zHv5cYvrdoyQnZmHiBNOGdMYt0wdpP5iAzyMwLTQGYNKZIGPb0dvGFNtzQkB2tdtHLx0bWwzSQ5bPg6//cDKyvB6mBTxBocBY3n1i9tGxqwIvGNcDF4zrgdE9ijBx3se6ZM7RuEu+LZR6tIL1/nk3k7xQyFq1+NZBZwaiUDhxHqSOEQn7qiyaacXjCe7Cx5VPIqcnUPv9PSnPySn3154g71w5kZ2HGjC6Z5Hj15QqkUnzUYCjch8Oh7F48WIsX74cFRUVaGhowN133y0xx29paUFbWxu8Xi/dIU/opi3chl8v+XXsWreFOxfiwws+1HhLm/e3v493t7+r2rh75vfE78b/Dl4Pv+tMAGDRzkWxvxdfuBh+jx9+jx85fv0TWzkz92cG/QzlQ2egV5GxXRA1eJvHRfOfddfNDabdUfYcbjSl2F97Qn/cdPJAVDe2WnI2PYrPpOfnlDwXVH7jQGKYSqHLOf0qyvHjiIxH9v87axjW/HgEfzxzGN5qdx4keGvhL1qBUHNXhOrYvJZryZSM3yPg7FHdVJV7o82pX+e8lAmTnCLflcGbsB4ReDV/n9eDHsXSu9ujfVl0h7xHcTZ+PKzt/ZrX9Ozpy8fH/mZN5vDuhfj5iCJgsVSSVAWS3ySSi3GMQ92oJWdcRSDI4coxpSNjiSR2pV5BwMkqRwQU40n4W7F/k9kVlSszuf6XVxbbUUdizjCtj8oREhfneThzUyuTK47ti8raZomjQ9k6I/OdJ0vdr0PyK3075eKKY/vGPq/4w8kYf/dHsc+d8wOorG3G8QM74YJxPfDx9/vxuzfWAgAm9SuN+Riyi0w7W68EF+X+3Xffxa9+9Svs3LlT8v1vfvMbiXL/1FNP4YYbbkBeXh727t2L3Fz9q0tExyUshmOKPQA0tOo3C5fjvpX3obKhUvO5s/udjZFlyk48kgmLYfxQ/QN6F/RWvBN+4c7I2cUBRQPQKVufmV2ycp28ODGm8xiMn/QbXWFahZWKuNtXXJPvyh7VoxDf/Vgt+Xzb6UMAQHJnd/LAy2OCxdUs3yWzsA9vOh6vrNiN+UvjVyU+fMkYBP1evLJit+Te3Ql9S3DlcX0l72eVfoqs0s8BALUb7wREI9f0aWSGhdYKp4/ogrNHdcWeI42xM9Pcdm0tQE9rfeCio/CX9zZg3oyRuDTB+WOiSbyc5FYrI0rBnzWqe7tyH2dc72KA421aVpUV9zwTRHizd0AMBxFuNndU0zp/FKKJCKytZJP7l+Jnk/vErmpz02K2k+jOBaEFOX0fgTcQmeNd+SmQOzAPjTt/wV02NRK9uTe0qPdfiRjxQJ/l8+BPZ0lN6uWU2obQESz9MWJ9srXuALy5O+DN2Z76YEK4yVdDHjugVHIkpyw/INmhf+3aSVi8sRI/Obon8gI+nDWqW0y5Z8XdMzx3Ylq5f/LJJ3HttdfGVjo7deqEgwcPyk52rr76avzxj39EdXU13nzzTVx22WVmoyc6EMlKnBmlrrKhEtuORLz6Hm6KrFT+dvxvUZCV6nDu/pX3o6qpCjtrd+KT3Z+gJZS66npM12MwpccUyXePfPsInlz7JM7pfw7uPu5uWTmiYU3rNc1wWqJE2+BFNbW4+uznUNbrONNhGkFtEpKpZvlqdC/O1n6oHaunb4kOg4zMZ1MWHFR+k74n3YFnjToktiDQ5b/w+I/gtR++BDABydff9SvLw6+mDZQo9wAwvk8JhncrkCj3srJ5E3aEPa1AKK7cs+4Y88Do5F0AcPXx/fDed/skDtFMy6OjgkzqV4obThrALe4opwwrl3WadqTB4WuMZHdOE0jo5+4+bzgwX/rsSUM6Y8mmSkv9MLC2TT3h6KHFtxU5fR4HANRt/iPEkPRec6Myqb3mpjFFSZbEdKulZdrQct2e6Ds2IvxFKyD4pTvPHl9NTLGPf1cHT7Z0M5IXLPW6ppHdym9wl3wT0qjz5v7f4t974/5OcnqpP/+Pi4/SHUevkhxcnrCz7/SRPKX4M21H35Ryv2XLFsyZMwcAcNJJJ+GRRx7BkCFDJHfMJ5KVlYULLrgATz/9NBYuXEjKPeEITW1NOPetc1HXWif5/vyB5yPXn2pN8uTaJ1HVVIUnv3sS26vlVzVf2/wavrz0S3iESN2vb63Hk2ufBAC8s+0dReU+OgHokmt+EI+btQNdsjsBnI8QJGNEGVGbfEUV/0zwgs8DK3LBrFm+3VS2fo+s4q8BAEv3b4Yn2B3hpp6Gw2PYYJeaymo9L7CGq+0F2ijMMjBEr0fExEdnHt0DkwewWR7x2Gkf3q2QOWwtTh1ejkN1LaavGYsIkCpBlwLp4p6AiD8NQRBcNaNMvm7KLCEhbp0keOtTlHt3JN1ZY3CeWWDlsGlF0E1tTcgqW4Csks8AQcQXdcMgii8ajtcT3INg1/8q/t5W3w9Ney7FMcd8iPWHI36bnJpqJFroaXHCoDLcN3M0fvP6GkmbESDAE9yNYJd3ADGekLf2Z6Nww7m4cPCFuGv5XdjfEDEb2tRai+yezQkBhFEXiij2Q0qGoLohhB8PN8KTdQCCN+E5DdLBoqRfWS62H6h3WgxbMaXcP/DAA2hra8OIESPw/vvvIytL25zx+OOPx9NPP41vv/1W81kifXhu/XP4tjJSpsNKh+EXo/ibPLGcg2OhpqUmptgPKo44TTq227Gyin0i0XeO6XoMhpVGTJ6a25rx0saX0Ngm3d1LPEufyHvb38Oyvcvg9/hxyZBLYt+nQwfJiupVeAnTGV7l6XaSc8NoSfOoIx7JDrrZ8/fGnhUEMM+qwpDucAiC/PWAqRYFguRflnecwI4WEG1mxw8sQ7fCIMoKgiln9PXmxdbDW7Gpbjl8+RGriPXVNcjdUYLdtbvhK9gPiKkL/E98twdVTVX4pu4QAuW1aK0+KkVGFk4a0hk9S/g525v/06Nx8yurTSv3yXZlqs+Kou7+7/4LRwNg3/llpS3chiW7l6BL9yPwFayLfb9oZytyAl5sbaiIlCmAT35sRnGVH76CNfEAwlloqx8sE7Lx2q2nPi6fexImzfvEcFzy8WsLYKT/eHbds2ju/hDyBBGhht4QW2/RIVTiny7ovEzy7vZ3EegUP8NS2bYWNS01EAQNPUIh4wVP5Mim2JaD1pox0h9FD1qrx0EM5cHniSxiseYgz5y+bGIvvPDlLvx8StwBnaZDPUHABeN64K3VeyS3ijSLNcjt+2jK8/tbgL+t+Bv+tuJvKb/58lK+AgA8f/rzeOfbA/jdG2shZB1AXv/7IIa0fbRE5E9NgXS8F5J+M7ApZHau2DGmmhJMKfeffPIJBEHATTfdxKTYA8CAARHTvd27d5uJmnAJ89fMx3+3/Bd76/fGvvt418c4t/+5KM/V72BGD0ZN8KIdhU/w4Y1z3tB8PtYZtUd3Us+TcOnQSwEA1c3VeGnjS/Fw2x9tamuShPH02qfRHGrGY2sei31X32rlSqL1g78jnbRNYTqGxcU2c1wPruFJzO1TfATwSIzdZSuNT6uOR9OolVJBYHvGCLK3UyR9/mLPF9hwaAPevekCtLXmYMJflT16e4K74PFXY8Wh3ahfn42s0nUAQoAQxj9WbcMrm15BTUsNACC7vTq9uivyHwBkK9yi9XDCen5WCeAN/gjgWrZEJtCnVHkR1mht0Zv1+p5PrlPGpBzbq1jfC4xCLtz1Dh5Z+/8ASMvuLyteif0d/f7eVUh5DgCa9p8O4Fxd4vEyoe9ayH7siRW9Ywpr231jyxuApw0CAF/udoSr6zTfMYMVQ2P0DHhLW+q1fXo40nyEgzSJRBIbbitA8/6zOYfNh7vOHYEbpw1CWb66Q3H1+hTGgYYDqA7FrUhbDh2HUENfQBCR3eMFydPd87rjhjE34PnlO7By55HY94HOH8Ljj3wOeANM8znW+uTmGZlSGtwssxFMKfc//hhZtR89ejTzO1Eneg0NfJyhEc4RCofw6OpHJYO03+NHa7gVrWFz1/24ET2TkeRnH1z1YMozTaEmUz1KylV4UbN2F/ZScoqekg+FTNiVSCQl6YwzwZQVbw7ZknivtNnwtJR3yVV4Kmf1eZBcZ5RM1d1WtzQVfl814GkBQtkpZs2sYQlCCNd+FFGin13/LAYUDkZWWQ5aDpwGeJqQ3fM5/PLTf2Bk2VCUYAxy+/4TAPD+vsh/gbjDZTy5Vroo0FYfOUs5oHMedtSvTfk+kYn9SuHz+LCvyoudzV9E0hWTUfqsHqWExyKSWnTyXqYVc5s5TqNm+bwtTqqaI96qy3PKUVdbGjMZntQ/4ihr56F67Gy/z31C3xIE/V4s3XwAAODJOghP1mF4fLXygbueqEM9C0KWKdqQKG95JAcviy9JGEYXEBNevOHEAXh0yVYO0kRoPTIO/qKVANgWVczmQ2wMcGCiJAhCimLPkp5NVZuw1/M6AuW1yCr5Aie9Fv8t1NQVzZVnxT7P7vYspoyIWLxlebMwotMI+D1+LPz6W7TVxDfh/MVfxpT76HFSKdbnj23Wc+4a9m3BlHIfbfB6FPVDhyIDSWGh/Jk5In0Q2/8HAPOOn4djux2L0944Da3hVksc26Qogzbv2MaUT4M90ul9T0fQG0R1czU+2f2JpO/kM0F1oVZvIZl4Nt9q5ZO3Qq/2STEMuZ1mbx0CXd6G4G1Ay6GpCNUPjPyQ0sYzt44n54qvcCWyu0VmcaIooHHnNQg19gE8TUD0eEJY22JORFyhqGmpwaoDKxDoBAQ6fRr7vrIR+HjXXgBS5X1U2Sis3OKHKHoBeHD5pH7wCB4MKBqA2gNjcPv/vgcAXDlpNJ7dOSfmk6Rx1zUpcvzrujMBADe89UpEubegLK1oPazDjCTuxJeSAhAc6KnV231Emqk9p2LN6pNQsTtyPOG+y09BcW4WHvxoMx5cvQUA8NdZJ6F7UTb6fPweACCr7EMEOi2BbFkmKk8yipQVfZ3eKYEZGfhILzlEzZ107C0Nl0nmTQdizPt6Hio9K5FVIv1eDHvRVjNK8l22twDHdB2oHaiooP6J5jNSLQSnp21Ox28XppT77t27Y8uWLdi+fTuOP/54pnc+/zxy5VC/fv00niTcTuIU5fjux6MwkB4LNqLB1XqtxQS1KVun7E64+7i74ff48cbmNyLKvcY7ekl0qGdLD2YgCjsWfdyG0V1rK3ZueMJTHm/eJvgL2nd+w340tiv3vD1q674ijrmwNMz3DcTtDcR3WQRBRE6fxyGGsyB4pJ7iN9bdiOrmUmys/hrevC0ARGyvPwJf4TYgHIDQ0i327Pgu47GiYoVsfOPKx6GqvgU/1MV34C8YeAG+WBrfaZp7zJmxv587uENfgqJpYXnG9CKUzufNRWc8YiNRmFhEszI+vbij37bXoZ6ZDQm3LGbzEiOaF2JC3rPUCdN9Q0J8bshRlhoRPcLZWj0a4ZZOePGyWXj98wBe+cb48ebWI0fDn7sd03qdFPnCgcxw04lKN8nCA1PK/dSpU7F582Y899xz+NnPfqb5fHV1NR5//HEIgoCTTjrJTNSES4kNQBY0lOSB0a7JQTRNeszGo7KO7TwWvxr7K/Qv7A9/1JFLUnisYSrJlRyn07BehZdcfG6RnzduMgXn7USPdaIlmVAJcu8lnN8U4n+n1gjt66XSCVXP/HLm4J7UK+AWHngIC19+CACQ036RwPsHgOx2nT7cEj+r/ei0R3GkPoRj73s1tpsqhn1YMGcGBnUpwPtr9+G335wJwaP/WBW70qmNWldgt1LIWreUHerJO5zSfc6f8XiA3nD1jGuKT3Buf2nanHVjdMxjrpOcxtT0KA9rFmr0LqpYY5ESSVtr9TiE6gdhfJfxeB3f6ZRLSlvNUbjymPNw49RhnKSMk5kzufRC/s46Rq655hoIgoBPP/0Uzz77rOqzhw4dwnnnnYeKigr4fD5ce61+RzqEy+BsVm43PDrhxHTLTTo7ZXfCuPJxKAoWyb5vxURVSPh/K+F9FZ4d8VuFnjO76vfBJ34wI5H9mHGol7RMpfA9X+TlSzah1gqD/Tmj9bX1yDjJ54Yd16D2+7+iYWfqjSShpnKEGqVXBQr+Gslnn8ePcEs5ws1dEG7uArG1EzwKVySy3jYg6DA2z4SJnxP3xrOSCflrFMNpN1Cgas5EncANMugh1l+I0jmUpF+xIFHpOFeVg0cy/J6ASn6wLq1bD894M3QPKQVTyv2YMWNw4403QhRFXHXVVbjooovw6quvxn5ftmwZXnrpJcyZMwcDBgzA0qVLIQgC/vSnP6F3796mhSfch9yVZ5bh4kaqln5eeeQm5TYR1avwEnpWXnXEHead7DCXWlKyeAzm0okTv7DkUHeoZ3HdjSrdOhZWnIBFnHBb0v3goVwAHoQa+qHl8DGS35r2XoyGHXPQLTg88Q0LpDJDNHyDu5YMnh501y8Oj+tZVDXTYzFbyyQtvmhJo+TAUz0+1ozjN9bxVM6M3lzAglzIyWMV80IvjLXK1DJ141ENp3bG2crert5QjXi9sU4ao4cV5JRltRDcNgbHSa95pBamzPIB4L777kNzczMee+wxvP7663j99ddjHcg118Sd6kQnejfddBP++Mc/mo2WcAFmzcqdIuZVXufZRbn3WK/K0pKFB3HzStGWHpT3zr1Zh4WZgNtTrqYss8ouW7ysnostySAT52AtneRaYdWj0V9xj5EtjtSUa/cTrkbDoV7moT9NVuygpdvRLqvl5WaWn9RAebRBOdncWH7uk8gm5I4B8Y7CgQlPR5lemtq5ByIT8UcffRQLFizA1KlTIQgCRFGU/AcAkyZNwnvvvYf777/ftNCEe7Fy597pSZ1m/DaL59Yz93JYYl7nelVYHua8SHnMXem1ZZA0sEsZeU2Q/Jv8PWvQrGWl9ZgAwUR+6XmR4aiBbHAJXzq5zpLu2LKoKv+3UeJ7ggLDYrWRkJP/1sa2CbgYT700fm0BNI/iGJNIOTytPsamTLM6Hs18NWtxZpEViFFYWkb8CIOlojDIkYqZLHTxlDXtMb1zH+WUU07BKaecgtraWnz77beorKxEKBRCaWkpjjrqKHTq1IlXVIRLcHrn3qiyr3eHOHnBQt9ZYuVnpWbL5vPP6cWPKKx1gZu8Lkg2r9pv58TJkBNHFWVZTxgsV3Ol/i33mTM2332s6lBP8R3j6p0bJrNyMvPOdf3e8s3nox6HekYSrNdhIbc+iVM4ZrDbn4PehXK5snHbYRieuPMGBYsc6nENzV2xso/DKk/pLDJDFp92nPJ1wTySJ6aU+yuvvBIAcPrpp2PmzJkAgPz8fEyZMsW8ZER60t5urdhFdnpnWi7+xI7KyMDE9So8iXw27CAZUBRYyjBdd+SVcIdCZT1mHOophmk6hKTwdCt9bOGx7HgaT4u5XBB0LFgYLUM9+cq0U6WjW+TRvPT2w2aiFGKLxAbf13jPLXPUdOj1YjK6sI9OlsjJcdESD/AJNVUUBV39FC8J0gVRwcKEJ/G+Xm1DyrLoCc6YUu6fe+45AMBFF13ERRgiveC986w7frM796w7ITJX12nGodILcrsKL+kd1+zcqznUkwzoJp0suXBCxoJRqbkkNyEMnuaNms8mxau6z5VoIc5YR1IsCgTpv+qone+2FnXxrDlzL3fmW5pPBs9CMMsQ/UNM/c6FsMomJPy/2pl7XYFyQrWoor5kDJVn9B2XjD16nzdRDtoORVlC4ZdvLElxZzvTaTWj9YCo/oTuq+10VypLH4+/587C1MSI3OmaVicxdea+rKwMAFBeXs5FGCJzcIuiyRW5OZrGVXhaCpBlV+GloUO9TCVlB0Yl29w6hsXW9FN2dhOfcav08nC5fcB8EAxhZdaOHZMSYnbhiaNZPvcjA5zDU4yHl2d0w9qHes7pyVf2o15GsadU7LY+dPNOq+JcwPKiEGT+UsbNeehWjNzooQaVgX5MKffDhg0DAOzcuZOLMER6IbfzbOUEP3kwsHvnl5c3d7ld7HTdhWZF/kyifHnqrUMdZcGAz8Z9wsTGpCm1vqvw9Egvv4uvLInMooPC67JS8FDyNcIQBBPxpLgdUAlIY9dKGzv6Ie041Lp2V0/02B0nmAjaWBvWEoXFn0bKd8zpcOkJdIXKZNd4rGuBg6PVlV7sio/p2J6iLG7uGJRxi9ROzEDt6ss74iKOKeX+sssugyiKMfN8gojiZoXL6KKALrP86LNavQrHbIorxywRO4PTfhOsQi1Veu54d/saDy/x3JtO5+qnIYVC5+ZXxJmhnNKmXyZeaxVuRy6d2kVl3FM8YRxew4tu3zAu6M/k6qSb52FOiWbn0Q1eyJ+513u8gGGxThqrrvCZ5bAkVCIZU8r9FVdcgWnTpuHtt9/G7bffnrETd0IeWQdzFvaEKTu9No0OsTPyGvfcM58PjuaRZIPSfL7ZPZAbcqjH9YxhegwTKXI6KLaVExVzDvXsUYYEnfNxrWfiDvW0Q9JfX613oiSPtfHF88FYObMslun3fq9bCr0vJGDW4ozvc+qWMlrtmLEsXdtVRxfh7dqpTzHDUXw2Od/dlIV8sytu1iRC1Gy7ir/LWLaoB9Sx9BVm/UzF+ktu/mZHLnaskuKDKYd6n332GX7zm9/gwIEDuOuuu/DKK6/goosuwqhRo1BcXAyv16v6PnnVz2Bc3BrNOuJjepbRxNyMsptyz71kkubcVEA2zTJzQKXFGqPHJZxEbdzkVRS8F87sXByR7DcI8rvHvMKPxpH4rzrOOdSzveayHB+QPO5MP6KWL+7eQ4gu3Ko71IsrMa5OjCtxk5LLEzuOAvCKgkcbdMO47QbYRijr88quKWNiPXeDVUSUTKuPppT7qVOnSgpq8+bNuOuuu5jeFQQBbW1tZqInXERsMm3lmXuHZnWsd3uzdg5yZ+55IDXLdyeZ1oHyxmplyvSJbJXdeWskd3d94VleKSElfCG9KoplJ1XfE0YUC8NXuXEMKx1xW1Jj4wZDISg+Ivu9ujWO+o0yjPFyw776Lw1ER/+mtTBnU83iXhbcunh98y/AHf2OXSMcex+v0+pJvyiEDZhS7oHMPUNLZDCx4/A6d4h11HVbVlqT5HeL4sx6FZ5pcV01qrAnxqjYvJNr3iO5hlWKxKGe0ThSQmWSRdlcPnFiJycUH1Nxud/lnjFfpnonYu7cLeY9jXBi0i4qRixzfM1EyUusYFRqOPNU3lX9qDxWTTMV1yr0Xpcm8528CTOfhDh9JI2PI0f5BR+PVtimV6j1Pm5/XifnbzyvOFu8qQZn/TjB3K5Jz9SNKeV+8eLFvOQg0hA1b/luUTR5YiRNapME7gtj0UUL2zyQ6h9o1NIcM8u3cNHFCXSlxuJ5hHnTT/PnQGXfUai0ri9bjuWlvJBh9/EJI7uY7O+IDOGnf7mr7EqzOls1iN6cM9rvov0t5Z/cXYZquKH+cVGgnU8GAwKU6pG+PGDrL836/NAiDdbKZDFa3/TmolvzJz3aCjumlPsTTjiBlxwEoRuzXu9ZJ6SxBQsZ80XJPfcmeoeMvwpPJn2ZuADEgluKmr8lAK8QUxcNE76Qj1vjc+x7TfNhtnBSn9OaUJrLb55lpceCQN1bvjmphMRyNhGU9Iow/o2L9XYBPQsjIkTDuoWxIxQG8sWQYpVexBdaXNIpq6DZx1iYBCvClpsvpV6PKyMLf1HSB4fdKjlmYWX2+uk0aN+8MeUtn+jYyN1jnexZ3qr4nMDQzr2c2WTigoAZh3oKZvlCJBLD4ZpFbRKSqd7y9TjUM3oVHo8iNb1vn6hI6Yo3cUHMvuppdX6aJbHaqNdnQeHv5PBYd63UhHI+Y4xOxqzwO2J63HH5lpCW81c3tJNMwe45THJsbhozJSQqrRZHZfXOvV5Y0mtvvREk/5gIQf43I+GaTL/TuoMTkHJPpC1mvd6zDnSxBQuZ98wMlnLHGsyQDlfhqRHrgHUG21EtANyAuvJsdrVdrmzNlbVLp7auIfX6LXt2wpOxezJmZ70wa5YvWebhsHglHc/Y49aH3C4tezxWLTDErUeS6r1tKxo6fLW4pPPi1Se4Ydx2w2KH87kQwYm8cJPO7SJRuGDaoV6U6upqvP7661i+fDkqKirQ0NCAf/3rX+jdu3fsmb179+LIkSMIBoPo168fr6gJF2JFx+2GwYAVR67Ck/SU7tq5Tz7akPx3uqOeEj5lwXtyZ96hnvrvag71UuuInNMxHQpKcvjRzQfdiRRVPqkJoPGzwG7enRo7v4IXBEHBmkgaK0uMblE2rEa23FjOfYhi+2c5x2FG5GB8Tme4Rq8g5S2HE5gZgTSPgzAEnlgP9GS/Nc45VeKzOHQ90mvWU03LI31pcVc/x74Qp/ym+ndxLPJJ4ECGdkSzfC7K/SOPPII//OEPqKurAxCZ1AmCgPr6eslzS5YswWWXXYZgMIgff/wRJSUlPKInHIL3zrPdMO/cR5/T6OvMXIWXljv3BmTOVLN8NfRc62S5SaJZhT7ls9Tc3kgYqQ/YvejjDl8Zbpx/sHrXNyS6heVsdV6a9XZvZQ2XC9tMdrAdFzFyZM1FqC6U80PP+Jfa17oHHrLInrk347eIdf7lpoxMM5zajsmcbSD7MG2W/+c//xk33ngjamtrkZWVhXHjxik+e/HFF6NLly5obm7GG2+8YTZqwoXY6S3fsEM9k474EjE9ubcqm1w2gslNEFOc55jy2uwsqmfukz+7Jnn2CcJgiG0u/JRMlY9R4gxT9gFjcmjvFZmo1WJSDGo7Uxq7ViyO/9ygRhitDUb7Y35tknHBWDCey+yysj1oXb+rXorGy9jgi3Jh8QsqBZb02T0W8IrPytmdVht2vneyH6uuwkvEsLd8E5XBPXMhdx0R4IEp5X7lypX4y1/+AgC47LLLUFFRga+//lo5Mo8HM2fOhCiKWLRokZmoCRdg986902b5eswX1Z5ltQQwCusKtlWoXv9nxXGNDOqVrfRObiaM6FtS5dioIiXoGNRtKFsHJxjmU2dAeFmTXv3hmNu7Nh+/HHb1BUz1NypLBvVPKTAnLfPygIfVDtf6KncygFPw0iMIfMKUw945nmhJWtLfBFxtDkKkC6aU+0ceeQSiKGLSpEl4/vnnUVhYqPnOpEmTAABr1641EzXhUqz0lp+MWYd6ensquTQlTkqdvgrPfidUBszyE8/cZ9CETy0tbh3s9YillDqp93wh6Tdz6ZZ/316HeqynNzV3mwQjk2Kl3RoTarVOISy5Wk7mO/59gTvbHBBZfDV25j5qV8F7Z1N+PIz7rTAVuGa0ctjWZ4rybYxH/LIhmNrl1H8EKn1gT1DmpT2V5CQ6s4HB5gTTTHlk8rqn05g6c7906VIIgoDrr7+e+Z0+ffoAAPbs2WMmasIFSDocGzpcp3Zo1bzlK8Gi7Jmd0KY41IvJF/9/t8CU5tgck9W01V1ptBweyeVp0qonrGTFQfXhxHps0EyeRTYmC5yOhdEmpe+96MPafUHaIckIMelfe7Gne1QpyzTunvXONfgvQ4JLAVq1gM6jaOVMzUVoXeapgqC0GJr0mM4Y0rgaa8Kjj7BjWm5LHGk76Mhjaud+3759AIDBgwczvxMMBgEAzc3NZqImCNM797yPEvC8s95Q/HYvfhgRObP6zxh6sl71nnsOsliJ2h6yHod6bHsCdiEq/K0N1/O/KZmbOFnlF5Gsp22F4FXrKtezz3wCs8vSwEwsZo9NaToL1/mOmLCoaut6qQs6u1h6DSSc00ES9vgczK+Oto7uNqJzy8SlDyfKJJOOQGY6ppT7rKwsAMCRI0eY39m/fz8AoKioyEzUhMuITs6sPHvvxpU1Y2dV47vYPNMk2bl3cDRmPXPfUQYKXiXBZeNeYkZvMiyN3yVX4ekxu4y+zxKJRhgp31vULFjM9+WviLRbErvCsE8EPYsVjiDrFdwBOQzCdAhA1hGlRiJdkQf2CmHlHIalj3VXs4j6pJD5TuEzYGxxS/q+Wp0ltK8S5IfRq24JbUwp97169QIAbNmyhfmdTz75BIC+3X4i/bBFETcaRXSngrHHMHKWmPWe+4RIdOO0V3neV+HpvW/Z6fQnYoXjIjehtMFlRF5zaeTXr7hBDU5MjWq9l0y4rKskjJcOmIuD5SE1i32VxmZ0wVCtLzFc4xRkMbFZnBqWShisweuxZDPc5+pUpJzuBh25i9vk73JYsYhkdc64dQx0ErdshMg6iJZdXOVsFWtH8t2RxdwwpdxPmzYNoiji8ccfZ3p+z549eOKJJyAIAqZPn24masIFyO3MuUnhsoWE5DIvaLS/I4oi105bGr+7yqGjWXQk4qbJitSMnl0wlhxO0QsNJ9z8mXslrDpvqZVUwczdZ3oEUQ0i2kfzwXibdlGDcAhDi2KcnzP6PHu47lqIkw03JqIBCzxB/m89GB277J5nWRtf/NgRS34oy8J25j5T0F0mckq4xMw/eRNLzuKIzQqJdV7rkjWLjMSUcn/99dfD7/djzZo1uOuuu1Sf3bRpE0477TRUV1cjJycH11xzjeF4H3vsMYwaNQoFBQUoKCjApEmT8MEHH6i+89prr2HIkCEIBoMYOXIk3n//fcPxE9q4ZaVRDr1n7lN27hne07sLbYQUOYxdAsAd1V0wF9cLq9BTBdTPODtdslJ0pUtzW0rfwph2fHrySs2axA0IjglitI9UQ5T5Sw8sZcuzpciFpSiDCxzqyZ65V7VMUB+rXNbt2AKPcUpWEUopHRXHuzrjS7dycmpRnodSzPFxJmJ5ZaPJPJHemFLu+/fvj7vvvhuiKOL222/HxIkT8be//S32+2uvvYa//vWvOPPMMzFixAhs2LABgiDgwQcfRFlZmeF4e/TogXvuuQcrV67EN998g5NOOgnnnnsu1q9fL/v8smXLcMkll+Cqq67Ct99+i/POOw/nnXce1q1bZ1gGQto5x88xWRhf8nksl0y9o7BOCCTX53F07md3fhhRNpnM8i1QKKxG9XaElGMdVkujjPSeeiPvp3yj8ptiKAZi5oP2bpteh3r80pJqEW+kPfNYHHK+XamlPLmfVdt9YsXeNmnsKrwo7OdUzSWKtRdWQiuJRrOAb78fO6PHMUx2dPkicb5ZxuC+0JywDqZ51aOL8iEzMZ/B3M3ybZjbukubMI+pq/AA4De/+Q1EUcQf//hHfP3111ixYkWsYO+8887Yc6Iowuv14u9//zuuuuoqU3GeffbZks933303HnvsMXz55ZcYPnx4yvMPPfQQTjvtNNx6660AgLvuuguLFi3CI488wnykgNCH2xRvOXhcuaZ957D2jgkvJOE56VBPJs1yecgr/R3FGsB9cxp1iaTHdvS82f6+jm+T0ZdX5usPS3OTPZvIHgPD34whqZyb1GWlZLBCxsNMXBw2FpZTKMub8EPMYZi7+yceC8xaC1E8TfTdQjouyBtus0mLolwsG5zauRdS+x/CmfmFXf1+mg0vXDC1cx/l1ltvxerVq3HFFVegU6dOsbPE0f8KCgpwySWX4Ntvv8WNN97II8oYoVAIL7/8Murr6zFp0iTZZ5YvX46TTz5Z8t2pp56K5cuXc5WloyHZuY+d57SvGRm+Cs+Fky1DXveT77m3OV2GHOq5MO95oJosnubrJjEavBD7l48VgmotSCMvxmbLS+pQz1xYyiHb96rhKDnFGXNkanFDMrXzbvg4glxYfNMpf6ODEescwExFsn+xJ7lfs0YAc+Of1kYCiwAmorcMAVzVLx2m625dVHTPETxXVhjdZEYq9GF65z7K0KFD8fTTTwMAdu3ahcrKSoRCIZSWlqJfv37weLisI8RYu3YtJk2ahKamJuTl5eHNN9/EsGHDZJ+tqKhAeXm55Lvy8nJUVFQoht/c3Izm5ubY55qaGj6CdxCsWJV1yxktqWmzAaW8/R3eim5sB0aMxOImEq//i8Jy7Y1GoIQJ9MwfmBzqqVm4aEamFEN6DMsspqSGqyunc5Y8FhCVn9MdtGqGuH0RkIune4M1QqtMhKR/HcM1CooyvCwK5MrSbBVWvQnB4azlonzKOWJL+lJfFuo/FpkuxPtDzot5DmcFaxuxYzhw+5ijF27KfSK9evWKXZNnFYMHD8bq1atRXV2N119/HT/72c/w6aefKir4epk3bx7uuOMOLmFlKhKz2/Zews4VR6ON0Q5nd3p3kIzI4tZBSvWe+wzrQFngVUpWX53FFgDHsPRGCIB5AsdiJi9XMq6yGjAgC8NCgNwTUp8pZk9b64dXXbJr/FGMRtahnr3IOtRzZKhwU1vSB8s4xSdPZfwWcYyP13hrbfURVD86jVvnWbxR9kFjdGHePXSMEpRiajv9ww8/RDgc5iWLLrKysjBgwACMGzcO8+bNw+jRo/HQQw/JPtulSxfs379f8t3+/fvRpUsXxfDnzp2L6urq2H+7d+/mKn/GY0F7d0wxTBl7Es0U438zO9SzyuQv8QoxG2ZzZgc9bmfu02BwcSM8Ji1qp7/5OPTiW7aJIsXqjQmFnquCqxBaQqtu/0JI/tFU+LzfYQk1gooTSh11x5wvA5YIeAZmPjirenZD3vJF7bJUfNUFC72xpNm0+mHlWOUeU2425PKC6So8s+NKemUTAPl8sS4dapsznGNiTEM6lpnTmFLuzzjjDHTr1g033ngjvvrqK14yGSIcDkvM6BOZNGkSPv74Y8l3ixYtUjyjDwCBQCB21V70P0KKbIeTDmfubfG86YDnd+fnSgAUzm0KqWb5ydhhUWEVVsxTjVzBaBWyu70m32d6z+CLtt8DrRGdAEHekR1/SbiH6ARqip8V3Ry/Lidx4UXdoZ7Vljis4etRso2LrPcWCsMRpRVWD9l2jEu8QrU/LNGStHSUuqsG7yywxSzf+ihsxfRB+MrKSjzyyCOYPHkyBg4ciNtvvx2bN2/mIZsic+fOxdKlS7Fjxw6sXbsWc+fOxZIlSzBr1iwAwOzZszF37tzY8zfeeCM+/PBD3Hfffdi4cSNuv/12fPPNN7j++ustlbMjk9Fn7hV32PSd+RLb/8cLaVg27NxzvgpPd/xposgY3YW0or6bzTPeu/NxRNm/rdzd41J/TKafzaEepx33qOW9yk63rgWbZIeejPXVY2G7dcNusBZCtOfX7WIkevRN6zk+sLRtTn5EHUPpzD1L2u1On5qVlOVxWxChXFsVRdFEXO5v+0aJ963WlXx8XmqezC2J9MGUcv/SSy/hzDPPhM/ngyiK2LZtG+666y4MHToU48ePx0MPPaTqtM4olZWVmD17NgYPHoxp06ZhxYoVWLBgAU455RQAEYd++/btiz0/efJkvPTSS3jiiScwevRovP7663jrrbcwYsQI7rJ1RCRm6mkwpMfOw3MxS3ZPeuPpch+xfEro9TPJLF/9nnt3wtuhXnJCpT452ONSi5PVAZZSfE5ZhRhzqOdUvdbfn+tJm9lUscRl/a0TctZJSk873z+pWtbrtDIzhrk8sK48OZ1Jl5GPLWS1cUNfot06zmgh6nQYav5Yi0dXOG7didcrlpvmqlHsWot1axlaiSmHehdffDEuvvhiVFVV4bXXXsOLL76IL774AqIoYuXKlVi1ahV+85vf4MQTT8Rll12G888/H3l5eaaFjnrlV2LJkiUp382cORMzZ840HTfBRjrsoFiJWvrlvOVb6cnaTTCdqXPhIGSG5GJS9SqvknY3ONST3BTBsf65pcQFnUtF9hmTKu3buSXnjLRbY6b3qrvFhuukTgVDTpkTk39Q2BWOHT/SFSUz8T0+I2OK7jeSYk2UI33nAG6cv7hpvOchCcuZe13F4J7s4Q6P+sjcHkXtPkwLt5nlu7A5Ww6X++lKSkpwzTXXxEzl77nnHowcORKiKCIUCuHjjz/GFVdcgfLyclx00UV455130NbWxiNqwkHkPMK7aQDSgtmTfbJZvkJ6nZ7M2O1QzzRmrwpy0WjeEQcPIFnNVG4nZsPmhdKBGlfCOQNiZt0qu84pZaYiA9eFDROBOdEL6IqTc+dgVb9n6dhteGdbJiiOYsaCMhIoDzmctO038aIVB8bkY5J50uwCdewPl/b7DuHElNGROJVmARlWHfhePg+gZ8+e+O1vf4s1a9Zg3bp1mDt3Lnr37g1RFNHY2IjXX38dM2bMQNeuXXlHTbgIS87cK5zRciOqzuEE6TOJ3+khuZNyenEhiupVeJZco8A/SJ7omYwnZl2qrwfnkItbjzyaeSCZaCWa87u8cNthuXvc+ERG0FGH3LPgpQfuXpgtzgemQzg2jE12T471LQCYuYUiaWyzuRuw7EabpISIOqYARpTelGzjkI+8ssaot3y1ECNojTUJf7q0u0wWi8eZezdthtiN1b6M3Ah35T6RYcOG4e6778b27dvx+eef47rrrkNBQQFEUURVVZWVURM2YM9ZPetgPk8qJCtZSit/Bq4D4tnRtAcVMfu0vkyMlHtiHqWa4KV3fUo3eGxW2T45Mqns8742jWv6U8IykVbZdOpfIGDvIxmDZgzTpWu2XBCS/mV+T5D+qxk+YwRcxnHNduXWAuXjqMz42wbNnh0eIu2K3oobmWh+EUfvIhZvxTiT+3mnsVS5j3L48GGsXbsW3333HWpra+2IkshAeK302rlyJ2sCy/idXtyyImn0Kjzd8Tg9w0lA/RywbWJoYlQWllJLDlvUsy1lME5WWdifseaMYcShHq+KYHJyq6L467LG4FiX3NRGWFAUV/bMvfP9snX3SdtTcHbXD5aFeq32bFZxSbc2oZd4Hgvxc94ij3Gdb8YZXYTjig1dicKWlXURuoxMS6kph3pqNDU14e2338aLL76IhQsXorW1FUC8QU+YMMGqqAmbUNtpdYuiyYOU9DF03izpN5tHqmb5NswM3KJcp1tdU8s1qWogKv/oEJJjAy6Qhw/8zIfNYPWOEnN7lazJ8JcpLoeKcbuL6pbaYqWh8AT91+BJ5VFHd9AJ47icXGxpTa8+OAU3VTgFtC02ZOZhnIrFqeyxpP9xw0DKAevmX8rhytUnU31ZZhSFK+Gq3IfDYSxatAgvvvgi3nrrLdTX1wOIK4EDBw7ErFmzMGvWLPTv359n1IRLSIeOk7f5t4DYzcWyv7GGYRa3+h8AlCYeSWb5an4KXI767QjuQXJtJYd8lvoISP5NkH1OnkSNMsExJGcS0+/i5iIl6aoodbHdVNuMo1Y2yb/J1WO9VVv383oeTpuKJiW2BGNYfvX33JAtrFdr8iJ1jmC3MwF7o2MlepCQ6TYdxTSIkn/YYnUelnZgxz33HYV00FF4wEW5//LLL/HSSy/h1VdfxYEDBwDEB4Ty8nJcfPHFmDVrFo4++mge0REuIdbhyLQVKxRNWbN8UbS9v2PqHFSSL3fnuyE5VGekznVg6aicuwWJQpxUhk4OSnIx21PMRs3k7c0rzV01QTCQX/JOBq1A6Ty36rWNyccw9MqYJt2E4csRuXvJl/tO7oiXPhLHcdbz/Jbj9JlyhsZquzNDA5mSUnfdoc8yY7VVHk1V4sTzImnFXsMglPU7M/CsBUp1ys2bY0Ywpdz/6U9/wn/+8x/88MMPAOKZk5+fjxkzZmDWrFmYNm0aPB5bjvYTLiAdFDu1RYl0RnIVng3o8gJvwdENN63ApsuwoLbTzvS+LQsOarnJz6GebFel02Ef12u5WMMSjUVqrcd9+9qiW4cYzR3F6HOmV3Vte0kb5rooY9XmgnIUZP4yGZAuVH216I3Qwvy0ppdP3I2OxCCKorb/Es2QNXLODRUvARZxeCxy8HAmy2qWz6onZ5g+7SpMKfd33303BEGAKIrw+/047bTTMGvWLJxzzjkIBoO8ZCQIAPIra3act9bylh9tAyzvJn6XKDuPAcctK49GFT6jXpvdkm5F3DWXMIQRh3qS3yyKUzE+PRFyuG5Pq85aVwWsrVzWLN7EDL4NvW1Fc1e3gWKbFYsikiqexgEKi5UM9uDV+12rztzrK0dr8kpJBt1jipyCo9MBsLb1jz6RAAuVpwwY01hx22KAvbh8bkUoYtos/9hjj8WsWbMwc+ZMlJSU8JCJSDMk53jToNe36go/tWverCBV/oSd+w7kUM8NqE2iUheD1J5V+Y1DdicGYegqPMnOP8/yT89JBM8cUDGAZw9EZSc12l7VdnCEhF00Lax0Juh2J5lO9nyaXto5x2fVmXs3ELOiMNAZ8qgDdp/55wYHsVk2a+QdPJqPOxpbuiC5WcBROdie4307hx0bOOlTG9gwpdzv2LEDvXr14iULkUFYMTnjdRWeXuR26tV+Zw1PFMX4hJtDp+3mHWy5hQDTZvlpsriQJmKqopgERmXfSFlFjvtZf26ZS7gMtqTmztw7gxVtjPcRHSealy5pLeyXeRRPVDyh/X9WxxeLV20XW+ZpO7FrbOGZKivPPkudo7rF4iQZ/bnpBm/87pm2CUn/ZibpsAHJA1OH4S+//HJMmzYN//rXv3jJQ6QRcl7n00Lhii3WWycr62TVlEKeJH5sbVeU+dECLLN80BmsO3b5+MiQWCWT0+WGliUofjCLvPO41CbKls/p0A0ZhyVx7s0AOyRzrvwTG7DCUS2jPVZ70KxO75j7Z5U+JxKfwczUcUTBeWxS5jlpcuw7nlyikxGAd2BKR0KMhqfyqxBVe9xbJ5WdlfKtp1bfKuJG3DFftB5Tyv1nn32GJUuWoE+fPpzEITIFS3aR5YJ0UTtlvQrPsjP3bsoMBtxsacATN42Hat74DYXH+blkuFcRTUGcdKiXFBhT2EYsItQsLHSEI0j/Zq1PcjmcbrspctKyOtRzArVyVbqClK0uRB/SPzi7ocw9JvxtuHcHOz1gscSU7/8VMoaD7xQncF05i4ofuI/HrpoCukkWDphS7jt37gwAKCoq4iELkWbITQrcMGCzwnwHvZZDPRNp5quQJ4Tl4IiRFtYbLsWtbcmsQz3tl3V9zRCc9pvxJ3g41NP6XdvkmT10tXD4nAS2h/SdTckeM9LpUM8qTDmi5OpHQelDBMNXDHKEm0M9HWEbxZBDPYPvpcRtPghlkvLJ6nFPb+hunc7wlsut6eSBm+ZSdmFKuR89ejQAYPPmzVyEITIHu87cOxlOSriJDvUYRnbeV8EZtGp3DYa95btASVB1qJeyOGQMHosmiSEYCk6Q/dMy+J+511Lpde7cW6UM6fqRNfx07RmkpBxXkT1rrC+tqk4u5cLXE3hS52D2ZCuztQzrgzLH63Qj+6rz/bIWMbFN9q2sKU3tz/jlUbq1blmHegzzJm5KqEsyzFW71wYwdbLUpjKQWMm6peAtxpRyf/XVV0MURTz++OO85CHSCKu8zhuRwUo0d+pVJ4bqeWPZzr2DqB1FINITpdKTWhpISZyoaRe//Jl7o9hd3ViusTLuUM+I6b26LKzv6enbWfsyljDTfcILgHsi3DAptUsGp8cLlvj5S+h8+bJgTbrlQzXWgtglpGmJHDw2EdIvY92wScQTU8r9+eefj8suuwyffvoprrzyStTX1/OSi0hz3HyeWs4RIPc4VD0BCxI5jJK6cx9Nlz1wn4DpzA43DSBqovPzWMw3DGMb9wnKvC3XLSZ/47KdLo5ZYHV2Wlle7mmJzhHJXhal0NpbQuR+VXsjtoRk1pRHIVy5T+7DXA22ov6rbSowx2ezAz89KM2RrPaWH89Ht9fJOLG5ophYB3RaJ2l8xxKarL8U00durEdqsecu2azC1FV4zz//PKZNm4bvvvsOzz33HN5++22cffbZGDVqFIqLi+H1elXfnz17tpnoCYeJKZM2XZPilqvwlHpBVod6knc4LjS4udPSc/2Vm5R2Qh31HWLjE5E4nHc/09R8WArDzreZNIlW9+fWT651n6u1tM9xpn4ZKTrjRxD0p9EN6/+87plnDiXpQZ6bIHZaOzg5Qrt9dpBZHugZjpeaGWpc0AdkKqaU+8svv1zSoRw+fBj//ve/md4VBIGU+wzGSkVTgOHLhAAoewc2I09KHA70WpI4nXSoZ/Pw6wYrEdUz98mfDZYNj1w1Gre8h/OUwA2FrQvGKIxLYrQuae+m2rM3qPxctF2qhqRDSK7KRFJQLmjSqsjuVulwqGdl+gyo2fG/dL/MYhNgHqu6FiUJ7Tv7zZ5Hhm8k1BULG9yrb3RjOtmfBu94oL/fcoPu7eaNGzn4X5PMNTjH4rATU8o9kNoJumGiTdiDkZ1qU/ElWApE/7alvqVs3Os505X6LOt3esO2ewDQlQ8MCyDpNoCx4tqVeQ47e/zSZk/Z8y4KnuGltpFUU0y7sbI/l4RsyimTWxtYOykO9cwex7IHN1lQWbVuHSsLh+qQ6+tuO9LjWPbFK28Gnh55xopbVKZ4viad3bNQPieK0k39mpWYUu5/+OEHXnIQBDNmG6eVHridRJouBxUCkz026/tuGuQzfVGTJadTVFNdDvUSA2pfuJP9kfFcpc1Vg82hHiehErJATFL8VX0/uKSfsgNXdA28HeopWQskPxf7l9WgPn40TDPfdFl2SGPRg9PFZ9XYknKPu8Gde9kNAtn42p/l7tzRPPHxIW7XZG4cZVsMTTxz73Q9UyLFn5IJ56pEx8SUct+7d29echDpSHTR26YOR847vyu85UdlYRyYYg71ICaYyhrYuVd4h9c5QqPxs5KpO/WpcDr+wbmZGatzCX9nzDwjcVtQ35tWmqZbtWWiJnPkyJMg+az8LFuYcnFE/jCe7ywyWYn8jSBsEggpf+iM25DFjdU5k559uWBCabJ83pMx/StfKFv4od0vZJ5lttK8MwOSJsGUt3yCSIaXJ3iNSExhh7f8KJoO9XhOilxy5l4OlsllOiv76Ss5G0rpU1X+zFpwmHpbIUyXtQtjqKTBQRN+N2BXO9RXjayTikefKecYl/1l4/Ut0ybTyeidA7m5a7LLW36KZYMFdUT3mXur13AYwucxn9a0LjMdg34yvQ9wElLuCcPwdkzHHJ/L1m7l0m/HVXhKcdq2e6VQ7mbLh/V9t9UDJVLOqRsPyaQkSaGZDC7FosVUeIqurQyGx7CDKvuIsfi0rZnN1FZONd1IIJY0sbhZrBFYuk3d10SpPM4aUqpDPWvQXjBuf87W7lGuUNzvUI9XBKxDeeq8QIdZvka5sx7bcDtmHAd2JPTmE3td0BewmQVGu/ooNy+cWYVph3pRtmzZgueffx7Lly9HRUUFGhsbsWDBAgwYMCD2zLp167Br1y7k5ubihBNO4BU14SKiCp/V3vKjmFGQmc92pygxCmb5hq4D4rdg4WaTKStvFHDDjr+Ls54ryXWfddB0y05nohiSI58cwjOLpRb+rL/pkKEjTpgMoeBQj0f28VzgZAmLJuMJaO2EpkUi9MMrVdJz5OzzRu1s1XFEyAVFxDJ3iOaLe70EJOFiMRWP1bpgHskT08p9OBzGb3/7Wzz00EMIh8MSE6+WlhbJs7t27cJZZ50Fn8+HH374Ad27dzcbPeEgtu+kxxzbuqvn0J3+9scTz9zzQKqrOJhHBqN28+KEGdxVW43BkgZTdVlu1ynh/5O/1QzO5kzX6pMiDvX0Buq84092KxoipXxV+zP9U0m5PJa/sYbtXXvJzL49ivyuOUua1Sz8lONgtiZJH3XQPIz9ZaJDvY6O1EmjwTCUB29uZJribQemzfKvueYaPPDAAwiFQujWrRt+8pOfKD57xhlnoG/fvgiFQnj99dfNRk24mExqjCm7lQwrf6yKauwdAx1rOl2FJ4eSvJlmls8L/g71zMngsjU2JuTld4dZoY79Ji6hWBuGM0Ql11suPPe/U+F7FR6rsGbTFM3DdGznrJhxqJcIs1l+yvWv/LBzPOSxwRLLi8RMEI0dP5CEa04sd6JyKwYrzEUmKn7IiLzNJN1EDVPK/ccff4ynn34aAPD73/8eO3bswKuvvqr6zsyZMyGKIj755BMzURMuwE7HdInwUip5ya3qfVr2yhprji7EysOms59uwQ07/qo+FjKgLJQd6rGh3daUdiDtLlvn61IqfPspfab3Kn2bhSqx0TZtn0M9uWNGKd+oh8FBDh5dn7nxMLoCoHHmPs26QJb6Z7eSmWZZ6FrkrnPXeMMiSdpDT/OC7SjKcrphyiz/iSeeABDZkf/LX/7C9M6ECRMAAOvXrzcTNeFSYmfuLVC45Bz4OXIVngWe3600mbUMheiNypWpg4SaeaWZcMxiZNFBUPg7Ep4pcRLCNX/rg763zO/ca8UnwK5FHnNx2CNh+pvFKuaTHQ71OuLOZQKWjHsOaVi2tDcOkVibPQLiS7nGHQ5Kw0sf9Jy5t/I2lHgZJ8aRJBxrcXAWk6c6oXyVNb843IAp5X758uUQBAFXXXUV8zs9evQAAFRUVJiJmnABZkzKncTU1T8M4SYifx9ywgIIVxES43dXoejKa/btYPeQYQNDMsrKDOP73MrKXEY7viCmQmobYUmre9PjJK7KFU6zRuuu8GIfD43nq/WL3cZQOBpmkTabctVbwmdexyisxupFSu0r25QeYD1zHzVYFuGyniJGuu/mE85jyiy/srISANCnTx/md/x+PwCgra3NTNSES7FyUHbqGIBL+/8U4vnjLG5WoJygowzUViTTqDWH3UchNKPjKo9Ra4aoWb7W1m/8d7W2bNj/goVl42RTEwRI06ah1ButoyxWIqlxGYqKUMHqLFW7mYS1PEWGM+xOIfGWn+GL42axyqpRd81wZ1UiZDCl3Ofm5gIADhw4wPzOjz/+CAAoKSkxEzXhAtTO6ll6FV6iWb6BXRGrvPxLHOoxxmHGikDVoZ4L7lvWejajrsKzIQ7eCquhE7ZqdvmmUHKumPyFPc7I2IPjF2DKMQduIeuQgbRAJsxc7WjaoR5nEvth/eWv9ryo8Ld7iE+Aqd6rYVe/wDQnoKJyDt5e8B3oFpTmi+7soYxjSrnv168fAGDDhg3M73zwwQcAgOHDh5uJmnAplu7c2331XjspZ+41PusNjxduUHIB8xMBt+40GCU1PemXPmWHesppkSgNeiJLUODtGfxdroQkn7NUFVFlp911fj34O82L/aZTbB4m73oc6tlTr/WXnfEF19T3tEJygyNUJfTKpnfsFQ2dnTZQnsZeswVpHnMUUiNvdd+k4UBfkkw8rxKsqixbqE40EXFvGyXUMaXcT58+HaIo4tFHH0U4HNZ8fsOGDXj22WchCALOOOMMM1ETbiB65F52pmOXCAZ27q2aVJjQEdw38bY+frOLEU6nPxE75qm8U2tscqBssm3F5o6Vzgd51B/W68KY5El5yN0TK/ep/dZgul4nJcbszj13XzHRcBlK1K4d3ORorNwykI2QgbSxcuHcmKxMtgjR0LGT6Nt6cUMRuniNi4l0k99N80YrMaXc/+pXv0Jubi62bduGa6+9VvUc/aJFizB9+nQ0NTWhpKQEP//5z81ETbgUKwc81+zc60ij6lV4nHtF6Zl7d3VgLGVm/Pord48ubphAmEUpCWppS6z7RvsF3mUrORLt8noTRemSQL0wOUwTpOGzlps+f5nRFRH2d9IBq9u5XPCsXSarbDz9AGgqaS7uGHXvvsoeO1N/o/0pzSfiMqnHl744NZ9Lj/4fsHCs0qjnRkuGd4naUVLptkihhSlv+eXl5Xj88ccxe/ZsPP3001iwYAHOPPPM2O8PPfQQRFHEF198gY0bN0IURXg8Hjz77LPIy8szLTzhLE6duQenCTqvyYXsnceMcjnmJJADSvlnNi1unvQpkS6KYiK865y5crNOibcDrby0TZ70q4a6UcvKuGGpk32IAEC0bLbImjJe3vJVg1E1gVZPv5vN8vWid7xPeJEbikdFODQFK1qTNM8Eme8yT+Fihcn1AIcFqKQHDOGg4S6hginlHgBmzZoFv9+Pa665Brt378b8+fNjle6pp54CEO/Q8vLy8Nxzz0kWAIjMIh285VuliMmFq3oVHkRTaUl+126HenowswCiJ8xMxg3JNeohXZ/oamf1zR7lSPxbRioHzxgq9wUuKHgZjNfH1J2zFOegKsXghomjvn472aFe+7/uLFaDyJSKZKfZDaWWihm53HFMyhmslFVrXO/YPn2clV1uzmZmAcaJTZF03Igxgimz/CgXXnghtm7dijvuuAPjxo2D1+uFKIqx/4YPH465c+di69atmDFjBo8oCZfjZm/5ZuIDZMz0uZzdNR9Gul+FZ7TOuH0HKOX8qNMFZABlh3ruRFcddKvTICa5Uox3zUVp6m3nsaskzTrUsxK9sZo57mYmhS5tdTZh4Iy/bTFpw2PIlRu3+YzlnC3SuIaWDiSm2Fh5cDfLt2WOl1k9kumd+yilpaX405/+hD/96U8Ih8OoqqpCKBRCSUlJ7G57IrNwyqScV3yWXoUX7YxUojC9AKIqvvVlYtqCQqHDTseVdbWxx6311UhwgsLf1sHbXN+iowiaFo+s592ZYuXyCMvL6dgWAYcX0ARB2iEkdQ7mHeqZel09bJO/S7FmV5xv+o071DMWmxiPVkiInwNWtlXpeX+XwrhIK/EFY5UsnLFlE8vyGOTitD/WdB3T9MJNuU/E4/GgU6dOVgRNpAlWdkZOLya4tXOI78C4DyvyzK3lkIkoGoxzcrgmuHX3PIPg317ctwjgtE4vQWUMNHIUWv5sq7K7Rc0jKNEwoov0gpFlBzPHytIbrT5NffFe/2Kf1EGp5uuWwiP++EKHAGlfYhbWEJz1ieEG5GW1LwUdxUTeCSxR7omOQUyZtHmkMRsfb4sDvQ4FJd7yOWadZEHFydGfMepU5znp29Gno+Tc78m1oMqltiPW3Rn+ssjGk/Sv4nOcdtKtRk2hIOJonApO+iy/c2+0z7BrIUVX0Wstzsn87oY+0+k9XNHiRU0RouMLAbzJsOTIkjI3kiyEOEcaT9E0ybS0mTpz39raig0bNmDDhg1obm5O+b2pqQm//vWv0bNnT2RnZ2PYsGF4+OGHzURJuJxEZ3G8cWqVL2UypdC/yimnlprKJYUd37l3YS+l5wh0hg3fvCZXbpikGb3eTp+SqOZQzxzy4SXG58K2owvlHHOLom7HVVQ8dsX5vuBm1NubW+qNm5FTDOS/c48DP31xWxe7KPnbem/5mVKfeXdZ8vli8eKTA8NtR7EWMKXcv/nmmxg5ciROOOEE2d9nzJiBBx98EHv27EFzczM2btyIm266Cddff72ZaDFv3jyMHz8e+fn56Ny5M8477zxs2rRJ9Z1nn30WgiBI/gsGg6bk6Oi4oZEYGSy5WxzoDEbOIWAmKbS27Sy5KM/01EO3SM1DDrvvXuYaA8fAND08GwrVKTeGbOb2Vs6R1cYWuz3py+YBU3tXf8Zo9ll9Z30y6WxRxYyBvDLX31lwTM0u3cxi5dh48BaZ2TuwGJByY2LsC+OydIRmTMQxpdwvWLAAoijivPPOQyAQkPz23nvvYcGCBQCAHj16YMaMGejevTtEUcRjjz2GZcuWGY73008/xZw5c/Dll19i0aJFaG1txfTp01FfX6/6XkFBAfbt2xf7b+fOnYZlIORJh6vwdJOyca+0/yem/G3nzn3yr1aja+dWdoKc/DEzR58M2SgAkHSOl2u6RIW/+SIns6kpOtcFAo5h2f2mrtcyqEHIkVyQKQ71DAZrJt9UXo2rDfrPgVuGTcdDrLTU0Styim+fDG8mEoVVFJK+M4PWQmvkd0EQM2YXnzdm563sC5CmojGE8vw9szB15n7VqlUQBEF25/6ZZ54BAAwaNAhff/018vPzUV1djcmTJ2Pjxo146qmnMHnyZEPxfvjhh5LPzz77LDp37oyVK1diypQpiu8JgoAuXboYipNIxTFv+Yk73waapNNKpFx+8bwKz2nMDwz63ndDup2XwBlsH5t5nlFNl0JLOWdpLNeNtEs3Tn7VRIr5QNApt+68YQnfhn7Jjq5PPS/VflMXzgXdtqXImuXD+A6skabIK4td2A3IkDkVKtXTjEzaOHRZVt2CwNq2mZ8zLkqHxZRyX1lZCQAYMGCA5PtwOIyPP/4YgiDghhtuQH5+PgCgsLAQ119/PebMmYPly5ebiVpCdXU1AKCkpET1ubq6OvTu3RvhcBhjx47FX//6VwwfPlz22ebmZokfgZqaGm7yZjLRSdLHuz7GD9U/cA17f8N+SRx2kbqiLsj+LncVntrESITINS2SAcCG0VhJ9lW7DqPqwI/48XAjBnfJR01jK6obW1Oee2r1a3hxzacAgIONlWgNR5558rPt6FfcjNLcAFpCYTQ0t8HjEXCwrhndi7IR8HkhCMDWmioAQHVjK55fvgPZfi9aQyJysrz4fl8NcgM+9CzJhigCOVk+NLS04XBDKzwCUJjth9cjoKElhLK8ANrCYVTWNqO2qQ19SnPh9QAHapuRneVDYXbkKs8Ne2vQsyQbfq8HLW1hVNQ0oUtBEAfrmrFs2yHd+aQX/g7wzE0wU1ROU/Il1t3Eb40N6yyyHG7ZB0+gAvC0yMvBEk90F4iDPHzgEBGj0yYOy5GKYama3rtgpheV1xPcDY8vMjeo8+bik13NQHYQEEPAvuVAdQmKj+zFUQnvRHeLeS+ceAIVEPxV2FpXg092/YD9NU3w5a0HBBFrqprhy9/S/mR7/L46iG15qIyOq4LA1yRa0PjdLlqbgHBb5O/mWqByA473fAcA6Cv+2P6QPcp2KjyvwlP6XgCEFvgL1uD1rfvhL9kc+211ZXcc1fko1XA3H96MNza+i6yy7QCATw98g3DRIWTlhgEAD6zcnPJOVtk22bASn/2+6vuU36uaqrCvbh8E32FAENEQDmBXzS6IEHGk+Qg2VW3Cxv2H4S/eAQhhCEIYQBjhlk7w+CN6gFaW8l+wDMOb8wMETxMAYFNNK05uOxXZvmwAwPqD67F492Kc2OtEDC+V1zfchwC5jLSmFZs/cqAFS5lX1jZZFr8TmFLuDx48CADIzs6WfL969WrU1NRAEASceeaZkt9GjBgBANi9e7eZqGOEw2HcdNNNOPbYY2NhyzF48GA888wzGDVqFKqrq/H3v/8dkydPxvr169GjR4+U5+fNm4c77riDi4zpxKHGQ1hVuQoLdyxEWAyrPlvbUgtA2nACvsjxjP9u+a9lMga88SMghnbuGRRvNa58dgUaquPKXN6gVghe4OT7PkW4JTJgBTr/gKxS4NHFW/HAq+9J3vdm70BOH+CHA5FjJJ4AcOHjXyLUWCF5zueJyNcWlk9jds+D8OXFP++rPQx4I5ubA/7wPto4XoYhJ4snUIHcfqnP/nPJNoTqpMd0cnrXw5sjfW7FwY9k43rmsx8hhg5ryuTN2Yac3sD+2ib839vrNZ93ilQP5M7IkUy0TBPplBfAwbpU56hKGE1LwMd2Iix1ASHyTfei7NSHE/B75cOPKuOvbnoVj2y9K6X+egIHIIpehJu7AaIPXQuD+OFgPYAQPFmHEG4pk0gVbu9LPFpn7jV+r26uxt1f/hXZPbfEvvMG98SkDoVFCF75FLHG5yqz6yScUv16FEfqkWa6hTb4i76E4KsDAFRlBZHdazN8udtjj1QAuHExgM7tmwxfxecPD2dnY2pjY+yzmrWR4K2DN3cLPIH9AMT2ayLD+Mfq7+D3CWgLhxAo3wEIIp7f/CVydnqxs2Yncvt9AwB4uyLyHwBk94z8+/RmIDt1mgMAWNc+lHkFr+zCSXZWvOIp1XNPcC/WH1rf/ndEYc7NbsCBFulzgq8Ggq8GgIiQPwtrD6yFCBFDeh/Gxl25gJjVLos0nkMJfZJWWwMAtDUD2xYDXz0ObF8c+3p9VhYOej24I7cN/VrbEgST9hc5WbKNTRc9S3KUfzRQ4c0sEvuLvkawy7t4bC0QLI9///OFH+Oziz9D0Kfsf+ruL+/GqspVCLTfbv1VFYBCIDrCP7Pu05R3Ago3Ycs9i3B8rnDzkpsBAHkDI5+XNAJL3kx9JahmgOtpU/ypvCCg+JsSWrnuL/oGwa7x+e5z24Fa77e4Y3Kk/V//yfU42HgQ/93yX3xy4Scp74toBQRpQwmJzWhsS+gvZBRgv0ffqerSvCzV3+XmAzl9H0bk9HYk/oe3+PH0zkjbyB0QkW9xoxfTXvVL5DwcaIW/+Di0Hj4OFfUVuOGTG3CoMT5nzh3QBI+/FhsA5A3xItxchk41t2LXodRNIB6wWHeu25NZG7imZv+BQABtbW0xJT/K0qVLAUTO2vfu3VvyW3QXPxQKmYk6xpw5c7Bu3Tp8/vnnqs9NmjQJkyZNin2ePHkyhg4divnz5+Ouu+5KeX7u3Lm45ZZbYp9ramrQs2dPLjK7CVEU8diax/D9oe8hQsSnP8p0vhoUBYpif99w1A146fvXIAhibGdahAhRBNpCIgI+D8LtHcDB2hZ89UO8wRcE/ahpakWW14OWUGRhITfgQ26WD7VNrSjJzcKh+lb8sGck/N3/BQAYd9cilARLIAKY0KcEeUEfvth6EKN6FCIv4MeGfTVobg1hZ1UDQmERvUtz4M/dCuQCW/bX4Vf/+Rb1zW34eGMlOuVloTQ3gOHdC7B+Tw3K8gPYfqAO2Z3KAL8ACCLCbXlorOuclIlGz6mKqu8qKfWxaMNJA7I3MkDkiZGc54msLDrSLYp+yedQY3eEGiPtyV+4GoI3smraXHkaxFCucUFdiEcQ8Isp/fDE0u3aD9tI0O/F32eOxt8XbEJFTST/rz+xPz7eWInqxlZM6l+K+Z9GZO5erK5MA/FpR3lBAPtrmjGmZ5His2eO6or3vtuHVfvXwF+4GlkliT5YwvAXfYmsQD221x+QvFfW6xO0dPkcuTlZOOvN+2PfDyoeBOBEAB70KM5GeYH8ZNXnFRAWw7jry0ifL4YCEEU/PO1KW3SS1lY3CI27r8Qd5wzHKQ8sRbD7S/AXrEfTvhloPXJMJCyPgIHlkdW1o3rF03r9iQPw6je7UVkbUUhGdC/AyUMjM+pfTu2Px5bEd7Wicn7646f4YMf7ksW6KGJbHsKtRfB6pYt/Z4/uhrUN2Ui0Gbny2L6obwL2ZPuxU90FjYQshcUWAQIev2wcbn19DR6+ZIzkt5ys+PQhxy9Vhib2K8GX26tin2+cNlASZvsfMfqUqihCyTIlvLe6cjVe3vQy/IVBtFaPR6f8yOS9OMePXiU52FXVIBvGAxeNxsZ9tTh7dDcAwCnDyvH05xFLs8sn90l53pe3EcEu78Y+HwbgS5grhxp6oVdpTmQCXbEeaI3EuyO3CDWhRuz3pSqLVfUtKd89u+5Z5A26T1bmV+PrPshqXz/4YFfqc71yh6AoJwvhsIjvfqyBKALDuxckTF4F+HJ2xJ4f23ksioPFOLHniXgPcWu7aDmdNqIL1u2pxsDyfATby/nen4zCra9/F3vW46vDxe9eDADI7Rv5bkeyYu8/iNz+97fvtgJNAC59v/3HHKDH8J7oXv8ndC0MStoTIFWUvQJQXbUNbaFmiKKIsCjizAE1+GrHYUzoU4Qf962C+PUTCK99DWEBEP0+hCFgVTCAuzrFrTtv2A8UikDP0kKg3wRgzzJcf0YIC76rw19nnJiasUkk1kOfN/7hxauPwcffV+KKY/ukvKPHLL88X9qHaa1pyC16HDugE174cic8/iMAgD4F/bBldwEAEf7CNWgKNaE51Kyo3IfFMFZVrgIAjCo8CaG2XIzqUYg1u6uxevdhDOmSj+MGlqW899GG/dhxKN4BdSvKxohuheiV1M7X7WrFpzWDkZdXCyH7U4gQ4YEHzW1hAAJ8Hg+Cfh888ABCZAFqaPFILN9Sh9YQAHjgK1gDIUGhD9X3Q2G2H789bTBe/HIXNuyrwQMXjcYnGw9g1jG98MWhNRGZCoPopbYAo8ENJw3Aw59sheCP9MDh1nxAzIIn6xD21++PPXewMaIfHWg8kBLG/7b9D7Vd/oT8cqk+dPnilEcBAB4BOPuoSJ917lHd8PnWgxjQOQ+9SnLw4EebERaBSf3lV1Z+NqkP1u2pwdhexXjgo4gFReIi2tjexZgxpjtaQ2HszO6NHTU/wOOXKry1bZH/AMDTPqVrFoHKRkgRIoserYePw9cVX2Nj1UZpOhKmg4IQgjdYgdtP7owrn9wT+35QeR4274+My3KLYc2hyPga8AYgiiL21O2JWX/Gws6K5HmrpwFCVqQ+1oYC+KE6G786tQAPL96Gk4d0xkcbK1GeH0BYDMMjmHJF5xpMKfe9e/fGhg0b8NVXX2HatGmx7//3v/9BEATZ8+9VVZEBv6wstUPQy/XXX493330XS5culd19V8Pv92PMmDHYunWr7O+BQCDFSWAmsq9+Hx5b81jK94OLB6NvYV+M6TxG5q04giBgUtf4osl9/2vFsm3HG5KlKenf6N/RyWviXq6/e/zvQ+2TpA/Xxye/+6rlTWx2HmqAt7EeOblAKCzinTV7Y78drGvBwboWbNofsUiI/ovqsYAwAhDCQNgPgGFVX0j5Q5P8oA8zx/XEM19EJljLbjsJu6oacPETX8o+31x5GsLNnSH4aiGGclGAevxKWIBT6uUntGb46vfT8OPhBlzwWPw4zfGDOmGV3BqdmNo5NldOR7i4CALCCLcWo+XgNET9eXpzt8Pbrty3HhnHXXa7SVycAiKTst+fMVRRudfahY6Fk1SXvJ7Iji4rH92S2h//ZFwP5AV8uPaFlQCALoXZ+PdVx8R+P2lwZ+ytbsTQrgUpMggCUJwT13D210QG28cuG4c3V+3BdSf2l8Q1dXAZvth6EGeP7orO+UG8/svJOOk/9+JAi7QP9vhrEOz6FgDghyQFtaYl0gvsS6riO2t24g/nXohte3Nwx7lS08d3rj8WD320BVcd1xd+rwdrD6yN/da090K01Q3HlCmv4dsDK+MyZB3E1MFlGFgeWYz2F0R2Jf1FK7D2138GANQ3t6E0LzJG5AV8+M/PJ+LDdftw9fF98ZtTB0OO3502BL87bQg+23IAzy/fib+cF7E2i1pB9cgZgn7+6WhobcNnWw5BbMtDqKE/mvZcihvPacZD70Xq1aXH9MJfZ4zEzYufx0cJCt7c04fC7/XjZx/IHbCUFQn/d9YwFLWXY5aMxcNpI7pg+rByeJJ2do7pV4LfnjYYHkHA0X1KgLiuh2evmIAL5y9H18Ig/jlrHLwyu0KJXDC2B77ZcRjLt0d6+3G9i1PlGN4FCzdU4OLx8UX2R759BF9VfIVgN+Dvp8+OtSVBEPDur47DqNsXpoSTk+XFjDE9gIShbWK/Umz76xkQgJR0CgKAdpPbcEsR2mqHY8qgMizdfACi6Efr4Yl485ozMbpHYcRiorUReGAE0HAQtxw1Couqk2/yibRZucWH7w5GMtEDD3L9uTh3wAwcqG1BcU4AeQE/BAjwCB54BE/EjD7h865DzchpGYvfnTIxZrlxsK4ZTa0h9CjOQZ/b4hZk+UNvAwCUBEvw3OnPJaQ19ShdQdCPO8+VWkXOPLonJvQtwZx3tmBn+3dl2WXweqRjY0V9fEz2ZB2EIIThE3zI9ZUg2++Ft60BbW3N2B9uQoNYgVeumQQ5JvcvxX0zR2NI13zc99o5eLZxh/QBP4CBwGcATo8Wec9usmFFeTi2g10NrPsnsK79Yz7wY1Nf9IS8LFECPi9+MaUfdh6qx9G944sGxw7ohGMHKGxdJ6HUe796zSSM7yNtA92KsjFlUGTeLDdmJNdbADhhUBkQ3A5vdqSTOKf/WdgSmgBBCOP9utma8n1/KG46/8hpt6M4GJGpdXwYq3cfwcjuhbEFn0RuGSdi75FG1Da1ITfgRe9ShQX78UB4hgiP5wLJ19G6euHRPfC3n4xOea3ppBDOfeSLyBxt30yMGPcydjasBgC8cvlMjOk8BoIg4KKje6K+OYTCHH+kzQNYVhXJpzNHdY31S98f+h4rKlbEwi8KFuH0PqfD7/VLFpYW3jwFW/bXYdrQzli2LaK0C+3HulqPjEe4pROyu7+qkJupLN+7HCGRbaNzQNEAPH7ruSjLi8gzsDwfb805Nvb7Fcf2QWVtM/qXyawQA+hcEMRzV04AABxpbMHOQw04aUh8oyro9+KBi44CADS0vowRdz+PxEEjP+jDP2eNi435Zz38OSACJw8rx80nD4r1OWsPrsWdy+9EtHZHd83HdB6D3x/zewDAGQ99htx+/5DIV5QXxvlju+O/q/bgkUvH4PllO6FEa6gVx/3nOBRkFWDRzEWYv2Y+/rnmnynP5bVPQSoB5BVG/v6wGvjwrfjvX7ZG/q0H0NR2LnL8xhd83IQp5f7EE0/E+vXr8fDDD2PGjBkYOnQo3nnnHSxZsgQAcMYZZ6S8s25dpAft2rWr4XhFUcQNN9yAN998E0uWLEHfvn11hxEKhbB27VpZGTsSLaFIxxT0BjH3mLkQIGBfZTH2H+wEHAE2HQFW7z6C0T2LcKC2GYcbWjC4fcLbEgrj3TX7cPZRtfB7IuWqdvbYCrJ7PA+x3WwoevIdACCICPo9aGqNdpzR78MQPOxmxzHELG62o1FJBW89Aj4PWtrDDfg8+PX0QfhsywEM7VqAbkXZ6FoYxNG9i/HNzlQzdbG1FC0HT4l97id8j9mB1wEAoaSLMLL9XjS2GreWKS8IJuRlhIl9emLlVqHdbDRCW0MfhBpT22O4qRea9/UyHL88LrFvT+AXU/rh0gm9sGl/La7590rtFwD8erq8IhjWMCUL+jyob2Ev0z5KEywVjulXKvks3RwS0C9hIlHfHFnSH9urGGN7pSpn/7p8PFpDomSXOD87jAMtwFn9zkJ5TjmeXvd07LfeBb1R5h2BZVtr0HJ4Ip68fChK8lInktd/fD1qW2sxbWhn/HxS6jmRUT2K8PTl42Ofa1trY3+31Q0BANxz3L1YsOtdvL5uOXY1RxawHpsVWWiSmuhGyiTo96ZMaif1L8Wk/tL8UuL4gWU4PmHHK2qCOa7rYPzluJ/jL+9uwOKaiKLVvSgb91wwEccPLMMvRoUgCIjFPaB4AD7alXC8pb18Tut7WmzHTY1j+pbgyuPi7XVY1wJ0zg/iUJJlq5zi4Pd6cN3UASnfAxH53rn+ONnfThnaBW9/ETl7Ht196Vrsw7NXjcHHGyrxwKLt+Omk3invPXLpGFQ1tKBzwo5m4k7NiD7S9lIQ9OPFq4/B7Ge+liyCje5RJCuX0gJEYjMMt5SjufJsnHL8KFzQx4+73t2AX58/CEclWqn4s4G+xwPr3wQObQN80qFjhGcHtgPoWhhUtCz4/TG/x0VDLpL9TQ+d8tQ3KMw4I+1dmotuBYXY2b659vZ5byM/K1/yzKXvzsbaQ9/i/MnN+HZHLSoADC4ZjJfPehloaQD+2hUVXi9O6dU9NYIEBEHABSOKgN1f4e7aHwBfpKw8ohgb6Tztn4Xo3xAgBPLh8fhiu3HZvmzsqYvvDpbnlEssD3+s+xH1rfXYV7+PKQ9+f8ZQpudkUtT+r3z+T+ib6j/K6xHwfLtyxsL4PsWobq6Gp9sTEITIOFEYKMTffjIabeE2vP9v7TDqW+Orq1HFHoi0/fF9lH1ceT2C+rGEBOT6Fi2Cfi+CCf1y35Ii7GxvStGrrgHA5/WgMEd9J1YURVz70bWoaqqSfP/ot4/ixF4n4ujyo7Hw5inI9nvRsyQHg9rnvycO7ozLj+2JN6raNzzE+EI363HRlnBk/n3LuFtw0WD19h70BVV3lfODfuQH/Yq/J/Lns9XP/uf4c/DzCSdgfvuGxFmjuuL+C4+SjN3hpsj4VODpgaGl8XZQ3Vwd+3vKoDKIiGyg5fnzMKQkMt7+eqoPj++SKvdXL7wareFW5A8F5n4LIBvIbw/2AICRzyGFpsYmtIXbYpYBQW8QWd54OdQ2tSEsRuYcLW2RhfEsnwfZCWN3KCyirrnNlc5jzWBKub/hhhvwxBNPoLKyEiNGjEBxcTEOHz4MURTRo0cPXHDBBSnvLFy4EIIgYNSoUYbjnTNnDl566SW8/fbbyM/PR0VFZHW4sLAwdv5/9uzZ6N69O+bNmwcAuPPOOzFx4kQMGDAAR44cwb333oudO3fi6quvNixHJhDthLK8WTh/4PmoaWrFUc8sRFiUbpmt+THeYBPNLQHgpa9kbAMtJtxaAI+/Bt4c5bhbAXhVarjYmqqAGEEEpAsLKX8D95w/Erf9t33HsP2MmeBtiin2YjgIeCPHEBbdEr99QhAE3HnuCJzxj8+Y5dnn7YZkxdeKfmtKv354ZOkv0OytgCfrIFprRiHcmDoptx7zqy6Jnb8e5p0/EnPby/WEQWWxyd6Wyjqm9/1eAQM6y6+0J+6KA3Jn9+UL9e4ZI/CHN9fJ/maWQeX5OGFQGdrCYQzvViD5rbktdaGhqqkKn+7+NMVcDohMIKJK7fkDz09R7k/qeRIml/wMi5d9iSyfB5N6jEFuILVB+zyR71gnVG3tzrW6Bgeitt0KpzhYgitGXIH120tjyn00e5ffNg3HvcYUNDN76/bikW8fQV1rpJ5EnY9GnTAlFu0Xt50U+zs76SzwFcOvwONrHk8J/6LBF+HFpSHsDPw/XXJ5PAKGlXfDZ3siRwdKgupOao0wpGu7AiiEcfQLR0t+EyDg5uk3Y3L/E1Le83k9EsU+GbkdsGMHdMKEPiUSi4C//cT43GNIlwJ0K+qK00d2QX7Qj9NGKBz+bW5v/801gE+q5MzwfoGPQrVoFFKVHytv/jhteBeJdRsPuvrHRCzIWkpSFHsAyMmK9GEf7Ho99eLlpD5Bs/2+cTWw+QOgazngC+Dhcbdh6ohZumU++bWTY855rx9zPc4bcF7stxs+vgFLflyiO0xmkpLYmP0x/retK87qdxb3Mfqi8b1Q2VAJQQhBDPvQemQ8Tu1zqq4wmkIRi5WhJUYXMazjlyf0x7UvrMSlx/RCbULeafomaP958e7F2Fu/F6FwKKbYn973dKw/uB67andhb/1evPj9i3h106tYfulyia8nIDL+Tj2qCm+0H6MPt8mP42pEN9dy/bmu2zGee8ZQ/HJqf3z1QxWOH9hJ8eiWEv075+LZc8fjne1vp/w258QBeDxJWZebI7CQWN6/nfBbzBw0M/Z5w94aLFhfgcsn98GYuxYBiBxd+7+zh8We+e7HIzjnkS/QrTDoujIwgynlfuDAgfj3v/+NK6+8EvX19TGT+6KiIvznP/9BVpZ0clpRUYFFiyIZfNJJJ6WEx8pjj0XMyKdOnSr5/l//+hcuv/xyAMCuXbvgSXA4cfjwYfz85z9HRUUFiouLMW7cOCxbtgzDhg0DEae5NYzoJsevThqAf3ySemxhQp8STOxXIvntVycNwNtr9mLnIelOxDmeZRjm2Yn/13YRxJTRnZ3kM8sNO6+FN5js6bb9X1GACAFTBpZh6ZaDke/F+O9ZXi9a2oBQI++dZGUSx/Rwczka9/4EnqyDOHloORauaUO4uTxiWihDSa66I5Qo0VwIm8hn9fClg2b/sjysvPWXmP301/h6R1XK85dN7IUXvmRY+DHqs4Aj157QH//4eIv2gyqcN0bdDDQKq2OkIV1SJ8uCvwrZ3V6G4G0ABAHB5k7w+GpjnnpF0Y+9TTcxywzoW/jJ8nlipn3JNLWG8dHOj/DMumdiCrScR2QlehX0wt+m/A0/VP+AoC+IGQNmoDhYjI9uOQEFQZ+sYm+E6ITK50ltcEGZs9GFOWy7IXp4e9vb+N/2/6V83yVX31WtOf4clOeUx5SVKB7Bg+cunYkT3/hbTGlirXd3TL4Dn+35DN3zuqNXAf8+sjS7FAOKBmDrkdSxRYSIL/Z+gStGXKE7XBbF+I1fGrt+N0q3omw8Om2s9oMjLgC2Lop9FD3SupsTrsMh2DuRvHvGCO7KvVfIQsuhqYq/XzniSvg9fny176vUyXtyeWmV36FIfRH9QQAihEL13X4lWHborL5e1SNE7kxoDWzA7z//PbrmGrdk3VS1CTcvuRk1LTXIGxjP4wc3+xHaFFnwEluLMMg3G4WBiG1yYl+gltaYZaeKwz0rUSuG00Z0wTd/PBmluVn4lcIZdTlKApEFy121u7CrNj4/6ZLbBX+b8jfcu+JePL/h+dj3reFW1LfWpyj3ACS7/W01o+DLX98ud+JNIILiwlV05z5xt9lNFOVk4dTh+sakaN3yeTwSqwyldveLUb/AKb1PQVgMozRYGlusB4BPNx/Ap5srccn43jH/NkDkGNvZb50NIDJmKOXvsG4FGJa0CTGhr/ymngsuYeGK6dnSzJkzccIJJ+C9995DRUUFunbtinPOOUf2WrrvvvsOl156KQB5k31WWDre6NGAKA888AAeeOABw3FmOtGGF20kHgG4ZfpgWeV+Uv9S3HzKIMlvt0wfjKqGFuw8JFXm/pH1CABgmmcVLmi5HTXg4yxNbC1BW6v6rlIXf0+E6mRuZfB7ETJhop6K2mQh8lv0OrXod23VkR2r6ScdhfeXrlYNvUuhvoFVzpmeVepz0O+VmLMmngP/6cQ+bMq9BB2S6uyNS3KzZJ1YARHnOnrw5m2AN+sAlh/ciKzSyIR5aeVGHPyuACJEbK2sQ1bpHrTVD8SOmq0I+XLgCURMPevFVngCBwGIEDwCNlVFzuQ2tjXicNNhCL4jENuKJGaHUXx5GyTWKv6sVCc9u5o/h997HNrCIgZ1zo/5jmCZ1JrZPRIE4IXvX8Dag2tTfhtcPBg98uN+UbYd2YYdNTvi77aX++l9T095V8myIR6vPqGjSoZXiA9/0SCmDS3H2weA3ECq2X3ys2ZoDUVkOKbLMTi1b2Q3LdeXixN7aTvySia62w9IJ+2lGibZgHwTKsspw/kDz9ctx2+O/g3mfDwHlw29TPU5n8eHN855Aw2t0oXghTsX4s/L/qw7Xi3MllfkfZ2dTXKk5cOB5nVAU7X88+3EFmIsMLUqzQtgTK8ifLvriOIzemPVEnNyt8mY3G0ypr02DZUNle1xJFmVRf/Qmte1tnvtKu4L1Gw3IK17GNZpMNZXrY59fnDVg2gsaUVe142AKODBlVtw07ibmML6Yu8X2F0bmeckdGmoThjq+hYMxf0npp5dVyMUDuH25bcDgKxi6waix0703CRw0ZCLUJpdKjlyAADju0SObkWcs0ppapP34RS1PDutz2l47Xt5Bd3n8cXGnFA4JPFLER0HsjzuVO7NENXTtPQ1r+CNmesnc/7oUpw/OvU3I4shn/32RGzYV4Ppw8ol39t9tbZdcNkK6dy5M664Qnulffr06Zg+fTqPKAlOGL1HWi8DPXtwjncZXgidov2wDOne/KLestVRTmWnvCwcrJNXTGNvC9aWJct8c3B5PvKCPngFAf3LtBdyepfmIFU9tYbcgBdVCh7E9eSc4D+EnJ6Rlf3FB4BAu0+ajyoi/0UJdAYCWIibPn80En/7cfDVIiRXsP3kfw9Jws/pm4P6LX9AeX58QrVszzIs2L4stjPQP38EttXGTe9ba0ZCDGUjq/hr5GR58M0fTkFrOIz7Fm6OO4a0iCFd8rGxohZTB5fhzf2RSdB1R12HEaURJ1xdc7tiQLH0bPY/Vv0DT659kqscrLttceU+dUc+OllktZYxSrTfHVg8UGJGGKVYR/xDS4diR80OFAeKU85kCoJg+upPVqb0mIIvLvkCBVkFms96BA/ysqSLNkFv+wKbjsZo1/iln3a/KrE6KQBnPwS8dnnCEwbKIxyOmKbXVQL1B4H+JwI9jtZ+r51nL5+A9Xurcc0X7TIk1Qm9uXntCf3x7nd7ccFYfQ6NdSOKQLvTSbFdZqP1WeIUNLkMosZ/lt3oHQn3wZPuRV1LHZ5a9xTe2/4e1hxYAwTboxeARTsXMSv30fY9rdc0/GrsryCGpTf7eeBBr4Jekr4hMe+U0rqxamPs/LQZywIzWNFlZfuycXb/sxV/P6f/ORhSMgQBbwA//eCnONJ8BP/v6/+X0l9tO7Itdv2jmmVDonI/5ZUpkryva4kc33Hrzr0a0XF/xhhjFjRRzCrXiTv3amH1LMlh9gORCfC7CJvIKNQGTqWftBppDuRXP9kEMv5qSlA2rxTkZnkZzyu5daLKTpbPg1cVPB4n8n9nDcPA8jzc9kbqLq9VeAQBVx7bN3YbgeFw2q8VEkNBHFV6PL7ZUQURAib2LUGfTpEJwLr9O9udvAgozvHD6xEk7eNA+0KNAMTOETeFmlDbUguPrwFXHt8j5lSpNdyKGxffiKZQE3zt6yXdcvtIlHuxtQBiKBK3CH2m5Gabw2OXjcOKHVWYMaY7Xn434iDtqLKjMKmbdj1wgne2vQNAXrl3C5dO6IUvth7EKQyLgndMvgPn9j8Xg4oHOX6ND4tin278ZFwPvBG5OcrwRFQEgOEzUPfqtcgTdIyDLQ3A0r8Byx8FQjKLu4v/AvxmK5DHdvtQYY4fkwd0AtqVe7UFMZZxsiw/gC/nTtOlaMefjU7INTi8E3jq5LjVQwZcVeURPBhQPABzJ8zF2M5j0RJqwQMfbUa9sA3+gu+0A0ggqtjkZ+WjX2GqQ1GjJB6juHX8rdzC1YPFpyNkEQQBg0siTm7LcspwpPkIPtmdej99ImXZ0vaXuGCS44v7lqlpSb1L3efxYUCRvGNSN/PWnGOx90ijxKkukGoFHPue4ySea1jRBb30n35L4K7cV1ZWYu3atbHz9yUlJRgxYgTKy1l2LgnbiTqRjy9Zmwgq9eVKsQidhSPtcdiNfIyWySHZORdj/xhZKOGNlbt2iUGzRnPemO4oyc1CfUub9sPKMet+2u81lw+Ctw45vSM7zuHmzji72034fHlkgWLGlKNwXvsq9sff78dVz30DAHjn1yekXE8TverH7xXwyd2RI0pNbU0Y/2LENPDX0wfHyiwUDsUcG5XjRCCci6tHX4rPKuL3bo/qUYQ1O1Pz0o761bdTLvp2iqw6RCeEes04edRPvQ71WsLKV0ZafeZWi6KcLLx49USmZ7N92Ti2+7Gyv6nuUroMpUmhGnaU09F9SnDb6UPw4BqeocrLnZKerYuAzzWOEzYeZlbutTBSQ5iO+zCErFjue1YC9RGTfpQOlF6SbQDpdZ7JRwTsbSOFgUJcOPhCAMCjb3+MIy3l8Bd8Z7lFip7y6JXfS9ZZoptQK1Mz3DvlXnz242cp5fFD9Q94c+ubACLOBi8fcTnuf1Xe6XF0wfXeKfdiUEmqyX9JoARFwSJuMttF0O9NUeydxu1jnJ1wUe5FUcT8+fPxz3/+E+vXr5d9ZtiwYbjuuutwzTXXSBzdEYTdZNqVF1EEm3f+MzQbVfHmbo79HW7hM6GWI3GSnzixeOfSe5Djz8Heur3SFxLKIvF5u3XU6NVmbjYzjE4AxhWfgWS7Ebv6BqcXDzIRKxWi6DEJ5slj1HS8/WPUZ6icPxRVWmV2+Sf/Clj2j9Tv0w29baB8BHDNUojvX2qNPAlYbZavVI90148EjFuVqKc1U+dLLPQv6o/+Rf1Tvv98z+cx5f7iIRcnWCylLlBG/+5b2JerZYXbiaZbr0NXFiTHSkSRi9Gre494GcO0ll1ZWYmJEydizpw5WL9+PURRlP1vw4YNuP7663HMMcfErq4j3IMQ65Sin7WfZQvXfQ2G/1DVPpHzNEDw1kPw1gOeVslvdiLKDMZWSiHZuWd9x8Avym/YWMeEyHV5YtiLporzknYMFF5RCS5xfmv1JEop9MR4jUjwh8//gLH/Houx/x6LivpI365XuTcz8AsyEyo1WCYbamGl0+7A1J5TY39P6THFOUEYcHO+8l6MUeqzUh3qyTw34Rcpb7kdaR+TvFseVQKUaP8luxjwyDu5NCyXi+pcoiRuWPxzgwysqNUvy+N2UR0iiCimdu6bm5tx0kkn4fvvv4coiigrK8OFF16ICRMmxMzw9+/fjxUrVuDVV19FZWUlVq5ciZNPPhkrV65EIOBOD5wdCTtXq8woYUY6ULsXnHP6PGEyBHcPEin3rLtc3mTUap/eeUyoYQAgKpuHmq17mrspKRNkAbFFJo5ysPDBDx9Izmd2yu6EHnnqDrbcsRsktwhm0869hV7RE3nwxAdjxxB8nvRwsaPLLN+1im1qWwSkfZDhorf0iJUb2qU6MQeRBtuq6jG56EJhGim2UYz5PdBOq5PjvFtLQTNPEgRPx7pkBt31hcNcya7xNJ0wNdo/8MAD2LBhAwRBwFVXXYUHH3wQubmpHrJ/+tOf4p577sHNN9+MJ598Et9//z0eeOAB3HbbbWaiJywg2g+ptRF9DvX4dGxc2yzn9t9WOwxZxV+nfC+GAgg16DHD4pNXZkz7nEZfDjiYzqidLUcRFE01dU4OnFJ4XjrjJZTllKE4WOzaq5MAtvx0r9Kon3RR6s1i5SQ6Vh/s8pGiJ6I0UB4k6UlZJI6g2ObSIH2sWHFzRUdTHuVIB98iHU3xTLkKz4a5kqGwokemMqwZmRr1X375ZQiCgFNOOQVPPql+rVFOTg7mz5+PnTt3YuHChXj55ZdJuXcBPAcb7d1Ge1E0Q+YcT3PF+WiumKEYm6Ci59jW4VsYjRFnNk6Mc6rm8czKHNtzbp1gWEE078pyytAltwvTOymWByYqRMwRm87R2cm5lhXnEA0K4h5ikyy+O/fdirKNSmScpDP3yb2PAFG2/qWkRzYvrDm7GonOTRUiiWTndxY0YMcVMFHfESMrcYMM6UfHGffdgiiyXYXX0TCl3G/duhUAcN111zG/c91112HhwoXYtm2bmagJwjDWDODOdyrxYw/WyJLiWdiKaETn89ENKE2somWg5uVZ7/yccjwByoy0RanN/OGMoWhpC2Pm0ebvYjd1FV57CIDJXaKUjtd4YKpX4RkOlTny9n+tjiizMavYkBKvH628lnOo11FIWTBME8U700rJlHIfCATQ2NiInj17Mr8TfTYry73elDsSyR0PS0PU00Td2JydXpxPxC5RrIzHmEM9FxUCTJzf1P0DfxmYw2cI3tDCV/J1mgZw5t1EixPpL2bv/2aXwF3twEmsyovi3Cz845IxpsKwbYKe0pas3blPCdmiwZHFbFo7h63fqbe6PaaToudqK44kJA71bJ7gMVsrUl/vSjK1XEx5yx8yZAgAYPfu3czvRJ+Nvkt0HMw51DPwTtq1WXMCW+01Plk612SvwOolXe03VnN7vijFqnQVnupAFPMD4P5JmePmr5DPS9sGevcXkWPoUoDSJB/FpH9N4YK2wwdjuWF2J5Dpjvc0UmyNysqaf27oq12HUpbEjFHSp/7wRm+75DHmplN7tQtTyv3ll18OURTx+OOPM7/z+OOPQxAEzJ4920zUhEXE2ggnh3q8FE6+u2VuhZdDvVTsGqA7zjxA3jyeQ5BcYQmSl2f/dJkEsky8OvLkLF2xsszMemhPhvkqPJYz92kwsVW/Ck+DNEgfD/R4sLcDN/SBRrLBrVfhZeoOsRJ23XNvPqzIvy5oclwxpdxfffXVOPXUU7FgwQJcd911aGpqUny2ubkZ119/PT788ENMnz4dv/hF8l2thBPwbHjag4G9rUcpTbYrIQYWSjhH4z44CMvzVgDmjp1xV5z9eII2iRO92Jl7jg7pnIDrQB89x6y3f3Ewy9JtQcQOjFxD5gYFRJZkh3pRp48M5umsYfMg1SzdxSQ5zuRxFZ5T/aiy7PrjN9qXkDm5cfSUnxsWajIZugpPHqYz90uXLlX87ZZbbkFVVRXmz5+Pt956CxdeeCHGjx+Pzp07QxCE2D33r732GioqKjB+/Hj8+te/xmeffYYpU6ZwSwjBB2tdsrmDTE4bYN1VeCn33GeoY0IW5NqJkfzgN+zH9wKdmkykyyRQ3YlYeqSBkMGGam+0z9O7CKGvHlrjUM8qkq0SGIzkrRRHIcaOo5CR8smfjpynybfXWHH9ozXjdGaVGZNyP3XqVKaC2b9/Px5++GHVZ7755huceuqpEAQBbW1tbFISlmGk4el61oUNxk2Le3YpE1amWW0nRPkdq6RRiVPtN87yGHHww5J3phzPKchhxCFiIkYmwlx3zGK6gnEFKiVfVYLiUVV4m3kblsNF/XNm77ok75AzXoVns0M9p2uDKIoq9YDXkQgO1hMW46Z2mQ5IytFuw0zm63/dWdcyCSPtOVOLhdlbfkdeiSL4YKYN8W20bm3N5uSy/Co8vWclXYaqQz3d3Zu1qVdyqBeLXdacNvqdQzv3aTJKqk2c0yUNRAStduIc8i0yalUlwsQZT9v8p1gdD2MGJGVUrJwtFM+KuiR3vIpLuAaPVrIeZ3LrAoiTaJVfh74Kz2A91PVOkn+KjpbHLDAp94sXL7ZaDsIlxHfy9b8r71CPD1zPptNYZR2m89bthSMdROhaND4Lv/b4/EiNlV9Y+rBDQSH4YZtDveS2ZLNDPauqo9pueeInEaJyHlswcLt1Mc8Nyko6begZsR7kFjertWIH6+yT67Bb0x/39eKwIJxhUu5POOEEq+UgHMaOhme3ib5buhK7zMHV8pdHx5V65j4ar4GwTEuTiPnE8a6ZZtOntPIfbaep7VWQfZ4FRwZdly6KuHUC0hEw4hjRDUqQLMkO9doRUx8xHDYP3KrcSpHfuefhUC/lN8fbv4H4Y+uEBnfuNSYH6VFH7EWXQz239lEuwWz9EiHG2wDV1RimvOUTmYfIMFDoaT+uPHPvtACKWHcVHmENZs+rS8Nyb81UI90mL/LHHNjfT9dyynTs2Gm070pRPfG436GebHpkHOqpy2Nfu7MiX+QWae2MXy9u6NdZZXB+UUaKG8rPaVgd6hnJK77X6rXLwS1Ed0DKfQfH3qvwjMOz63bT3NwuUax1qJfwN/M7HAQS9e5U2Adr8lgGNskzUWuJlDP3nMyFbcqkVNNc4xHHvPNytFiwyyzfbZNSJzFyFZ5d8KoPWjeZMDnUs7CRumls1MLoGKJ6RCCdMqAdw9eApV9SmbDHCtXQuVXCAiRX4VEmx2B2qKfEunXr8Nlnn2HNmjU4dOgQampqUFBQgNLSUowePRpTpkzB8OHDechKpDmmdvENDLqK3sFd2wGYkyv6tmVX4SV/TrOr8LiqDDoXFnQHb0hagzJxSop721US7tMdCYO413FVu1l+u0iWSubCxRA2GK/CS3aoZ0N63VWXrEUprR0pD6zEjYuVVpK82K7psNHI3N6CuUamlZNh5f6NN97APffcg1WrVmk+O27cONx22204//zzjUZH2Ixae1NqWPIO9dzXYNJwcT5tyPy85eskRq8jIN3nKtOkPPhYcpgPIhaUxQO9a67Cc1P37OK6ylvRERSufEvZgbK5gKyKTrJbrtLWVfPZis7MoTqndWabFGt9sNYvS+LmeNUtYT+ZWiq6zfLr6+txySWX4MILL8SqVasi1xBo/Ldy5UrMnDkTl156Kerr661IB2GQ2CTWhhqeqY3IDHY51OMUgZ6vmYJyjXLBWRCzA7kZ5VJyPZjF+Wv0eicnneCJKjuG6WiWm2nocqjnmg4kiZhDvfbdq9huVlx5My67PQ71LG8JzOmXf84KZclKBcyqump0IYDZyzv1iSko50nq4kxHW6ixZREjIYqonklI0bVz39LSgnPOOQdLliyJZebEiRNxzjnnYOzYsSgvL0deXh7q6uqwf/9+rFq1Cu+88w6+/PJLAMArr7yCyspKfPDBB/D7/fxTQ5iGRdfX51DPfbhRpgi8Oij3plATQ1ngXMcu8TdgYPFDr3Js9cDp1DzOmV0NQeavCGoTMr7+P5xtq5k0b7dyEs39KjxmD+UsaeJ4FZ5F9UHdQolVEcqgyqqEmPSvg6SVwuRg1aCr8NRx+3n4THWop0u5/93vfhe783706NGYP38+JkyYoPj89OnTcdttt2HFihW45pprsHr1aixevBi/+93vcP/995uTnOBCx3So5yazLZ6yONM9GbljNnaNnjv7ewbUBeeZLrlJlqxDOjF114BFDl6i6ulDeLZBvVeopdWk1WLclBVGrsJLN2IpEwQFD/IMgaRvp2kMhUrK4yo8no49jcpgFqMLT2mhlBroCtyqRBLWYdipZAbDbJa/detW/POf/4QgCJg2bRqWLVumqtgnMn78eCxbtgzTpk2DKIp49NFHsW3bNsNCE9ZhZSMxYzZuRJyO2s6tc6hnR4bqicOBAhbsUTzMKDh25kqmKWI0MUxf3HUVnvS5FMl4ysrpCI+VyLcrRod6sUDaF3/scKhn8VV4ciRWLTv7VaW0ZlrfbhVydVuSpx0sG5NvPdFqS0bGXGsc6nEP0lGYlfuXXnoJra2tKCkpwX/+8x9kZ2friigYDOLll19GaWkp2tra8MILL+gWluCPkQ5c7wlXt2G30q/qnDADdInEJDiTHifqmH0Jle7EC5J/E36wTR4ldO3cJz9rQnzB4KTfyVV+t5sqOoGb88IKRUde3WVxqGdlPllfBqrtTjabrfOHwrMf4oN+AazetXRzu4xit4yGFFIXjNFOYlX6RdHsVXiZWS7Myv2CBQsgCAKuuOIKdOrUyVBkpaWluOKKKyCKIhYsWGAoDMIa4hNkK+OwVwlzy6DEnqfm5NVzStNQ+ByzM766yy9MN+FkzdObpWYG3XQ0cVc/T++OPqNDo6NKuXZ3MeZQL0LC8pwDwijjqMKhu++QHtvo6MqSWZKvLEvBBU3LBSLoQ+ZonGv7KIuwo13SOK0Ns3K/Y8cOAMC0adNMRRh9Pxoe4U5UT4krOg5L/cGNTdC9c4KONQhkAkb8DaiGJ2fiJ1ENrK28ZsI3M6g7MVhLLU6SzKhVFA8ut/a5ZFHEHVKkD9wc6indLZ5SL+x1qGdHjUg55y6xYLZ5A0ChMVshh65bIAxZVFq0c+/eCVMMJ2Wkq/DkSb7n3q3pjznUc8mYzAtm5f7QoUMAgPLyclMRdu7cGQBQVVVlKhyCEzHv+OYbnuaZMhNhO3l1lun41Mzyecajkv/WWmTY846VcM8exvQZvwwr2RFU/P/tHKQMX8NkQQVgdqhHKq0r0dxJTCukO/dRuPhDsanzdE0fbeM1pW5VQNQw2t9rpdUN7dCQkbUdO8c64nBDPmYaifkvQuSqx2QKzMp9QUEBAODw4cOmIqyurgYA5OfnmwqHsIa4N9//3955x0dRdX38N7vpCQkEUiEhIL13CChFwVAEo6igKFUUBAWxPA+PAnZ8fEFUfLADdhAFREEUkSJKl1BEAaUKSaghhEDqvH8su9nZndmdmZ2+5+snsjt75947d24555Zz9MyFN0oqkIYRWtRCtbN3WiDnRSufC2G4A7VaSXN91ft3l8fJFeP+0X8Ord4chOHZaaRR52CUrcVmf/ecdqLDqrNYPHMWiHFZ78iVNKin/upvVRl6G9Tz+Q41nPxRe4LUr590E0zQGgk9lTp/u+2CDaleT/QeA51Y7Y2JVu5TU1MBANu3bw8oQef9zvgIfVF7+5eiQoxCaN2V+D7ioOSOBHW3EvtKWW46BunXZcjHSm3P9fguc4sf57sJBcOAjgNIFCbECM7BIpwZ6Sk9rSwbiYDz5Nz66X6Jp8p7TfpoblDPiCi8cu/LFZ6aA5LK1dqKBvXkFJkW+TWKhXczodbzs2C9+00JWPWtiFbue/bsCZZlsWDBApSXl8tKrLy8HPPnzwfDMOjZs6esOAh10Eaw0tqgnsB1jTVK8U+tTL5Uc4UnotykVp/Aqpse3bLI2WiReROKLViUSz3hq8/BLoCZDY7hKiNNCngY1FO3OcuPXKuxkLddufyzi4/FcZuxz/AGhna7E/x5GaExSCTu8+o8s3iG6pe0QINmac22ryyilfs777wTAHDw4EFMnTpVVmJPPfUUDhw4AAC46667ZMVBaIPv1Wahe/iEZeN1bMbtFoxXVuYg8HKTLOM6z3i5b4FX4BiI2C1+nkJ5YIbwOBEFEI8UMV35FbNAhCjvs9G+dsEol1cSUsyBqz4o+Lr4ovI2PqW1gTktEhH+ibcNq6gcCbU/tRVbXllJZtkb3WCZFvh0b6hGerLOiSqfDyPj5efeoEp/1S4phTOjM6KV+27duqF///5gWRavvvoqxo0bh6KiIlH3FhcXY8KECZg1axYYhkHfvn3RrVs32ZkmlMMlZLrOs6lHIIq+mftFnxMlKqSjdR9llK31gaC0/KhPmWi36uPCAAOiUc7sEYFhbYN6zmdToK5apr5LfM+eE5oqlIOaiqFR67VQvoyw4myEPASKUd+7meEY1HPzc09UIVq5B4B33nkHtWvXBsuyeO+999CoUSNMmzYNW7duRUlJCSdsSUkJtm3bhqeffhoNGzbE22+/DZZlkZqainfeeUfRhyCUx2gCs6IW2Y31aKZB0YkIwcjM8nKcwrtK58h4hBrxaelThkqcm9cS3jQ1yoZRDOqZHa39Scs3qOdpiNOfZxlfZ+69IpeNZz+jli7la2XV/Rv/O1TT1Yt6UVsFM/RRWufR306BYFY2PW3gGL0s9FoUU5sQKYFr166NH374AQMHDsThw4eRn5+Pl156CS+99BJsNhtiY2MRExODoqIiFBYWorKy0nUvy7KoV68eVqxYgTp16ij+IIQ8lGx4vNuHFYtdObTOk6+BR8kxSa8jEEZ8x1JReoVAbpkEpuR6r3yKqV+BpGkEV3hS8y8mz1ZYMTIrRhQGAzeox125F4xOVDIanZM3nFKnzFEkn+OxVmWrYDqWdoUnx0Ca4eqtg2A+NgGouBACVtOt/2ZB0so9ADRt2hQ5OTkYN24cwsLCHFsiWBYVFRW4cOEC/vnnH1y4cAEVFRWu38LCwvDggw8iJycHzZs3V+M5CIVwtREVGolRlGrNDeqJHnyVypc6z6fsRIRAZPrLE37wWIFzP3OvaCrehsLk1FstdVQrCC9aP4MVykxPhFxGqoXoNuhhUE9VV3gKxqXW0MhbbjLflxGUTjmIyjfrnPwJrqNVQphhkpXbh+vw/gxG1a6ja/8EcVnoiaSVeycxMTGYN28enn32WSxatAgbN27E7t27cfbsWVy6dAnVqlVDrVq10KpVK3Tv3h1Dhw5FYmKi0nkndEJQaSaDekGJGKNyYu6VkXIgN3Nj0mm2n9fNtIgm45Vfxm2eVkHDcv6QvXKvQiuU6grPV701qwIhFSMJXkae6FCqPrhiYfj7HFHHNQy6MikWX+9ZC4N6YoyvGald+EPtIz5Gbpd8GDW/Rt1RoBVqPn8gRiWrvI6ap82LQZZy7yQhIQEPP/wwHn74YaXyQ2iMltZ5AzKoZ+KOUett+aq5whMRr7au8JRD8W35Cr5YyQOXTk1FrzYqP13z9ilWxhpClsfqlefPBnlGfcdVeQb1AtnJ5D8JYxx50NQVXpBMZKoF9116l6k1+jPxSG1Dso/XgKG66wPJ2/KJ4MBXczOLSCyUT+POEwRPR6WsQT09yo277VZ2LDIj8GWUSmr8gbQHpQQXLZUMn27uNOrdjOIKz8yTpp4Y2aCe7HREGdRTcFu+YjH5SMPLjac/dFi5V6Eu+esrAzXWqFbd1LOPEn+gUePjVBbqN9XC06CeJi4K5azcuyZkrAUp90GOktZy+Q3qGa/JaH+u1tdvShoWc8C3cq/m5LHeCooSiC4eRlxIsWO/53vxVZZ+y5nnvKZRFw2UrfcqnHM0aLkpjZFWlawoMHuOf+59M9/TilMqrVdOcrDCuCOEESaqjNQ3SMKgm1GsXF/1gHHbyWPauqoiplTuZ86ciY4dO6JatWpITExEdnY2Dhw44Pe+JUuWoEmTJoiIiEDLli2xatUqDXJrDrxm01XxIatxAxR4BK1lSPFPbfDOX9EjBApggP48EHsDvtDaUJgSmEV44VtJcPZ3WimYLuHdHEVmWLRuG+KPxnDD8ZnWUA75ZeA1uR9oVgTwZVDP/RdepdYrj+boD30hXI9k1AxXVxLYyr8QZph0M7IrPCvUVyl4usIj9MGUyv2GDRswYcIEbNmyBWvWrEFZWRluvvlmXL58WfCeX3/9FXfffTfGjBmDXbt2ITs7G9nZ2di3b5+GObcGQv0ov0E9Qjt8bDdW8UWopeBaBSlCl9iw3uGqvkt3hef2WeILVMoVnhITA2q79STUxciTQ4FPIjgFXt+Cr7dLJ55wJu9kZb9nhZ7bve8R6u9U2ZZvsj7FCPmV88aN2o8YNV9aoaYrPFcaslwnXotH/+quKAEZ1NOL1atXc74vXLgQiYmJ2LlzJ7p37857z+uvv46+ffviiSeeAAA8//zzWLNmDd588028/fbbqufZ6Ghx7iSQpm1mecZn1k30XGLeQeAdpFkKxHc+A30K3pl/sxSNidHszL2G5xDNhhGUisDh2uTwfCZFd7EF4h1D8vl3mekIHEIQh+fRQTWN/hqjPWri1lGN40wKY9ycVeHPFZ6Ry9cIyN11YZS2alRMuXLvycWLFwEA8fHxgmE2b96M3r17c65lZWVh8+bNvOFLSkpQWFjI+QsmfJ8Tl4JCBrcCMJThdd2wMwXBMwgo8w6M8B6VzYPYcvEOxyieFzEoZlBPw7yLybM1FEz/mP0ptbZKLbvf4tmpwrtT3XPSh/eZjNDvBQDj+VXsO1Ro5d7Plmr/+TAWarnCM4rRTzFob0fJ+GWiN876o6Z3C08CeS9WG/NNr9xXVlZi8uTJ6NatG1q0aCEYLi8vD0lJSZxrSUlJyMvL4w0/c+ZMxMXFuf7S0tIUzbdR8KzQShvUMyJG6paVzEvVKpHGA53uBRp4vRNf75U1qCcF1QQKhaLV2xWeVIHcCJN8JCRWYeSVRNnHTwTjq5SfGasQ4Hs2mys8cZOKGp5XtmjXY9Q+1QjjjZVwHy/MontoiemV+wkTJmDfvn1YtGiRovFOnToVFy9edP2dOHFC0fiNhrdBPRXS0LgBCtoGMGwfa9iMAVBnIsK0OHfKcx5Euafya+TIIy2lUtbrvRhF8NHaFZ7eGKPU5WNYw1WK1Wcxz6ScQT21mqGv1XJ3ByRaG9QzSr8DyC/7QFfY/ZanjkUktpvU3KAex9CQ77CG6pc0wDXZ7uEKj9AWU565dzJx4kR8++232LhxI+rUqeMzbHJyMvLz8znX8vPzkZyczBs+PDwc4eHhiuXVqHhby/XfEIU6Un6DesZr2AYazxXFiGVNSIPX8q5E4U2vwVQNw4FSkPrcvvJgFOVbbQz1lM6d6MbKFQDl8sSyjJsyILOtW3UA84eG23rVroN+lVEDNAEjtkMxGGnChlARBgDrqKeBTHBZ1aCeKVfuWZbFxIkTsWzZMvz000+oV6+e33syMzOxdu1azrU1a9YgMzNTrWyaCl/Wt5VLI4B7FcyO5uezfMppaig56jyflEmdAFLR5JbA4RrMEvpdLsqdZRcTxtzn1KTmv6psee7TuC6RIKocRqiLVXD7B8/mzCiZVQUN6qkl4Pqq53JbgFG3X2uNXIOrYicyqJy9Ed6J4l2mxuqX1EdqfaH6pQ6Kr9xXVlbi/PnzKC4uRu3atWG325VOAhMmTMBnn32Gr7/+GtWqVXOdm4+Li0NkZCQAYPjw4ahduzZmzpwJAJg0aRJ69OiB2bNnY8CAAVi0aBF27NiBd999V/H8WR0p8qie24WF7jGuPB08g4DR3oH4ktdvRce3IsjAJVjoNAUtRVFVwxWekgSbQGZWtKrrARsXc+1K4P4rGNzncpKx2opUfO6Y8Vkyyjw3TaiJw0x9oNHGD6IKLTzDBOJRqOoYgbVQZOW+oqICH3zwAW644QZERUUhKSkJ9evXx4EDBzjhvv32Wzz55JN48cUXA0rvrbfewsWLF9GzZ0+kpKS4/hYvXuwKc/z4ceTm5rq+d+3aFZ999hneffddtG7dGl9++SWWL1/u0whfMBCMBvWMhLJr3jqVv27jqnEHdCXlR1FHZZRLznTIFRr0LDNyheeNVluiNcHPazXqESotzty7ECtseB4dVNGKu1aKv2DeWfltQPKKKeMx46RQvHpjOMv5rMhwhCSoPH0T8Mr96dOnkZ2dja1bt/qdRc/IyMCgQYPAMAwGDBiANm3ayEpTzGz9+vXrva7deeeduPPOO2WlGWyoM8YZxKCeprmwDopORAhWMHO9HffnUNKAo9+tkryu8KrulgLHNpDEvLr3xQENthq+dl9lSwKDeTGyK7yqFXt/x3mupVO1oZ8vEwKxy8iXDrt8fJWhz/woJJSIcYWnBmpNWqk2GWbM+Sdj4E+ft9oBbgl4ej3Rsizkucy2JgGt3FdUVGDgwIHYsmULGIbBXXfdhTfffFMwfIsWLdC5c2cAwLJlywJJmlAIL4N6ItqhlMZgyBUKzbfl+dpqrXwqfGfu1exf9e8ctRw8tMeKq7yqGNSTXMn1L0/aIlyFXJeGWhC4AuXxnlnWp597wj+quMLT+WiTEy3qgRnGEzmlYNTnCva+XunnV9wVnsW63oCU+w8//BDbt29HaGgoVq5ciUWLFuGhhx7yec+gQYPAsiw2bdoUSNKEwnifgVUhjQBajxpKsPEwbs4A452T1xffK3ABF5VJtkoaQRmRKjS4Jkv47OlpVMn1Vh6siLEUIg+DenLzJqaeKGhQT9t+xTlpKS5c1Tfztx2+fkZuyavlCq+qnzTGWOMLzV3hue/+4KRNBvWkusIziixjNQJS7j///HMwDIMHH3wQWVlZou5p27YtAHidxyeMgaiOSAdXePK22wjk06J9ia+ytuYzS1TqfFRF0cqWomaueaLn3XUhUshyndd0i0/DFy/JFZ4KSoVkIYqVuT3YQhjpMeXUAfMIzs62yXhc4SJOUTN3Zy6/rauwLV+gf1SjXonrU4yxc4BQnmBXYpV+fs4kQgB2DapMT1irzQWk3O/ZsweAYzVeLImJiQCAc+fOBZI0oRBaVujg7tr4UWV7skoKnfBkiTXerKK7QwxSJmKExEByasYB0QiCsxnLjZDAtfbvnAv0ftuK+sJTMCZ16iVvfyjXoJ4FjykpgdQxR+wRBNOVswbZFbTb4GrvQbxyb7b6YlECUu4LCgoAADVr1hR9T0VFBQCo4iKPkI9ng/Tpm13lvCiF+QzqBdcgoDVq6NscY3QKxutPOPBa+Waq9m0EIkzoZRTPKAKBUfJBiIOjmGjQfQY6aSfWFV5VNRRjUM9ceMkabp81MajHCChmYvOhRB5M0M+YSSk1Q3kGG3rUHzn9s1XrTkDKfXx8PADgxIkTou85dOgQACAhISGQpAmVUNoVnlLNxsyrqj4nShS1JcC6PmmJVbpGKc/BZ7RQkTyYTHA3wiq4E6nChE+r3SoLJmq68zIrRjFmxofcPAna5BCITm+FSq36yB+v2GcVCKfKZK167VHKu9XUFZ7F0NwVHs+Ze//hgg/VDOpBGYN6Bhx2AiIg5b558+YAgO3bt4u+Z/HixWAYBh07dgwkaUJhvGfTle+IjGI537hdrHFzBhh5wUjLesVNS63dIfwTZT5WnNzPj7u7pxPx0pQadAPpMwLJg5JCAynb5sWIBvWceOZMdJXlkzg9bza5VMpZuff5DpWZ/NHLFZ5aBFrvBQ3qmahe6avQe+NedmYqR0VwnmEX6Qov2Cc91CIg5T47Oxssy+LNN9/EhQsX/Ib/8ssv8c033wAABg8eHEjShELI6XiEFRrvH2wqGyDzheCqiUX7EudjqbWqbHbMMMbyCimi822e924MV3jaxOULEmyqkFMWWq10B5yOy2iTS/L16QrPCkqnIFIfTYeOW+16Jfx+LfzeZSBPPqUyNCKqGdRj2YB2wlUZ1LMWASn3Y8eORXp6OgoLC3HzzTdj//79vOFOnz6Np556Cvfccw8YhkGLFi1w1113BZI0oTKqnE+2XPMJHCsMQ7qNpT6sncsh0PNanLOcKpeJLxsZem3t1UshkZqu3lufjZIHo2KJsnEa1NMkMeXKS61+i3/SUqwrPGckVdtwBeMMEDX7ML8uwWQWfqATkVZwhac1UuqJJfozCVh6YtJEhARyc3h4OL7++mv07NkTO3fuRMuWLdG4cWPX7/feey+Kiopw+PBh1+xKzZo18dVXX1GHYTCqZsFEhDVL45Xgss8YKDUIBNdgIhYzdTmcbX0qC1kco4AalZERXOHx+pr2UCCsjtmfkq+dGBmuKzwf53Pd9mFZDV/Prck7FHFEWu+6FHRbuQOE9AnjIdbPvRLpBCIjWbXmBLRyDwCtW7fG9u3bkZmZCZZl8eeff7p+2717N/766y9UVlaCZVl06tQJW7duRYMGDQJNllAIJRuemo1Y0c5b49bsMzlF8+LsTMmgnhwkPYefXQNylVXjTjzxw0o8328EjCQ4m+19a4HeihUf8g3qCd0naFFPV9RqGvx9Q2AG9cy2cu+RkGLIVWzEPqvZ+ijNz9+LmS3yCkcYDSPJBUoQ0Mq9kwYNGuCXX37Bpk2bsGLFCuzYsQOnT59GRUUFatasibZt22LQoEHo06ePEskRGqBGN2SUrs0o+fDGuDkDzLXyrR4eA4BKZcLnCo8jHHik6/jNPKt9ShrwJKGJALQRzsQrUB4G9eRmTcyNATy3HgKtbFd4VYYLFEtfy75DVFkrfNRMDEL5MuIkm1mwmqIoBcajnRq+LCwqPgSk3B8/fhwAEBMTg/j4eFx//fW4/vrrFckYoQ2eHbiYDl28QT0FzwIqeI9VlVRfrvDU7F/Ns2Lr+zdpj6Ha4VTNCSRJQwmAIrMi5swuucLTHjmu8MxjUE/c2Kj3OWdNz9yLRUPlwFXueu+g0MAVnuh4TTK+OzGa5Xyp4ayKWq7wwCozDhhIklGEgLblZ2RkoF69eli0aJFS+SEMghodkVEM6hlJoDZSXvxh3Lzq6ApP6LOS2y55FEHvd8G4hVcubSnoZlAvyIUmwoB41ElP4VN8WxHTmOU3eF3bjtSOytOgngJ5t0LfIfvIiL9nN4C4JjYLRlPoXefNjb5qrQJWaFNWICDlPjIyEgDIZ70FkGZQz9wYt+8J1H+vj98M+8wEH/pZvJeG3Hx6CWMK1E+xefElcHluKVQbEoSUw1h+7vlx2kNhIOAKLwh2dPiq8/zvUNn3KqZs9XaFF4xKIWEt1Dao5278NiBXeBbtawNS7mvXrg0AqKioUCQzhPYo6hvaa3VCXxc9RpGbfeVDjTxqLRboV8wqbfPSLklpeWDEhZNuNV5ScB/xGKTBmQBDHWcwCEauP/InsZSNjz8y47vC4y8Iqfn2WLm3mFAu92kCNajn1xWeycpZi35EeBed8I4ds5Wj0gg9v1HKxWrzaQEp9zfffDMAYNOmTYpkhtAPLRpYIMq+krkzrgypVMbUOnunSrQBI9rElaL5v7YCp1ahsO4fxR4mN44xJn8o6QpPSc8ERlYwCf0RX9c8hXznv25Kgc9JX+cOEm1sP2gJb7vzZxBFyfQZIcXMONDknzSM1m8H8/vznDTSsixkucIzVtVRjICU+0mTJiEyMhKzZs3CyZMnlcoToSHeBvX8I9agnt5txugzhUrDuDpTzRM2BT7lR7CGeA65QopV67QURG/LFxFOC9+8hsBAyxX+VhL50Ew5VTgZoYluveuFaq7w+PonmWfu1UROHRQLx22oCv210nEGwxERucjxuBDs5aj086vZVq1AQMp9w4YN8dlnn6G4uBhdunTBZ599htLSUqXyRuiIVWezAGM9m4Gy4hc18mq+s4U+DOox/j+LT8W3KzyvSQCGcctNAGWqU4XUZ+WFZwXRVC2ScMdQrvAY59l6B6znv6KzamGDem64yklEwQT6nvVyhScO7fIjxzuFUTHMe/Rw/2aFspWKV70KviIwBAG5wrvxxhsBAAkJCThy5Ajuu+8+jBkzBg0bNkSNGjVgt9sF72UYBmvXrg0keUJBpHT0hulICQ56ucLTHcY6D6dP25KfppHOZappP0RpaFXMGzmKpnlWbcTtatP7ebTQ9aW/Z+3LRG+lzEiu8KiL8gOVjy646yyByCFWfX0BKffr16/ndNQsy6KkpAT79u0TvIdhGLAsa5gZ46BHYAxR4vUoa1DPvGdpfHU4ZmoHQnkN6Ly0gZ5fTk7EHlEJCNaZlvCKE8MJbhaFhyC80Vux4iNwg3qs0A+BY8Dy8oS/P3Tfpi4uFsddyrnC80pBxfGIY1xNwXRktxenWQeDHhG5lgnJaGI/SvD9+TCoZyBZR1WEZCIh+dFA5WIl3TQg5b579+6WKYhgp8qthJjAMtMIaPug7Ft54gq+OmvFR2YlVkRfZSBdPlK3QI2o4PhCsqVmJQ3qSUzbtWrOc5/WfUMw9kVqYQhlxIXne3WOr87t+qzvSV+X5mWkZ1If3neoYhkYtv0Z6LWbYXeRYd9jEKO6QT23yamAXOFZtO4EvHJPmBs1BSIlV+7lpS/tutmpOt+ptYKiaXI8iPVv7vt3cc+hvf9jUWfu3dcIjWenShOU7MvUnlwxliJqDGQZ1NOoHJVOR6h66T2pp5YixytAB9hRqWmUTpf2KfNxXHmVeL+/8jNrH6WvKzzCzLCsdeShgAzqEdZFnYHTGIOFkRqvkfLiDzWyqrcwKxuXPOWucIP3s7zo5ZRL4G9IaruX7QrP61hBACv3JFwR0Mignti65jSo57Ip5VzFUuO8uUn7UDd8G9RTzxWelojzea795IKRlXixeTPOGMB9f0YuW7XwfBealoFRqoEBIOWeAOBunEJMWLFxGhOj5itQGEZYgDSrDh1s+BI8fW/j1RfjCFf+EXNmV3WBhGdySA/M3i2YZ3LQczLLt+tNIyigSqLMSqd2ZWKeeqWeUVMy+ukDzkQ+lY8euO+yCaTfsurbC2hbPh8sy+Lw4cM4f/48ACA+Ph7169enBmBQhBqF0V6XksbOtMZXPgySRVEIG4+zBtKEmGsrc4LGYwLPjxNR7qE4prvcjfj4jz+QrJp7ZcIqNddaGFGxkp4nxu3/PK7wRCesUBhDIjLjHmUfqNLpa6IhmORU17Oatv7wY4TJCD43cMFSt6QaXjbC+3JipaagmHL//fff480338T69etRXFzM+S0qKgq9evXCxIkTcfPNNyuVJKEKIlzhyeykAmrCCnaMVu1kmarlQO/frPnIimG0Tt1sSrPUAVpJoVqqv2ZfZauVoKGmxe9gRYs2I/t9MZ5ffedVL4FXX0vjft6hyZuKe//krx5Jsjuh8go79VG+MZJyaiSc9VKtyVr3yamAXOFZ9PUFvC2/tLQU99xzD/r3749Vq1bh8uXLDr+Dbn+XL1/GypUr0a9fP9xzzz0oLS1VIu+EAgg1PDn13XNA0vuMvfAMImEUjCQ4GMGgHn+KAoogy3/e33hTFQThnyqPLdY3qCfU1chLx6TtPUChXxVXeHpLB6x26ev+rCqhtUE9d3zZ1LBqeYvFSLKeEEbcNSaXgFfu77nnHixbtgwsyyIkJAR9+vRB586dkZycDADIy8vDtm3bsGbNGpSVlWHx4sUoLy/HF198EXDmCeVRs27rrew7MVIfY6S8+EPYT6n8OAPqTDUUhIRgOJ/5t3vKGdTklYvMHTWM0CSBf1z5lHifkgO9bFd4PJk2gwBC8KPJyr1Eg3pO3E2peX/yhTHGTCXhbXc+7/DYlh9gmXD7O2O2dzn9v9xn8VeeZlBKjfYeyaCe+mWg1Phthvoth4CU+5UrV2Lp0qVgGAa9evXC/PnzUbduXd6wx48fx+jRo/HTTz/hq6++wqpVq9C/f/9AkicUQM72UKGQ3hawlWvUyjY/azZmJ1q7wtMf61hj9eUKT+x9Vpp9loLy7spY1YTGYBT6/CFLyNKoGJVuU0Kx6W7ETKVk+duRPDsGWqBG+/SvRBsL6qOEkWMg0qpKpN64+7kPPC7rENC2/IULFwIAWrdujdWrVwsq9gCQnp6O7777Dm3atAEALFiwIJCkCZVRZ8ubMZqO1pO81KWbA6mnxn3dpMtCgrN5aZi23LNuerrCI6GVUB+nQT3nuVPHVckTr2KEVgtM5rlcBvI9i6dBvQCtw5vBR7mWu1CMPBksNmtGe4+u8+ZBONZINZxntF0XViEg5X7Lli1gGAaPPfYYQkND/YYPDQ3F448/DpZlsWXLlkCSJhSmaiuNiLA6tEUl0zRuVxLgdkPXNiiemINvjJGE+PLR4cx9AKt5Yu4xbnuQhhJCKmcHhIrvWvcVWlc+dE2eg5yy4LwjDZ5FSYN6fHG5nscqjfIaiijUpATworYrPKvVRSUww9EOvVHdoB7P5JSsNmDR1xeQcn/mzBkAQLNmzUTf06RJEwDA2bNnA0maUBklDOrpDfW5hBQCPq+lQH2TlgeG97P7YKdVm9RbSZWDGfMcDBh5JVEqgjVM0Uc0aXlxhHJfz8DvCk+N5usy6qhiHfTd72hoUE+GAUszoGu/7mEHSIqHBKtjhue30NATmHIfHR0NADh37pzoey5cuADA4R6PMB7qGtQzBsbtY5TKGJ+hEYWiJkQRaHFLFbgYt/8Hgl7VJKCBX+qtPr1taVMCRnGFZ6V+wQwG9apOzog1YmYhadOJjyL0/Q6VqayBGjs1Gq5dQAo/i1o7AtTAaHm02oSJFLSaNHJPJ6DdjcaqOooRkHLfuHFjAMDixYtF3+MM67yX0Bc5jUKoMahrUM+iLVBBgq+ErPfEvuq5WpZglfB2YAQhWd6EiI/4rDSNL4CRHjGYXOEJVT5Z6RjpJQoQkEE9gWBqyASelr4J86GnKzyl7yG0xUrtPiDlftCgQWBZFgsWLHAZ1/PFxx9/jPnz54NhGGRnZweSNKESqm5507jhSDXsoVo+DKD4WBtl6pUcg3qCE130yn3i2Sb0Main/0sigU85jCWY8e+lcRnUE5tVEyjuSlC1T8HXthqFFG/OSSrt2p/UiVCtJhfJFZ56GKtP0hZPV3ha1COj1gM9CEi5f/jhh5GSkgKWZTFmzBjccsstWLp0KU6ePImysjKUl5fj5MmTWLp0KW655RaMHDkSlZWVSE1NxcSJE5V6BkIBpLnC074BUZv1T5VBPR5DTcE7xohC/CCskcDFc27eu935P3MfTCghSLn3g8EsmOmBLIN6Jq7rvGvZOu+EUStVXoN65n11quB45wEalVQQI7QtA2SBFzN4XAgW2Gv/ycWqby8gP/fR0dH49ttv0bt3b1y4cAHfffcdvvvuO8HwLMuiRo0a+Pbbb+nMvUEQahRKVHi9Xd/RhAAhCSn1hWeDi+datLwsyL1PfpqBoNSsvJbKTNV5d82SJCRgxEkVuYqO58l56bGIucN45SUV302R5f2qhCs8od+MUAdZsKoqjmZwhScHfZVtbplarWytjpVeV0Ar9wDQtm1b7N27F4MHD4bNZgPLsrx/NpsNd9xxB/bs2YPWrVsrkXdCQbRxhWehlmNAqiZTyKCekZBT9O7Cpdpu0/QwMKXks5hxJdAwrvDMVGh+0EKQFt0+vMJ526PxFZVmhh01kmbF9jG+82ONQUwtl3WqjQ8mEB40V6Ldj3aYoHy0xHM81sIVXkAr9xZ9fwGt3DtJTU3FkiVLkJubi/Xr12Pfvn04f/48ACA+Ph4tWrRAz549kZKSokRyhIIINTwlKrySTcaazY9QBIUqh5wz94ojO1p9tpMbxeq7FKr6PH/rhdZRfE2BUyaUIAya1qCekph2uUmsQT31ZBTBOFUoUjF1SMs3aaY+Wwp6T5gKYdXyDhSjvi+zo4hy7yQlJQV33323klESGmMlV3jUZRDKw20gQgO2KuO4hSq0koKO3Lj0lLWMMiliJcHKUK7wPMJVbct3HqAJTLFVCr3rnydaGNQziys8lmVF9flq9SWmcoWn43vkKx9DTwaqjKfXE7XqJ8c+jkF2whmJgLflExbB4G3CwGOwYag63xkshaXccxpt8YuzLV9QUBB6foku4QIoRiOdKZQsULF8x1fcrhnn0VTDSEIoCWYWFlLdtzFXbdEQebNx6qgS+Hy3PH0SYTws1z6DGKu+yYCV++PHj+P48eMoKSnxG/bq1auu8IGwceNGDBw4EKmpqWAYBsuXL/cZfv369WAYxusvLy8voHxYAU/hrmq2NnDIoB6hDQq5wpPjR17g7J3cqheY0KDAURqN2oznc2rqkspiyoLVMOL7kZsnxnnu1NOTiaKKrfHKSyqu1u/zUbgrgrIN6nH6af5+SI06KHUiVGoepJaHv2c160STvrsx+FetHb+YqxzlYhQX1HIw0FpFwASk3P/www+oV68eWrZsieLiYr/hi4uL0bx5c9SvXx/r16+Xne7ly5fRunVr/O9//5N034EDB5Cbm+v6S0xMlJ0HqyGl4cneBmsBIcTIVLnCI0wP6/5RQMhyW+XhunDTDiNs3VRSIHd/DjWVTCPteLAKhtqW7xWMZ5eIr9v1coWnUroBuQ4LkrZifNVHe8S2aa37U84EEa0i8cI3waEkfON+oAs0ViKgM/dLliwBy7LIzs5GjRo1/IaPj4/H4MGD8dFHH2Hx4sXo2bOnrHT79euHfv36Sb4vMTER1atXl5WmVRF0hadAhVdSmTfDrB9hbqTVecbt/0rmQUKM7kEFmppVBy4lIKHMWMjxeqCZUC8zGc8a5i8ar/FYzPOZVvlV7uy83Pu84pBh1FFGBkSh9mSVeM8PqmYjaKDxhh8jlYsRd43JJSDlfvPmzWAYBjfffLPoe7KysvDRRx9h8+bNgSQtizZt2qCkpAQtWrTAM888g27dugmGLSkp4Rw1KCws1CKLmsGyLB78eCe2n94NJAC/HbuAjH+vRLUIRW0schDbhCPgKPerCA8wPWN0Gkr3XW2Yv1CXyQMLG8pgxy+VLVCIaPcUlU3Q8FTCHn3A9a3iSl2gMkJ+dLZiRKZ9BFuIo82XF9dHSe4dAACGEdf5BzpgsWDxz6V/sProapwqOiUpzt1ncvD0pqcdn4sKEJFSBAB4elNVn8swDAbUH4AuKV2k541lsfyv5cgvzkd5ZbmkvAlhlAHeX59hCzuPj37/CJVsZUDpHL/kOJqm93OH2CxkdkchuWz9ifX49dSvnGuf//k5ACnvixtu3ZV/cPzHcShOrsAjheF4sPB1lBe/h7r2zni9YrACuTY3ztKatWMWYsJiUD+uPka3GA3m7EFgw8vXAl1bqTPgZMaJSycwad0kXLh6QTCMuD6jqt4cu3gMW3K34FjhMZ9Kx54ze67dKa8veWLDE4gIcYyXeZfzUFJRguvirkNhqbVkXiXxtxPl/JXzuGflPa6xO5hw9pGVbCXWHV+H44XXxjqF5VJnfH+c+wNXyq/ITsMoeoLSBKTJHT16FADQqFEj0fc0aNAAAHDkyJFAkpZESkoK3n77bXTo0AElJSV4//330bNnT2zduhXt2rXjvWfmzJl49tlnNcujFpRWlOJiyUUAwLmiUqw5eAj26EJEAnAOKpeuOoT1xsmxgvHwNoXCXFkrCINsv+Bm+04AQBEbgTcrbsOm8EkAgOuufowK2H0kGjhNkqvhz7xL6kR+jcZJ1QAAMeEhKCopx4CauRh16R1E4yrqfR2FNRGXUVnJIgY24M2Ia+XIcv79pvIKysIqEM9cQgxz1SuN82wMIlAGAIgItXv9flOTJHz12z+y8t+9UYLP39NqRFU9a3I1SXEXXi1H2KUWsEfko7w4Q1rGWIdiwtgqEJW+wHW5/HIDXDl+P+8tnevH452Nh3l/u//6epj1w0HYo44iJOqo63pY2HmU5A+ALfw0wuJ2X7vKoFNGvKCwb2PcP0s48uJW0f+7/b9Yf2K963uEPcIjbJU+45mPr//+2vU5tLrz2k5OmOV/LUdKdArKKioR3cAxofbElnCE7fCt8OVezvX7HP6IDIl0fWbAINQWGnCc83Lm4fM/P0dmSibGthrL+a24rBj7zu4DCxZlFY524qi3FxBm53/eGb/OgI3h/sbYr+D/dvxfwHl14vlOtWJ4Zl18tPkYJvRqoEv6fDjrcAVb4Rqn/FGJwCZZPLlcdhlT1k9BWWWZIvHVqqgAAORVFCPv5C9ANDAqOgljCy4CuAR79I94mF2PN96cDAA4HxUG2G3A0Z+BhLYiU5E+7vbN6IvVR1djaJOhnOs9GyXgt927sSTqZWDpCuD2dyTHzQevhXo3eSGuohKXbTb8dOIn17Wb0m9Cxp4vqiKJTVUkLzUja7o+x0fGC+YTcNQH5wQmAPx+9necvHzSK87nNj8nOv0G1fnb3IiuGXh6xRnX99tW3CY6TgCICYuRFD69WjqOFR5DfnG+129/X/zb9blOTB1J8SpBWnwkTpy/gg514/0HBhAVWiWDuI8tWsOWxwBgUFpZir1n97quZ8RmKDLGmQE7UyV/PrLuEddnm8dEcu2Y2jhZdBLd63SXlY6zH5n+63Sva0SAyn15uaPTs9u9lQkhnGGvXvVWUNSicePGaNy4set7165d8ffff2POnDn4+OOPee+ZOnUqpkyZ4vpeWFiItLQ01fOqFpfLLmPQskE4feW061pMQ+9wX47LRPWoUNSv5X+gqImLeDzkCwyy/wq8WoLnAZTYx+KLil4AhPXxWFzGSPv3aJ0A3FTwJee3prYqY4txuIzzcEwyFF6RLmwJtfPIsKr6+vGYzuj44o+i4gu1M5gzpA3OXy7F9K9/r4ov1I4+zZJw5Oxl7D1ZJZR++/D1+HzbcTzaxzH59c597bHgl6OYEb4KiX8edAQ6DzQEHNYvygGc5U87xRnGjd8r66K57RgAIJ4pcl1Pb9YR2FgVrlO9eDw1oCn6NEvEgl+OonFyNfx1ugi5F6/iyNnLAIC7OtTB5ZIKXC4tx/oDZzjpPDeouc9yGdu9Pn47fgHJcRF4/ObGPsN60iwlFvtze6PsYluwZY6jPRuf6IXu/7fO531LxmXizrd/RemFLrBHnHBctJXCHn4GtpACPNq7EVqlxWHUgu2c+3o1TsQ797XH1sPnMf8X7gTj+J4NMOuHg67jJBUlCbCHO8oiNO43RCR/4wrbo2EK/ntjexS41Uv36pZWIwq3tklFqN2GpFjpO1CKy4rxzyXHZEz3Ot2RFJWErIwsTpgaEdVxvsSRvzBbNGCrem+T2k2CjbGhvKIS+04VIjk2AslxDkXySvkVvL37bQBVirpT7jhXAsC/bVQvWtZqKSl8t9rdcH/L+3Gm+AzaJLZBtTBpk0LuJEUlAQD+KvgLALAjbwdGNB+BMHuYK8yEtROwI38H576GibF4f3iKq1wAIMwehsiQSFwpv4JvD38rmGZmSiYSonxPevmjRngN9ErrFVAccnlmYHOM73kdUuL0E4SFOHf1HK5fdL2seysqK/De3vc4CktcWBxGtRiFuPA4v/cXlBS4FPsHWj0AANh1ehe25zn6kUY1RC5kXBt8Bl8qQmJFBYraDcfv1Wrgsz8/AwC8V913XqJ+egnYPB9oLk3BE8uMzBnIbpCNTsmdONcHt6+D3pcuIW59LrBnEXDTNGDfUqDVEKBakuz0YsOrFgtiw7wXDt44fQabm/QGGvXF23vexuWyyyipKAHKHXJicWobLK+ZhEu730FpZanjJply/L86/QvdUrshNSYVtWNq84b54dgP+ObwNzh/9bykuEe1GIUB9Qb4DFMvrh7v9WGd09GqTjU8/MtcTrpN45uiS0oX1+o6H3Hhcehfr7+kvL7W6zXsP7e/6mw0y+L/tv8fCksLMaL5CNSLq4dQWyha1GohKV4l+GRMZ+ScKEBW82RR4W9veDuulF9B34y+nH5fS165oxW+25uLcX0+xOkrjrE7LjwOdWLqoHa12l4TxValRkQNjGg2ArvO7HJdiw6JxsD6AznhVmSvwKXSS5zJNikMazoMSw8tddXfOtXqoFnNZpLjCbUzeGuYY5FXaKLfjASk3NeqVQu5ubk4fPiw4Aq4J4cPO1bN4uPFzcipRadOnbBp0ybB38PDwxEeHti2cCNwtfwqhn47lDMTa2fsYAFUVDoaRbg9BKFsG3RslID2dWv4nf1y/pxt/wV3h3CVsGkhn7iU+z7NEgG3RdIhIevRzb4PdZhrGmxB1W+X2EhUY66gmo1fs2ibXp3zPQXn0MW2H3amEuWsHesq2+AiYtCzcQI2HDyDiR4rUoPb1cGh05dw9lIJHup5HdYdOI06NSJRMzoMD3Svj3c3HsbNzZIw667WaPXMD677nh3UHDNW/I7oMDv2PpMFm43Bnn+qMr7soa5om+5QSsvKSjF0+psIZxzCYYuSaLzYGkDez8Df69ANQLdkAJs/cdzceRzQqO+1AmUAxub2medfxgaAwYD/bcYBNg03NE7BgiENgMtuyrg9DDVqZOBofwZXSis4Exl9W6Sgb4sU3vJ1J+PfK7nfa0ULhHQQEWrHglGdfIYBgCf7NsYrqw9wrqVWj8T+3EKwZbVc19JrRqF+rWgcvjbxwEfHjHg0TorFgbxs1zV75FFEZTiU1l5NEtCqTnXOPQNbp4BhGGQ1T0ZW82R0zKiB8Z/+BgDo3TQJdptHva+sav+20KrtljfXvRlPdLwfNaLDOMq9OzYbg9eHCq+83dQkEWv/PI3b2vILl2N+GOP6PK7VOLRM8Fae5970Gjaf2oz4yHicPtUSbEXVxN39Ld12LrT2jv/epve6Jg/kEGILQcMaDXHh6gWUVZYhMUqacdLIkEhMajdJdvruTMuchj4ZfVBcVoynf3kaLFivLbAnixyrbXVi6iAiJAINazRERlwG6lfnDuZh9jC83ftt7D6zm3O9vLIcJ4tOomNyRzSJb4Lrql+nSN71wmZjDKfY142ti7qxdXGs8Jis+/+z6T94YuMTvL+lRKdgSJMhouOKDInEw20fdn0/e+UsKiorkBQtTcENBXBj8RWgzQMYWPM61I2t6zqSgcoKVFwthN1d6L90EnVO7kGz0jKg9ARwUX4b9UVMWAy61eY/lhgX6Sb3fD0BOLweyM0B7pgvO73HOzyOFjVbwG6zY0B9p/JbtXLfuLQMjWMaAC1G4qP9H+Fy2WWH0F7ukAeWJWXg5e3/5cQpd9dLbFgs+tfnV4TjIxxyqa/dSTGhMV6TIgBQN64uJrWdBLtN/GKXOwzDoFWdeCy7dRn+Lvgb8RHxqB9XX7XVyDB7GNoktuFc+/yWz1VJSyp1a0ajbk3fcoc7ydHJeKzDYyrmiAvfVu67OqThrg7OBUCxu26syeMdH/cbJsweJluxBxwTaaNajJJ9v5MQuw39WvqXi81GQMp9mzZtkJubi8WLF+OOO+4Qdc+iRYsAAC1aaD8b6E5OTg5SUqz3Qj05VvA3R7EffqUSTwz5Bqftyej00lrYGGDfTN8zzULYeLZEVmOu4C77OnxR0QsNEmI4yj2AKsUeABKaAA37YHd4e7zz/W+YF/YG0mtEADzH1qpFcLc0LYmdgzqlVZEvKe+OJ8rH4blBLZAYG46IUDu+2V113unpAU0RFxmKSpZFiN2G7U/1hp1hYLMx+E//pngiqzFCeWbtRnTNwN2d0hFic4QF+KyWs8CZAwhd/S98Fb6+6vpH3s/BoWEf4DrpK3e/sw4XjkUl5UBUvOOPB3fFXgpfPJiJu95R3ibGXR3S8P7PR9C5XlV+Zwxshj9yC3Gy4AonbM/GiTh89gha1I7FMwOb492Nh3F7uzoY90nV1vJP7u+MW+b+jPxC7oRQfEyol2IPwGsLcr+WKfjjub44W1SCtHjHtr6Fozriga/2Cz7DnY3uxPRMt21g/h+blzfvaYecEwVoV5ebT8+61aB6AzSowb+Ns1VCK7RKaAUAOFGrGN/v64SYiEo8fIP/bW5x4XGiVjL9EcjgrBTVwqqhT90q5R4QNowzq8csNK/leydKu6R2aJckbrKaUI7IkEh8k/0Nytly/4HdmPvbXCz4fQFnK32tyFoY2ngo1hxbgwMXDuBqhbidgkLnuWtF1uK9LojHdm9EVgfDMLin6T3i7p/VCCjKB35f6j8sWwksGw9cygWy5wHRiYDNLt/Yi91NLDy83vHvvq8CUu6To5MxMiQBWDcTWPcmEBYDtOYeCcDuxUCbYdxrFY6+vRCO4w314+qjXVI7NI1vGvDOGT6GNB6CxKhElFWWIcIegeToZNdqoLNvlqu8iyU+Ih7xyfoufhG+iQ6rmnioEeHfmDhBaE1Ayv2tt96KVatWYenSpViyZAnuvPNOn+G/+OILLF26FAzDIDs7W3a6RUVF+Ouvv1zfjxw5gpycHMTHxyM9PR1Tp07FyZMn8dFHDu3qtddeQ7169dC8eXNcvXoV77//Pn766Sf88MMPQklYh3zH9vH4igqs+CcXcZWVwIltYDMGAfB/RuWGhrXw86Gz6JhRA2eLSpF78QqaXDuP369VbeAP73s62Q7gi4peqLziraUXsNEYXvpvTBnYET27ZgIAWrEsHkMxsA4IESGPdKhbA3UuFQKlwInKBKTZziArw4am/a5Hes2qs1cDWqagZkwYasWEo0a0Y6uW7doA7anI8yn2TsJChH9jKkqBTwYDf6/lXP+zMg1NOOfPGaB6OpBwbVtnbB3gupv8P6wPpJzllkKnesKCRUpcBHIvyjtSUysmHFv/cxNC3FbI0+Kj8Mu/b0S3l3/iKPhPDWiKW9ukomFSDKLCQtAhw5Gnl25riZRr26gTqoWjU72anEkcAIiN4D/bFhXm3d1Fhtldij3gmFR4/tYWeGEHIMocvUwiw+zIvM63Yjyk8RA83eVpUfGlxUdhxcQeAHookDvrYSUruFaGYRiEMtLOpk7pMAX3NL3HdTY61BbqWmE/VngMBy4c0N4IW426QLdJwJGNQHpXIFri5EB4rEO5F8PeJcAf144MvdrU8W9aZ2DUakCOwUS+s8GMzTGJXXYFCIsCKsqBilJp8W7+H5BfdQ4ZJ7nHZHD5NPB+bzAN3I52lV9L45pC3SGpA6ZlTpOWrgQiQiLQr550T0xEcBEbFouP+n2E4rJi1I2tq3d2CMKLgJT7ESNGYObMmTh69CjuuecebN26FZMmTfI6m37ixAnMmTMHc+fOBcMwSEtLw/338xu8EsOOHTvQq1fViqfzbPyIESOwcOFC5Obm4vjxqrPbpaWleOyxx3Dy5ElERUWhVatW+PHHHzlxWBX22rY2O8s6FHuJfDymM4pKyhEdZkd5JYuyikqXktQuvQZXuY+tAxQ6thHeZNuJiXtme8VXyEZhD3sdiqtluK4xDIPrEnyfuXVXs9x12pWVnTHO9i1iI0LRojZ3JdJmY9D1OolClUTijn3PUexPxLTCLWcn4iJicPQheTsixKKHuhLodIKvSRR37DYGrdOqe12/p3O64D2sQv7OeRUBxvmPugZb3Cfb3I0EEf4hYzrBS3K0uLO5/lB0EqiPeCNrXjS4CTh3SFzYP77xvnZiK1B8FoiRdlwGAGDjEQvt4cDXE4GcT4C7FwHLxwM8k/eiiL8OqCxzTBYwNsdEyJFrRmJKi1zxsp/eCZy7Jscx6q6WE4RU2iYG99Z7wtgEpNyHhYVh6dKl6N69O4qKijBnzhzMmTMH6enpri3v7oo2y7KIiYnBsmXLAjrP3rNnT58z8QsXLuR8f/LJJ/Hkk0/KTs/cXCsnhgHqdAL+2SbZqn1MuKOahNoZ38rZtbODDFi0svF7Q+C6bJOHI/uez6C8qhuOUuD8ESAujbNV0V2HCCkpqPry5BF8sj4fFwWssRPCyNXL1FDn+PPCX7/cwyqhWzrPhgNA9fDqgUcYpHiOD67vpP8HFXInfHR3j9Tvv0DLO4H3r+3usoe7tqiLpuyK/zB88JVZSLhDsQeARfc4jgLI5dY3gbpdudcu5QOzr+1qK78KhIQ4jhk4uWYkjSbwCIIg/BOwU/M2bdpg69atuPfee7Frl8M64rFjxzgKvZP27dvj448/RpMmTQJNlhBJlausECC8ygK+IrKu5yQBT2SrIm/BJxdb4cPQ/yKUqcAb5bf5SVeqkq7OYF+XycPKsP8Ab1wF0jOB0asFUr+W32a3Os6+MyK3UiqAmmLOne3rYMnOf/D60DYqpmJMXKt3rHcJqy1ctqrVCnvOOvwWZzfIVjUtgggWTHkso04HYMqfjtXs4vPAwgFAfY9jNzXqORTtAjcjhM6JgHI/kwGVFcD3/wFCIoCbpru2vvMS4rYY41TsG/cHBr8v7lnKS4AF/R33Jrfy/r1aktcEBtssG7hyGYiqCbZWPeD0L+LSIgiCCHICVu4BoGnTpti5cyfWrFmDb7/9Frt27cLZsw7DabVq1UK7du0wcOBA3HRTYGeMCaVQW9BhXWkwYPBrZQu0K3kHVxGGMqEqJ1VpujaxwHp8V4outj+q/MmfyhFxh/YrCmq+xVfuaIVnBjVHdDj3fZlh5USd87UCK/cKv/dXe76Knfk70TW1K6pHVFc0bqvj612wbv0RQQhitDmAWDejv08edhiiA4A6HYF/tgPXPwq0HwHM7wcc/9Xxm9g+etu7wFaHdxHUyAA6XLM8zdd/xiR62wCIjAfCRO7EC4sGJmzxHcYWwt2dcMMUwOnaKmeeuHQIgiAIZZR7J3369EGfPn2UjJIIFAFFxyXsBrZ07/HdOzJniEsQeX5YhGLmnmdWJWGdMZyUpy0Mw3gp9kZEjbmGqskB7RXBpOgkQTdNBEHIQ+rKvSEnMSPc/MPf+xVw5gBQuwNPQGfe/Tyzu7Lu7k7VnZgkR7jk1kCemzG82DpA89vE5Fo8187685W8KXdeEARB6IQMU6qEmXANiozrf+rhJhBJV459501I1lJryOfm31cqOgodJO9wYZUxqOesa2Ji4Zy5p1VhXTGkQkaYCtMokRFxQFonfmv4YtuB4ET6tesNegNdxldddk4kDP0MmPI70LC36OyKwiPfpnkXBEEQBkN15f7cuXM4f/682skQgvAMkG6DurIKCeP2f2fqUuP3P6BrZVBPCF7ZiRSLgDBm8annCo9QFy/FwDXHaciKRqiE3Pdt7noiI++8ir5QPPqVjbnfC0EQhDaootzn5+fjgQceQK1atZCYmIiEhATUqFEDI0eO5LioI9THtcGYBUeDUuRYspdBPfeVe994KXMytTv1tuW7JyJcWIzHbyR8aIsapc27YqSRKzyCIJRHcz/3RkDRZ9Zw8pwnyaB8fwRBEDIRrdz/888/SE1NRWpqKt566y3BcIcPH0b79u3xwQcf4Pz582BZFizL4uLFi/j444/Rtm1b5OTkKJF3Qgy8Y6K6AyV3S7tKypDLoB7D+a4F/AoeKX1WgX97NwmXRse9XXq5wnPZGKF2Sghjia3gouu4wNEzVoPxmw9qmwRBEIogWrlfvXo18vLycP78edx1112C4YYOHYpTp065hKu0tDR07twZ1apVA8uyuHDhAu6++26Ul5cHnntCNJ4b5lnuDzLxb1BPepRSDeoRhBNlhEOXYqiDKzyCIJTD2V4lG9Qz9WStSIN6fqNxH2i1X7l3f2c0OUcQBCEe0cr95s2bAQC9evVCzZo1ecN8++232LFjBxiGQXx8PFavXo1jx45h8+bNyMvLw6hRDlcrBw8exFdffaVA9gn/KOHQXiQqGtTz/t1j5V5hNd+XQT2jyBd6rDIZ5dmd8Al7gW7hlPKIHIN6BiubYIOzcq+R60LCWlhi+7ccg3qswHjHiUvtsqG2SRAEoQSilfu9e/eCYRifru4+/fRT1+fZs2fj5ptvdn2PjIzE+++/j5YtWwIAvv76azn5JZSAY1BPmXjcY3NXjHkWP/1FKjFZgwgEpNkFhPGVLgsI/UGMJbZbE5KR3a8YvTvyiVJH1QQKQYexzjnpYvxxgiAIQn9EK/dHjx4FALRu3VowzPr16wEAcXFxuOeee7x+ZxgGo0ePBsuy2L17t7ScErLgDIocg3oqCLuSVu49BmnZBvXUgRE8g+g7B6Tj60+gipyv+0m4JAjrElyTQGLdvYr5XQGuDZ6M81SUFXZREARB6IBo5b6wsBAAUKtWLd7fjx49ivz8fDAMg+7duyM0NJQ3XNu2bQEAp06dkppXQhZ8A6R6KnHV/7nXFMfLE57OrvCCROkLhskL/mcU2OYdDAViFji2ODwM6pGiQAQLgXZJehnUEwH1twRBEP4Rrdw7O9XS0lLe37dt2+b63KFDB8F4qlevDgC4fPmy2KSJAHCO014G9ZzXlTSoJ8EVntgoeaL2sPOjxWDvayWXlAY9UWMiqUq2ZarqF0MGnQjCbLgM6kmc3DH3Dh2Rdmj8lYnmBvW4Ukpw7aIgCIJQDtHKvdOI3sGDB3l///XXX12fO3bsKBjPpUuXAAARERFikyZMhjxXeFKFKXUN6vmCV/DT5Ryi5kmqhpLFF6hQKMmgnsz7COXx5QrPFYYmZwgfWEKhlFPHxRjUc4WhNkQQBGFkRCv3zrP2fFbuWZbFihUrAAAhISHo1q2bYDzHjh0DACQlJUnKKCEX55l790vuBvUCGKgFDOoFhjSDekYTxUjsIQjjYQmljZCMc3yT7ArP1JNAKhvU0wGXKzwD5YkgCMKoiFbuBw0aBJZl8fXXX+Pjjz/m/DZr1iwcPXoUDMOgd+/eiImJEYzH6VKvcePGMrNMSIEVmoVXAwkG9byyItugnjrPJNqgHukM+sLz+gM+X823MUNAuDS1DkAQBJeg7c+NY1CvKsWgfRkEQRABIVq5v++++5CWlgYAGDlyJDp37oxhw4ahXbt2+Pe//+0KN2XKFME4WJbF8uXLwTAMunTpEkC2CdHwjo9qG9Rzc4UnVfkWq5h5hiODeppAKyeEURFTN6n+EmIwdT0JdMbRgAb1yCAmQRCEeELEBoyKisKiRYvQt29fXLp0CTt27MCOHTsAVHW8o0ePxk033SQYx6pVq3Dy5EnXCj+hPtzZbxMY1BOAc7aZYxVbg5V7MqhnEpQyqOd+tpSB4/3TeyYIsxJcyqEMg3p85aObQb1r9nSC6p0RBEEoh+iVewDIzMzEjh07MHjwYERERIBlWbAsi7p162LWrFl49913fd7//PPPAwCSk5Np5V4zeAZI1QbNQFzhyTWopz38C/fGWOFQG7UeU260fCts2hrUc3fjEFCyRIC4n5P2VAyc3029IkuojiW2ggfcSQsd5VNkRYAgCIJQGdEr904aNmyIJUuWoLKyEmfOnEFYWBhq1Kgh6t61a9c6Eg2RnCwRIJ7DcZWBmgDwOUkgV0iSZlBPf42Km1+Se6yEt2BrbkNbBBFcuFzhSTWop/u4EghiDer5252mcRkwfIsDDljqfwmCIEQjW8u22WySLd5HR0fLTY6QCVcPVtugXgBBefImZsu7etvy3RMxpgJvmF0LRoH1+FduNIz4CIxSFwhxkHJA+MISW8FVqeMWKBeCIIggQdK2fMKE8Aorbq7wFBUEvM/6sZJ32xvfoB4/pDTogaq6Ggu43iu9XsPD8XPvoYxYYrs1IRm5K/DmngSSceZe8LoxyoFc4REEQYiHlHvL4z4oehvUUyJuFxJc4Xnje9B2F7a4BvXUQaxBPVrRMBLytuB6wr96R8IlQZgVmtzxg6EM6l1Lkt4ZQRCELEi5D0ZUN6gXo6bT3AAATPZJREFUgCs8910FIsKxYlcpFIXPETopfYFgpJUyKQo8x4sDKf66wjGoJ7ByT++I8IUlFErRfanQswoY1ON4EdEHar8EQRD+IeXe4ggZzlNkmPaUDSS4whNU5kRMPHA8+PgNLQ+xOw8Yz/P4JHxYCMatgpFySBBmw9VeLaCzi0esQT13pBjfUwkfBvWC6/0RBEEEBin3wYTqq6N88YtM0xXMfeVeP4N6vuAvRu3zoYfxJyOtsAO+LSvLj1TC/cYqDuIagm2D3hfhg+BauRfAIGfuLWHckCAIQgdIubc6LN/Kvfu2O+WTdJzulzkwSzSox8papVASEkCsDWmDVoAUheBE7mSkNXboSDCoZ4j2IVzm5AqPIAhCPKTcWxzBU3WKDObCBvWk4/vsPOdssyYG9TwwhPDDxXg50ptrBvUCfVe+DOp51HF3JYDkToIwJmJX5GkSyA0hg3oqd3TOjVOW2EVBEAShA6TcWx6eAdLIBvU4eZNipV47QYB/V35waHZqPaXceDUr9uB4vabHOdlCBvWI4EXsbjahsVZIkVd5jPXRmbMstV+CIAixkHJvcdQ1qCffFV4g6XKTVWew98q/gKDkGU5LHZ/EHE+ULhFvwZaES4IwH1JXgWn7N8DX/2kJ7aIgCIKQByn3VodjG0dtt3E+rN36vdU7b2Li0cegXgCGAxVEF9HHBDJvwH7uJTwj6QAm4VqVoMkZwvKI7ZSElGeDGNTjxWDZIQiCMCKk3AcN/KNiYCsUvlbufSbrI0qpBvUk3ic+I36++7tOaIH6yhpJk2aCVlwJd1zHNCSOD9aYBJLwzIZYJRdeHKDz9wRBEOIh5d7icAfFqmFTnbE8kBVtPwb1GP7Puqzc833TQRY0hDxmRXwY1PN8z1ZQAYIJUv4JX1hDiRRbx/08q5BBPY16PWu8C4IgCO0h5d7yaGlQzwEDVhFXeL7j0M+gHjdZEkAUwfA6F71nMyC0UkuKQnAS1JM5kqq8QGBBBV8FfBnUI5snBEEQoiHl3uKw/AuOqHLtpUDkTiREFki6HPe8qhnU85GolPtUJJjlVidcY85KFYiwuUkSLgnCfEg2qBcM7dzvmKaPQb0gKHmCIAhVIeXe8mjnnzYgV3i8BvV8CBSqn7kXhr8YdTCop8NipBkEr4CtLPOeLhE6LqL2URdCDuQKj5CDJSy0u6q4zDP3upWB/7ZJ7ZcgCMI/pNxbHYGVe+EVfRmRO5HgCk9slP6Da+QKT8gWAG33tTieFhZIuDQy9H4Id+Qa1KNqpB98Y6olJl0IgiA0gpT7oEIPy+IKGdRzi0d/g3o8aeqwR57EHX4CPl/Ne7tQvSTMBCn/hC+sYZtBLYN6iqwIiIYUeoIgCHmQcm95nNtR3S+5bX1XQSkNKEaOQT2fAf18DwzxK/LBJYCoZaBKbqyqzqmwTLC9XvNzrT54GdQjRSEoCW6DenLrvJZH+dwQkVRQv0+CIAiRkHJvcYRWIhQRdUUY1BNaWfcKKmHQ1kVOF5uohsIHiTmeXNuCG2Dt5rvfWdaewiV3FwkpkARhBYJih4fBDepRf0oQBCEPUu6DBobXaJ06BOAKT7JBPYbzXQsMY1BP8xSDeUKBBE0zIWRQL4grMCECSyiUcsZ5QYN6mvp/EfyFDGISBEGIh5R7i+Pcjqq1QT35UUoTrjRzhScoKFlAGDQ12tqRIOHS2ND7IdxhZO7mCY7t33LHrmAoG4IgCPNiSuV+48aNGDhwIFJTU8EwDJYvX+73nvXr16Ndu3YIDw9HgwYNsHDhQtXzaQy0nIXnc4Un7V6hlXt3WYu7FVpfXFnRQxikc8S8aOoKjwRdU0Hvi/CJJbpUOXWc58E9DeqpXTYeY6gldlEQBEHogCmV+8uXL6N169b43//+Jyr8kSNHMGDAAPTq1Qs5OTmYPHky7r//fnz//fcq59SgsPxKc8DwuMKTHL9oxcxjW77aBvUE80UCiBIYc6XMiHkifEHKO6EElqhH/sZSMWOaQfplMohJEAQhnhC9MyCHfv36oV+/fqLDv/3226hXrx5mz54NAGjatCk2bdqEOXPmICsrS61sGgLh4VuBwdJrwJVgUM/zuoRzgpzjgXq4wvNx5l7T3BhE8NITPreIgbvCk3C/PjanCJkYcxKJUBuxymHQrhbzlo/WbYU7hpJCTxAEIQ9TrtxLZfPmzejduzfnWlZWFjZv3ix4T0lJCQoLCzl/poTlm4VXd9Bk4GZdXOrNYncVeA78egkCesofOjxz8OpGJGiaCXKFRwQtosd5gd91M6gnjMugXvAOQARBEKIJCuU+Ly8PSUlJnGtJSUkoLCzElStXeO+ZOXMm4uLiXH9paWlaZFVxnMO0kEG9wAZv+a7wAkGLM/fiDeoRVoXPFCUJl8aG3g/hjrM+SDaoZxClVjuk7FZSt2yc5k2CdhcFQRBEgASFci+HqVOn4uLFi66/EydO6J2lwGBc/1NxxbfKoJ50V3h8qw0itujrsi3f3baA66Lm+SDRR2BqKtCCkRAp6ZLGRMgVXvApbYQUrLHDI9Bx3ulKx8Ogntojjo/OtMrrD7VfgiAIf5jyzL1UkpOTkZ+fz7mWn5+P2NhYREZG8t4THh6O8PBwLbKnKizv+mMVahnU8x9W4LpEg3rC3wODDOrxo5ZwZUyRzTtXJFwaG3o/hDvkCs8HYsZaHcohCEqeIAhCVYJi5T4zMxNr167lXFuzZg0yMzN1ypEeeLiZUUIn9WFQj+Gb/feFmQzq+bgaDDKhYWG1N6hH9vTMBSn/hC8ssRVcziDk16CeFuXikW9e73zUfgmCIPxhSuW+qKgIOTk5yMnJAeBwdZeTk4Pjx48DcGypHz58uCv8uHHjcPjwYTz55JP4888/MW/ePHzxxRd49NFH9ci+trBuK/eaGdQLIH53g3pS4lF8O6XI+CyxjVM8wStbBdd7Njte2/KDrJ0SDoJ7Msd6BvUIgiAI/5hSud+xYwfatm2Ltm3bAgCmTJmCtm3bYvr06QCA3Nxcl6IPAPXq1cPKlSuxZs0atG7dGrNnz8b7779veTd4vvC3XV9sLBx4DeopD/cooFbCBwkXRkSVyQaG54vzH48Eee0vELpBK3sEL9R9+4F3mdztZw0UfsbDFR69NIIgCFmY8sx9z549fa7ELFy4kPeeXbt2qZgro8IzKKtuUE/O8O+9q8A9DiGZXY/h3z0vko8fEKoTqFDIf78Y446EUfByhUeutAgRWEOhFDnOC/4sZkVfH4J7J0bwUFFRgbKyMr2zQRCaExYWBpst8HV3Uyr3hHj8ucILSNb1HOx5I/N1Ql1EnH7CsDyTAkrglT+Rgg0JH/IwpM7FMq5z/FWO8YyYUYIg+CBXeCIRGt/IoB6hISzLIi8vDwUFBXpnhSB0wWazoV69eggLCwsoHlLurY6ms+08BvVE32omg3riJzHUxAALKbrDfRfXBPlAC0aKy2f32+h9GBZrrMgSamMJ2wyix1J/v+trUI+vzQbdpEuQ4VTsExMTERUVRTutiKCisrISp06dQm5uLtLT0wOq/6TcBwlCBvUUHSxdZ+aUEQSE4uG9rrBQ5p0GucIjCLNAyjwBkDIoHvcZcz/n63VQuCwx6UL4pKKiwqXY16xZU+/sEIQuJCQk4NSpUygvL0doaKjseExpUI8Qj7pDorArPOdv4lfWxQsMHDs/ou9SDv7TByREGoVAFTv++sW/LZ9jf4GqgO74U+ZI2QtOpCqHQbFi6K9MBA3qqYRHmZNCH1w4z9hHRUXpnBOC0A/ndvyKioqA4iHl3vK4n7pX26CeMyXlV+6FhC31ztyTYMGH0YReNbIjd3KAZFEDIXbjDUFYjkDHebf7GO8Jez1w9cnGGn4IFTCajEEQWqJU/Sfl3uIIKSqaGdQTSCCQCsxVvDUaCMQa1KNxSRbGXFF1mxBjSLg0AyQYEhycei4Z1PONIWYmlT3WRxAEEayQcm91dBonJYtGMoVyXbblu39mWZ6r2kDnij1R5h1I2Q7qrgTQ+zAOnu+CXOERYrBEGzatQT0u7u/C1X6DbdKFsAwMw2D58uV6Z4MIEki5tzhVgyIEDOopCI/gHIg1ezF3slVLM4rC9ySE8Rau1c2P0Z6WIAgpkDIoFgkG9ahMCYLDmTNnMH78eKSnpyM8PBzJycnIysrCL7/8okn6YicOGIZx/YWEhCA9PR1TpkxBSUmJ6nnMyMjAa6+9pno6hAOylh+kKLNCIWxQT/rWOn+GsIRyoIOg4e19jfbjG4jADTHx3S/CoB4JvbpDBvUIPsSOd86+Iyh2eBjUoJ6vkqf2SxiRwYMHo7S0FB9++CHq16+P/Px8rF27FufOnVM13dLSUsn+0BcsWIC+ffuirKwMu3fvxqhRoxAdHY3nn39e03wQ6kIr95bHbeXexAb1/F9X2aCeUJkZ4qwioTbidpFQXTAKQtvyCcL6yBjnOWEFDOrpONaR5fzghGVZFJeW6/Ints4VFBTg559/xn//+1/06tULdevWRadOnTB16lQMGjSIE/bs2bO47bbbEBUVhYYNG2LFihWc3zds2IBOnTohPDwcKSkp+Pe//43y8nLX7z179sTEiRMxefJk1KpVC1lZWcjIyAAA3HbbbWAYxvVdiOrVqyM5ORlpaWm45ZZbcOutt+K3335z/T5y5EhkZ2dz7pk8eTJ69uzpMx8sy+KZZ55x7V5ITU3FI4884gp/7NgxPProo66dAwBw7NgxDBw4EDVq1EB0dDSaN2+OVatWiSl2wg+0ch+kVBnUC2Am3IdBPX+xKjX/bpwh3/+qAyGMIgtlrDKlz/BuzaAzn2aA3g/hjrM+SHaFFxT1yDijpwPuGEoKPXGlrALNpn+vS9r7n8tCVJh/FSkmJgYxMTFYvnw5unTpgvDwcMGwzz77LF555RX83//9H+bOnYthw4bh2LFjiI+Px8mTJ9G/f3+MHDkSH330Ef7880+MHTsWEREReOaZZ1xxfPjhhxg/frxry398fDwSExNdK/J2u130Mx48eBA//fQTRo4cKfoeoXx89dVXmDNnDhYtWoTmzZsjLy8Pu3fvBgAsXboUrVu3xgMPPICxY8e64pgwYQJKS0uxceNGREdHY//+/YiJiZGcF8IbUu6tDq/BN7UGzQCs3co2qKeOEMYwws/AaFKW/iHZh39yKtBVWnKFZ348FYOg2m5NyMYSOzxEG9Rzhy+sMcY5d6j9EkYjJCQECxcuxNixY/H222+jXbt26NGjB4YOHYpWrVpxwo4cORJ33303AOCll17CG2+8gW3btqFv376YN28e0tLS8Oabb4JhGDRp0gSnTp3Cv/71L0yfPh02m2OjdcOGDfHKK6945cO5Iu+Pu+++G3a7HeXl5SgpKcEtt9yCqVOnSn5uz3ysXLkSycnJ6N27N0JDQ5Geno5OnToBcExA2O12VKtWjZPH48ePY/DgwWjZsiUAoH79+pLzQfBDyr3F4aj2ag+MihvU878tv8qgntrChzGEG70JLtnK+2FJuCQI80Dt1QeijpqJd28bMM4z9zTUEteIDLVj/3NZuqUtlsGDB2PAgAH4+eefsWXLFnz33Xd45ZVX8P7773NWxd2V/ejoaMTGxuL06dMAgD/++AOZmZmcPqtbt24oKirCP//8g/T0dABA+/btA3quOXPmoHfv3qioqMBff/2FKVOm4L777sOiRYskxeOZjzvvvBOvvfYa6tevj759+6J///4YOHAgQkKE1cxHHnkE48ePxw8//IDevXtj8ODBXhMihDzozL3lEfBzr0paKhrUE/hZF1d4vAaESYjUH3lbcL3huV9A4qTXbjAE+wnSGAj/WGMruEIT3pyFe3KFR2gPwzCICgvR5U/qxGBERAT69OmDadOm4ddff8XIkSMxY8YMTpjQ0FCv56usrJSUTnR0tKTwniQnJ6NBgwZo3LgxBgwYgGeffRaLFy/GX3/9BQCw2Wxe/WBZWZnffKSlpeHAgQOYN28eIiMj8dBDD6F79+689zq5//77cfjwYdx3333Yu3cvOnTogLlz5wb0fIQDUu6DBG0N6lUhtHIfmFKkhUE9z+gFlDsryIISIGWWMAOkzBMAKYO+EXJ/J2BQT/U2Re+KsA7NmjXD5cuXRYdv2rQpNm/ezFGsf/nlF1SrVg116tTxeW9oaCgqKipk5dN5Rv/KlSsAgISEBOTm5nLC5OTkiIorMjISAwcOxBtvvIH169dj8+bN2Lt3LwAgLCyMN49paWkYN24cli5disceewzvvfeerOcguJByb3GEhuOqM6iBRO7LoJ7E+GXnQ3uBQGC6wvF/kk+sAwv4M6hHyoOxIFd4BB9SJ3toO79+kEE9wkycO3cON954Iz755BPs2bMHR44cwZIlS/DKK6/g1ltvFR3PQw89hBMnTuDhhx/Gn3/+ia+//hozZszAlClTXOfthcjIyMDatWuRl5eHCxcu+AxbUFCAvLw8nDp1Chs2bMBzzz2HRo0aoWnTpgCAG2+8ETt27MBHH32EQ4cOYcaMGdi3b5/f/C9cuBAffPAB9u3bh8OHD+OTTz5BZGQk6tat68rjxo0bcfLkSZw9exaAwwr/999/jyNHjuC3337DunXrXPkgAoOUe6vjPkC65BUDGtTjjc1/POoN/2JjlralSklI9uFHN4N6AaVKKAmt3BNysES9Ud2gnvYTH2QQkzAqMTEx6Ny5M+bMmYPu3bujRYsWmDZtGsaOHYs333xTdDy1a9fGqlWrsG3bNrRu3Rrjxo3DmDFj8PTTT/u9d/bs2VizZg3S0tLQtm1bn2FHjRqFlJQU1KlTB3fffTeaN2+O7777znU2PisrC9OmTcOTTz6Jjh074tKlSxg+fLjfPFSvXh3vvfceunXrhlatWuHHH3/EN998g5o1awIAnnvuORw9ehTXXXcdEhISAAAVFRWYMGECmjZtir59+6JRo0aYN2+e37QI/5BBPYtTdVbN87oDRcdK95V75wkAFQQB3m3/pOlqgtFWPtWV9Rg3mZaES4IwK7QKzINcg3pqQX0rYULCw8Mxc+ZMzJw502c4vj6ooKCA871Hjx7Ytm2bYBzr16/nvT5w4EAMHDjQb17F9oPPPvssnn32WUn5yM7ORnZ2tuA9Xbp0cbnGc0Ln69WDVu6JAPDsKAIZnOVtp1XLFZ4veBU8EkwMwDWDehquvtFrNxa8rhHdBBqanCHEYLRJTGnImPDmC+veVgxiUI8gCILwDyn3Fsc1KDIM+AZ9RYUYpysb7kX+oCLSFQrBv11faYN6HvEJCjckdCgBKV2EolCzJED9im9EjGmaKvj0rgiCIJSAlPsgRZFx2kckks/dyxTCdHGF5+s3EiY1hXeSSEEPUK5vQq7wAkuKUBgyqEfwIdmgniXqiTlnuKosBnjn3xrvhSAIQl1Iubc6zoV7QKahHSk4LcYrc2ZPnEE9dQZ7ns29/OnreJbTnKKbsqixXZM/TiptM0Fbegk5WOJsvtghUcj9neujgEE9tSawfURrifdCEAShEaTcWxxhwVYBV3iecWvmE9ddcDeIQb0gWbEPkse8hvfDernCC64CMTy0ske446wPUpVDatdOjOJqliAIghALKfeWR0ulNxBXeH620wr8rN4eBB9HDhhx4dSGVjM8UdOgnohdJPQ+DIP7u2DVsjFCWA5r7PJwuarxA99qvXs0+hrU42aPvJUQBEGIhZT7IMEh1HoP+ooOle6u8PwkIGaMFlKcucef1T5q4IxeyG2QuskaDbVEK7nxqq+s0Uo9QZgVaq9yENqCTwb1CIIgzAAp91ZHQClVx6BeAIOzbIN6emwb5EvTIC75CG1d4WmWEiEGXld4bvWB2kxwIrZPsMbKvRM/zyJ05l5nnJP6ZFCPIAhCHqTcBwmaGNTjNVweiEE9/fDeNWAc4ceJLtvAg0A5IoN65sdaShpBSCDQPto1rgit2qtlUM/6YwtBEIQWkHJvcfx5sg1sJUtBV3gScI/b9UlLRZd34Z4EE91hlX4HYgzqKZwkQRCKIXelN+h2eBjIXgi5wiOsCMMwWL58uerpZGRk4LXXXlM9HT4WLlyI6tWr65K2Wri/t6NHj4JhGOTk5OiaJzGQcm95eAZt1QZyb4N64lfu/fmn5kc7V3hC6RtHKCIcqLKjQcDPPSdd5VMlZEKu8Ag5WMMoplgPMn5+9zSop3rZCI+61ngvhFU5c+YMxo8fj/T0dISHhyM5ORlZWVn45ZdfXGFyc3PRr18/HXPJj14K+YMPPgi73Y4lS5ZonrZc0tLSkJubixYtWuidFb+E6J0BQl24G+z4ViIVhGNQj/UZv5h0hVf/+SYPdDKoRyiCIRfKWMDLoB6tHBGEaZDrCi+40cCnPUFYiMGDB6O0tBQffvgh6tevj/z8fKxduxbnzp1zhUlOTtYxh8aiuLgYixYtwpNPPon58+fjzjvv1DtLorDb7aZ5j7Ryb3Usb1BPeRhGgis8j/OJJAtpC195a2pQj164oeCdfOHoKvS+CGFcLtfMPIkn1raOXIN6arUhj3g57izJFV5wwrJA6WV9/kQKyQUFBfj555/x3//+F7169ULdunXRqVMnTJ06FYMGDXKF49ve/cUXX+CGG25AZGQkOnbsiIMHD2L79u3o0KEDYmJi0K9fP5w5c8YVR8+ePTF58mRO+tnZ2Rg5cqRg/l599VW0bNkS0dHRSEtLw0MPPYSioiIAwPr16zFq1ChcvHgRDMOAYRg888wzAICSkhI8/vjjqF27NqKjo9G5c2esX7+eE/fChQuRnp6OqKgo3HbbbZzJDF8sWbIEzZo1w7///W9s3LgRJ06c4Pw+cuRIZGdnY9asWUhJSUHNmjUxYcIElJWVucJkZGTgpZdewujRo1GtWjWkp6fj3Xff5cRz4sQJ3HXXXahevTri4+Nx66234ujRo67ft2/fjj59+qBWrVqIi4tDjx498Ntvvwnm23Nb/vr168EwDNauXYsOHTogKioKXbt2xYEDBzj3vfDCC0hMTES1atVw//3349///jfatGkjqqzkQiv3Fsfdw7P6BvWqtuVXpRSIQT0J+VR9ZYZWfgDjWYdX47Xzr/LR+zcTtFJLEDLhTFgbrccngoqyYuClVH3S/s8pICzab7CYmBjExMRg+fLl6NKlC8LDw0UnMWPGDLz22mtIT0/H6NGjcc8996BatWp4/fXXERUVhbvuugvTp0/HW2+9JfsxbDYb3njjDdSrVw+HDx/GQw89hCeffBLz5s1D165d8dprr2H69OkuhTQmJgYAMHHiROzfvx+LFi1Camoqli1bhr59+2Lv3r1o2LAhtm7dijFjxmDmzJnIzs7G6tWrMWPGDFF5+uCDD3DvvfciLi4O/fr1w8KFCzFt2jROmHXr1iElJQXr1q3DX3/9hSFDhqBNmzYYO3asK8zs2bPx/PPP4z//+Q++/PJLjB8/Hj169EDjxo1RVlaGrKwsZGZm4ueff0ZISAheeOEF9O3bF3v27EFYWBguXbqEESNGYO7cuWBZFrNnz0b//v1x6NAhVKtWTXQZP/XUU5g9ezYSEhIwbtw4jB492nUk49NPP8WLL76IefPmoVu3bli0aBFmz56NevXqiY5fDrRyH6S4hN+Axm4FV+4l3CvvTL9yeKwvXLtIQpD+qGBQz6OK08qRsfH3fky9IktIx3n8XOLknCXqiZQz9waaDHOaNyFbGYQZCAkJwcKFC/Hhhx+ievXq6NatG/7zn/9gz549fu99/PHHkZWVhaZNm2LSpEnYuXMnpk2bhm7duqFt27YYM2YM1q1bF1D+Jk+ejF69eiEjIwM33ngjXnjhBXzxxRcAgLCwMMTFxYFhGCQnJyM5ORkxMTE4fvw4FixYgCVLluCGG27Addddh8cffxzXX389FixYAAB4/fXX0bdvXzz55JNo1KgRHnnkEWRlZfnNz6FDh7BlyxYMGTIEAHDvvfdiwYIFXhPyNWrUwJtvvokmTZrglltuwYABA7B27VpOmP79++Ohhx5CgwYN8K9//Qu1atVyldfixYtRWVmJ999/Hy1btkTTpk2xYMECHD9+3LUD4cYbb8S9996LJk2aoGnTpnj33XdRXFyMDRs2SCrjF198ET169HDtRvj1119x9epVAMDcuXMxZswYjBo1Co0aNcL06dPRsmVLSfHLgVbuLQ/P2Xe1BnLXyr0acfNf1sygnprHGwjZqKFr81tpFvGiqS4YElISCNFYoqoo1Cl6GtRTvXB8GNSzxoshpBIa5VhB1yttkQwePBgDBgzAzz//jC1btuC7777DK6+8gvfff9/nlvlWrVq5PiclJQEAR/FLSkrC6dOnpefdjR9//BEzZ87En3/+icLCQpSXl+Pq1asoLi5GVBT/M+7duxcVFRVo1KgR53pJSQlq1qwJAPjjjz9w2223cX7PzMzE6tWrfeZn/vz5yMrKQq1atQA4FPQxY8bgp59+wk033eQK17x5c9jtdtf3lJQU7N27lxOXe/k5Jyic5bV792789ddfXivwV69exd9//w0AyM/Px9NPP43169fj9OnTqKioQHFxMY4fP+7zGTxxz0dKSgoA4PTp00hPT8eBAwfw0EMPccJ36tQJP/30k6Q0pELKvcXhDomM13W11icYfyvaIhKWZoxP78HfAis9ItDouKWBMGzGCILwg8ugnu7jgwERPHOvr0E96nEJFwwjamu8EYiIiECfPn3Qp08fTJs2Dffffz9mzJjhU7kPDQ11fXbuOvO8VllZ6fpus9m8Vrjdz6F7cvToUdxyyy0YP348XnzxRcTHx2PTpk0YM2YMSktLBZX7oqIi2O127Ny5k6NgA1Xb9uVQUVGBDz/8EHl5eQgJCeFcnz9/Pke5dy8HwLss/IUpKipC+/bt8emnn3rlIyEhAQAwYsQInDt3Dq+//jrq1q2L8PBwZGZmorS0VNJz8b1Hz7xqDSn3VkfNpWUBg3rcVU6RQ7Vsg3rKiwJOcVDwd05ePadPSDQxAizLyt5CT4qAeeFrf+7vk9on4QtLGG5TxbaOjDFdKp4G9dyP37EWMHRIBBXNmjVT3K99QkICcnNzXd8rKiqwb98+9OrVizf8zp07UVlZidmzZ8Nmc5zCdm7JdxIWFoaKigrOtbZt26KiogKnT5/GDTfcwBt306ZNsXXrVs61LVu2+Mz/qlWrcOnSJezatYszabBv3z6MGjUKBQUFirnla9euHRYvXozExETExsbyhvnll18wb9489O/fH4DDAN/Zs2cVSd9J48aNsX37dgwfPtx1bfv27YqmwQedubc4VdZ/wTvoKyrE8LjCCwz/rvCqLgWfQT09jgQErWglws89YRxogoYASBkUjftgQgb1CEI0586dw4033ohPPvkEe/bswZEjR7BkyRK88soruPXWWxVN68Ybb8TKlSuxcuVK/Pnnnxg/fjwKCgoEwzdo0ABlZWWYO3cuDh8+jI8//hhvv/02J0xGRgaKioqwdu1anD17FsXFxWjUqBGGDRuG4cOHY+nSpThy5Ai2bduGmTNnYuXKlQCARx55BKtXr8asWbNw6NAhvPnmm3635H/wwQcYMGAAWrdujRYtWrj+nBbt+VbZ5TJs2DDUqlULt956K37++WccOXIE69evxyOPPIJ//vkHANCwYUN8/PHH+OOPP7B161YMGzYMkZGRiuUBAB5++GF88MEH+PDDD3Ho0CG88MIL2LNnj+oTyKTcBynKKIb6GNTj5IBV68y9j5V7988uw4QkBOmPCgb1yM+9qeBduWdVmswkTINU7wmWaOemMwjjYbPHfb7BCjsqCEsSExODzp07Y86cOejevTtatGiBadOmYezYsXjzzTcVTWv06NEYMWIEhg8fjh49eqB+/fqCq/YA0Lp1a7z66qv473//ixYtWuDTTz/FzJkzOWG6du2KcePGYciQIUhISMArr7wCAFiwYAGGDx+Oxx57DI0bN0Z2dja2b9+O9PR0AECXLl3w3nvv4fXXX0fr1q3xww8/4OmnnxbMS35+PlauXInBgwd7/Waz2XDbbbfhgw8+kFMsvERFRWHjxo1IT0/H7bffjqZNm2LMmDG4evWqayX/gw8+wIULF9CuXTvcd999eOSRR5CYmKhYHgDHJMPUqVPx+OOPo127djhy5AhGjhyJiIgIRdPxhLblBwmMl3EcVRJx/MNxhScQVKLwJBReM/FFyKCeVukTkmDByhbQ5brCo9Vi40Cu8Ag5WKMNq2RQj9oUQXgRHh6OmTNneinNnriPSRkZGV5jVM+ePb2ujRw5knNmPzQ0FPPmzcO8efME03H34w4Ajz76KB599FHOtfvuu4/z/a233vJytxcaGopnn30Wzz77rGBao0ePxujRoznXHnvsMd6wSUlJPu0DuD/TwoULvX5/7bXXON89nxOAy/+8k+TkZHz44YeCabZt29Zri/wdd9zB+e7rvfG9szZt2nhdmzZtGsfVX58+fdCgQQPBfCkBKfdBhbtBPR4r+lIROHOvFEKr5/yu8LQTPLiLB2qbJgwOzLJSRitH5sYs9YxQhuBur37GRBMa1KP2SxCEmSguLsbbb7+NrKws2O12fP755/jxxx+xZs0aVdMl5d7qaDnbzusKT22DesrDMPJFiKCWJXWGI56yrOz5Fv7VOxEr97SwpT88fs2tsRpLaIEldnyoMghpoPD7MKhHTZggCDPCMAxWrVqFF198EVevXkXjxo3x1VdfoXfv3qqma+oz9//73/+QkZGBiIgIdO7cGdu2bRMMu3DhQofS5van9pkHI8BZV+Y1qKd2yvIR4wrPtXKvt0E9HbR6PZSWYFoJY+nMPUGYGmqvvuAzouf+mQzqEQRBBEJkZCR+/PFHnDt3DpcvX8Zvv/2G22+/XfV0TavcL168GFOmTMGMGTPw22+/oXXr1sjKysLp06cF74mNjUVubq7r79ixYxrmWC8EFECrGNRTSfjwbVBPaa8AhCK4GVdUfuLDf3xBNO9hWPwZ1COCE7H9gaUMt5mu3nN3/rm3W0u9F4IgCJUxrXL/6quvYuzYsRg1ahSaNWuGt99+G1FRUZg/f77gPQzDIDk52fWXlJSkYY71oWoOXmuDeuy19PkHYzFjNKPJDgORCBnUM5v8ZDE0qxYiEqK6YBxoKz4RvIjsFf2dufcywkttiiAIwgyYUrkvLS3Fzp07OWcWbDYbevfujc2bNwveV1RUhLp16yItLQ233norfv/9dy2yayAcg/VfZ4qw9cj5a1cEBIHKSuDKBd/RiTCop45SLu9MtFL4Mqin9xyE2qj1fLpP3rhx6MIh74uMw8orrRyZG3p/wYXzfQfn7g0fz8yywGk3+edKAX843vaizZn7kooSFJcVo7isGBVshTppEgRBWBBTGtQ7e/YsKioqvFbek5KS8Oeff/Le07hxY8yfPx+tWrXCxYsXMWvWLHTt2hW///476tSp4xW+pKQEJSUlru+FhYXKPoQG7Dt5EdsOnwMigCulFViecxLZAFbvy8Xr5Q4Fxm7jGajz9gHv3QhUlgH3LQNqtwdO5QBgAcYOJDUHQiOBy2e4910bnKujCJXXPofYRc4f8QgR6cxp7GGv83mba2dAwXGgtBgIixKXnq+sgG+7vfGEQzPJq/50qshQu6LxTlo3CTbYEFkn33Xt4bWrOWGuVlzFjrwdnGvlbHnVF9YG5/ynLeSyIz0fgi1vWyI0xanMvZXzFv659A8qURmkih3hj5NFJ7Hoz0Uoqaga508VnQJg8rP6Qp0iywLF54CQcODkb44x08nfa/kicr9ZyRyKYvqv0zH91+map0sQBGF2TKncyyEzMxOZmZmu7127dkXTpk3xzjvv4Pnnn/cKP3PmTJ/+HY3KW0v/hW1n1qFaRQGASpwJsQEIx+XSclwoLnO98U4Z8ehTvBL3XVkEvMIC9lAgqQVww2PAgr5VEX50K5DaFji1y3/iNoeClmHLRwYcSlVmgyTUOh+Gwe3q4GxRKb767R8AQH7hVe694dW8ohtg34IEpgCj7atR451wbAq/iqOVSfgYU11hONv+N7wM9HnOfz49Of0HsGY6UHoZSG6JVrUHArnCwcPsNqTEReDS1XLEhF8r0GsCVajYyQyTInXhc+lDXfH8t/sx7ZZmPsO9PLgVxn28E+N7+p7M8aRPs2Ss3JOLUxevAmwY2IowMPZSbPxnIwAgxK1arf/nD9HxhiAStcN7oF+rnli47zPUjA5DRnwCeqb19Ap7d6c0/Jl3CZnX1ZSUd0J5nErZhn82eP2WEJmAMFuY1lkiDEAlW4lKtpJz7b097+GrQ1/xho8Ji9EiW9pxciew8BagrJj/d8YOfD3R8flUjvsPjn9ObFUzd5y0Mq9cwbbICK/phOjQaLROaK1BPgiCIMyNKZX7WrVqwW63Iz8/n3M9Pz8fycnJouIIDQ1F27Zt8ddff/H+PnXqVEyZMsX1vbCwEGlpafIzrRH/XDyIHRFXAIRzrkdXVK2M3tUhDROzM4H3pwH/uK2+X8oF/uLxvehU7EOjhIWDmg2AzuOBwlxs3b0HAFCvbl2kZ96BHTc7dlhculrmUu4TYrj5Q2wqcNfHwOn9wPqZAAAbWAy3/4A02xngElCHAerYzyIxZg1wzW4iRwA4Jnwkwye/fQQc+uFaHL/g5TuHoHDlJcD9UT8cCBTmXssX8CsAhADMrjJOVLe1rY3X1x5Cl/rx8vJiMdql18Cyh7r5DVevVjS+f7S75Pg71YvHr1NvwuEzRcj+3y+4NWUm2jYocv1eWcniz7xLqFcrGpFh3rsDGDBoVrMZakTUcF2zM3bUjKxS1Cd1vs9nHmbe3kpyvgl1yG6QjUV/LgILFuH2cDzb9VmE2R0KfcPqDWG3ydshQpibr//+Gl///TXvb33q9kH9uPqu73bGjqx6WVplTQXcPMiUFAFHfwY+H8oftNskYPt8oPQSsOtj7m+R1QHe7fAqreKf2AIAGHPxEoY1HorKaxP1zt04IbYQhNpC1UmbIAjCQphSuQ8LC0P79u2xdu1aZGdnAwAqKyuxdu1aTJw4UVQcFRUV2Lt3L/r378/7e3h4OMLDw3l/MzLXN7od1Y6tB8AgPf9H2CvLEMKy6Fq7Fyri44GDQGJFPnD6z6pt9T2nuhRqF3U6Af94uBac+g/w+zLgqzFV12o2BB7aAtivVaXB7+GpYxvw1+ki7B12MxBRNRhXiwjFmke74w+hVc5mgxx/8fWBpWORZXfbMn3HfFSumwnbuUNodPhD1+Xb29UBnEcHpS4rs6xjO+LJ3ziXGy3p5R32wlHOV05KjA1IaQMAqBEdhu1P9UaoXb1tnY2TquFA/iUMap2qWhpCzBjYHLfP+xWP3NRA87R9UT8hBjnTb4aNb2t8Y+3zQ+jDQ20ewkNtHtI7G4RBaFWrFUJtoSirLOP9PTo0GlPaT0Gdat5H80xP3l7gC4+Jye5PAPYwxxn7sGig0wNA4/7AsV+44cJigFZDvI/eAUBEdbVyXJVEpwcdiwkEYQFGjhyJgoICLF++HADQs2dPtGnTBq+99priaWVkZGDy5MmYPHmy4nET5sGUyj0ATJkyBSNGjECHDh3QqVMnvPbaa7h8+TJGjRoFABg+fDhq166NmTMdSutzzz2HLl26oEGDBigoKMD//d//4dixY7j//vv1fAzF6dftPvTrdm1A/3IMsO9Lx+fQcCD62mC5d4njz0nj/kDLO4FzfwNll4FqqY5z9j/PBta/5AjTeZxj233z27jK/YRtgI27Ff37yd1RWl7Ju1LaMKkaGiZ5b8HnwLclMq0zbDFJwDmusbMRPZpVKfcntgJvdgJCwoCO9wPtR3rHU1EOnD3oWJE4thn47gnfeXEnojowbhNPfqOBqKqV+rAQdbfmf/FgJnYeP4/uDRNUTYePFrXjsP+5LPG2FDSEV7EnCCJo6ZTSCZuGbuKcq3cnKjQK4XbzTeJ7wTexvfGVqs9hMcCAV4HWQ7zDxSQA6V34442sDjx5BCi5BBzZ6LBpk9hEkSwLEpMM1GqobhoEoRD+jLTOmDEDr7/+uk/bL1oq5M8884zryLHNZkNqair69euHl19+GfHxtOPUKphWuR8yZAjOnDmD6dOnIy8vD23atMHq1atdRvaOHz8Om5vSeeHCBYwdOxZ5eXmoUaMG2rdvj19//RXNmvk+C2xqGvcD/vgGAAs06O2YyXcnqqbDOF5iU8eZ+5oe553bDgPO/OkId+PTjmue21pt3kqe3cbwKvaiSe8CRCcCl6/tvWdsjgmHOI/VlUl7HNv53Tl7wPHvN5OAhKZAckuHQFJwAvhfZ8fkhSfRiUDDPkC1ZMeEhpPaHYCTbrsHwmOB6vofzYiLCsWNTfRz42hExZ4gCIKPqNAoRFl9FbhOx6rVd09l466PHTvi5BIV7/ir4ft4kmJ0fkCbdAhCAXJzqww0LV68GNOnT8eBAwdc12JiYhATYywbHs2bN8ePP/6IiooK/PHHHxg9ejQuXryIxYsX6501QiFMq9wDwMSJEwW34a9fv57zfc6cOZgzZ44GuTIQLe8Amt/u+GyzAfm/A+f+cijE/WdXbaUXIq4OcOcC7+vX3Qj8/RNw71Ll8ww4BIknDgGH1wNnDgBNbnHk/+YXgLROQGW5YwKgRl1H+KknHUq4LQSoKAM+znZcn3+zuPQa3ARkzwPy91cp94PeBNrdB/w3o8olYGR15Z6RIAiCIJSg9zMOK/ipbYFf51ZdT+sSmGKvJQ/+7JArMifonRPCILAsiyvlV3RJOzIkUpTrVHc7X3FxcWAYxsv2l+e2fHd69uyJY8eO4dFHH8Wjjz4KoMp156ZNmzB16lTs2LEDtWrVwm233YaZM2ciOjoaAHD69GmMGTMGP/74I5KTk/HCCy+IeraQkBBXHmvXro0777wTCxZUyfp8xways7NRvXp1LFy4EIBjt8EDDzyAv/76C0uWLEGNGjXw9NNP44EHaHLOCJhauSdE4L6yntQcGLZEOKxY7vnCYVgvIi7wuHxRv6fjz0lMAtBxjHe48BhuuDsWAF+O4o+zTidHGYTFAAv7O9z+Nb5mdyG6VlW4yGsG1m6ZA/zxrWP3QNthATwMQRAEQagAwwC9/uP4XFIEFOUDVwuBrg/rmy8ppLRy/BHENa6UX0HnzzrrkvbWe7ZqsuNn6dKlaN26NR544AGMHTvWdf3vv/9G37598cILL2D+/Pk4c+aMa0HTqYiPHDkSp06dwrp16xAaGopHHnkEp0+flpT+0aNH8f333yMsTLonmdmzZ+P555/Hf/7zH3z55ZcYP348evTogcaNydCR3pByT0jHHgrYVVbsA6HF7Q5hZ9t7Dr++vZ91CA2lxUCNjKodC2N+4N4XkwhkvQRc/Mexmg84bAw0v03T7BMEQRCELFrd6fgjCMLwxMfHw263o1q1apwV/5kzZ2LYsGGuc/gNGzbEG2+8gR49euCtt97C8ePH8d1332Hbtm3o2LEjAOCDDz5A06ZN/aa5d+9exMTEoKKiAlevOtxSv/rqq5Lz3r9/fzz0kMOA7b/+9S/MmTMH69atI+XeAJByT1gTuUo5bQkkCIIgCILQlciQSGy9Z6tuaevJ7t27sWfPHnz66aeuayzLorKyEkeOHMHBgwcREhKC9u3bu35v0qQJqlev7jfuxo0bY8WKFbh69So++eQT5OTk4OGHpe/yadWqaqeN8ziC1J0DhDqQck8QBEEQBEEQhGFgGMb6xjAFKCoqwoMPPohHHnnE67f09HQcPHhQdtxhYWFo0MDhzvjll1/GgAED8Oyzz+L5558H4LCi72ndv6zM251oaGgo5zvDMKisrJSdL0I5yOw1QRAEQRAEQRCExoSFhaGiooJzrV27dti/fz8aNGjg9RcWFoYmTZqgvLwcO3fudN1z4MABFBQUSE7/6aefxqxZs3Dq1CkAQEJCAscLQEVFBfbt2yfv4QhdIOWeIAiCIAiCIAhCYzIyMrBx40acPHkSZ8+eBeA4w/7rr79i4sSJyMnJwaFDh/D111+7PIQ1btwYffv2xYMPPoitW7di586duP/++xEZKf04QWZmJlq1aoWXXnoJAHDjjTdi5cqVWLlyJf7880+MHz9e1qQBoR+k3BMEQRAEQRAEQWjMc889h6NHj+K6665DQkICAMd59g0bNuDgwYO44YYb0LZtW0yfPh2pqamu+xYsWIDU1FT06NEDt99+Ox544AEkJibKysOjjz6K999/HydOnMDo0aMxYsQIDB8+HD169ED9+vXRq1cvRZ6V0AaG9TxYQfBSWFiIuLg4XLx4EbGxsXpnhyAIgiAIgiBMz9WrV3HkyBHUq1cPERERemeHIHTBVzuQoofSyj1BEARBEARBEARBmBxS7gmCIAiCIAiCIAjC5JByTxAEQRAEQRAEQRAmh5R7giAIgiAIgiAIgjA5pNwTBEEQBEEQBKErZOObCGaUqv+k3BMEQRAEQRAEoQuhoaEAgOLiYp1zQhD6UVpaCgCw2+0BxROiRGYIgiAIgiAIgiCkYrfbUb16dZw+fRoAEBUVBYZhdM4VQWhHZWUlzpw5g6ioKISEBKaek3JPEARBEARBEIRuJCcnA4BLwSeIYMNmsyE9PT3giS1S7gmCIAiCIAiC0A2GYZCSkoLExESUlZXpnR2C0JywsDDYbIGfmCflniAIgiAIgiAI3bHb7QGfOSaIYIYM6hEEQRAEQRAEQRCEySHlniAIgiAIgiAIgiBMDin3BEEQBEEQBEEQBGFy6My9SFiWBQAUFhbqnBOCIAiCIAiCIAgiGHDqn0591Bek3Ivk0qVLAIC0tDSdc0IQBEEQBEEQBEEEE5cuXUJcXJzPMAwrZgqAQGVlJU6dOoVq1aoF7H9QbQoLC5GWloYTJ04gNjZW7+wQhBdURwmjQ3WUMDpURwmjQ3WUMDpmqaMsy+LSpUtITU316y6PVu5FYrPZUKdOHb2zIYnY2FhDV1SCoDpKGB2qo4TRoTpKGB2qo4TRMUMd9bdi74QM6hEEQRAEQRAEQRCEySHlniAIgiAIgiAIgiBMDin3FiQ8PBwzZsxAeHi43lkhCF6ojhJGh+ooYXSojhJGh+ooYXSsWEfJoB5BEARBEARBEARBmBxauScIgiAIgiAIgiAIk0PKPUEQBEEQBEEQBEGYHFLuCYIgCIIgCIIgCMLkkHJPEARBEARBEARBECaHlHuL8b///Q8ZGRmIiIhA586dsW3bNr2zRFiQmTNnomPHjqhWrRoSExORnZ2NAwcOcMJcvXoVEyZMQM2aNRETE4PBgwcjPz+fE+b48eMYMGAAoqKikJiYiCeeeALl5eWcMOvXr0e7du0QHh6OBg0aYOHChWo/HmFBXn75ZTAMg8mTJ7uuUR0l9ObkyZO49957UbNmTURGRqJly5bYsWOH63eWZTF9+nSkpKQgMjISvXv3xqFDhzhxnD9/HsOGDUNsbCyqV6+OMWPGoKioiBNmz549uOGGGxAREYG0tDS88sormjwfYW4qKiowbdo01KtXD5GRkbjuuuvw/PPPw90WN9VRQms2btyIgQMHIjU1FQzDYPny5ZzftayTS5YsQZMmTRAREYGWLVti1apVij+vZFjCMixatIgNCwtj58+fz/7+++/s2LFj2erVq7P5+fl6Z42wGFlZWeyCBQvYffv2sTk5OWz//v3Z9PR0tqioyBVm3LhxbFpaGrt27Vp2x44dbJcuXdiuXbu6fi8vL2dbtGjB9u7dm921axe7atUqtlatWuzUqVNdYQ4fPsxGRUWxU6ZMYffv38/OnTuXtdvt7OrVqzV9XsLcbNu2jc3IyGBbtWrFTpo0yXWd6iihJ+fPn2fr1q3Ljhw5kt26dSt7+PBh9vvvv2f/+usvV5iXX36ZjYuLY5cvX87u3r2bHTRoEFuvXj32ypUrrjB9+/ZlW7duzW7ZsoX9+eef2QYNGrB333236/eLFy+ySUlJ7LBhw9h9+/axn3/+ORsZGcm+8847mj4vYT5efPFFtmbNmuy3337LHjlyhF2yZAkbExPDvv76664wVEcJrVm1ahX71FNPsUuXLmUBsMuWLeP8rlWd/OWXX1i73c6+8sor7P79+9mnn36aDQ0NZffu3at6GfiClHsL0alTJ3bChAmu7xUVFWxqaio7c+ZMHXNFBAOnT59mAbAbNmxgWZZlCwoK2NDQUHbJkiWuMH/88QcLgN28eTPLso7O2WazsXl5ea4wb731FhsbG8uWlJSwLMuyTz75JNu8eXNOWkOGDGGzsrLUfiTCIly6dIlt2LAhu2bNGrZHjx4u5Z7qKKE3//rXv9jrr79e8PfKyko2OTmZ/b//+z/XtYKCAjY8PJz9/PPPWZZl2f3797MA2O3bt7vCfPfddyzDMOzJkydZlmXZefPmsTVq1HDVWWfajRs3VvqRCIsxYMAAdvTo0Zxrt99+Ozts2DCWZamOEvrjqdxrWSfvuusudsCAAZz8dO7cmX3wwQcVfUap0LZ8i1BaWoqdO3eid+/erms2mw29e/fG5s2bdcwZEQxcvHgRABAfHw8A2LlzJ8rKyjj1sUmTJkhPT3fVx82bN6Nly5ZISkpyhcnKykJhYSF+//13Vxj3OJxhqE4TYpkwYQIGDBjgVY+ojhJ6s2LFCnTo0AF33nknEhMT0bZtW7z33nuu348cOYK8vDxO/YqLi0Pnzp05dbR69ero0KGDK0zv3r1hs9mwdetWV5ju3bsjLCzMFSYrKwsHDhzAhQsX1H5MwsR07doVa9euxcGDBwEAu3fvxqZNm9CvXz8AVEcJ46FlnTTq+E/KvUU4e/YsKioqOEIoACQlJSEvL0+nXBHBQGVlJSZPnoxu3bqhRYsWAIC8vDyEhYWhevXqnLDu9TEvL4+3vjp/8xWmsLAQV65cUeNxCAuxaNEi/Pbbb5g5c6bXb1RHCb05fPgw3nrrLTRs2BDff/89xo8fj0ceeQQffvghgKo65mtcz8vLQ2JiIuf3kJAQxMfHS6rHBMHHv//9bwwdOhRNmjRBaGgo2rZti8mTJ2PYsGEAqI4SxkPLOikURu86G6Jr6gRBmJ4JEyZg37592LRpk95ZIQgXJ06cwKRJk7BmzRpERETonR2C8KKyshIdOnTASy+9BABo27Yt9u3bh7fffhsjRozQOXcEAXzxxRf49NNP8dlnn6F58+bIycnB5MmTkZqaSnWUIAwKrdxbhFq1asFut3tZes7Pz0dycrJOuSKszsSJE/Htt99i3bp1qFOnjut6cnIySktLUVBQwAnvXh+Tk5N566vzN19hYmNjERkZqfTjEBZi586dOH36NNq1a4eQkBCEhIRgw4YNeOONNxASEoKkpCSqo4SupKSkoFmzZpxrTZs2xfHjxwFU1TFf43pycjJOnz7N+b28vBznz5+XVI8Jgo8nnnjCtXrfsmVL3HfffXj00Uddu6GojhJGQ8s6KRRG7zpLyr1FCAsLQ/v27bF27VrXtcrKSqxduxaZmZk65oywIizLYuLEiVi2bBl++ukn1KtXj/N7+/btERoayqmPBw4cwPHjx131MTMzE3v37uV0sGvWrEFsbKxL4M3MzOTE4QxDdZrwx0033YS9e/ciJyfH9dehQwcMGzbM9ZnqKKEn3bp183IhevDgQdStWxcAUK9ePSQnJ3PqV2FhIbZu3cqpowUFBdi5c6crzE8//YTKykp07tzZFWbjxo0oKytzhVmzZg0aN26MGjVqqPZ8hPkpLi6GzcZVFex2OyorKwFQHSWMh5Z10rDjv67m/AhFWbRoERseHs4uXLiQ3b9/P/vAAw+w1atX51h6JgglGD9+PBsXF8euX7+ezc3Ndf0VFxe7wowbN45NT09nf/rpJ3bHjh1sZmYmm5mZ6frd6Wbs5ptvZnNyctjVq1ezCQkJvG7GnnjiCfaPP/5g//e//5GbMUI27tbyWZbqKKEv27ZtY0NCQtgXX3yRPXToEPvpp5+yUVFR7CeffOIK8/LLL7PVq1dnv/76a3bPnj3srbfeyuvSqW3btuzWrVvZTZs2sQ0bNuS4dCooKGCTkpLY++67j923bx+7aNEiNioqityMEX4ZMWIEW7t2bZcrvKVLl7K1atVin3zySVcYqqOE1ly6dIndtWsXu2vXLhYA++qrr7K7du1ijx07xrKsdnXyl19+YUNCQthZs2axf/zxBztjxgxyhUcoz9y5c9n09HQ2LCyM7dSpE7tlyxa9s0RYEAC8fwsWLHCFuXLlCvvQQw+xNWrUYKOiotjbbruNzc3N5cRz9OhRtl+/fmxkZCRbq1Yt9rHHHmPLyso4YdatW8e2adOGDQsLY+vXr89JgyCk4KncUx0l9Oabb75hW7RowYaHh7NNmjRh3333Xc7vlZWV7LRp09ikpCQ2PDycvemmm9gDBw5wwpw7d469++672ZiYGDY2NpYdNWoUe+nSJU6Y3bt3s9dffz0bHh7O1q5dm3355ZdVfzbC/BQWFrKTJk1i09PT2YiICLZ+/frsU089xXEPRnWU0Jp169bxyqAjRoxgWVbbOvnFF1+wjRo1YsPCwtjmzZuzK1euVO25xcKwLMvqs2eAIAiCIAiCIAiCIAgloDP3BEEQBEEQBEEQBGFySLknCIIgCIIgCIIgCJNDyj1BEARBEARBEARBmBxS7gmCIAiCIAiCIAjC5JByTxAEQRAEQRAEQRAmh5R7giAIgiAIgiAIgjA5pNwTBEEQBEEQBEEQhMkh5Z4gCIIgCIIgCIIgTE6I3hkgCIIgCEI9Ll++jI8//hgrVqzA7t27ce7cObAsi9jYWGRkZKBly5bIzMxE3759kZaWpnd2CYIgCIKQCcOyLKt3JgiCIAiCUJ7Nmzdj6NChOH78uN+wSUlJyMvL41zr2bMnNmzYgB49emD9+vUq5ZIgCIIgCCWglXuCIAiCsCAHDx5EVlYWLl26BAAYNGgQ7rjjDjRq1AhhYWE4e/Ysdu/ejTVr1mDdunU655YgCIIgiEAh5Z4gCIIgLMhTTz3lUuwXLFiAkSNHeoXp06cPHn/8cZw5cwZffPGFxjkkCIIgCEJJyKAeQRAEQViMiooKrFy5EgDQoUMHXsXenYSEBEyYMEGDnBEEQRAEoRak3BMEQRCExThz5gyuXLkCAGjQoIHk+0eOHAmGYbBhwwYAwIYNG8AwDOcvIyOD996LFy9i5syZ6NatGxISEhAWFoaUlBQMHDgQX375JXyZ+nHG/cwzzwAAfvzxRwwaNAgpKSmIiIhA/fr1MXHiRJw8edJn/gsKCvDiiy8iMzMTNWrUQGhoKBISEtCsWTPcdttteOutt5Cfny+5XAiCIAjCyJBBPYIgCIKwGOfPn0fNmjUBAK1bt0ZOTo6k+0eOHIkPP/zQZ5i6devi6NGjnGtr167FkCFDcO7cOcH7+vfvj8WLFyMmJsbrN4ZhAAAzZszgKPmexMXF4ZtvvsENN9zg9dsff/yB3r1749SpUz7zP3fuXEycONFnGIIgCIIwE6TcEwRBEIQFycjIwLFjxwAAL7/8Mp544gnYbOI27J08eRIXLlzAqFGjsGPHDnTo0AELFizghAkLC0OjRo1c33/55Rf06tULZWVlSEpKwsMPP4zWrVsjNTUVp06dwuLFi/HJJ58AAG6//XZ89dVXXuk6lfsOHTpgx44daNy4MZ588km0atUKFy9exJIlS/Dee++hsrISsbGx2Ldvn5f7vg4dOmDnzp0IDQ3F2LFj0a9fPyQnJ6OyshL//PMPtmzZgmXLlmHSpEmk3BMEQRCWgpR7giAIgrAgs2fPxuOPP+76npGRgUGDBqFr167o1KkT6tWr5zcOsa7wysrK0KhRIxw9ehR9+/bFV199haioKK9w7733Hh544AEAwA8//IA+ffpwfncq9wDQrl07bNiwwWuF/+OPP8bw4cMBAHfeeSfHEODhw4dx3XXXAfC9Ms+yLAoKClCjRg0fT08QBEEQ5oLO3BMEQRCEBXn00UcxevRo1/ejR4/ijTfewNChQ1G/fn0kJydj6NCh+Oabb3yegxfDokWLcPToUUREROCjjz7iVewBYOzYsejUqRMAYOHChT7jfPfdd3m37t93333o168fAGDZsmXIy8tz/eb+uXv37oJxMwxDij1BEARhOUi5JwiCIAgLYrPZ8MEHH+CHH35A3759ERLC9X6bn5+PxYsXY9CgQejUqRP+/vtv2WmtWLECANCjRw8kJCT4DOtUujdv3iwYpmXLlmjfvr3g785Ji/Lycs6OgpSUFNdnf5MHBEEQBGE1SLknCIIgCAvTp08ffPfddzh37hxWrVqFZ599FgMHDkRcXJwrzI4dO3DDDTcgNzdXVho7duwAAHz//fdeVvU9/2bNmgWAu8ruSceOHX2m51z9B4C9e/e6PterV89lZG/OnDlo3rw5pk+fjp9++gnFxcWyno0gCIIgzAIp9wRBEAQRBMTGxqJfv36YPn06VqxYgfz8fMyfP9+1PT03NxfTpk2TFffp06cl3+N01cdHYmKiz3uTkpJcn8+fP8/57fPPP0dmZiYAYP/+/Xj++edx0003oXr16ujevTvefvttXL16VXJ+CYIgCMLohPgPQhAEQRCE1QgPD8eoUaOQmpqKvn37AgCWLl2Kd999V7RVfScVFRUAgH79+uGVV14JOG/uhvWkUrt2bfz6669Yu3Ytli5dig0bNmD//v0oKyvDzz//jJ9//hmzZs3CqlWrONb+CYIgCMLskHJPEARBEEFMVlYW0tLScOLECVy4cAHnzp3ze27ek5o1a+LUqVMoLS1FixYtAs5Tfn6+6N/j4+N5w9x000246aabAADnzp3Djz/+iHfffRc//fQT/v77bwwZMgS7du0KOK8EQRAEYRRoWz5BEARBBDmpqamuz+6r5mJX0Nu2bQvAcfa+tLQ04Pxs375d9O9iJhNq1qyJIUOGYO3atRg0aBAAICcnB4cOHQosowRBEARhIEi5JwiCIIggpri4GPv37wfgOJdfs2ZN128REREAgJKSEp9xOBXmixcvYsGCBQHnae/evT5X1efPnw8AsNvt6Nmzp6S4nav5AHD27FlZ+SMIgiAII0LKPUEQBEFYjKKiInTu3BnffvstKisrBcNVVlbi4YcfxqVLlwA4lHT31Xqna7nDhw+DZVnBeEaMGIG0tDQAwOOPP46NGzf6zN+mTZuwYcMGn2EeeOABXL582ev6Z599hlWrVgEAsrOzOe7vcnJykJOTIxgny7L48ccfATh2JWRkZPjMA0EQBEGYCYb1NVoTBEEQBGE6ioqKUK1aNQAOA3PZ2dnIzMxE3bp1Ua1aNRQUFGDXrl2YP3++y5VcXFwccnJyOArv+++/j7FjxwIAJk+ejHvvvdflQi80NBR169Z1hd2yZQt69uyJkpIS2O12DB06FNnZ2ahXrx4qKyuRm5uLnTt3YtmyZdi7dy/mzp2LiRMncvLtnFjo0KEDduzYgSZNmuBf//oXWrZsiYsXL+LLL7/EO++8g8rKSlSrVg179uzh5HfhwoUYNWoUOnbsiIEDB6Jdu3ZITk5GWVkZjhw5ggULFmDNmjUAgFtvvRXLly9XtNwJgiAIQk9IuScIgiAIi3H16lXUq1fPpy95dxo2bIjPP/8c7du351wvKipC69atcfjwYa976tati6NHj3KubdmyBXfddRdOnDjhN80PP/wQw4cP51xzKvczZswAADz77LO898bGxmLFihXo0aMH57pTufdH165dsWLFCs4RBIIgCIIwO2QtnyAIgiAsRkREBE6ePIktW7bgxx9/xJYtW3DgwAHk5+fj6tWriI6ORmpqKlq3bo1bb70VgwcPRlhYmFc8MTEx+PXXXzFz5kz88MMPOHbsGIqLiwXT7dKlCw4dOoSFCxfim2++wa5du3D27FnYbDYkJCSgadOm6NGjBwYPHozGjRv7fIZnnnkGmZmZmDt3Lnbs2IELFy4gNTUV/fv3x9SpU1GnTh2ve+6++24kJSVhzZo12L59O06ePIn8/HyUl5cjMTER7dq1w5AhQzB06FDJ7v4IgiAIwujQyj1BEARBEIbAfeX+mWee0TczBEEQBGEyaNqaIAiCIAiCIAiCIEwOKfcEQRAEQRAEQRAEYXJIuScIgiAIgiAIgiAIk0PKPUEQBEEQBEEQBEGYHFLuCYIgCIIgCIIgCMLkkCs8giAIgiAMATnwIQiCIAj50Mo9QRAEQRAEQRAEQZgcUu4JgiAIgiAIgiAIwuSQck8QBEEQBEEQBEEQJoeUe4IgCIIgCIIgCIIwOaTcEwRBEARBEARBEITJIeWeIAiCIAiCIAiCIEwOKfcEQRAEQRAEQRAEYXJIuScIgiAIgiAIgiAIk0PKPUEQBEEQBEEQBEGYnP8HIJrIaSa/yisAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(12,6))\n", - "plt.plot(scores_sb, label=\"Short Bursts\")\n", - "plt.plot(scores_anneal, label=\"Simulated Annealing\")\n", - "plt.plot(scores_tilt, label=\"Tilted Run\")\n", - "plt.xlabel(\"Steps\", fontsize=20)\n", - "plt.ylabel(\"Score Observered\", fontsize=20)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing for Compactness\n", - "\n", - "Another metric we may seek to optimize over is compactness. Below we look at minimizing the number of cut edges in a plan." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "num_cut_edges = lambda p: len(p[\"cut_edges\"])\n", - "optimizer = SingleMetricOptimizer(proposal, cons, part, num_cut_edges, maximize=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/10000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(12,6))\n", - "plt.plot(min_scores_sb, label=\"Short Bursts\")\n", - "plt.plot(min_scores_anneal, label=\"Simulated Annealing\")\n", - "plt.plot(min_scores_tilt, label=\"Tilted Run\")\n", - "plt.xlabel(\"Steps\", fontsize=20)\n", - "plt.ylabel(\"Min #CutEdges Observered\", fontsize=20)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - 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z3ym{;sw{BNR+EGR6IUd9TvTxo(~PjX2qh(Xct5Ol>(<1X4UYM{ z{`kY;Q59%^e(9_C?>8XaFX<4w5g5wjl7y5sp`kfl$@54scxVqMULUU9F4{MVqrj0j z>Z`J&QmehIIYVtt#_us9>ZVkUq1cSrHcMBnFn(IUEj^9e<>uy!m?r7qwM|<%z0EF& z7lGeG#Vww7Fz%F6^~#p)sK<=?l`>g^x0m`-OLuq~q6$Q7+Y~qEeDN<&zo%AiDW+@V z$Pc&9tgvKp#s6jH#H|mB$%bOg$74Tn;>3Tl0)7)*KSe}h`6@bp-dXRuQIV;|&#mqA zqU+QXTjtV{RdskYX90PX){RUegsBrflJw63$94MmkI6Gm&8pX;La~nU20&BcHJ9L@ zntCSWB4?@EuwhSttz1T}Ei<{_a9B9HT%v1bfAF%I`QIrp94-$;Co}ibrIvxK!n0_2 zsC!VbW1@r$#b$CPcA+Pl@gWh^f}?{K0VX3**8j45(UcHw?)>Gk6PPO zCV(?-?^9w_p&VwucI|{Nty_r0E`H~|dp-Gf;26PkrZp)J(>%llX!ga;EZ4oFLC^yQ z9_E8ld}?1=RTcd9?Xu5M<%Ql5wa`2_w_rljqKouw9c`kFyNWIi){>hrc+CO$gM>0y zz8Sy(A^6ZC9VrLhj5PWhQH_7%Cu14f)X6i2OF~0r$M)@0V?TjM#6qG#<|(g}lZS}Z zL$wrpQwS?#AQ3xb#izH6go!itOhknrSdA!Hq8MzcfnjhT312Se`IP$?!L&jkKjn3} z%4Ghk`r1(1x1O5YXxt{g$h<1fw}@|~l+kNGjo>+`#4Vz$M6=b5D}vuC+MviFy5K!8 zT1&gE<|ouRI5}03e}oh$()$}ZQvLMwj+Qsn!&~LuBsvZS-33!q5$V*oUwZ^nwm@Q4 zTnai0TEzKdFMEjXQx3fgx0+Bi$-_fImJ%+___O|iZRw!pH&v}Vi?Wluwtwtz?L_9| zx~5I*C@NtTPsO?51gF*)ZQ_}&4{Jg;@fEAiZpZ-?DwgQ;(|2vBe^*xj|C*+mwIci9 pa&jIbc7Yz>`FF72|1@mB{4`(RsdlUGg8>qMEY0oAj+xF2`Y%5P=N`_. +- **Simulated Annealing**: This method varies the probablity of accepting a worse plan + according to a temperature schedule which ranges from 0 to 1. +- **Tilted Runs**: This method accepts a worse plan with a fixed probability :math:`p`. + + +While sampling naively with GerryChain can give us an understanding of the neutral +baseline for a state, there are often cases where we want to find plans with +properties that are rare to encounter in a neutral run. Many states have +laws/guidelines that state that plans should be as compact as feasibly possible, maximize +preservation of political boundaries and/or communities of interest, some even look to +minimize double bunking of incumbents or seek proportionality/competitiveness in +contests. Heuristic optimization methods can be used to find example plans with these +properties and to explore the trade-offs between them. + + +.. raw:: html + +

+ + Download Example File + +
+
+ +The first thing we should do is set up our import statements + +.. code:: python + + from gerrychain import (GeographicPartition, Partition, Graph, MarkovChain, + proposals, updaters, constraints, accept, Election) + from gerrychain.optimization import SingleMetricOptimizer, Gingleator + from gerrychain.tree import recursive_seed_part + from functools import partial + import pandas as pd + import json + from networkx.readwrite import json_graph + import matplotlib.pyplot as plt + from tqdm import tqdm + import numpy as np + import random + + random.seed(2024) + + +And now we load in our file and set up our initial chain. + +.. code:: python + + graph = Graph.from_json("BG_05.json") + + POPCOL = "TOTPOP" + SEN_DISTS = 35 + EPS = 0.02 + TOTPOP = sum(graph.nodes()[n][POPCOL] for n in graph.nodes()) + + chain_updaters = { + "population": updaters.Tally(POPCOL, alias="population"), + "VAP": updaters.Tally("VAP"), + "BVAP": updaters.Tally("BVAP") + } + + initial_partition = Partition.from_random_assignment( + graph=graph, + n_parts=SEN_DISTS, + epsilon=EPS, + pop_col=POPCOL, + updaters=chain_updaters + ) + + proposal = partial( + proposals.recom, + pop_col=POPCOL, + pop_target=TOTPOP/SEN_DISTS, + epsilon=EPS, + node_repeats=1 + ) + + constraints = constraints.within_percent_of_ideal_population(initial_partition, EPS) + + +Using ``SingleMetricOptimizer`` +------------------------------- + +Now the `SingleMetricOptimizer` is set up as a wrapper around our basic `MarkovChain` +class, so interacting with it should be familiar. To set up our optimizer, we, we simply +pass it a proposal function, some constraints, an initial state, and the objective function: + +.. code:: python + + num_cut_edges = lambda p: len(p["cut_edges"]) + + optimizer = SingleMetricOptimizer( + proposal=proposal, + constraints=constraints, + initial_state=initial_partition, + optimization_metric=num_cut_edges, + maximize=False + ) + +And now we can run each of the optimization methods and collect some data! + +.. code:: python + + total_steps = 10000 + + # Short Bursts + min_scores_sb = np.zeros(total_steps) + for i, part in enumerate(optimizer.short_bursts(5, 2000, with_progress_bar=True)): + min_scores_sb[i] = optimizer.best_score + + # Simulated Annealing + min_scores_anneal = np.zeros(total_steps) + for i, part in enumerate( + optimizer.simulated_annealing( + total_steps, + optimizer.jumpcycle_beta_function(200, 800), + beta_magnitude=1, + with_progress_bar=True + ) + ): + min_scores_anneal[i] = optimizer.best_score + + # Tilted Runs + min_scores_tilt = np.zeros(total_steps) + for i, part in enumerate(optimizer.tilted_run(total_steps, p=0.125, with_progress_bar=true)): + min_scores_tilt[i] = optimizer.best_score + +We can then plot the results to see how each method performed: + +.. code:: python + + fig, ax = plt.subplots(figsize=(12,6)) + plt.plot(min_scores_sb, label="Short Bursts") + plt.plot(min_scores_anneal, label="Simulated Annealing") + plt.plot(min_scores_tilt, label="Tilted Run") + plt.xlabel("Steps", fontsize=20) + plt.ylabel("Min #CutEdges Observered", fontsize=20) + plt.legend() + plt.show() + + +This should give you something like: + +.. image:: ./images/single_metric_opt_comparison.png + :align: center + :alt: Single Metric Optimization Method Comparison Image + + +Using ``Gingleator`` +-------------------- + +Named for the Supreme Court case *Thornburg v. Gingles*, which created their precedent +as one of the litmus tests in bringing forth a VRA court case, **Gingles' Districts** are +districts that are 50% + 1 of a minority population subgroup (more colloquially called +majority-minority districts). It is common to seek plans with greater/maximal numbers +of gingles districts to understand the landscape of the state space. + +The `Gingleator` class is a subclass of the `SingleMetricOptimizer` class, so much of +the setup is the same: + +.. code:: python + + gingles = Gingleator( + proposal, + constraints, + initial_partition, + minority_pop_col="BVAP", + total_pop_col="VAP", + score_function=Gingleator.reward_partial_dist + ) + +Likewise, the methods are similar as well: + +.. code:: python + + total_steps = 10000 + + # Short Bursts + max_scores_sb = np.zeros(total_steps) + scores_sb = np.zeros(total_steps) + for i, part in enumerate(gingles.short_bursts(10, 1000, with_progress_bar=True)): + max_scores_sb[i] = gingles.best_score + scores_sb[i] = gingles.score(part) min_scores_sb = np.zeros(total_steps) + for i, part in enumerate(gingles.short_bursts(5, 2000, with_progress_bar=True)): + min_scores_sb[i] = gingles.best_score + + # Simulated Annealing + max_scores_anneal = np.zeros(total_steps) + scores_anneal = np.zeros(total_steps) + for i, part in enumerate( + gingles.simulated_annealing( + total_steps, + gingles.jumpcycle_beta_function(1000, 4000), + beta_magnitude=500, + with_progress_bar=True + ) + ): + max_scores_anneal[i] = gingles.best_score + scores_anneal[i] = gingles.score(part) + + # Tilted Runs + max_scores_tilt = np.zeros(total_steps) + scores_tilt = np.zeros(total_steps) + for i, part in enumerate(gingles.tilted_run(total_steps, 0.125, with_progress_bar=True)): + max_scores_tilt[i] = gingles.best_score + scores_tilt[i] = gingles.score(part) + +And we can plot the results again: + +.. code:: python + + fig, ax = plt.subplots(figsize=(12,6)) + plt.plot(max_scores_sb, label="Short Bursts") + plt.plot(max_scores_anneal, label="Simulated Annealing") + plt.plot(max_scores_tilt, label="Tilted Run") + plt.xlabel("Steps", fontsize=20) + plt.ylabel("Max Score Observered", fontsize=20) + plt.legend() + plt.show() + +This should give you something like: + +.. image:: ./images/gingleator_maxes.png + :align: center + :alt: Gingleator Optimization Method Comparison Image + +And we can see a little better how each method performs over the course of the run +by looking at all of the scores relative to the previous graph: + +.. image:: ./images/gingleator_all.png + :align: center + :alt: Gingleator Optimization Method Comparison All Steps Image + +From this we can observe that, throughout the entire run, the score function can dip all +the way back down to 0 even relatively shortly after being at the maximum of 4.5. \ No newline at end of file diff --git a/gerrychain/optimization/gingleator.py b/gerrychain/optimization/gingleator.py index ed07920e..8ba2164b 100755 --- a/gerrychain/optimization/gingleator.py +++ b/gerrychain/optimization/gingleator.py @@ -84,8 +84,6 @@ def __init__( initial_state.updaters.update(perc_up) minority_perc_col = min_perc_column_name - print(f"Perc_up keys: {perc_up.keys()}") - print(f"Minority_co: {perc_up.keys()}") score = partial( score_function, minority_perc_col=minority_perc_col, threshold=threshold ) diff --git a/gerrychain/optimization/optimization.py b/gerrychain/optimization/optimization.py index a411f644..40d98f54 100644 --- a/gerrychain/optimization/optimization.py +++ b/gerrychain/optimization/optimization.py @@ -59,8 +59,9 @@ def __init__( :type step_indexer: str, optional - Returns: - A SingleMetricOptimizer object + + :return: A SingleMetricOptimizer object + :rtype: SingleMetricOptimizer """ self._initial_part = initial_state self._proposal = proposal @@ -158,7 +159,6 @@ def _simulated_annealing_acceptance_function( """ Function factory that binds and returns a simulated annealing acceptance function. - :param beta_function: Function (f: t -> beta, where beta is in [0,1]) defining temperature over time. f(t) = 0 the chain is hot and every proposal is accepted. At f(t) = 1 the chain is cold and worse proposal have a low probability of being accepted relative to @@ -212,12 +212,69 @@ def linearcycle_beta_function( cls, duration_hot: int, duration_cooldown: int, duration_cold: int ) -> Callable[[int], float]: """ - IMPLEMENT THIS!!! + Class method that binds and returns a simple linear hot-cool cycle beta temperature + function, where the chain runs hot for some given duration, cools down linearly for some + duration, and then runs cold for some duration before warming up again and repeating. + + :param duration_hot: Number of steps to run chain hot. + :type duration_hot: int + :param duration_cooldown: Number of steps needed to transition from hot to cold or + vice-versa. + :type duration_cooldown: int + :param duration_cold: Number of steps to run chain cold. + :type duration_cold: int + + :return: Beta function defining linear hot-cool cycle. + :rtype: Callable[[int], float] + """ + cycle_length = duration_hot + 2 * duration_cooldown + duration_cold + + def beta_function(step: int): + time_in_cycle = step % cycle_length + if time_in_cycle < duration_hot: + return 0 + elif time_in_cycle < duration_hot + duration_cooldown: + return (time_in_cycle - duration_hot) / duration_cooldown + elif time_in_cycle < cycle_length - duration_cooldown: + return 1 + else: + return ( + 1 + - (time_in_cycle - cycle_length + duration_cooldown) + / duration_cooldown + ) + + return beta_function + + @classmethod + def linear_jumpcycle_beta_function( + cls, duration_hot: int, duration_cooldown, duration_cold: int + ): + """ + Class method that binds and returns a simple linear hot-cool cycle beta temperature + function, where the chain runs hot for some given duration, cools down linearly for some + duration, and then runs cold for some duration before jumping back to hot and repeating. + + :param duration_hot: Number of steps to run chain hot. + :type duration_hot: int + :param duration_cooldown: Number of steps needed to transition from hot to cold. + :type duration_cooldown: int + :param duration_cold: Number of steps to run chain cold. + :type duration_cold: int + + :return: Beta function defining linear hot-cool cycle. + :rtype: Callable[[int], float] """ cycle_length = duration_hot + duration_cooldown + duration_cold def beta_function(step: int): - pass + time_in_cycle = step % cycle_length + if time_in_cycle < duration_hot: + return 0 + elif time_in_cycle < duration_hot + duration_cooldown: + return (time_in_cycle - duration_hot) / duration_cooldown + else: + return 1 return beta_function @@ -226,12 +283,76 @@ def logitcycle_beta_function( cls, duration_hot: int, duration_cooldown: int, duration_cold: int ) -> Callable[[int], float]: """ - IMPLEMENT THIS!!! + Class method that binds and returns a logit hot-cool cycle beta temperature function, where + the chain runs hot for some given duration, down according to the logit function + + :math:`f(x) = (log(x/(1-x)) + 5)/10` + + for some duration, and then runs cold for some duration before warming up again + using the :math:`1-f(x)` and repeating. + + :param duration_hot: Number of steps to run chain hot. + :type duration_hot: int + :param duration_cooldown: Number of steps needed to transition from hot to cold or + vice-versa. + :type duration_cooldown: int + :param duration_cold: Number of steps to run chain cold. + :type duration_cold: int """ + cycle_length = duration_hot + 2 * duration_cooldown + duration_cold + + # this will scale from 0 to 1 approximately + logit = lambda x: (math.log(x / (1 - x)) + 5) / 10 + + def beta_function(step: int): + time_in_cycle = step % cycle_length + if time_in_cycle <= duration_hot: + return 0 + elif time_in_cycle < duration_hot + duration_cooldown: + value = logit((time_in_cycle - duration_hot) / duration_cooldown) + if value < 0: + return 0 + if value > 1: + return 1 + return value + elif time_in_cycle <= cycle_length - duration_cooldown: + return 1 + else: + value = 1 - logit( + (time_in_cycle - cycle_length + duration_cooldown) + / duration_cooldown + ) + if value < 0: + return 0 + if value > 1: + return 1 + return value + + return beta_function + + @classmethod + def logit_jumpcycle_beta_function( + cls, duration_hot: int, duration_cooldown: int, duration_cold: int + ) -> Callable[[int], float]: + cycle_length = duration_hot + duration_cooldown + duration_cold + # this will scale from 0 to 1 approximately + logit = lambda x: (math.log(x / (1 - x)) + 5) / 10 + def beta_function(step: int): - pass + time_in_cycle = step % cycle_length + if time_in_cycle <= duration_hot: + return 0 + elif time_in_cycle < duration_hot + duration_cooldown: + value = logit((time_in_cycle - duration_hot) / duration_cooldown) + if value < 0: + return 0 + if value > 1: + return 1 + return value + else: + return 1 return beta_function From a4366f970bb470e5a5c43afbdf675957c77e94ba Mon Sep 17 00:00:00 2001 From: peterrrock2 Date: Fri, 26 Apr 2024 17:05:50 -0600 Subject: [PATCH 24/28] All the doc updates --- README.rst | 4 +- docs/index.rst | 1 + .../reproducible_envs.rst | 196 ++++++++++++++++++ docs/topics/reporting.rst | 12 ++ docs/topics/reproducibility.rst | 124 ++--------- docs/user/data.rst | 2 +- docs/user/geometries.rst | 2 +- docs/user/images/gerrymandria_ensamble.gif | Bin 183039 -> 0 bytes docs/user/images/gerrymandria_ensemble.gif | Bin 0 -> 248503 bytes .../images/gerrymandria_region_ensamble.gif | Bin 236274 -> 0 bytes .../images/gerrymandria_region_ensemble.gif | Bin 0 -> 207150 bytes .../gerrymandria_water_and_muni_aware.png | Bin 51999 -> 53212 bytes .../gerrymandria_water_muni_ensamble.gif | Bin 178538 -> 0 bytes .../gerrymandria_water_muni_ensemble.gif | Bin 0 -> 228635 bytes .../images/jupyter_tutorial/jupyter_lab.png | Bin 0 -> 106735 bytes .../images/jupyter_tutorial/make_new_file.png | Bin 0 -> 113125 bytes .../images/jupyter_tutorial/select_kernel.png | Bin 0 -> 114937 bytes .../jupyter_tutorial/show_import_working.png | Bin 0 -> 79204 bytes .../vscode_tutorial/Make_a_new_file.png | Bin 0 -> 111374 bytes .../jupyter_extension_vscode.png | Bin 0 -> 502297 bytes .../vscode_tutorial/open_the_folder.png | Bin 0 -> 103074 bytes .../python_extension_vscode.png | Bin 0 -> 526161 bytes .../vscode_tutorial/select_kernel_vscode.png | Bin 0 -> 131367 bytes .../vscode_tutorial/select_python_env.png | Bin 0 -> 125812 bytes .../selecting_correct_venv.png | Bin 0 -> 139146 bytes .../show_gerrychain_import.png | Bin 0 -> 136331 bytes docs/user/install.rst | 104 +--------- docs/user/partitions.rst | 19 +- docs/user/quickstart.rst | 4 +- docs/user/recom.rst | 102 +++++++-- 30 files changed, 333 insertions(+), 237 deletions(-) create mode 100644 docs/repeated_subsections/reproducible_envs.rst create mode 100644 docs/topics/reporting.rst delete mode 100644 docs/user/images/gerrymandria_ensamble.gif create mode 100644 docs/user/images/gerrymandria_ensemble.gif delete mode 100644 docs/user/images/gerrymandria_region_ensamble.gif create mode 100644 docs/user/images/gerrymandria_region_ensemble.gif delete mode 100644 docs/user/images/gerrymandria_water_muni_ensamble.gif create mode 100644 docs/user/images/gerrymandria_water_muni_ensemble.gif create mode 100644 docs/user/images/jupyter_tutorial/jupyter_lab.png create mode 100644 docs/user/images/jupyter_tutorial/make_new_file.png create mode 100644 docs/user/images/jupyter_tutorial/select_kernel.png create mode 100644 docs/user/images/jupyter_tutorial/show_import_working.png create mode 100644 docs/user/images/vscode_tutorial/Make_a_new_file.png create mode 100644 docs/user/images/vscode_tutorial/jupyter_extension_vscode.png create mode 100644 docs/user/images/vscode_tutorial/open_the_folder.png create mode 100644 docs/user/images/vscode_tutorial/python_extension_vscode.png create mode 100644 docs/user/images/vscode_tutorial/select_kernel_vscode.png create mode 100644 docs/user/images/vscode_tutorial/select_python_env.png create mode 100644 docs/user/images/vscode_tutorial/selecting_correct_venv.png create mode 100644 docs/user/images/vscode_tutorial/show_gerrychain_import.png diff --git a/README.rst b/README.rst index 8728bcc2..7feb0258 100644 --- a/README.rst +++ b/README.rst @@ -66,7 +66,7 @@ Installation Supported Python Versions ------------------------- -The most recent version of GerryChain (as of February 2024) supports +The most recent version of GerryChain (as of April 2024) supports - Python 3.9 - Python 3.10 @@ -196,7 +196,7 @@ adjacencies or reading in shapefiles, then run This approach sometimes fails due to compatibility issues between our different Python GIS dependencies, like ``geopandas``, ``pyproj``, ``fiona``, and ``shapely``. If you run into this issue, try installing -the dependencies using the `geo_settings.txt `_ +the dependencies using the `geo_settings.txt `_ file. To do this, run ``pip install -r geo_settings.txt`` from the command line. diff --git a/docs/index.rst b/docs/index.rst index 57635f2c..0418c076 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -74,6 +74,7 @@ MGGG for the 2019 MIT IAP course `Computational Approaches for Political Redistr topics/reproducibility topics/tools topics/contributing + topics/reporting .. toctree:: diff --git a/docs/repeated_subsections/reproducible_envs.rst b/docs/repeated_subsections/reproducible_envs.rst new file mode 100644 index 00000000..56972b7c --- /dev/null +++ b/docs/repeated_subsections/reproducible_envs.rst @@ -0,0 +1,196 @@ +Making an Environment Reproducible +---------------------------------- + +If you are working on a project wherein you would like to ensure +particular runs are reproducible, it is necessary to invoke + +- **MacOS/Linux**: ``export PYTHONHASHSEED=0`` +- **Windows**: + + - PowerShell ``$env:PYTHONHASHSEED=0`` + - Command Prompt ``set PYTHONHASHSEED=0`` + +before running your code. This will ensure that the hash seed is deterministic +which is important for the replication of spanning trees across your runs. If you +would prefer to not have to do this every time, then you need to modify the +activation script for the virtual environment. Again, this is different depending +on your operating system: + +- **MacOS/Linux**: Open the file ``.venv/bin/activate`` located in your working + directory using your favorite text editor + and add the line ``export PYTHONHASHSEED=0`` after the ``export PATH`` command. + So you should see something like:: + + _OLD_VIRTUAL_PATH="$PATH" + PATH="$VIRTUAL_ENV/Scripts:$PATH" + export PATH + + export PYTHONHASHSEED=0 + + Then, verify that the hash seed is set to 0 in your Python environment by + running ``python`` from the command line and typing + ``import os; print(os.environ['PYTHONHASHSEED'])``. + +- **Windows**: To be safe, you will need to modify 3 files within your virtual + environment: + + - ``.venv\Scripts\activate``: Add the line ``export PYTHONHASHSEED=0`` after + the ``export PATH`` command. So you should see something like:: + + _OLD_VIRTUAL_PATH="$PATH" + PATH="$VIRTUAL_ENV/Scripts:$PATH" + export PATH + + export PYTHONHASHSEED=0 + + - ``.venv\Scripts\activate.bat``: Add the line ``set PYTHONHASHSEED=0`` to the + end of the file. So you should see something like:: + + if defined _OLD_VIRTUAL_PATH set PATH=%_OLD_VIRTUAL_PATH% + if not defined _OLD_VIRTUAL_PATH set _OLD_VIRTUAL_PATH=%PATH% + + set PATH=%VIRTUAL_ENV%\Scripts;%PATH% + rem set VIRTUAL_ENV_PROMPT=(.venv) + set PYTHONHASHSEED=0 + + - ``.venv\Scripts\Activate.ps1``: Add the line ``$env:PYTHONHASHSEED=0`` to the + end of the before the signature block. So you should see something like:: + + # Add the venv to the PATH + Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH + $Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH" + + $env:PYTHONHASHSEED=0 + + # SIG # Begin signature block + +After you have made these changes, verify that the hash seed is set to 0 in your +Python environment by running ``python`` from the command line and typing +``import os; print(os.environ['PYTHONHASHSEED'])`` in the Python prompt. + +Reproducible Environments in VSCode and Jupyter Lab +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In general, it is easier to use jupyter notebooks with bespoke virtual environments +through a text editor like `VSCode `_, but +we will show how to do this using the standard Jupyter Lab interface as well. + +Regardless of which method you prefer, you will need to make sure that you have +installed the ``ipykernel`` package into the virtual environment that you will be +working with: + +.. code:: console + + pip install ipykernel + + +VSCode +~~~~~~ + +First you will want to make sure that you have +`installed VSCode `_. +Then, you can click on "File" in the upper-left and select "Open Folder" to open the +folder with your project and virtual environment in it. This should look something like +this: + +.. image:: ../user/images/vscode_tutorial/open_the_folder.png + :align: center + +Notice that I have the folder ``.venv`` in my file explorer. This is just my virtual +environment that I created for this project. + + +To install the Python extension, open the extensions view by clicking on the square +icon on the left side of the window. Then, search for "Python" and install the +extension that is published by Microsoft. This will allow you to use the Python +interpreter in your virtual environment and will give you some helpful things like +code completion and debugging in the event that you would like it. + +.. image:: ../user/images/vscode_tutorial/python_extension_vscode.png + :align: center + +Likewise, we will want to install the "Jupyter" extension: + +.. image:: ../user/images/vscode_tutorial/jupyter_extension_vscode.png + :align: center + +Now let's make a new jupyter notebook. You can do this by right-clicking in the file +explorer (or by using the File menu) and selecting "New File". + +.. image:: ../user/images/vscode_tutorial/Make_a_new_file.png + :align: center + +.. raw:: html + +   + +Then, you will want to save the file with the extension ``.ipynb`` since we are making +a jupyter notebook. Then we will want to open the file and select the Python interpreter +that we would like to use: + + +.. image:: ../user/images/vscode_tutorial/select_kernel_vscode.png + :align: center + +.. image:: ../user/images/vscode_tutorial/select_python_env.png + :align: center + +.. image:: ../user/images/vscode_tutorial/selecting_correct_venv.png + :align: center + +And now we are done! We can now use all of the packages in our ``.venv`` virtual environment. + +.. image:: ../user/images/vscode_tutorial/show_gerrychain_import.png + :align: center + +Of course, if you would like to, you may also use a different name for your virtual environment, +or you can even use the same process to allow for multiple virtual environments in the same +project! + + +Jupyter Lab / Notebook +~~~~~~~~~~~~~~~~~~~~~~ + +In order to use Jupyter Lab or Jupyter Notebook, we will need to make sure that it is installed +for our virtual environment. This can be done by running the following command: + +.. code:: console + + pip install jupyter + +Then, we will want to install the kernel for our virtual environment. This can be done by running +the following command: + +.. code:: console + + python -m ipykernel install --user --name=.venv + + +Now, we can open Jupyter Lab by running the following command: + +.. code:: console + + jupyter lab + +You will now see that the ``.venv`` is available in your list of kernels: + +.. image:: ../user/images/jupyter_tutorial/jupyter_lab.png + :align: center + + +We can now make a new notebook and select the kernel that we would like to use: + +.. image:: ../user/images/jupyter_tutorial/make_new_file.png + :align: center + +.. image:: ../user/images/jupyter_tutorial/select_kernel.png + :align: center + +.. raw:: html + +   + +Lastly, we can import GerryChain and use it in our notebook: + +.. image:: ../user/images/jupyter_tutorial/show_import_working.png + :align: center \ No newline at end of file diff --git a/docs/topics/reporting.rst b/docs/topics/reporting.rst new file mode 100644 index 00000000..d65fb7b1 --- /dev/null +++ b/docs/topics/reporting.rst @@ -0,0 +1,12 @@ +================ +Reporting Issues +================ + +Sometimes, you may encounter a problem with the GerryChain, and that is okay! We would +love to hear about it so that we can fix it. If you can provide a minimal example that +reproduces the issue, please feel free to report it on the `GitHub issue tracker +`_. + +In the event that you would prefer not to post the issue publicly, you can also email +us at "code[at]mggg[dot]org". We will do our best to respond to your issue in a timely +manner. Thank you for your help in making the GerryChain better! \ No newline at end of file diff --git a/docs/topics/reproducibility.rst b/docs/topics/reproducibility.rst index 669453f5..32f71385 100644 --- a/docs/topics/reproducibility.rst +++ b/docs/topics/reproducibility.rst @@ -63,127 +63,33 @@ running process and is compatible across future and past releases of GerryChain. Use the same versions of all of your dependencies ------------------------------------------------- + You will want to make sure that anyone who tries to repeat your analysis by running your code will have the exact same versions of all of the software and packages that you use, including the same version of Python. -The easiest way to do this is to use `conda`_ to manage all of your dependencies. -You can use conda to export an ``environment.yml`` file that anyone can use to replicate your -environment by running the command ``conda env create -f environment.yml``. For instructions on -how to do this, see `Sharing your environment`_ and `Creating an environment from an environment.yml file`_ -in the conda documentation. - -If you've published your code on GitHub, it is a good idea to include your ``environment.yml`` -file in the root folder of your code repository. - -.. _`conda`: https://conda.io/projects/conda/en/latest/index.html -.. _`Sharing your environment`: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#sharing-an-environment -.. _`Creating an environment from an environment.yml file`: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file - - -Making Your Environment Reproducible ------------------------------------- - -If you are working on a project wherein you would like to ensure -particuluar runs are reproducible, it is necessary to invoke - -- **MacOS/Linux**: ``export PYTHONHASHSEED=0`` -- **Windows**: - - - PowerShell ``$env:PYTHONHASHSEED=0`` - - Command Prompt ``set PYTHONHASHSEED=0`` - -before running your code. This will ensure that the hash seed is deterministic -which is important for the replication of spanning trees accross your runs. If you -would prefer to not have to do this every time, then you need to modify the -activation script for the virtual environment. Again, this is different depending -on your operating system: - -- **MacOS/Linux**: Open the file ``.venv/bin/activate`` located in your working - directory using your favorite text editor - and add the line ``export PYTHONHASHSEED=0`` after the ``export PATH`` command. - So you should see something like:: - - _OLD_VIRTUAL_PATH="$PATH" - PATH="$VIRTUAL_ENV/Scripts:$PATH" - export PATH - - export PYTHONHASHSEED=0 - - Then, verify that the hash seed is set to 0 in your Python environment by - running ``python`` from the command line and typing - ``import os; print(os.environ['PYTHONHASHSEED'])``. - -- **Windows**: To be safe, you will need to modify 3 files within your virtual - environment: - - - ``.venv\Scripts\activate``: Add the line ``export PYTHONHASHSEED=0`` after - the ``export PATH`` command. So you should see something like:: - - _OLD_VIRTUAL_PATH="$PATH" - PATH="$VIRTUAL_ENV/Scripts:$PATH" - export PATH - - export PYTHONHASHSEED=0 - - - ``.venv\Scripts\activate.bat``: Add the line ``set PYTHONHASHSEED=0`` to the - end of the file. So you should see something like:: - - if defined _OLD_VIRTUAL_PATH set PATH=%_OLD_VIRTUAL_PATH% - if not defined _OLD_VIRTUAL_PATH set _OLD_VIRTUAL_PATH=%PATH% - - set PATH=%VIRTUAL_ENV%\Scripts;%PATH% - rem set VIRTUAL_ENV_PROMPT=(.venv) - set PYTHONHASHSEED=0 - - - ``.venv\Scripts\Activate.ps1``: Add the line ``$env:PYTHONHASHSEED=0`` to the - end of the before the signature bolck. So you should see something like:: - - # Add the venv to the PATH - Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH - $Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH" - - $env:PYTHONHASHSEED=0 - - # SIG # Begin signature block - -After you have made these changes, verify that the hash seed is set to 0 in your -Python environment by running ``python`` from the command line and typing -``import os; print(os.environ['PYTHONHASHSEED'])`` in the Python prompt. - -.. admonition:: A Note on Jupyter - :class: note - - If you are using a jupyter notebook, you will need to make sure that you have - installed the ``ipykernel`` package in your virtual environment as well as - either ``jupyternotebook`` or ``jupyterlab``. To install these packages, run - ``pip install `` from the command line. Then, to use the virtual - python environment in your jupyter notebook, you need to invoke - - .. code-block:: console - - jupyter notebook +The best way to do this is to create a `virtual environment <../user/install.html#virtual-envs>`_ +and then save all of the dependencies to a file. This will allow anyone to recreate the +exact same environment that you used to run your code. To save the packages that are in +your current virtual environment, simply run - or +.. code:: console - .. code-block:: console + pip freeze > requirements.txt - jupyter lab +and this will save the versions of all of your packages to a file called +``requirements.txt``. You can then share this file with anyone who wants to run your code, +and they can create the same virtual environment by running - from the command line of your working directory *while your virtual environment - is activated*. This will open a jupyter notebook in your default browser. You may - then check that the hash seed is set to 0 by running the following code in a cell - of your notebook: +.. code:: console - .. code-block:: python + pip install -r requirements.txt - import os - print(os.environ['PYTHONHASHSEED']) +Of course, you will both be responsible for making sure that your virtual environments +have the same ``PYTHONHASHSEED`` set. How to do this is detailed in the next section. -Of course, once this is all done, it would be a good idea to save the random seed -that you used somewhere so that others may replicate your work in the future. +.. include:: ../repeated_subsections/reproducible_envs.rst -.. _`environment variable`: https://en.wikipedia.org/wiki/Environment_variable .. _`pcompress`: https://github.com/mggg/pcompress .. _`install Cargo`: https://doc.rust-lang.org/cargo/getting-started/installation.html diff --git a/docs/user/data.rst b/docs/user/data.rst index f6007874..6fd2dbb3 100644 --- a/docs/user/data.rst +++ b/docs/user/data.rst @@ -24,7 +24,7 @@ Writing Data to JSONL .. raw:: html
- Download PA File + Download PA File

diff --git a/docs/user/geometries.rst b/docs/user/geometries.rst index f6c11875..7d80a7ba 100644 --- a/docs/user/geometries.rst +++ b/docs/user/geometries.rst @@ -35,7 +35,7 @@ Loading and Running a Plan .. raw:: html
- Download MN File + Download MN File

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