deap-1.0.1/0000755000076500000240000000000012321001644012645 5ustar felixstaff00000000000000deap-1.0.1/deap/0000755000076500000240000000000012321001644013556 5ustar felixstaff00000000000000deap-1.0.1/deap/__init__.py0000644000076500000240000000137112321001547015673 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . __author__ = "DEAP Team" __version__ = "1.0" __revision__ = "1.0.1" deap-1.0.1/deap/algorithms.py0000644000076500000240000004726412301410325016314 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """The :mod:`algorithms` module is intended to contain some specific algorithms in order to execute very common evolutionary algorithms. The method used here are more for convenience than reference as the implementation of every evolutionary algorithm may vary infinitely. Most of the algorithms in this module use operators registered in the toolbox. Generaly, the keyword used are :meth:`mate` for crossover, :meth:`mutate` for mutation, :meth:`~deap.select` for selection and :meth:`evaluate` for evaluation. You are encouraged to write your own algorithms in order to make them do what you really want them to do. """ import random import tools def varAnd(population, toolbox, cxpb, mutpb): """Part of an evolutionary algorithm applying only the variation part (crossover **and** mutation). The modified individuals have their fitness invalidated. The individuals are cloned so returned population is independent of the input population. :param population: A list of individuals to vary. :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution operators. :param cxpb: The probability of mating two individuals. :param mutpb: The probability of mutating an individual. :returns: A list of varied individuals that are independent of their parents. The variation goes as follow. First, the parental population :math:`P_\mathrm{p}` is duplicated using the :meth:`toolbox.clone` method and the result is put into the offspring population :math:`P_\mathrm{o}`. A first loop over :math:`P_\mathrm{o}` is executed to mate consecutive individuals. According to the crossover probability *cxpb*, the individuals :math:`\mathbf{x}_i` and :math:`\mathbf{x}_{i+1}` are mated using the :meth:`toolbox.mate` method. The resulting children :math:`\mathbf{y}_i` and :math:`\mathbf{y}_{i+1}` replace their respective parents in :math:`P_\mathrm{o}`. A second loop over the resulting :math:`P_\mathrm{o}` is executed to mutate every individual with a probability *mutpb*. When an individual is mutated it replaces its not mutated version in :math:`P_\mathrm{o}`. The resulting :math:`P_\mathrm{o}` is returned. This variation is named *And* beceause of its propention to apply both crossover and mutation on the individuals. Note that both operators are not applied systematicaly, the resulting individuals can be generated from crossover only, mutation only, crossover and mutation, and reproduction according to the given probabilities. Both probabilities should be in :math:`[0, 1]`. """ offspring = [toolbox.clone(ind) for ind in population] # Apply crossover and mutation on the offspring for i in range(1, len(offspring), 2): if random.random() < cxpb: offspring[i-1], offspring[i] = toolbox.mate(offspring[i-1], offspring[i]) del offspring[i-1].fitness.values, offspring[i].fitness.values for i in range(len(offspring)): if random.random() < mutpb: offspring[i], = toolbox.mutate(offspring[i]) del offspring[i].fitness.values return offspring def eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None, halloffame=None, verbose=__debug__): """This algorithm reproduce the simplest evolutionary algorithm as presented in chapter 7 of [Back2000]_. :param population: A list of individuals. :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution operators. :param cxpb: The probability of mating two individuals. :param mutpb: The probability of mutating an individual. :param ngen: The number of generation. :param stats: A :class:`~deap.tools.Statistics` object that is updated inplace, optional. :param halloffame: A :class:`~deap.tools.HallOfFame` object that will contain the best individuals, optional. :param verbose: Whether or not to log the statistics. :returns: The final population. It uses :math:`\lambda = \kappa = \mu` and goes as follow. It first initializes the population (:math:`P(0)`) by evaluating every individual presenting an invalid fitness. Then, it enters the evolution loop that begins by the selection of the :math:`P(g+1)` population. Then the crossover operator is applied on a proportion of :math:`P(g+1)` according to the *cxpb* probability, the resulting and the untouched individuals are placed in :math:`P'(g+1)`. Thereafter, a proportion of :math:`P'(g+1)`, determined by *mutpb*, is mutated and placed in :math:`P''(g+1)`, the untouched individuals are transferred :math:`P''(g+1)`. Finally, those new individuals are evaluated and the evolution loop continues until *ngen* generations are completed. Briefly, the operators are applied in the following order :: evaluate(population) for i in range(ngen): offspring = select(population) offspring = mate(offspring) offspring = mutate(offspring) evaluate(offspring) population = offspring This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`, :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be registered in the toolbox. .. [Back2000] Back, Fogel and Michalewicz, "Evolutionary Computation 1 : Basic Algorithms and Operators", 2000. """ logbook = tools.Logbook() logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit if halloffame is not None: halloffame.update(population) record = stats.compile(population) if stats else {} logbook.record(gen=0, nevals=len(invalid_ind), **record) if verbose: print logbook.stream # Begin the generational process for gen in range(1, ngen+1): # Select the next generation individuals offspring = toolbox.select(population, len(population)) # Vary the pool of individuals offspring = varAnd(offspring, toolbox, cxpb, mutpb) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # Update the hall of fame with the generated individuals if halloffame is not None: halloffame.update(offspring) # Replace the current population by the offspring population[:] = offspring # Append the current generation statistics to the logbook record = stats.compile(population) if stats else {} logbook.record(gen=gen, nevals=len(invalid_ind), **record) if verbose: print logbook.stream return population, logbook def varOr(population, toolbox, lambda_, cxpb, mutpb): """Part of an evolutionary algorithm applying only the variation part (crossover, mutation **or** reproduction). The modified individuals have their fitness invalidated. The individuals are cloned so returned population is independent of the input population. :param population: A list of individuals to vary. :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution operators. :param lambda\_: The number of children to produce :param cxpb: The probability of mating two individuals. :param mutpb: The probability of mutating an individual. :returns: A list of varied individuals that are independent of their parents. The variation goes as follow. On each of the *lambda_* iteration, it selects one of the three operations; crossover, mutation or reproduction. In the case of a crossover, two individuals are selected at random from the parental population :math:`P_\mathrm{p}`, those individuals are cloned using the :meth:`toolbox.clone` method and then mated using the :meth:`toolbox.mate` method. Only the first child is appended to the offspring population :math:`P_\mathrm{o}`, the second child is discarded. In the case of a mutation, one individual is selected at random from :math:`P_\mathrm{p}`, it is cloned and then mutated using using the :meth:`toolbox.mutate` method. The resulting mutant is appended to :math:`P_\mathrm{o}`. In the case of a reproduction, one individual is selected at random from :math:`P_\mathrm{p}`, cloned and appended to :math:`P_\mathrm{o}`. This variation is named *Or* beceause an offspring will never result from both operations crossover and mutation. The sum of both probabilities shall be in :math:`[0, 1]`, the reproduction probability is 1 - *cxpb* - *mutpb*. """ assert (cxpb + mutpb) <= 1.0, ("The sum of the crossover and mutation " "probabilities must be smaller or equal to 1.0.") offspring = [] for _ in xrange(lambda_): op_choice = random.random() if op_choice < cxpb: # Apply crossover ind1, ind2 = map(toolbox.clone, random.sample(population, 2)) ind1, ind2 = toolbox.mate(ind1, ind2) del ind1.fitness.values offspring.append(ind1) elif op_choice < cxpb + mutpb: # Apply mutation ind = toolbox.clone(random.choice(population)) ind, = toolbox.mutate(ind) del ind.fitness.values offspring.append(ind) else: # Apply reproduction offspring.append(random.choice(population)) return offspring def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, stats=None, halloffame=None, verbose=__debug__): """This is the :math:`(\mu + \lambda)` evolutionary algorithm. :param population: A list of individuals. :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution operators. :param mu: The number of individuals to select for the next generation. :param lambda\_: The number of children to produce at each generation. :param cxpb: The probability that an offspring is produced by crossover. :param mutpb: The probability that an offspring is produced by mutation. :param ngen: The number of generation. :param stats: A :class:`~deap.tools.Statistics` object that is updated inplace, optional. :param halloffame: A :class:`~deap.tools.HallOfFame` object that will contain the best individuals, optional. :param verbose: Whether or not to log the statistics. :returns: The final population. First, the individuals having an invalid fitness are evaluated. Then, the evolutionary loop begins by producing *lambda_* offspring from the population, the offspring are generated by a crossover, a mutation or a reproduction proportionally to the probabilities *cxpb*, *mutpb* and 1 - (cxpb + mutpb). The offspring are then evaluated and the next generation population is selected from both the offspring **and** the population. Briefly, the operators are applied as following :: evaluate(population) for i in range(ngen): offspring = varOr(population, toolbox, lambda_, cxpb, mutpb) evaluate(offspring) population = select(population + offspring, mu) This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`, :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be registered in the toolbox. This algorithm uses the :func:`varOr` variation. """ logbook = tools.Logbook() logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit if halloffame is not None: halloffame.update(population) record = stats.compile(population) if stats is not None else {} logbook.record(gen=0, nevals=len(invalid_ind), **record) if verbose: print logbook.stream # Begin the generational process for gen in range(1, ngen+1): # Vary the population offspring = varOr(population, toolbox, lambda_, cxpb, mutpb) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # Update the hall of fame with the generated individuals if halloffame is not None: halloffame.update(offspring) # Select the next generation population population[:] = toolbox.select(population + offspring, mu) # Update the statistics with the new population record = stats.compile(population) if stats is not None else {} logbook.record(gen=gen, nevals=len(invalid_ind), **record) if verbose: print logbook.stream return population, logbook def eaMuCommaLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, stats=None, halloffame=None, verbose=__debug__): """This is the :math:`(\mu~,~\lambda)` evolutionary algorithm. :param population: A list of individuals. :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution operators. :param mu: The number of individuals to select for the next generation. :param lambda\_: The number of children to produce at each generation. :param cxpb: The probability that an offspring is produced by crossover. :param mutpb: The probability that an offspring is produced by mutation. :param ngen: The number of generation. :param stats: A :class:`~deap.tools.Statistics` object that is updated inplace, optional. :param halloffame: A :class:`~deap.tools.HallOfFame` object that will contain the best individuals, optional. :param verbose: Whether or not to log the statistics. :returns: The final population. First, the individuals having an invalid fitness are evaluated. Then, the evolutionary loop begins by producing *lambda_* offspring from the population, the offspring are generated by a crossover, a mutation or a reproduction proportionally to the probabilities *cxpb*, *mutpb* and 1 - (cxpb + mutpb). The offspring are then evaluated and the next generation population is selected **only** from the offspring. Briefly, the operators are applied as following :: evaluate(population) for i in range(ngen): offspring = varOr(population, toolbox, lambda_, cxpb, mutpb) evaluate(offspring) population = select(offspring, mu) This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`, :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be registered in the toolbox. This algorithm uses the :func:`varOr` variation. """ assert lambda_ >= mu, "lambda must be greater or equal to mu." # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit if halloffame is not None: halloffame.update(population) logbook = tools.Logbook() logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) record = stats.compile(population) if stats is not None else {} logbook.record(gen=0, nevals=len(invalid_ind), **record) if verbose: print logbook.stream # Begin the generational process for gen in range(1, ngen+1): # Vary the population offspring = varOr(population, toolbox, lambda_, cxpb, mutpb) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # Update the hall of fame with the generated individuals if halloffame is not None: halloffame.update(offspring) # Select the next generation population population[:] = toolbox.select(offspring, mu) # Update the statistics with the new population record = stats.compile(population) if stats is not None else {} logbook.record(gen=gen, nevals=len(invalid_ind), **record) if verbose: print logbook.stream return population, logbook def eaGenerateUpdate(toolbox, ngen, halloffame=None, stats=None, verbose=__debug__): """This is algorithm implements the ask-tell model proposed in [Colette2010]_, where ask is called `generate` and tell is called `update`. :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution operators. :param ngen: The number of generation. :param stats: A :class:`~deap.tools.Statistics` object that is updated inplace, optional. :param halloffame: A :class:`~deap.tools.HallOfFame` object that will contain the best individuals, optional. :param verbose: Whether or not to log the statistics. :returns: The final population. The toolbox should contain a reference to the generate and the update method of the chosen strategy. .. [Colette2010] Collette, Y., N. Hansen, G. Pujol, D. Salazar Aponte and R. Le Riche (2010). On Object-Oriented Programming of Optimizers - Examples in Scilab. In P. Breitkopf and R. F. Coelho, eds.: Multidisciplinary Design Optimization in Computational Mechanics, Wiley, pp. 527-565; """ logbook = tools.Logbook() logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) for gen in xrange(ngen): # Generate a new population population = toolbox.generate() # Evaluate the individuals fitnesses = toolbox.map(toolbox.evaluate, population) for ind, fit in zip(population, fitnesses): ind.fitness.values = fit if halloffame is not None: halloffame.update(population) # Update the strategy with the evaluated individuals toolbox.update(population) record = stats.compile(population) if stats is not None else {} logbook.record(gen=gen, nevals=len(population), **record) if verbose: print logbook.stream return population, logbookdeap-1.0.1/deap/base.py0000644000076500000240000002547312301410325015053 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """The :mod:`~deap.base` module provides basic structures to build evolutionary algorithms. It contains the :class:`~deap.base.Toolbox`, useful to store evolutionary operators, and a virtual :class:`~deap.base.Fitness` class used as base class, for the fitness member of any individual. """ import sys from collections import Sequence from copy import deepcopy from functools import partial from operator import mul, truediv class Toolbox(object): """A toolbox for evolution that contains the evolutionary operators. At first the toolbox contains a :meth:`~deap.toolbox.clone` method that duplicates any element it is passed as argument, this method defaults to the :func:`copy.deepcopy` function. and a :meth:`~deap.toolbox.map` method that applies the function given as first argument to every items of the iterables given as next arguments, this method defaults to the :func:`map` function. You may populate the toolbox with any other function by using the :meth:`~deap.base.Toolbox.register` method. Concrete usages of the toolbox are shown for initialization in the :ref:`creating-types` tutorial and for tools container in the :ref:`next-step` tutorial. """ def __init__(self): self.register("clone", deepcopy) self.register("map", map) def register(self, alias, function, *args, **kargs): """Register a *function* in the toolbox under the name *alias*. You may provide default arguments that will be passed automatically when calling the registered function. Fixed arguments can then be overriden at function call time. :param alias: The name the operator will take in the toolbox. If the alias already exist it will overwrite the the operator already present. :param function: The function to which refer the alias. :param argument: One or more argument (and keyword argument) to pass automatically to the registered function when called, optional. The following code block is an example of how the toolbox is used. :: >>> def func(a, b, c=3): ... print a, b, c ... >>> tools = Toolbox() >>> tools.register("myFunc", func, 2, c=4) >>> tools.myFunc(3) 2 3 4 The registered function will be given the attributes :attr:`__name__` set to the alias and :attr:`__doc__` set to the original function's documentation. The :attr:`__dict__` attribute will also be updated with the original function's instance dictionnary, if any. """ pfunc = partial(function, *args, **kargs) pfunc.__name__ = alias pfunc.__doc__ = function.__doc__ if hasattr(function, "__dict__") and not isinstance(function, type): # Some functions don't have a dictionary, in these cases # simply don't copy it. Moreover, if the function is actually # a class, we do not want to copy the dictionary. pfunc.__dict__.update(function.__dict__.copy()) setattr(self, alias, pfunc) def unregister(self, alias): """Unregister *alias* from the toolbox. :param alias: The name of the operator to remove from the toolbox. """ delattr(self, alias) def decorate(self, alias, *decorators): """Decorate *alias* with the specified *decorators*, *alias* has to be a registered function in the current toolbox. :param alias: The name of the operator to decorate. :param decorator: One or more function decorator. If multiple decorators are provided they will be applied in order, with the last decorator decorating all the others. .. note:: Decorate a function using the toolbox makes it unpicklable, and will produce an error on pickling. Although this limitation is not relevant in most cases, it may have an impact on distributed environments like multiprocessing. A function can still be decorated manually before it is added to the toolbox (using the @ notation) in order to be picklable. """ pfunc = getattr(self, alias) function, args, kargs = pfunc.func, pfunc.args, pfunc.keywords for decorator in decorators: function = decorator(function) self.register(alias, function, *args, **kargs) class Fitness(object): """The fitness is a measure of quality of a solution. If *values* are provided as a tuple, the fitness is initalized using those values, otherwise it is empty (or invalid). :param values: The initial values of the fitness as a tuple, optional. Fitnesses may be compared using the ``>``, ``<``, ``>=``, ``<=``, ``==``, ``!=``. The comparison of those operators is made lexicographically. Maximization and minimization are taken care off by a multiplication between the :attr:`weights` and the fitness :attr:`values`. The comparison can be made between fitnesses of different size, if the fitnesses are equal until the extra elements, the longer fitness will be superior to the shorter. Different types of fitnesses are created in the :ref:`creating-types` tutorial. .. note:: When comparing fitness values that are **minimized**, ``a > b`` will return :data:`True` if *a* is **smaller** than *b*. """ weights = None """The weights are used in the fitness comparison. They are shared among all fitnesses of the same type. When subclassing :class:`Fitness`, the weights must be defined as a tuple where each element is associated to an objective. A negative weight element corresponds to the minimization of the associated objective and positive weight to the maximization. .. note:: If weights is not defined during subclassing, the following error will occur at instantiation of a subclass fitness object: ``TypeError: Can't instantiate abstract with abstract attribute weights.`` """ wvalues = () """Contains the weighted values of the fitness, the multiplication with the weights is made when the values are set via the property :attr:`values`. Multiplication is made on setting of the values for efficiency. Generally it is unnecessary to manipulate wvalues as it is an internal attribute of the fitness used in the comparison operators. """ def __init__(self, values=()): if self.weights is None: raise TypeError("Can't instantiate abstract %r with abstract " "attribute weights." % (self.__class__)) if not isinstance(self.weights, Sequence): raise TypeError("Attribute weights of %r must be a sequence." % self.__class__) if len(values) > 0: self.values = values def getValues(self): return tuple(map(truediv, self.wvalues, self.weights)) def setValues(self, values): try: self.wvalues = tuple(map(mul, values, self.weights)) except TypeError: _, _, traceback = sys.exc_info() raise TypeError, ("Both weights and assigned values must be a " "sequence of numbers when assigning to values of " "%r. Currently assigning value(s) %r of %r to a fitness with " "weights %s." % (self.__class__, values, type(values), self.weights)), traceback def delValues(self): self.wvalues = () values = property(getValues, setValues, delValues, ("Fitness values. Use directly ``individual.fitness.values = values`` " "in order to set the fitness and ``del individual.fitness.values`` " "in order to clear (invalidate) the fitness. The (unweighted) fitness " "can be directly accessed via ``individual.fitness.values``.")) def dominates(self, other, obj=slice(None)): """Return true if each objective of *self* is not strictly worse than the corresponding objective of *other* and at least one objective is strictly better. :param obj: Slice indicating on which objectives the domination is tested. The default value is `slice(None)`, representing every objectives. """ not_equal = False for self_wvalue, other_wvalue in zip(self.wvalues[obj], other.wvalues[obj]): if self_wvalue > other_wvalue: not_equal = True elif self_wvalue < other_wvalue: return False return not_equal @property def valid(self): """Assess if a fitness is valid or not.""" return len(self.wvalues) != 0 def __hash__(self): return hash(self.wvalues) def __gt__(self, other): return not self.__le__(other) def __ge__(self, other): return not self.__lt__(other) def __le__(self, other): return self.wvalues <= other.wvalues def __lt__(self, other): return self.wvalues < other.wvalues def __eq__(self, other): return self.wvalues == other.wvalues def __ne__(self, other): return not self.__eq__(other) def __deepcopy__(self, memo): """Replace the basic deepcopy function with a faster one. It assumes that the elements in the :attr:`values` tuple are immutable and the fitness does not contain any other object than :attr:`values` and :attr:`weights`. """ copy_ = self.__class__() copy_.wvalues = self.wvalues return copy_ def __str__(self): """Return the values of the Fitness object.""" return str(self.values if self.valid else tuple()) def __repr__(self): """Return the Python code to build a copy of the object.""" return "%s.%s(%r)" % (self.__module__, self.__class__.__name__, self.values if self.valid else tuple()) deap-1.0.1/deap/benchmarks/0000755000076500000240000000000012321001644015673 5ustar felixstaff00000000000000deap-1.0.1/deap/benchmarks/__init__.py0000644000076500000240000005464512117373622020035 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """ Regroup typical EC benchmarks functions to import easily and benchmark examples. """ import random from math import sin, cos, pi, exp, e, sqrt from operator import mul from functools import reduce # Unimodal def rand(individual): """Random test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization or maximization * - Range - none * - Global optima - none * - Function - :math:`f(\mathbf{x}) = \\text{\\texttt{random}}(0,1)` """ return random.random(), def plane(individual): """Plane test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - none * - Global optima - :math:`x_i = 0, \\forall i \in \\lbrace 1 \\ldots N\\rbrace`, :math:`f(\mathbf{x}) = 0` * - Function - :math:`f(\mathbf{x}) = x_0` """ return individual[0], def sphere(individual): """Sphere test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - none * - Global optima - :math:`x_i = 0, \\forall i \in \\lbrace 1 \\ldots N\\rbrace`, :math:`f(\mathbf{x}) = 0` * - Function - :math:`f(\mathbf{x}) = \sum_{i=1}^Nx_i^2` """ return sum(gene * gene for gene in individual), def cigar(individual): """Cigar test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - none * - Global optima - :math:`x_i = 0, \\forall i \in \\lbrace 1 \\ldots N\\rbrace`, :math:`f(\mathbf{x}) = 0` * - Function - :math:`f(\mathbf{x}) = x_0^2 + 10^6\\sum_{i=1}^N\,x_i^2` """ return individual[0]**2 + 1e6 * sum(gene * gene for gene in individual), def rosenbrock(individual): """Rosenbrock test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - none * - Global optima - :math:`x_i = 1, \\forall i \in \\lbrace 1 \\ldots N\\rbrace`, :math:`f(\mathbf{x}) = 0` * - Function - :math:`f(\\mathbf{x}) = \\sum_{i=1}^{N-1} (1-x_i)^2 + 100 (x_{i+1} - x_i^2 )^2` .. plot:: code/benchmarks/rosenbrock.py :width: 67 % """ return sum(100 * (x * x - y)**2 + (1. - x)**2 \ for x, y in zip(individual[:-1], individual[1:])), def h1(individual): """ Simple two-dimensional function containing several local maxima. From: The Merits of a Parallel Genetic Algorithm in Solving Hard Optimization Problems, A. J. Knoek van Soest and L. J. R. Richard Casius, J. Biomech. Eng. 125, 141 (2003) .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - maximization * - Range - :math:`x_i \in [-100, 100]` * - Global optima - :math:`\mathbf{x} = (8.6998, 6.7665)`, :math:`f(\mathbf{x}) = 2`\n * - Function - :math:`f(\mathbf{x}) = \\frac{\sin(x_1 - \\frac{x_2}{8})^2 + \ \\sin(x_2 + \\frac{x_1}{8})^2}{\\sqrt{(x_1 - 8.6998)^2 + \ (x_2 - 6.7665)^2} + 1}` .. plot:: code/benchmarks/h1.py :width: 67 % """ num = (sin(individual[0] - individual[1] / 8))**2 + (sin(individual[1] + individual[0] / 8))**2 denum = ((individual[0] - 8.6998)**2 + (individual[1] - 6.7665)**2)**0.5 + 1 return num / denum, # # Multimodal def ackley(individual): """Ackley test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - :math:`x_i \in [-15, 30]` * - Global optima - :math:`x_i = 0, \\forall i \in \\lbrace 1 \\ldots N\\rbrace`, :math:`f(\mathbf{x}) = 0` * - Function - :math:`f(\\mathbf{x}) = 20 - 20\exp\left(-0.2\sqrt{\\frac{1}{N} \ \\sum_{i=1}^N x_i^2} \\right) + e - \\exp\\left(\\frac{1}{N}\sum_{i=1}^N \\cos(2\pi x_i) \\right)` .. plot:: code/benchmarks/ackley.py :width: 67 % """ N = len(individual) return 20 - 20 * exp(-0.2*sqrt(1.0/N * sum(x**2 for x in individual))) \ + e - exp(1.0/N * sum(cos(2*pi*x) for x in individual)), def bohachevsky(individual): """Bohachevsky test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - :math:`x_i \in [-100, 100]` * - Global optima - :math:`x_i = 0, \\forall i \in \\lbrace 1 \\ldots N\\rbrace`, :math:`f(\mathbf{x}) = 0` * - Function - :math:`f(\mathbf{x}) = \sum_{i=1}^{N-1}(x_i^2 + 2x_{i+1}^2 - \ 0.3\cos(3\pi x_i) - 0.4\cos(4\pi x_{i+1}) + 0.7)` .. plot:: code/benchmarks/bohachevsky.py :width: 67 % """ return sum(x**2 + 2*x1**2 - 0.3*cos(3*pi*x) - 0.4*cos(4*pi*x1) + 0.7 for x, x1 in zip(individual[:-1], individual[1:])), def griewank(individual): """Griewank test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - :math:`x_i \in [-600, 600]` * - Global optima - :math:`x_i = 0, \\forall i \in \\lbrace 1 \\ldots N\\rbrace`, :math:`f(\mathbf{x}) = 0` * - Function - :math:`f(\\mathbf{x}) = \\frac{1}{4000}\\sum_{i=1}^N\,x_i^2 - \ \prod_{i=1}^N\\cos\\left(\\frac{x_i}{\sqrt{i}}\\right) + 1` .. plot:: code/benchmarks/griewank.py :width: 67 % """ return 1.0/4000.0 * sum(x**2 for x in individual) - \ reduce(mul, (cos(x/sqrt(i+1.0)) for i, x in enumerate(individual)), 1) + 1, def rastrigin(individual): """Rastrigin test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - :math:`x_i \in [-5.12, 5.12]` * - Global optima - :math:`x_i = 0, \\forall i \in \\lbrace 1 \\ldots N\\rbrace`, :math:`f(\mathbf{x}) = 0` * - Function - :math:`f(\\mathbf{x}) = 10N \sum_{i=1}^N x_i^2 - 10 \\cos(2\\pi x_i)` .. plot:: code/benchmarks/rastrigin.py :width: 67 % """ return 10 * len(individual) + sum(gene * gene - 10 * \ cos(2 * pi * gene) for gene in individual), def rastrigin_scaled(individual): """Scaled Rastrigin test objective function. :math:`f_{\\text{RastScaled}}(\mathbf{x}) = 10N + \sum_{i=1}^N \ \left(10^{\left(\\frac{i-1}{N-1}\\right)} x_i \\right)^2 x_i)^2 - \ 10\cos\\left(2\\pi 10^{\left(\\frac{i-1}{N-1}\\right)} x_i \\right)` """ N = len(individual) return 10*N + sum((10**(i/(N-1))*x)**2 - 10*cos(2*pi*10**(i/(N-1))*x) for i, x in enumerate(individual)), def rastrigin_skew(individual): """Skewed Rastrigin test objective function. :math:`f_{\\text{RastSkew}}(\mathbf{x}) = 10N \sum_{i=1}^N \left(y_i^2 - 10 \\cos(2\\pi x_i)\\right)` :math:`\\text{with } y_i = \ \\begin{cases} \ 10\\cdot x_i & \\text{ if } x_i > 0,\\\ \ x_i & \\text{ otherwise } \ \\end{cases}` """ N = len(individual) return 10*N + sum((10*x if x > 0 else x)**2 - 10*cos(2*pi*(10*x if x > 0 else x)) for x in individual), def schaffer(individual): """Schaffer test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - :math:`x_i \in [-100, 100]` * - Global optima - :math:`x_i = 0, \\forall i \in \\lbrace 1 \\ldots N\\rbrace`, :math:`f(\mathbf{x}) = 0` * - Function - :math:`f(\mathbf{x}) = \sum_{i=1}^{N-1} (x_i^2+x_{i+1}^2)^{0.25} \cdot \ \\left[ \sin^2(50\cdot(x_i^2+x_{i+1}^2)^{0.10}) + 1.0 \ \\right]` .. plot:: code/benchmarks/schaffer.py :width: 67 % """ return sum((x**2+x1**2)**0.25 * ((sin(50*(x**2+x1**2)**0.1))**2+1.0) for x, x1 in zip(individual[:-1], individual[1:])), def schwefel(individual): """Schwefel test objective function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - :math:`x_i \in [-500, 500]` * - Global optima - :math:`x_i = 420.96874636, \\forall i \in \\lbrace 1 \\ldots N\\rbrace`, :math:`f(\mathbf{x}) = 0` * - Function - :math:`f(\mathbf{x}) = 418.9828872724339\cdot N - \ \sum_{i=1}^N\,x_i\sin\\left(\sqrt{|x_i|}\\right)` .. plot:: code/benchmarks/schwefel.py :width: 67 % """ N = len(individual) return 418.9828872724339*N-sum(x*sin(sqrt(abs(x))) for x in individual), def himmelblau(individual): """The Himmelblau's function is multimodal with 4 defined minimums in :math:`[-6, 6]^2`. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Type - minimization * - Range - :math:`x_i \in [-6, 6]` * - Global optima - :math:`\mathbf{x}_1 = (3.0, 2.0)`, :math:`f(\mathbf{x}_1) = 0`\n :math:`\mathbf{x}_2 = (-2.805118, 3.131312)`, :math:`f(\mathbf{x}_2) = 0`\n :math:`\mathbf{x}_3 = (-3.779310, -3.283186)`, :math:`f(\mathbf{x}_3) = 0`\n :math:`\mathbf{x}_4 = (3.584428, -1.848126)`, :math:`f(\mathbf{x}_4) = 0`\n * - Function - :math:`f(x_1, x_2) = (x_1^2 + x_2 - 11)^2 + (x_1 + x_2^2 -7)^2` .. plot:: code/benchmarks/himmelblau.py :width: 67 % """ return (individual[0] * individual[0] + individual[1] - 11)**2 + \ (individual[0] + individual[1] * individual[1] - 7)**2, def shekel(individual, a, c): """The Shekel multimodal function can have any number of maxima. The number of maxima is given by the length of any of the arguments *a* or *c*, *a* is a matrix of size :math:`M\\times N`, where *M* is the number of maxima and *N* the number of dimensions and *c* is a :math:`M\\times 1` vector. The matrix :math:`\\mathcal{A}` can be seen as the position of the maxima and the vector :math:`\\mathbf{c}`, the width of the maxima. :math:`f_\\text{Shekel}(\mathbf{x}) = \\sum_{i = 1}^{M} \\frac{1}{c_{i} + \\sum_{j = 1}^{N} (x_{j} - a_{ij})^2 }` The following figure uses :math:`\\mathcal{A} = \\begin{bmatrix} 0.5 & 0.5 \\\\ 0.25 & 0.25 \\\\ 0.25 & 0.75 \\\\ 0.75 & 0.25 \\\\ 0.75 & 0.75 \\end{bmatrix}` and :math:`\\mathbf{c} = \\begin{bmatrix} 0.002 \\\\ 0.005 \\\\ 0.005 \\\\ 0.005 \\\\ 0.005 \\end{bmatrix}`, thus defining 5 maximums in :math:`\\mathbb{R}^2`. .. plot:: code/benchmarks/shekel.py :width: 67 % """ return sum((1. / (c[i] + sum((x - a[i][j])**2 for j, x in enumerate(individual)))) for i in range(len(c))), # Multiobjectives def kursawe(individual): """Kursawe multiobjective function. :math:`f_{\\text{Kursawe}1}(\\mathbf{x}) = \\sum_{i=1}^{N-1} -10 e^{-0.2 \\sqrt{x_i^2 + x_{i+1}^2} }` :math:`f_{\\text{Kursawe}2}(\\mathbf{x}) = \\sum_{i=1}^{N} |x_i|^{0.8} + 5 \\sin(x_i^3)` .. plot:: code/benchmarks/kursawe.py :width: 100 % """ f1 = sum(-10 * exp(-0.2 * sqrt(x * x + y * y)) for x, y in zip(individual[:-1], individual[1:])) f2 = sum(abs(x)**0.8 + 5 * sin(x * x * x) for x in individual) return f1, f2 def schaffer_mo(individual): """Schaffer's multiobjective function on a one attribute *individual*. From: J. D. Schaffer, "Multiple objective optimization with vector evaluated genetic algorithms", in Proceedings of the First International Conference on Genetic Algorithms, 1987. :math:`f_{\\text{Schaffer}1}(\\mathbf{x}) = x_1^2` :math:`f_{\\text{Schaffer}2}(\\mathbf{x}) = (x_1-2)^2` """ return individual[0] ** 2, (individual[0] - 2) ** 2 def zdt1(individual): """ZDT1 multiobjective function. :math:`g(\\mathbf{x}) = 1 + \\frac{9}{n-1}\\sum_{i=2}^n x_i` :math:`f_{\\text{ZDT1}1}(\\mathbf{x}) = x_1` :math:`f_{\\text{ZDT1}2}(\\mathbf{x}) = g(\\mathbf{x})\\left[1 - \\sqrt{\\frac{x_1}{g(\\mathbf{x})}}\\right]` """ g = 1.0 + 9.0*sum(individual[1:])/(len(individual)-1) f1 = individual[0] f2 = g * (1 - sqrt(f1/g)) return f1, f2 def zdt2(individual): """ZDT2 multiobjective function. :math:`g(\\mathbf{x}) = 1 + \\frac{9}{n-1}\\sum_{i=2}^n x_i` :math:`f_{\\text{ZDT2}1}(\\mathbf{x}) = x_1` :math:`f_{\\text{ZDT2}2}(\\mathbf{x}) = g(\\mathbf{x})\\left[1 - \\left(\\frac{x_1}{g(\\mathbf{x})}\\right)^2\\right]` """ g = 1.0 + 9.0*sum(individual[1:])/(len(individual)-1) f1 = individual[0] f2 = g * (1 - (f1/g)**2) return f1, f2 def zdt3(individual): """ZDT3 multiobjective function. :math:`g(\\mathbf{x}) = 1 + \\frac{9}{n-1}\\sum_{i=2}^n x_i` :math:`f_{\\text{ZDT3}1}(\\mathbf{x}) = x_1` :math:`f_{\\text{ZDT3}2}(\\mathbf{x}) = g(\\mathbf{x})\\left[1 - \\sqrt{\\frac{x_1}{g(\\mathbf{x})}} - \\frac{x_1}{g(\\mathbf{x})}\\sin(10\\pi x_1)\\right]` """ g = 1.0 + 9.0*sum(individual[1:])/(len(individual)-1) f1 = individual[0] f2 = g * (1 - sqrt(f1/g) - f1/g * sin(10*pi*f1)) return f1, f2 def zdt4(individual): """ZDT4 multiobjective function. :math:`g(\\mathbf{x}) = 1 + 10(n-1) + \\sum_{i=2}^n \\left[ x_i^2 - 10\\cos(4\\pi x_i) \\right]` :math:`f_{\\text{ZDT4}1}(\\mathbf{x}) = x_1` :math:`f_{\\text{ZDT4}2}(\\mathbf{x}) = g(\\mathbf{x})\\left[ 1 - \\sqrt{x_1/g(\\mathbf{x})} \\right]` """ g = 1 + 10*(len(individual)-1) + sum(xi**2 - 10*cos(4*pi*xi) for xi in individual[1:]) f1 = individual[0] f2 = g * (1 - sqrt(f1/g)) return f1, f2 def zdt6(individual): """ZDT6 multiobjective function. :math:`g(\\mathbf{x}) = 1 + 9 \\left[ \\left(\\sum_{i=2}^n x_i\\right)/(n-1) \\right]^{0.25}` :math:`f_{\\text{ZDT6}1}(\\mathbf{x}) = 1 - \\exp(-4x_1)\\sin^6(6\\pi x_1)` :math:`f_{\\text{ZDT6}2}(\\mathbf{x}) = g(\\mathbf{x}) \left[ 1 - (f_{\\text{ZDT6}1}(\\mathbf{x})/g(\\mathbf{x}))^2 \\right]` """ g = 1 + 9 * (sum(individual[1:]) / (len(individual)-1))**0.25 f1 = 1 - exp(-4*individual[0]) * sin(6*pi*individual[0])**6 f2 = g * (1 - (f1/g)**2) return f1, f2 def dtlz1(individual, obj): """DTLZ1 mutliobjective function. It returns a tuple of *obj* values. The individual must have at least *obj* elements. From: K. Deb, L. Thiele, M. Laumanns and E. Zitzler. Scalable Multi-Objective Optimization Test Problems. CEC 2002, p. 825 - 830, IEEE Press, 2002. :math:`g(\\mathbf{x}_m) = 100\\left(|\\mathbf{x}_m| + \sum_{x_i \in \\mathbf{x}_m}\\left((x_i - 0.5)^2 - \\cos(20\pi(x_i - 0.5))\\right)\\right)` :math:`f_{\\text{DTLZ1}1}(\\mathbf{x}) = \\frac{1}{2} (1 + g(\\mathbf{x}_m)) \\prod_{i=1}^{m-1}x_i` :math:`f_{\\text{DTLZ1}2}(\\mathbf{x}) = \\frac{1}{2} (1 + g(\\mathbf{x}_m)) (1-x_{m-1}) \\prod_{i=1}^{m-2}x_i` :math:`\\ldots` :math:`f_{\\text{DTLZ1}m-1}(\\mathbf{x}) = \\frac{1}{2} (1 + g(\\mathbf{x}_m)) (1 - x_2) x_1` :math:`f_{\\text{DTLZ1}m}(\\mathbf{x}) = \\frac{1}{2} (1 - x_1)(1 + g(\\mathbf{x}_m))` Where :math:`m` is the number of objectives and :math:`\\mathbf{x}_m` is a vector of the remaining attributes :math:`[x_m~\\ldots~x_n]` of the individual in :math:`n > m` dimensions. """ g = 100 * (len(individual[obj-1:]) + sum((xi-0.5)**2 - cos(20*pi*(xi-0.5)) for xi in individual[obj-1:])) f = [0.5 * reduce(mul, individual[:obj-1], 1) * (1 + g)] f.extend(0.5 * reduce(mul, individual[:m], 1) * (1 - individual[m]) * (1 + g) for m in reversed(xrange(obj-1))) return f def dtlz2(individual, obj): """DTLZ2 mutliobjective function. It returns a tuple of *obj* values. The individual must have at least *obj* elements. From: K. Deb, L. Thiele, M. Laumanns and E. Zitzler. Scalable Multi-Objective Optimization Test Problems. CEC 2002, p. 825 - 830, IEEE Press, 2002. :math:`g(\\mathbf{x}_m) = \\sum_{x_i \in \\mathbf{x}_m} (x_i - 0.5)^2` :math:`f_{\\text{DTLZ2}1}(\\mathbf{x}) = (1 + g(\\mathbf{x}_m)) \\prod_{i=1}^{m-1} \\cos(0.5x_i\pi)` :math:`f_{\\text{DTLZ2}2}(\\mathbf{x}) = (1 + g(\\mathbf{x}_m)) \\sin(0.5x_{m-1}\pi ) \\prod_{i=1}^{m-2} \\cos(0.5x_i\pi)` :math:`\\ldots` :math:`f_{\\text{DTLZ2}m}(\\mathbf{x}) = (1 + g(\\mathbf{x}_m)) \\sin(0.5x_{1}\pi )` Where :math:`m` is the number of objectives and :math:`\\mathbf{x}_m` is a vector of the remaining attributes :math:`[x_m~\\ldots~x_n]` of the individual in :math:`n > m` dimensions. """ xc = individual[:obj-1] xm = individual[obj-1:] g = sum((xi-0.5)**2 for xi in xm) f = [(1.0+g) * reduce(mul, (cos(0.5*xi*pi) for xi in xc), 1.0)] f.extend((1.0+g) * reduce(mul, (cos(0.5*xi*pi) for xi in xc[:m-1]), 1) * sin(0.5*xc[m]*pi) for m in reversed(xrange(obj-1))) return f def dtlz3(individual, obj): """DTLZ3 mutliobjective function. It returns a tuple of *obj* values. The individual must have at least *obj* elements. From: K. Deb, L. Thiele, M. Laumanns and E. Zitzler. Scalable Multi-Objective Optimization Test Problems. CEC 2002, p. 825 - 830, IEEE Press, 2002. :math:`g(\\mathbf{x}_m) = 100\\left(|\\mathbf{x}_m| + \sum_{x_i \in \\mathbf{x}_m}\\left((x_i - 0.5)^2 - \\cos(20\pi(x_i - 0.5))\\right)\\right)` :math:`f_{\\text{DTLZ3}1}(\\mathbf{x}) = (1 + g(\\mathbf{x}_m)) \\prod_{i=1}^{m-1} \\cos(0.5x_i\pi)` :math:`f_{\\text{DTLZ3}2}(\\mathbf{x}) = (1 + g(\\mathbf{x}_m)) \\sin(0.5x_{m-1}\pi ) \\prod_{i=1}^{m-2} \\cos(0.5x_i\pi)` :math:`\\ldots` :math:`f_{\\text{DTLZ3}m}(\\mathbf{x}) = (1 + g(\\mathbf{x}_m)) \\sin(0.5x_{1}\pi )` Where :math:`m` is the number of objectives and :math:`\\mathbf{x}_m` is a vector of the remaining attributes :math:`[x_m~\\ldots~x_n]` of the individual in :math:`n > m` dimensions. """ xc = individual[:obj-1] xm = individual[obj-1:] g = 100 * (len(xm) + sum((xi-0.5)**2 - cos(20*pi*(xi-0.5)) for xi in xm)) f = [(1.0+g) * reduce(mul, (cos(0.5*xi*pi) for xi in xc), 1.0)] f.extend((1.0+g) * reduce(mul, (cos(0.5*xi*pi) for xi in xc[:m-1]), 1) * sin(0.5*xc[m]*pi) for m in reversed(xrange(obj-1))) return f def dtlz4(individual, obj, alpha): """DTLZ4 mutliobjective function. It returns a tuple of *obj* values. The individual must have at least *obj* elements. The *alpha* parameter allows for a meta-variable mapping in :func:`dtlz2` :math:`x_i \\rightarrow x_i^\\alpha`, the authors suggest :math:`\\alpha = 100`. From: K. Deb, L. Thiele, M. Laumanns and E. Zitzler. Scalable Multi-Objective Optimization Test Problems. CEC 2002, p. 825 - 830, IEEE Press, 2002. :math:`g(\\mathbf{x}_m) = \\sum_{x_i \in \\mathbf{x}_m} (x_i - 0.5)^2` :math:`f_{\\text{DTLZ4}1}(\\mathbf{x}) = (1 + g(\\mathbf{x}_m)) \\prod_{i=1}^{m-1} \\cos(0.5x_i^\\alpha\pi)` :math:`f_{\\text{DTLZ4}2}(\\mathbf{x}) = (1 + g(\\mathbf{x}_m)) \\sin(0.5x_{m-1}^\\alpha\pi ) \\prod_{i=1}^{m-2} \\cos(0.5x_i^\\alpha\pi)` :math:`\\ldots` :math:`f_{\\text{DTLZ4}m}(\\mathbf{x}) = (1 + g(\\mathbf{x}_m)) \\sin(0.5x_{1}^\\alpha\pi )` Where :math:`m` is the number of objectives and :math:`\\mathbf{x}_m` is a vector of the remaining attributes :math:`[x_m~\\ldots~x_n]` of the individual in :math:`n > m` dimensions. """ xc = individual[:obj-1] xm = individual[obj-1:] g = sum((xi-0.5)**2 for xi in xm) f = [(1.0+g) * reduce(mul, (cos(0.5*xi**alpha*pi) for xi in xc), 1.0)] f.extend((1.0+g) * reduce(mul, (cos(0.5*xi**alpha*pi) for xi in xc[:m-1]), 1) * sin(0.5*xc[m]**alpha*pi) for m in reversed(xrange(obj-1))) return f def fonseca(individual): """Fonseca and Fleming's multiobjective function. From: C. M. Fonseca and P. J. Fleming, "Multiobjective optimization and multiple constraint handling with evolutionary algorithms -- Part II: Application example", IEEE Transactions on Systems, Man and Cybernetics, 1998. :math:`f_{\\text{Fonseca}1}(\\mathbf{x}) = 1 - e^{-\\sum_{i=1}^{3}(x_i - \\frac{1}{\\sqrt{3}})^2}` :math:`f_{\\text{Fonseca}2}(\\mathbf{x}) = 1 - e^{-\\sum_{i=1}^{3}(x_i + \\frac{1}{\\sqrt{3}})^2}` """ f_1 = 1 - exp(-sum((xi - 1/sqrt(3))**2 for xi in individual[:3])) f_2 = 1 - exp(-sum((xi + 1/sqrt(3))**2 for xi in individual[:3])) return f_1, f_2 def poloni(individual): """Poloni's multiobjective function on a two attribute *individual*. From: C. Poloni, "Hybrid GA for multi objective aerodynamic shape optimization", in Genetic Algorithms in Engineering and Computer Science, 1997. :math:`A_1 = 0.5 \\sin (1) - 2 \\cos (1) + \\sin (2) - 1.5 \\cos (2)` :math:`A_2 = 1.5 \\sin (1) - \\cos (1) + 2 \\sin (2) - 0.5 \\cos (2)` :math:`B_1 = 0.5 \\sin (x_1) - 2 \\cos (x_1) + \\sin (x_2) - 1.5 \\cos (x_2)` :math:`B_2 = 1.5 \\sin (x_1) - cos(x_1) + 2 \\sin (x_2) - 0.5 \\cos (x_2)` :math:`f_{\\text{Poloni}1}(\\mathbf{x}) = 1 + (A_1 - B_1)^2 + (A_2 - B_2)^2` :math:`f_{\\text{Poloni}2}(\\mathbf{x}) = (x_1 + 3)^2 + (x_2 + 1)^2` """ x_1 = individual[0] x_2 = individual[1] A_1 = 0.5 * sin(1) - 2 * cos(1) + sin(2) - 1.5 * cos(2) A_2 = 1.5 * sin(1) - cos(1) + 2 * sin(2) - 0.5 * cos(2) B_1 = 0.5 * sin(x_1) - 2 * cos(x_1) + sin(x_2) - 1.5 * cos(x_2) B_2 = 1.5 * sin(x_1) - cos(x_1) + 2 * sin(x_2) - 0.5 * cos(x_2) return 1 + (A_1 - B_1)**2 + (A_2 - B_2)**2, (x_1 + 3)**2 + (x_2 + 1)**2 deap-1.0.1/deap/benchmarks/binary.py0000644000076500000240000001135012117373622017544 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import math def bin2float(min_, max_, nbits): """Convert a binary array into an array of float where each float is composed of *nbits* and is between *min_* and *max_* and return the result of the decorated function. .. note:: This decorator requires the first argument of the evaluation function to be named *individual*. """ def wrap(function): def wrapped_function(individual, *args, **kargs): nelem = len(individual)/nbits decoded = [0] * nelem for i in xrange(nelem): gene = int("".join(map(str, individual[i*nbits:i*nbits+nbits])), 2) div = 2**nbits - 1 temp = float(gene)/float(div) decoded[i] = min_ + (temp * (max_ - min_)) return function(decoded, *args, **kargs) return wrapped_function return wrap def trap(individual): u = sum(individual) k = len(individual) if u == k: return k else: return k - 1 - u def inv_trap(individual): u = sum(individual) k = len(individual) if u == 0: return k else: return u - 1 def chuang_f1(individual): """Binary deceptive function from : Multivariate Multi-Model Approach for Globally Multimodal Problems by Chung-Yao Chuang and Wen-Lian Hsu. The function takes individual of 40+1 dimensions and has two global optima in [1,1,...,1] and [0,0,...,0]. """ total = 0 if individual[-1] == 0: for i in xrange(0,len(individual)-1,4): total += inv_trap(individual[i:i+4]) else: for i in xrange(0,len(individual)-1,4): total += trap(individual[i:i+4]) return total, def chuang_f2(individual): """Binary deceptive function from : Multivariate Multi-Model Approach for Globally Multimodal Problems by Chung-Yao Chuang and Wen-Lian Hsu. The function takes individual of 40+1 dimensions and has four global optima in [1,1,...,0,0], [0,0,...,1,1], [1,1,...,1] and [0,0,...,0]. """ total = 0 if individual[-2] == 0 and individual[-1] == 0: for i in xrange(0,len(individual)-2,8): total += inv_trap(individual[i:i+4]) + inv_trap(individual[i+4:i+8]) elif individual[-2] == 0 and individual[-1] == 1: for i in xrange(0,len(individual)-2,8): total += inv_trap(individual[i:i+4]) + trap(individual[i+4:i+8]) elif individual[-2] == 1 and individual[-1] == 0: for i in xrange(0,len(individual)-2,8): total += trap(individual[i:i+4]) + inv_trap(individual[i+4:i+8]) else: for i in xrange(0,len(individual)-2,8): total += trap(individual[i:i+4]) + trap(individual[i+4:i+8]) return total, def chuang_f3(individual): """Binary deceptive function from : Multivariate Multi-Model Approach for Globally Multimodal Problems by Chung-Yao Chuang and Wen-Lian Hsu. The function takes individual of 40+1 dimensions and has two global optima in [1,1,...,1] and [0,0,...,0]. """ total = 0 if individual[-1] == 0: for i in xrange(0,len(individual)-1,4): total += inv_trap(individual[i:i+4]) else: for i in xrange(2,len(individual)-3,4): total += inv_trap(individual[i:i+4]) total += trap(individual[-2:]+individual[:2]) return total, # Royal Road Functions def royal_road1(individual, order): """Royal Road Function R1 as presented by Melanie Mitchell in : "An introduction to Genetic Algorithms". """ nelem = len(individual) / order max_value = int(2**order - 1) total = 0 for i in xrange(nelem): value = int("".join(map(str, individual[i*order:i*order+order])), 2) total += int(order) * int(value/max_value) return total, def royal_road2(individual, order): """Royal Road Function R2 as presented by Melanie Mitchell in : "An introduction to Genetic Algorithms". """ total = 0 norder = order while norder < order**2: total += royal_road1(norder, individual)[0] norder *= 2 return total, deap-1.0.1/deap/benchmarks/gp.py0000644000076500000240000000741112250366562016674 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . from math import exp, sin, cos def kotanchek(data): """Kotanchek benchmark function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Range - :math:`\mathbf{x} \in [-1, 7]^2` * - Function - :math:`f(\mathbf{x}) = \\frac{e^{-(x_1 - 1)^2}}{3.2 + (x_2 - 2.5)^2}` """ return exp(-(data[0] - 1)**2) / (3.2 + (data[1] - 2.5)**2) def salustowicz_1d(data): """Salustowicz benchmark function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Range - :math:`x \in [0, 10]` * - Function - :math:`f(x) = e^x x^3 \cos(x) \sin(x) (\cos(x) \sin^2(x) - 1)` """ return exp(-data[0]) * data[0]**3 * cos(data[0]) * sin(data[0]) * (cos(data[0]) * sin(data[0])**2 - 1) def salustowicz_2d(data): """Salustowicz benchmark function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Range - :math:`\mathbf{x} \in [0, 7]^2` * - Function - :math:`f(\mathbf{x}) = e^{x_1} x_1^3 \cos(x_1) \sin(x_1) (\cos(x_1) \sin^2(x_1) - 1) (x_2 -5)` """ return exp(-data[0]) * data[0]**3 * cos(data[0]) * sin(data[0]) * (cos(data[0]) * sin(data[0])**2 - 1) * (data[1] - 5) def unwrapped_ball(data): """Unwrapped ball benchmark function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Range - :math:`\mathbf{x} \in [-2, 8]^n` * - Function - :math:`f(\mathbf{x}) = \\frac{10}{5 + \sum_{i=1}^n (x_i - 3)^2}` """ return 10. / (5. + sum((d - 3)**2 for d in data)) def rational_polynomial(data): """Rational polynomial ball benchmark function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Range - :math:`\mathbf{x} \in [0, 2]^3` * - Function - :math:`f(\mathbf{x}) = \\frac{30 * (x_1 - 1) (x_3 - 1)}{x_2^2 (x_1 - 10)}` """ return 30. * (data[0] - 1) * (data[2] - 1) / (data[1]**2 * (data[0] - 10)) def sin_cos(data): """Sine cosine benchmark function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Range - :math:`\mathbf{x} \in [0, 6]^2` * - Function - :math:`f(\mathbf{x}) = 6\sin(x_1)\cos(x_2)` """ 6 * sin(data[0]) * cos(data[1]) def ripple(data): """Ripple benchmark function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Range - :math:`\mathbf{x} \in [-5, 5]^2` * - Function - :math:`f(\mathbf{x}) = (x_1 - 3) (x_2 - 3) + 2 \sin((x_1 - 4) (x_2 -4))` """ return (data[0] - 3) * (data[1] - 3) + 2 * sin((data[0] - 4) * (data[1] - 4)) def rational_polynomial2(data): """Rational polynomial benchmark function. .. list-table:: :widths: 10 50 :stub-columns: 1 * - Range - :math:`\mathbf{x} \in [0, 6]^2` * - Function - :math:`f(\mathbf{x}) = \\frac{(x_1 - 3)^4 + (x_2 - 3)^3 - (x_2 - 3)}{(x_2 - 2)^4 + 10}` """ return ((data[0] - 3)**4 + (data[1] - 3)**3 - (data[1] - 3)) / ((data[1] - 2)**4 + 10) deap-1.0.1/deap/benchmarks/movingpeaks.py0000644000076500000240000004412712301410325020576 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """ Re-implementation of the `Moving Peaks Benchmark `_ by Jurgen Branke. With the addition of the fluctuating number of peaks presented in *du Plessis and Engelbrecht, 2013, Self-Adaptive Environment with Fluctuating Number of Optima.* """ import math import itertools import random from collections import Sequence def cone(individual, position, height, width): """The cone peak function to be used with scenario 2 and 3. :math:`f(\mathbf{x}) = h - w \sqrt{\sum_{i=1}^N (x_i - p_i)^2}` """ value = 0.0 for x, p in zip(individual, position): value += (x - p)**2 return height - width * math.sqrt(value) def sphere(individual, position, height, width): value = 0.0 for x, p in zip(individual, position): value += (x - p)**2 return height * value def function1(individual, position, height, width): """The function1 peak function to be used with scenario 1. :math:`f(\mathbf{x}) = \\frac{h}{1 + w \sqrt{\sum_{i=1}^N (x_i - p_i)^2}}` """ value = 0.0 for x, p in zip(individual, position): value += (x - p)**2 return height / (1 + width * value) class MovingPeaks: """The Moving Peaks Benchmark is a fitness function changing over time. It consists of a number of peaks, changing in height, width and location. The peaks function is given by *pfunc*, wich is either a function object or a list of function objects (the default is :func:`function1`). The number of peaks is determined by *npeaks* (which defaults to 5). This parameter can be either a integer or a sequence. If it is set to an integer the number of peaks won't change, while if set to a sequence of 3 elements, the number of peaks will fluctuate between the first and third element of that sequence, the second element is the inital number of peaks. When fluctuating the number of peaks, the parameter *number_severity* must be included, it represents the number of peak fraction that is allowed to change. The dimensionality of the search domain is *dim*. A basis function *bfunc* can also be given to act as static landscape (the default is no basis function). The argument *random* serves to grant an independent random number generator to the moving peaks so that the evolution is not influenced by number drawn by this object (the default uses random functions from the Python module :mod:`random`). Various other keyword parameters listed in the table below are required to setup the benchmark, default parameters are based on scenario 1 of this benchmark. =================== ============================= =================== =================== ====================================================================================================================== Parameter :data:`SCENARIO_1` (Default) :data:`SCENARIO_2` :data:`SCENARIO_3` Details =================== ============================= =================== =================== ====================================================================================================================== ``pfunc`` :func:`function1` :func:`cone` :func:`cone` The peak function or a list of peak function. ``npeaks`` 5 10 50 Number of peaks. If an integer, the number of peaks won't change, if a sequence it will fluctuate [min, current, max]. ``bfunc`` :obj:`None` :obj:`None` ``lambda x: 10`` Basis static function. ``min_coord`` 0.0 0.0 0.0 Minimum coordinate for the centre of the peaks. ``max_coord`` 100.0 100.0 100.0 Maximum coordinate for the centre of the peaks. ``min_height`` 30.0 30.0 30.0 Minimum height of the peaks. ``max_height`` 70.0 70.0 70.0 Maximum height of the peaks. ``uniform_height`` 50.0 50.0 0 Starting height for all peaks, if ``uniform_height <= 0`` the initial height is set randomly for each peak. ``min_width`` 0.0001 1.0 1.0 Minimum width of the peaks. ``max_width`` 0.2 12.0 12.0 Maximum width of the peaks ``uniform_width`` 0.1 0 0 Starting width for all peaks, if ``uniform_width <= 0`` the initial width is set randomly for each peak. ``lambda_`` 0.0 0.5 0.5 Correlation between changes. ``move_severity`` 1.0 1.5 1.0 The distance a single peak moves when peaks change. ``height_severity`` 7.0 7.0 1.0 The standard deviation of the change made to the height of a peak when peaks change. ``width_severity`` 0.01 1.0 0.5 The standard deviation of the change made to the width of a peak when peaks change. ``period`` 5000 5000 1000 Period between two changes. =================== ============================= =================== =================== ====================================================================================================================== Dictionnaries :data:`SCENARIO_1`, :data:`SCENARIO_2` and :data:`SCENARIO_3` of this module define the defaults for these parameters. The scenario 3 requires a constant basis function which can be given as a lambda function ``lambda x: constant``. The following shows an example of scenario 1 with non uniform heights and widths. .. plot:: code/benchmarks/movingsc1.py :width: 67 % """ def __init__(self, dim, random=random, **kargs): # Scenario 1 is the default sc = SCENARIO_1.copy() sc.update(kargs) pfunc = sc.get("pfunc") npeaks = sc.get("npeaks") self.dim = dim self.minpeaks, self.maxpeaks = None, None if hasattr(npeaks, "__getitem__"): self.minpeaks, npeaks, self.maxpeaks = npeaks self.number_severity = sc.get("number_severity") try: if len(pfunc) == npeaks: self.peaks_function = pfunc else: self.peaks_function = self.random.sample(pfunc, npeaks) self.pfunc_pool = tuple(pfunc) except TypeError: self.peaks_function = list(itertools.repeat(pfunc, npeaks)) self.pfunc_pool = (pfunc,) self.random = random self.basis_function = sc.get("bfunc") self.min_coord = sc.get("min_coord") self.max_coord = sc.get("max_coord") self.min_height = sc.get("min_height") self.max_height = sc.get("max_height") uniform_height = sc.get("uniform_height") self.min_width = sc.get("min_width") self.max_width = sc.get("max_width") uniform_width = sc.get("uniform_width") self.lambda_ = sc.get("lambda_") self.move_severity = sc.get("move_severity") self.height_severity = sc.get("height_severity") self.width_severity = sc.get("width_severity") self.peaks_position = [[self.random.uniform(self.min_coord, self.max_coord) for _ in range(dim)] for _ in range(npeaks)] if uniform_height != 0: self.peaks_height = [uniform_height for _ in range(npeaks)] else: self.peaks_height = [self.random.uniform(self.min_height, self.max_height) for _ in range(npeaks)] if uniform_width != 0: self.peaks_width = [uniform_width for _ in range(npeaks)] else: self.peaks_width = [self.random.uniform(self.min_width, self.max_width) for _ in range(npeaks)] self.last_change_vector = [[self.random.random() - 0.5 for _ in range(dim)] for _ in range(npeaks)] self.period = sc.get("period") # Used by the Offline Error calculation self._optimum = None self._error = None self._offline_error = 0 # Also used for auto change self.nevals = 0 def globalMaximum(self): """Returns the global maximum value and position.""" # The global maximum is at one peak's position potential_max = list() for func, pos, height, width in zip(self.peaks_function, self.peaks_position, self.peaks_height, self.peaks_width): potential_max.append((func(pos, pos, height, width), pos)) return max(potential_max) def maximums(self): """Returns all visible maximums value and position sorted with the global maximum first. """ # The maximums are at the peaks position but might be swallowed by # other peaks maximums = list() for func, pos, height, width in zip(self.peaks_function, self.peaks_position, self.peaks_height, self.peaks_width): val = func(pos, pos, height, width) if val >= self.__call__(pos, count=False)[0]: maximums.append((val, pos)) return sorted(maximums, reverse=True) def __call__(self, individual, count=True): """Evaluate a given *individual* with the current benchmark configuration. :param indidivudal: The individual to evaluate. :param count: Wether or not to count this evaluation in the total evaluation count. (Defaults to :data:`True`) """ possible_values = [] for func, pos, height, width in zip(self.peaks_function, self.peaks_position, self.peaks_height, self.peaks_width): possible_values.append(func(individual, pos, height, width)) if self.basis_function: possible_values.append(self.basis_function(individual)) fitness = max(possible_values) if count: # Compute the offline error self.nevals += 1 if self._optimum is None: self._optimum = self.globalMaximum()[0] self._error = abs(fitness - self._optimum) self._error = min(self._error, abs(fitness - self._optimum)) self._offline_error += self._error # We exausted the number of evaluation, change peaks for the next one. if self.period > 0 and self.nevals % self.period == 0: self.changePeaks() return fitness, def offlineError(self): return self._offline_error / self.nevals def currentError(self): return self._error def changePeaks(self): """Order the peaks to change position, height, width and number.""" # Change the number of peaks if self.minpeaks is not None and self.maxpeaks is not None: npeaks = len(self.peaks_function) u = self.random.random() r = self.maxpeaks - self.minpeaks if u < 0.5: # Remove n peaks or less depending on the minimum number of peaks u = self.random.random() n = min(npeaks - self.minpeaks, int(round(r * u * self.number_severity))) for i in range(n): idx = self.random.randrange(len(self.peaks_function)) self.peaks_function.pop(idx) self.peaks_position.pop(idx) self.peaks_height.pop(idx) self.peaks_width.pop(idx) self.last_change_vector.pop(idx) else: # Add n peaks or less depending on the maximum number of peaks u = self.random.random() n = min(self.maxpeaks - npeaks, int(round(r * u * self.number_severity))) for i in range(n): self.peaks_function.append(self.random.choice(self.pfunc_pool)) self.peaks_position.append([self.random.uniform(self.min_coord, self.max_coord) for _ in range(self.dim)]) self.peaks_height.append(self.random.uniform(self.min_height, self.max_height)) self.peaks_width.append(self.random.uniform(self.min_width, self.max_width)) self.last_change_vector.append([self.random.random() - 0.5 for _ in range(self.dim)]) for i in range(len(self.peaks_function)): # Change peak position shift = [self.random.random() - 0.5 for _ in range(len(self.peaks_position[i]))] shift_length = sum(s**2 for s in shift) shift_length = self.move_severity / math.sqrt(shift_length) if shift_length > 0 else 0 shift = [shift_length * (1.0 - self.lambda_) * s \ + self.lambda_ * c for s, c in zip(shift, self.last_change_vector[i])] shift_length = sum(s**2 for s in shift) shift_length = self.move_severity / math.sqrt(shift_length) if shift_length > 0 else 0 shift = [s*shift_length for s in shift] new_position = [] final_shift = [] for pp, s in zip(self.peaks_position[i], shift): new_coord = pp + s if new_coord < self.min_coord: new_position.append(2.0 * self.min_coord - pp - s) final_shift.append(-1.0 * s) elif new_coord > self.max_coord: new_position.append(2.0 * self.max_coord - pp - s) final_shift.append(-1.0 * s) else: new_position.append(new_coord) final_shift.append(s) self.peaks_position[i] = new_position self.last_change_vector[i] = final_shift # Change peak height change = self.random.gauss(0, 1) * self.height_severity new_value = change + self.peaks_height[i] if new_value < self.min_height: self.peaks_height[i] = 2.0 * self.min_height - self.peaks_height[i] - change elif new_value > self.max_height: self.peaks_height[i] = 2.0 * self.max_height - self.peaks_height[i] - change else: self.peaks_height[i] = new_value # Change peak width change = self.random.gauss(0, 1) * self.width_severity new_value = change + self.peaks_width[i] if new_value < self.min_width: self.peaks_width[i] = 2.0 * self.min_width - self.peaks_width[i] - change elif new_value > self.max_width: self.peaks_width[i] = 2.0 * self.max_width - self.peaks_width[i] - change else: self.peaks_width[i] = new_value self._optimum = None SCENARIO_1 = {"pfunc" : function1, "npeaks" : 5, "bfunc": None, "min_coord": 0.0, "max_coord": 100.0, "min_height": 30.0, "max_height": 70.0, "uniform_height": 50.0, "min_width": 0.0001, "max_width": 0.2, "uniform_width": 0.1, "lambda_": 0.0, "move_severity": 1.0, "height_severity": 7.0, "width_severity": 0.01, "period": 5000} SCENARIO_2 = {"pfunc" : cone, "npeaks" : 10, "bfunc" : None, "min_coord": 0.0, "max_coord": 100.0, "min_height": 30.0, "max_height": 70.0, "uniform_height": 50.0, "min_width": 1.0, "max_width": 12.0, "uniform_width": 0, "lambda_": 0.5, "move_severity": 1.0, "height_severity": 7.0, "width_severity": 1.0, "period": 5000} SCENARIO_3 = {"pfunc" : cone, "npeaks" : 50, "bfunc" : lambda x: 10, "min_coord": 0.0, "max_coord": 100.0, "min_height": 30.0, "max_height": 70.0, "uniform_height": 0, "min_width": 1.0, "max_width": 12.0, "uniform_width": 0, "lambda_": 0.5, "move_severity": 1.0, "height_severity": 1.0, "width_severity": 0.5, "period": 1000} def diversity(population): nind = len(population) ndim = len(population[0]) d = [0.0] * ndim for x in population: d = [di + xi for di, xi in zip(d, x)] d = [di / nind for di in d] return math.sqrt(sum((di - xi)**2 for x in population for di, xi in zip(d, x))) if __name__ == "__main__": mpb = MovingPeaks(dim=2, npeaks=[1,1,10], number_severity=0.1) print mpb.maximums() mpb.changePeaks() print mpb.maximums() deap-1.0.1/deap/benchmarks/tools.py0000644000076500000240000002556012117373622017430 0ustar felixstaff00000000000000"""Module containing tools that are useful when benchmarking algorithms """ from math import hypot, sqrt from functools import wraps from itertools import repeat try: import numpy except ImportError: numpy = False class translate(object): """Decorator for evaluation functions, it translates the objective function by *vector* which should be the same length as the individual size. When called the decorated function should take as first argument the individual to be evaluated. The inverse translation vector is actually applied to the individual and the resulting list is given to the evaluation function. Thus, the evaluation function shall not be expecting an individual as it will receive a plain list. This decorator adds a :func:`translate` method to the decorated function. """ def __init__(self, vector): self.vector = vector def __call__(self, func): # wraps is used to combine stacked decorators that would add functions @wraps(func) def wrapper(individual, *args, **kargs): # A subtraction is applied since the translation is applied to the # individual and not the function return func([v - t for v, t in zip(individual, self.vector)], *args, **kargs) wrapper.translate = self.translate return wrapper def translate(self, vector): """Set the current translation to *vector*. After decorating the evaluation function, this function will be available directly from the function object. :: @translate([0.25, 0.5, ..., 0.1]) def evaluate(individual): return sum(individual), # This will cancel the translation evaluate.translate([0.0, 0.0, ..., 0.0]) """ self.vector = vector class rotate(object): """Decorator for evaluation functions, it rotates the objective function by *matrix* which should be a valid orthogonal NxN rotation matrix, with N the length of an individual. When called the decorated function should take as first argument the individual to be evaluated. The inverse rotation matrix is actually applied to the individual and the resulting list is given to the evaluation function. Thus, the evaluation function shall not be expecting an individual as it will receive a plain list (numpy.array). The multiplication is done using numpy. This decorator adds a :func:`rotate` method to the decorated function. .. note:: A random orthogonal matrix Q can be created via QR decomposition. :: A = numpy.random.random((n,n)) Q, _ = numpy.linalg.qr(A) """ def __init__(self, matrix): if not numpy: raise RuntimeError("Numpy is required for using the rotation " "decorator") # The inverse is taken since the rotation is applied to the individual # and not the function which is the inverse self.matrix = numpy.linalg.inv(matrix) def __call__(self, func): # wraps is used to combine stacked decorators that would add functions @wraps(func) def wrapper(individual, *args, **kargs): return func(numpy.dot(self.matrix, individual), *args, **kargs) wrapper.rotate = self.rotate return wrapper def rotate(self, matrix): """Set the current rotation to *matrix*. After decorating the evaluation function, this function will be available directly from the function object. :: # Create a random orthogonal matrix A = numpy.random.random((n,n)) Q, _ = numpy.linalg.qr(A) @rotate(Q) def evaluate(individual): return sum(individual), # This will reset rotation to identity evaluate.rotate(numpy.identity(n)) """ self.matrix = numpy.linalg.inv(matrix) class noise(object): """Decorator for evaluation functions, it evaluates the objective function and adds noise by calling the function(s) provided in the *noise* argument. The noise functions are called without any argument, consider using the :class:`~deap.base.Toolbox` or Python's :func:`functools.partial` to provide any required argument. If a single function is provided it is applied to all objectives of the evaluation function. If a list of noise functions is provided, it must be of length equal to the number of objectives. The noise argument also accept :obj:`None`, which will leave the objective without noise. This decorator adds a :func:`noise` method to the decorated function. """ def __init__(self, noise): try: self.rand_funcs = tuple(noise) except TypeError: self.rand_funcs = repeat(noise) def __call__(self, func): # wraps is used to combine stacked decorators that would add functions @wraps(func) def wrapper(individual, *args, **kargs): result = func(individual, *args, **kargs) noisy = list() for r, f in zip(result, self.rand_funcs): if f is None: noisy.append(r) else: noisy.append(r + f()) return tuple(noisy) wrapper.noise = self.noise return wrapper def noise(self, noise): """Set the current noise to *noise*. After decorating the evaluation function, this function will be available directly from the function object. :: prand = functools.partial(random.gauss, mu=0.0, sigma=1.0) @noise(prand) def evaluate(individual): return sum(individual), # This will remove noise from the evaluation function evaluate.noise(None) """ try: self.rand_funcs = tuple(noise) except TypeError: self.rand_funcs = repeat(noise) class scale(object): """Decorator for evaluation functions, it scales the objective function by *factor* which should be the same length as the individual size. When called the decorated function should take as first argument the individual to be evaluated. The inverse factor vector is actually applied to the individual and the resulting list is given to the evaluation function. Thus, the evaluation function shall not be expecting an individual as it will receive a plain list. This decorator adds a :func:`scale` method to the decorated function. """ def __init__(self, factor): # Factor is inverted since it is aplied to the individual and not the # objective function self.factor = tuple(1.0/f for f in factor) def __call__(self, func): # wraps is used to combine stacked decorators that would add functions @wraps(func) def wrapper(individual, *args, **kargs): return func([v * f for v, f in zip(individual, self.factor)], *args, **kargs) wrapper.scale = self.scale return wrapper def scale(self, factor): """Set the current scale to *factor*. After decorating the evaluation function, this function will be available directly from the function object. :: @scale([0.25, 2.0, ..., 0.1]) def evaluate(individual): return sum(individual), # This will cancel the scaling evaluate.scale([1.0, 1.0, ..., 1.0]) """ # Factor is inverted since it is aplied to the individual and not the # objective function self.factor = tuple(1.0/f for f in factor) class bound(object): """Decorator for crossover and mutation functions, it changes the individuals after the modification is done to bring it back in the allowed *bounds*. The *bounds* are functions taking individual and returning wheter of not the variable is allowed. You can provide one or multiple such functions. In the former case, the function is used on all dimensions and in the latter case, the number of functions must be greater or equal to the number of dimension of the individuals. The *type* determines how the attributes are brought back into the valid range This decorator adds a :func:`bound` method to the decorated function. """ def _clip(self, individual): return individual def _wrap(self, individual): return individual def _mirror(self, individual): return individual def __call__(self, func): @wraps(func) def wrapper(*args, **kargs): individuals = func(*args, **kargs) return self.bound(individuals) wrapper.bound = self.bound return wrapper def __init__(self, bounds, type): try: self.bounds = tuple(bounds) except TypeError: self.bounds = itertools.repeat(bounds) if type == "mirror": self.bound = self._mirror elif type == "wrap": self.bound = self._wrap elif type == "clip": self.bound = self._clip def diversity(first_front, first, last): """Given a Pareto front `first_front` and the two extreme points of the optimal Pareto front, this function returns a metric of the diversity of the front as explained in the original NSGA-II article by K. Deb. The smaller the value is, the better the front is. """ df = hypot(first_front[0].fitness.values[0] - first[0], first_front[0].fitness.values[1] - first[1]) dl = hypot(first_front[-1].fitness.values[0] - last[0], first_front[-1].fitness.values[1] - last[1]) dt = [hypot(first.fitness.values[0] - second.fitness.values[0], first.fitness.values[1] - second.fitness.values[1]) for first, second in zip(first_front[:-1], first_front[1:])] if len(first_front) == 1: return df + dl dm = sum(dt)/len(dt) di = sum(abs(d_i - dm) for d_i in dt) delta = (df + dl + di)/(df + dl + len(dt) * dm ) return delta def convergence(first_front, optimal_front): """Given a Pareto front `first_front` and the optimal Pareto front, this function returns a metric of convergence of the front as explained in the original NSGA-II article by K. Deb. The smaller the value is, the closer the front is to the optimal one. """ distances = [] for ind in first_front: distances.append(float("inf")) for opt_ind in optimal_front: dist = 0. for i in xrange(len(opt_ind)): dist += (ind.fitness.values[i] - opt_ind[i])**2 if dist < distances[-1]: distances[-1] = dist distances[-1] = sqrt(distances[-1]) return sum(distances) / len(distances)deap-1.0.1/deap/cma.py0000644000076500000240000003337412301410325014700 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . # Special thanks to Nikolaus Hansen for providing major part of # this code. The CMA-ES algorithm is provided in many other languages # and advanced versions at http://www.lri.fr/~hansen/cmaesintro.html. """A module that provides support for the Covariance Matrix Adaptation Evolution Strategy. """ import numpy import copy from math import sqrt, log, exp class Strategy(object): """ A strategy that will keep track of the basic parameters of the CMA-ES algorithm. :param centroid: An iterable object that indicates where to start the evolution. :param sigma: The initial standard deviation of the distribution. :param parameter: One or more parameter to pass to the strategy as described in the following table, optional. +----------------+---------------------------+----------------------------+ | Parameter | Default | Details | +================+===========================+============================+ | ``lambda_`` | ``int(4 + 3 * log(N))`` | Number of children to | | | | produce at each generation,| | | | ``N`` is the individual's | | | | size (integer). | +----------------+---------------------------+----------------------------+ | ``mu`` | ``int(lambda_ / 2)`` | The number of parents to | | | | keep from the | | | | lambda children (integer). | +----------------+---------------------------+----------------------------+ | ``cmatrix`` | ``identity(N)`` | The initial covariance | | | | matrix of the distribution | | | | that will be sampled. | +----------------+---------------------------+----------------------------+ | ``weights`` | ``"superlinear"`` | Decrease speed, can be | | | | ``"superlinear"``, | | | | ``"linear"`` or | | | | ``"equal"``. | +----------------+---------------------------+----------------------------+ | ``cs`` | ``(mueff + 2) / | Cumulation constant for | | | (N + mueff + 3)`` | step-size. | +----------------+---------------------------+----------------------------+ | ``damps`` | ``1 + 2 * max(0, sqrt(( | Damping for step-size. | | | mueff - 1) / (N + 1)) - 1)| | | | + cs`` | | +----------------+---------------------------+----------------------------+ | ``ccum`` | ``4 / (N + 4)`` | Cumulation constant for | | | | covariance matrix. | +----------------+---------------------------+----------------------------+ | ``ccov1`` | ``2 / ((N + 1.3)^2 + | Learning rate for rank-one | | | mueff)`` | update. | +----------------+---------------------------+----------------------------+ | ``ccovmu`` | ``2 * (mueff - 2 + 1 / | Learning rate for rank-mu | | | mueff) / ((N + 2)^2 + | update. | | | mueff)`` | | +----------------+---------------------------+----------------------------+ """ def __init__(self, centroid, sigma, **kargs): self.params = kargs # Create a centroid as a numpy array self.centroid = numpy.array(centroid) self.dim = len(self.centroid) self.sigma = sigma self.pc = numpy.zeros(self.dim) self.ps = numpy.zeros(self.dim) self.chiN = sqrt(self.dim) * (1 - 1. / (4. * self.dim) + \ 1. / (21. * self.dim**2)) self.C = self.params.get("cmatrix", numpy.identity(self.dim)) self.diagD, self.B = numpy.linalg.eigh(self.C) indx = numpy.argsort(self.diagD) self.diagD = self.diagD[indx]**0.5 self.B = self.B[:, indx] self.BD = self.B * self.diagD self.cond = self.diagD[indx[-1]]/self.diagD[indx[0]] self.lambda_ = self.params.get("lambda_", int(4 + 3 * log(self.dim))) self.update_count = 0 self.computeParams(self.params) def generate(self, ind_init): """Generate a population of :math:`\lambda` individuals of type *ind_init* from the current strategy. :param ind_init: A function object that is able to initialize an individual from a list. :returns: A list of individuals. """ arz = numpy.random.standard_normal((self.lambda_, self.dim)) arz = self.centroid + self.sigma * numpy.dot(arz, self.BD.T) return map(ind_init, arz) def update(self, population): """Update the current covariance matrix strategy from the *population*. :param population: A list of individuals from which to update the parameters. """ population.sort(key=lambda ind: ind.fitness, reverse=True) old_centroid = self.centroid self.centroid = numpy.dot(self.weights, population[0:self.mu]) c_diff = self.centroid - old_centroid # Cumulation : update evolution path self.ps = (1 - self.cs) * self.ps \ + sqrt(self.cs * (2 - self.cs) * self.mueff) / self.sigma \ * numpy.dot(self.B, (1. / self.diagD) \ * numpy.dot(self.B.T, c_diff)) hsig = float((numpy.linalg.norm(self.ps) / sqrt(1. - (1. - self.cs)**(2. * (self.update_count + 1.))) / self.chiN < (1.4 + 2. / (self.dim + 1.)))) self.update_count += 1 self.pc = (1 - self.cc) * self.pc + hsig \ * sqrt(self.cc * (2 - self.cc) * self.mueff) / self.sigma \ * c_diff # Update covariance matrix artmp = population[0:self.mu] - old_centroid self.C = (1 - self.ccov1 - self.ccovmu + (1 - hsig) \ * self.ccov1 * self.cc * (2 - self.cc)) * self.C \ + self.ccov1 * numpy.outer(self.pc, self.pc) \ + self.ccovmu * numpy.dot((self.weights * artmp.T), artmp) \ / self.sigma**2 self.sigma *= numpy.exp((numpy.linalg.norm(self.ps) / self.chiN - 1.) \ * self.cs / self.damps) self.diagD, self.B = numpy.linalg.eigh(self.C) indx = numpy.argsort(self.diagD) self.cond = self.diagD[indx[-1]]/self.diagD[indx[0]] self.diagD = self.diagD[indx]**0.5 self.B = self.B[:, indx] self.BD = self.B * self.diagD def computeParams(self, params): """Computes the parameters depending on :math:`\lambda`. It needs to be called again if :math:`\lambda` changes during evolution. :param params: A dictionary of the manually set parameters. """ self.mu = params.get("mu", int(self.lambda_ / 2)) rweights = params.get("weights", "superlinear") if rweights == "superlinear": self.weights = log(self.mu + 0.5) - \ numpy.log(numpy.arange(1, self.mu + 1)) elif rweights == "linear": self.weights = self.mu + 0.5 - numpy.arange(1, self.mu + 1) elif rweights == "equal": self.weights = numpy.ones(self.mu) else: raise RuntimeError("Unknown weights : %s" % rweights) self.weights /= sum(self.weights) self.mueff = 1. / sum(self.weights**2) self.cc = params.get("ccum", 4. / (self.dim + 4.)) self.cs = params.get("cs", (self.mueff + 2.) / (self.dim + self.mueff + 3.)) self.ccov1 = params.get("ccov1", 2. / ((self.dim + 1.3)**2 + \ self.mueff)) self.ccovmu = params.get("ccovmu", 2. * (self.mueff - 2. + \ 1. / self.mueff) / \ ((self.dim + 2.)**2 + self.mueff)) self.ccovmu = min(1 - self.ccov1, self.ccovmu) self.damps = 1. + 2. * max(0, sqrt((self.mueff - 1.) / \ (self.dim + 1.)) - 1.) + self.cs self.damps = params.get("damps", self.damps) class StrategyOnePlusLambda(object): """ A CMA-ES strategy that uses the :math:`1 + \lambda` paradigme. :param parent: An iterable object that indicates where to start the evolution. The parent requires a fitness attribute. :param sigma: The initial standard deviation of the distribution. :param parameter: One or more parameter to pass to the strategy as described in the following table, optional. """ def __init__(self, parent, sigma, **kargs): self.parent = parent self.sigma = sigma self.dim = len(self.parent) self.C = numpy.identity(self.dim) self.A = numpy.identity(self.dim) self.pc = numpy.zeros(self.dim) self.computeParams(kargs) self.psucc = self.ptarg def computeParams(self, params): """Computes the parameters depending on :math:`\lambda`. It needs to be called again if :math:`\lambda` changes during evolution. :param params: A dictionary of the manually set parameters. """ # Selection : self.lambda_ = params.get("lambda_", 1) # Step size control : self.d = params.get("d", 1.0 + self.dim/(2.0*self.lambda_)) self.ptarg = params.get("ptarg", 1.0/(5+sqrt(self.lambda_)/2.0)) self.cp = params.get("cp", self.ptarg*self.lambda_/(2+self.ptarg*self.lambda_)) # Covariance matrix adaptation self.cc = params.get("cc", 2.0/(self.dim+2.0)) self.ccov = params.get("ccov", 2.0/(self.dim**2 + 6.0)) self.pthresh = params.get("pthresh", 0.44) def generate(self, ind_init): """Generate a population of :math:`\lambda` individuals of type *ind_init* from the current strategy. :param ind_init: A function object that is able to initialize an individual from a list. :returns: A list of individuals. """ # self.y = numpy.dot(self.A, numpy.random.standard_normal(self.dim)) arz = numpy.random.standard_normal((self.lambda_, self.dim)) arz = self.parent + self.sigma * numpy.dot(arz, self.A.T) return map(ind_init, arz) def update(self, population): """Update the current covariance matrix strategy from the *population*. :param population: A list of individuals from which to update the parameters. """ population.sort(key=lambda ind: ind.fitness, reverse=True) lambda_succ = sum(self.parent.fitness <= ind.fitness for ind in population) p_succ = float(lambda_succ) / self.lambda_ self.psucc = (1-self.cp)*self.psucc + self.cp*p_succ if self.parent.fitness <= population[0].fitness: x_step = (population[0] - numpy.array(self.parent)) / self.sigma self.parent = copy.deepcopy(population[0]) if self.psucc < self.pthresh: self.pc = (1 - self.cc)*self.pc + sqrt(self.cc * (2 - self.cc)) * x_step self.C = (1-self.ccov)*self.C + self.ccov * numpy.dot(self.pc, self.pc.T) else: self.pc = (1 - self.cc)*self.pc self.C = (1-self.ccov)*self.C + self.ccov * (numpy.dot(self.pc, self.pc.T) + self.cc*(2-self.cc)*self.C) self.sigma = self.sigma * exp(1.0/self.d * (self.psucc - self.ptarg)/(1.0-self.ptarg)) # We use Cholesky since for now we have no use of eigen decomposition # Basically, Cholesky returns a matrix A as C = A*A.T # Eigen decomposition returns two matrix B and D^2 as C = B*D^2*B.T = B*D*D*B.T # So A == B*D # To compute the new individual we need to multiply each vector z by A # as y = centroid + sigma * A*z # So the Cholesky is more straightforward as we don't need to compute # the squareroot of D^2, and multiply B and D in order to get A, we directly get A. # This can't be done (without cost) with the standard CMA-ES as the eigen decomposition is used # to compute covariance matrix inverse in the step-size evolutionary path computation. self.A = numpy.linalg.cholesky(self.C) deap-1.0.1/deap/creator.py0000644000076500000240000001400712320777200015600 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """The :mod:`~deap.creator` is a meta-factory allowing to create classes that will fulfill the needs of your evolutionary algorithms. In effect, new classes can be built from any imaginable type, from :class:`list` to :class:`set`, :class:`dict`, :class:`~deap.gp.PrimitiveTree` and more, providing the possibility to implement genetic algorithms, genetic programming, evolution strategies, particle swarm optimizers, and many more. """ import array import copy class_replacers = {} """Some classes in Python's standard library as well as third party library may be in part incompatible with the logic used in DEAP. To palliate this problem, the method :func:`create` uses the dictionary `class_replacers` to identify if the base type provided is problematic, and if so the new class inherits from the replacement class instead of the original base class. `class_replacers` keys are classes to be replaced and the values are the replacing classes. """ try: import numpy (numpy.ndarray, numpy.array) except ImportError: # Numpy is not present, skip the definition of the replacement class. pass except AttributeError: # Numpy is present, but there is either no ndarray or array in numpy, # also skip the definition of the replacement class. pass else: class _numpy_array(numpy.ndarray): def __deepcopy__(self, memo): """Overrides the deepcopy from numpy.ndarray that does not copy the object's attributes. This one will deepcopy the array and its :attr:`__dict__` attribute. """ copy_ = numpy.ndarray.copy(self) copy_.__dict__.update(copy.deepcopy(self.__dict__, memo)) return copy_ @staticmethod def __new__(cls, iterable): """Creates a new instance of a numpy.ndarray from a function call. Adds the possibility to instanciate from an iterable.""" return numpy.array(list(iterable)).view(cls) def __setstate__(self, state): self.__dict__.update(state) def __reduce__(self): return (self.__class__, (list(self),), self.__dict__) class_replacers[numpy.ndarray] = _numpy_array class _array(array.array): @staticmethod def __new__(cls, seq=()): return super(_array, cls).__new__(cls, cls.typecode, seq) def __deepcopy__(self, memo): """Overrides the deepcopy from array.array that does not copy the object's attributes and class type. """ cls = self.__class__ copy_ = cls.__new__(cls, self) memo[id(self)] = copy_ copy_.__dict__.update(copy.deepcopy(self.__dict__, memo)) return copy_ def __reduce__(self): return (self.__class__, (list(self),), self.__dict__) class_replacers[array.array] = _array def create(name, base, **kargs): """Creates a new class named *name* inheriting from *base* in the :mod:`~deap.creator` module. The new class can have attributes defined by the subsequent keyword arguments passed to the function create. If the argument is a class (without the parenthesis), the __init__ function is called in the initialization of an instance of the new object and the returned instance is added as an attribute of the class' instance. Otherwise, if the argument is not a class, (for example an :class:`int`), it is added as a "static" attribute of the class. :param name: The name of the class to create. :param base: A base class from which to inherit. :param attribute: One or more attributes to add on instanciation of this class, optional. The following is used to create a class :class:`Foo` inheriting from the standard :class:`list` and having an attribute :attr:`bar` being an empty dictionary and a static attribute :attr:`spam` initialized to 1. :: create("Foo", list, bar=dict, spam=1) This above line is exactly the same as defining in the :mod:`creator` module something like the following. :: class Foo(list): spam = 1 def __init__(self): self.bar = dict() The :ref:`creating-types` tutorial gives more examples of the creator usage. """ dict_inst = {} dict_cls = {} for obj_name, obj in kargs.iteritems(): if isinstance(obj, type): dict_inst[obj_name] = obj else: dict_cls[obj_name] = obj # Check if the base class has to be replaced if base in class_replacers: base = class_replacers[base] # A DeprecationWarning is raised when the object inherits from the # class "object" which leave the option of passing arguments, but # raise a warning stating that it will eventually stop permitting # this option. Usually this happens when the base class does not # override the __init__ method from object. def initType(self, *args, **kargs): """Replace the __init__ function of the new type, in order to add attributes that were defined with **kargs to the instance. """ for obj_name, obj in dict_inst.iteritems(): setattr(self, obj_name, obj()) if base.__init__ is not object.__init__: base.__init__(self, *args, **kargs) objtype = type(str(name), (base,), dict_cls) objtype.__init__ = initType globals()[name] = objtype deap-1.0.1/deap/gp.py0000644000076500000240000010733212320777200014553 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """The :mod:`gp` module provides the methods and classes to perform Genetic Programming with DEAP. It essentially contains the classes to build a Genetic Program Tree, and the functions to evaluate it. This module support both strongly and loosely typed GP. """ import copy import random import re import sys import warnings from collections import defaultdict, deque from functools import partial, wraps from inspect import isclass from operator import eq, lt ###################################### # GP Data structure # ###################################### # Define the name of type for any types. __type__ = object class PrimitiveTree(list): """Tree spefically formated for optimization of genetic programming operations. The tree is represented with a list where the nodes are appended in a depth-first order. The nodes appended to the tree are required to have an attribute *arity* which defines the arity of the primitive. An arity of 0 is expected from terminals nodes. """ def __init__(self, content): list.__init__(self, content) def __deepcopy__(self, memo): new = self.__class__(self) new.__dict__.update(copy.deepcopy(self.__dict__, memo)) return new def __setitem__(self, key, val): # Check for most common errors # Does NOT check for STGP constraints if isinstance(key, slice): if key.start >= len(self): raise IndexError("Invalid slice object (try to assign a %s" " in a tree of size %d). Even if this is allowed by the" " list object slice setter, this should not be done in" " the PrimitiveTree context, as this may lead to an" " unpredictable behavior for searchSubtree or evaluate." % (key, len(self))) total = val[0].arity for node in val[1:]: total += node.arity - 1 if total != 0: raise ValueError("Invalid slice assignation : insertion of" " an incomplete subtree is not allowed in PrimitiveTree." " A tree is defined as incomplete when some nodes cannot" " be mapped to any position in the tree, considering the" " primitives' arity. For instance, the tree [sub, 4, 5," " 6] is incomplete if the arity of sub is 2, because it" " would produce an orphan node (the 6).") elif val.arity != self[key].arity: raise ValueError("Invalid node replacement with a node of a" " different arity.") list.__setitem__(self, key, val) def __str__(self): """Return the expression in a human readable string. """ string = "" stack = [] for node in self: stack.append((node, [])) while len(stack[-1][1]) == stack[-1][0].arity: prim, args = stack.pop() string = prim.format(*args) if len(stack) == 0: break # If stack is empty, all nodes should have been seen stack[-1][1].append(string) return string @classmethod def from_string(cls, string, pset): """Try to convert a string expression into a PrimitiveTree given a PrimitiveSet *pset*. The primitive set needs to contain every primitive present in the expression. :param string: String representation of a Python expression. :param pset: Primitive set from which primitives are selected. :returns: PrimitiveTree populated with the deserialized primitives. """ tokens = re.split("[ \t\n\r\f\v(),]", string) expr = [] ret_types = deque() for token in tokens: if token == '': continue if len(ret_types) != 0: type_ = ret_types.popleft() else: type_ = None if token in pset.mapping: primitive = pset.mapping[token] if len(ret_types) != 0 and primitive.ret != type_: raise TypeError("Primitive {} return type {} does not " "match the expected one: {}." .format(primitive, primitive.ret, type_)) expr.append(primitive) if isinstance(primitive, Primitive): ret_types.extendleft(reversed(primitive.args)) else: try: token = eval(token) except NameError: raise TypeError("Unable to evaluate terminal: {}.".format(token)) if type_ is None: type_ = type(token) if type(token) != type_: raise TypeError("Terminal {} type {} does not " "match the expected one: {}." .format(token, type(token), type_)) expr.append(Terminal(token, False, type_)) return cls(expr) @property def height(self): """Return the height of the tree, or the depth of the deepest node. """ stack = [0] max_depth = 0 for elem in self: depth = stack.pop() max_depth = max(max_depth, depth) stack.extend([depth+1] * elem.arity) return max_depth @property def root(self): """Root of the tree, the element 0 of the list. """ return self[0] def searchSubtree(self, begin): """Return a slice object that corresponds to the range of values that defines the subtree which has the element with index *begin* as its root. """ end = begin + 1 total = self[begin].arity while total > 0: total += self[end].arity - 1 end += 1 return slice(begin, end) class Primitive(object): """Class that encapsulates a primitive and when called with arguments it returns the Python code to call the primitive with the arguments. >>> pr = Primitive("mul", (int, int), int) >>> pr.format(1, 2) 'mul(1, 2)' """ __slots__ = ('name', 'arity', 'args', 'ret', 'seq') def __init__(self, name, args, ret): self.name = name self.arity = len(args) self.args = args self.ret = ret args = ", ".join(map("{{{0}}}".format, range(self.arity))) self.seq = "{name}({args})".format(name=self.name, args=args) def format(self, *args): return self.seq.format(*args) class Terminal(object): """Class that encapsulates terminal primitive in expression. Terminals can be values or 0-arity functions. """ __slots__ = ('name', 'value', 'ret', 'conv_fct') def __init__(self, terminal, symbolic, ret): self.ret = ret self.value = terminal self.name = str(terminal) self.conv_fct = str if symbolic else repr @property def arity(self): return 0 def format(self): return self.conv_fct(self.value) class Ephemeral(Terminal): """Class that encapsulates a terminal which value is set when the object is created. To mutate the value, a new object has to be generated. This is an abstract base class. When subclassing, a staticmethod 'func' must be defined. """ def __init__(self): Terminal.__init__(self, self.func(), symbolic=False, ret=self.ret) def regen(self): """Regenerate the ephemeral value. """ self.value = self.func() self.name = str(self.value) @staticmethod def func(): """Return a random value used to define the ephemeral state. """ raise NotImplementedError class PrimitiveSetTyped(object): """Class that contains the primitives that can be used to solve a Strongly Typed GP problem. The set also defined the researched function return type, and input arguments type and number. """ def __init__(self, name, in_types, ret_type, prefix="ARG"): self.terminals = defaultdict(list) self.primitives = defaultdict(list) self.arguments = [] # setting "__builtins__" to None avoid the context # being polluted by builtins function when evaluating # GP expression. self.context = {"__builtins__" : None} self.mapping = dict() self.terms_count = 0 self.prims_count = 0 self.name = name self.ret = ret_type self.ins = in_types for i, type_ in enumerate(in_types): arg_str = "{prefix}{index}".format(prefix=prefix, index=i) self.arguments.append(arg_str) term = Terminal(arg_str, True, type_) self._add(term) self.terms_count += 1 def renameArguments(self, **kargs): """Rename function arguments with new names from *kargs*. """ for i, old_name in enumerate(self.arguments): if old_name in kargs: new_name = kargs[old_name] self.arguments[i] = new_name self.mapping[new_name] = self.mapping[old_name] self.mapping[new_name].value = new_name del self.mapping[old_name] def _add(self, prim): def addType(dict_, ret_type): if not ret_type in dict_: dict_[ret_type] for type_, list_ in dict_.items(): if issubclass(type_, ret_type): dict_[ret_type].extend(list_) addType(self.primitives, prim.ret) addType(self.terminals, prim.ret) self.mapping[prim.name] = prim if isinstance(prim, Primitive): for type_ in prim.args: addType(self.primitives, type_) addType(self.terminals, type_) dict_ = self.primitives else: dict_ = self.terminals for type_ in dict_: if issubclass(prim.ret, type_): dict_[type_].append(prim) def addPrimitive(self, primitive, in_types, ret_type, name=None): """Add a primitive to the set. :param primitive: callable object or a function. :parma in_types: list of primitives arguments' type :param ret_type: type returned by the primitive. :param name: alternative name for the primitive instead of its __name__ attribute. """ if name is None: name = primitive.__name__ prim = Primitive(name, in_types, ret_type) assert name not in self.context or \ self.context[name] is primitive, \ "Primitives are required to have a unique name. " \ "Consider using the argument 'name' to rename your "\ "second '%s' primitive." % (name,) self._add(prim) self.context[prim.name] = primitive self.prims_count += 1 def addTerminal(self, terminal, ret_type, name=None): """Add a terminal to the set. Terminals can be named using the optional *name* argument. This should be used : to define named constant (i.e.: pi); to speed the evaluation time when the object is long to build; when the object does not have a __repr__ functions that returns the code to build the object; when the object class is not a Python built-in. :param terminal: Object, or a function with no arguments. :param ret_type: Type of the terminal. :param name: defines the name of the terminal in the expression. """ symbolic = False if name is None and callable(terminal): name = terminal.__name__ assert name not in self.context, \ "Terminals are required to have a unique name. " \ "Consider using the argument 'name' to rename your "\ "second %s terminal." % (name,) if name is not None: self.context[name] = terminal terminal = name symbolic = True elif terminal in (True, False): # To support True and False terminals with Python 2. self.context[str(terminal)] = terminal prim = Terminal(terminal, symbolic, ret_type) self._add(prim) self.terms_count += 1 def addEphemeralConstant(self, name, ephemeral, ret_type): """Add an ephemeral constant to the set. An ephemeral constant is a no argument function that returns a random value. The value of the constant is constant for a Tree, but may differ from one Tree to another. :param name: name used to refers to this ephemeral type. :param ephemeral: function with no arguments returning a random value. :param ret_type: type of the object returned by *ephemeral*. """ module_gp = globals() if not name in module_gp: class_ = type(name, (Ephemeral,), {'func' : staticmethod(ephemeral), 'ret' : ret_type}) module_gp[name] = class_ else: class_ = module_gp[name] if issubclass(class_, Ephemeral): if class_.func is not ephemeral: raise Exception("Ephemerals with different functions should " "be named differently, even between psets.") elif class_.ret is not ret_type: raise Exception("Ephemerals with the same name and function " "should have the same type, even between psets.") else: raise Exception("Ephemerals should be named differently " "than classes defined in the gp module.") self._add(class_) self.terms_count += 1 def addADF(self, adfset): """Add an Automatically Defined Function (ADF) to the set. :param adfset: PrimitiveSetTyped containing the primitives with which the ADF can be built. """ prim = Primitive(adfset.name, adfset.ins, adfset.ret) self._add(prim) self.prims_count += 1 @property def terminalRatio(self): """Return the ratio of the number of terminals on the number of all kind of primitives. """ return self.terms_count / float(self.terms_count + self.prims_count) class PrimitiveSet(PrimitiveSetTyped): """Class same as :class:`~deap.gp.PrimitiveSetTyped`, except there is no definition of type. """ def __init__(self, name, arity, prefix="ARG"): args = [__type__]*arity PrimitiveSetTyped.__init__(self, name, args, __type__, prefix) def addPrimitive(self, primitive, arity, name=None): """Add primitive *primitive* with arity *arity* to the set. If a name *name* is provided, it will replace the attribute __name__ attribute to represent/identify the primitive. """ assert arity > 0, "arity should be >= 1" args = [__type__] * arity PrimitiveSetTyped.addPrimitive(self, primitive, args, __type__, name) def addTerminal(self, terminal, name=None): """Add a terminal to the set.""" PrimitiveSetTyped.addTerminal(self, terminal, __type__, name) def addEphemeralConstant(self, name, ephemeral): """Add an ephemeral constant to the set.""" PrimitiveSetTyped.addEphemeralConstant(self, name, ephemeral, __type__) ###################################### # GP Tree compilation functions # ###################################### def compile(expr, pset): """Compile the expression *expr*. :param expr: Expression to compile. It can either be a PrimitiveTree, a string of Python code or any object that when converted into string produced a valid Python code expression. :param pset: Primitive set against which the expression is compile. :returns: a function if the primitive set has 1 or more arguments, or return the results produced by evaluating the tree. """ code = str(expr) if len(pset.arguments) > 0: # This section is a stripped version of the lambdify # function of SymPy 0.6.6. args = ",".join(arg for arg in pset.arguments) code = "lambda {args}: {code}".format(args=args, code=code) try: return eval(code, pset.context, {}) except MemoryError: _, _, traceback = sys.exc_info() raise MemoryError, ("DEAP : Error in tree evaluation :" " Python cannot evaluate a tree higher than 90. " "To avoid this problem, you should use bloat control on your " "operators. See the DEAP documentation for more information. " "DEAP will now abort."), traceback def compileADF(expr, psets): """Compile the expression represented by a list of trees. The first element of the list is the main tree, and the following elements are automatically defined functions (ADF) that can be called by the first tree. :param expr: Expression to compile. It can either be a PrimitiveTree, a string of Python code or any object that when converted into string produced a valid Python code expression. :param psets: List of primitive sets. Each set corresponds to an ADF while the last set is associated with the expression and should contain reference to the preceding ADFs. :returns: a function if the main primitive set has 1 or more arguments, or return the results produced by evaluating the tree. """ adfdict = {} func = None for pset, subexpr in reversed(zip(psets, expr)): pset.context.update(adfdict) func = compile(subexpr, pset) adfdict.update({pset.name : func}) return func ###################################### # GP Program generation functions # ###################################### def genFull(pset, min_, max_, type_=__type__): """Generate an expression where each leaf has a the same depth between *min* and *max*. :param pset: Primitive set from which primitives are selected. :param min_: Minimum height of the produced trees. :param max_: Maximum Height of the produced trees. :param type_: The type that should return the tree when called, when :obj:`None` (default) no return type is enforced. :returns: A full tree with all leaves at the same depth. """ def condition(height, depth): """Expression generation stops when the depth is equal to height.""" return depth == height return generate(pset, min_, max_, condition, type_) def genGrow(pset, min_, max_, type_=__type__): """Generate an expression where each leaf might have a different depth between *min* and *max*. :param pset: Primitive set from which primitives are selected. :param min_: Minimum height of the produced trees. :param max_: Maximum Height of the produced trees. :param type_: The type that should return the tree when called, when :obj:`None` (default) no return type is enforced. :returns: A grown tree with leaves at possibly different depths. """ def condition(height, depth): """Expression generation stops when the depth is equal to height or when it is randomly determined that a a node should be a terminal. """ return depth == height or \ (depth >= min_ and random.random() < pset.terminalRatio) return generate(pset, min_, max_, condition, type_) def genHalfAndHalf(pset, min_, max_, type_=__type__): """Generate an expression with a PrimitiveSet *pset*. Half the time, the expression is generated with :func:`~deap.gp.genGrow`, the other half, the expression is generated with :func:`~deap.gp.genFull`. :param pset: Primitive set from which primitives are selected. :param min_: Minimum height of the produced trees. :param max_: Maximum Height of the produced trees. :param type_: The type that should return the tree when called, when :obj:`None` (default) no return type is enforced. :returns: Either, a full or a grown tree. """ method = random.choice((genGrow, genFull)) return method(pset, min_, max_, type_) def genRamped(pset, min_, max_, type_=__type__): """ .. deprecated:: 1.0 The function has been renamed. Use :func:`~deap.gp.genHalfAndHalf` instead. """ warnings.warn("gp.genRamped has been renamed. Use genHalfAndHalf instead.", FutureWarning) return genHalfAndHalf(pset, min_, max_, type_) def generate(pset, min_, max_, condition, type_=__type__): """Generate a Tree as a list of list. The tree is build from the root to the leaves, and it stop growing when the condition is fulfilled. :param pset: Primitive set from which primitives are selected. :param min_: Minimum height of the produced trees. :param max_: Maximum Height of the produced trees. :param condition: The condition is a function that takes two arguments, the height of the tree to build and the current depth in the tree. :param type_: The type that should return the tree when called, when :obj:`None` (default) no return type is enforced. :returns: A grown tree with leaves at possibly different depths dependending on the condition function. """ expr = [] height = random.randint(min_, max_) stack = [(0, type_)] while len(stack) != 0: depth, type_ = stack.pop() if condition(height, depth): try: term = random.choice(pset.terminals[type_]) except IndexError: _, _, traceback = sys.exc_info() raise IndexError, "The gp.generate function tried to add "\ "a terminal of type '%s', but there is "\ "none available." % (type_,), traceback if isclass(term): term = term() expr.append(term) else: try: prim = random.choice(pset.primitives[type_]) except IndexError: _, _, traceback = sys.exc_info() raise IndexError, "The gp.generate function tried to add "\ "a primitive of type '%s', but there is "\ "none available." % (type_,), traceback expr.append(prim) for arg in reversed(prim.args): stack.append((depth+1, arg)) return expr ###################################### # GP Crossovers # ###################################### def cxOnePoint(ind1, ind2): """Randomly select in each individual and exchange each subtree with the point as root between each individual. :param ind1: First tree participating in the crossover. :param ind2: Second tree participating in the crossover. :returns: A tuple of two trees. """ if len(ind1) < 2 or len(ind2) < 2: # No crossover on single node tree return ind1, ind2 # List all available primitive types in each individual types1 = defaultdict(list) types2 = defaultdict(list) if ind1.root.ret == __type__: # Not STGP optimization types1[__type__] = xrange(1, len(ind1)) types2[__type__] = xrange(1, len(ind2)) common_types = [__type__] else: for idx, node in enumerate(ind1[1:], 1): types1[node.ret].append(idx) for idx, node in enumerate(ind2[1:], 1): types2[node.ret].append(idx) common_types = set(types1.keys()).intersection(set(types2.keys())) if len(common_types) > 0: type_ = random.choice(list(common_types)) index1 = random.choice(types1[type_]) index2 = random.choice(types2[type_]) slice1 = ind1.searchSubtree(index1) slice2 = ind2.searchSubtree(index2) ind1[slice1], ind2[slice2] = ind2[slice2], ind1[slice1] return ind1, ind2 def cxOnePointLeafBiased(ind1, ind2, termpb): """Randomly select crossover point in each individual and exchange each subtree with the point as root between each individual. :param ind1: First typed tree participating in the crossover. :param ind2: Second typed tree participating in the crossover. :param termpb: The probability of chosing a terminal node (leaf). :returns: A tuple of two typed trees. When the nodes are strongly typed, the operator makes sure the second node type corresponds to the first node type. The parameter *termpb* sets the probability to choose between a terminal or non-terminal crossover point. For instance, as defined by Koza, non- terminal primitives are selected for 90% of the crossover points, and terminals for 10%, so *termpb* should be set to 0.1. """ if len(ind1) < 2 or len(ind2) < 2: # No crossover on single node tree return ind1, ind2 # Determine wether we keep terminals or primitives for each individual terminal_op = partial(eq, 0) primitive_op = partial(lt, 0) arity_op1 = terminal_op if random.random() < termpb else primitive_op arity_op2 = terminal_op if random.random() < termpb else primitive_op # List all available primitive or terminal types in each individual types1 = defaultdict(list) types2 = defaultdict(list) for idx, node in enumerate(ind1[1:], 1): if arity_op1(node.arity): types1[node.ret].append(idx) for idx, node in enumerate(ind2[1:], 1): if arity_op2(node.arity): types2[node.ret].append(idx) common_types = set(types1.keys()).intersection(set(types2.keys())) if len(common_types) > 0: # Set does not support indexing type_ = random.sample(common_types, 1)[0] index1 = random.choice(types1[type_]) index2 = random.choice(types2[type_]) slice1 = ind1.searchSubtree(index1) slice2 = ind2.searchSubtree(index2) ind1[slice1], ind2[slice2] = ind2[slice2], ind1[slice1] return ind1, ind2 ###################################### # GP Mutations # ###################################### def mutUniform(individual, expr, pset): """Randomly select a point in the tree *individual*, then replace the subtree at that point as a root by the expression generated using method :func:`expr`. :param individual: The tree to be mutated. :param expr: A function object that can generate an expression when called. :returns: A tuple of one tree. """ index = random.randrange(len(individual)) slice_ = individual.searchSubtree(index) type_ = individual[index].ret individual[slice_] = expr(pset=pset, type_=type_) return individual, def mutNodeReplacement(individual, pset): """Replaces a randomly chosen primitive from *individual* by a randomly chosen primitive with the same number of arguments from the :attr:`pset` attribute of the individual. :param individual: The normal or typed tree to be mutated. :returns: A tuple of one tree. """ if len(individual) < 2: return individual, index = random.randrange(1, len(individual)) node = individual[index] if node.arity == 0: # Terminal term = random.choice(pset.terminals[node.ret]) if isclass(term): term = term() individual[index] = term else: # Primitive prims = [p for p in pset.primitives[node.ret] if p.args == node.args] individual[index] = random.choice(prims) return individual, def mutEphemeral(individual, mode): """This operator works on the constants of the tree *individual*. In *mode* ``"one"``, it will change the value of one of the individual ephemeral constants by calling its generator function. In *mode* ``"all"``, it will change the value of **all** the ephemeral constants. :param individual: The normal or typed tree to be mutated. :param mode: A string to indicate to change ``"one"`` or ``"all"`` ephemeral constants. :returns: A tuple of one tree. """ if mode not in ["one", "all"]: raise ValueError("Mode must be one of \"one\" or \"all\"") ephemerals_idx = [index for index, node in enumerate(individual) if isinstance(node, Ephemeral)] if len(ephemerals_idx) > 0: if mode == "one": ephemerals_idx = (random.choice(ephemerals_idx),) for i in ephemerals_idx: individual[i].regen() return individual, def mutInsert(individual, pset): """Inserts a new branch at a random position in *individual*. The subtree at the chosen position is used as child node of the created subtree, in that way, it is really an insertion rather than a replacement. Note that the original subtree will become one of the children of the new primitive inserted, but not perforce the first (its position is randomly selected if the new primitive has more than one child). :param individual: The normal or typed tree to be mutated. :returns: A tuple of one tree. """ index = random.randrange(len(individual)) node = individual[index] slice_ = individual.searchSubtree(index) choice = random.choice # As we want to keep the current node as children of the new one, # it must accept the return value of the current node primitives = [p for p in pset.primitives[node.ret] if node.ret in p.args] if len(primitives) == 0: return individual, new_node = choice(primitives) new_subtree = [None] * len(new_node.args) position = choice([i for i, a in enumerate(new_node.args) if a == node.ret]) for i, arg_type in enumerate(new_node.args): if i != position: term = choice(pset.terminals[arg_type]) if isclass(term): term = term() new_subtree[i] = term new_subtree[position:position+1] = individual[slice_] new_subtree.insert(0, new_node) individual[slice_] = new_subtree return individual, def mutShrink(individual): """This operator shrinks the *individual* by chosing randomly a branch and replacing it with one of the branch's arguments (also randomly chosen). :param individual: The tree to be shrinked. :returns: A tuple of one tree. """ # We don't want to "shrink" the root if len(individual) < 3 or individual.height <= 1: return individual, iprims = [] for i, node in enumerate(individual[1:], 1): if isinstance(node, Primitive) and node.ret in node.args: iprims.append((i, node)) if len(iprims) != 0: index, prim = random.choice(iprims) arg_idx = random.choice([i for i, type_ in enumerate(prim.args) if type_ == prim.ret]) rindex = index+1 for _ in range(arg_idx+1): rslice = individual.searchSubtree(rindex) subtree = individual[rslice] rindex += len(subtree) slice_ = individual.searchSubtree(index) individual[slice_] = subtree return individual, ###################################### # GP bloat control decorators # ###################################### def staticLimit(key, max_value): """Implement a static limit on some measurement on a GP tree, as defined by Koza in [Koza1989]. It may be used to decorate both crossover and mutation operators. When an invalid (over the limit) child is generated, it is simply replaced by one of its parents, randomly selected. This operator can be used to avoid memory errors occuring when the tree gets higher than 90 levels (as Python puts a limit on the call stack depth), because it can ensure that no tree higher than this limit will ever be accepted in the population, except if it was generated at initialization time. :param key: The function to use in order the get the wanted value. For instance, on a GP tree, ``operator.attrgetter('height')`` may be used to set a depth limit, and ``len`` to set a size limit. :param max_value: The maximum value allowed for the given measurement. :returns: A decorator that can be applied to a GP operator using \ :func:`~deap.base.Toolbox.decorate` .. note:: If you want to reproduce the exact behavior intended by Koza, set *key* to ``operator.attrgetter('height')`` and *max_value* to 17. .. [Koza1989] J.R. Koza, Genetic Programming - On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, MA, 1992) """ def decorator(func): @wraps(func) def wrapper(*args, **kwargs): keep_inds = [copy.deepcopy(ind) for ind in args] new_inds = list(func(*args, **kwargs)) for i, ind in enumerate(new_inds): if key(ind) > max_value: new_inds[i] = random.choice(keep_inds) return new_inds return wrapper return decorator def graph(expr): """Construct the graph of a tree expression. The tree expression must be valid. It returns in order a node list, an edge list, and a dictionary of the per node labels. The node are represented by numbers, the edges are tuples connecting two nodes (number), and the labels are values of a dictionary for which keys are the node numbers. :param expr: A tree expression to convert into a graph. :returns: A node list, an edge list, and a dictionary of labels. The returned objects can be used directly to populate a `pygraphviz `_ graph:: import pygraphviz as pgv # [...] Execution of code that produce a tree expression nodes, edges, labels = graph(expr) g = pgv.AGraph() g.add_nodes_from(nodes) g.add_edges_from(edges) g.layout(prog="dot") for i in nodes: n = g.get_node(i) n.attr["label"] = labels[i] g.draw("tree.pdf") or a `NetworX `_ graph:: import matplotlib.pyplot as plt import networkx as nx # [...] Execution of code that produce a tree expression nodes, edges, labels = graph(expr) g = nx.Graph() g.add_nodes_from(nodes) g.add_edges_from(edges) pos = nx.graphviz_layout(g, prog="dot") nx.draw_networkx_nodes(g, pos) nx.draw_networkx_edges(g, pos) nx.draw_networkx_labels(g, pos, labels) plt.show() .. note:: We encourage you to use `pygraphviz `_ as the nodes might be plotted out of order when using `NetworX `_. """ nodes = range(len(expr)) edges = list() labels = dict() stack = [] for i, node in enumerate(expr): if stack: edges.append((stack[-1][0], i)) stack[-1][1] -= 1 labels[i] = node.name if isinstance(node, Primitive) else node.value stack.append([i, node.arity]) while stack and stack[-1][1] == 0: stack.pop() return nodes, edges, labels if __name__ == "__main__": import doctest doctest.testmod() deap-1.0.1/deap/tests/0000755000076500000240000000000012321001644014720 5ustar felixstaff00000000000000deap-1.0.1/deap/tests/__init__.py0000644000076500000240000000126512117373622017050 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . deap-1.0.1/deap/tests/test_logbook.py0000644000076500000240000000333512301410325017767 0ustar felixstaff00000000000000import sys import unittest sys.path.append("..") import tools class LogbookTest(unittest.TestCase): def setUp(self): self.logbook = tools.Logbook() print def test_multi_chapters(self): self.logbook.record(gen=0, evals=100, fitness={'obj 1' : {'avg' : 1.0, 'max' : 10}, 'avg' : 1.0, 'max' : 10}, length={'avg' : 1.0, 'max' : 30}, test={'avg' : 1.0, 'max' : 20}) self.logbook.record(gen=0, evals=100, fitness={'obj 1' : {'avg' : 1.0, 'max' : 10}, 'avg' : 1.0, 'max' : 10}, length={'avg' : 1.0, 'max' : 30}, test={'avg' : 1.0, 'max' : 20}) print(self.logbook.stream) def test_one_chapter(self): self.logbook.record(gen=0, evals=100, fitness={'avg' : 1.0, 'max' : 10}) self.logbook.record(gen=0, evals=100, fitness={'avg' : 1.0, 'max' : 10}) print(self.logbook.stream) def test_one_big_chapter(self): self.logbook.record(gen=0, evals=100, fitness={'obj 1' : {'avg' : 1.0, 'max' : 10}, 'obj 2' : {'avg' : 1.0, 'max' : 10}}) self.logbook.record(gen=0, evals=100, fitness={'obj 1' : {'avg' : 1.0, 'max' : 10}, 'obj 2' : {'avg' : 1.0, 'max' : 10}}) print(self.logbook.stream) def test_no_chapters(self): self.logbook.record(gen=0, evals=100, **{'avg' : 1.0, 'max' : 10}) self.logbook.record(gen=0, evals=100, **{'avg' : 1.0, 'max' : 10}) print(self.logbook.stream) if __name__ == "__main__": suite = unittest.TestLoader().loadTestsFromTestCase(LogbookTest) unittest.TextTestRunner(verbosity=2).run(suite) deap-1.0.1/deap/tests/test_pickle.py0000644000076500000240000001062312301410325017600 0ustar felixstaff00000000000000 import sys import unittest import array import pickle import operator from test import test_support sys.path.append("..") import numpy import creator import base import gp import tools def func(): return "True" class Pickling(unittest.TestCase): def setUp(self): creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("IndList", list, fitness=creator.FitnessMax) creator.create("IndArray", array.array, typecode='f', fitness=creator.FitnessMax) creator.create("IndTree", gp.PrimitiveTree, fitness=creator.FitnessMax) self.toolbox = base.Toolbox() self.toolbox.register("func", func) self.toolbox.register("lambda_func", lambda: "True") def test_pickle_fitness(self): fitness = creator.FitnessMax() fitness.values = (1.0,) fitness_s = pickle.dumps(fitness) fitness_l = pickle.loads(fitness_s) self.failUnlessEqual(fitness, fitness_l, "Unpickled fitness != pickled fitness") def test_pickle_ind_list(self): ind = creator.IndList([1.0, 2.0, 3.0]) ind.fitness.values = (4.0,) ind_s = pickle.dumps(ind) ind_l = pickle.loads(ind_s) self.failUnlessEqual(ind, ind_l, "Unpickled individual list != pickled individual list") self.failUnlessEqual(ind.fitness, ind_l.fitness, "Unpickled individual fitness != pickled individual fitness") def test_pickle_ind_array(self): ind = creator.IndArray([1.0, 2.0, 3.0]) ind.fitness.values = (4.0,) ind_s = pickle.dumps(ind) ind_l = pickle.loads(ind_s) self.failUnlessEqual(ind, ind_l, "Unpickled individual array != pickled individual array") self.failUnlessEqual(ind.fitness, ind_l.fitness, "Unpickled individual fitness != pickled individual fitness") def test_pickle_tree(self): ind = creator.IndTree([operator.add, 1, 2]) ind.fitness.values = (1.0,) ind_s = pickle.dumps(ind) ind_l = pickle.loads(ind_s) msg = "Unpickled individual %s != pickled individual %s" % (str(ind), str(ind_l)) self.failUnlessEqual(ind, ind_l, msg) msg = "Unpickled fitness %s != pickled fitness %s" % (str(ind.fitness), str(ind_l.fitness)) self.failUnlessEqual(ind.fitness, ind_l.fitness, msg) def test_pickle_population(self): ind1 = creator.IndList([1.0,2.0,3.0]) ind1.fitness.values = (1.0,) ind2 = creator.IndList([4.0,5.0,6.0]) ind2.fitness.values = (2.0,) ind3 = creator.IndList([7.0,8.0,9.0]) ind3.fitness.values = (3.0,) pop = [ind1, ind2, ind3] pop_s = pickle.dumps(pop) pop_l = pickle.loads(pop_s) self.failUnlessEqual(pop[0], pop_l[0], "Unpickled individual list != pickled individual list") self.failUnlessEqual(pop[0].fitness, pop_l[0].fitness, "Unpickled individual fitness != pickled individual fitness") self.failUnlessEqual(pop[1], pop_l[1], "Unpickled individual list != pickled individual list") self.failUnlessEqual(pop[1].fitness, pop_l[1].fitness, "Unpickled individual fitness != pickled individual fitness") self.failUnlessEqual(pop[2], pop_l[2], "Unpickled individual list != pickled individual list") self.failUnlessEqual(pop[2].fitness, pop_l[2].fitness, "Unpickled individual fitness != pickled individual fitness") def test_pickle_logbook(self): stats = tools.Statistics() logbook = tools.Logbook() stats.register("mean", numpy.mean) record = stats.compile([1,2,3,4,5,6,8,9,10]) logbook.record(**record) stats_s = pickle.dumps(logbook) logbook_r = pickle.loads(stats_s) self.failUnlessEqual(logbook, logbook_r, "Unpickled logbook != pickled logbook") if not sys.version_info < (2, 7): def test_pickle_partial(self): func_s = pickle.dumps(self.toolbox.func) func_l = pickle.loads(func_s) self.failUnlessEqual(self.toolbox.func(), func_l()) @unittest.expectedFailure def test_pickle_lambda(self): func_s = pickle.dumps(self.toolbox.lambda_func) func_l = pickle.loads(func_s) self.failUnlessEqual(self.toolbox.lambda_func(), func_l()) if __name__ == "__main__": suite = unittest.TestLoader().loadTestsFromTestCase(Pickling) unittest.TextTestRunner(verbosity=2).run(suite) deap-1.0.1/deap/tools/0000755000076500000240000000000012321001644014716 5ustar felixstaff00000000000000deap-1.0.1/deap/tools/__init__.py0000644000076500000240000000243312320777200017040 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """The :mod:`~deap.tools` module contains the operators for evolutionary algorithms. They are used to modify, select and move the individuals in their environment. The set of operators it contains are readily usable in the :class:`~deap.base.Toolbox`. In addition to the basic operators this module also contains utility tools to enhance the basic algorithms with :class:`Statistics`, :class:`HallOfFame`, :class:`Checkpoint`, and :class:`History`. """ from .crossover import * from .emo import * from .init import * from .migration import * from .mutation import * from .selection import * from .support import *deap-1.0.1/deap/tools/crossover.py0000644000076500000240000004216712312557122017336 0ustar felixstaff00000000000000from __future__ import division import random import warnings from collections import Sequence from itertools import repeat ###################################### # GA Crossovers # ###################################### def cxOnePoint(ind1, ind2): """Executes a one point crossover on the input :term:`sequence` individuals. The two individuals are modified in place. The resulting individuals will respectively have the length of the other. :param ind1: The first individual participating in the crossover. :param ind2: The second individual participating in the crossover. :returns: A tuple of two individuals. This function uses the :func:`~random.randint` function from the python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) cxpoint = random.randint(1, size - 1) ind1[cxpoint:], ind2[cxpoint:] = ind2[cxpoint:], ind1[cxpoint:] return ind1, ind2 def cxTwoPoint(ind1, ind2): """Executes a two-point crossover on the input :term:`sequence` individuals. The two individuals are modified in place and both keep their original length. :param ind1: The first individual participating in the crossover. :param ind2: The second individual participating in the crossover. :returns: A tuple of two individuals. This function uses the :func:`~random.randint` function from the Python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) cxpoint1 = random.randint(1, size) cxpoint2 = random.randint(1, size - 1) if cxpoint2 >= cxpoint1: cxpoint2 += 1 else: # Swap the two cx points cxpoint1, cxpoint2 = cxpoint2, cxpoint1 ind1[cxpoint1:cxpoint2], ind2[cxpoint1:cxpoint2] \ = ind2[cxpoint1:cxpoint2], ind1[cxpoint1:cxpoint2] return ind1, ind2 def cxTwoPoints(ind1, ind2): """ .. deprecated:: 1.0 The function has been renamed. Use :func:`~deap.tools.cxTwoPoint` instead. """ warnings.warn("tools.cxTwoPoints has been renamed. Use cxTwoPoint instead.", FutureWarning) return cxTwoPoint(ind1, ind2) def cxUniform(ind1, ind2, indpb): """Executes a uniform crossover that modify in place the two :term:`sequence` individuals. The attributes are swapped accordingto the *indpb* probability. :param ind1: The first individual participating in the crossover. :param ind2: The second individual participating in the crossover. :param indpb: Independent probabily for each attribute to be exchanged. :returns: A tuple of two individuals. This function uses the :func:`~random.random` function from the python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) for i in xrange(size): if random.random() < indpb: ind1[i], ind2[i] = ind2[i], ind1[i] return ind1, ind2 def cxPartialyMatched(ind1, ind2): """Executes a partially matched crossover (PMX) on the input individuals. The two individuals are modified in place. This crossover expects :term:`sequence` individuals of indices, the result for any other type of individuals is unpredictable. :param ind1: The first individual participating in the crossover. :param ind2: The second individual participating in the crossover. :returns: A tuple of two individuals. Moreover, this crossover generates two children by matching pairs of values in a certain range of the two parents and swapping the values of those indexes. For more details see [Goldberg1985]_. This function uses the :func:`~random.randint` function from the python base :mod:`random` module. .. [Goldberg1985] Goldberg and Lingel, "Alleles, loci, and the traveling salesman problem", 1985. """ size = min(len(ind1), len(ind2)) p1, p2 = [0]*size, [0]*size # Initialize the position of each indices in the individuals for i in xrange(size): p1[ind1[i]] = i p2[ind2[i]] = i # Choose crossover points cxpoint1 = random.randint(0, size) cxpoint2 = random.randint(0, size - 1) if cxpoint2 >= cxpoint1: cxpoint2 += 1 else: # Swap the two cx points cxpoint1, cxpoint2 = cxpoint2, cxpoint1 # Apply crossover between cx points for i in xrange(cxpoint1, cxpoint2): # Keep track of the selected values temp1 = ind1[i] temp2 = ind2[i] # Swap the matched value ind1[i], ind1[p1[temp2]] = temp2, temp1 ind2[i], ind2[p2[temp1]] = temp1, temp2 # Position bookkeeping p1[temp1], p1[temp2] = p1[temp2], p1[temp1] p2[temp1], p2[temp2] = p2[temp2], p2[temp1] return ind1, ind2 def cxUniformPartialyMatched(ind1, ind2, indpb): """Executes a uniform partially matched crossover (UPMX) on the input individuals. The two individuals are modified in place. This crossover expects :term:`sequence` individuals of indices, the result for any other type of individuals is unpredictable. :param ind1: The first individual participating in the crossover. :param ind2: The second individual participating in the crossover. :returns: A tuple of two individuals. Moreover, this crossover generates two children by matching pairs of values chosen at random with a probability of *indpb* in the two parents and swapping the values of those indexes. For more details see [Cicirello2000]_. This function uses the :func:`~random.random` and :func:`~random.randint` functions from the python base :mod:`random` module. .. [Cicirello2000] Cicirello and Smith, "Modeling GA performance for control parameter optimization", 2000. """ size = min(len(ind1), len(ind2)) p1, p2 = [0]*size, [0]*size # Initialize the position of each indices in the individuals for i in xrange(size): p1[ind1[i]] = i p2[ind2[i]] = i for i in xrange(size): if random.random() < indpb: # Keep track of the selected values temp1 = ind1[i] temp2 = ind2[i] # Swap the matched value ind1[i], ind1[p1[temp2]] = temp2, temp1 ind2[i], ind2[p2[temp1]] = temp1, temp2 # Position bookkeeping p1[temp1], p1[temp2] = p1[temp2], p1[temp1] p2[temp1], p2[temp2] = p2[temp2], p2[temp1] return ind1, ind2 def cxOrdered(ind1, ind2): """Executes an ordered crossover (OX) on the input individuals. The two individuals are modified in place. This crossover expects :term:`sequence` individuals of indices, the result for any other type of individuals is unpredictable. :param ind1: The first individual participating in the crossover. :param ind2: The second individual participating in the crossover. :returns: A tuple of two individuals. Moreover, this crossover generates holes in the input individuals. A hole is created when an attribute of an individual is between the two crossover points of the other individual. Then it rotates the element so that all holes are between the crossover points and fills them with the removed elements in order. For more details see [Goldberg1989]_. This function uses the :func:`~random.sample` function from the python base :mod:`random` module. .. [Goldberg1989] Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, 1989 """ size = min(len(ind1), len(ind2)) a, b = random.sample(xrange(size), 2) if a > b: a, b = b, a holes1, holes2 = [True]*size, [True]*size for i in range(size): if i < a or i > b: holes1[ind2[i]] = False holes2[ind1[i]] = False # We must keep the original values somewhere before scrambling everything temp1, temp2 = ind1, ind2 k1 , k2 = b + 1, b + 1 for i in range(size): if not holes1[temp1[(i + b + 1) % size]]: ind1[k1 % size] = temp1[(i + b + 1) % size] k1 += 1 if not holes2[temp2[(i + b + 1) % size]]: ind2[k2 % size] = temp2[(i + b + 1) % size] k2 += 1 # Swap the content between a and b (included) for i in range(a, b + 1): ind1[i], ind2[i] = ind2[i], ind1[i] return ind1, ind2 def cxBlend(ind1, ind2, alpha): """Executes a blend crossover that modify in-place the input individuals. The blend crossover expects :term:`sequence` individuals of floating point numbers. :param ind1: The first individual participating in the crossover. :param ind2: The second individual participating in the crossover. :param alpha: Extent of the interval in which the new values can be drawn for each attribute on both side of the parents' attributes. :returns: A tuple of two individuals. This function uses the :func:`~random.random` function from the python base :mod:`random` module. """ for i, (x1, x2) in enumerate(zip(ind1, ind2)): gamma = (1. + 2. * alpha) * random.random() - alpha ind1[i] = (1. - gamma) * x1 + gamma * x2 ind2[i] = gamma * x1 + (1. - gamma) * x2 return ind1, ind2 def cxSimulatedBinary(ind1, ind2, eta): """Executes a simulated binary crossover that modify in-place the input individuals. The simulated binary crossover expects :term:`sequence` individuals of floating point numbers. :param ind1: The first individual participating in the crossover. :param ind2: The second individual participating in the crossover. :param eta: Crowding degree of the crossover. A high eta will produce children resembling to their parents, while a small eta will produce solutions much more different. :returns: A tuple of two individuals. This function uses the :func:`~random.random` function from the python base :mod:`random` module. """ for i, (x1, x2) in enumerate(zip(ind1, ind2)): rand = random.random() if rand <= 0.5: beta = 2. * rand else: beta = 1. / (2. * (1. - rand)) beta **= 1. / (eta + 1.) ind1[i] = 0.5 * (((1 + beta) * x1) + ((1 - beta) * x2)) ind2[i] = 0.5 * (((1 - beta) * x1) + ((1 + beta) * x2)) return ind1, ind2 def cxSimulatedBinaryBounded(ind1, ind2, eta, low, up): """Executes a simulated binary crossover that modify in-place the input individuals. The simulated binary crossover expects :term:`sequence` individuals of floating point numbers. :param ind1: The first individual participating in the crossover. :param ind2: The second individual participating in the crossover. :param eta: Crowding degree of the crossover. A high eta will produce children resembling to their parents, while a small eta will produce solutions much more different. :param low: A value or an :term:`python:sequence` of values that is the lower bound of the search space. :param up: A value or an :term:`python:sequence` of values that is the upper bound of the search space. :returns: A tuple of two individuals. This function uses the :func:`~random.random` function from the python base :mod:`random` module. .. note:: This implementation is similar to the one implemented in the original NSGA-II C code presented by Deb. """ size = min(len(ind1), len(ind2)) if not isinstance(low, Sequence): low = repeat(low, size) elif len(low) < size: raise IndexError("low must be at least the size of the shorter individual: %d < %d" % (len(low), size)) if not isinstance(up, Sequence): up = repeat(up, size) elif len(up) < size: raise IndexError("up must be at least the size of the shorter individual: %d < %d" % (len(up), size)) for i, xl, xu in zip(xrange(size), low, up): if random.random() <= 0.5: # This epsilon should probably be changed for 0 since # floating point arithmetic in Python is safer if abs(ind1[i] - ind2[i]) > 1e-14: x1 = min(ind1[i], ind2[i]) x2 = max(ind1[i], ind2[i]) rand = random.random() beta = 1.0 + (2.0 * (x1 - xl) / (x2 - x1)) alpha = 2.0 - beta**-(eta + 1) if rand <= 1.0 / alpha: beta_q = (rand * alpha)**(1.0 / (eta + 1)) else: beta_q = (1.0 / (2.0 - rand * alpha))**(1.0 / (eta + 1)) c1 = 0.5 * (x1 + x2 - beta_q * (x2 - x1)) beta = 1.0 + (2.0 * (xu - x2) / (x2 - x1)) alpha = 2.0 - beta**-(eta + 1) if rand <= 1.0 / alpha: beta_q = (rand * alpha)**(1.0 / (eta + 1)) else: beta_q = (1.0 / (2.0 - rand * alpha))**(1.0 / (eta + 1)) c2 = 0.5 * (x1 + x2 + beta_q * (x2 - x1)) c1 = min(max(c1, xl), xu) c2 = min(max(c2, xl), xu) if random.random() <= 0.5: ind1[i] = c2 ind2[i] = c1 else: ind1[i] = c1 ind2[i] = c2 return ind1, ind2 ###################################### # Messy Crossovers # ###################################### def cxMessyOnePoint(ind1, ind2): """Executes a one point crossover on :term:`sequence` individual. The crossover will in most cases change the individuals size. The two individuals are modified in place. :param ind1: The first individual participating in the crossover. :param ind2: The second individual participating in the crossover. :returns: A tuple of two individuals. This function uses the :func:`~random.randint` function from the python base :mod:`random` module. """ cxpoint1 = random.randint(0, len(ind1)) cxpoint2 = random.randint(0, len(ind2)) ind1[cxpoint1:], ind2[cxpoint2:] = ind2[cxpoint2:], ind1[cxpoint1:] return ind1, ind2 ###################################### # ES Crossovers # ###################################### def cxESBlend(ind1, ind2, alpha): """Executes a blend crossover on both, the individual and the strategy. The individuals shall be a :term:`sequence` and must have a :term:`sequence` :attr:`strategy` attribute. Adjustement of the minimal strategy shall be done after the call to this function, consider using a decorator. :param ind1: The first evolution strategy participating in the crossover. :param ind2: The second evolution strategy participating in the crossover. :param alpha: Extent of the interval in which the new values can be drawn for each attribute on both side of the parents' attributes. :returns: A tuple of two evolution strategies. This function uses the :func:`~random.random` function from the python base :mod:`random` module. """ for i, (x1, s1, x2, s2) in enumerate(zip(ind1, ind1.strategy, ind2, ind2.strategy)): # Blend the values gamma = (1. + 2. * alpha) * random.random() - alpha ind1[i] = (1. - gamma) * x1 + gamma * x2 ind2[i] = gamma * x1 + (1. - gamma) * x2 # Blend the strategies gamma = (1. + 2. * alpha) * random.random() - alpha ind1.strategy[i] = (1. - gamma) * s1 + gamma * s2 ind2.strategy[i] = gamma * s1 + (1. - gamma) * s2 return ind1, ind2 def cxESTwoPoint(ind1, ind2): """Executes a classical two points crossover on both the individuals and their strategy. The individuals shall be a :term:`sequence` and must have a :term:`sequence` :attr:`strategy` attribute. The crossover points for the individual and the strategy are the same. :param ind1: The first evolution strategy participating in the crossover. :param ind2: The second evolution strategy participating in the crossover. :returns: A tuple of two evolution strategies. This function uses the :func:`~random.randint` function from the python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) pt1 = random.randint(1, size) pt2 = random.randint(1, size - 1) if pt2 >= pt1: pt2 += 1 else: # Swap the two cx points pt1, pt2 = pt2, pt1 ind1[pt1:pt2], ind2[pt1:pt2] = ind2[pt1:pt2], ind1[pt1:pt2] ind1.strategy[pt1:pt2], ind2.strategy[pt1:pt2] = \ ind2.strategy[pt1:pt2], ind1.strategy[pt1:pt2] return ind1, ind2 def cxESTwoPoints(ind1, ind2): """ .. deprecated:: 1.0 The function has been renamed. Use :func:`cxESTwoPoint` instead. """ return cxESTwoPoints(ind1, ind2) # List of exported function names. __all__ = ['cxOnePoint', 'cxTwoPoint', 'cxUniform', 'cxPartialyMatched', 'cxUniformPartialyMatched', 'cxOrdered', 'cxBlend', 'cxSimulatedBinary','cxSimulatedBinaryBounded', 'cxMessyOnePoint', 'cxESBlend', 'cxESTwoPoint'] # Deprecated functions __all__.extend(['cxTwoPoints', 'cxESTwoPoints'])deap-1.0.1/deap/tools/emo.py0000644000076500000240000005300712320777200016064 0ustar felixstaff00000000000000from __future__ import division import bisect import math import random from itertools import chain from operator import attrgetter, itemgetter from collections import defaultdict ###################################### # Non-Dominated Sorting (NSGA-II) # ###################################### def selNSGA2(individuals, k): """Apply NSGA-II selection operator on the *individuals*. Usually, the size of *individuals* will be larger than *k* because any individual present in *individuals* will appear in the returned list at most once. Having the size of *individuals* equals to *k* will have no effect other than sorting the population according to their front rank. The list returned contains references to the input *individuals*. For more details on the NSGA-II operator see [Deb2002]_. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list of selected individuals. .. [Deb2002] Deb, Pratab, Agarwal, and Meyarivan, "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II", 2002. """ pareto_fronts = sortNondominated(individuals, k) for front in pareto_fronts: assignCrowdingDist(front) chosen = list(chain(*pareto_fronts[:-1])) k = k - len(chosen) if k > 0: sorted_front = sorted(pareto_fronts[-1], key=attrgetter("fitness.crowding_dist"), reverse=True) chosen.extend(sorted_front[:k]) return chosen def sortNondominated(individuals, k, first_front_only=False): """Sort the first *k* *individuals* into different nondomination levels using the "Fast Nondominated Sorting Approach" proposed by Deb et al., see [Deb2002]_. This algorithm has a time complexity of :math:`O(MN^2)`, where :math:`M` is the number of objectives and :math:`N` the number of individuals. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :param first_front_only: If :obj:`True` sort only the first front and exit. :returns: A list of Pareto fronts (lists), the first list includes nondominated individuals. .. [Deb2002] Deb, Pratab, Agarwal, and Meyarivan, "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II", 2002. """ if k == 0: return [] map_fit_ind = defaultdict(list) for ind in individuals: map_fit_ind[ind.fitness].append(ind) fits = map_fit_ind.keys() current_front = [] next_front = [] dominating_fits = defaultdict(int) dominated_fits = defaultdict(list) # Rank first Pareto front for i, fit_i in enumerate(fits): for fit_j in fits[i+1:]: if fit_i.dominates(fit_j): dominating_fits[fit_j] += 1 dominated_fits[fit_i].append(fit_j) elif fit_j.dominates(fit_i): dominating_fits[fit_i] += 1 dominated_fits[fit_j].append(fit_i) if dominating_fits[fit_i] == 0: current_front.append(fit_i) fronts = [[]] for fit in current_front: fronts[-1].extend(map_fit_ind[fit]) pareto_sorted = len(fronts[-1]) # Rank the next front until all individuals are sorted or # the given number of individual are sorted. if not first_front_only: N = min(len(individuals), k) while pareto_sorted < N: fronts.append([]) for fit_p in current_front: for fit_d in dominated_fits[fit_p]: dominating_fits[fit_d] -= 1 if dominating_fits[fit_d] == 0: next_front.append(fit_d) pareto_sorted += len(map_fit_ind[fit_d]) fronts[-1].extend(map_fit_ind[fit_d]) current_front = next_front next_front = [] return fronts def assignCrowdingDist(individuals): """Assign a crowding distance to each individual's fitness. The crowding distance can be retrieve via the :attr:`crowding_dist` attribute of each individual's fitness. """ if len(individuals) == 0: return distances = [0.0] * len(individuals) crowd = [(ind.fitness.values, i) for i, ind in enumerate(individuals)] nobj = len(individuals[0].fitness.values) for i in xrange(nobj): crowd.sort(key=lambda element: element[0][i]) distances[crowd[0][1]] = float("inf") distances[crowd[-1][1]] = float("inf") if crowd[-1][0][i] == crowd[0][0][i]: continue norm = nobj * float(crowd[-1][0][i] - crowd[0][0][i]) for prev, cur, next in zip(crowd[:-2], crowd[1:-1], crowd[2:]): distances[cur[1]] += (next[0][i] - prev[0][i]) / norm for i, dist in enumerate(distances): individuals[i].fitness.crowding_dist = dist def selTournamentDCD(individuals, k): """Tournament selection based on dominance (D) between two individuals, if the two individuals do not interdominate the selection is made based on crowding distance (CD). The *individuals* sequence length has to be a multiple of 4. Starting from the beginning of the selected individuals, two consecutive individuals will be different (assuming all individuals in the input list are unique). Each individual from the input list won't be selected more than twice. This selection requires the individuals to have a :attr:`crowding_dist` attribute, which can be set by the :func:`assignCrowdingDist` function. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list of selected individuals. """ def tourn(ind1, ind2): if ind1.fitness.dominates(ind2.fitness): return ind1 elif ind2.fitness.dominates(ind1.fitness): return ind2 if ind1.fitness.crowding_dist < ind2.fitness.crowding_dist: return ind2 elif ind1.fitness.crowding_dist > ind2.fitness.crowding_dist: return ind1 if random.random() <= 0.5: return ind1 return ind2 individuals_1 = random.sample(individuals, len(individuals)) individuals_2 = random.sample(individuals, len(individuals)) chosen = [] for i in xrange(0, k, 4): chosen.append(tourn(individuals_1[i], individuals_1[i+1])) chosen.append(tourn(individuals_1[i+2], individuals_1[i+3])) chosen.append(tourn(individuals_2[i], individuals_2[i+1])) chosen.append(tourn(individuals_2[i+2], individuals_2[i+3])) return chosen ####################################### # Generalized Reduced runtime ND sort # ####################################### def identity(obj): """Returns directly the argument *obj*. """ return obj def isDominated(wvalues1, wvalues2): """Returns whether or not *wvalues1* dominates *wvalues2*. :param wvalues1: The weighted fitness values that would be dominated. :param wvalues2: The weighted fitness values of the dominant. :returns: :obj:`True` if wvalues2 dominates wvalues1, :obj:`False` otherwise. """ not_equal = False for self_wvalue, other_wvalue in zip(wvalues1, wvalues2): if self_wvalue > other_wvalue: return False elif self_wvalue < other_wvalue: not_equal = True return not_equal def median(seq, key=identity): """Returns the median of *seq* - the numeric value separating the higher half of a sample from the lower half. If there is an even number of elements in *seq*, it returns the mean of the two middle values. """ sseq = sorted(seq, key=key) length = len(seq) if length % 2 == 1: return key(sseq[(length - 1) // 2]) else: return (key(sseq[(length - 1) // 2]) + key(sseq[length // 2])) / 2.0 def sortLogNondominated(individuals, k, first_front_only=False): """Sort *individuals* in pareto non-dominated fronts using the Generalized Reduced Run-Time Complexity Non-Dominated Sorting Algorithm presented by Fortin et al. (2013). :param individuals: A list of individuals to select from. :returns: A list of Pareto fronts (lists), with the first list being the true Pareto front. """ if k == 0: return [] #Separate individuals according to unique fitnesses unique_fits = defaultdict(list) for i, ind in enumerate(individuals): unique_fits[ind.fitness.wvalues].append(ind) #Launch the sorting algorithm obj = len(individuals[0].fitness.wvalues)-1 fitnesses = unique_fits.keys() front = dict.fromkeys(fitnesses, 0) # Sort the fitnesses lexicographically. fitnesses.sort(reverse=True) sortNDHelperA(fitnesses, obj, front) #Extract individuals from front list here nbfronts = max(front.values())+1 pareto_fronts = [[] for i in range(nbfronts)] for fit in fitnesses: index = front[fit] pareto_fronts[index].extend(unique_fits[fit]) # Keep only the fronts required to have k individuals. if not first_front_only: count = 0 for i, front in enumerate(pareto_fronts): count += len(front) if count >= k: return pareto_fronts[:i+1] return pareto_fronts else: return pareto_fronts[0] def sortNDHelperA(fitnesses, obj, front): """Create a non-dominated sorting of S on the first M objectives""" if len(fitnesses) < 2: return elif len(fitnesses) == 2: # Only two individuals, compare them and adjust front number s1, s2 = fitnesses[0], fitnesses[1] if isDominated(s2[:obj+1], s1[:obj+1]): front[s2] = max(front[s2], front[s1] + 1) elif obj == 1: sweepA(fitnesses, front) elif len(frozenset(map(itemgetter(obj), fitnesses))) == 1: #All individuals for objective M are equal: go to objective M-1 sortNDHelperA(fitnesses, obj-1, front) else: # More than two individuals, split list and then apply recursion best, worst = splitA(fitnesses, obj) sortNDHelperA(best, obj, front) sortNDHelperB(best, worst, obj-1, front) sortNDHelperA(worst, obj, front) def splitA(fitnesses, obj): """Partition the set of fitnesses in two according to the median of the objective index *obj*. The values equal to the median are put in the set containing the least elements. """ median_ = median(fitnesses, itemgetter(obj)) best_a, worst_a = [], [] best_b, worst_b = [], [] for fit in fitnesses: if fit[obj] > median_: best_a.append(fit) best_b.append(fit) elif fit[obj] < median_: worst_a.append(fit) worst_b.append(fit) else: best_a.append(fit) worst_b.append(fit) balance_a = abs(len(best_a) - len(worst_a)) balance_b = abs(len(best_b) - len(worst_b)) if balance_a <= balance_b: return best_a, worst_a else: return best_b, worst_b def sweepA(fitnesses, front): """Update rank number associated to the fitnesses according to the first two objectives using a geometric sweep procedure. """ stairs = [-fitnesses[0][1]] fstairs = [fitnesses[0]] for fit in fitnesses[1:]: idx = bisect.bisect_right(stairs, -fit[1]) if 0 < idx <= len(stairs): fstair = max(fstairs[:idx], key=front.__getitem__) front[fit] = max(front[fit], front[fstair]+1) for i, fstair in enumerate(fstairs[idx:], idx): if front[fstair] == front[fit]: del stairs[i] del fstairs[i] break stairs.insert(idx, -fit[1]) fstairs.insert(idx, fit) def sortNDHelperB(best, worst, obj, front): """Assign front numbers to the solutions in H according to the solutions in L. The solutions in L are assumed to have correct front numbers and the solutions in H are not compared with each other, as this is supposed to happen after sortNDHelperB is called.""" key = itemgetter(obj) if len(worst) == 0 or len(best) == 0: #One of the lists is empty: nothing to do return elif len(best) == 1 or len(worst) == 1: #One of the lists has one individual: compare directly for hi in worst: for li in best: if isDominated(hi[:obj+1], li[:obj+1]) or hi[:obj+1] == li[:obj+1]: front[hi] = max(front[hi], front[li] + 1) elif obj == 1: sweepB(best, worst, front) elif key(min(best, key=key)) >= key(max(worst, key=key)): #All individuals from L dominate H for objective M: #Also supports the case where every individuals in L and H #has the same value for the current objective #Skip to objective M-1 sortNDHelperB(best, worst, obj-1, front) elif key(max(best, key=key)) >= key(min(worst, key=key)): best1, best2, worst1, worst2 = splitB(best, worst, obj) sortNDHelperB(best1, worst1, obj, front) sortNDHelperB(best1, worst2, obj-1, front) sortNDHelperB(best2, worst2, obj, front) def splitB(best, worst, obj): """Split both best individual and worst sets of fitnesses according to the median of objective *obj* computed on the set containing the most elements. The values equal to the median are attributed so as to balance the four resulting sets as much as possible. """ median_ = median(best if len(best) > len(worst) else worst, itemgetter(obj)) best1_a, best2_a, best1_b, best2_b = [], [], [], [] for fit in best: if fit[obj] > median_: best1_a.append(fit) best1_b.append(fit) elif fit[obj] < median_: best2_a.append(fit) best2_b.append(fit) else: best1_a.append(fit) best2_b.append(fit) worst1_a, worst2_a, worst1_b, worst2_b = [], [], [], [] for fit in worst: if fit[obj] > median_: worst1_a.append(fit) worst1_b.append(fit) elif fit[obj] < median_: worst2_a.append(fit) worst2_b.append(fit) else: worst1_a.append(fit) worst2_b.append(fit) balance_a = abs(len(best1_a) - len(best2_a) + len(worst1_a) - len(worst2_a)) balance_b = abs(len(best1_b) - len(best2_b) + len(worst1_b) - len(worst2_b)) if balance_a <= balance_b: return best1_a, best2_a, worst1_a, worst2_a else: return best1_b, best2_b, worst1_b, worst2_b def sweepB(best, worst, front): """Adjust the rank number of the worst fitnesses according to the best fitnesses on the first two objectives using a sweep procedure. """ stairs, fstairs = [], [] iter_best = iter(best) next_best = next(iter_best, False) for h in worst: while next_best and h[:2] <= next_best[:2]: insert = True for i, fstair in enumerate(fstairs): if front[fstair] == front[next_best]: if fstair[1] > next_best[1]: insert = False else: del stairs[i], fstairs[i] break if insert: idx = bisect.bisect_right(stairs, -next_best[1]) stairs.insert(idx, -next_best[1]) fstairs.insert(idx, next_best) next_best = next(iter_best, False) idx = bisect.bisect_right(stairs, -h[1]) if 0 < idx <= len(stairs): fstair = max(fstairs[:idx], key=front.__getitem__) front[h] = max(front[h], front[fstair]+1) ###################################### # Strength Pareto (SPEA-II) # ###################################### def selSPEA2(individuals, k): """Apply SPEA-II selection operator on the *individuals*. Usually, the size of *individuals* will be larger than *n* because any individual present in *individuals* will appear in the returned list at most once. Having the size of *individuals* equals to *n* will have no effect other than sorting the population according to a strength Pareto scheme. The list returned contains references to the input *individuals*. For more details on the SPEA-II operator see [Zitzler2001]_. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list of selected individuals. .. [Zitzler2001] Zitzler, Laumanns and Thiele, "SPEA 2: Improving the strength Pareto evolutionary algorithm", 2001. """ N = len(individuals) L = len(individuals[0].fitness.values) K = math.sqrt(N) strength_fits = [0] * N fits = [0] * N dominating_inds = [list() for i in xrange(N)] for i, ind_i in enumerate(individuals): for j, ind_j in enumerate(individuals[i+1:], i+1): if ind_i.fitness.dominates(ind_j.fitness): strength_fits[i] += 1 dominating_inds[j].append(i) elif ind_j.fitness.dominates(ind_i.fitness): strength_fits[j] += 1 dominating_inds[i].append(j) for i in xrange(N): for j in dominating_inds[i]: fits[i] += strength_fits[j] # Choose all non-dominated individuals chosen_indices = [i for i in xrange(N) if fits[i] < 1] if len(chosen_indices) < k: # The archive is too small for i in xrange(N): distances = [0.0] * N for j in xrange(i + 1, N): dist = 0.0 for l in xrange(L): val = individuals[i].fitness.values[l] - \ individuals[j].fitness.values[l] dist += val * val distances[j] = dist kth_dist = _randomizedSelect(distances, 0, N - 1, K) density = 1.0 / (kth_dist + 2.0) fits[i] += density next_indices = [(fits[i], i) for i in xrange(N) if not i in chosen_indices] next_indices.sort() #print next_indices chosen_indices += [i for _, i in next_indices[:k - len(chosen_indices)]] elif len(chosen_indices) > k: # The archive is too large N = len(chosen_indices) distances = [[0.0] * N for i in xrange(N)] sorted_indices = [[0] * N for i in xrange(N)] for i in xrange(N): for j in xrange(i + 1, N): dist = 0.0 for l in xrange(L): val = individuals[chosen_indices[i]].fitness.values[l] - \ individuals[chosen_indices[j]].fitness.values[l] dist += val * val distances[i][j] = dist distances[j][i] = dist distances[i][i] = -1 # Insert sort is faster than quick sort for short arrays for i in xrange(N): for j in xrange(1, N): l = j while l > 0 and distances[i][j] < distances[i][sorted_indices[i][l - 1]]: sorted_indices[i][l] = sorted_indices[i][l - 1] l -= 1 sorted_indices[i][l] = j size = N to_remove = [] while size > k: # Search for minimal distance min_pos = 0 for i in xrange(1, N): for j in xrange(1, size): dist_i_sorted_j = distances[i][sorted_indices[i][j]] dist_min_sorted_j = distances[min_pos][sorted_indices[min_pos][j]] if dist_i_sorted_j < dist_min_sorted_j: min_pos = i break elif dist_i_sorted_j > dist_min_sorted_j: break # Remove minimal distance from sorted_indices for i in xrange(N): distances[i][min_pos] = float("inf") distances[min_pos][i] = float("inf") for j in xrange(1, size - 1): if sorted_indices[i][j] == min_pos: sorted_indices[i][j] = sorted_indices[i][j + 1] sorted_indices[i][j + 1] = min_pos # Remove corresponding individual from chosen_indices to_remove.append(min_pos) size -= 1 for index in reversed(sorted(to_remove)): del chosen_indices[index] return [individuals[i] for i in chosen_indices] def _randomizedSelect(array, begin, end, i): """Allows to select the ith smallest element from array without sorting it. Runtime is expected to be O(n). """ if begin == end: return array[begin] q = _randomizedPartition(array, begin, end) k = q - begin + 1 if i < k: return _randomizedSelect(array, begin, q, i) else: return _randomizedSelect(array, q + 1, end, i - k) def _randomizedPartition(array, begin, end): i = random.randint(begin, end) array[begin], array[i] = array[i], array[begin] return _partition(array, begin, end) def _partition(array, begin, end): x = array[begin] i = begin - 1 j = end + 1 while True: j -= 1 while array[j] > x: j -= 1 i += 1 while array[i] < x: i += 1 if i < j: array[i], array[j] = array[j], array[i] else: return j __all__ = ['selNSGA2', 'selSPEA2', 'sortNondominated', 'sortLogNondominated', 'selTournamentDCD'] deap-1.0.1/deap/tools/init.py0000644000076500000240000000635012301410325016235 0ustar felixstaff00000000000000from __future__ import division def initRepeat(container, func, n): """Call the function *container* with a generator function corresponding to the calling *n* times the function *func*. :param container: The type to put in the data from func. :param func: The function that will be called n times to fill the container. :param n: The number of times to repeat func. :returns: An instance of the container filled with data from func. This helper function can can be used in conjunction with a Toolbox to register a generator of filled containers, as individuals or population. >>> initRepeat(list, random.random, 2) # doctest: +ELLIPSIS, ... # doctest: +NORMALIZE_WHITESPACE [0.4761..., 0.6302...] See the :ref:`list-of-floats` and :ref:`population` tutorials for more examples. """ return container(func() for _ in xrange(n)) def initIterate(container, generator): """Call the function *container* with an iterable as its only argument. The iterable must be returned by the method or the object *generator*. :param container: The type to put in the data from func. :param generator: A function returning an iterable (list, tuple, ...), the content of this iterable will fill the container. :returns: An instance of the container filled with data from the generator. This helper function can can be used in conjunction with a Toolbox to register a generator of filled containers, as individuals or population. >>> from random import sample >>> from functools import partial >>> gen_idx = partial(sample, range(10), 10) >>> initIterate(list, gen_idx) [4, 5, 3, 6, 0, 9, 2, 7, 1, 8] See the :ref:`permutation` and :ref:`arithmetic-expr` tutorials for more examples. """ return container(generator()) def initCycle(container, seq_func, n=1): """Call the function *container* with a generator function corresponding to the calling *n* times the functions present in *seq_func*. :param container: The type to put in the data from func. :param seq_func: A list of function objects to be called in order to fill the container. :param n: Number of times to iterate through the list of functions. :returns: An instance of the container filled with data from the returned by the functions. This helper function can can be used in conjunction with a Toolbox to register a generator of filled containers, as individuals or population. >>> func_seq = [lambda:1 , lambda:'a', lambda:3] >>> initCycle(list, func_seq, n=2) [1, 'a', 3, 1, 'a', 3] See the :ref:`funky` tutorial for an example. """ return container(func() for _ in xrange(n) for func in seq_func) __all__ = ['initRepeat', 'initIterate', 'initCycle'] if __name__ == "__main__": import doctest import random random.seed(64) doctest.run_docstring_examples(initRepeat, globals()) random.seed(64) doctest.run_docstring_examples(initIterate, globals()) doctest.run_docstring_examples(initCycle, globals()) deap-1.0.1/deap/tools/migration.py0000644000076500000240000000531412301410325017262 0ustar felixstaff00000000000000from __future__ import division def migRing(populations, k, selection, replacement=None, migarray=None): """Perform a ring migration between the *populations*. The migration first select *k* emigrants from each population using the specified *selection* operator and then replace *k* individuals from the associated population in the *migarray* by the emigrants. If no *replacement* operator is specified, the immigrants will replace the emigrants of the population, otherwise, the immigrants will replace the individuals selected by the *replacement* operator. The migration array, if provided, shall contain each population's index once and only once. If no migration array is provided, it defaults to a serial ring migration (1 -- 2 -- ... -- n -- 1). Selection and replacement function are called using the signature ``selection(populations[i], k)`` and ``replacement(populations[i], k)``. It is important to note that the replacement strategy must select *k* **different** individuals. For example, using a traditional tournament for replacement strategy will thus give undesirable effects, two individuals will most likely try to enter the same slot. :param populations: A list of (sub-)populations on which to operate migration. :param k: The number of individuals to migrate. :param selection: The function to use for selection. :param replacement: The function to use to select which individuals will be replaced. If :obj:`None` (default) the individuals that leave the population are directly replaced. :param migarray: A list of indices indicating where the individuals from a particular position in the list goes. This defaults to a ring migration. """ nbr_demes = len(populations) if migarray is None: migarray = range(1, nbr_demes) + [0] immigrants = [[] for i in xrange(nbr_demes)] emigrants = [[] for i in xrange(nbr_demes)] for from_deme in xrange(nbr_demes): emigrants[from_deme].extend(selection(populations[from_deme], k)) if replacement is None: # If no replacement strategy is selected, replace those who migrate immigrants[from_deme] = emigrants[from_deme] else: # Else select those who will be replaced immigrants[from_deme].extend(replacement(populations[from_deme], k)) for from_deme, to_deme in enumerate(migarray): for i, immigrant in enumerate(immigrants[to_deme]): indx = populations[to_deme].index(immigrant) populations[to_deme][indx] = emigrants[from_deme][i] __all__ = ['migRing']deap-1.0.1/deap/tools/mutation.py0000644000076500000240000002110112312557122017132 0ustar felixstaff00000000000000from __future__ import division import math import random from itertools import repeat from collections import Sequence ###################################### # GA Mutations # ###################################### def mutGaussian(individual, mu, sigma, indpb): """This function applies a gaussian mutation of mean *mu* and standard deviation *sigma* on the input individual. This mutation expects a :term:`sequence` individual composed of real valued attributes. The *indpb* argument is the probability of each attribute to be mutated. :param individual: Individual to be mutated. :param mu: Mean or :term:`python:sequence` of means for the gaussian addition mutation. :param sigma: Standard deviation or :term:`python:sequence` of standard deviations for the gaussian addition mutation. :param indpb: Independent probability for each attribute to be mutated. :returns: A tuple of one individual. This function uses the :func:`~random.random` and :func:`~random.gauss` functions from the python base :mod:`random` module. """ size = len(individual) if not isinstance(mu, Sequence): mu = repeat(mu, size) elif len(mu) < size: raise IndexError("mu must be at least the size of individual: %d < %d" % (len(mu), size)) if not isinstance(sigma, Sequence): sigma = repeat(sigma, size) elif len(sigma) < size: raise IndexError("sigma must be at least the size of individual: %d < %d" % (len(sigma), size)) for i, m, s in zip(xrange(size), mu, sigma): if random.random() < indpb: individual[i] += random.gauss(m, s) return individual, def mutPolynomialBounded(individual, eta, low, up, indpb): """Polynomial mutation as implemented in original NSGA-II algorithm in C by Deb. :param individual: :term:`Sequence ` individual to be mutated. :param eta: Crowding degree of the mutation. A high eta will produce a mutant resembling its parent, while a small eta will produce a solution much more different. :param low: A value or a :term:`python:sequence` of values that is the lower bound of the search space. :param up: A value or a :term:`python:sequence` of values that is the upper bound of the search space. :returns: A tuple of one individual. """ size = len(individual) if not isinstance(low, Sequence): low = repeat(low, size) elif len(low) < size: raise IndexError("low must be at least the size of individual: %d < %d" % (len(low), size)) if not isinstance(up, Sequence): up = repeat(up, size) elif len(up) < size: raise IndexError("up must be at least the size of individual: %d < %d" % (len(up), size)) for i, xl, xu in zip(xrange(size), low, up): if random.random() <= indpb: x = individual[i] delta_1 = (x - xl) / (xu - xl) delta_2 = (xu - x) / (xu - xl) rand = random.random() mut_pow = 1.0 / (eta + 1.) if rand < 0.5: xy = 1.0 - delta_1 val = 2.0 * rand + (1.0 - 2.0 * rand) * xy**(eta + 1) delta_q = val**mut_pow - 1.0 else: xy = 1.0 - delta_2 val = 2.0 * (1.0 - rand) + 2.0 * (rand - 0.5) * xy**(eta + 1) delta_q = 1.0 - val**mut_pow x = x + delta_q * (xu - xl) x = min(max(x, xl), xu) individual[i] = x return individual, def mutShuffleIndexes(individual, indpb): """Shuffle the attributes of the input individual and return the mutant. The *individual* is expected to be a :term:`sequence`. The *indpb* argument is the probability of each attribute to be moved. Usually this mutation is applied on vector of indices. :param individual: Individual to be mutated. :param indpb: Independent probability for each attribute to be exchanged to another position. :returns: A tuple of one individual. This function uses the :func:`~random.random` and :func:`~random.randint` functions from the python base :mod:`random` module. """ size = len(individual) for i in xrange(size): if random.random() < indpb: swap_indx = random.randint(0, size - 2) if swap_indx >= i: swap_indx += 1 individual[i], individual[swap_indx] = \ individual[swap_indx], individual[i] return individual, def mutFlipBit(individual, indpb): """Flip the value of the attributes of the input individual and return the mutant. The *individual* is expected to be a :term:`sequence` and the values of the attributes shall stay valid after the ``not`` operator is called on them. The *indpb* argument is the probability of each attribute to be flipped. This mutation is usually applied on boolean individuals. :param individual: Individual to be mutated. :param indpb: Independent probability for each attribute to be flipped. :returns: A tuple of one individual. This function uses the :func:`~random.random` function from the python base :mod:`random` module. """ for i in xrange(len(individual)): if random.random() < indpb: individual[i] = type(individual[i])(not individual[i]) return individual, def mutUniformInt(individual, low, up, indpb): """Mutate an individual by replacing attributes, with probability *indpb*, by a integer uniformly drawn between *low* and *up* inclusively. :param individual: :term:`Sequence ` individual to be mutated. :param low: The lower bound or a :term:`python:sequence` of of lower bounds of the range from wich to draw the new integer. :param up: The upper bound or a :term:`python:sequence` of of upper bounds of the range from wich to draw the new integer. :param indpb: Independent probability for each attribute to be mutated. :returns: A tuple of one individual. """ size = len(individual) if not isinstance(low, Sequence): low = repeat(low, size) elif len(low) < size: raise IndexError("low must be at least the size of individual: %d < %d" % (len(low), size)) if not isinstance(up, Sequence): up = repeat(up, size) elif len(up) < size: raise IndexError("up must be at least the size of individual: %d < %d" % (len(up), size)) for i, xl, xu in zip(xrange(size), low, up): if random.random() < indpb: individual[i] = random.randint(xl, xu) return individual, ###################################### # ES Mutations # ###################################### def mutESLogNormal(individual, c, indpb): """Mutate an evolution strategy according to its :attr:`strategy` attribute as described in [Beyer2002]_. First the strategy is mutated according to an extended log normal rule, :math:`\\boldsymbol{\sigma}_t = \\exp(\\tau_0 \mathcal{N}_0(0, 1)) \\left[ \\sigma_{t-1, 1}\\exp(\\tau \mathcal{N}_1(0, 1)), \ldots, \\sigma_{t-1, n} \\exp(\\tau \mathcal{N}_n(0, 1))\\right]`, with :math:`\\tau_0 = \\frac{c}{\\sqrt{2n}}` and :math:`\\tau = \\frac{c}{\\sqrt{2\\sqrt{n}}}`, the the individual is mutated by a normal distribution of mean 0 and standard deviation of :math:`\\boldsymbol{\sigma}_{t}` (its current strategy) then . A recommended choice is ``c=1`` when using a :math:`(10, 100)` evolution strategy [Beyer2002]_ [Schwefel1995]_. :param individual: :term:`Sequence ` individual to be mutated. :param c: The learning parameter. :param indpb: Independent probability for each attribute to be mutated. :returns: A tuple of one individual. .. [Beyer2002] Beyer and Schwefel, 2002, Evolution strategies - A Comprehensive Introduction .. [Schwefel1995] Schwefel, 1995, Evolution and Optimum Seeking. Wiley, New York, NY """ size = len(individual) t = c / math.sqrt(2. * math.sqrt(size)) t0 = c / math.sqrt(2. * size) n = random.gauss(0, 1) t0_n = t0 * n for indx in xrange(size): if random.random() < indpb: individual.strategy[indx] *= math.exp(t0_n + t * random.gauss(0, 1)) individual[indx] += individual.strategy[indx] * random.gauss(0, 1) return individual, __all__ = ['mutGaussian', 'mutPolynomialBounded', 'mutShuffleIndexes', 'mutFlipBit', 'mutUniformInt', 'mutESLogNormal'] deap-1.0.1/deap/tools/selection.py0000644000076500000240000001612212301410325017255 0ustar felixstaff00000000000000from __future__ import division import random from functools import partial from operator import attrgetter ###################################### # Selections # ###################################### def selRandom(individuals, k): """Select *k* individuals at random from the input *individuals* with replacement. The list returned contains references to the input *individuals*. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list of selected individuals. This function uses the :func:`~random.choice` function from the python base :mod:`random` module. """ return [random.choice(individuals) for i in xrange(k)] def selBest(individuals, k): """Select the *k* best individuals among the input *individuals*. The list returned contains references to the input *individuals*. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list containing the k best individuals. """ return sorted(individuals, key=attrgetter("fitness"), reverse=True)[:k] def selWorst(individuals, k): """Select the *k* worst individuals among the input *individuals*. The list returned contains references to the input *individuals*. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list containing the k worst individuals. """ return sorted(individuals, key=attrgetter("fitness"))[:k] def selTournament(individuals, k, tournsize): """Select *k* individuals from the input *individuals* using *k* tournaments of *tournsize* individuals. The list returned contains references to the input *individuals*. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :param tournsize: The number of individuals participating in each tournament. :returns: A list of selected individuals. This function uses the :func:`~random.choice` function from the python base :mod:`random` module. """ chosen = [] for i in xrange(k): aspirants = selRandom(individuals, tournsize) chosen.append(max(aspirants, key=attrgetter("fitness"))) return chosen def selRoulette(individuals, k): """Select *k* individuals from the input *individuals* using *k* spins of a roulette. The selection is made by looking only at the first objective of each individual. The list returned contains references to the input *individuals*. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list of selected individuals. This function uses the :func:`~random.random` function from the python base :mod:`random` module. .. warning:: The roulette selection by definition cannot be used for minimization or when the fitness can be smaller or equal to 0. """ s_inds = sorted(individuals, key=attrgetter("fitness"), reverse=True) sum_fits = sum(ind.fitness.values[0] for ind in individuals) chosen = [] for i in xrange(k): u = random.random() * sum_fits sum_ = 0 for ind in s_inds: sum_ += ind.fitness.values[0] if sum_ > u: chosen.append(ind) break return chosen def selDoubleTournament(individuals, k, fitness_size, parsimony_size, fitness_first): """Tournament selection which use the size of the individuals in order to discriminate good solutions. This kind of tournament is obviously useless with fixed-length representation, but has been shown to significantly reduce excessive growth of individuals, especially in GP, where it can be used as a bloat control technique (see [Luke2002fighting]_). This selection operator implements the double tournament technique presented in this paper. The core principle is to use a normal tournament selection, but using a special sample function to select aspirants, which is another tournament based on the size of the individuals. To ensure that the selection pressure is not too high, the size of the size tournament (the number of candidates evaluated) can be a real number between 1 and 2. In this case, the smaller individual among two will be selected with a probability *size_tourn_size*/2. For instance, if *size_tourn_size* is set to 1.4, then the smaller individual will have a 0.7 probability to be selected. .. note:: In GP, it has been shown that this operator produces better results when it is combined with some kind of a depth limit. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :param fitness_size: The number of individuals participating in each \ fitness tournament :param parsimony_size: The number of individuals participating in each \ size tournament. This value has to be a real number\ in the range [1,2], see above for details. :param fitness_first: Set this to True if the first tournament done should \ be the fitness one (i.e. the fitness tournament producing aspirants for \ the size tournament). Setting it to False will behaves as the opposite \ (size tournament feeding fitness tournaments with candidates). It has been \ shown that this parameter does not have a significant effect in most cases\ (see [Luke2002fighting]_). :returns: A list of selected individuals. .. [Luke2002fighting] Luke and Panait, 2002, Fighting bloat with nonparametric parsimony pressure """ assert (1 <= parsimony_size <= 2), "Parsimony tournament size has to be in the range [1, 2]." def _sizeTournament(individuals, k, select): chosen = [] for i in xrange(k): # Select two individuals from the population # The first individual has to be the shortest prob = parsimony_size / 2. ind1, ind2 = select(individuals, k=2) if len(ind1) > len(ind2): ind1, ind2 = ind2, ind1 elif len(ind1) == len(ind2): # random selection in case of a tie prob = 0.5 # Since size1 <= size2 then ind1 is selected # with a probability prob chosen.append(ind1 if random.random() < prob else ind2) return chosen def _fitTournament(individuals, k, select): chosen = [] for i in xrange(k): aspirants = select(individuals, k=fitness_size) chosen.append(max(aspirants, key=attrgetter("fitness"))) return chosen if fitness_first: tfit = partial(_fitTournament, select=selRandom) return _sizeTournament(individuals, k, tfit) else: tsize = partial(_sizeTournament, select=selRandom) return _fitTournament(individuals, k, tsize) __all__ = ['selRandom', 'selBest', 'selWorst', 'selRoulette', 'selTournament', 'selDoubleTournament']deap-1.0.1/deap/tools/support.py0000644000076500000240000006354512301410325017017 0ustar felixstaff00000000000000from __future__ import division try: import cPickle as pickle except ImportError: import pickle from bisect import bisect_right from collections import defaultdict from copy import deepcopy from functools import partial from itertools import chain from operator import eq def identity(obj): """Returns directly the argument *obj*. """ return obj class History(object): """The :class:`History` class helps to build a genealogy of all the individuals produced in the evolution. It contains two attributes, the :attr:`genealogy_tree` that is a dictionary of lists indexed by individual, the list contain the indices of the parents. The second attribute :attr:`genealogy_history` contains every individual indexed by their individual number as in the genealogy tree. The produced genealogy tree is compatible with `NetworkX `_, here is how to plot the genealogy tree :: history = History() # Decorate the variation operators toolbox.decorate("mate", history.decorator) toolbox.decorate("mutate", history.decorator) # Create the population and populate the history population = toolbox.population(n=POPSIZE) history.update(population) # Do the evolution, the decorators will take care of updating the # history # [...] import matplotlib.pyplot as plt import networkx graph = networkx.DiGraph(history.genealogy_tree) graph = graph.reverse() # Make the grah top-down colors = [toolbox.evaluate(history.genealogy_history[i])[0] for i in graph] networkx.draw(graph, node_color=colors) plt.show() Using NetworkX in combination with `pygraphviz `_ (dot layout) this amazing genealogy tree can be obtained from the OneMax example with a population size of 20 and 5 generations, where the color of the nodes indicate there fitness, blue is low and red is high. .. image:: /_images/genealogy.png :width: 67% .. note:: The genealogy tree might get very big if your population and/or the number of generation is large. """ def __init__(self): self.genealogy_index = 0 self.genealogy_history = dict() self.genealogy_tree = dict() def update(self, individuals): """Update the history with the new *individuals*. The index present in their :attr:`history_index` attribute will be used to locate their parents, it is then modified to a unique one to keep track of those new individuals. This method should be called on the individuals after each variation. :param individuals: The list of modified individuals that shall be inserted in the history. If the *individuals* do not have a :attr:`history_index` attribute, the attribute is added and this individual is considered as having no parent. This method should be called with the initial population to initialize the history. Modifying the internal :attr:`genealogy_index` of the history or the :attr:`history_index` of an individual may lead to unpredictable results and corruption of the history. """ try: parent_indices = tuple(ind.history_index for ind in individuals) except AttributeError: parent_indices = tuple() for ind in individuals: self.genealogy_index += 1 ind.history_index = self.genealogy_index self.genealogy_history[self.genealogy_index] = deepcopy(ind) self.genealogy_tree[self.genealogy_index] = parent_indices @property def decorator(self): """Property that returns an appropriate decorator to enhance the operators of the toolbox. The returned decorator assumes that the individuals are returned by the operator. First the decorator calls the underlying operation and then calls the :func:`update` function with what has been returned by the operator. Finally, it returns the individuals with their history parameters modified according to the update function. """ def decFunc(func): def wrapFunc(*args, **kargs): individuals = func(*args, **kargs) self.update(individuals) return individuals return wrapFunc return decFunc def getGenealogy(self, individual, max_depth=float("inf")): """Provide the genealogy tree of an *individual*. The individual must have an attribute :attr:`history_index` as defined by :func:`~deap.tools.History.update` in order to retrieve its associated genealogy tree. The returned graph contains the parents up to *max_depth* variations before this individual. If not provided the maximum depth is up to the begining of the evolution. :param individual: The individual at the root of the genealogy tree. :param max_depth: The approximate maximum distance between the root (individual) and the leaves (parents), optional. :returns: A dictionary where each key is an individual index and the values are a tuple corresponding to the index of the parents. """ gtree = {} visited = set() # Adds memory to the breadth first search def genealogy(index, depth): if index not in self.genealogy_tree: return depth += 1 if depth > max_depth: return parent_indices = self.genealogy_tree[index] gtree[index] = parent_indices for ind in parent_indices: if ind not in visited: genealogy(ind, depth) visited.add(ind) genealogy(individual.history_index, 0) return gtree class Statistics(object): """Object that compiles statistics on a list of arbitrary objects. When created the statistics object receives a *key* argument that is used to get the values on which the function will be computed. If not provided the *key* argument defaults to the identity function. The value returned by the key may be a multi-dimensional object, i.e.: a tuple or a list, as long as the statistical function registered support it. So for example, statistics can be computed directly on multi-objective fitnesses when using numpy statistical function. :param key: A function to access the values on which to compute the statistics, optional. :: >>> s = Statistics() >>> s.register("mean", numpy.mean) >>> s.register("max", max) >>> s.compile([1, 2, 3, 4]) {'max': 4, 'mean': 2.5} >>> s.compile([5, 6, 7, 8]) {'max': 8, 'mean': 6.5} """ def __init__(self, key=identity): self.key = key self.functions = dict() self.fields = [] def register(self, name, function, *args, **kargs): """Register a *function* that will be applied on the sequence each time :meth:`record` is called. :param name: The name of the statistics function as it would appear in the dictionnary of the statistics object. :param function: A function that will compute the desired statistics on the data as preprocessed by the key. :param argument: One or more argument (and keyword argument) to pass automatically to the registered function when called, optional. """ self.functions[name] = partial(function, *args, **kargs) self.fields.append(name) def compile(self, data): """Apply to the input sequence *data* each registered function and return the results as a dictionnary. :param data: Sequence of objects on which the statistics are computed. """ values = tuple(self.key(elem) for elem in data) entry = dict() for key, func in self.functions.iteritems(): entry[key] = func(values) return entry class MultiStatistics(dict): """Dictionary of :class:`Statistics` object allowing to compute statistics on multiple keys using a single call to :meth:`compile`. It takes a set of key-value pairs associating a statistics object to a unique name. This name can then be used to retrieve the statistics object. The following code computes statistics simultaneously on the length and the first value of the provided objects. :: >>> len_stats = Statistics(key=len) >>> itm0_stats = Statistics(key=itemgetter(0)) >>> mstats = MultiStatistics(length=len_stats, item=itm0_stats) >>> mstats.register("mean", numpy.mean, axis=0) >>> mstats.register("max", numpy.max, axis=0) >>> mstats.compile([[0.0, 1.0, 1.0, 5.0], [2.0, 5.0]]) {'length': {'max': 4, 'mean': 3.0}, 'item': {'max': 2.0, 'mean': 1.0}} """ def compile(self, data): """Calls :meth:`Statistics.compile` with *data* of each :class:`Statistics` object. :param data: Sequence of objects on which the statistics are computed. """ record = {} for name, stats in self.items(): record[name] = stats.compile(data) return record @property def fields(self): return sorted(self.keys()) def register(self, name, function, *args, **kargs): """Register a *function* in each :class:`Statistics` object. :param name: The name of the statistics function as it would appear in the dictionnary of the statistics object. :param function: A function that will compute the desired statistics on the data as preprocessed by the key. :param argument: One or more argument (and keyword argument) to pass automatically to the registered function when called, optional. """ for stats in self.values(): stats.register(name, function, *args, **kargs) class Logbook(list): """Evolution records as a chronological list of dictionaries. Data can be retrieved via the :meth:`select` method given the appropriate names. The :class:`Logbook` class may also contain other logbooks refered to as chapters. Chapters are used to store information associated to a specific part of the evolution. For example when computing statistics on different components of individuals (namely :class:`MultiStatistics`), chapters can be used to distinguish the average fitness and the average size. """ def __init__(self): self.buffindex = 0 self.chapters = defaultdict(Logbook) """Dictionary containing the sub-sections of the logbook which are also :class:`Logbook`. Chapters are automatically created when the right hand side of a keyworded argument, provided to the *record* function, is a dictionnary. The keyword determines the chapter's name. For example, the following line adds a new chapter "size" that will contain the fields "max" and "mean". :: logbook.record(gen=0, size={'max' : 10.0, 'mean' : 7.5}) To access a specific chapter, use the name of the chapter as a dictionnary key. For example, to access the size chapter and select the mean use :: logbook.chapters["size"].select("mean") Compiling a :class:`MultiStatistics` object returns a dictionary containing dictionnaries, therefore when recording such an object in a logbook using the keyword argument unpacking operator (**), chapters will be automatically added to the logbook. :: >>> fit_stats = Statistics(key=attrgetter("fitness.values")) >>> size_stats = Statistics(key=len) >>> mstats = MultiStatistics(fitness=fit_stats, size=size_stats) >>> # [...] >>> record = mstats.compile(population) >>> logbook.record(**record) >>> print logbook fitness length ------------ ------------ max mean max mean 2 1 4 3 """ self.columns_len = None self.header = None """Order of the columns to print when using the :data:`stream` and :meth:`__str__` methods. The syntax is a single iterable containing string elements. For example, with the previously defined statistics class, one can print the generation and the fitness average, and maximum with :: logbook.header = ("gen", "mean", "max") If not set the header is built with all fields, in arbritrary order on insertion of the first data. The header can be removed by setting it to :data:`None`. """ self.log_header = True """Tells the log book to output or not the header when streaming the first line or getting its entire string representation. This defaults :data:`True`. """ def record(self, **infos): """Enter a record of event in the logbook as a list of key-value pairs. The informations are appended chronogically to a list as a dictionnary. When the value part of a pair is a dictionnary, the informations contained in the dictionnary are recorded in a chapter entitled as the name of the key part of the pair. Chapters are also Logbook. """ for key, value in infos.items(): if isinstance(value, dict): self.chapters[key].record(**value) del infos[key] self.append(infos) def select(self, *names): """Return a list of values associated to the *names* provided in argument in each dictionary of the Statistics object list. One list per name is returned in order. :: >>> log = Logbook() >>> log.record(gen = 0, mean = 5.4, max = 10.0) >>> log.record(gen = 1, mean = 9.4, max = 15.0) >>> log.select("mean") [5.4, 9.4] >>> log.select("gen", "max") ([0, 1], [10.0, 15.0]) With a :class:`MultiStatistics` object, the statistics for each measurement can be retrieved using the :data:`chapters` member : :: >>> log = Logbook() >>> log.record(**{'gen' : 0, 'fit' : {'mean' : 0.8, 'max' : 1.5}, ... 'size' : {'mean' : 25.4, 'max' : 67}}) >>> log.record(**{'gen' : 1, 'fit' : {'mean' : 0.95, 'max' : 1.7}, ... 'size' : {'mean' : 28.1, 'max' : 71}}) >>> log.chapters['size'].select("mean") [25.4, 28.1] >>> log.chapters['fit'].select("gen", "max") ([0, 1], [1.5, 1.7]) """ if len(names) == 1: return [entry.get(names[0], None) for entry in self] return tuple([entry.get(name, None) for entry in self] for name in names) @property def stream(self): """Retrieve the formatted not streamed yet entries of the database including the headers. :: >>> log = Logbook() >>> log.append({'gen' : 0}) >>> print log.stream gen 0 >>> log.append({'gen' : 1}) >>> print log.stream 1 """ startindex, self.buffindex = self.buffindex, len(self) return self.__str__(startindex) def __delitem__(self, key): if isinstance(key, slice): for i, in range(*key.indices(len(self))): self.pop(i) for chapter in self.chapters.values(): chapter.pop(i) else: self.pop(key) for chapter in self.chapters.values(): chapter.pop(key) def pop(self, index=0): """Retrieve and delete element *index*. The header and stream will be adjusted to follow the modification. :param item: The index of the element to remove, optional. It defaults to the first element. You can also use the following syntax to delete elements. :: del log[0] del log[1::5] """ if index < self.buffindex: self.buffindex -= 1 return super(self.__class__, self).pop(index) def __txt__(self, startindex): columns = self.header if not columns: columns = sorted(self[0].keys()) + sorted(self.chapters.keys()) if not self.columns_len or len(self.columns_len) != len(columns): self.columns_len = map(len, columns) chapters_txt = {} offsets = defaultdict(int) for name, chapter in self.chapters.items(): chapters_txt[name] = chapter.__txt__(startindex) if startindex == 0: offsets[name] = len(chapters_txt[name]) - len(self) str_matrix = [] for i, line in enumerate(self[startindex:]): str_line = [] for j, name in enumerate(columns): if name in chapters_txt: column = chapters_txt[name][i+offsets[name]] else: value = line.get(name, "") string = "{0:n}" if isinstance(value, float) else "{0}" column = string.format(value) self.columns_len[j] = max(self.columns_len[j], len(column)) str_line.append(column) str_matrix.append(str_line) if startindex == 0 and self.log_header: header = [] nlines = 1 if len(self.chapters) > 0: nlines += max(map(len, chapters_txt.values())) - len(self) + 1 header = [[] for i in xrange(nlines)] for j, name in enumerate(columns): if name in chapters_txt: length = max(len(line.expandtabs()) for line in chapters_txt[name]) blanks = nlines - 2 - offsets[name] for i in xrange(blanks): header[i].append(" " * length) header[blanks].append(name.center(length)) header[blanks+1].append("-" * length) for i in xrange(offsets[name]): header[blanks+2+i].append(chapters_txt[name][i]) else: length = max(len(line[j].expandtabs()) for line in str_matrix) for line in header[:-1]: line.append(" " * length) header[-1].append(name) str_matrix = chain(header, str_matrix) template = "\t".join("{%i:<%i}" % (i, l) for i, l in enumerate(self.columns_len)) text = [template.format(*line) for line in str_matrix] return text def __str__(self, startindex=0): text = self.__txt__(startindex) return "\n".join(text) class HallOfFame(object): """The hall of fame contains the best individual that ever lived in the population during the evolution. It is lexicographically sorted at all time so that the first element of the hall of fame is the individual that has the best first fitness value ever seen, according to the weights provided to the fitness at creation time. The insertion is made so that old individuals have priority on new individuals. A single copy of each individual is kept at all time, the equivalence between two individuals is made by the operator passed to the *similar* argument. :param maxsize: The maximum number of individual to keep in the hall of fame. :param similar: An equivalence operator between two individuals, optional. It defaults to operator :func:`operator.eq`. The class :class:`HallOfFame` provides an interface similar to a list (without being one completely). It is possible to retrieve its length, to iterate on it forward and backward and to get an item or a slice from it. """ def __init__(self, maxsize, similar=eq): self.maxsize = maxsize self.keys = list() self.items = list() self.similar = similar def update(self, population): """Update the hall of fame with the *population* by replacing the worst individuals in it by the best individuals present in *population* (if they are better). The size of the hall of fame is kept constant. :param population: A list of individual with a fitness attribute to update the hall of fame with. """ if len(self) == 0 and self.maxsize !=0: # Working on an empty hall of fame is problematic for the # "for else" self.insert(population[0]) for ind in population: if ind.fitness > self[-1].fitness or len(self) < self.maxsize: for hofer in self: # Loop through the hall of fame to check for any # similar individual if self.similar(ind, hofer): break else: # The individual is unique and strictly better than # the worst if len(self) >= self.maxsize: self.remove(-1) self.insert(ind) def insert(self, item): """Insert a new individual in the hall of fame using the :func:`~bisect.bisect_right` function. The inserted individual is inserted on the right side of an equal individual. Inserting a new individual in the hall of fame also preserve the hall of fame's order. This method **does not** check for the size of the hall of fame, in a way that inserting a new individual in a full hall of fame will not remove the worst individual to maintain a constant size. :param item: The individual with a fitness attribute to insert in the hall of fame. """ item = deepcopy(item) i = bisect_right(self.keys, item.fitness) self.items.insert(len(self) - i, item) self.keys.insert(i, item.fitness) def remove(self, index): """Remove the specified *index* from the hall of fame. :param index: An integer giving which item to remove. """ del self.keys[len(self) - (index % len(self) + 1)] del self.items[index] def clear(self): """Clear the hall of fame.""" del self.items[:] del self.keys[:] def __len__(self): return len(self.items) def __getitem__(self, i): return self.items[i] def __iter__(self): return iter(self.items) def __reversed__(self): return reversed(self.items) def __str__(self): return str(self.items) class ParetoFront(HallOfFame): """The Pareto front hall of fame contains all the non-dominated individuals that ever lived in the population. That means that the Pareto front hall of fame can contain an infinity of different individuals. :param similar: A function that tels the Pareto front whether or not two individuals are similar, optional. The size of the front may become very large if it is used for example on a continuous function with a continuous domain. In order to limit the number of individuals, it is possible to specify a similarity function that will return :data:`True` if the genotype of two individuals are similar. In that case only one of the two individuals will be added to the hall of fame. By default the similarity function is :func:`operator.eq`. Since, the Pareto front hall of fame inherits from the :class:`HallOfFame`, it is sorted lexicographically at every moment. """ def __init__(self, similar=eq): HallOfFame.__init__(self, None, similar) def update(self, population): """Update the Pareto front hall of fame with the *population* by adding the individuals from the population that are not dominated by the hall of fame. If any individual in the hall of fame is dominated it is removed. :param population: A list of individual with a fitness attribute to update the hall of fame with. """ for ind in population: is_dominated = False has_twin = False to_remove = [] for i, hofer in enumerate(self): # hofer = hall of famer if hofer.fitness.dominates(ind.fitness): is_dominated = True break elif ind.fitness.dominates(hofer.fitness): to_remove.append(i) elif ind.fitness == hofer.fitness and self.similar(ind, hofer): has_twin = True break for i in reversed(to_remove): # Remove the dominated hofer self.remove(i) if not is_dominated and not has_twin: self.insert(ind) __all__ = ['HallOfFame', 'ParetoFront', 'History', 'Statistics', 'MultiStatistics', 'Logbook'] if __name__ == "__main__": import doctest from operator import itemgetter import numpy doctest.run_docstring_examples(Statistics, globals()) doctest.run_docstring_examples(Statistics.register, globals()) doctest.run_docstring_examples(Statistics.compile, globals()) doctest.run_docstring_examples(MultiStatistics, globals()) doctest.run_docstring_examples(MultiStatistics.register, globals()) doctest.run_docstring_examples(MultiStatistics.compile, globals()) deap-1.0.1/doc/0000755000076500000240000000000012321001644013412 5ustar felixstaff00000000000000deap-1.0.1/doc/_images/0000755000076500000240000000000012321001644015016 5ustar felixstaff00000000000000deap-1.0.1/doc/_images/genealogy.png0000644000076500000240000060323312117373622017520 0ustar felixstaff00000000000000‰PNG  IHDR Xšv‚psBIT|dˆ pHYsaa¨?§i IDATxœìw|çûøßge‰,{ÅĬ½‹5ÛRTm)jÖ¨­¨ÙR{”šE­Ú›ˆ½W¬AdÈ:9çÜ¿?ž‘“äœ$âÓïï~¿^Ï+9ϸÎõœû÷uß×P !‰D"‘H$‰D’ ¨ß·‰D"‘H$‰äÿ¤"‘H$‰D"‘H² i€H$‰D"‘H$’lC ‰D"‘H$‰$ÛˆD"‘H$‰D"É6¤"‘H$‰D"‘H² i€H$‰D"‘H$’lC ‰D"‘H$‰$ÛˆD"‘H$‰D"É6¤"‘H$‰D"‘H² i€H$‰D"‘H$’lC ‰D"‘H$‰$ÛˆD"‘H$‰D"É6¤"‘H$‰D"‘H² i€H$‰D"‘H$’lC ‰D"‘H$‰$ÛˆD"‘H$‰D"É6¤"‘H$‰D"‘H² i€H$‰D"‘H$’lC ‰D"‘H$‰$ÛˆD"‘H$‰D"É6¤"‘H$‰D"‘H² i€H$‰D"‘H$’lC ‰D"‘H$‰$ÛˆD"‘H$‰D"É6¤"‘H$‰D"‘H² i€H$‰D"‘H$’lC ‰D"‘H$‰$ÛˆD"‘H$‰D"É6¤"‘H$‰D"‘H² i€H$‰D"‘H$’lC ‰D"‘H$‰$ÛˆD"‘H$‰D"É6¤"‘H$‰D"‘H² i€H$‰D"‘H$’lC ‰D"‘H$‰$ÛˆD"‘H$‰D"É6¤"‘H$‰D"‘H² íûVàéÓ§œ8q‚€€îÞ½Kbb"9rä lÙ²øøøP½zuììì,–'„àâÅ‹øûûsþüyž?ŽJ¥"_¾|T©R…êÕ«S¼xq«tŒˆˆàäÉ“pëÖ-°··ÇËË jÔ¨AŽ9¬=õròäIΞ=Kpp0&“ *UªDÕªUñööF­þoÙŒ>|õÛa4qvv¦bÅŠT­Z•*Uª ÑhÞ·š’ "„àüùóœ9s†óçÏJ¥"þü¯î·bÅŠY%3""âÕ3áöíÛ¯î·2eʼºßÞÑI²ƒðððWm|çÎôz=ÉÚØÞÞþ}«ù^É®÷ÎûBÁåË—ñ÷÷çܹs„‡‡£R©pwwõì(Y²ä;ùnƒÁ€¿¿?\ºt‰èèh´Z- |õÛzxxX%3$$äÕ5}ÿþ} ŽŽŽ”/_ªU«†N§³XžÉdâìÙ³œ9s† .‰F£ÁÃÃ*UªP£F .l•ŽÏž=KÑß²··§lÙ²T­Z5Cý­K—.½êo½lüyó¾jÃ%JX¥cddä«ëþæÍ›¯®ûÒ¥K¿jGGG«t¼~ý:§OŸæܹs<}ú!¹sç¦råÊT«V2eÊ R©,–óJÇëׯ‡­­-%K–ÄÇÇ???œ­:ï;wîpêÔ©Wý?!®®®x{{ãëëKÅŠ­Ò1>>žS§NÀÕ«W‰‰‰A§ÓQ¬X±W:æÎÛ*³ñž8~ü¸øøãÎB«Ó @hsæšâµ„¦T}¡-RU¨mì \Ür‰!C†ˆû÷ï§)ïÅ‹böìÙ¢„——„J­¶eJ ›:µ”¥Ha@øÕ®-Ö¬Y# Cš2Ï;'zõê%lìì 4Ž.B[ÆO¨+ÖÚÒÕ„ÆÞQ"GN'Ñ¿qãÆ «£Ñ(þúë/Q÷ƒz¯ôs.’Gä¯SJxÔ+-r•ó*µZ¢x©’bÆŒ"**ÊêïÉNL&“غu«hÜ´é«s²)à!ljù ›ºµ„m…rB¥Õ @(\XLš4I<{öì}«-±‚èèh1kÖ,QºtqµQÎK'êùiDÝQ¤îUÛ×­[K¬]»6Ýû- @|úé§ÂÆÖVÂÖ%§pó+#rÕ¯$\«•ºÊ3!§³“øöÛoÅ­[·²él%YÅéÓ§EÝ…r}8¹Ù‰²5ÝD¥ú¹Di_Waç <\\ÅÀÅÝ»wß·ÊÙιsçDÏž=_½w´.Φº¯ÐÕ«-lªVǤ÷Ž“òÞ¹yóæûVÙ*bbbļyóDé²å”g„J%t½„¦B=¡©POè<Š¿zvT«á'V®\)³ä»CCCÅرcE^J?A«Ú‚ÞBSê¡)Q[h]ò+ëU*ѲUk±{÷na2™Ò”¹ÿ~Ѷm;¡Vk”ö²Í+49k ãBëXY¨5Êó,wž|bÔ¨QâÉ“'iÊ‹ˆˆÓ¦M…ŠKêËh„ν‚Ðü@h<ëK¡W¿Oƒ†Ä¦M›ÒÕñäÉ“¢K—®B«Mêo9äš5…¦àB›¿ªPë”g«³‹›øᇼ{÷Ò”#æÎ+J•)«è¢V ]¡2¯Û0±W:V÷«)V­Z•n^¸pA|þùçÂÖNÑEãà,´Åju©úB[´ºÐØ)×½ƒcNñõ×_‹ëׯ§)/!!A,^¼XTªäóJ®„Ðhj ¦¶ÐéJ¼Z_¡Be±hÑ"Ÿ¦Ì7nˆo¾ùF88æTt´sÚ¢Õ‹ù MåÝeg/zõê%Î;—¦<ƒÁ Ö¬Y#üjÖ~­£kQ¡)XOh Öº'¨?Phr¸;{1gÎa4SÈ ’-Ýc~M÷}'ŠuñVx*(öìÙ“é¶x}zºÆëÿbbb^½wt=…ÍØÂáâ)‘ãEhòggT°p8sTè'´yr ­˜0aÂâ>8räˆ(Z¢¤Pi4Bå÷‘à§]‚µÑÉß« ÁêpÁ…Ú»‘D•ª¾âòåËþ^“É$Ö¬Y#œ]Ý„ÚÖAP³¯àû‚_’÷;~‚q/Ú‚ÞmÚ¶ÁÁÁ)d†……‰?îœÔ¡//È7OPâ‘ÀË$(#^/^zA‘Óׯ„Fë(s:‹¥K—š5¶oß.ÜóyµÖFàÕCðÑAÁ—±‚þ"ùÒ;TÐx¥Ðxø @ÔoÐЬÑ)úöí«èèVBPûÁ§‚oLÉå}(è|^PyÐØ» [;{1kÖ,³ý­C‡‰ÂÅŠ+mX«ƒ`ÌnÁºæÛpÈz¡®ÔP¢jµêâêÕ«)äÅÅʼnÁƒ •Z-´n-Œ¼™²O8Ó(vUÐd¤Ð:çZNŒ7Nèõú2D™2¨„ZÝDÀZD¾µ<°N¨Õͨ„—WyáïïŸBž^¯?ÿü³ÐêtBëä.h2B0슢ӛ:Î2 FÞ´œ ´n…J¥ƒ ±±±)d^»vMøV«¡ ë š­ôy–²­û½´Ù#T%; •F+ .*8BžÑh³gÏvvB£qÐ]À§¼±œ°]À ¡Õ*ó½{÷‘‘‘)d¾kTB‘ùy˸yó& ›4åIÈSŒN¿>`‰KQ|4lûŽÎ§YólÜ°„Lœ8‘‘#G¢«å‡nÁ,ÔÅ-s÷0?‰áËïP= âÏÕ«ùè£ ¢a“¦Üº}S×ñðá÷ ±ÀSMkÇ¢Ú<•ê5üر}®®®©î¾}ûv:}Ü ›¼ŽÔXÜüõK[¤wtàSNô^Íãý×9r$ãƳjZî]rðàAZ·mK‚ƒš¹¿ mÖØ¢ãLO‚Ið‰;vÓ¿fÍšõŸs5ûÿ!cÇŽeìر|PSÍâ_L³ÐàÐ è5PË“P þ¹Ž6mÚðàÁ5mBà½{؃‚Z£²À%Ï—ÀÝŸVóð—-Ôª]‹í[·Y=å-ÉiÒ´‚ðéäb´øÊ&ýgV|¬‘•#î°õ×GÔoð[6ÿMÎœ9ß½ÂïGÑ°iSnß¹ƒvô0tß|J›þ{GÄÇ£Ÿ0 ì¹Ô¨Y“Û¶áââ’ [ÏôéÓ2dj¯¿Yž^–xã$Ú9=!ä.+–-£K—.V}¯Á` Oß¾,[ºUåNˆsÁ1Oú 6¢ÙøN:ÁζS½zuΟ?Oã&Íy‘€1Ïpê –¼‡á¨B¿GD¬àã;³bÅrlllB0dȦOŸŽºH3L,„œ-;Á{Ðìá9[6o¢qcå½{ûöm6nÊ£Ç!˜jNƒò_€Ê‚÷ªþœçÒ¸IS6oÚøÊÕoòäÉ 6 MÙZJ(e™Ž×Ž£ÛUØ}V­XA§Nxüø1š6ãÆõ˜šƒúƒ,ëo`÷xT{'SÕ×—];þÁÍÍ €E‹ѯߗ¨Te1ç-Ó‘Ëh4_!ÄeæÍ›K¿~ýxþü9-ZµæÔɈ†C¡éhÐYà¦f4ÀÁ™¨wŽ¢dÉìÛ³› °qãFºvëŽ)GA õ—€GmËTŒ¸æ@/ŒŽðóÏ?3|øpT*±±±´oß‘]»vþ€%î™&`3ͯäÏŸ›}ûöPª”…mšd›rçÎjÔªM„ÚCŸ«ˆõB®íB³¬uüª³sÇ?L™2…1cÆ`3bº¡²²Ó*bcÑõÆó×_Q³fMüj×!èE"†Q;¡PÙ èxͤ©P¼‡ìÇÉÉ)Å.ÿüómڶųuj¯ø£å>—ÂdâòÔ ¶…¡C‡2yòdëõÌb>Lã¦M5«c³âT®Ö½…þXFÂ÷CéÛ§¿ýöÛÿŒa%Q9r$&L`Âðã7–¼IL,|ú­Š-»Tlܸ‰ªU«R³NmÂI üŽÑäð²ð…ûG®pùÃñT(U†ý{÷ýŸí þWyðàµjû!l£½£JZ»sñ@8Ú^¡Rþݳ÷ÿ\üÏ“'Oð«S‡Ç‰zt›þD]ƲÁ¨71ž8Eb§T(Vœƒûö™}ï¼O¦L™Â?þ‡C—q`mÜŸ>Õü¾pp«W¯¶Ø1™LtïÞƒµý…輪}b½ò/ÂÐ,i‡mÈEî߇­­-µëÔ#ÖXcþ­ Ëo½Ì¨õ¨Ÿt§uë–¬_¿ŽÁƒó믿B™Pé[ËŒ™7ÑG¡ÞÓõ£½ìÜñÅ‹§FÍÚ„'æÄÐr8[‡ÀƒÑìúˆšÕ«²{×f̘ÁÈ‘#¡ÓHè<Æú6LˆC5¯ù“µþI½zõð«]‡‡áqJŸÐ£‚õ:Þ;…æV”+^Cöó×_ñÅ_½É€å17 ‰ÀàwæÏŸO·nݨW¿—nÞÃØ{õ³^Ç'WÐ.lNN=ÂñãÇéØ©”è„h°tV>Ï„ NƒÓc3f C‡¥yó–9r£q PÓz B«ý—XNžün`ýúõtèÐ!ãúf’ÐÐP¼Ê•ãEù2Øl\ƒÊŠ ¶·I\õ' _ `áÂ…ôéÓ' µ”d†Í›7óÑG1u$ þ*ãr èü¥Šûmð*Wž›!ñ>:»B&DÜæBýá|Ü®+–/ϸr’,Å`0àW³®3å¨7¹ dü¹pãT$#^ {×ÏX¸pajù~1™LÔ­_ŸÓ·o£Û» uáB–e¼x }Ó¶tjÕŠ5«Wg¡–™ãßÿ¥I“&ðñ(è:.ã‚L&Tsz¢9º–ógÏR®\¹t™>}:ƒ‡ O×AåŽÿî„4 ‘+î:­–àç¹08šL̺Fÿƒ*¨ mÛ~ÈæÍ›áƒP¡_Æåõ¨w´ÅþÙ1 ,Ä­'qÚ…ù2.óñ1Ô[Ó¢iC¶oß]ÆBçÑ—g2¡šõ šë©RŇ³×1 8¹3` ½äÉe4sêP¯º@ˆÏi@F00 •êw4lÀÁãþ¿9*e\ÇghçÔ¦R O.]¼Hbá6ˆÆ«A‰"w _/Û˵ž³Ø¶m­Zµzw?†Äb&OžÌˆÙz̯);j·ÎD±v| ·ü£ˆ‰4§tÍG» akŸòżó÷ æõ»ÎîÝ»•íÿæ̙À°ß¹MÝZ)¶Ï]@?f"ÆÓþ šj¾ØL¦By³òÿ\OBï¯Ø¼y3mÛ¶}×ê§KTT^åÊ’«$¦±{SŽì_:£˜=–©'¡TµäëôñhV¦‚»#§Ož@›†›ÚõëשXÉ›Ä}ÁÞÕº>†1¦T‚Ðëðá4h0ž?D5©,BÅn€M*£Äqgáéˆ;¦x°).}Á­Ê}ƒ¿ƒˆ¹P¢#¸–‚àSr" ÑR(cFÇðkpä{xr 46P¤%Ôžö¹!>õò˜ãࣃððßôe†œ†kË”ýž]“ú›^o˜'GBùºP¦6Ü<·NCL X Þ’·g\A7”}\ò‚WMè4Jñ*IˆCÝ·¦ˆP轞I¿mVþfú`î¥aø5ð_ «z¢R•AˆæÀY ˆæ]Í4” Xš´Üì ÀDÀ µÚ“é.tY á÷Ó×ñ»4\J5‚ƒá·f¨Š`êp.͵¬½/Î…‹ó *ìrCÉ¡ÆxÐÚ£Úà‡>tC™½9<œ“Îå+àí>T ð pe†¨6ð=à Ü@¥ú„Q£†3vìØÔÏ'‹xçixCBB˜4y2¢á)€a°g<¸†Þpû éZ®ú°±A7w†Y7ýŒ9˜Nù£ýèCtåË!‚CHüíbk5ÀþÀ.4eSúžšâ IŸ”Æ@³~àýÖ O˜`A?e¦ämã nWTGÖòíÀA´iÓµZÍŽ;8¸ÿwõÇ”`äÂø8vÃÍÛ“àƒ·R<œ 1 íµw¿¢”þ².vî9 ;~—ó?mçɾë4Ûÿ=Õçwæo¯qLž<™_~ù%íßípüøq6mÜˆí²…é¦ Çè§Í‚iN1ÛL‹~÷>ƌÒ%K²Ze‰•L:•„øHL)šíAbP|Ö <òAllØ=úý‡0âÛ”ò„€8=x~Õ‡Rœo6»Âîäô.Æóƒ—ÌÞþwF¬àþä änåK~Í1Æ%ðdÙ>.´G…ÃÉÿiCÂÖá»AiÑ¢…Œ!zÏ<þœqãÆòáwÍ÷.½`HíÜøõ×_ {Šiôá´ÝŠZ %|“¯Ëg¦ƒoc‡¡ÿ2Î ©ÁÚµkéÞ½{ª"‡ɹÔú&—³®qxDh¾MïžWÚ®z[pt…»ŠQâ¿f@ô P½§âvµ¸em£µUŒ7±Kz¶ÄE!ÆíQ:Ý#©Ëãk`=Šqò\ž*L$@ÙP²>Œ/ž¾Ž=V¥\÷Àý ^MÃE˜05\ †ËÚæØP8; Jvïï!ü \œ£üm³ ¡Ê¢—€F@ɤsø Å0Y¼¼—BPÜÓœ€o€X`%Š¶(Ÿ1qâ$¾þúkÜÝ3á­dïÜY¼x1&•ê4¿ƒ³ŒVܲÀ/¾æ÷{‰!N/F÷Y7Ôæ}/m|‰Úga² >mû6ÄV«GâŒÙhþ˜Ÿ\äú͆›ÿÎÒ5”åM®…„X¨×Íü1*¢ãñc÷îÝ4oÞœyóçá^µš–Ũ7ðqðìÝsò4à>Û}§¤¡±ÕÑòøä©ñzÖ¦Ôçµp,’‹s?mçñ¾ëx4ô¢DŸš,þc ãÇÏvÿèyóç£+^ mû6éî«þš¾`0 ž…§ºŸÊÙ Õ—Ÿ³zât¦OŸþ*¸L’ýÄÅűxñB>ïb$Þ”Û›7P–7ùú3ði W™7@Vm}‚ ð±qw¦NðJlÜ]ˆ ¸¿ï÷fõ^¾§j¥¨´õõô¿G¯Æ-ð)O–ïý…Gt" îPöïßO£F2q̲֒|ùr zÚ6?ƒyxm½‰1ÿxS°Œ,Ù´w„ ö¯xBL¤ÎÉ_O*•ŠŽÃ 1²ñ9Ž=J:uÞùy¼KÖ¬YCLt4öƒ˜Ý®7 r8à°竘:m—ÄVªA˜ دN98£R©Ðùž Z°gÏš5köNÏ!- ó~ûS½î·HÚ;—­~©L™¾Mé꨽1gÞüT Gñ÷Ö¿1µ› ¹‹[×Lj…Ýã¡Ñ°cTòmÕ{Â??Áó?y?c<þ[ƒç†ôÏÃÑk êàV>w €u©èxf"ã Ý9ÅÀÈ[ ¶4Vf1ž+FAåA`ãd™Ì _AÕa ±…ƒßÀó›oè˜WAÃÏqY0¸¸Ãíø!yýæ¥\W½- ªªx†ä- ±‘Ðd„uý?µ|ÌÌd‡­€:À- p¨ŸŠ°ÍÀŸÀj ¥™í{ÀM7‚sËt4§ÛÍý€ |ºÀŠnàQ Ô£>ý¶‰yçg€×'ÐxÙëõ.¥àP87Â/3CäÍgfàcd|Òº%@ððò…^e¦d;ÐèŠÉ´’Å‹3lØ0óçšE¼óá‘ÅK—còþRÉ¥µyb‰7صÝŠîóÏRÝESÝ7Euñb¨½Jaºq+Åþ‰«ÖOóô’orxr£×57µ—D©êhŠ{³lùrBBBؽk7%ú)Ù46ZìÝ“fS9mµN“ÌøxI¡¶Êè`äõ`J÷«CäóÅG3‰‰‰aýúõ¨zõH7€ñè [¶c3åg¥Ó½ÔöèJ¢ÁÀúõë³Re‰•ìرƒððHúõ°üµ<óCj5·–¬W“»•/vž¹QÛè°qOJXÆý¯¶·Å&Oò‘tmN49ìÐ8Øà\»,N劰\Ƽw–-_‚_»<¸æ³5»ÝÆ^y^8»'÷3vÍgƒZ£BkcþùP±+ž%sþŸhã%Ë—£mÒu!óÉŒÇO¢­_7YBuÞ¼hjùaܹk~^]­*ºòåÞûo´ÿ~B)é!ÄE+™ƒ,ÀÔìKNŸ<ÁÍ›7Ín_³f2bîÛÝú>ƶ!¯ø˜\Ôè Vˆ^â-]£Ö€1òLHR2Fñ”HèMÊ H¹>Š+•ƒ:ÞÞEZ½6> 6TÜ·nÿ×—+î\ö¹-—éà®æxø/Ä3ô…x IDAT+m¨³QŒE`êòÌ‘')]¢Vû—ƒWÅ0´ªm˜Lõ–ŽvèØ é1¨Šb|˜Pf?Þd ä÷†ÂÕ¬¿~^bH€ ¡ÄŠ!wû ”Oº,i›àŠ;\©ÎÉ×—Lú|ù7Påj¡Ìö¼=ŸP( Ü{cÝ~#íÍÑÄj@aàߤÏΘLMX¼x™E§™Þ©òüùsîÞ¾ ¥³p4òþ)pÏgu¦!"4 U®\É× ˜Î]€ŠVèhH„£W-È“FŒ‚J…±BCŽ<…¿¿?B<Y˜z0 â‚•Ð.·R 4g±<¸uçÔ©S™–m çϟǘˆ¦~½4÷F# ƒ†¡íÙìû›9ÔîyЕ/—íç$IΩS§(ä©£d:1‚±qð4î܃™ a÷Ab&X=!Î_¸4².X®ðx¶û,çn'î^1×rãë£ã(ø퇀2úëÜ°"ÇN´J¶$k‰åÒÅ+Tj”zòƽ֥ù+л)*šë_Ìãæ€ßÐåv¢ò¾ 8W}ýåô)ÎÕÙ[‰ŠŠúŸKEúÿ /^Äd2QÂ'õ”ȹ>ûÊ©gÖQ—,ñ´?Âdz5Ã,ôzŒþg•ÿŸ§~leoÍùçÏŸ§Yê]r& QÜ'íNœÎjvŸà”\-Ó•Xƒ)Ç¡˜™¸QGtÅ9{ö¬Y7¬SgÎb*aefH!`c¨ÒŠT‡g÷Ìïç™dPÅ€ÝÉô·”Y‘‡mÁ¥7¸O˜ð|˜" @òŒ˜$œ…ÂVô;bž(s˜q=Ï‘âŸ+ÿ»ga+ì¬Ò7ÊHG¼W0è•ÿóƒ ‡#"!Æú>¡³4 žU”Y¥«;áè|ºyJ€º˜R™ÅIA Jç|#Šáò3X€2‹¢ÓƒÌ÷[Ϭ­xw€'ANOp0ãÇœ®IƒµB7*Qþã´twa(îU Ä…˜‹ÕÍ°Ÿˆò›””>|ýú©¹±ežwj€„††*ÿ¸x¦½£5Ä„¢ò´Î1ݸEÂÀ¡¨kTCÛ-ùt–Kj”\Vèxx2-W»Súû&É}ôè9<]38yqâ.žì»ß‚.Ø8½¶Œ<]¹ú8S²­%44­{Ti¤kÏÂÑÿ<›@•Ë:ãK]ÀƒÐ]{3«¦$„…S.—i€ïû@§Öð8Vo‚oF€½|úÖ-öLùkë™v‚· Y”ë}çâÞ±6îjaˆŠåáÌ¿¹Øn>G¦àP<2¹OŸ>•È{âås?·gêiwŸ‡$ðSóóô_T†œ¹tøoÊ_îá’׆V_§^&·§-&“‰çÏŸÿg °°0TR¯!¡ëÛ“„o“ðåwØ|ÿ Âh$qê DˆòûŠ¸øTUðxõ=ïË  Ô+¢f^~ÊòßVŠAòmEX9 ~ڙꡦ\ž¯ûoñ,,ªZÙï8½ ž\†^é ”Ù9­3ÃÞRèˆXpýò%Í äl B¿Cžq`SâõþÆPp´b&À§ü5ç.¥yã^sÌÂþV\(”°~À€1»•Í¯Âß¿À˜&Ðsº²ÍÚ>a«‰É?Wîî¥àŸŠQcò°BØKw«çÀ>^§®mŽâÊ43c:¾I|\ýʶT®—¡ÃJyy*C¾êJ2ÇPà%ÚÁ/•x“žä®ToLA™Õy™òå,œ9ßh›7öÑŠ{Xj÷WVñN w“á×:™¦âÚwEåâ‚ÝêÅ) kuŒ{§þ†ÊM• é‘ô}&“)sÓy@àº3œµ•R½kQú‹·0U*å;²aA,G¸I¨r¹¡û²·õ_ R!²ùœ$É"eæ+s”.¡,ÝÛCÓ.ðÝOŠQbÿÆ ò«ÛÍŠ{Á¯çÆWóÉÕ¢*å× ~µ>O›œ(Ù—»#VP~íÐdr³©¾ªÄ –üökÇßãYP¿ßô#—‡Ò¡òk›“I°lèêuÉGN7óADª7Ÿ©ÿQ^ýFiܺÏ?Åô(ˆÄYó0¬^ €Ú§2ºï¿!qêLTŽiT:þ¸”ïÎÀ;/q¨ÞNlJ'^0õwžÕçÛ†AÃ!àb‰K” %và ÔI:§·Š$:wQ ¸“É kã(4IòfܹŒ©£™#×Où¤Qû*M•öì_öea\Òß+I^„ÖÔ:yùB*Lòº9€fÀºÌëva£R5ÉK&£÷aó°ëcØÛKù¬Ö( ì…°Ô¼Cžß¡ÌêLáõ=øÒp5çÑ kŸìyƾÓWÙ‹¢C²N¨½"$,ýýE|ÛΈèhì·¬C7¥µ¨rK2"",ÔñÔÐÇYæ~õ†Ü¼yóiÙ1fxüï5Ž|²ÏVðû-eØø(r¹å2sä»ÃÍÍ ÃÓg£ÑìvÓí»–®Dׯ7"è1¦û0Ý€ˆO@èõ˜ø¹Å²b¯?"ñY4y>¬žl½ÎÕçZeˆ8öÚ^‘¤÷ûõ•¼~îG„èSÝçêÑŠUv|e|¼¤zëÜ$Ĺ{>:Õc_ÊuqqIuŸÿu^^Ÿ/g3RÃö§á伊ýÞíØŸ>ŒÃ¡Ýô¼U—HÝUM$\¾Ï ‚¹Ý\QE¤î&–&¹<÷ø·ƒƒ_£Ž Iõüœ]ݬëw쟮 {wR\¯žÝƒÈGʶØpå³1©ã¦}¨ßúnmÒ(¼ö­~†&)ØØøÖ3Oã±VèøÒõê¥+Ö›Ä<›uB=¼1»˜în">ž¸ŽÝ0Ý Ä~ÃjÔ¥KšÝOíUJI×s×B­ûœPíCËö¿{–<ùòS»vm^GûÄz#$ìT ûÛýNîj…ùà¯Þf3NEœ §rfª`Z··7¦¸8³™ÅÄã'`2¡ÿa8±åª¾ZLgÎ"nÝ!¶¬ú)©×.Q¿HµÊ0JÞ•+Wåì¥TÒY¥ÁKï·/Õ e”uÑgï¤<(D¢òBÆ”£1"јl}ôÙÛ(TP¦n~T¬¨¸lÜ9›ºaL˜ÌŒ[•‘B£!õÃ;g£)Q²(9s¦cò¿N¹råÐhµ˜Î§ÿ.S¹8£©QíUãè< ¤úN0ž»ˆ»‡Ç{)Nû’*•+£ <›±Ñß»`cöŽæ·Ç½ ñÑ *§ò~¨ZÙÕ#+úÏBÜs¥^ÈøbÊ2»®²í߉ÊçऎÇ”8»·¾Û.)8<ñQòõ†¤Î¢æ­¶°ñ†+tt,öy Ô?å¶Ó;©¿–…ý­<Þp;‹ä%Ä)mª³kÚ&5â£!æ©’&W\á•á.ùQ\—Ìrö Î›q#ŸÀ­P©½’5 ”ú!‘¯ãt¬Å¹8xÔR2g…_…Ø`°Ë¼™!¥ àC`J¬7qG)6xÅÌ\ÞŒåUä¦veïÔÉ›7/ù „;æ+^gˆB¾ˆG1=x˜ê.Âh$þ“>˜üÏb·j1ßÔƒ‰T¶¶¨Ë•…+èöBv`“º󛨯¢F5_|}GúÃæ;ë©qí {[Î#g±Ü4Úþ5Û”Á¸àHžßxòê;²‹*Uª R©0;av»º\ìÖ.ÇnÝŠ×ËÚå¨Ëz¡*T»u+Ð~b~&IDEc¸p)ÛÏI’___n&dîYÍ똎7IL„ë!—+”{+?ƒ=”ñÒqø’Å:ä([µ­ŽµÉïÑøGO‰8r…œ•_§èŠ:t…¾ÕÞ!ÉFœ)Uº—§þ²-^%'wÎFt+y*ÙC† Ö¨(ZÑ|ÇSÁåCQTóõ3»ý¿‚å*VĘʳ357lÁtö<º¯¿H{Çc'¨ñžŸ¾¾¾ÂCàqï¼H3Ó«àôV¨œFµûkÇÀdJõýP­š/ê§ ÑBפzàó-É—NJ² ª÷T>ç*¢|¾}Ô¶`ûV5z§¤€·ˆ·ŠåEü*äø ùz{_¿ñ©×ÄJA‰ö¸])Hø’‡û”⥻‚SQÊÂþ–»/<‚à»–ío4 3÷ýÍÓðà2”««1´¦OhHPŒ·ÙTÛ¢|kq(u?,å#”Žú7Ö=vuÁTneðw<»ÉÓ8JºN_ga‚cC@—Cɨ¥:§|F`pÅí*µ¬e PŠ3¾9sx€RÄðÕIàáQð¿_ˆð“î]™>ç7Œ­'ƒm*>«‡ç*U"£’F .o…ç”ÿë HžJ¯\K°ÏIâÒ•Øþd¾p ~ØhŒ;v£iÑñ4œÄ?“×’Ðué˜ì³öãöèGWRƹ¤ñƒ]§äe¶ÔýêÁLWÑmôZ *„_­šÜZxŒ¢+#%×æDKìceVäáÖ‹Ä’wOëÖ­É‘ÃŽ?ÖÄóÓ ”Ûû†è¨[]©„ª¡ß¼ Kg‚ÆL†À파˜~ŒÄ_¿@—ˉ‡s·cˆxAÂcåÚÛzšøJǤà€Ñ:9Pðû6ÜŸ¼³ G§ÆèXÍß)!‘"Ôk$úü]"Nß ëЉ)¿T’­tëÚƒISÆÓ{FÊ‚‚ .Äñ¡ ­@«o<É馡ìzFÓ>©Ö¹åÅÝ ‘ÌžFý¥ÿŸtíÊáÃaOQåI™”ÁxôúÉÓÑ4ªÊÕãé3V­EÓ¤!º¯û¦*×xå‰'OÓm]ø²g‚&MšàäêFÔž…¯ƒßfÚÇ`ë¥ý”wïë°{!Ø9Â'“S•­Ú³ReËQ¡‚ùŽVçÎ3f œß ÔI¯áYùuv«—¼Ì‚•¯THòx0™àØ"pì !obç .½ b `‡ºs¢7@®á }+N!g{ùV) Xy \œ ¯Ýk·Â‹$+ P\¬ª‡[ëaS}ðþV©„~vä®ez*©Y/̆ZÓÀÆÑ2™Q÷áÆJe]èå¯ÿ@€C~%à~÷Bøt2ü3b" ê(†Ô›´HúÛ Ø›ô;tA©„¾¥zzë¤}âÐhvòÉ'_ó®Q‰w¥HñâÅífB=3e‘Æ…ç÷_ª”ô7)€í§@p}«ÖƆþpy59.û£rI^œ ®y[ŒGO˜ŸúU©p|ËÿP„?'¦xh3ºOHýd†Ö„@Xúز ÚÙ=Éui'>ÀÆƆ5kÖЭ[7ZùÿHÙPt$/TKÑ%)æ®}àÏ`l(6 •Êü©”ø¬ÕíÄß^ãèÜ¢=‹-J_§,fçδhÑ»mÐ6H»ÈKâš·E„?ÇáÔ!³ÛEBúêкb%6n° ¢¬äÒ¯_?þÞ¼˜G 8½åõ²îoXü'\ºÏžƒ“#T¯ûBÃT U‡=** ïB±1]9Vôsâï'ùð¾u#Ô ü»BÊ ÀÃÙ[ Z¸‹¸ÛOPÙhqªVŠ¢£:ãZOé„\é>qàî?@«}çc+’4xüø1… ¢ë¸"tVÄì>7OG²ú§@®@o"_1{~šŸöC £V›¾Nîx™‡gl¸s;9ëö?ijgÏððôD|û¶£SV6Þ#ỡ˜.\DD¿@]´0Ún£ëÿeŠB»o’з?Îûtÿ>6id(ÌÌÌßþÀ¸à–’f÷m¶ÏQÜšŸÜ†¸(pv‡Š ¡óOJÜ€9‚n ê_Ž¹³góÕWfŠ %Ñ°QcÝ|Šñ{ø¹¤u} P ñÅ Ít¨?PYwa,i…ƒ™ºÂO'*aÃcÐׯÁÍ|µ{‚º‚é(t¿kÊ)Ƥì|9“t ¿ G“£JF¬"-¡ö/Š{VôX^jMU‚•—M_棃°¹AÊ}.€Ð{ŠÛUî‚J¡çN#•Z /" §ÔéNI¿ÿg笤G¾w"ƒ0Bž’Ê Cƒ”Àì'Áö±@JqÁæåq¥î(úF‡P³«cPêƒèAíU[A·e–÷QCnÀ¤2P´™–¼­O,†µ} ëEÈUÞ²¶¹¶ÎÏ‚ÈÛ RCÞêà;BIË+¬(Qî(îW/gCÞF¼é¶w˜œGÉ~UÅuëeÜ䟨T¿p÷î]Š)bF^ÖñÎ P:0‹–¯Â4ääzÛ/-š¶KGì~Ÿ“iq"1‘8¯Ê˜BŸÂ/gRÏ=n çöÀ˜¦,X°€~ý”ê—‰‰‰øøVå±)œg†¢±É|'éÄ—kx¸2€Ë—.S´hü¶Vb2™¨[¿>§ÜÇæÔATŽ©øìZA˜ ˆYó8ê—$û ¤lÙÒtÿ(‘E© dZƒ^«ª‹PSíÜlËδ̧ÿøs¡ÕX7nÌæ͛ɑ# A’laàÀÌ_0‡_ÏWųtæÛãÄæP&|t‰•+Wš­ýð_dÔ¨QL˜<»£ÿ¢©P>ýÒÁ°w?ñm>æ÷ߧoßÔgI²‹àà`J—)Kt…¦ˆþ̼@£͈:Ô‡qåâ…4Ó0ŸB¥ª‡›ÈPöµdTªq¾Ü^3¯ã½SðkmT¹+":žu ’mªAQj™¤ô6±ž 4šÎôî݃ß~û- ä¥Í;yÉÔ©SÉ—'š•]”"4™!ø*šÝ£ñ­Rêµ$®ù+Sâ„èGÇJÑ¢EÑÌìªTðÌ aÐÎéIý ùâ‹×¾º:ŽË–q-ÿïÖg:Ebà_ÜøíÓ¦N{/Æ€Z­fù’%hBÃÐø!Óis {÷c˜1‡ŸF–ÆÇÿ?æ‹/¾ >>‘?ÖÀÚ-™“' «TM/\¸0׺L#1âE¦dÆsõ³™¸¸¹røða*T¨À¾}û2§¨$ÓüüóÏä÷(À”N—‰²4@Ô}²HÃÌ‘/_>æσ8²ödÁ ýšQ˜®Ÿdå²¥éÖ€©Q£? „zçh¸“Iß{£ÕŸŸá@<Î9P‡|¢Ô÷È qþ¨#gâçW.Ì„»[3'O8øS,ùÜó ÙÓô™{¶~ÿHªV©¬ÌTí]š9y«F ÂS¼D ´«ºÁ sY™¬ âÚM_S®BY„Ø€Yæ"Äu*T¬„vmÏ×îV%æšµ=)Z¤<=džf<5ïK®­€ ƒT­ê‹F3ef#3Ä¡ÑŒ _¾GV­\‘".ÂÛÛ›ùóæq}Áaü¿ß€ÉLfK\w†#Ý–Ò¹KçW3,ï‹âÅ‹³|éR mBÿåwˆÄô+g›Ã°{/‰?£Y³f š#V’L±eË*V¬ÈåË—Ùµk=zt§Çk6gLžÑ?Œƒ¹K¡W¯ÏÙþ÷VTA\l<}XÆž ±·‚¸PNj[¢#£ðööÆÃÃFI°ˆˆÔÓ}ú š×ö.É Ž&X56Må—éÓ•þ–!Í‚F•ÁtÍÏî¡W÷jvíØÁˆ#Pܨf“±&˜ŒdèСìÜñùœthçÕ‡§ìàG… YÐ'};þÙƬ™3áü 81L *Ï×–£Úß‹ž={±{÷.J•*ˆVû%`]’£×D£VÀÆ&M›þʶ ƒÙb€T«V÷ìÆ!ôÚ©àúË6aÿtÔ3«áU$öíÅÍÍ?-â³O?%¡Ï7$ôþ:Íš)DÞ $±ÅG$Θ͌30`^^^Ü¿×è'h¾­Ç7Z®£°gšï½ñtPsäà<<ÌWçìÛ·/óçÏçÚìì©;“È›–;úÈ8ŽöZÁ¡Î‹éо=+–¯@m&-ovÓ©S'V­Z…iízôu›b¼tÙâcEL ƒ‡ß¾+åJ—fãúõètÖ§~•d /^¼ OŸ>´k׎:uêpñâEš6mÊ’%KéÚµ;ݾ†^! ›áÖ]¨×^ÃŒßÁÇLJ¥K—rõêUìÛæ~8g*ô'l‹å„ÉÄ£;8Sù;òÙ:qîLû÷ïçÎ;1zôhÖ­[G¹råغ5“#‹«9räµk×&þülý{¡·Tô¯àÏéí–všL‚msòm"BøyüÄ÷šVö]Q¦LíßíýÄV©‰aË6‹B¸x9 ~ ðÔÙpä@êï÷…J¥bîܹôûâ ˜×Õô.eŨwÈ=Ô?5‚uã˜8q"C† ±øP;;;vlßF]¿j¨æ7RŠ Zš àþi´3|Ð^ÝʆõëiÙ²% 6dû¶­èôûÑ>ð†Ø£–Ëzƒêa]ªù–c÷î899±vퟴkó!ìh¿²næâÙ4›j¢ººˆ%K–УG|||Ø»g7Ñ—Ñ®«÷R¯(Ÿ“ÎÍD½Þ—R…ssèÀ>rçÎÍ‚ èÓ»7ÌùÕŒnÖyŠ„*m¸~S§NeàÀ”*UŠÃàfE3µœ³Â›E8±Í´Jxä0qäà<==?~<Æ F¡VwÆ|ªÝÔF¥ê gÈ!Lš4‰ (²sªÑL÷†ã‹¬›¹8¿Í´ ¸&<áàþ}xyy1`Àf̘SÐüÝ"­0lâÃQýÛö~ÆgŸ~Ê¢E qssãàÁýxyy¢V ¬D ¦·”hµqp¸Ã¿ÿî¢ZµlÌ")²™ÀÀ@Q¿ACµWcAï-‚zÁ¯"å2é¹ ý¡Íç%T*•8p ˆ‰‰I&Ïd2‰¥K— Ggg¡usºïû ‡K§…cLXŠ%Ç‹Paä_¡û¤«PÛÙ Ï"EÄþýûSè,Ú¶k'¡)W[ðß‚õq‚¿EÊem´à›?„¦xeˆ^Ÿ."""8Žü IDAT,ú->,Š•(.46:Qü“¢åÉ!âSã<ñ™Xbéø³¨8¼™pÈã$„èÙ³g–´IVâïï/Ê”//TZ­Ð|ÔFØïÞ*rü?öÎ;¬©óíãß“„½A‹8QQ‘夊8 (îA«uÕjÝ£nQq+*nÁ- Š7¸ (.dCî÷Þ¤„ Øö—Ïuyyñì“œ“óÜϽrÞ‹ü>ÔŸÜ%å93IÉȈ”UU©C‡¤¢¢B·oßþÑ—ñ?KBB™˜˜ºº:mݺ•¸\®@=—Ë¥mÛ¶‘¶¶éë1ôû$г› ÊþÇMÅŸ ÷©¨°¨Y³ÆKÅÅÅäííMl6›:DÔ·?@ú?·¥6g’cÁqêN§„þ9|;Df[&‘N»f€ÆŒC_¿~å¯ïÅ‹daaAššš´mÛ6êÓ§ ///ÊÊʪíó’£G’ŠŠ 999ñ ß¼yC½z»jã G¿nKÇ E|ÃÝéÐWš°É”ŒÛh200 ÒÓÓ£´´´|u5Cjj*©©©Q£&MÊÞ;lIe×ÒÈ~#ú]öþ©„¬%VëV€FŽ%õ{çGÁåriïÞ½¤­«Gl-=‚ûTÂÆ4BWø½Á%¬M&ôM,52j؈Ο?_幋ŠŠhñâÅÄQR"ŽAcBÿe„EïDï;Ö–&^$¦ÝbX,²´²¦{÷î ™ššJvv`ˆÑîOht–`VLhEÂÿZd~ZIµfÄfshîܹTXX(0^ii)“ªš:q4 ¶†¿&’ð¿I¥„Aצ>Ä°•È¤¥ÅÅÅ ­ñÕ«WÔ½‡sÙ~«qBŸc„ …¢Çó™à°8ủaúõ×_Eî·öìÙCZ:ºÿÿN#l~"zoÁ%¬N$8"–Š5hÜ„.^¼(´Æ÷ïßÓÀƒÊîûf ~û+óD7+¾†n'vc+@þÇÓçÏŸ…ÆŒˆˆ Cb³5 MÀM¾ðµÂ¿/Ä0–Øl-Ò×ÿ‰Ž;&4Þ—/_hD@@Ù·'xm#¬È½ÆUùÿpb7ïJÈÝ}½{÷NhÌèèhjØؘXJªó‚Ç-Â$®èïÆï)Áj±ÕõISK‡víÚ%ônÎÍÍ¥)S¦Ã0Äá4%`— Hñ/Ž€ÕÄbu$ääÔƒ^¾|)´Æš¦Ö¢²›xß¾}dcסìÁPR!Ž±-Áf¡cÁr)™†Ø }úAWW3äçç“……™ššRNN…††جÿ8ÄiÛš8žƒˆãçMœÁî¤dfJ`± C­Û´!†aèáÇ?ú2¤&33“¦M›FÚzeï5F»Áº7¡ûB÷ᄶNÄÒ({sTTiÁ‚‡ ÕáÁƒ4|øpRVQ-›[¯Á½lßaëGìæ]ˆ¥¢NÈ´UkÚ´i‹¯¤¤„¶mÛF­Û´+ÛË°Õˆ­Ù‘ ãKÐ h "%õæe×¢¤L>>Ã(%%EâŸ={F&L 5 ͲçD§¡™+¡Õ‚™?±9[E‹q3Z½z5åçç‹ËåRxx8Ùuèôÿ÷”2qŒl¦>eߤTÇŒÀ0Äb³iàÀAtãÆ ‰kLOO§©S§’¶®^Ùµé×#Ʀ/ÿ;dµëN­²ºz Ò¢E‹èÛ·oÇ"…™ò|üø‘fÏžMe{ Ç€¦;>øÃô §NÙÁ—þO4kÖ,úðáƒÄ1cbb¨wŸ¾Ä0 Å"¥­ V^ek´ö&N#KbØ@?ws¤ÈÈH‰ï›œœZ²d 5hT&ܨé«‘¡ÕpB«Ä4íK-#@ZÚº4eÊzûö­Ä5ÆÅÅÑ Aƒ‰ÅbÀ’’1=p# q8mˆÅR&dcÓᇾk% –$îܹƒØØX$%%áɳç(**†¦†:Ú¶i kkkôìÙS&•r^^Ο?ÄÄD$ݾìÏŸÁb±PßÐ6ÖÖèСe ᘚšŠK—.!)) aûöàNèèh#õÁ¬Y³ ¨v¸²eË–aÁ‚˜5kV®\ }]hjjâqj&ŒŸ'''¸¸¸@óÿ£L¼½½qúôi¤¤¤ Y31! ………hÒ¤  ///¸¸¸ eË–P×ÖFii)ÔUUqåòeüúë¯X°`ttþ¥üüùsX[[ÃÞÞÿó²ÿ:/_¾„¯¯/nܸY³faÞ¼y•šÀmß¾£GÆíÛ·ñìÙ3\¾|6ÃÔÔ¹¹¹øôé+f̘‰eË–aöìÙ˜3gŽÐ¥¥¥ðóóÃÁƒ±ÿ~xxxàáÇhݺ5Æ+W®àù«—hݺ5ÔÕÔк•9¬­­Ñ£G4i"9r—ËÅÂ… ±`Áxzz⯿þÂÌ™3qèÐ!ôë×›6mBÆ «õ¹)ø"Âœ9s°téRL™2«V­ùìz{{#!!ÇŽÕ+W°víZ¼ÏÊDAAš41Æ«—¯¡§§‡¾}ûb×®]€©S§bÓ¦M8uê† KKKœ;wî?c¦9iÒ$lÛ¶ °°°ÀÌ™3Šèèh$$$`Ó¦Mxòü9Z·iƒä¤$tîÐ7ÆáÇ‘žž[[[tèЇU/ KmóéÓ'ÂÉÉ é™xðèìììððþ=´iݽ{÷Ƽyó’’‚víÚÉuîììlœ?ãƃ¾A()«àéÓ'àæŠ.]º K—.èСƒÔ¾4D„ÄÄD\»v IIIxöì%Š‹K ­­‰víÚÂÚÚ...2™æääàܹsHLLÄí”;øüå+Øl650‚µµ5:uê{{{™Þ‘wïÞåï·?y†¢âbh¨«£msØØØÀÙÙ 4z¼üü|þ~+ùöm|Èþ‹#ß`cm ;;;899É=--¿ßz”ö…PSSEëVfüßY‚îáâÅ‹HHH@RR2²²²AD04¬+«ö°µµ…³³3TTDçÅË—/qñâE$%%áþÃGÈÏ/€ŠŠ2Z™¶„µµ5aff&õx%%%¸|ù2âã㑘”„ŒÌ÷àr¹0Ð׃µU{ØØØÀÅÅ¥ÒÀ åÉÈÈ@TT’’’pïÞ|ÿž eee´hÑ ÖÖÖpppûs%3?Dìù£­­M«V­¢û÷ïºråŠ\Æíر#¹»»—Ë%UUUZ·n¥¤¤ºyó¦È>_¾|¡¦M›’Éeò ,,ŒÐÇéÉ“'@H}Þ¨Q#š9s¦Èþ§N"´lÙ²ÚXîÿ4{÷î%mmmjÒ¤ ]½zUª>\.—Z·nM®®®ü²„„@ÉÉÉDÊÊÊÄåriРAdkk+v¬’’òññ!6›M¤%K–††åççÓ!C¨{÷îÕº¾Ã‡“ºº:YYYÑëׯéøñãdddDÚÚڪІȢ¢"1b U«V‰m—‘‘A‡Ö¬YÃ/spp ŽËÌŽ=JhØ°aÔ¢E ~›œœjÒ¤ õìÙ“.]ºDJJJ4vìØÿÄwÇ»æ7Q^^Д)Søm†JDDÔ´iSš9s&½zõŠ¯çiÓ““Ä%T™‹/º{÷.­X±‚tuu‰ˆÈÝÝzöìIEEET§Nš:ujÌŸ““Ch÷îÝtéÒ%@Ož<©‘¹(P ŒâxYˆ999ÐÒÒ‚‰‰ X,RSS«=nff&âããáîîŽ/_¾   õë×çŸDdddˆì§££ƒ 99Yä 󀈰~ýzôìÙ­ZµBrr2 }{Á,³VVVüºŠôíÛsæÌÁìÙ³Sãkþ_äË—/ðööÆ°aÃàêêŠ;wîHYæÂ… xðà¦L™Â/ãÝ£¼û¶¨¨ÙÙÙpwwÇ­[·žž.r,6›Ý»wÃËË ÞÞÞعs'zõêUUUdddTÛ¡vðàÁ¸~ý:>~ü[[[üôÓOxøð!† ‚±cÇ¢{÷îxöìYµæø_&77nnnØ»w/öîÝ‹©S§Šm eeeŒ1‚_–žž}}}@‹-øÿ?yò/^¼hjjbãƈŠŠBzz:6oÞŒÐÐPÕà•Õ<¯^½ÂÈ‘#1hÐ ~$Ã";;ãÇç·ËÈÈà¿ 4h€ŒŒ 4nÜVVVˆŒŒ„ŸŸZ´h?ÿüó‡\GU‰‰‰AݺuѦM|ûöyÇÌÌ ©©©PRR‚——öïßÒRYœj¥ãñãÇüùxsûöMîó(P @4 DrssADÐÒÒ‚ŠŠ š6m*ääÉ“`ýúõãoÔ4h(++‹Ý¼eÑÅ–.]Š+V *J†Èb5D\\ñË/e™_“““ѨQ#!Õ3O!1€óçχ““¼¼¼ðöíÛ_÷ÿW®\A»vípæÌ„‡‡#,,LÀ ®2Ö®]‹öíÛÃÁÁ_–žž‡ƒºuëò7KéééèÓ§Øl¶ÄHT…‘‘¿¿,æâ°´´Ä­[·Ð²eK8::âøñãضm.\¸€/^ mÛ¶X³fMlrþË|øðNNN¸zõ*Îœ9#1?GQQBCCáçç]]]e‡oß¾…ž^YÞ¦M›‚Ãá@CCçÏŸç÷ïÓ§¼¼¼0yòd¸¹¹aÚ´iøí·ßpö¬ Ñ}þAcèСÐÑÑÁÖ­[Á0 ˆ6l@¯^½ø ø4hЀÿ>pssÙ3gÀår±`Áœ>}7oJIîG'''0 Ã?ØÊ‚ׯ_#77¾¾¾ÈÌ̬‘ƒ(Þ»ÛÔÔ”?wNNŽÜçQ @hˆ ð~œ´µµ”ýP¦¥¥U{ÜÈÈHØÛÛÃÀÀ@à$™aÔ¯__¬„ÇÔ©Sáââ???¼_Åü%rbýúõ011AïÞeY9“““aee%ÔÎÊÊ ÙÙÙxóæÈqØl6öïßxxx ¨¨šIŸ ¨¨³fÍB·nÝ`llŒ»wïÂËËK¦1>|ˆsçÎaÊ”)öÑ022*ó·ú­EFFôõõáàà€ÈÈH‰ã²ÙlôèÑ Ã`ãÆ8pà€\4 <~úé'\¼xþþþÀ”)SЭ[7Ü»wcÆŒÁ´iÓйsg}BAA_QRRBóæÍñæÍtêÔI@€uëÖ¡¤¤Ó¦MÃ_ý…~ýúÁÓÓ÷ïKòûŸÂ¼yó€ððpþõ—Ù¨'aâĉüvD$𔸻»ãÛ·o¸|ù2<==ѦM›Œ&¼2¾}û†[·nÁÉÉ „ LCakk‹–-[",,LîkHMM…‘‘tttˆ?…"<õlEUquÈÉÉÁÅ‹áîîüÓ-Þ)pù/q°X,ìÞ½àççn5³‘W•·oßâÈ‘# ‹ÅI@ˆ5ÀºuëâðáÃHLLÄôéÓklÝÿ ¤¥¥¡sçÎXµj–.]Š˜˜4nÜXæqÖ­[###xzz ”—?¥­W¯†8©‰‰Áׯ’‡œ8qݺuƒ··7|||PTT$ eee„††bÆ FŸ>}P\\ŒuëÖáÚµkøöíÚ·o… *^ $''£sç΀7nˆ|¾+ŒîÝ»ÃÜÜœ_Æ»?xp†aвeK<~ü...ˆŽŽFq¹¤¦†††XµjvíÚ…ØØXìÛ·M›6Eÿþý‘••%ÏK¬Q.\¸€¿þú K–,A§Nøå!!!hÚ´)ÿð¾~ýŠ¼¼<‘¶mÛÂØؘ¬cÑ¢Eˆ‰‰ùW˜­^¹r¥¥¥èÞ½;€²÷ ï`ÏÔÔ@ÙoÃ0ðõõűcÇ››+×5¤¥¥ñçâÍ­0ÁR  öP 2À;á ¦¦¦xñâ dHlTóçÏ£¨¨nnnÊNëÔ©ÃÈ ({9ïÙ³QQQX½zu•×S6mÚuuu >ðæÍdgg‹Ü ÁÐÐP¢;vÄÚµk„ÔIJÿÓBCCѾ}{|ÿþqqqøý÷ßeŠÇãÇ äI“ ¬¬,PWþ”VII ?ýôÿ¾ussCqq1Î;'vìoß¾!&&îîîصköðáC™×) †a0qâD~t;;;{ö,Š‹‹áææ6›ÍwÂ7ouûöí`±XèSÞ'°üÿåÍ°222””†a°xñbܼyóïSÞÿ4Á­ š6mŠ®]»bïÞ½r›¿´´?æ Ã@SSS!€(PP‹(å Ê~ ÅÅÅ8}ú4_û”½pÊÛ½K«á±xñbXYYaèСµªNǧOŸN9“““ahhÈ7'«ˆ¤HXåa[¶l±±1 ¤xIHÁ¹sçжm[ܸq‘‘‘Ø´i444ª<^aa!BBBàïï¡úÊî[žÃ¬8Ó¦ÈÈHXZZòó{dff‚a :ÞÞÞ5"„4kÖ 7oÞD·nÝЯ_?¬X±DKKKÄÇÇcÉ’% ‚……bccå>ÿ¿"ªU«0lØ0øúú"""Bê{)++ À„ „´noß¾å›ëk@¸\.^¾| ggg‘Z3SSSÌ™3+W®ÄÝ»wÑ¡CìÚµ aaaø믿ªÁ5ÄŠ+pñâE„……ÁÐÐ_^RR‚M›6ÁÛÛ[èÙªh’Ë{Æxå]ºt¾¾>_¸ïÙ³'ìíí1gÎœfŠ[>|À;wøþ€d|}}qáÂdffÊe ¯_¿FAA@®---Å»E‚Úä‡ÿý—ÂËo‘——GDeùôõõiñâÅU=))‰_fccC£Fâÿ½oß> 1k{Ež={FÚÚÚäååU+±ò¹\.µk׎úöí+PÞ§OêÝ»·Ø~¼øRÍ“ššJZZZäááñŸÈPäååQ``  ^½zQff¦\Æݹs' ÔÔT¡ºÜÜ\@{öìá—;–Ú·oÏÿ›—Ó¦b>"¢ÂÂBÒÖÖ¦ùóçóË,X@†††TRRB¾¾¾Äb±èÀr¹–Š”––ÒìÙ³ ùøøðŸo¢²{®K—.€Æ'·ŒÌÿJKKiòäÉ€fÏž-ós·xñbRSS£ììl¡º€€²µµ¥­[·âr¹”‘‘A(""‚víÚE ÃPVV–PßÂÂB277§:PII Í›7ŸOäŸÆõë׉ÍfÓü!Twüøq¡÷%K–P:uøóžµ°°0~™¿¿?µnÝšÿwll, Ã‡Ëù*äáC‡€@FgcccÏfíÚµ¤¦¦F¥¥¥DDôéÓ'RVV¦Õ«WËe gÏž%ôòåK~Y«V­hòäÉr_•£@d`ãÆÄf³^Â;w¦aÆUi¼ÀÀ@jÔ¨‘ÀxFFF4wî\þß¼Iiii2NhûöíUZ›,\¾|™PTT”@y½zõhöìÙbû½xñ‚ЩS§¤žëÈ‘#€Ö­[WåõþWIII¡Ö­[“ŠŠ ÉMHãr¹daa!$`òà%›¼xñ"¿Œ'@”£I“&4a¡þQQQ€nß¾Í/3f _€)/„„‡‡ËåšDNjjjdkk+°9*--¥àà`ÒÐРFÑ™3gjl ÿ$ ÈÓÓ“†á'Ê“…¢¢"jРÀJy\\\hÀ€´eËâ…q¹\ÒÒÒ¢åË—ó…‘}ûö‰ìíÚ5@ÁÁÁü¾žžž¤®®.r3ÿ£ÈÎΦÆS—.]¨¸¸X¨¾{÷îÔ©S'‘}'L˜@eºººô×_ñÿ>vì˜P½ž={R«V­øÂÙ?‰qãÆ‘©©©@™¾¾¾@âÙ3gÎ ƒ "KKK¹¬¡¢€CDdggG#GŽ”Ëø (¨… – ðÔÄåÚššV)!""nnnüñJJJðþý{![zRûððòòB@@ñèÑ#™×' AAAhÕª•@(ÎÌÌL¼{÷Nb„œ&Mš@OOO*3,ƒ ÂÔ©S1mÚ4\¿~½Zëþ¯Àår±fÍØÙÙÍf#)) ÷iuˆ‰‰ÁÝ»w–‡gjUñ¾ÍÊÊâG1ªè0[žˆˆ4iÒíÚµ“7›ÍÆÎ;1lØ0øøøÔX0///\½z™™™°µµE||<€2ß„I“&áþýûhÕªúôé???dgg×È:þ |ýú½{÷FDDŽ9"OZ"""žž.ä|ÎãíÛ·hР€ßFyGt###XXXùðèÒ¥ ƇY³fáÍ›7`;wîD›6mп™LWk "¨Q£““ƒýû÷ƒÃáÔ?zôÑÑÑ¡wËSþ9àÁKFÈ£gÏžPUUu½hÑ"T 333|ÿþ]fç¸ÈÈHèèèd“u’¬¡¡™5 ¼¾@ZZZåÑظq#´´´àçç'Pžœœ }}ýJsMHëˆ^%%%=B×®]áááÊÍ9öG“ššŠÎ;ãÓ§O¸víìíí«4Î;wpõêU±Úïß¿ãëׯ"SSS~`¼{÷wïÞ9Ž®®.‚ƒƒqâÄ ~˜V###œ~üH~~~€œE>ÿtNœ8AjjjdooOŸ>}ªÖX?~$UUUZ²d‰Ø6ÑÑÑüû€÷»Ç#11‘PBBõíÛ—œœœ$ÎùúõkÒÔÔ¤ñãÇ ”/_¾\(jTmàïïObƒ‡p¹\jÕª 8Pì<'ü“'O ”‡„„’’’Ð{¢k×®äêê*P–––F,ë´ƒËåRÆ é·ß~(çEÈ‹(ÿóÏ?ÉÈÈHh >|x•×G(99Y ü÷ߧ¦M›Vy\ ȆB"¢|@”””`bb"“$22ÊÊÊèÕ«—@yzz:ŒŒŒã€êi@€2³‘;v@]]>>>|s’ê@D ‚««+Œê>~üˆ×¯_Kt@çѼyshiiÉl†ÅcêÔ©8p üýý«•òŸNII .\ˆ.]º N:HIIÁˆ#äæh.ŠÐÐPp¹\Œ7Nb;Q†aDÞ·={ö„ŠŠ "##ñìÙ3Ü¿_è”\œ&°ˆŠŠ‚žž^µÆÛ¾};ˆ£GÛ†—]œßÄÅÅ×®]Cnn®Øñ5j„eË–aÓ¦MÁ)¦OŸŽ#F`äÈ‘¸qãFµ®KZ°{÷nlÚ´‰-¹|ù2=z„I“&‰G’F¼¸¸XHkìæ憨¨(Ï©eË–ð÷÷ÇÒ¥K%~~µÁÓ§Oñöí[ÿ@8Á/333dff h†Á°aÃpôèÑ*û÷ðÞÕ¿…D‚ÚE!€È€( ì‡R–d„‘‘‘èÞ½»ÐX¢ì}Ù²¡‹C__û÷ïǵk×°xñâj”E2yøð!~ýõW¡ºÛ·o½", íÛ·¯²Â® 1hРîtZ¼xñX°`fÏž«W¯ÂÄĤFç,**† àççWiÆkYî[MMM8;;#22‘‘‘PQQAÏž=…Æãõ—DE!¤&#þ´hÑqqqèÒ¥ úô郵k× ™[õîÝ<€¿¿?~ýõWØÛÛW)B^mADX°`FñãÇãàÁƒPUU­Ö˜¥¥¥Ø¸q#¼¼¼$Þ7oß¾…¾¾>ÔÔÔ@DBшŒŒŒü@ŠŠŠ*5»?~<:tè€1cÆð^2 ƒÍ›7£C‡pwwÇË—/«u}•ñøñcŒ?~~~ðõõÛnÆ 077ð…¨ˆ¸ç b6tnnn(((@TT”@ùܹsñùógËr)r'&&l6[ȯH” >ѯrrrpâĉ*­#5572Vø€(PP»(ågm•Dvv6®\¹"Ò6ZÔI2ð·¤â†GVìíí1wî\,\¸W®\©ÖX¼ Ñåèy$''CKK Í›7—j¬ª8¢—GGGÇŽÃÓ§O1~üøµ~yˆ{öìA»ví™™‰«W¯bÁ‚~C5Å¡C‡™™‰É“'WºÆÊîÛŠ¸¹¹áúõë8|ø0œ¡©©)PŸžžeee‘×+ÂBx¾šBtttpâÄ L›6 ¿ýöPXX(ÐFKK !!!ˆÅ‡Ю];,]ºô‰(Ó¨;óçÏÇÒ¥K,”©¼*œÎŸ?/òÔ7//_¾|{’ Èž ] 6ÄŽ;‰7ÊÔ7'';vìÀ˜1c ¦¦&TÿåË<{öL&ÄÔÔjjjÕ2Ãâ±fÍXZZbðàÁÿºLÕEEE˜9s&ºwïܽ{µ¾Ž5kÖ uëÖpvv®´­$ àÛ·oøþý»PŠŠ X,–ÈçIœ&°2xBˆ¿¿?üüüj\±³³Cbb"4h{{{±›!ܺu óçÏÇêիѾ}ûZsˆ.Ï«W¯ÐµkW<}ú—/_FïÞ½å:þ† P¯^= 4¨Ò¶¼$„€x Hnn.ÿþrrr›Í–J ¢¦¦†ÐÐPÄÆÆ åÀpqqÁúõë±víZ¹iJ_¿~€€ 0&LØöÈ‘#ÈÊʪ´ ù9• (K¤Y¿~}P×<~ÿýw0O«-bbb ¢¢‚N: Õ‰;ØÄ èÛ·¯ÌI _¼xââb‘o ? j…"%………())¹aÒÓÓƒ¡¡a¥~ /^D^^žHÿI9CCC0 # 777Lš4 S§NÅ;w¤î·{÷näææbüøñ"ëSRRHç€ÎƒÃá ]»vr@TTTpøða|ÿþ]n¿jƒG¡cÇŽX»v-–/_Žèèh4jÔ¨Ö×ñäÉœâãã1}út©ýgäÍúõëQ§NøøøHÕ>##uëֹ駹»pá мys‘É1y> U…Åb !²ž’ÊŠªª*vïÞ•+W⯿þ‚»»»ØSTsss\¿~«W¯Æ¶mÛЦM\¸p¡F×wéÒ%üüóÏ022Â7j$‚ÚÎ;Q\\,6©^yx›_IfÍšÍf h•]\\-µCÿªU«Àf³1eÊ¡ºõë×ÃÑу ’ÊtVóæÍC||<ÂÃÃ+ _œœœŒ›7obâĉR] Pv¸ôâÅ Ü»wO¨î·ß~ƒŠŠ –.]*ÕäAII bccEúâ}+2íxqq±HÓ»~ýúAWWW&gô´´4hjjŠ<0Q Ô. DJÄ… äajj*Q)--ʼn'Dš_‚™EQÝ\ ¢PUUÅðæÍüòË/•¶?þ<žÉ¬GGGhkk‹îuuu1cÆ „††âÕ«W•®C$%%!''G¤ÿ YáÖ‰z·ª¨¨ÀÃÃ{÷î•:çÏ]”¦J!€(PP»()*‡™™?~,ö‡0..>|i~”mÒ455ÅŽ/\ ¢033Cpp0vìØQ©Cßúõëamm-ñe+«:+++‘Læ`•1{ölôîÝ>>>µö²•–3gΠmÛ¶HHHÀÉ“'uuõº¦­[·¢¤¤D¬y($i+444 ££#pß–””àäÉ“pww‡››²³³ü!$ie¥¶…èÓ§âããQ\\ [[[DGG‹mÛ¬Y3\¼x[·nÅÑ£GannŽãÇËm-ëׯ‡——<<~üˆÁƒË*ùýû÷6lºw3gVÚ>;;û÷ïÇøñã¥:œái‰$i@>|ø 2d¬²²2z÷î-Ò¡§§‡… Vºy ---ØØ؈¬—äÒ Ahhhˆ=ÜóõõÅëׯqõêU©Ö".ð÷»]á¢@Aí @¤D¬üü|¼yóFd}DD Ñ¡C‘õ•Ù½×„„Lj#àåå…1cÆàùóç"Û¤¦¦âüùóbCï@nn.RSS«$€˜››CYYYnfX@Ù4,, ÚÚÚ>¶¶¶pqqApp°Øhs Ã`Ô¨Qxøð!lmm1pà@xxxT)¼(.—‹3f`òäɘ>}:vïÞ]©_Fu†Ø߶Š¤§§ó…S@¼Â‹„ŃÍf£gÏž2 ¼D„YYY˜7ožP½‰‰ Ž;†«W¯"00Pª¨€\.~~~ "„……Ie.¹sçNp¹\Œ5JªuKŠ.W¾\œ/‡»»;’““E¾4551kÖ,ìÞ½»ZægÒ±‚—$†a$š7wéÒÆÆÆR?×’6› uuu…D‚ZB!€HIe&X’TÅD„ˆˆ¸ººŠ}YUf÷^Sàï—tݺu1tèP‘'ÁÁÁø駟àéé)vœ;wª$€(++£mÛ¶r@€² ðGŽÁ½{÷*MªWÓ¤¤¤ÀÆÆ;vì@HHN:U#&1UáÈ‘#HOOi// YïÛˆˆÁÖÖ, ®®®ˆˆˆàoüä©áñ#„===œ>}¿þú+~ùåìÜ¢hР"##ŽK—.ÁÜÜaaa2‡É.**‚ŸŸV­Z…uëÖaùòå5êO”––†óçÏ#00Pê\"¼ ¼ö3¡óhÙ²%ž?.𹹸¸ ))I¦P²Íš5Ãüùó±víZ‘¿/ Ehh(‚‚‚*oåÊ•ˆŠŠÂÞ½{¥29ãe‡÷ôô”˜¾<’‚’”/w(Õ»wo())‰4ÀqãÆ¡^½z˜?¾Të©*¸~ýºXó+@² P&ˆŠ‹0É0 † †Ã‡WÞúãÇøôé“DÿD---…¢@AmA ¤âÀ€¾}û&²¾¤¤„TUUiݺuBu< têÔ)±ãwíÚ•† &¶~Û¶m€ŠŠŠd_¼”ÄÇLJá3f”þü™444hîܹû“²²2ViþÑ£GS»víªÔ·2¶nÝJh÷îÝ52¾$JKKiÅŠ¤¤¤D–––ôðáÃZ_ƒ$¸\.ÙØØP=dêW\\L,‹¶lÙ"¶ŸŸuîÜ™?O³fÍhìرüú³gϺ{÷.-_¾œtttªp•SZZJÄ0 ………ÕȢرc)++S×®]éýû÷•¶ÏÊÊ"ooo@½{÷¦×¯_K5Ï·oßÈÙÙ™”••éàÁƒÕ]¶TRݺu©  @ê>äääÄÿ{éÒ¥d`` Ô.&&†Pjj*¿,##ƒÐþýûeZgQQµk׎¬¬¬¨¸¸Xd›éÓ§‹Å¢3gΈçÆÄf³é÷ß—zîS§NŠ‹‹“ºÏêÕ«ICCƒ¸\®Èúììl@‡;FÏž=©{÷îbë7oÞL Ã🽚àÒ¥K€RRRĶ111¡éÓ§‹­_¸p!Õ©SGl}ZZ C‡I\ËÕ«W Ý»w¯ÊkQ @üPh@¤„gª¡¡!²žÍf£E‹"5 ÐÐÐî$¯r—vvvXºt)V¬X!uÇŽ(**¸qã$öONNFÛ¶m«lîaee…ÔH¢¶‘#GbĈ7nîÞ½+÷ñÅñæÍôèÑ3gÎÄäÉ“'UÎÚäúõëHLL”*ñ`yÞ¿.—+µäþýûxþü¹€”££#´´´øfXÕ€% ‹…­[·bĈµ‹Çˆ#pùòeŹsç0qâD©MÔÉ9@€²ß3I‘°ÀÕÕ%%%8sæŒÈz%%%Ì›7HLL”zm²GGG‰ïMI> ÀßæÍ’ýúúúâìÙ³øðáƒØ6©©©hÚ´©Ä , j…"%•Ò¢sddd !!Alô+ ìd­°°P* HM9¢ó`±Xؽ{7ÀÏÏ'NœÀ‹/ð믿JìWPP€TKiÛ¶-Øl6’’’ª<†$ÔÔÔpôèQ|øðÇ—y#-ß¾}ÃðáÃááágggܽ{·Ö6…²òüùs?~S¦L‘ÙW@  ¸¸ÙÙÙˆˆˆ@ïÞ½…„0777$%%áÍ›75ªáÁBàïï_kBHÆ qåʸººÂÃÃóæÍ«4|¨ŽŽ¶lÙ‚èèh¼}ûX¹r%?tqBB:wî eeeܼyµq)àr¹Ø°a( Í¦_FF†T†a„"aeHff¦È<•±hÑ"`üøñ"ŸMMMœ8q0`_»yóf=zÛ·oG“&M¤žoãÆ000è;' I9@xHÊ”Ýo666bý@€²(afff˜3gŽL듆œœ$$$Hôÿ())A~~¾DÄÄÄ ÃH sÏû|:$¶Mjjj¥ù©ˆµ‡B‘’ÊNi€2$33_¿~å—8ql6}úôÛOš“d}}}¨¨¨Ô¸(˼¾gÏDEEaúôéèرc¥™“ïÝ»‡’’’j jjj077—»#zyš7oŽ={ö 22+W®”ûø7oÞ„¥¥%Ž=Š]»váàÁƒ•&(û‘A__¾¾¾2÷MOO‡’’êÔ©#¶ O˜HJJBRR’È<8}úô‡ÃÁ‰'j\ƒÅbaË–-µ.„¨««cÿþýXºt)-Z„ÁƒK•q…a IDAT ÝÉÉ ÷îÝÄ ðûï¿£S§Nظq#Ñ¢E \»vM¦qu¹xñ"ÒÒÒ¤½Ë#++ %%%Ri@áP¼ÐµkW¨««Ël†”m0CBBpîÜ9 @oÞ¼©òšªCii)5Š†©õ|1EEEHhüøñBù~òóóiàÀ"ó®ÓŠ+HUU•š5kF1115¶Îïß¿“®®®P® i™5k5iÒD lîܹ԰aC‘í¿|ù"2ï—Ë¥&MšP```•ÖATö}wíÚ•Z¶lIùùùu?&MMMòõõ¥¼¼<²³³£úõëÓÛ·o¥ÛÓÓ“Z´hA¥¥¥2¯ëüùó€^¼x!±]ll, GIlwåÊ@7oÞ”ØîâÅ‹€"""d]²GŽ‘ꙊŠ’êZ/_¾L*Í¡ÄkwíÚ5ò%K–žžžØ¼*<Ö¬YCêÿÇޙǟþÿÿ53Í´o””ƒÙqȾgW–>e_³e;â,BvαDi‘õP–Ð)ûÉIˆì»‡E!DRZfÞ¿?:3ª¹ï™ûžçóûÎóñ¸îûÚî{®îû~_ïÍÀ@e-Z´”Z G¸˜`_UÅùùù8vì˜Ê]_9\v’ÊÓ€"((LJ¥¥%<==1~üx̘1}YYYjý?n¶ô@‘iʧOŸ*\EÔÔÔnP«V-xzz*% ¼qãËeüÊpD/ÎôéÓB–ª\^^æ΋=z aƸsç†Zió,/.^¼ˆK—.á‡~иϟ?ããÇj×m^^2228ùÉ…™óçÏk<¯²"B&NœˆqãÆUª^^^ˆ‹‹Ãýû÷ñý÷ßãÔ©Sèر#îÝ»‡¸¸8 0€µmƒ pæÌcÏž=hÒ¤ Ž=Zns#"bàÀGÜ*DÞ¯ªC¦HX@‘Ö£Gszp¥Q£F˜?>V¯^¤¤$À?þˆ¤¤$ìß¿_ñÌ·¶¶Æ‘#GœœŒÑ£G«ü@ ƒD"Á˜1c4šSZZ,,,Ôæ ’ÿ½¨Û”‰D0`€J?9Ë—/ÇÝ»wU†³å‚ÜÿC\}@ªU«333µˆP(Ĉ#Y"ò—\Q‡|\"ÓiÑ¢¥lhŽpñŠvj ŽÚµksŠËÏGT|.€€tìØ-Z´P344ľ}ûððáCÌ›7¯Dý7nÀÞÞFFFå2¾““nݺUi»P[¶lAÍš51xð`Æ—ORRÚ´iƒÀÀ@¬]»±±±¼òü›ð÷÷Gýúõ9E7cƒKˆGAAJÛm9………H$œ>”*¡PˆÐÐÐo&„tìØW¯^…®®.zõê…7oÞàÂ… hÛ¶­Ú¶B¡S§NERR0`ÀŒ1Be‚6®œ9sIII¼CïçåË—J7š Ý»w‡H$*“~ùåÔ­[“&MBtt4°víÚÏ?hÞ¼9"""Åš=¼  ›7oƨQ£4Öäq E­¯¯sssNIÜÜÜœœ¬2”-P¤-éÛ·/üüü¹føòòåK$''s@>}ú]]]µ‰'Gt (Vff&Ž; h}ñ@´~ Z´T>>™Vüxöì:„Ù³g—é䈺u KKK¼ÿ^­"OPSf²RZÙ¹sg¥ŽŸ’’‚W¯^ÁÄÄ8pà/çàZµjáøñãصkNž<‰Æcß¾}er0 D“&M4NªIDi@ä¡xKÏÝÔÔmÛ¶-³¢««‹°°0$&&bäÈ‘puueuwuuÅêÕ«±bÅ ÆhKüñÒÓÓ5Ž,€W(j®¡Ù{ôè}}}µfX@Q„ÀGiœ'>>8­®–@Ñ:à"€4jÔ-[¶TÌ?==Ÿ>}R›„ø*€hshÑR |#ß“ÿ) 'Çh™LFzzz€“3hjj* £Gª­›Mh÷îÝœæ­ cÇŽ¥ZµjQAAãy™LFnnnT¥JJMM¥ÂÂB200 µk×–Û233 …‡‡—[Ÿ\‰ŒŒ$@éééÔ§O@Ó§O§œœœJŸOy3gÎ277§ììì2õAT:ßK¥R²¶¶¦Aƒzþü¹Ê>›7oN$téÒ¥2ͯ¼J¥4iÒ$´cÇŽJóСC¤««K]»v¥ÌÌLZºt) www~·W¯^‘»»; rv¤.γgÏH(RHHï¶rä×û÷ï/qü矦:uê°¶‹ŽŽ&”––¦tnÙ²edbb¢ä´Ï—‚‚ª^½: º{÷®Êº2™ŒÆG‰„.\¸Pâ\§N¨S§NešK«V­hâĉœêº¸¸(‚ž¨ÃÍÍÚ¶mË©î!C¨víÚôåËNõ‹3nÜ8jÖ¬§º?ÿü3Õ­[—SÝU«V‘©©©ZGr"¢ 6X,¦wïÞÑéÓ§ %''«mwûöíÕóG‹–ÿŸùßÜÆ­d¸† ŠTÅfffH$èر£Úú\w’"3(SSÓ Ó€¼yó{öì··7k(Y@€mÛ¶ÁÀÀ#FŒÀƒ““S®333Ô­[·Rý@主»ã‡~À?ü€FáÆ8~ü8Ëœãä[“••…-[¶`òäÉ044,S_iii066Vù7qõêU¤§§+üÔ­[yn‹ªU«~s3,9B¡!!!ðòòÂøñã+\Œ¡C‡*2X›™™aáÂ…ˆŠŠÂñãÇÑ¡C¤¤¤ðêÓÊÊ ‘‘‘ˆŠŠÂ•+WФIlݺ•—6$$$ÆÆÆ9r$ßKRÀ”„àf‚€Õ$++«ÌyP–,Y‚ŒŒ ˜™™ÁÏÏOe]@€ÐÐP´iÓnnn ”»wïâܹs…Þ-NEh@€"íÍ¥K—ðêÕ+µu—.]Š””lÛ¶SßrèŸ<5\̯î–@‘uÁÇñúõkµu===!“ɉäädèèèpÊͤ5ÁÒ¢¥òÐ à©CNnn.ÌÌÌ8åƒà’½8 +,, "‘'NTY¯J•*سg"ï%{é²âääÄ+î~y‘““ƒììlH¥R|ùòqqqèÓ§O¥Ï£"ؾ};rssËüpó[Š‰‰AÕªUÙÏU­Ûüü|¼yóß}÷ ÀÉT¤²(-„ìر£ÜÇ "øúúÂÛÛ³fÍž={J8!4‰‰‰ÈÌÌD«V­4rÔ4hîß¿ÁƒÃËË =zôÀÓ§OÕ¶ËÍÍÅ–-[0~üø2ùyɾ&Xvvv …Œˆ\`-‹V\\V¬X¥K—"44QQQj`‰D‚¨¨(cÀ€ÈÊÊBPPlll8a£°°¯^½âœŒ“K6t9ýû÷‡@ À‘#GÔÖmܸ1FŒåË—óÊ-óôéS¤¤¤p@ø˜`q„ Ý...ؽ{7’““Q¯^=µ~&€VÑ¢¥2Ñ à©(ÊŠýñãGäæærê;-- b±œêWT6ôüü|cÔ¨Q¨R¥ŠÚú;vÄ¢E‹ðÇÀÚÚºÜC§:99áæÍ›•ê pýúu899!<<«W¯†‰‰ fÎœùÿEHF©TŠ7ÂÃã\2s‰Ü ZµjÐÕÕU¹n廲5jÔ€««+@ê !“&M„ ÊU)((À„ °råJ¬Y³ëׯgôÏiÖ¬®^½ŠÆ£[·nغu+ï±ÌÍͱ}ûvüùçŸxòä °aÕk|ïÞ½ÈÌÌ,“_P$´ ¥ð²ê‰D‚:uê0:P‹D"ôìÙScäÍ›79r$ºu놟~ú îîîèׯ¦OŸ®ÖÀÂÂGŽAJJ ÜÝݱ{÷nLž<™Ó‡®ªùÈd2^ôôtNÏI tèгp¿xñbddd $$„S} (ú•H$BçÎ9Õç#€Ô­["‘ˆ“9£'&&âæÍ›œü?­ˆ-•‰Vᬘ˜ˆÅb|úôIÇ_/_¾„µµ5g‡àŠÒ€:tééé¼"Ü,X°&&&øðáÞ½{W®óqrrBVV§Ú²"•J±zõj8;;ÃÐÐ7nÜÀO?ý„ýû÷ãÌ™3¬oþ—ˆŽŽÆ³gÏÊz·8ê4 =ƒÔ™Š×º¸¸pv˜­L„B!‚ƒƒËUùüù3\]]±{÷n„‡‡cîܹ*?Æ---qêÔ)L˜0^^^˜9s¦FÑŠ\\\pïÞ=L˜0sæÌA‡pÿþ}¥zôOèݾ}ûÂÎÎŽ÷8ÅyñâªU«‰D¢4†º`l‘°€"3¬k×®qzÞG&“)Bꆇ‡C$A  ((>|€¯¯¯Ú>7nŒÈÈHœ:u _¾|QÊÑ®YÐåÔ¨Q………œ£œ¹¹¹á¯¿þâfÖÎÎãÇÇêÕ«9kNŸ>V­Zq¶à](DíììÔFò’ãêê cccܽ{—S, ( D"Ñj@´h©´ø ÑÑÑh×®pzPrÍ"§¢4 7nD÷îÝÑ´iSÎmbçt„ eŠ°S¹OIEû¤¤¤ {÷î˜?>æ΋‹/*^V]ºtÁªU«°råJNf ÿfüýýÑ©S'´lÙ²\úS·ncbb ¯¯ž={P¿n‹ûBÀÅÅå_ãRœòB222Э[7$$$àøñã1b§vb±!!!FHHz÷î÷ïßóßÈÈHHH@ff&Z´heË–¡  @QçÂ… ¸uëV™BïÊaJB ê6`ìííYþúë/^óY·nþüóOìÚµ Õ«WW¯]»6–/_Ž   \ºtIm?={ö„……d2Y™Ÿ\Ã[Ëáš DŽ««+òòò8kŒ,X€¬¬,lܸQm]"âœÿCœCñ€\]]ñáÃΠè=¯@´h©x´¸ú€dddàÂ… ððð€@ à$€pÍ"§FœUî\¹|ù2._¾Œ™3gòj÷ôéSdggãÇDLL ‚ƒƒËmN–––¨Y³f… ûöíC³fÍðôéSÄÇÇcÕªUJ»³óæ̓››FU)Ú˜ŠàÊ•+¸páB¹i?ˆH­£lLL \\\```@½æîåË—ÐÕÕU˜ÿ¹ººââÅ‹œN+›ÒBÈöíÛy÷ñôéS´oßÏŸ?ÇÙ³g‚¦NŠØØXܺu ­[·V$ÒãKûöíqëÖ-øøø`É’%hÕª•Âÿ*00 4Ðh~¥aʨ΄.§AƒxòäI áHŽ x™a]ºt óçÏÇO?ý„^½z)Ÿ1cZ¶l ///Æ1‹ó×_!##ƒ ‚··7NŸ>Íy¥yùò%ttt`iiÉ©>×lèrêÖ­ ÎÂ}Íš51uêT¬]»™™™*ë&%%áÍ›7èÞ½;§¾~&X?:tè¼Ìhµˆ-•ƒVáW£G‚ˆ0xð`ØÚÚrzPj¢)((àmn Š€€Ô­[—wb:¹pàííéÓ§ÃÇÇ·oß.·yUTFô¬¬,Œ=Æ CŸ>}pûömV›e@€;wÂÒÒC† áìÛóoÂßßvvv*³hóáýû÷ÈËËc]·¯_¿Fbb"\]]Ǹh@lll¢|f¿r!dòäɘ8q"/!äÆh×®ˆ‰‰‰eŠ ×µkW\½zúúúhÛ¶­Æ÷KOO+W®Ä•+W  ѦMx{{ãàÁƒ˜>}z¹ä½aÊp7Á*,,dÍzÞ«W/üù矜´°>|À°aÃЪU+,[¶Œ±ŽH$–-[ðàÁ¬]»Ve›6m‚££#öïßnݺaÈ!û/¥¥¥ñ2É­V­„B!/³\777;vL­`%ç—_~AAAÚûpúôiH$…ø öööxþü9gÇxyß/^ä<†±±±ÖD‹–J@+€pàÓ§OÐÓÓSÕ*&&íÚµƒ••ç®Yoå”w6ô´´4DFFbúôé‰D¼ÚÞ¸q5kÖ„¥¥%Ö¬Yƒ† ÂÃß?.—¹Éò4íºpáÝ»wcÏž=077WÙÆÔÔ‡ÂÇ1mÚ´rOE“ššŠ`Ö¬Y¼_6ÔÙ©=zýû÷Wã¢)ÞŸ¥¥%Ú·oÿ¯ó)ŽP(DPP/!äÔ©Sèܹ3jÕª…ÄÄDN¡AÕQ§N$&&¢{÷îŠDyš®Q'''\¹rK—.ÅæÍ›AD¨_¿~™ç°k@¸ s(^ HIOOÇÝ»wUöCDðòòBff&öîÝ«Òa¼yóæ ­ÐãÇë<{ö G…··7Äb1öïßêÕ«£ÿþ™Åñ}èèè zõê¼Ìr]]]‘™™‰„„Nõ­¬¬0sæLlܸoÞ¼a­‡víÚñ WÎÇ(Ò€ëïQšGÁÀÀÑÑÑj¡Ê111Ñj@´h©´¸ìÒäää 66V±ëËEùüù3>~üÈË«¼³¡oÞ¼ººº?~<ï¶Å3 ëééaß¾}HMMåmÊņ““Þ½{‡ÔÔÔ2÷UPP€… ¢S§N¨Q£nß¾‘#Gªýð‘Ó¬Y3„††bçμcãKadd„qãÆ•[ŸêìÔ£££Ñ¾}ûf$666øôéë‹É¤ËÍÍ §Nâä0û­à#„DDD oß¾èÔ©âãã9›ÙpÁØ؇‚ ðË/¿`ĈkëÄb1|||`jj +++ôéÓÞÞÞeú(ËÉÉÁû÷ï5@ä¾Alf­:t€¾¾¾Z3¬°°0}ªr×…OB9Õ«W‡@ ( H^^BCC1vìX˜ššòjKD% Hè ÄöíÛ±oß¾2ϯ¼Ñ?~Œ:`ÕªUX²d Îœ9ƒ:uêðîgôèј2e ¦OŸþMr”ð%;;aaa˜4iR™ò7”F.ü–§*óÔ©SJ¹Ôiî˜v~å³±±±å1í C.„L™2…Q!"¬]»#GŽÄ¨Q£]æDlóXºt)"##Ž;*roð%22ïß¿G\\6n܈;w¢iÓ¦‡»U¥5ã"€…BÔ¯_ŸU¢§§‡.]º¨œßÝ»w1{ölL:C† á4o„††âôéÓصkW‰s¹¹¹Ø¶mÆWâ÷´³³CTT0}út^Ú(¾€_. È¬ÔÕÕ111œçV¥Jøøø $$„qMݼyYYY¼ü?>þ "â%€T­Z–––œý@’““ѲeK4oÞáááœÚh-Z*­Â.‘:¢££Ñ¨Q#…¹BÆ !•JñäÉÖ6|#žE*w++«rÑ€ìß¿oÞ¼Ñ(ÂMjj*Þ½{§d¿>nÜ8xzzbÒ¤IevÚ¶¶¶†•••ÆaÛ¶mhÑ¢Þ¿ÄÄD,X°€S‚H66lØ :T#‹ÊdÇŽÈÎÎ.—FÅIKKCµjÕÍWbcc‘——WÂÿP¯¹cÚùµ³³CÓ¦Mÿ•Ñ°J# ±iÓ&%!D&“aÎœ9˜7o|}}±mÛ¶2å‰à‚»»;.\¸€7oÞ U«V¼ìßåÂÅÅ7ÆÌ™3qïÞ=4hн{÷Ƙ1cx¯}ùﮩPŠ(2ÃJHH`4ýüù3<<<РA¬[·º*” IDATŽÇÌ‹¢l9sæÌ)îvÿþýx÷î¦M›¦Ô¦S§Nؼy3ÂÂÂ8E’S H»˜’’‚[·nqn3{ölaùòåJçNŸ> CCC|ÿý÷œûã]²8\Í›e2>|ˆ† bäÈ‘8rä>|ø ¶ÖD‹–ÊA+€p@š¸°°GŽ)±ëË%k+ß,èrÊ#aãÆèÝ»7¯…räBAiD  44–––6lo„Ò}iêˆþîÝ; 2'N„§§'nÞ¼‰Ö­[k<9ººº8xð ²²²0jÔ¨JM”ÈyâAwwwÔ¬Y³\ûVµKƒ¦M›*åŒP.TnšÅÔ§««+Ž=ªQ®‹Ê¦¸2a„††bøðáظq#‚‚‚°|ùrÎ&e¥E‹¸zõ*êÕ«‡.]ºà÷ßçÜöòå˸zõj ÁµN:ˆÅ¶mÛƒFáàÁƒœûdË‚p@T…âŠüü|œ={VéÜÌ™3ñüùsìß¿Ÿ—‚œõë×æÌ™£˜ó¦M›Ð»woÔ«W±Í¸qã0oÞ<øøøàøñãjÇÈÍÍEfff…k@ sçÎ055ååcebb‚Ÿ~ú Û¶mSÚ\Š‹‹C§Nx ×\£K–ÆÞÞž“’ššŠÜÜ\ØÛÛcøðá(((ÀÔ¶Óú€hÑR9h¨3ÁJLLÄ»wïJ ÕªUƒ™™™ÊeZZŒyï•G.ÄÄDܸq³fÍÒ¨ýõë×aeeÅh†cjjŠ½{÷âÆX°`A™æéääÄÛÜéÔ©SpppÀÙ³g…­[·–« RíÚµ'N`åÊ•åÖoyräÈ>>œ¹ÀÀ@Ô­[}úô)q\ `üøñ¸ÿ>Úµkwww 2D‘Å^/^¼€™™£é ÈË—/Y}‚ìííQ»vm%3¬={ö`ûöí ✮4–––X¿~=ÂÃË+W®àúõë˜>}ºÊv«V­Bÿþýáéé‰{÷«‰F\^ÿíÛ·ÈËËãÜF,£_¿~¼µ‹ÞÞÞ°°°À’%KÇòòòpþüy^æW÷è’¥iØ°!>|¨vóGî'Ò°aCX[[£Gؽ{·Úþµ&XZ´TZ„ê4 ÑÑÑ°¶¶F«V­ÇZU±&ö¾@ùh@6n܈ ÀÅÅE£örÿ¶‡Ö­[cåÊ•øí·ßÊdÃïää„W¯^!==]mÝ/_¾`Îœ9pqqAÓ¦Mq÷î] 4Hã±UÑ»woøùùaÑ¢E8uêT…ŒQüýýѾ}ûrÑú”†mÝÊÚ•6¿’önUù´lÙ5jÔøŸ0Ã’“žžŽk×®A"‘ °°Pmþ„ŠDWW[¶lA@@6n܈þýû«œÏ«W¯ oooÖ¨i666ˆŠŠBdd$Ο?Æã÷ßWéOÀ à–ˆø ‹-’@ P„ã•ó÷ßcòäÉ1bÆŒ£v UŒ=ݺuÔ)S°qãFÔ©S½{÷VÙF$!""uëÖEÿþýUF‘â›]Ž¼>—gdq\]]qûömÖÐÆLÀ××áááxðà€"Ynn./t l&X999j7á’““¡««‹ÚµkF…„„µ×«@´h©´Tù€bbbàêêªôµ··W­C{_ ìÔÔTDEEaæÌ™Ç÷/í€Î„zõê…Ñ£GkœPŽ«#ú½{÷кukÁßß'OžÔèÞòaáÂ…èÕ«† †””” ‹ׯ_ǹsç*Dû°¯Û˜˜Ô¨Qƒ5Û:ÛºUµó«‰Ãì·$99íÚµÃû÷ïqýúuL:'NÄÖ­[¿Ùœf̘“'OâÊ•+pvvf}.………A,«Š'àîîŽû÷ï£_¿~;v,úôéƒçÏŸ3ÖgËpKD¨Å ™a=|øÏŸ?G^^<<|³¡Ë111A·nÝx ºººX´h8€›7oâôéÓèÚµ+ïg®¦> ¶¶¶H$œâþ†††4hvïÞ­R¸011L&ãœìP‹-š¡@8 Ê$&&ÆÆÆèÚµ«Ò¹† âãǬ»ÿeÑ€üUî@‘£cXX&L˜ ñK™Í +++ìÚµ ±±±¼£ÏªÑÓÓÓÑ·o_Ìž=S¦LÁÕ«WѬY3Þc”…ªU«âàÁƒ¸uë–ÂAõ[òòåKìß¿¿\çõë× "¥u{çÎ<þ\¥Ð£J¢êï K—.011ùW'%} «««tN•ª˜ˆ––¦±Ð,zDD2335úÈsãÆ T©REa[«üøã˜?>®\¹Â{¼–-[* 111pppÀíÛ·qòäIlØ°÷‹¬¼øþûïˆàà`αæ+ŠM›6A___£Ä’\`³SŽŽ†‰‰ :wîÌÚ¶FHOOgÜWõw ‘H4r˜­,¶nÝ 777ôíÛ±±±077/qþß&„È…¹üóæÍØ1cðåË¢k×®hÚ´©ÆýãÌ™3xõê±zõjäææâÕ«W¬¿qy àììŒK—.aÀ€å‚úÈ‘#HMM…¿¿?®_¿ŽM›6ñjïéé ???øúúâСC%ÎiªE€B*•âرc¼Úéèè`É’%8qâ yû_- 41“;¢³‘••…ôôt%¤{÷î°¶¶VéŒ.×kCñjÑRÁ•H¥R@aaaJçRRRíÙ³‡±m^^‰D" U:—‘‘AèàÁƒ¼çôöí[@àÕN&“‘ƒƒ 8÷˜Åqss£=zðj“ŸŸO­[·¦:uêЇˆˆèóçÏœÚΙ3‡PFFegg“—— WWWÊÈÈà=ÿŠ@&“ј1cH__ŸîܹCDD?~¤‚‚‚J›Cvv6™››Óœ9s*lŒC‡zóæM‰ã-Z´ aÆ©lEèõë×%Ž;;;Ó˜1cT¶Ý·o gÏži4ïò¢  €vìØA2™Œd2-^¼˜··7ªl+“ÉhÚ´i%ž'yyy_ 3gfÏž=¤§§GM›6%EqqqežÓçÏŸiîܹ$ ÉÁÁÐñãÇéåË—äééI©©©Šº&L Ö­[sêwõêÕdbbB2™¬Äñììl"*ú}š7oNèرceº&ºwïNmÛ¶%"¢éÓ§“¡¡!=þœˆHiNlÈd2òðð }}}ºví½ÿžœiĈÍ«cÇŽ4|øpÚ¶iӆ̻T*% ’H$œ¯½8 , š5kònGDäëëK5jÔ`=åÊ ¸¿Åñññ!KKKÊÏÏgl{íÚ5Ö¶Z´h)?´5È“Z×€\»v YYY8|ø0ttt”ÂUÊ‘H$°³³cÔ€h’]N•*U ««ËY²wï^œ={ñññ¸{÷.fÎœÉ{L™L¦áÉŽ4b±{÷îÅ»wï0eÊÄÄÄÀÖÖçÎcm“ &(bð¯[·-Z´@xx8ÂÂÂðÇÀ‚÷µTÁÁÁ¨_¿>† ‚‹/¢U«V˜?~…½aÃܼy»víÂÇË}×·8iii‹Å°°°À“'OðìÙ3<þ7oÞd~%‡MsÇEاOˆÅbÄÄÄ ++ W¯^-Û…h@NNŒqãÆáçŸÆäÉ“±xñb¬X±jMÞäšoooLš4 !!!ððð@÷îݱgÏžJºŠ¯H¥R¤¤¤ ** OŸ>…H$‚T*ÅСCѳgO„„„hÜ·Ö¬YƒK—.)ž¡QQQ˜222ÈÚÚZqïmmmI*•Ò»wïhíÚµäããCÈÅÅ…<<<èÏ?ÿä47-Z´ðC+€°pöìY¥—%Z±bãñÒíáÇ+ÕÑÕÕ¥¥K—2¶ñâ§yÕ¯__©­*s¨­[·*Õ·°° ½{÷òºaaaŒóŽåÜGrr29::*õáêêªøp-(( ¥K—2~¬ÈK›6mxͽ²øòå M:Ui¾ÆÆÆôðáà ÓÛÛ[i¼zõêÑ¥K—Êu¥q„B¡Ò±víÚ)µMOOgý-K—¥ö«W¯f¬{þüùr½F6þûßÿ’½½½Òø€"##y÷———GnnnŒ÷3<<¼®€™nݺqúM:wî¬drÇÒÓÓ£*Uª0ö/‰¨víÚœ>Ü¿|ùB€¶nÝJDD3gÎTê¯víÚäëëK Ì&šqqqJý’¿¿?9r„¾ûî;¥ó6làÔ÷–-[XŸsœú˜7oc{.ZùùùT³fM¥¶ßÿ½Êv«V­R¹^FŽ©²}§N”Ú8::rºÞû÷ï3Ž¹zõj"*2'+}ÎÞÞ^Ñþ×_%±X¬TçܹsôôéSƾýýý9ÍM‹-üК`±À¦~eS±·hÑ¢Äÿ™2îæåå1:Î …BXYYqšS”6çC"B@@€Òñ·oß²F¤aƒ- .S¬Ã‡3†R‰‰APPþûßÿ¢K—.X´h‘Êè2·nÝâ”ѹ²yôè¶oß®tüÓ§O2dˆÂ¥¼ÈÌÌÄŽ;”Ž§¤¤ÀÖÖ¶\ÇbZcLa•™rXZZrŽÈÅ´¾›7oÎX·2œÒoß¾víÚ1þÝ&OžÌÛYõÀŒs—Éd=z´Ú<åARRNŸ>Í©îÙ³gѺukܹsG£±^¼x‰DÂiInÒÓ®];$%%©ìKWW¶¶¶xøð!>Ìø|{þü9RRR@Dø믿4š³œ   ¥cŸ?FµjÕpùòe¼xñB鼯¯¯Ú¼@?~ÄÂ… YŸsoß¾å4?¶¨Y\̱Äb17n¬tüêÕ«*ÚÕ­›ˆˆ•¿#Ó»•kDF;;;Æg‰<Ü=“¹sñwqƒ PPP T'<<œuZS,-Z*ˆo+ÿü{‘«ÖK—Úµk+322¢ÜÜÜí###Û—6ã@ÖÖÖœçŤY155e¬Ï8‡-Zðvlݺ5ã½àƒT*U˜í”.b±˜ 8íÈ »wïò»²Ø²e‹ÊAMœ5Ù`Ó Œ7®ÜÆcnnÎéwa3W`Ú)f*LŽŸyyydbb¢T·^½zåz?KÇ8®¼XXXÐåË—y÷+“Éhþüù¬ýV†&¤´ —bhhHQQQ¼Çòôô¤öíÛÓèÑ£ÕŽ!‹iÉ’%*;zõêE...¬•&MšÐçÏŸ©iÓ¦4vìXïQJJ £–ÏÊÊŠòòò(77—Q# €úõë§vm^ºt‰tttÛQrr²Ú9²½gNŸ>ÍéCBBÛ3Öÿòå éëë«ý‡ Â:&“imß¾}9Í—ˆ¨AƒJíY5­?ýôS‰ù3=ËÌÌÌèÇŒíçÍ›ÇynZ´háŽVaaóæÍœ_ÌC‡UjçÎƺL*ï–-[rž×ܹsû•G)ΠAƒërµ·•“ŸŸOºººJý 4ˆW?DEæ8ÕªUãýñSºüþûï¼Ç® d27ŽuÞl/v¾äççS5ÇGá*/rrr8ý&&&&¬ŽL,SIKKclïááÁX?))©\¯UÎÞ½{M5äÅÖÖ¶Lfu\„Ý»w—ã}%33“QØ××ק;wªÝX²d /?£:(¢;?~œñXº888ÐÕ«Wû›>}:ëG°¾¾>Ý»wˆŠ¢ÙØØh,¤úúú2Ž±`ÁE¶MœÌóØÖ5ª_¿>½{÷NeûóçÏ3¶åºv^¾|ÉØÞÅÅ…±>›i2S¹~ý:cLÏNó%"8p R{333:}ú4ã>?~T:Ç©_X¸p!ãñ²˜`(‘Ý\·X#ã•®?jÔ(Æz»wïfœ‡ÖK‹– â[K@ÿVä{Ôzÿþ=cò(!êÊ”)S8ÏkÿþýŒ}”ŽÛϦ))¾{Ç•;w2ö¥iœý¼¼}8õ±lÙ2ÎóbS¹·ÏÎÎ&333¥::::Œ!ƒÕÁi¥§§óîëÁƒäää¤öž :TÉ¥_¿~J猌*$ÜlyÃz}...‡‘=wîcŸ 6¬{ÂæUúå-O0ÉÄÊ•+Õö¡N˜`ŠÄ@£5]š¼¼<1b„Êù >¼Ì!i™¨L!äèÑ£ŒctîÜY©n^^Mš4Iå=±´´¤„„Öñ^¿~MX}GrrranÕ•±cDzšoÖ­[—Q0ìÓ§朗DÄè«(oôÈ‘‡f*òÁLp5M\¿~=kLᓙ̂ÙHJJbÓÓÓ³D½ììlFŸ•^½z±®ß®]»–è#33“±žŸŸçù^¸p±&Mš(«V­clŒLf­]ºtá<7-Z´pGk‚Å×È6l¦!s$,&Ø"™ð©[\å¾{÷n|øðA©Ž»»;¯±ä0™;Y[[£zõêœû "„„„ÀÉɉ“ùTJJ ôôô0›zeggãñãÇœÇÿ 8¿üò ã¹ØØX^I»ŠãïïÏx|öìÙ¬&keAUD9]»v…©©)ëy.ëN]¶D‡‡VÛ·*>}ú„~ýú©Œ>5wî\ìÞ½›ÕĬ,,[¶ ,`>¾„ÙÛ{µ¬&XššªtŒíkÖ,"*™8«tÑ$7„T*eT¯÷ë×s¯_¿¦þýûsºÅË?þHD_¦•.{öìá}=•MAAÊ| |M¦þþûoF”ªU«ÒçÏŸ+ä¸hÕ9ן:uJmÞÞÞ*ûHMMel×»wo¯-==])‘]é¢j÷¹<‘Éd´`ÁÖy‚2iB’““û­Y³&¨lÇuJ^¦OŸNùùù%Ú>|˜öàDEÏÚnݺQ^^-]ºT¥ó?SY·nkß< tüøqÎ÷‰-ˆ„º5þôéSVþÅ‹+Õ/,,dÌ¢*2S€ &M;ß…l¦‡Å£Ú±å‘ X¾|9ãù6mÚ(LÈä‰$K—Í›7óš/“Ö‡©xyy±öÁ¦å*]êׯÏknZ´há†Va¡oß¾jLNNN*û8sæ §ߨE¥³çEö¯DD±±±Œchj+ÏöѲpáBNí;¦6êS¨Ëâ/ÀçÏŸ3ž›;w®F×TÙ¼~ýš5b•¹¹9ý÷¿ÿåÜ›9œ¯¯o…ÍŸ‹0®.‘&›™Gñ²bÅ µsaò}‹ù}“D IDATÅùe}RÛ~íÚµŒó”H$tÿþýuÓÒÒë6ŒÕÏ¥víÚ%2±M˜0qíò1ÇLHH`oêÔ©Š:-[¶T:off¦øm?}úÄ*9r„ˆˆ.^¼Èxžïß“/ SQ%œqê£zõê¼æ¦E‹nhÔ9`Eá(UñêÕ+N8u¡KÓ¬Y3¥>ä¨Ù4 Ý6Ûæ?þøCe»œœNŽÇMš4¡‹/2fšŠb§3 ]ݺuÓèš¾‰‰‰¬;›-[¶TÊ#ÃDff&1~l¨Úa.+êþÔeN&"ÖûÅ —ðÐl»¬ûöíãuM—/_V¹‹jllLqqq¼ú,/d2-\¸unš!YYYdll¬Ô—®®.¯€YYYŒaP‹—ºuë*BáúúúRÍš5Uöéîî®ä§QXXHëׯç”sbРA*׿——5lØÓõýöÛoŒc̘1ƒSû‚‚VZÇŽK×®]c¬·uëVÖy ¶mÛ–x^,Z´ˆ±Ÿìõ………ŒŽô666$•Jéýû÷ŒB‘››[‰~Ö­[Ç8GGG’J¥¬dGåLcÆŒ!gggÆë{öì:tˆ¨^½z´~ýz:qâ„âÃY&“qäaffFÛ·ogüÍ|H?üð™šÝðÐ57&= ³÷¥v±Ù’Åqaß¾}*…J@@vvv%B×fddЯ¿þJ¶u¾nðè‰É°Š.é|}n8µlA£FâôqX¼ôìÙSé÷ËÌÌ$‘HD¡¡¡C=]z)žq é[Ža±ìØÂ’ëC“(Zl~S‰„ŒŠ‡ç66"˜št¾ŽÙ±S':~ü8åææR§NX¯uÉ’%I;šÅéˆdh¦CúF_û³ªnI ,P¹iDôÕ_¡Ø߈ÐĘ„æf„âÏõžCL‚:cvuA±ç–H§è½ªÿõÙmdbJ>>>Œï•Ò¤§§ÓÈ‘# (þ,” GÀ×y "Úµk}ùò…µ¯«_TP22’¡‰PqÌØئM›FwïÞU¿´hÑ¢–ÿóHAA­^½šÄºº$Ò7$ôšLXtœ°ë !†ŠÊRBÐÂÌ„ÆEæ(¦æU•bœËyôèµëОySjðpc>Ê ¤±Bc)„†XO½ã ‡Ÿ{‘¥ EN¸LvÆR©”‚ƒƒÉÐÈĺ:Ôf”M‰éF«^þçŸÞÆR°l -y<˜ÆEt¢F½lPG@:::ôêÕ+•÷àîݻԪUÑgóVZ¢KgnëSz!ee=~oH‡þÒ£©sÄdb* ¡PH?þø£Â 55U¥³µ¼ 8Õ4@&“1ïH E-zæDB¢e_Ÿ/ ¾1„®£I(Ñ%}## ùWjD>}úÄ$@XôA¤W¯UûmÙ^ÞEö¹©Ý Ftƒì³/PíóÛÉbÉUÿª‘èСC¹Í-66–jÕ*À{uRø&Ðã é ¥•´› Ã;A£ÝAb1HG$ ÀÀ@F¡!;;[áw gaJµJNñ«¨ó‡ýÔŽRw:J]ó£©õÍj8…L›ùc8·kËše<11‘,þùÈn_´½ 辨pˆ¦•7c@'ú‚¦4éŠH"ÓŠ+hÉ’%*צ³³ó¿Nx•Éd¬&6òÒ«Wѵ^USª5o0µ8½’:eî+yoPƒMSÈ°é?þ"!5oÞ¼Ìó»~ý:}÷Ýw*ç× Aúôé…„„(žaΣU?Úöù®è&bö…PU )  Äšttt$S󢿑í÷„ÿ„~ºCX_@ØHEeÕ{‚÷_„.?tMýqÉh^šììlf-º© ‰gO'½ãáË¿Éès}Î Ãi¤)ž$ëW“¸YS@ß;;Sbb¢Bà.]„ÿl4éX…fmkD!÷)¦°Û?¿zwŠÈèHKO6§¾Sk‘˜ÄbZ¶l™R ¢¢ËóçÏ/P H~ûõWøøø(Î;¿ïÚ XÛ3¶;¨íy¹ÀÑîõCݺuqòØQØÙ=ˆÞ½{‡ݘx@]¨_CÀˆDÇ!•>Ã/¿ü‚+V(Þ?‘‘‘?~,Œ«H± Ì m{ª}WäþØú?½EÕ*V8|ø9ÌC‹-¥ù?+€¼yóí;uƳŒ(ü) hØ–_R)pp%°g|||°fÍlß¾'ND½1mÑ&ÈbC]^]f=ÉÀ¹Á[ }ùçÎœE­ZµÐ½G7Ü”„ ‘ÑØ¥¯þˆgƒ“q`æŒ9;wì,‘'ÂßßsæÌÁÄébø­‘@OOõ÷4ÉIRŒrÍGê3)¤Röz-[¶DDDküöÒÄÇÇ£[÷îEÿ™ô™¨y1(q3¢µÿAóFöˆû‹WœùÊ 22€@£~a³k)Dæü¥‚´ ¼òø…7â¯ØX´oÏáê2™ 'NÀï¿ÿÿ%„ãùßê¿Îî“E¨W¿ââÎàÅ‹èع «£IÔ/0¨ÏoÝJs¾àÑŒÍHÛ~ aaa˜8q"~úé'¬Y³~­€N€Ït'—_Nïó”…‰'"$$:::ü:­D…‘úu«ÃñÈ"Ú3çÙ`Cš›‡Ç³·àeØIaÚ´ieš_^^¦NŠ;v(ˆè‹0é`W4î¥Ù3,rÆeå|0`222иqcµBˆH$‚T*:Í\×:üžÉH¿íƒPE”ƒKÎóB’““áز% jXC?2Âʹ6TA¹¹ÈŸç‹‚»1cÆ ] ¶îW"ž®faå $˜ê[!áÜH¥R´íد¥…ÐÙ¿ "GÕ¹X”æXP€ü¥«Q°>K–,Á¢E‹°aÃüðÃ@_o`ÜZ@ÂEP(FÊ}èü:æ…Ÿ˜pæææèС?NTê 6¿þ@œ‚··7Ñ£G£÷0̵‚¡±ú·â¤=ËÇC_!í‰ññçмysžsÒ¢EËÿID*•¢cç.¸rÿ1¤+¸í¬³q4Ø2óæÍúuëPR{8S»“ÂF^ægœêººï ŽÍšáLBfÅ» –SU§xuïSì‘€eË–Á××pìØ1ôïß³~Ãw¥Dãù¾~%C¿v¹x‘BJBˆ@ ÀÏ?ÿŒÅ‹óJà¶lÙ2,òó|ö=5šàÉ ˆvE—îøãÐ!¯±"HJJB³æÍaèÚ 5ö­†@Ã_YN.^öÑ'¸ïïD“«W¯Æüù¿`w 0b°FSܺt*B»ö=p'é>>™ê yü ˆ«j¦}""<šŠ—ÁÇ1wî\¬Y³þí€ÙÍ4Ÿã“@Û?J !~~~ðóóûW­ U,^¼K–,DBØY£åùß ±dO© "Âã9[‘º!§NB=Ê47"B@@æÌ™™LVtPH t0çloÔnÉAeŵ}ÿÅöág±téׄcÇŽÅÅ‹‘]"«=ú­à/YËÉzÀŽ°5×Á›7 ¯¯¯¶Ivv67k†Wºü?öÎ;ªª#Zã¿[è]•" (‚»±bG{-Q“¨1–DMì%jì1Fcï]c½cÅ,¨ˆ¨H‘Þ.å–yÚÛ{É[á[Ë¥ž33gŸ9s÷ÌžÙûÛ†Çö#+ýnú[AÖ¸‰h–­Âßߟ“'Oòù|w:yÛEx¢ž¤3þ£[Tpô"#K˃ø8 NDîüvFl~dÍ[LÖÔ7n?ýôt ýæ¼{Ÿ'F£œÐ˜ò&àX¶ýˆVÛ°{g!8ÈèÑ£ùùçÅ´ïoɤUe‘ËßMÆÔd-_ù¿ þ…wîܧT©Rï![ JðßÃÒY¼x1£F†ëáÚA½ QÒN£§´sÓ$_¦Ôã«àìfxñÒÁº x5€î“ |øyò ›±©ê€¹kiâo>'=*…±–žeðÖ·>u •E§Ö²¿ÚL’‚£©5¯3UÇ´ õiªÎ$+5ƒN?ùÿ,‡g¢ˆšŠ™­ëÙññÌšØW’wB.ox̭ߟòüV<ªøLl+šS«gEZ|[#û~äôüûÞÀÉɉªU½pt‰Ã§¦Œ‹gt„?Õac+ïž‚fâZ)ogmÓ*5»6«yü@”(°+#£vc&âYEΣ`M|U¨ÕyïU¾|y6mÚDãÆßêÛܾ}¿ZµÐ6é F¦tbž‚…-xÖƒ>3õ ƇWáôzxxÂþöéòîÿµ~êÊÖ­[éÕ«×[Éò¿FCúõ¸‰I›ú¨ÎßDý4…­&õ|±Ÿ9ÃJå ­+ÔjB«õ$+8 ûy#±Óm|O½»á_«‡xãÅôÝ»w©Y³:}»hHJAõŒÁÓ † Ð7J„Ü­Vn†ÇOÁ@ U½`ìPhçûAÇ 7PbÓ²:ªàp2£bæéˆÓ°ö”íÓ4·½SòEÊVªEuªÆ­¦?xù>í´+àF,D©ÀXžÖ0¬*ôÉ7®¾„õ¤¿ãA«Ý—y÷/FA£}€LÆòåËùâ‹/Þ¨¯þM¨S§A·ðÙ;È5'H¹ñ¸È~B¹á1¿ÿEÊ­'hâS0®X†2=ãòmgd n·˜„ñãxîß¹ûAN ?N=HLL ãO~$£Ãrpvé}ÎýLì“TÌKá×£"Ϩ¡©’ýnpjÞ=®_Ä××—Ù³g3wî\BCC7n\ÁlìrT¨å ü¤DÒì=¡Ñ0¨õJ6ì¨û°w<¹ Cð€Ž Á¼4DÝC>¯ߎ)-°‹Á°aÃømíZd¢{ôýŒWrÇ`ÈgôìªW^èthÖl@½f#ºÇ`j‚ÂÇÃ93{W&«]g4—¯RÃßs%o¤•‰¡±GO3Ús¢iŸ²¹í-pÓ#_ GOS~»_ŸàËI|×0!ÀhÕRt×n =ÿ'ºgáÈJÙ ¨ã‡áä»»æÖM5·/ò}Í#35E{ô$¸ù!ÜüŠ×ÝBÀ±pl%D=…¸T…Nc¡V;F6²:B xñ@`8ÍW ;\G26â  Ð&ûïÃÈd×q¬h@ýÖ&žSùT•­ßz& iOùJy›eSDphc¯ÂÅÓßﻡ¦›÷S>nßM›6Ù?%(A â?g€ÄÆÆâT¾<™þƒ¡f8´D2&J9@¦ þÚ ÷.@ïÐ]:-à·a•.>`nÑ¡’Q’ž áôzäæQïמ<Ýsû®˜8X¡Ue¶û&ÑB¨1£Õ&´- ÏÝ…'¹9åš´,jÏïŒ÷hiòÁÊ \úb+žÍËœŒ_· 8úÚ©âìÒ`2SÕŒ½€ƒ· ©jF[n¡b}{|Ú;aaoLè_1\Þ‚{ã2Œ:Ýu¦–ŸüþÀÝ®*~5k±zõê|$¸sKÇÇÝ”xûʉŽ¬^ª&-Upô² ^ÞÒ±ô¸a™¤§ ªøȱ²‘ñ4TǦUÒR§Mqó³t^ÓÆfЫW/–-[†µµõ[ŸFMšréi,Úr’QÑ°Tð…øH8¼Têóy—¡¼·TaÛTØ=*VU2D>‚½úG1²ù=±¹šÏŸa\ŒÿõÿV¬XÁWC‡bÒĬaXtk‰±o%4‘±$,Ý.UE…Ë0ò.ˆ·p3±S~C—–ŽýüQØŽþ€”çÿd‡" àähÑ¢Ï/0{¼–ß6AƒZàPTé°û\¸3Æ„o¤ò?̆9K¡}KhßÒ3`ýN¸}ö¬†Nm¡Ï0ØyD‰e#¬ycäP ­*“—»/’xá.®3>¥â„Dm=[@¦äkxþóÜç}†Ë˜N¨B"¸RuÕ¤ª¡AYp0•v‡Â…H˜Q&Ô”êO½³o@5[HVã$оbcŒþ ~½¯$üEvvï³£ú¿ÿþ›jÕªáñóL*9¾ä V *ÙÏšÔtÎYvǪ¾¥Û×ÆÐޚĿîµáÖ«Róô,ÒŸDqÍçk¾1’Y³f}9ïܹCšÕñíèŒÐ B/żV‡ìwóîP³[<ýËy7‘óËàÙ¼_m™«ÃÜJ{sîìyöìÙC×®]yùò%vvvœ>}šÁƒ* ahÝ—AàV¨Ø,@­‚[»!ô´›­²u|b8Ì­¦6Ðxd¦Àéù`SÆ\•Ç'BöÇß¿‡‡G±ßH9¨?âi8Šzµ‘•+‹P©Ðì;ˆîâe 'áØQ¹u2†|fç”}z ¨[‘š†îï ”=º¢lÖ]ØST~Q¿­9Yé‚Ê ¬(å`D¦JËÅÝ/¹{!‘Og¸Òc‚ä"¶hÀ=.ìˆfÄšÊz²™Y)© D­ûˆ½‹^ oÑÝí ”?F^Õú·Õˆ´4LÎEQÅ õŽ=ÞUwã&ê_WbøãTtOÂÐlÜU›Â“[ÅëîM?Àž9P«=Ôn/ͱ§ÖCØm·êw‚½óaýw€àƒdD¤W,`ß0ÚÕçì2QÙu]³ÿ¿ëÒiÊhÙÍ‚J¾ÆÄFjر4UªŽ —+àæ-¹êMÁñÉL^SNï½Í­ä4 ŒõC™Ü?’ .ðÑGoëR‚”ø óæÍcüÄIèÖ„ƒe!.:ŒöƒÔxXý´è†߀1µ ëd§WãÑ­õ—÷.PLètô›Mf¼ŠnOÔ»—þ2…½žSñþ¶7'Ô3@tZ¿»MÄÅÛ”¯öûëùú¾ If¦Ï~jtuaà¦ÆhÕ:žÆáZO1uxÆmM¹Éˆ­ðòwàúŽ'¬éy SUÓæ%5jËQ*óvÌCCt4ñQÑ¡«’e›Š^¬ÿ}CK‹Z錙dÀ¸iFdf ¼Ë¦Q¯nkŽ=Zt¿½AAAøúúÂØ`ë•jƒ"ŸkRdŒð]aÔ&éZâK0³#X1Ž,Ó?xñ†z²qãFúöíûN²}(!ðöõá…»=6cûcR»ŠžûUVÈsB}ºcÑÕÇM3õêj^Æóس¶ßö#fÒ2=àYí¾|dWž£‡+Çýû÷©R¥ [—A¯Žïëtà×âàé5éšcMpv€Ë‡òÊ¥¤J×›7„}ë ô)¸7¯5ßà0°eÞ{ët\õ‰&>…†O Æ äÊ5h ëNòÑóu9H»›†-'sãÂ{é0Ì窭à·[r©zšÝ /ÓÁÊŒ0ü,»«—Ž›åL›9‹qãÆÛWÿ&|ùå—l:¸‡ºa«‘tÛ{µŸuj )!XÕóÒ+÷dÆvB§l¡Æ‰”ò¯ÎÑ+IÝr‘Èð½eœD!ظq#ýû÷gÚÃΤÆfàR»ôkuXR¤ŠÊï¢N7ú¯Ï[Äýõ>;¿¾ÂWüñiïLàÎ'¬îqŽ   „øúúòçŸæÆ?¥¥¥1~üx–þºZŒ…ö…T:Ì÷U$®^½Êý;w±úª+¦õ} ,P Ý1ªâJVpXº/Ç/ÁÈ«–í‚Ár IDAT} _¤XíÊñ£Gyþüy±r¬Y³»ÒJ:±Þ‘ËÁ©äû˜ƒÝ+æ`f ¦Ùnñ®.Ъ™ŒèÕGôÊÉärŒJ#+dÑœ]¦š—{.bÓÔ'×øpüª1©:?{EF8™ƒA>-fo"¯ƒ­1ôtÓ±zåo¯/ø/CFF7o¦ÌàV…P°ŸåÊÆ€]G‰5--8ú8!6Ž|YW­^‰W ì+YâZß¾@°ô«:,ôR B+¨ÕS?È;çÿ×·?$f]Öœ5kÖàîîŽL&ãáǹåÍĮ̀U« ƒúC N.—ŒŒü:þöðnŸg|xøƒÜÚ)ýßÀmÝÏY½v¦Ð¦ÓÓÓÙ¸y3²Ïú×%“Ë‘9:èý°Ô¿,G^Ûeû¶‘–VhÛƒ’“Á•ƒú÷r¹ŒÒNÆ( ^q½ÒâX•\¸¬æ64ím§NUîæŠÜË݃G…Ö™™höBѸ!º+×!9ZñæºÛЬ^™·L,ÀØLr¿I· y,…’¿/.ŽHƇ@:í( >(†Þœ¬wÕÙÝ×*F„ë×Ùý˜š\8ÓŠ\.£ËW–ìÙ³‡øøø"žY‚”àUü§ ØØXB=”Ž|ó#S%Ñ·F>†ý‹àæ1)ˆîU$ÇI;î!×aÉ@)ÄÔ'[JùæM\U±©$?Žáî¢SD»GÕ±-õšŠ¹ÆãW¨³¸[‘ò:TE“¡åùÍ8½ëB’£Ó‹¥²LŽJÈ-§4T`QÆwO9. ÿôBb¢¶¥ ÆÄÇ b^ê¸u]ˈØ•‘Ñk`ÞDÓ2@IRR*÷îÝ{­\EáÜŸÑTo Ê"Œ?! 1ºhãñ5Ðùpõê•w6Ž>.^¼ˆÂÄ³æµ ½/„@‡¢´¾ûZúÕ;$müƒ2‹¿-²mó€F!¸téÒÈqŽV5äßìV¥Cl<<ƒE+áØY)¾#ã‡K×–®…°çÃ~NA¾”W®ƒ¿ ñZê„T²b“P=ŽäÙ¢}Ä»ËØ.EÊ{ø:š$•^œ€yUÌJq1 TjˆM—Êý ÇžÃØw   (!¡aDGG¿}å·oß&=-ÒíõÇŽV•ñVý %Qf––b0Ì<°pwäâÅ‹ï-§Z­æÚÕkøv(:¨ùU¦É”w&úÖ£¡‰¤_žÝt ÒPW«2\¸xÊ—/σôê\¼x¥£ØVÈ»˜¥‚ÔXˆ} gAð1ðÏÖñ‰/ 5Ê×*(hùÚ~3ïÿUHIJäþýû…¾×íÛ·ÉP©P´m•÷®*"6]è²~ù íÉ3Ž.ÝKNAxEÍêdN™IZ9WÒÊT$­jm4¿ï×k[îY Wgî_L$C¥%)6‹ÈÇ*ö-zÆcqt«˜ž©ÒÒÝò=¬ÏÑËöˇ? #M]'ÀÍÓè"£ô® !/cÙ<¯=v’’QöèŠöòU)²¬ká… ÓÝ]ÆKsíK!:L2N~&‚´ÿ&¯\­Ð¥1ùÒ€ÚìLààœfgÿùéd$? Ñj+rãBzwŽ‹Ö`]Z f¨t4²|@ë‡4³}ÈœáQ¤§éoÐ5 0G­Öpýúõ"z«%(Á«ø÷òNþ/ 00Pú‡»Ÿþ5£áøJéß % ^­ Ù=ûÌ4Ù»#e\áÇs°â+J×rÖ+vmôn¬ü¹RNÝ%ÝñÒ(÷¾‚+_ï bÏZØÕ­Hj˜¾‘«ÊeQšðôz•ç^ÝJR„ŠgÖ|íûŸ„‰•!Þmó¨/µYüšmwîÞ¢!*BðýÌ‚CÃ×1¬ìׯà*ãÀ9Ê9æµUµ†™Lêç·åFÏÊÊânÐßп_Ñ…Îmø)˜ñmáæ‡:3“»wïþ£”‰˜Tó(’õ*yËa41ØÍÌ[ù !ˆúz.–=[cRׇ¬°Â”ö¥0v*K`` Ý»w/RFÃíÛAto£}ôT)À@©„%3`Hž‡ƒzƒB_Œƒ“¤k¥KÁ©P7ßPôóZÇýAKˆÙ+C2¥%CpòÊCó!jËYäƆØw-H'lZÇ“«W.3ú’`e¶}«”Ã’†0¤J‘M ¿ì×ÀÀ@)WÃÿ"7PbîSAïú£Ñkx±Rr{|“~x6wJ+SlÛæ-ºMý\¹øþ ¨»wï’™™Ey¿¢¯¯ê°2ž“×ã?£ñh’§ëB.Hbâ Uîµò~¶ìÛ~“¬¬,<<<ôN@._½ŽÆñ¿w4\ÊÖñr%tY ²u|rv°¶¥¾Ÿî5U¿Øö•nNÅö||<é陸½Âä9j0tïÑ°åw>Ar»êŸmËì:CÆB·öе=$§À¢UÐés¸°Ü*Hårþ¶iî‹Ó°2#â‰Þr–‡ÃCabD¹þþdÒ$«ˆû㶵QZLhìVŽgg¬l¤¡»D¤Á–G0üO0QBÿ7K1“‹ŠC軌Õ ÏŸ?ÇÌÉ®@BGçQŸ`ßý£7êg€°Y;‰?u¯åCõúÚÄ­O/_.´ÎÛÊ PÚ­p æÂtXùš¶T¨kÇñŸî`íh†GÓ²DÞOdÛW—QÈõÜPK»Y•¥&66OOOÎœ9£×~xx8Ô{å¨é(¨Ñ’" p ì&P§?¨³w Ëb}ʬN— c ”–öEŽ›çÏŸcèäˆ,ßÑ¢áð/Qvþ…fÇ2Gc úô„ÔlÝŸ€ñÙ£(üjH¢´Få]‹¬Ÿê ²Šˆ <ÅÈÕ|ÔÝžøˆLÎn‰æ·á12Qàß_2¢úÏÒŸ“u/ƒƒ‡)›&<æâî—4îQ;g#äJ9âEÞ¦†îÁ#2GC^¯Ê>…Ó ‹ä´GO hÓ™¥ºðð,$˜ ŠÖÝwÁ²!R°zƒ®‰ÈE0»̾ %1É%ËÂ^rñ$·«?‚Ìs6ºrܦґÓsŒ1O¤Sóè ¥HKÑ¡˜˜ÉxœÉOâ¨ÖÀ„ýóh­‡ÏÒgþjÕÝ’ò†ü:!†“»ShÝCãr¹ 'W£7r-A J á?å‚¥Ñh¤UÇ«4¥ŽžRÐ[ÓOaʨæ«GJ™Yó£j¨ÙZ2Xf‡Ä—ˆ¤8d ýn´ò,K¹æž¸}Z—–G†SÎß“«#w£ÍP“•œÎï÷Sul+Ì‹g‰’+h5O@RT:¿œÄÔƈÁ»›I·z}ÇLºIÃA4úBe&t¢€{.Hùdff(Ú´Dwý&B—ÏÝG©D«8yšQ­y)š}ZŽiGªSÍ߆•#’™^xœ@ÇQÎÈä2nŸÊ‹SÉdÈ•2„Fª§‹Ž&½KodÖÖoYSä£Ù231èÑ%çÅßNwgeÀoCÁ¯”ï©~gð?ž•¼ 6OÐoG¡t@*°‰Š·;#_ŽQnCžñ`x ¹gåçÛÉC‚Ø( ß<ÇÂFÁÜÝNÅÒ˜÷U ¹®ÒÕQ(ÿùù¥%øÿ„ÿ”bii)ù¢ª’__°~P%Iy?ŠBYWp­5Y ª¢Ë.]j•”Nâý(îÎ?‰N­¥B÷š¤†Å‘GZ¸ä¯"5,:oÉLPala@zRKÛž #9‹¯¶Äªláɰ`C¿ ø´w¢×o³»+$%èŸ%' z¶Í %Y°ã¨ eÊ?,*¸Ê©Z]ÎÍ«y“£N'HNÔ¾S.Ü:i ú7Ò’`Z[é›M9 6e V~¤&è?炵¥ºýñ§MJáYÛ¯Ñ%§â|t)ʲy~Òñó7Zƒe÷Vd…E&\rKÑÆ'‘È×’Rì;æÜO(Ho¯‡.”,Åz‡@\|ÜZ¿Œ54¬ ¯å!»]……þI†}—†h’T¨¼(ð¬¨-gQZ›ˆoÈ&!‹BBƒº¸BRÇÃÃÌÌLž=Ëc(0·°Õ+zäUTëIœçz•ãŠ•É‘`j›°®Ó¡S%9n,,,Ð%&¾ö);¶‡¤dtB•“ô™Ì¾ ‰ˆÌÎÔjHË7¿$&aRHÖî†]ìQ%ixñ è¹ÈÐXE)%©ñyú"C¥E“¡Efa†HJ&£cODJ &ûv /S¦È¶4;ö€µUn¬‹ÌÜüítwx0¤ÄAõë˜Û@农/)wΖ›‘â=>ÌóUÌùf…Hk†d¼äß,KG&ZðuÛg¤&ëXzÔ™Òe‹w 12–cYJAR¼¾±—œ û ¿Ÿ”à¿‚ÿ”’ë³ûäÖë feŸ|È‹éžÌt04%öfÁU~hÓ%…/“ËH{ž@f‚Š}Þ3Øí:‰Ý®“8ÒX¢tü{ÖQv»N"ñ¾4fĤö"‰2^V,ëpŠ˜d†jAY¯Â3?¹ÊN§q©SšA;›šáÕÐÂ[yFCF† O‡tž„èØrÈ„J^o>$ÒÓ%×Ü„=¤¦j%*Ý·„ e 4ß·ÉÊ€™$ lj‡ÀéuÔŠÅ T $}Ù>$|}|ÐÜÌóY×edò¼ÃHÔ!Ïq>ô3F^ú,@êçÑh’ õîÊc×d¯Qߪ ɱj,íò\rŸÜN•êUªDz·>èBŸ`²{ rÏJE5ƒ.*í¹?Q~ÒY6›—Ü·Šþ¼ZœîÖfAºBŠV­¯P¢B¥¼,ÜCŠõè ¼JDbd¤"q ’·y~»(œÝŒø¶s8ÏCÔü|È™Š^oF?–¢%1V‹]ž!˜’¤åùcÕÿ+}R‚üÓøO ^^^™˜Àƒl– ¤˜‚…4j8³QÊÞêì-)ÂÔBvÓ^…gwÀ¥*‰Aá¨Ó2Ɉ)¨ütj-7^ÁÈÖ ëªTÑŒæû¾ÐûÓ`…”?¤ÒÀú4ß÷æ¤àÍ——¤„Z—7„v%†Á»šQ±ná‰Ó"ï'òkÀIJ»Z0ìP Šà"Õf n_סV ´ZÁàܸ¢cÍ.cüê¬£Õ  îæݸª%øŽŽzòê\»$M5k¾>8¾(Ô­å‡üᥜüR2Âq»ôƒß.QÖÉùO<çççGúóHÔ/^"´Z^ôOÆ•;8îš‹IÝ‚A­¥FôÄißB½?eWHî V?ÆißB *8q3]–?¿×ƒæÊQKÒ·‹)d=«VÃÆ]`kÞžPÅŒŒ`»>1áRÂÂUó®úLËYbT®Tî5ZCÔÆÓØZ`î­Ÿå=zûy¢H÷+­*ƒäÛOrÇseÔÂƇ­nv.»7Æ¥h062¤J•wˆ`ÿ‡ó]“.IT¦Y1° ëç´ûϹ0 ײT?4¥@ €6#‹”›¡o4vŠƒ¥JÛzIÒ¯:­ŽÕ=Ϋà ¼‹NðûØëš)iôež+é“K±Ôö“¨Ë122Ò3@j×®…ìÙ5i!›ZˆŽ×ª¥œ¦¶P.;)^µ.p÷”0OAÌ#¨ž©0LÒOE鸜þÓ]¹†ˆ‰-p_¨Õh¶îÛR¹êÊnÏÃÑœ>—W.6ÍGP4Éˉ"22ÐÜ ÂÝOÿôE£Öqzc¶”÷6G©C•RÐhû i£Â¯M9@ð¥$P*PÏ_ŒîÚ Œ7¯AQûõc@³k/A¼lîŠÕàñuPg½™îv®"Qì^x%oFl8ܽ®yîhÜÏ9 ‰Cr»*Š]Í)Szh¾k* ÐßØ‘+ÂÈÌÐrçJsw9âS· Q—•©#-¥ ´j†ô]´É3 ï\‘ Úñû)A þ+øO¡+•J:~ò {N¯CÓi¬—žUK™Ð£¤ ôˆ‡0bäwššŸ;C£ž’Ò42ƒ§Apjt¤¨gçn‰k=;¢&ÿLÚñ:¼OËÕ‹ZŽÔÒºƒ‚øXÁ®ÍúµÝ>5 5ª;§Ñ±§Ï*rLÍdÜÒ±mû²2¾ù>o1³mà£ê¿ó"¿KçÎèß"Áá_áÚA¨ÝA¢H>»Y¿pÓœÌsOálvb«lŸ?ì+Hå²2PžßB}ÞI®‰Ö­[cdbBÒºhbI=xóÑÆ&´ù½²VŸ`\à ãú»‡9,XFÞnX|Ü$÷z⺔qp N‚ye^EçÎéÓgÁ!ðý,HIƒÆu¥LèQ/¥ ô‡¡°n‘ô303•‚Ôç,ÿîЩTgÙÈÌ‚ï¿Î–- Öl™½O¦oÃСYQ Dm9‹êaUÖ}ƒìù¨-g1r´Å¦iA zÛy´YZv?†”,p0ƒ(•„þ0 Ö5•Ø¹ž¦À¦ìµèõìµç7$/Ž R숰>ÆÀ Êç!œœœ¨Y»ÏÖž L·ò š”tlW-²Ÿ5)*n¶žŒ&1—±]ˆ9xU¯MS÷rXÕóâåÎ ¨ÓÒéرˆ@â·D/o.o¸JÇ9~ìHÐÁç¯Õa;¿¹‚&S‡S5´j׶>áéõXúoø'ɵ&úa.D1qƒ”|N¡Pàîî®GÅÛ©S'¦N Aàúfi÷Ü­1X9@r”„þò!ô^—·Ðò¸¹ ~iM¾É΄>|¡îÀܶW×R¯Qcl‹ §uvv¦z­ZÜݸUb‰JMEÑ°¾” =ú%ê»c´bIîïÀ`Ì7höì'£Ï@ ¿þJbÁZ³´: §æÅBhöìC¤grio Yé:J9’•ÅÙ-QDQÊ2³=Vi7©6± !ë.‘ô‚̸4 ,±«[‘*£ýqð/Ú}(5,ŽÝ®“ô2¡'?Ža¯ÇìÜ-ˆ I‘VM¯B&c™¶?qa©LrÍð+¤\½îô[ûÛ‡]&hG4•½YÚƒp.WþŠòc:QiÞgäZ-W«[Ô3ì!(^Êdniuía´/øçÛ=Íäv¡ÔFv÷6u€ÓÃÑgÐö0œ9s†¦M›ÛWÿ&¬[·ŽÏ>ûŒz÷–“ú÷"Öœ 5(¬È~N‹æ/×AEê†rü©¼z7êŒÁ¯” 'Ÿø röíÛ—Í›7ÓwmC®l|Ì£óѯÕa—6„pzñ=bB’‘ÉeT¬kG› ¾z´¼Û‡_&h{4/Â#06–¸;wîLjj*ÇÏ-W¿áG\–¡ûh\^ ‘AÆ–àRš–Ñó#êžD×ú§ÄˆåHîY å™W“íÛ·Ó£G"ß}íÚµ|>h†³¦¡=qÝÝ{ˆø°´@Q«&_…²™~âZ]ØS2˜ŠöìyPkPÔ«áôIÒÉ9Ô›ãÌSJ•1$,(•”85¦–J<êZÒqtyªûK'ŽiIV|ý€àËIÄGd¡Ó *™Ò´O:}›G{ëd<[ÞDîãîîý"¿yJ^®ÝÃT5`0b(F³¦êUµú]ŒNb|Ý pp‰DJCð¨Ý'I„/OnÃÈQ”Ÿe~}—ž %.tZ åÉÁï¡Õˆ"E¼®­LJ’–¹_Gt9˜5:-”¯dHÛ>–ôýÖ6·#Ÿ©ùÄ-”yó0räÈ"d,A Jð*þsˆ‚ÆM›qùá34‹ÿóâ+‡} `Ý·Ôš×™ªß¶,¾|q2êtk¾˜˜K¡L{Ð Û ï/cèå4<ÌìÙs¨Q£­ZµbÑ*#>ôþ;À m}5™i.ܺuCCÃâ+9sæðÄ ˆŸ.I“Ñû"ñ% õ䣚¾\8®øòÿxðà¾ÕªaþuÊÌû0VD¯ïáø5Þ¿½ý°eóçÏgìØïøs4(<öû­^ UiAè-…Ƽ-ž-ÜË£1k˜SÆÕ(¾|qHSƒï%UjsþÏ‹Å2ÞüÛžžNŸª$—5¡ú¹YN“ÞÏ9ÈÃ+8{ö¬´9óÐ¥K.þu•&…Iw;`a_8iÆÛàÉ•æ78̬Y³7n\îõï¿ÿžmÛ¶–{íرc´iÓz®†úŸ¿÷Êï›O IDAT³ÑªQ,®‡›iA·o¾VÇ¥§§SÙLJûÒÛÿA¾QÖ²Ud}÷}g¸ÒcbÅâ+ƒ •–á>Wˆy Fa›¼ÿ÷Éœò#êù‹aèŠÂóh½-´ø®„vúAçïŠP`#;˜³ø€s±¥‹ƒ‚í^ðäoSîÝ{€•UѱM%(A ôñŸŠ‰vpýÚ5(“£aÕ×…ïÒ¼ â^ <°— ÜšxØÀ§ï-ãÝ…§ˆ:÷ScvŽ¸ŠV£+¾ÒkŸÉ¦þQÓ¯&£G¦eË–|þùgL­ááý÷k`îÔ,þ¾¡fýúÍïe||ûí·T«^ÅÏý ½yh5È–~ŽR“ÁŸÎ3hÐ ’’Š¡~ú?€§§'3gÌ ~Á&ÒN^yïö’6ýAÒöc,_ºô€Q£FQ§N-ŒR_|ù×A«…Acd¨µfdƤðtÞïï× ró1O&lÆÁÁ·!<õýÚ¾ù "3¬]¿áÿñ`bbÂÆuëIøëa³w_¡¤üý„Ðñ6lØ3>^¼xA³¦Í0Flþü¯÷×a ’«Q³cƌѻçááÁ³gÏHOÏ£Moݺ5D±´Ätõ¾82ñâ6›7®/VÇ™˜˜°qíZÔ—® ^°ä½­ º‹fò *T¬À¡_"xù,ã½ÚB°fô#âÃ5¡F=yF±ÌjÅA÷$ ±n#+º¢X7žÝ{¯öØ1Ù“›)À1ôitß©(•qvvá¯#iü}9½ø*Å`ç¯ \<šÂÊ•kJŒ”à-ñŸ3@ÜÜÜX±|9œZ/åûнãä÷å”Ø™(8yòÕ«UçTë_‰»ñ¬øºEàÁoç¹þÝïŒ?žm[·s÷ð 6¸øÎxZ|&¿¶=EVlÞ´evî âââA×j¿ûâà—¹Y,úQÍ?Îz£Øƒâ T*Ù¶e3æi1(¦·”w\kÔÈ~€ìÆöíÙͪU«Ø¹s'>>>œ8ñaÜLÞ£G¦UëÖDtCÚÙwÏ>¼û$QŸM£ÿ€ôìYxÒ°¢ P(ظq ÑJZôà> ‡NÊؼy?üðǯçù¯‡Þ­A åv(·ž‚‹³3 $iä49ðîFˆðí%Xs–-ÿJ•Šfùù·£Q£FLž<™ÐI›yöóþâ+Ô 0‚ZMÁÛÓ‹9sæ|@ ¥d€îîîlÚ¸™»G"ØØÿO´ê÷ÐamN‘ [6oÍÕa9ððð@AHHˆÞõÅ‹á^ÑåòýJõâprœ˜ÍìY³¨]ûÍŽ 7nÌäÉ“Éš6‹¬e«ÞùÑÚ;÷PÜ ŸÊ•9yâ$–¦vLò¿ýÎFˆ‚õãsdÅ ~ýu?/^ŒzÙ*²¦Ï~g#Döuû®8Ù”âÔ©“x¸UD9µ<¿ÿNí°w>ì˜Á3g²aÃ:d²;HÉßÕIE¡ØŒ!'O§^½ú|Ó.‚;WßÝÙ¿.‘¹#¢9r$ïÜN Jð_ÅÒèׯË—/GöÇ/ȧ·˜·4.ïC9Æ;‘ƹӧpwwçØ‘£Tqõäh£…ÜÿåŒ~©b•¨ââg›¸ôÕ6¾ùæfÍšE@@[·nåÆŽ05>FÔƒ·Û½p:’™Õö¬âä‰SzñVVVœ8qÛRî´©›Éֵ귚€âbƒ{f2}\“&MÒs‰x_xyyqúä ,b£üÖþ>ýv „£˜ÐÅ_;Ù¾m 4ˆ   <<ê›÷À¦M›éС3gÎdÔ¨Q<þ÷,BøæVƒÐé_ö7ŽÃÖÄ‚ÇBððð K£#ZmDµ]ð{hñíäÇóTh{Dοá×_eÀ€o×À¿S¦Laìر<¹Š{} Žó±,„àÅŠ#Üh0–Jåœ9qì8ææ½E‚F£!** '''ÚµkǶmÛ¸±ó #*øíuØO~‡IÕpâøÉBc¸ £âIÇ9y·rÖ(×…+ëÞîÄ;5Ùúîpð{¦L™Âwß}÷V²O:•ï¾ûŽ¬ï~ sÐP)ä !„@½z=YþTqtâä±c¸¹¹qæô9 ´¥åÈŸ»¢‹o(â^d0½}{æ>åçŸfРA 6Œ¹s碞»ˆ¬ýÐE½y›BÔ;'«QKœ 9wê+VäÌɸ—µE1®œXóv}ž‹l~/Xÿ'NdüøñôèуիW#“Ý@.ß„çñ6xˆR¹ [[8wî :tïÊ5Üä9[Å¡Õ¾¹Œ©ÉZ~ü2ŠiŸE2dÈ,X°à-å)A J ˜:uêÔZˆ µjÕ¢^½zœØ¶–ô½‹ªd(ëæEd(×éàÖ dkFÂŽ´kåÏàââHGï½{õ&!.ž½Ó×y,¥…–öÈ É` ›Êý¥gù«ßFRoG±ì×e|ÿý÷¹‹IoooZ´hÁ¾­ðÇÜk¤Ä¤c[ÑóÒÆ…¶'„àñŸ/Ù;.ßÇ^ÇÚÌUšŠ¯¾úª€{Ž¹¹9}ú|ʳg‘ÌžÈùSZ¬Kɨè.+4‡HÓW.V3¼Ÿ†°3Ö¬YLj#>¸;K¹råèѽ×.œåÙúéÈ^ƒuY‰ ¨g…ÃŽÈ— ¦‚¥÷ï§uë¼ÌyÖÖÖôíÛ—²e˲`Á6lØ€®®®Tö7…¡¡!={ô@«VsâÇŸIÛ{LŒ0ôtAVD> mR ‰«÷ñ²ßd2Ï2÷§Ÿ˜;w.Šwð3Ÿ>}:Ó¦McàÀ\¿~»2X²*‘{e”µ‡òŽEwõƒøñgô­Ξäd)7B“&MÉd´jÕ ö/^Cøªch5ZL=Q˜>nuYj^îù‹‡ƒ—¾ò(•ÜÜyò8”Ö­[sñâEzõêEлؕsbå$®½;c¨hY´Œa)0÷ 8''ÉÈŽ»v¿3A¿ 2™Œ-ZàêêÊþ%ky¾âZµúõ}¬Ö³÷/~ñ+áË3hÀ@¶oÛŽÍ[rƒÈÈH,XÀ—_~‰‡‡ÞÞÞ´lÙ’ýÛsð§«o¦Ã.¾dïXI‡Õ®^—?.’2ÙÄÄ„%K–àééI£Fú¬ |Ú§7/ž…q{ótäÏ"Œ­¡´{Ñyž’"áÜb[ûa™ò„õëÖ1|øð·Öqù¿Ññ_~%sÝFt52w7df…%Ì“hzµþ@3| Y«×3xÀ¶oÛ–ûlllèÝ«÷î<`Õôó<¸’‚eiʺš)ßËg’Ñ1à™ñ¦l߶ƒ¾}ûæÞoØ°!Õ«WçøÊÕ¤-_…6M…ÜÝ™eáÉ…V‹öø)4£Ç“µx)Ý>éÈÞ={(WNÊÕbnnNß>}ˆ Æ­•Ó‘ß9ƒ0µ‡JE÷yB\Œòç~XÄ„°ní½y¥F4jÔˆ“'G¥:‡éHYÏ‹Š]@2Ùqà -[6ãðáC¹ºÞØؘ^½z“œœÊâ駹x8spñ0D¡,¼“âµìZ–À”þ/¹UÇÏ?/aÊ”)È?@¬[ JðŸ„(HLLcÆŒæ–V™L(ݪ Z~.èÿ“àóE‚^Sõ: eiˆÊU}ÄÖ­[…N§+²Í3gΈÆM›@˜”2N­½…Ï÷­Eí…]E­y…×ð¦¢l}7¡04†FF¢_ÿþâÙ³gE¶—––&&Mš$JÛÙ @”õ²õú»‹Ž³ýD×EuD‡é5DÍ®.¢´‹•„{%7±råJ‘’’"|||„§§§HNN.´í””aee%Ê”)-QÚÎ@´ 0£'ˆ ÅÔy†bàWJá[S.”J™055ƒQQQïÝ÷ÅA«ÕŠ+VˆŠî• ”e\ » zO—¾M¿Ù‚æý…ÂÅ[ÂÂÚFLžžÐC”j]Se÷±}Ù2âĉÿkò\¾|YâæÍ›z×U*•˜˜Ž JCCB! jÕC>†?N†sfƒ±£„A+a`[J¢A£F¯ýF:NìܹST«î#a[ÎTÔûÄNôžRQ ^TI œë.Zv•jZ ¹\&,,ÍÅ7ß|#âãã‹l366V >\˜˜› ™B! kTÊÏú çßf ƒï¿Êv­…A{ˆj~~bïÞ½¯}ï#GŽˆºõH}nUZÈjµt› øl¡`Àáêê*ìììÄøñãsCagg'Î;'”J¥˜9s¦ÐétbóæÍ5ªWf&FB© +s³ÿaï¼£¢ºÖ6þÌ0C—jA!"öÆ Á€QP°·ÄÞÀ‚5Œ"ÖÄŽ%Æ؃Š"ˆAìTšÒ;ó~ðÍ “™¡“ÀÍù­u×]ÙgŸ½÷9Œ3ûÙo#¤¢¢BÊÊÊ„Ò£P ¯Ñ;k¤¥¥ÑîÝ»iܸqÔÚÔ„yÇM[4'9lllhþüù¤  P¥ïœªröìY sŽüü|:}ú4MŸ>ºvïB|ñåy¤ÝX‹úXõ–úV&L KKËJõ}Ç}ogOªêšŽ©4R'cÓ¶4~üxÚ»w/¥§§WzîªàèèHººº4vìXÒjÖŒ8<)©ªRÓ-È~Ð Zµj={ö¬Òã …Bº{÷.-]º”llûSÓfIEE‰ÔÔQûmiòäÉtèС*°dff’Mš4‰LÛ·'UuuRRQ¡Æ::d3`yxxн{÷ªôÜ?¦+V½=5ÑiNJ*ª¤ª¦N­MÍhüøñ´gÏž*ý®äææÒï¿ÿNÓ¦M£:‘ªª)**“¦¦6YYõ£E‹Ñ­[·ªô[ýúõkZ»v- :„tõtHUU™ÔÔT¨µ±!¹¸¸ÐŽ;())©JÏÍÂÂ"V€40ÂÃà ݹs‡¶nÝJªªªDDtñâE@ Rïóõõ%´k×.±öÔÔTRSS£ùóçK½oÓ¦M¤¡QzšÔ±cG6lXí>P-2þ|ÒÑÑ¡âââ*ÝMVVV€æÎKÙÙÙu´Âooo@«W¯¦©S§’¢¢"=zôHjßǺzõ* âóù´{÷nºrå  ‡2ý¿~ýJ†††ÔµkWºrå õíÛ—ùÜøùù•»¡¼zõ*©©©QûöíÉÛÛ›8Íœ9“–-[F\.—nÞ¼IC‡¥–-[2Ö­Ý»wŸÏ§ììl3f õë׈ˆš5kF–––¤©©YjÑkܘ†^‹o±áÑ·o_rrr¢””RPP //¯:›kçΤ  PéÍŸ……M™2¥Fszzz’¶¶v•ïûøñ# 3gÎÔhþÊ››K***äééIDD .$33³:Ÿ—………¥>Â:/60 ¥¥…=z 99™‰ëˆ)ÂÙÙóçÏÇÂ… ùW5äM›6A(bùòåRïSRRB~~>”••Ñ¼ys„……AXݬauŒ««+¾|ù‚Û·«Imdd„ìܹD§NVG«ü÷ؾ};/^Œ+V@OO‡ÂÞ½{Ñ¥‹ô¯_—f±eŒâñxhݺ5^¿~ Y³føý÷ß™þpssÓ'O0hÐ dggãÂ… xüø1œœœ¤úJ~ýõW <}úôÁܹs±dÉüðÃ0`6n܈7¢¤¤—/_†··7”þ¿fApp0,,, ¢¢‚¬¬,&ÚÔÔ<ß¾}ŸÏ‡P(Ä… ˜çù/bnnŽÈÈH4nÜ£F¾}ûPRRRñÕ !!ººº•Ž™ ¢ǵiÓiiiHKK«Ò}zzzh×®k4e¸zõ*rrràì\ZY<557®óyYXXXê#¬i`ÀÞÞrrrbÄÐÐrrrxóFvºÉÍ›7£[·npvvFZZð믿ÂÍÍMfýEEE U«VPTTÄ·oßðêU-äx¯ pêÔ©*ßËår1oÞ<<}ú:::èÛ·/.\ˆÜÜÜ:Xé?èï¼téR >sçÎÅôéÓËͱüöfffˆŠŠÇÃرcqêÔ)âÒ¥K˜;w. K ¥MŸ>2ƒ4‹ŠŠ0kÖ,Ì›7 .„‹‹ fÎœ‰)S¦ÀÍÍ “&M‚££#.\ˆ… ¢OŸ>prr…Bܺu ÖÖÖ€ììl4jÔˆYãׯ_Áãñ`llŒ¯_¿BGG›7o®WÙ ˆGRRfÏžØØX\»v­NæJLL„žž^ÅÿŸÚ €ì˜ò°³³C```kaT„ŸŸ:uêĬ• ,,,ÿeXÒ€øôé?~Ìä/+@äååѪU«rˆ¼¼<|}}‘““ƒñãÇcíÚµPUU•(ìU%%% ‘““>Ÿ_e Ã?‡Ã‹‹ Ξ=‹ÂÂÂjallŒÐÐPlÙ²ûöíCçÎq÷îÝZ^é?Ëž={0oÞ<,Z´‹-‚““:vìˆ;w–{ßëׯajj*ÖfjjÊXÆŽ‹ääd˜ššÂÁÁ*** »wï0}útÌ™3H¯qòíÛ7 8ÄÁƒannŽ‰'b„ ؾ};œ¡££ƒß~û >>>xõê¶oßÎlTŸ?Ž´´4ôïß$, ÑÑÑ°°°@³fÍúúú8~ü8>~üXýÙ€Õè¹ÿ>ºuë†Ý»w×É\" He!¢gYéª+@ëô`%//—/_ƨQ£˜6V€°°°ü—aHâÊ•+àr¹°·· .@€R7¬Š~€¿ûî;œ8qW¯^ÅáDZ|ùr¨©©É쯨Xš*S__qqqèÞ½{½vOruuÅׯ_Tí1äääW"mmmôîÝ‹/«´ÜPØ¿?fÏž `ãÆ7n²³³áïï…r‚™™™X›™™qèÐ!Œ7››‹ÐÐPܺu 666àp8عs':uê„‘#G"55UlŒ·oߢGxüø1nܸ555Œ;cÇŽ…f̘÷ïßãܹs())ÁêÕ«1iÒ$tëÖ#88 èÑ£I HAAºvíŠ'Ož@CCoÞ¼A£F°mÛ¶¿Ó†ˆ¾¾>š6mŠÈÈHp8Ìž=×®]Ãû÷ïk}®ªZ@„Ba- ÊÊÊøî»ïÊ=€‘EŸ>} ¨¨X§nXw¿XÂÂÂò߆ ˆ?þø–––ÐÒÒP*@D'¼@©Be~€íììжm[…B‰ æßùÛëéé!..½zõÂíÛ·ëÜ]¡ºtèÐfffÕrÃú;&&&¸sç¼¼¼ð믿¢K—.ˆˆˆ¨…Uþ3|¸V-{iii(((¨² VmÊX€eagg‡Û·o×IÌWnn.þøã1÷«ÂÂBdee±–ÿ,¬i „††"''‡ 999ÈËË“ B¡111厵|ùr˜˜˜àæÍ›àóùpqqAQQ‘Ô¾" ˆhmmmp8œz£GFvv6®\¹Rkcš™™áîݻذa¶oߎnݺáþýûµ6~mrôèQL:?þø#~ýõWÄÆÆbüøñ6l<<<*5†(ÎãÞ½{077´nÝOŸ>…žž#ÀZ´h;vLê8¶¶¶X·nÖ¯_3f`ÆŒ@XXœ1|øp?~iiipvv†……¼½½îîîÐÓÓÃÂ… ÅÆ|üø1233™ø"³€p¹\˜˜˜àÝ»wèÓ§îß¿„‡‡cêÔ©øå—_““SÅ·Úðøöíãv5sæL¤§§ãôéÓµ6‡è0ãß²€¼{÷®ZYúìììPPPP'ßkÒܯDV8V€°°°üWaH! ß}÷:tèHJJ1"Ê®RžÖ­[·OOO4oÞgÎœADDV¬X!µ¿È¢¥¥999$%%¡cÇŽõ:¤M›6èÚµk­¸a•…ÇãÁÃÃ>„’’,--±bÅŠ -Nÿ$ÇÇäÉ“1uêTìÙ³ùùù1b´µµqôèÑJûæææÂÇÇ«W¯†¥¥%`É’%èر#“ KÄ„ pûömÄÅÅIŒ•‘‘Á|V”””°`Á\¿~NNN2dNž<  Ô%¨¤¤~~~àóù D@@¼½½™Ï ˆàà`¨¨¨0Â(??B¡±€eë²±±AXX¦L™‚ÂÂB ##¬Ú‹ý@ô¾DÂÙÈÈöööؽ{w­¹T&$$À¿biÓ¦ òóó«•hÀÌÌ zzzuâçç‡Î;K¸_¬aaaùï ! ƒf~¨“““ˆ 4jÔH¦aÙ²eèÖ­FŽ èÕ«6oÞ ooo\¸pA⑤¤¤úúúxÿþ=úôéS¯- @i0z@@233k}ìöíÛ#<<kÖ¬··7ºwïŽGÕúàóù̺0²kìß¿?òòò`ii .—‹ßÿcÆŒÁÖ­[«)­¡¢­­ ###±:@³gÏÆÇÅÚjBBB¸\.ttt*}Omº`ÕË„Åáp`oo_ëDä~UÖú°„………… €·oßâýû÷Œû ]€p8˜˜˜È´€\ºt ظq£Øþ‚ 0bÄLš4I"+Žèô9//FFFxÿþ=¬¬¬ÏŸ?×Ú3Ö6£FB~~>.^¼X'ãóù|¬\¹<ǃ@ ÀêÕ«ÿµM­¯¯/ƇqãÆÁÇÇ\.ÀÑ£GqàÀ‰ ñ²dddÀÓÓX¹r%†Ž®]»ÂÞÞ^ìÔV„(Í­ÈmOEE#GŽÄï¿ÿÎœ¤ß¾}………ˆˆˆ€££#/^Œ˜˜4mÚgÎœ¼¼<Ξ= ooolÞ¼VVVJ3w½~ý;vìؘ",,Œq¿Jã?ˆY@LMMñõëWèééAKK ·oßF·nÝðüùsÌ;?~¬u YC l :ØÛÛÃÐÐ{öì©•ñѼysðx¼JßS[¤eË–àóù5Šyýúu­¦j–æ~°„………ÿdÙu–ê±uëVRTT¤œœ¦íÀÄáp¨¸¸X¬ï˜1c¨W¯^cS»ví¨ÿþ$ %®§§§SëÖ­©sçΔ››Ë´úô‰P@@MŸ>:uêDŸ?&tæÌ™Z|ÊÚ§wïÞ4hР:Ÿ§°°Ö¬YC<:uêD?®ó9ËâïïOrrr4vìXæóA|>ŸfÏž-ó¾´´4Zµj©««“‚‚Í™3‡âããI(’¦¦&mØ°ˆˆRRR]¸pˆˆBBB½~ýšëÆ€"""èðáÃÄçó©_¿~”ššJDD7oÞ$EEEêÔ© ½{÷Òëׯ©Q£FäììÌ|&ÓÒÒHKK‹¦N*uÍwîÜ!tÿþ}¦íÉ“'€"##™¶—/_ ¥‘#GRÏž=ÉÏÏÐÊ•+ièСdffF%%%Õyå –mÛ¶‘¢¢"2m^^^¤  @)))5òäÉ$ªtO»víhÞ¼y5ž›ˆÈÌÌŒæÎ[­{¿~ýJ\.—|||je-DD£G¦Î;K´ïÙ³‡x<žÔïb–ÿ¬iX[[ÓÀÅÚ<==©I“&}×®]+µýèÑ£ÌQOž1׿|ùBèìÙ³D$)@DôüùóÌ=ÅÅŤ««K;w&ôÃ?0s‡„„’’ÙÙÙQ^^Í™3‡ø|>™™eff2ãÌ›75jD_¾|‘ú¼ëÖ­#uuu1áFèÕ«WL[AAÉÉÉÑþýû™Í^ff&)**’®®.ݽ{Wì™þ+ˆ\Y‘œ’’B ´iÓ¦ÿý÷ßÓˆ#ªtOÛ¶miþüù5ž›ˆÈÁÁìììª}¿¥¥%999ÕÊZrrrHYY™òeY·néèèÔÊ<,,,, Ö«ž# â-ë~H!abb‚””|ûöi+((ÀO?ýGGGXXXÈœ«S§Nسg|||pôèQÅ€äççÃÈÈÙÙÙHIIiq NNN …8{öì?2_—.]ðàÁ,[¶ ëÖ­ƒ……ž?^gó]ºt £F‚££#Ž;‡’’¸ºº¢  ~~~——gúþüîîî000À®]»0gÎÄÅÅaëÖ­binEæ²jÄ4kÖ bq ¹¹¹PRR“'Oàííýû÷C^^žùìöêÕ çÏŸ‡¢¢"¶lÙ555|øðdb7^¿~Ý»wcÅŠbõmÊ‚¾}ûBNNŽi¹`•‘——‡‘‘^¿~ ãöíÛ0`¡££+++lܸ±ÞÖ´© ºté99917¬ÆÃÅÅ{÷îeêµT—ªÖj§ºˆš¤âJÝ°‚‚‚P\\\ãµ\½z¹¹¹îW[„………… õœ7n ¸¸¸ÒDZ&¬ >>žžžÎ7yòdL™23gÎÄóçÏ™jÙ¢LÈóçÏÅ„N}£Y³f°±±©Õ4£!//uëÖáÞ½{(,,D·nÝ°aÆZÙД% NNNpppÀ‰'ŸûU«V!$$gÎœa6‚ ˜;w. qèÐ!¸»»#..7nD“&M$Æ~ýú5ää䘿÷ßáp8055e„ʇлwo|úô @iº^‡ƒ?ÿüD=pñâEFÌîÝ»iiiPSSêU«˜wãî-[bÁ‚RçÍËËÃÝ»wÅâ?€¿‚ÐËÆ€`Öhll ===ܼyëÖ­¬^½¸wïBCC+xÛÿ;(++£}ûöAç³gÏƇjœº:!!¡J°€Ú©„.¢M›6ˆ‹‹«v;;;¤§§×JŠm___‰ìW"Ø"„,,,ÿuXRÏ @Û¶ma`` Ö^‘fggÃÓÓ&L@Û¶m+5ç®]»`llŒ‘#G"++ Œ“ ‹ˆðçŸÖàéêܺuë˜ïÖ­>|ˆE‹1©l_¾|Y+c_»v #FŒÀàÁƒqêÔ)&ÔÅ‹±qãFlܸÖÖÖˆ‹‹ÃŒ3`dd„“'ObåÊ•ˆ‹‹ÃºuëÊÝüDEE¡uëÖbÖ“¿#Ê2@€ÌÌLÜ»w]»vűcÇ{{{˜››ãÒ¥KPVV„……aÑ¢EpwwÇùóçqëÖ-¬Zµ W¯^ÅÕ«Wáíí͈޿Ž‚‚±„€ô ô²käp8°±±ÁÍ›7ѹsg4nÜ—.]‚½½=:vìˆM›6UüÒÿ‡$,‹¹¹9ÌÍÍkŒž••…ÌÌÌjY@jS€‘D2ÊbnnMMÍgÃ’V|°,¬„……å¿+@ê1B¡W®\Á!C$®%''KuSQQQžžcÙ±cÒÓÓ±fÍšJÏ«¤¤|ùòÓ¦Mƒ’’òòòШQ#4iÒïß¿‡¡¡!tuuëu= 4E,ǃ¯¯ï?>·‚‚~þùg„‡‡#'']»v…——W¬!ׯ_ÇðáÃagg‡3gÎ0âãÝ»w˜0aáèèˆ)S¦ÀØØçÎúu뇕+WV˜Š(µ€˜šš–ÛÇÔÔÏŸ?G¿~ýЦMDFF¢}ûö?~<.]º;;;téÒ—/_fª’þü£FBïÞ½±iÓ&X[[cÓ¦MØ´i~øáôë׎ŽŽ2ç AãÆѾ}{±ö¬¬,(**Jd^255Ň›› <{ö ÉÉÉ5j233qëÖ-xxx 00?®ð½ü¯ ðòåK‰bŒ³f͵k×]­qEE«j©MR“T¼ ''[[Û òܯ€ÒB„¬aaaù/à zÌÇ‘œœ,á~ȶ€¥§€oÞ¼AZZ¼½½1sæL´lÙ²JsãÈ‘#ðóóƒP(d\D©x9NƒˆÑÔÔdjNü[ŸèÞ¼y¥"fíÚµ€õë×ÃÙÙ†††ðòòªìëið…B‰Ú5£G†––öîÝ[­q«S¨]Ò¤I¨««×8$22²F¾¾èÒ¥ Z·n-õ:kaaaù¯Ã zL@@444гgO±ö’’¤¦¦Ê ¢@ÌM›6¡¤¤Ë—/¯Öü#GŽÄÂ… ‘™™ÉœŠŠXYYáÁƒÈÍÍ­Öøÿ...ˆˆˆ@ll쿶EEExyyáÎ;HOOG—.]°eË–Jý†„„`Ø°aèׯüýý7%"¨Q£ðúõkÄÅÅáîÝ»øå—_ƒ… 2ևʒøøx™œœŒ5Š±(MŸ>YË£Gàââ hkk‹‰‚¥K—"""~~~b–»¯_¿âÇPWWÇŠ+$NåË®+22R"þ(µ€üÝý ó QQQhÑ¢ÌÌÌpóæM4nÜ­[·FXX¸\./^ ??¿jŸü74Ú¶m %%% 7,%%%L:‡®Ö¿éêTAjW€p8榺ØÙÙA("((¨Z÷Ë*>XV€°°°ü×aH=& vvv®%iii … ]»vÁÝÝ]f¿Êàåå%%%œ;w©©©b¤OŸ>(..FDDDµÇÿ'6l”••qæÌ™{)°´´Ä“'O0gÎ,Y²}úô©ð´644C† AïÞ½™LR@©…¬sçθrå ´´´°ÿ~DGGcöìÙLŸª"Z‹4 HZZ¬¬¬píÚ5øûûC^^ž Dòä lmmѦMlÛ¶ ‘‘‘̆Þ××Û·oǶmÛ$Äôš5kPRR‚K—.!66?þø£Ô¬TwîÜAqq±Dü Û¢®®ŽæÍ›3Ö&QÌœ9ÅÅÅ8xð &Mš„ÆcË–-UyU ‡nݺI­~>cÆ dddT«HcBB´µµ«üÙ«MÔ<–žžÚ¶m‹k×®Uëþ+W®”ë~•——‡œœV€°°°ü§aH=åóçÏxðàL÷+åº`@II îîî5ZŸÏGëÖ­Q\\ŒqãÆ¡U«VHJJBvv6Ú¶m --­z¢¢¢‚¡C‡þ«nXeQRR–-[†””têÔ Û·o—j ¹sç KKK\¼xŠŠŠˆˆˆÀàÁƒÑ½{w<{öŒI-ûã?Ê à®,¢ÍºÈ—¾,‹-BJJ îܹƒ‘#GÂØØQQQxöìlmmadd„ÀÀ@¸¸¸@MM ÇŽëW¯0eʸººbÎœ9bã½zõ {÷îŪU«`ee…C‡áäɓصk—ÄÜ!!!hÞ¼¹Ôuɲ€ËÖeccƒ˜˜ÄÅÅaÞ¼y““ÃÎ;¡¤¤„ àÈ‘#ÿx²‚ ièЪU+ 8»wï®rzâÄÄÄ*»_µ/@Ú´iS#”ZA«•¢ÙÏϯ\÷«´´4lt–ÿ6¬©§\½zööö×* ¢Øggg¨©©Õx-5‚••®_¿Ž;wîbbbÀårÑ»wïz”ºa=}ú´Z±uE¯^½ðôéSLŸ>îîîèׯŸ˜ÐÝ»w1pà@&“Ôýû÷1`ÀXZZâÝ»wÐÔÔD¯^½À£×‘»’ºº:ÓvñâE¥þõ‘‘‘èܹ3€R+Ƀ`cc}}}\¿~PRR‚³³3~ÿýw8::ÂÐÐ>>>b›L"‚›› 0oÞ<¥1 ,€››îÞ½+¶®àà`ôïß_êFU–D´FÑß\?róæMðx<¼~ý™™™˜9s&°sç콆ƒ@ @\\RRR$®Íž=?ƽ{÷ª4fuRðu#@þ^ ©ªØÙÙ!11¯^½ªÒ}•u¿XÂÂÂò߆ õ”€€XXXH­ÓP‘Ù¿?é§ØÕAII šššX³f <bq ááá(,,¬•¹êŠB]]½ÞXAD(++cÇŽ¸uë>}ú„Ž;â×_ÅÝ»waoo®]»ÂÝ݃ Bß¾}‘œœŒÓ§OCWWòòòðóó«5ñˆgÀ""¬_¿Ó¦MxzzBGG‡éÛ¸qcÜ¿ººº¸qã455™kãÆC\\pöìY‰X”«W¯"00[¶l³ÚlÞ¼=zô€³³3¾|ùHOOÇ£G¤º_[@Þ¾}‹’’hhh [·nŒÖòåËADðôô„††fÎœ‰½{÷"##£ª¯­ÁannRë]ØÛÛ£U«Vؽ{w•Æ¬NB nPýLX@é÷š¢¢b•³aUä~ü%@Ø: ,,,ÿeXR),,Ä7¤º_¥DQQQê¦ëÑ£GðóóCóæÍS+ëQTTD~~>V®\‰ï¿ÿž™(ÉË˓ȨSßPPP€££#NŸ>]/+_[YYáÙ³g˜:u*æÍ›+++èèè ??C‡Evv6.\¸€ÇãáÇ ƒ¯¯¯XóÚ@”+//cÇŽÅêÕ«±téR« …“'OB(âܹs›)‘{O¯^½˜ ¡ˆ¢¢"¸¹¹¡ÿþppp»Æçóáëë ¡P¦‚¹P(”€Tl),,dØØØ 88D„!C†@YYÇŽ,X°ùùùÕÎÕ044„¶¶¶T7,.—‹™3gÂ××—9ì¨ 5qÁª­Jè˜Â5 JJJèÛ·o•HEîWkaaaaXR/ CVVV¹¤iÓ¦RO —/_‚ûA‹Õár¹8qâø|>|||PTT„.]º@EE¥ÞÇ¥nXoß¾­·5TTT0qâD(**B(âÝ»wHIIÁüû÷ïÃÁÁçÏŸ‡··7¼½½aeeU«óãíÛ·hÑ¢¬­­qáÂøúúbÑ¢EbýÞ¾}‹þýû3(цJÄ­[·°lÙ2ôìÙ‘‘‘U©÷ìÙƒwïÞaûöíR?ÃÍ›7‡¯¯/îܹƒeË–!88-[¶„¡¡¡ÔuWd ’””Ä…´··Ç—/_ðæÍ4oÞ“&MÂŽ;——WÑëjÐp8™q 0yòdp¹\:t¨Rã 99¹Z.XµY (-H©««[£LX@©ÖíÛ·+ýY¨¨ø ˆÔÔTÈËËËü̲°°°ü`H=$ -Z´`|íÿŽ¬ ·nÝB`` <==affVã`" Pzjgii‰äädxxx€ÏçÃÒÒ²AÄØØØ qãÆõÎ (Ý„‰Üòóóaaa!C† 66Û¶mÇ…É“'cÔ¨QX°`A­¯!66EEEرcâããqûöm W’wïÞÁÚÚLšÒ²q5‰‰‰=z4úõë‡ ##—/_f®§¦¦bÍš5øá‡бcG™kéÓ§¼½½±eËœ?^¦õ(ߢ«« UUUf½zõ‚‚‚ㆵnÝ:ÀêÕ«‹/FJJ Ž=*s¾ÿîß¿/Õ"¨­­ ìÛ·¯R©¢EÁûõÁ ¨½@ôüüüJ·UÆý ø«am?3 KC‚ õ€€ >¾Üx¦¬¬,™„ÃáˆeÂRRRBÏž=Ò®];4mÚW®\´nÝNNNðöö®QÅú†€@ @jj*âââ¤^Ÿ={6âããPáX¢ õE€Ô4/P꾧§§Wi7,___tíÚFFFåöck€°°°°°¤ÞñîÝ;¼}ûV¦ûP*@Ês€Ë—/#""7n‡Ãa6lµQ\MQQQÌ ÁÈÈééé1b&Ož CCC¤§§ãÅ‹5ž«®quuÅÇþ¯®£¨¨¿ýöÌÌÌ0vìX|üø&&&ˆ‹‹Ã Aƒ˜~¶¶¶xöìš5k†/_¾@SS³FÙ}¤ADðòòÂÏ?ÿ ‡ððp©®4+W®„²²2‚ƒƒ™`ô²Y¦ÜÝÝñàÁøûû3yüøñ¸zõ*RRRðâÅ ìÛ·«W¯–š\áïp8Œ9pèÐ!deeIí—]®;KÙ5¥–°ÐÐPF`Œ;ÙÙÙL݇¥K—"&&þþþ®±!# D—å†Õ½{w‚J£W·!PwwïÞÕè Ãá0éx+"''Z?V€°°°°¬©w@^^6662ûüÝ"ªvÞ¿ØÚÚø+Vm¸aýÝbdd„’’¬^½Íš5ƒ——ø|~ƒˆéÕ«tuuÿ57¬ÂÂB8p&&&˜þŒ©S§J}LJ²²²Dôøøx >@iÞ¿Ÿp›™™!::Ó¦MÄ 0cÆ ±ë7Æ Aƒ°k×.ܸq[·n˦U!!!°··Ço¿ý???ìرCìzvv6Thùöí“Ñ©{÷îPSScÜ°444`jjŠððpæ3ááá§OŸV9 RCC"‹Q£FA[[»ÂÌ`¢ Õuå‚Ô,Pjär¹¸~ýz¹ýüüü*å~°„………`H½"++ ¡¡¡åº_åææ";;› ………X½z5ÅNžÚñƒăÐ@__rrrxÿþ=:t耽{÷"99ׯ_o'Æ®®®HNNÆ­[·ê|®ÜÜ\lß¾­ZµbÒë¾|ùëÖ­ÃÌ™3Ñ´iSI­ ðùógŒ5 ½{÷ÆÆêêê8tèðìÙ3´oßGŽ©ò{ñâ“)-$$cÆŒ«”n*­­­™±¥mšôôôPRR}}}ìÝ»WêFr̘1x÷î,--1tèÐJ¯ñÓ§OˆŠŠ‚µµ5FŽ‰E‹añâÅbBWä–U‘ø+ÇCß¾}sæÌAII öìÙ ´h¡@ À¦M›*½Þ†ˆ@ ÀÇeŠqEEEL:GŽANNŽÌqª›‚¨b``Wãï?MMM‚r…hNNN…ÅË V€Ô+‚‚‚PTTT®U.  >>žžž}MLLjÍ«¬„Ïç£eË–L1‰'ÂÎÎéééøã?j<_]Ó­[7Õ©VVV¼¼¼```€%K–ÀÞÞQQQ8~ü8x<¬­­¡¥¥…›7oJ‡(**¨Q£ÀårqæÌðx<±ëƒ ‹/ààà€)S¦`È!øôéS¥ÖvåÊôìÙ5Bdd$,--‘””„ŒŒ ÆòéÓ'X[[£¸¸.\:ŽP(Ä®]»óçχ²²²Ô~>|Pð]•¦H öë×°qãFôéÓ£Gf².UÆÒºukðx<‰8»wï2Ÿë3f€Çã1ñË–-Chhè¿/T—˜››#77·ÜŠß3fÌ@ff&N:%³Ou‹u#@x<ŒŒŒjåûÏÎÎAAA2EÚ•+W——W)BDHMMe‹²°°üçaH=" &&&åšñËVAÏÎÎÆúõë1a´mÛV¢o›6mðæÍ›[%þnJÝ°Dø+ƒÒ´iÓê}%i‡œ={¡|Ñ< IDATµ:vFF<==a``€U«VaĈx÷î> cccDGGÃÚÚêêê–YÍ~éÒ¥ˆˆˆ€ŸŸŸDššš8zô(.]º„G¡]»v8vì˜Ì¿7aÇŽ:t(úõë‡?ÿü-[¶ðW*]SSS|þüÖÖÖ(((@HHôõõ¥Žçåå…k×®AUUéééRû¤¤¤`Æ h×®«ŒvíÚ1ÏÏãñpúôip¹\8;;£¨¨¨R>Ÿ###Æ” ‚‚üùçŸ999ôìÙoß¾Åׯ_Æ ƒ©©)¼¼¼*½æ†F×®]ÁårË144ÄàÁƒ±{÷n™Ÿ-‘ Vu¨íB„"j#/P*@¾}û&ÓU­*îW¹¹¹ÈÏÏg- ,,,ÿyXRO "\¹rC† )·_Y²sçN¤§§cÍš5Rûš˜˜ ##ƒ±šT%%%””” ¨¨ˆiû»ÑÑÑAÛ¶mñíÛ7©ÁÂõ WWW¤§§WèÛ]Y¾~ýŠÕ«W£eË–ðôôĘ1cðþý{ìÛ·€˜˜X[[CEEÁÁÁ2…Å™3g°}ûvlÛ¶ ={ö¬pî¡C‡âåË—("-- [………ÄR/xøð! àààrû>|˜Ð§OŸHMMæÏŸ/³ï‹/ݾ}»Fk;}ú4 ÌÌL¦ÍÛÛ›TTTH(2móæÍ#@Û·o¯ÑœÿíÛ·'WWבœœL¤ªªJÊÊÊäææFŸ>}’èKúúúdllL‰‰‰2Ç{ùò%©¨¨«««Ø»­,çÏŸ§¦M›’––8q‚„B!¥¥¥Q¿~ýˆÏçÓáÇ¥Þ7wî\266¦¶mÛRóæÍéí۷̵””@.\ "¢øøxjܸ1}ÿý÷T\\LS¦L¡îÝ»KŒùìÙ3âr¹´mÛ6 …dddD“'O®ÔsÄÆÆ:þ¼Ôë¿þú+ www@ÉÉÉåŽçááAúúúbm®®®dnn.Ö¦ªªJMš4aþ»  €ôôôh„ •ZwCdêÔ©Ô¹sçrû”””‘‘3FâÚ§OŸ]¼x±ZókjjÒ¦M›ªuoy8p€¸\.åçç×x,'''êÑ£‡D»¯¯/ ÷ïßWjÑ÷üÇk¼&–† +@ê ëÖ­#555*,,,·ß¦M›HSS“/^L***”””$³o^^q8òññ©ÑÚ.^¼HÄæ:wî ÏŸ?3m~~~€f̘A<þüóÏÍ[×lØ°”••);;»Ê÷~úô‰ÜÜÜHYY™TUUÉÃÃCæ&8..Ž ÈÈȈ>~ü(sÌŒŒ 211¡öíÛWkM"RRRÈÅÅ…­­-’¶¶6…††Ê¼§oß¾¤¦¦F:::%1žH€äçç“@ }}}JMM%¢R1ªªª*&˜„B!ÙØØP›6m¨  €ˆˆÖ¬YCªªª”““Sá3>|˜8}ýúUêu¡PHcÆŒ!@¹¹¹åŽwôèQ@YYYLÛÁƒ‰ËåÒ·oߘ6Ñ{{þü9Ó¶}ûvâñxWáº"ûöí#99¹ ÿ.[¶l!>Ÿ/ñY£Mµ††yyyUëÞò¸uë W¯^Õx,âr¹ŸG'''êÖ­[¥Ç $ôáǯ‰………¥!à z‚@ ''§ û¹¹¹Q«V­HQQ‘V¯^]aCCCZ¼xqÖ&íGóéÓ§€îܹô}ùò…Љ'¨wïÞ¤««[áÉô¿Itt4 3gÎTúžøøxš3g)((ºº:­ZµŠÒÒÒÊíߪU+244¤øøx™ý„B!1‚ÔÔÔèÍ›7UzY¬ZµŠ8ÉÉÉÑ/¿ü"³_jj*ñù|RVV–ºY++@f̘Aòòòtÿþ}æúåË— €˜¸‰ÖË—/3mïß¿g>1~üxêÚµk¹}²³³©E‹€ÒÓÓËí{ïÞ=‰M²4+Ë»wï >œiËÊÊ"---š7o^…ënˆZ¾|9y{{Wh].øøx@?þø#1‚¶nÝJÙÙÙ¤¤¤T%ë͉'@¥D8 Ëÿ2¬©$%%‡Ã¡#GŽTØwܸq¤££CÚÚÚ”‘‘Qa{{{6lXÖwûöm v2ž••EèèÑ£b}Û´iC³fÍ¢„„jÒ¤ 0€Š‹‹k4]bnn.¶Ù”Ell,ýøãÄçóIKK‹Ö¯_/vr.„„222¢–-[Vxzîíí]®ËQUÙ½{7ÉÉÉQÿþýÉÁÁ³³³„ LKK£:úù知Ž% óæÍ#tàÀ±ë"!wýúu""ÊÏϧ֭[Ó÷ß/áFÖ»wo²··/wíB¡tuuÉÝݽÂç\²d  GGÇr]Ö222?~\¬½U«V4gα¶æÍ›“’’’XÛO?ýDJJJ”’’Ráš………¤¨¨HÛ¶m«°ï”)Sè»ï¾£¢¢"Z±bû_£F*üwATêÚö÷{Ð’%KjüUªo=ÈÖÖ–lmm%,•#//OªÄÃã&Á`ee%1¶¡¡aµÆÚ¶m›ÔµÊËËW9x~æ̙ԥK—j­ƒ………å Öò/STT„ÀÀÀr‹ŠxôèÒÓÓÑ¿Æ*Q&&&(..Flllµ×(ÍH¦âJ- ˜ú Ë—/‡½½=ÆŒƒ?V{ u…®®.úôéÃ%|öìF…víÚáöíÛøå—_ƒ È,´W–¤¤$ôïßYYY )·6@aa!œ!//S§NI¬ ÑÑÑ°´´Ddd$1}út‰>Íš5ÃÙ³gáãニ/âÉ“'X±brrr ««+µ–†P(ĬY³”Ö&QRR’:¿©©)¢¢¢°zõjp¹\&•íßÑÔÔÄСCñûï¿Ë|–˜››—[ÛCDvv65j„õë×ÃÚÚ...HHH(weYY‚ƒƒ™¶²©i ™v777deeÁÇǧÂu54Þ¿Ϥ‰-Y³f!((HêwJeSñRZ@€RËï߉‹‹«VÝ;;;©í¢¿U-BÈÂÂÂR +@þeþüóOdffVJ€,_¾0`À€J/ú!®‰–hÓY¶: ]€´lÙzzz¸}û6€ÒZ ÇŽƒ²²2F-¶¡«/¸ººâÆÆŽ‹­[·ÖËÏqMP)7,ggg4nÜXêBe‹Öµ111‘:çß¿¯*ƒ™™™TaÅçóahhX¥±X,–RXò/tíÚµÜ~·nÝb a5oÞ¼ÒãëêêBYY¹F¹Ê³€¤¤¤0Åà€Ò „(D„¶¶6üüüðàÁ,]º´Úë¨ """pöìY…B}ððáCäää0mضmvìØÿj¯¥¶¸}û6 KKK|úô ;v„‰‰ &Nœ>Ÿ_¥±ÒÒÒ`kk‹¤¤$ÃÔÔ´ÜþÏŸ?Ç?ü€ &`ÆŒÕZII ÜÝÝ1mÚ4L›6 W¯^…¦¦¦Ìþ999}ŠëׯÃÜÜ...àr¹044Ä°aÃ0iÒ$¤§§#..cÇŽÅÀáîî^áz’’’3gάðýñù|¸ººâĉÂÁÁÁàóù•ªüeaaa_~ù»wïÆñãÇÅúš™™€DÕv$$$àÝ»wL—Ë…••bbbœœÌ´·mÛðòò‚P(¬Ô‘‘‘•ê/Ëb[_, ²Hubd¹aI«Ž^¬„………¥V€ü‹ÄÄÄ **ªB÷«Ë—/#""“'OP5Ô<–È"Í  VVV(..FDD„XûìÙ³1zôhL™2¥F™êBD¸yó&úõ뇾}û"%%~~~xþü9ÜÜÜpçΙñ²e’JLLÄÍ›7ѶmÛrûgdd`Ĉ066ÆÞ½{«µáÊÌÌ„ƒƒvìØ_~ù{öì)wÓŸ››‹!C†àÑ£G¸v횘Å%&&ÅÅÅعs'Ž9‚óçÏ£mÛ¶0`ÔÕÕqìرrÅ10Ù¡´´´*õ ãÇÇçÏŸ%\Ÿ‚ƒƒaiiY©x@Ü"búôé˜8q"~üñG17¯æÍ›£Q£F§ÖVVVàñxkù駟Äþ_„‡‡Þ¼y#–%ësssDFFÊe‘%¾jj©è³VYZµj999‰öê~ïØÚÚJ][U±„………åÿaÈ¿H@@ø|~¹1%%%X¾|9ú÷ï}}}U &&&5Úðs¹\ÈËËKX@š4iUUU bffmmm&D‡ÃZ´h'''äææV{MUˆpõêUôêÕ ¶¶¶ÈÉÉÁÅ‹ñøñc899Ëåbøðá——‡¯¯o¥ÇMOOgÒØÞ¼yíÛ·/·¿P(Äĉ‘’’‚sçÎUz“]–¸¸8ôêÕ aaaÀܹsË1yyy6lîß¿«W¯JXDÖ€¶mÛbÒ¤Ixùò%äääŽ;JÝÄý_~ùñññÐÕÕ•°.È¢{÷î055sÃ*))Ahhh¥ã?I Pú9Û³gÚ´iƒ#F ==i733“Xc£F $HŸ>} ®®???±ö=z o߾شiS¥6ë @€ääd&-syÈê• ²LmY@äåå¥ÆgT÷ FSSSª«dhh¨Ä÷¢,²³³QXXÈ °ä_% VVVåfû9yò$^¾|‰Ÿþ)))àñxåÖŸ†‰‰ >þŒÌÌÌj¯UQQQÂÂáp`dd„˜˜‰ö>}úˆÅˆhÔ¨üýýY³fÕ鎈pñâE 4påÊDFFbØ°ab›uuu 4ˆÉ†U°³³Cll,‚‚‚бcÇ ïñòòÂÅ‹qüøñr³cÉâÏ?ÿ„@ @nn.ÂÃÃaoo_nÿüü|888 <<W®\AïÞ½%úDEEAMM :::JOt0iÒ$£}ûö ‘9GRRÖ¯_Y³f¡C‡•ö‰çp8?~<Î;‡ììlÀÓ§OñíÛ·JÇÒ-  ¬¬Œ³gÏ"-- &L`6¼Ò2a¥nX!!!c¤¥¥ááÇb툌ŒÄ­[·*½ÖúŽ¹¹9ðìu\TÙûÇ?w‚¡CBQE±À;VìLìîZ»[ìÄsÅÂDDWPqMD1é‰ç÷Ç0ÃÜ; ¸îºßß}¿^÷¥œ>÷Þ™9Ï9O@?Ct¶ À¯£‚°«aýÈF ›Vzz:ë÷ À <<<<àÔÔT\¹rE§úUVVfÏžŽ;¢fÍšøôéììì ü#­ú!þQCt¶>6OX€r÷øÖ­[¬†ª•*UB`` víÚ…íÛ·zL\( >|èСLLLðÇàÆhÙ²%çýëÞ½;ÂÃÃñüùsí'''£E‹xöì.^¼|ÇôÇ`æÌ™˜9s&Ú´iSà9íÙ³7FùòåqçÎ|U½222бcG„……©]6žþŒ¯_¿ª½µáž\üÃqGx²9qâ ˜˜Î2k×®%@@ÑÑÑDTø Vß¾}#´oß¾B×Éɉf̘¡•>iÒ$Ö_ááá€nÞ¼ÉÙæ!CH"‘Pddd¡Ç•™LFûöí£ *jÒ¤ ]½zUïú©©©dbbB~~~œe’““©N:daaAááázµK666Ô¬Y3’Édz‡ˆH.—Ó´iÓõïߟ233ó­“‘‘A­Zµ"CCCúã?t–õòò¢¾}ûRBB999‘§§'edd¨ó -_¾œ ]¼xQI ÃК5kˆˆ(00„B¡FýühÔ¨y{{Q«V­¨I“&z×U(Ä0 ê,7kÖ,b†.\¸@ÇŽ#¯Q&##ƒŒŒŒhéÒ¥ZõK–,I‰„ …FúEDDè=æ_Î;SÆ ó-צM­À|z÷óåËÖà~óçÏÿÑk²qãFÖ>îܹS¨ö’’’XÛ«T©’^õÏœ9CèíÛ·…Ꟈ‡‡ç þä_"$$eÊ”áôÖ’’’‚  OŸ>êÝnÕ HAQ©Øü¨!:× Hll,¤R©Fº‡‡LMMµì@r³zõjT¨P>>>øöí[¡Ç&•J±sçN¸¹¹Á××ÎÎθuë.^¼È¹óφ±±1Ú·oÏ©†¥rc… . Fù¶™™™ cÿþýzÙT¨HMM…–,Y‚eË–aÛ¶mùºVG»téNœ8oooβD„'Ož \¹rðõõEJJ ‚ƒƒ!‘HÔe†Aß¾}(wÈ›6mŠ#Fàû÷ï7nÊ—/¯öäåææ¹\žï RnúôéƒÐÐP¼zõ ×®]+úUZZˆ(߀…sæÌA³fÍУGXXXÐö„%‘HP¿~}-;@é’733SËÄÇÇ¥K—†¿¿¿ÞcþÕñòò½{÷´¼“å…í¤xñâz÷CÿÒ Px;sçα¦GEEqª¤åFuÂ"äááááU°þˆ!!!:UqV¯^¤¤$Ì;WVXøqOXFFFZ6 €R‘ËåxóæFºH$B:utêG"88 èß¿íA²²²°yóf”+Wýû÷GÅŠqïÞ=„„„ V­ZjKE÷îÝ…¨¨(ôÔÔT´iÓ>ÄùóçÕÛòcܸqxðàŽ9R …ÇÛ·oQ¯^=\¼x'NœÀĉó]œI¥RtëÖ çÏŸÇñãÇѬY3åãããñýûwµkÞ¨°1þ|¬_¿»ví‚‹‹ ®^½Š•+Wª=p©Ü$6BçÎahhˆåË—#%%¥@è*Û.,B¡ûö탩©)&Mš¡PÈirýúu-µÁ)S¦€a,]ºT#]$aòäÉÖpáû_ÆËË ©©©ù>C.,}ù7ª¢>|NNN¬yú¨a%$$ÀÄÄDíÖœ‡‡‡çÿ3¼ò/ððáC¼{÷ŽÓþãË—/Xºt)†®ñƒ÷ñãÇB ?ê K×  íŠPÚ„……éÜM-]º4vïÞcÇŽaÕªUz%##ëׯ‡‹‹ † OOO<|øÇŽCõêÕõœ;Í›7‡¥¥¥Æ)HZZÚ¶m‹{÷îáìÙ³z 7»ví¦M›°nÝ:½NKTܽ{^^^HLLÄ7жmÛ|ëH¥RôèÑ!!!8zôh¾ê@Î)À¡C‡àçç§Ó ô†6bÄÜ»wO|2$$DïÅÖÖÖÖÖ²177G‡ SSÓÝ'Õò;”»ÎÁÁÁˆŠŠ‚™™ë½½½‘––¦å>ÚØØîîŒÔú ôíÛ¶¶¶X¾|¹Þãþ•©^½:†Ñi’‘‘¡ÞÍÏMA\Xÿˆ*k^ ó=˜’’‚øúú²æë#€ðAyxxxrà˜ššrªùûû«ÝïææGN@THAOTp€”,Y"‘ˆUiР¾}û¦uš—víÚaòäɘ sçÎ8xð ˆéééhß¾=îܹ£vå«<À°aÃ0`À 4Hïþ:„† ¢T©R¸{÷®^ó’ÉdðõõÅÉ“'qäÈ‘|ãʨPÝëÖ­[cêÔ©zñäÉ“Éd˜>}:¶mÛ†*Uª¨Uí¸¼Lé¢OŸ>øôéÜÝÝ!‰ô®§ï ˆŠ5j`ݺuHJJbUµòðð€••kÞï¿ÿ…B¡%hbüøñعs'âããõû¯Š™™ÜÜÜt ïß¿gMûö­ÞÏþŸ@†a=)ÌIpHH2220hÐ V/^ÌWmÂÃÃÓ‹Ëøäÿ3µkצŽ;²æÅÅÅ‘¡¡!Íž=[#=33“ÐŽ; ÕçÉ“'ȲeË–œc.S¦ M˜0A+=== ÔFʺJ¥Ô A*^¼8}üøQ#/99™–,YB¶¶¶$‰¨ÿþôìÙ³BÍC.^¼HèúõëÔ¬Y3222¢Ë—/ë]?11‘J•*EÕªU£´´4½ê( š3g ___JOO׫žT*¥îÝ»“H$¢cÇŽé=ÆÔÔT²¶¶&±XL_¿~ÕYöóçÏ€Ž?NñœŒe& IDATññdjjJcÇŽ%"¢¿þú‹êÖ­K ÃÐرc©_¿~T­Z5½ÇAD”’’B¨~ýúªwíÚ5@OŸ>-P=www@÷ïß×ÊëÔ©Õ­[W+].—“999iå%%%‘¹¹9Mš4©@ãøUÉï^½z•ÕÛÌÌŒF­W>|`mcÉ’%×4ˆˆ¨k×®Z}“\./P;;w¦5jÑøñãYÇ~ûöí|ÛhÞ¼y¡çÂÃÃÃó¿/€üÃ|þü™†¡­[·²æ2„¬­­éÛ·oéqqq€BBB ÕoLL  K—.ª~§N¨E‹¬yÍ›7§öíÛ³æÕ«Wºté¢Wïß¿§¢E‹’··7Éd2úúõ+ÍŸ?ŸŠ)Bb±˜†J¯^½*Ôø ‚L&#;;;rvv&CCÃÝ3¹\N­[·&+++zùò¥^uÒÒÒÔ ¥… jy[Ò5N___ …¬÷ õíÛ—Ú•.r ¤"EŠÐ—/_4ƱråJ244$244,ÐOµ -R¤eeeé]/$$„P\\œÞuˆˆ¶nÝJÈÙÙYcDDëׯ'‘HDß¿ת׼ysN!~êÔ©djjJ‰‰‰˯Ȇ H$q Áû÷ïg]€ûúú’¹¹9ë½ËËû÷ïYÛð÷÷ÿ[ç2sæLÖ~ ²óýûw244T{H;wîk›óæÍÓÙNÆ É××÷‡æÃÃÃÃ󿯂õsîÜ9‘:0^nž={†mÛ¶aúôé077×Èûô适GAWQªT)ˆD¢B¢sـܱ@¥ȵk×ôRý²··ÇŠß~û ÎÎÎX¸p!zöì‰/^`Ó¦Mpvv.Ôø ‚L&ƒ±±1^¿~'NÈ3ÓÂ… qæÌìÛ·5s^âããÑ°aCœ:u ÁÁÁ˜>}º^j(r¹ ÀÁƒqàÀtîÜYï1nÞ¼»ví‚……EŒõ_¾|‰íÛ·cþüù(R¤ˆ:](büøñxðàlll‘‘¡C‡²ªì± 333$&&âÂ… zG¥‚¥ HnÜÝÝ_¿~E¯^½4‚z{{C&“±zo›7oí˜ 0vìXH¥Rlܸ±@cùñòò‚L&ÓŠ‰¢‚ËÖcØ°aHIIÁ¾}ûòíƒëûàïTÁ”ª§lä{P¥~åãã@©Zjhh¨U.?;^‹‡‡‡'^ù‡ AµjÕ`oo¯•7{ölØÛÛcĈZy?*€ˆÅb”.]ºÐ— u4t¶EEƒ ðñãG½\³~úô çÏŸ‡X,Æõë×Ѹqc¼zõ k×®EÉ’% 5î‚’••…nݺ©õÜÙ\œ;wsæÌÁœ9sвeË|ËGFFÂÓÓïß¿GXX˜ÞB„B¡ÀàÁƒ±wï^ìÝ»]ºtÑ{ŒwïÞŘ1c0xð`|ýúUí¹J¶nÝ 777 :”5¿\¹r8yò$¥¾‡‡‡–A7—/_FãÆQ¹reìÙ³Gïñ¨ŒÐMLLô®£' <çΠÔy®®®(Q¢«HÍš5aee¥œ˜›bÅŠ¡ÿþÐ[ðúU©\¹2 8í@¸\ÎÖ¨QmÛ¶ÅúõëóÝpø§¿ÃVPP<==Õ FFF¬ö{wîÜARRg;¼ÂÃÃÓ/€üƒÈd2œ;wŽÕHøþýû8tèæÎ˺èU ¶¶¶…îÿGhåÕ©S@g<øøxL˜0ÎÎÎX¿~=Æooo\½zU+¾ÈÏD*•¢{÷î8{ö,Ž9GGGΘ yyýú5|}}ѲeKÌš5+ßòGE½zõ`oo»wï¢Zµjzõ£P(0tèPìܹ»wïF÷îÝõª(£@ûøø ZµjêØnnnz×üø1V­Z¥ÓP¼téÒ044Äĉaii‰ºuëbÊ”)œïNZZnݺoooôîÝÇ×;&LJJ Œ äþPž˜888ÀÀÀsçÎżyópöìYÊ°··7«(Ý'%%áæÍ›Zy'NÄ—/_°cÇŽçWÃÀÀU«Vå@ØN@lll`hhˆ‘#GâÑ£G:Iÿ$%%gÎœÑòÙ¢¢ËårÎ÷†ˆÀÇááááQñïiýÿCe4ˉ·E‹T®\9’J¥¬u—/_Nfff?Ôÿĉ©téÒ…®ëêêÊš÷èÑ#µÑ6ÕªU£¾}ûj¥ÇÆÆÒ¨Q£H"‘……Íž=[­“ÿåËrrr"///½¢ÿ(YYYäããCb±˜N:EDÊ(ï666ùÚ%¤§§SõêÕ©T©RZ6yQ(´hÑ"@]ºt¡ÔÔT½Ç(—ËièСÄ0 íÚµKïzDJ; ooo²µµ¥·oßÒ®]»€^úúoß¾%j#Üü¨R¥ :”¤R)-^¼˜ ÈÍÍõ½WüGEEÑ»wïH pÚGåeÁ‚dgg§WÙ¼4iÒ„:vìHr¹œZµj¥a³£º7Ÿ>}Òª÷îÝ;@Íš5cm·{÷îäììÌù9þ¯0zôh*[¶,k^Íš5µì<<<ˆHùŽºººR·nÝt¶ËjG±bÅŠ¿}.vvvZý´jÕJ¯ºªh÷ymÏ¢¢¢XÇ?xð`Öv¾~ýJ(((èG§ÃÃÃÃó?/€üƒLž<™ììì´ t¯\¹BèðáÃ:ë–)Sæ‡úß¼y3 ÊÈÈ(pÝ™3g’££#k^jj* ;wjå}ûöêÔ©CVVVê´—/_Ò!CH,S‘"EhÁ‚”””¤U÷îÝ»$‹5<ëÔ{ r¹\mˆ*•J©[·n$‰èøñãê2÷ïß'tîÜ9m 4ˆ Y½*ÅÅÅÑãljH)¨ôêÕ‹Ðœ9sò‹B¡ ØØXõÿGŒA ÃÐöíÛõš£L&SÿÚ´i$ÔõS§N¥’%KêÕÎŒ3­_¿^¯òݺu£† ªÿŽŠŠ¢êÕ«“@  iÓ¦©ß¿¨¨(j×®ÙÚÚª ï›6mJ 4ЫŸ)S¦‹‹‹^eó2zôhrss#"m¯e*‡‡b­ëììL¬Ï/22’о}û 5®_…={öV£z­…w›6mÔù«V­"‘HDñññœí¿yó†u¿råÊ¿}.õêÕÓêGß÷¦S§Näéé©•®P(X£#«‰çÏŸ ýáùððððü/À ?™¬¬,µ+ÖŠ+ªObbb(00233©víÚT½zuÞúõëGuêÔù¡±¨¼ EGG¸îÂ… uî6ÛÛÛÓ¬Y³Ôýú•æÍ›GVVVêçêׯ …B²µµ%JNNÖÙïúõë Mîîîôùóç?7 …‚†N¶¶¶ôàÁêÙ³' …B:zô¨V9WWWêׯ½xñB«­-[¶pºG¾wï/^œœœœ(**ŠêÔ©C‰„8 ×8çÌ™CfffF£G&´e˽ê&$$PåÊ•éðáÃtüøq-C:t ¦M›rÖßµk?~œÞ½{GÆÆÆj/XúŽ;ï»’••E~~~$‹©B… tóæMª^½: µëeÕÂ÷Õ«W”œœ¬ÓëÙˆ#ÈÝÝ]¯1åEåíJuºuÿþ}244¤þýûSLL I$Î]ò… ªî´´4ú믿4ò[´hA•+WÖÛ›Ù¯ˆÊkÞ… 4Ò¥R) …B­…÷СCÕe¾~ýJÆÆÆ4þ|ÊÌ̤‹/jµÿúõkVdÕªUû\  Õ@ à¬X±B§ X½zõ¨gÏž”@3fÌ sssÖg{{{Zµj•ÞªG …‚ºuëFb±XÝFóæÍ }¢P(hÔ¨Qê¶$ ÎÓ§±cÇ’D"!@=RçÝ»w$ 2D«ÞáÇÉÈÈH£;;»|c¨˜?¾º®jî›6mÒ«®L&S»Œ@Ô¾}{’ËåÔ¼ys:t(ÙÚÚRûöíéõë×Z÷òÓ§Odaa¡~^ªg™Ÿ¢P(èãÇêx&Æ £-Zh,È>|HåË—×z/¬­­éСCôíÛ7’H$äîîNFFFÔºuk­~vïÞMþþþT³fMruu¥cÇŽÑÍ›7õº7DJõ¾5kÖ0`µnÝšnÞ¼I;wî$êç&èÍ›7ZõÓÒÒˆa²²²"sss­rÕ‰fa]fÿÛ( zóæ Sƒ ¨qãÆ4nÜ8"Êqž÷Z°`F=zô 333*V¬˜ZÅ.7¯^½bm' àoŸ¿¿¿Þ‚‘RÝÔØؘjÕª¥sÃ&(((ß9\¿~¦NªVvíÚE·nÝ*Ô)4Ïÿ¼ò“>|¸ÖT™2e´ÒªV­JçϟתÏvÌÏs#? ™››ÓâÅ‹ \wÆ $‹9ó»víJööödbbÂú£ €D"‘VÁüˆ§ hµ5þüÏA¡PÐرcµÚ255¥ÈÈH²111T§N­²3fÌ "å.©““yzzj,& -X°€uþ:uÒkW\µÃžû200 «W¯ê5ÏÙ³gkÕ¯W¯žZ¥,ï5sæLúÆ ÓÈgF½xÒÅÙ³gYÛÏ«Êôûï¿s¾#††† …B­w¦nݺZõªW¯®×½‰ˆˆ`íwõêÕ4mÚ4­twwwç»mÛ6rrrÒ¹˜U(T«V­VüU`{ï+W®LDDwîÜa½*µÀׯ_S§N´NIFŒ¡ÑÇË—/YÛÑ'hiA9vìk_lµB¡Ðz¾ÆÆÆ4`À­²‰‰‰$´ÚmÙ²¥º —ðSØ€°<<<<ÿ+ðÈOD¡P££#ëb’íGi̘1ZõÙÊr:ꃧ§'õïß¿Àõ¶oßN4ì ˆ”F¹ãÆÓ8¡Ðu-Z´¨@ýöìÙ“µ†aXU;¸P(:¾ÅŠÓ8•INNÖZ  Ò¥K“T*¥æÍ›“µµµÆyzz:çxU×ÆuŽ“kÁ(#M³FçæôéÓœõ5jÄš¾gÏuý‡².ªeD]¼xñ‚µ^îmR©”ótŒëÊ»+®ŠdžûÊms¢‹äädÖ>š5kÆÙnõ¢ÀÀ@Ö2Ó§O×èG¥ö¦×¸~%zôè¡5?@@)))täÈÖù«Tµ5NþT—©©©FpU•MDÞkíÚµû|¢££YûbS­º{÷.kÙ=z°¶­:%É}©ƒ8Nš4‰µ=•Z.ÏÿWx7¼?‘èèhÄÆÆj¥geei¥™˜˜`ÆŒiÉÉɬe  Pºâ-L,•k`•;ÕØØXŒ9¥K—F@@€Þîr«T©R ~W­Z…âÅ‹k¥zôèÁ-oÙ©S§bÅŠ¬ùb±›7o†±±±:ÍÌÌ mÚ´Ñ*ûòåK 6 .\Ààèèøðá5j„ýû÷sŽ£Y³f:Ýæ®X±S¦LaÍck×®Õé†ùåË—èÕ«k^ñâÅÑ´iSÖÏR¥JQbb"( êر#gŸGŽѪ3oÞ<ÎòB¡P¯ `½{÷f­åÊò÷÷Wh³]–––ùÚ©¨Ü s]ùÙŒmÚ´‰³~Ïž=Õ†ïöööZù*W¼Ë–-c­_¬X1µÇ"}•¬¼—Ênnï\ªK,SëÖ­ó}¯Š+¦¶­È{?>ß±åwÏUÂòÁƒ9ÇЪU+ÊÈÈ ­<333-ïn$ éõë×zïW@åõ-÷UªT)jØ°¡Vº*žJnöîÝËzÿ|||ˆˆèéÓ§…~ß Ã˜1cXû˽ñ3qâDÖ2ùy ôñña­GeË–ÕJç bÉÃÃÃóÿ ^ùI¨"èæw988p$nܸ‘µNnW°%%%…ö¸¹‰ŒŒ¤Î;ë5j·¤ª$ õŠ¾ÍFRRç.½¹¹9={öL]ÖÏÏs¬@ïS¤Ö6Ê•+§ó~”)S†ž>}ª³m.ƒfÕµnݺ|ÇwçÎN§•*U¢””õ½c+3zôhúøñ#§aøöíÛéóçÏè'€p½÷gÎœ¡ÌÌLuL‘ÜW½zõH¡PО={Xw“õ¹fÏž­×ó$"ºték¹iŽ?ž³¯yóæ©ã²ä½ò¾W)))dmm­Ló¿À!CXççì쬕֤I­údkk«UV(R\\=yò„µ}}]M.¡3<<œˆØ½_Jãùü ÆU§ÃlŸÜqTWÏž=ÊyxxxþKð6 ?‰Ó§OëUnÎœ9œ‰?ÃÄÄÄ%K–d5À€ððp´k×U«VUÛmèB(¢OŸ>xòä ¼½½‘˜˜ÈY¶~ýúËå¸uëV¡Ænaaàà`V›„äädøøø ==K–,ÁÌ™3YÛؽ{·NcðÜ4mÚ&&&Zéºìh~ûí7ܹs‡ÓžPÚ‘ :”3õêÕ9r¤Î±%$$ÀÇLJÕQ¹¹9Ž9¢{n;ŒÜ¸¹¹aÖ¬YHNNÖÊ«^½ºN»®öØxúô)ÂÃÑ––¦•×¸qc0 ƒ^½z!::šÕ:?~ÔD5Fþþþ¨W¯k¹¹sç¢L™2¬y»wïÖøÛÄÄcÆŒÁÖ­[ñùóg½ÇøoÃeÂf¢2@ÏD"ÁàÁƒµÒår9¶lÙòÚ€JÛ76TŸãððp¼yóF+¿mÛ¶ùŒsÙœ={–Õ&‹·áááááÐ r¹çÎË·œ««+úõëÇ™Ï&€X[[ÿÈðX=aݼy-[¶„——N:•ob±ƒ ³gÏ°k×.”+W...xñâgòåËÃÆÆׯ_/ôØ=<<°~ýzÖ¼?ÿü 6Ä´iÓXó†ÁÎ;Y½ôpQP£éÁƒãüùó(R¤g™;w².ÎT¬\¹cÆŒÑÙ\.G=ðöí[ÖüÝ»wk,ºòz¢Ra``€­[·²æ°zÒ…««+ë"òÉ“' e­Ó¸qcõÿ‹/Ž°°0˜››¨_}½`@±bÅXÛÏ}Äb1‚‚‚X?kD„¹sçÂÅÅE+ïÂ… øðáƒFÚÈ‘#!°víZ½ÇøoÃ%²9‘`ó*C‡e}6oÞÌ*4ÿ¼¢Úˆa3>€®]»æÛvÉ’%Y…Ú‹/² Z¼ÂÃÃÃà ?…;wîàË—/ù–óóóƒH$âÌg@lllX=B•',"•+Wàííºuëê%4‰ÅbŒ1ÏŸ?Ç–-[Pºtiuž‹‹ Þ¾}«sqQ¿~} oA…aÀ€œ‚[xx8gßÛ·oGïÞ½õîçóçÏðññÑy’¡B `ÕªU „X,æ,·{÷n 0€sxÙ²e?~|¾ýÍž=üñkÞÔ©SѾ}{4®íÛ·³ºÝíÚµ+ç €.ŒŒŒX==}ú”U144D­Zµ4ÒÄb1 P ~_½z¥wY†aXŒy½=ç)àׯ_YS¹\Žh¤Y[[cÈ!X·n« á_‘ *h¸¥ÖÛ 8::²z—‹ç|w–âààÀz’ñìÙ3Þ¯¸N7òÂVŽíôààm@~Ó§OÏWg½ZµjjÏ@\°|ª¼«ü$‹©^½zzëØ«‚òmÛ¶³]•n}LL g™U«V‘¡¡¡FtéššJ•+WÖ{ü[·n-Pû2™Œ¼½½ÉÆƆ5²xîËÌÌŒBBBòmsïÞ½:ÖõPâÄ Î6¼½½ÕºrÓ®];­²\Þž$‰†ÑtAl@ˆˆZ¶l©Õ¦ I$Öñ²Áµ]×5xð``wº`ó& )33S«l…  4ŽªU«jµñöí[‹Åœ'~EôõâwêÔ)Î6.\¸ÀZ§zõê¬éº¾_~”*Uª°>«Âx¿ÊËÙ³gõ~?:ôÓæÈÃÃÃó_?ùË刎ŽÆÙ³gqêÔ)}8O>üüü0yòdÄÄÄàܹs8uêþøã¼ÿ^£Ü_ýÅyŠãàà€hœª}ÿþ7nÜ`½ß\1Y&Nœ'''Ö<}`;]HHH`óÛo¿±¶ááá‘oàülÙ²•+Wfe¢Ïår9öîÝ«uÏ T U´ÈÈHìÞ½§OŸFHHÂÃÃaccƒ^½zaåÊ•zÅÎùà²É — x{{³ª?EDDz\……m?ƪU«XËë£~¥¢aÆê`­ùÁŸ€ððððü H!ÉÊÊ¢   jÜ´)²xö@‰XÓ5j¤vš›7oÞÐôéÓ©L)mo,ˆÈκmݺUËݧ.är9=z”ªU«¦÷.¹¹9͘1ƒ>þLDJ÷€nïY2™ŒÄb1­]»–³ŒT*%333ÖÝþ˜˜7n99g“‘‘„~kÔ€öï߯>Ab±˜nß¾MDDñññ´`Ár+[–õÄ…HÈ1{{{-/e9ÉÊÊ¢¡C‡’€ ö>D¹Òu¹ƒö÷÷׬'boa@B¡fÚðáÃ9½­Éd2š9s¦ò>pŒ±˜ õïߟÂÃÃéÏ?ÿ$¬^’ò^Bû —H$¤ •Ê—åWGå݉뾫òæÌ™£3®P@@€Þ߆¦¦Ô©sgºxñ"ëwdaIJJ¢fÍšDB×)d®1éãý*/M›6UÖg@!÷ûѲUKº~ýúß:?žÿ Ç–,''OžÄБ#ñ!.uj­[@PÕ‚2¥‘ø– ù£h(ÂïAº/HLȾշnÝÒÐ{ÿöí&þþ;¶ïØS‘=ìä¨kT5l ¯Óˆdàìà\`inÿeË1hÐ N½i¹\Žàà`øùùéÙÒÒãÆØ1c`ee¥‘'‹±fÍ >œ³¾««+Z·n͹«(#£ œ9s€ÒÖeôÆïª{ IDAT¨‘: kK!|›ÈQ»àQ°2¤2àÅ; "8}K€Ë +jƒví;aóæÍœý0B$'8ÕsDÙöe`_ÝEÊZA "#)Ÿ~BÜíwx¼ï ¾úŽöÚcâïÑ¢E 0 Ãq`‰1ШàVp©˜Y¤>¾^D@yŠˆ³0·°DÏîÝ°yófV; °±²@Â×o¨^TˆÎNrÔ°ÊY!ð] üù¸û ØÿR„·É2åû— 6 oß¾˜5kÖ¬^ :B: à.ìå è@¨8“=¤Ü#Û¹s§–ç«„„ØÚÚâøñãZ¶%¹9}ú4F ‚·ïâQ»(бPÃ(kˆÀ·,àáàöG`W ð%hÛº6nfÝEòä *T¨€‘Ы3PÏ ¨Z °)(À›8àÞŸÀÙKÀ¹+€@ÈåÊúÎÎÎرc5j¤nóêÕ«2hž=‰*E€ne”c,Ÿ}ÏS¤ÀŸ‰9÷<ö› ¿5l€‡¢àãヰÚq0 2`P¿[QTnh—jf°*f" !.Ï#’ñð¯¸uü3†ÁB¿E˜8qâÛsý ²²²°hÑ",ñ_¹LŠ¶>BÔo,‚{uŠÚ3||Ox¡ÀõPBŽFˆ)S¦aÆŒ000Ðh/)) öööÈJ¹Œ£D=ºBX½*Ê&&@FOc ¸ÿ8rYŸÂ­reìܺµP^ÑTÈår`Öœ9HÏÈ€°Usˆ¼APÕŒC €èÓg(ü yØ-È‚ŽR)Ê—+‡û÷ïçëKExx8:tl‡÷ï> „«1ùC™fp¬`‰±©r¼‰JÁ_áɸv ÀÓ«:¶oÛ‰J•*z~<<<<ÿUx¤ddd`ÈСس{7Ä-šB4w„•u«‰PVdÁÇ‘9eðí<«UÃÝ»wÕù7nÜ@÷.>HJø ?9–L¹íÒ¯Ò€/ï€fMš`ÿÁƒÞzd2<ˆ… rçÅÆÆ&LÀÈ‘#9=™™™aþüù:¤[¶l ±XŒ“'Or–Y´hüýý‘˜˜ˆóçÏ£oß^ Y2ü‡ÉáÛ0Ôö²«Á“×ÀŒ-À±«r.-]ÐÄÿ7ØUÖ­¶&Ï’#úÐc„Nº‚´Ä4(d mU)½MFù¸}ýø 88Ý 0¥€ÂBƒ,«©€W>šu2pò50柦)„ôéÓãÇG×Nûæ ¦(,ó±ç}O@€ Ø,•ÝÝqÿþ}-u£üŒŒ 6 ;wíBK'{*àž–I–8ü˜xG„ ¶lÛuþÍ›7ѽ»¾$ÄcáT`POÀTÛ²¯ßóW;)•Ì7jÔ(øùùaîܹ@ýB,ó’ÃËNùþp!W§Þîˆ÷]G',_¾;wV—ˆ0 Ðcv)´éS+nðå}&Ž,}ƒSkÞ«¦£dÉ’º'öòüùsøøtDTT4FMaØxØØê~‘>²°n© +VÄáÃGQ¶lYaõêÕø}âD³#$ËAؤ1*mDÅÛMÙÃG˜1}:æÍ›W`lqqqèܵ+îÞ¾ ñðAO}1u(餛·Cºd9J99ãhPÜÝÝuŽuÞ¼yðó[€Rîf¸Â•Zê4¦W(.&bëøˆžŽÅ‹—`„ ?ÍŸ‡‡‡çW„@ô$==­Ú¶Åõ›7!Z½¢žÝ ôƒA_‘1z'Ï`ÇŽèÛ·/.]º„6­[ÁÓT†=pÒo³M͹ w´ÅJ•Ååk×aaa={ö`Ñ¢E:í0rS´hQLš4 Æ cw‘;;;Œ?žÓÍ- \ì]¾|Ym3ÂÆõë×Ñ AøûûcúôihQ“°m*¡(·çZ-ˆ€ P ÿ" Kª\,#`  Ð:°%ªô©\ g”ö% !CÎâé±åq F”«L<Ø:ê?Hˆ8 ¬è ¤Ê­y€P¬¨ Œª °îøžŒ½ìÈö¢ìîk×¢m«VpÎLÇVF×ZvÝRý²n…‡ÃÑQsŽºŒŒ ´mÓa×®`C]ú•Ó½¨ÏKb0ì:ƒà—ÊØ(ýû÷Ghh(Ú´i…j•¥Ø»Vç®ÏÏ_zŒ’¿çœ†˜š˜ == ËjÆV.Ø=O‘n[ž(c€¤¦¦bÙ²e7L ªŒ’nùHGyxr3 Ëz<…–¸v5ŒÕsØ?ÍÓ§OѨQ}˜Y|CàªT+ØéÌ£H9†ö!9É—/_‡››fÎœ‰… B-- îÕ«ãMF:ÄçO@àÀm$¯ JþŽ¬Ý`òü%=x ånxÁ‚˜={6ÆnwCÓþ…|ùœˆÅ–ñaíÚµ5jT¡ÛááááùOñožü×8pà Éî­dšúY}Ì™N`TtÓH7MýL+S®,A"!¦¸=‰Ç '“ÏoÈ$ù‰Ö#C1•4“mœ÷ºS DÍ•WßâìeÊ›(óoÕTþ =.'''Ú¸q£ÚÀ4%%EïûàêêJ“&MÒYFå"6..Ž³Ljj*™K¨f†²®€(,çò¬4*®TZ3]q´q"¨ª+ÈÜdmjX² tzi®92ì×€;ýhMW_3åÓ¨å†æTÔÝŽDF"2²6"çÆN4äá š!›JŽ J#Š–&¥NPÎåë§4duª¤™~‚ëª6'šÌŠõ&ìùLXzK=Ƶõ@4,çòóR>¿JE4Óû–ãxî– ÅPPçÒ ‘Œ†ó=bŠ‘€’$  bPKÈ €*0 Y"Ð' è›!(Üd(иqã4ž—zPPÚ¨õî”3—Í @ ìAE@!ÈÑÔ½ (º«2_>ÔÄ!#C9Àõº¢÷ÚWÖe™å³•iwÏ€å=Ÿâò) *m2ŠH@µ‹‚özkÞs¦|>å-•ã,ašP”:PyÏ»¹€ Dd`$ä#Àv¿¯G§É›Fm.O•X’eQKdëhH º¥ ѵè4yS`Lm’‰hôèÑz½zù’¹…ˆ¾5¦Ïdª¾¦ûÀÜ* 4ÒÏß1¢þÃET¥š€D"åœUyÆ“©™„b1‰õÓþndùÞ4IùD’À5$lÕœ‡Tt#ƒ9ÓÉ$1Ž fL&PHáááùÎeìر$04$ãÈ[z}_KÖ­$A½ÚÄØÙ)¿¯K:¨KG2¾F&oŸ‘ØÞžš·l©a4~ÿþ}‰„Ôm¦3&oõÕÛ¯41 È©’‰FzîëxÖoTÒ͘4py:MÞÔf”IèÙ³g?ó1óðððü2rßùÿ)))>jÄÚAÜ9g÷Wñî=²–&ÆZ:'™3çC°¢Ní 5 Š'O!ݸŠÇ10:qL½:ȼ†©nÀ¨'ÀX'ÀÓB³_—<;¿°-­¢jãµ–%0¹°ô•¦æF{..˜>}:z÷î ±XŒ[·nPºuõððÐë^æ<·+^.÷œ‹/†L–…]3 â\o`Ü'`ÑÀÄP[gÆf`É^ M`X{ = Øyh3Ø72(Z³$b¯Å¢æXO÷ÔÜ‘´r±Ôøûä€ÓˆÚ ÷¾Uà5ÆY)Yøðà#Ò>§A  ÝζØTa3d®5•Fç*â€àE€ÄJe*hæMo˜Z}+Õ®Ž/Þ<üoBdb†:æß1"—ÙP\ °è>`"Öj €Ò0z[#Í4 åýiîy)ÇB!P4Ïa“Àx)àÄÅ …€‘RÀ‹Šw X$®*€S€«˜)P`ÖêÕèÓ§ªV­Ê2"%iii9|®¿ÆV<óر¸ä2%zðEùw‡R€•x™¬Ti:ýˆè ¸Zì —ÞÉ0e0f0và™çÕtqbÏÚíÀÛl﹪wÇÓ˜: X²Niÿð ЯPÜH“Á/Þ—€×ßÕ”u¦Ü–=º¸ã«Ñ_µQÊϵZ”½¡ÿ2X×<#aý°-eˆ"öJC¦WRPÌŵ:ØÂÔJ„/Óq~Ë{„ŸN@@„J¸£ÏâRX;n-úöí‹êÕ«sÞóŸEhh(öî݇µ;%(îó"½S `QŒM´?œ‘cß6*º àìÂàå_9ß<ö%p(Å &ц çhÔãüÞLMEæ°±Ôô„xp?0¶¶ß¾‹,?ȯ\ƒá‰ Ðés0dFDpª*Ý¿kÖ¬xñ|\Ëäß/ÅŸQ”r† M+0VP¼zÙŽ=H;{Æa— \·ç;÷Dpp0ºté"Âà!áXÑÝgå©%Äe hÑkšuªRZ‡„·Ù®˜³Ëõ[R÷Ï&aÄÈá¸x=H#Ïÿ¼’û÷ïÇ·¯_a´p®FzÖô9Öòd2ЗDuºâÃGH×n„¨g7n^«Ngʸ ë÷i†œöD×b@…l;æúV@§¢ºÇ!f€žöÜù³]€ ±Àw¹fzùòå1cÆ tïÞ]#>„*º÷³gÏ $€dddè,£ŠŒþâÅ 4hÐ@+?##›6®Ã°ö„ry´¤&®êTdr á›fÞ®³€—pÒ?'m@k DÀ•E¨1¼b¯Å¢dý’pëTžsŒƒžàÏÝÐõ˜ʵ׎ V¥,Qk‚n¬> JOÉ1:ß1(_Ë€ï š•‚YéÀ‚HÀ&[]£¬0§)°ýwÈR¿# ¥¦ýÁÄ[@bJ#ó–[+=˲ÏcÇS Åb÷|K¤èš­Ú'pÑðÌ%¨ôàÈ(…+r ‘!!Ö­[‡mÛ¶±w àÀHHLÄ°ÆJ¤¾=Щ4gq¬¯¯ÖÁ¨qØ÷0·°çàÓ¨”ýèê×:µânSŧ`A€RؘµT3oæX`ãàip¶µfÞÈJ@õ``óc¥Ÿ ¬üèSØ™+<‰«0:L),m}Tn`εƖ„Ì49ùæ:__N«\­¶_#Wö}€ï¼Òh3ª$N¯ŽÇÚµk°sç®ü'ü7³zõ*T¬"F·>š?s&fÁ³Ž2˜ ¹µÑ„c§‰!‘0˜:*/žIÕy=UàéŸ2H¶Îcªé¬ë{ ŒBÏ@èUC$îë‹,'G¥v B¿ÙxÔÖaaa¨_Ÿå…°víZˆJ:@PÛ&>‡[KÈe„äöx@IŸ²ppÁ+øLuÂÞY/Õé†&BôòsÂÒ—ðøñcT¨Pµ>Ïÿ | Â|X·iD-›Aà˜c +»ÙñÓ0ð÷S*‡çÚíRÜ ärˆºtÐhGìÓ Ý°ÒW±•kñM|—)¡\”ºòÉì±ã`,:Âì¡TªT ‡BTTzõê¥!|@‘"E`ccƒ˜˜˜üoB6FFFùž€¡xñâœFðGE—$ 神~ípä 06Ûô"Ï¢‘°Õ<Ä€™1`b¼ù”k[&E³uè Èüž Ç ½½òJÔ,rí]A BV*ûb¡êªJáãzv`ÉèkÀ­#À dR³ÂÍ#@69¸{Å]°Cð*&DÕ\Þ¡®½Ž¼ê°¶¦šŠò¹çâŸ_€[Ÿ€!¶C‡³=[ùdç‹MáCEëì4Õ¶ú“ û÷îÅ·oß´+d³iÃ:´vÀ>ç–ã{–îw8/NÙ1$Åàj<“ŒêŸ“O|O8â%ª™º(_ðí¤gd ð¶?2óçp0Uö·>*twÑ,×={#=0¸ñh=šÝ¾èêþ` aOÝž–ì²½MÅÊ'.2h1¬<ˆ¯_¿êžìßL\\NŸÁ€‘]û[×ä8}D¿ƒ¼_q[;$ö]þÝ›¥ÙX@Ô©Fº®ïMF,Ö>TÛ´(žýa£ú—-ƒ›6±ö›””„ý‚ÔL.÷ƺúåBPÒA=. îÛ7nàÑ£GØ´i#Š»˜¢j³¯Q×¾âæ‘OP â<Ù9õ9J–7ÖRUÔîd+;Cæ;>žÿ:¼¢ƒ¤¤$<ŠŒ„ C[uÉåÈü}Dý{CXA{—2•+EÆ0•‘R]Cñ( ÖF"Ô͵˜îX\Œþ‡,ë¾49`~ °¼X‡£©yf]Šr–/_Ž‡¢k×®:ã ¸ººHÑçPªaq —/_F•²"Ó¹½ ܨÈa$<µpþ.°îð:xú¹øž $}ÊûäìžìK-V`±ÑRìi¼ññê¼ÌäL¼GñÅ:ý –Z¬€¿Ùr¬sـLJŸhôiédâ^À£ËÊAn 4 8²¸^þòHþ ”Ñ^D¡L %ÝJ嬀å `ô `°PQ‡÷¯4`¾ °ÜXïF]R¥Àåw€„Z°|‚¥“µ d>k­OÙ‚‡u®r@FVîܹÃZçÛ·o¸wÿ:—Ê‘6ú_,¶F[Æ'ˆÏìý}É>¥÷>)ë5ú—B߶VÊSu›ã‹r€Qi q âOíöîF»ó¸çèÓHL}Ò¤@B:ðâ°êOàü[`rö J@1Ês.l”ýº÷‰Ôl«ícX&U ,èÜêZÂÎQÛYAò)’>eá¯{Éèÿ–E дΑf];dff©U#ÿ)®]»…‚ЦsΤår´љè=X„ò îãÊehÕŒ$ÇŸv~ß›\ÐÇOÆÚZ麷Cüqù2kÙÛ·o#+#¢öú_kôõ%ŠOŸ!¿ÿÃÆ€±³…¨w€°e3 påÊ„^¾„Ú­!È>Ê”Ë £Ÿ¡ùàâpªÈížûÙÝoÝýƒØO]ÅÔìP¡—y,žÿ}x,Ü¿ ¬–£¢$ݺŠ¸8Í:ÊZGPN©/#¿uÂúuÔéò›·•ÿIú/kFi×áShe ؈è`ùk þ]àfMÀ#[‡¾¸˜R ¨f®Ôí?ûØðxø¸â•sêáa®ÜÕµ°°ÐË]e¹råôPèw(.7¼÷î †«¦ä´éû ÄÝæ ¶J×µC—c”i6À’áJážF1d|Í€›Oy”mUF6Fø€ÛËï`gý=è³/ŠyÅ×_ADˆ>ø±M–7†Ä\‚»«Ãq´ûqHÌ%piž£KT¢†>¿ ÅùMÀçX`A(û¿f 9EXôä„Ê]TœP-Øôˆý„¶Õ.®¢¸10Ũf“ýÜc ÑJ['3 ’@y²‘—K à+rÔ¯t ,4Éõº”bs¡hÖ¬™VÈÈHÊ~ß³ŸÒ@+'ÀƈN–?êŸnv<ò¬ÕKìQÆ€ÒæÀÕv@  "¨î®Üœ–(U±Z5VŒ~,ßÔïÜ< xdÛB£gÝÛ5«)〰á^AùîD$[Ÿ*U®@$ÖÔ†dË®å³ãn†}æ2!ºþAùï—L Tcˆ%ÚŸ­ûçñ=Qʺ³ }K„A–¥ØŠ•6Â’«Õa]"GP)ZÊfVDDD U+=ôÎþ&"""àTÊErI ;7I«ÀÑЂ» ËÈ ü%…¨Ÿ¦Zg~ß›\HÖæ5ó«ºãó²ÄÇÇÃÞ^󳑅9—œ]Œ‚ô›Z¶ •½TÊFçOBP\Ùc`Q¥ ¸~ý:ÞÅÅ£lƒ¼³›âð)6 C«q¶MDØ4út/Šr5-ðñ5û÷h™ê游-éééz{'äáááù/ :xþü9À0`Ê*u2èK"²üüa0u"kömk¡{e<«#kå0Å‹AX¿.1Ï9v2 YY¨mÓ\Û¨ëwºàS ¨r˜öp6ÛuQž ³®ÅW`Æ_@ð [öï°±p2+Ç­åʕÑ#G@:Trchhˆäää|˹¸¸p"|þâzäÈeøò ˜½˜Ý°¶`­8 Y tù ðù HNVffB·vµ#`àS;GýɵMY¸ù”Çæ*[:í2zžíŽ¬å#=1n÷U«»¶+‹µ¥6àºß ÄÆÍŠ ‘ÀþÙ@·Ù€y.)"7™Ù KÅÌT9*G_2”.]g׬uxö]TSóï®.J{„w©@ ‡å€€ù +² ÐW‰ á¾WÀeÿ½ókê|ÿð„-àžˆËrÏZ7­uÕm[ÔÖºêÞ¶vY­«Õ⨶uT­uâjµNÐ*Vq+KTÅ È’$$ïï#QL ÒñíïÜ×ååå9ï:''ñ}Îç*E¾ÏQ®ºåSBÚÄ7{nÏÝ¥ªdÔÝ ÓOÇ]ìï Ù:ˆJo.Â{àx7¸žž>çÍK c¶—TŒºþ0}ü¾A:þÓfˆˆí+Í_§£#Tsƒk¥Àò¾5àn¦{2긤x ô†e iy˜w^2Šü*At Œ8&¹iiõàVËÅäG¹’V}MW”œ¹¿>Úl=·¢2ÙñÍ->yã<ó7¢LeéP(Tö)fñw·¨¸ví¯ø:íâïxòVý†ÒMúÐ{{!©¤•}$ßôGZ6|Ç»ŸVǵtþE(ýtøˆ >Þ^ÇìÜ}‹¡Óé¸yó&¾¾¾®YFFFæÙË ¥­­ÁŸX=sŠÒ¥°aæU=àðË”uj¡>–¬ZÉî€Mï(ëÕ…3w½†t+ ¡ÉùWø_UÚ(ÎO‰½RZ·%xyy‘––ƃ,jo’œœLjjªÑ9F‹ƒÝ³ÏøQR2F÷2jj [ #B§fðËçг êG–Hë ¥T€Ð¥j”Äë-On†Æ#„ÀÆQÚ$”¨^"O¦,»bvxvy…»áwÏ•WÙ«¤’Ú.e ËèüiÿômeŽÚøœFr[+ñÔ6™.©£k7-ˆñu¥Ï=E#¹`½H†€½zðWBI3ûÇí:˜•U0ØÄk{!ò}Ž4 *¥›|žãÅ¡[5½kü ·©Ü¥ëø£›äŽ5󬤊ØÛ™N³të¡'¤1ÓÒ%cdÊHp3“œ!{ÐèÁ»´uƒ^’qäïãÂàÉSQ.ø ¨WÞ  ðÖ>)&¤Aé¾Ûš¸éO2r8¹+‰†Jå[ ½N›’4ìPšîã«0ïF_÷s ###ów#+ fprrB¯Ñ 4Ä­rÖ¬Çnþ,Ä»†t·"[ÐhÐߺÂÅEÉ(+Pb IDATVÀéàoèãn $¢¨á²\Y2kÔFagG†ÎÄ&õ9*;H›¥L8çó 9¨ ”-$kóÏÔa±tÿ|&¬ Ì΂u1 ½‘|1­¨“£O¤E_½ ?þ ߎ„çb²ÕRuóøûàê$™?z oµÌ;OIWðt‡ WA§Ñ¡²3ýÊßÕÝF‡6S‹K%ÉGÛ°þÅÊC§Õ¡ÉÔ`ï"mdRo> Èé2ZJ³›‹&r4N®Pòé.8ùF™ÿ²b†\ \#½rÕëÑLúͤŒÚfÕl„í¨aØÏýÒpLéQ<$d}ôÉyŃ )7¼úM÷D |ÍÏø)kÖC ”}î­qŠngh-NáX£F ”J%W®\1™2÷E¬Q@À´âë[“Ë×¥àæ;%qaÌ·Ïâ:ž§z×úµ—þ­3ÞûÔ”¤È$*40½LKÅÖÑ;g;ìœíp®àLút£véwÓ±u´5÷Ï= øqŒôçE†V‡®ãàý…àZ®6n“Àåd©®‡^À˜ãÒ£kÞãêÂÂæÆç@Š¹x˜-¹?E™ÈµE.@Ç|Ô‰3z诅F øÉ6oJà\rÄäè˜Ïs”û½¸œl\û#—¸tɨÈÏøÈåIŽ´_W¸e¾m\¼”ËÁ¹ܺ)©PËϸÝW‹¥?BÝšð8 âqF\ƒòñâ}¨Q\ú• ÷³$äÖãçæȆ8º¨hú–qpz~hžè AÌ 2ÇG¦S£g 3½Š___V®ú€{wz=|4FÃGcŒßÀ7ªžÅ°q¶|¹0ë¨\E£}d4øû!îÞ³êwSwú,ÙïBÙ¸ëWJçÏ¡ˆ2¬ÛÔµälØ€mNŽÕóšäÉ“<†*"Š†¯¾Æ¥ ¸y9ƒrUzÁ÷cbù~L¬Q÷÷«Ÿà­±îd¦æ‘’ÃÈZ'Úlùê&[¾ºÉâ ¯R½® 7.e`ooGõêùdã‘‘‘ù  f¨W¯J¥ý™sØôx ‡Mkó¦pÍÌ9ˆŒLìÌFQ½šÉq„^zÆPÌ ›.9»l9:!©e_p;¹˜»¡sYéßj½ôfÜå…Oê˧I¦Þ|nÏsæéËzK‹™ÙÛÛS­Z5‹3aYª€”*UŠâÅ‹›Ì„Õ¨ñ«ìÝq!r¨ã;¾2º¥Ìø2ž@ÐX¨áÕ+‚½-l: CŸÕ‚$!"ž¦Ò¿~—Ê.+›WÙxpñWvÇâÙùYQ²Zoûr*è4q‡nàÑNú>ëa±»®R­mÞJwÉ7Ò¡^»¼îWBÀ†ð$>‚ O7Í{AÈZI)ÉMÅ{ñ0$ÞDagOx¢š‘µ`G×|2´ÔB*اÖI ‚Ë ÏÇ—g¥¿ý*IÁÔ6àüt¬‡Žè%õÃÁ„aqE}4PM›íL»pD ÈÖëó}ŽêÖ­‹J¥$sQú»QÙ¼íµ:X+ÅáÔ*iú^èL9)Š|ç˜zꉤT¸J_ÈÇI.JƯìòÊN:àIZŽ‘[Vløcâ#2é<ò™ÛÎí¨L´Ùz‹Ý!‹ŠF1{¶–;·mñ­£dí£gsÎ ™‚ÙAöT«a>.ÄÆFA­ú*.Ÿ•x(kùZü»©‰åI¯~(«WÅqÛ/y²hå¢?wwJ–4þÀ5j„þÉôQÑÏ+t:HK7RBtgΡŠÁv¨”Z¤g ‰‰¥Åø‰;JlxM:—áãuî×ú×ÉÎÐ14È‹ ŽähÍzä}øRhX6,†vƒ+òZ·²”¯&}‰®žN§n½:ØÚÎNFFFæÙ1ƒ““-Û´áäæ`CaÓ¥£QíR)'½Mç7 ÇÔ“?B¨5¨êÔBhsÈÙŒþÜìXŠÒÛ“ŒEKÙ“ßÆKãÍŠC9;ˆÊ„ÀYsŸäÞSCƒRBï§{ëýá÷‡Ð± t{î ôÏ÷¡zw¼¼L§y4…··7±±ÆoïLa©¢P(òMÅûæ›o²dÉ£ i-èf¢žØ¢-ÒßÏ»\[ª„î?z´†ô,øn¨µPÏ "¾LÔæhllqkæF±rN<Œzȹ.`çlGÛ¹Ï*˵˜Þœ¨-Ñl뵦^ÅÞÕžs+Ρ×éiû•Ÿ¡ÝƒK‰$G'”aðjÞšì^$ýýüñÞAØV˜ñ:t+¹^íXÕê"*¼ÂÚ˜]Ìh˜C7/7]~zÍÕ¤¿o¦Cƒ­RBï§{£ý·¥LX«À”R&­í:È­·]: ¯ õ#]@O <ƪ`ß œÇsuB¶è TqWš4ib<Òs𺟢Ž§ÃÉF D/ç —ÿ-)s_{:·Ü×Køš%¥ÍüåG°æŠdxLo ) …索æNŽÐ¬”+Q±ðÃÏ’ò1÷éËìu¤?Ï“ëŠUËÞêðìøÏÁ’3& ÚT”ܬîgIAè±aŸ”% `l˜düÕ+-ÿrUJ¿»¶-´ªÓNÁÑè8Ì €c› × üúW}’žÃ ÷0Z¿S÷šÅp(¦âæå ­¹KÉ vô™þÌØ ýù>ö¶ìرƒ¯¿þúoÛ€¶iÓ;¶mÈaì4;:v3þ/aÅ"ÉeòÍ·ž»¯gëzI>ºpF’&ÍÖ ¸UÔsqïïˆÇi(J—²èwS¤gð¤[_H}ŒÍøQäìÝŸ§½²Fu”õëBð.ºö40öꫯâZ²$O6cÿÕç–Í›ú˜LïúØôîŽÒÇE1'ô‘Ñh×oDQ¾¶“ƳmAûöí¹té?o^Éo¨.]FU×D…¨°•²~]´K¿'gó6P*Q5n„ãží†´¼v êtë=ÊÁ†{°(^*0XÎNJËûY ðxê ^Òº–ƒƒ`í]©Î‡§Ìñ„IÏmdÕ°ù¾’™_²(o.ÞÞÞìÝ»×¢¶–* -:P½š;K‚oÓÔD9 轘”ë«aP¡ü°[ª˜ng¯Ö„Ÿ?ÄèûéšO}ø#·8µ(uššbåŠáÛÛ‡ÖŸµ¤¤Ç³7¦ÅÊcàñ@M:Ì©Eáèµz*7w£Ç/Ý(Wç™EwfÙY”¥Ë£oÚ c•,Sf…Õ`Ý4°µ— ¾÷ ÜŽâúŸÁJ€öîVÒºV“âÖ^yú¹‡9MaR=iÃÜɾ¿Ok«mÕA9ÀÏÄÇŸ Üy:Çç&\·ú«$$SÀÏ ï0‡üfG~8Šž=C˜\þ¸'ÕÔHÓHñ½=à³ÆRŒ@1©ÞIè]Ø'¹=¹;Ã{>0£¡T ¤‚¼V¯„žaÃvXô¤eHFHï.ðÙð¨šï’Lò06í„îU¥4ºË£¤,d®vд,m þÏÅ7,ß^’Œ¥Bjs¸ë³´¼]ªÁž%·éðA%”JG~y@‰òvÔogœeÉ¡˜ŠTâRh aÛQ?ÑQÖÝöïUâíÕ(Y^zߥãÐêôíóëׯgóæÍ 0Àº -$¥J•âwú±vÅFNØšÈëlâ'Ž[7s?Õä9?çéßMš+Qh4h×oÄnÔ0Ó¿0¨HNFܹ šOŒÝ¢l¼ƒÊÿu´÷0bÄ“C:::2dð`ÿ´ññdŌ㼌.¦˜¶ƒ ;FÎÎ_áI6ŠÊnØöÃvÊx”åË!ôzÄkèÔ¥ U«VeĈ,Y²„ã[ðú ‹² ¾HȺ{¨Ÿèøàƒ n,###ó?ŽBs¹–d´Z-^5kr§tIìþš§ÂnaÑ|„æÓYl«½,‹[-~—ìËt!öÚuÊ”±Ü}ÅŠŒ=š¬¬¬ߺ.[¶Œ‰'Zd„LŸ> 6pëÖ-“ãŒ5Š£K¡u}­D£…’•”¬SAa(U/ŸÜíNø]V¿ö´ €ñë^~‘B  (‡2…K}òÞ¶†õ±"’¿WD¯¦já'•ÑW®P­Z5>|HÙ²eÙ¹s'ݺu###ƒY³f±pÁ<ê—†?{Š5 [võ·¡Šé ºV±é*¼{†/õ¢Ë‡&,ÉB°râU~_vŸ¨¨hFM||<—.]²êEÂËpéÒ%6lÀäÏl˜ø‰™TdVжÑ.Ç:âtáO”_þGN}ßfÞü,¼ó_bÓ®ÑA¿Ã Î>T°-x;nÕªÑ]Øpó%Œ3zЫh÷úëŒc"Û’ª5`Àš5kFtt4kÖ¬áã?æãpX]ø¹3µÐm¿’øl{¶ïØ…ƒcYÞx׆{/qËýF+éÝ»'ß,\HÐe˜s®ðãitÐ?DÁ©$%[·m£zU>j{ž{׳ =fìé4fw‹ _kÆ@Ë–-iÞ¼9sçÎ-üb Á§Ÿ~J½zõx§£–èˆÂ 1‘:Þ騥^½zl\¿žœC¡hFM(´"„~w7lbù²eT­Z°ÞرcñkÛí»ƒÐ>[¨yô7ãÑvíMµÊ•™?~žsîîî,_þ=G6Ügõäk…6BôzÁ²áW8³÷«Wýdõ $™ÿUdÄBfϞͰaÃP‹ú£Ïc?." Mû·°=sžßû ???…!Ù¾¯±áhrÁcäOÀ×7 Ëþíß`ÝúŸ åw\©R%Š+f‘’`m*^Sxzz²oßA¢o;Ñ|„Š³1V,Pk¤*èý¾€þýû³hÑ"fÏžÍÐaCùõ½=š|ímÁ=ǃK‰¬kµÄÓIìݳ—6mÚzè ¥ÔÉØL} "ŽZ·Hv,@1»+ÚµcýºµŒ?ž?þ˜©'aÈQ)n®?ÿ=J~»­b[ðvÞxã ††b[É7ô6ì·rŸ'¬É·t*ê4iÂö]»P=çf˜˜˜È¸qãÈÊÊ"$$„mÛ¶’™3gòÞàÁ|p&œ€,ën9ÉÐêW'“íùõ·½Ò÷âÐR3JÑì-Žœ°n<¾Y•øùµcýú Œ3†Ï>ûŒÂáý#ðØJƒ?. ÚïU²û–ŠÍ[¶Ò¡C8Œ6ƉMÏpjwRÁƒ<‡‚ý+ïðñë¨]³»vî6Üs…BÁ´iÓ ãøqyšÿ"ìííùý÷¸Uò¡k+-Á¿h­ÚT !رIK×VZ*Vðæ÷ßЭ[7Ö­]KÎÏ›Ðôêþ®u²–xøÍ ¡h.fáÂ…ZÔO¥R±{ÇšÔ©ƒ¦s/´«×Ym äìݦm'Üìì =xWWW£6ýû÷'((ˆßÜb^ßH'Y÷e~tWÍ—]/spÕ=V¯^M×®Eäw(###ó?€l€XˆB¡`ùòåÒ›°«Ð4÷G»9Q€ ¿€zÆLÔ-Ûá¡ÍáøjnøøøpâT8•ë6Áï4| Q¨:{’ ÅiS®*=v<;víÂÞDÊJK¯ËËËË¢LX¹ ˆ%1 nnnØÙÙåk€€”µæøñ?±sõ¥éP¿âî˜W›[C ñ*æý¢â‹/fòÓOkQ©T(•J^©!¥Û=t–U ~"bc$:ù]ùã[94%„U×PB[‚ãÇŽÓª•”žË××—S'ÂhR£2|ìK?€[¬ÐéàÌTÓš£X;•ñcF³kçìííQ(Ìš5‹)S¦°&jmQ²:¦àûý,øâ Ô٦䎭‡CB wwwŽŸˆáÝ·pèàa£ÍmçΩU«Öß®‚”)S†#GŽñf‡ž ï¯& ›†ð:³›w!á'ttÓ0ô]5o´ïÁ‘#Ç([VÊì4`ÀöîÙCɈ(4[¢Y´ñÈüÛ‘–ŽæûU¨›´ÂñðQ6mÚdPˆ,ÅÅÅ…Cû÷3ðwPžˆ¦korŽ+ÐÑ]¸Dö ¡d÷@»F9yü8îîùÇúŒ3†-[¶"Åpü¶ô6Yi&²<-À£¸”–**&Þ„ëg±¹tœû7©ß¨1ËѼyÞª‚YYYÔ«W2ÓÓˆ½v*:UÖѨ¬”v×^%Õ¹ôN%)8plíì6|_~ù%ÎÎÎFkB°bÅ Æ~ø!Z!¨§TÐR!¨¯„²Hu­\pTiÃ5m5½¼X¸x1:<Ë[»oß>ÆÇÕ«W>|8cÇŽÅÛÛÛ„þ|˜råÊ1fÔ‡<BÙb6¼é–Cã²Rö.[%<ÖÀÅGp2QÁ¡Û‚bNŽŒ7žO>ùÄdÆ-½^Ïwß}ǬYŸóàÁ#š7QÒ¢‰ž†µ¡t)é–Ç'Hu>³áÆ­6¬KPÐ2Z¶li4@||<ãÆŽa×î_)î ¤£›tÏ}^¸ç§“ìO•-C‡ gÖ¬Yã,—˜˜|}}™0a[¶n"áö]^iXœZ­]y¥‘ %ÊÛ!¾^,üæ[:v4N›Ëúõë äâÅ‹Ô­[7ßvÛ¶mcÚ´I\¿OÍ:v4÷Ôk¤¤|EIe}pOpñ¬žã¡‚˜ˆ<<ª0wî×ôéÓÇäx)))L™:•ŸÖ®E¯P |ÃeÃú(kù‚£#¨Õèc® ?wq ñä ½ûôáÛ… ©XÑD¦)+Ø¿?c&L 6* ;/OômZ¢jPEåJ€‘ôý…‹(Žÿ‰æüE*º»óÕÌ™ 8ÐbUùþýûL˜8-›·`ç ¤aÇ’x6v¡J-g씨³tÄGdrõL:ç~OFè0þJ•2Π&###óŸGÈš¨¨(1~üxÑ Iako/öx¥Ë—ovê$V¬X!ÒÓÓ-O­V‹7ŠÝ»‹*nóŒ§R)EÝZ¾bèСâÔ©SB¯×Ùu|öÙg¢|ùò¶;qâ„DDD„EãvêÔItîÜÙ¢¶ç΀˜:uªèÒ¥³¨X¡lžë·µµÖcÆŒ—.]2êŸ Ê•+'Ú¶m+´Z­áxdd¤?~¼hÔ¤‘°µ³Í3fÙòeEÇN­úŒ²³³Å/¿ü"º÷è!*TvÏ3žR¥¾uêŠaƉS§Nå;Æرc…ƒƒƒ8sæŒ(^¼¸2dˆ˜6mšhÖ´‰ptxá9*Y\¼Ñ¾X´h‘HII)p}ß|óP©TbåÊ•ÂÑÁA”rqÉ»F¥RÔôôƒ GŽÉóÅÆÆŠ.]º@øùù‰‹/ !„HJJ€Ø¹sgž¹V­Z%±lÙ²<Ç£¢¢Ä„ „{¥ BùÜÜ€°U)DûvþÂÆÆFÌœ9Ó¢{®V«Å¦M›DÏž=D•*y¿ ­R91tèPqòäI‹¿qqqbúôé¢ùkM…“£Cž1mˆvm_ .ÉÉÉùŽ¡×ëE¥J•Äĉ…V«»vío¿ý¶¨îQå…5*„¯§hÞ¼¹P(âöíÛ®O£Ñˆ*Uªˆþýû[t=:NìÛ·Oôïß_8»ä½G€pvqîîîbß¾}B§ÓY4fbb¢˜;w®ðkÛV8/žgýôSÃgœÙÙÙôìÙ¥RIpp°Ùä÷îÝ£|ùò†{zz¢T*‰‰‰¡}ûö”/_žõë×[}­†ëñõõ%&&æ¥k1äR¶lYJ•*ELŒeiÙüýý)>ʉˆˆàâÅ‚SE;991fÌV­ZEbb¢EãÿDFFP»vmœœœé7 Y³f¨T*þøãlm22222ÿ›Èˆ ...T¬X±ÀLX¹Â–dÁ‚‚Sñ¾HëÖ­IOO·hspùòe>øà>|¸E}þ)Ž9ÂÒ¥K™3g5jÔ`þüù¼õÖ[øúúÉø{÷î%22’©S§"„ 44Ô±*##ô~ÿþ}LÓ¦MÉÎÎæèQ)ËP•*U œCÁˆ#¸pá;vì B…ü+\ !ÈÈÈÀÃÃÃpÌÞÞ¢££±±±¡_¿~üòË/ää˜Ïô"ééé†ëñññáñãÇÜ¿ߪ1òC¡PàããCt´e…M*W®Œ··7‡¶¨ýo¼AÙ²e-6¼>üðCT*‹/¶¨ý_ADDxxx Ô¬¬,œiܸqÉ+dddddd^D6@d)õiQ+ Õ«WG¡PXl€4nÜ{{{Ž+¸‚ñãÇéÙ³'žžž¬X±¢P5Pþ.222~~~Œ9Œ…š5kÒ¹sç"™#&&†]»v1yòd”J%:ŽÐÐP4 µk×6ËëСƒai ™™™†*Í'NÄÆÆÆ¢~‘‘‘ØØØP¢D‰<Ç}||Bâú÷ïZ­fÛ¶m¯éy$wÌ¢V@‹Ç,Uª 6´ØÉ5¼vîÜi±[ÕäÉ“¹ÿ~¡‚ö_†„„ÒÒÒ¨]»¶á˜£££á%DñâÅ©_¿¾ˆ.####c²"HX– ë¯T@r3똊¹}û6ï¾û.íÚµãóÏ?·xÌŠÉ“'óðáCV¯^R©$**ŠÝ»wŒ…¢`Á‚TªT‰þýû£×ë™9s&?æܹs|þùçDEEaooo”Ë„ <˜›7oàêêjqß[·n™¬ðœ»¹ÏU,ÜÜÜð÷÷·jcý¼ÖÇlDµjÕ°··/Tˆ¥±ýû÷';;›àà`‹ÚûøøУGæÏŸoU–¸—%7ÖóÆ«“““Agq 222222–" 2€´é²µµµ(–µ Hjj*)))µwvv¦aÆFoTÕj5½{÷ÆÉɉ 6X¿ðwsàÀ¾ÿþ{,X@õêÕÉXpss£_¿~E2Ç;wX¿~=ãÆãܹs4mÚ”™3g¢R©ˆŠŠâã?ÆÁÁÁH1°”9sæ°uëV–.]juß””“™µJ–,Iùòåó¨ =zÔ`è„)$!!ôôt«×i •J…———UªŠ¿¿?÷ïß·¸»»;mÛ¶µ*þeÚ´i\½z•;vXÜçe‰ˆˆ X±bT­ZÕpÌÉÉ)ÏKˆ6mÚopÕ“‘‘‘‘‘)Ù‘ÀÆƆ5jX¤€Xk€€å©xáYÈóŒ7Ž .L™2e,ëŸàñãÇ 2† H®,6l`üøñØÙÙÉ>ÞUhíÚµ¬X±‚¥K—Ò¸qc‹Çù§˜4i)))¬ZµÊ°qûöÛoqrrâƒ>(’9îß¿ÏâÅ‹ÉÎÎ&44”ü‘°°0._¾løm­C¿~ýxë­·øì³Ï¬^[nQ¾ü C¾³áììL¯^½X·n]kµZV«Ís=¹îƒEbÍxNNN4kÖÌ*¤gÏžV^ © gÏžµjž—!"""Oü+ eÊ”¡víÚ²–ŒŒŒŒŒÅȈŒ//¯]°¬U@J–,IÉ’%­2@rߨ;vŒ .0|øpÞ{ï=† bñÿûöícåÊ•|óÍ7·•ÔÔT¾ÿþ{FŽiU…)„ìÞ½›Zµj¡V«4h±±± 2„ .‘‘a(@ ÓéÈÊʲXIMM¥[·nT®\™uëÖ*V%,, xV%üE|}}‰ÍË@ll,§OŸ6;vFF@žëqqq¡råÊEž ëÁƒ»‚t½GŽ±¸°¢‹‹ =zô`ýúõ+þþþ4jÔˆ9sæX¼®Â¢×뉊Š2J^𢒖¬€ÈÈÈÈÈXŠl€ÈðööææÍ›f k°>½téÒÔªU‹ƒÒ³gO|}}Yºté¿ºØ H›÷!C†Ð¾}û}úðäÉvìØQ$q6GŽ¤Í¨9|}}‰ŽŽ6Êþôî»ï¢Óéزe‹É~ù]O… (^¼x‘gŠ‹‹C­V[ܧvíÚ”-[Öê4¹üúë¯VeÝ:t(ÅŠcÑ¢EVÍe :Žèèh£tÈ_Éù·geddddþydD&…R©R%ìíí 4@FMdd$Û·o§T©R´nÝšóçÏ“––fÕ|ÉÉÉ :”Î;3pàÀ<çŽ=ÊéÓ§ T?„lß¾š5k2sæLFŽIll,ƒ62&æÏŸO:uèرcžãYYYœüðC¾ÿþû|£ÂrýúuÔjµU ˆ»»;Õ«W—Ý°ddddd D6@dòàííMrr2>4y¾0 ˆR©ÄÃÃì²jÕ*V®\ÉòåËiР UD×ëõüùçŸVÍ÷w1f̲³³ùþûï\—æÍ›GݺuÍtGDDЮ];zõêEÍš5‰ˆˆ`Á‚&Óß^¾|™½{÷2eÊ£¹ÂÂÂÐjµ& \ÅÀÉÉ)ÏñU«V±lÙ2–,YB«V­,¾æ‚ˆ‰‰ÁÞÞÞh¾1• +—ÀÀ@Nœ8a2›¹¢ŠE ËÜÍáïïÏéÓ§yüø±Å}*V¬Hûöí­Ê†Ò3¨ÓéX¶l™Uý """ _Ä”’ ò¿à6)####óÏ" 2y((Va0Ÿ ëìÙ³|øᇠ:”AƒŽ{zzR®\¹å†fçÎlØ°   £,A/^dß¾}&”“Ñ£GS¿~}سg{öì1¸=þ|ªT©ÂÛo¿mt.44”òåË6ÌÏ“»a^M9qâ#FŒ`èС >ÜšË.;wîP¦L™Û½òÊ+¨T*“ŠE·nÝpqqáçŸ6:—‘‘‘oAAA†š/EAdd$¥K—¦\¹rFçòsÁÉõîâÅ‹V¹’ÉÈÈÈÈüÿC6@dòàéé‰B¡È× «0 äo€—6mÚ „ ,,¬ÈÖ"####óßC6@dòàèèH•*Uò5@^F¹qãFžM‹N§£ÿþddd°mÛ6“)w[µjÅ©S§ eôüUŒ=FcÒõêæÍ›lÚ´ÉÈX8zô( 6däÈ‘¼õÖ[ÄÆÆ2iÒ$£XS,Z´WWWÞÿ}£siiiœ9sÆ(ýn.éééÅ ;;›=z`ccCpp°Es[Ã7BP§N‹Ú›‹Ù ..Î(›TA üó™°@RA:dU'''z÷îmÒð2GõêÕyçwøú믋¬J~°r× ˜ü¨^½:nnnrˆŒŒŒŒŒYdDÆs™°rkÝ\jÔ¨F£áÎ;†c3gÎäÀlܸ1ß èÖ­[£Ñh8}ú´UóýU³qãF–,YBÅŠÎ/Z´ˆâÅ‹Œ…øøxúöí‹ŸŸNNN„‡‡³zõj‹«¢?zôˆü‘Q£F™|óìØ1t:Éøx¦€!>|8—/_fÇŽ”/_ÞŠ«¶ŒÜ X-Z´°¨½9u¡uëÖT©RÅ(5­9ÄÃÃ;;»"¹rå z½Þª~þþþDEEY­Ærýúu«ãž¦NÊ­[·Ø´i“UýL¡Ñh¸råŠÉøÀ-ÍTˆB¡ëÈÈÈÈȈl€Èa.VîæÚԤ`œŠwÏž=Ìœ9“/¿ü’öíÛçÛ¯N:¸ººþ+â@’’’1bÝ»w§_¿~Fç=zÄÊ•+5j …‚/¾øŽ?κuë ³(%ïó|÷ÝwèõzFeò|HH•+W6ÜßÉU@‚‚‚X»v-?þø#7¶j –’«VøùùYÔÞ××—øøx“± J¥’°eË–<ê—9ÄÆÆOOÏ"U@|}}ÉÊÊâöíÛVõË5CBB¬êצMÜÝÝ­F¯S§;wfÞ¼yVK/rõêUrrr ¥€€d<ž;wÎMFFFFFæEdDÆooo®]»fÒÇÛÁÁÀj—¨jÕª¡P(¸~ý:qqq 0€®]»2}út³ýT*-[¶üWÄŒ5 ½^ÏŠ+LúÆ/[¶ ½^O•*Uðõõ嫯¾bìر\¹r…€€£´ºñäÉ/^Ì{ï½g2¤ô¶mÛæ[@0##µZͤI“˜8q" °j Ö‰R©4© ™"×e*?µ- €ÔÔT~ûí7Ã1s Hî˜E©€ä®ÑÚ1Ë—/OíÚµ­ŽÉ5¼6oÞlµ‘?mÚ4"##Ù³gUý^$7V~ˆ9$#J§ÓYUŒQFFFFæÿ²"c„——†øøx£s¹›k {{{ÜÝ݉‰‰¡W¯^”.]šuëÖY´)oÕªaaaäääX5gQ²uëV¶lÙÂÒ¥KMº/eee±hÑ"J—.Í!C¨_¿>‘‘‘Ì;7ß7ö±fÍ’““™8q¢ÉóÉÉÉ\¸p!ßø€ÄÄDΟ?OÛ¶m™;wn¡Öa)7oÞ¤D‰WS/(fÃÇLJ&MšäQÌ) ð¬ÀaQQµjU røða«ÝHII±ÚhÙ²%-Z´`Îœ9/• ,22’ *Pºti“çsü ooïmö:™²"c„··7€I7¬\¤0èlÛ¶+W®°}ûvJ”(aQ¿Ö­[“‘‘ÁÅ‹­ž³(HLLdäÈ‘ôêÕËdÜGÑ¡CRSSqpp`ß¾}ìÚµ‹W^y¥Ðsæääðõ×_Ó§OŸ|³J=z!D¾HFF.\ÀÞÞžM›6accSèõX£GòTƒ/ˆâÅ‹S±bE³êB`` {÷î5,´D¹{÷n‘¯T*•x{{JUñ÷÷çÖ­[V¥ÕɈjܸ±Qü‹%LŸ>?ÿü“ãÇ[Ý7sèP° Vnö:9DFFFF&?dDÆwwwL …U@´Z-ñññüðÃÔ­[×â~7ÆÁÁáy£*„`äÈ‘€ñüÛýœœ–.]Š§§'aaa4hЀèèh³Å-%88˜7n0eÊ”|Û„„„àááAÕªUM®{РAdggÓ½{wJ•*õÒk2Gjj*Z­–š5kZÕ¯ ÅâwÞ0W[¢€€õ.S/³ÆühÓ¦ *•Êj7,T½{÷æ[4?:uêDíÚµ_JíÊMÁ›¹`tíáááfÛÈÈÈÈÈüÿE6@dŒP*•xzzšôÍ/¬ÎÉ“'±³³£ÿþVõµ³³ãµ×^ûGâ@6oÞLpp0ß}÷]ž8Œ4hÀ˜1c¨_¿>BV­Ze²N‡µ!˜7oíÚµ£aÆù¶Ëÿ0ÅìÙ³ ¦téÒ& ”¢&×ßßÚ û‚b6Ê”)C§N nX) ¹ê]QÇf|X¤o¶Í!„`Ĉ¨T*–.]Jff&Ÿ|ò >>>œ:uŠ 6püøqC ¸9W)k™7o 4Àßß?ß6¡¡¡€ñ†?::šн{w>ùä£JèçÏŸ0»q5…†›7oæÛ¦K—.”(Q‚5kÖx=ÿ–LX  =*”È©S§òM‰666Lš4‰Í›7gUßÈÈH³®t–ü(•JZµj%¢ËÈÈÈȘD6@dLâååÅ;wÈÈÈÈsÜZäÓO?%$$„M›6Ѽys pÈk¯½†J¥úÛ647ndçÎ|÷Ýw:t,XÀ¤I“ˆ‰‰¡_¿~( æÏŸO£Fòu…²–sçÎqèÐ!¦Nj6›Thh(¾¾¾yRÞ¦¤¤Ð­[7C?­Vû·) W¯^ÅÉÉÉêêê¹1æ {{{Þ~ûm6nÜPàõu&,/// E¡ÆlÖ¬…rÃêÚµ+Å‹ç矶ºïàÁƒ)]º4_ýµUý"""pww§xñâù¶Q©TØÛÛßѺukþüóO«Ó ËÈÈÈÈü÷‘ “äúÒ_½z5Ïqk]»vñÕW_1gÎÚ¶mKñâÅ)]ºt¡ ggg5jô·ÄÜ»wQ£FÑ¡C‚‚‚èׯMš4!**ŠY³fÞÀŸ={–Çh,XÃüùóñðð W¯^fÛ…„„äÉ~•«4%%%±sçN\\\ Æãßa€Ü¿ßâêîÏS©R%\\\ T¸{÷.Pðõøøøpýúu´Z­Õë1…ƒƒÕ«W/”âàà@Ë–- e€888зo_Ö¯_oµ —““cÇŽeõêÕOÂ{ÏÜixÏ=Í•ÀÀ@Ž?ž¾ˆ°±± &&†¨¨(âââÒÇL›6‰'âééI›6m(\¸0k×®Íd(S¦ ÁÁÁ\¾|™X4§>ø€ÐÐP|||˜9s&—.]Ê…+}KJJ ½{÷æÈ‘#¨Õj-ZÄ•+WhÑ¢…Qß{÷î±cÇƇF£É•óÏŸ?ú÷ïo¶Ÿ!þ£iÓ¦€è.æíí··7­ZµJï÷¿²€*gרQ#GãåÄl¨Tªôç]=“w‰Ù0'3§òš7oN\\kÖ¬aúôé£ùgeÕªU ,[¶Ìl?½^/8;; 3fÌÈÑ|¤X½zµ V«…„„Ùct:°`ÁÁËË+ÓýëÞ½»¬ñ/^¼.\()R$ÓøK—.É* <8ÓØÆ›ìáÂ.\¸­ìªU« #FŒÈ¶ß¬Y³Yßù¿Ã7ôWOEAAAáo…bQ0"((ˆÀÀ@t:]zÛÝ»wyýúµ‘ÛEbb"< W¯^FrÆŒÃO?ýˆÕÄÏŸ?Ÿi×455•lçcggÇÂ… ÓkMÈ ×Žû÷ïÓ±cGZµjEDD...œ;wWWW“cÂÃÃY»v-_ýuzLÌ»²téRÔj5_}õ•Ù~‚ ¤Ç¼xñ‚Ž;R¹reV­Ze‡ò¿²€\¸p€zõêåh|ÅŠ‰ŠŠâÅ‹fû\¯víÚe¶ŸÁ­+·- z½Þ(&Êjµš•+WY|}}eY1>̨Q£ ËÔ.'5¯^¯§iÓ¦¬Zµ*SûéÓ§MVI—“Ë€ ˆV˸¸¸\·V*((((ü³Q# è —t}‰¥sçÎDEEÓjµénBeÊ”‘<—œx­V›žA+#çÎË‘_=ˆ‹óï¾ûŽJ•*qéÒ%†Jdd$k׮Ͷjø’%KÐjµ|ùå—9:·Ô\–.]Ê_|‘í¹>|HHH|ð]»v%))‰]»vI*Bÿ«[·n¡ÕjÍ*mæ“ Äû¤R©øóÏ?yþüy¶2s3–Á­ËR™R©” 5\²ãÓO?ÅÁÁÁ¨}óæÍf릀¨üŒ1BòØìÙ³%Ûoܸ»»»ä9³"' N:ØÛÛ+nX ™P# èY‘J±êëëË•+W$ûÏŸ??]qxÄÔ¬$&&rñâEYã ‚ÀÆ©P¡ ,`âĉ;vŒÍ›7Ó§OÚµkgv|ll,?ÿü3ƒÎVYËêÕ«‰e̘1ÙöõõõE­V³ÿ~Ξ=ËŽ;(Q¢„d_ƒä}+ Ož<ÁÅÅ%ÇãË”)ƒV«ÍÖba¨‚nee•ž’× ˆKƒ\\\(P €ÅV©ø!@VV,Élhááá=z4Ûñƒ ’|G÷íÛÇõë×Úƒ‚‚d×q±··—e±²²¢aÆJ º‚‚‚‚B&DÁ) HÿšrIéÑ£ÇOÿý]&MšH¶[’èâÅ‹4jÔˆÞ½{Ó°aCn߾͔)Søúë¯É“' .ÌVÆêÕ«yóæ £G–}^s¤¤¤0þ|ºwïNÉ’%³íïççGÉ’%Y³f K—.¥qãÆ&ûÆÆÆbmmmqmKÐëõDEEQªT©Ë°²²¢lÙ²ÙZ H»víX¿~½Ù¾¼yó†§OŸæx^R2-µ€4kÖ µÚøϬܴ¼}úô‘lÏîúAT/…ÿŠ¢`D±bÅ$c 丳8::²cÇI?r)+ˆ\ÄÎÎŽºuëµûûû›ÜYMNNfÞ¼y”/_žÝ»w³téR.]º”^À/$$„1cÆп¼¼¼²ÃÖ­[yòä‰,eA‚ àíí——U«VͶÿñãljˆˆÀÃÃ#½¶ƒ9 ƒ÷‰! Y*FÇäX ×óÉ'Ÿàââb6ÛÝÝkkë\ñðð 88Ø⢀Rq 111²Ü5 ={ö4jOLLÄÇÇ'Ûñ®®®|ñÅFí:Žyóæ¥ÿ~óæM€÷bñôôÄÆÆF‰QPPPPHGQ@ŒP«Õ’q r*K¯_¿Þd ‰)D®Ÿ¾Tˆ©ÕƒR­Z5ÆOïÞ½¹{÷.Æ Kw#AƒáèèÈüùó³=·^¯ÇÛÛ›6mÚÈÞ%ÎŽƒrãÆ ÆŸmß„„úõëÀÞ½{e¹UÅÆƾw ˆ!3™9W09T¬X‘ÐÐÐô¸) kkk>ÿüs6mÚdRùÔh4”/_>×- ²2·eDJùnX½{÷–l—ã†b6:)÷ÉÕ«W§g»qãjµ:Ýr”–X@lmm©W¯ž¢€(((((¤£( ’H¹ae—qjâĉtèÐÁäq)$...Ûô«äÄܽ{—¶mÛâååEÑ¢E¹rå K—.5 Æýå—_8zô(«W¯–t ËÊÁƒ b„ ²æ*oooêÕ«'©XeD† “'O¨^½:îîî²ä¿yóæ½[@®]»†J¥2ã#C&¬àà`“}2ZtúôéógÏðõõ5+3·- `y&¬F¥ð̈\¤råÊÔªU˨ýĉ<~ü8Ûñ%K–4iEY²d Æ”-[[[[Ys²Ä¢ÖÉ“'s-)€‚‚‚‚Â?EQDÊŠaÎõ¤yóæLŸ>ݬÌw DoØ°¡d@ïÉ“'‰e„ T®\™7nàããñcÇ$]›?~Ìرc4hü±¬sÏž=›úõë¿óN¿óçÏsâÄ ÆoT¿#+ .dÆ ØÛÛÓ¾}{Ùçø_X@>|HÞ¼y³­Nž…לÅ"cLKݺu©P¡‚Y+@n×)Q¢öööË´³³“tQ;sæŒl+‚©`ô7ÊoÊʶtéRbcc¹qã†Eq<–X@@´^FDD¤»z)((((ü·QI, D/^¼8[¶lÉvú® ˆ““Õ«W7j?zô(åÊ•cÉ’%|ÿý÷ܺu‹Î;K.ìA`àÀ8;;gò7ÇÙ³g9uê&LÈVYËìÙ³)_¾¼Y‹ˆ×6nÜ8úöíË›7oÒãWäð¿°€¼|ù’bÅŠ½³GGGŠ+fֺѢR©èÝ»7;wî4é¶åááAXXÑÑÑï¼{HÁ‚iݺµQ{pp0²dLœ8Q²}îܹ<þÜ" ˆ¥.XÔ©SG‰QPPPPDÁ¦ɳ’7o^êÕ«'«o±bÅ$}áåZ@@: S§NÙÖÑxøð!ãÆcÈ!´lÙRÖùn߾͞={øæ›o$Ý¿rÂܹs)T¨½zõ2Ù'66–:àââ–-[8~ü8 6”í£ïßréÒ%@¬vd³‘5«—››M›65™ Ë åvHNäÕ©SGRá•«€€é`tsÙÀ2R¿~}š5kfÔn¨*o©D§ÓÉJLa iÓ¦œ8qB‰QPPPPPiœœœ(T¨P®ÊT«Õ’Ôr¤¤$“U׳síÐëõ 0WWWæÌ™#ë| * … 6«,XBXXëׯgÔ¨Q&• ½^Oß¾}yüø1{öì!oÞ¼œ8q‚>úÈ¢s½o ˆa7;» z¹T¬X‘»w!UפOŸ>;vŒ'Ožõ···ÇÍÍ-WŠ+òêÕ+^½zeÑ8­VKÓ¦MÚ‰ŠŠ’%£}ûö’JÌ–-[²MaÀ” téÒ²déVFKÑŸ?ν{÷dQPPPPøw¢( &)R¤H¶}’’’,’™“Z ‚ °oß>ªT©ÂO?ý$Y00;dùòå?~œ5kÖÈ^”?{öŒ 60jÔ(IËMNX´h666 2ÄdŸü‘]»v±qãF*UªÄåË—‰‰‰±(þþw©¸œœàááAJJŠ¤Kž ’uMºté‚ ›7o6)37]° ™°rË K¯×süøqYãíììèÚµ«Q{DD‡’%£U«VÔ¨QCòØþýûeÉÒ‹ZˆÞ¨Q#Ôjµâ†¥     ( Ò„‡‡sçÎlû%''›¬Å …”n2888///ÚµkG©R¥¸zõ*;w6ê÷èÑ#BCC%eÜ¿ŸñãÇóå—_šôÅ—báÂ…ØÚÚšU,!::šåË—3tèP“©÷ìÙÃ?üÀÔ©SÓƒ†}}}qpp,ÄhŽ÷m¹sç666²ci²Ã³!e±HJJB§Ó]££#;vdýúõ’®=+VÌU H¹råP«Õ¹ˆ–¹a™Ê†%·&ˆJ¥2i™5k–l÷(ƒb‰ÄÑÑ‘š5k*è Š¢`Ljj*Ÿþ¹ìÅ…%V¹èÑÑÑŒ;–*UªÌ®]»8rä•+W6éò“±ˆƒëUÁ‚ñöö–=ÏèèhV¬XÁСCqrr’=ΫV­"!!‘#GJ ¢W¯^têÔ‰I“&¥·ûùùѸqcYÅ èt:Þ«$,,Œ‚ æš¼"EŠàèè(i]ˆ¼žÞ½{$éžçááÁƒ,¶Ô™ÂÆƆҥKçÈR¹reI·FKÆãææfÔ¾wï^^¿~-KFçÎ%¿ÃÀÀ@Ùs1¸`YbÑ]O±€(((((( ˆ‚ÿ÷ÿ'Û-,[„d—ŠW¯×³fÍÊ—/ÏŠ+˜:u*7oÞ¤cÇŽé)på$4°téRNž<ÉÚµk-ZŒ¯X±‚¤¤$F%{Œ9’’’X°`½{÷–L[ûúõk:vìH©R¥øí·ßÒÞ“““9uê”Åñ‹Òû²€$&&òæÍ‹â²C¥R™´X˜»žV­ZQ°`AI+@ÅŠÑét¹wS«ŠJ¥’|Ž·nÝâÙ³g²d¨ÕjÉ`ôääd¶mÛ&K†V«å›o¾‘<6kÖ,Y2rb1$$$„GY4NAAAAáß…¢€(dbçÎY @\ŒÊÅœræÌ<==4h-[¶äÎ;|÷ÝwFÁÚnnn”(QÂHFV׎»wï2qâD†.™ýÇIII,\¸>}úÈŠƒ‘æM› “\ø,NìÞ½;“¢@||üßN1T,7OSLÅl, R×£ÕjéÑ£›7o6 `—˜ Kç(SnXæ*ºgÅT6,¹nX}ûö5i‘“Ö7§Ãæ↥   ðßFQ@Ò ¦_¿~³dâîî.YÌoÕªU4jÔ6nÜh²ÀJ¥’´‚ܼy3=;‘N§£ÿþ)RDö®® 6θqã,g ½^··7:tHsÈÈ·ß~ËŸþɶmÛŒ4___œœœ¨Y³¦Eç4ç²”¬M†g–[¬ Yc •©ëéÓ§/^¼àÈ‘#™Ú]]]ÉŸ?®gÂzôè‘Å‹oÈ8òåËK¦¾>sæŒìŒr¶¶¶Œ=ZòØìÙ³³ŸS Hþüù©Zµªâ†¥   ðGQ@qשS§ô…«%Xb±±±¡xñâFí=bõêÕ\¸p† f+ÇTÈéÓ§X¼x1þþþ¬]»ÙóÓëõÌ™3‡Ž;æZ½½{÷Ì„ ŒŽmÚ´‰¹sç2wî\Z´hatÜÏϦM›š-X(Åû¶€œ?OOÏ\•ëááALLLzm æ, Zb*W®lTC¥R½—LX‚ ÈJÒ•R¥JIº­;vÌ¢ú¦‚ÑåÖ:t(VVVFí;wîL·p™"'ix 4mÚT±€((((üÇQA`РAܼy3Gã-Ý –rÃ*V¬”]ìÏ\ˆÁukäÈ‘רسgwîÜaüøñ3… Ìž=›ÆU1 dРAôîÝ[2Ö$11‘3gÎXœ~Þ¿$((µZ-é ÷.˜Ê„•D¥RѧOvïÞMtt´‘Ìܶ€HÍQ.RVÐÐP‹âTºuë&©DÅ‹óûï¿K“SÙœÄßߟÀÀÀôßoÞ¼)¹sjQUe\]]ÚOžÍÙ³g¬ááátìØ‘ªU«²råJI·´³gÏ’””dqü¼ HHHÎÎιVÞ@éÒ¥±²²2²XÈQ¨zöìIRR>>>™Ú ½^Ÿ+stvv¦P¡B¹b‰–‹‹ mÚ´1jðàgÎœ‘%C¢££ÑjµFÇÖ¯_ÏÓ§OMŽ5ÄeåÄbØ“¥`äÈ‘œ?ž;wÅÂÀ‡~ȨQ£ðññI¯-—   öíÛ—nuzòä Ïž=˵Ý€£G¢×ë)[¶l®É4 ×ëqssÃßߟE‹1xð`öïßϵk×°³³ãÎ;„……™ß»woNœ8‘žÁl÷îÝ”*U €åË—3yòd‹2NI!%K–$ €¥K—2vìX‹â7 (@µjÕŒÚ}}}-zNmÚ´ÁÙÙÙ¨ý÷ß7k•]([¶¬Q߬ãÇ—ã9.\¸PÈ›7¯‘Ìððp‹äŒ=Zò~Z$gèС’r¶oßnrÌÈ‘#3õU«Õ’×Ó§O7occcÔoèС²æ{øða¡víÚFÏõÌ™3]÷ßÝ»wKÞÏãÇÿÕSSPPPø[ X@þ£L˜0A²pÀ”)Shݺ5 ºK8::š•uÿþ}Z¶lI§Nxøð¡dŸÄÄD~ùå ûb„r)Q¢„dìD@@€¬‚‰‰‰,\¸0S[LL “&M2ªÌ.—7íÐ;vŒŸ~ú‰Ó§O3|øp† Æ_|!9>ëî¼ ™bh²C*‹™V«ÅÆÆF¶ K ˵Z)öööF1‚„eA*®ÅÊÊŠõë×rK¹½KF¬¼yóJÞcKeæFä,VPPP¦ßõz=ÖÖÖ’}-ZdôL¤\åÆ‚X[[hô\•x…ÿŠòdÛ¶m&ý»½¼¼˜4iRúï*•ŠòåË›•÷ý÷ßg»pÊ“'Oº»Q¾|ùÈŸ?¿QK1cƘt}‘DZqãF£t¯ .k×®mÑ\àm _) @çÎiØ°¡‘Ò“???£6µZ-;›—!ø<#yòä‘TÔrÉd9AªFŠRq-*•ÊdLDVÞ%#–©9Z*óƒ> þ¶T©_¿¾¤+Üxùò¥ä)w©5j¤o:däÕ«Wüú믙ڤb–ä* õêÕ“TvDAAAá¿…¢€üǸyó& <æîîΆ ŒüÁ³«‡‘ßz¿~ý¸{÷n&«„”Äd×®]é1&R˜‹1èÝ”²Ó ô?þøC²~BµjÕ˜?>666lß¾Ýd µN§ãøñãFíµjÕ"_¾|²æ µ;ÿ¾2aÁÛJãÿKY¦®§W¯^²Æ?|øТÚ51¥€XjÉ›7¯d •S§NYaJñJMM•Ìl)©tW®\™‰'Jžcîܹ™2ÔIY@ä£ÛÙÙI^·¿¿¿d,š‚‚‚‚¿EùC§Nˆ‹‹3:fkkËŽ;$-9-ÈW¯^=Ο?ÏÚµk)\¸p¦cR ȃdó¾zõ*Ûì\¦ÜË üñÇ’é€kÔ¨!™(;„´¾RäÍ›—Û·o³{÷nɺ ®^½JTT”Q»%ix¥÷U ä[-äPºtiI«@VL) îîî&ëÃdD¯×s÷î]‹çbr©g˜«Š”V||<çγHŽ)ÅkýúõFmYݯ T®\ÙdúéÇgRfÞÅb¬ÄÆÆråÊÙ2þÙ( ÈA0`€É ÇË—/§fÍš’DzsÁÊJáÂ…ùí·ß8sæŒÉJÙR H\\áááÙÊ1b/^¼0ÛÇ\ŠOsÊÂøñãsä®túôiÉ…£³³3þþþ¬Y³†Zµj™•a*;“%…¥\°þ)+++Ê•+—m?s •©˜ˆ¬¼KˆÔ5çD^nÅ”.]Z2%u@@€Ñ¼Le«ªR¥ *•Ê¤dÖ¬Yé–Îw±€€tö:0ÿÍ*((((ü»PÿóæÍcÇŽ’dž B¿~ý$%$$Èö϶²²büøñܹs‡>}ú˜M›Ó@t¶nÝší\üýýÑét’ÇNž<ÉùóçÚÝÝÝéÚµk¶²¥0¥ÐDEE1~üxºwïž­ )D«ÕJ..MñO¶€È•gN¡êÒ¥‹¬€ûÜŽyüø±¤eÑõë×—´&Xª€€üš ¦, •*U ]»véÿŸuܱÞø IDAT€w·€4lØFcÔ®Ä((((üwPÿÇ7¹³Y·n]-ZdÔ.>>>xxx°|ùòlÏѶm[‚‚‚˜={¶¬÷œ( /_¾dØ°aÙÊq!~õêUÉcÞÞÞ’ícÇŽ•å”•7n°ÿ~£v•JEóæÍeUdOII‘tóôô´Hø'[@äÊ3w?òåËG‡²•‘Û@Ò¥Ï666’Ö€óçÏK>GstíÚURñÚ¸qc¦-) HñâÅÓcŒÔj5&L<ǬY³€w·€äÉ“G2ÉéS§rµ^‚‚‚‚ÂßEù—óôéSºuë&i pqqÁÇÇÇháríÚ5>úè#ºvíÊãdz=GëÖ­Ù»w¯,÷9Q@¾úê+ÉÌ>¦¤R úëׯ§ïäfÄÕÕ•þýû›<·9L³;;;³}ûvÉÝÞ¬\¼xQrÑiIüüo- ¶¶¶”,Y2We¾«ä¹aý2a´VjjªÅîHÎÎδoßÞ¨=$$$]– ’ HåÊ•3ýÞ½{wÉ*÷þþþœ>}úÒðŠyýúµÅ þ™( È¿˜ääd>ûì3Éx µZÍÖ­[3- #""øꫯ¨Y³¦d6&Sä$ k‘"E°µµ5j7¥€lÛ¶íÛ·K[°`¤»—Ô"Δ²0bÄÉ…Uv„††šÌƵwï^ÙÙ«¤Òï‚eñ𿵀T¨PA–re ïjhÕª 0Û'888Ç»í¦æøWÆ€i7,C0ú‹/ˆˆˆ0:^¥J•L¿[YY1nÜ8IY³fÍ’tÁ²ÄÒ (nX ÿä_̸qã8sæŒä±éÓ§Ó¢E @ÜqýùçŸ)_¾<Ë–-³xa–“”¦jµšÒ¥KµK) ááá&]¯:tèÀÀ©Q£†Ñ±S§Neʪ–-[ŒúÙÛÛóÕW_Y2ýt,X ™>´M›64lØP¶©ø4h [Frr²d ×÷eÉíø—q-;…ÊÊÊŠ=z˜í“@HHˆEs3P¼xqIe5'5jHfžË‰ÒºukÉÍâããÍfÀÊÊÀqqq1jß¿¿¤²a©¤Q£F’É”@t…ÿŠò%&&†GñøñcÉ]ïM›6±dÉɱíÛ·O ñóó£V­Z >œÈÈÈÍÅÒŇ)7¬;wîd’'Æ “ܹ͟??+V¬@¥RIúÒ¿|ù’?ÿü“°°0A`þüù’Ê Aƒ$[Y‰M¿ç±±±DFF²jÕ*£~jµš¥K—f+O¯×óìÙ3nß¾ÍéÓ§Ž7hÐ@r·Ù¦âÞ—$·ã?@œ«¡`¥)ä(TrŠž>}Úd¢s¨ÕjIåëÚµkDGG[,KÊÊuõêU.^¼È“'Od×Ç°²²’LvËöíÛM¦¦Îjpppà믿–ì/å&•˜˜hÑÆE¾|ù$7 üüüxðà¯^½’-KAAAAሠð 55UØ·oŸÐ½{w¡Œ[IHÿQ©TBùÒîB¯^½„#GŽW®\ìíí3õ1ü”)SFxýúµððáC¡sçÎ’}²þ-ZT4h‚:Ëq[+­Ðªe aÑ¢EÂëׯe]Orr²Ð¦M¬Ô™å©Õ*¡RÅrBÿþý…É“'›œ×–-[Òåíر#½]£AÐd‘éäè XiÕF24ðèÑ#“÷|ÿþýB=„²nnFc :;KΫG&¯ûùóçÂÌ™3…fMšŽ™¯›Ì÷vÚ´iÙÞG½^/øúú }ûöʺÏÑJP½zua×®]BJJŠ¬g)ÌŸ?_hРž Ñf}6âý„E‹É’g :N¨Q£†ä{ahkРpðàAA§Ó™”£×ë…J•*Ï=‹L[¡q£Â?ü „††Êš£^¯š7onrŽ¥Jºvé"ìܹSÖ=_¶lY†oA›E¦­Pß³Ž0aÂáÞ½{feÍG ‚J¢Íðÿ¯^½’”õêÕ+Á!Ë;j˜£Z«2j«^³º0jÔ(áƲîãÈ‘#ß>­JP©²|_… ^m¼„Õ«W qqq²dþÕÏ’ 0|£µE§bk¥%1Åx×ÔÎÎ???öïßÏœ9s²u²¶¶fàÀ<¼CGŽâh íÝ v¨¬Õ“×"àÂKGBÁÚƆ¡_cÚ´i888H^ÏêÕ«™2yÏž¿ vQhVj…iÝC¢!ð)üù@ËÝ—©hÕšesµS§Nøøø R©xöìÆ å={±³ƒ6-À³x”[ˆ‹‡Ápá2ð]*èÓÞúž={²qãF£9nß¾‰ãÆñ04”*VšèuTWƒ¡œbpE~z¸#€0ÜñË—/íîFDD0þ›oØ°a½ž–*=µTà¡; ¸-Àe=ÒCТEs6lÜD¡B…$ŸÏÁƒ;z$·‚ïR¡ –æ¥S©]J8‰Ç_ÄÁ¥gpü¡šKOõ/Z˜é3~¢oß¾’î/±±±Lš4‰_~YIjj ~¬¦–§ªjòä…ä$¸{[ÏÕ@=‡÷¦÷FEûömY´h nnn’s´&ŽÇý‡ñ(-ËAí¢P$Í€ ÏÀ÷šÏõ”qwc–÷\ºté")oòäÉLŸ>µ ´ø¤xz@•Òà` ‰Épó\¼ ÎiHHèÚ¥ ó,¤H‘"’2>ÌØÑ# ºLiGh]BüJ¤½»/áÒK8®áb¸ŽbE 1íÇ™ôïß_òž¿yó†ádzaýo¨€ËA½’P­0䱆d¿¯ûÀ] QñzÚ¶ñbÑâ%¸»»ÉR¥Jñ4$PøX5TPR%š¼_WõpF(àìÌw“'3bÄ£˜ž1cÆ°`ÁTèÊT„2 R¼º3¶NÖèS^Þ‹áñÅn #:<Ž?jÆÒ%?K¦ôÑÂÓ£gwnÝ"O[ª¶-NÉÚ.(ãˆZ«"!*™'W"yxöÁ~a8:åeì˜qL˜0kkkI™%ÏŸ?gô˜Ñl߶ ; µ>q¦\GJTrÀÚNMR¼ŽÇ7â¸Ã¥ƒ¯ÑëD+·÷YÖW…Šò7&""‚A°{Ï|ZHÅDwºŽ`ªNž €üx{,1lØ0þøãžE°w4ý‡262…?×>cÛ¡ØÙ8²ö×uxyyýg¬   ð×£( SÂÃÃù¨é„?¾ÏŠ :ºÎ~ŒA€aðåMHÔ!î„)BXXX¶c=<ù„GñaÓ&$ÇñjS)úmlLñjÆÁò¦HIÔ±wòeŽÎ¹Á_ bÅŠ•‚@ÿýÙ¸a#­¿«Ê'ßWÇÊFþGr)‚õ}üyó4•Ã‡ŽP¯^=ùøžX¼x1#GŽ¤q—B|¹¼}: àÊ•+Ô­[‡qUuüôŽŒ‰©ÐtŸ†àx;„”.¥£´üMTIßÖëÀÖÆŠŸ¦°z^Δ:´é­æt€ ) ‰Ó T{ÇÜp—ôÐ"ìlmù 5‰M!GʇA€Á©°GcMrJ2¾Ÿ™w!ø%Ô^¦FmmG¥jÉìô͹òa`Òè$Ö.¸|ùªIßÿŒ|÷ÝwÌ3‹sCj{§Søê¯[;;>¨šÈ³„)úÍ€íÇ­HHLáh[ha>9W¶Ü‹†;ÔXÙÚãᜀß@69T> Œ;‹ÎªIÕéYkÞ±,K¸tR°+¢aü/lóæLù0pÄû:»&ðÙâz|8âݲ§%D'3«öÊöàä‰S¹^‹F!!!T©R‰Úíó2vC¥)ôz;\çÁy·n種’‚‚‚Â? Eù›@½zõ˜]Nàw¸S‰z(mƒ‹Ã7q‘ôÛ3ØWb!2ÜíàóÂ0ÎlÒÒ“îÂÌb*–Œ4nܘŋS³fM@ôÏ®S«Ñnò0ZÚfr®x|ûû­×0ú ø?ƒÒÛ¸Áüàš–=vÍ-t¾is3ÍŠ2‡’¾›!Ö]‚ó¡pí9èÐÏÈÜßÍt¶°j´5QôúÜ>ð¬™v¶Á΃påDF{Iø¼Œ 66pȼzÁZh¦ïTQ‰ˆŠ« «FhÀ.m±.¶éà®Ñ@AÀS ãµP1íž7L‚'¬µ‚Õ:óò2’"@£d1°}ºF¤-P¯é¡i Œl=ªÃt?xщP2ŸØ6® ØeX+.= ?Ÿƒ‡¯ÁÕºUƒé-À>ÍKdÙ9øêX±É†Î=¬¸vI‡÷”d.øëHJ·ÒjzÖòÅqÀ†_Rؾ1…ûÁÑQ ©¨ÛPÃØï­©PIMb¢ÀG5“qÉWƒ3gΛ]”]½z•Zµj2µ¹À¤áÒS1ÖÇÿ±¨¸–Îƒëˆ ¥T ®x+/ÀW`o%Æ-ð‚jE Ö¸ ·7CW¸ S~ÿëbÀyé¢0¸=ŒH §Q719=ZÔíÓ¡bO°KYʊ̸ß_€Êùáúg™éXySü¹ öZ¨î Â7gáx¬î}}¤Ïñ{Èî9&¥BµÅð<bMü%?f µÓÞKA€µ:X§ƒ‡iÉ*©a¤Z¥­ã÷ê W 4X–3¿Þ“”9þ\JyŠ¾mÃÔëLÞÇŠ-Š2dׇŒ/¸•Rõ 0êØÇ„^Ždÿ”+Ü÷AJ¢×Òyh<¸B&Å$ìV>£¸ïŽÖZC•6Åé2¿.y\Åâ¥÷N…3¿é!/^ÌðáÃMžÿ}Ѷ]Î_>ÎÒuÈ“ïíG÷ûŒ‡lüþ%+;ðóõúéííÔ¦ëºÔh‘Ÿ1ë+ñUåÚyuaã†&û*(((ü[xÇý7…Üfþ¼y”Í£aL©TŽ¼‚v— ¶L.y4p/ž&‰}ãt0à4È_–€‚Öp& ~¸Ç"Á·®Øïû2ðËx‘V£®xñâÌ™3‡nݺeZ$;vŒËW¯³°¡¨TŒ¬ u fž_™ ¼OÞÀ{ÀÙ~ª±ÉbÐíõ¸Ð ¬4ðû}¨S >)/* #BÝ,»Ýe2ø¿¸k.Š Ë2ùán–`ú+Ï $ ¶­û4%gä ¨›¥¤@™´¤Lqñ0` 4¨_ö…‚®p&~˜ ÇNƒïvظÜ4Ð\%Z.Š¨àK-8#f š™*f¼Úœ¶p¿.€»Ú¨ Ÿ éÅ ÞÃÉpÂlUpK€Ñèž’½¼Œ¬ÔÁÓ´…dÆåûVä³Ï«AãUP4/ŒjùíàLüpL´ìN+1áÌ9]«ÀèFô–œ… p8Ô_ì3ÔV\€Ý[SÉ甆W»Dª×V3n²5yT<¼§çùÓ·«ÚWô”*£Æ«£'gèÙðK*GöÅs,О2åÕÌX¤á³ð÷÷§qãÆƘƂó)‘OÄR9rÚ­‡ÚÅ`òGÇîEÀÓ˜Ìcì„ÍW¡oMøº¼I†+að2žÅÀµp˜7BT>Ž\€v v˜ÜòØÁ½'ðôå[y'Ï+à,Ú{‚S˜?zL… HQÙñ½Ÿy ¬2?£ôyÏûV€¯«Â›¸ò Ç™pQ7d*Ëî{Èî9ÚhaY{hñ«8—/5P+‹Ï=Ã$§¥Â´VC˜}m³>K @;øó¡nàÑ•p«›yWÞµÌ[Ól¿Æ5x¼ÂoÑM*}\”Ë;“¯£ÇŠÜ:úŒåíŽQ²¶ ^“«c“GËË{±D=}[ûçõ“8æp{g:þT›ÄØþœÄÓ믙x¡-+5e›³WiæΛ×_~ù?µ‚ܺu‹ýû0vC¥LÊÇ«'‰l›ù[‘ò=v£qÐüÝ€þXJ­óã\؆SÝøeÔfý4+ÛZ8 ÿtäoDxx8;vìÀ»L*q:èsÚãz]€há8Sêgˆ©XJÙÁ÷àX4wû +ÓÀØoÆ3yòdɹ˗-£Š«–j.bBÙ&E “WŸ™—!!.wâi‰q< BË}°.>,GŸÀo]ÞºI5)Ì$°V¾m*.¬†ÿw²( ËÏCÑ‚ðé'púBšÌzÐÉDk8óÔ¯áu‡R%D%dÇ~ؾ¾WÁn=$Û¬Hé‹è¾¶EÑ8©`^V ´Ñ@³dÑ2¢ìeÉ‘gà¥sRa´~ÌE9A€M|QöÜS³îï iÊá ºâ®ûúË¢E$>柆>5a]† µå]`Ä>ØwÚVµ†×‡¡{tœÓñq; ¿ú˜.|8ûg£6¯ŽZZÔIÀgS ¦Úд…†2å¬X¶ìg“ Hdd$[·laꇩ$¤@ŸíЮ"øô4yj¶]¯oWOè áÝ5õØZC¿OÄŒV}~„vÀçGÓ2{´4nó ßÕîiÇ:7ƒB‹`y,M³˜Œ; ‹©¡_eÉd½í>¬†]C‡,Yr—‰ßËÐz¢‚濇°yÏñ£2PÒBb Ú›Y‹oÑAmlÍ üöÒ€G’x¬]ÚØ!jøü^,¨ l“BÔìd:Ͳgã?Á¾a RQ§{iVuòãUQ Û1ÿƒCTmW‚Á>ÆÅ šy”£.ŒsqñïT)OW·<ÂÙu÷hü…˜Á¢éWñÞ°ŸC‡ѦMÓË,_¾ç‚v4îš9 h͸{x4̇.U æUr¦cÍzg¹æû• >è.û¨OÖû_~ùÅâd ÿ4”Jè#|}}IIM¥gQØ&Z,f¤ÅXÆe¨]aÀJYù0Ð1maz;îm[¯¢¢;SÓ¦M%•NÇáÇèY&5]Y­YkpØñÚº½U>š‡òù`Û8*αkÕ·ÇAÌâ“j"*¾`ÌúźÝ:‚6CA€Ø7 U0ÚÊ*³òa ckñ¿ŽAr*|–Á%ª@–mí‚€0—ߦdÚ+ü©/õ[åB®¼)©P.ÍE+#ˆÒCÏo]¬ fÉ„Z8˜õÈZgCÅgýy–ŸWÿ»õÚÛ¶îim‘¯à»¢‚' Ïú²™ ¸›ø'ÄÊJ¼HµZEçž*:€)ïÎãÇ“”œBÏ¢EãEÌh%‹K]­²2ßꕽ^ì—‘Cw¡]cÑj±ù(¼x 3§ÉL–™•¤dØqšÕ„¢iþÖVЭz*þ~òìx ŠßGV Èü«P¯¨|èˆKÉ0ÇPhêÅßfh6û=È}Ž*•X3ÄsÆ jâñÙªÀ5ˤóªD…9£êÙJ †÷PHŒMAgêAR’t\Ùñ˜òÍ ce«ááù—xö,MÀæ‡Ä¾H ýŒZ$Å¥H¾g—w<¦JÛéÊ@ÅæE)XÞ‰ÀmÓÛJyºR¨l>:$k^¹ÅÁCûhØÕ+›·ÿ|Þ8ùš3;^ðÅÂr ÙÆ„¤$éñßñ‚ªÍœq)*~wöŽZ<;¸pðÐþ÷:…¿Šò7"00Ry­(` F€£B¡Â)È{ œŽÁ°›”Í:àyš‹–k†úÒvàl£!00PrLpp0ñ ‰™â;úûÓ¯`·>ú3¸¯<ƒ— PG"ÅiÝb „À—Pµp渄þ;ÀiØý­݆äò*B"Å"ƒé?œ*€]iø¨+^“Ÿ‘çi×ò2ŠYAa•¸\ž×õb ÇNè3?T"f#R­—õ0,Eû™‚ÑçÞyzqz–D¼ï½GP¹ ¨ …òÀÀp5 B£à÷k¢+Õ× Å{”¦ˆÙe‘e¨ár)Ã=Ïk#º†aóD%ÃÎBôè™Å2R×îGADŒð‡/<Þºce$&^ŠßÄwçÅo'ï(³¶ß‡‹/1Ê\fî{°ä9VHû‡¥B‰$(”í’Åw3#£5pL«Rá±wô06Þ º Ш ,€ëûû3Æi_Ûm`ÁG‡xøÊôM‚É ¨TP-/ܼySúÜ·nF¦‹C IDATP5¿˜­§KiðrW[Ñÿ}îUh²Ît„®bÑA€")‚‹ØCd"½†ªi®Ì6ZèR¼Êƒ«ƒèÃ>÷4Yg†@¢2îOšÒP5m¡ic ]Ú‚×Gàš‚îÀÜåФ£èvU£Š™{ô389BJ*TÒ1Vã°5tM&ªßhàÿ$ƒŠI¢‹@)°~ëvUI%_ž À7)ÐY uÔâÂ0#w¨TP¬æ]Ôü‡€×:¨¹ômŸIÍ`ZÚ¹bÚs?ýHÜq7pê‘ø߬±DëQߎ‰ôdÅäÙNûéX½$…è(X¹Ù6SÿjÅâHN»žR¥UüqÂŽ"ÅÞîeTJK#vëÖ-J—6vϹuëU Š λ¢…­ãFTf—¿bœCT"lî÷#EkÃÖ«b\Ñ\/QiYt>ß*t'¦@Õ´SÝ •’ŽßÁ ¶0»&ø]‚%; *6O1š›ŽŠn\]²xU+#þ÷§Ë ¾&2¥ÞI«MrO´üÍmŽV°è:|þ§h¨šæµ#ç{¨f…‘ó îx½5ð¡ZŒAZ’ Ÿ$ÃkÒ3»õÕŠÏ{T*ŒOSp\€?¬Åw/#evÐòÙâzäqµáYPνÁ¼&ùæLJÔNiwaÓ´¶jvqãÜo÷ÑZ«)XÞ‘wcЧêYÙÑ—FƒÊÓqvaîø…q|É-¢’°¹)Ña 81vt*bO|dº}záÂbÕòãwð–ôyPªê[EãàŠ'¼Id†o-ÙrŽozŽµ­šF]2Ù¹WËCRR2>¤|y Š%)(((üÃP¿ñqo(’¶x“ ñ:1¸|aE±­c!H`e(L+eíeÌ| Æ~,¯$ZP2â Ò‘ ynC{+hPXü1ÐÖMTHªm‡oÏÃÁ6æ2b#aC³M;o| äIó3jPRüI—YQ\€U[ ߃ýÌÜÃÓ6ó¤)= êˆ?é2[B—6P­9|ûÜ$-gæb1}ù,ðùò¤-ø_Ð%틵_‡õ0WUðE–û¹Ó’¸-ÀÒTø4¤õÉ£’/o“N\0n4áã• ˆV €ðXødøÿ¿| .öb,ÀŒã¢eä«P³¨è®4û$sSöÞz_îÆ Y\Õt‚è¢Ô­¯3Š'òê¨%%Yà·•©L˜¦§tÙ·zÛa;îÜÔ³l^ ][%°ï´E‹‹}òˆ¦“ïZ|–ýÊPµm juqãÇj°çÛ@†4¢IˆIæÆþ'TiS;GkRR±¶×¢V«Hz“Jr|*|Y‘® =¨Ñ±$©ÉzN¯ ¦í´š¤¤ýaÑJ(´²Û’R±³/È&–„xé÷ì}/ËÛæ瑦Éè>ÙGyéŠãcR ØAÝ6.FÓmÓüÞL}; ÿ¬¿Ö6¶$¦¹LØ¥ýûÛ½Hæ>iñŠœ‹2ÿ{|W´˜ ‘(R—$¨±¶–^åÚ“LÄf”qR~ÏÄ]^Ãü¤ÜÁÓF6š·n$’2]DŸ~¿¢Ìì°6œ3ÉŒÌRÐácð;#-ó÷=ð½7 êCzƒµÍ[+†w*„ °Ïúh¡­–XA ü ¯³Èk¬†æøJ+Z?^ °!íþ%É”#ˆ Ä‘Z(jÂmÜZ%îðƒ˜~÷i ø ‚uÄÅúêNзL8 ‘iÉ„vô„ê…ÅÌQ¥çBû b,AÍ¢o•B†%ŸvϼêÔ]\PžËüR4lªá£µ mÍ'íxõB`î´·&žÄDñF™|×llÒß3ƒ{Q÷,q†Ø”s!bº]wç·Ê€ƒµ¸p¿.&kJw¯²K[äwϲ>îÞ"Mfñœv‡¤c÷«Œrl`„«šá›pÏ›9{œƒ´NûÌxÖH}rŸ£á;˸vW‹±H§ô¢¼DAt·j¥†5Öb°zOø~&#fÈÊH2 µÎüOD2ŽTïP‚`¿ç’1>—w<&5I‡gOÑ¥±Ö’¤C¬ÒnPóë¦ýþðÜËô>©ˆRÅ6k;m¦6+ëw«Sb †w:%íõ†I÷qtµ¦ÝùY«üw¼ %Ioä~’dþÛQPøöÎ;®ªúÿãÏ;ØÓ" )) ®«–¹r䀖}µLraiÙÏRÓÔ´2Gjéukš£ q¯T4GD8AEAÙpïç÷Ç«lT ­ó|<î£î9Ÿó>ŸÏ¹çàçu>ï!#óoA /¯ÖªEX†ôiN\"öùþ²Ëùþ ßDfoœ”5«[XTD ¸KiJ^}õÕB÷Õ¬)ù™\zPtÿœ,¤ L)Ù\¯î¦lw7UzK\ËFzc[Õ¬slã›oècŽÇÇ¥ÂK<²é™™R ÞÇÙ{ÞÝÚârlº@xÎÄñ¸º8W.IÿŸ{Oå§fÍš\ŠË¹Ïs²¹ÚçsÏ ²Vt; É…K©€K‘Ò¶Ürû ùÚæ|ð° µ{ÁÖºµ(¸/0'ljX]¸"U5x( íL½”^÷A8æ<ö…$˧.¸/O»|ÏCiÇ\÷Äùî5G…$$R\ùâÎùþêWP@s%œÈw‡•ÝlÈm5 t™:2R ¾]Z{3[cêw“W•šVd¥éˆ¿™‚­ƒ´DeïYÙIßSdb“Ó&×ëqï¦bQÉÄà~})‘š¯~Ÿ=rÿ~ÞK!êJ*{–Þ¡û'Õˆ»ALD1id¦ëÉÎÔs/2äü¨‘ܯ,lÕhº,8xëR *• —ç=™rE /7&,)‹‡ÙÐ$g‚y;_šÏ;9oÿ«<&LN$@ï3ÐÔ64 ÐªÛÑp'%›Æ I Ô«W#µšS±…îàúC)ÖÒHšlU1“‚nósòž'Ò¸ „Ü-:ãÀõø›3¼ š T±‚Sg‹ow=ÌL¹jœ8 ½IìK)h{Àµ,)%nPXWs§YÅ j*¨7„”Ò^4ËÏ éÓ9gò9['}¯¬€„L)"K'¹Lå'+çDùÕ¬-œ¥ ýÅ{ 훯efK“]…îÜÎ{pôé{åüi¼ò‘–ŠÇþ’œ ÖcnnÊk¯½Vhû&Mšp=.‹øT©F ÀíļmîäÄ7T±€W¬%ñ‘?vàNÎ=Yÿ8–c?ç´·óÝ›wrb§«äËw7NŠñnF…8¥ËY1™pj¬}ô9y.'€ëZ˜,‰r{sˆJ-hãNªô\ž+A€õ<”ô;GI®^Ë÷SE)»•¥¢øû8ÿ½*œV*pÒœ$Ç]ˆ‘™S˼+‰wS ?MCogƒH¨ÞX &¿y*Žê9ånç}k‘xGº`VUL±u0DzŠ)‘AÝ#OÆQ-_ÜÉíà4›2¢çƒŽÕ^áÊ©‡ÄGe ô‚Å#/óQ¿ ŸË'“ˆºœŠ¯ëßü65o"†ø»œ;ð€Þv¨ þó{58‰:uÝ03+:¶ŒŒŒÌ¿Y€¼@´iÓ!`S ôÍY_ž/KÔ²ÛRª×6¹«ÉÐõ´”åj{£Âc2þˆµJUdmÞlñ#UÄâ~|6¶F@ÇÇ< ¼kÀöH©0[.ûoÕDèSÞr”ÞÊï¹"‹+`ó.l ƒŽµ ïs~ hë·I¤ØûÛœ½[ÿ„Ž­m»tº€ΰ}µTý<—Ö¯Kv·éÀSg\Ë7‰÷×I»îJiò_Ø‚@°. ©CK%ìÒI+"ÅÙ«§„¡*Xg”÷37gÜ?g_G%˜(`ãyhä§ïÀ•|ó³õ¡Ræ"‚^€ãñÅ.ÉhX³GÛ·…=ZY»<ïí5˲12‚7Ú¨ÐéD¡+!§Oê;¯§yËG>ûÛü¥tÏE‡kÕª …‚ oNŠæåù’³-;%Å9´É ,×n&JY³r‰K-¥:m]aëaÉ]ªï[96·ç³¹MmæÝþÛ~é~ú_Ç‚}Õëáø9hõ lîôè³ém)–³•ôÝ7Gô¼[S Tßwû±~¦Á–¨“õ+!í韇¢~Çu!Ò½§~L€œÓÃN=¼•ó7ÁM&@@¾û1JÀ1ý£@u fÞ˸µÍëzûl<¡[oQ§cÁ¬§~»BÐô”‘­ƒ9ön6„lŒ¤q_ÉÕêïåWòwtÙeTFJjµ‘nÞ†ÞÎœÛ~;P Û‡{W’hÔÇÅ°-ær"7ÏÆÒ¶mÑ5EžoµmDZ÷qr·à«MLØüèóÕ&ª»[`çlÊ„ÍtðÍ{ýƒî~•©çÄæxÞjÛþŸŠŒŒŒL¹!¡¿@Ô¨QƒŽíÛ³ øÇ5:9Š()V« p0üc`| ¨j³áí`HȆ/\a[¾7¾¯šKuB„í5½{÷ÂÞÞ¾ð“C‡ûñî»‡è¶ ª˜Jèv¦pñ,¹$­|Loþ¨ýø†RzѶۤªé3aæYð¨ºI‹W‘‚„3ŽH¾ü¯WôwÉIÉ}úÛlF>€_ÏHÿ*G|}{@Ê‚äb+Uï~k9ü¦Î•ª¡¿ÞXªp~ñ2,Y#­|L/û0Þî Ið…lÛ›ï¹@§Ö°ä0,PÁV=tÊ„!9•Ëw륺¨À^ Ü3À[%Mè,Òî®Õ=0Z ÷,ÑA}%l/Áž½ò'4‹@¶ô6»KÎÞ[ Úc°ù}ØxAÊ–ôñëR%ôíaRŒÁM jŽ»Ò¨íR\€ç+ÒêȺ³Òõ\å“·Å‚м…’š¯)Y·"›ììt^o¥âèAÛü³ùt¼öU•$&8¥Ðë]5nu•˜[(¸tNÏú•YØUU0jœô6<ô´Žà™lÙâWä}æääD·®]Xpr7!~:5†ÁÒêM+8xüÏÃøÖÆ3®5l8Þkaô›R¬E'%Aø]Gé^›{üJBbPWX±CZ}kå C¤}ã@Õ|^×þ ŽU €ýÁp=Võ„7óÅcÍ9'ý·‡Ë£mãIŽÿ„Ñ`m ‹.€NÚ–ðöøå4l½Tºç¡4¿ã™;t(`V6TÂü¢“îÏÉ9å-৒*¡÷È„nJ)ýî²l)fiôcÿ,Õ‘‰’Ày‰8‹•w/&pdÉeL,Õôš^p%õäÚëØ:šS»MÞÉuËanlúâ>sšòú Z[q]¶ V+{.Œ&Ä?‚Nã=°©*½õï4ÞƒÓD0§ínÞU—ô‡YìyGŠ¼ñá#uvxQ8+UÀÛÛ»à÷6l8¿þº†kÁiÞ³`ò-snЬGÁ}×FSÉÑ„úm*Øw,àî¥3tèвﴌŒŒÌ †BU-L¦\عs']»vå7ð¶‡ïnÀÊ(¸“.U8QFæ%ŽHƒ‡¤bh…ýˆaE=Xr †^„ǹ™™É«5\°HÆÆHp5Iªm`gíaR¨‘/öàb<Œ>GîJAç]aöë’{À‚óðñ©Âyð©tR†ä»ß®&Lj5óª8x]ð¨È[îØÚ¸Â~_¨6ìœ``X¿®Þ€¤dI„´{&–V;"nAæÒ*GawúÀw GGÉ=k™‘äCÿ]6œÔK.U. )h|”Jr¡Éðu¶Ø{S@’Ÿ}%ŒQKÙ­šeHq‹Ôð£®h{…)$׫ijø8gRxJí²`NWx£:LÚGJq5*JAè_´|äZ¶ê´4!¿z_:O3'øªmÞt®Û.IAÍóV˜Ðw€š¹ße±~eÑwN. |G1xdNÐm–`Ê™= ãf„žô4ptRЮ³šÏ&ag¯D¯ôn›IL”áá׊\Ø¿?íÛ·g•¼ç ßý+ƒ%×+— 0¢¹T×äqnÄÃÿí‚ýפÉøÎÒd½qŽ—ÇORÁÆ‹k¤tºßý +wJ®W.Ua„Œì“×føM¨ó?øü]˜9"ﾬlh4c!ò=È_[®íV)³Uhß|ýL’ª¥ï‚,½T5}z3IŒ·Û !`LK©ª}IÏCI¿£^/=/'# .pCH™­ª­•ð¥:of+€EÙ’8¹.¤b˜sîÝ7sÚÒCûL¨ÓÉ´YÜ»šDzRVv¦¼ÖîºLj@•VylF‡'òMM´ÿܯ™š<ûRd0ö•ßiò®+ý—µ`÷w¡[y•Ä;©Tr±¤õˆ×h;2oàÚÝ‹ øâÚ‘Ô&*êu­†÷l VUL çû¾Á6Æ|þ%Ó¦Sîþ9 „@Ó´12¯3;¨Fùõǵ=ÍÃûYÌm–gûíð†×9NïÏ«3hfÞe®ôŸxœÂ½f3öþ¹ï¹AFFF¦¼‘È HìØÌùæ:ª–"6¢8"ÒÀ㸊¾ý?`Ùòå%¶ßµk]ºtaq+RD0{i¼öÀ®;jì,¡ë°}F×æóÑÐp^ð͘ðé³Ùú¬X¯ÄBN‰AãOÊ-Ͳ”d¾ =3Ê IÏ´,˜¥W`ªVò±ÞPxîi¹Ÿ õ~RŸ]¼,YoRbõæ’XúS&ãGeX*·˜þÿû;6ÿιOtyVež†Û‰Pgž’Œlø¨»íçÏf`ò ˜ú‹•Îúê|iýD<È€ºT$d)èæ¦cû¢€¨yR´ÇaÄVI¬ÿi,¥á}Ò´Ö«¸©PâôzeFíï€RõlFOûG°´ÏAü¶µ3¨?-ºl=s[ÿ ±œ=Š¹y!ùÈŸ3!!!4mªÁ{¬¦>{ü¢OÂÙ¿<–³gÏQ«V)}Reddd^bäíÂ…YW ËñÏP7::ŸQSÉþfÿøc©Žéܹ3Cæ㣠vÝ|ús cŽÃæøjÂ×<Ô›Óc’äbRè–DÄèò«š×ÜÜøò˱|=Ö<½= .Yý¼O²^à•­àþ3HòX>z5•ªVåÛï¿g±“Š¸4üš 3u0qâDª»¸ÒåW5· IÃ\ZÓ¡Û¯*²ÔVÌ™û›Ïæ» ™…¦U--»¶d3qt#GŽ,µOþO?ÿŒ¥m:¯VQZbS ój¶íøö»é,Ü?þöôöVí‚)+`ܸñÔªU‹Î»ÕÜ,${ViIÊ„n»UdYòãÜŸð?'·§t駋bÛ%µCÁ°aÃx½Y3úéU„“©­$Ò¼¯SrKeÄü…‹¸väk‡C¯úN^=ÃêŽâíãM·î]YùÞnœ(&ÓE è²õüúáQ"NÆòËÊUå">6lÈ×_Oâ÷iì]yç™lm™{“íóo3cÆ,Y|ÈÈÈügPMžû3gÎ0ûÇ9¬ôÿ“íazÚ¸ *=á¼áàuèºFiGöø‹ž={róæM&}µÞhòÈý¨4¤¥Á˜©0m :”ß~ûÖmÛr->žY4G`ÿ„o§Côà#Ô<´±%ðÐ!ºwïNZZS%U@‹|AÂ%‘%`†ÆgÃðaØ9s&=zöbů¿³âDMõT·-ÙÎã\ºÝר¸šdÎÞ}ôìÙKKK¦~½›;·áÍ·T—¾“z½`ɼ,>ý(///–,YŠ²”?„™™]ºvC»|ëNgÒÂIðŠuÉÇ=Îé(èú«Šx½-ѳgO²²²˜8û0S¡µ'¨Ÿ Ò-+¾]ŸÎƒÁƒ?âÇçУg/V­ßÀÒ³©h*ëq¶*ÙÎã„=€îª¸œjÆž½ûèÕ«¶¶¶L\²‡›‰R½ÉôQ¯—*ÅûnRУGOV¬\‰—·7·leáýÔ‚šOøjé–€:%Ç•j¶lÛ†®®®ÌŸ¼’;¡pk÷ Ææ¥ï¤‚¿^cù»‡y£ùlô߈——7ûöîcóŒ#TrµÀ¡ží­º=¼—ÆÊ÷svÓ-Ö­[GçΟleL«V­ˆŽ¾Ë‚¯÷P§… JUéÇ“™®cÕ¸k¬tƒ/¿ü’qãÆ=ó*¤ŒŒŒÌË‚,@^Pìííéѳ'ìü“ïCãÉÒA}+0/Ú­€˜ øæø^PàêîÁî?÷Y¡(T*>>}ˆ»Ï”AŽQân+p°(þ¸l=l¼ÝvAÐ}:ý£¢ZÛ·ogÖ¬Ùì?ÊŒ}1R êW}T5½(n'J•¡?Þ š²kÏ^P(tïÞƒ¬¬l¦Î8®@¯½*¨îX¼XÒëaç~èý‘ŠýGÔL›ö-6l jÕªüùçŸôéûîeÖ½ûd )S•y s‚{B*:8B§ÀÙ½»÷î¥V­Z( Ú·o••Ó÷²%5„Eñ}BŠ1ù@¨Ø¬W0yòd~˜1…B­­->}ú²gÿa¦n">M*TgU‚@}³ÀÿþPbQÙ…»ÿ¤aC)êú7ÞÀÉɉ93w³au6ŽÕ¡F-Ê¢‚T&˜§Oêö^«—f1jÔ§,^¼õ“ÌöÊ•+Ó³Wo6íäÛ±¤gÇ+R‘Á⸗,%'¸Q£k]vÿ¹ÚµkðÖ[oakkËw?ïgÃ%5ôÔt(ùš:}¾Vñû~˜0a"³gÿˆB¡ÀÆƆ>}ßaOà!¦í".<*JæÅ‘?†B¿@%fvÎìصÇ»yó游¸0sÕ~9 Õ­õÔ®T²ˆº ýýU,:!ññ',[¶µZ¹¹9ïôëljӧùþÊ5"¸+¤Å‘,`…>ЫH¯bÇæmÛ +Xžžžxzzò˼ß8´( K{ì_³A¥.¾“Qç°ö£cü9ãýú½ÇoëÇÌÌ cccÞ}ç]ÂÂÂYóÍNî„&àP¿‚!®£(²ÒuœX}%½’%øý÷ ôîÝ»øý( ºví†B¡@;m+AÛâqt3ÃÎÙ´X!¡× Nï‰ç{¯ ïzÀŒ3˜8q¢,>dddþSÈ1 /8|óÍ7Ìš9…^‡· mEhl-eÂBª œñ 6Å‚ÚȘ¯&Lä‹/¾ÀÈèÙöíÛÇÐÁ¾\¸Iã*àSW7[)è<)ÎÞ—j"üz¢SA­T 22fÔ¨Q̘1¥RIÕªU‰eÕªUœ8q‚ æc¬¼SOOK)½¬…p~3Aªk°ïš¨kfjBzF&L`Ê”)úxüøq>úh .„S¿Žš>ݲiâ ukƒ© $§Àùp:ë7«¹™Ío4cÑ¢¥|öÙgœ9s†   \]¥ÈÞÌÌL¦M›ÆŒéÓA§£‡BÏ› h „ª ©ÑÎèá°P°U*##ÆO˜Àرc ½æçÏŸÇwà@NSÃH>›†J¨£²¥az©~È&…š°¬l<ÝÝYöË/4iÒ¤€=NÇܹs™ôõDÒÓÓéYÚÕ4v')& &YJÙ{(6œW’-”Œ9Š©S§ZgàÚµk âK`à_8:áý4lª¢®‡K+ÈÌ€+azΞҳ}£àìé,jÕreéÒ•´nݺ€½'!33“ï¾ûŽ¦^—·»ž6®R€yn!Â;¥ûâà ØxA‰R¥æ˱ã?~|¡•£/\¸ÀG¾rüD.¯@ÿŽ ©õ\Á Ò2àb„T?dÃ5®gS¿^–¯X…F£)`oܸqÌœ9cµŠŒÌLzº(hï(h\œ,¥k~/ ‚ãàð]X²Q2rÔ(¦M›V¨»Ð72Ø—}ûP­‚š÷êgÓ´š”õÊÊ2tRÊà(¸¤âÔ-¯Öpaɲ…ºº !X¹r%ÿ÷Ùg$$%ÑN­¤=z*¡ºBJÿ|_@¨ enDE²^Ï Aƒ˜5{666ƒqîÞ½‹ß?6oÚŒßsƵyª5¨ˆ¹­1º,=÷®$yê>¶Eqùð]_a¡v=zô(ô÷ö÷÷çãOF}·6Ôëæ@õÆ•©òª*#%©2¹rŸÇc9µ6‚‡÷ÓèÓ·óž]¡6Ë“S§N1Èw çB/à\ך}+Q«‰5ÕÝ-06S’‘¢'ò|2WN%qx}QW“iÚ¬ Ë—­¤^½zåÝ}™!óµ IDATRpïÞ=Q¿~}aif*”J¥@š>*•R4ô¨/æÌ™#âããËôÜÙÙÙ⧟~J*Î kKsÑ´iSadd$&Mš$Ñ»woáíí-LLL„B¡¯¾úªP(béÒ¥"**JL™2E¼V[Ú–ßžDCÏúÂÚÚZøúúŠ¯¾úJ‹°°°Bû¨ÓéÄÞ½{…—WoakkUhíì*Šú‹cÇŽ ½^/>ûì3¡R©D```¡6cccÅŒ3„Gݺ…_s¥R4¨WOÌž=[Ü¿¿Äë¨×ëÅ‘#GÄ{ýú‰Ê¶¶…ö±¢µµèãã#…^¯/ÑfBB‚øùçŸE“F „Z­*x Q÷µÚbÚ´i"::ºD{BqêÔ)áëë+^y¥J῵µ…èÞ½«Ø±c‡ÈÎÎ.•ÍÒ'fÍš%<ë»yŸ{Öw³fÍqqq%Ú»té’D‹os3ãBÇS¡‚•ðññû÷ï/òš_¸pÁpo¿ÿþûÂÖÖV4¨_O( ±g'GQ£F ѤI“R;88X|ôÑG¡já×ÜH¥ݺvÛ¶m+Õ5OII+V¬-š7&FF…Ú|¥re1~üxQª>^¼xQ|òÉ'¢ZuÇBí™™›‰víß6l™™™%ÚËÈÈëׯmßj#LÍL µYÝÅI|úé§"<<¼T},Oôz½ >}|D…Š6…Ž§r•Šâ½÷ú‰#GŽ”êù–‘‘‘ù·"¯€¼$¤¤¤`ggÇĉùä“O %>>…BAåÊ•©_¿þs­ž;yòdfÎœIjj*£G¦C‡deeannάY³ÈÈÈ`Æ T«V)S¦°`ÁnݺE`` £GææÍ›ÄÇÇÓ¨Q#NŸ>Í÷ßÏ—_~‰B¡àáÇ„††2pà@jÕªÅàÁƒñòòbçÎ?~œü‘«W¯Ò¢E œœœ ,Ö]AÁ7¸rå ™™™˜ššR§N Ç­^½š>ø€yóæ1räÈÇŸššJhh(÷ïßG¡PP©R%<<<žúš !¸uëááᤧ§cllLíÚµqqqyjWŒôôtÎ;Gll,z½žŠ+âáᥥåSÙpwwÇÕÕ•¡C‡bddDÍš5©Y³f©ã<ž…ǯ9`¸æOxüÙgŸ±fÍnß¾‡‡Í›7§ÿþOtÍõz=mÚ´!::šC‡Q£F Ƨ§'=zô`ëÖ­ÌŸ?Ÿˆˆ–-[F—.]øꫯ¨]»6ÞÞÞ„„„РAƒR÷ùîÝ»œ8q‚Þ½{3aÂT*Ó¦M#99SÓâÝ• #++‹ .p÷î]t:ÖÖÖ 0ooo~,erŠüÄÆÆrþüy’““Q«ÕT¯^×^{­ØÔËÅ‘MXX·nÝ";;+++êׯO¥J•J>øDADD—/_6ü rssÃÉÉIvµ’‘‘‘yäeÁßß_âòåËår~Q³fMˆ7näÙ÷í·ß kkk¡ÓéÄ€„«««8tèDµjÕĵkׄ½½½puu€h×®ÄèÑ£…N§3ØiÒ¤‰:t¨Ðëõ¢B… bÊ”)âÖ­[B¥R‰ ˆ={ö@¬Zµê™ÆrâÄ abb">üðCù-d1èt:ajj*~üñÇòîÊS‘œœ,lllÄرcEBB‚Ä/¿üòÄv–/_.±ÿ~1wî\¡V«ÅÝ»wÅĉE•*U„^¯¾¾¾¢Y³fB!Ú´i#¼¼¼DVV–pppC† yâsÆÇÇ @lܸQ @;vì‰íEß¾}Å›o¾Yföddddddž9 ïK‚¿¿?žžžå’¦ñêÕ«„††’™™‰±±1...yök4’’’¸|ù2~~~ܸqƒäädºwïÎíÛ·Y»v-›6m"** OOOöïßOß¾}™3g$++o®a…BA“&M ¢ZµjôìÙ­VK‡x÷ÝwùüóÏ oÅŸ”èèh¼¼¼hР .”ßFÃÍ›7IOOçµ×^+ï®<¿ýöIII :”àà`€Bc;Š#66–1cÆпÚ¶m‹V«ÅÛÛ›ªU«„F£)pi4‚‚‚P«Õ :”5kÖ˜˜øÔãðððÀØؘ   §¶‘FÃéÓ§ÉÎ~ÆÑ222222O,@^ÒÒÒؾ};>>>årþM›6ajjJ\\Õ«W/°?7H:((ˆf͚ѰaC´Z-Ë–-ÃØؘo¾ùGGG–-[ÆÙ³giÕª6l`Ø°a¬_¿žÞ½{“šššÇfî$NŸŸ.\àðáÃÌ™3‡¬¬,¾üòË'GFFÞÞÞèõz01yÆ*ÿrÂÃÃ^J"„`Á‚tíÚ‚‚‚°´´ÄÍÍí‰ìŒ3!³gÏ&00Ð ²…’Fí[·ˆ‰‰á£>"33“Õ«W?õXŒñôô,s’ššÊ¥K—Ê̦ŒŒŒŒŒLi‘ÈKÀŸþIrrr¹ €€Ú¶mKZZZ¡® *ðꫯ„B¡ÀÏÏ;v’’ÂÔ©SÉÎÎæƒ>`À€|ùå—>|˜Ž;²dÉÆÇèر#:Î`S£ÑÃíÛ·yë­·psscÁ‚T­Z•ï¿ÿžåË—søðáRAÁÇÌ©S§x¢º(ÿUÂÂÂ055-Tt¾èœ8q‚üüüI7nÜø‰b<ȪU«øᇰ³³C«ÕâîîNË–-‰ˆˆàþýûE Üs:88лwo´Zí3zÌäeE£FP(ejSFFFFF¦´Èä%Àßßww÷ry}çÎŽ?N•*UŠL«ùø©_¿~X[[³xñbFMõêÕ9xð ›6mâÛo¿¥[·n;vŒöíÛóÃ?ðý÷ßséÒ% +!Oâ Ç' €»wï2tèPš5kÆ°aÃÈÌÌ,Õ8.\ȲeËX´hÍ›7ÖËòŸ ,,ŒÚµk?u`qy¢Õjquuåí·ß(rµ¢(2226l-Z´À××—Û·o³eËüüüòLÜ ³éììLåÊ• müüü ãÀO=FCxx8IIIOmãq¬¬¬¨S§Ž,@dddddÊY€¼àddd°uëÖr[ýؼy3jµš˜˜ hÒ´iSÎœ9CVV 8åË—“ͪU«øè£HKKcíÚµ8;;såÊZ·nÍرc™7oÙÙÙlÞ¼™«W¯âè舃ƒƒa‚ôÁ`ddIJeËP*•,^¼˜ððpfÏž]âþúë/FÅ'Ÿ|‡~XFWæßOXXØ»,½ÄÅÅñûï¿3|øp”J%÷îÝãæÍ›4mÚ´Ô6f̘Áµk×X¼x1J¥’¥K—bffFÿþýIÐ8;;Z“B¡PдiSýۺukêÔ©ƒV«}ê15mÚ!„!–¥,x¼222222ÿ$²yÁÙ·oIIIåî~uþüy¬­­‹LªÑhHOOçüùó >œ¸¸8üýýiÓ¦ Ý»w'>>ž±cÇbeeÅÖ­[ILL$33FÈ#pqqA©TÒ¢E BBBò¬ªØÚÚò¿ÿýÅ‹“§§'Ÿ}öß|ó ׯ_/²ÿ‘‘‘øøøвeËR‰™G„‡‡¿”ñ+V¬@¡PÄfq«…qåʾýö[þïÿþwww²²²X²d  ÀÚÚÚ`³8{Ç0åº%nÞ¼™¨¨¨§“››–––eJFFF™Ù”‘‘‘‘‘) ²yÁñ÷÷ÇÍÍ ww÷üÜ÷ïßçàÁƒôîÝ›»wïû6¼aƨT*ÃÉÍÍvíÚÞú.\¸###´Z-gÎœÁÕÕ•€€Ž=JíÚµyíµ×¸~ý:mÚ´¡zõê´iӆʕ+sêÔ)ôz= ‰š¨¨(¶mÛHµIììì1bD¡þõ©©©ôîÝKKK6lØðÌUáÿK$&&r÷îÝ—N€èt:-ZÄ;ï¼CåÊ•I,T®\ggçÏMzàààÀĉ) Ctt4Ç7œ#88¸DGdd$ ÀÔÔ”¥K—>Õ¸T*7.s’••ÅÙ³gË̦ŒŒŒŒŒLiÈ Lff&›7oÆÇǧ\ÒÅnß¾½^O5 ÅØŠÂÜÜww÷<$???Ž;FHHŽŽŽLž<!ï¿ÿ>:ŽV­Zb3|||066fïÞ½,_¾FÃêÕ«ILLäêÕ«€8Û¼ysƒ¨±°°`þüùìÞ½›?þø#O„øúúÎæÍ› “Q™Òñ²fÀÚ³g7nÜ0ŸE¦Ë-Œõë׳oß>,X`XíÓjµ´lÙ’úõëÒµINN.Q€äžÀÆƆþýû³dÉ’i§KKY¢{xx`dd$»aÉÈÈÈÈüãÈäæÀ$$$”«ûÕ믿N`` @‰ýÐh4œÿüsœœœ8wî‹-¤¸Q£F1nÜ8P«ÕôêÕ‹%K–йsg@òÇÏÅÏÏ}ûö&ÈÝ»w§wïÞŒ5*O­…3fðÛo¿ñË/¿àééYWã¿EXXµk×.çž<Z­–Fâ=„œ£OŸ>†{ïÂ… üõ×_yÍÉ“'Q(4nܸH[vvvT¯^=Ïó0|øpî޽˖-[žjl†ÈÈHîÝ»÷TÇçÇÄÄOOÏ<}”‘‘‘‘‘ù'È Œ¿¿?5kÖ,— trr2{öìÁËË‹¿þú ¥Ri¨÷Q† .2Yåb[»v- ˜˜˜\PÆŒÃÝ»w˜5kíÚµ#""‚Ö­[“M·nÝX¼x1ÖÖÖ,_¾œ~ø!}úô¡råÊðÓO?‘œœÌW_}À®]»7nãǧOŸ>Ïãòüë £ZµjXZZ–wWJÍ7عs§!SH1@qqq¥ cÇŽ%==¹sç¶-\¸{{{¼¼¼ Û‚‚‚pss3ăEþ OOOZ´hñÔÁèùWUÊ‚²^U‘‘‘‘‘‘) ²yAÉÍU^îW»ví"##ƒÞ½{FÕªUQ*‹¿]4 :ŽöÁƒç)ÄööÛoÓ¹sg222øä“OI¨üþûïqèÐ!¸ÿ>]ºt¡C‡T«V±cÇ2fÌŒñõõeåÊ•¤¤¤P­Z5¦NŠV«Åßߟ~ýúÑ¥K¦Núœ®Î¿Ÿ—1}ñâÅØØØЯ_?öÒ ÿý÷ß,Y²„o¿ýÖP#æáǬ^½šÁƒcllœÇfiF£!888O}›#FpàÀ§*èââB¥J•Ê\€„……ñðáÃ2³)#####S²yA9tèqqqåZý¼AƒØÛÛ“˜˜Hƒ J<¦~ýú˜˜˜ä™ ½òÊ+xyyå)Ķ`Á”J%7nd÷îÝ€”åªV­Z¤¥¥1aÂvíÚÅ7&..Ž9sæ0{öl Ä AƒHJJâ·ß~3œçã?¦~ýúôïߟªU«²víÚ“LÑ„……½T$==åË—óá‡æÉÔ„““öööE›••ÅСCiÒ¤‰!Ð`íÚµ¤¤¤0dÈöÌÌLΞ=[j’œœlpðòòÂÎÎÎà–ø$(Š2_±Ðh4ežÞWFFFFF¦$äÚ Š¿¿?ÎÎÎÅú™?/222ؾ};½{÷fÇŽtêÔ©Ä㌌ŒhРA ’ŸŸááá†Bl®®®Œ?…BÁ!C .[&&&tèÐ}ûö±fÍvïÞMtt4ééé¼ñƬ[·Žµk×òùçŸÓ©S',X`5J¥222èÕ«666eyIþSdggsåÊ•—ªˆ¿¿?qqq 6,ÏöÒ¬VÌ™3‡‹/²xñbCÑE!Z­–=zàäädhjH]¹Ïîãσ‰‰ }ô«V­"99¹ÔãËåñô¾eA:u°°°Ý°dddddþQdò¢Óé(7÷«ýû÷óðáC¼¼¼Øºu+@©c) {CÛªU+êÖ­›Ç÷}ìر888Å´iÓ Û«U«Æ¼yó˜3gçÏŸgãÆ 6Œ>}ú°mÛ6‰ŒŒ$$$Ä@;iÒ$Ž9B·nݘ?>7oÞ|¦kð_æÆdee½T+ Z­–:ä š×ëõ%¦Ëˆˆ`òäÉŒ9’F¶=z”sçÎå >IL¨ÕêR­ÚØØàææVày2dÉÉɬ[·®´Ã3 Ñhˆ-³û[¥RѨQ#Y€ÈÈÈÈÈü£ÈääèÑ£ÄÄÄ”kö«ZµjáîîΉ'055¥jÕª¥:V£ÑpåÊ Û +ÄfffÆÏ?ÿŒ^¯gÆŒ\¸pÁÐÞÏÏaÆ1|øp¬¬¬pvvæÌ™3 4ˆŽ;²oß>îܹƒ±±1³gÏÆßߟiÓ¦ñÝwß±fͬ­­9rdÙ^”ÿ/[ ÞŽ;V@,„‡‡óðáÃ"ˆ‚?þ˜J•*ñÍ7ßäÙ§Õj©U«íڵ˳=((ˆúõëcjjZª¾&ÈéÖ­[ž¼Ò"¢ËÈÈÈÈüÈ ˆ¿¿?ÕªU3¤ý'ÑétlÙ²Åõ'22WW×RŸ;A:uêTží…bëÕ«íÛ·G©Tæñ³W(üôÓO´hÑ///š6mŠ³³3kÖ¬á“O>¡yóæ9rSSSþøã À;ï¼Ã—_~‰ óæÍcË–-Oîô¿NXX8::–wWJÅÂ… ©V­ݺu˳=wR]”c@@;vìàçŸÆÊÊÊ°=&&†^ Ž¨´è¹h4Îœ9Cfffží~~~„††rìرRÛ¨Zµ*ÕªU+sAlll™Ù”‘‘‘‘‘)Y€¼`èõz6n܈··w¹Q9r„¸¸8z÷împÅyýõ×K}¼››VVV&HÖÖÖ 0 O!6…BÁüùóÑëõüý÷ßÄÅÅÚáïïµµ5GŽáÖ­[ÌŸ?­VËøñãqwwgÓ¦M€y*?ûÿ:¹èåáþ÷¤$$$°víZ†ŠZ­Î³/((ˆÚµkckk[ฤ¤$FŽI=èÕ«Wž}Ë—/G­V3pàÀ<ÛSRR¸xñâ ÌÌLÎ;—g{‡¨Y³æS¥ä}èP𥌌ŒŒŒÌóB /ÇçÎ;åê~åèèˆF£1TÏ?A+¥RIãÆ VˆÍÍÍÑ£G£R©¸uëééé†}•*UbÛ¶m$&&¢ÓéðððàÇdúôéL›6o¿ýcccT*o¿ý6ýõ › ǤI“žöRüg {iÐW¯^Mff&}ôQ}Å­VLœ8‘ÄÄD~þùç<Ûu:‹-¢_¿~T¨P!ϾӧO£×ëŸH€4hеZ]àyP*• >œ?þø㉠æ¦÷ÕëõOt\QÔ¨QƒŠ+ÊnX22222ÿ²yÁð÷÷ç•W^á7ÞøÇÏ-„`Ó¦MôîÝ¥RÉÞ½{hß¾ýÙ)ê ­‡‡o¾ùf·¾'N¤R¥Jèõú.)uëÖ5ëN™2…Ï>ûŒÉ“'3qâD<Èœ9sÈÊÊ¢F¼ýöÛqãêêʤI“˜7ogÎœy¢þÿ×yYj€äfªòöö.£”™™É™3g  ÁÁÁÌŸ?Ÿ)S¦P½zõ<ûvìØÁ­[· Ä“€$hÌÌÌpww/uÍĮ̀W¯^¡ÏÃÀQ*•¬X±¢Ôö@z¾’’’¸|ùòW …‚&MšÈDFFFFæC /Büýýñòò*÷«àà`nݺeˆÿ ¥B… ˜™™=‘FÃíÛ·‰ŽŽ.°ÏÏϯ@!6+++æÌ™ƒ‚Ë—/ÒõæÒ³gOœœœ éys'z½sss6lˆ££#Ý»wÇËË‹•+W0zôhêÔ©ÃСCóƒ“)š¸¸8âââ^ ràÀÂÃà  çÏŸ'##£€ÉÎÎfÈ!Ô¯_ŸQ£F8N«ÕÒ´iÓBãF‚‚‚hØ°aW¯’(JWªT‰wß}—E‹=ÑýÙ¤ICÊŠ²Nï+#####S²y âÖ­[åê~U©R%Z¶lIff&qqqOô¶7—â2õUˆ­_¿~XZZ¢R©6lyöwïÞ ÄСCñõõ5|š5kÆ®]»˜>}:ƒfРAÌœ9###/^ÌÉ“'Y¼xñã¿ÈË”K«ÕâîîNË–- ì B¥RH—»`ÁBBBX¼xq!qõêUöìÙS¨ Éµù$îW¹h4.\¸@JJJ}~~~DFF²k×®RÛË-ÚYÖ$&&†Û·o—™M™¢È „¿¿?UªT)tBõ¼B°qãFzôèZ­æèÑ£!xë­·žØ–³³3•+W.t‚TT!6…B³³3:Ž«W¯òÃ?ä9N£Ñ˜˜hø>vìX/^Lß¾}Y¶læææ,]º”… 2a¾øâ ÆŒÃ믿ÎàÁƒ7nwïÞ}â±ü× C¡Pðꫯ–wWŠåöíÛlÞ¼??¿Bƒåƒ‚‚¨W¯^žªè·oßf„ 6ŒfÍš8fÑ¢ET¬X‘¾}ûØϵkמZ€èõzBBB ÝפI,XðÄ6ŸG ºì†%####óO „Çݯr«1ÿ“\ºt‰Ë—/ܯr >ÍjŒB¡(v‚”[ˆmíÚµy¶çú׫Õj¾ûî;®\¹bØçéé HîZvvvôíÛ—ôôtV¯^MçÎIOOgÑ¢Eddd0uêTæ͛ǬY³4hÓ¦MÃÄÄ„Ï>ûì‰Çò_#,, —'v»û§Yºt)fffôïß¿Ðý…­VŒ5 KKK¾ûî»íÓÒÒX±bƒ *tì¹¢žF€¸»»cjjZäóàççÇîÝ»¹víZ©mæ¦÷ÍÍ(÷¬888ààà ™Y€¼ „„„pãÆrs¿Ú´i–––†€óÇ£R©žÊ Š÷)Ï-ĦÕj ì×h4XYYaddÄðáÃB „0d+êׯ;vìàòåË 8•Jņ hÒ¤ ‰‰‰Ìœ9€‘#G²fÍÖ¬YÃàÁƒ™>}:¿ÿþ;{öìyªñüWxг²²X²d  ÀÚÚºÀþÔÔT.\¸G,lß¾€€æÌ™ShZÞßÿ0lØ°BÏ„ÍS­ Ñ°aÃ"'÷ï¼ó*T`Ñ¢E¥¶©ÑhHOOçüùóOÜŸâlÊDFFFFæŸ@ /þþþTªT‰Ö­[—ËùèÒ¥‹¡ÂóÕ«Wqtt|ê`xFÃýû÷‰ˆˆ(tn!¶¿ÿþ;Ïv¦OŸNrr2û÷ïgݺu,X°€•+WR»vm¢££ñôôdÍš5øûûóÍ7ß`jjʾ}û°¶¶æ›o¾1LÊþ÷¿ÿ±uëVöîÝËÊ•+iݺ5Ç'55õ©Æô_ ·È‹ÌæÍ›‰ŽŽføðá…î A§ÓHJJ #FŒ cÇŽ¼óÎ;…£ÕjéÔ©5kÖ,tPPMš4y¦ç¡¨É½¹¹9~ø!+V¬ --­Tö6lˆJ¥*s7¬S§N•Yz_™¢È €‚?þøƒ^½zaddôŸ?""‚Ó§OܯbbbHMM5dÛyJò)/®Û AƒÐh4ØÚÚ2bÄ>ýôSFE÷îÝ özõêÅ·ß~Ë”)SØ°a–––üüóÏdggÓ¦M®^½ @çÎÙ·oçÏŸ'&&†¨¨(¦M›öÔãú7“‘‘Áõë×_ø Z­––-[R¿~ýB÷ajjJ½zõ)}ó½{÷ÐjµEÆ‹|žÛæiܯrÑh4\½z•ºØ°aÄÇdzaÆRÙ377ÇÝݽÌHbb¢áÙ‘‘‘‘‘‘y^Èäàܹs\½zooïr9ÿ¦M›066¦K—.lÛ¶ €®]»>µM{{{œœœŠœ WˆM©T2þ|HLLÄÞÞžY³f¡Ñh¸yó¦¡ý¸qãèׯ$88˜÷Þ{ªU«’Mûöí }Þxã >LRRÌœ9“ .<õØþ­\»v N÷B¯€\¼x‘ƒ–(4h€‘‘¡¡¡üøãLœ8±ÈÕ… R½zuÃýŸŸ;wîpçÎg PtµñZµjѱcÇ'ªŒ^Ö.SÏ#½¯ŒŒŒŒŒLaÈäÀßßÚµkW.çß´i;vÄÊÊ Š±tëÖí™ì–4AúðÃQ©T,_¾¼À>www*V¬ˆB¡àÎ;œ8q¢ÀªŠB¡`ùòåÔ«Wž={ËðáÃÉÌÌD§ÓÑ¡CƒX©W¯G¥bÅŠ!0`€ìj’—!ïÂ… ±··7¬ÖFîj…^¯gèС¸¹¹ñÿ÷…¶gýúõ 6¬Èä¹÷Û³Zµjamm]ìóàççÇÉ“'‹)ùÑh4œ?¾Ì\ +V¬HÍš5e"îþ9 IDAT####óÜ‘È €¿¿?={öÄØØø?wLL GŽ¡wïÞ†mÁÁÁ˜››cgg÷L¶5 ÁÁÁEY«X±"ýúõ+PˆMÁ AƒHOOÇÜÜœ*Uª0lØ0ªU«F¥J•òLÌÌÌؼy3BzõêEÿþýÉÊÊbàÀ$$$ðöÛo“€‹‹ ÿý75jÔ $$„qãÆ=Óøþm„……akkûÌ¿ûó"99™U«V1xðà"Ÿ•„„®\¹‚F£aéÒ¥?~œE‹Ùþ—_~A¯×ãëë[äyƒ‚‚°··§ZµjOÝw¥RYbµñnݺQ½zõ5rŠB£Ñ Óé8sæÌS÷«0›²‘‘‘‘‘yÞȤœ¹xñ"—.]*·ìW[¶lA¡PУGt:QQQÔªUë™mk4’““ oÖ ÃÏÏ›7o²sçNöéÓ§³aÃV­ZÅ÷ßOll,/^dÎœ9…Nâزe çÎã믿ÆËË‹?þøƒ?ÿü“›7oÒ¥KCÍ;;;N:EÕªU™1ckÖ¬yæqþ[È @/,NâE`íÚµ¤¤¤0dÈ"Û䮸ºº2vìX Td]½^V«¥OŸ>ÅŠ®Ü•g½.%MîU*C‡eݺuÑ\õë×ÇÄĤÌã@BBBÈÎÎ.3›222222ù‘H9ãïï••:t(—óкuk*W® ÀÙ³gÑëõ¼ùæ›Ïl»qãÆ@ñ>åMš4A£Ñ|ß###ùꫯ˜0a>>> >œúõëcooÏ”)S  ó§ïmÒ¤ ¿üò k×®ÅÆƆððpbccÙ½{7çΣW¯^¤§§`mm͉'022âý÷ßç—_~yæ±þ {aÐ…hµZzôè““S‘í‚‚‚°¶¶F«Õ¢V«™1cF‘m÷îÝ˵k׊'BpêÔ©gr¿ÊE£ÑUlAL___t:ëÖ­+Ñž‘‘ 4(s’––&ÇHÉÈÈÈÈ¥RɪU«pssC¯×3wî\@ª²víZ~ûí7üüüB T*Y´hIIItêÔ‰3fàëëûŸô ^Ì X÷îÝã?þ`øðáÅŒŽŽ&**Š‡²páÂb…Å’%K°´´ä½÷Þ+öÜAAA¸¸¸\Ÿ…BQ*AÞ¢E êÖ­[*›¹Â(W˜—r ºŒŒŒŒÌóF 刿¿?]»v-³Éþ“ššÊ®]»ò¸_åÖÝ(ªÀÛÓ ÑhÈÌÌäܹsy¶=z”=zдiSüüü¸qã÷ïß/ÔŽ££#_ýµ¡¿¿‘ç´°°`ûö혚šòÓO?ÐûôéÃÒ¥KY¼x1_|ñBÜÝÝ3f `æÌ™üúë¯øøø”º"õËNDDÑÑÑ\ºt •JE5Ê»KX¾|9jµšÛ.×}ÐÏϯX!•‘‘Á²eËøàƒ°´´,Öæ³ ÌOî侸•6…BÁ Aƒˆ‹‹+Öž››VVVerîÜ9CÌ”ŒŒŒŒŒL™#dÊ…«W¯ @lØ°¡\οiÓ&ˆððpö€€ˆéÓ§—ÙyRRR„J¥ .4l»uë–°··€áóÁkkkQ¥J•"meddˆÚµk‹Š+ @œ8q¢Øs¯^½Z¢mÛ¶B¯×¶Ï;WbêÔ©†>ºººŠvíÚ‰íÛ· 333ѪU+‘ðŒ£ññññ€066¦¦¦âƒ>sçÎ-ïnÈÎÎÕ«Wƒ *¶^¯ÎÎÎB¥R‰ÔÔÔbÛ®[·NââÅ‹%žÛÂÂB̘1£Ä~úúúŠfÍš•Ønǎ׮]+¶]dd¤„O‰6Û´i#¼¼¼JlWZ‚‚‚ Ž;Vf6edddddG^)'6n܈™™;w.—óàîîNíÚµ Û¶lÙ`HÉ[˜››S¯^=ÃÚôôt¼¼¼ˆ‰‰ÉÓnõêÕ¨Õjbcc‹,ÄfllÌO?ýD||< 2,Î]jÀ€Ô®]›\±FÅÔ©S™8q"óæÍÃÜÜ­VËþýûIHH`ïÞ½„††Ò¦M›ýü·‘›"933“ôôtV­ZÅêի˹Wعs'7oÞ,1ø|ÕªUDFFÒ¸qãWµZ-mÛ¶¥N:Ŷ»té)))e¾%WÏ- ºwïÞBÝóÛ,ËŒe7,™ç†,@Ê :wî\¢ Èó 33“mÛ¶¨&}ìØ1Ôju™§b}ÜídÈ!…Nl„ciiYl!¶·ß~›.]ºR¼Ê‚ Š=÷äÉ“øüóÏÙµk—aûW_}Ř1cøôÓOY±b:u¢oß¾Œ=šºuërèÐ!bbbhÑ¢E©‚_6t:—/_.°ýEŠÑjµ4mÚÔÒ¹0âââøüóÏ111)QЇ††räÈ‘ H"A¡P{î'¥J•*8;;—zrŸ˜˜È¦M›Šm£Ñh¸uëV™‰eccc<==e"####óÜH9IPPP¹e¿:xð yÈÿ³wÞáQT_~7›M¯„zè5t)RCQ”^’  ØÀ†ÒDA„ФI$*U‘&Zè]Z(!„Þvï÷Çì†lv6Ù„Pü~ó>Ï>¸·ž¹;ï™{Š‚ÿý—råÊåêè[|}}9wî³fÍbõêÕ²m:wîL©R¥¨^½:ëÖ­ãñãÇfÇ›?>*•ŠbÅŠñÅ_pçγm{õê…§§'åÊ•£ÿþ\¸pììgÎœ‰¿¿?#FŒà·ß~cîܹ¤¦¦2aÂjÕªÅáÇQ©T4oޜӧO?Û"¼‚ܼy“´´4“òWE¹ví;vìÈSYøä“OÈÈÈ ---ÏÓŠ… R²dIºwïžçüøøødFù9±¨^½zVŽœÜƃ¼OUòƒâˆ®    ðŸ¯¯/:ŽÏ>ûL¶¾jÕª¬[·•J…Z­–+V˜¯|ùòÔ¨Qƒ˜˜ìíí;v¬Ù¶¶¶¶ >œØØXJ•*…ŸŸ_–£»J¥bÁ‚¼õÖ[ 0€“'O2}út–,Y¡C‡ðööæСC”,Y’V­ZqðàÁg[ˆW Cä«œ¼*É.\H‘"EèׯŸÙ6û÷ïgÅŠôïßÈ=\n||<«W¯fäÈ‘h4š<ç/lt¾¾¾?~­V›gÛŽ;²ÿ~“ Ù)W®E‹-täÒ¥KÄÇÇÚ˜ ä%L‡dÃÍ>o´Z-aaaôìÙÓ(LéÖ­[Âõÿ0`°É7äßÈŽ‹‹ ›6mÂÕÕ5«mŸ>}X¸p¡l{ï¾û.^^^„……±e˳mGŽIbb" ..Ž~ýúe%$´²²bÅŠtéÒ…Þ½{ããワ¯/þþþdddàééɾ}û¨W¯íÛ·ÏužÿæWá$%%…åË—3tèP³>éééøûûÓ¬Y3\\\([¶,žžžfÇ\½z5©©©Œ1"ÏùÓÒÒˆŒŒ|n HRR’ÙõÏNãÆ)Q¢D®f‰–†÷ͯŒBˆB ï«     `@Q@^0QQQ>|ø¥™_9r„˜øìر ÏDoù%11‘¾}ûÊÖ©T*Ö­[gòÆ=00«W¯²gϳã6oÞ!‘‘‘Ô¯_Ÿ÷Þ{¤¤$Ù¶åÊ•£k×®üöÛo„„„°ÿ~Þÿý¬zFÃúõëiÑ¢=zô`̘1œ?ž~ø”¤ßÿN:ѳgOV­Z•ßex%18 gG¥RQ¹rå— 16làñãÇøûû›móÝwßqåÊ-ZıcÇr=½BD=(UªTžóŸ>}ú¹6hЕJe‘Â`mm͈#X½zu®§5Ê3¼o~ðññÁÉÉI1ÃRPPPPx.( È &44FƒŸŸßK›¿D‰4mÚÔ¨üÔ©S899Q¬X±B›K§Ó1hРΞ=+[?mÚ4Y3´æÍ›S«V­\mßëÕ«‡Z­¦zõê<|øèèè,‡s99}ú4ÖÖÖ,\¸   £ñíìì§víÚŒ7ŽðÍ7ßdå±³³cãÆ 2„Áƒóý÷ß[¸ ¯.roཽ½_J^šœѱcG*V¬([íÚ5¦Nš4àøñ㹞Vìß¿ŸóçÏ[ä|’ù•F£12S,,\\\ðññ±xs?räHRRRX³fÙ6¾¾¾ÄÄÄpóæÍB‘Q­VÓ AEQPPPPx.( È &88˜öíÛãææöÂçBF=ŒÍ“““‰ŽŽ.tÓ›iÓ¦e%‡ËI¿~ý˜0a‚lJ¥"00-[¶pëÖ-Ù6Ô¨Qƒ5jpÿþ}Z´hÁœ9sˆŒŒ”mß¾}{*V¬HPPÇgܸqŒ;–½{÷fµqttdÛ¶m”+WŽ;vàêêÊ{ï½—õVY­V³dÉ&NœÈG}Ä„  íóË@Ny̯"""ˆˆˆ0«,! ¤xñâ|õÕW\ºt‰ÄÄÄ\   ªV­Ê믿n± µk×ÆÖÖ¶@×ù1™*]º4ݺu#((Èìý¦8¢+((((ü—PÈýû÷9pàÀK3¿ŠŒŒäßÿ¥gÏžFåG°xsf ›6m⫯¾’­«S§Ë—/7òAÉÉ€pttdÉ’%fÛøúúrùòe>øàÖjµôïߟ«W¯ÊÖ·mÛ6Ë©ÛjÔ¨Aë֭;õ­U«¶¶¶DDDðÖ[oѲeK®^½ŠF£aüøñ²}FÍ­[·Ø¾};UªTá·ß~cÏž=|òÉ'Fm+V¬ÈüK–,᯿þ2¯k×®ìÞ½›S§NѺuëBËFý¼yNÇÂ… éÛ·¯l8Ý#GŽ°xñb¦NšÉ*===×p¹[¶l!**ÊbçsªU«V° ±;;;j×®¯ÍýàÁƒÑh4üüóϲõ¾¾¾$&&š=ÝÊ/Ï#¼¯‚‚‚‚‚( È Ã`æÔ£G—2XXööö¼ñÆFåwîÜ!99™zõê=ó'Nd×®]²uC‡eÔ¨Qܽ{—»wïZOó+¾¾¾\¸pAV©5G@@wîÜÉJšWWWªV­Zè ÈÇÍF£SPPPPP(Šòxôè{÷î}iæWwîÜáèÑ£²æW†LΓ‘üpüøq³6û%K–$$$[[[Ê”)ƒ§§g¾6H†‘#GšMÄæëëË•+W²œÈ'OžŒܾ}›éÓ§›ôyóÍ7qwwgÑ¢EYe*•ŠyóæѼyszõêerŠ1räHºvíʹsçèÓ§¬}ùòå9xð %J” U«V<_¹< ÷Ñ‹P@ªW¯Ž½½}¾Ÿ‡ÀÀ@vïÞÍåË—Mê|}}9uê”ÙÈqù¥D‰”.]ZQ@ EyÓ²eKJ”(ñRæ ¥M›6&Q€ÒÓÓ¹sç*T0JLh)éééôéÓ‡Û·oËÖ/^¼˜Æ•ùúúrìر|Ù”—.]šîÝ»Ë&b«Zµ*ÎÎÎF¤!C†dEòöö–Uüýý‰å·ß~3*÷ðð`Ë–-ܼy“t¸ººfšüüóÏ|øᇲ×áêêÊŽ;èر#Ý»wgõêÕ_ë‹ÀœúËÊ’žžÎÒ¥K4hNNNFuááálÙ²…yóæáììlT—””d6\þJ||<£FÊ—,¸»»›ÍÀ^˜X[[S¿~ý|oîûö틇‡‡Ñ žCxßÂ4TÑ EyÎÄÅű{÷î—f~Ã_ý%k~‰N§£Y³f{ܸqf£;–Áƒ›”ûúúËõë×ó5—¹DlVVV4hÐÀhƒdeeÅüùó9}ú4:ubÿþý¬\¹Ò¨_¥J•èСƒ¬)KõêÕY¿~=[·nå‹/¾0ªëׯ:tÀÝÝ9sæ0yòdYyíììظq#ƒfàÀÌ™3'_×ûÌ!CøðÃYºt©Y¹g̘ÁìÙ³ùöÛo5jT¡%Š+(ry"*V¬h’ òEpæÌ8`¢,œ8q‚yóæñõ×_S®\9Ù¾Ô«WÏ$\nPPåË—§C‡ù’%**Šû÷ï¿pU‚s£B… têÔ‰ ˜(µ…m2Õ°aC@qDWPPPP(<ä9ÏÎ;_šùUBB»ví2ëü~æÌ\\\òøöíÛôîÝ[6­ƒƒ›6m¢hÑ¢¹ŽQÐ Ò Aƒd±™;U)V¬“'OfÍš5²råJ£d‚jµšQ£F±nÝ:?~l2ŸJ¥bÑ¢E4hЀ=zùº|ôÑGT­Z• .À¨Q£LÞFgçÃ?dåÊ•,_¾œ~ýúÉ:í¿„fCð¾ .\HÉ’%Nè´Z-£F¢F¼ÿþûfûÊVÄÄÄ°aÃòí×d¸^¤R©R%ÜÜÜ ô<râÄ “¾¾¾¾œ;wŽ¤¤¤B‘ÑÍÍÊ•++ ˆ‚‚‚‚B¡¡( Ï‘mÛ¶‘––FïÞ½_ÊüÛ·o'==]Vyøð!Ož<¡fÍš—’’BÏž=‰ŽŽ–­_¹r%µk×Îs___Nœ8‘ï“s‰ØÊ•+GÑ¢Ee7HÔ¬Y“½{÷Ò¬Y3üýýIKK˪6lZ­ÖÄGÄ€­­-aaaØÚÚÒ½{÷¬M ‹-âÈ‘#ÔªU‹wÞy‡wß}—-[¶˜•РA„……±}ûv:wîlVøEÙ×?|øPVÙzèñññ¬^½š‘#G˜qüøq/^lö$-66–k×®™( Ë—/G¥R1dÈ|ËAÉ’%_èi¥J¥¢aÆÚÜwìØooo³DCxß“'O–˜Š#º‚‚‚‚B¡¢( Ï‘àà`|}}Íš—¹r’žž.J”(aÑú-ZÔD±MJJ2QØÌ} `‘LS§N•íìØ1‹ú?‹’˜˜hV±·D™6mšÅ÷ãÉ“'-’)'Z­V¸¸¸˜Œ×®]»§      „ŠH!ræÌ®^½jRÞ¹sg^˜™™™lÞ¼™^½zåOøçŸdÛ7iÒD¶ùä“ÉÖ¨Q£õ3ðæ›oâîîžg»›7oòðáC“ò¥K—âèèhÑ\9ZYY±zõj‹ßŽ¯^½šï¾ûŽäädÚ·oOll¬Q½««+;vì GfÇ7n_~ùe¡¼m¾zõªì8/B9þ<ûöí³¨í¼yópvv6)·thÑI×Ý»w¹{÷®Iù³Þ£ùáe*ä–bNF%,¯‚‚‚‚BAQBâüùó²ae;tè »™z^èt:ÂÂÂèÙ³§Ñ&L«ÕrôèQ“ö76IØÎ×_-;~ݺuY¶l™Å¦L9©P¡EŠ)P_{{{†jQ[¹ ’‹/¶¨ÿš5kxòä‰Q™³³3›7o¶(Ì«‚o¾ù†ßÿû÷ïÓ±cGLäÙ¸q#Æ 3;ÎÔ©S xæ êæB𾈠æ²ÍçÄÏÏϬBfɆ×ÅÅ…·ß~Û¢¹Ì÷22¡”æÍ›[¤Ÿ9s†”””Í¡( …¢€¯ŠùUDDQQQ&æWçÏŸ'11Ѥ}Nó«sçÎñî»ïÊŽ]´hQÂÃßɟÅxíY°¨¹ Ò€,J˜˜œœÌ/¿übRîííMXX˜Eö÷§OŸf×®]ìܹ“K—.áççg²´¶¶féÒ¥L˜0Áì8‹/¦ÿþFIóËËÊ’˜˜ÈªU«òlçàà`ÖO'..Ž+W®ä9Æ Aƒprr²H®WA©]»ö3™{©T*Fg;­VkbRh)eÊ”ÁÓÓÓ¤\Q@ Š¢€!!!&e??¿*GXXÅŠ£E‹Få–øÄÆÆÒ½{wYEÅÚÚšàààBIªø¬¼Š+Ò±cÇ<Ûå¶AÚ¼y³ÉÉAAA²fK-Z´`Ñ¢Eyö˜4ilß¾ˆˆúôéCzzºQ•JÅ·ß~Ë÷ßovœàà`ºtébrŠb)r ˆl¢ÊÂdíÚµÉüÍ7ߘ½¿ ‰óÂRåäïggçšÞÆƆ:uê<Ó °è”µ  ƒ!ÑgNNœ8a’´SAAAAAÁ¤¸|ù2§OŸ6)ã7L"ùÜbgWK( ¤sçΔ-[6×6>äÖ­[fëúé'ÜÜÜòœ+· ô¬Y³,:Ù²e ááá´oßžõë×ʈ#dC8°°0ìììdÇ:vì-Z´ ïk!—.]2)ÞæW‡–Uγ£R©X¼xq®&m–¼½ÏÏ=zíÚ5ÙðÒ/Òüª0ç´ääGqDWPPPPxUPB@ÎÿÃÚÚšnݺ½P9BCCqvv¦mÛ¶Fåqqqœ?Þ¤½ÁüjíÚµfMš6mÊüùó ìt.‡——^^^Ï4†Z­Æßß?Ïv¹mÔjµ¬GNÂÂÂd£%ÆX¿~½Eù1cÆ@Ïž=Y¹r%+V¬àƒ>=¥ñóóc÷îÝfOÐ._¾LóæÍ-~}÷î]YÓºçmndÉéǨQ£Ìæ¢1×F×ËËËâɹ÷_U@ªW¯žgžšË—/W ñDAAAA¡0Qgäúõëœ8q¤¼mÛ¶Ïí© „……ѵkWlmmÊÍmš4i±cÇ>|¸l½——!!!&ã…±é²$[^$??¿,34shµZ–.]j¶ÞÕÕ•-[¶ä8**Š¯¾ú í1oÞ<&Mš$Û¾E‹ìß¿Ÿ%J˜¯eË–fÍë²ó2У££Ù¸qc®mŠ/ηß~›k›{÷î•k›‘#GZœ”^-ÄÇÇÇâÐйaÉ ÐñãÇ 4v±bÅdýsDAAAA¡@¼¤ ìÿ9ÒÒÒÄÆ…¿¿¿ðmXO¸»9 ;áäh'ÔV0þ,]ºô…ÊwíÚ5ˆ 6ˆ¿þúK|ôÑGâõ×Z‰bEÜ…­ÆZ¨AXçq×®]¢téÒ&²ÂÖÖVüóÏ?ÏMÞ©S§Ígm…P«¤Æ QªT)ñÃ?ˆû÷ïç:ÎÛo¿-+¿µ•þ£¶E\EófMÄرcŶmÛDff¦Ñ÷ïßjµZvÃÇÎÎN”+í%œí…³“ƒ(_®´èÕ«—˜1c†¸yó¦Bˆ½{÷æ9ŽJ¥sçÎcÆŒM7Nö¶Â „£­hÔ ¾ !!!"===K¾k×®‰Š+šÓÁÁAüþûïF×”‘‘!ÂÂÂÄèÑ£EãF ³îSk+Œî×Ç?Óž.‚ƒƒE@@€hä[_z.ìíDwá]®L®kˆuëÖ™ŒyóæM1cÆ Ñ«W/á]¶”p°·Íº/äÆP«Õ"**ʬŒ™™™bëÖ­b̘1¢Y“Æ¢ˆ›‹°V«„µþ~3Œãáá!t:E×}ïÞ=ñý÷ß ///¡Éqï::8ˆÉ“'‹K—.Y¼Ž-[¶Ì’ÃJ•íyÐÿf-Z´k×®)))fÇHOO%J”]#ƒŒ¶káU¼˜èÔ±ƒøòË/ÅéÓ§-–±wïÞ¦2Z!\]E­š>bðàÁbÙ²e"!!Áâ1þ7Q QÎþ“––ÆìÙ³™ÿÓ\î?ˆ¡ZykVɤzy°·…Äd8sþ> ·€µ´:÷îÝ£xñâ/LÎÙ³g3qâD*W(Ï…ËW(mgM#M&ulÁE .§ÃßÉp>Ô€§——YÓ¢U«V1pàÀç"ëáÇyot 'OEâ¬ÆžÐÐ<íA¸•Uœx¤B+¬èÓ§S§M£B… &c:t(+â—µdê RqhVª•; $¥ÁÙ;ñ¯†k÷2(ï]†ñ~B@@@–³÷§Ÿ~ʬY³ŒÆV!í¶ì5Ш4ø–/g©ìn<¿§&â¤dèèæçÇÔiÓÙ¿®o¢­U) ’³_Ç j:ƒ£’µp>Ž%k¸ø$¯âžŒyÿÆMV‘ÈÈHùq­­ùå—_èÓ§sçÎåǹßu÷UKihX>ƒ¥ÁÁFZ‹swàðe¸ñJ{•àýñ1nܸ|9£§¥¥ñý÷ß3ÿ§¹Ü»ÿje44ôΠz)°·ÄT8sþ¾ ·=ým²óÆo°cÇŽ,ó¾3gÎðÅ矱uÛ6ì5V4*-hà¥ËZó{ pô6½iúàKÉ×Jî¤E«Õ²`Áæ|ÿ7nÝ¡¢»_ j'kHÑÂùÇð÷}¸¶6|þÕ$>üðC³§~W¯^å‹Ï?'$4k• ‡ßbPÆ T*x DZGjž¤jiß® “§L3kb&„`ÅŠ|úñxbbŸPÒš•…:%ÁÙVºÎK1eÅÙû:<Š¸1Ê?Ï>ûLöÔdÒ¤IYYä­TÒwÝ¢Ò3VÞÔ*ˆI…“TüóPÍ£äLZ4kʤo&Ó®];³2óÁûcˆºû€bÎÒóU×Ü C WÀ±­9uC‹“£C†gÒ¤I% UPPPPøßCQ@ráرc 8€+W¯0¢«  'Ô4ÝÒÆùèy˜kwC“ÆX¹j5UªTyîrÞ½{—Z5jGwcÜm¤ ‘ÿ¦Ãâ8˜+)&9i~ðÁüðÃ….gJJ _|ñsæÌ¡~1+>¨¥¥O°•øÄã4Xu 朳æQ†53gÍ6ÉrAë×Zž–ÎȶØj”‘O8z ì‚5¡i“ƬXù UªTA§§'111€¤|Tò€OZÁ[uÀÑLª†Ä4X ?Vs㱊I_ÃíÛ·MBôª0Ð Þ+ s Žv:‚nÁ²»*ªW«Æª5k©[·.Ož<¡[·nìß¿ßlß2¥½¸{ïCZ F¿!mÍqâ_i-VîWQ¯nV®ZMÍš5Íw0ô;q‚ApéÒ%†µ¶‡Z¹Äˆ¸?í„5ÀÊ ´:)ãÙ³g©X±"™™™|ûí·L™2™ŠE`|³LÞª Nf,ÿÓ`]$|w®=‚ÁC†°dÉ#êâÅ‹ ø.GãÝÊð^Mð5Me‘Å™Gt–]RQµjUV­^Kýúõ³êu:óçÏg§Ÿài«e|ÍLV732¦fÂÆë0笚ÈãÇÈ”)SŒ ܺu‹aC‡°gïô© cšBKoóÏí•Xtµ¢¤WiV¬ZM«V­²ê…Ì;—?»-|T†ú@1{ùñ2´°ù&Ì9kÅ¡»:FŒÁ÷ßoÒ7::š€BCÃèTWŸŽ‚öµ¤ßQŽ[1°d/Ìß­ÆÁ¹K^A—.]ä+((((üÏ¢( fزe }ûö¦fy+?ÓšU<ä8t†|kMt¼¿ÿ¾‹¦M›>79/]ºD›V­ÈxͲ’à—¤ë7ÒaÈ=Ø—ü´¬]»vüþûï…š5>>ž.:r,â¦6Ôñ~-P[蔘Ÿ‘6ˆÃ‡ cñ’%XYY±yófúõëC /-«tÔ4£xÈqð" ]jÍÃ${¶ÿ¾“¦M›²}ûvºvé‚JŸ·†/^ —!5¾ùfPѽ[7âžÄó矒òQÞÖÔ‚ÆyÝÊâT< ¾ ærª5aá›èС©©©ôïߟM›6™´·RA•’°f44ÈÇýq †,±æFŒ†-[·ñú믛m»mÛ6z÷îIu/+Gi©´0‡/û àf 6‚%K–––Æ›ýú²uëV&´|Ùl-\ó´L˜üÌܯ¢k×®løm#¶¶¶:tˆÎ;PÂ&•­2i*ïB#ËéG0ø/5ž¨ £sçÎhµZF Ί•+y¯&Ìh Žº›dêà‡ÓðÕ1+š4mÆ–mÛqvvæôéÓ´o÷:¶™ñüÜ#“7*[.ãÕG04TÍá[‚•+WñÎ;ï „àÓO?å»ï¾ãÝ*ðcsp·ÐuKXr>úGMeŸìܽ‡bÅŠqýúuÚµmMâã» ÑÒ'wW)#¢baÄÏVü~RÇ?þÈرc-ﬠ   ðÿE‘á?þ S§tmªeÝ$mÇ'ߧVœºnχ-ʼ_nß¾MÓF¾¸Ä=doi%-÷ÁÍB'`ìXðXr4½páBVhÞÂ"==ömÛyì;:iiR@Ë´_.Á}*G¦gÏžtìØnõµ¬{OX¬(d'>ºÎ¶"òŽ=í?È7_Oâ÷m›Ùôt(àÁÕ– ÐçW+ºtíÆшîGEѶ֗Ì~òKªúQ±;NÃî={iÑ¢™™™Œ5ŠåË—gµ³RAûZö¡d•_’Ó ç+^¶áÏ}ѨQ#“6ûöí£C‡öt®£eýmî·„è2 NÝq`ß_˜>m[·„ö¶ŽN ȵýôZg…_·ž|öùç¼Ö² ÜSÙü†ç¬Ej&ôß«â÷;jvíÞÃú_eÉ’%¬n#x;ŠBv݇Î;Ô4lÒ‚ ѪesÊØ=aç -E à®ÕÁˆ0XuREHH('OždòäÉÌkcjLÆ3 Ýv5¥+×dcH(mÛ¼†&ã>{&fR¶hþÇ>]ßm…Ÿþ™aÆL0…ÿw( H?~LêU©^úÛ¿ÓaS€M–„dh9ZÖ¦2ÇŽŸ*ÔhRBÞh׎‹‡÷s´tf”§cÁÀ»šf˹‹ =3ö¤I“˜>m ûüÍóñ6ZŽ…ç 𸻹РL"Û?Ñ¡y†Ãš„hñš‡éE¹wï›ß?ËrÙ™%ä,ôYE=ŠP:=–ƒÀñdLÓÁ'¬¸aW‚3ç/àââ‚‚ &0kÖ,ÔVаìûì °á6œm¦©‰Ñ–æô™ó888dÕÅÅÅQ³†Uܲc‚®@ ŸÄTh9YMtªwïEöô¨^ðñÂÏCÏ5P¢¸'%Å#öûiqz†g"M ·[q:Ù…ØÇq,i#žQÆýw¡ÍV^¥J¡I¹Ç?£ ¦|ÐêàÍõ°ë_’’™Ö>«Ÿw¿ÜˆŒ¦›¬(^ª )O¢ˆ˜’I™gx!,ƒU‡l8uêô Í2¯    ð óÂÝÞ_q$\Õ¢ïë•Êüçn8B4þ¤ïCTó–êg–Ê"W"4•øòË/ UÎ¥K— @´w@¨0ÿ¹[ !ª!þñF¸!êÛIÑ°THå†O|DY;kÑöõÖG²„S§N kµZÔõÈCÎwÂ?÷6íK#t£]ÊJQ}Î}‡¿"ŽOGøÕGqB8Ø"j–AÌ$Õ>? Føx!l5ˆREã» ’VJuÛ?•"U-šÇo>!¦#tÓ »#êy!\lˆ×Ê#¶ ’êÅtÄÒ˜4DˆˆãM~ÅE45¢¦bžT': to VGÔsF¸X#<4ˆ×ÜÛêKõÿ¶B8iÔÂßßßh}›7o&ì4ˆ^ò=èéZœŸèPád'­Ù»-Ku—~@ØÙX‰>øÀhžaÆ gµ¸51 Ñ­¢¤›´Þ>^ˆÉ}É«žÎ¡[‡X8 QÏábðpF¼V ±í©þÌ,)ŠR½’ˆˆ@D·jˆ’Î §br;Dò7O×3·kk_Ij3¼¡´æíK[v¯ ÄO->n[5¢”#b|mDÒ0©.²$cwoÄÑ^ˆnÞˆ’k©Ïd_Dòð§c Äù7Ê œ4ˆ"¶ˆw« zZÿy}I†U}$™Føù ŠØK×^³8b^ק×=¨¾ü5û“ê}(ê€(j‡ÐŽBïð+'Íí`¨Y1¯¹±Œ†OúD5wIžÙMŸ–ÿØ\Št5íMËž¯¦ Ú#ê—GX«%ù²?{I+•½¬EÓ&„V«-´¿- ÿ]”lܼy“òåË3ï}AC¸ž#@”Nþ³¡|I8#“¿î‡õ0i$¥ÂìÑ0¾¿T>a!,ØdÏÝ»Œ< ŠV«¥¢w9š%D1Ö®§çð¿åmàŒÞàë‡ðí#¨c ñ:¸’Úoú7'@÷;R”ªÂò[y«Ží ayËLn'åS€ÿ~):Ï™~RÙº+¦cDDÃg໦ðaˆJ‚ kaú[’ó³ßwР<¼Ùœìàê}):ÒŒ·¤þ3¾¡mM) ÔÂÝЦì˜cWºðÛ[p?AFÆMPÞÎŒ“Ê>Û 3öCתÐÕR2`å ˆ¼!oCÏ—Þ³`”´-~' +¼YœÔp5Y/£ÞÔë³Ë0ã_èZLú¤è`eD&@H]èY¾¿®ª¹uû6%K–äÁƒ”)SšÉ½3i]®Gç]þË ¼'œÑøºóêMwGÛRaöV(ëG§‚Ʀ„´Í¢¢îáááÁíÛ·ñö.ÇïÚÔß/ÀËüÛAGÉ·cå~èÖÂ?Ô_Ïz˜±ºÖƒ®õ!%Vþ‘· äèé+µ™»MZc/ðo Eìáð-i=»ù@ø»úûâ”Ì}?†ï:‡-!>ÊÍ‚N¥ «wÞ÷Ú§Gà»Sз"´-çK'lmJÁŽ.0ã$|} ~ï ¶ƒ—ø×€"¶pø>¬¼ݼ!¼£~m¡^°ä{1¶$¤ÃìH(ëG{F-¬TýZV‚wëß/Р¼YKrº¿úH:5˜¡sp0l8 Ëz_«tñyº6~ƒ…-aÜ!hP Þ¬N)º—0C&ב0é$eÀì¦0¾ÎÓµj ž^ða×¼Ÿ¯¯ƒáÛMP§ħÀ•{ ]g<מ3Ð~:ìÞ½Ûl´-…ÿ ×Óø?Î’%Kpv´bp'-NФ†qýÁÓœ Þ0íý¦¬‚ ïÀ—?×½×f¯OeÍš5<³œ;vìàæ(~ó†FöÐ$G”›ƒÉ,`€ËÓ²@w˜è¶VðÞ})$oNº:A{k‚,(äÁƒ„„„0«Q&-eŸ¼É™0 ›]½œýQRd ·*IßK9J›Æ;!9üêCðò2Ü{ ?l‡-ae¶¥¯RƬ„à#°j?Œnm*ÊÈxCšc@ݧe«NJay7g‹R<´!”š«NH ˆ›½T¶ô(¬Š?O®k2üÓ1ïB#W؜̈́fh)(µOªëY†—‚¯® –.]ÊW_}ÅòåËQ«tŒl Eœ IŽµ;x’Óa@ó§eÓ7IÊÀÉo¡´Þ´¦QEis¸r?Œh#)Sõi5 IDAT,_¾œ?þ˜¥K—âhgÅÐÖZfl†ôLØö‰æ`xiÓúËx’ ®Òš6ª›?Îv=­¡Ôh©®§/Œ~fè}é· ‚jú(UÃ}õã„'©Òfûm™µû㺭ì-ýÆÙÅNê»ì(üܲ™`å¼×î%IÎá«ÂÊl>÷U\aÌAØü/,>/Ýs{î@º¶u†jú¨²Ã«IŠþ/—àI¸ÚÂô“’ 'û@i'ýÚzBû­’²2¢ºñmtMøü(ì¼~><ÀÜ]!¡QË_¿¾µàÃí0þ0øyC°ÌߧœD§À”ã0¡|yÔ¸ÎJï׆Á‰¹?_ Ež›Øl5ðÞ ¸|Ï´MÛšP£¬5AA DAAAAAÉ„žÐ ô{]R>äX·[Ú¿ÝÞ´nÂ"ð)+¯œ”ö„ö¾\8r††RÝÁ_;ùúuO¤ÙÛÙB½zZKÊGnX©`°c&áa¡ètºÜ[ÀÖ­[ÉÔJáJe弪_ÏJæÇHÓBÈuhí^Ùìå‡T…c :¦½)•%¥Joý³ó÷ÉV¾3ãòþzýjþ.é­ífdŒÔ¯e§eöÖP,‡í¾³­ª×!›Æð8¢Óaš~ã›”)m®sboÅrøo8[K9Bô¿›«zÓºñ7BC~£gCEœÌÈ~X/{6$ä¨t"Q:›]Ûš’Bö›>¡z1èV_Ghˆaž ôm¤ÅÙþ©ƒ»g6å „›ÕÌàbo#ct=öàhûtJÊ%¥·éž9®¡„“”³ÂÆLˆæ´LÉצuéôÄÀÐRøæý96Á9ﵿèï‹Jg}ý¢óp#AºÏìõ×ä™CÑ/a¯¿f½Œ!סk¹§Ê@ÛÒPÅ ~»þ´lHUÈÐALLÓÿ½HJ7½w !ÕŧÊ×kÔP»„”×dª>‘{R†ü}f`Âðq7Vþ³Ó¯"h¬àQBîÏ€§+y$P©`HËL¶mÛFzºÌÛ…ÿ)DOBB—._§™™4™ðÛм”ÍÅéèyøeÌÍ%ÒdÓ‚ã'"( ‹·ãÿ¡©M¦l¾€ ¿%@s{(['ܦ˜œÂåË—Ÿ]ÎãÇññÐPDFQÊÐÂo× y (›‹UÚö[ð$Ýt£ÔXÿØiàö#¨:œ‡‚ë0\iR½á_ûkaØHŸ»RÎYÏ@órP6[øÜ ¯Io¯çÿ 7ÃŇ0z3$¤Á¸lŠN O)ñ ­ÜN…ªÀy/¸î…Àó’syÖ˜`g Ì¿7Ràb"Œ> Z—-ÔmSW8wá"ñññœ>s–¦f6™’BѼ*YŒ¢báa¼ä°žß pòF¶y*Cäé3ÅÀÍŽÇd[ ™{-M+ýkŸã Ø^¯Lœ|$)@¾žR.â0lŸä¤};6\•””±5¥1¢’àa 4,&³¶Åàd6yŠÚKæQ5Ü~UçoÀu2nzšdÑ@r¸L·)à1ÞÛ,),ÙIÐ?#™x9/×åRÀõ8 ¿\†¹9”r£u°)±jnÏW~hZÒÓ38{ölþ;+((((ü¿B1ÁÒsþüy„Ô5³¡ÛùÄÆ›žpcæBÿ¶Ð¸Ü1?¨W?N **ŠÒ¥KXN­V˹‹—a&,æÎDˆÕ€\ÝåF=} ®Ó§OãããS°Aôœ‰Y26€Éú‡{zÿ¦’2Q­J:Hãeh%¥¤ßZ«ƒk`xC˜Yþ¼?ý q©°Nêàå Ÿ¶‚ú¥¤Ó‡ß/CÐ?’²¶oÄÓ\:q)Ò¿C÷Á¨ê0Ó þ¼ ?¸4X§·zB21ë_IRâoäðyÊI¦.÷ç+?ÔÕ+Ò§OŸ6Jò¨    ð¿‡¢€è‰ÀÝÌÛøu»ÁFýräh[¹Î^‡Ði¹o7!!ÿãçAjj*™Z-îfLSÖŃ ú¹È×ç…aÜg• þɳÉÐÖ]‘ˆ~2~YýÓaÛMèRNz›’mþˆ60WïÑÃWòQX¼¦ôƒúå¥ ïÌ-’ÉOëêp!J ªQK›Bw3™¢×EêeÌ‘Waã&ÙÞ÷©)™ÆÌ9$…=0*f;M@¦€‘^0W¯Ïõ(é߆ɕ¡’l¼#ÏAßâЧÄgÂœ›Ðóhõfîú¨¨(é»™0®ëIæPý²9§èß’Ë™ËÂ÷¦¤K ˆa܇Íó :Í”þ{éðp‚­'aZ˜t22Z¯ o<#–ÿû4–ÌÜæl‡žßï¡¢þËp ¨ÕÁÒžÒiÔÖ‹0mw‚Ñ2®Hñ©°ít©*ù}äÄÝâ³Ý¾r÷Z½¢Ò|æIɧ¨µ\x $Ó£LÝÓD~’¡Ó6ý5¿v°õ&L;Åí%ŸŽý)ƒœ™£þ¯lJ6DÐ Tæv•ÊzT×ß»0¹t*7½ƒñXýjC•¢ðùn> oêÓ ¥êOMZ•„¹z¥³GyéùX|^R”*¹J¾(gc!4Ǹrèt’œƒZÉ?_“ûB¥|„Õv°[U¡ümQPPPPøo£˜`é1dþÖÊØ8'&æƒÐ¡¸gÛØÇ'ÁÄÅðÉÛPJÆô";™ZãyžUÎLK®DlJ€Ž˜UPòÂ`ýQ™Ð­­­ååÌ€M7 C™Ü³5‡\—ÌGÌ’-ÞÊaJbøþ·ÞŠ,ä¨S†.† ã ÛlÉ'¤ž7X[=ýmŒdLƒMç¡Cec%5C2“é\UzKÝ« n ½N×JÃìXé…|«dõ·#qR²ÁÀóй¨t"Ò«8 .û|!]Ÿg‹ fXÏ¥K—fî×TØt:Ô÷lþó(9ó™Ôtã6™úqmllŒæ™&™rýù% {]Úþ‹™˜Òe½¹–`z‹…ÿ+E ²ÄüÊͺ–5­BÚ ªÞ¼gÇà ýXoãå.½u¿2L‚¨RÑ[1àæ×›Ž~^/cƒ‹áQ tËÂØÝ^ò9tÓ¸Üà\<Ç Ž§þûã ¸˜2 ›gŽ15ÐÜ Å=-»¦ß|t®=‘=B:ÉÈn~OM¯îÅ™ö¹'f’:Æ­S§ŽÑ÷ƒ—$ÅÍËݸ¿_)âÖÉpñ.­FKuÓWKß/ܾ¿Õ|*áäd&d‘…¨T*ê7lH„Ìeíp¶‚nÏ0Åq}¤Â°Ñnа!ÇcTäô»_{œm [9ù~ mäþ¼ ½+äظéù7áé[ú;±Æuwõ EÎ(L‹KNÙž®pþÜ ËùR.##õ2æP42ôsʆ=Ç g+°Ë±IÛ¿5þ,Öç=ÒYúî]´Zøýkš·h](²–)[Ž}ÉR„¤,93aOôt»gøU·%©¨^¥ îîîy7΃-Zð8EËßÙÞÒ?L=QгüSÛx9Ö_“Þ>›;%ÙvSÿ†VõÔŒÇÀÏJ›ÓÖÕåzJ¶íŸ¬“BÂŽï"mˆwf3'y˜{®Jù<ìrlèª{J¹ÖŸ6.¿óÜ0~“ÿ{¶@bËrœ¬ý|4*h]ª9J¾ëïç3<†zzÿ!!`[¬šæ-[Ò½{wê7ð%<#ïa<ì9+åÙ°“ñ›éÝHòÙ¸“ÍI{ïY¸r_ò×Èš'ÒšæÍ_C¥RÑüÿØ»ï°(®.€Ã¿Ý¥‰ì%vQŠ=죱&±&Æž˜ø#»±ÄKLTìJÅ[4Æ(±ÆŠb "`¡îýþ°À,ì.eÕÜ÷yx¢sgî–ÅÌÙsKÝzl;¡ü^Ô(©ì q9YebÝßÊjÅ¡RQeHÐú#ɾŸ0eò¿sIåïz½2Ÿ ..?LÖßiåç[-Ùƒõ§•y5††_íº¤<à×)dü{-‰^ÀȣݾuV†9m»5òAÐC¸œ¬r´îrâ÷œ8ççÃÒÊùIU€}·àrt~e…áÊÐ:€_¿Þç/ÿ(sP–V/ˆŠIçÄýÊ[¼²¼õ³ÄÊåêdâ~9ŸØ_esÄÍ-^ÿZÒ@9¯wåï%ßkçÕÉëLÿý2dû (Y¢EŠ¨l $I’$ý§ÈÁ¸¯è×ÿ3:uÚÍ©`pJ;½a¿ò€ª¶¿‡syåëUI«`U.íê)Þþ7\»O¿~ý2,Öh«#á‹Ä6üz^| ×Ã'˜¡² ÒÚËÐcŸ2ºÞû0e3,?¨ )™_Y‰ih‹—ç{ÿ sv@ð=åSm÷²ð]‡—Ëò®8½+É?(uCH8Üe8ÆyÃϦ¬°äV ¼+×ì¹ Í–ÃújðaA˜ËoÃh(™ ‡¡É†Í»?ß‚àgÊJfn¹Á«Œ’Ä-Nh ¶/Æ¥+WÑétèõz*V(Gëö}'Ðj¡ÎXywŽýÜ-øj•2ŸÃÖZ;ìžÊ½NÒòP”áßsÑh4ÄÇÇS¦t *ç eû7z¯À¸ßàïKʤüÒ”Iè#Û*K¿ø~vÁÏû”×ÝÆJIô¼<•×=>jyAT¬ìß¯+¶K;*“ÐGÖ½¿‹ âQf´Lù}í¿¢$…«CÏòi¿×¼/œәø¾(ßÕx¹,ïÞ[Ê.æš*•qð÷½ÄsÁ§ï+ºµ¯ô}î|uDÙyÝV§¬à6«¶2< àäCpÙ?´PöŒ™r–W†^•t€Aµ”ýO@Ù~ÈVeBú(%™*—OIŒ¿®ÿr’ø§>ʦŒ·{ÂÜ3°ü"ÜyªÄ<¨ŠRù0äZ”2ôjfmø*1ቄJ`P e®TZ¿_ÎAãIÊŸSüÛR ö‘k`Þk®]»A¡B&,%I’$½“d’Œ¿¿?íÛ·gåø¸EÚç§eê*³TÑ#GqssKwAAAÔ¬Y“U«V1à³þ|lÍ¢ øÿù‰ ÊüùóAuܾ)BŸA¥ZGëY9Pùt5½&ùÁ÷>PÜQÇ™! dWºdŠÈh¨}^K><<<øüóÏùrµ–}éÜ`z÷iøf­–Áƒ¿–|´mÛ–O>þ˜Ëµü¥²Pƒ)¶‡ï6jèׯ?‘ñ6tY¯M±û·)bâá£õZÅÚЧo?¼þÑ°ízÚ×¥æÐ]tXKçÎÑÙçÁsŽ'fìþ$A}j8®aàaÌù¼ƒÒãé»Ðó75kTG/­wÀÃTVÏJ‹ðåßpðŽàËß°î0ÌÚ–¾¯ÞƒçXáêR“¯¾ú*}I’$Iï ™€¨X²äg\\kÓì+-þ™~½ðë6èä¥ÁÓ³#“&MÊ°Ø|}}i×®ÖÖÖ 0€aÆ10¦®GA“í:.<µcÇ®ßY¹j5]jÑ|šÿãi_ŸœðË~è4GCGÏŽüôÓOøúmfw°–v«µ<4q‰Q€{QÐz•–?¯[±Å‹-¢e«ÖxžÒ²ò6)V3ƦPhyBKÝú°à§ŸTÏ™;w. 6¡Í -Ž¨ž’*!”y1ífiñðhƬY³TÏ[¼d îµêÒâ›Í»Ïòðá íÛw`áÂ…lÞâÏþmWiy`Æk~ÿ ´Y¥å~›ýY´hmÛ¶£ãn +.š÷šû…@‹ZÜk×e…·7Ûwîâl”-ÛuÜ|’öõÉEÄ@—½ÖÚµk™={6ýúõ¥÷&˜ý—2ìÍTû¯@Ãe:r: èÄ)ê5hH˜Î¶YqIe™å´<‹ƒÏþ„ygয়~â‡~`ôèÑ|½¼6ªï•“–À+Ð`¢öÅؼe«ÜÿC’$IzA& *ìííÙ±ãw<šµ¦ý(øt’†{Ò¾ ä´ýŸ–~Ó W¯>¬[·.ãŽ]¸póçÏÓ±cG@Y’wöìÙxyy1框·tœ‰N£“D`Ä=¨Ê:»pð¯ÃäË—/í ÍÔ­[76nôaû];ªn²bÇuãc`ñ¿På7÷¬ pàà!jÔ¨½½=;wí¦±GKÚÏ„O)»t#ä>´©¥ÿRèÝ»/kFÍ›7gûŽ>ÈI¥y:6œ6îá0A«O@åù:Î<ÎÍ®ßwÓ¨Q#¬­­ñùmÝzôäÓ³Ðñ´†F~B}7ºŸÑÐé´nß­Û·cg§²í7J•ióÚwø®óࣹÊfƸçjè±:uꊯßæ›&—-[6¶ïØE³mðü>^¨!ÔÈ×üÚh7SKŸ%ðñǽX¿~VVVxxx°s×ï…å¢Ò\ëN©/ß›\‚ÖR†ºËÅÎ]¿ãáá••6úðñ'½èýtحẑo‡>ƒžû5tüš·jËö»È–-®®®üqàOniòQÙGÇÏç^ß/Ã!`ë5¨²ÉŠÝ÷³±i“/;wF£Ñ°dÉÏŒñ5_í€&˵\0²Jöè òWæºØçÎÏÍÛw9r${÷îåÐá#Äæ.†Ó&-³N½Ü1­ÿ¸ Õ}­XuÅšeË–ñÅÊ"“&MbÊ”)LÙ¢¡ö8A*K>«‰zß®ƒZc5.Y•?ý-ç}H’$I¯’Az½^¬X±BäÎCX[kDWðŒ¸± ¡?„)ÿ½²±î{DÛzZ¡Ñ Ì'¶lÙ’áñL™2EdÏž]<{ö,EÛ¡C‡D¹Ò¥ æЉå…çK#* DEå+´bû{ˆþyöVZakc-f̘!âãã3Ã×æ½þZ„ÌCl†èè¦:FäËë 6nÜhôÏP¯× ooo‘'wNa¥Óˆ.µ5bÓ—)_ó+së‡"Ú¹h„V« ä~~~ª}†††Š;v€(™×JL6ðšÿ=1¹¢„£ò¾ø°cGqïÞ=Õ>ýüüDÁüy…V£íJiÄzÄ•îýçÊûLÿ9âzĦfˆ.e•×}:Í>ÃÂÂRþŒ²éD¾ÜVª?£'Ož¤ã‘#GÄÇ÷¶6Ö:­FäÍa%³[ ­VéÏJ§9rä‡J3ÆÈÈH±hÑ"QµRÅñd·Ö‰|Ù¬DkÝ‹cÊ•sçÎáááF¿¦¯züø±øñÇEÎÙR¼Ùí^Þ§r¥÷ÅO?ý$"""̺ϣGÄìÙ³Eùr¥S}ͪUK–,1ê÷âèÑ£âÓO?v¶6Z-Â1û믹­øôÓOÅÑ£GÓì/**J,Y²D8U­ü"{­È—ÝJä°}ùZ”/[ZÌž=[O:%  rdW^_áho%òÚ[ +­FÂÚÊJ|Ô¹³8pà@š äóçÏ…···pwsyO6­È—ÓJäÊö2ÆïÆ ï½÷ž(P €8|ø°j3fÌ:NôíÛW8äÎõâz‡lJŒÖ:Mâk«mÛ´;wî ©Æ+|||DÃê¿èÏÖZ+ò嶹³¿Œ±PÁ|ÂËËKܼy3Í×Q’$Iúï’«`™@Á;wøçŸ8tè³fÍbØ°a„††²ÿ~îÝ»—îev ¹qã%J”`íÚµtëÖ-ÍóýõWúõ뇗—'N¤gÏžXYY±víZ¢¢¢ ³ÉjÏŸ?çôéÓœÅÆƆҥKS³fMNŸ>M÷îÝ)]º4þþþ”(‘r{÷§OŸRªT)Ú·oÏÒ¥KIHHàÒ¥K?~œ;wî@îܹqrr¢zõêdϞʖë<~ü˜'NpöìY¢¢¢°¶¶¦xñ⸸¸PºtéLû7P’$IzwÈÄLgÏž¥jÕª9r„Ю];®]»¦úPæÎËÈ‘#yðà¹r¥½ÖæW_}Å–-[8~ü8øøøP¸paêիlj'¨^ÝÀVÒ’ÉFżyó8þ|¦ýüÍõèÑ#6lHXX‡¢ti;øIo,!³fÍbäÈ‘´oßžU«V‘#GÕsg͚ŨQ£¸té¥JeÀúÏ’$I’” ä$t3%­%„ÀÝÝ€€€€L»ŸŸŸF%I±¸¹¹‘”_j4ªW¯ŽV«%0ÐŒ%Œ$ƒÆŒƒƒƒ#FŒ°t()8::²{÷nìííñððàöíÛ–I2ALL }úôá›o¾aÔ¨QlÚ´É`òñìÙ3¦OŸÎ§Ÿ~*“I’$é&3% 3Ðëõ(P€’%KfZrÿþ}:„§§§QçÇÅÅô"1JŠ7{öìTªT‰þù'Sâü¯Ê‘#3gÎdÓ¦MìÙ³ÇÒá¤P¨P!öîÝK||5cç?)ÓùûûS§N ¤V­Zi^³dÉ>|Èwß}—J’$IRúÈÄLÉ+ îîîDGGsúôé ½ODDûöí{±ù 1prrÂÎÎ.EâââB|||†Ç)AµjÕ4hßÿ=wïÞµt8ªªU«Æ®]»8yò$žžžÄÄÄX:$)‘‚~ø:ЬY3þúë/Þ{ï½4¯{þü9Ó§Oçã?–ÕI’$é­ 3%¯€8;;cmmáó@vìØAll,:t0úšcÇŽ½6ÿ^Æ[­Z5¬¬¬ä0¬L2a²eËÆÈ‘#-ŠAîîîlݺ•?ÿü“®]»oÄ–ÙR¦ŠŽŽ¦W¯^Œ5Šï¾û£—ÈýùçŸyðà¬~H’$Io ™€˜)yÄÎÎŽêÕ«gø<___\\\(^¼¸Qç?~ü˜ .¼H@’ϱ³³£Zµj2É$yòäaÚ´i¬^½š¿þúËÒáÔ¨Q#~ûí7¶mÛFïÞ½_$ÒRÖ»wï7fÆ ¬]»–‰'¦:ÙüUÏŸ?gÚ´iôìÙ“²eËfr¤’$I’”1db¦äP>YÎÈ ÈóçÏÙ±c‡Iï’–ØussH1 ”aXr)ÞÌÓ«W/ÜÜÜ|hépRÕ·o_fÏžÍôéÓ™:uª¥Ãyç !˜:u*žžž´nÝšC‡Q¬X1“ûYºt)÷îÝ“ÕI’$é­#3©U@Ê–-‹££c†Ì‰‹‹Ãßßߤꇂ€€€×& «}¢]¹relmmå0¬L6eÊ„oÅäàáÇ3~üx¾ûî;,X`épÞYÑÑÑ|üñÇŒ=šqãƱ~ýzìííÍê'©ú‘Tí”$I’¤·…•¥x[©U@4M†Í9xð ááá&% ×®]ãÁƒª È«kkkªW¯.'¢g²üùó3qâD†Jÿþýqqq±tH©òòò"22’!C†3gN>ýôSK‡ôN ¥C‡œ:uŠõë×Ó¥K³ûúå—_ •ÕI’$é­$+ fR«€€2 ëرcéKïççG‰%pvv6úš¤ÄçÕ±äj Èѳʀ¨Zµ*ƒ~ãWšÒh4̘1ƒþýûÓ§O6mÚdéÞ'NœÀÕÕ•›7orèСt%IÕîÝ»S¾|ù ŒR’$I’²†L@̤V% ãÊ•+f÷­×ëñóóÃÓÓ3EâšcÇŽQºtiòçÏo0Þ$®®®\¼x‘ˆˆ³ã”ÒfeeÅ‚ ÀÛÛÛÒá¤I£Ñ°hÑ"ºtéB·nÝصk—¥CzëmÚ´‰zõêQ¨P!Ž;–îJد¿þÊÝ»weõC’$IzkÉÄL†* IÕ‡ô à àîÝ»& ¿Jº.ù„†*1IAAAAæ)­~ýútïÞQ£FñøñcK‡“&N‡··7-Z´ cÇŽüù矖é­$„`âĉtêÔ‰¶mÛrðàAŠ-š®>cbb˜:u*ݺu3iq I’$Iz“ÈÄL†* ŽŽŽ”/_>]Ñ}}})P uêÔ1úš¸¸8‚‚‚R,åihV… Èž=»†•Ef̘Á³gÏøþûï-ŠQ¬­­Ù¸q#µkצM›6ò}b¢çÏŸÓ½{wÆŽË„ X·nY“Í“ûõ×_¹s玬~H’$Io5™€˜)é^­Âž‰èBüüüèС:ÎèëNŸ>MtttŠ Hòx“èt:jÔ¨!,³H‘"E;v, ,àìÙ³–Ç(vvvlÙ²…Ê•+Ó¼yó·&nK»sç|ð[¶lÁÇÇ///“†R’TýèÚµ+*TÈ€H%I’$É2db&CC°@I@Nž¢C‡}ÊG}ĤI“˜:u*+W®L±äuF[¹r%×®]“ÕI’$é#3¥UeȱcÇR= ,,Œ˜=ü ÀÍÍMµ=­!XFα òåËóÕW_1eÊ®]»fépÌÒ¹sg~ùå~þùgFŽùÎ%!·nÝ¢~ýúìÚµ ???F•)“Í_ÇäÉ“éÔ©U«VÍÔ{I’$IRV“ ˆ™Òª€€2$22’ .¤Ú×Ö­[Ñëõ´oßÞä8(]º4ùóçWmO+e<``à;÷àø¶3f ŽŽŽŒ1ÂÒ¡˜­wïÞÌ;—™3g¾uË §& WWWÂÂÂ8|ø°Y¿£æXµj×®]cìرYr?I’$IÊJ21“14MšÃ°üüü¨S§ŽY›—;v,ÍùzâââÂýû÷å×’#GfÎœ‰¯¯/{öì±t8f:t(“&MÂËË‹¹sçZ:œt[»v-|ð¥K—æرc899eÉ}ãââ˜4i;v”ÕI’$é$3%=ЧV5È•+•*UJ5yòä ¿ÿþ;žžž&ÇGPPPª ˆ1U 9ÝòºvíJƒ :t(±±±–Çl£GfäÈ‘ >œeË–Y:³èõz¾ûî;zôèA—.]Ø¿? ̲û¯^½šYý$I’ÞY2IF“æüŽ´&¢ïܹ“˜˜³Ó§Ompþ7«hÑ¢.\XîˆnA†ùóçsùòeæÍ›gép̦Ñh˜6m  ÿþlܸÑÒ!™äÉ“'têÔ‰©S§2}útV¬X­­m–Ý?©úáéé™eI’$IÊjV–àm¦ÕjÓ¬0ÔªU ooož>}ªºÁ ¯¯/Õ«W§téÒ&ß? kkkœ žcLrCÂ7AµjÕ4hãǧ{÷î)RÄÒ!™E£ÑðÓO?ñäÉzôèAŽ9hÕª•¥ÃJÓ7h×®W®\aóæÍ´k×.ËcX³f W¯^eÓ¦MY~oI’$IÊ*²’ÆV@8~üxŠ¶˜˜¶oßnö.Ê899µ¨± ˆœˆnYãÇ'[¶lüïÿ³t(é¢ÕjY¾|9­[·æÃ?äÀ–)UGŽÁÍ͈ˆþþûo‹$ñññLš4‰:dê憒$I’di2Ic* •+W&{öìªÃ°öíÛGTT”Yï£6 46¡pqq!<<œ³b‘2Fž¡GìÝ»×àrÖ™-©úѾ}ûT‡TJ’$IÒ»@& é ÑhŒª0¼:ýÆtéÒ…"EŠàííM±bÅLª:¬_¿ž ¼HZ.\¸À‘#Gˆ‹‹K3ÖÔ(P€âÅ‹Ëy o­VËرc9uê7æôéÓ 2$ÍŸñ›*{öìlß¾GGG>ùä""" ¡iÓ¦>>œ?^V?$I’¤ÿ,™€¤ƒ±¤ÝÏ“3vø¨' yóæ¥L™2©^gjrGô7ÑìÙ³U+‡fÍš5ˆ(ã´mÛ–U«V|öéÓ‡M›6¼þêÕ«Ô®]›Ã‡³sçN†úFM6URõ£E‹F-Ÿ-I’$Iï"™€¤ƒ1¸¸8üýýS/V¬...FÝ'..Žãǧ8îææfôƒ–)d®®®œ:uJuçjÉ2Ê”)ÃÈ‘#UÛ¾ùæ"##³8¢ŒÕ­[7–,Y¢Ú¦×ëéÖ­»víJÑvðàAÜÜ܈åèÑ£4kÖ,³CM—ß~ûsçÎÉê‡$I’ôŸ&t0¦rðàAÂÃÃSïر£ÑIÁÙ³gyþüyŠãÆ|‚jÎðbcc9sæŒÉ×J™gÔ¨Q/^<ÅñÐÐP&Nœhˆ2Vÿþý™5k–j[\\;väСC/Ž-]ºœœœ B… YªYôz=&L yóæÔªUËÒáH’$I’ÅÈ$Œ©€øúúªïСƒÑ÷QÛÿLK@L©€899¡Óéä0¬7Œ½½=?þø£jÛœ9sÞ‰•Ë¾úê+ƒÕçÏŸÓºuk>|8Ÿ}öýû÷g×®]8::fq¤¦“ÕI’$IRÈ$Òª€èõz6oÞ¬ÚöÞ{ï}CÐYAÇœÄÞޞʕ+Ëä Ô±cG<<ø€£GuÀÀ@Õ{˜š€˜Zqqq!!!S§N™t”5ÆŽK¡B…R¿qãÓ¦M³@DO§Ó±jÕ*ªV­ªÚ~íÚ5š6mÊýû÷³82ÓøùùqöìYYý$I’¤D2I‡Ô†`9s†«W¯¦8ÞºukêÔ©CPPQËÜš»¡Z¬¦¨Zµ*666rÖ*W®\̘1Cµmúôéªï½·M||<#FŒàÌ™3-ZTõœ .мys?~œÅÑ'©úѤIêÕ«gép$I’$é tH­bhøUÇŽqww'&&†Ó§O§yµÄÆÆ'''£b4w–­­-ÕªU“Ñß`=zô nݺ)ŽÇÄÄœÄý¶§U«V,\¸E‹qþüyƒI÷É“'iÕªOž<Éâ(Ó¶yófÎœ9#«’$I’ô ™€¤CjµÄÖÖ––-[R½zu¬­­Óœ"„P=ÇÙÙYuWlCŽ€-ö IDAT}$Åj*¹#ú›M£Ñ°`Á´Ú”¿ÆþþþìܹÓQ¥ß¥K—¨U«ÿüó»wïfÀ€äÌ™“;vP­Z5ÕkŽ9B‡ˆŽŽÎâh KZùªqãÆÔ¯_ßÒáH’$IÒC& é`¨¬º‰_óæÍÉ‘#vvv8;;§9äæܻ͛w/ÅqS†_¥79þüùɲ¤¨^½: Pm6l111YQúìÛ·www4 4nÜøE›££#»wï¦\¹r¯íÚµ+qqqYnª¶lÙ©S§dõC’$I’’‘ H:ª€Z ÕÓÓóÅŸÝÝÝÓ¬€dÔü0/quuEAPPÉ×JYgâĉäÍ›7ÅñË—/3{öl Ddœ˜˜zõêŪU«X¸p!Í›7ÇÍÍ£Gª& dïÞ½÷ÑÙ²e ½zõJsƒÐÌ(‰ÿ„ hÔ¨ 4°H,’$I’ô¦’ H:ª€¨ ¿Òét´mÛöÅßÝÝݹ|ù2—.]bÛ¶mܼy3Å5éÙ€0Iz6¦«T©vvv=z”   >lv_RæqttdÊ”)ªm'N|#—ª½ÿ>7ÆÛÛ›~ýúñá‡2hÐ  ÄöíÛÉ“'Ák‹/ξ}û(X° jûÚµk8p E6eìÓ§žžžÌž=›“'OÊê‡$I’$©’ÉêÖ­+Ê–-+lllD®\¹DÉ’%ÅàÁƒ…BܺuK)¾š4i"„âñãÇbÎœ9¢M›6¯µÿôÓO)îS¯^½ýäÍ›Wèõú4cܵk—(Uª”(\¸°DñâÅEÙ²eE`` Qߣ8p È‘#‡Ðjµµk×6áU’²R||¼¨Y³¦ê{¯k×®–ï5§NÅ‹OçÔ©SMîÇÁÁAõ{Ä×_mÔïJF¹xñâ‹ß@äË—OeÙý%I’$ém!+ f¸zõ*ÁÁÁÄÆÆɵk×^ìE°yófÕk:vì(‰ádzmÛ¶×Ú“W;âââTwKwss3j8Õ“'O y±û76z’îÊ•+Y¸p!Ož¤F,Y²$Ëb$I’¤·L@Ì ¶êHîahþGûöíÈ“'*THÑž|Bº¡]ÈÓ»º±sA\]]S‹ŽŽæܹsF]/e½ZµjÑ«W/Õ¶!C†X4yB0uêT<==yúô©ê9W¯^5ùýåîîÎÖ­[±³³Sm÷òòbÞ¼y&ÇkªË—/³fÍšÇmllhݺu¦ß_’$I’Þ&21ƒÚC¼^¯',,Lõ“æZµj½¶‘šZqéÒ%ÂÃÃ_ü=#' ¿ÊØÄÅÅEõ¸\–÷Í6mÚ4råÊ•âøÙ³gY¸p¡"R×O>ù„Ñ£GLŒK—.ÍÑ£GMšß”¤aÆüöÛoXYY©¶6ŒåË—›Ü¯)’W?’ôë×bÅŠeê½%I’$ém#3ª€lݺ•„„„mIï’J"Ž;öâÏé€nèAÏX†¹3ú›­`Á‚L˜0AµmìرªË:g¦ÐÐP5jÄêÕ« žóÁ@¥J•Ì¾OëÖ­Y³fêï&(‰€Ùý§&88Ø`õcÔ¨Q™rOI’$Iz›ÉÄ †* †v?uù]P*"j^M:^MF’”+WGGG£bLï¬üùóS¢D‰ÇeäÍ7hÐ ªT©’âxdd$ß~ûm–ÅqòäÉKêÒ¿vïÞM¾|ùÒ}¿>úˆ¥K—ª¶éõzzôèÁŽ;Ò}Ÿä&Ož¬úÁCß¾} .,I’$Iÿe21ƒÚC|\\»wïNq¼jÕª”-[6űlÙ²¥87éA-22’óçϧh7gÂäLÙD­ rúôé·ns»ÿ+++æÏŸ¯Ú¶|ùò47À̾¾¾Ô­[WuyiPªˆsæÌaÉ’%ØØØdØ}ûôécp¸8>üðC<˜a÷»råÊ‹}L^emm¥Éž$I’$½Mdbµa¡¡¡ªæɇ_ò€X³fÍÇ;†‚ÀÀ@Õ"#öÿHo§ºË»ôfiØ°!]ºtQmÏ>ûLµ=<<œ¦M›rùòe³ï1eÊÕêGŸ>}(^¼¸ÙýJ’$IÒ»N& fP«"¨=ˆxzz¬8ªf=zT5qvvÆÖÖÖè3b–ƒƒCŠù+ ·É—_~I¹råR 3˜œ˜*((WW×T‡5 0€ßÿ¼yófÈ=¡ÑhX¸p!Ý»wWm¿wï穤æêÕ«x{{§8.«’$I’”6™€˜ÁÐRŸÉ~ʧӅ NqüСCªÕS‡_eDêðþý÷_ƒãû¥7‹­­-sçÎUm[¼x1'OžLWÿ¿ýöõêÕãöíÛªí:Žùóç³páB¬­­Óu/sèt:V¬XA»víTÛoܸ‡‡‡É˪~ôîÝ[uõ8I’$I’^Rß¹KRËÞ½{yøð!V‰Ïò ’?î.\8Õ¤A£ÑàîîÎæÍ›_9ÿž=…•â“=Û˜’€œ9sæÅŠ\:@è¿üýý)T¨Ñ›£¹¸¸¼6¬Fh¨åâBérå¨Q³&îîî4iÒÄàFp’eµlÙ’víÚáïïÿÚq½^Ï_|Á7ß|Ã?ÿüÃÉA„? C£ÑP°pjÖtÁÝÝF¥øÙ !˜8q"ãÆ3xßܹsãããCÓ¦M3åû2–µµ56l uëÖìß¿?Eû¥K—hÖ¬ÀÁÁ€ˆˆvíÚÅ?ÿüÃéS'‰G§ÓQ´x J•*ÅŠ+Rôcee%«’$I’d !¥),,LŒ3FÌŸWÂÁÑ´¢÷ûÊW³bG[ ¬4Ê{÷îjŸW¯^îîîB§UÎ/äˆh[ѧ5âÓ–ˆúNˆìv‰}ê£FÏž=3ØŸ^¯ëׯµÝÜ l´QÝÑ3¢oD—\ˆ²Ö V+<;t‡N5Æ;wîˆ.]º’_‰|:DËìˆÞ¹•/œZá`£€(Z¨ ?~¼ˆˆˆ0ë5–2ו+W„­­­ ñg™ô¥K|¿Éc%ÚTЈÞ5½k"š”ÕŠ<öÊÏö½¢…ŤI“DTT”BˆgÏž‰.]º¤èëÕ¯råʉ .Xø»~]TT”¨U«–Á˜ÝÝÝʼn'ÄgŸ}&ìíì Jä° "úE|ZÑ ¯Nd·Ò¼ö»žôÕ¿K‹’$I’ôVБ³•ßaþþþ|Þ¿/O"Âù´l*Ce¥Zñ*!àÂcXr~96ö9Yºl9~øákçéõz-ZÄÿþ÷5ÙmãÐ>¾m xÁ”÷Öë!àüä ÿÐPºTiVx¯N1ýÆôïÛ—Ý{÷Ò$§–A¹õ´Î6*£­"`m$ü©ãßg 2„©S§’={öW¾ÁêÕ«:xšçÏè›3Ï œÊv BÀÉX«¢´ä/Tˆ_–¯°ø§ÞRJcÇŽeâĉè4øAW(£25Cº‹`Í)-… aúÌ™>}zªó€š4i‚Ï‹j›$<<œFqêÔ)Õv+­†v:>/OߢPÔ.å9z‡ÃÂàŠRÔê¦dÉ’™¾$I’$½db€‚±cÇ2iÒ$Z—Ô²¤¾ž¢ÙÓ¾ ô|qHÃæÁˆ#˜1c†ØØXzöìÏo èÓBΔ«¤ª:½§êøç‚ž%K~¦_¿~€²jVËfÍ°yÆÏâi•Ã¸þôæ‡Ã·µ”)_žÝûöS¸pa’\Š:·t”®ZÀãÇù¥0f%¯úö>üðHáC‡¨[·nú:“ÒmÙ²eôíÛ—•ácgóû¾Ù ?þõrá|||hÒ¤I†ÄšU‚ƒƒqvvæé“'ì¨ -ò™ß—^À'g5ø>²áô™³ªËWK’$I’¤ H2‘‘‘T®ø>u÷™ìªgRÞ‡ˆX(žº—ƒ¯ [âc÷aÅE¸§A‚ô^ö'4Þ¦áÐ]ÁNУL\ç!â©2÷£{Søºd³UÎ÷Þ ¾áäex¥ C×&Ê9¶6E:hxöD°®¬ˆ€Àhe~Gqkèž ¾Î Ù^Y-X/`ÉcX—bÁ^ N¶0» T³ƒß"¡óm¨› æT*©õÙ묌Hùú½oçË(+ƒÕ¿¥ãaâœ1|Þí†PØz•wR¶¿ŸÎ׃§ñP-ÀŠ¢NnøóÑËuK’$IÒ\75™E‹ñàþ}~i®§þ(bë£-ü ãáøØÜB9Ç øõ<8å…2¹àr²‡ròÚ å…nPo ÉÃ?Ç\ð÷Y÷+¿›§ÂÓçÐg*Ô®_xBøû Œ[ûŽÃþyð0"ž†9@·;PÄ †;‚£þ~® : ›ß{GŸ»°6>ÍCá‰NFÃÄå~;傉‰L½ëÆõi«_“me’;ñ™K§å¨r•+W2`À$Ëøᇰ%†…íàÁÓ×ÛôlR/“€>¾°ö|ê CkÓX8yW¹ÞJ«:Cµy0dÈ·2ùø~Ü8JÚkXPnżަ0à”ʦ$Ilµðk•×ÏÍø¯hv+XZ!ž&‡ÿf×®]´jÕ*s¿I’$IzKÉä ,^¸€®eôüyb`{+¨˜¸˜O¿ŠÊ^+/BD 䶅•á[g°ÕÁàCpéñë}>x[oÀä°å/ˆƒíÓ¡bÉÄ>Û*Ÿ6¯ÜOÀÞþ^ µ*¿ì£o(YXITöýO‚½NÙã#VÀö÷ bâCR?‡Ä#”êEnlŒTþîW Úç4üýÌ u®+rÓê”qóÝsîï}[h—ΟÇçŸnò&ˆRú=yòïËêOS•<á¯kð,zTylãiXyüz@ûJêýV)­*hY±üWüÖýloß¾Íæ-›™[^OÇ”í…óè‘,Á¶Ö@÷”û‡¾ÐÈj8èX¸`L@$I’$É9FàGåÚ[ô¯ørˆUd«Ê:-Øè^¶Û¦2ÿÂ7DŽÔ»•2Ä ”ªÆk}:&öi ÖV¯'I:$ιpÖî‚n9^&’Ý¿•’œ$-Ãûc¸gS’½€§2ª•MéKÑ'(çéD¦Üú…Ïr Îœ;Ï¿ÿþkø$)Ólß¾¨'ÏèŸrC{@©rh€îN/ýxÜ‹)ɇ^OcÕ¯ýÌUOЉS\ºt)ÃãÎl>>>XQo_{7ñuI–l¼xÏÇ«_§ÑÀg…رkááá±$I’$½;dòŠÀÀ@l­´¸å‡>  =ô=§ÂÍ'°!Ÿƒ¡U^&(iöy*—„¼¹¡Ok(è}§Á©`¸y6ìƒÅ[`hç— ŠšÐıûv6p%ØCŸÜPÐ úÞ…SÑp36D*{r uTækD&(ê\ì`ô}È} r^„2Áàùú=4h™C)‹¥Ög’gzÈuò\‚¼—`phÊä¦n6åA.00иLÊPw´¦„Ê–q °ñ Ô-ňŒ†À[àR Fÿ¹'@ÎñPf&øœyýúú%_ÞãmHõÜr©üÇéac(Ôu€âÉ>€x–¹öAž}w? >§ÌýxUeÕ±   Ìû$I’$é-&‡`½âäÉ“TͧÅZ§§Hv8ÜZíçß^ž3¦&L0ði²jŸ †‹òç"ùàðBhõ 8÷~¥ÏOaB¿Ôû™¾rçP€vPÄ—€V7Á9ä•þòÁ„ÄåD¯Ä)ŸÚ®T†ü¿½;ªÊÓ8þ­%`€€dˆ„DdFYEQPtÐÆ­›€=öðàtóÐ8íÒí´Ow à¢H@xtD6a¥…†dA‘f KH¨˜}­ºóÇ©˜ÊF‡F.Æy?ÿ$¹¹÷äÖ­›ç¹oó;çõHsÂxø¬ù~TÀÚ!·‡À;yp¤¼á6ÁÔˆ¼aÎÃ|\ sMhÙÑÕÔ€´rA¯ ÒÓÓùÉO^´Ø"#=Øõþnë1ð”Ô~uÜã¿_@ ^ aÍáÝðð{æûQ½Ì¾m[@·vAddd4¹õ/2öïcxhý]w[sÀSQwøUTsx¡ĆùïùlXxÀŽ„ê{¾W(„¹ÈÈÈhr3ƒ‰ˆˆØA$€Çã¡CsóqfV1ŒÙd¶/Á°ñ¼’í[@RÿË4Øf™bå1þe1–¼a°q7¼²Â‹¤êoãÕ¦}ÑL(ó‡éà†¬JsÆß^GˆpÁÆBx%Ú» É_làñBJ4 ö¢{o+èö ¼œS3€T-vîµnàÕÈšç81 z5ƒÙÙð~L (hnïòáñ\fú%¹f<—²éÙÀ┫˜¡„o®ÞVè¿¿<%ò3ÜÙü|o_èö{xù³êÐ>Ôj’ï­'×C‡ê—V‡fN˜Ø¡æöW{Õüyb6fƒ÷/À$`q9 ]sg“¼."""vP àp8¨AôR*œ-‚¯(ÿ èã»™ñß/¤À#= <¸1mš©uÁL¿{6¾^mzCÆó·¹ÈÌ’Vóø5ÛaÎRS¬þì}fz^0m¾”g+àëî¦7`¼¿Îã…‹ðHkháÿT¶[PuøuÂ=-!9ßìïôï·Æ?,ëƒÎpKpým†7Póò|8ÌɆíE5ˆš’ô:q8߭ר° >úFõ¬¹zÕÐÂnm«Ã@h3¸§$g˜ºª·Óòÿ¦ÆápPßä…•ðQ6ŒŠ€¶XÌýù®0çØî© Ðt¯‹ˆˆˆôT 22’Ìó¶ë jW>ªŒ‹†âJȸÔÈ6ƒ!3Û|¿ë êY>¾ks—šu?}²¦¼ ÷ Åþž“ªöÌJØU ƒ‚«ÃÇwíµ‚bËL³ÛÉÿ@Ù¾ž¨醊ZEéüÓ‘öiVsßÀ6ì4áÄSkdKf¥“nø;–˜–«Ù¾#™ùu„×} %•ðè-5·GùƒcûzzM"CM}DQÀˆ®Ì|‘‘‘uwþ‹ŒŒ¬3õ.Àº‹Pâ…G(N¯-ØáAfÈV• d•x›äu±ƒH€ØØXçx)©4Þz>!­ð?¬W60“T6# õˆÿØJ³Pa6ýì•î_†û }aíoª?qØÃôª¤–šðPß(ö ÿyWZÐ1È„³õÌÚs®Òô®¢^胇Yï ¡6Rà…/ÜÐžÇ 'J*ˆ‹‹kø@¹fbãâI=ïªóiòhÕÌ ­ ZÂÙZœ+0=$­ü“%dÀÙÜ bcc¯ÍÉ_C±ƒI-ª›Ê“ÏC+7ÜÛȼ\P 9åpC@`?\e^_“¼."""vP ˜˜H¥ÏâóóÛÒràX­u=V3Sæˆhd›‘ðU¦™ñ*¶7¤} ÇÎÔjs›¿Íîæç#'áîYpSlü³úy•–!Я l+2ài¥p¬Ö4©«óÌ”¹üC¨ƒÓæ˜*9•ðQŒ èáñY&h”Y—o³ÌgÂFm/嘯£>=ÿÔÿ7.s•äZILLäB^%_^¬Þ–]Û¾ûûAp=ÃŒ& €ÓyfŸ*9EfÈÖÈîÕÛ¶7_›â{›˜˜ÈÁØ¥•pS<Þf ¬.ÚÞqF®7ßWÕœV]ÑQðé½0y||¾YÇÏšÍwÿ/”–›^ŽÇÇÀ¬Éf˜ÕÉópÓÄšÅëž ¿ŸQ÷¿´ ­`n6ì.R n ‚Ç[›UÍCÿO”ÃÌ‹¦@¼Â‚!!ð» Î_€ì³`Ø)øÖ Ë:‹9 ·™ç…YRç*Ì0°žÍàÑ0˜Q=iJ Üv’““™Mÿ˜¾Ll[ÌÒ~Wß^v9ôKqsûèq¼ÿÁšKDD¤ª©¥yóæ¼½b%.Yü|wý½eY0+Ò³-~ù«9¬úÄbá_ÝùUTÂc/;q¸[п_ g¹9_ÿ:s¶»ff;¹çî»I+±ø÷œ«kÏgÁ³YNz¼ýî» ×YÛ¶mù¯¥o±ù¨ßþùêÚòúàÉœ+t±üíM6|téÒ…?üé –eÂ[™W×V™9äÄÜ’…‹)|ˆˆˆ\†ëÅ_|ñzŸÄMTT:uæ×ËÖ“U wu6EâW¢Â ÿºþtæϟϬY³ÈÏÏgÎS [û5<ü¥!ùE0i®“ÿÙçdݺõLŸñKV®bUvcC| ®Ïq9ÛŠ`Ü9q·ÝÆG6вeKælØN…#BjãjŒ<•å`e¬LNfĈW~Rò½ëÕ«N§“9Ëv0,úÊï¿ârxâkÁêÕïý(† 4ˆ¬¬,ænÙO» vå×åÛ xà “]n6lÜD¿~ßCwŠˆˆÈ˜HbccéÒ¥ ¿y{ëN9¹5Ò¢cHãŽ=x Æmu±á´ƒ ””„Ãá஻¤„¹ü {;>Тu«T×öÉ>3ÓÍ‘3ÍX·n=£F¢M›6ŒàV|ð!¯Ÿ)¤µÃ"îî!mIDAT>¸qPE>˜™Ó³`ÄÈ‘|´a#¡¡¡ :”ÐÐPænØƶRCƒ-Ú5²cO1Œ9çfW™‹ääd&MšÔ¸ÅÆ #((ˆ¹Ë>å³.n¶oä=½ë$ŒYáfïY7«ß[Ä ®é¹ÚÅáp0vìXrs¿eî¦/H-p2¼­EX#ïùÍÙ0:ÃÍ o07¬À-""ÒªùÒÓÓybÊc:ü%£»8y¶QкyÍýòËáóóðæ'›NùˆéÓ›·W¬$>>¾N››7o晧§’ÍCwøxj$Æ@‹ZmfçÂÖ½°p‹=‡¼Œ¼cËÞZNtttý ˜5k‹/¦{ 7?mUÉ„VT3ŒTZðe¬Èƒ· \”:Ýüöµ×˜1cF•ÊwîÜÉÔǧpâä)îkÏ´¶ø‡hY«'(×k Ûå;ù´ÀGB\ËW¬ &&æJ/µØdÇŽL}b §Ïdr?x&ÞbHW³Úy O±™ŠwÑ>;Ž{¹-1åﬠwïÞ×çį±õë×óìSO’ëñðpOv2="µ§äÍ*ƒ-9°ð¬‹½¹^FÝu'K–.ãÆo¬¿a©A¤ÊËËINNfá‚ùìOK GxQ-LAïù'__2…ôgÚôç˜2e Í›7o°Í¼¼<–.]Ê¢…ó9þ×S¸\úF»‰óá³àä'g.˜6ÿqä¦%Í`üøñu‚B ½{÷2Þ<Ö®]CyE%áÍÜônͱ(ÄÁá%^á­Ã˜úô3$%%Õ 3Š‹‹yçwøÏyopøèW8€Þ¡A´wú°€L¯“¿›s’˜È´3˜4i’j>š€¢¢"–/_ÎÂó8òÕ1œNèDd¨Ë‚ÓyNNúïéÛ‡ágÓ¦3qâÄ&]óѹ¹¹,Y²„E æsòL&n§ƒ¾anÂÝæÿòD©“LÿRð£î¼“iÓ§3nÜ8Õ|ˆˆˆ\+tôèQöíÛGZZ999X–EDD±±± <˜¾}û^ÑÈÏç#==ÔÔT222ÈËËÃårѱcGâââHLL¤k×®WtŽ‡””RSS9~ü8ååå„„„C\\ ´hÑø©³,ËâСCìß¿Ÿôôt<N§“ÈÈHbccILL¤GWtŽòÃ`Yüî½ÍÍÍÅétÒ¾}ûïî•îÝ»ÿí†~d¼^/iii¤¦¦ràÀòóóq¹\DEEObb¢z–ÚIEND®B`‚deap-1.0.1/doc/_images/gptree.png0000644000076500000240000003634712117374514017043 0ustar felixstaff00000000000000‰PNG  IHDRúúˆìZ=sBIT|dˆ pHYsaa¨?§i IDATxœíg\”WÚ‡/fèUaAT°‚•bÅn°DÑ$ÆÄØ[Ôw7»É›5ÙÔMÙ$‚5ö’²ÑhTDQc(¤¨Q»¢XhÒa˜™çý`tc”83L“9×ïç‡9ç¾o˜ÿ<ç9ÿóœc#I’„@ ¨×ÈÌ€@ 0>Bè „.ÐI’wz&¶æN@`yH’Ä¡C‡HJJ"--ôÔTr®^EYSƒL&ÃÍÅ…:IDDC† ÁËËËÜi þ1'¸Cii)ëÖ­caL ÇOžÄÙÞž0…‚pOOZ¸»ãdk‹Z£¡°ºš#……¤r®¨Gž;–™3giî2@]€$I|ûí·Ìž5‹¢[·x"0!!ô÷÷G.û󻻬<}šÅÙÙ\*.fÄðá,^²???e/Ð!t+çÆLŸ6Í[¶ðlË–ü»Kš¸ºêÜZ£aã… Ì9t¥\ÎW11<ÿüóØØØ!k®¡[1§Nâ±P³¨GFչςª*æìßφ³gyíµ×øôÓO…Ø-1g¥œ:uŠÞQQøÚØ°säHü]\ Ò¯—£#ëû÷§‡¯//þ9ÕÕÕ,X°@ˆÝÌ¡[!yyy<6`>À¾¡Cñrt4xŒYíÚa/“ñRl,~~~¼ùæ›!Ð1t·BžyúiöÆÅqôÉ'il +ym¼uø0;Fjj*aaaF%¨!t+ã¿ÿý/Ï<ó ßôïϳ-[=žR­¦ËO?¡ññ!-#{{{£ÇÜXgETUUñòÌ™ŒjÞœ1-Z˜$¦½\Ϊ^½È:uŠ¯¾úÊ$1÷#„nE|ÿý÷ÜÌÏç£ÈH“NŽuR(ײ%±  V«MWð?„Эˆ…11 jÚ”V&=3$„‹—/oòØ!t«á×_åÐáÃÌ6KüHÂ}|X¶t©Yâ[;Â^³RRR°•ÉøáüyÞ:|˜Ï»wçõƒ9[\L„·7«ûöÅÕÎŽÉÉ$\¹‚·“DFòÌo÷òÛ/_æ‹_åXa!U*! òNx8ƒš4¹cäÎüZXÈѧžÂÕ΀oÏžå¹½{‰:”aMš›œŒ$IÂW71âŠn%¤§§ÓN¡ÀV&ãZEóäÿÂÂXß¿?JKynï^žÚ½›Î^^lŠŽ&\¡`ÜÏ?s¹¬ €‹¥¥ kÚ”µýú±):šž¾¾ ç—ÜÜ»1–õîM™JÅ+û÷[^ÎÌ”f´mKt@á ù……äää˜åw`͈+º•vèá ¢–$Š”J’FŒ ¤aCr+*˜’Â:ñæo^w„·7›.\`óÅ‹ÌißžYíÚÝíK#IôñóãDQKO¢¿?ÞNN,éÕ‹Q»v1¢Y3eeáíèȧݺ®PÜÎ%-¦M›š²|«GÝJÈÍÍetPgKJhìì|WäÀÝɹß}ÍÃÞ''®”—p¥¬Œ7fOn.×**în@áí}Oœ‘ŒoÝšgöìA#I$“í홿‹ ör9¹¿Lƒº•P­Tâ(—ÐÀÁសÙÿö(ê}¯ËåT©TH’Ĉ;)­©á_´twÇÙÖ–·ÓÒÈùí‹à÷ŒmÑ‚5§OÓÅLJ®>>÷üÌÉÎŽÊÊJC–&Ð!t+ÁÖÖÕoWa]Cž).æHA[ bx³fw_¯P©î{oyM ³RRèèåÅἎ I;v4lq‚‡"„n%„‡‡“^P€ ¾¢ÿÙkör9›¢£qÉx:!ùéé¼F?¿»ï)¬ªbjb"c[¶äéæÍpËYÛ¯IׯóÕñã¤çåaogGûöíW¨àˆ§×¬„øøx† Âñ§ž¢§§YrxvÏÎyxp8=Ý,ñ­qE·úõë‡ÂÓ“¯³³Í?¿ªŠ/]bÌرf‰oí¡[ L}é%Vž9CyMÉã¯8u ™ŒI“&™<¶@ݪ˜6m%ÕÕ,;uʤqËkjˆ9uŠ1cƈƒÌ„ºÈôéÓy3=ó%%&‹ûFj*ùJ%ÿ÷öÛ&‹)¸!t+ãã?ÆÛ×—ÉIIhL0ûKn.1'NðáGÑÒ[W Œ˜u·BöîÝË€˜Ê¿»v5Ú#£çKJˆÚ¶–;²/1ÙCN}ñ›·Bú÷ïÏ_|Á§ÇŽñiiF9!õ|I vìÀÅLJÿnÜ(DnfÄZw+eîܹ(•Jþú׿r¥¼œ¯zôÀÝ@;´î¾r…qóõe÷Þ½øúú¤_þˆ¡»•³fÍfÍœIC¹œ¯£¢ˆл¯R¥’¿:Ä’¬,úõéÃ7ß}'Dn!¡ ¸téS§La÷ž= nÚ”Y!! iÒä¡'©Þájy9ËNbIv6¥ Ÿ|ú)Ó¦MÃu B]Ü~€eýúõÌš1ƒ’²2šyx0, €…‚pooZ¸»ã(—ßÞ¡¦ºš#¤çå±ÿæMvää€$1hÈbcc 4w9‚? îÑÀíX‚ƒƒ))+ã“O>!;;›Ýûö±01±ÖÉ:wWWÂÂÂøbÞ<–/_ŽF£"·P„ÐwY°`¼úê«ÈÛ¦´´”ÌÌL._¾LUU¶¶¶¸¹¹J‹-îÏÝÝÝ™0a§OŸ¦uëÖæ,CðÄÐ]ÀÍ›7iÒ¤ ï¿ÿ>óæÍÓ¹}uu5Mš4áÙgŸG/Y b¶DÀÒ¥K±µµeÊ”)zµwpp`Ú´i¬\¹’.¯h‡º€šš.\È /¼@Ãßí«+Ó§O§ªªŠÕ«W0;!B°qãF®]»ÆìÙ³ëÔOãÆyꩧX°`Æ@Ù ¸GУGœœœØ³gOûÚ¿?={ö$..Ž!C† ;!B·rÒÒÒˆŒŒdóæÍ<ñÄuîO’$"##ñööfÇŽÈP`ÄÐÝʹc© 6Ì ýÙØØ0gÎâãã9}ú´AúÔ!t+ææÍ›|ûí·Ìš5ë®onÆŒƒ··7111ëSP7„Э˜ºZjµ!¬6ËCÝJ1”¥Vw¬¶U«V¼oî¡[)†²ÔjãŽÕ#¬6 @̺[)†´ÔjCXm–ƒºbhK­6„Õf9ˆ¡»bhK­6„Õf9¡[ƲÔjCXm–º•a,K­6„Õf¡[ƶÔjCXmæGÝŠ0¶¥VÂj3?bÖÝŠ0…¥VÂj3/BèV‚©,µÚV›yCw+ÁT–ZmüÞjËÎÎ6KÖŒº`jK­6îXm±±±fËÁZB·Lm©Õ†°Ú̇z=Ç\–Zm«Í<¡×sÌe©Õ†°Ú̃˜u¯ç˜ÓR« aµ™!ôzŒ¹-µÚV›éC÷zŒ¹-µÚV›éB¯§XŠ¥VÂj3-BèõK±ÔjCXm¦E½bi–Zm«Ít¡×C,ÍR« qV›é³îEEE”––¢R©pttÄÛÛ;;»{Þc‰–Zmü™ÕvëÖ-JJJîÖªP(°··7S¦6¶æN@ðçœ8q‚M›6‘vø0é‡sõúõ{~î`oOh‡DtéB¯^½hÚ´)`óæÍfÊX7ºwïNxx8_}õAAAlܸ‘´´4ÒSSÉÉͽç½övv„vè@xd$QQQŒ='''3eþh!®èˆJ¥bÓ¦M,Œ‰á—¤$89áåE„BA'//:8 ·±¡J­æ\I iyy¤’UP€“ƒv¤§§Ó²eKs—òPT*¯¾ú* ccÑHD*„{yÑÙË OGGä66TÿVkz~>i……œÈϧ¡‡“§NeÆŒ´hÑÂÜ¥X4BèÆÑ£G™4a™GÒ»qcfód` öZXd§oÝbqV+NŸ¦R£áŸóçó׿þ[[˸>ÌnßžÑAA8hQë¹’Ÿ<ÉŠ3g(©©áÍ7ßäÿø‡Úׂº… R©øàƒø׿þEpƒ,Š¢‹^}U¨T¼—‘ÁÇGÒ¹S'Ö¬[GÛ¶m œ±þh4þýïóϷߦ¹›Ë£¢èѨ‘^}UªT|tä9B»víX½v-;v4pÆ>Bè@uu5ÏË–-[x£cGþ/,L««ÚÃ8|ó&“’¸R]Ͷ¸8zõêe€lëFMM Æç»ï¾c^h(ãh€Gf~>9_QÁ–Ÿ~bÀ€ȶþ „nfjjjxú©§ˆ‹ã¿0¼Y3ƒö_ªT2r÷näç“°{7={ö4hÿº V«ûì³lþñG¾é×ÑÍ›´ÿ •ŠÑ»wóóõë숧_¿~íÿQFÝÌÌš5‹¥‹óSt4Cš65JŒJ•Š!;wr¤¤„Ì£G 2Jœ‡ñúë¯óå_ðÃÀŒ 4JŒjµš»v±¿°ôŒ Z·nm”8Bèf$!!èèhb{ödf»vFU¬Tºi-:wf÷Þ½Èd¦]+•˜˜HŸ>}ø¬[7^ 5j¬òš:mÞŒO›6$&'[äZS#Ÿ?þ|s'a”””08:š¾êÑ£Æs”ËéРïîÞ¯¯/‘‘‘F÷{ÊËËM[''GE!3r­ör9==ù×Ï?ãááA÷îÝïQ@,5ü1…yy,ïÕËèü; à¥þö׿šôA’Ï?ÿœÜ«WYÙ»7r$zùù1»];ÞzóM LÓ’B7ÕÕÕ,[²„)­[èæfÒØo‡…QYYÉÚµkM¯¦¦†Å 2¾eKZyx˜$æÞìܵJ%šAÝ,lܸ‘¼‚f˜ÁÛnìâÂÈÀ@ÆÄ`Šé™­[·’{ý:3BBŒëø89ñtP‹bc­þ¡!t3°rÅ úÜ ^í÷åæ"[ºTïø3BB8yê‡Ö»mY¹bÝýüè¤PèÕÞµž»pääd½û¨¡›FáƒyÌÏÏl9ôjÔ{¹œƒ5Ž$I"„nbª««qÐñ!ŽÍš‘6jÔÝÿ§åå1=)éž×üt”ƒ\NµR©Sºr§V]¥V™ÌèµZ:Bè&ÆÞÞ¥Z­SOGG<7(ùíCV‡{_¥Fƒý¶ 24ööö(U*$IÒZìj­–Ž˜Œ31^^^T(•w?ÀæâFe¥ÑwˆõôôD#IäWU5ÎøQY‰§ï†k „ÐML§N€ÛÏO›“ôÂB:GD5ÆZ3Ì]kAÑkµt„ÐMLHHNŽŽ¤×ñÃ_—‡`Ô ™ùùDùÃߢE <ÜÜÌZ«$Id½VKGÝÄØÚÚƾk×ô¿?ê_Ô»}Z~>J¥ÑŸ`“ÉdDvé¾?ì\« u­õXa!E••&}ZÏB7Ï¿ðÛ/_&§¬Ì,ñ—deÑ4 €¨¨(£Çz~Ü8rr8g¦c—–deÑÈLJþýû›%¾¥ „nžþy\œYš•eòØ…UU|sþ<ÓgÎ4Ɇ cÆŒ¡¡‡‹Ož4z¬?R¢T²öÜ9^š>ý¾C.¬ !t3àææÆø‰Y”mòéÏý ˜ìðE'''&Oʲӧ¹^Qa’˜wøêøq*U*^¬Ãп¾ „n&Þzë-${{^NI1YÌŒü|>:z”¿ÿãø蹕´>¼ñÆ8¸º2=%Å$Æ/,äÝÌLæÍ›G@@€IbZ2bÏ83²aÞþy~8Ðà;¢þ¥ZMÄ–-ÈüüHMK3ùA›6mbôèѬë×ç[µ2j,•FC÷­[©hØôÌLuXn\_Wt32vìXF=ù$“’’HÏË3ZµFÃø}ûÈ..fÕš5f9ÍdÔ¨Q<7v,ÓRR8pã†Ñâh$‰“’8RPÀÊÕ«…ÈCÝŒØØØ°zÍÚ††OêÍ›Q£Ñ0ñ—_øï… |óí·w±˜ƒe_MXd$Cvî$¥–[m¨5^LLdõéÓ¬Y³†.]º<Æ£Šº[7nÜ ¸MªËÊøªgO¦´ic]a/•–29)‰Äë×Y·ncÆŒ1@¶u£¤¤„Æ‘zð ŸuíÊ´ƒlŽy¥¬Œ)‰‰$\¹Â«¯½ÆgŸ}f€lëâŠnf$Iâ7Þ ¼¢‚~ÑѼ˜˜È;ëä±K’ÄÒ¬,ÚoÚÄIbçÎ!rwwwvìÜÉøÉ“™™œÌc;vp¡»$I¬ÌΦý¦MW©èÚ­K—.åèÑ£ÌúÑGÝÌ|øᇬZµŠ+V°=.ŽíÛ·s¼¦†VßÏÄ}ûH½ySë™êb¥’ÇÓvãF¦%%ñì¸qüzâ„Å-qrrbñâÅ$$$pþáÆýü3û¯_׺Ö¥’…'NÐaÓ&&ÿò #Ÿy†ã'O²{÷nZ·nÍ°aÃÈýÃùêÖŒº›‘ï¾ûŽgŸ}–ùóçóÏþóîëÅÅÅ,Y²„E±±\¼|™ö Ý Â :)4´·ÇV&£R¥úß™áùùì»~¥FÓ#G2gî\“¬|«+¥¥¥,]º”E±±œ»p//z(„{{ß>ÝÁ[™ìîYðéùù¤ÿVk¥JÅ#F0gî\úôés·ÏÜÜ\ºv튷·7‰‰‰¸j¹AE½F˜…””ÉÁÁA7nœ¤Ñhø•J%mß¾]š4i’Ú®$—Ë%à¾> …4dð`éý÷ß—®^½jâJ ƒZ­–âãã¥)S¦H:tlmmX«ÂÓS-½ûî»RNNN­ý=zTruu•†.©T*Vb™¡›sçÎI …BêÕ«—TUU¥u»ŠŠ )==]š>}ºäââ"%''K—/_®õ‹âQ¦²²RÊÈÈæÎ+ÙÙÙIÉÉÉÒ¥K—tª5..N’ÉdÒ+¯¼bÄL „ÐMLaa¡Ô¦M©U«VR~~¾^}|òÉ'’‡‡‡3³L,X 988èÝ>66V¤˜˜fõè!¶’2!J¥’Ñ£G“——ÇÁƒñòò2wJõž™3gröìYæÌ™CPPC‡5wJfA̺›I’˜6m)))lÞ¼™VF^*øŸ|ò Æ c̘1Vk» ¡›ˆ;6ÚòåËéÕ«—¹Ó±*är96l°jÛMÝ|÷Ýw¼ùæ›ÌŸ?ŸqãÆ™;«ÄÅÅ…­[·0lØ0Ê̴釹B72û÷ïg„ Œ7Ž·ß~ÛÜéX5þþþlß¾3gÎðÜsÏ¡ÖqÛíG!t#rþüyžxâ ºtéÂ×_mõ낺Ê÷ßÏöíÛ™7ož¹Ó1BèF¢¨¨ˆ¡C‡Ò°aC~üñGÌ’à7† ‚ øâ‹/ˆ5w:&AØkF@Øh–µÙnâŠn`„öè`M¶›º6Ú£ƒ5ÙnBèDØhÖb» ¡a£=ºXƒí&„n„öèSßm7!ô:"l´úC}¶Ý„½V„Vÿ¨¯¶›¸¢ë‰$ILŸ>]Øhõúh» ¡ëɇ~ÈÊ•+…V©¶›º­þSßl7!t6šõPŸl7!t6šõQ_l7!t-6šõRl7a¯i°Ñ3gÎäÌ™3¬í&®èAØh‚;|ú駬í&„þ„&¸Ã£l» ¡ÿ ÂFü‘GÕvB¯a£ jãQ´Ý„Ѐ°ÑãQ³Ý„Ðÿ€°ÑÚò(ÙnÂ^ûÂFèÊ£b»‰+úoM /‚í&„þÂFèË£`»=òC÷ŠŠ ²²²(--E­VãèèH³fÍhܸ±Ö“hŠVYYIVVçÎC¥R‘’’BÓ¦M ¨w†UUUdeeqæÌ4 ÉÉÉ4iÒ„¦M›Zd­wl·®]»2lØ0quuÕªmuu5YYY£R©ppp qãÆ®V³žÎ®555ÒæÍ›¥ &H킃%™L&÷ýóQ(¤!ƒK}ô‘týúõZûKII‘¤qãÆIÆ„•<•J%mÛ¶Mš4i’Ô¶C[I.—?°V/o/iÐàAÒûï¿/]½zÕÜië…Z­–vìØ!M™2Ejß±½dkkûÀZz5”‹~Lz÷Ýw¥œœs§}G•\]]¥áÇK*•êïQ«ÕRBB‚4uêT©C§µÖÚÀ³4`àiþüùÒÅ‹ë”—$I’a¾2ŒKaa!‹-bÉ¢Eä\½J¨·7Ý "¼½éèéICd66T©Õœ).&=?Ÿô‚~ÎÍE%IŒ=š¹¯¼B·nÝîöyþüyºvíJHH 3Ã^\\ÌâÅ‹Y¸x!—/^FÑ^woá ¼:yáÐй ê*5%çJÈOÏ'?-Ÿkû®¡®R3òÉ‘¼2÷¢¢¢Ì]ÊC)--eÉ’%Ä.Šåâù‹x…xáÝóv­ŠÎ <«µúwµ¦çsýçë¨*U 1œ¹sæÒ·o_s—r—;v0lØ0æÌ™Ãþ󟻯———³lÙ2bÅröôY<ÛxâÝÓïo¼:{áèåx·ÖÒ ¥÷Ôª,Sòø°Ç™;g. Ð9§GBè[¶laÚ‹/RrëϵhÁŒ½½µj[T]ÍêÓ§YxêgŠŠ˜1cÿþ÷¿©©©¡{÷îh48`13ìqqqL}i*yùy´Û‚™!øDúhÕVY¬äôÚÓd/̦ «€)S§ðÙ§Ÿáááaä¬õ#!!ÉS'sýúu‚ž ¢í̶øtóÑj¸ª,UrvÝY²b³(8QÀ ã_àË/¾¤aÆ&Èüá,\¸Y³fìY³Ø·o'OäÊ•+=u»Vßž¾ZÕZSVÃÙ g9{Š¼cyŒyv 1 bP(ZçcÑB/))aÆôéløæ†5kÆ’¨(ü]\ôêK#I,:y’¿>Œ¯/  .]ºÄÁƒ-b†½¼¼œY/ÏbõªÕ4Ô”¨eQ¸6ÑîïH’Ä©¯O‘úZ*^ ¼X»z-ýû÷7pÆúSYYÉ+¯¼ÂÒ¥K è@¯å½p tÓ«/I’8½ú4‡^9„‡³«W®fРAÎX?^}õU¾üòK†>>”íÛ¶ãßÛŸ^Ë{áÑR¿/^I’8»á,gÄÅÎ…_¯`øðáZµµX¡ççç38:š3YYÄtëƸV­ 21q¾¤„ñûöqàúuÞ}ï=Þ|óMd[7ŠŠŠòø2eÒíËn´™ÜÆ µ–^*%irׯ³nÝ:ÆŒc€lëFII œC‡Ñõó®„L 1H­eWÊHžšÌÕÝWY±bãÇ7@¶u£¸¸˜V­[QPT@·ÏºÑnV;ldu¯µâZI/%qyûe–,Y‹/¾øÐ6)ôââbúõéÕ³gÙ5ht¢hƒR­fܾ}lºx‘Í›73lØ0ƒö¯ eeeôØŸãgŽ3(~ÖÃtmѨ4$NNäìú³|ÿý÷Œ=Ú ýëBEE zŒŒ_3ˆÞM£ž Ú¿F­!yz2Ù˳Y¿~=cÇŽ5hÿºPUUÅÐLJ’’šBôÖhüûú´I#±Î~NÄž`ÅŠLš4éOßoqB—$‰a?Î}ûøåñÇéàéi”8*†göìaGn.é´mÛÖ(qþ I’ýÔhââúóP¼Ãµ›wÐZþqû¸´é©‡RéÔ©“Qâ<ŒçžŽ[62d÷|»ù%†¤‘Hœ’ȹuçHII¡K—.F‰ó0&O™Ìº ë¼s0~½ýŒC’$’g$“½,›_~ùåO'_-Nè+V¬`Ê”)l<˜Ç›65j¬J•Š°Í›q bÿÁƒØÚšvYÁ† xþùçøÃ@šnnÔXj¥š-[ð“ù‘–š†½½½Qãý‘M›61zôhú­ëG«ç;'¢QiØÚ}+ Êp$㎎ŽF÷GâââxüñÇéýuo‚§5–F­a{Ÿí8Ýpâ×£¿âììüÀ÷ÉçÏŸ?ߨ™è@NNO ÎØ  þÒ±£ÑãÙÉdDxyñÞ¾}899™ÔŽº~ý:C‡ ¥ñˆÆ„¿nôx2¹ ï®Þ$½Ÿ„ÌFfR;*??ŸÁCã;Зˆ÷#Œ¾àÅFfƒO|teµ’5Þï¹uëу£ñìéI×O»š¤Vß(_ýû%Å% <øﳨ%°ï¼óÎÀç¿óºM7__^íÐwßy‡ÂÂB“Å}ï½÷PÚ(éÓÓd1«J¨¯}IDATa :¾Ñ‘>ü€›7oš,îG}DYu=÷4Ùª6Ïöžtú¿N|úé§\¹rÅ$1áöº÷¢’"¢–F™¬Ö­þ^8_}õçÏŸà{,FèEEElX¿ž—ƒƒihâ…+ EUSÃêÕ«M¯´´”UkVÑfF¦Vvx­È`ùòå&‰WYYÉ×+¾¦õ‹­qnôàa¥±h?§=¶N¶,[¶Ì$ñ”J%K–-¡åÄ–z[£úÒvF[<X²dÉn1B_½z5ªš¦÷žæAø:;óTP‹bcÑh4F·~ýz*Ê+y)Äè±þˆ£§#ÍÇ6gáâ…&Ùå»ï¾£¸¨˜¶ÓM?ÙiïnO‹Z°xébjjjŒoÓ¦Mäß̧í Ó×jëlK«I­X¶|UUU÷ýÜb„¾nÍF6kF£Z&ŒÍ´à`Îœ;ÇáÇkíúµ4}¼©NßúÊ%šm áé„{^OšžÄjÅj*®WhÝWÈ´®\¾Brr²ÖmôeÝúu4y¬ î-ܵncèZo^¿ÉÞ½{µn£/ë֯ÿ—? Ûj¿:ÏеßÏ,BèUUUýõWúúéoCLÜ·_êðx`w__ìår£ ]¥R‘‘ž__Ýjµw·§ïª¾\Üt‘3ëÎpyÇe²–fÑkQ/†ÅŠö.öF¯U£Ñz8•F}uóË Y«g¨'N Œ^«$IJ=dÖZ´i€k#×ÖjBÿõ×_Q©TZ¯_¿Ã7Ø|ñ"w¦=Êjjø03•ŽCp{¹œ ééé:µÓ•¬¬,ª*«P„ë¾È¿Ÿ?íf·#ev ùGòIœ’HËçZÒüiݬ9™\†¢“‚´´4sÐ…sçÎQZ\jÖZmllð ó2z­W¯^%ÿf¾Ykð ÷"-ýþZ-BèGŽA.“ªã☦®®l¹x‘'vîäjy9[/_¦ïÖ­xØÛ£Ï|g¸§'™Fþ@9rEgýVûuù¨ Î~Îlé¶[¢bõ³=Ã=IË4Q­aæ­Õ+ÜËèµfff–QkFfÆ}¯[„Ð ðptÄIÇ+]\XÙ·/cZ´`On.?^¸À¶Áƒ™Ù®r™î¥ù:9Ýb+((ÀÞÙ{wý¬Ø:ÚøD j¥š–ϵÄÞC¿~œ|(**Ò«­¶b#³ÑÛY0h­…ƯÐÛY0d­· oÝ÷ºE]©Tb¯‡0sËË™òË/|wîüýy2(ˆá;w²ääI4z,ø³—ÉP*•:·Ó¥R‰Ü^®wû‚cûüŠ0Ç¿:έS÷ÿQµAf/£FiÜ™h¥R‰­½­Þ~ò£V+€­yk•ÛËQ©T÷¹G!tªõ°z.––2¼Y3¶ DcF4kƾaÃ(¬®F­‡Ð«Õj£o>áàà€ªZ¥W[µRÍÏ/üŒo7_F‰g{O~ÿ3µî– ºZ½ƒq—ÁÞ©UŸUÖ†¬US­1I­š=þþ»ÚÚÙ"ûÃ…Ó"„îããíª*Ju¼šöhÔˆ‘÷¼æbgÇß;wÆN•òr|| ûôØñõõ¥¦²†ª‚û½Î‡‘öv¥J鳪2;ýÖô£ðx!™ïeêÜWù•r¼uœüÔ$I¢"W{‹è¯ÕÇøµ”ç”ëÜÖе*|îŸ'°¡‡……!I™z÷±²o_z×ÁžH/*",2²N}<Œ°°0òÓóujw}ÿuŽ}zŒnŸuÃ=è¶'Ý ¸]>ìBæ™ägèÖ_Qz]Âûd×ZóÒótjgèZ Ò ¬ªÖˆ°ˆû^·ˆ]`Û¶m‹£ƒéyyu«¾T¨TœÈÏgvÄý¿$CÒ²eK\Ü\ÈKÏ# :@ëvz4âEÕý t˜Ûs;蔃Z©&ïháãŒû0M“&MðTx’ŸžOàˆ@­Û²VZC~F¾ÑòññÁ¿‰?ùéù´x¦…Öí Y«$Iä§åùÊý+‹¸¢ÛÙÙN‚÷Ãþ97$ѵkW£Æ‘ÉdtëÚk ׌çϸžtµR}ÏF™ÆÀÆƆîݺ›µÖ›o¢,W½V€Ýz»+W¯9 CŸ‘OeQå?Ã!t€IS¦Ÿ“Ãù’³Ä_”•EX§Ntè Û·¨>Lž4™+?_¡(˸–Omd-Ê"¸m0‘F¾MÛµ^;pü#º A EÖ¢,‚Z™ääI'‘w$¼T݆ï†"kqþþÜ%Öb„þì³ÏâáæÆ’¬,“ǾPRBÜåËÌ|ùe“ýüóZ·á1ûÛßðñò!iJ’Æ4µ^Ù}…¬¥Y|üÑǸ»kÿ4Y]yíµ×h@â¤D½¼a}¸–t Nðþ{ï›tÏþÙ³gÓ²eK’&&¡Q™¦Ö›©79öÉ1æÿs>=ø¡‹Û3nÛ¶m >œ}ú0©M£Æ*¬ª¢Ã?Ò±gO¶ÇÅ™üL¯„„¢££éÛ“v3Û5–²XɦÐMtnÑ™½»÷šd(û{éÓ§Ý>ëFèk¡FUS^ÃæN› ö &é—$ärýW"êáC‡èÑ£áÿ §ó?:5–ªRÅ–ˆ-4snÆ¡‡jÝ÷ТöŒhݺ59—/óÁ¶môðñ¡¹‘®<å55 Ù¹“ q;v˜å4“-Z——Çæ÷7£ˆPàÑÊ89¨*U춋êKÕÄÇÅãi¤uÿŒfÍšQZZÊÆ÷6âêIƒàF‰£®V“02²¬2vÄí0ú¢ €R©äûw¿§Ap<Ûç÷­©Ñ°û©Ýe±#nG­Ws°@¡ 2„C‡ñáîÝ„+´4°‹•J†îÚű’öì!$Äô;½ÜaРA9r„ïï¤ahC´1¬”¥J†'P˜ZÈÎø„†÷júg 8'O÷¯8„4Ðiƒm¨)¯a÷“»¹ñË vÄí <Üø›nÖF¿~ý8wþ[ßÙŠ{Kw<;Vìª*{žÞÃÕWÙúÓVºwïþ§ï·H¡ËårFMFf&ïî܉$IôlÔ¹†Öû¯_gØ®]\T*Ù¹k—Ñ}ó‡!“Éõä(Ž?NÜ;q¨«Õ4Šj„̶îC뛩7I–@yv9ñqñf?tQ&“ñäÈ'9sæ [ÿ¹U¹ŠF½ Sk~F> Ã(>ZÌömÛéׯŸ2ÖF Á¥K—ØòÏ-(o)ñëí‡Ì®îµ+`÷ðݤ°eó­Ž ²H¡ÃíE4O=ý4j†÷øŸ._¦‹·7~zN˜•ÕÔð÷ÔT^JN¦U‡l‹‹3ÛAÄÖÖ–§F?…\.ç¿þ—K›.áá…KcýΙSU¨H{;_&ÿBp³`â¶ÅaäÚ"—ËyòÉ'qrrâû¾çÂ÷ð óÔ{3EU¥ŠŒe°oü>šû6'n[œIÇhƒL&cĈ¸»»óý¿¿ç܆s4ìØPïsæÔÕjŽ|t„}Ïï£Iƒ&lûi½{÷Öª­ÅMÆ=ˆŒŒ &Žϯ'NÐÛÏ—Ûµcd` V®d±èäIV9ƒxïý÷yõÕWM>A£-ÇŽcüÄñÍ<Š/‚g4*H«G[o¾EÖ¢,ά:ƒºBÍ;óßá/ù‹É¦Ð–“'O2~âxÒ§ã×Ãà™Á4ª9r‡‡×Z|¶˜¬ÅYœYy†š’Þzë-þþ÷¿›ü` mÉÎÎfâä‰ÜF]Ý®õ™æØ:=üoSr¡„¬%YœY~†ê¢jþö·¿ñöÛoëô¤å#!t¸ý¼ï‹/¾È†uëPi44pr"R¡ ÜË‹N^^4tp@ncC¥Ju÷|ô´¢"² pvpÀÑÅ…ôôtÿð´›%¢R©øñljYCâ¾D=QD(ðŠðº{>ºÌV†ªRuûÌð´| Ó )È* ¡WC^šúÓ§Odjý駟ˆYÃÏ{~ÆÑý·Zýðêì…ƒçíZ—žGQZù'óiàÙ€©“§2}útZ´Ð~}¹¹P«Õl߾؅±ìÚ¹ W‡»µ*Â8xý®Ö ¿û»ž(ÀÕÝ•)“¦0cÆ Z·n­sìGFè†Ö­[Ó¥KÞxã ~üñGÒÓÒH?|˜Ü7îy¯£ƒ:v$<2’Þ½{£P(0`ñññs¤®¶œ8q‚M›6‘žžNjz*׮ܻnÜÞÁžÐŽ¡t‰èBTTO>ù¤É 2§NbãÆwk½zùê=?·³·#´c(‘á‘ôìÙ“Ñ£Gãääd¦lëÆ™3gøá‡HKK#5=•+—î=dÂÎÞŽvÚÑ%¼ËÝZ]ô<2!¡oß¾aƱÿþûf‹ŠŠ(--E¥Ráää„··÷=ÃUI’ ÃßߟíÛ·›:uƒRTTDYY*• GGG vvvæNË(ܺu‹ÒÒRjjjprrÂËËËb‡æu¥¸¸˜’’£ÕúÈ}ðàÁššª×–•+W2yòdNŸ>M«VÆ=äO °4,j lmœ:uŠ;w2gνW¯;…BAll¬³,ŸGBè111øøøðÌ3Ïè݇££#/½ô+V¬ ´Ô´’æÆâ…^\\̪U«˜6mZ7nœ1c&;LQ °,^è«V­¢ººšéӧ׹¯€€FELLŒIS,‹ºF£aÁ‚<ýôÓøûû¤Ï9sæMBBÂÃß,Ô,zÖýÏ,5}©OV›@ --ôºZjµ!¬6µa±CwCXjµ!¬6µa±B7„¥VÂjX)tCZjµ!¬65a‘B7¤¥VÂjX'tcXjµ!¬6µ`q³îÆ°ÔjCXmkÁâ„n,K­6„Õ&°,jènLK­6„Õ&°,JèÆ´ÔjCXmkÀb„n K­6„Õ&¨ïXŒÐMa©Õ†°Úõ‹º)-µÚV› >c³î¦´ÔjCXm‚úŒEÝÔ–Zm«MP_1ûÐÝ–Zm«MP_1»ÐÍa©Õ†°Úõ³ Ýœ–Zm«MP1«ÐÍi©Õ†°Úõ³ Ý,µÚV› ¾a¶YwK°ÔjCXm‚ú†Ù„n)–Zm«MPŸ0ËÐÝ’,µÚV› >a¡[’¥VÂjÔ'L.tK´ÔjCXm‚ú‚ÁîÑoݺEFFW¯^¥ºº[[[<<< ¥yóæw‡è_~ù%óæÍãÒ¥K7Ûþ žyæŽ;ÆÉ“'‘Én/“™™INNÎÝZÝÝÝ ¥E‹{;"°b$=Ñh4Rbb¢4aÂ)°E ÔúϽ»Ô@iÉ’%RPP4vìX}Úœ¤¤$ þóŸÿH“&M’š·jþ§µºy¸I}û÷•.\(•””˜;}@’$IÒùŠ®V«Y¹r%_|õ'~=AÃV x<E¸E¸· 7är$µDUA™ä¥åq#ùWv]<5ê)¾øâ ‹¿¢k4V¯^ͬ9³¨,«¤Aó û_­î-ÜïÖZ]XM~f>ùéùÜH¹AN|ÎÎÎL?7Þxƒ&Mš˜»£“г³³™8y"‡"ð‰@Bf…иcldÚ UK/–’µ4‹ÓËNc«²å«/¾büøñ9Ô=wî'O$91™¦7¥ÝËíˆкֲœ2N-;Eö’l¨„/>ÿ‚)S¦Xd­‚úÖBåõy¯ãàL¯•½hÕHï U…U|å §×žfèãCY·v 6Ô»?C³lÙ2æ¾2_¢–GáßOÿ‘‡²XÉ×½"›Ç¢cÃú ( f+<œ‡ ]’$Þzë->øàڽ܎®wÅÖÙÖ Á/m½DâÄDš7nΞ„=øúú¤ßºð¯ý‹·ß~›—BèöY7ì\í ÒoN|‰ã P°'a76H¿6ïfÐthS\›ê6sçwmìZÖÍ=CwI’(-.ÅÑËQ¯Î‚_ fïs{)Ï-çìú³´¥½^ý܉_RR¢W{m©K­ß4û†òÜÛî€_|;@ç>ì=챑Ù½VusÏ]’$4 2[ýv˜ |"‡†ì»—ê¢jÚLÔϲ²±½=”®©©Ñ«½¶¨Tª»±ôaHüFIïe½)½PJü°x4j݆î666ÈíäF¯U`ÝÜ£h™L†½ª*•~ÙÊh=¡5×’®€Kc½úQW©Û4G‡»±ôÁ³½'>]}žLô–h®%^ãâuêCÒH¨ªUF¯U`ÝÜwénäßˆÒ úïz82€6“õ_€r'¾±åôó÷«S­¿Ç³ƒ'2;%çu‚—^¼ßÒwÛ<ÚÜ'ô.á](L/ԻÜ98* |"Pï>òÒó Ó»mèѵNµþž›‡n¢©ÑàÞÜ]§vùéù„‡‡$àAÜ磇‡‡³íÃmHIëm“neßâVö-Ž/8NûÙíµ^U÷ òÓó j„‡‡‡Þ}hCxx8ßýðšNùîµ ïHo<;xbëdKÁÑŽ~r¯Ž^wG4Ú’—ž‡_€ŸElº!¨¿Ü'ô¨¨(ªK«¹–t ÿ>Ú'“¦'qóàMš iB§¿wÒ;!I#q5î*#zŽÐ»m‰ŠŠ¢¦ª†«{¯Òdö›7útõáÜwç8òÑЀ[m§µ%t^¨N™’$quûUú÷ì¯OúÖÜ÷<º$I´ iƒ¦“F/»¨®\I¸B\tÉÉÉôìÙÓ¨±$I¢c玔–ñØæÇŒëA\KºÆÖÞ[Ù½{7˜þw-°î»üØØØ0kÆ,.l¼@Åõ “'”µ0‹ö¡íéÑ£‡ÑcÙØØðòÌ—¹¸õ"e—ËŒïd-Ì¢U›Vôï/®èãòÀqæ„ pvr&í­4“&s-é·\dîì¹&Ûbé¹çžÃÝÃÔ¿§š$Þn¦ÞäÜ÷ç˜ó²å4)¨?çÔòSw×®U…ŠäÉÉtëÑI“&™$&€««+ ¾\ÀÙ g¹¸ù¢IbªªT$ML¢SçNLŸ>Ý$1ÖM­{ÆI’Ä Áƒ8xâ O}²NKEµ!ååÎ.?˱£ÇhݺµQcýI’ñÄö¥îcäÑ‘8ûwWƒó’µ ‹Œô Ú·×o™°@  µNÛØØ°üëåØ+íÙ9d'ÊRÝ6UÐ…£ŸåDì >ûô3“‹n׺tÉRœmœÙ9x'Õ·ªëø‚ãûì~ð¡¹Àdü©Ô¤Iv&Pqº‚vPUPeÐà’$‘ùA&‡þrˆ7ß|“Y³f´]ðóócOÂj.××/ŽÊ›•qìóc쟳Ÿ×_×^{Íàý µ¡Õ‘L™™™<6è1”öJ¢¾Ž¢ÉàºXq­‚äéÉ\üé"ï¾û.o½õ–ELJ?~œÑ)—Êé¹´'͆7«sŸ•7+I™•ÂùÎóüƒ÷Þ{Ï"jXZŸ½–““Ô©SHØ•@›Émˆ|?R¯b55ά?Cêk©¸Ú»²tñRFŽ©s?Æ$77—_z‘¸íq´~¡5‘Fêõ€ŽF¥áÜwç8ôÊ!qdñÂÅ<ýôÓFÈX øst:MU’$–/_Îk¯¿FyE9A£‚™‚_/¿‡.—-»RFö×Ùd/ͦìZÏŽ}–˜1xyé¾É…)$‰µk×2{îlÊJËHÈŒüúú!“ÿùê·òÜr²—gszéiJ®”0ú©Ñ,Œ]ˆ‰²îEçóÑáöÉ&kÖ¬!fa g²Ïàà怢³ÏpOÜ‚Ü°u²ES£¹{>zaz!Å—Šqvqfü ã™1c¡¡¡Æ¨Çà”””°víZbÆpêä)ì]ìïÖêÞÂýµÞ®µ(½ˆ[oáèäȸçÇ1sæL:w6ÞqV6è%ô;H’Drr2)))¤§§“šžÊõÜë(«•ÈårÜ<Ü %2<’ˆˆ†Š»»nOwY ’$±ÿþ{jͽ’{·VWw×»µ†‡‡3tèP4h`î´ ŽB¯ cïÞjIXS­‚G£] Xú?4.„Ð+àÿ>ÎÕÍý4%¢IEND®B`‚deap-1.0.1/doc/_images/gptypederrtree.png0000644000076500000240000002605212117374514020612 0ustar felixstaff00000000000000‰PNG  IHDR»»=ïjsBIT|dˆ pHYsaa¨?§i IDATxœíyXçÚÿïÉLbBÂ""ˆ@cÕ¢•= µ«•¶zl_Äz¬U\~¾*VÛcÕ¶.ØCi­ÇÖ µbÕ—žJµT-êñ”uy%P,Ø…€  „"»†$“¹hrŒ d™0Dçs]ßëÒáÉ3÷ý<ß™LæÉ!x}‘(++ëSPP P«Õ>Z­¶‹H$ÒuëÖíFDDDa¿~ý~'IÒÀuœy=¢Òjµ¢ýë_“^~ùåÜÝÝ›»tébpss£Åb±Á¸M*•jžþùSiiiÿÛÜÜ,ã:vGˆóx±¯¦¦&×åË—èíí}pØ°atJJ þûßÿÆšš¼›úúzÌÎÎÆ5kÖà+¯¼bŒ››[Ëüùó7VWWûp ›â<^ì*+++F.—Wº¸¸ÐóçÏÇ’’´†Ë—/ãÒ¥KÑÓÓ“îÖ­[ý¾}û&2 Cpâ<^ìH¯×So¾ùf*àðáÃéòòr´µZñññ `||ü––)×9Ú+Îàe¿Z[[»Œ7î EQLjj* d‹ôôt”Édô³Ï>›W__ïÁu®öˆóxÙ'š¦Éøøø"‘È™™‰Ž //=<<èçž{îÌÍ›7]¸ÎÙVq/û”œœ¼B 0DGræÌ”J¥ôÌ™3·s³­â<^¶«¨¨(T(ê—.]ŠAZZ;vlùÚ+‘_TrFhš¦žyæ™|N”ŸŸOuéÒÅáûDD9r$ó믿Ö÷wwwotøNYDÀu<¶‘‘‘WPP¶cÇŽ1:A°}ûvÁŸþé½mÛ¶ÿí²fwR†~Ÿý駟ȎÞ÷Ô©SñÔ©S•*•ª—@ `:zÿ6Ãõu/ëU\\¸oß>l‹o¿ýÃÂÂP,cÏž=qáÂ…ØÚÚŠˆˆ999H=zcccQ*•¢¯¯/~üñÇmö‰xûÃ*àÑ£GÿÂõXX#Îàe½’““WxxxÐZ­Æ¡C‡ œ>Þlûk¯½§OŸ6m‹5k3~üx¨ªª‚ÊÊÊ6cˆŠŠ‚’’’þ­­­b«àÞìNF]]gUU•wxxøCÛ444"‚ÙvwwwèÒ¥ ÔÕÕ™¶y{{›µ1¾æúõëmÆ4M“¿þúëÓÖæÀ¼ÙŒææfWOOχ¶ñðð‚  ¦¦Æl{cc#hµZ³×ÞÛF­V€¯¯o›qû0Æã ðfw2hš¦Hòáwe2„‡‡ÃþýûͶóÍ7mÚ–‘‘aÖæÀàççþþþmÆAQ”Y<΀ÓÊs±XÜ  ÑhÚl÷ÁÀ¸qã`Ê”)0yòdøý÷ßaÙ²eAAA›› 999°dÉxñÅá‡~€½{÷–-[Úøc>>pðàA«ãP*•àíí]çïïßöm›NÍî„DFFæ)•J=—1(•J&22òANó}ÞìNÈСCO–””KKKíê‡ ›^×Øع¹¹}Ò®:®¯£xY¯[·nI<==Þyçä‚7"EQtUUUO[âçJü™Ý ‘H$š3f|±sçNí[·:t߈[¶l¡ccc¿ëÙ³çµݹ½p}´ñ²Meee½;²JɈ±ZéäÉ“ÑŽÈË‘â<^¶+99yI’Ìùóç±#¸|ù2Êd2§­Cå‹7œ½^/ùä“wE"‘öúõë=Ñ¿F£{{{«çλ…ë\íçð²OZ­VÔ³gÏ*G—Ê%''¯H$·jkk»q³­â<^öi÷îÝSKJJ8r?þù§—D"¹õá‡.ç:g[Åßztb‘ÿÙÏϯêèÑ££½¿¹sç~ž‘‘wåʹSý ©®6^¶ë‡~x°£êBÿý÷@‚ ˜íÛ·Ïä:w[ÄŸÙ˜¿üå/Ç®_¿î[XXÑQ >cÇŽ=¤R©ú;Ý"×G/Ûd|<äž={ÞèÈýž8qb(8ác!ù3»Ó2sæÌÇyéÒ¥'…Ba‡ý¢/"Ï<óLž››[SVVÖ‹µ_Vàúhãe½®_¿ÞC$iW¯^½„‹ýïÛ·o"`aaa8×ca8€—õZ¶lYŠL&k®¯¯÷àbÿz½ž’Ëå—ßxã=\…5â<^Ö©¥¥Eêééyã­·ÞZÏek×®ýEQúŠŠ ®ÇÄRq/ëdo})[rÆ:UÎàe¹Øª/eKÎV§Êy¼,[õ¥lÉÙêTù[N›õ¥láTuª\m¼,Ûõ¥lÉ™êTù3»“àˆúR¶pš:U®6^íËQõ¥lÉYêT9€WûrT})[r–:UÎàÕ¶]_Ê–œ¡N•óxµ-G×—²%g¨Så<^WGÕ—²¥Î^§Êy¼®Žª/eK½N•¿õØIÁ®/e‹N]§ÊõÑÆëÁêèúR¶Ô™ëTù3{'…‹úR¶è´uª\m¼îWõ¥l©³Ö©ògöNWõ¥lµN•ë£—¹¸®/eK±N•óx™‹ëúR¶ÔëTù˘@­VûäççG]¹rE®Ñh$$I\]]›ƒƒƒ‹CCC/H$ €ùóKׯ_ÿ6×qÛ˺uëþvïóTu:¨¤¤$¨¨¨(¬±±Ñ¦iJ,·öìÙóZdd¤2  Âaj¹>ÚE1 CüôÓOÏ¿þúëÿòóóSRŸººÒ...†;Š$IŠW®\ùÁG}ô~g¨/eKÆ:ÕùóçoøôÓOß4hR$éŒã!‘H ®®®´P(dŒÛºwï^7nܸƒ™™™¯Ð4M²çò(É`0¶oß>3((è7ÀÀÀ@ý»ï¾‹û÷ïÇòòrd´¶¶âùóçqëÖ­˜€R©Ô@TDp R©T}‚ƒƒ(‰ &L`6lØ€§N––ÓX0 ƒ•••xèÐ!LJJB…BAöêÕ«bÍš5‹´Z­ˆx8GE*•ªÏ°aÃNÆÆÆ2YYYfænÆÆFÜ´iÒ$IV¬X‘ÌÖ$w´ ƒ`ãÆó]\\4þþþôªU«P­V[< Ã`^^&$$ EQLHHH‰R©TØçó(hçÎÓ]\\4½zõÒggg·;™m¡ÕjqåÊ•HQZ\ZZÚ—ëü¬‘Z­ö6ô‰‰‰ØÜÜl×xbXXM’¤á£>ZÊ0 aklœŽ³kõêÕKgÎœi÷ÄÞMaa!ê}||j/\¸Âuž–èêÕ«e=zô°û ¿­V‹Ë–-CÀyóæm²Õðœ3kÆ —/_nÕ%‹¥ÔÔÔ`xx8ííí]Û™‹"oŸÑËär¹^¥R±>ˆˆiii¸páÂÏl1<çƒä¬ÊÍ͸hÑ"‡ÝHMM öïß_?`À€ß5˜ë¼$†aˆ˜˜˜l}ii©ÃÆ155ÁƯRp>PΨ––iïÞ½¯DGGÓƒÁ≲• . P(dÞ{ï½U\çþ mݺu6àñãÇ:F&MšÄtíÚµñÚµk¾ÖÄÉù@9£,X°A"‘}»›””̹sçvDŽ–êòåËr™LvkÖ¬YÿR[[‹>>>ú1cÆ|oM¬œ–³©²²Ò$IÃêÕ«­˜ûÑëõB=ú˜#ò²UsçÎÝâãã£ollt𘓞žŽ€Öüœ–³iåÊ•H¥Rº£'qÇŽHSVVÖ›\ìUcc£›L&»•””äðÜï…¦i”ËåúiÓ¦}ii¼œ˜3I§Ó }}}kæÌ™cͼØL}}=»víBDÄ›7o¢‡‡ÝY~&zóæ͉$I2ˆˆ˜€ÁÁÁ˜““ƒááá(•JqРA¨T*M91 ƒkÖ¬Á§žz »t邽{÷ÆuëÖÝ—{FF¢X,ÆÁƒ£R©DwwwüàƒLmV­Z…b±Xki7çæL2%œ;wî¾Éq÷š111{÷î}ÕQ9Z£#FäŒ=Út+jÚ´ièåå…¡¡¡øõ×_ã‘#G044Ÿxâ ¤içÏŸ...øñÇã?þˆÉÉÉ(‰pëÖ­¦ $IŒ‹‹ÃcÇŽá矎}ûöE‰D‚ÿûßMíª««p÷îÝS,‰—ós&­]»öob±Ø ×ë±#xÙ÷ìÙƒ€uuu]•§%b†pwwoNII1Å–€/^¼hÚ–››‹AàéÓ§Q¥R¡@ ÀíÛ·›åùÞ{¯¯éÿ&LÀÀÀ@³6{÷îE‚ ÌÌŽˆØ§O¥O!8ä«”(J¥22<<œ¡(Ê´íÌ™30fÌðóó™L°wï^ÓßsssA @VV¼þúëàææ½zõ‚5kÖÜ×ÿ_|½zõ©T /¾ø"¨TªûÚDEE@AAÂ)ZLYYYŸÆÆF™1#~~~ðôÓO›þoüwee%dee@ll,Ð4mRLL TWWCEEœ?^}õU³~ÇŒóÀ8¢¢¢„J¥r%1óf·‚_~ùENݽíÊ•+0dÈøâ‹/ 33Æ3g΄ݻw›½vÎœ9п8xð üÏÿü¼ûî»püøqÓß333aöìÙ„˜˜˜0aÂ}1‚D"a.\¸ê 4-â—_~ 7Ûîááaö‘H­­­pãÆ @Dðòò‘HdÒË/¿ A˜Ì^]] Ý»w7ëÇÕÕÄbñ}q„‡‡ƒ¥cAµß„ÇHSS“›§§§Ù¶¿þõ¯¦#"DGGCEE¤¥¥ÁÔ©SM‹‡¤¤$1b9r8#GŽ€””xþùçaÇŽðÒK/Akk+|øá‡fûàááahnnvuH’ÒÔÔäpïx >¼îÂÓÓ‚€Ó§O›‚» ___¨©©1û[ss3´¶Þÿ34žžžÐÜÜ삈D{Eü™Ý hš¦H’4ÛV__ ,¹\n:S}ñÅPZZjÖîå—_6ûÿÓO? •••`0   bccÍÚŒ?þqP4Msz¢2îÿÞñ ⡯‰‰‰€ÚÚZP(÷I&“ÀÀ!33ÓìÀ9xðàû¤( ‘`¦]/ógv+‹Å­ÆlÛ´iÓàÌ™3°råJ 777زe ¤§§›µ»÷í](BSSüùçŸ@Ó4x{{›µñññy`†àú׶Œû×h4 •JMÛÛ:³?õÔS0oÞ<˜2e ,^¼ z½þøãÈÍÍ…ï¾ûÞÿ}8p Œ?fÍšW®\Ï>û Äb1æžÖh4  iKÐÀ›Ý .—••=ÀíëÐ#GŽÀºuë`Þ¼y¦vƒ¡Í3ܽtïÞ(Šºï­[­Vß׶¡¡jkk©€€€ Ó`ãþËÊÊ 4ôö%3Aíæ½qãFèׯ¤¥¥Arr2Èd2èß¿¿Ùç“ððpøæ›oàý÷߇¸¸8 ]»vÁðáÃÁÝÝݬ?•J×-‰™7»DEEKOOB­V ÀP(4µinn†Ã‡[Õ/I’ P( ##Þzë-ÓöÜ׶  ÀK¾-9°EDDD!A¨T* £Ù¿üòËûÚyxxÃ0fÛæÍ›gvrx±±±f—u?þø#Ð4}ßb¥RiˆŠŠ:cIÌü5»DFF*¯^½*¬­­www8p |òÉ'ðí·ßÂÁƒᥗ^6ßÎÜÝfÙ²epòäI˜1c?~>þøc³[˜F”J%H¥ÒÖÀÀÀ?ØËÌz\]]›ûõëW®T*Òbb"|ûí·›› ›7o†É“'ƒB¡€¡Cÿû @†a °°"##- ‚Ë… gSEE…ÿí4-j¨T*Œ‰‰A©TŠr¹?ûì3üàƒÐÕÕsrrP ˜-™#"Ž7_xá³miiiøÄO D"Á^xÏ;wߢRtt4ýÒK/eq=ˆ³fÍÚ&—ËõÆÕQ6™4iöìÙE"vïÞ§NŠ555fm²²²ðôéÓC,‰—ós6=ú˜B¡ Y°ñ0ŠŠŠpÿþýñläb¯Î;7ðûï¿wxî"..Ž úÍÒª%ÎÌÙ”™™ù àÙ³g­š6˜={6úúúÖèt:!¹°¡¨¨¨‚Q£F9¾‚å***$Ifóæ͉–ÆÊù`9›hš&Ë:ªJɈ±Z)%%eYGäi©öìÙót`•’cµRcc£›¥±r>XÎ(cý郾šêt:* zÀ€¿·¶¶vá"ç‡ÉX ohhpðHÜ&##æ:TÎËY5þü‰ÄPRRbñ$ÙJRRR§,É3ÊXš—àÐâsDÄëׯ£··7=f̘ÃÖþÂçå¬jii‘_ôóóÓ———[gΜϓüóÏ?[6‹÷ Õjq×®]èååEwëÖ­~ß¾}ÉèFeeeÅÈåòJ©TJ¯[·oÞ¼iÓx¨T*ÓA?~üøoÕjµ·=qq>0Ψ¦¦&W…B¡ôõõ½v÷o©geeÅ<ùä“WŸ{î9ú«¯¾2ûiæÁ0 –——ãŠ+°GzÀøøøöN,×jjjr3gÎçA0ôÛo¿%%%í^Ïk4<|ø0Ž5Ê·¯ý[=ÿä +Ñét¢W^yåȹsç22Rx§Ê 444@QQ£T*µZMÉd2ÍÔ©S¿œ;wîçÁÁÁÅ\åÇ6—.]z2--mööíÛg߸qãk×®´B¡( A·nÝ@(Bkk+”——ƒR©¤‹‹‹Iš¦‰$&&¦Nœ81Ýød{áÍn æL™²çÀñÇ9|øðܶڗ––>uâĉç•Jed~~þà+W®Èëê꺊D"½§§g}HHHaddd~dd¤rĈÙnnnM”J‡ÓÚÚ*ÎÉÉyáÎXDEÕÖÖvÓh4bOOÏú;™É‹ŠŠÊ2dÈÿ………±×owΤ… ~FóÍ7ßL°µ€€€«+V¬Hæ:—Π;vÌ4 ‚ŽØÿ}v ùì³ÏÞY»víÂÔÔÔù&LØÏu<<Ößݾúê«É‹-útéÒ¥¿ùæ››¸Ž‡Ç6x³·ÃþóŸ—§M›öÏéÓ§™’’²œëxxl‡7{äççGÅÅÅeŒ9òø¶mÛþ×aÏçäéx³?•JÕwôèÑGƒƒƒ‹ÓÓÓ'REs}ðfjµÚgäÈ‘Ç===ë233_•J¥7¹Ž‰Ç~ø»1÷ÐÜÜì:zôè£Fòã?ÆxyyÕr;ðf¿ N'Š‹‹ËP©T}Ož<9´W¯^—¹Ž‰‡=x³ßaÁôéÓ¿><ÖfçWG/[³ó«£¥ÙùÕÑÇ“ÇÎìüêèãËcu7†_}¼ylÌίŽò<fçWGy³ó«£<ÙùÕQ#ôÝ~u”çnY³ó«£<÷òHš_åyœÙùÕQž‡ñH™_åi‹Gæn ¿:ÊÓ„ÙùÕQKpz³ó«£<–âôfçWGy,Å©ÍίŽòXƒÓÞáWGy¬Å)ÍίŽòØ‚Ó™_屧2;¿:ÊcNcv~u”Ç^œân ¿:ÊÃÞìüê([8Äì:NTRRTTTÖØØèNÓ4%‹[ï<ëRPaÉËGeuT­VûäççG]¹rEÞÔÔäš——÷ÌŽ;f‡††^`롶ÎÀÝÞøá‡^زeK¢ŸŸ_•5Þ° ¶ž1Y[[ÛíÓO?}gРAJ‘H¤”H$WWWZ(2ÆmÝ»w¯7nÜÁÌÌÌWhš&ùÜQ.Ä0 ñÓO?=ÿúë¯ÿËÏÏOmÌ›¢(ÆÅÅ% sgB‘$ICXXXñÊ•+?¨¬¬ôã:vGÈä …B) MÞã€B‚ø¯7ºv­7fL»Þ°Evw R©úL›6íK±X¬‰D† &06lÀS§NaKK a+++ñСC˜””„ …‚ìÕ«WÅš5kiµZÑÝý~úé§ï¦¦¦¾ÉõdY*ƒÁ ؾ}ûÌ   ßõï¾û.îß¿ËËË‘aÓx´¶¶âùóçqëÖ­˜€R©Ô@’¤aüøñßDp 2yC$ÒŠÃfžÀ€Ûö@+ð& ‚$o{ÃßÿÞ°UvMìÆ绸¸hüýýõ«V­BµZ–Â0 æååaBBRÅ„„„”(•J"ÂÞ½{'.]ºô#®'Ìš‰6lØIÀØØX&++ËÌÜíÑØ؈›6mÂ~ýúéI’4¬X±"™­Iîh™¼!kü)J¿ Õw™»=1˜€ H2`€ÉöȦ©ÕjoãÄ&&&bss³Å“ú 1,,Œ&IÒ0mÚ´$Iê§OŸ¾“a‚뉳D;wîœîââ¢éÕ«—>;;Û®±Ðjµ¸råJ¤(Š -.--íËu~ÖH­V{‹Ž¾í l¶ÂäR!†‘$M †>úh©=ž°úW¯^  ,ëÑ£‡Ý{7Z­—-[†€r¹ü’N§£¸œ4Kµzõê%€3gδû ¿›ÂÂB ÔûøøÔ^¸p!„ë<-ÑÕ«W{÷.ëAQúl;M~·´¸ìÎ5ý¼yó6Ùjx««ÕjïÀÀÀ2¹\®W©TvOèƒHKKCÀ… ~ÖÙÏì6lX¸|ùr«.Y,¥¦¦ÃÃÃiooïÚ?þøã)Hl¡Ë ÀIDAT®ómKjµÚ;°wï29EéU,ýn¥Ý1¼­Þ°¸!Ã0DLLL¶¾´´”•É|©©©¸gÏž7ì™G*77wà¢E‹bt#555Ø¿ý€~×h4b®ó~†!b^x!Û‡¢ô¥2ºQ©w o‹7,n¸uëÖÙ€ÇgiÛfÒ¤IL×®]¯]»ækmRŽVKK‹´wïÞW¢££iƒÁàØ@Ä . P(dÞ{ï½U\çþ ™¼á`£5‰ ˜®nnV{âF—/_–Ëd²[³fÍbmÛ£¶¶}||ôcÆŒùÞš„:B ,Ø ‘H Ž~‡»›””̹sçvDŽ–êòåËr™DrkV°}(J?æÕW­ò†EæλÅÇÇGßØØÈâôµOzz:v¦ ®¬¬ô#IÒ°zõjG§o†^¯ÇzôèÑÇ‘—­š;wîŠÒ7v ÙÓï\ÎXãv466ºÉd²[III¬Nž%Ð4r¹\?mÚ´/Û‹³£´råʤR)ÝÑ>"âŽ; ¦¬¬¬7¹Ø«ÆÆF7™Dr+©ƒŽH œ¢¬òF» 6oÞœH’$SQQÁîÌYȪU«P,kkkk»µ«£¥Ó鄾¾¾5sæ̱8þÇ#Axï%O]]ŠÅbüüóÏ-îëæÍ›èááA/Y²d5×cFoSa¥Q÷à0ì €=p.˜¯¨ZªU(‰,öF» FŒ‘3zôhÇÝnh‡êêjܽ{÷”öbu´Nœ81ôÎ[§Åñ ô÷÷Ç÷ßßlû¦M›ÐÅÅ­}‡HLLÄÞ½{_åz,F –3š kMê·ï›çàç(ÀÙ6˜½úÎ¥Œ¥Þh³x ¥R5dÈš/—555\.‡ &˜mŸ3gxyyAuuµÅ}ùøø@Ÿ>}ôJ¥2ÒšA~~~”X,f""",~@ €éÓ§ÃîÝ»aÓö;wB\\¸¹¹YóÏ> åååõõõ]­z!˘¼h•7~€s`±!è#Z„ÒÒÒ¾€ÿþ÷¿­:û "fgg£@ À={ö "âÑ£G‘ üæ›o¬îkâĉý¶œ}ØÔäÉ“÷·Q’¤!êÙg-óF{Ÿ`û÷ﯚ7ožUw NŸ>$Iâ¶mÛ̶¯_¿E"*•J‹û2 (“ÉèÕ«W/±õÎ[Ú·oßDÀ?ÿüÓªñøí·ßðСCèáá+V¬°êµ÷òü¥R©†íbd[Ô¿OÕ<ï¢\@wÛq'Ç€2’´Øí6˜5kÖ6¹\®§iÚ®I²•¬¬,<}úôöbu´***ü ‚`¶oßnUÇG±XŒ±±±xëÖ-Gâ6ÑÑÑôK/½”ÕQ9·¥Y³fm“S”ž¶Á¨—îÜrÜe‡Ù³n_ Yìvœ;wn à÷ßo×$ÙJ\\ô[g©Z=zô1…BA;²`ãa!àþýûãÙÈÅ^™¼aç½v[GLP¿~{⤢¢¢ Fåø*…{¨¨¨@’$™Í›7'ZgG(33óÀ³gÏ::ýû˜={6úúúÖèt:!¹°¡¨ˆˆ‚Q¡£^€$AXå ‹íÙ³ç €Ž«R2b¬Vjllt³4!G‹¦i200°¬£ª”Œ«•RRR–uDž–Êä6»±ZÉoXÔÈX ohh`sJFFtÎ:Tcýéºuë;wÐét¨P(èüÞÚÚÚ…‹œ&cýiEé:Èè`[ªÅ ¥y -0FD¼~ý:z{{ÓcÆŒ9ÜY®ÕïÕüùó7J$CII‰CÇ1))©S–äe,ÍK€Û?päH£_@o’¤Ç¼úªÕÞ°*©]»vM\¶l™Ã ãÆ Ñ÷ìÙSÝ‹­jii‘_ôóóÓ———;d,·mÛ†€ÉÉÉ+¸Î¹-™¼á@ÃßÀ’Ô÷ôö¶ÉV'eüQ Å‹#Û׬UUU¬÷òòª+))ÀŤY£ªªªž}úô¹ìïﯿxñ"«cˆ¸qãF°ï‡:R&oÀí{àl½ ƒIRïÕµ«ÍÞ°)©õë׿8|øpš­³Zzz:zyyÑ~~~Õ/^|šë‰³TUUU=ƒ‚‚~•J¥ô¦M›X9¨ÕjŒgßyçOÁèF™¼!Ðå,=½H’öóñ±Ë6'•••#—Ë+¥R)½nÝ:¼yó¦M«R©L;~üøoÕjµ7×f­ššš\çÌ™ó¹ñðóÏ?Û4Z­wíÚ…^^^t·nÝê÷íÛ7Ñ™ŒnTVVVŒÜϯRJ’ô:¼i£ÉUçç¬ÇÇÅÙí V&™ ÆÃÃ~ûí·±¤¤¤ÝëyFƒ‡ÆQ£F»wï~ÃY'öÞI~òÉ'¯>÷ÜsôW_}eö³Ý‚a,//Ç+V`=ô€ñññœñ ¿[fÞ Iúm,±àz^€‡p”@pÛžž¬yƒ@D;¿ŠpéÒ¥'ÓÒÒfoß¾}ö7<ºvíJ+ B¡tëÖ „B!´¶¶Byy9(•Jº¸¸˜¤iš8p`AbbbêĉÓ•§OèõzááÇÇlÙ²åÍìììáú÷﯌Œ‚D"ƒÁ PTTÄ(•JF­VS2™L3uêÔ/çÎûyppp1×y°…Éii³o44xt%IZ(P0Œ  Ê@IQt±Á@ÒˆÄÀˆˆ‚Ä Xõ+f7ÒÚÚ*ÎÉÉyA©TFæççGE566ºéõzJ"‘3“•?dÈÿ +bmçÒÒÒ§Nœ8ñüñ|åʹF£“$iÉd·BBB ###ó###•#FŒÈvsskâ:fGaæó磊”ʨƦ&7=MS±¸µ§¯ïµÈÁƒê VÍÎÃÓ™ùÿ\·] ¢²IEND®B`‚deap-1.0.1/doc/_images/gptypedtree.png0000644000076500000240000004037512117374514020105 0ustar felixstaff00000000000000‰PNG  IHDRúúˆìZ=sBIT|dˆ pHYsaa¨?§i IDATxœíy\Tõþÿ_g``PvY4@sß@Aíj%iWÁ¥´¯˜[b×=ëÞ®Þ4·p»–Vfnh.™K]×ÂÝÄå–JX¨)æ‚f ¢"ÛÌyýþð7\‘mf˜ø<y<äœÏçý~ŸçuÞç|Îç|>IB ÔhU€@ °ùÄ*>MEܺ lž„„¤¦¦bþüùV9øûûcÒ¤IX»v-îß¿o5¿¦ „.°y–-[†6mÚà¹çž³ºï¿ýíoxøð!¾üòK«û6!tMsûömìرcÇŽ­’!ªþþþˆŠŠB\\œÕ}ƒºÀ¦9uêt:"##«,†ÈÈHœ>}999UCE¡ lFOOO4jÔÈd#FŒÀÑ£GM®¯V«!Ë2NŸ>m² K#„.°i¡V«¾mÿᇰsçN(ªûàÁÌŸ?Z­Ö([mÛ¶…££#4Qõ¬‰ºÀ¦ÉÈÈ@ãÆ®×°aCìÚµ }ûöÅõë×ñÍ7ß <<nnnF_4”J%üýýqýúu£ã°¶1PW (ƒ¼¼<¨T*£ëùùùaíڵشi†Š‹/â¿ÿý/êׯoRNNNÈËË3©®5]`ÓØÛÛ}« <ºˆ‰‰ÁÖ­[ѽ{wôïßQQQX¹r%dY6Ú^aa!”J¥Ñõ¬…ºÀ¦qssÃ;wŒ®wùòeDEEa×®]ðóóCŸ>}€ÌÌLèt:£l‘Ä;wàêêjtÖBܺ lšöíÛcÿþýF×ëܹs‰muëÖÅ»ï¾k´­ëׯãöíÛhß¾½Ñu­…Èè›F­VãüùóÈÎÎ6ÙÆÚµkÑ­[7“ëë{ÛÕjµÉ6,ºÀ¦ ðèuYUqâÄ øúúÂÏϯÊb¨!tMÓ¦M´mÛ«W¯®ÿùùùØ°aþïÿþ¯ZÏ+„.°i$I¸qã°sçÎ*y½}ûvܺu cÇŽµºocߣ lž{÷îÁÏÏûÛß°hÑ"«ùÕétèر#ÜÜÜðÝwßYͯ)ˆŒ.°y\]]1uêT|üñÇøñÇ­æwÑ¢EHNNÆœ9s¬æÓTDFÔ´Z-þò—¿ ''III&–3†_ý!!!˜0a>üðC‹ú2Bè‚ÃÙ³gŠà‹/¾€Ba™ÖÛ·o£[·neÉÉÉprr²ˆs"Ìj mÛ¶ÅÆ1hÐ 8::"..ÎìSKýùçŸèÙ³'îܹƒãÇÛ„È!tA càÀÈÏÏLj#pëÖ-¬Y³¾¾¾f±˜˜ˆÁƒãÞ½{8xð Z´ha»Ö@tÆ jC† ÁîÝ»ñã?¢m۶زeK¥Ö?ÏÏÏÇ´iÓðÌ3ÏÀÅÅ'Nœ@»ví̱°öPµ¸uë_}õUà_þònÞ¼Ù •Tõܾ}›|ð¨T*ksk®éq‚ I4mÚÙÙÙÈÌÌ„úõëµZ]l}tY–qïÞ=¤¤¤@£ÑàÔ©Søæ›o Ë2HbðàÁX·n]UŽÉ¡ j4;wîDÿþý‘ooo¬\¹GŽÁ/¿üRæç¨uêÔApp0¢¢¢ƒ¥K—âƒ>@zzºÙž÷­º ÆB¡¡¡ððð(1ríáÇHIIÁÅ‹‘——;;;Ô©SmÛ¶E«V­ŠõÖgeeá駟Æo¼aïÌKC]Pcy<›Wvq‡™3gÚtVBÔHÊËæ¦`ëY]¼^ÔHvíÚ…Ó§OcæÌ™f±çáá·Þz Ë–-Ãüa›ÖDdtAÃÜÙ\-gu‘Ñ5sgs=¶œÕEFÔ(,•ÍõØjV]P£°T6×c«Y]dtAÁÒÙ\-fu‘Ñ5Kgs=¶˜ÕEFÔ¬•ÍõØZV]P#°V6×ckY]dtÍcíl®Ç–²ºÈè›ÇÚÙ\-eu‘Ñ6MUes=¶’ÕEFØ4U•ÍõØJV]`³Tu6×c Y]dtÍRÕÙ\-du‘Ñ6IuÉæzª{V]`“T—l®§ºgu‘Ñ6GuËæzªsV]`sT·l®§:gu‘Ñ6EuÍæzªkV]`ST×l®§ºfu‘Ñ6CuÏæzªcV]`3T÷l®§:fu‘Ñ6­ds=Õ-«‹Œ.° l%›ë©nY]dtAµÇÖ²¹žê”ÕEFT{l-›ë©NY]dtAµÆV³¹žê’ÕEFTkl5›ë©.Y]dtAµÅÖ³¹žêÕí«Ä« VóàÁdee¡  ŽŽŽ¨W¯œœœJ”Ógó„„ëiFôYýƒ>À?ÿùÏR×WÏÎÎFvv6´Z-T*¼¼¼ààà`¶DFXœëׯ㫯¾Â?þFƒ .Û¯P(ЦM¨Õj<óÌ3xõÕWáááQ#²¹ž'³ú… ðõ×_#11W®\)V^©T"((jµÏ>û,^yåÔ­[×dÿBè‹@ X¶lvìØ¥R‰ÐÐP„……!44õëׇR©DAA®^½ FFƒÓ§OÃÞÞ]ºtÁáÇ‘€çž{®ªÇ,LŸ> .DçΑ„……A­V#$$^^^°³³C~~>ÒÓÓ‹.©©©puuň#0nÜ8´hÑÂxçÌLFF£¢¢€mڴ᧟~Ê»wïT÷æÍ›œ;w.ýüü€£F2¸nu&--;v$vèÐëׯçÇ ª{éÒ%þë_ÿ¢——íìì8uêTæååå_]`V6nÜHwwwúúúrÛ¶m”eÙ$;Z­–+W®¤‹‹ ýýýyàÀ3Gjt:-ZD•JÅfÍš1!!Ád[yyyŒ¥R©dÛ¶m™˜˜hp]!tYe™Ó§O'<˜·oß6‹ÝßÿT(\³fYlZ‹ÂÂBŽ1‚’$ñ­·ÞbNNŽYìž9s†!!!trrâÞ½{ ª#„.0 ï½÷pÁ‚f·­Óé8vìX°±ët:2„vvvüòË/Ín?77—}úô¡R©ä¾}û*,/„.¨4qqqÀ>øÀb>dYæرc©P(lâ6~êÔ©”$‰[·nµ˜‚‚FEEÑÉɉ©©©å–BTŠôôt:;;sÔ¨Q÷¥ÓéAÿjÝAwòäI* Î;×â¾>|È6mÚ0,,Œ………e–B˜Œ,ËìÞ½;6lÈììl«ø¼|ù2]\\¬ra1…ÜÜ\¶jÕŠ:t(WxæäÔ©S^X„ÐkÇg```Ñß``` éîî^aýäädÎœ9³ÄkŸµk×R’$Þ¹s§Øö¯¿þš¬~+½bÅ  F£)·ÜÌ™3éììl¥¨±páB*•Ê o¥ÍÍ”)SèààÀ7n”º_½qñâEþüóÏEûùù±wïÞœAAA¼}û6:DI’8cÆ ž:uŠ—.]*·îŸþÉéÓ§S’$8p€§NâéÓ§IþOèmÚ´áÇÌC‡ÑÛÛ›ø믿Ùøúë¯iggÇ7Þxƒû÷ïçÚµkéëëËAƒ•Ñ ½iÓ¦œ={6>Ì &P’$ƒÞë™8q" CCCyøða¾ÿþûtppàŠ+ŠÊ<)ôÙ³gÓÅÅ…Ë—/ç±cǸmÛ6Ž3†·nÝ"Iæçç3,,Œ5âÚµkyàÀ:”J¥²Ø#QYpòäÉÅGI’Ê|¦&É-Zðµ×^+¶­sçÎìÕ«W©åeY¦‡‡gÏž]bŸz âñ謬,J’Äõë×\¿¢gôåË—“|ôš«nݺtppàœ9sH>:É5jTâÄÜ·o ùå’ÿú”)SŠ•kܸ±Á=é¿ýö »uëV¬óñ_ÿú4hPô÷“BïÝ»7 P¦ÝÏ?ÿœJ¥²ØÅ‹$Ÿyæ¾úê«åÆôÇ€ÁïÍ·mÛFI’øûï¿—ºÿâÅ‹”$‰»ví*¶}É’%tttdAAA©õ^|ñEFEE•Ø.žÑÓ£GÀ… ““ƒ àúõë€óçÏãÊ•+8p ´Zmѯ[·nP(HLL,Õ–žÖ­[ãÚµkÅqèÐ!@ÿþýqöìYÜ¿Z­Ý»wÇÍ›7qõêÕRë©ÕjÄÇÇcöìÙø駟 Ër±ý@PPš7o^ì"""ðÓO?•SRRR‘²ÐétÈËËCRRÞÿ}ôêÕ 6,µlZZ U«VŶ·jÕ HOO/óõ±<Ž˜xB`0îîî€[·nêÔ©ƒ¼¼<ÀíÛ·<ß“H’TB|z[z”J¥ASz_$ñ÷¿ÿ$áææVÂW@@@‰zÓ¦MƒB¡Àúõë1{ölx{{cüøñ˜1cF‘Ýääd(•ÊuííË—Š¾MJó«§Q£FÈÈÈ„‡‡cË–-e–Õw2>ÙN€ÌÌÌRëùûûÅò8Bè£ÉÏÏðHTz<==Ÿ}ö:uêT¢Nƒ ÌæßÓÓ’$áã?Æ›o¾‰;wÂßß¿hYßk;88`æÌ™˜9s&.]º„5kÖ`Ö¬YhÒ¤ † ‚zõê¡]»vøüóÏŽIß&¥]$ôìÛ·999HMMÅœ9s‰Ã‡ÃÎÎÎheáèèˆÂÂB,öÿ#„.(B?uQnn®AåøØ««V­ZÁßß/^Äرc-$€ˆˆÿËj¡¡¡Å„nMš4Áܹs±råÊ¢Û䈈ìÙ³ 40ú¤o­V[¦Øõ¯>;uê„: 88;vìÀ€J”Õgîììløøøm×gzý…õI `oo_L䀺à1Ú´iàQVîÛ·/êÔ©ƒ   åô'™>s²ûâÅ‹1xð`äää W¯^¨[·.~ÿýwìÙ³óæÍCóæÍËõOßy7oÞãÇÇÂ… ÉÉÉøùçŸqþüy$$$`ÇŽ¥ÖëׯÂÂÂŒºuëâ›o¾AVV^xáÀ°aðråJ„‡‡ãwÞAóæÍq÷î]$''£°°óæÍ+3&}›Ü¼y³ÜÛw=AAAP*•¸téR©ûõÏæiiiÅÚ--- hÒ¤I©õþøãR/Bè5I’J\É!88³fÍÂêÕ«±páB4lØ°èD|Ün«V­ R©››[lû€àîsçbãÆ€§Ÿ~ýë_‹MˆXZŒÆƾdɤ¤¤à‡~ÀÀáììŒV­ZaàÀeÚ|öÙgñÕW_aÑ¢EÐjµhÕª6mÚT$t|÷Ýw˜5kæ΋7nÀËË ¡¡¡ ð¨SΡŸ:u ………e ¶I“&hÑ¢¾úê+DEEmߺu+"""Êì3Ðh4E±àwÁtêÔ‰C† ©Òþò—¿0::ºJcÐ#Ë2}||8}úôûú÷ïÏyóæñ›o¾á¡C‡¸hÑ"Ö¯_ŸÁÁÁE¾Œ9’öööÅêéÌÌœ9“GŽá˜1cèààP怒¬_¿>§NZb»Èè“èر#víÚNgÖÎ$CÉÎÎFrrr± ^•H’„Ž;–:5u§N°uëV,X°²,£qãÆ=z4Þyç¢Ì,Ër‰×}ƒ ÂDZ`Á,X°­ZµÂŽ;Jíì€sçÎáæÍ›èСCÉ•¼ lY–YXXXæÏXNž8booÏëׯ›Ý¿©lÞ¼™xöìÙ*ñÿöÛo³^½zÌÍÍ-±O½–0sæÌcÙÿ3‚Ž|tá eïÞ½Ígzzz¹q>ÿüó”e™­[·æÀÍ껲äççÓÇLJ&L°ºïœœº»»—9WÌë^K¸qãnܸQæþ§Ÿ~ºÌW6e±fͼñÆø駟Êf ………øùçŸËÜïââ‚Ÿþ¯¼ò Ž9‚ððp³ø5ï½÷>ùäüòË/uÊ™‹E‹áŸÿü'~ûí·R;ø„Ð&SXXˆN:¡°°‰‰‰ptt´¸Ï;wî mÛ¶èÔ©vîÜY©· –àîÝ» D`` öîÝk•øΟ?öíÛcôèÑøøãK/dÅ» A äÌ™3´··ç´iÓ¬â/::šÌÈÈ°Š?Sˆ'®^½Úâ¾´Z-»téÂfÍš•;´º ÒÄÆÆR’$nÞ¼Ù¢~æÏŸOܸq£Eý˜ƒ‘#GR¥RñèÑ£ó!Ë2GM…BÁãÇ—[V]Pit:ƒƒƒiggÇM›6YÄÇÂ… €ýû÷·ˆ}s“››Ë^xÎÎÎnØ°Á,¶-Íýû÷AGGG~òÉ'Ôétf±›‘‘ÁÈÈHJ’ÄU«VTG]P)ô"¥N§+Z,00ШµÁJcÿþý  ³³3ãââ¨Óé8jÔ(J’d3bÏËËã„ €]»vå… L¶%Ë27nÜHúøøp÷îÝ×B˜Ìã"ýÚ`’$±W¯^üöÛo‹ºTD~~>7mÚÄgŸ}–ÁË—/í·E±“fÖiܸ1•J% ÄcÇŽ¼åýû÷¹bÅ ¶k׎môÚvBè“(Käz ¸f͆††6lÈ×^{‹/æÑ£G™––Æ‹/ò×_åÁƒ¹`Á0 h.ºçŸžÿùÏJƒ­ŠýÁƒüè£Ø¢E `‹-8räH~öÙgüþûïyîÜ9^¼x‘gÏžå·ß~ËÙ³g³OŸ>tuu¥B¡`ß¾}yèÐ!“| ¡ Œ¦"‘?Ž,ËSÄœ9s0}útÄÆÆâ½÷Þ«êpŠP(X¹r%`øðအC‡VeHÕ!tA…TW‘ëb¯!tA¹Tw‘ëb/!tA™ØŠÈõ±—º TlMäz„ØKG]P[¹!ö’¡ Šaë"×#Ä^!tA5Eäz„Øÿ‡º@͹!öG¡ j¬Èõ± ¡×zjºÈõÔv± ¡×bj‹ÈõÔf± ¡×Rj›ÈõÔV± ¡×Bj«ÈõÔF± ¡×2j»ÈõÔ6± ¡×"„È‹S›Ä.„^K"/Ú"v!ôZ€yùÔ± ¡×p„È £¦‹]½#Dn5YìBè5!rÓ¨©bB¯‘WŽš(v!ô†¹y¨ibB¯A‘›—š$v!ô‚¹e¨)bB¯‘[–š v!tGˆÜ:غ؅Ðm!rëbËbB·Q„È«[»º "D^µØ¢Ø…Ðm !òê­‰]݂ȲŒ .àÖ­[ÈÏχƒƒ<==ѪU+ØÛßô5AäZ­iiiHIIüôÓO(,,D³fÍ P(ª8:ã0—Øóóóñ믿";;Z­ŽŽŽðóóÃÓO? I’ÌlU/Ð^Ó8{ö,'OžÌnݺÑÙÙ™JüT*;uêĉ'òäÉ“”e¹B»±±±ÀØØX+…ùe™'OžäĉÙ©S'ªTªRÛÄÅŅݺuãäÉ“yöìÙªÛ(t:GEI’¸aÃÊ7oÞÌ®]»»wï^élf-ÊËìGŽaãÆ©T*ÍãÇt'G’÷ïßçÊ•+Ù®];à Aƒø矛z%ˆ‹‹£³³3xàÀJÙJLLd`` •J%ccc©ÓélNäú˜íííÈÄÄÄJÙ;pàèìì̸¸83EiYž{^^ÇOìÖ­/\¸`²mY–¹qãFzxxÐÇLJ»wï6¸®º ȲÌiÓ¦GÅììl³ØÍËËã´iÓ(IƒƒƒmJä………:t(%Iâ´iÓ˜——g»ÙÙÙ5jð½÷Þ38 V%z±``` ¹dÉêt:³ØÏÈÈ`dd$%IâªU« ª#„n3fÌ .\¸Ð"ö7mÚD;;;›íä°$:ŽC‡¥½½=7oÞl .$Îœ9Ó"öÍMNNýüüèääÄ#GŽ˜Ý¾N§+ºSøüóÏ+,/„n$7n$þûßÿ¶¨ŸM›6Q’$›Èè±±±”$Éb"׳`ÁàÆ-êÇŒ9’*•ŠGµ˜Y–9zôh* ?~¼Ü²BèF‘‘Awww<Ø*þ¦NJ¥RY¢ƒ®:qúôiÚÛÛsÚ´iVñMfddXÅŸ)ÄÇÇW¯^mq_Z­–]ºta³f͘““Sf9!t‘e™QQQôõõåíÛ·­â3// aAAU|CAACBBh¶gòŠ¸}û6}}}Ù§OŸjù¼ž••Å§žzŠ/½ô’Õâ;wîU*'MšTf!tùî»ï€Û¶m³ªßÄÄDJ’Ä5kÖXÕ¯!¬^½š’$UºwÝX¶mÛFyö­,Ó¦M£³³3¯\¹bU¿~ø!%IâÅ‹KÝ/„n  `›6mª$‹ôêÕ‹¡¡¡Õ*ƒÉ²ÌöîÝ»J|·nÝš´ºïòÈÏϧ'L˜`uß999twwçäÉ“KÝ/„n×®]£?ýôÓ*ñÿí·ßO:U%þKãäÉ“Àøøø*ñ¿dÉÚÛÛóúõëUâ¿46oÞLF áݽ{7%I*ñ~=33“*•ŠË—/7ØÖÛo¿Ízõê177·Ä>!tü\° IDATX¼x1U*ïÞ½kq_Z­–………%¶pâĉ÷o('NdÆ ñfnîÞ½K•JÅÅ‹W‰ÿÒˆŒŒd×®]ª£ÓéèïïÏwß}·Øö¥K—²N:FÑHKK#îر£Ä>Ûú\¨ŠøñÇ 77·¢m÷îÝC£F0pàÀbeÇŒ///ܼy™™™9r$¼½½Q§NtéÒÇ/V><<QQQX¿~=Z¶l •JUôe—;;;tëÖ ?ýô“åÒH~üñGtëÖ vvv&Û1bŽ=jR]777„„„T›6!‰üáááFÕS(xýõ×±aÃȲ\´ýóÏ?ÇË/¿ WWWƒmµlÙõë×/µM„Ð @£Ñ ,,¬Ø6WWW¬[·Û·oÇÆ{÷îŪU«°bÅ øøøà¯ý+âãã±páB|ýõ×pvvÆ‹/¾ˆ¤¤¤";’$!11‹-Âœ9s°wï^øûû—ˆA­VãÌ™3Ðjµ–=XÐjµ8sæ ÔjµÑuøáìܹŠ>VyðàæÏŸoô±………A£Ñƒ%¸~ý:nݺeR›ÄÄÄàÆØ·o %%ÉÉɈ‰‰1Ú–Z­.½MŒºÏ¨…Ü¿Ÿ¸nݺR÷Oš4‰îîîLNNfƒ øÚk¯‘$wíÚEI’Š/,,d£FøÊ+¯m{î¹çèèèÈk×®•ÇÑ£G €©©©f8ªÊñóÏ?€IƒA®]»Æ#F°OŸ>|ñÅùüƒjµšŸ}ö™ÑëÖ­#Þ¿ßè8ÌÍîÝ» ÀäÞöž={òå—_&ùèœjÚ´©Ivf̘AŸÛ…Ð+àÊ•+À}ûö•º?77—­[·¦££#Šžãßy纻»—(ÿöÛoÓ××·èïçž{Ž:t¨0ýó×±cÇL<ó¡¿èœ;wÎd_~ù% ›4iÂ7n˜dcïÞ½À«W¯š‡¹Ð_tLïðŸÿü‡¼~ý:½¼¼8gΓì|öÙg´··/±]ܺW@AA”ú8¨T*ôíÛ}€ÌÌLèt:£lUÇ6),,4©¾½½=†ŽãÇ£Gðóó3ÉN~~>”Je‰i¹„Ð+ ^½zP(¸zõj‰}:t(žyæüðà İaàÓéеkWÜ»w,*¯Õj±cÇ<ûì³FÇqíÚ5îª} ¥µIEtîÜýúõ+¶­nݺx÷Ýw¾C¸ví <==ŽÃÜT¦MôèÛeäÈ‘&Û¸víZ©çˆz899¡uëÖ¥ödΘ1éééX·n”J%6lØ€ÔÔTÌ™3½{÷FÇŽ1dȬ]»ñññˆŒŒÄü©S§³CfóÒh4pqqA³fÍÌvl¦Ò¼ys8;;WºÇ{íÚµèÖ­›ÉõѦM899U*s •j“½{÷ÂËË }ûö5Ù†F£)Šåq„Ð  ´×8ßÿ=>üðC,Z´7´jÕ óçÏǼyópúôiìÙ³½{÷Æ?ÿùO 0<ÀRdG’$ƒnÝZ-fJU( ­òW[¥½ö¬*|||`R›œ;w»wïƧŸ~Š1cƘÔ÷û,Ÿþy«û­ˆððp£Çv›ýØÿ‘#GZÝwyhµZ6nܘÆ ³ºï¬¬,:99•9#‘ºh4àÊ•+­êwÿþýÀÿüç?Võk_ý5Tz\cY±bP£ÑXÕ¯!,\¸J¥Òê#§L™B‡2 ¡ALL ]\\øûï¿[ÅßÝ»wÀˆˆˆjõ-ºY–Ù½{w˜m&ÜŠ¸|ù29jÔ(«ø3ýHÉ°°°Ro¡-Á©S§¨P(8wîÜ2Ë¡ÁÝ»wéïïψˆ«ÌÎCggçj½€AzzºÕ„§ÓéaÕ ‹)œ I''§ „ÐM _¿~À±cÇZ$³ÏŸ?¿hÁ[á½÷Þ#.X°Àì¶u:ÇŒCìׯŸÙí[ý\÷vvvüòË/Ín?77—QQQT*•Ü¿…å…ÐD¿LR¿~ý¨P(a¶[ëÛ·o3::ºh¡‚êø\^²,-lm¶™r/_¾Ìˆˆ*Š¢ ¬-ÌuO>ú,yĈ”$‰o½õV¹Ó1Ù3gB'''îÝ»× :BèFðäZh ¿¿?]\\¸bÅ “_½É²ÌmÛ¶Ñ××—ÉÖB¿6˜¯¯/·mÛfòÅJ«ÕrÅŠtqq)¶¶-®G·hÑ"ªT*6kÖŒ &ÛÊËËcll,•J%Û¶mkÔì»BèRÖ v÷îÝ¢u¶6lȹsç<‚îîÝ»\²d [·nMìÓ§Oµ^˜ÀP222Elݺ5—,Ybð|{7oÞäœ9sØ°aâµíž¬kkb'Í'йsg`ÇŽ¹nÝ:ƒGÁ]ºt‰S¦L¡——íìì8uêT£çÑ룀!ë“'%%á³Ï>æM› ÕjѾ}{¨Õj„……Áßß¿h}ô?þø‰‰‰HNN†V«Eÿþý1nÜ8<÷Üs6±¸!ÄÑ£G±lÙ2lß¾J¥!!! ƒZ­†¯¯oÑúè×®]Cbb"4 Îœ9¥R‰Áƒcܸq¥~ Øæšñ:ñññX¶löïßggç¢ö E½zõ`oo¼¼<¤§§µÉÙ³gáêêŠ×_cÇŽE‹-Œwnô¥©–alö¸sç—/_Î#F000 …‚ŠýZ´hÁèèh.^¼¸ZMWl)®_¿ÎÅ‹3::š-Z´(Ñ …‚AAA1b—/_nð°V[ÌìzΟ?Ïyóæñå—_f£FJ´‰ƒƒÕj5ÿö·¿qýúõ|ðàA¥ü ¡—ƒ9N¤‡òûï¿'nÙ²¥ZÌoVÕÜ¿Ÿ[¶l!~ÿý÷•úÖÅþ8wïÞå•+WxñâEfdd˜ýc!!ô20ç téÒ%àáÇÍYÍàðáÃÀK—.UÚVM»%1m‚«Ž->ÿÕfôÿGÓ§O/ö·à¡?¹m"Ä^>Bè!DnÛ±—úÿGˆ¼f Ä^:Bè"¯i±—¤Ö ]ˆ¼f"Ä^œZ-t!òšûÿ¨µB"¯±?¢V ]ˆ¼v!Ä^ ….D^;©íb¯UB"¯ÝÔf±×¡ ‘ €Ú+öZ!t!rÁãÔF±×x¡ ‘ J£¶‰½F ]ˆ\PµIì5VèBäC¨-b¯‘B"Cm{º¹ÀjºØk”Ð…È•¡&‹½Æ]ˆ\`jªØk„Ð…Èæ¤&ŠÝæ….D.°5Mì6-t!r%©Ib·Y¡ ‘ ¬AM»M ]ˆ\`Mj‚ØmNèB䂪ÀÖÅnSB"T%¶,v›º¹ :`«b· ¡ ‘ ª¶(öj/t!rAuÄÖÄ^­….D.¨ÎØ’ØÍ&ô»wï")) ׯ_G~~>ìííáææ†víÚ¡I“&$É({5Aä·nÝBRRΞ= Ø¿?rrr???£ÛÄÖ!‰ëׯ#99'Nœlß¾mÛ¶Ehh(|||ª8B㩬ØIâÒ¥KHIIAvv6´Z-áçç‡ÐÐP¸»»›'PSV—e™ÇŽãðáÃÙ´iS(óçîîÎîÝ»sÕªU|ðàA…¶mua{­VËøøx8嶉###¹yófæççWuè#??Ÿ›7ofdd$}||Êm“€€8ñññÔjµUºQsÎ>xð€«V­b÷îÝéîî^n›4mڔÇçñãÇ)˲Éñ-t­V˸¸8›7oηÞz‹_|ñùåæææR–eòæܻ͛w/cccÙ³gOJ’DWWW¾ù曼~ýz©ömQä¹¹¹\¸p!7nL æäÉ“¹uëVþöÛoÌÏϧ,Ë,((à•+W¸sçNNŸ>]»v%úúúrÚ´iÌÊʪêC1YYYœ6mZ‘¸»víÊéÓ§sçμrå (Ë2óóóùÛo¿qëÖ­œvì›5kF•JÅ¥K—\¯B¡Ë²Ì©S§'L˜ÀœœœJú8»w獵§'4h`S"/,,ä!C(IçÎËÂÂB³Ø•e™«W¯¦J¥b÷îÝyÿþ}³Øµ÷ïßç /¼@•JÅÕ«WWêyòq 9wî\J’Ä¡C‡š­­-^ì 4 §§'wïÞm6Û999?~<pêÔ©µu…BŸ5kðƒ>0KOröìYz{{³AƒÌÌÌ´ˆs¢Óé8bÄÚÙÙqË–-ñ‘@ggg¾øâ‹ÌË˳ˆs’——ÇîÝ»ÓÙÙ™Gµˆ-[¶ÐÎÎŽ¯¿þºMdö;wî°Aƒôöö6êÑÅ>øààìÙ³+,[®Ð·oßNœ7ožÙ‚+³gÏÒÓÓ“½{÷6[&°‹-¢$IüòË/-êçÈ‘#tttä„ ,êÇŒ?žŽŽŽ>žÝºu£··7Ù¤Iþýïgvvv…6W¯^ÍæÍ›S¥R±}ûöüöÛoË,;sæLÖ­[·T»%„¾iÓ&àÕ«W 96’dvv66lÈÛ>zôhÖ«WÏèçüž={2""¨:–dîܹtqqáǪ·k×®¢ñÛíÚµcPPIþeYf‹-øÆo˜Tߌ5Š-[¶4ê.C§ÓÑßߟï¾ûn±íK—.e:u :ñõ<|øÎÎÎ#d Ý»wçK/½TbûÆ9eÊnß¾GåÒ¥KéååÅ=z”koóæÍT(œ1c8fÌ*•JžÜäŒN’#GŽdHHˆÉõÍMpp0GŽit½éÓ§ÓÏϯXç]hh(‡ b´­ððp¾üòËF׳²,ÓÝÝÝàA_qqq”$©Ü$Ø¢E ¾öÚkŶuîÜ™½zõ*³N£Føü£ÄvÅ“_³i4„……ûžþyLœ8'NÄéÓ§ƒÁƒcàÀFÛR«ÕÈÊÊBzzºÑu-F£Z­6ºž9?CU«ÕHMME~~¾ÙlšJ^^RSSM:ObbbpãÆ ìÛ·’’‚äädÄÄÄmK­VC£Ñ]Ï\ºt wïÞ5¸M<==eÚ»pá^}õÕbÛ „Ç£°°°ÔzeµI ¡§¤¤ $$Ä `ŸdÁ‚hРžyæØÛÛã³Ï>3ÉŽÞÿ™3gLªoN²³³qåÊ“ÛÄ\„„„ °°¿þúk•ÆiiiÐjµ&µI£Fðâ‹/bÍš5€Ï?ÿMš4Axx¸Ñ¶BCCñûï¿#;;Ûèºæ&%%ÊmN‡¼¼<$%%áý÷ßG¯^½Ð°aÃR˦¥¥ZµjUl{«V­PPPPf )ŠåqŠ $²³³Q¯^½r©lT*úöí‹‚‚ <nnn&ÙÑû¿wïžIõ͉þ$òòòªÒ8ªc›˜zž¼ñÆøöÛo‘‘‘/¿ü¯¿þºIvl­M5j„:uê ,, žžžØ²eK™e³²² ÄÄ€ÌÌÌRëÕ«W¯Ô _ ¡Ë² {{Ó&žIIIÁâÅ‹Š%K–]•ŒE￬Ûk¢ÕjvvvUGMj“¾}ûÂÃÃÑÑÑÈÊʈ#L²ckm²oß>üðÈ‹‹Czz:"##¡Óé̇R©„N§ÉbÛ‹ ]¡PÀÁÁyyyF;(((ÀСCñÌ3Ïà‡~@`` † fÒèý«T*£ëš} Uýl\ÚÄÑÑ€émbooáÇãøñãèÑ£üüüL²£o'''“ê›CÚ$00:uBLL víÚ…cÇŽaÇŽ¥–Õgî'³³>ÓëŸñŸ$77ŽŽŽ%ú‡J<£?õÔS&u‚͘1éééX·n”J%6lØ€ÔÔTÌ™3Çh[zÿ¦žæÄËË J¥²Ê;«S›èc¨L›ôë×0räH“m¤§§ÃÁÁÁäGsbl›A©TâÒ¥K¥î×?›?yWœ––4iÒ¤ÔzéééxꩧJl/!tSz2¿ÿþ{|øá‡X´h7n\èüùó1oÞ<$%%eOï?44Ô¨z–ÀÁÁAAAHLL¬´­ÊôÂk4xzz¢Q£F•Ž£²<ýôÓððð¨T÷Þ½{áåå…¾}ûšl#11AAAppp0Ù†¹ÐŸ«†¶É©S§PXXX¦`›4i‚-Z૯¾*¶}ëÖ­ˆˆˆ(óñº¬7D%J«ÕjÌŸ?²,C¡(q(•Î;=£<ΤI“0iÒ$ƒl<l³fÍLîÌ37jµ'Ož4º^nn.âãã ¨wxÛ¶m ‰ððp£:øôÿÕaæXI’L~µuîÜ9œ;wŸ~ú)&Nœ¥Rir;w6¹¾9qwwGÓ¦M¡Ñh0dÈbû^~ùetèÐAAAprr™3gðÁ }ûöEw6111Ø°aC±þ†Y³fáµ×^CÓ¦MŽ­[·â§Ÿ~±cÇJA–e$%%aÚ´i%w>ùb]?ܲª>Ô¡>|x•ø/õë×S’$^ºtɨzééé”$‰’$Q¡PP¡PýÛ˜cîß¿OWW×*–\³fÍ¢«««Ñß$„‡‡S¥R±ÿþF4|œK—.Q’¤jõYóðáÃÙ¼yó_ò-X°€!!!tuu¥³³3ƒ‚‚8sæÌb31‚ …¢„Í5kÖ°yóætttdûöí_¦ý'ÃÇŽ+±¯„ÐeYfË–-ùÿ÷F¤¹8pàðĉUâ¿4þøcÚÛÛ[õ[t=ýúõc»víªÍðW=“&M¢———Õ§v’e™íÚµcß¾}­ê×úôéÃöíÛ[ýÿ*//^^^V›ÄPdYfPPûõëguß´··/sR…ž••EÆÄÄX4¸'9vìXµûrMϹsçhggÇ9sæXÕïÆ«Ý—kz:DŸVëIbcciggW-g™Ña¯ìLÉÆ2räHººº–¹6@™3̬ZµŠ¸wï^‹÷8ú™CºtéR-g!}+¯T*™’’b†ÌRÕ 4Ȫ39s†J¥²J¾ù6ýLDÍš5³ÚcÅž={ ÜY¦ÐeYf=èççWî\TæbüøñÕ~.°¼¼<¶iÓ†¡¡¡Ÿw]§Ó-cdö7ýÜ‚‘‘‘ŸN*77—!!!lÛ¶mµžW?· 5&ö¼}û6ýüüسgÏr¡ÊöÊ•+ôööf‡xïÞ=³©G?m­1ÒW‰‰‰trrbŸ>},6,Ë7n%I²Êƒ•E?¡Äøñã-ö¼^PPÀ¨¨(:99111Ñ">ÌÉÒ¥K-:M:IÞ»w:t ··w…‹eT8¯{RRÝÜÜØ¡C³gY–‹¦«6mšYm[’½{÷R©T2**ªR¯ˆJC«ÕrôèÑPe=Ú¦ Ô3fŒÙ½>|ÈÈÈH*•J«=JšýÂ'sçÎ5ûðöíÛìСÝÜܘ””Tayƒ–dJJJ¢··7ýüüÌÖÐìÓ§ðý÷߯v½ì±oß>:99±M›6üñÇÍbóܹsìÒ¥  ×®]k›ÖäóÏ?§B¡`—.]xþüy³ØZ¶l‰qãÆaøðá&}b’Ðõĉ'ðßÿþ·è?*##ùùù°³³ƒ››ÚµkµZ°°0ôêÕ ®®®¦º³ ´Z->ŒS§N!11III¸uë aooOOÏ¢‹b‡ðÒK/U‹oÌ-Inn/l‰S‡IDAT.öïߟ~ú©èäÍÌÌ„V«…R©„BCC†N:¡{÷î&O~b+dggcïÞ½HLL„F£AJJ ²³³¡Óéàè舧žzªè¢Ø¥K<û쳕ú ©RB/ ’Õâ+«ê„1_ÖD›”ÄRÚ±ˆÐAõB\N‚Z€º@P øƒŸÑ^½·IEND®B`‚deap-1.0.1/doc/_images/gptypedtrees.png0000644000076500000240000005236012117374514020265 0ustar felixstaff00000000000000‰PNG  IHDR&ú•­Œ; CiCCPICC profilexÚSwX“÷>ß÷eVBØð±—l"#¬ÈY¢’a„@Å…ˆ VœHUÄ‚Õ Hˆâ (¸gAŠˆZ‹U\8îܧµ}zïííû×û¼çœçüÎyÏ€&‘æ¢j9R…<:ØOHÄɽ€Hà æËÂgÅðyx~t°?ü¯opÕ.$ÇáÿƒºP&W ‘à"ç RÈ.TÈÈ°S³d ”ly|B"ª ìôI>Ø©“ÜØ¢©™(G$@»`UR,À ¬@".À®€Y¶2G€½vŽX@`€™B,Ì 8CÍ L 0Ò¿à©_p…¸HÀË•Í—KÒ3¸•Ðwòðàâ!âÂl±Ba)f ä"œ—›#HçLÎ ùÑÁþ8?çæäáæfçlïôÅ¢þkðo">!ñßþ¼ŒNÏïÚ_ååÖpÇ°u¿k©[ÚVhßù]3Û  Z Ðzù‹y8ü@ž¡PÈ< í%b¡½0ã‹>ÿ3áoà‹~öü@þÛzðqš@™­À£ƒýqanv®RŽçËB1n÷ç#þÇ…ýŽ)Ñâ4±\,ŠñX‰¸P"MÇy¹R‘D!É•âé2ñ–ý “w ¬†OÀN¶µËlÀ~î‹XÒv@~ó-Œ ‘g42y÷“¿ù@+Í—¤ã¼è\¨”LÆD *°A Á¬ÀœÁ¼ÀaD@ $À<Bä€ ¡–ATÀ:ص° šá´Á18 çà\ëp`žÂ¼† AÈa!:ˆbŽØ"ΙŽ"aH4’€¤ éˆQ"ÅÈr¤©Bj‘]H#ò-r9\@úÛÈ 2ŠüŠ¼G1”²QÔu@¹¨ŠÆ sÑt4]€–¢kÑ´=€¶¢§ÑKèut}ŠŽc€Ñ1fŒÙa\Œ‡E`‰X&ÇcåX5V5cX7vÀžaï$‹€ì^„Âl‚GXLXC¨%ì#´ºW ƒ„1Â'"“¨O´%zùÄxb:±XF¬&î!!ž%^'_“H$É’äN !%2I IkHÛH-¤S¤>ÒiœL&ëmÉÞä²€¬ —‘·O’ûÉÃä·:ňâL ¢$R¤”J5e?奟2B™ ªQÍ©žÔªˆ:ŸZIm vP/S‡©4uš%Í›Cˤ-£ÕКigi÷h/étº ݃E—ЗÒkèéçéƒôw † ƒÇHb(k{§·/™L¦Ó—™ÈT0×2™g˜˜oUX*ö*|‘Ê•:•V•~•çªTUsU?Õyª T«U«^V}¦FU³Pã© Ô«Õ©U»©6®ÎRwRPÏQ_£¾_ý‚úc ²†…F †H£Tc·Æ!Æ2eñXBÖrVë,k˜Mb[²ùìLvûv/{LSCsªf¬f‘fæqÍƱàð9ÙœJÎ!Î Î{--?-±Öj­f­~­7ÚzÚ¾ÚbírííëÚïup@,õ:m:÷u º6ºQº…ºÛuÏê>Ócëyé õÊõéÝÑGõmô£õêïÖïÑ7046l18cðÌcèk˜i¸Ñð„á¨Ëhº‘Äh£ÑI£'¸&î‡gã5x>f¬ob¬4ÞeÜkVyVõV׬IÖ\ë,ëmÖWlPW› ›:›Ë¶¨­›­Äv›mßâ)Ò)õSnÚ1ìüì ìšìí9öaö%ömöÏÌÖ;t;|rtuÌvlp¼ë¤á4éĩÃéWgg¡só5¦KË—v—Sm§Š§nŸzË•åîºÒµÓõ£›»›Ü­ÙmÔÝÌ=Å}«ûM.›É]Ã=ïAôð÷XâqÌ㧛§Âóç/^v^Y^û½O³œ&žÖ0mÈÛÄ[à½Ë{`:>=eúÎé>Æ>ŸzŸ‡¾¦¾"ß=¾#~Ö~™~üžû;úËýø¿áyòñN`Áå½³k™¥5»/ >B Yr“oÀòùc3Üg,šÑÊZú0Ì&LÖŽ†Ïß~o¦ùLé̶ˆàGlˆ¸i™ù})*2ª.êQ´Stqt÷,Ö¬äYûg½Žñ©Œ¹;Ûj¶rvg¬jlRlc웸€¸ª¸x‡øEñ—t$ í‰äÄØÄ=‰ãsçlš3œäšT–tc®åÜ¢¹æéÎËžwç|þü/÷„óû€9%bKGDÿÿÿ ½§“ pHYsaa¨?§itIMEݶv6 IDATxÚìy\Tõþÿ_g˜AY$Dsà T—«©”¤ý\J»bšŠv]ÒônzÃ=ļšZfæšf®õ5—ÄÝÄå–¸…†šàŠK(¢"Û̼~$|x^‚'$$”¨ƒÁÀÝ»w³~ýú´µµåÇL½^/…‰&‚ ¾’ÂDŠÇ7ؼysÚÛÛsÙ²e4 ¥nóÑ£G;v,0,,ŒÙÙÙR˜Ha"â«r@î1,Š›7o¢cÇŽÈÎÎƾ}ûиqã2móæÍ CHH¾þúk¨Õæqÿ¸Üc"â+³ñ•$º`)ddd mÛ¶¸wïŽ=Š_|±\úÙ±cúôéƒaÆaÉ’%R˜Ha"â«2Džc"X “'OÆ… °{÷îrKr Á¢E‹°téRDGGKàA_•å@JF ‚%ð¿ÿý;vÄÿû_üûßÿ®ˆ‘;ºwïŽ3gÎ >>NNN¦è2c"â+sñ•$º`ÿX¡E‹°³³ÃÑ£GaeeU!ý^¿~Í›7Ç!CðÉ'ŸHa"…‰ ˆ¯Ê¹”#˜=111ˆÇ‡~XaIµk×Ƹqã°jÕ*<|øP„ â+)LX¼x1š5k†Î;WxßûÛßðøñc¬[·N„ â+)L„ªNJJ ¶lÙ‚Q£FUÊ#˜k×®ÐÐP,_¾\† â+)L„ªÎñãÇ¡×ëRiû‚S§N!==]ˆ â+)L„ªL\\œQ·nÝ·1dÈ:t¨ÄÛÀ`0àÔ©Sr@A_Ia"Tebcc`ô´è?þˆ­[·@Þ¶=‡~NgT[Í›7‡ âââ䀂 ¾’ÂD¨Ê$''£^½zFoW§NlÛ¶ ½zõÂ7ðÝwß!((5jÔ0Zµk×Æ7䀂 ¾*%j9Us&33Z­Öèí<==±jÕ*¬_¿ƒ Â¥K—ð¿ÿýµjÕ*Ñ~ØÚÚ"33Sˆ â«R"3&‚Y£V«žÊ̹ 6 ›6mB—.]ЧO„††béÒ¥0 F·—““F#Dñ•&BU¦F¸{÷®ÑÛ]¾|¡¡¡Ø¶m<==ѳgOÄÄÄàÞ½{ÐëõFµEwïÞ…ƒƒƒAÄW¥-àäTÌ™–-[bÏž=Fo×¾}ûg–U¯^ï¿ÿ¾Ñmݸq)))hÙ²¥AÄW¥DfL³& .\@ZZZ‰ÛXµj:uêTâísïn"‚øJ ¡*àŸÓUG…»»;<==倂 ¾’ÂD¨Ê4kÖ Í›7ÇŠ+*¥ÿ¬¬,¬Y³ýë_a!/ðA|%…‰ ”EQ0zôhlݺµR~—ÿí·ßâÎ;5j” AÄWe'’”ÓE0gˆ‰‰««+–.]Šƒâܹs…¾.¼ZµjhÕªBCC1lØ0,Z´sçÎERRR™]ÿ•ÂD A_Ia"TÁч¿¿?œœœžyÒáãÇqæÌ\ºt ™™™°²²BµjÕмys4iÒ$ßÝñ©©©xñÅñÎ;ï˜Å3¤0ñ•YûJ]¨ £Î;—ª­iÓ¦™õ(D A_Ia"&:ú( æ> ‘ÂDÄWæ‚ü\X°H¶mÛ†S§NaÚ´ieÒž““ÆÅ‹ãöíÛ`AÄWå5’ˆ £Ë…ÈŒ‰ ˆ¯Ì™1dôQEF!‚ ˆ¯Ìb %#AF–? ‘A_™ 2c"È裊ŒBA_™Å@JF ‚Œ>,"3&‚ ¾2_ÉŒ‰ £*2 A|e)2ú°üQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|%3&‚Œ>ªÈ(Dñ•9øJfL}TQˆÌ˜‚øÊ\|¥–ÓI¨h=z„ÔÔTdggÃÆÆ5kÖ„­­m¡£˜˜³þ{sG!sçÎÅ¿ÿýo¸»»?³NZZÒÒÒ Óé Õjáââkkk9YA|Uå|%3&B¹sãÆ |ýõ×ø駟‡‹/æû^¥R¡Y³f@»víðæ›oÂÉÉÉ"F…B.^¼ˆo¾ù±±±ˆ‹‹ÃÕ«Wó­¯Ñhàëë‹€€¼ôÒKxã7P½zu™1_ â+Ë÷•$ºPNÿ€ &&‹/Æ–-[ ÑhàïïÀÀ@øûû£V­ZÐh4ÈÎÎƵkׇ¸¸8œ:u jµ:tÀƒÎ;[DL¦L™‚9sæ }ûöˆ‰‰½½=???¸¸¸ÀÊÊ YYYHJJÊ“@||<0dÈŒ=ÞÞÞR˜‚øÊb} B“œœÌÐÐP`³fÍø駟òþýûÅÚöÖ­[ŒŠŠ¢§§'pøðáÅÞÖ”IHH`›6m€­[·æ—_~ÉÇkÛÄÄDþç?ÿ¡‹‹ ­¬¬ÁÌÌL£ú· @A|eé¾’ÓR(KÖ®]KGGGº»»sóæÍ4 %jG§ÓqéÒ¥´··gíÚµ¹wï^³Œ‡^¯ç¼yó¨ÕjÙ°aCÆÄÄ”¸­ÌÌLFFFR£Ñ°yó挕ÂDÄW–ç+95…²À`0pÊ”)À0%%¥LÚ½rå ƒƒƒ©R©¸råJ³ŠINN‡ BEQ8~üx¦§§—I»§OŸ¦ŸŸmmm¹k×.)LA|eY¾’ST( &OžLœ={v¹Tñ£F"³Iv½^ÏÒÊÊŠëÖ­+óö322سgOj4îÞ½[ A_YŽ¯äJËòåË €sçÎ-×ΨQ£¨R©Ìbš4""‚Š¢pÓ¦MåÖGvv6CCCikkËøøx)LA|e¾’ÓT( III´³³ãðáÃ+¤ªfíÚµMú³cÇŽQ¥R1**ªÜûzüø1›5kÆÀÀ@æääHa"â+ó÷•œªBiF]ºta:u˜––V!}^¾|™ööö"–’‘‘Á&Mš°uëÖÏM¼²äøñãEŠE A|%¾2_Ééj9 <˜>>>yÿ½wï^úøøÐÆƆŽŽŽEnòäIN›6홟…­ZµŠŠ¢ðîÝ»ù–óÍ7PáS•K–,!ÆÅÅ=w½iÓ¦ÑÎήB÷mÎœ9Ôh4ENU–5'N¤µµ5oÞ¼)…‰ ¾_™·¯$=,‡K—.ñ—_~ÉûoOOOöèуG-2)ž—Ð…- bÇŽ+üïÔét¬S§ÃÃËLt{{û ݯzõêñí·ß®ð˜¤¦¦ÒÖÖ–‘‘‘R˜â+ñ•YûJÞ.lAÔ¯_>>>þx¿Crr2Þxã tèÐþþþF=±(Î;‡˜˜Œ=ºÂÿN+++Œ1ëׯGjjj©ÿ–²bÏž=HJJz&&;wîDçÎáææ­V‹ àŸÿü':u‚J¥BlllmåÒ´iS\¿~½Xû±ÿ~@Ÿ>}pöìY<|ø:]ºtÁ­[·píÚµ· @tt4f̘Ÿþƒ!ß÷{÷î…¯¯/5j”ïoÆÏ?ÿüÜ}:qâD^…¡×ë‘™™‰'Nàƒ>@÷îÝQ§N×MHH4iÒ$ßò&Mš ;;III…þ¹û"â+ñ•9úJ-§¯P\wîÜT«V ™™™€”””¼ä{EQžI¾Ü¶rÑh4ź¹*·/’øÇ?þ’¨Q£Æ3}yyy=³Ý¤I“ R©ðå—_bÆŒpuuŻﾋ©S§æµ{òäIh4šgEýüTÉIAýæR·n]$''‚‚‚°qãÆB×ͽIîé8999îÝ»Wàvµk×ÎÛA_‰¯ÌÑWR˜F“•••—T¹8;;>ûì3´mÛö™m<<<ʬggg(Š‚?þï½÷¶nÝŠÚµkç}ïíí]àvÖÖÖ˜6m¦M›†ÄÄD¬\¹Ó§OGýúõ1pà@Ô¬Y-Z´À_|Qâ˜$‰\vïÞôôtÄÇÇcæÌ™ Á`eeUf±±±±ANNHæ;>‚ ¾_™‹¯¤0ò%dddk½?ߺФIÔ®]—.]¨Q£Êu?ƒƒƒóUáþþþù½8Ô¯_QQQXºtiÞ4dpp0vîÜ £Å”NWh²çþ4²mÛ¶hݺ5Zµj…-[¶ o߾Ϭ›;ÒHKKƒ››Û3#“\±>Mvv6Ôjµ%‚øJ|e¶¾’ÂDÈ£Y³fy£ˆ^½z¡Zµjðõõ-p ¯ÒÍÌŸ? @zz:ºwïŽêÕ«ãÊ•+عs'fÍš…F=·ÿâÞ£Ù¨Q#¼û3gàäÉ“øå—_páÂÄÄÄ`Ë–-n×»wo¢U«V¨^½:¾ûî;¤¦¦â•W^¼ýöÛXºt)‚‚‚ð¯ý 5Âýû÷qòäIäää`Ö¬YÏÀ­[·ž;=š‹¯¯/4  ü>÷ZmBBB¾¸%$$ÀÚÚõë×/p»Û·o*A_‰¯ÌÁWR˜XŠ¢”j¤ÜªU+LŸ>+V¬Àœ9sP§N¼ñÏí6iÒZ­ù–÷íÛŽŽŽˆŠŠÂÚµk/¾ø"þßÿûpwwÏ·Ÿ¥Ý÷… âÌ™3øñÇѯ_?ØÙÙ¡I“&èׯ_¡m¾ôÒKøúë¯1oÞ<èt:4iÒëׯÏKtkkk|ÿý÷˜>}:¢¢¢póæM¸¸¸Àßß¿È3ùùùø㦲â$úñãÇ‘““ShÂÖ¯_ÞÞÞøú믚·|Ó¦M.ôr\\\Þ¾‚øJ|e–¾’­ %¡mÛ¶8p`¥îÃ_þò†……™D< ÝÜÜ8eÊ”g¾ëÓ§gÍšÅï¾ûŽû÷ïç¼yóX«V-¶jÕ*ïÅYáááT«Õ>°hÚ´i}šjµš“&MªþÂÂÂèääÄääd“Itt4pÅŠåÞ—N§c‡Ø°aÃ|¯k—K9‚ ¾2[_É©*”–ÈÈH*ŠÂ 6”k?~ø!píÚµ&“ððpjµZ:t¨Üú0 1bU*9òÜu¥0ñ•ÙøJNS¡´èõz¶jÕŠVVV\¿~}¹ô1gÎ`Ÿ>}Ì"&|å•Whggǃ–KÌGM\µjU‘ëKa"â+³ñ•œ¦BYŒ@°U«VT…ÌÌÌ,“¶ÓÒÒ8lØ°¼öpÍš5f—‡288˜666üä“O¨×ëˤÝääd†„„PQ.[¶¬XÛHa"â+³ñ•œ¦BY$ydd$õz=###©ÑhèããÃØØØRµ½gÏzyyÑÎÎŽË—/§^¯çðáé(ŠÙ${ff&ÇŒCìر#/^¼Xⶠ×®]K'''º¹¹qûöíÅÞV A_™¯äTÊ"ÉÿÌéÓ§éççGEQؽ{wîر#ïACE‘••Åõë×ó¥—^"óòåËyß›c²“äÁƒY¯^=j4öïߟ‡¦Á`(öHfÉ’%lÑ¢0,,Œ)))Fõ/…‰ ¾_™¯ätÊ2ÉsÉÎÎæÊ•+éïïO¬S§ßzë-Ο?Ÿ‡bBB/]ºÄ_ý•ûöíãìٳٷo_ººº_~ùeþßÿý_É`®ÉþèÑ#.X°€ÞÞÞ@ooo†‡‡ó³Ï>ã?üÀóçÏóÒ¥K<{ö,wìØÁ3f°gÏžtpp J¥b¯^½¸ÿþõ-…‰ ¾_™¯ä”Ê:ÉŸžÎ;~ü8ÇŽËvíÚQ«ÕÀ3{{{vîÜ™&L(Ö#’Í5Ùsc²ÿ~¾óÎ;ôóó£F£)0&ÎÎÎ|õÕW9}út^¹r¥T}Ja"ˆ¯ÄWfã+9m…òJò‚ÈÉÉa||<,X@Ü´iÏŸ?_¢­Ì9ÙÿLff&Ož<ÉC‡qÿþýùG:¨cGñUYúJÒ´t$%%ñ•W^!vìØ‘6l`VVV±¶ÕétŒŽŽf÷îÝ©( ýüüxúôi³OòS§N±U«VT…=zô`tt4u:]±¶ÍÊÊ↠رcG`—.]J]}›B²«Õjúøø066¶TííÝ»—^^^´³³ãòåËÍ2Ù333ùî»ï;uêÄ‹/–j¤»víZ:99ÑÍÍÛ·o—ÂD_•ÖWVVô±²bl1‹‘Â>{z©Õ´³µ_•ÖW’®% ø¤I“€Ã‡gZZZ™´›™™ÉI“&QQ¶jÕʬ’<''‡ƒ ¢¢(œ4i333ˤݴ´4>œ8yòd³˜*ÍMvôññ¡ .\H½^_&í'''3$$„Š¢pÙ²eR˜â«’øjà@*'Ì,eQ’ûI8üÉ=â«RøJÒÖx¦NJœ3gN¹´¿~ýzZYY±U«Vevr”÷‰=hÐ ªÕjnØ°¡\ú˜3gpÚ´ifqŽ¤§§ÓÓÓ“¶¶¶>>ôóó+ô§y•Ivv6ýüüèããSf÷”EJJ ÝÝÝÙ³gO“¼~›ššÊ^x¯½öZ…íßùóç©Õj9nÜ8)LñÕó|Õ¢}Ôê2»§¤¨O @wµš=CBÄWÆøJR¸x|ÿý÷ÀÍ›7Wh¿±±±T…+W®4¹˜¬X±‚Š¢”ú×7Ʋyóf(—k¡¥eÒ¤I´³³ãÕ«W+´ß>úˆŠ¢ðÒ¥KR˜â«Â|”ú×7Æ~6?¹¤#¾2ÂW’ÂÅ£oß¾lÖ¬Y¥T½Ý»w§¿¿¿IU܃~~~ìÑ£G¥ôÝ´iSöë×ϤΑ¬¬,º¹¹q̘1Þwzz:9aÂ)LñUA¾jÑ‚=Tª -Jа©ZÍ~}ûŠ¯Šë+Iᢹ~ý:­¬¬ø駟VJÿ;vì ?~ÜdbrìØ1`ttt¥ô¿páBªÕjÞ¸qÃdb²aÃ0êÕÛ·o§¢(Ï>¾ÒcòË/¿@‰Æsýúu2„={ö䫯¾ÊþóŸ àgŸ}fôe¡Õ«W>|Xé1Ù¾};”øîönݺñõ×_Ï;§4hP¢v¦NJ777)LÄW⫧}UÈ¥—qžèð­'Ë·=)RöþiÝ€u¾ñ§eŸ+׋¸Ä³úɯsÄWEûJ.åAjj* V­Z~?{ölxxx ]»vP«Õøì³ÏGŽA5ðꫯ歫V«ñúë¯ãèÑ£ùÚhÑ¢<==Ÿ»îîî€{÷îUzLr÷¡°˜<OOO¬Zµ ýë_qàÀlÙ²;vìÀèÑ£¾,”“û÷ï›uLàwÞÁŽ;œœŒuëÖaèС%jÇÝÝÝ$ÎA|er¾*äûÙ<´ ðÙ“åGž\‚yõO몼àèSm´àYTn>ù_ñUѾ’¤²³³¦ÀïµZ-zõê…ììl 0 ïºnjj*\]]ŸYßÍÍ홑›ÄÏ#·ÿÜý1å˜<ääd 6 ›6mB—.]ЧO„††béÒ¥ù®YSŒ‰Z­.Ñö½zõ‚““ššŠ!C†”¨kkkèt:£c)ˆ¯,ÞW…|¯Ð @6€OŠHÅ÷–<üqïIAEÇscòÔþˆ¯ ÷•&E`ccóÜ“éÌ™3˜?>üýý±páB$$$œqçÎgÖ¿}û6jÖ¬™o™¢(Å>‘r÷Ç”cò<._¾ŒÐÐPlÛ¶ žžžèÙ³'bbbpïÞ=èõú%—)Å$''§DÛ«Õj <GŽA×®]‹‘FVV4 T*Imñ•ø*_L ùþ €ùü,ðd¹3€;¬@ͧ–)ÅØì§öG|U¸¯Ä^EP³fM¨T*\»v­Àä4hÚµk‡ü>>>xûí·¡×ëѱcG|8ßúAAA -rúöíËÎ;›LL:uêTé„à!CL&&^^^…>b¹¨ mÛ¶ŽŽŽœ2eJ‰û7 trrâŒ3äW9â+ñÕŸ}Õ¡û=õ+™ÿ´¸ì©å´~ò“໇¬ P °ÀÃO­0´^ P«ÅWÅõ•¤qÑ|þùçT«Õ¼uëV¥ôŸ™™IWW×DåÅ„ èêêʬ¬¬JéÿÖ­[T«ÕF=¹¼éׯ[µjeô;B‚‚‚¨ÕjÙ§O>~ü¸ÄýÇÆÆwïÞ-…‰øJ|õ´¯ÔjfUÒ#éoT+Šøª¸¾’4.š»wïR«ÕrÖ¬Y•Òÿúõë €çÎ3™˜œ={–¸aÆJé?**Š¶¶¶¼wïžÉÄ$::šxìرJéøðá¬]»vµ’ÂD|%¾7TRaÐÖÆF|U\_I¡C‡VÚ»a^zé%¾üòË&“   £ßµP例#<<ܤâ¡ÓéX¯^=¾ýöÛÞwjj*mmmYà÷R˜ˆ¯ª¼¯:vdG+« /Jt½Ôjñ•1¾’.qqqÀ¥K—Vh¿{öì!þßÿýŸÉÅä›o¾!€|O‹¬–,YBù•m*Ì™3‡¦ÂŸx9qâDZ[[óæÍ›R˜â«çùª‚ “%Ožø*¾2ÂW’ÂÅgØ°a´··ç•+W*¤¿û÷ïÓËË‹ÁÁÁF_¬ »téB///£Þ*Y._¾L;;;>Ü$Ï‘ŒŒ 6mÚ”öžãÇS¥R1**ªÐu¤0_‰¯ ìòòËôR«™VAEÉe€vVVâ+c}%ék\âÕ®]›ÁÁÁÔëõ";;;^¾|Ùdc’””Ta…‚^¯gppp…B%áرcE&^Yñøñc6kÖ¬H±Ha"¾_=ñ•­-‡W@Q¢¬RÑËÃC|e¬¯$}cÏž=T©T5jT¹Ž æÌ™C\±b…ÉÇdùòåÀ9sæ”ëhgÔ¨QT©T~é¨$DDDPQnܸ±ÜúÈÎÎfHHmmm‹œŠ•ÂD|%¾zÊWåX”Ž¨zêňâ«búJR×xz÷îM5jT¹ŒD>üðCàäÉ“Í&&“'O&Ξ=»\fJFŽIìÝ»·YÄC¯×sРA´²²âºuëʼýŒŒ †††R£ÑpÏž=E®/…‰øJ|U€¯Êi¦dä“ûJÄW%ô•¤­qDFFæp*•ŠÁÁÁe6u™’’°°0à´iÓLò:íóf4¦NJ cJJJ™´{ùòeS¥Rå ¶°;¹Mœœ2„Š¢püøñLOO/“vOŸ>M???ÚÚÚr×®]ÅÚF ñ•øª_) SÊðž’`•Š*E_•ÆW’ºÆ'ywï^Ö®]›ööö\²dI‰šg0¸yófº»»ÓÉÉ©\*ÖŠbíÚµtrr¢»»;7oÞ\bYét:.Y²„öööôòòÊ›}ú˜ÃHdÞ¼yÔjµlØ°!cbbJÜVff&###©ÑhؼysÆÆÆ{[)LÄWâ«B|åà@wµš›Ÿ\‚)éO‚—´·²¢—‡‡øª´¾’ô-Y’çrÿþ}>œX§NFEEû‰‹÷ïßçÂ… Ù´iS`Ïž=™œœlö±JNNfhh(°iÓ¦\¸p!ïß¿_¬moݺř3g²N:ÀáÇ?³­¹%;ùÇcœÛ·oOlÓ¦ W¯^]ì§&&&&râĉtqq¡••#""˜™™iTÿR˜ˆ¯ÄWÏñUøJ­æB€÷x¢ëL€uÔjñUúJ±$i•3gÎÄ”)S‰É“'¸Î‰'ðÙgŸaýúõÐéthÙ²%ˆÚµkÃÚÚ999¸}û6âââ‹“'OB§Ó¡OŸ>=z4:wî\¬WŠ›É? 8tè/^Œo¿ý~~~ D@@ÜÝÝ¡Ñhëׯ#66qqq8}ú44  €Ñ£GúBºâSC¯×#::‹/Æž={`gg—Ô¬Yjµ™™™HJJÊ‹ÉÙ³gáàà€¡C‡bÔ¨Qðöö6ºoÅBN,ñ•øª\}õÙgø €Ÿ¢ P¯Gwüñ’¿lüñ–àXqj5NëõÐX[cÀÀâ«2ô•Œ@J8ò(Œ»wïòóÏ?ç!CèããC•JE<¹*÷ãííÍ°°0Ο?Ÿ7nÜ°øÞ¸qƒóçÏgXX½½½Ÿ‰‡J¥¢¯¯/‡ ÂÏ?ÿ¼Øm6Ç‘H..\à¬Y³øú믳nݺÏÄÄÚÚšüÛßþÆ/¿ü’=*U2c"¾_•ÀWõêì«&MÄWåé+Iåò=‘?~Ì~ø¸qãF>|ø°ÊÇõáÇܸq#ð‡~(Õ‹ Ì9ÙŸž&¿zõ*/]ºÄäää29¢&â+ñ•øÊl|%é\þ'Pbb"ðÀØ'8p€˜˜˜hRÇÊR‘ÂD|%¾_™ j¹"k׫2¹ÇhÊ”)ùþ[ÄW‚øÊüÂD’\’]ÄW‚øJ IrA’]ÄWâ+ñ•&’ä’ì‚ ¾ÄWR˜H’ ’ì‚ ¾ÄWR˜H’K²K² â+A|%…‰$¹ É.â+A|%…‰$¹ É.ˆ¯ñ•&’ä‚$» ˆ¯ñ•&’ä‚$» ¾ÄWR˜H’ ’ì‚ ¾ÄWR˜H’ ’ì‚øJ_Ia"I.H² ‚øJ_Ia"I.H² R”â«*ã+µ$¹ É.ʼn ¾ÄWR˜H’ ’ì‚ ¾ÄWU¡0‘$$Ùñ• ¾’ÂD’\dñ• ¾’ÂD’\dÄW‚øJ IrA’]ÄW‚øJ IrA’]_ ‚eúJ-I.Rœâ+A|%…‰$¹ É.HQ"â+K)L$ÉIvA|%–ç+µ$¹ Hq"ˆ¯ñ•&’ä‚$» E‰ ˆ¯Ì¹0‘$$Ùñ• X¶¯Ô’ä‚ Å‰ ¾ÄWR˜H’ ’ì‚%‚ ¾2·ÂD’\d¤(„ªã+µ$¹ Hq"ˆ¯ñ•Å&÷ïßlj'pãÆ deeA­V£FhÑ¢êׯEQª\’ß¹s'NœÀÙ³g{öìAzz:üüüàééitLÌ’¸qãNž<‰£G¾ýö[4oÞþþþpss«rÉN‰‰‰8sæ ÒÒÒ Óé`ccOOOøûûÃÑÑQŒZˆ¯ÄWâ+öKˆÁ`àáÇ9xð`6hЀ ý8::²K—.\¶l=zTdÛ‘‘‘ÀÈÈHš:ŽÑÑÑìׯ½¼¼ž777†„„pÆ ÌÊÊ¢¥’••Å 60$$„nnnω——ûõëÇèèhêt:³ú;9g=zÄeË–±K—.ttt|nL4hÀÁƒóÈ‘#4 %Þ? úÇB|%¾_Yº¯Jr2/_¾œ¾¾¾ÀFqüøñüꫯxîÜ9fddÐ`00''‡·nÝâ®]»ÉnݺQQ:88ð½÷Þã7,&É3228gÎÖ«WتU+N˜0›6mâo¿ýƬ¬, fggóêիܺu+§L™ÂŽ;ÝÝÝ9iÒ$¦¦¦ZL‚§¦¦rÒ¤IyÉݱcGN™2…[·nåÕ«W™MƒÁÀ¬¬,þöÛoÜ´i'L˜ÀV­ZëÕ«Ç9sæ0##Ãb’ýúõë|ï½÷èàà@•JÅ×^{‘‘‘ܵkoݺŜœ fddðܹsüꫯ8~üx6jÔˆèëëËåË——H‚Uµ0_‰¯ÄWfè+cVNHH`ûöí©( {÷îÍ}ûöQ¯×{û¤¤$¾ÿþûtqq¡££#W¯^¯ª2Ç$?vì›6mJF÷ß~›ÇŽ3ªRŒç˜1chggGOOOîܹÓì“<::šžžž´³³ã˜1cxöìY£F¶ÇŽãÛo¿MFæM›òøñãf샫W¯f5èâ∈^¾|¹ØmêõzîÝ»—½{÷¦¢(lß¾=ÏŸ?/…‰øJ|%¾²L_wÅE‹Q«Õ²aÆ̆ R«ÕrÑ¢ER˜ˆ¯ÄWâ+ËóUq*ˆˆà˜1c˜žž^fÙ¾};éááaVIž““ÃRQFEE1''§LÚ5 \±bµZ-»t釚M’?|ø¯¼ò µZ-W¬XQªë‹OÇ:**ŠŠ¢pРAeëŠJv:;;sûöíeÖvzz:ß}÷]`DDD±b]U ñ•øJ|e¾*j…éÓ§çÎ[.9{ö,]]]éááÁ{÷î™ÅÈcÈ!´²²âÆË¥˜˜ÚÙÙñÕW_eff¦ÉÇ$33“]ºt¡:T.}lܸ‘VVV:t¨YŒDîÞ½Kººº55l sçÎ%Θ1C ñ•øJ|e9¾zÞ—ß~û-pÖ¬Y唳gÏÒÙÙ™=zô(³Êµ¼˜7oEáºuëʵŸƒÒÆƆcÆŒ1ù“úÝwߥ O*½zõ¢»»;SRRªla"¾_‰¯,ÈW…}FgggÞ¼y³Â4pà@:::úÓ¼ÊD§Ó±}ûölÔ¨Q™^·~z½ž;vdýúõ‹õ<…ŠæÑ£G¬W¯;uêTaÓ•ééélØ°!Û·oo’ϸ~ý:kÔ¨ÁAƒUXŸÉÉÉtrrbXXX•-LÄWâ+ñ•ùª …§OŸ&®^½ºBƒt÷î]º¸¸ð½÷Þ3¹¸uëV(·k’…qñâEj4.X°Àäb²`Áj4^¼x±Bû‰‰!nÛ¶ÍäbòÞ{ïÑÅÅ¥Âï?XµjðôéÓU®0_‰¯ÄWæ«‚Ž1‚/¼ð³³³+kÔ¨arw×®]Ù¶mÛJé»ÿþôöö6©ëÙz½ž5znÕ[ž´iӆݺu3¹™ƒƒ#""*¼ïììlzxxpäÈ‘U®0_‰¯ÄW櫧Ü¿ŸÕ«WçôéÓ+%X—/_¦¢(\¾|¹ÉÀ .”èzòÅ‹9bĶlÙ’VVVôññ)Qÿ‡&îß¿ßdb²oß>0êÛ·o§¢(ÏŒXîÝ»G­VËÏ?ÿ¼Ømå^­èÑÏóX¶lU*Õ3#ŠŽŽf§NèêêJÖ¯_ŸÿøÇ?˜––Vd›+V¬`£F¨ÕjÙ²eKîر£Ðu§M›ÆêÕ«Ø®¥&â+ñ•øÊ}õô‚õëׯ]»Vì?0--uêÔaß¾}ŸÉÔ¬YÓèë¾Ýºucpp°ÉÀ¨¨(ÚÛÛóñãÇFm·mÛ¶¼÷)´hÑ‚¾¾¾%êß`0ÐÛÛ›ï¼óŽÉÄdøðálܸ±Q£"½^ÏÚµkóý÷ßÏ·|Ñ¢E¬V­Z±Nü\?~L;;»rÿ†1téÒ…¯½öÚ3Ë×®]ˉ'òÛo¿å¡C‡¸hÑ"º¸¸°k×®ÏmoÆ T©Tœ:u*cbb8räHj4;v¬Àõ¯]»FÜ°aC•)LÄWâ+ñ•úêéÿøÇ?X·n]£ÿÈï¿ÿž*•Š_}õIrçÎT…_ýµÑmEFFÒÉÉÉd¦ûôé×_~¹D šËàÁƒK<!Éððpúùù™ÌIݪU+†‡‡½Ý”)Sèéé™ïæ38Ð趂‚‚øú믛D< ‹ýЭåË—SQ”çþ#èííÍ·Þz+ß²öíÛ³{÷î…nS·n]þóŸÿ¬2…‰øJ|%¾²<_©žNü¸¸8-Œ—_~cÇŽÅرcqêÔ) 6  @¿~ýŒn+ ©©©HJJ2 ÆÅÅ! ÀèíÊò5áˆGVVV¥Ç#33ñññ%:O† †›7ob÷îÝ€3gÎàäÉ“6lX‰bgçHbb"îß¿_ì˜8;;²³³ mïâÅ‹xóÍ7ó-ïß¿?8€œœ“IEå¦øJ|%¾²,_=S˜œ9s~~~%úcgÏž ´k×jµŸ}öY‰ÚÉíÿôéÓ•~ÓÒÒpõêÕǤ¬ðóóCNN~ýõ×JIBBt:]‰bR·n]¼úê«X¹r%à‹/¾@ýúõdt[þþþ¸rå ÒÒÒ*=&gÎœÉwî„^¯Gff&Nœ8>øÝ»wG:u 14iÒ$ßò&Mš ;;»Ðýüüòö¥* ¾_‰¯,ÏWª§¦I‘––†š5k–èÕjµèÕ«²³³1`ÀÔ¨Q£DíäöÿàÁ“Htpqq©Ôý0Ř”ôTöµRSŠ‰M©b¢V«1xð`9r]»v…§§g©bbkkk1ñññAÛ¶m1lØ0lÛ¶ ‡Æ–-°‹•… IDAT[ \7w¤ñôh"wd’{Í÷i222`ccS¦÷ ˜*â+ñ•øÊ2}õÌ=&/¼ðB‰nâš:u*’’’°zõjh4¬Y³ñññ˜9s¦Ñmåö_Ò ,qqqF£©ôÛL)&¹ûPš˜ôîÝ^ª˜X[[—xŠ¶2câëë FƒÄÄĿϽVûô(>!!ÖÖÖ¨_¿~¡1yá…PU_‰¯ÄW–ç«g “’Ü9üÃ?à£>¼yóP¯^½¼ýðÃ1kÖ,œ8q¨örû÷÷÷¯ôhmm ___ÄÆÆ–º­ÒŒbãââàì쌺uëVzL^|ñE899•êó]»vÁÅŽzõ*q±±±ðõõ…µµu¥Ç$÷\-nLŽ?ŽœœœB¶~ýúðööÆ×_où¦M›\èå‹’þ"Ã\_‰¯ÄW–ç+uA‰þá‡Â`0@¥Rk§Û·oŸwÍêÏŒ7ãÆ+ÑIÝ°aÃߌVò;vì˜ÑÛedd ::òîÆÞ¼y3H"((ȨÔr )LÑ+ŠR⟾?çϟǧŸ~Š±cÇB£Ñ”J~íÛ·7‰sÄÑÑ 4@\\˜ï»×_­[·†¯¯/lmmqúôiÌ;-[¶Ì‰ 6 kÖ¬Éwýyúôéx뭷РAaÓ¦MøùçŸqøðá÷Á`0àĉ˜4iR•*LÄWâ+ñ•…ùª°Ç WÖ««sßi0xð`“y8Ï—_~IEQ˜˜˜hÔvIIIT…Š¢P¥RQ¥Råýc^®õðáC:88TÚc· búôétpp0ú!AAAÔjµìÓ§ÑO¦ü3‰‰‰TŤ^;?xð`6jÔè™7—Ξ=›~~~tpp }}}9mÚ4>|ø0o!C†P¥R=ÓæÊ•+Ù¨Q#ÚØØ°eË–ŒŽŽ.´ÿÜWº>|¸Ê<`M|%¾_Y ¯ z"\ãÆù׿þµR‚µwï^àÑ£GMæ>zôˆ5jÔàþóŸJééÒ¥T©T¼råŠÉÄäòåËT©T\¶lY¥ô?qâDÖ¨Q£Â^é^Ž9BÜ·o_¥ôÿæ›oúØmK-LÄWâ+ñ•úª  >þøcªÕj£ßQôîÝ›-Z´0©7S’ä¸qãèââÂÌÌÌ tp‹-Ø«W/š={ödË–-+üXeffÒÅÅ…ãÇ7©x úúú²wïÞÞwrr2Õj5?ùä“¿·ÔÂD|%¾_Y ¯ Z˜ššJ{{{6¬BwöðáÃ&÷¦Î\Ο?O+++Îœ9³Bû]»v­É½©3—ýû÷×­[W¡ýFFFÒÊÊŠçÏŸ7¹˜ä¾S˜·˜–ááátpp`jjj•+LÄWâ+ñ•…ùª° —-[FܵkW…ìhzz:6lÈ:P§ÓÑyÿý÷©ÑhxæÌ™ «*œœFS¥ÿþtvv®°ÑêéÓ§©Ñha’ñÐétlß¾=6lXaÓ¶;wî$€çþiÉ…‰øJ|%¾²0_=oš§k×®ôôôdJJJ¹ïì»ï¾K­Vk’Ue.™™™lÖ¬ýýý™‘‘Q®}éõz†„„ÐÍÍ­Bâ_R~ÿýwº¹¹1$$ä™›¨ÊšŒŒ úùù±yóæ>Em Ôjµ3fL¹÷•’’BOOOvëÖí¹SÔ–^˜ˆ¯ÄWâ+ òÕó¹zõ*]]]Ùºuk>xð ÜvvîܹÀE‹ÑÔ‰¥­­-{öìÉìììréÃ`0pôèÑT…Û·o7ù˜lß¾Š¢ðÝwß-·ë·ÙÙÙ ¥­­-cccM>&‹-"Î;·Üúxðà[·nMWWW^½zõ¹ëZza"¾_‰¯,ÈWE5vâÄ Ö¨Qƒ­[·.óJØ`00**Š8iÒ$š »ví¢F£ahhh©~BVØÔÚˆ# Òî / ¹Sé#GŽ,ó©íÇ3$$„¦Â¦êË‚ˆˆ`TTT™ 0%%…­[·f5xâĉ"ׯ …‰øJ|%¾²_§Ñ'NÐÕÕ•žžžeèäädöìÙ“øÁ˜Ü]íE±{÷nÚÚÚ²Y³fü駟ʤÍóçϳC‡T©T\µjÍ/¾ø‚*•Š:tà… ʤÍãdzY³f´µµåž={Ì*ƒ3fÌ öêÕ«Ì®kïܹ“žžžtuu-V’W¥ÂD|%¾_Y€¯ŠÛøÕ«WÙµkW`xxx‰w:;;›«V­¢““ÝÝݹeËš+ñññ  J¥âĉ ½Ã¸(ÒÓÓùÑGQ«Õ²aÆ~‡tYräÈ6lØZ­–}ôQ‰o¨JMMåĉ©R©Èøøx³É–-[èææF'''®ZµªÄSê7oÞdxx8°[·nEN‡VÕÂD|%¾_™¹¯Œ­¨–/_NªÕj¾ù曌‰‰)ÖMD×®]ã´iÓèááA 3雤ŠKNN£¢¢hmmÍjÕªqøðáŒ-Öˆ*!!ÿûßéèèHEQ8nÜ8“zOIIOOç¸qã¨( œœø÷¿ÿ Å:¿bcc9|øpÚÚÚÒÚÚšQQQÌÉÉ1û˜¤¤¤0,,ŒèááÁiÓ¦ñÚµkEn§×ëyðàA¾ùæ›T«Õtppàòå˱WµÂD|%¾_™¯¯”’HëþýûX³f /^ŒóçÏÃÞÞ~~~@½zõ`kk‹œœܽ{'OžD\\®\¹‚êÕ«cРA5jZ´haQïì¸uëV¬X¥K—âúõëpvvF@@P»vmØØØ ;;·oßF\\âââpëÖ-Ô¬YÆ È# }A’¹’˜˜ˆ%K–`åÊ•¸wïjÕª•wwwX[[#++ ׯ_ϋɽÿßÞ£( EQþ Z)(ˆˆ`i'h"ÚÙˆ›°7ä\„XØi!& Ù€…E ˆ1’é,¦˜QaÌœo 7y‡ó’âzår™ÑhÄp8¤X,Æj&®ë2™L˜N§œÏg*• ¦iR¯×Éçó†Aì÷{Ça·Ûq:¨V«ŒÇcƒÁ¯v²|ü…¥%O*&Ê+å•ò*æyõÈm*Š"–Ë%«Õêþ ‡—Ë…D"A6›¥V«aš&–eÑï÷Éd2ÄY†, Öë5¶m³Ýn9\¯W’É$¹\îŠÍf“^¯G:ŽõL‚ `>Ÿ³Ùlî/¯çy„aˆa –eÑjµèv»ßn£Œ ß÷™Ífضã8¸®‹ïûÜn7R©¥RéŠív›N§óÐB´ÿ\L”WÊ+åÕ›åÕ+>óFQDLrði~²ýT3ù¿^uvTL”W:›šÉÛäUœþ?‹ˆŠ‰ˆ¼7Õ?Q1ùêâ`¼ˆî™VIEND®B`‚deap-1.0.1/doc/_images/more.png0000644000076500000240000000273612117373622016511 0ustar felixstaff00000000000000‰PNG  IHDR szzôsBIT|dˆtEXtSoftwarewww.inkscape.org›î<pIDATX…Å–[lUÇÿß™ÙݶÔÞì…R‹)¡Þ¸GB¸T MVcˆFÑYDž|‘G£QLLÔø¤‰KbÀ„¨$„5ATB@*Ò…‚”ZJé…Þwév/s;Ÿìn¶ën¡ÀÙ|9çÌœ9¿ßùæÌÎ3ãAq¯x|Ú—Ÿv×óܳ€íBÁ¯Ÿæ~P´pöŠUBÁ)O+yX<sYÝú:hõø´9÷]@‚ÚªzeíÓ/–“ÍŸ¶ø¾ *FÍnÌ,­—¿R õ¨Ç§5Þ" Œ<”_Ϫ7r5§ëÏ.mÛm¯ŸÎÿ€Ç§åh W‰èYf.¬ÎØÖøúõ °Ù„Ír•b¥âç¦ïññ¯¼¥ï¸gO[MûfWkµÕOæÏ(£<­9Î|Rѽ‹MH6a± —ȃŠþ{øf(p%6û½º9mOs’Àgîò- ‹_vW-DÔ Bç0Lƒ)£08 › ØlÁf1;„þXTr¡ÌY‹ó—ÎE‡oœe‰ü^=tÇŸVN,{lCÕòù5:¢ò&$[°ØHÖ‰Ô'®EšµƒMä¡Òµÿt\Ô{úº¯±Ä¿WO0Ô©V/|³tþúGWÎkTBÖ0nm6àÖîOÔ”r$d 'á«Ã4-ŒŽŒXö§Â§ðø´µUŵë—ÌÝ Í>“ €“?${‰0¤w$ç(qÖ@³ qâôñˆnÆÞ÷oÑ¿Hçdˆß÷ÝëžÚä Û£°X‡€ ƒ˜àRf@!GÊ„¾ØyŒ›ƒ0dP¦Íé98Ñr–îJÀmòþþž‹gliÁjî@è²tHMZ|°íGëùã;­XÚ99â²…þ'@DâÛwí s4òÉ‘KSFPúHiQål±:ÀyìsKçoÒÎOm«‰:ž¤ˆH)ÊÆ‘þÎŽãáÁ`aq…(|DÛðprÒ&wN AMȤ¯>N„’D$€”·aü€—J¶·(iœ[³èíXtÜlÿ½íëCŸÇN Â@idò6a§÷‰™OÉ$pj{ãÇEUËê_›c΋MWù0Ô%3(Òàö4ÚvzÒ’ýâ9ŠsÍ;•Õ³•š¡«7Ní t´Yf<©È”‰ô•K63Û“>H2܆D$3äÈ!Q±PuÏQÝŠJ°WêÝ'õ˜Çá™DÒ¥,f–“ö@Še§Ö‰U§?Vœ!R%lf¶'ñ¦ú$‹Ë¤ e‚S|òLI8gMë³,>ö˜tÿýRÙ²ÒèÑÒ!ÒÒ7J|àߘâINNNv:Y¾ÜV<ÆŒ‘úöµcGJ—^*•,™qòÏ?Ò‰RT”4{¶tÛmÒ×_K¥½ß‘#RR»vÒÛo§ß¼YªXQêÝÛ „cǤýûíCåÊ•RݺR|¼Ô©SÚûÅÄH\ }÷]ʱC‡ìx³fÒ‡ú5l'¸jdöl)W.©G”cyóJݺÙö¨mÛÎþØÈHK>Î%W.)Oû·—Z‰¶—/ßùÅ€–ú`Fç÷ó峆©,hg°óçϹøˆ«~*W¶d"µºuízõêì¿F®\Ò#XÂûæ›ÒÖ­Ò?ÚÖ¯¢EÓ&?p™¤ùó¥ l‹ÌúõÒØ*HïÞNG繜ÀŸvìJ—>ó¸÷Øöí¾y'Ÿ´$¸cÇ”¸bEÛâ›þcþþûoÍŸ?_±±±ÊÇ2 @ÀILLÔæ͛ղeK/^üüž¤{w+B¾ç©W/;V¼¸ôÅÒWø.Øæª$1Ѷ\.""åv_xþyiØ0éÞ{¥k¯µÄç™g¤6m¤Å‹¥bÅÎ|ÌüùóÕ¡C߀3sæLÝyçç÷àwßµ-1·ÞjEÃڇǶmíƒâ…ú6Øäª$_>+:?Ý‘#)·g×öí¶²öÀÒøñ)ǯ¹ÆÚòŽmÉÈé*T¨ ÉþAW­Z5û¸HŸ>}4nÜ8§Ã*üÌÎ?·¬ãgv~ø¹e?³óÃÏ-kÖ­[§:œúÜ–eGŽX¬V­l¯¾W›6ÒEY‡¬·ÞòM°ÌU HéÒéo³Ú±Ã®Ë”Éþk,_.?.µnöx¥JRÕªÒ’%é?.â¿e˜ªU«ªV­ZÙÄE¢¢¢ø™e?³óÃÏ-ëø™~nYÇÏìüðs;?ÞÏmY¶~½´gÏ™‹‘48÷\ˆáª"ôš5mÇ¡Ci/[f×Þ–ÍÙqü¸]ŸÜ®þÙ®§O—-²¯¶ë /”Ú··ç=xPjѶx½ø¢µvîÓ'Çß&œ0a‚ #ôîùŸ3Ç>hJÖñªP!顇¬ ¸ys+p s±û‘«É’†!C¤3¤}û¤Ë/—æΕ6L¹Çc—Ó jÇ““ízÊ””û{É’Ê•¥™3¥?¶¹#W_-=õ”ÕÁ·âââœ!èð3;?üܲŽŸÙùáç–uüÌÎ?7;Vڲžöx¤>Þß¾îÔÉ‘#¥èhÛ’Ó¯ŸÍn¨WÏ>8¦þ@Â<ÉÉj„¿¬ZµJµk×ÖÊ•+)@|^ó WÕ€p ¿!à7$ ü†€ß€ð~ãºA„œÓ–-ÒcIÇKùóŸû’/ß™ÇÊ”‘rñgNÇoFRÛ´IjÚÔ’ªU¥?þ¥ÿM{9r$ãç©][úì3©HÿÄ A‚¯ß~“š5“rç–/–.¸àì÷MJ²Ä$½äd×.é믷$¤`Aÿ½p$ HÒ¯¿ÚÊG¾|ÒW_I11ß?,L*PÀ.鉵d憤yól[€"t´aƒÔ¸±%_}îä#3jÕ’>ýTZµJºé¦soÙ— ¸ÛúõR“&Rá–|”)ã»ç¾òJ)!Á¶sÝz«tì˜ïž‚ À½þïÿ,ù(ZÔ¶]EGûþ57–>üPZ°@ºóNéÄ ß¿€;ýô“Õ|”,)}ù¥TªTνVË–Ò»ïZ"Ò¥‹tòdν8ŠÐî³v­ˆÇÄHŸ./žó¯ÙºµôÆR\œ!Mš$y<¾}ädéãme§pa»*”òµ÷R° îÛ×€L"¸Ëš5RóæR¹rÖ"·X1ÿ½öm·Y1zçÎÖmküxß%!K–HýúIK—JQQÒ¡C¯´,˜~r- 3ÛÑ@$ 7YµJjÑBªPÁ¹!:YrÏ=–„<óLö’ ¤¥÷ß—jÔ°Z“-l5äߥ¤ƒí:½KêÛvï–¾øBzï=Û.V»¶ïÞ7ü‡à+WJ×\#]t‘}HŠr.–=l€aŸ>6äñdzþ»vIO>)½úªTº´4}º¹‡ýWÞéñ¤Ì)ÉJg¯mÛ¤¶m¥† ¥©S¥öí³d€ú¾ÿÞVªVµ¡€… ;‘Ô»·%!ÚJÈ£fîqÿþ+=ÿ¼ôì³–lŒ!õêeu%¾#-\hIR\œÕË<õTJbÙDmß}g]¨.½Ô*ätD) °„¢KBzö<û}Ož”â㥡Cm«ÔƒJ=–35,ùòÙŠJõêÛÚµÒÌ™õ³´8]K–H×^k¤çÍ ÌÐO§#Êœ°0N˜˜h«!ÑÑö}—.R®øs]¤ˆ”`5!½zI?þ(½ô’”'Ó‘2ð ™>]êÚUj×ξ¶ÇááÒ´iÒí·[Ëà@[¹É•K;Vºì2›c²~½Í )YÒéÈj@¡á¥—lûR—.VGlɇWîÜR›6—|¤Ö¹³ôõ×ÒÆR:ÒêÕNG ˆ€‚[r²4r¤Õ$<ü°m[ w:ªÐwå•ÒŠ¶úqÕU¶Ý 2¼’“m–ÆcÙTðÑ£)Œö§²e¥Å‹­àÿ¶ÛlFIR’ÓQpÔ€‚SR’ôÀÒĉҸq6Yþ—/Ÿmy«^]4HZ°À¶hÝzkÎÎ)´H@ÌÚµÒ±cNG\ °zH.rü¸}È}ë-éõ×­ðÎñxl%ªNøàƒ6Õýúë¥;îZ·–òçw:J‚$ÀtîìtÁ髯¤&MœŽ€_9b]¢>ýÔ[ou:"x]s]ví’ÞyÇVFââ¤ÈH©m[éÎ;m°b ´à~˜wÞ‘ªUs:ŠàróÍÒk¯‘€®ðÏ?Ö!jÉé£ì ;OÉ’¶ òàƒÒo¿Ù@Å7ÞfÌ°Ûn¿Ý’‘zõ¨Ù\ˆ$À\x¡tÉ%NG\ºu³ºÇ lN€µw¯Ôª•ôÿ'ÍŸ/5jätDÈŒ /”† ‘–~øÁ‘Y³lò{¥J¶EëÎ;¥Ê•Ž€ŸÐ A¯S'éäIû› DíÜiËœ¿þ*}ù%ÉG0òx¤ZµláHŸ.]}µ5¸øb©n]iüxÛb ¤‘€ èEGK7ÞhÛ°’“Ž€ÏmÝj ÇßK‹Y¡3‚[x¸Õ‚L™bÉåìÙÒHé–[¤÷ß·Á†ÉÉ–d>ÿ<3E€E‚в¥cÝ8„ˆ?þ°-:‘‘6ì®bE§#BN«^]úþ{›ïÒ·¯tíµÒŸ:#AHÈ•ËZ¿ù¦tø°ÓÑð‰‡–¤… í Ü!"Âf‰|ö™´n%%ï¾ëtT|Èu ÈÑ£RÿþR™26©~}«ƒ;—;mÆRÓ¦RÁ‚)ÏæØ1iäH©J[]öÖ)lÛæ»÷‚´ºv•´­Ä‚Ü_؇ÎÑ£¥%œŽN¸æ«iÞ\ºí6é®»ì—<€ çº¤sgÛVÚ±£ô Vת•ôí·?nýziÔ(iÇ;#½uùñãÒ 7XÒª•ôÊ+Ò£Ú.~w朊íïÛ°€ wü¸Ô«—Ô µg…{-j²¦M“>ø@ºür«Ô\5dùréí·¥1clk©d‰È¥—Z‚QR§Žµ Š²3ìK—žý¾Ï?oZ¾ý–f-þÖ­›µ”ÿåëê M˜`g}V®dHìß@§NVÔ±£Ô¸±mIxüq)O§£p\µ2{¶Õ ôè‘r,o^ûкtiÆÛ£"#-ù8—¤$kc~óÍ–|œ8!ýûoöcGæ´mkÃY‚Ô_IÆI÷Ü#Õ¨át4$*ØÞ秞²- W]e‰*€ ãªä‡lÐjddÚãuëÚõêÕÙÿû?Û¦uÙe–è(`¯wùåÒ×_gÿù‘±ˆ;A6mšíâd rç–†w:¢ðpiÐ ;kxè 6|å†@AÆU ÈŽRéÒg÷Û¾=û¯±q£]{·a½öš4uª v½î:f+ùC·nÒ®]ÒܹNG K–.•âã¥#lï?p6uêH«VYaçý÷KÿûŸ­ž ®ªIL´-W§‹ˆH¹=»þù'åzõê”Α͚I•*ÙªñŒg|Ÿ>}uÚ^¯¸¸8ÅÅÅe?8—¨^ÝVµ&O¶-Y‚ÀÉ“RÏžvF»{w§£A0(P@zùeëúÒµ«m=˜2ÅZNh®J@òå³6¼§;r$åv_¼†dÍ[R·­¿àæ»dIÆ7nœjÕª•ý@\®{wé¾ûl~UÙ²NGàœ^݊Η,±m6@fÝpƒm/èÖÍVB~XzúiÛÊøÓáÃv¦yÙ2ë|´¿mƒ¹ë®3ï›”$½úª]6l°Ù—_n[h¼íVC˜«¶`•.þ6«;ìºL™ì¿†÷9J•:ó¶%ìß"r^ûö¶²ït$Îiï^Û××]Ò•W: ‚QÉ’Òœ96Àpüx©Q#i˧£‚ÛìÞmM~ù%¥‰ÆÙ:ùuí*õîm[6&L†•Ê—·çpW­€Ô¬i…à‡Ù0A¯eËìÚ W.»ÌNº¤×QkûvæiùK¡B6·êõ×ísM˜«Rm È bÓ[ŸyÆéHÌ<顇¬;Öí·Ûýøx©uk§#ƒ[”)c“«K–´]o—£Ó½óŽ4}ºÍ¶iÓÆ¿1W},k×ζOš”rìèQ[«_?eËÔÎÖÙïĉ¬¿FÁ‚)ƒ ù%åøºu¶³ E‹ì½d^÷îÒæÍÒW_9 €³Z½Zš8ÑZïFG; BÁWXÛËFìÃÝÃ[‚ ä´Ü.ï½gßOŸžr,µ‘#­¤Y3éÙgí¤^óæRñâv6þqÕU6Œpòd§#®äd+<¿øb»|¥H;»4pÕY?8dÿ~+úýà©OÛž'Ïù?߉Ò_H3gJï¿/ýû¯}˜|ï=ëÄ…0kÖ,Íš5+ͱhÑ¢Eçþ¼¶b…m½‰—:uJ9¾x±%ªôÝw)u"‡K*ØïÅÅ‹}ÿfŒ«¶`Á}J–´úÃÉ“” ”§ž²…cÇ: Ü *Ê’ƒñ㥗^²m›6eí9’“­¶¤o_ë­Ýuö!ó±Çl ñ7Z±é'ŸäÌ{€ßÅÅÅiÎœ9i.Ï?ÿ|öžÔ;¯¡B…´Eê Ø¿¡åË­.$Ä‘€ äuï.ýô“m¹Ö¯·^÷ƒIåÊ9 ÜÂã‘zõ².1ÿm[²>øàÜÛºÕŠ9/½Ôe¾ñ†uÙZ±Bú¿ÿ³Ç•*Ù–šë¯· ¸óæåüûApÊh^CÉ’Òñã®(J'AÈ»öZFH1:’“m?d¹rR¿~NG7ª[WZµÊŠ3o¾Ùþ=ž>¥øÀëãÞ´©ÍfxòIëÕÿÉ'ÖgÜ8©ví´£yòX{Õk¯•nºIúì3ÿ¾/‡2e¬ãßÙæ5äË—vVDˆ"AÈ —ºt‘fͲZAúè#iÁûát4p«¨(iölë5q¢Õ"ýò‹“ßv›¾ûn)W.iÚ4鯿låãúëíØÙäÉcÏÛ¼¹íÿýâ ÿ½'Ûo·•µÏ?O9ö÷ßöû±Y3çâò#×uÁ‚;uíjÉÞ}×’HL´Aq×_o{'y<ÒƒJW^iIG•*vüòËíF\\Ê€°¬È›×êMnºÉ Òl%î0a‚Õ·yg9Ì™cɆd[ ²yï¼#Ýr‹Õ*d‰ðÉ“6ËÁH@à ±±Ò5×Ø6,À!£FÙå Òïu8¡vmÛ’õöÛ–Œ\vYöŸ3""eÊõ7ÚÖ-ZôºÃرҖ-öµÇcÿÞß¾îÔÉ’’%­y¿~Vwü¸µ‹~óMßüû $ pnݬÅöºu6«€mÞl…¼}ûJ]ät4@Z… K=zøö9óå“>üÐVAn¸Á ÓSChÊlwµ R&[»5 p›n’ŠµºB~Ö·¯M}ì1§#ü'~éã­ðýúë¥%KœŽ$ p¼ymõsÚ4éØ1§£\dÁÛ†0fŒét4€åÏ/Íkm¯»Î†Ï.GWéÖÍM|ü±Ó‘.qì˜^6nl_7*PÀê@ªW—Z¶d0\®ré¥ÒW0ð‹ädiÄé×_¥^ ðî)}ú©tÉ%6+dåJ§#C×éÞ]š??¥+€ðï¿RçÎ6Àí±ÇìÌ/àv Z1úÅK-ZH?üàtD€#H@à:·ßn[rããŽQ6HõëÛ@¶éÓ¥'žp:" p*dgÁ*U²þðkÖ8àw$ p‚- yýu›ùÀ‡fÏ–êÔ±ÚeˤŽŽ<… [kSÓ×®u:"À¯˜WêÞ]š2Eúâ ÛŠ ›Ž—ú÷·¡Z·Ýf…V :¸Š‘>ûÌFlb©R)—èè´ß-*…qÞ¡®T¿¾ #|ýu ÛþüÓ–—/—Æ—zö¤àÈŒ¢E¥Ï?—žzʆu®_/-\(ýõ—ÕQ¥–+—T¢DúÉIÕªÖi.~GÞU$ p%ÇVA °¶¼Å‹;¤>ÿ\ºã´³h‘ÅyÅŠIãÆyüŸ,ñ^vîLûýÆÒ7ßØñÇ¥E©ØXøÉ?ÿH:؇•{ïµbór圎 @V…‡KõêÙ¶Éo¾±.,o¾igµî½×ºkÝq‡´`»eýù§Ô·¯%Ï>k–¿þ*M›&U«ætt!‰ H•/ÏÊ9ü`ÿ~;;ºe‹4k–Ô¾½Óð•¨().Î.Û¶Y½C|¼ý¿#uê$Ýu—tñÅNGš3~ùÅê;fÌ zHêÕ‹†~@©ØXé“OœŽ!ïÃ¥u뤴¶žBSLŒ }ôQéûï-yåéé§mûQçÎ6ï'*Ê7¯—”d[žrå’òå³VÞ¾j|ü¸<Ù·ïÌkï×ë×Kl«>#GJ÷ÜÃðT?"‚Tl¬”NN¦Õ;rPB‚mÙ ùÜÁã±ÿçëÕ“ž{Nš3Ƕ"Ý¿Ô»·Ô¶­%#×\“qŠƒ¥­[S.ü‘öûmÛ,QH-o^KF""R.}âDúÉÅáÃéÇn TT”T²¤áwêd¯ ¿"‚Tl¬u[üë/;øܱc¶¼_?§#à„ˆé¶Ûì²}»õ¬·a‡eÊX;îK.±äâôãàÁ”ç ·–rå¤ .°¥åÊYC‹“'íYêKbâÙ¿OL´D#1Ñž·HûƒX£†}uöë‚9c H@€ k×›7“€ ‡|ó}ˆ¸á§#à´2elÀa¿~ÒÊ•–ˆLšdÉ@±b–X”+'5i’òµ÷m[­€ÿð¯RåËÛõæÍtDIH°3”5k: €@ájX§ŽÍÊ8vÌ ¸, ‚TᶢL',䘄©U+¶,H_îÜLTÇyaĘ‚óÛoÖ¢’íW#‚ rLB‚”'uºÀ‡H@€ F‚“ 5nL_|€Ï‘€AÌ; =9ÙéHRþùGúúk¶_r Äbc­úîÝNG‚òùçÖÙ†H@€ –zà3 RåÊR¥JNGA$ @#Ï%'KŸ|Âê Ç€A,*Êæ€ÀgV¯–¶o'ä ÈÑ >•`¯®¾ÚéH!ŠrÞNX€O$$H×^k3@È$ @c>³{·´lÛ¯9Êu ÈÑ£RÿþR™2RþüRýúÖqò\vî” š6µÝ aaÒÂ…ç~ÜþýRÉ’vÿ÷ÞË~üÀé¼ ³@mŸ~jÿ®¿ÞéH!Ìu HçÎÒóÏK;J/¼ …‡K­ZIß~›ñãÖ¯—F’vìªW·cϹ_oèP›Óàñdîþ@VÅÆJÿþ+ýý·Ó‘ è%$HuêHÑÑNGa®J@–/—Þ~[zæéÙg¥îÝ¥/¿´=ô>šñcëÔ‘öîµD䡇2÷z?ý$Mœh+t‡ IDAT.œFN¡/|âøqiþ|¶_rœ«Ù³¥\¹¤=RŽåÍ+uë&-]*mÛvöÇFFZËÓ¬èÝ[ºùfšÉ g•/o×$ È–%K¤H@9.—ÓøÓ?ØpßÈÈ´ÇëÖµëÕ«¥˜ß¼Ö»ïZR³~½ôûï¾yN =EŠX]°- R©RRíÚNGq®ZÙ±C*]úÌãÞcÛ·ûæu¥~ý¤¾}¥rå|óœÀÙxkÃ{º#GRnϮ͛¥1c¤—_¶6¿Y5nÜ8ÕªU+ûÀUbc­¡p^¤Ü¹¥-œŽà®Zk/]:ýmV;vØu™2Ù¡C­Ž¤qcKF6o¶"’´kó3Ê—çß²!!Áºe*ät$pÕ HÍšÒ×_K‡YѮײev]£Fö_ã?¤_•*V<ó¶ûï·ëýûù;ߊ•¶VÑÅŠ9 ‚ÊáÃÒW_I#G: À%\•€´kgÛ£&M’~ØŽ=*Mjѽ°vî´$¡R%kÛ›ÇK{ö¤=¶v­4dˆÍ¹òÊóÛšd$õ,dÉ—_Ú/BÚïüÄU H½zÒ­·JÚv¨ /”¦M“¶nµ$ÄkÀiútû0—º‹ÕðávýóÏv=}º´h‘}=x°]7hpæëzW;êÖ•Z·öé[$¥M@袊,IH°_†•+; À%\•€H–4 "͘!íÛ']~¹uŸlØ0å>]N7t¨ON¶ë)SRîïM@Î&½ç|¥hQ›oC',dIr²% ·ÜÂ/)€ß¸ª]²6¼£FY1zb¢ôÝwg6~™:ÕÚèž>Ã#)ÉŽ§¾ö~‘&Mì>7ßìÓ·œâñ¤¢™öãÒŸ²ý |áðaéñǥ뮳3ƒaa¶Õ&#ÇKÕªÙ}ÇŽõOœÀu ª˜‚,KH 5r:~»wÛ<¥_~Iélt®Õå_´F™¹o!BDl¬´e‹ÓQ ¨$$ØpzZYS¦Œu2Ú´I=úÜ÷ߵ˖r>¶C„ï ³@){öØT¶_€oäÉ#•,i_gæñ€R•*Òwæl\ÈuEè@¨Šµ7ûöÙÖS CóæY[«VNGî³|¹uFúö[§#q+ @ˆHÝŠ8§¹sm:k™2NGî’œ,õì)µo/]q…ÓÑ8‚ D”/o×›7Kµj9 ݉¶Ò³§Ó‘@Àš5k–fÍš•æزÿÄññÒO?Iï¿Ÿýç R$ @ˆ(^\ÊŸŸBtdÂÒ¥ÒþýÔ@âââ—æتU«T;;´‰Ø>*ÅÄd3ÂàE„‡V¼È¤„©D ©n]§#w3ÆfÜv[Êì?ÿ´ë½{íXLŒ”;·Sú5 @!A¦$$H×_oƒ¯þóÇÖ-æ’K¤ŠíâÅ4r¤}¿n³1ú+ @‰•¾ùÆé(жnµ½ÇC†8 ¸O¯^RÛ¶iýõ—tÏ=R—.R›6)]eB BÊ——fδ.¨Š¬HHÂÃ¥k¯u:=&XÝöíöýœ9vâG²ä£fM»¤æݺpÉ%RëÖ~ ÕI$ @‰µú¶ýû¥"EœŽiî\©aC)*ÊéH ôŒ›Ò Æã‘>øÀº]ybb¤—_¶Ž 1 Ä¥úõ“Ö­“Þ~[*XÐéhHEè@*UJÊ›—ÄUÞ{Ïj>^~YªQÃéh8+V@€&•+Gâ›6IݺI·Übõ0 D1 Ä%Ž“Ú·—Š‘&O¦îðØ‚„¨ØXiåJ§£@Ž4HZµJúö[)*Êéh8'V@€K¬— +=û¬T¯žÓÑ)$ @ˆŠ•öì‘r:äˆ?ÿ”îºKºñF顇œŽ€L#BTùòvÍ*H:qBºã)_>)>žº@P!B³@BØOHK–H³fIÅŠ9 YB:¢J—–rç& 9Ÿ.! .5lèt4d™+W@Ž•ú÷—Ê”‘òç—ê×·¿éç²s§4`€Ô´©  “.<ó~‰‰6ìÚkí5 ’jÕ’&N”’’|ÿ~€ô„…Ù6,¶`…;¥¤k®±_F!W& ;KÏ?/uì(½ð‚.µje],3²~½4j”´c‡T½ºKoëõo¿I½zÙm?lMj*Tî¿_êÚÕço8+f„“'-ù¤3,à ¹n ÖòåÒÛoKcÆH}ûÚ±Ž¥K/•}4ã$¤Niï^kµ?{¶´tiú÷+]Zúé'©jÕ”cwßmƒŠ§N•† ‘.¼Ðwï 8›òå¥5kœŽ>ñÌ3Ò—_J H¥J9 çÍu§ÐfÏ–rå’zôH9–7¯%K—JÛ¶ý±‘‘™›óU¬XÚäÃ릛ìzýú¬Å œ/V@BÄêÕÒСÒcÙö+‚˜ë~*W¶d"µºuízõêœ{í;íºxñœ{ µØXé￥ÇŽÙ2p T©’%!9×% ;vØ©Óymßž3¯{ì˜4nœT±bJ²ä4o+^ уØ×_KóæYç«Ü¹Ž€ls] Hb¢m¹:]DDÊí9áÁ¥uë¤O>ɸv´OŸ>Š:mŸW\\œâââr&0„´Ô³@ªUs2œ—ädëvU§ŽtË-NG€O¸.É—ÏÚðžîÈ‘”Û}môhiòdkÛÝußwܸqªU«–+1 $È}ô‘´l™õ gÚ9 D¸n VéÒéo³Ú±Ã®Ë”ñíëÅÇÛ Ìûî“ òísç.]p HP:qÂ~i\sÔ¼¹ÓÑà3®K@jÖ”6lJ{|Ù2»®QÃw¯õÑGR÷î¶s⥗|÷¼@VÐ +H͘aû6Ÿ~ÚéHð)×% íÚÙ<¯I“RŽ=jó9ê×—bbìØÎÖ.÷ĉó{E‹¤öí¥&M¤7ÞÈvØÀy# BGŽH?.Ýz«ÕB\WR¯žýM8PÚµËN›&mÝjIˆ×€ÒôéöÁ­\¹”ãÇÛõÏ?Ûõôé–lHÒàÁv½e‹Ôºµ›ßr‹ >LíòË¥Ë.Ë‘·œ!6Vš;×é(%/¿l{E½¿p!®K@$K† ±ûöYB0w®Ô°aÊ}<žôk>‡µãÉÉv=eJÊý½ ȦMÒÁƒvìÒ>Þã±›$ ð—ØXK¶ÿýWÊŸßéhpNH#GÚtÔÊ•ŽŸse’7¯4j”]ÎfêÔ´+"^IIç~þ&M2w?ÀÊ—·ë-[¤ªU™0v¬MŽdè D¹®p›Ô³@àþúKzî9©w4B âÊ”‘rå" ÇÛà–þýŽ€C„¸\¹lÈ–-NG‚ ýþ»ôê«–|)ât4äÀhņ•Š—zõr:r”+‹Ð·)_ÞfÚ!@­Y#½ù¦ôÊ+´*„Õúða[íÞ³Gºï>iüx©^=K\®¿Þÿ1;„ÀR' Uª8 RûøciÉiÁ;S^eÊH;wJ%KJ+WJuëžyŸ¼yí÷~ýú)Ǻu³?Ô?.}ñ…Ô¼¹ßBv ñˆ‰‘Âé ('OZíG³fÒ5×8 »òä±äC²îôäÎ6ùðºé&»^¿>gb 0¬€.+—T¶, H@yã é知)SÎ\¢¸ËÎv]¼¸³qø + €KÐ +€=js?n¹ÅöþÜmÔ(©pa×Ô°¸Dl¬ÕÅ!Lœ(ýñ‡4ožÓ‘ÎbÖ¬Yš5kVšcðý iµ¯¼"*äûç@$ €KÄÆZ­3vð µÜíÒ…ŽÀâââ—æتU«T»vmß½ÈÛoKC†HÝ»K÷Üã»ç plÁ\"6VÚ±C:rÄéH\î¹ç¤C‡¤aÃœŽà¤Ï>“:u’n¼ÑVÆ]„p‰òåízëVgãpµ]»¤±c¥ž=­+À–-“Ú¶µ:ÀwÞq]+vw½[Àż³@(DwЈÖyà@§#8eÝ:醤Š¥¹sm6ˆËP¸DÙ²v‚…Ä!›7[á°aRÑ¢NGÈ &Hû÷KÛ·Û÷sæ¤l=èÕËÚ®·li÷yôQH›Z¥JéÏ 1$ €KäÎÍ,G=ù¤T¬˜Ô»·Ó‘rÊرҖ-öµÇ#}ðôþûöu§NRR’ôçŸöý€g>¾sg¡%66å÷"ühÏéÍ7¥'ž p:@NÙ´éÜ÷IJÊù85 €‹”/Ï ˆ#¦M³?8]»: Ž#\„ièHN¶öŠíÚI%J8 Ž#\$6ÖêâŽu:ùê+iãFéÞ{Ž€€@¸ˆ·/³@ühâD©jUéê«Ž€€@¸ˆ7¡ÝOvî´(÷ÞkO à&eËÚç`ê@üdÊëÜ©“Ó‘0H@É“GŠ‰!ñ‹“'¥W_•â⤨(§£ `€.C',?™7ÏŠm(> ÀeH@üdâD©V-©N§#  €.Câ[¶H ŸÀeÊ—·Y ÇŽ9I›ù$GÞ%ÑÑv½k—µåE6Mœh?Ô6mœŽÚ¸q£*³µ 6„ø«åË¥·ß¶d o_;Ö±£t饶Zñí·gl:¶½;*ʶz/]zöû$+&}ýuÊ äØXéÏ>“Z´ðÕ;Î7Ù¹“$ÛþùÇ–T{÷–rçv:8È»ò1sæLU­ZÕáhdźuëÔ¡CV0ýÄU ÈìÙR®\ikDó敺u³¤aÛ6)&&ýÇz‰s9xPúüsKpR?¦S'[uyç8/u‚lš5ËödÞ}·Ó‘ @T­ZUµjÕr: X®*BÿáÛBuz2Q·®]¯^ý×X»V:qÂVLRË[ªQÃbœV¢„F’mÉÉ6ù¼U+©\9§£ (¸*Ù±C*]úÌãÞcÛ·ûæ5R?gjÑѾy »ÂÃ¥âÅI@²mÅ ;«pï½NG@ÐpÕ¬ÄÄô;þxg†%&úæ5¤³¿Î¹^£OŸ>ŠŠŠJs,..NqqqÙH…V¼>0q¢­|\wÓ‘4‚"Ù²Å:I5l˜rlõjiìXéØ1©}{©mÛs?O¾|ÖvôtGŽ¤Üž]Þç8ÛëäÏŸñãÇÇÞaø H6íßoõ=fKJ S‚"éÝÛÍ|þ¹}ÿ×_R³f–|DFZqù;ïH·Ü’ñó”.þ(ﶩ2e²«wë•÷9O_¼à ÑÑÒ¯¿:E›>]:~ܺX€L ŠåËÓ œ>]ú÷_[Ù¾ÝfnŒ{îç©YSÚ°A:½ÃÚ²ev]£Föc½ôRë´õý÷i;fñúâ5_`$’“mûUÛ¶)-Å@¦E²w¯TªTÊ÷sçÚòJ•¬“OÛ¶Òºuç~žví¤“'¥I“RŽ=jÃë×OiÁ»s§ éòËmE%u»Çc—Ó jÇ““ízÊ””ûœr¿š5­^¥HX¨Ô½»ôôÓYï@NI=Œ°R%gc *»vYáÙ3Ϥÿ‹À)¹råÒ‰'ôúë¯kĈgܾqãF-\¸ðÔýNÿð;}út%ú Eåƒ>¨¸¸8]Ù !ªI“&jÒ¤ÉǽÝ7Ÿ~úi 8PeR¬zÿ›¸5yCΊä駭v£_?)O«÷¨XÑn;rDzûméŽ;2÷\yóÚjGêÓMj—Ó%%e>æ ¤o¾Éüý#9Oññ¶÷󮻜Žx¥J•RéÒ¥5uêT=ùä“ ?­cÜäÉ“%IÿûßÿôÁœñx_% ÅŠS±bÅ|ò\Á¬I“&:tèYoŽŽVôiumÉÉÉi®_Š-XÑÑÒ·ßÚ¶¦¬+–Wr²ôÅÒO8ŒR' Ȥ¤$éÕW¥Ûn“ø0œ“ÇãÑÝwß­;wjîܹin;~ü¸âããÕ AU«V-ÝǧWòõ×_+,,LO<ñ„V¯^­n¸AQQQ*P €š4i¢¥K—žñ<Þ-H‹-Js<,,LM›6Õ®]»ÔµkWEGG+22R 4ÐâÅ‹%I‡ÖÃ?¬råÊ)""B—\r‰fÏžé×ξÉ[s±yófM˜0A—\r‰òçϯØØX9òÔýÞyçÕ­[W‘‘‘*Uª”zöì©#Þ>tz È°aÃTñ¿3¾Ó¦MK³mË»]î|þ{HÒ‰'ôòË/«~ýú*T¨ ( Zµj饗^J7Ù™3gŽš7o®Ò¥K+""B111jÒ¤‰^yå•4÷ûý÷ßÕ£GUªTIùóçW±bÅT½zuÝwß}Ú»w¯/\Ȇ Xñ:m>Ÿ$›»Ag) 뢢lE‘$ >ûLúýwÛà SâââÔ·o_Mž«÷ß?Ëq§gÕªUZ»v­Jÿ7LìñÇW¥J•4vìXåÏŸ_«V­ÒÅ_,I1b„jÖ¬©)S¦è‰'žP‰%2ý:_}õ•’ÒÙOÞ¥K•/_^RÚ­V7Vllì©$£í[Yùï1bÄ-X°@={öÔ¸qãN%=IIIêÑ£‡¦L™¢víÚ©uëÖ’¤W_}UyóæÕš5kT¼xñ4¯›zUcöìÙÚ·oŸÆ¯ž={¦¹_bb"u,$('žÊ•Kù~íZë~Y½ºtá…Ò‹/Ú©œ‹F¥J‘€dÚ¯¿Jl¿pø#Z¿^ª]Ûÿ¯»r¥T«–^ëî»ïÖ믿®)S¦hÈ!Ú²e‹>ûì3Ýwß}Šˆˆ8¯çlØ°aš»’ÔµkW=ðÀúþôa\(P €FæØwÜ¡.]ºèСC?~ü©äÃûºå˗ך5kÎ+îô 2äTò!I… VëÖ­5uêT=òÈ#§’IÊ“'n¿ýv 6LëׯÏR²páÂ3Z{<5kÖìTrºÌÖ~dö¿GRR’^|ñE•.]ZÏ?ÿ|š¤ ,,LcÆŒÑÔ©SõÆoœJ@$)<<\¹rù±5õŠ†÷¹Òû7•/_¾L½øGP$ ëÖI7ßœòýŒRÁ‚Ò¢ER–ŒÌ˜Ad+ ™tô¨-µ–//uìèt41UªX2àÄëúK½zõtÙe—iÊ”)U¤HI:ÕÂ6µ+Vø;ŸñÎnÉʪRFªV­ª¨¨(-]ºô¼V‘ .¬ë¯¿^“&MRçεwïÞS-“S W­Zµôè£jÖ¬Y’¤>ú(Ûñ”?–n¹E*QÂ:3¥®×)QÂ’’øx§¢ËPP$ ƒY}ÇÖ­¶û£¤ÿþ?מ=6=U€LŠŽ–Nœ²°]Ú=Ž—Ú··ýŸS¦PxøÀðáÃõá‡jþüùgÔ];oKÞ©S§¦ù°þÇœu«Q0ð&V[¶lñÉ󅇇«gÏžÚ±c‡zõê•î<“;v¤©ùꫯÒ}®¿þúK’”ÿ¿vn«V­ÒθßÎÿ–úóû£í›?=ù¤tõÕ6ùúþûϼýÊ+­l Šܹ¥#ìrºbÅì .€¬K=Œ¹z§4È*ƒ¿ù&匀l¹à‚ ²<Ý}úøìç~úIzî¹³ß^ªTÀ~HŠ$µ «±Zêä’Kœ% $$XÍǘ1RÈ:Ǔ³?×såÔóœëù>úè#=òÈ#úè£4aÂU®\Y£GV‹-ÒM@2Šë|oËÎýÏv¿3f衇Ò¼yóNmg*W®\¦ôž/W®\úðÃ5sæLÅÇÇ+!!AÿüóJ–,© *høðáºóÎ;OÝÿÙgŸÕüùóµjÕ*}òÉ'ŠˆˆPll¬F¥ûî»ïÔ6±;î¸CÇŽÓ’%K´råJ%&&ªlÙ²ºãŽ;NuÑ )ùó[§¦³Ù´)`Ï.z’õÔÂi>üÐÑlÜh;!>ûLjÖÌjlZ´\jÛÖé(ÏߪU«T»vm­\¹RµÜÐáðaKägΔRý®w·?þ°Â½«®’æÌaë2ßã@ðÊìÿ¿õÿy»vÒ/¿X1ôRÉ’6½»Y3;³xÙeÒ 7dHPÔ€q Ð °™:tÂúω6ï#~û¥BòTÇÛI³ºu¥W_µcóçK=&]z©u˜yüqgc<‹ H@‚¸Æx´âMeèPé»ï¤·Þ Øek@€:|Ø>ð_wT´¨&M›–þ}×­³û,ho:u’RµΔ*U¤o¿•Š·¿_’4z´ôôÓRõêöÁ¹B…콧5 A\c¼R¥H@$ÙY£§Ÿ¶KƒNG6»wKO=e-[kÔ¾þ:ý•ô?ÿ”5²'O?-:d5‡k×Úܹ3ÿš—\bÛ®öîµayIIRÅŠ¶+€EÄ56@ÀcDÒöíRÇŽRË–Ò£: •)cPK–´.Šu릿‘#¥ÄDÛ¾S¶¬«WÏŠšã㥻ïÎÜëmÝj…œE‹Ú¥^½´·ÿû¯­ª”+wÞo)§Å¬¦Mmëøñ3oÛ¹Ó†¦žŒ ó\Ÿ€œ8!Ýq‡”+—4}º-™Uyò¤¬$©ys›ª•vͱ±–\¼ñFú·¿ÿ~ÀnÁ Š¿´A\c<×' O=%-^,ÍšðKÖ€ ·m›mÕªSçÌÛêÖÍzQs‰¶‚ߧ} >]€6» Š$ˆkl€€m+´é­0†¼/¾°dØ0©qc§£„º;ìºté3o+]Új9²òyøpéÙg¥ le÷nßę¢D Ú ày‡îÞmÛW]cçN~Ò¬™M= •Y³f¼èuàÀì=ib¢]çÍ{æm)÷Él!ºÇ#=òˆ­¨Ü~»]¿÷^ú+,$h¯ôjlœ¿ÔÓÐ]“€œ<)uè`_Ïœ)ý7E¯¸¸8ÅÅÅ¥9æDxÞòå³ë£GϼíÈ‘´÷ÉŠ¦Mm a»vÖa륗¬&%@Mrâ„Õ}lÚ$íÛ—þ–6ïö,™—:q‘#¥/¿”>û,å@Nón½ònÅJmÇkëš•6¼©•-+-Z$õê%uë&U«°uƒ"Y±Bºùfk›œ ë¼Û]“€,\h5ƒÛ~Yü%&Æ Ç¿ÿþÌÛ–/·ù!Ù‘'4q¢tÅ6½›"ôówÿý¶*õÑGÒž=Vÿ‘Þ@ÖåÎm \‘€ìÞm-w¯¾šÖygÜr‹4wnÚ3ë_|!mÜ(ÝzkæŸ')Éþ¦¥§Kk!ûûïÙ‹5‡Å È?Z‘ÿÿþçt$@hrM+Þž=¥cǤ7ߤîà{&Hû÷Û€[Iš3ÇJ¶5ªP!k|òî»V·Ñ»·MB=ÚZ»véâ»XŠ·K Š$&Æé€ÐVª” mÀÓĉ.ª¶€ñññêÚµ«¦Nª»îºËépBÓرҖ-öµÇ#}ð ôx¤N,)[Ö¶÷í+ `±n¼Ñ›QýG—.ö<¯½f'Ѽߟ˔)¾yo> HÿþÒ˜16™¾pa§£BOttÊ š5v¬¼têät$€ëŒ1BC† ‘$­_¿^•+Wv8¢ÐpòäIM™2E3gÎÔÚµkõÏ?ÿ¨H‘"ŠŽŽV½zõÔºuký/ÕöÇsꂲiSæîW­š4o^Öžû«¯,áHJ²Äû½Wzõúß:(C‡¤‚¥‹.²ÇåÊ¥¿{¢o_ÿÇ„‚èh«} Y;wJññV÷áí³À/’““5yòäSß¿öÚk=z´ƒ…†“'OêÆoÔüùóU¤HÝxã*[¶¬Ž;¦Ÿ~úIo¾ù¦~ùå—4 HÛ¶muå•W*šîÁió挿"A‘€<òHÊ×/½töû‘€ç'äk@^|Ñ–µï½×éH×Y°`¶lÙ¢N:éÓO?Õ´iÓ4räHå>ßV£dCòæÏŸ¯5jháÂ…*X°àÿ³wßáQmÇ¿»„@ @è¡Yh$†^UÄR¥?A@ņ4¬ØPxôA@Œh¨ÒT¬@ŠÒ¤Wi¡'@rÞ?ÆM²É&¤îÙdŸëÚkÍì™Ý{'dï™{Ü¿pá«S|³T¼xqŠ/îÍ0Å›þú ¢¢L9ßë®3S´|ôÿwž¨‚µsgÆn"’5¡¡f¤ñüy»#ÉgÎÀĉп?”,iw4"~gòäÉôïߟûî»cÇŽ1gηcZ·nÓédÆ ŸcæÌ™8N† æÖ~âÄ ž{î9n¸áŠ)BHHwÞy'ß}÷]ªçøôÓOq:L:•%K–ТE J”(Ó™ôQhîܹôêÕ‹š5kLpp0õë×çƒ>ÀJ£œé¶mÛèÒ¥ %K–$88˜fÍš±hÑ"·×Kiÿþý 0€ªU«R¸paÊ”)CÇŽùý÷ßÓ?™Éüúë¯ôíÛ7UòÄm·Ý–æ9péÛ·/N§3ÍÛµ×^›ê¹###¹ýöÛ !((ˆo¼‘7Þxƒ‹/f8~É¢ñã¡fM8v̽ýë¯M ß—_6k‡ :uRç#òÄÈ5×ØHþæ?r<ü­ÉÛ>þΞ…ÁƒíŽDÄï9r„ùóçS³fMš6mJ±bÅ;v,“&M¢{÷î‰ÇõíÛ—o¿ý–iÓ¦1f̘TÏ3uêT}ûöMlÛ³g-Z´`Ïž=Üzë­´iÓ†³gϲ`ÁZ·nÍG}ÄC=”ê¹¢¢¢X²d mÚ´áñÇgkÁ0ðÜsÏQ @š4iB¥J•8uêË–-cРA¬Y³†iÓ¦¹=×–-[hÚ´)111´k׎°°0þþûo:uêD›6mR­·X»v-wß}7'Ož¤uëÖtíÚ•£G2wî\š7oÎœ9s¸çž{®xnËü[ÝhëÖ­W<6¥ä1uêÔ‰ªU«¦:fÆ Ìž=›¢E‹ºµ?øàƒ|úé§T©R…nݺÂÊ•+y饗X¶lß}÷Te0÷ÌŸU«ºW·º|zȬO˜4 êÕƒE‹à…LÙ±cí‹7-VàpXÖŒi?iYN§÷âÉ ÑÑÑ`EGGÛŠø¡- ,ë×_íŽ$‡]¼hY•+[VŸ>vG"~ ËÿŽŸ;gYÑÑÞ¿;—;'"™·ÞzËr8Ö[o½•ØV·n]ËétZ;vìHl‹µBBB¬ÐÐPëòåËnÏqèÐ!«@VýúõÝÚo»í6«@ÖÌ™3ÝÚcbb¬ððp+((È:räHbû”)S,‡Ãa(PÀúæ›o<Æ»sçÎTm Öý÷ßo9kÕªUnµlÙÒr8Ö‡~èÖ¾xñbËápX‡Ãš:ujbû¥K—¬jÕªYAAAÖ?þèÖçàÁƒV¥J•¬ *XqqqãKnݺuV`` åt:­Þ½{[³g϶vïÞn×9H“'ûöí³*Uªd)RÄí=»úwéÒÅŠuëóÊ+¯X‡Ãzÿý÷¯»/Êèï¯íŸ×*U²¬W^qoûö[óaùùçÝÛ{ö´¬š5½[&ä‹ä³Ï”€ˆdÇÑ£&™=ÛîHrØ´iæmÜhw$â²üïxt´¹N½}Ëå¿7 VµjÕ¬€€ëÀ‰í|ðåp8¬áǻ߿ËápX .tk=z´åp8¬>ø ±mýúõ–Ãá°ºwïîñµçÎk9kâĉ‰m®Ï;wÎô{‰ŽŽ¶‡õúë¯'¶íÝ»×r8VÍ4>àÝu×]©>ì»â6l˜Ç>cÇŽµ‡µhÑ¢ Åõå—_Z*THLv‡UªT)«S§NÖ×_êøŒ$ §OŸ¶Â¬ X³fÍr{,<<Ü ´N:•ªßåË—­2eÊX 6ÌPì¾&Ï$ … [Ö'Ÿ¸· f>,¯YãÞ>~¼e*ä½Ø2!OLÁJÏ©Sðí·>»ÏŠHžPªä³…è–£FAÛ¶pÓMvG#’¶ë¯‡èh{^7}ÿý÷ìܹ“Ö­[S1ÙÞ;={öä駟æÓO?eäÈ‘˜"}ûöeòäÉL:5qú˜éWôL¶ãóÊ•+ˆ‰‰á•W^IõÚG௿þJõXÆ ÓŒùøñãŒ=šE‹±sçNΧXwàÀÄÿ^¿~=Mš4ñø\Íš5céÒ¥nm®¸wïÞí1îíÛ·'Æ‘iXݺu£S§N,_¾œ_~ù…uëÖñóÏ?3wî\æÎKŸ>}øôÓO¯ø<.ñññtïÞ72zôh:wîœøØùóçùã?([¶,ï¾û®ÇþϹä Owýô)bÖ€$hn>ÈgW_57×4Å^½Ì--O>é¸Dò#§3nF¸d lÚ”~é<_P¤Ô­kw9nÒ¤I©6¼+UªíÚµcöìÙÌ›7.]ºæƒ|Íš5™?>111„„„°víZ6oÞL§N(UªTâs?~€ï¾ûÎã‚s0ëÎ;—ª=­´1114hЀݻwÓ¨Q#úöíK©R¥àäÉ“¼ÿþûÄÅÅ%êÔ)Ê—/ïñù<µ»âþꫯ<öI/î´p×]wq×]wÀ¬Y³xðÁ™6m:u¢cÇŽz®'žx‚o¾ù†G}”§žzÊí±“'O&¹{íµ×Ò_rQýú0u* `*\mÞ kÖ@‡æ›Ää¶n5›ú ŸM@4€Ç7ÿ=q"Üu—Ù$9‡Š5ÿ/’%é"’ù®ï¨QШÜr‹Ý‘ˆø×¢j€ˆˆ"""<7iҤĠOŸ>¼øâ‹Ìœ9“Gy$±ZSÊ$¦Ä¿»7Žd*¶´> üñlj##FŒp{låÊ•¼ÿþûnm®r¶GŽñø|žÚ]qÏŸ?ŸvíÚe*îŒr:tëÖ72räH–/_ž¡dÔ¨QLš4‰6mÚ0ÁÃ7®ØëÖ­›©j]’Ã^~Ù|ð­YjÕ×ÿ‹çžK}ìœ9pûíÞ/ƒ|6iÓÆÜÀ°yôQhÜØÞ˜Dò³|•€¬^ +VÀ¬Y>» ¬H~6uêT.]ºDýúõ O9-ä_óæÍcéÒ¥ìÞ½›kþ-wÙ§OFŒÁ´iÓèׯ‘‘‘”-[–¶mÛºõuM{úñÇ3€¤eÇŽn ‘Ë?üª­N:€IN,ËJ•ØüüóÏ©ú$;·—àà`€4Ë'ųÏ>Kxx83gÎô˜¤S«V-6mÚÄÉ“')©²æö¨]Ûì€þÆð÷ßФ <ý´IJ’[¾‚‚ [7{⼂<±ȧŸ*ùÉmùj Ö¨QfÈ4ƒÓD$gMž<‡ÃÁĉ™4i’ÇÛ#<’j—ôÊ•+Ó²eKV®\Éرc9vì={öLUÖµ^½zÜrË-Ìž=›)S¦xŒaãƉkA2µßÅòåËÝÚ×­[Ç[o½•êø*UªÐ¢E ¶oßÎG}äöØ’%KX¶lYª>;v¤ZµjL˜0Å‹{ŒcåÊ•\¸páŠñFFF²téR ÆáÇ÷_¹õÖ[Ó}ž•+WÒ»wo*W®ÌÂ… S•ÝMnèС\¼x‘|0q Zr'OždݺuWŒ]²©iSX¸¶l1Óï¼3õ1·ßn¦!·jåýø2À'G@¦N5_Zöêe榧(»¦>}r7.‘ü,4R¬—Ì›¶o‡Ù³ÍFLªE/âu+V¬`ûöí„……Q?å·²Éôëב#G2eÊ^}õÕÄ$ãþûïgéÒ¥<ÿüó‰?{òùçŸÓ²eKúõëǸqãhØ°!!!!ìß¿Ÿ 6°yóf~ûí7Ê–-›¡¸ûôéÃèÑ£Ö¬Yî]»8|ø0AAAéÆ»zõjÞÿ}BCCiÞ¼yâ(Ò®]»X¸p!±±±Ü{ï½Gt’ëׯqqq4lØ0U"P²dI À<@tt4'N¤Zµj´jÕŠ*UªpâÄ víÚÅO?ýă>Èĉ¯tºÅßÙ[„Ë3‡Ã”Õu•Áv82vËËl/ë&~oÜ8Ë ´¬„»#ɦG±¬òå-ë»#?£Çûî»Ïr:nesÓr÷Ýw[N§Óš;wnbÛùóç­%JXN§Ó K·ÿ™3g¬7ß|ÓªW¯žlYU«VµÚµkgMž<Ù:—l¯“O?ýÔr:é– ýóÏ?­:XåÊ•³Š-jÕ¯_ßúä“O¬Ý»w[‡ÃzàRõÙ²e‹Õ¹sg+$$Ä*Z´¨Õ´iSkÑ¢E‰åƒçÍ›—ªÏ?ÿüc=ûì³ÖM7Ýd)RÄ ¶jÖ¬iuëÖÍš1cFª½P<Ù·oŸ5a«S§NÖu×]g/^Ü ´*V¬hµmÛÖšáaÿOçàšk®±œN§[)ßä·k¯½6Õó,X°Àj×®U®\9+00ЪP¡‚Õ¨Q#륗^²¶nÝzÅØ}Qž)ÛO8,+“½l÷n³–æÙgáæ›ÍÏ`FEÒ‹6#;¦ÇÅÁˆ0}:ÄÄ@X˜Ù$ÒÓèUJ110l˜YÓsá4lï¼cvºO.6Öl:9}:ìÙÁÁ¦ÀÉK/™©zž¬]»–zõêMÝ|X E|ßW_A÷îpò$„„ØM9W_méyZ'’‹ôï¸$wß}÷ÉÖ­[©‘²ŠŽøœŒþþê÷ͧ—^2·äÜó±Äǧÿ|.˜)W).œôxZbc3Þ÷½÷à•WÌ:Ø»ï6ÉËÿköDûé'(]:í×›€üï9ÿœAA¦ oJ±±Ig·ïÁƒ¦|ðOÀûï'wçf„dôh“Œ¤eìر*ë&¶É“ È’%f·×ñãíŽDDDD2Àg¾}sþ9+Tð<ÍêÐ!s_±böû®^m¦£wèà~\õêpà ð믙[Ä[ʗσ×è¨QШh±§ˆˆHžàWU°êÔ1kHÎœqo_µÊ܇‡§Ý7<ܬGIY xÕ*(ZÔ,n‡¤jXž¦ƒ]¼—/g-voÈs# «WʦŽ¶Ãaw4"""’>;’ºv5{€Lšd63­jÊhÜ8©ÖáÃfÑ{õꦪ•«oTÌž ]º˜¶cÇÌîÑíÛ›½Ï i!|d¤Yÿá²v­I~y$÷ߧHV…†ÂÑ£&.PÀîh2`ôh¨QÃ,°ñýõ—Ý!ˆH&é÷Ö»ü*iØÐløÜsfójÕÌf{÷š$ÄåÙgaÚ4ؽ®ºÊ´uíj’”€?ÿ4 É'N4#"¯¾šÔ·Z5øÏÌóž> wÝe¦i}ð)qª- Ä—…†BB‚I®Ë—·;š+رfÍ‚?Ì#Ù’äwÅŠ W¯^6G""Yåú=–ÜåW ˜Ä⥗`út8yn¾,0ûu¸8©gs8°h‘ÙãcÜ8SõªaCó|5j¸;uª™’õÙgðõ×f…[n1{¤ž€œ= &@ÿþ&XÉS”€ˆˆŸ/ÅûñÇ& 2ÄîHDDD$ ”€ˆˆŸN@.^„wß…ž=¡J»£‘,P""n|6IH€‡6u­ŸyÆîhDDD$‹”€ˆˆŸL@,Ëìv>mš¹Ýt“݉ˆˆH)7åËû`2j¼óŽÙ„'"ÂîhDDD$”€ˆˆ›ÐPˆ‰¸8»#ù×'ŸÀ³ÏšDŸ|ÒîhDDD$›”€ˆˆ×nèGŽØsçšr»> ¯¾jw4"""’”€ˆˆWbû4¬+à?ÿ®]aüxp8lHDDDr‚qã ȺuСÜr‹Yt^ €ÁˆˆˆHNR""nÊ”§ÓÆdûvhÝ®¿fÏ6Û³‹ˆˆH¾¡DDÜ(åÊÙ”€<wß %K¢EP¬˜ AˆˆˆHn °;ñ=¶ìrò$´j—/Ã?˜¡Éw”€ˆH*^O@Ο‡öíÍÈÏ?ÃUWyñÅEDDÄ›4KDRñjrétïnž/Z7Üà¥;(‘T¼–€$$@¿~ðí·fÁy£F^xQ±“¦`‰H*åË›IJrqû Ë‚§Ÿ†Ï>ƒÏ?7ë?DDD$ßÓˆˆ¤ .ÀÙ³¹ø"o¿ ï½|`6¿ DDRÉõÍ?þž{^~žx"—^DDDÄË~ÿ:v„Š¡hQ³®ñõ×Í·z’HS°D$•ä H9üäóæÁ#Àã›DDD$?ظš77ÉÇàÁPªüú«ù[ sçÚ¡ÏP""©äÚHL <ôtèãÆåâ/ûâ ¸x.LªèøÐC¦àÊ´ipê”(aoŒ>BS°D$•% P¡\H@^{Í CO˜`¶\É/‚‚Ì}¹rîí¡¡æo^` ÷còQJ@D$‡#JñnÝjœ¿ð‚žÉO|Д‘ì×þøö탙3áç\çu IDATaàÀ¤E4KD<ËñdèP¨R† ÉÁ'ñ+Â/¿@›6P§NRû‹/š’H ˆˆx”£ ÈâÅf—óY³ pázR‘ÜIdd¤[Û©S§ÒîpäÜsùïÉ“¡tiX°ÞxÃŒŒ¨êc"% "âQh(¬Y“Oté’ýhÑ:uÊ'É}DDD¸µ­]»–zõêyîðúëpàlÛ–4ÕøÞ{Í"ôáÃ!"ÂTÆ­ÏrldâDóñرªz%""ù×Ï?›©W)×9¶oçÏÃúõöÄ僔€ˆˆG¡¡f49!!Orì¼ò <ü0Ü|sN…&""â{.]‚øxÏí—/{7¦DD<*_Þü;zâD6ždÄ°,3,-""’ŸÕ­ k×Âöíîí‘‘¦ oX˜=qù ­’oFX¦Lž`Ãøè#3Ê–ÍÑØDDD|Î3Ϙb+·Ü˜õ À’%f&€ë«hDD<ËÖnè–ƒCªú!""þ!, V¬0ë@F6eçwí‚7ß„ÿû?»£ó)Ê—7÷YJ@æÎ…åËaáBíü*""þ£aCSz^Ò¥ñ¨H(^< Hl,<ý´©…Þ¦M®Ä&"""y—F@D$MY*Å;v,ìÝkF?DDDDRЈˆ¤)Ó È¡CfÇ×àúës-.É»”€ˆHš2€<ÿ<*dÊˆˆx DDÒ”©dÍøôS9J–ÌÍ°DDD$S""iÊpâ*»[»6<ôP®Ç%"""y—ß% qq0|8T¬hªü4n K—f¬oL ôïoöT †–-aÝ:ÏÇ^¼hÊ>_=™ríÚÁ9÷^Dr[h(?—.]áÀ/¾€_5 ÐTÛBDDDÒæwŸúö5›TböH›2ÅT ]¾š5K»_B´mk6w6 J—†‰¡E ˆŽ†êÕ“Ž½tÉ»r¥IXÂÂàÄ X½NŸ†J•rû]Šä ×^ ÿü“Îu{þ¼ù¥èÔÉdå""""éð«dõj˜9ÆŒ¡CM[ïÞpÓMæóÓ/¿¤Ý7*Ê$QQй³iëÞjÖ„—_†3’Ž}ï=øñGó|õëçÞûÉmÉwCO35Êd(cÆx-.É»üj VT”™Ò¿R[¡BЯŸI.Ò›e>Œ¹’€2eL2o^Ò•„xÿ}s\ýúpù²ù‚X$/Jž€x´w¯I@†…ªU½—ˆˆˆä]~•€¬[gF,‚ƒÝÛ40÷ëק߷nÝÔí ˜cÛ6óóŸš­j×6‰NÑ¢æõn¾V¬È‘·!â5e˂ÑN2|8”(aÊˆˆd€_% ‡A… ©Û]mf¿ïöíæÞ5 kòd³Î$6Z·†³¿ˆ·,hFú<& ?ÿlŸ¿õ+æõØDDD$oò«5 .˜)W).œôxZbc3Ö÷ìÙ¤ûõë“æÍ·liªÓ§§ý:ƒ&$$Ä­-""‚ˆˆˆ´;‰ä"¥xLÙÝúõ¡O[⑼ɯ  S†7¥ØؤdzÛ×u߬™û¢Ý*U ysS©4=cÇŽ¥®§¹^"6ñ˜€LjÊ¿ýò 8ýj UDDD²É¯>9T¨àyšÕ¡Cæ¾bÅì÷uݻʗ&W¶¬ÙKD$/I•€?Ï=={BÓ¦¶Å%"""y“_% uê˜Åâgθ·¯ZeîÃÃÓîkך ŸSö-ZÔ,n³ø¼`Aϵ4IˆH^â–€œ8wÞi¦`½ý¶­q‰ˆˆHÞäW H×®“&%µÅÅ™Eâ'M™:|¶l1%t“÷=rfÏNj;v ¾ú Ú·7I˜µ¸mÚ˜™)[·&û×_fúÕ]wåÞûÉ ‰ ˆ+ùØ¿¾ÿ*W¶;4Ƀüj HÆЭ›™=òÏ?P­š™Ê¾w¯IB\ž}¦MƒÝ»áª«L[×®&IyàSj×µºeÁ«¯º¿Î›o²efáùÀæ˜qãL5!U+•¼&4Ξ$þŽ»(°o/,_nvïÉ¿J@À$/½d*QøÀî¨|ŠF@DRºt † ƒ±c¡woøè#³± jr1ñàÛo¡}{¨WFŒ€à`ر°;2Ÿ¢D$¹þ1ûyüò ŒøízO4"""’†Ó§ÍaíÛCT”ÝÑø4% ".kÖ@çÎpñ",[·ÞjwD>'4Ô,‡‰…Â…íŽFDDć|þ¹ù"ó7ÌÏçΙN­xHIgDàÿƒ[nJ•ÌÜM%¹v@?rÄÞ8DDD|ÎÒ¥P¼8ìÛ×]ÅŠA‰ðøãgwt>E ˆø·‹á±Ç _?3lúÃ& \ ˆ¦a‰ˆˆ¤°};\¾ ÷Þkö ›=|>üxÀîè|Š¦`‰ÿ:tÈl,øûï0i<ü°Ýù<% ""â/"##‰ŒŒtk;uêTÚΞ…óçÍ›cÇš¶{ï5_v~ô¼öT¯ž‹çJ@Ä?­\ ]º˜æ?üÛQžPº4( DDDò¿ˆˆ"""ÜÚÖ®]K½zõL ˆø‹áÑG¡xè!øþû¤9E’a*Å+""âAýúæ~ÿ~÷öƒÍ}Ù²ÞLJ)ÿpèÜ~;L™ 'B` ÝQåIJ@DDD<èÞÝÜò‰{ûÇCÁ‚Т…×CòUZ"ùŸÖ{ä¨ÐPøë/»£ñ1áá¦êÕÿþgªaÝz+¬Xa6%|þyͺHF ˆäo“'ÃO@Ææýòg[h(,_nw""">èÃ᪫̌‹9sàškLE¬íŽÌ§øݬ¸8>ܬ*RÄ|¾tiÆúÆĘåeËBp0´l ëÖ]¹O¹rfñî¬YÙ_2Hë=rMh¨ÙˆÐ²ìŽDDDÄÇÀˆ°k—ùйu«’ü.éÛÞ{z÷†qãLIÑ6mà—_Òï—mÛBd¤¹ŽF‚þ1ÓùvìH»ßˆpá‚™ýãpää;‘4i½G® 5×ô™3vG""""y‘_% «WÃÌ™ðßÿÂÛo'}1~õÕ0lXú}£¢ÌR‚©SᥗàñÇÍ´¾àå—=÷Ù´ÉŒÄ ®o‹½fåJ¨Wvï6ë=úõ³;¢|Ç5tè½qˆˆˆHÞäW HT”ëß?©­P!óuåJ8p ý¾¡¡Ð¹sR[™2¦àÁ¼ypéRê>ƒ™ão¹%çÞƒ¤Cû{xŵךûíÛíCDDDò&¿J@Ö­ƒš5Íúä40÷ëק߷nÝÔí Àùó°m›{ûW_™¤fÔ(~ä:­÷ðªÊ•!$6l°;É‹ü*9t*THÝîjsí“ݾ.ÀÓOÃС¦‚䢓'µÞÃË S""""YãWex/\0S®R*\8éñ´ÄÆf¼ïÿ ññ¦äsf <˜·¶ˆˆ"""2ÿdùÝ… СlÙ¢ý=¼,, –-³; É‹ü* 2ÑRŠMz<»}wï†1cÌñEŠd>ƱcÇR×Ó\/q÷ÝgÖz|ÿ½’/ ƒÿû?sý»’p‘Œð«)X*xžfåªæS±böûŽ•*™µÐ»w›ÛáÃæ±þ1?kMH6Y<ù$ÌŸ_~©äÃaa&ÔŽè"""’Y~5R§Ž){æ +–Ô¾j•¹O»ox8üô“ùì›|?U« hQ³¸`ß>³/HÕª©ŸãñÇÍ}L /ž­·âßÞ|Ó|ý>y2´kgw4~©V-ó{°aƒù½É(¿éÚÕ|k;iRR[\œY¿Ü¸±¹3b±e \¾ìÞ÷Ș=;©íØ1Síª}{(Xд sçºß^Ý<6|¸ù9+S³ä_S¦À‹/Âk¯™ŠWb‹à`¨VM ÑEDD$óüj¤aCèÖ ž{ÎL‡ªVÍl,¸w¯ù\ëòì³0mš™.åªbÕµ«IRxþüJ—6ë<, ^}5©o³f©_×5ÚÑ Y3-Y´p!<ü0<òˆIBÄVª„%"""YáW# `‹Áƒaút³Q`|<,XÍ›'ãp¸O³p:aÑ"èÑÆ3;§—+gÖ?רqå×Mù|’I«V™]Ûµƒ tB}€É ‡eiI´/X»v-õêÕ#::ZU°RÚ¶ š6…뮃¥KÓ/W&^3gtîl¦,–/ow4"""¹OŸ×r†ß€Hsø0´je†›¾þZɇ©]ÛÜkDDDD2C ˆø®Ó§¡M¸x–,R¥ìŽH’©ZÕTP""""™áW‹Ð%¹xÑÌïÙ¹ÓÔ?vUŸátšQ% """’J@Ä÷$$˜rc?ýß|“4×G|NX¬Ycw"""’—h –øžaà 2>û Z´°;IGX˜)K}é’Ý‘ˆˆˆH^¡D|Ë{ïÁ;ïÀûï›M[ħ……™ÙrÛ¶Ù‰ˆˆˆäJ@Äw|ñ jv‚|òI»£‘ P%,É,% â–-ƒ>}ÌíÍ7íŽF2¨dI¨RE ˆˆˆˆdœ±_t4tê-[ÂÇk—óüP¹rP¾¼ÖˆˆˆÈ•iD²îȸ㈋3 ^{­Ý‰Â´ˆˆˆˆ\™F@$kŽƒ;øþ{%BíÚ‘+S"™wßmF@–-Ó¤ÌÈßÃÙ³vG""""¾L ˆdΙ3к5ìÙK—Â7Ú‘ø×BôM›ìCDDD|›ɸsç m[øë/øöÛ¤Oœ"À 7@š†%"""éÓ"tɘ  CX·Î$õêÙ‘ø˜Â…M4% """’% reqqÐ¥ ¬\ K–@“&vG$>*,L ˆˆˆˆ¤OS°$}—.A¦ÒÕüùpë­vG$>Ì•€X–Ý‘ˆˆˆˆ¯R"i»|î»-‚Ù³MÙ]‘t„…Á©S°oŸÝ‘ˆˆˆˆ¯R"žÅÇØÄãË/¡M»#’<ÀU—@Ó°DDD$-J@$µ„xôQøüs˜1î½×îˆ$¨\BB”€ˆˆˆHÚ´]ÜY Ÿ|S§šõ"äph!ºˆˆˆ¤O# ’IJàé§aÂøè#èÝÛîˆ$R""""éQ"I^z Þ}ƃ‡¶;É£ÂÂ`ëVˆµ;ñEJ@.^„aÃà7`ôhxòI»#’<,,Ì,#úóO»#±Ùo€Ó µkÛ‰OQâïþø6„÷Þƒ1cÌ,‘l¨UˬÑ4,ñkû÷ÛoBÑ¢æ£$Râ¯.]‚×^ƒúõÍ×Õ«VÁSOÙ•äÁÁP­šñsO? M›šÏZÚ¡×´q#4jdgŸ…߇ºuíŽJò-D¿öã0kŒk’€¸QâO._6sëÕƒ¸8øí7xýu ´;2ÉgÂÂÌì>}á#""~'>Þ¬§}øa3/YRÑ> þbófèÛÖ®…áÃáå—¡P!»£’|*, Žƒ#G 4ÔîhDDD¼èÃaï^øþ{»#ñYJ@ò»Ë—áw`ĨZV®4‹ÎEr‘«Ødž J@DD$oŠŒŒ$22Ò­íÔ©Séw:~Ü|æ1J—ÎÅèò6¿œ‚g*V„"E qcXº4c}cb ([Ö,¶mÙÖ­s?æ³—ßÝw›×(^Ü,±øðC³ÞÛk¶læÍáùçaÐ ¨’ñ‚ªUÍï–ÖˆˆH^ÁüùóÝnï½÷^ú^|ʔіWà— Hß¾¦êlïÞfϽ Møå—ôû%$@Û¶ ¨QðÏ?ТìØ‘tÜß›ÇSXêwàÚkáñÇáÁsóý+>Þ”Ô ‡“'áçŸM°… {áÅE’Jž+¿±};Lžl’ýûa÷ns‹5{®íÙc>—‰ÿMÁZ½fÎ4ŸÏ‡5m½{ÃM7™½øÒKB¢¢Ì ¦¨(èÜÙ´uï5kš%3f˜¶ `Ó&¸á†¤¾? ýúÁ”)fÃñjÕrçý±m›É°~û † ‘#!((—^L$maaæ÷MDDÄ/8`¾­8ÐÜRºöZ<Þ}×û±ù¿K@¢¢ ÀL£r)TÈ$Ï?o®J•Òîš”|€eëÞ>ûÌl­Q° ™òçiÚß½÷šdË–\H@Îœ1Y÷ /@åʦü[óæ9ü""S§&ý^ˆˆˆäkµkÃœ9î%w-ËLË:{Þ?¿Î[ü.Y·ÎŒX»·7h`îׯO;Y·Îóv À¤Ifð!½jk‡›û2e2·G{÷Â×_Ãüù°b…Þ4ÈìºY¤H½ˆHÖ„…™KòJ¿"""ùBéÒбcêv׺‘¼ó»5 ‡™)R)¹Ú̾/š½hªVMJv2-!Ö¬1•ÂÃáê«ÍP^|<Œ ;wšQò!> y%,¿åph#ÂünäÂÏÛ_¸Ög_¸vßØج÷0þú -2 tÓ2xð`BBBŒçÉoä¶S§`Á“•,iVÍ?÷´n %J¤ý„"6)YªT1 HD„ÝшˆˆØdùr»#ð9~—€™2¼)ÅÆ&=žÓ}G†?6ëÁ[·N?¾±cÇR·bEX¸ÐL­Z¾Üd-Õ«›Oq:@³ff!‹ˆ Óˆˆˆˆ¸ó»O±*xž*u蹯X1gû~ú)<û,<ö˜Y䞞ǀëî¿ß”Ðr:¡iSxå“t\w†ï$Ï ƒéÓíŽBDDD|‰ß% uê˜õÚgÎ@±bIí«V™ûðð´û†‡ÃO?™‚ÉsU« hQ³¸=¹yóࡇ K³1á•Ô.–/OÑgž1S¬rlµºˆ=ÂÂL)ô' T)»£_àw‹Ð»v5k¶'MJj‹‹3åq7Nª€uø°)—{ù²{ß#G`ö줶cÇ૯ }{÷R£?þÿù٤е?È• v}ú(ù|!,ÌÜoÜho"""â;ün¤aCèÖͬßþçSŽyêTSÑvÊ”¤ãž}¦M3X^u•iëÚÕ$)<þiª­MœhFD^}5©ïž=fÖ”ÓiF?fÎtá曓*‰äg5kB` YrÛmvG#"""¾Àï0‰ÅK/™¹é'Oš„`Á÷}ûÞîHDDDÄ(‘\çÏÃÎvG""""¾À/×€ˆˆ÷¸*amØà¾V*«.]²o4¥@÷jw"""’yJ@D$W•+åË›¤K—ì=ׂн»)a§î¼"" S'(Qž8DDDò2% "’ë²_ kÍèÑZ¶4{ìØáäI˜5Ë”â~ôQh×zö4û†.lOL"""yÉuµkÃܹYï¿s§ù°_~ EŠä\l™õä“°oŸÙß'2ÒŒê/nî#"àöÛMùañL‹ÐE$×……™$âÌ™Ì÷=~ÜŒ0/óçÛ›|¸T©O? ÑѦÄðàÁðÓOp÷ÝP¹2 dʫȹˆˆHjJ@D$×¹¢oÚ”¹~±±Ð±£IB/†²es>¶ìºþzxõUضÍLëÙ¾ú 7†êÕáÅáÏ?íŽRDDÄw(‘\wà ¦‚TfÖ$$@Ÿ>f”áë¯Í‡y_æp@ýúðî»fŠÖ²ef½Ê„ f7øðp˜>Ýî(EDDì§DDr]áÂpÝu™K@† ƒ¨(øüs3š—(`’É“áða³þåÚkMBÕ¿?ÄÅÙ¡ˆˆˆ}”€ˆˆWd¦ÖÀ;ïÀر¦Üm^V¨™F6g|ò L›·ÞjFIDDDü‘ñ Wr¥…ÙsçšEÜC‡ÂÀÞ‰Í[|~þÙŒŠÔ«+VØ‘ˆˆˆ÷)¯ ƒÓ§aïÞ´ùí7SʶK=Ú{±ySýúðûïæ|Üy§Y3¢jY""âO”€ˆˆW¸*amÜèùñ; }{320}ºÙu<¿*[–,§ž2·ˆ8wÎî¨DDD¼#ÿ‰_R¹2„„x^rô(Üs”. óæùÇ®âðöÛ¦dï‚f¡ýöívG%""’û”€ˆˆW8ž¢Ÿ?:˜éY‹™$ÄŸtí «WÃŋРIFDDDò3% "â5)øxèÕË´-XU«Ú›n¼Ñ$!-Z˜ih¯¼böAÉ”€ˆˆ×„…ÁÖ­f‡sË2•®æ̓/¾0ßþû³%`öl9^{Í$"'OÚ•ˆˆHÎS""^f¾ÙÿóO³ÇǸq0~¼ù°-fáý /˜©h+Wš¤,­Eû"""y•ñšZµÌZ‘#Mõ§áÃá±ÇìŽÊ÷´nmJõ›Åé‘‘vG$""’sì@DüGp0T«fvˆ€7ß´;"ßUµ*üú+<òôìi60lÜØ¦Y3ÿ]ß#""©P-·{IDAT)¯ºã¸öZ˜2%ïõ‘ŠiÓ Q#xúi˜8Ñ¦\9X»*U²;ñJ@DÄ«þïÿ̽Ãaoy…ÃÀ£šªayÍñãа¡)7üÃhwD""b7% "âUJ<²& ÀÜòšŠ!* n½ÕT=?ÞîˆDDÄnš!""¹ªqcSñl˜>ÝîhDDÄnJ@DD$×=òÜ?ôïë×ÛˆˆØI ˆˆˆä:‡Ã¬ÿ¹þzèÒE›,Šˆø3% ""âAA0k–I>zõ2›RŠˆˆÿQ"""^Sµ*̘‹› )EDÄÿ(¯ºçxås[¼ØîhDDÄÛ”€ˆˆˆ×½ø¢IDî»vî´;ñ&% ""âuN'|ö”,i¥_¸`wD""â-J@DDÄ%KÂìÙ°e <öX–݉ˆˆ7(ÛÜ|3|ôLjîED$ÿ °;ño}úÀªU0p Ô©Ù‘ˆˆä&€ˆˆˆíÞ{êÕ3ëAþùÇîhDD$7)ÛBT\ºÿù\¾lwD""’[”€ˆˆˆO¨T f΄„^°;É-J@DDÄg´ho¿ £FÁ¬YvG#""¹Áï¸8>*V„"E qcXº4c}cb ([‚ƒ¡eKX·Îó±¿þ Í›CÑ¢P¡ çÎåÜû$‘‘‘v‡çèœeÎ[æeåœ  ݺAß¾¦D¯?Òµ–y:gY£ó–ƒÖ¬ V-óAñê«¡GؾÝîÈ|ŽßUÁêÛ×|«6dÔ¨S¦@›6°|94k–v¿„hÛ6l€aàti˜8Ñ|[ Õ«'»~=Üq‡¹þÞ{öíƒ1cÌõ·hQn¿CÿIDD„Ýaä):gY£ó–yY9g|ò‰©†Õ¾=´k—KÁ]A` ù¢éöÛÍ{“®µÌÓ9Ë·ôöÛ°r¥ù%, ‚ñã¡n]øí7óÁP?K@V¯6ó‹ÇŒ1ß°ôî 7Ýd’Š_~I»oT”¹¦¢¢ sgÓÖ½;Ô¬ /¿ 3f$ûüó&AY±Â$À×\? ß}wÝ•ïND$ÿ(VÌlRøàƒðí·öÄpê”™ V¼¸ùêÞ{áž{Ll""©<õ4hÉ>^÷èµkÃÿ Ó§Û›ñ«$*Ê\ýû'µ*ýú™¤áÀ³2­¾¡¡IÉ@™2& ùì3S¹¥`A8}ÚLé:4)ùSç~ÈøòK% ""qýõf:«], 6m‚9s`î\ó9"0î¼Ó$#:@ùòöÅ'">¦I“ÔmÕ«Ã7úï|Ò4øÕuë̈EòÄL² fêTz}ëÖMÝÞ œ?Û¶™Ÿ7n4å#ë×w?®`AO{͈ˆˆø‡Ã|q9b¬] »w›‘óçáÑGÍú¾fĮ́úŽvG+">ɲàÈó­µ$ò«C‡ÌŒ”\m¦ß·E‹ôûÖªeŽKÞž\h(üü³çç௿þJ;ñ(&&†µk×ÚFž¢s–5:o™—ßÎÙ-·˜[LŒ)¼b…)üÌ3Pµªù;Ѳ¥½q8²þ:ùí¼yƒÎYÖè¼eŽësÚ… 2ÖaÆ ó!qäÈ\Œ*ïñ«äÂ3å*¥Â…“OKllÆúºîÓ:6­×صk½zõJ;IS½zõì!ÏÑ9Ë·Ìó—s¶s§¹ýï9ó|þrÞr’ÎYÖè¼eÞîÝ»i–^õ"0Ó®žxš6…ûï÷N`y„_% AA¦ oJÿ>”ý¾®û´Ž-RÄóó·jÕŠÏ>ûŒk®¹† ô[ÄÆƲk×.Zµj•þ‡›ê%Kš…ÄÙ͇ü*©PÁó4+×´©Š³ß×5õÊÕžòØ´^£L™2Üwß}i """"¶kÚ´iúœ:eJæ> ?ýdæà‹¿Z„^§ŽY,~æŒ{ûªUæ><<í¾ááf¢e¥î[´¨Yܦ¤o@€Ù‹&¹‹Í"÷ô^CDDDDò°ØX³ÑŽ°`Y&©øUÒµ+ÄÇäIImqqf3ÂÆ“Jð>l¦í]¾ìÞ÷ÈS—ÞåØ1øê+s,hÚJ”0%?û ΞM:vút³z·n¹÷þDDDDÄ&ññ¦^÷ªUæb£FvGä³–•ò;ýü­GSÓ}ȨV ¦N…߇eË yssLß¾0mš)¹xÕU¦-!Á<¾i“©vâÚ }ÿ~3ÚQ£FÒk¬[gÖÝx£Ù|pÿ~x÷]¸í6X¼ØÛïXDDDDrÝàÁ0nœùfÚÓ7Î*4”ȯF@À$ƒ›‰AƒL²º`ARòfPʵBN',Zd˜qãÌÎéåÊÁ÷ß»'`¦z-]j¤  =dÖ ¥ÇðáéX±"EŠ¡qãÆ,]º4çßx>²bÅ œN§ÇÛêÕ«íÏvçÎãå—_¦uëÖ”*U §ÓÉÔ©S=û×_ѺukŠ+FéÒ¥éӧǎórľ!£ç­oß¾¯½n¸Á†¨íµfÍ @­Zµæꫯ¦Glß¾=Õ±ºÖ’dô¼éZK²yófºuëFµjÕ(Z´(¥K—¦iӦ̘1#Õ±ºÖ’dô¼éZKßo¼Óé¤víÚ©K~½ý4a €õõ×fêä7UÁrãW‹ÐÁ”Ç5ÊÜÒ2eŠ¹¥“'›Û•4k–öžÉõíÛ—Y³f1dÈjÔ¨Á”)ShÓ¦ Ë—/¿ry7?7hÐ ¸v‘üWµjÕlŠÆw=z”×_«¯¾šððpV¬XÃCõýû÷së­·R²dIÞzë-Îœ9Ø1cظq#«W¯¦ k^¡ŸÈèy(T¨Ÿ|ò‰[[‰%¼¦Oyûí·Y¹r%ݺu#,,ŒC‡1~üxêÖ­Ëo¿ýF­Zµ]k)eô¼®5—½{÷röìYúöíKÅŠ9þœºuë’ÀâÅ‹™8q"üñ+V¬ @v‡h«3fpðàAFŽ èZ˨”ç t­y2tèP&ý[K? €qãÆ%Î"е–¶ôÎèZóäÃ?dïÞ½|ÿý÷×õ–=J@ltá *”ª½pá‰KjMš4¡I“&‰?·k׎®]»ÆsÏ=ÇbÕ:¾"×µu¥ëOÿp¦öæ›oºýܽ{wjÖ¬É /¼@TT=zô°)2ûmÙ²…'žx‚¦M›rÿ¿_t­]™§óºÖ<2dÝ»wçàÁƒÌ˜1ƒÄý÷߯k-é7е–ÒñãÇ1b#FŒ téÒÑõ–=š‚e£   âââRµÇÆÆ&>.S­Z5:vìÈòå˯8_’®-]9cÈ!8N–-[fw(¶9|ø0mÛ¶¥dÉ’DEE%Εֵ–¾´Î[ZüýZ»îºëhÙ²%½zõbñâÅÜqÇ <˜ØØX]kéHë¼¥÷E§?_k/¾ø"eÊ”áÉ'ŸLó]oÙ£ÄF*Tð8ÍÊ5¬W±bEo‡”§U®\™‹/úõœÕŒr »®µä:DéÒ¥õ­M&.\˜R¥JqâÄ »C±Å©S§¸çž{8}ú4K–,!444ñ1]kiKï¼¥Å߯µ”ºté©S§Ø²e‹®µLp·­[·¦yŒ¿^kÛ·ogòäÉ<ùä“ìß¿ŸÝ»w³{÷nbcc¹xñ"{öìáäÉ“ºÞ²I ˆêԩömÛ8sæŒ[ûªU«´–!“vîÜIPPPªEý’Z¥J•([¶,kÖ¬IõØêÕ«uíeÒ™3g8vìeË–µ;¯‹¥}ûöìرƒ pýõ×»=®kͳ+·´øóµæ‰ë|§Ó©k-’Ÿ·´øëµvàÀ8p U«VM¼­^½šmÛ¶qíµ×òúë¯ëzË&% 6êÚµ+ñññ‰ ÃÀ åM™2…ÆS©R%£ó]GMÕöÇ0þ|î¾ûn"Ê›ºté‚ Ø¿bÛ²eËؾ};ݺu³12ß—ê €×_€Ö­[{;$[ÅÇÇÓ£GV­ZÅW_}E£F<§kÍ]FΛ®5wžþÝ¿téÓ¦M£téÒ‰UŠt­¹ËÈyÓµæ®víÚÌ™3‡¹sç&ÞæÌ™C­Zµ¸úê«™;w.ýúõt½e‡ÃÒ„y[õèу9sæ0dȪU«ÆÔ©Sùý÷ßY¶lÍ›7·;<ŸÔ²eKŠ)B“&M(W®þù'“&M¢P¡B¬\¹’ë®»Îîm7~üxbbb8xð ~ø!;wNü6fàÀ/^œýû÷S§NBBB4hgÎœaôèÑ\uÕU¬Y³Æ/‡Ž¯tÞNœ8A:uèÙ³gâuöÍ7ß°xñbî¹ç.\hgø^7xð`ÆGûöí=þ±íÕ«€®µ2rÞvïÞ­k-™N:qæÌn½õV*V¬ÈáÇ™1cÛ¶mcÊ”)ôéÓе–RFΛ®µŒiÑ¢ÇgãƉmºÞ²ÁžýÅ%66Özæ™g¬ *X… ¶5jd}ûí·v‡åÓÆg5jÔÈ*]º´U°`A«R¥JVŸ>}¬¿ÿþÛîÐ|Æ5×\c9ËápXN§Ór:‰ÿ½gÏžÄã6oÞlµjÕÊ*Z´¨UªT)«wïÞÖ?ÿüccäöúÿöî?¤ªóãøçÜkíæõªËYýr­¬†ÉFEÑ•.[i«mˆ,fY$ÁúyëY…A…eFPDŠ+Äbe+ÈØ ÀÚ+ËfZiëwê³?úz×íÚÒ-Oß;Þ/8àyžçœû<‡ÞÏyÎ}Ùukjj2sæÌ1Æ 3n·Û¸\.“œœl LKKËëî¾í|>_à=¿9Ž ¶ÜkéÌuã^ VVVf¦L™bL=L\\œ™6mšùöÛoCÚr¯ý¥3×{­s|>ŸINN)ç~ûg˜`Ö€° €m lC`Û@؆K‡Cùùù¯»€."€@ª©©‘ßï×ðáÃåv»åv»õî»ïÊï÷ýRo¸;vìØ߆ ˲lì àUà‡ Ì=zTŸþ¹zöì©ììl¥¤¤Èápè·ß~Ó¡C‡tåÊ]¾|Y|Ý]ý×ü~¿ŠŠŠÔÖÖR÷øñc9N9Î×Ð3À?ñº;輋/*++K‰‰‰úî»ïÔ·oß ú7ª¸¸øÿvfàþýûŠŒŒìÒ1/KÏž=_E—6ã,#………ºÿ¾vïÞ>$ÉétÊï÷kÀ€²ªª*}öÙgŠ‹‹S¯^½4vìX•——·gÏ9ýøãÊÍÍU||¼¢¢¢”™™©ÆÆÆÏ9~ü¸RSS¥èèh}üñÇúõ×_ƒÚäääÈãñèÒ¥Kš6mš¢££•-I:uê”f̘¡ÁƒËåriРAÊÍÍÕǃŽ/**’1F‡#°µëh È™3gôÑG)&&FG“'OÖéÓ§ÿÕX¯3 FŽ=ªaÆiìرjþüyMœ8QT^^žÜn·öï߯ŒŒ |¨Å‹+..N§OŸÖ¶mÛtõêU8p@’´páBÕÕÕ©¢¢Bûöíëp|ÏÎŽœ?^©©©ŠÕÊ•+¡’’ù|>}ÿý÷7n\—Ç èš››eY&333¤îöíÛ¦¡¡!°=xðÀċ~hRRRÌãǃÚOœ8Ñ ><°¿{÷ncY–IKK j—››k"""Ì;wŒ1ÆüñÇ&66Ö,X° ¨]}}½‰5óçÏ”Í;×X–eV­ZÒßöþ=«  À8S[[([²d‰±,«ÃëaY–ÉÏÏìgdd—Ëejjjeuuu&::ÚLš4©ËctÁ€0qçÎIRTTTHÏçSŸ>}[QQ‘nݺ¥“'OjÆŒjnnVccc`KKKSuuµêêê‚Î3þü }¯×«ÖÖV]¹rE’TQQ¡ææfeeeÏáphܸq:yòdHß-ZRær¹ß»wOš0a‚Œ1:{öl—¯Mkk«Nœ8¡ŒŒ 2$Pž Y³fé‡~ÐÝ»w»4V@÷à,G’B¾HKÒŽ;t÷î]]¿~]³gÏ–$]¸pAÆ­^½Z«W¯9Ʋ,ݸqCýúõ ” 4(¨Í›o¾)Iº}û¶$©ººZ’ôÁtØǘ˜˜ ý=z­GiW[[«5kÖèÈ‘#jjj ªknnîðܧ¡¡A>¾SçìèË|ee¥ª««õÕW_fk¤§w=¯³¯ŽWdd¤ªªªBꪪªäp8þ¿‹ÿ¬€0²bÅ EFFjÞ¼yºqãFHý³_øãããåóùTRR¢ëׯ‡´mhhèòçO:UÑÑÑZ¿~½ZZZBêŸmG¢}æáÙ™cŒ¶nÝÒÖívKzùcYN§Siii:|øpÐŽúúz•––^ xý˜€0òÎ;勤´T3gÎTRR’²³³5zôhcTSS£ÒÒR9ÎÀº‹íÛ·Ëëõ*99Y_|ñ…U__¯Ÿ~úI×®]ëò‚oÇ£ââbÍ™3Gï¿ÿ¾²²²ôÖ[o©¶¶Vß|ó¼^¯¶mÛhßÑ ÈÈ‘#5tèP}ùå—ºvíš<<²D’ÆŒ#IZ¶l™ÒÒÒät:•••ÕaßÖ­[§ŠŠ y½^-^¼XN§S%%%zòä‰ »4N@÷!€@˜ùôÓOUYY©Í›7ëĉÚµk—,ËÒ!CôÉ'ŸháÂ…JNN–ôôËþ/¿ü¢üü|íÙ³G7oÞTß¾}õÞ{ïiíÚµAç}ÑãNÏ—Ïœ9Sýû÷WAA6mÚ¤GiÀ€JMMÕ¼yó‚Žëèœ*//ײeË´aù\.effjÉ’%JII j›™™©¥K—ª¬¬,ð[ / £FÒ©S§”——§ 6¨­­MãÇWiiiÈãj+àÕ³ «íØ„5 lC`Û@؆À6¶!€° €m lC`Û@؆À6¶!€° €m lC`Û@ØæO*žyÉDù2öIEND®B`‚deap-1.0.1/doc/_static/0000755000076500000240000000000012321001644015040 5ustar felixstaff00000000000000deap-1.0.1/doc/_static/copybutton.js0000644000076500000240000000467712117373622017635 0ustar felixstaff00000000000000$(document).ready(function() { /* Add a [>>>] button on the top-right corner of code samples to hide * the >>> and ... prompts and the output and thus make the code * copyable. */ var div = $('.highlight-python .highlight,' + '.highlight-python3 .highlight') var pre = div.find('pre'); // get the styles from the current theme pre.parent().parent().css('position', 'relative'); var hide_text = 'Hide the prompts and output'; var show_text = 'Show the prompts and output'; var border_width = pre.css('border-top-width'); var border_style = pre.css('border-top-style'); var border_color = pre.css('border-top-color'); var button_styles = { 'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0', 'border-color': border_color, 'border-style': border_style, 'border-width': border_width, 'color': border_color, 'text-size': '75%', 'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '0.2em', 'border-radius': '0 3px 0 0' } // create and add the button to all the code blocks that contain >>> div.each(function(index) { var jthis = $(this); if (jthis.find('.gp').length > 0) { var button = $('>>>'); button.css(button_styles) button.attr('title', hide_text); jthis.prepend(button); } // tracebacks (.gt) contain bare text elements that need to be // wrapped in a span to work with .nextUntil() (see later) jthis.find('pre:has(.gt)').contents().filter(function() { return ((this.nodeType == 3) && (this.data.trim().length > 0)); }).wrap(''); }); // define the behavior of the button when it's clicked $('.copybutton').toggle( function() { var button = $(this); button.parent().find('.go, .gp, .gt').hide(); button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden'); button.css('text-decoration', 'line-through'); button.attr('title', show_text); }, function() { var button = $(this); button.parent().find('.go, .gp, .gt').show(); button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible'); button.css('text-decoration', 'none'); button.attr('title', hide_text); }); }); deap-1.0.1/doc/_static/deap_icon_16x16.ico0000644000076500000240000000217612301410325020326 0ustar felixstaff00000000000000 h(  Ðа°šš¢„„Sªª¬¬ØÀƒƒÿ‚‚¬……¤¤¬¬Î¬¬ÿÀƒƒÿƒƒÿƒƒí„„]½½›ººÌººÌŸ¤°~~Ì~~Ì~~Ì~~Ìww–``ÖÖoÖÖÿÝÝÞ›Ö`¼Dj>ÀYYÿYYÿYYÿYYÿYYaØدüüÀẋ®ÿÿ.z|ÂYYúYYÿYYÿYY«ÿÿžÿˆÿÿÿÿ‰µÑYZìYYÿYYñ@@žÿˆÿÿÿÿÿÿ ”ÜäV\ ÙYYÿXX@žÿˆÿÿÿÿÿÿÿÿšôôNa ÈYYŠžÿˆÿÿÿÿÿÿÿÿÿÿþþ/yy§žÿˆÿÿÿÿÿÿÿÿÿÿÿì›ÿ@žÿˆÿÿÿÿÿÿÿÿÿÓ›ÿ!žÿˆÿÿÿÿÿÿÿ¯•ÿ žÿˆÿÿÿÿžÿƒÿÿžÿˆÿõÿVœÿjœÿ1ñÿàÀ?ÀÀàèøøøøøøø?øÿùÿdeap-1.0.1/doc/_static/deap_long.png0000644000076500000240000010142212117373622017511 0ustar felixstaff00000000000000‰PNG  IHDRuÐàˆþ?ðiCCPICC Profile(‘UÝoÛT?‰o\¤? ±Ž‹¯US[¹­ÆI“¥éB¹ÍØ*¤ÉunS×6¶ÓmUŸöo ø€²xB Äö²í´ISAÕ$¤=tÚ@h“ö‚ªp®¯S»]Ƹ‘¯9çw>ïÑ5@ÇWšã˜I`Þò]5Ÿ‘Ÿ˜–;V! ÏA'ô@§¦{Nº\..Æ…GÖÃ_!ÁÞ7ÚëÿsuV©§$žBlW=}ñi€”©;® ÞFùð)ßAÜñ<â.&ˆXax–ã,Ã38Sê(b–‹¤×µ*â%Äý31ùl ó‚µ#O-êºÌzQvíšaÒXºOPÿÏ5o6Zñzñéòæ&â»Õ^wÇÔ®k¹IÄ/#¾æø&ñ½Æ\%x/@ò™š{¤ÂùÉ7ëSï Þ‰¸jø…©P¾hÍ”&¸mryÎ>ª†œkº7Š=ƒߪÓB‘ç#@•fs¬_ˆ{ë±Ð¿0î-LæZ~ë£%îGpßÓÆˈ{èÚêÏYX¦f^åþ…+Ž_sÖ-³Tä>‰D½ Æ@îקƸ-9àã!r[2]3ŽBþ’c³ˆ¹‘ónC­„œš›Ës?ä>µ*¡ÏÔ®ª–e½D|Ž%4 `à î:X°2¨‡ ¾pQSL”PÔR”‰§aeíyå€ÃqĘ ¬×™5FiÏáî„›t…ìÇç )’Cd˜Œ€LÞ$o‘Ã$‹ÒrpÓ¶‹ÏbÝÙôó>4Ð+ãƒÌ¹žF_ï¬{ÒЯô÷kû‘œi+ŸxÀô˜ñú¯.ý°+ò±B.¼{³ëêL<©¿©Û©õÔ î«©µˆ‘ú=µ†¿UHcnfÑ<>F‡Ë ^Ãe||ÐpÿyvŒ·%bÍ:×iX'襇%8ÛI•ß”?•å å¼rw[—ÛvIøTøVøQøNø^ødá’pYøI¸"|#\ŒÕãçcóìƒz[Õ2M»^S0¥Œ´[zIÊJ/H¯HÅÈŸÔ- IcÒÔìÞ<·x¼x-œÀ½ÕÕö±8¯‚ZNxA‡-8³mþCkÒK†HaÛÔ³Yn1Äœ˜Ó ‹{ÅqHg¸•Ÿ¸u#¸ç¶Lþ˜ hŒ¯s ˜:6«Ìz!Ðy@}zÚgí¨íœqÙº/ïS”×å4~ª¨\°ôÁ~Y3M9Py²K=ê.Ðê °ï ¿¢¨Á÷-±óz$óß8ôÞY7"Ùtàk ûµHÖ‡wⳟ\8 7Ü…ðÎO$~ðjû÷ñ]¼›n5›ð¾êø`ããfóŸåfsãKô¿pÉüYqxÄ(Â@ pHYs  šœ IDATxœìÝMnIÚ.좶p¦=ïI jNO$J›iÔéo.zö ôb,Yï ÒãÂ;:Ó>èÎ ߀þ‘e‰$ó'"óº€%ZJþ“qçó<1¥¨Ûfî O¨С@„: ê4@¨С@„: ê4@¨С@„: ê4@¨С@„: ê4@¨С@„: ê4@¨С@„: ê4@¨С@„: ê@­>Ä»¹€zu V)ìÂûøÇ܇@„:P·.¼)üW|3÷0/¡´`zÁÀº u ûÐkÇ°^BhKÞÇ?Tí¬PÚÓiÇ°>BhÕ>ôáC¼›û0˜†PZ–ÂÎœ€uê@ûÌÙX¡,ƒ9; 'Ô%1g`±„:°4æì,’P–Éœ€…êÀr™³° BXº}èµchŸPÖ¡ì´M¨ëÑ…÷1iÇÐ&¡¬9;MêÀ™³Ð¡¬—9; êÀº™³Ð¡`Î@„:À9;UêOæì¨Ú¨ŽPx®ÓŽ  >BàeûЇñnîÃà@¨¼.…9;uê9æìT@¨”0g`fB œ9;³ê§1g`BàæìLL¨œËœ€ u€ËìC¯Àø„:À:ÁÀ¸„:ÀPºð>&íØÆ!Ô†eÎÀ(„:ÀðÌÙœP‹9;êc2g` B`|ûЇñnîÃh™P˜F ;íØÎ'Ô¦t˜³£ÀÉ„:ÀÔº°½`à4B`æìœD¨ÌÇœ€bB`næìê50g C¨ÔÃœ€W u€º˜³ð"¡P£.¼I;6€ï„:@½ÌÙøF¨ÔmzíØ„:@:Á°vB æì«&ÔÚbΰRB =æì+$ÔZeΰ*B eæì«!ÔÚ·}øïæ> €1 u€eHa§°dB`Isv´cH¨,Mö¡ìK#Ô–Éœ`a„:Àr™³,ˆPX:sv€Eêk`ÎÐ<¡°æì êëbÎÐ(¡°F]x“vl@K„:Àz™³4D¨¬Û>ôÚ±-êÚ± v€ª uÌÙª&ÔxÊœ RB€çÌÙ*$ÔxÙaÎŽª B€×uÚ±µêäìC>Ä»¹X7¡@‰væìsê”3g˜Pà4æì³êœÃœ`bB€s™³LH¨psv€Iu.gÎ0:¡ÀPö¡×Ž ‹P`X`ƒP`x]x“vlÀ„:c1gP`LæìêŒÏœàbB€i˜³\D¨0%sv€3 u¦fÎp¡À<svTí…„:óé´cJ uæ¶}øïæ>  nB€¤°3g8F¨Psv€W uêbÎð"¡@ÌÙžêÔÊœà ¡@ÝÌÙBB€˜³uš±½vl°^B€¶t‚X'¡@{ºð>&íØ`]„:­2gVE¨Ð2sv`5„:í3gV@¨° æìÀ u–dúð!ÞÍ}Àð„:K“ÂN;6X¡À2æìhÇ‹!ÔX®.ìC/Ø€eê,9;°B€50gš'ÔXsv aB€u1g%ÔX#sv 9B€µ2gš"ÔX·.¼I;6¨ŸP€`ÎÔO¨ÀÁ>ôÚ±@½„:<Õ v NBž3g*$Ôàeæì@U„:¼Îœ¨†P€sv BJ˜³3êPnúð!ÞÍ}°FBN“ÂN;6˜žP€sæìhÇ“êp®.ìC/Ø€iu¸Œ9;0 ¡—3gF'Ô`(æìÀˆ„: Éœ‰P€á™³ƒê0sv`PBÆÔ…÷1iÇ—ê0>svàbB¦±½vlp>¡Sê;p¡S3gÎ Ô`æìÀI„:ÌÇœ(&Ô`n‡9;ªvà(¡5è´c€ã„:Ôcúð!ÞÍ}P#¡uIagÎüL¨@ÌÙ€g„:ÔÊœxB¨@ÝÌÙ€‚P€˜³BšaΫ&Ô %æì°ZBÚ³½vl¬P€Vu‚ÖD¨@˺ð>&íØX¡í3g€ê° æì°pB–ÄœK¨ÀÒ˜³À" uX&svX¡ËeÎ "Ô`ésvTíÐ8¡kÐiÇ@ë„:¬Ç>ôáC¼›û0àBÖ%…9;´H¨À™³@s„:¬•9;4E¨Àº™³@#„:`Î êÀ9;TM¨ß™³@µ„:ðÜ>ôÚ±P¡¼¬ìP¡¼® ïcÒŽ €u Çœ* Ô€æì03¡”3g€Ùuà4æì0 ¡œcúð!ÞÍ}¬‡PΕÂN;6¦"Ô€ËæìhÇÀÈ„:p¹.ìC/Ø`LBŠ9;ŒH¨C2g€‘u`xæì08¡ŒÃœ%Ô€1™³À@„:06sv€P¦Ñ…÷1iÇÀ¹„:0%sv8“P¦¶½vlœ*¦”æ>à%ï£7',_þ™ÞÎ}@âõÍÝáÿ¤ÝѦ¸ !„ôéþÝØÇÀ¼„:P+¡¬Ç&tá÷ôyîÃ`ñúæ.»IŠ/ú!Å>=~læó$n·“驪‡8ÕßÂE¯‘w5†;S>ߧšëõ1îcûB_ãká%kZ&»¯)îjþ\È®s¯eSÿ½ìñX0ø¹Ñ¥fü,užH í×`næìp‰˜v!¦]Øìû¸Ý¦oÕ4'^ݾ‰Ûmºh3'¦]ÜnS¼ºun±j©ûöZØÞüáõ°B>€œ'.”Pj`ÎCùº‘ëK{Sâöæ°Ù÷ƒýÂ;Ûç„R6ûÞš°b1í„{@ÁyâBu `€ÁØÄkÆ!|IÝð¿9u‚¾ù²&Ì}ÌåK¸ç3Áybã„:P—.¼I;6†a¯vã:_¥ÎÕ¸|'è[=Ÿ À7Î[%Ô€íC>D0 Ãö*–1¯(í<ÿ|'ØY=Ÿ ÀSÖ„æu V)ì´c`0CÎkábñêöMˆiWxë>¤¸ ûM—bzxˆ!Å]H±ðßÏ?ÏvVÏ&.ð”ó„¦ü2÷GæìlÂ.üž>Ï}0Œ/=<ÄÒÛÆ«Û7a“v¥Õq{óGz¸{î± ©ä~Æë›»£ÁGŠ»ôéþÝÇ5™MI û°»ôøñ§s€'÷û]i ·x}s7×ãuÊëú¹E¿Ž8õ1ûvåaaêâÕí›—^_S[ÓzðÚ}ÛmzýåïÛéÏøºUñ™°6ÖÄ-ñ>i-牧Rê×…}èÍÙà¹ôøñsz¸ö›.„Øü‹Î•Ùó;<¹ –ا‡û·%îß_¹?|Âf/ÍI?§O÷ï¾Ur•peöbüðüûL‚óÄ%ê@+ÌÙàß¾´—|a/ªaTÏÁ©WʦǟK6òs|Xºôéþ]Qм&–Ègð”5ay„:Ðsv8¢ì »«0ç—©Ò)ÜŒÿé·~º—}þc8ëwÓžôøñ³ ®uó™„؇w!Å]Øoºôð¿þo°¿ÃbXNÐ<á<±m¿Ì}ÀÀö¡ïcþ™ÞÎ}(Ì-ö®´\§/›5—ÏçÊyb«TêÀ2uæì½2Û\X”Œ ÀŠ8Ol–P–Ëœ€µËG`•œ'6K¨KgÎÀjŽÞWÄqžØ.¡¬ÁaÎŽvlüÄ\X ¡áºyþ38O¬“PÖã0gGÕÀÊh­Q—ãχÍf£ Ϫ¥O÷ïæ>`Öþ u`]:íØV&7—i %ŽW·>§™œ6<Ë%,^å<±IBX£}èÇèËL­¤b£Mî\ ÝÑŸ§¸›ä8„PÖ*…9;˜³0­C5D.ØI]ÜÞøŒf81u™ôSÓ;z™ç_ë=à5Ϋ$Ô€u3g`élÖÕg_rU|êâv›TX0ˆl¥†ö;K¯n߄;Ïܪ×zVÌyb“„:€9; f³®>eÕ:_lö½y\N¥ÆÚ”:¡0d–Êyb›„:À9;0™ôpÿ¶øÆ1íâöæU;œ£$´©·ñêöMÜÞüQ褸óÜ´ç—¹¨ÈaÎNþ™Ê7š€óì7]ÑÆk!„Ô…MêãõÍ.}º7æa±‡jÌ<„¤R£z1tñú&{›Ã:QüK{k @›„:Às‡9;›° ¿'WîÀHÒãÇÏñêö„`'|­ÚéÂÞöÈ:!h½Ö„Ôeç"$ö'U ÂÜbÚÅív7ÊïNÑÅ4Gû5à%æìÀÒãÇÏa¿éNüW]Øìû¸½ùcŒc¢}ßZpåfé„Ø W&Å@ mBàuûЇ÷цŒè{°sjÅDêâv›Jf¦°ñúæîPý• tB{­×Ö%ö*³Ú§ýshÇfÎŒæKµÄÛx}sbAˬ§´d[¤“ú¯¯›Xxûäõ²>_fsmo´_h˜P(Ñ…÷1…MèÌÙ€ñ¤O÷ïâÕm˜…RPiñý_6k¯oÌXŠSýÓ~yïu²f‡*¿°ßt‚=€öh¿”3gF—?~N÷oC:£5Ö¡jçxuëóšWDUlö½µ =*u€ÓæìôÚ±À¸¾TR¼;½%Û—ª«[WáóŒ@§M±)ôe7=±ÂëìX+"ÔÎaÎLäì–l›½vl|—¢×B«R8¥]Þ»¾ÌdŠ¡+Z3;ÔÎú?Ð~ 8×aÎŽvl0ºo-Ùö›î¤ø¥Û(E#bö›Î†èº¤O÷ïNZ36û~Ìã`8Bà2æìÀdáÎCw[ë@„:Àåsv\ IŸîß®ÀÏoÔ†;¢·%ÅÝÑÿ•ü a_ÍR:u³0SÊaÎÎ&ìÂïÉŒìˆýÛC{µ²¹!„8òa1\»´¸½éŽ>ï‡ z-×øn¿érmÖâõÍV}uS© ©ÓŽ ¦•îß–Vn˜¯³ ûüs®O‚àLu_ Ý$ÀÙ„:Àðö¡¢M˜Hútÿ®,ØI6lËP¶A¯ÏdÃÀ‚ª?f%ÔÆ‘ÂÎœ˜Î¡eRÁŒþÅH¡ÏÝDµO•ÌYòš¨›PÓaÎŽvl0‰Ã0ô\°ãJü¥(š}¢Z‡Ÿ„¿TK¨ŒÍœ€iµµBf­¬KAÛ=Ï7?(¨ðÖÁçC›„:À4ÌÙ˜GT•±6E³VXŒ²jÐ$,†ê.€ª u€é˜³ÓÈ]‰o“Y²Õ:©SµË Ô¦fÎ@M Z7Ñž|õ† ®%)ªÖÙ¨¾à èõ‚óÄJ u€9˜³0°ªu8•óÄ& u€ùìC¯ÀØTd¬—¹:k¢Z€Ó9OlÑ/s°z‡vlÿLoç>€UJ6þ§¯nß„ød%†.¤ÐÒ)…Þ¸+“âîøûCµNzüøy²c¢:ñêö˼"Ϋ$ÔjÐ…÷1…MèÂïÉ&À@âõÍ]ˆÇocswq»M!„—ûe‚3.¸Xútÿ.n·»£7:T븘fÍ¢+óç‰-“Ëõ0gà2fëp1WæÔL¨ÔÅœ€ámà 6išÖk«d¶K¡Ÿû€ 8Ol–P¨Q'Ø`YŽ_ù¯oî†ÿ›Z,­–jŽÉmäP5¡P«ÃœíØÎR]ÑÏ0\ù΄TëpƒÑañœ'¶M¨Ômúð!Žpõ2ÀÂe¯Ä¶iW•¯œÏVah©²|ªuxAÑFîƒÑsÁÑÐÕDù6Sý jç<±iB ~)ì´c(WÔÊKåȤJ®vtƒ=j½¶vªuxQvÖÖ4¹“G'¨íx`LÎÛ'ÔZq˜³£@^ÁÎZjThÈ &óðü¯Enƒ^µÎšžëÌ:SÑFîP³ÆÆ™Y sžØ<¡Ð’.ìC/Øx]ÜÞä+µÞšGîq¨Ý L¾Ù¼×Uu­Çfßçn2éFnvMÌUÊ·^;þsXç‰Ë ÔÚcÎÀ‹_Ôó´®¾œIÁ̆¢Í–cÿþêö L¾:´”šxv U*[[&ž¡‘]SwiH]ÖfÊìÖÁyâru€6™³ðƒÒ/ê6ôçS´ÁRwn°¯nßTw%>ó+¨ÖQݵlÅŸ%•]*šcÓîÜE!wéq@ãœ'.‹Ph™9;ÀêÅë›»¸Ý¦¢/ê!ö6ôgV4¯âô`§4бY³>ªuÖëäχ9Â’5i³ïO ãõÍ5œ'.Õ/sÀ…¾ÎÙéÂïÉUv@óŠ7®¾nÂÆ~ùÄWaSt?sóbèâõMö×Ô´A‘>Ý¿‹Û›.¿¹’º¸Ý¦â.¤×7[ãõÍ]ˆ¡ ›²¹(5=Lhwa“úc7‰×7ws½>Ö´¼z_­å…÷íéíCHÝ)Ÿéáþmù­‡sX·»ì cÚÅíMR8ºé|XÓ®ì¾ÛÀnÆ©ïKùÜÛZÎ9N¨,ÃaÎÎ.üïä‹ж±®˜ßoºª6(¹Ÿ©+¤]×gCÁû71íBL!n·¯üü”¿»éN¸5 ’?~ŽÛ›þh˜xxOÎó^YÓzpÖ}-¾oç™{mØoº¢ªš/ÃÑÈöBüøÙ.„PÏ9ÓSk9Oä(í×€å0gàe¾¨W%=~ü<}ËŸz¯:f"fëð’ >Êæ ýGãnîû Õ¨`à4B`iºð>&sv¾ðE½J‡–?SmbÆ~®ÖJÔÃl~RÑçÃašpMÔv *Z('Ô–é0gG°¬XìÓÃCôE½^Ólb txBµ!„b_ãFî$kbŠ;k"„à<±mB`¹ö¡×Ž XŸ/›u6­šîߎ֊Íæ%ϨÖáëºPëFnz¸;ÚŒŸý¦S¡Î—à—¹`d]xÿÿLNZ‹}HA;™F¥O÷ïâÕm6iwt}±Ø‡½y¼bwa“ú–âÎ:²Dm}F|Y»b¼¾¹$dôºfõÚZÈêkp˜³³ ]ø=Ùàæ•bBØ ò«Zþr>VuJ£¾lb¾ áKû«ºÓžoØäÞ3iâëC™é=?~ŽÛ›þ‡××Ü›ÞkZ¦¼¯)ö-‡»_^“﬉Ï,qMðÜhs>†Î)SJsð’÷Ñ›` ‚¯nß„xd#³ñM[æ¯n߄;Ÿ=ÌY„:P/¡À˜zíØ€Ölæ>€æì4D¨¬ÕaÎÎÅ7s@ ¡°nûЇñnîÃÈꤰӎ ¨Pàà0gG;6 RB€ïº°½`¨‘Pà9sv€ u^bÎP¡ÀëÌÙª!Ô8Îœ  B€æì3ê”2g˜‘Pà4]x“vlÀÔ„:ç0g˜˜Pà\ûÐkÇLE¨p™N°LA¨p9sv€Ñ u†bÎ0"¡ÀÌÙF"ÔÞaÎŽª`@B€qtÚ±CêŒiúð!ÞÍ}@û„:cKagÎp)¡À4ÌÙ."Ô˜Ž9;ÀÙ„:S3g8ƒP`æì'êÌÇœ ˜P`^æìE„:5؇^;6à¡@=:Áð¡@]ºð>&íØ€ç„:52gxF¨P+sv€'„:u3g!uZ`Î Ôh†9;°jB€–˜³«%ÔhÏaÎŽªX¡@›:íØ`]„:-Û‡>|ˆws0>¡@ëRØ™³Ë'ÔXsv`á„:ËaÎ,˜P`iÌÙ€Eê,‘9;°8B€å2gD¨°læìÀBuÖ`zíØ mB€õè;Ð.¡Àºtá}LÚ±@{„:kdÎ4G¨°Væì@S„:ëfÎ4B¨€9;СûЇñnîÃ^&Ôà»vÚ±@„:à%ï£7'mØ„.üž>Ï}@ýâõÍ¡Ê/†.„ÒÃýÛÉa»M!Ä>¤Ð‡Bútÿnêc8×·Çï-Ý€sXa=âõÍ]ˆ¡ )ôs¼¿ãÕí›°Ù÷!Å]ë‹Pj%Ô %‚(¯nß„˜º¹ã›ûôøq”÷ï·/äáåû›â÷øñ¤Ý+?íçÚ8xÍ·×Ê«Çü‚wc>§c8¶QÛÚ})•[†~f׉ç£Ïùœfx½ «~Œ IDATMúX4ö~ZêXz0ÆçQîõVÓgà)æ|LÇ6æQÛãñò{>öó\tóÇ篱û¸«emùeî`ö¡ïcþ™&?á†æœºA5¾]aœ/¨™û¯oîêÙPH]ˆ1„f?žÃÕ¡i6g„1íBL!noªÚ|8êøëdÆz}Î)¿ û:Ìÿ½]˜òq®k |j¦~½MûXìBï§Å¯…çñúføM÷üßý3ð,¥éÕmÕß‹F\#âv»«á¢–o½øžO]¼º}3ýóöüXR6©Ûm¨¡zÇL†Ò™³ü(ösÁr#_Z²Í)noþ›}ÿZuS¹Ô…;?\i Ðkàõ†ŸõùÒÖ5»Š*¥«qÄâv›f«¢Œiwô=¿™ö½}*xo uRÞÇþ+¾™û@€ äB’ ¾ÿ ÍBÅ«Û7?·úBêšÞÔVcÌ50^Ý6ynÚêqO¯ðuSþ¬UL»YÞ/)î27è¦8Œo²åŽw|B†·½`˜3$y®äêÓ¹Z²|Æ;Ú¦…`¨Û8ÎW_ªvZ HT–dö¼z<󦿔´2›ªŠ¨èïTp~+Ô`‡9;6aÅJB’ÉZ}ä®ÎóªËIÚŠv€:è<1q §AÔVÑZ£ƒ¯&ý9L„æÎæz/d«¹bs™„:ŒÉœX»ì—ô©Z¡Ôyuîd›™!„R7[¿|€Ö¤ ×ÀÃm!DƩ窟ÊMìÔP­s¸¯™×Ç~þÖk!„ðËÜÀâæìlB~O³_Õõ‹}vÍÆn!‘bb:vƒnÔ¿¾¶7;~›’Í„¡Ž«ôþÇ>ìãCãÕ훃Žó¿&íâÕmW™²b—TÆÅÐ_7.XCçh©SúX}Þç Z=õe ÜÞº)ô/­Õ'­!uñêöMSkà!„hçx'wâyÄ!šü34§¬§Ùõó‡÷èÛ“þÍÙbôøÆ~î²ëQU:!u˜Àã_!\ÿÏuŸ~qîcê½²iÕªôøñsÜnÞfô ¶ìU¹3mtnö÷õ0ç«/?û¯nûÆDæþNºA?»d‹×7ǯÌol -=Ö¸Ýî^ÿ%mÝçoJý¦+YCko;礇°ª½çw%mülY•:'¾÷ß…pFz}s7ɳ»°IýÑcõœ1óÚ˜ò¢« í×Õ—@gÞyÀÌ2¡ÉØ­P²ót¦ÿ’^ÖB$öéáþméæEzüø9=͇ܿT‡M‘’ß 0†¢6G!öéá!ž²[¶N8Ïm ÖìWœyþ°öÇ3=~üœ>Ý¿K±è–‰æÙÞë™ãi6VÉšPSx.Ô`4¿þùÛ!Ð ¡º–À„r¡É蛹+/gXŸ²÷ùèœó«‹65[,GÉtæ슢5pª¡ëC±f¿ìܹ|æê|S]š½Ðf¤ç.·&Tv¢P€Á=þutþÏÿû_ßþ[-ý‡éÍyecÑ•—¯OE#âÍBªu€y” #?Þr-§d£º­j!ÄËÎ|\Î ƒª¦ ´äœqè÷nÉùPMU:!uØ×vkOÙæU͘msmŽ+/³íàŽÏÐ)–»o®TæP0çl50Ž7¶±ßV5¾ìFü~Ó½þCŸ?)¸˜d²‹Aòç/Ç~ª\%\eU:!uÐ?þý·ïíÖžªh¨$0“¹¾çÛiô“Ç´;úÓ®ÍþžÖÚË[{.¬Tü*?Ÿ£±ýÆB¨ÑeÂÁ\0¨ZõGEól&ºdÊj¢ÊÁ Ûˆ uįþþõŸ¿¿ø³ÚÊÕä¾0µÓ¨®õÚÀ™0ͦ0¥É×åÌ…EUV¿¼ºn7BíXÈUr!‰jÕŸÍ>ñ‰lµÎ@!çT•ƒêp‘—æç¿Ž½žT«þ¬¦Š”ì± 4p¢ÊÁ¡ u8ÛËósž©°10—‰†ììš9ZC^Ö.æä¿–û}6µ€)eÖœqª»ÛjÁv´ V!Ô r›ùºœ§¦Š”)ÚÁ•„¤5=&O u8Ë«ósž«éŠ/`^Ù¶CoVM3»¦TþŠÒ‘ÖKá:°f¹l5¶¡|õ˜ë ¡fQ¸™ŸûœWùtºIß/¹*™K/LÉÏ]¼ì÷H¨ÀÉŽÍÏy®Ö«›€éåC”á6«ªÜ¤Ë¶š£r¨ÒÇ XœìZ3Öjƒm(}^ "Âåót8ß„ï—¢Ö½g¾Šæ{U\ñ%Ô ØÉós|©~r|sm°Íª‰ç6 b¬ÊÆ74ši­i÷£×Z°i›yÒE æê´-w¾vîs¸i·J'¡…ŠæçäLU’o©1Íq<•›%1ÒÆc»šÀªÌÕ²·Öý#Ÿ—k®°,¨øê§9¦PR-sêûápûºZôžJ¨@Ö¯þV6?ç™ÚO†ä«Fv“† ã»Z74e™)ØnÕÑóè5WXfîûóב¹: «šÉUÝ<—}ÿÔ u8ê¤vk?¨ÿd˜Þ›vù šק `!*o¥ô:-Ø~rlžŽÏÒñÍP U2“ñ¤jÜûg_ÿz!ÔàE…ÿû‚vk3 üÙ\ýªÙÖ§|»k&À\ªmg¦Û ŽTY¼öx™«3˜ù*ꆙXráO UƒB~òu~Î%´^Îvô*Ü’Ÿiñ3Çú4w«œf¯RVaî5jî5úZ°ý(»!ožÎråªgJùTé„ Ôà™sçç”*i£qîïÎ_¹lÃç%f c²Æ”ûùsL ¶R¯VXd¯σš‡Ãs{ÙóXRÝÖB•NBž8~Î3s_m 4 óÅüܶ2¹+—µ9 fÏ?ÇŽT¬®ÛÑ ëõóŠV6ê«7÷w¼Ü9\.èÜd~>÷ý;P€Ëçç<§õ“ÿbÞõ{s­Û¬O4äh ‘Û¤^“ìEG>ÿU=Tþ8”´Ï}-è<ü÷ãç–-µê¬Üósžs5• WΞ«“ùÂn} 5¯V¬g®ÎÅótTêUÔš¬†Ð#WMóZЙ­än§J'¡Àª2?§±b`ùpåôª‚ ŸÝ©¿s0¹«_Uðš#Ÿ5ÏA™Rö¼Â\ã²U_uœ§”Ìe|1 Êœ‡UX@¨°B 8?à\™eM,*ˆfT{Û©cŸgW¶6æÂçÈçìëç[ Í$Ì]¤ó¬*'>YG`uŠ_æ>¦5F»µ§Z»Ê ªÓ.n·»Q~wŠ»E¿Gv&|“âîåϹõ´`{Uq%nì_}¼íêÎâÕí›’ÙLUC¥Ø‡˜^ÿùóç2|îÛë4¡R`Eþñï¿è´x•0ŸìÁ WN[ŸhØŠ[° vÿjª6©@¼¾¹ ›}Ÿ½aeíµUWeíô­ØŽŸ±o±ŠK¨°¿þù[ø×þ>îñE 8YæKyÁàÞJæéXŸh—l¯ªŠdéáX‡óªx}s·ÛTvñL쫪Òù*W]óõ=‘«BjôüPû5€…{ü+„ÿïÿN3?§Ê~ n)ôG7£}ÑóWPæ6´2’ zkmÁ6Pånútÿn´·3‰Ûí‘>d/8µÄ£ÒÖdéñã總é_í§îKk¹W~þåV~U©°`_ççLèœ#ß‚­ôêãÌ—ö[k,ÆÊ+I†rì3s U&/²5Ø m_`¿éª>ÊUÙäZËUÖVîB€…~Î3 Ÿ5Ë_}œmÑf}˜ÙÂ+I&õJåéB‰ÁÃ*çejtÂåU6­Vé„ ÔX¤Iæç<§µp®ÌK6´‰™ÍBëÀ̬Ã9RP:‡nI†Þ˜_mÅÓS :ßœÒ5° _Û­Í¡™¨]Š»–¯ÌM¥ÎPŽÎ…)C×’+²su­×uöMìÃ>îZ:o:{NRãü¨ÔXˆ9Ö¯tæ•Ý<¸hC§í/íð³uµ`{Õ(ßAV@¦¸K÷o[ t¾;õ\/ömÞÏï„: ð럿Íè â¼ð%Û&%7D€ñ¹hX+iÁ6Z+´KÛ¾.É~Ó¥‡‡Øt•øþÄõåÔÛWH¨и_ÿü-üŸÿ÷¿f=†¦¿uÈ„/çnìXŸXš£Ÿm¹9sK2V ­†Ãôð_û_Øoºì/hè¾¾æPuSúZh¿J'¡@³ÿ !þ÷õìŽÖFÀ²áË¡Çý ÿ}­fVuU0XA ¶‘fæ}Îј¢°c)¯—Òê›…Tp u4ëüœçrb Ôîç+I³AHKí~p¥,@³Zú¼øêÈ&ö:.ó²}&„£µ¹›PiÀ·” î_æ>NSC»µ§–rb T Åݱ+FãÕ훾´ç‚±Ú²ÀÌÒãÇÏq»}ù‡›´ !¼òx†¯oîB0ºE†3µnhR›¥]!„ÙäÕÛGîG[kÊ´!Jö{ÍÂæê„Pø]n÷{I„:3ùÇ¿ÿ®ÿçzîÃX”ªCç¹6k»ê™¼%^96‘/rt-odMɇOsTUµñØ,{ñLêÚ ×E¨0ƒ¶æç<³È«çÚœ44ÞBg´9 Ýš’_×GZ«Vl7Þ‚->uqÄÊæê„Pøºß4òºY!¡À„ÿ !þw{íÖ~`0˜×›Ê[è̱qXçUÏ/gó|EÁvë-ØrÕZs}i¤Òédªuš%Ô˜È×ù9­»h€9À)N iš'ž«3×UÏÔ%w•~ï!·‰<ðæy~žÎ²*г-ت¿¿ÇŸÿѾ‡äÖÀ…¶T­Ó.¡À~ýó·E:õ–äÔÊ–&Bç\ˆ2ôÆQf3Ym‡’Ý@œv­‰÷ë0ôk¿ÖÊQµÙ‚mÎÊÒü¸ÐJTë4J¨0²_ÿü­ívk-h%tÎn ·y’¯úYâff¦ QlÆQ“’ y°5ðêöÍl•sÚù<¬¹âdöÊÒL%íB×RÕ:mêŒdósžqE70¹Ò°¦‘«­ˆ™cjó${…ºÖk5¬_îõÔJ Êrä^sC­+}íª*®8™»ª*[I[ñcw)Õ:ÍêŒ`)ós~ÔƆ)°0…›8M]m S.ß<9™+ÔõóÊÏ9þó%• 0¹’ŠÅ CÍ¢×~#œ¥ÉÀj檪ÊÚbNIµN{„:[Ìüœç\Ñ Ì l§­¹²Í“}îïW·o²›OMnø-LÁ†rÜÞüqÑß(Ø„î1µ¢ŠÅ˜vç†ÛñêöM~ }Sœª±ÀjÎy:_-úõPBµNS„:Zòü›>À|2›9-†Î¡Ê9úe›™Öô”m ¦îÜ`çðïrU:mmü² Çæ¾|µÙ÷§n"—®E¿aͳÏÓù*3Wg¨¶˜R­Ó¡À–8? ™ÍœŠ²cN]ÜnSé¦f¼¾¹+ÚÌT¥S¢çâì¿®nß”:añÛÔ«¨Z'„C°S¸‘^¼.½Jç«–ÖúÜ<©´x‘ÈTë4ã—¹ uÿø÷ß¿þó÷¹c\-})„ÖÅ´‹Ûín²¿—â®öP$}º7éc2•ý¦+Ú€Üìû¸ÝÖâôãfä·ÍΘv!–ýÙÚŸïLý~øj¢÷Åáµ}Óå˜Ô…Mêãö¦)ô¯¿B6…3tRÜ­bc»rq»M—ÿ’Â÷Imëý>îÂ&õÙÛ}½_ÎGŸÞ‡sÖÀÕ„™)ö!^þòšF%3àrÙ¡µi=ï¡oªuÞNq<¼N¨p%·[ûAc}¹i8tN?ÇíM_<È>¦]ˆ)ÄíöÉ;ñî7e‹é”nl‡BH]ˆ¡»øub_Õæ>«”?~Ž×7»âô_n÷æókàZÂÌÃgÌ6ÙZæÍ}­àgfê«ñø—@§H ý܇üL¥° Ú­ ©Ô€ u€ÅûÇ¿ÿþõŸ¿Ï}ÍH?Ï} ÀÏ„:À¢i·v¢wsð23u€E2?çLZ¯@µTê‹c~Îù´^€z u€E1?çªt fB`1´[»P ý܇¼ÎL yæç #ýÿìÝ¿n\Wº7赇FÇgŽÚð7€O7ÀR"‘º £Ñ±““«t-N{Îø&):p1ºåpÐÒ³¶>Ì@Aƒ1×EҒ̪½«jï½þìçÜÝn•ÈUdÕ®ªõ[ïû¾<{–{ Àf*u€ª™Ÿ3­× tB Zªs¤õOû5 J%•:P:•:@U´[Gºxq™{ Àv*u€jtF’â2÷€n*u€*h·6"­×  *u€â tÆ¥õÔA¥P,íÖ¦ Jj¡R(’@g")¬r/è'¦”r¯¸Ï_¢''0[Ú­M'ŸÇÜkúQ©ãâ½@gZZ¯@MÌÔŠ ÝZZ¯@U„:@vß¾ý"|ÿËW¹—1?I¥ÔD¨d¥ÝZ>éâÅeî5ý™©da~Nf).s/ØJ`ræç@ë5¨ŽP˜”ù9eÐz ê#Ô&£ÝZ)Té@„:Àè´[+L «ÜKvSJ¹×Üç/Ñ“h‚@§<éü<æ^°;•:Àh´[+‘ÖkP«£Ü Ú$Ð)”ÖkP-•:À ´[+\R©µR© F S¾tñâ2÷€ý¨Ô¡ÝZR\æ^°?•:ÀÁ:•Ðz ª¦RØ›vkuÑz ê&ÔöòíÛ/Â÷¿|•{ô¦Jj'Ôv¦ÝZ…RXå^p3u€Þ.Þ tj•^ž=˽à0*u€^ÌÏ©™ÖkСÐÉüœÊi½Mê[i·Ö€¤RZ`¦p/ósÚ‘.^\æ^p8•:ÀÓ—¹— C¨|ÄüœÆh½Íêw´[kÖkС ÝZ³Té@KŽr/ÈK Ó°V¹— '¦”r¯¸Ï_¢''0:íÖÚ–ÎÏcî5ÃQ©3%ÐiÖkÐ3u`f´[› ­× 9*u`F:3’Tê@kTêÀLh·6/éâÅeî5ÃR©3 Й™—¹— O¥4L»µ™Òz š$Ô€F}ûö‹ðý/_å^h½mê@ƒ´[›3U:Ð*3u !ï:³—Â*÷€qÄ”Rî5÷ùKôävb~!„ÎÏcî50Žøøôéoÿ’–ýaŠwÿž^ž=›jMS‰ž‡˜ëù侇¸º;ÔâJR€úܽÆÅ°áæzëö5Î5BB(—PØù9¬ÅU:?{˜{Àpâ£'ÇáèzÕu»ZÝxr²ù=oŠËÉ]­iù» ®î¿¹ ×qYóæ×]󻧇Š?[Ÿ‡¸Ù­æçðøôé^¿ë=yÝœú¾ìdÂçF×Ïa¬ßA)¯-}Cÿú^SJxï°÷k\ŠKÏýF{M¹ÇØ¡~÷eøÏÀ¹®]»Ð~ *÷õ«Ö´^ƒöÄ~›ñÑ“ã‘WÂÄâ£'Çñäô§u¨·k BiŽ®Wñäô§w÷}ßñ˜–ñä$}TÝ471-ï~•>`†|nÖòZtm°¤‹—éüìa¸>Z þýGpB,{Ür•ÎÏvmÔÜÝÿ>§º<Ñ›.^\¦—gÏÒ˳géü<®ý~¨^‚Ò‚­ÐÖk½+Qû¼ÆÝ^Ûú\×´a›§BŸcê@¡ÌÏ¡¯OQãÕe§Í®Nž¦‹—ýƒŒ­júl¾¤¸Lçgwù²éåÙ³>÷¿õçËN!WH J9±å IDATmØ ·6¤K ªû„+»¾ÆŸ=ìv\Óf©ÔçÁ„:Pós˜Ó‡ÒYa³ëÖzC¿g‹¦\·ÎïWû\H/.;ÃŒ™<Ïú†\N¶C~û„ÍeÔAÕž¯qáºWEæöïM“ææ u  æç°³‡mØr:u&›Ô-èU¥“6Îé¯WÅÎô³ÙúlBîZ¡ó»¿ÿòìY×}+{3t8ëÇQ÷Éö)Öl±O ¶CÚ¶¨ûú÷ž{#¸g£™„yB(„ù9ì%M¿Œ«s¤ÏéTÊ×cÓaˆöš­Ø®‡†'{éÚlêÐB×ó¥ÐÍÐQô¸vÌ%ä‚rí³!]è&v×õõÀ¹ }^#çRµÁfæ u ³‹÷æç°¿ƒOp•é>ÕjS¶S…aC•FŠËt~s¼ŽôÙdj^\w…J¡›¡#èW­Lcósq—×ñ²_ó·__¹ÎwWël]5ÛòšA˜'Ô€ŒnÛ­ tØ‹ÖkЦm›ýw§ZmÌÖlÊPãîëÝUãÄU¸>Z ýõwÒµÉ6ôë[Çið²7EÖu2~&'œ!»mÏÅ]*·ÝvŸººÌ©sn¶=‡f0#N¨™|ûö íÖØÍíæÅÖÍ ö?ÈVoêPãöËžŸÇt~ö0{•ggKžaCˬVaü,  ¯õ»T—l¸méÎ@ëë~=S©Ó´AžCuê@_¿z¾ÿå«ÜË r6f =]w›æiÕ­»õÚj’udÓÑ’gêÐI ä°åZߧ‚pëmz¼ŽŒY¥8iäGûqR\†—ë™qçq²u0½ŸC5êÀ„ÌÏa8­oø¿÷ÛóÞ\zõj½–»’fD÷¬Ó奟Z/Èf@ ¶^ëû´ Ûr›–_G>•^ž=[W¢ÞT£¾<{–^ž=›ÓÏ`®~UL¨1?‡AuõÄêÔkžUËÔz­…­„~ Ðß4é öQ•¶^ƒ!Í´›P&`~CÓz غi£T¹?9Ú©ãþöúÖ|K; J{¶:´õ4c¦-Ø„:02ósè£sžŽ0·ódšÿ=ç99ÛÙ†gîaÅÞí£ o½Öþk¥˜k 6¡ŒÄüF£¥º7LZ>Ãk»M 0­^ƒ÷jUyë5Å i†-Ø„:0ós•– Ц­ót*Ù¤a«îM¾¹_ßç~ÿËà„=LlÇöQ­´^‹žç^mØöºÕê!'¡ ìëWÌÏaT%´T a®N[RXå^˜:7VF¿ÿõlvó±sû¨Â[¯ý¦ãš{ä} CÚðxkôý°P¤Ý£sZšdžL #4šÃ©ñ9ÜG¨ÒNí£*i½ÖÔ§E«Ud°åñÖâkŸPpñ>„ø£vkLË\Ê4zZ´±ÝÞûw:ï£j&È¢g ¶šZ¯õ:ӲŠw¦·õñÖàë»Pt;?¦à´>4j¬y:[Z´P×xF×uMh¼ ”ªw ¶jZ¯ÝèóæèzON14åÞçÂ|Z° uàæç0­²NßÃ8ø„êÖ “öN&‡è¸&vÒf¥W ¶JZ¯ÝèX!-;ìæžçÂŒZ° u`Oæç09§g¡M]-!ÜdmíC,°Ÿ>í‹<ésÑÑ‚­¦Öké8¥E<9IÞ·°¯9µ`êÀŽÌÏ!my Qí:7Y»6rûKÅ:ÚŸxYgû™‚7…a:[°ÕÖzíÆúÚ¾ÃõE;62lŸå^ÔÄü†·-téÞI/.ãÉÉ–/!›å™ÅǧOC̽ æª×éu™í›hWŸê±ý¾ð(_u).ïß|Þò^aS%LŠ«Ó Ë:T:?{¸¾õ=h²®Ú ).þìä:.ÃQZÝ÷GñÑ“ã’Ð]u 'íÖȪÐ>ÙÀaâ£'Ç[û'ôn»W›7JTêÔA•ãè·‘W­ltQ‘ÆNÎbŸ fCÅn硉íì„bZÆ“ÓE¸ŽK×(úØú¸?JËÂÃ)×3í× ÃÅ{(¹O6°¿¡æét„?úÓWÀÜ4=9J&µ©úd\ìö¾¿¦°#Ÿ=Üý°ZZhÉÆN6>ÆÚ9è¤R¶ÐnRÔôa ØÁ¡ótînØq²w¹Ž@…öjQÃ"õݼR¥EIaÕõþà·ÛÈ®+¥&om–^ž=‹ž¬ÂÑõjÇ¿¹nÉv}´pÍb«-ï‹ããÓ§-´ôêÀß¾ý"|ÿËW¹—Z¯AÓ›§s÷UÌÕvÚ¢*®ÒùY­h éåÙ³xr²ìwã:«ùoB™¸s;¶ÂMÕÎJK66Ùú¾¸‘÷ÄB¸‡vkŒm¸y:w_qe®NûâãÓ§ƒoòD=_Ú®A¡¶½¦ÿ¦öP#Ÿ=\¿JËÝÞ«¤E8J«ø艪î—âòþ÷Km¼'6S>`~%²ÑjžÎÝíÍÕv …”«ÏÁŽFªùÓÅ‹Ëýfí„`Ömy½W[Ó¨Ô€æçP¦:[*= 5O§÷÷3WAË5Š1V(1jËÂiôjÁViëµMn²=Û½"5-âÉéOÚ±ñ¡Ö[° u ˜ŸCÁvn¿ÔcØö@ |€¤Õë±Ø{͘ –¶µ`‹«VŒôòìY|ôdµ[K6íظGÃ-Ø´_`ö¾~õ@ C±l¼@›:[¡rz¹þ°ÀžR\¦óóè}TdÛá®Æ~ݵd»>Zìô®WÚÍrkÛk^í-Ø„:ÌÖÅûâÍÏ`z]ótöÕÙè€ ¥¸ÜøOŸ6­×G aÔgÛóv.Ïéu¸swjI}Tû=†´á±Sy›Fíט%ós¨B#ÃO{tÍÓk³Æ\¨NÇõàY<9I[¿ÀzƒÓ ¨Ò}-ØÚš¥ÓG:?{=9G׫·^Ä“ÓŸÌ#„°®jÛð¾;>zr\k»>•:ÌÎׯt¨CcÃOS©Óu„IdÔów“^ž=Kççq×F^ý jo…’Mç!´P¥•º¯ÍZã­×6I/.û·csÝcmë{ã±*ç' R€YùúÕíÖ¨F­§†€íÖ'M·ÞbuÐæöÖíûz?¼V/ÅUˆÛ *ünØOzyö¬s0¼j¨Ò}Ïï¹´^»ÏÍç£ONê|Ý\WõTq¨±ÝWñn[°Uù|ê0 Ú­Q­× ]§ÓbÌŠšš[MÔ,]¼¸Œ''¹—A«R\nŸ°>µî¹5úpCZ%!„ë¸ GiÕu³øøôéœC0n4Ø‚Mû5š'Р(¹[ UÜj‚ÚÙŒMŸ–­†‡C>l·6ÓÖkŸêÝŠ-÷{.ŠÐb 6¡M3?‡Z9Q-Ëüáѹ،Íú”qW°cÆÔèÃÏ>#ü¦ïuoŠµPƒ •­U®åêÐ,ós¨—“ÌЪ26Tmp@“®{´n­ôD2W>#ü^:?ëœvÐœBÚ±å5²Œ÷ç»êÐœ‹÷!Ä t¨—“ÌЮB6TküðÚ†ír6žFÖQ¥Vû ø^§Ö+=‘ ³—ÂÊg„M„]tÛ:7§Âö¤BšòíÛ/´[£zµo*[”Òú¬pivlÈe6ƒÇ}já!Ô'½<{6Æg„&yt½¶–òÞ‹üÒ¦×ÈúÞ|–{0íÖ(߶qØS¸ÛNä¯ÿL€LY<.–.^\Æ“ÓÕÖkŸ3pk}ÈcsCÒ˳gñäd¹å‹©ÖBáR\…˜îý£øøôiM‡+…:Tïâ}ÿùß±ñôP»øøôiˆ[nÂjÈ“ñäta#£0[6B·§‰«ÙPØU÷ƃ¸ŽËp”VÛnRÛæ°¯¸ò^ÖÖNîÿÃÊÞƒi¿@Õ.Þ‡ðøoæçФ'40Žª-˜¦·µŸûú‹I2C-†:Ta¶p§¶ŸññéÓøøôiíÞ¨[#-Ø„:TËüZÔ½áT«cÕ©yBhd¾ÁF»ÿs›#e¶¬ÝC\ñÑ“ãøøôi<9IþbZÞü³È½FfnË!Êš^…:TéëWÂ÷¿|•{0¬†N ùu†DNêçÑu­o}Ãkî÷"ªu ŸîÊÀ2ªgÆ.^\nŸé“-î±õeEO¡U¹x¿t´[ &'ÿ„ºÜªhC¡*?×&+åTë@Âé-ül(@-Ø„:TÃüZ×ä†WGXdCwzÝ×úz6öÒ5;n´ê‘Æ®÷P­¿·í½X-ï…:TÁüÚ×nom ˜§Ã'¶_ókÙPØGŽÙq³®”S­L®°×¸–¯ñ`Ãã´’ÃBŠg~³Ðpom su ÕuÍoþ÷2ñ†ßŒ[Ú©Ö&7ñçÁ4{Ùö8­à}ƒP€b]¼!þ¨Ýóà”>´kÖUÜ«Ï5¿sÐvÍ&µ¶·^kþ5XµPA2¨ýµ_¨@‘nçç2W§PÕGínzMju?¾ÛoÚ«åMV` “\Ðâ–½mzPþ>¡ÅùúÕóâ”>´Íf÷鬞H‹¶·‰B­®°¢GKz¼×hûñLkšk¼ë©¸ºP€¢|ýêvkÌOjÿ”0¹:eê;ë¤Ù6l„Zñäô§®Ûôªbi@¯ð¸‚@%:7ËÓâÐ×·øèÉqç{‡çØ¢æƒUBŠ`~s6— %˜#­ŸØªO•ÈÑõêà¯O2j­Žö)sÛì뼿‡o²„Ðs³üÐ×·Õ>5oÚ3•:ß‹ uÈÎüfmnJÀÇ&kû°ýk‰›þsÐ+Øaï¯øèÉq<9ý©Øj¬‘B­^N˜ßf_¿MÖB+@…Æy}»{më¼Î×¹YÏÄ*mÃ*Ô +óshZw[Õ$머gxëÒùÙÃ^7<º^Å“ÓŸúl~ÅǧOãÉéOáèzUò°ßB­÷½ÿF_áú¨û6-R­L¥ïfùÑõªïá’øøôiï׶J7ë™V­]3>˽æËü˜ß)aàc“}LqbÚüçëð©¬ëQLËxr²œüû¦¸œüÚ|}´XoRuI‹p”VñääþÍù!¤Eˆ¯oL×qŽÒªû†]÷=-û[«Z7q•^ž=ë|^­«uú…ŠOŸöª*ëx|Ç““-»ß¤óóšž)0Ž_ÿÓÅ‹ËxrºêÀ|øš¿éBçóÿ·o—s½Î÷ºvý{¾kæ:šâ²Øªæ „:LN»5¸¥%´l½A¹õ«‰–r³±r2Õ·cG;m|ݪlóa“tñâ2>zÒ3Ôºqà}ï]ÕªÎÍ«uµÎ\7Dá¤ó³‡½+(oüúWα“®ÃOÒ~ €I}ûö ÜÒ æmòk€¹:%[ Âþ•IŸ.¡o¶!̵íÚÌÖ&5i´¸š}pÏÎj<Ä Ô`2_¿z¾ÿå«ÜË€bäÞDFVÊ<»ï'H.Ý´ÁN\…ë£E)¯E“Ü÷ë£E7£0[˜Hºxq9M .Ðᯋeê0º‹÷æçÀ§&ß\î ‘içU»t~öpôͯ—éüìaiÇx÷ý&À*ìþæ¤Z˜ÒoÁÎXá½@‡M}Øê@BFu;?G Ÿ¨ì$°›îVfÓp´¡]Ñ6¿R\¦óóXJuÎ}¿ï…XEØú^$®¦m™ qHâ^éâÅe:?{8øç ë£…@‡CÕö>A¨ÀhÌÏ-*; ì(ÅÕÖM‹[¡¥¸Ôn©w›_‡).×^e‡9ä¾W`åvÿÏ榪IŒ$½<{–ÎÏãaáÎú}V:?®U ¦¢ƒ—1¥”{ À}þ=9©švk°]:?¹×L#>zrbZ„!¤E!ÛpúøøôéÝ â›®6½ëq÷X aÃI𸺠S\µ´ÑõQõÛ}÷ýv#¦±û=¶ß® ëÊ?;`j¿¶}ð^éCl¶{ßB(—P‡J]¼á?ÿ[ Ûéù s=9εqz[‰c㸕óšìN¨¥êP¡Ûù9@‡L'ô€º u TB*£Ýô§õ°£Ü  ~ØÅî€Yû,÷¨—vk°‡ÛÖ;R©À^:°§¤RØJ™‹!‡!„B0°èE»5Ø_ºxq™{ @„:3_½>Gaþøeø<Ä–ë?Xÿ·¸@â2÷€zi¿6gG7=ýß¾ùøÿOaRXÆRL!ŞƞN¿@ $ïCˆ?>èÀ!´^SJ¹×@ño?ÿBZÜýŸÂ|Ùó/«ä™Ä_¢''Å0?†‘ÎÏcî5õÒ~m†âß~úQ BWW!üó]ÿþoÝ_ ­CÚµ­BXÿ“B0'£Ý E•p¡ÎÌÜÌÑYÞû‡ï~]Wìüáóݾh ‹Â"„âú?VAÈÕ»xÂþ·@“nÚžìIûµ™‰{Ýý ð?þ¦Bž½h¿FFÚ­Áð´^¥RgFÖstzøÇ›þóuúPÉUùöíáû_¾Ê½ hŒÖkÀá„:3±t>™£³ÉÕÕðÁ·„_g›CžBx6þós`dI¥p83uwè¬öþürš`§K Ëfò˜©ÃDÌÏñ™§ á(÷ÙQ\ô÷ßý:Ì:•Â2¤°Œ)¤˜BŠ!6Wò„³<‹bn´[ƒ‚¤¸Ì½ =BŠM:Gg“9Ì×ÙÇÇ!Ï2¬ÿÇrýGB†'ÐÂh½Œ@ûµŠÅ¿½.ç—7§ù:Cé y´_£'íÖ ÌÔÉlè4>Gg“««þñ&„ÿø2÷J`TÚ­Aô^&¤R'£øêõñl[WWڰѬ‹÷hÖkÀ”Têd_½>žÝMÞ¾ á_†ð‡Ïs¯sñ>h·ÍS¥LK¥N.GzðÄ|b~ÌDr8˜–J f=Ggóuh„vk0éåÙ³ÜkæE¥ÎÄÌÑÙÂ|*f~ÌÖkÀôTêLÈÌסBæçÀ i½d RgJæèôóöMî@oæçÀL%•:ÀôTêLÄ™¯C´[ƒùJ/.s¯˜•:ˆÿù©@gGWW!üó]îUÀ½.Þ‡|,йJ*o€<„:#‹¯^‡”–¹×Q¥w¿†ð¯«Ü«€˜Ÿh½ä¢ýÚØŽ R>ÈÛ7!<øŸ¹W!íÖ€5­×€\TêŒh=G‡ƒýãMî€@¸¡JÈG¨3std¾™Ÿ|$©ÀòêŒÀ˜¯CæçŸJ/Ïžå^0_fêŒÁq¼}¿ áŸç^ 3 ÝP•:3Ggdï~ͽf@ Ü+Åeî%ó&ÔÐ:Ð1GgTWW!üãMîUÐ(ós€­R\å^0oBÄW¯:¹º2_‡Á}ûö ós€­ÒÅ‹ËÜkhA|ôä8>>}:×ï‡0SgñÕëcst&f¾Òn è¤õÀÁâãÓ§!†E8Z†‹OCzyölò…¥eiON–!ÄU¸ŽKÁ=¥» "cZvÜrRX…W×Ð&¡ÎŽâ2„”{óóî×þðeîUP±‹÷!üç t€éÅ““>@Ülæ†',¹G|ôä8Ä´1-CüôÓ2„0é:ã£'Ç·¡ÒZZ„£´Š''!¤¸ûç6Îï}Ü÷[ëpoK01ÒZº¾o:?ÿôZ„»u÷^]Z„!¦0ÕãzƒCÿ^r=þ`HگȌÌ×áïCxü7ós€~|¸êpž¤e<9Iñäô§øèÉqÖ%]¯¶m N¾¾¸å³ÛÍÏmºÅÀfñÑ“ãxr’úÛ¿PZq-Ø“V‰ð{B˜£Sóu؃ù9Ànâ*÷ ö“áèz•wC·ãztà†õ®º6ȵۤñäô§pt½î+Þ\ j HbXä^”F¨³'st òö`‡Þ¾~õ |ÿËW¹—Ô$y½j÷[¸3ý·îº†¦ÅTSŸ m•™ä¶~žŽtxô¦jg”¯=šé®P ¡Î¾Žœà*Ê[mØØîâý:ÐÑn Ø• > i1õ†n¯k趖hCê3`25й“ÕUìLu€Juö`ŽN¡Ì×aósnMìt¶4;tfH½Nú_;¸G>ë eº€³ªê— ®P¡ÎŽâߞº áŸïr¯‚Â|ýêù9ÀþÌVš4m°Ó§ZgôÊÎÙ=q•.^\ŽºØ >zr¼Sp‘â2¤¸Lçç1ŸÇp}´¸ýÿzAgöŒ¯ª FöYîÔäfŽÎ2÷:Øâݯ!|þyø<÷J(€vkÀÁ’V<@éü<îrûøøôéz xßhë9“).·nZ¯ÿlÄv—?—‰ªtvý½~hý;Þò3Lq©eh¥:CÇ~Ç7ÏãÛçò³ÎÇÊøøôi5™u 6Á+•:»92(¹ æëÌÞÅûâÚ­‡sj¨Ezyö,Ÿ= ×G‹Þ³a¦<©ß#$ë$~Ÿ* ×{rY?î{„±×G‹¾Lzyöl}-èúæµaÓ‚ îuzZÏÑ¡æëÌÖíü€ƒi½T(]¼¸ü-Üé6ÕÀôuhÒìô­VØU×f°ë=9õyÜ_-v ÓÅ‹Ë^×±žw#¨&€€‘ uz0G§BæëÌ’ù9k½7t§<ýÞÙâlÝnÈoÙ«J§–öS4§W•Îέ~׊ö:bEk€ u:ÄW¯Cªçä x÷kÿºÊ½ &b~04›|@ízUÇ„éN¿÷ZÏЛ¶1lÿzªtÈ©óñW‡¶ì󼛪bï`Z°@A¨Óͺ½}#Øiœù9À8zΣ(]guL˜öô{êø|5à¦mŸ*>Yu=Þû<ûèú:Å…%›ß‡U@Àˆ„:[˜£Óˆw¿æ^#1?Mצ#@%úUÇtT³ ¨Oˆ2ئmç¬>ùt?ίҹիZ§¤y5ÛÞ‡Mx½€R u6X:úµ6áê*„¼É½ f~0&'·¦tÕîéjy6XÕ@׬­×(ØÐLº«äƒ~¿CÄ´Ü|(h‰PçñÕëîa…ÔåêJ¶F\¼7?`¥Õ½ªu2& ¤IDAT¬˜² öÒ^ý¼íüz¥UÀ$-Ø`¡Î'â«×Çæè4Ê|êݶ[è£24`­Ï:[§u}ùŽ¿¯Í&E«5ද[ÖÁÖ­¡kiLL¨ó©#9M3_§Zß¾ýB»5`[N†´jò“ï­Ïöß`îs_J«^b^:£c…Ž_·¨¹:!l9hSVSê|À0_§J_¿z¾ÿå«ÜËfB;€ñõܾoÐÔuŠ_E&ܯ¤¹:!hÁun˜£3#æëTÃü`r6ú¦ÓU­ÓÕBí¾¿òèIçç:U:d×<Žõ­í±¿5üÕ‚ €êstfÉ|♟ж>•‘;ŸÆïšÅ#¼§…(-1(ÙØ2.-Škê„:3õV¶R™ŸäRÛ V€Áäš'Ö²ì¼ÉܱYnnlQ^Ø´õ½Yiíâ`"³uÖst˜-óuŠc~>`¾rÍëÓûŸÆï®ê‰+sÓÈ­óñ¬šì›Z°íÞ¢Z0ëP'þýç§%žDaBWW!üó]îUÌÏ °±½£êÚÄîj©v«kƒ·k†L!wuI¡Ñ–÷hZ°0G³ uâ«×Ç!9ÕAáݯæëdf~P­×€Ví<—fj-Ѻ7ÁUéÀ0J I´`€Í6Ô1G‡˜¯“Íׯ˜ŸUÞö“ë°eû:C›®Ù;ª1¡ŸbC-ØàÖ,Cst¸—ù:“Ón (F­HúêÚô,!ðèj¶å>¬+ ¶oD«Æ„ÊiÁwf꘣ÃFæëLæâ}ñGíÖ€‚t¶þ¨S¯ÍήUëtUî¡zZ°Àof꘣C'óuFw;? $æ,Í:êþüSÌ5°«bhS‹µŽJ$U:¥³U`þµX›Z-ؘ™Ù„:ñÕëcstèåíÁÎHÌÏŠä7ШueK=U,ÝáKZ|ZyÔ9k§ ûk­K YK´%ðê¼@C>˽€ÉÅe)÷*¨Å»_Cø×¹WÑós¦=9îS¥S\U@ŠË­§î×÷éáÝ¿wÎ *ìþÑ-¦e<9YÿuÿŠL,]¼¸Œ''÷ÿáºJU³0‹Jø·Ÿ2G‡\]…ð7¹WÑós€ÒiË´&>>}Ž®W=n¹*­* OµÎíÿêžTÞý´±úΞóÑ|¨_½>öâÎ^®®´a;ù9@ùœàÚŸ>''©÷|‰ëB[“u´L»k³ÔU‰Têýö§´Ý~ÍööMü2„?|ž{%ÕÑn ¨B×Pn€LzoNÆ°¸;ĶK{©—ÅV±¤¸ qKë옖ñÑ“U82›æF 6h<Ô1G‡A˜¯³“‹÷!üç t€:h½«oµÍ~_|Uòõo½i{ºÚÚq¡«½\GµP±³·ti`šm¿fŽƒ1_§·Ûvk€RÅU:?{˜{lVrhH 6f®ÉPÇg¾N§oß~a~P§¸Ù©$Ð ¶Ns}‡¦m½>¬[°@Óšk¿fŽ£1_g#ós€*m9å Ðœ—ÕU¯ll±Ôñ×j»Ÿ|l¤Çj||útܶ†LjK ¶øèɱ™Z´¬½Jcz« Û‡.Þ t€zù°ÌC\…ë£EAÇ~kØÃl½>D[h[S•:ë9:0²¼ á?¾Ì½ŠìnççTIk iqRXÕäüήÕ:ÎâjW÷¶Þ__3ê¿þÀÍ„:ñï??5G‡I\]…ðÏw!üû¿å^I6ß¾ý"|ÿËW¹—Ю=Âç&BœO¤—gÏâÉɲ߭ãJ&ÌH «M3t´` eM„:7st–¹×ÁŒ¼û5„Ï?Ÿå|íÖ€´¸ñ ´ÅuêCNãJ•ÌÊÖÐwÝ‚M¨@“Ú˜©cŽ9Ìl¾ÎÅûâ:@Ì[¨JÏ°Æ©|ZŸ>ͽ†ºlx_·KÛF¨Lõ¡Ž9:dõy;æçMIƒÔdÖtòf¥Ázr¼õµ·ªÕ‚ €™(>Ô1G‡j]]…ðÏw¹Wñ;ïÍÏf(iá ”bûa“шØöÞŠlÌEÑ¡Ž9:TïݯEÍ×¹xÂã¿™ŸÌÖk@1J=lÒ9ï§[Z°uV*@%Šu:4ámmØÌϨÀXU%1-·ýq ó~¶äi¼R €ù(6ÔYÏÑFdž¯c~0kµ÷‡šÒ]Aœ#|h JçΆûÒj@-Š uâß~jŽMÉ4_Çü€ÐF+`V†nÖ9§§Ô–pûØr_´` Å…:ñÕë㜞 AÏ×1?`­…V"@cº*‰‡nÖÕÒ­¡C0Z°кâBsthÚDóuÌϸ¡õP¢®eÀVaëê”íaF{‡`´` ]E…:æè0 #Ï×1? l}B”Ζi}u-‚i©|¢˜PÇfãêj”`çâ}ñGíÖ>Ô=ˆ “îlÛÿ¼‡>U:-µ^»å= -+"Ô1G‡Ù¹ºt¾Îíü>ÔÞÐŽ>ÁC<9=¬£ÉÑõªã;¬Úk½vË{AÚ”=Ô‰¯^›£Ã,½}3H°óõ«€ûh»¯+xH‹}ƒ^ïºÁÖk·Z¾oÌZöP'y‘eÆÞýzÐ_ÿúÕíÖ6i°•И^ÁÃnÁN|ôäx}û®÷-Wéô›[5ÊêÄ¿ýÜãM4lÏù:æçtóA(]ºxqÙ9[g}ËE<9IññéÓõœœß» sŽ®W½öZæPÉÒëg uù,×7¾i»¶Èõý¡·óuþðy¯››ŸЃð@%Ò˳gñätÑ+ˆ‰ib ñää÷¶Ë±Ý—³8“â*Ä”{0¨,•:æèÀ'zÎ×1? '­×€šLZ5WéåÙ³é¾_>³®˜<í×ÌÑß똯c~@>À5I/.ÃõÑbüïWéüìáøߧ *¸hÌä¡Ž9:°Á†ù:æçìJ•PŸñƒ:!¨à 9“†:ñÕëclquÂ?ßÝýë·o¿Ðn `WI‹W N¿;C3 t‚ nÚ3Y¨cŽôôî×þuþúÿ<ßÿòUîÕTg.=â€6¥‹—éüìá0mÃâ*\-æèÜÑ‚ €†|6éwûuûÌàÆíså«ÿ‘wÕIËÜ+èÅãnÖí“–™W‘W×Ï –S¹îG…¡›ƒ*ÏâãÓ§!†ÅNOR\†WÅV©LüûH/ÏžÅǧ=nXÉó€Y‹)¥é¾Ù_81­&û†P³ÿõ¿BøÿþßÜ«¨DZ…—éùó27.=9qs¸£bÚ7i¨‚`zêô”–é»ïl`Í›<Ô A°½u:¤Uúî»y÷‡få(Ç7Mþæ2¤¸Èñ½€Ä°ès“¥RçØÍTêÜC«5`¾²TêÜR±ô“V7Õ9`¶²VêÜ-â¿~ø)¤´È½(ŠJ€ªsB($Ô A°¿#Ôf/­ÌÍøMÖökJúæaˆq•{@Ö­Ö:(¦Rç–Š¸¡R˜%Õ9›S©sKÅÌQZ©ÎØ®¸J[*v˜=•:Àl¤eúî»g¹WPºâ*un©Ø€ÖÝUçtz(¶Rç–ŠfK¥вéùóËÜ˨Iñ¡N‚fJ¨4)­ÌÍØO±í×>¤Ôî®Õš@`OUTêÜR±Ã¬¨Ôš‘–æ殊J[*v &wÕ9€TU©sKų R¨Y ‹ôüùeîe´¤ÊP'Á3 Ôª”Væ挣ªökÒŠ ³nµ&ÐIµ•:·TìÐ,•:@5ÒÒÜ€ñU[©sç:,s/æ)­nªs:¨¾R'„â_81­r¯¥R(šê€©5ê„ Ø¡AB Hien@õ·_»‘þüÍeHq‘{Ьu«5@&ÍTêÜR±C3TêÅÐj  ÍTêÜR±CI«›ê@š«Ô¹¥b‡ê©Ô²RPšæ*un©Ø€}¨Î(U³•:·TìP-•:ÀÔbX¤çÏ/s/€û5ê„ Ø¡RB`2i•¾ûîaîU°]³í×>¤Üç®Õš@ ³¨Ô¹¥b‡ª¨ÔF•–ææÔe•:·TìÀ]uŽ@ 2³ªÔ¹¥b‡*¨Ô†Ã"=~™{ìg–¡N‚* Ô“VææÔoVí×>¤í»kµ&ÐhÀl+unÅÿúá§Ò"÷:àwTêIKssÚ2ÛJ[éOß< 1®r¯†qW#ÐhÌì+un©Ø¡8*u€©Îh™Pç‚Š"ÔzK+ssÚ7ûökÒŠ €ê¬[­ tf@¥Î=TìP•:ÀVZ­ÌJ{¨Ø \iuS#И•:[¨Ø!+•:Àï¨Î˜3¡NÁÙu€;ienÚ¯uЊ €¬Ö­Ö:¨ÔéKÅ“S©3§ÕS©Ó“Š¦‘V7Õ9>¢RgG*v˜ŒJ˜!Õ9l¦RgG*vžêº©ÔÙ“ŠF§Ræ!†Ezþü2÷2(ŸPç‚F%ÔÆ¥Uú¹W@=´_;ÄuXæ^µ¹kµ&Ð`'*uÿúÃqˆi•{4H¥4(-ÍÍ`_*u”þüÍeHq‘{”ì®:G ÀÞTê DŃS©mˆa‘ž?¿Ì½ ê'Ô`‡A u rienCÒ~m@Z±ðA«5ƒR©3; B¥T(-ÍÍ`,*uF b`nîªs:ŒF¥ÎˆTìp•:P Õ9LC¨32Á{ê@áÒÊܦ¤ýÚÈ´bhк՚@€I©Ô™ˆŠv¦R ¤Õù¨Ô™ˆŠ€š¥ÕMuŽ@€lTêLLŽ©ÔB¨Î  B ;ô"ÔÌÒÊÜJ¢ýZZ±nÝjM @QTêdÿ뇟BJ‹Üë P*u ­Ö(—JŒÒŸ¾yb\å^iuS#РX*u  b‡{©Ô‰¨Î *u  b Õ9ÔE¥NATìð•:0žéùóËÜË€]u #ØáŽPFVé»ïæ^ìCûµÂhÅ0†»Vkª¥R§P*vP©CIKsshJB©Ø8Ô]uŽ@€&¨Ô)œŠS©û‹a‘ž?¿Ì½ ’P§‚™êÀÒÊÜZ¥ýZ´bèr×jM @³TêTDÅĮ̂ԞÒÒÜæ@¥NETì|è®:G À,¨Ô©Š™P©[¨Î`~„:•ìÌ€Pî‘Vææ0WÚ¯Õê:,s/`RëVkfK¥NÅâ_81­r¯ƒ‘¨ÔZ­@*uª–þüÍeHq‘{ãH«›ê•:MP±Ó(•:Ìšêø”P§‚ u˜¥´27î§ýZ#´bª·nµ&Ѐ Tê4FÅNCTê0Z­@*u£b¨GZÝTçt •:R±Ó•:4MuìJ¥N£TìeRûR©Ó8;S©CkbX¤çÏ/s/j%Ô™ÁN¥„:4#­Òwß=̽ ¨ök3 Ç]«5 @¥ÎŒ¨Ø©ŒJª––ææÀ°Tê̈Š`|wÕ9˜JR±S •:Ô&†Ezþü2÷2 UB™ŠÿõÃO!¥Eîu°…P‡j¤•¹90>í×f*ý雇!ÆUîu•[·ZèÀTêÌœŠ‚©Ô¡hiinLK¥ÎÌ©Øv“V7Õ9˜˜JB*vŠ¤R‡â¨Î€œ„:ÜìF¨C1ÒÊÜÈOû5îhÅüκ՚@  R‡ßQ±S•:d¥Õ”F¥¿£bæ,­nªs:P•:l¤b'3•:LNu”L¨ÃV‚Œ„:L&­ÌÍ€òi¿ÆVZ±AãÖ­Ö:P•:ô¢b'•:ŒJuÔF¥½¨ØV¤•ê¨“Jv¢bgB*u\Z¦ï¾{–{À~Tê°;P£»êTì³Ü  B×abXå^ÐC ‹ôü»ËÜ˧ý{‰ýá8ĴʽŽ¦i¿ÆAÒÊÜh‹ökì%ýù›Ëâ"÷:€OݵZè@cTêp;#R©ÃÎÒÒÜh—J¢bJpW#Ѐ†©Ôa*vF R‡>bX¤çÏ/s/ŸP‡Áv&Ôa«´27æEû5£LdÝjM 3£R‡Á©ØˆJ~'-ÍÍ€ùR©ÃàTìÀÐÒê¦:G 3¦R‡Ñ¨Ø9JBªs€[BF%Ø9€PgæÒÊÜàCÚ¯1*­Ø`ëVkà#*u˜„Š=¨Ô™!­Ö€ÍTê0 ;°MZÝTçt€Tê0©øþ_OCËÜ먂J™Pô#Ôarñ¿~ø)¤´È½Žâ u—Vææ»Ð~É¥?}ó0ĸʽÈfÝjM ìD¥Ù¨Øé R§Aªs€ý©Ô!;ÌGZ©Î¥R‡ìTìl R§i™¾ûîYîUõS©Cv*vhÓ]uŽ@„JŠ¡bç*uêÃ"=~™{@[„:E°ó¡N…ÒÊÜ`,Ú¯Q­Ø¨Ó]«50•:IÅNP©S´47˜‚JŠ¤b‡òÝUçt€I¨Ô¡h³®ØQ©S®éùóËÜËæE¨Cñfìu ”Vææ¹h¿Fñ´b£ëVk •:Tcv;*u ‘–ææ%P©C5ÒŸ¾Q%Á„Òê¦:G á³Ü €¤¸1­r/ƒÖ©ÎÊ£ýÕ‰ýáxÁŽök¤•¹9@©´_£:éÏß\†¹×AcÖ­Ö:@±TêP­æ+vTêLD«5€ÿ¿½»»m#Ã0ʶ¯È¥8NêˆÜ€T@⨺ ‚ôÀ½° `•uô73äGžsmÃß¾x>1(uK±Ã}òx¨s :@JÂk¶ØQêÌHÄcÔ¡ M;FäÑ»9@TίѧØ8ëíÔšAK©CSš*v”:QçmPêÐÅ¿åQ´D©C“š(v”:wÈë¼Ý>—þ €))uh’b§WÇ:Ç 4G©CÓB;Jë i•7›}éϘ‹Q‡æ…vŒ:Ê£ws€8¿FóœbkÕñÔšAè‚R‡n ß¾I)­KÇÅ”:‘×ÞÍz£Ô¡ùÓÇç4 céïàÇ:Ç tG©Cw†—ÝkÊyUú;ÎRêœPç}3êХÎQç ÞÍp~Nå§Ç§Øx;µfÐHJ:Wu±Óu©ãÔÀ)¥]SìÔ&‡:Ç pB©©Òb§»RGð7F8¨nØéfÔÉ£wsÎs~ œb+àíÔšAàJ8QM±Ót©ãÔÀµ”:pB±3§<êƒÀ•”:ðŽâÅNs¥Ž:àJx‡bg*ê€)(uàŒbÅN ¥ÎVy³Ù—þ €uàE†Ð£NóvûPú+Zâü\ ?=(.r<µæÿ0±J„‘‡UòXú3ê•×Þ͘R.”??îSV¥¿£>Ç:Ç 0#oêÀ•†¯»‹;ÞÔÒ*o6ûҟУÜ`‘a§êQ'ÞÍX–ókpƒ~O±O­t¦Ô;ÌZìTWêäµwsÊQêÀú(vŽuŽA  ¥L`–b§ŠRGP £Ldòa§è¨“GïæÔÅù5˜H3§ØÞN­t*£Ô‰MVì,^ê8µP3¥L,^±“ÇCcШ˜Rfrw±³H©£ÎˆÂ¨3ºkØ™uÔÉ£wsbq~ fTå)¶·Sk€`”:°€áÛ÷/)¥õU¿4y©ãÔ@dJX@þôñ9 ÃX诇:Ç ˜R4¼ì^SΫ‹~x’RGÐ ¥,(?=>,Sì¨sZ£Ô.*vn-u†´Ê›Íþ¶/ VF(äì°sõ¨“ǼÝ>ÜýaTÉù5(dºSlÇSk€†)u °w‹‹J¼ön@”:PØmÅαÎ1ètB©•ø£Øy¯ÔÒ*o6ûå¾ €u "ÿvþuòèÝ€~9¿ùÿSlÇSk€Ž)u BÃËî5ýø±J¿~®½›@JF¨Öðu÷!~ôv)%£@ÞÔÀ¨€Q £@F€Œ:u0ê`ÔÀ¨€Q £@F€Œ:u0ê`ÔÀ¨€Q £@F€Œ:u0ê`ÔÀ¨€Q £@F€Œ:u0ê`ÔÀ¨€Q £@F€Œ:ü ¡XJKµ\öIEND®B`‚deap-1.0.1/doc/_static/deap_orange_icon_16x16.ico0000644000076500000240000000217612301410325021661 0ustar felixstaff00000000000000 h(  ××›ÿžÿƒœÿ‰™ÿ žÿžÿÔÿ¦ÿÿÿàžÿL¡ÿÿÑÿÿÿ¦ÿÿÿÿÿÿÿ®›ÿÿ€žÿ«žÿ«›ÿsžÿ©žÿ«žÿ«žÿ«ÿ¡œÿ;ÿsÿÿÿÕœÿ„ÿ¡ÿæÿÿÿÿÿÿÿõªÿ žÿ˜ÿšœÿ‰ÿÿžÿ¿ÿÄÿÿÿÿÿÿœÿM›ÿ@ÿÿÿÿžÿáÿ¡ÿþÿÿÿœ›ÿ@ÿÿÿÿÿÿÿùœÿ–ÿïÿé€ÿ›ÿ@ÿÿÿÿÿÿÿÿÿÿÿ®ÿÑžÿ:›ÿ@ÿÿÿÿÿÿÿÿÿÿÿÿžÿÒžÿO›ÿ@ÿÿÿÿÿÿÿÿÿÿÿÿÿÄÿ›ÿ@ÿÿÿÿÿÿÿÿÿÿÿ™ÿ›ÿ@ÿÿÿÿÿÿÿòœÿU›ÿ@ÿÿÿÿÿÕÿ'›ÿ@ÿÿžÿ¥¢ÿ ÿ9ÿkðÿàÀÀÀàøøøøøøøø?øùÿdeap-1.0.1/doc/_static/lvsn.png0000644000076500000240000030660612301410325016541 0ustar felixstaff00000000000000‰PNG  IHDRù"¢#ÉgAMA± üasRGB®Îé cHRMz&€„ú€èu0ê`:˜pœºQ<bKGDÿÿÿ ½§“ pHYsgŸÒR€IDATxÚì}w|Çyö3»{è{ï½S¬"EQ…¢zï¶ä&¹Ä±cljãÄqlÇÉ;Ž[\e+–­.KV¯¤(±I¬bï½$Dow»óý±;3ïìî¡’šG? ‡ÝÙ™Ù¹Ã=oy^Æ9ç0000000000000008ïauõ 2Cò .’o`````````````pÀ|ƒ †ä\ 0$ßÀÀÀÀÀÀÀÀÀÀÀÀÀà!ù É70000000000000¸@`H¾Á§«``p®!™J¢1•BÊsáº.\Îáz.<×CŠ»p=žüä)×çç8çð88À¼–?æ߈ù¯£Çü׳`Y 6³`Y6l‹Áb6lÛ‚ÅìàµcÙH8 $l ÇAÂvÀëê%40000000000è"’oð¡AÒM¢´¢§«*PV]ÊÚÔ4Ô¡¦¾u õ¨O6 !•8‡OÙ}ð€œsqˆA]Ãýß¹º\^ ÔOÐTœáP¿ýÈöÁÈ8ÃOw–ƒ¬D9YÙÈÉÊB^Vºç G~zuëŽÞ=з°ºåæuõ#1008GP\Q†Ö¯‚ë¹­j7{ÔxÌ3©«‡o``````@À8e$ç9jêëpòìWœÁÙê*TÖU£²¶µ uhH% aç`qäœmƘï¡ç>!÷¯çúyÁò5Òδþå[L´<üþòî¤/€{Ê"Àsÿ˜¹ëÆ…ðqb ÆiÛò³sŸ›‡ÂÜ<ô*(Dÿî½1°g îÕyÙÙ]ý8 :;‹ãÿu -ºþS—_O_qCWÛÀÀÀÀÀÀ Cò ÎK”×TãÄÙR”TœÅéªrTÔT£²¶ ©ÆX/8%êù \ò”¸Ó6ò(%Úa@¾‹¸æ{‡$íi#¸v%'í9ç`` ´žØB‘ºQ@Ÿi÷ÔÆÂi2¾@Fä&²Ñ½ ½ º£O·îУ7†õ€Á½ú²Œœ‡Á…ˆ··€ÿxáÑf¯»dìdü×G?ßÕÃ5000000ˆ ×78çQRqÇÎãäÙ38]y5ÕhH6ú®mè–‹ôv͹Ž#ð´ƒénoÆüv‚0"N ¼$ÒL7H÷=ÀY4:²«àr¦ ´ð‚˜p}?i_§Þ}9$. LŸs0i¨¡ÿZÊc~›ºdjËêq²ì ™3àX6ztCÿî½0¸w? í3£ú 2áÿMž‰?-ÇËJ›¼îã ®íꡤ!ùçÎÖTâ`ñ )ÁéÊrTÖÕ åy’8{Äê˜$ª€"»º§*¤žë„šLCï•G¤_ŸÈGÚËÐ|DCóãc*”_k/ÇË4¿»XCRèœÁ´ãôœ¿LŠµ3ƤqB­™?1oåñ÷@–T®™ˆk–ò\”V–£´â,¶=(ŸC~Núvë¡}úcÌÀ!7p¸!þçc¸|ÂExòý·Ó^Ó« “‡Œèꡤ!ù]×sqìt Žœ>…“e§q¦ªõÉFDCÓ‹ö‰ªçl”z¡!òÝO> i÷›r¬ræin=c\#í#aABºå±¨g^„‡žœ÷8ñÚSƒn‰2zù„´«CÔø ÝŒŒ!C"ðjYÐDOs óVs%i¡ ¦¾5õu8Tr+vnGünÜ«FöŒq‡bd¿Á°M¨¿Á9IÍøIƒ‡wõ š€!ù×sq¤ô¡¨¬å5Up½€°K"NòåE89%¾\ºèÕu¾Çð‚Ðx?|ŸG®“%ëàPhH=<íz=¤¾I I„y¤_-û_„ð“Þè¸=e5ÒDØ¿ ñLiˆöŒÉöáëyØp"ïíþ¢>þPJÌ ô  Ï«¢¦ å5UØ~ô À±m èÙcúƤ¡£0qð8¶Ý®}e``Y êÑ»Éóº÷êê!4Cò :ÇΔàà©"/+AYUE@ê¹äꊄzA"¼ t²ø„_qKzOÖ¥w]\O<ä2¬_K؇ c§ÇeTdŸaEÊy¨½04hôœ äù¬œÑHB]­  c ºA*ðy¿Xµ~ž :PVâ½gÒn R„®Œö€hÄ×ópüt1Ž.Æ»;6±m îÕ£ ÆÔa£1nàpãé70èbô,èÖäùù]=Dƒ&`H¾AFQÛPÝE‡q¨ä$ŠËËtSŠö8 ½ˆlT(Ž„¼û5Ñ;-´"¦.;kcÔûÍCÍsí{‹…Xœì“zßCtŠ,sBÇUä:1*H¢JÙpˆªö~h»Fõcú×ÆÄ”r¾Þ“^ymb~< þq%ðB¤>N €ãQŠÏCëï·Wy÷¾>AÐVk®GY†Òö÷¯QÏ•kÍ£Ï5ô½yè}"Æø¦Tø#—Sõ|Õži³âŠÌ†‰¾Ê\yå y–jû@L$É“¯s°–i¿ts}CF(Õ@… ¨$ ºþÀÁâ8xêž[óFôˆùã§âÒñÓš300¸pò\4¦RM^“e;+Û™tSHºn«Ú0øDº«PV]‰5ûvbóÑýØwò8Nœ=†TR»&ÛI`hï~Õf¹£'¶»¢Bc*…”×úµÊrªQÛØÐäyÛ²ZmÙ}â(jê0¼Ïäeçd~Ðœ£!•DqE¶ƒÑý·º‹£§‹±öÀ.l9²‡JOáTùUâ8@AN.†õîñ‡bÎè ˜9rrÚù7¶¹õHضfdhë{/á8p¬¶¿ÿRÉȺ´ @Êuq¢ü ÆÚîþÒ¡®±­5ÉvôûŽsŽºdcFúbJ+ËQ“‹^…2Þó†äªëk±õÈ(.Bu}òRklÁ ˆZîþ)ª˜Nó®%Ñ£žmzÊ,ÂÝyØ /<ý¹ÆÓ£ä,ˆÉ×ú¦Ü‘Œ_¶'uæEÝyÁêE ø°ì¼¦(O"´ˆq¯jÙ ƒ¥ô2 V._ˆúGÔëtO¾Ç¹N…cPÏV†F‘B yñU{åVçÚê“|ý0©ï ²ŽTDP¾ÖÄ Éœèó ­?c¾÷á@ñ (.Â3ￃ)CGâÊÉ31uØè}ÿt=jêñëÅ/â­­ëÒ^sûìøÚ ÷dä~'ΞÁžÿ3öµªÅò³sѯ{Œ8 S‡ŽÂ/ê`ÃÁÝxvírl8°.oš¤4¤’Ø_\„ýÅEX¼u=lËÂüqSp÷ÅWà¢ácÚtÿƒ%'ðƒçÿŒ¢³§[ݶ07#úÄôác0wÌDL2²CÖˆsŽ76¯Åo–¼–È èÑ Oå»­ê÷¹µËðöö:dÌaüìÛâkSž‹%[7àÅ «°çäÑf¯¯®¯Ã΢ÃØYt/lX‰¼¬l\=e6î½d!†ôêÛ¦ñ¾µe~µøù&‰óÈ~ñ½;Àð¾ä±¢²Óøþó¡’“­¾gAv.†õééÃÇ`Îè ˜1bl‹Û,>þÏ8Y~¦Mó cêÐQøå—‘¾â°jÏ6üôõ¿ ®…Æ DzÑ3¿ý»÷Âð¾ý1aÐpL>C{÷kט\Ïß–¿¿¬y7cóüéÇ¿lH~ ìï}ï{ßëêAt <Ïî¢#X±sÖíÛSåeH&S~y·šðnS†Ì„G8â¹gAwE>µp|’bÚw8-^„¸ûRäÊÃ/ÏG ¿8ÍH¼7§Ç…k[üŸAó>‹‰R='íèýí#hÏDÔÁgtÑñÓkÉ‘ ‚‡ ˆm¯òÿYP®O¬¿Ê±—çBe ƒeUåõ´ç¨“‘éÏ?üümM5Å‚˜ýM—@Ì$úƒWRí'0¤\§ÊÏ`õÞíX¾sʪ+ѯ{OääÂÀÀ =R®‹'Þ[’öüœQ0ehÇ­ö ;‘…Ë&Lò]›Q^[{͉³§qÏÜ…°2à¥ê‘W€KÇOÅë›ÖÈp÷–€hL%q¶¦ ûNÇ{{·á¯ëVàtU&™Ñpù½'á;ÏþO¼÷6ŠÊJ£ÕPZ2^Îqôt1Þܲ;ŽÆ„AÃÐ=¯užý>ݺcΨ xyã{­N÷ð½Ôg±åè¼¾y Öîß…=zaP†K¬2Æ0iðpÔ'±ýØ¡Økªëë0sä8ôïÞ«Åýþüg3æ¹l Žeãk7ÜÝ¢H•»¶àŸŸù=ÞÚºgª+Út¿¤ëbÏÉcxiÃ*”VU`êÐQÈN´nïN< ÛÁ‡öÆžÏMdáןþ:‡Œ=ò pé¸)xmÓêV{ôÝJ«Ê±íØA¼µuVìÞŠ^…Þ§³mûöÀœÑðJöqúwÏkw?é0ºÿ èÑ +voiu[sÔ66 ¤²ûNÇê};ðÂú•xgÇFT××cXŸ~mŠJ², sFOÀ‰ò38P|"#ó¼nÚÅسw‡­ãù Cò?„¨¨­Áš};°lÇ&*)Bmcƒæð ‡} ÐÜi&s©…Ç’Ðú×rÒŽx¾ƒ¶z®w”Ð ‚*o,•öõ‘»hcÞ¨!ê5æòR ãƒ4hîymþ²½–ÆÙ·Y w# ’éêuL´×Êþ‘u”¾ÏèíÕüe?Y²þʯž¥&Æ"ÿ°C…óä“%ã—êú,ô,éú3säùQãPèÙFÓAT®¾š?‰L †‚úÆF(>Ž%[×cë‘°-†!½ûÁŠ5|¸q¾’|M‡÷áÈéâØs ©$ÆŠa-øBßäeeãtUv8Ò®~Rž‹Ý'ŽâÍ-ë0eÈHôëÞ³Ýýý~é«øÑ+O¢¤²<#s|#Ék›V#'+“‡ŒhUÛîy8Xr"í³i)NWU`ñ¶õ¨O6bæÈqi ýmÇ™êJ¬Ú³5íyÛ¶1Ü”õu¨ä$ž^ýNFÇ—Ç­³4yMe] þýù?ãÑ•oùQ›ÇÞ“ÇðÖÖõÙo`„7‡Ñã¹µËb½ù×_t1®™6'¶]~vJ*ÏbÏÉcíÿÙš*¼»sNWU`ö¨ ͆©÷È+À¡’“8|úT»×®£I>Œè;¯n\Ý&o~*êj°éð>üuírTÔÕ` amJÛÖ»^Ü°*#c2$?&\ÿC„½'aDZƒ(­8ë¼³‚´…Üh¾8wWâz~>7 rîÂt*óšžÌË$œÄ^àðóëiø¾`ü:çç¤G1· °ŸVvŽKr)óæåœi¤.HËÞ)!<%HG‰²òØÓu%íå‚Šû«¤Z»ž õQBåäfj¦Zj„¦X—ë,„ç7¤”1Àa=É ÏŸ“( ª¯W8`Ú5Œ¶§!ýäE…ÉúS½m(ª¿ƒ%E8°ô8žy).?7L¿Ä„y\@(ãÒ`ñ¶õ¸tüÔŒÝoƈ±x~ýŠ´ç³l}»÷DϼxÜÑÓŨi¨½ölMþñÉßâ§ÿ2&Þ¦ñ”×Vã;ù?l;v°É1M6 c Á€½—•ƒ”ëâlmŸÀ‡ö ¢¶&¶m£›Â¯¿€='Žâ›·Ü,§å_%gŒ‹å»Ò{ ²sÑ»[!r³²Q]_‡¢²Ói£ž^ýSI|õú»Û´NéP\QÖäùe;7á«×ÝÕ¢yphOFÇÖ¦5“–¶ÿTþõ/ãTó+ÈÉÅôác1ªß@ô-ìœDê“(­,ÇÞ“Ç°é𾈆ƒ@Yu%þéɇð…«oÅG/¹ªÅãÎËÊÆÄÁñùÈþȹ™#Ç5Ùvƈqxéƒ÷šì»O·îÈËÎAmcŽŸ)I[äÕM«QÛØ€ïÜñÉf G³FDz]›[<Ç®„Å,L2²MÞü¦ò\üuÝr,Ù¶ãGpå¤é­j?¼ïô.(Ä™êÊ®^¢ †ä_à¨O6bÓ¡=ØwòxPÏ7‘7- ° K¤I‚(¤ó€ü)çt¨Þ¹úï÷ö¤‡sò#áÞÔ Ìù$2 $4®¯“yFÆ/¯•^h•W.ÚHŸ7m/‡O¼å$O†»«±ˆµb©–ëLæ$R™UÏN–ý£Q !ï<ÂFíYŠœzH€Rò}BEhÚ*^äg4‡rÎrŽ!â®".ô=NW`r#t£’ÒlT†bKc™#c •u5xkóZ,Ù²“†ŒÀuÓçâ¢á-ÏÍ30¸PÑþ Ô®CEm ö<Þä5«÷î@u}]ÆRw÷Š¿qú<rÉU¸jòÌ&½obŒ­\ŒE‡c¯y{û¨ª¯Ãîùl‹‰~ºûÌ[ˆûæ_ø«ª«Å[[×á±U‹cϯ_‰‰ƒGàš©³[µNMaÃÁ¦‰yu}ÖìÛË'^Ôl_" aìÀ!˜2d$úö@ANn$ZïéÕKq¼¬T;Ö=7^uK¤ß†T#ʪ+±áàéɾ¨ ’¿ýØ!|멇PÝ｟5rîž{%.=±IOv}²ooûO¼·$6/ƒãwo¿„šú:|váM-^óA=ûÄ’ü¡½šÎÿN÷Þ»nÚÅøüU7£w·îÚñÚ†z,ݱ®x¥UÑ4…wvlÄ„AÃpï¼…MÞ7A¿Âž˜:ÔÄ)ÌÍG–£„>’Iœ©ªÀ΢#Mà:zħ—$l£ú ÆØCÐ+¿ sóp4$“(«®Äñ²ÓØsâHìZ TÖÕâ{}w»síí°XËS¡†ôêKòg‡!½ú¡WA7YÙÁ±m4¦R¨m¨Ç±3ÅXpÎÖTuê:žo0$ÿÅÙê*|ppŽž)çyÒ¯"CÞe M¥Õ4±t/ZöÎÿ !UwE€©~¼'û¢^oÙ¹%}d$t]¿?U××£Ù5C†‘Ó¿ÑjF„w›†§«øzhhÂxDƒ‚´‡×2$ ¨yÁÃDVÜ“¬%}~ÄûïÿÊ"Æ©¯­Y_bPà$/?l4‰3à¨sLžŒY_?õ¼h΃êOšÈøUK½€bxý©®¶#kéïEÛŽĶcѯ{O\=u6®™zqƸ :«ölÕ„å¦ ‰íÇi×4º),Û¹7ϼ$#÷,Ì/ÛÙ3¿ Bð_¡ý’±“1{Ôx|÷¹GðþÞí‘kŽž)Áâ­ëqÓŒ–±ª®ßxü×8z¦$r.ÛIàó‹nÅí³´H5[Œñ’±“ñÚ¦Õøå[Ï£>&¯|íþøÅ÷ïþt‹Âæ sóbìÙ;VÁ¿[nîž{%Nšo=ý{ì;5àüáWqÅÄé­Š(H‡3Õ•ØvT°žùÝ“ÈŠÚÅÛÖ7KòSž‹­GàÆéópÿ¥W7+N·dÛúÉÏÍÎnrŸ>xÕ-X¼u=~ôÊ“˜2tTì5Š‹ðOþ66\»O·îøÇ›?Š¹c&µh}rY¸yæ%¸fÚlüiùxêý¥±×=¶j1 rrñ‘zôÓ½‡º7S·{šv} »G>äeçà–™óqåÄéøγÿkXxtå[¸á¢¹è–f¯~È~n5_pm³ó=zº?yí™´Q™F~QÏG¾øÏ-M<\z oo߀W>xuñ>Ï­[Ž³5UøöŸh1Ñ{ÏÛ–…Ÿ|üËͶMy.^ùà}üöí—:e ÏGt^]ƒNÁÑÓ§ðò†•x~Ý2.=Oæ8qÍ£+rÇ)ÙD8¹ðpó@ D(†Ž«ûJßnäK £1â=Ö´åO-]FHâFø¸æÝÉq×îÃõ{JOº½¥èYŠ€êóÕCùµù“Ð}FîÏ#ãÕÓúõõãQ/8Í]—O‰>?2xNî/׌<­J åW‡U{®=?­ÎžŒ¢ )\]'¢èúiaúê†tLÊ$Ä"{†®…ØâYÊuãgúZÒöê:¿»âò2<±r1¾òÇŸáé÷ßFM†r Î'œÏJK¶mÐ~ÿäå×a\L‰ªÅÛÖgìžm5&lÿvç§ÒzÙÞݹ©Å}¹ž‡ï<û±¿O·îøõ§¿Ž».¾¼Me±nšq ~õÀ×Ò¦5­Ø½®|«ekeµˆ÷îÖÿ}ÿc&%•g±vÿŽ6õÆ;;6já…“fàê)³"×­Ù·•iÈŽÀñ3¥ø·;?…oÞr_›Õç[‚k§ÍÁ×n¸'62ålM¾õÔC±Òàxøóßl1Á§Èvø¢[ñý»?¶$ÝïÞ~ëìjQ‰4fD(m4ìtËÍÃÿûȃYR]_‡wvnl²}sãjÃúôÇO>ñe,È`ÚPSH÷¾·[HÆGô€Ï-¼O}õ»¸þÕiõŒ–î؈_¾ù|‹ÇÕžut,w̹ ÿ}ÿ»´,é¹ Cò/ì9qÏ®~oo]ÒŠrý$ñK…x®ç®ë¡éâzF99N<á>ûRck³‡r°f%†¢B×´¯—D„N}áß%Ìšz¡¡ŽÅ‰ã)rÌUsR› Áë ¨Mš“®g0ÒžµÓûsµn*„\o¯&FW]´‰æµ‡+!ÐIP#‚4ÎpJúUl~œ'†ŠøInMCæŹÈþQÏ_85Ð9‡ï¯GƒH£ #·—ëO4$˜Zù¤å•1J½ÖË ŠöÕ µxõƒ÷ðÕG~†ß/}¥•ga``pn£´²[Ž¿÷È+ÀÌ‘ã°hòÌȵ[À©ò²ÖtŸípÆå$²pÿü«cÏím… Øc+ߊõJöÊò+3 õµÓ)Æ ŒŸ}âoÓzNÿ¼âÍ´aýúRµ}±zæwÃW\{nõ¾íšŸÀÛ!#Ñ¢)³°(†ä§<ïìhÚ3¢ï€6è¶à–™ócÿ×KOĆZO<?ùøߤõH·WLœŽïßýéXÒÇÁñמhÖ"®m Ú£pŸ—ƒ/,º5öÜš í§¦àXv‹#Ú‹L äeeãó‹nÁO?ñ·iÓ^Ø°²ÓJFÀôácÚ¬_r¡Ãüó;ÂÓï½÷voEU]­–Ïí#`»q i,qQÒ(íâ7r5üê_¯B»©Ì=§¬UŽKDÕv"8'…hä@($›ií•'™r¬Êæ…ÖEØB‚Jp¯™ïsRÏ Ú¯H7ü^÷Å«0z5~=Ô_¯IŒŸ?ÐHMÄ*f@[ÿÐì™X_¤™¿z:PûJï%lôÄ_FˆçJ|éúóÔÊ/ªûkºŒÐÌÚúé)$ʨ#çÏ¡ÛBóg¤}C*…;7áïý%~úêÓ8RÚ~5]ƒŽÁÒ/¬cÙXCòàííZÚu“ðš©;ß.;9öxe]-’-(Íw ¸(Ö“n3 ÿ~Ïg3æEÞ§?¾{ב\r 8~ôò“MÖ;×µWMžë•<œÏ棧‹5¥ö=zcòÑwFö¹~ñÖÌEƒt^ß¼&Ö“Þ» ÿù‘3æý¼tüT|náͱçÊjªð›%/6ÛG:²ÞÜ{«½eì柂‚ì(Ymn?µÇXÕhïgTÓ‡ÁÏ>ñ·i÷ÐÏ^ÿ Êkª›í'e ÒÃüóžçaÛÑxú½%X»§ ÂnÂKÉ4r¯{ÈDˆ¼€–GN¸ ¦*4ß?v>û·`µÎIÀ¸—ä{*ß[ý®ÂÄyˆju×E8: cw¨ñËþH[åM±]êÝvFæÏÂ}ÇDP#„x&"}ŸÑùsÕ9I/?Ùdòw±’Ý“Û«rtâ&êÉé÷'Ùz¾˜( ˜PíUh¿ô›ËñCFiÈ|qxjô!c‹^jOk/×_í&ª ö£Ü rÿ‰²jþto«MËå~Fè¸ÿžãøàà|û™ßã^yÇbBb ºKB!øWOõ=°ý {Ä*Ÿ+$­O·îiÃiãu¿|ëùXò|ÿ¥Wg¼ÔáÌ‘ãpϼ+cÏ9]Œ×7¯éеꖓ‡a½£å3!Àµ$âÅWÆ¡E“£ÞüE‡QTvºCçÛVÔ4Ôãï¼{î›·Ü›öÐÜ7¦¦ÑxkËz(.j²}Wq=Dz1vàÈñf÷“!§;`¾}û'bÏÕ4Ôãáe¯5Û‡YÅŽ…!ùç<ÏÖÃûðÌê·ñÁÁ=¨O6’z%U&ÚµwñÄRÕú4öHšs.šÔÌ$¡‹|ÖQq¼È)¥ÞÎXˆî…"¤W—\êÑÖJª­ %‰ëFJèHÈ—t.ƒ „_7:0hîe:gÚžD;ÄΟUèši24BBŠæ±èse˜¡i4B"¬š/nOÃÛµGA"8äz39ÀUz„UAÖ"‚˜Fõäû+uýðó#ë'®f¡õgúóÑ„åHÃiªs¹ŸÝÿŠì{Øxh/¾õäoñ?¯<…ã†ìœ8TrŠOÈßôè…ÉCÁ¥„Màè™ì>q´«‡ Þ£Õ3¿[³ùþڦߧ[w|ü²æÀÚ‚O_qz¥!‰­\ܬ7¿½ˆcKd@(uI(²ƒûEi¢A–dPÛ!“xaýÊX¢:o̤I!`Œáë7Ük”âàxlåâ®^’´ˆ3xáÝ–aÁø©ißol^ƒÓM¨òt< É?O°ëøa<»æ]l>¼ É1a ´ÁA­-õxJO0WdXzPÙ§ò³2® êǦ×jwŒ½?i®y’U»æõ ~R²¦RðYèþú-cö"Þ¤T¸ö‡I¨ ®VüшFÛÑ$`@’[¹üZ}tüáù5-ûnC ¶¯!" „-ˆê„ÛË㊄ oºÒuˆ‹{þäV2¯ž…­š7]®6S¿QÓÖ)d¬Šæ Ñ$tXZ$‰>g–îýÃÔ.âœcã¡=øÖ“¿ÅO^}E!EdƒÎEÄ ò¼^1qz¬ÈÔ¹àÍ?rº8Ö?mبfÛ>ýþ;±Çï·ÙíK‡Ü¬l|lÁ5±çJ*ÏbÕž­¸Z@c*šÂ§¤Þl?vHÓhÕo¢?°goLŠÉû ï»s©ž_·<öÜ'/¿®Ãî;ªÿ \5yFì¹»· ¤âlW/M,SÉȱ>íÜO&|náͱŸ­®çᥠ«ºzxj’Žã`qž[ó.ÖØå‡åkyëõSlXØLóÙ¬=]˜ŒôöŠ6*:^\A^iîs´D™¸¿j'I‹^G]Ç"e@„®Ë0x"’ “ùøÚkN"׉QA’Km0ÄVÎVgšc\£út.¡’rTàOk¡»¾IûPn¸¶V”ÔÇ=Dm^:YfÄè R$ ]«bï! ¸®f¯"%¨@ÙŒôw"M‚“}§åÓk造^®…ÖžkWH=ÁÉûC%dqQL‘ûO—«Ä9\αáÀn|ó‰ßào<‹²˜z¯ ÎyDè)¬ˆÞ#¯³F´}gÇF¤<·KÇÿæ–µ±Ç›+ŸWRYŽõwGŽ'l7\4·CÇ|ÃôyiËr½Õ†ÎylºÔä!#ÚÕo$Õ#Fl/N€¯èìiì•hìj¬Ý¿e1^üÑýaÒàö­SsH—ÊáżGÏ9]9Öì~jA¹È öì⫼³£é*f;†äŸ£8~¦/­_•»· ¶¡ž„`Ò+¯åx“oݹ ÑLq6寢jç ŒôG2©!àqjfqJëa¯,#Þnmð¢ô\8”õ)ÔÀA²¼é Tóñ!lÑ.ùñráµðø|ðèúÑ‘iyó’<‡ž Ï€äÉóÐ:sH+^Âu/5Õ[Pååè:ǬáÿÒû-½Ú¡Èí¹#d„`2_?º7x膪íRÙžŽUi@äßk{ŒŒŸ vRhúÑ}«µ——1yÏó°fïv|ýÏÿ‹ÇV¼‰ú˜rEƒ­G „TÀÝP¬PZ\Xiym56خ¦ÃûðìÚe‘ãsÇLÂÅ£'6Ù6ÝèÙ£&4Yã;ÈËÊƧǞÛp`7j;è3pÛ±ƒ¨ª¯¿t\ÛK‘¥\7R®ðª˜½r失ÑkçB4ÅÒíñûbᤙ­ì©õ˜0h8†÷é{nù®-]º.q8~¦$ÖhÔì~29ù®:'öxÑÙÓMŠšUìX’Ž¡¼¶¯ozïìØ€ŠÚj•g ùVùåJ°N–b£%Ë$9®Q~L®;(äòþŠ[2™K-CÎCáô຀šRz§^tÀµû‡rªIÉ8ÂÛTn8•»åõˆ<]+=4]©µ‹p}mþ"AÞKÌO5@òüCf ùáÏÉó㊀Š¦b !‡¥Ú35.9þ`ŽÒCMóôAž‰tP.ŸöãYùüI7´Ä Í]—{ĘÀUD…ôìkF(:&e8¥ I‡ ðºq…F±¨¾¥ÄŒ4åõȚзƒHÚöYÞ”ì#ÏJ„Tºµ|øhH%ñÚÆ÷ñ•?ý¯n|^ç¦DC¦ã¼°pÙ„iȲ£uµwQÈõ’mðígþÉaÞ§?¾}ûÇ›m¿>M ò9£'tÊøÓ‘üF7…Ç:Æ»ýøªhn÷Ô¡£ÚUBkÝ]¨¬S†ƒ)CFb@^‘ëzbƈ±‘ãïîÜ„”ÛµÑ ÷°áàžØswñ¾Ø{òªëëºjibW•bXŸþ˜7¶sJ^(˜9r+^Ç`Û±ƒ]=¼- É?GL%±r÷¼¶ñ=œ©ªÐüÑTž†H ¯ŠÜ&jçi Cìí•’[zL÷YË»HRB©74ìÑ'=ÂE úýõÜláá]Hn+½ø!µ}Áö˜no×FÊÑf ä˜Ì-´jÔ®-O¼ÇÚÓ ÷Ð ôþá‘êkA >þÀ#OËF<êôùË[3mœ4w>ÝR:J¯ĸ œäѱ+¾O#Ï/nè8å–Ò×?Nû@0/mèý£åD—^ôÎÝ×Çï7¨ª«Åc+ÞÄ×ý_¬Ù·ƒ¤›Â²]›µcW¥‚ÊËÎÁ¼˜ru«ölCmC}§Œ÷Lu%^ß¼_ü¿Ÿà?_|,âñž3j~õÀ×P˜¦½@Ês±ãøáØs 딹\4|t§~±qÃ*¬ E]XŒ¥­uÞR,NS•!q{«²®köw|]õ–à`ñIT7D‰t¶1ªÿ NÃ옴ÀÿŽ°³èpW.†å»6Çø¾¸èVX¬ãèQKÊbžoÈÍÊƘƒcÏí>q$ã÷s=/ãe/D8íï ½Øvôv;ˆ”çju¾q£¹ÎÊA®¼¡ÂÓ¨ò–A¼˜T}ž¶×2àuõv¦÷ÿvD¨Ì'3 aVC¤í)+ÆHÏIZ-<åz=v‘… óëå5žš+™@tdþt¨o¦M\Ž_¶çÊ{Ï ĸš¿¦H=½dýÔí•Ç=¸¯èSÒÕ'ß#G'î$¤?4êÉ}kÞ|Òø÷ Òý † ½ P^l­½8©§9ÈçO×ú^Œé\WÔ×t@ö61‘ýDן1±~*}„jFHð&ÛÓy¨È •j Îx¡gÐ=æϹ¸¼ ?}íL<Ÿ_t+e¨^µ5ûvhÞÁ©CG¡÷^i¯_4e&VìÖÆSI,ßµ7LÏlûÒ±çä1pÎQÛЀ⊲Ø\iÀ× øâÕ·âúæÒŸ(;údcì¹}¶¨ö"'‘…ÑýiµåŽÆä9·¯l|¿xã¹ÈñO^v]»ÊÖ4Ôãý=Ûåï6³påÄi¯¿bâEøùÏFto]ãÛž2)(9{|Hï~i 2™ÆøAÃ`ÉïO:Žœ.n6 ¥3°lçfüÇ FŽß6kæ›Òæ~ßز.¶ÚàÓªºZŒ8߼微^‚ŒctÿA±ÕJZ[fÒõ<üÿ‰=ǹÿy}ìL ~ûÙ¿ÇÀ½»zÚç4 ÉïB?S‚õv¡¦¡áÌEˆ§‘qh3€s¦óNâÝ„qs¨*¾ÈÆÕë`ÂJ&h=c4ž::€t÷çà\'A¶e!ËI ;‘…Üì,ä&²‘°$YŽƒ„@v"˲`1 v൷,KZñ<Ãõ<¸ÜEc2‰Æ”ø—Bc²uÉF4ÿSI4$“Ê(Aæ¯-_ð?IT¥‚~8€‘~t?<×òǃùKã‹è@ôsÈxˆ<ÒÔP)‚1G´„=%÷{à‰§"z)D}ÿ‰ý%êŒS¹C5\F#F£Ý+ò:„çŒRK`ÚXé¤ ñ¼ÀYÈ(TV {Už<ˆ4a’Ñ,ò^‹II"&v8Šxü7¸fÚ|lÁÕÈr²```Ð~,ÙÜk óÆNFnV¶/dK°xÛúŒ“üSåešb{S(¯­Æsë–ãlMn™y) rr›¼þxšŠÙ¹ÈËÊÎè<šÂ¨~ñ$ÿDù™Œô_ZYŽ_/~!­7ÏœO]~}»ú_¾k3‰guÖ¨ñè‘_öún¹y˜3zV‡"´VïÛŽªúZtËéX-„æ®ÒK¿Âž6†l'!½úâhL®û‰V¾L£¼¶-}olŽŠ]^1ñ"|åú;ÛÕQYi³ÕvÆÒ¥kÐQHG¸‹ÛPU!]ʉAë`H~ .Ù€÷voEqyô@  …xû/HØz˜Ø©úÞ¢¼š¤^\…+ƒ‰ÓŒ4ì[éŒÓ0}ßìÑö€$DÂ{ëØ6r³²‘—ƒn¹yè‘_€žùÝÑ+¿zäwCNVçÏóPQ[³5•(«®BEmÊkªQ^]…ªúZÔ6ÔyÔš{žüBì…{(Ã@©ãû×jÊÔ ÀxÌÃ!„Qó:· Œ”A”·—ve\‘Qˆqyäóµ‘9êª=Ý?ࡽAÆÏC{j4 0<ˆÔ-¬>D¸i¬†–Bõ™yÿDŒïkñüD_Lö-W¡õFÆ9<ùPÉþGLôBpßwñƦÕX³o>qùuX0~Z§ïƒ ÕõuX½xa­¦½°€O@.›0-"–¶ùð~”T–£_a.›ÏþSEت­\Œ®¸wϽ"mØðÙ4Í2¸Üu¨¬«isŸ)×Åæ#û±xÛz¼³=ZýÀb Ÿºüz|ò²ëb„n[‡··5]•!‹¦ÌŠü¤ëbÙÎ͸eæüv§½H)Ò­“÷Eÿî½bI~eŒhbGÃõ<ì8~K¶mÀ[[×Ç–Ì»gî•øÂÕ·vZ´Ã…ˆ^…±Ç;+Ê Cò;ÛÄö£ò<-O8Jî9å2ÁEÂ`¹!$M9F\Õœ~,N«ümêy—^Háý¤Ñ÷>‹‘í³ ägç¢0?½»uG¿ÂžØ£òsrp®Á²,ô,(DÏ‚BŒŠ€õ<gª+PTVŠSgÏ ´ò,ʪ+PYSD;pù càLx»¹F”•Çž…Ö›kÆ—% ÅíT9:±þ”´Ò-¡òÝ©ql,E;åkåuf~ÿL¥FèÉ*"Aî³€õSLçÏ W é*9È5‹ é}pí¾Úú C £kÐ ,ÄõëÄú±˜ñ‡Ò „FªBÿåW)êI”UWâ¯ýK¶®ÇL¿A›±|×f$‰àÙœQÐ=/¿Ùv‹&ÏŠ|Ž··mÀý—^ÝÕÓBmc~³äE¬Þ·ÿyïçSª.z}–Ó¹_ëzæw‹=^ßØØÊž|£ÍwŸû#v‰DZL<_¾övLÒö}ÒÊrl:¼Oþžå$°`BóÆ×KÇMA¶“@Cˆ,.Þº¾ËI~]B•èä}‘Žðuf噤›ÂwŸ{[H+ø7ºÿ |éêÛ0{Tçˆ^ÈÈMÄG¥K+2èx’ßI8S]÷÷lCU]mD)@$YyÔ‰ê8 ™—$UA9@<¿4/P¤‘qOq4\¦ÓS2”“ÈBa^>úöÀÀ½1´wÿs’Ì·–e¡oaOô-ì ŒPÇ]×ű3Å8\z'ÊJQZqå5ÕÁZo/ÉS{~â5ƒþLô( (])§?;&îÃÕµÒDE¨Ñ@{–"§Ä6¤WR{ç!{Õ¥•>œC9g9ÇqWb}zؼ¦!ãTŠ€¦¿ÐÀ•IKìcaŒ¢s {ùu u|fòöTS‚ÊèZ¸>z)ŒaÇñCøÆc¿Æí_†{æ.„eTƒÖ ¬ª?¨g¬Ý¿«Ùv®ç"aÛš@ô—I’¿`üTÜ2s> ròÐ-'yÙ9ð8GM}Žœ>…‡öaùîͨ¨÷zo:¼_ì×øŧ¾‚œ„ù–®ljc'‹ze'â#òì6|žää¢_aO|phoäÜ•“¦ãŽÙ—á¢ác26ö¥;6jGFöˆmG[&8²ßÀHþñ¶cqòì ìÙuyÂ^š}‘Luî¾ÈI$b;vçyʶƒ}àý½Û#ç掙„».¾sFMhw4Åà^}1(¶îqµ5(*+EÝLxÓ’ÚòÝfNÈèÂÁјJ¡¸¢¬MáÿV’ßÁp=ëöï‘ÒR¨ÎøÒÃ{g£¤"Š¼A„{¢_<#TãL8‡E—DÕœ†6‡¼ËÁE¶ÅP˜—¾…=1¤w? íÓyY¡o lÛƈ~ƒ0¢ŸR§­olľSÇp°ø8ŠÎ”àte\ÏUŠ‡ŸGhù#bƒaÈ Ö B@Éó#Þÿ×(‘BõZè¿–2¢ œš ‘^ õ!F,µhAƬ8 N¡Rv´?=YD­™LÙÙÐ@ÓZÔ©¸µ¤"…”„“F´UXó0¬F ¿ÿh¾ }èÁkÆRž‹gW¿‹µûvâ+×ß…‘ý:GýØÀà|GIeyDàêùõ+ðüúmîóPéIì;u~Ùµxkë:œ©®Ô×£®³FŽo;ÁOÓ.ÆÇ\{.é¦ðêÆÕ8P\Ô©kÐYˆKƒ€ÜDëRsmËÂ?ö¥´çž.Æ_~²«§{^À¸Ž:'ËÏàå «p¸ôd Ä­h‹öù@òzi¯ðn20åEõú’>hxÊyÔM4®ºŸø•Á±môíÞ ƒ›g]ŠÏ-º½ô,š:ã ûÐütÈÉÊÂÔa£qÛœ+ð7×߃¹ëÓøè‚k1sôô.èx‘¯7y~€xþœóa}úãÿ}äÁfËŒ’ß!p=«÷nDzQŸlÔBQ’< м·Tñ\ ïq%ˆ¦DÊ8!€j‹5.Šøor-8‡Åzæwä!#qÓÌùøÌ›q×Ü+1üT éÝÏ„·Žmc⑸ýâ+ñw7ߺãÜ4kFŠ¬ tM‘Q¯³Nþ)Wò ñyíauxy-t#B8ôœä@„ö‡AEü$·¦!óâÂû7h/ö0¦pzˆ"ì,¶½JàrItíŠPh<´wº– ß=Èk½¬ ª@î<+*œ¨ê´÷otí¤¼Ñ׌6ÁÏóðÊïákþ_ì:‡j œ‹ˆ«o ,ݱ®×ùõ—G÷ŒÝ÷Å´Â_O¬Z¢ý>¨‰ðC¥';mÜe!©Àà^}ÚÜç]_¡½ûEŽÿfÉ‹8Rz*#ã^ÒAûçxY)vé¾[‚A=ã×ýØ™’N5 §ß¯AsÍÔÙ˜òü ï<ó0YöºŸþa`` á`ñ *é"[V]‰19á1ãc ®‰=·þànÍC?¸gz²Ô™$ódšRyÃûôoeO Žmão¯½#r¼1•Ä^xÉvêpÎñööÚÕGSèJoþ4$:å¹Ø_|¼ÓÆÑû¢­`Œá«×ß­Œü<Îñƒm[äK<ù:—ÇÞg@Ú6f;&'?Cð<ëìÂáÒ“2'›æ;SPQ;e­+Û‹ZÝLÐj¦-Ê摈@ ¾ 'ƒzöÅØC0°ç…iA<×aYÆ †qƒ†•aãÁ=Ø{â(ªëkÂîLEnpEZ©Ñ˜LÓÒ•¢?4qE=OR(Pyè!õ"d$_ÝNTb¨ |øœaúö€hÄ‘Ösò_e"Fä/l«"‰öT¾4aÏæ•ïaë‘ýøÆ-÷¥ýâf`ðaDØ‹?vÀ )/Ɖ'V“ïûѲ[ºŠ¹Ò1×Óeh3‡ç)BEëªäæchŸ~˜2ÿjÌ7=Ú¨jkе°m³FMÀ—®»_¿õã¸xìä$”'‰“ýŘÆàuPopˆ,KÒÉ”×ò âº×š A<š¯/ä” \«ž*h½žÁ¢ë(‚>z™@2øµ+äûK0x-òÊ(!;½I-Á°ÇŸæÞмûHÊè•xóý¨H£ŒÖ^+—j/—ÇŸí–#ûñ•?ÿÚ“ùÍg`pž ,˜6qÐpôëÞ³Íý¥#iKº¤¥«=®C={ÔæÆ~ví²iµonÿbõ”Ù»Ç'/».6]étU~üêÓ­îo;š'—áò6zñ` a±"ˆïïÙŽê.*[·(Ížü}Ñ‘Hº)¼³ccä¸cÙ¸rbÛ)™Âís.äÁÑ”…ê†:üç‹Ãã-Ý49ùeÕ•x# É¿nÚœ&ÛšUìX’ßœ*?ƒ×7¯Áéª -÷YóÕiñ»ô ·f¦9ÖÔc©ˆ‹à%ŒYèÛ½'L¼÷/¸WM™Õ®/9çúöÀóâ_ïù,îœw÷îGbèuOo$´Bä©‹_Ix¸*/‡Pºn¡&üžD›ˆ+õÎdí°ê[D¯DTôy膪íRÙžŽ•…¼íLÑñk)4LŽ_Ÿ~xl¡öò2¥Q ÆÅH4JGK8½NÿÂyÝž £²®ÿù£xtå›00è(´¹vtp¸ õôÆT ËwmÖŽ]ÑNïàˆ¾0²ïÀÈñ‡öâL(b ³P]_{<\–αmÜ2s~ìµGOw˜‚<à‹n858ïÓgPÏ 7+ÿvç§bóÛWîÞŠ×6­nUa=‡)CG¢w·îícœ¡¨ÑîÕÎÂð¾0sĸøùo]›â‘)¼¹e*ë¢û÷ši³Ñ#Ã¥ÔÚDzñ;>…¼˜Tœ­GàÉ÷ÞnYGÆ“/ñÐÒWÐSJ³oaf?ŸÍ*v, Éo%6Þ‹•»·"™Jª˜ã \WÕ>§„…ˆŠŒ@x+¥‘”ÿR9Ï¡!F´ì΋¯@Vš<ç?¼óJÚÚÕíÅïßy%öøÇ.½&ãûgÜÀ¡ø\aÅ_¾õ<Ž·´VÕ×bMHÈ/!äiCö»Peÿ¾ù‹b»ÜÃÿ¾ù×¹g]cClíy›Y¸ï’Emè±c0°go|ýÆ{cÏ=²ü ìŽÑÈÀxò+vmÁ[i"z>uÙõÍŠOšUìX†ØB$S)¼³cC $KÕ¶˜îÌ—^xš£+_¤‚xå‚€´÷éÓ­L¼5掌ÜìÎ2èzôëÑ ™5þå®pù¤™r|¸l[œ¥YÚ·(QçŸS¹óé¾¢©*xb‡* m¾7©]/îC0òRï-s]óÌÓ|{•J 9(‡¼.lI»”±á`œÐµáÀ„pî¾>~½AX|(Ü^[¿PÙ?:Tê€1†E‡ñÕ?ÿÛŽ€ÁùŒcgJð7ü ïîÜ„Kõø8,Ù¦“îñ‡b`5˜[Š…“ãÈ»"¯úPÉÉXOkßnÝ1¢o´Uï‚B|ô’«bû*­ªÀ/Þ|.ãc|wÇ&¬Ù¿3r|üÀa¸fjæBõ)>2o!fŠ íÕ'ñƒkQ øe;7GDü.o‡žƒÀ¨~ƒbŸÍ–£Z—çAÌ=!v½¿ãK¬Êø=~÷5”V–GŽß6ûR ë‚ÒyMáš©³qmÌ^u=?xá1Ô-ƒxì,:ŒÿzéñØsc Á3ævõ?ô0$¿ðóMÖ ¬ªJÅ’Þ@@–*“e´¸NjhN0 ‹¢ 3¦¼€cÞwnŸs9nš9£ûîêe08G—‹›g/À¿ÞóY\?c> sóUù¸àI.Cåà¡ëÒKÍu‘8Y….”ß/‰3£÷QDZób‹±H:W˜á8E}bðyOéü—xò¥¡È›ãײ¶‡ÖžÎô5çš @ UEJ¨Š¤ ¢\=bõä«ñÓ5QíÕœÂv›òÚjüÛ³ÄÓï/í¤Ýgða€Û” Ô4Ôãáw_Ågú!öž<†Ë&LK›^]_‡µûu/ì§gdCzõŸQ5øÅ'p°¤y¡²Lz®Ÿz?>\øú‹æÂbñ_Ùî¿ôê´ÆŽÅ[×ã¹ æa)=…Ç”´- ߸éÞf×ÂJkBnºc ÿ|ÛÇÑ=/?rnÏÉ£xhéËÍŽ}i(2còè[Ø##ë²0x_Ø0ÕÜ3‰¯^wWÚ(_¾ù<¶fÐ@¼lç&üuÝòÈñ¾…=ðé+olÁÜÛ¸&iÎ34¿–_»ážX=…¢²RüÏkÏ´mÀ@&ŸKGaíþ]ø‡Çƒºdcä\–“À·oÿDÚÏ+ŠŽZÅÓU8UÞ5¶s †ä7ƒý§ŽcÙ®MhL¥d½Nx„ß”i>;MU›©<}-Z^¬\§YN‡ŒÀ½—\…+&Í@aÌ4HØ®š:ÿrקq÷ü«Ñ‡|Y¡Buj{1¼‹‘Þ’4 @ìuÎåI¾ù˜f”ð“?ÒLåp)9½}ú®‰2¥IÐqI@‡ "cÄÛO‘uMøþ*G]+çODÿ¨ > ³¤$ÈõUJøâ¿Ähå5™'Páûž|ÿmüç‹¢!ýƒk`ÐZÔ'›ÞG•u5(¯­†ëµP¤*„}'ã7K^Ľ¿ø._µÉÀ¨°hrúV+wo•× dŠäM‘´æóÚ[B(Z‚e;7ErÆ  ;·Éº÷9‰,|çÎOÂNó¥úW‹_À+ßo÷øŽ)Á?<ñ­šÀçÞk(‰¬U:RÖ‚%ì]Pˆ¾õc±çž]»¬Éðø3Õ•ØrD'OíQÕcQG 8¶#žyXŸþø›kn=—ò\|멇°ãø¡vÏ}õ¾øÏ‹·÷oÿD ÕÕÛFÖ­´û©ù •—ƒïÜñ©Ø÷ÍÒíDJ#¶f\mAC²?{ý/±ZíE¦ HõÉFüîí—𭧊ý€oÜô‘ØÈ–ŽÅöc‡ðõÇ~…žç€DW£ý5N.Pxž‡µvâÄÙÓÄÛ©ÇóJO‚óy½\ó:ú|ˆKbå_­Ô³Áü›ICFbÜ€¡&ÏÞ U°, sÇNÆܱ“±õÈ>¼±q5J+ËHŽ½¸’K²‹`?êo®·‘9ꪽŒL'¹æêzEN‰¹ç´º„oàA ‰@ˆÜÊ£47ÒX î«qlq®÷Ò ð*^ôÅdßAdýD8>ãž!ÿ4__ÝŸ@î¡î ˜ÐŒJhŸ@æécí¾]øÆc¿ÆwïútƼSN”UW5yþ‰÷ÞÆ@UAv. óòP˜›ÂÜ|tÏË^ç¡ 'ŽmÁõ<œ©ªÄ±3%ØYtØ­ ¡{^>f—öžonY«ý>¦ÿ` îÕ'cs¾jòŒXoð[[×ã³WÞÇNŸ[êzí‹|àœã¥Vá—o>{þÁE7Çz°)& ¿»ánüôõ¿ÄžÿÉkÏàø™{öœ8Š7Bï¨mlÀ×ýþþ¦{qýE­­æœã/kÞÅCK_Ž-¥ø•ëîÂôácZÔW:U{·µ{/;ié{râàáøô•7âáw_œû풗Ч[÷XÍ…ö¾çÃë¸jÏ6<´ôe/+Åï?÷뻹uj)ªêkñæ–uxúý¥8S]™öºÏ_uK³Šú™EYu%þ¼â-¼²ñ=\2vrÚ¨° ÉAC2‰»7¡ªN”?Qâz´ žúÌà©°]PÑ»ð&Üœ¦¼–ASØqüH§ßóê©éCõŸ[õÚÎ16ãc¸|âE’Yó.®ž2+mhiœØà œ­?¸[;–Ÿƒl'ÚƆfÓ"晄¾ích >}Å ÈÍÊÆïÞ~)öü¡’“ø‡'~‹ÑýaÑäY¸høhŒì;yÙ9ü/ÝÇÏ”`ûñCxoÏv|p(}èðU“gâ_nûx«"ÎTÅ{K*Ê[ÜG–ãàßîø|ø"¥»RIüóSá‡÷}Dÿ½=Û"ù¹-õ0·—O¸(6dÅ®-8U^†=z5Ù¾4f RI”V–·+:˱lüàžÏâ¿^zKcjØ~Èýê};0käx\9i:&Á½û"ÛI€sŽÊºZ9} ›ìÇò]›ÓV€|#LK#;äÜ«Êc—T–cHï~iÛNSæ²$Í{2³ðíÛ?Ï<ô£H¤ŠË=|ÿ¯Â÷ïþ4.¯ˆcº÷üÁ’ô:¹YÙÈËÊFÒM¡ª®.YóÇfÖx)p2M~úÿ¼ö´|°mØ–†d2vléÐ3¿þå¶cNJg–Tž=žN±ßf rsáX6ªëëÐSºá’qé\?L0$ŸàÄÙR¬?°[™Òz:.;êõ ‘!Ú%¾ÀääaæȱÒÛ{ƒŽ…eY¸bÒ \:~–n[;6¢.Ù r⃜pÔKï¸ïÔVt”ô"D@©g> Ô¢©_¯Þ?Ôa­¢ôþTP½4ÐKâéï?¤ykê}iùšòý«ºæ´‡¤FD^~d„~R*ô‘~…'Ÿ3¸¾~ú¼I?èsýAOÎâZú2Žœ>…/_{g¦·–Á {X¹{K§Þ³÷ž˜2ddì¹}'ãÍ-Ñ/|™PÕ#ñÛwê8Þز7NŸ{~ÏÉ£-¾GMC}‹ÊÚÝ3÷J|ñêÛ`·!}\…á}úãÿ¥ ¯|QAJÔD^rsáÑâÚÏ]us«‰œXËö®!àׂÿÚõwã¿cDë’ø‡'~ƒoßþI\2n2Z-÷7¨gæR=÷êƒ>ݺGˆ§Ë=üzÉ øÁ=ŸMÛ¶¸¢ 1>Œµûwáæ™—´klŽmã_ïø$Æ ‚‡ßy5ísþàÐÍ°“°m¤\¯E¤¯[N¾}ûÇ1olë Ö¾4‘,{NÅÌ‘éÓxÒEÀì9ѺýÔ·°¾uëýøö_ŽœKy.¾óìÿáë7Þ‹[Ãyk÷+àG4a°`|æ=Ð)ÏÅöc›½.éºÝ“æpÃô¹øÂU·¢Gòß’8\zªUm\î¡¢¶¦Ék& ŽžùÝ2°rç?Lòw€ÝEG°vÿÎ ÏFÏ—(mAxç@®SÂZZÔm Ú•“ÈƼ±“qëì†àt*ÛÆuÓçá;÷|óÆMõ54råW‡…8œôß Ò+“ó!(/¶ôÑCŠJʼüz˜¾º!“Š`š2½jOSg„~€º·8.rþãÚ«ë ¥Ò¨ ßžŽ›zöcÖOˆÒê½9„ïr®ÚËuæ\¿HäçÞزß{î¾p¨A °dëmy¸~&pÕä™±^òªºZ|ÿù?ÅŒtž¿ö )BýË7ÿŠC%'cϽ½½å êÍa aøßO}_¾öŽ6|KÆNÆ#_ü®l¡8¡Ë½üñ‡áwŸûF›¾Ç=¼»sSì¹Ý'ŽÆ–l 7Θ‡]zu칤ëâûýþîÏÿ+C¡)NwÀþVQ|åî­xqCú²uM©ð?ùþ4$Û/ªÊÃ}óá·Ÿý{Œ8¬Em’®Û"‚å¤éøÓ—þ¹MëÑ(Mã‘o.¤´ª[Bá÷ÍáÒñSÓ zœã'¯=ƒÿ{÷5¤<K·olUß-A^V6¦È|„É;Û7¢¼¶:cýÙ–…k¦ÎÆÿ}þ›ø§[îoÁ€wwnj³xkS0¡ú ö÷¾÷½ïuõ ºëöïÂÁ’"Z h„žzÐñÝɲxä¿Ò÷f9 L6—M¼=›È+20èh8¶ÉCGaÚð±8YvåµU’ CFœD•ò9” _¸¬‡bȺR|8×^yÊ#0žNêôCÏó°jÏV_)2 zmQ¶Jy…·R:2…ç/8ï‚^c3`¦kÔò ÎIl:¼/¯[á‹óIAIý} HrH‚–„$P¡ÿ1bsâ(Bë‰ü}-$(Ü3¡sß^ê_Í<=÷@F,ˆÜN;àÑöá÷\„CŒ$Ÿœ¸êOOg ×é@$þŸ…ÖŒŸ&ÑÀðüµçFLâó«žøÁ=ŸÃà^};i‡œO(©8«ÀÝÑÈNdᣗ\¥󸇿®[KÚ( ròpÛìKÓ öµ)ÏÅKV¡º¾®Ùk-fáö9 Ð-'Š‹°j϶VÝ+'‘…¼ìäee£oa ëÝ¿Íž°ÖâÄÙÓX²mÖìÛ‰½'5ë¹Ú»æŒ«§ÎƤÁ#Úuïǵ¨4Ø€î½pÝE·º×ó°«è”¡ª®¶EB‹Y¸aú\ôi£Ñ‚b;Ø“&t<Œùã¦`ì€!ü’dϯ[¤Û|´ÕØC2.8 »ŠŽ`ɶ ØphŽž.nfÍÆ Šyc&áÚisÚöðêÆÕ8S]Ñìu—Ž›Š1”@õþSExooóï½ÂÜüV pαçä1ì;uµ5UÓO‡‹GOŒ€Ú‹W6¾²&ÔðãÀó?§²rп{O îÕ{ôÎX¹»¤›ÂóëVtÁÏrÜ7ÿêöwtàCKò“©–ïÚŒê†Z€䜔¼ ^EH‹:ÏCש/áƒzôÆœ±“›h½UÓÀ 3áº.ÞؼËwlDR†tSRŠy Γ <`§J¥?*ˆ§§æGƒÿ¸ü²Éb µè#‚—qaò¡± ƒ\\n>öóïÖÐSÔ!NŒ}þ}<ÎÉêÐy>²(áörNÄA ‘ñsí)…Œ2áãjLÝròð¯w|S†Æç?|8PÛØ€£§‹q¼¬Uuµ¨kl@–ã ?;{öƈ>:Íø`pî ¼¶GOãÄÙÓ¨i¨GC2‰œ¬,tËÉÃ^}1¼O)Òh``pnãCIòë°b÷fÔ76ÊòVŠ ¨ò`z­j*ø(áIïfa^.3 ½MX¾Áy†òš*<¹ò-ì=q$\½@ÈcŒr-® {ýåñ¨‡L‘céAx°U{éÕ17ÈÈ„"˜"ÞLEØ„Í \{ÿ3ýýC¢£íŠ #çŒñЇƯ“ùÐZUSŠüÑñCªð“û“’Ÿbœ '¾íc˜;fR'ì,ƒÎƇŽäWÖÕ`Õî­Hº©q¨ç^å {ñƒkƒW¶eaÚ°12üÊÀà|Åúý;ñâÚe¨n¨ yñݳM¼àM–²‹†®º r­ÅãÊ, ÞÂíш!ÛÍ…Ó>¢=úþgQ2ŸfÎ4E1& ‰4Ê!]È~¸}$2tüáU€öÌÂ)Œ´·™¯ßtNšÙÛÈÀÀÀÀÀÀÀÀ  ñ¡"ùgª*°fÿ$]76l8ê9“¿4âa ÈÃÞ}1gÔD$SÐàÂ@mCž^µ[ŽìSÄ“äx¯6|ÑÉ®}ßsApN÷¦Ë6M¶ßßCØÛ³§ãŒõªÇô,Ç—w/õbÂäi8Í÷×ÖQŒ/¦î}èþ< ô¸Uç‡H9J߶~A$Æß\s;nšÑ¾ÒLç>4$¿´ªk÷íPb,44– {……Æèxâ=ËÏÎÅÅc&fL™ÕÀà\Ãöcð—÷ÞFEmµÜû±¤9,^("®…–’¶‡JøùâpD}ž¤ÇõxIÞƒûëi‹„·k9ùa£‰ˆMCçä‡åøÄè¡úý© XhíÅgKh>¡0þØT¹~d`d=Â÷W‚†ÊØñÀå7àÞy ;iWt4>$¿¸â,Öíß!½aòËwLž½RâŽÉÉ|Ç‚‹†g¾–¥Áù Î9’)$“H5¦àº.<×çžëY˜Å`Y,‹!‘•€“å ‘p`;í/‰ÓQhL%ñÜêw°vïöç=*Χ¼è¡tq­`ÛD1>6÷<ì…¡ûT}>è7*ê&ø¡÷tL¸{\{2^H@¤ÊSi Ô=æšHa¨=%òj-ÂqcŠ‰Ï‹®?IƒPŸsj}ï·Ÿ¾âÆ®ÞnÀOòOž=ƒ ‡vû!®}áÖ¾ðƒ†P.o@ò»åæá’±SP˜—ßÕÓ2è¸)E‡Nâؾã8²¿%E%(9q§ŸFÅÙJ0X`ŒÁbˆ)z.ê—Gœ¾ÈÎÍFßA½ƒ}0hÄ ;CÇ F^·¼®ž6`ç±Cx|囨ª­!G…'ß­ÙXá¼ðûÚy]°.Jþ£ï_hçiØ|:’Þ¤¾mö"¯÷¥“ë¸\{/¦½\?DÂéiƯyï¡Œ3,f}ïœs9¼ê–ŽÚB„ šä••bãá½²<—¿¢Jú*ß‘:ÓôÚñƒ†còSvêÄ3Åg±gó>ìÙ´{6ïÃѽÇá¹"ÝÃÒ/¶ c –­%ÊchqQqUï½1fê(Œ»h4ÆN¡cò¬õ‘iÔ6ÔáÑå¯cÇуz8Í× Ðñ·jƒÈëHꌼF7ÒAþ€Dëï_2:96z¯p{e@€¡Ð}®k„u ƨ!Bö=êÉ×æ&ê¡5 ip­}8Í äÕGŒ€|þÝuñøÜ›»`wd ,É?^V‚M‡öÆ~Áçþªó*/7?'—Ž›Šn¹Æ{¡#™La×{°qÕ6lyoŽ: °8qÌ3õlE¼õLxëc\ù>Ég ü<ß—µç•·Ë/ÌÔ¹“0í’ɘ2wº÷îür+wmÆ‹k—¡ÑMD?D*…€†‘#ü~ÓÉ!b¼æº·;.‡]¼‰&QìZ(;´ßC†¿`í=pí)jZéÆ3&úym Í÷&ÿ´1€Ô¡?¿–"î‡÷Ì»Ÿ½ò¦ÌlƒNÇIòOž=íQ_j„e³„Øù%ßí_;ªß Ì1®«§cÐH%SغzV/^ Ë6£®º1±ö`,ÆÔ©&<ò›çµgA€äYLCž~ÆN- AÛqÓFcö¢˜}åtöêÖiëvº²ÿ÷ÎË8vº@8¯=¾”§.ó€°ëäÚŸ[T]?ì±QÑ\}*’ ¯'¥ø„‡>Á£•ÿS㤣7ôÄ ˆâ¿¸*m¥Íã?Öá„ÀG„÷èüCºäDÚ£¹NØ#8î½d!>crô ÎK\p$¿¸â,6Ü Î=’ª×ÙÖ‚n©.8—å$pñ˜‰èWس«§cÐA8º÷8–>¿«^_šªšXÏ<@¼óLsÊ"Ö ¼üAn>åëêw á,q¡%ÚB¿†=ÝÀ‰'˜4g.¿õL_0µSÄü<Ïà ë–cÙöàðBJúz¸¾\ ˜q½$ÆþM /íû—*lhNúðq=§>œ·Äe®NG4JAœcÚýU¾V¶“áñ4ã׌AJ‰?nüúú‹qi9ù!%~êØW(øèü«ðÀå7tø>200000000È,.(’¦ªkìT_p=OzKÃaø€úÂKC{ûöÀ¼1SàØç®â¹AÛJ¦°zñz¼ùäRØ~0Øñä^€!Òó~±Nä­ L_cAl¿¿ÿ|ònY”¤3­[qŽ‰ÖŠôŠ>¥°Fpç®ë¡[¯\vó<\u×åοåÈ~<¾üuÔ56ÄæÐÓaFæežyœ°\¸uƦ*FØ ŽÎQ=Åkè€1ZŽ+i„…J@PM†^+M![…Z¿ôkѤÂ>IIPéQc†ˆ~àiŽ ƒÀ—߀ûæ/êÐýc````````Y\0$ÿlMÖìßsÍ[º“ûÊ0yðHŒ8´«§baÔV×aÉsËðæKPVR!I6#Ä™‚’&|ù!á¼pc˜E¢ú}önY¶:&É?§wð“ÿeȸ2 ø†aŽ1€ÜÅ÷s/ŲŸ‚íؘ{õL\ý‘…ùü ‡äË~B† Õ‘EÁ´µ”ge„žr`1 ÿtË}¸rÒôÛ;™ÃyOòë°r÷$]7öËoÜôh™¬¾ÝºcÞØ)°»¨,™Aæ‘L¦°ø©¥xáá×PU^-û‘ð2qžúÐ5Hš(õðD>ƒm«Pz%µO…ô˜ ñgä^*”_yäÁ}âoÙbðÛÚ ¾ëÞó MìÕP* çRI€s¤\³NÇmŸ½½úwœÞĺ};ðÔª%hH5C"¹ö@ì{Ò/sI<Û!×ig4Œ?J~š Ïš–ø¬@”œóйPm¿ÄD)ÐûSqÀ81@ª­è×éQ ñbzéÆOÇÆcÛëéJ$[@‹ÈHؾ{ט3jB&¶ˆAâ¼&ù©T ËvoFc*ù«BP™"û¡pÞ±†bÒ]= ƒ bòÍøóOžFÉ‘’È9&óá IoŠK3ÙΗj`ñgZŒ\+ȾhgÙ<Ó–N¼õ"\ßòAgŒ10Ë H¾%½÷¼™±s|Î9Ü” Ïãp Wß»×Ýw²r²:ä9œ(+ůßú+Ê«+cª\¨Ñ‹2uz‰8åU–ö4í)Éuî5†J<ü’§Òn/½û1•8èý#¯Cí¤ŸVáÁpI»ˆ! $>(Ή&ùš` xóù§÷ ù¡(•ÊÊ`”“ÈÂ?ú & Ù!{ÆÀÀÀÀÀÀÀÀ 38oI¾çyX¹g jêÄ帒]нa–Å0kÄ ìÙ»«§a!/ÅþãQl{GäœÒ¹ó‰²Ô¶ó¦½^ñ,Qãžøþ³¡‚yâ¨WžyrY~;Ë’Þ}1 Ûb€mÆ¡ÐÏa×ú÷ƒn\ˆàœ#•ôÀ=ž«Ù´Ë<…½ºáÎoÂE—NîgRÛP‡_¿ùW*9!I¨¦0¯ [Ôtg±„Zt-g<.¯=FdS+±‡8á:¦" Bâ} $ZâÒp!ž= Ã’øP)¿˜örNÄïUWÆ›¦+@ï7²Þz|€ìZ1¹þ9¹øÅ'¾‚!½ûe~Ãdç-É_½o;Êk«Uè½æÉò!Õõ¡ÂQ³ÙX0~*ò³s»z €çzxý‰ÅxúW/ ±¾16_‘zUÚŽ5IòãÄõ¨w€Å|ƒÈ—WÌÞ÷´3æ;í=fYw^|˶ä}˜/¨gÛ~˜¾í_o¤_œgŒ©’{Áð¹PàJÏó”×õßžðH{®ë{õṘ:wîúÒÍèÞ+óJüžçáñUoaõî­{˜ÁHêLD]?š?VÇ%1%ï¦WGKÎÉöP„;½º~ØÃ/Žêˆ„î3ë=ÜÆ{òCklÓf]1­CžÕ;Û7à…µËr]|ÚôPtY?ž+²)‰w¬gš†Ã“a/:5rÚG” ë¡üZBäþœ§b}—ĵ§{zûHdèøã´Ô<Ã) tN¡vô9„Æâã iãÆÏ?ñ8¶Ó!ûÅÀÀÀÀÀÀÀÀ í8ïHþž“Gq ¸HWêàQoTH¹{hï~˜1b\Wß CxçùøÓžDC]£v\‹^§uq “ü8¾Ì©—œÝ"W°@,ÏöÉ:cP‰ô„Ö[A_Üò=þ–¥<û,ñ³KÞÓ±-ßcxð}O¾ ‹Ù’û·£¹ œÃ—~¿{À9\p/çw}OªÑçÜ”Ïõ0cÁÜýÅ›“—“ñg¶»è0þ°ôeÔÔ×Ñ@x9æˆ?âÈ0 ¥à¨ç'Bî©·;fOEúb½ê1=ËqÄåÝ‹Tˆ¸0yÍrVÐKçÅ)ÿë÷<î5ñù—¾mýˆñ!öÚ˜bz…†yc'áßïþLÆ÷‰Aûp^‘ü姱åÈ~ù»þ‡© oؤÁ#0fÀ®¾AP_[ßþÛ±fñ†Øó*ÿðëË“_ƒWéI¾ÐlÐCõ©š½¼¶,–Å‚¼ú ©mû!ö–OÐm†€ÈŒùç˜c„]äßvà½g6C"ß³²œ@]_D V0  €ùSŒ€s𔚗Ç]IR}’¸®ÉTžÇá%/¿ë¢{Ÿxà›÷bè˜A~Åågð‹×ÿ‚³5U¡Òyç^¨ðey­<!ízš»"Óá’sŠkóˆž ˉÏÕ_8'?,Ç'F}0ÐïO……Z{‘cšO(Œ?6U@®Yðýµp}õ$…IDt²Ïé‰à™Ü9{¾tÍíß'mÇyCò+ëj°zÿŸ¤4¡vMCç˜9jö0{ŠŸÄ¿ö+œÉ©×Âñ£í™æ±Ðpüˆè]ÈcNEîx¨=%òj-ÂqcŠ‰ÏK] ¥Aˆ¾£Ÿ ¡±†> ?“àÕß^sn›½ ãûÄÀÀÀÀÀÀÀÀ m8/H~2•Âò=›‘rÝô¡¨þù¥Ø¶,Ì3=ó»uõð 2€õË7áßú=j`ÅìX=LZ¨¾"ù‘|á­—Êõ¢?f+Q|a,ŠÚÛ–(oÇ|bxõe_¢ì] ÈÏ‚|Ç„÷læçé[€ã¡ÿ¶[ÿ ]pX’O'¼< `ðž0Šð\.C÷¹ÿn*¿¼žOðS®‡TcJz÷ç]3·?xƒmA4¦’øÍ[Åî¢#Áður _$—Ç’‡éIz4úG¶ÖÛÇ…½Ç„Èë}éä:.×Þ‹i/cx¸OÚ^õ¤¤¿æ½‡ÊÕg1ë«/¤5²ƒ@6b[6~øÑÏã¢ác2ºG Ú†ó‚äÓRy€ŸWL¿ä†¿à& Æý o<ù6þü£§Ð’­*I| É+›—¶<¸_O†ï3¤= û–Cˆ¹¬eÜÏö‰¾/–gù×2ÿZ?Ìßò‰¼ÅàX`ùb{‚ä[É·-Ëãd_L’é~|™¢xè=îùï σ}O|x®üô{Ÿër$“)x®‡TÒ…ç¹5a8>ùÍ{‘“›Ñçêy]ñVï٦σ<¦xò/"ǘ Ѻǚ¬ã ·WáÅ…îCöâ+§Œa£„Ù÷‰×ç&êjÜq•D¸Ö>œfòê#Æ=¡yó#çBFæçã·ü=úuï‘Ñ=b````````Ðzœó$ã¡=(­®Þ,õÅR÷¬‰Id; \6þ"d']=tƒv‚sŽGü^üí·ÑH~àù¶yW× a=]Iß÷ Ñ<‹t„ïÛV ¼çÀr,X"ÄÞ²|roù}±ÀÓo;¢Æ½Ë;ðú;–ÿš1X +ðÞ;°ƒóB¸Ï±Àóo©R}b¤4ß Þžçÿt¹ôä»®È÷¤!ÀKùQ1.wýß]¸‡ç¹p“®Çá&Ẓ)ôÜŸù—ûÑ£wæËì½¼a%^ûà=ùÌõ¼xmGÄ«ÂËvŠä M¢ØµPvh¿ÓÜ};\7¤@ôÈÈšRÍ'eè¢í£¡ùá>ÃäŸvÀ#’:ày¡Õ‹I÷£-Z µþ!# ™ÉÈ~ñ«O} YŽQÜ70000000èJœÓ$ÿ`é ì;y\ûª{ðô0Ô‚œ\\:~+³¡ÅÏõð›ï<Œ•¯®iU;Jò}±:FxŠ",¾Ê}+¿~ÿSåå[A[QÛÞ²„ðC"áøäÞq`‹úõÄ o^|Çr²/¼öaÀ¶`%lÙÎ ró„£" œ„/¾g ±@¦DõÃ$Ÿs¤ag¶±÷•ð­D,ÛïËêÝÃv`Y Y¶_bÙœüð|'[ˆíÙ~i<Ûü„°¤'_(ô[6Âþ•¸Ÿ ÌšO•Ë0|Ÿà{àðÜ€”zžÝsSnàåJè%]x® ×ó׿.•rÁƒ°ýÆd Ér²øüw?‰þCúfü¹¿¿{+[ù¦?-R‡sbrIÇ{¡uå|ª:%±a¢LÃõ¡ ¨Øˆ—\µ‚Œ ÇIž¼ÂãiƯ#‚”V×NÐð{Ý ¢V1\¡@d¿Þoìú1à3—߀û/½&ãûã|ÄCÿù0ÞxfqWÃÀÀÀÀÀÀà<ÇÐÑCðË~ÚâëÏIFœrSØxhÉ•ÕIšPL÷¿þÝóò Á¿@J¦ðÓ¿ÿ5Ö¾½±m¨JxÍ\@ÃõI©{r ”ò¥!Àaüð«óÞuqÞ¶á«îaû¾1ÁÏÕ÷së²v&Ž9Ä»o[~y>›ÿlÙƶmÿŸh×øé~º€m[ò¼åm]`Ë{[éŸù†fûãô¥þ›Ùd,þª*ëðÛïþÅÇJ3þìçO˜†ÝDzBi¡?À¥ÓL|”à’vRÎïPõä~è•1EêiÿL„‡pòYÄäˆ$‘f¤/¥TÏUu‡ ÐUÐJ÷i6%2¨î©ÒIÔç_”`Ë(2.¨ÕSýh¡úbþd-͸¡2^hZ‹¾þ,¨¿÷§oaÓá}ß-Ã9™<¹þàn¤5ÝR”Ðùí‚Y"G_¨ì‹œw‹ùù÷"Ï>‘å׶g v@Àí„_ϱX6`96ÛÑwl–mŽï¹O8vàÅ·ae%dY=f1ØO>|å}ÆÀx@°mæ‡í3eèÒ˜š+<¼þ{Æ¡ù.‡+ˆ+w}¿Ë‘̲á¹'ßõà$-¸_2•ôùà‚¹©´,¿ÄŸ¸)øÇñ¥½ûg6tæ¨ñørÖÝøÝ[/ !Ù..,ͧb}‚Ì#šã㵧^wIÂC‰ÿZÀ{T+O ¢g`â㊜$Þ2ñ©€¿'u+U$ŸŒ_ÍŸ‘{ÏŒD=0Òž¦ˆ×rÈÑÜ’ÅŠ(!ûÄàqÿï¥Çñ‡Ïý#zädt4sŽï>q•õµÁoÊ D=ú=ó»aþØ)†à_àœãwß{k¯o[wÝ©þk¾™ï%õɱòà3‹ W}ðšICc~ <¨›10Û÷ìÛÌÿi±Àµ/rñ³m0Û†•°¯»;á—Ìó¾7Þ îç8 ¸~ ÊÏÀ¡ÞUbÏfA@ \k9~¹?Gxí_KÀÏÿ·°,82šÀöõl[  ㆨ`[þ¸kªëð‡ÿxå§+2¾&…¿»é#ÈÉÊÈó‰DôP­Ž€ÜÓð~qœþ¸öê:ÈöZ†ˆÌöµáÂéĹ2ȈñsrÿH%èvyûkÑMÁ\…žÌ_ê ˆ¹Ft.âz²~\Ÿ€¿þ‹œ¼œŒï‰C%'ð³WŸF]c=h¦»þ‰¥«Ó$ß>F±¢—8UyÚ^Ëßýø‰ àA³“Îð°B¾>-tЯ «Üljé¥?m­'#‚ƒ‘¹2íþª½Ò=øèüExpáÍ™Ý維ß#{tõ0 ÎstëÑ WÜtY‹¯?gH~2•Âʽ[rƒ0}MíZy˜|ýù&ÿ‚ÁÒVà¡ï>Ò"Ï;!ë"D›zËŠöE <“aùÁOË‚Åü|uX~¨¾“pfÁv½¶=c` Gz˳œ˜eÁIø9ö¶mÁɲý’xNOŸÈ‚mûªü¶ã‡øgegùFÇöü¾?· ªÀñçÃ÷sçÀíÇãÊ) ‚&”÷Cñ=ÿ¸›ò ¼ÛDcÊC2É‘J6Âs=$}1¾T£ÿ»—ôt}Á¾dc )×E]MFM†Ï~û>Øvæ«Z¢_ÛP¯ÏWÍ(˜'d{¡%{-z>¤V©×•èy„G^‡Ú…Ãáéu"º£Ï-$>(Î)•äk‚âUPºOê[,@Ô°냒 ÔæKîEÇo[þãÞÏáâÑ3¾7 âqΰäGö"åzJ؉æ«^YÆ€|Cð/(l[»¿ÿ÷?·­1‹¾n*H_ØÂ]!7™¯/z Ôö•¸‚~ád·óßEAؾ"Ö'Äõ,?dÞ/ÅgùíLök*ý¾Ÿ*¿g9ð A¨¾¸Tâ€`düL„ö«õÏ7Bø‘–Ô`øŸm!H)ðS ,¿ÅA›Yr|¶Å`;öm=ˆçz½CöÇÈ~ƒðõ›?ŠÜìl•*œS„V„Ê“0ö`!D(»’ëSQQ"]ç ’}Ï”pÙÖbŒ´Ô" @7]¸=#;–¦ÖËŽ… IB‘!ø!s?tÒMjKªöáñ“¡2Bð…±U½?bîOŒtþ®çá¿^~gk*;dDqNxò•œÀþ’"Âë¤{Õÿ enV6.7Íü §OÁ·îýªÊkZÕNr&&“ð•X^(Dß¿ÞmÛ~â¾ô’3B~c~¾”±åìÕ{Û¶À,;å·üütËòËâY ‰l_dÏI$à$lØ ‰DÂ'Å ¶ÍàdÙpœ„í‹ðYÌÙ³ìD``úø…ÈžHA`ÜŠ+üÚíþ«PÔ>uÓú¥ö<©$óº¾G?•rÑXï‡ð76¦à¥<464ÂM¥L¦€ ô^2•w}~]m=Ü”‹Û?s#æ^3³CöŠïÑ µ º'Ÿz ‰ˆóCЃõóbJæÉöˆ†¸§+¯m-A_†«3ë=ÜÆ{òI ‚¸äþ¡Ð|Õ‹– Œ¤ˆrxLË¡ÆNº§iê<õñÇGBh^ý`£N6?ùø—;doèèr¶\]_‡%' Ä*ê$D«8²÷n×€IDATœ.cDö.$“øé×Ýj‚@‰ì‘¼{-?|¹(s…ØN¾Øž²ÈcNlቢzÂSÕŽY`Ì’%çì@dÙŽå‡é¥è£?ADô‚þƒ¾m›”ÅžtRhÙVðÏÿݶXPÊɈ!ÊŠôfÙ°lq?ß`a[œ €#ø‚1;¶(¯çƒž~Ûq`'¼ðoàè¾¢Ù/#û Â×o¹¹YÙJä  ®çà :T©:®4 $$ÿUžg*ŠGAS‡ÂûL:ÎÓˆÒ1ú¹†¸öŒ²jÙ†îõpN~¸T½'“}R¾¾4ˆ  C› ÓTùé Aür¬,4!ã’ ûYù>‘Êá96óö­Tõ}r Ë‚… ¾ÛO;p¾  eOüì9ÔÕÔuÈžÙw¾zã½H8 éGV¡éŒxÓ9É1' [ä²KÎLD Ä& ú¡{Y²PzóÅå‘€y5*â͆.<ì´½&bjüB½æTP0 š3“s¢¡ûJ)?4{¢@<ôéÄ›O=üáhZe@¦KøïÃ?.‡JNtÈÞ000000000PèR’¿ýØA4ºIY¢ PùÐ ¢‹ÇLDNVVWÕ ƒØ°l?ónÛÇäÔ«<ø˜Ë™~5#„Ý?ϤW›Iì—¾ó}ÿLæáƒIG¶ÿ?+È÷}/=³‚wË H» 'Áà$l'áÇ–¥òX@¶Un=”ŸíGÅ™2`Y°AÉ»„ü ŽeùxKD ؤ¼ž¥<ò"R€1?ÿÞrÿ^ŽßãXÊ`åìDV"xøƒÈÀ8!Ž9Žƒªò<ûÛW;lïŒ8_¾î.8Ž ê×½íÄm-ö”‹x¥5Áxš¢nRÖsy’ß.£%è& P4^¡ÿœ` ŽíÀ±mro9A ?Ë‚å00[À °=˜‹mû÷a6pü1'¶à8°mŽí€9˜ÃüPËŸ—Oðì„ïáß²z'Ö¿»¹ÃöДa£ð¹E·Áµî¶¬OrÚY® yr×¥r" AÌÉãQ…}!J¨´"„±@_ÎhÎxþ 8rº¿^òB‡í ƒ."ùžçaÛñƒPŠàÊG½K“‡ŒB¯‚®^#ƒ â¡ï=‚겪f¯£Jñqªø±aú„Ð3$Úßê2g™¨¥‹}È-N·§÷I½ïÑôó}猪“¤ØÓÈ·ã+Ð;Á9??ÞòÓÀràG9ùv ÐÏO¿¯·E†‚-ºQAŸØ#ðÌËœ~Ç‚ Çò_;Ž ÇQêúòûmÛVùúq á0$¾ ¡ã$`; ß``ùêÿ~4€ 'aÁɲ•mãå?¾‰Š3§¨>{Ô|jáMÒ@èµâÕç‰ '‹“=n¯¥À‡láÜ}F:`¡¡¾P{µ¹¹N •…:¯ä9¡¢uÂd†‚Rõ—+ î¯AĽé¸v#z§÷ Žkº ÁU¯l|›ìkÛƒ700000000h]BòwöC69ˆ'Ÿæ{Ã{Àà^}»z} 2ˆ÷ÞZ‡Ë·€51kâ_ô×f;’¼ž´´B„ÐBHtŒäõ«Ðj”’#¡þÌR¤<÷…÷üpxXá¼~A¶ÓÚÊPƒ ÷ß÷´[ÑÁ‚ä °` VøŸ¥Jø‰ùضOØÅØ}ãd:“!üa6³ˆxŸ¥¥ ø¥øDõ@´Y`Ì746&ñׇ^ëÐýtéøiøè¥W"rDPNªà3ÎTN¾„ð„CkOó×A_S¡:„óC÷ãÊOuõ¨@žòä«ñË‘iíÕœâŒ]äöZ:©fî@øãåýI3©{ ×Ú”ØSÁªÖ/Ã8Y/¿êÁ¿ú4S©ÝVt:É/¯­FqåY™«<« ½ 1~а®^ƒ ¢º²ü×ðo‚™GÅõâþ€"³¬…»ØW²<ô $FÅKÃ/ ÐV¤˜YÁ© ¬Ý¶•wÞÎòÕåí¬,XYY°í„/X—°IÔ€˜8€„ "|¢/'xÙ-¨Ùœœäþû!ÿ¶ ÿ ¼ù¶ãÀv¹l~8}ðÏv`[B¨OU€ã§ 8|ᾄ;Kˆðù‘ Ž#Ò,0'dìà$,$²²á86v}°›ßÛÑ¡ûêê©spãÌùòI’UVi?ÄÀ£ êÅä°3yF]+¢BD8¾"ìµ'ùþ¤„S›ZD¿ˆ~þ‰>i“š“€¦lÏ"èêûb„TœO|þJ÷=´ë9}_ȈEÞ¥Á„ÅÝ^Ì_–ïµSågð¶o```````Ð!èT’ïy¶=RåZæ¤Â÷ôägç`ÖÈ ]½.Æ“?e- ß늅Ýö-H×­Êd&ì¥á2%_’šz’«ˆ¡|~KH½ñ…p‰5ÿ”¡‚‘”’Z`¥öD XL–êS*qÊÃ,njے„ò M¿B€8c—÷°ðâß@C]C‡î­»ç-Ä%㧒0v®çãËy¢ÄúÙC<ú¢=ÉW}¨p|š—¯…à?©'ž&ºÓp~¡ ´üiºUÐ×räÉ{@ê ÈAªùËH B)ŸiQòÞ$š@ÍŸæþ7Ž1˜ˆu`Ô˜!ÜÿÁ¿pûW6½/ÿdJòwŸ<Š¤› ¾œ O’úÒçX6.5©«×Ä Ã8¼÷–¾°Â'Œ`¼ý}¶«(BÕm¢ ¡Âó-!²`Æü’y²n¼Íd>»ã$å$•eûeòLzÆA–$„þ,Ø–íÿsu~Û"DÜ(p„¨^Bí1ßÓo;¾G=֓Ϥðžm1_!?a+eËlÛWÓ—âQ@Xdx>ÑJû¶ +áø¢~¶”´àXvÞD#d9¨)¯Å’gWtøSýì›1yèHåáV6Š—eEB•ÔT{´J !ýtY9—ÏàG¯>iÂö 2ŒN#ùÕõµ8Y~F•–’ß©Å—BŽY#Ç!á8]½&Æü¸ÇÁÚß•ŽÁ=€’õh቗éï ¯‚Ÿ‰»Þ’%îÄõ¢\ŸRš.aIï=³™–ŠbÙäzÇoøžqËÁáÂoÊÀ¡DÿD¤€m1ª/Ⱦ̓—¹û$¢@D8„\ ³©ñ‚öi `–˜«æÌ òü-âÑ'ªþ–íˆUÐ"Þ},i5NŸ*ëÐ=fY¾rýÞw€¦®/öƒ «4D?ì‰Ö¢¤[U¦ ÞòêpÄC(Ø+‘;M˜.¸¿Tî×rò¹L7P÷T!û" Z£Qu}ª- ß¡ü{9чxC‰—$u@»¿ újžÂ»O”Q#Èí?qö zç¥Ý6tÉßvì ´ÏPÙªq‡¡{^AW¯‡A†±~ù&l_¿¬<ø4LZæÇžò÷\”/Äç´¼fYbÎo¤êÙ[DÝRú£Ö&±II>yMÃæŽT™:2BºÃãä°ˆöÚ<8ÅSi þtmRågª¸¿&l¨Þ%ÔÀHsMxú³í_úà=ì?UÔ¡{ÃÀÀÀÀÀÀÀàÄNq›;S‚ºd“bD˜ è[ØÃz÷ïêµ0È08çxöW/ á^ðÍßj'ѧ9Ѻyp>¦mOóí-É“abÄ+.Tí™OÈaBs±·âmÙ¶ïù¶ý*w~Z: Óþc–$í,Ðø³¥|ï+ÖN ¨o'‚zô‰*°-I®<σÇ=ØX<|RæÁJø6Õhgð8Ï¿­eÁƒOÝJý©RXÒ7BØÌO©ñàÁMùdѳ-8¶/0ÈS.8¢ýźY·-«wáÄáS4b@‡î¹üì\|ãæûðƒçÿ„šúº˜}å÷I3“¡è"?^î³à… ¸Z{øKÈ‘áê$ ^xá…Çž1_Y^Dxx4o^ñéȆeÁ¸èg&‹¦?@ÜóÒ‹ª]ÏéK/- Í LM t}ýèe*r <ÚÞßi*€#årüÏëOãwŸùF‡î ƒ ¥•å8UQ†âŠ³¨i¨CC2 ÈrÈMd¡w·BôíÖzôBv"««‡k``ÐIèp’ŸòR8PR¾*¾Ü›ÈÆEÃÆtõ:tÖ,Ý€Ã{ŽÿtfÐ|oM]ÁÒŸ;,=÷ä5Éݧb|`Ô¯û{éQækÔ)XD'ÀO‡—7³œÀÀ”éóKÛÙJ”ÌS%Þ4xÂëÊÀ8cž¦¸îÛ¨O• [ƒß/WuÜ,0æéË'Æm±Àxã_/J÷‰y²àÚןXŠÏ}ûcÛé0 goüíuw⧯>”çjÑC’\’ײ<\MACßq.0P/6ü ©t…ÁW!íºj¾º¹%í@£Ýº¡ò¯Ÿr/¶¶NúUH?ƒ08×Je­ˆÌ•®#v¦ïC™ÊÀ˜f ¡cb Ø}â^X¿w̹¼Ã÷GG¢¦¡eÕ-í]Pˆ¼ìœGÊuq²üL‹®Ívè×½g§­‘AûP\Q†U{¶aýÝØuâ*jkZÔÎb C{÷ÃØC0}øXÌ5zôêêét:œäï,:¢„ö ×t¶ì‘ã»z :œs<óÛq× x­òÅ%3H‡ei¥ó”(ŸOYE8¼È‹·,Q¢.(gˆñ %{ÞxÆ|¯>`%üPvˆ°yX°$±÷óõQ;$²üˆ'+ ¶í€9–çqx®ïÍçð<Âëqžç%ý÷žåX@Š®ïn·¼…€ÏY ¶g îy`Ìf°AŽ>¸ž ×c°RAŸBo xÍ9ó#\Àv9ÛË/éaçÚ=8~à†ŒÔáÏ~â‘øÄå×ã‘e¯C©å1ýF-=ö åçCç±÷á¡ôpz±C*ø´=UäÓ $4%´Šê瑽­ú£z4c€G߈ÔËšJgÐÃìéòEÌkD^÷ú“…¶‚\ñNš‰ùçoÚVmC=¾ÿ×?áTEóß¾í¸dÜäÇ{{·áÉ÷–úŸ Mà¦é—àK×ÜÖ%ëe``Ð2xÜÃ{{¶ã¹µËp°ä¤v®[n^“méß‹³5ÕXw`7ÖØß¿ó úwï‰?<ø]==ƒ@‡’üŠÚjœ©®„ú¢¬rN98&:tAbóªm(Ú"£ŸfÊG‰F|>‘+óÅçdØ<RªL‰ñùáêByÞÏ«"u,È··ì@\Žxô}§·öÃò!óù•_– ³õµ¸Oò=æHؾð!wUy<Üà‹1pÆÁmæªðn&Œ –hXg°,_1ßàásÜ`½]ý´aðÀÃ;ϯÄ'ÿñ#²/Ÿ4'ËÏàÍÍk¤WÚwP /6õs{àœižf„ª°z&Êæ‘p{™î ö& s×úd,¶=§ì7þî7çºÓñïÎN)ÐŒP/i…9~q2® g@ý0OqÿèüIVL\{Õuuøù[Ïâ{w~ºSöGG oaüàžÏâÁ?üÕ uM^[Ÿlì°q$l½d,fá7K^lòÚË&L능200h!vÆ/Þx{NËxß5Í|Nœ¿èPá½E‡ƒW\óqpô/ì‰=zwõü :¯<ú¦ áÎd¸Ì]¦¥ïbòð%ñf‘šááœ~eB Rl´½å}Ûñ™ºš„ðÒ( ]ú4÷? úBÐÏÍó ¾“p`gÛÈÊÎF"; NVŽãÀIåëBÿœ„'a¯i™<õO– „÷D4gjQý°ü ïß‚Š.`dŽ–ŠÒ÷#,õ:œ¾ æ`ËÊí8[RÞiûð#ó¯ÆÔa£Ixº"–jOð{éÙ»ú•‡š“öœë'š—®¼çº ¾ÞžÝp¨×:U㧢¥¡-¬ìL§Ð$’@pZÞ¶§*/„Ž?,ZyWV]25¿p4Bÿî=Ñ· {¦·@§c`ÏÞøÚ÷4{Ý¡Ò“-è­}¸sÎåèÑ„ m¿Âž˜ó÷xú+ßÅ—¯½³c6A'ãê)³0mXÓã¶;ÔáãplS‡J{þªÉ3¢Æ"ƒ.çÿõòxøÝ×"QO.þ¼âM¼ºñ}<øð±ûÄÑ®ŽÁyŽ ×÷<‡JN±)?Ü”s‹3GŽëêyt –<ûnÝ™…VÈÆÀD@u„—é ‚×Âk-I1õT Å|Rn/^t/Ü¿è›ÉŸ ûm[–É“¥ý,;0"4ýE\ò½³n[pÁÁlÛµáY ®• BøÅhÑ41Y™ÎÔœÂuÚå*F¦Ë5O¾\{y?ÎÖ,þ7?pYRÜÙ‰,üý-Å÷þò0jˆ°^¤„;[“'€Pù¸°§š:ÞÅ9Ïôc²=ÙT OS®‡ð¸ó@$ÏÓKïÉáG+D^³ÔÐø©ØŸ&¤ý{\{=䞦&„ï¥Îû‚ssGOÄÓ/Á¤ÁÃ;et>}Å øúc¿J{~û±ƒhH6v¸Úu·œôùºWMžÙeëc``¿^üo]ߢkËÆ„AÃ0²ß@ô.(Dv"ºÆF”V–ã`ÉIì;u,òyopîaïÉcxê½¥üŠ _ýÓ/ð÷7Ý‹ë/šÛÕC38OÑ!ß²÷‡Ë=Í#$¾BŽè;yY£(lÐõ¨¯mÀª7Ö@¹;\F‹(*ʤH¸ÿ5B ßóLC؃`ô€¬2YÎÎ'âA > ÍÆį6ƒšoÙ6X³,$¬„ƒììl8YlÛ'ü~z€­Âýˆª 0åÙuì€Ø¹`̆Ë,]<Àm´ÁmßÛny8ù8š|tkùyÚâ>Ì,‡ž¤,°€lÒrƒ"_¬§"EMU-6­ÜŠ‹uÁèWØ_ºöNü쵧ázDTJ_ –„‘’t@¨Äž ¯Á ’ý!š‘p&š/Û«°‚èæŒ ƒ”Ý#I4^+oGïR¾§SUŠÿÁ-¹2†ÑP£xxý¢)1ân½ò 0gÔD\;m6¦ÛiϽ+1cÄXŒé?û‹‹bÏ7º)|ph/æ›Ò¡ãhL%cîÕãíêe200aíþxnÝòf¯ܳ>¶à\>á"ä䦽®º¾«÷íÀk›Vcó‘ý]==ƒ¤\?|ùI¸D(µÑMá‡/?‰='ŽáË×ÞǶ»z˜ç2NòS)œ,?£…éó@¬ '#ûìê9t Þ_¼u5õè†&½èM5±ÈkúRKög’è2+Ôž€ ºLkƒ2!Äg1?äßf°l¶å{óµði©òaÀHv(€V– °,+ _\‘/o¥ôZ´Œé *±¾H³pœv¨êé^ùÊšN%ù0uØhÜ9÷J<»úM±^/?§<æÊcŒ:Ís ¥êÄþµç‰›*ÝÓµÐJõÉûû{Fu™'SŒ=è˜F„=ëô>*Õ_ÇtU~òŠDd‘EY9!<Ø=7sFOÀuÓæ`ƈgôÖµÓæ`ÿ’¢´çWîÙÚá$ÿtUEìñ«&ÍèÒµ100ˆ"åºøÉkiöº{æ^‰/\}+«yâW“‹k¦ÎÆ5SgcgÑaüvÉKØvì`WOÕ€`ÙÎM8T¯Óò†•Ø_\„ïßýiô*(ìê¡œGÈ8Éßuâpà9 ¾’Šï–ŒaƇăóaÆò—WÁ Ä»xñ|J²cÄÃÓåÑxì9•iLxèÃ9Ô‚€«Xþðáùa¤9ÆÀ`{Ä‹ËüùrφÇ<ØŽÎ9<ÏÎ5ŸHÚö•ñ­`]ßSo[`)Ÿ€Š¯L®ƒsc‘¾9㾪½çoŽ=÷¡¬ä,zõëÙ1›$ nžy)öŸ:ŽM‡ö*Å}¦“ýHŽ(Q=&mA2€ÜO–9eAôCš7°^y€^ÆæåcöÈqX4y挚ËêPm×s‹¦ÌÂïÞ~)m¨ì{{¶!u“Û¢/êmÅÁ’±ÇM¨¾Á¹‡e;7¡¤òl“×||Á5øÜ›ÛÔÿ¤Á#ð¿Ÿú*^ú`~õÖ &ßÿÁÕSgcæÈqX±{+–íÜ„­Gh7¶;ˆ/<ü?ø÷{>‹‰pš›Af‘Q’_]_‹³µU„— RàjÂÀ¡Èr]=_ƒÄÙÒrìÜ´·cK6‘ªfâ—ˆ‚>‹åóÍt*:¦B}J]?rŽö ¥ÉÓßÔÛ,‹Áa–_./Õ³±:©Ž¦ê™5ÙgdÒðK‚ùeëD„€¯´ïJƒFWÄZP®³þM¸î£Wuú¾tÍøÎ3¿Ç©ò2mL~'¹ì‹­R$¨ À“ÞmB–I¾Ø‹/<Ü"äÝ£ž|„Œ Z{=?>VJbˆ*¬>¼à<|yk®Êö‘fÒ›¯#yúÁv/ÈÎÅÌ‘ãpõ”™˜;zÒ‡žØSô.(ÄŒãðÁ¡=±ç+ëj±ùð>Ì5¡Cî_Tv•uµ‘ã#ûÄÈ~&ªÎÀà\Ãk›×4y~ aøÌ•7¶ëŒ1Ü>û2Œ0ß~æᮞ²A€^…¸}öÜ>{ÎÖTaù®-á/­ªÀWÿü |ý†{qãŒy]=\ƒó%ù»O*%Hú…±07ƒzöíê¹t0Þ{=<Îau0}ŒóÞÇú(‘%ž÷Pì´ðäR %/r̃Å,°°œ>é„$…Ë«ùýù%ó‹ÉÜ|'Èï·í oˆxçnzãd°‚ÜjÀ²8 ̶c… [eÈoÔ‚\ÿ†ëßÞØ%$?;‘…¿»ñ#øþ³D}²!"V'Œ8žÐ'¹óŠ SáFÕžl n/$Hmz.ûÕÅøä2‡=õB€%þ‘çÄÀ=•V!î©Rˆ×̲г ×—óãgÈÏÊÆŒc±pòLÌ;vz¢Ïw\9izZ’Ëvnî0’Ÿî¾Æ‹o`pî!馰£™ªwϽÒ7âg“‡ŒÄïÿRWOÛ =ó»i„Åî-xw‡OøÿûÕ§°çä1|åº;Mž¾A“Èɯ¨­FuC½üâ)ÓD9ô¡£ÚÝ¿Á¹ÕKZ¦ÛVxœ^žòpb=€ ¢}Fd‡’ëU.È÷Isl˜$Ì]ÊÊÙyÙÁ¹¯L òï¡lG4__’¹Î ÷V ’/£Ÿ8y-îNAþ»üœ F,îÉcøHî½l/Îpeô‰Ó€œD6¦ƒE“gbþ¸)æËE ±`üTüôµ¿¤ ‹]¹g+¾~ã½°; bÝ]±ÇN6ùøç¡ÑM5yÍÌ™M{;pHWOÛ ôÌï†Ûf-Àm³á_¶s3¾ñø¯ñow=€Þ&Oß 2Fò÷œ:¯ºâkïȾL˜þ‡5UµØ½e_§Ü+,bx'Gå¹Ö…ñS 9":ÖÚ46O5ãÐqµÆ3Z?ݲ˜¯3Í3h~>J{°mž|¦0m¿õýXxÇe™Ÿx 0üTl?v«vo!䘤I¨‹©„õ„è@Ô¢ˆ—ß#Ç¥7]¶è4ãÐìB )–/I7!á¢üÔ@ˆÜ‡Œ2÷ž ˆis îÍý–ãdaڰѸrÒt\1á"8vç”=¼Ð3¿¦ …-GÄž¯¨­ÁÖ£0#Ã_Þë“Xw`wäø¸C1¤—‰¬308×PV]ÕäyÛ²ŒðÚ‡a¿«èfš€,Çüm6ˆ"#»âlMê´/¡þĆ÷ÐÕs4èl]³Hu|¨> kÝÉ’y‚o‘“²!\ª¬õ»úààAº¹Þ Té-D®•¿y~W4ä™[\M~pîy‘¤j‹YMF'´{½†meùü›ZRÉ<ÏŸš'¸¾ÇÓ èýàÀ½´—‰GhüÃË8cšžÛ¶Õ;»ŒäÀgÞŒƒ%E(:SªràI(<@…õ–!üH>­/Ò'¤^ÀÓ<ùzÞ;'Jû!zäžu}ª- ŸŒžÛ¯"ücYvS‡ŽÄ“¦ãŠ‰!ÛéØ:î\9iFZ’ø!û™&ùk÷ïŒ-Ÿ×¡úu 8TzŠO ¸¢ µ õ¨kl„cÛÈÍÊFŸnÝ1°G/Œî?ƒzöéôñµõÉFì8~KNâDÙiTÖÕ ÑMÁb ¹YÙè‘× ÃúôÃØC0¦ÿàf£¯ :)×í´¨£”×>ÍŠÚš&Ï{A¥*³·|¸ž‡£§‹q°äŽ—•¢º¾5 õ°Cv" =ó п{/Œè;#û Lûl8çp¹×¡¨íÝ#=ó»uxuƒ¦ÑJâpé)(.©ò2T×ס.ÙDz‘“ÈBïnÝ1 {OŒê7C{÷ëô÷nFHþÞSÇäké}0mØèNŒA×aÓªmšoº«ÁZp&r É£×<Ûñžn®þqF¼«áKhL4÷Ù4áõÜ`³#ê,cÄ?ÝgŠn²Ð§ -ó‡—ÆíOÒíc—jìÜ°©” ÇéšÐoǶñw7Ü‹ï<ó0R’„Óykë¤kåEªøŸá“Q<åÉ'Â}’è‡öãSÁ$4&‚¼r÷ ¾1!8ÏHûh¾?G–í`Ê°Q¸|Â4,œ8ÙY†Øg—O˜†_¼ù\Úó+÷lÅßÝpWÆrmàÝ›b§+—r]<²üuœªhZÙ;'‘À§.¿ý {4y]c*…¥Û?Àò]›±á़¦S{ só0gÔ\3u6æ{MC²\ö:NWW6ÙW^V6>}Å mö~6$±|×¼µu=6Ù×óZÔ®G^ŒŸŠ›f\Òf%l{xø×PÜ„Òú7n¼yÙ9m꟢1•ÂÞye5M{’o›uiƾ×í=y Ï­]7í–Á'{ÕõuøñÇ¢ùåO½¿¯mZ} ¿‰ZòíAC2‰ÒÊrÜ8c.n›µ ísiæO>Çñ²R íݯCæAñæ–µXP×ôÈI$Я°'æŒñ‡¶}½RI<²ì ”V•7yݽóÆÞgÃÁ=Xºã¬Ú½ Uõµh ²lS‡Â%c'ã΋/×>k÷Ÿ*Â×ÿÆô쇾ws]u5(ÌÍÃ7núþ¸ìuTÕ×µ«O1ÊcgJñƒ{?Ûìg2œ©ªÀÃË^Ccªé´;f_†)CGft ^ß¼ÚÛä5“À]_¿†ž‡?¯xEgO·{,c(«®Äs.Ç‚ñS[Õ6å¹X±k Þݹ k÷ïŠ5¦Ç!?;3GŽÃU“fvZÊ\»Iþéª Ô'#y¦zôB^Vûÿøœغ~¬ó¢ ߣžPò‚‹m8¬¿ÈY˜;÷8<ûóN3wqÊs=_½Ýó­ñžësxź®~ï„L—ÃÏ) ¶ Ï÷‚\}—ÃsýùzœÃõü22Aæó·0î?@}mí<Œ±ÓºÎ 8°g|âòëððÒW4·x!rX O¨ë“"ò—ž©Q¤z¿|Í9]>&ýäþ!¢®ÆFŒ3Â8AÃõE¤ Ê Z& Ë'^„+'Î@^vv—=ƒ ½»uÇÔ¡£ÒÖ¦.«®Äöc‡2F j°zߎÈñ)CF¢_÷ž±mÛÆ}ó¯Æ—þøS;Seãøj“_&=îáÍkñ§o¢´²¼Õc¯¬«ÅÒQZU‘–äg'²pßü«ñù‡ÿ'm¹±l'ß|æëm"ø©ž_¿Ϭ~g›!¾q(¯­Æ«›VãÕM«1uè(}Å ­_YŽƒ^r¾úè/QTV{Í—¯½#£Ž›q‡bî˜IøÁ f¤¿ÁiRP>8´§*Êpª¢,ccO‡ºõ¾vµ/ÈÉköšU{¶â¾ùWwø\®›v1Ž—•âñUK"çYþîºø |åº;ÛÔw¶“À½ó®Äÿï§iß¿c Æ؃µc[Àï—¾‚íǵä6Ý>8´Ú‹[f]ŠlG}ßúàð^T××aó‘ý¾®Ÿ[x sóqï¼…øÂÃ?i±‘¢9pÞ2dïnÝqíÔ9øÆã¿N[Ú†öê›q’¿fßN¬Ø½%íù=zã›·¤Ù–…ûæ/—ùyÚÒ°­Å-3ç·êúwwlÂÿ-{ ÇÓ|N6…š†z¬Ü½GNwÉo·Û`Éq­æ2ƒÿåq\ÿ¶[ù Î/œ--Gɉö[Ö€€/z€/®‹ÉÉ|*FÆÞŒ' –zYL”Æd›ä\ƒ: Båù¦2¶]ÑV/ y¹'§†@†¹ƒs¸ž ×uáºRn@šI4A{m&-ÑUí=aÐ DÞsƒi*÷>RI©¤ Ïõ|Òïú Ïóä#™‡:¤‹úÑaíÝr]+&ÍÀì14Ò-à;͉Â=tu}û^o*B*‰6Ôk™ÏÓu$qº LnFŽ ǾÔ``zˆ*°mS‡ŒÂÝŠ§þö»øá}_ÀÓç‚ß ¸rÒô&ϯص¥eµïïÝŽ†¸Pý)M‡êääâ ‹nM{þú‹.ƤÁ#Òž¯ª¯Å?>ñ;üøÕ§ÛDð[ƒùøl¥Än›½£ûnE>6Þ‡Ï<ôCüîí—ÚDðÃØvì ¾úçÿÅ¿òª[é¹ë‘W€¿û3iE_µ8c_v{wëŽÜý•ÒEpå¤é¸gŢ)³2ž¦BѤXtºåäadßö•¥Ü‚T•¿¬Y†š†úŸc Ÿ[x3î¾øŠØó]·¼]ŸY½ ñÀåק=ÿµëï‘ÞvÎ9~÷U|õÏÿÛ&‚ß66Qý$Ó†²A=ûàþKuÚ})fŒ‹Ë'\Ôä5ËvmÎø}wŸ8Úäù§ÏCv3n¹YÙøÛkïèð5 £!Ùˆïýõ|ÿù?µ‰àwÚEò˪+ÑJ鹡àÛˆ©•ü!ÂîÍ ¸Ãr5ñ»vö•þ=¡÷•ï9ã2Û#$¤[&« ÙàIã i÷ßýˆß«lCü—‘¥L§š/EÜ"É9™`pÎ ¾Àã<>ðòrOu?•æOCÝ€}çÉ€Ï/º ½ò»…òÝQ¯n$ý7¦ö1zÊæ /b~Õ D8Ošššß"Œ_(ç3øÖï ƒ†áÁ«nÆãûüèþ/â–Y—"?ÇDZu&šûRµ|÷–f –-ŲÑP}†+&No¶í¼1“•F`±©öµ õøúc¿j²\ à‡,ëÓ#ûD¼‚vÍsÁ„iiÿ&\1ñ¢VõÅ9ÇŸ–¿¿ì×Í~‰ëž—±†`Ú°Ñ?ph‹Ô­_ß¼ŸøÇi½òé0¬O\:.>œÔõ<üèå'[œ ÑFõ„9£'FŽô’Ž##Í¿ÚƒmÇ"éffmšÃÔa£Úo;¬Oäf5mp=[S…ÿyõéŒ}V4‡/_{¦{î¡w^Aªë;¼o¼f×µÓæh^䟿ñ\lDE¶1¸W_Œê7ý»÷lñwÅ”ëbëу-º¶½HØ6& Ré;WNêon¦kºêÙáÒSØYt8c÷;X|"mÔ†À¨~-3’Í1…¹ÍG½d I7…o=ý{,Û¹¹ÉërYÖ»FöxÎd¶+\ÿ@I‘®Ì ?+zôîêyt"„ª¾ qçŠy‡KhëþðJŽëºà°Àmœqx̳=pXð<Ìõ{v-nY°¸?wîq0¸8˜ci+âqÀå€ë©zÜõ°m–mŽ¯‚ï¹~94+иàŒùVíVLÅó€TŠK]€TÊ…ë¹ð<8\0߈áÇuG¤ ‰L¦'1 œƒ§Ïåà©<7å÷éyÿñ À#„<˜x(¯õ\!ù¹YÙøÒµwâ¿^x4È gßCËu§HDTõÒSA-®JèYŒaìÀ!¸dì\=e6ºçåwõò~èÑ·°&ÇÇž/­,Ç®Gšô”·5 õX»gäøôcZDFÛÆ°¾ý±ÿTQäܘé=ãÿóÚ3±m_ êî¹WàŠ‰Ó#Êþgkª°áà,ÞºëîFkŸƒ=zádù™èX[áÅO¹.þóÅÇðnŒqðÿ†ÌíëÌ9.ö \Yu%ÖìÛ‰×6¯NûŒOœ=ƒ¿ùãOñ£û¿ˆ©­(-E¬F2EQÐ¥y,ÚìŸßPTägç 3-©))H ¤@3tT«QTZ#ÆÔŠW§MÄâÍþÎÌœôLœ3ä8ßûh´/l.Ì¡´ª‹6­Ã´ ðëÚÓ#ê 5&ùeU¨Ö5ÐVï6‰Íá8ô±yͶÚ7’Dð^y'ÌÞ½\½Õo{¸Õ´D׈bíƒbç´{Ä lï½-cÝÃöŽ« Da˜PËx Äs‡ó”›¢¨›3 Ât ]1LÇ;.Äžpªÿtl®yš6I'¦ ƒÐPtº9¡é°Óû…¼^ñÝ…Òƒe(.*A~a^²ôhÝ'5?,žë%Ñ.E{!Lž–\êú‚tŸçz.N_êsÉßÓþ9c€[a_QtjÖ #ºõÁØ>‘Ÿ›ìå”paTÏ£Â@À Ù¯íªÙk–ûz/ãQÕ/ÈÉÃzˆ„= ¨a=ïK·¬ÇŒ•‹| íÒ ?û ä„=kœ‹û‰}aÝîíxî‡/Ãjøµ‘‡äçdd"=56ñHÃ4ñÏ/ÞÂœµ+|îÔ·œx:Fñ25Éi„S Ã)†aÁÆ?ðÌ÷Ÿû DU†‚øóG¯àéËnY”/ZÅæLÃÐ.=’3ß2¿‰ë}A*BææûîÛg îr#ä©ï8P„)Ëæáóßgú_´y-2RÓpÚ€á8¡Ï@tmÙ&¬²ù_>yÍ£c‘ž‰W¯ûSØþ7î݉×gLÂoëV¢ûÄèŒï?4*É€‰‹æ`_Y1þvÖåa?[‰B«Æ…¸è˜ðîÏ?xŽ½óódŒë7¸F³þXâÙwõèS˜­*ÄËÓ¾ö½¶E~xáê;bÒ™0ÿgßɦòñøE7Ä–߶ ž¸ä&Ü6îXE'jÔSe¨!Køƒ°†ôl›UZâðÁÁ¢”,·œÕ|û0°<Ýâæ Õ8‹tû‘Dþ<.'žz’úoÞñØžžÅ7Ã5Ç*GXoŽØà 05žt‹)Ü<¯e$zÚ ±Å÷ è! º¦A…˜’½®Œ¼[•ß³™&3Xcrþ5 aèÐu†¦Ã°Eó {3©2€OT(Ïã3-CÚӉݧm@0i߶Ÿ3Bàî… ÛÖm¯³g4^T·Ž;‡ÕV¦¥î¨ÛžÏ»wÏ̹µŽ7ßRӷɺ⺞Ëý÷\o¿¡¤Ÿ–h$„ }Ó¸ð˜ãñú Æ3WÜŽ ‡‚œäGBHDF³¼ÆèÁ{»»øÖîªydTYu%lôæÄêÔ2cOÙð#óátvtÓÀ¼õþÞ‰KGŒÛ ¤( ®5×Å~î7ÖXDMY:ß,ôz÷RÔ¹ðº‡!Ö÷Ͻãûõ=~ ¢ õ®õ=)èóÙSrÿ›üyÇËÖÎuŸëºÆ|"ô›Ú6ǿλZ·À£^‡Æ1õ‚Z=®yÖä¢u»ä4'¿E^¤¤Ôº"ŸDÃŽ;9ïdôÂoÄgó@áÚR¼ Zen[€Uïê›Ë9§y„ÌGv­jwD¸Ž&Ø›ºE~©—Û4¬’sÄg l¬T¼Î´<÷ºfÀÐ4è! š¦YÄ_7@ˆMÈuË}w6Ã4¡& X67 Ã66h!Ýj_7˜AÁ0Òn‰)ØÆê'Ð-µ}g3­þìq;J€Ð²€&±Úµ£ hÙ=!• vnÚUgÏhMж°9ÎÄýÀgœ› êq ö¶€÷Ô ö.–`Ï‘~z½`@pAÿm[Ð ç …×®¿Ï^y;.9æD4¡.®Ä¡…hâwµñœÌþc¹¯[<¡ú€˜Vâìô?wÇþ}aó]ª…rz¬9ë~c%÷qwñüï‡/|ýùô‹1ÑÔ'Mgoáú‰Jodü.«ª +8 }ÝÿmKâþöé:J«+…/f@çfñ—ª‘høØ»£ˆ…µ àI;-CÏêÚ3ï¹ÉÞó…{>„ÐÐsÓ[‡M¤ 膦%ä¡€n庛”œª¡o{¹MJÀ)¹·È»¡ÐuºfyÜCvh½Eîí0|ƒ„õâb‘n]7­vtº¦! ÁiЃ!hšEö5Í°¼úºS¾Ï´Ãï ÛH@h=:wb€]VÏ6(è:í_‡i8õöèu†‹nýK˺±ogÍÿˆÔÎ4š¶°Çkyá­{ÞÛNÃùn¿âëqJ÷|î>¡Æ"v¹s”†ñ·jRˆ³‡¯¾Ï_u'.=ö$4Ïkë$AD'ùKjÜöÌUÞPý´@ ŽíÞ7®vL_âìÿ- B™¹üìÚ©ç×t¬±T&yéǯQ zöŸrÔ0œÔopÂÆG=½á<ÊïÌšŒâÊòˆm¸½º›µôR<3ùs_q°C‰ª"s¸á’cã~Cº†~†‹Ÿïÿ2•>Ïwm **n>ñLßc“—Ì©œcU(ˆ§Nöe¤¦á–Ïòœî»%^¯õ¡Œ$h±A7 •‰Â}Íó‡:úaé¼÷µlëìã¢<(ÓkšúäÔä×Ã÷ÆÁ$ÿmKâ&ù›öíûj£}a Y2ïÅÞ‡~½H‡\¹ ¾èÕ„Έ w0¶SîŽ8¹è¦­ÏÂãM;UÀu§ŸËyÍŸ×-¹AsüMÃÔaê:LÝʵ§DÞò”;¡ûüFˆ“ût]ã¢lci*,7¼a‘zpFƒPnÜ°Œ„+³Ç¢LŽ°št­ò{lMâûWmßN¯@U²¡ª*n:ñl¤¦8V>0ƒ¯IOçG#›ø§†‡ûzN( }[æâŒ#ðìUwà…«ïÂå#Ç¡U˜ô -ò› {Ëvao?°¯FµÏK*+°p£WmxH—^ž<ÔDÂ0Ûx‹Jã á­/¬Ø¶ÉWí9/+7=³-FF^V6nwŽï±ÊPF)æFFJ8ûrß´„Š`5û惘Ò$mÜÆ%8kбq_W¬Â›?}‡‹Ÿû>ùm‚œz|m1°cw íÒ˳Ÿ€àÕéßF½þƒÙSÒWŒç•f†‰Ü£–…„?ö–ôü6iÖ(Çtë㫃ñÃÒ¹5Ž¸™æ Õïß¾3š6ÊGa®—ä(/EHד½< ÿ¶Å™ž’,ÄÍÌ‹ÊK…è’€ª¢]AódÏC"IØ»s¿oþ|}ƒøpG«¤™“wN÷ŠMv°V…«q¼µ†CØ À$V˜<Ñ æ 7t†®;eãˆUòŒ˜°¼ãvé:C×auhÕBUBš† ¦Áš0BÖq]3ÒtèÁ ´ê ‚Á BÁµÙ^øn@Ó è®[aùL¤O·ÂþƒUÕVV"XQ‰ê †ê #d§èº=`ÐCºfyæ-Ñ<“)ðö~ÃÖ šƒXï­É40ˆ³4}À žÜÿpfëý{z ´)h†S U»„ð™í‚RwGdÄñÆóºzÌ0Þ“OЬQcœvôp(.:t­Y\9¶ƒEìàÅÎ<²‰S*ÎäKÉqÊôTèξˆåòN­xÓÉ­7uGÈŽö1Ó “'„ØÄÚQÏ7 Ë°@_3òÌ<ú¶p͹ç &w‹(0ÁúrÒ¬ü|ƒ+"ÁtŒ'¦i;áy5}NXÐd͸‰¼£_@£Ük®(€i˜(+‰²š,œ;t Z5)`áønA}>ÌÞ±8åñEôÖÓë›6ÊÇÉý‡â‰ËnÁK×þ W>íj &$ÑðMe¿&!û~¡ú©iÞ­ö¹å‘Ц iØZÝŸÏY§b`@üa›»Š÷û z¥¥¤âô£GÔéX/>f¬ïþÊP0¦Sn\:âDô S¶øíYßã[ët>õƒ †ÁË×Þö…5s¨••à©ï>Åu¯?%[Ö×z<š¶°ß^¼<í›°^ßç¦|éÑ ¹ýäs‘ðÏ'WÕbéÖ 1•”ð‡;§y^cözüQÃ|¯ù~Iü| 6þ!TGૼ4õñäñåå×5 róÂýI‹¯QÄ]}#.’¿ãà>0‰5EA@QÃæ…I¨(¶”õù-ÜçF;ß1pdýAÈ â±|ë “SN *2OBHÈ®ceälo¶å§Äßâ³<ì:‚Õ‚Õ!„‚´ =hB³_kºå¡×5G O×Lk_uZu¡ªjæÕׂš¯¯³ÍÔ ˜ºªª*TUT¡º¢ÕÕ¨®Bê0B¶§ß¤¹ü”¸Ûåúl¡>Ó4AtË‹OˆÞ¯kÖµša€h&LÍÖ°0ìÆì™ š0tû˜­¸Ïý0§teŇ&ɨ*®;þ ¨p„Fù¼|!Ÿ=sŽ'Ÿ3)Èi„û ÆcÝ€—¯»×zÌe€$´l\€na¼º°yßnl-Ús{Åå¾´Ýú #5Ž#!3-G…!*­ªÄß>}Åu÷َד?# ™Ñ­O×Ù£š„ÉÍÿiÕâ¸Û ¨*þvÖå¾e¸ ÓÄ#ÞCuCµ%’‡®-ÚàþŒŽ?=lÙÁhØ´wî|ïy<ö͵.·xͨñ¾ãX»k›GM°"æmKýÛ½†øˆ½Q ‹ ~ùø7`åö͵šÃ‘ ·áµGò»·l‹ŽM½Æ•_V/û™q‡êéÜ“ ݆ ŽGa¿>0Ì'5°ô/øìC^ÿ$f’_RUÍ°s%l¯_M­Š‡Ê–Õ¾‘ZÀI§'‚õ8’w‡€÷^Ûžxˆ9óà<ÞN¾ÉÄüˆMXù|zÂJØñ¹êÖ¦ëNˆ»NI½>o{>Þ`¢~¡-¤CÓthA ¡ê´`z0ˆ`° ¡`5´`Z0ˆP0#h 4Í€®ëÐiZ]:Ï4è¼L{#NÕ[Ÿ•´ü ðŒœäª+ÀkØ`*ý„ñy AxŽoÇ>Øý•W$õYŠ„î­ÚáØžýbÏJã)ì_'h_ôòçgçbLŸxè‚kñÊõ÷âúÎ@×OâÈ@´ºõñ„ìÏúc‰¯Ý˜„êñ{ÇOì;0ì±õ{vàæ·ž®UiÀDŽ5œ0júÑ`y²Žò=¶dóz_!ÀhhÙ¸wœ|®ï±íöá—È™DÃEj —Œ‹÷o}§ ^ãzÛS—ÍÇÕ¯<1´>šä4ÂÅ#ü#SÞ˜ù[ÔB±½´”TÜzÒYûÝó¨°i •¡ îzÿL©…(Ü‘ 7‘nÞ¨±ð~üQ^¾¡cúÊ…ˆÕZsÖ,öÑP}ÈNÏð5í9„<ù0¶ï °ÇvÜ[Þz:!Ñ1u…˜¿!6Ûa €Ô”T4oÔ­K/þ‘ŽòÒz fÄõo˜¢é‚ÐügëD€ÉÜü¼¸ 9·=ò&5"˜NÉ=“+AGUï‰i€XLغ¦•/¯ÛaôºE¼5Í€Òah&Œ -¤AéVd€fÂд =¨#¨ëi´P¡ê BÕÕ¨® ¢º*­*„PU¡ª µU ¡Ùeø˜!A§ùúÔ‹ïtgχóÓ5[O€ ºÉŒ¼àŸ°6v5N4à¤Xß©@¸ûV/ÏR-pùÈñN¹…I9Úö "xñeæพGáïç^…W¯¿7==ZµOö$!$ReæJ¯8;=#¢—,âõŽŸÔo°¯÷‡bWñ~ÜüÖÓxsæw ÷,Ç3ÖÊPëvù—åê×®sBÇC»ø߃˜X½cKÚ<¹ÿа) “ýŠÙ®Û …¹y¸ç´‹ðîÍÁ }ÖH—b_Y îùð%LZô[ÇqÁ°1¾j»K`ÂüŸÙû~î!–—Ž‹–ùÛo‘ßgF éŸøaƒð¨Jð„ëç‹{Nì;È×€ôýâØCö]»UÜw}FjFtë#œãçͯëô®x1¤sψ%T”áÎ÷žÇs?|²êº)]YÄ\X13=-ò Ð47_*éK0„ªµÚ7ÂAáëŒ1P*e=w&!P @Uœ*TüÌ0T(Šþ%¶•J\Xµ-ÀgØžWbg脨ÐM@1M;_\BL¨ÃvÓÒpmU!Ð5@UhŠ•~ ¨†Ý ¢ª ŠÅT`ª Ht&BÕªªBA*Ì€]ö/@ (Pu«~ºÒ¡Ó;´Ý qwª¶ÓÉÚ*ý:UÒ7aêNE¹8}Åö¶L`Xôˆ½a"2¡ëVÈ¿Ò,=Í*Ù»Äá¼ú&áóö9>!1ìƒÞRzZðÐ+ÍJOÇyÃÆà홓„ð|ú:73 ýÛwÅÈžýë0H4\´iÒ]š·Æú=;|¯ß³;¡Uãˆíì//ÅÒ-<ûGöèwídŠx½ãª¢â¾3.Áíï>‡îÿ7Á0M¼?{*~X:׌>'õ6—¿®Æºv×6_qÂœôL´È¯ŸÒ”ýÚu†ÅwÜkwoÇѻըݻO½+¶oòU}þï·£gëö(Èi8¥Çv,Â/øG³¦‰òêjõÎ2*!ýÕT=<™hSÐ ?û \qìIx÷—)˜¹rq\ŸÃ4ñäwŸ ¤ª—Ž81îþ3RÓpíèSñŸo?ò{öTœrÔ0”WWáã9¢ÐeËü\tÌ 1õqÍèS°`ãØ!}iöšå˜·~5Î6s‚¯B|CBU°:ì³o‚jMæ½;qS +¸½ånO~ãì\ éÒ¿­[)ì_³k+6íÝV+‡;TD·>ž´¢ÂÜʪ*1oýjaªOá'¾W[OþÃÞ‹zΈî}ãj³M“¦xøükqÿǯ"¨‡wl–TUàù)_âóßg⊑ãpRÿÁ5ªŠ‘HÈ_¤5†Šß‹o*ÎåLnó§öÄ)QïãcD”óÚ˜\^8/»o§‘[¢y†Áؽ‚ou`„BôVXºn{´iè:S¿§áíV˜¾©0¨ŸnBÓLƒ¦3o ïévy½PµfmU‚Uª«B¨ª ¡ª*ˆªÊª*ƒ¨,¯Feyå!T”QŶjTUQUe ü…th¶Hža¶¢?-•g æ:©à ë†tèš#¤!¤„4z(ÍÞ [ ФºVÚ‚IX•ÐM@7@ {³—ÝZGQ‰_×}’^|#ž»ú.¼pÍÝxý†ûpÛÉça@‡n’àKÄè!ûÑóòýBõó2³kì® F÷: _tCL?®·íÁƒ_¾ƒë^¿G!s‘ç2\Ho81¼ºB›0å1‹ÊkW­f`Çî8èhßcó7þ¯¸j‰Ãš·Â#\‡g¯¸ ]Z´ŽëÚ'&}Œõ»wÄu `EñÜ|¢?Ùü|îOžJC»ôÂ1®°íhhݤ/^}':ÄP}¦¼º oÌü?ÿ/|>÷§Cªîú¡€ýe¥#ls’?¼kßò¨?._Ý0 ³V/ª(äddú¦Žøü½%ŇdTÍ€]ñÌÿ‡ü¬œ¨çî.9€'&}Œ+_~ÜŠ®Iâ|ä¯R‰è˺æ>a7|#û]ˆ^kVOÙcb8ÂjT”Î"®€aˆbsÔ3OKàQºnF€i½xÓpH6õ²ëº]7mU}ÝçÓ q q[ЀQm@·_ëAÝú7D7ëzÓ&áÖFœvh¾fXz´2€nBÓuf ÐuKIß¿îˆÚÕ¡A'2Á ¢ Ý7 .‡Ÿ®©Sr‡Y—ÏS¡( róPÛpB_%M´-h±ºÂ;·Fôlì+-Æòm=ûGE¬ªk éÜoÞpú¶íÓùöìÄýŸ¼†»Þ›öîªÓ±…«EŸ^ÇÜW>ª&Â{n\¡bÇ«Ó&6ˆ²OµCÿö]ðÚu÷àæ±g"5Û÷€ašxúûOkÔßÀŽÝ1ÔG…Ü-šà¶qçÔ¨æyMðêµ™éü’Ê ¼8u®xéÑ°¡ïG"Üáði4ö1r¦¾¢sÅ•åQ#l¦­X ¼Ýó(¤ø<‡M}ròuÓÀþòÒd/“/zµî€·n¼Ï÷Y÷ÃŽûð¯¯ÞÁMo>U;6'eÌ’ä"رy>{}¿û)¼ÿü'ÕÀK^ßHIMrΉ^8ÏS¦ŒŠÄÁÎÃ7 K%ß`Eßí¼qZ:pÖe ¯uÓÚ Û›OX™9‡L³Zõ¦ à Ðu¦®YÄÛ0¡kÄ"ëö¦³#óAJô5šî(·KúYFˆ0‘?#d‰û±Í4í2¹7 Ý*§§†a h;¶¡‚èbÓ$ gì0€IìX Zb®)ßsnV ÙÏ“„DP•ýŸÂˆóé= ©sjÙ¸Ï]y;î=í¢˜<°xó:\÷ÚxsæwQ=E5E¸PËúö´d¥gøî”æ+ÒRRðÀÙ—û’»¡ã‘ ïKïæUQqáðãñêµ÷Ĭ7±jÇÌw•¹‹7=j””‡ †_«²Ûé©i¸ë”óñÂUw¢k‹61]³»äþþù[¸ÿãWYòXŸØ]¼_xïªOqr˜ôÉKà ðí/+ñhÄø…êᇚÂ>&9ðŸ‹oăç^…fÇtÍš][qË[Ïà“?¯÷’¦òWu±qõ&|ûñX¹p5´ÆrÂ֯܈ÝÛ÷âÞÿÜžì!FDJj@»¥æ}´sèo­Ú¦Ç±:å–¿Éú!&¬|wâäP¢¯¨¶—Ÿ€˜–P’a˜Öàˆ• P SB`h ¦aBM5¡˜ Ô@ª¢€H€€(Šf1¨Šj…óRT[|PŒSàóóUzXš›£êZ8¥˜ºS-€zÖ-㆕z@•õuÝòÖaÞÓ6VNµ=Ã3L0ì*š¦ƒè¶C³ŒLÀ4]¹ûâó$!q¤at¯x{Öä°Ç^½ ã{l¦OÍ÷&9п}ò…EÁ©†cL¯ø|îOøô·¨Œâ©6ˆ%Î7oÃxìÂë|Ã9kƒpB„‰ð ÇƒpÞÕŒEtjÖ 7œp†§llÜ»¯Ïø6j^m²1þ¨¡¸ê¸“Ù{*ÞZZ]‰­E{0qáœd±A SóVxñª;qÏG/Ç)3yé\ îÜ#î~:4mS ǤE¿úoÚ(—<)!sêÓ¶#^»îL_¹oÿô½¯¾…¿¯_…«_ymDµôC­âéËoeïé³_­…°«x?~Y³ ÓW,ªQۻݢ{H~×m|Åaç®[…å¥hâ#ä9}å"ÁySáïQan¾ïþ=%лM‡ÍïÝ›ÿŠŒTGïÁ$š®coi1o^‹/ç%&eit¯Þ­¾Y0Îþ%UÑ«C}½`6mZ‹Ç.º¡VÆ®x I~=cë†m˜ðî$¬Zô‚Õ!›ˆz™ïêÅ`óÚ­èЭ]²‡ªªBµÉl2À+œ û½7ÇX@Cõ*ÔÄ]ÍéôÓ€B ÀõDQ,!=»V¼ “(ØvS7@V§†bG(€Pé‚YjÒ&Ý„‘¢Z†@ X9ÿª­œ¯²ñQ>¯Ö„‘÷®»bzƒs¨—Ê „žXé ºAɽÂÂêM¦–ÀÖ`‘Œ°›LãÀ¤¥ §´ 1,ÁÓ'Öljóù¨ë§¦Æ&Ä#qxÃ0 ¬]¾[ÖoC¨:ˆãÆ@~A~²‡Ugh_Øš¶Àæ}»}SÅtw©ª=%°Ê§äÚ˜^ Ö°~v] +=Ww2Î|>û}&¾˜7+*©^³k+nzëi<{ÅmQ« ăÌ0Z‘½êá<é9™ ëã¼!£ðûºUX¸içØçsÂÐ.=1¨Süd®¾–æy^tK {˶8¡Ïј¼$ö’^G2 róð䥷àÆ7žŒú¬/ØX3O>\3j<¦/_ ”N£¸õijfÄ,#âØ>1º×QøqÙ¼÷Ëìry©Ý(­ªÄ=¼„¿ŸsET=”d"ø>ûбYKÓ­úµ­™!×þÉ“XÞü\ÆBƒ˜˜²l>.ö©à6>ßûè° Ãzòk.¾×<¯±ïsÖ®°9u²ÒJb}Œ†ô”T\0l N;úL˜ÿ3>ýmJ«"—ÑÛº/nyëi½é™õ›+qèaÆ·³ðÓ¤Ÿ±ïäæå -#í°&ø££üàüÙ'§tæª%¾çÖ·ª~¬ÈÍ̵cNÅÇ·ý5Wx_i1þüÑ+(ÁC+ür@`Wñ„„ÊÇŠpõ”[&°ŒŸ¢(øË™—†-'öø7¢¤2qk[ßP§žìa4ä4ŸN½0êy¥U•56z5Éi„º{ög¦¥ct¯£êd^)jãŠ÷oýþtê…¾Þeºià‘ ïaÙÖ 1öphbüQCkt›@·È‹ü3¶ï ßR§~!ûÛìÚ][…}áBõëyñ«Ž²»¸îÂõûµë“€c<ÈJKÇ¥#NÄÇ·ýWwrTcViU%îûäÕZWˆ’ä×voß‹W7~'þrõƒ˜÷Ó„ªCbi8Û›©(|˜ºÂË¥Kñ퇓k6€zBN^lù–‰FM>ïù>'PÑ=‡øS˜öET îQ²Oïå#6‰5‰ÁÊã&%ÖñÀRÞ·óÛu†ÃÏùtƒ‰ß™a縦#d§›–‘Á¤aòöf˜¶Š¾½Y!ö–Ê¿nXzºaB7Mèö9ºiU°*ØýÛb{¦®YÄÜ0]Ž¨›†eû°Dôìêö˜¸ÎZñÞ{.Ò‚÷æ'ëY’84PY^ ]ב‘™ÎþìbjèˆæUúÙ'/ßOU¿y^ã‡9òˆ§,]¼ÈÏÊÁÍ'ž…woþ«¯â2íöáåiß$l¬­û‡H†t [ŠvÇÜNm±·¤Ø| á,ÌÍÃ=§ù»ýå¥xò»OêmÎÉÇð®½Ñ½eôèеÈ]÷+EX¾Ÿ5€Ó>Üú.~BD}Í0ðø7F,‡v¸Â®-¯&Iòˆ¢ÝûñúïâÆÓïÄŸ¯ø;~ûñwTUT9®ebÿ ¡ß= ‹ÂÊyæóõ{“PQvèZÛsó³¡âÐyü„÷x7šONM&!,ìÜfçNå>»Ìœåá·rÕ Ó°Åå¨ ½U Ï0,u{]#Ð4šfyÄ5MG(¤A mÏ~u¡ê BÁjhÁjÇ Ô Wë0ªuË»¯Û*ø¶§\³ôƒšŽPHG(H7 ¡ `ÈÚBAÃòÔ T‡ «u¹sµfÅjG«Ö k!hÁ´ê ´`Z0d•ÒÓ¬HCÓ-Cî³–AÂRÆ75b4tÝ2+«G €]ÊŽhÿ,I¹øyòdeg¬A(°1ƒ“=¬zAÇf-Ñ®°yØã˶nÀÁŠ2ö~×Áý Œé•/~TAŸV °" ì(b †Žoobj„ëN¸ö‰Ï’Ä‘„Šò ¨)ªmðT@PVRŽÜüú­_žLD Ù7 Áì5ËÙû«üE—bùQu¨á¬A#ñÈ…×ù†„R¸K2ñˆÇ Q›‡V |ý¥,T¢°§ä€`°¡h”™…ö =µÁmãÎAË|ÿy¿0å+l?°¯^æ.‘|Dò¬R„«þÐлMG¼xõ]Ãѧ.ŸŸìaÖ+Š+Ê=Ñ Ñrò`H—ž¾URf¬Xˆ ­¿°n÷vlÝ¿W8>6B¨>EÓFÞ¼|·!¢¡blŸxê²["†ïÿáo[" I~ Pr°o?ýn9ëO¸ãü?ã§I¿ ¢¬’ý8åÁ{)P14*>FBÎŽã߶~~vh Ì4ã¾ ˆXü\!Þ(ÞÍuÃðí¦¹|pnŸuK‹ÐÛeà,:«Ö;LÓ*‘Ç…¹ÓÍ0l¯> ×­stÛ#¯‡thš]× ëºM¾5š†fÓ4j$ÐYH¿©™Ì+®ë&tÝËól:¦‡äk†a`ÁÐtè!FÈ€©0B:ÌPÈîG·ÆDÇ"0Bº¡Ù¡û†î¯ÛªûÄ„AìÔƒ@׉U2Ð.'Hà0x“xo§õü›lOZF²IOþ‘ŠYßÏAf³ž¾ƒ{'{XõŠhyù?q9ø3WyCõÛ4iZ/B>uá]{ãº1§…=>Ú8Z‹ŒÁaÄænZS«0åX1/L‰²cºõñ uN²Ò3ðÀÙ—û†0Wi!<2á=èfÝ”-”8´¥¤¥-b ~ -ò›àÁó®k@\¿{GƒÖ¥ˆ~òXÊÀ¥¨œØwge(ÈJ¼º½ø]Z´ŽFá'¾W †Õ-ihèÛ¶î^Øã jX²2VH’#ÊJÊñÁ ŸâÖ³ÿ„[ÏúfLüåÅe¶ ºå£¶Ê¶V±68áù žl« ˜8œb Åñ±ú\‰7ZNQðÎÓ"¬ßúŠ± i«BNp¯ÞT÷¬RrŠ·G^xÏã=æŒ42ßç$–woè´¾½í9×t@7Í€Aœ|xMסÑúôºÉˆµfØ„^· ~H·Äíª5hA;œ?dÀ¨Ò Wk0¨X_È€ A×B0BÖ>-¤Ã°7-¨‰¢{v8½Ò ‡ìèÃ2$h! FH³I¾]ӠѨÝ„ÒlƒƒÎÂìMC·®™,õÀÐ4èAÍÖ Ð¡k&‹X0mõ}¾Ôži˜,ZBá¢$ÂGQ؆.“  ùáñ£B"~(AjjŠ]í=ˆŽÝÚ'{hõŠNÍ[E,«³dó:”VU`ûþ½X¿{‡çø¡*¸+.6&lÝäH9šñêŒ ³N&!õR–Íýc˜¢®ï_ï6qÙ±þ%ÌþعïÎú¡Îç.‘|„¢ä¡·+l†ôªà'=ZµÃØ>ƒÂ¯ñ³Cî¹6ÉÎ [VÔpBß/ù„Lw•s´æ<•ÑÛ[r0¦ëNî?šù§Ší++Iê.=F’ü¨,¯Äǯ|ÿ;çÜtúøáói(9PÊÊÞYDž’…óäS=Î{©ØµÛ]uÚuwˆåô¨±€*×›&žúË¡¶ß¬µUÞˆpÿ¯(ì¿ÈÃX)7.R_8›+óFL뇟#¼g†åÕ7Y ¿•‡N B ˜¶×Ÿ¶qÀ¤µä +ä]§õä‰E¬5;-€·=÷¶ÁÀûl/¿}¾g³Ãè 'ðÇÄüt+qg{ß +Ÿè\ù;b¥*Ã`åö ;zØ"€†KïóJKX›m D+ÿ–(lÚ»+âñhb˜ '÷ö˜Iêówkr±Ç-ºGÄF§f­Ð­e[Ïþ%[ÖcʲyØWZ,ì?>ŠðEÓ°eô’ãú…תËgP’|ª*«ñÉk_áöóþŒëÆ߆ï>úÅûK˜·ÝQ·Ëß1¶õSƒz‘¬Ž¾Ÿ“ïDÍ9åó¨€9´À“`ýÊø}fÝæoÄ‹æmšÁTL…$Y€ÏI †*ìÆ×h'09ÃŒÉαRÈ —§€€iÚÞm;÷Þ&¾ÐuK˜ýø$¶ÇŸ8ùö: ‹·<ýº¦C3ìÐüM·Êç…B‚!ÁPÁ`¡`ÁJk«®ÖXɼ`µsÜÚB…BÐB![|/M !¤iÐBV‰=]3ì|BÕÕЪCvy¿Œ`È*ëg‡õ!Ó.ó:8ï¾]72L+š ÈU0 ˆ®hND`ØÆ@7¬ Î]¢÷Ã6¢˜Ö]iÖ*±ªÒ ;6ïD#;ïžØ_|ŠJ0ø¸†MXkŠh*û³V/õÍÇïج%Ú'¸,P2СÐyÙ‰ÓëPçã{¬¤ªÌžZgó{ïgoùÃÇ„­%H¤¨ìð~žXz¼¶0¢*xó¿ï¡ªòÐù#ܦs«Ú7 ˆû5¯šñRÑ7¿fù ˆéøžY´¡¦ûj$ áç& E§$–,9ò3Më|ƒyÛíhj$°óúuÓŠ 0ìòzÄ°=ìL<Šß“ “7¸s Ã.¹Ç‹÷™¬˳ϓ †U­`š°ëÛ›1™¸ž¥šo["éóÏœS<ˆr›Út©§çHâ¼Y p†_Wó8º¶hÖË ó7¬öõÄ5t/>C˜œôNÍÙ1þ¨¡h¦&ýg¿ÍÄš]Û>µ¥[Ö{BZKKáÔ£ê¯Þ{›&Mñ'ã{lwɼ2}b½åPI²G·[˶xqê<2á½:3´¬Ú±sÖ®{¼G«vèÑêÈI“ÊJKZ'þp‚»|^ó8ç~BŸ£‘êSÖ¶J y΋Måûî?\Ä÷øÿmë˜à¿mn±$_ éøæýïq×E÷ãšoÅWo}ƒ¢]Ev·“"´§(Šçw½P@LOÜó¿d/CVN&š¶ªãPkÆM;Ü&Û¶Ÿ¢ð‡x>6§9ç5‚·:1Åó™6œMةʾáÔ®‡ICÞ-RLL+ß°ì­¼xƒ ÛÑ|ý-§Ñrxö¬¡º*d—ÕÓ¬"T²¶ -¨!X­!Ô¬{»$ŸV­Ùz»4ž½iÕVþ¾4  hAÝn‹æó;"}º-Ú§ëºfB7ìýv‚µYs Â€D7ìùÓbˆ•“Ïò9K ¯›@Ñ®kà “¨9V/Yƒ&Mó퀔–”aø CkÛtƒF¤ýpa}‰ÎçöKƒò…ÛWZŒýe% ëwýîí¾û‡t Bì;Ö(i\é)©¸eìY¾Ç bâÁ/ÞN¨ WiUýúßc·;)ÈžœD—4<íèáÑ­ï±m.…소j-v£º=Œ4bÚŠ…¸îõ'0?Á‚\+ÊðÏ/ÞŽxÎucNMêðøcçÖÚ7bcýž¾ûuê‘ôû^W0L!]öíª¥'¿Qf6Ž ó½APTŒî[¨>à/¼{"è°Ô5ªµʪ+Zudýž0Ûê8=æˆ"ùº®ãÛ'ãî‹ÿŠËÇÜ€O_ý{¶ïcd›^Hû‡{¯°p|bçóáú<‘dW3/¼–“Oxr¯€OÅ÷æèsf[ˆoëÆm˜öÍOÉ^N†v]Z'À—Å Î=âJÈNÏ@fZºgÿžâÈ$¿.î…IL¼2m"¶íENz&þüÑËX¼y]­Û5Lß.úÕw'õ‹M °¦8ìI¾išøáói¸çÒpù¨ðÑKŸc×ÖÝLóžÐlêAW¸yÆùÇÉë°· ;ÍÛ·Ýþ©çÒö¹Fœ—¼3_qåè»;´†ªâ“W¾À½‡F8K§ž úE!o=>çÞ‰‚pD0pžhÓdÆbrçG„‘iìRrÛLXd†%`GˆCp 1í°w;t^§áúD³ß3Q>ëµUמW³×­|ÓRǧdÜ°CøM[<Ï`‚y–X ´:˜Ðži{åiƒ¥G»Dž8w˜ 9 ÌXSDû„æmš!+'Gf}?Mš6fψKètä¸ú [>Tѹykti»Ñ«.rh}9¤Ï¾¬ô ¤§¤âÖ·žÁ³„fèQÛ‡§¿ÿ ¥UÞ²Iç …ìu»kÃwï>å´-hæ{lՎ͸ëý"£h8P^Š;ß{+·oöëÞ²-n=鬘ÚÑ ±¼Aj_î.?+÷qi­Û©-&¯›¾œ÷3nxý¿a­cE]ª^ûÁ-ŠøËËpõ«ÿÆŸ½‰ùþˆ{<ºaà‹¹?áÆ7ŸòÔ0çÑ47wrAÍË0ã_ÇÎÍ[á‰Iã__¾S«Ò–?._à›¢ÐµE ëÒ+®¶Â=ª5™×j-®õbá¦5¸öµ' † œ¿¯´ØÙ.T>wîÉŒQ~Æp ~ÞühU-V·¥hîx÷yL_¹][¶¢(hž×w¿ÿ"^™61®¨ 7^>»}ÒÆõwÊD¼ˆ­vBƒiš˜>q~üj&¶lØ…qe‡¼[^vªvo ÞÁ.ñel‹àÙç+'žpmYùùöõTÏõWlýö¹„)ñ[#rúí±ÑsY£|_¶8•ÞõžúðÑd/7zÕ !0™-ÂN.¨G®/ê$Ø+IøÒ„GWUþ>€Ýb˜ö=§Ï‰ Û DT(ö3£¨Ö5¦aZa °&n**—²A *°ò (ˆªÊH±¢êà¬;ÖS¨ZïU=`Ýþ€éšTë¥ÊrGTk=MÇð@i¿7 ºœQƒéRrN#IˆeÍÇ· º‹ð[‘ ë“0ˆõB£Q§Ö>µtëߥþ‰¤£¬¤•å•ÈÊÍr¢¥&Ò3ÒkßÁa€s‡'&}Ó¹£cT1ŽAŸ2[AÝÿGNVí±¥hÞ˜9 S–ÍÕnj1½„­MíiW á©ï?ÃÔeó=ÇZä7Á¥Çžñz¿’`A-r™0Šìô †¾n9býN€î¶ÁcæªÅ˜»~.>f,Î<9±;¾^0ÏOùÒ³_UÜuÊùqß×pÏj°Ð÷¹ …ðÇέhš›‡Æ9¹Q£]4CǼõ«ñÕüŸ±pÓZÀUÇ,œã—þP“Hž€ªâäþCñáœ=ÇÒSR1¢{߸Û,ÌÍó¤ ¨(CPמ’ê{M¸ïÀÕ;¶ UãääEOƒ"+·oÆÄ…³1mÅB˜„àìA#Ùñ­Úañæuøä·é˜¾r!®y2ÆõŒÔ@lÔY7 ¼<í|9o–çX^f6®?þ´¸×*^V$ÿ§ï~ÁäÏ~Ä–õÛ=^tž°+ Ÿ!Ï)Úsµë=%álÂΫê+6aq¿}ªŸË^qÎu\ûV ¸?(Dl‚'øP€Òƒ¥xûépõÝ—%uÝ»õëÌR’Bc ¸$Ù.ËÀÅ6ØmQ (6)fb˜ô Õ:3ÀÙg`·Eb›Ô˜\ž@% †ýRµ<5¦i3{(PM€( ˆJŸÓòð™71IÓ"Õ&­Y‘uÓ4ꔋF°çgšÌCÏìÕ¦­O@†‰ëq!ùB¥ÖëÝH’$aÆÄYh\˜I¥* ª*«1òäc’=´C'õŒæLÅ΃‘ËjõmÛ Íjà‰†âŠrÏ>Í0P¬F–Ë«Þ£U;f¿mÿ^<2á=¼>ã[Œë7Çvï‹®-Úøþ˜® VcúÊExï—)¾Þò´”Tý}&Î:#ºõñ¬[·ÊrÌþc9¾^ðKØ<à¶ÍðÔe·¢Io˜îšÑEµˆ.p㦱g`ÑæµØR´'amƃbã `U’˜µz)ëYhš›‡ÂÜ<6ÊGAN#d§g@7 ì//ņ=;±n÷6Á»×£U»°ÊÝþãð>G†iâ`EY\÷ª6èÙº]ÄãûÊJðÍÂ9øfá@NF&Zæ ;=i))(©¬ÄŽûP¬Š©¿ŒÔ4üçâ}K£Õ%>ŸG‚}¥ÅaÅ.ýÀG5T†‚xó§ïðѯÓ0¦×ß{ú¶ëìK ÓÄ‚kðÁì©aKB^üéèÕºCÜs ÷¬î«….ÉAŸçnoéAÜôæS¬ô‡&9¹(ÌÍGa£<4ÍÍCNFTEAIe¶íÁê›ñ;UQ0¬ko¡ÍU;6{úÉH‹ÍÈèÆ™Gà“ߦ{"ŽéÖ'ê÷µ2Â;7ïÛîažÍƒaîÅ]ï¿À^7Îε¾3róÐ47yYÙH P^]…íöaõŽ-(v=¯#º;†AþÜWZŒ'¿ûoýôNì;Çõ< =ZµóýÔB˜µz)ÞûeŠo^¿ª(øëY—×Ë÷Jƒ'ù³§ü†ï>‚Mk¶ØùÀÕã¼áÄMžâxÇÞŸnSF&ê済YŽ=áÎçò¾/?a¤Ñ ½‡§ûlžÅ³yYçQ(aæüø;†Ž„^jŸKUSdfg¢m÷6ØöÇözÏk£¡á*ÕîR¢Ÿð÷Œ&ð«,ŠÂ D[áÎU¨—[…uõì+0uzŽa_§²ÁöÃ0LkŸi7¬*0ìóšía( ösa}y*Ì` @{\pÄÙóëš7%ô–@¡ÂÖ‹#Ù†)¬¥Mà(¸ïôKbÒbI$ÉÿmÆù”|ÆñÃ^Ï‘Pî:+úÛ/gý+xö/ãŸþ™Yáóëý‡öÁ¶?jöǦ¾Á? –AÆ]÷Àzoë•Ê,>€GÝPV‡Ý°¢ØD]µŸ@ú,š¦}ï쫉bq`Gý[ ¶e0a `ØãqŒQ(¢pϽ0G.7ÞbÁyÝMj3™v€BÃö)y§Jù„X‚ƒœdeX3];b€Þ}gÅè+D!Èkš6¥èÞ‘Ó4±lÞr4)´Ä~è·tEY%F2²v†Ú¥.~>ùmºïqUQ"*ñ׫¶oAEÁ¹¹ëWyH~çæ­‘¢ ›þyâåÕUX¾mcX¯š³sñ¯ó®F¿v£ž»dóº°ù°sׯŠ™äV9©®¾o̘„/æ΂&ÿY7 ¬Ùµ-.±Á´@ ®8îd\2ℸ…æV”á'ŸÚæß.šƒÛO>/®¶Â¡SóV¸÷´‹|źêó¬"OMœÇ²­ÂæÜ~»hN½‘üÜÌ,´n\—1£&8¶{_üéÔ Ñ8Á‹«vlH9¯æ͉}Æüü+Š‚î-Ûaá¦5¾Ç5ÃÀºÝÛc&pEÅcÏÀÃÆÔhnš¡‡a›µz n{FÜÆ»¹V×h,ÑàNSY¸i ¶ú_ÖïÞ^ãH°kÇœŠŸÿXÊþVä¤gÖˆ¸ê†õ»ý£ž¦.Ÿ ‡ñ<3Û÷ïåV íÒKñož×³sÃF T†‚X¹}³¯æŠ²Ó3ð·³.뻩¶h0Â{ ~YŒnxs žùë‹X»b½Mð©B½(£NÃõ­·”‹BvNN¾ÂêÑŽ`»‚ªëÞ#O­ ÛÄðdÂÕ¸§íÖ=KÅf)"M£ã,tŽT¡¤Ï>Ç4L'|ßù×ùìÀÑ1ÁQ#zÇz%8¦Oü Mš6æÂNìô§@™ÙÉ3Pʸñ„ÓÃzˆtèšð鄼ûóa½`¶çOj ›'ÆPwBŸxû¦ûc"ø†iâý_¦†=þÅÜY(‹àÑóCj 7Ÿx^½îž„”6R`bÞºé~\vì‰q|Ý4ðø7¢*ôûfÁœ„–Y;±ï zÉå±yßnÌX¹0áí¶Ì/ˆYtÏ$fÄg~Þ†?0ѯ S .´iÒŸ ¹àº:ùîxsæ÷a¯Ùµ ïÌú!Žý=©5A×mðâ5wÕ˜àÀWó~[V³2Ä&~—_P ùæµ'<É× /Là{Þ'¿Í¨±h`anî=íböþ¸žýcÎUçñý’ßÃz½7íÝ…æLóìkÖä:_7ŠD=ƒÃ»öÆÛ7Þ_¯’hiÓbñoËðõ{ßaͲµ0tËK`ÿ,dyóÔsO½¨Žž(^'ˆî‰ñù, žæë YÛa⿙ןå×ûˆ³ðV!MŸï_áõõXÿ ×/w48o>@0öÌѸðÆs“r¯4MÇUÇÝ‚`U q¬j O—_Ù½H×)*m^üÁÄû³€‰ØÁ}êÎë0øöΨäÚ×Z÷RµÃèí¶TÛh£R9G8çP=Ò¦_Ÿ¾šB¢²×‘ʈø dS×¾«2 §gò¡ùÔKO÷ ±¢üíX}Ãt¼øVû†]:F¸,&LÓ€NLüéÉ[1üÄ!щ=;öbÉïËž‘Æ}ge8á¬1HO¯Ynà‘‚†×¦Oâî=í"œ: qÕtÃÀK?NÀÏ,‹x^‡¦-pßé—ÊÌ{K‹1så"ÌX¹(îrziŒìÙsèpP á¹)_bîúÈž°®-Úàϧ_\cB³v×6|»èWÌ\µ8¬‡ÒyYÙ8±Ï œ~ô1hß´Eúé:žøö#,Ù²>üÚ¥¤à®ñ`pçÄ¥ë}5ÿg¼0å+–ß>°cwæï­ÊPsÖ,Çô ±hÓÚ°é=‘æ}îQÕ³­Ä§.›7ú.*!ر;î=í¢¨‚oeU•xêûO±bÛ¦)Zæàù«î`ï_Ÿ1É·4)ÅÎ=qÇøó ÜEÃçs‹S'àéËnÅÑ»Åuí÷‹Ç{¿L X¿n>ñ,ßûh¦‰×gL´ ¾nŠ¢àíï÷;W”cæªÅ˜¾b!VíØ—ª@U1¼ko\0lLL†ëºÀ!Gò—Í_‰ ï|‹?–®…Ò)g¤ži§L™Ï· ³ÊH½CÔ¨B¾ãÐWüÛ'î\x€éÆ ¡ÒŽê=OÔùvèE *ŸƒOeÙœ}"yûâMhM€IDATÿåçÊŒ„àîÇoCÏþñ}Ð…'î~óf,ª{’Ï, ª³ŒüqŸ…䀪zï«okžg@E€ˆìgEå \)Fë> ¨–`Ÿb[)buê(Š~*Ėٷ槄5ø/¶EÞ»“I ½pŽËo°9;ìØ|NÄî³KîûB[Qß4iĽj°H>R¼ùÓ Èήœ,Ñ°ñåÛQÐ,ß•Ò¤  aìY5÷®IX¼y™ðö——" ªøúîGcR¯o”UWbå¶MX³kvïÇ®ƒûQ¬Bµ¦Á0 d¦¥£in>:4m>m:bPç5kªOè¦5;·bÉ–õزo7v,Byu‚º†ŒÔ4ä¤g¢u“¦èдtè‚n-ÛÖYý÷úÀê[ðßIŸ`ãÞuFò%bCIeVØé.›öíÆ®ƒEØU¼šáOŒŠŠæùÑ£U{ôk× Çtë['âœõ Íбfç6¬Ú±;ìÃ΃ûQ\YŽ BHב–šŠ&Ù¹hÓ¤)z¶nÁz Y^ãdûˆÀŒ•‹0º×Q ú;/T†‚Xµ}þع ;íÏaYU%ªµtÓ@fj: r¡]AsônÓC:÷LúßèC‚ä¯Z´_½3+ýP0ð„˜”…Û»I™³ßëHW|_SÀuµÏõ>×ñ½ ùû\@,ž|VªO €^ãßû‡8/XËü{PTOô(²“ b5燹øß}/si ´È[dÄKòÙÒ¨v?nƒ,RÌl,>d?V«®¢(Ì@Û¦ÚÔ#IÅ5Üx•E[Ð’{ÌÐÞ=±ÔmÏ¿Êñy.’@ƒä›&__”Ï»7œ4b?Q¶žzã‰ÉlmÓöôÛ^}“Ø*þ ¾3¦a`Àèþ¸ÿ¹;c¼DƒÄ/Sƒ¡é`ÑN¶E¬¼¤ã΋”´) “W–ã??!ÿ¾øÆdGâ0†ILÌ´=É×z²‡#Á‚’ª T‡B¨ÖBêÒ)ÈJOGAn^yë%$$’Fò×®\/ÞøˬB°*èCEO½ZoŸŽ»÷1ï<œ¶ÀMàúƒË«n3!M@0,(^ÂÎp¦ÄÑ/†ú»Âþ…¨ÂÆHC£ù°–š‚ü‚|<ñÞCõ~O«+ƒ¸vÌmU…P/$ŸÞOñ¦$˜ä‹^Ãõf¿UY) Åy>ì ™¡€ÞsE±¼úÎΈƒP :Ù \÷aKè…[k†O¯1iȾéˆô™T†Â>fX:ø¦pOl¾Aìªyw¼÷ÑIþ­]ãN“eÓgíÙù³"#+Cø,@ %Çž”¸pó# {K‹ „„„„„DÝ ^](ÿØŒÏßüKæ®@UE•kì„5óD˜Ð:u¶ÀâzÍ8W¢Ž½åCåirÅyíJèwÄÀl‚­ØíE$Üt?Ëã¦íÛ£çEù¨j? À D#Ó}mMßI Þ`GD !g—[ƒ+9P‚W{7ýõÚú¼½ÈÈJÇÑ£úcÞóÙ>Jàc!íñ‚@!Än g/AEY…˜¿Î»Ãݹ÷óõÖœ{˹Óðzï.ÿ¼zP²ídWsž}çµG½Ÿµå”Ñsë—r»“b´‰9øœrº=.ç¸×€¡ðÞ|—]Ã2.Ø"pªŠ^øº¶E».±)'ýé&-›`ßž"(PM{u\Žð„ÂEˆ©™È–·£ymæÍZ„òÒrAÐŽò …#èô¥Ê‘V‹g»¼ÕIS˜+£ÕNÞ:¸ž¯·„†¾;žuoÿ”l¹’Šyc²/²2ž|;‘<åS•Á‡MO±'Ÿzú…hÚCwý¿ïyO}ô2³ê§µªª8þœ‘øôå °äåìÑ$QÞ‘‘ 3{Çë4¨tŸ×*ÀKX8†kñç9 é,ÂÝ~n ˜,‡_Qœt}'­Ä¯oE!>ËÉrA•ð½!ýtL|‹N¬ á> Ät"¨] ËèazÖõB…üìëL´nß½uOÐÝ•8Ô`~Ÿ9M óöým}階aÇNö%$$$$$$$kÔŠäïÛU„O_Ÿ€ßš²ƒål7çÁäë{ÓMN?L ¸Ž×œ¡Îî°h¶ßj™½"Ö92ì„ÁÛçs 0A;Wã„q}z.¨í ƶ,ˆ¹õð0!'\ÿD$徂\ Ô›+Ì_£hÀ0tÞò8þóοjs«ã‰çŒÆ—¯O„®ÜÜ"ÄŸ×îÐ|ÅyOñÒ›4‹œ¥¢øoÖ† ÷Hï{¸É‡ƒ‰š€)í»4 Ä1Dž(óïsiã.S£ñH4 Lúx æ³h-þûª¢¬ ­Ú·Lö%$$$$$$$kÔ¸¨áâ_—áÚSnÃÔ¯f ì`¹ÖËÂámÏ7õ°Îí'êïN@¼Vasš÷ìäÂÓ2^ÔiêPwÞcϼ«Ä!Þ`׃oœE °f8‘„z]¹Üy:'vµí…w¼ œgŸ‹àªð¹j‹;㧠A•ö GîY´qîo,(.*Æsÿ|¥ÞªÆMó1â”a0A`*€á6€Ô L¸É,!–š|,›ð}ŽìšòÄ ŽÎç£-gí³¼×ÎbXõé‰bš0t†aÂ0MK™Þ´®7L† «þ¼gƒ}ŒßH„ÍYa>¦ Ó5>Ó‡nØc# bÚáý&óÆ6Wú¹óòíý&ýø˜©€¦20æì‘õö,JÔ/Î^‚ìl+jˆFðЯêŠòJŒ9]†éKHHHHHHHÔ5jLòûé”+€ŠÓQbï—cÂtôb··ÚÑ©ƒãþ÷¨š…¡‰Žª=Ÿû¯ð ³n±/Û³NÙ;5,¡óvlµ Ò~˜;_ô¦ óâg#ŽŸ'ìrÏuèŽ$ ƒƒ_4ÀÊ…«ñÍßÕôvÇ3¯OWÐÚ¢ÀwSˆwó;/"§„Ç Ë\ ÊV·ùÂô!攼Û%ì¨1†ìc†I<ÄÛoCÜ›54Óµ9sqÄõ »†Éö™vX>Oê}ÜáøÜãníPˆ &N: 2ê)uD¢~±ïìܺ JÀú³Âì°°^d7ÊAn^N²‡)!!!!!!!qØ£Æ$?%%€+n»ÈË×y5n.žåš3×¾@sííÿ1o¶â£L˜%øÓ“½ùÆ n¯º+—šŽ…Îóâhœ'S·§é|““ÑgŽŽÞ ×w~üzÇÏ<É´QÖ?ÿÞ‰ðòy^MÄ÷ŸLÅ‚_×í»´ÅÑÇö·É=áùŸgS|6¿óê ”üúõϳ|®<lá|\5XDÞñxSO¹IL˜†iyÙMÞSž˜Mˆ\ðLƒØ¥õ¸©ßµ«=ÞÐæ)üz©©©8õ’“êóNJÔLÓÄŒ‰³•!XÀè÷н0jüˆdSBBBBBBBâˆ@I>œuÅ©hÖª)'Û¡­0êìV¢ïñŒs9ùô=ŸwO5̸0v…§ƒŠÓRe3ázb¼#ÞÇ;©ŸR›p=œ}Â]ÏyãÅ €óìS5}>Ç^̲wª 8{-›òî…`a¨‚Ê€Ðæëÿy[7l¯Ím—Ür®óF`•Ö.•Ø%îBl§5±H2¿ ^íš ˆíývDóè.¬ß¶žpS¯¸áõÔsĈ-Õ Ö-¬™…&샦 ¦=N^ƒ·6tim˜öüLÓJE0ìóÇ]p<š°’j‡&}üš4m,DBQº#ŽJö%$$$$$$$ŽÔŠäÀ?ž¿OŒD·IŽ'¯œóš3ï´O{–SÑ!‚B=—À{?z‚à wòòÅ1:„Ù‰ä'œò8O¸2îä¿‹¤'yÔ›/\ãêß óO˜£×ÏÔÚ ·&éç®eªé\è?Miø÷ÝO£¬´¢öOOtîÕCÆÍÍ«üò$ÊfÆpNX.L„Ëãš+<ÞwÀÄäw}lNh¾cqH½iº½ö‘—^Ð7 Žvzf:ιöôº»ïIÃï3æ#++ôK‰}½Áúþ¬(¯BÇní“=L ‰#µ&ùm;µÆ¨SFp¤Òú--çxÈmð¤ÓÞ%ˆÍϯ'Þt‡s³7îë©7‰~oŽ:m—æäa<¼ÑÀ1€O»à¨ÿ3±ANeßÝ'oq~{ÇïZ&O8;¯þïœ#^¯(¢qÂÓoš&þqÃ#Ðu½¶·?*.¾åKå_ UQ¬­>!P¦ïæ¤mðyg½!ˆô‘°¬Ø9nðn&Í—ž›ŸˆÍ4 Änè ëNš€³~Ñ, àÖN»Äª¤¡BÁé—œ„¼&êõ¾KÔ=¶®ß†ý{÷{ôFèG¼¼¤'ž}|²‡)!!!!!!!qD¡Ö$î|øfdçdYoø¼t^€î£y^$ÏâéVÎP ð×ÕÓî"Õ.ÒîxñyVÆG(BÊŸ+Î"ÀU `€OÁO>ÍÉwze†…cüáŒÌdÁë pùøaCü¹9óë¡(|J…‚ªÊ*üë–'âöGDû®mqâ9£áu£'5ñäs×:»Ýâ|µH¤œþºÛèXj–Î@×B\#ç¿ü‚F8ëÚS“w¿%ê•å•˜ÿËb¤g¤ vDþ#Ó´e•§/!!!!!!!!QoHÉWUyên`dšrp·º>ïuf¡ùàÂiܼ½§/Ùõ¼\þñöÏ…â;VN/€é xÉ¿h´öí.Âï{.@D\|ë9ÈÉ΂JS‰Ÿ+ Ìݯ1X8;· ¢Z=Kû`~$Þíùv§¥ìH½l„ص8²O# …(žØ™°Äéƒñç!3;3É7P"јôñh”o©åóß_öcØñC’=L ‰# !ùÐoHo 3€7”žåÀƒ#Í®(r®ºõž¿Þ†s=¥¶ežj®÷õ®¼|÷kFT\QüÈÎM¼äœ#<ô Ÿ— N€5æNI peëÇ÷¯Ø"†|ŸÎ¼xÇwýˆcp°ç¼qõ&¼ñä{‰z |‘פοñLkü\úF²|ú‘õQ/G~]W²ÝÑÊxˆ½þ/´•Èy¿ú£ŸUNÔ1–M\"•;$èÜ»#Ž;CªªnøúýI–О áû‡TWTã„3F%{˜G$FòàÏOÜŽŒ¬t'×¼R>—dnç¯3rÍçÓÑ3χ¥³}âxºv'‚ßQ½õfI žzÎõN˜=»†7'0b×ÐsròÅyØ©lvœD£͵wY?¸j44Ú‰”•ø)á÷Ë Wà2pØË·`Ö"|ýÞ¤D> œréIèØ£ƒp?ˆŽÖ¢…ãG½žc·®ì´/p|?b\«&zóNË1˜‘6BËñ¡9êÉW*nüçUPÕ„~ÍH$3¾ýÙ9YÂw3eÚß{š4BãÂüdUBBBBBBBâˆDB}§¤¤àþÿÞ ®ÏÊÆñáôÖÿÄîh‘ÌrÌi©7ê*t ï`$Tb÷Ð~…{-„¼+¼è\Œ_q"øþFøo”âD ^u{¿s¹Âý°u{󩲿û,…i8‚{|«cÝ ,âæè+ÌXÁb èœx%}»M¶€>ãwÅ7°5ÿäÕ¯ðûŒù‰~$:õl3¯8…›+|^70¸¦Abþ/ÿzÝÛ»‰£#‘.ŽÔ€VíZàü›Îª‡™HÔ¶nØŽÍë¶ %5…¥]ñéCAuUcN;.ÙC•8¢Q'q´yúnd7²Ôöœ|ê}]ñBù;®¦½.¤ØÉ8çé„‘xOÙ;áz®>÷™ÆßsFfl`ã'®‘p£áÄïøy»K‡÷°¨Ñãï™?w-‹xpNv›öÅN\ñà\€ãq#âq{€€Š·ŸùK~_Và¢[ÎF§îm»¬žõÖwÔ~BÀnèÁŽz u·QÛ µwYš‘LnO=<››ˆˆ}Ƨ@ûœ¾ bm©ÜùØMHÏHKö’HöïÙy³ 3+Ô ÉÿKM™ÙrPؼ ÙÕ8¢Q'$_UU<øâýö;·g,oÓ)#ÇÎÃ/^×õô ˜'Â.Q”Rc+D'äê+Œ33“„Жã ç+ÐV¾Þ™î²o8Mr‘¶À‰ Âøékf,àE÷ÙrDùÜóûé[.ý€ PðÊcocͲuuñh 55wüçf¤f¤Å:é©áŽÚOÀ„j^–/z›$¢±Àgnüé$Ü)4l[Á…7Ÿ‰Î}:&tìÉCEY¦Oœ…Üu@qåÇ{ˆSý'Θ™QÀ§ÀµåŠ–à¾47BUUðìß_Ʀµ[êäÙhݱ%®üóÅ,ç^W-‚XWeòê2—ŸÞBÝí$v~Lm ÒàKó±-L™;¦×H#\¯V@ìƒ1Ź)€õÕ¡ 0†b‚(&z펳®95AwN"ÙCøö£׸‘'Ú†%APQZqçžìáJHHHHHHHH I>\ó§ËѲm3ÎéÎ×€wÂáEO»CÀù´{b-z§E>WŸ8îpQ­Dõ>*îþY¼s.ççš#¬o…‹NpÇ> 9ùÜNf|`sS¼óøŠ’®ù(v„P.kå÷; ÀÍë[žøó³Øºa{Ÿ'ž7£Ïˆîõ3•ð„”çc"}®kc%ð ï3a Ñ™â&ÆÎÇN±£‰ø"žiK4t•ÀPL˜ A~ó|ÜõŸ[¤šþaÓ4ñջߢqaž „ÊŸ[$²C×h”ß(ÙC–@“|xìÍBUU''ÜöÜû©ê3O:×s<Ö G¼/§MÎ[ÏRÌY0>#¼ÄiÈÑÿã¼úTìÎÉcç;s™¢4®Ïω]ͼðpÆ^wÀ?Ë“öäèsž~&ÄG#h8ÂoÄU†»NqõáDð³ØXTEÁãzºÎˆþ \‰Ž=Ú»Ÿ€†e‹«™ÄüSÇÑÆJ\çÁ‰n~àZðþc«ZÔšãêy AîaÄÙˆ“#Ê+öó ³n±/êY·Ù;5,¸Õö™â>7|ÑaOÝáœî>àÌ‹Ÿ8~ž°{È=×!q_M¨ñÄéHQüWJq:qþå•©KŸ‹†°ˆþ3uBôÓÒÓpï3·£qãF Å16ÐZìõ *.àÞœ¨Z.Ï€µY nüËÕèÞ¯K²' ‘ |ñæ×h”—ÇêÿX\T‚S/>9ÙÕàP/qµcÏ!£†à†BÈùœ|\½“—ïäÔ;!ꔻȠǫî“÷Î…êÓÜyâºV§ÀåýÓ(j\ zãáÌÑñÂ;áúNª¿wüÔÏå´xá¡œ 8yßH á=ß­;õÏ&°Ç¯*À¿ëˆè7kUˆ¿<Ò2Réì]¢s‡)Ñæí7ýZDDàß®â<_Š™rcì9£’½¢ ÂWïL´ª£ØÂ)LÿÄ¥€¬bèñƒ‘’Hö%$$$$$$$$8(Ä/n¾Žpõ¸[Q\TâÊo‡Çóì„–ÛLÔ%ÔÇ„ëv\ø ÊçøÛ/(awòæ yVËžyÂ]<ØUÇžyü‰xœWÌwþá”ð©QÀ5~¡æ½óê~ïÌÅ­ÔÏïSø ðkÊæࢄqØ+D¯!&ÁýOÝ…vÛ$üÙ˜7sþ{×s0쑨6T‰3Rw¾|4¡¾Ú êÕ• ¯ùDDåÎ5}H½âg鉦¿@ ÷Ž Ø‚šjD§0ò¤¡¸ý?7…‘hXøæýïž‘æ|î…ï,1b*=3Ç|L²‡|Hã»ÀüŸ${ ÍZ7Ã-ÿ¸!æóë•äïÛU„›Î¼ ¦a²|u‹/»…áìÁ)9†ë<ÅE`Ã_ïxÃÝd™ÿ+8¶9’Ì—÷£cæÛR¸s¼knÔà¶bw\ÛŒ¼»û¤5Ô9w9=Á8fþÌPµŒ\óç×…‚»¹]zwNøó1í«Yxé¡·¬1+@8à„=xH»­T\ µA]ÑÖ¸ÚU¹³ýÔó8트íc¯€c`°¼÷& ÅqõÞ÷?wRRSêh¥$ê_¿? iii–mÇ•åØÿ¬÷e%å8÷ê3“=äC¯>ú&:5ÙÃhàhÛ¹ žŸðtÌç׫ vÓ–…¸ã¡›ˆêù‚Øø—>Q¢ö5Î÷õ4¯ž†ß{½Œ„ýˆ¥¡õ‚š?¸>…:füXWJ°p~'Àà ÝIKð?;ŸËÁWøë9½çñzÞðàN`£ç³ —ÀTü]ýÓѪŠ‚§þö"–Í_™ðçcì9£pͽ—Xý*„#”~qäÑ"וZnu…X"õÙ|X‰<æ¸xNÌ[,ƒäÚ§b“½öÀ½ÏÜ! þa‚¯Þ™ˆôô4®/ðI¿GŠ÷—àŒKOIö%$$$$$$$$ Þk]wò7~„RorÑÏÕµgÂz4×wæê…Äa ¬N]9ùv8:ëß•-OõXª½} /XǧyñN{g^´{'üð3 0“¯/à!ã` y®rðÅ(A9ìshïÑçôì6¯<ú&æÖAêi—ŽÃe·ŸÏÖKW]µþe$Ó‡¨ú•Ð“ˆ[€:4Å€©X| !èÞ· þúÜ]HÏHKöˆ%€/Þü™Üwýþg´þ­ª¬ÆÈ“G 5-5ÙÖƒ¤¸áîzä¬[µ»¶îv(#ïNH:@ßÛ/A‰>Ÿ7ïܹÃÓëYÀ»E쉟OÂȬÂGÀï€p¤YáÌB8¼ë_¾TÓ¿mf Sv‡ÇÃEÎÁ™i Øó¢ã§‚€ùç£,¢ÆŒt,¼Þ€kžüÅ´  ªªx÷¡²¼ cN™ÐçãœkNCZz*ÞúïG‚KQp HĆX–Œp/l QAÆ>CzâþgïDFVF²g#‘|öúWÈi” öùÀ¼XZÓ4Ѫ]K´lÛ<ÙÃn0hÕ¾ú îìaHHHHHHH4p4oÝ,®óë5'ŸGUe5®=ùVTW=yë|ˆ=õ¶3R/i—0N¯òm‚žS9£'ð'´ïÉ¿9·xŠ£°Ïu¾z‘†ª¸˜?/¼çÈú‰òÿòsÂóýÄú\seËF×Bqõ˧2Ðñû2¦ibü'â´:(£õãW?ᵇÞaF Ó¾Óžg”\ôdyö£¥È;éñµå÷IåÍ‹¸c¹ð ûµi3>¢`à±ýqïÓ·#-]zq:tÍÀçoN@£¼(ª" s«_¢( ‚Õ!¦/!!!!!!!Ñ4’›×nÅ=—=Ó4ËâÑ],ÜÞM¶ý~v~¯ùDÅïzŸëøÞEE}' @ñÀãÉwóœy¸ÓæŵàæKœNÍ{Cë»ßØÜz¦ÚÏ•µEÏw{öíù˜„`ؘA¸ü¶‹ñX˜7c!þ÷—WªØyªÉT#^{DüX¸”úýf¹î {€¦­‰`ÂÄØ3GáÆ®’9ø‡ª*«ðÕ;ß"¿I#¶bbŸuÕy¸J”âÂÎIöÐ%$$$$$$$$b@RI>̘ø3^xè5‚,zêµ{.µžîìBJ *œ¶Àlo>×ÜÞmÂ<òœ©A06x;GÀ%|p|]ð»¼åBTac¤yóÞÒ|á !Îxøj€Bõš*ÀŸ7øUB#:õì„»½%áÏȺåðøíÿCùþ2˨£šü¡JòÞY";$!¦åÑ·IþEÿw.λþŒzœ”D]¡ô`)¾ýx2òò9߇Eå¢`ï³v”—áôKOA¦LÑhH:É€—}Ó¾þI Â^O4À“W¡Ü ÁëL‰3?£¼D×N—wŒ t ɹпàÍÓœ9¸Cñ~!Gß×›îDаNsi) \;¼ !7~VªÏEðù~ÏúhÒ´1þþŸ‘’’XïÞExòÎç°eÍVªm6±ÇÀÞÒ°Õ%É÷†doE½°5ö–•Š›ÿu Ž7´'$QWسc/¦Oœ…Fù¹à‰îˆÞ¸WUYá' E‹6ñåIHHHHHHHH$‡É€û®ü'Ö¯Þ$Ä$ó!ú¼Ççê⹮ɹ~¼:jút'÷—wîxò}Œ ÂK…ãÙaòòùk8ï¹£Ò＞sÿTO~= 4|òú}sò]iîHA´OUE·~lüv¿éøçK±½‡Pu¯?ü¦÷•ÉçóõɯÑuñ’|» É·vÂ*n=wª¢ E»f¸ç™ÿCÛ.mêsé$ê,]‹¥s—#+'Ëù.RçFU-½.#†a C·è3°g²‡/!!!!!!!!’oš&n<íN(*öIÞY/„λ<óÏ “Î)ñ»Ãóuÿ0Ñô0'¶gïeí(‰p8˜ pçLpZâ»ÇïÊ·¸¾M—!Û¯ºÉ¡âËÝ©Ñ<ûªªâ¾ÿÞ‰Ví[&üY™òé ¼óäÇj!(v!·(ŸÊÍÔäׂÔKûAI`[¢ ã…GÌ0iIDëco(€•¯ @ †„¼ Y¹Y‰œD’ð봹ؽ}RSS¸¼{·®ÀþEAzf:Æœv\²‡/!!!!!!!!'’奸ñ´;P]ôõ¼»‰?ïíöÏ'àí猼×Ûɵw‘i®1‘Lóþ~ÎSî ­ròéõ¶¸•;5A ÛœÂ!Ó>¡÷œR¾UF|ÏWï@Œš d]0pãòY?W¿i˜¸ò®K1x䀄?+[×ïÀ3y[×n·I¾õL;T¿¡‘|5'ùnDúD7ɧ• de¤ášû.ÅñgKbw¸à»O~€®é¶§^á?DýUU­çÂþn©®áì+NKöð%$$$$$$$$j€CŠäÀέ»pÇ…÷ÃÔM'÷]o÷‹Nö’Oz"ó83ÂK< (¾¤š¾åH¶KŸvçÎÝ÷¿ÞmP*Š]‰ ¸¢üCæ#M'àç”äÛð‹ ð¿Ç À.Xk—‚QãGà¼kϪÅSá-¤á“ç¿Âwïÿ &Ë.W  ÅôžO#ù–¯H“|Ó£Æo*¶‘ƒXåñøÈŽý:ã–G®GËö²úá]3ðÅ[“w~ý¤Ÿ[Šml´N)-.Ã…7œ›ì)HHHHHHHHHԇɀ¥sWàáÛþãx™áÊsçêܳc4wœ+åÎiç ªEvÆÔˆ·Œ=„¾¬ËTËŸOÏ ù;Íòž}ÎhእçKZñÄž¯_Ï{Ή/ãÛóŒðð}ÎÛOû ,|êƒÉí3Q'GßRkïеþôømuò¼lX¹ /?ø6¶¬Ý S!_&^úmr3Tˆ"®WM>5%ù|Ç‘öÅ bOÀqЃØÿ™ìž[ù÷ · ²²³pñíçàÄ Æ°¼l‰†ý{àûO¦"Ï.‘礹"{T÷g(>PŠ ®?))dOCBBBBBBBB¢†8$I>Lùb:^âxÂâ=$ßEέºN˜-çZâºÎ w]Ïd\œáÀ¯®Ÿ[ïRì'žñsݺs{‰8.¾AX«ÛþuºôìX'ÏLqQ >|ösÌœø‹ç˜©Xd^%Š3.Ÿu«m(Tþž$’¯@B´ïÚWßw)z鑸1H$S'Ì@éR¤¦¥œ°¨¶ç%ÿY¯,¯ÂñgŒBA3ið‘hè8¤I>¼öŸw0åËéP‰Uä=êø€CŠÙ,m² JϤåa4À{ï¹¾(AW!íÕ à ¸OÙ:×ø1>ðû\?Ι'ßEÎýÂÿù»"<^x'g—9xƒ…Û@!Ì×/’ÀU^œxÎñ8ýâ“ëì¹Ù¸z3>~þK,ž³œí3X òÄ Ý·Üü‽$?>BÉOH6?7PbóÃ,’O’߬uS\pÙuú¨š¸ X—o}m•Ç„üzÀk0¤8úT]Äàã¢]gY.QBBBBBBBâpÀ!Oòà©¿¾€ß¦Íó-«g½vÞx<÷,‰>LȺâ„Ù‹xᮼpß*ïxù¶Ü9øn;€)v‡á }rž|¯b¿8¹HóS hÜyÜüÝ¥=¶ !Czœ… ÃK~[wh‰»û?¤¤¤$ü™¡X»t=>{q–ÿ¾ €¥&O=ú€¨¼ïº PíR‹ª˜î..³ˆ_t’ï¿;Ú'‘J ªöX¨xžUQ0­»àô š7ÁÙן†1gDJjÝ­µDýcóº-øeò¯ÈÍÏeF<Þƒ/~çqŸ_{w(Bï½Ð½o×dOEBBBBBBBB"Ah$¼åq¬\ø‡ÃÆÁçäóï!äÕS¢-–“£y¢.^ï©+ï {õæÁ»ú÷ËÉÛ.Ýãퟟ ÝO|Îw·ï§ÐïQå÷ôÓsa‘ýEÙx¸ò¾ý»«#ˆ÷+ÀŸû?´jß2IXl\µ“Þ‚_œÍ4¸<õðר„Šô¹I¾m±=òq“ü0ðû$®WJè)É'¶·žWÀ´FPU´ëÚ§]y2†,ÉýaˆßþŒ}»ö!--•yè@U½ß9ô5Ÿn£iºõéŠÞ{&{* Dƒ!ùpïåÇæµ[żw!ÔÞ59Áf…ÜýîÜv®1wßÕ®¦c¡øvÝ^w1ŒoßI#pzO>¾7¤ßkÔðóÈÏX<"€v;ÖkNI_XJqýø5D÷ÃÞ ¦aà¬+NÃñ§×}}ö¢]ûñãç31ãë_pp±pŒ(¢·ž'ïô˜J,b ÎH«õ©q†à{ ÜGÑ ¦'0lC QH Z ÒSR0xÌÑ{þô*ÉÛሪÊ*|õöDdeg ŸkbvÝÆ6°J‰páúZ(„ŽÝ:à¨áý’= ‰£A‘|¸û’¿bÛ†áîÜáé./¶ .LJäs¡®ü‚(aܲb¾Oî¼}ОWÿ"Ñ÷ ôd³{Ú'ž ±œž›ð{Jíùüæ)íÁ£òïàs1œýÎ=#¦‰N=:àÖ\_§áû†n`áÏK0ýë_°ä×eÐuƒ­Ï-cRý*]'ŽäÓXx²Ü‰Ó¤¸“õmùíMÛ B„¯ó§ }ç6}ÆŒ>ýX4j’[çë'‘¬X¸‹æ,ANn6µ‘q©;ŠË˜æþüahݾ5::ÙÓ‘¨¾œ÷3~^½$êy5€Ç/ºé©iu:žwþ‹6­ùüVM ñ§S/DŠ*KVJHHHH$ ŽäÀ]ÿÛ7íp<Ý|x¾ÍEU©e¤’+Ç+Ê ÞwOþ=à1àX—°×½ˆ•­ '\Ç‘n‚ð¡õbŽ?|Kè…Sëw8œZ®~9µ}6þp>‚$lÿpÖšˆƒtÝ·!ö¸²t~ׇ Í÷SÜçóÌcñäó^7Ú•_¸>ßû‡8/Üùó‘Jßùÿz¯(ã‡ï|ø}ŠÛx€ð÷†h°™„`訸ä–ók÷ÔÁª VÎÿKç,DzßWa×–=.ï¾ë9âÉxœŸ$Oˆ"ƈŠ Ù²ÑkP75¢/ŽÑWÖ·?B°uÃ6̘ø ró²Eb.¥†óâû‰‚*Š]3PТ#Ç Oö”$$ŠÇ¿ùS–Í‹xNzJ*Þºñ~´nRX§c!„àÁ/ßƬÕKÞӮ°9^¹ænd¥g$e½$âÇÞÒb|þûLÜrâYa#<%$$$%4X’w^x?vnÙeMÄ77^ 5·"ÛÅÄk‡*ÂhÀíݦ?¤E=…cñÂÎpA _<ä.o¹U@Ø)éó–æT|H$7^>ª€WÒw•Þ"\T%N{üKnƒg>ñ¤8ãSıq¡þYÙY¸ûÑ[QÐÎtl²‡$!!! šä›¦‰»/ù›Eôù2m‚×™þÐ8F,z¯]DWÍß‘Ô ÎtJ×}òÅ4v½'”=zMy`žÇ›Î‰kÙçáð< ¼^yÅ{€^ÁçCú=ëG¯`8`ë ôÏÏ^\_ éèؽŽÑ?ÙS“¨Sü¼z)þñÅ[QÏËHMÃ×ß‹6ÍêtOLqp.W„ã„30ÀEðÞÿ¨„-Œ÷þ©ª‚EñÃwŸLMè³SW¤š–ŠôÌtdfg"#+ié©HIM‘? $|±ø×exÿ¹a&RRSlÁEÅõ¼ð_& uç[Ÿ)"~ÖBÁºöé, ¾ÄŽÍb­ÖBxxÂûÐM#¦ókŠMýÇë8%’‡½¥Å¸íg} >¬Ú± 㨢 !!!‘ $þùâý|Ü@Ó+„F±‰%%ðÔ›n“b‘RÂ1`*kÎíç‰0aÇiÛD 츚k7óÆÙ:<é%ð'ÀŒ8s`ýÎ äˆö¯ep¦&¨ÿsó`¸\yÖ?Ÿ ðF Ç*"DKØíÂÏ‘7Ðë|†ÏÍÙÚ¯T̘8 ¿ñìݹ¯¾- ‰:ÅÁ¢ƒx÷Ù°vù:dçf9ØG†ÿô±<û³îóCØ笪ª½öB¿!}’== ‰zAANìQ^kvmÅÛ?M®Óñ4Éö/eZ“W¯ë"–nY^ÿ/ÖìÚ*ìOQÞµ7þræeøúOIM ‰Cu_¼qÏnÃ+½‰¶vpTÉärÚYî´]¨ÏÎàˆ7õx{Êã‰!ò´-ç:GiŸž:jêWÄ°]…~! ßEêYw&êå´ÇŸš „ÜÚ?¿=.š?L¸uÆω‡)Îr3vï,pó¨X0{1–Ì]Ž«ï¾ÝûvMö£'!3ÎY‚%¿.CN£l¤g¦ÁñËó©DŽÁQ…ßçÏ!÷ô3UVRŽñœˆÂæÉž¢„D½BQ1c:Ÿ€àѯßÇ›7ÜW'µêSÔ€ïþÔÀa÷³«Á#¨kxæûÏðÃÒy(*u²ˆýq=ú#7S{ ‰†‰Ãò¯Íeÿw!šµlŠ·žz_Ø/äÉÛÿ' Pˆóc™W¡ˆ8øs§MˑƹÃΛ.ŽÑyK@ˆÂ(‚7›zéœpxq4–m"šb?×6¹ß¤\9ùŠ¢X¼ŸïçZ#ŽÂøYŽ¾ânZ€{œh^#Á1nðÐC^}ü´lÛ7ýåäæçÔüÁ‘¨clÛ¸?N˜Ì¬ ä4Êàˆp:¹wbŒø+}¯Ø¡üÖg¾´¸ç\srr³“=M ‰äÀý1‰‚=%ñÌäÏñ÷³¯¨ƒ±H핆€½¥Åxð‹·‘šŽ?z!Föè‡ü,ù;BBB¢áã°$ùpҹǣyë¦ø÷=Ï€˜D ’‚GXP¤;ÿÌ-F8Ž}…ý8§%úœ´by¤]ÞoÇ O‰8Ï´¹4¾î½MtY_4=€ÏÅ@„1²½<‘vwËœñÄòä+üÝQö1z¾ I FðçøUðŽÜòäÓT :GþžñåékU!Ø»s/þqóc:z .ºQ†ðKZ(+)ÇÄ'ƒ²²2™…ûÌ"_èw…Ã>€óÁµ>e¥•¸øæóš–šì©JH$ Š)9œ˜búŠ…Ö¥Nì;¨~Æ(Éÿ!ƒªPìØ‚G/¼Ãh(HHHH4T6Â{~è?¬/þûþ#HËHËÁ)4CÞEB­£Üa>aœ¿N;áI,h àÜúì_*TçüHw üÉçξãÈóªâù±#èðÇݹõBb¼ÐïsW`d\×Ï-6èÓçŽòŘ€;ì¬5;æAhZ@JŠŠ…³ãÏWüó^TW•„D̨®ªÆ—o}ƒ/ßú €””«oÏ—êd–6ÅŸ8é>-wi]F i.½õIð%$|páðã¹Ô:üoòçØ]| ÙC•¨gd¦¥ã¸žý%ÁO!(*+Áƽ;±rûf¬ÛµÛìCHד=´#&1±¯´›÷íƪ›±f×6lÙ·eÕ•ÉšDâ°õäS´éØ /Nx w_üW”—”[;m—1ó.s¢zԿϜڜ–“#Ï/ˆËYÿ* a^i§<ž‹¼+œOœýþ+(ŠË{n“Z¢X)¬¡ ÑH‰°èÉ'IW%'ŠÌ+ÏréiD0t!÷Á™ƒ;¯Ÿ¸«Ϋϋýñœ1p÷…æ.pºÎ}!øôµ/ñÃçÓpÍ=—£u{Y²H¢~¡i&6E»ŠXùDA£ƒ3¸±Ï¢»²„Ë`¦p†6À0 ääᤳOöt%$ øyì{·i+FŽÃ;?ÿöºŠ`5ýú}<{åmP•äø>tÓÀªí›c:·M“¦hG5ØSr{JÆtn81Â’Ê l)Úõúv…Íò¾ýÀ>(/xŽª¨èÓ¶£ï1Ã4±rû¦„ç@y)Ú¶ˆX1–1û!-%³sдQ~žÇåÛ6zKû y^c4ÏkRã50‰‰%›×cÞ†ÕX²e=6ïÛj-ä9O‚æùѧMGÝ¡ŽíÑ2kžV¶¿¬;ÅtnŸ¶ã^Ã5»¶!è37ò²rо°¹°oÙÖ 5ž—¥U•hœ‹Þm:Ä}­IL,Ûº?¯^ŠU;6cÓÞ]êšï¹YiéèÔ¬º·j‡»áèŽÝ‘*Bö$rórðòħñ×k¶ ÛA™" ƒ%ÎhÀõ››åŃó(TLÎ:Åí¡vBmÁö8\˜å°×ÃC|iD¡4VL?jd Þx'¤—áÉ<O{Rˆoÿü>é^·ÁÁéž×¿~LÓ€ [&îv¹ó©™£ÆoE*”—ᙿ½€mšãê»/CA³šÿ1“ˆ†a`Ú„™Ø¶q;²²3‘‘•Á=¶ŠøJaÚ¢ Öi®Hþ{Áú UWÑóè0¬_²§,!qÈãò‘ã0oÃj¬Ú±%ì9Ë·mć³§áò‘'%eŒ)jÊËðÈ„÷ ›FØóÎx,n=éì¸ÛÏÉÈ S'à—?–…='#5 MØãYé阼d.&/±¯»N9g<6¡ëóÞ/S0uÙüˆë÷¿+n {< ª(©,ÇC_½ Í¿¾ñàáó¯‰HòsÒ3ñÌ÷Ÿaᦵ5j?;=}ÛvÂ.=qrÿ¡ÈŠ±‚„ašxàÓ7P¬ {ÎÈýpÿ—Öh\¥UøjÞϘ´ø7••D=Ÿ€`wñì.>€i+âéï?Èî}pñ1' G«öq÷Ÿ“‘‰©ËæcÒâßž“àŸç^]##I@QñðWïb_„¹õhÕçý¬¦‰>{Áê­­·Ÿ|n\$_7 |»èW|0{*öÇh`ª ±bû&¬Ø¾ _Λ…&9ðÕ]'düÉEàÁ|0Ùƒ¨¨ªŠÏƒ}»‹°yÝV‘PrµÞxá+![^qÿÐV¸ý<…gZq"i·ÿÇÂõYh;ýWlŸ/¾-Ú†+´Þs=ï¹Ú3Zðaÿüly² FÀ-%}…fcqÏ•76Àw|ÌáRØÓù¸k¹µ ìN89ÌVI±JÌþá7¬Y¶½ô@z†´PJ$ZHÃäÏ~ÄÏßÏÒ‘šš*¤ý(ö þóC_û~ˆPýBeÕ=”—Ubô©Ç¡{ß.Éž¶„Ä!…÷~™âñ`ß{:4m‰£ÚwÅä%¿G$Ð˶lÀÐ.=Q˜›_ë±h†çüèÙÊQÃÐ<¯±ï5š¶Á’-ë}woÙ_p RþÊý‘–’‚cºõÁKç¡2ô=經ÏÁ ½†m# ªÖµf­^‚’ÊŠð}R1¦÷€Z¯!çü‘PžrÔ0œ5xdÄ6Ú¶@j PcÒíÆñ½ }a‹°Ç3ÒÒpL·>øvѯ5 Y× ÛìÃÜõ«ñÍÂÙ€Þm;DÕuh‘ß90{ÍrßãMsóð¿+n‹»ì¤fèøø×éøÇçobÁ¦5aŸ£h0 Á–¢=˜´ø7¬Û½½Zwˆ«ŠAJ €¡]zá×µ+p ¢Ì÷œkÇœŠÓ ¯Ñøšä4B§f­0u¹¿Q)5À‹Wß…f>Ÿã–ù(ÌÉ »öñbh—^èÙ:6CÈÊí›q÷/`ÚŠ…¨ªá½¡ó»ø˜± ¿DrqXçäûáæ¿]‹ëî½ÒzÃy±ÁòÍ9±:úÞ#Žåx¹["„îóäSLs'Ü%ÄãhÈ.û¡/º° ç gI”Œ3}ò.:Á=Á§íS¢ìðãwÖÏ#ö':üÖÏ3$¢1Å>Àʉ"Ìß#]¨ˆ"KÔ$ Tlß´ÿºõq¼þÄ»VÕü PB‚¢º:„¯ß›„wžþÅEÅÈÎá¨Ðïþ3Á=³öçŸîÄh çí ´¸ ç\}:Útl•ì©KHrðÞ£hݤ·‹,ÊjOx¿V?k‹Hð3S«t‚ŒÔ4Œë7$챓ûÚFŠÀè^‘ ü¯kWD4Ä‹’Ê ¬Ýµ-â9Guˆ­ŒîéG@ S2r3²pBï£kÝNyu^›ñ-îùàe”WWE=ÿ„>‘žá{ìäþC‘g8öÆ=;qÝkOà™“PC({¬˜³v®|åqLZô[\×T§íOâ(8ýècj5®Aº£u“¦¾ÇŽíÞM函ö„¾‘›Q¿¥ùcî|ï9ì<¸¿^û•8´qD„ë»1ö¬ÑèØ£=þuË¿¡…4NùކϫYó|›ª]ƒýç*¾9ùóöEŠu‰@˜¹äwNH‹h…//†¨;}a¿ïõp‰ëXáÁ,­ÞQþç+0±;Å~V-€ ýúúw¢¨¯Qà7ÄÔ¾MÚ§£‡ 0#Ÿ“ÏÆéQê'H °aÕFüíú‡ÐopœÃ9È̌Ϛ-!q`_1¦~9 e%eÈÌÌ@Vv&¯¨Á"_¨A‹~>ÜŸ_úF4.ŠúÄ~¸ “àŠ;.†ªq¶Y ‰˜IEN0 ¿®[‰Ùk‡¬ï8°/Lý ÷žvq-G-?ùÙ9hݤ)vØç9Ö·m§Z¯Qß09ë=ZµCzJlâÑ„êtÓÀËç㼡£k=^ø}ÝJ˜QÖ3/ÆLtjÞ ëvoOÈØbAŸ¶ðÍÂ9¾Ç sóж  só•–Žòê*¬Þ¹%,Y[´y-þñù[xâ’›"Ft¤èÞ²möF-ômßs4mù<ñíÇþѪ¢ w›ŽС+:4mÆÙ¹HKIAE°{Jâ[ðÛÚ•a½î!]Óß}‚Õ;7ãîS.D Æ¿q}ÚøÏ£CÓµÊùgëÔ¶£ïç°ûίKQèÕ¦æ®_Uë1Ä‚?vnÁÃÒPò³r0²G?ônÓÍóš -%¥U•ØR´K6¯Ã‚k"F8I4\‘$:÷舗¾y÷_ù ìÝïÔªçæ1OŸòo‘`Û9òDf× jûtþÜع)))XµøüóƇѡ[\|óyh\ŸÌGR¢`ã›ðÓ¤Ù–R~Z 233œÏ¤Pò’Æ?·ƒ‘Oi±>ÿô{‡0ƒ V‡Ð¡k;Œ8iX²§/!ÑàqïiaÕŽÍѾ[ü;†ué…‘=ú'eŒ-òšø’‹– jßv¾ñ´‹'yòÒ¹ #ùÓW.ŒzN8é‡V |IþyCF¡CÓ–(Èm„¬´t¤¦¤ UMAÈÐQªÆ¶ýû0wý*Ì]¿:ªA‰G¸ôŒO8=lXô²­ðâÔ¯±f×VϱE›×âÃ9?âÊãNŽØo‹|¢Vù±ßë/æþ„¦Nð=––’Šó†ŒÂYƒG¢YÏöéG“˜øuíJ¼óód¬ß½Ã÷¼ïÿŽ’Ê ¨—}þíÚlŸðùáÓ^˜à'}èUzÀÑëP^RŽãÏ…vÛ&{ $$ äeeã/g\Š{?z9âyÿô z¶î€Âܼšu%o:ü¬ÓSR‘¨ýO¶œŒLßýñ„ï.‰^npÞX»kºµ¬ÝwWqe9n½Ñ¼ÑŸbO cŠ6×ë?-bû N=pöà‘X·k;üòí˜ûËI÷_ó@"Û¯]g¼xõxôë÷1sÕbÏñO~›3±ŠA¸pýœïõ÷‹Kðjß9óÒ˜ÕùUEűÝûbx×Þøì÷xcÆw0ˆ—ÄÎ^³ONú$&QÀpó‹'¿?Â~V23£^n —{RXã ìØg mû÷â__¾µŸ_þXŠÍûü+^üí¬ËqBŸQÛÈËÊÆU£Æã”ÃñØ×ïcãÞ Y?‰äCÆ}¸ëÑ[qó×R,æQ¼ê4LœqM®^»›¸‚ó. g)Nø8'¼Ç tñÆ>m€–Ãcµêyá=¡†ŸÂ<ÚtÜîñ‹1ßž¥&ÇÀLŠ3~çjN4ˆé,ââù¼c^È€e&Ј:T.*€i'psâK‚_O¶þ¢’? ù§Ë£( E{Šðƒ¯âÑ;žÄK#Ì#Ñpq`ßA|þƼòèX¿r=2m¥|ÏóÀùüóº®Ï/ ËþóO[AEE.ºùy¹²Yx:õ¸;Bu<'bçÔ´¦å°`·ARo´‘ÐR¯2ÀýØ·ÏÄéø‹hï¬/ \L¿pãUwÍ„ é¹ÆÏBÝâxŠx9ËÛsôYŸl}ÜëÇ÷/Š±»-N3ÎÔ %KÜrñBgži‹Ý³>,5þr¼û¿ñ÷ÁwO®Ë\¥# g/Ákÿ~Þ™ˆÊ² dfYÞw|lH&Ç¢DèYôóã\äÎzqž?… zè!ٹٸòŽK‘™ ‰ØIxÏN8š¶ˆxÎÂMkðùÜŸê}ujXH@Ó{J ïOì;È×;9mÅ©Êó˜¾B ÕÓk€o8¶{Lu¦òѵe›ØN®ÅýL ¤àº1§ú›·au”nI\û)J*+ðð„÷|uN0wŸzA­ +*âÑ ¯ +‚øÊ´obò(Ç“6ÑЛ™…þí#WÑY±m£ïþ±q|Š5€ÛO>·F×Jz$ŸC³VMñʤgÑoHo&š'(¾ƒWÂKáѯ±üœs ÇO]!ýŠèeŸ£ïVÜæBè9ødAÁŸF>pÑ ~ºŒøðàr塈װ5wN§A¶fΈðcÏSa€€C¼øÈ EQaê~ýñwÇË¿Žås—#=-))–_x~ùh¾”&çÉ·Oƒcü³;á¢dx°s AEY%:§^4.ÙK"!qX#=%œuET¯ßë3¾Å†=õûÝïG®EüÑ¢xšwêùMpr¯jYu%æÔ¢”Øž’Xî"1'ôè›BɯïàŒh¢Ñ0¤KOdù”»ÛEI=\¿Ñˆñ«Ó'ú–+ìݦî<åü„­Ë N=pãØ3|i†g'ñúš1b©áú€I¢çÓ×ÕZÅ•å¾ÇZ5Ž® qøC’|Ü÷ä]¸úO—ÆeÞ|Ž;¡ï,qÜõžp¡ÿœxš—ïòÐC<™P'7]û'œ!×øfc&®0aN™ž]Âçó{…íx/º3|'ßÞ Ñç¶Ý.!üyýÎÉLvG Ðe…x¹³~pH»YáŒ.#3ÄP‘3ÞhŸ{¡”voÛ—~ÿ¸éQüðÅ4˜1¢HÚ¨(­À¤À‹½†©_üˆ`e2l¯={îÜÏ/ûü)âsÇ…ã; 0ç<'IAJöù·¶`PÃ%·^€®½;Ç8 ‰Ú K‹Ö¸þøÓ"ž£žð‚º–Ô±&Š—Ö†¸ÀÁŠ2„\kÑ<¯qØÒ|ß/ù½Æc±R Enž×}ÚtDan¾çÜø<ù Ëû›HAÛ‚æžýá^m¦¿f×6ß{–HÁßκzx‚ÁAôËa‡o‰:¡<Òè0\¦3À ƒ9};Ù<¡§áðŽA"ÁRî}<ë®þ4Þ(A#&ÙA‡Z¶ø|ž4/T¨°}®ž@ ¿OÔGÏVU†¦cö”_ñ×kþ…çÿõ*Ö­Ø÷³%‘ z–äÎúg9÷à \jkƒï_ðV:‚~ü:ðÞa¨Ä³*ð„y๧ݞv9é ¼fïùgkÌÝSÚáæϯ?+ýG€””önß‹wÿ÷¸î!¼óÌØ»«(Ù¬„*J+0å‹ixéá×ñÁócû¦HËHEJj 'HIXŠŽóÙpBvœç—{Vë“èé'ÀI%-¨gâó[^ZÏ;#O>&ÙË$!qDBQüåÌËÂ*iS|5ÿgÌ]¿:ÆV_¸îšqåáüBö ¦,w?›÷íƆ=bN6Í3.l”ç{M,ªÿ AoÙ¦ÊÖÕ;a¶OzEZ  Sgsëܼ5Fõìï{lÅöMظGª½û!+-Ý×ø_Ïÿ•ÁêdQ"É$?\qÇÅxèÕ¿±P^¯Ãžs[Á”à!¸ôxß:ø²â%÷Bé-×Õ4DžëÈSNÛÂå9g2(YvææÓ8{…8~&ô'F ¸á^'Vjˆ†Œ0ó¢äâ¹Äu%Íýçí¼kªüú‡[SºÞWoÂóÿx ÿ¸ñ¼ó¿°kënH$Û6nÇçoLÀó¾‚^øÛ7nGzz*R\ªÂDÐÞ°öñÕ"ì=Üÿ=Š}Å8øO†˜Ï5 i:2³3qí½W Eko¦„„Dý¡Y£|üé” £ž÷Ÿ‰Ö>GòëÙß×»üÃÒøUöÝ‚{í ›£ss«¼­_¸>ì­gñ½X ¤tÓÀ&Ÿ2iGµïZƒÖÂãÇåó}÷ìÙùÙ9q¶"U»˜¾rQ-Yð‹ž€exì›ëE@âÐ…$ù1¢sÏŽxí»gÑoh_˜¦-pGB xã©KXáDæ<^<{'zN¯È=çEvÞ;^îI<žrëz—rrφãpDþÀEðéñ®ñqü ;‡×/pÚöŽ,ʵͭ)¯#@ÇÄ^ÇÅ"ˆØ/¬v\ÉÜ(Üý#ÄEÁ‰^oŸ¿qÕ¼ôÈëøû àÕ¿•‹þHÐS(¡êæüø;Þzê}<û÷—ðÃgSQ\TŒŒŒtñóãz¦ˆ)-‚'ßýü:ˆ ´ç\Ì G,oŸKË! (/­ÀqãGàŒËNIö²IHHØÓ{Nê¹tÔŠ2<ñíÇÉjR±§ØíÉw”î3RÓ0¦÷Ï5;aé–õqõ3}¥Hòù²^Mrr}‰ó¡êɯm¸þü øæ_ìÑ/¡ãœ¹j±ïþÑ=ª³µ¡è×®³oÕøuÝ¡——¨àØî}Û½fþþÙ›¨ÖBɦD’ I~PU÷þçvÜ÷ß;‘–™æ„ù2Âo'öñéï4O\píÑäë¼ógRÏ6SÖW8g£‹œ»ÿò„Uð6ºsë…Äx¡NUÜy\Y=W$[lІÀÄïø:ötØZ+=§¤Mq¯???×ú³þ¹4g´vÝrõž¨ñyþŽ ½· óü«ª‚›và³×¾Ä×?„ÿÞ÷,&þ#ÊKŽlOP"`š&–Í[_üÏþã%¼ñÄ;X¹`ô†¬ìL¸Ãf<ÏŸâe€§ñÕeP8 ú!Pxñ=Âöûëj(ÐB2³3qÍ=—£}—¶É^B î>ZD©ýýëÚ˜¸pN²‡š4¸‰tóF…÷ãûó½nrÞüU;6c§K9þøÞG³×)jsr=×Õw½XQO¾nxó§ï<ûíÞ7j Èxp ¼[ŠöøŽ}`Çn5h1~Œìîo´Ø´wʪ*ëe £{ @ë&Mß³v®}í ¬Ø¶)ÙC•H$ɯú íƒ×¿{ƒF ’Ë«dS_2% Ä^>ù ÇGxe|Øm»r€ pÙ~åôœ’sN迆ìX¨QÂñÂG)_(o§ˆC÷X!À®åóú¹ËÙXøùRÎ-xâùHiÂ(8çH%B£\ƒ À<öDðÚŠQ¼aÃ5îž9Ð!…ÔKk++ˆQDœt @Ei9æΘ'þü,þyÓ£xñ¡×ðóä_Q]^!UÂBuu¿Ï˜÷ŸÿÏýý%¼ô¯WñÛ¿£¼¤™™HI ÀË8 ¼1Â9»·ÜÆEŸ×Ó{Ë>€öç‡>{Ü÷ýü”••cäøcpÖ§AUå×®„Ä¡ˆìô üõ¬Ë¢’²§NÀVBt$`w„œ|èÓ¶#ÚøŽŸV-Ae(¶¿uÓWˆáÙ=ZµCë&¢è[ÓZ–Ñ« ö•Ç<µñä¿:}"ÖïK8f¤¦áÆΨa‹þGÛ6CVzFBû ‡H˜kwo«—1$Å•å(©¬ˆùü”@·tvÄsv؇ÿ{çxü›}K#J¾H©}G&TUÅíÿº kWnÀÓ÷?ÊòJ!t[a¡âtH2@z=ܼ=@H¡W¨ß#­”Ð 5º9Bϗ˃¸› ¶bÐÛ?¿Ï!¶Ö^'ôÙÛXnP4:¸çÏ‹æ±jîv¹ó)iã=ùÞ0ñþ‰ý»ÆÏ‘rÑ9íš“M„Ÿ…Ni>ï ªªõoÑîý˜ùíO˜þÍL4k‚®}:cÀ1ýѲ푛§mš&֯܀5Ë×cזݨ(«@JZ Ríºõié©‚x‚÷óÃß)îyà>TDqßø|~Ù[çzΆÃ?¿ EUhæ»åÁ`…Í pÑMçIr/!ÑЯ]g\2b,>œócØs‚º†G¾~/]}R‰-)v¨Ã®ßÂEòàäþCñÆÌI¾j-„ŸV.Æ)†Elß$&fºr°©àÂÜ<¬Ù%¿=µ ×?ÿÙúxBºŽj-„·n¸š·Š«ÝšxòuÃÀ S¿Â× f{ŽÝ5þ|´-hV«¹º±u¿¿Ñʯt_]¡W›aí8P„»×ÛXê ׿þ_ïoM×Q¥…ðßKnÆàÎ=bnsX×Þ¸zÔx¼=krÄó¦,›‡™«ãœÁ#qñ1c‘—•ìå¨cH’_Ktëݯ|û?¼ñÄ»˜õýÛ3HÅÜOàÃõíý @ˆ7¼×ñ\;D…††{ËÁ¹*¼ž{:…vÊ{áí„õê&&L¦¨¢ç“8óaáOçg-žŒ‰ºüø£çPeýsÆžŠ‹ÞZшà=Ú.á»·‡/Þ-¡’ ÍSŽÏŸÒÆiJ³~Ìæ (PV‹%Š±àç…XðË"蚎´ô4¶(@§îÐã¨nh×ùð ñ®(­Àšåë±eýVìÙ¾•åUHIQ-å{{mÓ¹Ô–áj‡O¹÷$ )ÄõùóÓ¤à=÷΃îzf¼é´ì‚ÕAœqé)(l™ø²Cu‡«GÇ‚xH$µ»¶á­Ÿ¾Ç 'œžìáÖʪ+=žìf>$ÿ¤~ƒñæÌï<ìï—ü•ä/Ú´*ÊØ{ Æôòæùû‰ïÕVx¯´ŽBÂãõä/ظ/Lý ›}Äönw.ÆùT1¨-¶Ø绿0·Q¬‰ò³rŸ•ã+nY[Ρ‚òêªÚ7âƒ+;„¼óóÏ é>ùm¾Y8ç ‡ìzŠÔ¨H’Ÿ \÷ç+qÚ%ãñäýÏa^A.>œ°$s‹&sDS ã|¸<õBr¢^€H@YÉ.ˆ¹øŒ|òyät,\N:늸¼ó4Ä™ËÙ§Q~ü¼fžâ&?\»LÔ#м߯ï“£Ï0ø?¨T\ó ³þ˜ÎX8¶h< }òþz×úY‹Æ­?<ëG¤¤¦€‚¢]E(ÚU„y³ÀÐ @Vn6š4k‚Vm›£}·öèÜ£Ò3_/7Q0tÛ6íĶ Û°gû^í)BeyLb"--•y¸ÈÈLž¾t{¦ˆht»ÿ ·Üb~½sWÓ3ºñ×[7–=‡ŽÉ ¬Ö! Šx¢(¨¬¨BßÁ½0ìøÄÿ“¨{¤øÛY—ãú×ÿ‹ ®…=ïã_§cH—ž8ª}—d¹^°§X$Ñ 4õ!ÛÍåcP§î˜¿Q]±}¶íßÑ íÜС+ |Bó›ú”Ñ+©ª@µBF„úáÉ@,žü­E{ð뺕˜ºl>6îõ–ŒËÉÈÄ}§_’p±=ŠpŽÌ´úýmÑ<¯‰/ɯ Ê´Æh¸jÔx´-h†g¾ÿåÁÈÆ„ªPïý2_/ø—‡³DŠzdE% $?hѦžüàÌš<ï=û ôÆré)æEß—¸Ë»ÈÅž»kv ¤†‘C‘ìø^—¸q‹‡DS&Å”ä)yñ„4;çÀáDb?BÿŠ@ÄÄHÞ¸!¦ðmÒ>½žrŽ¨scó¤"p†BTNc€. ñvZüæϧ.Pリ¨\†5ºÎlΪ©ÁfÉÖÆ)ϦÀÊëWÁª vmÙ…Ý[waÑœ% 0 Š¢ -= YÙ™hÔ¸æ#¿ Mš6Fa‹¶(Djjb?ò†n`÷Ž½Ø½mŠv¡ø@ J‹ËPUQ…`U„¤¦¦@ X9óª¢ ’€¢¤²5ç#UèóBïûð°ÏâsÿªîÖ°…pŸŸë¨ì£éóüò÷Ÿ»^× ¤f¤áò;.AFÆ¡õ#SBB">´+lŽ[O:OÿYØsýú}¼uã}ÈÍÈŠ£õ† ·7µ ·QØt…“ûñ|˜¼dnØ臮ãçÕK…}'ô9Ú÷ÜBâXéí(HWר VãÊWǾÒbßãUÅi†ãŠ‘ã|‰BU­w¥¦ºF¸R}ÕºTˆ'ôˆ¾m;á¹)_böšåQÏ/­ªÄ‹S'àÛE¿âžS/D¿v“=‰B’ü:À¨ñÇbä¸cðÊcoaîŒù^o¾?¯äT,ËO°u–‘qN™Œ”o¿@09;àð|Ž|qýs12ïh ¸rà>ø߉Nàç/D4Øc=û<ùì.M@´‹ˆ¡ñÂ8AfT`QØn•~jqȹÐ?[?¾m*ÊçoÁwRŸõw¥;3€Ý—~CJJ 0 e¨(«À®­»¸ptkMLÓ„i: ¿©vUΨÁ—c$Ž¢œu®ª"%`F A@jZŠãQ·ÿ/êGˆÆ º&Nê?!ÄÄ“gïD‡@ ì,ƒ_|ÎÀåNégb¿Îó«ˆç¨ª¬Â¸óND»Îm !!qxàŒ#ðÛº•ømÝÊ°çì+-ÆÓß}†ž{U²‡[çð(ëû„êSÛ£rÒ3=Å)ËæáÚ1§"à£Q2wýJT«Ùû5€ãzö÷mß/\ö”¬1É¿hø H 8ã"ÄÊÅßWZŒe[7iñ R¸~VzN0Ü7Ÿúü¡£qÙ±'ÕKît82Ô´8[ª2RS}÷gbÑ5ÅYƒŽENF&{O úÿ·wæáYU×Ï—9!$@˜ †0 Q( R§ªµhÕÞj[k[oµÕÛÚZ{[{í­­œ®ª8+2a†„9 „$2}Ó¹ä;{¯½Ï>™C ®ßÓ§$ç;{<'&k­w­ã§N"ÿP!ŽU¶.å¨O¡yäšEXW°þè-ì)>Üh›ƒ¥%¸÷ŸOâÚ)áö™ó?ŸÌ¹ùí„ÏçÃÝ?]„…7/À<‰â#ÇDÁ5™_\ŸOKÙ)–4ÍW·©´Ø­8DôW3.mñ`tÀ wIëE‡PœÊü‰}eŒn@V¦‡­Éé…Å®£Dy¥Ôî‘s¿Œ¾Êd>¼% W%Z¯(”p:ÐiJÕ½#&¯_›<’n¨ÓžˆÆIÚhïŒg0hi!wÛ&d:'ÇABžctt´tà ЯÕ„Àò‰~”z TBox}Eô›(7,åùëF¿X²ÚŸ%^ý¾Œ¹›VN®Û|îԙ¢:ÔöT@ÓIäûWUU‹qSGã¼ Ÿ¯Í0̹Éç ·ü﯌b‡O¶oÄä!¹¸lô¤Žžn»R\¡ùÞÇ ÆEÇ`fîx¼¹A=nðÄéJ¬Ý·“‡äºÚèUõÏ<ÜS!‘éÉoE^þ-Óg#ÎØ „‚øã{¯·¨ßÆäú×M™‰w6­rÍ}]Á.,š9¯Åëi^²üò:6ZJ(lv6té$J™k§ÌDVZ7µ‡ñìò÷Úl¬‰ÙÃ0aàP,Û¹ÿX¶…ÇŠ¼ß†—¾øûJŽâá«n9c§*0í»jÚ™ž}ºã×Ïý÷<|'»$Jù8‰~ªçr™²RNÊÌ-P£LŽ¨‘“áUiCêáaMÚ,¢ËZAgå}Ôäï¦íe_´„päÈ&64çY¿¬’ï¼ÿòUµ” 48²œ}¢ï?õB÷Çå"QÎm„2õüµ~¤vKÃí?º™ |†éĤ'%ãþ×7zßïß]Œ"íl÷Άn„vOIoðþ9cÏ7^wój×µêºZ¬Ü¯\»d¤÷[3RÒŒ×uGD[oÏ^ˆ¬ônÍnÛXὸ˜XÜuÉ®ë…Ç‹ðçßh—õè$'˜èö>–P§6`Nð’ñw&¢|>Ü2}†÷îßf}Z–…éÃÇâowÜŸ-¼ýšp*ÃÚ‚xpñ3†B½%L+a#ÿ 1ñ‚qxjɸú¶+á‹Šö•C«UçëÿJ¤©åqkÒ±Î@4ýJU”lç~'Ê ÑŸÈ9¦…ΨéSD¥IdVŒ)¬5˜ÍEú§Æ¥ ÝZ¤ry²ĸ†ÛØŒ•¶Öý’n¯£8yâd~ŽáI#ÎÎÞÛN®6‰ø‹¨sý=´f€­=¥î‚ò¡%Ç Q~u7åþƒŽOdðÊú-uÕç'sò•¯åòíÈÿäþ¸ªÎGÞ'ç TëCÀ5?µš½þ̤®tB¶Îr7Š¤‚ØâýךчïV¶hwÛ‚‘â‡Wß± nœËÇâ1Ì—€)Crqù„¼ï©ö×á‘%Ï"wôtÛ’fÈõ`xïþè—á>‚mÅ®|×YàËwn?ß'ÄÄbJN®gßIqñF w[ȽˆöEµ¨]S ïÍ1ÖXÀñµµË±zïöv[“C/èòþãEg4/ßë÷ìîÍ;¶ð\†ÊùÛ Ë²pQî8üýÎð9W#%¡aeÄúÂÝxúã·:z+˜V¡ža¾òµËð—wþ€ .›¹¢F2eu|@›j¤VÛªªŠˆÙÝU$Ìη"pm»ÉŠóÁ` +Ìmš£oƒúÔ ç–ûÉ•—‘[-‚N<Jv öL¯Šî^?I•Í…)«ÖCp@PŒEwŒqe­Tîù;ÒÒÖo‘lÃó×U5-ק–úµ¾ýŽrƒì…zf¼Žw¢ùrZêþKµ‡>éQßòfY2B¿æ˜@¢RŸÒ†ëùËw†8 „cLúºœç¦ìŸe!#aε—áë÷^Ôô3w¬Ã0Ï]³®hôlòm‡÷ãùÏßï詶ÍÉÉw˜3Æ͆Cø0ríÃ|µª~ÞÐQVÉ7EóÛ+’&¸ç²¯Âgp6ÿêÍQQuº=6¯w»&àÇÒ’3²þP8쩆ÔãËcä·'Q>®˜8 ÏÞõäåŒlðÞWWf<Æ‘9w`#¿ðù|¸í7á¯ý#ÆC˜xþ]²."—¹éTÞNäÔÂÐôE ¦)9õ"êí("E>?5`ÉøÔ(³¨SÂÖ!­¬ïôë=G.’ÊèF•6}²frÝÙµ•öÄ@Î¥r[Ç£&³~nÓ „tŒ¯Ÿ& Hù•ÇJ$åôÙG‡­ìÈ\,-è/#ì®h¹£fpÞ¢ÒpÒ+„QîÐ÷GÎO—þ+ûäÒìCQkGŠØ‹Œãñü¢§hkuT´¤]—¢†!',¸Ú«¯l¨©©Ã…s§á¶Ü„¬¾î¨Ã0Ÿø˜XüôÊ›-Hõì²÷°ýÈþVe2ô:Z!Pð»¢ïM1ò/5Ѹžw6­_WTƆÂÝÊç—ŒœÐhߦ ûíÉoo²{ôƒb¤¼ê{ëÅv;·Ï@ÏÏÖî:#ë?PZl<²r@fOcm³ú…YfÞiI]ð起ㆼYž÷„ì0ÞÚ°²£§Ê´6ò;¤ä$üà±{ðûÅaèè!¤8œzŸÌV#Ÿ"ªK.¨S$æZ.²í´«ÿÞ¢’›ªÏ‰ádiF½ÕÀ·„=Ef u^Ĩ£·Q§„“N­I#¢uË’k°h´Ø’yïZlÛù׶Õk&É·2%ÜMêgçl:*j Ò›ò³£:µèœvTm!ÚGú·m ¶º§OÀ7¸ÙüÿbæËÁЬ¾¸eúÜï Ùa<òú³¨!U⛋Éx ƒgü83J‰!BÞ½ F~·äTœ7h¸ëú¾’£ØST_õû“í²¥#%!³‡5Ú·©ø^iåÉwˆè4–“O¹uÆ\£”ú‹=Û°dÝòv›cÏ´®ž5–ïÜÒþ›¸=y9£<Û˜R!LŽÆÌí3çáò Ó?q'Îg\¹ý–kÿäû#¿–)ë†mÕU5}þ(ÜùÓE9ÁýÇ)Ã0_^®Ï»£úf7xÏÑòxò½×Z<†)òmÃFeMu«çßR7A±V€-!6γò½Îì1æ|N4_—îÏ>ÑQQök:F/d‡Qzª¢ÕûÔQ¤$$áÖfGÒS¼í(Ÿ¾pØhãõMöž‘4ˆe΄ †yù¦Ÿ]qÒRšãœ9—ùÖ¥WzžVq¼²§kkšÙ#s¶ÀFþYDz·T<ðÄ÷ðø bЈl)ã—¡\W¾²cÄÉ`°/ ¸‹…uM¨J4] Ç›z“i¡$Ñlª=0ÏJË‘w¤äŽ^ÚÒGs5&±tí^[kéȽ•È4©E -•Ë^{*êŠùëÆ¥ž6õ µ‡Œ(ëÕÞ-úüé;  ´Z=Ý?9šÇþ5„±€6&/€òü,õ¹JwÌrÙpØ„|8†¹;þ ºtúTi`jﬡ¶ÎINÀÝÞŽñycÀ0 £ã³|øÉ_GR#ÇK}ºcS‹Çˆ6Ÿ®çÄ·„–F¹õ¢{=8>O'oèH£CàÃüõ8XZ‚m‡÷+×/9¾Iýf¤˜ “3]¾­Y0!ã q]÷øÅëÏ"@ ¶%7"ñêšÏÚu͇OÖƒû\×÷ìa½ú{¶‹vŸÞV‰³MÒ^ÄFGcÞø©žŸWûëšÑs6ÁFþYH·î]ñãß}¿ø(FŒ*£œ´Z;‰ÌªØ®H/ æ/»%çDúOåó–}U"î yú2·ŸFþ-HO¨~tŸeŒTDú°mÐè³3­# Œ`YiM™—ÌÇVûqrKÁeÔÚ&sÔU ¢o:§H{µ¦‘l]:œáAD"ù"R-ö_ÆüŠÅ¸T6y¶ºSÇÐ'{ëDÃI¤œú¤Á/ ošCOóö]J±WÎÄÛ ö’VƧÏ_MI‘júNŠn,¹ñþØ€¿.€©³¦à®Ÿ,¨ó¼«93 ÃõÒæ{g_Õný{9 JŽ¶ºïS5-‹rêF~S¤ú1QÑÆûSµÕø¯7žW®e&§bt¿AMê×+úx®ùŽ#É$Ûß[rùøívwxïþÖ«Ÿñ³7ׯÀñÊŠv[ó +?4^¿æü‹lgªD_|² U­H—q¨l#EÀ¹€éd‡„F `2g/läŸÅtÍHÇ÷ùmüiɘrñyjn: vpçz䛜JŽºž[/ÂÉjŸ ?U P‡ƒ®$КkG*³ÓrеÙ;Êm1v"»ôD‹¶w«”ñIš‚œm$¬E¬Õù«yþT .T VCíAÆpŠÖC_l°k‹”ñÕ`;1Ø•H>…TFÐü÷Ès½?î4øÈ^“H½|ÿ4ƒßTTVÏ_5üÕÄ#ªâKÉuˆ{éâ”WEŽ … Û˜>ïBÜù“Û0ll†9·1ÉkÛ+ýÒÑ“03·içæ’éqüÆ{ZÝ÷žâÃ-j§Ëõ{xäo{1{¬Y²¿óèAåû™¹ã=•s:&¹>àvHœ‹d$§âþù×?ûתO°® }Šá]?õãõº`O}°¤]ÆÜS|ïm^ãºÞ;=£ÑŸ1¯w`ó½­ž×Þ’#í²Þ³¯Ô›äøD$'4--‡9û`#ÿ .!‹~pþòΓ˜{íeˆŽ‹ùãÂ`¤t›ü±Cç£2nRœ" *ÒÔãÝhÞ¼R]MŒïÎë'ÍÅ\ĽÙJ$ž†lma‚±­tJDR @£ßdýªÊ:6´õ;Ñ_gË,9g'¯\Ú»jý‘[o\¿X%TwŒ­ýEJÔJ°ßV®)6­ùVk8È4 E ÙÄ"/“øÉIôË4ýo2çþ Ïý'9²÷Êi dÍÊQƒ¶|¦²X¥ªåïI©¨«õ#61_½írÜ~ÿÍß¹þ`ë¤Ú^EÔ;˽¸B=Ú¬9‘| ¾háÀîYÞwqªê;dtÒH¾CÞÐQ¸b¢¹(Ú/ßx¾å¹ç <ê ‡ÁHJûŸl߈·®C[R à±7_4þœÞ3û«Öfè›aþYùxÛ†VÍëTm56îo½S­½…öá)¥§O¯ê—ÝÌž˜³ 6òÏ1Þ<Zò;|ýÞëž‘* Zý805/Û9u¾Æ•¨FOœJ„ª1îüQ ‘ÅhT‡WóÈ¥D^=âωˆÛÄ àê—| ØÓN¤Ä Õ#»PçkS#VÉWé8û£N{ØŠ­,ëóéyíd_„c¤3h•ã?ƒ%Æ—Ï:MtÙ®_©~/úµÉ3ròòyE!"P@®ñ©Cƒ(¤/BK'ûIóê©£FõE(Ï_)d(Ÿ”0ö- ¦º™½»cÑoÆߺ]3›žOÊ0ÌÙ?4FòÛ+¨rýøò5›ËˆÞŒ×«ýu®"uÍaËÁ}X±;ßøYcÁó£ÚùåM9>OgŽG>‡¾Ýº#'«o“ûëÚ%ÅXx­±H~S•ÍeOÑaqbÅg™ÿÔnÊ<îšufº#'NWâÑ%Ï"l{ç7ZŸÉƒï̹Ñ>³qýë·_ÂŽ#Úd¿lÛÆoß~É1Ÿ>| Î<¢Ñ>¼~V>ßµ'Nl´½ÿô]ÔüÆÏ|Møyo¯wìpÙql>°>Ë£¯?‡ê6HK€O·o2^¿hĸvYsf`#ÿåÂÙyøÍsàO?ˆ¡£‡ !#–æÓPŒ.Zñf›×ßfŽÊ­#ÝRR?gä‚;oÚ¯E­a@™Ÿ8!À‰Ü‹à¹“¯gÒ“ô{ˆWXê éj±7Þ&' (’pý¤4@ÕÖ2Š Ȫþb,:‹Œo“' òÌe^¼ ó/hýŠM¾pZÉTyšNàÖªÄV"ïD)AÚØäký/DUá_žL ;~ öŒ¼AÊþÁr Rÿ&‡Ã6üµ 7 ß~øN,¸a.bã9·Œa:#•¹æmQ‘¾!Æ ‚k&ÏhÓ>Ó’ºxFSÿúñÛ-úþ¼ê]ò\‹æSí¯CyÕ)åZKŒüY£&"Êòþ³óâf¦?Dù|HOJv]ïˆHþ;›Vᾟ2F–[cðÅEÇà?~ÃXŒq;øó‡oz¶õµ±ù îÙ·]d®ðïpß O!ÿPa«ö+áñ¿„÷ Ê€>]3ñƒy_kR?y9#×k~üùã·Z4·•»óñÚÚe­Z_[;þ€zηþö;ôîš Ë²p²º ·ÿå7®”—沯ä>غÖu=39Ó‡móu0g6òÏq²úöÀ}¿ºzó ̾úÄ'Ä#¶F”°s©¤›Zƒ4×Râ¯ä°ÓÊtĘÖiúžQtK©£þH{ú¯T W©¬/KXJ4Ç¢.ÅèwškÊ''N U ½Bàj¯+#d*‚í¤À{|÷üéúe=ßæÇ+=*F$e@ËW·Õ t™jà|£8”éšCËè{æV ¸k9ˆ‰P¹¾þ,œÝ²€ºÚ:$$%`Á׿‚oýìäÍš †a:7EšœÜáLäg/š9ƒ{ônÓ>ç{T».«:…?Ó,Ù~ÉÉ2|ïùÿiÐø †¼¥¿û ‘Ö®]Rš½¦ô¤dLñ0Ê€æIõL9ÙùÁ6¬š¾åà>ÜûÏ?à×oýrû D¼¡HYÈCVÝОSvÏÂݳ®0~öòªOð¯UŸ4kMQ·\7åbÏ|øªºZ|÷¹'ñúÚåÆô»Æ8v²÷=ÿþ½q•ë³.q xôÚÛõLd¦¤a²GÄÿý-kñÊêO›5·ÏwmÅC¯ü½Á{š"“oKÑî¢Cx१ñàâg•ÞM¤© ëÕGÊKq÷ßžÀïß}¥Eé‡OÃO^þ+†wñö™ó§0çüô: ÑÑÑXxó,¼yvlÞ…Wþú¨²1¥¸ž X>iQIº‘h“sË‚e0B…¤Þ‘tÛÚ8 †˜Œü:F%-ç—–èêòÕsaÔ;Ö¹e C·Ô ! ·‘‚m>RcÀ" p*á;-5|Më§9ùNÄÙgù Ž÷#F2-xX¿ÿä\zg•boä© –¶OˆãÁWòß-u_"ý®ý§u¨\¾¾zÝR÷E›lÔ»mù&8n%|B•`Y>e<›>@êT ¹ âùiϨ7îs'ŽÀô¹ÓÕ„3–†é<èÇ°9´•´¸!b¢¢ñà•7á›ÏüÖSâÛ\.5/¬üKK\Ÿ­/Üï=÷GÜ7ï: ÈìéÙG0ÂÒMkðôÇo6ªh8]ç}övÃÞzêjŒË'äáó]îº9Y}=k4DzR×µÚ€Ç++<ë%œ®5ïÅ}™)iÈLNCFr*2S"ÿ&§!51 ÑQQ8][ƒÃeDZíð~|¾k çãU®ñ8‚¬9ŠŒ+&Nú‚ø|×V×gO}°1QѸrÒÊõ*gZU[‹´Ä.h˲pÿ‚ëq²º ë ÝEþ¡~¿ô|°un1³‡5º†òªSx}írükÕ'ÆŸ“®]Rðøõw¡F&ï Ü2c.VïÝaL×ùãû¯£äd9n™>‰ {YQuûô¼¹aE£ã5åÜx¯{î{áO虚Ž ñŽ¥Š¯Ó“º 6:Uuµ8Z^ŠG`Åî|ì.:$ÚçåŒ_;'!„m¯¯[Žw7¯Æ¼qSð•qS­ ‡ðö†/ð×ß6þìO>—ŽžÔ¬çÀœ}°‘ß >f(|ò‡ƒxçå°ìÏqêäi³QçØr–Z%_-Ü&ïXÒ˜·±k¤oçs§ Çp¥j :V1=»ÝÉ˦ÉôÌuZ#Ω- ]2 NÅ׫ô;iœ+ãÓ{ÄþѾ¢|9q¨¶ ûO¤ðZf͇—Ø°mƒóDn:½Sq¢ç„m)Š÷1wêxÊÉøÂA!ž?mmiã«Î §L°\F? ŽQ[ëGzffΛŽ>ÙmIcæÜá£üõÆëù‡ Qzê¤g‘¶¶¢fO|ÿ+׶X¯åóáǗ߈oýýwÆóºó⶧Ãyƒ†cjÎHô隉ÔÄ$„Ba­(ÅÖƒølÇ&×ò’GöˆüÃn©uQ¹·âa}áî6Û§Iƒ†!»{/S¼¤Q| ¾p›‰üC…¸(לOìuŽúÒÅho,Lõ0ò½Æ+n¦Êä‡ó¿†G¢Ôkþû¥¯À†…“.l¸'лkF£ãÅEÇà—×ÝŸ¿ö£s¶Ùû^øz§g`jÎH ëÕYéÝ0ˆ²ªJ+ÂÆý»±¾p·çÙóÙÝ{áÑk!+½[³÷~hV_\Ÿw1^Xa>†oñêOñþÖµ¸`ØL˜ƒŒä4t‰O@U] ö/ÆÚ‚X±k«+šíù³â¡R÷ؼ÷GÊŽãHÙñf¯Ñ!o¨|Çôãk~¼²æ3¼²æ3ôËè‰s0¤g_ôîš.ñ …Ã(®8- ðѶ (;]icX¯~øÑ‚ZËåt€Ür%C¼/–…@]1ñq˜tÁxL¸ÀüGÃ0_–íØìy4œ ÿøì]Ü7ïºvŸÇ¬QQx¬/zœùÝ\œ?¶½¡p_ìÙ†/ölkR9Y}ñ£×ãƧu}¶óèC!W5óc•ÆHåHMLjѺî˜9?zéiñ½«Å¾{L+vo5ùµ?ö·ýÑh#úô7Ö¼Õ$Û dC¤$$á?~ß}îFcùK_ÅñÊ Ü1s>BvX‰SòbÂÀ¡M36:¿¸ú6<»ü=üsÙRÏÓ*Ž”—bq3¥ñí‹ÂÓfáú¼Y­’†ß:ã+8xâ˜çé'««ðö†•x{ÃÊ&õwã´YèÖ%Õhäç*À‚ yžmÃvب~i-YiÝݽ—ø¾{j:º&%£L«—KKŒ* ÆÛ0¹fbãÚ|þÌ™‡sò¿$tíÞß|àüÏk¿Åüì›È6À/. äE¢Ë4â "õVìrz.»š`‹6Ôˆ£ÎKZ®Ð »QßQ89ìNþ½EÆGÊÅ‘–Ë5ª…÷Üs£Rq*Ï×rÈIѹz¼êlP°D¾¹1X‘TEwOžšºÿÂYAF‡V4O¬S˜GT¢ ¡³ýÊóÓë Ðô ùŽ‡m/kŠ–Êz–Ó•æœ ë§… EEgÜpþ@‡eã›?^„;|ø Ãà`i ~ûï—¼çí_´ùÑ_^Ü>sæŒ=¿õE˜5j"~~Õ­Hˆi]ÑÐiCGá¿oú¶§dÙ«rÿ?—½k4(?ݱ©Ås™<$³FMßé?¨EGî)>Œã•ÆÏ>Ù¶ÑhÌ/ݼºMs 2jJmÀ=T&GËO`C3U£û ƒWÞ„”´ÿ[ùyýY,Ý´ÕiK7¯iVθeYøÆ…³ñÔ­ßkóÚÁpÉ ‰­ÎýŽòùð³…7cÁø¼Võ…o_¶‹.šçyÏg;6£¢ê´ççŸïÜŠŠjïÏ[Š)d¨Ío)>ËÂÓfáñïnr=æì'ꡇz¨£'ÁœYº÷ÊDÞ¥“1ïk—Ý›Y‡ WIDAT¡ßà>8Q\†Š²“JÄØ1–…áåó©s@5ö(9‰ŠÓ"jº³€Ë8U$ü†ÏE5¡w¤@ -;p¿Ä àª2/R li„“Š|–ÏRóã#ÃPC[¶·¤³Á‰Ø‹½Õ r’ë®å˜»>Wœ¶¡/êÐÐç©î­yÿ»šŒo©ÎÕó"ž)}®ŽF_«|OhÊ=¡A>ãPÈF0D¿!ý°àëó0}î4 ‘¨hηgØt`/xéé&ýA½bW>’ââ1¢Ï€v“eY˜š“‹S5ÕØa¨z=wìäfW¦ïŸÙ—Œœˆ’ÊòfGçz§gàÞ9WaÑEó^ö(Ô¶iÿ^Œé?ÝSêç·lÇf<íQ¡|û‘ýÈîž…þÞ5â¼Añ¾`7JOÄ Ófah3ŽÎê%Ó½òÏgoÃÆê}Û‘—3É õõ6Ø‹ÇÞ|±]Œü{f_åÊs¯®«Å½ñ¼gD6îß‹Iƒ†!-©KcCdfaZÎ(?uGËJ]¹è…Ç‹°rO¾gûÓµ58v²ç îyTž‰ŒäTÌ?ý3zàhù O¹wsY³o’âÛÊŸMŸÏ‡)9¹Ö«vÂɚ梛ã™î…ÇŠš”¿Ódù¼N¿J‹q²ºÚ³Š¿ý3z6)7ß‹=E‡±|׬+؉½ÅGà7¨¢,úgöD]0Ðh.ú-ÓçàÎnñ|(a;ŒÕ{wà£üõØ°Ñ!aÁ ½0iÐ0\:j’«X]QÅ +ò#39 C²ú(×VïÝÑìçÐTrÊ)+ÅÚ‚Øz¨;@qEB¶¹þAlT4†öê‡óÀŹã[T97`#Ÿ1²w{!>xýcìÎß‹Úš:©¦J{jtê†>±# †¿m>‹ô# }ÅY@¤û–ˆ§ƒ–V@U®jübJ´½ª’wGÅ Úúh„š:ÜέŸ¢zЖ‡±íÌÛô÷ì+Û/ ’ý·,Ÿëù‰ùÙîg&Üíµý‡¦|P‘ùù, þ:?¢cc0hx6f̆¤”–åy2 ÃœmUœÀÓ½…O¶oÐz#ŸRí¯CQy)*kªaÛ6ã⑕֭ŹòAØsô°¶Ã(©(GEõiTûk‹”„$tOMG\¤bü.þ›±¾eᤠð—-lów¢ìt%JN–£Ú_‹h_R’Ð+½ÛsøiBá0ŽU–£²¦µþ:B!ÄÇÄ¢k—dôHíª8þ˜Î ùL£TªÂK>ņ›QZ\ _”O«Og©_;PƒÎà ·jQmÍ&¯0ˆåÑk †»Mï'Ñv%ÔßÐøn£^uDÖ᳨ZTÎŽÆÚ‘_^6½I¬M‘¸;Ÿ)»ÁýsÏßéIÊÜ)Òy#wOŸ¦èí©óÇ´¦yûëHë–†±“Gaü´±ðñ/†a:1ûJŽàÕ5Ë°`Bž«*6Ã|†Bøí;/ãÝM«¼oÊ\<¸ðHäâo Ó*ØÈgšÍ†•›±âýUØ¿çjª«ëÏ$WÏÉ#ÑlÀ]@NÞãü«5—ÒoC¿®ö rzçvKLF®õˆ{}¢x?q˜ â™dì¦ù‹‚z¢Š¾©‰ÊƒÌÛ)N§« ¹¾¶¶*áWsüUI½+õÁ‰Ú›Rô}ÕRdŠ¹ ž3V0„/ʇ¾Ù}7k2zõoø W†a†a:/¬øùøíïØ= õôËèÑÑÓe˜s6ò™V ±ú“õX»lî;­¿>ÒA·î¶^ùé–Î×Óh͸TÛ#ò™®Ç‡"µGc‘hxË×i~º3ž¹=(¼çnOj·º|ß=Ouò9hÍ=&ŠÆ_+b¨©DüÉþC!Àzô鱓G!wÂpŽÖ3 Ã0 ƒ»¶â‘%Ï5Xó!!&ß™{ .=©£§Ë0ç$lä3mŠ¿Î•­ÁÆ•[Pt°ÕU5ðE …¸ªÅ\‘~—±îeÔ7’[o«Ù“ÛOsßÝã©a¯DÑ•Úúg‘x½½²q7MAÐ"ùã+Žýte¨a|µÐa) ÂÑâ³H–„jôƒ!À²Ñ£FNŽqycÓº#q†a†éœ8^ŒŸ.~‡Nkð¾ó Çwç\Í⦙°‘Ï´;{·`íòØ»mN/ƒ²ë FEŒ^Íà–«qq‘¤(Eåhs+"É'Gí ¨LÝ1œI¹ãTˆ|)㈰ޖkñJÐóÚi=G ¬Ik¯ÖPs×5KqýŒÜìäÿëÒ§^Ö pœ)@ I èÝ? £'åb蘜Ž~͆a†9‡¨ö×á¿ß]Œ÷·¬mð¾Øè\>!×L¾™)i=m†9'`#Ÿ9ãÔÕÔaÝç±}ã.9P„ʲJ„B!X>ˆí*š§õVÿµ)¯Þ¹Ä‹Ü+›é²wMeàÏš/Âé—¶uªñÛZ{y¤œ>Ž ª+'¨7FŠØÓÊæˆ̪YxÐ6¨'”½4åR9üAÄÆÅ"£{7 Èé‡qSG#­[ÚyW†a†éܼ¿e-þðÞ«8][Óà}Ѿ(Ì13sÇclÿÁHŒ‹ïè©3ÌY ùÌYÞü}È_¿…»àxñ TWUÃçóEÎV÷Η—ß“ëôØ=-²ïÝžJÐÝáeÑ9[s XFǃ Ý°7§å½ë™¤º>ç@CÕïéZlgÊZáPáp ‰ èÞ+ÙÃbÔ¤HNå³ê†a†i?ÊNWâK_ŧ;65é~ z¦uEzR2nœ6 SsFvô欂|æ¬Æ_çÇžü}س½GöEiIª*«ðà‹"HsÙ»HܹýòKõ^Ú†$¶[ ¨lc{wu}Å7ÔÃÓÆ2¨ %=Á4/Ïú „`[@b—DtëÞ}ôÂÜAè7¸oG?n†a†a¾Ä¬+؉ÿýèMì->Òè½ 1±¸ûÒ+1üÔŽž6Ãœu°‘Ïœ³ÔTÕ`ç–=8TpÇŠN üx9NWžFmu-‚ ,Ÿ…¨HÑ?cß`è7hÄÛæëJEüÆÚ[nãž 4ñg˜?•ý«Uþ-„Ba„CaDEG!±K"R»+½ôÂÀœþHÏHëèGÇ0 Ã0 ãÉê½ÛñÆúX»o¡òYblfšˆ›.œn]R:zª sVÂF>Ó©ñûý¨­®CmMu~ÔÕùð¨ Ôÿ"à "‡µ£î¤_—ükb{È\y"‡Á>ÈŒ÷}T”ÑÑQˆŠŽBtl bãbƒ¸¸XÄÄÇ!>!qqqˆKˆCLlLGo;Ã0 Ã0L« †C¨¬®B] X@blÃ0 Ã0 Ã0 ÃtþàžuVQšPf%tEXtdate:create2013-05-13T18:16:51-04:00oÕì‚%tEXtdate:modify2013-05-13T18:09:43-04:00u8ÁÄIEND®B`‚deap-1.0.1/doc/_static/sidebar.js0000644000076500000240000001206012117373622017021 0ustar felixstaff00000000000000/* * sidebar.js * ~~~~~~~~~~ * * This script makes the Sphinx sidebar collapsible. * * .sphinxsidebar contains .sphinxsidebarwrapper. This script adds in * .sphixsidebar, after .sphinxsidebarwrapper, the #sidebarbutton used to * collapse and expand the sidebar. * * When the sidebar is collapsed the .sphinxsidebarwrapper is hidden and the * width of the sidebar and the margin-left of the document are decreased. * When the sidebar is expanded the opposite happens. This script saves a * per-browser/per-session cookie used to remember the position of the sidebar * among the pages. Once the browser is closed the cookie is deleted and the * position reset to the default (expanded). * * :copyright: Copyright 2007-2011 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ $(function() { // global elements used by the functions. // the 'sidebarbutton' element is defined as global after its // creation, in the add_sidebar_button function var bodywrapper = $('.bodywrapper'); var sidebar = $('.sphinxsidebar'); var sidebarwrapper = $('.sphinxsidebarwrapper'); // original margin-left of the bodywrapper and width of the sidebar // with the sidebar expanded var bw_margin_expanded = bodywrapper.css('margin-left'); var ssb_width_expanded = sidebar.width(); // margin-left of the bodywrapper and width of the sidebar // with the sidebar collapsed var bw_margin_collapsed = '.8em'; var ssb_width_collapsed = '.8em'; // colors used by the current theme var dark_color = '#AAAAAA'; var light_color = '#CCCCCC'; function sidebar_is_collapsed() { return sidebarwrapper.is(':not(:visible)'); } function toggle_sidebar() { if (sidebar_is_collapsed()) expand_sidebar(); else collapse_sidebar(); } function collapse_sidebar() { sidebarwrapper.hide(); sidebar.css('width', ssb_width_collapsed); bodywrapper.css('margin-left', bw_margin_collapsed); sidebarbutton.css({ 'margin-left': '0', //'height': bodywrapper.height(), 'height': sidebar.height(), 'border-radius': '5px' }); sidebarbutton.find('span').text('»'); sidebarbutton.attr('title', _('Expand sidebar')); document.cookie = 'sidebar=collapsed'; } function expand_sidebar() { bodywrapper.css('margin-left', bw_margin_expanded); sidebar.css('width', ssb_width_expanded); sidebarwrapper.show(); sidebarbutton.css({ 'margin-left': ssb_width_expanded-12, //'height': bodywrapper.height(), 'height': sidebar.height(), 'border-radius': '0 5px 5px 0' }); sidebarbutton.find('span').text('«'); sidebarbutton.attr('title', _('Collapse sidebar')); //sidebarwrapper.css({'padding-top': // Math.max(window.pageYOffset - sidebarwrapper.offset().top, 10)}); document.cookie = 'sidebar=expanded'; } function add_sidebar_button() { sidebarwrapper.css({ 'float': 'left', 'margin-right': '0', 'width': ssb_width_expanded - 28 }); // create the button sidebar.append( '
«
' ); var sidebarbutton = $('#sidebarbutton'); // find the height of the viewport to center the '<<' in the page var viewport_height; if (window.innerHeight) viewport_height = window.innerHeight; else viewport_height = $(window).height(); var sidebar_offset = sidebar.offset().top; var sidebar_height = sidebar.height(); //var sidebar_height = Math.max(bodywrapper.height(), sidebar.height()); sidebarbutton.find('span').css({ 'display': 'block', 'margin-top': sidebar_height/2 - 10 //'margin-top': (viewport_height - sidebar.position().top - 20) / 2 //'position': 'fixed', //'top': Math.min(viewport_height/2, sidebar_height/2 + sidebar_offset) - 10 }); sidebarbutton.click(toggle_sidebar); sidebarbutton.attr('title', _('Collapse sidebar')); sidebarbutton.css({ 'border-radius': '0 5px 5px 0', 'color': '#444444', 'background-color': '#CCCCCC', 'font-size': '1.2em', 'cursor': 'pointer', 'height': sidebar_height, 'padding-top': '1px', 'padding-left': '1px', 'margin-left': ssb_width_expanded - 12 }); sidebarbutton.hover( function () { $(this).css('background-color', dark_color); }, function () { $(this).css('background-color', light_color); } ); } function set_position_from_cookie() { if (!document.cookie) return; var items = document.cookie.split(';'); for(var k=0; k-B‰²£y³ªÕK9xÐ ` RjpªNW _à [Ñ(Ât[ÿÄøjÁ$Ûuf +–ì9ªW .dàPNbˆ0Ñ€À"€H ¡N…ÅO!"†éܾ=‡8†ÆŽ!Ë(@9ˆÄ"2ˆ BD‘ÏéÞ]Õ‹… åd°ÀÀ §˜Á€¹tšÏH±@ù 6„]q++ÀsïÅG°n „‰Ü yËÿ8âA©†r¦0@À@êàñgž:|^y¬ àyp×Ý9&„Ð_ Õ àW2hà Õ%¸ |Í'"@/˜RÀù1XàW,¶H€êáâŒ=lç`F+ÎÈ"Œéè c‹3€8âþüÀÁ¥˜Îÿ H8#p…ZNº(!ç8OFsUÙ"Hu•:^)$‘h‚sÂÄ ZèXZçxPÂl:ÆYYZb4%ž3®GçZ\ Ü@–ni6z‹,"’Y`@…ŽP)LPJÀرØ\rƒö™7z©¨š~ iš…úUq¤¦ôW|Ž^å€D Ò¤Äi=a¡CÂ!áDª_Ýà„!DÐZƒZ&ä©°…Ò5íÉž£‚¥Î›è´!„+£ŽœPÀ»‹ŒðÈûn’‰Øâ îÂ;ˆ¾/,»0ü;ïÁpB!ý"| 0rA òÁÿ (2ïƒ$ì±Ã„|œ0!"g­ qκ`ðÞ9ÄM  ë}uòêšzÍl0®¨+ßàz°,‹00®ºì6R€)‹` ¾H] +bu‡<] !˜‚_"[ûZ5,ûz½ ½ˆTPñ*c WJˆ½Ð ÉçmГòÇŸr xÎ~]h°Ÿß¤ A ‰¿“-Ì{›P^d؃sèDe !ð'á‹ ¾.®Ž¨õÔ„òÁé_k 5!Fñ!”’![·’¶‰ï~€J›†À ú ¸[JÀùÍM2/v¿‚7:À€=8f}¼æô€Ædœq® €ÿ œgPD\‘ÃQ• ŒÕU‡ýkOpO“ëU#ùç5͈éZCîPq;²½®DL‘€D¼­`ƒØZKw@8à§È!ì PÍ/0ÛòHWˆ0&rĤRb'C}ÊP&QÀ«‚”  Oë¹AJÀ©yü ShA ¦„&Œ†‡Z¹S¥`(Ì°†MLÕzâ¤$Þj_lKAµÜ•x‡ØZ×*(ˆ·­î @Ü!ÁG$p&´]½LÁ1*OqŽ¤îȈ¬ŒBÉ™@,0½v0€'Ad!m4€0-@Z@ƒ!¸@HQ g3ÈE®ƒÿˆË¹ÿ-âah¾2¬Æ C¶%„¨ÜZ^ul  Ê@R€âp³-DàA"ùMß" <‚HûË€¤ø½ë`qfäÅ·±Èõê .Z†6–ά ÀªFDx1J5“M¤Fä‘­5ªˆGÈŠ/)-'½Ç^Õ!·â”d‡àƒ# vÕcb>Öœæ3E⸹|ls»ÖÏZ8<ã,5 ÌÐjõu%Š®#. ¶éŽxÀä ó€q˜!6yŒ°N+Á¨“S È ÔŒDh _@< B9ÏÑ™OËFÔ¤6u§RíRЬ¡ÿWÜC°« ±)PôäŽ!ƒ´ÀÛ ZYGز öv"^à]ÊrÄÒ…vy› €á,ÑLÎ6 ƒæ¤@ÄÁ@L¿Ùa¤ š„%$! D(¢HGJœÌ”Ž¡ws”™ï"l N HA½]ægHú³>Dì(°«„ø«¦x@Y°EÐ"T‹T.kã•Z $u™>&»s¡þp¨IH˜œ2W‚Ÿ/`Bg¤<¦ÁtDÇç úW—¿ýÇúŠŠØí`´¶œµÕDLMv:ÑF2w£_nYäñŒˆ¹ ?,fukZ…*b‘€šÄ"ñ¥Cz<†”îÿé]@UÂÒ_O¹¯ÔˆÙ›>¶çnˆÉS·À¨å²ÉÛ^yTؼåâv¶Øeú#Û½Ýè~èéo>L1iï3R¼^ï"(Þ(©ì½t e÷zQÇ!Y† 4ї爳n~2%;ÒWz]g±yS&6܇Ûwix‡7'U"!¤Â)ž","!TtRê×BÉ1`¡gÒëQ%å±G yDDu±wÑ–æ|’Çe¬3}n¥ÿ|¤•rAÅ°O SŒFw˜6fç¶sçá)`Ý1°é²H(1`.%ð„ 3Žs#°ò"LÈ" PH§.,c!F`.7ð„Õr-F–±ÓAó’a^ü7;øŠsÜU}²P€9e Ì%K\ÆÅ“aÆw„E¸z#æ Ï3&P†³Â$Ð2Ú9=s4_Q4—3#4ã2’˜ã24˜è3åg-¸Hm˜“· ,^†vƒÊgvH6$‹•¥;~XS Áaž7„ˆˆsFHwŒÈiçA{òt”:Lršãœƒ8)à÷–%Ÿ¨Œ"ÀŒS:yƒÀÿ7Ó‰˜sE—p Pij”6eóÒ0•gŠPøU"s0rÕ0ˆ@ió’$#`<ª“º¸1‹Pï‚!ÓøØnýcïc=úŠ%@uëB‘³ë$`æó‰;ñ€×!/ÂL%°=Á‘ÕS) pýS¹r“¢ 3£…¢|1>×¢ qCù“(?!”[È ”¡…H`aL‰;ÙBq’C)”ꈓ\i ~¤©Âj4îp4‰ô)×’”¢‘Iaù)ÕâIêP‰!¨p9:Ø•x™ åp3åc*•‹Aj!  ˜ë ~èÀÿ—~ ˜‰9˜h‰˜Š¹Rw™—š‰´Åk) l÷Ÿ©Ÿˆ£I pšSa“›Ùš‘ÀTÃÅŒ›æš¶é °I›··ý¥›)x›À™ ¹é›Ø…Ĺš™œÊ™[ÐÎùœÐÒ9Ôùœ}Õ™ÚI[`“àžâ™Ü† ß9žèÉè)ž{8 ë9žå çùžà©‹ZCŸàUÕ'þùŸª%(`“Œ§ Ÿ”0o¨ íÙ­`Ÿ„¬ðy‡à‚®°‡à_– *)ý  ¢": ÉiAªˆ™àŽÃþ‡ ÂH w˜ ×U Š kÿ JWXñ¡Üñ("jK°ë¢> ¤Êš† Z 9ʤŒp¢7j ‘– ñ9pµ ʨ£–ÀŸÜa.ÐCp_>àvæŸ9àD ;0QpIþ)¦dj¦hÊ$J ]ZŒ–ð¤§pˆÏÇ ) WV\̧ %ªzŠ `*KÀ4N¸Æ£ÜáH4 X“DL §Zò¨‘*IJÀ”zy: ¯x  P£VÊ¢«¨”P¨[š¯p¥´è¥•Ð¨nR…£êÞ$Uû基*U½z¥* U ¥ž0P­°œ• $— WF¡Šà]­z«ªÐ¡Q¬ôY°«C@ÿNZâ< UJRZ’S®? Ç …Jyš¬¤ SJ;Zš 9:£…p¨«­“° ÚŠBÜ*GD»:DE*A@_»jXbRI»: KªJº¤|ª§¿“¯µš}–P£´*¦]©ð¢` »T 6Ц€©à$X-p g?P¦1ëQ4fDnñ²%³’I- cFñ®¯M* YZe-šz´ #ËÌ µ x $wª¿4 *Šƒ·Ú²ŸZ®C‹<IJpXF!±’D¶eUÀiºM·nIö•¶G›±Jë Ú@Ð7mŒ@¯tt þ«‚ø§ðÿ¬¨ v=e«”€«‘t¶õecp ¶àµUöU–¬G+X–Up›¹{›¨‹¢•À¸ÙꪅPµ ²žGµ©1ªÊr’е+k`ûW-Pkß·BÐB€¤F!;_÷%I+o:·ñòUcôUºÍk©ûª«°´pª¼+Ÿ«¡ ªà¸àºÑ·ô wMk Së‹°šÛX©Í[_ú[Êk9àpF¼¿…K·ù³ôEýû¿“Ë·ßË 1:Yvh­µñ[ ã+ 9:µ]*L{Ú¾—­Œª¹CÐ5©4€-0í* TkL@ÿIWÐ'.PÃ+ŒJðM0쮬 àë‡z£O[e\Z¾{ ›Ö´;^ªðÄ f;ÊQ°>ðHL`KÃZb\L5PDàV@½VŒÅZÌÅ^ܽ“з›Àa¡¸¼Z{Á‘¿ˆ» rž_ôg$\ÅéüŸ6ð?p³z„Ü'H¡¬ WÆmºi~|$“° Ô«J£ö È‹*È":ʤì ìpœ -ºÁ $µõÄJ‘À¾üJ§ªT©œƒõ+ ÀÐ˾üËÀÌÂ<̾Ü'_@ÌÈœÌÃÌAœ C¼´«¾Ú¦ ²»|× „;µÖÚž!ÌÊ¢ÿÊ_Ú™Ç9›Çù›„ɘ°¡÷*w,ÅjÁ ]ºi‡ºÉ„@¸¯‹ËTÎå,›¥ÙÏ[yÎB¬ ]z—Œ²„pÄ…ÖW&Í„Àa³L»¦Ï}«âLœäÜÏ­˜|Êz ñK«]»Î„@Ç\×¢"½‹ùŒ¯ÐÊv¹üšâ¬ÿEêõbmaœÕÓÓ@f—Þ;ИàŽÕYèŒh®Œ»¨°-:`ñŠ¸ØuԌڙ C¶ÓÐ!ÕE@ÓjYxìÕS=\íb ¡º·L ìÛ¤ðì͆€Ï^;nœ´‹¸! ƒÎÝsñ“{†feÓé #x|Ö=í̘pª'­¨¨0ÿ²Ĉ h-m€Øj½´¾ûÔp cl³A†¦˜l¬¦£f»yá1 «–ýÙ¡­£V¦¶Ñ|¸±Ðl­T«0˪ h¦0Ü‚Ð¥À]ÁÔLÑk° ’o@pH'€øön2Àù#qóvÚ‘qó¦ ’èÙqpæ ¯C= þj¾‚ ªî<ל&Íí m¾•Í™éÐs R! u¢³x€H?Î ŒùtEuI—û‘à î >|ìÓ•Ð¥A]¼ÐD­ ºH»ûèŽwIÒ'Èü =‡÷U$=’ÿ4>xY{.n ˆ·#¢ã0Î"ÐîÍáoLØ×ê¹ Í‹À¾L=zضH×‘Ë º÷#Âç{´÷%Y®6-#À·#/ö{:b{í´ï ù ~Š —,×íØŒpªö¼ûm- 95~í”ìÇ"q&7Λ‚)í÷׳W~-r~b‚Û„†ªd}²±|ßx~Ú¸» É-Pð<`ÿÝâQäB{ǵ‚ž²¥þB¥ò‰-Ô«¾)¨î“«Ž‚BŽæDŽ£© ß… Ð#믖–Ð6Êþ:¿½¾ ƒ:åN à](+Є7`ר.ãbÎn…+ Y8Cêçÿ) {ÒŽ,‹áìMXÙ>–lØÌ- !ü ,î@QìéË®'*ï4×÷^×;.ó36#M8s‰2¹™X~œžhœ(ð1‰ Ð2/sð(—›È«xænä’ìú¾Ò‹ ÐDUìòКó^å豌“Ó ƒÐ˜äh Øh¦XœJ 9% zÓ"Î(:Ñ~=gŽÕ¨íæ é(­Û†ò¬«$½/öØÂîô­€Ð¡îïëÒ÷v’Ö#>‡¤‘ƦÕã‘’#96à> È¢æàõå#‡Ä’IöI )’ÊiÞ—lõ ‹'ªÿAk܃OøŒM¿û¬õͤ€•jA/MMt'E¹'2¹Hó°¦DÀÀDàœ‘?ù¬ž–ïÓc•x¢(ÿ÷ ø¬Êø‚ê¾ܵ[L¶¿ Òœõ,_>i)êòIT‚Ö²˜ñpY€ÅÃ8›IK*Å_qB*›=ObMûŠ0Áj‘⢾ØcþûØ¥ þâ_aMÿøÁO˜)@jPp}F˜÷_(å"„…‡.8Œ8.ˆ…„)3% ”Ÿ"—œžŸ¬­®¯¯³´³°¸¯µ³¹¿¸¼´Àö»¼ÿÆ̵/ÆÓÉΰÉ ÔÆŸ” *âç„äæè‘><4K‘“çä èöåù©«Þ±×nýc%¬…bq%¸Ö ˜¬dl«ÅbÚÄZÙêºv×ÅZ 5º—¯¤É|ˆ~$¯†DõNÊ4ÙO$€‡ÃþÃI+¢MW®=˜Vp؈a0ŒåUñg+’ÔÆÍ)+’3³¢LÄ„Q x-¾ùq(¦Ö³„jŠäYK§7F­¶‚›Ì°…ÉŠÎj ¯¤r]³ëê#­?±¢]|HÊW_[2‚rDÒâÅj5²¥åvÚÒZ|øæï×çk‡AåE!0«£y©ClSñÿ9gq£t±Á";Ôþõ‘etº³&?—9áfeÿüFsM0 1º¨geÌ…·VGêÖê¾2<‹6µ ˆmSRÑ£ 2IÄ(!c«ãÁ…0ÞBH ÊÇ}ž{ð$}ŠÓÜ@ÏÐÙ/#¼ B®uÇKi¹@ƒZS¿¬Ö uË5^UÞðBx#…sNÐÀ)&­Ðâ 0~rH(@ÁÈ"¾12Q”uŽ -¾(“Œ.ÖHÉ‚;]óAPF)%”á%ák§ñòà+QeØ/' óˆx8Œ4­ÀS¶ e—µ¤H‰˜ A l (l @$Ù矆 ÿr…="ÐU RØS!–؉' àâ”zg¡€pè …0Ybv¨zI&Øñò…¸„‰Zh¹ÀÆ aÔIÇZFj¦Ššœ…0A2€Ð¦ ˜`A&|ÂÀè@Bž ø9A¦”D’Ã(ÁR °DôBB {l¦Ê‚³Ðê ‚˜nµ‚–ªŠM ú:몌vÍ•°ØJb.TIËTdÌ˘¬ôêï0À²`€Ä„ðDž¨XªznŒ)…†œm$‡ØÐD•±àc@(|ܢȄ¤BÆ&/€È<›JM¿óݪzñâS. ˆpÔ³<ÿM&Õ=Eœtª p@‹“Œ)Ø1 6$€q‹:¬,óÛ“¦u¶Ø)°‚Ù-¢7Øõé‹!ƒ[ûºˆ“¬®hHQ‡X;pˆ: qàtÍ"ؘƒ-Á'£džùæ‰Â wLDzŽ9è…Ì`úéKî»åÙý b–Ã<°¥×|ù pºúx™¨RÐ4ì´t= Çžc|©h0 È™Z@‚¢‹Óñ¦sld!£Ôy¡%$gôïä—oþù«Ðv%%@¯çyn/€Çzv¡òW?z%¸h:½ !p=™.~0úÈÀ:p$(ýP‚ô¯HP Að‘zÿÀ‚`û`ÿB:ý•k~Ǫà]”Al ðXl_‹Th€ ¬Ãk°ÚwÈÃlš@‚cµ(0Áµ8(€g-à xŸû6….&šPfõPÁB@D#êi˜òÕÄ<‘RœRPÅBDocs for other versions

Other resources

deap-1.0.1/doc/_templates/layout.html0000644000076500000240000000246512301410325017757 0ustar felixstaff00000000000000{% extends "!layout.html" %} {%- block extrahead %} {{ super() }} {% if not embedded %}{% endif %} {% endblock %} {% block rootrellink %}
  • Project Homepage{{ reldelim1 }}
  • {{ shorttitle }}{{ reldelim1 }}
    • {% endblock %} {% block footer %}
    © Copyright {{ copyright|e }}.
    Built on {{ last_updated|e }}. Found a bug?
    Created using Sphinx {{ sphinx_version|e }}.
    {% endblock %} deap-1.0.1/doc/_themes/0000755000076500000240000000000012321001644015036 5ustar felixstaff00000000000000deap-1.0.1/doc/_themes/pydoctheme/0000755000076500000240000000000012321001644017177 5ustar felixstaff00000000000000deap-1.0.1/doc/_themes/pydoctheme/static/0000755000076500000240000000000012321001644020466 5ustar felixstaff00000000000000deap-1.0.1/doc/_themes/pydoctheme/static/pydoctheme.css0000644000076500000240000000541712301410325023346 0ustar felixstaff00000000000000@import url("default.css"); body { background-color: white; margin-left: 1em; margin-right: 1em; } div.related { margin-bottom: 1.2em; padding: 0.5em 0; border-top: 1px solid #ccc; margin-top: 0.5em; } div.related a:hover { color: #41cdce; } div.related:first-child { border-top: 0; border-bottom: 1px solid #ccc; } div.sidebar { background-color: #eeeeee; } div.sphinxsidebar { background-color: #eeeeee; border-radius: 5px; line-height: 130%; font-size: smaller; } div.sphinxsidebar h3, div.sphinxsidebar h4 { margin-top: 1.5em; } div.sphinxsidebarwrapper > h3:first-child { margin-top: 0.2em; } div.sphinxsidebarwrapper > ul > li > ul > li { margin-bottom: 0.4em; } div.sphinxsidebar a:hover { color: #41cdce; } div.sphinxsidebar input { font-family: 'Lucida Grande','Lucida Sans','DejaVu Sans',Arial,sans-serif; border: 1px solid #999999; font-size: smaller; border-radius: 3px; } div.sphinxsidebar input[type=text] { max-width: 150px; } div.body { padding: 0 0 0 1.2em; } div.body p { line-height: 140%; } div.body h1, div.body h2, div.body h3, div.body h4, div.body h5, div.body h6 { margin: 0; border: 0; padding: 0.3em 0; } div.body hr { border: 0; background-color: #ccc; height: 1px; } div.body pre { border-radius: 3px; border: 1px solid #ac9; } div.body div.admonition, div.body div.impl-detail { border-radius: 3px; } div.body div.impl-detail > p { margin: 0; } div.body div.seealso { border: 1px solid #dddd66; } a.reference em { font-style: normal; } div.body a { color: #167171; } div.body a:visited { color: #f38b28; } div.body a:hover { color: #41cdce; } tt, pre { font-family: monospace, sans-serif; font-size: 96.5%; } div.body tt { border-radius: 3px; } div.body tt.descname { font-size: 120%; } div.body tt.xref, div.body a tt { font-weight: normal; } .deprecated, .deprecated-removed { background-color: #ffe4e4; border: 1px solid #f66; padding: 7px } .deprecated { border-radius: 3px; } table.docutils { border: 1px solid #ddd; min-width: 20%; border-radius: 3px; margin-top: 10px; margin-bottom: 10px; } table.docutils td, table.docutils th { border: 1px solid #ddd !important; border-radius: 3px; } table p, table li { text-align: left !important; } table.docutils th { background-color: #eee; padding: 0.3em 0.5em; } table.docutils td { background-color: white; padding: 0.3em 0.5em; } table.footnote, table.footnote td { border: 0 !important; } div.footer { line-height: 150%; margin-top: -2em; text-align: right; width: auto; margin-right: 10px; } div.footer a:hover { color: #41cdce; } deap-1.0.1/doc/_themes/pydoctheme/theme.conf0000644000076500000240000000111712117373622021163 0ustar felixstaff00000000000000[theme] inherit = default stylesheet = pydoctheme.css pygments_style = sphinx [options] bodyfont = 'Lucida Grande', 'Lucida Sans', 'DejaVu Sans', Arial, sans-serif headfont = 'Lucida Grande', 'Lucida Sans', 'DejaVu Sans', Arial, sans-serif footerbgcolor = white footertextcolor = #555555 relbarbgcolor = white relbartextcolor = #666666 relbarlinkcolor = #444444 sidebarbgcolor = white sidebartextcolor = #444444 sidebarlinkcolor = #444444 bgcolor = white textcolor = #222222 linkcolor = #0090c0 visitedlinkcolor = #00608f headtextcolor = #1a1a1a headbgcolor = white headlinkcolor = #aaaaaa deap-1.0.1/doc/about.rst0000644000076500000240000000375212301410325015263 0ustar felixstaff00000000000000.. image:: _static/deap_long.png :width: 300 px :align: right :target: index.html .. image:: _static/lvsn.png :width: 175 px :align: right :target: http://vision.gel.ulaval.ca/ .. image:: _static/ul.gif :width: 175 px :align: right :target: http://www.ulaval.ca/ About DEAP ========== Main Contributors ----------------- In alphabetical order - `François-Michel De Rainville `_ - `Félix-Antoine Fortin `_ - `Christian Gagné `_ - Olivier Gagnon - Marc-André Gardner - Simon Grenier - Yannick Hold-Geoffroy - Marc Parizeau DEAP is developed at the `Computer Vision and Systems Laboratory (CVSL) `_ at `Université Laval `_, in Quebec city, Canada. Publications on DEAP -------------------- - Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau and Christian Gagné, "DEAP: Evolutionary Algorithms Made Easy", Journal of Machine Learning Research, pp. 2171-2175, no 13, jul 2012. - François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau and Christian Gagné, "DEAP: A Python Framework for Evolutionary Algorithms", in EvoSoft Workshop, Companion proc. of the Genetic and Evolutionary Computation Conference (GECCO 2012), July 07-11 2012. Citation -------- Authors of scientific papers including results generated using DEAP are encouraged to cite the following paper. .. code-block:: latex @article{DEAP_JMLR2012, author = " F\'elix-Antoine Fortin and Fran\c{c}ois-Michel {De Rainville} and Marc-Andr\'e Gardner and Marc Parizeau and Christian Gagn\'e ", title = { {DEAP}: Evolutionary Algorithms Made Easy }, pages = { 2171--2175 }, volume = { 13 }, month = { jul }, year = { 2012 }, journal = { Journal of Machine Learning Research } } deap-1.0.1/doc/api/0000755000076500000240000000000012321001644014163 5ustar felixstaff00000000000000deap-1.0.1/doc/api/algo.rst0000644000076500000240000000300412117373622015647 0ustar felixstaff00000000000000Algorithms ========== .. automodule:: deap.algorithms Complete Algorithms ------------------- These are complete boxed algorithms that are somewhat limited to the very basic evolutionary computation concepts. All algorithms accept, in addition to their arguments, an initialized :class:`~deap.tools.Statistics` object to maintain stats of the evolution, an initialized :class:`~deap.tools.HallOfFame` to hold the best individual(s) to appear in the population, and a boolean `verbose` to specify wether to log what is happening during the evolution or not. .. autofunction:: deap.algorithms.eaSimple(population, toolbox, cxpb, mutpb, ngen[, stats, halloffame, verbose]) .. autofunction:: deap.algorithms.eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen[, stats, halloffame, verbose]) .. autofunction:: deap.algorithms.eaMuCommaLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen[, stats, halloffame, verbose]) .. autofunction:: deap.algorithms.eaGenerateUpdate(toolbox, ngen[, stats, halloffame, verbose]) Variations ---------- Variations are smaller parts of the algorithms that can be used separately to build more complex algorithms. .. autofunction:: deap.algorithms.varAnd .. autofunction:: deap.algorithms.varOr Covariance Matrix Adaptation Evolution Strategy =============================================== .. automodule:: deap.cma .. autoclass:: deap.cma.Strategy(centroid, sigma[, **kargs]) :members: .. autoclass:: deap.cma.StrategyOnePlusLambda(parent, sigma[, **kargs]) :members: deap-1.0.1/doc/api/base.rst0000644000076500000240000000056712301410325015635 0ustar felixstaff00000000000000Base ==== .. automodule:: deap.base Toolbox ------- .. autoclass:: deap.base.Toolbox .. automethod:: deap.base.Toolbox.register(alias, method[, argument[, ...]]) .. automethod:: deap.base.Toolbox.unregister(alias) .. automethod:: deap.base.Toolbox.decorate(alias, decorator[, decorator[, ...]]) Fitness ------- .. autoclass:: deap.base.Fitness([values]) :members:deap-1.0.1/doc/api/benchmarks.rst0000644000076500000240000001226612301410325017037 0ustar felixstaff00000000000000========== Benchmarks ========== .. automodule:: deap.benchmarks ============================= ============================= ============================ ===================== Single Objective Continuous Multi Objective Continuous Binary Symbolic Regression ============================= ============================= ============================ ===================== :func:`cigar` :func:`fonseca` :func:`~binary.chuang_f1` :func:`~gp.kotanchek` :func:`plane` :func:`kursawe` :func:`~binary.chuang_f2` :func:`~gp.salustowicz_1d` :func:`sphere` :func:`schaffer_mo` :func:`~binary.chuang_f3` :func:`~gp.salustowicz_2d` :func:`rand` :func:`dtlz1` :func:`~binary.royal_road1` :func:`~gp.unwrapped_ball` :func:`ackley` :func:`dtlz2` :func:`~binary.royal_road2` :func:`~gp.rational_polynomial` :func:`bohachevsky` :func:`dtlz3` .. :func:`~gp.rational_polynomial2` :func:`griewank` :func:`dtlz4` .. :func:`~gp.sin_cos` :func:`h1` :func:`zdt1` .. :func:`~gp.ripple` :func:`himmelblau` :func:`zdt2` .. .. :func:`rastrigin` :func:`zdt3` .. .. :func:`rastrigin_scaled` :func:`zdt4` .. .. :func:`rastrigin_skew` :func:`zdt6` .. .. :func:`rosenbrock` .. .. .. :func:`schaffer` .. .. .. :func:`schwefel` .. .. .. :func:`shekel` .. .. .. ============================= ============================= ============================ ===================== Continuous Optimization ======================= .. autofunction:: deap.benchmarks.cigar .. autofunction:: deap.benchmarks.plane .. autofunction:: deap.benchmarks.sphere .. autofunction:: deap.benchmarks.rand .. autofunction:: deap.benchmarks.ackley .. autofunction:: deap.benchmarks.bohachevsky .. autofunction:: deap.benchmarks.griewank .. autofunction:: deap.benchmarks.h1 .. autofunction:: deap.benchmarks.himmelblau .. autofunction:: deap.benchmarks.rastrigin .. autofunction:: deap.benchmarks.rastrigin_scaled .. autofunction:: deap.benchmarks.rastrigin_skew .. autofunction:: deap.benchmarks.rosenbrock .. autofunction:: deap.benchmarks.schaffer .. autofunction:: deap.benchmarks.schwefel .. autofunction:: deap.benchmarks.shekel Multi-objective --------------- .. autofunction:: deap.benchmarks.fonseca .. autofunction:: deap.benchmarks.kursawe .. autofunction:: deap.benchmarks.schaffer_mo .. autofunction:: deap.benchmarks.dtlz1 .. autofunction:: deap.benchmarks.dtlz2 .. autofunction:: deap.benchmarks.dtlz3 .. autofunction:: deap.benchmarks.dtlz4 .. autofunction:: deap.benchmarks.zdt1 .. autofunction:: deap.benchmarks.zdt2 .. autofunction:: deap.benchmarks.zdt3 .. autofunction:: deap.benchmarks.zdt4 .. autofunction:: deap.benchmarks.zdt6 Binary Optimization =================== .. automodule:: deap.benchmarks.binary .. autofunction:: deap.benchmarks.binary.chuang_f1 .. autofunction:: deap.benchmarks.binary.chuang_f2 .. autofunction:: deap.benchmarks.binary.chuang_f3 .. autofunction:: deap.benchmarks.binary.royal_road1 .. autofunction:: deap.benchmarks.binary.royal_road2 .. autofunction:: deap.benchmarks.binary.bin2float Symbolic Regression =================== .. automodule:: deap.benchmarks.gp .. autofunction:: deap.benchmarks.gp.kotanchek .. autofunction:: deap.benchmarks.gp.salustowicz_1d .. autofunction:: deap.benchmarks.gp.salustowicz_2d .. autofunction:: deap.benchmarks.gp.unwrapped_ball .. autofunction:: deap.benchmarks.gp.rational_polynomial .. autofunction:: deap.benchmarks.gp.rational_polynomial2 .. autofunction:: deap.benchmarks.gp.sin_cos .. autofunction:: deap.benchmarks.gp.ripple Moving Peaks Benchmark ====================== .. automodule:: deap.benchmarks.movingpeaks .. autoclass:: deap.benchmarks.movingpeaks.MovingPeaks(self, dim[, pfunc][, npeaks][, bfunc][, random][, ...]) :members: .. automethod:: deap.benchmarks.movingpeaks.MovingPeaks.__call__(self, individual[, count]) .. autofunction:: deap.benchmarks.movingpeaks.cone .. autofunction:: deap.benchmarks.movingpeaks.function1 Benchmarks tools ================ .. automodule:: deap.benchmarks.tools :members: convergence, diversity .. autofunction:: deap.benchmarks.tools.noise .. automethod:: deap.benchmarks.tools.noise.noise .. autofunction:: deap.benchmarks.tools.rotate .. automethod:: deap.benchmarks.tools.rotate.rotate .. autofunction:: deap.benchmarks.tools.scale .. automethod:: deap.benchmarks.tools.scale.scale .. autofunction:: deap.benchmarks.tools.translate .. automethod:: deap.benchmarks.tools.translate.translatedeap-1.0.1/doc/api/creator.rst0000644000076500000240000000023712301410325016354 0ustar felixstaff00000000000000Creator ------- .. automodule:: deap.creator .. autofunction:: deap.creator.create(name, base[, attribute[, ...]]) .. autodata:: deap.creator.class_replacersdeap-1.0.1/doc/api/gp.rst0000644000076500000240000000070612301410325015324 0ustar felixstaff00000000000000Genetic Programming =================== .. automodule:: deap.gp .. autoclass:: deap.gp.PrimitiveTree :members: .. autoclass:: deap.gp.PrimitiveSet :members: .. autoclass:: deap.gp.Primitive :members: .. autoclass:: deap.gp.Terminal :members: .. autoclass:: deap.gp.Ephemeral :members: .. autofunction:: deap.gp.compile .. autofunction:: deap.gp.compileADF .. autoclass:: deap.gp.PrimitiveSetTyped :members: .. autofunction:: deap.gp.graph deap-1.0.1/doc/api/index.rst0000644000076500000240000000027312301410325016024 0ustar felixstaff00000000000000Library Reference ================= Description of the functions, classes and modules contained within DEAP. .. toctree:: :maxdepth: 2 creator base tools algo gp benchmarks deap-1.0.1/doc/api/tools.rst0000644000076500000240000001656012320777200016074 0ustar felixstaff00000000000000Evolutionary Tools ================== .. automodule:: deap.tools .. _operators: Operators --------- The operator set does the minimum job for transforming or selecting individuals. This means, for example, that providing two individuals to the crossover will transform those individuals in-place. The responsibility of making offspring(s) independent of their parent(s) and invalidating the fitness is left to the user and is generally fulfilled in the algorithms by calling :func:`toolbox.clone` on an individual to duplicate it and ``del`` on the :attr:`values` attribute of the individual's fitness to invalidate it. Here is a list of the implemented operators in DEAP, ============================ =========================================== ========================================= ============================ ================ Initialization Crossover Mutation Selection Migration ============================ =========================================== ========================================= ============================ ================ :func:`initRepeat` :func:`cxOnePoint` :func:`mutGaussian` :func:`selTournament` :func:`migRing` :func:`initIterate` :func:`cxTwoPoint` :func:`mutShuffleIndexes` :func:`selRoulette` .. :func:`initCycle` :func:`cxUniform` :func:`mutFlipBit` :func:`selNSGA2` .. .. :func:`cxPartialyMatched` :func:`mutPolynomialBounded` :func:`selSPEA2` .. .. :func:`cxUniformPartialyMatched` :func:`mutUniformInt` :func:`selRandom` .. .. :func:`cxOrdered` :func:`mutESLogNormal` :func:`selBest` .. .. :func:`cxBlend` .. :func:`selWorst` .. .. :func:`cxESBlend` .. :func:`selTournamentDCD` .. .. :func:`cxESTwoPoint` .. :func:`selDoubleTournament` .. .. :func:`cxSimulatedBinary` .. .. .. .. :func:`cxSimulatedBinaryBounded` .. .. .. .. :func:`cxMessyOnePoint` .. .. .. ============================ =========================================== ========================================= ============================ ================ and genetic programming specific operators. ================================ =========================================== ========================================= ================================ Initialization Crossover Mutation Bloat control ================================ =========================================== ========================================= ================================ :func:`~deap.gp.genFull` :func:`~deap.gp.cxOnePoint` :func:`~deap.gp.mutShrink` :func:`~deap.gp.staticLimit` :func:`~deap.gp.genGrow` :func:`~deap.gp.cxOnePointLeafBiased` :func:`~deap.gp.mutUniform` :func:`selDoubleTournament` :func:`~deap.gp.genHalfAndHalf` .. :func:`~deap.gp.mutNodeReplacement` .. .. .. :func:`~deap.gp.mutEphemeral` .. .. .. :func:`~deap.gp.mutInsert` .. ================================ =========================================== ========================================= ================================ Initialization ++++++++++++++ .. autofunction:: deap.tools.initRepeat .. autofunction:: deap.tools.initIterate .. autofunction:: deap.tools.initCycle .. autofunction:: deap.gp.genFull .. autofunction:: deap.gp.genGrow .. autofunction:: deap.gp.genHalfAndHalf .. autofunction:: deap.gp.genRamped Crossover +++++++++ .. autofunction:: deap.tools.cxOnePoint .. autofunction:: deap.tools.cxTwoPoint .. autofunction:: deap.tools.cxTwoPoints .. autofunction:: deap.tools.cxUniform .. autofunction:: deap.tools.cxPartialyMatched .. autofunction:: deap.tools.cxUniformPartialyMatched .. autofunction:: deap.tools.cxOrdered .. autofunction:: deap.tools.cxBlend .. autofunction:: deap.tools.cxESBlend .. autofunction:: deap.tools.cxESTwoPoint .. autofunction:: deap.tools.cxESTwoPoints .. autofunction:: deap.tools.cxSimulatedBinary .. autofunction:: deap.tools.cxSimulatedBinaryBounded .. autofunction:: deap.tools.cxMessyOnePoint .. autofunction:: deap.gp.cxOnePoint .. autofunction:: deap.gp.cxOnePointLeafBiased Mutation ++++++++ .. autofunction:: deap.tools.mutGaussian .. autofunction:: deap.tools.mutShuffleIndexes .. autofunction:: deap.tools.mutFlipBit .. autofunction:: deap.tools.mutUniformInt .. autofunction:: deap.tools.mutPolynomialBounded .. autofunction:: deap.tools.mutESLogNormal .. autofunction:: deap.gp.mutShrink .. autofunction:: deap.gp.mutUniform .. autofunction:: deap.gp.mutNodeReplacement .. autofunction:: deap.gp.mutEphemeral .. autofunction:: deap.gp.mutInsert Selection +++++++++ .. autofunction:: deap.tools.selTournament .. autofunction:: deap.tools.selRoulette .. autofunction:: deap.tools.selNSGA2 .. autofunction:: deap.tools.selSPEA2 .. autofunction:: deap.tools.selRandom .. autofunction:: deap.tools.selBest .. autofunction:: deap.tools.selWorst .. autofunction:: deap.tools.selDoubleTournament .. autofunction:: deap.tools.selTournamentDCD .. autofunction:: deap.tools.sortNondominated .. autofunction:: deap.tools.sortLogNondominated Bloat control +++++++++++++ .. autofunction:: deap.gp.staticLimit Migration +++++++++ .. autofunction:: deap.tools.migRing(populations, k, selection[, replacement, migarray]) Statistics ---------- .. autoclass:: deap.tools.Statistics([key]) :members: .. autoclass:: deap.tools.MultiStatistics(**kargs) :members: Logbook ------- .. autoclass:: deap.tools.Logbook :members: Hall-Of-Fame ------------ .. autoclass:: deap.tools.HallOfFame .. automethod:: deap.tools.HallOfFame.update .. automethod:: deap.tools.HallOfFame.insert .. automethod:: deap.tools.HallOfFame.remove .. automethod:: deap.tools.HallOfFame.clear .. autoclass:: deap.tools.ParetoFront([similar]) .. automethod:: deap.tools.ParetoFront.update History ------- .. autoclass:: deap.tools.History .. automethod:: deap.tools.History.update .. autoattribute:: deap.tools.History.decorator .. automethod:: deap.tools.History.getGenealogy(individual[, max_depth]) deap-1.0.1/doc/code/0000755000076500000240000000000012321001644014324 5ustar felixstaff00000000000000deap-1.0.1/doc/code/benchmarks/0000755000076500000240000000000012321001644016441 5ustar felixstaff00000000000000deap-1.0.1/doc/code/benchmarks/ackley.py0000644000076500000240000000127612117373622020304 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks def ackley_arg0(sol): return benchmarks.ackley(sol)[0] fig = plt.figure() # ax = Axes3D(fig, azim = -29, elev = 50) ax = Axes3D(fig) X = np.arange(-30, 30, 0.5) Y = np.arange(-30, 30, 0.5) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = ackley_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/benchmarks/bohachevsky.py0000644000076500000240000000131512117373622021334 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks def bohachevsky_arg0(sol): return benchmarks.bohachevsky(sol)[0] fig = plt.figure() ax = Axes3D(fig, azim = -29, elev = 50) # ax = Axes3D(fig) X = np.arange(-15, 15, 0.5) Y = np.arange(-15, 15, 0.5) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = bohachevsky_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/benchmarks/griewank.py0000644000076500000240000000121512117373622020634 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks def griewank_arg0(sol): return benchmarks.griewank(sol)[0] fig = plt.figure() ax = Axes3D(fig, azim = -29, elev = 40) # ax = Axes3D(fig) X = np.arange(-50, 50, 0.5) Y = np.arange(-50, 50, 0.5) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = griewank_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/benchmarks/h1.py0000644000076500000240000000126212117373622017337 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks def h1_arg0(sol): return benchmarks.h1(sol)[0] fig = plt.figure() # ax = Axes3D(fig, azim = -29, elev = 50) ax = Axes3D(fig) X = np.arange(-25, 25, 0.5) Y = np.arange(-25, 25, 0.5) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = h1_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/benchmarks/himmelblau.py0000644000076500000240000000111012117373622021136 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks def untuple(sol): return benchmarks.himmelblau(sol)[0] fig = plt.figure() ax = Axes3D(fig, azim = -29, elev = 49) X = np.arange(-6, 6, 0.1) Y = np.arange(-6, 6, 0.1) X, Y = np.meshgrid(X, Y) Z = np.array(map(untuple, zip(X,Y))) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/benchmarks/kursawe.py0000644000076500000240000000154112117373622020510 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks X = np.arange(-5, 5, 0.1) Y = np.arange(-5, 5, 0.1) X, Y = np.meshgrid(X, Y) Z1 = np.zeros(X.shape) Z2 = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z1[i,j], Z2[i,j] = benchmarks.kursawe((X[i,j],Y[i,j])) fig = plt.figure(figsize=(12,5)) ax = fig.add_subplot(1, 2, 1, projection='3d') ax.plot_surface(X, Y, Z1, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") ax = fig.add_subplot(1, 2, 2, projection='3d') ax.plot_surface(X, Y, Z2, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0) plt.show()deap-1.0.1/doc/code/benchmarks/movingsc1.py0000644000076500000240000000133412117373622020735 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import matplotlib.pyplot as plt try: import numpy as np except: exit() import random rnd = random.Random() rnd.seed(128) from deap.benchmarks import movingpeaks sc = movingpeaks.SCENARIO_1 sc["uniform_height"] = 0 sc["uniform_width"] = 0 mp = movingpeaks.MovingPeaks(dim=2, random=rnd, **sc) fig = plt.figure() ax = Axes3D(fig) X = np.arange(0, 100, 1.0) Y = np.arange(0, 100, 1.0) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = mp((X[i,j],Y[i,j]))[0] ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/benchmarks/rastrigin.py0000644000076500000240000000117112117373622021030 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks def rastrigin_arg0(sol): return benchmarks.rastrigin(sol)[0] fig = plt.figure() ax = Axes3D(fig, azim = -29, elev = 50) X = np.arange(-5, 5, 0.1) Y = np.arange(-5, 5, 0.1) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = rastrigin_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/benchmarks/rosenbrock.py0000644000076500000240000000130612117373622021175 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks def rosenbrock_arg0(sol): return benchmarks.rosenbrock(sol)[0] fig = plt.figure() # ax = Axes3D(fig, azim = -29, elev = 50) ax = Axes3D(fig) X = np.arange(-2, 2, 0.1) Y = np.arange(-1, 3, 0.1) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = rosenbrock_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/benchmarks/schaffer.py0000644000076500000240000000121712117373622020610 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks def schaffer_arg0(sol): return benchmarks.schaffer(sol)[0] fig = plt.figure() ax = Axes3D(fig, azim = -29, elev = 60) # ax = Axes3D(fig) X = np.arange(-25, 25, 0.25) Y = np.arange(-25, 25, 0.25) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = schaffer_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/benchmarks/schwefel.py0000644000076500000240000000121712117373622020627 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks def schwefel_arg0(sol): return benchmarks.schwefel(sol)[0] fig = plt.figure() # ax = Axes3D(fig, azim = -29, elev = 50) ax = Axes3D(fig) X = np.arange(-500, 500, 10) Y = np.arange(-500, 500, 10) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = schwefel_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/benchmarks/shekel.py0000644000076500000240000000157712117373622020313 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt try: import numpy as np except: exit() from deap import benchmarks #NUMMAX = 5 #A = 10 * np.random.rand(NUMMAX, 2) #C = np.random.rand(NUMMAX) A = [[0.5, 0.5], [0.25, 0.25], [0.25, 0.75], [0.75, 0.25], [0.75, 0.75]] C = [0.002, 0.005, 0.005, 0.005, 0.005] def shekel_arg0(sol): return benchmarks.shekel(sol, A, C)[0] fig = plt.figure() # ax = Axes3D(fig, azim = -29, elev = 50) ax = Axes3D(fig) X = np.arange(0, 1, 0.01) Y = np.arange(0, 1, 0.01) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = shekel_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) plt.xlabel("x") plt.ylabel("y") plt.show()deap-1.0.1/doc/code/tutorials/0000755000076500000240000000000012321001644016352 5ustar felixstaff00000000000000deap-1.0.1/doc/code/tutorials/part_1/0000755000076500000240000000000012321001644017540 5ustar felixstaff00000000000000deap-1.0.1/doc/code/tutorials/part_1/1_where_to_start.py0000644000076500000240000000413512301410325023364 0ustar felixstaff00000000000000## 1.1 Types from deap import base, creator creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) ## 1.2 Initialization import random from deap import tools IND_SIZE = 10 toolbox = base.Toolbox() toolbox.register("attribute", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attribute, n=IND_SIZE) toolbox.register("population", tools.initRepeat, list, toolbox.individual) ## 1.3 Operators def evaluate(individual): return sum(individual), toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", evaluate) ## 1.4 Algorithms def main(): pop = toolbox.population(n=50) CXPB, MUTPB, NGEN = 0.5, 0.2, 40 # Evaluate the entire population fitnesses = map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit for g in range(NGEN): # Select the next generation individuals offspring = toolbox.select(pop, len(pop)) # Clone the selected individuals offspring = map(toolbox.clone, offspring) # Apply crossover and mutation on the offspring for child1, child2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values for mutant in offspring: if random.random() < MUTPB: toolbox.mutate(mutant) del mutant.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # The population is entirely replaced by the offspring pop[:] = offspring return pop if __name__ == "__main__": main() deap-1.0.1/doc/code/tutorials/part_2/0000755000076500000240000000000012321001644017541 5ustar felixstaff00000000000000deap-1.0.1/doc/code/tutorials/part_2/2_1_fitness.py0000644000076500000240000000033412117373622022242 0ustar felixstaff00000000000000## 2.1 Fitness from deap import base from deap import creator ## FitnessMin creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) ## FitnessMulti creator.create("FitnessMulti", base.Fitness, weights=(-1.0, 1.0)) deap-1.0.1/doc/code/tutorials/part_2/2_2_1_list_of_floats.py0000644000076500000240000000115112117373622024015 0ustar felixstaff00000000000000## 2.2.1 List of floats import random import array import numpy from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) IND_SIZE=10 toolbox = base.Toolbox() toolbox.register("attr_float", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=IND_SIZE) creator.create("Individual", array.array, typecode="d", fitness=creator.FitnessMax) creator.create("Individual", numpy.ndarray, fitness=creator.FitnessMax)deap-1.0.1/doc/code/tutorials/part_2/2_2_2_permutation.py0000644000076500000240000000067312117373622023366 0ustar felixstaff00000000000000## 2.2.2 Permutation import random from deap import base from deap import creator from deap import tools creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) IND_SIZE=10 toolbox = base.Toolbox() toolbox.register("indices", random.sample, range(IND_SIZE), IND_SIZE) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices) deap-1.0.1/doc/code/tutorials/part_2/2_2_3_arithmetic_expression.py0000644000076500000240000000120412301410325025402 0ustar felixstaff00000000000000## 2.2.3 Arithmetic expression import operator from deap import base from deap import creator from deap import gp from deap import tools pset = gp.PrimitiveSet("MAIN", arity=1) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.sub, 2) pset.addPrimitive(operator.mul, 2) creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin, pset=pset) toolbox = base.Toolbox() toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) deap-1.0.1/doc/code/tutorials/part_2/2_2_4_evolution_strategy.py0000644000076500000240000000150012117373622024755 0ustar felixstaff00000000000000## 2.2.4 Evolution Strategy import array import random from deap import base from deap import creator from deap import tools creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode="d", fitness=creator.FitnessMin, strategy=None) creator.create("Strategy", array.array, typecode="d") def initES(icls, scls, size, imin, imax, smin, smax): ind = icls(random.uniform(imin, imax) for _ in range(size)) ind.strategy = scls(random.uniform(smin, smax) for _ in range(size)) return ind IND_SIZE = 10 MIN_VALUE, MAX_VALUE = -5., 5. MIN_STRAT, MAX_STRAT = -1., 1. toolbox = base.Toolbox() toolbox.register("individual", initES, creator.Individual, creator.Strategy, IND_SIZE, MIN_VALUE, MAX_VALUE, MIN_STRAT, MAX_STRAT) deap-1.0.1/doc/code/tutorials/part_2/2_2_5_particle.py0000644000076500000240000000125112117373622022616 0ustar felixstaff00000000000000## 2.2.6 Particle import random from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Particle", list, fitness=creator.FitnessMax, speed=None, smin=None, smax=None, best=None) def initParticle(pcls, size, pmin, pmax, smin, smax): part = pcls(random.uniform(pmin, pmax) for _ in xrange(size)) part.speed = [random.uniform(smin, smax) for _ in xrange(size)] part.smin = smin part.smax = smax return part toolbox = base.Toolbox() toolbox.register("particle", initParticle, creator.Particle, size=2, pmin=-6, pmax=6, smin=-3, smax=3) deap-1.0.1/doc/code/tutorials/part_2/2_2_6_funky_one.py0000644000076500000240000000111312117373622023006 0ustar felixstaff00000000000000## 2.2.6 Funky one import random from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() INT_MIN, INT_MAX = 5, 10 FLT_MIN, FLT_MAX = -0.2, 0.8 N_CYCLES = 4 toolbox.register("attr_int", random.randint, INT_MIN, INT_MAX) toolbox.register("attr_flt", random.uniform, FLT_MIN, FLT_MAX) toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_int, toolbox.attr_flt), n=N_CYCLES) deap-1.0.1/doc/code/tutorials/part_2/2_3_1_bag.py0000644000076500000240000000102712117373622021542 0ustar felixstaff00000000000000## 2.3.1 Bag import random from deap import base from deap import creator from deap import tools creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) IND_SIZE = 20 toolbox = base.Toolbox() toolbox.register("attr_int", random.randint, -20, 20) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_int, n=IND_SIZE) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.population(n=100) deap-1.0.1/doc/code/tutorials/part_2/2_3_2_grid.py0000644000076500000240000000122212117373622021734 0ustar felixstaff00000000000000## 2.3.2 Grid import random from deap import base from deap import creator from deap import tools creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) IND_SIZE = 20 toolbox = base.Toolbox() toolbox.register("attr_float", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=IND_SIZE) N_ROW, N_COL = 20, 10 toolbox.register("row", tools.initRepeat, list, toolbox.individual, n=N_COL) toolbox.register("population", tools.initRepeat, list, toolbox.row, n=N_ROW) population = toolbox.population() population[9][9] deap-1.0.1/doc/code/tutorials/part_2/2_3_3_swarm.py0000644000076500000240000000147512117373622022153 0ustar felixstaff00000000000000## 2.2.6 Particle import random from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Particle", list, fitness=creator.FitnessMax, speed=None, smin=None, smax=None, best=None) creator.create("Swarm", list, gbest=None, gbestfit=creator.FitnessMax) def initParticle(pcls, size, pmin, pmax, smin, smax): part = pcls(random.uniform(pmin, pmax) for _ in xrange(size)) part.speed = [random.uniform(smin, smax) for _ in xrange(size)] part.smin = smin part.smax = smax return part toolbox = base.Toolbox() toolbox.register("particle", initParticle, creator.Particle, size=2, pmin=-6, pmax=6, smin=-3, smax=3) toolbox.register("swarm", tools.initRepeat, creator.Swarm, toolbox.particle) deap-1.0.1/doc/code/tutorials/part_2/2_3_4_demes.py0000644000076500000240000000111112117373622022103 0ustar felixstaff00000000000000## 2.3.4 Demes import random from deap import base from deap import creator from deap import tools creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) IND_SIZE=10 toolbox = base.Toolbox() toolbox.register("indices", random.sample, range(IND_SIZE), IND_SIZE) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices) toolbox.register("deme", tools.initRepeat, list, toolbox.individual) DEME_SIZES = 10, 50, 100 population = [toolbox.deme(n=i) for i in DEME_SIZES] deap-1.0.1/doc/code/tutorials/part_2/2_3_5_seeding_a_population.py0000644000076500000240000000122412117373622025204 0ustar felixstaff00000000000000# 2.3.5 Seeding a population import json from deap import base from deap import creator creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) def initIndividual(icls, content): return icls(content) def initPopulation(pcls, ind_init, filename): contents = json.load(open(filename, "r")) return pcls(ind_init(c) for c in contents) toolbox = base.Toolbox() toolbox.register("individual_guess", initIndividual, creator.Individual) toolbox.register("population_guess", initPopulation, list, toolbox.individual_guess, "my_guess.json") population = toolbox.population_guess() deap-1.0.1/doc/code/tutorials/part_2/my_guess.json0000644000076500000240000000006312117373622022301 0ustar felixstaff00000000000000[ [1,2,3,4,5,6], [0,0,0,0,1,1], [1,1,1,1,1,1] ] deap-1.0.1/doc/code/tutorials/part_3/0000755000076500000240000000000012321001644017542 5ustar felixstaff00000000000000deap-1.0.1/doc/code/tutorials/part_3/3_6_2_tool_decoration.py0000644000076500000240000000140412117373622024202 0ustar felixstaff00000000000000from deap import base from deap import creator from deap import tools toolbox = base.Toolbox() MIN, MAX = -5, 5 def checkBounds(min, max): def decorator(func): def wrapper(*args, **kargs): offspring = func(*args, **kargs) for child in offspring: for i in xrange(len(child)): if child[i] > max: child[i] = max elif child[i] < min: child[i] = min return offspring return wrapper return decorator toolbox.register("mate", tools.cxBlend, alpha=0.2) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=2) toolbox.decorate("mate", checkBounds(MIN, MAX)) toolbox.decorate("mutate", checkBounds(MIN, MAX)) deap-1.0.1/doc/code/tutorials/part_3/3_6_using_the_toolbox.py0000644000076500000240000000356312301410325024323 0ustar felixstaff00000000000000## 3.6 Using the Toolbox from deap import base from deap import tools toolbox = base.Toolbox() def evaluateInd(individual): # Do some computation result = sum(individual) return result, toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", evaluateInd) ## Data structure and initializer creation import random from deap import creator creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox.register("attr_float", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, 10) toolbox.register("population", tools.initRepeat, list, toolbox.individual) pop = toolbox.population(n=100) CXPB, MUTPB, NGEN= 0.7, 0.3, 25 ## 3.6.1 Using the Tools for g in range(NGEN): # Select the next generation individuals offspring = toolbox.select(pop, len(pop)) # Clone the selected individuals offspring = map(toolbox.clone, offspring) # Apply crossover on the offspring for child1, child2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values # Apply mutation on the offspring for mutant in offspring: if random.random() < MUTPB: toolbox.mutate(mutant) del mutant.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # The population is entirely replaced by the offspring pop[:] = offspringdeap-1.0.1/doc/code/tutorials/part_3/3_7_variations.py0000644000076500000240000000307612301410325022747 0ustar felixstaff00000000000000## 3.7 Variations import random from deap import base from deap import creator from deap import tools ## Data structure and initializer creation creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_float", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, 10) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def onemax(individual): return sum(individual), toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", onemax) pop = toolbox.population(n=100) CXPB, MUTPB, NGEN= 0.7, 0.3, 25 fitnesses = toolbox.map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit from deap import algorithms for g in range(NGEN): # Select and clone the next generation individuals offspring = map(toolbox.clone, toolbox.select(pop, len(pop))) # Apply crossover and mutation on the offspring offspring = algorithms.varAnd(offspring, toolbox, CXPB, MUTPB) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # The population is entirely replaced by the offspring pop[:] = offspringdeap-1.0.1/doc/code/tutorials/part_3/3_8_algorithms.py0000644000076500000240000000203212301410325022731 0ustar felixstaff00000000000000## 3.7 Variations import random from deap import base from deap import creator from deap import tools ## Data structure and initializer creation creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_float", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, 10) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def onemax(individual): return sum(individual), toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", onemax) pop = toolbox.population(n=100) CXPB, MUTPB, NGEN= 0.7, 0.3, 25 fitnesses = toolbox.map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit from deap import algorithms algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=50) deap-1.0.1/doc/code/tutorials/part_3/3_next_step.py0000644000076500000240000000275412117373622022372 0ustar felixstaff00000000000000## 3.1 A First Individual import random from deap import base from deap import creator from deap import tools IND_SIZE = 5 creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("attr_float", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=IND_SIZE) ind1 = toolbox.individual() print ind1 # [0.86..., 0.27..., 0.70..., 0.03..., 0.87...] print ind1.fitness.valid # False ## 3.2 Evaluation def evaluate(individual): # Do some hard computing on the individual a = sum(individual) b = len(individual) return a, 1. / b ind1.fitness.values = evaluate(ind1) print ind1.fitness.valid # True print ind1.fitness # (2.73, 0.2) ## 3.3 Mutation mutant = toolbox.clone(ind1) ind2, = tools.mutGaussian(mutant, mu=0.0, sigma=0.2, indpb=0.2) del mutant.fitness.values print ind2 is mutant # True print mutant is ind1 # False ## 3.4 Crossover child1, child2 = [toolbox.clone(ind) for ind in (ind1, ind2)] tools.cxBlend(child1, child2, 0.5) del child1.fitness.values del child2.fitness.values ## 3.5 Selection selected = tools.selBest([child1, child2], 2) print child1 in selected # True ## 3.5 Note LAMBDA = 10 toolbox.register("select", tools.selRandom) population = [ind1, ind2]*10 selected = toolbox.select(population, LAMBDA) offspring = [toolbox.clone(ind) for ind in selected] deap-1.0.1/doc/code/tutorials/part_3/logbook.py0000644000076500000240000000254512301410325021554 0ustar felixstaff00000000000000import pickle from deap import tools from stats import record logbook = tools.Logbook() logbook.record(gen=0, evals=30, **record) print(logbook) gen, avg = logbook.select("gen", "avg") pickle.dump(logbook, open("logbook.pkl", "w")) # Cleaning the pickle file ... import os os.remove("logbook.pkl") logbook.header = "gen", "avg", "spam" print(logbook) print(logbook.stream) logbook.record(gen=1, evals=15, **record) print(logbook.stream) from multistats import record logbook = tools.Logbook() logbook.record(gen=0, evals=30, **record) logbook.header = "gen", "evals", "fitness", "size" logbook.chapters["fitness"].header = "min", "avg", "max" logbook.chapters["size"].header = "min", "avg", "max" print(logbook) gen = logbook.select("gen") fit_mins = logbook.chapters["fitness"].select("min") size_avgs = logbook.chapters["size"].select("avg") import matplotlib.pyplot as plt fig, ax1 = plt.subplots() line1 = ax1.plot(gen, fit_mins, "b-", label="Minimum Fitness") ax1.set_xlabel("Generation") ax1.set_ylabel("Fitness", color="b") for tl in ax1.get_yticklabels(): tl.set_color("b") ax2 = ax1.twinx() line2 = ax2.plot(gen, size_avgs, "r-", label="Average Size") ax2.set_ylabel("Size", color="r") for tl in ax2.get_yticklabels(): tl.set_color("r") lns = line1 + line2 labs = [l.get_label() for l in lns] ax1.legend(lns, labs, loc="center right") plt.show()deap-1.0.1/doc/code/tutorials/part_3/multistats.py0000644000076500000240000000340012301410325022320 0ustar felixstaff00000000000000import operator import random import numpy from deap import algorithms from deap import base from deap import creator from deap import gp from deap import tools random.seed(0) stats_fit = tools.Statistics(key=lambda ind: ind.fitness.values) stats_size = tools.Statistics(key=len) mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size) mstats.register("avg", numpy.mean) mstats.register("std", numpy.std) mstats.register("min", numpy.min) mstats.register("max", numpy.max) creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin) pset = gp.PrimitiveSet("MAIN", 1) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.mul, 2) pset.addEphemeralConstant("rand101", lambda: random.randint(-1,1)) pset.renameArguments(ARG0='x') def evalSymbReg(individual, points): # Transform the tree expression in a callable function func = toolbox.compile(expr=individual) # Evaluate the mean squared error between the expression # and the real function : x**4 + x**3 + x**2 + x sqerrors = ((func(x) - x**4 - x**3 - x**2 - x)**2 for x in points) return sum(sqerrors) / len(points), toolbox = base.Toolbox() toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evalSymbReg, points=[x/10. for x in range(-10,10)]) toolbox.register("compile", gp.compile, pset=pset) pop = toolbox.population(n=100) # Evaluate the individuals fitnesses = map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit record = mstats.compile(pop) print(record) deap-1.0.1/doc/code/tutorials/part_3/stats.py0000644000076500000240000000262212301410325021252 0ustar felixstaff00000000000000import random import numpy from deap import algorithms from deap import base from deap import creator from deap import tools random.seed(0) stats = tools.Statistics(key=lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) def evalOneMax(individual): return sum(individual), creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evalOneMax) pop = toolbox.population(n=100) # Evaluate the individuals fitnesses = map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit record = stats.compile(pop) print(record) stats = tools.Statistics(key=lambda ind: ind.fitness.values) stats.register("avg", numpy.mean, axis=0) stats.register("std", numpy.std, axis=0) stats.register("min", numpy.min, axis=0) stats.register("max", numpy.max, axis=0) record = stats.compile(pop) print(record) pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=0, stats=stats, verbose=True)deap-1.0.1/doc/code/tutorials/part_4/0000755000076500000240000000000012321001644017543 5ustar felixstaff00000000000000deap-1.0.1/doc/code/tutorials/part_4/4_4_Using_Cpp_NSGA.py0000644000076500000240000001160512301410325023263 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random from deap import algorithms from deap import base from deap import creator from deap import tools from deap import cTools import sortingnetwork as sn INPUTS = 6 def evalEvoSN(individual, dimension): network = sn.SortingNetwork(dimension, individual) return network.assess(), network.length, network.depth def genWire(dimension): return (random.randrange(dimension), random.randrange(dimension)) def genNetwork(dimension, min_size, max_size): size = random.randint(min_size, max_size) return [genWire(dimension) for i in xrange(size)] def mutWire(individual, dimension, indpb): for index, elem in enumerate(individual): if random.random() < indpb: individual[index] = genWire(dimension) def mutAddWire(individual, dimension): index = random.randint(0, len(individual)) individual.insert(index, genWire(dimension)) def mutDelWire(individual): index = random.randrange(len(individual)) del individual[index] creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() # Gene initializer toolbox.register("network", genNetwork, dimension=INPUTS, min_size=9, max_size=12) # Structure initializers toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.network) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evalEvoSN, dimension=INPUTS) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", mutWire, dimension=INPUTS, indpb=0.05) toolbox.register("addwire", mutAddWire, dimension=INPUTS) toolbox.register("delwire", mutDelWire) toolbox.register("select", cTools.selNSGA2) def main(): random.seed(64) population = toolbox.population(n=300) hof = tools.ParetoFront() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) CXPB, MUTPB, ADDPB, DELPB, NGEN = 0.5, 0.2, 0.01, 0.01, 40 # Evaluate every individuals fitnesses = toolbox.map(toolbox.evaluate, population) for ind, fit in zip(population, fitnesses): ind.fitness.values = fit hof.update(population) stats.update(population) # Begin the evolution for g in xrange(NGEN): print "-- Generation %i --" % g offspring = [toolbox.clone(ind) for ind in population] # Apply crossover and mutation for ind1, ind2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(ind1, ind2) del ind1.fitness.values del ind2.fitness.values # Note here that we have a different sheme of mutation than in the # original algorithm, we use 3 different mutations subsequently. for ind in offspring: if random.random() < MUTPB: toolbox.mutate(ind) del ind.fitness.values if random.random() < ADDPB: toolbox.addwire(ind) del ind.fitness.values if random.random() < DELPB: toolbox.delwire(ind) del ind.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit print " Evaluated %i individuals" % len(invalid_ind) population = toolbox.select(population+offspring, len(offspring)) hof.update(population) stats.update(population) print " Min %s" % stats.Min[0][-1][0] print " Max %s" % stats.Max[0][-1][0] print " Avg %s" % stats.Avg[0][-1][0] print " Std %s" % stats.Std[0][-1][0] best_network = sn.SortingNetwork(INPUTS, hof[0]) print best_network print best_network.draw() print "%i errors, length %i, depth %i" % hof[0].fitness.values return population, stats, hof if __name__ == "__main__": main() deap-1.0.1/doc/code/tutorials/part_4/4_5_home_made_eval_func.py0000644000076500000240000001271012301410325024523 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import sys import random import logging import time logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) from deap import algorithms from deap import base from deap import creator from deap import tools import SNC as snc # ... import sortingnetwork as sn INPUTS = 11 def evalEvoSN(individual, dimension): fit,depth,length= snc.evalNetwork(dimension, individual) return fit, length, depth def genWire(dimension): return (random.randrange(dimension), random.randrange(dimension)) def genNetwork(dimension, min_size, max_size): size = random.randint(min_size, max_size) return [genWire(dimension) for i in xrange(size)] def mutWire(individual, dimension, indpb): for index, elem in enumerate(individual): if random.random() < indpb: individual[index] = genWire(dimension) def mutAddWire(individual, dimension): index = random.randint(0, len(individual)) individual.insert(index, genWire(dimension)) def mutDelWire(individual): index = random.randrange(len(individual)) del individual[index] creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() # Gene initializer toolbox.register("network", genNetwork, dimension=INPUTS, min_size=9, max_size=12) # Structure initializers toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.network) toolbox.register("population", tools.initRepeat, list, toolbox.individual) # ... toolbox.register("evaluate", evalEvoSN, dimension=INPUTS) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", mutWire, dimension=INPUTS, indpb=0.05) toolbox.register("addwire", mutAddWire, dimension=INPUTS) toolbox.register("delwire", mutDelWire) toolbox.register("select", tools.selNSGA2) def main(): random.seed(64) population = toolbox.population(n=500) hof = tools.ParetoFront() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) CXPB, MUTPB, ADDPB, DELPB, NGEN = 0.5, 0.2, 0.01, 0.01, 10 # Evaluate every individuals fitnesses = toolbox.map(toolbox.evaluate, population) for ind, fit in zip(population, fitnesses): ind.fitness.values = fit hof.update(population) stats.update(population) # Begin the evolution for g in xrange(NGEN): t1 = time.time() print "-- Generation %i --" % g offspring = [toolbox.clone(ind) for ind in population] t2 = time.time() # Apply crossover and mutation for ind1, ind2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(ind1, ind2) del ind1.fitness.values del ind2.fitness.values t3 = time.time() # Note here that we have a different sheme of mutation than in the # original algorithm, we use 3 different mutations subsequently. for ind in offspring: if random.random() < MUTPB: toolbox.mutate(ind) del ind.fitness.values if random.random() < ADDPB: toolbox.addwire(ind) del ind.fitness.values if random.random() < DELPB: toolbox.delwire(ind) del ind.fitness.values t4 = time.time() # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit print " Evaluated %i individuals" % len(invalid_ind) t5 = time.time() population = toolbox.select(population+offspring, len(offspring)) t6 = time.time() #hof.update(population) stats.update(population) t7 = time.time() print stats print("Times :") print("Clone : " + str(t2-t1) + " (" + str((t2-t1)/(t7-t1)) +"%)") print("Cx : " + str(t3-t2) + " (" + str((t3-t2)/(t7-t1)) +"%)") print("Mut : " + str(t4-t3) + " (" + str((t4-t3)/(t7-t1)) +"%)") print("Eval : " + str(t5-t4) + " (" + str((t5-t4)/(t7-t1)) +"%)") print("Select : " + str(t6-t5) + " (" + str((t6-t5)/(t7-t1)) +"%)") print("HOF + stats : " + str(t7-t6) + " (" + str((t7-t6)/(t7-t1)) +"%)") print("TOTAL : " + str(t7-t1)) best_network = sn.SortingNetwork(INPUTS, hof[0]) print best_network print best_network.draw() print "%i errors, length %i, depth %i" % hof[0].fitness.values return population, stats, hof if __name__ == "__main__": main() deap-1.0.1/doc/code/tutorials/part_4/installSN.py0000644000076500000240000000036412117373622022042 0ustar felixstaff00000000000000from distutils.core import setup, Extension module1 = Extension('SNC', sources = ['SNC.cpp']) setup (name = 'SNC', version = '1.0', description = 'Sorting network evaluator', ext_modules = [module1]) deap-1.0.1/doc/code/tutorials/part_4/SNC.cpp0000644000076500000240000001371112117373622020710 0ustar felixstaff00000000000000/* This file is part of DEAP. DEAP is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. DEAP is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with DEAP. If not, see . */ /* This file is an example of a C evaluation function interfaced with DEAP; * It implements the evaluator of the sorting network problems, making it * about five times faster (up to 16x if used in combination with the * optimized cTools.nsga2 selection algorithm). * * To compile it, you may want to use distutils to simplify the linking, like * the installSN.py script provided with this package. * You may then import it like any other Python module : import SNC SNC.evalNetwork(dimension, individual) * See 4_5_home_made_eval_func.py for a comprehensive example. */ #include #include #include #include // Set of connectors that can be applied in parallel. typedef std::map Level; static PyObject* evalNetwork(PyObject *self, PyObject *args){ // Retrieve arguments (first : network dimensions; second : list of connectors [tuples]) PyObject *inDimensions = PyTuple_GetItem(args, 0); PyObject *listNetwork = PyTuple_GetItem(args, 1); std::vector mNetwork; const unsigned int inputs_size = (unsigned int)PyInt_AS_LONG(inDimensions); const unsigned int lNbTests = (1u << inputs_size); unsigned long lCountMisses = 0; unsigned int inWire1, inWire2; const unsigned long lgth = PyList_Size(listNetwork); // Network creation for(unsigned int k = 0; k < lgth; k++){ // Retrieve endpoint values inWire1 = (unsigned int)PyInt_AS_LONG(PyTuple_GetItem(PyList_GetItem(listNetwork,k),0)); inWire2 = (unsigned int)PyInt_AS_LONG(PyTuple_GetItem(PyList_GetItem(listNetwork,k),1)); // Check values of inWire1 and inWire2 if(inWire1 == inWire2) continue; if(inWire1 > inWire2) { const unsigned int lTmp = inWire1; inWire1 = inWire2; inWire2 = lTmp; } // Nothing in network, create new level and connector if(mNetwork.empty()) { Level lLevel; lLevel[inWire1] = inWire2; mNetwork.push_back(lLevel); continue; } // Iterator to the connector at current level, after mWire1 bool lConflict = false; Level::const_iterator lIterConnNext = mNetwork.back().begin(); for(; (lIterConnNext != mNetwork.back().end()) && (inWire1 > lIterConnNext->first); ++lIterConnNext); if(lIterConnNext != mNetwork.back().end()) { // Check if conflict with next connector and inWire2 if(inWire2 >= lIterConnNext->first) lConflict = true; } if(lIterConnNext != mNetwork.back().begin()) { // Iterator to the connector at current level, before mWire1 Level::const_iterator lIterConnPrev = lIterConnNext; --lIterConnPrev; // Check if conflict with previous connector and inWire1 if(inWire1 <= lIterConnPrev->second) lConflict = true; } // Add connector if(lConflict) { // Add new level of connectors Level lNextLevel; lNextLevel[inWire1] = inWire2; mNetwork.push_back(lNextLevel); } else { // Add connector to current level mNetwork.back()[inWire1] = inWire2; } } // Network test for(unsigned int i=0; i lSeqIOrig(inputs_size, 0.0); for(unsigned int j=0; j lSeqISorted(lSeqIOrig); for(unsigned int z=0; zfirst; const unsigned int lId2 = lIter->second; if(lSeqISorted[lId1] > lSeqISorted[lId2]) { const double lTmp = lSeqISorted[lId1]; lSeqISorted[lId1] = lSeqISorted[lId2]; lSeqISorted[lId2] = lTmp; } } } bool lIsSorted = true; bool lLastWasOne = false; for(unsigned int w=0; w. try: from itertools import product except ImportError: def product(*args, **kwds): # product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy # product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111 pools = map(tuple, args) * kwds.get('repeat', 1) result = [[]] for pool in pools: result = [x+[y] for x in result for y in pool] for prod in result: yield tuple(prod) class SortingNetwork(list): """Sorting network class. From Wikipedia : A sorting network is an abstract mathematical model of a network of wires and comparator modules that is used to sort a sequence of numbers. Each comparator connects two wires and sort the values by outputting the smaller value to one wire, and a larger value to the other. """ def __init__(self, dimension, connectors = []): self.dimension = dimension for wire1, wire2 in connectors: self.addConnector(wire1, wire2) def addConnector(self, wire1, wire2): """Add a connector between wire1 and wire2 in the network.""" if wire1 == wire2: return if wire1 > wire2: wire1, wire2 = wire2, wire1 try: last_level = self[-1] except IndexError: # Empty network, create new level and connector self.append({wire1: wire2}) return for wires in last_level.iteritems(): if wires[1] >= wire1 and wires[0] <= wire2: self.append({wire1: wire2}) return last_level[wire1] = wire2 def sort(self, values): """Sort the values in-place based on the connectors in the network.""" for level in self: for wire1, wire2 in level.iteritems(): if values[wire1] > values[wire2]: values[wire1], values[wire2] = values[wire2], values[wire1] def assess(self, cases=None): """Try to sort the **cases** using the network, return the number of misses. If **cases** is None, test all possible cases according to the network dimensionality. """ if cases is None: cases = product(range(2), repeat=self.dimension) misses = 0 ordered = [[0]*(self.dimension-i) + [1]*i for i in range(self.dimension+1)] for sequence in cases: sequence = list(sequence) self.sort(sequence) misses += (sequence != ordered[sum(sequence)]) return misses def draw(self): """Return an ASCII representation of the network.""" str_wires = [["-"]*7 * self.depth] str_wires[0][0] = "0" str_wires[0][1] = " o" str_spaces = [] for i in xrange(1, self.dimension): str_wires.append(["-"]*7 * self.depth) str_spaces.append([" "]*7 * self.depth) str_wires[i][0] = str(i) str_wires[i][1] = " o" for index, level in enumerate(self): for wire1, wire2 in level.iteritems(): str_wires[wire1][(index+1)*6] = "x" str_wires[wire2][(index+1)*6] = "x" for i in xrange(wire1, wire2): str_spaces[i][(index+1)*6+1] = "|" for i in xrange(wire1+1, wire2): str_wires[i][(index+1)*6] = "|" network_draw = "".join(str_wires[0]) for line, space in zip(str_wires[1:], str_spaces): network_draw += "\n" network_draw += "".join(space) network_draw += "\n" network_draw += "".join(line) return network_draw @property def depth(self): """Return the number of parallel steps that it takes to sort any input. """ return len(self) @property def length(self): """Return the number of comparison-swap used.""" return sum(len(level) for level in self) deap-1.0.1/doc/conf.py0000644000076500000240000001736612301410325014724 0ustar felixstaff00000000000000# -*- coding: utf-8 -*- # # DEAP documentation build configuration file, created by # sphinx-quickstart on Sat Jan 30 13:21:43 2010. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, time # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.append("..") #sys.path.append(os.path.abspath('_ext/')) import deap # -- General configuration ----------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.todo', 'sphinx.ext.pngmath', 'sphinx.ext.intersphinx', 'sphinx.ext.extlinks'] try: import matplotlib except: pass else: extensions += ['matplotlib.sphinxext.only_directives', 'matplotlib.sphinxext.plot_directive'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8' # The master toctree document. master_doc = 'index' # General information about the project. project = u'DEAP' copyright = u'2009-%s, DEAP Project' % time.strftime('%Y') # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = deap.__version__ # The full version, including alpha/beta/rc tags. release = deap.__revision__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. #unused_docs = [] # List of directories, relative to source directory, that shouldn't be searched # for source files. exclude_trees = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'default' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, the todo will be printed in the documentation todo_include_todos = True # Search in python documentation intersphinx_mapping = {'python' : ('http://docs.python.org/', None), 'numpy' : ('http://docs.scipy.org/doc/numpy', None)} # Reload the cached values every 5 days intersphinx_cache_limit = 5 # -- Options for pyplot extension ---------------------------------------------- # Default value for the include-source option plot_include_source = False # Code that should be executed before each plot. #plot_pre_code # Base directory, to which ``plot::`` file names are relative # to. (If None or empty, file names are relative to the # directory where the file containing the directive is.) #plot_basedir # Whether to show links to the files in HTML. plot_html_show_formats = True # -- Options for extlinks extension ---------------------------------------------- import subprocess branch = str(subprocess.check_output(["hg", "branch"])[:-1]) extlinks = {'example': ('https://code.google.com/p/deap/source/browse/examples/%s.py?name='+branch, 'examples/')} # -- Options for HTML output --------------------------------------------------- # Add any paths that contain custom themes here, relative to this directory. html_theme_path = ["_themes"] # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. html_theme = 'pydoctheme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = {'collapsiblesidebar': True} # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = "" # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = "deap_orange_icon_16x16.ico" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. html_use_smartypants = True # Custom sidebar templates, maps document names to template names. html_sidebars = { 'index': 'indexsidebar.html', } # Additional templates that should be rendered to pages, maps page names to # template names. html_additional_pages = {} # If false, no module index is generated. #html_use_modindex = True # If false, no index is generated. html_use_index = True # If true, the index is split into individual pages for each letter. html_split_index = True # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = '' # Output file base name for HTML help builder. htmlhelp_basename = 'DEAP-doc' # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('contents', 'DEAP.tex', u'DEAP Documentation', u'DEAP Project', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # Additional stuff for the LaTeX preamble. latex_preamble = '\usepackage{amsmath,amssymb}' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_use_modindex = True deap-1.0.1/doc/contributing.rst0000644000076500000240000000323512117373622016671 0ustar felixstaff00000000000000Contributing ============ Reporting a bug --------------- You can report a bug on the issue list on google code. ``_ Retrieving the latest code -------------------------- You can check the latest sources with the command:: hg clone https://deap.googlecode.com/hg If you want to use the development version, you have to update the repository to the developing branch with the command:: hg update dev Contributing code ----------------- Contact us on the deap users list at ``_. Coding guidelines ----------------- Most of those conventions are base on Python PEP8. *A style guide is about consistency. Consistency with this style guide is important. Consistency within a project is more important. Consistency within one module or function is most important.* Code layout +++++++++++ Same as PEP8. Imports +++++++ First imports in a file are the standard library module, then come the imports of eap module, and finally the custom module for a problem. Each block of imports should be separated by a new line. :: import system from deap import base import mymodule Whitespace in Expressions and Statements ++++++++++++++++++++++++++++++++++++++++ Same as PEP8. Comments ++++++++ Same as PEP8 Documentation Strings +++++++++++++++++++++ Same as PEP8 Naming Conventions ++++++++++++++++++ - **Module** : use the lowercase convention. - **Class** : class names use the CapWords? convention. - **Function** / Procedure : use the mixedCase convention. First word should be an action verb. - **Variable** : use the lower_case_with_underscores convention. deap-1.0.1/doc/examples/0000755000076500000240000000000012321001644015230 5ustar felixstaff00000000000000deap-1.0.1/doc/examples/bipop_cmaes.rst0000644000076500000240000000411612301410325020243 0ustar felixstaff00000000000000.. _bipopcma-es: ================================================ Controlling the Stopping Criteria: BI-POP CMA-ES ================================================ A variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [Hansen2001]_ implies to control very specifically the termination criteria in the generational loop. This can be achieved by writing the algorithm partially invoking manually the :meth:`generate` and :meth:`update` inside a loop with specific stop criteria. In fact, the BI-POP CMA-ES [Hansen2009]_ has 9 different stop criteria, which are used to control the independent restarts, with different population sizes, of a standard CMA-ES. As usual, the first thing to do is to create the types and as usual, we'll need a minimizing fitness and an individual that is a :class:`list`. .. literalinclude:: /../examples/es/cma_bipop.py :lines: 34-35 The main function includes the setting of some parameters, namely the number of increasing population restarts and the initial sigma value. Then, the instanciation of the :class:`~deap.base.Toolbox` is done in the main function because it will change with the restarts. Next are initialized the :class:`~deap.tools.HallOfFame`, The :class:`~deap.tools.statistics` and the list of :class:`~deap.tools.Logbook` objects, one for each restart. .. literalinclude:: /../examples/es/cma_bipop.py :lines: 37-53 Then the first loop controlling the restart is set up. It encapsulates the generational loop with its many stop criteria. The content of this last loop is simply the generate-update loop as presented in the :func:`deap.algorithms.eaGenerateUpdate` function. .. literalinclude:: /../examples/es/cma_bipop.py :lines: 62,101-110,114-130,151-188,192-194 Some variables have been omited for clarity, refer to the complete example for more details :example:`es/cma_bipop`. .. [Hansen2001] Hansen and Ostermeier, 2001. Completely Derandomized Self-Adaptation in Evolution Strategies. *Evolutionary Computation* .. [Hansen2009] Hansen, 2009. Benchmarking a BI-Population CMA-ES on the BBOB-2009 Function Testbed. *GECCO'09*deap-1.0.1/doc/examples/cmaes.rst0000644000076500000240000000270712301410325017056 0ustar felixstaff00000000000000.. _cma-es: =============================================== Covariance Matrix Adaptation Evolution Strategy =============================================== The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [Hansen2001]_ implemented in the :mod:`~deap.cma` module makes use of the generate-update paradigm where a population is generated from a strategy and the strategy is updated from the population. It is then straight forward to use it for continuous problem optimization. As usual the first thing to do is to create the types and as usual we'll need a minimizing fitness and an individual that is a :class:`list`. A toolbox is then created with the desired evaluation function. .. literalinclude:: /../examples/es/cma_minfct.py :lines: 28-32 Then, it does not get any harder. Once a :class:`~deap.cma.Strategy` is instantiated, its :meth:`~deap.cma.Strategy.generate` and :meth:`~deap.cma.Strategy.update` methods are registered in the toolbox for uses in the :func:`~deap.algorithms.eaGenerateUpdate` algorithm. The :meth:`~deap.cma.Strategy.generate` method is set to produce the created :class:`Individual` class. The random number generator from numpy is seeded because the :mod:`~deap.cma` module draws all its number from it. .. literalinclude:: /../examples/es/cma_minfct.py :lines: 34,36,37,41-50,52,54 .. [Hansen2001] Hansen and Ostermeier, 2001. Completely Derandomized Self-Adaptation in Evolution Strategies. *Evolutionary Computation* deap-1.0.1/doc/examples/cmaes_plotting.rst0000644000076500000240000000473612301410325021002 0ustar felixstaff00000000000000===================================================== Plotting Important Data: Visualizing the CMA Strategy ===================================================== With this example we show one technique for plotting the data of an evolution. As developpers of DEAP we cannot make a choice on what data is important to plot and this part is left to the user. Although, plotting would all occur the same way. First the data is gathered during the evolution and at the end the figures are created from the data. This model is the simplest possible. One could also write all data to a file and read those file again to plot the figures. This later model would be more fault tolerant as if the evolution does not terminate normally, the figures could still be plotted. But, we want to keep this example as simpe as possible and thus we will present the former model. Evolution Loop ============== The beginning of this example is exactly the same as the :ref:`CMA-ES ` example. The general evolution loop of function :func:`~deap.algorithms.eaGenerateUpdate` is somewhat insufficient for our purpose. We need to gather the required data on each generation. So instead of using the :func:`~deap.algorithms.eaGenerateUpdate` function, we'll develop it to get a grip on what is recorded. First, we'll create objects to record our data. Here we want to plot, in addition to what the :class:`~deap.tools.Logbook` and :class:`~deap.tools.HallOfFame` objects contain, the step size, the axis ratio and the major axis of the covariace matrix, the best value so far, the best coordinates so far and the standard deviation of the all coordinates at each generation. .. literalinclude:: /../examples/es/cma_plotting.py :lines: 59-64 Once the objects are created, the evolution loop, based on a generational stopping criterion, calls repeatedly the :meth:`generate`, :meth:`evaluate` and :meth:`update` methods registered in the toolbox. .. literalinclude:: /../examples/es/cma_plotting.py :lines: 66-75 Then, the previoulsy created objects start to play their role. The data is recorded in each object on each generation. .. literalinclude:: /../examples/es/cma_plotting.py :lines: 88-93 Now that the data is recorded the only thing left to do is to plot it. We'll use `matplotlib `_ to generate the graphics from the recorded data. .. literalinclude:: /../examples/es/cma_plotting.py :lines: 95-124 Which gives the following result. .. plot:: ../examples/es/cma_plotting.py :width: 67%deap-1.0.1/doc/examples/coev_coop.rst0000644000076500000240000000556312301410325017745 0ustar felixstaff00000000000000======================= Cooperative Coevolution ======================= This example explores cooperative coevolution using DEAP. This tutorial is not as complete as previous examples concerning type creation and other basic stuff. Instead, we cover the concepts of coevolution as they would be applied in DEAP. Assume that if a function from the toolbox is used, it has been properly registered. This example makes a great template for implementing your own coevolutionary algorithm, it is based on the description of cooperative coevolution by [Potter2001]_. Coevolution is, in fact, just an extension of how algorithms works in deap. Multiple populations are evolved in turn (or simultaneously on multiple processors) just like in traditional genetic algorithms. The implementation of the coevolution is thus straightforward. A first loop acts for iterating over the populations and a second loop iterates over the individuals of these population. The first step is to create a bunch of species that will evolve in our population. .. literalinclude:: /../examples/coev/coop_evol.py :lines: 72 Cooperative coevolution works by sending the best individual of each species (called representative) to help in the evaluation of the individuals of the other species. Since the individuals are not yet evaluated we select randomly the individuals that will be in the set of representatives. .. literalinclude:: /../examples/coev/coop_evol.py :lines: 77 The evaluation function takes a list of individuals to be evaluated including the representatives of the other species and possibly some other arguments. It is not presented in detail for scope reasons, the structure would be, as usual, something like this :: def evaluate(individuals): # Compute the collaboration fitness return fitness, The evolution can now begin. .. literalinclude:: /../examples/coev/coop_evol.py :lines: 85-96,103-106,113-114 The last lines evolve each species once before sharing their representatives. The common parts of an evolutionary algorithm are all present, variation, evaluation and selection occurs for each species. The species index is simply a unique number identifying each species, it can be used to keep independent statistics on each new species added. After evolving each species, steps described in [Potter2001]_ are achieved to add a species and remove useless species on stagnation. These steps are not covered in this example but are present in the complete source code of the coevolution examples. - :example:`Coevolution Base ` - :example:`Coevolution Niching ` - :example:`Coevolution Generalization ` - :example:`Coevolution Adaptation ` - :example:`Coevolution Final ` .. [Potter2001] Potter, M. and De Jong, K., 2001, Cooperative Coevolution: An Architecture for Evolving Co-adapted Subcomponents. deap-1.0.1/doc/examples/eda.rst0000644000076500000240000000437112301410325016516 0ustar felixstaff00000000000000======================================= Making Your Own Strategy : A Simple EDA ======================================= As seen in the :ref:`cma-es` example, the :func:`~deap.algorithms.eaGenerateUpdate` algorithm is suitable for algorithms learning the problem distribution from the population. Here we'll cover how to implement a strategy that generates individuals based on an updated sampling function learnt from the sampled population. Estimation of distribution ========================== The basic concept concept behind EDA is to sample :math:`\lambda` individuals with a certain distribution and estimate the problem distribution from the :math:`\mu` best individuals. This really simple concept adhere to the generate-update logic. The strategy contains a random number generator which is adapted from the population. The following :class:`EDA` class do just that. .. literalinclude:: /../examples/eda/fctmin.py :pyobject: EDA A normal random number generator is initialized with a certain mean (*centroid*) and standard deviation (*sigma*) for each dimension. The :meth:`generate` method uses numpy to generate *lambda_* sequences in *dim* dimensions, then the sequences are used to initialize individuals of class given in the *ind_init* argument. Finally, the :meth:`update` computes the average (centre) of the `mu` best individuals and estimates the variance over all attributes of each individual. Once :meth:`update` is called the distributions parameters are changed and a new population can be generated. Objects Needed ============== Two classes are needed, a minimization fitness and a individual that will combine the fitness and the real values. Moreover, we will use :class:`numpy.ndarray` as base class for our individuals. .. literalinclude:: /../examples/eda/fctmin.py :lines: 28-29 Operators ========= The :func:`~deap.algorithms.eaGenerateUpdate` algorithm requires to set in a toolbox an evaluation function, an generation method and an update method. We will use the method of an initialized :class:`EDA`. For the generate method, we set the class that the individuals are transferred in to our :class:`Individual` class containing a fitness. .. literalinclude:: /../examples/eda/fctmin.py :pyobject: main The complete :example:`eda/fctmin`. deap-1.0.1/doc/examples/es_fctmin.rst0000644000076500000240000000457112301410325017736 0ustar felixstaff00000000000000=========================== Evolution Strategies Basics =========================== Evolution strategies are special types of evolutionary computation algorithms where the mutation strength is learnt during the evolution. A first type of strategy (endogenous) includes directly the mutation strength for each attribute of an individual inside the individual. This mutation strength is subject to evolution similarly to the individual in a classic genetic algorithm. For more details, [Beyer2002]_ presents a very good introduction to evolution strategies. In order to have this kind of evolution we'll need a type of individual that contains a :attr:`strategy` attribute. We'll also minimize the objective function, which gives the following classes creation. .. literalinclude:: /../examples/es/fctmin.py :lines: 33-35 The initialization function for an evolution strategy is not defined by DEAP. The following generation function takes as argument the class of individual to instantiate, *icls*. It also takes the class of strategy to use as strategy, *scls*. The next arguments are the minimum and maximum values for the individual and strategy attributes. The strategy is added in the :attr:`strategy` member of the returned individual. .. literalinclude:: /../examples/es/fctmin.py :pyobject: generateES This generation function is registered in the toolbox like any other initializer. .. literalinclude:: /../examples/es/fctmin.py :lines: 56-57 The strategy controls the standard deviation of the mutation. It is common to have a lower bound on the values so that the algorithm don't fall in exploitation only. This lower bound is added to the variation operator by the following decorator. .. literalinclude:: /../examples/es/fctmin.py :pyobject: checkStrategy The variation operators are decorated via the :meth:`~deap.base.Toolbox.decorate` method of the toolbox and the evaluation function is taken from the :mod:`~deap.benchmarks` module. .. literalinclude:: /../examples/es/fctmin.py :lines: 59,60,63-65 From here, everything left to do is either write the algorithm or use one provided in :mod:`~deap.algorithms`. Here we will use the :func:`~deap.algorithms.eaMuCommaLambda` algorithm. .. literalinclude:: /../examples/es/fctmin.py :lines: 67,69-81 The complete :example:`es/fctmin`. .. [Beyer2002] Beyer and Schwefel, 2002, Evolution strategies - A Comprehensive Introduction deap-1.0.1/doc/examples/es_onefifth.rst0000644000076500000240000000021212117373622020261 0ustar felixstaff00000000000000.. _one-fifth: ============== One Fifth Rule ============== Soon! .. The one fifth rule consists in changing the mutation strength when deap-1.0.1/doc/examples/ga_knapsack.rst0000644000076500000240000000573412301410325020233 0ustar felixstaff00000000000000===================================== Knapsack Problem: Inheriting from Set ===================================== Again for this example we will use a very simple problem, the 0-1 Knapsack. The purpose of this example is to show the simplicity of DEAP and the ease to inherit from anyting else than a simple list or array. Many evolutionary algorithm textbooks mention that the best way to have an efficient algorithm to have a representation close the problem. Here, what can be closer to a bag than a set? Lets make our individuals inherit from the :class:`set` class. .. literalinclude:: /../examples/ga/knapsack.py :lines: 41-42 That's it. You now have individuals that are in fact sets, they have the usual attribute :attr:`fitness`. The fitness is a minimization of the first objective (the weight of the bag) and a maximization of the second objective (the value of the bag). We will now create a dictionary of 100 random items to map the values and weights. .. literalinclude:: /../examples/ga/knapsack.py :lines: 34-39 We now need to initialize a population and the individuals therein. For this we will need a :class:`~deap.base.Toolbox` to register our generators since sets can also be created with an input iterable. .. literalinclude:: /../examples/ga/knapsack.py :lines: 44,47,50-53 Voilà! The *last* thing to do is to define our evaluation function. .. literalinclude:: /../examples/ga/knapsack.py :pyobject: evalKnapsack Everything is ready for evolution. Ho no wait, since DEAP's developers are lazy, there is no crossover and mutation operators that can be applied directly on sets. Lets define some. For example, a crossover, producing two children from two parents, could be that the first child is the intersection of the two sets and the second child their absolute difference. .. literalinclude:: /../examples/ga/knapsack.py :pyobject: cxSet A mutation operator could randomly add or remove an element from the set input individual. .. literalinclude:: /../examples/ga/knapsack.py :pyobject: mutSet We then register these operators in the toolbox. Since it is a multi-objective problem, we have selected the SPEA-II selection scheme : :func:`~deap.tools.selSPEA2` .. literalinclude:: /../examples/ga/knapsack.py :lines: 83-86 From here, everything left to do is either write the algorithm or use provided in :mod:`~deap.algorithms`. Here we will use the :func:`~deap.algorithms.eaMuPlusLambda` algorithm. .. literalinclude:: /../examples/ga/knapsack.py :lines: 88,90-107 Finally, a :class:`~deap.tools.ParetoFront` may be used to retrieve the best non dominated individuals of the evolution and a :class:`~deap.tools.Statistics` object is created for compiling four different statistics over the generations. The Numpy functions are registered in the statistics object with the ``axis=0`` argument to compute the statistics on each objective independently. Otherwise, Numpy would compute a single mean for both objectives. The complete :example:`ga/knapsack`. deap-1.0.1/doc/examples/ga_onemax.rst0000644000076500000240000002334112301410325017721 0ustar felixstaff00000000000000.. _ga-onemax: =============== One Max Problem =============== This is the first complete example built with DEAP. It will help new users to overview some of the framework possibilities. The problem is very simple, we search for a :data:`1` filled list individual. This problem is widely used in the evolutionary computation community since it is very simple and it illustrates well the potential of evolutionary algorithms. Setting Things Up ================= Here we use the one max problem to show how simple can be an evolutionary algorithm with DEAP. The first thing to do is to elaborate the structures of the algorithm. It is pretty obvious in this case that an individual that can contain a series of `booleans` is the most interesting kind of structure available. DEAP does not contain any explicit individual structure since it is simply a container of attributes associated with a fitness. Instead, it provides a convenient method for creating types called the creator. First of all, we need to import some modules. .. literalinclude:: /../examples/ga/onemax.py :lines: 16-20 ------- Creator ------- The creator is a class factory that can build at run-time new classes that inherit from a base classe. It is very useful since an individual can be any type of container from list to n-ary tree. The creator allows build complex new structures convenient for evolutionary computation. Let see an example of how to use the creator to setup an individual that contains an array of booleans and a maximizing fitness. We will first need to import the :mod:`deap.base` and :mod:`deap.creator` modules. The creator defines at first a single function :func:`~deap.creator.create` that is used to create types. The :func:`~deap.creator.create` function takes at least 2 arguments plus additional optional arguments. The first argument *name* is the actual name of the type that we want to create. The second argument *base* is the base classe that the new type created should inherit from. Finally the optional arguments are members to add to the new type, for example a :attr:`fitness` for an individual or :attr:`speed` for a particle. .. literalinclude:: /../examples/ga/onemax.py :lines: 22-23 The first line creates a maximizing fitness by replacing, in the base type :class:`~deap.base.Fitness`, the pure virtual :attr:`~deap.base.Fitness.weights` attribute by ``(1.0,)`` that means to maximize a single objective fitness. The second line creates an :class:`Individual` class that inherits the properties of :class:`list` and has a :attr:`fitness` attribute of the type :class:`FitnessMax` that was just created. In this last step, two things are of major importance. The first is the comma following the ``1.0`` in the weights declaration, even when implementing a single objective fitness, the weights (and values) must be iterable. We won't repeat it enough, in DEAP single objective is a special case of multiobjective. The second important thing is how the just created :class:`FitnessMax` can be used directly as if it was part of the :mod:`~deap.creator`. This is not magic. ------- Toolbox ------- A :class:`~deap.base.Toolbox` can be found in the base module. It is intended to store functions with their arguments. The toolbox contains two methods, :meth:`~deap.base.Toolbox.register` and :meth:`~deap.base.Toolbox.unregister` that are used to do the tricks. .. literalinclude:: /../examples/ga/onemax.py :lines: 25-31 In this code block we registered a generation function and two initialization functions. The generator :meth:`toolbox.attr_bool` when called, will draw a random integer between 0 and 1. The two initializers for their part will produce respectively initialized individuals and populations. Again, looking a little closer shows that their is no magic. The registration of tools in the toolbox only associates an *alias* to an already existing function and freezes part of its arguments. This allows to call the alias as if the majority of the (or every) arguments have already been given. For example, the :meth:`attr_bool` generator is made from the :func:`~random.randint` that takes two arguments *a* and *b*, with ``a <= n <= b``, where *n* is the returned integer. Here, we fix ``a = 0`` and ``b = 1``. It is the same thing for the initializers. This time, the :func:`~deap.tools.initRepeat` is frozen with predefined arguments. In the case of the :meth:`individual` method, :func:`~deap.tools.initRepeat` takes 3 arguments, a class that is a container -- here the :class:`Individual` is derived from a :class:`list` --, a function to fill the container and the number of times the function shall be repeated. When called, the :meth:`individual` method will thus return an individual initialized with what would be returned by 100 calls to the :meth:`attr_bool` method. Finally, the :meth:`population` method uses the same paradigm, but we don't fix the number of individuals that it should contain. The Evaluation Function ======================= The evaluation function is pretty simple in this case, we need to count the number of ones in the individual. This is done by the following lines of code. .. literalinclude:: /../examples/ga/onemax.py :lines: 33-34 The returned value must be an iterable of length equal to the number of objectives (weights). The Genetic Operators ===================== There is two way of using operators, the first one, is to simply call the function from the :mod:`~deap.tools` module and the second one is to register them with their argument in a toolbox as for the initialization methods. The most convenient way is to register them in the toolbox, because it allows to easily switch between operators if desired. The toolbox method is also used in the algorithms, see the :ref:`short-ga-onemax` for an example. Registering the operators and their default arguments in the toolbox is done as follow. .. literalinclude:: /../examples/ga/onemax.py :lines: 37-40 The evaluation is given the alias evaluate. Having a single argument being the individual to evaluate we don't need to fix any, the individual will be given later in the algorithm. The mutation, for its part, needs an argument to be fixed (the independent probability of each attribute to be mutated *indpb*). In the algorithms the :meth:`mutate` function is called with the signature ``mutant, = toolbox.mutate(mutant)``. This is the most convenient way because each mutation takes a different number of arguments, having those arguments fixed in the toolbox leave open most of the possibilities to change the mutation (or crossover, or selection, or evaluation) operator later in your researches. Evolving the Population ======================= Once the representation and the operators are chosen, we have to define an algorithm. A good habit to take is to define the algorithm inside a function, generally named ``main()``. ----------------------- Creating the Population ----------------------- Before evolving it, we need to instantiate a population. This step is done effortless using the method we registered in the toolbox. .. literalinclude:: /../examples/ga/onemax.py :lines: 42, 45 ``pop`` will be a :class:`list` composed of 300 individuals, *n* is the parameter left open earlier in the toolbox. The next thing to do is to evaluate this brand new population. .. literalinclude:: /../examples/ga/onemax.py :lines: 50-53 We first :func:`map` the evaluation function to every individual, then assign their respective fitness. Note that the order in ``fitnesses`` and ``population`` are the same. ----------------------- The Appeal of Evolution ----------------------- The evolution of the population is the last thing to accomplish. Let say that we want to evolve for a fixed number of generation :data:`NGEN`, the evolution will then begin with a simple for statement. .. literalinclude:: /../examples/ga/onemax.py :lines: 57-59 Is that simple enough? Lets continue with more complicated things, selecting, mating and mutating the population. The crossover and mutation operators provided within DEAP usually take respectively 2 and 1 individual(s) on input and return 2 and 1 modified individual(s), they also modify inplace these individuals. In a simple GA, the first step is to select the next generation. .. literalinclude:: /../examples/ga/onemax.py :lines: 61-64 This step creates an offspring list that is an exact copy of the selected individuals. The :meth:`toolbox.clone` method ensure that we don't own a reference to the individuals but an completely independent instance. Next, a simple GA would replace the parents by the produced children directly in the population. This is what is done by the following lines of code, where a crossover is applied with probability :data:`CXPB` and a mutation with probability :data:`MUTPB`. The ``del`` statement simply invalidate the fitness of the modified individuals. .. literalinclude:: /../examples/ga/onemax.py :lines: 66-76 The population now needs to be re-evaluated, we then apply the evaluation as seen earlier, but this time only on the individuals with an invalid fitness. .. literalinclude:: /../examples/ga/onemax.py :lines: 78-82 And finally, last but not least, we replace the old population by the offspring. .. literalinclude:: /../examples/ga/onemax.py :lines: 87 This is the end of the evolution part, it will continue until the predefined number of generation are accomplished. Although, some statistics may be gathered on the population, the following lines print the min, max, mean and standard deviation of the population. .. literalinclude:: /../examples/ga/onemax.py :lines: 89-100 A :class:`~deap.tools.Statistics` object has been defined to facilitate how statistics are gathered. It is not presented here so that we can focus on the core and not the gravitating helper objects of DEAP. The complete :example:`ga/onemax`. deap-1.0.1/doc/examples/ga_onemax_numpy.rst0000644000076500000240000000201512301410325021144 0ustar felixstaff00000000000000============================ One Max Problem: Using Numpy ============================ The numpy version one max genetic algorithm example is very similar to one max short example. The individual class is inherited from the :class:`numpy.ndarray`. .. literalinclude:: /../examples/ga/onemax_numpy.py :lines: 18,26 The first major difference is the crossover function that implements the copying mechanism mentionned in the :doc:`/tutorials/advanced/numpy` tutorial. .. literalinclude:: /../examples/ga/onemax_numpy.py :pyobject: cxTwoPointCopy This crossover function is added to the toolbox instead of the original :func:`deap.tools.cxTwoPoint` crossover. .. literalinclude:: /../examples/ga/onemax_numpy.py :lines: 67 The second major difference is the use of the *similar* function in the :class:`~deap.tools.HallOfFame` that has to be set to a :func:`numpy.array_equal` or :func:`numpy.allclose` .. literalinclude:: /../examples/ga/onemax_numpy.py :lines: 80 The complete source code: :example:`ga/onemax_numpy`.deap-1.0.1/doc/examples/ga_onemax_short.rst0000644000076500000240000000253012301410325021135 0ustar felixstaff00000000000000.. _short-ga-onemax: =============================== One Max Problem: Short Version =============================== The short one max genetic algorithm example is very similar to one max example. The only difference is that it makes use of the :mod:`~deap.algorithms` module that implements some basic evolutionary algorithms. The initialization are the same so we will skip this phase. The algorithms impemented use specific functions from the toolbox, in this case :func:`evaluate`, :func:`mate`, :func:`mutate` and :func:`~deap.Toolbox.select` must be registered. .. literalinclude:: /../examples/ga/onemax_short.py :lines: 41-44 The toolbox is then passed to the algorithm and the algorithm uses the registered function. .. literalinclude:: /../examples/ga/onemax_short.py :lines: 46, 49-58 The short GA One max example makes use of a :class:`~deap.tools.HallOfFame` in order to keep track of the best individual to appear in the evolution (it keeps it even in the case it extinguishes), and a :class:`~deap.tools.Statistics` object to compile the population statistics during the evolution. Every algorithms from the :mod:`~deap.algorithms` module can take these objects. Finally, the *verbose* keyword indicate wheter we want the algorithm to output the results after each generation or not. The complete source code: :example:`ga/onemax_short`. deap-1.0.1/doc/examples/gp_ant.rst0000644000076500000240000000606712301410325017241 0ustar felixstaff00000000000000.. _artificial-ant: ====================== Artificial Ant Problem ====================== The Artificial Ant problem is a more sophisticated yet classical GP problem, in which the evolved individuals have to control an artificial ant so that it can eat all the food located in a given environment. This example shows how DEAP can easily deal with more complex problems, including an intricate system of functions and resources (including a small simulator). For more information about this problem, see :ref:`refPapersAnt`. Primitives set used =================== We use the standard primitives set for the Artificial Ant problem : .. literalinclude:: /../examples/gp/ant.py :lines: 150-156 - ``if_food_ahead`` is a primitive which executes its first argument if there is food in front of the ant; else, it executes its second argument. - :func:`prog2` and :func:`prog3` are the equivalent of the lisp PROGN2 and PROGN3 functions. They execute their children in order, from the first to the last. For instance, prog2 will first execute its first argument, then its second. - :func:`move_forward` makes the artificial ant move one front. This is a terminal. - :func:`turn_right` and :func:`turn_left` makes the artificial ant turning clockwise and counter-clockwise, without changing its position. Those are also terminals. .. note:: There is no external input as in symbolic regression or parity. Although those functions are obviously not already built-in in Python, it is very easy to define them : .. literalinclude:: /../examples/gp/ant.py :lines: 62-75, 122-123 Partial functions are a powerful feature of Python which allow to create functions on the fly. For more detailed information, please refer to the Python documentation of :func:`functools.partial`. Evaluation function =================== The evaluation function use an instance of a simulator class to evaluate the individual. Each individual is given 600 moves on the simulator map (obtained from an external file). The fitness of each individual corresponds to the number of pieces of food picked up. In this example, we are using a classical trail, the *Santa Fe trail*, in which there is 89 pieces of food. Therefore, a perfect individual would achieve a fitness of 89. .. literalinclude:: /../examples/gp/ant.py :pyobject: evalArtificialAnt Where `ant` is the instance of the simulator used. The :func:`~deap.gp.evaluate` function is a convenience one provided by DEAP and returning an executable Python program from a GP individual and its primitives function set. Complete example ================ Except for the simulator code (about 75 lines), the code does not fundamentally differ from the :ref:`Symbolic Regression example `. Note that as the problem is harder, improving the selection pressure by increasing the size of the tournament to 7 allows to achieve better performance. The complete :example:`gp/ant` .. _refPapersAnt: Reference ========= *John R. Koza, "Genetic Programming I: On the Programming of Computers by Means of Natural Selection", MIT Press, 1992, pages 147-161.* deap-1.0.1/doc/examples/gp_multiplexer.rst0000644000076500000240000000344512301410325021026 0ustar felixstaff00000000000000.. _mux: ======================= Multiplexer 3-8 Problem ======================= The multiplexer problem is another extensively used GP problem. Basically, it trains a program to reproduce the behavior of an electronic `multiplexer `_ (mux). Usually, a 3-8 multiplexer is used (3 address entries, from A0 to A2, and 8 data entries, from D0 to D7), but virtually any size of multiplexer can be used. This problem was first defined by Koza (see :ref:`refPapersMux`). Primitives set used =================== The primitive set is almost the same as the set used in :ref:`Parity `. Three Boolean operators (and, or and not), imported from :mod:`operator`, and a specific if-then-else primitive, which return either its second or third argument depending on the value of the first one. .. literalinclude:: /../examples/gp/multiplexer.py :lines: 56-62 As usual, we also add two terminals, a Boolean true and a Boolean false. Evaluation function =================== To speed up the evaluation, the computation of the input/output pairs is done at start up, instead of at each evaluation call. This pre-computation also allows to easily tune the multiplexer size, by changing the value of *MUX_SELECT_LINES*. .. literalinclude:: /../examples/gp/multiplexer.py :lines: 32-54 After that, the evaluation function is trivial, as we have both inputs and output values. The fitness is then the number of well predicted outputs over the 2048 cases (for a 3-8 multiplexer). .. literalinclude:: /../examples/gp/multiplexer.py :pyobject: evalMultiplexer The complete :example:`gp/multiplexer`. .. _refPapersMux: Reference ========= *John R. Koza, "Genetic Programming I: On the Programming of Computers by Means of Natural Selection", MIT Press, 1992, pages 170-187.* deap-1.0.1/doc/examples/gp_parity.rst0000644000076500000240000000377712301410325017774 0ustar felixstaff00000000000000.. _parity: =================== Even-Parity Problem =================== Parity is one of the classical GP problems. The goal is to find a program that produces the value of the Boolean even parity given n independent Boolean inputs. Usually, 6 Boolean inputs are used (Parity-6), and the goal is to match the good parity bit value for each of the :math:`2^6 = 64` possible entries. The problem can be made harder by increasing the number of inputs (in the DEAP implementation, this number can easily be tuned, as it is fixed by a constant named PARITY_FANIN_M). For more information about this problem, see :ref:`refPapersParity`. Primitives set used =================== Parity uses standard Boolean operators as primitives, available in the Python operator module : .. literalinclude:: /../examples/gp/parity.py :lines: 49-55 In addition to the *n* inputs, we add two constant terminals, one at 0, one at 1. .. note:: As Python is a dynamic typed language, you can mix Boolean operators and integers without any issue. Evaluation function =================== In this implementation, the fitness of a Parity individual is simply the number of successful cases. Thus, the fitness is maximized, and the maximum value is 64 in the case of a 6 inputs problems. .. literalinclude:: /../examples/gp/parity.py :pyobject: evalParity `inputs` and `outputs` are two pre-generated lists, to speedup the evaluation, mapping a given input vector to the good output bit. The Python :func:`sum` function works on booleans (false is interpreted as 0 and true as 1), so the evaluation function boils down to sum the number of successful tests : the higher this sum, the better the individual. Conclusion ========== The other parts of the program are mostly the same as the :ref:`Symbolic Regression algorithm `. The complete :example:`gp/parity`. .. _refPapersParity: Reference ========= *John R. Koza, "Genetic Programming II: Automatic Discovery of Reusable Programs", MIT Press, 1994, pages 157-199.* deap-1.0.1/doc/examples/gp_spambase.rst0000644000076500000240000000656312301410325020253 0ustar felixstaff00000000000000.. _spambase: =================================== Spambase Problem: Strongly Typed GP =================================== This problem is a classification example using STGP (Strongly Typed Genetic Programming). The evolved programs work on floating-point values AND Booleans values. The programs must return a Boolean value which must be true if e-mail is spam, and false otherwise. It uses a base of emails (saved in *spambase.csv*, see :ref:`refPapersSpam`), from which it randomly chooses 400 emails to evaluate each individual. Primitives set ============== Strongly-typed GP is a more generic GP where each primitive, in addition to have an arity and a corresponding function, has also a specific return type and specific parameter(s) type. In this way, each primitive is someway describe as a pure C function, where each parameter has to be one of the good type, and where the return value type is specified before run time. .. note:: Actually, when the user does not specify return or parameters type, a default type is selected by DEAP. On standard GP, because all the primitives use this default type, this behaves as there was no type requirement. We define a typed primitive set almost the same way than a normal one, but we have to specify the types used. .. literalinclude:: /../examples/gp/spambase.py :lines: 37-68 On the first line, we see the declaration of a typed primitive set with :class:`~deap.gp.PrimitiveSetTyped`. The first argument remains the set name, but the next ones are the type of the entries (in this case, we have 57 float entries and one Boolean output; we could have written `float` 57 times, but it is fairly quicker and more understandable to use the :func:`itertools.repeat` function). The last argument remains the entries prefix. After that, we define the primitives themselves. The definition of a typed primitive has two additional parameters : a list containing the parameters type, in order, and the return type. The terminals set is then filled, with at least one terminal of each type, and that is for the primitive set declaration. Evaluation function =================== The evaluation function is very simple : it picks 400 mails at random in the spam database, and then checks if the prediction made by the individual matches the expected Boolean output. The count of well predicted emails is returned as the fitness of the individual (which is so, at most, 400). .. literalinclude:: /../examples/gp/spambase.py :pyobject: evalSpambase Toolbox ======= The toolbox used is very similar to the one presented in the symbolic regression example, but notice that we now use specific STGP operators for crossovers and mutations : .. literalinclude:: /../examples/gp/spambase.py :lines: 88-92 Conclusion ================ Although it does not really differ from the other problems, it is interesting to note how Python can decrease the programming time. Indeed, the spam database is in csv form : with many frameworks, you would have to manually read it, or use a non-standard library, but with Python, you can use the built-in module :mod:`csv` and, within 2 lines, it is done! The data is now in the matrix *spam* and can easily be used through all the program : The complete :example:`gp/spambase` .. _refPapersSpam: Reference ========= Data are from the Machine learning repository, http://www.ics.uci.edu/~mlearn/MLRepository.html deap-1.0.1/doc/examples/gp_symbreg.rst0000644000076500000240000001514612301410325020125 0ustar felixstaff00000000000000.. _symbreg: =============================================== Symbolic Regression Problem: Introduction to GP =============================================== Symbolic regression is one of the best known problems in GP (see :ref:`refPapersSymbreg`). It is commonly used as a tuning problem for new algorithms, but is also widely used with real-life distributions, where other regression methods may not work. It is conceptually a simple problem, and therefore makes a good introductory example for the GP framework in DEAP. All symbolic regression problems use an arbitrary data distribution, and try to fit the most accurately the data with a symbolic formula. Usually, a measure like the RMSE (Root Mean Square Error) is used to measure an individual's fitness. In this example, we use a classical distribution, the quartic polynomial :math:`(x^4 + x^3 + x^2 + x)`, a one-dimension distribution. *20* equidistant points are generated in the range [-1, 1], and are used to evaluate the fitness. Creating the primitives set =========================== One of the most crucial aspect of a GP program is the choice of the primitives set. They should make good building blocks for the individuals so the evolution can succeed. In this problem, we use a classical set of primitives, which are basic arithmetic functions : .. literalinclude:: /../examples/gp/symbreg.py :lines: 29-43 The redefinition of the division is made to protect it against a zero division error (which would crash the program). The other functions are simply a mapping from the Python :mod:`operator` module. The number following the function is the *arity* of the primitive, that is the number of entries it takes. On the last line, we declare an :class:`~deap.gp.Ephemeral` constant. This is a special terminal type, which does not have a fixed value. When the program appends an ephemeral constant terminal to a tree, the function it contains is executed, and its result is inserted as a constant terminal. In this case, those constant terminals can take the values -1, 0 or 1. The second argument of :class:`~deap.gp.PrimitiveSet` is the number of inputs. Here, as we have only a one dimension regression problem, there is only one input, but it could have as many as you want. By default, those inputs are named "ARGx", where "x" is a number, but you can easily rename them : .. literalinclude:: /../examples/gp/symbreg.py :lines: 44 Creator ======= As any evolutionary program, symbolic regression needs (at least) two object types : an individual containing the genotype and a fitness. We can easily create them with the creator : .. literalinclude:: /../examples/gp/symbreg.py :lines: 46-47 The first line creates the fitness object (this is a minimization problem, so the weight is negative). The `weights` argument must be an iterable of weights, even if there is only one fitness measure. The second line create the individual object itself. Very straightforward, we can see that it will be based upon a tree, to which we add a fitness. If, for any reason, the user would want to add any other attribute (for instance, a file in which the individual will be saved), it would be as easy as adding this attribute of any type to this line. After this declaration, any individual produced will contain those wanted attributes. Toolbox ======= Now, we want to register some parameters specific to the evolution process. In DEAP, this is done through the toolbox : .. literalinclude:: /../examples/gp/symbreg.py :lines: 49-67 First, a toolbox instance is created (in some problem types like coevolution, you may consider creating more than one toolbox). Then, we can register any parameters. The first lines register how to create an individual (by calling gp.genRamped with the previously defined primitive set), and how to create the population (by repeating the individual initialization). We may now introduce the evaluation function, which will receive an individual as input, and return the corresponding fitness. This function uses the `compile` function previously defined to transform the individual into its executable form -- that is, a program. After that, the evaluation is only simple maths, where the difference between the values produced by the evaluated individual and the real values are squared and summed to compute the RMSE, which is returned as the fitness of the individual. .. warning:: Even if the fitness only contains one measure, keep in mind that DEAP stores it as an iterable. Knowing that, you can understand why the evaluation function must return a tuple value (even if it is a 1-tuple) : .. literalinclude:: /../examples/gp/symbreg.py :pyobject: evalSymbReg :emphasize-lines: 9 Returning only the value would produce strange behaviors and errors, as the selection and stats functions relies on the fact that the fitness is always an iterable. Afterwards, we register the evaluation function. We also choose the selection method (a tournament of size 3), the mate method (one point crossover with uniform probability over all the nodes), the mutation method (an uniform probability mutation which may append a new full sub-tree to a node). At this point, any structure with an access to the toolbox instance will also have access to all of those registered parameters. Of course, the user could register other parameters basing on his needs. Statistics ========== Although optional, statistics are often useful in evolutionary programming. DEAP offers a simple class which can handle most of the "boring work". In this case, we want to compute the mean, standard deviation, minimum, and maximum of both the individuals fitness and size. For that we'll use a :class:`~deap.tools.MultiStatistics` object. .. literalinclude:: /../examples/gp/symbreg.py :lines: 75-81 Note that a simple :class:`~deap.tools.Statistics` object can be used, as in previous examples when statistics over a single key are desired. Launching the evolution ======================= At this point, DEAP has all the information needed to begin the evolutionary process, but nothing has been initialized. We can start the evolution by creating the population and then calling a complete algorithm. In this case, we'll use :func:`~deap.algorithms.eaSimple`. .. literalinclude:: /../examples/gp/symbreg.py :lines: 72,73,83-84 The hall of fame is a specific structure which contains the *n* best individuals (here, the best one only). The complete :example:`gp/symbreg`. .. _refPapersSymbreg: Reference ========= *John R. Koza, "Genetic Programming: On the Programming of Computers by Means of Natural Selection", MIT Press, 1992, pages 162-169.* deap-1.0.1/doc/examples/index.rst0000644000076500000240000000202312301410325017064 0ustar felixstaff00000000000000Examples ======== This section contains some documented examples of common toy problems often encountered in the evolutionary computation community. Note that there are several other examples in the ``deap/examples`` sub-directory of the framework. These can be used has ground work for implementing your own flavour of evolutionary algorithms. Genetic Algorithm (GA) ---------------------- .. toctree:: :maxdepth: 1 ga_onemax ga_onemax_short ga_onemax_numpy ga_knapsack coev_coop .. _gpexamples: Genetic Programming (GP) ------------------------ .. toctree:: :maxdepth: 1 gp_symbreg gp_parity gp_multiplexer gp_ant gp_spambase Evolution Strategy (ES) ----------------------- .. toctree:: :maxdepth: 1 es_fctmin es_onefifth cmaes bipop_cmaes cmaes_plotting Particle Swarm Optimization (PSO) --------------------------------- .. toctree:: :maxdepth: 1 pso_basic pso_multiswarm Estimation of Distribution Algorithms (EDA) ------------------------------------------- .. toctree:: :maxdepth: 1 eda deap-1.0.1/doc/examples/pso_basic.rst0000644000076500000240000001135312301410325017725 0ustar felixstaff00000000000000================================== Particle Swarm Optimization Basics ================================== The implementation presented here is the original PSO algorithm as presented in [Poli2007]_. From Wikipedia definition of PSO `PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae. The movements of the particles are guided by the best found positions in the search-space which are updated as better positions are found by the particles.` Modules ======= Before writing functions and algorithms, we need to import some module from the standard library and from DEAP. .. literalinclude:: /../examples/pso/basic.py :lines: 16-24 Representation ============== The particle's goal is to maximize the return value of the fonction at its position. PSO particles are essentially described as a positions in a search-space of D dimensions. Each particle also has a vector representing the speed of the particle in each dimension. Finally, each particle keeps a reference to the best state in which it has been so far. This translates in DEAP by the following two lines of code : .. literalinclude:: /../examples/pso/basic.py :lines: 26-28 Here we create two new objects in the :mod:`~deap.creator` space. First, we create a :class:`FitnessMax` object, and we specify the :attr:`~deap.base.Fitness.weights` to be ``(1.0,)``, this means we want to maximise the value of the fitness of our particles. The second object we create represent our particle. We defined it as a :class:`list` to which we add five attributes. The first attribute is the fitness of the particle, the second is the speed of the particle which is also goind to be a list, the third and fourth are the limit of the speed value, and the fifth attribute will be a reference to a copy of the best state the particle has been so far. Since the particle has no final state until at has been evaluated, the reference is set to ``None``. The speed limits are also set to ``None`` to allow configuration via the function :func:`generate` presented in the next section. Operators ========= PSO original algorithm use three operators : initializer, updater and evaluator. The initialization consist in generating a random position and a random speed for a particle. The next function create a particle and initialize its attributes, except for the attribute :attr:`best`, which will be set only after evaluation : .. literalinclude:: /../examples/pso/basic.py :pyobject: generate The function :func:`updateParticle` first computes the speed, then limits the speed values between ``smin`` and ``smax``, and finally computes the new particle position. .. literalinclude:: /../examples/pso/basic.py :pyobject: updateParticle The operators are registered in the toolbox with their parameters. The particle value at the beginning are in the range ``[-100, 100]`` (:attr:`pmin` and :attr:`pmax`), and the speed is limited in the range ``[-50, 50]`` through all the evolution. The evaluation function :func:`~deap.benchmarks.h1` is from [Knoek2003]_. The function is already defined in the benchmarks module, so we can register it directly. .. literalinclude:: /../examples/pso/basic.py :lines: 50-54 Algorithm ========= Once the operators are registered in the toolbox, we can fire up the algorithm by firstly creating a new population, and then apply the original PSO algorithm. The variable `best` contains the best particle ever found (it known as gbest in the original algorithm). .. literalinclude:: /../examples/pso/basic.py :pyobject: main Conclusion ========== The full PSO basic example can be found here : :example:`pso/basic`. This is a video of the algorithm in action, plotted with matplotlib_. The red dot represents the best solution found so far. .. _matplotlib: http://matplotlib.org/ .. raw:: html
    References ========== .. [Poli2007] Ricardo Poli, James Kennedy and Tim Blackwell, "Particle swarm optimization an overview". Swarm Intelligence. 2007; 1: 33–57 .. [Knoek2003] Arthur J. Knoek van Soest and L. J. R. Richard Casius, "The merits of a parallel genetic algorithm in solving hard optimization problems". Journal of Biomechanical Engineering. 2003; 125: 141–146 deap-1.0.1/doc/examples/pso_multiswarm.rst0000644000076500000240000000602412301410325021047 0ustar felixstaff00000000000000Moving Peaks Benchmark with Multiswarm PSO ========================================== In this example we show how to use the :class:`~deap.benchmarks.movingpeaks.MovingPeaks` benchmark. A popular algorithm on this benchmark is the Multiswarm PSO (MPSO) [Blackwell2004]_ which achieve a great offline error and is able to follow multiple peaks at the same time. Choosing the Scenario --------------------- The moving peak benchmark allows to choose from the 3 original scenarios proposed in the `original studies `_. This is done by retreiving one of the constants defined in the :mod:`~deap.benchmarks.movingpeaks` module. Here we will use Scenario 2. .. literalinclude:: /../examples/pso/multiswarm.py :lines: 37,41 Once the scenario is retrieved, we need to set a few more constants and instantiate the benchmark, here the number of dimensions and the bounds of the problem. .. literalinclude:: /./examples/pso/multiswarm.py :lines: 43-46 For a list of all the variables defined in the ``SENARIO_X`` dictionaries see :class:`~deap.benchmarks.movingpeaks.MovingPeaks` class documentation. Initialization -------------- As in every DEAP example we are required to create the objects. The moving peak benchmark is a max problem, thus we need a maximizing fitness. And, we associate that fitness to a particle as in the :doc:`pso_basic` example. .. literalinclude:: /../examples/pso/multiswarm.py :lines: 48-51 Then, the particle generator is defined. It takes the particle class object :data:`pclass` into which to put the data. Remeber that :class:`creator.Particle`, which is gonna be give to this argument in the toolbox, inherits from :class:`list` and can be initialized with an iterable. The position (elements of the list) and the speed (attribute) of the particle is set to randomly generated numbers between the given bounds. .. literalinclude:: /../examples/pso/multiswarm.py :pyobject: generate The next function update the particle position and speed. .. literalinclude:: /../examples/pso/multiswarm.py :pyobject: updateParticle Thereafter, a function "converting" a particle to a quantum particle with different possible distributions is defined. .. literalinclude:: /../examples/pso/multiswarm.py :pyobject: convertQuantum Finally, all the functions are registered in the toolbox for further use in the algorithm. .. literalinclude:: /../examples/pso/multiswarm.py :lines: 97-104 Moving Peaks ------------ The registered evaluation function in the toolbox refers directly to the instance of the :class:`~deap.benchmarks.movingpeaks.MovingPeaks` benchmark object :data:`mpb`. The call to :func:`mpb` evaluates the given individuals as any other evaluation function. Algorithm --------- The algorithm is fully detailed in the file :example:`pso/multiswarm`, it reflects what is described in [Blackwell2004]_. .. [Blackwell2004] Blackwell, T., & Branke, J. (2004). Multi-swarm optimization in dynamic environments. In *Applications of Evolutionary Computing* (pp. 489-500). Springer Berlin Heidelberg.deap-1.0.1/doc/index.rst0000644000076500000240000000404612320777200015266 0ustar felixstaff00000000000000.. image:: _static/deap_long.png :width: 300 px :align: right DEAP documentation ================== DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanism such as multiprocessing and `SCOOP `_. The following documentation presents the key concepts and many features to build your own evolutions. .. warning:: If your are inheriting from :class:`numpy.ndarray` see the :doc:`tutorials/advanced/numpy` tutorial and the :doc:`/examples/ga_onemax_numpy` example. .. sidebar:: Getting Help Having trouble? We’d like to help! * Search for information in the archives of the `deap-users mailing list `_, or post a question. * Report bugs with DEAP in our `issue tracker `_. * **First steps:** * :doc:`Overview (Start Here!) ` * :doc:`Installation ` * :doc:`Porting Guide ` * **Basic tutorials:** * :doc:`Part 1: creating types ` * :doc:`Part 2: operators and algorithms ` * :doc:`Part 3: logging statistics ` * :doc:`Part 4: using multiple processors ` * **Advanced tutorials:** * :doc:`tutorials/advanced/gp` * :doc:`tutorials/advanced/checkpoint` * :doc:`tutorials/advanced/benchmarking` * :doc:`tutorials/advanced/numpy` * :doc:`examples/index` * :doc:`api/index` * :doc:`releases` * :doc:`contributing` * :doc:`about` .. toctree:: :hidden: overview installation porting tutorials/basic/part1 tutorials/basic/part2 tutorials/basic/part3 tutorials/basic/part4 tutorials/advanced/gp tutorials/advanced/checkpoint tutorials/advanced/benchmarking tutorials/advanced/numpy examples/index api/index releases contributing about deap-1.0.1/doc/installation.rst0000644000076500000240000000217312301424454016656 0ustar felixstaff00000000000000Installation ============ Requirements ------------ DEAP is compatible with Python 2 and 3. The most basic features of DEAP requires Python 2.6. In order to combine the toolbox and the multiprocessing module Python 2.7 is needed for its support to pickle partial functions. The computation distribution requires SCOOP_. CMA-ES requires Numpy_, and we recommend matplotlib_ for visualization of results as it is fully compatible with DEAP's API. .. _SCOOP: http://code.google.com/p/scoop .. _Numpy: http://www.numpy.org/ .. _matplotlib: http://www.matplotlib.org/ Install DEAP ------------ We encourage you to use easy_install_ or pip_ to install DEAP on your system. Linux package managers like apt-get, yum, etc. usually provide an outdated version. :: easy_install deap or :: pip deap If you wish to build from sources, download_ or clone_ the repository and type:: python setup.py install .. _download: https://pypi.python.org/pypi/deap/ .. _clone: https://code.google.com/p/deap/source/checkout .. _easy_install: http://pythonhosted.org/distribute/easy_install.html .. _pip: http://www.pip-installer.org/en/latest/ deap-1.0.1/doc/Makefile0000644000076500000240000000610712301410325015054 0ustar felixstaff00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = _build # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . .PHONY: help clean html dirhtml pickle json htmlhelp qthelp latex changes linkcheck doctest help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " changes to make an overview of all changed/added/deprecated items" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" clean: -rm -rf $(BUILDDIR)/* html: PYTHONPATH=${PWD}/../ $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/EAP.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/EAP.qhc" latex: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @echo "Run \`make all-pdf' or \`make all-ps' in that directory to" \ "run these through (pdf)latex." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." deap-1.0.1/doc/overview.rst0000644000076500000240000000700312320777200016021 0ustar felixstaff00000000000000Overview ======== If your are used to an other evolutionary algorithm framework, you'll notice we do things differently with DEAP. Instead of limiting you with predefined types, we provide ways of creating the appropriate ones. Instead of providing closed initializers, we enable you to customize them as you wish. Instead of suggesting unfit operators, we explicitly ask you to choose them wisely. Instead of implementing many sealed algorithms, we allow you to write the one that fit all your needs. This tutorial will present a quick overview of what DEAP is all about along with what every DEAP program is made of. Types ----- The first thing to do is to think of the appropriate type for your problem. Then, instead of looking in the list of available types, DEAP enables you to build your own. This is done with the :mod:`~deap.creator` module. Creating an appropriate type might seems overwhelming but the creator makes it very easy. In fact, this is usually done in a single line. For example, the following creates a :class:`FitnessMin` class for a minimization problem and an :class:`Individual` class that is derived from a list with a fitness attribute set to the just created fitness. .. literalinclude:: /code/tutorials/part_1/1_where_to_start.py :lines: 2-4 That's it. More on creating types can be found in the :doc:`tutorials/basic/part1` tutorial. Initialization -------------- Once the types are created you need to fill them with sometimes random values, sometime guessed ones. Again, DEAP provides an easy mechanism to do just that. The :class:`~deap.base.Toolbox` is a container for tools of all sorts including initializers that can do what is needed of them. The following takes on the last lines of code to create the initializers for individuals containing random floating point numbers and for a population that contains them. .. literalinclude:: /code/tutorials/part_1/1_where_to_start.py :lines: 7-16 This creates functions to initialize populations from individuals that are themselves initialized with random float numbers. The functions are registered in the toolbox with there default arguments under the given name. For example, it will be possible to call the function :func:`toolbox.population` to instantly create a population. More initialization methods are found in the :doc:`tutorials/basic/part1` tutorial and the various :doc:`examples/index`. Operators --------- Operators are just like initalizers, excepted that some are already implemented in the :mod:`~deap.tools` module. Once you've chose the perfect ones simply register them in the toolbox. In addition you must create your evaluation function. This is how it is done in DEAP. .. literalinclude:: /code/tutorials/part_1/1_where_to_start.py :lines: 19-25 The registered functions are renamed by the toolbox to allows genericity so that the algorithm does not depend on operators name. Note also that fitness values must be iterable, that is why we return tuple in the evaluate function. More on this in the :doc:`tutorials/basic/part2` tutorial and :doc:`examples/index`. Algorithms ---------- Now that everything is ready, we can start to write our own algorithm. It is usually done in a main function. For the purpose of completeness we will develop the complete generational algorithm. .. literalinclude:: /code/tutorials/part_1/1_where_to_start.py :lines: 28-64 It is also possible to use one of the four algorithms readily available in the :mod:`~deap.algorithms` module, or build from some building blocks called variations also available in this module. deap-1.0.1/doc/pip_req.txt0000644000076500000240000000004012117373622015617 0ustar felixstaff00000000000000deap numpy>=1.5 matplotlib>=1.0 deap-1.0.1/doc/porting.rst0000644000076500000240000000660212312561303015635 0ustar felixstaff00000000000000============= Porting Guide ============= DEAP development's high velocity and our refusal to be at the mercy of backward compatibility can sometime induce minor headaches to our users. This concise guide should help you port your code from the latest version minus 0.1 to the current version. General ======= #. The algorithms from the :mod:`~deap.gp.algorithms` module now return a tuple of 2-elements : the population and a :class:`~deap.tools.Logbook`. #. Replace every call to DTM by calls to `SCOOP `_. #. Statistics and logging of data are accomplished by two distinct objects: :class:`~deap.tools.Statistics` and :class:`~deap.tools.Logbook`. Read the tutorial on :doc:`logging statistics `. #. Replace :class:`~deap.tools.EvolutionLogger` by :class:`~deap.tools.Logbook`. #. Replace usage of :func:`tools.mean`, :func:`tools.var`, :func:`tools.std`, and :func:`tools.median` by their Numpy equivalent. #. If the fitness has multiple objectives, add the keyword argument ``axis=0`` when registering statistical function. Genetic Algorithms (GA) ======================= #. Replace every call to the function :func:`~deap.tools.cxTwoPoints` by a call to :func:`~deap.tools.cxTwoPoint`. #. Remove any import of cTools. If you need a faster implementation of the non-dominated sort, use :func:`~deap.tools.sortLogNondominated`. #. When inheriting from Numpy, you must manually copy the slices and compare individuals with numpy comparators. See the :doc:`tutorials/advanced/numpy` tutorial. Genetic Programming (GP) ======================== #. Specify a ``name`` as the first argument of every call to :func:`~deap.gp.PrimitiveSet.addEphemeralConstant`. #. Replace every call to :func:`~deap.gp.lambdify` and :func:`~deap.gp.evaluate` by a call to :func:`~deap.gp.compile`. #. Remove the pset attribute from every :func:`~deap.creator.create` call when creating a primitive tree class. #. In the toolbox, register the primitive set as the ``pset`` argument of the following mutation operator: :func:`~deap.gp.mutUniform`, :func:`~deap.gp.mutNodeReplacement` and :func:`~deap.gp.mutInsert`. #. Replace every call to the function :func:`~deap.gp.genRamped` by a call to :func:`~deap.gp.genHalfAndHalf`. #. Replace every call to :func:`~deap.gp.stringify` by a call to :func:`str` or remove the call completely. #. Replace every call to :func:`~deap.gp.lambdifyADF` by a call to :func:`~deap.gp.compileADF`. #. Replace the decorators :func:`~deap.gp.staticDepthLimit` and :func:`~deap.gp.staticSizeLimit` by :func:`~deap.gp.staticLimit`. To specify a limit on either depth, size or any other attribute, it is now required to specify a `key` function. See :func:`~deap.gp.staticLimit` documentation for more information. Strongly Typed Genetic Programming (STGP) ----------------------------------------- #. :class:`~deap.gp.PrimitiveSetTyped` method now requires type arguments to be defined as classes instead of string, for example ``float`` instead of ``"float"``. Evolution Strategy (ES) ======================= #. Replace every call to the function :func:`~deap.tools.cxESTwoPoints` by a call to :func:`~deap.tools.cxESTwoPoint`. Still having problem? ===================== We have overlooked something and your code is still not working? No problem, contact us on the deap users list at ``_ and we will get you out of trouble in no time.deap-1.0.1/doc/releases.rst0000644000076500000240000001142412321001441015744 0ustar felixstaff00000000000000================== Release Highlights ================== Here is the list of changes made to DEAP for the current release. API enhancements ++++++++++++++++ - algorithms: Every algorithm now return the final population and a logbook containing the evolution statistical data. - base: Fitness objects are now hashable. - base: Added a ``dominates`` function to the Fitness, which can be replaced. This function is now used in most multi-objective specific selection methods instead of ``isDominated``. - base: Fitness - implementation of a ``__repr__`` method. (issue 20) - examples: Removed prefix (ga, gp, pso, etc.) from examples filename. - gp: Added ``pset`` to mutation operators that require it. - gp: Replaced the :func:`~deap.gp.stringify` function by :func:`PrimitiveTree.__str__`. Use ``str`` or ``print`` on trees to read their code. - gp: Added an explicit description of the error when there are no available primitive/terminal of a certain type. - gp: Added symbolic regression benchmarks in ``benchmarks.gp``. - gp: Removed the ephemeral generator. - gp: Added a :func:`~deap.gp.PrimitiveTree.from_string` function to :class:`~deap.gp.PrimitiveTree`. - gp: Added the possibility to name primitives added to a PrimitiveSet in :func:`~deap.gp.PrimitiveSet.addPrimitive`. - gp: Added object oriented inheritance to strongly typed genetic programming. - gp: :class:`~deap.gp.PrimitiveSetTyped` now requires real classes as type instead of string. See the :doc:`Spambase example `. - gp: Replaced :func:`~deap.gp.evaluate` and :func:`~deap.gp.lambdify` by a single function :func:`~deap.gp.compile`. - gp: Replaced :func:`~deap.gp.lambdifyADF` by :func:`~deap.gp.compileADF`. - gp: New :func:`~deap.gp.graph` function that returns a list of nodes, edges and a labels dictionnary that can then be feeded directly to networkx to draw the tree. - gp: Renamed :func:`deap.gp.genRamped` as :func:`deap.gp.genHalfAndHalf`. - gp: Merged :func:`~deap.gp.staticDepthLimit` and :func:`~deap.gp.staticSizeLimit` in a single function :func:`~deap.gp.staticLimit` which takes a key function in argument than can be return the height, the size or whatever attribute the tree should be limited on. - tools: Revised the :class:`~deap.tools.HallOfFame` to include only unique individuals. - tools: Changed the way statistics are computed. See the :class:`~deap.tools.Statistics` and :class:`~deap.tools.MultiStatistics` documentation for more details and the tutorial :doc:`logging statistics ` (issue 19). - tools: Replaced the :class:`EvolutionLogger` by :class:`~deap.tools.Logbook`. - tools: Removed :class:`~deap.tools.Checkpoint` class since it was more trivial to do simple checkpointing than using the class. The documentation now includes an example on how to do checkpointing without Checkpoint. - tools: Reorganize the operators in submodule, tools now being a package. - tools: Implementation of the logarithmic non-dominated sort by Fortin et al. (2013), available under the name :func:`~deap.tools.sortLogNondominated`. - tools: Mutation operators can now take either a value or a sequence of values as long as the individual as parameters (low, up, sigma, etc.). - tools: Removed DTM from the sources. - tools: Removed the cTools module. It was not properly maintained and rarely used. - tools: Renamed :func:`~deap.tools.cxTwoPoints` as :func:`~deap.tools.cxTwoPoint` - tools: Renamed :func:`~deap.tools.cxESTwoPoints` as :func:`~deap.tools.cxESTwoPoint` - tools: Bounds as well as some other attribute related parameters now accept iterables or values as argument in crossovers and mutations. Documentation enhancements ++++++++++++++++++++++++++ - Major overhaul of the documentation structure. - Tutorial are now decomposed in two categories: basic and advanced. - New tutorial on :doc:`logging statistics ` - New tutorial on :doc:`checkpointing ` - New tutorial on :doc:`inheriting from Numpy ` Bug fixes +++++++++ - creator: Issue 23: error in creator when using unicode source. - creator: create does not handle proper slicing of created classes inheriting from ``numpy.ndarray`` anymore. This was bug prone and extremely hard to maintain. Users are now requested to include ``numpy.copy()`` operation in their operators. A tutorial on inheriting from numpy is on its way. **Release 1.0.1**: - tools: issue #26: Operators with bounds do not work correctly when bounds are provided as list instead of iterator. rev: `b172432515af`, `9d4718a8cf2a`. - tools: add missing arguments to sortLogNondominated (`k`, `first_front_only`). rev: `f60a6520b666`, `4de7df29dd0f`. - gp: issue #32: :meth:`~deap.gp.PrimitiveTree.from_string` used incorrect argument order with STGP. rev: `58c1a0711e1f`. deap-1.0.1/doc/tutorials/0000755000076500000240000000000012321001644015440 5ustar felixstaff00000000000000deap-1.0.1/doc/tutorials/advanced/0000755000076500000240000000000012321001644017205 5ustar felixstaff00000000000000deap-1.0.1/doc/tutorials/advanced/benchmarking.rst0000644000076500000240000000556612301410325022401 0ustar felixstaff00000000000000Benchmarking Against the Bests (BBOB) ===================================== Once you've created your own algorithm, the structure of DEAP allows you to benchmark it against the best algorithms very easily. The interface of the `Black-Box Optimization Benchmark `_ (BBOB) is compatible with the toolbox. In fact, once your new algorithm is encapsulated in a main function, there is almost nothing else to do on DEAP's side. This tutorial will review the essential steps to bring everything to work with the very basic :ref:`one-fifth`. Preparing the Algorithm ----------------------- The BBOB makes use of many continuous functions on which the algorithm will be tested. These functions are given as an argument to the algorithm. The toolbox shall thus register the evaluation in the main function. The evaluation functions provided by BBOB return a fitness as a single value. The first step is to put each fitness in its own tuple, as required by DEAP's philosophy on single objective optimization. We will use a decorator for this. .. literalinclude:: ../../../examples/bbob.py :pyobject: tupleize The algorithm is encapsulated in a main function that receives four arguments: the evaluation function, the dimensionality of the problem, the maximum number of evaluations and the target value to reach. As stated earlier, the toolbox is initialized in the main function with the :func:`update` function (described in the example) and the evaluation function received, which is decorated by our tuple-izer. Then, the target fitness value is encapsulated in a :class:`FitnessMin` object so that we can easily compare the individuals with it. The last step is to define the algorithm, which is explained in the :ref:`one-fifth` example. .. literalinclude:: ../../../examples/bbob.py :pyobject: main Running the Benchmark --------------------- Now that the algorithm is ready, it is time to run it under the BBOB. The following code is taken from the BBOB example with added comments. The :mod:`fgeneric` module provides a :class:`LoggingFunction`, which take care of outputting all necessary data to compare the tested algorithm with the other ones published and to be published. This logger contains the current problem instance and provides the problem target. Since it is responsible of logging each evaluation function call, it is not even needed to save the best individual found by our algorithm (call to the :func:`main` function). The single line that is related to the provided algorithm in the call to the :func:`main` function. .. literalinclude:: ../../../examples/bbob.py :lines: 26,27,28,90-137 Once these experiments are done, the data contained in the :file:`ouput` directory can be used to build the results document. See the `BBOB `_ web site on how to build the document. The complete example is available in the file :example:`bbob`. deap-1.0.1/doc/tutorials/advanced/checkpoint.rst0000644000076500000240000000634412320777200022104 0ustar felixstaff00000000000000============= Checkpointing ============= In this tutorial, we will present how persistence can be achieved in your evolutions. The only required tools are a simple :class:`dict` and a serialization method. Important data will be inserted in the dictionary and serialized to a file so that if something goes wrong, the evolution can be restored from the last saved checkpoint. It can also serve to continue an evolution beyond the pre-fixed termination criterion. Checkpointing is not offered in standard algorithms such as eaSimple, eaMuPlus/CommaLambda and eaGenerateUpdate. You must create your own algorithm (or copy an existing one) and introduce this feature yourself. Starting with a very basic example, we will cover the necessary stuff to checkpoint everything needed to restore an evolution. We skip the class definition and registration of tools in the toolbox to go directly to the algorithm and the main function. Our main function receives an optional string argument containing the path of the checkpoint file to restore. :: import pickle def main(checkpoint=None): if checkpoint: # A file name has been given, then load the data from the file cp = pickle.load(open(checkpoint, "r")) population = cp["population"] start_gen = cp["generation"] halloffame = cp["halloffame"] logbook = cp["logbook"] random.setstate(cp["rndstate"]) else: # Start a new evolution population = toolbox.population(n=300) start_gen = 0 halloffame = tools.HallOfFame(maxsize=1) logbook = tools.Logbook() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("max", numpy.max) for gen in range(start_gen, NGEN): population = algorithms.varAnd(population, toolbox, cxpb=CXPB, mutpb=MUTPB) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit halloffame.update(population) record = stats.compile(population) logbook.record(gen=gen, evals=len(invalid_ind), **record) population = toolbox.select(population, k=len(population)) if gen % FREQ == 0: # Fill the dictionary using the dict(key=value[, ...]) constructor cp = dict(population=population, generation=gen, halloffame=halloffame, logbook=logbook, rndstate=random.getstate()) pickle.dump(cp, open("checkpoint_name.pkl", "w")) Now, the whole data will be written in a pickled dictionary every *FREQ* generations. Loading the checkpoint is done if the main function is given a path to a checkpoint file. In that case, the evolution continues from where it was in the last checkpoint. It will produce the exact same results as if it was not stopped and reloaded because we also restored the random module state. If you use numpy's random numbers, don't forget to save and reload their state too. deap-1.0.1/doc/tutorials/advanced/gp.rst0000644000076500000240000002724012320777200020361 0ustar felixstaff00000000000000.. _genprogtut: Genetic Programming =================== Genetic programming is a special field of evolutionary computation that aims at building programs automatically to solve problems independently of their domain. Although there exists diverse representations used to evolve programs, the most common is the syntax tree. .. image:: /_images/gptree.png :align: center For example, the above figure presents the program :math:`\max(x + 3 * y, x + x)`. For this tree and further examples, the leaves of the tree, in green, are called terminals, while the internal nodes, in red, are called primitives. The terminals are divided in two subtypes: the constants and the arguments. The constants remain the same for the entire evolution while the arguments are the program inputs. For the last presented tree, the arguments are the variables :math:`x` and :math:`y`, and the constant is the number :math:`3`. In DEAP, user defined primitives and terminals are contained in a primitive set. For now, two kinds of primitive set exists: the loosely and the strongly typed. Loosely Typed GP ---------------- Loosely typed GP does not enforce a specific type between the nodes. More specifically, primitives' arguments can be any primitives or terminals present in the primitive set. The following code define a loosely typed :class:`~deap.gp.PrimitiveSet` for the previous tree :: pset = PrimitiveSet("main", 2) pset.addPrimitive(max, 2) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.mul, 2) pset.addTerminal(3) The first line creates a primitive set. Its arguments are the name of the procedure it will generate (``"main"``) and its number of inputs, 2. The next three lines add functions as primitives. The first argument is the function to add and the second argument the function arity_. The last line adds a constant terminal. Currently, the default names for the arguments are ``"ARG0"`` and ``"ARG1"``. To change it to ``"x"`` and ``"y"``, simply call :: pset.renameArguments(ARG0="x") pset.renameArguments(ARG1="y") .. _arity: http://en.wikipedia.org/wiki/Arity In this case, all functions take two arguments. Having a 1 argument negation function, for example, could be done with :: pset.addPrimitive(operator.neg, 1) Our primitive set is now ready to generate some trees. The :mod:`~deap.gp` module contains three prefix expression generation functions :func:`~deap.gp.genFull`, :func:`~deap.gp.genGrow`, and :func:`~deap.gp.genHalfAndHalf`. Their first argument is a primitive set. They return a valid prefix expression in the form of a list of primitives. The content of this list can be read by the :class:`~deap.gp.PrimitiveTree` class to create a prefix tree. :: expr = genFull(pset, min_=1, max_=3) tree = PrimitiveTree(expr) The last code produces a valid full tree with height randomly chosen between 1 and 3. Strongly Typed GP ----------------- In strongly typed GP, every primitive and terminal is assigned a specific type. The output type of a primitive must match the input type of another one for them to be connected. For example, if a primitive returns a boolean, it is guaranteed that this value will not be multiplied with a float if the multiplication operator operates only on floats. :: def if_then_else(input, output1, output2): return output1 if input else output2 pset = PrimitiveSetTyped("main", [bool, float], float) pset.addPrimitive(operator.xor, [bool, bool], bool) pset.addPrimitive(operator.mul, [float, float], float) pset.addPrimitive(if_then_else, [bool, float, float], float) pset.addTerminal(3.0, float) pset.addTerminal(1, bool) pset.renameArguments(ARG0="x") pset.renameArguments(ARG1="y") In the last code sample, we first define an *if then else* function that returns the second argument if the first argument is true and the third one otherwise. Then, we define our :class:`~deap.gp.PrimitiveSetTyped`. Again, the procedure is named ``"main"``. The second argument defines the input types of the program. Here, ``"x"`` is a :class:`bool` and ``"y"`` is a :class:`float`. The third argument defines the output type of the program as a :class:`float`. Adding primitives to this primitive now requires to set the input and output types of the primitives and terminal. For example, we define our ``"if_then_else"`` function first argument as a boolean, the second and third argument have to be floats. The function is defined as returning a float. We now understand that the multiplication primitive can only have the terminal ``3.0``, the ``if_then_else`` function or the ``"y"`` as input, which are the only floats defined. The previous code can produce the tree on the left but not the one on the right because the type restrictions. .. image:: /_images/gptypedtrees.png :align: center .. note:: The generation of trees is done randomly while making sure type constraints are respected. If any primitive has an input type that no primitive and terminal can provide, chances are that this primitive will be picked and placed in the tree, resulting in the impossibility to complete the tree within the limit fixed by the generator. For example, when generating a full tree of height 2, suppose ``"op"`` takes a boolean and a float, ``"and"`` takes 2 boolean and ``"neg"`` takes a float, no terminal is defined and the arguments are booleans. The following situation will occur where no terminal can be placed to complete the tree. | .. image:: /_images/gptypederrtree.png :align: center In this case, DEAP raises an :class:`IndexError` with the message ``"The gp.generate function tried to add a terminal of type float, but there is none available."`` Ephemeral Constants ------------------- An ephemeral constant is a terminal encapsulating a value that is generated from a given function at run time. Ephemeral constants allow to have terminals that don't have all the same values. For example, to create an ephemeral constant that takes its value in :math:`[-1, 1)` we use :: pset.addEphemeralConstant(lambda: random.uniform(-1, 1)) The ephemeral constant value is determined when it is inserted in the tree and never changes unless it is replaced by another ephemeral constant. Since it is a terminal, ephemeral constant can also be typed. :: pset.addEphemeralConstant(lambda: random.randint(-10, 10), int) Generation of Tree Individuals ------------------------------ The code presented in the last two sections produces valid trees. However, as in the :ref:`next-step` tutorial, these trees are not yet valid individuals for evolution. One must combine the creator and the toolbox to produce valid individuals. We need to create the :class:`Fitness` and the :class:`Individual` classes. We add a reference to the primitive set to the :class:`Individual` in addition to the fitness. This is used by some of the gp operators to modify the individuals. :: creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin, pset=pset) We then register the generation functions into a :class:`~deap.base.Toolbox`. :: toolbox = base.Toolbox() toolbox.register("expr", gp.genFull, pset=pset, min_=1, max_=3) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) Calling :func:`toolbox.individual` readily returns an individual of type :class:`~deap.gp.PrimitiveTree`. Evaluation of Trees ------------------- In DEAP, trees can be translated to readable Python code and compiled to Python code objects using functions provided by the :py:mod:`~deap.gp` module. The first function, :func:`~deap.gp.stringify` takes an expression or a PrimitiveTree and translates it into readable Python code. For example, the following lines generate a tree and output the code from the first example primitive set. :: >>> expr = genFull(pset, min_=1, max_=3) >>> tree = PrimitiveTree(expr) >>> stringify(tree) 'mul(add(x, x), max(y, x))' Now, this string represents the program we just generated, but it cannot yet be executed. To make it executable, we have to compile the expression to a the Python code object. Since this function has two inputs, we wish to compile the code into a callable object. This is possible with :func:`~deap.gp.compile`. The function takes two arguments: the expression to compile and the associated primitive set. The following example compiles the previous tree and evaluates the resulting function for :math:`x=1` and :math:`y=2`. :: >>> function = compile(tree, pset) >>> function(1, 2) 4 When the generated program has no input argument, the expression can be compiled to byte code using the same :func:`~deap.gp.compile` function. An example of this sort of problem is the :ref:`artificial-ant`. Tree Size Limit and Bloat Control --------------------------------- Since DEAP uses the Python parser to compile the code represented by the trees, it inherits from its limitations. The most commonly encountered restriction is the parsing stack limit. The Python interpreter parser stack limit is usually fixed between 92 and 99. This means that an expression can at most be composed of 91 succeeding primitives. In other words, a tree can have a maximum depth of 91. When the limit is exceeded, Python raises the following error :: s_push: parser stack overflow Traceback (most recent call last): [...] MemoryError Since this limit is hard-coded in the interpreter, there exists no easy way to increase it. Furthermore, this error commonly stems from a phenomena known in GP as bloat. That is, the produced individuals have reached a point where they contain too much primitives to effectively solve the problem. This problem leads to evolution stagnation. To counteract this, DEAP provides different functions that can effectively restrain the size and height of the trees under an acceptable limit. These operators are listed in the GP section of :ref:`operators`. Plotting Trees -------------- The function :func:`deap.gp.graph` returns the necessary elements to plot tree graphs using `NetworX `_ or `pygraphviz `_. The graph function takes a valid :class:`~deap.gp.PrimitiveTree` object and returns a node list, an edge list and a dictionary associating a label to each node. It can be used like following with pygraphviz. :: from deap import base, creator, gp pset = gp.PrimitiveSet("MAIN", 1) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.sub, 2) pset.addPrimitive(operator.mul, 2) pset.renameArguments(ARG0='x') creator.create("Individual", gp.PrimitiveTree) toolbox = base.Toolbox() toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) expr = toolbox.individual() nodes, edges, labels = gp.graph(expr) ### Graphviz Section ### import pygraphviz as pgv g = pgv.AGraph() g.add_nodes_from(nodes) g.add_edges_from(edges) g.layout(prog="dot") for i in nodes: n = g.get_node(i) n.attr["label"] = labels[i] g.draw("tree.pdf") Using NetworkX, the last section becomes: :: import matplotlib.pyplot as plt import networkx as nx g = nx.Graph() g.add_nodes_from(nodes) g.add_edges_from(edges) pos = nx.graphviz_layout(g, prog="dot") nx.draw_networkx_nodes(g, pos) nx.draw_networkx_edges(g, pos) nx.draw_networkx_labels(g, pos, labels) plt.show() Depending on the version of graphviz, the nodes may appear in an unpredictable order. Two plots of the same tree may have sibling nodes swapped. This does not affect the primitive tree representation nor the numerical results. How to Evolve Programs ---------------------- The different ways to evolve program trees are presented through the :ref:`gpexamples` examples. deap-1.0.1/doc/tutorials/advanced/numpy.rst0000644000076500000240000001007012301410325021103 0ustar felixstaff00000000000000===================== Inheriting from Numpy ===================== DEAP's :class:`~deap.creator` allows to inherit from :class:`numpy.ndarray` so that individuals can have the properties of the powerful `Numpy `_ library. As with any other base class, inheriting from a :class:`numpy.ndarray` is no more complicated than putting it as a base class. :: import numpy from deap import base, creator creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", numpy.ndarray, fitness=creator.FitnessMax) What You Should be Concerned With! ================================== Inheriting from :class:`numpy.ndarray` is an appealing feature, but some care must be taken regarding validity of the data and performance of the system. Copy and Slicing ---------------- Slicing a :class:`numpy.ndarray` should be done with care. The returned element is a :func:`numpy.ndarray.view` of the original object. This leads to bug prone code when swapping data from one array to another. For example, the two points crossover use the following for swapping data between two lists. :: >>> a = [1,2,3,4] >>> b = [5,6,7,8] >>> a[1:3], b[1:3] = b[1:3], a[1:3] >>> print(a) [1, 6, 7, 4] >>> print(b) [5, 2, 3, 8] With :class:`numpy.array`, the same operation leads to a single resulting individual being changed. :: >>> import numpy >>> a = numpy.array([1,2,3,4]) >>> b = numpy.array([5,6,7,8]) >>> a[1:3], b[1:3] = b[1:3], a[1:3] >>> print(a) [1 6 7 4] >>> print(b) [5 6 7 8] The problem is that, first, the elements in ``a`` are replaced by the elements of the view returned by ``b`` and the element of ``b`` are replaced by the element in the view of ``a`` which are now the one intially in ``b`` leading to the wrong final result. One way of to circumvent this problem is to explicitely copy the view returned by the ``__getitem__``. :: >>> import numpy >>> a = numpy.array([1,2,3,4]) >>> b = numpy.array([5,6,7,8]) >>> a[1:3], b[1:3] = b[1:3].copy(), a[1:3].copy() >>> print(a) [1 6 7 4] >>> print(b) [5 2 3 8] Thus, care must be taken when inheriting from :class:`numpy.ndarray`; **none** of the operators in the :mod:`~deap.tools` module implement such copying. See the One Max with Numpy example for the complete two points crossover. Comparing Individuals --------------------- When one wants to use a :class:`~deap.tools.HallOfFame` or :class:`~deap.tools.ParetoFront` hall-of-fame. The *similar* function should be changed to a compare all function. Using the regular :func:`operator.eq` function will result in a vector of comparisons :: >>> a = numpy.array([1, 2, 3]) >>> b = numpy.array([1, 2, 3]) >>> operator.eq(a, b) array([ True, True, True], dtype=bool) This cannot be used as a condition :: >>> if operator.eq(a, b): ... print "Gosh!" ... Traceback (most recent call last): File "", line 1, in ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() One must replace the *similar* operator by a numpy function like :func:`numpy.array_equal` or :func:`numpy.allclose`. :: hof = tools.HallOfFame(1, similar=numpy.array_equal) Now the condition can be computed and the hall-of-fame will be happy. :: >>> if numpy.array_equal(a, b): ... print "Yeah!" "Yeah!" Performance ----------- If your intent is performance, `DEAP Speed `_ reveals that using an :class:`array.array` should be prefered to :class:`numpy.ndarray`. This is mostly because the creation (also required by the deep copy) of new arrays is longer for the :class:`numpy.array` than for :class:`array.array`. What You Don't Need to Know =========================== The creator replaces systematically several functions of the basic :class:`numpy.ndarray` so that - array instances can be created from an iterable; - it deep copies the attributes added in the ``__dict__`` of the object; - pickling includes the dictionary of attributes. See the implementation of :class:`~deap.creator._numpy_array` in the :mod:`~deap.creator` module for more details. deap-1.0.1/doc/tutorials/basic/0000755000076500000240000000000012321001644016521 5ustar felixstaff00000000000000deap-1.0.1/doc/tutorials/basic/part1.rst0000644000076500000240000003152212320777200020314 0ustar felixstaff00000000000000.. _creating-types: Creating Types ============== This tutorial shows how types are created using the creator and initialized using the toolbox. Fitness ------- The provided :class:`~deap.base.Fitness` class is an abstract class that needs a :attr:`~deap.base.Fitness.weights` attribute in order to be functional. A minimizing fitness is built using negatives weights, while a maximizing fitness has positive weights. For example, the following line creates, in the :mod:`~deap.creator`, a ready to use single objective minimizing fitness named :class:`FitnessMin`. .. literalinclude:: /code/tutorials/part_2/2_1_fitness.py :lines: 6 The :func:`~deap.creator.create` function takes at least two arguments, a name for the newly created class and a base class. Any subsequent argument becomes an attribute of the class. As specified in the :class:`~deap.base.Fitness` documentation, the :attr:`~deap.base.Fitness.weights` attribute must be a tuple so that multi-objective and single objective fitnesses can be treated the same way. A :class:`FitnessMulti` would be created the same way but using: .. literalinclude:: /code/tutorials/part_2/2_1_fitness.py :lines: 9 This code produces a fitness that minimize the first objective and maximize the second one. The weights can also be used to vary the importance of each objective one against another. This means that the weights can be any real number and only the sign is used to determine if a maximization or minimization is done. An example of where the weights can be useful is in the crowding distance sort made in the NSGA-II selection algorithm. Individual ---------- Simply by thinking about the different flavors of evolutionary algorithms (GA, GP, ES, PSO, DE, ...), we notice that an extremely large variety of individuals are possible, reinforcing the assumption that all types cannot be made available by developers. Here is a guide on how to create some of those individuals using the :mod:`~deap.creator` and initializing them using a :class:`~deap.base.Toolbox`. .. warning:: Before inheriting from :class:`numpy.ndarray` you should **absolutely** read the :doc:`/tutorials/advanced/numpy` tutorial and have a look at the :doc:`/examples/ga_onemax_numpy` example! .. _list-of-floats: List of Floats ++++++++++++++ The first individual created will be a simple list containing floats. In order to produce this kind of individual, we need to create an :class:`Individual` class, using the creator, that will inherit from the standard :class:`list` type and have a :attr:`fitness` attribute. .. Then, we will initialize this list using .. the :func:`~deap.tools.initRepeat` helper function that will repeat ``n`` times .. the float generator that has been registered under the :func:`attr_float` alias .. of the toolbox. Note that the :func:`attr_float` is a direct reference to the .. :func:`~random.random` function. .. literalinclude:: /code/tutorials/part_2/2_2_1_list_of_floats.py :lines: 2,5-18 The newly introduced :meth:`~deap.base.Toolbox.register` method takes at least two arguments; an alias and a function assigned to this alias. Any subsequent argument is passed to the function when called (à la :func:`functools.partial`). Thus, the preceding code creates two aliases in the toolbox; ``attr_float`` and ``individual``. The first one redirects to the :func:`random.random` function. The second one is a shortcut to the :func:`~deap.tools.initRepeat` function, fixing its :data:`container` argument to the :class:`creator.Individual` class, its :data:`func` argument to the :func:`toolbox.attr_float` function, and its number of repetitions argument to ``IND_SIZE``. Now, calling :func:`toolbox.individual` will call :func:`~deap.tools.initRepeat` with the fixed arguments and return a complete :class:`creator.Individual` composed of ``IND_SIZE`` floating point numbers with a maximizing single objective :attr:`fitness` attribute. Variations of this type are possible by inheriting from :class:`array.array` or :class:`numpy.ndarray` as following. .. literalinclude:: /code/tutorials/part_2/2_2_1_list_of_floats.py :lines: 20,21 Type inheriting from arrays needs a *typecode* on initialization, just as the original class. .. _permutation: Permutation +++++++++++ An individual for the permutation representation is almost similar to the general list individual. In fact they both inherit from the basic :class:`list` type. The only difference is that instead of filling the list with a series of floats, we need to generate a random permutation and provide that permutation to the individual. .. First, the individual class is created the exact same way as the .. previous one. Then, an :func:`indices` function is added to the toolbox .. referring to the :func:`~random.sample` function. Sample is used instead of .. :func:`~random.shuffle` because the latter does not return the shuffled list. .. The indices function returns a complete permutation of the numbers between ``0`` .. and ``IND_SIZE - 1``. Finally, the individual is initialized with the .. :func:`~deap.tools.initIterate` function which gives to the individual an .. iterable of what is produced by the call to the indices function. .. literalinclude:: /code/tutorials/part_2/2_2_2_permutation.py :lines: 2- The first registered function ``indices`` redirects to the :func:`random.sample` function with its arguments fixed to sample ``IND_SIZE`` numbers from the given range. The second registered function ``individual`` is a shortcut to the :func:`~deap.tools.initIterate` function, with its :data:`container` argument set to the :class:`creator.Individual` class and its :data:`generator` argument to the :func:`toolbox.indices` alias. Calling :func:`toolbox.individual` will call :func:`~deap.tools.initIterate` with the fixed arguments and return a complete :class:`creator.Individual` composed of permutation with a maximizing single objective :attr:`fitness` attribute. .. _arithmetic-expr: Arithmetic Expression +++++++++++++++++++++ The next individual that is commonly used is a prefix tree of mathematical expressions. This time, a :class:`~deap.gp.PrimitiveSet` must be defined containing all possible mathematical operators that our individual can use. Here, the set is called ``MAIN`` and has a single variable defined by the arity_. Operators :func:`~operator.add`, :func:`~operator.sub`, and :func:`~operator.mul` are added to the primitive set with each an arity of 2. Next, the :class:`Individual` class is created as before with the addition of a static attribute :attr:`pset` to remember the global primitive set. This time, the content of the individuals will be generated by the :func:`~deap.gp.genHalfAndHalf` function that generates trees in a list format based on a ramped procedure. Once again, the individual is initialized using the :func:`~deap.tools.initIterate` function to give the complete generated iterable to the individual class. .. literalinclude:: /code/tutorials/part_2/2_2_3_arithmetic_expression.py :lines: 2- .. _arity: http://en.wikipedia.org/wiki/Arity Calling :func:`toolbox.individual` will readily return a complete individual that is an arithmetic expression in the form of a prefix tree with a minimizing single objective fitness attribute. Evolution Strategy ++++++++++++++++++ Evolution strategies individuals are slightly different as they contain generally two lists, one for the actual individual and one for its mutation parameters. This time, instead of using the list base class, we will inherit from an :class:`array.array` for both the individual and the strategy. Since there is no helper function to generate two different vectors in a single object, we must define this function ourselves. The :func:`initES` function receives two classes and instantiates them generating itself the random numbers in the ranges provided for individuals of a given size. .. literalinclude:: /code/tutorials/part_2/2_2_4_evolution_strategy.py :lines: 2- Calling :func:`toolbox.individual` will readily return a complete evolution strategy with a strategy vector and a minimizing single objective fitness attribute. Particle ++++++++ A particle is another special type of individual as it usually has a speed and generally remember its best position. This type of individual is created (once again) the same way as inheriting from a list. This time, :attr:`speed`, :attr:`best` and speed limits attributes are added to the object. Again, an initialization function :func:`initParticle` is also registered to produce the individual receiving the particle class, size, domain, and speed limits as arguments. .. literalinclude:: /code/tutorials/part_2/2_2_5_particle.py :lines: 2- Calling :func:`toolbox.individual` will readily return a complete particle with a speed vector and a maximizing two objectives fitness attribute. .. _funky: A Funky One +++++++++++ Supposing your problem have very specific needs. It is also possible to build custom individuals very easily. The next individual created is a list of alternating integers and floating point numbers, using the :func:`~deap.tools.initCycle` function. .. literalinclude:: /code/tutorials/part_2/2_2_6_funky_one.py :lines: 2- Calling :func:`toolbox.individual` will readily return a complete individual of the form ``[int float int float ... int float]`` with a maximizing two objectives fitness attribute. .. _population: Population ---------- Population are much like individuals. Instead of being initialized with attributes, it is filled with individuals, strategies or particles. Bag +++ A bag population is the most commonly used type. It has no particular ordering although it is generally implemented using a list. Since the bag has no particular attribute, it does not need any special class. The population is initialized using the toolbox and the :func:`~deap.tools.initRepeat` function directly. .. literalinclude:: /code/tutorials/part_2/2_3_1_bag.py :lines: 17 Calling :func:`toolbox.population` will readily return a complete population in a list, providing a number of times the repeat helper must be repeated as an argument of the population function. The following example produces a population with 100 individuals. .. literalinclude:: /code/tutorials/part_2/2_3_1_bag.py :lines: 19 Grid ++++ A grid population is a special case of structured population where neighbouring individuals have a direct effect on each other. The individuals are distributed in grid where each cell contains a single individual. However, its implementation is no different than the list of the bag population, other that it is composed of lists of individuals. .. literalinclude:: /code/tutorials/part_2/2_3_2_grid.py :lines: 20-21 Calling :func:`toolbox.population` will readily return a complete population where the individuals are accessible using two indices, for example ``pop[r][c]``. For the moment, there is no algorithm specialized for structured population, we are awaiting your submissions. Swarm +++++ A swarm is used in particle swarm optimization. It is different in the sense that it contains a communication network. The simplest network is the complete connection, where each particle knows the best position that have ever been visited by any particle. This is generally implemented by copying that global best position to a :attr:`gbest` attribute and the global best fitness to a :attr:`gbestfit` attribute. .. literalinclude:: /code/tutorials/part_2/2_3_3_swarm.py :lines: 11,23 Calling :func:`toolbox.population` will readily return a complete swarm. After each evaluation the :attr:`gbest` and :attr:`gbestfit` should be set by the algorithm to reflect the best found position and fitness. Demes +++++ A deme is a sub-population that is contained in a population. It is similar has an island in the island model. Demes, being only sub-populations, are in fact no different than populations, aside from their names. Here, we create a population containing 3 demes, each having a different number of individuals using the *n* argument of the :func:`~deap.tools.initRepeat` function. .. literalinclude:: /code/tutorials/part_2/2_3_4_demes.py :lines: 17-20 Seeding a Population ++++++++++++++++++++ Sometimes, a first guess population can be used to initialize an evolutionary algorithm. The key idea to initialize a population with not random individuals is to have an individual initializer that takes a content as argument. .. literalinclude:: /code/tutorials/part_2/2_3_5_seeding_a_population.py :lines: 2- The population will be initialized from the file ``my_guess.json`` that shall contain a list of first guess individuals. This initialization can be combined with a regular initialization to have part random and part not random individuals. Note that the definition of :func:`initIndividual` and the registration of :func:`individual_guess` are optional as the default constructor of a list is similar. Removing those lines leads to the following: :: toolbox.register("population_guess", initPopulation, list, creator.Individual, "my_guess.json") deap-1.0.1/doc/tutorials/basic/part2.rst0000644000076500000240000002535112320777200020320 0ustar felixstaff00000000000000.. _next-step: Operators and Algorithms ======================== Before starting with complex algorithms, we will see some basis of DEAP. First, we will start by creating simple individuals (as seen in the :ref:`creating-types` tutorial) and make them interact with each other using different operators. Afterwards, we will learn how to use the algorithms and other tools. A First Individual ------------------ First import the required modules and register the different functions required to create individuals that are a list of floats with a minimizing two objectives fitness. .. literalinclude:: /code/tutorials/part_3/3_next_step.py :lines: 2-16 The first individual can now be built by adding the appropriate line to the script. .. literalinclude:: /code/tutorials/part_3/3_next_step.py :lines: 18 Printing the individual ``ind1`` and checking if its fitness is valid will give something like this .. literalinclude:: /code/tutorials/part_3/3_next_step.py :lines: 20-21 The individual is printed as its base class representation (here a list) and the fitness is invalid because it contains no values. Evaluation ---------- The evaluation is the most personal part of an evolutionary algorithm, it is the only part of the library that you must write yourself. A typical evaluation function takes one individual as argument and return its fitness as a :class:`tuple`. As shown in the in the :ref:`core` section, a fitness is a list of floating point values and has a property :attr:`~deap.base.Fitness.valid` to know if this individual shall be re-evaluated. The fitness is set by setting the :attr:`~deap.base.Fitness.values` to the associated :class:`tuple`. For example, the following evaluates the previously created individual ``ind1`` and assign its fitness to the corresponding values. .. literalinclude:: /code/tutorials/part_3/3_next_step.py :lines: 24-32 Dealing with single objective fitness is not different, the evaluation function **must** return a tuple because single-objective is treated as a special case of multi-objective. Mutation -------- The next kind of operator that we will present is the mutation operator. There is a variety of mutation operators in the :mod:`deap.tools` module. Each mutation has its own characteristics and may be applied to different type of individual. Be careful to read the documentation of the selected operator in order to avoid undesirable behaviour. The general rule for mutation operators is that they **only** mutate, this means that an independent copy must be made prior to mutating the individual if the original individual has to be kept or is a *reference* to an other individual (see the selection operator). In order to apply a mutation (here a gaussian mutation) on the individual ``ind1``, simply apply the desired function. .. literalinclude:: /code/tutorials/part_3/3_next_step.py :lines: 35-37 The fitness' values are deleted because they not related to the individual anymore. As stated above, the mutation does mutate and only mutate an individual it is not responsible of invalidating the fitness nor anything else. The following shows that ``ind2`` and ``mutant`` are in fact the same individual. .. literalinclude:: /code/tutorials/part_3/3_next_step.py :lines: 39-40 Crossover --------- The second kind of operator that we will present is the crossover operator. There is a variety of crossover operators in the :mod:`deap.tools` module. Each crossover has its own characteristics and may be applied to different type of individuals. Be careful to read the documentation of the selected operator in order to avoid undesirable behaviour. The general rule for crossover operators is that they **only** mate individuals, this means that an independent copies must be made prior to mating the individuals if the original individuals have to be kept or is are *references* to other individuals (see the selection operator). Lets apply a crossover operation to produce the two children that are cloned beforehand. .. literalinclude:: /code/tutorials/part_3/3_next_step.py :lines: 43-46 .. note:: Just as a remark on the language, the form ``toolbox.clone([ind1, ind2])`` cannot be used because if ``ind1`` and ``ind2`` are referring to the same location in memory (the same individual) there will be a single independent copy of the individual and the second one will be a reference to this same independent copy. This is caused by the mechanism that prevents recursive loops. The first time the individual is seen, it is put in the "memo" dictionary, the next time it is seen the deep copy stops for that object and puts a reference to that previously created deep copy. Care should be taken when deep copying containers. Selection --------- Selection is made among a population by the selection operators that are available in the :mod:`deap.operators` module. The selection operator usually takes as first argument an iterable container of individuals and the number of individuals to select. It returns a list containing the references to the selected individuals. The selection is made as follow. .. literalinclude:: /code/tutorials/part_3/3_next_step.py :lines: 49-50 .. warning:: It is **very** important here to note that the selection operators does not duplicate any individual during the selection process. If an individual is selected twice and one of either object is modified, the other will also be modified. Only a reference to the individual is copied. Just like every other operator it selects and only selects. Usually duplication of the entire population will be made after selection or before variation. .. literalinclude:: /code/tutorials/part_3/3_next_step.py :lines: 56-57 Using the Toolbox ----------------- The toolbox is intended to contain all the evolutionary tools, from the object initializers to the evaluation operator. It allows easy configuration of each algorithms. The toolbox has basically two methods, :meth:`~deap.toolbox.Toolbox.register` and :meth:`~deap.toolbox.Toolbox.unregister`, that are used to add or remove tools from the toolbox. This part of the tutorial will focus on registration of the evolutionary tools in the toolbox rather than the initialization tools. The usual names for the evolutionary tools are :func:`~deap.mate`, :func:`~deap.mutate`, :func:`~deap.evaluate` and :func:`~deap.select`, however, any name can be registered as long as it is unique. Here is how they are registered in the toolbox. .. literalinclude:: /code/tutorials/part_3/3_6_using_the_toolbox.py :lines: 2-8,10-15 Using the toolbox for registering tools helps keeping the rest of the algorithms independent from the operator set. Using this scheme makes it very easy to locate and change any tool in the toolbox if needed. .. _using-tools: Using the Tools +++++++++++++++ When building evolutionary algorithms the toolbox is used to contain the operators, which are called using their generic name. For example, here is a very simple generational evolutionary algorithm. .. literalinclude:: /code/tutorials/part_3/3_6_using_the_toolbox.py :lines: 30- This is a complete algorithm. It is generic enough to accept any kind of individual and any operator, as long as the operators are suitable for the chosen individual type. As shown in the last example, the usage of the toolbox allows to write algorithms that are as close as possible to the pseudo code. Now it is up to you to write and experiment your own. Tool Decoration +++++++++++++++ Tool decoration is a very powerful feature that helps to control very precise thing during an evolution without changing anything in the algorithm or operators. A decorator is a wrapper that is called instead of a function. It is asked to make some initialization and termination work before and after the actual function is called. For example, in the case of a constrained domain, one can apply a decorator to the mutation and crossover in order to keep any individual from being out-of-bound. The following defines a decorator that checks if any attribute in the list is out-of-bound and clips it if it is the case. The decorator is defined using three functions in order to receive the *min* and *max* arguments. Whenever the mutation or crossover is called, bounds will be check on the resulting individuals. .. literalinclude:: /code/tutorials/part_3/3_6_2_tool_decoration.py :lines: 8- This will work on crossover and mutation because both return a tuple of individuals. The mutation is often considered to return a single individual but again like for the evaluation, the single individual case is a special case of the multiple individual case. |more| For more information on decorators, see `Introduction to Python Decorators `_ and `Python Decorator Libary `_. Variations ---------- Variations allows to build simple algorithms using predefined small building blocks. In order to use a variation, the toolbox must be set to contain the required operators. For example in the lastly presented complete algorithm, the crossover and mutation are regrouped in the :func:`~deap.algorithms.varAnd` function, this function requires the toolbox to contain the :func:`~deap.mate` and :func:`~deap.mutate` functions. The variations can be used to simplify the writing of an algorithm as follow. .. literalinclude:: /code/tutorials/part_3/3_7_variations.py :lines: 33- This last example shows that using the variations makes it straight forward to build algorithms that are very close to the pseudo-code. Algorithms ---------- There is several algorithms implemented in the :mod:`~deap.algorithms` module. They are very simple and reflect the basic types of evolutionary algorithms present in the literature. The algorithms use a :class:`~deap.base.Toolbox` as defined in the last sections. In order to setup a toolbox for an algorithm, you must register the desired operators under a specified names, refer to the documentation of the selected algorithm for more details. Once the toolbox is ready, it is time to launch the algorithm. The simple evolutionary algorithm takes 5 arguments, a *population*, a *toolbox*, a probability of mating each individual at each generation (*cxpb*), a probability of mutating each individual at each generation (*mutpb*) and a number of generations to accomplish (*ngen*). .. literalinclude:: /code/tutorials/part_3/3_8_algorithms.py :lines: 33- The best way to understand what the simple evolutionary algorithm does, it to take a look at the documentation or the source code Now that you built your own evolutionary algorithm in python, you are welcome to gives us feedback and appreciation. We would also really like to hear about your project and success stories with DEAP. .. |more| image:: /_images/more.png :align: middle :alt: more info deap-1.0.1/doc/tutorials/basic/part3.rst0000644000076500000240000002264112301410325020307 0ustar felixstaff00000000000000Computing Statistics ==================== Often, one wants to compile statistics on what is going on in the optimization. The :class:`~deap.tools.Statistics` are able to compile such data on arbitrary attributes of any designated object. To do that, one need to register the desired statistic functions inside the stats object using the exact same syntax as the toolbox. .. literalinclude:: /code/tutorials/part_3/stats.py :lines: 12 The statistics object is created using a key as first argument. This key must be supplied a function that will later be applied to the data on which the statistics are computed. The previous code sample uses the :attr:`fitness.values` attribute of each element. .. literalinclude:: /code/tutorials/part_3/stats.py :lines: 13-16 The statistical functions are now registered. The ``register`` function expects an alias as first argument and a function operating on vectors as second argument. Any subsequent argument is passed to the function when called. The creation of the statistics object is now complete. Predefined Algorithms --------------------- When using a predefined algorithm such as :func:`~deap.algorithms.eaSimple`, :func:`~deap.algorithms.eaMuPlusLambda`, :func:`~deap.algorithms.eaMuCommaLambda`, or :func:`~deap.algorithms.eaGenerateUpdate`, the statistics object previously created can be given as argument to the algorithm. .. literalinclude:: /code/tutorials/part_3/stats.py :lines: 50-51 Statistics will automatically be computed on the population every generation. The verbose argument prints the statistics on screen while the optimization takes place. Once the algorithm returns, the final population and a :class:`~deap.tools.Logbook` are returned. See the :ref:`next section ` or the :class:`~deap.tools.Logbook` documentation for more information. Writing Your Own Algorithm -------------------------- When writing your own algorithm, including statistics is very simple. One need only to compile the statistics on the desired object. For example, compiling the statistics on a given population is done by calling the :meth:`~deap.tools.Statistics.compile` method. .. literalinclude:: /code/tutorials/part_3/stats.py :lines: 38 The argument to the compile function must be an iterable of elements on which the key will be called. Here, our population (``pop``) contains individuals. The statistics object will call the key function on every individual to retrieve their :attr:`fitness.values` attribute. The resulting array of values is finally given the each statistic function and the result is put into the ``record`` dictionary under the key associated with the function. Printing the record reveals its nature. >>> print(record) {'std': 4.96, 'max': 63.0, 'avg': 50.2, 'min': 39.0} How to save and pretty print the statistics is shown in the :ref:`next section `. Multi-objective Statistics -------------------------- As statistics are computed directly on the values with numpy function, all the objectives are combined together by the default behaviour of numpy. Thus, one need to specify the axis on which to operate. This is achieved by giving the axis as an aditional argument to the register function. .. literalinclude:: /code/tutorials/part_3/stats.py :lines: 41-45 One can always specify the axis even in the case of single objective. The only effect is to produce a different output, as the objects are numpy arrays. >>> print(record) {'std': array([ 4.96]), 'max': array([ 63.]), 'avg': array([ 50.2]), 'min': array([ 39.])} Multiple Statistics ------------------- It is also possible to compute statistics on different attributes of the population individuals. For instance, it is quite common in genetic programming to have statistics on the height of the trees in addition to their fitness. One can combine multiple :class:`~deap.tools.Statistics` object in a :class:`~deap.tools.MultiStatistics`. .. literalinclude:: /code/tutorials/part_3/multistats.py :lines: 14-16 Two statistics objects are created in the same way as before. The second object will retrieve the size of the individuals by calling :func:`len` on each of them. Once created, the statistics objects are given to a MultiStatistics one, where the arguments are given using keywords. These keywords will serve to identify the different statistics. The statistical functions can be registered only once in the multi-statistics, as shown below, or individually in each statistics. .. literalinclude:: /code/tutorials/part_3/multistats.py :lines: 17-20 The multi-statistics object can be given to an algorithm or they can be compiled using the exact same procedure as the simple statistics. .. literalinclude:: /code/tutorials/part_3/multistats.py :lines: 54 This time the ``record`` is a dictionary of dictionaries. The first level contains the keywords under which the statistics objects have been registered and the second level is similar to the previous simple statistics object.:: >>> print(record) {'fitness': {'std': 1.64, 'max': 6.86, 'avg': 1.71, 'min': 0.166}, 'size': {'std': 1.89, 'max': 7, 'avg': 4.54, 'min': 3}} .. _logging: Logging Data ============ Once the data is produced by the statistics (or multi-statistics), one can save it for further use in a :class:`~deap.tools.Logbook`. The logbook is intended to be a chronological sequence of entries (as dictionaries). It is directly compliant with the type of data returned by the statistics objects, but not limited to this data. In fact, anything can be incorporated in an entry of the logbook. .. literalinclude:: /code/tutorials/part_3/logbook.py :lines: 7-8 The :meth:`~deap.tools.Logbook.record` method takes a variable number of argument, each of which is a data to be recorded. In the last example, we saved the generation, the number of evaluations and everything contained in the ``record`` produced by a statistics object using the star magic. All record will be kept in the logbook until its destruction. After a number of records, one may want to retrieve the information contained in the logbook. .. literalinclude:: /code/tutorials/part_3/logbook.py :lines: 12 The :meth:`~deap.tools.Logbook.select` method provides a way to retrieve all the information associated with a keyword in all records. This method takes a variable number of string arguments, which are the keywords used in the record or statistics object. Here, we retrieved the generation and the average fitness using a single call to select. A logbook is a picklable object (as long as all inserted objects are picklable) providing a very nice way to save the statistics of an evolution on disk. .. literalinclude:: /code/tutorials/part_3/logbook.py :lines: 1,14 .. note:: Every algorithm returns a logbook containing the statistics for every generation and the number of evaluation for the whole evolution. Printing to Screen ------------------ A logbook can be printed to screen or file. Its :meth:`~deap.tools.Logbook.__str__` method returns a header of each key inserted in the first record and the complete logbook for each of these keys. The row are in chronological order of insertion while the columns are in an undefined order. The easiest way to specify an order is to set the :attr:`~deap.tools.Logbook.header` attribute to a list of strings specifying the order of the columns. .. literalinclude:: /code/tutorials/part_3/logbook.py :lines: 20 The result is:: >>> print(logbook) gen avg spam 0 [ 50.2] A column name containing no entry in a specific record will be left blank as for the ``spam`` column in the last example. A logbook also contains a stream property returning only the yet unprinted entries. :: >>> print(logbook.stream) gen avg spam 0 [ 50.2] >>> logbook.record(gen=1, evals=15, **record) >>> print(logbook.stream) 1 [ 50.2] Dealing with Multi-statistics ----------------------------- The logbook is able to cope with the dictionary of dictionaries return by the :class:`~deap.tools.MultiStatistics` object. In fact, it will log the data in :attr:`~deap.tools.Logbook.chapters` for each sub dictionary contained in the record. Thus, a *multi* record can be used exactly as a record. .. literalinclude:: /code/tutorials/part_3/logbook.py :lines: 29-30 One difference is the column ordering, where we can specify an order for the chapters and their content as follows: .. literalinclude:: /code/tutorials/part_3/logbook.py :lines: 32-34 The resulting output is:: >>> print(logbook) fitness size ------------------------- --------------- gen evals min avg max min avg max 0 30 0.165572 1.71136 6.85956 3 4.54 7 Retrieving the data is also done through the chapters. .. literalinclude:: /code/tutorials/part_3/logbook.py :lines: 38-40 The generations, minimum fitness and average size are obtained, chronologically ordered. If some data is not available, a :data:`None` appears in the vector. Some Plotting Sugar ------------------- One of the most common operation when an optimization is finished is to plot the data during the evolution. The :class:`~deap.tools.Logbook` allows to do this very efficiently. Using the select method, one can retrieve the desired data and plot it using matplotlib. .. literalinclude:: /code/tutorials/part_3/logbook.py :lines: 38-41,42-61 When added to the symbolic regression example, it gives the following graphic: .. image:: /_images/twin_logbook.png :width: 50% deap-1.0.1/doc/tutorials/basic/part4.rst0000644000076500000240000001236612301410325020313 0ustar felixstaff00000000000000.. _distribution-deap: Using Multiple Processors ========================= This section of the tutorial shows all the work that is needed to distribute operations in DEAP. Distribution relies on serialization of objects which is usually done by pickling, thus all objects that are distributed (functions and arguments, e.g. individuals and parameters) must be pickleable. This means that modifications made to an object on a distant processing unit will not be made available to the other processing units (including the master one) if it is not explicitly communicated through function arguments and return values. Scalable Concurent Operations in Python (SCOOP) ----------------------------------------------- SCOOP_ is a distributed task module allowing concurrent parallel programming on various environments, from heterogeneous grids to supercomputers. It has an interface similar to the :mod:`concurrent.futures` module introduced in Python 3.2. Its two simple functions :func:`~scoop.futures.submit` and :func:`~scoop.futures.map` allow to distribute computation efficiently and easily over a grid of computers. In the :ref:`second part `, a complete algorithm was exposed with the :func:`toolbox.map` left to the default :func:`map`. In order to distribute the evaluations, we will replace this map by the one from SCOOP. :: from scoop import futures toolbox.register("map", futures.map) Once this line is added, your program absolutly needs to be run from a :func:`main` function as mentioned in the `scoop documentation `_. To run your program, use scoop as the main module. .. code-block:: bash $ python -m scoop your_program.py That is it, your program has been run in parallel on all available processors on your computer. .. _SCOOP: http://scoop.googlecode.com/ Multiprocessing Module ---------------------- Using the :mod:`multiprocessing` module is similar to using SCOOP. It can be done by replacing the appropriate function by the distributed one in the toolbox. :: import multiprocessing pool = multiprocessing.Pool() toolbox.register("map", pool.map) # Continue on with the evolutionary algorithm .. warning:: As stated in the :mod:`multiprocessing` guidelines, under Windows, a process pool must be protected in a ``if __name__ == "__main__"`` section because of the way processes are initialized. .. note:: While Python 2.6 is required for the multiprocessing module, the pickling of partial function is possible only since Python 2.7 (or 3.1), earlier version of Python may throw some strange errors when using partial function in the multiprocessing :func:`multiprocessing.Pool.map`. This may be avoided by creating local function outside of the toolbox (in Python version 2.6). .. note:: The pickling of lambda function is not yet available in Python. .. Parallel Evaluation .. ------------------- .. The multiprocessing example shows how to use the :mod:`multiprocessing` module .. in order to enhance the computing power during the evaluations. First the .. toolbox contains a method named :func:`~deap.map`, this method has the same .. function as the built-in :func:`map` function. In order to use the .. multiprocessing module into the built-in :mod:`~deap.algorithms`, the only .. thing to do is to replace the map operation by a parallel one. Then the .. difference between the `Multiprocessing One Max Example .. `_ and the `Regular One .. Max Example `_ is the .. addition of these two lines .. :: .. .. # Process Pool of 4 workers .. pool = multiprocessing.Pool(processes=4) .. tools.register("map", pool.map) .. .. Parallel Variation .. ------------------ .. .. The paralellization of the variation operators is not directly supported in .. the algorithms, although it is still possible. What one needs is to create its .. own algorithm (from one in the algorithm module for example) and change the .. desired lines in order to use the :meth:`~deap.toolbox.map` method from the .. toolbox. This may be achieved for example, for the crossover operation from .. the :func:`~deap.algorithms.eaSimple` algorithm by replacing the crossover part .. of the algorithms by .. :: .. .. parents1 = list() .. parents2 = list() .. to_replace = list() .. for i in range(1, len(offspring), 2): .. if random.random() < cxpb: .. parents1.append(offspring[i - 1]) .. parents2.append(offspring[i]) .. to_replace.append(i - 1) .. to_replace.append(i) .. .. children = tools.map(tools.mate, (parents1, parents2)) .. .. for i, child in zip(to_replace, children): .. del child.fitness.values .. offspring[i] = child .. .. Since the multiprocessing map does take a single iterable we must .. bundle/unbundle the parents, respectively by creating a tuple in the .. :func:`tools.map` function of the preceding code example and the following .. decorator on the crossover function. .. :: .. .. def unbundle(func): .. def wrapUnbundle(bundled): .. return func(*bundled) .. return wrapUnbundle .. .. tools.decorate("mate", unbundle) deap-1.0.1/examples/0000755000076500000240000000000012321001644014463 5ustar felixstaff00000000000000deap-1.0.1/examples/bbob.py0000644000076500000240000001215412301410325015742 0ustar felixstaff00000000000000 # This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import math import random import time from itertools import chain from deap import base from deap import creator from deap import benchmarks import fgeneric import bbobbenchmarks as bn creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode="d", fitness=creator.FitnessMin) def update(individual, mu, sigma): """Update the current *individual* with values from a gaussian centered on *mu* and standard deviation *sigma*. """ for i, mu_i in enumerate(mu): individual[i] = random.gauss(mu_i, sigma) def tupleize(func): """A decorator that tuple-ize the result of a function. This is useful when the evaluation function returns a single value. """ def wrapper(*args, **kargs): return func(*args, **kargs), return wrapper def main(func, dim, maxfuncevals, ftarget=None): toolbox = base.Toolbox() toolbox.register("update", update) toolbox.register("evaluate", func) toolbox.decorate("evaluate", tupleize) # Create the desired optimal function value as a Fitness object # for later comparison opt = creator.FitnessMin((ftarget,)) # Interval in which to initialize the optimizer interval = -5, 5 sigma = (interval[1] - interval[0])/2.0 alpha = 2.0**(1.0/dim) # Initialize best randomly and worst as a place holder best = creator.Individual(random.uniform(interval[0], interval[1]) for _ in range(dim)) worst = creator.Individual([0.0] * dim) # Evaluate the first individual best.fitness.values = toolbox.evaluate(best) # Evolve until ftarget is reached or the number of evaluation # is exausted (maxfuncevals) for g in range(1, maxfuncevals): toolbox.update(worst, best, sigma) worst.fitness.values = toolbox.evaluate(worst) if best.fitness <= worst.fitness: # Incease mutation strength and swap the individual sigma = sigma * alpha best, worst = worst, best else: # Decrease mutation strength sigma = sigma * alpha**(-0.25) # Test if we reached the optimum of the function # Remember that ">" for fitness means better (not greater) if best.fitness > opt: return best return best if __name__ == "__main__": # Maximum number of restart for an algorithm that detects stagnation maxrestarts = 1000 # Create a COCO experiment that will log the results under the # ./output directory e = fgeneric.LoggingFunction("output") # Iterate over all desired test dimensions for dim in (2, 3, 5, 10, 20, 40): # Set the maximum number function evaluation granted to the algorithm # This is usually function of the dimensionality of the problem maxfuncevals = 100 * dim**2 minfuncevals = dim + 2 # Iterate over a set of benchmarks (noise free benchmarks here) for f_name in bn.nfreeIDs: # Iterate over all the instance of a single problem # Rotation, translation, etc. for instance in chain(range(1, 6), range(21, 31)): # Set the function to be used (problem) in the logger e.setfun(*bn.instantiate(f_name, iinstance=instance)) # Independent restarts until maxfunevals or ftarget is reached for restarts in range(0, maxrestarts + 1): if restarts > 0: # Signal the experiment that the algorithm restarted e.restart('independent restart') # additional info # Run the algorithm with the remaining number of evaluations revals = int(math.ceil(maxfuncevals - e.evaluations)) main(e.evalfun, dim, revals, e.ftarget) # Stop if ftarget is reached if e.fbest < e.ftarget or e.evaluations + minfuncevals > maxfuncevals: break e.finalizerun() print('f%d in %d-D, instance %d: FEs=%d with %d restarts, ' 'fbest-ftarget=%.4e' % (f_name, dim, instance, e.evaluations, restarts, e.fbest - e.ftarget)) print('date and time: %s' % time.asctime()) deap-1.0.1/examples/coev/0000755000076500000240000000000012321001644015417 5ustar felixstaff00000000000000deap-1.0.1/examples/coev/coop_adapt.py0000644000076500000240000001173412301410325020106 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """This example contains the adaptation test from *Potter, M. and De Jong, K., 2001, Cooperative Coevolution: An Architecture for Evolving Co-adapted Subcomponents.* section 4.2.3. A species is added each 100 generations. """ import random try: import matplotlib.pyplot as plt except ImportError: plt = False import numpy from deap import algorithms from deap import tools import coop_base IND_SIZE = coop_base.IND_SIZE SPECIES_SIZE = coop_base.SPECIES_SIZE TARGET_SIZE = 30 NUM_SPECIES = 1 noise = "*##*###*###*****##*##****#*##*###*#****##******##*#**#*#**######" schematas = ("1##1###1###11111##1##1111#1##1###1#1111##111111##1#11#1#11######", "1##1###1###11111##1##1000#0##0###0#0000##000000##0#00#0#00######", "0##0###0###00000##0##0000#0##0###0#0000##001111##1#11#1#11######") toolbox = coop_base.toolbox if plt: toolbox.register("evaluate_nonoise", coop_base.matchSetStrengthNoNoise) def main(extended=True, verbose=True): target_set = [] stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "species", "evals", "std", "min", "avg", "max" ngen = 300 adapt_length = 100 g = 0 add_next = [adapt_length] for i in range(len(schematas)): target_set.extend(toolbox.target_set(schematas[i], int(TARGET_SIZE/len(schematas)))) species = [toolbox.species() for _ in range(NUM_SPECIES)] # Init with random a representative for each species representatives = [random.choice(s) for s in species] if plt and extended: # We must save the match strength to plot them t1, t2, t3 = list(), list(), list() while g < ngen: # Initialize a container for the next generation representatives next_repr = [None] * len(species) for i, s in enumerate(species): # Vary the species individuals s = algorithms.varAnd(s, toolbox, 0.6, 1.0) r = representatives[:i] + representatives[i+1:] for ind in s: ind.fitness.values = toolbox.evaluate([ind] + r, target_set) record = stats.compile(s) logbook.record(gen=g, species=i, evals=len(s), **record) if verbose: print(logbook.stream) # Select the individuals species[i] = toolbox.select(s, len(s)) # Tournament selection next_repr[i] = toolbox.get_best(s)[0] # Best selection g += 1 if plt and extended: # Compute the match strength without noise for the # representatives on the three schematas t1.append(toolbox.evaluate_nonoise(representatives, toolbox.target_set(schematas[0], 1), noise)[0]) t2.append(toolbox.evaluate_nonoise(representatives, toolbox.target_set(schematas[1], 1), noise)[0]) t3.append(toolbox.evaluate_nonoise(representatives, toolbox.target_set(schematas[2], 1), noise)[0]) representatives = next_repr # Add a species at every *adapt_length* generation if add_next[-1] <= g < ngen: species.append(toolbox.species()) representatives.append(random.choice(species[-1])) add_next.append(add_next[-1] + adapt_length) if extended: for r in representatives: # print individuals without noise print("".join(str(x) for x, y in zip(r, noise) if y == "*")) if plt and extended: # Do the final plotting plt.plot(t1, '-', color="k", label="Target 1") plt.plot(t2, '--', color="k", label="Target 2") plt.plot(t3, ':', color="k", label="Target 3") max_t = max(max(t1), max(t2), max(t3)) for n in add_next: plt.plot([n, n], [0, max_t + 1], "--", color="k") plt.legend(loc="lower right") plt.axis([0, ngen, 0, max_t + 1]) plt.xlabel("Generations") plt.ylabel("Number of matched bits") plt.show() if __name__ == "__main__": main() deap-1.0.1/examples/coev/coop_base.py0000644000076500000240000000737712301410325017737 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """This is the base code for all four coevolution examples from *Potter, M. and De Jong, K., 2001, Cooperative Coevolution: An Architecture for Evolving Co-adapted Subcomponents.* section 4.2. It shows in a concrete manner how to re-use initialization code in some other examples. """ import random from deap import base from deap import creator from deap import tools IND_SIZE = 64 SPECIES_SIZE = 50 def initTargetSet(schemata, size): """Initialize a target set with noisy string to match based on the schematas provided. """ test_set = [] for _ in range(size): test = list(random.randint(0, 1) for _ in range(len(schemata))) for i, x in enumerate(schemata): if x == "0": test[i] = 0 elif x == "1": test[i] = 1 test_set.append(test) return test_set def matchStrength(x, y): """Compute the match strength for the individual *x* on the string *y*. """ return sum(xi == yi for xi, yi in zip(x, y)) def matchStrengthNoNoise(x, y, n): """Compute the match strength for the individual *x* on the string *y* excluding noise *n*. """ return sum(xi == yi for xi, yi, ni in zip(x, y, n) if ni != "#") def matchSetStrength(match_set, target_set): """Compute the match strength of a set of strings on the target set of strings. The strength is the maximum of all match string on each target. """ sum = 0.0 for t in target_set: sum += max(matchStrength(m, t) for m in match_set) return sum / len(target_set), def matchSetStrengthNoNoise(match_set, target_set, noise): """Compute the match strength of a set of strings on the target set of strings. The strength is the maximum of all match string on each target excluding noise. """ sum = 0.0 for t in target_set: sum += max(matchStrengthNoNoise(m, t, noise) for m in match_set) return sum / len(target_set), def matchSetContribution(match_set, target_set, index): """Compute the contribution of the string at *index* in the match set. """ contribution = 0.0 for t in target_set: match = -float("inf") id = -1 for i, m in enumerate(match_set): v = matchStrength(m, t) if v > match: match = v id = i if id == index: contribution += match return contribution / len(target_set), creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("bit", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.bit, IND_SIZE) toolbox.register("species", tools.initRepeat, list, toolbox.individual, SPECIES_SIZE) toolbox.register("target_set", initTargetSet) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=1./IND_SIZE) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("get_best", tools.selBest, k=1) toolbox.register("evaluate", matchSetStrength) deap-1.0.1/examples/coev/coop_evol.py0000644000076500000240000001433112301410325017756 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """This example contains the evolving test from *Potter, M. and De Jong, K., 2001, Cooperative Coevolution: An Architecture for Evolving Co-adapted Subcomponents.* section 4.2.4. The number of species is evolved by adding and removing species as stagnation occurs. """ import random try: import matplotlib.pyplot as plt plt.figure() except: plt = False import numpy from deap import algorithms from deap import tools import coop_base IND_SIZE = coop_base.IND_SIZE SPECIES_SIZE = coop_base.SPECIES_SIZE NUM_SPECIES = 1 TARGET_SIZE = 30 IMPROVMENT_TRESHOLD = 0.5 IMPROVMENT_LENGTH = 5 EXTINCTION_TRESHOLD = 5.0 noise = "*##*###*###*****##*##****#*##*###*#****##******##*#**#*#**######" schematas = ("1##1###1###11111##1##1111#1##1###1#1111##111111##1#11#1#11######", "1##1###1###11111##1##1000#0##0###0#0000##000000##0#00#0#00######", "0##0###0###00000##0##0000#0##0###0#0000##001111##1#11#1#11######") toolbox = coop_base.toolbox toolbox.register("evaluateContribution", coop_base.matchSetContribution) def main(extended=True, verbose=True): target_set = [] species = [] stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "species", "evals", "std", "min", "avg", "max" ngen = 300 g = 0 for i in range(len(schematas)): size = int(TARGET_SIZE/len(schematas)) target_set.extend(toolbox.target_set(schematas[i], size)) species = [toolbox.species() for _ in range(NUM_SPECIES)] species_index = list(range(NUM_SPECIES)) last_index_added = species_index[-1] # Init with random a representative for each species representatives = [random.choice(species[i]) for i in range(NUM_SPECIES)] best_fitness_history = [None] * IMPROVMENT_LENGTH if plt and extended: contribs = [[]] stag_gen = [] collab = [] while g < ngen: # Initialize a container for the next generation representatives next_repr = [None] * len(species) for (i, s), j in zip(enumerate(species), species_index): # Vary the species individuals s = algorithms.varAnd(s, toolbox, 0.6, 1.0) # Get the representatives excluding the current species r = representatives[:i] + representatives[i+1:] for ind in s: # Evaluate and set the individual fitness ind.fitness.values = toolbox.evaluate([ind] + r, target_set) record = stats.compile(s) logbook.record(gen=g, species=j, evals=len(s), **record) if verbose: print(logbook.stream) # Select the individuals species[i] = toolbox.select(s, len(s)) # Tournament selection next_repr[i] = toolbox.get_best(s)[0] # Best selection if plt and extended: # Book keeping of the collaborative fitness collab.append(next_repr[i].fitness.values[0]) g += 1 representatives = next_repr # Keep representatives fitness for stagnation detection best_fitness_history.pop(0) best_fitness_history.append(representatives[0].fitness.values[0]) try: diff = best_fitness_history[-1] - best_fitness_history[0] except TypeError: diff = float("inf") if plt and extended: for (i, rep), j in zip(enumerate(representatives), species_index): contribs[j].append((toolbox.evaluateContribution(representatives, target_set, i)[0], g-1)) if diff < IMPROVMENT_TRESHOLD: if len(species) > 1: contributions = [] for i in range(len(species)): contributions.append(toolbox.evaluateContribution(representatives, target_set, i)[0]) for i in reversed(range(len(species))): if contributions[i] < EXTINCTION_TRESHOLD: species.pop(i) species_index.pop(i) representatives.pop(i) last_index_added += 1 best_fitness_history = [None] * IMPROVMENT_LENGTH species.append(toolbox.species()) species_index.append(last_index_added) representatives.append(random.choice(species[-1])) if extended and plt: stag_gen.append(g-1) contribs.append([]) if extended: for r in representatives: # print final representatives without noise print("".join(str(x) for x, y in zip(r, noise) if y == "*")) if extended and plt: # Ploting of the evolution line1, = plt.plot(collab, "--", color="k") for con in contribs: try: con, g = zip(*con) line2, = plt.plot(g, con, "-", color="k") except ValueError: pass axis = plt.axis("tight") for s in stag_gen: plt.plot([s, s], [0, axis[-1]], "--", color="k") plt.legend((line1, line2), ("Collaboration", "Contribution"), loc="center right") plt.xlabel("Generations") plt.ylabel("Fitness") plt.show() if __name__ == "__main__": main() deap-1.0.1/examples/coev/coop_gen.py0000644000076500000240000001142612301410325017564 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """This example contains the generalizing test from *Potter, M. and De Jong, K., 2001, Cooperative Coevolution: An Architecture for Evolving Co-adapted Subcomponents.* section 4.2.2. Varying the *NUM_SPECIES* in :math:`[1, \ldots, 4]` will produce the results for one to four species respectively. """ import random try: import matplotlib.pyplot as plt except ImportError: plt = False import numpy from deap import algorithms from deap import tools import coop_base IND_SIZE = coop_base.IND_SIZE SPECIES_SIZE = coop_base.SPECIES_SIZE NUM_SPECIES = 4 TARGET_SIZE = 30 noise = "*##*###*###*****##*##****#*##*###*#****##******##*#**#*#**######" schematas = ("1##1###1###11111##1##1111#1##1###1#1111##111111##1#11#1#11######", "1##1###1###11111##1##1000#0##0###0#0000##000000##0#00#0#00######", "0##0###0###00000##0##0000#0##0###0#0000##001111##1#11#1#11######") toolbox = coop_base.toolbox if plt: # This will allow to plot the match strength of every target schemata toolbox.register("evaluate_nonoise", coop_base.matchSetStrengthNoNoise) def main(extended=True, verbose=True): target_set = [] stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "species", "evals", "std", "min", "avg", "max" ngen = 150 g = 0 for i in range(len(schematas)): size = int(TARGET_SIZE/len(schematas)) target_set.extend(toolbox.target_set(schematas[i], size)) species = [toolbox.species() for _ in range(NUM_SPECIES)] # Init with random a representative for each species representatives = [random.choice(s) for s in species] if plt and extended: # We must save the match strength to plot them t1, t2, t3 = list(), list(), list() while g < ngen: # Initialize a container for the next generation representatives next_repr = [None] * len(species) for i, s in enumerate(species): # Vary the species individuals s = algorithms.varAnd(s, toolbox, 0.6, 1.0) # Get the representatives excluding the current species r = representatives[:i] + representatives[i+1:] for ind in s: ind.fitness.values = toolbox.evaluate([ind] + r, target_set) record = stats.compile(s) logbook.record(gen=g, species=i, evals=len(s), **record) if verbose: print(logbook.stream) # Select the individuals species[i] = toolbox.select(s, len(s)) # Tournament selection next_repr[i] = toolbox.get_best(s)[0] # Best selection g += 1 if plt and extended: # Compute the match strength without noise for the # representatives on the three schematas t1.append(toolbox.evaluate_nonoise(representatives, toolbox.target_set(schematas[0], 1), noise)[0]) t2.append(toolbox.evaluate_nonoise(representatives, toolbox.target_set(schematas[1], 1), noise)[0]) t3.append(toolbox.evaluate_nonoise(representatives, toolbox.target_set(schematas[2], 1), noise)[0]) representatives = next_repr if extended: for r in representatives: # print individuals without noise print("".join(str(x) for x, y in zip(r, noise) if y == "*")) if plt and extended: # Do the final plotting plt.plot(t1, '-', color="k", label="Target 1") plt.plot(t2, '--', color="k", label="Target 2") plt.plot(t3, ':', color="k", label="Target 3") plt.legend(loc="lower right") plt.axis([0, ngen, 0, max(max(t1), max(t2), max(t3)) + 1]) plt.xlabel("Generations") plt.ylabel("Number of matched bits") plt.show() if __name__ == "__main__": main() deap-1.0.1/examples/coev/coop_niche.py0000644000076500000240000000657112301410325020106 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """This example contains the niching test from *Potter, M. and De Jong, K., 2001, Cooperative Coevolution: An Architecture for Evolving Co-adapted Subcomponents.* section 4.2.1. Varying the *TARGET_TYPE* in :math:`\\lbrace 2, 4, 8 \\rbrace` will produce the results for the half-, quarter- and eight-length schematas. """ import random import numpy from deap import algorithms from deap import tools import coop_base IND_SIZE = coop_base.IND_SIZE SPECIES_SIZE = coop_base.SPECIES_SIZE TARGET_SIZE = 200 TARGET_TYPE = 2 def nicheSchematas(type, size): """Produce the desired schemata based on the type required, 2 for half length, 4 for quarter length and 8 for eight length. """ rept = int(size/type) return ["#" * (i*rept) + "1" * rept + "#" * ((type-i-1)*rept) for i in range(type)] toolbox = coop_base.toolbox def main(extended=True, verbose=True): target_set = [] species = [] stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "species", "evals", "std", "min", "avg", "max" ngen = 200 g = 0 schematas = nicheSchematas(TARGET_TYPE, IND_SIZE) for i in range(TARGET_TYPE): size = int(TARGET_SIZE/TARGET_TYPE) target_set.extend(toolbox.target_set(schematas[i], size)) species.append(toolbox.species()) # Init with a random representative for each species representatives = [random.choice(s) for s in species] while g < ngen: # Initialize a container for the next generation representatives next_repr = [None] * len(species) for i, s in enumerate(species): # Vary the species individuals s = algorithms.varAnd(s, toolbox, 0.6, 1.0) # Get the representatives excluding the current species r = representatives[:i] + representatives[i+1:] for ind in s: ind.fitness.values = toolbox.evaluate([ind] + r, target_set) record = stats.compile(s) logbook.record(gen=g, species=i, evals=len(s), **record) if verbose: print(logbook.stream) # Select the individuals species[i] = toolbox.select(s, len(s)) # Tournament selection next_repr[i] = toolbox.get_best(s)[0] # Best selection g += 1 representatives = next_repr if extended: for r in representatives: print("".join(str(x) for x in r)) if __name__ == "__main__": main() deap-1.0.1/examples/coev/hillis.py0000644000076500000240000001315712301410325017262 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random import sys import numpy from deap import algorithms from deap import base from deap import creator from deap import tools sys.path.append("../ga") import sortingnetwork as sn INPUTS = 12 def evalNetwork(host, parasite, dimension): network = sn.SortingNetwork(dimension, host) return network.assess(parasite), def genWire(dimension): return (random.randrange(dimension), random.randrange(dimension)) def genNetwork(dimension, min_size, max_size): size = random.randint(min_size, max_size) return [genWire(dimension) for i in range(size)] def getParasite(dimension): return [random.choice((0, 1)) for i in range(dimension)] def mutNetwork(individual, dimension, mutpb, addpb, delpb, indpb): if random.random() < mutpb: for index, elem in enumerate(individual): if random.random() < indpb: individual[index] = genWire(dimension) if random.random() < addpb: index = random.randint(0, len(individual)) individual.insert(index, genWire(dimension)) if random.random() < delpb: index = random.randrange(len(individual)) del individual[index] return individual, def mutParasite(individual, indmut, indpb): for i in individual: if random.random() < indpb: indmut(i) return individual, creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Host", list, fitness=creator.FitnessMin) creator.create("Parasite", list, fitness=creator.FitnessMax) htoolbox = base.Toolbox() ptoolbox = base.Toolbox() htoolbox.register("network", genNetwork, dimension=INPUTS, min_size=9, max_size=12) htoolbox.register("individual", tools.initIterate, creator.Host, htoolbox.network) htoolbox.register("population", tools.initRepeat, list, htoolbox.individual) ptoolbox.register("parasite", getParasite, dimension=INPUTS) ptoolbox.register("individual", tools.initRepeat, creator.Parasite, ptoolbox.parasite, 20) ptoolbox.register("population", tools.initRepeat, list, ptoolbox.individual) htoolbox.register("evaluate", evalNetwork, dimension=INPUTS) htoolbox.register("mate", tools.cxTwoPoint) htoolbox.register("mutate", mutNetwork, dimension=INPUTS, mutpb=0.2, addpb=0.01, delpb=0.01, indpb=0.05) htoolbox.register("select", tools.selTournament, tournsize=3) ptoolbox.register("mate", tools.cxTwoPoint) ptoolbox.register("indMutate", tools.mutFlipBit, indpb=0.05) ptoolbox.register("mutate", mutParasite, indmut=ptoolbox.indMutate, indpb=0.05) ptoolbox.register("select", tools.selTournament, tournsize=3) def cloneHost(individual): """Specialized copy function that will work only on a list of tuples with no other member than a fitness. """ clone = individual.__class__(individual) clone.fitness.values = individual.fitness.values return clone def cloneParasite(individual): """Specialized copy function that will work only on a list of lists with no other member than a fitness. """ clone = individual.__class__(list(seq) for seq in individual) clone.fitness.values = individual.fitness.values return clone htoolbox.register("clone", cloneHost) ptoolbox.register("clone", cloneParasite) def main(): random.seed(64) hosts = htoolbox.population(n=300) parasites = ptoolbox.population(n=300) hof = tools.HallOfFame(1) hstats = tools.Statistics(lambda ind: ind.fitness.values) hstats.register("avg", numpy.mean) hstats.register("std", numpy.std) hstats.register("min", numpy.min) hstats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "evals", "std", "min", "avg", "max" MAXGEN = 50 H_CXPB, H_MUTPB = 0.5, 0.3 P_CXPB, P_MUTPB = 0.5, 0.3 fits = htoolbox.map(htoolbox.evaluate, hosts, parasites) for host, parasite, fit in zip(hosts, parasites, fits): host.fitness.values = parasite.fitness.values = fit hof.update(hosts) record = hstats.compile(hosts) logbook.record(gen=0, evals=len(hosts), **record) print(logbook.stream) for g in range(1, MAXGEN): hosts = htoolbox.select(hosts, len(hosts)) parasites = ptoolbox.select(parasites, len(parasites)) hosts = algorithms.varAnd(hosts, htoolbox, H_CXPB, H_MUTPB) parasites = algorithms.varAnd(parasites, ptoolbox, P_CXPB, P_MUTPB) fits = htoolbox.map(htoolbox.evaluate, hosts, parasites) for host, parasite, fit in zip(hosts, parasites, fits): host.fitness.values = parasite.fitness.values = fit hof.update(hosts) record = hstats.compile(hosts) logbook.record(gen=g, evals=len(hosts), **record) print(logbook.stream) best_network = sn.SortingNetwork(INPUTS, hof[0]) print(best_network) print(best_network.draw()) print("%i errors" % best_network.assess()) return hosts, logbook, hof if __name__ == "__main__": main() deap-1.0.1/examples/coev/symbreg.py0000644000076500000240000001107212301410325017440 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random import sys import numpy from deap import base from deap import creator from deap import tools # GP example "symbreg.py" already defines some useful structures sys.path.append("..") import gp.symbreg as symbreg creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("IndGA", list, fitness=creator.FitnessMax) toolbox_ga = base.Toolbox() toolbox_ga.register("float", random.uniform, -1, 1) toolbox_ga.register("individual", tools.initRepeat, creator.IndGA, toolbox_ga.float, 10) toolbox_ga.register("population", tools.initRepeat, list, toolbox_ga.individual) toolbox_ga.register("select", tools.selTournament, tournsize=3) toolbox_ga.register("mate", tools.cxTwoPoint) toolbox_ga.register("mutate", tools.mutGaussian, mu=0, sigma=0.01, indpb=0.05) toolbox_gp = symbreg.toolbox def main(): pop_ga = toolbox_ga.population(n=200) pop_gp = toolbox_gp.population(n=200) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "type", "evals", "std", "min", "avg", "max" best_ga = tools.selRandom(pop_ga, 1)[0] best_gp = tools.selRandom(pop_gp, 1)[0] for ind in pop_gp: ind.fitness.values = toolbox_gp.evaluate(ind, points=best_ga) for ind in pop_ga: ind.fitness.values = toolbox_gp.evaluate(best_gp, points=ind) record = stats.compile(pop_ga) logbook.record(gen=0, type='ga', evals=len(pop_ga), **record) record = stats.compile(pop_gp) logbook.record(gen=0, type='gp', evals=len(pop_gp), **record) print(logbook.stream) CXPB, MUTPB, NGEN = 0.5, 0.2, 50 # Begin the evolution for g in range(1, NGEN): # Select and clone the offspring off_ga = toolbox_ga.select(pop_ga, len(pop_ga)) off_gp = toolbox_gp.select(pop_gp, len(pop_gp)) off_ga = [toolbox_ga.clone(ind) for ind in off_ga] off_gp = [toolbox_gp.clone(ind) for ind in off_gp] # Apply crossover and mutation for ind1, ind2 in zip(off_ga[::2], off_ga[1::2]): if random.random() < CXPB: toolbox_ga.mate(ind1, ind2) del ind1.fitness.values del ind2.fitness.values for ind1, ind2 in zip(off_gp[::2], off_gp[1::2]): if random.random() < CXPB: toolbox_gp.mate(ind1, ind2) del ind1.fitness.values del ind2.fitness.values for ind in off_ga: if random.random() < MUTPB: toolbox_ga.mutate(ind) del ind.fitness.values for ind in off_gp: if random.random() < MUTPB: toolbox_gp.mutate(ind) del ind.fitness.values # Evaluate the individuals with an invalid fitness for ind in off_ga: ind.fitness.values = toolbox_gp.evaluate(best_gp, points=ind) for ind in off_gp: ind.fitness.values = toolbox_gp.evaluate(ind, points=best_ga) # Replace the old population by the offspring pop_ga = off_ga pop_gp = off_gp record = stats.compile(pop_ga) logbook.record(gen=g, type='ga', evals=len(pop_ga), **record) record = stats.compile(pop_gp) logbook.record(gen=g, type='gp', evals=len(pop_gp), **record) print(logbook.stream) best_ga = tools.selBest(pop_ga, 1)[0] best_gp = tools.selBest(pop_gp, 1)[0] print("Best individual GA is %s, %s" % (best_ga, best_ga.fitness.values)) print("Best individual GP is %s, %s" % (best_gp, best_gp.fitness.values)) return pop_ga, pop_gp, best_ga, best_gp, logbook if __name__ == "__main__": main() deap-1.0.1/examples/de/0000755000076500000240000000000012321001644015053 5ustar felixstaff00000000000000deap-1.0.1/examples/de/basic.py0000644000076500000240000000543012301410325016506 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import random import array import numpy from deap import base from deap import benchmarks from deap import creator from deap import tools # Problem dimension NDIM = 10 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("attr_float", random.uniform, -3, 3) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, NDIM) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("select", tools.selRandom, k=3) toolbox.register("evaluate", benchmarks.sphere) def main(): # Differential evolution parameters CR = 0.25 F = 1 MU = 300 NGEN = 200 pop = toolbox.population(n=MU); hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "evals", "std", "min", "avg", "max" # Evaluate the individuals fitnesses = toolbox.map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit record = stats.compile(pop) logbook.record(gen=0, evals=len(pop), **record) print(logbook.stream) for g in range(1, NGEN): for k, agent in enumerate(pop): a,b,c = toolbox.select(pop) y = toolbox.clone(agent) index = random.randrange(NDIM) for i, value in enumerate(agent): if i == index or random.random() < CR: y[i] = a[i] + F*(b[i]-c[i]) y.fitness.values = toolbox.evaluate(y) if y.fitness > agent.fitness: pop[k] = y hof.update(pop) record = stats.compile(pop) logbook.record(gen=g, evals=len(pop), **record) print(logbook.stream) print("Best individual is ", hof[0], hof[0].fitness.values[0]) if __name__ == "__main__": main() deap-1.0.1/examples/de/dynamic.py0000644000076500000240000001327312301410325017055 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """Implementation of the Dynamic Differential Evolution algorithm as presented in *Mendes and Mohais, 2005, DynDE: A Differential Evolution for Dynamic Optimization Problems.* """ import array import itertools import math import operator import random import numpy from deap import base from deap.benchmarks import movingpeaks from deap import creator from deap import tools scenario = movingpeaks.SCENARIO_2 NDIM = 5 BOUNDS = [scenario["min_coord"], scenario["max_coord"]] mpb = movingpeaks.MovingPeaks(dim=NDIM, **scenario) def brown_ind(iclass, best, sigma): return iclass(random.gauss(x, sigma) for x in best) creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_float", random.uniform, BOUNDS[0], BOUNDS[1]) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, NDIM) toolbox.register("brownian_individual", brown_ind, creator.Individual, sigma=0.3) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("select", random.sample, k=4) toolbox.register("best", tools.selBest, k=1) toolbox.register("evaluate", mpb) def main(verbose=True): NPOP = 10 # Should be equal to the number of peaks CR = 0.6 F = 0.4 regular, brownian = 4, 2 stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "evals", "error", "offline_error", "avg", "max" # Initialize populations populations = [toolbox.population(n=regular + brownian) for _ in range(NPOP)] # Evaluate the individuals for idx, subpop in enumerate(populations): fitnesses = toolbox.map(toolbox.evaluate, subpop) for ind, fit in zip(subpop, fitnesses): ind.fitness.values = fit record = stats.compile(itertools.chain(*populations)) logbook.record(gen=0, evals=mpb.nevals, error=mpb.currentError(), offline_error=mpb.offlineError(), **record) if verbose: print(logbook.stream) g = 1 while mpb.nevals < 5e5: # Detect a change and invalidate fitnesses if necessary bests = [toolbox.best(subpop)[0] for subpop in populations] if any(b.fitness.values != toolbox.evaluate(b) for b in bests): for individual in itertools.chain(*populations): del individual.fitness.values # Apply exclusion rexcl = (BOUNDS[1] - BOUNDS[0]) / (2 * NPOP**(1.0/NDIM)) for i, j in itertools.combinations(range(NPOP), 2): if bests[i].fitness.valid and bests[j].fitness.valid: d = sum((bests[i][k] - bests[j][k])**2 for k in range(NDIM)) d = math.sqrt(d) if d < rexcl: if bests[i].fitness < bests[j].fitness: k = i else: k = j populations[k] = toolbox.population(n=regular + brownian) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in itertools.chain(*populations) if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit record = stats.compile(itertools.chain(*populations)) logbook.record(gen=g, evals=mpb.nevals, error=mpb.currentError(), offline_error=mpb.offlineError(), **record) if verbose: print(logbook.stream) # Evolve the sub-populations for idx, subpop in enumerate(populations): newpop = [] xbest, = toolbox.best(subpop) # Apply regular DE to the first part of the population for individual in subpop[:regular]: x1, x2, x3, x4 = toolbox.select(subpop) offspring = toolbox.clone(individual) index = random.randrange(NDIM) for i, value in enumerate(individual): if i == index or random.random() < CR: offspring[i] = xbest[i] + F * (x1[i] + x2[i] - x3[i] - x4[i]) offspring.fitness.values = toolbox.evaluate(offspring) if offspring.fitness >= individual.fitness: newpop.append(offspring) else: newpop.append(individual) # Apply Brownian to the last part of the population newpop.extend(toolbox.brownian_individual(xbest) for _ in range(brownian)) # Evaluate the brownian individuals for individual in newpop[-brownian:]: individual.fitness.value = toolbox.evaluate(individual) # Replace the population populations[idx] = newpop g += 1 return logbook if __name__ == "__main__": main() deap-1.0.1/examples/de/sphere.py0000644000076500000240000000735312301410325016721 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import random import array import numpy from itertools import chain from deap import base from deap import benchmarks from deap import creator from deap import tools # Problem dimension NDIM = 10 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) def mutDE(y, a, b, c, f): size = len(y) for i in range(len(y)): y[i] = a[i] + f*(b[i]-c[i]) return y def cxBinomial(x, y, cr): size = len(x) index = random.randrange(size) for i in range(size): if i == index or random.random() < cr: x[i] = y[i] return x def cxExponential(x, y, cr): size = len(x) index = random.randrange(size) # Loop on the indices index -> end, then on 0 -> index for i in chain(range(index, size), range(0, index)): x[i] = y[i] if random.random() < cr: break return x toolbox = base.Toolbox() toolbox.register("attr_float", random.uniform, -3, 3) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, NDIM) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("mutate", mutDE, f=0.8) toolbox.register("mate", cxExponential, cr=0.8) toolbox.register("select", tools.selRandom, k=3) toolbox.register("evaluate", benchmarks.griewank) def main(): # Differential evolution parameters MU = NDIM * 10 NGEN = 200 pop = toolbox.population(n=MU); hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "evals", "std", "min", "avg", "max" # Evaluate the individuals fitnesses = toolbox.map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit record = stats.compile(pop) logbook.record(gen=0, evals=len(pop), **record) print(logbook.stream) for g in range(1, NGEN): children = [] for agent in pop: # We must clone everything to ensure independance a, b, c = [toolbox.clone(ind) for ind in toolbox.select(pop)] x = toolbox.clone(agent) y = toolbox.clone(agent) y = toolbox.mutate(y, a, b, c) z = toolbox.mate(x, y) del z.fitness.values children.append(z) fitnesses = toolbox.map(toolbox.evaluate, children) for (i, ind), fit in zip(enumerate(children), fitnesses): ind.fitness.values = fit if ind.fitness > pop[i].fitness: pop[i] = ind hof.update(pop) record = stats.compile(pop) logbook.record(gen=g, evals=len(pop), **record) print(logbook.stream) print("Best individual is ", hof[0]) print("with fitness", hof[0].fitness.values[0]) return logbook if __name__ == "__main__": main() deap-1.0.1/examples/eda/0000755000076500000240000000000012321001644015214 5ustar felixstaff00000000000000deap-1.0.1/examples/eda/fctmin.py0000644000076500000240000000575512320777200017071 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random import numpy from operator import attrgetter from deap import algorithms from deap import base from deap import benchmarks from deap import creator from deap import tools creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", numpy.ndarray, fitness=creator.FitnessMin) class EDA(object): def __init__(self, centroid, sigma, mu, lambda_): self.dim = len(centroid) self.loc = numpy.array(centroid) self.sigma = numpy.array(sigma) self.lambda_ = lambda_ self.mu = mu def generate(self, ind_init): # Generate lambda_ individuals and put them into the provided class arz = self.sigma * numpy.random.randn(self.lambda_, self.dim) + self.loc return list(map(ind_init, arz)) def update(self, population): # Sort individuals so the best is first sorted_pop = sorted(population, key=attrgetter("fitness"), reverse=True) # Compute the average of the mu best individuals z = sorted_pop[:self.mu] - self.loc avg = numpy.mean(z, axis=0) # Adjust variances of the distribution self.sigma = numpy.sqrt(numpy.sum((z - avg)**2, axis=0) / (self.mu - 1.0)) self.loc = self.loc + avg def main(): N, LAMBDA = 30, 1000 MU = int(LAMBDA/4) strategy = EDA(centroid=[5.0]*N, sigma=[5.0]*N, mu=MU, lambda_=LAMBDA) toolbox = base.Toolbox() toolbox.register("evaluate", benchmarks.rastrigin) toolbox.register("generate", strategy.generate, creator.Individual) toolbox.register("update", strategy.update) # Numpy equality function (operators.eq) between two arrays returns the # equality element wise, which raises an exception in the if similar() # check of the hall of fame. Using a different equality function like # numpy.array_equal or numpy.allclose solve this issue. hof = tools.HallOfFame(1, similar=numpy.array_equal) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) algorithms.eaGenerateUpdate(toolbox, ngen=150, stats=stats, halloffame=hof) return hof[0].fitness.values[0] if __name__ == "__main__": main() deap-1.0.1/examples/eda/pbil.py0000644000076500000240000000534512301410325016521 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random import numpy from deap import algorithms from deap import base from deap import creator from deap import tools class PBIL(object): def __init__(self, ndim, learning_rate, mut_prob, mut_shift, lambda_): self.prob_vector = [0.5] * ndim self.learning_rate = learning_rate self.mut_prob = mut_prob self.mut_shift = mut_shift self.lambda_ = lambda_ def sample(self): return (random.random() < prob for prob in self.prob_vector) def generate(self, ind_init): return [ind_init(self.sample()) for _ in range(self.lambda_)] def update(self, population): best = max(population, key=lambda ind: ind.fitness) for i, value in enumerate(best): # Update the probability vector self.prob_vector[i] *= 1.0 - self.learning_rate self.prob_vector[i] += value * self.learning_rate # Mutate the probability vector if random.random() < self.mut_prob: self.prob_vector[i] *= 1.0 - self.mut_shift self.prob_vector[i] += random.randint(0, 1) * self.mut_shift def evalOneMax(individual): return sum(individual), creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("evaluate", evalOneMax) def main(seed): random.seed(seed) NGEN = 50 #Initialize the PBIL EDA pbil = PBIL(ndim=50, learning_rate=0.3, mut_prob=0.1, mut_shift=0.05, lambda_=20) toolbox.register("generate", pbil.generate, creator.Individual) toolbox.register("update", pbil.update) # Statistics computation stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) pop, logbook = algorithms.eaGenerateUpdate(toolbox, NGEN, stats=stats, verbose=True) if __name__ == "__main__": main(seed=None) deap-1.0.1/examples/es/0000755000076500000240000000000012321001644015072 5ustar felixstaff00000000000000deap-1.0.1/examples/es/cma_1+l_minfct.py0000644000076500000240000000376612301410325020225 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import numpy from deap import algorithms from deap import base from deap import benchmarks from deap import cma from deap import creator from deap import tools N=5 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) # See http://www.lri.fr/~hansen/cmaes_inmatlab.html for more details about # the rastrigin and other tests for CMA-ES toolbox = base.Toolbox() toolbox.register("evaluate", benchmarks.sphere) def main(): numpy.random.seed() # The CMA-ES One Plus Lambda algorithm takes a initialized parent as argument parent = creator.Individual((numpy.random.rand() * 5) - 1 for _ in range(N)) parent.fitness.values = toolbox.evaluate(parent) strategy = cma.StrategyOnePlusLambda(parent, sigma=5.0, lambda_=10) toolbox.register("generate", strategy.generate, ind_init=creator.Individual) toolbox.register("update", strategy.update) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) algorithms.eaGenerateUpdate(toolbox, ngen=200, halloffame=hof, stats=stats) if __name__ == "__main__": main() deap-1.0.1/examples/es/cma_bipop.py0000644000076500000240000002011612301410325017373 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """Implementation of the BI-Population CMA-ES algorithm. As presented in *Hansen, 2009, Benchmarking a BI-Population CMA-ES on the BBOB-2009 Function Testbed* with the exception of the modifications to the original CMA-ES parameters mentionned at the end of section 2's first paragraph. """ from collections import deque import numpy from deap import algorithms from deap import base from deap import benchmarks from deap import cma from deap import creator from deap import tools # Problem size N = 30 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) def main(verbose=True): NRESTARTS = 10 # Initialization + 9 I-POP restarts SIGMA0 = 2.0 # 1/5th of the domain [-5 5] toolbox = base.Toolbox() toolbox.register("evaluate", benchmarks.rastrigin) halloffame = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbooks = list() nsmallpopruns = 0 smallbudget = list() largebudget = list() lambda0 = 4 + int(3 * numpy.log(N)) regime = 1 i = 0 while i < (NRESTARTS + nsmallpopruns): # The first regime is enforced on the first and last restart # The second regime is run if its allocated budget is smaller than the allocated # large population regime budget if i > 0 and i < (NRESTARTS + nsmallpopruns) - 1 and sum(smallbudget) < sum(largebudget): lambda_ = int(lambda0 * (0.5 * (2**(i - nsmallpopruns) * lambda0) / lambda0)**(numpy.random.rand()**2)) sigma = 2 * 10**(-2 * numpy.random.rand()) nsmallpopruns += 1 regime = 2 smallbudget += [0] else: lambda_ = 2**(i - nsmallpopruns) * lambda0 sigma = SIGMA0 regime = 1 largebudget += [0] t = 0 # Set the termination criterion constants if regime == 1: MAXITER = 100 + 50 * (N + 3)**2 / numpy.sqrt(lambda_) elif regime == 2: MAXITER = 0.5 * largebudget[-1] / lambda_ TOLHISTFUN = 10**-12 TOLHISTFUN_ITER = 10 + int(numpy.ceil(30. * N / lambda_)) EQUALFUNVALS = 1. / 3. EQUALFUNVALS_K = int(numpy.ceil(0.1 + lambda_ / 4.)) TOLX = 10**-12 TOLUPSIGMA = 10**20 CONDITIONCOV = 10**14 STAGNATION_ITER = int(numpy.ceil(0.2 * t + 120 + 30. * N / lambda_)) NOEFFECTAXIS_INDEX = t % N equalfunvalues = list() bestvalues = list() medianvalues = list() mins = deque(maxlen=TOLHISTFUN_ITER) # We start with a centroid in [-4, 4]**D strategy = cma.Strategy(centroid=numpy.random.uniform(-4, 4, N), sigma=sigma, lambda_=lambda_) toolbox.register("generate", strategy.generate, creator.Individual) toolbox.register("update", strategy.update) logbooks.append(tools.Logbook()) logbooks[-1].header = "gen", "evals", "restart", "regime", "std", "min", "avg", "max" conditions = {"MaxIter" : False, "TolHistFun" : False, "EqualFunVals" : False, "TolX" : False, "TolUpSigma" : False, "Stagnation" : False, "ConditionCov" : False, "NoEffectAxis" : False, "NoEffectCoor" : False} # Run the current regime until one of the following is true: ## Note that the algorithm won't stop by itself on the optimum (0.0 on rastrigin). while not any(conditions.values()): # Generate a new population population = toolbox.generate() # Evaluate the individuals fitnesses = toolbox.map(toolbox.evaluate, population) for ind, fit in zip(population, fitnesses): ind.fitness.values = fit halloffame.update(population) record = stats.compile(population) logbooks[-1].record(gen=t, evals=lambda_, restart=i, regime=regime, **record) if verbose: print(logbooks[-1].stream) # Update the strategy with the evaluated individuals toolbox.update(population) # Count the number of times the k'th best solution is equal to the best solution # At this point the population is sorted (method update) if population[-1].fitness == population[-EQUALFUNVALS_K].fitness: equalfunvalues.append(1) # Log the best and median value of this population bestvalues.append(population[-1].fitness.values) medianvalues.append(population[int(round(len(population)/2.))].fitness.values) # First run does not count into the budget if regime == 1 and i > 0: largebudget[-1] += lambda_ elif regime == 2: smallbudget[-1] += lambda_ t += 1 STAGNATION_ITER = int(numpy.ceil(0.2 * t + 120 + 30. * N / lambda_)) NOEFFECTAXIS_INDEX = t % N if t >= MAXITER: # The maximum number of iteration per CMA-ES ran conditions["MaxIter"] = True mins.append(record["min"]) if (len(mins) == mins.maxlen) and max(mins) - min(mins) < TOLHISTFUN: # The range of the best values is smaller than the threshold conditions["TolHistFun"] = True if t > N and sum(equalfunvalues[-N:]) / float(N) > EQUALFUNVALS: # In 1/3rd of the last N iterations the best and k'th best solutions are equal conditions["EqualFunVals"] = True if all(strategy.pc < TOLX) and all(numpy.sqrt(numpy.diag(strategy.C)) < TOLX): # All components of pc and sqrt(diag(C)) are smaller than the threshold conditions["TolX"] = True if strategy.sigma / sigma > strategy.diagD[-1]**2 * TOLUPSIGMA: # The sigma ratio is bigger than a threshold conditions["TolUpSigma"] = True if len(bestvalues) > STAGNATION_ITER and len(medianvalues) > STAGNATION_ITER and \ numpy.median(bestvalues[-20:]) >= numpy.median(bestvalues[-STAGNATION_ITER:-STAGNATION_ITER + 20]) and \ numpy.median(medianvalues[-20:]) >= numpy.median(medianvalues[-STAGNATION_ITER:-STAGNATION_ITER + 20]): # Stagnation occured conditions["Stagnation"] = True if strategy.cond > 10**14: # The condition number is bigger than a threshold conditions["ConditionCov"] = True if all(strategy.centroid == strategy.centroid + 0.1 * strategy.sigma * strategy.diagD[-NOEFFECTAXIS_INDEX] * strategy.B[-NOEFFECTAXIS_INDEX]): # The coordinate axis std is too low conditions["NoEffectAxis"] = True if any(strategy.centroid == strategy.centroid + 0.2 * strategy.sigma * numpy.diag(strategy.C)): # The main axis std has no effect conditions["NoEffectCoor"] = True stop_causes = [k for k, v in conditions.items() if v] print("Stopped because of condition%s %s" % ((":" if len(stop_causes) == 1 else "s:"), ",".join(stop_causes))) i += 1 return halloffame if __name__ == "__main__": main() deap-1.0.1/examples/es/cma_minfct.py0000644000076500000240000000426712301410325017553 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import numpy from deap import algorithms from deap import base from deap import benchmarks from deap import cma from deap import creator from deap import tools # Problem size N=30 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("evaluate", benchmarks.rastrigin) def main(): # The cma module uses the numpy random number generator numpy.random.seed(128) # The CMA-ES algorithm takes a population of one individual as argument # The centroid is set to a vector of 5.0 see http://www.lri.fr/~hansen/cmaes_inmatlab.html # for more details about the rastrigin and other tests for CMA-ES strategy = cma.Strategy(centroid=[5.0]*N, sigma=5.0, lambda_=20*N) toolbox.register("generate", strategy.generate, creator.Individual) toolbox.register("update", strategy.update) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) #logger = tools.EvolutionLogger(stats.functions.keys()) # The CMA-ES algorithm converge with good probability with those settings algorithms.eaGenerateUpdate(toolbox, ngen=250, stats=stats, halloffame=hof) # print "Best individual is %s, %s" % (hof[0], hof[0].fitness.values) return hof[0].fitness.values[0] if __name__ == "__main__": main() deap-1.0.1/examples/es/cma_plotting.py0000644000076500000240000001034612301410325020126 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import numpy from deap import algorithms from deap import base from deap import benchmarks from deap import cma from deap import creator from deap import tools import matplotlib.pyplot as plt # Problem size N = 10 NGEN = 125 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("evaluate", benchmarks.rastrigin) def main(verbose=True): # The cma module uses the numpy random number generator numpy.random.seed(64) # The CMA-ES algorithm takes a population of one individual as argument # The centroid is set to a vector of 5.0 see http://www.lri.fr/~hansen/cmaes_inmatlab.html # for more details about the rastrigin and other tests for CMA-ES strategy = cma.Strategy(centroid=[5.0]*N, sigma=5.0, lambda_=20*N) toolbox.register("generate", strategy.generate, creator.Individual) toolbox.register("update", strategy.update) halloffame = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "evals", "std", "min", "avg", "max" # Objects that will compile the data sigma = numpy.ndarray((NGEN,1)) axis_ratio = numpy.ndarray((NGEN,1)) diagD = numpy.ndarray((NGEN,N)) fbest = numpy.ndarray((NGEN,1)) best = numpy.ndarray((NGEN,N)) std = numpy.ndarray((NGEN,N)) for gen in range(NGEN): # Generate a new population population = toolbox.generate() # Evaluate the individuals fitnesses = toolbox.map(toolbox.evaluate, population) for ind, fit in zip(population, fitnesses): ind.fitness.values = fit # Update the strategy with the evaluated individuals toolbox.update(population) # Update the hall of fame and the statistics with the # currently evaluated population halloffame.update(population) record = stats.compile(population) logbook.record(evals=len(population), gen=gen, **record) if verbose: print(logbook.stream) # Save more data along the evolution for latter plotting # diagD is sorted and sqrooted in the update method sigma[gen] = strategy.sigma axis_ratio[gen] = max(strategy.diagD)**2/min(strategy.diagD)**2 diagD[gen, :N] = strategy.diagD**2 fbest[gen] = halloffame[0].fitness.values best[gen, :N] = halloffame[0] std[gen, :N] = numpy.std(population, axis=0) # The x-axis will be the number of evaluations x = list(range(0, strategy.lambda_ * NGEN, strategy.lambda_)) avg, max_, min_ = logbook.select("avg", "max", "min") plt.figure() plt.subplot(2, 2, 1) plt.semilogy(x, avg, "--b") plt.semilogy(x, max_, "--b") plt.semilogy(x, min_, "-b") plt.semilogy(x, fbest, "-c") plt.semilogy(x, sigma, "-g") plt.semilogy(x, axis_ratio, "-r") plt.grid(True) plt.title("blue: f-values, green: sigma, red: axis ratio") plt.subplot(2, 2, 2) plt.plot(x, best) plt.grid(True) plt.title("Object Variables") plt.subplot(2, 2, 3) plt.semilogy(x, diagD) plt.grid(True) plt.title("Scaling (All Main Axes)") plt.subplot(2, 2, 4) plt.semilogy(x, std) plt.grid(True) plt.title("Standard Deviations in All Coordinates") plt.show() if __name__ == "__main__": main(False) deap-1.0.1/examples/es/fctmin.py0000644000076500000240000000557412301410325016735 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random import numpy from deap import algorithms from deap import base from deap import benchmarks from deap import creator from deap import tools IND_SIZE = 30 MIN_VALUE = 4 MAX_VALUE = 5 MIN_STRATEGY = 0.5 MAX_STRATEGY = 3 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode="d", fitness=creator.FitnessMin, strategy=None) creator.create("Strategy", array.array, typecode="d") # Individual generator def generateES(icls, scls, size, imin, imax, smin, smax): ind = icls(random.uniform(imin, imax) for _ in range(size)) ind.strategy = scls(random.uniform(smin, smax) for _ in range(size)) return ind def checkStrategy(minstrategy): def decorator(func): def wrappper(*args, **kargs): children = func(*args, **kargs) for child in children: for i, s in enumerate(child.strategy): if s < minstrategy: child.strategy[i] = minstrategy return children return wrappper return decorator toolbox = base.Toolbox() toolbox.register("individual", generateES, creator.Individual, creator.Strategy, IND_SIZE, MIN_VALUE, MAX_VALUE, MIN_STRATEGY, MAX_STRATEGY) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("mate", tools.cxESBlend, alpha=0.1) toolbox.register("mutate", tools.mutESLogNormal, c=1.0, indpb=0.03) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", benchmarks.sphere) toolbox.decorate("mate", checkStrategy(MIN_STRATEGY)) toolbox.decorate("mutate", checkStrategy(MIN_STRATEGY)) def main(): random.seed() MU, LAMBDA = 10, 100 pop = toolbox.population(n=MU) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) pop, logbook = algorithms.eaMuCommaLambda(pop, toolbox, mu=MU, lambda_=LAMBDA, cxpb=0.6, mutpb=0.3, ngen=500, stats=stats, halloffame=hof) return pop, logbook, hof if __name__ == "__main__": main() deap-1.0.1/examples/es/onefifth.py0000644000076500000240000000535312301410325017252 0ustar felixstaff00000000000000 # This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random from deap import base from deap import creator from deap import benchmarks from deap import tools IND_SIZE = 10 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) def update(ind, mu, std): for i, mu_i in enumerate(mu): ind[i] = random.gauss(mu_i,std) toolbox = base.Toolbox() toolbox.register("update", update) toolbox.register("evaluate", benchmarks.sphere) def main(): """Implements the One-Fifth rule algorithm as expressed in : Kern, S., S.D. Muller, N. Hansen, D. Buche, J. Ocenasek and P. Koumoutsakos (2004). Learning Probability Distributions in Continuous Evolutionary Algorithms - A Comparative Review. Natural Computing, 3(1), pp. 77-112. However instead of parent and offspring the algorithm is expressed in terms of best and worst. Best is equivalent to the parent, and worst to the offspring. Instead of producing a new individual each time, we have defined a function which updates the worst individual using the best one as the mean of the gaussian and the sigma computed as the standard deviation. """ random.seed(64) logbook = tools.Logbook() logbook.header = "gen", "fitness" interval = (-3,7) mu = (random.uniform(interval[0], interval[1]) for _ in range(IND_SIZE)) sigma = (interval[1] - interval[0])/2.0 alpha = 2.0**(1.0/IND_SIZE) best = creator.Individual(mu) best.fitness.values = toolbox.evaluate(best) worst = creator.Individual((0.0,)*IND_SIZE) NGEN = 1500 for g in range(NGEN): toolbox.update(worst, best, sigma) worst.fitness.values = toolbox.evaluate(worst) if best.fitness <= worst.fitness: sigma = sigma * alpha best, worst = worst, best else: sigma = sigma * alpha**(-0.25) logbook.record(gen=g, fitness=best.fitness.values) print(logbook.stream) return best if __name__ == "__main__": main() deap-1.0.1/examples/ga/0000755000076500000240000000000012321001644015052 5ustar felixstaff00000000000000deap-1.0.1/examples/ga/evoknn.py0000755000076500000240000000546512301410325016737 0ustar felixstaff00000000000000#!/usr/bin/env python2.7 # This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import csv import random import numpy import knn from deap import algorithms from deap import base from deap import creator from deap import tools # kNN parameters import knn FILE="heart_scale.csv" N_TRAIN=175 K=1 # Read data from file data = csv.reader(open(FILE, "r")) trainset = list() trainlabels = list() rows = [row for row in data] random.shuffle(rows) for row in rows: trainlabels.append(float(row[0])) trainset.append([float(e) for e in row[1:]]) classifier = knn.KNN(K) classifier.train(trainset[:N_TRAIN], trainlabels[:N_TRAIN]) def evalClassifier(individual): labels = classifier.predict(trainset[N_TRAIN:], individual) return sum(x == y for x, y in zip(labels, trainlabels[N_TRAIN:])) / float(len(trainlabels[N_TRAIN:])), \ sum(individual) / float(classifier.ndim) creator.create("FitnessMulti", base.Fitness, weights=(1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMulti) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, classifier.ndim) toolbox.register("population", tools.initRepeat, list, toolbox.individual) # Operator registering toolbox.register("evaluate", evalClassifier) toolbox.register("mate", tools.cxUniform, indpb=0.1) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) def main(): # random.seed(64) MU, LAMBDA = 100, 200 pop = toolbox.population(n=MU) hof = tools.ParetoFront() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean, axis=0) stats.register("std", numpy.std, axis=0) stats.register("min", numpy.min, axis=0) stats.register("max", numpy.max, axis=0) pop, logbook = algorithms.eaMuPlusLambda(pop, toolbox, mu=MU, lambda_=LAMBDA, cxpb=0.7, mutpb=0.3, ngen=40, stats=stats, halloffame=hof) return pop, logbook, hof if __name__ == "__main__": main() deap-1.0.1/examples/ga/evoknn_jmlr.py0000644000076500000240000000361112301410325017747 0ustar felixstaff00000000000000#!/usr/bin/env python2.7 # This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import knn, random from deap import algorithms, base, creator, tools def evalFitness(individual): return knn.classification_rate(features=individual), sum(individual) creator.create("FitnessMulti", base.Fitness, weights=(1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMulti) toolbox = base.Toolbox() toolbox.register("bit", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.bit, n=13) toolbox.register("population", tools.initRepeat, list, toolbox.individual, n=100) toolbox.register("evaluate", evalFitness) toolbox.register("mate", tools.cxUniform, indpb=0.1) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) population = toolbox.population() fits = toolbox.map(toolbox.evaluate, population) for fit, ind in zip(fits, population): ind.fitness.values = fit for gen in range(50): offspring = algorithms.varOr(population, toolbox, lambda_=100, cxpb=0.5,mutpb=0.1) fits = toolbox.map(toolbox.evaluate, offspring) for fit, ind in zip(fits, offspring): ind.fitness.values = fit population = toolbox.select(offspring + population, k=100) deap-1.0.1/examples/ga/evosn.py0000644000076500000240000001167512301410325016566 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random import numpy from deap import algorithms from deap import base from deap import creator from deap import tools import sortingnetwork as sn INPUTS = 6 def evalEvoSN(individual, dimension): network = sn.SortingNetwork(dimension, individual) return network.assess(), network.length, network.depth def genWire(dimension): return (random.randrange(dimension), random.randrange(dimension)) def genNetwork(dimension, min_size, max_size): size = random.randint(min_size, max_size) return [genWire(dimension) for i in range(size)] def mutWire(individual, dimension, indpb): for index, elem in enumerate(individual): if random.random() < indpb: individual[index] = genWire(dimension) def mutAddWire(individual, dimension): index = random.randint(0, len(individual)) individual.insert(index, genWire(dimension)) def mutDelWire(individual): index = random.randrange(len(individual)) del individual[index] creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() # Gene initializer toolbox.register("network", genNetwork, dimension=INPUTS, min_size=9, max_size=12) # Structure initializers toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.network) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evalEvoSN, dimension=INPUTS) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", mutWire, dimension=INPUTS, indpb=0.05) toolbox.register("addwire", mutAddWire, dimension=INPUTS) toolbox.register("delwire", mutDelWire) toolbox.register("select", tools.selNSGA2) def main(): random.seed(64) population = toolbox.population(n=300) hof = tools.ParetoFront() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean, axis=0) stats.register("std", numpy.std, axis=0) stats.register("min", numpy.min, axis=0) stats.register("max", numpy.max, axis=0) logbook = tools.Logbook() logbook.header = "gen", "evals", "std", "min", "avg", "max" CXPB, MUTPB, ADDPB, DELPB, NGEN = 0.5, 0.2, 0.01, 0.01, 40 # Evaluate every individuals fitnesses = toolbox.map(toolbox.evaluate, population) for ind, fit in zip(population, fitnesses): ind.fitness.values = fit hof.update(population) record = stats.compile(population) logbook.record(gen=0, evals=len(population), **record) print(logbook.stream) # Begin the evolution for g in range(1, NGEN): offspring = [toolbox.clone(ind) for ind in population] # Apply crossover and mutation for ind1, ind2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(ind1, ind2) del ind1.fitness.values del ind2.fitness.values # Note here that we have a different sheme of mutation than in the # original algorithm, we use 3 different mutations subsequently. for ind in offspring: if random.random() < MUTPB: toolbox.mutate(ind) del ind.fitness.values if random.random() < ADDPB: toolbox.addwire(ind) del ind.fitness.values if random.random() < DELPB: toolbox.delwire(ind) del ind.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit population = toolbox.select(population+offspring, len(offspring)) hof.update(population) record = stats.compile(population) logbook.record(gen=g, evals=len(invalid_ind), **record) print(logbook.stream) best_network = sn.SortingNetwork(INPUTS, hof[0]) print(stats) print(best_network) print(best_network.draw()) print("%i errors, length %i, depth %i" % hof[0].fitness.values) return population, logbook, hof if __name__ == "__main__": main() deap-1.0.1/examples/ga/heart_scale.csv0000644000076500000240000005716412117373622020071 0ustar felixstaff000000000000001.0,0.708333,1.0,1.0,-0.320755,-0.105023,-1.0,1.0,-0.419847,-1.0,-0.225806,0,1.0,-1.0 -1.0,0.583333,-1.0,0.333333,-0.603774,1.0,-1.0,1.0,0.358779,-1.0,-0.483871,0,-1.0,1.0 1.0,0.166667,1.0,-0.333333,-0.433962,-0.383562,-1.0,-1.0,0.0687023,-1.0,-0.903226,-1.0,-1.0,1.0 -1.0,0.458333,1.0,1.0,-0.358491,-0.374429,-1.0,-1.0,-0.480916,1.0,-0.935484,0,-0.333333,1.0 -1.0,0.875,-1.0,-0.333333,-0.509434,-0.347032,-1.0,1.0,-0.236641,1.0,-0.935484,-1.0,-0.333333,-1.0 -1.0,0.5,1.0,1.0,-0.509434,-0.767123,-1.0,-1.0,0.0534351,-1.0,-0.870968,-1.0,-1.0,1.0 1.0,0.125,1.0,0.333333,-0.320755,-0.406393,1.0,1.0,0.0839695,1.0,-0.806452,0,-0.333333,0.5 1.0,0.25,1.0,1.0,-0.698113,-0.484018,-1.0,1.0,0.0839695,1.0,-0.612903,0,-0.333333,1.0 1.0,0.291667,1.0,1.0,-0.132075,-0.237443,-1.0,1.0,0.51145,-1.0,-0.612903,0,0.333333,1.0 1.0,0.416667,-1.0,1.0,0.0566038,0.283105,-1.0,1.0,0.267176,-1.0,0.290323,0,1.0,1.0 -1.0,0.25,1.0,1.0,-0.226415,-0.506849,-1.0,-1.0,0.374046,-1.0,-0.83871,0,-1.0,1.0 -1.0,0,1.0,1.0,-0.0943396,-0.543379,-1.0,1.0,-0.389313,1.0,-1.0,-1.0,-1.0,1.0 -1.0,-0.375,1.0,0.333333,-0.132075,-0.502283,-1.0,1.0,0.664122,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.333333,1.0,-1.0,-0.245283,-0.506849,-1.0,-1.0,0.129771,-1.0,-0.16129,0,0.333333,-1.0 -1.0,0.166667,-1.0,1.0,-0.358491,-0.191781,-1.0,1.0,0.343511,-1.0,-1.0,-1.0,-0.333333,-1.0 -1.0,0.75,-1.0,1.0,-0.660377,-0.894977,-1.0,-1.0,-0.175573,-1.0,-0.483871,0,-1.0,-1.0 1.0,-0.291667,1.0,1.0,-0.132075,-0.155251,-1.0,-1.0,-0.251908,1.0,-0.419355,0,0.333333,1.0 1.0,0,1.0,1.0,-0.132075,-0.648402,1.0,1.0,0.282443,1.0,0,1.0,-1.0,1.0 -1.0,0.458333,1.0,-1.0,-0.698113,-0.611872,-1.0,1.0,0.114504,1.0,-0.419355,0,-1.0,-1.0 -1.0,-0.541667,1.0,-1.0,-0.132075,-0.666667,-1.0,-1.0,0.633588,1.0,-0.548387,-1.0,-1.0,1.0 1.0,0.583333,1.0,1.0,-0.509434,-0.52968,-1.0,1.0,-0.114504,1.0,-0.16129,0,0.333333,1.0 -1.0,-0.208333,1.0,-0.333333,-0.320755,-0.456621,-1.0,1.0,0.664122,-1.0,-0.935484,0,-1.0,-1.0 -1.0,-0.416667,1.0,1.0,-0.603774,-0.191781,-1.0,-1.0,0.679389,-1.0,-0.612903,0,-1.0,-1.0 -1.0,-0.25,1.0,1.0,-0.660377,-0.643836,-1.0,-1.0,0.0992366,-1.0,-0.967742,-1.0,-1.0,-1.0 -1.0,0.0416667,-1.0,-0.333333,-0.283019,-0.260274,1.0,1.0,0.343511,1.0,-1.0,-1.0,-0.333333,-1.0 -1.0,-0.208333,-1.0,0.333333,-0.320755,-0.319635,-1.0,-1.0,0.0381679,-1.0,-0.935484,-1.0,-1.0,-1.0 -1.0,-0.291667,-1.0,1.0,-0.169811,-0.465753,-1.0,1.0,0.236641,1.0,-1.0,0,-1.0,-1.0 -1.0,-0.0833333,-1.0,0.333333,-0.509434,-0.228311,-1.0,1.0,0.312977,-1.0,-0.806452,-1.0,-1.0,-1.0 1.0,0.208333,1.0,0.333333,-0.660377,-0.525114,-1.0,1.0,0.435115,-1.0,-0.193548,0,-0.333333,1.0 -1.0,0.75,-1.0,0.333333,-0.698113,-0.365297,1.0,1.0,-0.0992366,-1.0,-1.0,-1.0,-0.333333,-1.0 1.0,0.166667,1.0,0.333333,-0.358491,-0.52968,-1.0,1.0,0.206107,-1.0,-0.870968,0,-0.333333,1.0 -1.0,0.541667,1.0,1.0,0.245283,-0.534247,-1.0,1.0,0.0229008,-1.0,-0.258065,-1.0,-1.0,0.5 -1.0,-0.666667,-1.0,0.333333,-0.509434,-0.593607,-1.0,-1.0,0.51145,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.25,1.0,1.0,0.433962,-0.086758,-1.0,1.0,0.0534351,1.0,0.0967742,1.0,-1.0,1.0 1.0,-0.125,1.0,1.0,-0.0566038,-0.6621,-1.0,1.0,-0.160305,1.0,-0.709677,0,-1.0,1.0 1.0,-0.208333,1.0,1.0,-0.320755,-0.406393,1.0,1.0,0.206107,1.0,-1.0,-1.0,0.333333,1.0 1.0,0.333333,1.0,1.0,-0.132075,-0.630137,-1.0,1.0,0.0229008,1.0,-0.387097,-1.0,-0.333333,1.0 1.0,0.25,1.0,-1.0,0.245283,-0.328767,-1.0,1.0,-0.175573,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.458333,1.0,0.333333,-0.320755,-0.753425,-1.0,-1.0,0.206107,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.208333,1.0,1.0,-0.471698,-0.561644,-1.0,1.0,0.755725,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,-0.541667,1.0,1.0,0.0943396,-0.557078,-1.0,-1.0,0.679389,-1.0,-1.0,-1.0,-1.0,1.0 -1.0,0.375,-1.0,1.0,-0.433962,-0.621005,-1.0,-1.0,0.40458,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.375,1.0,0.333333,-0.320755,-0.511416,-1.0,-1.0,0.648855,1.0,-0.870968,-1.0,-1.0,-1.0 -1.0,-0.291667,1.0,-0.333333,-0.867925,-0.675799,1.0,-1.0,0.29771,-1.0,-1.0,-1.0,-1.0,1.0 1.0,0.25,1.0,0.333333,-0.396226,-0.579909,1.0,-1.0,-0.0381679,-1.0,-0.290323,0,-0.333333,0.5 -1.0,0.208333,1.0,0.333333,-0.132075,-0.611872,1.0,1.0,0.435115,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,-0.166667,1.0,0.333333,-0.54717,-0.894977,-1.0,1.0,-0.160305,-1.0,-0.741935,-1.0,1.0,-1.0 1.0,-0.375,1.0,1.0,-0.698113,-0.675799,-1.0,1.0,0.618321,-1.0,-1.0,-1.0,-0.333333,-1.0 1.0,0.541667,1.0,-0.333333,0.245283,-0.452055,-1.0,-1.0,-0.251908,1.0,-1.0,0,1.0,0.5 1.0,0.5,-1.0,1.0,0.0566038,-0.547945,-1.0,1.0,-0.343511,-1.0,-0.677419,0,1.0,1.0 1.0,-0.458333,1.0,1.0,-0.207547,-0.136986,-1.0,-1.0,-0.175573,1.0,-0.419355,0,-1.0,0.5 -1.0,-0.0416667,1.0,-0.333333,-0.358491,-0.639269,1.0,-1.0,0.725191,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,0.5,-1.0,0.333333,-0.132075,0.328767,1.0,1.0,0.312977,-1.0,-0.741935,-1.0,-0.333333,-1.0 -1.0,0.416667,-1.0,-0.333333,-0.132075,-0.684932,-1.0,-1.0,0.648855,-1.0,-1.0,-1.0,0.333333,-1.0 -1.0,-0.333333,-1.0,-0.333333,-0.320755,-0.506849,-1.0,1.0,0.587786,-1.0,-0.806452,0,-1.0,-1.0 -1.0,-0.5,-1.0,-0.333333,-0.792453,-0.671233,-1.0,-1.0,0.480916,-1.0,-1.0,-1.0,-0.333333,-1.0 1.0,0.333333,1.0,1.0,-0.169811,-0.817352,-1.0,1.0,-0.175573,1.0,0.16129,0,-0.333333,-1.0 -1.0,0.291667,-1.0,0.333333,-0.509434,-0.762557,1.0,-1.0,-0.618321,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.25,-1.0,1.0,0.509434,-0.438356,-1.0,-1.0,0.0992366,1.0,-1.0,0,-1.0,-1.0 1.0,0.375,1.0,-0.333333,-0.509434,-0.292237,-1.0,1.0,-0.51145,-1.0,-0.548387,0,-0.333333,1.0 -1.0,0.166667,1.0,0.333333,0.0566038,-1.0,1.0,-1.0,0.557252,-1.0,-0.935484,-1.0,-0.333333,1.0 1.0,-0.0833333,-1.0,1.0,-0.320755,-0.182648,-1.0,-1.0,0.0839695,1.0,-0.612903,0,-1.0,1.0 -1.0,-0.375,1.0,0.333333,-0.509434,-0.543379,-1.0,-1.0,0.496183,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,0.291667,-1.0,-1.0,0.0566038,-0.479452,-1.0,-1.0,0.526718,-1.0,-0.709677,-1.0,-1.0,-1.0 -1.0,0.416667,1.0,-1.0,-0.0377358,-0.511416,1.0,1.0,0.206107,-1.0,-0.258065,1.0,-1.0,0.5 1.0,0.166667,1.0,1.0,0.0566038,-0.315068,-1.0,1.0,-0.374046,1.0,-0.806452,0,-0.333333,0.5 -1.0,-0.0833333,1.0,1.0,-0.132075,-0.383562,-1.0,1.0,0.755725,1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.208333,-1.0,-0.333333,-0.207547,-0.118721,1.0,1.0,0.236641,-1.0,-1.0,-1.0,0.333333,-1.0 -1.0,-0.375,-1.0,0.333333,-0.54717,-0.47032,-1.0,-1.0,0.19084,-1.0,-0.903226,0,-0.333333,-1.0 1.0,-0.25,1.0,0.333333,-0.735849,-0.465753,-1.0,-1.0,0.236641,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.333333,1.0,1.0,-0.509434,-0.388128,-1.0,-1.0,0.0534351,1.0,0.16129,0,-0.333333,1.0 -1.0,0.166667,-1.0,1.0,-0.509434,0.0410959,-1.0,-1.0,0.40458,1.0,-0.806452,-1.0,-1.0,-1.0 -1.0,0.708333,1.0,-0.333333,0.169811,-0.456621,-1.0,1.0,0.0992366,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,0.958333,-1.0,0.333333,-0.132075,-0.675799,-1.0,0,-0.312977,-1.0,-0.645161,0,-1.0,-1.0 -1.0,0.583333,-1.0,1.0,-0.773585,-0.557078,-1.0,-1.0,0.0839695,-1.0,-0.903226,-1.0,0.333333,-1.0 1.0,-0.333333,1.0,1.0,-0.0943396,-0.164384,-1.0,1.0,0.160305,1.0,-1.0,0,1.0,1.0 -1.0,-0.333333,1.0,1.0,-0.811321,-0.625571,-1.0,1.0,0.175573,1.0,-0.0322581,0,-1.0,-1.0 -1.0,-0.583333,-1.0,0.333333,-1.0,-0.666667,-1.0,-1.0,0.648855,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.458333,-1.0,0.333333,-0.509434,-0.621005,-1.0,-1.0,0.557252,-1.0,-1.0,0,-1.0,-1.0 -1.0,0.125,1.0,-0.333333,-0.509434,-0.497717,-1.0,-1.0,0.633588,-1.0,-0.741935,-1.0,-1.0,-1.0 1.0,0.208333,1.0,1.0,-0.0188679,-0.579909,-1.0,-1.0,-0.480916,-1.0,-0.354839,0,-0.333333,1.0 1.0,-0.75,1.0,1.0,-0.509434,-0.671233,-1.0,-1.0,-0.0992366,1.0,-0.483871,0,-1.0,1.0 1.0,0.208333,1.0,1.0,0.0566038,-0.342466,-1.0,1.0,-0.389313,1.0,-0.741935,-1.0,-1.0,1.0 -1.0,-0.5,1.0,0.333333,-0.320755,-0.598174,-1.0,1.0,0.480916,-1.0,-0.354839,0,-1.0,-1.0 -1.0,0.166667,1.0,1.0,-0.698113,-0.657534,-1.0,-1.0,-0.160305,1.0,-0.516129,0,-1.0,0.5 -1.0,-0.458333,1.0,-1.0,0.0188679,-0.461187,-1.0,1.0,0.633588,-1.0,-0.741935,-1.0,0.333333,-1.0 -1.0,0.375,1.0,-0.333333,-0.358491,-0.625571,1.0,1.0,0.0534351,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,0.25,1.0,-1.0,0.584906,-0.342466,-1.0,1.0,0.129771,-1.0,0.354839,1.0,-1.0,1.0 -1.0,-0.5,-1.0,-0.333333,-0.396226,-0.178082,-1.0,-1.0,0.40458,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,-0.125,1.0,1.0,0.0566038,-0.465753,-1.0,1.0,-0.129771,-1.0,-0.16129,0,-1.0,1.0 -1.0,0.25,1.0,-0.333333,-0.132075,-0.56621,-1.0,-1.0,0.419847,1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.333333,-1.0,1.0,-0.320755,-0.0684932,-1.0,1.0,0.496183,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.0416667,1.0,1.0,-0.433962,-0.360731,-1.0,1.0,-0.419847,1.0,-0.290323,0,-0.333333,1.0 1.0,0.0416667,1.0,1.0,-0.698113,-0.634703,-1.0,1.0,-0.435115,1.0,-1.0,0,-0.333333,-1.0 1.0,-0.0416667,1.0,1.0,-0.415094,-0.607306,-1.0,-1.0,0.480916,-1.0,-0.677419,-1.0,0.333333,1.0 1.0,-0.25,1.0,1.0,-0.698113,-0.319635,-1.0,1.0,-0.282443,1.0,-0.677419,0,-0.333333,-1.0 -1.0,0.541667,1.0,1.0,-0.509434,-0.196347,-1.0,1.0,0.221374,-1.0,-0.870968,0,-1.0,-1.0 1.0,0.208333,1.0,1.0,-0.886792,-0.506849,-1.0,-1.0,0.29771,-1.0,-0.967742,-1.0,-0.333333,1.0 -1.0,0.458333,-1.0,0.333333,-0.132075,-0.146119,-1.0,-1.0,-0.0534351,-1.0,-0.935484,-1.0,-1.0,1.0 -1.0,-0.125,-1.0,-0.333333,-0.509434,-0.461187,-1.0,-1.0,0.389313,-1.0,-0.645161,-1.0,-1.0,-1.0 -1.0,-0.375,-1.0,0.333333,-0.735849,-0.931507,-1.0,-1.0,0.587786,-1.0,-0.806452,0,-1.0,-1.0 1.0,0.583333,1.0,1.0,-0.509434,-0.493151,-1.0,-1.0,-1.0,-1.0,-0.677419,0,-1.0,-1.0 -1.0,-0.166667,-1.0,1.0,-0.320755,-0.347032,-1.0,-1.0,0.40458,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.166667,1.0,1.0,0.339623,-0.255708,1.0,1.0,-0.19084,-1.0,-0.677419,0,1.0,1.0 1.0,0.416667,1.0,1.0,-0.320755,-0.415525,-1.0,1.0,0.160305,-1.0,-0.548387,0,-0.333333,1.0 1.0,-0.208333,1.0,1.0,-0.433962,-0.324201,-1.0,1.0,0.450382,-1.0,-0.83871,0,-1.0,1.0 -1.0,-0.0833333,1.0,0.333333,-0.886792,-0.561644,-1.0,-1.0,0.0992366,1.0,-0.612903,0,-1.0,-1.0 1.0,0.291667,-1.0,1.0,0.0566038,-0.39726,-1.0,1.0,0.312977,-1.0,-0.16129,0,0.333333,1.0 1.0,0.25,1.0,1.0,-0.132075,-0.767123,-1.0,-1.0,0.389313,1.0,-1.0,-1.0,-0.333333,1.0 -1.0,-0.333333,-1.0,-0.333333,-0.660377,-0.844749,-1.0,-1.0,0.0229008,-1.0,-1.0,0,-1.0,-1.0 1.0,0.0833333,-1.0,1.0,0.622642,-0.0821918,-1.0,0,-0.29771,1.0,0.0967742,0,-1.0,-1.0 -1.0,-0.5,1.0,-0.333333,-0.698113,-0.502283,-1.0,-1.0,0.251908,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.291667,-1.0,1.0,0.207547,-0.182648,-1.0,1.0,0.374046,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,0.0416667,-1.0,0.333333,-0.226415,-0.187215,1.0,-1.0,0.51145,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.458333,1.0,-0.333333,-0.509434,-0.228311,-1.0,-1.0,0.389313,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.166667,-1.0,-0.333333,-0.245283,-0.3379,-1.0,-1.0,0.389313,-1.0,-1.0,0,-1.0,-1.0 1.0,-0.291667,1.0,1.0,-0.509434,-0.438356,-1.0,1.0,0.114504,-1.0,-0.741935,-1.0,-1.0,1.0 1.0,0.125,-1.0,1.0,1.0,-0.260274,1.0,1.0,-0.0534351,1.0,0.290323,1.0,0.333333,1.0 -1.0,0.541667,-1.0,-1.0,0.0566038,-0.543379,-1.0,-1.0,-0.343511,-1.0,-0.16129,1.0,-1.0,-1.0 1.0,0.125,1.0,1.0,-0.320755,-0.283105,1.0,1.0,-0.51145,1.0,-0.483871,1.0,-1.0,1.0 1.0,-0.166667,1.0,0.333333,-0.509434,-0.716895,-1.0,-1.0,0.0381679,-1.0,-0.354839,0,1.0,1.0 1.0,0.0416667,1.0,1.0,-0.471698,-0.269406,-1.0,1.0,-0.312977,1.0,0.0322581,0,0.333333,-1.0 1.0,0.166667,1.0,1.0,0.0943396,-0.324201,-1.0,-1.0,-0.740458,1.0,-0.612903,0,-0.333333,1.0 -1.0,0.5,-1.0,0.333333,0.245283,0.0684932,-1.0,1.0,0.221374,-1.0,-0.741935,-1.0,-1.0,-1.0 -1.0,0.0416667,1.0,0.333333,-0.415094,-0.328767,-1.0,1.0,0.236641,-1.0,-0.83871,1.0,-0.333333,-1.0 -1.0,0.0416667,-1.0,0.333333,0.245283,-0.657534,-1.0,-1.0,0.40458,-1.0,-1.0,-1.0,-0.333333,-1.0 1.0,0.375,1.0,1.0,-0.509434,-0.356164,-1.0,-1.0,-0.572519,1.0,-0.419355,0,0.333333,1.0 -1.0,-0.0416667,-1.0,0.333333,-0.207547,-0.680365,-1.0,1.0,0.496183,-1.0,-0.967742,0,-1.0,-1.0 -1.0,-0.0416667,1.0,-0.333333,-0.245283,-0.657534,-1.0,-1.0,0.328244,-1.0,-0.741935,-1.0,-0.333333,-1.0 1.0,0.291667,1.0,1.0,-0.566038,-0.525114,1.0,-1.0,0.358779,1.0,-0.548387,-1.0,0.333333,1.0 1.0,0.416667,-1.0,1.0,-0.735849,-0.347032,-1.0,-1.0,0.496183,1.0,-0.419355,0,0.333333,-1.0 1.0,0.541667,1.0,1.0,-0.660377,-0.607306,-1.0,1.0,-0.0687023,1.0,-0.967742,-1.0,-0.333333,-1.0 -1.0,-0.458333,1.0,1.0,-0.132075,-0.543379,-1.0,-1.0,0.633588,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.458333,1.0,1.0,-0.509434,-0.452055,-1.0,1.0,-0.618321,1.0,-0.290323,1.0,-0.333333,-1.0 -1.0,0.0416667,1.0,0.333333,0.0566038,-0.515982,-1.0,1.0,0.435115,-1.0,-0.483871,-1.0,-1.0,1.0 -1.0,-0.291667,-1.0,0.333333,-0.0943396,-0.767123,-1.0,1.0,0.358779,1.0,-0.548387,1.0,-1.0,-1.0 -1.0,0.583333,-1.0,0.333333,0.0943396,-0.310502,-1.0,-1.0,0.541985,-1.0,-1.0,-1.0,-0.333333,-1.0 1.0,0.125,1.0,1.0,-0.415094,-0.438356,1.0,1.0,0.114504,1.0,-0.612903,0,-0.333333,-1.0 -1.0,-0.791667,-1.0,-0.333333,-0.54717,-0.616438,-1.0,-1.0,0.847328,-1.0,-0.774194,-1.0,-1.0,-1.0 -1.0,0.166667,1.0,1.0,-0.283019,-0.630137,-1.0,-1.0,0.480916,1.0,-1.0,-1.0,-1.0,1.0 1.0,0.458333,1.0,1.0,-0.0377358,-0.607306,-1.0,1.0,-0.0687023,-1.0,-0.354839,0,0.333333,0.5 -1.0,0.25,1.0,1.0,-0.169811,-0.3379,-1.0,1.0,0.694656,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,-0.125,1.0,0.333333,-0.132075,-0.511416,-1.0,-1.0,0.40458,-1.0,-0.806452,0,-0.333333,1.0 -1.0,-0.0833333,1.0,-1.0,-0.415094,-0.60274,-1.0,1.0,-0.175573,1.0,-0.548387,-1.0,-0.333333,-1.0 1.0,0.0416667,1.0,-0.333333,0.849057,-0.283105,-1.0,1.0,0.89313,-1.0,-1.0,-1.0,-0.333333,1.0 1.0,0,1.0,1.0,-0.45283,-0.287671,-1.0,-1.0,-0.633588,1.0,-0.354839,0,0.333333,1.0 1.0,-0.0416667,1.0,1.0,-0.660377,-0.525114,-1.0,-1.0,0.358779,-1.0,-1.0,-1.0,-0.333333,-1.0 1.0,-0.541667,1.0,1.0,-0.698113,-0.812785,-1.0,1.0,-0.343511,1.0,-0.354839,0,-1.0,1.0 1.0,0.208333,1.0,0.333333,-0.283019,-0.552511,-1.0,1.0,0.557252,-1.0,0.0322581,-1.0,0.333333,1.0 -1.0,-0.5,-1.0,0.333333,-0.660377,-0.351598,-1.0,1.0,0.541985,1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.5,1.0,0.333333,-0.660377,-0.43379,-1.0,-1.0,0.648855,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.125,-1.0,0.333333,-0.509434,-0.575342,-1.0,-1.0,0.328244,-1.0,-0.483871,0,-1.0,-1.0 -1.0,0.0416667,-1.0,0.333333,-0.735849,-0.356164,-1.0,1.0,0.465649,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,0.458333,-1.0,1.0,-0.320755,-0.191781,-1.0,-1.0,-0.221374,-1.0,-0.354839,0,0.333333,-1.0 -1.0,-0.0833333,-1.0,0.333333,-0.320755,-0.406393,-1.0,1.0,0.19084,-1.0,-0.83871,-1.0,-1.0,-1.0 -1.0,-0.291667,-1.0,-0.333333,-0.792453,-0.643836,-1.0,-1.0,0.541985,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.0833333,1.0,1.0,-0.132075,-0.584475,-1.0,-1.0,-0.389313,1.0,0.806452,1.0,-1.0,1.0 -1.0,-0.333333,1.0,-0.333333,-0.358491,-0.16895,-1.0,1.0,0.51145,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,0.125,1.0,-1.0,-0.509434,-0.694064,-1.0,1.0,0.389313,-1.0,-0.387097,0,-1.0,1.0 1.0,0.541667,-1.0,1.0,0.584906,-0.534247,1.0,-1.0,0.435115,1.0,-0.677419,0,0.333333,1.0 1.0,-0.625,1.0,-1.0,-0.509434,-0.520548,-1.0,-1.0,0.694656,1.0,0.225806,0,-1.0,1.0 1.0,0.375,-1.0,1.0,0.0566038,-0.461187,-1.0,-1.0,0.267176,1.0,-0.548387,0,-1.0,-1.0 -1.0,0.0833333,1.0,-0.333333,-0.320755,-0.378995,-1.0,-1.0,0.282443,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.208333,1.0,1.0,-0.358491,-0.392694,-1.0,1.0,-0.0992366,1.0,-0.0322581,0,0.333333,1.0 -1.0,-0.416667,1.0,1.0,-0.698113,-0.611872,-1.0,-1.0,0.374046,-1.0,-1.0,-1.0,-1.0,1.0 -1.0,0.458333,-1.0,1.0,0.622642,-0.0913242,-1.0,-1.0,0.267176,1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.125,-1.0,1.0,-0.698113,-0.415525,-1.0,1.0,0.343511,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,0,1.0,0.333333,-0.320755,-0.675799,1.0,1.0,0.236641,-1.0,-0.612903,1.0,-1.0,-1.0 -1.0,-0.333333,-1.0,1.0,-0.169811,-0.497717,-1.0,1.0,0.236641,1.0,-0.935484,0,-1.0,-1.0 1.0,0.5,1.0,-1.0,-0.169811,-0.287671,1.0,1.0,0.572519,-1.0,-0.548387,0,-0.333333,-1.0 -1.0,0.666667,1.0,-1.0,0.245283,-0.506849,1.0,1.0,-0.0839695,-1.0,-0.967742,0,-0.333333,-1.0 1.0,0.666667,1.0,0.333333,-0.132075,-0.415525,-1.0,1.0,0.145038,-1.0,-0.354839,0,1.0,1.0 1.0,0.583333,1.0,1.0,-0.886792,-0.210046,-1.0,1.0,-0.175573,1.0,-0.709677,0,0.333333,-1.0 -1.0,0.625,-1.0,0.333333,-0.509434,-0.611872,-1.0,1.0,-0.328244,-1.0,-0.516129,0,-1.0,-1.0 -1.0,-0.791667,1.0,-1.0,-0.54717,-0.744292,-1.0,1.0,0.572519,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.375,-1.0,1.0,-0.169811,-0.232877,1.0,-1.0,-0.465649,-1.0,-0.387097,0,1.0,-1.0 1.0,-0.0833333,1.0,1.0,-0.132075,-0.214612,-1.0,-1.0,-0.221374,1.0,0.354839,0,1.0,1.0 1.0,-0.291667,1.0,0.333333,0.0566038,-0.520548,-1.0,-1.0,0.160305,-1.0,0.16129,0,-1.0,-1.0 1.0,0.583333,1.0,1.0,-0.415094,-0.415525,1.0,-1.0,0.40458,-1.0,-0.935484,0,0.333333,1.0 -1.0,-0.125,1.0,0.333333,-0.339623,-0.680365,-1.0,-1.0,0.40458,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.458333,1.0,0.333333,-0.509434,-0.479452,1.0,-1.0,0.877863,-1.0,-0.741935,1.0,-1.0,1.0 1.0,0.125,-1.0,1.0,-0.245283,0.292237,-1.0,1.0,0.206107,1.0,-0.387097,0,0.333333,1.0 1.0,-0.5,1.0,1.0,-0.698113,-0.789954,-1.0,1.0,0.328244,-1.0,-1.0,-1.0,-1.0,1.0 -1.0,-0.458333,-1.0,1.0,-0.849057,-0.365297,-1.0,1.0,-0.221374,-1.0,-0.806452,0,-1.0,-1.0 -1.0,0,1.0,0.333333,-0.320755,-0.452055,1.0,1.0,0.557252,-1.0,-1.0,-1.0,1.0,-1.0 -1.0,-0.416667,1.0,0.333333,-0.320755,-0.136986,-1.0,-1.0,0.389313,-1.0,-0.387097,-1.0,-0.333333,-1.0 1.0,0.125,1.0,1.0,-0.283019,-0.73516,-1.0,1.0,-0.480916,1.0,-0.322581,0,-0.333333,0.5 -1.0,-0.0416667,1.0,1.0,-0.735849,-0.511416,1.0,-1.0,0.160305,-1.0,-0.967742,-1.0,1.0,1.0 -1.0,0.375,-1.0,1.0,-0.132075,0.223744,-1.0,1.0,0.312977,-1.0,-0.612903,0,-1.0,-1.0 1.0,0.708333,1.0,0.333333,0.245283,-0.347032,-1.0,-1.0,-0.374046,1.0,-0.0645161,0,-0.333333,1.0 -1.0,0.0416667,1.0,1.0,-0.132075,-0.484018,-1.0,-1.0,0.358779,-1.0,-0.612903,-1.0,-1.0,-1.0 1.0,0.708333,1.0,1.0,-0.0377358,-0.780822,-1.0,-1.0,-0.175573,1.0,-0.16129,1.0,-1.0,1.0 -1.0,0.0416667,1.0,-0.333333,-0.735849,-0.164384,-1.0,-1.0,0.29771,-1.0,-1.0,-1.0,-1.0,1.0 1.0,-0.75,1.0,1.0,-0.396226,-0.287671,-1.0,1.0,0.29771,1.0,-1.0,-1.0,-1.0,1.0 -1.0,-0.208333,1.0,0.333333,-0.433962,-0.410959,1.0,-1.0,0.587786,-1.0,-1.0,-1.0,0.333333,-1.0 -1.0,0.0833333,-1.0,-0.333333,-0.226415,-0.43379,-1.0,1.0,0.374046,-1.0,-0.548387,0,-1.0,-1.0 -1.0,0.208333,-1.0,1.0,-0.886792,-0.442922,-1.0,1.0,-0.221374,-1.0,-0.677419,0,-1.0,-1.0 -1.0,0.0416667,-1.0,0.333333,-0.698113,-0.598174,-1.0,-1.0,0.328244,-1.0,-0.483871,0,-1.0,-1.0 -1.0,0.666667,-1.0,-1.0,-0.132075,-0.484018,-1.0,-1.0,0.221374,-1.0,-0.419355,-1.0,0.333333,-1.0 1.0,1.0,1.0,1.0,-0.415094,-0.187215,-1.0,1.0,0.389313,1.0,-1.0,-1.0,1.0,-1.0 -1.0,0.625,1.0,0.333333,-0.54717,-0.310502,-1.0,-1.0,0.221374,-1.0,-0.677419,-1.0,-0.333333,1.0 1.0,0.208333,1.0,1.0,-0.415094,-0.205479,-1.0,1.0,0.526718,-1.0,-1.0,-1.0,0.333333,1.0 1.0,0.291667,1.0,1.0,-0.415094,-0.39726,-1.0,1.0,0.0687023,1.0,-0.0967742,0,-0.333333,1.0 1.0,-0.0833333,1.0,1.0,-0.132075,-0.210046,-1.0,-1.0,0.557252,1.0,-0.483871,-1.0,-1.0,1.0 1.0,0.0833333,1.0,1.0,0.245283,-0.255708,-1.0,1.0,0.129771,1.0,-0.741935,0,-0.333333,1.0 -1.0,-0.0416667,1.0,-1.0,0.0943396,-0.214612,1.0,-1.0,0.633588,-1.0,-0.612903,0,-1.0,1.0 -1.0,0.291667,-1.0,0.333333,-0.849057,-0.123288,-1.0,-1.0,0.358779,-1.0,-1.0,-1.0,-0.333333,-1.0 -1.0,0.208333,1.0,0.333333,-0.792453,-0.479452,-1.0,1.0,0.267176,1.0,-0.806452,0,-1.0,1.0 1.0,0.458333,1.0,0.333333,-0.415094,-0.164384,-1.0,-1.0,-0.0839695,1.0,-0.419355,0,-1.0,1.0 -1.0,-0.666667,1.0,0.333333,-0.320755,-0.43379,-1.0,-1.0,0.770992,-1.0,0.129032,1.0,-1.0,-1.0 1.0,0.25,1.0,-1.0,0.433962,-0.260274,-1.0,1.0,0.343511,-1.0,-0.935484,0,-1.0,1.0 -1.0,-0.0833333,1.0,0.333333,-0.415094,-0.456621,1.0,1.0,0.450382,-1.0,-0.225806,0,-1.0,-1.0 -1.0,-0.416667,-1.0,0.333333,-0.471698,-0.60274,-1.0,-1.0,0.435115,-1.0,-0.935484,0,-1.0,-1.0 1.0,0.208333,1.0,1.0,-0.358491,-0.589041,-1.0,1.0,-0.0839695,1.0,-0.290323,0,1.0,1.0 -1.0,-1.0,1.0,-0.333333,-0.320755,-0.643836,-1.0,1.0,1.0,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.5,-1.0,-0.333333,-0.320755,-0.643836,-1.0,1.0,0.541985,-1.0,-0.548387,-1.0,-1.0,-1.0 -1.0,0.416667,-1.0,0.333333,-0.226415,-0.424658,-1.0,1.0,0.541985,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.0833333,1.0,0.333333,-1.0,-0.538813,-1.0,-1.0,0.267176,1.0,-1.0,-1.0,-0.333333,1.0 -1.0,0.0416667,1.0,0.333333,-0.509434,-0.39726,-1.0,1.0,0.160305,-1.0,-0.870968,0,-1.0,1.0 -1.0,-0.375,1.0,-0.333333,-0.509434,-0.570776,-1.0,-1.0,0.51145,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.0416667,1.0,1.0,-0.698113,-0.484018,-1.0,-1.0,-0.160305,1.0,-0.0967742,0,-0.333333,1.0 1.0,0.5,1.0,1.0,-0.226415,-0.415525,-1.0,1.0,-0.145038,-1.0,-0.0967742,0,-0.333333,1.0 -1.0,0.166667,1.0,0.333333,0.0566038,-0.808219,-1.0,-1.0,0.572519,-1.0,-0.483871,-1.0,-1.0,-1.0 1.0,0.416667,1.0,1.0,-0.320755,-0.0684932,1.0,1.0,-0.0687023,1.0,-0.419355,-1.0,1.0,1.0 -1.0,-0.75,-1.0,1.0,-0.169811,-0.739726,-1.0,-1.0,0.694656,-1.0,-0.548387,-1.0,-1.0,-1.0 -1.0,-0.5,1.0,-0.333333,-0.226415,-0.648402,-1.0,-1.0,-0.0687023,-1.0,-1.0,0,-1.0,0.5 1.0,0.375,-1.0,0.333333,-0.320755,-0.374429,-1.0,-1.0,-0.603053,-1.0,-0.612903,0,-0.333333,1.0 1.0,-0.416667,-1.0,1.0,-0.283019,-0.0182648,1.0,1.0,-0.00763359,1.0,-0.0322581,0,-1.0,1.0 -1.0,0.208333,-1.0,-1.0,0.0566038,-0.283105,1.0,1.0,0.389313,-1.0,-0.677419,-1.0,-1.0,-1.0 -1.0,-0.0416667,1.0,-1.0,-0.54717,-0.726027,-1.0,1.0,0.816794,-1.0,-1.0,0,-1.0,0.5 1.0,0.333333,-1.0,1.0,-0.0377358,-0.173516,-1.0,1.0,0.145038,1.0,-0.677419,0,-1.0,1.0 1.0,-0.583333,1.0,1.0,-0.54717,-0.575342,-1.0,-1.0,0.0534351,-1.0,-0.612903,0,-1.0,1.0 -1.0,-0.333333,1.0,1.0,-0.603774,-0.388128,-1.0,1.0,0.740458,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,-0.0416667,1.0,1.0,-0.358491,-0.410959,-1.0,-1.0,0.374046,1.0,-1.0,-1.0,-0.333333,1.0 -1.0,0.375,1.0,0.333333,-0.320755,-0.520548,-1.0,-1.0,0.145038,-1.0,-0.419355,0,1.0,1.0 1.0,0.375,-1.0,1.0,0.245283,-0.826484,-1.0,1.0,0.129771,-1.0,1.0,1.0,1.0,1.0 -1.0,0,-1.0,1.0,-0.169811,-0.506849,-1.0,1.0,0.358779,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,-0.416667,1.0,1.0,-0.509434,-0.767123,-1.0,1.0,-0.251908,1.0,-0.193548,0,-1.0,1.0 -1.0,-0.25,1.0,0.333333,-0.169811,-0.401826,-1.0,1.0,0.29771,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.0416667,1.0,-0.333333,-0.509434,-0.0913242,-1.0,-1.0,0.541985,-1.0,-0.935484,-1.0,-1.0,-1.0 1.0,0.625,1.0,0.333333,0.622642,-0.324201,1.0,1.0,0.206107,1.0,-0.483871,0,-1.0,1.0 -1.0,-0.583333,1.0,0.333333,-0.132075,-0.109589,-1.0,1.0,0.694656,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,0,-1.0,1.0,-0.320755,-0.369863,-1.0,1.0,0.0992366,-1.0,-0.870968,0,-1.0,-1.0 1.0,0.375,-1.0,1.0,-0.132075,-0.351598,-1.0,1.0,0.358779,-1.0,0.16129,1.0,0.333333,-1.0 -1.0,-0.0833333,-1.0,0.333333,-0.132075,-0.16895,-1.0,1.0,0.0839695,-1.0,-0.516129,-1.0,-0.333333,-1.0 1.0,0.291667,1.0,1.0,-0.320755,-0.420091,-1.0,-1.0,0.114504,1.0,-0.548387,-1.0,-0.333333,1.0 1.0,0.5,1.0,1.0,-0.698113,-0.442922,-1.0,1.0,0.328244,-1.0,-0.806452,-1.0,0.333333,0.5 -1.0,0.5,-1.0,0.333333,0.150943,-0.347032,-1.0,-1.0,0.175573,-1.0,-0.741935,-1.0,-1.0,-1.0 1.0,0.291667,1.0,0.333333,-0.132075,-0.730594,-1.0,1.0,0.282443,-1.0,-0.0322581,0,-1.0,-1.0 1.0,0.291667,1.0,1.0,-0.0377358,-0.287671,-1.0,1.0,0.0839695,1.0,-0.0967742,0,0.333333,1.0 1.0,0.0416667,1.0,1.0,-0.509434,-0.716895,-1.0,-1.0,-0.358779,-1.0,-0.548387,0,-0.333333,1.0 -1.0,-0.375,1.0,-0.333333,-0.320755,-0.575342,-1.0,1.0,0.78626,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,-0.375,1.0,1.0,-0.660377,-0.251142,-1.0,1.0,0.251908,-1.0,-1.0,-1.0,-0.333333,-1.0 -1.0,-0.0833333,1.0,0.333333,-0.698113,-0.776256,-1.0,-1.0,-0.206107,-1.0,-0.806452,-1.0,-1.0,-1.0 -1.0,0.25,1.0,0.333333,0.0566038,-0.607306,1.0,-1.0,0.312977,-1.0,-0.483871,-1.0,-1.0,-1.0 -1.0,0.75,-1.0,-0.333333,0.245283,-0.196347,-1.0,-1.0,0.389313,-1.0,-0.870968,-1.0,0.333333,-1.0 -1.0,0.333333,1.0,0.333333,0.0566038,-0.465753,1.0,-1.0,0.00763359,1.0,-0.677419,0,-1.0,-1.0 1.0,0.0833333,1.0,1.0,-0.283019,0.0365297,-1.0,-1.0,-0.0687023,1.0,-0.612903,0,-0.333333,1.0 1.0,0.458333,1.0,0.333333,-0.132075,-0.0456621,-1.0,-1.0,0.328244,-1.0,-1.0,-1.0,-1.0,-1.0 -1.0,-0.416667,1.0,1.0,0.0566038,-0.447489,-1.0,-1.0,0.526718,-1.0,-0.516129,-1.0,-1.0,-1.0 -1.0,0.208333,-1.0,0.333333,-0.509434,-0.0228311,-1.0,-1.0,0.541985,-1.0,-1.0,-1.0,-1.0,-1.0 1.0,0.291667,1.0,1.0,-0.320755,-0.634703,-1.0,1.0,-0.0687023,1.0,-0.225806,0,0.333333,1.0 1.0,0.208333,1.0,-0.333333,-0.509434,-0.278539,-1.0,1.0,0.358779,-1.0,-0.419355,0,-1.0,-1.0 -1.0,-0.166667,1.0,-0.333333,-0.320755,-0.360731,-1.0,-1.0,0.526718,-1.0,-0.806452,-1.0,-1.0,-1.0 1.0,-0.208333,1.0,-0.333333,-0.698113,-0.52968,-1.0,-1.0,0.480916,-1.0,-0.677419,1.0,-1.0,1.0 -1.0,-0.0416667,1.0,0.333333,0.471698,-0.666667,1.0,-1.0,0.389313,-1.0,-0.83871,-1.0,-1.0,1.0 -1.0,-0.375,1.0,-0.333333,-0.509434,-0.374429,-1.0,-1.0,0.557252,-1.0,-1.0,-1.0,-1.0,1.0 -1.0,0.125,-1.0,-0.333333,-0.132075,-0.232877,-1.0,1.0,0.251908,-1.0,-0.580645,0,-1.0,-1.0 -1.0,0.166667,1.0,1.0,-0.132075,-0.69863,-1.0,-1.0,0.175573,-1.0,-0.870968,0,-1.0,0.5 1.0,0.583333,1.0,1.0,0.245283,-0.269406,-1.0,1.0,-0.435115,1.0,-0.516129,0,1.0,-1.0deap-1.0.1/examples/ga/knapsack.py0000644000076500000240000000701512301410325017220 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random import numpy from deap import algorithms from deap import base from deap import creator from deap import tools IND_INIT_SIZE = 5 MAX_ITEM = 50 MAX_WEIGHT = 50 NBR_ITEMS = 20 # To assure reproductibility, the RNG seed is set prior to the items # dict initialization. It is also seeded in main(). random.seed(64) # Create the item dictionary: item name is an integer, and value is # a (weight, value) 2-uple. items = {} # Create random items and store them in the items' dictionary. for i in range(NBR_ITEMS): items[i] = (random.randint(1, 10), random.uniform(0, 100)) creator.create("Fitness", base.Fitness, weights=(-1.0, 1.0)) creator.create("Individual", set, fitness=creator.Fitness) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_item", random.randrange, NBR_ITEMS) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_item, IND_INIT_SIZE) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalKnapsack(individual): weight = 0.0 value = 0.0 for item in individual: weight += items[item][0] value += items[item][1] if len(individual) > MAX_ITEM or weight > MAX_WEIGHT: return 10000, 0 # Ensure overweighted bags are dominated return weight, value def cxSet(ind1, ind2): """Apply a crossover operation on input sets. The first child is the intersection of the two sets, the second child is the difference of the two sets. """ temp = set(ind1) # Used in order to keep type ind1 &= ind2 # Intersection (inplace) ind2 ^= temp # Symmetric Difference (inplace) return ind1, ind2 def mutSet(individual): """Mutation that pops or add an element.""" if random.random() < 0.5: if len(individual) > 0: # We cannot pop from an empty set individual.remove(random.choice(sorted(tuple(individual)))) else: individual.add(random.randrange(NBR_ITEMS)) return individual, toolbox.register("evaluate", evalKnapsack) toolbox.register("mate", cxSet) toolbox.register("mutate", mutSet) toolbox.register("select", tools.selNSGA2) def main(): random.seed(64) NGEN = 50 MU = 50 LAMBDA = 100 CXPB = 0.7 MUTPB = 0.2 pop = toolbox.population(n=MU) hof = tools.ParetoFront() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean, axis=0) stats.register("std", numpy.std, axis=0) stats.register("min", numpy.min, axis=0) stats.register("max", numpy.max, axis=0) algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats, halloffame=hof) return pop, stats, hof if __name__ == "__main__": main() deap-1.0.1/examples/ga/knn.py0000644000076500000240000000655212117373622016235 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import numpy import csv import operator import random class KNN(object): def __init__(self, k): self.k = k self.data = None self.labels = None self.ndim = 0 def train(self, data, labels): self.data = numpy.array(data) self.labels = numpy.array(labels) self.classes = numpy.unique(self.labels) self.ndim = len(self.data[0]) def predict(self, data, features=None): data = numpy.array(data) if features is None: features = numpy.ones(self.data.shape[1]) else: features = numpy.array(features) if data.ndim == 1: dist = self.data - data elif data.ndim == 2: dist = numpy.zeros((data.shape[0],) + self.data.shape) for i, d in enumerate(data): dist[i, :, :] = self.data - d else: raise ValueError("Cannot process data with dimensionality > 2") dist = features * dist dist = dist * dist dist = numpy.sum(dist, -1) dist = numpy.sqrt(dist) nns = numpy.argsort(dist) if data.ndim == 1: classes = dict((cls, 0) for cls in self.classes) for n in nns[:self.k]: classes[self.labels[n]] += 1 labels = sorted(classes.items(), key=operator.itemgetter(1))[-1][0] elif data.ndim == 2: labels = list() for i, d in enumerate(data): classes = dict((cls, 0) for cls in self.classes) for n in nns[i, :self.k]: classes[self.labels[n]] += 1 labels.append(sorted(classes.items(), key=operator.itemgetter(1))[-1][0]) return labels # Create a default internal KNN object # Read data from file FILE="heart_scale.csv" N_TRAIN=175 K=1 data_csv = csv.reader(open(FILE, "r")) trainset = list() trainlabels = list() rows = [row for row in data_csv] random.shuffle(rows) for row in rows: trainlabels.append(float(row[0])) trainset.append([float(e) for e in row[1:]]) _knn = KNN(K) _knn.train(trainset[:N_TRAIN], trainlabels[:N_TRAIN]) def classification_rate(features): """Returns the classification rate of the default KNN.""" labels = _knn.predict(trainset[N_TRAIN:], features) return sum(x == y for x, y in zip(labels, trainlabels[N_TRAIN:]))/float(len(trainlabels[N_TRAIN:])) if __name__ == "__main__": trainset = [[1, 0], [1, 1], [1, 2]] trainlabels = [1, 2, 3] knn = KNN(1) knn.train(trainset, trainlabels) print("Single Data ===========") print(knn.predict([1, 0], [1, 1])) print("Multiple Data ===========") print(knn.predict([[1, 3], [1, 0]], [1, 1])) deap-1.0.1/examples/ga/kursawefct.py0000644000076500000240000000560412301410325017605 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import logging import random import numpy from deap import algorithms from deap import base from deap import benchmarks from deap import creator from deap import tools creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_float", random.uniform, -5, 5) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, 3) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def checkBounds(min, max): def decorator(func): def wrappper(*args, **kargs): offspring = func(*args, **kargs) for child in offspring: for i in range(len(child)): if child[i] > max: child[i] = max elif child[i] < min: child[i] = min return offspring return wrappper return decorator toolbox.register("evaluate", benchmarks.kursawe) toolbox.register("mate", tools.cxBlend, alpha=1.5) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=3, indpb=0.3) toolbox.register("select", tools.selNSGA2) toolbox.decorate("mate", checkBounds(-5, 5)) toolbox.decorate("mutate", checkBounds(-5, 5)) def main(): random.seed(64) MU, LAMBDA = 50, 100 pop = toolbox.population(n=MU) hof = tools.ParetoFront() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean, axis=0) stats.register("std", numpy.std, axis=0) stats.register("min", numpy.min, axis=0) stats.register("max", numpy.max, axis=0) algorithms.eaMuPlusLambda(pop, toolbox, mu=MU, lambda_=LAMBDA, cxpb=0.5, mutpb=0.2, ngen=150, stats=stats, halloffame=hof) return pop, stats, hof if __name__ == "__main__": pop, stats, hof = main() # import matplotlib.pyplot as plt # import numpy # # front = numpy.array([ind.fitness.values for ind in pop]) # plt.scatter(front[:,0], front[:,1], c="b") # plt.axis("tight") # plt.show() deap-1.0.1/examples/ga/nqueens.py0000644000076500000240000000643312301410325017106 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random import numpy from deap import algorithms from deap import base from deap import creator from deap import tools #Problem parameter NB_QUEENS = 20 def evalNQueens(individual): """Evaluation function for the n-queens problem. The problem is to determine a configuration of n queens on a nxn chessboard such that no queen can be taken by one another. In this version, each queens is assigned to one column, and only one queen can be on each line. The evaluation function therefore only counts the number of conflicts along the diagonals. """ size = len(individual) #Count the number of conflicts with other queens. #The conflicts can only be diagonal, count on each diagonal line left_diagonal = [0] * (2*size-1) right_diagonal = [0] * (2*size-1) #Sum the number of queens on each diagonal: for i in range(size): left_diagonal[i+individual[i]] += 1 right_diagonal[size-1-i+individual[i]] += 1 #Count the number of conflicts on each diagonal sum_ = 0 for i in range(2*size-1): if left_diagonal[i] > 1: sum_ += left_diagonal[i] - 1 if right_diagonal[i] > 1: sum_ += right_diagonal[i] - 1 return sum_, creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) #Since there is only one queen per line, #individual are represented by a permutation toolbox = base.Toolbox() toolbox.register("permutation", random.sample, range(NB_QUEENS), NB_QUEENS) #Structure initializers #An individual is a list that represents the position of each queen. #Only the line is stored, the column is the index of the number in the list. toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.permutation) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evalNQueens) toolbox.register("mate", tools.cxPartialyMatched) toolbox.register("mutate", tools.mutShuffleIndexes, indpb=2.0/NB_QUEENS) toolbox.register("select", tools.selTournament, tournsize=3) def main(seed=0): random.seed(seed) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", numpy.mean) stats.register("Std", numpy.std) stats.register("Min", numpy.min) stats.register("Max", numpy.max) algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=100, stats=stats, halloffame=hof, verbose=True) return pop, stats, hof if __name__ == "__main__": main() deap-1.0.1/examples/ga/nsga2.py0000644000076500000240000001170212301410325016435 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random import json import numpy from math import sqrt from deap import algorithms from deap import base from deap import benchmarks from deap.benchmarks.tools import diversity, convergence from deap import creator from deap import tools creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) toolbox = base.Toolbox() # Problem definition # Functions zdt1, zdt2, zdt3, zdt6 have bounds [0, 1] BOUND_LOW, BOUND_UP = 0.0, 1.0 # Functions zdt4 has bounds x1 = [0, 1], xn = [-5, 5], with n = 2, ..., 10 # BOUND_LOW, BOUND_UP = [0.0] + [-5.0]*9, [1.0] + [5.0]*9 # Functions zdt1, zdt2, zdt3 have 30 dimensions, zdt4 and zdt6 have 10 NDIM = 30 def uniform(low, up, size=None): try: return [random.uniform(a, b) for a, b in zip(low, up)] except TypeError: return [random.uniform(a, b) for a, b in zip([low] * size, [up] * size)] toolbox.register("attr_float", uniform, BOUND_LOW, BOUND_UP, NDIM) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.attr_float) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", benchmarks.zdt1) toolbox.register("mate", tools.cxSimulatedBinaryBounded, low=BOUND_LOW, up=BOUND_UP, eta=20.0) toolbox.register("mutate", tools.mutPolynomialBounded, low=BOUND_LOW, up=BOUND_UP, eta=20.0, indpb=1.0/NDIM) toolbox.register("select", tools.selNSGA2) def main(seed=None): random.seed(seed) NGEN = 250 MU = 100 CXPB = 0.9 stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean, axis=0) stats.register("std", numpy.std, axis=0) stats.register("min", numpy.min, axis=0) stats.register("max", numpy.max, axis=0) logbook = tools.Logbook() logbook.header = "gen", "evals", "std", "min", "avg", "max" pop = toolbox.population(n=MU) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in pop if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # This is just to assign the crowding distance to the individuals # no actual selection is done pop = toolbox.select(pop, len(pop)) record = stats.compile(pop) logbook.record(gen=0, evals=len(invalid_ind), **record) print(logbook.stream) # Begin the generational process for gen in range(1, NGEN): # Vary the population offspring = tools.selTournamentDCD(pop, len(pop)) offspring = [toolbox.clone(ind) for ind in offspring] for ind1, ind2 in zip(offspring[::2], offspring[1::2]): if random.random() <= CXPB: toolbox.mate(ind1, ind2) toolbox.mutate(ind1) toolbox.mutate(ind2) del ind1.fitness.values, ind2.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # Select the next generation population pop = toolbox.select(pop + offspring, MU) record = stats.compile(pop) logbook.record(gen=gen, evals=len(invalid_ind), **record) print(logbook.stream) return pop, logbook if __name__ == "__main__": optimal_front = json.load(open("pareto_front/zdt1_front.json")) # Use 500 of the 1000 points in the json file optimal_front = sorted(optimal_front[i] for i in range(0, len(optimal_front), 2)) pop, stats = main() pop.sort(key=lambda x: x.fitness.values) print(stats) print("Convergence: ", convergence(pop, optimal_front)) print("Diversity: ", diversity(pop, optimal_front[0], optimal_front[-1])) # import matplotlib.pyplot as plt # import numpy # # front = numpy.array([ind.fitness.values for ind in pop]) # optimal_front = numpy.array(optimal_front) # plt.scatter(optimal_front[:,0], optimal_front[:,1], c="r") # plt.scatter(front[:,0], front[:,1], c="b") # plt.axis("tight") # plt.show() deap-1.0.1/examples/ga/onemax.py0000644000076500000240000000717712301410325016725 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), # Operator registering toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) def main(): random.seed(64) pop = toolbox.population(n=300) CXPB, MUTPB, NGEN = 0.5, 0.2, 40 print("Start of evolution") # Evaluate the entire population fitnesses = list(map(toolbox.evaluate, pop)) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit print(" Evaluated %i individuals" % len(pop)) # Begin the evolution for g in range(NGEN): print("-- Generation %i --" % g) # Select the next generation individuals offspring = toolbox.select(pop, len(pop)) # Clone the selected individuals offspring = list(map(toolbox.clone, offspring)) # Apply crossover and mutation on the offspring for child1, child2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values for mutant in offspring: if random.random() < MUTPB: toolbox.mutate(mutant) del mutant.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit print(" Evaluated %i individuals" % len(invalid_ind)) # The population is entirely replaced by the offspring pop[:] = offspring # Gather all the fitnesses in one list and print the stats fits = [ind.fitness.values[0] for ind in pop] length = len(pop) mean = sum(fits) / length sum2 = sum(x*x for x in fits) std = abs(sum2 / length - mean**2)**0.5 print(" Min %s" % min(fits)) print(" Max %s" % max(fits)) print(" Avg %s" % mean) print(" Std %s" % std) print("-- End of (successful) evolution --") best_ind = tools.selBest(pop, 1)[0] print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values)) if __name__ == "__main__": main() deap-1.0.1/examples/ga/onemax_island.py0000644000076500000240000001233112301410325020243 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random import numpy from collections import deque from multiprocessing import Event, Pipe, Process from deap import algorithms from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), def migPipe(deme, k, pipein, pipeout, selection, replacement=None): """Migration using pipes between initialized processes. It first selects *k* individuals from the *deme* and writes them in *pipeout*. Then it reads the individuals from *pipein* and replace some individuals in the deme. The replacement strategy shall not select twice the same individual. :param deme: A list of individuals on which to operate migration. :param k: The number of individuals to migrate. :param pipein: A :class:`~multiprocessing.Pipe` from which to read immigrants. :param pipeout: A :class:`~multiprocessing.Pipe` in which to write emigrants. :param selection: The function to use for selecting the emigrants. :param replacement: The function to use to select which individuals will be replaced. If :obj:`None` (default) the individuals that leave the population are directly replaced. """ emigrants = selection(deme, k) if replacement is None: # If no replacement strategy is selected, replace those who migrate immigrants = emigrants else: # Else select those who will be replaced immigrants = replacement(deme, k) pipeout.send(emigrants) buf = pipein.recv() for place, immigrant in zip(immigrants, buf): indx = deme.index(place) deme[indx] = immigrant toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) def main(procid, pipein, pipeout, sync, seed=None): random.seed(seed) toolbox.register("migrate", migPipe, k=5, pipein=pipein, pipeout=pipeout, selection=tools.selBest, replacement=random.sample) MU = 300 NGEN = 40 CXPB = 0.5 MUTPB = 0.2 MIG_RATE = 5 deme = toolbox.population(n=MU) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "deme", "evals", "std", "min", "avg", "max" for ind in deme: ind.fitness.values = toolbox.evaluate(ind) record = stats.compile(deme) logbook.record(gen=0, deme=procid, evals=len(deme), **record) hof.update(deme) if procid == 0: # Synchronization needed to log header on top and only once print(logbook.stream) sync.set() else: logbook.log_header = False # Never output the header sync.wait() print(logbook.stream) for gen in range(1, NGEN): deme = toolbox.select(deme, len(deme)) deme = algorithms.varAnd(deme, toolbox, cxpb=CXPB, mutpb=MUTPB) invalid_ind = [ind for ind in deme if not ind.fitness.valid] for ind in invalid_ind: ind.fitness.values = toolbox.evaluate(ind) hof.update(deme) record = stats.compile(deme) logbook.record(gen=gen, deme=procid, evals=len(deme), **record) print(logbook.stream) if gen % MIG_RATE == 0 and gen > 0: toolbox.migrate(deme) if __name__ == "__main__": random.seed(64) NBR_DEMES = 3 pipes = [Pipe(False) for _ in range(NBR_DEMES)] pipes_in = deque(p[0] for p in pipes) pipes_out = deque(p[1] for p in pipes) pipes_in.rotate(1) pipes_out.rotate(-1) e = Event() processes = [Process(target=main, args=(i, ipipe, opipe, e, random.random())) for i, (ipipe, opipe) in enumerate(zip(pipes_in, pipes_out))] for proc in processes: proc.start() for proc in processes: proc.join() deap-1.0.1/examples/ga/onemax_island_scoop.py0000644000076500000240000000444012301410325021450 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random import numpy from functools import partial from deap import algorithms from deap import base from deap import creator from deap import tools from scoop import futures creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("map", futures.map) def main(): random.seed(64) NISLES = 5 islands = [toolbox.population(n=300) for i in range(NISLES)] # Unregister unpicklable methods before sending the toolbox. toolbox.unregister("attr_bool") toolbox.unregister("individual") toolbox.unregister("population") NGEN, FREQ = 40, 5 toolbox.register("algorithm", algorithms.eaSimple, toolbox=toolbox, cxpb=0.5, mutpb=0.2, ngen=FREQ, verbose=False) for i in range(0, NGEN, FREQ): results = toolbox.map(toolbox.algorithm, islands) islands = [pop for pop, logbook in results] tools.migRing(islands, 15, tools.selBest) return islands if __name__ == "__main__": main() deap-1.0.1/examples/ga/onemax_mp.py0000755000076500000240000000435312301410325017415 0ustar felixstaff00000000000000#!/usr/bin/env python2.7 # This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import multiprocessing import random import sys if sys.version_info < (2, 7): print("mpga_onemax example requires Python >= 2.7.") exit(1) import numpy from deap import algorithms from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) if __name__ == "__main__": random.seed(64) # Process Pool of 4 workers pool = multiprocessing.Pool(processes=4) toolbox.register("map", pool.map) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats, halloffame=hof) pool.close() deap-1.0.1/examples/ga/onemax_multidemic.py0000644000076500000240000000621012301410325021124 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random import numpy from deap import algorithms from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("migrate", tools.migRing, k=5, selection=tools.selBest, replacement=random.sample) def main(): random.seed(64) NBR_DEMES = 3 MU = 300 NGEN = 40 CXPB = 0.5 MUTPB = 0.2 MIG_RATE = 5 demes = [toolbox.population(n=MU) for _ in range(NBR_DEMES)] hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "deme", "evals", "std", "min", "avg", "max" for idx, deme in enumerate(demes): for ind in deme: ind.fitness.values = toolbox.evaluate(ind) logbook.record(gen=0, deme=idx, evals=len(deme), **stats.compile(deme)) hof.update(deme) print(logbook.stream) gen = 1 while gen <= NGEN and logbook[-1]["max"] < 100.0: for idx, deme in enumerate(demes): deme[:] = toolbox.select(deme, len(deme)) deme[:] = algorithms.varAnd(deme, toolbox, cxpb=CXPB, mutpb=MUTPB) invalid_ind = [ind for ind in deme if not ind.fitness.valid] for ind in invalid_ind: ind.fitness.values = toolbox.evaluate(ind) logbook.record(gen=gen, deme=idx, evals=len(deme), **stats.compile(deme)) hof.update(deme) print(logbook.stream) if gen % MIG_RATE == 0: toolbox.migrate(demes) gen += 1 return demes, logbook, hof if __name__ == "__main__": main() deap-1.0.1/examples/ga/onemax_numpy.py0000644000076500000240000000620312301410325020142 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random import numpy from deap import algorithms from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", numpy.ndarray, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), def cxTwoPointCopy(ind1, ind2): """Execute a two points crossover with copy on the input individuals. The copy is required because the slicing in numpy returns a view of the data, which leads to a self overwritting in the swap operation. It prevents :: >>> import numpy >>> a = numpy.array((1,2,3,4)) >>> b = numpy.array((5.6.7.8)) >>> a[1:3], b[1:3] = b[1:3], a[1:3] >>> print(a) [1 6 7 4] >>> print(b) [5 6 7 8] """ size = len(ind1) cxpoint1 = random.randint(1, size) cxpoint2 = random.randint(1, size - 1) if cxpoint2 >= cxpoint1: cxpoint2 += 1 else: # Swap the two cx points cxpoint1, cxpoint2 = cxpoint2, cxpoint1 ind1[cxpoint1:cxpoint2], ind2[cxpoint1:cxpoint2] \ = ind2[cxpoint1:cxpoint2].copy(), ind1[cxpoint1:cxpoint2].copy() return ind1, ind2 toolbox.register("evaluate", evalOneMax) toolbox.register("mate", cxTwoPointCopy) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) def main(): random.seed(64) pop = toolbox.population(n=300) # Numpy equality function (operators.eq) between two arrays returns the # equality element wise, which raises an exception in the if similar() # check of the hall of fame. Using a different equality function like # numpy.array_equal or numpy.allclose solve this issue. hof = tools.HallOfFame(1, similar=numpy.array_equal) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats, halloffame=hof) return pop, stats, hof if __name__ == "__main__": main() deap-1.0.1/examples/ga/onemax_short.py0000644000076500000240000000403712301410325020134 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random import numpy from deap import algorithms from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) def main(): random.seed(64) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats, halloffame=hof, verbose=True) return pop, log, hof if __name__ == "__main__": main() deap-1.0.1/examples/ga/pareto_front/0000755000076500000240000000000012321001644017554 5ustar felixstaff00000000000000deap-1.0.1/examples/ga/pareto_front/dtlz1_front.json0000644000076500000240000007051012117373622022733 0ustar felixstaff00000000000000[[0.0000000000,0.5000000000], [0.0005005005,0.4994994995], [0.0010010010,0.4989989990], [0.0015015015,0.4984984985], [0.0020020020,0.4979979980], [0.0025025025,0.4974974975], [0.0030030030,0.4969969970], [0.0035035035,0.4964964965], [0.0040040040,0.4959959960], [0.0045045045,0.4954954955], [0.0050050050,0.4949949950], [0.0055055055,0.4944944945], [0.0060060060,0.4939939940], [0.0065065065,0.4934934935], [0.0070070070,0.4929929930], [0.0075075075,0.4924924925], [0.0080080080,0.4919919920], [0.0085085085,0.4914914915], [0.0090090090,0.4909909910], [0.0095095095,0.4904904905], [0.0100100100,0.4899899900], [0.0105105105,0.4894894895], [0.0110110110,0.4889889890], [0.0115115115,0.4884884885], [0.0120120120,0.4879879880], [0.0125125125,0.4874874875], [0.0130130130,0.4869869870], [0.0135135135,0.4864864865], [0.0140140140,0.4859859860], [0.0145145145,0.4854854855], [0.0150150150,0.4849849850], [0.0155155155,0.4844844845], [0.0160160160,0.4839839840], [0.0165165165,0.4834834835], [0.0170170170,0.4829829830], [0.0175175175,0.4824824825], [0.0180180180,0.4819819820], [0.0185185185,0.4814814815], [0.0190190190,0.4809809810], [0.0195195195,0.4804804805], [0.0200200200,0.4799799800], [0.0205205205,0.4794794795], [0.0210210210,0.4789789790], [0.0215215215,0.4784784785], [0.0220220220,0.4779779780], [0.0225225225,0.4774774775], [0.0230230230,0.4769769770], [0.0235235235,0.4764764765], [0.0240240240,0.4759759760], [0.0245245245,0.4754754755], [0.0250250250,0.4749749750], [0.0255255255,0.4744744745], [0.0260260260,0.4739739740], [0.0265265265,0.4734734735], [0.0270270270,0.4729729730], [0.0275275275,0.4724724725], [0.0280280280,0.4719719720], [0.0285285285,0.4714714715], [0.0290290290,0.4709709710], [0.0295295295,0.4704704705], [0.0300300300,0.4699699700], [0.0305305305,0.4694694695], [0.0310310310,0.4689689690], [0.0315315315,0.4684684685], [0.0320320320,0.4679679680], [0.0325325325,0.4674674675], [0.0330330330,0.4669669670], [0.0335335335,0.4664664665], [0.0340340340,0.4659659660], [0.0345345345,0.4654654655], [0.0350350350,0.4649649650], [0.0355355355,0.4644644645], [0.0360360360,0.4639639640], [0.0365365365,0.4634634635], [0.0370370370,0.4629629630], [0.0375375375,0.4624624625], [0.0380380380,0.4619619620], [0.0385385385,0.4614614615], [0.0390390390,0.4609609610], [0.0395395395,0.4604604605], [0.0400400400,0.4599599600], [0.0405405405,0.4594594595], [0.0410410410,0.4589589590], [0.0415415415,0.4584584585], [0.0420420420,0.4579579580], [0.0425425425,0.4574574575], [0.0430430430,0.4569569570], [0.0435435435,0.4564564565], [0.0440440440,0.4559559560], [0.0445445445,0.4554554555], [0.0450450450,0.4549549550], [0.0455455455,0.4544544545], [0.0460460460,0.4539539540], [0.0465465465,0.4534534535], [0.0470470470,0.4529529530], [0.0475475475,0.4524524525], [0.0480480480,0.4519519520], [0.0485485485,0.4514514515], [0.0490490490,0.4509509510], [0.0495495495,0.4504504505], [0.0500500501,0.4499499499], [0.0505505506,0.4494494494], [0.0510510511,0.4489489489], [0.0515515516,0.4484484484], [0.0520520521,0.4479479479], [0.0525525526,0.4474474474], [0.0530530531,0.4469469469], [0.0535535536,0.4464464464], [0.0540540541,0.4459459459], [0.0545545546,0.4454454454], [0.0550550551,0.4449449449], [0.0555555556,0.4444444444], [0.0560560561,0.4439439439], [0.0565565566,0.4434434434], [0.0570570571,0.4429429429], [0.0575575576,0.4424424424], [0.0580580581,0.4419419419], [0.0585585586,0.4414414414], [0.0590590591,0.4409409409], [0.0595595596,0.4404404404], [0.0600600601,0.4399399399], [0.0605605606,0.4394394394], [0.0610610611,0.4389389389], [0.0615615616,0.4384384384], [0.0620620621,0.4379379379], [0.0625625626,0.4374374374], [0.0630630631,0.4369369369], [0.0635635636,0.4364364364], [0.0640640641,0.4359359359], [0.0645645646,0.4354354354], [0.0650650651,0.4349349349], [0.0655655656,0.4344344344], [0.0660660661,0.4339339339], [0.0665665666,0.4334334334], [0.0670670671,0.4329329329], [0.0675675676,0.4324324324], [0.0680680681,0.4319319319], [0.0685685686,0.4314314314], [0.0690690691,0.4309309309], [0.0695695696,0.4304304304], [0.0700700701,0.4299299299], [0.0705705706,0.4294294294], [0.0710710711,0.4289289289], [0.0715715716,0.4284284284], [0.0720720721,0.4279279279], [0.0725725726,0.4274274274], [0.0730730731,0.4269269269], [0.0735735736,0.4264264264], [0.0740740741,0.4259259259], [0.0745745746,0.4254254254], [0.0750750751,0.4249249249], [0.0755755756,0.4244244244], [0.0760760761,0.4239239239], [0.0765765766,0.4234234234], [0.0770770771,0.4229229229], [0.0775775776,0.4224224224], [0.0780780781,0.4219219219], [0.0785785786,0.4214214214], [0.0790790791,0.4209209209], [0.0795795796,0.4204204204], [0.0800800801,0.4199199199], [0.0805805806,0.4194194194], [0.0810810811,0.4189189189], [0.0815815816,0.4184184184], [0.0820820821,0.4179179179], [0.0825825826,0.4174174174], [0.0830830831,0.4169169169], [0.0835835836,0.4164164164], [0.0840840841,0.4159159159], [0.0845845846,0.4154154154], [0.0850850851,0.4149149149], [0.0855855856,0.4144144144], [0.0860860861,0.4139139139], [0.0865865866,0.4134134134], [0.0870870871,0.4129129129], [0.0875875876,0.4124124124], [0.0880880881,0.4119119119], [0.0885885886,0.4114114114], [0.0890890891,0.4109109109], [0.0895895896,0.4104104104], [0.0900900901,0.4099099099], [0.0905905906,0.4094094094], [0.0910910911,0.4089089089], [0.0915915916,0.4084084084], [0.0920920921,0.4079079079], [0.0925925926,0.4074074074], [0.0930930931,0.4069069069], [0.0935935936,0.4064064064], [0.0940940941,0.4059059059], [0.0945945946,0.4054054054], [0.0950950951,0.4049049049], [0.0955955956,0.4044044044], [0.0960960961,0.4039039039], [0.0965965966,0.4034034034], [0.0970970971,0.4029029029], [0.0975975976,0.4024024024], [0.0980980981,0.4019019019], [0.0985985986,0.4014014014], [0.0990990991,0.4009009009], [0.0995995996,0.4004004004], [0.1001001001,0.3998998999], [0.1006006006,0.3993993994], [0.1011011011,0.3988988989], [0.1016016016,0.3983983984], [0.1021021021,0.3978978979], [0.1026026026,0.3973973974], [0.1031031031,0.3968968969], [0.1036036036,0.3963963964], [0.1041041041,0.3958958959], [0.1046046046,0.3953953954], [0.1051051051,0.3948948949], [0.1056056056,0.3943943944], [0.1061061061,0.3938938939], [0.1066066066,0.3933933934], [0.1071071071,0.3928928929], [0.1076076076,0.3923923924], [0.1081081081,0.3918918919], [0.1086086086,0.3913913914], [0.1091091091,0.3908908909], [0.1096096096,0.3903903904], [0.1101101101,0.3898898899], [0.1106106106,0.3893893894], [0.1111111111,0.3888888889], [0.1116116116,0.3883883884], [0.1121121121,0.3878878879], [0.1126126126,0.3873873874], [0.1131131131,0.3868868869], [0.1136136136,0.3863863864], [0.1141141141,0.3858858859], [0.1146146146,0.3853853854], [0.1151151151,0.3848848849], [0.1156156156,0.3843843844], [0.1161161161,0.3838838839], [0.1166166166,0.3833833834], [0.1171171171,0.3828828829], [0.1176176176,0.3823823824], [0.1181181181,0.3818818819], [0.1186186186,0.3813813814], [0.1191191191,0.3808808809], [0.1196196196,0.3803803804], [0.1201201201,0.3798798799], [0.1206206206,0.3793793794], [0.1211211211,0.3788788789], [0.1216216216,0.3783783784], [0.1221221221,0.3778778779], [0.1226226226,0.3773773774], [0.1231231231,0.3768768769], [0.1236236236,0.3763763764], [0.1241241241,0.3758758759], [0.1246246246,0.3753753754], [0.1251251251,0.3748748749], [0.1256256256,0.3743743744], [0.1261261261,0.3738738739], [0.1266266266,0.3733733734], [0.1271271271,0.3728728729], [0.1276276276,0.3723723724], [0.1281281281,0.3718718719], [0.1286286286,0.3713713714], [0.1291291291,0.3708708709], [0.1296296296,0.3703703704], [0.1301301301,0.3698698699], [0.1306306306,0.3693693694], [0.1311311311,0.3688688689], [0.1316316316,0.3683683684], [0.1321321321,0.3678678679], [0.1326326326,0.3673673674], [0.1331331331,0.3668668669], [0.1336336336,0.3663663664], [0.1341341341,0.3658658659], [0.1346346346,0.3653653654], [0.1351351351,0.3648648649], [0.1356356356,0.3643643644], [0.1361361361,0.3638638639], [0.1366366366,0.3633633634], [0.1371371371,0.3628628629], [0.1376376376,0.3623623624], [0.1381381381,0.3618618619], [0.1386386386,0.3613613614], [0.1391391391,0.3608608609], [0.1396396396,0.3603603604], [0.1401401401,0.3598598599], [0.1406406406,0.3593593594], [0.1411411411,0.3588588589], [0.1416416416,0.3583583584], [0.1421421421,0.3578578579], [0.1426426426,0.3573573574], [0.1431431431,0.3568568569], [0.1436436436,0.3563563564], [0.1441441441,0.3558558559], [0.1446446446,0.3553553554], [0.1451451451,0.3548548549], [0.1456456456,0.3543543544], [0.1461461461,0.3538538539], [0.1466466466,0.3533533534], [0.1471471471,0.3528528529], [0.1476476476,0.3523523524], [0.1481481481,0.3518518519], [0.1486486486,0.3513513514], [0.1491491491,0.3508508509], [0.1496496496,0.3503503504], [0.1501501502,0.3498498498], [0.1506506507,0.3493493493], [0.1511511512,0.3488488488], [0.1516516517,0.3483483483], [0.1521521522,0.3478478478], [0.1526526527,0.3473473473], [0.1531531532,0.3468468468], [0.1536536537,0.3463463463], [0.1541541542,0.3458458458], [0.1546546547,0.3453453453], [0.1551551552,0.3448448448], [0.1556556557,0.3443443443], [0.1561561562,0.3438438438], [0.1566566567,0.3433433433], [0.1571571572,0.3428428428], [0.1576576577,0.3423423423], [0.1581581582,0.3418418418], [0.1586586587,0.3413413413], [0.1591591592,0.3408408408], [0.1596596597,0.3403403403], [0.1601601602,0.3398398398], [0.1606606607,0.3393393393], [0.1611611612,0.3388388388], [0.1616616617,0.3383383383], [0.1621621622,0.3378378378], [0.1626626627,0.3373373373], [0.1631631632,0.3368368368], [0.1636636637,0.3363363363], [0.1641641642,0.3358358358], [0.1646646647,0.3353353353], [0.1651651652,0.3348348348], [0.1656656657,0.3343343343], [0.1661661662,0.3338338338], [0.1666666667,0.3333333333], [0.1671671672,0.3328328328], [0.1676676677,0.3323323323], [0.1681681682,0.3318318318], [0.1686686687,0.3313313313], [0.1691691692,0.3308308308], [0.1696696697,0.3303303303], [0.1701701702,0.3298298298], [0.1706706707,0.3293293293], [0.1711711712,0.3288288288], [0.1716716717,0.3283283283], [0.1721721722,0.3278278278], [0.1726726727,0.3273273273], [0.1731731732,0.3268268268], [0.1736736737,0.3263263263], [0.1741741742,0.3258258258], [0.1746746747,0.3253253253], [0.1751751752,0.3248248248], [0.1756756757,0.3243243243], [0.1761761762,0.3238238238], [0.1766766767,0.3233233233], [0.1771771772,0.3228228228], [0.1776776777,0.3223223223], [0.1781781782,0.3218218218], [0.1786786787,0.3213213213], [0.1791791792,0.3208208208], [0.1796796797,0.3203203203], [0.1801801802,0.3198198198], [0.1806806807,0.3193193193], [0.1811811812,0.3188188188], [0.1816816817,0.3183183183], [0.1821821822,0.3178178178], [0.1826826827,0.3173173173], [0.1831831832,0.3168168168], [0.1836836837,0.3163163163], [0.1841841842,0.3158158158], [0.1846846847,0.3153153153], [0.1851851852,0.3148148148], [0.1856856857,0.3143143143], [0.1861861862,0.3138138138], [0.1866866867,0.3133133133], [0.1871871872,0.3128128128], [0.1876876877,0.3123123123], [0.1881881882,0.3118118118], [0.1886886887,0.3113113113], [0.1891891892,0.3108108108], [0.1896896897,0.3103103103], [0.1901901902,0.3098098098], [0.1906906907,0.3093093093], [0.1911911912,0.3088088088], [0.1916916917,0.3083083083], [0.1921921922,0.3078078078], [0.1926926927,0.3073073073], [0.1931931932,0.3068068068], [0.1936936937,0.3063063063], [0.1941941942,0.3058058058], [0.1946946947,0.3053053053], [0.1951951952,0.3048048048], [0.1956956957,0.3043043043], [0.1961961962,0.3038038038], [0.1966966967,0.3033033033], [0.1971971972,0.3028028028], [0.1976976977,0.3023023023], [0.1981981982,0.3018018018], [0.1986986987,0.3013013013], [0.1991991992,0.3008008008], [0.1996996997,0.3003003003], [0.2002002002,0.2997997998], [0.2007007007,0.2992992993], [0.2012012012,0.2987987988], [0.2017017017,0.2982982983], [0.2022022022,0.2977977978], [0.2027027027,0.2972972973], [0.2032032032,0.2967967968], [0.2037037037,0.2962962963], [0.2042042042,0.2957957958], [0.2047047047,0.2952952953], [0.2052052052,0.2947947948], [0.2057057057,0.2942942943], [0.2062062062,0.2937937938], [0.2067067067,0.2932932933], [0.2072072072,0.2927927928], [0.2077077077,0.2922922923], [0.2082082082,0.2917917918], [0.2087087087,0.2912912913], [0.2092092092,0.2907907908], [0.2097097097,0.2902902903], [0.2102102102,0.2897897898], [0.2107107107,0.2892892893], [0.2112112112,0.2887887888], [0.2117117117,0.2882882883], [0.2122122122,0.2877877878], [0.2127127127,0.2872872873], [0.2132132132,0.2867867868], [0.2137137137,0.2862862863], [0.2142142142,0.2857857858], [0.2147147147,0.2852852853], [0.2152152152,0.2847847848], [0.2157157157,0.2842842843], [0.2162162162,0.2837837838], [0.2167167167,0.2832832833], [0.2172172172,0.2827827828], [0.2177177177,0.2822822823], [0.2182182182,0.2817817818], [0.2187187187,0.2812812813], [0.2192192192,0.2807807808], [0.2197197197,0.2802802803], [0.2202202202,0.2797797798], [0.2207207207,0.2792792793], [0.2212212212,0.2787787788], [0.2217217217,0.2782782783], [0.2222222222,0.2777777778], [0.2227227227,0.2772772773], [0.2232232232,0.2767767768], [0.2237237237,0.2762762763], [0.2242242242,0.2757757758], [0.2247247247,0.2752752753], [0.2252252252,0.2747747748], [0.2257257257,0.2742742743], [0.2262262262,0.2737737738], [0.2267267267,0.2732732733], [0.2272272272,0.2727727728], [0.2277277277,0.2722722723], [0.2282282282,0.2717717718], [0.2287287287,0.2712712713], [0.2292292292,0.2707707708], [0.2297297297,0.2702702703], [0.2302302302,0.2697697698], [0.2307307307,0.2692692693], [0.2312312312,0.2687687688], [0.2317317317,0.2682682683], [0.2322322322,0.2677677678], [0.2327327327,0.2672672673], [0.2332332332,0.2667667668], [0.2337337337,0.2662662663], [0.2342342342,0.2657657658], [0.2347347347,0.2652652653], [0.2352352352,0.2647647648], [0.2357357357,0.2642642643], [0.2362362362,0.2637637638], [0.2367367367,0.2632632633], [0.2372372372,0.2627627628], [0.2377377377,0.2622622623], [0.2382382382,0.2617617618], [0.2387387387,0.2612612613], [0.2392392392,0.2607607608], [0.2397397397,0.2602602603], [0.2402402402,0.2597597598], [0.2407407407,0.2592592593], [0.2412412412,0.2587587588], [0.2417417417,0.2582582583], [0.2422422422,0.2577577578], [0.2427427427,0.2572572573], [0.2432432432,0.2567567568], [0.2437437437,0.2562562563], [0.2442442442,0.2557557558], [0.2447447447,0.2552552553], [0.2452452452,0.2547547548], [0.2457457457,0.2542542543], [0.2462462462,0.2537537538], [0.2467467467,0.2532532533], [0.2472472472,0.2527527528], [0.2477477477,0.2522522523], [0.2482482482,0.2517517518], [0.2487487487,0.2512512513], [0.2492492492,0.2507507508], [0.2497497497,0.2502502503], [0.2502502503,0.2497497497], [0.2507507508,0.2492492492], [0.2512512513,0.2487487487], [0.2517517518,0.2482482482], [0.2522522523,0.2477477477], [0.2527527528,0.2472472472], [0.2532532533,0.2467467467], [0.2537537538,0.2462462462], [0.2542542543,0.2457457457], [0.2547547548,0.2452452452], [0.2552552553,0.2447447447], [0.2557557558,0.2442442442], [0.2562562563,0.2437437437], [0.2567567568,0.2432432432], [0.2572572573,0.2427427427], [0.2577577578,0.2422422422], [0.2582582583,0.2417417417], [0.2587587588,0.2412412412], [0.2592592593,0.2407407407], [0.2597597598,0.2402402402], [0.2602602603,0.2397397397], [0.2607607608,0.2392392392], [0.2612612613,0.2387387387], [0.2617617618,0.2382382382], [0.2622622623,0.2377377377], [0.2627627628,0.2372372372], [0.2632632633,0.2367367367], [0.2637637638,0.2362362362], [0.2642642643,0.2357357357], [0.2647647648,0.2352352352], [0.2652652653,0.2347347347], [0.2657657658,0.2342342342], [0.2662662663,0.2337337337], [0.2667667668,0.2332332332], [0.2672672673,0.2327327327], [0.2677677678,0.2322322322], [0.2682682683,0.2317317317], [0.2687687688,0.2312312312], [0.2692692693,0.2307307307], [0.2697697698,0.2302302302], [0.2702702703,0.2297297297], [0.2707707708,0.2292292292], [0.2712712713,0.2287287287], [0.2717717718,0.2282282282], [0.2722722723,0.2277277277], [0.2727727728,0.2272272272], [0.2732732733,0.2267267267], [0.2737737738,0.2262262262], [0.2742742743,0.2257257257], [0.2747747748,0.2252252252], [0.2752752753,0.2247247247], [0.2757757758,0.2242242242], [0.2762762763,0.2237237237], [0.2767767768,0.2232232232], [0.2772772773,0.2227227227], [0.2777777778,0.2222222222], [0.2782782783,0.2217217217], [0.2787787788,0.2212212212], [0.2792792793,0.2207207207], [0.2797797798,0.2202202202], [0.2802802803,0.2197197197], [0.2807807808,0.2192192192], [0.2812812813,0.2187187187], [0.2817817818,0.2182182182], [0.2822822823,0.2177177177], [0.2827827828,0.2172172172], [0.2832832833,0.2167167167], [0.2837837838,0.2162162162], [0.2842842843,0.2157157157], [0.2847847848,0.2152152152], [0.2852852853,0.2147147147], [0.2857857858,0.2142142142], [0.2862862863,0.2137137137], [0.2867867868,0.2132132132], [0.2872872873,0.2127127127], [0.2877877878,0.2122122122], [0.2882882883,0.2117117117], [0.2887887888,0.2112112112], [0.2892892893,0.2107107107], [0.2897897898,0.2102102102], [0.2902902903,0.2097097097], [0.2907907908,0.2092092092], [0.2912912913,0.2087087087], [0.2917917918,0.2082082082], [0.2922922923,0.2077077077], [0.2927927928,0.2072072072], [0.2932932933,0.2067067067], [0.2937937938,0.2062062062], [0.2942942943,0.2057057057], [0.2947947948,0.2052052052], [0.2952952953,0.2047047047], [0.2957957958,0.2042042042], [0.2962962963,0.2037037037], [0.2967967968,0.2032032032], [0.2972972973,0.2027027027], [0.2977977978,0.2022022022], [0.2982982983,0.2017017017], [0.2987987988,0.2012012012], [0.2992992993,0.2007007007], [0.2997997998,0.2002002002], [0.3003003003,0.1996996997], [0.3008008008,0.1991991992], [0.3013013013,0.1986986987], [0.3018018018,0.1981981982], [0.3023023023,0.1976976977], [0.3028028028,0.1971971972], [0.3033033033,0.1966966967], [0.3038038038,0.1961961962], [0.3043043043,0.1956956957], [0.3048048048,0.1951951952], [0.3053053053,0.1946946947], [0.3058058058,0.1941941942], [0.3063063063,0.1936936937], [0.3068068068,0.1931931932], [0.3073073073,0.1926926927], [0.3078078078,0.1921921922], [0.3083083083,0.1916916917], [0.3088088088,0.1911911912], [0.3093093093,0.1906906907], [0.3098098098,0.1901901902], [0.3103103103,0.1896896897], [0.3108108108,0.1891891892], [0.3113113113,0.1886886887], [0.3118118118,0.1881881882], [0.3123123123,0.1876876877], [0.3128128128,0.1871871872], [0.3133133133,0.1866866867], [0.3138138138,0.1861861862], [0.3143143143,0.1856856857], [0.3148148148,0.1851851852], [0.3153153153,0.1846846847], [0.3158158158,0.1841841842], [0.3163163163,0.1836836837], [0.3168168168,0.1831831832], [0.3173173173,0.1826826827], [0.3178178178,0.1821821822], [0.3183183183,0.1816816817], [0.3188188188,0.1811811812], [0.3193193193,0.1806806807], [0.3198198198,0.1801801802], [0.3203203203,0.1796796797], [0.3208208208,0.1791791792], [0.3213213213,0.1786786787], [0.3218218218,0.1781781782], [0.3223223223,0.1776776777], [0.3228228228,0.1771771772], [0.3233233233,0.1766766767], [0.3238238238,0.1761761762], [0.3243243243,0.1756756757], [0.3248248248,0.1751751752], [0.3253253253,0.1746746747], [0.3258258258,0.1741741742], [0.3263263263,0.1736736737], [0.3268268268,0.1731731732], [0.3273273273,0.1726726727], [0.3278278278,0.1721721722], [0.3283283283,0.1716716717], [0.3288288288,0.1711711712], [0.3293293293,0.1706706707], [0.3298298298,0.1701701702], [0.3303303303,0.1696696697], [0.3308308308,0.1691691692], [0.3313313313,0.1686686687], [0.3318318318,0.1681681682], [0.3323323323,0.1676676677], [0.3328328328,0.1671671672], [0.3333333333,0.1666666667], [0.3338338338,0.1661661662], [0.3343343343,0.1656656657], [0.3348348348,0.1651651652], [0.3353353353,0.1646646647], [0.3358358358,0.1641641642], [0.3363363363,0.1636636637], [0.3368368368,0.1631631632], [0.3373373373,0.1626626627], [0.3378378378,0.1621621622], [0.3383383383,0.1616616617], [0.3388388388,0.1611611612], [0.3393393393,0.1606606607], [0.3398398398,0.1601601602], [0.3403403403,0.1596596597], [0.3408408408,0.1591591592], [0.3413413413,0.1586586587], [0.3418418418,0.1581581582], [0.3423423423,0.1576576577], [0.3428428428,0.1571571572], [0.3433433433,0.1566566567], [0.3438438438,0.1561561562], [0.3443443443,0.1556556557], [0.3448448448,0.1551551552], [0.3453453453,0.1546546547], [0.3458458458,0.1541541542], [0.3463463463,0.1536536537], [0.3468468468,0.1531531532], [0.3473473473,0.1526526527], [0.3478478478,0.1521521522], [0.3483483483,0.1516516517], [0.3488488488,0.1511511512], [0.3493493493,0.1506506507], [0.3498498498,0.1501501502], [0.3503503504,0.1496496496], [0.3508508509,0.1491491491], [0.3513513514,0.1486486486], [0.3518518519,0.1481481481], [0.3523523524,0.1476476476], [0.3528528529,0.1471471471], [0.3533533534,0.1466466466], [0.3538538539,0.1461461461], [0.3543543544,0.1456456456], [0.3548548549,0.1451451451], [0.3553553554,0.1446446446], [0.3558558559,0.1441441441], [0.3563563564,0.1436436436], [0.3568568569,0.1431431431], [0.3573573574,0.1426426426], [0.3578578579,0.1421421421], [0.3583583584,0.1416416416], [0.3588588589,0.1411411411], [0.3593593594,0.1406406406], [0.3598598599,0.1401401401], [0.3603603604,0.1396396396], [0.3608608609,0.1391391391], [0.3613613614,0.1386386386], [0.3618618619,0.1381381381], [0.3623623624,0.1376376376], [0.3628628629,0.1371371371], [0.3633633634,0.1366366366], [0.3638638639,0.1361361361], [0.3643643644,0.1356356356], [0.3648648649,0.1351351351], [0.3653653654,0.1346346346], [0.3658658659,0.1341341341], [0.3663663664,0.1336336336], [0.3668668669,0.1331331331], [0.3673673674,0.1326326326], [0.3678678679,0.1321321321], [0.3683683684,0.1316316316], [0.3688688689,0.1311311311], [0.3693693694,0.1306306306], [0.3698698699,0.1301301301], [0.3703703704,0.1296296296], [0.3708708709,0.1291291291], [0.3713713714,0.1286286286], [0.3718718719,0.1281281281], [0.3723723724,0.1276276276], [0.3728728729,0.1271271271], [0.3733733734,0.1266266266], [0.3738738739,0.1261261261], [0.3743743744,0.1256256256], [0.3748748749,0.1251251251], [0.3753753754,0.1246246246], [0.3758758759,0.1241241241], [0.3763763764,0.1236236236], [0.3768768769,0.1231231231], [0.3773773774,0.1226226226], [0.3778778779,0.1221221221], [0.3783783784,0.1216216216], [0.3788788789,0.1211211211], [0.3793793794,0.1206206206], [0.3798798799,0.1201201201], [0.3803803804,0.1196196196], [0.3808808809,0.1191191191], [0.3813813814,0.1186186186], [0.3818818819,0.1181181181], [0.3823823824,0.1176176176], [0.3828828829,0.1171171171], [0.3833833834,0.1166166166], [0.3838838839,0.1161161161], [0.3843843844,0.1156156156], [0.3848848849,0.1151151151], [0.3853853854,0.1146146146], [0.3858858859,0.1141141141], [0.3863863864,0.1136136136], [0.3868868869,0.1131131131], [0.3873873874,0.1126126126], [0.3878878879,0.1121121121], [0.3883883884,0.1116116116], [0.3888888889,0.1111111111], [0.3893893894,0.1106106106], [0.3898898899,0.1101101101], [0.3903903904,0.1096096096], [0.3908908909,0.1091091091], [0.3913913914,0.1086086086], [0.3918918919,0.1081081081], [0.3923923924,0.1076076076], [0.3928928929,0.1071071071], [0.3933933934,0.1066066066], [0.3938938939,0.1061061061], [0.3943943944,0.1056056056], [0.3948948949,0.1051051051], [0.3953953954,0.1046046046], [0.3958958959,0.1041041041], [0.3963963964,0.1036036036], [0.3968968969,0.1031031031], [0.3973973974,0.1026026026], [0.3978978979,0.1021021021], [0.3983983984,0.1016016016], [0.3988988989,0.1011011011], [0.3993993994,0.1006006006], [0.3998998999,0.1001001001], [0.4004004004,0.0995995996], [0.4009009009,0.0990990991], [0.4014014014,0.0985985986], [0.4019019019,0.0980980981], [0.4024024024,0.0975975976], [0.4029029029,0.0970970971], [0.4034034034,0.0965965966], [0.4039039039,0.0960960961], [0.4044044044,0.0955955956], [0.4049049049,0.0950950951], [0.4054054054,0.0945945946], [0.4059059059,0.0940940941], [0.4064064064,0.0935935936], [0.4069069069,0.0930930931], [0.4074074074,0.0925925926], [0.4079079079,0.0920920921], [0.4084084084,0.0915915916], [0.4089089089,0.0910910911], [0.4094094094,0.0905905906], [0.4099099099,0.0900900901], [0.4104104104,0.0895895896], [0.4109109109,0.0890890891], [0.4114114114,0.0885885886], [0.4119119119,0.0880880881], [0.4124124124,0.0875875876], [0.4129129129,0.0870870871], [0.4134134134,0.0865865866], [0.4139139139,0.0860860861], [0.4144144144,0.0855855856], [0.4149149149,0.0850850851], [0.4154154154,0.0845845846], [0.4159159159,0.0840840841], [0.4164164164,0.0835835836], [0.4169169169,0.0830830831], [0.4174174174,0.0825825826], [0.4179179179,0.0820820821], [0.4184184184,0.0815815816], [0.4189189189,0.0810810811], [0.4194194194,0.0805805806], [0.4199199199,0.0800800801], [0.4204204204,0.0795795796], [0.4209209209,0.0790790791], [0.4214214214,0.0785785786], [0.4219219219,0.0780780781], [0.4224224224,0.0775775776], [0.4229229229,0.0770770771], [0.4234234234,0.0765765766], [0.4239239239,0.0760760761], [0.4244244244,0.0755755756], [0.4249249249,0.0750750751], [0.4254254254,0.0745745746], [0.4259259259,0.0740740741], [0.4264264264,0.0735735736], [0.4269269269,0.0730730731], [0.4274274274,0.0725725726], [0.4279279279,0.0720720721], [0.4284284284,0.0715715716], [0.4289289289,0.0710710711], [0.4294294294,0.0705705706], [0.4299299299,0.0700700701], [0.4304304304,0.0695695696], [0.4309309309,0.0690690691], [0.4314314314,0.0685685686], [0.4319319319,0.0680680681], [0.4324324324,0.0675675676], [0.4329329329,0.0670670671], [0.4334334334,0.0665665666], [0.4339339339,0.0660660661], [0.4344344344,0.0655655656], [0.4349349349,0.0650650651], [0.4354354354,0.0645645646], [0.4359359359,0.0640640641], [0.4364364364,0.0635635636], [0.4369369369,0.0630630631], [0.4374374374,0.0625625626], [0.4379379379,0.0620620621], [0.4384384384,0.0615615616], [0.4389389389,0.0610610611], [0.4394394394,0.0605605606], [0.4399399399,0.0600600601], [0.4404404404,0.0595595596], [0.4409409409,0.0590590591], [0.4414414414,0.0585585586], [0.4419419419,0.0580580581], [0.4424424424,0.0575575576], [0.4429429429,0.0570570571], [0.4434434434,0.0565565566], [0.4439439439,0.0560560561], [0.4444444444,0.0555555556], [0.4449449449,0.0550550551], [0.4454454454,0.0545545546], [0.4459459459,0.0540540541], [0.4464464464,0.0535535536], [0.4469469469,0.0530530531], [0.4474474474,0.0525525526], [0.4479479479,0.0520520521], [0.4484484484,0.0515515516], [0.4489489489,0.0510510511], [0.4494494494,0.0505505506], [0.4499499499,0.0500500501], [0.4504504505,0.0495495495], [0.4509509510,0.0490490490], [0.4514514515,0.0485485485], [0.4519519520,0.0480480480], [0.4524524525,0.0475475475], [0.4529529530,0.0470470470], [0.4534534535,0.0465465465], [0.4539539540,0.0460460460], [0.4544544545,0.0455455455], [0.4549549550,0.0450450450], [0.4554554555,0.0445445445], [0.4559559560,0.0440440440], [0.4564564565,0.0435435435], [0.4569569570,0.0430430430], [0.4574574575,0.0425425425], [0.4579579580,0.0420420420], [0.4584584585,0.0415415415], [0.4589589590,0.0410410410], [0.4594594595,0.0405405405], [0.4599599600,0.0400400400], [0.4604604605,0.0395395395], [0.4609609610,0.0390390390], [0.4614614615,0.0385385385], [0.4619619620,0.0380380380], [0.4624624625,0.0375375375], [0.4629629630,0.0370370370], [0.4634634635,0.0365365365], [0.4639639640,0.0360360360], [0.4644644645,0.0355355355], [0.4649649650,0.0350350350], [0.4654654655,0.0345345345], [0.4659659660,0.0340340340], [0.4664664665,0.0335335335], [0.4669669670,0.0330330330], [0.4674674675,0.0325325325], [0.4679679680,0.0320320320], [0.4684684685,0.0315315315], [0.4689689690,0.0310310310], [0.4694694695,0.0305305305], [0.4699699700,0.0300300300], [0.4704704705,0.0295295295], [0.4709709710,0.0290290290], [0.4714714715,0.0285285285], [0.4719719720,0.0280280280], [0.4724724725,0.0275275275], [0.4729729730,0.0270270270], [0.4734734735,0.0265265265], [0.4739739740,0.0260260260], [0.4744744745,0.0255255255], [0.4749749750,0.0250250250], [0.4754754755,0.0245245245], [0.4759759760,0.0240240240], [0.4764764765,0.0235235235], [0.4769769770,0.0230230230], [0.4774774775,0.0225225225], [0.4779779780,0.0220220220], [0.4784784785,0.0215215215], [0.4789789790,0.0210210210], [0.4794794795,0.0205205205], [0.4799799800,0.0200200200], [0.4804804805,0.0195195195], [0.4809809810,0.0190190190], [0.4814814815,0.0185185185], [0.4819819820,0.0180180180], [0.4824824825,0.0175175175], [0.4829829830,0.0170170170], [0.4834834835,0.0165165165], [0.4839839840,0.0160160160], [0.4844844845,0.0155155155], [0.4849849850,0.0150150150], [0.4854854855,0.0145145145], [0.4859859860,0.0140140140], [0.4864864865,0.0135135135], [0.4869869870,0.0130130130], [0.4874874875,0.0125125125], [0.4879879880,0.0120120120], [0.4884884885,0.0115115115], [0.4889889890,0.0110110110], [0.4894894895,0.0105105105], [0.4899899900,0.0100100100], [0.4904904905,0.0095095095], [0.4909909910,0.0090090090], [0.4914914915,0.0085085085], [0.4919919920,0.0080080080], [0.4924924925,0.0075075075], [0.4929929930,0.0070070070], [0.4934934935,0.0065065065], [0.4939939940,0.0060060060], [0.4944944945,0.0055055055], [0.4949949950,0.0050050050], [0.4954954955,0.0045045045], [0.4959959960,0.0040040040], [0.4964964965,0.0035035035], [0.4969969970,0.0030030030], [0.4974974975,0.0025025025], [0.4979979980,0.0020020020], [0.4984984985,0.0015015015], [0.4989989990,0.0010010010], [0.4994994995,0.0005005005], [0.5000000000,0.0000000000]]deap-1.0.1/examples/ga/pareto_front/dtlz2_front.json0000644000076500000240000007051012117373622022734 0ustar felixstaff00000000000000[[0.0000000000,1.0000000000], [0.0369034559,0.9993188355], [0.0554689064,0.9984604150], [0.0701154707,0.9975388818], [0.0825460836,0.9965872486], [0.0944933533,0.9955254925], [0.1052322500,0.9944476726], [0.1149755587,0.9933683209], [0.1240790518,0.9922723361], [0.1323880288,0.9911979670], [0.1401883532,0.9901248536], [0.1470407828,0.9891304303], [0.1540288320,0.9880663535], [0.1602847298,0.9870708209], [0.1664128767,0.9860561619], [0.1723284939,0.9850395374], [0.1779913023,0.9840320606], [0.1834929251,0.9830210305], [0.1888536496,0.9820052439], [0.1940749455,0.9809867051], [0.1990808625,0.9799830663], [0.2038239780,0.9790075516], [0.2085914966,0.9780028566], [0.2132114339,0.9770060821], [0.2178376178,0.9759850267], [0.2222055155,0.9749998507], [0.2265423242,0.9740013220], [0.2308470222,0.9729900577], [0.2350620607,0.9719803638], [0.2391443145,0.9709840353], [0.2431809463,0.9699809418], [0.2471722125,0.9689715669], [0.2510553101,0.9679727430], [0.2548588786,0.9669782583], [0.2586462636,0.9659721064], [0.2623167872,0.9649818149], [0.2660016761,0.9639725662], [0.2696215002,0.9629663788], [0.2731796937,0.9619630216], [0.2766542506,0.9609695238], [0.2801268556,0.9599629913], [0.2835449083,0.9589589590], [0.2869030002,0.9579596382], [0.2902298788,0.9569569570], [0.2935083252,0.9559565173], [0.2967507945,0.9549549550], [0.2999530859,0.9539539540], [0.3031103626,0.9529554597], [0.3062375464,0.9519551277], [0.3093316711,0.9509542141], [0.3124021328,0.9499499499], [0.3154217281,0.9489515970], [0.3184020635,0.9479557616], [0.3213660178,0.9469550584], [0.3243243243,0.9459459459], [0.3272221858,0.9449474277], [0.3300954464,0.9439475601], [0.3329379853,0.9429487250], [0.3357662943,0.9419453252], [0.3385707395,0.9409409409], [0.3413325513,0.9399425990], [0.3440838109,0.9389389389], [0.3468031496,0.9379379379], [0.3494914651,0.9369395476], [0.3521544727,0.9359418931], [0.3548192039,0.9349349349], [0.3574365344,0.9339374304], [0.3600347561,0.9329388910], [0.3626332503,0.9319319319], [0.3651931617,0.9309317669], [0.3677307710,0.9299322986], [0.3702561890,0.9289296822], [0.3727563119,0.9279292710], [0.3752418848,0.9269269269], [0.3777033956,0.9259266412], [0.3801498169,0.9249249249], [0.3825762444,0.9239239239], [0.3849807434,0.9229246054], [0.3873757477,0.9219219219], [0.3897494803,0.9209209209], [0.3921062878,0.9199199199], [0.3944464735,0.9189189189], [0.3967701220,0.9179180085], [0.3990771074,0.9169173694], [0.4013664253,0.9159175687], [0.4036459287,0.9149152771], [0.4059080659,0.9139139139], [0.4081514925,0.9129142124], [0.4103859950,0.9119119119], [0.4126030930,0.9109109109], [0.4148059255,0.9099099099], [0.4169947186,0.9089089089], [0.4191696921,0.9079079079], [0.4213310601,0.9069069069], [0.4234790309,0.9059059059], [0.4256138074,0.9049049049], [0.4277302163,0.9039064454], [0.4298445625,0.9029029029], [0.4319409211,0.9019019019], [0.4340248458,0.9009009009], [0.4360965147,0.8998998999], [0.4381524982,0.8989006554], [0.4402007852,0.8978993645], [0.4422397046,0.8968968969], [0.4442548729,0.8959004453], [0.4462716759,0.8948975312], [0.4482769549,0.8938947207], [0.4502691215,0.8928928929], [0.4522432984,0.8918946121], [0.4542173715,0.8908908909], [0.4561739547,0.8898906242], [0.4581228473,0.8888888889], [0.4600583792,0.8878886685], [0.4619863237,0.8868870484], [0.4639032196,0.8858858859], [0.4658097686,0.8848848849], [0.4677064034,0.8838838839], [0.4695932443,0.8828828829], [0.4714828031,0.8818752556], [0.4733427410,0.8808783398], [0.4751952113,0.8798803960], [0.4770406081,0.8788812538], [0.4788811021,0.8778797697], [0.4807150328,0.8768768769], [0.4825364754,0.8758758759], [0.4843489995,0.8748748749], [0.4861527050,0.8738738739], [0.4879476896,0.8728728729], [0.4897340493,0.8718718719], [0.4913662950,0.8709530206], [0.4930308523,0.8700118267], [0.4944409928,0.8692111968], [0.4958731824,0.8683949487], [0.4972038803,0.8676337369], [0.4984028062,0.8669455824], [0.4995380883,0.8662919244], [0.5006154830,0.8656697628], [0.5014559265,0.8651831909], [0.5022666475,0.8647127933], [0.5031221290,0.8642153223], [0.5038378201,0.8637982699], [0.5045948993,0.8633562344], [0.5052461589,0.8629752713], [0.5058661023,0.8626120139], [0.5064909416,0.8622452818], [0.5071004080,0.8618869858], [0.5077948321,0.8614780371], [0.5085028146,0.8610603275], [0.5091884560,0.8606550507], [0.5098623244,0.8602560143], [0.5104395997,0.8599136091], [0.5110220546,0.8595676004], [0.5116045741,0.8592210192], [0.5121824280,0.8588766853], [0.5127456864,0.8585405413], [0.5133164602,0.8581994009], [0.5139171891,0.8578398002], [0.5145203163,0.8574781887], [0.5151429121,0.8571042995], [0.5157917619,0.8567139886], [0.5164264251,0.8563315640], [0.5170709193,0.8559425591], [0.5176871000,0.8555700243], [0.5182871208,0.8552066770], [0.5188617486,0.8548581671], [0.5194335140,0.8545108686], [0.5200757604,0.8541201341], [0.5206731708,0.8537560830], [0.5212974684,0.8533750344], [0.5219719126,0.8529626735], [0.5226403853,0.8525532403], [0.5232753102,0.8521636872], [0.5238980396,0.8517809836], [0.5245113037,0.8514034839], [0.5251433508,0.8510137843], [0.5257516020,0.8506381446], [0.5263766517,0.8502515043], [0.5269964664,0.8498674746], [0.5276272887,0.8494759821], [0.5282513099,0.8490880717], [0.5288973285,0.8486858170], [0.5295458214,0.8482813348], [0.5301624602,0.8478960819], [0.5308048452,0.8474940804], [0.5314704031,0.8470768623], [0.5320732387,0.8466983339], [0.5326753017,0.8463196931], [0.5332994236,0.8459265481], [0.5339121576,0.8455399506], [0.5345345345,0.8451466331], [0.5351594859,0.8447510430], [0.5357890366,0.8443518865], [0.5364106345,0.8439571264], [0.5370230863,0.8435675461], [0.5376334769,0.8431786552], [0.5382034505,0.8428149535], [0.5387686643,0.8424537533], [0.5393584806,0.8420762611], [0.5399531958,0.8416950436], [0.5405405405,0.8413179684], [0.5411155897,0.8409482259], [0.5417229553,0.8405571008], [0.5423260737,0.8401680961], [0.5429669464,0.8397540682], [0.5435996064,0.8393446657], [0.5442146081,0.8389460413], [0.5448272687,0.8385482976], [0.5454368027,0.8381519517], [0.5460469885,0.8377545502], [0.5466735397,0.8373458312], [0.5473023850,0.8369349433], [0.5479290378,0.8365248171], [0.5485370289,0.8361262632], [0.5491247912,0.8357403686], [0.5497296206,0.8353426508], [0.5503275986,0.8349488213], [0.5509453978,0.8345412924], [0.5515814517,0.8341210356], [0.5521934967,0.8337159842], [0.5527651897,0.8333370537], [0.5533539033,0.8329462514], [0.5539294444,0.8325636136], [0.5545085146,0.8321780502], [0.5551259226,0.8317663194], [0.5557448952,0.8313528802], [0.5563254221,0.8309645147], [0.5568964635,0.8305819219], [0.5574640036,0.8302011110], [0.5580346413,0.8298176541], [0.5586159811,0.8294264197], [0.5592211739,0.8290185032], [0.5598067495,0.8286231974], [0.5603754700,0.8282386930], [0.5609717717,0.8278349300], [0.5615702950,0.8274290325], [0.5621655663,0.8270247131], [0.5627583491,0.8266214614], [0.5633666495,0.8262070069], [0.5639663209,0.8257977894], [0.5645982471,0.8253658700], [0.5652180106,0.8249415740], [0.5658627611,0.8244994455], [0.5665077826,0.8240563890], [0.5671386419,0.8236223411], [0.5677312926,0.8232139330], [0.5683074749,0.8228162699], [0.5688783385,0.8224216899], [0.5694700803,0.8220120606], [0.5700453735,0.8216132132], [0.5706220363,0.8212128176], [0.5711883696,0.8208190096], [0.5717565184,0.8204233564], [0.5723164842,0.8200328298], [0.5728546187,0.8196569928], [0.5734517451,0.8192393399], [0.5740430940,0.8188250889], [0.5746447945,0.8184029327], [0.5752422136,0.8179831268], [0.5758351001,0.8175658612], [0.5764334119,0.8171441254], [0.5770385373,0.8167169195], [0.5775944950,0.8163238324], [0.5781852812,0.8159054974], [0.5787508416,0.8155044227], [0.5793238635,0.8150974550], [0.5798826174,0.8147000368], [0.5804466148,0.8142983037], [0.5810258563,0.8138850990], [0.5816122879,0.8134661312], [0.5822313119,0.8130231850], [0.5828532438,0.8125774401], [0.5834723222,0.8121330243], [0.5840505573,0.8117172824], [0.5846356701,0.8112959591], [0.5851890576,0.8108968904], [0.5857589197,0.8104853410], [0.5863346929,0.8100689032], [0.5869043514,0.8096562741], [0.5874699609,0.8092459732], [0.5880140385,0.8088507220], [0.5885899410,0.8084317419], [0.5891712380,0.8080082007], [0.5897805084,0.8075635900], [0.5903722347,0.8071311074], [0.5909731865,0.8066912004], [0.5915521608,0.8062667307], [0.5921208026,0.8058492137], [0.5926768075,0.8054403776], [0.5932266964,0.8050354568], [0.5937712979,0.8046338582], [0.5943236246,0.8042259814], [0.5948727212,0.8038199087], [0.5954451305,0.8033959774], [0.5960177897,0.8029712289], [0.5965880254,0.8025476484], [0.5971592963,0.8021226682], [0.5977293661,0.8016979512], [0.5983197190,0.8012574579], [0.5989034379,0.8008212485], [0.5994961891,0.8003776104], [0.6000862860,0.7999352782], [0.6006803491,0.7994892859], [0.6012714947,0.7990447982], [0.6018623738,0.7985998266], [0.6024410249,0.7981633990], [0.6030140078,0.7977305976], [0.6035853496,0.7972983919], [0.6041675960,0.7968572745], [0.6047483389,0.7964166288], [0.6053359718,0.7959700756], [0.6059088682,0.7955340617], [0.6064873862,0.7950931080], [0.6070598467,0.7946561159], [0.6076082992,0.7942368379], [0.6081585019,0.7938156187], [0.6087008225,0.7933998416], [0.6092493277,0.7929787240], [0.6098030527,0.7925529869], [0.6103731258,0.7921140368], [0.6109665199,0.7916564353], [0.6115512499,0.7912048210], [0.6121478031,0.7907433637], [0.6127075106,0.7903097535], [0.6132794115,0.7898660415], [0.6138526102,0.7894206566], [0.6144516716,0.7889544621], [0.6150361282,0.7884989290], [0.6155715884,0.7880809727], [0.6161249092,0.7876484598], [0.6166805932,0.7872134691], [0.6172303276,0.7867825130], [0.6177829555,0.7863486631], [0.6183391364,0.7859113896], [0.6188979332,0.7854714179], [0.6194366405,0.7850466536], [0.6200040267,0.7845986279], [0.6205590118,0.7841597496], [0.6211145615,0.7837197851], [0.6216575860,0.7832891201], [0.6222173611,0.7828445284], [0.6227721219,0.7824032747], [0.6233246414,0.7819631650], [0.6239067112,0.7814988264], [0.6244931263,0.7810303037], [0.6250711759,0.7805677582], [0.6256102179,0.7801357928], [0.6261334159,0.7797159390], [0.6266502055,0.7793006608], [0.6271902164,0.7788661197], [0.6277586298,0.7784080566], [0.6283003785,0.7779708442], [0.6288407357,0.7775341338], [0.6293810109,0.7770968685], [0.6299102714,0.7766679149], [0.6304645970,0.7762180054], [0.6310086733,0.7757757758], [0.6316174909,0.7752801720], [0.6322085094,0.7747982967], [0.6328081424,0.7743086303], [0.6333784888,0.7738421609], [0.6339594258,0.7733663080], [0.6345165953,0.7729092381], [0.6350706268,0.7724540756], [0.6355849311,0.7720309549], [0.6361115568,0.7715971017], [0.6366398489,0.7711612690], [0.6371659714,0.7707266214], [0.6377124530,0.7702745143], [0.6382694686,0.7698130198], [0.6388120988,0.7693627898], [0.6393722504,0.7688973439], [0.6398901502,0.7684663920], [0.6404140943,0.7680298092], [0.6409410450,0.7675901099], [0.6414990653,0.7671238161], [0.6420366757,0.7666739248], [0.6425993635,0.7662023610], [0.6431668598,0.7657260544], [0.6437381844,0.7652458102], [0.6442759163,0.7647931378], [0.6447880509,0.7643614128], [0.6453596100,0.7638789000], [0.6458819044,0.7634373357], [0.6463891541,0.7630079039], [0.6469019786,0.7625731637], [0.6474281745,0.7621264717], [0.6479731485,0.7616631794], [0.6485163733,0.7612007052], [0.6490706137,0.7607281633], [0.6496196821,0.7602593430], [0.6501586847,0.7597984501], [0.6506855595,0.7593472873], [0.6512624614,0.7588525591], [0.6518486201,0.7583491125], [0.6524062744,0.7578694169], [0.6529552921,0.7573964527], [0.6534930151,0.7569325460], [0.6540287990,0.7564696491], [0.6545710321,0.7560005053], [0.6551098937,0.7555336043], [0.6556243355,0.7550872338], [0.6561249582,0.7546522638], [0.6566328735,0.7542103615], [0.6571569666,0.7537537538], [0.6576988639,0.7532809598], [0.6582401174,0.7528080418], [0.6587921703,0.7523249806], [0.6593218692,0.7518608068], [0.6598342919,0.7514111439], [0.6603684227,0.7509417729], [0.6608832360,0.7504887397], [0.6614001260,0.7500332482], [0.6619247438,0.7495702993], [0.6624657526,0.7490922016], [0.6630165568,0.7486047324], [0.6635694406,0.7481146954], [0.6641188509,0.7476270139], [0.6646432296,0.7471608778], [0.6651597065,0.7467011215], [0.6656823687,0.7462352069], [0.6661962464,0.7457764821], [0.6667464410,0.7452846324], [0.6672525404,0.7448315564], [0.6677715372,0.7443662903], [0.6682979983,0.7438936655], [0.6688166721,0.7434273731], [0.6693261124,0.7429687445], [0.6698347461,0.7425102107], [0.6703298317,0.7420632835], [0.6708735590,0.7415717550], [0.6714082569,0.7410876821], [0.6719538916,0.7405929837], [0.6725252041,0.7400742191], [0.6730822426,0.7395676404], [0.6736282685,0.7390703322], [0.6741566569,0.7385883847], [0.6746726115,0.7381171094], [0.6751854676,0.7376480085], [0.6756756757,0.7371990106], [0.6761607313,0.7367541418], [0.6766421534,0.7363120237], [0.6771586217,0.7358370751], [0.6776737898,0.7353626551], [0.6782096396,0.7348684813], [0.6787475351,0.7343716931], [0.6792935083,0.7338666974], [0.6798276103,0.7333719522], [0.6803548488,0.7328828553], [0.6808864775,0.7323889709], [0.6813888171,0.7319216351], [0.6819012364,0.7314442588], [0.6824316575,0.7309494051], [0.6829608407,0.7304549884], [0.6834903026,0.7299595922], [0.6840397074,0.7294447743], [0.6845764967,0.7289410265], [0.6850882559,0.7284600755], [0.6856044637,0.7279742573], [0.6860900097,0.7275166655], [0.6865771574,0.7270569489], [0.6870726626,0.7265887119], [0.6875677777,0.7261202043], [0.6880811093,0.7256337830], [0.6885950807,0.7251460645], [0.6891212070,0.7246460944], [0.6896491273,0.7241436882], [0.6901706210,0.7236466776], [0.6906770753,0.7231633133], [0.6911701553,0.7226920620], [0.6916916917,0.7221929130], [0.6922143667,0.7216919498], [0.6927315117,0.7211955718], [0.6932482963,0.7206988273], [0.6937626037,0.7202037556], [0.6942619006,0.7197224558], [0.6947787469,0.7192235347], [0.6952929865,0.7187264173], [0.6958120217,0.7182239418], [0.6963341710,0.7177177177], [0.6968524650,0.7172145021], [0.6973644298,0.7167167167], [0.6978707343,0.7162237347], [0.6983696435,0.7157372710], [0.6988439807,0.7152741367], [0.6993121350,0.7148164364], [0.6997858586,0.7143526805], [0.7002729450,0.7138752010], [0.7007571441,0.7133999054], [0.7012619608,0.7129036838], [0.7017843038,0.7123894939], [0.7023035839,0.7118775710], [0.7028540416,0.7113340961], [0.7033812989,0.7108127379], [0.7038927077,0.7103063114], [0.7044176915,0.7097856830], [0.7049148983,0.7092918906], [0.7053934668,0.7088159543], [0.7058758101,0.7083356131], [0.7063725126,0.7078402881], [0.7068638323,0.7073496466], [0.7073496466,0.7068638323], [0.7078402881,0.7063725126], [0.7083356131,0.7058758101], [0.7088159543,0.7053934668], [0.7092918906,0.7049148983], [0.7097856830,0.7044176915], [0.7103063114,0.7038927077], [0.7108127379,0.7033812989], [0.7113340961,0.7028540416], [0.7118775710,0.7023035839], [0.7123894939,0.7017843038], [0.7129036838,0.7012619608], [0.7133999054,0.7007571441], [0.7138752010,0.7002729450], [0.7143526805,0.6997858586], [0.7148164364,0.6993121350], [0.7152741367,0.6988439807], [0.7157372710,0.6983696435], [0.7162237347,0.6978707343], [0.7167167167,0.6973644298], [0.7172145021,0.6968524650], [0.7177177177,0.6963341710], [0.7182239418,0.6958120217], [0.7187264173,0.6952929865], [0.7192235347,0.6947787469], [0.7197224558,0.6942619006], [0.7202037556,0.6937626037], [0.7206988273,0.6932482963], [0.7211955718,0.6927315117], [0.7216919498,0.6922143667], [0.7221929130,0.6916916917], [0.7226920620,0.6911701553], [0.7231633133,0.6906770753], [0.7236466776,0.6901706210], [0.7241436882,0.6896491273], [0.7246460944,0.6891212070], [0.7251460645,0.6885950807], [0.7256337830,0.6880811093], [0.7261202043,0.6875677777], [0.7265887119,0.6870726626], [0.7270569489,0.6865771574], [0.7275166655,0.6860900097], [0.7279742573,0.6856044637], [0.7284600755,0.6850882559], [0.7289410265,0.6845764967], [0.7294447743,0.6840397074], [0.7299595922,0.6834903026], [0.7304549884,0.6829608407], [0.7309494051,0.6824316575], [0.7314442588,0.6819012364], [0.7319216351,0.6813888171], [0.7323889709,0.6808864775], [0.7328828553,0.6803548488], [0.7333719522,0.6798276103], [0.7338666974,0.6792935083], [0.7343716931,0.6787475351], [0.7348684813,0.6782096396], [0.7353626551,0.6776737898], [0.7358370751,0.6771586217], [0.7363120237,0.6766421534], [0.7367541418,0.6761607313], [0.7371990106,0.6756756757], [0.7376480085,0.6751854676], [0.7381171094,0.6746726115], [0.7385883847,0.6741566569], [0.7390703322,0.6736282685], [0.7395676404,0.6730822426], [0.7400742191,0.6725252041], [0.7405929837,0.6719538916], [0.7410876821,0.6714082569], [0.7415717550,0.6708735590], [0.7420632835,0.6703298317], [0.7425102107,0.6698347461], [0.7429687445,0.6693261124], [0.7434273731,0.6688166721], [0.7438936655,0.6682979983], [0.7443662903,0.6677715372], [0.7448315564,0.6672525404], [0.7452846324,0.6667464410], [0.7457764821,0.6661962464], [0.7462352069,0.6656823687], [0.7467011215,0.6651597065], [0.7471608778,0.6646432296], [0.7476270139,0.6641188509], [0.7481146954,0.6635694406], [0.7486047324,0.6630165568], [0.7490922016,0.6624657526], [0.7495702993,0.6619247438], [0.7500332482,0.6614001260], [0.7504887397,0.6608832360], [0.7509417729,0.6603684227], [0.7514111439,0.6598342919], [0.7518608068,0.6593218692], [0.7523249806,0.6587921703], [0.7528080418,0.6582401174], [0.7532809598,0.6576988639], [0.7537537538,0.6571569666], [0.7542103615,0.6566328735], [0.7546522638,0.6561249582], [0.7550872338,0.6556243355], [0.7555336043,0.6551098937], [0.7560005053,0.6545710321], [0.7564696491,0.6540287990], [0.7569325460,0.6534930151], [0.7573964527,0.6529552921], [0.7578694169,0.6524062744], [0.7583491125,0.6518486201], [0.7588525591,0.6512624614], [0.7593472873,0.6506855595], [0.7597984501,0.6501586847], [0.7602593430,0.6496196821], [0.7607281633,0.6490706137], [0.7612007052,0.6485163733], [0.7616631794,0.6479731485], [0.7621264717,0.6474281745], [0.7625731637,0.6469019786], [0.7630079039,0.6463891541], [0.7634373357,0.6458819044], [0.7638789000,0.6453596100], [0.7643614128,0.6447880509], [0.7647931378,0.6442759163], [0.7652458102,0.6437381844], [0.7657260544,0.6431668598], [0.7662023610,0.6425993635], [0.7666739248,0.6420366757], [0.7671238161,0.6414990653], [0.7675901099,0.6409410450], [0.7680298092,0.6404140943], [0.7684663920,0.6398901502], [0.7688973439,0.6393722504], [0.7693627898,0.6388120988], [0.7698130198,0.6382694686], [0.7702745143,0.6377124530], [0.7707266214,0.6371659714], [0.7711612690,0.6366398489], [0.7715971017,0.6361115568], [0.7720309549,0.6355849311], [0.7724540756,0.6350706268], [0.7729092381,0.6345165953], [0.7733663080,0.6339594258], [0.7738421609,0.6333784888], [0.7743086303,0.6328081424], [0.7747982967,0.6322085094], [0.7752801720,0.6316174909], [0.7757757758,0.6310086733], [0.7762180054,0.6304645970], [0.7766679149,0.6299102714], [0.7770968685,0.6293810109], [0.7775341338,0.6288407357], [0.7779708442,0.6283003785], [0.7784080566,0.6277586298], [0.7788661197,0.6271902164], [0.7793006608,0.6266502055], [0.7797159390,0.6261334159], [0.7801357928,0.6256102179], [0.7805677582,0.6250711759], [0.7810303037,0.6244931263], [0.7814988264,0.6239067112], [0.7819631650,0.6233246414], [0.7824032747,0.6227721219], [0.7828445284,0.6222173611], [0.7832891201,0.6216575860], [0.7837197851,0.6211145615], [0.7841597496,0.6205590118], [0.7845986279,0.6200040267], [0.7850466536,0.6194366405], [0.7854714179,0.6188979332], [0.7859113896,0.6183391364], [0.7863486631,0.6177829555], [0.7867825130,0.6172303276], [0.7872134691,0.6166805932], [0.7876484598,0.6161249092], [0.7880809727,0.6155715884], [0.7884989290,0.6150361282], [0.7889544621,0.6144516716], [0.7894206566,0.6138526102], [0.7898660415,0.6132794115], [0.7903097535,0.6127075106], [0.7907433637,0.6121478031], [0.7912048210,0.6115512499], [0.7916564353,0.6109665199], [0.7921140368,0.6103731258], [0.7925529869,0.6098030527], [0.7929787240,0.6092493277], [0.7933998416,0.6087008225], [0.7938156187,0.6081585019], [0.7942368379,0.6076082992], [0.7946561159,0.6070598467], [0.7950931080,0.6064873862], [0.7955340617,0.6059088682], [0.7959700756,0.6053359718], [0.7964166288,0.6047483389], [0.7968572745,0.6041675960], [0.7972983919,0.6035853496], [0.7977305976,0.6030140078], [0.7981633990,0.6024410249], [0.7985998266,0.6018623738], [0.7990447982,0.6012714947], [0.7994892859,0.6006803491], [0.7999352782,0.6000862860], [0.8003776104,0.5994961891], [0.8008212485,0.5989034379], [0.8012574579,0.5983197190], [0.8016979512,0.5977293661], [0.8021226682,0.5971592963], [0.8025476484,0.5965880254], [0.8029712289,0.5960177897], [0.8033959774,0.5954451305], [0.8038199087,0.5948727212], [0.8042259814,0.5943236246], [0.8046338582,0.5937712979], [0.8050354568,0.5932266964], [0.8054403776,0.5926768075], [0.8058492137,0.5921208026], [0.8062667307,0.5915521608], [0.8066912004,0.5909731865], [0.8071311074,0.5903722347], [0.8075635900,0.5897805084], [0.8080082007,0.5891712380], [0.8084317419,0.5885899410], [0.8088507220,0.5880140385], [0.8092459732,0.5874699609], [0.8096562741,0.5869043514], [0.8100689032,0.5863346929], [0.8104853410,0.5857589197], [0.8108968904,0.5851890576], [0.8112959591,0.5846356701], [0.8117172824,0.5840505573], [0.8121330243,0.5834723222], [0.8125774401,0.5828532438], [0.8130231850,0.5822313119], [0.8134661312,0.5816122879], [0.8138850990,0.5810258563], [0.8142983037,0.5804466148], [0.8147000368,0.5798826174], [0.8150974550,0.5793238635], [0.8155044227,0.5787508416], [0.8159054974,0.5781852812], [0.8163238324,0.5775944950], [0.8167169195,0.5770385373], [0.8171441254,0.5764334119], [0.8175658612,0.5758351001], [0.8179831268,0.5752422136], [0.8184029327,0.5746447945], [0.8188250889,0.5740430940], [0.8192393399,0.5734517451], [0.8196569928,0.5728546187], [0.8200328298,0.5723164842], [0.8204233564,0.5717565184], [0.8208190096,0.5711883696], [0.8212128176,0.5706220363], [0.8216132132,0.5700453735], [0.8220120606,0.5694700803], [0.8224216899,0.5688783385], [0.8228162699,0.5683074749], [0.8232139330,0.5677312926], [0.8236223411,0.5671386419], [0.8240563890,0.5665077826], [0.8244994455,0.5658627611], [0.8249415740,0.5652180106], [0.8253658700,0.5645982471], [0.8257977894,0.5639663209], [0.8262070069,0.5633666495], [0.8266214614,0.5627583491], [0.8270247131,0.5621655663], [0.8274290325,0.5615702950], [0.8278349300,0.5609717717], [0.8282386930,0.5603754700], [0.8286231974,0.5598067495], [0.8290185032,0.5592211739], [0.8294264197,0.5586159811], [0.8298176541,0.5580346413], [0.8302011110,0.5574640036], [0.8305819219,0.5568964635], [0.8309645147,0.5563254221], [0.8313528802,0.5557448952], [0.8317663194,0.5551259226], [0.8321780502,0.5545085146], [0.8325636136,0.5539294444], [0.8329462514,0.5533539033], [0.8333370537,0.5527651897], [0.8337159842,0.5521934967], [0.8341210356,0.5515814517], [0.8345412924,0.5509453978], [0.8349488213,0.5503275986], [0.8353426508,0.5497296206], [0.8357403686,0.5491247912], [0.8361262632,0.5485370289], [0.8365248171,0.5479290378], [0.8369349433,0.5473023850], [0.8373458312,0.5466735397], [0.8377545502,0.5460469885], [0.8381519517,0.5454368027], [0.8385482976,0.5448272687], [0.8389460413,0.5442146081], [0.8393446657,0.5435996064], [0.8397540682,0.5429669464], [0.8401680961,0.5423260737], [0.8405571008,0.5417229553], [0.8409482259,0.5411155897], [0.8413179684,0.5405405405], [0.8416950436,0.5399531958], [0.8420762611,0.5393584806], [0.8424537533,0.5387686643], [0.8428149535,0.5382034505], [0.8431786552,0.5376334769], [0.8435675461,0.5370230863], [0.8439571264,0.5364106345], [0.8443518865,0.5357890366], [0.8447510430,0.5351594859], [0.8451466331,0.5345345345], [0.8455399506,0.5339121576], [0.8459265481,0.5332994236], [0.8463196931,0.5326753017], [0.8466983339,0.5320732387], [0.8470768623,0.5314704031], [0.8474940804,0.5308048452], [0.8478960819,0.5301624602], [0.8482813348,0.5295458214], [0.8486858170,0.5288973285], [0.8490880717,0.5282513099], [0.8494759821,0.5276272887], [0.8498674746,0.5269964664], [0.8502515043,0.5263766517], [0.8506381446,0.5257516020], [0.8510137843,0.5251433508], [0.8514034839,0.5245113037], [0.8517809836,0.5238980396], [0.8521636872,0.5232753102], [0.8525532403,0.5226403853], [0.8529626735,0.5219719126], [0.8533750344,0.5212974684], [0.8537560830,0.5206731708], [0.8541201341,0.5200757604], [0.8545108686,0.5194335140], [0.8548581671,0.5188617486], [0.8552066770,0.5182871208], [0.8555700243,0.5176871000], [0.8559425591,0.5170709193], [0.8563315640,0.5164264251], [0.8567139886,0.5157917619], [0.8571042995,0.5151429121], [0.8574781887,0.5145203163], [0.8578398002,0.5139171891], [0.8581994009,0.5133164602], [0.8585405413,0.5127456864], [0.8588766853,0.5121824280], [0.8592210192,0.5116045741], [0.8595676004,0.5110220546], [0.8599136091,0.5104395997], [0.8602560143,0.5098623244], [0.8606550507,0.5091884560], [0.8610603275,0.5085028146], [0.8614780371,0.5077948321], [0.8618869858,0.5071004080], [0.8622452818,0.5064909416], [0.8626120139,0.5058661023], [0.8629752713,0.5052461589], [0.8633562344,0.5045948993], [0.8637982699,0.5038378201], [0.8642153223,0.5031221290], [0.8647127933,0.5022666475], [0.8651831909,0.5014559265], [0.8656697628,0.5006154830], [0.8662919244,0.4995380883], [0.8669455824,0.4984028062], [0.8676337369,0.4972038803], [0.8683949487,0.4958731824], [0.8692111968,0.4944409928], [0.8700118267,0.4930308523], [0.8709530206,0.4913662950], [0.8718718719,0.4897340493], [0.8728728729,0.4879476896], [0.8738738739,0.4861527050], [0.8748748749,0.4843489995], [0.8758758759,0.4825364754], [0.8768768769,0.4807150328], [0.8778797697,0.4788811021], [0.8788812538,0.4770406081], [0.8798803960,0.4751952113], [0.8808783398,0.4733427410], [0.8818752556,0.4714828031], [0.8828828829,0.4695932443], [0.8838838839,0.4677064034], [0.8848848849,0.4658097686], [0.8858858859,0.4639032196], [0.8868870484,0.4619863237], [0.8878886685,0.4600583792], [0.8888888889,0.4581228473], [0.8898906242,0.4561739547], [0.8908908909,0.4542173715], [0.8918946121,0.4522432984], [0.8928928929,0.4502691215], [0.8938947207,0.4482769549], [0.8948975312,0.4462716759], [0.8959004453,0.4442548729], [0.8968968969,0.4422397046], [0.8978993645,0.4402007852], [0.8989006554,0.4381524982], [0.8998998999,0.4360965147], [0.9009009009,0.4340248458], [0.9019019019,0.4319409211], [0.9029029029,0.4298445625], [0.9039064454,0.4277302163], [0.9049049049,0.4256138074], [0.9059059059,0.4234790309], [0.9069069069,0.4213310601], [0.9079079079,0.4191696921], [0.9089089089,0.4169947186], [0.9099099099,0.4148059255], [0.9109109109,0.4126030930], [0.9119119119,0.4103859950], [0.9129142124,0.4081514925], [0.9139139139,0.4059080659], [0.9149152771,0.4036459287], [0.9159175687,0.4013664253], [0.9169173694,0.3990771074], [0.9179180085,0.3967701220], [0.9189189189,0.3944464735], [0.9199199199,0.3921062878], [0.9209209209,0.3897494803], [0.9219219219,0.3873757477], [0.9229246054,0.3849807434], [0.9239239239,0.3825762444], [0.9249249249,0.3801498169], [0.9259266412,0.3777033956], [0.9269269269,0.3752418848], [0.9279292710,0.3727563119], [0.9289296822,0.3702561890], [0.9299322986,0.3677307710], [0.9309317669,0.3651931617], [0.9319319319,0.3626332503], [0.9329388910,0.3600347561], [0.9339374304,0.3574365344], [0.9349349349,0.3548192039], [0.9359418931,0.3521544727], [0.9369395476,0.3494914651], [0.9379379379,0.3468031496], [0.9389389389,0.3440838109], [0.9399425990,0.3413325513], [0.9409409409,0.3385707395], [0.9419453252,0.3357662943], [0.9429487250,0.3329379853], [0.9439475601,0.3300954464], [0.9449474277,0.3272221858], [0.9459459459,0.3243243243], [0.9469550584,0.3213660178], [0.9479557616,0.3184020635], [0.9489515970,0.3154217281], [0.9499499499,0.3124021328], [0.9509542141,0.3093316711], [0.9519551277,0.3062375464], [0.9529554597,0.3031103626], [0.9539539540,0.2999530859], [0.9549549550,0.2967507945], [0.9559565173,0.2935083252], [0.9569569570,0.2902298788], [0.9579596382,0.2869030002], [0.9589589590,0.2835449083], [0.9599629913,0.2801268556], [0.9609695238,0.2766542506], [0.9619630216,0.2731796937], [0.9629663788,0.2696215002], [0.9639725662,0.2660016761], [0.9649818149,0.2623167872], [0.9659721064,0.2586462636], [0.9669782583,0.2548588786], [0.9679727430,0.2510553101], [0.9689715669,0.2471722125], [0.9699809418,0.2431809463], [0.9709840353,0.2391443145], [0.9719803638,0.2350620607], [0.9729900577,0.2308470222], [0.9740013220,0.2265423242], [0.9749998507,0.2222055155], [0.9759850267,0.2178376178], [0.9770060821,0.2132114339], [0.9780028566,0.2085914966], [0.9790075516,0.2038239780], [0.9799830663,0.1990808625], [0.9809867051,0.1940749455], [0.9820052439,0.1888536496], [0.9830210305,0.1834929251], [0.9840320606,0.1779913023], [0.9850395374,0.1723284939], [0.9860561619,0.1664128767], [0.9870708209,0.1602847298], [0.9880663535,0.1540288320], [0.9891304303,0.1470407828], [0.9901248536,0.1401883532], [0.9911979670,0.1323880288], [0.9922723361,0.1240790518], [0.9933683209,0.1149755587], [0.9944476726,0.1052322500], [0.9955254925,0.0944933533], [0.9965872486,0.0825460836], [0.9975388818,0.0701154707], [0.9984604150,0.0554689064], [0.9993188355,0.0369034559], [1.0000000000,0.0000000000]]deap-1.0.1/examples/ga/pareto_front/dtlz3_front.json0000644000076500000240000007051012117373622022735 0ustar felixstaff00000000000000[[0.0000000000,1.0000000000], [0.0023789418,0.9999971703], [0.0035957625,0.9999935352], [0.0046461501,0.9999892066], [0.0056164443,0.9999842277], [0.0065159141,0.9999787712], [0.0074534964,0.9999722223], [0.0083258899,0.9999653392], [0.0091587956,0.9999580574], [0.0099918644,0.9999500801], [0.0108327355,0.9999413242], [0.0117233814,0.9999312788], [0.0126708842,0.9999197211], [0.0135594612,0.9999080663], [0.0144810937,0.9998951435], [0.0154426100,0.9998807558], [0.0163588209,0.9998661855], [0.0172796659,0.9998506954], [0.0182956723,0.9998326202], [0.0192861105,0.9998140057], [0.0202946195,0.9997940430], [0.0212767154,0.9997736251], [0.0222383250,0.9997526979], [0.0232425368,0.9997298558], [0.0241988150,0.9997071658], [0.0251821942,0.9996828783], [0.0261513463,0.9996579951], [0.0271274637,0.9996319826], [0.0281038746,0.9996050081], [0.0291089804,0.9995762438], [0.0301146996,0.9995464496], [0.0311420800,0.9995149678], [0.0321305605,0.9994836802], [0.0331494292,0.9994504066], [0.0341224678,0.9994176590], [0.0351196254,0.9993831157], [0.0361104817,0.9993478039], [0.0371030349,0.9993114453], [0.0381098792,0.9992735547], [0.0391060032,0.9992350677], [0.0400819542,0.9991963956], [0.0410766009,0.9991560003], [0.0420738233,0.9991145046], [0.0430885441,0.9990712574], [0.0440880109,0.9990276509], [0.0450918416,0.9989828456], [0.0460950834,0.9989370567], [0.0471008766,0.9988901378], [0.0481098764,0.9988420495], [0.0491071431,0.9987935164], [0.0500806316,0.9987451779], [0.0510749448,0.9986948233], [0.0520726250,0.9986433005], [0.0530671044,0.9985909485], [0.0540624909,0.9985375542], [0.0550624603,0.9984829120], [0.0560756802,0.9984265211], [0.0570766944,0.9983697967], [0.0580787531,0.9983120046], [0.0590846125,0.9982529782], [0.0600803499,0.9981935441], [0.0610750549,0.9981331763], [0.0620820036,0.9980710520], [0.0630804729,0.9980084438], [0.0640708640,0.9979453514], [0.0650811265,0.9978799762], [0.0660806623,0.9978142844], [0.0670828705,0.9977474072], [0.0680916171,0.9976790725], [0.0690876065,0.9976105967], [0.0700892996,0.9975407210], [0.0710876698,0.9974700713], [0.0720852865,0.9973984718], [0.0730871962,0.9973255545], [0.0740941746,0.9972512488], [0.0750929511,0.9971765384], [0.0760929028,0.9971007322], [0.0770905309,0.9970240970], [0.0780853028,0.9969466814], [0.0790890122,0.9968675580], [0.0800843181,0.9967880928], [0.0810920505,0.9967066165], [0.0820960362,0.9966244232], [0.0831026587,0.9965409917], [0.0840977867,0.9964575065], [0.0850989512,0.9963725049], [0.0860976797,0.9962867005], [0.0871044729,0.9961991823], [0.0881031458,0.9961113571], [0.0891070246,0.9960220571], [0.0901066082,0.9959321258], [0.0911009665,0.9958416611], [0.0921012781,0.9957496445], [0.0930979201,0.9956569576], [0.0941009818,0.9955626576], [0.0951016757,0.9954675642], [0.0961036555,0.9953713314], [0.0971077489,0.9952738744], [0.0981081000,0.9951757637], [0.0991041628,0.9950770648], [0.1001077606,0.9949766009], [0.1011117730,0.9948750722], [0.1021127434,0.9947728322], [0.1031140974,0.9946695345], [0.1041165595,0.9945651020], [0.1051226787,0.9944592613], [0.1061153429,0.9943538274], [0.1071120699,0.9942469535], [0.1081102128,0.9941389148], [0.1091150101,0.9940291316], [0.1101122560,0.9939191572], [0.1111111111,0.9938079900], [0.1121136994,0.9936953851], [0.1131139276,0.9935820245], [0.1141231545,0.9934666102], [0.1151229650,0.9933512485], [0.1161257655,0.9932345174], [0.1171251493,0.9931171630], [0.1181284450,0.9929983235], [0.1191229956,0.9928795052], [0.1201258342,0.9927586736], [0.1211211211,0.9926377355], [0.1221221221,0.9925150816], [0.1231288797,0.9923906887], [0.1241286609,0.9922661314], [0.1251279986,0.9921406070], [0.1261333960,0.9920132894], [0.1271359168,0.9918853052], [0.1281380543,0.9917563406], [0.1291448225,0.9916257433], [0.1301428579,0.9914952529], [0.1311375140,0.9913641876], [0.1321343226,0.9912318199], [0.1331352793,0.9910978748], [0.1341341341,0.9909631850], [0.1351420164,0.9908262388], [0.1361438408,0.9906890807], [0.1371434507,0.9905511970], [0.1381422984,0.9904123916], [0.1391432443,0.9902722644], [0.1401403250,0.9901316525], [0.1411430141,0.9899892169], [0.1421455635,0.9898457651], [0.1431449617,0.9897017328], [0.1441476716,0.9895561878], [0.1451516891,0.9894094133], [0.1461565556,0.9892614726], [0.1471579737,0.9891130020], [0.1481567787,0.9889638866], [0.1491583681,0.9888133197], [0.1501545067,0.9886625431], [0.1511624053,0.9885089414], [0.1521608817,0.9883557386], [0.1531664898,0.9882003979], [0.1541589451,0.9880460615], [0.1551583027,0.9878896199], [0.1561619849,0.9877314587], [0.1571610807,0.9875729820], [0.1581629812,0.9874130196], [0.1591635308,0.9872522324], [0.1601631179,0.9870905610], [0.1611652964,0.9869274275], [0.1621621622,0.9867641224], [0.1631631632,0.9865990990], [0.1641641642,0.9864330323], [0.1651651652,0.9862659217], [0.1661661662,0.9860977666], [0.1671671672,0.9859285665], [0.1681710294,0.9857578328], [0.1691691692,0.9855870292], [0.1701726043,0.9854142706], [0.1711754161,0.9852405680], [0.1721785211,0.9850657627], [0.1731774838,0.9848906331], [0.1741768392,0.9847143894], [0.1751792598,0.9845365544], [0.1761813346,0.9843577283], [0.1771811334,0.9841782592], [0.1781785649,0.9839981702], [0.1791819245,0.9838159573], [0.1801815240,0.9836333760], [0.1811811812,0.9834497341], [0.1821849176,0.9832642858], [0.1831859705,0.9830782778], [0.1841861550,0.9828913777], [0.1851863886,0.9827034148], [0.1861889937,0.9825139483], [0.1871879123,0.9823241245], [0.1881881882,0.9821329879], [0.1891901325,0.9819404736], [0.1901908766,0.9817471316], [0.1911911912,0.9815528149], [0.1921921922,0.9813573056], [0.1931958360,0.9811602157], [0.1941991204,0.9809621306], [0.1951970208,0.9807640507], [0.1961969398,0.9805645113], [0.1971971972,0.9803638434], [0.1981997319,0.9801616531], [0.1992018002,0.9799584903], [0.2002040952,0.9797542142], [0.2012014977,0.9795498749], [0.2022030700,0.9793436161], [0.2032032032,0.9791365881], [0.2042060303,0.9789279326], [0.2052074063,0.9787185093], [0.2062097106,0.9785078208], [0.2072096854,0.9782965533], [0.2082098806,0.9780841710], [0.2092092092,0.9778709050], [0.2102102102,0.9776562113], [0.2112112112,0.9774404454], [0.2122138358,0.9772232539], [0.2132132132,0.9770056938], [0.2142177690,0.9767859271], [0.2152194718,0.9765657064], [0.2162268763,0.9763431456], [0.2172247210,0.9761216218], [0.2182252142,0.9758984352], [0.2192262943,0.9756740398], [0.2202286523,0.9754482768], [0.2212286337,0.9752219704], [0.2222264292,0.9749950842], [0.2232287358,0.9747660907], [0.2242272055,0.9745368953], [0.2252294622,0.9743057474], [0.2262295232,0.9740740233], [0.2272287819,0.9738414043], [0.2282282282,0.9736076601], [0.2292304065,0.9733721902], [0.2302302302,0.9731361884], [0.2312329865,0.9728984047], [0.2322325765,0.9726602852], [0.2332332332,0.9724208240], [0.2342342342,0.9721801909], [0.2352352352,0.9719384672], [0.2362373060,0.9716953922], [0.2372398441,0.9714511086], [0.2382404413,0.9712062047], [0.2392410008,0.9709602173], [0.2402405576,0.9707133843], [0.2412412412,0.9704651789], [0.2422450093,0.9702151078], [0.2432457261,0.9699646987], [0.2442479682,0.9697128080], [0.2452518361,0.9694594044], [0.2462527863,0.9692056362], [0.2472490611,0.9689519605], [0.2482482482,0.9686964474], [0.2492504764,0.9684390533], [0.2502502503,0.9681811877], [0.2512512513,0.9679219022], [0.2522522523,0.9676615117], [0.2532532533,0.9674000154], [0.2542542543,0.9671374123], [0.2552575598,0.9668730931], [0.2562597600,0.9666079533], [0.2572592603,0.9663424201], [0.2582593220,0.9660756299], [0.2592592593,0.9658077637], [0.2602609197,0.9655383233], [0.2612612613,0.9652681251], [0.2622648187,0.9649959403], [0.2632641986,0.9647237748], [0.2642660612,0.9644498167], [0.2652652653,0.9641754711], [0.2662678984,0.9638990644], [0.2672685868,0.9636220745], [0.2682693526,0.9633439440], [0.2692712328,0.9630643816], [0.2702722259,0.9627839446], [0.2712735368,0.9625022952], [0.2722722723,0.9622202501], [0.2732732733,0.9619364418], [0.2742761494,0.9616509730], [0.2752786807,0.9613644720], [0.2762795701,0.9610773117], [0.2772772773,0.9607899414], [0.2782792613,0.9605002097], [0.2792800783,0.9602096843], [0.2802823235,0.9599176106], [0.2812817558,0.9596252257], [0.2822831454,0.9593311346], [0.2832842485,0.9590359923], [0.2842880068,0.9587389265], [0.2852925352,0.9584404882], [0.2862915788,0.9581425426], [0.2872909969,0.9578433500], [0.2882899977,0.9575431464], [0.2892909832,0.9572412063], [0.2902902903,0.9569386330], [0.2912929148,0.9566339100], [0.2922922923,0.9563290312], [0.2932942328,0.9560222241], [0.2942942943,0.9557148468], [0.2952952953,0.9554060334], [0.2962969827,0.9550958581], [0.2972984499,0.9547845996], [0.2982993300,0.9544723724], [0.2993011060,0.9541587121], [0.3003006996,0.9538445837], [0.3013025019,0.9535286059], [0.3023030034,0.9532118831], [0.3033033033,0.9528940687], [0.3043043043,0.9525748739], [0.3053053053,0.9522545198], [0.3063067368,0.9519328669], [0.3073073073,0.9516103293], [0.3083083083,0.9512864905], [0.3093093093,0.9509614877], [0.3103108868,0.9506351317], [0.3113113113,0.9503079856], [0.3123123123,0.9499794838], [0.3133133133,0.9496498132], [0.3143154356,0.9493186014], [0.3153153153,0.9489869609], [0.3163163163,0.9486537767], [0.3173173173,0.9483194188], [0.3183187733,0.9479837333], [0.3193204370,0.9476468005], [0.3203210408,0.9473090472], [0.3213221831,0.9469699333], [0.3223223223,0.9466299808], [0.3233233233,0.9462885546], [0.3243243243,0.9459459459], [0.3253253253,0.9456021535], [0.3263272005,0.9452568742], [0.3273293622,0.9449103072], [0.3283288453,0.9445634808], [0.3293293293,0.9442151200], [0.3303303303,0.9438653892], [0.3313313313,0.9435144667], [0.3323335732,0.9431619141], [0.3333333333,0.9428090416], [0.3343343343,0.9424545362], [0.3353353353,0.9420988339], [0.3363363750,0.9417419194], [0.3373389033,0.9413832717], [0.3383385080,0.9410244705], [0.3393393393,0.9406640276], [0.3403411793,0.9403020162], [0.3413413413,0.9399394069], [0.3423423423,0.9395752874], [0.3433433433,0.9392099598], [0.3443451363,0.9388431323], [0.3453453453,0.9384756749], [0.3463466065,0.9381066188], [0.3473473473,0.9377365410], [0.3483483483,0.9373651520], [0.3493493493,0.9369925465], [0.3503510626,0.9366184564], [0.3513524033,0.9362432850], [0.3523523524,0.9358674157], [0.3533533739,0.9354899215], [0.3543548782,0.9351110203], [0.3553564497,0.9347308670], [0.3563595686,0.9343488952], [0.3573589347,0.9339671256], [0.3583583584,0.9335841082], [0.3593593594,0.9331992557], [0.3603603604,0.9328131703], [0.3613624468,0.9324254297], [0.3623623624,0.9320372945], [0.3633653536,0.9316467248], [0.3643651514,0.9312561605], [0.3653670219,0.9308635449], [0.3663671270,0.9304703801], [0.3673673674,0.9300759202], [0.3683693446,0.9296795286], [0.3693724147,0.9292814532], [0.3703712393,0.9288838168], [0.3713719774,0.9284841702], [0.3723723724,0.9280834102], [0.3733733734,0.9276811543], [0.3743743744,0.9272776433], [0.3753753754,0.9268728756], [0.3763774806,0.9264664010], [0.3773777160,0.9260594255], [0.3783792359,0.9256506651], [0.3793793794,0.9252412045], [0.3803803804,0.9248301283], [0.3813813814,0.9244177854], [0.3823823824,0.9240041740], [0.3833845378,0.9235888134], [0.3843877245,0.9231717485], [0.3853870001,0.9227550380], [0.3863891830,0.9223358386], [0.3873891583,0.9219162869], [0.3883907844,0.9214947632], [0.3893942570,0.9210711767], [0.3903917375,0.9206488425], [0.3913933222,0.9202234877], [0.3923929945,0.9197976614], [0.3933933934,0.9193702399], [0.3943943944,0.9189412722], [0.3953975550,0.9185100835], [0.3963971073,0.9180791542], [0.3973973974,0.9176466142], [0.3983997498,0.9172118836], [0.3994003620,0.9167766090], [0.4004012107,0.9163399317], [0.4014014014,0.9159022409], [0.4024024024,0.9154628920], [0.4034043517,0.9150218189], [0.4044067845,0.9145792216], [0.4054054054,0.9141370014], [0.4064064064,0.9136924170], [0.4074074074,0.9132465190], [0.4084084084,0.9127993054], [0.4094094094,0.9123507744], [0.4104115392,0.9119004159], [0.4114128541,0.9114491009], [0.4124129881,0.9109969963], [0.4134134134,0.9105434364], [0.4144144144,0.9100882886], [0.4154154154,0.9096318116], [0.4164164164,0.9091740032], [0.4174186674,0.9087142874], [0.4184201372,0.9082535928], [0.4194194194,0.9077925703], [0.4204204800,0.9073293889], [0.4214229047,0.9068642321], [0.4224241375,0.9063982834], [0.4234234234,0.9059318984], [0.4244244244,0.9054633664], [0.4254254254,0.9049934847], [0.4264264264,0.9045222512], [0.4274274274,0.9040496636], [0.4284284284,0.9035757200], [0.4294294294,0.9031004181], [0.4304304634,0.9026237401], [0.4314314314,0.9021457310], [0.4324330951,0.9016660237], [0.4334351935,0.9011847386], [0.4344353208,0.9007030321], [0.4354354354,0.9002199629], [0.4364364364,0.8997350927], [0.4374374374,0.8992488467], [0.4384384384,0.8987612229], [0.4394394394,0.8982722188], [0.4404404404,0.8977818323], [0.4414414414,0.8972900611], [0.4424437834,0.8967962414], [0.4434434434,0.8963023555], [0.4444444444,0.8958064165], [0.4454463415,0.8953086378], [0.4464477251,0.8948097165], [0.4474474972,0.8943102019], [0.4484484484,0.8938086983], [0.4494494494,0.8933057665], [0.4504518510,0.8928007224], [0.4514526015,0.8922951017], [0.4524531394,0.8917881793], [0.4534534535,0.8912799591], [0.4544544545,0.8907699753], [0.4554554555,0.8902585737], [0.4564564565,0.8897457521], [0.4574574575,0.8892315079], [0.4584589324,0.8887155941], [0.4594594595,0.8881987419], [0.4604604605,0.8876802151], [0.4614614615,0.8871602559], [0.4624625580,0.8866388117], [0.4634641023,0.8861156955], [0.4644644647,0.8855917575], [0.4654654700,0.8850660406], [0.4664664665,0.8845388831], [0.4674674675,0.8840102753], [0.4684701105,0.8834793464], [0.4694714720,0.8829476412], [0.4704729272,0.8824144291], [0.4714726513,0.8818806830], [0.4724724725,0.8813454276], [0.4734734735,0.8808080778], [0.4744744745,0.8802692617], [0.4754756000,0.8797289093], [0.4764764765,0.8791872197], [0.4774778479,0.8786437872], [0.4784784785,0.8780992801], [0.4794798626,0.8775528824], [0.4804804805,0.8770054207], [0.4814814815,0.8764562642], [0.4824824825,0.8759056194], [0.4834834835,0.8753534836], [0.4844852498,0.8747994300], [0.4854857936,0.8742445563], [0.4864864865,0.8736881014], [0.4874879901,0.8731296922], [0.4884884885,0.8725703391], [0.4894900830,0.8720088639], [0.4904904905,0.8714465438], [0.4914914915,0.8708823766], [0.4924924925,0.8703166923], [0.4934934935,0.8697494880], [0.4944944945,0.8691807608], [0.4954954955,0.8686105076], [0.4964967353,0.8680385889], [0.4974980710,0.8674650825], [0.4984992707,0.8668901182], [0.4995004652,0.8663136183], [0.5005005005,0.8657362468], [0.5015018074,0.8651565969], [0.5025047918,0.8645744238], [0.5035043517,0.8639926897], [0.5045045045,0.8634090600], [0.5055055055,0.8628233793], [0.5065065065,0.8622361387], [0.5075086402,0.8616466678], [0.5085100628,0.8610560470], [0.5095106642,0.8604643416], [0.5105105105,0.8598715129], [0.5115122540,0.8592759824], [0.5125125125,0.8586797567], [0.5135135135,0.8580815063], [0.5145154430,0.8574811128], [0.5155159317,0.8568799940], [0.5165165165,0.8562772262], [0.5175175303,0.8556726043], [0.5185196118,0.8550657356], [0.5195197838,0.8544584216], [0.5205205205,0.8538491598], [0.5215220771,0.8532377881], [0.5225238556,0.8526246655], [0.5235243128,0.8520107358], [0.5245245245,0.8513953389], [0.5255260434,0.8507775136], [0.5265265265,0.8501587010], [0.5275275275,0.8495379378], [0.5285306324,0.8489142304], [0.5295307330,0.8482907537], [0.5305305305,0.8476658281], [0.5315315315,0.8470385062], [0.5325325325,0.8464095355], [0.5335347999,0.8457781135], [0.5345345345,0.8451466331], [0.5355355355,0.8445126939], [0.5365365365,0.8438770911], [0.5375375375,0.8432398210], [0.5385393670,0.8426003502], [0.5395395395,0.8419602635], [0.5405405405,0.8413179684], [0.5415415415,0.8406739908], [0.5425425425,0.8400283266], [0.5435435435,0.8393809721], [0.5445445445,0.8387319232], [0.5455455455,0.8380811761], [0.5465465465,0.8374287268], [0.5475475475,0.8367745713], [0.5485485485,0.8361187056], [0.5495495495,0.8354611257], [0.5505505506,0.8348018276], [0.5515517276,0.8341406907], [0.5525525526,0.8334780601], [0.5535542448,0.8328131231], [0.5545552970,0.8321468756], [0.5555555556,0.8314794193], [0.5565565566,0.8308097251], [0.5575579506,0.8301380197], [0.5585585586,0.8294650907], [0.5595595596,0.8287901419], [0.5605605606,0.8281134330], [0.5615615616,0.8274349597], [0.5625625626,0.8267547177], [0.5635646789,0.8260719416], [0.5645645646,0.8253889098], [0.5655655656,0.8247033352], [0.5665665666,0.8240159741], [0.5675675676,0.8233268223], [0.5685685686,0.8226358750], [0.5695695696,0.8219431279], [0.5705717985,0.8212477231], [0.5715715716,0.8205522156], [0.5725727651,0.8198539069], [0.5735747959,0.8191531929], [0.5745745746,0.8184522333], [0.5755755756,0.8177485902], [0.5765765766,0.8170431147], [0.5775775776,0.8163358022], [0.5785785786,0.8156266477], [0.5795807837,0.8149147901], [0.5805810088,0.8142024885], [0.5815815816,0.8134880847], [0.5825827335,0.8127714061], [0.5835835836,0.8120530777], [0.5845851229,0.8113323820], [0.5855855856,0.8106105859], [0.5865869027,0.8098862918], [0.5875886488,0.8091597987], [0.5885907985,0.8084311176], [0.5895895896,0.8077029874], [0.5905905906,0.8069713466], [0.5915915916,0.8062377991], [0.5925932684,0.8055018425], [0.5935954680,0.8047635804], [0.5945945946,0.8040256638], [0.5955966761,0.8032836357], [0.5965965966,0.8025412768], [0.5975981234,0.8017957863], [0.5985985986,0.8010491357], [0.5995995996,0.8003001438], [0.6006006006,0.7995491971], [0.6016028124,0.7987953781], [0.6026031993,0.7980409665], [0.6036036036,0.7972845726], [0.6046053195,0.7965252084], [0.6056056056,0.7957649467], [0.6066066875,0.7950020922], [0.6076076076,0.7942373670], [0.6086086086,0.7934705801], [0.6096099182,0.7927015502], [0.6106113274,0.7919304306], [0.6116116116,0.7911581615], [0.6126129903,0.7903830236], [0.6136141937,0.7896059911], [0.6146146146,0.7888275322], [0.6156156717,0.7880465372], [0.6166172857,0.7872630583], [0.6176189981,0.7864774461], [0.6186186186,0.7856914182], [0.6196196196,0.7849022404], [0.6206206206,0.7841109904], [0.6216216216,0.7833176620], [0.6226231309,0.7825218444], [0.6236236236,0.7817247444], [0.6246246246,0.7809251426], [0.6256256256,0.7801234367], [0.6266270828,0.7793192536], [0.6276276276,0.7785136871], [0.6286295540,0.7777048822], [0.6296302197,0.7768949649], [0.6306306306,0.7760831191], [0.6316316316,0.7752686515], [0.6326326326,0.7744520335], [0.6336340432,0.7736329229], [0.6346346346,0.7728123191], [0.6356356356,0.7719892089], [0.6366378099,0.7711629523], [0.6376376376,0.7703364480], [0.6386386386,0.7695067831], [0.6396396396,0.7686749192], [0.6406406406,0.7678408491], [0.6416427965,0.7670035995], [0.6426442698,0.7661646967], [0.6436463401,0.7653230618], [0.6446457845,0.7644814010], [0.6456456456,0.7636371522], [0.6466466466,0.7627896921], [0.6476487003,0.7619390796], [0.6486486486,0.7610879914], [0.6496496496,0.7602337356], [0.6506506507,0.7593771993], [0.6516516517,0.7585183748], [0.6526526527,0.7576572543], [0.6536536537,0.7567938300], [0.6546546547,0.7559280939], [0.6556556557,0.7550600381], [0.6566566567,0.7541896547], [0.6576576577,0.7533169355], [0.6586597139,0.7524409487], [0.6596596597,0.7515644573], [0.6606606607,0.7506846818], [0.6616616617,0.7498025377], [0.6626626627,0.7489180165], [0.6636639380,0.7480308666], [0.6646655423,0.7471410288], [0.6656656657,0.7462501066], [0.6666666667,0.7453559925], [0.6676676677,0.7444594586], [0.6686687686,0.7435604064], [0.6696700121,0.7426587877], [0.6706708568,0.7417550821], [0.6716716717,0.7408489492], [0.6726726727,0.7399401837], [0.6736747602,0.7390279544], [0.6746746747,0.7381152236], [0.6756756757,0.7371990106], [0.6766766767,0.7362802967], [0.6776776777,0.7353590723], [0.6786786787,0.7344353281], [0.6796815187,0.7335073504], [0.6806834933,0.7325776286], [0.6816825186,0.7316481011], [0.6826831850,0.7307144920], [0.6836836837,0.7297784737], [0.6846846847,0.7288394079], [0.6856869699,0.7278965443], [0.6866885605,0.7269517321], [0.6876876877,0.7260066420], [0.6886886887,0.7250571633], [0.6896899925,0.7241047674], [0.6906910369,0.7231499786], [0.6916916917,0.7221929130], [0.6926940408,0.7212315619], [0.6936946854,0.7202691743], [0.6946963858,0.7193030874], [0.6956971197,0.7183352405], [0.6966966967,0.7173658152], [0.6976988128,0.7163912106], [0.6986986987,0.7154160527], [0.6996997298,0.7144370428], [0.7007007007,0.7134553441], [0.7017037971,0.7124687931], [0.7027036467,0.7114826667], [0.7037039479,0.7104933172], [0.7047047047,0.7095007253], [0.7057057057,0.7085050860], [0.7067070125,0.7075063240], [0.7077077077,0.7065053435], [0.7087087087,0.7055012163], [0.7097098302,0.7044941142], [0.7107107107,0.7034843891], [0.7117122799,0.7024710888], [0.7127127127,0.7014560493], [0.7137147039,0.7004365220], [0.7147148498,0.6994159589], [0.7157157157,0.6983917341], [0.7167167167,0.6973644298], [0.7177177177,0.6963341710], [0.7187187187,0.6953009445], [0.7197197197,0.6942647370], [0.7207207207,0.6932255353], [0.7217217217,0.6921833257], [0.7227227227,0.6911380948], [0.7237243839,0.6900891364], [0.7247258515,0.6890373286], [0.7257257257,0.6879841357], [0.7267277147,0.6869256355], [0.7277277277,0.6858661344], [0.7287287287,0.6848024824], [0.7297297297,0.6837357103], [0.7307318389,0.6826646172], [0.7317335427,0.6815908028], [0.7327341160,0.6805150368], [0.7337337337,0.6794371258], [0.7347348303,0.6783544274], [0.7357370699,0.6772672766], [0.7367367367,0.6761796956], [0.7377382823,0.6750868291], [0.7387404285,0.6739900439], [0.7397397397,0.6728930951], [0.7407409969,0.6717907230], [0.7417423300,0.6706849602], [0.7427429352,0.6695766813], [0.7437437437,0.6684648410], [0.7447447447,0.6673494326], [0.7457457457,0.6662306528], [0.7467468652,0.6651083516], [0.7477496769,0.6639807382], [0.7487507698,0.6628516310], [0.7497509003,0.6617201731], [0.7507507508,0.6605855813], [0.7517517518,0.6594462099], [0.7527527528,0.6583033444], [0.7537537538,0.6571569666], [0.7547547548,0.6560070580], [0.7557557558,0.6548536002], [0.7567572714,0.6536959785], [0.7577608951,0.6525323179], [0.7587598282,0.6513704961], [0.7597610024,0.6502024448], [0.7607615002,0.6490315399], [0.7617617618,0.6478572515], [0.7627636728,0.6466773380], [0.7637637638,0.6454958661], [0.7647647648,0.6443095953], [0.7657667975,0.6431183498], [0.7667667668,0.6419257943], [0.7677679668,0.6407279837], [0.7687687688,0.6395268408], [0.7697707260,0.6383204755], [0.7707728607,0.6371100354], [0.7717732667,0.6358978101], [0.7727727728,0.6346827882], [0.7737737738,0.6334620328], [0.7747757435,0.6322361484], [0.7757757758,0.6310086733], [0.7767767768,0.6297760229], [0.7777777778,0.6285393611], [0.7787799647,0.6272971916], [0.7797808234,0.6260526075], [0.7807810914,0.6248046793], [0.7817817818,0.6235521195], [0.7827827828,0.6222950385], [0.7837855860,0.6210315251], [0.7847858628,0.6197670123], [0.7857860662,0.6184983898], [0.7867876755,0.6172237468], [0.7877909140,0.6159427537], [0.7887915250,0.6146608253], [0.7897914009,0.6133755318], [0.7907924484,0.6120843925], [0.7917917918,0.6107910923], [0.7927927928,0.6094912532], [0.7937961556,0.6081839059], [0.7947956983,0.6068770864], [0.7957972547,0.6055631507], [0.7968006001,0.6042423385], [0.7978006176,0.6029213668], [0.7988012156,0.6015950614], [0.7998009797,0.6002652688], [0.8008020100,0.5989291617], [0.8018018018,0.5975900523], [0.8028033501,0.5962438940], [0.8038051655,0.5948926423], [0.8048048048,0.5935395742], [0.8058066380,0.5921787417], [0.8068068068,0.5908153489], [0.8078078078,0.5894459650], [0.8088090292,0.5880713854], [0.8098098098,0.5866924850], [0.8108108108,0.5853083197], [0.8118118118,0.5839191572], [0.8128128128,0.5825249620], [0.8138151346,0.5811238480], [0.8148148148,0.5797213275], [0.8158158158,0.5783118144], [0.8168174358,0.5768962441], [0.8178205144,0.5754733757], [0.8188210157,0.5740489040], [0.8198215946,0.5726190296], [0.8208227641,0.5711829741], [0.8218230662,0.5697427910], [0.8228285404,0.5682897088], [0.8238349478,0.5668297617], [0.8248384140,0.5653685442], [0.8258532896,0.5638850451], [0.8268672457,0.5623971533], [0.8278771762,0.5609094233], [0.8288727624,0.5594371670], [0.8298619650,0.5579687438], [0.8308483376,0.5564989128], [0.8318485214,0.5550027364], [0.8328354465,0.5535206582], [0.8338338338,0.5520155229], [0.8348361362,0.5504985247], [0.8358364463,0.5489785379], [0.8368382625,0.5474502009], [0.8378390119,0.5459173839], [0.8388388388,0.5443798329], [0.8398398398,0.5428342688], [0.8408408408,0.5412824405], [0.8418423655,0.5397234770], [0.8428431932,0.5381592252], [0.8438438438,0.5365888251], [0.8448448448,0.5350113907], [0.8458473234,0.5334250702], [0.8468468468,0.5318368340], [0.8478478478,0.5302395939], [0.8488516396,0.5286311512], [0.8498513361,0.5270224916], [0.8508508509,0.5254072988], [0.8518541278,0.5237790994], [0.8528538201,0.5221497502], [0.8538543001,0.5205120884], [0.8548548549,0.5188672057], [0.8558575680,0.5172115846], [0.8568599769,0.5155492024], [0.8578590836,0.5138849995], [0.8588601162,0.5122102116], [0.8598598599,0.5105301376], [0.8608635220,0.5088359229], [0.8618636728,0.5071400295], [0.8628679780,0.5054293745], [0.8638649378,0.5037235047], [0.8648656649,0.5020033683], [0.8658683931,0.5002718519], [0.8668677905,0.4985380967], [0.8678678679,0.4967950925], [0.8688718730,0.4950370373], [0.8698736211,0.4932746529], [0.8708723750,0.4915092130], [0.8718718719,0.4897340493], [0.8728728729,0.4879476896], [0.8738752272,0.4861502723], [0.8748774666,0.4843443181], [0.8758783709,0.4825319465], [0.8768768769,0.4807150328], [0.8778800077,0.4788806658], [0.8788808536,0.4770413453], [0.8798798799,0.4751961668], [0.8808817021,0.4733364839], [0.8818818819,0.4714704088], [0.8828841587,0.4695908456], [0.8838838839,0.4677064034], [0.8848864682,0.4658067607], [0.8858858859,0.4639032196], [0.8868880801,0.4619843432], [0.8878890006,0.4600577383], [0.8888888889,0.4581228473], [0.8898916606,0.4561719328], [0.8908916277,0.4542159262], [0.8918927976,0.4522468768], [0.8928951469,0.4502646518], [0.8938938939,0.4482786036], [0.8948948949,0.4462769623], [0.8958977603,0.4442602876], [0.8968979645,0.4422375395], [0.8979002899,0.4401988976], [0.8988988989,0.4381561018], [0.8998998999,0.4360965147], [0.9009023300,0.4340218795], [0.9019052986,0.4319338287], [0.9029056424,0.4298388081], [0.9039065779,0.4277299363], [0.9049132506,0.4255960630], [0.9059130618,0.4234637228], [0.9069101087,0.4213241682], [0.9079098397,0.4191655079], [0.9089127246,0.4169864014], [0.9099115880,0.4148022446], [0.9109114570,0.4126018875], [0.9119136867,0.4103820513], [0.9129129129,0.4081543990], [0.9139139139,0.4059080659], [0.9149167765,0.4036425300], [0.9159174342,0.4013667322], [0.9169192282,0.3990728367], [0.9179194591,0.3967667660], [0.9189213809,0.3944407379], [0.9199263475,0.3920912078], [0.9209313246,0.3897248970], [0.9219314736,0.3873530148], [0.9229364461,0.3849523562], [0.9239322618,0.3825561078], [0.9249289645,0.3801399881], [0.9259311199,0.3776924162], [0.9269299287,0.3752344697], [0.9279344792,0.3727433464], [0.9289377295,0.3702359987], [0.9299354536,0.3677227926], [0.9309309309,0.3651952927], [0.9319337420,0.3626285985], [0.9329401493,0.3600314956], [0.9339416807,0.3574254287], [0.9349441928,0.3547948088], [0.9359447383,0.3521469108], [0.9369519997,0.3494580809], [0.9379468684,0.3467789961], [0.9389476600,0.3440600119], [0.9399442674,0.3413279571], [0.9409457092,0.3385574874], [0.9419483491,0.3357578111], [0.9429462076,0.3329451148], [0.9439470603,0.3300968758], [0.9449524570,0.3272076619], [0.9459521587,0.3243062033], [0.9469537918,0.3213697499], [0.9479517402,0.3184140360], [0.9489543944,0.3154133119], [0.9499565125,0.3123821768], [0.9509555279,0.3093276320], [0.9519640614,0.3062097742], [0.9529639469,0.3030836780], [0.9539654496,0.2999165233], [0.9549600502,0.2967343973], [0.9559616024,0.2934917627], [0.9569658084,0.2902006919], [0.9579643450,0.2868872838], [0.9589720906,0.2835004929], [0.9599723687,0.2800947184], [0.9609664523,0.2766649194], [0.9619764369,0.2731324492], [0.9629758058,0.2695878288], [0.9639714133,0.2660058539], [0.9649758435,0.2623387533], [0.9659811043,0.2586126567], [0.9669917798,0.2548075701], [0.9679901121,0.2509883323], [0.9689941110,0.2470838173], [0.9700076093,0.2430745522], [0.9710116043,0.2390323500], [0.9720159574,0.2349148325], [0.9730324483,0.2306682781], [0.9740358201,0.2263939513], [0.9750207543,0.2221137741], [0.9760222141,0.2176709386], [0.9770250551,0.2131244746], [0.9780318938,0.2084553064], [0.9790453947,0.2036421252], [0.9800580079,0.1987116031], [0.9810811984,0.1935966999], [0.9821315948,0.1881954583], [0.9831871921,0.1826005072], [0.9841812952,0.1771642691], [0.9852222690,0.1712807072], [0.9862351536,0.1653487882], [0.9872695178,0.1590562770], [0.9883054593,0.1524871113], [0.9893535395,0.1455320372], [0.9904196033,0.1380905840], [0.9914345875,0.1306042062], [0.9925216962,0.1220683524], [0.9936003062,0.1129532269], [0.9946000689,0.1037819972], [0.9956227614,0.0934629176], [0.9966372661,0.0819399766], [0.9976101518,0.0690940309], [0.9985118883,0.0545344751], [0.9993366830,0.0364169478], [1.0000000000,0.0000000000]]deap-1.0.1/examples/ga/pareto_front/dtlz4_front.json0000644000076500000240000007051012117373622022736 0ustar felixstaff00000000000000[[0.0000000000,1.0000000000], [0.0024376240,0.9999970290], [0.0036802178,0.9999932280], [0.0047339714,0.9999887947], [0.0057080250,0.9999837091], [0.0065972495,0.9999782379], [0.0074750663,0.9999720613], [0.0082930392,0.9999656122], [0.0092130046,0.9999575594], [0.0101694069,0.9999482902], [0.0110864071,0.9999385439], [0.0119747142,0.9999283005], [0.0128499955,0.9999174354], [0.0137610854,0.9999053118], [0.0145690681,0.9998938655], [0.0155405037,0.9998792391], [0.0164983942,0.9998638922], [0.0174200448,0.9998482595], [0.0183730015,0.9998312022], [0.0193122038,0.9998135020], [0.0202840371,0.9997942578], [0.0212768482,0.9997736222], [0.0222809149,0.9997517496], [0.0232513323,0.9997296512], [0.0242029392,0.9997070660], [0.0251831875,0.9996828532], [0.0261748439,0.9996573801], [0.0271739884,0.9996307190], [0.0281525004,0.9996036398], [0.0291354196,0.9995754736], [0.0301282126,0.9995460424], [0.0311013086,0.9995162373], [0.0321139185,0.9994842151], [0.0330989146,0.9994520808], [0.0340917610,0.9994187070], [0.0351106506,0.9993834310], [0.0361201345,0.9993474550], [0.0371240356,0.9993106654], [0.0381147359,0.9992733695], [0.0391119534,0.9992348348], [0.0401116218,0.9991952051], [0.0411149177,0.9991544243], [0.0420874618,0.9991139302], [0.0430838499,0.9990714598], [0.0440956172,0.9990273152], [0.0450929276,0.9989827966], [0.0460960293,0.9989370131], [0.0471014257,0.9988901119], [0.0480916857,0.9988429255], [0.0490864970,0.9987945313], [0.0500776278,0.9987453285], [0.0510828172,0.9986944206], [0.0520683706,0.9986435224], [0.0530737986,0.9985905927], [0.0540673112,0.9985372932], [0.0550745741,0.9984822439], [0.0560703438,0.9984268208], [0.0570812977,0.9983695335], [0.0580806256,0.9983118956], [0.0590791197,0.9982533033], [0.0600760736,0.9981938015], [0.0610794219,0.9981329091], [0.0620921520,0.9980704207], [0.0630949569,0.9980075282], [0.0640874302,0.9979442877], [0.0650982022,0.9978788624], [0.0661023217,0.9978128497], [0.0670930983,0.9977467194], [0.0680862574,0.9976794383], [0.0690824980,0.9976109505], [0.0700963842,0.9975402232], [0.0710925827,0.9974697212], [0.0720982253,0.9973975365], [0.0730953602,0.9973249562], [0.0740907900,0.9972515003], [0.0750785689,0.9971776213], [0.0760807102,0.9971016626], [0.0770868071,0.9970243849], [0.0780948102,0.9969459367], [0.0790960303,0.9968670012], [0.0800934694,0.9967873575], [0.0810943834,0.9967064267], [0.0820953907,0.9966244763], [0.0830961419,0.9965415351], [0.0840875863,0.9964583673], [0.0850850851,0.9963736891], [0.0860864750,0.9962876687], [0.0870964432,0.9961998843], [0.0880968714,0.9961119120], [0.0890971860,0.9960229372], [0.0900939480,0.9959332711], [0.0910945671,0.9958422465], [0.0920937126,0.9957503443], [0.0931010419,0.9956566657], [0.0941055363,0.9955622271], [0.0951045577,0.9954672888], [0.0961077250,0.9953709385], [0.0971077215,0.9952738771], [0.0981104624,0.9951755308], [0.0991152388,0.9950759616], [0.1001131241,0.9949760612], [0.1011094437,0.9948753090], [0.1021111442,0.9947729963], [0.1031117383,0.9946697791], [0.1041118913,0.9945655906], [0.1051191129,0.9944596382], [0.1061118212,0.9943542032], [0.1071091685,0.9942472660], [0.1081100177,0.9941389360], [0.1091122245,0.9940294374], [0.1101121508,0.9939191689], [0.1111126274,0.9938078205], [0.1121164374,0.9936950762], [0.1131187770,0.9935814724], [0.1141166455,0.9934673579], [0.1151151151,0.9933521582], [0.1161181012,0.9932354135], [0.1171252355,0.9931171528], [0.1181243744,0.9929988077], [0.1191263594,0.9928791017], [0.1201254734,0.9927587172], [0.1211220864,0.9926376178], [0.1221272126,0.9925144553], [0.1231235628,0.9923913484], [0.1241241241,0.9922666989], [0.1251280973,0.9921405945], [0.1261297051,0.9920137587], [0.1271271271,0.9918864318], [0.1281318463,0.9917571426], [0.1291357881,0.9916269199], [0.1301339324,0.9914964244], [0.1311348537,0.9913645395], [0.1321321321,0.9912321119], [0.1331335013,0.9910981136], [0.1341341341,0.9909631850], [0.1351373794,0.9908268712], [0.1361361361,0.9906901395], [0.1371401854,0.9905516491], [0.1381430255,0.9904122902], [0.1391426332,0.9902723502], [0.1401426651,0.9901313213], [0.1411464108,0.9899887326], [0.1421459659,0.9898457074], [0.1431458244,0.9897016080], [0.1441441441,0.9895567016], [0.1451451917,0.9894103665], [0.1461492859,0.9892625467], [0.1471496571,0.9891142393], [0.1481506966,0.9889647977], [0.1491491491,0.9888147103], [0.1501501502,0.9886632047], [0.1511511512,0.9885106623], [0.1521521522,0.9883570825], [0.1531541342,0.9882023129], [0.1541562342,0.9880464845], [0.1551551552,0.9878901142], [0.1561561562,0.9877323802], [0.1571617368,0.9875728776], [0.1581673166,0.9874123252], [0.1591644644,0.9872520819], [0.1601629302,0.9870905915], [0.1611634143,0.9869277349], [0.1621710749,0.9867626576], [0.1631766408,0.9865968700], [0.1641805745,0.9864303011], [0.1651864816,0.9862623517], [0.1661850043,0.9860945920], [0.1671808690,0.9859262432], [0.1681738832,0.9857573459], [0.1691691692,0.9855870292], [0.1701714936,0.9854144624], [0.1711732166,0.9852409502], [0.1721738929,0.9850665717], [0.1731731732,0.9848913910], [0.1741741742,0.9847148608], [0.1751764980,0.9845370458], [0.1761761762,0.9843586516], [0.1771771772,0.9841789715], [0.1781803976,0.9839978384], [0.1791847030,0.9838154513], [0.1801862087,0.9836325179], [0.1811844530,0.9834491314], [0.1821862264,0.9832640433], [0.1831883610,0.9830778323], [0.1841859485,0.9828914164], [0.1851886409,0.9827029904], [0.1861889928,0.9825139485], [0.1871875126,0.9823242006], [0.1881907775,0.9821324917], [0.1891891892,0.9819406554], [0.1901901902,0.9817472646], [0.1911921208,0.9815526338], [0.1921964651,0.9813564688], [0.1931985642,0.9811596785], [0.1941986011,0.9809622334], [0.1952024094,0.9807629782], [0.1961971621,0.9805644668], [0.1971989718,0.9803634864], [0.1982001985,0.9801615588], [0.1991991992,0.9799590191], [0.2002002002,0.9797550101], [0.2012025911,0.9795496503], [0.2022022022,0.9793437953], [0.2032033034,0.9791365673], [0.2042056070,0.9789280209], [0.2052052052,0.9787189708], [0.2062068572,0.9785084221], [0.2072096983,0.9782965506], [0.2082132451,0.9780834548], [0.2092124293,0.9778702160], [0.2102141420,0.9776553659], [0.2112112112,0.9774404454], [0.2122143762,0.9772231365], [0.2132145302,0.9770054064], [0.2142175608,0.9767859728], [0.2152166788,0.9765663219], [0.2162183061,0.9763450436], [0.2172172172,0.9761232917], [0.2182182182,0.9758999996], [0.2192192192,0.9756756295], [0.2202202202,0.9754501805], [0.2212212212,0.9752236519], [0.2222223137,0.9749960222], [0.2232237113,0.9747672413], [0.2242253312,0.9745373266], [0.2252252252,0.9743067268], [0.2262276870,0.9740744497], [0.2272300563,0.9738411069], [0.2282282282,0.9736076601], [0.2292300768,0.9733722679], [0.2302307812,0.9731360580], [0.2312322399,0.9728985822], [0.2322324473,0.9726603160], [0.2332332332,0.9724208240], [0.2342342342,0.9721801909], [0.2352352352,0.9719384672], [0.2362362362,0.9716956523], [0.2372386447,0.9714514015], [0.2382405006,0.9712061902], [0.2392412812,0.9709601482], [0.2402402402,0.9707134629], [0.2412412412,0.9704651789], [0.2422422422,0.9702157987], [0.2432432432,0.9699653213], [0.2442442442,0.9697137460], [0.2452459588,0.9694608913], [0.2462462462,0.9692072979], [0.2472472472,0.9689524234], [0.2482482482,0.9686964474], [0.2492492492,0.9684393692], [0.2502502503,0.9681811877], [0.2512526720,0.9679215334], [0.2522522523,0.9676615117], [0.2532532533,0.9674000154], [0.2542542543,0.9671374123], [0.2552574003,0.9668731352], [0.2562571120,0.9666086553], [0.2572572573,0.9663429534], [0.2582583893,0.9660758792], [0.2592592593,0.9658077637], [0.2602613898,0.9655381965], [0.2612612613,0.9652681251], [0.2622622623,0.9649966351], [0.2632651100,0.9647235261], [0.2642654661,0.9644499798], [0.2652680004,0.9641747186], [0.2662662663,0.9638995152], [0.2672677791,0.9636222986], [0.2682682683,0.9633442460], [0.2692696608,0.9630648212], [0.2702702703,0.9627844935], [0.2712712713,0.9625029337], [0.2722722723,0.9622202501], [0.2732732733,0.9619364418], [0.2742742743,0.9616515078], [0.2752752753,0.9613654471], [0.2762762763,0.9610782586], [0.2772788149,0.9607894976], [0.2782782783,0.9605004944], [0.2792792793,0.9602099167], [0.2802802803,0.9599182072], [0.2812812813,0.9596253648], [0.2822822823,0.9593313886], [0.2832832833,0.9590362774], [0.2842849564,0.9587398310], [0.2852852853,0.9584426462], [0.2862867741,0.9581439782], [0.2872872873,0.9578444626], [0.2882882883,0.9575436611], [0.2892892893,0.9572417182], [0.2902902903,0.9569386330], [0.2912917893,0.9566342527], [0.2922930540,0.9563287984], [0.2932932933,0.9560225123], [0.2942966170,0.9557141315], [0.2952971643,0.9554054557], [0.2962962963,0.9550960710], [0.2972976632,0.9547848446], [0.2982994581,0.9544723324], [0.2993013384,0.9541586392], [0.3003003003,0.9538447094], [0.3013013013,0.9535289853], [0.3023023023,0.9532121055], [0.3033033033,0.9528940687], [0.3043053572,0.9525745375], [0.3053053053,0.9522545198], [0.3063080000,0.9519324604], [0.3073089810,0.9516097888], [0.3083083083,0.9512864905], [0.3093096236,0.9509613855], [0.3103103103,0.9506353198], [0.3113113113,0.9503079856], [0.3123123123,0.9499794838], [0.3133133133,0.9496498132], [0.3143143969,0.9493189453], [0.3153153153,0.9489869609], [0.3163163163,0.9486537767], [0.3173182203,0.9483191167], [0.3183188165,0.9479837188], [0.3193193193,0.9476471771], [0.3203219689,0.9473087333], [0.3213213213,0.9469702258], [0.3223223223,0.9466299808], [0.3233233233,0.9462885546], [0.3243243243,0.9459459459], [0.3253264361,0.9456017713], [0.3263263263,0.9452571760], [0.3273278545,0.9449108295], [0.3283283283,0.9445636605], [0.3293293293,0.9442151200], [0.3303303303,0.9438653892], [0.3313313313,0.9435144667], [0.3323345267,0.9431615781], [0.3333363614,0.9428079710], [0.3343376385,0.9424533641], [0.3353359355,0.9420986203], [0.3363363363,0.9417419333], [0.3373373373,0.9413838329], [0.3383383383,0.9410245315], [0.3393393393,0.9406640276], [0.3403403403,0.9403023199], [0.3413413413,0.9399394069], [0.3423426276,0.9395751834], [0.3433447556,0.9392094435], [0.3443443443,0.9388434228], [0.3453453453,0.9384756749], [0.3463469230,0.9381065019], [0.3473473473,0.9377365410], [0.3483492393,0.9373648209], [0.3493493493,0.9369925465], [0.3503505674,0.9366186417], [0.3513513514,0.9362436798], [0.3523523524,0.9358674157], [0.3533533534,0.9354899292], [0.3543553204,0.9351108528], [0.3553562021,0.9347309611], [0.3563563564,0.9343501203], [0.3573595451,0.9339668921], [0.3583592842,0.9335837528], [0.3593610561,0.9331986023], [0.3603608116,0.9328129960], [0.3613613614,0.9324258504], [0.3623623624,0.9320372945], [0.3633633634,0.9316475010], [0.3643643644,0.9312564684], [0.3653653654,0.9308641951], [0.3663687537,0.9304697396], [0.3673682839,0.9300755582], [0.3683695078,0.9296794640], [0.3693701930,0.9292823363], [0.3703703704,0.9288841633], [0.3713713714,0.9284844126], [0.3723723724,0.9280834102], [0.3733742155,0.9276808153], [0.3743743744,0.9272776433], [0.3753753754,0.9268728756], [0.3763769050,0.9264666348], [0.3773787553,0.9260590019], [0.3783783784,0.9256510157], [0.3793793794,0.9252412045], [0.3803804304,0.9248301077], [0.3813817812,0.9244176204], [0.3823850997,0.9240030495], [0.3833864307,0.9235880276], [0.3843850740,0.9231728521], [0.3853853854,0.9227557124], [0.3863863864,0.9223370102], [0.3873888039,0.9219164358], [0.3883883884,0.9214957731], [0.3893901834,0.9210728989], [0.3903903904,0.9206494138], [0.3913913914,0.9202243089], [0.3923923924,0.9197979182], [0.3933933934,0.9193702399], [0.3943943944,0.9189412722], [0.3953972258,0.9185102252], [0.3963992697,0.9180782205], [0.3973994635,0.9176457194], [0.3984020005,0.9172109060], [0.3994007345,0.9167764467], [0.4004009036,0.9163400659], [0.4014014014,0.9159022409], [0.4024024024,0.9154628920], [0.4034034034,0.9150222370], [0.4044044044,0.9145802741], [0.4054054054,0.9141370014], [0.4064064064,0.9136924170], [0.4074074074,0.9132465190], [0.4084089819,0.9127990488], [0.4094094094,0.9123507744], [0.4104128790,0.9118998129], [0.4114124307,0.9114492920], [0.4124124124,0.9109972569], [0.4134134134,0.9105434364], [0.4144152103,0.9100879262], [0.4154170659,0.9096310578], [0.4164164164,0.9091740032], [0.4174174174,0.9087148616], [0.4184184184,0.9082543846], [0.4194194194,0.9077925703], [0.4204204204,0.9073294165], [0.4214221831,0.9068645674], [0.4224237180,0.9063984789], [0.4234234234,0.9059318984], [0.4244250516,0.9054630724], [0.4254254254,0.9049934847], [0.4264264264,0.9045222512], [0.4274287698,0.9040490290], [0.4284284284,0.9035757200], [0.4294303298,0.9030999900], [0.4304320727,0.9026229727], [0.4314314314,0.9021457310], [0.4324335813,0.9016657905], [0.4334334334,0.9011855851], [0.4344353513,0.9007030174], [0.4354354354,0.9002199629], [0.4364364364,0.8997350927], [0.4374385065,0.8992483267], [0.4384397109,0.8987606021], [0.4394394394,0.8982722188], [0.4404404404,0.8977818323], [0.4414416607,0.8972899532], [0.4424424424,0.8967969029], [0.4434434434,0.8963023555], [0.4444444444,0.8958064165], [0.4454454454,0.8953090836], [0.4464464464,0.8948103545], [0.4474474474,0.8943102268], [0.4484484484,0.8938086983], [0.4494497560,0.8933056122], [0.4504504505,0.8928014290], [0.4514514515,0.8922956836], [0.4524524525,0.8917885278], [0.4534535412,0.8912799145], [0.4544550743,0.8907696590], [0.4554554555,0.8902585737], [0.4564564565,0.8897457521], [0.4574574575,0.8892315079], [0.4584584585,0.8887158387], [0.4594608197,0.8881980383], [0.4604611149,0.8876798757], [0.4614624797,0.8871597262], [0.4624641660,0.8866379730], [0.4634634635,0.8861160297], [0.4644650912,0.8855914290], [0.4654654655,0.8850660430], [0.4664674311,0.8845383744], [0.4674674675,0.8840102753], [0.4684684685,0.8834802171], [0.4694704188,0.8829482011], [0.4704704705,0.8824157390], [0.4714719089,0.8818810799], [0.4724733641,0.8813449496], [0.4734749367,0.8808072913], [0.4744752965,0.8802688186], [0.4754756692,0.8797288718], [0.4764775552,0.8791866351], [0.4774786626,0.8786433445], [0.4784784785,0.8780992801], [0.4794794795,0.8775530917], [0.4804804805,0.8770054207], [0.4814822202,0.8764558584], [0.4824824825,0.8759056194], [0.4834834835,0.8753534836], [0.4844844845,0.8747998538], [0.4854854855,0.8742447274], [0.4864873961,0.8736875949], [0.4874886019,0.8731293507], [0.4884897154,0.8725696522], [0.4894894895,0.8720091970], [0.4904904905,0.8714465438], [0.4914914915,0.8708823766], [0.4924931862,0.8703162997], [0.4934938730,0.8697492727], [0.4944954278,0.8691802298], [0.4954954955,0.8686105076], [0.4964964965,0.8680387255], [0.4974975104,0.8674654040], [0.4984994477,0.8668900164], [0.4995003265,0.8663136983], [0.5005016274,0.8657355953], [0.5015035026,0.8651556142], [0.5025037718,0.8645750166], [0.5035035189,0.8639931750], [0.5045045045,0.8634090600], [0.5055067750,0.8628226356], [0.5065065065,0.8622361387], [0.5075075075,0.8616473349], [0.5085085085,0.8610569649], [0.5095099539,0.8604647621], [0.5105105105,0.8598715129], [0.5115122721,0.8592759717], [0.5125132777,0.8586792999], [0.5135135135,0.8580815063], [0.5145145145,0.8574816700], [0.5155155155,0.8568802444], [0.5165165165,0.8562772262], [0.5175177849,0.8556724503], [0.5185185185,0.8550663986], [0.5195195195,0.8544585823], [0.5205215857,0.8538485105], [0.5215215215,0.8532381277], [0.5225225225,0.8526254825], [0.5235235235,0.8520112208], [0.5245245245,0.8513953389], [0.5255255255,0.8507778335], [0.5265265265,0.8501587010], [0.5275277980,0.8495377698], [0.5285285285,0.8489155403], [0.5295295295,0.8482915049], [0.5305311399,0.8476654467], [0.5315319825,0.8470382232], [0.5325333534,0.8464090190], [0.5335347073,0.8457781719], [0.5345345345,0.8451466331], [0.5355355355,0.8445126939], [0.5365365365,0.8438770911], [0.5375375375,0.8432398210], [0.5385389105,0.8426006420], [0.5395411221,0.8419592494], [0.5405413821,0.8413174278], [0.5415420331,0.8406736741], [0.5425425425,0.8400283266], [0.5435435435,0.8393809721], [0.5445445356,0.8387319290], [0.5455455455,0.8380811761], [0.5465465465,0.8374287268], [0.5475475475,0.8367745713], [0.5485491382,0.8361183188], [0.5495495495,0.8354611257], [0.5505517055,0.8348010659], [0.5515520726,0.8341404625], [0.5525525526,0.8334780601], [0.5535535536,0.8328135826], [0.5545556718,0.8321466258], [0.5555555556,0.8314794193], [0.5565565566,0.8308097251], [0.5575575576,0.8301382837], [0.5585589897,0.8294648004], [0.5595600109,0.8287898372], [0.5605607480,0.8281133061], [0.5615615616,0.8274349597], [0.5625625626,0.8267547177], [0.5635635636,0.8260727025], [0.5645648651,0.8253887042], [0.5655655656,0.8247033352], [0.5665665666,0.8240159741], [0.5675675676,0.8233268223], [0.5685685686,0.8226358750], [0.5695695696,0.8219431279], [0.5705705706,0.8212485763], [0.5715715716,0.8205522156], [0.5725737337,0.8198532305], [0.5735735736,0.8191540488], [0.5745745746,0.8184522333], [0.5755755756,0.8177485902], [0.5765765766,0.8170431147], [0.5775775776,0.8163358022], [0.5785794568,0.8156260247], [0.5795795796,0.8149156465], [0.5805814353,0.8142021843], [0.5815815816,0.8134880847], [0.5825825826,0.8127715143], [0.5835848928,0.8120521368], [0.5845855244,0.8113320927], [0.5855855856,0.8106105859], [0.5865865866,0.8098865207], [0.5875875876,0.8091605693], [0.5885893149,0.8084321977], [0.5895896677,0.8077029304], [0.5905905906,0.8069713466], [0.5915915916,0.8062377991], [0.5925936476,0.8055015635], [0.5935947019,0.8047641455], [0.5945968537,0.8040239932], [0.5955962815,0.8032839283], [0.5965965966,0.8025412768], [0.5975982942,0.8017956590], [0.5985985986,0.8010491357], [0.5995995996,0.8003001438], [0.6006006006,0.7995491971], [0.6016018645,0.7987960920], [0.6026026026,0.7980414171], [0.6036036036,0.7972845726], [0.6046046046,0.7965257511], [0.6056056056,0.7957649467], [0.6066066066,0.7950021540], [0.6076076076,0.7942373670], [0.6086086086,0.7934705801], [0.6096096096,0.7927017875], [0.6106106106,0.7919309832], [0.6116116116,0.7911581615], [0.6126126126,0.7903833164], [0.6136136136,0.7896064420], [0.6146152013,0.7888270751], [0.6156156156,0.7880465810], [0.6166175888,0.7872628209], [0.6176176176,0.7864785302], [0.6186192566,0.7856909159], [0.6196196196,0.7849022404], [0.6206206206,0.7841109904], [0.6216216216,0.7833176620], [0.6226226226,0.7825222488], [0.6236236240,0.7817247442], [0.6246246246,0.7809251426], [0.6256256256,0.7801234367], [0.6266266266,0.7793196204], [0.6276280342,0.7785133593], [0.6286286286,0.7777056302], [0.6296296296,0.7768954431], [0.6306312736,0.7760825966], [0.6316316316,0.7752686515], [0.6326326326,0.7744520335], [0.6336348665,0.7736322486], [0.6346346346,0.7728123191], [0.6356361249,0.7719888061], [0.6366375449,0.7711631711], [0.6376376376,0.7703364480], [0.6386386386,0.7695067831], [0.6396396396,0.7686749192], [0.6406408378,0.7678406846], [0.6416425329,0.7670038201], [0.6426426426,0.7661660615], [0.6436436436,0.7653253295], [0.6446446446,0.7644823622], [0.6456458656,0.7636369663], [0.6466479317,0.7627886027], [0.6476482022,0.7619395029], [0.6486486486,0.7610879914], [0.6496496496,0.7602337356], [0.6506506507,0.7593771993], [0.6516527191,0.7585174578], [0.6526530179,0.7576569397], [0.6536536537,0.7567938300], [0.6546546547,0.7559280939], [0.6556568938,0.7550589630], [0.6566575390,0.7541888865], [0.6576576577,0.7533169355], [0.6586586587,0.7524418724], [0.6596596597,0.7515644573], [0.6606606607,0.7506846818], [0.6616629033,0.7498014420], [0.6626635918,0.7489171944], [0.6636667064,0.7480284104], [0.6646659255,0.7471406879], [0.6656668809,0.7462490225], [0.6666666667,0.7453559925], [0.6676676677,0.7444594586], [0.6686686687,0.7435604962], [0.6696704909,0.7426583560], [0.6706728119,0.7417533143], [0.6716716717,0.7408489492], [0.6726741610,0.7399388307], [0.6736754328,0.7390273413], [0.6746746747,0.7381152236], [0.6756757933,0.7371989028], [0.6766781542,0.7362789387], [0.6776776777,0.7353590723], [0.6786786787,0.7344353281], [0.6796796797,0.7335090545], [0.6806814878,0.7325794920], [0.6816816817,0.7316488809], [0.6826847773,0.7307130044], [0.6836872970,0.7297750886], [0.6846856371,0.7288385133], [0.6856856857,0.7278977541], [0.6866866867,0.7269535022], [0.6876876877,0.7260066420], [0.6886886887,0.7250571633], [0.6896906033,0.7241041857], [0.6906906907,0.7231503093], [0.6916927176,0.7221919305], [0.6926926927,0.7212328566], [0.6936936937,0.7202701294], [0.6946960628,0.7193033994], [0.6956978996,0.7183344851], [0.6966972731,0.7173652554], [0.6976976977,0.7163922966], [0.6986998634,0.7154149152], [0.6996996997,0.7144370723], [0.7007007007,0.7134553441], [0.7017017017,0.7124708568], [0.7027027027,0.7114835990], [0.7037040193,0.7104932464], [0.7047047047,0.7095007253], [0.7057057057,0.7085050860], [0.7067076778,0.7075056594], [0.7077090516,0.7065039974], [0.7087087087,0.7055012163], [0.7097097097,0.7044942356], [0.7107116143,0.7034834762], [0.7117117117,0.7024716645], [0.7127127127,0.7014560493], [0.7137148537,0.7004363694], [0.7147147147,0.6994160969], [0.7157157157,0.6983917341], [0.7167167167,0.6973644298], [0.7177177177,0.6963341710], [0.7187187187,0.6953009445], [0.7197201077,0.6942643348], [0.7207218083,0.6932244045], [0.7217238051,0.6921811534], [0.7227227227,0.6911380948], [0.7237237237,0.6900898287], [0.7247247247,0.6890385137], [0.7257261578,0.6879836800], [0.7267267267,0.6869266807], [0.7277277277,0.6858661344], [0.7287291544,0.6848020295], [0.7297310644,0.6837342859], [0.7307318073,0.6826646511], [0.7317330380,0.6815913447], [0.7327327327,0.6805165262], [0.7337343109,0.6794365026], [0.7347351592,0.6783540711], [0.7357357357,0.6772687260], [0.7367381340,0.6761781732], [0.7377377377,0.6750874242], [0.7387387387,0.6739918960], [0.7397406562,0.6728920876], [0.7407407407,0.6717910055], [0.7417425852,0.6706846781], [0.7427440214,0.6695754765], [0.7437437437,0.6684648410], [0.7447447447,0.6673494326], [0.7457463363,0.6662299917], [0.7467467467,0.6651084846], [0.7477477477,0.6639829107], [0.7487502106,0.6628522626], [0.7497497497,0.6617214767], [0.7507507508,0.6605855813], [0.7517525774,0.6594452687], [0.7527527528,0.6583033444], [0.7537537538,0.6571569666], [0.7547547548,0.6560070580], [0.7557557558,0.6548536002], [0.7567572359,0.6536960195], [0.7577577578,0.6525359611], [0.7587587588,0.6513717418], [0.7597597598,0.6502038968], [0.7607617770,0.6490312155], [0.7617629059,0.6478559062], [0.7627635738,0.6466774548], [0.7637637638,0.6454958661], [0.7647658552,0.6443083010], [0.7657657658,0.6431195783], [0.7667667668,0.6419257943], [0.7677677678,0.6407282222], [0.7687687688,0.6395268408], [0.7697703971,0.6383208721], [0.7707707708,0.6371125638], [0.7717717718,0.6358996244], [0.7727727728,0.6346827882], [0.7737737738,0.6334620328], [0.7747747748,0.6322373355], [0.7757757758,0.6310086733], [0.7767767768,0.6297760229], [0.7777783334,0.6285386735], [0.7787787788,0.6272986639], [0.7797797798,0.6260539075], [0.7807820152,0.6248035249], [0.7817817818,0.6235521195], [0.7827827828,0.6222950385], [0.7837837838,0.6210337996], [0.7847863227,0.6197664300], [0.7857867449,0.6184975275], [0.7867867868,0.6172248797], [0.7877884300,0.6159459307], [0.7887887888,0.6146643366], [0.7897897898,0.6133776063], [0.7907926824,0.6120840902], [0.7917931700,0.6107893057], [0.7927931709,0.6094907614], [0.7937937938,0.6081869885], [0.7947952646,0.6068776543], [0.7957974082,0.6055629489], [0.7967972425,0.6042467661], [0.7977997788,0.6029224767], [0.7987992098,0.6015977248], [0.7997997998,0.6002668409], [0.8008025015,0.5989285046], [0.8018018018,0.5975900523], [0.8028029418,0.5962444436], [0.8038038038,0.5948944822], [0.8048048048,0.5935395742], [0.8058058058,0.5921798741], [0.8068077651,0.5908140403], [0.8078095627,0.5894435600], [0.8088105678,0.5880692693], [0.8098138065,0.5866869683], [0.8108114218,0.5853074733], [0.8118136242,0.5839166374], [0.8128128128,0.5825249620], [0.8138138138,0.5811256976], [0.8148148148,0.5797213275], [0.8158169878,0.5783101611], [0.8168184406,0.5768948215], [0.8178186541,0.5754760195], [0.8188188188,0.5740520377], [0.8198198198,0.5726215705], [0.8208208208,0.5711857667], [0.8218218218,0.5697445859], [0.8228238876,0.5682964455], [0.8238243384,0.5668451813], [0.8248248248,0.5653883695], [0.8258258258,0.5639252658], [0.8268275482,0.5624555142], [0.8278292609,0.5609801376], [0.8288298715,0.5595007096], [0.8298298298,0.5580165352], [0.8308308308,0.5565250493], [0.8318371804,0.5550197342], [0.8328370086,0.5535183078], [0.8338364467,0.5520115762], [0.8348366113,0.5504978043], [0.8358384509,0.5489754857], [0.8368390892,0.5474489372], [0.8378378378,0.5459191859], [0.8388388388,0.5443798329], [0.8398414856,0.5428317226], [0.8408438187,0.5412778146], [0.8418439693,0.5397209755], [0.8428428428,0.5381597739], [0.8438438438,0.5365888251], [0.8448452503,0.5350107504], [0.8458458458,0.5334274131], [0.8468468468,0.5318368340], [0.8478487676,0.5302381231], [0.8488498054,0.5286340964], [0.8498498498,0.5270248881], [0.8508508509,0.5254072988], [0.8518541147,0.5237791207], [0.8528550532,0.5221477360], [0.8538538539,0.5205128205], [0.8548555562,0.5188660502], [0.8558578594,0.5172111024], [0.8568570450,0.5155540751], [0.8578597590,0.5138838720], [0.8588588589,0.5122123198], [0.8598598599,0.5105301376], [0.8608643134,0.5088345840], [0.8618632894,0.5071406810], [0.8628648541,0.5054347076], [0.8638671547,0.5037197029], [0.8648698076,0.5019962309], [0.8658694806,0.5002699697], [0.8668740170,0.4985272697], [0.8678697721,0.4967917659], [0.8688688689,0.4950423100], [0.8698722052,0.4932771499], [0.8708724574,0.4915090670], [0.8718718719,0.4897340493], [0.8728733747,0.4879467919], [0.8738759654,0.4861489453], [0.8748748749,0.4843489995], [0.8758758759,0.4825364754], [0.8768797248,0.4807098379], [0.8778795992,0.4788814147], [0.8788788789,0.4770449835], [0.8798798799,0.4751961668], [0.8808831295,0.4733338274], [0.8818854618,0.4714637127], [0.8828865393,0.4695863699], [0.8838866019,0.4677012668], [0.8848848849,0.4658097686], [0.8858858859,0.4639032196], [0.8868868869,0.4619866339], [0.8878878879,0.4600598858], [0.8888910506,0.4581186529], [0.8898922851,0.4561707147], [0.8908921884,0.4542148265], [0.8918918919,0.4522486630], [0.8928928929,0.4502691215], [0.8938952094,0.4482759803], [0.8948967283,0.4462732860], [0.8958976713,0.4442604670], [0.8968968969,0.4422397046], [0.8979013757,0.4401966827], [0.8989033242,0.4381470229], [0.8999065407,0.4360828109], [0.9009078226,0.4340104782], [0.9019070573,0.4319301564], [0.9029043232,0.4298415792], [0.9039052369,0.4277327703], [0.9049049049,0.4256138074], [0.9059059059,0.4234790309], [0.9069073164,0.4213301786], [0.9079106989,0.4191636468], [0.9089089089,0.4169947186], [0.9099123421,0.4148005903], [0.9109118139,0.4126010994], [0.9119119119,0.4103859950], [0.9129153075,0.4081490430], [0.9139171372,0.4059008085], [0.9149181522,0.4036394119], [0.9159183186,0.4013647140], [0.9169169169,0.3990781471], [0.9179199782,0.3967655650], [0.9189243362,0.3944338530], [0.9199199199,0.3921062878], [0.9209237083,0.3897428942], [0.9219256800,0.3873668036], [0.9229272591,0.3849743816], [0.9239276539,0.3825672364], [0.9249261890,0.3801467414], [0.9259296651,0.3776959826], [0.9269332918,0.3752261618], [0.9279340293,0.3727444664], [0.9289356506,0.3702412148], [0.9299412641,0.3677080981], [0.9309390654,0.3651745561], [0.9319389049,0.3626153299], [0.9329391161,0.3600341729], [0.9339474741,0.3574102904], [0.9349400788,0.3548056497], [0.9359359359,0.3521703051], [0.9369418354,0.3494853316], [0.9379434473,0.3467882491], [0.9389480540,0.3440589367], [0.9399461273,0.3413228350], [0.9409458343,0.3385571399], [0.9419450762,0.3357669929], [0.9429524712,0.3329273751], [0.9439566796,0.3300693671], [0.9449532058,0.3272054994], [0.9459634153,0.3242733677], [0.9469570607,0.3213601176], [0.9479562419,0.3184006336], [0.9489550553,0.3154113235], [0.9499623323,0.3123644780], [0.9509578453,0.3093205078], [0.9519580020,0.3062286115], [0.9529605489,0.3030943619], [0.9539580838,0.2999399514], [0.9549602688,0.2967336937], [0.9559631744,0.2934866424], [0.9569620350,0.2902131348], [0.9579638589,0.2868889072], [0.9589662177,0.2835203579], [0.9599641443,0.2801229046], [0.9609627600,0.2766777437], [0.9619627470,0.2731806607], [0.9629684589,0.2696140707], [0.9639745872,0.2659943518], [0.9649749823,0.2623419211], [0.9659803434,0.2586154985], [0.9669725468,0.2548805479], [0.9679833242,0.2510145097], [0.9690027864,0.2470497925], [0.9699937542,0.2431298355], [0.9709965510,0.2390934921], [0.9719965453,0.2349951404], [0.9730039315,0.2307885378], [0.9740234618,0.2264471149], [0.9750130104,0.2221477650], [0.9760117713,0.2177177584], [0.9770276696,0.2131124886], [0.9780334118,0.2084481841], [0.9790432917,0.2036522355], [0.9800456180,0.1987727009], [0.9810352692,0.1938293078], [0.9820827955,0.1884499477], [0.9830927312,0.1831083882], [0.9840943996,0.1776463136], [0.9851091672,0.1719300108], [0.9861321242,0.1659621451], [0.9871738308,0.1596490769], [0.9882408359,0.1529053638], [0.9892959260,0.1459231674], [0.9903307490,0.1387263765], [0.9913727364,0.1310728709], [0.9924590538,0.1225766151], [0.9935239974,0.1136224742], [0.9945787371,0.1039862289], [0.9956080275,0.0936197396], [0.9966472117,0.0818189182], [0.9976157780,0.0690127481], [0.9985299609,0.0542025562], [0.9993397922,0.0363315259], [1.0000000000,0.0000000000]]deap-1.0.1/examples/ga/pareto_front/zdt1_front.json0000644000076500000240000007051012117373622022557 0ustar felixstaff00000000000000[[0.0000000001,0.9999894843], [0.0002103152,0.9854977511], [0.0006297138,0.9749059012], [0.0011612996,0.9659221532], [0.0017723350,0.9579008909], [0.0024447099,0.9505559926], [0.0031687919,0.9437079768], [0.0039380100,0.9372464341], [0.0047489797,0.9310871584], [0.0055918795,0.9252211292], [0.0064691896,0.9195687275], [0.0073740671,0.9141276114], [0.0082978126,0.9089076698], [0.0092456824,0.9038455287], [0.0102191774,0.8989100531], [0.0112003220,0.8941684261], [0.0122039498,0.8895285116], [0.0132172119,0.8850338664], [0.0142420309,0.8806600198], [0.0152859804,0.8763635151], [0.0163211988,0.8722455529], [0.0173715220,0.8681989301], [0.0184382204,0.8642125910], [0.0194878329,0.8604011717], [0.0205429616,0.8566718395], [0.0216119982,0.8529898023], [0.0226874279,0.8493765361], [0.0237651448,0.8458405214], [0.0248553652,0.8423441558], [0.0259467988,0.8389198994], [0.0270374622,0.8355692784], [0.0281393522,0.8322521170], [0.0292192881,0.8290634968], [0.0302957541,0.8259432445], [0.0313819561,0.8228504697], [0.0324638454,0.8198227390], [0.0335461426,0.8168439391], [0.0346247771,0.8139226584], [0.0357119454,0.8110239556], [0.0367916279,0.8081885616], [0.0378793499,0.8053738202], [0.0389433189,0.8026593834], [0.0399997201,0.8000006998], [0.0410631898,0.7973594567], [0.0421245632,0.7947573067], [0.0431927077,0.7921714464], [0.0442485743,0.7896465491], [0.0453108161,0.7871366259], [0.0463678279,0.7846680983], [0.0474309334,0.7822135602], [0.0484718366,0.7798367956], [0.0495183888,0.7774727236], [0.0505548969,0.7751558387], [0.0515956857,0.7728531626], [0.0526417769,0.7705620413], [0.0536804267,0.7683096317], [0.0547163017,0.7660848407], [0.0557571264,0.7638705304], [0.0567949321,0.7616831268], [0.0578289419,0.7595235108], [0.0588553007,0.7573988856], [0.0598860753,0.7552836840], [0.0609083639,0.7532038009], [0.0619349783,0.7511326089], [0.0629606370,0.7490804173], [0.0639905072,0.7470365498], [0.0650075274,0.7450342624], [0.0660281646,0.7430405391], [0.0670527767,0.7410544909], [0.0680657495,0.7391058653], [0.0690825195,0.7371644630], [0.0700995404,0.7352368221], [0.0711165289,0.7333231751], [0.0721371795,0.7314163454], [0.0731538057,0.7295303978], [0.0741739887,0.7276509800], [0.0751937914,0.7257851364], [0.0762103252,0.7239378237], [0.0772231944,0.7221093842], [0.0782394068,0.7202869206], [0.0792528891,0.7184811034], [0.0802645371,0.7166900335], [0.0812793931,0.7149045895], [0.0822923094,0.7131336384], [0.0833020611,0.7113790356], [0.0843148914,0.7096297340], [0.0853240314,0.7078972246], [0.0863361730,0.7061698228], [0.0873494798,0.7044505459], [0.0883580677,0.7027491503], [0.0893676528,0.7010557698], [0.0903801054,0.6993671585], [0.0913873872,0.6976965312], [0.0923974601,0.6960304948], [0.0934058712,0.6943762587], [0.0944170189,0.6927264754], [0.0954268425,0.6910876460], [0.0964393518,0.6894531407], [0.0974469856,0.6878350026], [0.0984532027,0.6862274667], [0.0994620038,0.6846240278], [0.1004686769,0.6830320570], [0.1014778843,0.6814440642], [0.1024834037,0.6798697083], [0.1034889593,0.6783030008], [0.1044969695,0.6767400899], [0.1055029432,0.6751878340], [0.1065113264,0.6736392695], [0.1075171046,0.6721019905], [0.1085246735,0.6705691674], [0.1095345919,0.6690398938], [0.1105412889,0.6675224987], [0.1115502884,0.6660085504], [0.1125586220,0.6645024262], [0.1135692240,0.6629996676], [0.1145703720,0.6615175455], [0.1155736397,0.6600387673], [0.1165790942,0.6585631915], [0.1175826917,0.6570966730], [0.1185884398,0.6556332772], [0.1195925525,0.6541784384], [0.1205987817,0.6527266470], [0.1216048952,0.6512810657], [0.1226117877,0.6498403398], [0.1236138187,0.6484124309], [0.1246178887,0.6469874100], [0.1256223393,0.6455675814], [0.1266288059,0.6441505854], [0.1276331204,0.6427422214], [0.1286375304,0.6413392544], [0.1296439087,0.6399390208], [0.1306492659,0.6385456241], [0.1316565646,0.6371549028], [0.1326627997,0.6357709516], [0.1336661888,0.6343961312], [0.1346714681,0.6330238861], [0.1356757096,0.6316581621], [0.1366794375,0.6302981775], [0.1376834503,0.6289427937], [0.1386893002,0.6275898763], [0.1396927198,0.6262451073], [0.1406979479,0.6249027488], [0.1417017801,0.6235670311], [0.1427051064,0.6222367059], [0.1437102024,0.6209087150], [0.1447137172,0.6195874381], [0.1457167842,0.6182713212], [0.1467215834,0.6169574653], [0.1477250332,0.6156498560], [0.1487301929,0.6143444634], [0.1497321205,0.6130476509], [0.1507357298,0.6117530041], [0.1517373003,0.6104652772], [0.1527405289,0.6091796718], [0.1537442813,0.6078976138], [0.1547476447,0.6066202285], [0.1557526398,0.6053449104], [0.1567551035,0.6040768970], [0.1577568569,0.6028138259], [0.1587602057,0.6015527567], [0.1597621685,0.6002973999], [0.1607657072,0.5990440084], [0.1617683085,0.5977956881], [0.1627724681,0.5965492990], [0.1637763198,0.5953071291], [0.1647798412,0.5940691670], [0.1657848951,0.5928330870], [0.1667889969,0.5916019137], [0.1677946146,0.5903725906], [0.1687971345,0.5891507156], [0.1698011474,0.5879306521], [0.1708037274,0.5867159240], [0.1718067926,0.5855041706], [0.1728113261,0.5842941832], [0.1738141690,0.5830897351], [0.1748184625,0.5818870218], [0.1758228774,0.5806876136], [0.1768242806,0.5794952073], [0.1778252033,0.5783067427], [0.1788275384,0.5771199480], [0.1798308764,0.5759352922], [0.1808348055,0.5747532416], [0.1818367667,0.5735767752], [0.1828401120,0.5724019271], [0.1838421355,0.5712318395], [0.1848455281,0.5700633441], [0.1858496519,0.5688971679], [0.1868523609,0.5677357742], [0.1878564187,0.5665759366], [0.1888596307,0.5654201677], [0.1898641784,0.5642659315], [0.1908653578,0.5631185998], [0.1918678536,0.5619727707], [0.1928709340,0.5608292655], [0.1938736345,0.5596891615], [0.1948776348,0.5585505297], [0.1958799520,0.5574167287], [0.1968825267,0.5562855348], [0.1978863809,0.5551557791], [0.1988885986,0.5540307201], [0.1998908284,0.5529084787], [0.2008943177,0.5517876422], [0.2018966761,0.5506708599], [0.2029002818,0.5495554620], [0.2039023420,0.5484445305], [0.2049056364,0.5473349622], [0.2059090098,0.5462280200], [0.2069098050,0.5451266055], [0.2079116420,0.5440267091], [0.2089146888,0.5429281361], [0.2099165426,0.5418334991], [0.2109195946,0.5407401666], [0.2119213054,0.5396508875], [0.2129237064,0.5385634318], [0.2139272900,0.5374772546], [0.2149292816,0.5363953391], [0.2159324438,0.5353146830], [0.2169335265,0.5342387666], [0.2179354426,0.5331644373], [0.2189385128,0.5320913414], [0.2199411096,0.5310212056], [0.2209443987,0.5299527698], [0.2219479223,0.5288865080], [0.2229497945,0.5278244029], [0.2239527947,0.5267634896], [0.2249543898,0.5257064308], [0.2259571022,0.5246505473], [0.2269598923,0.5235969225], [0.2279637924,0.5225444603], [0.2289652239,0.5214968925], [0.2299677528,0.5204504689], [0.2309696262,0.5194070056], [0.2319710025,0.5183663192], [0.2329726679,0.5173275771], [0.2339754122,0.5162899502], [0.2349757427,0.5152570344], [0.2359771401,0.5142252167], [0.2369784365,0.5131956897], [0.2379807928,0.5121672492], [0.2389819466,0.5111422021], [0.2399841512,0.5101182274], [0.2409854406,0.5090973206], [0.2419877723,0.5080774732], [0.2429890820,0.5070607725], [0.2439914253,0.5060451181], [0.2449932324,0.5050320895], [0.2459960658,0.5040200954], [0.2469978573,0.5030112101], [0.2480006667,0.5020033467], [0.2490023296,0.5009986678], [0.2500037119,0.4999962881], [0.2510060990,0.4989949112], [0.2520081964,0.4979958203], [0.2530090762,0.4969999242], [0.2540106605,0.4960052971], [0.2550132341,0.4950116496], [0.2560153554,0.4940204002], [0.2570169550,0.4930316035], [0.2580190482,0.4920442458], [0.2590211470,0.4910587981], [0.2600222448,0.4900762363], [0.2610243081,0.4890946192], [0.2620263758,0.4881148803], [0.2630278416,0.4871375997], [0.2640302625,0.4861612486], [0.2650327522,0.4851866822], [0.2660342736,0.4842148959], [0.2670367394,0.4832440234], [0.2680384765,0.4822756752], [0.2690402992,0.4813090524], [0.2700420822,0.4803442657], [0.2710447960,0.4793803730], [0.2720467529,0.4784189872], [0.2730496341,0.4774584857], [0.2740510135,0.4765011810], [0.2750524520,0.4755455673], [0.2760548037,0.4745908226], [0.2770557516,0.4736391432], [0.2780568760,0.4726890140], [0.2790589031,0.4717397393], [0.2800605035,0.4707925704], [0.2810630011,0.4698462477], [0.2820640714,0.4689029548], [0.2830660316,0.4679604981], [0.2840671392,0.4670205077], [0.2850691304,0.4660813447], [0.2860712583,0.4651437032], [0.2870727459,0.4642082999], [0.2880751084,0.4632737118], [0.2890765211,0.4623416316], [0.2900778233,0.4614112670], [0.2910799911,0.4604817046], [0.2920820420,0.4595538491], [0.2930830765,0.4586285227], [0.2940849672,0.4577039856], [0.2950867089,0.4567811593], [0.2960893022,0.4558591155], [0.2970902136,0.4549401743], [0.2980913730,0.4540225527], [0.2990933745,0.4531057008], [0.3000947068,0.4521909942], [0.3010968758,0.4512770500], [0.3020989432,0.4503647181], [0.3031018428,0.4494531420], [0.3041017718,0.4485457663], [0.3051025241,0.4476391359], [0.3061030785,0.4467341702], [0.3071044517,0.4458299433], [0.3081053668,0.4449276022], [0.3091070962,0.4440259933], [0.3101083583,0.4431262636], [0.3111099191,0.4422277176], [0.3121122872,0.4413298941], [0.3131149498,0.4404332481], [0.3141158017,0.4395396520], [0.3151174522,0.4386467670], [0.3161181726,0.4377561271], [0.3171196864,0.4368661914], [0.3181217641,0.4359771599], [0.3191235216,0.4350898110], [0.3201260665,0.4342031579], [0.3211272782,0.4333190685], [0.3221292714,0.4324356676], [0.3231305050,0.4315543078], [0.3241321845,0.4306739207], [0.3251346391,0.4297942134], [0.3261355068,0.4289172505], [0.3271364512,0.4280415651], [0.3281381625,0.4271665490], [0.3291396056,0.4262931013], [0.3301418116,0.4254203175], [0.3311425891,0.4245500986], [0.3321436033,0.4236809883], [0.3331453728,0.4228125324], [0.3341459404,0.4219464208], [0.3351472582,0.4210809571], [0.3361489823,0.4202164350], [0.3371507677,0.4193531471], [0.3381524780,0.4184912056], [0.3391549311,0.4176299019], [0.3401567307,0.4167704305], [0.3411582977,0.4159124229], [0.3421606009,0.4150550446], [0.3431622914,0.4141994441], [0.3441647139,0.4133444674], [0.3451667125,0.4124910958], [0.3461677626,0.4116397680], [0.3471695375,0.4107890552], [0.3481717360,0.4099392099], [0.3491724318,0.4090918584], [0.3501738456,0.4082451135], [0.3511740724,0.4074005802], [0.3521750124,0.4065566477], [0.3531759396,0.4057139245], [0.3541775770,0.4048717979], [0.3551786772,0.4040313119], [0.3561801620,0.4031916874], [0.3571823517,0.4023526527], [0.3581834097,0.4015157398], [0.3591851682,0.4006794112], [0.3601857453,0.3998452322], [0.3611863629,0.3990121774], [0.3621875029,0.3981798417], [0.3631893357,0.3973480808], [0.3641910201,0.3965175892], [0.3651921942,0.3956886612], [0.3661940554,0.3948603009], [0.3671954009,0.3940334985], [0.3681962477,0.3932082337], [0.3691974477,0.3923837990], [0.3701989322,0.3915602476], [0.3712010950,0.3907372529], [0.3722026586,0.3899158594], [0.3732040997,0.3890956706], [0.3742062136,0.3882760316], [0.3752075512,0.3874581229], [0.3762095577,0.3866407597], [0.3772115816,0.3858244701], [0.3782126106,0.3850100728], [0.3792143028,0.3841962140], [0.3802158394,0.3833835557], [0.3812180365,0.3825714321], [0.3822199836,0.3817605775], [0.3832214717,0.3809511557], [0.3842236149,0.3801422623], [0.3852249508,0.3793350737], [0.3862261312,0.3785290585], [0.3872279612,0.3777235653], [0.3882290167,0.3769197349], [0.3892307183,0.3761164224], [0.3902315329,0.3753148530], [0.3912329899,0.3745137972], [0.3922341711,0.3737139862], [0.3932356676,0.3729149439], [0.3942368997,0.3721171290], [0.3952387682,0.3713198204], [0.3962404890,0.3705236390], [0.3972416358,0.3697289188], [0.3982426535,0.3689353017], [0.3992443010,0.3681421829], [0.4002458160,0.3673501632], [0.4012476451,0.3665588859], [0.4022491451,0.3657688551], [0.4032503499,0.3649800398], [0.4042521769,0.3641917137], [0.4052537047,0.3634045989], [0.4062556224,0.3626181502], [0.4072569386,0.3618331421], [0.4082588710,0.3610486161], [0.4092606576,0.3602651662], [0.4102617331,0.3594832296], [0.4112624413,0.3587025329], [0.4122637590,0.3579223108], [0.4132653697,0.3571428077], [0.4142662300,0.3563648316], [0.4152676955,0.3555873252], [0.4162692852,0.3548106594], [0.4172703805,0.3540353102], [0.4182720770,0.3532604257], [0.4192733529,0.3524867933], [0.4202752273,0.3517136225], [0.4212756052,0.3509425255], [0.4222765645,0.3501718962], [0.4232781177,0.3494017233], [0.4242787754,0.3486331484], [0.4252800238,0.3478650264], [0.4262810561,0.3470979736], [0.4272826768,0.3463313708], [0.4282838288,0.3455660241], [0.4292855666,0.3448011244], [0.4302867936,0.3440375060], [0.4312875044,0.3432751684], [0.4322886274,0.3425134013], [0.4332899081,0.3417523960], [0.4342917680,0.3409918301], [0.4352931925,0.3402324708], [0.4362941875,0.3394743097], [0.4372952371,0.3387169766], [0.4382968603,0.3379600765], [0.4392987002,0.3372038774], [0.4403003992,0.3364486461], [0.4413025499,0.3356939336], [0.4423036524,0.3349408655], [0.4433045196,0.3341888259], [0.4443059523,0.3334372105], [0.4453071467,0.3326866203], [0.4463088270,0.3319365097], [0.4473098391,0.3311877400], [0.4483114119,0.3304393889], [0.4493127632,0.3296920385], [0.4503146730,0.3289451044], [0.4513157024,0.3281996558], [0.4523164657,0.3274552315], [0.4533177831,0.3267112187], [0.4543188312,0.3259682269], [0.4553201613,0.3252258443], [0.4563210560,0.3244845997], [0.4573225003,0.3237437614], [0.4583238268,0.3230038207], [0.4593257008,0.3222642840], [0.4603267214,0.3215261823], [0.4613282868,0.3207884816], [0.4623293171,0.3200519747], [0.4633308899,0.3193158663], [0.4643320495,0.3185808562], [0.4653337494,0.3178462421], [0.4663344235,0.3171131693], [0.4673354687,0.3163806112], [0.4683365246,0.3156488294], [0.4693381161,0.3149174385], [0.4703397160,0.3141868214], [0.4713408502,0.3134573209], [0.4723419756,0.3127286012], [0.4733436321,0.3120002674], [0.4743446608,0.3112731596], [0.4753462181,0.3105464351], [0.4763472245,0.3098208751], [0.4773487573,0.3090956960], [0.4783501167,0.3083714026], [0.4793520007,0.3076474881], [0.4803525678,0.3069252798], [0.4813533187,0.3062036908], [0.4823545902,0.3054824766], [0.4833559019,0.3047619819], [0.4843571489,0.3040422794], [0.4853589138,0.3033229487], [0.4863606119,0.3026044079], [0.4873615883,0.3018871235], [0.4883630792,0.3011702073], [0.4893643031,0.3004542166], [0.4903654765,0.2997389940], [0.4913671616,0.2990241363], [0.4923677196,0.2983108098], [0.4933687866,0.2975978456], [0.4943702513,0.2968853214], [0.4953720027,0.2961733149], [0.4963729128,0.2954626250], [0.4973743280,0.2947522931], [0.4983753948,0.2940429228], [0.4993769647,0.2933339086], [0.5003777125,0.2926261861], [0.5013789612,0.2919188173], [0.5023800189,0.2912122893], [0.5033815757,0.2905061130], [0.5043826400,0.2898009856], [0.5053842016,0.2890962079], [0.5063854717,0.2883923330], [0.5073872373,0.2876888058], [0.5083889007,0.2869860445], [0.5093897165,0.2862845689], [0.5103909713,0.2855834749], [0.5113927177,0.2848827245], [0.5123935702,0.2841832845], [0.5133949120,0.2834841858], [0.5143963044,0.2827857332], [0.5153968896,0.2820885224], [0.5163976842,0.2813918424], [0.5173989641,0.2806954997], [0.5184004520,0.2799996861], [0.5194012903,0.2793049949], [0.5204018643,0.2786111560], [0.5214029196,0.2779176504], [0.5224037735,0.2772249496], [0.5234051073,0.2765325803], [0.5244057131,0.2758413757], [0.5254067967,0.2751505007], [0.5264080749,0.2744601493], [0.5274095904,0.2737702909], [0.5284107867,0.2730813066], [0.5294124578,0.2723926486], [0.5304134599,0.2717051010], [0.5314145860,0.2710181168], [0.5324161841,0.2703314560], [0.5334174758,0.2696456505], [0.5344186562,0.2689605645], [0.5354198349,0.2682761211], [0.5364214821,0.2675919975], [0.5374231840,0.2669084750], [0.5384241045,0.2662261217], [0.5394254907,0.2655440853], [0.5404269200,0.2648626523], [0.5414278987,0.2641821566], [0.5424285603,0.2635025049], [0.5434296837,0.2628231666], [0.5444310258,0.2621443056], [0.5454323428,0.2614660856], [0.5464335108,0.2607885886], [0.5474343417,0.2601119398], [0.5484352321,0.2594358690], [0.5494365797,0.2587601065], [0.5504375180,0.2580852354], [0.5514389117,0.2574106709], [0.5524404694,0.2567366083], [0.5534411993,0.2560637129], [0.5544423819,0.2553911215], [0.5554438198,0.2547189659], [0.5564448574,0.2540476843], [0.5574461165,0.2533768578], [0.5584478256,0.2527063324], [0.5594491886,0.2520366395], [0.5604500000,0.2513679142], [0.5614512125,0.2506995179], [0.5624528717,0.2500314195], [0.5634549518,0.2493636355], [0.5644561107,0.2486970579], [0.5654577141,0.2480307759], [0.5664582806,0.2473657724], [0.5674592894,0.2467010624], [0.5684602005,0.2460370032], [0.5694615527,0.2453732362], [0.5704629576,0.2447100175], [0.5714641608,0.2440475142], [0.5724654756,0.2433855172], [0.5734672286,0.2427238096], [0.5744680511,0.2420632935], [0.5754693100,0.2414030649], [0.5764697757,0.2407439327], [0.5774704561,0.2400852311], [0.5784715703,0.2394268146], [0.5794722894,0.2387692273], [0.5804734408,0.2381119237], [0.5814749025,0.2374549833], [0.5824759068,0.2367989081], [0.5834765886,0.2361436073], [0.5844776999,0.2354885875], [0.5854784090,0.2348343911], [0.5864795462,0.2341804742], [0.5874804650,0.2335272575], [0.5884814714,0.2328745400], [0.5894829039,0.2322220999], [0.5904841227,0.2315703528], [0.5914852917,0.2309191904], [0.5924868846,0.2302683035], [0.5934884263,0.2296179998], [0.5944895742,0.2289684998], [0.5954904730,0.2283197080], [0.5964917927,0.2276711888], [0.5974928613,0.2270233760], [0.5984940903,0.2263760020], [0.5994956347,0.2257289656], [0.6004967023,0.2250827771], [0.6014981875,0.2244368578], [0.6024993232,0.2237917012], [0.6035002904,0.2231471887], [0.6045016730,0.2225029434], [0.6055025383,0.2218595639], [0.6065038176,0.2212164501], [0.6075050713,0.2205738834], [0.6085062356,0.2199319032], [0.6095078120,0.2192901871], [0.6105086888,0.2186494456], [0.6115099762,0.2180089667], [0.6125114577,0.2173688879], [0.6135126807,0.2167294971], [0.6145143125,0.2160903671], [0.6155151153,0.2154522862], [0.6165163252,0.2148144645], [0.6175175368,0.2141771594], [0.6185185992,0.2135404657], [0.6195195973,0.2129043277], [0.6205210002,0.2122684466], [0.6215226154,0.2116329437], [0.6225233172,0.2109985316], [0.6235244214,0.2103643743], [0.6245257479,0.2097305852], [0.6255267345,0.2090975190], [0.6265281218,0.2084647059], [0.6275295273,0.2078323869], [0.6285308933,0.2072005971], [0.6295318748,0.2065695526], [0.6305332546,0.2059387589], [0.6315339696,0.2053088842], [0.6325350814,0.2046792588], [0.6335364007,0.2040500011], [0.6345377382,0.2034212291], [0.6355385583,0.2027932776], [0.6365397727,0.2021655732], [0.6375407380,0.2015385182], [0.6385419343,0.2009118107], [0.6395432199,0.2002855385], [0.6405448978,0.1996595113], [0.6415457759,0.1990344727], [0.6425470447,0.1984096778], [0.6435481693,0.1977854593], [0.6445496837,0.1971614835], [0.6455511954,0.1965379938], [0.6465521894,0.1959153096], [0.6475530688,0.1952931784], [0.6485543353,0.1946712874], [0.6495554858,0.1940499483], [0.6505563874,0.1934292422], [0.6515576742,0.1928087747], [0.6525587103,0.1921889390], [0.6535596358,0.1915696469], [0.6545609448,0.1909505918], [0.6555621420,0.1903320792], [0.6565628695,0.1897143285], [0.6575639075,0.1890968569], [0.6585649820,0.1884798326], [0.6595660804,0.1878632625], [0.6605675589,0.1872469263], [0.6615690601,0.1866310431], [0.6625702503,0.1860158169], [0.6635718189,0.1854008231], [0.6645734446,0.1847862583], [0.6655743074,0.1841726240], [0.6665755394,0.1835592248], [0.6675771477,0.1829460558], [0.6685778451,0.1823339037], [0.6695786084,0.1817221692], [0.6705795914,0.1811107575], [0.6715809484,0.1804995739], [0.6725815516,0.1798893053], [0.6735824170,0.1792793307], [0.6745836545,0.1786695826], [0.6755849960,0.1780602236], [0.6765863940,0.1774512817], [0.6775870496,0.1768432412], [0.6785880749,0.1762354251], [0.6795890291,0.1756281002], [0.6805903522,0.1750209989], [0.6815913005,0.1744145710], [0.6825923463,0.1738085293], [0.6835936242,0.1732027914], [0.6845952689,0.1725972752], [0.6855961635,0.1719926549], [0.6865974237,0.1713882552], [0.6875983946,0.1707844703], [0.6885995083,0.1701810389], [0.6896009860,0.1695778266], [0.6906018299,0.1689754336], [0.6916030367,0.1683732588], [0.6926040693,0.1677716243], [0.6936051583,0.1671703906], [0.6946066087,0.1665693738], [0.6956081144,0.1659687570], [0.6966087417,0.1653690985], [0.6976097286,0.1647696554], [0.6986107311,0.1641706328], [0.6996120925,0.1635718247], [0.7006128849,0.1629737848], [0.7016140349,0.1623759585], [0.7026149662,0.1617786890], [0.7036162542,0.1611816322], [0.7046173934,0.1605850886], [0.7056187329,0.1599888495], [0.7066196879,0.1593932620], [0.7076209976,0.1587978854], [0.7086222330,0.1582029740], [0.7096231569,0.1576086676], [0.7106242107,0.1570147032], [0.7116250435,0.1564212879], [0.7126259416,0.1558282511], [0.7136271913,0.1552354226], [0.7146280968,0.1546432133], [0.7156293530,0.1540512113], [0.7166303756,0.1534597614], [0.7176317479,0.1528685179], [0.7186332155,0.1522776306], [0.7196340181,0.1516875469], [0.7206351688,0.1510976683], [0.7216358525,0.1505084741], [0.7226367795,0.1499195453], [0.7236380533,0.1493308203], [0.7246394866,0.1487424088], [0.7256403528,0.1481547366], [0.7266412523,0.1475674500], [0.7276424968,0.1469803655], [0.7286437738,0.1463936658], [0.7296449298,0.1458074398], [0.7306464295,0.1452214149], [0.7316471101,0.1446362703], [0.7326481332,0.1440513256], [0.7336491728,0.1434667708], [0.7346501133,0.1428826724], [0.7356513949,0.1422987730], [0.7366525763,0.1417153291], [0.7376535200,0.1411324200], [0.7386546873,0.1405497761], [0.7396557111,0.1399676105], [0.7406570737,0.1393856417], [0.7416580993,0.1388042619], [0.7426591227,0.1382232756], [0.7436604837,0.1376424850], [0.7446618415,0.1370620871], [0.7456626999,0.1364823685], [0.7466638944,0.1359028444], [0.7476649673,0.1353237789], [0.7486659356,0.1347451615], [0.7496671382,0.1341667954], [0.7506682343,0.1335888769], [0.7516696643,0.1330111510], [0.7526706712,0.1324340537], [0.7536717145,0.1318573191], [0.7546730904,0.1312807759], [0.7556745018,0.1307045946], [0.7566754738,0.1301290476], [0.7576762216,0.1295540100], [0.7586773001,0.1289791621], [0.7596783202,0.1284047269], [0.7606796703,0.1278304808], [0.7616811144,0.1272565587], [0.7626823795,0.1266831162], [0.7636839735,0.1261098619], [0.7646847839,0.1255374314], [0.7656859219,0.1249651882], [0.7666872205,0.1243932273], [0.7676885121,0.1238216437], [0.7686896065,0.1232505452], [0.7696910270,0.1226796326], [0.7706919092,0.1221093979], [0.7716931166,0.1215393483], [0.7726940288,0.1209698362], [0.7736952654,0.1204005085], [0.7746962081,0.1198317160], [0.7756974744,0.1192631072], [0.7766986881,0.1186948950], [0.7776994921,0.1181272813], [0.7787005425,0.1175598930], [0.7797015898,0.1169928710], [0.7807029587,0.1164260310], [0.7817041419,0.1158596594], [0.7827056459,0.1152934691], [0.7837066697,0.1147279120], [0.7847075981,0.1141627700], [0.7857088459,0.1135978081], [0.7867097986,0.1130333723], [0.7877110700,0.1124691160], [0.7887118670,0.1119054853], [0.7897129817,0.1113420333], [0.7907138703,0.1107790655], [0.7917150758,0.1102162758], [0.7927158703,0.1096540727], [0.7937169808,0.1090920469], [0.7947177613,0.1085305607], [0.7957188569,0.1079692511], [0.7967200529,0.1074082384], [0.7977208339,0.1068478103], [0.7987219291,0.1062875580], [0.7997228517,0.1057277530], [0.8007240878,0.1051681232], [0.8017250764,0.1046089813], [0.8027261482,0.1040501419], [0.8037271570,0.1034916860], [0.8047284777,0.1029334040], [0.8057294740,0.1023756498], [0.8067307814,0.1018180689], [0.8077319714,0.1012608992], [0.8087331365,0.1007040885], [0.8097339845,0.1001477985], [0.8107351420,0.0995916804], [0.8117364375,0.0990358290], [0.8127375240,0.0984804361], [0.8137385638,0.0979254112], [0.8147399117,0.0973705568], [0.8157408920,0.0968162468], [0.8167421797,0.0962621068], [0.8177431682,0.0957084717], [0.8187444633,0.0951550059], [0.8197455532,0.0946019919], [0.8207469489,0.0940491466], [0.8217481147,0.0934967652], [0.8227495857,0.0929445520], [0.8237510513,0.0923926778], [0.8247522421,0.0918412903], [0.8257537368,0.0912900700], [0.8267544619,0.0907396072], [0.8277552475,0.0901894442], [0.8287562618,0.0896394880], [0.8297572276,0.0890898905], [0.8307584954,0.0885404587], [0.8317597138,0.0879913850], [0.8327606867,0.0874427762], [0.8337619605,0.0868943323], [0.8347627055,0.0863465069], [0.8357636616,0.0857988943], [0.8367649176,0.0852514457], [0.8377660281,0.0847044040], [0.8387667224,0.0841579162], [0.8397676397,0.0836116327], [0.8407688554,0.0830655119], [0.8417696709,0.0825199343], [0.8427707840,0.0819745189], [0.8437714494,0.0814296709], [0.8447724117,0.0808849845], [0.8457735560,0.0803405217], [0.8467747674,0.0797963446], [0.8477754203,0.0792527924], [0.8487763687,0.0787094005], [0.8497772153,0.0781663842], [0.8507783567,0.0776235277], [0.8517792627,0.0770811180], [0.8527804629,0.0765388677], [0.8537814690,0.0759970406], [0.8547825058,0.0754555144], [0.8557838358,0.0749141468], [0.8567851958,0.0743730796], [0.8577865119,0.0738323522], [0.8587879522,0.0732918732], [0.8597887845,0.0727520372], [0.8607898296,0.0722124006], [0.8617911659,0.0716729209], [0.8627921776,0.0711339291], [0.8637930359,0.0705953325], [0.8647941843,0.0700568919], [0.8657951783,0.0695188458], [0.8667961226,0.0689811373], [0.8677973561,0.0684435841], [0.8687986808,0.0679062918], [0.8697993534,0.0673696588], [0.8708001677,0.0668332584], [0.8718011741,0.0662970633], [0.8728024680,0.0657610220], [0.8738039536,0.0652251856], [0.8748050259,0.0646898771], [0.8758060549,0.0641548980], [0.8768073370,0.0636200894], [0.8778089051,0.0630854334], [0.8788095456,0.0625515771], [0.8798103062,0.0620179606], [0.8808113516,0.0614844958], [0.8818125165,0.0609512704], [0.8828136959,0.0604183400], [0.8838144803,0.0598859217], [0.8848153253,0.0593537725], [0.8858164535,0.0588217738], [0.8868176417,0.0582900438], [0.8878189258,0.0577585629], [0.8888198590,0.0572275678], [0.8898210742,0.0566967221], [0.8908220833,0.0561662841], [0.8918233738,0.0556359951], [0.8928243253,0.0551061831], [0.8938254322,0.0545765858], [0.8948268196,0.0540471367], [0.8958277659,0.0535182168], [0.8968289919,0.0529894447], [0.8978298680,0.0524611522], [0.8988308562,0.0519330951], [0.8998318860,0.0514053099], [0.9008331944,0.0508776715], [0.9018345438,0.0503503047], [0.9028354051,0.0498234874], [0.9038365440,0.0492968161], [0.9048375013,0.0487705317], [0.9058384620,0.0482445367], [0.9068395794,0.0477187499], [0.9078404128,0.0471934022], [0.9088415223,0.0466681993], [0.9098422743,0.0461434729], [0.9108432594,0.0456189129], [0.9118445198,0.0450944969], [0.9128455886,0.0445704691], [0.9138465602,0.0440467793], [0.9148478062,0.0435232328], [0.9158489542,0.0430000239], [0.9168497246,0.0424772982], [0.9178507100,0.0419547453], [0.9188519685,0.0414323349], [0.9198527005,0.0409104836], [0.9208537049,0.0403887741], [0.9218546764,0.0398673652], [0.9228559198,0.0393460978], [0.9238569337,0.0388252325], [0.9248581429,0.0383045477], [0.9258591557,0.0377842468], [0.9268604392,0.0372640865], [0.9278612824,0.0367444356], [0.9288623956,0.0362249248], [0.9298637098,0.0357055897], [0.9308645785,0.0351867650], [0.9318657163,0.0346680797], [0.9328668805,0.0341496594], [0.9338681670,0.0336314538], [0.9348693061,0.0331136023], [0.9358707132,0.0325958894], [0.9368716642,0.0320786891], [0.9378725827,0.0315617817], [0.9388735835,0.0310451076], [0.9398748514,0.0305285711], [0.9408762012,0.0300122675], [0.9418775077,0.0294962609], [0.9428785621,0.0289806582], [0.9438795318,0.0284653728], [0.9448807671,0.0279502240], [0.9458817890,0.0274354577], [0.9468830759,0.0269208275], [0.9478839859,0.0264066630], [0.9488851602,0.0258926342], [0.9498859806,0.0253790580], [0.9508870648,0.0248656171], [0.9518880089,0.0243525181], [0.9528892163,0.0238395540], [0.9538902006,0.0233269736], [0.9548914475,0.0228145276], [0.9558923190,0.0223025422], [0.9568933803,0.0217907278], [0.9578947035,0.0212790472], [0.9588958951,0.0207677012], [0.9598973482,0.0202564886], [0.9608982736,0.0197458117], [0.9618994598,0.0192352679], [0.9629006974,0.0187249634], [0.9639021955,0.0182147916], [0.9649037413,0.0177048604], [0.9659055471,0.0171950615], [0.9669069401,0.0166857369], [0.9679082355,0.0161767255], [0.9689097900,0.0156678457], [0.9699112462,0.0151592787], [0.9709126936,0.0146509788], [0.9719142405,0.0141428904], [0.9729159660,0.0136349733], [0.9739176212,0.0131273531], [0.9749193101,0.0126199769], [0.9759212564,0.0121127309], [0.9769232360,0.0116057285], [0.9779252391,0.0110989741], [0.9789272710,0.0105924647], [0.9799293395,0.0100861959], [0.9809313029,0.0095802390], [0.9819335223,0.0090744113], [0.9829358524,0.0085687858], [0.9839384382,0.0080632892], [0.9849411070,0.0075580082], [0.9859440311,0.0070528558], [0.9869468965,0.0065479898], [0.9879500168,0.0060432520], [0.9889530664,0.0055388060], [0.9899563704,0.0050344878], [0.9909598639,0.0045303300], [0.9919635394,0.0040263360], [0.9929674602,0.0035224738], [0.9939714188,0.0030188473], [0.9949756310,0.0025153480], [0.9959800294,0.0020120094], [0.9969846811,0.0015087977], [0.9979895491,0.0010057312], [0.9989946569,0.0005027980], [1.0000000000,0.0000000000]]deap-1.0.1/examples/ga/pareto_front/zdt2_front.json0000644000076500000240000007051012117373622022560 0ustar felixstaff00000000000000[[0.0000000000,1.0000000000], [0.0037548734,0.9999859009], [0.0056323101,0.9999682771], [0.0072002711,0.9999481561], [0.0085975093,0.9999260828], [0.0098827783,0.9999023307], [0.0111021022,0.9998767433], [0.0122644749,0.9998495827], [0.0133717654,0.9998211959], [0.0144556768,0.9997910334], [0.0154989513,0.9997597825], [0.0165198262,0.9997270953], [0.0175091577,0.9996934294], [0.0184792182,0.9996585185], [0.0194383563,0.9996221503], [0.0203994415,0.9995838628], [0.0213502373,0.9995441674], [0.0222798620,0.9995036077], [0.0232135253,0.9994611322], [0.0241284123,0.9994178197], [0.0250545142,0.9993722713], [0.0259635001,0.9993258967], [0.0268835323,0.9992772757], [0.0277878213,0.9992278370], [0.0286988944,0.9991763735], [0.0295955060,0.9991241060], [0.0305002006,0.9990697378], [0.0313914778,0.9990145751], [0.0322999824,0.9989567111], [0.0331957101,0.9988980448], [0.0341024348,0.9988370239], [0.0350154214,0.9987739203], [0.0359165055,0.9987100046], [0.0368284765,0.9986436633], [0.0377291560,0.9985765108], [0.0386233194,0.9985082392], [0.0395347862,0.9984370007], [0.0404357461,0.9983649504], [0.0413838286,0.9982873787], [0.0423210511,0.9982089286], [0.0432598654,0.9981285840], [0.0441884928,0.9980473771], [0.0451274643,0.9979635120], [0.0460566673,0.9978787834], [0.0469936799,0.9977915940], [0.0479285927,0.9977028500], [0.0488543871,0.9976132489], [0.0498041848,0.9975195432], [0.0507558612,0.9974238426], [0.0516986155,0.9973272532], [0.0526435088,0.9972286610], [0.0536006512,0.9971269702], [0.0545492479,0.9970243796], [0.0554916186,0.9969206803], [0.0564497778,0.9968134226], [0.0573998053,0.9967052624], [0.0583678442,0.9965931948], [0.0593370928,0.9964791094], [0.0602984253,0.9963640999], [0.0612621068,0.9962469543], [0.0622182087,0.9961288945], [0.0631922258,0.9960067426], [0.0641587364,0.9958836565], [0.0651270265,0.9957584704], [0.0660978578,0.9956310732], [0.0670615596,0.9955027472], [0.0680279356,0.9953722000], [0.0690027495,0.9952386206], [0.0699706776,0.9951041043], [0.0709476107,0.9949664365], [0.0719178176,0.9948278275], [0.0728887192,0.9946872346], [0.0738666736,0.9945437145], [0.0748381543,0.9943992507], [0.0758104622,0.9942527738], [0.0767933220,0.9941027857], [0.0777698921,0.9939518439], [0.0787528783,0.9937979842], [0.0797353113,0.9936422801], [0.0807116919,0.9934856228], [0.0816950256,0.9933259228], [0.0826724413,0.9931652675], [0.0836520716,0.9930023309], [0.0846324853,0.9928373424], [0.0856122078,0.9926705499], [0.0865915946,0.9925018957], [0.0875654427,0.9923322932], [0.0885486165,0.9921591425], [0.0895263321,0.9919850359], [0.0905159446,0.9918068638], [0.0915001473,0.9916277230], [0.0924829805,0.9914468983], [0.0934713885,0.9912630995], [0.0944545706,0.9910783341], [0.0954460041,0.9908900603], [0.0964322884,0.9907008137], [0.0974231726,0.9905087254], [0.0984090176,0.9903156653], [0.0993933392,0.9901209641], [0.1003891393,0.9899220207], [0.1013800005,0.9897220955], [0.1023716462,0.9895200461], [0.1033584889,0.9893170228], [0.1043457257,0.9891119695], [0.1053366579,0.9889041885], [0.1063229292,0.9886954347], [0.1073171467,0.9884830300], [0.1083067588,0.9882696460], [0.1092942220,0.9880547730], [0.1102871736,0.9878367393], [0.1112792090,0.9876169376], [0.1122702940,0.9873953811], [0.1132570045,0.9871728509], [0.1142428580,0.9869485694], [0.1152287028,0.9867223461], [0.1162242907,0.9864919142], [0.1172176143,0.9862600309], [0.1182067291,0.9860271692], [0.1192005741,0.9857912231], [0.1201937044,0.9855534734], [0.1211827316,0.9853147456], [0.1221738421,0.9850735523], [0.1231674304,0.9848297841], [0.1241570110,0.9845850366], [0.1251513739,0.9843371336], [0.1261417866,0.9840882497], [0.1271305733,0.9838378173], [0.1281188446,0.9835855617], [0.1291120346,0.9833300825], [0.1301014046,0.9830736245], [0.1310981670,0.9828132706], [0.1320911402,0.9825519307], [0.1330828331,0.9822889595], [0.1340757168,0.9820237022], [0.1350667537,0.9817569720], [0.1360640059,0.9814865863], [0.1370576036,0.9812152133], [0.1380482074,0.9809426924], [0.1390442299,0.9806667021], [0.1400366851,0.9803897268], [0.1410285212,0.9801109562], [0.1420297497,0.9798275502], [0.1430279818,0.9795429964], [0.1440227304,0.9792574531], [0.1450201414,0.9789691586], [0.1460141225,0.9786798760], [0.1470106774,0.9783878607], [0.1480038546,0.9780948590], [0.1490008035,0.9777987606], [0.1499987966,0.9775003610], [0.1509934697,0.9772009721], [0.1519892171,0.9768992779], [0.1529845440,0.9765957293], [0.1539824730,0.9762893980], [0.1549812527,0.9759808113], [0.1559768141,0.9756712335], [0.1569732565,0.9753593967], [0.1579682628,0.9750460279], [0.1589635779,0.9747305809], [0.1599602369,0.9744127226], [0.1609537910,0.9740938772], [0.1619508157,0.9737719333], [0.1629447714,0.9734490015], [0.1639420183,0.9731230146], [0.1649362321,0.9727960394], [0.1659285601,0.9724677129], [0.1669263423,0.9721355962], [0.1679242718,0.9718014389], [0.1689192361,0.9714662917], [0.1699143812,0.9711291031], [0.1709136645,0.9707885193], [0.1719100267,0.9704469427], [0.1729091065,0.9701024409], [0.1739075198,0.9697561746], [0.1749039959,0.9694085922], [0.1758976333,0.9690600226], [0.1768930813,0.9687088378], [0.1778885982,0.9683556466], [0.1788857928,0.9679998731], [0.1798809668,0.9676428378], [0.1808733880,0.9672848175], [0.1818737507,0.9669219388], [0.1828713622,0.9665580649], [0.1838675279,0.9661927322], [0.1848647298,0.9658250317], [0.1858592422,0.9654563421], [0.1868578763,0.9650841341], [0.1878538419,0.9647109341], [0.1888480200,0.9643364254], [0.1898479302,0.9639577634], [0.1908452072,0.9635781069], [0.1918426183,0.9631964098], [0.1928374365,0.9628137231], [0.1938386413,0.9624265811], [0.1948372605,0.9620384419], [0.1958345643,0.9616488234], [0.1968348666,0.9612560353], [0.1978338730,0.9608617587], [0.1988303570,0.9604664891], [0.1998255835,0.9600697362], [0.2008227242,0.9596702334], [0.2018185255,0.9592692828], [0.2028169428,0.9588652877], [0.2038160948,0.9584589995], [0.2048127978,0.9580517178], [0.2058088595,0.9576427134], [0.2068084964,0.9572302458], [0.2078057174,0.9568167838], [0.2088052907,0.9564003506], [0.2098024715,0.9559829230], [0.2107996014,0.9555635281], [0.2118015707,0.9551400946], [0.2128016905,0.9547154405], [0.2138009672,0.9542891464], [0.2147979086,0.9538618584], [0.2157940366,0.9534329338], [0.2167962249,0.9529993969], [0.2177980650,0.9525640029], [0.2187976010,0.9521276098], [0.2197968130,0.9516893610], [0.2207968137,0.9512487671], [0.2217945498,0.9508071777], [0.2227934740,0.9503630679], [0.2237901588,0.9499179648], [0.2247867449,0.9494709193], [0.2257873735,0.9490200620], [0.2267874112,0.9485674701], [0.2277870983,0.9481130378], [0.2287845918,0.9476576106], [0.2297817570,0.9472003441], [0.2307815350,0.9467398831], [0.2317791474,0.9462784268], [0.2327802895,0.9458133368], [0.2337802672,0.9453467867], [0.2347788662,0.9448788840], [0.2357753414,0.9444099884], [0.2367704676,0.9439397457], [0.2377710737,0.9434649165], [0.2387695744,0.9429890903], [0.2397695434,0.9425105660], [0.2407685174,0.9420305210], [0.2417654190,0.9415494822], [0.2427650570,0.9410651271], [0.2437638578,0.9405791816], [0.2447606123,0.9400922427], [0.2457599204,0.9396020615], [0.2467571968,0.9391108858], [0.2477530477,0.9386184274], [0.2487530373,0.9381219264], [0.2497510170,0.9376244295], [0.2507525225,0.9371231724], [0.2517543782,0.9366197331], [0.2527542404,0.9361152940], [0.2537544657,0.9356086711], [0.2547540027,0.9351003981], [0.2557521225,0.9345908518], [0.2567536907,0.9340775423], [0.2577544535,0.9335626417], [0.2587538466,0.9330464469], [0.2597513096,0.9325292572], [0.2607499713,0.9320094525], [0.2617490401,0.9314874400], [0.2627462021,0.9309644333], [0.2637450953,0.9304385247], [0.2647450974,0.9299100334], [0.2657445968,0.9293798093], [0.2667422165,0.9288485899], [0.2677393514,0.9283156397], [0.2687367900,0.9277805377], [0.2697371886,0.9272418491], [0.2707357322,0.9267021633], [0.2717368092,0.9261591065], [0.2727361892,0.9256149711], [0.2737365126,0.9250683217], [0.2747350083,0.9245206752], [0.2757357028,0.9239698222], [0.2767345816,0.9234179714], [0.2777350726,0.9228632295], [0.2787337615,0.9223074902], [0.2797339559,0.9217489139], [0.2807342099,0.9211883034], [0.2817336042,0.9206261763], [0.2827326525,0.9200622472], [0.2837299357,0.9194973236], [0.2847306145,0.9189284771], [0.2857312198,0.9183576700], [0.2867316128,0.9177849822], [0.2877302606,0.9172112971], [0.2887297852,0.9166351111], [0.2897292567,0.9160569578], [0.2907278411,0.9154773224], [0.2917247107,0.9148966932], [0.2927247433,0.9143122247], [0.2937230677,0.9137267595], [0.2947242214,0.9131376333], [0.2957236747,0.9125475082], [0.2967222911,0.9119558819], [0.2977226397,0.9113612298], [0.2987226420,0.9107647832], [0.2997209704,0.9101673399], [0.3007211623,0.9095667826], [0.3017196908,0.9089652282], [0.3027213448,0.9083597874], [0.3037216001,0.9077531896], [0.3047224279,0.9071442419], [0.3057227202,0.9065336184], [0.3067213760,0.9059219975], [0.3077219257,0.9053072164], [0.3087217834,0.9046908604], [0.3097200220,0.9040735080], [0.3107204423,0.9034528067], [0.3117192522,0.9028311078], [0.3127202311,0.9022060571], [0.3137200906,0.9015797047], [0.3147183568,0.9009523559], [0.3157189242,0.9003215609], [0.3167186443,0.8996893003], [0.3177167867,0.8990560435], [0.3187167327,0.8984196443], [0.3197151101,0.8977822484], [0.3207150263,0.8971418719], [0.3217133837,0.8965004987], [0.3227122877,0.8958567794], [0.3237134545,0.8952095994], [0.3247145488,0.8945604618], [0.3257140999,0.8939103251], [0.3267132688,0.8932584400], [0.3277109098,0.8926055596], [0.3287103536,0.8919495035], [0.3297082779,0.8912924515], [0.3307106586,0.8906304603], [0.3317115202,0.8899674674], [0.3327117172,0.8893029132], [0.3337120990,0.8886362350], [0.3347117763,0.8879680268], [0.3357131074,0.8872967095], [0.3367129451,0.8866243926], [0.3377145017,0.8859489154], [0.3387145731,0.8852724380], [0.3397137097,0.8845945954], [0.3407131349,0.8839145597], [0.3417134322,0.8832319303], [0.3427127583,0.8825479653], [0.3437106274,0.8818630046], [0.3447112096,0.8811741820], [0.3457119036,0.8804832797], [0.3467111493,0.8797913789], [0.3477113852,0.8790967926], [0.3487101824,0.8784012087], [0.3497105997,0.8777024965], [0.3507095860,0.8770027863], [0.3517093257,0.8763005502], [0.3527082230,0.8755969094], [0.3537068614,0.8748914562], [0.3547084670,0.8741819034], [0.3557086584,0.8734713503], [0.3567096721,0.8727582098], [0.3577092813,0.8720440701], [0.3587087780,0.8713280126], [0.3597068822,0.8706109589], [0.3607042064,0.8698924755], [0.3617058245,0.8691688965], [0.3627060558,0.8684443171], [0.3637073052,0.8677169961], [0.3647071765,0.8669886754], [0.3657070569,0.8662583485], [0.3667086267,0.8655247831], [0.3677099490,0.8647893934], [0.3687099346,0.8640529841], [0.3697105071,0.8633141409], [0.3707097257,0.8625742993], [0.3717101517,0.8618315631], [0.3727095392,0.8610875994], [0.3737075868,0.8603426395], [0.3747084124,0.8595936056], [0.3757092915,0.8588425283], [0.3767088375,0.8580904518], [0.3777096188,0.8573354439], [0.3787090743,0.8565794370], [0.3797085811,0.8558213934], [0.3807097313,0.8550601005], [0.3817099675,0.8542975007], [0.3827088932,0.8535339031], [0.3837103797,0.8527663445], [0.3847105592,0.8519977857], [0.3857114715,0.8512266608], [0.3867110851,0.8504545367], [0.3877100118,0.8496809468], [0.3887116145,0.8489032808], [0.3897119267,0.8481246142], [0.3907130047,0.8473433479], [0.3917134253,0.8465605924], [0.3927125683,0.8457768387], [0.3937119675,0.8449908866], [0.3947140174,0.8442008444], [0.3957147954,0.8434098007], [0.3967148913,0.8426172950], [0.3977152137,0.8418226088], [0.3987156539,0.8410258274], [0.3997162157,0.8402269469], [0.4007155251,0.8394270679], [0.4017167430,0.8386236584], [0.4027176025,0.8378185326], [0.4037172184,0.8370124076], [0.4047181532,0.8362032164], [0.4057191940,0.8353919356], [0.4067189998,0.8345796552], [0.4077185048,0.8337656208], [0.4087181103,0.8329495063], [0.4097169711,0.8321320036], [0.4107155685,0.8313127218], [0.4117172434,0.8304889115], [0.4127176998,0.8296641003], [0.4137174051,0.8288379087], [0.4147187587,0.8280083512], [0.4157189034,0.8271777934], [0.4167185316,0.8263456654], [0.4177183326,0.8255113946], [0.4187195083,0.8246739733], [0.4197194872,0.8238355521], [0.4207198251,0.8229948287], [0.4217204761,0.8221518401], [0.4227199399,0.8213078524], [0.4237197207,0.8204615983], [0.4247210901,0.8196119956], [0.4257212790,0.8187613926], [0.4267222242,0.8179081434], [0.4277232266,0.8170528414], [0.4287233936,0.8161962518], [0.4297236851,0.8153375545], [0.4307234570,0.8144773036], [0.4317220687,0.8136160554], [0.4327227208,0.8127510469], [0.4337242582,0.8118832678], [0.4347246393,0.8110144880], [0.4357256663,0.8101431438], [0.4367255433,0.8092707998], [0.4377254253,0.8083964521], [0.4387259858,0.8075195094], [0.4397258095,0.8066412125], [0.4407267822,0.8057599035], [0.4417266182,0.8048775948], [0.4427282448,0.8039917012], [0.4437287384,0.8031048067], [0.4447285233,0.8022165406], [0.4457296196,0.8013251062], [0.4467295917,0.8004326719], [0.4477300390,0.7995378122], [0.4487308600,0.7986406153], [0.4497305649,0.7977424190], [0.4507311698,0.7968414125], [0.4517306641,0.7959394071], [0.4527309640,0.7950346742], [0.4537301589,0.7941289429], [0.4547317747,0.7932190131], [0.4557322873,0.7923080823], [0.4567331553,0.7913948249], [0.4577329265,0.7904805680], [0.4587341907,0.7895629423], [0.4597349938,0.7886437354], [0.4607358668,0.7877224611], [0.4617356526,0.7868001871], [0.4627355127,0.7858758453], [0.4637356656,0.7849492325], [0.4647373456,0.7840191996], [0.4657391298,0.7830870630], [0.4667403516,0.7821534442], [0.4677404995,0.7812188252], [0.4687410432,0.7802818344], [0.4697414800,0.7793429420], [0.4707408513,0.7784030509], [0.4717417862,0.7774596872], [0.4727416591,0.7765153237], [0.4737421366,0.7755683880], [0.4747424913,0.7746195670], [0.4757417920,0.7736697474], [0.4767427173,0.7727163815], [0.4777431196,0.7717615117], [0.4787424745,0.7708056431], [0.4797437215,0.7698459617], [0.4807446946,0.7688845387], [0.4817454071,0.7679213628], [0.4827450802,0.7669571875], [0.4837444982,0.7659912605], [0.4847441394,0.7650231193], [0.4857427498,0.7640539810], [0.4867439287,0.7630803479], [0.4877440779,0.7621057145], [0.4887446887,0.7611286293], [0.4897442752,0.7601505450], [0.4907434877,0.7591708293], [0.4917429738,0.7581888477], [0.4927440231,0.7572033277], [0.4937440555,0.7562168077], [0.4947448247,0.7552275584], [0.4957450165,0.7542368786], [0.4967458918,0.7532435189], [0.4977457588,0.7522491596], [0.4987468469,0.7512515827], [0.4997469302,0.7502530058], [0.5007479399,0.7492515007], [0.5017479491,0.7482489956], [0.5027480910,0.7472443570], [0.5037472381,0.7462387201], [0.5047479628,0.7452294940], [0.5057476955,0.7442192685], [0.5067487937,0.7432056600], [0.5077489031,0.7421910514], [0.5087482942,0.7411751731], [0.5097494511,0.7401554971], [0.5107504552,0.7391339725], [0.5117513863,0.7381105186], [0.5127513385,0.7370860649], [0.5137512217,0.7360596822], [0.5147525094,0.7350298540], [0.5157529053,0.7339989406], [0.5167523310,0.7329670284], [0.5177543851,0.7319303967], [0.5187554695,0.7308927629], [0.5197570929,0.7298525644], [0.5207577512,0.7288113646], [0.5217592124,0.7277673242], [0.5227597126,0.7267222829], [0.5237611018,0.7256743082], [0.5247624920,0.7246243270], [0.5257629266,0.7235733450], [0.5267641490,0.7225195313], [0.5277644199,0.7214647171], [0.5287656287,0.7204069099], [0.5297673309,0.7193465751], [0.5307680861,0.7182852388], [0.5317684741,0.7172222900], [0.5327690726,0.7161571152], [0.5337707582,0.7150887777], [0.5347715039,0.7140194386], [0.5357718028,0.7129485753], [0.5367721463,0.7118756630], [0.5377728983,0.7108003098], [0.5387727192,0.7097239570], [0.5397735992,0.7086444616], [0.5407735512,0.7075639664], [0.5417749442,0.7064799098], [0.5427754119,0.7053948523], [0.5437751045,0.7043086357], [0.5447764053,0.7032186682], [0.5457767860,0.7021276999], [0.5467779259,0.7010338997], [0.5477781493,0.6999390992], [0.5487797162,0.6988408231], [0.5497803692,0.6977415456], [0.5507808823,0.6966404197], [0.5517822930,0.6955363012], [0.5527827950,0.6944311816], [0.5537827522,0.6933246634], [0.5547837348,0.6922150076], [0.5557839327,0.6911042202], [0.5567832306,0.6899924341], [0.5577845788,0.6888763636], [0.5587850282,0.6877592923], [0.5597850731,0.6866406719], [0.5607861014,0.6855189485], [0.5617862362,0.6843962249], [0.5627868694,0.6832709396], [0.5637866132,0.6821446548], [0.5647869812,0.6810156659], [0.5657864632,0.6798856781], [0.5667863677,0.6787532134], [0.5677880813,0.6776166947], [0.5687889112,0.6764791744], [0.5697903369,0.6753389720], [0.5707914239,0.6741971504], [0.5717916330,0.6730543284], [0.5727922091,0.6719090851], [0.5737919114,0.6707628424], [0.5747928556,0.6696131731], [0.5757940029,0.6684612663], [0.5767942797,0.6673083589], [0.5777954536,0.6661524138], [0.5787957601,0.6649954681], [0.5797958927,0.6638367229], [0.5807957294,0.6626763207], [0.5817966026,0.6615127133], [0.5827969968,0.6603476605], [0.5837965324,0.6591816087], [0.5847986687,0.6580105171], [0.5857999463,0.6568384229], [0.5868017357,0.6556637229], [0.5878026701,0.6544880211], [0.5888030814,0.6533109313], [0.5898044159,0.6521307510], [0.5908049003,0.6509495698], [0.5918060868,0.6497655556], [0.5928064265,0.6485805407], [0.5938072464,0.6473929541], [0.5948087191,0.6462025877], [0.5958099128,0.6450105478], [0.5968102653,0.6438175072], [0.5978103153,0.6426228269], [0.5988105996,0.6414258658], [0.5998121828,0.6402253454], [0.6008129297,0.6390238235], [0.6018134084,0.6378206215], [0.6028143007,0.6366149188], [0.6038147499,0.6354077478], [0.6048143703,0.6341995775], [0.6058156411,0.6329874090], [0.6068165160,0.6317737159], [0.6078165655,0.6305590228], [0.6088174526,0.6293413094], [0.6098175170,0.6281225960], [0.6108184435,0.6269008291], [0.6118185500,0.6256780619], [0.6128191996,0.6244526286], [0.6138190323,0.6232261956], [0.6148189560,0.6219976513], [0.6158192562,0.6207666437], [0.6168200734,0.6195329971], [0.6178200786,0.6182983505], [0.6188212651,0.6170602419], [0.6198216417,0.6158211325], [0.6208229481,0.6145788672], [0.6218243862,0.6133344327], [0.6228252818,0.6120886684], [0.6238253731,0.6108419038], [0.6248249263,0.6095938115], [0.6258256208,0.6083422924], [0.6268255152,0.6070897735], [0.6278268897,0.6058333966], [0.6288274656,0.6045760185], [0.6298277409,0.6033170168], [0.6308284625,0.6020554509], [0.6318283903,0.6007928852], [0.6328297731,0.5995264782], [0.6338303637,0.5982590701], [0.6348314384,0.5969890449], [0.6358317238,0.5957180191], [0.6368322957,0.5944446271], [0.6378334062,0.5931685459], [0.6388337311,0.5918914641], [0.6398346033,0.5906116804], [0.6408352031,0.5893302425], [0.6418350217,0.5880478049], [0.6428361808,0.5867616446], [0.6438365603,0.5854744836], [0.6448382648,0.5841836123], [0.6458398106,0.5828909390], [0.6468411991,0.5815964631], [0.6478418124,0.5803009861], [0.6488417767,0.5790043488], [0.6498430841,0.5777039660], [0.6508436201,0.5764025821], [0.6518444272,0.5750988428], [0.6528453459,0.5737929544], [0.6538454973,0.5724860657], [0.6548457623,0.5711770276], [0.6558466176,0.5698652142], [0.6568467091,0.5685524007], [0.6578483101,0.5672356009], [0.6588491486,0.5659177994], [0.6598495399,0.5645985847], [0.6608506818,0.5632763764], [0.6618520295,0.5619518910], [0.6628526198,0.5606264044], [0.6638532352,0.5592988821], [0.6648546560,0.5579682864], [0.6658557484,0.5566361223], [0.6668561595,0.5553028625], [0.6678570275,0.5539669908], [0.6688577411,0.5526293222], [0.6698577061,0.5512906536], [0.6708589585,0.5499482579], [0.6718604722,0.5486035059], [0.6728617433,0.5472570744], [0.6738622695,0.5459096417], [0.6748631339,0.5445597505], [0.6758642604,0.5432075016], [0.6768646453,0.5418542519], [0.6778657070,0.5404980833], [0.6788660295,0.5391409141], [0.6798673730,0.5377803551], [0.6808679791,0.5364187950], [0.6818689868,0.5350546849], [0.6828700058,0.5336885552], [0.6838702912,0.5323214249], [0.6848716407,0.5309508358], [0.6858722582,0.5295792455], [0.6868733810,0.5282049585], [0.6878737742,0.5268296708], [0.6888743691,0.5254521036], [0.6898754989,0.5240717961], [0.6908762384,0.5226900232], [0.6918770254,0.5213061817], [0.6928770886,0.5199213402], [0.6938772011,0.5185344298], [0.6948778114,0.5171448272], [0.6958789205,0.5157525280], [0.6968793095,0.5143592280], [0.6978801979,0.5129632293], [0.6988809228,0.5115654558], [0.6998811742,0.5101663419], [0.7008807109,0.5087662290], [0.7018824250,0.5073610614], [0.7028834243,0.5059548918], [0.7038844436,0.5045466901], [0.7048847511,0.5031374877], [0.7058852015,0.5017260823], [0.7068859163,0.5003123013], [0.7078859228,0.4988975203], [0.7088875851,0.4974783917], [0.7098885397,0.4960582612], [0.7108888828,0.4946369963], [0.7118903868,0.4932120771], [0.7128911864,0.4917861563], [0.7138919825,0.4903582373], [0.7148930803,0.4889278837], [0.7158942397,0.4874954376], [0.7168953773,0.4860610180], [0.7178958158,0.4846255976], [0.7188962346,0.4831882039], [0.7198965507,0.4817489563], [0.7208968372,0.4803077501], [0.7218969152,0.4788648438], [0.7228980439,0.4774184181], [0.7238984794,0.4759709916], [0.7248985473,0.4745220961], [0.7258985725,0.4730712625], [0.7268995657,0.4716170214], [0.7278998696,0.4701617798], [0.7289000165,0.4687047659], [0.7299007776,0.4672448548], [0.7309013965,0.4657831485], [0.7319013305,0.4643204424], [0.7329017974,0.4628549554], [0.7339029342,0.4613864831], [0.7349033882,0.4599170100], [0.7359041834,0.4584450328], [0.7369050893,0.4569708893], [0.7379053155,0.4554957453], [0.7389060278,0.4540178821], [0.7399060623,0.4525390189], [0.7409068101,0.4510570987], [0.7419084637,0.4495718315], [0.7429094411,0.4480855623], [0.7439105660,0.4465970698], [0.7449110172,0.4451075765], [0.7459121306,0.4436150934], [0.7469125722,0.4421216095], [0.7479130782,0.4406260275], [0.7489143233,0.4391273363], [0.7499154898,0.4376267582], [0.7509159879,0.4361251791], [0.7519164094,0.4346217133], [0.7529177058,0.4331149283], [0.7539191070,0.4316059801], [0.7549198431,0.4300960305], [0.7559213352,0.4285829350], [0.7569224886,0.4270683463], [0.7579229798,0.4255527567], [0.7589246123,0.4240334328], [0.7599255838,0.4225131070], [0.7609265638,0.4209907644], [0.7619278812,0.4194659038], [0.7629290969,0.4179391932], [0.7639296555,0.4164114814], [0.7649301145,0.4148819199], [0.7659303794,0.4133506538], [0.7669302055,0.4118180599], [0.7679308016,0.4102822839], [0.7689307459,0.4087455081], [0.7699310321,0.4072062058], [0.7709329274,0.4056624214], [0.7719341717,0.4041176345], [0.7729352533,0.4025710942], [0.7739363623,0.4010225071], [0.7749368238,0.3994729192], [0.7759377998,0.3979205308], [0.7769383207,0.3963668458], [0.7779381974,0.3948121611], [0.7789392652,0.3932536211], [0.7799405976,0.3916926642], [0.7809414697,0.3901304209], [0.7819417004,0.3885671771], [0.7829428248,0.3870005331], [0.7839433091,0.3854328881], [0.7849447045,0.3838618109], [0.7859461881,0.3822885894], [0.7869470336,0.3807143663], [0.7879475411,0.3791386725], [0.7889477963,0.3775613747], [0.7899486202,0.3759811774], [0.7909491668,0.3743994155], [0.7919490806,0.3728166538], [0.7929497720,0.3712306591], [0.7939498320,0.3696436642], [0.7949503135,0.3680539990], [0.7959503011,0.3664631181], [0.7969515721,0.3648681917], [0.7979528927,0.3632711811], [0.7989535850,0.3616731691], [0.7999548002,0.3600723177], [0.8009553888,0.3584704652], [0.8019561006,0.3568664128], [0.8029563271,0.3552611368], [0.8039576438,0.3536521070], [0.8049589254,0.3520411284], [0.8059595843,0.3504291485], [0.8069609103,0.3488140893], [0.8079616150,0.3471980287], [0.8089628692,0.3455790762], [0.8099635779,0.3439590025], [0.8109647699,0.3423361420], [0.8119658521,0.3407114550], [0.8129663173,0.3390857670], [0.8139677166,0.3374565563], [0.8149684999,0.3358263441], [0.8159695655,0.3341936681], [0.8169700171,0.3325599912], [0.8179705438,0.3309241895], [0.8189717626,0.3292852520], [0.8199729459,0.3276443680], [0.8209735179,0.3260024829], [0.8219740559,0.3243586514], [0.8229745108,0.3227129546], [0.8239760623,0.3210634487], [0.8249770051,0.3194129410], [0.8259776978,0.3177608427], [0.8269788523,0.3161059779], [0.8279800667,0.3144490092], [0.8289809674,0.3127905557], [0.8299812639,0.3111311016], [0.8309812489,0.3094701639], [0.8319817746,0.3078063267], [0.8329830448,0.3061392471], [0.8339838437,0.3044709485], [0.8349840421,0.3028016494], [0.8359858560,0.3011276486], [0.8369870696,0.2994526454], [0.8379877848,0.2977764725], [0.8389888231,0.2960977546], [0.8399892643,0.2944180358], [0.8409905999,0.2927348108], [0.8419913394,0.2910505844], [0.8429918039,0.2893648186], [0.8439929114,0.2876759655], [0.8449934252,0.2859861113], [0.8459945215,0.2842932696], [0.8469950254,0.2825994269], [0.8479960615,0.2809026797], [0.8489965067,0.2792049316], [0.8499975511,0.2775041632], [0.8509980060,0.2758023938], [0.8519985154,0.2740985298], [0.8529995778,0.2723917202], [0.8540000529,0.2706839097], [0.8550002400,0.2689745897], [0.8560004378,0.2672632505], [0.8570019396,0.2655476756], [0.8580028562,0.2638310988], [0.8590037053,0.2621126343], [0.8600039714,0.2603931692], [0.8610051818,0.2586700768], [0.8620058102,0.2569459832], [0.8630067094,0.2552194195], [0.8640080614,0.2534900698], [0.8650088331,0.2517597186], [0.8660097076,0.2500271864], [0.8670106433,0.2482925444], [0.8680112546,0.2465564619], [0.8690112892,0.2448193792], [0.8700130267,0.2430773334], [0.8710141875,0.2413342852], [0.8720152654,0.2395893770], [0.8730164118,0.2378423446], [0.8740175616,0.2360933021], [0.8750181379,0.2343432583], [0.8760187188,0.2325912043], [0.8770192391,0.2308372543], [0.8780206123,0.2290798044], [0.8790214144,0.2273213529], [0.8800219663,0.2255613389], [0.8810225877,0.2237991999], [0.8820239210,0.2220338029], [0.8830246858,0.2202674043], [0.8840256459,0.2184986575], [0.8850260393,0.2167289098], [0.8860270515,0.2149560640], [0.8870274983,0.2131822173], [0.8880283489,0.2114056515], [0.8890286356,0.2096280851], [0.8900302999,0.2078460652], [0.8910318396,0.2060622609], [0.8920328163,0.2042774546], [0.8930340663,0.2024901564], [0.8940347550,0.2007018568], [0.8950360844,0.1989104077], [0.8960368536,0.1971179570], [0.8970377732,0.1953232334], [0.8980385729,0.1935267217], [0.8990397954,0.1917274463], [0.9000404604,0.1899271697], [0.9010406568,0.1881257348], [0.9020418368,0.1863205247], [0.9030424612,0.1845143133], [0.9040437450,0.1827049072], [0.9050446075,0.1808942585], [0.9060456802,0.1790812254], [0.9070468502,0.1772660116], [0.9080474676,0.1754497966], [0.9090481832,0.1736314005], [0.9100489200,0.1718109631], [0.9110499684,0.1699879550], [0.9120504668,0.1681639460], [0.9130512773,0.1663373650], [0.9140523715,0.1645082622], [0.9150529174,0.1626781584], [0.9160534651,0.1608460490], [0.9170547161,0.1590106478], [0.9180554204,0.1571742451], [0.9190562956,0.1553355256], [0.9200571762,0.1534947925], [0.9210575125,0.1516530588], [0.9220585205,0.1498080847], [0.9230589853,0.1479621097], [0.9240597622,0.1461135559], [0.9250608477,0.1442624280], [0.9260614803,0.1424101348], [0.9270615722,0.1405568413], [0.9280630850,0.1386989102], [0.9290640575,0.1368399771], [0.9300655418,0.1349780880], [0.9310664869,0.1331151969], [0.9320675835,0.1312500198], [0.9330687497,0.1293827084], [0.9340693788,0.1275143956], [0.9350700730,0.1256439587], [0.9360702317,0.1237725214], [0.9370710634,0.1218978221], [0.9380713607,0.1200221222], [0.9390721206,0.1181435523], [0.9400723473,0.1162639819], [0.9410738335,0.1143800399], [0.9420747868,0.1124950960], [0.9430764485,0.1106068123], [0.9440775782,0.1087175264], [0.9450787494,0.1068261575], [0.9460800707,0.1049324998], [0.9470808622,0.1030378405], [0.9480818340,0.1011408360], [0.9490822775,0.0992428306], [0.9500834795,0.0973413820], [0.9510845102,0.0954382545], [0.9520850141,0.0935341259], [0.9530859384,0.0916271940], [0.9540863371,0.0897192613], [0.9550868011,0.0878092024], [0.9560877695,0.0858961770], [0.9570882140,0.0839821507], [0.9580884059,0.0820666065], [0.9590886162,0.0801490263], [0.9600896778,0.0782278105], [0.9610902176,0.0763055936], [0.9620904461,0.0743819735], [0.9630915722,0.0724546236], [0.9640921779,0.0705262724], [0.9650923980,0.0685966633], [0.9660923668,0.0666655389], [0.9670928311,0.0647314561], [0.9680927779,0.0627963734], [0.9690928236,0.0608590993], [0.9700925336,0.0589204763], [0.9710924163,0.0569795191], [0.9720917842,0.0550375631], [0.9730915101,0.0530929130], [0.9740914078,0.0511459293], [0.9750907922,0.0491979469], [0.9760898892,0.0472485283], [0.9770885645,0.0452979371], [0.9780877027,0.0433444458], [0.9790863306,0.0413899573], [0.9800852744,0.0394328550], [0.9810837091,0.0374747557], [0.9820819259,0.0355150907], [0.9830801314,0.0335534553], [0.9840781166,0.0315902604], [0.9850755958,0.0296260706], [0.9860728806,0.0276602742], [0.9870696611,0.0256934842], [0.9880665739,0.0237244456], [0.9890629838,0.0217544141], [0.9900591295,0.0197829200], [0.9910547741,0.0178104347], [0.9920503191,0.0158361644], [0.9930453645,0.0138609040], [0.9940402226,0.0118840359], [0.9950345858,0.0099061731], [0.9960284521,0.0079273226], [0.9970219902,0.0059471511], [0.9980151238,0.0039658127], [0.9990078085,0.0019833986], [1.0000000000,0.0000000000]]deap-1.0.1/examples/ga/pareto_front/zdt3_front.json0000644000076500000240000007123412117373622022565 0ustar felixstaff00000000000000[[0.0000000000,1.0000000000], [0.0001669867,0.9870767898], [0.0003556074,0.9811384730], [0.0005020830,0.9775848727], [0.0005936368,0.9756242656], [0.0008526856,0.9707763811], [0.0009594493,0.9689961054], [0.0015383844,0.9607034357], [0.0020570965,0.9545119258], [0.0026049104,0.9487487404], [0.0030767688,0.9442344352], [0.0034180675,0.9411694253], [0.0036717564,0.9389823762], [0.0038889639,0.9371644873], [0.0042634278,0.9341357353], [0.0045677159,0.9317619051], [0.0048025025,0.9299780777], [0.0049491266,0.9288835705], [0.0052231746,0.9268752322], [0.0056348254,0.9239422323], [0.0059965327,0.9214397209], [0.0062303750,0.9198555571], [0.0064301391,0.9185217235], [0.0066199433,0.9172701090], [0.0068214844,0.9159570195], [0.0072291603,0.9133477888], [0.0076741700,0.9105653571], [0.0080639223,0.9081795586], [0.0085268555,0.9054020802], [0.0088785809,0.9033292734], [0.0091366688,0.9018273795], [0.0093681110,0.9004935189], [0.0095528069,0.8994374843], [0.0096573496,0.8988429411], [0.0099784197,0.8970308931], [0.0102322266,0.8956126871], [0.0105317498,0.8939543318], [0.0107778336,0.8926036594], [0.0110849122,0.8909324019], [0.0114248107,0.8890999667], [0.0116816063,0.8877270886], [0.0119375977,0.8863679309], [0.0122450122,0.8847476272], [0.0124563098,0.8836411373], [0.0126694291,0.8825308265], [0.0128683452,0.8814995256], [0.0130583286,0.8805189165], [0.0131986790,0.8797971649], [0.0134103869,0.8787126395], [0.0136429688,0.8775268110], [0.0139332104,0.8760549727], [0.0141327011,0.8750482925], [0.0143068967,0.8741724532], [0.0144956544,0.8732266709], [0.0149293257,0.8710661595], [0.0153483399,0.8689943461], [0.0156313482,0.8676032942], [0.0158346655,0.8666079009], [0.0162010255,0.8648223436], [0.0166327204,0.8627311017], [0.0170537110,0.8607043403], [0.0173191595,0.8594325272], [0.0175344871,0.8584042124], [0.0177394099,0.8574283093], [0.0181096151,0.8556718157], [0.0185920954,0.8533947644], [0.0189556499,0.8516876796], [0.0192777942,0.8501810417], [0.0194690215,0.8492892820], [0.0198037163,0.8477330068], [0.0202974665,0.8454473371], [0.0206546959,0.8438009602], [0.0209831653,0.8422923698], [0.0212487014,0.8410763951], [0.0215018774,0.8399199344], [0.0217359231,0.8388533410], [0.0219358993,0.8379438680], [0.0221884994,0.8367974771], [0.0224118827,0.8357858922], [0.0225142106,0.8353231873], [0.0227053361,0.8344600970], [0.0228607177,0.8337595036], [0.0229579167,0.8333217372], [0.0230225099,0.8330310289], [0.0231956360,0.8322526674], [0.0232116861,0.8321805673], [0.0235123826,0.8308316289], [0.0238751954,0.8292086653], [0.0242269208,0.8276400578], [0.0245668626,0.8261283879], [0.0250313925,0.8240695616], [0.0254135798,0.8223815507], [0.0256564042,0.8213117880], [0.0258458064,0.8204788343], [0.0259543603,0.8200020095], [0.0261712464,0.8190505805], [0.0262662654,0.8186342762], [0.0265035580,0.8175960166], [0.0269069841,0.8158353717], [0.0272859376,0.8141866940], [0.0276270372,0.8127069664], [0.0279624818,0.8112557002], [0.0281386232,0.8104952023], [0.0281838469,0.8103001203], [0.0288243220,0.8075448957], [0.0293430341,0.8053239043], [0.0297674992,0.8035134265], [0.0301836989,0.8017443297], [0.0305296931,0.8002782929], [0.0307107548,0.7995127936], [0.0308331559,0.7989959623], [0.0312153919,0.7973854390], [0.0314860627,0.7962481634], [0.0317119832,0.7953009439], [0.0320680775,0.7938117197], [0.0323274831,0.7927297883], [0.0325584125,0.7917687206], [0.0327545581,0.7909539735], [0.0329089323,0.7903137492], [0.0330877498,0.7895732754], [0.0332574191,0.7888718023], [0.0334145865,0.7882229944], [0.0337083799,0.7870127140], [0.0339404353,0.7860591181], [0.0341096686,0.7853650013], [0.0344325575,0.7840437769], [0.0347528391,0.7827373008], [0.0349601076,0.7818940097], [0.0353433487,0.7803393361], [0.0355824439,0.7793724529], [0.0358127931,0.7784431724], [0.0360432832,0.7775155381], [0.0362240647,0.7767895244], [0.0362597010,0.7766465731], [0.0364984920,0.7756900814], [0.0366765299,0.7749785273], [0.0370172041,0.7736207936], [0.0375036794,0.7716907923], [0.0378698896,0.7702448701], [0.0382213553,0.7688628704], [0.0385632853,0.7675238068], [0.0388163408,0.7665362934], [0.0389235753,0.7661187323], [0.0392235353,0.7649536098], [0.0394193855,0.7641951960], [0.0397264145,0.7630099790], [0.0398992225,0.7623449129], [0.0400762209,0.7616652420], [0.0402551451,0.7609797537], [0.0405720649,0.7597695091], [0.0408019665,0.7588947413], [0.0409289064,0.7584128930], [0.0411088328,0.7577313321], [0.0411312365,0.7576465840], [0.0414266470,0.7565315470], [0.0417815920,0.7551978412], [0.0420921689,0.7540363331], [0.0423322940,0.7531418556], [0.0424672908,0.7526403583], [0.0427556349,0.7515725323], [0.0429379854,0.7508995993], [0.0433199764,0.7494959371], [0.0437319122,0.7479914750], [0.0440034342,0.7470051430], [0.0441726619,0.7463925646], [0.0445044030,0.7451965726], [0.0447799423,0.7442081408], [0.0450253475,0.7433316240], [0.0453463382,0.7421906230], [0.0456599823,0.7410818081], [0.0460450198,0.7397289095], [0.0463325491,0.7387246698], [0.0465637319,0.7379210202], [0.0468612153,0.7368919134], [0.0471432534,0.7359215072], [0.0474087176,0.7350128587], [0.0475834041,0.7344174548], [0.0476606316,0.7341548746], [0.0479047065,0.7333276034], [0.0480227605,0.7329288973], [0.0482432416,0.7321867712], [0.0486030764,0.7309826655], [0.0488902048,0.7300282123], [0.0490264087,0.7295774426], [0.0491226194,0.7292598080], [0.0492906317,0.7287066721], [0.0494557620,0.7281649519], [0.0494762709,0.7280978048], [0.0496805787,0.7274305171], [0.0499937537,0.7264134172], [0.0501414608,0.7259361441], [0.0503484317,0.7252700226], [0.0504477231,0.7249515612], [0.0507211686,0.7240782418], [0.0509941463,0.7232118873], [0.0514111655,0.7218990444], [0.0516993609,0.7209993629], [0.0519333555,0.7202734989], [0.0520138186,0.7200248592], [0.0523655440,0.7189438029], [0.0527094385,0.7178960327], [0.0530505014,0.7168659813], [0.0533247669,0.7160442935], [0.0535522030,0.7153674210], [0.0536961857,0.7149410436], [0.0539226786,0.7142736870], [0.0542729460,0.7132497722], [0.0545718753,0.7123838129], [0.0548422347,0.7116069183], [0.0549807337,0.7112112661], [0.0552522995,0.7104400938], [0.0554245608,0.7099541031], [0.0554642028,0.7098426148], [0.0557355095,0.7090831390], [0.0559251621,0.7085559256], [0.0562772464,0.7075852652], [0.0567959585,0.7061745652], [0.0572856348,0.7048641986], [0.0576507424,0.7039008251], [0.0578722749,0.7033220194], [0.0579826175,0.7030353467], [0.0583233699,0.7021569099], [0.0585107057,0.7016783979], [0.0586683163,0.7012782580], [0.0589152172,0.7006559391], [0.0591712059,0.7000165547], [0.0593313410,0.6996196213], [0.0595679261,0.6990374856], [0.0596879886,0.6987440297], [0.0597919273,0.6984910564], [0.0598532005,0.6983423927], [0.0601420422,0.6976462759], [0.0603736874,0.6970936087], [0.0605760782,0.6966148402], [0.0607647336,0.6961720225], [0.0608835727,0.6958947984], [0.0611481301,0.6952824352], [0.0613933597,0.6947207334], [0.0616252405,0.6941948726], [0.0617114801,0.6940006080], [0.0620790585,0.6931805939], [0.0623719468,0.6925365176], [0.0625860127,0.6920710273], [0.0630987308,0.6909742466], [0.0635061169,0.6901211399], [0.0637844296,0.6895477285], [0.0640918984,0.6889231656], [0.0644701284,0.6881677698], [0.0647023437,0.6877110775], [0.0650249530,0.6870855944], [0.0652330033,0.6866877833], [0.0653423543,0.6864804475], [0.0654488482,0.6862796926], [0.0656172300,0.6859646177], [0.0656567874,0.6858910156], [0.0658327234,0.6855655908], [0.0660074892,0.6852454527], [0.0661995406,0.6848972472], [0.0663074061,0.6847033331], [0.0665094730,0.6843432793], [0.0667699400,0.6838853547], [0.0671436157,0.6832406132], [0.0673621585,0.6828702314], [0.0676548746,0.6823819064], [0.0680478574,0.6817403361], [0.0683270518,0.6812943328], [0.0687335562,0.6806595643], [0.0690300965,0.6802074678], [0.0693293144,0.6797606892], [0.0695505216,0.6794364778], [0.0697532285,0.6791439326], [0.0700261347,0.6787569641], [0.0703917670,0.6782509315], [0.0707729007,0.6777386210], [0.0712916128,0.6770663438], [0.0716774616,0.6765849831], [0.0719773117,0.6762219633], [0.0722712346,0.6758755200], [0.0724960238,0.6756168535], [0.0728875441,0.6751793644], [0.0732364471,0.6748034735], [0.0735861732,0.6744399355], [0.0738379780,0.6741864037], [0.0740166562,0.6740106769], [0.0742215332,0.6738134542], [0.0745353683,0.6735201959], [0.0748324729,0.6732524483], [0.0752210672,0.6729167612], [0.0755965740,0.6726080145], [0.0758688632,0.6723937510], [0.0760783642,0.6722343998], [0.0762090398,0.6721374295], [0.0762720685,0.6720913239], [0.0764187907,0.6719856749], [0.0765747129,0.6718759756], [0.0766011447,0.6718576424], [0.0767497593,0.6717559818], [0.0768078496,0.6717168998], [0.0770485469,0.6715588866], [0.0774273984,0.6713229792], [0.0776993770,0.6711632724], [0.0782800840,0.6708492640], [0.0788525184,0.6705756699], [0.0792997562,0.6703867107], [0.0797034248,0.6702348093], [0.0799846997,0.6701394109], [0.0801524418,0.6700865978], [0.0804359408,0.6700042596], [0.0806711539,0.6699425386], [0.0809076056,0.6698865116], [0.0811192571,0.6698414733], [0.0814211472,0.6697855776], [0.0816908262,0.6697439295], [0.0819181881,0.6697148794], [0.0823765250,0.6696731419], [0.0828473667,0.6696536196], [0.1824747079,0.6682957828], [0.1827373042,0.6668379181], [0.1829934200,0.6654064856], [0.1832008830,0.6642401022], [0.1835325807,0.6623625566], [0.1840130923,0.6596151795], [0.1844136563,0.6573003083], [0.1846547945,0.6558960797], [0.1850327646,0.6536790223], [0.1855514767,0.6506049186], [0.1859032316,0.6484997804], [0.1862571443,0.6463652113], [0.1865781222,0.6444151024], [0.1867381357,0.6434379418], [0.1870567377,0.6414825070], [0.1873506813,0.6396669210], [0.1875908212,0.6381755498], [0.1877572846,0.6371374939], [0.1878386950,0.6366285640], [0.1882765200,0.6338774632], [0.1888441256,0.6302759901], [0.1892961923,0.6273799277], [0.1896479177,0.6251099884], [0.1899216837,0.6233332085], [0.1901596799,0.6217815704], [0.1905021558,0.6195374578], [0.1907437681,0.6179463286], [0.1908541931,0.6172169635], [0.1910015634,0.6162414722], [0.1910592217,0.6158591627], [0.1913795526,0.6137285628], [0.1916022091,0.6122410756], [0.1918542490,0.6105508918], [0.1920630625,0.6091454935], [0.1922707349,0.6077432472], [0.1923574812,0.6071561924], [0.1925399478,0.6059188204], [0.1927127826,0.6047436241], [0.1932256466,0.6012386596], [0.1937303630,0.5977640158], [0.1944123056,0.5930305273], [0.1949310177,0.5894011703], [0.1952907647,0.5868698611], [0.1955428228,0.5850895151], [0.1957338951,0.5837362693], [0.1959506900,0.5821970824], [0.1961476249,0.5807954737], [0.1963512801,0.5793426559], [0.1965346412,0.5780317096], [0.1968033756,0.5761054945], [0.1970607366,0.5742554420], [0.1974781249,0.5712441366], [0.1978230478,0.5687457361], [0.1983417599,0.5649722025], [0.1987585410,0.5619264912], [0.1990311925,0.5599276545], [0.1992358082,0.5584243672], [0.1993614322,0.5575000768], [0.1995662000,0.5559913203], [0.1997751609,0.5544489585], [0.1999667287,0.5530326194], [0.2002564937,0.5508860807], [0.2003811045,0.5499614639], [0.2006038409,0.5483065276], [0.2010021822,0.5453399268], [0.2012337900,0.5436111052], [0.2015290071,0.5414034185], [0.2018860370,0.5387276261], [0.2020864756,0.5372227120], [0.2024146698,0.5347545517], [0.2027721744,0.5320604855], [0.2029652911,0.5306029304], [0.2036494328,0.5254273064], [0.2037918467,0.5243476959], [0.2040759367,0.5221919202], [0.2042545974,0.5208347687], [0.2043734498,0.5199313558], [0.2046445322,0.5178691539], [0.2048561060,0.5162581076], [0.2054073512,0.5120547358], [0.2054972178,0.5113687330], [0.2056650614,0.5100869645], [0.2060618135,0.5070545512], [0.2063499033,0.5048506040], [0.2063806988,0.5046149187], [0.2065289984,0.5034797026], [0.2065386776,0.5034055960], [0.2067652829,0.5016701953], [0.2068756268,0.5008248564], [0.2072025889,0.4983189841], [0.2079865360,0.4923056583], [0.2080552744,0.4917781338], [0.2082324576,0.4904182206], [0.2082767918,0.4900779188], [0.2084572594,0.4886925802], [0.2084982003,0.4883782832], [0.2089079600,0.4852323538], [0.2090520981,0.4841256760], [0.2096320525,0.4796731020], [0.2097606455,0.4786859815], [0.2103218452,0.4743792398], [0.2105433294,0.4726802577], [0.2106133311,0.4721433877], [0.2112569439,0.4672101715], [0.2114660166,0.4656089731], [0.2123187022,0.4590871651], [0.2131713877,0.4525824143], [0.2140240733,0.4460991906], [0.2141744858,0.4449581302], [0.2147429805,0.4406531527], [0.2148767588,0.4396419800], [0.2150226572,0.4385400544], [0.2151610011,0.4374960306], [0.2155880417,0.4342787333], [0.2156715366,0.4336506749], [0.2157294444,0.4332152810], [0.2157694979,0.4329142234], [0.2158193744,0.4325394410], [0.2164019203,0.4281712993], [0.2165821300,0.4268236007], [0.2165868632,0.4267882264], [0.2166382981,0.4264039060], [0.2166716890,0.4261544874], [0.2168971922,0.4244716742], [0.2174348155,0.4204714516], [0.2177286617,0.4182923973], [0.2182875011,0.4141633474], [0.2191401866,0.4079037996], [0.2192092469,0.4073990920], [0.2193259541,0.4065469676], [0.2197739050,0.4032858086], [0.2198406810,0.4028009851], [0.2199928722,0.4016973140], [0.2200434960,0.4013306013], [0.2203664505,0.3989959983], [0.2208455577,0.3955483864], [0.2211857673,0.3931121154], [0.2216982433,0.3894614995], [0.2224841626,0.3839100178], [0.2225509288,0.3834411184], [0.2230708562,0.3798047124], [0.2234036144,0.3774916878], [0.2236749044,0.3756144141], [0.2241705672,0.3722046966], [0.2242562999,0.3716176274], [0.2247012462,0.3685838327], [0.2251089855,0.3658233284], [0.2254846966,0.3632966806], [0.2256380391,0.3622702351], [0.2259616710,0.3601131499], [0.2268143566,0.3544914153], [0.2269037613,0.3539072694], [0.2276670421,0.3489624078], [0.2281103951,0.3461256564], [0.2284526376,0.3439541530], [0.2285197277,0.3435303676], [0.2293200719,0.3385237273], [0.2293396461,0.3384024239], [0.2293724132,0.3381994876], [0.2294968854,0.3374300133], [0.2296800171,0.3363020183], [0.2297577720,0.3358245769], [0.2300275251,0.3341751270], [0.2302250988,0.3329739099], [0.2304780201,0.3314447659], [0.2309139435,0.3288321015], [0.2310777843,0.3278577220], [0.2316447522,0.3245183980], [0.2317466886,0.3239234228], [0.2319304699,0.3228549533], [0.2322646184,0.3209262765], [0.2322675102,0.3209096643], [0.2326165189,0.3189148858], [0.2327831554,0.3179695717], [0.2334122828,0.3144425877], [0.2336185363,0.3133009319], [0.2336358410,0.3132054792], [0.2344885265,0.3085665094], [0.2350785884,0.3054315538], [0.2353412121,0.3040564232], [0.2353574766,0.3039716723], [0.2357106365,0.3021433829], [0.2361494584,0.2999037148], [0.2361938976,0.2996789056], [0.2362157389,0.2995685501], [0.2362451566,0.2994200560], [0.2365222345,0.2980294084], [0.2365486157,0.2978977571], [0.2370465832,0.2954375624], [0.2372412379,0.2944887902], [0.2373547984,0.2939386609], [0.2374943975,0.2932658180], [0.2378992687,0.2913359166], [0.2387519543,0.2873774051], [0.2391475268,0.2855905375], [0.2395096834,0.2839825392], [0.2395801353,0.2836728516], [0.2396046398,0.2835653755], [0.2404573254,0.2799030828], [0.2411490497,0.2770443042], [0.2413100109,0.2763936863], [0.2414336135,0.2758978465], [0.2420882410,0.2733267747], [0.2421626965,0.2730402465], [0.2425730674,0.2714828065], [0.2430153820,0.2698457218], [0.2432027902,0.2691651976], [0.2433419448,0.2686649669], [0.2433651935,0.2685818156], [0.2433674014,0.2685739249], [0.2437105387,0.2673609416], [0.2437217900,0.2673216175], [0.2437320374,0.2672858265], [0.2438680676,0.2668129659], [0.2439830416,0.2664165644], [0.2447207531,0.2639447247], [0.2448928051,0.2633861641], [0.2449517244,0.2631964526], [0.2451769624,0.2624786152], [0.2452816039,0.2621491187], [0.2453810174,0.2618384393], [0.2455734387,0.2612436334], [0.2455736491,0.2612429878], [0.2458092287,0.2605265523], [0.2458778269,0.2603203767], [0.2463140002,0.2590353079], [0.2464141094,0.2587466935], [0.2464261242,0.2587122139], [0.2472788098,0.2563528720], [0.2473870886,0.2560657000], [0.2475759375,0.2555715859], [0.2480189656,0.2544461828], [0.2480891828,0.2542721739], [0.2481314953,0.2541678948], [0.2482020169,0.2539950606], [0.2486844706,0.2528451466], [0.2489841809,0.2521594483], [0.2492553014,0.2515581637], [0.2493371796,0.2513801353], [0.2493457504,0.2513615955], [0.2494104516,0.2512222219], [0.2494378405,0.2511635346], [0.2496888176,0.2506343931], [0.2497552029,0.2504970399], [0.2498368664,0.2503295748], [0.2498807861,0.2502401945], [0.2500901454,0.2498207203], [0.2505251969,0.2489839818], [0.2506895520,0.2486801906], [0.2510571995,0.2480251737], [0.2515422375,0.2472130838], [0.2516620739,0.2470216019], [0.2518406640,0.2467429910], [0.2522596072,0.2461212008], [0.2523115515,0.2460472180], [0.2523949231,0.2459299122], [0.2524376295,0.2458705099], [0.2525663442,0.2456942908], [0.2532476086,0.2448322009], [0.2535325365,0.2445069822], [0.2535639434,0.2444724130], [0.2535644347,0.2444718742], [0.2539076627,0.2441107097], [0.2541002942,0.2439213408], [0.2546621954,0.2434238529], [0.2549529797,0.2431985863], [0.2550488590,0.2431291323], [0.2551936932,0.2430287561], [0.2553742605,0.2429112738], [0.2558056653,0.2426650537], [0.2559100389,0.2426127938], [0.2564216354,0.2423979110], [0.2565766752,0.2423463457], [0.2566583508,0.2423217197], [0.2566588832,0.2423215649], [0.2570180981,0.2422341244], [0.2572989269,0.2421894148], [0.2575110364,0.2421694194], [0.4098582019,0.2348828551], [0.4100040285,0.2329368690], [0.4101417500,0.2311003352], [0.4101986798,0.2303415450], [0.4104141778,0.2274713125], [0.4108833219,0.2212344319], [0.4109944356,0.2197597237], [0.4111714943,0.2174118150], [0.4112404293,0.2164983809], [0.4113085988,0.2155954751], [0.4114829782,0.2132875875], [0.4118471211,0.2084766701], [0.4120396284,0.2059381324], [0.4126998067,0.1972592212], [0.4135524922,0.1861154077], [0.4139115213,0.1814471928], [0.4140105118,0.1801627115], [0.4141069858,0.1789119971], [0.4144051778,0.1750532381], [0.4151431065,0.1655519011], [0.4151986117,0.1648400932], [0.4152578633,0.1640806927], [0.4156037319,0.1596573475], [0.4156466759,0.1591092789], [0.4157214432,0.1581556824], [0.4158089490,0.1570406116], [0.4161105489,0.1532057175], [0.4165466837,0.1476836195], [0.4169632344,0.1424362181], [0.4178159200,0.1317800534], [0.4183217051,0.1255159076], [0.4186686055,0.1212450299], [0.4186787865,0.1211200063], [0.4186814505,0.1210872954], [0.4187573183,0.1201562445], [0.4195212911,0.1108388954], [0.4196912985,0.1087802178], [0.4197446990,0.1081347040], [0.4203739766,0.1005693330], [0.4212266622,0.0904439550], [0.4220793477,0.0804702970], [0.4228425261,0.0716783516], [0.4229320333,0.0706558123], [0.4229376574,0.0705916230], [0.4230191873,0.0696619194], [0.4231561604,0.0681034315], [0.4237847188,0.0610078653], [0.4240658757,0.0578643885], [0.4245002961,0.0530451040], [0.4246374044,0.0515337262], [0.4246507852,0.0513864762], [0.4254169068,0.0430308882], [0.4254393427,0.0427884507], [0.4254579173,0.0425878348], [0.4254900899,0.0422405649], [0.4259170918,0.0376570251], [0.4263427755,0.0331354455], [0.4271954610,0.0242253203], [0.4274585653,0.0215164472], [0.4277141542,0.0189035410], [0.4280481466,0.0155170246], [0.4287973840,0.0080371276], [0.4289008321,0.0070172706], [0.4290926681,0.0051344203], [0.4291478856,0.0045944951], [0.4292687761,0.0034155882], [0.4297050276,-0.0008021133], [0.4297535177,-0.0012673577], [0.4298439841,-0.0021334331], [0.4299046629,-0.0027129362], [0.4300707096,-0.0042929680], [0.4306062032,-0.0093304099], [0.4307991437,-0.0111234744], [0.4309630779,-0.0126377513], [0.4310458812,-0.0133993772], [0.4314191740,-0.0168057931], [0.4314588888,-0.0171655750], [0.4318711408,-0.0208701208], [0.4323115744,-0.0247666858], [0.4323640925,-0.0252270614], [0.4323778282,-0.0253473187], [0.4324984386,-0.0264005891], [0.4326632565,-0.0278321036], [0.4331642599,-0.0321277250], [0.4332164032,-0.0325699452], [0.4332559566,-0.0329047785], [0.4332908224,-0.0331994892], [0.4333819941,-0.0339681867], [0.4334426579,-0.0344780954], [0.4337340703,-0.0369100367], [0.4338258496,-0.0376699375], [0.4339001649,-0.0382831180], [0.4340169455,-0.0392428295], [0.4345476674,-0.0435446602], [0.4346628819,-0.0444655343], [0.4348478610,-0.0459342397], [0.4348696310,-0.0461062958], [0.4353749557,-0.0500527542], [0.4355632364,-0.0514998544], [0.4357223166,-0.0527125847], [0.4362539063,-0.0566986161], [0.4363378884,-0.0573189226], [0.4363807794,-0.0576347267], [0.4364646315,-0.0582501762], [0.4365750021,-0.0590563260], [0.4374276877,-0.0651323234], [0.4374529676,-0.0653083224], [0.4378940237,-0.0683402368], [0.4380374175,-0.0693101147], [0.4382803732,-0.0709355587], [0.4383534569,-0.0714201053], [0.4383787512,-0.0715873315], [0.4387398436,-0.0739478443], [0.4391330588,-0.0764611964], [0.4391332880,-0.0764626443], [0.4393852604,-0.0780416508], [0.4393888542,-0.0780639924], [0.4396288252,-0.0795444322], [0.4397822029,-0.0804788439], [0.4398085693,-0.0806385438], [0.4399857443,-0.0817045878], [0.4401025811,-0.0824008119], [0.4404724574,-0.0845692636], [0.4408384299,-0.0866612754], [0.4411810648,-0.0885713055], [0.4415263797,-0.0904484668], [0.4415561080,-0.0906078198], [0.4416911154,-0.0913269970], [0.4416966088,-0.0913561036], [0.4417812955,-0.0918032620], [0.4417851622,-0.0918236092], [0.4421658579,-0.0937970853], [0.4422985945,-0.0944712620], [0.4423032247,-0.0944946493], [0.4424199989,-0.0950815637], [0.4424415629,-0.0951893336], [0.4425438010,-0.0956976891], [0.4426833911,-0.0963848289], [0.4427447461,-0.0966843135], [0.4433964865,-0.0997694912], [0.4434666353,-0.1000910500], [0.4437950990,-0.1015694125], [0.4438830561,-0.1019576390], [0.4442000924,-0.1033300618], [0.4442491721,-0.1035387491], [0.4447257997,-0.1055125495], [0.4451018576,-0.1070020186], [0.4458893498,-0.1099259077], [0.4459545432,-0.1101560686], [0.4461220094,-0.1107389276], [0.4461342828,-0.1107811704], [0.4462568346,-0.1111994144], [0.4468072287,-0.1129978846], [0.4468862824,-0.1132454372], [0.4470300929,-0.1136888213], [0.4470858412,-0.1138582850], [0.4472320330,-0.1142962628], [0.4472735211,-0.1144188641], [0.4476087225,-0.1153819101], [0.4476167235,-0.1154042984], [0.4476599143,-0.1155246713], [0.4483596140,-0.1173609793], [0.4485125998,-0.1177338558], [0.4487221788,-0.1182279460], [0.4491526219,-0.1191819743], [0.4493652854,-0.1196230903], [0.4496493598,-0.1201810691], [0.4497353421,-0.1203428969], [0.4497536984,-0.1203770201], [0.4498039672,-0.1204697004], [0.4501515607,-0.1210798084], [0.4502179709,-0.1211902547], [0.4508104132,-0.1220884801], [0.4508762980,-0.1221786821], [0.4510706565,-0.1224334587], [0.4513843057,-0.1228089196], [0.4517361098,-0.1231775371], [0.4519233420,-0.1233510443], [0.4522228486,-0.1235958014], [0.4524547621,-0.1237575671], [0.4525470994,-0.1238152261], [0.4527760276,-0.1239415880], [0.4528032170,-0.1239550242], [0.4531242504,-0.1240884232], [0.4531581815,-0.1240998012], [0.4531961700,-0.1241119223], [0.4536287131,-0.1242039021], [0.6184020756,-0.1243106190], [0.6184454865,-0.1250679966], [0.6190497100,-0.1355538383], [0.6196671059,-0.1461578293], [0.6199023956,-0.1501687797], [0.6199587998,-0.1511277725], [0.6207550811,-0.1645600367], [0.6207934439,-0.1652020771], [0.6209983076,-0.1686226116], [0.6212508782,-0.1728208152], [0.6216077667,-0.1787169427], [0.6219727086,-0.1847017514], [0.6219876207,-0.1849453356], [0.6224604522,-0.1926289728], [0.6225746064,-0.1944723125], [0.6233131378,-0.2062857523], [0.6239825747,-0.2168220720], [0.6239839713,-0.2168438789], [0.6240710760,-0.2182024938], [0.6240967403,-0.2186022478], [0.6241658233,-0.2196770641], [0.6243192383,-0.2220574690], [0.6245378035,-0.2254332346], [0.6246631475,-0.2273609009], [0.6250185089,-0.2327928572], [0.6252210187,-0.2358662376], [0.6252555762,-0.2363890811], [0.6256415168,-0.2421959227], [0.6256591138,-0.2424592645], [0.6258711944,-0.2456232543], [0.6259532367,-0.2468423375], [0.6267238800,-0.2581585591], [0.6275765655,-0.2703892646], [0.6277270264,-0.2725151175], [0.6284292511,-0.2823060601], [0.6286961317,-0.2859699288], [0.6287325749,-0.2864677680], [0.6292819366,-0.2938998386], [0.6299394401,-0.3026135805], [0.6301346222,-0.3051617041], [0.6304099012,-0.3087251433], [0.6307546121,-0.3131368690], [0.6309873077,-0.3160829787], [0.6310487510,-0.3168565668], [0.6313051128,-0.3200646101], [0.6318399933,-0.3266552094], [0.6318999444,-0.3273851798], [0.6321276444,-0.3301415343], [0.6325151072,-0.3347727595], [0.6326926788,-0.3368701750], [0.6332478210,-0.3433248264], [0.6334204619,-0.3453002275], [0.6335453644,-0.3467198926], [0.6336573460,-0.3479858928], [0.6336971764,-0.3484346386], [0.6339773887,-0.3515684940], [0.6343980499,-0.3561966244], [0.6344388464,-0.3566405571], [0.6348607985,-0.3611808023], [0.6352507355,-0.3652928835], [0.6356154423,-0.3690655298], [0.6361034210,-0.3740014404], [0.6364452642,-0.3773822703], [0.6368583045,-0.3813819896], [0.6369561066,-0.3823153290], [0.6369705750,-0.3824529550], [0.6370845178,-0.3835327566], [0.6375080245,-0.3874830959], [0.6378087921,-0.3902278522], [0.6382020221,-0.3937398281], [0.6382194765,-0.3938936953], [0.6386614777,-0.3977325875], [0.6387380999,-0.3983867820], [0.6389638372,-0.4002946383], [0.6391918052,-0.4021917537], [0.6395141632,-0.4048233927], [0.6403668488,-0.4114944107], [0.6404651097,-0.4122359285], [0.6407627079,-0.4144471837], [0.6407638189,-0.4144553413], [0.6412195343,-0.4177400749], [0.6420292062,-0.4232721666], [0.6420722199,-0.4235551138], [0.6422532320,-0.4247336695], [0.6424991181,-0.4263030876], [0.6429249054,-0.4289345559], [0.6437775910,-0.4338737337], [0.6437796731,-0.4338852516], [0.6438234593,-0.4341268612], [0.6446302765,-0.4383682882], [0.6448007468,-0.4392131548], [0.6449820746,-0.4400921146], [0.6454829621,-0.4424141729], [0.6460598213,-0.4448949556], [0.6463356476,-0.4460076574], [0.6464264773,-0.4463636359], [0.6471883332,-0.4491453310], [0.6475091364,-0.4502071611], [0.6478721399,-0.4513301403], [0.6480410188,-0.4518241062], [0.6483680612,-0.4527291844], [0.6486736402,-0.4535133600], [0.6488937043,-0.4540412215], [0.6490445958,-0.4543852976], [0.6490769649,-0.4544572142], [0.6496691777,-0.4556546720], [0.6497141963,-0.4557365144], [0.6497463899,-0.4557942443], [0.6502262359,-0.4565758810], [0.6504014060,-0.4568243693], [0.6505990754,-0.4570810740], [0.6508915100,-0.4574147149], [0.6512142266,-0.4577189410], [0.6513561638,-0.4578314766], [0.6514517610,-0.4578999436], [0.6518148357,-0.4581062052], [0.6523044465,-0.4582494224], [0.8236942423,-0.4656399170], [0.8244611317,-0.4810280805], [0.8245469278,-0.4827307625], [0.8247849896,-0.4874350785], [0.8249379580,-0.4904421186], [0.8252764784,-0.4970524946], [0.8253052504,-0.4976115054], [0.8253996134,-0.4994417560], [0.8254900917,-0.5011921484], [0.8256108952,-0.5035222955], [0.8259901515,-0.5107859382], [0.8262522989,-0.5157603793], [0.8263781215,-0.5181343938], [0.8263998990,-0.5185443940], [0.8264879094,-0.5201986387], [0.8266323925,-0.5229049146], [0.8266609167,-0.5234378040], [0.8267493168,-0.5250863775], [0.8269624824,-0.5290434770], [0.8271049845,-0.5316743727], [0.8271058621,-0.5316905391], [0.8271631321,-0.5327445643], [0.8279576700,-0.5471717444], [0.8286341178,-0.5591619086], [0.8286675882,-0.5597480764], [0.8288103556,-0.5622407796], [0.8289117204,-0.5640031175], [0.8293682480,-0.5718628113], [0.8296630411,-0.5768700500], [0.8302180569,-0.5861507110], [0.8305157267,-0.5910484223], [0.8308126715,-0.5958781397], [0.8310499835,-0.5996973866], [0.8313684122,-0.6047650671], [0.8322210978,-0.6180094673], [0.8326425958,-0.6243788564], [0.8330737833,-0.6307714262], [0.8331690621,-0.6321670428], [0.8335579400,-0.6377991851], [0.8335962178,-0.6383479833], [0.8339264689,-0.6430410760], [0.8343756460,-0.6493032557], [0.8345459403,-0.6516407149], [0.8347791544,-0.6548088851], [0.8349253950,-0.6567760459], [0.8356318400,-0.6660656663], [0.8357570559,-0.6676751432], [0.8364845255,-0.6768025834], [0.8368088552,-0.6807482529], [0.8368332286,-0.6810416698], [0.8370031084,-0.6830746718], [0.8371853783,-0.6852324055], [0.8373372111,-0.6870111593], [0.8379621683,-0.6941530781], [0.8380928583,-0.6956098636], [0.8381898966,-0.6966832817], [0.8382094851,-0.6968991112], [0.8387351664,-0.7025835533], [0.8387982285,-0.7032514909], [0.8390425822,-0.7058112109], [0.8393742696,-0.7092132857], [0.8397852094,-0.7133118602], [0.8398312494,-0.7137629928], [0.8398952677,-0.7143875855], [0.8400976177,-0.7163410894], [0.8407479533,-0.7224054283], [0.8410918730,-0.7254797457], [0.8412498750,-0.7268611987], [0.8416006388,-0.7298581530], [0.8417113899,-0.7307843563], [0.8424533244,-0.7367395687], [0.8433060099,-0.7430438863], [0.8438789555,-0.7469530783], [0.8441586955,-0.7487657224], [0.8442747933,-0.7494994499], [0.8448382803,-0.7529055420], [0.8450113810,-0.7539001049], [0.8458139940,-0.7581921713], [0.8458640666,-0.7584424769], [0.8462369389,-0.7602417031], [0.8464098786,-0.7610374021], [0.8467167521,-0.7623887010], [0.8467297492,-0.7624442189], [0.8467865333,-0.7626851412], [0.8475694377,-0.7657350633], [0.8475873208,-0.7657987988], [0.8479958367,-0.7671824333], [0.8481982545,-0.7678166157], [0.8484221232,-0.7684782767], [0.8486716850,-0.7691666322], [0.8488883339,-0.7697220602], [0.8489466629,-0.7698649017], [0.8490424709,-0.7700933541], [0.8492748088,-0.7706154841], [0.8498940763,-0.7717863694], [0.8498956098,-0.7717888699], [0.8501274943,-0.7721442618], [0.8509801799,-0.7730626213], [0.8511956627,-0.7731978855], [0.8513782046,-0.7732818778], [0.8517201435,-0.7733636553], [0.8518328654,-0.7733690123]]deap-1.0.1/examples/ga/pareto_front/zdt4_front.json0000644000076500000240000007051012117373622022562 0ustar felixstaff00000000000000[[0.0000000001,0.9999894726], [0.0002105478,0.9854897362], [0.0006312647,0.9748750180], [0.0011630803,0.9658960376], [0.0017746306,0.9578736358], [0.0024505398,0.9504970728], [0.0031768967,0.9436360339], [0.0039503274,0.9371483698], [0.0047628349,0.9309867048], [0.0056133085,0.9250779837], [0.0064844270,0.9194740603], [0.0073825092,0.9140784704], [0.0083046512,0.9088701410], [0.0092513217,0.9038162085], [0.0102235345,0.8988885045], [0.0112037024,0.8941524567], [0.0122063005,0.8895178727], [0.0132289862,0.8849826699], [0.0142533197,0.8806127324], [0.0152866946,0.8763606266], [0.0163381500,0.8721792270], [0.0173920785,0.8681209701], [0.0184482339,0.8641757241], [0.0195148773,0.8603043403], [0.0205965083,0.8564851634], [0.0216666513,0.8528040377], [0.0227383129,0.8492077161], [0.0238229080,0.8456532864], [0.0249019551,0.8421964667], [0.0259929519,0.8387767019], [0.0270660633,0.8354823314], [0.0281500276,0.8322203004], [0.0292333708,0.8290223089], [0.0303128734,0.8258940741], [0.0314021618,0.8227934486], [0.0324911066,0.8197471037], [0.0335765797,0.8167608674], [0.0346709704,0.8137985757], [0.0357370146,0.8109576380], [0.0368111297,0.8081377324], [0.0378895903,0.8053475139], [0.0389532784,0.8026341508], [0.0400243289,0.7999391870], [0.0410894955,0.7972945598], [0.0421616561,0.7946669628], [0.0432213442,0.7921025634], [0.0442876083,0.7895537876], [0.0453412088,0.7870652476], [0.0463909832,0.7846143384], [0.0474467646,0.7821772174], [0.0484983540,0.7797765817], [0.0495450062,0.7774129245], [0.0505853782,0.7750880658], [0.0516311551,0.7727751001], [0.0526673294,0.7705063631], [0.0537086508,0.7682487307], [0.0547368762,0.7660408665], [0.0557699761,0.7638433229], [0.0568037616,0.7616646026], [0.0578314646,0.7595182655], [0.0588637743,0.7573814221], [0.0598984973,0.7552583048], [0.0609298646,0.7531602451], [0.0619606711,0.7510809949], [0.0629836759,0.7490345126], [0.0640108688,0.7469963068], [0.0650299931,0.7449902099], [0.0660450761,0.7430076342], [0.0670640897,0.7410326474], [0.0680825901,0.7390735925], [0.0691035230,0.7371245105], [0.0701179135,0.7352021270], [0.0711359997,0.7332866714], [0.0721531501,0.7313866160], [0.0731706023,0.7294993488], [0.0741806823,0.7276386915], [0.0751924292,0.7257876203], [0.0762062164,0.7239452655], [0.0772233979,0.7221090179], [0.0782425560,0.7202812914], [0.0792510937,0.7184842922], [0.0802628607,0.7166929921], [0.0812759972,0.7149105453], [0.0822884477,0.7131403693], [0.0833012730,0.7113804009], [0.0843171957,0.7096257661], [0.0853334216,0.7078811516], [0.0863411931,0.7061612805], [0.0873519226,0.7044464133], [0.0883637196,0.7027396435], [0.0893732618,0.7010463885], [0.0903856712,0.6993579018], [0.0913949697,0.6976839903], [0.0924070702,0.6960146875], [0.0934200164,0.6943531181], [0.0944275417,0.6927093530], [0.0954377690,0.6910699610], [0.0964475333,0.6894399682], [0.0974559260,0.6878206830], [0.0984669407,0.6862055758], [0.0994732780,0.6846061542], [0.1004805510,0.6830133268], [0.1014903611,0.6814244813], [0.1024949925,0.6798516087], [0.1035020978,0.6782825809], [0.1045074114,0.6767239394], [0.1055120333,0.6751738415], [0.1065190579,0.6736274247], [0.1075236424,0.6720920215], [0.1085305843,0.6705601963], [0.1095359091,0.6690379038], [0.1105435514,0.6675190963], [0.1115512301,0.6660071407], [0.1125570012,0.6645048418], [0.1135639375,0.6630075113], [0.1145731157,0.6615134925], [0.1155784246,0.6600317301], [0.1165859290,0.6585531828], [0.1175892437,0.6570871193], [0.1185930860,0.6556265313], [0.1195990615,0.6541690275], [0.1206040643,0.6527190413], [0.1216111695,0.6512720695], [0.1226168930,0.6498330498], [0.1236222729,0.6484004083], [0.1246297053,0.6469706736], [0.1256343337,0.6455506613], [0.1266409786,0.6441334821], [0.1276471982,0.6427225193], [0.1286513413,0.6413200016], [0.1296564281,0.6399216362], [0.1306634703,0.6385259756], [0.1316668478,0.6371407329], [0.1326721441,0.6357581242], [0.1336779496,0.6343800476], [0.1346824492,0.6330089249], [0.1356853479,0.6316450789], [0.1366873372,0.6302874938], [0.1376911695,0.6289323922], [0.1386940586,0.6275834878], [0.1396967886,0.6262396642], [0.1407006623,0.6248991305], [0.1417038828,0.6235642382], [0.1427088852,0.6222317044], [0.1437113389,0.6209072159], [0.1447140663,0.6195869794], [0.1457185366,0.6182690258], [0.1467241957,0.6169540553], [0.1477281975,0.6156457395], [0.1487317722,0.6143424158], [0.1497370456,0.6130412870], [0.1507395611,0.6117480700], [0.1517437491,0.6104569997], [0.1527491415,0.6091686534], [0.1537521086,0.6078876327], [0.1547567167,0.6066086977], [0.1557592375,0.6053365516], [0.1567622432,0.6040678806], [0.1577668585,0.6028012356], [0.1587687449,0.6015420413], [0.1597722169,0.6002848303], [0.1607742633,0.5990333389], [0.1617765541,0.5977854377], [0.1627804021,0.5965394665], [0.1637844766,0.5952970514], [0.1647863012,0.5940612100], [0.1657896532,0.5928272441], [0.1667919637,0.5915982815], [0.1677957846,0.5903711624], [0.1687999261,0.5891473183], [0.1698025772,0.5879289173], [0.1708067129,0.5867123122], [0.1718093417,0.5855010957], [0.1728134375,0.5842916436], [0.1738186323,0.5830843822], [0.1748211768,0.5818837759], [0.1758239238,0.5806863659], [0.1768265821,0.5794924708], [0.1778306658,0.5783002658], [0.1788332145,0.5771132368], [0.1798358264,0.5759294559], [0.1808398397,0.5747473225], [0.1818418499,0.5735708149], [0.1828452443,0.5723959257], [0.1838468916,0.5712262933], [0.1848493707,0.5700588753], [0.1858532128,0.5688930379], [0.1868559327,0.5677316427], [0.1878592674,0.5665726503], [0.1888620073,0.5654174333], [0.1898660817,0.5642637475], [0.1908674683,0.5631161845], [0.1918701717,0.5619701247], [0.1928737645,0.5608260430], [0.1938761620,0.5596862914], [0.1948789191,0.5585490751], [0.1958829695,0.5574133198], [0.1968839484,0.5562839327], [0.1978851741,0.5551571355], [0.1988876695,0.5540317618], [0.1998903999,0.5529089579], [0.2008922998,0.5517898932], [0.2018954520,0.5506722221], [0.2028989078,0.5495569871], [0.2039007023,0.5484463462], [0.2049011516,0.5473399160], [0.2059028251,0.5462348348], [0.2069049004,0.5451319967], [0.2079081919,0.5440304923], [0.2089091658,0.5429341778], [0.2099113416,0.5418391749], [0.2109132670,0.5407470555], [0.2119152403,0.5396574750], [0.2129184008,0.5385691809], [0.2139198230,0.5374853267], [0.2149224200,0.5364027394], [0.2159250466,0.5353226425], [0.2169277369,0.5342449818], [0.2179315886,0.5331685651], [0.2189353258,0.5320947469], [0.2199402161,0.5310221582], [0.2209406566,0.5299567503], [0.2219422321,0.5288925472], [0.2229444987,0.5278300108], [0.2239468923,0.5267697259], [0.2249504102,0.5257106261], [0.2259511071,0.5246568533], [0.2269529145,0.5236042459], [0.2279546225,0.5225540632], [0.2289564315,0.5215060799], [0.2299583451,0.5204602779], [0.2309608549,0.5194161312], [0.2319644550,0.5183731164], [0.2329659947,0.5173344899], [0.2339686130,0.5162969785], [0.2349707108,0.5152622247], [0.2359718198,0.5142306929], [0.2369739928,0.5132002539], [0.2379751637,0.5121730186], [0.2389762694,0.5111480087], [0.2399774526,0.5101250643], [0.2409787263,0.5091041594], [0.2419805638,0.5080848002], [0.2429834403,0.5070664951], [0.2439855244,0.5060510913], [0.2449886396,0.5050367290], [0.2459906664,0.5040255386], [0.2469925755,0.5030165239], [0.2479955026,0.5020085316], [0.2489970506,0.5010039573], [0.2499996077,0.5000003923], [0.2510019167,0.4989990851], [0.2520038840,0.4980001156], [0.2530057956,0.4970031853], [0.2540087011,0.4960072410], [0.2550103471,0.4950145080], [0.2560129786,0.4940227489], [0.2570152630,0.4930332723], [0.2580171398,0.4920461244], [0.2590199910,0.4910599338], [0.2600208175,0.4900776358], [0.2610226089,0.4890962822], [0.2620252361,0.4881159935], [0.2630267801,0.4871386346], [0.2640292794,0.4861622052], [0.2650319605,0.4851874511], [0.2660333481,0.4842157931], [0.2670347118,0.4832459852], [0.2680357884,0.4822782712], [0.2690371011,0.4813121352], [0.2700393471,0.4803468973], [0.2710418231,0.4793832282], [0.2720440694,0.4784215597], [0.2730454143,0.4774625235], [0.2740471733,0.4765048488], [0.2750498495,0.4755480485], [0.2760526149,0.4745929055], [0.2770562927,0.4736386292], [0.2780569657,0.4726889289], [0.2790577919,0.4717407910], [0.2800595170,0.4707935025], [0.2810605780,0.4698485329], [0.2820625321,0.4689044040], [0.2830633674,0.4679630018], [0.2840650890,0.4670224310], [0.2850662377,0.4660840537], [0.2860682669,0.4651464996], [0.2870698581,0.4642109948], [0.2880712825,0.4632772759], [0.2890735788,0.4623443678], [0.2900757018,0.4614132365], [0.2910783508,0.4604832248], [0.2920798864,0.4595558434], [0.2930822820,0.4586292564], [0.2940842385,0.4577046575], [0.2950856983,0.4567820894], [0.2960870729,0.4558611640], [0.2970886374,0.4549416202], [0.2980910475,0.4540228508], [0.2990936425,0.4531054558], [0.3000950324,0.4521906971], [0.3010967550,0.4512771601], [0.3020993122,0.4503643824], [0.3031014386,0.4494535092], [0.3041029020,0.4485447416], [0.3051051913,0.4476367216], [0.3061067375,0.4467308635], [0.3071080050,0.4458267373], [0.3081100901,0.4449233476], [0.3091118903,0.4440216818], [0.3101127726,0.4431223001], [0.3111144638,0.4422236436], [0.3121167514,0.4413258988], [0.3131186269,0.4404299625], [0.3141199527,0.4395359488], [0.3151211702,0.4386434554], [0.3161229768,0.4377518547], [0.3171255784,0.4368609600], [0.3181264009,0.4359730495], [0.3191280118,0.4350858368], [0.3201301025,0.4341995913], [0.3211313107,0.4333155104], [0.3221333005,0.4324321182], [0.3231351783,0.4315501972], [0.3241358160,0.4306707315], [0.3251372272,0.4297919439], [0.3261379206,0.4289151372], [0.3271393827,0.4280390025], [0.3281401366,0.4271648260], [0.3291410306,0.4262918594], [0.3301426868,0.4254195559], [0.3311443335,0.4245485829], [0.3321446186,0.4236801074], [0.3331456580,0.4228122853], [0.3341469535,0.4219455445], [0.3351488093,0.4210796175], [0.3361510327,0.4202146667], [0.3371524512,0.4193516975], [0.3381546145,0.4184893685], [0.3391566314,0.4176284421], [0.3401593896,0.4167681511], [0.3411604553,0.4159105759], [0.3421620297,0.4150538233], [0.3431643380,0.4141976972], [0.3441651948,0.4133440576], [0.3451662555,0.4124914847], [0.3461676576,0.4116398572], [0.3471697849,0.4107888452], [0.3481709012,0.4099399173], [0.3491727381,0.4090915993], [0.3501746238,0.4082444560], [0.3511757655,0.4073991516], [0.3521774192,0.4065546199], [0.3531797861,0.4057106882], [0.3541826948,0.4048674982], [0.3551827727,0.4040278759], [0.3561835555,0.4031888443], [0.3571849903,0.4023504453], [0.3581857802,0.4015137594], [0.3591872702,0.4006776575], [0.3601882868,0.3998431148], [0.3611899999,0.3990091516], [0.3621908668,0.3981770470], [0.3631924261,0.3973455168], [0.3641936381,0.3965154202], [0.3651955391,0.3956858936], [0.3661969049,0.3948579465], [0.3671984667,0.3940309689], [0.3682007123,0.3932045548], [0.3692019399,0.3923801024], [0.3702038472,0.3915562087], [0.3712055375,0.3907336071], [0.3722079045,0.3899115601], [0.3732089877,0.3890916700], [0.3742100083,0.3882729299], [0.3752116992,0.3874547370], [0.3762133237,0.3866376897], [0.3772148257,0.3858218291], [0.3782157057,0.3850075564], [0.3792172488,0.3841938221], [0.3802181001,0.3833817225], [0.3812196110,0.3825701570], [0.3822206018,0.3817600775], [0.3832222488,0.3809505280], [0.3842235710,0.3801422978], [0.3852251764,0.3793348919], [0.3862274336,0.3785280106], [0.3872282277,0.3777233512], [0.3882296692,0.3769192113], [0.3892307415,0.3761164039], [0.3902324583,0.3753141123], [0.3912346802,0.3745124460], [0.3922368074,0.3737118814], [0.3932378133,0.3729132330], [0.3942394571,0.3721150925], [0.3952405845,0.3713183759], [0.3962423467,0.3705221635], [0.3972435960,0.3697273637], [0.3982454771,0.3689330645], [0.3992472187,0.3681398741], [0.4002480479,0.3673483992], [0.4012489559,0.3665578512], [0.4022496843,0.3657684301], [0.4032503529,0.3649800374], [0.4042516431,0.3641921335], [0.4052527434,0.3634053539], [0.4062544628,0.3626190599], [0.4072557686,0.3618340587], [0.4082576907,0.3610495398], [0.4092589953,0.3602664654], [0.4102609131,0.3594838698], [0.4112626065,0.3587024041], [0.4122636316,0.3579224100], [0.4132652652,0.3571428890], [0.4142674430,0.3563638893], [0.4152688799,0.3555864062], [0.4162709214,0.3548093915], [0.4172720122,0.3540340472], [0.4182737041,0.3532591677], [0.4192747583,0.3524857080], [0.4202764107,0.3517127097], [0.4212772980,0.3509412214], [0.4222787805,0.3501701911], [0.4232804027,0.3493999672], [0.4242813186,0.3486311962], [0.4252828255,0.3478628783], [0.4262837535,0.3470959079], [0.4272846573,0.3463298559], [0.4282856329,0.3455646457], [0.4292871941,0.3447998824], [0.4302888245,0.3440359579], [0.4312904321,0.3432729394], [0.4322910343,0.3425115710], [0.4332919868,0.3417508171], [0.4342935181,0.3409905023], [0.4352953978,0.3402307996], [0.4362973516,0.3394719146], [0.4372982381,0.3387147075], [0.4382996979,0.3379579335], [0.4393006265,0.3372024242], [0.4403021258,0.3364473451], [0.4413034896,0.3356932263], [0.4423054221,0.3349395350], [0.4433068964,0.3341870410], [0.4443085740,0.3334352440], [0.4453096867,0.3326847172], [0.4463107974,0.3319350351], [0.4473124701,0.3311857731], [0.4483130258,0.3304381837], [0.4493141404,0.3296910113], [0.4503155791,0.3289444292], [0.4513163666,0.3281991615], [0.4523176354,0.3274543618], [0.4533194590,0.3267099741], [0.4543204053,0.3259670592], [0.4553219036,0.3252245532], [0.4563237931,0.3244825738], [0.4573242750,0.3237424492], [0.4583253047,0.3230027292], [0.4593262806,0.3222638562], [0.4603278024,0.3215253856], [0.4613291449,0.3207878498], [0.4623310314,0.3200507141], [0.4633320623,0.3193150051], [0.4643336345,0.3185796932], [0.4653356544,0.3178448458], [0.4663361052,0.3171119380], [0.4673370931,0.3163794231], [0.4683378402,0.3156478683], [0.4693391225,0.3149167040], [0.4703402348,0.3141864431], [0.4713410174,0.3134571991], [0.4723423318,0.3127283420], [0.4733434528,0.3120003977], [0.4743451036,0.3112728380], [0.4753467082,0.3105460797], [0.4763473095,0.3098208135], [0.4773484369,0.3090959279], [0.4783495419,0.3083718182], [0.4793511712,0.3076480871], [0.4803522870,0.3069254824], [0.4813539248,0.3062032539], [0.4823555688,0.3054817722], [0.4833565520,0.3047615143], [0.4843580541,0.3040416291], [0.4853584322,0.3033232943], [0.4863589651,0.3026055886], [0.4873600131,0.3018882517], [0.4883612140,0.3011715418], [0.4893620785,0.3004558066], [0.4903634552,0.2997404372], [0.4913642855,0.2990261877], [0.4923653881,0.2983124712], [0.4933670001,0.2975991172], [0.4943688830,0.2968862944], [0.4953700904,0.2961746734], [0.4963718042,0.2954634117], [0.4973732519,0.2947530561], [0.4983752042,0.2940430578], [0.4993770732,0.2933338319], [0.5003793995,0.2926249937], [0.5013798719,0.2919181743], [0.5023808440,0.2912117072], [0.5033817843,0.2905059660], [0.5043832226,0.2898005755], [0.5053841880,0.2890962175], [0.5063856496,0.2883922081], [0.5073868334,0.2876890894], [0.5083877579,0.2869868459], [0.5093891756,0.2862849479], [0.5103907542,0.2855836269], [0.5113920834,0.2848831680], [0.5123939033,0.2841830518], [0.5133954642,0.2834838005], [0.5143963096,0.2827857296], [0.5153972877,0.2820882452], [0.5163983179,0.2813914015], [0.5173998337,0.2806948952], [0.5184013999,0.2799990278], [0.5194023355,0.2793042698], [0.5204032304,0.2786102091], [0.5214043454,0.2779166631], [0.5224054528,0.2772237879], [0.5234070402,0.2765312445], [0.5244083215,0.2758395748], [0.5254100811,0.2751482351], [0.5264109890,0.2744581411], [0.5274123732,0.2737683750], [0.5284133357,0.2730795534], [0.5294144273,0.2723912952], [0.5304153500,0.2717038034], [0.5314167455,0.2710166357], [0.5324178038,0.2703303461], [0.5334193331,0.2696443790], [0.5344202693,0.2689594612], [0.5354216747,0.2682748640], [0.5364229975,0.2675909630], [0.5374247881,0.2669073809], [0.5384256524,0.2662250670], [0.5394265638,0.2655433547], [0.5404279400,0.2648619585], [0.5414289726,0.2641814268], [0.5424304684,0.2635012095], [0.5434313875,0.2628220109], [0.5444327680,0.2621431250], [0.5454332114,0.2614654975], [0.5464341140,0.2607881805], [0.5474353065,0.2601112878], [0.5484366190,0.2594349326], [0.5494372200,0.2587596746], [0.5504379207,0.2580849640], [0.5514390766,0.2574105599], [0.5524403307,0.2567367017], [0.5534419480,0.2560632097], [0.5544431444,0.2553906095], [0.5554444467,0.2547185453], [0.5564456248,0.2540471699], [0.5574472537,0.2533760962], [0.5584478796,0.2527062963], [0.5594489541,0.2520367963], [0.5604499619,0.2513679395], [0.5614512555,0.2506994892], [0.5624529959,0.2500313367], [0.5634541722,0.2493641547], [0.5644550290,0.2486977779], [0.5654563297,0.2480316963], [0.5664573800,0.2473663707], [0.5674588728,0.2467013389], [0.5684603141,0.2460369279], [0.5694611285,0.2453735172], [0.5704623831,0.2447103979], [0.5714634373,0.2440479927], [0.5724649304,0.2433858775], [0.5734659813,0.2427246331], [0.5744674695,0.2420636772], [0.5754682081,0.2414037911], [0.5764693822,0.2407441919], [0.5774704071,0.2400852633], [0.5784718661,0.2394266202], [0.5794729710,0.2387687795], [0.5804745086,0.2381112229], [0.5814755874,0.2374545342], [0.5824770975,0.2367981280], [0.5834781770,0.2361425676], [0.5844796864,0.2354872883], [0.5854812475,0.2348325363], [0.5864819621,0.2341788968], [0.5874826373,0.2335258404], [0.5884833481,0.2328733168], [0.5894844847,0.2322210704], [0.5904856555,0.2315693554], [0.5914866485,0.2309183083], [0.5924878516,0.2302676754], [0.5934891833,0.2296175084], [0.5944909378,0.2289676156], [0.5954917979,0.2283188496], [0.5964925865,0.2276706748], [0.5974937953,0.2270227718], [0.5984953253,0.2263752038], [0.5994965587,0.2257283689], [0.6004982106,0.2250818039], [0.6015002183,0.2244355486], [0.6025013431,0.2237904000], [0.6035022941,0.2231458991], [0.6045033654,0.2225018550], [0.6055046739,0.2218581916], [0.6065063968,0.2212147942], [0.6075067706,0.2205727932], [0.6085075567,0.2199310565], [0.6095084202,0.2192897976], [0.6105096950,0.2186488018], [0.6115107578,0.2180084669], [0.6125122307,0.2173683940], [0.6135136639,0.2167288695], [0.6145149966,0.2160899308], [0.6155167376,0.2154512523], [0.6165178715,0.2148134798], [0.6175194122,0.2141759661], [0.6185202474,0.2135394178], [0.6195210710,0.2129033916], [0.6205222991,0.2122676222], [0.6215235142,0.2116323737], [0.6225247801,0.2109976045], [0.6235264489,0.2103630905], [0.6245270350,0.2097297709], [0.6255276077,0.2090969669], [0.6265285809,0.2084644159], [0.6275298171,0.2078322040], [0.6285311785,0.2072004172], [0.6295320318,0.2065694537], [0.6305332832,0.2059387409], [0.6315344158,0.2053086034], [0.6325354850,0.2046790051], [0.6335369506,0.2040496557], [0.6345379412,0.2034211017], [0.6355390288,0.2027929825], [0.6365405109,0.2021651105], [0.6375414081,0.2015380985], [0.6385422167,0.2009116340], [0.6395434177,0.2002854149], [0.6405441118,0.1996600024], [0.6415451971,0.1990348340], [0.6425464899,0.1984100238], [0.6435472938,0.1977860050], [0.6445484870,0.1971622287], [0.6455498066,0.1965388581], [0.6465509890,0.1959160560], [0.6475520639,0.1952938027], [0.6485535260,0.1946717899], [0.6495552002,0.1940501255], [0.6505561784,0.1934293717], [0.6515571679,0.1928090883], [0.6525585422,0.1921890430], [0.6535599267,0.1915694670], [0.6545608471,0.1909506523], [0.6555621504,0.1903320740], [0.6565630740,0.1897142023], [0.6575643794,0.1890965660], [0.6585650162,0.1884798116], [0.6595656632,0.1878635194], [0.6605666900,0.1872474608], [0.6615674655,0.1866320233], [0.6625684896,0.1860168984], [0.6635698921,0.1854020058], [0.6645709906,0.1847877634], [0.6655724665,0.1841737523], [0.6665742784,0.1835599970], [0.6675753583,0.1829471508], [0.6685768138,0.1823345343], [0.6695776224,0.1817227717], [0.6705788052,0.1811112376], [0.6715797575,0.1805003005], [0.6725807373,0.1798898018], [0.6735820898,0.1792795301], [0.6745834687,0.1786696957], [0.6755850054,0.1780602179], [0.6765862213,0.1774513867], [0.6775878078,0.1768427806], [0.6785892295,0.1762347243], [0.6795910208,0.1756268922], [0.6805918094,0.1750201158], [0.6815929661,0.1744135623], [0.6825942324,0.1738073878], [0.6835950796,0.1732019112], [0.6845962934,0.1725966561], [0.6855974192,0.1719918967], [0.6865989106,0.1713873579], [0.6875995449,0.1707837768], [0.6886002694,0.1701805802], [0.6896013578,0.1695776028], [0.6906025356,0.1689750090], [0.6916034324,0.1683730209], [0.6926046915,0.1677712505], [0.6936054767,0.1671701995], [0.6946062606,0.1665695826], [0.6956072227,0.1659692915], [0.6966085451,0.1653692163], [0.6976100449,0.1647694660], [0.6986105718,0.1641707281], [0.6996114572,0.1635722044], [0.7006121260,0.1629742382], [0.7016131523,0.1623764854], [0.7026143784,0.1617790396], [0.7036156464,0.1611819945], [0.7046166041,0.1605855588], [0.7056179175,0.1599893349], [0.7066188136,0.1593937821], [0.7076197944,0.1587986006], [0.7086211294,0.1582036295], [0.7096225483,0.1576090288], [0.7106241994,0.1570147099], [0.7116252814,0.1564211469], [0.7126263761,0.1558279938], [0.7136278225,0.1552350490], [0.7146287960,0.1546427998], [0.7156301202,0.1540507579], [0.7166316652,0.1534589997], [0.7176326872,0.1528679636], [0.7186340584,0.1522771335], [0.7196350244,0.1516869537], [0.7206363388,0.1510969792], [0.7216369658,0.1505078189], [0.7226379399,0.1499188628], [0.7236388529,0.1493303503], [0.7246401122,0.1487420413], [0.7256407605,0.1481544973], [0.7266416984,0.1475671883], [0.7276429812,0.1469800816], [0.7286443305,0.1463933397], [0.7296457222,0.1458069760], [0.7306471410,0.1452209987], [0.7316479446,0.1446357825], [0.7326490906,0.1440507663], [0.7336502408,0.1434661473], [0.7346517328,0.1428817277], [0.7356528242,0.1422979397], [0.7366542565,0.1417143503], [0.7376554901,0.1411312731], [0.7386560386,0.1405489900], [0.7396569262,0.1399669040], [0.7406577527,0.1393852472], [0.7416589175,0.1388037869], [0.7426598805,0.1382228359], [0.7436611811,0.1376420806], [0.7446621188,0.1370619264], [0.7456633931,0.1364819671], [0.7466643144,0.1359026013], [0.7476655714,0.1353234296], [0.7486667367,0.1347446986], [0.7496678093,0.1341664078], [0.7506692164,0.1335883101], [0.7516700907,0.1330109051], [0.7526712983,0.1324336923], [0.7536726537,0.1318567781], [0.7546736671,0.1312804439], [0.7556750126,0.1307043008], [0.7566759716,0.1301287615], [0.7576772617,0.1295534125], [0.7586783077,0.1289785837], [0.7596796842,0.1284039444], [0.7606807290,0.1278298738], [0.7616821035,0.1272559920], [0.7626835892,0.1266824236], [0.7636844218,0.1261096054], [0.7646855826,0.1255369747], [0.7656866915,0.1249647484], [0.7666881279,0.1243927091], [0.7676890053,0.1238213622], [0.7686898894,0.1232503839], [0.7696910995,0.1226795913], [0.7706921334,0.1221092702], [0.7716934926,0.1215391343], [0.7726940395,0.1209698301], [0.7736949106,0.1204007102], [0.7746957206,0.1198319930], [0.7756964987,0.1192636611], [0.7766975999,0.1186955124], [0.7776986208,0.1181277753], [0.7786999640,0.1175602208], [0.7797013908,0.1169929837], [0.7807028390,0.1164260988], [0.7817032335,0.1158601731], [0.7827038182,0.1152945020], [0.7837047229,0.1147290116], [0.7847058172,0.1141637752], [0.7857067437,0.1135989939], [0.7867076609,0.1130345774], [0.7877088967,0.1124703404], [0.7887101225,0.1119064675], [0.7897110968,0.1113430939], [0.7907121409,0.1107800380], [0.7917135020,0.1102171602], [0.7927145190,0.1096548315], [0.7937158522,0.1090926803], [0.7947168235,0.1085310866], [0.7957181102,0.1079696697], [0.7967194474,0.1074085776], [0.7977202934,0.1068481129], [0.7987214536,0.1062878240], [0.7997222983,0.1057280624], [0.8007234564,0.1051684760], [0.8017248350,0.1046091161], [0.8027255034,0.1040505018], [0.8037264838,0.1034920615], [0.8047276559,0.1029338620], [0.8057284549,0.1023762175], [0.8067295650,0.1018187460], [0.8077305471,0.1012616915], [0.8087314389,0.1007050323], [0.8097326406,0.1001485453], [0.8107340433,0.0995922905], [0.8117355382,0.0990363280], [0.8127364320,0.0984810418], [0.8137376341,0.0979259265], [0.8147389434,0.0973710932], [0.8157398773,0.0968168085], [0.8167411185,0.0962626938], [0.8177419646,0.0957091372], [0.8187431170,0.0951557499], [0.8197441544,0.0946027643], [0.8207451668,0.0940501301], [0.8217464845,0.0934976644], [0.8227473602,0.0929457788], [0.8237482445,0.0923942241], [0.8247494330,0.0918428368], [0.8257506293,0.0912917799], [0.8267520451,0.0907409362], [0.8277530787,0.0901906361], [0.8287544152,0.0896405022], [0.8297557396,0.0890907073], [0.8307573662,0.0885410781], [0.8317586416,0.0879919729], [0.8327598709,0.0874432232], [0.8337614014,0.0868946384], [0.8347625903,0.0863465699], [0.8357636924,0.0857988775], [0.8367650944,0.0852513490], [0.8377664088,0.0847041960], [0.8387670887,0.0841577163], [0.8397680672,0.0836113994], [0.8407691192,0.0830653681], [0.8417702211,0.0825196345], [0.8427716209,0.0819740630], [0.8437722831,0.0814292171], [0.8447732422,0.0808845327], [0.8457744425,0.0803400397], [0.8467755021,0.0797959454], [0.8477768577,0.0792520119], [0.8487777194,0.0787086675], [0.8497788763,0.0781654832], [0.8507800883,0.0776225890], [0.8517815949,0.0770798545], [0.8527822920,0.0765378773], [0.8537832828,0.0759960591], [0.8547841926,0.0754546022], [0.8557852306,0.0749133929], [0.8567865615,0.0743723419], [0.8577873019,0.0738319257], [0.8587883345,0.0732916670], [0.8597894874,0.0727516582], [0.8607903014,0.0722121463], [0.8617914065,0.0716727913], [0.8627922701,0.0711338793], [0.8637934242,0.0705951236], [0.8647943790,0.0700567872], [0.8657955010,0.0695186724], [0.8667969127,0.0689807130], [0.8677978471,0.0684433205], [0.8687990703,0.0679060829], [0.8697998649,0.0673693845], [0.8708007436,0.0668329498], [0.8718016519,0.0662968074], [0.8728028478,0.0657608188], [0.8738040727,0.0652251219], [0.8748049300,0.0646899284], [0.8758060737,0.0641548880], [0.8768071610,0.0636201834], [0.8778082382,0.0630857893], [0.8788096010,0.0625515475], [0.8798105006,0.0620178570], [0.8808113618,0.0614844904], [0.8818125074,0.0609512753], [0.8828136140,0.0604183835], [0.8838146098,0.0598858528], [0.8848155274,0.0593536651], [0.8858167283,0.0588216278], [0.8868175377,0.0582900990], [0.8878186297,0.0577587200], [0.8888199055,0.0572275431], [0.8898209001,0.0566968143], [0.8908221764,0.0561662348], [0.8918229674,0.0556362103], [0.8928240393,0.0551063344], [0.8938248923,0.0545768713], [0.8948256992,0.0540477289], [0.8958267862,0.0535187344], [0.8968278265,0.0529900600], [0.8978288316,0.0524616992], [0.8988301158,0.0519334856], [0.8998311310,0.0514057079], [0.9008324247,0.0508780770], [0.9018335024,0.0503508530], [0.9028343789,0.0498240274], [0.9038355329,0.0492973478], [0.9048368316,0.0487708837], [0.9058381419,0.0482447048], [0.9068391628,0.0477189686], [0.9078400350,0.0471936004], [0.9088411833,0.0466683771], [0.9098423648,0.0461434255], [0.9108431288,0.0456189813], [0.9118440801,0.0450947272], [0.9128453063,0.0445706168], [0.9138461746,0.0440469810], [0.9148473171,0.0435234885], [0.9158485399,0.0430002404], [0.9168497027,0.0424773096], [0.9178508027,0.0419546970], [0.9188520076,0.0414323145], [0.9198534854,0.0409100744], [0.9208542194,0.0403885060], [0.9218552254,0.0398670793], [0.9228562900,0.0393459051], [0.9238575408,0.0388249167], [0.9248584690,0.0383043782], [0.9258596681,0.0377839806], [0.9268606115,0.0372639970], [0.9278618253,0.0367441538], [0.9288627097,0.0362247618], [0.9298638640,0.0357055097], [0.9308651963,0.0351864448], [0.9318666150,0.0346676142], [0.9328674020,0.0341493894], [0.9338684575,0.0336313035], [0.9348693830,0.0331135625], [0.9358705765,0.0325959600], [0.9368714974,0.0320787752], [0.9378726857,0.0315617285], [0.9388740816,0.0310448506], [0.9398748957,0.0305285483], [0.9408758181,0.0300124650], [0.9418770069,0.0294965189], [0.9428783036,0.0289807913], [0.9438793675,0.0284654574], [0.9448803501,0.0279504385], [0.9458814245,0.0274356451], [0.9468827639,0.0269209878], [0.9478839160,0.0264066989], [0.9488853326,0.0258925457], [0.9498863156,0.0253788861], [0.9508875625,0.0248653618], [0.9518884093,0.0243523129], [0.9528895194,0.0238393988], [0.9538907769,0.0233266785], [0.9548920663,0.0228142110], [0.9558929996,0.0223021941], [0.9568941951,0.0217903113], [0.9578951777,0.0212788049], [0.9588964219,0.0207674322], [0.9598976316,0.0202563439], [0.9608986212,0.0197456345], [0.9618998716,0.0192350579], [0.9629009653,0.0187248269], [0.9639023193,0.0182147285], [0.9649038050,0.0177048280], [0.9659050567,0.0171953110], [0.9669065681,0.0166859260], [0.9679076734,0.0161770112], [0.9689090376,0.0156682279], [0.9699104799,0.0151596678], [0.9709121808,0.0146512390], [0.9719141140,0.0141429546], [0.9729154699,0.0136352247], [0.9739169262,0.0131277052], [0.9749186401,0.0126203161], [0.9759204079,0.0121131604], [0.9769224329,0.0116061347], [0.9779241666,0.0110995163], [0.9789260691,0.0105930720], [0.9799281993,0.0100867718], [0.9809305859,0.0095806010], [0.9819330847,0.0090746321], [0.9829355173,0.0085689549], [0.9839382056,0.0080634065], [0.9849410844,0.0075580197], [0.9859437889,0.0070529778], [0.9869466316,0.0065481231], [0.9879497292,0.0060433967], [0.9889528489,0.0055389153], [0.9899562232,0.0050345618], [0.9909597362,0.0045303942], [0.9919633539,0.0040264291], [0.9929672257,0.0035225915], [0.9939712250,0.0030189445], [0.9949754780,0.0025154247], [0.9959799285,0.0020120600], [0.9969846132,0.0015088317], [0.9979894950,0.0010057583], [0.9989946210,0.0005028159], [1.0000000000,0.0000000000]]deap-1.0.1/examples/ga/pareto_front/zdt6_front.json0000644000076500000240000007051012117373622022564 0ustar felixstaff00000000000000[[0.2807753191,0.9211652202], [0.2814990682,0.9207582746], [0.2822222294,0.9203506133], [0.2829444640,0.9199424303], [0.2836657932,0.9195337178], [0.2843873868,0.9191238143], [0.2851090310,0.9187128404], [0.2858297620,0.9183013472], [0.2865498629,0.9178891761], [0.2872696154,0.9174761681], [0.2879897830,0.9170618849], [0.2887090502,0.9166470843], [0.2894296667,0.9162304680], [0.2901500283,0.9158129611], [0.2908694956,0.9153949365], [0.2915887137,0.9149760220], [0.2923081531,0.9145559436], [0.2930283871,0.9141343644], [0.2937477359,0.9137122677], [0.2944686574,0.9132882098], [0.2951886965,0.9128636335], [0.2959092187,0.9124377343], [0.2966288636,0.9120113173], [0.2973486046,0.9115838073], [0.2980687980,0.9111549917], [0.2987889944,0.9107251368], [0.2995087588,0.9102945034], [0.3002276584,0.9098633531], [0.3009485794,0.9094299526], [0.3016692216,0.9089956808], [0.3023890030,0.9085608909], [0.3031093708,0.9081247093], [0.3038288826,0.9076880101], [0.3045484000,0.9072502720], [0.3052688710,0.9068109164], [0.3059884919,0.9063710428], [0.3067086687,0.9059297926], [0.3074280000,0.9054880248], [0.3081481037,0.9050447462], [0.3088673660,0.9046009502], [0.3095875091,0.9041555742], [0.3103068146,0.9037096808], [0.3110269959,0.9032622078], [0.3117467791,0.9028139457], [0.3124657314,0.9023651667], [0.3131855124,0.9019148348], [0.3139057114,0.9014632044], [0.3146260227,0.9010104659], [0.3153455094,0.9005572097], [0.3160651118,0.9001028451], [0.3167855450,0.8996469184], [0.3175051591,0.8991904740], [0.3182256463,0.8987324380], [0.3189453180,0.8982738841], [0.3196653185,0.8978140842], [0.3203854336,0.8973531739], [0.3211047395,0.8968917463], [0.3218255490,0.8964283160], [0.3225455513,0.8959643673], [0.3232654163,0.8954994706], [0.3239847460,0.8950338844], [0.3247054027,0.8945664015], [0.3254252596,0.8940984004], [0.3261461526,0.8936286872], [0.3268662488,0.8931584554], [0.3275857368,0.8926875850], [0.3283053726,0.8922155824], [0.3290261297,0.8917418060], [0.3297460975,0.8912675112], [0.3304663686,0.8907919792], [0.3311867047,0.8903153667], [0.3319062574,0.8898382363], [0.3326254653,0.8893602998], [0.3333458929,0.8888805157], [0.3340655420,0.8884002136], [0.3347857558,0.8879184977], [0.3355051949,0.8874362642], [0.3362254101,0.8869524736], [0.3369448539,0.8864681655], [0.3376647811,0.8859824956], [0.3383839408,0.8854963086], [0.3391042500,0.8850083076], [0.3398242021,0.8845195116], [0.3405445374,0.8840294180], [0.3412641108,0.8835388067], [0.3419831543,0.8830475222], [0.3427033174,0.8825544363], [0.3434237715,0.8820601132], [0.3441437123,0.8815651053], [0.3448629001,0.8810695801], [0.3455825476,0.8805727028], [0.3463021370,0.8800748299], [0.3470224033,0.8795754516], [0.3477430093,0.8790747995], [0.3484628686,0.8785736292], [0.3491830692,0.8780711842], [0.3499030062,0.8775678863], [0.3506225462,0.8770638301], [0.3513413480,0.8765592572], [0.3520618447,0.8760524575], [0.3527822893,0.8755446564], [0.3535019983,0.8750363372], [0.3542229553,0.8745260979], [0.3549431787,0.8740153399], [0.3556629548,0.8735038626], [0.3563825690,0.8729914645], [0.3571025403,0.8724777757], [0.3578231915,0.8719625637], [0.3585431169,0.8714468333], [0.3592634354,0.8709297840], [0.3599830318,0.8704122168], [0.3607027073,0.8698935570], [0.3614216648,0.8693743802], [0.3621420657,0.8688531243], [0.3628623050,0.8683309476], [0.3635818296,0.8678082532], [0.3643011961,0.8672846385], [0.3650204511,0.8667600703], [0.3657405850,0.8662338245], [0.3664600099,0.8657070611], [0.3671795890,0.8651791494], [0.3679004541,0.8646492559], [0.3686206718,0.8641188003], [0.3693408003,0.8635873732], [0.3700602269,0.8630554285], [0.3707799355,0.8625222394], [0.3714989456,0.8619885334], [0.3722182638,0.8614535641], [0.3729368870,0.8609180783], [0.3736579327,0.8603797493], [0.3743782827,0.8598409014], [0.3750981475,0.8593013797], [0.3758179965,0.8587608335], [0.3765371560,0.8582197702], [0.3772581153,0.8576763144], [0.3779783857,0.8571323399], [0.3786980305,0.8565878017], [0.3794175516,0.8560423215], [0.3801382362,0.8554949214], [0.3808582376,0.8549470028], [0.3815790568,0.8543974234], [0.3822997011,0.8538469385], [0.3830196662,0.8532959353], [0.3837394596,0.8527440272], [0.3844598890,0.8521905938], [0.3851796433,0.8516366424], [0.3858993552,0.8510816877], [0.3866183959,0.8505262160], [0.3873376386,0.8499695537], [0.3880574538,0.8494114126], [0.3887771485,0.8488523288], [0.3894961770,0.8482927281], [0.3902162679,0.8477312643], [0.3909361552,0.8471689225], [0.3916553797,0.8466060635], [0.3923760036,0.8460410718], [0.3930959657,0.8454755618], [0.3938168295,0.8449083048], [0.3945370336,0.8443405291], [0.3952574482,0.8437715497], [0.3959773500,0.8432019383], [0.3966973406,0.8426312199], [0.3974166779,0.8420599841], [0.3981367187,0.8414871532], [0.3988565577,0.8409134464], [0.3995757472,0.8403392223], [0.4002952696,0.8397636971], [0.4010157076,0.8391864023], [0.4017360769,0.8386081245], [0.4024558003,0.8380293288], [0.4031754576,0.8374495504], [0.4038957197,0.8368682476], [0.4046156350,0.8362861879], [0.4053351080,0.8357034502], [0.4060539424,0.8351201959], [0.4067746522,0.8345343823], [0.4074947236,0.8339480502], [0.4082149745,0.8333605346], [0.4089345900,0.8327725011], [0.4096543402,0.8321833216], [0.4103750895,0.8315922859], [0.4110955980,0.8310004093], [0.4118154751,0.8304080145], [0.4125357176,0.8298142817], [0.4132556326,0.8292197822], [0.4139759287,0.8286239305], [0.4146955982,0.8280275609], [0.4154156792,0.8274298134], [0.4161359398,0.8268308796], [0.4168555769,0.8262314280], [0.4175758225,0.8256304325], [0.4182959856,0.8250284684], [0.4190155288,0.8244259866], [0.4197356963,0.8238219453], [0.4204556855,0.8232170165], [0.4211750583,0.8226115703], [0.4218951128,0.8220045138], [0.4226149810,0.8213965778], [0.4233350181,0.8207874625], [0.4240544428,0.8201778295], [0.4247740379,0.8195670167], [0.4254945300,0.8189544050], [0.4262144120,0.8183412750], [0.4269344998,0.8177269329], [0.4276543475,0.8171117591], [0.4283735893,0.8164960680], [0.4290925939,0.8158795459], [0.4298128945,0.8152608757], [0.4305332794,0.8146410954], [0.4312530616,0.8140207969], [0.4319729298,0.8133993879], [0.4326929082,0.8127768472], [0.4334127928,0.8121533510], [0.4341323320,0.8115291183], [0.4348523102,0.8109034683], [0.4355716924,0.8102773008], [0.4362915142,0.8096497146], [0.4370114625,0.8090209816], [0.4377314683,0.8083911616], [0.4384514632,0.8077603144], [0.4391708669,0.8071289497], [0.4398914795,0.8064954862], [0.4406115019,0.8058615044], [0.4413321519,0.8052259317], [0.4420523419,0.8045897270], [0.4427723130,0.8039526788], [0.4434916987,0.8033151132], [0.4442122201,0.8026755036], [0.4449321570,0.8020353756], [0.4456517440,0.8013945231], [0.4463719076,0.8007521201], [0.4470920801,0.8001086719], [0.4478119674,0.7994644419], [0.4485322737,0.7988187994], [0.4492525001,0.7981721911], [0.4499721492,0.7975250649], [0.4506924144,0.7968763476], [0.4514128145,0.7962264709], [0.4521326398,0.7955760760], [0.4528531164,0.7949240550], [0.4535730198,0.7942715157], [0.4542928659,0.7936179920], [0.4550123930,0.7929637222], [0.4557317919,0.7923085339], [0.4564515473,0.7916519850], [0.4571716388,0.7909940927], [0.4578911632,0.7903356827], [0.4586102372,0.7896766503], [0.4593303281,0.7890156497], [0.4600498545,0.7883541313], [0.4607701063,0.7876909092], [0.4614897951,0.7870271691], [0.4622097775,0.7863621216], [0.4629295537,0.7856962283], [0.4636496779,0.7850289762], [0.4643692428,0.7843612063], [0.4650895898,0.7836916734], [0.4658093790,0.7830216224], [0.4665294545,0.7823502681], [0.4672489743,0.7816783961], [0.4679691586,0.7810048666], [0.4686894119,0.7803302351], [0.4694091118,0.7796550857], [0.4701288822,0.7789788341], [0.4708481705,0.7783020004], [0.4715686236,0.7776230333], [0.4722890871,0.7769430182], [0.4730094404,0.7762620693], [0.4737292452,0.7755806023], [0.4744495231,0.7748976500], [0.4751700634,0.7742134108], [0.4758900574,0.7735286532], [0.4766099258,0.7728429787], [0.4773299031,0.7721561636], [0.4780493374,0.7714688310], [0.4787688821,0.7707803576], [0.4794891935,0.7700901134], [0.4802089638,0.7693993511], [0.4809292340,0.7687070719], [0.4816489649,0.7680142746], [0.4823688992,0.7673202451], [0.4830890552,0.7666249647], [0.4838086745,0.7659291665], [0.4845292173,0.7652314376], [0.4852495068,0.7645329161], [0.4859692618,0.7638338766], [0.4866887396,0.7631340707], [0.4874087623,0.7624326984], [0.4881282532,0.7617308085], [0.4888477781,0.7610278498], [0.4895672547,0.7603239032], [0.4902871640,0.7596184968], [0.4910074998,0.7589116351], [0.4917273072,0.7582042553], [0.4924475871,0.7574953740], [0.4931673402,0.7567859746], [0.4938872506,0.7560753837], [0.4946073802,0.7553635394], [0.4953278463,0.7546503247], [0.4960477883,0.7539365917], [0.4967681530,0.7532214022], [0.4974879954,0.7525056944], [0.4982080396,0.7517887492], [0.4989275636,0.7510712863], [0.4996469268,0.7503529485], [0.5003672351,0.7496326300], [0.5010872988,0.7489115190], [0.5018072564,0.7481894774], [0.5025266975,0.7474669183], [0.5032460344,0.7467434288], [0.5039665115,0.7460177553], [0.5046869068,0.7452911261], [0.5054067880,0.7445639786], [0.5061264450,0.7438360217], [0.5068461670,0.7431069630], [0.5075665046,0.7423762434], [0.5082863309,0.7416450058], [0.5090058345,0.7409130605], [0.5097259023,0.7401795045], [0.5104454615,0.7394454308], [0.5111652901,0.7387100462], [0.5118846119,0.7379741441], [0.5126049237,0.7372361921], [0.5133252710,0.7364971661], [0.5140453835,0.7357573437], [0.5147656997,0.7350162744], [0.5154855121,0.7342746869], [0.5162058432,0.7335315274], [0.5169256718,0.7327878498], [0.5176453130,0.7320433299], [0.5183651464,0.7312975750], [0.5190844799,0.7305513027], [0.5198049085,0.7298028571], [0.5205249908,0.7290537339], [0.5212452963,0.7283033411], [0.5219651041,0.7275524301], [0.5226851358,0.7268002488], [0.5234050484,0.7260471553], [0.5241244659,0.7252935443], [0.5248444325,0.7245383217], [0.5255645614,0.7237818917], [0.5262841970,0.7230249439], [0.5270034717,0.7222673409], [0.5277238579,0.7215075298], [0.5284437525,0.7207472005], [0.5291634061,0.7199860896], [0.5298839437,0.7192230062], [0.5306039914,0.7184594043], [0.5313237776,0.7176950433], [0.5320435301,0.7169296821], [0.5327632502,0.7161633192], [0.5334834468,0.7153954120], [0.5342036586,0.7146264511], [0.5349233850,0.7138569722], [0.5356433433,0.7130862088], [0.5363628178,0.7123149277], [0.5370834134,0.7115414070], [0.5378035746,0.7107673151], [0.5385232536,0.7099927053], [0.5392435519,0.7092163917], [0.5399633692,0.7084395599], [0.5406841037,0.7076607000], [0.5414044614,0.7068812091], [0.5421243399,0.7061012001], [0.5428439770,0.7053204166], [0.5435631904,0.7045390581], [0.5442835200,0.7037554498], [0.5450033730,0.7029713234], [0.5457234746,0.7021858893], [0.5464435527,0.7013994437], [0.5471631564,0.7006124803], [0.5478830807,0.6998241299], [0.5486025320,0.6990352619], [0.5493225900,0.6982446921], [0.5500421761,0.6974536045], [0.5507624228,0.6966607536], [0.5514821986,0.6958673847], [0.5522019002,0.6950730614], [0.5529222531,0.6942769820], [0.5536421368,0.6934803843], [0.5543624929,0.6926822264], [0.5550823810,0.6918835503], [0.5558026852,0.6910833751], [0.5565225227,0.6902826817], [0.5572423527,0.6894809603], [0.5579623525,0.6886780132], [0.5586818878,0.6878745483], [0.5594016781,0.6870697626], [0.5601210053,0.6862644594], [0.5608410932,0.6854572681], [0.5615612694,0.6846489408], [0.5622809837,0.6838400954], [0.5630012083,0.6830296395], [0.5637214336,0.6822181453], [0.5644411988,0.6814061331], [0.5651609659,0.6805930827], [0.5658815721,0.6797780463], [0.5666017196,0.6789624914], [0.5673214448,0.6781463783], [0.5680414563,0.6773289040], [0.5687610114,0.6765109119], [0.5694809629,0.6756914329], [0.5702009041,0.6748709290], [0.5709203907,0.6740499074], [0.5716398683,0.6732278610], [0.5723600084,0.6724040208], [0.5730802102,0.6715790726], [0.5737999595,0.6707536064], [0.5745202005,0.6699265392], [0.5752404531,0.6690984211], [0.5759602548,0.6682697849], [0.5766805417,0.6674395528], [0.5774003789,0.6666088025], [0.5781207176,0.6657764359], [0.5788406075,0.6649435511], [0.5795606178,0.6641094903], [0.5802801809,0.6632749117], [0.5809999913,0.6624390101], [0.5817195652,0.6616023475], [0.5824386939,0.6607651678], [0.5831592491,0.6599252902], [0.5838793591,0.6590848940], [0.5845996542,0.6582432444], [0.5853195055,0.6574010765], [0.5860394054,0.6565578153], [0.5867588632,0.6557140364], [0.5874792607,0.6548681182], [0.5881992165,0.6540216817], [0.5889192043,0.6531741708], [0.5896388234,0.6523260580], [0.5903589673,0.6514762897], [0.5910786721,0.6506260034], [0.5917980249,0.6497750977], [0.5925178026,0.6489226536], [0.5932383267,0.6480682877], [0.5939584133,0.6472134033], [0.5946785809,0.6463573854], [0.5953983126,0.6455008494], [0.5961184062,0.6446428458], [0.5968386041,0.6437836807], [0.5975583674,0.6429239975], [0.5982786461,0.6420626616], [0.5989984913,0.6412008075], [0.5997185778,0.6403376274], [0.6004386868,0.6394733834], [0.6011583640,0.6386086214], [0.6018783330,0.6377424723], [0.6025978714,0.6368758054], [0.6033178491,0.6360075730], [0.6040373972,0.6351388228], [0.6047571169,0.6342688296], [0.6054765079,0.6333981983], [0.6061972873,0.6325248489], [0.6069176382,0.6316509805], [0.6076376926,0.6307764345], [0.6083577064,0.6299009010], [0.6090776041,0.6290244722], [0.6097977013,0.6281467635], [0.6105173733,0.6272685369], [0.6112372454,0.6263890298], [0.6119573665,0.6255081816], [0.6126774577,0.6246263328], [0.6133971258,0.6237439661], [0.6141169374,0.6228603872], [0.6148363272,0.6219762908], [0.6155566476,0.6210900136], [0.6162765465,0.6202032182], [0.6169966257,0.6193151639], [0.6177165894,0.6184262152], [0.6184361335,0.6175367488], [0.6191562255,0.6166455684], [0.6198758989,0.6157538700], [0.6205958158,0.6148608334], [0.6213162428,0.6139661264], [0.6220366679,0.6130703838], [0.6227569013,0.6121738418], [0.6234767183,0.6112767817], [0.6241969342,0.6103781873], [0.6249167347,0.6094790748], [0.6256361485,0.6085794096], [0.6263561775,0.6076779389], [0.6270758762,0.6067758455], [0.6277953706,0.6058729727], [0.6285151297,0.6049687318], [0.6292344767,0.6040639734], [0.6299540887,0.6031578461], [0.6306743052,0.6022499208], [0.6313941104,0.6013414773], [0.6321144403,0.6004313344], [0.6328343597,0.5995206732], [0.6335545093,0.5986086837], [0.6342745561,0.5976957875], [0.6349950220,0.5967813220], [0.6357150792,0.5958663381], [0.6364353898,0.5949499947], [0.6371555351,0.5940328241], [0.6378752735,0.5931151355], [0.6385950657,0.5921963421], [0.6393144522,0.5912770312], [0.6400336516,0.5903569248], [0.6407539188,0.5894344156], [0.6414738017,0.5885113617], [0.6421940438,0.5875868101], [0.6429138820,0.5866617404], [0.6436340793,0.5857351719], [0.6443539131,0.5848080347], [0.6450739684,0.5838795752], [0.6457940691,0.5829500203], [0.6465137683,0.5820199474], [0.6472337655,0.5810884528], [0.6479533622,0.5801564404], [0.6486738154,0.5792222813], [0.6493939433,0.5782875063], [0.6501136720,0.5773522134], [0.6508332792,0.5764160427], [0.6515537377,0.5754777269], [0.6522737979,0.5745388926], [0.6529938173,0.5735990745], [0.6537134398,0.5726587387], [0.6544335756,0.5717166951], [0.6551533153,0.5707741335], [0.6558734667,0.5698299956], [0.6565932228,0.5688853397], [0.6573130176,0.5679395969], [0.6580329040,0.5669926972], [0.6587529885,0.5660445002], [0.6594726793,0.5650957852], [0.6601926480,0.5641456675], [0.6609122242,0.5631950319], [0.6616318052,0.5622433544], [0.6623517813,0.5612901179], [0.6630717484,0.5603358565], [0.6637921402,0.5593799947], [0.6645121410,0.5584236144], [0.6652319562,0.5574664445], [0.6659518938,0.5565080752], [0.6666714422,0.5555491882], [0.6673913968,0.5545887235], [0.6681109630,0.5536277411], [0.6688313419,0.5526646361], [0.6695513328,0.5517010128], [0.6702709451,0.5507368602], [0.6709910465,0.5497710155], [0.6717107616,0.5488046528], [0.6724308805,0.5478367110], [0.6731513504,0.5468672595], [0.6738716051,0.5458970599], [0.6745914748,0.5449263421], [0.6753111307,0.5439548768], [0.6760311021,0.5429819489], [0.6767506902,0.5420085033], [0.6774711204,0.5410328810], [0.6781913842,0.5400564464], [0.6789112655,0.5390794936], [0.6796312006,0.5381014312], [0.6803507544,0.5371228510], [0.6810708573,0.5361424873], [0.6817905796,0.5351616056], [0.6825101891,0.5341798417], [0.6832304223,0.5331961901], [0.6839502757,0.5322120203], [0.6846705176,0.5312262824], [0.6853903806,0.5302400262], [0.6861106120,0.5292522281], [0.6868304654,0.5282639117], [0.6875505019,0.5272743074], [0.6882706788,0.5262834728], [0.6889904789,0.5252921200], [0.6897104199,0.5242995367], [0.6904301616,0.5233061919], [0.6911498808,0.5223118422], [0.6918700377,0.5213158509], [0.6925899260,0.5203191944], [0.6933094401,0.5193220203], [0.6940291111,0.5183235929], [0.6947492452,0.5173234862], [0.6954690061,0.5163228615], [0.6961892740,0.5153204947], [0.6969091694,0.5143176096], [0.6976294392,0.5133131655], [0.6983493609,0.5123081701], [0.6990695134,0.5113018154], [0.6997892950,0.5102949426], [0.7005093077,0.5092867098], [0.7012294810,0.5082772150], [0.7019494236,0.5072670067], [0.7026689971,0.5062562806], [0.7033889333,0.5052440085], [0.7041088861,0.5042306765], [0.7048284709,0.5032168266], [0.7055483281,0.5022015568], [0.7062680573,0.5011854312], [0.7069874199,0.5001687882], [0.7077076588,0.4991498697], [0.7084275312,0.4981304331], [0.7091478574,0.4971093164], [0.7098682030,0.4960871344], [0.7105881831,0.4950644341], [0.7113083272,0.4940404636], [0.7120281068,0.4930159751], [0.7127478884,0.4919904476], [0.7134678376,0.4909636447], [0.7141874236,0.4899363239], [0.7149074533,0.4889073332], [0.7156276836,0.4878770185], [0.7163475514,0.4868461856], [0.7170676867,0.4858139326], [0.7177876267,0.4847809230], [0.7185072056,0.4837473955], [0.7192273635,0.4827119996], [0.7199473773,0.4816757739], [0.7206674670,0.4806384020], [0.7213871970,0.4796005120], [0.7221072699,0.4785610907], [0.7228269838,0.4775211515], [0.7235469640,0.4764797909], [0.7242665860,0.4754379124], [0.7249868992,0.4743939960], [0.7257068545,0.4733495613], [0.7264266556,0.4723043140], [0.7271468324,0.4712574841], [0.7278666526,0.4702101360], [0.7285864734,0.4691617507], [0.7293059386,0.4681128479], [0.7300261037,0.4670618879], [0.7307463976,0.4660097023], [0.7314663366,0.4649569985], [0.7321865760,0.4639028180], [0.7329064611,0.4628481192], [0.7336260877,0.4617927634], [0.7343462725,0.4607355520], [0.7350661042,0.4596778225], [0.7357860871,0.4586188341], [0.7365063252,0.4575584329], [0.7372263975,0.4564972388], [0.7379462953,0.4554352652], [0.7386658419,0.4543727740], [0.7393856459,0.4533088666], [0.7401050995,0.4522444417], [0.7408252424,0.4511779602], [0.7415452445,0.4501106504], [0.7422652220,0.4490423402], [0.7429848503,0.4479735122], [0.7437049028,0.4469030176], [0.7444246067,0.4458320049], [0.7451444102,0.4447598079], [0.7458643206,0.4436864152], [0.7465843437,0.4426118177], [0.7473041778,0.4415364658], [0.7480236652,0.4404605962], [0.7487438310,0.4393826755], [0.7494641180,0.4383035359], [0.7501840588,0.4372238780], [0.7509045014,0.4361424297], [0.7516245985,0.4350604629], [0.7523448935,0.4339771613], [0.7530648436,0.4328933413], [0.7537851005,0.4318080223], [0.7545050132,0.4307221850], [0.7552250702,0.4296350934], [0.7559452401,0.4285467939], [0.7566655525,0.4274572417], [0.7573855220,0.4263671711], [0.7581054373,0.4252761459], [0.7588250108,0.4241846029], [0.7595446221,0.4230919671], [0.7602645552,0.4219978060], [0.7609844179,0.4209027157], [0.7617045887,0.4198061195], [0.7624244191,0.4187090052], [0.7631440889,0.4176110996], [0.7638639016,0.4165119398], [0.7645836183,0.4154118906], [0.7653029962,0.4143113240], [0.7660233826,0.4132081772], [0.7667434303,0.4121045120], [0.7674632302,0.4110001902], [0.7681828732,0.4098950734], [0.7689032031,0.4087878643], [0.7696231956,0.4076801368], [0.7703434003,0.4065710457], [0.7710632683,0.4054614363], [0.7717833008,0.4043505365], [0.7725029975,0.4032391188], [0.7732231120,0.4021260191], [0.7739428912,0.4010124012], [0.7746627350,0.3998976470], [0.7753828112,0.3987814961], [0.7761029323,0.3976642385], [0.7768227193,0.3965464628], [0.7775425517,0.3954275802], [0.7782623628,0.3943076947], [0.7789822031,0.3931867273], [0.7797017108,0.3920652423], [0.7804218564,0.3909417260], [0.7811419884,0.3898171940], [0.7818617884,0.3886921438], [0.7825819105,0.3875655533], [0.7833020847,0.3864378441], [0.7840219278,0.3853096167], [0.7847421200,0.3841798050], [0.7854619818,0.3830494751], [0.7861818099,0.3819181617], [0.7869019836,0.3807852681], [0.7876221980,0.3796512732], [0.7883420831,0.3785167599], [0.7890617585,0.3773815412], [0.7897813435,0.3762454295], [0.7905014234,0.3751074995], [0.7912211754,0.3739690516], [0.7919411977,0.3728291395], [0.7926610027,0.3716885347], [0.7933807013,0.3705470629], [0.7941004800,0.3694044276], [0.7948208839,0.3682597625], [0.7955409613,0.3671145790], [0.7962610153,0.3659683955], [0.7969811363,0.3648210684], [0.7977009319,0.3636732232], [0.7984207949,0.3625242342], [0.7991410558,0.3613735729], [0.7998611314,0.3602221705], [0.8005813064,0.3590695719], [0.8013011574,0.3579164551], [0.8020211083,0.3567621419], [0.8027408959,0.3556070540], [0.8034609458,0.3544505085], [0.8041806731,0.3532934450], [0.8049006140,0.3521350017], [0.8056203791,0.3509758048], [0.8063403053,0.3498153120], [0.8070599102,0.3486543014], [0.8077796764,0.3474919943], [0.8084999692,0.3463277999], [0.8092199410,0.3451630870], [0.8099397717,0.3439975663], [0.8106599806,0.3428303958], [0.8113798696,0.3416627071], [0.8120998833,0.3404937796], [0.8128195778,0.3393243340], [0.8135391027,0.3381541285], [0.8142591218,0.3369820826], [0.8149790796,0.3358090998], [0.8156987194,0.3346355991], [0.8164183952,0.3334610039], [0.8171385539,0.3322845838], [0.8178583951,0.3311076455], [0.8185785261,0.3299291966], [0.8192986947,0.3287496488], [0.8200185468,0.3275695829], [0.8207381323,0.3263889181], [0.8214584471,0.3252060197], [0.8221784461,0.3240226028], [0.8228985981,0.3228378973], [0.8236186938,0.3216522473], [0.8243384746,0.3204660792], [0.8250581998,0.3192789669], [0.8257778827,0.3180908884], [0.8264981981,0.3169007285], [0.8272181996,0.3157100502], [0.8279382728,0.3145182164], [0.8286580329,0.3133258645], [0.8293777385,0.3121325668], [0.8300976355,0.3109379155], [0.8308172204,0.3097427464], [0.8315377029,0.3085450486], [0.8322578733,0.3073468323], [0.8329779310,0.3061477664], [0.8336979931,0.3049476563], [0.8344177441,0.3037470283], [0.8351375001,0.3025453560], [0.8358576647,0.3013419643], [0.8365775191,0.3001380545], [0.8372976172,0.2989327002], [0.8380175773,0.2977265401], [0.8387372281,0.2965198622], [0.8394573693,0.2953113251], [0.8401772016,0.2941022699], [0.8408974576,0.2928914658], [0.8416174052,0.2916801433], [0.8423371703,0.2904680916], [0.8430568787,0.2892550993], [0.8437770792,0.2880402406], [0.8444970735,0.2868246928], [0.8452167610,0.2856086270], [0.8459370934,0.2843904340], [0.8466571628,0.2831716486], [0.8473769261,0.2819523452], [0.8480968712,0.2807316970], [0.8488165108,0.2795105310], [0.8495366200,0.2782875313], [0.8502564239,0.2770640135], [0.8509765722,0.2758388736], [0.8516966670,0.2746127874], [0.8524164574,0.2733861832], [0.8531364033,0.2721582774], [0.8538560454,0.2709298537], [0.8545756644,0.2697004339], [0.8552959489,0.2684688398], [0.8560159302,0.2672367273], [0.8567357685,0.2660038229], [0.8574553685,0.2647702911], [0.8581754563,0.2635348862], [0.8588952420,0.2622989633], [0.8596154269,0.2610613179], [0.8603353101,0.2598231542], [0.8610553354,0.2585837094], [0.8617754763,0.2573430284], [0.8624955528,0.2561014215], [0.8632153286,0.2548592964], [0.8639350406,0.2536162455], [0.8646553356,0.2523711506], [0.8653753306,0.2511255372], [0.8660953514,0.2498788422], [0.8668150730,0.2486316292], [0.8675352658,0.2473825626], [0.8682554825,0.2461324171], [0.8689754005,0.2448817533], [0.8696951640,0.2436303217], [0.8704149170,0.2423778723], [0.8711351117,0.2411236172], [0.8718552522,0.2398684191], [0.8725750954,0.2386127029], [0.8732949565,0.2373559189], [0.8740145210,0.2360986172], [0.8747342740,0.2348399499], [0.8754543919,0.2335796077], [0.8761746912,0.2323179106], [0.8768946943,0.2310556951], [0.8776145736,0.2297926602], [0.8783346712,0.2285282053], [0.8790546531,0.2272629169], [0.8797743401,0.2259971105], [0.8804939120,0.2247304709], [0.8812138446,0.2234621601], [0.8819337161,0.2221929204], [0.8826535948,0.2209226316], [0.8833731799,0.2196518251], [0.8840929474,0.2183796604], [0.8848132759,0.2171054668], [0.8855336076,0.2158302299], [0.8862536463,0.2145544745], [0.8869734963,0.2132780168], [0.8876933734,0.2120004748], [0.8884133130,0.2107217854], [0.8891329608,0.2094425781], [0.8898530169,0.2081616083], [0.8905731261,0.2068795070], [0.8912929442,0.2055968875], [0.8920131251,0.2043125847], [0.8927330152,0.2030277635], [0.8934529241,0.2017418725], [0.8941730851,0.2004544939], [0.8948929561,0.1991665971], [0.8956128046,0.1978777042], [0.8963327750,0.1965875565], [0.8970527813,0.1952963075], [0.8977724988,0.1940045404], [0.8984924094,0.1927113902], [0.8992125684,0.1914167569], [0.8999325260,0.1901214486], [0.9006521957,0.1888256224], [0.9013716648,0.1875291219], [0.9020917071,0.1862305520], [0.9028114621,0.1849314639], [0.9035313368,0.1836311235], [0.9042514045,0.1823293975], [0.9049711855,0.1810271534], [0.9056912515,0.1797233570], [0.9064110312,0.1784190425], [0.9071314180,0.1771125904], [0.9078515188,0.1758056198], [0.9085715015,0.1744978266], [0.9092916706,0.1731886578], [0.9100115925,0.1718789016], [0.9107316949,0.1705677798], [0.9114515127,0.1692561399], [0.9121715956,0.1679429801], [0.9128913943,0.1666293022], [0.9136109934,0.1653149527], [0.9143309149,0.1639989781], [0.9150508896,0.1626818694], [0.9157705811,0.1613642428], [0.9164906707,0.1600448506], [0.9172108055,0.1587243383], [0.9179309281,0.1574028113], [0.9186507682,0.1560807660], [0.9193705967,0.1547577059], [0.9200905249,0.1534334261], [0.9208101714,0.1521086283], [0.9215305408,0.1507814624], [0.9222506286,0.1494537780], [0.9229708262,0.1481248540], [0.9236907427,0.1467954119], [0.9244106520,0.1454649464], [0.9251306075,0.1441333591], [0.9258505602,0.1428007402], [0.9265702329,0.1414676034], [0.9272899034,0.1401334351], [0.9280100090,0.1387974231], [0.9287298353,0.1374608930], [0.9294498354,0.1361230034], [0.9301695567,0.1347845958], [0.9308898667,0.1334440560], [0.9316098981,0.1321029978], [0.9323298397,0.1307610700], [0.9330496743,0.1294183052], [0.9337696518,0.1280742373], [0.9344893518,0.1267296514], [0.9352095347,0.1253831262], [0.9359296283,0.1240357309], [0.9366497522,0.1226872416], [0.9373695994,0.1213382342], [0.9380893274,0.1199884138], [0.9388092215,0.1186372456], [0.9395292431,0.1172848013], [0.9402489889,0.1159318389], [0.9409686707,0.1145779608], [0.9416885002,0.1132227687], [0.9424086551,0.1118659268], [0.9431285349,0.1105085666], [0.9438485784,0.1091498611], [0.9445685689,0.1077902186], [0.9452882851,0.1064300581], [0.9460079488,0.1050689608], [0.9467280640,0.1037059728], [0.9474482126,0.1023418845], [0.9481680874,0.1009772779], [0.9488879960,0.0996115710], [0.9496083099,0.0982440577], [0.9503283507,0.0968760259], [0.9510483007,0.0955071297], [0.9517679782,0.0941377157], [0.9524880536,0.0927665077], [0.9532078569,0.0913947816], [0.9539280500,0.0900212754], [0.9546482500,0.0886467188], [0.9553681782,0.0872716440], [0.9560881137,0.0858955189], [0.9568080998,0.0845182601], [0.9575278150,0.0831404834], [0.9582479488,0.0817608687], [0.9589679894,0.0803803954], [0.9596877596,0.0789994040], [0.9604074374,0.0776175543], [0.9611276344,0.0762336704], [0.9618476359,0.0748491252], [0.9625673680,0.0734640621], [0.9632875712,0.0720770552], [0.9640075052,0.0706895300], [0.9647272650,0.0693013042], [0.9654473401,0.0679114335], [0.9661671467,0.0665210446], [0.9668871452,0.0651292484], [0.9676068757,0.0637369341], [0.9683268611,0.0623430901], [0.9690465788,0.0609487281], [0.9697663836,0.0595531612], [0.9704859373,0.0581570455], [0.9712052242,0.0567604125], [0.9719258029,0.0553602336], [0.9726461145,0.0539595359], [0.9733664680,0.0525577189], [0.9740865550,0.0511553833], [0.9748066103,0.0497520726], [0.9755266577,0.0483477401], [0.9762464394,0.0469428896], [0.9769663969,0.0455366594], [0.9776863258,0.0441294483], [0.9784061080,0.0427214878], [0.9791256255,0.0413130095], [0.9798456390,0.0399025237], [0.9805655893,0.0384911251], [0.9812857853,0.0370782076], [0.9820057169,0.0356647719], [0.9827258942,0.0342498168], [0.9834460511,0.0328338646], [0.9841659443,0.0314173941], [0.9848857478,0.0300000638], [0.9856057979,0.0285812112], [0.9863255849,0.0271618406], [0.9870451228,0.0257419255], [0.9877653545,0.0243196044], [0.9884853237,0.0228967649], [0.9892051324,0.0214732061], [0.9899251805,0.0200481371], [0.9906449667,0.0186225500], [0.9913647890,0.0171958551], [0.9920848096,0.0157677306], [0.9928045688,0.0143390881], [0.9935245161,0.0129090360], [0.9942442024,0.0114784659], [0.9949642472,0.0100461467], [0.9956841510,0.0086130714], [0.9964038065,0.0071794544], [0.9971233996,0.0057449260], [0.9978427330,0.0043098803], [0.9985620044,0.0028739234], [0.9992811316,0.0014372201], [1.0000000000,0.0000000000]]deap-1.0.1/examples/ga/sortingnetwork.py0000644000076500000240000001051512117373622020540 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . from itertools import product class SortingNetwork(list): """Sorting network class. From Wikipedia : A sorting network is an abstract mathematical model of a network of wires and comparator modules that is used to sort a sequence of numbers. Each comparator connects two wires and sort the values by outputting the smaller value to one wire, and a larger value to the other. """ def __init__(self, dimension, connectors = []): self.dimension = dimension for wire1, wire2 in connectors: self.addConnector(wire1, wire2) def addConnector(self, wire1, wire2): """Add a connector between wire1 and wire2 in the network.""" if wire1 == wire2: return if wire1 > wire2: wire1, wire2 = wire2, wire1 index = 0 for level in reversed(self): if self.checkConflict(level, wire1, wire2): break index -= 1 if index == 0: self.append([(wire1, wire2)]) else: self[index].append((wire1, wire2)) def checkConflict(self, level, wire1, wire2): """Check if a connection between `wire1` and `wire2` can be added on this `level`.""" for wires in level: if wires[1] >= wire1 and wires[0] <= wire2: return True def sort(self, values): """Sort the values in-place based on the connectors in the network.""" for level in self: for wire1, wire2 in level: if values[wire1] > values[wire2]: values[wire1], values[wire2] = values[wire2], values[wire1] def assess(self, cases=None): """Try to sort the **cases** using the network, return the number of misses. If **cases** is None, test all possible cases according to the network dimensionality. """ if cases is None: cases = product((0, 1), repeat=self.dimension) misses = 0 ordered = [[0]*(self.dimension-i) + [1]*i for i in range(self.dimension+1)] for sequence in cases: sequence = list(sequence) self.sort(sequence) misses += (sequence != ordered[sum(sequence)]) return misses def draw(self): """Return an ASCII representation of the network.""" str_wires = [["-"]*7 * self.depth] str_wires[0][0] = "0" str_wires[0][1] = " o" str_spaces = [] for i in range(1, self.dimension): str_wires.append(["-"]*7 * self.depth) str_spaces.append([" "]*7 * self.depth) str_wires[i][0] = str(i) str_wires[i][1] = " o" for index, level in enumerate(self): for wire1, wire2 in level: str_wires[wire1][(index+1)*6] = "x" str_wires[wire2][(index+1)*6] = "x" for i in range(wire1, wire2): str_spaces[i][(index+1)*6+1] = "|" for i in range(wire1+1, wire2): str_wires[i][(index+1)*6] = "|" network_draw = "".join(str_wires[0]) for line, space in zip(str_wires[1:], str_spaces): network_draw += "\n" network_draw += "".join(space) network_draw += "\n" network_draw += "".join(line) return network_draw @property def depth(self): """Return the number of parallel steps that it takes to sort any input. """ return len(self) @property def length(self): """Return the number of comparison-swap used.""" return sum(len(level) for level in self) deap-1.0.1/examples/ga/test.py0000644000076500000240000000041612320340110016375 0ustar felixstaff00000000000000from nsga2 import* from deap import tools ind1 = toolbox.individual() ind2 = toolbox.individual() ind1.fitness.values = toolbox.evaluate(ind1) ind2.fitness.values = toolbox.evaluate(ind2) import pdb pdb.set_trace() print(tools.sortLogNondominated([ind1, ind2], k=2)) deap-1.0.1/examples/ga/tsp/0000755000076500000240000000000012321001644015660 5ustar felixstaff00000000000000deap-1.0.1/examples/ga/tsp/gr120.json0000644000076500000240000021543212117373622017430 0ustar felixstaff00000000000000{ "TourSize" : 120, "OptTour" : [1, 76, 59, 15, 30, 29, 120, 32, 92, 28, 45, 78, 86, 94, 81, 22, 66, 31, 117, 85, 18, 19, 25, 108, 43, 79, 52, 33, 100, 58, 91, 68, 65, 69, 113, 107, 20, 46, 50, 44, 75, 14, 87, 74, 105, 40, 72, 38, 7, 56, 41, 42, 98, 17, 118, 49, 13, 51, 11, 23, 9, 103, 119, 3, 82, 2, 115, 21, 93, 53, 64, 109, 88, 97, 12, 95, 77, 39, 63, 5, 27, 80, 101, 102, 48, 110, 112, 106, 114, 73, 57, 83, 67, 37, 62, 99, 10, 35, 104, 36, 84, 6, 89, 55, 47, 71, 26, 4, 34, 116, 70, 8, 54, 90, 96, 111, 24, 60, 16, 61], "OptDistance" : 6942, "DistanceMatrix" : [[0, 534, 434, 294, 593, 409, 332, 232, 464, 566, 552, 802, 633, 257, 187, 91, 412, 400, 472, 389, 610, 340, 510, 153, 511, 269, 525, 150, 80, 130, 401, 134, 666, 259, 505, 453, 627, 339, 710, 243, 376, 449, 505, 322, 185, 353, 324, 388, 447, 360, 605, 656, 573, 293, 372, 330, 610, 598, 214, 154, 70, 606, 631, 642, 503, 372, 641, 561, 478, 247, 317, 272, 575, 219, 293, 54, 648, 211, 568, 497, 290, 475, 654, 445, 375, 268, 261, 710, 396, 295, 651, 175, 585, 250, 717, 246, 788, 426, 596, 634, 507, 463, 408, 529, 192, 529, 434, 535, 630, 446, 166, 471, 442, 523, 566, 235, 432, 435, 369, 121], [534, 0, 107, 241, 190, 351, 320, 354, 124, 508, 80, 316, 432, 641, 577, 450, 624, 752, 805, 665, 76, 730, 152, 447, 844, 283, 157, 539, 507, 520, 791, 524, 942, 281, 447, 358, 334, 275, 283, 353, 520, 594, 781, 611, 575, 638, 314, 234, 664, 606, 133, 932, 113, 384, 283, 479, 297, 874, 604, 401, 464, 349, 228, 129, 779, 762, 348, 837, 754, 316, 336, 382, 276, 479, 683, 529, 188, 601, 844, 150, 680, 65, 341, 387, 765, 658, 519, 184, 291, 424, 927, 565, 67, 640, 221, 454, 302, 596, 575, 910, 209, 186, 169, 389, 412, 286, 710, 811, 108, 252, 518, 313, 718, 230, 45, 313, 822, 653, 167, 511], [434, 107, 0, 148, 137, 240, 232, 261, 88, 397, 127, 336, 479, 541, 477, 357, 531, 659, 712, 572, 183, 630, 134, 354, 751, 190, 95, 446, 414, 427, 691, 431, 849, 188, 336, 247, 251, 187, 254, 260, 427, 501, 688, 518, 482, 545, 191, 127, 571, 513, 180, 839, 101, 274, 199, 386, 234, 781, 504, 308, 371, 266, 175, 176, 686, 662, 265, 744, 661, 223, 212, 289, 199, 386, 583, 429, 182, 501, 751, 67, 580, 42, 278, 276, 665, 558, 426, 271, 180, 278, 834, 472, 146, 540, 251, 361, 322, 503, 330, 817, 111, 79, 105, 278, 319, 231, 617, 718, 191, 145, 425, 202, 625, 147, 139, 220, 722, 560, 79, 418], [294, 241, 148, 0, 374, 190, 139, 113, 171, 347, 259, 509, 552, 407, 337, 210, 384, 512, 565, 425, 317, 490, 217, 207, 604, 42, 232, 299, 267, 280, 551, 284, 702, 40, 286, 234, 408, 94, 491, 113, 280, 354, 541, 371, 335, 398, 106, 124, 424, 366, 312, 692, 280, 143, 153, 239, 391, 634, 364, 161, 224, 387, 412, 349, 539, 522, 422, 597, 514, 76, 95, 142, 356, 239, 443, 289, 355, 361, 604, 204, 440, 182, 435, 226, 525, 418, 279, 417, 177, 183, 687, 325, 292, 400, 424, 213, 495, 356, 377, 670, 201, 155, 116, 310, 172, 310, 470, 571, 337, 227, 277, 252, 478, 304, 273, 73, 582, 413, 77, 271], [593, 190, 137, 374, 0, 258, 494, 372, 202, 331, 234, 222, 586, 706, 636, 509, 690, 818, 871, 731, 192, 789, 248, 470, 910, 332, 42, 598, 566, 579, 850, 583, 1008, 364, 354, 265, 168, 313, 117, 419, 586, 660, 847, 677, 634, 704, 275, 219, 730, 672, 287, 998, 79, 319, 229, 545, 107, 940, 663, 460, 523, 183, 38, 121, 845, 821, 152, 903, 820, 360, 289, 448, 86, 545, 742, 588, 68, 660, 910, 70, 739, 137, 151, 294, 824, 717, 585, 239, 198, 308, 993, 624, 135, 699, 137, 373, 208, 662, 237, 976, 139, 157, 242, 236, 477, 177, 776, 877, 165, 162, 437, 166, 784, 81, 228, 371, 881, 719, 205, 570], [409, 351, 240, 190, 258, 0, 310, 188, 328, 171, 365, 470, 723, 522, 452, 325, 506, 634, 687, 547, 442, 605, 370, 280, 726, 148, 257, 414, 382, 395, 666, 399, 824, 180, 110, 59, 239, 281, 375, 235, 402, 476, 663, 493, 450, 520, 91, 113, 546, 488, 418, 814, 329, 129, 39, 361, 275, 756, 479, 276, 339, 216, 296, 369, 661, 637, 262, 719, 636, 176, 105, 264, 240, 361, 558, 404, 316, 476, 726, 257, 555, 282, 319, 50, 640, 533, 401, 487, 53, 118, 809, 440, 385, 515, 385, 183, 456, 478, 179, 792, 172, 161, 298, 127, 293, 165, 592, 693, 424, 111, 247, 99, 600, 188, 383, 187, 697, 535, 259, 386], [332, 320, 232, 139, 494, 310, 0, 208, 188, 467, 249, 588, 417, 184, 375, 248, 210, 338, 391, 251, 396, 394, 175, 246, 430, 169, 316, 279, 305, 318, 474, 314, 528, 142, 406, 354, 528, 45, 611, 89, 106, 180, 367, 197, 231, 224, 225, 289, 250, 192, 264, 518, 359, 262, 273, 65, 511, 460, 402, 199, 262, 507, 532, 428, 365, 456, 542, 423, 340, 170, 218, 84, 476, 167, 238, 327, 434, 231, 430, 288, 317, 261, 555, 346, 384, 231, 141, 496, 297, 302, 513, 311, 371, 277, 503, 332, 574, 182, 497, 496, 408, 251, 131, 430, 160, 430, 296, 397, 416, 347, 336, 372, 304, 424, 352, 168, 441, 239, 153, 309], [232, 354, 261, 113, 372, 188, 208, 0, 284, 345, 372, 584, 621, 391, 321, 141, 408, 536, 589, 449, 430, 474, 330, 113, 628, 63, 355, 283, 251, 264, 535, 268, 726, 72, 284, 232, 406, 184, 489, 133, 300, 378, 565, 395, 319, 422, 104, 168, 448, 390, 425, 716, 403, 60, 151, 259, 389, 658, 348, 78, 208, 385, 410, 472, 563, 506, 420, 621, 538, 39, 79, 162, 354, 259, 427, 273, 430, 345, 628, 327, 424, 295, 433, 224, 509, 402, 299, 530, 175, 100, 711, 309, 405, 384, 499, 130, 570, 380, 375, 694, 286, 216, 229, 308, 154, 308, 494, 595, 450, 225, 114, 250, 502, 302, 386, 43, 566, 437, 190, 255], [464, 124, 88, 171, 202, 328, 188, 284, 0, 485, 61, 392, 411, 372, 507, 380, 398, 526, 579, 439, 202, 582, 46, 377, 618, 213, 160, 469, 437, 450, 662, 454, 716, 211, 424, 335, 300, 143, 319, 283, 294, 368, 555, 372, 419, 412, 244, 215, 438, 380, 112, 706, 163, 314, 324, 253, 322, 648, 534, 331, 394, 354, 240, 232, 553, 644, 314, 611, 528, 246, 266, 234, 287, 317, 426, 459, 238, 479, 618, 155, 505, 65, 366, 364, 572, 419, 329, 300, 268, 354, 701, 495, 175, 465, 307, 384, 378, 370, 552, 684, 213, 167, 57, 366, 342, 319, 484, 585, 220, 233, 448, 290, 492, 235, 121, 243, 629, 427, 97, 441], [566, 508, 397, 347, 331, 171, 467, 345, 485, 0, 522, 502, 874, 679, 609, 482, 663, 791, 844, 704, 515, 762, 527, 437, 883, 305, 330, 571, 539, 552, 823, 556, 981, 337, 70, 182, 166, 438, 354, 392, 559, 633, 820, 650, 607, 677, 248, 270, 703, 645, 575, 971, 402, 286, 196, 518, 248, 913, 636, 433, 496, 147, 306, 428, 818, 794, 189, 876, 793, 333, 262, 421, 250, 518, 715, 561, 362, 633, 883, 335, 712, 439, 266, 125, 797, 690, 558, 546, 218, 275, 966, 597, 458, 672, 417, 340, 488, 635, 100, 949, 329, 318, 455, 125, 450, 182, 749, 850, 483, 268, 404, 210, 757, 255, 540, 344, 854, 692, 416, 543], [552, 80, 127, 259, 234, 365, 249, 372, 61, 522, 0, 386, 354, 433, 595, 468, 459, 587, 640, 500, 141, 643, 72, 465, 679, 301, 167, 557, 525, 538, 723, 542, 777, 299, 461, 372, 348, 204, 351, 368, 355, 429, 616, 446, 480, 473, 332, 254, 499, 441, 55, 767, 157, 402, 324, 314, 331, 709, 622, 419, 482, 363, 272, 226, 614, 705, 362, 672, 589, 334, 354, 295, 296, 378, 487, 547, 232, 480, 679, 164, 566, 85, 375, 410, 633, 480, 390, 249, 305, 442, 762, 583, 147, 526, 301, 472, 372, 431, 589, 745, 223, 206, 118, 403, 430, 328, 545, 646, 188, 272, 536, 358, 553, 174, 60, 331, 690, 488, 185, 529], [802, 316, 336, 509, 222, 470, 588, 584, 392, 502, 386, 0, 738, 915, 845, 718, 892, 1020, 1073, 933, 233, 998, 438, 715, 1112, 544, 254, 807, 775, 788, 1059, 792, 1210, 549, 566, 477, 331, 543, 202, 621, 788, 862, 1049, 879, 843, 906, 487, 431, 932, 874, 439, 1200, 235, 531, 441, 747, 254, 1142, 872, 669, 732, 357, 210, 187, 1047, 1030, 313, 1105, 1022, 572, 501, 650, 266, 747, 951, 797, 154, 869, 1112, 282, 948, 321, 298, 506, 1033, 926, 787, 168, 410, 520, 1195, 833, 249, 908, 95, 585, 23, 864, 407, 1178, 351, 369, 437, 447, 680, 379, 978, 1079, 190, 374, 649, 378, 986, 293, 314, 583, 1090, 921, 435, 779], [633, 432, 479, 552, 586, 723, 417, 621, 411, 874, 354, 738, 0, 390, 572, 661, 227, 524, 413, 274, 492, 444, 380, 659, 407, 582, 521, 488, 572, 543, 524, 530, 446, 555, 819, 767, 700, 458, 679, 502, 340, 256, 289, 359, 462, 282, 638, 606, 188, 273, 313, 444, 509, 675, 686, 378, 693, 370, 599, 612, 567, 715, 640, 578, 245, 506, 714, 311, 261, 583, 631, 497, 672, 474, 352, 595, 584, 466, 348, 516, 506, 437, 755, 759, 434, 420, 422, 616, 710, 715, 407, 504, 499, 466, 653, 745, 724, 253, 941, 414, 575, 558, 468, 755, 573, 680, 346, 336, 540, 624, 749, 679, 300, 596, 411, 581, 491, 220, 537, 518], [257, 641, 541, 407, 706, 522, 184, 391, 372, 679, 433, 915, 390, 0, 196, 228, 169, 151, 257, 146, 723, 125, 359, 345, 296, 382, 638, 112, 196, 167, 238, 154, 423, 372, 618, 566, 740, 229, 823, 209, 146, 206, 262, 69, 86, 110, 437, 501, 204, 117, 448, 413, 686, 451, 485, 119, 723, 355, 223, 298, 191, 719, 744, 755, 260, 209, 754, 318, 235, 360, 430, 180, 688, 83, 50, 219, 761, 74, 325, 610, 139, 588, 767, 558, 160, 53, 43, 823, 509, 491, 408, 128, 698, 99, 830, 472, 901, 183, 709, 391, 620, 576, 315, 642, 228, 642, 191, 292, 743, 559, 435, 584, 199, 636, 679, 348, 217, 192, 482, 142], [187, 577, 477, 337, 636, 452, 375, 321, 507, 609, 595, 845, 572, 196, 0, 158, 351, 270, 342, 328, 653, 185, 553, 275, 381, 312, 568, 96, 88, 59, 238, 63, 605, 302, 548, 496, 670, 382, 753, 286, 322, 388, 444, 261, 124, 292, 367, 431, 386, 299, 648, 595, 616, 381, 415, 276, 653, 537, 49, 228, 121, 649, 674, 685, 442, 209, 684, 500, 417, 290, 360, 315, 618, 172, 232, 92, 691, 81, 507, 540, 98, 518, 697, 488, 245, 138, 200, 753, 439, 421, 590, 76, 628, 89, 760, 402, 831, 365, 639, 573, 550, 506, 451, 572, 235, 572, 373, 474, 673, 489, 365, 514, 381, 566, 609, 278, 302, 374, 412, 99], [91, 450, 357, 210, 509, 325, 248, 141, 380, 482, 468, 718, 661, 228, 158, 0, 383, 371, 443, 360, 526, 311, 426, 98, 482, 185, 441, 120, 77, 101, 372, 105, 637, 175, 421, 369, 504, 250, 626, 159, 306, 420, 476, 293, 156, 324, 240, 304, 418, 331, 520, 627, 489, 201, 288, 260, 526, 569, 185, 63, 27, 522, 547, 558, 474, 343, 557, 532, 449, 163, 233, 188, 491, 149, 264, 82, 564, 182, 539, 413, 261, 391, 570, 361, 346, 239, 232, 626, 312, 241, 622, 146, 501, 221, 633, 237, 704, 397, 512, 605, 423, 379, 325, 445, 108, 445, 405, 506, 546, 362, 150, 387, 413, 439, 482, 151, 403, 406, 286, 84], [412, 624, 531, 384, 690, 506, 210, 408, 398, 663, 459, 892, 227, 169, 351, 383, 0, 167, 220, 53, 700, 223, 385, 500, 259, 365, 615, 267, 351, 322, 303, 309, 357, 338, 602, 550, 724, 255, 807, 285, 112, 45, 196, 94, 241, 61, 421, 485, 36, 46, 474, 347, 663, 458, 469, 150, 707, 289, 378, 453, 346, 703, 728, 732, 141, 285, 738, 252, 116, 366, 414, 280, 672, 253, 131, 374, 738, 243, 259, 587, 285, 565, 751, 542, 213, 199, 211, 800, 493, 498, 342, 283, 675, 245, 807, 528, 818, 27, 693, 325, 604, 553, 341, 626, 383, 626, 125, 226, 720, 543, 590, 568, 81, 620, 656, 364, 270, 29, 459, 297], [400, 752, 659, 512, 818, 634, 338, 536, 526, 791, 587, 1020, 524, 151, 270, 371, 167, 0, 57, 112, 828, 67, 513, 488, 96, 493, 743, 255, 339, 310, 138, 297, 280, 466, 730, 678, 852, 350, 935, 413, 240, 204, 119, 142, 226, 125, 549, 613, 202, 150, 602, 270, 791, 586, 597, 278, 835, 212, 319, 441, 334, 831, 856, 860, 151, 120, 866, 175, 132, 494, 542, 408, 800, 235, 101, 362, 866, 189, 182, 715, 155, 693, 879, 670, 48, 132, 183, 928, 621, 626, 265, 271, 803, 178, 935, 656, 1006, 181, 821, 248, 732, 681, 469, 754, 371, 754, 82, 94, 848, 671, 578, 696, 137, 748, 784, 492, 105, 190, 587, 285], [472, 805, 712, 565, 871, 687, 391, 589, 579, 844, 640, 1073, 413, 257, 342, 443, 220, 57, 0, 165, 881, 139, 566, 560, 39, 546, 796, 327, 411, 382, 126, 369, 217, 519, 783, 731, 905, 436, 988, 466, 293, 257, 136, 224, 298, 178, 602, 666, 255, 203, 655, 200, 844, 639, 650, 331, 888, 197, 391, 513, 406, 884, 909, 913, 168, 108, 919, 192, 149, 547, 595, 461, 853, 339, 174, 434, 919, 261, 150, 768, 227, 746, 932, 723, 59, 204, 294, 981, 674, 679, 282, 343, 856, 250, 988, 709, 1059, 234, 874, 185, 785, 734, 522, 807, 443, 807, 142, 76, 901, 724, 650, 749, 184, 801, 837, 545, 69, 243, 640, 357], [389, 665, 572, 425, 731, 547, 251, 449, 439, 704, 500, 933, 274, 146, 328, 360, 53, 112, 165, 0, 741, 168, 426, 477, 204, 406, 656, 244, 328, 299, 248, 286, 279, 379, 643, 591, 765, 296, 848, 326, 153, 101, 118, 84, 218, 38, 462, 526, 88, 63, 515, 269, 704, 499, 510, 191, 748, 211, 355, 430, 323, 744, 769, 773, 116, 230, 779, 174, 91, 407, 455, 321, 713, 230, 108, 351, 779, 220, 181, 628, 229, 606, 792, 583, 158, 202, 178, 841, 534, 539, 264, 260, 716, 220, 848, 569, 919, 82, 734, 247, 645, 594, 382, 667, 360, 667, 47, 129, 761, 584, 567, 609, 53, 661, 697, 405, 215, 64, 500, 274], [610, 76, 183, 317, 192, 442, 396, 430, 202, 515, 141, 233, 492, 723, 653, 526, 700, 828, 881, 741, 0, 806, 213, 523, 920, 359, 188, 615, 583, 596, 867, 600, 1018, 357, 538, 449, 360, 351, 272, 429, 596, 670, 857, 687, 651, 714, 390, 310, 740, 682, 193, 1008, 131, 460, 413, 555, 302, 950, 680, 477, 540, 375, 233, 98, 855, 838, 344, 913, 830, 392, 412, 458, 289, 555, 759, 605, 177, 677, 920, 216, 756, 141, 346, 478, 841, 734, 595, 108, 382, 500, 1003, 641, 57, 716, 190, 530, 203, 672, 421, 986, 275, 262, 245, 420, 488, 344, 786, 887, 43, 346, 594, 350, 794, 265, 81, 389, 898, 729, 243, 587], [340, 730, 630, 490, 789, 605, 394, 474, 582, 762, 643, 998, 444, 125, 185, 311, 223, 67, 139, 168, 806, 0, 569, 428, 178, 465, 721, 195, 279, 250, 101, 237, 336, 455, 701, 649, 823, 439, 906, 439, 296, 260, 175, 146, 166, 181, 520, 584, 258, 206, 658, 326, 769, 534, 568, 334, 806, 268, 234, 381, 274, 802, 827, 838, 207, 72, 837, 231, 188, 443, 513, 464, 771, 228, 105, 302, 844, 129, 238, 693, 88, 671, 850, 641, 42, 72, 162, 906, 592, 574, 321, 211, 781, 96, 913, 555, 984, 237, 792, 304, 703, 659, 525, 725, 311, 725, 145, 154, 826, 642, 518, 667, 209, 719, 762, 431, 99, 246, 555, 225], [510, 152, 134, 217, 248, 370, 175, 330, 46, 527, 72, 438, 380, 359, 553, 426, 385, 513, 566, 426, 213, 569, 0, 423, 605, 259, 206, 515, 483, 496, 649, 500, 703, 257, 466, 377, 346, 130, 365, 226, 281, 355, 542, 372, 406, 399, 290, 232, 425, 367, 89, 693, 209, 360, 370, 240, 368, 635, 580, 377, 440, 400, 286, 278, 540, 631, 360, 598, 515, 292, 312, 221, 333, 304, 413, 505, 284, 406, 605, 201, 492, 111, 412, 406, 559, 406, 316, 321, 310, 400, 688, 541, 221, 452, 353, 430, 424, 357, 460, 671, 259, 213, 72, 408, 388, 365, 471, 572, 266, 279, 494, 332, 479, 281, 132, 289, 616, 414, 111, 487], [153, 447, 354, 207, 470, 280, 246, 113, 377, 437, 465, 715, 659, 345, 275, 98, 500, 488, 560, 477, 523, 428, 423, 0, 599, 170, 438, 237, 205, 218, 489, 222, 754, 172, 376, 324, 504, 248, 587, 157, 338, 537, 593, 410, 273, 441, 238, 302, 535, 448, 517, 744, 486, 151, 241, 297, 487, 686, 302, 47, 162, 483, 508, 555, 591, 460, 518, 649, 566, 133, 213, 186, 452, 263, 381, 227, 561, 299, 656, 410, 378, 388, 531, 316, 463, 356, 342, 623, 273, 191, 739, 263, 498, 338, 631, 167, 701, 514, 467, 722, 384, 376, 322, 400, 167, 406, 522, 623, 543, 323, 90, 348, 530, 400, 479, 137, 520, 523, 283, 209], [511, 844, 751, 604, 910, 726, 430, 628, 618, 883, 679, 1112, 407, 296, 381, 482, 259, 96, 39, 204, 920, 178, 605, 599, 0, 585, 835, 366, 450, 412, 165, 408, 194, 558, 822, 770, 944, 475, 1027, 505, 332, 296, 129, 263, 337, 217, 641, 705, 294, 242, 694, 177, 883, 678, 689, 370, 927, 157, 430, 552, 445, 923, 948, 952, 162, 147, 958, 186, 159, 586, 634, 500, 892, 378, 213, 473, 958, 300, 127, 807, 266, 785, 971, 762, 98, 243, 333, 1020, 713, 718, 276, 382, 895, 289, 1027, 748, 1098, 273, 913, 162, 824, 773, 561, 846, 482, 846, 145, 75, 940, 763, 689, 788, 194, 840, 876, 584, 108, 282, 679, 396], [269, 283, 190, 42, 332, 148, 169, 63, 213, 305, 301, 544, 582, 382, 312, 185, 365, 493, 546, 406, 359, 465, 259, 170, 585, 0, 274, 274, 242, 255, 526, 259, 683, 27, 244, 192, 366, 136, 449, 94, 261, 335, 522, 352, 310, 379, 64, 128, 405, 347, 354, 673, 322, 101, 111, 220, 349, 615, 339, 136, 199, 345, 370, 391, 520, 497, 380, 578, 495, 37, 53, 123, 314, 220, 418, 264, 390, 336, 585, 246, 415, 224, 393, 184, 500, 393, 260, 459, 135, 141, 668, 300, 334, 375, 459, 171, 530, 337, 335, 651, 202, 176, 158, 268, 153, 268, 451, 552, 379, 185, 235, 210, 459, 262, 315, 48, 557, 394, 119, 246], [525, 157, 95, 232, 42, 257, 316, 355, 160, 330, 167, 254, 521, 638, 568, 441, 615, 743, 796, 656, 188, 721, 206, 438, 835, 274, 0, 530, 498, 511, 782, 515, 933, 272, 353, 264, 179, 271, 159, 344, 511, 585, 772, 602, 566, 629, 227, 163, 655, 597, 220, 923, 57, 318, 228, 470, 149, 865, 595, 392, 455, 194, 80, 132, 770, 753, 193, 828, 745, 307, 248, 373, 127, 470, 674, 520, 100, 592, 835, 28, 671, 95, 193, 293, 756, 649, 510, 241, 197, 307, 918, 556, 131, 631, 169, 372, 240, 587, 236, 901, 87, 115, 200, 235, 403, 159, 701, 802, 161, 161, 436, 165, 709, 75, 189, 304, 813, 644, 163, 502], [150, 539, 446, 299, 598, 414, 279, 283, 469, 571, 557, 807, 488, 112, 96, 120, 267, 255, 327, 244, 615, 195, 515, 237, 366, 274, 530, 0, 63, 56, 256, 34, 521, 264, 509, 458, 632, 286, 715, 190, 220, 304, 360, 177, 40, 208, 329, 393, 302, 215, 610, 511, 578, 343, 377, 174, 615, 453, 123, 190, 83, 611, 636, 647, 358, 227, 646, 416, 333, 252, 397, 193, 580, 79, 148, 119, 653, 105, 423, 502, 144, 480, 659, 450, 230, 123, 98, 715, 401, 383, 506, 32, 590, 105, 722, 364, 793, 281, 601, 489, 512, 468, 413, 534, 119, 534, 289, 390, 635, 451, 402, 476, 297, 528, 571, 240, 287, 290, 375, 35], [80, 507, 414, 267, 566, 382, 305, 251, 437, 539, 525, 775, 572, 196, 88, 77, 351, 339, 411, 328, 583, 279, 483, 205, 450, 242, 498, 63, 0, 25, 340, 29, 605, 232, 478, 426, 600, 312, 683, 216, 322, 388, 444, 261, 124, 292, 297, 361, 386, 299, 577, 595, 546, 311, 345, 276, 583, 537, 115, 158, 47, 579, 604, 615, 442, 311, 614, 500, 417, 220, 290, 245, 548, 139, 232, 31, 621, 150, 507, 470, 176, 448, 627, 418, 314, 207, 200, 683, 369, 351, 590, 76, 558, 189, 690, 332, 761, 365, 569, 573, 480, 436, 382, 502, 165, 502, 373, 474, 603, 419, 295, 444, 381, 496, 539, 208, 371, 374, 343, 29], [130, 520, 427, 280, 579, 395, 318, 264, 450, 552, 538, 788, 543, 167, 59, 101, 322, 310, 382, 299, 596, 250, 496, 218, 412, 255, 511, 56, 25, 0, 311, 22, 576, 245, 491, 439, 613, 325, 696, 229, 293, 359, 415, 232, 95, 263, 310, 374, 357, 270, 591, 566, 559, 324, 358, 247, 596, 508, 86, 171, 64, 592, 617, 628, 413, 282, 627, 471, 388, 233, 378, 258, 561, 134, 203, 43, 634, 121, 478, 483, 164, 461, 640, 431, 285, 178, 171, 696, 382, 364, 561, 47, 571, 160, 703, 345, 774, 336, 582, 544, 493, 449, 394, 515, 178, 515, 344, 445, 616, 432, 383, 457, 352, 509, 552, 221, 342, 345, 356, 42], [401, 791, 691, 551, 850, 666, 474, 535, 662, 823, 723, 1059, 524, 238, 238, 372, 303, 138, 126, 248, 867, 101, 649, 489, 165, 526, 782, 256, 340, 311, 0, 298, 416, 516, 762, 710, 884, 519, 967, 500, 376, 340, 255, 247, 227, 261, 581, 645, 338, 286, 738, 406, 830, 595, 629, 414, 867, 348, 287, 442, 335, 863, 888, 899, 287, 29, 898, 311, 268, 504, 574, 544, 832, 289, 206, 363, 905, 190, 318, 754, 140, 732, 911, 702, 90, 185, 275, 967, 653, 635, 401, 272, 842, 151, 974, 616, 1045, 317, 853, 384, 764, 720, 605, 786, 372, 786, 225, 202, 887, 703, 579, 728, 285, 780, 823, 492, 77, 326, 626, 286], [134, 524, 431, 284, 583, 399, 314, 268, 454, 556, 542, 792, 530, 154, 63, 105, 309, 297, 369, 286, 600, 237, 500, 222, 408, 259, 515, 34, 29, 22, 298, 0, 563, 249, 494, 443, 617, 321, 700, 225, 253, 346, 402, 219, 82, 250, 314, 378, 344, 257, 595, 553, 563, 328, 362, 207, 600, 495, 90, 175, 68, 596, 621, 632, 400, 269, 631, 458, 375, 237, 382, 228, 565, 112, 190, 58, 638, 108, 465, 487, 136, 465, 644, 435, 272, 165, 131, 700, 386, 368, 548, 30, 575, 147, 707, 349, 778, 323, 586, 531, 497, 453, 398, 519, 154, 519, 331, 432, 620, 436, 387, 461, 339, 513, 556, 225, 329, 332, 360, 36], [666, 942, 849, 702, 1008, 824, 528, 726, 716, 981, 777, 1210, 446, 423, 605, 637, 357, 280, 217, 279, 1018, 336, 703, 754, 194, 683, 933, 521, 605, 576, 416, 563, 0, 656, 920, 868, 1042, 573, 1125, 603, 430, 394, 161, 361, 495, 315, 739, 803, 392, 340, 792, 42, 981, 776, 787, 468, 1025, 84, 632, 707, 600, 1021, 1046, 1050, 201, 398, 1056, 154, 226, 684, 732, 598, 990, 507, 385, 628, 1056, 497, 98, 905, 424, 883, 1069, 860, 326, 401, 455, 1118, 811, 816, 174, 537, 993, 447, 1125, 846, 1196, 371, 1011, 32, 922, 871, 659, 944, 637, 944, 238, 175, 1038, 861, 844, 886, 261, 938, 974, 682, 383, 380, 777, 551], [259, 281, 188, 40, 364, 180, 142, 72, 211, 337, 299, 549, 555, 372, 302, 175, 338, 466, 519, 379, 357, 455, 257, 172, 558, 27, 272, 264, 232, 245, 516, 249, 656, 0, 276, 224, 398, 102, 481, 67, 234, 308, 495, 325, 300, 352, 95, 159, 378, 320, 352, 646, 320, 132, 143, 193, 381, 588, 329, 126, 189, 377, 402, 389, 493, 487, 412, 551, 468, 34, 88, 96, 346, 193, 408, 254, 395, 326, 558, 244, 405, 222, 425, 216, 490, 383, 233, 457, 167, 172, 641, 290, 332, 365, 464, 202, 535, 310, 367, 624, 278, 210, 155, 300, 126, 300, 424, 525, 877, 217, 189, 242, 432, 294, 313, 32, 547, 367, 116, 236], [505, 447, 336, 286, 354, 110, 406, 284, 424, 70, 461, 566, 819, 618, 548, 421, 602, 730, 783, 643, 538, 701, 466, 376, 822, 244, 353, 509, 478, 491, 762, 494, 920, 276, 0, 95, 162, 377, 345, 331, 498, 572, 759, 589, 546, 616, 187, 209, 642, 584, 514, 910, 425, 225, 135, 457, 239, 852, 575, 372, 435, 139, 293, 465, 757, 733, 185, 815, 732, 272, 201, 360, 237, 457, 654, 500, 412, 572, 822, 353, 651, 378, 262, 64, 736, 629, 497, 583, 157, 214, 905, 536, 481, 611, 481, 279, 552, 574, 91, 888, 268, 257, 394, 80, 389, 137, 688, 789, 520, 207, 343, 156, 696, 284, 479, 283, 793, 631, 355, 482], [453, 358, 247, 234, 265, 59, 354, 232, 335, 182, 372, 477, 767, 566, 496, 369, 550, 678, 731, 591, 449, 649, 377, 324, 770, 192, 264, 458, 426, 439, 710, 443, 868, 224, 95, 0, 177, 325, 337, 279, 446, 520, 707, 537, 494, 564, 135, 120, 590, 532, 425, 858, 336, 173, 83, 405, 231, 800, 523, 320, 383, 154, 261, 376, 705, 681, 200, 763, 680, 220, 149, 308, 205, 405, 602, 448, 323, 520, 770, 264, 599, 289, 254, 57, 684, 577, 445, 494, 67, 162, 853, 484, 392, 559, 392, 227, 463, 522, 117, 836, 141, 168, 305, 68, 337, 106, 636, 737, 431, 141, 291, 61, 644, 195, 390, 231, 741, 579, 266, 430], [627, 334, 251, 408, 168, 239, 528, 406, 300, 166, 348, 331, 700, 740, 670, 504, 724, 852, 905, 765, 360, 823, 346, 504, 944, 366, 179, 632, 600, 613, 884, 617, 1042, 398, 162, 177, 0, 425, 183, 453, 620, 694, 881, 711, 668, 738, 309, 219, 764, 706, 401, 1032, 236, 353, 263, 579, 77, 974, 697, 494, 557, 22, 138, 260, 879, 855, 23, 937, 854, 394, 323, 482, 82, 579, 776, 622, 194, 694, 944, 184, 773, 272, 100, 193, 858, 751, 619, 378, 215, 342, 1027, 658, 303, 733, 246, 407, 317, 696, 72, 1010, 173, 219, 344, 112, 511, 75, 810, 911, 315, 162, 471, 135, 818, 104, 366, 405, 915, 753, 305, 604], [339, 275, 187, 94, 313, 281, 45, 184, 143, 438, 204, 543, 458, 229, 382, 250, 255, 350, 436, 296, 351, 439, 130, 248, 475, 136, 271, 286, 312, 325, 519, 321, 573, 102, 377, 325, 425, 0, 430, 96, 151, 225, 412, 242, 276, 269, 196, 229, 295, 237, 219, 563, 314, 233, 244, 110, 408, 505, 409, 201, 269, 447, 351, 383, 410, 501, 439, 468, 385, 146, 189, 91, 373, 174, 283, 334, 389, 276, 475, 243, 362, 216, 452, 317, 429, 276, 186, 451, 268, 273, 558, 318, 326, 322, 458, 303, 529, 227, 468, 541, 252, 206, 86, 401, 162, 370, 341, 442, 371, 272, 295, 311, 349, 321, 307, 144, 486, 284, 108, 316], [710, 283, 254, 491, 117, 375, 611, 489, 319, 354, 351, 202, 679, 823, 753, 626, 807, 935, 988, 848, 272, 906, 365, 587, 1027, 449, 159, 715, 683, 696, 967, 700, 1125, 481, 345, 337, 183, 430, 0, 536, 703, 777, 964, 794, 751, 821, 392, 336, 847, 789, 404, 1115, 176, 436, 346, 662, 106, 1057, 780, 577, 640, 209, 79, 161, 962, 938, 165, 1020, 937, 477, 406, 565, 141, 662, 859, 705, 95, 777, 1027, 187, 856, 254, 103, 411, 941, 834, 702, 279, 315, 425, 1110, 741, 215, 816, 117, 490, 188, 779, 259, 1093, 256, 274, 359, 299, 594, 231, 893, 994, 216, 279, 554, 276, 901, 193, 308, 488, 998, 836, 322, 687], [243, 353, 260, 113, 419, 235, 89, 133, 283, 392, 368, 621, 502, 209, 286, 159, 285, 413, 466, 326, 429, 439, 226, 157, 505, 94, 344, 190, 216, 229, 500, 225, 603, 67, 331, 279, 453, 96, 536, 0, 181, 255, 442, 272, 207, 299, 150, 214, 325, 267, 421, 593, 392, 187, 198, 140, 436, 535, 313, 110, 173, 432, 457, 461, 440, 471, 467, 498, 415, 95, 143, 29, 401, 126, 248, 238, 467, 310, 505, 316, 389, 294, 480, 271, 474, 367, 166, 529, 222, 227, 588, 222, 404, 349, 536, 257, 607, 257, 422, 571, 333, 282, 228, 355, 71, 355, 371, 472, 449, 272, 247, 297, 379, 349, 385, 93, 531, 314, 189, 220], [376, 520, 427, 280, 586, 402, 106, 300, 294, 559, 355, 788, 340, 146, 322, 306, 112, 240, 293, 153, 596, 296, 281, 338, 332, 261, 511, 220, 322, 293, 376, 253, 430, 234, 498, 446, 620, 151, 703, 181, 0, 82, 269, 99, 212, 126, 317, 381, 152, 94, 370, 420, 559, 354, 365, 46, 603, 362, 349, 291, 295, 599, 624, 628, 267, 354, 634, 325, 242, 262, 310, 142, 568, 154, 140, 327, 634, 204, 332, 483, 290, 461, 647, 438, 286, 204, 114, 696, 389, 394, 415, 256, 571, 250, 703, 424, 774, 84, 589, 398, 500, 449, 237, 522, 207, 522, 198, 299, 616, 439, 428, 464, 206, 516, 552, 260, 343, 141, 356, 268], [449, 594, 501, 354, 660, 476, 180, 378, 368, 633, 429, 862, 256, 206, 388, 420, 45, 204, 257, 101, 670, 260, 355, 537, 296, 335, 585, 304, 388, 359, 340, 346, 394, 308, 572, 520, 694, 225, 777, 255, 82, 0, 233, 106, 278, 82, 391, 455, 68, 58, 444, 384, 633, 428, 439, 120, 677, 326, 415, 490, 383, 673, 698, 702, 231, 322, 708, 289, 206, 336, 384, 250, 642, 290, 168, 411, 708, 280, 296, 557, 322, 535, 721, 512, 250, 236, 248, 770, 463, 468, 379, 320, 645, 282, 777, 498, 848, 19, 663, 362, 574, 523, 311, 596, 420, 596, 162, 263, 690, 513, 627, 538, 126, 590, 626, 334, 307, 78, 429, 334], [505, 781, 688, 541, 847, 663, 367, 565, 555, 820, 616, 1049, 289, 262, 444, 476, 196, 119, 136, 118, 857, 175, 542, 593, 129, 522, 772, 360, 444, 415, 255, 402, 161, 495, 759, 707, 881, 412, 964, 442, 269, 233, 0, 200, 334, 154, 578, 642, 231, 179, 631, 151, 820, 615, 626, 307, 864, 93, 471, 546, 439, 860, 885, 889, 44, 237, 895, 65, 55, 523, 571, 437, 829, 346, 224, 467, 895, 336, 63, 744, 263, 722, 908, 699, 165, 240, 294, 957, 650, 655, 155, 376, 832, 286, 964, 685, 1035, 210, 850, 129, 761, 710, 498, 783, 476, 783, 77, 58, 877, 700, 683, 725, 90, 777, 813, 521, 222, 219, 616, 390], [322, 611, 518, 371, 677, 493, 197, 395, 372, 650, 446, 879, 359, 69, 261, 293, 94, 142, 224, 84, 687, 146, 372, 410, 263, 352, 602, 177, 261, 232, 247, 219, 361, 325, 589, 537, 711, 242, 794, 272, 99, 106, 200, 0, 151, 46, 408, 472, 130, 48, 461, 351, 650, 445, 456, 126, 694, 293, 288, 363, 256, 690, 715, 719, 198, 218, 725, 256, 173, 353, 401, 267, 659, 136, 41, 284, 725, 143, 263, 574, 195, 552, 738, 529, 169, 109, 67, 787, 480, 485, 346, 193, 662, 155, 794, 515, 865, 83, 680, 329, 591, 540, 328, 613, 232, 613, 129, 230, 707, 530, 500, 555, 137, 607, 643, 351, 226, 123, 446, 207], [185, 575, 482, 335, 634, 450, 231, 319, 419, 607, 480, 843, 462, 86, 124, 156, 241, 226, 298, 218, 651, 166, 406, 273, 337, 310, 566, 40, 124, 95, 227, 82, 495, 300, 546, 494, 668, 276, 751, 207, 212, 278, 334, 151, 0, 182, 365, 429, 276, 189, 495, 485, 614, 379, 413, 166, 651, 427, 151, 226, 119, 647, 672, 683, 332, 198, 682, 390, 307, 288, 358, 159, 616, 621, 122, 147, 689, 37, 397, 538, 116, 516, 695, 486, 201, 86, 90, 751, 437, 419, 480, 56, 626, 76, 758, 400, 829, 255, 637, 463, 548, 504, 362, 570, 136, 570, 263, 364, 671, 487, 363, 512, 271, 564, 607, 276, 258, 264, 411, 70], [353, 638, 545, 398, 704, 520, 224, 422, 412, 677, 473, 906, 282, 110, 292, 324, 61, 125, 178, 38, 714, 181, 399, 441, 217, 379, 629, 208, 292, 263, 261, 250, 315, 352, 616, 564, 738, 269, 821, 299, 126, 82, 154, 46, 182, 0, 435, 499, 96, 31, 488, 305, 677, 472, 483, 164, 721, 247, 319, 396, 287, 717, 742, 746, 152, 243, 752, 210, 127, 380, 428, 294, 686, 194, 72, 315, 752, 184, 217, 601, 226, 579, 765, 556, 171, 140, 142, 814, 507, 512, 300, 224, 689, 186, 821, 542, 892, 67, 707, 283, 618, 567, 355, 640, 324, 640, 83, 184, 734, 557, 531, 582, 91, 634, 670, 378, 228, 84, 473, 238], [324, 314, 191, 106, 275, 91, 225, 104, 244, 248, 332, 487, 638, 437, 367, 240, 421, 549, 602, 462, 390, 520, 290, 238, 641, 64, 227, 329, 297, 310, 581, 314, 739, 95, 187, 135, 309, 196, 392, 150, 317, 391, 578, 408, 365, 435, 0, 64, 461, 403, 385, 729, 353, 83, 54, 276, 292, 671, 394, 191, 254, 288, 313, 422, 576, 552, 323, 634, 551, 92, 21, 179, 257, 276, 473, 319, 333, 391, 641, 199, 470, 255, 336, 127, 555, 448, 316, 490, 78, 114, 724, 355, 365, 430, 402, 157, 473, 393, 278, 707, 138, 112, 188, 211, 209, 211, 507, 608, 410, 128, 221, 153, 515, 205, 346, 103, 612, 450, 149, 301], [388, 234, 127, 124, 219, 113, 289, 168, 215, 270, 254, 431, 606, 501, 431, 304, 485, 613, 666, 526, 310, 584, 232, 302, 705, 128, 163, 393, 361, 374, 645, 378, 803, 159, 209, 120, 219, 229, 336, 214, 381, 455, 642, 472, 429, 499, 64, 0, 525, 467, 307, 793, 220, 147, 72, 340, 236, 735, 458, 255, 378, 232, 257, 330, 640, 616, 233, 698, 615, 156, 85, 243, 201, 340, 537, 383, 277, 455, 705, 135, 534, 169, 280, 149, 619, 512, 380, 448, 53, 151, 788, 419, 261, 494, 346, 221, 417, 457, 221, 771, 74, 48, 160, 169, 273, 155, 571, 672, 306, 50, 285, 93, 579, 149, 266, 167, 676, 514, 121, 365], [447, 664, 571, 424, 730, 546, 250, 448, 438, 703, 499, 932, 188, 204, 386, 418, 36, 202, 255, 88, 740, 258, 425, 535, 294, 405, 655, 302, 386, 357, 338, 344, 392, 378, 642, 590, 764, 295, 847, 325, 152, 68, 231, 130, 276, 96, 461, 525, 0, 82, 514, 382, 603, 498, 509, 190, 747, 324, 413, 488, 381, 743, 768, 772, 174, 320, 778, 287, 149, 406, 454, 320, 712, 288, 166, 409, 778, 278, 294, 627, 320, 605, 791, 582, 248, 234, 246, 840, 533, 538, 377, 318, 715, 280, 847, 568, 918, 65, 733, 360, 644, 593, 381, 666, 418, 666, 160, 261, 760, 583, 625, 608, 111, 660, 696, 404, 305, 34, 499, 332], [360, 606, 513, 366, 672, 488, 192, 390, 380, 645, 441, 874, 273, 117, 299, 331, 46, 150, 203, 63, 682, 206, 367, 448, 242, 347, 597, 215, 299, 270, 286, 257, 340, 320, 584, 532, 706, 237, 789, 267, 94, 58, 179, 48, 189, 31, 403, 467, 82, 0, 456, 330, 645, 440, 451, 132, 689, 272, 326, 401, 294, 685, 710, 714, 177, 268, 720, 235, 152, 348, 396, 262, 654, 201, 89, 322, 720, 191, 242, 569, 243, 547, 733, 524, 196, 157, 159, 782, 475, 480, 325, 231, 657, 203, 789, 510, 860, 35, 675, 308, 586, 535, 323, 608, 331, 608, 108, 209, 702, 525, 538, 550, 116, 602, 638, 346, 253, 75, 441, 245], [605, 133, 180, 312, 287, 418, 264, 425, 112, 575, 55, 439, 313, 448, 648, 520, 474, 602, 655, 515, 193, 658, 89, 517, 694, 354, 220, 610, 577, 591, 738, 595, 792, 352, 514, 425, 401, 219, 404, 421, 370, 444, 631, 461, 495, 488, 385, 307, 514, 456, 0, 782, 210, 455, 377, 329, 384, 724, 675, 471, 534, 416, 325, 279, 629, 720, 415, 687, 604, 387, 407, 310, 349, 393, 502, 600, 285, 495, 694, 217, 581, 138, 428, 454, 648, 495, 405, 310, 358, 495, 777, 636, 200, 541, 354, 525, 425, 446, 642, 760, 276, 259, 169, 456, 482, 381, 560, 661, 241, 325, 589, 380, 568, 297, 112, 384, 705, 503, 238, 581], [656, 932, 839, 692, 998, 814, 518, 716, 706, 971, 767, 1200, 444, 413, 595, 627, 347, 270, 200, 269, 1008, 326, 693, 744, 177, 673, 923, 511, 595, 566, 406, 553, 42, 646, 910, 858, 1032, 563, 1115, 593, 420, 384, 151, 351, 485, 305, 729, 793, 382, 330, 782, 0, 971, 766, 777, 458, 1015, 85, 622, 697, 590, 1011, 1036, 1040, 199, 388, 1046, 152, 224, 674, 722, 588, 980, 497, 375, 618, 1046, 487, 96, 895, 414, 873, 1059, 850, 316, 391, 445, 1108, 801, 806, 173, 527, 983, 437, 1115, 836, 1186, 361, 1001, 33, 912, 861, 649, 934, 627, 934, 228, 160, 1028, 851, 834, 876, 259, 928, 964, 672, 373, 370, 767, 541], [573, 113, 101, 280, 79, 329, 359, 403, 163, 402, 157, 235, 509, 686, 616, 489, 663, 791, 844, 704, 131, 769, 209, 486, 883, 322, 57, 578, 546, 559, 830, 563, 981, 320, 425, 336, 236, 314, 176, 392, 559, 633, 820, 650, 614, 677, 353, 220, 603, 645, 210, 971, 0, 375, 300, 518, 186, 913, 643, 440, 503, 262, 117, 75, 818, 801, 231, 876, 793, 355, 375, 421, 165, 518, 722, 568, 81, 640, 883, 85, 719, 92, 230, 365, 804, 697, 558, 184, 269, 364, 966, 604, 74, 679, 150, 429, 221, 635, 308, 949, 144, 172, 208, 307, 451, 231, 749, 850, 104, 233, 493, 237, 757, 147, 158, 352, 861, 692, 206, 550], [293, 384, 274, 143, 319, 129, 262, 60, 314, 286, 402, 531, 675, 451, 381, 201, 458, 586, 639, 499, 460, 534, 360, 151, 678, 101, 318, 343, 311, 324, 595, 328, 776, 132, 225, 173, 353, 233, 436, 187, 354, 428, 615, 445, 379, 472, 83, 147, 498, 440, 455, 766, 375, 0, 90, 313, 336, 708, 408, 138, 268, 332, 357, 430, 613, 566, 367, 671, 588, 83, 62, 216, 301, 313, 487, 333, 377, 405, 678, 292, 484, 325, 380, 165, 569, 462, 353, 560, 122, 40, 761, 369, 435, 444, 446, 70, 517, 430, 316, 744, 233, 195, 259, 249, 246, 255, 544, 645, 480, 172, 134, 197, 552, 249, 416, 95, 626, 487, 220, 315], [372, 283, 199, 153, 229, 39, 273, 151, 324, 196, 324, 441, 686, 485, 415, 288, 469, 597, 650, 510, 413, 568, 370, 241, 689, 111, 228, 377, 345, 358, 629, 362, 787, 143, 135, 83, 263, 244, 346, 198, 365, 439, 626, 456, 413, 483, 54, 72, 509, 451, 377, 777, 300, 90, 0, 324, 246, 719, 442, 239, 302, 242, 267, 340, 624, 600, 277, 682, 599, 139, 68, 227, 211, 324, 521, 367, 287, 439, 689, 228, 518, 241, 290, 75, 603, 496, 364, 458, 32, 79, 772, 403, 356, 478, 356, 144, 427, 441, 212, 755, 143, 120, 269, 225, 256, 165, 555, 656, 395, 82, 208, 107, 563, 159, 342, 150, 660, 498, 230, 349], [330, 479, 386, 239, 545, 361, 65, 259, 253, 518, 314, 747, 378, 119, 276, 260, 150, 278, 331, 191, 555, 334, 240, 297, 370, 220, 470, 174, 276, 247, 414, 207, 468, 193, 457, 405, 579, 110, 662, 140, 46, 120, 307, 126, 166, 164, 276, 340, 190, 132, 329, 458, 518, 313, 324, 0, 562, 400, 303, 250, 249, 558, 583, 587, 305, 396, 593, 363, 280, 221, 269, 96, 527, 108, 167, 281, 593, 166, 370, 442, 252, 420, 606, 397, 324, 166, 76, 655, 348, 353, 453, 210, 530, 212, 662, 383, 733, 122, 548, 436, 459, 408, 196, 481, 161, 481, 236, 337, 575, 398, 372, 423, 244, 475, 511, 219, 381, 179, 315, 222], [610, 297, 234, 391, 107, 275, 511, 389, 322, 248, 331, 254, 693, 723, 653, 526, 707, 835, 888, 748, 302, 806, 368, 487, 927, 349, 149, 615, 583, 596, 867, 600, 1025, 381, 239, 231, 77, 408, 106, 436, 603, 677, 864, 694, 651, 721, 292, 236, 747, 689, 384, 1015, 186, 336, 246, 562, 0, 957, 680, 477, 540, 103, 69, 191, 862, 838, 59, 920, 837, 377, 306, 465, 35, 562, 759, 605, 125, 677, 927, 167, 756, 255, 44, 311, 841, 734, 602, 309, 215, 325, 1010, 641, 245, 716, 169, 390, 240, 679, 153, 983, 156, 202, 327, 193, 494, 125, 793, 894, 246, 179, 454, 170, 801, 87, 335, 388, 898, 736, 288, 587], [598, 874, 781, 634, 940, 756, 460, 658, 648, 913, 709, 1142, 370, 355, 537, 569, 289, 212, 197, 211, 950, 268, 635, 686, 157, 615, 865, 453, 537, 508, 348, 495, 84, 588, 852, 800, 974, 505, 1057, 535, 362, 326, 93, 293, 427, 247, 671, 735, 324, 272, 724, 85, 913, 708, 719, 400, 957, 0, 564, 639, 532, 953, 978, 982, 125, 330, 988, 77, 150, 616, 664, 530, 922, 439, 317, 560, 988, 429, 30, 837, 356, 815, 1001, 792, 258, 333, 387, 1050, 743, 748, 90, 469, 925, 379, 1057, 778, 1128, 303, 943, 52, 854, 803, 591, 876, 569, 876, 170, 140, 970, 793, 776, 818, 185, 870, 906, 614, 315, 312, 709, 483], [214, 604, 504, 364, 663, 479, 402, 348, 534, 636, 622, 872, 599, 223, 49, 185, 378, 319, 391, 355, 680, 234, 580, 302, 430, 339, 595, 123, 115, 86, 287, 90, 632, 329, 575, 523, 697, 409, 780, 313, 349, 415, 471, 288, 151, 319, 394, 458, 413, 326, 675, 622, 643, 408, 442, 303, 680, 564, 0, 255, 148, 676, 701, 712, 469, 258, 711, 527, 444, 317, 387, 342, 645, 199, 259, 84, 718, 160, 534, 567, 147, 545, 724, 515, 294, 187, 227, 780, 466, 448, 617, 103, 655, 138, 787, 429, 858, 392, 666, 600, 577, 533, 478, 599, 262, 599, 400, 501, 700, 516, 392, 541, 408, 593, 636, 305, 351, 401, 439, 126], [154, 401, 308, 161, 460, 276, 199, 78, 331, 433, 419, 669, 612, 298, 228, 63, 453, 441, 513, 430, 477, 381, 377, 47, 552, 136, 392, 190, 158, 171, 442, 175, 707, 126, 372, 320, 494, 201, 577, 110, 291, 490, 546, 363, 226, 396, 191, 255, 488, 401, 471, 697, 440, 138, 239, 250, 477, 639, 255, 0, 115, 473, 498, 509, 544, 413, 508, 602, 519, 92, 184, 139, 442, 216, 334, 180, 515, 252, 609, 364, 331, 342, 521, 312, 416, 309, 295, 577, 263, 178, 692, 216, 452, 291, 584, 174, 655, 467, 463, 675, 374, 330, 276, 396, 120, 396, 475, 576, 497, 313, 137, 338, 483, 390, 433, 94, 473, 476, 237, 162], [70, 464, 371, 224, 523, 339, 262, 208, 394, 496, 482, 732, 567, 191, 121, 27, 346, 334, 406, 323, 540, 274, 440, 162, 445, 199, 455, 83, 47, 64, 335, 68, 600, 189, 435, 383, 557, 269, 640, 173, 295, 383, 439, 256, 119, 287, 254, 378, 381, 294, 534, 590, 503, 268, 302, 249, 540, 532, 148, 115, 0, 536, 561, 572, 437, 306, 571, 495, 412, 177, 247, 209, 505, 153, 227, 53, 578, 145, 502, 427, 224, 405, 584, 375, 309, 202, 195, 640, 326, 308, 585, 109, 515, 184, 647, 289, 718, 360, 526, 568, 437, 393, 339, 459, 110, 459, 368, 469, 560, 376, 252, 401, 376, 453, 496, 165, 366, 369, 300, 55], [606, 349, 266, 387, 183, 216, 507, 385, 354, 147, 363, 357, 715, 719, 649, 522, 703, 831, 884, 744, 375, 802, 400, 483, 923, 345, 194, 611, 579, 592, 863, 596, 1021, 377, 139, 154, 22, 447, 209, 432, 599, 673, 860, 690, 647, 717, 288, 232, 743, 685, 416, 1011, 262, 332, 242, 558, 103, 953, 676, 473, 536, 0, 153, 275, 858, 834, 45, 916, 833, 373, 302, 461, 97, 558, 755, 601, 209, 673, 923, 199, 752, 287, 122, 170, 837, 730, 598, 393, 206, 321, 1006, 637, 318, 712, 272, 386, 343, 675, 47, 989, 208, 254, 391, 89, 490, 77, 789, 890, 348, 175, 450, 147, 797, 119, 381, 384, 894, 732, 352, 583], [631, 228, 175, 412, 38, 296, 532, 410, 240, 306, 272, 210, 640, 744, 674, 547, 728, 856, 909, 769, 233, 827, 286, 508, 948, 370, 80, 636, 604, 617, 888, 621, 1046, 402, 293, 261, 138, 351, 79, 457, 624, 698, 885, 715, 672, 742, 313, 257, 768, 710, 325, 1036, 117, 357, 267, 583, 69, 978, 701, 498, 561, 153, 0, 122, 883, 859, 122, 941, 858, 398, 327, 486, 56, 583, 780, 626, 56, 698, 948, 108, 777, 175, 113, 332, 862, 755, 623, 240, 236, 346, 1031, 662, 176, 737, 125, 411, 196, 700, 212, 1014, 177, 195, 280, 231, 515, 155, 814, 915, 177, 200, 475, 191, 822, 108, 266, 409, 919, 757, 243, 608], [642, 129, 176, 349, 121, 369, 428, 472, 232, 428, 226, 187, 578, 755, 685, 558, 732, 860, 913, 773, 98, 838, 278, 555, 952, 391, 132, 647, 615, 628, 899, 632, 1050, 389, 465, 376, 260, 383, 161, 461, 628, 702, 889, 719, 683, 746, 422, 330, 772, 714, 279, 1040, 75, 430, 340, 587, 191, 982, 712, 509, 572, 275, 122, 0, 887, 870, 244, 945, 862, 424, 444, 490, 178, 587, 791, 637, 66, 709, 952, 160, 788, 161, 235, 405, 873, 766, 627, 118, 309, 419, 1035, 673, 62, 748, 92, 484, 173, 704, 334, 1018, 219, 247, 277, 353, 590, 247, 818, 919, 55, 273, 548, 277, 826, 192, 155, 421, 930, 761, 275, 619], [503, 779, 686, 539, 845, 661, 365, 563, 553, 818, 614, 1047, 245, 260, 442, 474, 141, 151, 168, 116, 855, 207, 540, 591, 162, 520, 770, 358, 442, 413, 287, 400, 201, 493, 757, 705, 879, 410, 962, 440, 267, 231, 44, 198, 332, 152, 576, 640, 174, 177, 629, 199, 818, 613, 624, 305, 862, 125, 469, 544, 437, 858, 883, 887, 0, 269, 893, 66, 25, 521, 569, 435, 827, 344, 222, 465, 893, 334, 103, 742, 295, 720, 906, 697, 197, 272, 292, 955, 648, 653, 162, 374, 830, 318, 962, 683, 1033, 208, 848, 169, 759, 708, 496, 781, 474, 781, 76, 91, 875, 698, 681, 723, 60, 775, 811, 519, 254, 137, 614, 388], [372, 762, 662, 522, 821, 637, 456, 506, 644, 794, 705, 1030, 506, 209, 209, 343, 285, 120, 108, 230, 838, 72, 631, 460, 147, 497, 753, 227, 311, 282, 29, 269, 398, 487, 733, 681, 855, 501, 938, 471, 354, 322, 237, 218, 198, 243, 552, 616, 320, 268, 720, 388, 801, 566, 600, 396, 838, 330, 258, 413, 306, 834, 859, 870, 269, 0, 869, 293, 250, 475, 545, 526, 803, 260, 177, 334, 876, 161, 300, 725, 111, 703, 882, 673, 72, 156, 246, 938, 624, 606, 383, 243, 813, 122, 945, 587, 1016, 299, 824, 366, 735, 691, 587, 757, 343, 757, 207, 184, 858, 674, 550, 699, 267, 751, 794, 463, 66, 308, 587, 257], [641, 348, 265, 422, 152, 262, 542, 420, 314, 189, 362, 313, 714, 754, 684, 557, 738, 866, 919, 779, 344, 837, 360, 518, 958, 380, 193, 646, 614, 627, 898, 631, 1056, 412, 185, 200, 23, 439, 165, 467, 634, 708, 895, 725, 682, 752, 323, 233, 778, 720, 415, 1046, 231, 367, 277, 593, 59, 988, 711, 508, 571, 45, 122, 244, 893, 869, 0, 951, 868, 408, 337, 496, 66, 593, 790, 636, 178, 708, 958, 198, 787, 286, 77, 216, 872, 765, 633, 362, 238, 356, 1041, 672, 287, 747, 228, 421, 299, 710, 95, 1024, 187, 233, 358, 135, 525, 89, 824, 925, 299, 176, 485, 149, 832, 118, 380, 419, 929, 767, 319, 618], [561, 837, 744, 597, 903, 719, 423, 621, 611, 876, 672, 1105, 311, 318, 500, 532, 252, 175, 192, 174, 913, 231, 598, 649, 186, 578, 828, 416, 500, 471, 311, 458, 154, 551, 815, 763, 937, 468, 1020, 498, 325, 289, 65, 256, 390, 210, 634, 698, 287, 235, 687, 152, 876, 671, 682, 363, 920, 77, 527, 602, 495, 916, 941, 945, 66, 293, 951, 0, 91, 579, 627, 493, 885, 402, 280, 523, 951, 392, 56, 800, 319, 778, 964, 755, 221, 296, 350, 1013, 706, 711, 96, 432, 888, 342, 1020, 741, 1091, 266, 906, 122, 817, 766, 554, 839, 532, 839, 133, 113, 933, 756, 739, 781, 126, 833, 869, 577, 278, 275, 672, 446], [478, 754, 661, 514, 820, 636, 340, 538, 528, 793, 589, 1022, 261, 235, 417, 449, 116, 132, 149, 91, 830, 188, 515, 566, 159, 495, 745, 333, 417, 388, 268, 375, 226, 468, 732, 680, 854, 385, 937, 415, 242, 206, 55, 173, 307, 127, 551, 615, 149, 152, 604, 224, 793, 588, 599, 280, 837, 150, 444, 519, 412, 833, 858, 862, 25, 250, 868, 91, 0, 496, 544, 410, 802, 319, 197, 440, 868, 309, 120, 717, 276, 695, 881, 672, 178, 253, 267, 930, 623, 628, 187, 349, 805, 299, 937, 658, 1008, 183, 823, 194, 734, 683, 471, 756, 449, 756, 51, 88, 850, 673, 656, 698, 35, 750, 786, 494, 235, 112, 589, 363], [247, 316, 223, 76, 360, 176, 170, 39, 246, 333, 334, 572, 583, 360, 290, 163, 366, 494, 547, 407, 392, 443, 292, 133, 586, 37, 307, 252, 220, 233, 504, 237, 684, 34, 272, 220, 394, 146, 477, 95, 262, 336, 523, 353, 288, 380, 92, 156, 406, 348, 387, 674, 355, 83, 139, 221, 377, 616, 317, 92, 177, 373, 398, 424, 521, 475, 408, 579, 496, 0, 70, 124, 342, 221, 396, 242, 418, 314, 586, 279, 393, 257, 421, 212, 478, 371, 261, 492, 163, 123, 669, 278, 367, 353, 487, 153, 558, 338, 363, 652, 274, 204, 191, 296, 127, 296, 452, 553, 412, 213, 150, 238, 460, 290, 348, 12, 535, 395, 152, 224], [317, 336, 212, 95, 289, 105, 218, 79, 266, 262, 354, 501, 631, 430, 360, 233, 414, 542, 595, 455, 412, 513, 312, 213, 634, 53, 248, 397, 290, 378, 574, 382, 732, 88, 201, 149, 323, 189, 406, 143, 310, 384, 571, 401, 358, 428, 21, 85, 454, 396, 407, 722, 375, 62, 68, 269, 306, 664, 387, 184, 247, 302, 327, 444, 569, 545, 337, 627, 544, 70, 0, 172, 271, 269, 466, 312, 347, 384, 634, 220, 463, 277, 350, 141, 548, 441, 309, 512, 92, 93, 717, 423, 387, 423, 416, 132, 487, 386, 292, 700, 159, 133, 211, 225, 202, 225, 500, 601, 432, 142, 196, 167, 508, 219, 368, 92, 605, 443, 172, 294], [272, 382, 289, 142, 448, 264, 84, 162, 234, 421, 295, 650, 497, 180, 315, 188, 280, 408, 461, 321, 458, 464, 221, 186, 500, 123, 373, 193, 245, 258, 544, 228, 598, 96, 360, 308, 482, 91, 565, 29, 142, 250, 437, 267, 159, 294, 179, 243, 320, 262, 310, 588, 421, 216, 227, 96, 465, 530, 342, 139, 209, 461, 486, 490, 435, 526, 496, 493, 410, 124, 172, 0, 430, 97, 219, 267, 496, 196, 500, 345, 275, 323, 509, 300, 454, 227, 137, 558, 251, 256, 583, 225, 433, 235, 565, 286, 636, 252, 451, 566, 362, 311, 177, 384, 74, 384, 366, 467, 478, 301, 276, 326, 374, 378, 414, 122, 511, 309, 218, 249], [575, 276, 199, 356, 86, 240, 476, 354, 287, 250, 296, 266, 672, 688, 618, 491, 672, 800, 853, 713, 289, 771, 333, 452, 892, 314, 127, 580, 548, 561, 832, 565, 990, 346, 237, 205, 82, 373, 141, 401, 568, 642, 829, 659, 616, 686, 257, 201, 712, 654, 349, 980, 165, 301, 211, 527, 35, 922, 645, 442, 505, 97, 56, 178, 827, 803, 66, 885, 802, 342, 271, 430, 0, 527, 724, 570, 112, 642, 892, 132, 721, 220, 79, 276, 832, 699, 567, 296, 180, 290, 975, 606, 232, 681, 181, 355, 252, 644, 156, 958, 121, 167, 292, 175, 459, 99, 758, 859, 233, 144, 419, 135, 766, 52, 314, 353, 863, 701, 253, 552], [219, 479, 386, 239, 545, 361, 167, 259, 317, 518, 378, 747, 474, 83, 172, 149, 253, 235, 339, 230, 555, 228, 304, 263, 378, 220, 470, 79, 139, 134, 289, 112, 507, 193, 457, 405, 579, 174, 662, 126, 154, 290, 346, 136, 621, 194, 276, 340, 288, 201, 393, 497, 518, 313, 324, 108, 562, 439, 199, 216, 153, 558, 583, 587, 344, 260, 593, 402, 319, 221, 269, 97, 527, 0, 134, 170, 593, 99, 409, 442, 178, 420, 606, 397, 263, 130, 69, 655, 348, 353, 492, 104, 530, 138, 662, 383, 733, 267, 548, 475, 459, 408, 260, 481, 96, 481, 275, 376, 575, 398, 353, 423, 283, 475, 511, 219, 320, 276, 315, 104], [293, 683, 583, 443, 742, 558, 238, 427, 426, 715, 487, 951, 352, 50, 232, 264, 131, 101, 174, 108, 759, 105, 413, 381, 213, 418, 674, 148, 232, 203, 206, 190, 385, 408, 654, 602, 776, 283, 859, 248, 140, 168, 224, 41, 122, 72, 473, 537, 166, 89, 502, 375, 722, 487, 521, 167, 759, 317, 259, 334, 227, 755, 780, 791, 222, 177, 790, 280, 197, 396, 466, 219, 724, 134, 0, 255, 797, 125, 287, 646, 154, 624, 803, 594, 128, 68, 82, 859, 545, 527, 370, 164, 734, 114, 866, 508, 937, 145, 745, 353, 656, 612, 369, 678, 264, 678, 153, 176, 779, 595, 471, 620, 161, 672, 715, 384, 185, 154, 518, 178], [54, 529, 429, 289, 588, 404, 327, 273, 459, 561, 547, 797, 595, 219, 92, 82, 374, 362, 434, 351, 605, 302, 505, 227, 473, 264, 520, 119, 31, 43, 363, 58, 628, 254, 500, 448, 622, 334, 705, 238, 327, 411, 467, 284, 147, 315, 319, 383, 409, 322, 600, 618, 568, 333, 367, 281, 605, 560, 84, 180, 53, 601, 626, 637, 465, 334, 636, 523, 440, 242, 312, 267, 570, 170, 255, 0, 643, 173, 530, 492, 190, 470, 649, 440, 337, 230, 223, 705, 391, 373, 613, 99, 580, 212, 712, 354, 783, 388, 591, 596, 502, 458, 403, 524, 187, 524, 396, 497, 625, 441, 317, 466, 404, 518, 561, 230, 394, 397, 364, 60], [648, 188, 182, 355, 68, 316, 434, 430, 238, 362, 232, 154, 584, 761, 691, 564, 738, 866, 919, 779, 177, 844, 284, 561, 958, 390, 100, 653, 621, 634, 905, 638, 1056, 395, 412, 323, 194, 389, 95, 467, 634, 708, 895, 725, 689, 752, 333, 277, 778, 720, 285, 1046, 81, 377, 287, 593, 125, 988, 718, 515, 578, 209, 56, 66, 893, 876, 178, 951, 868, 418, 347, 496, 112, 593, 797, 643, 0, 715, 958, 128, 794, 167, 169, 352, 879, 772, 633, 179, 256, 366, 1041, 679, 120, 754, 69, 431, 154, 710, 268, 1024, 197, 215, 283, 287, 526, 211, 824, 925, 121, 220, 495, 224, 832, 139, 213, 429, 936, 767, 281, 625], [211, 601, 501, 361, 660, 476, 231, 345, 479, 633, 480, 869, 466, 74, 81, 182, 243, 189, 261, 220, 677, 129, 406, 299, 300, 336, 592, 105, 150, 121, 190, 108, 497, 326, 572, 520, 694, 276, 777, 310, 204, 280, 336, 143, 37, 184, 391, 455, 278, 191, 495, 487, 640, 405, 439, 166, 677, 429, 160, 252, 145, 673, 698, 709, 334, 161, 708, 392, 309, 314, 384, 196, 642, 99, 125, 173, 715, 0, 399, 564, 79, 542, 721, 512, 164, 57, 90, 777, 463, 445, 482, 57, 652, 39, 784, 426, 855, 257, 663, 465, 574, 530, 362, 596, 182, 596, 265, 276, 697, 513, 389, 538, 273, 590, 633, 302, 221, 266, 436, 96], [568, 844, 751, 604, 910, 726, 430, 628, 618, 883, 679, 1112, 348, 325, 507, 539, 259, 182, 150, 181, 920, 238, 605, 656, 127, 585, 835, 423, 507, 478, 318, 465, 98, 558, 822, 770, 944, 475, 1027, 505, 332, 296, 63, 263, 397, 217, 641, 705, 294, 242, 694, 96, 883, 678, 689, 370, 927, 30, 534, 609, 502, 923, 948, 952, 103, 300, 958, 56, 120, 586, 634, 500, 892, 409, 287, 530, 958, 399, 0, 807, 326, 785, 971, 762, 228, 303, 357, 1020, 713, 718, 112, 439, 895, 349, 1027, 748, 1098, 273, 913, 66, 824, 773, 561, 846, 539, 846, 140, 110, 940, 763, 746, 788, 155, 840, 876, 584, 285, 282, 679, 453], [497, 150, 67, 204, 70, 257, 288, 327, 155, 335, 164, 282, 516, 610, 540, 413, 587, 715, 768, 628, 216, 693, 201, 410, 807, 246, 28, 502, 470, 483, 754, 487, 905, 244, 353, 264, 184, 243, 187, 316, 483, 557, 744, 574, 538, 601, 199, 135, 627, 569, 217, 895, 85, 292, 228, 442, 167, 837, 567, 364, 427, 199, 108, 160, 742, 725, 198, 800, 717, 279, 220, 345, 132, 442, 646, 492, 128, 564, 807, 0, 643, 88, 211, 293, 728, 627, 482, 269, 197, 281, 890, 528, 159, 603, 197, 346, 268, 559, 241, 873, 59, 87, 172, 240, 375, 164, 673, 774, 189, 108, 410, 139, 681, 80, 182, 276, 785, 616, 135, 474], [290, 680, 580, 440, 739, 555, 317, 424, 505, 712, 566, 948, 506, 139, 98, 261, 285, 155, 227, 229, 756, 88, 492, 378, 266, 415, 671, 144, 176, 164, 140, 136, 424, 405, 651, 599, 773, 362, 856, 389, 290, 322, 263, 195, 116, 226, 470, 534, 320, 243, 581, 414, 719, 484, 518, 252, 756, 356, 147, 331, 224, 752, 777, 788, 295, 111, 787, 319, 276, 393, 463, 275, 721, 178, 154, 190, 794, 79, 326, 643, 0, 621, 800, 591, 130, 86, 176, 856, 542, 524, 409, 112, 731, 40, 863, 505, 934, 299, 742, 392, 653, 609, 448, 675, 261, 675, 233, 242, 776, 592, 468, 617, 293, 669, 712, 381, 177, 308, 515, 175], [475, 65, 42, 182, 137, 282, 261, 295, 65, 439, 85, 321, 437, 588, 518, 391, 565, 693, 746, 606, 141, 671, 111, 388, 785, 224, 95, 480, 448, 461, 732, 465, 883, 222, 378, 289, 272, 216, 254, 294, 461, 535, 722, 552, 516, 579, 255, 169, 605, 547, 138, 873, 92, 325, 241, 420, 255, 815, 545, 342, 405, 287, 175, 161, 720, 703, 286, 778, 695, 257, 277, 323, 220, 420, 624, 470, 167, 542, 785, 88, 621, 0, 299, 318, 706, 599, 460, 229, 222, 365, 868, 506, 104, 581, 236, 395, 307, 537, 506, 851, 147, 121, 110, 320, 353, 252, 651, 752, 149, 187, 459, 244, 659, 168, 97, 254, 763, 594, 108, 452], [654, 341, 278, 435, 151, 319, 555, 433, 366, 266, 375, 298, 755, 767, 697, 570, 751, 879, 932, 792, 346, 850, 412, 531, 971, 393, 193, 659, 627, 640, 911, 644, 1069, 425, 262, 254, 100, 452, 103, 480, 647, 721, 908, 738, 695, 765, 336, 280, 791, 733, 428, 1059, 230, 380, 290, 606, 44, 1001, 724, 521, 584, 122, 113, 235, 906, 882, 77, 964, 881, 421, 350, 509, 79, 606, 803, 649, 169, 721, 971, 211, 800, 299, 0, 355, 885, 778, 646, 353, 259, 369, 1054, 685, 289, 760, 213, 434, 284, 723, 172, 1037, 200, 246, 371, 212, 538, 148, 837, 938, 290, 223, 498, 214, 845, 131, 379, 432, 942, 780, 332, 631], [445, 387, 276, 226, 294, 50, 346, 224, 364, 125, 410, 506, 759, 558, 488, 361, 542, 670, 723, 583, 478, 641, 406, 316, 762, 184, 293, 450, 418, 431, 702, 435, 860, 216, 64, 57, 193, 317, 411, 271, 438, 512, 699, 529, 486, 556, 127, 149, 582, 524, 454, 850, 365, 165, 75, 397, 311, 792, 515, 312, 375, 170, 332, 405, 697, 673, 216, 755, 672, 212, 141, 300, 276, 397, 594, 440, 352, 512, 762, 293, 591, 318, 355, 0, 676, 569, 437, 523, 97, 154, 845, 476, 421, 551, 421, 219, 492, 514, 133, 828, 208, 197, 334, 84, 329, 168, 628, 729, 460, 147, 283, 118, 636, 224, 419, 223, 733, 571, 295, 422], [375, 765, 665, 525, 824, 640, 384, 509, 572, 797, 633, 1033, 434, 160, 245, 346, 213, 48, 59, 158, 841, 42, 559, 463, 98, 500, 756, 230, 314, 285, 90, 272, 326, 490, 736, 684, 858, 429, 941, 474, 286, 250, 165, 169, 201, 171, 555, 619, 248, 196, 648, 316, 804, 569, 603, 324, 841, 258, 294, 416, 309, 837, 862, 873, 197, 72, 872, 221, 178, 478, 548, 454, 832, 263, 128, 337, 879, 164, 228, 728, 130, 706, 885, 676, 0, 107, 197, 941, 627, 609, 311, 246, 816, 138, 948, 590, 1019, 227, 827, 294, 738, 694, 515, 760, 346, 760, 135, 123, 861, 677, 553, 702, 195, 754, 797, 466, 57, 236, 600, 260], [268, 658, 558, 418, 717, 533, 231, 402, 419, 690, 480, 926, 420, 53, 138, 239, 199, 132, 204, 202, 734, 72, 406, 356, 243, 393, 649, 123, 207, 178, 185, 165, 401, 383, 629, 577, 751, 276, 834, 367, 204, 236, 240, 109, 86, 140, 448, 512, 234, 157, 495, 391, 697, 462, 496, 166, 734, 333, 187, 309, 202, 730, 755, 766, 272, 156, 765, 296, 253, 371, 441, 227, 699, 130, 68, 230, 772, 57, 303, 627, 86, 599, 778, 569, 107, 0, 90, 834, 520, 502, 386, 114, 709, 46, 841, 483, 912, 213, 720, 369, 631, 587, 362, 653, 239, 653, 210, 219, 754, 570, 446, 595, 270, 647, 690, 359, 164, 222, 493, 153], [261, 519, 426, 279, 585, 401, 141, 299, 329, 558, 390, 787, 422, 43, 200, 232, 211, 183, 294, 178, 595, 162, 316, 342, 333, 260, 510, 98, 200, 171, 275, 131, 455, 233, 497, 445, 619, 186, 702, 166, 114, 248, 294, 67, 90, 142, 316, 380, 246, 159, 405, 445, 558, 353, 364, 76, 602, 387, 227, 295, 195, 598, 623, 627, 292, 246, 633, 350, 267, 261, 309, 137, 567, 69, 82, 223, 633, 90, 357, 482, 176, 460, 646, 437, 197, 90, 0, 695, 388, 393, 440, 134, 570, 136, 702, 423, 773, 225, 588, 423, 499, 448, 272, 521, 165, 521, 223, 324, 615, 438, 432, 462, 231, 515, 551, 259, 254, 234, 355, 146], [710, 184, 271, 417, 239, 487, 496, 530, 300, 546, 249, 168, 616, 823, 753, 626, 800, 928, 981, 841, 108, 906, 321, 623, 1020, 459, 241, 715, 683, 696, 967, 700, 1118, 457, 583, 494, 378, 451, 279, 529, 696, 770, 957, 787, 751, 814, 490, 448, 840, 782, 310, 1108, 184, 560, 458, 655, 309, 1050, 780, 577, 640, 393, 240, 118, 955, 938, 362, 1013, 930, 492, 512, 558, 296, 655, 859, 705, 179, 777, 1020, 269, 856, 229, 353, 523, 941, 834, 695, 0, 427, 537, 1103, 741, 121, 816, 162, 630, 138, 772, 452, 1086, 328, 350, 345, 471, 588, 365, 886, 987, 80, 391, 694, 395, 894, 310, 189, 489, 998, 829, 343, 687], [396, 291, 180, 177, 198, 53, 297, 175, 268, 218, 305, 410, 710, 509, 439, 312, 493, 621, 674, 534, 382, 592, 310, 273, 713, 135, 197, 401, 369, 382, 653, 386, 811, 167, 157, 67, 215, 268, 315, 222, 389, 463, 650, 480, 437, 507, 78, 53, 533, 475, 358, 801, 269, 122, 32, 348, 215, 743, 466, 263, 326, 206, 236, 309, 648, 624, 238, 706, 623, 163, 92, 251, 180, 348, 545, 391, 256, 463, 713, 197, 542, 222, 259, 97, 627, 520, 388, 427, 0, 111, 796, 427, 325, 502, 325, 176, 396, 465, 185, 779, 112, 101, 238, 133, 280, 137, 579, 680, 364, 51, 240, 67, 587, 128, 323, 174, 684, 522, 199, 373], [295, 424, 278, 183, 308, 118, 302, 100, 354, 275, 442, 520, 715, 491, 421, 241, 498, 626, 679, 539, 500, 574, 400, 191, 718, 141, 307, 383, 351, 364, 635, 368, 816, 172, 214, 162, 342, 273, 425, 227, 394, 468, 655, 485, 419, 512, 114, 151, 538, 480, 495, 806, 364, 40, 79, 353, 325, 748, 448, 178, 308, 321, 346, 419, 653, 606, 356, 711, 628, 123, 93, 256, 290, 353, 527, 373, 366, 445, 718, 281, 524, 365, 369, 154, 609, 502, 393, 537, 111, 0, 801, 409, 438, 484, 435, 65, 506, 470, 305, 784, 222, 199, 299, 238, 286, 244, 584, 685, 474, 161, 129, 186, 592, 238, 456, 135, 666, 527, 260, 355], [651, 927, 834, 687, 993, 809, 513, 711, 701, 966, 762, 1195, 407, 408, 590, 622, 342, 265, 282, 264, 1003, 321, 688, 739, 276, 668, 918, 506, 590, 561, 401, 548, 174, 641, 905, 853, 1027, 558, 1110, 588, 415, 379, 155, 346, 480, 300, 724, 788, 377, 325, 777, 173, 966, 761, 772, 453, 1010, 90, 617, 692, 585, 1006, 1031, 1035, 162, 383, 1041, 96, 187, 669, 717, 583, 975, 492, 370, 613, 1041, 482, 112, 890, 409, 868, 1054, 845, 311, 386, 440, 1103, 796, 801, 0, 522, 978, 432, 1110, 831, 1181, 355, 996, 142, 907, 856, 644, 929, 622, 929, 223, 205, 1023, 846, 829, 871, 222, 923, 959, 667, 368, 365, 762, 536], [175, 565, 472, 325, 624, 440, 311, 309, 495, 597, 583, 833, 504, 128, 76, 146, 283, 271, 343, 260, 641, 211, 541, 263, 382, 300, 556, 32, 76, 47, 272, 30, 537, 290, 536, 484, 658, 318, 741, 222, 256, 320, 376, 193, 56, 224, 355, 419, 318, 231, 636, 527, 604, 369, 403, 210, 641, 469, 103, 216, 109, 637, 662, 673, 374, 243, 672, 432, 349, 278, 423, 225, 606, 104, 164, 99, 679, 57, 439, 528, 112, 506, 685, 476, 246, 114, 134, 741, 427, 409, 522, 0, 616, 96, 748, 390, 819, 297, 627, 505, 538, 494, 439, 560, 151, 560, 305, 406, 661, 477, 353, 502, 313, 554, 597, 266, 303, 306, 401, 47], [585, 67, 146, 292, 135, 385, 371, 405, 175, 458, 147, 249, 499, 698, 628, 501, 675, 803, 856, 716, 57, 781, 221, 498, 895, 334, 131, 590, 558, 571, 842, 575, 993, 332, 481, 392, 303, 326, 215, 404, 571, 645, 832, 662, 626, 689, 365, 261, 715, 657, 200, 983, 74, 435, 356, 530, 245, 925, 655, 452, 515, 318, 176, 62, 830, 813, 287, 888, 805, 367, 387, 433, 232, 530, 734, 580, 120, 652, 895, 159, 731, 104, 289, 421, 816, 709, 570, 121, 325, 438, 978, 616, 0, 691, 154, 505, 235, 647, 364, 961, 218, 225, 220, 363, 463, 287, 761, 862, 41, 289, 569, 293, 769, 208, 93, 364, 873, 704, 218, 562], [250, 640, 540, 400, 699, 515, 277, 384, 465, 672, 526, 908, 466, 99, 89, 221, 245, 178, 250, 220, 716, 96, 452, 338, 289, 375, 631, 105, 189, 160, 151, 147, 447, 365, 611, 559, 733, 322, 816, 349, 250, 282, 286, 155, 76, 186, 430, 494, 280, 203, 541, 437, 679, 444, 478, 212, 716, 379, 138, 291, 184, 712, 737, 748, 318, 122, 747, 342, 299, 353, 423, 235, 681, 138, 114, 212, 754, 39, 349, 603, 40, 581, 760, 551, 138, 46, 136, 816, 502, 484, 432, 96, 691, 0, 823, 465, 894, 259, 702, 415, 613, 569, 408, 635, 221, 635, 241, 265, 736, 552, 428, 577, 301, 629, 672, 341, 195, 268, 475, 135], [717, 221, 251, 424, 137, 385, 503, 499, 307, 417, 301, 95, 653, 830, 760, 633, 807, 935, 988, 848, 190, 913, 353, 631, 1027, 459, 169, 722, 690, 703, 974, 707, 1125, 464, 481, 392, 246, 458, 117, 536, 703, 777, 964, 794, 758, 821, 402, 346, 847, 789, 354, 1115, 150, 446, 356, 662, 169, 1057, 787, 584, 647, 272, 125, 92, 962, 945, 228, 1020, 937, 487, 416, 565, 181, 662, 866, 712, 69, 784, 1027, 197, 863, 236, 213, 421, 948, 841, 702, 162, 325, 435, 1110, 748, 154, 823, 0, 500, 81, 779, 322, 1003, 266, 284, 352, 362, 595, 294, 893, 994, 147, 289, 564, 293, 901, 208, 247, 498, 1005, 836, 350, 694], [246, 454, 361, 213, 373, 183, 332, 130, 384, 340, 472, 585, 745, 472, 402, 237, 528, 656, 709, 569, 530, 555, 430, 167, 748, 171, 372, 364, 332, 345, 616, 349, 846, 202, 279, 227, 407, 303, 490, 257, 424, 498, 685, 515, 400, 542, 157, 221, 568, 510, 525, 836, 429, 70, 144, 383, 390, 778, 429, 174, 289, 386, 411, 484, 683, 587, 421, 741, 658, 153, 132, 286, 355, 383, 508, 354, 431, 426, 748, 346, 505, 395, 434, 219, 590, 483, 423, 630, 176, 65, 831, 390, 505, 465, 500, 0, 571, 500, 370, 814, 287, 269, 329, 303, 294, 309, 614, 715, 550, 226, 80, 251, 622, 303, 486, 165, 647, 557, 290, 336], [788, 302, 322, 495, 208, 456, 574, 570, 378, 488, 372, 23, 724, 901, 831, 704, 818, 1006, 1059, 919, 203, 984, 424, 701, 1098, 530, 240, 793, 761, 774, 1045, 778, 1196, 535, 552, 463, 317, 529, 188, 607, 774, 848, 1035, 865, 829, 892, 473, 417, 918, 860, 425, 1186, 221, 517, 427, 733, 240, 1128, 858, 655, 718, 343, 196, 173, 1033, 1016, 299, 1091, 1008, 558, 487, 636, 252, 733, 937, 783, 154, 855, 1098, 268, 934, 307, 284, 492, 1019, 912, 773, 138, 396, 506, 1181, 819, 235, 894, 81, 571, 0, 850, 393, 1164, 337, 355, 423, 433, 666, 365, 964, 1065, 160, 360, 635, 364, 972, 279, 284, 569, 1076, 907, 421, 765], [426, 596, 503, 356, 662, 478, 182, 380, 370, 635, 431, 864, 253, 183, 365, 397, 27, 181, 234, 82, 672, 237, 357, 514, 273, 337, 587, 281, 365, 336, 317, 323, 371, 310, 574, 522, 696, 227, 779, 257, 84, 19, 210, 83, 255, 67, 393, 457, 65, 35, 446, 361, 635, 430, 441, 122, 679, 303, 392, 467, 360, 675, 700, 704, 208, 299, 710, 266, 183, 338, 386, 252, 644, 267, 145, 388, 710, 257, 273, 559, 299, 537, 723, 514, 227, 213, 225, 772, 465, 470, 355, 297, 647, 259, 779, 500, 850, 0, 665, 339, 576, 525, 313, 598, 397, 598, 139, 240, 692, 515, 604, 540, 108, 592, 628, 336, 284, 56, 431, 311], [596, 575, 330, 377, 237, 179, 497, 375, 552, 100, 589, 407, 941, 709, 639, 512, 693, 821, 874, 734, 421, 792, 460, 467, 913, 335, 236, 601, 569, 582, 853, 586, 1011, 367, 91, 117, 72, 468, 259, 422, 589, 663, 850, 680, 637, 707, 278, 221, 733, 675, 642, 1001, 308, 316, 212, 548, 153, 943, 666, 463, 526, 47, 212, 334, 848, 824, 95, 906, 823, 363, 292, 451, 156, 548, 745, 591, 268, 663, 913, 241, 742, 506, 172, 133, 827, 720, 588, 452, 185, 305, 996, 627, 364, 702, 322, 370, 393, 665, 0, 979, 208, 251, 388, 52, 480, 80, 779, 880, 394, 167, 434, 128, 787, 161, 607, 374, 884, 722, 349, 573], [634, 910, 817, 670, 976, 792, 496, 694, 684, 949, 745, 1178, 414, 391, 573, 605, 325, 248, 185, 247, 986, 304, 671, 722, 162, 651, 901, 489, 573, 544, 384, 531, 32, 624, 888, 836, 1010, 541, 1093, 571, 398, 362, 129, 329, 463, 283, 707, 771, 360, 308, 760, 33, 949, 744, 755, 436, 983, 52, 600, 675, 568, 989, 1014, 1018, 169, 366, 1024, 122, 194, 652, 700, 566, 958, 475, 353, 596, 1024, 465, 66, 873, 392, 851, 1037, 828, 294, 369, 423, 1086, 779, 784, 142, 505, 961, 415, 1003, 814, 1164, 339, 979, 0, 890, 839, 627, 912, 605, 912, 206, 143, 1006, 829, 812, 854, 229, 906, 942, 650, 351, 348, 745, 519], [507, 209, 111, 201, 139, 172, 408, 286, 213, 329, 223, 351, 575, 620, 550, 423, 604, 732, 785, 645, 275, 703, 259, 384, 824, 202, 87, 512, 480, 493, 764, 497, 922, 278, 268, 141, 173, 252, 256, 333, 500, 574, 761, 591, 548, 618, 138, 74, 644, 586, 276, 912, 144, 233, 143, 459, 156, 854, 577, 374, 437, 208, 177, 219, 759, 735, 187, 817, 734, 274, 159, 362, 121, 459, 656, 502, 197, 574, 824, 59, 653, 147, 200, 208, 738, 631, 499, 328, 112, 222, 907, 538, 218, 613, 266, 287, 337, 576, 208, 890, 0, 46, 183, 156, 391, 131, 690, 791, 248, 49, 351, 80, 698, 69, 244, 285, 795, 633, 144, 484], [463, 186, 79, 155, 157, 161, 251, 216, 167, 318, 206, 369, 558, 576, 506, 379, 553, 681, 734, 594, 262, 659, 213, 376, 773, 176, 115, 468, 436, 449, 720, 453, 871, 210, 257, 168, 219, 206, 274, 282, 449, 523, 710, 540, 504, 567, 112, 48, 593, 535, 259, 861, 172, 195, 120, 408, 202, 803, 533, 330, 393, 254, 195, 247, 708, 691, 233, 766, 683, 204, 133, 311, 167, 408, 612, 458, 215, 530, 773, 87, 609, 121, 246, 197, 694, 587, 448, 350, 101, 199, 856, 494, 225, 569, 284, 269, 355, 525, 251, 839, 46, 0, 137, 199, 341, 177, 639, 740, 270, 66, 333, 123, 647, 115, 218, 215, 751, 582, 98, 440], [408, 169, 105, 116, 242, 298, 131, 229, 57, 455, 118, 437, 468, 315, 451, 325, 341, 469, 522, 382, 245, 525, 72, 322, 561, 158, 200, 413, 382, 394, 605, 398, 659, 155, 394, 305, 344, 86, 359, 228, 237, 311, 498, 328, 362, 355, 188, 160, 381, 323, 169, 649, 208, 259, 269, 196, 327, 591, 478, 276, 339, 391, 280, 277, 496, 587, 358, 554, 471, 191, 211, 177, 292, 260, 369, 403, 283, 362, 561, 172, 448, 110, 371, 334, 515, 362, 272, 345, 238, 299, 644, 439, 220, 408, 352, 329, 423, 313, 388, 627, 183, 137, 0, 336, 287, 314, 427, 528, 265, 203, 393, 260, 435, 240, 178, 188, 572, 370, 39, 386], [529, 389, 278, 310, 236, 127, 430, 308, 366, 125, 403, 447, 755, 642, 572, 445, 626, 754, 807, 667, 420, 725, 408, 400, 846, 268, 235, 534, 502, 515, 786, 519, 944, 300, 80, 68, 112, 401, 299, 355, 522, 596, 783, 613, 570, 640, 211, 169, 666, 608, 456, 934, 307, 249, 225, 481, 193, 876, 599, 396, 459, 89, 231, 353, 781, 757, 135, 839, 756, 296, 225, 384, 175, 481, 678, 524, 287, 596, 846, 240, 675, 320, 212, 84, 760, 653, 521, 471, 133, 238, 929, 560, 363, 635, 362, 303, 433, 598, 52, 912, 156, 199, 336, 0, 413, 87, 712, 813, 393, 115, 367, 76, 720, 160, 421, 307, 817, 655, 297, 506], [192, 412, 319, 172, 477, 293, 160, 154, 342, 450, 430, 680, 573, 228, 235, 108, 383, 371, 443, 360, 488, 311, 388, 167, 482, 153, 403, 119, 165, 178, 372, 154, 637, 126, 389, 337, 511, 162, 594, 71, 207, 420, 476, 232, 136, 324, 209, 273, 418, 331, 482, 627, 451, 246, 256, 161, 494, 569, 262, 120, 110, 490, 515, 590, 474, 343, 525, 532, 449, 127, 202, 74, 459, 96, 264, 187, 526, 182, 539, 375, 261, 353, 538, 329, 346, 239, 165, 588, 280, 286, 622, 151, 463, 221, 595, 294, 666, 397, 480, 605, 391, 341, 287, 413, 0, 413, 405, 506, 508, 330, 257, 355, 413, 407, 444, 115, 403, 406, 248, 169], [529, 286, 231, 310, 177, 165, 430, 308, 319, 182, 328, 379, 680, 642, 572, 445, 626, 754, 807, 667, 344, 725, 365, 406, 846, 268, 159, 534, 502, 515, 786, 519, 944, 300, 137, 106, 75, 370, 231, 355, 522, 596, 783, 613, 570, 640, 211, 155, 666, 608, 381, 934, 231, 255, 165, 481, 125, 876, 599, 396, 459, 77, 155, 247, 781, 757, 89, 839, 756, 296, 225, 384, 99, 481, 678, 524, 211, 596, 846, 164, 675, 252, 148, 168, 760, 653, 521, 365, 137, 244, 929, 560, 287, 635, 294, 309, 365, 598, 80, 912, 131, 177, 314, 87, 413, 0, 712, 813, 317, 98, 373, 70, 720, 84, 346, 307, 817, 655, 275, 506], [434, 710, 617, 470, 776, 592, 296, 494, 484, 749, 545, 978, 346, 191, 373, 405, 125, 82, 142, 47, 786, 145, 471, 522, 145, 451, 701, 289, 373, 344, 225, 331, 238, 424, 688, 636, 810, 341, 893, 371, 198, 162, 77, 129, 263, 83, 507, 571, 160, 108, 560, 228, 749, 544, 555, 236, 793, 170, 400, 475, 368, 789, 814, 818, 76, 207, 824, 133, 51, 452, 500, 366, 758, 275, 153, 396, 824, 265, 140, 673, 233, 651, 837, 628, 135, 210, 223, 886, 579, 584, 223, 305, 761, 241, 893, 614, 964, 139, 779, 206, 690, 639, 427, 712, 405, 712, 0, 82, 806, 629, 612, 654, 55, 706, 742, 450, 192, 148, 545, 319], [535, 811, 718, 571, 877, 693, 397, 595, 585, 850, 646, 1079, 336, 292, 474, 506, 226, 94, 76, 129, 887, 154, 572, 623, 75, 552, 802, 390, 474, 445, 202, 432, 175, 525, 789, 737, 911, 442, 994, 472, 299, 263, 58, 230, 364, 184, 608, 672, 261, 209, 661, 160, 850, 645, 656, 337, 894, 140, 501, 576, 469, 890, 915, 919, 91, 184, 925, 113, 88, 553, 601, 467, 859, 376, 176, 497, 925, 276, 110, 774, 242, 752, 938, 729, 123, 219, 324, 987, 680, 685, 205, 406, 862, 265, 994, 715, 1065, 240, 880, 143, 791, 740, 528, 813, 506, 813, 82, 0, 907, 730, 713, 755, 123, 807, 843, 551, 145, 249, 646, 520], [630, 108, 191, 337, 165, 424, 416, 450, 220, 483, 188, 190, 540, 743, 673, 546, 720, 848, 901, 761, 43, 826, 266, 543, 940, 379, 161, 635, 603, 616, 887, 620, 1038, 877, 520, 431, 315, 371, 216, 449, 616, 690, 877, 707, 671, 734, 410, 306, 760, 702, 241, 1028, 104, 480, 395, 575, 246, 970, 700, 497, 560, 348, 177, 55, 875, 858, 299, 933, 850, 412, 432, 478, 233, 575, 779, 625, 121, 697, 940, 189, 776, 149, 290, 460, 861, 754, 615, 80, 364, 474, 1023, 661, 41, 736, 147, 550, 160, 692, 394, 1006, 248, 270, 265, 393, 508, 317, 806, 907, 0, 319, 614, 323, 814, 238, 124, 409, 819, 749, 263, 607], [446, 252, 145, 227, 162, 111, 347, 225, 233, 268, 272, 374, 624, 559, 489, 362, 543, 671, 724, 584, 346, 642, 279, 323, 763, 185, 161, 451, 419, 432, 703, 436, 861, 217, 207, 141, 162, 272, 279, 272, 439, 513, 700, 530, 487, 557, 128, 50, 583, 525, 325, 851, 233, 172, 82, 398, 179, 793, 516, 313, 376, 175, 200, 273, 698, 674, 176, 756, 673, 213, 142, 301, 144, 398, 595, 441, 220, 513, 763, 108, 592, 187, 223, 147, 677, 570, 438, 391, 51, 161, 846, 477, 289, 552, 289, 226, 360, 515, 167, 829, 49, 66, 203, 115, 330, 98, 629, 730, 319, 0, 290, 39, 637, 92, 284, 224, 734, 572, 164, 423], [166, 518, 425, 277, 437, 247, 336, 114, 448, 404, 536, 649, 749, 435, 365, 150, 590, 578, 650, 567, 594, 518, 494, 90, 689, 235, 436, 402, 295, 383, 579, 387, 844, 189, 343, 291, 471, 295, 554, 247, 428, 627, 683, 500, 363, 531, 221, 285, 625, 538, 589, 834, 493, 134, 208, 372, 454, 776, 392, 137, 252, 450, 475, 548, 681, 550, 485, 739, 656, 150, 196, 276, 419, 353, 471, 317, 495, 389, 746, 410, 468, 459, 498, 283, 553, 446, 432, 694, 240, 129, 829, 353, 569, 428, 564, 80, 635, 604, 434, 812, 351, 333, 393, 367, 257, 373, 612, 713, 614, 290, 0, 315, 620, 367, 550, 154, 610, 613, 354, 299], [471, 313, 202, 252, 166, 99, 372, 250, 290, 210, 358, 378, 679, 584, 514, 387, 568, 696, 749, 609, 350, 667, 332, 348, 788, 210, 165, 476, 444, 457, 728, 461, 886, 242, 156, 61, 135, 311, 276, 297, 464, 538, 725, 555, 512, 582, 153, 93, 608, 550, 380, 876, 237, 197, 107, 423, 170, 818, 541, 338, 401, 147, 191, 277, 723, 699, 149, 781, 698, 238, 167, 326, 135, 423, 620, 466, 224, 538, 788, 139, 617, 244, 214, 118, 702, 595, 462, 395, 67, 186, 871, 502, 293, 577, 293, 251, 364, 540, 128, 854, 80, 123, 260, 76, 355, 70, 654, 755, 323, 39, 315, 0, 662, 87, 345, 249, 759, 597, 221, 448], [442, 718, 625, 478, 784, 600, 304, 502, 492, 757, 553, 986, 300, 199, 381, 413, 81, 137, 184, 53, 794, 209, 479, 530, 194, 459, 709, 297, 381, 352, 285, 339, 261, 432, 696, 644, 818, 349, 901, 379, 206, 126, 90, 137, 271, 91, 515, 579, 111, 116, 568, 259, 757, 552, 563, 244, 801, 185, 408, 483, 376, 797, 822, 826, 60, 267, 832, 126, 35, 460, 508, 374, 766, 283, 161, 404, 832, 273, 155, 681, 293, 659, 845, 636, 195, 270, 231, 894, 587, 592, 222, 313, 769, 301, 901, 622, 972, 108, 787, 229, 698, 647, 435, 720, 413, 720, 55, 123, 814, 637, 620, 662, 0, 714, 750, 458, 252, 77, 553, 327], [523, 230, 147, 304, 81, 188, 424, 302, 235, 255, 174, 293, 596, 636, 566, 439, 620, 748, 801, 661, 265, 719, 281, 400, 840, 262, 75, 528, 496, 509, 780, 513, 938, 294, 284, 195, 104, 321, 193, 349, 516, 590, 777, 607, 564, 634, 205, 149, 660, 602, 297, 928, 147, 249, 159, 475, 87, 870, 593, 390, 453, 119, 108, 192, 775, 751, 118, 833, 750, 290, 219, 378, 52, 475, 672, 518, 139, 590, 840, 80, 669, 168, 131, 224, 754, 647, 515, 310, 128, 238, 923, 554, 208, 629, 208, 303, 279, 592, 161, 906, 69, 115, 240, 160, 407, 84, 706, 807, 238, 92, 367, 87, 714, 0, 262, 301, 811, 649, 201, 500], [566, 45, 139, 273, 228, 383, 352, 386, 121, 540, 60, 314, 411, 679, 609, 482, 656, 784, 837, 697, 81, 762, 132, 479, 876, 315, 189, 571, 539, 552, 823, 556, 974, 313, 479, 390, 366, 307, 308, 385, 552, 626, 813, 643, 607, 670, 346, 266, 696, 638, 112, 964, 158, 416, 342, 511, 335, 906, 636, 433, 496, 381, 266, 155, 811, 794, 380, 869, 786, 348, 368, 414, 314, 511, 715, 561, 213, 633, 876, 182, 712, 97, 379, 419, 797, 690, 551, 189, 323, 456, 959, 597, 93, 672, 247, 486, 284, 628, 607, 942, 244, 218, 178, 421, 444, 346, 742, 843, 124, 284, 550, 345, 750, 262, 0, 345, 854, 685, 199, 543], [235, 313, 220, 73, 371, 187, 168, 43, 243, 344, 331, 583, 581, 348, 278, 151, 364, 492, 545, 405, 389, 431, 289, 137, 584, 48, 304, 240, 208, 221, 492, 225, 682, 32, 283, 231, 405, 144, 488, 93, 260, 334, 521, 351, 276, 378, 103, 167, 404, 346, 384, 672, 352, 95, 150, 219, 388, 614, 305, 94, 165, 384, 409, 421, 519, 463, 419, 577, 494, 12, 92, 122, 353, 219, 384, 230, 429, 302, 584, 276, 381, 254, 432, 223, 466, 359, 259, 489, 174, 135, 667, 266, 364, 341, 498, 165, 569, 336, 374, 650, 285, 215, 188, 307, 115, 307, 450, 551, 409, 224, 154, 249, 458, 301, 345, 0, 523, 393, 149, 212], [432, 822, 722, 582, 881, 697, 441, 566, 629, 854, 690, 1090, 491, 217, 302, 403, 270, 105, 69, 215, 898, 99, 616, 520, 108, 557, 813, 287, 371, 342, 77, 329, 383, 547, 793, 741, 915, 486, 998, 531, 343, 307, 222, 226, 258, 228, 612, 676, 305, 253, 705, 373, 861, 626, 660, 381, 898, 315, 351, 473, 366, 894, 919, 930, 254, 66, 929, 278, 235, 535, 605, 511, 863, 320, 185, 394, 936, 221, 285, 785, 177, 763, 942, 733, 57, 164, 254, 998, 684, 666, 368, 303, 873, 195, 1005, 647, 1076, 284, 884, 351, 795, 751, 572, 817, 403, 817, 192, 145, 819, 734, 610, 759, 252, 811, 854, 523, 0, 293, 657, 317], [435, 653, 560, 413, 719, 535, 239, 437, 427, 692, 488, 921, 220, 192, 374, 406, 29, 190, 243, 64, 729, 246, 414, 523, 282, 394, 644, 290, 374, 345, 326, 332, 380, 367, 631, 579, 753, 284, 836, 314, 141, 78, 219, 123, 264, 84, 450, 514, 34, 75, 503, 370, 692, 487, 498, 179, 736, 312, 401, 476, 369, 732, 757, 761, 137, 308, 767, 275, 112, 395, 443, 309, 701, 276, 154, 397, 767, 266, 282, 616, 308, 594, 780, 571, 236, 222, 234, 829, 522, 527, 365, 306, 704, 268, 836, 557, 907, 56, 722, 348, 633, 582, 370, 655, 406, 655, 148, 249, 749, 572, 613, 597, 77, 649, 685, 393, 293, 0, 488, 320], [369, 167, 79, 77, 205, 259, 153, 190, 97, 416, 185, 435, 537, 482, 412, 286, 459, 587, 640, 500, 243, 555, 111, 283, 679, 119, 163, 375, 343, 356, 626, 360, 777, 116, 355, 266, 305, 108, 322, 189, 356, 429, 616, 446, 411, 473, 149, 121, 499, 441, 238, 767, 206, 220, 230, 315, 288, 709, 439, 237, 300, 352, 243, 275, 614, 587, 319, 672, 589, 152, 172, 218, 253, 315, 518, 364, 281, 436, 679, 135, 515, 108, 332, 295, 600, 493, 355, 343, 199, 260, 762, 401, 218, 475, 350, 290, 421, 431, 349, 745, 144, 98, 39, 297, 248, 275, 545, 646, 263, 164, 354, 221, 553, 201, 199, 149, 657, 488, 0, 347], [121, 511, 418, 271, 570, 386, 309, 255, 441, 543, 529, 779, 518, 142, 99, 84, 297, 285, 357, 274, 587, 225, 487, 209, 396, 246, 502, 35, 29, 42, 286, 36, 551, 236, 482, 430, 604, 316, 687, 220, 268, 334, 390, 207, 70, 238, 301, 365, 332, 245, 581, 541, 550, 315, 349, 222, 587, 483, 126, 162, 55, 583, 608, 619, 388, 257, 618, 446, 363, 224, 294, 249, 552, 104, 178, 60, 625, 96, 453, 474, 175, 452, 631, 422, 260, 153, 146, 687, 373, 355, 536, 47, 562, 135, 694, 336, 765, 311, 573, 519, 484, 440, 386, 506, 169, 506, 319, 520, 607, 423, 299, 448, 327, 500, 543, 212, 317, 320, 347, 0]] }deap-1.0.1/examples/ga/tsp/gr17.json0000644000076500000240000000310312117373622017343 0ustar felixstaff00000000000000{ "TourSize" : 17, "OptTour" : [15, 11, 8, 4, 1, 9, 10, 2, 14, 13, 16, 5, 7, 6, 12, 3, 0], "OptDistance" : 2085, "DistanceMatrix" : [[0, 633, 257, 91, 412, 150, 80, 134, 259, 505, 353, 324, 70, 211, 268, 246, 121], [633, 0, 390, 661, 227, 488, 572, 530, 555, 289, 282, 638, 567, 466, 420, 745, 518], [257, 390, 0, 228, 169, 112, 196, 154, 372, 262, 110, 437, 191, 74, 53, 472, 142], [91, 661, 228, 0, 383, 120, 77, 105, 175, 476, 324, 240, 27, 182, 239, 237, 84], [412, 227, 169, 383, 0, 267, 351, 309, 338, 196, 61, 421, 346, 243, 199, 528, 297], [150, 488, 112, 120, 267, 0, 63, 34, 264, 360, 208, 329, 83, 105, 123, 364, 35], [80, 572, 196, 77, 351, 63, 0, 29, 232, 444, 292, 297, 47, 150, 207, 332, 29], [134, 530, 154, 105, 309, 34, 29, 0, 249, 402, 250, 314, 68, 108, 165, 349, 36], [259, 555, 372, 175, 338, 264, 232, 249, 0, 495, 352, 95, 189, 326, 383, 202, 236], [505, 289, 262, 476, 196, 360, 444, 402, 495, 0, 154, 578, 439, 336, 240, 685, 390], [353, 282, 110, 324, 61, 208, 292, 250, 352, 154, 0, 435, 287, 184, 140, 542, 238], [324, 638, 437, 240, 421, 329, 297, 314, 95, 578, 435, 0, 254, 391, 448, 157, 301], [70, 567, 191, 27, 346, 83, 47, 68, 189, 439, 287, 254, 0, 145, 202, 289, 55], [211, 466, 74, 182, 243, 105, 150, 108, 326, 336, 184, 391, 145, 0, 57, 426, 96], [268, 420, 53, 239, 199, 123, 207, 165, 383, 240, 140, 448, 202, 57, 0, 483, 153], [246, 745, 472, 237, 528, 364, 332, 349, 202, 685, 542, 157, 289, 426, 483, 0, 336], [121, 518, 142, 84, 297, 35, 29, 36, 236, 390, 238, 301, 55, 96, 153, 336, 0]] }deap-1.0.1/examples/ga/tsp/gr24.json0000644000076500000240000000562512117373622017354 0ustar felixstaff00000000000000{ "TourSize" : 24, "OptTour" : [16, 11, 3, 7, 6, 24, 8, 21, 5, 10, 17, 22, 18, 19, 15, 2, 20, 14, 13, 9, 23, 4, 12, 1], "OptDistance" : 1272, "DistanceMatrix" : [[0, 257, 187, 91, 150, 80, 130, 134, 243, 185, 214, 70, 272, 219, 293, 54, 211, 290, 268, 261, 175, 250, 192, 121], [257, 0, 196, 228, 112, 196, 167, 154, 209, 86, 223, 191, 180, 83, 50, 219, 74, 139, 53, 43, 128, 99, 228, 142], [187, 196, 0, 158, 96, 88, 59, 63, 286, 124, 49, 121, 315, 172, 232, 92, 81, 98, 138, 200, 76, 89, 235, 99], [91, 228, 158, 0, 120, 77, 101, 105, 159, 156, 185, 27, 188, 149, 264, 82, 182, 261, 239, 232, 146, 221, 108, 84], [150, 112, 96, 120, 0, 63, 56, 34, 190, 40, 123, 83, 193, 79, 148, 119, 105, 144, 123, 98, 32, 105, 119, 35], [80, 196, 88, 77, 63, 0, 25, 29, 216, 124, 115, 47, 245, 139, 232, 31, 150, 176, 207, 200, 76, 189, 165, 29], [130, 167, 59, 101, 56, 25, 0, 22, 229, 95, 86, 64, 258, 134, 203, 43, 121, 164, 178, 171, 47, 160, 178, 42], [134, 154, 63, 105, 34, 29, 22, 0, 225, 82, 90, 68, 228, 112, 190, 58, 108, 136, 165, 131, 30, 147, 154, 36], [243, 209, 286, 159, 190, 216, 229, 225, 0, 207, 313, 173, 29, 126, 248, 238, 310, 389, 367, 166, 222, 349, 71, 220], [185, 86, 124, 156, 40, 124, 95, 82, 207, 0, 151, 119, 159, 62, 122, 147, 37, 116, 86, 90, 56, 76, 136, 70], [214, 223, 49, 185, 123, 115, 86, 90, 313, 151, 0, 148, 342, 199, 259, 84, 160, 147, 187, 227, 103, 138, 262, 126], [70, 191, 121, 27, 83, 47, 64, 68, 173, 119, 148, 0, 209, 153, 227, 53, 145, 224, 202, 195, 109, 184, 110, 55], [272, 180, 315, 188, 193, 245, 258, 228, 29, 159, 342, 209, 0, 97, 219, 267, 196, 275, 227, 137, 225, 235, 74, 249], [219, 83, 172, 149, 79, 139, 134, 112, 126, 62, 199, 153, 97, 0, 134, 170, 99, 178, 130, 69, 104, 138, 96, 104], [293, 50, 232, 264, 148, 232, 203, 190, 248, 122, 259, 227, 219, 134, 0, 255, 125, 154, 68, 82, 164, 114, 264, 178], [54, 219, 92, 82, 119, 31, 43, 58, 238, 147, 84, 53, 267, 170, 255, 0, 173, 190, 230, 223, 99, 212, 187, 60], [211, 74, 81, 182, 105, 150, 121, 108, 310, 37, 160, 145, 196, 99, 125, 173, 0, 79, 57, 90, 57, 39, 182, 96], [290, 139, 98, 261, 144, 176, 164, 136, 389, 116, 147, 224, 275, 178, 154, 190, 79, 0, 86, 176, 112, 40, 261, 175], [268, 53, 138, 239, 123, 207, 178, 165, 367, 86, 187, 202, 227, 130, 68, 230, 57, 86, 0, 90, 114, 46, 239, 153], [261, 43, 200, 232, 98, 200, 171, 131, 166, 90, 227, 195, 137, 69, 82, 223, 90, 176, 90, 0, 134, 136, 165, 146], [175, 128, 76, 146, 32, 76, 47, 30, 222, 56, 103, 109, 225, 104, 164, 99, 57, 112, 114, 134, 0, 96, 151, 47], [250, 99, 89, 221, 105, 189, 160, 147, 349, 76, 138, 184, 235, 138, 114, 212, 39, 40, 46, 136, 96, 0, 221, 135], [192, 228, 235, 108, 119, 165, 178, 154, 71, 136, 262, 110, 74, 96, 264, 187, 182, 261, 239, 165, 151, 221, 0, 169], [121, 142, 99, 84, 35, 29, 42, 36, 220, 70, 126, 55, 249, 104, 178, 60, 96, 175, 153, 146, 47, 135, 169, 0]] }deap-1.0.1/examples/ga/tsp.py0000644000076500000240000000464612301410325016242 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random import json import numpy from deap import algorithms from deap import base from deap import creator from deap import tools # gr*.json contains the distance map in list of list style in JSON format # Optimal solutions are : gr17 = 2085, gr24 = 1272, gr120 = 6942 tsp = json.load(open("tsp/gr17.json", "r")) distance_map = tsp["DistanceMatrix"] IND_SIZE = tsp["TourSize"] creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='i', fitness=creator.FitnessMin) toolbox = base.Toolbox() # Attribute generator toolbox.register("indices", random.sample, range(IND_SIZE), IND_SIZE) # Structure initializers toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalTSP(individual): distance = distance_map[individual[-1]][individual[0]] for gene1, gene2 in zip(individual[0:-1], individual[1:]): distance += distance_map[gene1][gene2] return distance, toolbox.register("mate", tools.cxPartialyMatched) toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", evalTSP) def main(): random.seed(169) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) algorithms.eaSimple(pop, toolbox, 0.7, 0.2, 40, stats=stats, halloffame=hof) return pop, stats, hof if __name__ == "__main__": main() deap-1.0.1/examples/ga/xkcd.py0000644000076500000240000001036412301410325016357 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """This example shows a possible answer to a problem that can be found in this xkcd comics: http://xkcd.com/287/. In the comic, the characters want to get exactly 15.05$ worth of appetizers, as fast as possible.""" import random from operator import attrgetter from collections import Counter # We delete the reduction function of the Counter because it doesn't copy added # attributes. Because we create a class that inherit from the Counter, the # fitness attribute was not copied by the deepcopy. del Counter.__reduce__ import numpy from deap import algorithms from deap import base from deap import creator from deap import tools IND_INIT_SIZE = 3 # Create the item dictionary: item id is an integer, and value is # a (name, weight, value) 3-uple. Since the comic didn't specified a time for # each menu item, random was called to generate a time. ITEMS_NAME = "Mixed Fruit", "French Fries", "Side Salad", "Hot Wings", "Mozzarella Sticks", "Sampler Plate" ITEMS_PRICE = 2.15, 2.75, 3.35, 3.55, 4.2, 5.8 ITEMS = dict((name, (price, random.uniform(1, 5))) for name, price in zip(ITEMS_NAME, ITEMS_PRICE)) creator.create("Fitness", base.Fitness, weights=(-1.0, -1.0)) creator.create("Individual", Counter, fitness=creator.Fitness) toolbox = base.Toolbox() toolbox.register("attr_item", random.choice, ITEMS_NAME) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_item, IND_INIT_SIZE) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalXKCD(individual, target_price): """Evaluates the fitness and return the error on the price and the time taken by the order if the chef can cook everything in parallel.""" price = 0.0 times = list() for item, number in individual.items(): price += ITEMS[item][0] * number times.append(ITEMS[item][1]) return abs(price - target_price), max(times) def cxCounter(ind1, ind2, indpb): """Swaps the number of perticular items between two individuals""" for key in ITEMS.keys(): if random.random() < indpb: ind1[key], ind2[key] = ind2[key], ind1[key] return ind1, ind2 def mutCounter(individual): """Adds or remove an item from an individual""" if random.random() > 0.5: individual.update([random.choice(ITEMS_NAME)]) else: val = random.choice(ITEMS_NAME) individual.subtract([val]) if individual[val] < 0: del individual[val] return individual, toolbox.register("evaluate", evalXKCD, target_price=15.05) toolbox.register("mate", cxCounter, indpb=0.5) toolbox.register("mutate", mutCounter) toolbox.register("select", tools.selNSGA2) def main(): NGEN = 40 MU = 100 LAMBDA = 200 CXPB = 0.3 MUTPB = 0.6 pop = toolbox.population(n=MU) hof = tools.ParetoFront() price_stats = tools.Statistics(key=lambda ind: ind.fitness.values[0]) time_stats = tools.Statistics(key=lambda ind: ind.fitness.values[1]) stats = tools.MultiStatistics(price=price_stats, time=time_stats) stats.register("avg", numpy.mean, axis=0) stats.register("std", numpy.std, axis=0) stats.register("min", numpy.min, axis=0) algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats, halloffame=hof) return pop, stats, hof if __name__ == "__main__": _, _, hof = main() from matplotlib import pyplot as plt error_price = [i.fitness.values[0] for i in hof] time = [i.fitness.values[1] for i in hof] plt.plot(error_price, time, 'bo') plt.xlabel("Price difference") plt.ylabel("Total time") plt.show() deap-1.0.1/examples/gp/0000755000076500000240000000000012321001644015071 5ustar felixstaff00000000000000deap-1.0.1/examples/gp/__init__.py0000644000076500000240000000126412301410325017203 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see .deap-1.0.1/examples/gp/adf_symbreg.py0000644000076500000240000001420212320777200017733 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random import operator import math import numpy from deap import base from deap import creator from deap import gp from deap import tools # Define new functions def safeDiv(left, right): try: return left / right except ZeroDivisionError: return 0 adfset2 = gp.PrimitiveSet("ADF2", 2) adfset2.addPrimitive(operator.add, 2) adfset2.addPrimitive(operator.sub, 2) adfset2.addPrimitive(operator.mul, 2) adfset2.addPrimitive(safeDiv, 2) adfset2.addPrimitive(operator.neg, 1) adfset2.addPrimitive(math.cos, 1) adfset2.addPrimitive(math.sin, 1) adfset1 = gp.PrimitiveSet("ADF1", 2) adfset1.addPrimitive(operator.add, 2) adfset1.addPrimitive(operator.sub, 2) adfset1.addPrimitive(operator.mul, 2) adfset1.addPrimitive(safeDiv, 2) adfset1.addPrimitive(operator.neg, 1) adfset1.addPrimitive(math.cos, 1) adfset1.addPrimitive(math.sin, 1) adfset1.addADF(adfset2) adfset0 = gp.PrimitiveSet("ADF0", 2) adfset0.addPrimitive(operator.add, 2) adfset0.addPrimitive(operator.sub, 2) adfset0.addPrimitive(operator.mul, 2) adfset0.addPrimitive(safeDiv, 2) adfset0.addPrimitive(operator.neg, 1) adfset0.addPrimitive(math.cos, 1) adfset0.addPrimitive(math.sin, 1) adfset0.addADF(adfset1) adfset0.addADF(adfset2) pset = gp.PrimitiveSet("MAIN", 1) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.sub, 2) pset.addPrimitive(operator.mul, 2) pset.addPrimitive(safeDiv, 2) pset.addPrimitive(operator.neg, 1) pset.addPrimitive(math.cos, 1) pset.addPrimitive(math.sin, 1) pset.addEphemeralConstant("rand101", lambda: random.randint(-1, 1)) pset.addADF(adfset0) pset.addADF(adfset1) pset.addADF(adfset2) pset.renameArguments(ARG0='x') psets = (pset, adfset0, adfset1, adfset2) creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Tree", gp.PrimitiveTree) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register('adf_expr0', gp.genFull, pset=adfset0, min_=1, max_=2) toolbox.register('adf_expr1', gp.genFull, pset=adfset1, min_=1, max_=2) toolbox.register('adf_expr2', gp.genFull, pset=adfset2, min_=1, max_=2) toolbox.register('main_expr', gp.genHalfAndHalf, pset=pset, min_=1, max_=2) toolbox.register('ADF0', tools.initIterate, creator.Tree, toolbox.adf_expr0) toolbox.register('ADF1', tools.initIterate, creator.Tree, toolbox.adf_expr1) toolbox.register('ADF2', tools.initIterate, creator.Tree, toolbox.adf_expr2) toolbox.register('MAIN', tools.initIterate, creator.Tree, toolbox.main_expr) func_cycle = [toolbox.MAIN, toolbox.ADF0, toolbox.ADF1, toolbox.ADF2] toolbox.register('individual', tools.initCycle, creator.Individual, func_cycle) toolbox.register('population', tools.initRepeat, list, toolbox.individual) def evalSymbReg(individual): # Transform the tree expression in a callable function func = toolbox.compile(individual) # Evaluate the sum of squared difference between the expression # and the real function : x**4 + x**3 + x**2 + x values = (x/10. for x in range(-10, 10)) diff_func = lambda x: (func(x)-(x**4 + x**3 + x**2 + x))**2 diff = sum(map(diff_func, values)) return diff, toolbox.register('compile', gp.compileADF, psets=psets) toolbox.register('evaluate', evalSymbReg) toolbox.register('select', tools.selTournament, tournsize=3) toolbox.register('mate', gp.cxOnePoint) toolbox.register('expr', gp.genFull, min_=1, max_=2) toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr) def main(): random.seed(1024) ind = toolbox.individual() pop = toolbox.population(n=100) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "evals", "std", "min", "avg", "max" CXPB, MUTPB, NGEN = 0.5, 0.2, 40 # Evaluate the entire population for ind in pop: ind.fitness.values = toolbox.evaluate(ind) hof.update(pop) record = stats.compile(pop) logbook.record(gen=0, evals=len(pop), **record) print(logbook.stream) for g in range(1, NGEN): # Select the offspring offspring = toolbox.select(pop, len(pop)) # Clone the offspring offspring = [toolbox.clone(ind) for ind in offspring] # Apply crossover and mutation for ind1, ind2 in zip(offspring[::2], offspring[1::2]): for tree1, tree2 in zip(ind1, ind2): if random.random() < CXPB: toolbox.mate(tree1, tree2) del ind1.fitness.values del ind2.fitness.values for ind in offspring: for tree, pset in zip(ind, psets): if random.random() < MUTPB: toolbox.mutate(individual=tree, pset=pset) del ind.fitness.values # Evaluate the individuals with an invalid fitness invalids = [ind for ind in offspring if not ind.fitness.valid] for ind in invalids: ind.fitness.values = toolbox.evaluate(ind) # Replacement of the population by the offspring pop = offspring hof.update(pop) record = stats.compile(pop) logbook.record(gen=g, evals=len(invalids), **record) print(logbook.stream) print('Best individual : ', hof[0][0], hof[0].fitness) return pop, stats, hof if __name__ == "__main__": main() deap-1.0.1/examples/gp/ant/0000755000076500000240000000000012321001644015653 5ustar felixstaff00000000000000deap-1.0.1/examples/gp/ant/AntSimulatorFast.cpp0000644000076500000240000002641412117373622021641 0ustar felixstaff00000000000000#include "AntSimulatorFast.hpp" AntSimulatorFast::AntSimulatorFast(unsigned int inMaxMoves) : mMaxMoves(inMaxMoves), mNbPiecesAvail(0), mRowStart(0), mColStart(0), mDirectionStart(AntSimulatorFast::eAntEast), mNbMovesAnt(0), mNbPiecesEaten(0), mRowAnt(0), mColAnt(0), mDirectionAnt(AntSimulatorFast::eAntEast) { } void AntSimulatorFast::parseMatrix(char* inFileStr){ std::fstream lFileHandle; lFileHandle.open(inFileStr, std::fstream::in); mOrigTrail.resize(ROWS_NBR); mExecTrail.resize(ROWS_NBR); for(unsigned int i = 0; i < ROWS_NBR; i++){ mOrigTrail[i].resize(COLS_NBR); mExecTrail[i].resize(COLS_NBR); } char lBuffer; for(unsigned int i=0; i> lBuffer; switch(lBuffer) { case eStart: { mOrigTrail[i][j] = eStart; mRowStart = i; mColStart = j; mExecTrail[i][j] = eStart; break; } case eEmpty: case eFoodPiece: { mOrigTrail[i][j] = lBuffer; mExecTrail[i][j] = lBuffer; break; } case ePassed: { mOrigTrail[i][j] = eEmpty; mExecTrail[i][j] = ePassed; break; } case eEatenPiece: { mOrigTrail[i][j] = eFoodPiece; mExecTrail[i][j] = eEatenPiece; break; } case eAntNorth: case eAntEast: case eAntSouth: case eAntWest: { mOrigTrail[i][j] = eEmpty; mExecTrail[i][j] = lBuffer; break; } default: { } } } } lFileHandle.close(); } void AntSimulatorFast::turnLeft(void){ if(mNbMovesAnt >= mMaxMoves) return; ++mNbMovesAnt; switch(mDirectionAnt) { case eAntNorth: { mDirectionAnt = eAntWest; break; } case eAntEast: { mDirectionAnt = eAntNorth; break; } case eAntSouth: { mDirectionAnt = eAntEast; break; } case eAntWest: { mDirectionAnt = eAntSouth; break; } default: { } } } void AntSimulatorFast::turnRight(void){ if(mNbMovesAnt >= mMaxMoves) return; ++mNbMovesAnt; switch(mDirectionAnt) { case eAntNorth: { mDirectionAnt = eAntEast; break; } case eAntEast: { mDirectionAnt = eAntSouth; break; } case eAntSouth: { mDirectionAnt = eAntWest; break; } case eAntWest: { mDirectionAnt = eAntNorth; break; } default: { } } } void AntSimulatorFast::moveForward(void){ if(mNbMovesAnt >= mMaxMoves) return; ++mNbMovesAnt; switch(mDirectionAnt) { case eAntNorth: { if(mRowAnt == 0) mRowAnt = (mExecTrail.size()-1); else --mRowAnt; break; } case eAntEast: { ++mColAnt; if(mColAnt >= mExecTrail.front().size()) mColAnt = 0; break; } case eAntSouth: { ++mRowAnt; if(mRowAnt >= mExecTrail.size()) mRowAnt = 0; break; } case eAntWest: { if(mColAnt == 0) mColAnt = (mExecTrail.front().size()-1); else --mColAnt; break; } default: { } } switch(mExecTrail[mRowAnt][mColAnt]) { case eStart: case ePassed: case eEatenPiece: break; case eEmpty: { mExecTrail[mRowAnt][mColAnt] = ePassed; break; } case eFoodPiece: { mExecTrail[mRowAnt][mColAnt] = eEatenPiece; ++mNbPiecesEaten; break; } default: { } } } void AntSimulatorFast::ifFoodAhead(PyObject* inIfTrue, PyObject* inIfFalse){ unsigned int lAheadRow = mRowAnt; unsigned int lAheadCol = mColAnt; switch(mDirectionAnt) { case eAntNorth: { if(lAheadRow == 0) lAheadRow = (mExecTrail.size()-1); else --lAheadRow; break; } case eAntEast: { ++lAheadCol; if(lAheadCol >= mExecTrail.front().size()) lAheadCol = 0; break; } case eAntSouth: { ++lAheadRow; if(lAheadRow >= mExecTrail.size()) lAheadRow = 0; break; } case eAntWest: { if(lAheadCol == 0) lAheadCol = (mExecTrail.front().size()-1); else --lAheadCol; break; } default: { } } PyObject_CallFunctionObjArgs((mExecTrail[lAheadRow][lAheadCol] == eFoodPiece) ? inIfTrue : inIfFalse, NULL); } void AntSimulatorFast::run(PyObject* inWrappedFunc){ this->reset(); while(mNbMovesAnt < mMaxMoves) PyObject_CallFunctionObjArgs(inWrappedFunc, NULL); } void AntSimulatorFast::reset(void){ mExecTrail = mOrigTrail; mNbMovesAnt = 0; mNbPiecesEaten = 0; mRowAnt = mRowStart; mColAnt = mColStart; mDirectionAnt = mDirectionStart; } /* * * Python wrappers * * */ typedef struct { PyObject_HEAD AntSimulatorFast *mInnerClass; } AntSimulatorWrapper; static int wrapAntSimulatorConstructor(AntSimulatorWrapper *self, PyObject *args, PyObject *kwargs){ int lMaxMoves; const char *keywords[] = {"max_moves", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, (char *) "i", (char **) keywords, &lMaxMoves)) { return -1; } self->mInnerClass = new AntSimulatorFast(lMaxMoves); return 0; } static PyObject* wrapTurnLeft(AntSimulatorWrapper *self){ self->mInnerClass->turnLeft(); Py_INCREF(Py_None); return Py_None; } static PyObject* wrapTurnRight(AntSimulatorWrapper *self){ self->mInnerClass->turnRight(); Py_INCREF(Py_None); return Py_None; } static PyObject* wrapMoveForward(AntSimulatorWrapper *self){ self->mInnerClass->moveForward(); Py_INCREF(Py_None); return Py_None; } static PyObject* wrapIfFoodAhead(AntSimulatorWrapper *self, PyObject *args){ self->mInnerClass->ifFoodAhead(PyTuple_GET_ITEM(args, 0), PyTuple_GET_ITEM(args, 1)); Py_INCREF(Py_None); return Py_None; } static PyObject* wrapRun(AntSimulatorWrapper *self, PyObject *args){ PyObject* func = PyTuple_GetItem(args, 0); self->mInnerClass->run(func); Py_INCREF(Py_None); return Py_None; } static PyObject* wrapParseMatrix(AntSimulatorWrapper *self, PyObject *args){ self->mInnerClass->parseMatrix(PyString_AsString(PyFile_Name(PyTuple_GetItem(args, 0)))); Py_INCREF(Py_None); return Py_None; } static PyObject* wrapGetEaten(AntSimulatorWrapper *self, void *closure){ PyObject *py_retval; py_retval = Py_BuildValue((char *) "i", self->mInnerClass->mNbPiecesEaten); return py_retval; } // Getters and setters (here only for the 'eaten' attribute) static PyGetSetDef AntSimulatorWrapper_getsets[] = { { (char*) "eaten", /* attribute name */ (getter) wrapGetEaten, /* C function to get the attribute */ NULL, /* C function to set the attribute */ NULL, /* optional doc string */ NULL /* optional additional data for getter and setter */ }, { NULL, NULL, NULL, NULL, NULL } }; // Class method declarations static PyMethodDef AntSimulatorWrapper_methods[] = { {(char *) "turn_left", (PyCFunction) wrapTurnLeft, METH_NOARGS, NULL }, {(char *) "turn_right", (PyCFunction) wrapTurnRight, METH_NOARGS, NULL }, {(char *) "move_forward", (PyCFunction) wrapMoveForward, METH_NOARGS, NULL }, {(char *) "if_food_ahead", (PyCFunction) wrapIfFoodAhead, METH_VARARGS, NULL }, {(char *) "parse_matrix", (PyCFunction) wrapParseMatrix, METH_VARARGS, NULL }, {(char *) "run", (PyCFunction) wrapRun, METH_VARARGS, NULL }, {NULL, NULL, 0, NULL} }; static void AntSimulatorWrapperDealloc(AntSimulatorWrapper *self){ delete self->mInnerClass; self->ob_type->tp_free((PyObject*)self); } static PyObject* AntSimulatorWrapperRichcompare(AntSimulatorWrapper *self, AntSimulatorWrapper *other, int opid){ Py_INCREF(Py_NotImplemented); return Py_NotImplemented; } PyTypeObject AntSimulatorWrapper_Type = { PyObject_HEAD_INIT(NULL) 0, /* ob_size */ (char *) "AntC.AntSimulatorFast", /* tp_name */ sizeof(AntSimulatorWrapper), /* tp_basicsize */ 0, /* tp_itemsize */ /* methods */ (destructor)AntSimulatorWrapperDealloc, /* tp_dealloc */ (printfunc)0, /* tp_print */ (getattrfunc)NULL, /* tp_getattr */ (setattrfunc)NULL, /* tp_setattr */ (cmpfunc)NULL, /* tp_compare */ (reprfunc)NULL, /* tp_repr */ (PyNumberMethods*)NULL, /* tp_as_number */ (PySequenceMethods*)NULL, /* tp_as_sequence */ (PyMappingMethods*)NULL, /* tp_as_mapping */ (hashfunc)NULL, /* tp_hash */ (ternaryfunc)NULL, /* tp_call */ (reprfunc)NULL, /* tp_str */ (getattrofunc)NULL, /* tp_getattro */ (setattrofunc)NULL, /* tp_setattro */ (PyBufferProcs*)NULL, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT, /* tp_flags */ NULL, /* Documentation string */ (traverseproc)NULL, /* tp_traverse */ (inquiry)NULL, /* tp_clear */ (richcmpfunc)AntSimulatorWrapperRichcompare, /* tp_richcompare */ 0, /* tp_weaklistoffset */ (getiterfunc)NULL, /* tp_iter */ (iternextfunc)NULL, /* tp_iternext */ (struct PyMethodDef*)AntSimulatorWrapper_methods, /* tp_methods */ (struct PyMemberDef*)0, /* tp_members */ AntSimulatorWrapper_getsets, /* tp_getset */ NULL, /* tp_base */ NULL, /* tp_dict */ (descrgetfunc)NULL, /* tp_descr_get */ (descrsetfunc)NULL, /* tp_descr_set */ 0, /* tp_dictoffset */ (initproc)wrapAntSimulatorConstructor, /* tp_init */ (allocfunc)PyType_GenericAlloc, /* tp_alloc */ (newfunc)PyType_GenericNew, /* tp_new */ (freefunc)0, /* tp_free */ (inquiry)NULL, /* tp_is_gc */ NULL, /* tp_bases */ NULL, /* tp_mro */ NULL, /* tp_cache */ NULL, /* tp_subclasses */ NULL, /* tp_weaklist */ (destructor) NULL /* tp_del */ }; PyObject* progn(PyObject *self, PyObject *args){ for(Py_ssize_t i = 0; i < PyTuple_Size(args); i++) PyObject_CallFunctionObjArgs(PyTuple_GET_ITEM(args, i), NULL); Py_INCREF(Py_None); return Py_None; } static PyMethodDef AntC_functions[] = { {"progn", progn, METH_VARARGS, "Boum"}, {NULL, NULL, 0, NULL} }; PyMODINIT_FUNC initAntC(void) { PyObject *m; m = Py_InitModule3((char *) "AntC", AntC_functions, NULL); if (m == NULL) { return; } /* Register the 'AntSimulatorWrapper' class */ if (PyType_Ready(&AntSimulatorWrapper_Type)) { return; } PyModule_AddObject(m, (char *) "AntSimulatorFast", (PyObject *) &AntSimulatorWrapper_Type); } deap-1.0.1/examples/gp/ant/AntSimulatorFast.hpp0000644000076500000240000000331012117373622021634 0ustar felixstaff00000000000000#include #if PY_MAJOR_VERSION >= 3 #define PY3K #endif #include #include #include #include #include #include #include #include #define ROWS_NBR 32 #define COLS_NBR 32 class AntSimulatorFast { public: enum State {eStart='S', eEmpty='.', ePassed='x', eFoodPiece='#', eEatenPiece='@', eAntNorth='^', eAntEast='}', eAntSouth='v', eAntWest='{'}; AntSimulatorFast(unsigned int inMaxMoves); void parseMatrix(char* inFileStr); void turnLeft(void); void turnRight(void); void moveForward(void); void ifFoodAhead(PyObject* inIfTrue, PyObject* inIfFalse); void run(PyObject* inWrappedFunc); unsigned int mNbPiecesEaten; //!< Number of food pieces eaten. private: void reset(void); std::vector< std::vector > mOrigTrail; //!< Initial trail set-up. unsigned int mMaxMoves; //!< Maximum number of moves allowed. unsigned int mNbPiecesAvail; //!< Number of food pieces available. unsigned int mRowStart; //!< Row at which the ant starts collecting food. unsigned int mColStart; //!< Column at which the ant starts collecting food. unsigned int mDirectionStart; //!< Direction at which the ant is looking when starting. std::vector< std::vector > mExecTrail; //!< Execution trail set-up. unsigned int mNbMovesAnt; //!< Number of moves done by the ant. unsigned int mRowAnt; //!< Row of the actual ant position. unsigned int mColAnt; //!< Column of the actual ant position. char mDirectionAnt; //!< Direction in which the ant is looking. }; deap-1.0.1/examples/gp/ant/buildAntSimFast.py0000644000076500000240000000046412117373622021275 0ustar felixstaff00000000000000from distutils.core import setup, Extension module1 = Extension('AntC', sources = ['AntSimulatorFast.cpp']) setup (name = 'AntC', version = '1.0', description = 'Fast version of the Ant Simulator (aims to replace the AntSimulator class)', ext_modules = [module1]) deap-1.0.1/examples/gp/ant/santafe_trail.txt0000644000076500000240000000204112117373622021240 0ustar felixstaff00000000000000S###............................ ...#............................ ...#.....................###.... ...#....................#....#.. ...#....................#....#.. ...####.#####........##......... ............#................#.. ............#.......#........... ............#.......#........#.. ............#.......#........... ....................#........... ............#................#.. ............#................... ............#.......#.....###... ............#.......#..#........ .................#.............. ................................ ............#...........#....... ............#...#..........#.... ............#...#............... ............#...#............... ............#...#.........#..... ............#..........#........ ............#................... ...##. .#####....#............... .#..............#............... .#..............#............... .#......#######................. .#.....#........................ .......#........................ ..####.......................... ................................ deap-1.0.1/examples/gp/ant.py0000644000076500000240000001537512301410325016236 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """ This example is from "John R. Koza. Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge, MA, USA, 1992.". The problem is called The Artificial Ant Problem. The goal of this example is to show how to use DEAP and its GP framework with with complex system of functions and object. Given an AntSimulator ant, this solution should get the 89 pieces of food within 543 moves. ant.routine = ant.if_food_ahead(ant.move_forward, prog3(ant.turn_left, prog2(ant.if_food_ahead(ant.move_forward, ant.turn_right), prog2(ant.turn_right, prog2(ant.turn_left, ant.turn_right))), prog2(ant.if_food_ahead(ant.move_forward, ant.turn_left), ant.move_forward))) Best solution found with DEAP: prog3(prog3(move_forward, turn_right, if_food_ahead(if_food_ahead(prog3(move_forward, move_forward, move_forward), prog2(turn_left, turn_right)), turn_left)), if_food_ahead(turn_left, turn_left), if_food_ahead(move_forward, turn_right)) fitness = (89,) """ import copy import random import numpy from functools import partial from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp def progn(*args): for arg in args: arg() def prog2(out1, out2): return partial(progn,out1,out2) def prog3(out1, out2, out3): return partial(progn,out1,out2,out3) def if_then_else(condition, out1, out2): out1() if condition() else out2() class AntSimulator(object): direction = ["north","east","south","west"] dir_row = [1, 0, -1, 0] dir_col = [0, 1, 0, -1] def __init__(self, max_moves): self.max_moves = max_moves self.moves = 0 self.eaten = 0 self.routine = None def _reset(self): self.row = self.row_start self.col = self.col_start self.dir = 1 self.moves = 0 self.eaten = 0 self.matrix_exc = copy.deepcopy(self.matrix) @property def position(self): return (self.row, self.col, self.direction[self.dir]) def turn_left(self): if self.moves < self.max_moves: self.moves += 1 self.dir = (self.dir - 1) % 4 def turn_right(self): if self.moves < self.max_moves: self.moves += 1 self.dir = (self.dir + 1) % 4 def move_forward(self): if self.moves < self.max_moves: self.moves += 1 self.row = (self.row + self.dir_row[self.dir]) % self.matrix_row self.col = (self.col + self.dir_col[self.dir]) % self.matrix_col if self.matrix_exc[self.row][self.col] == "food": self.eaten += 1 self.matrix_exc[self.row][self.col] = "passed" def sense_food(self): ahead_row = (self.row + self.dir_row[self.dir]) % self.matrix_row ahead_col = (self.col + self.dir_col[self.dir]) % self.matrix_col return self.matrix_exc[ahead_row][ahead_col] == "food" def if_food_ahead(self, out1, out2): return partial(if_then_else, self.sense_food, out1, out2) def run(self,routine): self._reset() while self.moves < self.max_moves: routine() def parse_matrix(self, matrix): self.matrix = list() for i, line in enumerate(matrix): self.matrix.append(list()) for j, col in enumerate(line): if col == "#": self.matrix[-1].append("food") elif col == ".": self.matrix[-1].append("empty") elif col == "S": self.matrix[-1].append("empty") self.row_start = self.row = i self.col_start = self.col = j self.dir = 1 self.matrix_row = len(self.matrix) self.matrix_col = len(self.matrix[0]) self.matrix_exc = copy.deepcopy(self.matrix) ant = AntSimulator(600) pset = gp.PrimitiveSet("MAIN", 0) pset.addPrimitive(ant.if_food_ahead, 2) pset.addPrimitive(prog2, 2) pset.addPrimitive(prog3, 3) pset.addTerminal(ant.move_forward) pset.addTerminal(ant.turn_left) pset.addTerminal(ant.turn_right) creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("expr_init", gp.genFull, pset=pset, min_=1, max_=2) # Structure initializers toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr_init) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalArtificialAnt(individual): # Transform the tree expression to functionnal Python code routine = gp.compile(individual, pset) # Run the generated routine ant.run(routine) return ant.eaten, toolbox.register("evaluate", evalArtificialAnt) toolbox.register("select", tools.selTournament, tournsize=7) toolbox.register("mate", gp.cxOnePoint) toolbox.register("expr_mut", gp.genFull, min_=0, max_=2) toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset) def main(): random.seed(69) trail_file = open("ant/santafe_trail.txt") ant.parse_matrix(trail_file) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) algorithms.eaSimple(pop, toolbox, 0.5, 0.2, 40, stats, halloffame=hof) return pop, hof, stats if __name__ == "__main__": main() deap-1.0.1/examples/gp/multiplexer.py0000644000076500000240000000635312301410325020022 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import random import operator import numpy from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp def if_then_else(condition, out1, out2): return out1 if condition else out2 # Initialize Multiplexer problem input and output vectors MUX_SELECT_LINES = 3 MUX_IN_LINES = 2 ** MUX_SELECT_LINES MUX_TOTAL_LINES = MUX_SELECT_LINES + MUX_IN_LINES # input : [A0 A1 A2 D0 D1 D2 D3 D4 D5 D6 D7] for a 8-3 mux inputs = [[0] * MUX_TOTAL_LINES for i in range(2 ** MUX_TOTAL_LINES)] outputs = [None] * (2 ** MUX_TOTAL_LINES) for i in range(2 ** MUX_TOTAL_LINES): value = i divisor = 2 ** MUX_TOTAL_LINES # Fill the input bits for j in range(MUX_TOTAL_LINES): divisor /= 2 if value >= divisor: inputs[i][j] = 1 value -= divisor # Determine the corresponding output indexOutput = MUX_SELECT_LINES for j, k in enumerate(inputs[i][:MUX_SELECT_LINES]): indexOutput += k * 2**j outputs[i] = inputs[i][indexOutput] pset = gp.PrimitiveSet("MAIN", MUX_TOTAL_LINES, "IN") pset.addPrimitive(operator.and_, 2) pset.addPrimitive(operator.or_, 2) pset.addPrimitive(operator.not_, 1) pset.addPrimitive(if_then_else, 3) pset.addTerminal(1) pset.addTerminal(0) creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("expr", gp.genFull, pset=pset, min_=2, max_=4) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("compile", gp.compile, pset=pset) def evalMultiplexer(individual): func = toolbox.compile(expr=individual) return sum(func(*in_) == out for in_, out in zip(inputs, outputs)), toolbox.register("evaluate", evalMultiplexer) toolbox.register("select", tools.selTournament, tournsize=7) toolbox.register("mate", gp.cxOnePoint) toolbox.register("expr_mut", gp.genGrow, min_=0, max_=2) toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset) def main(): # random.seed(10) pop = toolbox.population(n=40) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) algorithms.eaSimple(pop, toolbox, 0.8, 0.1, 40, stats, halloffame=hof) return pop, stats, hof if __name__ == "__main__": main() deap-1.0.1/examples/gp/parity.py0000644000076500000240000000561512301410325016760 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import random import operator import numpy from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp # Initialize Parity problem input and output matrices PARITY_FANIN_M = 6 PARITY_SIZE_M = 2**PARITY_FANIN_M inputs = [None] * PARITY_SIZE_M outputs = [None] * PARITY_SIZE_M for i in range(PARITY_SIZE_M): inputs[i] = [None] * PARITY_FANIN_M value = i dividor = PARITY_SIZE_M parity = 1 for j in range(PARITY_FANIN_M): dividor /= 2 if value >= dividor: inputs[i][j] = 1 parity = int(not parity) value -= dividor else: inputs[i][j] = 0 outputs[i] = parity pset = gp.PrimitiveSet("MAIN", PARITY_FANIN_M, "IN") pset.addPrimitive(operator.and_, 2) pset.addPrimitive(operator.or_, 2) pset.addPrimitive(operator.xor, 2) pset.addPrimitive(operator.not_, 1) pset.addTerminal(1) pset.addTerminal(0) creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("expr", gp.genFull, pset=pset, min_=3, max_=5) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("compile", gp.compile, pset=pset) def evalParity(individual): func = toolbox.compile(expr=individual) return sum(func(*in_) == out for in_, out in zip(inputs, outputs)), toolbox.register("evaluate", evalParity) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("mate", gp.cxOnePoint) toolbox.register("expr_mut", gp.genGrow, min_=0, max_=2) toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset) def main(): random.seed(21) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) algorithms.eaSimple(pop, toolbox, 0.5, 0.2, 40, stats, halloffame=hof) return pop, stats, hof if __name__ == "__main__": main() deap-1.0.1/examples/gp/spambase.csv0000644000076500000240000253473612117373622017440 0ustar felixstaff000000000000000,0.64,0.64,0,0.32,0,0,0,0,0,0,0.64,0,0,0,0.32,0,1.29,1.93,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.778,0,0,3.756,61,278,1 0.21,0.28,0.5,0,0.14,0.28,0.21,0.07,0,0.94,0.21,0.79,0.65,0.21,0.14,0.14,0.07,0.28,3.47,0,1.59,0,0.43,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0,0,0,0,0,0,0,0,0.132,0,0.372,0.18,0.048,5.114,101,1028,1 0.06,0,0.71,0,1.23,0.19,0.19,0.12,0.64,0.25,0.38,0.45,0.12,0,1.75,0.06,0.06,1.03,1.36,0.32,0.51,0,1.16,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0.12,0,0.06,0.06,0,0,0.01,0.143,0,0.276,0.184,0.01,9.821,485,2259,1 0,0,0,0,0.63,0,0.31,0.63,0.31,0.63,0.31,0.31,0.31,0,0,0.31,0,0,3.18,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.137,0,0.137,0,0,3.537,40,191,1 0,0,0,0,0.63,0,0.31,0.63,0.31,0.63,0.31,0.31,0.31,0,0,0.31,0,0,3.18,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.135,0,0.135,0,0,3.537,40,191,1 0,0,0,0,1.85,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.223,0,0,0,0,3,15,54,1 0,0,0,0,1.92,0,0,0,0,0.64,0.96,1.28,0,0,0,0.96,0,0.32,3.85,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.054,0,0.164,0.054,0,1.671,4,112,1 0,0,0,0,1.88,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.206,0,0,0,0,2.45,11,49,1 0.15,0,0.46,0,0.61,0,0.3,0,0.92,0.76,0.76,0.92,0,0,0,0,0,0.15,1.23,3.53,2,0,0,0.15,0,0,0,0,0,0,0,0,0.15,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0.271,0,0.181,0.203,0.022,9.744,445,1257,1 0.06,0.12,0.77,0,0.19,0.32,0.38,0,0.06,0,0,0.64,0.25,0,0.12,0,0,0.12,1.67,0.06,0.71,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0.04,0.03,0,0.244,0.081,0,1.729,43,749,1 0,0,0,0,0,0,0.96,0,0,1.92,0.96,0,0,0,0,0,0,0.96,3.84,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0.462,0,0,1.312,6,21,1 0,0,0.25,0,0.38,0.25,0.25,0,0,0,0.12,0.12,0.12,0,0,0,0,0,1.16,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.022,0.044,0,0.663,0,0,1.243,11,184,1 0,0.69,0.34,0,0.34,0,0,0,0,0,0,0.69,0,0,0,0.34,0,1.39,2.09,0,1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.056,0,0.786,0,0,3.728,61,261,1 0,0,0,0,0.9,0,0.9,0,0,0.9,0.9,0,0.9,0,0,0,0,0,2.72,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.083,7,25,1 0,0,1.42,0,0.71,0.35,0,0.35,0,0.71,0,0.35,0,0,0,5.35,0,0,3.21,0,2.85,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.102,0,0.357,0,0,1.971,24,205,1 0,0.42,0.42,0,1.27,0,0.42,0,0,1.27,0,0,0,0,0,1.27,0,0,1.7,0.42,1.27,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,1.27,0,0,0.42,0,0,0,0,0,0,0,0,0,0.063,0,0.572,0.063,0,5.659,55,249,1 0,0,0,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,2.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.428,0,0,4.652,31,107,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.11,0,0.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.975,0.37,0,35.461,95,461,1 0,0,0.55,0,1.11,0,0.18,0,0,0,0,0,0.92,0,0.18,0,0.37,0.37,3.15,0,0.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.182,0,0.455,0,0,1.32,4,70,1 0,0.63,0,0,1.59,0.31,0,0,0.31,0,0,0.63,0,0,1.27,0.63,0.31,3.18,2.22,0,1.91,0,0.31,0.63,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,1.59,0,0,0,0,0,0,0,0,0,0.275,0,0.055,0.496,0,3.509,91,186,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.729,0,0.729,0,0,3.833,9,23,1 0.05,0.07,0.1,0,0.76,0.05,0.15,0.02,0.55,0,0.1,0.47,0.02,0,0,0,0.02,0.13,2.09,0.1,1.57,0,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0.1,0,0,0,0,0,0,0,0,0,0,0,0.042,0.101,0.016,0.25,0.046,0.059,2.569,66,2259,1 0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.404,0.404,0,0.809,0,0,4.857,12,34,1 0,0,0,0,1.16,0,0,0,0,0,0,0.58,0,0,0,1.16,0,1.16,1.16,0,1.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.133,0,0.667,0,0,1.131,5,69,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.44,0,0,0,0,0.196,0,0.392,0.196,0,5.466,22,82,1 0.05,0.07,0.1,0,0.76,0.05,0.15,0.02,0.55,0,0.1,0.47,0.02,0,0,0,0.02,0.13,2.09,0.1,1.57,0,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0.1,0,0,0,0,0,0,0,0,0,0,0,0.042,0.101,0.016,0.25,0.046,0.059,2.565,66,2258,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.44,0,0,0,0,0.196,0,0.392,0.196,0,5.466,22,82,1 0,0,0,0,0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.368,0,0,2.611,12,47,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.352,0,0.352,0,0,4,11,36,1 0,0,0,0,0.65,0,0.65,0,0,0,0.65,0.65,0,0,0,0.65,1.3,0,1.3,5.22,1.3,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.459,0,0.091,0,0,2.687,66,129,1 1.17,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,1.17,0,3.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0,0,0.886,0,0,1.966,10,59,1 0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.352,0,2.112,0,0,3.909,11,43,1 0,0,0,0,1.89,0.27,0,0,0,0,0,0.81,0,0,0,0.27,0,0,3.51,0,2.7,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.045,0,0,0.091,0,1.39,11,89,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.83,4.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.302,0,1.7,5,17,1 0,0.68,0,0,0,0,0,0,0,0.68,1.36,0,0,0,0,0,0,0,2.04,0,0.68,0,0,0,0.68,0,0,0.68,0,0,1.36,0,0,0,0.68,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0.185,0,0,0,3.826,30,264,1 0,0,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.308,0,1.543,0,0,2.777,6,25,1 0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.436,0.436,0,0.873,0,0,4.142,12,29,1 0,0,0.48,0,1.46,0,0.48,0,0,0,0,0.97,0,0,0,0.48,0.97,0,2.43,0,2.43,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0.081,0,0.488,0.244,0,5.431,78,239,1 0,0.48,0.48,0,0.48,0,0,0,0,0,0,0.97,0,0,0,0.48,0,0.97,1.46,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0.963,0,0,3.1,61,186,1 0,0.41,1.66,0,0.41,0,0,0,0,0,0,0.41,0,0,0,0.41,0,0.83,2.08,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.068,0,0.75,0,0,3.851,121,285,1 0.3,0,0,0,0.61,0.92,0,2.45,0,0,0,0.3,1.53,0,0,0,0,0.3,2.76,0,0.61,0,0.3,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0.051,0,0.207,0.207,0,2.132,30,226,1 0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.335,0.335,0,0.671,0,0,4,12,28,1 0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.355,0.355,0,0.711,0,0,4,12,28,1 0,0,0.55,0,1.11,0,0.18,0,0,0,0,0.18,0.92,0,0.18,0,0.37,0.37,3.15,0,0.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.182,0,0.426,0,0,1.283,4,68,1 0,0,0,0,0.52,0,0.26,0.52,0,0.26,0.26,0.52,0,0,0,0.26,1.56,0.26,1.82,2.08,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.073,0,0.813,0.036,0.147,2.145,38,339,1 0.15,0.45,1.05,0,0.45,0,0,1.81,0.6,0.75,0,0.9,0.3,0,0.3,0,0,0,4.07,0,1.51,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0,0,0.25,0,1.318,0.068,0,5.301,130,774,1 0.18,0,0.18,0,1.57,0.36,0.06,0.06,0.06,0.12,0.06,0.54,0.3,0.06,0,0,0.72,0.06,4.54,0.24,1.09,0,0.84,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.06,0,0,0,0.01,0.052,0,0.01,0.167,0,1.733,12,442,1 0.49,0,0.99,0,0,0.99,0,0,0,0.99,0.99,2.48,0.49,0,0,4.97,0.99,0,3.48,0,1.99,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0.17,0,0,1.468,8,94,1 0.46,0.3,0.46,0,0.05,0.12,0.05,0.28,0.43,0.74,0.25,0.97,0.56,1.23,0,0.25,0.43,0.02,3.22,0,1.46,0,1.05,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.065,0,0.325,0.756,0.153,5.891,193,3040,1 0.46,0.46,0.26,0,0,0.33,0.06,0.33,0,1.12,0.39,0.73,0.79,0,0.26,0.26,0,0.26,3.51,0,0.66,0,0.19,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.036,0.084,0,0.278,0.23,0.084,3.887,40,898,1 0,1.92,0,0,1.92,0,0,0,0,0,0,1.92,0,0,0,0,0,0,1.92,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.75,12,33,1 0.73,0.36,1.09,0,0,0.73,0.73,1.09,0.36,0.36,0,0.36,0,0,0,1.09,0.36,0.36,2.19,2.19,2.19,0,1.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0.498,0.332,0,3.254,30,179,1 0.06,0.12,0.77,0,0.19,0.32,0.38,0,0.06,0,0,0.64,0.25,0,0.12,0,0,0.12,1.67,0.06,0.7,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0.04,0.03,0,0.244,0.071,0,1.732,43,750,1 0,1.26,0,0,0,1.26,0,0,0,0,0,1.26,0,0,0,0,0,0,0,0,1.26,0,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0.198,0,0.198,0.596,0,3.833,17,69,1 0.73,0.36,0.73,0,0,0.73,0.73,1.1,0.36,0.36,0,0.36,0,0,0,1.1,0.36,0.36,2.2,2.2,2.2,0,1.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.111,0,0.5,0.333,0,3.259,30,176,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,1.08,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.478,0,0,2,30,106,1 0,0,0,0,0,0,1.04,0,0,0,0,1.04,0,0,0,0,1.04,0,3.66,0,2.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.061,0.246,0,0.615,0.061,0.061,3.318,59,146,1 0,0,1.26,0,0,0,0,0,0,0,0,2.53,0,0,0,0,0,0,2.53,0,5.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.642,8,51,1 0,0.45,0.45,0,0.45,0,0,0,0,0,0,0.45,0,0,0,0.45,0,0.91,1.36,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.501,0,0,2.777,61,200,1 0,0.42,1.68,0,0.42,0,0,0,0,0,0,0.42,0,0,0,0.42,0,0.84,2.1,0,1.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0.066,0,0.669,0,0,3.837,121,284,1 0,0.59,0,0,0,0,0.59,0,0,0.59,0,0.59,0,0,0,0,0,1.18,1.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.257,0,0,0,0,8.586,66,249,1 0.23,0,0.47,0,0.23,0,0,0,0,0,0,0,0,0.23,0,0.23,0.23,0,7.1,0,1.89,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0.043,0.043,0,0.175,0,0,1.294,11,66,1 0,0,0.46,0,1.39,0,0.93,0.93,0,0,0.46,0.93,0,0,0,1.39,0,0.46,0.93,0,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0,0.069,0,0,0,0,0.069,1.442,8,75,1 0,0.34,0,0,0.68,0,0.68,0,0,0.34,0.34,0,0,0,0,0.34,0,1.36,3.42,0,2.73,0,0,0,0.34,0.34,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.048,0.048,0,1.411,15,96,1 0.12,0.24,0.12,0,1.32,0.36,0,0.36,0,0,0.36,0.72,0,0,0,0,0,0,4.1,0,3.01,0,0.12,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0.12,0,0,0.12,0,0,0,0,0,0,0,0,0,0.059,0,0.019,0.019,0,1.714,34,180,1 0.66,0,0.66,0,0,0,0,0,0,0.66,0,0,0,0,0,1.98,1.32,0,1.32,0,1.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.205,0,0,3.184,34,121,1 0,0.48,0.48,0,1.46,0,0.48,0,0,0.97,0.48,0,0,0,0,0.48,0,0,0.97,0.48,1.95,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,1.46,0,0,0.48,0,0,0,0,0,0,0,0,0,0.073,0,0.589,0.294,0,4.85,47,194,1 0,0,0,0,0,0,1.47,0,0,1.47,0,1.47,0,0,0,0,0,0,5.88,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,1.214,3,17,1 0.3,0,0.61,0,0,0,0,0,0,0.92,0.3,0.92,0.3,0.3,0,2.15,0.61,0,5.53,0,1.23,0,0,0.3,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0.1,0,1.053,0.351,0.25,3.884,66,303,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0.201,0,0,0.1,0,4.548,59,141,1 0,0,0,0,1.26,0,2.53,1.26,1.26,1.26,1.26,1.26,0,0,0,0,5.06,0,2.53,1.26,3.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.537,0,0,72.5,287,290,1 0,0.53,0.53,0,0.53,0,0,0,0,0,0,0.53,0,0,0,0.53,0,1.06,1.6,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.087,0,0.877,0,0,3.4,61,187,1 0,0.44,0.89,0,0.44,0,0,0,0,0,0,0.44,0,0,0,0.44,0,0.89,2.24,0,1.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0.073,0,0.807,0,0,3.849,121,281,1 0,0.46,0.46,0,0.46,0.46,0.46,0,0,0,0.46,0.46,0,0,0,0.92,0,0.92,2.76,0,1.38,0,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0.46,0,0,0,0,0,0,0.298,0.223,0,2.156,13,110,1 0,0,0.48,0,1.44,0,0.48,0,0,0,0,0.96,0,0,0,0.48,0.96,0,2.41,0,2.41,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0.081,0,0.486,0.243,0,5.13,78,236,1 0,0.94,0.94,0,0,0,0,0,0,0.94,0,0,0,0,0,2.83,0,0,0.94,0,0.94,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.366,0,0,26.5,245,318,1 0,0,1.77,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0,0.59,4.14,0,1.18,0,0,0,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.165,0,0.165,0.082,0,2.325,16,100,1 0.75,0.18,0.37,0,0.18,0.12,0,0.25,0.75,0.31,0.25,1.51,0.31,0.37,0,0.37,0.44,0.12,2.96,0.69,1.26,0,0.44,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0.12,0,0,0.06,0,0,0,0,0,0,0.085,0.053,0.437,0.234,0.064,3.675,45,1066,1 0,0.41,0.2,0,1.67,0.2,0.2,0,0,1.04,0.2,0,0.2,0,0,0.83,0.2,0,2.09,0,0.62,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0.62,0,0.2,0,0,0,0.132,0,0,1.65,15,175,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.26,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.338,0,1.666,5,10,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.431,0,0,4.071,29,114,1 0,0,0.23,0,0,0,0.23,0,0,0.95,0,0.47,0,0.23,0,0.23,0.95,0,2.38,0,1.9,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0.23,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0.123,0,0.197,0,0.024,5.038,280,519,1 0,0.72,0.72,0,0,0,0,1.45,0,0,0.72,0,0,0,0,2.91,0,0.72,1.45,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.123,0,0.495,0,0,1.525,8,61,1 0,0,1.28,0,1.28,1.28,0,0,0,0,0,0,0,0,0,1.28,0,0,2.56,0,1.28,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.181,0,0.724,0,0,3.071,9,43,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.278,0,0.834,0,0,5.13,27,118,1 0,0.46,0.46,0,1.4,0,0.46,1.86,0,0.93,0.46,0,0,0,0,1.86,0,0,0.93,0.46,1.4,0,0,0.46,0,0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0.46,0,0,0,0,0,0,0,0,0,0.071,0,0.571,0.214,0,4.63,64,213,1 0,0,0.38,0,1.15,0.76,0,0,0,0,0,0.38,0.38,0,0,0.38,0,0.38,2.69,0,2.3,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.122,0,0.061,0.061,0,1.775,20,158,1 0,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0.79,1.58,1.58,3.96,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.79,0,0,0,0,0.268,0,0.268,0,0,2.815,26,107,1 0.06,0.06,0.47,0,0.4,0,0,0,0.67,0.06,0,0.33,0.13,0,0,0.2,0,0,1.14,0.13,1.21,0,0,0.06,0,0,0,0,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0.06,0,0,0,0.021,0.107,0,0.096,0.085,0.01,3.353,144,845,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.153,0,0,0,0,3.8,23,38,1 0,0.56,1.12,0,2.24,0,1.12,0,0,0,0,0.56,0.56,0,0,0.56,2.8,0,3.93,0,1.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,1.083,0.333,0,4.974,140,194,1 0.47,0.31,0.47,0,0.05,0.13,0.05,0.26,0.44,0.76,0.26,0.97,0.58,1.26,0,0.26,0.44,0,3.25,0,1.5,0,1.05,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0.004,0.066,0,0.322,0.764,0.159,6.1,193,3038,1 0.59,0.44,0.29,0,0.14,0.03,0.03,0.14,0.56,0.67,0.29,0.67,0.59,1.23,0.03,0.22,0.44,0.07,3.43,0,1.53,0,0.59,0.63,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0.075,0,0.613,0.532,0.137,7.3,763,2453,1 0.59,0.44,0.29,0,0.14,0.03,0.03,0.14,0.56,0.67,0.29,0.67,0.59,1.23,0.03,0.22,0.44,0.07,3.43,0,1.53,0,0.59,0.63,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0.075,0,0.612,0.531,0.137,7.3,763,2453,1 0.46,0,0.46,0,0,0,0,0.46,0,0,0,1.38,0,0,2.31,0,0.46,0.46,2.77,0,2.31,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0.494,0,0.082,0.823,0,3.4,12,102,1 0,0,0.46,0,0,0,0.46,0,0,0,0.46,0,0,0,0,0,0,1.4,1.87,0,0,0.93,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,0,0,0,2.676,32,91,1 0,0.35,0.7,0,0.35,0,0,0,0,0,0,0.7,0,0,0,1.05,0,0.7,2.11,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0.122,0,1.284,0,0,3.639,61,313,1 0,0.43,0.43,0,0.43,0,0,0,0,0,0,0.43,0,0,0,0.43,0,0.86,1.29,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.147,0,0.736,0,0,2.81,61,222,1 0,0,0,0,0,0.6,0,0,0,1.21,0,0,0,0,0,0.6,0,0,1.21,0,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.207,0.518,0.414,0.31,0,0,4.897,17,191,1 1.24,0.41,1.24,0,0,0,0,0,0,0,0,0.41,0,0,0,0.41,0,0.82,3.73,0,1.24,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.065,0,0.461,0.527,0,3.166,19,114,1 0,0,0,0,4.25,0,0.7,0,0,0,0,0,0,0,0,2.83,0,0,4.96,0,1.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,1.153,3,30,1 0,0,0.64,0,0,0.64,0,0,0,0,0,0,0,0,0,0.64,0,0,2.59,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0.094,0.189,0.284,0.662,0,0,10.068,131,292,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0.305,0.611,0,1.529,0,0,5.5,22,66,1 0,0,0.64,0,0,0.64,0,0,0,0,0,0,0,0,0,0.64,0,0,2.59,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0.094,0.189,0.284,0.662,0,0,10.068,131,292,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0.305,0.611,0,1.529,0,0,5.5,22,66,1 0,0,0.64,0,0,0.64,0,0,0,0,0,0,0,0,0,0.64,0,0,2.59,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0.094,0.189,0.284,0.662,0,0,10.068,131,292,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0.305,0.611,0,1.529,0,0,5.5,22,66,1 0,0,0,0,0,0.79,0,0,0,0,0,0,0,0,0,0.79,0,0,1.58,0,0,0,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0.79,0,0,0,0,0,0,0,0,0,0,0,0.115,0.231,0.347,0.462,0,0,5.793,22,168,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0.305,0.611,0,1.529,0,0,5.5,22,66,1 0,0,0,0,0,0,1.96,0,0,1.96,0,1.96,0,0,0,0,0,0,3.92,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.166,60,74,1 0,0,0,0,0,0,2.46,0,0,0,0,0,0,0,0,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.907,0,0,1.285,7,36,1 0,0,0,0,0,0.79,0,0,0,0,0,0,0,0,0,0,0,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.147,0,0,0,0,2.913,27,67,1 0,0,0.76,0,0.38,0,0.76,0,0,0,0,0.38,0,0,0,0,0,0.76,1.52,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.177,0.059,3.836,79,211,1 0,0,0,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0.95,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.365,0,0,0,0,1.238,6,78,1 0.12,1.76,0.63,0,0.88,0,0.12,0.5,0.25,3.9,0.5,0.88,0.12,0,0,0.25,0.12,0,2.9,0.25,1.38,0,1.13,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0,0.019,0.379,0.159,0,0.119,0,4.155,38,507,1 0,0,1.02,0,0.51,0,0,0,0,0,0,0,0,0,0,0.51,0,0,1.53,0,1.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0,0,0,0,0.09,0,0.542,0,0,1.972,19,146,1 0.05,0.3,0.4,0,0.1,0.05,0,0.05,0.1,0,0,0.3,0.2,0,0.05,0,0,0.5,1.55,0.3,0.75,0,0.15,0.2,0.05,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0.05,0,0,0,0,0.045,0,0.054,0.118,0,2.37,96,588,1 0.05,0.3,0.4,0,0.1,0.05,0,0.05,0.1,0,0,0.3,0.2,0,0.05,0,0,0.5,1.55,0.3,0.75,0,0.15,0.2,0.05,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0.05,0,0,0,0,0.036,0,0.054,0.118,0,2.379,96,583,1 0,0,0,0,1.28,0,2.56,1.28,1.28,1.28,1.28,1.28,0,0,0,0,5.12,0,2.56,1.28,5.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.542,0,0,102.666,304,308,1 0,0.55,0.55,0,2.23,0,1.11,0,0,0,0,0.55,0.55,0,0,0.55,2.79,0,3.91,0,1.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.248,0,1.158,0.331,0,4.875,140,195,1 0.05,0.3,0.4,0,0.1,0.05,0,0.05,0.1,0,0,0.3,0.2,0,0.05,0,0,0.5,1.55,0.3,0.75,0,0.15,0.2,0.05,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0.05,0,0,0,0,0.045,0,0.054,0.118,0,2.37,96,588,1 0.05,0.3,0.4,0,0.1,0.05,0,0.05,0.1,0,0,0.3,0.2,0,0.05,0,0,0.5,1.55,0.3,0.75,0,0.15,0.2,0.05,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0.05,0,0,0,0,0.036,0,0.054,0.118,0,2.379,96,583,1 0.5,0.46,0.34,0,0.15,0.03,0,0.19,0.57,0.65,0.3,0.73,0.65,1.27,0.03,0.23,0.42,0,3.08,0,1.34,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0.011,0.077,0,0.335,1.281,0.125,7.202,595,2413,1 0,0.32,0.8,0,0.32,0,0.16,0,0,0.48,0.16,0,0.16,0,0.16,0.16,0,0.8,0.16,0.16,0.64,0,0,0,0,0,0,0.16,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.271,0.024,0.049,5.709,149,982,1 0,0,0,0,0.92,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0.61,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.046,0,0,0.092,0.322,0,2.074,49,278,1 0.16,0,0.67,0,0.33,0.16,0.33,0.84,0.16,0.5,0.33,1.51,0,0,0,0,1.68,0.33,2.18,1.68,3.69,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0.19,0,1.194,0.054,0,5.567,101,657,1 0.5,0,0.5,0,1.51,0,0,0,0,0,0.5,1.01,0,0,0,0,0,0,4.04,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,1.01,0,0,0,0,0.089,0,0.089,0.178,0,3.416,53,164,1 0,0,0,0,0,0,0.59,0,0,0,0,1.19,0,0,0,0,0,0.59,4.76,0,1.19,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.427,0,0,10,33,170,1 0,0,0,0,1.6,0,0.4,1.2,0,0.4,0,0.8,0,0,0,0,1.6,0.4,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.706,0.212,0,1.838,13,114,1 0.41,0,0.41,0,0,0.41,0,0,0,0,0,2.07,0,0,0,0.41,0,0,7.05,0,2.48,0,0.82,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0.149,0,32.142,335,450,1 0,0,0.38,0,0.76,0,0.38,0,0,1.14,0,0,0,0,0,0.38,0.76,0,3.04,0,1.52,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.14,0,0,0,0,0.299,0,0.598,0.179,0,4.523,78,285,1 0,0,0,0,0.4,0.4,0.4,0.4,0,0,0.4,0,0,0,0,0.4,0,0,4,0,2,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.121,0,0,1.979,12,95,1 0,0,1.12,0,0.56,0,0,0,0,0.56,0,0,0,0,0,0.56,0,0,2.25,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.56,0,0,0,0,0.101,0,0.606,0,0,2.36,19,144,1 0,0,0.8,0,1.44,0.16,0.16,0,0,0,0,0.64,0.8,0,0,0,0.16,0.16,1.6,0,0.47,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.024,0,0.299,0.174,0,1.891,24,174,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0.215,0,0.215,0.431,0,4,25,76,1 0,0.39,0.39,0,0.19,0,0,0.19,0,0,0.39,0.39,0,0,0,0.98,0.19,0.39,0.59,0,0.78,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0.128,0,0.16,0.16,0,2.128,31,730,1 0,0.39,0.39,0,0.19,0,0,0.19,0,0,0.39,0.39,0,0,0,0.98,0.19,0.39,0.59,0,0.78,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0.128,0,0.16,0.16,0,2.128,31,730,1 1,0,0.33,0,0.66,0.66,0,0,0,0,0,0.33,0.66,0,0,0.66,0.66,0,2.33,0,0.33,0,1.66,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.12,0.541,0,5.428,21,304,1 0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,2.98,0,1.49,0,0,1.49,0,0,0,1.49,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.1,2,11,1 0,0,0,0,1.65,0,0,0,0.82,0,0,1.65,0,0,0,0.82,0,0,1.65,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.415,0,0,0,0,1.769,11,69,1 1,0,0.33,0,0.66,0.66,0,0,0,0,0,0.33,0.66,0,0,0.66,0.66,0,2.33,0,0.33,0,1.66,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.12,0.541,0,5.428,21,304,1 0,0,0,0,0,0,1.58,0,0,0,0,0,1.58,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.558,0.279,0,3.272,23,36,1 0.5,0.46,0.34,0,0.15,0.03,0,0.19,0.57,0.65,0.3,0.73,0.65,1.27,0.03,0.23,0.42,0,3.08,0,1.34,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0.011,0.077,0,0.335,1.281,0.125,7.202,595,2413,1 0,0,0,0,0,0,1.58,0,0,0,0,0,1.58,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.558,0.279,0,3.272,23,36,1 0,0,1.38,0,0,0,0,0,0,0,0,1.38,0,0,0,2.77,0,4.16,4.16,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.715,0,0,1.181,2,13,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0.215,0,0.215,0.431,0,4.277,27,77,1 1,0,0.33,0,0.66,0.66,0,0,0,0,0,0.33,0.66,0,0,0.66,0.66,0,2.33,0,0.33,0,1.66,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.12,0.541,0,5.428,21,304,1 0,0.29,0.72,0,0.29,0,0.14,0,0,0.43,0.29,0,0.14,0,0.14,0.14,0,0.72,0.58,0.14,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0.14,0,0,0,0,0,0,0.865,0.023,0.046,5.133,132,1001,1 0.36,0,1.09,0,0,0,0,0,0,0,0,0.72,1.81,0,0,0,0,0,0.72,0,1.09,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.063,0.126,0,0.063,0.126,0,2.562,35,123,1 0,0,0.27,0,0.81,0.81,0,2.98,0.54,0.81,0.27,0.54,0.27,0,0,0.81,1.63,0.27,2.17,1.35,2.44,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.04,0,0.565,0.121,0,1.617,18,131,1 0.39,0,0.39,0,0,0.39,0,0,0,0,0,0.39,0.78,0,0,0,1.17,0.78,3.13,0,1.17,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.261,0,0,1.461,19,114,1 0,0.56,0.56,0,2.25,0,1.12,0,0,0,0,0.56,0.56,0,0,0.56,2.82,0,3.95,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.247,0,1.32,0.33,0,5.135,140,190,1 0.67,0,0.67,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0.67,0.67,4.05,0,1.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,1.064,3,33,1 0,0,0.62,0,0.62,0,0,0,0,0.62,0,0,0,0,0,0.62,0,0,1.24,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.105,0,1.055,0,0,2.033,16,120,1 0,0,1.68,0,0.33,0,0,0,0,0.33,0,0,0,0,0,0.33,0,0,2.02,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0.06,0,0.484,0,0,1.796,19,203,1 0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,4.76,0,0,4.76,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.375,11,38,1 0,0,0,0,1.31,0,1.31,1.31,1.31,1.31,0,0,0,0,0,0,1.31,0,1.31,1.31,3.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.117,0.117,0,48.5,186,291,1 0,0,0,0,1.36,0.45,0.45,0,0,0,0,0,0.45,0,0,0.45,0.45,0.45,1.81,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.135,0,0.135,0,0,5.571,46,117,1 0.42,0,0,0,0.85,0.85,0,0,0,0.42,0,2.13,0,0,0,0,1.7,0,0.85,0,0.85,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.088,0,0,5.714,107,200,1 0,0,0,0,0.27,0,0,0,0,0.83,0,0,0,0,0,0,0,0,0.27,0,0.27,8.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.092,0,0.185,0.232,7.313,99,607,1 0,0,0,0,0.43,0,0,0,0,0.65,0,0,0,0,0,0.43,0,0.21,0.21,0,0.43,6.75,0,0,0.21,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.073,0.146,0.146,0.183,6.233,99,642,1 0.46,0,0.46,0,0,0,0,0.46,0,0,0,1.38,0,0,2.31,0,0.46,0.46,2.77,0,2.31,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0.49,0,0.081,0.816,0,3.4,12,102,1 0.14,0.14,0.29,0,0.29,0.29,0,0.29,0,0,0.29,0,0.14,0,0,0.87,0.29,0.43,3.66,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0.58,0,0,0,0,0.024,0,0.265,0,0,3.121,38,437,1 0,0.34,0.68,0,0,0,0.34,0,0,0.34,0,0,0,0,0.34,0.68,0,1.37,1.03,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.094,0,0,0,0,3.131,13,119,1 0.46,0,0.46,0,0,0,0,0.46,0,0,0,1.38,0,0,2.31,0,0.46,0.46,2.77,0,2.31,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0.49,0,0.081,0.816,0,3.4,12,102,1 0.62,0,0.62,0,0,0,0.62,0,0,0,0,3.1,0,0,0,0,1.24,1.24,5.59,0,1.86,0,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0.517,0,0,3.363,22,111,1 0,0,0,0,2.1,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,34,108,1 0,0.71,0.35,0,0.35,0,0,0,0,0,0,0.71,0,0,0,0.35,0,1.42,1.77,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.058,0,0.7,0,0,3.768,61,260,1 0,0.3,0.61,0,0.3,0,0.15,0,0,0.45,0.15,0,0.15,0,0.15,0.15,0,0.76,0.15,0.15,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0,0,0,0,0,0,0,0,0,0.567,0.024,0.049,5.425,132,944,1 0,0,0,0,0,0,0.57,0,0,0.57,0,1.15,0.57,0,0,0,0,0.57,4.62,0,1.15,0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.518,0,0,10.117,33,172,1 0.52,0,2.38,0,0.26,0,0.26,0,0.52,0,0.26,0,0,0,0,0.79,0,0,1.32,0,1.05,0,0,0.52,0,0,0,0,0,0,0,0,0.26,0,0,0.26,0.26,0,0.52,0,0,0,0,0,0,0,0,0,0,0.656,0,0.31,0,0,5.549,71,566,1 0.17,0,0.08,0,0.42,0.08,0.08,0.42,0.08,0.08,0,0.6,0.17,0.17,0,0,0.17,0.08,1.2,0,3.17,0,0.34,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.081,0.027,0.095,0.013,0,4.07,48,574,1 0,0,1,0,0.5,0,0,0,0,0.5,0,0,0,0,0,0.5,0,0,2.5,0,1.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0.357,0,0.892,0,0,2,19,172,1 0,0,0.54,0,0.54,0,0,0,0,0.54,0,0,0,0,0,0.54,0,0,1.64,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0.096,0,1.443,0,0,1.969,16,130,1 0,0,0,0,0,0.78,0,2.34,0,0.78,0.78,1.56,0,0,0,0,0.78,0,3.12,0,0.78,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.889,0,0,2.13,15,49,1 0,0,0,0,0,0,0,2.04,0,0,1.02,0,0,0,0,0,0,0,4.08,0,1.02,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.968,0,0,2.179,18,85,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.1,2,11,1 0.44,0,0,0,0.89,0,0,0,0,0.44,0,1.34,0,0,0,0.44,0,0,4.03,0,1.79,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0.944,0.145,0.072,2.451,28,152,1 0,0.66,0.66,0,0.33,0,0,0,0,0,0,0.66,0,0,0,0.33,0,1.32,2.64,0,1.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0.053,0,0.583,0,0,4.024,121,326,1 0,0,0,0,0,0,0,2.04,0,0,1.02,0,0,0,0,0,0,0,4.08,0,1.02,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.969,0,0,2.179,18,85,1 0.34,0.25,0.25,0,0.08,0.43,0.08,0.25,0.08,1.46,0.34,0.51,0.94,0,0.17,0.08,0,0,3.01,0,0.77,0.17,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0.048,0,0.258,0.258,0.113,5.297,300,694,1 0.34,0.26,0.26,0,0.08,0.43,0.08,0.26,0.08,1.47,0.34,0.52,0.95,0,0.17,0.08,0,0,3.03,0,0.78,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0.048,0,0.259,0.259,0.064,3.335,62,537,1 0.43,0,0,0,0.87,0.87,0,0,0,0.43,0,2.18,0,0,0,0,1.74,0,0.87,0,0.87,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.835,0,0,5.114,107,179,1 0.44,0,0,0,0.89,0,0,0,0,0.44,0,1.33,0,0,0,0.44,0,0,4.46,0,1.78,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,1.083,0.144,0.072,2.428,28,153,1 0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,2.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0.059,0,0.118,0,0,1.307,7,68,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.754,0,0,1,1,7,1 0,0.41,0.53,0,0.11,0.05,0,0.05,0.11,0,0,0.17,0.05,0,0,0.05,0,0.53,1.19,0.35,0.53,0,0.23,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.064,0.01,0.032,0.14,0,1.364,14,303,1 0,0,0,0,6.25,0,3.12,0,0,0,0,3.12,0,3.12,0,3.12,0,0,6.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.428,60,66,1 2.12,0,0,0,0.53,0.53,0,0,0,1.59,0,1.59,0,0,0,1.59,0.53,0.53,6.91,0,1.59,0,0.53,0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.467,0,0.28,0.186,0,2.823,85,240,1 0,0,0,0,1.4,0.46,0.93,0,0,0,0,0,0.46,0,0,0.46,0.46,0,1.87,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.135,0,0.135,0,0,4,46,96,1 0,1.12,0.56,0,0.56,0.56,1.12,1.12,0,0,0.56,2.25,0,0,0,2.25,0,1.12,2.25,0,2.82,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.416,5,51,1 0,0,1.32,0,0.66,0,0,0,0,0,0,0.66,0,0,0,0,0.66,0,5.29,2.64,5.29,0,0,1.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0.83,0.069,0,3.215,43,164,1 0,0.8,0,0,0.8,0,0,0,0,0.8,0,0.8,0,0,0,1.61,0,0.8,0.8,0,2.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0,1.192,0,0,1.463,12,101,1 0,0.29,0.87,0,0.29,0,0.14,0,0,0.43,0.14,0,0.14,0,0.14,0.14,0,0.72,0.43,0.14,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0,0,0,0.585,0.046,0.046,5.02,132,979,1 0.17,0,0.08,0,0.42,0.08,0.08,0.42,0.08,0.08,0,0.6,0.17,0.17,0,0,0.17,0.08,1.2,0,3.17,0,0.34,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.081,0.027,0.095,0.013,0,4.07,48,574,1 0,0,0,0,0,0,0,0,0,0.81,0,0.81,0,0,0,0,0,0,1.63,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.147,0,0,0.294,0.147,0,2.333,11,63,1 0.54,0,1.08,0,0.54,0,1.08,0,0,0,0,0.54,0,0,0,0.54,0.54,0,4.32,0,1.08,0,1.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,1.18,0.252,0,5.323,68,181,1 0.17,0,0.08,0,0.42,0.08,0.08,0.42,0.08,0.08,0,0.6,0.17,0.17,0,0,0.17,0.08,1.2,0,3.17,0,0.34,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.081,0.027,0.108,0.013,0,4.07,48,574,1 0.53,0,1.07,0,0.53,0,1.07,0,0,0,0,0.53,0,0,0,0.53,0.53,0,4.3,0,1.07,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0,0,1.183,0.253,0,5.454,68,180,1 0.51,0.51,0,0,0,0,0.51,0,0,0.51,0,0,0,0,0.51,2.07,0,2.07,1.03,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.135,0,0.067,0,0,2.676,17,91,1 0,0.54,0.54,0,2.19,0,1.09,0,0,0,0,0.54,0.54,0,0,0.54,3.29,0,3.84,0,1.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.241,0,1.045,0.321,0,5.047,140,212,1 0,0,0.38,0,1.15,0,0,0,0,0.77,0,0.38,0,0,0,0.38,0.77,0,2.7,0,1.15,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.15,0,0,0,0,0.061,0,0.985,0.184,0,3.923,78,255,1 0,0,0.39,0,1.17,0,0,0,0,0.78,0,0.39,0,0,0,0.39,0.78,0,2.73,0,1.17,0,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0.062,0,0.869,0.186,0,4,78,256,1 0.43,0,0.43,0,0.43,0,0.86,0,0,0,0,0.43,0,0,0,0,0.86,0.43,1.29,0,4.76,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0.915,0,0,3.891,47,144,1 0.45,0,0,0,0.68,0.45,0,0.45,0,0.22,0.22,0,1.6,0,0.45,0,0.91,1.83,1.83,0,0.68,0,1.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,0,0.037,0,0.187,0.112,0,3.184,30,363,1 0,0,1.12,0,0.56,0,0,0,0,0.56,0,0,0,0,0,0.56,0,0,2.25,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.56,0,0,0,0,0.102,0,0.615,0,0,2.403,19,137,1 0,0,0.55,0,0.55,0,0,0,0,0.55,0,0,0,0,0,0.55,0,0,1.67,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0.099,0,0.893,0,0,2.122,16,121,1 0,0,1.31,0,0.65,0,0,0,0,0,0,0.65,0,0,0,0,0,0,5.26,1.97,4.6,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0.816,0.068,0,3.173,43,165,1 0,0,0.61,0,0,0,0.61,0,0,0,0,0,0,0,0,0,1.23,1.85,2.46,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.176,0,0.353,0,0,2.25,13,81,1 0.22,0.22,0.22,0,1.77,0.22,0.44,0.44,0.22,2.88,0,0.88,0.22,0,1.11,0.44,0,0.44,3.33,0,3.33,0,0.44,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0.563,0.15,0,86.65,1038,1733,1 0.34,0.42,0.25,0,0.08,0.42,0.08,0.25,0.08,1.63,0.34,0.51,0.94,0,0.17,0.08,0,0,3,0,0.94,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0.063,0,0.287,0.223,0.079,3.314,62,537,1 0,0,0,0,0,0,1.04,0,0,0,0,0,0,0,0,2.08,0,0,2.08,0,2.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.812,11,61,1 0,0,0,0,0,0,1.33,0,0,0,0,0,0,0,0,0,0,0,1.33,0,5.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.202,1.417,0,29.125,223,233,1 0.54,0,1.08,0,0.54,0,1.08,0,0,0,0,0.54,0,0,0,0.54,0.54,0,4.32,0,1.08,0,1.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,1.182,0.253,0,5.454,68,180,1 0,0,0,0,2.5,0,0,0,0,0,0,0.62,0,0,0,0,1.25,0,3.12,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,2.111,18,57,1 0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,1.81,3.63,0,2.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0.165,0.165,0,6.266,41,94,1 0.07,0.02,0.15,0,0.25,0.2,0,0.02,0.15,0,0,0.25,0.25,0.07,0,0.05,0.22,0,0.05,0,0.02,0,0.37,0.02,0,0,0,0,0.02,0,0,0,0,0,0,0.05,0.3,0.02,0,0.02,0,0,0.02,0,0.02,0,0,0,0.011,0.022,0,0,0.022,0,1.423,20,965,1 0.07,0.02,0.15,0,0.25,0.2,0,0.02,0.15,0,0,0.25,0.25,0.07,0,0.05,0.22,0,0.05,0,0.02,0,0.37,0.02,0,0,0,0,0.02,0,0,0,0,0,0,0.05,0.3,0.02,0,0.02,0,0,0.02,0,0.02,0,0,0,0.011,0.022,0,0,0.022,0,1.423,20,965,1 0.17,0.26,1.07,0,0.35,0.62,0.53,0.17,0.62,0.8,0.26,1.25,0.17,0,0.62,0.62,0.08,1.43,2.5,0.17,1.16,0,0.89,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0.08,0,0,0,0,0.066,0,0.212,0.185,0.013,6.815,583,1329,1 0,0,0.48,0,0.96,0,0,0,0.48,0,0,0,0,0,0,0.96,0.96,0,1.44,0,0.48,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0.133,0.066,0.468,0.267,0,3.315,61,242,1 0.46,0,0.46,0,0,0,0,0.46,0,0,0,1.38,0,0,2.31,0,0.46,0.46,2.77,0,2.31,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0.49,0,0.081,0.816,0,3.4,12,102,1 1.03,0,0.68,0,1.03,0,0.68,0,0,0.68,0,0.68,0,0,0.34,0.68,0,0,5.86,0,1.37,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0.366,0.061,0,1.895,12,91,1 0,0,0.18,0,0.18,0,0,0,0.54,0.36,0.36,0.9,0,0.36,0,0.72,0,0.18,2.7,0.18,0.72,0,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0.18,0,0,0,0,0,0,0,0,0.633,0.063,0,9.043,363,841,1 0.26,0.26,0.52,0,0.39,0,0.39,0.13,0,0.26,0,0.78,0.26,0,0,1.57,0,0.26,2.61,0,1.57,0,0.13,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0,0,0,0,0,0,0,0.129,0,0.779,0.021,0.021,2.689,49,476,1 0,0,0.32,0,0.65,0.65,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.28,0,3.25,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.104,0,0,0.157,0.052,1.537,10,143,1 0,0,0.32,0,0.64,0.64,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.27,0,3.24,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.106,0,0,0.159,0.053,1.537,10,143,1 0.19,0.19,0.39,0,0.19,0,0,0.59,0,0,0,0.39,0,0,0,0.59,0.39,1.37,4.52,0,3.14,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.258,0.032,0,3.112,43,305,1 0.46,0,0,0,0.69,0.46,0,0.46,0,0.23,0.23,0,1.61,0,0.46,0,0.92,1.84,1.84,0,0.69,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.92,0,0,0,0,0,0,0,0,0,0.037,0,0.188,0.112,0,3.105,30,354,1 0,0,0.71,0,0.71,0,0,0,0,0,0,0,0,0,0,0.71,0,0,1.42,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0,0.121,0,1.094,0,0,2.021,16,95,1 0,1.49,0,0,0,0,2.98,0,0,1.49,0,0,0,0,0,1.49,2.98,0,0,0,2.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.171,0,0,0.171,0.171,13,140,156,1 0,0,0.16,0,0.33,0,0.16,0,0.5,0,0.16,0,0,0,0,0.5,0,1.5,0.66,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.088,0,0.884,0.752,0.022,5.328,47,1087,1 0,0,1.1,0,0.55,0,0,0,0,0.55,0,0,0,0,0,0.55,0,0,2.2,0,1.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0.097,0,0.683,0,0,2.338,19,145,1 0.16,0.32,0.65,0,0.32,0,0.16,0,0,0.49,0.16,0,0.16,0,0.16,0.16,0,0.81,0.32,0.16,0.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0,0,0.773,0.08,0.08,6.586,132,955,1 0,0,0.72,0,1.81,0,0,0,0,0.36,0,0.36,0,0,0,0,0.72,0,0.72,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.053,0.265,0,0,0,0,1.543,13,88,1 0.84,0.84,0,0,0,0,1.69,0,0.84,0.84,0,0.84,0,0,0,10.16,0.84,0,0.84,0,2.54,0,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.113,0.278,0.092,173,418,519,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.103,3,32,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.103,3,32,1 0.17,0,0.08,0,0.43,0.08,0.08,0.43,0.08,0.08,0,0.6,0.17,0.17,0,0,0.17,0.08,1.2,0,3.14,0,0.34,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.081,0.027,0.088,0.013,0,4.16,48,1140,1 0,0.54,0.54,0,1.09,0.54,2.18,0,0,0.54,0,0.54,0,0,0,0,0,0.54,3.27,0,1.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0.157,0,0.471,0,0.078,15.08,147,377,1 0,0,0.42,0,0,0,0,0,0,0,0,0.85,0,0,0,0.85,0,0.85,4.7,0,0.85,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0.082,0,0,0.082,0.248,7.17,42,294,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,2.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,1.806,0,0,1.293,5,75,1 0,0,0.45,0,0.22,0.22,0,0,0.67,0.45,0.22,0.9,0,0,0,0.22,0,0,1.35,0,1.12,0.22,0.22,0.22,0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0.064,0.258,0,0.129,0.193,0,7.258,71,617,1 0,0.55,0.55,0,1.11,0.55,2.23,0,0,0.55,0,0.55,0,0,0,0,0,0.55,3.35,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0.159,0,0.479,0,0.079,16.739,147,385,1 0,0,0,0,0,1.12,0,2.24,0,0,1.12,1.12,0,0,0,0,0,0,4.49,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.814,0,0,2.6,15,39,1 0,0,0.32,0,0.65,0.65,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.28,0,3.26,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.105,0,0,0.158,0,1.494,10,139,1 0,0,1.43,0,0.71,0,0,0.71,0,0.71,0,0,0,0,0,0,2.87,2.87,1.43,0,3.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.937,0,0,11.888,116,214,1 0,0.55,0.55,0,1.11,0.55,2.23,0,0,0.55,0,0.55,0,0,0,0,0,0.55,3.35,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0.159,0,0.479,0,0.079,16.739,147,385,1 0,0,0,0,0,0,1.07,0,0,0,0,0,0,0,0,2.15,0,0,0,0,2.15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0.145,0.437,0.291,1.823,10,62,1 0,0,0.47,0,0.95,0,0,0,0.47,0,0,0,0,0,0,0.95,0.95,0,1.42,0,0.47,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0.131,0.065,0.461,0.263,0,3.493,61,255,1 0,0,0.15,0,0.31,0,0.15,0,0.63,0.15,0.15,0,0,0,0,1.11,0,1.27,0.79,0,0,0,0,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0.15,0,0,0,0,0,0,0,0,0,0.088,0,0.862,0.707,0.022,5.423,51,1128,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0.39,0,0,0,3.58,0.39,0,0,0,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0.39,0,0,0,0,0,0,0,0,0,2.5,21,130,1 0,0,0,0,0,2.3,0,0,0,0,0,0.76,0.76,0,0,0,0,0,2.3,0,1.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.829,0,2.766,0.829,0,5.607,25,157,1 0.08,0.16,0.32,0,1.38,0.16,0.08,0,0.24,0.08,0,1.3,0,0.08,0,0.48,0.08,0.08,3.5,0,0.73,0,0.08,0.16,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.085,0.061,0.39,0.097,0.012,5.594,119,1561,1 0.48,0.2,0.55,0,0.27,0.2,0,0.27,0.27,0.97,0.41,1.04,0.13,0,0,1.11,0.69,0.06,2.37,0,1.04,0,0.06,0.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0.13,0,0,0,0,0.105,0,0.75,0.305,0,3.401,94,966,1 0.48,0.2,0.55,0,0.27,0.2,0,0.27,0.27,0.97,0.41,0.97,0.13,0,0,1.11,0.69,0.06,2.23,0,0.97,0,0.06,0.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0.13,0,0,0,0,0.105,0,0.75,0.305,0,3.401,94,966,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.453,0,0,4.153,26,54,1 0,0,1.42,0,0.71,0,0,0.71,0,0.71,0,0,0,0,0,0,2.85,2.85,1.42,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.931,0,0,12.055,117,217,1 0.8,0,0.8,0,1.6,0,0,0,0,0,0,0,0,0,0,0.8,0.8,0,1.6,0,2.4,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.62,0.347,0,2.604,22,125,1 0,0,0.33,0,0.99,0.99,0.33,0.33,0,0,0,0.33,0.33,0,0,0.33,0.33,0,1.98,0,3.3,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0.108,0,0,0.162,0.054,2.195,50,202,1 0.07,0.37,0.81,0,0.51,0.29,0.07,0,0.07,0.37,0.07,1.48,0.14,0,0.07,0,0.14,0.44,3.55,0,1.85,0,0,0.14,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0,0,0,0.049,0.069,0,0.159,0.159,0.009,3.456,44,802,1 0,0,0.33,0,0.99,0.99,0.33,0.33,0,0,0,0.33,0.33,0,0,0.33,0.33,0,1.98,0,3.3,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0.107,0,0,0.161,0.053,2.195,50,202,1 0,0,0.15,0,0.31,0,0.15,0,0.63,0.15,0.15,0,0,0,0,1.11,0,1.27,0.79,0,0,0,0,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0.15,0,0,0,0,0,0,0,0,0,0.088,0,0.862,0.707,0.022,5.423,51,1128,1 0,0,0.62,0,1.24,0.62,0,0,0,0,0,0,0,0,0,0.31,0,0,2.48,0,0.93,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0.086,0,0.043,0,0,1.741,14,155,1 0,0.34,0.69,0,0.34,0,0.17,0,0,0.51,0.17,0,0.17,0,0.17,0.17,0,0.86,0.17,0.17,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0,0,0,0,0,0,0,0.665,0.083,0.083,6.294,132,963,1 0,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0.79,0.79,1.58,3.17,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.79,0,0,0,0,0.271,0,0.271,0.135,0,3.257,26,114,1 0.14,0.14,0.29,0,0,0,0,0,1.17,0.29,0.14,0.58,0,0,0,0.14,0,0.14,2.35,0.14,0.88,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0.204,0,0.204,0.127,0.102,2.962,73,400,1 0,0,0,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.784,0,0,5.687,39,91,1 0,0,1,0,0,0.25,0,0.25,0,0,0,1.5,0.25,0,0,0.25,0.5,0,2.5,0,1.5,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.203,0.203,0,2.866,34,129,1 0.58,0,0,0,2.33,0,1.16,0,0,0,0.58,0,0,0.58,0,0.58,0,0.58,2.92,1.16,2.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0.09,0.09,0,1.829,9,75,1 0.14,0.14,0.29,0,0,0,0,0,1.17,0.29,0.14,0.58,0,0,0,0.14,0,0.14,2.35,0.14,0.88,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0.204,0,0.204,0.127,0.102,2.962,73,400,1 0.14,0.14,0.29,0,0,0,0,0,1.17,0.29,0.14,0.58,0,0,0,0.14,0,0.14,2.35,0.14,0.88,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0.204,0,0.204,0.127,0.102,2.962,73,400,1 0,0,0.58,0,1.17,0,0.58,0,0,0,0,0.58,0,0,0,0.58,0,0,1.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.829,0,0,2.529,8,86,1 0.25,0.25,0,0,0.75,0,0,0,0.25,0.75,0,1.51,0,1.26,0,0,0.5,0,3.29,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.082,0,0.041,0.124,0.124,3.181,32,210,1 0,0,0,0,6.25,0,3.12,0,0,0,0,3.12,0,0,0,0,0,0,6.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.428,60,66,1 0,0.57,0.57,0,1.14,0.57,2.28,0,0,0.57,0,0.57,0,0,0,0,0,0.57,3.42,0,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0.081,0,0.487,0,0.081,16.217,147,373,1 0,0.17,0,0,0,0,0.17,0.52,0,0.17,0.35,0.52,0,0,0,0,0.17,0.7,0.88,0,0.7,1.93,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.028,0.143,0.028,0.085,0.057,0.229,3.564,39,417,1 0,0,0.47,0,0.95,0,0,0,0.47,0,0,0,0,0,0,0.95,0.95,0,1.42,0,0.47,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0.133,0.066,0.401,0.267,0,3.459,61,256,1 0,0.57,0.57,0,1.14,0.57,2.28,0,0,0.57,0,0.57,0,0,0,0,0,0.57,3.42,0,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0.081,0,0.487,0,0.081,16.217,147,373,1 0,0.34,0.69,0,0.34,0,0.17,0,0,0.51,0.17,0,0.17,0,0.17,0.17,0,0.86,0.34,0.17,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.315,0,0.026,6.364,149,942,1 0,0.57,0.57,0,1.14,0.57,2.28,0,0,0.57,0,0.57,0,0,0,0,0,0.57,3.42,0,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0.081,0,0.487,0,0.081,16.217,147,373,1 0,1.63,0,0,0,0,3.27,0,0,0,0,0,0,0,0,1.63,1.63,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0.54,0.18,0.18,14.818,140,163,1 0,0,0.14,0,0.29,0,0.14,0,0.58,0,0.29,0,0,0,0,0.87,0,1.46,0.58,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0.764,0.784,0.02,4.979,45,1200,1 0,0.38,0.76,0,0.38,0,0.19,0,0,0.57,0.19,0,0.19,0,0.19,0.19,0,0.95,0.19,0.19,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0.632,0.03,0.09,6.789,132,869,1 0.4,0,0.6,0,0.2,0.6,0.2,0.6,0.2,0.2,0.2,1.2,0,0,0,0.4,1.61,0.4,2.21,1.81,2.62,0,0.2,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.096,0,1.453,0.129,0,3.946,64,513,1 0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,0,0.91,0,2.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.132,0,0.531,0,0,2.9,28,87,1 0,0,0.15,0,0.3,0,0.15,0,0.61,0,0.3,0,0,0,0,0.92,0,1.53,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0.083,0,0.732,0.753,0.02,5.058,45,1128,1 0,0.52,0.52,0,0.52,0,0,0,0,0,0,0,0,0,0,0.52,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.192,0,0.867,0,0,2.22,20,131,1 0,0,0.85,0,0.42,0,0,0,0,0,0,0,0,0,0,0.42,0,0,2.14,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0.159,0,1.117,0,0,1.206,7,117,1 0.18,0,0.18,0,1.57,0.36,0.06,0.06,0.06,0.12,0.06,0.54,0.3,0.06,0,0,0.72,0.06,4.48,0.24,1.15,0,0.84,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.06,0,0,0,0.01,0.052,0,0.01,0.167,0,1.733,12,442,1 0.1,0.1,0.73,0,0.2,0.1,0.2,0.62,0.1,0.31,0.31,1.04,0,0,0,0.1,1.14,0.31,2.4,0.93,2.92,0,0,0.2,0.1,0.1,0,0,0,0,0,0,0,0,0,0,0.1,0,0.1,0.1,0,0,0,0,0,0,0,0,0,0.163,0,0.785,0.065,0,4.064,92,817,1 0,0,0,0,0,0,5.4,0,0,0,0,0,0,0,0,5.4,0,0.9,1.8,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.841,0.21,0,24.785,295,347,1 0.17,0.17,0.71,0,0.53,0.17,0.17,0.89,0.17,0.53,0.35,1.61,0,0,0,0,1.79,0,1.97,1.61,4.12,0,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0,0,0,0,0,0.115,0,1.158,0.057,0,5.163,63,599,1 0.08,0.17,0.34,0,1.46,0.17,0.08,0,0.25,0.08,0,1.37,0,0.08,0,0.51,0.08,0.08,3.43,0,0.77,0,0.08,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.077,0.064,0.348,0.103,0.012,5.392,119,1456,1 0,0.46,0,0,1.15,0,0.23,0.23,0,0.46,0,0.69,0.23,0,0,0,0.69,0.69,2.76,0,1.84,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,1.15,0,0,0,0,0,0,0,0,0,0,0,0,0,0.036,0.036,0.841,0.036,0,1.862,52,285,1 0,0,0.39,0,0.78,0,0,0.06,0.06,0.19,0.13,0.26,0.13,0,0,0,0,0,0.32,0,0.06,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0,0,0.032,0,0,0.032,0,1.206,15,240,1 0,0,0,0,0,0,0,1.05,0,0,0.52,1.05,0.52,0,0,1.05,0,0,3.7,1.05,1.05,0,1.58,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.702,0.263,0,6.487,47,266,1 0,0,0.32,0,0.64,0.64,0.32,0.64,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.27,0,3.24,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.105,0,0,0.157,0,1.494,10,139,1 0.54,0,0.54,0,1.63,0,0,0,0,0,0,0.54,0,0,0,0.54,0.54,0,2.17,0,5.97,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.715,0.318,0,2.345,22,129,1 1.63,0,1.63,0,0,0,0,0,1.63,0,0,0,0,0,0,1.63,0,0,3.27,0,3.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.759,0.253,0,2,16,36,1 0,1.32,0.56,0,0,0.94,0,0.18,0.37,0.75,0,2.07,0,0,0,0,0.37,0,2.45,0,0.94,0,0,0.18,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0.242,0.107,0,2.623,35,244,1 0.35,0,0.35,0,0.35,0.7,0.35,1.41,0,0,0.35,1.06,0,0,0,0.7,1.06,0,5.3,2.82,2.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0.411,0,0,2.917,60,213,1 0.34,1.03,0.34,0,1.03,0,2.41,0.34,0,1.72,2.06,2.06,0.68,0,0.34,0,0,3.44,4.13,0,2.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.116,0,0,0,0,1.888,6,68,1 0,1.32,0.56,0,0,0.94,0,0.37,0.37,0.75,0,2.07,0,0,0,0,0.37,0,2.45,0,0.94,0,0,0.18,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0.295,0.107,0,2.542,34,239,1 0.64,0,0.64,0,1.28,0,0.64,0,0,0,0,0.64,0,0,0,0.64,0.64,0,1.28,0,3.2,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.551,0.459,0,2.333,22,119,1 0,0,0.32,0,0.65,0.65,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.28,0,3.26,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.106,0,0,0.159,0,1.494,10,139,1 0.64,0,0.64,0,1.28,0,0.64,0,0,0,0,0.64,0,0,0,0.64,0.64,0,1.28,0,2.56,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.554,0.369,0,2.333,22,119,1 0,0,0.56,0,0,0.18,0,0,0,1.32,0,0.75,0.75,0.18,0,0.18,0,0,0.94,0,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0.07,0.07,0,2.616,23,191,1 0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,0,0.91,0,2.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.133,0,0.532,0,0,2.9,28,87,1 0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.309,0,0,1.333,11,56,1 0,0,0,0,1.29,0.43,0.43,0,0,0,0,0,0.43,0,0,0.43,0.43,0.43,1.72,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.129,0,0.129,0,0,5.8,46,116,1 0,0,0.86,0,0.43,0,0,0,0,0,0,0,0,0,0,0.43,0,0,2.17,0,1.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0.161,0,1.133,0,0,1.2,6,114,1 0,0.68,0.34,0,0.34,0,0,0,0,0,0,0.68,0,0,0,0.34,0,1.37,1.72,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.055,0,0.718,0,0,3.718,61,264,1 0,0,0.16,0,0.16,0,0.16,0,0.65,0.16,0.16,0,0,0,0,1.64,0,0.65,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0.066,0,0.906,0.663,0,5.289,52,1116,1 0.18,0,0.18,0,1.57,0.36,0.06,0.06,0.06,0.12,0.06,0.54,0.3,0.06,0,0,0.72,0.06,4.49,0.24,1.09,0,0.85,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.06,0,0,0,0.01,0.052,0,0.01,0.167,0,1.74,12,442,1 0,0.11,0.23,0,0.58,0.34,0.11,0,0.34,0,0.23,0.92,0.46,0,0,0.46,0.23,0.34,0.58,0,0.58,0,0.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0,0.075,0.037,0,0.322,0.094,0.018,2.576,48,389,1 0,0,0,0,0,0,0,0,0,0,1.23,1.23,0,0,0,0,0,0,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.284,0,0,1.357,5,19,1 0,0,0,0,0.91,0,0.91,0,0,0.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.134,0,0.672,0.269,0,4.35,31,87,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.199,0,4.818,25,53,1 0,1.31,0.56,0,0,0.93,0,0.18,0.37,0.75,0,2.06,0,0,0,0,0.37,0,2.44,0,0.93,0,0,0.18,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0.268,0.107,0,2.698,42,251,1 0,1.32,0.56,0,0,0.94,0,0.18,0.37,0.75,0,2.07,0,0,0,0,0.37,0,2.45,0,0.94,0,0,0.18,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0.241,0.107,0,2.623,35,244,1 0,0,0.32,0,0.65,0.65,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.28,0,3.25,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.105,0,0,0.157,0,1.494,10,139,1 0,0,0,0,0,0,0,0.67,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.34,0,0,0,0,0,0,0,0,0,0,0,0.17,0.511,0.085,0.511,0,0,4.617,27,217,1 0,0.62,1.24,0,0.31,0,0,0,0,0,0,0.62,0,0,0,0.31,0,1.24,2.49,0,1.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0.05,0,1.152,0,0,4.592,121,349,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.266,4,19,1 0,0,0,0,0,0,0,0.67,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.34,0,0,0,0,0,0,0,0,0,0,0,0.17,0.511,0.085,0.511,0,0,4.617,27,217,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.266,4,19,1 0,0,0.17,0.52,0.17,0,0.17,0,0.69,0.17,0.17,0,0,0,0,1.74,0,0.69,1.04,0,0.17,0,0,0,0.17,0,0,0,0,0,0,0,0,0.17,0,0,0.34,0,0,0.17,0,0,0,0,0,0,0,0,0,0.072,0,0.754,0.681,0,4.74,52,967,1 0,1,1,0,2,0,1,0,0,0,0,0,0,0,0,0,2,3,2,0,4,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.275,0.137,0,2.538,11,33,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,1.05,0,3.15,0,2.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0.351,0,0.351,0.175,0,3.343,28,107,1 0,0,0.16,0.16,0.32,0,0.16,0,0.65,0.16,0.16,0,0,0,0,2.13,0,0.65,0.98,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0.16,0.32,0,0,0.16,0,0,0,0,0,0,0,0,0,0.089,0,0.693,0.67,0,4.835,52,1030,1 0,0,1.53,0,0,0,0,0,0,0,1.53,0,0,0,0,0,0,0,3.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.53,0,0,0,0,0,0,1.434,0,0,7.055,75,127,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.201,0,4.5,25,54,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,3.84,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0.23,0.23,0,5.538,41,72,1 0,0,0.47,0,0,0,0.94,0,0,0,0,0.47,0,0,0,0,0.47,0,0.94,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0.072,0,0.217,0,0,1.48,11,77,1 0.25,0,0,0,0.51,0.51,0,0,0.25,0,0.25,0,0.25,0,0,0,0.25,0,2.81,0,0.25,0,0.25,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.041,0,0.209,0.209,0,2.776,75,211,1 0,0,0.73,0,0.36,0,0,0,0,0,0,0.73,0,0,0,0.36,0.73,0,1.09,0,1.46,0.36,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0.604,0.181,0,3.787,58,356,1 0.64,0,0.64,0,1.93,0,0,0,0,0,0,1.29,0,0,0,1.29,0.64,0,1.93,0,2.58,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.462,0.37,0,2.44,22,122,1 0,0,0,0,0,0,0,1.29,0,0.43,0,0,0,0,0,0.43,0,1.73,0.43,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0.124,0,0.31,0.062,0,1.477,8,65,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,3.84,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0.23,0.23,0,5.538,41,72,1 0,0,0,0,0,0.6,0.6,0,0,0,0.6,0,0,0,0,0,0,1.21,1.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.095,0,0,0,0,1.583,11,38,1 0,0,0.32,0,0.65,0.65,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.28,0,3.25,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.106,0,0,0.159,0,1.494,10,139,1 0,0,1.29,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,1.29,0,5.19,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.198,0,4.23,25,55,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,3.84,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0.23,0.23,0,5.538,41,72,1 0.63,0,0.63,0,1.27,0,0.63,0,0,0,0,0.63,0,0,0,0.63,0.63,0,1.27,0,2.54,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0.501,0.3,0,2.458,22,118,1 0.65,0,0.65,0,1.3,0,0,0,0,0,0,0.65,0,0,0,1.3,0.65,0,1.96,0,2.61,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.834,0.463,0,2.44,22,122,1 0.19,0.19,0.19,0.19,1.16,0,0,0,0.58,0.38,0,0,0,0,0,0,0,0,0.19,0.38,0.58,0,0,0,0.19,0,0,0.19,0,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,1.121,0,1021.5,2042,2043,1 0,0,0,0,0,0,0,1.29,0,0.43,0,0,0,0,0,0.43,0,1.73,0.43,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0.124,0,0.31,0.062,0,1.477,8,65,1 0.08,0,0.08,0,0.16,0,0,0,0,0,0,0.23,0,0,0,0.08,0.23,0,0.4,0.16,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0.16,0,0,0,0,0,0.228,0,0.406,0.038,0,2.811,67,1254,1 0.64,0,0.64,0,1.93,0,0,0,0,0,0,1.29,0,0,0,1.29,0.64,0,1.93,0,2.58,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.462,0.37,0,2.44,22,122,1 0,0,0,0,0,0,0,1.29,0,0.43,0,0,0,0,0,0.43,0,1.73,0.43,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0.124,0,0.31,0.062,0,1.477,8,65,1 0,0,0.73,0,0.36,0,0,0,0,0,0,0.73,0,0,0,0.36,0.73,0,1.09,0,1.46,0.36,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0.604,0.181,0,3.787,58,356,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.06,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.342,0,0,0,0,2.217,10,51,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,3.84,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0.23,0.23,0,5.538,41,72,1 0,0,0.32,0,0.65,0.65,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.28,0,3.25,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.105,0,0,0.158,0,1.494,10,139,1 0.18,0,0.18,0,1.57,0.36,0.06,0.06,0.06,0.12,0.06,0.54,0.3,0.06,0,0,0.72,0.06,4.49,0.24,1.09,0,0.85,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.06,0,0,0,0.01,0.052,0,0.01,0.167,0,1.736,12,441,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.201,0,4.5,25,54,1 0,0,0,0,0,0,0.45,0.91,0.45,0.91,0,0,0,0,0,0,0.45,0.45,0.91,0,0.45,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.254,0,0.063,0.127,0,4.735,46,161,1 0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,4.65,2.32,0,3.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,21,1 0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,6.25,0,0,3.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,19,1 0,0.02,0.05,0,0.02,0,0,0.05,0,0.35,0,0.02,0,0,0,0.05,0.1,0.38,0.07,0.2,0.17,0,0,0,0.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.004,0,0.107,0.017,0.017,3.922,489,3271,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,1,1,2,1 0.48,0,1.45,0,0.48,0,0,0,0,0,0,0,0,0,0,0.48,0,0,4.36,0,1.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0.09,0,2.443,0,0,1.227,8,81,1 0,0,0.71,0,0.23,0,0,0,0.23,0.23,0.23,1.9,0,0,0,0.23,0,0,3.81,0.23,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.109,0,1.018,0.036,0,4.022,97,543,1 0,0,0.32,0,0.65,0.65,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.28,0,3.25,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.105,0,0,0.158,0,1.494,10,139,1 0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.588,0,0,0,0,1,1,6,1 0,0,0.71,0,0.23,0,0,0,0.23,0.23,0.23,1.9,0,0,0,0.23,0,0,3.81,0.23,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.109,0,1.018,0.036,0,4.022,97,543,1 0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,5,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,3.178,62,89,1 0.05,0,0.29,0,0.23,0.17,0.05,0,0,0,0.65,0.82,0,0,0,0.76,0.11,0.11,1.53,0.29,1.3,0,0.23,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.009,0.129,0,0.102,0.259,0,1.493,8,660,1 0,0,0.32,0,0.64,0.64,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.27,0,3.24,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.104,0,0,0.157,0,1.494,10,139,1 0,0,1.34,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0.67,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0.332,0.11,0,2.315,12,132,1 0,0.02,0.05,0,0.02,0,0,0.05,0,0.35,0,0.02,0,0,0,0.05,0.1,0.38,0.07,0.2,0.17,0,0,0,0.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.004,0,0.107,0.017,0.017,3.922,489,3271,1 0,0,1.35,0,0.67,0,0,0,0,0.67,0,0,0,0,0,0,0,0.67,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0.802,0.114,0,2.527,20,139,1 0.2,0.81,0.61,0,0,0,0,0,0.2,0,0,0.4,0,0,0,0.2,0,0,0.2,0,0.2,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.831,0.338,0.03,1102.5,2204,2205,1 0,0,1.22,0,1.22,0,0,0,0,0,0,0,0,0,0,0.61,0,0.61,1.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0.517,0.103,0,2.966,28,178,1 0,0,0,0,1.48,0.74,1.48,0,0,0.74,0.74,0.74,0.74,0,0,0.74,0.74,0,2.22,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.108,0,0,2.346,12,61,1 0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.076,0.153,0,0,3.317,11,136,1 0.35,0.46,0.31,0,0.15,0.03,0,0.35,0.58,0.66,0.31,0.7,0.62,1.28,0.03,0.23,0.42,0,3.12,0,1.36,0,0.46,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0.005,0.088,0,0.319,0.479,0.124,6.11,116,2218,1 0,0.35,0.7,0,0.7,0,0.35,0.35,0,0.35,0.7,0,0,0,0,0.7,0,0.35,4.25,0,1.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0.113,0,0.397,0,0,3.388,58,183,1 0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0,2.43,0,0,3.65,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,31,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.199,0,4.818,25,53,1 0.6,0,0.36,0,1.44,0,0,0,0.24,1.32,0.72,2.52,0.6,0,0,0.6,0.24,0,4.44,0,1.8,0,0.72,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0.04,0,0.101,0.202,0,3.548,54,479,1 0,0,1.33,0,1.78,0.44,0,0.44,0,0,0,0,0,0,0,0,0,0,4.46,0.89,0.89,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0.078,0.078,0,0,0,0,1.541,5,37,1 0.08,0.08,0.76,0,0.85,1.02,0.25,0.17,0.59,0.08,0.17,0.59,0.17,0,2.22,0.25,0.08,0.94,1.62,0.17,0.42,0,0.85,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.17,0.08,0.08,0.08,0,0,0,0.063,0,0.42,0.114,0.012,7.497,669,1402,1 0,0,0.46,0,0.46,0,0,0,0,0,0,0,0,0,0,0.46,0,0,2.8,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0.082,0,0.663,0,0,1.428,20,120,1 0,0,0.14,0,0.14,0,0.14,0,0.57,0.14,0.14,0,0,0,0,0.86,0,0.57,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.826,0.745,0,5.456,72,1315,1 0.18,0,0.18,0,1.59,0.36,0,0.06,0.06,0.06,0.06,0.55,0.3,0.06,0,0,0.73,0,4.4,0.24,1.1,0,0.85,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0,0.01,0.052,0,0.01,0.169,0,1.748,12,444,1 0.18,0,0.18,0,1.59,0.36,0,0.06,0.06,0.06,0.06,0.55,0.3,0.06,0,0,0.73,0,4.4,0.24,1.1,0,0.85,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0,0.01,0.052,0,0.01,0.169,0,1.775,12,451,1 0.76,0.19,0.38,0,0.19,0.12,0,0.25,0.76,0.31,0.25,1.52,0.31,0.38,0,0.38,0.44,0.06,2.98,0.69,1.26,0,0.44,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0.06,0,0,0,0,0,0,0.085,0.053,0.429,0.236,0.064,3.664,45,1059,1 0.08,0.08,0.35,0,1.52,0.17,0.08,0,0.35,0.17,0,1.43,0,0.08,0,0.53,0.08,0,3.58,0,0.89,0,0.08,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.054,0.068,0.369,0.109,0.027,4.911,119,1277,1 0.08,0,0.93,0,1.52,0.33,0,0.08,0.67,0,0.25,0.67,0.16,0,1.69,0.08,0,1.1,1.86,0.16,0.42,0,1.1,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0.16,0,0.08,0.08,0,0,0.012,0.101,0,0.356,0.101,0.012,11.32,669,1834,1 0,0,0.48,0,0.48,0.48,0.48,0,0,0.96,0,0,0,0,0,0,0.96,0,3.36,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.163,0,0.163,0,0,1.696,17,95,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.578,0,0,0,0,3.8,15,19,1 0,0,0.59,0,0.59,0,0,0.59,0,0,0,1.19,0,0,2.38,0,0.59,0.59,2.97,0,2.97,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0.105,0,0.105,0.42,0,3.428,12,72,1 0.6,0,0,0,1.21,0,0.6,0,0,0,0,0.6,0,0,0,0,0,0.6,3.65,0,1.21,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.101,0.304,0,3.217,30,74,1 0.76,0.19,0.38,0,0.19,0.12,0,0.25,0.76,0.31,0.25,1.52,0.31,0.38,0,0.38,0.44,0.06,2.98,0.69,1.26,0,0.44,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0.06,0,0,0,0,0,0,0.085,0.053,0.428,0.235,0.064,3.702,45,1070,1 0,0,0,0,0,0,0,0,0,0,0,0,0,2.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0.163,0,0.163,0.326,0,3.545,21,78,1 0,0,0.33,0,0.33,0,0.33,0.33,0,0,0,0.33,0,0,0,1.65,0,1.65,2.64,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.161,0.537,0,0,2.517,9,141,1 0,0,0.67,0,0,0,0.67,2.02,0,0,0,0,0,0,0,0,0.67,0,3.37,0,1.35,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.095,0,0.286,0,0,5.558,39,189,1 0.11,0.23,0.11,0,0.46,0.46,0,0.11,0.93,1.74,0.11,0.34,0.23,0.11,2.09,0,0.46,0,3.49,0,1.28,0,0.46,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0.34,0,0,0,0.019,0.172,0,0.23,0.134,0,4.281,144,655,1 0,0,0,0,1.55,0,0,0,0,0.31,0,0.31,0,0,0,0.31,0.62,0,2.79,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.93,0,0,0,0,0.048,0,0,0,0,2.09,22,115,1 0,0,0,0,0.96,0,0.96,0,0,0,0.96,0,0,0,0,0,0,0,2.88,0,2.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.123,0,0.616,0,0,1.181,3,13,1 1.05,0,0.7,0,1.05,0,0.7,0,0,0.35,0,0.7,0,0,0.35,0.7,0,0.35,5.96,0,1.4,0,0.35,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0.367,0.061,0,1.88,12,94,1 0,0,0.55,0,0.55,0,0,0,0,0,0,0,0,0,0,0.55,0,0,3.31,0,1.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0.104,0,0.524,0,0,1.229,7,75,1 0.29,0,0.29,0,0.29,0,0,0.29,0,0,0.29,0,0,0,0,0,2.93,0.58,1.75,0,1.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.361,6,113,1 0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,0,0,0,0.89,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0.52,0,0,3.5,46,105,1 0.08,0.08,0.35,0,1.52,0.17,0.08,0,0.35,0.17,0,1.43,0,0.08,0,0.53,0.08,0,3.58,0,0.89,0,0.08,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.054,0.068,0.369,0.109,0.027,4.896,119,1278,1 0,0,1.16,0,3.48,0,0,0.58,0.58,0,0,0.58,0,0,0,1.74,0,0,1.16,0,3.48,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.082,0,0.165,0.082,0,2.17,12,102,1 0.1,0,0.03,0,0.1,0.03,0,0,0,0.1,0.1,0.43,0,0,0,0.37,0.1,0,0.43,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0.13,0.06,0,0,0,0,0.06,0,0.03,0,0,0,0.2,0.014,0.078,0,0.034,0.019,0.019,4.93,113,3550,1 0,0,0,0.42,0.84,0,0,0.42,0,0,0,0,0,0,0,0,0,0.42,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.488,0,2.636,0.683,0,3.168,36,301,1 0.25,0,0.51,0,0.25,0.51,0.25,0,0,0,0,0.76,0,0,0,0.25,0,0.76,2.29,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.044,0,0.132,0.354,0,0,2.593,14,153,1 0.08,0.08,0.76,0,0.85,1.02,0.25,0.17,0.59,0.08,0.17,0.59,0.17,0,2.21,0.25,0.08,0.93,1.61,0.17,0.42,0,0.85,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.17,0.08,0.08,0.08,0,0,0,0.063,0,0.394,0.114,0.012,7.484,669,1407,1 0,0.24,0.72,0,0.24,0,0.12,0,0,0.36,0.12,0,0.12,0,0.12,0.12,0,0.6,0.36,0.12,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0.018,0,0.34,0,0.018,5.634,158,1234,1 0,0,0.43,0,0.87,0,0,0,0,0,0,0,0,0,0,0.43,0.87,0,2.62,0,1.31,0.43,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0.072,0,0.577,0.216,0,6.274,90,320,1 0.14,0.14,0.29,0,0,0,0,0,1.02,0.29,0.14,0.58,0,0,0,0,0,0.14,2.35,0.14,1.02,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0.14,0,0,0,0,0.204,0,0.153,0.153,0.102,2.705,73,368,1 0,0,0.14,0,0.28,0,0.14,0,0,0,0,0,0,0,0,0,0,0,2.89,2.31,2.02,7.97,0.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0.053,0,0.269,0.08,0.484,15.086,74,1222,1 0.62,0,0.62,0,1.25,0,0.62,0,0,0,0,0.62,0,0,0,0.62,0.62,0,1.25,0,2.51,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.456,0.273,0,2.52,22,121,1 0.16,0,0.67,0,0.33,0.16,0.33,0.84,0.16,0.5,0.33,1.51,0,0,0,0,1.68,0.33,2.02,1.68,3.87,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0.218,0,1.118,0.054,0,4.928,63,621,1 0,0,0.14,0,0.28,0,0.14,0,0,0,0.14,0.14,0,0,0,0,0,0,2.86,2.14,2,3.86,0.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0.048,0,0.241,0.072,0.435,6.238,37,1229,1 0,0,0.15,0,0.15,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,2.26,2.11,4.07,0.15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0,0,0.051,0,0.255,0.076,0.46,6.3,37,1216,1 0.51,0.43,0.29,0,0.14,0.03,0,0.18,0.54,0.62,0.29,0.65,0.65,1.2,0.03,0.21,0.43,0.03,3.03,0,1.35,0,0.51,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0.012,0.078,0,0.443,0.51,0.133,6.59,739,2333,1 0,0,0,0,0,0.68,0,1.36,0.68,0.68,0,0,0,0,0,0.68,2.73,0.68,1.36,3.42,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,1.143,0.519,0,3.737,75,228,1 0.33,0,0.66,0,0.22,0,0,0,0.44,0.11,0,0.33,0,0,0,0.55,0,0,1.76,0,1.1,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0.11,0,0,0,0,0,0,0.173,0,0.367,0.193,0.077,2.559,75,389,1 0,0,0.49,0,1.48,0,0.49,0,0,0,0,0.99,0,0,0,0.49,0.99,0,2.47,0,2.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0.082,0,0.497,0.165,0,5.113,78,225,1 0,0,0.94,0,0.94,0,0,0,0,0,0,0,0,0,0,0.94,0,0,4.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,3.571,0,0,1.3,7,52,1 0.49,0.28,0.4,0,0.09,0.11,0.02,0.21,0.42,0.75,0.23,0.89,0.54,1.06,0,0.16,0.33,0.02,3.23,0,1.46,0,1.03,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.058,0,0.382,0.847,0.141,5.783,193,3210,1 0.33,0,0.66,0,0.22,0,0,0,0.44,0.11,0,0.33,0,0,0,0.55,0,0,1.76,0,1.1,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0.11,0,0,0,0,0,0,0.173,0,0.367,0.193,0.077,2.559,75,389,1 0,0,1.56,0,0,0,1.56,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0.233,0,0.7,0,0,2.125,12,34,1 0,1.11,1.11,0,1.11,0,2.22,0,0,0,0,0,0,0,0,3.33,0,0,3.33,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.146,0,0,2.058,5,35,1 0,0,3.03,0,0.43,0,0.86,0,0,0,0.43,0.43,0,0,0,2.16,0,1.29,3.46,0,1.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.801,0,0,4.77,41,353,1 0,0,0.91,0,1.82,0.45,0,0,0,0,0,0.45,0,0,0,1.36,0,0,2.28,0,4.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.073,0,0,0,0,2.032,12,126,1 0,0,0.76,0,0.76,0,0.5,0.5,0,1.01,0,0.25,1.52,0,0.76,0,0,1.52,2.03,0,1.52,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0.074,0,0.412,0.412,0,2.441,19,249,1 0,0,1.44,0,0,0,0,0,0,0,0,2.89,0,0,0,1.44,0,0,5.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.277,0,0,1.312,3,21,1 0,0,0.76,0,0.76,0,0.5,0.5,0,1.01,0,0.25,1.52,0,0.76,0,0,1.52,2.03,0,1.52,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0.074,0,0.412,0.412,0,2.441,19,249,1 0,0.71,0.71,0,0.35,0.35,0,0,0,0,0,0.71,0,0,0,0.35,0,1.43,1.79,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.057,0,1.257,0,0,3.895,61,261,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.88,0,5.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,4,9,1 0.6,0,0.36,0,1.44,0,0,0,0.24,1.32,0.72,2.52,0.6,0,0,0.6,0.24,0,4.44,0,1.8,0,0.72,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0.04,0,0.101,0.222,0,3.577,54,483,1 0,0,0.88,0,0.88,0,0,0,0,0,0,0.88,0,0,0,0,0,0,0.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.935,0,0,3.417,51,229,1 0,0,0.36,0,0.6,0.12,0.12,0,0,0,0.12,0.48,0.12,0.12,0,0.12,0,0.6,2.41,0,0.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.056,0.037,0,0.056,0.094,0,1.246,14,389,1 0,0,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,1.31,0,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.251,0,1.007,0,0,1.44,8,36,1 0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,3.61,0,0,3.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.421,0.21,0,3.454,17,38,1 0,0,0,0,0,0,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0.19,0,0.19,0.38,0,3.6,16,72,1 0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,1.92,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.336,0,0,1.21,3,23,1 0.59,0.09,0.09,0,0.29,0.09,0,0.59,0.59,2.09,0.29,0.09,0.29,0,0.39,0.09,0.79,0.39,3.19,0.09,1.69,0,1.39,0.99,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0.09,0,0,0,0,0,0.19,0,0,0,0.044,0.078,0,0.334,0.133,0.011,15.493,1171,2541,1 0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.125,0,2.411,10,41,1 0.27,0,0.41,0,0,0,0.13,0.13,0,0,0,0.41,0,0,0,0,0,0.41,0.69,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0.133,0,0,0,0,1.531,20,144,1 0,0.62,0.62,0,0.31,0,0,0,0,0,0,0.62,0,0,0,0.31,0,1.25,2.51,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0.051,0,0.777,0,0,3.39,61,278,1 0,0,0,0,0.26,0,0.26,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0.52,17.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0.451,0.082,0.082,0.369,0,1.026,13.82,104,1078,1 0.33,0,0.67,0,0.22,0,0,0,0.44,0.11,0,0.33,0,0,0,0.56,0,0,1.79,0,1.12,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0.11,0,0,0,0,0,0,0.157,0,0.373,0.196,0.078,2.576,75,389,1 0.12,0.12,0.24,0,1.34,0.12,0,0.12,0,0,0.36,0.85,0,0,0,0.24,0.24,0,2.33,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.98,0,0,0,0.12,0,0,0,0,0.12,0,0,0,0.061,0.02,0,0.041,0.041,0,2.351,69,254,1 0.12,0.12,0.24,0,1.34,0.12,0,0.12,0,0,0.36,0.85,0,0,0,0.24,0.24,0,2.33,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.98,0,0,0,0.12,0,0,0,0,0.12,0,0,0,0.061,0.02,0,0.041,0.041,0,2.351,69,254,1 0.31,0.31,0.31,0,0,0,0.31,0,0.31,0.31,0.31,0.31,0,0,0,0.94,0,0,0.31,0,2.51,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0.75,0,0.89,0.046,0.046,12.382,138,421,1 0,0,0.51,0,0.51,0,0,0,0,0,0,1.03,0,0,0,0,0,0,1.54,0,1.03,0,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0.177,0,3.125,12,100,1 0,0.48,0.48,0,0.48,0,0,0.48,0,0,0,0.96,0,0,1.92,0,0.48,0.96,2.88,0,2.88,0,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0.084,0,0.084,0.336,0,3.2,12,80,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.45,0,0,3.22,0,6.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,1 0,0,0.89,0,1.79,0.44,0,0,0,0,0,0.44,0,0,0,1.34,0,0,2.24,0,4.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.073,0,0,0,0,2.25,12,144,1 0,0,0.71,0,0.17,0,0.35,0.35,0,0.17,0.17,0.35,0,0,0,0.35,0,0.17,0.53,0,0.17,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0.057,0,0.057,0.171,0,1.974,34,229,1 0,1.72,0,0,0,0,0,0,0,1.72,0,0,0,0,0,1.72,0,0.86,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0.251,0.251,0,2.022,12,91,1 0,0,0,0,0,0,0,2.53,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0.186,0,0.186,0.186,0,4,23,84,1 0,0,0,0,0.42,0.42,0.42,0,0,0,0,0.42,0,0,0,0,0,0,0.84,0,0.42,8.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.062,0,0.187,0,1.002,7.951,74,493,1 0,0,0,0,0.45,0.45,0.45,0,0,0,0,0.45,0,0,0,0,0,0,0.9,0,0.45,9.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.203,0,1.084,8.517,72,477,1 0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,3.01,0,0,1.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.176,0,0,4.476,20,94,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.385,0,0,20,169,180,1 0,0,1.25,0,2.5,0,0,0,0,0,0,0,0,0,0,1.25,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,4,36,1 0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0.194,0,0,0.389,0,3.6,16,72,1 0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.486,0,0,2.681,11,59,1 0.21,0.1,0.52,0,1.26,0.1,0,0,0.42,0.52,0.21,0.52,0.42,0,0,0,0.52,0,4.53,0,2,0,0.31,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.028,0,0.141,3.305,0,11.288,193,1016,1 0,0.23,0,0,0.23,0.47,0,0.47,0,0.95,2.61,1.66,0,2.61,0,0,0,0,3.8,0,0.95,0,0.23,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0.95,0,0,0,0,0.23,0,0,0.47,0,0,0,0,0.121,0.04,0,0.04,0,3.78,55,189,1 0.09,0.18,0.36,0,0.09,0,0.09,0,0.55,0.27,0.09,0.83,0.36,0,0,0,0,0.09,3.69,0.55,1.56,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0,0,0,0,0,0.09,0,0,0,0,0.056,0,0.341,0.085,0,7.273,103,1171,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.546,0,0,2.3,9,23,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.631,0,0,1.666,5,15,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.844,0,0,1.666,5,15,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.081,0,0,2.3,9,23,1 0,0,0.64,0,0.64,0,0,0,0,1.29,0,0,0,0,0,2.59,0,0,3.24,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0.574,0,0,5.833,30,105,1 0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0,0,0,0.93,0,3.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.134,0,0.536,0,0,2.166,23,65,1 0,0,0.32,0,0.64,0.64,0.64,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0.32,2.27,0,3.24,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.156,0,0,0.156,0,1.688,19,157,1 0,0,0,0,0,0,0,1.08,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0.191,0,0.191,0.383,0,3.95,23,79,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.448,0,0,2.666,11,24,1 0,0,0,0,0,0,0,1.08,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0.191,0,0.191,0.383,0,3.95,23,79,1 0,0,0,0,0,0.05,0,0.34,0,0,0.11,0.81,0.05,0.11,0,0,0.75,0,0,0,0,0,0.05,0,1.16,0,0,0,0,0,0,0,0.05,0,0,0.23,0.05,0,0,0,0,0,0,0,0,0,0,0,0.283,0.107,0,0,0.053,0,1.864,32,910,1 0,0,0,0,0.88,0,0,0,0,0,0.44,0.44,0,0,0,0,0,0.44,1.32,0,1.32,0,0,0,0.44,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.072,0.072,0,0.291,0,0,1.348,3,58,1 0,0,0.41,0,0.82,0.61,0.2,0,0.2,0.61,0.41,1.23,0.2,0,0,0.61,0,0,2.89,3.09,1.23,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.098,0.065,0,0.816,0.065,0,3.716,45,301,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.347,0,0,1,1,2,1 0.1,0,0.43,0,0.1,0.1,0.1,0.53,0.1,0,0,0.64,0,0.32,0,0,0.1,0,0,0,0,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.049,0,0.016,0.065,0,1.901,29,329,1 0.65,0.49,0.32,0,0.32,0.16,0,0.49,0.65,0.49,0.16,1.3,0,0,0.16,1.14,1.3,0.16,3.6,0.49,1.8,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0.225,0,0.902,0.225,2.233,5.833,47,595,1 0.09,0,0.09,0,0.39,0.09,0.09,0,0.19,0.29,0.39,0.48,0,0.58,0,0.87,0.19,0,1.66,4.1,1.66,0,0.39,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.136,0,0.318,0.151,0,6.813,494,1458,1 0,0,0,0,0,0,0,3.33,3.33,0,0,0,0,0,0,0,3.33,0,0,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.396,0,0.396,3.714,11,26,1 0.1,0,0.1,0,0.4,0.1,0.1,0,0.2,0.2,0.4,0.5,0,0.6,0,0.91,0.2,0,1.72,4.26,1.72,0,0.4,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.093,0,0.297,0.156,0,6.8,494,1428,1 0,0,0.37,0,1.11,0.74,0,2.96,0,2.96,0,0,0.74,0,0,0,2.22,0,5.18,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.149,0,1.096,0,0,5.16,107,289,1 0.08,0.08,0.76,0,0.85,1.02,0.25,0.17,0.59,0.08,0.17,0.59,0.17,0,2.21,0.25,0.08,0.93,1.61,0.17,0.42,0,0.85,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.17,0.08,0.08,0.08,0,0,0,0.063,0,0.394,0.114,0.012,7.484,669,1407,1 0.25,0,0.51,0,0.25,1.28,0,0,0.77,0.51,0,0.25,0,0,0,0,0,0.51,1.79,0,0.77,0,2.05,0,0.51,0.51,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0.086,0,0.26,0.173,0,3.298,16,287,1 0,0,0,0,1.05,2.1,1.05,0,0,0,0,0,0,0,0,0,0,0,3.15,0,1.05,0,2.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.171,0.171,0,2.05,6,41,1 0,0.1,0.3,0,1.02,0.3,0.1,0.4,0,0.2,0.1,0.92,0,0.1,0,1.94,0.92,0.4,1.94,0.4,0.61,0.92,0.51,0.1,0,0,0,0,0,0,0,0,0,0,0,0.3,0.1,0,0,0.1,0,0,0,0,0,0,0,0,0,0.048,0.016,0.518,0.162,0.34,8.181,283,1890,1 0.1,0,0.1,0,0.4,0.1,0.1,0,0.2,0.2,0.4,0.5,0,0.6,0,0.91,0.2,0,1.72,4.26,1.72,0,0.4,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.093,0,0.297,0.156,0,6.8,494,1428,1 0,0,0,0,0.44,0.44,0.44,0,0,0,0,0.44,0,0,0,0,0,0,0.88,0,0.44,9.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,1.017,8.033,72,474,1 0,0.1,0.62,0,0.31,0,0.1,0,0.2,0.62,0.1,0.62,0.41,0,0,0.1,0.1,0.2,3.43,0.1,1.66,0,0.1,1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0,0,0.611,0.264,0.049,3.794,69,702,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0.422,0,0.422,0.634,0,4.066,17,61,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.363,11,26,1 0,0.62,0.62,0,0,0.62,0,2.82,0,0.31,0.31,2.5,0,0,0,2.5,0,0,5.32,0.31,1.56,0,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.055,0,0.111,0.111,0.055,1.672,6,92,1 0.23,0.29,0.64,0,0.17,0.17,0.11,0.05,0.05,0.47,0.11,1.17,0.47,0.05,0.17,0.05,0.11,0.29,3.93,0,2.05,0,0.47,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.133,0,0.492,0.338,0.092,6.033,87,1460,1 0.51,0.43,0.29,0,0.14,0.03,0,0.18,0.54,0.62,0.29,0.65,0.65,1.2,0.03,0.21,0.43,0.03,2.99,0,1.35,0,0.51,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0.012,0.078,0,0.478,0.509,0.127,6.518,611,2340,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,16,33,1 0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0.204,0,0.408,0.408,0,4.1,25,82,1 0,0,0.48,0,0.48,0,0,0.48,0,0,0,0.96,0,0,1.93,0,0.48,0.48,2.41,0,2.41,0,3.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0.48,0,0,0,0,0,0,0,0,0,0.084,0,0.084,0.761,0,5.322,46,165,1 0.08,0.08,0.76,0,0.85,1.02,0.25,0.17,0.59,0.08,0.17,0.59,0.17,0,2.21,0.25,0.08,0.93,1.61,0.17,0.42,0,0.85,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.17,0.08,0.08,0.08,0,0,0,0.063,0,0.394,0.114,0.012,7.54,669,1410,1 0,0,0.19,0,0.19,0,0,0.19,0.19,0.19,0,0.19,0.19,0,0,0.76,0,0,0.95,0,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0.19,0,0,0,0.38,0,0.19,0,0,0,0,0.058,0,0.264,0,0,4.053,93,381,1 0,0.35,0.35,0,1.07,0,0,0.35,0,1.07,0,0.71,0,0,0,0,0.71,0.71,2.85,0,2.5,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0.35,0,0,0,0,0.233,0,0.233,0.233,0,3.414,25,140,1 0,0,0,0,0,0,0.31,0,0,0,0,1.26,0,0,0,0,0,0.31,1.9,0,0.31,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0.043,0.086,0,0.13,0.173,0.26,3.244,60,279,1 0.4,0.4,0.26,0,0.13,0.2,0.06,0.33,0,1.14,0.33,1.07,1,0,0.26,0.4,0.06,0,4.1,0,0.94,0,0.53,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.088,0,1.06,0.151,0.05,4.623,123,1045,1 0,0.39,1.18,0,0.39,0,0,0,0,0.78,0.78,0.78,0,0,0.39,3.54,0,0,1.18,0,1.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0.064,0,0.843,0.129,0.064,5.87,42,364,1 0,0,0,0,0,0,0,4.62,0,0,0,0,0.92,0,0,0,0.92,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.919,0.367,0,2.84,16,71,1 0.32,0.28,0.57,0,0.12,0.2,0.16,0.2,0,0.32,0.08,0.98,0.41,0.04,0.04,0,0,0.41,3.74,0,1.64,0,0.45,0.53,0.04,0.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0.171,0,0.507,0.493,0.028,5.608,133,1991,1 0,0.43,0.87,0,0,0,0,0,0.43,0.43,0.43,0,0,0,0,0,0,0,6.14,0,0.43,0,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.075,0.075,0,0,0.151,0,5.086,33,117,1 0,0,0.53,0,0.53,0,0,0.53,0,0,0,1.06,0,0,2.12,0,0.53,0.53,2.65,0,2.65,0,1.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0.53,0,0,0,0,0,0,0,0,0,0.186,0,0.093,0.466,0,5.038,60,131,1 0.35,0.08,0.35,0,0.35,0,0,0.52,0.61,1.76,0.17,0.26,0.79,0,0.26,0,0.7,0.35,2.64,0,2.03,0,0.61,0.7,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.08,0.17,0,0,0,0,0.17,0,0,0,0,0.081,0,0.556,0.069,0.011,19.234,1170,3116,1 0.51,0.17,0.51,0,1.7,0.34,0,0,0.85,0.17,0,0.68,0.17,0.34,0,0.17,0.17,0,2.9,0,2.05,0,0.68,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.387,1.961,0.025,11,183,660,1 0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0,1.6,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.062,0,0.311,0,0,1.954,11,43,1 0.26,0.72,0.85,0,0,0.19,0.06,0.33,0.72,0.46,0.72,0.79,0.19,1.05,0.06,0.59,0.19,0.33,3.5,0.06,1.52,0,0.06,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0.06,0,0,0,0,0.131,0,0.101,0.101,0.202,4.398,79,1280,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0.417,0,0.208,0.626,0,4.066,17,61,1 0,0,0.52,0,0,1.05,0.52,0,0,0,0,0,0,0,0,1.05,0,0,2.63,0,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.194,0,0.291,0,0,3.333,43,120,1 0.31,0,0,0,0,0,0,0,0,0,0.31,0.31,0.31,0,0,0,0.31,0,2.79,0,1.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0.114,0,0,0.057,0,0,2.972,18,110,1 0,0,0,0,0,1.29,0,0.64,0,0,0,0,0,0,0,0,0,0,3.87,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.116,0.111,0,1.8,12,63,1 0,0,0,0,0,1.28,0,0.64,0,0,0,0,0,0,0,0,0,0,3.84,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.103,0.11,0,1.777,12,64,1 0,0,0.15,0,0.62,0,0.31,0,1.09,0,0,0,0.46,0,0,0.15,0.15,1.4,2.19,0,1.09,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.252,0,0.378,4.017,0,3.278,23,259,1 0,0.11,0.35,0,1.18,0.47,0.23,0.35,0,0.11,0.11,0.95,0,0.11,0,2.13,0.95,0.23,1.9,0.35,0.35,0,0.59,0.11,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0.11,0,0,0,0,0,0,0,0,0,0.057,0,0.42,0.191,0.21,8.026,283,1509,1 0.09,0,0.27,0,0.36,0.09,0,0.18,0.09,0,0,0.73,0,0.36,0,0,0,0,2.01,0,3.38,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.015,0.046,0.031,0.249,0.031,0.031,3.689,69,535,1 0,0,0.47,0,0.47,0,0,0.47,0,0,0,0.94,0,0,1.88,0,0.47,0.47,2.83,0,2.35,0,1.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0.47,0,0,0,0,0,0,0,0,0,0.164,0,0.082,0.41,0,5.074,60,137,1 0.14,0.14,0.29,0,0,0,0,0,1.03,0.29,0.14,0.59,0,0,0,0,0,0.14,2.36,0.14,0.88,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0.205,0,0.153,0.128,0.102,2.686,73,368,1 0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0,3.871,26,151,1 0.14,0.14,0.29,0,0,0,0,0,1.03,0.29,0.14,0.59,0,0,0,0,0,0.14,2.36,0.14,0.88,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0.205,0,0.153,0.128,0.102,2.686,73,368,1 0.5,0.4,0.33,0,0.13,0.03,0.13,0.1,0.54,0.77,0.3,0.7,0.54,1.14,0.03,0.27,0.43,0.03,3.2,0,1.45,0,0.37,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0.071,0,0.456,0.5,0.11,6.049,129,2220,1 0,0,0.2,0,0.4,0.2,0,0,0,0,0,0,0,0,0,0,0.61,0.4,2.45,0.2,0.61,4.49,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0.382,0.223,0.478,7.538,55,490,1 0.57,0,0.57,0,0,0,0,0.57,0,0,0,1.14,0,0,0,0,0,0,5.14,0,1.14,0,2.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.211,0.74,0,2.9,32,116,1 0.59,0,0.59,0,0,0,0,0.59,0,0,0,1.18,0,0,0,0,0,0,5.32,0,1.18,0,2.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.109,0.763,0,3,32,114,1 0,0,0.2,0,0.81,1.01,0,0,0,0,0.2,1.21,0,0,0,0,0,0.2,1.21,0,0,0,0.6,0.4,0,0,0,0,0,0,0,0,0,0,0,1.62,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0.152,0,0.121,0.121,0,2.61,10,261,1 0.19,0.19,0,0,1.55,0.19,0.77,0,0.19,0.19,0,0.77,0.58,0,0,0.19,0.58,2.33,0.77,0,0.38,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0.03,0.061,0.03,0.185,0.216,0,1.948,11,113,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0.421,0,0.21,0.632,0,3.75,15,60,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0.407,0,0.203,0.61,0,4.133,17,62,1 0,0,0.53,0,0.21,0.1,0.1,0.53,0.1,0.21,0,0.64,0,0,0,0,0.1,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.097,0,0.016,0.065,0,2.104,29,381,1 0.9,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0.9,0.9,1.81,0,2.72,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.537,0,0,2.782,19,64,1 0,0,0,0,0,0,1.02,0,0,0,0,2.04,0,0,0,2.04,0,2.04,3.06,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0.17,0,1.47,4,25,1 0,0.56,0.28,0,0,0,0.56,0,0,0.56,0.28,0.56,0.28,0,0,1.41,0.28,0,1.97,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0.114,0,0.153,0,0.153,9.25,394,555,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.92,0.46,0.92,1.85,0.46,1.85,0.46,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.142,0,0.142,0.5,0.285,1.636,10,126,1 0.28,0,0.28,0,1.43,0.28,0,0.14,0,0,0,1.14,0,0,0,0.14,0.42,0,3.86,0,1.28,0,0.14,0.42,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0.047,0,0.094,0.118,0.023,1.42,27,250,1 0,0,0,0,0.87,0,0,1.16,0,0,0.29,1.74,0,0,0,0,0.87,0,4.95,0,2.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.151,0,0,0,0,0,1.095,4,46,1 0,0.55,0.55,0,2.23,0.55,0,0.55,0,0,0.55,0,0,0,0,0,0,1.11,1.67,0,2.23,0,0,0.55,0,0,0,0,0,0,0.55,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0.265,0,0.088,0.353,0,2.571,11,108,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.5,0,0,0,0,12.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,1 0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.121,0,0,3.871,26,151,1 0,1.47,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,1.47,4.41,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.333,12,21,1 0.26,1.07,0,0,1.61,0,1.07,0.26,0.26,0,0,0.8,0,0,0,0,0,1.61,3.5,0,1.34,0,0,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0.092,0,0.324,0,0,7.369,52,339,1 0.33,0.67,0,0,0,0,0.67,0.33,0.33,0.33,0,0.67,0,0,0,0,0.67,1.01,2.02,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.048,0,1.204,6,59,1 0,0,0.4,0,0.4,0,0.4,0,0,0,0,0,0.4,0,0,0,0,0,2.04,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0.11,0,0,1.594,11,118,1 0.41,0,0.61,0,0.41,0.61,0,0.82,0.2,0.2,0.2,1.44,0,0,0,0.41,1.03,0.2,1.65,1.65,3.09,0,0.2,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.099,0,1.689,0.132,0,4.913,102,565,1 0,0.9,1.81,0,0,0,0.9,3.63,0,1.81,0,0.9,0,0,0,0,0.9,0,2.72,0,3.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.774,0,0,1,1,18,1 0,2.66,0,0,2,0,0,0.66,0,0,0,2,1.33,0,0.66,0,0,6.66,3.33,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.111,0,0,2.133,7,32,1 0.5,0.43,0.28,0,0.14,0.03,0,0.18,0.54,0.61,0.28,0.65,0.65,1.19,0.03,0.21,0.43,0.03,2.96,0,1.34,0,0.5,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0.012,0.078,0,0.439,0.505,0.132,6.683,798,2426,1 0.56,0,0.84,0,0.28,0.84,0,0.84,0.28,0.28,0.28,1.41,0,0,0,0,1.41,0,0.84,1.98,2.83,0,0.28,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.128,0,1.289,0.042,0,3.979,47,386,1 0.33,0.16,0.33,0,0,0.16,0,0.16,0.16,0.08,0.16,0.57,0.24,0,0,0.16,0.24,0.24,3.47,0,2.06,0,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0,0,0.213,0.113,0,3.15,76,441,1 0,0.34,1.02,0,0.68,0.34,0.34,0,0,0,0,0.34,0,0,0,2.04,0,0.34,4.76,0,2.38,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.161,0,0.215,0,3.879,6.978,56,328,1 0.64,0,0.25,0,0,0.38,0,0,0,0.25,0.64,0.25,1.03,0,0,0.77,0.9,0.12,1.93,0,0.51,0.12,0.12,1.03,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0,0.12,0,0,0,0,0.12,0,0,0,0,0.161,0,1.082,0.299,0.092,5.274,146,981,1 0,0,0.78,0,1.17,0,0,0,0,0,0,0.39,0,0,0,0.78,0,0,1.56,0,1.96,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.092,0,4.111,20,222,1 0,0,0.49,0,1.48,0,0,0,0.49,0,0,0,0,0.16,0,0.66,0.33,0,0.82,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0.197,0,0.616,0,0,5.778,128,549,1 0,0,0.68,0,0,0,0,1.36,0,0,0.68,0.68,0,0,0,0,0,0,3.4,0,1.36,0,0.68,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.232,0.232,0,2.232,19,96,1 0,0,0.32,0,0.64,0.64,0.64,0.32,0.32,0,0,0.32,0.32,0,0,0.32,0.32,0.32,2.25,0,3.21,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.156,0,0,0.156,0,1.752,19,149,1 0,0.45,0,0,0.91,0,1.36,0,0,0,0,0.45,0,0,0,1.82,0.45,0,2.73,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.368,0,0,1.68,17,158,1 0,1.25,0.62,0,0,0,1.25,0,0,0,0.62,0.62,0,0,0.62,2.5,0,1.25,5,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.045,0,0.225,0,0,2.35,29,134,1 0,1.25,0.62,0,0,0,1.25,0,0,0,0.62,0.62,0,0,0.62,2.5,0,1.25,5,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.044,0,0.223,0,0,2.35,29,134,1 2.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.666,13,44,1 0,0,0,0,0,0,2.1,0,0,0,0,1.05,0,0,0,0,0,0,4.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.695,15,62,1 0,0,0.99,0,0.24,0,0,0,0.24,0.49,0,0.49,0,0,0.24,0.24,0,0,0.24,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.035,0,0,0,0,5.555,209,400,1 0.52,0.34,0.4,0,0.14,0.17,0.05,0.14,0.46,0.52,0.31,0.89,0.4,1.16,0.05,0.11,0.23,0.11,2.9,0,1.1,0,0.63,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0.073,0,0.363,0.535,0.132,6.171,159,2771,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.53,6.32,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.649,0,0.432,5.875,46,94,1 0,0,0,0,0,0,0,3.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.04,0,0,0,0,0.189,0,0.189,0.189,0,3.857,25,81,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.66,5.33,1.33,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.699,0,0.466,9.2,46,92,1 0.5,0,0.75,0,0.25,0.25,0.25,0.5,0,0,0.5,2.26,0,0,0,0.5,1,0.25,4.03,0,2.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.034,0,0.375,0.034,0,4.2,60,231,1 1.03,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,1.03,0,0,3.62,0,1.03,0,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.176,0.176,0,2.766,26,83,1 1.18,0.39,0.59,0,0,0.98,0.19,0.19,1.38,0.39,0,0.98,0,0.19,0,0.98,0,0,2.56,0.39,1.38,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.231,0,0.745,0.308,0.025,6.652,76,632,1 1.18,0.39,0.59,0,0,0.98,0.19,0.19,1.38,0.39,0,0.98,0,0.19,0,0.98,0,0,2.56,0.39,1.38,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.232,0,0.749,0.31,0.025,6.652,76,632,1 1.18,0.39,0.59,0,0,0.98,0.19,0.19,1.38,0.39,0,0.98,0,0.19,0,0.98,0,0,2.56,0.39,1.38,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0.741,0.306,0.025,6.652,76,632,1 0,0,0,0,0,0,2.1,0,0,0,0,1.05,0,0,0,0,0,0,4.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.695,15,62,1 0,0,0,0,0,0,0,0,0,0,0,0,0,2.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0.169,0,0,0.338,0,4.047,29,85,1 0.13,0.13,0.13,0,0.55,0.27,0.27,0.13,1.1,0.27,0,0.97,0.27,0,0.13,0,0,0,3.88,0.13,2.77,0,0.13,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0,0,0,0,0.017,0,1.316,0.177,0,4.947,232,757,1 0,0,0.46,0,0,0,0,0.15,0,0,0,0.15,0,0,0,0,0,0.46,0.93,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0,0.93,0,0,0,0,0,0,0,0,0.071,0.071,0,0.095,0.023,0,62.75,1505,2761,1 0.09,0,0.27,0,0.36,0.09,0,0.18,0.09,0,0,0.72,0,0.36,0,0,0,0,2,0,3.27,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.015,0.045,0.03,0.242,0.03,0.03,3.816,69,542,1 0.13,0.13,0.13,0,0.55,0.27,0.27,0.13,1.11,0.27,0,0.97,0.27,0,0.13,0,0,0,3.91,0.13,2.65,0,0.13,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0,0,0,0,0.018,0,1.294,0.182,0,4.745,232,726,1 1.18,0.39,0.59,0,0,0.98,0.19,0.19,1.38,0.39,0,0.98,0,0.19,0,0.98,0,0,2.56,0.39,1.38,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.232,0,0.749,0.31,0.025,6.652,76,632,1 0,0,0,0,0.28,0.86,0,0,0,0,0,0.57,0.28,0,0,0,0.28,0,0.28,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.084,0.126,0,0,0,0,27.479,772,1319,1 0,0,0.36,0,0.36,0,0,0,0.36,0.36,0,0.36,0,1.09,0,1.81,0,0,3.63,0,1.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0.125,0,0,1.287,5,94,1 0,0.23,0.47,0,1.18,0,0.23,0,0.7,0.7,0,0.47,0.23,0,0,0.23,0.7,0,2.83,0,1.89,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.072,0.108,0,2.438,20,178,1 2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.277,0,0,3.2,13,48,1 0,0,0,0,0,0,0,0,0,1.51,0,1.51,0,0,0,0,0,0,7.57,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.769,15,36,1 0,0.54,0,0,0,0,1.08,0,0,0.54,0.54,0.54,0,0,0,2.17,0,0.54,3.26,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0.334,0,0,1.325,5,53,1 0.45,0.68,0.68,0,1.92,0,0.56,0.45,0,0.45,0.22,1.81,0,0,0.79,0.22,0.11,1.81,2.38,0,1.36,0,0.11,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0,0.11,0,0,0.019,0.057,0,0.574,0.134,0.019,3.155,94,385,1 0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0.68,0.68,0,4.76,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.255,0,0,2.818,21,124,1 0,0,0.55,0,0.22,0.22,0.11,0,0.11,0.22,0,0.33,0.33,0,0,0,0.22,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.155,0,0.034,0.12,0,1.961,14,302,1 0,0,0,0,0,0,0.76,0,0,0,0,0,0.76,0,0,0,0,0,0.76,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0,0,0,0.274,0,0,11.035,110,309,1 0.68,0.11,0.11,0,0.45,0.11,0,0.57,0.79,2.73,0.34,0.11,0.22,0,0.45,0.11,0.68,0.45,3.07,0,1.71,0,1.82,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0.11,0,0,0,0,0.11,0,0,0,0.067,0.118,0,0.388,0.236,0.016,9.827,164,1592,1 0,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,3.77,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.307,16,30,1 0,0,0,0,1.28,0,1.28,0,0,0,0,0,0,0,0,0.64,0,1.28,1.28,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.085,0.17,0,2.466,18,111,1 0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,2.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.392,0,3.333,0,0,2.551,12,74,1 0.9,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0.9,0.9,1.81,0,2.72,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.546,0,0,2.818,19,62,1 0.49,0.32,0.46,0,0.05,0.16,0.05,0.24,0.46,0.79,0.27,1.01,0.6,1.23,0,0.21,0.38,0,3.3,0,1.5,0,1.09,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.064,0,0.322,0.626,0.165,6.896,193,3269,1 0.39,0,0,0,0,0.39,0.79,0,0,0,0,0.79,0,0,0,0,0.39,0,2.37,0,2.76,0,1.18,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.061,0,0.612,0.183,0,2.678,13,75,1 0,0.58,0.58,0,0,0,0,0.58,0.58,7.55,0.58,1.16,0,0,0,0,0.58,0,4.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.192,0,0.192,0,0.096,1.526,10,58,1 0.17,0.17,0.69,0,0.34,0.17,0,0.86,0.17,0.69,0.34,1.38,0,0,0,0,1.73,0.34,2.07,1.55,3.8,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0,0,0,0,0,0.194,0,1.718,0.055,0,5.175,63,621,1 0.51,0,0.77,0,0.25,0.25,0,0,0,0.51,0,1.55,0,0,0,0.77,1.55,0,4.9,0,2.58,0,0.77,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0.179,0,0.359,0.403,0.134,5.774,56,358,1 0,0,1.24,0,1.24,0.62,0,0,0,0,0,0,0,0,0,1.24,0.62,0,0.62,0,1.86,0.62,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0.107,0,0.321,0.107,0.107,3.846,30,150,1 0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.45,14.5,42,87,1 0,0.84,0.84,0,0,0,0.84,0,0,1.68,0.84,0,0,0,0,0.84,0,0,3.36,0,0.84,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.519,0,0,5,43,125,1 0,0,0,0,0,0,0,0.71,0,0,0,0.71,0,0,0,1.43,0,0,4.31,0,1.43,0,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.15,0,0,0,0,0.265,0,0.132,0,0,2.322,16,72,1 0.09,0,0.27,0,0.36,0.09,0,0.18,0.09,0,0,0.82,0,0.36,0,0,0,0,2.01,0,3.38,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.015,0.046,0.03,0.246,0.03,0.03,3.771,69,528,1 0.11,0.22,0.11,0,0.45,0.45,0,0.11,1.02,1.59,0.11,0.34,0.22,0.11,2.16,0,0.45,0.11,3.53,0,1.25,0,0.45,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0.45,0,0,0,0.018,0.17,0,0.265,0.132,0,4.215,144,666,1 0.44,0,0.88,0,0.44,1.32,0.44,0,0,0,0,0,0,0,0,0,0,0.44,1.76,0,2.2,0,2.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.216,0,0,0.433,0.361,0,2.375,16,133,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.107,0,0.474,0.152,0.015,8.55,669,1351,1 0.11,0.22,0.11,0,0.45,0.45,0,0.11,1.02,1.59,0.11,0.34,0.22,0.11,2.16,0,0.45,0.11,3.53,0,1.25,0,0.45,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0.45,0,0,0,0.018,0.17,0,0.265,0.132,0,4.215,144,666,1 0.42,0.46,0.38,0,0.19,0.11,0,0.07,0.58,0.62,0.34,0.77,0.5,1.32,0.03,0.23,0.54,0,3.06,0,1.51,0,0.38,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0,0.076,0,0.438,0.585,0.127,6.134,153,2184,1 0,0,0.9,0,0.45,0,0,0,0,0,0,0.9,0.45,0,0,0.45,0.9,0,4.52,0,0.9,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,2.115,0.07,0,2.651,14,114,1 0.33,0,0.67,0,0.22,0,0,0,0.44,0.11,0,0.33,0,0,0,0.56,0,0,1.79,0,1.12,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0.11,0,0,0,0,0,0,0.157,0,0.392,0.176,0.078,2.606,75,391,1 0,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,3.77,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.307,16,30,1 0,0,0,0,0,1.27,0,0.63,0,0,0,0,0,0,0,0,0,0,3.82,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.096,0.109,0,1.916,12,69,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.37,0,0,0,2.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.218,0,0,1.827,11,53,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,8.84,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,1.126,7.054,37,261,1 0,0.47,0.47,0,1.41,0,0.47,0,0,0.47,0.47,0.94,0,0,0,0.94,0,0,1.88,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.218,0,0,1.102,6,54,1 0,0,0,0,0,0,0,0,0,1.47,0,1.47,0,0,0,0,0,0,7.35,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.769,15,36,1 0.19,0.19,0.29,0,1.07,0.19,0.19,0.97,0.87,0.58,0.09,1.07,0.19,0.87,0.09,0,0,1.17,3.71,0.68,1.75,0,0.09,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0.09,0,0,0,0,0,0,0,0,0,0,0.194,0.404,0.224,0.029,4.285,49,870,1 0,0,0,0,0.82,0,0,1.65,0,0.82,0,0,0,0,0,0,0.82,0,1.65,0,2.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.113,0,0.113,0,0,1.25,4,50,1 0.79,0.19,0.09,0,0,0,0,0.09,0.29,0.09,0.29,0.59,0.69,0,0,0.09,0,0.59,4.09,0,0.89,0,0.39,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.267,0,0.19,0.247,0,2.324,19,365,1 0,0,0,0,0,0.68,1.37,0.68,0,0,0,0.68,0,0,0,0,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0.34,0,0,0,0,0,0,0,0.103,0,0.206,0.309,0,4.029,69,270,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0.85,0,0,0,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.126,0,0,0,0,3.925,51,106,1 0,0,1.47,0,0,1.1,0.36,0,0,0,0.36,0.36,0,0,0,0.36,0,0,2.21,1.1,2.95,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.118,0,0.414,0.888,0.177,3,33,177,1 0,0,0.31,0,0.62,0.62,0.62,0.31,0,1.88,0.62,1.25,0,0,0.31,1.56,0.31,0,3.76,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.77,0,0,2.481,11,134,1 0.1,0.1,0.71,0,0.61,0.3,0.4,0.1,1.42,0.81,0.1,0.5,0,0,0,0.1,0,1.01,2.34,0.5,2.03,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.256,0.928,0.384,0.032,3.179,56,1043,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.55,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,13,1 0.52,0.42,0.35,0,0.14,0.03,0.03,0.1,0.56,0.8,0.28,0.7,0.56,1.19,0.03,0.24,0.45,0,3.18,0,1.47,0,0.38,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0.075,0,0.452,0.528,0.116,6.152,260,2184,1 0,0,0,0,0,0,0,0,0,1.49,0,1.49,0,0,0,0,0,0,7.46,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.692,15,35,1 0,0.35,0.71,0,0.35,0,0.17,0,0,0.53,0.17,0,0.17,0,0.35,0.17,0,1.07,0.17,0.17,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0,0.167,0.027,0.055,7.527,149,956,1 0,0.64,0.64,0,0.32,0,0,0,0,0,0,0.64,0,0,0,0.32,0,1.29,1.62,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.053,0,1.065,0,0,3.932,61,291,1 0.56,0,0.84,0,0.28,0.84,0,0.84,0.28,0.28,0.28,1.41,0,0,0,0,1.41,0,0.84,1.98,2.83,0,0.28,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.129,0,1.294,0.043,0,3.979,47,386,1 0,0.64,1.29,0,0.32,0,0,0,0,0,0,0.64,0,0,0,0.32,0,1.29,2.59,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.053,0,0.531,0,0,4.337,121,334,1 0.34,0.05,0.58,0,0.63,0.17,0,0,0.75,0.23,0.34,1.27,0.34,0,0,0.58,0.05,0.17,3.01,2.61,1.5,0,0.17,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.201,0,0.127,0.182,0.027,4.225,131,1107,1 0,0,0.63,0,0,1.27,1.27,0.63,0,0,0,0.63,0,0,0,0,0.63,0,4.45,3.18,3.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.983,0.089,0,3.488,59,157,1 0.44,0,0.88,0,0.44,1.32,0.44,0,0,0,0,0,0,0,0,0,0,0.44,1.76,0,2.2,0,2.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.215,0,0,0.43,0.358,0,2.403,16,137,1 1.26,0.42,1.26,0,0,0,0,0,0,0,0,0.42,0,0,0,0.42,0,0.84,3.79,0,1.26,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0.067,0,0.472,0.472,0,3,19,108,1 0,0,0,0,0,0,2.94,1.47,1.47,1.47,1.47,0,0,0,0,0,2.94,0,0,1.47,4.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.141,0,0.425,0.141,0,140,279,280,1 0,0.57,0,0,0.57,0,0.57,0,0,0.57,0,0.57,0,0,0,0,0,0.57,4.57,0,1.14,0,0,0,0.57,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0.072,0,0,0.072,0.289,0.144,7.512,114,293,1 0.89,0,0.89,0,0,0,1.78,0,0,0,0.89,1.78,0,0,0,0,0,0,6.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,1.344,0,0,5.25,16,84,1 0,0,0,0,0,0,4.08,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.257,0,0,4.181,26,46,1 0,0,0,0,0,0,2.94,1.47,1.47,1.47,1.47,0,0,0,0,0,1.47,0,0,1.47,4.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0.142,0,0.427,0.142,0,92.333,274,277,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.52,0,0,2.17,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.149,0,0,9.1,33,91,1 0.13,0.26,0.52,0,0.26,0,0.13,0,0,0.39,0.13,0.13,0.13,0,0.26,0.13,0,0.78,0.39,0.13,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.366,0,0.04,7.138,149,1235,1 0,0,0,0,0,0,1.94,0,0,0,0,0,0,0,0,2.91,3.88,0,1.94,0,1.94,0,0,1.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0.97,0,0,0,0,0.13,0,0.52,0.13,0,6.266,26,94,1 0,0.43,0,0,0.43,0,0.86,0,1.3,0.86,0,1.3,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0.063,0.126,0,0,0.063,0,4.297,30,159,1 0,0.44,0.44,0,0,0,0,0,0,0,0,0.88,1.32,0,0,0,0,0.88,3.96,0,3.08,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.074,0,0.669,0.297,0,3.666,82,165,1 0,0.65,0.98,0,0.32,0,0,0,0,0,0,0.65,0,0,0,0.32,0,1.3,2.61,0,1.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.053,0,0.477,0,0,4.273,121,312,1 0,0,0,0,0.89,0,0,0,0,0,0,0.89,0,0,0,0,0,0,1.78,0,2.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.121,0,1.336,0,0,6.611,51,238,1 0.33,0,0.33,0,0,0,0.66,0,0,0,0,1.32,0,0,0,0.66,0.99,0,2.64,0,0.99,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0,0,0,0,0,0,0.116,0,0.406,0.464,0.348,6.932,43,513,1 0,0,0.94,0,0,0,0.94,0,0,1.88,0,1.88,0,0,0,0,0,0,4.71,0,0.94,0,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.163,1.305,0,2.571,20,36,1 0.73,0,0.36,0,0.36,0.36,1.1,0,0,0,0,0.36,0,0,0,0.36,1.84,0.73,2.58,0,1.1,0,0.36,1.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.186,0,0.435,0.062,0,4.411,190,300,1 0,0.66,0.66,0,1.33,0.33,0.33,0,0.33,0,0.33,0.33,0,0,0,0.33,0.66,1,1,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0.266,0,0.372,0.159,0,1.894,14,161,1 0,0.3,0.75,0,0.3,0,0.15,0,0,0.45,0.15,0,0.15,0,0.15,0.15,0,0.75,0.15,0.15,0.6,0,0,0,0,0,0,0.15,0,0,0,0,0.15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0.352,0.02,0.041,5.938,149,1057,1 0.57,0,1.72,0,0,0,0,0.57,0,0,0,0.57,1.72,0,0,0,0.57,0,4.59,0,0,0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.099,0,0.396,0.099,0,2.333,11,70,1 0,0,0,0,0,0,1.04,0,0,0,0,1.04,0,0,0,0,1.04,0,3.66,0,2.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.062,0.248,0,0.621,0.062,0.062,3.902,59,160,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,1 0,0.28,0.84,0,0.28,0,0.14,0,0,0.42,0.14,0,0.14,0,0.14,0.14,0,0.7,0.42,0.14,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0,0,0,0.583,0,0.067,5.415,132,991,1 0.51,0.25,0.49,0,0.04,0.23,0.04,0.32,0.38,0.81,0.21,0.9,0.79,1.24,0.02,0.21,0.36,0.04,3.49,0,1.54,0,1.09,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0.1,0,0,0,0,0.058,0,0.308,0.672,0.128,5.459,193,3243,1 0.7,0,0.35,0,0.7,0.35,0.7,0,0.7,0,0,0,0.7,0,0,0,1.05,0,3.16,0,1.4,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.173,0,0.23,0,0,4.596,60,262,1 0.3,0,1.23,0,1.54,0.92,0.61,0.92,0.3,0.3,0,0.3,0,0,0,0,0.3,0,2.47,0.92,0.92,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0.045,0,0.728,0.182,0,4.339,60,243,1 0,0,0.84,0,0.56,0,0,0.56,0,0,0,0,0,0,0,0.28,0,0,1.13,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0,0,0,0,0.278,0,0.046,0,0,1.661,6,118,1 0,0.7,1.05,0,0.35,0,0,0,0,0,0,0.7,0,0,0,0.35,0,1.4,2.46,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0.056,0,0.897,0,0,4.43,121,350,1 0.56,0,0.32,0,1.13,0.08,0,0,0.16,0,0.08,0.72,0.56,0,0,0.24,1.13,0,4.6,0,2.01,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.121,0,0.337,0.054,0,3.502,79,606,1 0,0.26,0.26,0,0.39,0,0.13,0,0,0.26,0,0.26,0.26,0,0.13,0.26,0,0.13,3.14,0.26,1.44,7.33,0.13,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0,0.022,0.022,0.889,12.454,107,1096,1 0,0,1.29,0,0,1.29,0,0,0,0,0,0,0,0,0,2.59,0,0,1.29,0,1.29,1.29,2.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.439,0,1.098,0,0.439,3.571,36,125,1 0.09,0.38,0.57,0,0.48,0.38,0,0,0,0.38,0,1.53,0.19,0,0.09,0,0.09,0,3.55,0,1.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.038,0.038,0,0.246,0.894,0.012,4,70,640,1 0.34,0,1.7,0,1.02,0,0,0,0.68,1.02,0,0,0,0,0,0,0,0,0.34,1.02,0.68,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.902,0.106,0,664,1327,1328,1 0,0.8,0,0,0.8,0,0.8,0,0,0.8,0,0,0,0,0,0.8,0.8,0.8,1.61,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.134,0,0.269,0,0,3.115,19,81,1 0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.111,0,0,1.409,10,31,1 0,0,0.28,0,0.16,0.18,0,0,0,0,0.02,0.09,0.11,0,0,0,0,0,0.14,0,0.02,0,0,0.04,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0.02,0,0,0,0,0,0,0,0,0,0,0.116,0.021,0.03,0,0,16.644,154,9088,1 0.82,0,0,0,0.41,0,0.82,0,0,0,0,0.82,0,0,0,0,0.41,0,2.46,0,1.23,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0.171,0,0.513,0.114,0,2.953,44,189,1 0,0.42,0,0,0.42,0.42,0,0,0,0,0,0,0,0,0,0.42,0,0.42,4.2,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0.08,0,0.644,0.161,0,2.522,14,111,1 0.1,0.1,0.7,0,0.6,0.2,0.4,0.1,1.41,0.8,0.1,0.5,0,0,0,0.1,0,1.11,2.22,0.4,1.92,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.26,0.991,0.39,0.032,3.173,56,1044,1 0,0,0.28,0,0.16,0.18,0,0,0,0,0.02,0.09,0.11,0,0,0,0,0,0.14,0,0.02,0,0,0.04,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0.02,0,0,0,0,0,0,0,0,0,0,0.116,0.021,0.034,0,0,16.587,154,9090,1 0.87,0.17,0.52,0,0,0.32,0,0.04,0.29,0.42,0.39,1.37,0.87,1.69,0,0.32,0.54,0.22,3.47,0.29,1.32,0,0.34,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0.07,0,0.04,0,0.016,0.058,0,0.64,0.166,0.183,3.697,117,3498,1 0.43,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0.43,1.29,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0.122,0,0.061,0,0,1.456,13,67,1 0,0.81,0.61,0,0,1.02,0,0.2,0.4,0.61,0,2.25,0,0,0,0,0.61,0,2.86,0,1.02,0,0,0.2,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.095,0,0.158,0.063,0,2,31,156,1 2.32,0,0.77,0,1.55,0,0,0,0,0,0,0.77,0,0,0,0.77,0,0,2.32,0,0.77,0,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.77,0,0.134,0,0.671,0,0,129.5,515,518,1 0.08,0.08,0.48,0,0.16,0.24,0,0,0.24,0.08,0,0.56,0,0,0,0,0,0.08,0.88,0.08,0.48,4.57,0.4,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.233,0.202,0.326,8.763,102,1481,1 0.07,0,0.55,0,0.63,0.23,0.07,0.23,0,0.23,0.07,0.55,0.63,0,0,0.47,0.31,0.31,2.76,0,1.49,0,0.55,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.012,0,1.132,0.077,0.012,3.382,77,707,1 0.52,0.42,0.35,0,0.14,0.03,0.03,0.1,0.56,0.8,0.28,0.7,0.56,1.19,0.03,0.24,0.45,0,3.19,0,1.43,0,0.38,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0.077,0,0.453,0.543,0.119,6.305,286,2207,1 0.4,0.18,0.32,0,0.25,0.18,0.03,1.01,0.4,0.4,0.1,0.72,0.65,0.36,0.25,0.54,0.36,0.36,3.05,0.14,1.41,0,0.29,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0,0.07,0,0,0,0.012,0.042,0.073,0.337,0.141,0,3.305,181,1613,1 0,0,2.22,0,0,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0.439,0,3,11,24,1 0,0,0,0,1.91,0,0.31,0.31,0,0.31,0.63,1.59,0.63,0,0.63,0,0.63,2.23,3.19,0,1.59,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.63,0,0,0,0,0,0,0,0,0,0.092,0,0.323,0,0,2.15,18,86,1 0,0.75,0.25,0,0,0,0,0.5,0,0.5,0.25,0.75,0,0,0,1.5,0,1.5,4.26,0,4.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.045,0.18,0,0,0,0,1.023,3,86,1 0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,2.23,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.109,0,0,2.263,24,86,1 0,0,1.29,0,0,1.29,0,0,0,0,0,0,0,0,0,2.59,0,0,1.29,0,1.29,1.29,2.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.439,0,1.098,0,0.439,3.571,36,125,1 0,0,0,0,0,0,0,0,0,0,0,0,2.58,0,0,0,0,0,2.58,0,1.72,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.142,0,0,3.851,0,0,13.266,70,199,1 0,0,0,0,0.59,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.18,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0.052,0,0.052,0.105,0,2.886,27,127,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.11,0,0.49,0.158,0.015,8.55,669,1351,1 0,0.33,0.33,0,1.65,0.33,0.66,0,0,0.16,0.16,0.99,0,0,0,0.82,0.33,0.16,2.81,0,0.99,0,0.49,0.33,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.024,0.074,0.248,0.049,0.049,6.161,350,727,1 0.4,0.26,0.93,0,0,0.8,0,0.8,0.8,1.2,0,0.8,0.4,0,1.46,0,0.26,2.26,2.4,0.53,1.06,0,0.8,0.93,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.593,0.217,0.039,11.463,525,1112,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.11,0,0.49,0.158,0.015,8.55,669,1351,1 0.15,0.21,0.58,0,0.15,0.15,0.05,0.1,0,0.42,0.1,0.95,0.42,0.05,0.05,0,0,0.36,3.16,0,1.58,0,0.52,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0.184,0,0.406,0.388,0.036,4.829,116,1589,1 0.1,0.1,0.71,0,0.51,0.2,0.2,0.1,1.43,0.82,0.1,0.51,0,0,0,0.1,0,1.02,2.15,0.41,1.84,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.264,0.974,0.396,0.033,3.163,56,1028,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.118,17.481,49,472,1 0,0,0.71,0,0.71,0,0,0,0,0,0,0,0,0,0,0.71,0,1.43,2.15,0,2.15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.256,0.128,0,0,1.779,11,105,1 0,0.81,1.47,0,1.3,0,0.98,0.98,0.32,1.79,0,0.81,0,0,0.32,0.49,0.65,0,0.98,0.16,1.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0.247,0,0.179,0.674,0,2.922,113,640,1 0.1,0.1,0.7,0,0.6,0.2,0.4,0.1,1.41,0.8,0.1,0.6,0,0,0,0.1,0,1.01,2.22,0.4,2.02,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.265,0.977,0.397,0.033,3.16,56,1046,1 0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.166,19,49,1 0,0,0,0,0.84,1.27,0.42,0,0,0.42,0.42,0.42,0,0,0,0,0,0.42,2.11,0,1.27,0,3.38,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0.42,0,0,0,0.097,0.097,1.171,0.244,0.39,0,26.405,363,977,1 0.11,0.11,0.47,0,0,0.11,0.23,0,0.35,0.35,0.11,0.94,0.11,0,0,0.11,0,0,3.76,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0.233,0,0.287,0.107,0.035,3.439,64,509,1 0,0.37,0,0,0,0.74,1.12,0,0,0,0.74,1.49,0.74,0,0,0.37,0,1.49,4.49,0,1.87,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.223,0.223,0,2.021,10,93,1 0.24,0,0.99,0,0.99,0,0.49,0.99,0,0.24,0,0.49,0,0,0,0.49,0.99,0.74,1.98,0.74,0.99,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.179,0,0.847,0.077,0,3.219,114,499,1 0.4,0.14,0.32,0,0.25,0.18,0.03,1.01,0.4,0.4,0.1,0.72,0.65,0.36,0.25,0.54,0.36,0.32,3.05,0.14,1.45,0,0.29,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0,0.07,0,0,0,0.012,0.042,0.073,0.343,0.141,0,3.328,181,1621,1 0.95,0,0.47,0.95,0,0.95,0,0,0.47,0,0.47,0,0,0,1.42,0.47,0.47,2.38,0,0,0.95,0,0,0.47,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0.791,0,0.169,0.452,0.113,9.64,259,723,1 0,0,0,0,0,0.63,0,1.58,0.31,0.63,0,0.95,0,0,0,0,0,0,1.26,0,0.63,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.103,0,0.206,0.206,0,4.171,76,292,1 0,0,0,0,0.47,0,1.41,0,0,0,0.47,0.47,0,0,0,0.47,0,1.88,1.41,0.47,1.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.145,0.145,0,4.041,54,194,1 0,0.81,1.47,0,1.3,0,0.98,0.98,0.32,1.79,0,0.81,0,0,0.32,0.49,0.65,0,0.98,0.16,1.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0.246,0,0.179,0.673,0,2.922,113,640,1 0,0,0,0,0.47,0,1.41,0,0,0,0.47,0.47,0,0,0,0.47,0,1.88,1.41,0.47,1.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.144,0.288,0,3.745,54,191,1 0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,1.96,0,1.96,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.334,0,0,3.214,22,45,1 0,0,0.47,0,0.7,0,0.7,0.23,0,0,0,0.47,0,0,0,1.65,0.7,0.23,2.12,0,1.65,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0.037,0.037,1.362,0.037,0,5.236,111,576,1 0,0,0,0,0.38,0.38,0.38,0.38,0,0,0.38,0,0,0,0,0.38,0,0,3.5,0,1.94,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.122,0,0,2.08,12,104,1 0.33,0,1.65,0,0.99,0,0.33,0,0.66,1.32,0,0,0,0,0,0,0,0,0.33,0.99,0.66,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.859,0.101,0,337.25,1146,1349,1 0.32,0,1.64,0,0.98,0,0.32,0,0.65,1.31,0,0,0,0,0,0,0,0,0.32,0.98,0.65,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.858,0.101,0,337.25,1146,1349,1 0,0.2,0.61,0,1.03,0,0.41,0.2,0,0.2,0,0.41,0.2,0,2.06,0.2,0,2.47,2.06,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0.2,0,0,0,0,0,0,0.238,0.034,0,3.632,32,247,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.11,0,0.49,0.158,0.015,8.55,669,1351,1 0.16,0.24,1.24,0,0.41,0.58,0.49,0.33,0.66,0.66,0.24,1.24,0.16,0,0.66,0.82,0.16,1.57,2.32,0.16,1.16,0,0.91,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0.08,0,0,0,0,0.132,0,0.25,0.224,0.013,5.872,581,1339,1 0.16,0.24,1.24,0,0.41,0.58,0.49,0.33,0.66,0.66,0.24,1.24,0.16,0,0.66,0.82,0.16,1.57,2.32,0.16,1.16,0,0.91,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0.08,0,0,0,0,0.132,0,0.25,0.224,0.026,5.872,581,1339,1 0.93,0,0.93,0,0.93,0.93,0,0.93,0,0,0,0,0.93,0,0,0,0,0,3.73,0,2.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.343,0,0.343,0.171,0,2.235,15,38,1 0,0,1.63,0,0,0.65,0,0,0,0,0.32,0.32,0,0,0,0.32,0,0,1.96,0.98,2.94,0,1.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0.276,0.83,0.166,3.8,33,228,1 0,0,0,0,0.39,0.39,0.39,0.39,0,0,0.39,0,0,0,0,0.39,0,0,3.52,0,1.96,0,0,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.124,0,0,1.94,12,97,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.055,3,38,1 0,0.36,0,0,0,0.36,1.47,0,0,0.36,0.36,0.73,0,0,0,0.36,0,1.1,2.2,0,0.73,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.183,0,0.367,0.061,0.122,4,36,264,1 0,0,0.24,0,0.72,0,0,0,0.48,0,0,0.48,0,0,0,0,0,0.96,0.96,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,0,0,0,0.328,0,0,1.74,48,141,1 0.17,0,0.17,0.17,1.44,0.34,0.05,0.05,0.05,0.05,0.05,0.51,0.28,0.05,0,0,0.69,0.05,4.14,0.23,1.09,0.17,0.74,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.05,0,0.01,0.161,0.03,2.051,51,521,1 0.98,0,0.32,0,0.98,0,0,0,0,0,0,0,0.98,0,0,0.65,0,0,3.6,0,3.93,0,0.32,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.529,0.411,0,3.964,132,222,1 0.07,0.64,0.64,0,0.35,0.71,0.57,0.14,1.14,0.5,0.07,0.35,0.21,0,1,0.14,0.07,1.14,1.5,0,1.14,0,0.35,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0,0.07,0,0,0,0.08,0,0.309,0.103,0,4.923,117,1295,1 0,0,0.71,0,0.89,0.17,0.17,0,0,1.24,0.17,0,0,0,0.89,0,0.17,0.35,1.24,0.17,1.42,6.41,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.278,0.216,0.836,8.523,58,895,1 0,0,0.38,0,1.15,0.38,0,0.19,0.19,0,0,1.72,0,0,0,0,0.19,0,4.03,0,2.3,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0.031,0,0.349,0,0,5.886,105,312,1 0,0,0.72,0,2.91,0,0.72,0,0,0,0,0,0,0,0,1.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0.373,0,0.124,0.124,0,1.781,12,114,1 0,0,0.22,0,0.67,0,0,0,0.44,0,0,0.44,0,0,0,0,0,0.89,0.89,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0.305,0,0,1.895,48,163,1 0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,1.35,0,0,1.35,0,0,0,2.7,0,1.35,1.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.836,0,0,3.285,19,92,1 0,0.53,0.53,0,0.8,0,0.26,0.26,0,0.26,0,0.53,0.53,0.53,0,0,0,0,2.15,0.26,0.8,0,0,0,0.26,0.26,0,0,0,0,0,0,0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.04,0,2.779,21,164,1 0,0,0.89,0,1.79,0.44,0,0,0,0,0,0.44,0,0,0,1.34,0,0,2.24,0,4.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.075,0.075,0,0,0,1.968,11,124,1 0,0,0.34,0,0,0,0.34,0,0,0.34,0,0.34,0,0,0,0.68,0.34,0.34,0.68,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0.34,0,0,0.393,0,0.224,0.056,0,2.257,17,158,1 0,0,0.52,0,1.58,0,1.05,0,0,1.05,0.52,1.58,0,0,0,0.52,0,0,1.05,0,0.52,0,0,0,0.52,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,2.82,23,110,1 0,0,0,0,0.43,0.43,0.43,0,0,0.43,0,0.43,0,0,0,0,0,0,0.87,0,0,9.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.065,0,1.047,9.269,89,482,1 0,0,0.67,0,0.27,0.27,0.13,0,0.13,0.27,0,0.4,0.4,0,0,0,0.27,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.109,0,0.021,0,0,1.728,12,204,1 0.24,0,0.24,0,0,0.48,0.24,0,0,0.48,0.24,0.72,1.2,0,0,1.68,0.72,0,1.92,0,1.68,0,0.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.677,0.828,0.15,4.333,52,429,1 0,0.18,1.1,0,0.73,0.73,0.73,0.09,0.83,0.27,0.27,0.64,0.27,0,1.47,0.09,0,1.2,1.38,0.18,0.64,0,0.55,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18,0,0.09,0.09,0,0,0,0.094,0,0.432,0.135,0.013,8.445,696,1478,1 0,0,0,0,0,0,0,0,0,0,0,6.25,0,0,0,0,0,0,12.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,1 0,0,0.34,0,0,0,0.34,0,0,0.34,0,0.34,0,0,0,0.68,0.34,0.34,0.68,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0.34,0,0,0.393,0,0.225,0.056,0,2.257,17,158,1 0.68,0.17,0.51,0,0.34,0,0.51,0,0,0.51,0,0.51,0.51,0.17,0.17,0.34,0.17,1.02,4.96,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0.063,0.095,0,0.126,0,0,2.285,40,224,1 0,1.15,0.86,0,0.57,0.28,0.57,0,0,0.28,0,0.57,0,0,0,1.72,0,0.86,4.32,0,2.01,0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.142,0,0.19,0,3.423,6.584,56,349,1 0,0,0,0,0,0,7.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.291,0,0,2.444,8,44,1 0,0,0.75,0,0.75,0,0.5,0.25,0,1.01,0,0.25,1.51,0,0.75,0,0,1.51,2.02,0,1.51,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0.078,0,0.432,0.432,0,2.375,19,247,1 0,0,0.32,0,0.65,0.32,0.32,0.32,0,0,0.65,1.3,0,0,0,0.98,0,0.65,2.61,2.61,3.26,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.051,0,0.103,0,0.103,5.85,137,234,1 0,0.67,0.67,0,0.5,0,0.16,0.16,0,0,0,0.33,0.67,0.67,0.5,0,0,0,2.52,0.5,1.51,0,0,0.16,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.079,0.105,0.052,2,32,260,1 0,0.66,0.66,0,0.49,0,0.16,0.16,0,0,0,0.33,0.66,0.49,0.66,0,0,0,2.47,0.49,1.48,0,0,0.16,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0.076,0.101,0.05,2.03,32,264,1 0,0.69,0.69,0,0.51,0,0.17,0.17,0,0,0,0.34,0.69,0.69,0.69,0,0,0,2.59,0.51,1.55,0,0,0.17,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.079,0.106,0.053,2,32,260,1 0,0,1.47,0,0,1.1,0.36,0,0,0,0.36,0.36,0,0,0,0.36,0,0,2.21,1.1,2.95,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.124,0,0.435,0.932,0.186,2.932,33,173,1 0.46,0.33,0.2,0,0.13,0.53,0.06,0.2,0,1.13,0.33,0.66,0.93,0,0.2,0,0,0,3.6,0,1.13,0,0.13,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.012,0.038,0,0.41,0.192,0.115,4.754,268,813,1 0,0,0.56,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,1.69,0,0.56,2.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.493,0.197,0.394,0,0.394,3.537,22,237,1 0,0.67,0.67,0,0.5,0,0.16,0.16,0,0,0,0.33,0.67,0.67,0.5,0,0,0,2.37,0.5,1.52,0,0,0.16,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0.107,0.053,2.015,32,258,1 0,0,0,0,0.76,0.38,0.38,0.38,0,0,0.38,0,0,0,0,0.38,0,0,3.46,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0,0.117,0,0,2.061,12,101,1 0,0,0,0,0.93,0,0,0,0,0.93,0,0,0,0,0,0,0,0,2.8,0,2.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.066,73,166,1 0,0,0,0,3.69,0.56,0,0.56,0.56,0.56,0,0,0,0,0,0,3.4,0,0.85,1.13,0.56,0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.092,0,0.371,0.046,0,14.58,97,452,1 0,0,0,0,0.94,0,0,0,0,0.94,0,0,0,0,0,0,0,0,2.83,0,2.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.533,65,158,1 0.26,0.08,0.26,0,0.53,0.08,0.08,0.08,0.97,0.62,0.08,1.15,0.08,0.7,0.17,0.35,0.08,0,4.16,0.26,2.21,0,0.17,1.5,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.08,0,0,0,0,0,0,0,0,0,0,0,0.044,0,0.339,0.162,0.014,4.137,74,753,1 0,0.47,0.47,0,1.41,0,0.47,0,0,0.47,0.47,0.94,0,0,0,0.94,0,0,1.88,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.224,0,0,1.102,6,54,1 0,0.89,0,0,0.89,0,0,0,0,0,0,0,0.89,0,0,0.89,0,0,6.25,2.67,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0.89,0,0,0,0,0,0,1.129,0.483,0,1.826,10,42,1 0,0,0.18,0,0.55,0.37,0.18,0.18,0,0.18,0,0.18,0,0,0,0,0,0,0.74,0,0.37,0,0.18,0,0,0,0,0,0,0,0,0,0.55,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0.081,0,0,0.027,1.625,2.326,11,363,1 0.17,0,0.17,0,1.45,0.34,0.05,0.05,0.05,0.05,0.05,0.52,0.29,0.05,0,0,0.69,0.05,4.24,0.23,1.04,0,0.75,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.051,0,0.02,0.163,0,1.796,12,460,1 0,0,0,5.03,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0.77,0,0,7.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.431,0,0,0.215,0,0.539,7.552,43,506,1 0,0,0.44,0,1.32,0,0.44,0,0,1.32,0,0,0,0,0,0,0.44,0,4.42,0,3.09,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.301,0,0.301,0,0,2.787,19,131,1 0.49,0,0.74,0,0.24,0.24,0.24,0.49,0,0,0.49,2.24,0,0,0,0.49,0.99,0.24,3.99,0,1.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.035,0,0.315,0.035,0,4.071,60,228,1 0,0,0,0,0.52,0,0,0,0,0,0,0,0,0.52,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.52,0,0,0,0,0,0,0,0,0,0.52,0,0,1.56,0,0,0,0.077,0,0.077,0,0,1.388,11,75,1 0,0,0,0,0,1.29,0,0.64,0,0,0,0,0,0,0,0,0,0,3.87,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.179,0.117,0,1.8,12,63,1 0.42,0,0.42,0,2.53,0.42,0.42,0,0,0.84,0.42,0.84,0,0,0,1.68,0,0,2.95,0,2.1,0,2.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.291,0,0.072,1.457,0.072,2.632,12,179,1 0,0,0,0,3.98,0.44,0,0.44,0,0.88,0,0,0,0,0,0.88,0,0,0.88,0.44,1.32,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.257,0,0.064,0.128,0,2.215,23,113,1 0,0,0,0,0.49,0,0.98,0,0.49,0,0,0,0,0,0,0,0,0,1.47,0,2.46,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0.466,0,0,0,0,2.373,12,197,1 0.09,0.19,0.98,0,0.78,0.78,0.49,0,0.78,0.19,0.29,0.68,0.29,0,1.57,0.09,0,1.08,1.28,0.19,0.68,0,0.59,0.09,0.09,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0.09,0.09,0,0,0,0.102,0,0.393,0.145,0.014,8.323,669,1415,1 0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.496,0,0,0,0.248,1.985,3.15,12,63,1 0.17,0,0.17,0,1.47,0.35,0,0.05,0.05,0.05,0.05,0.52,0.29,0.05,0,0,0.7,0,4.17,0.23,1.11,0,0.76,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.051,0,0.01,0.165,0,1.79,12,453,1 0,0,0,0,0,0,0,0,0,0,0,6.06,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.4,14,24,1 0.09,0,0.27,0,0.36,0.09,0,0.18,0.09,0,0,0.81,0,0.36,0,0,0,0,1.99,0,3.35,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.015,0.047,0.031,0.253,0.031,0.031,3.771,69,528,1 0,0,0.55,0,0.22,0.22,0.11,0,0.11,0.22,0,0.33,0.33,0,0,0,0.22,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0.035,0.124,0,1.98,14,305,1 0,0,0,0,0.64,0,0.64,0,0,0,0,0.64,0.64,0,0,0,0,0,3.89,1.29,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.64,0,0,0,0,0.115,0,0.921,0.345,0,1.833,11,55,1 0,0.95,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0.47,1.9,0,0,0.47,0,0,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.066,0.2,0,0.267,0,0,4.18,45,464,1 0,0,0.54,0,0.21,0.21,0.1,0,0.1,0.21,0,0.21,0.32,0,0,0,0.21,0,0,0,0,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.152,0,0.033,0.118,0,1.987,14,306,1 0.09,0,0.27,0,0.36,0.09,0,0.18,0.09,0,0,0.72,0,0.36,0,0,0,0,2,0,3.27,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.015,0.047,0.031,0.252,0.031,0.031,3.816,69,542,1 0.09,0,0.27,0,0.36,0.09,0,0.18,0.09,0,0,0.72,0,0.36,0,0,0,0,2,0,3.27,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.015,0.047,0.031,0.252,0.031,0.031,3.816,69,542,1 0,0.54,0,0,0,0,1.08,0,0,0.54,0.54,0.54,0,0,0,2.17,0,0.54,3.26,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0.347,0,0,1.325,5,53,1 0.32,0,0.32,0,0.98,0.32,0.65,0,0,0.32,0,0.98,0.32,0,0,0,0.65,0,2.61,0,2.28,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.288,0.173,0,3.065,28,141,1 1.18,0.39,0.59,0,0,0.98,0.19,0.19,1.38,0.39,0,0.98,0,0.19,0,0.98,0,0,2.56,0.39,1.38,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.238,0,0.766,0.317,0.026,6.652,76,632,1 0,0.31,0,7.18,0,0,0.31,0.62,0,1.25,0,0,0,0,0,0,0,0.62,0.93,0,0.62,0.31,0,0.31,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0.183,0,0.61,0,0.122,9.218,51,507,1 1.01,0.33,0.5,13.63,0,0.67,0,0.16,1.34,0.33,0,0.67,0,0.16,0,0.5,0,0.16,2.02,0.33,0.84,0,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.204,0,0.613,0.229,0.051,9.652,151,888,1 0,0.56,0,0,0.56,0,0,0,1.01,0.56,0.11,1.79,0.22,0.11,0,0.11,0.22,0.89,1.79,0,2.8,0,0,0,0.11,0.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.186,0,0.056,0.056,0,2.153,53,532,1 0.72,0,0,0,1.45,0.72,0.72,0,0,1.45,0,0,0,0,0,0,0,0,1.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0.404,0,0.134,0,0,3.066,14,92,1 0.09,0,0.27,0,0.36,0.09,0,0.18,0.09,0,0,0.72,0,0.36,0,0,0,0,2,0,3.36,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.015,0.047,0.031,0.237,0.031,0.031,3.758,69,530,1 0,0.27,0.82,0,1.37,0,0.82,0,0,0.82,0,0.82,0,0,0,0.82,0.27,0,2.75,0,1.1,0,0.82,0.27,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.235,0,0.235,0.188,0,5.622,124,298,1 0,0,0.74,0,1.85,0.37,0.37,0,0,0.74,0,0.37,0,0,0,1.11,0,0,1.85,0,3.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.264,0,0,0,0,2.492,12,172,1 0,0,0.68,0,0,0,0,1.36,0,0,0.68,0.68,0,0,0,0,0,0,3.4,0,1.36,0,0.68,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.238,0.238,0,2.232,19,96,1 0,0.5,0.25,0,0.5,0,1.01,0,0,0.76,0.76,0.5,0.25,0,0,1.26,0.25,0.25,1.77,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0.113,0,0.189,0,0.151,8.972,447,646,1 0.07,0.22,0.82,0,0.52,0,0.07,0,0.67,0.59,0.22,0.82,0.07,0,0.14,0,0.07,0,3.29,0.22,1.87,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.095,0,0.119,0.071,0.167,3.429,74,974,1 0.08,0.08,0.76,0,0.85,1.02,0.25,0.17,0.59,0.08,0.17,0.59,0.17,0,2.21,0.25,0.08,0.93,1.61,0.17,0.42,0,0.85,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.17,0.08,0.08,0.08,0,0,0,0.065,0,0.408,0.118,0.013,7.55,669,1412,1 0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,1.28,0,2.56,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0,3.809,28,80,1 0,0.56,0.28,0,0,0,0.56,0,0,0.56,0.28,0.56,0.28,0,0,1.41,0.28,0,1.97,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0.118,0,0.158,0,0.158,9.25,394,555,1 0,0,0,0.81,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,2.45,0,0,0,0,2.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.249,0,0,0.124,0,0,3.707,14,152,1 0.34,0,1.7,0,1.02,0,0,0,0.68,1.02,0,0,0,0,0,0,0,0,0.34,1.02,0.68,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.898,0.105,0,443.666,1325,1331,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0.443,0,0.221,0.665,0,3.812,15,61,1 0,0.51,0,0,0.51,0.51,1.02,0,0,0,0,0,0,0,0,0,0,0.51,0.51,0,0.51,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.142,0,0.071,1.212,0,7.025,130,281,1 0.48,0.97,0.97,0,0.48,0,0,0.48,0,1.95,0,2.43,0,0.48,0,0.48,0,0,1.95,0,5.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,1.739,0.173,0.086,56.538,636,735,1 0.34,0,1.7,0,1.02,0,0,0,0.68,1.02,0,0,0,0,0,0,0,0,0.34,1.02,0.68,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.898,0.105,0,443.333,1325,1330,1 0.09,0,0.27,0,0.36,0.09,0,0.18,0.09,0,0,0.72,0,0.36,0,0,0,0,2,0,3.36,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.015,0.047,0.031,0.237,0.031,0.031,3.758,69,530,1 0.08,0.08,0.76,0,0.85,1.02,0.25,0.17,0.59,0.08,0.17,0.59,0.17,0,2.21,0.25,0.08,0.93,1.61,0.17,0.42,0,0.85,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.17,0.08,0.08,0.08,0,0,0,0.065,0,0.408,0.118,0.013,7.55,669,1412,1 0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.48,0,2.32,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.733,42,131,1 0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,0,0,0,2.7,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.636,0,0,3.809,28,80,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.57,0,0,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.184,0,8.161,31,253,1 0.43,0.43,0.43,0,0.14,0.1,0.03,0.07,0.54,1.01,0.28,0.79,0.47,1.19,0.03,0.25,0.39,0,3,0,1.3,0,0.39,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.011,0.077,0,0.458,2.33,0.113,6.601,266,2370,1 0.23,0.34,0.58,0,0.46,0.11,0.11,0.23,1.04,0.93,0,0.46,0,0.23,0.23,0,0.11,0,3.72,0.46,1.74,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0.075,0,0.131,0.056,0.018,4.47,74,675,1 0,0.67,0.33,0,0.33,0.33,0.33,0.33,0,0,0.67,1,0,0,0,1,0.33,0.33,2.68,2.68,3.02,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.053,0,0.16,0,0.107,6.111,139,275,1 0.47,0.95,0.95,0,0.47,0,0,0.47,0,1.9,0,2.38,0,0.95,0,0.47,0,0,1.9,0,5.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.253,0,1.687,0.168,0.084,57.076,634,742,1 0.46,0.93,0.93,0,0.46,0,0,0.46,0,1.86,0,2.33,0,0.46,0,0.46,0,0,1.86,0,5.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.252,0,1.683,0.168,0.084,57.076,634,742,1 0.09,0,0.27,0,0.36,0.09,0,0.18,0.09,0,0,0.73,0,0.36,0,0,0,0,2.01,0,3.38,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.016,0.048,0.032,0.257,0.032,0.032,3.689,69,535,1 0,0,0.73,0,0,0,0.73,0,0,0,0,0,0,0,0,2.2,0,0,1.47,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0.088,0,0,3.048,29,125,1 0,0,0,1.26,0,0,0.63,0,0,1.26,0,0,0,0,0,0.63,0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.195,0,0,0.979,0,0.293,8.476,68,356,1 0,0,0.74,0,0,0,0.74,0,0,0,0,0,0,0,0,2.22,0,0,1.48,0,1.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0.088,0,0,3.048,29,125,1 0.71,0,0.11,0,0.47,0.11,0,0.59,0.71,2.86,0.23,0.11,0.23,0,0.47,0.11,0.59,0.47,3.21,0,1.66,0,1.9,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0.11,0,0,0,0,0.11,0,0,0,0.072,0.127,0,0.418,0.254,0.018,9.705,148,1514,1 0,0,0.73,0,0,0.73,0.73,0,0,0,0,0,0,0,0,0.73,0,0,1.47,0,0,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.875,0.109,13.129,2.08,12,52,1 0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,2.04,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0.375,0,1.001,0,0.25,4.551,32,132,1 0.5,0.4,0.33,0,0.13,0.03,0.13,0.1,0.54,0.78,0.3,0.71,0.54,1.15,0.03,0.27,0.44,0.03,3.19,0,1.42,0,0.37,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0.074,0,0.445,0.519,0.119,6.029,136,2213,1 0.67,0.16,1.35,0,1.01,0,0,0,0,0.16,0.16,1.69,0.5,0,0,0.33,0,0.16,5.77,0,1.35,0,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.061,0,0.123,0.278,0.03,3.774,46,268,1 0,0,0,0,2.38,0,0,2.38,2.38,2.38,0,0,0,0,0,0,7.14,0,0,2.38,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.396,0,0,22.714,149,159,1 0.18,0,0.09,0,0.36,0.09,0,0.36,0.09,0,0,0.63,0.09,0.36,0,0,0.09,0,1.27,0,3.38,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0.03,0.03,0.015,0,4.192,48,566,1 0,0,0.53,0,0.53,0,0,0.53,0,0,0,1.06,0,0,2.12,0,0.53,0.53,2.65,0,2.65,0,1.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0.53,0,0,0,0,0,0,0,0,0,0.191,0,0.095,0.478,0,5.038,60,131,1 0,0,0.52,0,0.52,0,0,0.52,0,0,0,1.05,0,0,2.11,0,0.52,0.52,2.64,0,2.64,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.52,0.52,0,0,0,0,0,0,0,0,0,0.19,0,0.095,0.475,0,5.038,60,131,1 0,0,0.51,0,0.51,0,0,0.51,0,0,0,1.02,0,0,2.05,0,0,0.51,2.56,0,2.56,0,1.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0.51,0,0,0,0,0.51,0,0,0,0,0.189,0,0.094,0.473,0,5.038,60,131,1 0,0.1,0.31,0.1,1.05,0.42,0,0.31,0,0.1,0.1,0.84,0,0.1,0,2,0.84,0.21,1.69,0.31,0.31,0,0.52,0.1,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0.054,0,0.384,0.182,0.201,8.851,299,1726,1 0,0,1.11,0,0,0,1.11,0,0,0,1.11,1.11,0,0,0,2.22,0,0,3.33,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.138,0,0.138,0.138,0,2.7,11,54,1 0.31,0,0.63,0,0.47,0.47,0.15,0.79,0.15,0.63,0.31,1.42,0,0,0,0,1.58,0,2.05,1.58,3.95,0,0.15,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0,0,0.15,0,0,0,0,0.076,0,1.3,0.127,0,5.241,97,650,1 0,0,0,0,0,0,0.91,0,0,0,0,0.91,0,0,0,0.91,0,1.83,4.58,0,1.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.151,0,0.455,0,0,2.842,10,54,1 0.31,0,0.63,0,0.47,0.47,0.15,0.79,0.15,0.63,0.31,1.42,0,0,0,0,1.58,0,2.05,1.58,3.95,0,0.15,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0,0,0.15,0,0,0,0,0.076,0,1.3,0.127,0,5.241,97,650,1 0,0,0.32,0,0.64,0.64,0.64,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0.32,2.27,0,3.24,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.166,0,0,0.166,0,1.688,19,157,1 0,0,0,0,1.26,0,1.26,0,0,0,0,0,0,0,0,0,0,1.26,0,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0.431,0,0,0,0.215,1.724,3.529,13,60,1 0.1,0,0.1,0,0.4,0.1,0.1,0,0.2,0.2,0.4,0.5,0,0.6,0,0.91,0.2,0,1.72,4.26,1.72,0,0.4,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.096,0,0.336,0.16,0,6.758,494,1426,1 0,0,0,0,0.09,0,0,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.032,0,0.016,0,0,24.375,135,3315,1 0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0.212,0,0.424,0.424,0,4.1,25,82,1 0.59,0,0,0,0,0,1.18,0.59,0.59,1.18,0,1.18,0,0,0,0,2.95,0,4.14,2.36,2.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.421,0,0,6.275,46,182,1 0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,9,18,1 0.06,0.65,0.71,0,0.39,0.65,0.52,0.19,1.04,0.52,0.06,0.39,0.32,0,1.17,0.13,0.06,1.1,1.3,0,1.04,0,0.52,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0,0.06,0,0,0,0.085,0,0.287,0.106,0,4.742,117,1342,1 1.23,0,0,0,0,0,0,0,0,0,0,2.46,0,0,0,0,0,0,6.17,0,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.843,0,0,2,19,106,1 0,1.5,1.5,0,0.75,0,0,0,0.75,3.75,0,2.25,0,0,1.5,0,1.5,0,0.75,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.224,1.223,0,107.4,412,537,1 0,1.51,1.51,0,0.75,0,0,0,0.75,3.78,0,2.27,0,0,1.51,0,0.75,0,0.75,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.249,1.237,0,105.8,404,529,1 0.1,0.2,0.52,0,0.31,1.14,0.2,0.62,1.04,0.52,0.2,0.62,0,0,1.66,0,0.2,1.45,2.08,0.2,1.25,0,1.14,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0.095,0.143,0,0.334,0.175,0.031,7.439,689,1287,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.46,0,0,0,0,0,0,1.235,0,0,4.466,10,134,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0.441,0,0.441,0.662,0,4.066,17,61,1 0,0,0.31,0,0.31,0.31,0.31,0.31,0,0,0.63,0.95,0,0,0,0.95,0.63,0.31,2.54,2.54,3.5,0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.051,0,0.102,0,0.102,5.708,138,274,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,1.02,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.174,0,0.174,0,0,1.787,7,59,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.96,0,0,0.98,0.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.173,0,0.173,0,0,1.787,7,59,1 0,0.39,1.17,0,0.39,0,0,0,0,0.78,0.78,0.78,0,0,0.39,3.51,0,0,1.17,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0.066,0,0.864,0.132,0.066,5.87,44,364,1 0,0,0,0,0,0,0,0,0,0,0,1.42,0,0,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.058,5,35,1 0.09,0,0.09,0,0.39,0.09,0.09,0,0.19,0.29,0.39,0.48,0,0.58,0,0.87,0.19,0,1.66,4.1,1.66,0,0.39,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0.326,0.155,0,6.813,494,1458,1 0.1,0,0.41,0,0.1,0.1,0.1,0.52,0.1,0,0,0.62,0,0.2,0,0,0.1,0,0,0,0,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.048,0,0.016,0.064,0,1.915,29,339,1 0.14,0,0.28,0,0.09,0.24,0.04,0.04,0.24,0,0,0.52,0.04,0.09,0,0,0.14,0,0.24,0.04,0.28,0,0.38,0.14,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0.04,0,0,0,0.04,0.09,0,0,0,0,0.061,0,0.007,0.099,0,1.867,14,521,1 0.36,0.27,0.63,0,0.82,0.36,0,0.36,0.27,4.1,0.09,1.27,0.45,0,1.27,1.18,0.27,2.1,2.73,0,2.83,0,0.09,0.27,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0.09,0,0,0,0,0,0,0,0,0,0.101,0,0.611,0.014,0,3.707,127,875,1 0,0,1.11,0,1.11,0,0.74,0,0,0,0.74,0.37,0,0,0,0,0.37,0,3.35,2.98,2.61,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.368,0.552,0,1.58,7,79,1 0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0.235,0.235,1.882,6.266,41,94,1 0,0.37,1.11,0,0.37,0,0,0,0,0.74,0.37,0.74,0,0,0.37,3.34,0,0,0.74,0,1.48,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.292,0,0.878,0.175,0.058,5.985,58,425,1 0,0.37,1.11,0,0.37,0,0,0,0,0.74,0.37,0.74,0,0,0.37,3.34,0,0,0.74,0,1.48,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.292,0,0.878,0.175,0.058,5.985,58,425,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.733,0,0,2.666,11,24,1 0.17,0,0.51,0,0.17,0,0.17,0.34,0.17,0,0,0,0,0.34,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.026,0,0,0,0,1.559,10,145,1 0.17,0,0.17,0.17,1.43,0.34,0.05,0.05,0.05,0.05,0.05,0.51,0.28,0.05,0,0,0.69,0.05,4.2,0.23,1.03,0.17,0.74,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.05,0,0.02,0.161,0.03,2.093,51,536,1 0,0,0,0,1.46,0,0.83,0,0.2,1.04,0,0.41,0,0,0,0,0,1.46,1.04,0,0.2,0,0.2,0,1.46,1.46,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0.182,0.401,0.109,0.182,0.146,0,3.791,26,364,1 0,0.37,0,0,0.37,0.37,0.37,0.74,0.37,0.37,0,0.74,0.37,0,0,0.37,1.49,0,3.73,2.61,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0.37,0,0,0,0,0,0,1.199,0.505,0,3.337,64,267,1 0.45,0,0.67,0,0.22,0.67,0,0.67,0.22,0.22,0.22,1.35,0,0,0,0.45,1.35,0.22,1.57,1.57,3.37,0,0.22,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.111,0,1.599,0.148,0,4.947,102,564,1 0,0.19,0.57,0,0.09,0.28,0.09,0.09,0.38,0.19,0,0.57,0.57,0,0,0.19,0,0,2.01,0,1.43,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.035,0.23,0,0.088,0.124,0,2.405,50,368,1 0,0,0.44,0,0.88,0.22,0,0,0,0,0,0.44,0,0.22,0,0,0,0,0.66,0,0.44,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0.037,0.224,0,0,0.187,0.149,3.384,21,264,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.843,0,0,1.666,5,15,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,1 0,0,0,0,0,0,0,2.2,0,0,1.47,0.73,0,0,0,2.94,0,0,5.14,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.6,8,48,1 0.46,0.92,0.92,0,0.46,0,0,0.46,0,1.85,0,2.31,0,0.46,0,0.46,0,0,1.85,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.249,0,1.663,0.249,0.083,49.866,636,748,1 0.47,0.94,0.94,0,0.47,0,0,0.47,0,1.88,0,2.35,0,0.47,0,0.47,0,0,1.88,0,5.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.253,0,1.687,0.168,0.084,57.23,636,744,1 0.9,0,0.9,0,0.9,0,0.9,0,0,0,0,0,0,0,0,0,0,0,5.45,0,0,2.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.153,0,2.143,0.612,0.459,10.125,54,162,1 0.07,0.22,0.82,0,0.52,0,0.07,0,0.67,0.6,0.22,0.82,0.07,0,0.15,0,0.07,0,3.3,0.22,1.87,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.095,0,0.131,0.071,0.167,3.446,74,972,1 0,0,1.28,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,1.28,0,6.41,0,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.204,0,5.181,25,57,1 0,0,0.98,0.49,0,0,0.49,0,0,0.98,0,0.98,0,0,0,2.94,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0.156,0,0,0.862,0,0,12.148,272,571,1 0.47,0.95,0.95,0,0.47,0,0,0.47,0,1.91,0,2.39,0,0.95,0,0.47,0,0,1.91,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.252,0,1.683,0.168,0.084,57.23,636,744,1 0.47,0.94,0.94,0,0.47,0,0,0.47,0,1.88,0,2.35,0,0.47,0,0.47,0,0,1.88,0,5.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.252,0,1.68,0.168,0.084,57.23,636,744,1 0,0,0.89,0,1.79,0.44,0,0,0,0,0,0.44,0,0,0,1.34,0,0,2.24,0,4.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.074,0,0,0,0,2.25,12,144,1 0,0,0.89,0,1.78,0.44,0,0,0,0,0,0.44,0,0,0,1.33,0,0,2.23,0,4.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.074,0,0,0,0,2.25,12,144,1 0.34,0,1.7,0,1.02,0,0,0,0.68,1.02,0,0,0,0,0,0,0,0,0.34,1.02,0.68,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.899,0.105,0,667,1333,1334,1 0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0.194,0,0,0.389,0,3.476,16,73,1 0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,0,0.91,0,2.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.143,0,0.572,0,0,2.9,28,87,1 0,0,0,0,0,0,0,0,0,0,0,1.33,0,0,0,1.33,0,0,2.66,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.326,0,0,2.2,11,44,1 0,0,0,0,0.73,0,0,0,0,0,0,0.36,1.1,0,0,0.36,0,0,3.69,0,0.73,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.119,0,0.238,0.059,0,2.93,29,211,1 0,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,2.24,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0.18,0.27,0.045,0,0,0,2,14,178,1 0.41,0.41,0.41,0,0.13,0.1,0.03,0.06,0.52,0.94,0.27,0.76,0.45,1.15,0.03,0.24,0.41,0.03,2.99,0,1.25,0,0.34,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.011,0.076,0,0.425,0.573,0.112,5.761,131,2224,1 0.31,0,0,0,0.94,0,0,0,0,0,0.31,0,0,0,0,0.31,0,0.31,3.76,0.31,0.62,0,0,0.62,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0.055,0,0.055,0.111,0,2.358,32,125,1 0,0,1.13,0,1.13,0.56,0.56,0,0,0.56,0,1.13,0,0,0,3.97,0,0,2.84,0,0.56,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.288,0,0.768,0,0,11.685,296,409,1 0,0,0.14,0,0.29,0,0,0,0,0,0,0.89,0,0,0,0.14,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0,0.44,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0.022,0.067,0,0,0.022,0,2.227,11,294,1 0,0,0.55,0,0,0.55,0,0.27,0,0,0.55,0.27,0.27,0,0,1.1,0.27,0.83,2.49,0,3.04,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0.194,0,0.582,0.291,0.582,2.309,35,291,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,4.5,25,54,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,4.5,25,54,1 0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,4.65,0,4.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.701,0.35,0,1.434,6,33,1 0,0,2.99,0.42,0.42,0,0.85,0,0,0,0.42,0.42,0,0,0,0.42,0,1.28,3.41,0,1.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.204,0,0.819,0,0,4.84,42,363,1 0,2.08,0,0,3.12,0,1.04,0,0,0,0,2.08,0,0,0,0,0,4.16,2.08,0,1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.263,0,0,1.428,4,20,1 0.29,0.04,0.04,0,0.14,0.04,0,0.29,0.29,0.94,0.14,0.04,0.14,0,0.19,0.04,0.39,0.19,1.6,0.04,0.79,9.53,0.69,0.47,0,0,0,0,0,0,0,0,0,0,0,0.04,0,0,0.04,0,0,0,0,0,0.19,0,0,0,1.117,0.053,0,0.356,0.09,0.011,12.332,1171,9163,1 0,0,0.76,0,0.76,0,0.5,0.5,0,1.01,0,0.25,1.52,0,0.76,0,0,1.52,2.03,0,1.52,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0.078,0,0.433,0.433,0,2.441,19,249,1 0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0.182,0,0,0.182,0,3.545,21,78,1 0.31,0,0.63,1.91,0.21,0,0,0,0.42,0.1,0,0.31,0,0,0,0.53,0,0,1.7,0,1.06,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0.1,0,0,0,0,0,0,0.169,0,0.358,0.188,0.075,2.847,75,447,1 0,0.75,0.37,0,0,0,0.75,0,0,0.37,0,0.75,0,0,0,1.87,0.37,0,2.63,0,1.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0,0,0.242,0,0.145,9.584,332,508,1 0,1.96,0.98,0,0,0,1.96,0,0,0,0,0,0,0,0,0.98,0,0,0.98,0,0.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.98,0,0,0,0,0.168,0,1.011,0,0,2.888,12,52,1 0.51,0.43,0.29,0,0.14,0.03,0,0.18,0.54,0.62,0.29,0.65,0.65,1.2,0.03,0.21,0.43,0.03,3,0,1.35,0,0.51,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0.012,0.08,0,0.454,0.523,0.136,6.59,739,2333,1 0.2,0.4,0.4,0,0,0.4,0,0.2,1.43,0.61,0,0.2,0,0,0,0,0,0,2.66,0.2,2.04,0,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0.029,0.059,0.447,0.298,0.149,0.029,11.96,376,909,1 0,0,0,0,0,0,0,0,0,0,0,0,0,2.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0.17,0,0.17,0.341,0,3.809,24,80,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,4.5,25,54,1 0,0,0,0,0,0.54,1.63,0,0,0,0.54,0.54,0,0,0,0.54,2.73,0.54,4.91,0,2.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.171,0,2.592,32,70,1 0,0,0,0,0,0.65,0,1.3,0.65,0.65,0,0,0,0,0,0.65,2.61,0.65,1.3,3.26,1.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,1.154,0.524,0,3.89,78,249,1 2.35,0,0,0,0,0,2.35,0,2.35,0,0,1.17,0,0,0,1.17,0,0,2.35,0,0,0,2.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.168,0.336,0,4.576,17,119,1 0,0,0.32,0,0.65,0.65,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.28,0,3.25,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.112,0,0,0.169,0,1.494,10,139,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,8.29,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.218,0.087,0,0.174,0.174,0.437,9.186,126,937,1 0,0,0.24,0,0.49,0,0,0.24,0,0.24,0.24,0.49,0,0,0,0.99,0.24,0,2.47,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.478,0,0,2.868,44,175,1 0,0,0.32,0,0.64,0.64,0.32,0.64,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.27,0,3.24,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.112,0,0,0.168,0,1.494,10,139,1 0,0,0.32,0,0.64,0.64,0.32,0.64,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.27,0,3.24,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.112,0,0,0.168,0,1.494,10,139,1 0.78,0,0.78,0,1.56,0,0,0,0,0,0,0,0,0,0,0.78,0.78,0,1.56,0,2.34,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.792,0.339,0,2.627,22,113,1 0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0.4,0.4,0,0.4,7.63,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.133,0,0.601,0.133,1.068,10.578,108,603,1 0.78,0,0.78,0,1.57,0,0,0,0,0,0,0,0,0,0,1.57,0.78,0,1.57,0,2.36,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.682,0.341,0,2.555,22,115,1 0,0.75,0.37,0,1.51,0,0,0.37,0,0.37,0.75,1.89,0,0,0,0.75,0.37,1.13,6.06,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0.415,0.138,0,1.937,11,93,1 0,0,0,0,0.96,0,0.96,0,0,0.48,0.48,0.96,0,0,0,1.44,0,0,3.36,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.149,0,0.149,0.074,0,2.586,44,150,1 0,0,0,0,0.43,0.43,0.43,0.43,0,0,0,0.43,0,0,0,0,0,0,0.87,0,0,9.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.395,0,1.121,7.983,72,495,1 0.78,0,0.78,0,1.57,0,0,0,0,0,0,0,0,0,0,1.57,0.78,0,1.57,0,2.36,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.682,0.341,0,2.555,22,115,1 0,0.81,1.62,0,2.43,0,0,0,0,0.81,0,0,0,0,0,0.81,0,0.81,2.43,0.81,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.121,0,0.121,0,0,4.035,43,113,1 0.38,0.46,0.31,0,0.15,0.03,0,0.19,0.58,0.66,0.31,0.66,0.58,1.24,0.03,0.23,0.38,0,3.11,0,1.32,0,0.46,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0.006,0.091,0,0.36,0.524,0.137,6.186,122,2227,1 0.43,0,0.87,0,0.87,0,0.87,0,0,0,0,0.43,0,0,0,0,0.43,0.43,4.38,0,1.31,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0.145,0,1.021,0.218,0,3.35,59,134,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,1.23,0,4.93,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,0.234,0,0,4.176,41,71,1 0,0,1.58,0,1.58,0,1.58,0,0,0,0,1.58,0,0,0,1.58,0,0,3.17,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.371,0,0,3.538,21,46,1 0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0.9,0,3.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.139,0,0.559,0,0,2.9,28,87,1 0,0,0,40.13,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0.32,0.98,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.185,0,0.185,0.061,0,10.585,124,434,1 0,0.47,0,0,0.94,0,0.94,0,0,0,0,0.47,0.47,0,0.47,0,0,0,1.89,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0.074,0.074,0,0,0,2.125,11,102,1 0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,0,0.91,0,2.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.141,0,0.565,0,0,2.9,28,87,1 0.05,0.05,0.4,0,0.34,0,0,0,0.57,0.05,0,0.28,0.11,0,0,0.17,0,0,1.15,0.05,0.92,0,0,0.05,0,0,0,0,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0.05,0,0,0,0.019,0.099,0,0.089,0.079,0.009,4.913,95,1312,1 0.05,0.05,0.4,0,0.34,0,0,0,0.57,0.05,0,0.28,0.11,0,0,0.17,0,0,1.04,0.05,0.92,0,0,0.05,0,0,0,0,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0.05,0,0,0,0.019,0.099,0,0.089,0.079,0.009,4.924,95,1310,1 0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0.86,0,1.73,3.47,0,1.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.152,0,0.457,0,0,2.75,10,55,1 0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0.86,0,1.73,3.47,0,1.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.151,0,0.759,0,0,2.75,10,55,1 0.38,0,1.9,0,1.14,0,0,0,0.38,0.38,0,0,0,0,0,0,0,0,0.38,0.76,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.008,0.059,0,295,1177,1180,1 0.38,0,1.9,0,1.14,0,0,0,0.38,0.38,0,0,0,0,0,0,0,0,0.38,0.76,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.005,0.059,0,295,1177,1180,1 0.38,0,1.9,0,1.14,0,0,0,0.38,0.38,0,0,0,0,0,0,0,0,0.38,0.76,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.008,0.059,0,589,1177,1178,1 0,0,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.205,0,0.034,0,0,3.168,15,339,1 0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.204,0,0.034,0,0,2.588,15,277,1 0,0.65,0,0,0.65,0,1.31,0,0,0,0,0.65,0,0,0.65,0,0,0,3.28,0,0.65,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.104,0.522,0,0,1.69,11,71,1 0,0,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.205,0,0.034,0,0,3.168,15,339,1 0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.204,0,0.034,0,0,2.588,15,277,1 0,0,0.3,0,0.3,0.3,0.3,0.3,0,0,0.6,0.9,0,0,0,0.9,0.6,0.3,2.4,2.7,3,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.048,0,0.144,0,0.096,5.403,139,281,1 0,0,0.32,0,0.64,0.64,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.27,0,3.24,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.111,0,0,0.166,0,1.494,10,139,1 0.08,0.08,0.76,0,0.85,1.02,0.25,0.17,0.59,0.08,0.17,0.59,0.17,0,2.22,0.25,0.08,0.94,1.62,0.17,0.42,0,0.85,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.17,0.08,0.08,0.08,0,0,0,0.065,0,0.435,0.118,0.013,7.497,669,1402,1 0,0,1.83,0.91,0,0,0.45,0,0,0.91,0,0,0,0,0,2.75,0,0,1.83,0,0.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.134,0,0,2.077,0,0.134,12.176,338,621,1 0,0,0,0,3.09,0,1.03,1.03,0,1.03,0,1.03,0,0,0,2.06,0,0,2.06,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,0,10.692,65,139,1 0,0,0,0,3.12,0,1.04,1.04,0,1.04,0,1.04,0,0,0,2.08,0,0,2.08,0,1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.04,0,0,0,0,0,0,0,0,0,10.692,65,139,1 0,0,0,0,3.09,0,1.03,1.03,0,1.03,0,1.03,0,0,0,2.06,0,0,2.06,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,0,10.692,65,139,1 0,0.46,0.46,0,2.8,0,0,0,0,0,0,1.4,0,0,0,1.4,0,1.4,1.86,0,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0.148,0,0.74,0,0,2.673,21,139,1 0,0,0,0,0,0,1.21,0,0,1.21,0,1.21,1.21,0,0,1.21,0,0,4.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.676,15,57,1 0,0,1.31,0,2.63,0,0,0.65,0,0,0,0.65,0,0,0,1.97,0,0,1.31,0,2.63,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0.198,0,0.198,0.099,0,2.195,12,101,1 0,0,0,0,0,0,0,0,0,0,0.26,0.26,0,0,0,0,0,0,1.05,1.32,0.26,10.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.306,0.043,0.087,0.175,0.043,0.35,8.271,69,885,1 0.74,0,0,0,0,0,0.74,0,0,1.49,0.74,0.74,0,0,0,0.74,3.73,0,4.47,0,0.74,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.355,0.118,0.237,2.095,7,44,1 0,0,1.29,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,1.29,0,5.19,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.208,0,4.818,25,53,1 0,0.02,0.05,0,0.02,0,0,0.05,0,0.35,0,0.02,0,0,0,0.05,0.1,0.38,0.07,0.2,0.17,0,0,0,0.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.004,0,0.112,0.018,0.018,3.922,489,3271,1 0,0,1.33,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0.66,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0,0,0.355,0.118,0,2.315,12,132,1 0,0,1.35,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0.67,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0.356,0.118,0,2.315,12,132,1 0,0,0,0,0.53,0,1.07,0,0,0.53,0,0,0,0,0,0,0,1.61,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.335,0,0,0,0,2.333,14,119,1 0,0,0,0,0,0,0,0,0,0.27,0,0.27,0,0,0,0.27,0,0,1.09,1.36,0.27,10.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.333,0.047,0.095,0.142,0.047,0.381,2.353,13,273,1 0,0,0,0,0.53,0,1.07,0,0,0.53,0,0,0,0,0,0,0,1.61,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.336,0,0,0,0,2.333,14,119,1 1.23,0,0,0,0,0.46,0,0.15,0,0.61,0,0.3,1.07,0,0,0,0,0,1.84,0,0.92,0,0.76,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.057,0,0.52,0.289,0.144,4.33,84,446,1 0,0,0.71,0,0.23,0,0,0,0.23,0.23,0.23,1.9,0,0,0,0.23,0,0,3.81,0.23,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.112,0,1.046,0.037,0,4.022,97,543,1 0.95,0,0.23,0,0.23,0.23,0.23,0,0,0.23,0,0.23,0,0,0,0,0.71,0,3.8,0,1.9,0,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.238,0,0,3.184,38,242,1 0,0,0.71,0,0.23,0,0,0,0.23,0.23,0.23,1.9,0,0,0,0.23,0,0,3.81,0.23,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.112,0,1.046,0.037,0,4.022,97,543,1 0,0,0.57,0,0.28,0,0,0.57,0,0,0,0.28,0,0,0,0.57,1.15,0,0.86,2.31,2.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.047,0,1.147,0.191,0.191,11.735,489,622,1 0.9,0,0,0,0,0,0.9,0,0,1.8,0.9,0.9,0,0,0,0.9,4.5,0,5.4,0,0.9,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.391,0.13,0.26,2.15,7,43,1 0.74,0,0,0,0,0,0.74,0,0,1.49,0.74,0.74,0,0,0,0.74,3.73,0,4.47,0,0.74,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.355,0.118,0.236,2.15,7,43,1 0,0.16,0,0,0.16,0.16,0,1.14,1.3,0.32,0.32,0.48,0,0,0,1.95,0,0.32,0.81,0.48,1.46,2.93,0.16,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.673,0.35,0.053,0.134,0.107,0.026,5.216,57,1038,1 0.27,0.27,0.27,0,0,0,0,0.54,0,0.27,0,0.27,0,0,0,1.08,0,0.27,1.08,0,0.27,0,0.27,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.229,0,0.458,0.504,0,2.934,64,578,1 0,0,0.85,0,0.85,0.21,0.21,0,0,1.5,0,0,0,0,1.07,0,0.21,0,0.64,0.21,1.71,7.08,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.465,0.271,0.969,9.052,58,869,1 0.64,0,0.64,0,1.29,0,0.64,0,0,0,0,0.64,0,0,0,0.64,0.64,0,1.29,0,3.22,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.104,0,0.522,0.313,0,2.511,22,113,1 0,0,0,0,0,0.54,1.63,0,0,0,0.54,0.54,0,0,0,0.54,2.73,0.54,4.91,0,2.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.171,0,2.592,32,70,1 0,0,0,0,0,0,0,0,0.52,0.52,0,2.08,0,0,0,0,0,0,4.16,0,4.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.073,0.073,0,0.367,0.073,0.073,2.34,27,103,1 0,1.32,0.56,0,0,0.94,0,0.18,0.37,0.75,0,2.08,0,0,0,0,0.37,0,2.65,0,0.94,0,0,0.18,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.084,0,0.31,0.112,0,2.548,34,237,1 0,0,1.07,0,0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0.53,1.07,8.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.265,0.088,1.151,11.066,67,332,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.213,0,4.818,25,53,1 0,0,0,0,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,3.36,0,0,12.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.587,0,1.468,7,35,273,1 1.36,0,0.68,0,0,0,0.68,0,0,0,0,4.1,0.68,0,0,1.36,0,0,2.73,0,2.05,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,1.706,0.2,0,4.281,38,137,1 0,1.16,0.38,0,0,0,1.16,0,0,0.77,0.38,0.77,0,0,0,1.93,0,0.38,2.32,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0,0.198,0,0.148,9.266,332,556,1 0,0,0.27,0,0.27,0.27,0.27,0.27,0,0,0.54,0.82,0,0,0,0.82,0.54,1.09,2.46,2.46,2.73,0,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.043,0,0.13,0,0.086,4.6,139,276,1 0,0,0.94,0,0.31,0,0,0,0.31,0,0,0.62,0,0,0,1.25,0.62,0,3.14,0,1.25,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0.146,0.048,0.39,0.438,0.097,3.322,61,319,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.09,1.09,0,3.29,0,2.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.09,0,0,0,0,0.371,0,0.371,0,0,3.096,28,96,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.111,0,0.491,0.158,0.015,8.55,669,1351,1 0,0,0.81,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0.81,0,3.27,0,0.81,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,0.651,13.5,86,189,1 1.24,0,0,0,0,0,0,0,0,0.62,0,1.24,0,0,0,0.62,0,0,1.86,0,3.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.24,0,0,0,0,0.1,0,1.105,0.201,0,12.904,155,271,1 0,0,0,0,0,0,0,1.25,0,0.41,0,0,0,0,0,0.41,0,1.67,0.41,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0.312,0.062,0,1.477,8,65,1 0,0.41,0.41,0,2.06,0,1.65,0.82,0,0,0,0,0,0,0,2.47,0,0.82,2.47,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.265,0,0.199,0,0,15.892,226,445,1 0,0.41,0.41,0,2.06,0,1.65,0.82,0,0,0,0,0,0,0,2.47,0,0.82,2.47,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.265,0,0.199,0,0,15.892,226,445,1 0,0.41,0.41,0,2.06,0,1.65,0.82,0,0,0,0,0,0,0,2.47,0,0.82,2.47,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.265,0,0.199,0,0,15.892,226,445,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,3.89,0,3.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,0.236,0,0,7.181,41,79,1 0.45,0.9,0.9,0,0.45,0,0,0.45,0,1.8,0,2.25,0,0.45,0,0.45,0,0,1.8,0,5.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.243,0,1.626,0.162,0.081,69.727,706,767,1 0.45,0.9,0.9,0,0.45,0,0,0.45,0,1.8,0,2.26,0,0.45,0,0.45,0,0,1.8,0,4.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.244,0,1.631,0.244,0.081,64.416,708,773,1 0.45,0.91,0.91,0,0.45,0,0,0.45,0,1.83,0,2.29,0,0.91,0,0.45,0,0,1.83,0,5.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.245,0,1.633,0.245,0.081,64.416,708,773,1 0.82,0,0.82,0,0.41,0,0.41,0.82,0.41,1.23,1.65,0.41,0,0,0,2.47,1.65,0,1.23,1.23,2.06,0,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0.132,0.132,6.404,76,301,1 0.09,0.49,0.59,0,0.39,0.19,0,0,0.09,0.39,0,1.57,0.19,0,0,0,0.09,0,3.75,0.09,1.08,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.768,0.037,0,5.848,1.313,0,5.96,54,757,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.212,0,4.818,25,53,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.212,0,4.818,25,53,1 0,0,0.6,0,0,0.6,0,0,0.6,0,0,1.8,0,0,0,0.3,0,0,2.7,0,1.2,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.094,0.047,0.189,0.141,0,1.932,31,201,1 0.47,0,0.94,0,0.94,0,0.94,0,0,0,0,0.47,0,0,0,0,0.47,0,4.24,0,0.94,0,1.41,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0.073,0,1.254,0.221,0,5.918,91,219,1 0,0.72,1.81,0,0,0.36,0,0.36,0.72,1.08,0.36,0.72,0,0.36,0,0.36,0.36,0.36,1.08,0,2.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0.36,0,0,0,0,0.334,0,1.203,0.467,0.066,18.4,393,736,1 1.47,0,0,0,0,0,0,0,0,1.47,0,0,1.47,0,0,7.35,0,0,2.94,0,1.47,0,0,4.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.272,0.254,0,6.294,63,107,1 1.47,0,0,0,0,0,0,0,0,1.47,0,0,1.47,0,0,7.35,0,0,2.94,0,1.47,0,0,4.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.272,0.254,0,6.055,63,109,1 0,0,0.51,0,0.51,0.51,0.51,0,0,0,0,0,0,0,0,0,1.03,1.03,3.1,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0.18,0,0,1.773,17,94,1 0,0,0.5,0,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,1.01,1.01,3.04,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.089,0,0.178,0,0,1.792,17,95,1 0,0.74,1.85,0,0,0.37,0,0.37,0.74,1.11,0.37,0.74,0,0.37,0,0.37,0.37,0.37,1.48,0,2.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.11,0,0,0,0,0,0,0,0.37,0,0,0,0,0.336,0,1.211,0.471,0.067,18.4,393,736,1 0.09,0.49,0.59,0,0.39,0.19,0,0,0.09,0.39,0,1.57,0.19,0,0,0,0.09,0,3.75,0.09,1.08,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.767,0.037,0,5.84,1.311,0,5.96,54,757,1 0,0.72,1.81,0,0,0.36,0,0.36,0.72,1.08,0.36,0.72,0,0.36,0,0.36,0.36,0.36,1.08,0,2.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0.36,0,0,0,0,0.334,0,1.203,0.467,0.066,18.4,393,736,1 0,0,0.15,0,0.9,0.15,0,0,0.9,0,0,0.75,0.15,0,0,0,0.3,0,2.26,0,0.9,0,0.15,0.3,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0.376,0.05,0.025,2.271,49,427,1 0.15,0.15,0.3,0,0.75,0,0,0,0,0,0,0.15,0.15,0,0,0,0,0.75,1.51,0,0.45,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.058,0.145,0,0.087,0,0,1.54,18,208,1 0.12,0.19,0.7,0,0.44,0,0.06,0,0.57,0.5,0.25,0.95,0.06,0,0.19,0,0.06,0,3.82,0.19,2.48,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0,0,0.06,0,0,0,0,0,0,0,0.102,0,0.133,0.041,0.143,3.29,74,1030,1 0,0,0,0,0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.143,0,0.573,0,0,2.884,18,75,1 0.05,0.05,0.4,0,0.34,0,0,0,0.57,0.05,0,0.28,0.11,0,0,0.17,0,0,1.09,0.05,0.92,0,0,0.05,0,0,0,0,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0.05,0,0,0,0.019,0.099,0,0.099,0.079,0.009,4.906,95,1310,1 0,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,1.19,0,0,3.57,0,3.57,0,0,0,1.19,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0.204,0,0,8.636,41,95,1 0,0,0.44,0,1.34,0,0.44,0,0,0,0,0,0,0,0,0.44,0.89,0,2.24,0,1.34,0,0.44,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.34,0,0,0,0,0.068,0,0.482,0.896,0,6.77,78,325,1 0,0,0,0,0.77,0,0,0,0,0,0,1.55,0,0,0,0.77,0.77,0,2.32,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.77,0,0,0,0,0.268,0,0.672,0.403,0,2.794,29,109,1 0.28,0.14,0.14,0,0,0,0.14,0,0.42,0,0.84,0.98,0,0,0,0,0.28,0,1.82,2.53,1.12,10.82,0.84,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.137,0.045,0.342,1.233,14.88,79,1622,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.11,0,0.49,0.158,0.015,8.55,669,1351,1 0,0,0,0,0.8,0,0,0,0,0,0,1.6,0,0,0,0.8,0.8,0,2.4,0,2.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0.274,0,0.823,0,0,2.815,29,107,1 1.63,0,1.63,0,0,0,0,0,1.63,0,0,0,0,0,0,1.63,0,0,3.27,0,3.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0.266,0,2,16,36,1 0.69,0,0,0,1.39,0,0.69,0,0,0,0,0.69,0,0,0,0,0,0,3.49,0,1.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.196,0,0,6.1,57,183,1 0,0,0,0,0,0,0,1.04,0,0,0.52,1.04,0.52,0,0,1.04,0,0,3.66,1.04,1.04,0,1.57,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.539,0.269,0,5.787,47,272,1 0,0,0.32,0,0.65,0.65,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.28,0,3.25,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0.111,0,0,0.166,0,1.494,10,139,1 0,0,0.32,0,0.65,0.65,0.32,0.32,0,0,0,0.32,0.32,0,0,0.32,0.32,0,2.28,0,3.25,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0.111,0,0,0.166,0,1.494,10,139,1 0.08,0.08,0.76,0,0.85,1.02,0.25,0.17,0.59,0.08,0.17,0.59,0.17,0,2.21,0.25,0.08,0.93,1.62,0.17,0.42,0,0.85,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.17,0.08,0.08,0.08,0,0,0,0.065,0,0.486,0.118,0.013,7.561,669,1414,1 0.17,0,0.17,0,1.52,0.35,0.05,0.05,0.05,0.05,0.05,0.52,0.29,0.05,0,0,0.64,0.05,4.21,0.23,1.11,0,0.82,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0,0.05,0,0,0,0.01,0.052,0,0.01,0.167,0,1.818,13,462,1 0.27,0,0.27,0,0,0,0,0,0,0,0,1.62,0.27,0,0,0,0.27,0,4.87,0,0.81,0.27,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0.874,0.051,0.051,5.582,61,374,1 0,1.32,0,0,0,0.44,0,0,1.32,0,0,2.65,0,0.44,0,0.44,0,0.44,3.53,0,1.76,0,0,1.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.066,0,0.663,0.132,0.066,8.666,123,442,1 0,0,0,0,2.29,0,0,0,0,0,0,1.14,0,0,0,4.59,0,0,3.44,0,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.14,0,0,0,0,0.646,0,1.939,0,0,8.461,30,110,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.606,0.606,0,3.111,10,28,1 0.54,0,0,0,2.16,0,0,0.54,0,1.08,0,0,0,0,0,0,1.08,0,2.7,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0.051,0,0,0,0,1.49,19,82,1 0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,0,0.91,0,2.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.141,0,0.567,0,0,2.9,28,87,1 1.06,0,0.7,0,1.06,0,0.7,0,0,0.7,0,0.7,0,0,0.35,0.7,0,0,6,0,1.41,0,0.35,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0.381,0.063,0,2.021,12,95,1 0.68,0.68,0.68,0,0.68,0,2.73,0,0,0.68,0,2.05,0,0,0,0,0,0.68,4.1,0,2.73,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.244,0,0,2.472,9,89,1 0,0,0,9.16,0.27,0,0.55,0.27,0.27,0.27,0,0.27,0,0,0,0,1.11,0,0.55,0.27,0.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.387,0,0,0.301,0,0.043,19.482,694,1130,1 0,0,0.09,0,0.58,0.29,0.09,0,0.38,0,0.29,0.48,0.38,0,0,0,0.19,0,0.77,0,0.67,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0,0,0.09,0,0.09,0,0,0,0.063,0.047,0,0.559,0.047,0.031,1.694,23,432,1 0,0,1.61,0,0,0,1.61,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0.268,0,0.804,0,0,2.466,17,37,1 0,0,0,0,0.97,0,0.97,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0.97,0,0,0,0.97,0.97,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0.503,0.167,0,0,0.167,1.342,3.5,13,77,1 0,0.56,0.56,0,1.12,0.56,2.25,0,0,0.56,0,0.56,0,0,0,0,0,0.56,3.38,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.56,0,0,0,0,0.083,0,0.503,0,0.083,16.304,148,375,1 0.1,0.1,0.71,0,0.61,0.3,0.4,0.1,1.42,0.81,0.1,0.5,0,0,0,0.1,0,1.11,2.23,0.5,2.03,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.264,0.976,0.397,0.033,3.186,56,1042,1 0.8,0,0.8,0,1.61,0,0,0,0,0,0,0,0,0,0,0.8,0.8,0,1.61,0,2.41,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.562,0.36,0,2.638,22,124,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.212,0,4.818,25,53,1 0,1.47,0,0,0,0,1.47,0,0,0,0,0,0,0,0,1.47,1.47,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0.338,0,0.508,0.169,0.169,10.625,140,170,1 0.05,0.05,0.4,0,0.34,0,0,0,0.57,0.05,0,0.28,0.11,0,0,0.17,0,0,1.04,0.05,0.92,0,0,0.05,0,0,0,0,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0.05,0,0,0,0.019,0.099,0,0.099,0.079,0.009,4.881,95,1313,1 0,0.4,0,0,0.81,0,0.81,0,0,0.4,0,0,0,0,0,0,0,0,1.22,0,0.81,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.123,0.246,0.061,0,0.123,0.185,4.793,23,302,1 0.29,0.58,0.58,0,0.87,0,0.58,0,0,1.16,0,0.87,0,0,0,0,0.87,0,2.62,0,1.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.054,0,0,0.271,0,1.67,14,162,1 0.18,0,0.18,0,1.57,0.36,0.06,0.06,0.06,0.06,0.06,0.54,0.3,0.06,0,0,0.72,0.06,4.41,0.24,1.08,0,0.84,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0,0.01,0.052,0,0.01,0.169,0,1.766,12,447,1 0.06,0,0.24,0,0.1,0,0,0.17,0.17,0.17,0,0.1,0.03,0,0,0.03,0,0,0.45,0,0.2,0,0.03,0,1.18,1.22,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0.06,0,0.45,0,0,0,0.179,0.305,0.029,0.029,0.011,0.023,2.813,26,2510,1 1.24,0.41,1.24,0,0,0,0,0,0,0,0,0.41,0,0,0,0.41,0,0.82,3.73,0,1.24,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.068,0,0.481,0.549,0,3.166,19,114,1 0.08,0,0.32,4.31,0.08,0.16,0.08,0.08,0,0,0.08,0.24,0.32,0,0,0.08,0,0.32,1.87,0,0.57,0,0.16,0.24,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0,0.16,0,0,0,0.344,0.068,0,0.55,0.082,0.151,15.547,339,2923,1 0.1,0.1,0.71,0,0.6,0.3,0.4,0.1,1.42,0.81,0.1,0.5,0,0,0,0.1,0,1.01,2.23,0.5,2.03,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.264,0.977,0.397,0.033,3.166,56,1045,1 0,0,0,0,0,0,0.45,0,0,0.45,0.22,0.22,0,0,0.22,0.22,0,0.22,1.58,0,1.13,13.34,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.037,0.15,1.584,13.936,114,1324,1 0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,5.26,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.212,0,4.818,25,53,1 0,0,0,0,0,0.4,0,0,0,0.81,0,0,0,0,0,0.4,0,0,1.22,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.198,0.529,0.33,0.198,0,0,5.019,18,261,1 0,0,0,0,0.38,0.38,0.38,0.38,0,0,0.38,0,0,0,0,0.38,0,0,3.43,0,2.29,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0,0.121,0,0,2.08,12,104,1 0,0,0,0,0,0,1.78,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.544,0,0,1.777,5,16,1 0,0.06,0.2,0,0.61,0.13,0,0,0.75,0,0.27,0.75,0.27,0,0,0,0.2,0.13,1.16,0,1.23,0,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.06,0,0,0,0.034,0.057,0,0.472,0.092,0.023,2.086,104,703,1 0,1.36,0,0,0,0,1.36,0,0,0,0,0,0,0,0,1.36,1.36,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0.17,0.17,0.17,9.411,128,160,1 0,0,0,0,0,2.3,0,0,0,0,0,0.76,0.76,0,0,0,0,0,2.3,0,1.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.868,0,2.894,0.868,0,5.607,25,157,1 1.63,0,0,0,2.45,0,0,0,0,0,0,0,0,0,0,0.81,0,0,3.27,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.258,0,0,2.826,20,65,1 0.16,0,0.67,0,0.33,0.16,0.33,0.84,0.16,0.5,0.33,1.51,0,0,0,0,1.68,0.33,2.02,1.68,3.87,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0.224,0,1.151,0.056,0,4.928,63,621,1 0.09,0.49,0.59,0,0.39,0.19,0,0,0.09,0.39,0,1.58,0.19,0,0,0,0.09,0,3.75,0,1.08,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.778,0.037,0,5.213,0.979,0,5.781,54,740,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.597,0,0,4.153,26,54,1 0.17,0,0.17,0,1.45,0.34,0.05,0.05,0.05,0.05,0.05,0.52,0.29,0.05,0,0,0.69,0.05,4.25,0.23,1.04,0,0.75,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.05,0,0.01,0.162,0,1.794,12,454,1 0,0,0.27,0,0.54,0.27,0.27,1.08,0,0.81,0,0,0,0,0,0,0,0,2.45,0,1.36,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.04,0,0.489,0.04,0,2.121,19,227,1 1.61,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0.8,0,0,3.22,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.511,0,0,2.909,20,64,1 0,0.55,0.55,0,1.1,0.55,2.2,0,0,0.55,0,0.55,0,0,0,0,0,0.55,3.31,0,1.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0.165,0,0.496,0,0.082,16.782,148,386,1 0,0.55,0.55,0,1.1,0.55,2.2,0,0,0.55,0,0.55,0,0,0,0,0,0.55,3.31,0,1.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0.165,0,0.496,0,0.082,16.826,148,387,1 0,0,0.31,0,0.63,0.63,0.31,0.31,0,0,0,0.31,0.31,0,0,0.31,0.31,0,2.55,0,3.19,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0.108,0,0,0.162,0.054,1.515,10,144,1 1.04,0,0.69,0,1.04,0,0.69,0,0,0.69,0,0.69,0,0,0.34,0.69,0,0,5.9,0,1.38,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0.379,0.063,0,2.042,12,96,1 0,1.56,0,0,0,0,1.56,0,0,1.56,0,0,0,0,0,1.56,1.56,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0.183,0,0,0.183,0.183,11.714,140,164,1 0,0.54,0.54,0,1.08,0.54,2.16,0,0,0.54,0,0.54,0,0,0,0,0,0.54,3.24,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0.162,0,0.488,0,0.081,15.16,148,379,1 0.14,0,0.57,0,0.28,0.14,0.28,0.28,0,0.43,0.14,0.28,0,0,0,1.88,0.14,0.14,1.01,0,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0,0.07,0,0.21,0,0,2.293,32,477,1 0,0,0.44,0,0.22,0.22,0,0,0.66,0.44,0.22,0.88,0,0,0,0.22,0,0,1.32,0,1.1,0.22,0.22,0.22,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0.065,0.261,0,0.13,0.196,0,7.4,75,629,1 0,0,0.29,0,0.88,0.14,0,0,0.88,0,0,0.73,0.14,0,0,0,0.29,0,2.2,0,0.88,0,0.14,0.29,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.097,0,0.388,0.048,0.024,2.264,49,428,1 0.07,0.37,0.81,0,0.51,0.29,0.07,0,0.07,0.37,0.07,1.48,0.14,0,0.07,0,0.14,0.44,3.55,0,1.85,0,0,0.07,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0,0,0,0.052,0.073,0,0.167,0.167,0.01,3.412,44,795,1 0,0,0.31,0,0.63,0.63,0.31,0.31,0,0,0,0.31,0.31,0,0,0.31,0.31,0,2.55,0,3.19,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0.108,0,0,0.163,0.054,1.515,10,144,1 0,0,0.31,0,0.63,0.63,0.31,0.31,0,0,0,0.31,0.31,0,0,0.31,0.31,0,2.55,0,3.19,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0.108,0,0,0.163,0.054,1.515,10,144,1 0.17,0,0.17,0,1.52,0.35,0.05,0.05,0.05,0.05,0.05,0.52,0.29,0.05,0,0,0.64,0.05,4.21,0.23,1.11,0,0.81,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0,0,0,0,0,0.01,0.052,0,0.01,0.167,0,1.838,13,467,1 0.48,0,0.97,0,0.48,0,0.97,0,0,0,0,0.48,0,0,0,0,0.48,0.48,4.36,0,1.45,0,1.45,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,1.085,0.232,0.077,5.166,58,186,1 1.24,0.41,1.24,0,0,0,0,0,0,0,0,0.41,0,0,0,0.41,0,0.82,3.73,0,1.24,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.068,0,0.48,0.549,0,3.166,19,114,1 0.34,0.42,0.25,0,0.08,0.42,0.08,0.25,0.08,1.62,0.34,0.51,0.94,0,0.17,0.08,0,0,3,0,0.94,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0.065,0,0.261,0.294,0.065,3.282,62,535,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.233,0,0.233,9.5,84,323,1 0,1.63,0,0,0,0,1.63,0,0,1.63,0,0,0,0,0,0,0,0,1.63,0,3.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,2,12,1 0.17,0,0.08,0,0.43,0.08,0.08,0.43,0.08,0.08,0,0.6,0.17,0.17,0,0,0.17,0.08,1.2,0,3.14,0,0.34,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.085,0.028,0.092,0.014,0,4.16,48,1140,1 0.17,0,0.08,0,0.43,0.08,0.08,0.43,0.08,0.08,0,0.6,0.17,0.17,0,0,0.17,0.08,1.2,0,3.14,0,0.34,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.085,0.028,0.092,0.014,0,4.16,48,1140,1 0,0.34,0.69,0,0.34,0.69,0.34,0,0,1.04,0.34,1.38,0,0,0,0.69,0,0.69,4.86,0,1.73,0,0,1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.058,0,0.638,0.174,0,2.17,8,89,1 0,0,0.29,0,0.29,0.29,0.29,0.29,0,0,0.58,0.87,0,0,0,0.87,0.58,0.29,2.61,2.61,2.9,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.046,0,0.14,0,0.14,4.892,139,274,1 0,0,0,0,0.45,0.45,0.45,0,0,0,0,0.45,0,0,0,0,0,0,0.9,0,0,9.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.072,0,1.083,7.857,45,440,1 0,0,0.4,0,0,0,0.2,0,0.8,0.2,0,0.4,0,1.41,0.2,0.4,0,0,3.44,3.03,2.22,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.033,0.066,0,0.133,0.066,0,2.704,30,192,1 0,0,0,0,0,0,1.33,0,0,0,0,0,0,0,0,0,0,0,1.33,0,5.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.213,1.492,0,29.125,223,233,1 0.22,0.22,0.22,0,1.77,0.22,0.44,0.44,0.22,2.88,0,0.88,0.22,0,1.1,0.44,0,0.44,3.32,0,3.32,0,0.44,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0.588,0.156,0,86.7,1038,1734,1 0,0.9,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,1.81,6.36,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.146,0,0.292,0,0,3,38,75,1 0.74,0,0,0,0.74,0,0.74,0,0.74,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0.467,0.233,0,1.846,12,72,1 0,0,0,42.81,1.28,0,0.28,0,0,0,0,0.28,0,0,0,0.14,0,0,1.7,0,0.85,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0.026,0.078,0,0.13,0,0,7,137,826,1 0,0,0.37,0,1.13,0,0.37,0,0,0.75,0,0.37,0,0,0,0.37,0.75,0,2.65,0,1.13,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.13,0,0,0,0,0.063,0,0.882,0.189,0,4.08,78,253,1 0,0,0,0,0,0,0,0,0,0,0,0,1.33,0,0,0,2.66,5.33,2.66,0,2.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.799,0.514,0,1.823,11,62,1 0,0,0.72,0,1.45,0.36,0,0,0,1.45,0,1.09,0,0,0,0.72,0,0,2.54,1.81,0.72,0,0,0,0.36,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.056,0,0.963,0.113,0.17,2.622,47,139,1 0.54,0,1.08,0,0.54,0,1.08,0,0,0,0,0.54,0,0,0,0.54,0.54,0,4.32,0,1.08,0,1.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,1.218,0.261,0,5.323,68,181,1 0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,1.81,1.81,0,0,1.81,0,0,0,5.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.628,0,0.628,0.943,0,2.944,9,53,1 0,0,0.48,0,0.96,0,0.48,0,0,0,0,0,0,0,0,0.48,0.96,0,1.92,0,1.44,0,0.48,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.44,0,0,0,0,0.073,0,0.515,0.957,0,6.833,78,328,1 0,0,0,0,0.98,0,0,0,0,0.98,0.98,0.98,0,0,0,0.98,0,0.98,2.94,0,1.96,0,0,0,0.98,0.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.98,0,0,0,0,0,0.278,0,0,2.95,18,59,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.833,0,0,1.375,3,11,1 0,0,0,19.16,0.18,0,0.18,0,0,0,0,0,0,0,0,1.89,0,0,0.56,0,0,9.48,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.294,25.22,180,1261,1 0,0,0.6,0,0,0.6,0,0,0.6,0,0,1.82,0,0,0,0.3,0,0,2.74,0,1.21,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.143,0.047,0.191,0.143,0,2.041,31,196,1 0,0,0,0,0,0,0,0,0,0,0,1.33,0,0,0,1.33,0,0,2.66,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.326,0,0,2.2,11,44,1 0.37,0.17,0.3,0.06,0.23,0.17,0.03,0.95,0.37,0.37,0.1,0.64,0.61,0.34,0.2,0.51,0.34,0.34,2.75,0.13,1.36,0,0.27,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0,0.03,0,0,0,0.011,0.041,0.071,0.379,0.136,0,3.341,181,1955,1 0,0,0.6,0,0,0.6,0,0,0.6,0,0,1.81,0,0,0,0.3,0,0,2.72,0,1.21,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.142,0.047,0.19,0.142,0,2.03,31,199,1 0.58,0,0,35.46,0.58,0,0.58,0.58,0,0,0,0,0,0.58,0,0.58,0.58,0.58,0.58,0,1.74,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.239,0.239,0,3.338,123,207,1 0,0,1.4,0,0.46,0,0.46,1.4,0,0.46,0,0,0,0,0,0,0,0,2.8,0,1.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0,0,0,0.123,0,0.37,0,0,6.137,54,313,1 0,0,0.3,0,0.3,0.91,0,0.3,0,0,0,0.3,0.3,0,0,0.3,0.3,0.3,2.12,0,3.03,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0.211,0,0,0.211,0.052,1.745,11,185,1 0,0,0.3,0,0.3,0.9,0,0.3,0,0,0,0.3,0.3,0,0,0.3,0.3,0.3,2.11,0,3.02,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0.21,0,0,0.21,0.052,1.738,11,186,1 1.19,0.59,0,0,0.59,0,0,0.59,0,0,0,0,0.59,0,0,0,0,0.59,3.57,0,6.54,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.103,0,0,1.437,11,161,1 0.5,0.25,0.42,0,0.08,0.23,0.02,0.35,0.35,0.69,0.21,0.9,0.5,0.92,0.02,0.33,0.42,0.02,3.05,0,1.43,0,0.94,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.02,0,0.02,0,0,0.069,0,0.325,0.523,0.124,6.723,445,4128,1 0.58,0,0,35.46,0.58,0,0.58,0.58,0,0,0,0,0,0.58,0,0.58,0.58,0.58,0.58,0,1.74,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.239,0.239,0,3.338,123,207,1 0,0,0.3,0,0.3,0.91,0,0.3,0,0,0,0.3,0.3,0,0,0.3,0.3,0.3,2.12,0,3.03,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0.211,0,0,0.211,0.052,1.752,11,184,1 0,0,0.3,0,0.3,0.91,0,0.3,0,0,0,0.3,0.3,0,0,0.3,0.3,0.3,2.12,0,3.03,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0.211,0,0,0.211,0.052,1.752,11,184,1 0.47,0,1.19,0,0.23,0.23,0,0,0,0.47,0,1.43,0,0,0,0.71,1.43,0,5.26,0,2.63,0,0.71,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0.178,0,0.402,0.402,0.089,5.681,49,392,1 0,0,1.79,0,0,0.59,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.103,10,204,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.347,0,0,3,7,9,1 0,0,0.48,0,0.72,0.48,0,0,0.24,0,0.48,0.24,0,0,0,0.48,0,0,1.2,0,1.44,0,0.48,0.24,0,0,0,0,0,0.24,0,0,0,0,0,0,0,0,0,0.24,0,0,0.24,0,0,0,0,0.24,0,0.036,0,0.036,0.184,0,2.336,66,264,1 0,0,0.48,0,0.72,0.48,0,0,0.24,0,0.48,0.24,0,0,0,0.48,0,0,1.2,0,1.44,0,0.48,0.24,0,0,0,0,0,0.24,0,0,0,0,0,0,0,0,0,0.24,0,0,0.24,0,0,0,0,0.24,0,0.036,0,0.036,0.184,0,2.336,66,264,1 0.34,0.25,0.25,0,0.08,0.43,0.08,0.25,0.08,1.47,0.34,0.51,0.95,0,0.17,0.08,0,0,3.03,0,0.77,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0.049,0,0.263,0.263,0.065,3.076,62,526,1 0.43,0,0,0,0.87,0.87,0,0,0,0.43,0,2.18,0,0,0,0,1.74,0,0.87,0,0.87,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.802,0,0,5.114,107,179,1 0.43,0,0,0,0.87,0.87,0,0,0,0.43,0,2.18,0,0,0,0,1.74,0,0.87,0,0.87,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.874,0,0,5.114,107,179,1 0,0,0.29,0,0.29,0.29,0.29,0.29,0,0,0.58,0.87,0,0,0,0.87,0.58,0.29,2.61,2.61,2.9,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.046,0,0.14,0,0.14,4.892,139,274,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.4,7.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.543,0,0,5,15,50,1 0,0,0,0,0.36,0.36,0,0.36,0.36,0.36,0,0.36,0,0,0,0,0.73,0,2.94,0,4.04,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0.653,0.118,0,1.53,13,75,1 0,0,0.97,0,0.38,0.19,0,0,0,0.19,0,1.16,0,0,0,0,0,0,0.58,0,0.38,0,0.77,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0.208,0,0.364,0.312,0,7.541,192,543,1 0.17,0,0.08,0,0.42,0.08,0.08,0.42,0.08,0.08,0,0.6,0.17,0.17,0,0,0.17,0.08,1.2,0,3.17,0,0.34,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.085,0.028,0.099,0.014,0,4.07,48,574,1 0.52,0,2.38,0,0.26,0,0.26,0,0.52,0,0.26,0,0,0,0,0.79,0,0,1.32,0,1.05,0,0,0.52,0,0,0,0,0,0,0,0,0.26,0,0,0.26,0.26,0,0.52,0,0,0,0,0,0,0,0,0,0,0.69,0,0.327,0,0,5.549,71,566,1 0.46,0.31,0.46,0,0.05,0.13,0.05,0.26,0.44,0.75,0.26,0.96,0.57,1.22,0,0.1,0.44,0,3.21,0,1.48,0,1.01,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.068,0,0.346,0.793,0.159,6.05,199,3213,1 0.18,0,0.54,0,1.09,0.18,0.54,0,0.54,0.54,0,0.18,0,0,0.18,0.36,0.18,0.54,1.82,0,2,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18,0,0,0,0,0,0,0,0.166,0,0.249,0.305,0,3.921,59,447,1 0.17,0,0.08,0,0.42,0.08,0.08,0.42,0.08,0.08,0,0.6,0.17,0.17,0,0,0.17,0.08,1.2,0,3.17,0,0.34,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.085,0.028,0.099,0.014,0,4.07,48,574,1 0,0,1.26,0,0,0,0,0,0,0,0,0,1.26,0,0,0,2.53,5.06,2.53,0,3.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.9,0.475,0,1.763,11,67,1 0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,0.36,0,0,3.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.73,0,0,0,0,0,0,0.053,0.053,0,18.37,134,496,1 0.37,0.75,1.13,0,0.37,0,0,0.37,0.37,1.88,0.37,2.64,0,0.37,0,0.37,0,0,2.26,0,4.52,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.197,0,1.248,0.197,0.065,58.705,842,998,1 0,0.57,0,0,0,0,0,0,0,0,0.57,0.57,1.15,0,0,0,0,1.73,3.46,0,1.15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.107,0,0,1.421,7,54,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.411,0,0,4.307,15,56,1 0.09,0.49,0.59,0,0.39,0.19,0,0,0.09,0.39,0,1.59,0.19,0,0,0,0.09,0,3.79,0,1.09,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.778,0.037,0,5.924,1.33,0,5.8,54,725,1 0,0,0,0,0.38,0.38,0.38,0.38,0,0,0.38,0,0,0,0,0.38,0,0,3.87,0,1.93,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.123,0,0,2.062,12,99,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.17,0,3.17,0,3.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.298,0.597,0,3.333,12,30,1 0,0.49,1.97,0,2.46,0,0,0,0,0,0,0,0.49,0,0,0.49,1.47,0.49,4.43,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.424,0,0,12.692,152,330,1 0,0,0,0,0.38,0.38,0.38,0.38,0,0,0.38,0,0,0,0,0.38,0,0,3.87,0,1.93,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.123,0,0,2.062,12,99,1 0.06,0.12,0.77,0,0.19,0.32,0.38,0,0.06,0,0,0.64,0.25,0,0.12,0,0,0.12,1.67,0.06,0.7,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0.041,0.031,0,0.25,0.073,0,1.764,37,766,1 0.74,0.74,0.74,0,0,0,0.37,0,0.37,1.12,1.12,1.12,0,0,0,0,0,0.74,2.99,0,2.24,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.061,0.061,0.122,0,4.727,57,208,1 0,0,0,0,1.58,0,0.39,1.19,0,0.39,0,0.79,0,0,0,0,1.58,0.39,3.96,0,1.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.705,0.211,0,1.903,13,118,1 1.24,0,0.82,0,0,0,0.41,0,0,0.41,0,0.41,0,0,0,1.65,0.41,0,2.9,0,0.41,0,0.41,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.193,0,0.904,5.3,0,7.478,92,344,1 0,0.09,0.14,0,1.04,0.09,0.09,0,0.79,0,0.04,0.29,0.19,0,0,0,0.14,0.04,1.53,0.24,1.23,0,0.29,0.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.04,0,0.04,0,0,0,0.015,0.119,0.007,0.431,0.111,0,3.37,87,1645,1 1.24,0,0.82,0,0,0,0.41,0,0,0.41,0,0.41,0,0,0,1.65,0.41,0,2.9,0,0.41,0,0.41,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.193,0,0.904,5.3,0,7.478,92,344,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.722,57,85,1 0,0,0,0,1.21,0,1.21,1.21,1.21,1.21,1.21,1.21,0,0,0,0,4.87,0,2.43,1.21,4.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.555,0,0,104.666,311,314,1 1.44,0,0,0,0,0,0,0,0,0.48,0,2.4,0,0,0,0.96,0,0,6.73,0,1.92,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0.174,0.087,0,1.612,12,50,1 0.47,0.31,0.47,0,0.05,0.13,0.05,0.26,0.42,0.76,0.26,0.97,0.57,1.23,0,0.1,0.47,0,3.23,0,1.49,0,0.99,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.067,0,0.328,0.858,0.157,5.928,199,3160,1 0,0,0,0,1.47,1.47,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.562,0,0,5,95,170,1 0.53,0,1.06,0,0.53,0,1.06,0,0,0,0,0.53,0,0,0,1.06,0.53,0,4.25,0,1.06,0,1.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0,0,1.208,0.259,0,5.558,76,189,1 1.24,0.41,1.24,0,0,0,0,0,0,0,0,0.41,0,0,0,0.41,0,0.82,3.73,0,1.24,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.068,0,0.48,0.549,0,3.166,19,114,1 0,0.55,0.55,0,2.23,0,0.55,0,0,0,0,0.55,0.55,0,0,0.55,2.79,0,3.91,0,1.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.259,0,1.208,0.345,0,4.761,140,200,1 0,0.55,0.55,0,2.23,0,0.55,0,0,0,0,0.55,0.55,0,0,0.55,2.79,0,3.91,0,1.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.258,0,1.207,0.345,0,4.761,140,200,1 0.37,0.75,1.13,0,0.37,0,0,0.37,0.37,1.89,0.37,2.65,0,0.37,0,0.37,0,0,2.27,0,4.54,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0.196,0,1.246,0.196,0.065,62.5,845,1000,1 0.34,0,0.69,0,0.17,0.51,0,0.51,0.17,0.17,0.17,1.38,0,0,0,0.34,1.03,0.17,1.9,1.55,3.81,0,0.17,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0.17,0,1.275,0.141,0,5.598,78,711,1 0,0.89,1.15,0,0.12,0,0,0.12,0.25,0.12,0.12,0.38,0.12,0,1.15,0,0.12,2.04,2.81,0.12,1.27,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0.164,0,0.371,0.061,0,2.89,84,477,1 0,0.47,0.47,0,1.89,0,1.18,0.23,0,0.47,0.23,0.7,0.23,0,0.47,0.23,1.41,0,2.83,0,1.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,1.844,13,83,1 0.47,0.31,0.47,0,0.05,0.13,0.05,0.26,0.44,0.76,0.26,0.97,0.58,1.26,0,0.26,0.44,0,3.24,0,1.5,0,1.02,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0.009,0.067,0,0.329,0.78,0.162,6.045,193,3059,1 0,0,0,0,1.35,0.45,0,0,0,0,0,0,0.45,0,0,0.45,0.45,0.45,1.8,0,1.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.138,0,0.138,0,0,5.809,46,122,1 0,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.939,0,0,1.379,8,40,1 0,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.947,0,0,1.379,8,40,1 0,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.949,0,0,1.379,8,40,1 0.19,0,0,0,0.09,0.09,0.19,0,0,0.09,0.09,0.69,0.09,0,0,0,0,0.19,1.38,0,0.49,0,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0.09,0.017,0.068,0,0.586,0.189,0.017,2.349,31,477,1 1.03,0,0.68,0,1.03,0,0.68,0,0,0.68,0,0.68,0,0,0.34,0.68,0,0,5.86,0,1.37,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0.503,0.062,0,1.82,12,91,1 0.27,0,0.27,0,0,0,0,0,0,0.27,0.27,0.55,0,0,0,0,0,0,2.2,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.136,0.182,0,8.207,30,435,1 0,1.09,0,0,0,0,0,1.09,0,0,0,0,0,0,0,0,0,0,0,0,1.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.09,0,0,0,0,0,0,0,1.09,0,0,0,0.173,0.519,0,0,0.692,0,4.941,25,84,1 0,0.89,1.14,0,0.12,0,0,0.12,0.25,0.12,0.12,0.38,0.12,0,1.14,0,0.12,2.04,2.8,0.12,1.27,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0.165,0,0.371,0.061,0,2.878,84,475,1 0,0,0,0,1.2,0,1.2,1.2,1.2,1.2,1.2,1.2,0,0,0,0,4.81,0,2.4,1.2,3.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.331,0,0,50.166,295,301,1 0.49,0,0.74,0,0.24,0.74,0.24,0.74,0.24,0.24,0.24,1.23,0,0,0,0,1.23,0,1.23,1.73,2.47,0,0.24,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.116,0,1.634,0.155,0,3.975,47,485,1 0,0,0.6,0,0.6,0,0.6,0,0,0,0,0,0.6,0,0,0,0,0.6,1.81,0,1.21,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.412,0,0.619,0.103,0,6.166,33,259,1 0,0.46,0.46,0,1.38,0,0,1.85,0,0.92,0.46,0,0,0,0,0.92,0,0,0.92,0.46,1.38,0,0,0.46,0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0.46,0,0,0,0,0,0,0,0,0,0.072,0,0.795,0.217,0,4.869,66,224,1 0.67,0,0.67,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,1.35,0,1.35,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.362,0,0,3.384,37,132,1 0,0,0,0,0,0,1.47,1.47,1.47,1.47,1.47,0,0,0,0,0,2.94,0,0,1.47,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0.147,0,0.294,0.147,0,72,281,288,1 0,0.49,0.49,0,1.49,0,0,0,0,0.99,0.49,0,0,0,0,0.49,0,0,0.99,0.49,1.99,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0.49,0,0,0,0,0,0,0,0,0,0.078,0,0.625,0.312,0,4.75,47,190,1 0,0.53,0,0,0,0.53,0.53,0,0,0,0,0,0,0,0,0,0,1.6,2.67,0,1.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.255,0,0,0,0,2.131,12,81,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.473,0,0,4.071,29,114,1 0,0.56,0.56,0,2.27,0,0.56,0,0,0,0,0.56,0.56,0,0,0.56,3.4,0,3.97,0,1.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.262,0,1.135,0.349,0,5.105,140,194,1 0.23,0.59,0.23,0,0.23,0.11,0,0,0.82,1.18,0.11,2,0.23,0,0,0,0.11,0,4.84,4.96,1.77,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0.186,0,0.13,0.168,0.018,5.76,175,795,1 0,0,0.56,0,1.12,0,0,0,0,0,0,0,0.93,0,0.18,0,0.37,0.37,3.18,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.189,0,0.441,0,0,1.372,4,70,1 0,0,0.47,0,1.42,0,0,0,0,0,0,0.95,0,0,0,0,0.95,0,2.38,0,2.38,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0.082,0,0.496,0.248,0,5.187,80,249,1 0,0,0.56,0,1.12,0,0,0,0,0,0,0,0.93,0,0.18,0,0.37,0.37,3.18,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.189,0,0.441,0,0,1.372,4,70,1 0,0,0.48,0,0.72,0.48,0,0,0.24,0,0.48,0.24,0,0,0,0.48,0,0.24,1.21,0,1.45,0,0.48,0.24,0,0,0,0,0,0.24,0,0,0,0,0,0,0,0,0,0.24,0,0,0.24,0,0,0,0,0.24,0,0.036,0,0.036,0.184,0,2.276,66,255,1 0,0.36,0.72,0,1.44,0,0.36,0,0,1.44,0.72,0.36,0.36,0,0,0,0,0,2.89,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.517,6.685,60,234,1 0.67,0,0.67,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,1.35,0,1.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.413,0,0,3.384,37,132,1 0,0.47,0,0,0.47,0,0,0,0,0,0.47,0,0,0,0,0.47,0,0.95,1.9,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.056,0,0,0,0,7.18,182,359,1 0,0.47,0,0,0.47,0,0,0,0,0,0.47,0,0,0,0,0.47,0,0.95,1.9,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.056,0,0,0,0,7.18,182,359,1 0.43,0.28,0.43,0,0.04,0.11,0.04,0.21,0.4,0.69,0.23,0.88,0.52,1.14,0,0.23,0.4,0,2.93,0,1.36,0,0.97,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.064,0,0.311,0.734,0.145,5.328,144,3016,1 0,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.303,0,0.909,0,0,2.857,11,40,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.11,0,0.49,0.158,0.015,8.55,669,1351,1 0,0,0,0,0,0,1.47,1.47,1.47,1.47,1.47,0,0,0,0,0,2.94,0,0,1.47,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0.147,0,0.294,0.147,0,71.5,281,286,1 0,0.56,0.56,0,2.25,0,0.56,0,0,0,0,0.56,0.56,0,0,0.56,3.38,0,3.95,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,1.217,0.347,0,5.105,140,194,1 0,0,0,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,11,20,1 0,0,0.55,0,1.11,0,0,0,0,0,0,0,0.92,0,0.18,0,0.37,0.37,3.14,0,0.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.186,0,0.434,0,0,1.377,4,73,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.462,0,0.462,0,0,3.125,6,25,1 0.47,0.31,0.47,0,0.05,0.15,0.05,0.23,0.44,0.76,0.26,0.97,0.58,1.27,0,0.26,0.44,0,3.25,0,1.5,0,1.11,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.068,0,0.344,0.784,0.154,6.094,193,3029,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,4.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.176,0,0.706,0,0,8.411,55,143,1 0,0.47,0,0,0,0.47,0,0,0.23,0.23,0,1.19,0.47,0,0,0.23,0,0.47,2.63,0,0.47,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0.23,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.327,1.357,0.046,5.769,72,450,1 0,0,0,42.73,0,0,0.42,0,0,0.42,0,0.42,0,0,0.42,0,0,1.28,2.99,0,2.13,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.349,0,7,105,441,1 0,0,0.54,0,1.08,0,0,0,0,0,0,0.18,0.9,0,0.18,0,0.36,0.36,3.06,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.029,0.178,0,0.416,0,0,1.373,6,92,1 0,0,0.58,0.58,0,0,0,0.29,0,0,0,0,0.29,0,0,0,0.29,0.58,2.91,0.87,1.74,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,1.434,0,0.047,3.281,64,361,1 0,0,0.48,0,1.44,0.48,0,0,0,0,0,0.96,0,0,0,0,0.96,0,2.41,0,2.41,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0.083,0,0.502,0.251,0,5.488,80,247,1 0,0,0.48,0,1.45,0,0,0,0,0,0,0.97,0,0,0,0,0.97,0,2.42,0,2.42,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0.084,0,0.421,0.252,0,5.173,80,238,1 0,0,0.49,0,1.47,0,0,0,0,0,0,0.98,0,0,0,0,0.98,0,2.45,0,2.45,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0.084,0,0.677,0.254,0,5.2,80,234,1 0.72,0,0,0,0,0,1.45,0,0,0,0,0.72,0,0,0,0,1.45,0,2.18,1.45,5.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.105,0,0,2.689,25,78,1 0.45,0.28,0.42,0,0.04,0.11,0.04,0.21,0.4,0.69,0.23,0.88,0.52,1.14,0,0.23,0.4,0,2.93,0,1.36,0,1,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.065,0,0.318,0.754,0.152,5.349,144,3033,1 1.17,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,1.17,0,3.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0,0,0.924,0,0,1.966,10,59,1 0,0,0,0,0.64,0,0,0,0,0,0.64,0.64,0,0,0,0,1.29,0,1.29,5.19,1.29,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.468,0,0.093,0,0,2.755,66,135,1 0,0,0,0,0.64,0,0,0,0,0,0.64,0.64,0,0,0,0,1.29,0,1.29,5.19,1.29,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.468,0,0.093,0,0,2.755,66,135,1 0,0,0,0,0.64,0,0,0,0,0,0.64,0.64,0,0,0,0,1.29,0,1.29,5.19,1.29,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.468,0,0.093,0,0,2.755,66,135,1 0,0,0.3,0,0,0,0,0,0,0.3,0,0.3,0,0,0.3,0.3,0,0.15,0.15,0,0.15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0,0,0,0,0,0.3,0.472,0.067,0,0,0.044,0.067,1.607,16,418,1 0.41,0,1.25,0,0.2,0.2,0,0,0,0.41,0,1.25,0,0,0,0.62,1.25,0,4.6,0,2.3,1.67,0.62,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0.153,0,0.345,0.345,0.306,5.132,37,426,1 0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.166,28,79,1 0.47,0.31,0.47,0,0.07,0.13,0.05,0.26,0.44,0.76,0.26,0.97,0.57,1.26,0,0.26,0.44,0,3.22,0,1.47,0,1.1,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.068,0,0.331,0.79,0.159,6.073,193,3043,1 0,0,0.55,0,1.11,0,0,0,0,0,0,0,0.92,0,0.18,0,0.37,0.37,3.15,0,0.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.185,0,0.464,0,0,1.392,4,78,1 0,0.63,0,0,1.59,0.31,0,0,0.31,0,0,0.63,0,0,1.27,0.63,0.31,3.18,2.22,0,1.91,0,0.31,0.63,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,1.59,0,0,0,0,0,0,0,0,0,0.278,0,0.055,0.501,0,3.509,91,186,1 0,0.56,0.56,0,2.25,0,1.12,0,0,0,0,0.56,0.56,0,0,0.56,3.38,0,3.95,0,2.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,1.13,0.347,0,4.875,140,195,1 0,0,0.55,0,1.11,0,0,0,0,0,0,0,0.92,0,0.18,0,0.37,0.37,3.15,0,0.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.185,0,0.464,0,0,1.392,4,78,1 0,0,0,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,2.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.443,0,0,4.652,31,107,1 0.17,0,0.17,0.44,0.17,0,0,0,0,0,0,0.35,0.52,0.17,0,0.08,0.52,0,4.04,0,2.64,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0,0,0,0,0,0,0,0.709,0.105,0,0,0,0,2.039,18,414,1 0,0,0,0,0,0,0,0,0,0.33,0,0.67,0,0,0,0,0,0,1.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0.33,0.33,0,0,0.28,0.28,0,0.112,0.336,0,2.96,19,222,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.88,2.65,0,0.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.88,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,1.512,7,62,1 0,0,0.12,0,0.36,0.24,0,0,0,0,0.12,0.12,0.12,0,0,0,0,0,1.21,0,0.96,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.043,0,0.693,0,0,1.335,11,195,1 0.19,0.19,0.29,0,1.07,0.19,0.19,0.97,0.87,0.58,0.09,1.07,0.19,0.87,0.09,0,0,1.17,3.81,0.68,1.75,0,0.09,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0.09,0,0,0,0,0,0,0,0,0,0,0.202,0.405,0.233,0.031,4.32,49,877,1 0,0,0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,1.56,6.25,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0.754,0.188,0,5.551,119,161,1 0.73,0,0.36,0,0.36,0.36,1.09,0,0,0,0,0.36,0,0,0,0.36,1.83,0.73,2.56,0,1.09,0,0.36,1.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.183,0,0.427,0.061,0,4.42,192,305,1 0,0,0.22,7.07,0,0,0,0.45,0,0,0,0,0,0,0,0.45,0,0,0.22,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0.22,0,0,0,0.153,0.092,0,0,0,0.03,2.47,27,425,1 0,0.19,0,0,0.68,0.09,0.09,0,0.29,0.09,0.48,0.77,0.09,1.65,0,0.58,0.87,0.19,3.21,0,2.43,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0.06,0.045,0,1.597,20,329,1 0,0.42,0.42,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0.84,2.95,0,2.53,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.075,0,0.6,0.3,0,4.02,82,197,1 0,0.42,0.42,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0.84,2.95,0,2.53,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.075,0,0.6,0.3,0,4.02,82,197,1 0,0.4,0.4,0,0.4,0,0.4,0,0,2.4,0,0,0,0,0.4,0.8,0,0,2,0.4,2,0,0,0,0,0,0,0,0,0,0.4,0,0.4,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0.232,0,0.116,0.116,0,4.058,54,207,1 0,0,0,0,0,0.63,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,1.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.613,0,1.251,12,174,1 0,1.25,0,0,0,0,1.25,0,0,0,0,0,0,0,0,1.25,1.25,1.25,1.25,0,3.75,0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.199,0,0,0.298,0,0,3.976,32,171,1 0,0,0.79,0,0.26,0,0.26,0.26,0,0,0,1.31,0,0,0,0,0,0.26,1.58,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.52,0.26,0,0,0.26,0,0,0,0,0,0,0,0.26,0.038,0.038,0,0.077,0,0,1.8,29,171,1 0,0.7,0,0,2.83,0,0,0,0,0.7,0,0.7,0,0,0,1.41,1.41,0,7.09,0,5.67,0,0,0,0,0,0,0,0,0,0,0,0.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.126,4.176,44,142,1 0,0.55,0.55,0,2.22,0,0.55,0,0,0,0,0.55,0.55,0,0,0.55,3.88,0,3.88,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.255,0,1.191,0.34,0,4.59,140,202,1 0,0,0.72,0,0.72,0,0.72,0,0,0,0,0,0.72,0,0,0,0,0,1.45,0,1.45,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.364,0,0.729,0.121,0,7.781,32,249,1 0,0,0.84,0,0.84,0,0.84,0,0,0,0,0,0.84,0,0,0,0,0,2.54,0,1.69,0,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.388,0,0.776,0.129,0,10.375,168,249,1 0,0,0.72,0,0.72,0,0.72,0,0,0,0,0,0.72,0,0,0,0,0,1.45,0,1.45,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.364,0,0.729,0.121,0,7.781,32,249,1 0,1.22,0.81,0,0.4,0,0.81,0.4,0,0.81,0,0.4,2.04,0,0,3.27,0,1.22,0.81,0,0.4,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0.64,0.8,0,7.651,181,505,1 0.34,0.05,0.58,0,0.63,0.17,0,0,0.75,0.23,0.34,1.27,0.34,0,0,0.58,0.05,0.17,3.01,2.61,1.5,0,0.17,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.204,0,0.13,0.186,0.027,4.225,131,1107,1 0.71,0,0,0,5,0,0,0,0,0,0,0,0,0,0,2.85,0,0,2.14,0,0,0,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.417,0,0,3.029,12,103,1 0.65,0,0,0,1.3,0,0,0,0,0,0.65,1.3,0.65,0,0,1.3,1.3,0,2.61,0,3.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,0.515,0.103,0,2.04,12,51,1 0,0,0,0,0,0,1.61,0,0,1.61,0,1.61,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,4.941,60,84,1 0,0,0,0,0.32,0,0,0.32,0.32,0.64,0,1.28,0,0,0,2.56,0.96,0,3.84,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.098,0.049,0.492,0,0,2.184,25,166,1 0,0.64,0.64,0,0.64,0,0.64,0,2.59,1.29,1.29,1.94,0,0,0,0.64,0.64,0.64,3.24,0,2.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.233,0,0,1.136,4,25,1 0,0,0,0,0.49,0,0.98,0,0,0,0,0.98,0,0,0,0,0.98,0,2.45,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.064,0.258,0,0.645,0.064,0.064,3.552,25,135,1 0.44,0,0.88,0,0.44,1.32,0,0,0,0,0,0,0,0,0,0,0,0.44,1.76,0,2.2,0,2.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.222,0,0,0.444,0.37,0,2.413,16,140,1 0,0,0.69,0,0.69,0,0.69,0,0,0,0,0,0.69,0,0,0,0,0,1.38,0,2.08,0,0,0.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0.701,0.116,0,8.781,34,281,1 0.44,0,0.88,0,0.44,1.32,0,0,0,0,0,0,0,0,0,0,0,0.44,1.76,0,2.2,0,2.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.222,0,0,0.444,0.37,0,2.413,16,140,1 0.44,0,0.88,0,0.44,1.32,0,0,0,0,0,0,0,0,0,0,0,0.44,1.76,0,2.2,0,2.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.222,0,0,0.444,0.37,0,2.413,16,140,1 0.44,0,0.88,0,0.44,1.32,0,0,0,0,0,0,0,0,0,0,0,0.44,1.76,0,2.2,0,2.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.222,0,0,0.444,0.37,0,2.448,16,142,1 0,0,0,0,0,0,0,0,0,0,0,0.59,0,0,0,0.59,0,0,1.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.177,0,0.443,0.088,0,1.693,16,83,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.11,0,0.49,0.158,0.015,8.55,669,1351,1 0,0,1.66,0,1.66,0,1.66,0,0,0,0,1.66,0,0,0,3.33,0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.284,0,1.424,0,0,24.333,59,146,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.11,0,0.49,0.158,0.015,8.55,669,1351,1 0.17,0.17,0,0,0.52,0,0,0.43,0,0.17,0.17,0.35,0,0,0,0.87,0,0,1.4,0.17,0.87,0,0,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.376,0,0.25,0.721,0,2.742,35,617,1 0,0,0.8,0,0.8,1.61,0,0,0,0,0,0.8,1.61,0,0,0,0,0,4.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.875,0,0,6,48,138,1 0,0.52,1.05,0,2.63,0.52,1.05,0,0,0,0.52,1.05,0,0,0,1.05,1.05,1.05,4.21,0,1.57,0,0.52,0,0,0,0,0,0,0,0,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.091,1.276,0.729,0.091,3.062,19,98,1 0.17,0,0.17,0,1.45,0.34,0.05,0.05,0.05,0.05,0.05,0.52,0.29,0.05,0,0,0.69,0.05,4.24,0.23,1.04,0,0.75,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.051,0,0.02,0.163,0,1.796,12,458,1 0.17,0.17,0,0,0.52,0,0,0.52,0,0.17,0.17,0.34,0,0,0,0.87,0,0,1.39,0.17,0.87,0,0,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.373,0,0.342,0.716,0,2.973,35,336,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.46,0,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.415,0,0,1.909,9,42,1 0.1,0.3,0.4,0,0.2,0.9,0.2,0.5,0.8,0.8,0.2,0.8,0,0,1.5,0,0.2,1.6,2.2,0.2,1,0,0.1,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0.175,0,0.307,0.175,0.014,6.937,669,1214,1 0,0,1.04,0,1.04,0,0,1.39,0.34,0,0,0.34,0,0,0,0,0,0,3.83,2.09,1.04,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.105,0,0.157,0.105,0,2.366,60,142,1 0,0.89,1.14,0,0.12,0,0,0.12,0.25,0.12,0.12,0.38,0.12,0,1.14,0,0.12,2.04,2.8,0.12,1.27,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0.185,0,0.371,0.061,0,2.878,84,475,1 0,0.89,1.15,0,0.12,0,0,0.12,0.25,0.12,0.12,0.38,0.12,0,1.15,0,0.12,2.04,2.81,0.12,1.27,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0.185,0,0.37,0.061,0,2.878,84,475,1 0.29,0.19,0.68,0,0,0.58,0,0.58,0.58,0.77,0,0.58,0.38,0,0.97,0,0.19,1.46,1.75,0.38,0.77,0,0.58,0.68,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.034,0,0.569,0.207,0.034,12.064,691,1689,1 0.31,0.2,0.72,0,0,0.62,0,0.62,0.62,0.93,0,0.62,0.41,0,1.04,0,0.2,1.56,1.87,0.41,0.83,0,0.62,0.72,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.548,0.199,0.033,14.301,685,1516,1 0,0.3,0.3,0,0.61,0.3,0,0,0,0.3,0.3,0.3,0,0,0,0.92,0,0,0.61,0,0,0,0,0,0,0,0,0,0,3.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0.094,0,0,0,0,2.141,38,212,1 0,0,0,0,1.13,0,1.13,0,0,0,0,0,0,0,0,1.13,1.13,0,1.13,0,0,0,0,0,0,0,0,0,0,0,0,0,1.13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.319,0,0,0,0.479,3.925,17,106,1 0.17,0,0.17,0,1.45,0.34,0.05,0.05,0.05,0.05,0.05,0.52,0.29,0.05,0,0,0.69,0.05,4.24,0.23,1.04,0,0.75,0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.051,0,0.02,0.163,0,1.796,12,458,1 0,0,0,0,0.32,0.64,0,0,0,0.64,0,0.32,0,0,0,0,0,0,1.94,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.052,0,0.263,0.105,0,3.418,110,188,1 0.31,0.2,0.72,0,0,0.62,0,0.62,0.62,0.93,0,0.62,0.31,0,1.14,0,0.2,1.56,1.87,0.41,0.83,0,0.62,0.72,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.548,0.199,0.033,14.283,685,1514,1 0,0.39,0.99,0,0.39,0,0.19,0,0.19,0.19,0,0.39,0,0,0,0,0.19,0.19,0.59,0.59,0.39,0,0.19,0.39,0,0,0,0.59,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0.074,0,0.174,0.548,0,4.965,97,993,1 0,0,0,0,0.43,0.86,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.068,0,0,0,0,2.923,55,114,1 0.1,0.5,0.6,0,0.3,0.2,0,0,0.1,0.4,0,1.6,0.2,0,0,0,0.1,0,3.81,0,1.1,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.78,0.037,0,5.942,1.334,0,5.838,54,724,1 0.39,0,0,0,0,0.39,0,0,0,0,0,1.19,0,0,0,0.39,0.39,0,2.39,0,2.78,0,1.19,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0.256,0,3.5,30,112,1 0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.098,0,0.392,0,0,3.965,67,115,1 0,0,0,0,0.54,0.27,0,1.62,0,1.62,0,0,0,0,0.54,0,0,0.27,2.16,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,1.62,0,0,0,0,0,0,0,0,0,0.038,0.038,0.463,0,0,7.941,65,405,1 0,0,0.26,0,0.26,0,0,0,0,0,0.26,1.06,0,0.26,0.26,0.8,0,0.26,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.299,0,0.471,0,0,2.088,15,188,1 0,0.9,0,0,0.9,0,0.9,0,0,0.9,0,0,0,0,0,1.81,0,1.81,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0.319,0,1.492,0,19.829,5.3,66,106,1 0.44,0.44,0,0,0,0,0,0,0,2.64,0,1.76,0,0,0,0,0,0.44,2.64,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.231,0,0,0.231,0,5.977,70,263,1 0,0.55,0.55,0,0.55,0,0,0.55,0,0,0,1.11,0,0,0,1.11,0,0.55,1.66,0,2.22,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0.484,0.08,0,8.375,85,201,1 0,0,0,0,1.21,0,0.8,0,0,0.8,0.4,0.8,0.4,0,0,1.61,0,0,1.61,0,1.21,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0.21,0,0.07,4.49,24,229,1 0,0.53,0,0,1.06,0,1.6,0,0,0.53,0,0,0,0,0.53,0,0,0.53,2.13,0,0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0.239,0.079,0.159,0,0,4.555,51,123,1 0,2.35,0,0,1.17,0,0,0,0,2.35,0,1.17,0,0,0,1.17,0,0,2.35,0,3.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.25,20,42,1 0,0,0,0,0,0,0,6.06,0,0,0,0,0,0,0,0,0,0,6.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0.48,0,1.684,10,32,1 0,0.8,0,0,0.8,0,0.8,0,0,0.8,0,0,0,0,0,0.8,0.8,0.8,1.6,0,1.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.135,0,0.27,0,0,3.115,19,81,1 0,0.8,0,0,0.8,0,0.8,0,0,0.8,0,0,0,0,0,0.8,0.8,0.8,1.6,0,1.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.134,0,0.269,0,0,3.115,19,81,1 0.59,0,0.35,0,1.66,0,0,0,0.23,1.3,0.71,2.49,0.59,0,0,0.59,0.11,0,4.51,0,1.66,0,0.47,0.83,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0.11,0,0,0,0,0.11,0,0,0,0,0.038,0,0.155,0.233,0.019,3.625,54,504,1 0.17,0.26,1.21,0,0.43,0.6,0.43,0.26,0.69,0.52,0.26,1.3,0.17,0,0.6,0.78,0.17,1.39,2.43,0.17,1.13,0,0.95,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0.08,0,0,0,0,0.108,0,0.271,0.243,0.013,6.395,583,1375,1 0.1,0.1,0.7,0,0.6,0.2,0.4,0.1,1.41,0.81,0.1,0.5,0,0,0,0.1,0,1.11,2.22,0.4,1.92,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.26,0.994,0.391,0.032,3.176,56,1042,1 0.22,0,0,0,0,0.22,0.22,0,0,0.22,0,0.22,0,0,0,0.22,0,0,2.03,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.271,0,0.647,0,0,1.869,21,215,1 0.1,0.3,0.4,0,0.2,0.9,0.2,0.5,0.8,0.8,0.2,0.8,0,0,1.6,0,0.2,1.7,2.2,0.2,1,0,0.1,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0.174,0,0.334,0.174,0.014,6.896,669,1200,1 0.49,0.49,0.49,0,0,0,0.49,0,0,0,0,1.98,0,0,0,0.49,0,0.49,3.46,0,0.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0.195,0,0.845,0.195,0,7.205,47,281,1 0,0,0.65,0,0.65,0,0.65,0,0,0,0,0.65,0,0,0,0.65,0,0,4.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0.233,0,0,2.5,23,135,1 0,0,1.25,0,1.25,0.62,0,0,0,0,0,0,0,0,0,1.25,0.62,0,0.62,0,1.88,0.62,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0.11,0,0.331,0.11,0.11,3.897,30,152,1 0.9,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0.9,0.9,1.81,0,2.72,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.564,0,0,2.818,19,62,1 0,0.29,0,0,0,0.29,0.29,2.04,0,0,0.29,1.16,0.29,0,0.29,1.16,2.33,1.16,2.33,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.132,0,0.044,0,0,1.559,24,145,1 0,0.95,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0.47,1.91,0,0,0.47,0,0,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.067,0.202,0,0.269,0,0,4.18,45,464,1 0.54,0.13,0.38,0,0.05,0.16,0,0.05,0.35,0.16,0.24,1.11,0.38,1.19,0.13,0.19,0.43,0.48,3.56,0,0.81,0,1.14,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0,0.02,0,0,0.086,0,0.268,0.15,0.159,6.761,195,3313,1 0.54,0.13,0.38,0,0.05,0.19,0,0.05,0.35,0.16,0.24,1.11,0.38,1.19,0.13,0.19,0.43,0.48,3.56,0,0.81,0,1.14,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0,0.02,0,0,0.086,0,0.273,0.15,0.159,6.789,195,3327,1 0.27,0.27,0.55,0,0.27,0.27,0,1.39,0.27,0.83,0.27,0.55,0,0,0,0,1.39,0.55,1.67,1.95,3.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0.279,0,2.001,0.093,0,3.706,63,341,1 0,0,0,0,0,0,0,0,0,0,0,0,0,2.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0.177,0,0,0.354,0,4.047,29,85,1 0.1,0.1,0.03,0,0.07,0.03,0,0.03,0,0.1,0,0.53,0,0,0,0.17,0.03,0,0.81,0.03,1.35,0,0.1,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0,0.03,0,0,0.071,0,0.013,0.065,0,2.11,46,3220,1 0.49,0.33,0.33,0,0.08,0.41,0.08,0.24,0,1.4,0.33,0.57,0.9,0,0.24,0,0,0,2.89,0,0.9,0,0.16,0.41,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.063,0,0.271,0.191,0.095,4.904,264,667,1 0,0.27,0.27,0,1.09,0,0,0,0.82,0.54,0,0.27,0.27,0,0,0.27,0.54,0,2.46,0,2.19,0,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.085,0.128,0,2.484,20,164,1 1.18,0.39,0.59,0,0,0.98,0.19,0.19,1.38,0.39,0,0.98,0,0.19,0,0.98,0,0,2.56,0.39,1.38,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.237,0,0.765,0.316,0.026,6.652,76,632,1 0,0,0,0,3.84,0,0,1.28,0,0,0,1.28,0,0,0,0,0,0,2.56,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.203,0,0,1.956,10,45,1 0.33,0.44,0.37,0,0.14,0.11,0,0.07,0.97,1.16,0.11,1.42,1.76,1.27,0.03,0.03,0.07,0.07,4.38,0,1.49,0,0.33,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0.03,0,0,0,0.006,0.159,0,0.069,0.221,0.11,3.426,72,819,1 0,0,0,0,0,0,0,3.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.04,0,0,0,0,0.198,0,0.198,0.198,0,3.857,25,81,1 0,0,0.78,0,1.17,0,0,0,0,0,0,0.39,0,0,0,0.78,0,0,1.56,0,1.96,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.094,0,4.111,20,222,1 0,0.34,1.02,0,0.68,0.34,0.34,0,0,0,0,0.34,0,0,0,2.04,0,0.34,4.76,0,2.38,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.167,0,0.222,0,4.008,6.978,56,328,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.25,0,2.12,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,26,45,1 0,0,0.48,0,1.45,0,0,0,0.48,0,0,0,0,0.16,0,0.64,0.32,0,0.8,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0.198,0,0.594,0,0,5.683,128,557,1 0.28,0.28,0.56,0,0.28,0.28,0,1.4,0.28,0.84,0.28,0.56,0,0,0,0,1.4,0.56,1.69,1.97,3.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0,0,0,0,0.284,0,1.282,0.094,0,3.725,63,339,1 0.3,0,0,0,0.3,0.3,0.61,0,0.61,0.61,0,0.61,0,0,0,0.3,0.3,0.61,1.84,0,0.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.051,0,0.103,0.051,0,6.125,64,343,1 0,0,0,0,0,0,0,0,0,3.77,0,0,0,0,0,0,0,0,1.88,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.264,0,0,0,0,0,4.333,13,78,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.537,0,0,2.777,12,25,1 0,0,0,0,0,0,0,0,0,3.77,0,0,0,0,0,0,0,0,1.88,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.264,0,0,0,0,0,4.333,13,78,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.537,0,0,2.777,12,25,1 0,0,0,0,0,0,0,0,0,3.77,0,0,0,0,0,0,0,0,1.88,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.264,0,0,0,0,0,4.333,13,78,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.537,0,0,2.777,12,25,1 0,0,0.53,0,0.21,0.1,0.1,0.53,0.1,0.21,0,0.64,0,0,0,0,0.1,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.099,0,0.016,0.066,0,2.104,29,381,1 0,0,0,0,0,0,1.15,0,0,0,1.15,0.76,0.76,0,0,0.38,0,0.38,4.61,0.38,0.76,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.486,0.347,0,1.608,6,74,1 0,0,0.68,0,0.68,0,0.68,0,0,0.68,0,0.68,0,0,0,0,0,4.1,4.1,0,0.68,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,1.089,0.242,0,3.488,60,157,1 0,0,0.51,0,1.03,0.51,0,0,0,0,0.51,1.03,0,0.51,0,0,0.51,0.51,2.59,0,5.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.074,0,0.373,0.149,0,7.233,71,217,1 0,0.35,0.17,0,0,0,0,0,0.17,1.25,0,0.53,0,0,0,0,0,0.17,3.21,0,1.25,7.32,0,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0,0,0.066,0,0,0.099,0.63,16.418,158,903,1 0,0,0,1.33,0,0,0,1.33,0,0,0,0,0,0,0,1.33,0,0,0,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.684,0,0.228,3,12,69,1 0,0,0.27,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0.82,0,1.1,1.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0.187,6.693,49,328,1 0,0,0,0,0,1.09,0,0,0,0,0,0,0,0,0,0,0,0,2.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.403,0,3.427,0,0,2.678,12,75,1 0.09,0,0.27,0,0.36,0.09,0,0.18,0.09,0,0,0.73,0,0.36,0,0,0,0,2.01,0,3.38,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0.016,0.048,0.032,0.257,0.032,0.032,3.689,69,535,1 0.73,0,0.36,0,1.59,0,0,0,0.24,1.35,0.73,2.58,0.61,0,0,0.61,0.12,0,4.55,0,1.72,0,0.49,0.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0.041,0,0.104,0.229,0.02,3.705,54,478,1 0.73,0,0.36,0,1.59,0,0,0,0.24,1.35,0.73,2.58,0.61,0,0,0.61,0.12,0,4.55,0,1.72,0,0.49,0.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0.041,0,0.104,0.229,0.02,3.705,54,478,1 0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0.176,0,0.176,0.352,0,3.857,25,81,1 0.66,0,0.26,0,0.26,0,0.13,0,0.66,0.26,0,0,0.79,0.13,0,0,0,0,3.98,0,0.53,0,0,1.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0.109,0,0.414,0.021,0,5.955,65,667,1 0.18,0,0.09,0,0.36,0.09,0,0.36,0.09,0,0,0.63,0.09,0.36,0,0,0.09,0,1.27,0,3.38,0,0.36,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059,0.029,0.029,0.014,0,4.192,48,566,1 0,0,1.15,0,0.38,0.38,0,0,0,0,0,0.38,0,0,0,1.54,0,0,5.4,0,2.31,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.196,0,0.261,0,0,5.666,56,272,1 0,0,0,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.05,0,0,0,0,0,0,0,0,0,0,0,0,0.088,0,0,0.088,0,6.718,33,215,1 0,0,0.53,0,0.53,0,0,0.53,0,0,0,1.06,0,0,2.12,0,0.53,0.53,2.65,0,2.65,0,1.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0.53,0,0,0,0,0,0,0,0,0,0.191,0,0.095,0.478,0,5.038,60,131,1 0,0.11,0.35,0,1.18,0.47,0.23,0.35,0,0.11,0.11,0.95,0,0.11,0,2.14,0.95,0.23,1.9,0.35,0.35,0,0.59,0.11,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0.11,0,0,0,0,0,0,0,0,0,0.059,0,0.434,0.197,0.217,8.026,283,1509,1 0,0.35,0.35,0,1.07,0,0,0.35,0,1.07,0,0.71,0,0,0,0,0.71,0.71,2.85,0,2.5,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0.35,0,0,0,0,0.24,0,0.24,0.24,0,3.414,25,140,1 0,0.76,0,0,0,0,0,0,0.57,0.19,0,0,0,0,0,0.57,0,0.19,0.19,0.38,0.57,10.17,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0.099,0,0.232,0.066,0.928,20.432,213,1655,1 0.1,0,0.1,0,0.4,0.1,0.1,0,0.2,0.2,0.4,0.5,0,0.6,0,0.91,0.2,0,1.72,4.26,1.72,0,0.4,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.096,0,0.336,0.16,0,6.758,494,1426,1 0.39,0.46,0.31,0,0.15,0.03,0,0.19,0.58,0.66,0.31,0.7,0.62,1.29,0.03,0.23,0.43,0,3.16,0,1.36,0,0.5,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0.006,0.078,0,0.381,0.496,0.133,7.192,543,2424,1 0.32,0,0.64,0,0.32,0.32,0,1.61,0.32,0.64,0.32,0.64,0,0,0,0,1.61,0,1.29,2.58,3.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0.16,0,1.178,0.107,0,3.613,63,318,1 0.1,0,0.1,0,0.4,0.1,0.1,0,0.2,0.2,0.4,0.5,0,0.6,0,0.91,0.2,0,1.72,4.26,1.72,0,0.4,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.096,0,0.336,0.16,0,6.758,494,1426,1 0.08,0.08,0.76,0,0.85,1.02,0.25,0.17,0.59,0.08,0.17,0.59,0.17,0,2.21,0.25,0.08,0.93,1.61,0.17,0.42,0,0.85,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.17,0.08,0.08,0.08,0,0,0,0.065,0,0.403,0.117,0.013,7.484,669,1407,1 0.09,0.49,0.59,0,0.29,0.19,0,0,0.09,0.39,0,1.59,0.19,0,0,0,0.09,0,3.67,0.09,1.09,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0.766,0.037,0,5.836,1.31,0,5.792,54,753,1 0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,1.92,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.416,6,17,1 0.1,0,0.1,0,0.4,0.1,0.1,0,0.2,0.2,0.4,0.5,0,0.6,0,0.91,0.2,0,1.72,4.26,1.72,0,0.4,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.096,0,0.352,0.16,0,6.918,494,1439,1 0,0,0,0,1.26,0,0,1.26,0,0,0,0,0,0,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.411,0,0.926,0,0,3.558,25,121,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.714,0,0,0.238,0,0,4.333,11,104,1 0,0.08,0.25,0,0.84,0.25,0.08,0.33,0,0.16,0.08,0.76,0,0.08,0,1.6,0.76,0.33,1.6,0.33,0.5,0.84,0.42,0.08,0,0,0,0,0,0,0,0,0,0,0,0.25,0.08,0,0,0.08,0,0,0,0,0,0,0,0,0,0.047,0.015,0.502,0.157,0.329,7.24,292,2049,1 0,0.08,0.25,0,0.84,0.25,0.08,0.33,0,0.16,0.08,0.76,0,0.08,0,1.61,0.76,0.33,1.52,0.33,0.5,0.84,0.42,0.08,0,0,0,0,0,0,0,0,0,0,0,0.25,0.08,0,0,0.08,0,0,0,0,0,0,0,0,0,0.047,0.015,0.518,0.157,0.33,7.277,292,2045,1 0,0,0,0,1.05,2.1,1.05,0,0,0,0,0,0,0,0,0,0,0,3.15,0,1.05,0,2.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.176,0.176,0,2.05,6,41,1 0,0,0,0,1.25,0,0,1.25,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.409,0,1.023,1.023,0,3.485,25,122,1 0.09,0,0.09,0,0.39,0.09,0.09,0,0.19,0.29,0.39,0.48,0,0.58,0,0.87,0.19,0,1.66,4.1,1.66,0,0.39,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.139,0,0.31,0.155,0,6.813,494,1458,1 0,0,0,0,0,0,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.11,0,0,0,0,0.208,0,0.208,0.416,0,3.95,23,79,1 0,0.55,1.11,0,0.55,0.55,0,0,0,0,0.55,0,0,0,0.55,1.11,0,0,1.67,0,1.67,0.55,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.086,0.086,0.517,4.166,18,125,1 0,0,0.29,0,0.59,0.29,0.29,0,0.29,1.78,0,0.89,0,0,0,0,0.59,0.29,4.16,0,0.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.137,0,0.045,0.045,0,12.2,163,488,1 0.65,0.49,0.32,0,0.32,0.16,0,0.49,0.65,0.49,0.16,1.3,0,0,0.16,1.14,1.3,0.16,3.6,0.49,1.8,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0.231,0,0.925,0.231,2.29,5.833,47,595,1 0,0.64,0.64,0,1.29,0.64,0,0.64,0,0.64,0,1.94,0,0.64,0,3.89,0,0.64,3.24,0,3.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.728,0.08,0.08,6.612,129,205,1 0,0,0.96,0,0,0,0,0,0,0,0,0.48,0,0,0,0.96,0,0.48,5.79,0,1.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0.086,0,0.26,0.086,0,1.117,4,38,1 0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,1.85,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.291,0,3.79,0,0,4.833,29,87,1 0,0,0.38,0,0.38,0.38,0.38,0,0.38,1.94,0,1.16,0,0,0,0.38,0.77,0.77,2.72,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0.057,0.057,0,7.121,70,235,1 0,0.85,0.42,0,0.42,0,0.42,0,1.27,0.85,0,0.42,0.42,0,0,0,0,0,2.55,0,2.12,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0.221,0,0.177,0.221,0.177,8.777,54,553,1 0,0.6,0,0,0,0.6,0,0,0,0.3,0,1.21,0,0,0,0,0.3,0,0.3,0,0.3,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.146,0,0,0.097,0,3.23,77,210,1 0,0,0.18,0,1.68,0.18,0.37,0.56,0,0,0.37,1.5,0.18,0,0,1.12,0,0.18,3.18,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.066,0,0.563,0.165,0.033,3.106,34,292,1 0,0,0,0,0.91,0,0,0,0,0.45,0,0.45,0,0,0,0,0,0,3.21,0.45,0.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.37,0,0,0,0,0,0,0.164,0,0,1.076,4,42,1 0,0,0,0,1.82,0.36,0.36,0.72,0.36,0.36,0,0,0,0,0,0,0,0.36,2.91,0,2.18,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0.36,0,0,0,0,0,0.297,0.059,0.178,0,0,2.446,11,115,1 0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0,0,0,0.93,0,3.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0.561,0,0,2.166,23,65,1 0.42,0.39,0.36,0,0.13,0.09,0.09,0.06,0.49,0.91,0.26,0.55,0.42,1.08,0.03,0.26,0.42,0.03,2.75,0,1.27,0,0.32,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0.032,0.104,0.016,0.345,0.515,0.109,5.632,134,2501,1 0,0.33,1.34,0,0,0,1.34,0.33,0,0.33,0.33,0.33,0,0,0,0.67,0.67,0.33,0.67,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0.161,0,0.053,0,0.053,2.036,12,167,1 0,0,0,0,0.13,0,0,0,0,0.13,0,0.06,0,0,0,0,0,0,0.2,0,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,1.03,0,0,1.611,0.01,7.549,278,3752,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.38,0,0,3.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.872,0,0,2.2,5,11,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.333,0,0,1.666,5,15,1 0.29,0,0.29,0,0,0,0,0,0.44,0.29,0,0.44,0,0,0,0.14,0,0,3.14,0,1.64,0,0,0.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0.066,0,0.083,0.05,0,3.075,60,326,1 0.6,0,0.36,0,1.44,0,0,0,0.24,1.32,0.72,2.53,0.6,0,0,0.6,0.24,0,4.45,0,1.8,0,0.72,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0.04,0,0.102,0.224,0,3.656,54,479,1 0.43,0.43,0.43,0,0.43,0,0,0,0,1.31,0,0.87,0.43,0,0,2.63,0,0,1.75,0,2.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.201,0,0.941,0.067,0,2.329,28,226,1 0,0.45,0,0,0.45,0.45,0.45,0.45,0,1.8,0,0.45,0,0,0,0,0,0,1.8,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.281,0,0.21,0,0,2.368,14,135,1 0.29,0.29,0,0,0.29,0,0,1.46,0,0,0,0.29,0,0,0,0.58,2.04,0.29,2.04,1.16,1.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0.046,0.046,2.228,34,234,1 0.12,0.12,0.24,0,1.34,0.12,0,0.12,0,0,0.36,0.85,0,0,0,0.24,0.24,0,2.33,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.98,0,0,0,0.12,0,0,0,0,0.12,0,0,0,0.063,0.021,0,0.042,0.042,0,2.351,69,254,1 0,0.33,0.33,0,0.66,0,0,0.33,0,0.33,0,0.33,0,0,0,0.66,1,0,1,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0.109,0,0.054,2.825,34,113,1 0.62,0.62,0,0,0,1.86,0,0,0,0,0,0.62,0.62,0,0,0,0,0.62,2.48,0,1.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.327,0,2.295,0.218,0,5.166,28,155,1 0,0,0.78,0,0.78,0,0.52,0.52,0,1.04,0,0.26,1.56,0,0.78,0,0,1.56,2.08,0,1.56,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.52,0,0,0,0,0,0,0,0,0,0.08,0,0.443,0.402,0,2.41,19,241,1 0,0.72,0,0,2.89,0,0,0,0,0,0.72,0.72,0,0,0,0,0,0,2.17,0,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.379,7,40,1 0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,4.91,0,0,3.27,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.695,0,0,2.315,12,44,1 0,0,0,0,0.26,0,0.26,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0.52,17.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0.462,0.084,0.084,0.378,0,1.051,13.82,104,1078,1 0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.26,0,2.444,10,44,1 0.25,0,0.25,0,0.5,0,0.25,0,0,0,0.5,0.76,0,0,0,0.5,0,0,1.52,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0.25,0,0,0,0,0,0,0,0.041,0,0.082,0.041,0.041,1.89,18,225,1 0.25,0.5,0.5,0,0,0,0,0,0,0.25,0.25,1,0.25,0,0,0,0,0.5,3,0,2.75,0,1.25,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.181,0,0.407,0.997,0,3.417,49,270,1 0,0,0.35,0,0,0.7,0.35,0.35,0,0,0.35,1.06,0,0,0,0.7,0,1.41,2.12,2.82,2.47,0,0,0.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.061,0,0.061,0,0.122,2.302,21,99,1 0,0,0,0,2.48,0,0,0.62,0,0,0,1.24,0,0,0,0,0,0,2.48,0,3.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.179,0,0.089,3.702,53,174,1 0,0,0.77,0,0.77,0,0.51,0.51,0,1.03,0,0.25,1.54,0,0.77,0,0,1.54,1.8,0,1.54,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0,0.079,0,0.514,0.434,0,2.441,19,249,1 0,0,0.74,0,0.74,0,0,0.74,1.49,0,0,0,0,0,0,0,0,0,6.71,0,2.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.99,0,0,1.666,12,60,1 0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0.188,0,0,0.188,0,3.545,21,78,1 0.49,0.28,0.4,0,0.09,0.11,0.02,0.21,0.42,0.75,0.23,0.89,0.54,1.06,0,0.16,0.33,0.02,3.23,0,1.46,0,1.03,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0.059,0,0.391,0.868,0.144,5.783,193,3210,1 0,0,1.56,0,0,0,1.56,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0.256,0,0.769,0,0,2.125,12,34,1 0,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.647,0,0,1,1,13,1 0,0,0,0,0,0,0,0,0,0,0,0,0,2.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0.171,0,0.171,0.342,0,3.809,24,80,1 0,0.19,0.39,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0.19,2.36,0,1.18,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0.152,0,0,1.357,19,148,1 0,0.57,0.57,0,0.14,0.14,0,0,0.14,0,0,0.43,0.14,0,0,0,0.14,0,3.31,0,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.156,0,0,1.394,6,159,1 0,0.17,0,0,0,0,0.17,0.17,0,0.17,0,0,0,0.35,0,0,0,0,0,0,0.17,0,0.17,0,3.37,1.77,0,0,0,0.17,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0,0.35,0,0,0,0.108,0.216,0.061,0.046,0.03,0,4.259,85,3318,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.11,0,0.488,0.157,0.015,8.55,669,1351,1 0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,1.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.265,0,0.797,0.885,0,9.29,75,288,1 0,0,0,1.29,1.29,0,0,0,0,0,0,0,0,0,0,1.29,0,0,2.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.187,0,0,0.936,0,0,4.586,24,133,1 0.84,0,0,0,0,2.54,0,0,0,0,0,0.84,0.84,0,0,0,0,0,2.54,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.452,0,2.865,0.301,0,5.037,23,136,1 0,0,0.76,0,0.76,0,0.76,0.51,0,1.02,0,0.25,1.53,0,1.02,0,0.25,1.79,1.53,0,1.79,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0,0.083,0,0.458,0.499,0,2.455,19,248,1 0,0,1.06,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,3.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0.353,0,3.904,12,82,1 0.08,0.08,0.76,0,0.85,1.02,0.25,0.17,0.59,0.08,0.17,0.59,0.17,0,2.21,0.25,0.08,0.93,1.61,0.17,0.42,0,0.85,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.17,0.08,0.08,0.08,0,0,0,0.065,0,0.403,0.117,0.013,7.484,669,1407,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.273,0,0,5.75,25,69,1 0,0,1.16,0,3.48,0,0,0.58,0.58,0,0,0.58,0,0,0,1.74,0,0,1.16,0,3.48,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.085,0,0.171,0.085,0,2.17,12,102,1 0.74,0.28,0.31,0,0.07,0.21,0,0.14,0.49,0.35,0.17,0.74,0.56,1.48,0,0.17,0.49,0.03,3.24,0,1.23,0,0.56,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0.07,0,0.03,0,0.006,0.054,0,0.678,1.05,0.162,5.648,154,3084,1 0.32,0,0.64,0,0.32,0.32,0,1.6,0.32,0.64,0.32,0.64,0,0,0,0,1.6,0,1.28,2.57,3.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0.213,0,1.174,0.106,0,3.584,63,319,1 0.09,0.49,0.59,0,0.29,0.19,0,0,0.09,0.39,0,1.59,0.19,0,0,0,0.09,0,3.67,0.09,1.09,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0.766,0.037,0,5.836,1.31,0,5.792,54,753,1 0,0,0,0,1.56,0,0,0,0,0.31,0,0.31,0,0,0,0.31,0.62,0,2.82,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.94,0,0,0,0,0.05,0,0,0,0,2.132,22,113,1 0,0,0,0,0.96,0,0.96,0,0,0,0,0,0,0,0,0.96,0,0,0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.96,0,0,0,0,0,0,0,0.824,0,0,3.025,67,118,1 0,0,0.93,0,0,0,0,0,0,2.8,0.93,0,0,0,0,0,2.8,0,4.67,0.93,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0.464,0.154,0,1.612,10,50,1 0,1.14,1.14,0,0,0,0,0,1.14,0,0,1.14,0,0,0,0,0,0,0,0,3.44,0,0,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.197,0,3.681,35,81,1 0,0,0,0,0,0,0,0,0,0,0,0,0,2.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0.17,0,0.51,0.34,0,3.761,23,79,1 0,0.81,0,0,2.03,0,0,0.4,0,1.21,0,0.81,0,0,0,0.4,0,0,3.65,0,1.62,0,1.62,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0.272,0,3.192,34,166,1 0.6,0,0,0,1.21,0,0.6,0,0,0,0,0.6,0,0,0,0,0,0.6,3.65,0,1.21,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.105,0.315,0,3.217,30,74,1 0.25,0,0.25,0,0,0,0.25,0,0.77,1.55,0,0.51,0,0,0,0.25,0,0,1.55,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0.404,0,0.161,0.161,0.04,9.633,110,578,1 0.76,0.19,0.38,0,0.19,0.12,0,0.25,0.76,0.31,0.25,1.52,0.31,0.38,0,0.38,0.44,0.06,2.98,0.69,1.26,0,0.44,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0.06,0,0,0,0,0,0,0.087,0.054,0.439,0.241,0.065,3.702,45,1070,1 0,0,0.47,0,0.47,0.47,0.47,0,0,2.38,0,0.95,0.47,0,0,0,0.47,0,1.9,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0.266,0,0.621,0.799,0.088,36.642,148,513,1 0,0,0,0,0,0,0,0.42,0,0.42,0.42,0,0,0,0,0,0,0,2.52,0,2.94,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0.129,0,0.129,0.194,0,1.859,20,119,1 0.62,0,0,0,1.24,0,0.62,0,0,0,0,0.62,0,0,0,0,0,0.62,3.72,0,1.24,0,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,1.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.107,0.322,0,3.318,30,73,1 0.33,1.01,0,0,1.35,0,0.33,0,0,0,0.67,0.67,0.33,0,0,1.01,0,1.68,2.36,0,3.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0.06,0.06,0,193.5,1013,1161,1 0,0,0,0,0.97,0,0.97,0,0,0,0,0,0,0,0,0.97,0,0,0,0,1.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0.834,0,0,3.052,68,116,1 0.14,0,0.21,0,1.72,0.43,0,0,0.07,0.14,0.07,0.57,0.35,0.07,0,0,0.28,0,4.31,0.28,0.64,0,1,0.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0,0.012,0.064,0,0,0.206,0,1.711,10,380,1 0.6,0,0.36,0,1.44,0,0,0,0.24,1.32,0.72,2.52,0.6,0,0,0.6,0.24,0,4.44,0,1.8,0,0.72,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0.041,0,0.102,0.205,0,3.548,54,479,1 0.2,0.1,0.7,0,1.1,0.2,0,0.3,0,1.2,0.3,1.1,0.1,0,0.1,0.4,0.2,0.1,2.61,0,2.51,0,0,0,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0,0,0,0,0.017,0.159,0,0.53,0.406,0.123,9.781,84,851,1 0,0,0,0,0,0,0,0,0,0,0,5.26,0,0,0,0,0,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.048,0,0.349,3.333,10,30,1 0.35,0.46,0.31,0,0.15,0.03,0,0.35,0.58,0.66,0.31,0.7,0.62,1.28,0.03,0.23,0.42,0,3.12,0,1.36,0,0.46,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0.006,0.09,0,0.324,0.486,0.126,6.11,116,2218,1 0.3,0.2,0.3,0,0.2,0.4,0.2,0.3,0.4,1.71,0.1,1.91,0.2,0,0.5,0.6,0,0.8,3.43,0,1.51,0,0.9,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.2,0,0,0,0,0.017,0,0.275,0.206,0.017,4.923,103,1029,1 0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.59,0,2.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.093,0.186,0.559,0.279,0,2.297,12,108,1 0.19,0.19,0.29,0,1.07,0.19,0.19,0.97,0.87,0.58,0.09,1.07,0.19,0.87,0.09,0,0,1.17,3.81,0.68,1.75,0,0.09,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0.09,0,0,0,0,0,0,0,0,0,0,0.201,0.402,0.232,0.03,4.295,49,872,1 0,0,0,0,0,0,0,0,0,0,0,1.42,0,0,0,1.42,0,0,2.14,0,0.71,0,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0.099,0,0.899,0,0,3.066,36,138,1 0.15,0.3,0.45,0,0.76,0.3,0,0,1.52,1.52,0.15,1.98,0.3,0,0.61,0.3,0,1.52,2.14,0.15,2.44,0,0.76,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.025,0,1.051,0.225,0.05,6.686,217,896,1 0,0,0.28,0,0.84,0.84,0.28,0,0.28,0.28,0,0.28,0,0,0,0.56,0,0.56,2.52,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0.28,0,0,0,0,0.05,0,0.05,0,0,2.083,34,150,1 0.09,0.09,1.14,0,0.38,0,0,0.09,0,0.19,0.38,0.19,0,0,0,0.66,0,0,1.52,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0.044,0.059,0,0.591,0,0,3.28,31,771,1 0,0,0,0,0,0,1.11,0,0,1.11,0,0,0,0,0,0,0,0,2.22,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.202,0,0.202,0,0,4,16,40,1 0,0.51,0,0,0,0,0,0,0,0.51,1.02,0.51,0,0,0,0.25,0.76,1.27,2.04,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0.457,0,0.29,0,0.124,2.614,66,149,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.169,0,0,3,12,36,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,3.33,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.653,0,0,8,38,80,1 0.7,0,1.05,0,0,0,0,1.4,0.35,0.35,0,0.35,0,0,0,2.1,0.7,0.35,2.1,3.15,2.1,0,0.35,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.116,0,0.348,0,0,1.166,13,189,1 0,0,0,0,0,0,0,1.2,0,0,1.2,0,0,0,0,6.02,0,0,1.2,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.443,0,0,3.782,32,87,1 0,0,0.53,0,0.53,0,0.53,0,0,0.53,0,0,0,0,0,0,0.53,0,5.85,0,3.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0.361,0,0,2.437,19,78,1 0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.416,0,0,9.785,42,137,1 0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0.609,0,1.524,0,0.304,1,1,36,1 0.32,0.16,0.56,0,0.32,0.23,0.04,1.24,0.4,0.4,0.11,0.68,0.52,0.36,0.28,0.72,0.4,0.4,3.08,0.16,1.32,0,0.44,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.04,0,0,0,0,0.11,0,0,0,0.019,0.052,0.065,0.413,0.164,0,3.533,181,1643,1 0,0,0,0,0,0,0,1.21,0,0,1.21,0,0,0,0,6.09,0,0,1.21,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.407,0,0,3.454,32,76,1 0.28,0,0.28,0,0,0.28,0.28,0.28,0.28,1.15,0,0.86,0.86,0,0,0,0,0,2.89,0,1.44,0.86,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0,0,0.554,0.221,0.166,5.328,140,341,1 0.09,0,0.67,0,0.29,0,0,0,0.19,0.38,0.09,1.35,1.06,0,0,0.29,0.19,0,2.51,0,1.35,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0.29,0,0,0.19,0,0.149,0,0.374,0.059,0,9.039,148,1148,1 0,0,0.4,0,0.4,0.2,0,0,0,1.01,0.2,0.4,0,0,0,0.2,0.4,0.2,0.8,0,0.4,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0.03,0,0,0.302,0,1.727,11,190,1 0,3.05,0.38,0,1.14,0.19,0,0,0,1.52,0.19,0.76,0.19,0,0,0,1.72,0.38,3.05,0.38,2.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0.171,0,0.294,0.147,0.024,17.074,430,1144,1 0,0,1.55,0,0,0.77,0,0.38,0,0,0.38,1.16,0,0,0,0.38,0,1.16,1.93,0,0.38,0,1.16,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.609,0.121,0,2.666,22,160,1 0,0.82,0.32,0,1.14,0.32,0,0.16,0,0.65,0,2.13,0,0,0,0.16,0,0,1.47,0,1.47,0,0.98,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0.29,0.029,2.257,13,158,1 0,0,0,0,0,0,0,0,0,0,0,0,2.63,0,0,0,0,0,2.63,0,1.75,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.144,0,0,3.907,0,0,13.928,70,195,1 0.1,0,0.7,0,0.2,0,0,0,0.2,0.3,0.1,1.3,1.1,0,0,0.3,0.2,0,2.61,0,1.2,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0.3,0,0,0.2,0,0.141,0,0.352,0.056,0,9.601,148,1133,1 0.35,0.1,0.55,0,2.15,0.15,0,0,0.1,0.75,0.35,0.85,0.25,0,0,0.15,0.3,0,5,0,1.75,0,0.05,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0.008,0.035,0,0.149,0.131,0.008,3.629,127,617,1 0,0.19,1.08,0,0.79,0.79,0.49,0,0.89,0.29,0.29,0.69,0.29,0,1.58,0.09,0,1.08,1.38,0.19,0.69,0,0.59,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0.09,0.09,0,0,0,0.092,0,0.417,0.154,0.015,8.323,669,1365,1 0.61,0,0,0,1.22,0.61,0.61,0,0.61,0,0,1.22,0,0,0,1.22,0,0,5.52,0,0.61,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0,0,0,0.184,0,0.829,0,0,4.45,34,89,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,2.22,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.145,0.145,0.291,0,2.95,11,59,1 0,0,0.7,0,0,0,0,0,0,0,0,0,0,0,0,0.7,0,0,0,0,0,0,0,0,0.7,0.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.328,0,0,1.333,4,16,1 0,0.26,0.78,0,0.26,0.26,0.08,1.04,0.52,1.56,0.26,0.69,0.17,0.08,0.69,0.86,0.34,0,1.82,0.17,1.3,0,0.08,0.34,0,0,0,0,0,0,0,0,0.08,0,0,0.08,0,0,0,0,0,0,0,0,0.08,0.08,0,0,0.096,0.234,0,0.358,0.261,0.11,3.554,54,981,1 0.17,0.17,0.25,0,0.43,0.08,0.08,0.08,0.69,2.41,0,0.34,0.17,0,1.46,0.34,0.08,0,2.76,0.43,1.55,0,0.17,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0.34,0.08,0,0,0,0.107,0,0.308,0.067,0.026,4.215,82,1214,1 0.71,0,0.35,0,0.17,0.17,0.35,0,0,0.35,0.17,0.53,0,0,0,0.35,0.71,0.35,3.76,0,1.97,0,0,0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.029,0,0.234,0.029,0,3.519,97,359,1 0,0,0.71,0,0.23,0,0,0,0.23,0.23,0.23,1.9,0,0,0,0.23,0,0,3.81,0.23,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.111,0,1.045,0.037,0,4.022,97,543,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.094,0,0,1.428,5,40,1 0,0.26,0,0,0.26,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0.26,0,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.407,0.067,0,0.033,0,0,5.009,55,506,1 0.27,0.27,0.27,0,0,0,0,0.54,0,0.27,0,0.27,0,0,0,1.08,0,0.27,1.08,0,0.27,0,0.27,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.225,0,0.451,0.496,0,2.934,64,578,1 0.16,0,0.24,0,1.63,0.49,0,0,0,0.16,0.08,0.65,0.4,0.08,0,0,0.32,0,3.68,0.32,0.65,0,1.14,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0.08,0,0,0,0.014,0.058,0,0,0.232,0,1.725,10,333,1 0,0,1.29,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,1.29,0,5.19,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.208,0,4.23,25,55,1 0.19,0,0.38,0,0,0.19,0,0,0,0,0.19,0.19,0,0,0,0.38,0,0.19,1.14,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.01,0,0,0,0.003,2.383,21,15841,1 0.19,0,0.19,0,0.87,0.48,0.09,0,0.09,0.39,0.48,0.68,0.19,0,0.09,0.29,1.07,0.39,3.51,0.78,1.56,0,0.09,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0.015,0.18,0,0.045,0.015,0,2.133,40,303,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.219,0,3.875,11,31,1 0,0,0,0,0,0,0,1.25,0,0.41,0,0,0,0,0,0.41,0,1.67,0.41,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0.312,0.062,0,1.477,8,65,1 0.86,0,0.86,0,0,0,0,0,0,0,0,0.43,0,0,0,0.86,0.86,0,3.47,0,1.73,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24,0,1.765,0.481,0.08,7.059,159,473,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,1.05,0,3.15,0,2.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0.365,0,0.365,0.182,0,3.343,28,107,1 0.76,0.38,0,0,0.38,0.38,0,0,0,0.38,0,1.53,0,0,0,0,0,0,1.92,0,3.07,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.124,0,0.124,0,0.062,89.9,735,899,1 0,0,0.94,0,0.31,0,0,0,0.31,0,0,0.62,0,0,0,1.25,0.62,0,3.14,0,1.25,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0.145,0.048,0.485,0.388,0.097,3.322,61,319,1 0,0,0,0,1.56,0,1.56,0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.767,0.255,0,8.083,81,97,1 0.52,1.31,0.26,0,2.9,0.26,0.79,0.26,0,0.79,1.05,1.58,0.79,0,0,0,0,1.31,3.16,0,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.138,0,0.046,0,0,2.934,60,135,1 0.47,0,0.95,0,0.95,0,0.95,0,0,0,0,0.47,0,0,0,0.47,0.47,0,4.28,0,0.95,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0.076,0,1.306,0.23,0,6.027,91,217,1 0,0,0.47,0,1.43,0,0,0,0,0,0,0.95,0,0,0,0.47,0.95,0,3.34,0,1.91,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.43,0,0,0,0,0.076,0,0.536,0.306,0,4.653,78,242,1 0.49,0,0.99,0,0.99,0,0.99,0,0,0,0,0.49,0,0,0,0.49,0.49,0,4.45,0,0.99,0,1.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,1.118,0.239,0,5.228,69,183,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,1.19,0,1.19,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.181,0.787,0,3.875,31,93,1 1.63,0,1.63,0,0,0,0,0,1.63,0,0,0,0,0,0,1.63,0,0,3.27,0,3.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.802,0.267,0,2,16,36,1 0.27,0,0.16,0,0.27,0,0,0.05,0,0.21,0.1,0.93,0.1,0,0,0.38,0.1,0,2.85,0,1.2,0,0.21,0.16,0,0,0,0,0,0,0,0,0.05,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.068,0.029,0,0.019,0.058,0.009,3.389,56,539,1 0.33,0,0,0,0,0.33,0,0,0,0,0,1.01,0.67,0,0,0,0.67,0,3.05,0,2.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.682,21,69,1 0.23,0.23,0.47,0,0.7,0.23,0.23,1.41,0.23,0.47,0.23,0.47,0,0,0,0,1.41,0.47,0.94,1.89,3.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0.23,0,0,0,0,0.075,0,1.289,0.151,0,6.529,276,666,1 0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,0.212,0.212,0,0,0.212,0,3.272,24,72,1 0,0.17,0,0,0,0,0.17,0.52,0,0.17,0.35,0.52,0,0,0,0,0.17,0.7,0.87,0,0.7,1.92,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.029,0.147,0.029,0.117,0.058,0.235,3.521,39,419,1 0,0.74,0,0,0,1.49,0.74,0,0,0,0,0,0,0,0,0,0,2.23,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.307,0,0,0,0,3.39,45,139,1 0,0.56,0.56,0,1.12,0.56,2.25,0,0,0.56,0,0.56,0,0,0,0,0,0.56,3.38,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.56,0,0,0,0,0.083,0,0.502,0,0.083,16.304,148,375,1 0.8,0,0.8,0,1.6,0,0,0,0,0,0,0,0,0,0,0.8,0.8,0,1.6,0,2.4,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.566,0.361,0,2.638,22,124,1 0,0,0,0,0.87,0,0,0,0,0,0,0,0,0.87,0,0,0,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.132,0,0,0,0,3.851,51,104,1 0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,2.4,0,0,12.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.709,0,1.56,7.82,39,305,1 0,0,0,0,1.52,0,2.29,0,0,0,0,0,0,0,0,0,0.76,0.76,0.76,0,2.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.85,19,77,1 0,0,0,0,0,1.36,0,0,1.36,0,0,0,0,0,0,1.36,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,1.36,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,1.777,0.222,0,9.727,63,107,1 0.28,0.28,0.28,0,0.57,0.28,0.28,0,0,0,0,0.86,0.28,0,0,0,0.57,0.28,2.88,0,2.01,0,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.131,0,0.218,0.218,0,3.694,40,218,1 0,0.5,0,0,1.25,0,0,0.25,0,0.75,0.25,0.75,0,0,0,0.25,0,0,2.01,0,1.76,0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0.25,0,0,0,0,0,0.25,0.25,0,0,0,0,0.222,0.095,0.031,0,0,5.5,114,616,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.33,0,0,0,0,0,0,0,0,1.33,0,0,0,0,0.213,0,0.426,0.213,0,4.6,23,69,1 0.16,0.16,0.5,0,0.33,0,0,0,0.5,0.84,0,0.84,0,0.33,0,0,0,0.16,2.37,0,0.5,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0.16,0,0,0,0,0,0,0.143,0,0.458,0.143,0.028,6.298,247,781,1 0,0,0,0,0,0.41,0,0,0,0.82,0,0,0,0,0,0.41,0,0,1.23,0,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.199,0.53,0.331,0.199,0,0,5.019,18,261,1 0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,1.81,3.63,0,2.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0.17,0.17,0,6.266,41,94,1 0,0,0,0,0,0,0,0,0,0,0,1.14,0,0,0,0,0,0,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.545,4,17,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0.212,0,0.424,0.212,0,4.125,21,66,1 0.49,0.21,0.56,0,0.28,0.21,0,0.28,0.28,0.98,0.42,0.98,0.14,0,0,1.12,0.7,0.07,2.24,0,0.98,0,0.07,0.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0.14,0,0,0,0,0.108,0,0.768,0.312,0,3.401,94,966,1 0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,1.81,3.63,0,2.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0.17,0.17,0,6.266,41,94,1 0,0,1.78,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.351,0,0.27,32,75,160,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.413,0,0,4.047,22,85,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.412,0,0.268,20,137,180,1 0,0.33,0.33,0,1.65,0.33,0.66,0,0,0.16,0.16,0.99,0,0,0,0.82,0.33,0.16,2.81,0,0.99,0,0.49,0.33,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.025,0.075,0.252,0.05,0.05,6.269,350,721,1 0,0.55,0.55,0,1.1,0.55,2.2,0,0,0.55,0,0.55,0,0,0,0,0,0.55,3.31,0,1.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0.165,0,0.495,0,0.082,16.826,148,387,1 0,0,0,0,0.86,0,0.86,0,0,0,0,0,0,0,0.86,0,0,1.72,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0.272,0,0,0.136,0,4.541,31,109,1 0.63,0.63,0.63,0,0,0,0.63,0.63,0.63,0,0,0.63,0,0,0.63,1.26,0,0.63,1.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.398,0,0,2.625,19,126,1 0,0,0,0,0,1.12,0,0,0,1.12,0,0,0,0,0,0,0,1.12,2.24,0,1.12,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.375,0,0,6.003,0,3.75,14,45,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.33,0,0,0,0,0.208,0,0.417,0.208,0,3.812,16,61,1 0.15,0,1.22,0,0.45,0,0.15,0,0.61,0.61,0,0.76,0.3,0,0.3,0.61,0.61,0,1.83,0.45,2.75,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.112,0,0.698,0.067,0,5.101,63,801,1 0,0,0,0,2.17,0,0,0,0,0,0,2.17,0,0,0,2.17,0,2.17,6.52,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.333,5,16,1 0.16,0,0.32,0,1.3,0.65,0,0.65,0,0,0,0.16,0,0,0.16,0.32,1.63,2.45,1.79,0,1.14,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0.027,0,0.622,0.027,0,1.25,12,165,1 0,0,0,0,2.17,0,0,0,0,0,0,2.17,0,0,0,2.17,0,2.17,6.52,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.333,5,16,1 0,0,0,0,0,0,1.96,0,0,0,0,0.98,0,0,0,0,0.98,1.96,2.94,0,0.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.163,0.489,0,0.326,0,0,2.3,12,46,1 0.87,0.17,0.52,0,0,0.32,0,0.04,0.29,0.42,0.39,1.37,0.87,1.69,0,0.32,0.54,0.22,3.47,0.29,1.32,0,0.34,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0.07,0,0.04,0,0.016,0.058,0,0.639,0.165,0.182,3.697,117,3498,1 0,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,15,49,1 0.56,0,0.56,0,2.25,0,0,0,0,0.56,0,0,0,0,0,0.56,0.56,0,1.69,0,1.69,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.91,0.273,0,2.283,22,121,1 0.07,0,0.15,0,0.07,0.15,0,0.07,0.07,0,0,0.46,0,0,0,0,0.15,0,0.15,0,0.07,0,0,0.07,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0.07,0,0,0.07,0,0,0,0,0,0.011,0.047,0,0,0.023,0,1.263,10,264,1 0.54,0,1.08,0,0.54,0,1.08,0,0,0,0,0.54,0,0,0,0.54,0.54,0,4.32,0,1.08,0,1.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,1.216,0.26,0,5.454,68,180,1 0,1.65,0,0,0,0,1.65,0,0,1.65,0.82,0,0,0,0,0.82,0,0,3.3,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,15.5,68,186,1 0.41,0,0.41,0,0,0,0.41,0,0,0,0.41,0,0.41,0,0,0,0,0,2.05,0,1.23,0,0,0.41,0,0,0,0,0,0,0,0,0.41,0,0,0,0.41,0.41,0,0,0,0,0,0,0,0,0,0,0,0.067,0,0.067,0,0,1.863,14,41,1 0.14,0,0.29,0,1.17,0.58,0.14,0.58,0,0.43,0,0.14,0,0,0.14,0.29,1.46,2.05,1.9,0,1.02,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0.097,0,0.558,0.024,0,1.517,12,217,1 0,0.29,0.29,0,0,0.59,0.29,1.04,1.04,2.22,0.14,1.04,0,0,1.04,0.29,0.74,0,1.63,0.44,0.59,0,1.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0.29,0,0,0,0,0,0,0,0,0,0.084,0,0.105,0.21,0.021,10.817,887,1244,1 0.17,0,0.08,0,0.42,0.08,0.08,0.42,0.08,0.08,0,0.6,0.17,0.17,0,0,0.17,0.08,1.2,0,3.17,0,0.34,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.084,0.028,0.098,0.014,0,4.049,48,575,1 0.22,0,0.78,0,0,0.11,0.11,0,0.22,0.11,0.11,0.22,0.89,0,0,0.44,0.44,0,4.68,0,1.56,0,0.11,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0.44,0,0,0,0,0.142,0,0.775,0.224,0.142,5.782,103,798,1 0.58,0,0.58,0.58,0.58,0,0,0,0,0,0,1.17,0,0,0,0,0,0,4.11,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.098,0,0.197,0,0,3.807,61,297,1 0.26,0.05,1.45,0,0.37,0.1,0,0,0.1,0.1,0.21,1.07,0,0,0,0,0,0,3.38,0,1.39,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0,0,0,0,0.076,0,0.262,0.186,0.025,11.793,289,2288,1 0.44,0,0,0,0.89,0,0,0,0,0.44,0,1.33,0,0,0,0.44,0,0,4.46,0,1.78,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,1.131,0.15,0.075,2.428,28,153,1 0.43,0,0,0,0.87,0.87,0,0,0,0.43,0,2.18,0,0,0,0,1.74,0,0.87,0,0.87,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.942,0,0,5.114,107,179,1 0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.554,0,0.518,2.111,15,38,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.428,4,10,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0.215,0,0,0.215,0,3.937,18,63,1 0,1.63,0.81,0,0,0,1.63,0,0,1.63,1.63,0,0,0,0,0.81,0,0,4.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.212,0,0,15.916,71,191,1 0.52,0,2.38,0,0.26,0,0.26,0,0.52,0,0.26,0,0,0,0,0.79,0,0,1.32,0,1.05,0,0,0.52,0,0,0,0,0,0,0,0,0.26,0,0,0.26,0.26,0,0.52,0,0,0,0,0,0,0,0,0,0,0.689,0,0.326,0,0,5.549,71,566,1 0.32,0,0.8,0,0.8,0.32,0.16,0,0.64,0,0.32,1.44,0.16,0,0,0,0.32,0,3.37,0,1.28,0,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0,0.05,0.075,0,1.419,15,159,1 0,0,1.2,0,0,0,0,0,0,0,0,2.4,0,0,0,0,0,0,2.4,0,4.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,16,72,1 0.58,0,0.19,0,1.75,0.39,0.58,0,0,0.19,0.39,0.78,0.39,0,0,0.58,0.58,0.58,4.29,0,0.39,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0.271,0,0.067,0.135,0,3.015,21,190,1 0,0,0,0,0.73,0,0,0,0,0,0,0,0,0,0,0.73,0,0,2.94,0,2.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.73,0,0,0,0,0.105,0,0.211,0,0,1.333,7,48,1 0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.666,12,23,1 0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.317,0,0,0.952,0,0,4.823,13,82,1 0,1.05,0,0,0,0,1.05,0,0,0,0,0,0,0,0,3.15,0,1.05,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,4.947,24,94,1 0,4.76,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.225,38,162,1 0,0,3.48,0,0,0,0,1.16,1.16,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.405,0,0,9,28,72,1 0.5,0.19,0.57,0,0.25,0.38,0,0,0.5,0.06,0.12,0.63,0.19,0,0,0.69,0.5,0.38,3.49,0.06,1.27,0,0.31,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0.06,0,0,0.067,0,0.435,0.592,0.022,5.335,73,1590,1 0.09,0.09,1.14,0,0.38,0,0,0.09,0,0.19,0.38,0.19,0,0,0,0.66,0,0,1.52,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0.044,0.059,0,0.591,0,0,3.28,31,771,1 0,0,1.07,0,3.22,0,0,0,0,0,0,0,0,1.07,0,1.07,0,0,2.15,0,2.15,0,1.07,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,2.395,0.598,0.998,82.25,295,329,1 0,0,0,0,0.68,0,0,0,0,1.81,0,0.68,0,0,0,0.22,0,0,3.4,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0.22,0,0,0,0.159,0.558,0.159,0.199,0,0,6.091,83,530,1 0,0,0,0,0.47,0,1.43,0,0,0,0.47,0.47,0,0,0,0.47,0,1.91,1.91,0.47,1.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.073,0.295,0,3.361,54,158,1 0,0.2,1.83,0,0.81,0.2,0.61,0.4,0,0,1.22,1.01,0.2,0,0,0.2,0.4,0.2,1.83,0,1.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.232,0,0,4.159,142,287,1 0,0,0,0,0.68,0,0,0,0,1.81,0,0.68,0,0,0,0.22,0,0,3.4,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0.22,0,0,0,0.159,0.558,0.159,0.199,0,0,6.091,83,530,1 0,0,0,0,0,1.4,0,0,0,0,0,0,0,0,0,2.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0.963,0,0,3.8,17,57,1 0.77,0.38,0.77,0,0,0.57,0,0.57,1.15,1.15,0,0.38,0.38,0,1.15,0.19,0.19,2.12,2.12,1.15,1.15,0,1.35,0.77,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0.027,0,0.438,0.191,0.054,14.619,525,921,1 0,0,0,0,1.09,0,0,0.54,0,0,0.54,1.63,0,0.27,0,0,0.27,0.54,2.18,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0.208,0,0.166,0.083,0,3.521,114,243,1 0.17,0.26,1.24,0,0.53,0.62,0.44,0.17,0.79,0.79,0.26,1.33,0.17,0,0.62,0.62,0.08,1.33,2.66,0.17,1.15,0,0.79,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0.08,0,0,0,0,0.07,0,0.225,0.211,0.014,6.725,583,1345,1 0.13,0.13,0.26,0,0.26,0.26,0,0.13,0.39,0.13,0.13,0.39,0,0,0,0.13,0,0,2.35,0,0.13,0,0,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.137,0,0,0.068,0,2.736,30,468,1 0,0,0.83,0,1.66,0.41,0,0,0,0,0,0.41,0,0,0,0.41,0,0,2.08,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.218,0,0,0,0,2.35,12,134,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.675,0,0,2.23,12,29,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.62,0.62,0,1.25,3.12,3.12,1.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.075,0,1.285,0.075,0.226,6.722,101,363,1 0.58,0,0.19,0,1.75,0.39,0.58,0,0,0.19,0.39,0.78,0.39,0,0,0.58,0.58,0.58,4.29,0,0.39,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0.271,0,0.067,0.135,0,3.015,21,190,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0.73,0,0,0,0,0,0,0,0.73,0,0,0,0,0,0,0,0,0,0.36,0,0,0.21,0.21,0,0,0.105,0,1.866,22,112,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.714,0,0,0.238,0,0,4.333,11,104,1 0,0.38,0.38,0,0,0,0,0.38,0.38,0,0,0,0,0,0,0.38,0,0.38,0.38,2.67,0,0,0.76,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0.35,0,2.162,8,80,1 0.99,0.49,0,0,0,0,0,0,0,0.49,0,0.49,0,0,0,0,0,0,2.48,0,1.99,2.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.356,0,0.446,10.366,64,311,1 0.52,0,1.05,0,0,1.05,0,0,0,0.52,0,0.52,1.05,0,0,1.05,0.52,0,3.15,0,0.52,0,1.05,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.295,0.698,0,2.016,14,125,1 0.08,0,0.32,0,0.24,0.32,0,0.16,0.16,0,0,0.65,0,0,0,0,0,0,4.67,0,0.65,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0.32,0,0,0.24,0,0,0,0,0.045,0,0.36,0.03,0,1.42,10,196,1 0,0,0,0,1.9,0,0.95,0,0,0.95,0,0.95,0,0,0,0,0,0,5.71,3.8,2.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.147,0,0,1.4,6,21,1 0.85,0,0,0,0,0,0,0,0,0,0,0.85,0,0,0,4.27,0,0,3.41,0,4.27,0,0,5.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.146,0,0.881,0,0,5,17,150,1 0.77,0.38,0.77,0,0,0.57,0,0.57,1.15,1.34,0,0.38,0.38,0,1.15,0.19,0.19,1.92,2.11,1.15,1.15,0,1.34,0.77,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0.027,0,0.438,0.191,0.054,14.619,525,921,1 0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,1.85,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.289,0,3.768,0,0,4.833,29,87,1 0,2.43,0,0,1.21,0,0.6,0,0.6,0,0,0,0,0,0,0,0,2.43,1.82,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.031,71,129,1 0.32,0.16,0.56,0,0.32,0.24,0.04,1.16,0.4,0.4,0.12,0.68,0.52,0.4,0.28,0.64,0.36,0.4,3.06,0.16,1.28,0,0.36,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.04,0,0,0,0,0.12,0,0,0,0.019,0.052,0.066,0.37,0.152,0,3.225,181,1500,1 0.28,0,0,0,0,0,0,0.28,0,0,0,0.84,0.56,0,0.84,0.84,0.28,4.51,2.54,0,2.54,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.091,0,1.147,0.045,0,7.178,104,524,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.102,0,0.102,0.716,0,4.512,43,185,1 0.09,0.09,1.14,0,0.38,0,0,0.09,0,0.19,0.38,0.19,0,0,0,0.66,0,0,1.52,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0.044,0.059,0,0.591,0,0,3.28,31,771,1 0,0,0.42,0,0.42,0,0.21,0,0,0,0.21,0.21,0,0,0,0,0,0.42,0.42,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0.126,0,0.031,1.269,0.412,13.017,183,1484,1 0.32,0.09,0.6,0,2.04,0.13,0,0,0.09,0.69,0.32,0.79,0.27,0,0,0.13,0.32,0,4.92,0,1.81,0,0.04,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.04,0,0,0,0,0.18,0,0.09,0,0.008,0.032,0,0.145,0.121,0.008,3.575,127,640,1 0.14,0.28,0.84,0,0.14,0.14,0,0.84,0.42,0.14,0,0.56,0.28,0.14,0.42,0.14,0.14,0.28,4.34,0.14,2.1,0,0.14,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0.025,0,0.381,0.05,0,2.322,15,216,1 0.13,0.27,0.83,0,0.13,0.13,0,0.83,0.41,0.13,0,0.55,0.27,0.13,0.41,0.13,0.13,0.27,4.31,0.13,2.08,0,0.13,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.025,0,0.379,0.05,0,2.329,15,219,1 0.34,0.17,0.17,0,1.38,0.69,0.17,0.17,0,0.17,0,0.86,0,0,0.34,1.55,0.34,0.17,2.94,0,2.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.115,0,0,0.086,0,4.792,38,508,1 0.77,0.38,0.77,0,0,0.57,0,0.57,1.15,1.15,0,0.38,0.38,0,1.15,0.19,0.19,2.12,2.12,1.15,1.15,0,1.35,0.77,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0.027,0,0.438,0.191,0.054,14.619,525,921,1 0.4,0.18,0.32,0,0.25,0.18,0.03,1.01,0.4,0.4,0.1,0.72,0.65,0.36,0.25,0.54,0.36,0.36,3.05,0.14,1.41,0,0.29,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0,0.07,0,0,0,0.012,0.042,0.072,0.334,0.139,0,3.305,181,1613,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.102,0,0.102,0.72,0,4.512,43,185,1 0.77,0.38,0.77,0,0,0.57,0,0.57,1.15,1.15,0,0.38,0.38,0,1.15,0.19,0.19,2.11,2.11,1.15,1.15,0,1.34,0.77,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0.027,0,0.437,0.191,0.054,14.406,525,922,1 0.32,0,0.64,0,0,0,0,0,0,0,0.64,0.97,0,0,0,2.58,0,0,2.58,0.32,1.94,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.233,0.058,0,0.116,0.116,0,2.926,20,240,1 0,0.17,1.03,0,0.68,0.17,0.68,0,0,0.17,0,0.17,0.17,0,0.34,1.03,0.34,0.17,3.44,0,1.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0,0,0,0,0,0.084,0,0.056,0.196,0,2.26,53,208,1 0,0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,1.21,2.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,0,0,0,1.627,0,0.465,2.591,31,127,1 0.77,0.38,0.77,0,0,0.57,0,0.57,1.15,1.15,0,0.38,0.38,0,1.15,0.19,0.19,2.12,2.12,1.15,1.15,0,1.35,0.77,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0.027,0,0.438,0.191,0.054,14.619,525,921,1 0.14,0.29,0.44,0,0.88,0.29,0,0,1.47,1.47,0.14,1.91,0.29,0,0.58,0.29,0,1.62,2.35,0.14,2.35,0,0.73,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.023,0,1.003,0.215,0.047,6.602,217,898,1 0.14,0.29,0.44,0,0.88,0.29,0,0,1.47,1.47,0.14,1.91,0.29,0,0.58,0.29,0,1.62,2.35,0.14,2.35,0,0.73,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.023,0,1.004,0.215,0.047,6.602,217,898,1 0,0.17,0,0,0.34,0.34,0,0,0,0.17,0,0,0.17,0,0,0.17,0.17,0,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0.054,0,0,0.027,0,2.073,11,170,1 0,0,0.36,0,0.73,0,0,0,0,0.73,0,0.36,0,0,0,0,0,0,0.73,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,2.13,12,228,1 0,0,0.58,0,1.16,0,0,0,0,0.58,0,0,0,0,0,0.58,0,0,0.58,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.101,11,145,1 0,0,0,0,0.23,0,0,0,0,0,0,0.93,0,0,0,0.11,0,0.11,0.35,0,0.23,0,0,0,0,0,0,0.35,0.11,0.11,0,0,0,0,0,0.58,0,0.11,0,0,0,0.35,0,0,0,0.46,0.11,0.11,0,0.381,0,0.016,0,0,2.47,41,504,1 0,0,0,0,0,0.59,0,2.95,0,0,0,0.59,0.59,0,0.59,5.91,2.95,0.59,1.77,0,1.18,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.293,0,0,1.69,15,93,1 0.77,0.38,0.77,0,0,0.57,0,0.57,1.15,1.34,0,0.38,0.38,0,1.15,0.19,0.19,1.92,2.11,1.15,1.15,0,1.34,0.77,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0.027,0,0.438,0.191,0.054,14.619,525,921,1 0.43,0.26,0.43,0,0.78,0.26,0,0.17,0.34,4.09,0.08,1.22,0.43,0,0.78,1.13,0.26,1.91,2.35,0,2.35,0,0.08,0.43,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0.08,0,0,0,0,0,0,0,0,0.056,0.241,0.042,0.709,0.056,0,4.319,126,1123,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0,2.5,33,125,1 0.85,0,0,0,0.85,0,0,0,0,0,0,0.85,0.42,0,0,1.28,0,0,3.86,0,0.85,0,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.066,0,0.535,0.133,0,11.592,110,313,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.041,0,0,0,0,1.938,33,95,1 0,0,1.55,0,0,0.77,0,0.38,0,0,0.38,1.16,0,0,0,0.38,0,1.16,1.93,0,0.38,0,1.16,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.601,0.12,0,2.666,22,160,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,3.33,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.986,0,0,8,38,80,1 0,0.13,0.13,0,0,0.13,0,0,0.13,1.5,0,0.4,0,0,0.27,0.27,0,0.4,1.09,0,2.32,10.38,0.13,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0,0,0,0,0,0,0,0.021,0.042,0,0.364,0.064,0.686,13.884,107,1444,1 0.87,0.17,0.52,0,0,0.32,0,0.04,0.29,0.42,0.39,1.37,0.87,1.69,0,0.32,0.54,0.22,3.47,0.29,1.32,0,0.34,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0.07,0,0.04,0,0.016,0.058,0,0.638,0.165,0.182,3.697,117,3498,1 0,0.27,0.54,0,0.27,1.64,0,0.27,0.54,0.54,0,1.09,0.27,0,0,0,0,0.27,1.37,0,1.09,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0.472,0.128,0,10.877,93,533,1 0.4,0,0,0,0.8,0,0.4,2.8,0,1.2,1.2,2.8,0,0,0,0.4,0,0,4,0,0.8,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.052,0,0,0.105,0.052,0.052,1.194,5,129,1 4.54,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,9.09,0,0,4.54,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.076,0,0,1.428,4,10,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.699,0.932,0,5.083,39,122,1 0,0,0,0,2.63,0,0,0,0,0,0,0,0,0,0,1.31,0,0,1.31,0,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.818,46,106,1 0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.403,0,0,9.785,42,137,1 0.4,0.34,0.27,0,0.13,0.4,0.06,0.2,0,1.36,0.27,0.68,0.95,0,0.2,0,0,0,3.68,0,0.81,0,0.13,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.013,0.052,0,0.474,0.197,0.065,3.286,53,608,1 0,0,0,0,0.12,0,0,0,0,0,0,0.9,0,0,0,0.12,0,0.12,0.12,0,0.12,0,0,0,0,0,0,0.25,0.12,0.12,0,0,0,0,0,0.64,0,0.12,0,0,0,0.38,0,0,0,0.38,0,0,0,0.391,0,0,0,0,2.417,41,481,1 0,0,0,0,0.12,0,0,0,0,0,0,0.99,0,0,0,0.12,0.12,0.12,0.12,0,0.12,0,0,0,0,0,0,0.24,0.12,0.12,0,0,0,0,0,0.62,0,0.12,0,0,0,0.37,0,0,0.12,0.37,0,0,0,0.365,0,0,0,0,2.376,41,492,1 0,0,0,0,0.12,0,0,0,0,0,0,0.96,0,0,0,0.12,0.12,0.12,0.12,0,0.12,0,0,0,0,0,0,0.24,0.12,0.12,0,0,0,0,0,0.6,0,0.12,0,0,0,0.36,0,0,0.12,0.36,0,0,0,0.352,0,0,0,0,2.337,41,505,1 0.19,0.19,0.19,0,1.08,0.19,0.19,0.98,0.89,0.59,0.09,1.08,0.19,0.89,0.09,0,0,1.18,3.85,0.59,1.78,0,0.09,0.29,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0.09,0.09,0,0,0,0,0,0,0,0,0,0,0.19,0.412,0.222,0.015,4.195,49,814,1 0.87,0.17,0.52,0,0,0.32,0,0.04,0.29,0.42,0.39,1.37,0.87,1.69,0,0.32,0.54,0.22,3.47,0.29,1.32,0,0.34,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0.07,0,0.04,0,0.016,0.058,0,0.639,0.165,0.182,3.697,117,3498,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.862,0,8.5,17,34,1 0,0,0,0,0,0,0,0,0,0,0,2.1,0,0,0,0,0,0,2.1,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.178,0,0,0,0,1.275,7,51,1 0.21,0.21,0.42,0,0.42,0.21,0,0.42,0.42,0.21,0,0.64,0,0,0,0.85,1.07,0,4.07,1.07,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.216,0,0.432,0.18,0.072,4.391,36,303,1 0,0.29,0.29,0,0.58,0,0.58,0,0,0.58,0.29,0.29,0,0,0,1.46,0.29,0.87,1.16,0.87,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0.086,0,0.606,0,0.043,3.591,37,352,1 0.22,0.88,0.44,0,0.22,0,0,0,1.32,1.54,0,0.88,0.66,0,1.1,0.66,0,1.54,2.87,0,1.54,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0.085,0,0.659,0.114,0.028,9.1,65,728,1 0,0,0,0,1.63,0,0,0,0,1.63,0,0.81,0,0,0,0,0,0,3.27,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,1.558,11,53,1 0,0,0,0,0,0,0,0,0,0.76,0,2.29,0,0,0,0,0,0,3.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.76,1.52,0,0,0,0,0,0,0,0,1.216,4,45,1 0.22,0.88,0.44,0,0.22,0,0,0,1.32,1.54,0,0.88,0.66,0,1.1,0.66,0,1.54,2.87,0,1.54,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0.085,0,0.659,0.114,0.028,9.1,65,728,1 0.1,0,0.74,0.21,0.21,0,0.1,0.1,0,0,0.1,0.31,0,0,0,0,0,0.21,0.63,0,0.31,0,0.21,0,0,0,0,0,0,0.1,0,0,0,0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0.101,0,0.05,0.609,0.253,7.887,126,1609,1 0,0.32,0,0,0,0,0,0,0.32,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.037,0,0,0,0,2.391,36,110,1 0.43,0,0.43,0,0.43,0.43,0,0,0,0.87,0,0.43,0,0,0,0,3.49,0,1.31,0,1.74,0,1.31,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.298,0.149,0.074,2.955,47,133,1 0.43,0.26,0.43,0,0.78,0.26,0,0.17,0.34,4.09,0.08,1.22,0.43,0,0.78,1.13,0.26,1.91,2.35,0,2.35,0,0.08,0.43,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0.08,0,0,0,0,0,0,0,0,0.056,0.241,0.042,0.709,0.056,0,4.319,126,1123,1 0.7,0,1.06,0,0,0,0,1.41,0.35,0.35,0,0.35,0,0,0,2.12,0.7,0.35,2.12,3.18,2.12,0,0.35,1.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.117,0,0.353,0,0,1.209,13,196,1 0.43,0.4,0.37,0,0.15,0.09,0.06,0.12,0.5,0.97,0.25,0.69,0.4,1.06,0.03,0.15,0.25,0,2.57,0,1.41,1.28,0.31,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0.031,0.122,0.01,0.345,0.42,0.266,8.016,178,3303,1 0,0,0.19,0,0,0,0.19,0,0,0,0,0.19,0,0.09,0,0,0,0.09,0.19,0,0.09,0,0,0,0.09,0,0,0,0,0,0,0,0.19,0,0,0,0,0.09,0.19,0,0,0,0,0,0,0,0.09,0,0.015,0.137,0,0.061,0,0,3.626,44,990,1 0,0.24,1.45,0,0.36,0.6,0.6,0,0.6,1.45,0.12,0.85,0.48,0,1.94,0.12,0,0,1.33,0.12,0.6,0,0.48,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0.12,0,0,0,0.117,0,0.234,0.234,0,4.493,39,746,1 0.35,0.1,0.55,0,2.15,0.15,0,0,0.1,0.75,0.35,0.85,0.25,0,0,0.15,0.3,0,5,0,1.75,0,0.05,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0.008,0.035,0,0.149,0.131,0.008,3.629,127,617,1 0,0,0,0,0.45,0,0.45,0,0.9,0.45,0.45,0.9,0.45,0,0,1.81,0,0.45,1.36,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0.45,0,0,0,0,0.16,0,0.64,0.16,0,3.607,71,184,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0.83,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,0,0,0.121,0.605,0,0,0,0,2.222,22,100,1 0,0.45,1.35,0,1.35,0,0.9,0.45,0,1.35,0,0.45,2.71,0,0,0,0,0.9,2.26,0,1.8,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.82,0.41,0,2.446,12,137,1 0.4,0.4,0,0,0,0,0,0,1.2,4.81,0.4,0,0,0,4.41,0,0,0,1.2,0,1.2,0,4.01,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.855,0.794,0,4.152,41,353,1 0.1,0.1,0.03,0,0.07,0.03,0,0.03,0,0.1,0,0.53,0,0,0,0.17,0.03,0,0.81,0.03,1.35,0,0.1,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0,0,0,0,0.03,0,0,0.071,0,0.006,0.065,0,2.106,46,3214,1 0.14,0.18,0.79,0,0.04,0.14,0.18,0.28,0.28,0.84,0.18,0.46,0.61,0.09,0.32,0.89,0.37,0.46,3.8,0.04,1.87,0,0.46,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0.28,0.04,0,0,0,0.101,0,0.522,0.109,0.062,5.759,116,2062,1 0,0,0.21,0,0.21,0,0,0,0,0,0,0.84,0,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0.42,0,0,0.21,0,0,0,0,0,0,1.48,0,0,0,0.057,0,0,0,0,2.807,39,379,1 0.33,0.42,0.75,0,0,0.25,0,0.08,0.16,1.09,0.33,1.09,0.16,0,0,0.67,0.67,0.08,2.52,0,0.92,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0.08,0,0,0,0.014,0.029,0,0.523,0.378,0,3.631,67,897,1 0,0.82,0.32,0,1.14,0.32,0,0.16,0,0.65,0,2.13,0,0,0,0.16,0,0,1.47,0,1.47,0,0.98,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0.29,0.029,2.257,13,158,1 0,0,0,0,1.21,0,0,0.6,0,0.6,1.21,0,0,0,0,1.82,0,0,4.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0.193,0,0,1.861,26,67,1 0.33,0.16,0.16,0,1.35,0.67,0.16,0.33,0,0.16,0,0.84,0,0,0.33,1.52,0.33,0.16,2.88,0,2.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.113,0,0.028,0.084,0,4.971,40,532,1 0,0,0,19.73,0,0,0,0,0,0,0,0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0.53,0,0,0,0,0,0,0,0,0,0,0.087,0,0,0,0,4.786,152,292,1 0,1.11,0.55,0,0,0,0,0,0,0,0.55,0,1.11,0,0,3.35,0,0,0.55,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0.19,0,0,1.429,0.095,0,2.861,36,186,1 0,0,0.24,0,0.72,0,0,0,1.69,0,0.48,1.21,0,0,0,0.24,0,0,2.91,0,1.21,0,0,0.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,0,0,0,0.24,0,0,0,0,0.036,0,1.021,0.291,0.109,7.092,67,461,1 0,0,0,0,0.67,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.114,0.114,0,0.228,0.228,0,2.847,16,168,1 0.15,0,0.3,0,1.23,0.61,0,0.61,0,0.15,0,0.3,0,0,0.15,0.3,1.54,2.32,1.85,0,1.08,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0.128,0,0.615,0.025,0,1.377,13,186,1 0.13,0.1,0.55,0,0.02,0.13,0.02,1.11,0.23,0.29,0.05,0.34,0.42,0.07,0.55,0.87,0.45,0.66,3.95,0.05,1.59,0,0.39,0.34,0,0,0,0,0,0,0,0,0.02,0,0,0,0,0,0,0.31,0,0,0,0.05,0.23,0.02,0,0,0.03,0.083,0,0.538,0.145,0.07,5.108,116,3525,1 0,0,0,0,0,1.05,0,0,0,0,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.057,0,0,0,0,2.675,36,99,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0.364,0,0,0,0,3.23,38,126,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.4,0,0,2.4,0,2.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.151,0.302,0,2.611,11,47,1 0,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0.47,0,0,0,0,0,0,0.94,0.47,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0,0,0.94,0,0,0,0.332,0,0,0,0,1.518,15,161,1 0.98,0.16,0.41,0,0.08,0.24,0,0.08,0,0.49,0.08,0.57,0.9,0,0.16,0,0,0.32,2.46,0,1.14,0,0.49,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.095,0,0.751,0.255,0.095,4.163,84,712,1 0,0.22,0.22,0,0,0,0,0,0.22,2.75,0,0.68,0,0,0.68,0.45,0,1.37,2.06,0,4.12,0,0.45,0.22,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0.028,0.114,0,0.919,0.229,0.028,4.444,138,400,1 0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0,0.68,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.309,0,0,1.6,4,32,1 0.26,0.46,0.99,0,0.53,0,0,0.53,0.19,1.12,0.26,0.73,0.66,0,0.06,0.26,0.13,0.26,3.78,0,3.32,0,0.39,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.011,0.023,0,0.449,0.265,0.034,13.235,272,1575,1 0,0.26,0.78,0,0.26,0.26,0.08,1.04,0.52,1.56,0.26,0.69,0.17,0.08,0.69,0.86,0.34,0,1.82,0.17,1.3,0,0.08,0.34,0,0,0,0,0,0,0,0,0.08,0,0,0.08,0,0,0,0,0,0,0,0,0.08,0.08,0,0,0.096,0.234,0,0.358,0.261,0.11,3.56,54,979,1 0.14,0,0.29,0,0.14,0,0,0,0,0,0,0.14,0.29,0,0,0.29,0,0,2.19,0,1.02,0,0,0.43,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0.05,0.382,0,0.764,0,0,2.468,28,469,1 0,0.26,0.78,0,0.26,0.26,0.08,1.04,0.52,1.56,0.26,0.69,0.17,0.08,0.69,0.86,0.34,0,1.82,0.17,1.3,0,0.08,0.34,0,0,0,0,0,0,0,0,0.08,0,0,0.08,0,0,0,0,0,0,0,0,0.08,0.08,0,0,0.096,0.234,0,0.358,0.261,0.11,3.554,54,981,1 0,0,0,0,0.53,0,0,0.26,0,0,0,0.26,0.26,0,0,0.53,0,0,1.33,0,0,9.33,0.53,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.555,0,1.157,19.26,107,886,1 0,0,2.15,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,1.07,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.192,0,0,2.333,19,49,1 0,0,0,0,0,0,0,0,0,0,0,0,0,2.05,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.73,0,0,0.098,0.589,0,0,0,0,2.044,22,92,1 0,0.18,0.37,0,0.18,0,0,0,0,0,0.18,0.56,0,0.18,0.18,0.56,0.18,0.56,0.56,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.277,0,0.493,0.061,0.03,1.874,13,253,1 0,0,0,0,1.04,1.04,0,0,0,0,0,0,0,0,0,6.25,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.555,0,0,3.275,14,95,1 0,0.28,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0,0,0.85,0,0,0.57,0,0,0,0,0,0,0.57,0,0,0,0.103,0,0,0,0,2.417,33,162,1 0.09,0.49,0.59,0,0.49,0.19,0,0,0.09,0.39,0,1.57,0.19,0,0,0,0.09,0,3.74,0.09,1.08,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.765,0.037,0,5.803,1.284,0,5.944,54,755,1 0,0.55,0.55,0,0.55,0.55,0,0.27,1.94,1.67,0,1.39,0.83,0,0.83,0.27,0,1.94,2.5,0,2.22,0,0.55,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.273,0.364,0.045,6.641,48,352,1 0.58,0,0.34,0,0.11,0.11,0,0,0,0.23,0.23,0.93,0.93,0,0,0.58,0.23,0.11,4.19,0,1.51,0,0.58,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0.11,0,0,0.11,0,0.125,0,0.733,0.104,0.335,8.192,326,1360,1 0,0.18,0.18,0,0.74,0,0.18,0,0,0.55,0.18,0.18,0,0,0.18,0,0,0,1.11,0,0.74,0,0,0,0,0,0,0,0,0.18,0,0,0.37,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0.058,0,0,0.029,1.57,2.166,11,208,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.013,0,0,0,0,3.5,28,42,1 0.49,0,0.24,0,0.24,0,0,0.73,0,0,0,0.49,0,0,0,0,0,0,4.9,0,1.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.544,0.077,0,2.055,22,111,1 0,0,0,0,0,0,0.91,0,0,0,0.91,2.75,0,0,0,0,0,0,6.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.941,12,33,1 0,0,0.29,0,0.87,0,0.29,0,0.87,0,0,1.45,0,0,0,0,0,0.29,5.24,0,1.45,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.042,0,0,0.085,0,5.145,33,247,1 0,0,0.57,0.57,0,0,0,0.28,0,0,0,0,0.28,0,0,0,0.28,0.57,2.89,0.86,1.73,0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.461,0,1.385,0,0.046,3.535,64,396,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,1 1.06,0.16,0.4,0,0.16,0.24,0,0.16,0,0.49,0.08,0.57,0.9,0,0.16,0,0,0.32,2.37,0,1.22,0,0.49,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.095,0,0.845,0.255,0.095,4.194,84,713,1 0,0.26,0.79,0,0.26,0.26,0.08,1.06,0.53,1.59,0.26,0.71,0.17,0.08,0.71,0.88,0.44,0,1.86,0.26,1.24,0,0.08,0.35,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0,0,0,0,0,0.26,0.08,0,0,0.098,0.226,0,0.353,0.254,0.113,3.591,54,966,1 0.98,0.16,0.41,0,0.16,0.24,0,0.16,0,0.49,0.08,0.57,0.9,0,0.16,0,0,0.32,2.37,0,1.23,0,0.49,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.096,0,0.736,0.256,0.096,4.123,84,701,1 0.58,0,0.34,0,0.11,0.11,0,0,0,0.23,0.23,0.93,0.93,0,0,0.58,0.23,0.11,4.19,0,1.51,0,0.58,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0.11,0,0,0.11,0,0.125,0,0.733,0.104,0.335,8.192,326,1360,1 0,0,0,0,0,0,0,0,0,4.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.287,0,0,0,0,0,4.333,13,78,1 0.41,0,0.41,0,0.41,0,0,0,0.41,0.83,0,0,0,0,0,0,0.41,0,1.66,0,1.25,3.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.676,9.444,54,255,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.333,11,13,1 0,0.26,0.79,0,0.26,0.26,0.08,1.06,0.53,1.59,0.26,0.71,0.17,0.08,0.71,0.88,0.44,0,1.86,0.26,1.24,0,0.08,0.35,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0,0,0,0,0,0.26,0.08,0,0,0.098,0.226,0,0.353,0.254,0.113,3.598,54,968,1 0,0.32,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0.075,0,0,0,0,2.269,33,118,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.32,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0.254,0,0,0,0,1.987,28,153,1 0,0,0,0,0.44,0,0,0,0,0.88,0,0,0,0,0,0.44,0,0,1.32,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,1.841,10,186,1 0.09,0.49,0.59,0,0.39,0.19,0,0,0.09,0.39,0,1.57,0.19,0,0,0,0.09,0,3.74,0.09,1.08,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.765,0.037,0,5.828,1.308,0,6.047,54,768,1 0.36,0.29,0.36,0,0,0.58,0.07,0.14,0.66,1.25,0.14,1.39,0.58,1.1,0.14,0.14,0,0,2.35,0,1.25,0.07,0.58,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0,0,0,0,0.319,0.266,0.279,4.689,145,1163,1 0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0.24,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0.186,0,0,0,0,2.823,38,240,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.4,0,0,2.4,0,2.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.145,0.291,0,2.5,11,45,1 0.17,0.22,0.62,0,0.11,0.22,0.05,0.11,0,0.39,0.11,1.02,0.45,0.05,0.05,0,0,0.39,3.46,0,1.76,0,0.56,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.2,0,0.441,0.421,0.04,4.945,116,1449,1 0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,1.16,0,1.16,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,1.16,0,0,0,0.381,0,0,0,0.19,2.652,28,61,1 0,0.26,0.78,0,0.26,0.43,0.08,1.12,0.43,1.47,0.26,0.69,0.17,0.08,0.69,0.86,0.6,0,1.82,0.6,1.39,0,0.08,0.26,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0,0,0,0,0,0.26,0.08,0,0,0.097,0.222,0,0.444,0.25,0.111,3.138,54,929,1 0,0,0,0,0,0,0,0,0,0,1.23,0,0,0,0,2.46,0,0,2.46,0,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.232,0.465,0,2.687,12,43,1 0,0,0,0.6,0.6,0,0,0,0,0,0,0,0.6,0,0,2.42,0,0.6,0,0,0.6,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.412,0.206,0.103,2.3,20,237,1 0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,0,0,0.6,0,1.2,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,1.8,0,0,0,0.299,0,0,0,0.199,2.465,28,106,1 0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0.24,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0.185,0,0,0,0,2.802,38,241,1 0,0,0,0,0,0.27,0,0,0.82,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0,0.54,0,0,0.54,0,0,0,0,0,0,0,0,0,0.037,0.226,0,0.037,0,0,2.666,33,208,1 0,0.68,0,0,4.08,0,0.68,0,0,0.68,1.36,1.36,0,0,0,0,0.68,0.68,2.72,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,6.588,68,112,1 0,0.68,0,0,4.08,0,0.68,0,0,0.68,1.36,1.36,0,0,0,0,0.68,0.68,2.72,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.111,0,6.588,68,112,1 0.7,0,0.7,0,2.83,0,0,0,0,0,0,0,0,0,0,0,0,0.7,3.54,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,1.083,3,26,1 0.2,0.41,0.2,0,1.44,0,0,0.41,0.41,0.62,0,1.86,0.2,0.2,0,0.2,0.41,0,2.69,1.03,2.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.249,0,0.996,0.106,0,7.836,116,384,1 0,0,0,0,0,0,0,0,0,0,0,0,1.44,0,0,1.44,0,0,5.79,0,1.44,0,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.543,0.271,0,2.157,11,41,1 0,0.28,0,0,1.4,0,0.28,0.28,0,0.56,0,0.84,0,0,0,0.28,0,0,1.68,0,1.96,0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0.28,0,0,0,0,0,0.28,0.28,0,0,0,0,0.137,0.068,0.034,0,0,5.635,114,603,1 0,0,0,0,1.03,0,1.03,0,0,0,0,2.06,0,0,0,2.06,0,0,3.09,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,60,84,1 0,0.49,0,0,0,0,0,0,2.48,0,0,0,0,0,0,0,0,0,0.49,0,0.99,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.177,0,0,0.265,0.088,10.159,114,447,1 0.4,0.48,0.37,0,0.14,0.14,0.03,0.07,0.55,0.66,0.29,0.89,0.44,1.04,0.03,0.26,0.37,0.07,3.16,0,1.41,0,0.48,0.59,0,0,0,0,0,0,0,0,0.03,0,0,0,0,0.03,0,0,0,0,0,0,0.07,0,0,0,0,0.082,0,0.433,0.529,0.114,6.482,140,2379,1 0,0,0,0,0,0,0,0,0,3.57,0,1.78,0,0,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.252,0,0,0.757,0,0,4.157,13,79,1 0,0,0,0,0.64,0,0.64,0,0,0,0,0.64,0,0,0,0,0,0,5.8,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,1.44,0,0,2.875,21,115,1 0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.261,0,0,0.785,0,0,4.333,13,78,1 0,0,0,0,0.65,0,0.65,0,0,0,0,0,0,0,0,0,0,0,5.22,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,1.461,0,0,2.973,21,113,1 0.1,0.1,0.71,0,0.61,0.3,0.4,0.1,1.42,0.81,0.1,0.5,0,0,0,0.1,0,1.11,2.23,0.5,2.03,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.264,1.01,0.397,0.033,3.199,56,1043,1 0.15,0,0.3,0,1.23,0.61,0,0.61,0,0.15,0,0.3,0,0,0.15,0.3,1.54,2.32,1.85,0,1.08,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0.128,0,0.615,0.025,0,1.377,13,186,1 0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,1.85,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.258,0,0,1.55,0,0,4.555,13,82,1 0,0,0,0,0.65,0,0.65,0,0,0,0,0,0,0,0,0.65,0,0,5.88,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,1.765,0,0,3.025,21,118,1 0,0,0,0,0,0,1.43,0,0,0.47,0,0.95,0.47,0,0,0,0,0,2.87,0,0.47,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.694,0,0,7.709,164,239,1 0.19,0.19,0.29,0,1.07,0.19,0.19,0.97,0.87,0.58,0.09,1.07,0.19,0.87,0.09,0,0,1.17,3.81,0.68,1.75,0,0.09,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0.09,0,0,0,0,0,0,0,0,0,0,0.202,0.404,0.233,0.031,4.32,49,877,1 0,0,0,0,0,0,0,0,0,0,0,0,1.44,0,0,1.44,0,0,5.79,0,1.44,0,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.542,0.271,0,2.157,11,41,1 0,0,2.43,0,0,0,0,0,0,0,0,0,0,0,0,2.43,0,4.87,2.43,0,2.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.896,0,0,5.538,58,72,1 0,0,1.14,0,0,0,1.14,0,0,0,0,0,0,0,0,0,0,0,2.29,0,2.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.197,0,0,0,0,1.227,6,27,1 0,1.63,0,0,0.81,0,1.63,0,0,0,0,0,0,0,0.81,0,0,0.81,1.63,0,2.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.134,0,0,0,3.294,11,56,1 0,0,0,0,0,0,2.3,0,0,0,0.76,2.3,0,0,0,0.76,0,0.76,3.07,0,2.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.518,0,0,11.312,142,181,1 0,0,1.06,0,0,1.06,1.06,0,0,0,0,1.06,1.06,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.909,78,108,1 0,0,1.03,0,1.03,0,0,0,0,0,0,0,0,0,0,0,2.06,1.03,4.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.149,0,0,1.461,3,19,1 0.27,0,0.83,0,1.11,1.11,0.27,0,0,0,0,0.83,0,0,0,0.83,1.11,0.27,1.38,0,1.11,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.638,0,0,2.512,17,196,1 0,0,0,0,0,0,0,0,0,3.92,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.55,3,31,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.375,0.525,0.225,0,0,4.906,39,211,1 0,0,0,0.04,0,0,0,0,0,0,0,0,0,0,0,0.02,0,0,0.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.024,9.752,0.003,1.542,1.785,1.998,239.571,9989,10062,1 0.1,0.1,0.71,0,0.61,0.3,0.4,0.1,1.42,0.81,0.1,0.5,0,0,0,0.1,0,1.11,2.23,0.5,2.03,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.264,0.975,0.396,0.033,3.186,56,1042,1 0,0,1.63,0,0.54,0,0.54,0,0.54,1.09,0,2.18,0,1.09,0,0,0,0,2.73,0,2.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.086,0,1.466,0.258,0.086,31.388,392,565,1 0,1.2,0.4,0,0.4,0,0.8,0.4,0,0,0,0.8,0.4,0,0,0.8,0.4,1.2,3.62,0,1.61,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0.069,0,0.552,0.207,0.138,6.652,69,153,1 0.22,0.44,0,0,1.33,0.22,1.33,0,0,0.22,0.44,0.66,0.22,0,0,1.11,0,1.11,2.66,0,1.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0,0.134,0,0.067,0.067,0,1.946,22,183,1 0.07,0,1,0,0.3,0.46,0.07,0.23,0.23,0,0.3,1.31,0.15,0,0.07,1.39,0.15,0.85,2.24,0,0.77,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0,0,0,0,0,0,0.024,0.183,0,0,0.183,0,3.211,84,700,1 0,0.5,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,0.5,0,1,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.178,0,0,0.267,0.089,10.372,114,446,1 0.19,0.76,0.19,0,0.19,0.19,0.19,0,0.95,0.38,0.19,0.57,0,0,2.86,0.19,0,3.43,1.71,0,2.09,0,3.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0.151,0.303,0.212,0.303,0,11.242,132,742,1 0,0.37,0,0,0,0.74,1.12,0,0,0,0.74,1.49,0.74,0,0,0.37,0,1.49,4.49,0,1.87,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.223,0.223,0,2.021,10,93,1 0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,1,1,4,1 0.17,0.26,1.21,0,0.43,0.6,0.43,0.26,0.69,0.52,0.26,1.3,0.17,0,0.6,0.69,0.08,1.47,2.43,0.17,1.04,0,0.95,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0.08,0,0,0,0,0.107,0,0.256,0.242,0.013,6.142,583,1339,1 0.96,0,0.48,0,0,0.96,0,0,0.48,0,0.48,0,0,0,1.44,0.48,0.48,2.41,0,0,0.96,0,0,0.48,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0.818,0,0.175,0.467,0.116,9.56,259,717,1 0,0,0,0,0.67,0,2.01,0,0,0,0,0,0,0,0,0,0,2.01,1.34,0.67,2.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.305,0,0,2.162,14,93,1 0.09,0.49,0.59,0,0.29,0.19,0,0,0.09,0.39,0,1.58,0.19,0,0,0,0.09,0,3.76,0.09,1.09,0,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.765,0.037,0,5.831,1.309,0,6,54,756,1 0,0,0,0,0.68,0,2.04,0,0,0,0,0,0,0,0,0,0,2.04,1.36,0.68,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.309,0,0,2.111,14,95,1 0,0,0.37,0,0,0,0.37,0,0,0,0,0.37,0,0,0,0.74,0.37,0.37,0.74,0.37,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0,0,0.37,0,0,0.302,0,0.241,0.06,0,2.166,18,143,1 0.16,0.24,1.23,0,0.41,0.57,0.49,0.32,0.65,0.49,0.24,1.23,0.16,0,0.65,0.9,0.08,1.56,2.38,0.16,1.07,0,0.9,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0.08,0,0,0,0,0.114,0,0.241,0.228,0.012,6.544,683,1466,1 0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,3.26,0,0,5.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.152,0,0,5.21,36,99,1 0,0.96,0.96,0,1.44,0,0.48,0,0.48,1.92,0.48,0.96,0.48,0,1.92,0,0,0,0.96,0,0.96,0,4.32,0.48,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0.061,0,0.43,0.43,0,25.964,305,727,1 0,0.18,1.1,0,0.73,0.73,0.73,0.09,0.83,0.27,0.27,0.64,0.27,0,1.47,0.09,0,1.2,1.38,0.18,0.64,0,0.55,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18,0,0.09,0.09,0,0,0,0.094,0,0.43,0.134,0.013,8.445,696,1478,1 0,0,0,0,0.4,0.4,0.4,0.4,0,0,0.4,0,0,0,0,0.4,0,0,3.6,0,2,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.124,0,0,1.94,12,97,1 0,0,0,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0,2.52,0,1.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0.139,0,0,0,0,1.304,6,30,1 0,0,0,0,0,0,0,0.85,0,0,0,0.85,0,0,0,0,0,0,2.56,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0.144,0,0,0,0,1.333,6,28,1 0,0.21,0.43,0,0.65,0,0.21,0.21,0.87,0.65,0.43,0.87,0,0,0,0.43,0,0.87,3.71,0,1.09,0.65,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0.21,0,0,0,0,0.032,0,0.96,0.128,0.128,8.08,70,501,1 0,0,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0.64,0,1.29,2.58,0.64,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0.224,0,2.354,0,0,2.09,13,69,1 0,0.5,0,0,0,0,2,0,0,0.5,0.5,0.5,0,0,0,0.5,0,1.5,3,0,1.5,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.267,0,0.445,0.979,0,4.685,28,164,1 0.27,0.27,0.55,0,0.27,0.27,0,1.37,0.27,0.82,0.27,0.55,0,0,0,0,1.37,0.55,1.65,2.2,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0.28,0,1.029,0.093,0,3.621,63,344,1 0.87,0.17,0.52,0,0,0.32,0,0.04,0.29,0.42,0.39,1.37,0.87,1.69,0,0.32,0.54,0.22,3.47,0.29,1.32,0,0.34,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.02,0.07,0,0.04,0,0.016,0.058,0,0.639,0.165,0.182,3.697,117,3498,1 0,0.78,2.34,0,0.78,0,1.56,0,0,0,0,1.56,0,0,0,0,0,0.78,7.03,0,2.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.363,0,0,1.348,29,147,1 0.71,0.35,0.71,0,1.79,0,0,0,0,0.35,0,1.43,0,0,0,0.35,0,0,3.94,0,1.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.061,0,0,0,0,8.086,153,186,1 0.33,0.84,0.67,0,0.67,0.33,0.67,0,0.33,0,0.16,0.84,0.16,0,0,0.67,0,0.5,3.03,0.33,2.18,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.183,0,0.156,0.104,0.026,6.5,525,858,1 0.42,0,0.42,0,1.71,0,0.42,0,0,0.21,0.21,0.85,0.21,0,0,0,1.92,0.42,3.21,0,1.49,5.78,0.21,0.21,0,0,0,0,0,0,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.075,0.263,0.075,0.639,53.433,494,1603,1 0,0,1.01,0,0,0,0.5,0,0,2.02,1.51,1.51,0,0,0,0.5,0,0,3.53,0,1.01,0,1.51,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.089,0,1.431,0.536,0,4.09,23,225,1 0.86,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0.86,3.44,0,4.31,0,0.86,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.928,0.154,0.154,2.409,7,53,1 0.25,0.17,0.34,0,0,0.08,0,0,0.08,0.08,0.08,0.86,0,0,0,0.08,0,0.25,4.66,0,1.2,0,0,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0.015,0,0.094,0.015,0,2.531,89,319,1 0.27,0.27,0.55,0,0.27,0.27,0,1.37,0.27,0.82,0.27,0.55,0,0,0,0,1.37,0.55,1.65,2.2,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0.279,0,1.023,0.093,0,3.621,63,344,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.81,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.763,21.428,62,150,1 0,0,0,0,0,0,7.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.297,0,0,2,8,52,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.11,0,0.488,0.157,0.015,8.55,669,1351,1 0.2,0,0.1,0,0,0.1,0.2,0,0,0,0,0.72,0,0,0,0.1,0.2,0.1,4.17,0,1.35,0,0.52,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.057,0,0.095,0,0,2.717,24,318,1 0,0,1.47,0,0,1.1,0.36,0,0,0,0.36,0.36,0,0,0,0.36,0,0,2.21,1.1,2.95,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.123,0,0.433,0.928,0.185,3,33,177,1 0.15,0.15,0.31,0,0.15,0,0.46,0,0,0,0.62,0.62,0.15,0,0,0.31,0.15,0.93,2.63,0,2.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.092,0,0.123,0,0,6.268,196,608,1 0.93,0,0,0,0.93,0,1.86,0,0,0,0,2.8,0.93,0,0,0,0,0,8.41,0,1.86,0,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.164,0,2.306,0.164,0,8.312,29,133,1 0,0.22,0.45,0,0.68,0,0.22,0.22,0.9,0.68,0.45,0.9,0,0,0,0.68,0,0.9,3.86,0,1.13,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0.22,0,0,0,0,0.033,0,1.103,0.133,0.033,7.166,54,430,1 0,0,0.27,0,0.54,0,0.27,0,0,0.27,0,0.54,0,0,0,1.35,0,0,1.08,0,2.44,10.86,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.329,0.141,1.41,44.72,252,1118,1 0.76,0,0.38,0,0.12,0.25,0,0.12,0.12,0,0,0.25,0.38,0,0,0.38,0,0.25,2.92,0,2.92,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.022,0,0.661,0.088,0,2.256,21,325,1 0,0,0,0,0,0,0,3.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0.207,0,0.207,0.207,0,3.761,25,79,1 0,0,0,0,0,1.29,0,0.64,0,0,0,0,0,0,0,0,0,0,3.87,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0.116,0,1.8,12,63,1 0,0,0,0,0,0,0,3.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0.207,0,0.207,0.207,0,3.761,25,79,1 0,0,1.35,1.35,0,0,0,1.35,0,0,0,0,0,0,0,1.35,0,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,3.588,0,0,2.516,17,78,1 0,1.03,0,0,1.03,0,1.03,0.51,0,0.51,0,1.03,0,0,0,0.51,0,0.51,2.07,0,1.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.257,0,0.6,0.429,0,1.447,4,55,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0.29,0,0.29,1.79,0,0.59,0,0.29,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,0.248,0,0,0.049,0,2.47,30,168,1 0,0,0.68,0,0,0,0,1.36,0,0,0.68,0.68,0,0,0,0,0,0,3.4,0,1.36,0,0.68,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.238,0.238,0,2.232,19,96,1 0.1,0.2,1.01,0,0.8,0.8,0.5,0,0.8,0.1,0.3,0.7,0.3,0,1.61,0.1,0,1.11,1.31,0.2,0.7,0,0.6,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0.1,0.1,0,0,0,0.11,0,0.488,0.157,0.015,8.55,669,1351,1 0,0,0.66,0,0.33,0,0.33,0.33,1.33,2,0,0.66,0,0.33,1,0.33,0,0.66,2.67,0,1,0,2,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0.23,0,0.057,0.23,0,5.279,82,227,1 0,0,0,0,0,0.23,0,0,0,0,0,0.46,0,0,0,0.46,0.46,0.23,3,0,0.69,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.077,0.038,0,0,0,0.038,2.6,42,182,1 0.39,0,0,0,0,0.39,0.79,0,0,0.39,0,0.79,0,0,0,0,0.39,0,2.37,0,2.76,0,1.18,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.064,0,0.64,0.192,0,2.74,13,74,1 0,0,0.77,0,0.38,0.38,0.38,0,0,0.77,0.38,0.38,0,0,0,0.77,0.77,0.77,2.31,0,1.15,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.063,0.127,0.255,0.51,0,0,3.685,62,258,1 0,0,0,0,0.53,0,0.53,0,0.53,0,0,1.07,0,0,0,0,0,0,2.15,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.082,0,0,4.391,66,101,1 0,0.31,0.42,0,0,0.1,0,0.52,0.21,0.52,0,0.52,0.63,0.1,0.1,0.21,0.31,0.21,2.53,0.42,1.69,0.31,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0.016,0,0.887,0.032,0.049,3.446,318,1003,1 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.022,0.022,0.019,0.022,0.022,0.022,3.482,5,5902,0 0,0,0,0,0,0,0,0,0,0.85,0,0,0,0,1.7,0,0,0,2.56,0,1.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.299,0,0,0.149,0,0,1.04,2,26,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,33.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.12,0,0,0,0,0,0,0,0,0,0,0,1.28,0,2.56,0,0,0,0,0,0,0,0,0,0,0.131,0,0.262,0,0,1.625,7,65,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0.07,0,0,0,0.07,0,0,0,0,0,0,0.07,0,0,0,0,0,0,0,0,0,0.104,0.324,0,0,0.011,4.411,28,1866,0 0,0,0,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.08,2.04,2.04,2.04,2.04,2.04,2.04,2.04,0,2.04,2.04,2.04,0,0,0,2.04,0,4.08,0,0,0,0,0,0,0,0.671,0,0,0,0,2.5,11,35,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0.84,0,0,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.273,0.136,0,0,0.136,3.571,28,150,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.87,0,0,0,0,0,0,0,0,0,0,0,0,0.393,0,0,1.75,7,28,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.729,0,0,2.285,7,16,0 0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,0,0,0,0.24,0,0,0,0,0,9.33,3.93,0.24,0,0,0.73,0,0,0,0,0.24,0.24,0,0,0.24,0,0,0.73,0,0.49,0,0,0,0,0,0.037,0,0.149,0,0,10.012,251,791,0 0.9,0,0,0,0.9,0,0,0,0,0,0,1.8,0,0,0,0,0,0,3.6,0,1.8,0,0,0,0.9,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0.149,0,0,0,0,2.766,12,83,0 0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,1.85,0,0,0,0,3.7,0,0,0,0,0,0,0,0.308,0,0,0,0,2,11,26,0 0.08,0,0.08,0,0,0.08,0,0.49,0,0,0.08,1.48,0.08,0.08,0,0,0.08,0,0,0,0,0,0,0,3.3,0,0,0,0,0,0,0,0,0,0,0.41,0.08,0,0,0,0,0,0.08,0,0,0,0,0.16,0.098,0.153,0,0,0.032,0,2.324,18,709,0 0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0.284,0,0,0,0,1.8,5,27,0 0,0,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.89,0,0,2.89,0,0,0,0,0,0,0,0.247,0,0,0,0,2.38,8,50,0 0,0,0,0,0,0,0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.546,0,0,2,4,16,0 0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0.8,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0.8,0.8,0,0,0,0,0,1.6,0,1.6,0,0,0,0,0,0.115,0,0.115,0,0,3.388,28,122,0 0,0,0,0,1.51,0,0,0,0,0,0,3.03,0,0,0,0,0,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.51,0,0,0,0,0,0.547,0,0,0,0,1.75,5,28,0 0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,2.32,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,2.32,0,0,0,0,0,0.375,0,0,1.444,5,13,0 0,0.63,0,0,1.27,0,0,0,0,0,0,1.27,0,0,0,0,0,0.63,3.18,0,0.63,0,0,0,0,0,1.27,1.27,0,0,0,0.63,0,0.63,0,0,0,0,0,0,0,0,0,0,2.54,0,0,0,0,0.218,0,0,0,0,2.215,22,113,0 0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0.58,0.58,0,0,0,0,0,1.16,0.58,1.16,1.74,0.58,0.58,0.58,0.58,0,0.58,0.58,0.58,0,0,0,0.58,0,0,0,0,0.58,0,0,0,0,0.658,0,0.282,0,0,1.932,11,114,0 0.18,0.06,0.24,0,0.18,0,0,0.18,0,0.12,0,0.6,0,0,0,0.24,0.12,0,0.78,0,0.72,0,0.06,0.42,1.93,0.66,0,0.18,0,0.12,0.3,0,0,0,0.42,0,0.18,0,0.24,0,0,0.12,0,0,0.18,0,0,0.12,0,0.196,0,0.044,0.026,0,1.873,29,843,0 0,0,1.88,0,0,0,0,0,0,0,0,1.88,0,0,0,0,0,0,1.88,0,1.88,0,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,0,0,0,0.323,0.323,0,0,0,0,1,1,12,0 0,0,2.12,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.647,16,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.1,5.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,0,0,0,0,1.142,2,8,0 0,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,0,0.336,0,0,0,0,1.909,5,21,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.315,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0.86,0.86,0,0,0,0,0,0,0,0,0,0,0,3.47,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.322,11,72,0 0,0,0,0,0,0,0,0,0,1.44,0,0,0,0,0,0,0,1.44,0,0,0,0,0,0,2.89,1.44,0,1.44,0,1.44,1.44,0,0,0,1.44,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0.156,0,0.313,0,0,1.689,10,49,0 0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.538,4,20,0 0,0,0.54,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,3.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0.27,0,0.54,0,0.27,0,0.27,0.27,0,0,0,0.188,0.047,0,0,0,1.745,12,89,0 0,0,0.75,0,0,0,0,0,0,0,0,0.75,0,0,0,0,0,0,3.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.262,0,0,0,0,1.437,3,23,0 0,0,0.79,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0,0.39,3.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0.39,0,0,0,0.39,0.39,0,0,0,0.237,0,0,0,0.059,2.51,12,123,0 0.08,0.16,0.08,0,0.2,0,0.04,0.04,0.04,0.49,0.12,0.32,0.12,0.04,0,0.08,0,0,0.77,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.69,0.65,0,0,0.04,0,0.08,0,0.16,0,0.28,0,0.89,0.016,0.243,0,0.033,0,0.016,2.747,86,1995,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.125,17,25,0 0,0,0,0,0,0,0,0.26,0.39,0,0.13,0.52,0.26,0,0,0,0,0,0,0,0,0,0,0,4.22,0.13,0,0,0,0,0,0,0,0,0,0.13,0.13,0,0,0,0,0,0,0.13,0,0,0,0,0.017,0.107,0,0,0.071,0,2.848,26,433,0 0,0,1.58,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,1.58,0,1.58,0,0,0,1.58,3.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.538,4,20,0 0,0,0.21,0,0.42,0,0,0,0.21,0,0,0,0,1.27,0,0,0.21,0,0.21,0,1.06,0,0,0,0.21,0,0,0.21,0,0,0,0,0,0,0.21,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0.161,0,0.161,0,0.182,2.813,121,723,0 0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.684,0,0,2,7,16,0 0,0,1.21,0,0,2.43,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,1.21,0,0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.211,0,0.211,0,0,1,1,11,0 0,0,0,0,0,0,0,0,0,0,0,1.41,0,0,0,0,0,0,0.47,0,0.94,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0.137,0,0.068,0,0,3.195,21,147,0 0,1.28,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,2.56,0,1.28,0,0,0,5.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.196,0,0,0,0,1.952,10,41,0 0.29,0,0.29,0,0.29,0,0,0,0,0,0,0,0.29,0,0,0,0.29,0,0,0,1.75,0,0,0,2.63,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0.108,0,0.072,0,0,2.847,60,242,0 0.26,0,0,0,0,0,0,0,0,0.53,0,3.76,0,0,0,0,0,0,0.26,0,0,0,0,0,3.76,2.68,0,0,0,0.26,0,0,0,0,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0.045,0,0,1.837,11,158,0 0,0,0,0,0,0,0,0,0,0,0,0.91,0,0,0,0,0.91,0,2.75,0,0,0,0,0,1.83,0,0,0,0,0,0,0,0,0,0,0,0.91,0,0,0,0,0.91,0,0,0,0,0,0,0.301,0,0,0.301,0,0,1.942,8,68,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,7.14,0,0,0,0,0,0,0,0,5.5,10,11,0 0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,3.22,0,0,0,0.526,0,0,0,0,1.571,3,11,0 0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.613,0,0,1,1,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,8.69,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.048,0,0,1,1,8,0 0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,1.02,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0.167,0,0,0,0,2.195,17,90,0 0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,0,2.63,0,3.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.235,5,21,0 0,0,0.78,0,0,0,0,0,0,0,0,0.78,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.78,0.145,0,0,0.725,0,0,1.187,4,19,0 0.11,0,0.11,0,0.11,0.11,0,0,1.03,0,0,0.34,0,0,0,0,0,0,0.45,0,0.22,0,0,0,0.57,0.68,0.11,0,0,0,0,0,0.34,0,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0.078,0.171,0.031,0,0.031,0,3.407,41,535,0 0.67,0,0,0,1.01,0,0,0,0,0,0,0.67,0.67,0,0,0,0,0,1.35,0,1.68,0,0,0,0.33,0.33,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0.67,0,0,0.33,0,0,0.33,0.097,0.048,0,0.048,0,0,2.326,22,107,0 0.02,0,0.15,0,0.24,0.31,0,0.04,0.22,0,0.02,0.08,0,0,0.02,0,0,0.02,0.08,0,0.06,0,0,0,0.44,0.47,0.02,0,0,0,0,0,0.11,0,0,0,0,0.02,0,0,0,0.02,0,0,0,0,0,0,0.185,0.15,0.044,0,0.006,0,2.838,52,2078,0 0.51,0,0.51,0,0,0.51,0,0,0,0,0,0.51,0,0,0,0,0,0,0.51,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0,0.51,0,0.51,0,1.02,0,0,0.51,0,0,0,0.161,0.08,0.08,0,0,1.885,12,66,0 0,0,0.65,0,0.32,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.061,0.061,0.061,0,0,1.392,11,71,0 0,0,0.1,0,0.1,0.1,0.2,0.2,0.1,0,0,0.2,0.1,0.2,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0.2,0.013,0.097,0,0,0.027,0,2.214,22,423,0 0,0,0.23,0,0.23,0,0,0,0.23,0,0,0,0,1.43,0,0,0.23,0,0.23,0,2.14,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,1.9,0,0,0,0,0,0,0.117,0.235,0,0.117,0,0.164,2.616,160,683,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.15,0,0,0,0,0,0,0,4.5,1.8,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0.557,0,0,0.123,0,0,2.063,34,130,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.751,0,0,0,0,2,4,10,0 0,0.16,0.32,0,0.16,0,0,0.16,0.16,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0.96,0.48,0.16,0,0,0,0,0,0,0,3.21,0,0.16,0,0,0,0,0.96,0,0,0.32,0.16,0.16,0,0,0.124,0,0,0,0.11,4.771,63,1064,0 0,0.54,0,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.09,0,0,0,0,0.182,0.091,0.091,0,0,1.212,5,40,0 0,0.37,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0,1.51,0,0,0,0,0,5.68,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.065,0.261,0,0,0,0,1.114,5,39,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,7.14,3.57,0,3.57,0,3.57,3.57,0,0,0,3.57,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0.24,0,0,1.687,10,27,0 0.3,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0.3,0,0,0,0,0,0.3,0,0,0,0,0.3,0,0,0.3,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.426,6,97,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,5.55,2.77,0,2.77,0,2.77,2.77,0,0,0,2.77,2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0.218,0,0.218,0,0,1.687,10,27,0 0,0,0,0,0,0,0,0,0,0,0,3.92,0,0,0,0,0,0,0,0,0,0,0,0,1.96,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.303,0.303,0,0,0,0,1.6,9,24,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,3.7,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.645,0,0,0.645,2.888,8,26,0 0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,2.22,0.74,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0.414,0,0.31,0.103,0,2.034,11,59,0 0,0,0.06,0,0.89,0.13,0,0.2,0,0,0,0.13,0.06,0,0,0,0.96,0,0,0,0,0,0,0,1.1,0,0,0,0,0,0,0,0,0,0,0,0.13,0.06,0,0,0,0.06,0,0,0.34,0,0,0,0.018,0.047,0,0,0.085,0,2.924,52,617,0 0,1.35,0.19,0,0,0,0,0,0,1.74,0,0.19,0,0,0,0,0,0,0,0,0.38,0,0,0,2.32,0.96,0,0,0,0,0,0,0,0,0.58,1.16,0.38,0,0,0,0,0,0.19,0,0,0,0,0.58,0,0.337,0,0,0,0,3.937,44,693,0 0.07,0,0.15,0,1.53,0.15,0,0.46,0,0,0.07,0.46,0.46,0,0,0,0.07,0,0.76,0,0.38,0,0,0,0.69,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0,0,0.07,0,0,0.61,0,0,0,0,0.022,0,0,0.033,0,1.705,36,220,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.886,0,0,1.375,4,11,0 0,0,0,0,0.69,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0.69,0,0.69,0,0,0,1.38,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,2.488,15,112,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,4,0 0,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,2.15,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,1.416,6,68,0 0,0,0,0,0,0,0,0,0.91,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0.109,0.254,0,0,0,0,3.606,119,357,0 0,0,0,0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,2.34,0,0,0,0,0,0.93,0,0,0,0,0.46,0,0,0,0,0,0,0,0,3.75,0,0,0,0,0,0,0,0,0,0,0.438,0,0,0,0,2.448,37,120,0 0,0,0,0,0,0.25,0,1,0,0,0,0.25,0,0,0,0,0,0.25,0,0,0,0,0,0,0.75,0.25,0,0,0,1,0,0,0,0,0,0.25,1.25,0,0,0,0,0,0,0,0,0.5,0,0,0,0.153,0,0,0,0,1.958,26,329,0 0.11,0.05,0.22,0,0.22,0.05,0,0,0.05,0.11,0.11,0.56,0.05,0,0,0.11,0.16,0,1.35,0,0.73,0,0,0,1.69,1.3,0,0.05,0,0.11,0.16,0,0.05,0,0.33,0.05,0.33,0,0,0.05,0,0.11,0,0.11,0.05,0,0,0.05,0.025,0.085,0,0.042,0,0,2.031,22,971,0 0,0,0,0,0,0,0,0,0.14,0,0,0.43,0,0,0,0,0.14,0,0,0,0.14,0,0,0,0.14,0.57,0,0,0,0,0,0,0.14,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0.14,0.058,0.156,0,0,0,0,1.687,24,496,0 0,0,0,0,0,0,0,0,0.29,0,0,0,0,1.75,0,0,0.29,0,0.29,0,0.29,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,1.75,0,0,0,0,0,0,0.156,0.052,0,0.052,0,0.235,2.721,38,566,0 0,1.36,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0.45,0,0.9,0,0.45,0,0,1.81,0.45,0,0,1.36,0,0,0.069,0.069,0,0,0,0,2.186,15,164,0 0,2.4,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,2.4,0,0,0,0,0,1.6,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,1.6,0,0,0,0.8,0,0,0.12,0,0,0,0,0,1.696,15,56,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,7.14,0,0,0,0,0,0,0,0,5.5,10,11,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0.1,0,0,0,0,0,0,0.1,0.1,0.96,0.1,0,0,0,0,0,0,0,0,0,0,0,3.52,0.1,0,0,0,0,0,0,0.74,0,0,0.1,0.21,0.1,0,0,0,0,0,0,0,0,0,0,0.014,0.117,0,0,0,0,2.204,24,496,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,7.14,0,0,0,0,0,0,0,0,5.5,10,11,0 0,0,0,0,0,0,0,0,0,0,0,1.01,0,0,0,0,0,0,0,0,1.01,0,0,0,1.01,0,0,0,0,0,0,0,0,0,2.02,1.01,0,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.108,25,115,0 0,0,0,0,0.68,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0.34,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0.051,0,0,0,0,1.062,4,137,0 0,0,0,0,0.14,0,0,0.14,0,0,0,0.73,0,0,0,0,0,0,0.14,0,0,0,0,0,2.48,1.6,0,0,0,0.14,0,0,0,0,0,0,1.16,0,0.29,0,1.16,0,0,0,0.14,3.07,0,0,0.144,0.433,0.082,0.02,0,0,4.113,52,654,0 0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,1.666,4,15,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,4.34,2.17,0,4.34,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.529,0,0,0,0,4,11,68,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.163,0,0,0,0,1.5,5,24,0 0,0,0,0,0.26,0.26,0,0,0,0.26,0,0.26,0,0,0,0.26,0,0,2.08,0,2.6,0,0,0,0.26,0,0,0,0,0,0.26,0,0,0,0,0,0,0,0,0,0,0,0.26,0,0.26,0,0,0,0.037,0,0,0,0,0,2.545,18,168,0 0.31,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0,0,0.31,0,0,0,0,0,0.31,0,0,0,0,0.31,0,0,0.31,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.446,6,94,0 0,0,0,0,0,0,0,0,0,0,0,0.96,0,0,0,0,0,0,1.44,0,0.48,0,0,0,2.89,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0.48,0,0,0,0.48,0,0,0,0,0.48,0,0.371,0.074,0.074,0,0,2.534,18,185,0 0.32,0,0,0,0,0,0,0,0,0.64,0,3.23,0,0,0,0,0,0,0.32,0,0,0,0,0,3.88,2.58,0,0,0,0.32,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.492,0,0,0,0,1.89,11,138,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,7.14,0,0,0,0,0,0,0,0,5.5,10,11,0 0.15,0.1,0,0,0.3,0.15,0,0.3,0,0,0,0.6,0,0,0,0,0,0,2.06,0,0.85,0,0.05,0,0.2,0,0,0,0,0,0,0,0.25,0,0.15,0,0.35,0,0,0,0,0,0,0.05,0,0,0,0,0.073,0.234,0,0.073,0,0,2.206,49,1026,0 0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.142,3,16,0 0,0,0,0,0,0.27,0,0,0,0.27,0,0.27,0,0,0,0.27,0,0,2.18,0,2.73,0,0,0,0.27,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.593,18,166,0 0.07,0,0.15,0,0.09,0.05,0,0.03,0.05,0.05,0,0.41,0.03,0,0,0.01,0.09,0.13,0.03,0.01,0.07,0,0.01,0,0,0,0.03,0,0.01,0,0,0,1.21,0,0,0.07,0.95,0,0.01,0.11,0.13,0.01,0,0,0,0.39,0.03,0.51,0.042,0.173,0.002,0.008,0.005,0.002,2.145,71,2954,0 0.16,0.08,0,0,0,0,0,0,0.16,0.33,0,0.67,0,0,0.08,0,0.5,0.33,0.58,0.16,0.42,0,0,0.08,1.34,0.58,0,0,0,0.08,0,0,0,0,0,1.09,1.34,0,0.16,0,0,0,0,0.08,0,0,0,0,0.084,0.408,0.06,0,0.012,0,4.179,104,1655,0 1,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0.17,0,0,1.692,4,22,0 0,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,2.35,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0.193,0,0,0,1.974,16,77,0 0,0,1.06,0,0,0.35,0,0,0,0,0,2.13,0,0,0,0,0.71,0,0.71,0,0,0,0,0,4.62,0,0,0,0.35,1.06,0,0,0,0,0,0.35,0.35,0,0.35,0,0,0,0.35,0,0.71,0,0,0,0,0.055,0.055,0,0,0,2.239,17,206,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,7.14,3.57,0,3.57,0,3.57,3.57,0,0,0,3.57,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0.239,0,0.239,0,0,1.687,10,27,0 0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,2,0,2,0,2,2,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18,0,0,0,0,1.611,10,29,0 0.23,0,0,0,0,0.23,0,0,0,0,0,0.92,0.46,0,0,0,0,0,2.76,0,2.76,0,0,0.69,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.445,0,0.202,0.121,0,1.945,7,142,0 0,0,0,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,1.666,7,15,0 0,0,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,1.78,0,0,0,0.89,0,0,0,0,0.89,0,0,0,0.269,0.269,0,0,0,2.27,16,84,0 0,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,3.44,0,1.72,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.058,5,35,0 0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,1.16,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0.744,0,0.148,0,0,1.972,18,71,0 0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,1.36,1.36,0,0,0,0,0,0,1.36,0,0,0,1.36,0,0,0,0,0,1.36,0,0,0,0,0,0,0.404,0.202,0,0,0,3.533,17,53,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,0,0,0,0,0,0,1.169,0,0,0,0,2.533,21,76,0 0,0,1.18,0,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,1.77,0,1.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.313,0.313,0,7.588,66,129,0 0,0,0,0,0,0.57,0,0,0,0,0,1.73,0,0,0,0,1.15,0,0.57,0,0,0,0,0,2.31,0,0,0,0.57,1.73,0,0,0,0,0,0.57,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0.086,0,0,0,0,1.5,5,72,0 0,0,0,0,0,0,0,0,0.62,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,3.34,2.3,0,0,0,0,0,0,0,0,0.2,0,0.62,0,0.2,0,0,0,0.41,0,0,0,0,0,0.085,0.198,0.056,0,0,0.454,3.414,108,536,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.96,0,1.96,0,0,0,0.98,0,0,0,0,0.98,0,0.377,0,0.125,0,0,2.925,27,158,0 0,0,0.25,0,0,0.25,0.5,0.25,0,0,0,0,0,0,0,0,0,0,1.25,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0.18,0,0.045,0,0,1.324,5,98,0 0.05,0,0.45,0,0.16,0.11,0,0,0.62,0,0,0.16,0,0,0,0,0,0.05,0.22,0,0.16,0,0,0,0.62,0.67,0.05,0,0,0,0,0,0.9,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0.121,0.162,0.04,0,0.016,0,2.887,45,875,0 0,0,0.35,0,0.35,0.71,0,0,0,0,0,0.71,0,0,0,0,0,0,0.71,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0.35,0,0.35,0,0,0,0.35,0,0.35,0,0,0,0,0.124,0,0.372,0,0,1.641,12,110,0 0.25,0.25,0,0,0.25,0,0.25,0,0,0.25,0.25,0,0.25,0,0,0.25,0,1.02,2.05,0,2.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0.51,0.25,0,0,0,0,0,0.413,0,0.165,1.78,13,146,0 0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0,0.214,0,0.214,0,0,1.263,4,24,0 0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,1.38,0,1.38,0,0,0,1.38,1.38,2.77,0,0,0,0,0,0,0,0,0,1.38,0,1.38,0,0,0,1.38,0,0,0,0,0,0,0,0.224,0.448,0,0,1.451,12,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.234,0,0,3,5,15,0 0,0,0,0,0,0,0,0,0,0,0,1.52,0,0,0,0,0,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0.76,0,0,0,0.118,0,0,0,0,1.735,10,59,0 0,0,0,0,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.92,0,0,0,0,0,0,0,0.92,0,0,0,0,0.92,0,0,0,0,0,0,0,0.165,0,0.165,0,0,1.666,7,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,3,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.42,0,1.42,0,0,0,0,0,0,0,0,1.42,0,0.361,0,0,0,0,2.025,7,81,0 0,0,0,0,0,0,0,0,0,0.42,0,0.42,0,0,0,0,0,0,3.4,0,0.42,0,0,0.42,1.27,0.85,0,0.85,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0.155,0,0,0,0,2.555,11,92,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,2.307,9,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0.724,0,0,2.285,8,16,0 0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0.7,0,1.4,0,1.4,0,0,0,0,0,0.7,0,0,0,0.7,0,0,0,0,0,0,0,0,2.11,0,0,0,0,0,0,0,0,0,0.266,0.066,0,0,0,18,200,378,0 0,0,0,0,0,0,0,0,0.14,0,0,0.43,0,0,0,0,0.14,0,0,0,0.14,0,0,0,0.14,0.57,0,0,0,0,0,0,0.14,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0.14,0.058,0.156,0,0,0,0,1.566,13,462,0 0,0,0.13,0,0.26,0,0,0.65,0.13,0,0,0.78,0.26,0,0,0,0.13,0,0,0,0,0,0.13,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0,0,0,0,0,0.13,0,0.105,0,0,0.052,0,2.165,20,446,0 0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0.286,0,0,0,0,1.461,4,38,0 0.67,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,1.01,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.34,0,0,0,0,0,0,0,0.055,0,0,0,0,1.644,13,74,0 0.42,0,0,0,0,0,0,0,0,0.42,0,0.42,0.42,0,0,0,0,0,0.42,0,0.42,0,0,0,1.28,2.57,0,0,0,0.42,0,0,0.42,0,0,0.42,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.151,0,0,1.533,9,69,0 0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,3.92,1.96,0,3.92,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.475,0,0,0,0,2.95,11,59,0 0,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0.96,1.93,0,0,0,0.48,0,0,0,0,0,0.96,0.48,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0,1.353,7,88,0 0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,1.66,0,0,0,0,0,3.33,1.66,0,1.66,0,1.66,1.66,0,0,0,1.66,1.66,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0.167,0,0.167,0,0,1.533,10,46,0 0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0.81,0,0,0,0,0,2.45,2.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0.12,0,0,0,0,2.473,11,47,0 0,0,0,0,0,0,0,0,0,0,0,2.15,0,0,0,0,0,0,1.07,0,0,0,0,0,5.37,1.07,0,0,3.22,0,1.07,0,0,0,0,0,1.07,0,0,0,0,3.22,1.07,0,0,0,0,0,0,0.388,0.194,0,0,0.194,2.666,13,104,0 0.06,0,0.19,0,0.06,0.06,0,0,0,0.13,0,0.26,0.06,0,0,0,0,0,0.52,0,0.46,0,0,0,2.57,0.92,0,0.06,0.13,0.52,0.32,0,0.06,0,0.26,0.13,0.32,0,0.26,0,0,0,0,0,0.06,0,0,0.13,0.009,0.152,0,0.047,0.028,0,1.948,23,836,0 0,0,0,0,0,0,0,0,0,0,0,5.12,0,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,0,5.12,0,0,0,0,0,0,0,0,0,0,0,0,5.12,0,0,0,0,0,0,0,0.5,0,0,0,0,2.266,7,34,0 0,0,0,0,0,0,0,0,0,0,0.38,0.19,0,0,0,0,0,0,1.33,0,0.38,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0.154,0,0.03,0,0.03,2.852,12,388,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,4.54,4.54,4.54,4.54,4.54,4.54,4.54,0,4.54,4.54,4.54,0,0,0,4.54,0,0,0,0,0,0,0,0,0,1.169,0,0,0,0,3.1,11,31,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,2.43,0,0,0,0,0,0,0,0,0,0,0,0,0,2.43,0,0,0,0,0,0,0,2.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.442,0,0,0,0,1.2,3,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,6,66,0 0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0.22,0,0,0,3.875,28,93,0 0.22,0,0.22,0,0.67,0,0,0,0.22,0,0,0,0.22,1.34,0,0,0.44,0,0.67,0,1.56,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.23,0,0,0,0,0,0,0,0.156,0,0.134,0,0.156,3.08,121,693,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.161,0,0,1.25,3,15,0 0,0,0,0,0.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0.69,0,0,0,0,0,2.08,0.69,0,0,0,0,0,0,0,0,0,0,0.69,0,0,0,0,0,0,0,0,0,0,0,0,0.284,0,0.284,0.094,0,2,11,60,0 0.13,0,0.13,0,0.13,0,0,0,1.18,0,0,0.52,0,0,0,0,0,0,0.52,0,0.26,0,0,0,0.65,0.79,0.13,0,0,0,0,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.128,0.164,0.036,0,0.036,0,3.185,32,481,0 0.08,0,0.22,0,0.04,0,0,0,0.44,0.04,0,0.22,0.04,0,0,0,0.04,0,0.17,0,0.08,0,0,0,0.39,0.44,0.04,0,0,0,0,0,0.57,0,0,0,0.08,0.08,0,0,0,0,0.04,0.04,0,0,0,0,0.163,0.197,0.058,0,0.011,0,3.851,64,1583,0 0,0.28,0.28,0,0.86,0,0,0,0,0.28,0.28,0,0,0,0,0,0,0,2.87,0,1.72,0,0,0,0.28,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0.137,0,0,0,0,1.95,15,156,0 0.09,0.04,0.04,0,0.04,0,0,0,0.66,0,0,0.33,0.04,0,0,0,0,0.04,0.19,0,0.14,0,0,0,0.62,0.66,0.04,0,0,0,0,0,0.14,0,0,0.04,0,0,0,0,0,0,0,0.09,0,0,0,0.04,0.145,0.152,0.053,0,0.013,0,3.685,85,1463,0 1.14,0,0,0,1.14,0,0,0,0,0,0,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.14,0,2.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.14,0,0,0,0,0,0,0,0,0,1.19,3,25,0 0,0,0,0,3.22,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,6.45,0,0,0,0,0.512,0,0,0,0,2.4,11,24,0 0.2,0,0.1,0,0,0,0,0.1,0,0,0,0.4,0.1,0,0,0,0.2,0,0,0,0,0,0.1,0,4.5,0.1,0,0,0,0,0,0,0.1,0,0,0.1,0.1,0.1,0,0,0,0.6,0,0,0,0,0,0,0.092,0.079,0,0,0.013,0,2.361,26,562,0 0,0,1.94,0,0,0,0,0,0,0.97,0,0.97,0,0,0,0,0,0,1.94,0,0,0,0,0,0.97,0.97,0,1.94,0,0.97,0,0,0,0,1.94,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0.379,0,0,0,0,8.125,75,195,0 0,0,0,0,0,0,0,0,0,1.23,0,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0.796,0,0,0,0,2.142,8,60,0 0.81,0,0.81,0,0.81,0,0,0,0,0,0,0.81,0,0,0,0,0,0,1.62,0,1.62,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,1.62,0,0,0,0,0,0,0,0.123,0,0.37,0,0,5.375,69,129,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.38,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,7,17,35,0 0.54,0,0.27,0,0,0,0,0,0.27,0.54,0,0.81,0,0,0,0,0,0.27,1.08,0,0.81,0,0,0,0.81,0.81,0,1.08,0,0.54,0,0,0.27,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0.039,0.318,0.079,0,0,0,4.971,76,517,0 0.56,0,2.24,0,0,0.56,0,0,0,0,0,1.12,0,0,0,0,0,1.12,4.49,0,0,0,0,0,1.12,0,0,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.56,0,0,0,0.111,0.111,0,0.111,0,0,1.23,4,32,0 0.36,0,1.09,0,0,0,0,0,0,0,0,0.72,1.81,0,0,0,0,0,0.72,0,1.09,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.063,0.126,0,0.063,0.126,0,2.562,35,123,0 0,0,0,0,0,0,0,0,0,0,0,0,2.38,0,0,0,0,0,2.38,0,0,0,0,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,2,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,17,20,0 0.27,0,0.27,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0.54,0,0.27,0,0,0,9.83,0.81,0.54,0.27,0.27,0.27,0.27,0.27,0,0.27,0.27,0.27,0.54,0,0.27,0.27,0,0.54,0.54,0,0.54,0,0,0,1.411,1.411,0.041,0,0,0,4.891,20,675,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.43,0,0,0,0,0,0,0,0,2.43,0,0,0,0,0,0,0,0,0,0,0,0,1.625,6,13,0 0.84,0,0,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0.082,0.414,0,0,0,0,3.34,7,167,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,5,0,0,0,0,0,0,0,0,0,1.125,2,9,0 0,0,0,0,1.75,0,0,0,0,0.87,0,0,0,0,0,0,0,0,1.75,0,0.87,0,0,0,0.87,0.87,0.87,0,0,0,0,0,0,0,0,0,0.87,0,0.87,0,0,0,0.87,2.63,0.87,0,0,0,0.469,0,0.156,0,0,0,1.466,12,44,0 0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0.48,1.93,0,0,0.48,0,0,0,0,0,0,0,1.44,0,0,0,0,0,0,1.44,0,0,0,0,0,0.304,0,0,0.365,0,3.016,10,187,0 1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,1.02,0,0,0.352,0,0.176,0,0,1.241,3,36,0 0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,1.81,0.45,0,0,0,0.9,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,1.444,5,104,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,1.333,3,12,0 0,0,0.76,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0.76,0,1.52,0.76,0.76,1.52,0.76,0.76,0,0.76,0.76,0.76,0.76,0,0,0.76,0,0.76,0,0,2.29,0,0,0,0,0.254,0,0.127,0,0,1.755,11,79,0 0,0,0,0,2.46,0,0,0,0,1.23,0,0,0,0,0,0,0,0,2.46,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.46,0,0,0,0,0.245,0,0,0,0,0,1.166,3,14,0 0,0,0,0,0,0.69,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.92,0.92,0,0,0,0,0,0,0.46,0,0,0,0.23,0,0.23,0.23,0,0,0.23,0,0,0.69,0,0,0.033,0,0.033,0,0,0,1.554,13,143,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.04,1.04,0,0,0,0,0,1.04,0,0,0,0,0,0,0,0,0,0,0,1.04,0,1.04,0,0,0,1.04,0,0,0,0,0,0.179,0.358,0,0.179,0,0,2.037,13,55,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.08,0,0,0,0,0,2.08,0,0,0,0,0.393,0,0,0,0,1.545,6,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0.142,0,1.857,10,65,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.6,4,8,0 0.46,0,0,0,0,0,0,0,0,0,0,2.32,0.46,0,0,0,0,0.46,5.11,0,0.46,0,0,0,0.93,1.39,0,0,0.46,0,0,0,0,0,0,0,0,0,1.39,0,0,0.93,0,0,0,0,0,0,0,0,0,0.065,0,0,2,13,86,0 0,0,0.58,0,0.58,0,0,0,0,2.35,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,1.76,1.17,1.76,0.58,0.58,0.58,0.58,0.58,0,0.58,0.58,0.58,0.58,0,0,0.58,0,0,0.58,0,0.58,0,0,0,0.188,0.566,0.094,0,0,0,2.246,13,146,0 0,0,0,0,0,0,0,0,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.75,0,0.75,0,0,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0.133,0,0,0.133,4.472,33,161,0 0,0,0,0,0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0.51,0,0.51,0,0,0,3.06,2.04,0.51,2.04,0.51,1.02,0.51,0.51,0,0.51,1.02,0.51,0,0,0.51,0.51,0,1.02,0,0,0.51,0,0,0,0.158,0.553,0,0,0,0,4,37,216,0 0.85,0.85,0,0,1.7,0,0,0,0,0.85,0,0.85,0,0,0,0,0,0,0,0,0.85,0,0,0,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.275,0,0,0,0,1.55,5,31,0 0,0,0.52,0,0.52,0,0,0,0,2.11,0,0,0,0,0,0,0,0,0.52,0,0.52,0,0,0,1.58,1.05,1.05,0.52,0.52,0.52,0.52,0.52,0,0.52,0.52,0.52,0.52,0,0.52,0.52,0,0,0.52,0,0.52,0,0,0,0.171,0.513,0.085,0,0,0,2.225,13,158,0 0.39,0.39,0,0,0.39,0,0,0,0,0.39,0,1.19,0,0.39,0,0,0,0,1.19,0,0,0,0,0,2.77,1.98,1.19,1.19,0.39,0.39,0.39,0.39,0,0.39,1.19,0.39,0.39,0,0,0.39,0,0.39,0.39,0,0.39,0,0,0,0.125,0.377,0.439,0,0,0,2.238,13,141,0 0,0,0,0,0,0,0,0.86,0,0,0,2.58,0,0,0,0,0.86,0,2.58,0,0,0,0,0,1.72,0,0,0,0,0.86,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.141,0,0,1.535,8,43,0 0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0.58,1.75,1.16,0,0.58,0,0.58,0.58,0,0,0,0.58,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059,0,0,0,0.178,2.506,11,183,0 0,0.68,0.34,0,0,0,0,0.34,0,0,0.34,0,0,0,0,1.72,0,0,1.03,0,2.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0.34,0,0,0,0,0.046,0,0,0,0,3.344,107,194,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,1.46,0.29,0,0.58,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0.018,0,0,0,0,251,1488,1506,0 0.3,0,0.3,0,0,0,0,0,0,0,0,0.15,0,0,0,0,0,0.15,1.38,0,0.61,0,0,0,1.38,0.3,0.61,0.15,0.15,0.15,0.15,0.15,0.3,0.15,0.15,0.15,0.3,0,0.15,0.15,0,0,0.3,0,0.61,0,0,0,0.131,0.183,0.052,0,0,0,1.837,13,305,0 0,0,0.29,0,0.29,0,0,0,0.29,0,0,0.29,0,1.19,0,0,0.29,0,0.29,0,0.29,0,0,0,0,0,0,0.29,0,0,0.29,0,0,0,0.29,0,0.29,0,0,0,0,0.89,0,0,0,0,0,0,0,0.156,0.031,0.376,0,0.125,3.338,157,611,0 0,0,0,0,0,0,0,0,0.43,0,0,2.17,0,0,0,0,0,0,0.86,0,0,0,0,0,0.86,0.43,0,1.3,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.248,0,0,0,0,2.119,15,142,0 0,0,0,0,0,0,0,0,0,0.55,0,0.55,0,0,0,0,0,0,1.11,0,0.55,0,0,0,2.77,2.22,1.11,0.55,0.55,0.55,0.55,0.55,0.55,0.55,0.55,0.55,1.66,0,0.55,0.55,0,0,1.11,0,1.11,0,0,0,0,0.603,0.086,0,0,0,2.113,13,167,0 0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,1.49,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,2,8,36,0 0,0,0.89,0,0.44,0.44,0,0,0,0,0,0.89,0,0.44,0,0,0.89,0,0,0,0,0,0,0,1.79,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0.131,0,0,0,0,1.61,13,95,0 0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0.6,0.6,0,0.6,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.173,0,0,0.129,0,3.266,31,196,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.666,3,15,0 0.35,0,0.35,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,1.42,0,0.71,0,0,0,0,0,0.35,0,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.292,4,84,0 0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,1,1,19,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0.294,0,0,0,0,1.25,2,15,0 0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0.65,0,0.65,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.327,0.109,0,0,0.109,3.857,28,162,0 0.36,0,1.47,0,0.36,0,0,0,0.36,0,0.36,3.32,0,0,0,0,0,0,1.1,0,0.36,0,0,0,0.36,0.36,0,0,0,0,0,0,0,0,0,0,1.1,0,0,0,0,1.1,0,0,0,0,0,0,0,0.051,0,0,0,0,2.293,45,172,0 0,0,0,0,0.83,0.41,0,0.83,0,0,0,1.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0.068,0,0,0,0,1.673,5,82,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,16,0 0,0,0.87,0,0.87,0,0,0,0,0.87,0,0,0,0,0,0,0,0,0,0,0.87,0,0,0,1.75,0.87,2.63,0,0,0,0,0,0,0,0,0,0.87,0,0,0,0,0,0.87,0,0.87,0,0,0,0,0.283,0.141,0,0,0,1.785,15,75,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.272,0.272,0,0,0,4.19,26,88,0 0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,1.182,0,0,0,0,2.057,13,72,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0,0,0,1.75,5,21,0 0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.4,0,0.56,0,0,0,3.37,1.4,0.28,0.28,0.28,0.28,0.28,0.28,0,0.28,0.28,0.28,0.56,0,0,0.28,0,0.28,0.56,0,0.28,0,0,0,0,0.14,0.093,0,0,0,2.464,15,207,0 0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0.5,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.505,0.168,0,0,0.084,4.068,28,236,0 0,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.33,0,0,0,0,0,0,2.66,1.33,0,1.33,0,1.33,1.33,0,0,0,1.33,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0.288,0,0.144,0,0,1.857,10,39,0 0,0,0,0,0.35,0,0,0,0,0.35,0,0,0,0,0,0,0,0,2.1,0,0.7,0,0,0,2.8,1.05,1.4,0.35,0.35,0.35,0.35,0.35,0,0.35,0.35,0.35,0.7,0,0,0.35,0,0,0.7,0,0.7,0,0,0,0,0.233,0.116,0,0,0,1.746,13,145,0 0,2.07,0,0,0,0,0,0,1.55,0,0,0.51,0,0,0,0,0,0,1.03,0,0,0,0,0,0.51,0,2.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0.141,0.211,25.812,104,413,0 0,1.36,0.9,0,0,0,0,0,0,1.81,0,0.45,0,0,0,0,0,1.81,0,0,3.18,0,0,0,0.45,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.199,0,0,0,0,3.382,53,159,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.14,0,0,0,0,0,2.29,1.14,0,3.44,0,0,0,0,0,0,2.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.163,0,0,0,0,3.28,15,82,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,1.08,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.263,4,24,0 0.1,0,0.1,0,0.1,0.1,0,0.4,0,0,0.1,0.8,0,0,0,0,0,0.1,0.1,0,0,0,0,0,0.1,0,0,0.1,0,0,0,0,0.1,0,0,0,0.3,0,0.1,0,0,0.4,0.2,0.2,0,0.8,0,0,0.015,0.136,0.015,0,0.015,0,1.636,18,527,0 0,0,0,0,0.67,0,0,0,0,0.67,0,0.67,0,0,0,0,0,0,0.67,0,0,0,0,0,4.05,4.05,0,2.02,0,0,0,0,0,0,0.67,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0.613,0,0,0,0,2.976,24,128,0 0.9,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0.9,0,0,0,0,0,0,0,0.9,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0,0,3.225,22,129,0 1.19,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,1.19,1.19,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,3.09,11,68,0 0,0,0,0,0.34,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,3.06,0,0,0,0.34,0,0,0,0.34,0,0,0,0,0,0,0.34,0.088,0.132,0,0,0,0,1.25,7,85,0 0,0,0,0,0,0.32,0,0.64,0,0,0,1.6,0,0.32,0,0,0,0.32,0.32,0,0,0,0,0,0.32,0.32,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,4.295,87,262,0 0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,1.85,0,3.7,0,0,0,1.85,0,0,3.7,0,0,0,0,1.85,0,1.85,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0.636,0,0.318,0,0,2.695,15,62,0 0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0.76,0,0.76,0,0,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.123,0.123,0,0,0.123,3.7,28,148,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,3.33,0,0,0,2.22,1.11,2.22,1.11,1.11,1.11,1.11,1.11,0,1.11,1.11,1.11,1.11,0,1.11,1.11,0,0,1.11,3.33,1.11,0,0,0,0,0.353,0,0.176,0,0,2.1,12,63,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,3.57,3.57,3.57,3.57,3.57,3.57,3.57,0,3.57,3.57,3.57,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0.956,0,0,0,0,3.6,11,36,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.44,0,4.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0.383,0,0,1.333,3,8,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0,0.33,0,0,0,5.66,2.66,2,0.66,0.66,0.66,0.66,0.66,0,0.66,0.66,0.66,0.66,0,0.33,0.66,0,0,0.66,0,0.66,0,0,0,0.101,0.254,0.101,0.05,0.05,0,2.725,15,248,0 0.2,0,0.2,0,0.2,0,0,0,0,0,0,1,0.2,0,0,0,0,0.2,0.4,0,0,0,0,0,2.61,1.2,0,0.4,0,0,0,0,0.8,0,0.4,0,0.8,0,0,0,0,0,0,0,0,0.2,0,0,0.061,0.462,0.061,0,0,0,2.61,24,308,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.08,0,4.16,0,0,0,0,0,2.08,0,2.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.08,0,0,0,0,0,0,0,0,1.181,3,13,0 0,0,0,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,0,0,0,0,2.63,2.63,2.63,1.31,1.31,1.31,1.31,1.31,0,1.31,1.31,1.31,1.31,0,1.31,1.31,0,0,1.31,0,2.63,0,0,0,0,0.407,0.203,0,0,0,2.151,12,71,0 0,1.32,0,0,0,0,0,0,0.66,0.66,0,0.22,0,0,0,0,0,0.88,0.66,0,0.88,0,0,0,1.76,0,1.54,0,0,0.44,0,0,0.44,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0.023,0,0.023,0.047,0.094,8.76,161,876,0 0,2.07,0,0,0,0,0,0,1.55,0,0,0.51,0,0,0,0,0,0,1.03,0,0,0,0,0,0.51,0,2.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0.141,0.211,25.812,104,413,0 0.34,0,0,0,0,0,0,0,0.34,0.68,0,1.02,0,0,0,0,0,0,1.36,0,0.68,0,0,0,2.38,1.7,0.68,1.7,0.68,0.34,0.34,0.34,0,0.34,0.34,0.34,0.68,0,0.68,0.34,0,0,0.68,0,0.34,0,0,0,0.052,0.42,0.052,0,0,0.052,2.604,13,250,0 0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0.47,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.074,0.297,0,0,0.074,4.308,28,293,0 0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,4.54,0,0,0,0,0,0,0,0,0,0,2,5,16,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1.428,3,10,0 0,1.86,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.8,1.86,1.86,0.93,0.93,0.93,0.93,0.93,0,0.93,0.93,0.93,0.93,0,0.93,0.93,0,0.93,0.93,0,0.93,0,0,0,0,0.457,0.152,0,0,0,2.097,13,86,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0.47,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.074,0.298,0,0,0.074,4.268,28,286,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,1.625,6,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.27,0,0,0,0,0,2.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.052,2,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.2,4,11,0 0.66,0,0,0,0,0,0,0,0.66,0.66,0,2,0,0,0,0,0,0,2,0,1.33,0,0,0,0.66,0.66,0,1.33,0,0,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,2.529,11,86,0 0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,1.02,2.04,2.04,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,1.02,0,2.04,0,0,0,0,0.323,0,0,0,0,2.682,13,110,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0.34,0,0,0,5.86,2.75,1.37,0.68,0.68,0.68,0.68,0.68,0,0.68,0.68,0.68,0.68,0,0.34,0.68,0,0,0.68,0,0.68,0,0,0,0.11,0.276,0.11,0.055,0.055,0,2.87,15,244,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0.26,0,0,2.2,10,44,0 0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.727,5,19,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.38,0,0,0,2.38,0,0,4.76,0,0,0,0,2.38,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.749,0,0.374,0,0,2.85,15,57,0 2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,1.38,0,0,0,0,0,0,0.213,0,0,1.75,6,49,0 0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.733,9,26,0 0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.592,8,43,0 0,0,0.17,0,0.17,0,0,0.17,0.08,0,0.08,0.25,0,0,0,0.08,0,0,0.94,0,0.6,0,0.25,0,1.89,0.43,0,0.08,0,0.25,0.34,0,0,0,0.25,0,0.17,0,0,0,0,0,0,0,0,0,0,0.08,0,0.127,0,0.051,0.038,0,1.838,24,605,0 1.05,0,0,0,1.05,0,0,0,0,0,0,2.1,0,0,0,0,0,0,4.21,0,2.1,0,0,0,1.05,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,0.171,0,0,0,0,2.541,12,61,0 0,0,0,0,0.59,0,0,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,1.79,1.49,0,0.59,0,0.89,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0.141,0,0,0,0,1.87,24,174,0 0,0,0,0,0,0,0,0,0.27,0,0,0.82,0.27,0,0,0,1.64,0,1.36,0,0.54,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0.54,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0.045,0,0,1.465,8,85,0 0,0,0,0,0,0.8,0,0,0,0,0,1.61,0,0,0,0,0,0,0.8,0,0.8,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.417,0,0.139,0,0,1.411,5,24,0 0,0,0,0,1.69,0,0,0,0,0,0,1.69,0,0,0,0,0,0,1.69,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0,0,0,0.552,0,0,1.461,4,19,0 0,0,0,0,0,0,0,0,0,0,0,1.6,0,0,0,0,0,0,0,0,0,0,0,0,9.62,0,0.53,0,0,0,0,0,2.13,0,0,0,1.06,0,1.6,0,0,0.53,1.06,0,1.06,0,0,0,0.425,0,0.17,0,0,0,2.567,15,172,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.93,0,0.46,0,0,0,4.2,2.8,1.4,0.46,0.46,0.46,0.46,0.46,0,0.46,0.46,0.46,0.46,0,0,0.46,0,0,0.46,0,0.46,0,0,0,0.151,0.227,0.075,0.075,0.075,0,2.482,12,139,0 0.35,0,0,0,1.41,0,0,0,0,0,0,1.76,0,0,0,0,0.35,0,0,0,0,0,0,0,1.06,1.06,0,0.7,0,0,0.35,0,0,0,0.7,0,1.06,0,0.7,0,0,0,0,1.41,0,0,0,0.35,0,0.104,0,0,0,0,2.108,24,213,0 0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0.68,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0.68,0,0,5.47,0,0,0,0,0,0.68,0,0.68,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0.138,0,0,0,0,1.1,3,22,0 0,0,0,0,0.59,0,0,0,0,1.18,0,0.59,0,0,0,0,0,0,1.77,0,0,0,0,0,0.59,0.59,0,1.18,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.215,0,0.107,0,0,2.741,11,85,0 0,0.55,0,0,0,0,0,0,0,0,0,1.67,0,0,0,0,0,0,2.79,0,1.67,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.083,0,0,0,0,1.392,4,39,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.098,0,0,0,2.375,5,19,0 0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,7.27,0,1.81,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.316,0,0,0,0,1.125,2,9,0 0,0,0.16,0,0.83,0,0.16,0,0.16,0,0,0.66,0,0.66,0,0,0.16,0,0,0,0.16,0,0,0,3,0.83,0.33,0.5,0.16,0.16,0.5,0.16,0,0.16,0.5,0.16,0.5,0,0.16,0.16,0,0.66,0.33,0.16,0,0,0,0,0,0.162,0.04,0.02,0,0.02,2.604,28,758,0 0.33,0.33,0.99,0,0,0.66,0,0,0,0,0,0.33,0,0,0,0,0,0,2.65,0,0.33,0,0,0,1.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0.33,0,0,0,0,0,0,0.051,0,0,1.786,28,134,0 0,0,0,0,0,0,0,0.08,0,0,0,0.08,0,0,0,0,0.08,0,0,0,0,0,0,0,0.08,0.08,0.08,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0.08,0,0,0,0,0,0,0.34,0.081,0.451,0,0,0,0,1.833,18,935,0 0,0,0.2,0,0.6,0.2,0,0,0.1,0,0,0.5,0,0,0,0,0.1,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0.049,0,0,0,0,1.133,10,263,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.7,0,0.85,0,0,0,0.85,0,0,0,0,0,0,0,0.85,0,0,0,0,0,0,0,0,0.85,0,0.85,0,0,0,0,0,0,0.138,0,0,0,1.228,4,43,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,2,5,0 0,0,0.23,0,0.92,0,0,0,0.23,0,0,0.92,0,0.92,0,0,0.23,0,0,0,0.23,0,0,0,0.23,0,0,0.23,0,0,0.23,0,0,0,0.23,0,0.23,0,0,0,0,0.92,0,0,0,0,0,0,0,0.13,0.026,0.026,0,0.026,2.222,23,480,0 0,0,0.33,0,0.08,0,0,0.16,0,0,0,1,0.08,0,0,0,0.25,0,0.16,0,0,0,0,0,2.68,0,0,0,0,0,0,0,0.08,0,0,0.08,0.08,0,0,0,0,0.25,0,0,0.16,0,0,0,0.134,0.089,0,0,0,0,2.432,24,557,0 0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0,0,1.62,0,0,0,0,0,1.62,1.08,1.08,1.62,0.54,0.54,0.54,0.54,0,0.54,0.54,0.54,0.54,0,0,0.54,0,0,0.54,0,0.54,0,0,0,0,0.559,0,0,0,0,3.039,13,155,0 0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,1.538,8,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,3.714,16,26,0 0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,3.84,3.84,0,0,0,0,0,0,2.56,0,0,0,1.28,0,0,0,0,0,1.28,0,0,0,0,0,0,0.194,0.194,0,0,0,3.631,17,69,0 0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,3.84,3.84,0,0,0,0,0,0,2.56,0,0,0,1.28,0,0,0,0,0,1.28,0,0,0,0,0,0,0.194,0.194,0,0,0,3.631,17,69,0 0,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.583,8,38,0 0,0,0,0,0,0,0,0,0,4.34,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0.23,0,0.46,0,0,0,0.23,0,0,0,0,1.39,0,0,0.23,0,0,0,0.69,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,1.86,0,0,0,0,0,0,0,0.113,0,0.09,0,0.203,2.43,121,666,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.333,11,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,1.458,0,0,1.066,2,16,0 0.08,0,0,0,0.08,0,0,0,0,0.08,0,0,0.08,0,0,0,0.08,0,0.08,0,0.08,0,0,0,0.16,0,0,0,0,0,0,0,0.16,0,0.24,0.16,0.08,0,0,0,0,0,0,0.24,0,0,0,0,0,0.085,0,0,0,0.007,4.858,60,2026,0 0.09,0,0.09,0,0,0.09,0,0.09,0.87,0,0,0.29,0,0,0,0,0,0,0.38,0,0.19,0,0,0,0.58,0.68,0.09,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.173,0.173,0.053,0,0.026,0,3.704,48,726,0 0,0,0.09,0,0.09,0,0,0.55,0,0.09,0,0.73,0.09,0,0,0,0.55,0,0.09,0,0,0,0.36,0.09,3.48,0,0,0,0,0,0.09,0,0,0,0,0.09,0.09,0,0,0,0,0,0,0.55,0,0,0,0,0.012,0.1,0,0,0.1,0,2.188,22,510,0 0.05,0,0.15,0,0.05,0.05,0,0,0.52,0,0,0.15,0,0,0.05,0,0,0.05,0.31,0,0.15,0,0,0,0.78,0.83,0.05,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0.05,0.1,0.1,0,0,0,0.223,0.162,0.084,0,0.015,0,2.725,38,1150,0 0,0.24,0,0,0.24,0,0,0.24,0,0.49,0,0,0,1.49,0,0,0,0,0.99,0,0,0,0,0,0.49,0,0.24,0,0,0,0.24,0,0,0,0.24,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.163,0,9.876,235,1116,0 0,0,0.29,0,0.59,0,0,0,0.29,0,0,0,0,1.79,0,0,0.29,0,0,0,0.59,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,2.69,0,0,0,0,0,0,0,0.052,0,0.078,0,0.235,3.153,121,618,0 0,0,0,0,0,0,0,0,0,0.46,0,1.84,0,0,0,0,0,0.46,1.38,0,0.46,0,0,0,1.84,1.38,0.92,0.92,0.46,0.46,0.92,1.38,0,1.38,0.92,0.46,0,0,0,0.92,0,1.38,0,0,0.46,0,0,0.92,0,0.362,0,0,0,0,4.153,34,162,0 0.67,0,0.22,0,0.45,0,0,0,0,0.22,0.45,1.12,0.22,0.22,0,0,0,0,1.12,0,1.35,0,0,0,2.03,0,0,0.45,0,0,0.22,0,0,0,0.45,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0.072,0,0.072,0.072,0.036,3.242,38,347,0 0,0.33,0.16,0,1.15,0.33,0.16,0,0,1.32,0,0.16,0,0.16,0.16,0.99,0,0,2.8,0,2.31,0,0.33,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0.33,0,0,0,0,0.126,0,0.076,0.076,0.025,3.401,37,364,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,4,14,0 0.53,0,0,0,0.88,0,0,0,0,0,0,0.71,0.35,0,0,0,0,0,1.06,0,1.06,0,0,0,2.13,1.06,0.17,0.17,0.17,0.17,0.17,0.17,0,0.17,0.17,0.17,0.53,0,0,0.17,0,0.71,0.17,0,0.53,0,0,0.35,0.052,0.131,0.026,0.026,0,0,2.941,34,353,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,2,5,0 0,0,0,0,0,0,0,0.79,0,0,0,0.79,0,0,0,0,0,0,0.39,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.309,8,55,0 0.29,0,0.51,0,1.62,0,0,0,0,0,0,0.73,0.14,0,0,0.07,0.81,0,1.54,0,0.07,0,0,0,0.95,0,0,0,0,0.07,0,0,0,0,0,0.14,0.07,0.07,0,0,0,0.07,0,0,0.07,0,0,0,0,0.032,0,0,0.01,0,1.588,51,243,0 0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,1.05,0,2.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.563,0,0,0,0,3.571,11,75,0 0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.72,1.72,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.588,0,0.294,0,0,3.714,11,78,0 0.26,0,0.26,0,0,0,0,0,0,0,0,0.26,0.52,0,0,0,0,0,1.56,0,0,0,0,0,0.78,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0,0,0,0,0.123,0.041,0,0.041,0,0,1.517,4,44,0 0.6,0,0,0,0.91,0,0,0,0,0,0,0.91,0.6,0,0,0,0,0,1.21,0,1.82,0,0,0,0.3,0.3,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0.91,0,0,0.3,0,0,0.3,0.088,0.044,0,0.044,0,0,2.222,22,120,0 0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0.81,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.43,0,0,0,3.25,0,0,0,0,2.43,0,0,0,0.142,0,0.285,0,0,2.136,7,47,0 0,0,0.76,0,0.15,0,0,0,0,0.15,0,1.07,0,0,0,0,0,0,1.99,0,0.46,0,0,0,0.92,0.15,0,0.3,0,0,0,0,0,0,0,0,0.15,0,0,0,0,0,0,0,0,0,0,0,0,0.264,0,0,0,0.026,2.891,28,347,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,1.69,0,0,0,0,0,0,3.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0.296,0,0,0,0,3.315,13,63,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,1.29,1.29,1.29,0,0,0,1.29,0,0,0,0,0,0,0,1.29,0,0,0,0,0,1.29,0,0,0,0,0.234,0,0,0,0,1.857,8,39,0 0,0,0,0,0,0,0,0,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.01,0,0,0,0,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.312,0,0,0,4.03,28,133,0 0,0,0,0,0,0,3.07,0,0,0,0,0,0,0,0,0,0,0,3.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.251,0,0,3.214,12,45,0 1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.04,0,0,0,0,0,0,0,3.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.04,0,0,0,0,0,0,0,0.191,0,0,0,0,1,1,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0.088,0,0,0,0,1.607,4,45,0 0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,3.57,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.545,3,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0.19,0,0,0,0.09,0,0,0,0,0,0,0.09,0,0,0,0,0,0,0,0,0,0.046,0.341,0,0,0.031,4.413,28,1399,0 0,0,0,0,0,0.32,0,0.65,0,0,0,1.62,0,0.32,0,0,0,0.32,0,0,0,0,0,0,0.32,0.32,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.081,0,0,0,0,4.093,87,262,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0,2.54,2.54,0,1.69,0,0,0,0,0,0,1.69,0,0,0,0.84,0,0,0,0,0,0.84,0,0,0.84,0,0.123,0,0.123,0,0.371,5.515,34,182,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0,1.66,1.66,0,3.33,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0.53,4.052,22,77,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.333,3,8,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.666,3,10,0 0,0,0,0,0.78,0,0,0,0.78,0.78,0,0.78,0,0,0,0.78,0,0,1.56,0,0,0,0,0,0.78,0.78,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.544,0,0,0.136,0,2.62,11,76,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.42,0,0.94,0,0,0,0.47,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.94,0,0,0,0,0.079,0,0,0,0,2.315,17,88,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,3.7,1.85,1.85,1.85,1.85,1.85,1.85,1.85,0,1.85,1.85,1.85,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,2.526,11,48,0 0,0,0,0,4.76,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,0,0,0,1.23,3,16,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.71,2.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,2,10,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,4.44,2.22,0,0,0,0,0,4.44,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,3.578,20,68,0 0.09,0.09,0.36,0,0.91,0.18,0,0,0,0,0,3.66,0.09,0,0,0,0.82,0,0.82,0,0.45,0,0,0,1.37,0.09,0,0,0,0.82,0,0,0,0,0,0.18,0,0,0.09,0,0,0,0,0,0,0,0,0,0.027,0,0,0,0,0,1.263,4,192,0 0,0,1.96,0,0,0,0,0,0,0,0,3.92,0,0,0,0,0,0,0,0,0,0,0,0,1.96,3.92,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.476,0,0,0,0,2.318,25,51,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,10.86,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.798,0,0,2.615,13,34,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.69,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,1.44,1.44,0,1.44,0,0,0,0,0,0.222,0,0,0,5.357,28,150,0 0.08,0.17,0.17,0,0.8,0.08,0,0.26,0,0,0,3.39,0.17,0,0,0.08,0,0,0,0,0,0,0,0,2.68,0,0,0,0,0,0,0,0,0,0,0.35,0.08,0,0,0,0,0.08,0.08,0,0,0,0,0,0.023,0.046,0,0,0.023,0,2.658,57,436,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,3.84,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0.751,2.333,13,21,0 0,0,0.69,0,0,0,0,0,0,0,0,1.39,0,0,0,0,0,0,0,0,0,0,0,0,2.79,0,0,0,0,0,0,0,0.69,0,0,0,0,0,0,0,0,0,0,1.39,0,0,0,0,0,0,0,0,0,0,1.268,4,52,0 0,0,0,0,0,0,0,0.82,0,0,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0,0,0,0.41,0,0,0,0,0,0,0,0.41,0,0,0.41,0,0,0,0,0,0,1.394,12,53,0 0,0,0,0,0.31,0,0.31,0,0.31,0,0.31,0.31,0,0,0,0,0,0.31,0.63,0,0.63,0,0,0,0,0,0.95,0,0,0,0,0.31,0,0.63,0,0,0.31,0,0,0,0,0,0,0,0.63,0,0,0,0,0.255,0.102,0,0,0.255,3.547,46,259,0 0.07,0.07,0.07,0,0.14,0,0,0.43,0,0,0.14,1.43,0.07,0,0,0,0.93,0,0,0,0,0,0,0,4.3,0,0,0,0,0.07,0,0,0,0,0,0.43,0.14,0,0,0,0,0,0,0,0,0,0,0.14,0.056,0.094,0,0,0.028,0,2.394,24,881,0 0,0,0,0,0,0,0,0,0,0.72,0,0.72,0,0,0,0,0,0,4.37,0,0,0,0,0,1.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.107,0,0,0,1.48,9,37,0 0,0,0.32,0,0,0,0.32,0,0.32,0,0,0.65,0,0,0,0,0,0.32,0.98,0,2.63,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.128,5.633,118,338,0 0.9,0,0.9,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0.9,0.9,0,0,0,0,0,1.81,1.81,0,0.9,0,0.9,0.9,0,0,0,0.9,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0.112,0,0.225,0,0,1.807,10,47,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,8.1,0,0,0,0,0,0,0,0,0,0.473,2.25,14,27,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.432,0,0,2,16,60,0 0,0.1,0,0,0,0,0,0.2,0,0,0,0.3,0,0,0,0,0.05,0.05,0.05,0,0,0,0,0,0.3,0.2,0,0.05,0,0.05,0,0,0.05,0,0,0.2,0.41,0,0,0,0,0,0,0.1,0.05,0.2,0,0.35,0,0.141,0,0,0,0,1.997,87,1620,0 0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0,0,0.6,0,1.21,0,0,0,3.63,1.21,1.21,0.6,0.6,1.81,0.6,0.6,0,0.6,0.6,0.6,0,0,0,0.6,0,0,0,0,0.6,0,0,0,0,0.132,0,0,0,0,4.536,52,186,0 0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0.74,1.49,0,0,0,0,0,4.47,2.23,0,0.74,0,0.74,0.74,0,0,0,0.74,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0.102,0.204,0.102,0,0.204,2.121,10,87,0 0,0,0.91,0,0,0,0,0,0,0,0,1.83,0,0,0,0,0,0.91,1.83,0,0,0,0,0,1.83,0.91,0,0.91,0,0.91,0.91,0,0,0,0.91,0.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0.232,0,0.116,0,0,1.619,10,68,0 0,0,0,0,0.57,0,0,0,0,0,0,1.71,0,0,0,0,0,0.57,0,0,0,0,0,0,1.71,0.57,0,0.57,0,0.57,0,0,0,0,0.57,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0.077,0,0,0,0,1.947,12,111,0 0.22,0,0.22,0,0.45,0,0,0,0.22,0,0,0,0,1.35,0,0,0.22,0,0,0,0.67,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,2.02,0,0,0.22,0,0,0,0,0.042,0,0.063,0,0.232,3.133,121,749,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,1.4,3,7,0 0,0,0.12,0,0.12,0,0,0.29,0.08,0.04,0,0.8,0.04,0,0,0,0.08,0,0.88,0,0.63,0,0.08,0,1.9,0.5,0,0.08,0,0.12,0.21,0,0,0,0.08,0,0.21,0,0.21,0,0,0,0,0.08,0,0,0,0.04,0.038,0.115,0,0.044,0.051,0,1.664,27,1263,0 0,0,0.24,0,0.49,0,0,0,0.24,0,0,0,0,1.49,0,0,0.24,0,0,0,0.74,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,0,2.23,0,0,0,0,0,0,0,0.046,0,0.069,0,0.255,2.776,121,622,0 0,0,0,0,0.51,0,0,0,0,0,0,3.09,0,1.03,0,0,0.51,0,0,0,0,0,0,0,1.03,0.51,0,0,0,0.51,0,0,2.06,0,0,0,0,0,0,0,0,0,0,2.57,0,0,0,0,0,0,0,0,0,0,1.586,6,92,0 0,0,1.5,0,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.116,0,0,0,0,1.833,8,22,0 0,0,1,0,1.5,0,0,0,0,1,0.5,2,0,0,0,0,0,0,6.5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.145,0,0,1.342,14,51,0 0,0,0.77,0,0,0,0,0,0,0,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.77,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0.77,0,0.102,0,0.102,0,0,4.771,26,167,0 0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,1.17,0,0,0,0,0,0.58,0,0.29,0.29,0,0,0,0,0.178,0,0.044,0,0,1.666,10,180,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.222,2,11,0 0.01,0.01,0.13,0,0.13,0.01,0,0,0.01,0.13,0.03,0.45,0.03,0.07,0,0.11,0.53,0.07,0.07,0,0.03,0,0.01,0,0,0,0,0,0.01,0,0,0,1.57,0,0,0.11,0.86,0,0,0.03,0,0.03,0.03,0.01,0.01,0.23,0,0.15,0.008,0.111,0,0.002,0,0.01,2.106,58,3027,0 0,0,0,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0,0,2.38,0,1.19,0,0,0,1.19,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.103,0,0,0,0,3.086,55,142,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,33.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0.78,0,0,0,0,0,0,0,0,1.57,0,0,0,0,0,0,3.14,0,0,0,0,0,0,0,0,1.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0.78,0,0,0,0.78,0,0,0,0.437,0,0.087,0,0,2.812,13,90,0 0,0.44,0,0,0,0,0,0,0,0.29,0,0.29,0,0,0,0,0.14,0,0,0,0.29,0,0,0,0.44,0,0,0,0,0.89,0,0,0,0,0,0,0.89,0,0,0,0.59,0,0.14,0,0,0.89,0,0.44,0.101,0.135,0.016,0,0,0,2.297,46,680,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.888,5,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,0.25,0,0,2.619,9,55,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,3,7,0 0,0,0,0,0.57,0,0,0,0,0,0,1.72,0,0,0,0,0,0.57,0,0,0,0,0,0,1.72,0.57,0,0.57,0,0.57,0,0,0,0,0.57,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0.077,0,0,0,0,1.964,12,110,0 0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,16,0 0,0,0,0,0,0,0,0,0,0,0,0.99,0,0,0,0.99,0,0,2.97,0,1.98,0,0,0,0.99,0.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.262,0,0,1.565,14,36,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,1.666,7,25,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.333,2,4,0 0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,1.45,0,2.18,0,0,0,1.45,0.72,1.45,0.72,0.72,0.72,0.72,0.72,0,0.72,0.72,0.72,0.72,0,0.72,0.72,0,0,0.72,0,0.72,0,0,0,0,0.367,0,0,0,0,1.897,12,74,0 0,0,0,0,0,0,0,0,0,0,0,0.58,0.58,0,0,0,0,0,1.17,0,2.35,0,0,0,1.17,0.58,1.17,0.58,0.58,0.58,0.58,0.58,0,0.58,0.58,0.58,0.58,0,0.58,0.58,0,0,0.58,0.58,0.58,0,0,0,0,0.301,0,0,0,0,1.76,12,81,0 0,0,1.47,0,0,0,0,0,0,0,0,0,0.73,0,0,0,0,0,3.67,0,0.73,0,0,0,1.47,0.73,0.73,0.73,1.47,0.73,0.73,0.73,0,0.73,0.73,0.73,0.73,0,0,0.73,0,0,0.73,0,0,0,0,0,0,0.363,0.121,0,0,0,2.171,12,76,0 0,0,0,0,0,0,0,0,0,1.41,0,0,1.41,0,0,0,0,0,1.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.7,0,0,0,0,0.246,0,0,0,0,1.56,6,39,0 0,0,2.5,0,0,0,0,0,0,0,0,0,2.5,0,0,0,0,0,2.5,0,2.5,0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.222,3,11,0 1.04,0,0.52,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,2.09,0,0.52,0,0,0,2.09,2.61,1.04,0.52,0.52,0.52,0.52,0.52,0,0.52,0.52,0.52,0,0,0,0.52,0,0,0,0,1.04,0,0,0,0,0.309,0,0.309,0,0,3.973,34,151,0 0,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0,2,2.66,0,0.66,0,0,0,0,0,0,0.66,0,1.33,0,0.66,0,0,0,0.66,0,0,0,0,0,0,0.104,0.209,0.104,0,0,2.152,17,127,0 0,0,1.29,0,0,0,0,0,0,1.29,0,1.29,0,0,0,0,0,0,2.59,0,0,0,0,0,2.59,2.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,1.35,4,27,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,2.71,0,0.67,0,0,0,0,0,0.67,0,0,0,0,4.4,0,0,0,0,0,0,0,0,0,0.555,3,14,348,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0.4,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0.4,0,0,0,13.93,0.81,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,2.053,1.932,0.06,0,0,0,6.113,20,593,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.01,0,0.5,0,0,0,4.02,2.01,1,0.5,0.5,0.5,0.5,0.5,0,0.5,0.5,0.5,0.5,0,0.5,0.5,0,0,0.5,0,0.5,0,0,0,0,0.176,0.088,0,0,0,2.319,12,109,0 0,0,0,0,0,0,0,0,0,0.37,0,0.75,0,0,0,0,0,0,2.63,0,0.75,0,0,0,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.066,0,0,0,0,1.433,5,86,0 0,0,0.28,0,1.73,0,0,0,0,0,0,0.28,0.57,0.28,0,0,0,0,1.15,0,0.57,0,0,0,0.28,0,0.57,0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0.28,0.57,0,0,0,0,0.051,0,0.103,0,0,1.411,4,24,0 2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.1,2,11,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.408,0,0,0,0,0,2.6,6,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.204,0,0,0,0,0,1.285,2,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.96,2.97,1.98,2.97,0.99,0.99,0.99,0.99,0,0.99,0.99,0.99,0,0,0,0.99,0,0,0.99,0,0.99,0.99,0,0,0,0.479,0,0.239,0,0,2.688,13,121,0 0,0,0,0,0,0,0,0,0.27,0,0,0.27,0,1.36,0,0,0.27,0,0.81,0,0.54,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,1.09,0,0,0,0,1.91,0,0,0,0,0,0,0.23,2.521,31,517,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.123,0,0,2.6,16,26,0 0.48,0,0,0,0,0,0,0,0,0,0,0.96,0,0,0,0,0,0.48,0.96,0,0,0,0,0,2.88,0.96,0.96,0.96,0.48,0.96,0.96,0.48,0,0.48,0.96,0.96,0,0,0,0.48,0,0,0,0,0.48,0,0,0,0,0.276,0,0.138,0,0,1.986,11,147,0 0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,2.32,0,4.65,0,2.32,0,0,0,0,4.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.453,0,0,0,0,11.687,75,187,0 0.43,0,0.43,0,0,0.21,0,0,0,0.21,0,0.21,0.21,0,0,0,0,0,1.08,0,0.43,0,0,0,0.43,0.43,0,0.43,0,0.21,0,0,0,0,0.43,0,0,0,0,0.21,0,0,0,0,0,0,0.65,0,0.034,0.238,0.136,0,0,0,3.372,75,344,0 0,0,0.93,0,0.93,0,0,0,0,0,0,0,0,0,0,0.93,0,0,2.8,0,0,0,0,0,0,0.93,0,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,1.771,5,62,0 0.42,0,0,0,0,0,0,0,0,0,0,0,0.85,0,0,0,0,0,4.25,0,0.85,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.374,0,0,0.124,0,1.772,18,78,0 0,0,0.11,0,0.11,0,0,0.11,0,0,0,0,0.11,0.23,0,0,0.11,0,0,0,0,0,0.11,0,3.45,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0,0,0,0,0,0,0,0.047,0.157,0,0,0.078,0,2.351,28,508,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.75,0,1.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,0,0,0,1.913,6,44,0 0.39,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0,0,0,0,2.39,0,0,0,0,0,5.57,1.59,1.19,0.39,0.39,1.19,0.39,0.39,0,0.39,0.39,0.39,0.39,0,0.79,0.39,0,0,0.39,0,0.39,0,0,0,0,0.104,0.052,0,0,0.052,3.153,57,246,0 0,0,0.15,0,0.3,0,0,0.15,0.15,0,0.15,2.76,0,0,0,0,0,0.46,1.69,0,0,0,0,0,0.46,0.15,0,0,0,0,0,0,0.15,0,0,0.15,0.15,0,0,0,0,0,0,0.15,0,0,0,0,0.023,0.023,0,0,0,0,2.677,58,415,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,7.6,2.17,2.17,1.08,1.08,1.08,1.08,1.08,0,1.08,1.08,1.08,1.08,0,0,1.08,0,0,1.08,0,0,0,0,0,0,0.364,0.182,0,0,0,2.421,13,92,0 0,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,1.23,0,1.23,0,0,0,0,0,3.7,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0.392,0,0,0,0,2.142,10,75,0 0,0,0,0,0,0,0,0,0,0,0,2.4,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,2.4,0,0,0,0,0,0,0,0.166,0,0,0,0,2.2,22,55,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.35,0,0,0,0,0,1.17,0,1.17,2.35,0,0,0,0,1.17,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0.203,0,0,2.541,15,61,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.25,4,18,0 0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,2.916,7,35,0 0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,0.97,0,0.1,0,0,0,0,2.59,69,386,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.272,0,0,1.75,7,14,0 0,0,0,0,0,0,0,0.13,0,0.13,0,1.1,0.13,0,0,0,1.24,0,0,0,0,0,0,0,3.17,0,0,0,0,0,0,0,0.69,0,0,0.27,0.41,0,0,0,0,0,0,0,0,0,0,0.13,0.07,0.07,0,0,0,0,2.064,23,322,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.337,0,0,0,0.337,2.95,7,59,0 0,0,0,0,0,0,0,0,0,0,0,1.44,0,0,0,0,0.48,0,0,0,0,0,0,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.058,0,0,0.058,0.058,1.755,9,79,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.286,0,0,2.434,17,56,0 0,0,3.79,0,0,0,0,0,0,0,0,3.79,0,0,0,0,0,1.26,0,0,0,0,0,0,3.79,2.53,0,1.26,0,1.26,1.26,0,0,0,1.26,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0.147,0,0.147,0,0,1.962,10,53,0 0,0,0,0,0.42,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0.85,0,0,0,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0,2.161,5,294,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0.277,0,0,3,17,51,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.26,0,0.65,0,0,0,0,0,1.3,0,0,0,0,4.57,0,0,0,0,0,0,0,0,0,0.657,3.041,14,219,0 0,0.31,0,0,0,0,0,0.31,0,0,0,0.62,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.62,0,0.31,0,1.24,0,0.31,0,0,1.24,0,0,0,0.088,0.044,0,0,0,3.086,34,250,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,1.38,0,1.38,0,0,0,2.77,1.38,1.38,1.38,1.38,1.38,1.38,1.38,0,1.38,1.38,1.38,0,0,0,1.38,0,0,0,0,0,0,0,0,0,0.464,0,0,0,0,2.333,11,42,0 0,0,0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,2.5,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.416,3,17,0 0.09,0,0.19,0,0.09,0,0,0.39,0,0,0,1.27,0.19,0.09,0,0,0.49,0,0.29,0,0,0,0.29,0,2.74,0,0,0,0,0,0,0,0,0,0,0.29,0.19,0,0,0,0,0,0.09,0.09,0,0,0,0,0.067,0.067,0,0,0.026,0,2.247,18,481,0 0.44,0.22,0.22,0,0.44,0,0,0.22,0,0.22,0,0.44,0,0,0,0,0,0,1.57,0,0,0,0,0,0.44,0.22,1.12,0.22,0.22,0.22,0.22,0.22,0,0.22,0.22,0.22,0.22,0,0,0.22,0,0.22,0.22,0,0.67,0.44,0,0,0.033,0.169,0.033,0.033,0.033,0,2.28,12,203,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.342,0,0,0,0.342,2.75,7,55,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0.19,0,0,0,0.09,0,0.09,0.59,0,0,0,0.09,0.39,0,1.77,0,0.98,0,0.09,0,1.57,0.78,0,0,0,0.09,0.19,0,0.09,0,0.19,0.09,0.39,0,0.29,0.09,0,0,0,0.09,0,0,0,0.19,0,0.096,0.027,0.068,0,0,2.059,25,593,0 0,0,0.32,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0.055,0.334,0,0.055,0,0.055,1.685,6,59,0 0,0,0.91,0,0,0.45,0,0,0,0,0,0.45,0,0,0,0,0,0.45,2.28,0,1.36,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.416,0,0.486,0,0,3.782,31,87,0 0.76,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0,1.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0.76,0,0,0,0.135,0,0,0,0,0,1.411,5,24,0 0,0.44,0.44,0,0.44,0,0.22,0,0,2.43,1.1,0.44,0,0,0,0,0,1.55,2.88,0,2.21,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0,0.036,0,0.073,0.146,0.036,2.574,22,224,0 0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0.29,0,0,0,0.1,0.353,0.05,0,0,0,1.227,4,27,0 0.37,0.18,0.18,0,0.37,0,0,0.18,0,0.18,0,0.55,0,0,0,0,0,0,0.92,0,0,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0.55,0,0.18,0,0,0.37,0,0,0.74,1.48,0,0,0.116,0.29,0.029,0.029,0.029,0,3.455,24,387,0 0.17,0.11,0.05,0,0.4,0.11,0,0.4,0,0,0,0.34,0.11,0,0,0,0,0,1.15,0,0.57,0,0.05,0,0.52,0,0,0,0,0,0,0,0.23,0,0.17,0,0.63,0,0,0,0,0,0,0.05,0,0,0,0,0.007,0.304,0,0.053,0.03,0,2.548,49,1134,0 0,0,0,0,0.93,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,3.73,0,0,0,0,0,0,0,3.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.533,7,46,0 0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,3.06,4.08,0,0,0,0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.217,0,0,0,0,1.718,12,122,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,1.19,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.082,0,0,0,0.216,3.478,7,80,0 0,0,0.85,0,0,0,0,0,0,0,0,0.85,0,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.85,0,0,0,0,0.85,0,0,0,0,0,0,0,0.331,0,0,1.842,6,35,0 0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,1.78,0,0,0,0,0,0,0,1.78,1.78,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,1.72,11,43,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,1.66,0,0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.884,0,0,0,0.294,3.368,7,64,0 0,0.19,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,2.86,0,0,0.38,0.19,0,0,0,0,0,0,0,0,0,0.19,0.19,0,0.201,0,0,0,0,2.217,9,204,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,4.16,4.16,4.16,4.16,4.16,4.16,4.16,0,4.16,4.16,4.16,0,0,0,4.16,0,0,0,0,0,0,0,0,0,1.092,0,0,0,0,3.333,11,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.341,0,0,0,0.341,3.166,7,57,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.337,0,0,0,0.337,2.95,7,59,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,1.19,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.082,0,0,0,0.216,3.478,7,80,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,4,9,0 0,0,0,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0.55,1.65,0,1.65,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0.55,0,0,0,0,0,0,0.104,0.314,0,0.052,0,6.894,97,393,0 0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0.58,0,0.58,0,0,0,3.51,2.34,0.87,2.34,0.58,1.17,0.58,0.58,0,0.58,1.17,0.58,0.29,0,0.87,0.58,0,0.87,0.29,0,0.58,0,0,0,0.091,0.637,0.045,0,0,0,3.552,37,373,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,1.58,0,0,0,0,0,0,0,0,0.79,0,0,0,0,0,0,1.58,0,0,0,0,0,0.79,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.149,0,0.149,0,0,1.482,10,43,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.375,4,11,0 0,0,0,0,0.33,0,0,0,0,0,0,0.33,0,0,0,0.33,0,0.33,0.33,0,0.33,0,0,0,0.99,0.33,0,0.66,0,0.33,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0.13,0.043,0,0,0,2.016,19,125,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.325,0,0,0,0,0,1,1,14,0 0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0.24,0,0,0,0.49,0,0.49,0,0,0,1.72,1.23,0.24,0.24,0.24,0.24,0.24,0.24,0,0.24,0.24,0.24,0.24,0,0,0.24,0,0,0.24,0,0.24,0,0,0,0,0.312,0.039,0,0.117,0,1.89,13,189,0 0,0,0,0,0,0,0,0,0,0,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,8.08,5.88,0.73,0.73,0.73,0.73,0.73,0.73,0,0.73,0.73,0.73,0.73,0,0.73,0.73,0,0,0.73,0,0.73,0,0,0,0.388,0.259,0.129,0,0,0,2.666,13,96,0 0,0,0.15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0.62,0,0.31,0,0,0,1.56,0.31,0.93,0.15,0.15,0.15,0.15,0.15,0.46,0.15,0.15,0.15,0.31,0,0.31,0.15,0,0,0.31,0,0.31,0,0,0,0.078,0.235,0.052,0,0,0,1.945,12,323,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,3.03,0,3.03,0,0,6.06,3.03,0,0,0,0,0,0,0,0,0,0,0,2,12,42,0 0.12,0,0.12,0,0,0,0,0,1.11,0,0,0.37,0,0,0,0,0,0,0.49,0,0.24,0,0,0,0.61,0.74,0.12,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.119,0.17,0.034,0,0.034,0,3.237,32,505,0 0,0,0,0,0,0,0,0,0,0.69,0,0,0,0,0,0,0,0,0,0,0.69,0,0,0,0,0,0.69,0,0.69,0,0,0,0.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.228,0.114,0,0,0.114,3.651,28,157,0 0,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18,0,0,0,0,1.5,4,63,0 0,0,0.31,0,0.31,0,0,0,0,1.27,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,2.87,1.27,1.91,0.63,0.63,0.63,0.63,0.63,0,0.63,0.63,0.63,0.95,0,0.95,0.63,0,0,0.95,0,0.95,0,0,0,0.097,0.534,0.242,0,0.048,0,2.23,13,261,0 0,0.16,0,0,0,0,0,0.16,0.16,0,0,0,0,0,0.16,0,0,0,0.48,0,0.16,0,0,0,0.81,0.48,0.16,0.32,0,0,0,0,0,0,3.4,0,0.16,0,0,0,0,0.48,0,0,0,0.32,0.16,0,0,0.123,0,0,0,0.095,4.438,50,932,0 0.18,0.14,0.25,0,0,0,0,0.07,0,0.14,0.03,0.77,0.07,0.03,0,0,0.03,0.18,0.11,0,0.25,0.07,0,0,0,0,0,0,0.03,0.11,0,0,0.03,0,0,0.37,0.62,0,0,0,0.18,0,0.03,0,0,0.22,0,0.18,0.019,0.414,0,0.004,0,0,2.393,40,1795,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.3,3,13,0 0.04,0.02,0.14,0,0.25,0.08,0,0.08,0.02,0.12,0,0.27,0,0,0.02,0,0.08,0.23,0.17,0,0.06,0.29,0,0,0,0,0,0.04,0,0,0,0,1.4,0,0,0.12,1.04,0,0,0,0.17,0.04,0,0.06,0.06,0.27,0,0.02,0.046,0.149,0.005,0.014,0,0.002,2.35,46,3006,0 0,0,0,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,1.3,0,0,0,0,0,0,0,0,0,0.18,0.93,0,0.18,0,1.3,0,0,0,0,1.49,0,0,0.182,0.339,0.13,0,0,0,3.628,44,479,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,6,0 0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,3.09,0,0,0,0,0,1.03,0,0,0,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,0,1.666,7,25,0 0.09,0,0.09,0,0.56,0.09,0,0,0,0.18,0,0.46,0,0.09,0,0,0.37,0,0.56,0,0.65,0,0,0,1.86,0.46,0,0.09,0,0.09,0.28,0,0,0,0.37,0,0.28,0,0.09,0,0,0.28,0,0.18,0,0,0,0,0,0.081,0,0,0,0,1.983,25,601,0 0,0,1.23,0,0,0,0,0,0,0,0,2.46,0,0,0,0,0,2.46,1.23,0,1.23,0,0,0,2.46,1.23,0,1.23,0,1.23,1.23,0,0,0,1.23,1.23,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0.139,0,0.279,0,0,1.736,10,66,0 0,0,0.57,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0,0,0.57,0,0.57,0,0,0,0.57,0,0,0,0,0,0,0,1.15,0,0,0,0,0,0,0,0,0,0,1.73,0,0,0,0,0,0.093,0,0,0,0,1.136,3,25,0 0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,3.26,0,0,0,0,3.26,0,0,0,0,0,0,0,0,3.066,10,46,0 0,4.16,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0.709,0,0,2.09,6,23,0 0,0,0.74,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,1.48,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,2.595,31,122,0 0,0,0,0,0,0,0,0,0.48,0,0.48,0,0,0,0,0.48,0.48,0,1.44,0,2.88,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0.076,0,0.305,0.381,0,1.884,9,98,0 0,0,0,0,0,0,0,0,0.48,0,0.48,0,0,0,0,0.48,0.48,0,1.44,0,2.88,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0.076,0,0.305,0.381,0,1.884,9,98,0 0,0,0,0,0,0,1.78,0,0,1.78,0,0,0,0,0,1.78,0,1.78,5.35,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,35,63,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0 0.15,0.31,0,0,0,0,0,0,0,0.63,0.31,0.31,0,0,0,0,0,0.63,0.95,0,0.47,0,0,0,3.34,0.63,0.47,0.15,0.15,0.15,0.15,0.15,0,0.15,0.15,0.15,0.47,0,0.47,0.15,0,0,0.31,0,0.15,0,0,0,0.149,0.199,0.049,0.174,0,0,4.026,100,608,0 0,0,0.43,0,0,0,0,0,0,0,0,0.43,1.29,0,0,0,0,0,1.29,0,0.43,0,0,0,0.86,0,0,0,0,0,0,0,0.43,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0.146,0,0,1.341,6,55,0 0,0.25,0.12,0,0.37,0,0,0.12,0,0.37,0.25,0.37,0.12,0,0,0,0.12,0,0.37,0,0.12,0,0.12,0,2.51,0,0,0,0,0.25,0,0,0.12,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0.016,0.05,0,0.05,0,0,2.414,25,367,0 0,0,0.61,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0.61,0,0,0,0,0,5.52,1.22,1.22,0.61,0.61,1.84,0.61,0.61,0,0.61,0.61,0.61,0,0,1.22,0.61,0,0,0,0,0.61,0,0,0,0,0.143,0,0,0,0,3.682,51,151,0 0,2.59,1.29,0,1.29,0,0,0,0,0,0,1.29,0,0,0,0,0,0,2.59,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,0,0,0,1,1,13,0 0.33,0.33,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0.99,0.33,0,0.66,0,0,0,4.98,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0.66,0,0,0,0,0,0,0,0,0,0.306,0.204,0,0.306,0,0,5.525,100,431,0 0,0,2.41,0,0,0,0,0,0.26,0,0,2.14,0,0,0,0,0,0,0.26,0,1.6,0,0,0,0.26,0.53,0,0,0.26,0,0,0,0.26,0,0,0,0,0,0,0.26,0,0,0,0,0,0,0,0,0,0.339,0,0,0,0,2.36,12,177,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.48,0,1.48,0,0.74,0,0,0,2.96,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.105,0,0,0.105,0.105,2.555,12,69,0 0.04,0.14,0.29,0,0.04,0.04,0,0.09,0,0.19,0.09,1.04,0,0,0,0,0,0.24,0.09,0,0.04,0,0,0,0.04,0,0,0,0,0.09,0,0,0,0,0,0.09,0.24,0,0,0,0,0,0.04,0,0,0,0,0,0.02,0.16,0.006,0,0,0,2.667,185,1763,0 0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.666,4,16,0 0,0,0,0,0.82,0,0,0,0,0,0,1.65,0,0,0,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.82,0,0,0,0,0,0,3.3,0,0,0,0,0,0,0,0,0,0,2.06,8,68,0 0.18,0,0.55,0,0.18,0,0,0,0,0,0,0.37,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18,0,0,0,0,0,0,0,0.18,0,0,0,0.031,0.127,0.031,0,0,0,1.428,5,80,0 0,0,0,0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0,0,0.57,0,2.31,0,0,0,0,0.089,0.179,0,0.089,0,0,2.204,10,97,0 0.37,0,0.63,0,0.25,0.12,0,0,0,0,0,0.12,0.12,0,0,0,0,0.12,1.51,0,0.25,0,0,0,0,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0.107,0,0.193,0,0,1.181,4,104,0 0,0,0.1,0,0.1,0,0,0,0,0,0,0.1,0,0.1,0,0,0,0,0,0,0,0,0,0,0.4,0.1,0,0.1,0.2,0.2,0,0.1,0.7,0,0.1,0.1,0,0,0,0.1,0,0,0,0.1,0,0,0,0.6,0,0.096,0,0,0,0.012,2.037,18,913,0 0,0,0,0,1.38,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.94,0,0,0,0,0,0,0,0,0,0,0,0,6.94,0,0,0,0,0,0,0,0.238,0,0,0,0,1.578,4,30,0 0.51,0,0,0,0,0,0,0,0,0,0,0.25,0.51,0,0,0,0,0,2.3,0,1.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0.25,0,0,0,0,0.333,0.047,0,0,0,1.196,5,67,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,1.35,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.087,0,0,0.087,0.087,4.23,24,110,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.191,0,0,0.095,0.095,1.688,11,103,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,2.27,2.27,2.27,2.27,2.27,2.27,2.27,0,2.27,2.27,2.27,0,0,0,2.27,0,0,0,0,0,0,0,0,0,0.664,0,0,0,0,3.157,11,60,0 0,0,0.74,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,1.48,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,2.425,23,114,0 0.12,0,0.12,0,0,0,0,0,1.12,0,0,0.37,0,0,0,0,0,0,0.49,0,0.24,0,0,0,0.62,0.74,0.12,0,0,0,0,0,0.37,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0.12,0.189,0.034,0,0.034,0,3.302,41,535,0 0.08,0,0.16,0,0,0,0,0,0.82,0,0,0.24,0,0,0,0.08,0,0,0.32,0,0.16,0,0,0,0.49,0.57,0.08,0,0,0,0,0,0.74,0,0,0,0.16,0,0,0,0,0,0,0,0.08,0,0,0,0.221,0.188,0.044,0,0.033,0,2.816,32,628,0 0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,1.2,0,0,0,0,0.202,0,0,0,0,1.533,5,23,0 0.12,0,0.12,0,0,0.06,0,0,0.56,0,0,0.31,0,0,0,0.06,0,0.06,0.25,0,0.18,0,0,0,0.63,0.69,0.06,0,0,0,0,0,0.82,0,0,0,0.63,0,0,0.06,0,0,0,0.06,0,0,0,0,0.187,0.16,0.035,0,0.017,0,2.829,47,815,0 0,0,0,0,0,0,0.49,0.99,0,2.48,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,3.48,2.48,0.49,0,0,0,0,0,0,0,0,0,1.99,0,0,0,0,0,0,0,0,0,0,0,0.336,0.588,0.168,0,0,0,5.61,42,331,0 0,0,0,0,0,0,0.49,0.99,0,2.48,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,3.48,2.48,0.49,0,0,0,0,0,0,0,0,0,1.99,0,0,0,0,0,0,0,0,0,0,0,0.336,0.588,0.168,0,0,0,5.61,42,331,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,1.5,3,6,0 0.31,0,0.31,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,1.24,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0.31,0,0,0,0.31,0,0.31,0,0.31,0.31,0,0,0,0,0.051,0,0,0,1.409,12,62,0 0,0,0,0,0,0,0,0,0,0,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,1.11,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.183,0,0,0,0,1.8,4,36,0 0.4,0,0.4,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.148,3,31,0 0.69,0,0.69,0,0,0,0,0,0,0.69,0,0,0,0,0,1.38,0,0,1.38,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.378,0,0,3.315,37,126,0 0,0,0,0,0,0,0,0,0,2.38,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.442,0,0,2.125,10,17,0 0,0,0.73,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,1.47,0,0.73,0,0,0,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0.238,0,0,0,0,1.827,5,53,0 0,0,0.17,0,0,0.08,0,0,0,0,0.08,0.87,0.08,0.08,0,0,0.78,0,0,0,0,0,0,0,3.05,0,0.08,0,0,0,0,0,0.61,0,0,0.08,0.08,0,0,0,0,0,0,0,0,0,0,0,0.079,0.068,0,0,0.022,0,2.432,24,540,0 0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,3.7,0,0,0,0,0,0,7.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0.371,0,0,2.25,8,27,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,1.75,0.87,1.75,1.75,1.75,0.87,0.87,0.87,0,0.87,1.75,0.87,0,0,0,0.87,0,0,0,0,0.87,1.75,0,0,0,0.749,0,0.107,0,0,2.454,11,81,0 0.03,0.01,0.15,0,0.09,0.03,0,0.03,0.03,0.11,0,0.25,0.11,0.05,0.01,0.03,0.05,0.03,0.13,0,0.15,0,0.07,0,0,0,0,0,0,0,0,0,1.84,0,0,0.11,0.91,0,0,0.05,0.19,0.01,0.03,0.03,0,0.09,0,0.23,0.038,0.19,0,0.002,0.005,0,2.143,107,3168,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.086,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.13,0,2.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.13,0,0,0,0,0,0.194,0,0,0,1.909,5,42,0 0,0,0,0,1.47,0,0,0,0,0,0,0.73,0.73,0,0,0,0,0,0.73,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0.73,0,0.276,0,0,0,0,1.379,4,40,0 0,0,1.61,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,4.83,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.769,8,23,0 0,0,1.31,0,0,0,0,0,0,0,0,2.63,0,0,0,0,0,0,5.26,0,1.31,0,0,0,1.31,0,0,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,0,0.242,0,0,0,0,1.266,3,19,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0.72,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.047,0,0,0,0.13,3.475,7,139,0 0,0,0,0,0.44,0,0,0,0,0,0,0.88,0,0,0,0,0,0,1.32,0,0.44,0,0,0,1.76,1.32,0.88,0.44,2.64,0.44,0.44,0.44,0,0.44,0.44,0.44,0.88,0,0.88,0.44,0,2.64,0.88,0,0.88,0,0,0,0,0.146,0.073,0,0,0,1.955,13,133,0 0,0,0,0,1.75,0,0,0,0,0,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,0,0,0,0,0.955,0,0,1.5,5,24,0 0,0,0.94,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0.94,0,0,0,0,1.42,0,0,0,0,0,0.94,0,0,0,0,0,0,1.766,4,53,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.558,0,0,2,7,28,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0,0,0,0.9,0,0,0,0,0,0,1.8,0,0,0,0,0,0,1.8,0,0.9,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,1.631,8,31,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,2.77,2.77,1.38,2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0.355,0,0.355,0,0,2.666,12,64,0 0,0,0,0,0.96,0,0,0,0,0.48,0,0.48,0,0,0,0,0.48,0,1.93,0,0,0,0,0,0.96,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.223,0,0,0,0,0,1.375,5,55,0 0,0.22,0.33,0,0.22,0.11,0,0,0,0,0.11,0.44,0,0,0,0,0,0,0.44,0,0.11,0.11,0,0,0.11,0.11,0,0,0,0,0,0,0.11,0,0,0,0,0,0.11,0,0,0,0,0,0.66,0,0,0,0.019,0.253,0,0,0,0,2.068,11,395,0 0,0,2.43,0,0,0,0,0,0.27,0,0,2.16,0,0,0,0,0,0,0.27,0,1.62,0,0,0,0.27,0.54,0,0,0.27,0,0,0,0.27,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0.344,0,0,0,0,2.319,12,167,0 0,0,0,0,0,0,0,0,0,0,0,1.48,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0.74,0,0,0,0.74,0,0,0,0,0,0,1.48,0,1.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.228,53,148,0 0,0.18,0,0,0,0,0,0,0,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0.18,0,0,0,0,0,0,0,2.8,0,0,0.37,0.18,0,0,0,0,0,0,0,0,0,0.18,0.18,0,0.187,0,0,0,0,2.141,9,212,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.787,0,0,1.875,7,15,0 0,0,1.81,0,0,0,0,0,0,0,0,3.63,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0.3,0,0,0,0,1.652,8,38,0 0,0,0,0,4.16,0,0,0,0,0,0,4.16,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0.689,0,0.689,0,0,1.3,4,13,0 0,0,0,0,1.43,0,0,0,0,0,0,0,0,0,0,0,0,0,1.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0,0,0,0,0,2.15,0,0,0,0,0,0,0,0.138,0,0,0,0,1.863,5,41,0 0,0,0,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,1.55,0,0.77,0,0,0.77,0,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.55,0.77,0,0,0,0.49,0,0.196,0,0,3.16,10,79,0 0,0,0,0,3.07,0,0,0,0,0,0,4.61,0,0,0,0,0,0,0,0,1.53,0,0,0,0,0,0,0,6.15,0,0,0,0,0,0,0,0,0,0,0,0,6.15,0,0,0,0,0,0,0,0,0,0,0,0,1.529,4,26,0 0.29,0.58,0.29,0,0.29,0,0,0.29,3.23,0.88,0.29,0.88,0,0,0,0,0,0.88,1.76,0.29,2.64,0,0,0,0.29,0.29,0.29,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0.075,0.113,0,0.113,0.265,0.113,2.285,16,208,0 0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.062,8,33,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.777,14,25,0 0,0,0,0,0,0,0,0,0,0,0.61,0.61,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0.61,0,0,0.61,0,0,0.61,0.61,0,0,0,0,0.61,0,0,0,0,0,0,0.179,0,0,0,0,0,1.24,6,67,0 0,0,0.26,0,0,0,0,0,0,0,0.26,0,0,0,0,0,0,0.26,0,0,0,0,0,0,0.26,0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0,0,1.06,0,0.26,0,2.4,0,0,0.036,0.109,0,0,0.036,0,1.632,11,307,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0,0,0,0,1.567,6,428,0 1.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.29,2.19,0,1.09,0,0,0,0,0,0,1.09,0,0,0,0,0,0,0,0,0,1.09,0,0,0,0,0.353,0,0,0,0,2.304,10,53,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.386,0,0,1.6,4,16,0 0,0,1.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.41,0.97,0.48,0.48,0,0,0,0,0,0,0.48,0,0.48,0,0,0,0,0,0.48,0,0.97,0,0,0,0.471,0.55,0,0.078,0,0,2.552,16,171,0 0,0,0.08,0,0.17,0,0,0.08,0.08,0,0,0.43,0.08,0,0,0,0,0,0,0,0,0,0.08,0,3.54,0,0,0,0,0,0,0,0,0,0,0.77,0.17,0,0,0,0,0.08,0,0.17,0,0,0,0.17,0.08,0.045,0,0,0.011,0,2.45,25,566,0 0,0,2.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.25,1.5,0,0.75,0,0,0,0,0,0,0.75,0,0,0,0,0,0,0,0,0,0.75,0,0,0,0,0.369,0,0,0,0,2.032,10,63,0 0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0,2.56,0,0,0,0,0,0,0.473,0,0,2.454,15,27,0 0,0,0,0,0,0,0,0,0,0,0,0,1.01,0,0,0,0,0,2.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.01,0,2.02,0,0,0,1.01,0,2.02,1.01,0,0,0,0,0.188,0.376,0,0,2.31,15,67,0 0,0,1.06,0,1.06,0,0,0,0,0,0,1.06,0,0,0,0,0,0,4.25,0,0,0,0,0,0,0,1.06,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.186,0,0,1.25,4,25,0 0,0,0,0,0.54,0,0,0,0,1.63,0.54,0.54,0.54,0,0,0,0,0,2.18,0,1.09,0,0,0,1.09,0.54,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0.09,0,0,0,1.969,16,65,0 0,0,0,0,0,0,0,0,0,0,0,1.09,0,0,0,0,0,0,2.19,0,0,0,0,0,0,0,2.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.409,11,53,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0.68,0.68,0,0,2.9,18,29,0 0,0,0,0,0,0,0,0.56,0,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0,1.12,0.56,0,0,0,0.181,0.09,0.181,0,0,4.5,34,153,0 0,2.12,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.3,4,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,0,0,1.88,0,0,0,0,0,0,0.366,0,0,2,15,28,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0.229,0,0.114,0,0,1.8,17,36,0 0.39,0,0,0,0.78,0.39,0,0,0,0,0,0.39,0,0,0,0,0,0,0.39,0,0.39,0,0,0,3.14,0.39,1.18,0.39,0.39,0.39,0.39,0.39,0.39,0.39,0.39,0.39,0.78,0,0.78,0.39,0,1.96,0.78,0,0.78,0,0,0,0.645,0.581,0,0.129,0,0,2.895,16,249,0 0.05,0,0,0,0,0.1,0,0,0,0.1,0.05,0.48,0,0,0.05,0.21,0.1,0,1.62,0.05,1.08,0,0.21,0.05,2.05,0.48,0.05,0.16,0,0.16,0.27,0,0,0,0.21,0,0.27,0,0.16,0,0,0,0,0,0.05,0,0,0.1,0,0.289,0.015,0.062,0.046,0,2.007,32,1026,0 0.06,0,0,0,0,0.12,0,0,0,0.12,0,0.19,0,0,0.06,0.19,0.12,0,1.74,0.06,1.23,0,0.25,0.06,2.26,0.38,0.06,0.19,0,0.19,0.32,0,0,0,0.25,0,0.32,0,0.19,0,0,0,0,0,0.06,0,0,0.12,0,0.33,0.018,0.064,0.055,0,2.024,25,897,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0.97,0,0,0,1.94,0.97,0,2.91,0,0,0,0,0,0,1.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.141,0,0,0,0,3.178,15,89,0 0,0,0.85,0,1.36,0,0,0,0,0.17,0,0.34,0.17,0,0,0,0,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.034,0,0,0,0,0,1.085,3,89,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,3.7,0,0,3.7,0,0,0,0,0,0.689,0,0,0,1.888,5,17,0 0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,2.23,0.74,0,0,0,0.74,0,0,0,0,0,0,1.49,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,45,140,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.2,17,26,0 0,0,0,0,1.92,0,0,0,0,0,0,2.88,0,0,0,0,0,0,0,0,0,0,0,0,0.96,0.96,0,0,0,0,1.92,0,0,0,0.96,0,0.96,0,0.96,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0.161,2.307,14,90,0 0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0.45,0,1.35,0,0,0,1.35,1.35,1.35,1.35,0.9,0.45,0.45,0.45,0,0.45,1.35,0.45,0.45,0,0.45,0.45,0,0.45,0.45,0,0.45,0,0,0,0,0.358,0.43,0,0,0.071,2.236,12,161,0 0,0,0.36,0,0.73,0,0,0,0,0,0,0.36,0.18,0,0,0.36,0,0,1.28,0,0.36,0,0,0,0.36,1.28,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0.18,0,0.18,0,0,0,0.027,0,0,0.055,0,0,3.176,51,270,0 1.03,0,0,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.09,0,0,0,0,0,0,0,0.185,0,0.37,0,0,2.277,11,41,0 0.72,0,0,0,0,0,0,0,0,0,0,1.45,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,1.407,6,38,0 0,0,0,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0.82,0,0,0,0,0,4.13,2.47,1.65,0.82,0.82,0.82,0.82,0.82,0,0.82,0.82,0.82,0,0,0,0.82,0,0,0,0,0.82,0,0,0,0,0.361,0,0.24,0,0,4.666,34,126,0 0,0,0.34,0,0.34,0,0,0,0,0,0,0.34,0.34,0,0,0,0,0,0.34,0,0.34,0,0,0,0.34,0.69,0,0,0,0,0,0,0,0,0,0.34,1.04,0,0,0,0,0,0.34,0,0,0,0,0,0,0.149,0,0,0,0,2.35,14,188,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0.24,0,0,2.833,12,68,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,1.78,1.78,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,9,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0.9,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,3.472,28,125,0 0,0,0,0,0,0,0,0,2.29,0,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,5.34,1.52,1.52,0.76,0.76,2.29,0.76,0.76,0,0.76,0.76,0.76,0,0,0.76,0.76,0,0,0,0,0.76,0,0,0,0,0.157,0,0,0,0,4.242,52,140,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,1.5,4,18,0 0.97,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.91,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.166,0,0,2.185,11,59,0 0,0,0,0,0,0,0.46,0,0,0,0,0.46,0,0,0,0,0,0,0.46,0,0,0,0,0,0.46,0,0.92,0,0,0,0,0,2.3,0,0,0,0.92,0,0.92,0,0,0,0.92,0,0.46,0,0,0,0.163,0.163,0,0.163,0,0.081,2.343,13,150,0 0,0,0,0,0.54,0.54,0,0,0,0,0,1.09,0,0,0,0,0,0,1.63,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0.299,0.199,0,0,0,0,1,1,14,0 0,0.07,0.14,0,0.14,0.07,0,0,0,0,0,1.34,0.07,0.14,0,0,0.63,0,0.14,0,0,0,0.07,0,3.03,0,0,0,0,0,0,0,0,0,0,0.07,0.21,0,0,0,0,0,0,0,0,0,0,0,0.084,0.177,0,0,0,0,2.25,26,855,0 0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,9,21,0 0,0,0,0,0,0,0,0,0,0,0,0.99,0,0,0,0,0,0,0,0,0,0,0,0,2.97,3.96,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,0,0,0,1.736,12,125,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,4.47,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,16,0 0.53,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0,0,0.26,0,0.26,0,0,0,1.61,0.8,1.88,0.53,0.53,0.53,0.53,0.53,1.88,0.53,0.53,0.53,0.8,0,0.8,0.53,0,0,0.8,0,0.8,0,0,0,0,0.412,0,0.091,0,0,2.225,12,227,0 0,0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.475,0.158,0,0,0,4.393,33,145,0 0.58,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0.58,0,0,0,0,0,1.76,1.17,1.76,0.58,0.58,0.58,0.58,0.58,1.76,0.58,0.58,0.58,0.58,0,0.58,0.58,0,0,0.58,0,0.58,0,0,0,0,0.414,0,0.103,0,0,2,12,94,0 0.31,0.31,0.94,0,0,0.62,0,0,0,0,0,0.31,0,0,0,0,0,0,2.83,0,0.31,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0.31,0,0,0,0,0,0,0.096,0,0,2.368,45,180,0 0.12,0,0.12,0,0.12,0.12,0,0,1.08,0,0,0.36,0,0,0,0,0,0,0.48,0,0.24,0,0,0,0.6,0.72,0.12,0,0,0,0,0,0.36,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0.117,0.151,0.033,0,0.033,0,4.134,78,645,0 0.05,0,0.1,0,0,0.1,0,0.05,0.49,0,0,0.27,0,0,0,0,0.38,0,0.21,0,0.1,0,0,0,0.49,0.54,0.05,0,0,0,0,0,0.38,0,0,0.38,0.21,0,0,0,0,0,0,0,0,0,0,0,0.308,0.136,0.078,0,0.014,0,3.715,107,1386,0 0,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.216,0,0.216,0,0.216,2.166,6,39,0 0.75,0,0.37,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0,1.12,0,1.87,0,0,0,0.75,0.37,1.87,0.37,0.37,0.37,0.37,0.37,0,0.37,0.37,0.37,0.75,0,0.37,0.37,0,0,2.63,0,0.75,0,0,0,0,0.305,0,0.061,0,0,1.903,13,118,0 0,0,0,0,0,0,0,0,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.2,2.2,1.47,2.2,1.47,0.73,0.73,0.73,0,0.73,2.2,0.73,0.73,0,0.73,0.73,0,0.73,0.73,0,0.73,0,0,0,0,0.555,0.666,0,0,0.111,2.351,12,127,0 0.68,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,1.37,0,2.06,0,0,0,0,0,1.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0.332,0,0,0,0,1.125,2,18,0 0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,1.12,0,1.12,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.24,0,0,0,0,0,0,0.203,0,0.203,2.222,20,40,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,8.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.125,6,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,4,0 0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0,0.57,0,0,2.87,0,4.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.14,0,0,0,0,0.84,0,0,0,0.076,3.583,31,129,0 0.24,0,0.49,0,0,0,0,0,0,0.24,0,0.24,0.24,0,0,0,0,0,1.23,0,0.24,0,0,0,0.24,0.24,0,0.49,0,0.24,0,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0.029,0.119,0.119,0,0,0,3.574,75,336,0 0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0.564,0,0,1.818,9,20,0 0,0,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0.49,0,0.49,0,0,0,0,0.195,0,0.097,0,0,2.3,18,69,0 0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0.68,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,0,0,0,0,3.4,0,0,0,0.68,0,0.086,0,0,0,0,1.41,5,79,0 0,0,2.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.389,0,0.389,0,0,1.26,3,29,0 0,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0.231,0,0.231,0,0,1.761,17,37,0 0,0,0.79,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,2.38,0,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.137,0,0,0,0,0,1.09,3,24,0 0,0,0,0,0,0,0,0,0,0,0,1.04,0,0,0,0,0,0,5.2,0,0,0,0,0,0,0,1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.195,0,0,0,0,1.071,2,15,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,1.28,0,0,0,1.28,0,0,0,0,1.28,0,0,0,0,1.28,0,0,0,0,0,2.56,1.28,1.28,1.28,1.28,1.28,1.28,1.28,0,1.28,1.28,1.28,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0.398,0,0,0,0,2.21,11,42,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0,2.54,0,0,0,1.69,0.84,1.69,1.69,0,0.84,0,0,0,0,0.84,0,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0,0,0,0,1.777,11,64,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.51,0,0,0,0,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.51,0,0,0,0.3,0,0,0,0,0,1.611,5,29,0 0.48,0,0,0,0.48,0,0,0,0,0,0,0,0.48,0,0,0,0,0,4.39,0,0,0,0,0,0.48,0,0.48,0,2.92,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0.085,0,0,0,0,1.275,3,37,0 0.12,0,0.25,0,0,0,0,0.38,1.28,0,0,0.38,0,0,0,0,0,0,0.51,0,0.25,0,0,0,0.64,0.76,0.12,0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.054,0.162,0.036,0,0.036,0,3.167,32,491,0 0.08,0.08,0.25,0,0,0.25,0,0,0.76,0,0,0.25,0,0,0,0,0,0,0.33,0,0.16,0,0,0,0.5,0.59,0.08,0,0,0,0,0,0.42,0,0,0.25,0.08,0,0,0,0,0.08,0,0,0,0,0,0,0.148,0.136,0.045,0,0.022,0,3.995,55,807,0 0,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,3.57,0,2.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.375,23,38,0 0.24,0,0.12,0,0,0.12,0.24,0,0,0,0,0.37,0,0,0,0,0,0,0.86,0,0.24,0,0,0,1.24,0.62,0.49,0.24,0.24,0.24,0.24,0.24,0.37,0.24,0.24,0.24,0.24,0,0.24,0.24,0,0.12,0.24,0.86,0.24,0,0,0,0.018,0.297,0.055,0,0,0,1.801,13,227,0 0,0,0,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,0,0,0,0,0,3.75,3,2.25,0.75,0.75,0.75,0.75,0.75,0,0.75,0.75,0.75,0.75,0,0.75,0.75,0,0.75,0.75,0,0.75,0,0,0,0,0.222,0,0,0,0,1.833,12,77,0 0.1,0,0.21,0,0,0,0,0.21,0.31,0.1,0,1.06,0.21,0,0,0.1,0.21,0,0,0,0,0,0.21,0,3.5,0.1,0,0,0.1,0.1,0,0,0,0,0,0.21,0.21,0,0,0.1,0,0,0,0.21,0,0,0,0,0.043,0.143,0,0,0.057,0,2.409,23,571,0 0,3.68,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0.61,0,1.22,0,0,0,1.22,0.61,3.06,0.61,0.61,0.61,0.61,0.61,0,0.61,0.61,0.61,1.84,0,0.61,0.61,0,0,1.84,0,1.84,0,0,0,0,0.189,0.094,0,0,0.094,2.283,13,169,0 0,0,0,0,0,0,0,0,0,0,0,3.12,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0.27,0,0,1.5,4,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.33,0,1.33,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.211,0,0,0.211,0,0,1.38,4,29,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.94,0,0,0,0.48,0,0.48,0,0,0,0,0,0,0,0,0,0,0.057,0,0,0,0,6.526,83,248,0 0.51,0,0,0,0,0,0,0,0,0,0,1.54,0,0,0,0,2.06,0,0.51,0,0,0,0,0,3.6,2.06,0,0,0,0.51,0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0,1.574,4,74,0 0,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,0.89,0,0,0,0,0,1.78,1.78,0,0.89,0,0,0,0,0,0,0.89,0.89,0,0,0,0,0,0,0,0,0,0,0,0.89,0,0.537,0,0,0,0.268,2.292,12,94,0 0,0,0,0,0,0.78,0,0,0,0,0,0.78,0,0,0,0,0,0,0.78,0,0,0,0,0,0.78,0.78,0,0.78,0,0,0,0,0,0,0.78,0.78,0,0,0,0,0,0,0,0.78,0,0,0,0,0,0.451,0,0,0,0.112,2.714,22,133,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,1.29,6.49,0,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0.19,0,0,1.857,4,26,0 0,7.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,1.75,0,0,0,0,0,3.5,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,1.75,0,1.75,0,0,0,0,0,0,0,0,0.286,1.826,13,42,0 0,5.47,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,1.36,0,2.73,0,0,0,0,0,2.73,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,1.36,0,1.36,0,0,0,0,0,0,0,0,0.232,2.035,13,57,0 0,0,0,0,0.87,0,0.87,0,0,0,0,0,0,0,0,2.63,0,0.87,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.798,0.159,0,18.454,136,203,0 0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0.7,0,1.4,0,1.4,0,0,0,0,0,0.7,0,0,0,0.7,0,0,0,0,0,0,0,0,2.11,0,0,0,0,0,0,0,0,0,0.266,0.066,0,0,0,18,200,378,0 0.3,0,0.15,0,0,0.15,0.3,0,0,0,0,0.3,0,0,0,0,0,0,0.75,0,0.3,0,0,0,0.75,0.3,0.3,0.15,0.15,0.15,0.15,0.15,0.45,0.15,0.15,0.15,0.15,0,0.15,0.15,0,0,0.15,0.75,0.15,0,0,0,0,0.328,0.046,0,0,0,1.703,12,155,0 0.41,0,0.41,0,1.25,0,0.41,0,0,0.2,0,1.04,0.2,0,0,0.41,0.41,0,3.96,0,2.29,0,0.2,1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0.83,0,0,0,0,0.069,0,0.866,0.103,0,5.052,214,485,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0.32,0,0,0,0.32,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0.54,0.108,0,0,0.054,3.787,28,375,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.92,0,0,0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,0,1.96,0,1.96,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,1.785,6,25,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.87,0,0,0,0,0,0,0,7.31,0,0,0,0,0,0,0,0,0,2.43,0,0,0,0,0,0,0,2.43,0,0,0,0,0,0,0,0,0,1.461,5,19,0 0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,4.44,0,0,0,0,0,0,0,8.88,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0,0,0,1.3,5,26,0 0,0,0.7,0,0,0.14,0,0,0.28,0,0,3.08,0.14,0.28,0,0,0.14,0,0,0,0,0,0,0,0.98,0,0,0,0.14,0.14,0,0,0,0,0,0.7,0.28,0,0,0,0,0,0,0,0,0,0,0,0.054,0.199,0,0,0,0,1.82,18,304,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,1.562,5,25,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.285,2,9,0 0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,5.55,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,10,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.285,2,9,0 0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,1.4,0,0,0,0,0,0,0,0,0,1.4,1.4,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0,1.4,0,0,0,0,0,0,0.205,0.205,0,0,0,4.533,21,68,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.285,2,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,2.56,0,1.28,0,0,0,0,0,0,1.28,1.28,0,0,0,0,0,0,0,2.56,0,0,0,0,0,0.552,0,0,0,0,2.093,11,90,0 0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0.64,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.314,0.209,0,0,0.104,4.062,28,195,0 0,0,0,0,1.26,0,0,0,0,0,0,1.26,0,0,0,1.26,0,0,2.53,0,0,0,0,0,0,0,1.26,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,1.26,0,0,0,0,0,0,0,0,0,1.285,5,18,0 0,0.25,0,0,0,0,0,0,0,0.51,0.77,0.25,0,0,0,0,0,0,1.02,0,0.51,0,0,0,0.25,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.124,0,0.207,0,0,10.409,343,635,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.857,18,41,0 0,0,0.38,0,0,0,0,0,0,0,0,1.53,0,0.38,0,0,0.76,0,0.76,0,0,0,0,0,3.84,1.53,0.38,0.38,1.53,0.38,0.38,0.38,0,0.38,0.38,1.15,0.38,0,0,0.38,0,0,0.38,0,0.76,0,0,0,0,0.163,0.054,0,0,0,2.297,17,193,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0.58,0,0.58,0,0,0,3.51,2.34,0.87,2.34,0.58,1.17,0.58,0.58,0,0.58,1.17,0.58,0.29,0,0.87,0.58,0,0.87,0.29,0,0.58,0,0,0,0.091,0.637,0.045,0,0,0,3.552,37,373,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,1.58,0,0,0,0,0,0,0,0,0.79,0,0,0,0,0,0,1.58,0,0,0,0,0,0.79,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.149,0,0.149,0,0,1.482,10,43,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.375,4,11,0 0,0,0,0,0.33,0,0,0,0,0,0,0.33,0,0,0,0.33,0,0.33,0.33,0,0.33,0,0,0,0.99,0.33,0,0.66,0,0.33,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0.13,0.043,0,0,0,2.016,19,125,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.325,0,0,0,0,0,1,1,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.3,3,13,0 0.34,0,0,0,0,0,0,0,0.34,0.68,0,1.02,0,0,0,0,0,0,1.36,0,0.68,0,0,0,2.38,1.7,0.68,1.7,0.68,0.34,0.34,0.34,0,0.34,0.34,0.34,0.68,0,0.68,0.34,0,0,0.68,0,0.34,0,0,0,0.052,0.42,0.052,0,0,0.052,2.604,13,250,0 0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,4.54,0,0,0,0,0,0,0,0,0,0,2,5,16,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1.428,3,10,0 0,1.86,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.8,1.86,1.86,0.93,0.93,0.93,0.93,0.93,0,0.93,0.93,0.93,0.93,0,0.93,0.93,0,0.93,0.93,0,0.93,0,0,0,0,0.457,0.152,0,0,0,2.097,13,86,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,1.625,6,13,0 0.26,0,0.26,0,0,0,0,0,0.53,0,0.53,2.94,0,0,0,0,0,0.26,4.27,0,2.4,0,0,0,0,0.26,0.53,0,0,0,0,0,0,0,0,0,0.26,0,0.53,0,0,0.8,0,0,0,0,0,0.53,0,0.03,0,0,0,0,1.58,8,128,0 0,0,0,0,0.13,0,0,0.55,0,0,0,0.13,0.13,0,0,0,0.27,0,0,0,0,0,0.41,0,2.79,0,0,0,0,0,0,0,0,0,0,0.13,0.27,0,0,0,0,0,0,0,0,0,0,0,0.071,0.143,0,0,0.053,0,2.662,22,418,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,2,2,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0.176,0,0,1.758,7,51,0 0,0,1.23,0,0,0,0,0,0,0,0,2.46,0,0,0,0,0,2.46,1.23,0,1.23,0,0,0,2.46,1.23,0,1.23,0,1.23,1.23,0,0,0,1.23,1.23,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0.139,0,0.278,0,0,1.736,10,66,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.166,2,7,0 0.17,0.35,0,0,0,0,0,0,0,0.35,0,0.17,0,0,0,0,0,0,1.94,0,0.7,0,0,0.17,0.17,0.17,0.88,0,0,0.17,0,0.17,0,0.17,0,0,0.35,0,0,0,0,0,0,0,0.53,0.17,0,0,0,0.031,0,0.031,0,0,1.564,21,194,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,1.75,3,14,0 0,0,0,0,0.1,0,0,0,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0,2.06,1.19,0,0,0,0.1,0,0,1.3,0,0,0.1,1.08,0,0,0,0.65,0,0,0,0,2.6,0,0.1,0.14,0.5,0.093,0,0,0,4.06,51,1003,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,1.6,7,16,0 0.17,0,0.51,0,0.17,0,0,0,0,1.36,0,0.17,0,0,0,0.17,0.34,0,1.19,0,0.85,0,0,0,1.53,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0.111,0,0.167,0,0,1.894,22,216,0 0,0,0,0,0,0.44,0,0,0,0,0,0.44,0.44,0,0,0,0,0,1.32,0,0,0,0,0,0,0,0.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0.44,0,0,0,0.15,0,0,0,0,1.613,11,71,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,1.142,2,8,0 0,0.24,0,0,0.24,0,0,0.24,0,0.49,0,0,0,1.48,0,0,0,0,0.99,0,0,0,0,0,0.49,0,0.24,0,0,0,0.24,0,0,0,0.24,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,9.31,235,1108,0 0,0,0,0,0.44,0,0,0,0,0,0.44,0.89,0,0,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,0,0,1.33,0,0,0,0.139,0,0,0,0,1.731,16,116,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0.86,0,0,0,0,0,0,0,0.86,0.86,0,0,0,0,0,0.86,6.95,0,4.34,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.047,2,22,0 0.25,0,0,0,0.25,0.25,0,0,0,0,0,0.51,0,0.25,0,0,0,0.25,0.51,0,0.25,0,0,0,0,0.25,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0.25,0,0,0,0,0.25,0,0.25,0,0.082,0,0,0,0.041,1.287,4,85,0 0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,1.56,0,0,0,0,0,0,0,0,1.75,3,21,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.12,0,0,0,0,0,0,0,3.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.502,0,0,0,0,1,1,8,0 0,1.61,3.22,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,3.22,3.22,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,1.61,0,0,0,0,0,0,0,0,1.083,2,13,0 0,0,0,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,4.63,0,3.31,0,0,0,2.64,1.98,1.32,0.66,0.66,0.66,0.66,0.66,0,0.66,0.66,0.66,0,0,0.66,0.66,0,0,0,0,0.66,0,0,0,0,0.293,0,0,0,0,3.968,34,127,0 0,0,0,0,0,0,0,0.77,0,0,0,0,0,0,0,0,0,1.55,2.32,0,0,0,0,0,3.1,3.87,3.1,0.77,0,0.77,0.77,0,0,0,1.55,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0.198,0,0.099,0,0,2.325,30,93,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.501,0.167,0,0,0.083,3.983,28,239,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.125,17,25,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.453,0.181,0,0,0.09,4.037,28,214,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.666,3,5,0 0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0.34,0,0,0,0,0,0.68,0.34,0,0.68,0,0.34,0,0,0.34,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0.048,0,0,0,0,0,2.147,11,131,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.395,2.333,8,119,0 0,0,0,0,1.04,0,0,0,0,0,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,1.56,1.04,0,0.52,0,0,0,0,2.08,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.274,0,0,0,0,1.848,10,61,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,5.6,0,4,0,0,1.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.122,0.244,0,0,0,0,1.909,6,21,0 0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,1.36,0,5.47,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.307,8,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.396,0,0.396,2.533,10,38,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.63,0,0,0,0,0,2.63,0,0,0,0,0,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.248,0,0,0,0,4.166,14,50,0 0,0.28,0,0,0.56,0,0,0,0.28,0,0,0.56,0,0,0,0,0,0.56,3.41,0,1.13,0,0,0,0.56,0.56,1.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.56,0,0.85,0,0,0,0.046,0.281,0.046,0,0,0,1.834,15,200,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,50,99,100,0 0,0,0,0,0.32,0.32,0,0,0,0,0,0.32,0,0,0,0,0,0,1.3,0,0.98,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0.257,0,0,0,0,1.3,7,104,0 0,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,1.19,3.57,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.791,71,115,0 0,0,0,0,2.25,0,0,0,0,0.75,0,0,0,0,0,0,0,0,1.5,0,0,0,0,0,0.75,0.75,1.5,1.5,0,0,0,0,0,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.128,0,0,0.128,0.128,3.657,28,128,0 0,1.96,0,0,0,0,0,0,0,0,0,0,0,0.98,0,0,0,0,1.96,0,0.98,0,0,0,1.96,1.96,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.98,0,0,3.92,0,0,0,0,0,0,0,0,3.129,17,97,0 0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0.87,0,0.29,0,0,0,0.29,0.29,0.29,0.58,0,0,0,0,0,0.29,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,2.038,0,13.562,351,434,0 0,0,0,0,0,0,0,0,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.615,4,21,0 0,0,0.59,0.11,0,0,0,0,0.11,0.23,0,0.11,0,0,0,0.11,0,0,0.95,0,0.47,0,0,0,0.23,0,0.71,0,0,0,0,0,0,0.11,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0.227,0.322,0.113,0.056,0.075,0,2.546,38,601,0 0.39,0,0,0,1.17,0,0,0,0,0.39,0,1.17,0,0,0,0,0,0.39,3.12,0.39,1.17,0,0,0,0,0,0.39,0.78,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0,0,0,0,0.07,0.07,0,0.07,0,0,2.069,13,89,0 0,0,0,0,1.17,0,0,0,0,1.17,0,0,0,0,0,0,0,0,2.35,0,0,0,0,0,0,0,1.17,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.551,10,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,4.8,19,24,0 0,0,0,0,0,0,0,0,0,0,0,0.52,0,0,0,0,0,0,1.05,0,1.05,0,0,0,0.52,2.11,1.58,1.05,0,0.52,0,0,0,0,0.52,0,0,0,0,0,0,0,0,0,0.52,0,0,0,0,0.164,0,0,0,0,2.173,11,113,0 0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,1.58,1.58,3.17,0,1.58,0,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0.601,0,3.36,13,84,0 0,0,0,0,0.56,0,0,0,0,0,0,2.27,0,0,0,0,0,0,1.98,0,0.28,0,0,0,0.85,0.85,0.85,0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0.28,0,0,0,0,0.09,0.135,0,0,0,1.962,15,155,0 0,0,0.16,0,0.64,0,0,0.16,0,0,0,1.91,0,0,0,0.16,0,0,3.04,0,1.76,0,0,0,0,0.32,0.32,0,0,0,0.16,0,0,0,0,0.16,0,0,0,0.16,0,0,0,0,0.32,0,0,0,0,0.055,0,0,0.055,0,1.798,7,196,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,2.98,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.761,5,37,0 0,0,0,0,0,0.65,0,0.65,0,0.65,0,1.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0.108,0.108,0,0,0.108,0,1.924,9,102,0 0,0,0,0,0,0,0,0,0,0,0,1.74,0,0,0,0,0,0,2.9,0,0.58,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.201,0,0,0,0,1.794,6,70,0 0,0,1.49,0,0,0.37,0,0,0,0,0,0.74,0.37,0,0,0,0,0,2.24,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,1.79,5,111,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.344,0,0,0,0,1.88,13,47,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20.83,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.555,18,64,0 0.87,0.43,0,0,0,0,0,0,0,0.43,0,0.87,0,0,0,0,0,0,3.5,0,1.31,0,0,0,1.31,0.43,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,2.085,25,73,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.29,0,0,0,0,0,2.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0.258,0,0,0,0,3.74,53,101,0 0,6.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,1.58,1.58,1.58,1.58,1.58,1.58,1.58,3.17,0,3.17,1.58,1.58,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0.431,0,0,0,0.215,3.461,12,90,0 0.32,0,0,0,0.32,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0.96,0,2.56,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.234,0,0.058,0,0,1.068,3,47,0 0,1.23,0,0,0,0,0,0,0,1.23,0,0.61,2.46,0,0,0,0,0,3.08,0,1.23,0,0,0,0,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.224,0,0,0,0,1,1,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,6,0 0.28,0,0,0,0.28,0,0,0,0.28,0,0.28,3.97,0,0,0,0,0,0,3.97,0,0.85,0,0,0,0.28,1.13,0,0,0,0,0,0,0,0,0,0,0.28,0,0.28,0,0,0,0,0.28,0,0,0,0.28,0,0,0,0.08,0,0,2.396,16,139,0 0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,1.533,5,23,0 0,0,0,0,0,0,0,0,0,0,0,5.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.166,2,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.72,6.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.466,13,37,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.53,6.15,0,0,0,0,0,0,0,0,0,0,1.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.714,6,36,0 0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.56,0,1.56,0,3.12,0,1.56,0,1.56,1.56,0,0,0,0.215,0.215,0,0,0,1.666,12,30,0 0,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,1,1,15,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.552,0,0,0,0,1,1,4,0 0,0,0.97,0,0,0,0,0,0,1.94,0,0.97,0,0,0,0,0,0.97,5.82,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.293,0,0,0,0,2.187,14,70,0 0,0,0,0,0,0,0,0,0,1.19,0,2.38,0,0,0,0,0,0,1.19,0,0,0,0,0,1.19,1.19,0,2.38,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0.621,0,0,0,0,2.617,11,89,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0.34,0,0,0.34,0,1.7,0,1.36,0.34,0,0,0,0.34,0,1.36,0,0,0,0,0,0.34,0.34,1.02,0,0,0,0,0,0,0,0.34,0,0.34,0,0,0,0,0,0,2.38,0,0,0,0,0,0.055,0.11,0,0,0,1.421,8,91,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,0.578,1.734,0,0,0,0,3.083,24,37,0 0,0,1.33,0,0,0,0,0,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.33,1.33,0,2.66,0,0,0,0,0,0,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0.413,0,0,0,0,4.36,46,109,0 0.23,0,0.46,0,0,0,0,0.23,0,0.23,0,0,0,0,0,0,0,0,3.69,0,0.69,0,0,0,1.84,0.23,0,0,0,0.23,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0.253,0,0,0.031,0,2.016,19,244,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.52,4.76,4.76,4.76,4.76,4.76,4.76,4.76,0,4.76,4.76,4.76,0,0,0,4.76,0,0,0,0,0,0,0,0,0,1.257,0,0,0,0,3.333,11,30,0 0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0.22,0,0,0.33,0.11,0,0,0,0,0.11,0,0,0,0,0,0,0.053,0.16,0,0,0,0,2.367,24,651,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.552,0,0,0,0,1.6,4,8,0 0,0,0,0,0,0,0,0,0,0,0,3.38,0,0,0,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.204,0,0.408,0,0,6.187,47,99,0 0,0,0.32,0,0.32,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0.32,0,0,0,0,0,0.64,0.64,0,0,0,0,0,0,0,0,0,0.32,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0.04,0,0,0,0,1.677,10,156,0 0.23,0,0.23,0,1.17,0,0,0,0,0,0,1.41,0,0,0,0,0.11,0,0.47,0,0.7,0,0.11,0,1.29,0.11,0,0,0.11,0.23,0,0,0,0,0,0,0.11,0,0,0,0,0.11,0,0,0.23,0,0,0,0,0.015,0,0,0,0.015,1.486,7,162,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,0,0,0,0,0,0,0,0,0,1.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.6,7,64,0 0,0,0,0,0,0,0,0,0,0,0,0.98,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0.159,0,0,1.45,7,74,0 0.29,0,0.44,0,0.73,0,0,0,0,0,0,0.58,0,0,0,0.14,0,0,0.73,0.14,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0.14,0,0.14,0,0,0,1.32,0.02,0.321,0.18,0.14,0,0,1.891,24,522,0 0,0,0.91,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,2.28,0,0,0,0,0,0.91,0.91,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.32,7,103,0 0,0,1.09,0,0,0,0,0,0,1.09,0,2.19,0,0,0,0,0,0,0,0,0,0,0,0,1.09,1.09,0,2.19,0,0,0,0,0,0,0,1.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0.325,0,0,0,0,4.586,51,133,0 0,0.51,0,0,1.02,0,0,0.51,0,0,0,0,0,0,0,0.51,0.51,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0,0,0,0.51,0,0,0,0.51,0,0,0,0,0,0.071,0,0,0,0,2.076,9,108,0 0,0.61,0,0,1.22,0,0,0,0,3.68,0,0,0,0,0.61,0,0,0,1.84,0,1.84,0,0,0,0.61,0.61,0,0,0,2.45,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.476,8,62,0 0,0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,1.83,0,0,0,0,0,1.83,0.91,2.75,0.91,0.91,0.91,0.91,0.91,0,0.91,0.91,0.91,0.91,0,0.91,0.91,0,0,0.91,0,0.91,0,0,0,0,0.46,0,0,0,0,1.918,13,71,0 0,0,0,0,0,0,0,0,0,0,0,4.58,0,0,0,0,0.91,0,0,0,0.91,0,0,0,1.83,0,0,0,0,0.91,0,0,0,0,0,0.91,0,0,0.91,0,0,0,0,0,0,0,0,0,0,0.12,0,0.241,0,0,3.541,26,85,0 0,0,0.36,0,0.36,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0,6.25,5.51,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0.279,0.767,0.139,0,0,0,3.722,20,268,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0.18,0,0,0.09,0,0,0,0,0,0.94,0.37,0,0,0,0.28,0,0,0,0,0,0,0,1.41,0,0,0,0,0,0,0,0.84,0,0,0.47,0.09,0.09,0,0,0,0,0,0,0,0,0,0,0.052,0.065,0,0,0,0,2.022,19,451,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.44,0,0,0,1.44,0,0.72,0,1.44,1.44,0,0,0,0,0.114,0.114,0,0.114,1.645,12,51,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,1.62,0,0.81,0,1.62,1.62,0,0,0,0,0.137,0,0,0.137,1.636,12,36,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,1,1,8,0 0,0,0,0,0,0,0,0,0,0,0,0.85,0,0,0,0,0,0.85,2.56,0,0,0,0,0,0.85,0.85,0,0.85,0,0,0,0,0,0,0.85,0,1.7,0,0,0,0,0,0.85,0,0.85,0,0,0,0.142,0,0.142,0,0,0,1.717,12,67,0 0,0,0,0,0,0,0,0,0,1.22,0,0,0,0,0,0,0,0.61,1.84,0,0,0,0,0,2.45,1.84,1.22,1.22,0.61,0.61,0.61,0.61,0,0.61,1.22,0.61,0.61,0,0,0.61,0,0,0.61,0,0.61,0,0,0,0.095,0.38,0.19,0.19,0,0,1.857,12,104,0 0,0,0,0,0,0,0,0,0,0,0,2.81,0,0,0,0.35,0,0.35,0.35,0,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0.293,0,0,0,0,1.226,5,146,0 0,0,0,0,0,0,0,0,0,0,0,0.54,0.54,0,0,0,0,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0.54,0,0,2.7,0,0.54,0,0,0,0,0,0.087,0,0.087,0,0,2.363,5,52,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,2.22,2.22,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.769,8,23,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,3.63,0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,1.181,3,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,2.04,0,0.68,0,0,0,1.36,0.68,0,0.68,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.241,0,0,0,0,2.461,17,96,0 0,0,0,0,6.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6,10,0 0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.87,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,0,0,0,0.87,0,0,0,0,0,0,1.795,11,79,0 0,0,0,0,0,0,0,0,0,0.9,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0.9,0,1.81,0,0,0,0,0,0,0,0.9,0,0,0.9,0,0,0,0,0,0,0,0,0,0,1.208,0,0,0,0,5.111,58,138,0 0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0.35,0,1.06,0,0,0,1.41,1.06,0.7,0.35,0.35,0.7,0.35,0.35,0.35,0.35,0.35,0.35,0.35,0,0,0.35,0,0,0.35,0,0.7,0,0,0,0,0.222,0.055,0,0,0,1.506,12,119,0 0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,2,7,26,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,1.01,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0.08,0.564,0,0,0.161,0,1.712,20,137,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0.33,0,0.042,0,0,0,0,2.519,46,131,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.56,0,1.12,0,0,0,0,0,0,0,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.56,0,0,0,0,0.096,0,0,0,0,1.15,4,46,0 0,0,0.18,0,0.56,0,0,0,0,0,0,0.75,0.37,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0.18,0.18,0,0,0,0,0,0.18,0,0.18,0,0,0,0,0.056,0,0,0.112,0,2.188,19,232,0 0,0,0.8,0,2.42,0,0,0,0,0,0,0.4,0,0,0,0,0,0.4,5.26,0,1.61,0,0,0,0.4,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0.27,0,0,2.36,35,59,0 0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,3.84,3.84,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0.581,0,0,1.615,4,21,0 0.21,0,0.21,0.21,0.63,0,0,0,0,0,0.42,0,0.21,0,0,0.84,0,0.42,1.9,0,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0.031,0,0.374,0.062,0,2.892,71,405,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0,4.7,2.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.882,21,49,0 0,0,0.22,0,0,0,0,0,0,0,0,0.68,0.9,0,0,0.22,0.22,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0.22,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0.094,0,0,0,0,2.182,42,203,0 0,0.08,0.08,0,0,0.08,0,0.16,0,0,0,0.81,0.16,0,0,0,0.08,0,0,0,0,0,0.08,0,3.49,0.48,0,0,0.32,0.24,0,0,0,0,0,0.32,0.08,0,0,0,0,0.08,0,0,0,0,0,0.08,0.022,0.111,0,0,0.055,0,2.145,21,693,0 0.22,0,0.22,0,0.45,0,0,0,0,0,0,0,0.68,0,0,0.22,0,0,0.68,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0.68,0,0.22,1.83,0.22,0.91,0,0,0,0.267,0.038,0,0,0,1.649,13,94,0 0,0,0,0,0,0,0,0,0,0,0,1.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.6,7,64,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,0,0,1.149,0,0,1.5,3,12,0 0,0,0.81,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.09,2,12,0 0,1.17,0,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,2.35,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0,0,0,1.17,0.376,0,0,0,0,0,2.925,16,117,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,1,1,8,0 0,0,0,0,0,1.34,0,0,0,0,0,0,0,0,0,0,0,0.67,1.34,0,0,0,0,0,0,0,0.67,0,0,0,0,0,1.34,0,0,0,0,0,0,0,0,0,0,0.67,0.67,0,0,0,0,0.111,0,0,0,0,1.285,5,27,0 0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.65,0,0.55,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0,1.1,0.55,0,0,0,0,0.092,0,0,0,0,1.84,5,46,0 0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,2.7,0,1.35,0,0,0,0,1.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,0,0,0,0,0,1.88,5,47,0 0,0,1.56,0,1.56,0,0,0,0,1.56,0,6.25,0,0,0,0,0,1.56,1.56,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.278,0,0,0,0,1,1,11,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.687,0,0,0,0,1.903,17,59,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.52,4.76,4.76,4.76,4.76,4.76,4.76,4.76,0,4.76,4.76,4.76,0,0,0,4.76,0,0,0,0,0,0,0,0,0,1.257,0,0,0,0,3.333,11,30,0 0,0,0,0,0.27,0,0,0,0,0.27,0,0.54,0,0.54,0,0,0.54,0,1.63,0,0,0,0,0,4.89,1.35,0.27,0.27,0.27,0.27,0.27,0.27,0,0.27,0.27,0.27,0,0,0.27,0.27,0,0,0.27,0,0.81,0,0,0,0,0.192,0.153,0,0,0,4.608,35,424,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.63,0,0,0,0,0,0,2.63,0,0,0,0,0,0,0,0,0,0.414,0,0,2.705,13,46,0 0,0,0.2,0,0,0,0,0.2,0,0.2,0,0,0,0,0,0,0,0.2,0.2,0,0,0,0,0,0.2,0.2,0,0.41,0,0,0,0,0.2,0,0.2,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0.148,0,0,0,0,1.669,15,187,0 0,0.22,0,0,0.66,0.22,0,0.44,0.44,0.89,0,0,0.22,0.22,0,1.33,0,0,0.89,0,0.44,0,0,0.22,3.34,3.56,0.66,0.22,0.22,0.22,0.22,0.22,0,0.22,0.22,0.22,1.11,0,0,0.22,0,0,0.22,0,0.22,0,0,0,0.148,0.372,0.111,0.372,0.223,0,3.425,42,411,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.66,0,0,0,0,0,1.33,0,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.478,0,0,0,0,2.166,18,52,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.333,5,7,0 0,0,0.62,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0,0.62,1.25,0,0,0,0,0,1.25,0.62,0,0.62,0,0.62,0.62,0,0.62,0,0.62,0.62,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0.084,0,0.169,0,0,1.863,10,82,0 0,0.04,0.25,0,0.04,0.04,0,0,0.16,0.08,0.2,0.62,0,0,0,0.16,0.04,0,0.71,0,0.41,0,0.12,0,2.01,0.41,0,0.12,0,0.08,0.12,0,0,0,0.04,0,0.2,0,0,0,0,0,0,0.08,0.08,0,0,0.04,0.012,0.274,0.012,0.031,0.056,0,1.83,23,1479,0 0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0.62,0,0,0,0.62,0,0,0,3.1,0,0,0,0,0.62,0,0,0,0,0,0.62,0,0,0.62,0,0,0,0,0,0.62,0,0,0,0,0.166,0,0.333,0,0,4.255,34,200,0 0,0.39,0.19,0,0.19,0.09,0,0,0,0,0,0.29,0,0,0.29,0,0,0.29,0.89,0,0.29,0,0,0,0.49,0.49,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,4.75,0,0.09,0,0.09,5.74,0,0,1.352,0.08,0,0.016,0,0,1.679,17,178,0 0,0.39,0.19,0,0.19,0.09,0,0,0,0,0,0.29,0,0,0.29,0,0,0.29,0.89,0,0.29,0,0,0,0.49,0.49,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,4.75,0,0.09,0,0.09,5.74,0,0,1.353,0.08,0,0.016,0,0,1.679,17,178,0 0,0.39,0.19,0,0.19,0.09,0,0,0,0,0,0.29,0,0,0.29,0,0,0.29,0.89,0,0.29,0,0,0,0.49,0.49,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,4.75,0,0.09,0,0.09,5.74,0,0,1.353,0.08,0,0.016,0,0,1.679,17,178,0 0,0,0.93,0,0.31,0,0,0,0.31,0,0.31,0.93,0,0,0,0,0.62,0,3.75,0,3.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0.054,0.108,0,0.054,0,0.054,2.735,14,145,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.729,0,0,1.875,4,15,0 0,0,0,0,0,0,0,0,0,0.84,0,0.84,0,0,0,0,0,0,4.2,0,0,0,0,0,1.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.119,0,0,0,1.454,9,32,0 0,0,0,0,0,0,0,0,0,0,0,4.58,0,0,0,0,0.91,0,0,0,0.91,0,0,0,1.83,0,0,0,0,0.91,0,0,0,0,0,0.91,0,0,0.91,0,0,0,0,0,0,0,0,0,0,0.124,0,0.249,0,0,2.576,14,67,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.333,3,12,0 0,0,0.86,0,0,0,0,0.86,0,0,0,1.73,0,0,0,0,0,0,0,0,0,0,0,0,3.47,5.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0.125,0,0,0,0,1.8,9,72,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.41,4.41,0,1.47,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.565,10,59,0 0.06,0,0.19,0,0.26,0.06,0,0.19,0,0.06,0,1.12,0.06,0.19,0,0,0.52,0,0,0.59,0.06,0,0.39,0,3.23,0,0,0,0,0,0,0,0.06,0,0,0.19,0.13,0,0,0,0,0,0,0.06,0,0,0,0,0.072,0.117,0,0,0.063,0,2.121,25,751,0 0,0,1.09,0,0,0,0,0,0,0,0,1.09,0,0,0,0,0,0,3.29,0,0,0,0,0,0,0,0,0,0,2.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.111,2,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.44,0,1.44,0,2.89,1.44,0,0,0,0,0.227,0,0,0,1.64,12,41,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.166,3,14,0 0,0,0,0.14,0.42,0,0,0.14,0,0,0,0.98,0,0.14,0,0,0.7,0,0,0,0,0,0,0,1.82,0.28,0,0,0.28,0.7,0,0,0,0,0,0.28,0.14,0,0,0,0,0,0,0,0.14,0,0,0,0,0.077,0,0,0,0,1.502,6,257,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6,24,0 0,0,0,0,0,0,0,0,0,0,0,4.25,0,0,0,0,0,0,6.38,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.133,3,17,0 0,0,0,0,0.68,0,0,0,0,0.68,0,0.68,0,0,0.68,0,0,0.68,1.36,0,0.68,0,0,0,2.72,1.36,1.36,0.68,0.68,0.68,0.68,0.68,0,0.68,0.68,0.68,0.68,0,0.68,0.68,0,0,0.68,0.68,0.68,0,0,0,0.104,0.418,0.104,0,0,0,2.102,12,82,0 0,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0,2.35,0.39,1.17,0.39,0.39,0.78,0.39,0.39,0,0.39,0.39,1.56,0.39,0,0,0.39,0,0.39,0.39,0,0.39,0,0,0.39,0,0.314,0,0.125,0,0,1.955,13,133,0 0,0,0,0.15,0.46,0,0,0.15,0,0,0,0.92,0,0.15,0,0,0.46,0,0,0,0,0,0,0,2.15,0.3,0,0,0.3,0.92,0,0,0,0,0,0.3,0.15,0,0,0,0,0,0,0,0.15,0,0,0,0,0.085,0,0,0,0,1.535,6,238,0 0,0,0,0,0.68,0,0,0,0,0.68,0,0,0,0,0.68,0,0,0,0.68,0,0,0,0,0,2.72,2.72,2.04,2.04,0.68,0.68,0.68,0.68,0,0.68,2.04,0.68,0.68,0,0.68,0.68,0,0,0.68,0.68,0.68,0,0,0,0,0.828,0.621,0,0,0,2.277,12,123,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,0.64,0,0,0,0,3.2,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0.213,0,0,0.106,0,0,2.714,47,95,0 0,0,0.2,0,0.2,0,0,0,0,0.8,0,1,0,0,0,0,0,0,0.2,0,0.2,0,0,0,1.4,1.6,0.2,0.2,0.2,0.2,0.2,0.2,0,0.2,0.4,0.2,1,0,0.2,0.2,0,0,0.2,0.8,0,0,0,0.2,0,0.429,0.03,0,0,0,2.703,50,346,0 0.87,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.493,0,0,0,0,1.344,4,39,0 0,1.12,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,2.24,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,1.12,0.361,0,0,0,0,0,2.875,16,115,0 0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,6.92,3.89,0,0,0,0,0,0,0,0,0,0,2.16,0,0,0,1.29,0,0,0.43,0,0,0,0,0.318,0.717,0.159,0.079,0,0,4.411,19,300,0 0.05,0,0.1,0,0.15,0.05,0,0,0.57,0,0,0.26,0,0,0,0,0,0.05,0.21,0,0.15,0,0,0,0.63,0.68,0.05,0,0,0,0,0,0.73,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0.05,0,0.193,0.17,0.059,0,0.014,0,3.461,66,1170,0 0.07,0,0.14,0,0.07,0,0,0,0.74,0,0,0.22,0,0.07,0,0,0,0.07,0.29,0,0.22,0,0,0,0.74,0.81,0.07,0,0,0,0,0,0.22,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0.116,0.2,0.042,0,0.021,0,2.79,36,681,0 0.12,0,0.6,0,0.6,0,0,0,0,0,0,0.12,0.12,0,0.12,0,0.73,0,0.6,0,0.48,0,0,0,1.58,0,0,0.24,0,0,0.48,0,0,0,0.36,0,0.12,0,0,0,0,1.33,0,0.12,0.12,0,0,0.12,0.016,0.148,0,0.033,0.016,0,2.056,65,364,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.369,0,0,0,0,1.25,2,5,0 0,0,0.42,0,0.42,0.14,0,0,0,0,0,1.56,0.14,0,0,0,0.28,0,0.14,0,0.14,0,0,0,3.12,0,0.14,0,1.27,0.42,0,0,0,0,0,0.56,0.28,0,0.14,0,0,0,0.14,0,0.14,0,0,0,0.058,0.019,0.019,0,0,0,2.345,17,333,0 0,0,2.04,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.08,0,0,0,0,0,0,0,0.722,0,0,0,0,1.1,2,11,0 0,0,0,0,0,0,0,0,0,0,0,1.13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.13,0,0,3.4,0,0,0,0,0,0,0.198,0.396,0,0,0,0,2.076,5,27,0 0,0,0,0,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,2.77,0,0,0,0,0,5.55,2.77,2.77,2.77,5.55,2.77,2.77,2.77,0,2.77,2.77,2.77,0,0,0,2.77,0,0,0,0,0,0,0,0,0,1.229,0,0,0,0,3.25,11,39,0 0,0,0,0,0,0,0,0,0,0,0,1.42,0,0,0,0,0,0,0,0,4.28,0,0,0,2.85,1.42,4.28,1.42,1.42,1.42,1.42,1.42,0,1.42,1.42,1.42,1.42,0,1.42,1.42,0,0,1.42,0,1.42,0,0,0,0,0.419,0,0,0,0,2.133,12,64,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0.185,0,0,0,0,1.904,11,80,0 0.2,0.06,0.2,0,0.4,0,0,0,0,0,0,0.95,0.27,0,0,0.06,0.06,0,0,0,0,0,0,0,3.47,0,0,0,0,0,0,0,0.06,0,0,0.34,0.06,0,0,0,0,0,0.13,0.06,0.06,0,0,0.13,0.028,0.093,0,0,0.018,0,2.423,26,693,0 0,0,0,0,0.38,0,0,0,0,0,0,2.28,0,0,0,0,0,0,0.76,0,0,0,0,0,1.14,0.76,0,0,0.38,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.352,3,46,0 0.06,0.04,0.29,0,0.08,0.06,0,0.13,0.02,0.11,0,0.47,0,0.02,0.02,0.13,0.13,0.08,0.24,0,0.17,0,0,0,0,0,0,0.02,0.02,0,0,0,1.7,0,0,0.22,0.83,0.02,0,0,0.06,0.04,0.02,0.06,0,0.29,0.02,0.15,0.032,0.176,0,0.003,0.003,0,2.201,79,2631,0 0,0,0,0,0,0,0,0,0,2.5,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.436,0,0,1.7,8,17,0 0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.769,0,0,1.428,4,10,0 0.2,0.06,0.2,0,0.4,0,0,0,0,0,0,0.95,0.27,0,0,0.06,0.06,0,0,0,0,0,0,0,3.47,0,0,0,0,0,0,0,0.06,0,0,0.34,0.06,0,0,0,0,0,0.13,0.06,0.06,0,0,0.13,0.027,0.09,0,0,0.018,0,2.423,26,693,0 0.79,0,0.79,0,0.79,0,0,0,0,0,0,0.79,0,0,0,0,0,0,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.147,1.166,4,42,0 0,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,1.19,0,2.38,0,0,0,0,0,0,0,1.19,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0.404,0,0,0,0,3.379,11,98,0 0,0,0.32,0,0.49,0.16,0,0,0,0,0,1.48,0,0,0,0,0.32,0,0.16,0,0.16,0,0,0,1.31,0,0,0,1.31,0.49,0,0,0,0,0,0.65,0.16,0,0,0,0,0,0,0,0.16,0,0,0,0,0.022,0,0,0,0,1.638,6,154,0 0,0,0,0,0.31,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0.63,0,0.63,0,0,0.63,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.045,0,0,0,0,1.813,11,107,0 0,0,0,0,0,0,0,0.67,0,0,0,1.35,0,0,0,0.67,0,0,4.05,0,2.02,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0.386,0,0,0,0,3.27,19,121,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.222,9,29,0 0,0,1.63,0,0,0,0,0,0,0,0,1.63,0,0,0,1.63,0,0,1.63,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.181,3,13,0 0,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.727,4,19,0 0,0,0,0,0.33,0,0,0,0,0.66,0,0,0,0,0,0,0,0,0.66,0,0.33,0,0,0,7.61,2.64,0,0,0,0,0,0,0.33,0,0,0,1.32,0,0,0,2.31,0.33,0,0.33,0,0,0,0,0.349,0.524,0.116,0,0,0,3.627,19,341,0 0,0,0,0,1.4,0,0,0,0,0,0,2.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.239,0,0,1.923,7,50,0 0,0,0,0,0,0,0,0,0,0.53,0,2.68,0,0,0,0,0,0,0.53,0,0,0,0,0,0.53,0.53,0,1.07,0,0,0,0,0,0,0,0.53,0,0,1.07,0,0,0,0,0,0,0,0,0,0,0.194,0,0,0,0,3.731,21,153,0 0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,10.71,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0.51,0,0,0,0,0,0,0,0.51,0,0,0,0,0,1.55,0,0,0,0,0,0,0,0.51,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.281,0,0,1.363,5,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0.925,0,0,1.833,6,11,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.52,4.76,4.76,4.76,4.76,4.76,4.76,4.76,0,4.76,4.76,4.76,0,0,0,4.76,0,0,0,0,0,0,0,0,0,1.257,0,0,0,0,3.333,11,30,0 0,0,0,0,0,0,0,0,0,0,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0.42,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.536,8,106,0 0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,1.6,4,8,0 0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.92,0,0,3.92,0,0,0,0,0,0,0,0,0,1.647,4,28,0 0,0,0,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0,0,1.06,0,0,0,0,0,3.19,1.06,0,0,0,0,0,0,0,0,0,0,1.06,0,2.12,0,0,0,0,0,0,0,0,0,0,0.168,0,0.168,0,0,1.75,7,63,0 0,0,0,0,0,0,0,0,0,0,0,3.19,0,0,0,0,1.06,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,0.143,0,0,0,0,2.714,13,76,0 0.64,0,0.64,0,0,0,0,0,0,0,0,0.64,0,0,0,0.64,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0.64,0,0.64,0,0,0,0,0.309,0.619,0,0,0,0,1.727,5,57,0 0,0,0.47,0,1.91,0,0,0,0,0,0,1.91,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.43,0,0,0.95,0,0,0,0,0,0,0,0,0,1.233,4,37,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,1.333,2,4,0 0,0,0,0,0.76,0.25,0,1.27,0,0,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.14,5,65,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,1.38,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,1.38,0,0,1.38,0,0,0,0,0,0,1.666,9,35,0 0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.349,0,0,1.47,4,25,0 0,0,0.59,0,0.29,0.59,0.59,0.29,0,0.29,0.29,0,0,0,0,0,0,0.89,3.58,0,1.49,0,0,0,0.29,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0.089,0,0,0.044,0.134,0,1.6,15,120,0 0,0,0,0,0,0,0,0,0,0.28,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,1.13,0.84,0,0.56,0,0.84,0,0,0,0,0.56,0,0.56,0,0,0,0,0,0,0,0,0,0,0.28,0,0.262,0,0,0,0,3.25,75,286,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0.523,0,0,2.571,10,36,0 0,0,0.08,0,0,0.25,0,0.25,0.08,0,0,1.17,0.08,0.25,0,0,0.42,0,0,0,0,0,0,0,3.11,0,0,0,0,0,0,0,0.16,0,0,0.25,0.25,0,0,0,0,0,0,0,0,0,0,0,0.034,0.08,0,0,0,0,2.023,27,694,0 0,0,1.36,0,0.68,0.68,0,0,0,0,0,0,0,0,0,0,0,0,2.05,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0.357,0,0,0,0,1.294,5,44,0 0,0,0,0,0,0.14,0,0,0,0.14,0,0,0,0,0,0,0,0.29,0.74,0,0.14,0,0,0,0.14,0.14,0.59,0,0,0,0,0,0.14,0,0,0,0.59,0,0,0,0,0,0,0,0.44,0,0,0,0,0.297,0,0,0,0,1.803,27,238,0 0.03,0.03,0,0,0,0,0,0,0.06,0.09,0.03,0.15,0,0,0,0,0.03,0.12,0.03,0,0,0,0,0,0.46,0.27,0,0,0.03,0.06,0,0,0,0,0,0.03,0.15,0,0,0,0.36,0,0.03,0,0.12,1.19,0,0,0.024,0.178,0,0.128,0,0,3.427,49,1827,0 0,0,0.27,0,0,0,0,0,0,0.83,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0.27,0,0.55,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0.082,0.164,0,0,0,0,2.235,51,199,0 0,0,2.27,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.27,0,0,0,0,0,0,0,0,0,0,0,0,0.296,0,0,5.25,32,63,0 0,0,0.7,0,0,0.7,0,0,0,0,0,0,0,0,0,0,0,0,3.54,0,0.7,0,0,0,0,0,1.41,0,0,0,0,0,0,0,0,0,0.7,0,0,0,0,0,0,0,0.7,0,0,0,0.126,0.252,0,0,0,0,1.375,5,55,0 0,0,0.64,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0.64,0,0,0,1.28,0.64,1.92,0.64,0.64,0.64,0.64,0.64,0,0.64,0.64,0.64,0.64,0,0.64,0.64,0,0,0.64,0,1.28,0,0,0,0,0.225,0,0.225,0,0,1.902,12,78,0 0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,6.34,0,0,0,0,0,0,0,0,0,0,1.259,3,34,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,2.98,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,1.49,1.49,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,1.49,0,0,0,0,0,0,0.209,0.209,0,0,0,3.5,17,49,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,0,0,0,0,0,0,0,0,1.123,0,0,1.3,4,13,0 0,0,0,0,0,0,0,0,0,0,0,2.18,0,0,0,0,0,0,1.45,0,0,0,0,0,2.18,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.45,0,0,0,0,0,0.122,0,0,0,0,1.785,18,75,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.187,5,35,0 0,0.99,0,0,0.49,0,0,0.49,0,0,0,0.49,0,0,0,0,0,1.98,2.97,0,1.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.081,0,0,1.348,4,58,0 0,0,0,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0.52,0,0,0.52,0,0,0,0.52,0.52,0.52,1.05,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0.52,0,0,0,0,0,0,0,0.166,0,0,0,0,3.888,55,140,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0,0,2.3,0.76,0,0,0,1.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.3,7,138,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0.2,0.2,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0.62,0.41,0,0,0,0,0,2.49,0.62,0,0,0,0,0,0,0,0,0,0.2,0.2,0,0,0,0,0,0,0,0,0,0,0.2,0,0.087,0,0,0,0,2.797,127,512,0 0.04,0.09,0.31,0,0.04,0.22,0.04,0,0,0.58,0.09,1.17,0,0,0,0.13,0.04,0,1.3,0,1.17,0,0.04,0,0.9,0.54,0,0.04,0,0.18,0.18,0,0,0,0.18,0.04,0.31,0,0.22,0,0.04,0,0,0,0.13,0.04,0,0.09,0.013,0.224,0,0.027,0.006,0,1.784,29,1192,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.99,0,0.99,0,0,0,0,0,1.98,0,0,0,0,0,0,0,0,0,0.99,0,0,0,0,0,0,0.99,0.99,0,0,0,0,0,0,0,0,0,1.478,5,34,0 0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0.7,0,1.4,0,1.4,0,0,0,0,0,0.7,0,0,0,0.7,0,0,0,1.4,0,0,0,0,2.11,0,0,0,0,0,0,0,0,0,0.267,0.066,0,0,0,17.952,200,377,0 0,0,0.59,0,0.59,0,0,0,0,0,0,2.38,0,0,0,0,0,1.19,0.59,0,0,0,0,0,1.78,1.19,0,0.59,0,0.59,0.59,0,0,0,0.59,0.59,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0.177,0,0.088,0,0,1.8,10,81,0 0,0.26,0.26,0,0.26,0,0,0.26,0,0,0.26,1.07,0,0,0,0,0.53,0,1.07,0,1.07,0,0,0,1.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.111,0,0,0.037,0,1.564,8,194,0 0,0,5.1,0,2.04,0,0,0,0,0,0,1.02,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.094,0,0,0,0,1.29,5,40,0 0.27,0,0.13,0,0.82,0,0,0,0,0,0,0.55,0.41,0,0,0,0,0,1.24,0,1.1,0,0,0,1.65,0.82,0.13,0.13,0.13,0.13,0.13,0.13,0,0.13,0.13,0.13,0.41,0,0,0.13,0,0.41,0.13,0,0.41,0,0,0.27,0.041,0.102,0.02,0.02,0,0,2.78,34,367,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,33.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,4.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.454,5,27,0 0,0,0,0,0,0,0,0.39,0,0,0,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0.39,0.39,0,0,0.39,0,0,0.39,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0.049,0,0,0,0,2,38,124,0 0,0,0,0,1.58,0.79,0,0,0,0,0,3.17,0,0,0,0,0,0.79,0,0,0,0,0,0,1.58,1.58,0,1.58,0,0,0.79,0,0,0,0.79,0,0,0,0,0,0,0,0,3.17,0,0,0,0,0,0.263,0,0,0,0,2.575,15,103,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0.68,0,0.68,0,0.136,0,0,0,0,4.341,46,178,0 0,0,0,0,3.27,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.91,0,0,0,0,0,0,0,0,0,0,0,0,1.545,5,17,0 0,0,0,0,0,0,0,0.4,0,0,0,0.81,0.4,0,0,0,0,0,1.22,0,0,0,0,0,2.86,2.45,0,0,0.4,0.4,0,0,0,0,0,0,0.4,0,0.4,0,0,0,0,0.4,0,0,0,2.45,0.126,0.063,0.063,0.063,0,0,1.611,12,116,0 0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,3.33,0,0,0,0,0,0,0,0,0,1.3,4,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,1.166,2,21,0 0,0,1.16,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,3.48,0,0,1.16,0,0,0,2.32,1.16,0,1.16,0,1.16,1.16,0,0,0,1.16,1.16,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0.139,0,0.139,0,0,1.515,10,50,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0.163,0,0,0,0,1.153,3,15,0 0,0.24,0,0,0.24,0,0,0.24,0,0.49,0,0,0,1.48,0,0,0,0,0.99,0,0,0,0,0,0.49,0,0.24,0,0,0,0.24,0,0,0,0.24,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,9.31,235,1108,0 0,0,0,0,0,0,0,0.5,0,0,0,1,0.5,0,0,0,0,0,1.5,0,0,0,0,0,1,0.5,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,2.5,0,0.075,0,0.075,0,0,1.483,6,89,0 0,0,0,0,2.08,0,0,0,0,0,0.83,0.83,0,0,0,0.83,0,1.66,2.91,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.131,0,0.329,0,0.065,2.962,11,157,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,1.12,0,1.12,0,0,0,1.12,1.12,0,2.24,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.326,0,0,0,0,4.689,55,136,0 0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0.32,1.28,0,0.32,0,0,0,4.48,3.52,0.96,0.96,0.64,0.32,0.32,0.32,0,0.32,0.64,0.32,0.32,0,0,0.32,0,0,0.32,0,0.96,0,0,0,0.264,0.211,0.105,0.052,0,0.105,2.258,15,192,0 0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0.5,1.5,0,0.5,0,0,0,2.01,1.5,1,1,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0.168,0.084,0.084,0,0.168,2.303,15,129,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.682,0,0,0,0,4.208,15,101,0 0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0,1.19,0,0,0,4.76,2.38,0,1.19,0,1.19,1.19,0,0,0,1.19,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0.286,0,0.286,0,0.143,2.724,13,79,0 0,0,0,0,0.73,0.24,0,0,0,0.24,0,0.49,0,0,0,0,0,0,2.46,0,0.49,0,0,0,1.23,0.73,1.47,0.49,0.49,0.49,0.49,0.49,0,0.49,0.49,0.49,0,0,0.49,0.49,0,0,0.73,0,0.73,0,0,0,0,0.287,0.041,0.041,0,0.041,1.792,12,224,0 0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,1.56,1.56,0,3.12,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0.484,0,0,0,0,3,11,81,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,4.76,0,0,0,0,0,0,1.8,5,9,0 0.01,0,0.03,0,0.33,0.03,0,0,0.23,0.01,0,0.09,0,0,0,0.13,0,0.01,0.07,0,0.05,0,0,0,0.53,0.55,0.01,0,0,0,0,0,0.47,0,0.01,0.01,0.45,0.01,0,0,0,0,0.01,0,0,0,0.05,0,0.2,0.127,0.064,0,0.005,0,2.589,38,2349,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,2,4,16,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.6,4,8,0 0.05,0,0.1,0,0.16,0.05,0,0,0.48,0,0,0.16,0,0,0,0,0,0.05,0.21,0,0.16,0,0,0,0.64,0.69,0.05,0,0,0,0,0,0.26,0,0,0.16,0.75,0,0,0,0,0,0,0,0.05,0,0,0,0.172,0.195,0.062,0,0.015,0,2.758,47,1073,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0,0,0,0,0,0,0,0,1.36,0,2.73,0,0,0,0,0,0,1.36,0,0,0,0,0,1.36,1.36,0,2.73,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,3.142,11,88,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.37,0,1.01,0,0,0,0.67,1.69,1.01,0.33,0,0.67,0,0,0,0,0.33,0,0.33,0,0,0,1.01,0,0.33,0,1.01,1.01,0,0,0,0.108,0,0,0,0,1.851,13,100,0 0,0,0.38,0,0.38,0,0,0,0,0,0,0.38,0.38,0,0,0,0,0,1.14,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0.414,0,0.276,0,0,1.104,2,53,0 0.26,0,0,0,0,0.26,0,0,0.26,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.35,0,0,0,0,0,0,0,0.26,0,0,0,0,0.52,0,0,0,0.033,0,0,0,0,2.921,61,111,0 0,0,3.44,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.609,0,0,0,0,1.5,4,15,0 0,0,0,0,0,0.13,0,0.27,0,0,0,0.54,0.13,0,0,0,0.68,0,0,0,0,0,0,0,1.9,0.13,0,0,0,0,0,0,0.13,0,0,0.54,0.27,0,0,0,0,0,0,0,0,0,0,0,0.161,0.143,0,0,0,0,2.296,21,473,0 0,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0,0.84,0,1.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0.84,0,0,0,0.84,0,0.84,0.84,0,0,0,0,0.137,0.413,0,0.137,3.052,13,116,0 0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0.42,0,0.42,2.12,0,0.42,0,0,0,1.7,0.42,0.85,0.85,0.42,1.7,0.42,0.85,0,0.85,0.42,0.42,0.85,0,0.85,0.42,0,0.42,0.85,0,0.85,0,0,0,0,0.403,0.134,0.134,0,0,2.202,17,163,0 0,0,0.26,0,0,0,0,0,0,1.05,0,1.31,0,0,0,0,0,0,0.26,0,0.26,0,0,0,0.26,1.05,0,0,0,0,0,0,0,0,0.26,0,1.05,0,0,0,0,0,0,1.05,0,0,0,0.26,0,0.439,0,0,0,0,2.724,50,237,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,1.333,3,8,0 0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.363,0,0,0,0,1,1,10,0 0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.542,0,0.217,0,0,1.34,14,67,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,4,10,0 1.17,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.607,8,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,1.16,0,0,0,0,0,0,1.16,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.408,0,0,0,0,2.125,17,51,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.38,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.8,17,34,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.125,2,9,0 0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.655,8,48,0 1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.27,0,0,1.63,0,0,0,0,0.571,0,0,0,0,1.181,3,13,0 0,0,0,0,1.13,0,0,0,0,1.13,0,0,0,0,0,0,0,1.13,2.27,0,3.4,0,0,0,0,0,1.13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.13,0,0,0,0,0,0,0,0,0,0,0,0,1,1,11,0 0,0,0.87,0,0,0,0,0,0,0.87,0,1.75,0,0,0,0,0,0,0,0,0,0,0,0,0.87,0.87,0,1.75,0,0,0,0,0,0,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0.608,0,0,0,0,2.941,11,100,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.26,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.735,0,0.735,0,0,2.571,10,18,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,0,0,0,0,0.465,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.555,3,14,0 0,0,1.33,0,0,0,0,0,0.66,0,0,0,0,0,0,0,0,0,0,0,1.33,0,0,0,2,1.33,2,0.66,0.66,0.66,0.66,0.66,0,0.66,0.66,0.66,0,0,0.66,0.66,0,0,0.66,0,0.66,0,0,0,0.3,0.2,0.1,0,0,0,1.979,12,97,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0.175,0,0,0,0,0,1.873,8,118,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0.202,0,0,0,0,2,11,82,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,2.17,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,8,0 0,0,3.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.222,3,11,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.25,6,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.666,6,8,0 0,0,0,0,1.31,0.65,0,0,0,0.65,0,1.31,0,0,0,0,0,0,3.28,0,0,0,0,0,1.31,0.65,1.97,0.65,0.65,0.65,0.65,0.65,0,0.65,0.65,0.65,0,0,0,0.65,0,0,0.65,0,0.65,0,0,0,0,0.35,0,0.116,0,0,2,12,88,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,0,0,0,0,0,0,0,0.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.83,0,0,0,0,0,0.83,0,0,0,0,0,0,0,0,0,0,0,0.83,0,0,0,0,0.131,0.262,0,0,0,4.128,28,161,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,6,0 0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.464,7,41,0 0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,1.61,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0.253,1.518,0,0.506,0,0,2.047,6,43,0 0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.275,8,91,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0.272,0,0,0,4.382,28,298,0 0,0,0,0,0,0.23,0,0,0,0.23,0,1.18,0,0,0,0,0.23,0,1.18,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0,0.069,0,2.216,44,215,0 0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,3.37,0,0.67,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,0,0,0,1.87,7,58,0 0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0.86,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,1.564,7,61,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.333,5,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.333,5,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.333,5,7,0 0,0,0,0,0,0,0,0,0,0.87,0,2.63,0,0,0,0,0,0,0.87,0,0,0,0,0,0.87,0.87,0,1.75,0,0,0,0,0,0,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0.308,0,0,0,0,3.029,11,103,0 0,0,0.2,0,0,0.1,0,0.51,0,0.1,0,1.33,0.1,0.2,0,0,0.82,0,0,0,0,0,0,0,2.97,0,0,0,0,0,0,0,0.1,0,0,0.2,0.1,0,0,0,0,0,0,0,0,0,0,0,0.08,0.16,0,0,0.053,0,2.224,19,574,0 0,0,0.87,0.87,0.87,0.43,0,0,0,0,0,0,0,0,0,0,0,0.43,0.43,0,0,0,0,0,1.74,1.74,0,1.74,0,0,0,0,0,0,0.43,0,1.31,0,0.43,0,0,0,0.43,0,0.43,0,0,0,0,0.298,0.059,0.059,0,0,2.554,15,212,0 0,0,1.58,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.482,7,43,0 0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0.36,0,0.36,0,0,0,0,0.36,0,0,0,0,0,0,0,0,1.646,12,107,0 0.39,0,0.39,0,0.59,0,0,0,0,0,0,0.19,0,0,0,0,0,0.19,0.59,0,0.19,0,0,0,1.39,0,0,0.39,0,0,0,0,0.59,0,0.39,0,0.19,0,0,0,0,0,0,0,0.39,0.19,0,0,0,0.191,0,0,0,0,2.566,34,349,0 0,0,0,0,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,2.02,0,1.01,0,0,0,2.02,1.01,3.03,1.01,1.01,1.01,1.01,1.01,0,1.01,1.01,1.01,0,0,0,1.01,0,0,0,0,1.01,0,0,0,0,0.476,0,0,0,0,1.875,11,45,0 0,0,0,0,0,0,0,0,0,0,0,0.28,0.28,0,0,0,0,0,0.57,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0.28,0,0,0,0.28,0,0,0,0.216,0,0.043,0,0,1.3,7,52,0 0,0,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.072,0,0,0,0,1.486,10,55,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,4.83,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,1.705,7,29,0 1.16,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,1.16,1.16,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.176,0.176,0,0,0,0,1.476,7,31,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0.344,0,0,3.25,17,52,0 2.27,0,2.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.401,0,0,0,0,1,1,5,0 0,0,0.55,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0.55,3.31,0,0,0,0,0,2.2,1.65,0.55,0,0,0,0,0,0.55,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0.55,0,0.27,0.18,0,0,0,3.596,34,187,0 0,0.77,0.77,0,0.77,0.38,0,0,0,0,0,1.16,0,0,1.16,0,0,0.38,3.48,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0.067,0,0,0,0,1.225,5,49,0 0.1,0.05,0.1,0,0.31,0.1,0,0,0,0.05,0,0.31,0.05,0,0,0.1,0.1,0,0.84,0.05,0.63,0,0,0.05,1.47,0.36,0,0.05,0,0.21,0.1,0,0,0,0.1,0.15,0.21,0,0.36,0,0,0,0,0,0.1,0,0,0.15,0.007,0.168,0,0.038,0.061,0.007,1.704,25,939,0 0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,1.142,2,8,0 0,0,1.58,0,0,0,0,0,0,1.58,0,0,0,1.58,0,0,0,0,3.17,0,1.58,0,0,0,1.58,0,3.17,0,1.58,1.58,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,0,1.4,5,35,0 0,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,0,0,1.31,0,1.31,0,0,0,1.31,0,2.63,0,1.31,1.31,0,0,0,0,0,1.31,1.31,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,0,0,0,1.75,15,42,0 0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0.7,0,1.4,0,1.4,0,0,0,0,0,0.7,0,0,0,0.7,0,0,0,0,0,0,0,0,2.11,0,0,0,0,0,0,0,0,0,0.266,0.066,0,0,0,18,200,378,0 0,0,0.65,0,0,0,0,0,0,0,0,2.61,0,0,0,0,0,0,0.65,0,0.65,0,0,0,1.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0.109,0,0,0,0,0,1.411,4,48,0 0,0,0,0,2.17,0,0,0,0,0,0,0,0,4.34,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.367,0,0,0,0,1,1,8,0 0,0,0,0,0,0.49,0,0,0.99,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0.99,0,0,0,0.49,0,2.48,0,0.49,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0.062,0,0,0,0,2.824,29,161,0 0,0,0.53,0,0.53,0,0,0.53,0,1.07,1.07,0,0,0,0,1.07,0,0,3.76,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0.294,0,0.367,0,0,2.161,21,67,0 0,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0,0.71,0,0,5,0,0,0,0,0,0,0,1.42,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0,0.71,0,0,0.71,0,0,0,0,0.121,0,0,0,0,1.387,5,43,0 0,0,0,0,0,0,0,0,0,0,0,0.96,0,0,0,1.92,0,0,3.84,0,0,0,0,0,0,0,2.88,0,0,0,0,0,0,0,0,0,0.96,0,0,0,0,0,0,0,0.96,0,0,0,0.343,0,0,0.171,0,0,1.291,5,31,0 0,0.56,0,0,0.56,0,0,0,0,0,0,1.7,0,0,0,0,1.7,0,1.13,0,0,0,0,0,0,0,0,0,1.13,0.56,0,0,0,0,0,0,0,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.028,13,71,0 0.19,0,0,0,0,0,0,0,0,0,0,0.96,0.38,0,0,0,0.58,0,0,0,0,0,0.38,0,3.48,0,0,0,0,0,0,0,0,0,0,0.19,0.19,0.19,0,0,0,0,0,0,0,0,0,0,0.027,0.108,0,0,0.108,0,2.634,23,303,0 0,0,0,0,0,0,0,0,0,0,0,3.17,0,0,0,0,0,0,0,0,0,0,0,0,1.58,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.216,0,0,0,0,1.92,6,48,0 0,0,0,0,0,0,0,0,0,0.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.227,0.227,0,0,0,4.043,28,186,0 0,0,0,0,0,0,0,0,0,0,0,5.88,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.211,9,115,0 0,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,3.03,0,3.03,0,0,0,0,0,0,0,0,0,0,1.51,0,0,0,0,0,0,0,0,0,0,0.428,0,0,0,0,2.321,22,65,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,1.428,4,10,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.69,4.34,4.34,4.34,4.34,4.34,4.34,4.34,0,4.34,4.34,4.34,0,0,0,4.34,0,0,0,0,0,0,0,0,0,1.162,0,0,0,0,3.333,11,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0.416,0.416,0,0,0,0,1,1,9,0 0,0,1.58,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,2.11,0,0,0,0,1.58,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0.209,0,0,0,0,1.78,7,73,0 0.16,0,0,0,0.66,0,0,0,0,0,0,0.66,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0.16,0,0.16,0.33,0,0,0,0,0.118,0.047,0.023,0,0,0,1.983,19,240,0 0.12,0.12,0.12,0,0.12,0.12,0,0.37,0.12,0,0.12,0.74,0,0,0,0,0.24,0.12,0,0.24,0,0,0,0,0.49,0,0,0.12,0.12,0,0,0,0,0,0,0.98,0.24,0,0.12,0,0,0.49,0,0,0,0.74,0,0,0.017,0.089,0,0,0.017,0,1.403,18,456,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.77,0,0,0,0.442,0,0,0,0,0,1.363,3,15,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,2.32,0,0,0,0,0.409,0,0,0,0,1,1,10,0 0.24,0,0.12,0,0,0,0,0.36,0,0.12,0,1.09,0.12,0,0,0,0.6,0,0,0,0,0,0.12,0.12,3.63,0,0,0,0,0,0,0,0,0,0,0.12,0.12,0,0,0,0,0,0,0,0,0,0,0,0.016,0.05,0,0,0.016,0,2.309,25,425,0 0,0,0,0,0,0,0,0,0.66,0,0.66,0,0,0,0,1.98,0,1.98,1.98,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,1.382,5,47,0 0,0,0,0,0.27,0,0,0.27,0,0,0,0.27,1.91,0,0.27,0.27,0,0.54,0,0,0,0,0,0,0.27,0.27,0,0.54,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0.039,0.117,0,0,0,0,2.52,55,189,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.186,0.186,0,0,0,3.862,28,112,0 0,0,0,0,0,0,0,0,0,0,0.56,0.56,1.12,0,0,0,0,0,2.82,0,0,0,0,0,1.12,0.56,0,0,0,0.56,0.56,0,0,0,0,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0.183,0.367,0,0,0,0,1.583,7,57,0 0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.4,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.393,9,46,0 0,0,0,0,0,0,0,0,0,0.29,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,1.18,0.59,0,0.59,0,0.88,0,0,0,0,0.59,0,0.59,0,0,0,0,0,0,0,0,0,0,0.29,0,0.273,0,0,0,0,3.317,75,282,0 0,0,0,0,0,0,0,0,0,0.78,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0.78,0.78,0,1.56,0,0,0,0,0,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0.278,0,0,0,0,2.472,11,89,0 0,0,0,0,0,0.76,0,0,0,0,0,0.76,0,0.76,0,0,0,0,1.53,0,0.76,0,0,0,0,0,0.76,0,0,0,0,0,0.76,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0,0.133,0.133,0,0,0,0,1.269,4,33,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0.704,0,0,0,0,1.428,4,10,0 0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,1.73,0,0.86,0,0,0,6.08,3.47,0.86,0.86,0.86,0.86,0.86,0.86,0,0.86,0.86,0.86,0.86,0,0,0.86,0,0,0.86,0,0.86,0,0,0,0,0.267,0.133,0.133,0,0,2.607,13,73,0 0,0,0,0,2.85,0,0,0,0,0,0,0,0,2.85,0,0,0,2.85,0,0,2.85,0,0,0,0,0,2.85,0,0,0,0,0,2.85,0,0,2.85,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,3.8,29,38,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0.925,0,0,1.3,4,13,0 0,0,0,0,0.82,0,0,0,0,0.82,0,0,0,0,0,0,0,0,1.65,0,0.82,0,0,0,0,0,0.82,0,0.82,0.82,0,0,3.3,0,0,0,0,0,0,0,0,0.82,0,0,1.65,0,0,0,0,0.301,0.15,0,0,0,1.678,5,47,0 0.07,0,0.31,0,0,0,0,0,0.71,0,0,0.31,0,0,0,0,0,0,0.31,0,0.15,0,0,0,0.55,0.63,0.07,0,0,0,0,0,0.79,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0.096,0.234,0.064,0,0.021,0,3.617,42,890,0 0.05,0,0.11,0,0.05,0.02,0,0,0.35,0,0,0.14,0,0,0.02,0,0,0.02,0.11,0,0.08,0,0,0,0.5,0.53,0.02,0,0,0,0,0,0.14,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0.02,0.203,0.182,0.049,0,0.008,0,2.95,52,1617,0 0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0,2.18,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,0,0,0,0,0.24,0,0,0,0.067,0.067,0,0,0,0,1.98,59,204,0 0.03,0,0.03,0.13,0.06,0.03,0,0,0.32,0,0,0.09,0,0,0,0,0,0.03,0.13,0,0.09,0,0,0,1.4,1.44,0.03,0,0,0,0,0,0.09,0,0,0.03,1.27,0.03,0,0,0,0,0,0.06,0,0,0,0,0.226,0.235,0.181,0,0.009,0,2.754,34,2688,0 0.06,0,0.06,0,0,0.06,0,0,0.54,0,0,0.18,0,0.12,0,0,0,0.06,0.24,0,0.18,0,0,0,1.14,1.2,0.06,0,0,0,0,0,0.18,0,0,0,0.9,0,0,0,0,0,0,0,0.12,0,0,0,0.115,0.221,0.115,0,0.017,0,3.015,38,1345,0 0,0,0,0,0,0,0,0,0,1.05,0,1.05,0,0,0,1.05,0,0,1.05,0,0,0,0,0,0,1.05,3.15,0,0,0,0,0,0,0,0,0,1.05,0,1.05,0,0,0,1.05,2.1,1.05,0,0,0,0,0.677,0,0.338,0,0,1.468,12,47,0 0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,1.61,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.222,0,0,0,0,1.958,6,47,0 0,0,0.26,0,0,0,0,0,0,0,0,0.26,0.53,0,0,0,0.53,0,0.53,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0.043,0,0,0.043,0,1.487,4,61,0 0.01,0.03,0.2,0,0.09,0.03,0,0,0.05,0.05,0,0.47,0.03,0.17,0,0.09,0.18,0.13,0.35,0.03,0.15,0,0,0.03,0,0,0.05,0.03,0.01,0,0,0,1.47,0,0,0.11,0.9,0,0,0.03,0,0.07,0,0.13,0.05,0.18,0,0.15,0.038,0.263,0.005,0.016,0,0.005,2.23,102,3168,0 0,0,0,0,0,0,0,0,0,1.01,0,0,0,0,0,0,1.01,0,0,0,0,0,0,0,2.02,1.01,2.02,1.01,1.01,1.01,1.01,1.01,0,1.01,1.01,1.01,1.01,0,1.01,1.01,0,0,1.01,4.04,1.01,0,0,0,0,0.814,0,0.162,0,0,2.125,12,68,0 0,0,0,0,0,0,0,0,0,3.33,0,3.33,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.156,0,0,2.333,10,21,0 0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0,1.07,2.15,0,0,0,0,0,2.15,3.22,0,2.15,0,0,0,0,0,0,2.15,0,0,0,0,0,0,2.15,0,0,0,0,0,0,0,0,0,0,0,0,1.718,11,55,0 0,0,1.47,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,1.47,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.928,16,41,0 0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.595,0,0,1.5,4,15,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.763,0,0,2.222,8,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.44,0,0,0,0,0,0,0,0,0,0,0,0,1.764,6,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0.86,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0.86,0.86,0,2.58,0,0,0,1.72,0.86,0,0.86,0,0.86,0.86,0,0,0,0.86,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0.11,0,0,1.812,10,58,0 0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,2.99,1.87,0,1.87,0,0.74,0.74,0,0,0,1.49,0.74,0.37,0,0,0,0,0,0.37,0,0,0,0,0,0,0.131,0.043,0.043,0,0,2.468,15,195,0 0,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,0,0,0,4.08,2.04,0,2.04,0,2.04,2.04,0,0,0,2.04,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0.189,0,0,0,0,1.681,10,37,0 0,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,1.23,0,0,1.23,0,0,0,0,0,0,1.23,2.46,0,0,0,0,0,0,0,0,0,1.23,0,1.23,0,0,0,1.23,2.46,1.23,0,0,0,0,0.77,0,0.192,0,0,1.535,12,43,0 0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.476,0,0,0,0,1,1,7,0 0,0.66,0,0,0,0,0.66,0,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,6,1.33,0,0,2,0,0,0,0,0.66,0,0.66,0,0,0,2,0,0,0,0,0,0,0,0,0.228,0,0,0,0,2.673,18,139,0 0,0,0,0,0,0,0,0,0,0,0,0.81,1.62,0,0,0,0,0,1.62,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,1.125,3,18,0 0,0,0.68,0,0,0,0,0,0,0.68,0,2.06,0,0,0,0,0,0,0.68,0,0,0,0,0,1.37,0.68,0,1.37,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0.114,0.342,0,0,0,0,2.727,11,90,0 0,0,0,0,0,0,0,0,0,0,0,1.15,0,0,0,0,0,0,2.89,0,0.57,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0,0,0,0,0,0,1.379,5,40,0 0,0,1.03,0,2.06,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,1.03,0,0,0,0.373,0.186,0,0,0,0,1.529,5,26,0 0,1.25,0,0,0,0,0,0,0.62,1.25,0,0,0,0,0,0,0,0,1.87,0,1.25,0,0,0,1.87,1.87,1.25,1.87,0.62,1.87,0.62,0.62,0,0.62,1.87,0.62,1.87,0,0.62,0.62,0,0,0.62,0,1.87,0,0,0,0,0.475,0.57,0,0,0,2.238,12,141,0 0,0,0.94,0,0,0,0,0,0,0,0,0.94,0,0,0,0,0,0,1.89,0,0.94,0,0,0,1.42,0.94,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.24,13,112,0 0,0,0.18,0,0.09,0,0,0,0,0,0.27,2.31,0.27,0,0,0,0.27,0,0,0,0.18,0,0,0,3.06,0,0,0,0,0,0,0,0.27,0,0,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0.143,0.117,0,0,0.039,0,2.313,24,590,0 0,0,0,0,0,0,0,0,0,0,0,1.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.342,8,89,0 0.84,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,1.69,0,0,0,1.69,0,0.84,0,0,0,0,0.136,0,0,0,0,1.619,12,68,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.25,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,1.11,0,0,0.55,0,3.91,0,0,0,0,0,0.55,0,0,1.67,0,2.23,0,0,0,0.55,0.55,0,0,0,2.79,0,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.085,0,0,1.142,5,48,0 0,0,1.04,0,1.04,0,0,0,0,0,0,1.04,0,0,0,0,0,0,0,0,0,0,0,0,2.08,2.08,0,2.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.313,0,0,0,0,2.108,22,78,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,4.54,0,4.54,0,0,0,0,0,0,0,0,0,0.675,1.351,0,0,0,0,3.7,26,37,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.523,0,0,2.272,9,25,0 0,0,0.5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1.25,0,0,0,0.5,0.25,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0.083,0,0,0,0.041,1.732,11,227,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0.06,0.03,0.46,0,0.13,0.06,0,0.03,0.03,0.16,0.19,0.59,0.06,0.03,0,0.19,0,0,1.23,0.19,1.06,0,0,0,1.53,0.23,0,0.06,0,0.06,0.36,0,0,0,0.13,0.09,0.13,0.16,0.19,0,0,0,0,0.06,0.03,0,0,0.13,0.024,0.231,0,0.019,0.009,0.004,1.885,25,1738,0 0,0,0.28,0,0.28,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0.18,0,0,0,0,1.523,11,160,0 0.52,0,1.05,0,0.52,0,0,0,0,0,0,3.17,0,0,0,0,0,0,0.52,0,0,0,0,0,2.64,2.64,0.52,0,0,0,0,0,0,0,0,0.52,1.05,0,2.64,0,0,0.52,0.52,1.58,0.52,0,0,0.52,0.084,0.169,0.084,0,0,0,1.577,12,112,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.12,0,0,0,0,0,6.25,3.12,3.12,3.12,3.12,3.12,3.12,3.12,0,3.12,3.12,3.12,0,0,0,3.12,0,0,0,0,0,0,0,0,0,0.913,0,0,0,0,3.454,11,38,0 0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0.58,0.58,0,0,0,0,0,0,0,0,0,1.16,1.74,0,0.58,0,0,0,0.58,0,0,0,0,1.74,0,0,0.118,0,0,0,6.428,98,315,0 0.16,0,0.67,0,0.33,0.16,0.33,0.83,0.33,0.67,0.33,1.51,0,0,0,0,1.67,0.33,2.01,1.67,3.85,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0.205,0,1.027,0.051,0,4.984,70,638,0 0,1.93,0.77,0,0.77,0,0,0.38,0,0,0.38,0,1.54,0,0,0.38,0.38,1.15,4.24,0,6.17,0,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.109,0,0,1.043,4,95,0 0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,2.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.01,0,0,0,1.01,0,1.01,0,0,1.01,0,0,0.271,0.09,0.181,0.181,0,0,2,12,122,0 0,0,0.29,0,0.29,0,0,0,0,0,0,2.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0.187,0,0,0,0,1.484,11,147,0 0,0,0.13,0,0,0,0,0,0.13,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0.13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.072,0,0.024,0,0,1.666,8,190,0 0,0,0,0,0,0,0,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.94,0,0,0,0.94,0.94,0,0,0,0.203,0,0,0,0,1.416,6,34,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,1.4,1.4,0,0,0,0.133,0,0,0,0,1.5,6,30,0 0,0,0.88,0,0.88,0,0,0,0,0,0,4.42,0,0,0,0,0,0,0.88,0,0,0,0,0,1.76,1.76,0.88,0,0,0,0,0,0,0,0,0.88,0.88,0,3.53,0,0,0.88,0,1.76,0,0,0,0.88,0.139,0.279,0,0,0,0,1.326,6,61,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,8,0 0,0,0.28,0,0,0,0,0.28,0.28,0.57,0,0.57,0,0,0,0,0,0,0,0,0.57,0,0,0,5.2,6.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0.28,0,0,0.04,0,0,0,0,1.883,9,211,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0 0,0,0,0,0.9,0,0,0,0,0,0,2.7,0,0,0,0,0,0,4.5,0,0,0,0,0,0.9,0,0.9,0,1.8,0,0,0,0,0,0,0,0,0,0,0,0,1.8,0,0,0,0,0,0,0,0,0,0,0,0,1.45,4,29,0 0,0,0.52,0,0.13,0,0,0,0,0,0,2.22,0.65,0.13,0,0,0.13,0,0.13,0,0.13,0,0.13,0,2.09,0,0,0,0,0,0,0,0.78,0,0,0.26,0.26,0,0,0,0,0,0,0,0,0,0,0,0.018,0.073,0,0,0,0,2.556,23,317,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.12,0,0,0,0,0,0,0,0,2.56,2.56,0,0,0,0,0,0,0,2.56,0,0,2.56,0,0,0,0.375,0,0,2.25,7,36,0 0,0,0,0,0.3,0,0,0,0,0,0,0.3,0.91,0,0,0,0.6,0,1.21,0,0.3,0,0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0.3,0,0.3,0,0,0.6,0.3,0,0.6,0,0,0,0,0.042,0.042,0.042,0.042,0,1.183,13,168,0 0.43,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,4.34,0,0.86,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.374,0,0,0.124,0,1.974,18,77,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.336,0,0,0,0,3.38,7,71,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,3.52,0,0,0,0,0,0,0,0,2.35,0,0,0,0,1.17,0,0,0,1.17,0,0,0,0,0,0,0,0,1.17,0,0,2.35,0,0,0,0,0,0,0,0,0,0,0,0,2,12,54,0 0.18,0,0.18,0,0,0,0,0,0.94,0,0,0.37,0,0,0,0.09,0,0,0.37,0,0.18,0,0,0,0.56,0.66,0.09,0,0,0,0,0,0.37,0,0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0.18,0.167,0.051,0,0.025,0,4.434,87,909,0 0.11,0,0.22,0,0,0,0,0,1.02,0,0,0.34,0,0,0,0,0,0,0.45,0,0.22,0,0,0,0.56,0.68,0.11,0,0,0,0,0,0.34,0,0,0.11,0.22,0,0,0,0,0,0,0,0,0.11,0,0,0.076,0.198,0.03,0,0.03,0,4.211,81,678,0 0.06,0,0.06,0,0.19,0,0,0,0.73,0,0,0.19,0,0,0,0,0,0.06,0.26,0,0.19,0,0,0,0.79,0.86,0.06,0,0,0.06,0,0,1.06,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0.06,0,0.363,0.143,0.057,0,0.019,0,2.716,37,880,0 0.05,0,0.45,0,0.15,0.1,0,0,0.55,0,0,0.15,0,0,0,0,0,0.05,0.2,0,0.15,0,0,0,0.65,0.7,0.05,0,0,0,0,0,1.16,0,0,0,0.81,0.05,0,0,0,0,0,0,0,0,0,0,0.203,0.195,0.05,0,0.014,0,2.88,45,1080,0 0,0,0,0,0,0,0,0,0,1.21,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,1.21,1.21,0,2.43,0,0,0,0,0,0,0,1.21,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0.441,0,0,0,0,3.193,11,99,0 0,0,0,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,7,12,0 0.1,0.1,0.1,0,0.21,0.1,0,0,0.1,0.31,0,0.84,0.21,0,0,0.1,0,0.21,1.78,0,0.63,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,0,0.035,0.177,0.035,0.07,0.053,0,1.744,29,417,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.88,0,0.88,0,0,0,0,0,1.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.88,0,0,0,0,0.139,0.139,0,0,0,1.763,9,67,0 0,0,0,0,0.37,0,0,0,0,0,0,0.37,0.37,0,0,0,0.75,0,1.12,0,0.37,0,0,0,0,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.75,0,0,0.37,0,0,0,0,0.054,0,0,0.054,0,1.066,4,128,0 0.1,0,0,0,0,0.1,0,0,0,0,0,1.66,0.1,0.31,0,0,0.41,0,0,0,0,0,0,0,2.07,0,0,0,0,0.1,0,0,0,0,0,0.1,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0.117,0,0,0.043,0,2.272,24,525,0 0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0.595,0,0,0,0,1.25,2,10,0 0,0,0.24,0,0,0,0,0,0,0.48,0,0.24,0,0,0,0,0.48,0.24,0.72,0.48,0.72,0,0,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.034,0,0,0,0,3.202,87,285,0 0.29,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0.29,0.29,0,2.38,0,0.29,0,0,0,1.19,0.59,2.38,0.29,0.29,0.29,0.29,0.29,0,0.29,0.29,0.29,0.89,0,0.89,0.29,0.29,0,0.89,0,0.59,0.29,0,0,0,0.196,0.049,0.344,0,0.049,1.843,17,212,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,2,10,0 0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.04,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.896,2.142,8,60,0 0,0,0.42,0,0,0,0.21,0,0,0.21,0,0.42,0,0,0,0,0,0.21,1.49,0,0.42,0,0,0,0.21,0.21,0,0,0,0,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.034,0.139,0.034,0,0.069,0,3.151,37,312,0 0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.04,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.896,2.142,8,60,0 0,0,0,0,0.15,0,0,0.07,0.07,0.07,0,0.83,0.15,0,0,0,0.15,0,0,0,0,0,0.07,0,4.42,0,0,0,0,0,0,0,0.07,0,0,0.22,0.07,0,0,0,0,0,0,0,0,0,0,0.07,0.068,0.049,0,0,0.009,0,2.356,27,707,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.735,0,0,0,0,3,7,48,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0,0,0,0.054,0.353,0,0,0,4.438,28,1589,0 0,0,0,0,0,0,0,0,0,18.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,39,40,0 0.08,0,0.16,0,0.58,0.08,0,0,0.08,0,0,2.24,0.08,0.16,0,0,0.08,0,0.99,0,0.74,0,0.08,0,0.74,0.66,0,0,0.82,0.99,0,0,0,0,0,0.08,0.08,0,0.16,0,0,0.24,0,0,0.08,0,0,0.08,0.08,0.011,0,0,0,0,2.1,60,500,0 0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,3.12,0,0,0,0,0,3.12,1.56,1.56,1.56,1.56,1.56,1.56,1.56,0,1.56,1.56,1.56,0,0,0,1.56,0,0,0,1.56,0,0,0,0,0,0.53,0,0,0,0,2.533,11,38,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.555,0,0,0,0,1.647,4,28,0 0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0.28,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0.28,0.28,0.28,0,0.28,0,0.043,0,0,0,0,1.641,8,110,0 0,0,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.7,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.196,0,0,0,0,1.294,3,22,0 0,0,0.21,0,0,0.21,0,0,0,0.21,0.21,1.28,0,0,0,0,0.21,0,1.28,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.097,0,2.451,55,255,0 0,0,0,0,1.16,0,0,0,0,1.16,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,1.16,1.16,0,2.32,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0,3.379,11,98,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.25,0,0,0,0,0,0,0,0,0,0,2.714,10,38,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0.925,5.857,16,41,0 0.86,0,0,0,0,0,0,0,0,0,0,2.6,0,0,0,0,0,0,2.6,0,0,0,0,1.73,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0.86,0.561,0.14,0,0,0,0,1.352,6,23,0 0,0,0.24,0,0,0,0,0,0,0.48,0,0.24,0,0,0,0,0.48,0.24,0.72,0.48,0.72,0,0,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.035,0,0,0,0,3.179,87,283,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0.62,0,0.31,0,0,0,0,0.31,1.24,0,0,0,0,0.31,0,0,0.31,0,0,0,0,0,0,0,0,0,0,2.607,11,219,0 0,0,0,0,1.19,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,3.57,3.57,0,0,3.57,0,0,0,0,0,0,0,1.19,0,0,0,0,3.57,0,0,1.19,0,0,0,0,0,0,0,0,0,1.733,14,52,0 0,0,0.71,0,0,0,0,0,0,0,0,1.43,0,0,0,0,0,1.43,0.71,0,2.87,0,0,0,2.15,0.71,1.43,0,0,1.43,0,0,0,0,2.15,0,0,0,0,0,0.71,0,0,0,0,0,0,0,0.08,0.322,0,0,0,0,3.9,27,156,0 0,0,1.31,0,0,0,0,0,0,0,0,2.63,0,0,0,0,0,1.31,0,0,0,0,0,0,2.63,1.31,0,1.31,0,1.31,1.31,0,0,0,1.31,1.31,3.94,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0.15,0,0,1.906,10,61,0 0.1,0,0.21,0,0.31,0,0,0.1,0,0,0,0.63,0.21,0,0,0,0.53,0,0,0,0,0,0,0,3.82,0,0.1,0.1,0,0,0,0,0.42,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.215,0.043,0,0,0,0,2.221,18,511,0 0,0,0,0,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,2.63,0,2.63,0,0,0,5.26,2.63,2.63,2.63,2.63,2.63,2.63,2.63,0,2.63,2.63,2.63,0,0,0,2.63,0,0,0,2.63,0,0,0,0,0,0.793,0,0,0,0,3.076,11,40,0 0,0,0,0,5.55,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,1.222,3,11,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.63,0,0,0,0,0,0.24,0,0,0,0,2,7,48,0 0,0,0,0,0,0,0.62,0,0,0,0,0,0,0,0,0,0,0.62,1.25,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0.049,0,0.247,0.049,0,3.732,90,321,0 0,0,1.78,0,0.25,0.51,0,0,0,0.25,0,0.76,0.25,0,0,0,0,0,1.27,0,0.76,0,0,0,1.27,1.02,0,0.25,0.51,0.51,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0.76,0.186,0.26,0,0,0,0.037,1.794,10,183,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.77,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.77,0,1.55,0,0,0,0,0.247,0.247,0,0,0,0,1.611,12,29,0 0,0,0.25,0,0,0,0,0,0,0,0,0.25,0.25,0,0,0,0,0,4.02,0,4.02,0,0,0,0.75,0.75,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0.111,0,0,0,0,4.446,29,209,0 0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,1.61,0,0,0,1.61,1.61,0,0,0,0,0,0,0,0,0,0,0,4.83,0,0,0,0,0,0,0,0.283,0,0,0,0,1.666,4,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.86,0,0,0,0,0,3.73,0,1.86,0,0.93,3.73,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0.173,0,0,0,0,1.9,5,38,0 0,0,1.96,0,0,0,0,0,0,0,0,0,0,1.96,0,0,0,0,1.96,0,1.96,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.142,11,30,0 0,0,0.59,0,0,0,0,0.59,0,0,0,2.99,0,0,0,0,0,0,1.19,0,0,0,0,0,1.19,0.59,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.391,0,0,0,0,1.836,7,90,0 0,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0,0,2.53,1.26,0,1.26,0,1.26,1.26,0,0,0,1.26,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0.141,0,0,0,0,2.28,10,57,0 0.1,0.05,0.35,0,0.15,0,0.05,0.05,0.05,0.2,0.15,0.61,0,0,0,0.1,0.05,0,0.71,0.05,0.46,0,0.05,0,1.84,0.3,0,0.1,0,0.15,0.15,0,0,0,0.1,0.25,0.15,0,0,0,0,0,0,0.05,0.05,0,0,0.15,0,0.153,0,0.029,0.021,0,1.871,25,1123,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,0.436,0,0,3.071,13,43,0 0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0.6,0,1.21,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,1.21,0,1.21,0,0,0,1.21,0,1.21,0,0,0,0,0,0.1,0,0,0,1.535,13,86,0 0.04,0.14,0.29,0,0.04,0.04,0,0.09,0,0.19,0.09,1.03,0,0,0,0,0,0.24,0.09,0,0.04,0,0,0,0.04,0,0,0,0,0.14,0,0,0,0,0,0.09,0.24,0,0,0,0,0,0.04,0,0,0,0,0,0.02,0.157,0.013,0,0,0.006,2.655,185,1787,0 0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0.62,0.62,0,0.62,0,0,0,0,0,1.87,0.62,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0,0,0,0.62,0,0,0,0,0,0.103,0,0.103,0,0,1.347,4,31,0 0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0,0.76,0,1.53,0,0,0,10.76,0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0.76,0,0,0,0,0,0.377,0,0.094,0,0,0,4.807,31,274,0 0,0,0,0,0.36,0,0,0,0,0,0,1.09,0,0,0,0,0.36,0.36,0,0,0,0,0,0,1.81,0.72,0,0.72,0,0.72,0,0,0,0,0.36,0,0.36,0,0,0,0,0,0,0.36,0,0,0,0.36,0,0.201,0,0.05,0,0,2.293,11,211,0 0,0,0,0,0,0,0,0,0,0,0,0.86,0,0.51,0,0,0,0,1.55,0,3.79,0,0,0,0.69,0.69,0,0,2.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0.078,0.052,0,0,0,1.203,5,183,0 0,0.39,0,0,0,0,0,0,0.39,0,0,1.97,0.79,0,0,0,1.18,0,0.79,0.39,0,0,0,0,0.39,0,0,0,0,0,0,0,0,0,0,1.97,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0.192,0,0.128,0,0,1.229,6,75,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.53,0,0,0,3.07,0,1.53,0,1.53,3.07,0,0,0,0.253,0.253,0,0,0,2.235,12,38,0 0,0,0.39,0,0,0,0,0.39,0,0.79,0,0.79,0,0,0,0,0,0,0,0,0,0,0,0,6.74,7.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0.053,0,0,0,0,1.8,9,153,0 0,0,0,0,0,0,0,0,0,0.85,0,0.85,0,0,0,1.28,0,0,0.85,0,0.42,0,0,0,1.7,1.28,0.85,1.28,0.42,0.42,0.42,0.42,0,0.42,0.42,0.85,0.42,0,0,0.42,0,0,0.42,0,0.42,0,0,0,0,0.369,0.073,0,0,0,2.44,12,144,0 0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.345,0,0,0,0,1.666,6,55,0 0.27,0,0,0.55,0.13,0,0,0,0.13,0,0,1.1,0.55,0,0,0,0,0,0.13,0,0,0,0,0,0.13,0.13,0,0,0,0,0,0,0,0,0,0.13,0.27,0,0,0,0,0,0,0.27,0,0,0,0.13,0,0.04,0,0,0,0,2.496,16,322,0 0,0,0,0,0.62,0.62,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0.224,0.224,0,0,0,0,2,5,54,0 0,0,0,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0.09,0,0,0,0,1.357,6,38,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.613,0,0,0,0,2,3,6,0 0.47,0,0.62,0,0,0,0,0,0.15,0,0,0.15,0,0,0,0,0,0,0.15,0.15,0,0,0.15,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.052,0,0.105,0.079,0.026,1.962,13,155,0 0,0,0.83,0,0.41,0,0,0,0,0,0,0,0.41,0,0,0,0,0,3.33,0,0,0,0,0,0,0,1.25,0,0,0,0,0.41,0,0.41,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0.065,0,0.195,0,0,1.444,22,91,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,4,0,0,0,0,0,0,0,0,0,1.111,2,10,0 0.05,0,0.15,0,0.1,0,0,0,0.52,0,0,0.15,0,0,0,0,0.1,0,0.21,0,0.1,0,0,0,0.47,0.52,0.05,0,0,0,0,0,0.15,0,0,0.05,0.36,0,0,0,0,0,0,0.1,0,0,0,0.05,0.164,0.171,0.068,0,0.013,0,3.591,35,1329,0 0,0,0,0,0.13,0.26,0,0,0,0.13,0,1.17,0.13,0.13,0,0,0.52,0,0,0,0,0,0,0,3.64,0.65,0,0,0.13,0.52,0,0,0,0,0,0.39,0.13,0,0,0,0,0,0,0,0,0,0,0,0.135,0.101,0,0,0,0,1.915,19,387,0 0.07,0,0.07,0,0,0,0,0.46,0.69,0,0,0.23,0,0,0,0,0.07,0.07,0.3,0,0.23,0,0,0,0.69,0.76,0.07,0,0,0,0,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.142,0.175,0.032,0,0.021,0,3.007,60,791,0 0.05,0,0.05,0,0,0,0,0,0.53,0,0,0.23,0,0,0,0,0,0.05,0.23,0,0.17,0,0,0,0.65,0.71,0.05,0,0,0,0,0,0.53,0,0,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0.115,0.173,0.041,0,0.016,0,2.853,47,896,0 0.03,0.05,0.03,0,0.09,0.05,0,0.01,0.16,0,0,0.09,0.01,0,0,0.01,0.01,0.01,0.07,0.01,0.05,0,0,0,0.56,0.58,0.01,0,0,0,0,0,1.43,0,0,0.05,0.49,0.03,0,0,0.03,0.01,0.01,0.07,0,0,0.01,0,0.221,0.129,0.063,0,0.005,0,3.364,66,3334,0 0,0.19,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0,2.86,0,0,0.38,0.19,0,0,0,0,0,0,0,0,0,0.19,0.19,0,0.199,0,0,0,0,2.204,9,205,0 0,0,0,0,0,0,0,0,0,0,0,9.67,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.564,0,0,0,0,1.692,5,22,0 0.41,0,0,0,0,0.82,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,3.3,0,0,0,0,1.65,0,0,0,0,0,0.82,0,0,0,0,0,0,0,0.41,0,0,0,0.41,0,0.198,0,0,0,0,1.569,7,113,0 0,0,0,0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0.63,0,0,0,0,0,3.16,0,0,0,0,0,0,0,0,0,0.63,0,0,0,0,0,0,0,0,0.63,0,0,0,0,0,0,0,0,0,0,1.56,9,64,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.714,6,24,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.125,17,25,0 0,0,1.81,0,2.01,0,0,0,0,0,0.2,0,0,0,0,0.4,0,0.2,3.62,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0.301,0,0,1.576,17,164,0 0,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,3.44,1.72,1.72,1.72,1.72,1.72,1.72,1.72,0,1.72,1.72,1.72,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0.487,0,0,0,0,2.533,11,38,0 0,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,3.44,1.72,1.72,1.72,1.72,1.72,1.72,1.72,0,1.72,1.72,1.72,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0.487,0,0,0,0,2.533,11,38,0 0,0,0,0,0,0,0,0,0,0,0,1.67,0,0,0,0,0.41,0,0.83,0,0,0,0,0,1.25,0,0,0,0.83,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0,0,0,0.139,0,0,0.069,0,1.804,6,74,0 0,0,1.19,0,0,0,0,0.59,0,0,0,0,0,0,1.19,0,0,0,2.97,0,1.19,0,0,0,1.78,1.19,2.38,0.59,0.59,0.59,0.59,0.59,0,0.59,0.59,0.59,0,0,0.59,0.59,0,0,0.59,0,1.19,0,0,0,0,0.197,0.098,0,0,0,2.203,12,119,0 0,0,0.36,0,0,0.09,0,0.09,0,0,0.09,0.36,0.09,0,0,0,0.27,0,0,0,0,0,0.09,0,3.2,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0,0,0.1,0.176,0,0,0.125,0,2.356,21,641,0 0,0,1.12,0,0,0,0,1.12,0,0,0,0,0,0,2.24,0,0,0,3.37,0,2.24,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,2,5,44,0 0,0,0,0,0,0.74,0,0,0,0.74,0,0.37,0,0,0,0,0,0,2.61,0,1.49,0,0,0,0.37,0.37,0,0.74,0,0,0,0,0.37,0,0,0.37,0.37,0,0.37,0,0,0.37,0,0.74,0.37,0,0,0,0,0.405,0,0,0,0,2.28,11,130,0 0,1.52,0,0,0.76,0,0,0,0,0.76,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0.76,0.76,1.52,0,0,0,0,0.76,0,0.76,0,0,1.52,0,0.76,0,0,0.76,0.76,0,0.76,0,0,0,0.121,0.365,0.121,0.487,0,0,1.956,22,90,0 0,0,1.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.6,3.2,0,2.4,0,0,0,1.6,0.8,2.4,0.8,0.8,0.8,0.8,0.8,0,0.8,0.8,0.8,0,0,1.6,0.8,0,0,1.6,0,1.6,0,1.6,0,0,0.25,0,0,0,0,2.065,12,95,0 0,0,0.56,0,0,0,0,0,0,1.12,0,0.56,0,0,0,0,0,0,1.12,0,1.12,0,0,0,0.56,0.56,0,1.12,0,0,0,0,0,0,0,0.56,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0.311,0,0,0,0,2.486,11,92,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.769,0,0,0,0,3.75,9,15,0 0.3,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,1.81,2.11,0,0,0,0,0,0,0.3,0,0,0.3,1.51,0,0,0,2.11,0,0,0,0,2.11,0,0,0.358,0.666,0.256,0,0,0,3.923,18,408,0 0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,8.69,0,6.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,0,1.333,3,8,0 0,0,2.04,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,0,2.04,0,2.04,0,0,0,0,0,0,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.428,3,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,4.54,0,4.54,0,0,0,0,0,0,0,0,0,0.675,1.351,0,0,0,0,3.7,26,37,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.77,0,2.32,0,0,0,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,1.156,3,37,0 0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,1.61,3.22,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.181,3,13,0 0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,0.79,0,0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.79,0,0,0,0,0,0.124,0.124,0,0,0,0,1.8,8,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,3.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.526,7,87,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,7.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0.8,0,0,0.6,0,0,0.2,0,0.2,0,0,0,0,0,1.8,0,2.2,1.8,0,2.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0.2,0.2,0,0,0,0,0,0.06,0,0,2.55,43,227,0 0.05,0.02,0.18,0,0.02,0.02,0,0.02,0,0.07,0,0.38,0.02,0.2,0.02,0,0.33,0.12,0.31,0,0.12,0,0,0,0,0,0,0.02,0,0,0,0,2.17,0,0,0.2,0.59,0,0,0.1,0,0.07,0.02,0.28,0,0.15,0.05,0.05,0.011,0.144,0.003,0,0,0,2.255,55,1825,0 0,0,0,0,0,0,0,0,0,1.07,0,1.07,0,0,0,0,0,0,0.53,0,0,0,0,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0,0.53,0,0.53,0,0,1.07,0,0.18,0,0.09,0,0,1.825,7,73,0 0,0,0.24,0,0.24,0,0,0,0,0,0,0.72,0,0,0,0.48,0.48,0,1.44,0.24,0.48,0,0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.823,143,464,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,5.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.4,0,0,0,0,0,0,0,0,0,0,0,0,1.4,3,7,0 0,0,0,0,0,0,0,0,0,1.27,0,1.27,0,0,0,0,0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.27,0,0.111,0,0.111,0,0,1.73,7,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0.719,0,0,0,0,1.571,3,11,0 0,0,0,0,1.81,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.615,0,0,0,0,1.388,5,25,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.497,0.124,0,0,0.124,3.904,28,164,0 0,0,0,0,0,0,0,0,0,0,0,1.22,0,0,0,0,0,0,1.84,0,0.61,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0.61,0,0,0,0.109,0.109,0.327,0,0,1.068,2,47,0 0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0.28,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0.28,0,0.28,0,0.28,0,0.043,0,0,0,0,1.651,8,109,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.769,0,0,0,0.384,3.187,7,51,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.026,0.343,0,0,0.026,4.326,28,822,0 0,4.1,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,1.36,6.84,0,1.36,0,0,0,0,0,2.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0.21,0,0.42,0,0,1.387,7,43,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.176,0.264,0,0,0.088,4.25,28,238,0 0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,3.12,3.12,1.56,3.12,3.12,0,0,0,0,0,1.56,0,0,0,0,0,0,3.12,0,0,0,0,0,0,0,0.515,0,0,0,0,3.285,24,69,0 0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0,1.58,0,1.58,0,0,0,0,0,0,2.227,7,49,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.67,0,0,0,0,0,3.91,2.23,0.55,0.55,0.55,0.55,0.55,0.55,0,0.55,0.55,0.55,0.55,0,0,0.55,0,0,0.55,0,0.55,0,0,0,0,0.275,0.091,0.367,0,0,2.208,13,106,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.689,0,0,0,0,1.666,3,10,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,7.4,0,0,0,0,0,0,0,0,0,0,0,0,7.4,0,0,0,0,0,0,0,0,0,0,0,1.503,3.875,16,31,0 0.17,0,0.35,0,0.53,0,0,0.35,0,0,0.17,1.78,0.17,0.17,0,0,0.53,0,0,0,0,0,0,0,3.2,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,0,0,0,0,0,0,0,0.071,0.143,0,0,0,0,2.464,80,244,0 0,0,0,0,0,0.37,0,0,0,0,0,0.75,0.37,0,0,0,0.37,0,0,0,0,0,0,0,2.26,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0.387,0,0,0.331,0,2.287,14,167,0 0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0.7,0,1.4,0,1.4,0,0,0,0,0,0.7,0,0,0,0.7,0,0,0,0,0,0,0,0,2.11,0,0,0,0,0,0,0,0,0,0.266,0.066,0,0,0,18,200,378,0 0,1.07,0,0,1.79,0.35,0,0,0,0.35,0,0,0.35,0,0,0,0,0,0.71,0,1.07,0,0,0,0.35,0.35,0.71,0,0,0,0,0.35,0,0.35,0,0,0.71,0,0.71,0,0,0,0.71,0,0.35,0,0,0,0,0.244,0.061,0.244,0,0,1.974,22,152,0 0,0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0,0,0,2.81,0,0,0,0,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.125,2,18,0 0.59,0,1.19,0,0.59,1.19,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0.59,0,0,0,0.59,0,1.19,0,0,0.59,0,0.59,0,0.59,0,0,0.59,0,0,0,0,0,0,0,0.59,0,0,0,0,0.312,0,0.312,0,0,1.818,22,80,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.95,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0.262,0,0,0,0,1.625,7,26,0 0.02,0.02,0,0,0,0,0,0,0.05,0.08,0.02,0.17,0,0,0,0,0.02,0.11,0.02,0,0,0,0,0,0.44,0.26,0,0,0.02,0.05,0,0,0,0,0,0.02,0.14,0,0,0,0.35,0,0.02,0,0.11,1.15,0,0,0.024,0.17,0,0.126,0,0,3.637,49,2015,0 0,0,0,0,0.4,0,0,0,0,0,0,2.04,0,0,0,0,0,0,2.44,0,0.81,0,0,0,1.22,3.26,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0.137,0,0.068,0,0,2.282,21,89,0 0,1.1,0,0,0.55,0,0,0,0,1.1,0,0,0,0,0,0,0,0.55,0,0,1.1,0,0,0,0.55,0.55,2.2,0,0,0,0,0.55,0,0.55,0,0,1.65,0,0.55,0,0,0,1.1,0,0.55,0,0,0,0.088,0.355,0.088,0.177,0,0,1.867,22,127,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0.666,0,0,1.222,3,11,0 0,1.28,0,0,0.64,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0.64,0.64,1.92,0,0,0,0,0.64,0,0.64,0,0,1.28,0,0.64,0,0,0,0.64,0,0.64,0,0,0,0.104,0.418,0,0.209,0,0,1.888,22,102,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.714,4,12,0 0,0,0.37,0,0.37,0,0,0,0,0,0,0.37,0,0,0,0.37,0,0,0.37,0,0.37,0,0,0,0.37,0.74,0,0,0,0,0,0,0,0,0,0.37,0.74,0,0,0,0,0,0.37,0,0,0,0,0,0,0.162,0,0,0,0,2.643,34,193,0 0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,2.23,0,0.37,0,0,0,1.11,0.37,1.86,0.37,0.37,0.37,0.37,0.37,0,0.37,0.37,0.37,0.74,0,0.74,0.37,0.37,0,0.74,0,0.37,0.37,0,0,0,0.192,0.064,0.32,0,0.064,1.923,17,177,0 0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0,0,1.19,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.38,0,0,0,0,0,0,0,0.22,0,0,0,0,2,12,34,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.06,0,0,0,0,0,2.06,1.37,1.37,0.68,0.68,0.68,0.68,0.68,0,0.68,0.68,0.68,0.68,0,0,0.68,0,0,0.68,0,0.68,0.68,0,0,0,0.216,0.108,0.216,0,0.108,2.754,25,157,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0,0,0,0,0,0,0,0,0,0,1.14,0,0,0,0,0,0,2.29,0,0,0,0,0,1.14,1.14,0,0,0,0,1.14,0,0,0,0,0,0,0,0,0,0,0,0,2.29,0,0,0,0,0,0,0,0.596,0,0.198,2.133,14,64,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0.314,0,0,2.473,10,47,0 0,0,2.63,0,0,0,0,0,0,0,0,2.63,0,0,0,0,0,0,2.63,0,0,0,0,0,2.63,2.63,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,8,0 1.02,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,1.02,0,2.04,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0.531,0,0,0,0,2.967,11,92,0 0,0,0,0,0.6,0,0,0,0,0,0,0.6,0,0,0,0,0,0,3.01,0,0,0,0,0,0,0,1.8,0,0,0,0,0.6,0,0.6,0,0,0.6,0,0.6,0,0,0,0.6,0,1.2,0,0,0,0,0.085,0.085,0.085,0,0,1.735,22,92,0 1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,2,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0.324,0,0.487,0,0,2.291,22,55,0 0.06,0,0.36,0,0.12,0,0,0.06,0.06,0,0.12,0.66,0,0,0,0.06,0.18,0.06,0.6,0,0.78,0,0,0,1.99,0.42,0,0,0,0.18,0.18,0,0,0,0.06,0,0.18,0,0,0,0,0.06,0,0,0,0,0,0.24,0.008,0.099,0,0,0.008,0.016,1.972,27,941,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.833,0,0.416,1.937,8,31,0 0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,4.47,2.98,0,1.49,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0.229,0,0,0,0,2.333,10,49,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,1.33,0,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.33,0,0,0,0,0,1.33,0,0,0,0,1.33,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.681,0,0.227,0,0,2.037,22,55,0 0,3.27,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.27,0,0,0,0,0,1.63,0,0,0,0,1.63,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.527,0,0.263,0,0,2.12,22,53,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.62,0,0.81,0,0,0,0.81,0,2.43,0,0,0,0,0,0,0,0,0,0.81,0,0.81,0,0,0,0.81,0,0,0,0,0,0,0.135,0,0.406,0,0.135,1.958,17,94,0 0,0,0,0,0.7,0,0,0,0,0,0,0.7,0,0,0,0,0,0,3.52,0,0,0,0,0,0,0,1.4,0,0,0,0,0.7,0,0.7,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0.1,0,0.1,0,0,1.682,22,69,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.52,0,0,0,0,0.793,0,0,0,0,1.25,2,5,0 0,0,0,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0,0.61,0,0,0,0.196,0.098,0,0.098,0,0,1.312,6,63,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 1.07,0,1.07,0,1.07,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.07,0,1.07,0,0,1.07,0,1.07,0,1.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0.185,0,0,2.24,22,56,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,8,0 0,0.25,0.75,0,1,0.25,0,0,0,0,0.25,0.25,1.25,0,0,0.25,0,1.25,2.51,0,1.75,0,0.25,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.042,0,0,1.204,7,118,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,8,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0.34,0,0,0.68,0,0.68,0,0,0.34,0.34,0,0,0,0,0.34,0,1.36,3.42,0,2.73,0,0,0,0.34,0.34,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.048,0.048,0,1.411,15,96,0 0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,2.5,1.25,1.25,0,1.25,2.5,0,0,0,0,0.209,0,0,0,3.3,13,66,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,2.56,2.56,0,0,0,0,0,0,0,0,3.333,7,20,0 0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,1.736,8,33,0 0,0,0,0,0,0,0,0,0,0.72,0,0.72,0,0,0,0,0,0,4.37,0,0,0,0,0,1.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.107,0,0,0,1.48,9,37,0 0,0,0.36,0,0.72,0,0,0,0,0,0,0.36,0.18,0,0,0.36,0,0,1.44,0,0.36,0,0,0,0.36,0.9,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0.18,0,0.18,0,0,0,0.026,0,0,0.107,0,0,2.988,51,263,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,2.94,0,0,0,1.47,0,1.47,2.94,0,0,0,0,1.47,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.484,0,0.484,0,0,2.5,15,65,0 0,0,0,0,0.09,0,0,0,0,0.09,0,0.18,0,0,0,0,0,0,0.37,0,0,0,0,0,2.43,1.21,0.28,0.09,0.09,0.18,0.09,0.09,1.12,0.09,0.09,0.18,1.12,0,0,0.09,0.56,0,0.18,0,0.09,2.24,0,0.09,0.123,0.479,0.095,0.013,0,0,3.625,51,1131,0 0,0,0,0,0.24,0,0,0,0,0,0,0.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0.24,0,0,0,0,0,0,0,0,0,0.24,0,0.24,0,0.195,0,0,0,0,2.192,35,239,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,1.78,1.78,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0.307,2.227,14,49,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,8,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,8,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.333,5,7,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,4,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.166,4,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,0,0,0,0,7.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,2.142,5,15,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0.23,0,0,0.23,0,0,0,0,0.47,0,0.23,0,1.67,0,0,0,0,1.19,0,0,0,0,0,0.47,0,0.23,0,0,0,0.23,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.158,0,10.036,235,1094,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0.12,0.12,0,0,0,0,0.12,0,0,0,0.12,0,0,0.12,0,0,0,0.12,0,0,0,0,0,0.9,0.38,0.38,0,0,0,0,0,0,0,4.11,0,0,0,0,0,0,0.9,0,0,0,0.12,0.12,0,0,0.149,0,0,0,0.074,5.264,53,1232,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,1.5,3,12,0 0,0.14,0.14,0,0,0,0.14,0.14,0,0,0,0.14,0,0,0.14,0,0,0,0.28,0,0,0,0,0,1.13,0.42,0.28,0,0,0,0,0,0,0,2.69,0,0,0,0,0,0,0.84,0,0,0,0.14,0.14,0,0,0.16,0,0,0,0.072,5.331,80,1029,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,4.4,16,22,0 0,0,0.56,0,0.08,0.16,0,0,0,0.16,0,0,0,0.24,0,0,0,0,0.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0.08,0.08,0,0,0,0,0,0,0,0,0,0,0,1.54,0.164,0.505,0,0.01,0.021,0,2.729,55,1122,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,2.04,2.04,2.04,2.04,2.04,2.04,2.04,2.04,0,2.04,2.04,2.04,0,0,0,2.04,0,0,0,0,0,0,0,0,0,0.536,0,0,0,0.268,2.529,11,43,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0 0.38,0,0.64,0,0.12,0.25,0,0,0,0,0,0.25,0,0,0,0,0.25,0.12,1.03,0,0.38,0,0,0,0.9,0.38,0.25,0.25,0.64,0.25,0,0,0,0,0.12,0.51,0,0,0,0,0,0.12,0,0.25,0,0,0,0.25,0,0.082,0,0.02,0,0,1.491,11,267,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0.97,0.97,0.97,1.94,0,0.97,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.261,0,0,0,0,2.03,11,67,0 0.44,0,0,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,2.22,0,0,0,0,0.44,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.265,0,0,0,0,1.48,7,74,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0.86,0.86,0.86,1.73,0.86,0.86,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.131,0,0,0,0,1.74,11,47,0 0,0,0.64,0,0.32,0.32,0,0,0,0,0,0,1.29,0,0,0,0.32,0,0.97,0,0.32,0,0,0,0.32,0.32,0.32,0.64,0,0.32,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.371,0,0,0,0,2.05,11,82,0 0.13,0,0.13,0,0,0,0,0,0.13,0.13,0,0.66,0,0.66,0,0,0.13,0,1.06,0,0.66,0,0,0,0.13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0.13,0,0.93,0,0.014,0.042,0,0,0,0.183,5.603,57,1160,0 0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,3.333,14,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.754,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.333,14,20,0 0,0,0,0,0,0,0,0,0.3,0,0,0.3,0,1.82,0,0,0.3,0,0.6,0,0.91,0,0,0,0,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,0,1.51,0,0,0.057,0,0,0,0.231,2.011,28,358,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,1.857,5,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,2.428,5,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,1.25,2,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,3,5,15,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.754,0,0,0,0,1,1,7,0 0.21,0,0.42,0,2.54,0,0,0,0,0,0,1.05,0,0,0,0,0.21,0,0,0,0,0,0,0,0.21,0,0.63,0.21,0,0,0,0,0.21,0,0,0,0,0,0.21,0,0,1.27,0,0,0,0,0,0.21,0.028,0.115,0,0,0,0,2.457,45,258,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18.18,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,4,0 0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.333,2,4,0 0,0.35,0.35,0,0.17,0,0,0,0.17,0.35,0,1.23,0,0.88,0,0,0.17,0,1.41,0,0.7,0,0,0,0.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.7,0,0,0.17,0,0.88,0,0,0.038,0,0.019,0,0.095,2.059,28,447,0 0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,5.88,0,1.47,0,0,1.47,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.037,15,82,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,4.333,20,26,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.353,0.353,0,0,0,0,1,1,21,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0.289,0,0,0.289,0,0,1.076,2,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.666,15,20,0 0.01,0.01,0.07,0,0.05,0,0,0.01,0.03,0.13,0.05,0,0,0.05,0,0,0.01,0.07,0.01,0,0.01,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0.13,0,0,0,0.01,0.46,0,0,0.03,0,0.8,0.01,0.07,0.05,0.301,0.131,0.002,0.09,0.002,2.577,82,5395,0 3.94,0,0,0,0,0,0,0,0,0,0,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,5,36,0 0,0,1.13,0,0,0,0,0,0,0,0,1.13,0,0,0,0,0,1.13,1.13,0,1.13,0,0,0,2.27,1.13,0,1.13,0,1.13,1.13,0,0,0,1.13,1.13,1.13,0,0,0,0,0,0,0,0,0,0,0,0,0.136,0,0.136,0,0,1.812,10,58,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,3.44,0,0,0,0,0,0,0,0,0,0,6.89,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,2.818,8,31,0 0,0,0,0,0,0,0,0,0,0,0,3.17,0,0,0,0,0,0,3.17,0,0,0,0,0,1.58,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,6.34,0,0,0,0,0,0,0,0,0,0,0,0,1.384,4,18,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.476,0,0,2.642,9,37,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0.17,0,0,0,0,0,0,0.17,0,0.17,0,0,0,0,0,0,0.17,0,0,0,0,0,1.57,1.4,0,0,0,0.17,0,0,0.17,0,0,0,1.92,0,0,0,2.8,0,0,0,0,2.8,0,0,0.267,0.802,0.118,0,0,0,4.808,20,601,0 0.19,0,0.39,0,1.24,0.13,0,0.06,0.32,0,0,0.45,0.26,0,0,0,0.13,0,1.24,0,0.39,0,0.06,0,1.04,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0.03,0,0,0.03,0,1.571,42,297,0 0,0,0,0,0,0,1.78,0,0,0,0,1.78,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.586,0,0,1.307,4,17,0 0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0.58,0,0,0,0,0,1.76,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0.107,0,0.107,0,0,1.531,6,49,0 0.13,0,0.2,0,0.54,0.13,0,0.13,0.4,0,0,0.06,0.06,0,0,0,1.01,0,0,0,0,0,0,0,1.08,0,0,0.06,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0.06,0,0,0,0.009,0.068,0,0,0.166,0,2.804,45,617,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.373,0.373,0,0.373,0,0,1.714,4,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0 0.26,0,0.53,0,0,0.26,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0,0,0.039,0,0,0,0,2.646,77,172,0 0.26,0,0.53,0,0,0.26,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0,0,0.039,0,0,0,0,2.646,77,172,0 0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0.55,0,0,0,0.55,0,0,0,0.55,0,0,0,0,0,0,0,0.55,0,0,0,0,0.55,0,0,0,0,0,0,0,0.25,0,0,0,0,1.601,8,173,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,2.56,1.28,2.56,1.28,1.28,1.28,1.28,1.28,0,1.28,1.28,1.28,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0.632,0,0,0,0,2.142,11,45,0 0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,0,0.29,0,0,0,0,0,1.75,0.29,0,0.58,0,0,0,0,0,0,0.29,0.29,0.58,0,0,0,0,0,0,0,0.29,0,0,0,0,0.091,0,0.045,0,0,2.333,15,175,0 0,0,0.6,0,0.6,0,0,0,0,0,0,0,0,0,0,0.6,0,0,2.4,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0.11,0,0,0,0,1.074,3,29,0 0,0.32,0.32,0,0.16,0.16,0,0,0.16,0.32,0,0,0,0,0.32,0,0.32,0.32,0.8,0.32,2.08,0,0,0.16,0,0,0.16,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,1.309,0,0.022,0.377,0,3.918,157,772,0 0.04,0.08,0.24,0,0.04,0.16,0,0.04,0.16,0.52,0.08,1.04,0,0,0,0.24,0.04,0,0.68,0,0.88,0,0.04,0,1.76,0.6,0,0.16,0,0,0.16,0,0,0,0.36,0,0.24,0.04,0,0,0,0.04,0,0,0.04,0,0,0,0.029,0.142,0,0.071,0.071,0.011,1.983,23,1361,0 0,0,0,0,0.7,0,0,0.88,0,0.17,0,0.52,0.17,0,0,0,0,0,2.46,0,1.93,0,0,0,0.52,0.35,0.35,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0,0,0.17,0,0,0,0,0.17,0,0,0,0,0.086,0,0.057,0,0,1.472,15,162,0 0.09,0.09,0.09,0,0.29,0,0,0,0.87,0,0,0.29,0.09,0,0.19,0,0,0,0.39,0,0.19,0,0,0,0.58,0.68,0.09,0,0,0,0,0,0.29,0,0,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0.111,0.153,0.069,0,0.041,0,3.298,41,686,0 0.02,0.08,0.1,0,0.27,0.07,0,0,0.14,0,0,0.05,0,0.02,0.01,0.02,0,0.01,0.05,0,0.04,0,0,0,0.48,0.49,0.04,0,0,0,0,0,0.36,0,0,0.01,0.45,0,0,0.01,0,0,0.04,0,0.01,0,0,0.02,0.221,0.152,0.056,0,0.004,0,2.63,38,3086,0 0.05,0,0.17,0,0.28,0,0,0,0.51,0,0,0.17,0,0.05,0,0,0,0.05,0.22,0,0.17,0,0,0,0.96,1.02,0.05,0,0,0,0,0,0.28,0,0,0.11,0.73,0,0,0,0,0,0,0,0,0,0,0,0.165,0.182,0.091,0,0.016,0,2.777,38,1161,0 0.05,0,0.11,0,0.16,0.05,0,0,0.5,0,0,0.16,0,0,0,0,0,0.05,0.22,0,0.16,0,0,0,0.62,0.67,0.05,0,0,0,0,0,0.56,0,0,0,0.73,0,0,0,0,0,0,0.05,0,0,0,0,0.073,0.211,0.04,0,0.016,0,2.787,47,1090,0 0,0,0,0,0,0.05,0,0.34,0,0,0.11,0.81,0.05,0.11,0,0,0.75,0,0,0,0,0,0.05,0,1.16,0,0,0,0,0,0,0,0.05,0,0,0.23,0.05,0,0,0,0,0,0,0,0,0,0,0,0.283,0.107,0,0,0.053,0,1.864,32,910,0 0,0,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,2.11,0,2.81,0,0,0,0,0,0.7,0,0,0,0,0.35,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.047,0,0,0,0.047,2.232,12,163,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,1.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.481,5,40,0 0.27,0.27,0,0,0.83,0,0,0,0,0,0,0.27,0.27,0,0,0,0,0,1.1,0,0.27,0,0,0,1.93,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0.084,0,1.231,6,101,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.38,0,1.69,0,0,0,1.69,1.69,1.69,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0,0,1.69,0,0,0,0,0.315,0,0,0.63,0,3.083,12,37,0 0,0,0.87,0,0,2.63,0,0,0,0,0,0.87,0,0,0,0,0,0.87,0.87,0,0,0,0,0,0,0,0.87,0,0,0,0,0,0,0,0,0,0.87,0,0,0,0,0,0,0,0.87,0,0,0,0.317,0.317,0,0,0,0,1.269,5,33,0 0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.491,0.163,0,0,0,4.312,33,138,0 0,0,0,0,0,0,0,0,0,0,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0.42,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.652,9,114,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,1.333,4,12,0 0,0,0.73,0,0,0,0,0.73,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,2.94,4.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.73,0,0,0.107,0,0,0,0,1.695,9,78,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.55,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0.07,0,0,0,0,1.541,4,37,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,0.28,0,0,0,0,0,0,0.57,0,1.43,0,0,0,0,0,0,0,0,0.28,0,0,0,0.28,1.14,0,0,0,0,0,0,0,0,0.28,0,1.14,0,0,0,0,0,0,1.14,0,0,0,0.28,0,0.43,0,0,0,0,2.902,55,238,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,7.46,2.98,0,0,0,2.98,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.961,11,51,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,1.666,3,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,6,0 0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0.8,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,1.35,3,54,0 0,0,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,2.24,0,0.56,0,0,0.56,0,0,1.12,0,0,0,0,0,0,0,0,0,0.56,0,0,0.56,0,0,0.56,0,0.56,0,0,0,0,0.299,0,0,0,0,2.236,13,85,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.439,0,0,0.219,0,1.911,11,65,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.333,8,10,0 0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0.74,0,0,0,0.134,0.672,0,0,0,0,1.863,5,41,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,1.59,5,35,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.081,0,0,0,1,1,3,0 0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0.97,0,0,0,0,0.76,0,0,0,0,2,5,38,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,2.12,0,0,4.25,0,0,0,0,2.12,0,2.12,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0.714,0,0,0,0,2.708,15,65,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.98,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0.98,0,0,0,0,0,0,0,0,0,0,1.96,0,0.98,0,0,0,0,0,0,0,0,2,13,42,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.239,0,0,0,0,2.166,5,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.714,3,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.863,0.143,0,0,0,4.484,33,148,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.52,4.76,4.76,4.76,4.76,4.76,4.76,4.76,0,4.76,4.76,4.76,0,0,0,4.76,0,0,0,0,0,0,0,0,0,1.257,0,0,0,0,3.333,11,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0.34,1.36,0,0,0,0,0,0,0.34,1.7,0,0,0,0,0,0,0,0,1.7,0,0.34,0,0,0,1.36,0.68,1.02,0.34,0.34,0.34,0.34,0.34,0,0.34,0.34,0.34,0.34,0,0.34,0.34,0,0,0.34,0,0.34,0,0,0,0,0.244,0,0,0,0,1.696,13,112,0 0,0,0,0,0,0,0,1.57,0,1.57,0,1.57,0,0,0,0,0,0,1.57,0,1.57,0,0,0,3.14,2.36,0.78,0.78,0.78,0.78,0.78,0.78,0,0.78,0.78,0.78,0,0,0.78,0.78,0,0,0,0,0.78,0,0,0,0,0.372,0,0,0,0,3.971,34,139,0 0,0,0.88,0,0,0,0,0,0.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.315,0,0,0,0,1.166,3,21,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.88,5.88,0,5.88,0,0,0,0,5.88,0,0,0,0,0,5.88,0,0,0,0,5.88,0,0,0,0.763,0,0,0,0,2.285,10,16,0 0,0,0,0,0,0,0,0,0,0.31,0,0.31,0,0,0,0,0,0,0.95,0,0.95,0,0,0,1.27,0.63,1.91,0.63,0.63,0.63,0.63,0.63,0,0.63,0.63,0.63,0.95,0,0.63,0.63,2.22,0,0.63,0,0.63,1.91,0,0,0.05,0.304,0.101,0,0,0,2.186,15,164,0 0,0.18,0,0,0.18,0,0,0.37,0,0,0,0.94,0,0,0,0,1.89,0,0.18,0,0,0,0,0,0.37,0.18,0,0,0,0.18,0,0,0,0,0,0.37,0,0,0,0,0,0,0,0,0.56,0,0,0,0,0.244,0,0,0,0,1.663,10,168,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.176,0,0,0,2.142,8,15,0 0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0.97,0,0.97,0,0,0,1.29,0.64,1.94,0.64,0.64,0.64,0.64,0.64,0,0.64,0.64,0.64,0.97,0,0.64,0.64,2.26,0,0.64,0,0.32,1.94,0,0,0.051,0.255,0.102,0,0,0,2.197,15,156,0 0,0.46,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,1.38,0,1.85,0,0,0,2.31,0.46,0,0,0,0.46,0,0,0,0,0,0,0.46,0,0.46,0,0,1.38,0,0,0,0,0,0,0,0.155,0,0,0,0,2.982,28,167,0 0.1,0,0.3,0,0.05,0.15,0,0.25,0.3,0.1,0.2,0.65,0,0,0,0.25,0.05,0,0.55,0,0.65,0,0.05,0,1.3,0.35,0,0.15,0,0.25,0.2,0,0,0,0.2,0.05,0.25,0,0,0.05,0,0,0,0.3,0.15,0,0.05,0,0.014,0.139,0,0.022,0.058,0,1.979,23,1081,0 0,0,0,0,0.81,0,0,0,0,0,0,0.81,0.81,0,0,0,0,0,1.62,0,0,0,0,0,0.81,0,1.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0.143,0,0.143,0,0,1.055,2,19,0 0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0.7,0,1.4,0,1.4,0,0,0,0,0,0.7,0,0,0,0.7,0,0,0,0,0,0,0,0,2.11,0,0,0,0,0,0,0,0,0,0.267,0.066,0,0,0,17.952,200,377,0 0,0,0.61,0,0,0,0,0,0,0,0,1.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0.079,0.158,0,0,0,0,2.508,17,143,0 0.78,0,0,0,0.39,0,0,0,0,0.39,0,0,0,0,0,0,0,0,0.78,0,0.39,0,0,0.39,0,0,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0,0.073,0.146,0,0,0,0,1.354,8,42,0 0,0,0,0,0,0,0,0,0,0,0,0,2.94,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.6,5,16,0 0.22,0,0.07,0,0.07,0.07,0,0.14,0,0.36,0,0.51,0.44,0.07,0,0,0.29,0.07,0.07,0,0.07,0,0,0,1.99,0,0,0,0.29,0.29,0,0,0,0,0,0.14,0.07,0.07,0,0,0,0,0,0,0,0,0,0,0.041,0.031,0,0.031,0,0,1.912,22,568,0 0,0,0,0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0.22,0,0,0,0,0,0,0.22,0.22,0,0.45,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0.22,0,0,0,0.22,0,0.154,0,0,0,0,1.768,15,122,0 0,0.33,0,0,0.33,0,0,0,0,0,0,0.33,0,0,0,0,0,0.33,0,0,0,0,0,0,0.33,0.33,0,0.67,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0.33,0,0,0,0.33,0,0.088,0,0,0,0,1.87,15,116,0 0.49,0,0,0,0.49,0.49,0,0.49,0,0,0,0.49,0.99,0,0,0,0,0,0.49,0,0,0,0,0,2.48,0.99,0,0,0.99,0.99,0,0,0,0,0,0.49,0.49,0,0,0,0,0,0,0,0,0,0,0.49,0,0.145,0,0,0,0,1.641,10,87,0 0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,1.28,0,1.28,0,0,0,0.85,0.42,1.7,0.42,0.42,0.42,0.42,0.42,0,0.42,0.42,0.42,0.85,0,0.42,0.42,1.7,0,0.42,0,0.42,1.28,0,0,0,0.204,0.068,0,0,0,2.108,15,97,0 0,0,0.51,0,1.54,0,0,0,0.25,0,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0.77,0.25,0,0,1.03,1.8,0,0,0,0,0,0,0.25,0.25,0,0,0,0,0,0,0.25,0,0,0,0,0.039,0,0,0,0,1.767,7,99,0 0,0,0,0,0.5,0,0,0,0,0.5,0,1.01,0,0,0,0,0,0,2.53,0,1.01,0,0,0,1.52,1.01,1.52,0.5,0.5,0.5,0.5,1.01,0,1.01,0.5,0.5,0.5,0,0.5,0.5,0,0,0.5,0,1.01,0,0,0,0.09,0.272,0.09,0,0,0,1.836,13,101,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.78,0,0.89,0,0,0,0,0,2.67,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0.89,0,0,0,0,0.89,0,0,0,0.15,0,0,0,0,1.85,15,37,0 0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,4.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.769,5,46,0 0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.709,0,0.709,0,0,2.3,9,23,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.52,4.76,4.76,4.76,4.76,4.76,4.76,4.76,0,4.76,4.76,4.76,0,0,0,4.76,0,0,0,0,0,0,0,0,0,1.257,0,0,0,0,3.333,11,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0.602,4.7,23,47,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,4.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.48,6,37,0 0,0,0,0,0,0,2.22,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.665,0,0,0.665,0,4.571,25,64,0 0,0,0,0,0,0,2.22,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.665,0,0,0.665,0,4.571,25,64,0 0,0,0.33,0,0,0.49,0,1.32,0.16,5.12,0,0,0,0.66,0,0,0.33,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0.16,0,0,0,0.33,0,0,0,0.07,0.023,0,0,0.023,1.552,10,149,0 0,0,0,0,1.06,0,0,0,0,0,0,1.06,0,0,0,0,0,0,1.06,0,1.06,0,0,0,1.06,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.19,0,0,0,0,0,0.181,0,0,0,0,1.4,4,28,0 0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0.88,0,0.88,0,0,0,1.32,0.88,0.88,0.88,0.44,0.44,0.44,0.44,0,0.44,0.88,0.44,0,0,0,0.44,0,0,0,0,0.44,0,0,0,0,0.207,0,0,0,0.207,2.588,40,132,0 0,0,0,0,0,0,0,0,0,0,0,0.51,0,0,0,0,0,0,1.03,0,0.51,0,0,0,1.54,1.03,1.54,1.03,0.51,0.51,0.51,0.51,0,0.51,1.03,0.51,0,0,0,0.51,0,0,0,0,0.51,0,0,0,0,0.24,0,0,0,0.48,2.6,40,130,0 0,0,0,0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0.91,0.91,0,0.45,0,0,0,2.73,3.19,0.91,0.45,0,0,0,0,0,0,0.45,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0.075,0.151,0,0,0,0,2.158,20,136,0 0.05,0,0.31,0,0,0.05,0,0.05,0.47,0,0,0.15,0,0,0,0,0.26,0.05,0.21,0,0.15,0,0,0,0.79,0.85,0.05,0,0,0,0,0,0.47,0,0,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0.112,0.202,0.067,0,0.014,0,3.117,52,1303,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.7,0,0,0,0,0,0.35,0.35,0,0.7,0.35,0.35,0,0,0,0,0.35,0,0,0,0.7,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0,0,1.506,11,113,0 0.23,0,0.23,0,0.69,0,0,0,0,0,0,1.39,0,0,0,0,0,0,0.23,0,0,0,0,0,0.23,0.23,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0,0.067,0,0,0,0,1.433,9,86,0 0.23,0,0,0,0.23,0.23,0,0,0,0,0,0.23,0,0,0,0.23,0,0,0.47,0,0,0,0,0,0.47,0.23,0,0,0,0.47,0.23,0,0.47,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0.106,0.106,0,0,0,0,1.588,8,143,0 0,0,0,0,0,0,0,0,0,1.31,0,1.31,0,0,0,0,0,0,6.57,0,0,0,0,0,2.63,1.31,2.63,1.31,1.31,1.31,1.31,1.31,0,1.31,1.31,1.31,1.31,0,0,1.31,0,0,1.31,0,1.31,0,0,0,0,0.649,0,0,0,0,2.214,13,62,0 0.05,0,0.05,0,0.05,0.05,0,0,0.5,0,0,0.16,0,0.05,0,0,0,0.05,0.22,0,0.16,0,0,0,0.62,0.67,0.05,0,0,0,0,0,0.45,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0.185,0.233,0,0,0.016,0,2.972,46,963,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0.719,0,0,0,0,1,1,4,0 0.13,0.4,0,0,0,0,0,0,0,0.53,0,0,0,0,0,0,0.13,0,0.8,0,0.53,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,1.2,0,0,0,0,0,0,0,0,0.53,0,0.13,0,0.25,0,0.014,0.427,0.044,5.473,143,1538,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.909,0,0,0,0,1,1,1,0 0.1,0,0.1,0,0.1,0,0,0,0.94,0,0,0.31,0,0,0,0,0,0,0.41,0,0.2,0,0,0,0.52,0.62,0.1,0,0,0,0,0,1.15,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0.132,0.251,0.029,0,0.029,0,2.784,32,490,0 0.11,0,0.22,0,0,0.11,0,0,1.01,0,0,0.33,0,0,0,0,0,0,0.44,0,0.22,0,0,0,0.78,0.67,0.11,0,0,0,0,0,0.56,0,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0.171,0.233,0.031,0,0.031,0,3.189,32,571,0 0,0,0.09,0,0,0,0,0.09,0,0,0.09,1.49,0.27,0.09,0,0,0.37,0,0,0.09,0,0,0,0,2.51,0,0,0,0.09,0.27,0,0,0,0,0,0.37,0.18,0,0,0,0,0,0,0.09,0,0,0,0,0,0.106,0,0,0,0,2.277,27,558,0 0.02,0,0.1,0,0.05,0.05,0,0.13,0.3,0,0,0.13,0,0,0,0,0,0.02,0.1,0,0.08,0,0,0,0.46,0.49,0.02,0,0,0,0,0,0.27,0,0,0,0.41,0,0.13,0,0,0,0,0,0,0,0,0,0.166,0.158,0.047,0,0.007,0,2.984,52,1758,0 0,0,1.06,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0,1.06,0,0,0,0,0,0,2.12,1.06,0,1.06,0,1.06,1.06,0,1.06,0,1.06,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0.121,0,0,0,0,2.151,10,71,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,0,0.35,0,0,0,0,1.461,6,19,0 0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0.74,0,0,2.166,7,26,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0.26,0,0.26,0.13,0,0,0,0,0,0.52,0,0,0,0,0.39,0,1.05,0,1.05,0,0,0,0.39,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0,0,0,0,0,0,0,0.017,0.089,0.017,0.035,0.053,0.053,5.189,107,685,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,0,0,0,0,0,0,0,0,1.25,2,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,2.94,0,2.94,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,1,1,10,0 0,0,0.48,0,2.18,0,0,0,0.48,0,0,1.69,0,0,0,0,0.24,0,0.48,0,0.48,0,0,0,1.69,0.24,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0.24,0,0,0,0,0.036,0,0,0,0,2.364,73,227,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.06,3.03,3.03,3.03,3.03,3.03,3.03,3.03,0,3.03,3.03,3.03,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,2.75,11,33,0 0,0,0.95,0,0.31,0,0,0,0,0,0,0.31,0,0,0,0,0,0,1.26,0,0,0,0,0,0.63,0.95,0,0,0,0,0,0,0,0,0,0.31,0.31,0,0.31,0,0,0,0.31,0,0,0,0,0,0.048,0.339,0,0.048,0,0,1.99,14,215,0 0,0,0,0,0,0,0,1,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,2,0.5,0,0.5,0.5,1,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,2.017,13,117,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.888,5,17,0 0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.505,0,0,2.375,4,19,0 0,0,0,0,0,0,0,0,0.83,1.66,0,0,0,0,0,0,0,0,0.83,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0.118,0,0,0,0,1.475,11,59,0 0,0,0.57,0,0.85,0,0,0,0.28,0,0.57,0.28,0,0,0,0.85,0,0.57,1.42,0,0.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0.282,0.242,0,2.46,26,278,0 0.71,0.14,0.42,0,1,0.14,0.14,0,0,3,0.14,0.85,0,0,0,0,0,0.28,0.85,0,0.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0,0.022,0,0,0,0.022,1.931,9,168,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.4,3,7,0 0,0,0,0,0,0,0,0,5.26,0,0,0,0,0,0,0,2.63,0,2.63,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.058,11,35,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,1.806,11,56,0 0,0,0,0,0,0,0,1.08,0,0,0,1.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0,0.54,0.54,0,0,0,0,0,0,0,0,0,0.54,0,0.166,0,0.083,0,0,1.528,13,81,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,2.41,0,0.8,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0.8,0,0,0,0,0.45,0,0,0,0,1.2,4,30,0 0,0,0,0,0.27,0,0,0.55,0,0.55,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,6.64,4.15,0.83,0.27,1.66,0.27,0.27,0.27,0,0.27,0.27,0.27,1.38,0,0,0.27,0,0.27,0.27,0,0.55,0,0,0,0.183,0.549,0.137,0,0,0,4.257,57,430,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.543,0,0.271,0,0,3,18,72,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0.28,0,0,0,1.43,1.15,0.86,0.28,0.28,0.28,0.28,0.28,0.14,0.28,0.28,0.28,0.28,0,0.14,0.28,0,0,0.43,0,0.57,0.28,0,0,0.023,0.324,0.046,0,0,0,2.24,12,372,0 0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,0,6.79,3.88,0.97,0.97,0.97,0.97,0.97,0.97,0,0.97,0.97,0.97,0.97,0,0,0.97,0,0,0.97,0,1.94,0,0,0,0,0.299,0.149,0,0,0,2.666,13,72,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,1.35,0,1.35,0,1.35,0,1.35,1.35,0,0,0.205,0,0.205,0,0,0,1.722,12,31,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.666,5,20,0 0,0,1.19,0,0,0,0,0,0,0,0,2.38,0,0,0,1.19,0,0,2.38,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0.2,0.4,0,0.2,0,0,1.461,4,19,0 0,0,0,0,1.81,0,0,0,0,1.81,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,1.81,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.3,3,13,0 0,0,0,0,0,0,0,0,0,0.45,0,0.45,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0.45,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.082,0.165,0,0,0,0,1.666,4,40,0 0,0,0.26,0,0.53,0,0,0,0,0,0,0.26,0,0,0,0,0.26,0,0,0,0,0,0,0,1.61,0.8,0,0,0.53,0.8,0,0,0,0,0,0.8,0.26,0,0,0,0,0,0,0,0.26,0,0,0,0.128,0.042,0,0,0,0,1.635,6,139,0 0,0,0.32,0,0,0.16,0,0,0,0,0,0.64,0,0,0,0,0.48,0,0.96,0,0.96,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0.021,0.105,0,0.021,0.063,0.063,3.789,39,432,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,1.93,1.69,0.72,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0,0,0.24,0,0,0.24,0,0.24,0.48,0,0,0,0.148,0.074,0,0,0,2.386,12,210,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.97,1.72,0.49,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0,0,0.24,0,0,0.24,0,0.24,0.49,0,0,0,0.15,0.075,0.037,0,0,2.367,12,206,0 0,0,0.09,0,0,0.09,0,0.27,0,0,0.18,1.49,0.09,0.09,0,0,0.46,0,0,1.49,0,0,0.09,0,2.42,0,0,0,0,0,0,0,0,0,0,0.09,0.18,0,0,0,0,0,0,0,0,0,0,0,0.066,0.118,0,0,0.066,0,2.156,26,552,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,2.56,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,0,1.5,4,24,0 0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.73,2.73,1.36,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.541,7,37,0 0,0,0,0,0,0,0,0,0,1.25,0,1.25,0,0,0,0,0,0,6.25,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.246,0,0,0,0,1.076,2,14,0 0,0.08,0.17,0,0,0.08,0,0.34,0,0.17,0.08,0.34,0,0,0,0,0.87,0,0.26,0,0,0,0,0,2.79,0.69,0,0.08,0,0,0,0,2.35,0,0,0.26,0.78,0,0.17,0,0,0,0,0,0,0,0,0,0.133,0.306,0.053,0,0.013,0,3.205,57,904,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.625,9,26,0 0,0,0,0,0,0,0,0,0,4.54,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.51,0,0,0,0,0,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.286,0,0,0,0,2.277,12,41,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,5,18,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.545,4,17,0 0.35,0,0.35,0,0.71,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0.35,0,0.17,0,0,0,0.53,0.35,0.53,0.17,0.17,0.17,0.17,0.17,0.35,0.17,0.17,0.17,0,0,0,0.17,0,0.71,0.17,0.17,0.35,0,0,0,0.123,0.309,0.03,0,0,0,2.241,13,204,0 0,0.13,0.55,0,0.27,0.13,0,0,0,0.27,0,1.38,0,0,0,0.13,0,0,1.94,0,0.97,0,0,0,0.13,1.11,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0,0.13,0,0,0,0,0,0.075,0.025,0,0.025,0,0,5.695,82,598,0 0,0,0.48,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0.48,0,1.44,1.93,0,0,1.44,1.44,0,0,0,0,0,0,1.44,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0.119,0.059,0,6.145,115,338,0 0,0,0.24,0,0.09,0.04,0,0,0.04,0,0.04,0.69,0,0,0,0.14,0.19,0,0.69,0,0.64,0,0,0,2.04,1.09,0,0.04,0,0.19,0.14,0,0.04,0,0.29,0.09,0.34,0,0,0,0,0,0,0,0.04,0,0,0,0.014,0.148,0,0.014,0.044,0.007,2.112,26,1223,0 0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,1.92,0,1.92,0,1.92,0,1.92,1.92,0,0,0.394,0.098,0.295,0,0,0,1.813,13,107,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.09,7,23,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.94,0,1.94,0,1.94,0,1.94,0,1.94,1.94,0,0,0.147,0.147,0.294,0,0,0,1.789,12,68,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.72,0,1.72,0,1.72,0,1.72,0,1.72,1.72,0,0,0,0,0.265,0,0,0,1.65,12,33,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.09,7,23,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,2,0,0,0,0.687,0,0,0,0,1.888,9,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.777,0,0,0,2,4,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.777,0,0,0,2,4,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,4.16,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.751,0,0,0,1.428,4,10,0 0,0,0.08,0,0,0.16,0,0.08,0.08,0,0.08,0.92,0.08,0.08,0,0,0.16,0,0,0,0,0,0,0,3.53,0,0,0,0,0,0,0,0.25,0,0,0,0.08,0,0,0,0,0,0,0.16,0,0,0,0,0.069,0.103,0,0,0.011,0,2.44,18,598,0 0,0,0,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,2.38,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,4,15,0 0,0,0,0,0.84,0,0,0,0,0,0,1.68,0,0,0,0.42,0,0.42,1.68,0,0.42,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,2.95,0,1.26,0,0,0,0,0.145,0.217,0,0,0,0,1.487,8,61,0 0,0,0,0,0,0,0,0,0,0.84,0,0.84,0,0,0,0,0,0,4.2,0,0,0,0,0,1.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.119,0,0,0,1.454,9,32,0 0.08,0.08,0.57,0,0.48,0,0,0.08,0,0,0,0.81,0.08,0,0.08,0,0.81,0,0.65,0,0.4,0,0,0,1.38,0,0,0,0,0,0.16,0,0.16,0,0.08,0,0.08,0,0,0.08,0,0.89,0,0.24,0.08,0,0,0.08,0.011,0.034,0,0.057,0.022,0,1.875,65,542,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.304,0,0,2.125,9,34,0 0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,1.78,1.78,0,0,0,0,0,1.78,0,0,1.78,0,0,0,0,1.78,0,1.78,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0.319,0,0,0,0,2.391,10,55,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0.86,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.944,8,35,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.09,7,23,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,2.85,0,0,0,0,0,0,2.85,0,0,0,0,0.543,0,0,0,0,1,1,10,0 0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0.9,4.5,0,0.9,0,0,0,0,0,0.9,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.318,0,0,0,0,1.772,4,39,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.52,4.76,4.76,4.76,4.76,4.76,4.76,4.76,0,4.76,4.76,4.76,0,0,0,4.76,0,0,0,0,0,0,0,0,0,1.257,0,0,0,0,3.333,11,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.375,6,44,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.03,0,0,0,0,0,2.53,2.53,1.52,0,0,0,0,0,1.01,0,0,0,0.5,0,0.5,0,0,0,0.5,3.04,0.5,0,0,0,0.094,0,0.094,0.094,0,0,1.26,12,63,0 0,0,0,0,0,0,0,0,0,1.2,0,2.4,0,0,0,0,0,0,4.81,0,1.2,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0.227,0,0,0,0,1.062,2,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.09,0,0,0,0,0,1.03,1.03,2.06,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,0,0,3.09,0,0,0,0,0,0,0,0.193,0,0,1,1,23,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,1.2,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.237,0,0,2.583,8,62,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.73,0.86,3.47,0.86,0.86,0.86,0.86,0.86,0,0.86,0.86,0.86,1.73,0,1.73,0.86,0,0,1.73,0,1.73,0,0,0,0,0.289,0,0,0,0,1.978,12,91,0 0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0.645,0,0,0,0,1,1,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.837,0,0,0,0,3.789,10,72,0 0.23,0.23,0,0,0.23,0.23,0,0.47,0.23,0.23,0.23,0.23,0,0,0,0.23,0,0,2.87,0,1.91,0,0.23,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0.37,0.205,0.041,2.281,24,146,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0.11,0,0.11,0,0.34,0.22,0,0,1.02,0,0,0.45,0.11,0,0,0,0,0,0.45,0,0.22,0,0,0,0.68,0.79,0.11,0,0,0,0,0,0.34,0,0,0.11,0.22,0,0,0,0,0,0,0,0,0,0,0,0.096,0.192,0.08,0,0.032,0,2.829,32,549,0 0.11,0,0.11,0,0,0,0,0,1.15,0,0,0.34,0,0,0,0,0,0,0.46,0,0.23,0,0,0,0.57,0.69,0.11,0,0,0,0,0,0.34,0,0,0.11,0,0,0,0,0,0,0,0,0,0,0,0,0.047,0.159,0.031,0,0.031,0,3.196,32,505,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.272,3,14,0 1.19,0,0.59,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,2.97,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.102,0,0,1.52,7,38,0 0.08,0,0.08,0,0,0,0,0,0.79,0,0,0.26,0,0,0,0,0,0.08,0.35,0,0.26,0,0,0,0.88,0.97,0.08,0,0,0,0,0,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.149,0.162,0.049,0,0.024,0,2.9,40,673,0 0.05,0,0.05,0,0.3,0,0,0,0.51,0,0,0.15,0,0,0,0,0,0.05,0.2,0,0.15,0,0,0,0.67,0.72,0.05,0,0,0,0,0,0.2,0,0,0,0.46,0,0,0,0,0,0,0.1,0,0,0,0,0.209,0.158,0.05,0,0.014,0,3.764,85,1423,0 0.17,0.08,0.08,0,0.17,0.08,0,0,0.76,0,0,0.25,0,0,0,0.08,0,0.08,0.34,0,0.25,0,0,0,0.76,0.85,0.08,0,0,0,0,0,0.34,0,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0.157,0.205,0.036,0,0.024,0,2.883,47,715,0 0,0,1.16,0,0,0,0,0,0,0,0.58,4.09,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0.58,0,0,1.16,0,0,0,0,0,0,0,0,0,0.091,0,0,0,0,0,1.21,4,46,0 0,0.15,0.07,0,0.23,0,0,0.07,0.07,0.07,0,1.48,0.15,0.23,0,0.07,1.01,0,0.15,0.07,0,0,0.15,0.07,3.11,0,0,0,0,0,0,0,0.15,0,0,0,0.15,0,0,0,0,0.46,0,0,0.23,0,0,0,0.185,0.098,0,0,0.043,0,2.013,24,576,0 0,0,0,0,0,0,0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.098,0,0,2.142,9,15,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.08,0,0,0,0,0,0,0,0,0,0,2.08,2.08,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0.173,0,0,1.777,6,48,0 0,0,0,0,0.74,0,0,0,0,1.49,0,0,0,0,0,0,0,0,1.49,0,0.74,0,0,0,2.98,2.23,1.49,2.23,0.74,0.74,1.49,0.74,0,0.74,0.74,1.49,0.74,0,0,0.74,0,0,0.74,0,0.74,0,0,0,0,0.557,0.111,0,0,0,2.607,12,133,0 0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.75,8,19,0 1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.178,0,0,1.272,3,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,2.77,2.77,2.77,0,2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.857,11,26,0 0,0.13,0.52,0,0,0.13,0,0.79,0,0,0,0.13,0,0,0,0.13,0,0,0.26,0,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0,0.02,0.061,0,0.04,0.04,0,1.823,26,372,0 0,0,0,0,0,0,0,0,0,0.8,0,1.07,0,0,0,0.26,0,0,1.07,0,1.88,0,0,0,2.15,1.61,1.07,0.26,0.26,0.26,0.26,0.26,0,0.26,0.26,0.26,0.53,0,0,0.26,0,0,0.53,0.53,0.53,0,0,0,0.174,0.437,0,0.043,0,0,2.879,19,262,0 0,0,0.36,0,0.36,0.72,0,0,0,0,0,1.09,0,0,0,0,0.36,0,0.36,0,0.72,0,0,0,1.09,1.09,0,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.114,0.114,0,0,0,0,2.075,7,110,0 0.68,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0.34,0.34,0.68,0.34,0,0.34,0,0,0,0,0.34,0,0,0,0.34,0,0.34,0,0,0,1.02,0.34,0,0,0,0.172,0,0.387,0,0,1.5,15,84,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0.86,0,0,0,6.03,3.44,0.86,0.43,0.43,0.43,0.43,0.43,0,0.43,0.43,0.43,0.86,0,0.43,0.43,0,0,0.43,0,0.43,0,0,0,0,0.13,0.065,0.065,0,1.043,2.983,40,179,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,4,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.391,0,0,1.333,4,20,0 0,0,0,0,0,0,1.23,0,0,0,0,1.23,0,0,0,0,1.23,0,0,0,0,0,0,0,2.46,0,0,0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.403,0,0,2.045,6,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0.628,0,0,0,0,1.5,5,15,0 0,0,0,0,0,0,0,0,0,0,0,1.41,0,0,0,0,0.7,0,1.41,0,1.41,0,0,0,0,0,0.7,0,0,0,0.7,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0.267,0.066,0,0,0,17.857,199,375,0 0,0.07,0.15,0,0.12,0.02,0,0.02,0,0.12,0,0.3,0.12,0,0,0.02,0.17,0.12,0.22,0.02,0.12,0,0,0,0,0,0,0,0,0,0,0,2.16,0,0,0.15,0.68,0,0,0.02,0.1,0.02,0.02,0.02,0,0.33,0,0.43,0.037,0.225,0.007,0.015,0.041,0.003,2.198,25,2458,0 1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,0,0.61,3.7,0,2.46,0,0,0,0,0,1.23,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0.226,0,0,0,0,1.3,3,26,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.166,2,7,0 0,0,0,0,1.54,0,0,0.51,0,0.51,0,0.51,0.51,0,0,0.51,0,0,1.54,0,1.03,0,0,0,0.51,0.51,1.54,0.51,0,0.51,0,0,0,0,0.51,0,0.51,0,0,0,0.51,0,0,0,0,0.51,0,0,0,0.158,0,0.079,0,0,1.711,15,77,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.166,2,7,0 1.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,1.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0.317,0,0,0,0,1.125,2,9,0 0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,13.04,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.407,3.555,19,96,0 0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,3.7,0,0,0,0,0,0,3.7,0,0,0,0,3.7,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.466,6,22,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.181,0,0,2,7,22,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.319,0,0,0,0,2.636,9,29,0 0,0,0,0,0.22,0.22,0,0,0,0.45,0,0.9,0,0,0,0,0,0,0.67,0,0.22,0,0,0,0.67,0,0,0.67,0,0,0.45,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.024,0,4.223,157,359,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.166,2,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.166,2,7,0 0,0,0,0,0,0.49,0,0.49,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0.228,0,0,0,0,1.962,5,106,0 0,0,0.32,0,0.32,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0.32,0,0,0,0,0,0.64,0.64,0,0,0,0,0,0,0,0,0,0.32,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0.04,0,0,0,0,1.902,10,175,0 0,0,2.5,0,0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,1,1,15,0 0,0,0,0,0,0,0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.961,0,0,2.333,9,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.431,0,0,0,0,2.733,7,41,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.96,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.613,0.306,0,0,0,1.611,7,29,0 0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.178,21,61,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.12,2.56,2.56,2.56,2.56,2.56,2.56,2.56,0,2.56,2.56,2.56,0,0,0,2.56,0,0,0,0,0,0,0,0,0,0.881,0,0,0,0,2.5,11,40,0 0.77,0,0,0,0.25,0,0,0,0,0,0,1.28,0,0,0,0,0,0,2.05,0,2.31,0,0,0,0.25,2.57,0,0,0.51,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0.117,0,0.039,0,0,4.016,45,237,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.45,0,0,0,0,0,0.72,6.56,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.124,0,0,2.361,30,85,0 0.07,0,0.22,0,0.03,0.07,0,0,0.03,0.22,0,0.71,0.03,0,0,0.03,0,0,0.9,0,0.56,0,0,0,1.58,0.26,0,0.11,0.11,0.11,0.18,0,0.03,0,0.22,0.07,0.18,0,0,0.03,0,0,0,0,0,0,0,0.03,0.028,0.078,0,0.028,0.016,0,1.765,20,1356,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0.719,0,1.25,2,10,0 0,0,0.34,0,0.34,0,0,0,0.34,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.78,0,0,0,0,0,0,0,0,0,0,1.583,6,95,0 0.89,0,0,0,1.49,0.29,0,0,0,0,0,1.19,0,0,0,0,0,0,0.89,0,0.89,0,0,0,0,0,0.29,0,0,0,0,0,0.29,0,0,0,0,0,0.29,0,0,0.59,0,0.59,0,0,0,0,0.325,0.162,0,0,0,0,1.583,9,76,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0.724,0,1.25,2,10,0 0,0,0,0,0,0,0,0,0,0,0,3.72,0,0,0,0,0,0,3.1,0,0,0,0,0,0.62,0,0,0,1.24,0,0,0,0,0,0,0,0,0,0,0,0,1.24,0,0,0,0,0,0,0,0.11,0,0,0,0,1.47,8,50,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,4,16,0 0,0,0,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0.35,0,1.43,0,0,0,3.95,0.71,0,0,0,0,0,0,0,0,0.71,0,0.35,0,0,0,0,0,0.35,0,0.35,0,0,0,0,0.113,0.113,0.056,0,0,2.969,16,193,0 0,0,0,0,0,0,0,0,0,1.37,0,2.75,0,0,0.68,0,0,0,0,0,0,0,0,0,2.06,2.06,1.37,0,0.68,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,1.37,0,0,0,0,0,0.235,0,0,0,0,1.531,11,49,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.66,0,1.88,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,2.15,8,43,0 0,0,0.25,0,0.12,0.12,0,0.12,0.25,0,0.12,1.14,0.25,0,0,0,0.25,0,0,0,0,0,0.25,0,3.04,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0.017,0,2.444,24,418,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0.465,0,0,0,0,1.769,6,23,0 0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0.9,0.9,0,0,0,0,0,0.9,0.9,0,0,0.9,0,0.9,0,0.9,0,0,0.9,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0.471,0,0,0,0,1.424,8,47,0 0,0,0,0,0,0,0,0,0,0.58,0,2.33,0,0,0,0.58,0,0,1.75,0,3.5,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0.195,0,0.097,0,0,2.157,11,41,0 0,0,0.2,0,0,0,0,0,0.2,0.41,0,0,0,0,0,0,0,0,1.45,0,0.2,0,0,0,0,0.2,0.2,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.45,0,0,0,0,0,0.329,0,0.109,0,0.365,1.187,11,114,0 0,1.16,0,0,0,0,0,1.16,0,1.16,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,1.25,3,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.34,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0.06,0,0,0,0,2.426,76,199,0 0.23,0,0,0,0.23,0.23,0,0,0,0,0,0.23,0,0,0,0.23,0,0,0.47,0,0,0,0,0,0.47,0.23,0,0,0,0.47,0.23,0,0.47,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0.107,0.107,0,0,0,0,1.595,8,142,0 0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0.684,0,0,0,0,1,1,8,0 0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,1.5,4,18,0 0,0,0,0,0,0,0,0,0,0,0,0.46,0.46,0,0,0,0,0,1.38,0,0.46,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0,0,0.92,0,1.38,0,0.92,0.46,1.38,0,1.38,0.92,0,0,0.149,0.074,0.149,0,0,0,1.76,12,132,0 0,0,0.8,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,4.8,0,1.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,1,1,11,0 0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.854,0,0,0,0,1.823,8,31,0 0,0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,1.4,2.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.125,2,9,0 0.21,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,2.11,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.084,0,0.028,0.084,0.084,4.11,62,411,0 0,0,0,0,0,0,0,0,0.68,0,0.68,0,0,0,0,0.68,0,2.04,4.08,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.523,0,0,1.218,5,39,0 0.11,0,0.22,0,0.55,0,0,0,0,0,0,0.55,0,0,0,0.11,0.11,0,1.22,0,0,0,0,0,1.22,0.44,0,0,0,0.11,0,0,1.89,0,0,0,0,1.22,0.11,0,0,0,0,0,0.22,0,0,0.11,0.052,0.156,0.034,0.017,0,0.052,3.061,38,600,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.35,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0.06,0,0,0,0,2.444,76,198,0 0.75,0,0,0,0,0,0.37,0,0,0.37,0,0,0,0,0,0,0.75,0,3.75,0,3.38,0,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.099,0.597,0,0,0,2.125,13,85,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.72,3.44,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.611,7,29,0 0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,2.7,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.444,0,0,2.8,7,28,0 0,0,0,0,0,0.67,0,0.67,0,0,0,2.02,0,0,0,0,0,0,0,0,0,0,0,0,2.02,1.35,0,1.35,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.205,0,0,0,0,2.84,24,142,0 0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,9.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,1.5,4,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,1.714,6,12,0 0,0,0,0,0,1.03,0,2.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.144,0.072,0,0,0,1.523,11,64,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,2.63,3.5,2.63,1.75,1.75,0.87,0.87,0,0.87,1.75,0.87,0.87,0,2.63,0.87,0,0,0.87,0,1.75,0,0,0,0,0.49,0.122,0.122,0,0,2.203,12,130,0 0.06,0,0.4,0,0.13,0.13,0,0.13,0,0,0,1.4,0.2,0.06,0,0,0.2,0,0.06,0,0,0,0,0,2.54,0,0,0,0,0,0,0,0,0,0,0.06,0.06,0,0,0,0,0,0,0,0.06,0,0,0,0.028,0.085,0,0,0,0,2.341,22,665,0 0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,1.02,2.04,0,0,0,0,0,2.04,1.02,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0.188,0,0,0,0,3.9,13,78,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.8,12,28,0 0.26,0.26,0,0,0.52,0.26,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.83,1.57,0,0,0.26,0,0.26,0,0,0,0.26,0.26,0.26,0,0,0,0,0,0.52,0,0,0,0,0,0.065,0,0.032,0,0,0,1.455,12,115,0 0.13,0,0.26,0,0.65,0,0,0,0,0,0,0.52,0,0,0,0.13,0.13,0,1.18,0,0,0,0,0,0.52,0.26,0,0,0,0.13,0,0,2.1,0,0,0,0,1.44,0.13,0,0,0,0,0,0.26,0,0,0.13,0,0.188,0.041,0,0,0.062,2.876,38,420,0 0,0,0,0,0,0,0,0,0,0,0,3.63,0,0,0,0,0,0,0,0,0,0,0,0,0.9,3.63,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.153,0,0,1.933,7,58,0 0,0,0,0,0,0,0,0,0,0,0,6.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.645,0,0,1.666,7,15,0 1.17,3.52,0,0,0,0,0,0,0,1.17,0,1.17,0,0,0,0,0,3.52,2.35,0,3.52,0,0,0,3.52,2.35,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0.414,0,0,1,1,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,1.4,3,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,2.4,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,1.6,0,0.8,0.8,1.6,0,1.6,0.8,0,0,0.128,0,0.128,0,0,0,1.596,12,83,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.25,0,0,0,0,0,0,0,6.25,0,0,0,0,0,6.25,0,0,0,0,0,0,0,0,0,0,0,6.25,0,0,0,0,0,0,0,0,0,1.285,3,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,11.11,0,0,0,0,1.492,0,0,0,0,1.571,4,11,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,2,5,0 0,0,0.44,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,1.76,1.76,0,0,0,0,0,0,0,0,0,0,0.88,0,0.88,0,0,0,0.44,0,0,0,0,0.44,0,0,0.061,0,0,0,1.949,17,230,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.25,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,1.142,2,8,0 3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,3.03,0,0,0,0,0.609,0,0,0,0,1.181,3,13,0 0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0,0.42,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0.061,0,0,0,0,2.288,11,103,0 0,0,0.32,0,0,0,0,0,0.32,0,0,1.3,0,0,0,0,0,0,0.97,0,0.32,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0.32,0,0.32,0,0.65,0,0.32,0.32,0,1.3,0,0,0.047,0.094,0.047,0,0,0,1.973,17,148,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0.47,0,0,0,2.83,2.35,1.88,2.35,1.41,1.41,0.47,0.47,0,0.47,1.41,0.47,0.47,0,0,0.47,0,0,0.47,0,1.41,0,0,0,0,0.144,0.072,0.072,0,0,2,13,168,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.53,0,0,0,0,0,1.26,1.26,1.26,2.53,1.26,1.26,0,0,0,0,1.26,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0,0,0.208,0,0,1.758,11,51,0 0.11,0.11,0.34,0,0.11,0,0,0,1.02,0,0,0.45,0,0,0,0.11,0,0,0.45,0,0.22,0,0,0,0.56,0.68,0.11,0,0,0,0,0,0.34,0,0,0,0.22,0,0,0.11,0,0.11,0,0,0,0,0,0,0.103,0.177,0.029,0,0.029,0,4.296,81,653,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.25,10,17,0 0,0,0.58,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0.58,0,2.33,0,0,0,0,0,2.33,0,0.58,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0.58,0.58,0,0,0,0,0.203,0,0.407,0.407,0,3.294,17,112,0 0,0,0,0,0,0,0,0,0,0,0,4.65,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.448,0,0,2,4,28,0 0,0,0,0,0,0,0.88,0,0,0,0,0.88,0,0,0,0,0,0,0.88,0,0,0,0,0,0,0,0,0,0,0,0,0,1.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.133,0,0,0,0,1.629,9,44,0 0,0,0,0,0,0,0,0,0,0.63,0,0.63,0,0,0,0,0,0,0,0,0.63,0,0,0,2.54,1.91,1.91,0.63,0.63,0.63,0.63,0.63,0,0.63,0.63,0.63,0.63,0,0.63,0.63,0,0,0.63,0,0.63,0,0,0,0,0.279,0.093,0,0,0,1.981,12,105,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,3.84,3.84,3.84,3.84,3.84,3.84,3.84,0,3.84,3.84,3.84,0,0,0,3.84,0,0,0,0,0,0,0,0,0,1.092,0,0,0,0,2.909,11,32,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,4,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0.54,0,0.54,0,0,0,0,0,0,0.54,0,0,0,2.71,1.63,0.54,0.54,0.54,0.54,0.54,0.54,0,0.54,0.54,0.54,0,0,0,0.54,0,0,0,0.54,0.54,0,0,0,0,0.531,0,0,0,0,4.114,35,251,0 0,0,0,0,0,0,0,0,0,0,0,2.38,0,0,0,0,0,0,2.38,0,0,0,0,0,0,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.38,0,0,0,0,0,0,0,0,0,1.666,9,25,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.33,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0.06,0,0,0,0,2.481,76,201,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,0,0,1.31,0,0,0,2.63,1.97,1.31,0.65,0.65,0.65,0.65,0.65,0,0.65,0.65,0.65,0,0,0,0.65,0,0,0,0.65,0.65,0,0,0,0,0.507,0,0,0,0,3.041,34,146,0 0,0,0.32,0,0.32,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0.32,0,0,0,0,0,0.64,0.64,0,0,0,0,0,0,0,0,0,0.32,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0.04,0,0,0,0,1.677,10,156,0 0,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,1.96,1.96,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.373,0,0,0,0,1.857,11,26,0 0,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,1.96,1.96,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.373,0,0,0,0,1.857,11,26,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,2.4,1.2,1.2,1.2,1.2,1.2,1.2,1.2,0,1.2,1.2,1.2,0,0,0,1.2,0,0,0,0,1.2,0,0,0,0,0.57,0,0,0,0,2.312,11,37,0 0,0,0,0,0,0,0,0,0,1.11,0,3.33,0,0,0,0,0,0,1.11,0,0,0,0,0,2.22,1.11,0,0,0,3.33,0,0,0,0,0,1.11,0,0,0,0,0,0,0,0,0,0,0,1.11,0,0.191,0,0,0,0,1.454,7,48,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.343,0,0,0,0,4.226,8,317,0 0,0,0.33,0,0.66,0,0,0.33,0,1.32,0,0,0,0.33,0,0,0.33,0,1.32,0,0.33,0,0,0,1.98,0.66,0.66,0,0,0,0,0,0.33,0,0,0,0.99,0,0,0,0,0,0.33,0.33,0.33,0,0,0,0.168,0.392,0,0.224,0.336,0,4.115,42,321,0 0.51,0,0,0,0.17,0.17,0,0,0.34,0.17,0,2.07,0,0,0,0.17,0,0,2.24,0,1.03,0,0,0,0.34,0.69,0.17,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0.17,0.34,0,0,0,0,0.466,0.248,0,0,0,0.062,2.926,48,319,0 0,0.1,0,0,0.1,0.21,0,0.1,0,0,0,1.19,0,0,0,0,0,0,0.1,0,0,0,0,0,0.87,0,0,0,0,0.1,0,0,0.1,0,0,0.43,0,0,0,0,0,0,0,0.1,0,0,0,0,0,0.047,0,0,0.031,0,1.793,12,391,0 0.09,0,0,0,0,0.09,0,0.28,0,0,0,0.76,0.09,0,0,0,0.38,0,0,0,0,0,0,0,2.66,0,0,0,0,0,0,0,0.38,0,0,0,0.09,0,0,0.47,0,0.09,0,0,0,0,0,0,0.026,0.093,0,0.013,0.12,0,2.658,24,577,0 0,0,0,0,0,0,0,0,0,0,0,2.89,0,0,0,0.57,0,0,0,0,1.73,0,0,0,2.31,0,0,0,0,3.46,0,0,0,0,0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0.163,0,0,0,0,1.9,12,76,0 0.3,0.3,0,0,0.6,0.3,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0.3,0,0,0.3,0,0.3,0,0,0,0.3,0.3,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,1.389,8,82,0 0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0.7,0,1.4,0,1.4,0,0,0,0,0,0.7,0,0,0,0.7,0,0,0,0,0,0,0,0,2.11,0,0,0,0,0,0,0,0,0,0.267,0.066,0,0,0,17.952,200,377,0 0,0,0,0,0,0.24,0,0,0,0,0,0.72,0,0,0,0,0,0,0.24,0,0,0,0,0,2.65,1.2,0,0,0,0,0,0,0.24,0,0,0,0.96,0,0,0,0,0,0,0,0,0.48,0,0.24,0.067,0.371,0.067,0,0,0,3.322,44,319,0 0.23,0,0.23,0,0.69,0,0,0,0,0,0,1.39,0,0,0,0,0,0,0.23,0,0,0,0,0,0.23,0.23,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0,0.068,0,0,0,0,1.483,9,89,0 0,0,0,0,0,0,0.68,0,0,0.68,0,0,0,0,0,0,0,0,1.37,0,2.06,0,0,0,0,0,0.68,0,0,0,0,0.68,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.144,0,0,0,0.072,3.369,19,155,0 0.58,0,0,0,0.19,0.19,0,0,0.38,0.19,0,2.32,0,0,0,0.19,0,0,2.51,0,1.16,0,0,0,0.19,0.58,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0,0,0,0.38,0,0,0,0,0,0.251,0,0,0,0.071,2.08,11,156,0 0,0,0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,1.26,0,0,0,0,0,0,0,0.31,0,0,0,0.31,0,0,0,0,0.14,0,0,0,0,1.592,7,129,0 0,0,0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,1.26,0,0,0,0,0,0,0,0.31,0,0,0,0.31,0,0,0,0,0.14,0,0,0,0,1.592,7,129,0 0,0,0.42,0,0.64,0,0,0,0,0,0,0.21,0,0,0,0,0,0,0.85,0,0.21,0,0,0,2.13,0.21,0.21,0,0,0,0,0,2.13,0,0,0,0.42,0,0.21,0.21,0,0,0.42,0.21,0.64,0,0,0,0.238,0.443,0.068,0,0,0,2.524,18,260,0 0,0,0,0,0.24,0.49,0,0,0,0.49,0,0.24,0,0,0,0,0,0,0.99,0,0.49,0,0,0,0.74,0,0,0.74,0,0,0.49,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.054,0,0,0.027,0,4.634,157,380,0 0,0.23,0,0,0.47,0,0.23,0,0,0,0.23,0,0,0,0,0,0,0.23,0.23,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0.23,0,0,0,0.298,0,0.149,0,0,1.533,18,184,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,1.272,4,14,0 0,0,0,0,0,0,0,0,0,0,0,3.75,0,0,0,0,0,0,0,0,0,0,0,0,1.25,1.25,0,1.25,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0.224,0,0,0,0,2.379,18,69,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0.39,0,0.19,0,0,0,0,0,0,0.19,0.19,1.98,0,0.19,0,0,0,0.19,0.19,0,0.19,0,0,0,1.58,1.19,0,0.19,0,0.39,0.19,0,0.59,0,0.39,0.39,1.19,0,0.19,0,0,0.19,0.19,0,0,0,0,0.39,0.28,0.14,0.028,0.112,0,0,2.101,17,311,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0.88,0,0,0.88,0.88,2.65,0,1.76,0,0,0,0.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.37,3,37,0 0.13,0.06,0,0,0.13,0.13,0,0,0.13,0.27,0.06,0.41,0.06,0,0,0.27,0.06,0,1.04,0.13,0.83,0,0,0.06,1.46,0.48,0,0.13,0,0.06,0.27,0,0,0,0.13,0,0.2,0,0,0,0,0,0,0,0.06,0,0,0.48,0,0.194,0,0.029,0.048,0.009,1.793,23,888,0 0.09,0.09,0.28,0,0.28,0,0,0.28,0,0,0,0.09,0.18,0,0,0,0.18,0.28,1.22,0,0.37,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0.28,0,0,0.09,0,0,0,0.28,0.37,0.09,0,0,0.014,0.084,0,0.042,0,0.042,1.877,18,552,0 0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0,1.37,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0,0,0.68,0,0.68,0,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,1.488,12,64,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,4,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.934,0,0,0,0,3.2,7,16,0 0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2,2,2,2,4,2,2,0,2,2,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0.682,0,0,0,0,2.705,11,46,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.354,0,0,0,0,2.187,5,35,0 0.9,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,3.6,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0.9,0,0.479,0,0,0,0,2.166,8,52,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.333,8,10,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.35,0,0,0,1.17,1.17,2.35,0,0,0,0,4.7,0,4.7,0,0,0,0,1.17,0,0,0,0,0,2.35,0,0,0,0.185,0.743,0,0,0,0,4.476,14,94,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,3.84,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,1.85,3.7,0,3.7,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.544,1.634,0,0,0,2.352,11,40,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0.42,0.85,0,0,0,0,2.14,0,2.14,0,0,0,0,0,0,0,0,0,0,0.85,0,0,0,0.332,0.73,0,0,0,0,5,14,270,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,1.27,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0,0,0.325,0.781,0,0,0,0,4.758,14,276,0 0,0,0.24,0,0,0,0,0.12,0.12,0,0,0.6,0.12,0.12,0,0,0.72,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0.12,0.12,0,0,0,0,0,0,0,0,0,0,0,0.105,0.06,0,0,0,0,1.827,23,466,0 0.67,0,0,0,0,0,0,0,0.33,0.33,0.33,0.33,0.33,0,0,0,0,0.33,1.35,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.056,0.28,0.168,0.056,0,0,1.866,17,112,0 0.1,0,0.1,0,0,0,0,0,0.92,0,0,0.4,0,0,0,0,0.1,0,0.4,0,0.2,0,0,0,0.51,0.61,0.1,0,0,0,0,0,0.3,0,0,0,0.1,0,0,0,0,0,0,0.1,0,0,0,0,0.014,0.154,0.028,0,0.028,0,2.785,32,507,0 0.04,0.02,0.12,0,0.08,0.02,0,0.08,0,0.06,0.02,0.5,0.06,0,0.02,0.02,0.14,0.12,0.25,0,0.19,0,0.04,0,0,0,0.1,0,0.02,0,0,0,1.97,0,0,0.19,0.97,0.02,0,0.02,0.1,0.02,0,0.14,0,0.33,0.02,0.1,0.024,0.198,0,0,0.018,0.003,2.43,81,3337,0 0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0,3.33,0,1.66,0,0,1.66,1.66,0,1.66,0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0.29,1.722,7,31,0 0,0,0.5,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0.5,0,1.52,2.03,0,0,1.52,1.52,0,0,0,0,0,0,1.01,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0.122,0.061,0,4.309,38,237,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,6,18,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.98,0,0.99,0,0,0,2.97,1.98,0,0.99,0,0,0,0,0,0,0.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0,0,0,0,2.347,10,54,0 0,0.11,0,0,0.11,0.22,0,0.11,0,0,0,1.32,0,0,0,0,0,0,0.22,0,0,0,0,0,0.99,0,0,0,0,0.22,0,0,0.11,0,0.11,0.44,0,0,0,0,0,0,0,0.11,0,0,0,0,0,0.047,0,0,0.031,0,1.614,12,339,0 0,0,0.21,0,0,0,0,0.21,0,0.21,0,0,0,0,0,0,0,0.21,0,0,0,0,0,0,0.21,0.21,0,0.43,0,0,0,0,0.21,0,0.21,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0.149,0,0,0,0,1.79,15,188,0 0,0,0,0,0,0.3,0,0,0,0,0.3,2.42,0,0,0,0.3,0,0.9,3.63,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.087,0,0,2.74,64,148,0 0,0,0,0,0,0,0,0,0,0,0,2.9,0,0,0,0.58,0,0,0,0,1.74,0,0,0,2.32,0,0,0,0,3.48,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0.165,0,0,0,0,1.7,12,68,0 0,0,0,0,0,0,0,0,0,5.26,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.952,0,0,3.2,12,16,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.22,0,1.4,0,0,0,1.4,0,1.4,2.81,0,0,0,0,1.4,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,2.81,0,0.458,0,0.229,0,0,2.653,15,69,0 0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.265,0,0,0,3.85,26,77,0 0,0,1.28,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,2.56,0,0,0,0,0,2.56,1.28,0,0,0,0,0,0,2.56,0,0,0,1.28,0,0,0,0,3.84,0,0,0,0,0,0,0,0.148,0.148,0,0,0,2.034,13,59,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.96,0,1.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0.268,0,0.134,0,0,2.75,8,22,0 0,0,0,0,0.19,0,0,0,0,0,0.19,0.77,0.19,0,0,0.19,0,0.19,0.38,0.19,0,0,0,0,0.19,0,0,0.38,0,0,0,0,0,0,0,0.19,0.38,0,0.19,0,0,0.38,0,0,0,0,0,0,0.068,0.113,0,0.022,0.045,0,1.74,21,395,0 0,0,2.12,0,1.06,0,0,0,0,1.06,0,1.06,0,0,0,0,0,0,4.25,0,0,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,1.785,6,25,0 0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0.237,0,0,0,0,1.8,9,36,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,4,4,4,4,4,4,4,0,4,4,4,0,0,0,4,0,0,0,0,0,0,0,0,0,1.117,0,0,0,0,3.333,11,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,4.16,4.16,4.16,4.16,4.16,4.16,4.16,0,4.16,4.16,4.16,0,0,0,4.16,0,0,0,0,0,0,0,0,0,1.142,0,0,0,0,3.333,11,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.2,4.05,0,0,0,0,0,0,0.9,0,0,0,2.25,0,0,0,1.35,0.9,0,0,0.9,0,0,0,0.332,0.747,0.166,0,0,0,4.054,19,296,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,1.214,4,17,0 0,0,0.36,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,0.36,1.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.236,0,0,0,0,1.277,3,69,0 0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,0,0,1.21,0,0,0,0.238,0,0,0.238,0,0,1,1,16,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,0,0,1.21,0,0,0,0,0,0,2.43,0,0,0,0,0,0,0,0,1.21,0,0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.43,0,1.21,0,0,0,0,0.567,0.378,0,0,0,0,1.333,3,24,0 0,0.5,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0.5,0,0,0,0,0,0,0,1.5,1,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1.468,5,69,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,5.43,6.52,2.17,1.08,1.08,1.08,1.08,1.08,0,1.08,1.08,1.08,0,0,1.08,1.08,0,0,0,0,1.08,0,0,0,0,0.472,0,0,0,0,5.291,34,127,0 0.06,0,0.25,0,0.25,0.25,0,0.5,0,0,0,0.56,0.12,0.06,0,0,0.5,0,0.12,0,0,0,0,0,2.06,0,0,0,0,0,0,0,0.06,0,0,0.75,0.06,0,0,0,0,0.06,0,0.06,0,0,0,0.06,0.104,0.069,0,0,0.043,0,2.148,23,623,0 0,0,0,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,1.31,0,0,0,0,0,0,2.63,0,0,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0.431,0,0,0,0,2.176,8,37,0 0,0,0,0,0.19,0,0,0,0,0,0.19,0.76,0.19,0,0,0.19,0,0.19,0.38,0.19,0,0,0,0,0.38,0,0,0.38,0,0,0,0,0,0,0,0.19,0.38,0,0.19,0,0,0.38,0,0,0,0,0,0,0.066,0.111,0,0.022,0.044,0,1.759,21,403,0 0.75,0,0,0,0,0,0,0,0.75,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,0,0,0,0,0,0,1.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.846,39,100,0 0,0,1.69,0,0,0,0,0,0,0,0,1.69,0,0,0,0,0,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,1,1,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.714,5,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0.33,0,0,0,1.444,5,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.384,4,18,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0.99,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0.28,0,0,0,0,1.363,5,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.526,0,0,0,0,1.529,6,26,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.285,7,32,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,3,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.833,5,11,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,3,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,7,10,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.25,6,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,3,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,1.5,3,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.4,2,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.6,4,8,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.333,8,10,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.142,2,8,0 0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.34,0,0,0,0,0,0,0,0.44,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,3.901,33,398,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.941,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,6,0 0,0,0,0,0,0,0,0,0,0,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,2.63,2.63,0,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,7.89,0,0,0,0,0,0,0,0,0,0,0,0,1.4,3,14,0 0,0.15,0,0,0.15,0,0,0,0.15,0.15,0.3,0.46,0,0,0,0,0,0.15,0.3,0,1.07,0,0,0,0,0,0,0,0,0.15,0,0,0.61,0,0,0.15,1.22,0,0,0,0,0,0,0,0,0.61,0,0.15,0.019,0.137,0,0,0,0,2.276,20,485,0 0.36,0.36,0,0,1.8,0,0,0,0,0,0,1.44,0,0,0,0,0.72,0,0.36,0,1.08,0,0,0,1.8,0,0,0,0.72,0.36,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.636,12,54,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.88,0,0,0,0,0.28,0,0,0.28,0,0,0,0.14,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0.037,0,0,12.43,30,2051,0 0,0,0,0,2.02,0,0,0,0,0,0,0,1.01,0,0,0,0,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.02,0,0,0,0,0.166,0.166,0.166,0,0,0,1.428,6,40,0 0,0,0,0,0.3,0,0,0,0,0,0,0.76,0,0,0,0,0,0,0.15,0,0,0,0,0,0.3,0.15,0,0,0.6,0,0,0,0,0,0,1.21,0.15,0,0,0,0,0,0,0,0,0.15,0,0,0,0.022,0,0,0,0,1.59,37,272,0 0,0,0,0,0,0,0,0,0,1.08,0,1.08,0,0,0,0,0,0,2.17,0,2.17,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,0.173,0,0,0,0,2.1,18,42,0 0,0,0.61,0,0,0,0,0,0,0,0,1.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0.079,0.158,0,0,0,0,2.508,17,143,0 0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,1.29,0,0,0,0,0,0,4.51,3.22,3.22,1.29,0,1.29,1.29,0,0,0,1.29,1.29,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.324,0.194,0.129,0,0.194,2.142,10,150,0 0,0,0,0,0.53,0,0,0,0,0,0,0.53,0.53,0,0,0,0,0,0.53,0,1.06,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0.188,0,0,0,0,1.142,3,40,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.869,0,1.739,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0.14,0,0,0,0,0,0,0,0,0.14,0,0,0,5.16,0,0,0,0.14,0.44,0,0,0.14,0,0,0,1.47,0,0.59,0,0,0,0,0,0.29,0,0,0,0.186,0.538,0.124,0,0,0,4.454,55,931,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,0,3.84,0,0,0,1.92,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0.178,0,0,1.666,7,50,0 0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0.636,0,0,2,10,18,0 0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,0.444,0,0,2.333,12,28,0 0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,1.35,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0,0,1.533,6,23,0 0.11,0,0.23,0,0.23,0.11,0,0,1.15,0,0,0.34,0,0,0,0.11,0,0,0.46,0,0.23,0,0,0,0.57,0.69,0.11,0,0,0,0,0,0.34,0,0,0.34,0.23,0,0,0,0,0,0,0,0,0,0,0,0.048,0.194,0.032,0,0.032,0,3.275,33,511,0 0.17,0,0.17,0,0,0,0,0,0.8,0,0,0.26,0,0,0.08,0,0,0,0.35,0,0.17,0,0,0,0.62,0.71,0.08,0,0,0,0,0,0.26,0,0,0.08,0.44,0,0,0,0,0,0,0,0,0,0,0,0.253,0.168,0.084,0,0.024,0,4.665,81,1031,0 0.07,0,0.29,0,0.07,0.07,0,0,0.74,0,0,0.22,0,0.07,0,0,0,0.07,0.29,0,0.22,0,0,0,0.67,0.74,0.07,0,0,0,0,0,1.63,0,0,0,0.59,0,0,0,0,0,0.07,0,0,0,0,0,0.163,0.228,0.032,0,0.021,0,3.03,45,706,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,4,2,2,4,0,2,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,2.095,11,44,0 0,0,0,0,0,0,0,0,0.75,0,0,0,0,0,0,0,0,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.75,0,0,0,0,0.255,0,0,0,0,1.842,6,35,0 0.83,0,0.41,0,0,0,0,0,0,0,0.41,0.83,0,0,0,0,0,0,2.91,0,1.66,0,0,0,0.41,0.41,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0,0.41,0,0,0,0,0,0,0,0,0.283,0,0,2.022,13,91,0 0,0,0.06,0,0,0,0,0.06,0.13,0.13,0.13,1.67,0.26,0.33,0,0.13,0.13,0,0,0.06,0.06,0,0,0,2.54,0.13,0,0,0.2,0.26,0.13,0,0,0,0.06,0.2,0.13,0.06,0,0.06,0,0,0,0,0,0,0,0,0.028,0.131,0,0,0,0,1.997,20,787,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,2.32,0,0,0,0,0,4.65,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,1,1,11,0 0,0,0.38,0,0.38,0.38,0,0,0.38,0,0,1.55,0,0,0,0,0,0,1.16,0,0.38,0,0,0,0.77,0.77,0.38,0,0,0,0,0,1.93,0,0,0,0,0,0.38,0,0,0,0,0,1.16,0,0,0,0,0.061,0,0,0,0,2.953,34,127,0 0,0,0,0,0,0.47,0,0,0,0.23,0,0,0,0,0,0,0,0,2.6,0,0,0,0,0,5.45,0,0.23,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0,0,0.94,0,1.18,0,0,0,0.119,0.158,0.119,0,0,0,2.565,19,295,0 0,0,0,0,0,0,0,0,0,0,0,0.88,0,0,0,0,0,0.22,0,0,0,0,0,0,0.22,0.22,0,0.44,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0.22,0,0,0,0.22,0,0.172,0,0,0,0,1.729,15,128,0 0,0,0,0,0,0.57,0,0,0,0.28,0,0,0,0,0,0,0,0,2.86,0,0,0,0,0,4.58,0,0.28,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0,0.85,0,0.85,0,0,0,0.144,0.192,0.096,0,0,0,2.306,19,203,0 0.41,0,0.83,0,0,0.41,0,0,0,0,0,0.83,0,0,0,0,0,0,1.67,0,0.41,0,0,0,0,0,0.83,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,1.12,3,56,0 0,0,0.15,0,0.13,0.03,0,0.08,0,0.06,0.03,0.64,0.08,0.01,0,0.05,0.22,0.01,0.15,0.03,0.33,0,0,0,0,0,0.01,0,0.03,0.01,0,0,1.33,0,0,0.1,0.76,0,0.01,0.05,0.06,0.03,0,0.05,0,0.1,0,0.37,0.024,0.254,0.002,0.002,0.007,0,2.128,36,3467,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0.675,0,0,0,0,0,1,1,3,0 0,0.33,0,0,0.33,0,0,0,0,0,0,0.33,0,0,0,0,0,0.33,0,0,0,0,0,0,0.33,0.33,0,0.67,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0.33,0,0,0,0.33,0,0.132,0,0,0,0,1.857,15,117,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,2.333,8,28,0 0,0,0.52,0,0,0,0,0,0,0,0,1.56,0,0,0,0.52,0,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,5.72,0,0,0,1.56,0,0,0,0.52,1.04,0,0,0,0.52,0,0,0,0.075,0.151,0,0,0,2.416,18,116,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.449,0,0,0,2,5,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,2,5,0 0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,4.74,0,0,0.86,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0.062,0,0,0,0,1.21,6,69,0 0,0.13,0.54,0,0.27,0.13,0,0,0,0.27,0,1.21,0,0,0,0.13,0,0,1.89,0,0.94,0,0,0,0.13,0.94,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0.13,0,0,0,0,0,0.073,0.048,0,0.024,0,0,5.15,82,582,0 1.26,0,0,0,0,0,0,0,0,1.26,0,1.26,0,0,0,0,0,1.26,2.53,0,0,0,0,0,0,0,0,2.53,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,1.26,2.53,0,0,0,0,0,0,0,0,2.842,11,54,0 0,0,0,0,0.64,0,0,0,1.28,0,0,0.64,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0.197,0,0,2.35,13,94,0 0,0,0,0,0.5,0,0,0,0,0,0,0.5,0,0,0,0,0,0,1.01,0,0,0,0,0,1.01,0.5,5.55,0.5,0.5,0.5,0.5,0.5,0,0.5,0.5,0.5,0.5,0,0.5,0.5,0,0,0.5,0,0.5,0,0,0,0.083,0.167,0,0.502,0,0,1.547,11,113,0 0,0,0,0,0,0,0,0,0,0,0,1.33,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0.44,0,0,0,0,0,0,1.33,0,0.44,0,0.89,0,0,0,0,0,0,0,0,0,0,0,0,0.397,0,0,0,0,1.936,10,122,0 0,0,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,1.37,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0.143,0,0,0,0,1.784,18,141,0 0,0,0,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,1.37,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0.143,0,0,0,0,1.784,18,141,0 0,0,0,0,0.9,0,0,0,0,0,0,0,1.8,0,0,0.9,0,0,0.9,0,0,0,0,0,2.7,0.9,0.9,0.9,0.9,0.9,0.9,0.9,0,0.9,0.9,0.9,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0.449,0,0,0,0,2.15,11,43,0 0,0,0,0,0,0,0,0.99,0,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,1.98,0.49,0,0.49,0.49,0.99,0,0,0,0,0.49,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0.119,0,0,0,0,2.135,13,126,0 0,0,0,0,0,0.23,0.23,0.23,0,0,0,0.46,0,0.46,0,0,0,0,0.23,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0.073,0,0,0,0,0,3.184,74,207,0 0,0,0,0,0,0,0,0,0,0,0,0,0.86,0.86,0,0,0,0,0,0,0,0,0,0,3.44,2.58,1.72,0.86,0.86,0.86,0.86,0.86,0,0.86,0.86,0.86,1.72,0,1.72,0.86,0,0,1.72,0,1.72,0,0,0,0,0.27,0.135,0.135,0,0,2.288,13,103,0 0.1,0,0,0,0,0.1,0,0.52,0,0.1,0,1.9,0.1,0.1,0,0.1,0.21,0,0,0,0,0,0,0,3.17,0,0,0,0,0,0,0,0,0,0,0.1,0.1,0,0,0,0,0,0,0.1,0,0,0,0,0.027,0.138,0,0.041,0.041,0,2.321,31,469,0 0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0.53,0,0,0,0,0,1.61,0.53,0,0,0.53,0,0,0,0,0,0,0.53,0,0,0,0,0,0.53,0,1.07,0,0,0,0.53,0,0,0,0,0,0,1.375,5,99,0 0,0,0.41,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0.41,0,0,0,0,0,0,0.41,0,0.41,0,0,0,0,0,0,0,0,1.522,11,67,0 0,0,0,0,0.43,0,0,0,0,0,0,0.43,0,0,0,0,0,0,2.19,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.079,0.158,0,0,0,0,1.115,2,29,0 0.23,0,0.23,0,0.69,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0.23,0,0,0,0,0,0.23,0.23,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0,0.066,0,0,0,0,1.412,9,89,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,9,15,0 0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0.395,0,0,0,1.523,6,32,0 0,0,0,0,0,0,0,0,0.75,0,0,0.75,0,0,0,0,0,0,2.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.75,0,0,0,0,0.263,0,0,0,0,1.176,3,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,6,0 0,0,0.19,0,0.19,0.19,0,0,0,0.19,0,0.38,0,0,0,0,0,0.38,1.54,0,0.96,0,0,0,2.69,1.54,0.77,0.57,0.19,1.15,0.19,0.19,0,0.19,0.57,0.38,0.38,0,0,0.19,0.38,0,0.38,0,0.38,0,0,0.19,0.026,0.404,0.053,0.026,0,0,2.894,45,411,0 0,0,0,0,0,0,0,0.65,0,1.3,0,0,0,0,0,0,0,0.32,0.32,0,0.65,0,0,0,4.9,4.24,0.32,0,0,0.65,0,0,0,0,0,0,1.63,0,0,0,0.98,0,0,0,0.65,0,0,0,0.153,0.562,0.102,0,0,0,5.555,42,500,0 0.25,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0.25,0,0,0.25,0,0,0,0,0,2.06,1.03,0.25,0.25,0.25,0.25,0.25,0.25,2.83,0.25,0.25,0.25,0.25,0,0,0.25,0,0,0.25,0,0.25,0,0,0,0.301,0.473,0.043,0.043,0,0,2.111,17,190,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0.44,0,0,0,0,0,0,0.44,0.44,0,0.88,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0.123,0,0,0,0,1.857,15,104,0 0,0,0.44,0,0.44,0,0,0,0,0.44,0,0.88,0,0,0,0,0,0.88,2.22,0,2.22,0,0,0,1.33,0.44,0.88,0.88,0,0.88,0,0,0,0,0.88,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0.506,0,0.05,0,0,3.772,45,249,0 0.33,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.448,0,0.056,0,0,1.788,6,93,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.166,2,7,0 0,0,1.1,0,0,0,0,0,0,0.27,0.27,0.55,0,0,0,0,0,0,1.1,0,0.83,0,0,0,1.1,0.27,0,0,0.55,0.27,0,0,0,0,0,0,0.83,0,0,0,0,0,0,0,0,0,0,1.1,0.543,0.349,0,0,0,0,2.724,79,316,0 0,0.29,0.29,0,0.29,0,0,0.29,0,0,0.29,1.45,0,0,0,0,0.58,0,1.16,0,1.45,0,0,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.037,0.113,0,0,0.037,0,1.531,7,147,0 0,0,2.56,0,0,0,0,0,0,0,0,0,0,5.12,0,0,0,0,2.56,0,0,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0.485,0,0,0,0,1,1,11,0 0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0.374,0,0,1.375,5,22,0 0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.666,5,16,0 0,0,0,0,2.22,0,0,0,0,0,0,3.33,0,0,0,0,0,0,1.11,0,1.11,0,0,0,1.11,1.11,0,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,22,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.26,0,0,5.26,0,0,0,0,0,0,0,0,0,1.25,2,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,7,18,0 0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0.286,0,0,0,0,2.277,9,41,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0,0,1.69,0,0,0,0,0,0,1.629,7,44,0 0,0,0,0,0.93,0,0,0,0,0.93,0,0.46,0,0,0,0,0,0,1.4,0,0,0,0,0,4.22,1.87,0.93,0.46,0.93,0.46,0.46,0.46,0,0.46,0.46,0.46,0.46,0,0,0.46,0,0,0.46,0,0.93,0,0,0,0,0.2,0.066,0,0,0,5.593,42,330,0 0,0,0,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,0,0,1.23,0,0,0,0,0.404,0,0,0,0,1.187,4,19,0 0,0,1.49,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0.238,0,0.238,0,0,2,8,50,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.32,0,0,0,0,0,1.98,3.97,0,0,0,0.66,0,0,0,0,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.102,0,0,2.531,30,81,0 0,0.23,0,0,0,0.23,0,0.46,0,0,0,0.92,0,0,0.23,0,0,0.23,0.23,0,0,0,0,0,1.15,0.92,0,0,0,0.23,0,0,0.23,0,0,0.23,0.23,0,0,0,0,0.23,0.23,0,0,0.23,0,0,0.063,0.063,0,0.159,0,0,1.616,13,173,0 0,0,0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,1.23,1.23,0,0,0,0,0.468,0,0,0,0,1.058,2,18,0 0,0.8,0,0,0,0,0,0,0,1.6,0,0,0,0,0,2.4,0,0,5.6,0,1.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.235,0,0,1.38,4,29,0 0.07,0,0.07,0,0,0.07,0,0,0,0,0.15,1.07,0.15,0.07,0,0,0.53,0,0,0,0,0,0.22,0,1.83,0,0,0,0,0,0,0,0,0,0,0.22,0.07,0,0,0,0,0,0,0,0,0,0,0,0.127,0.174,0,0,0.023,0,2.182,24,659,0 0.2,0,0.2,0,0.4,0,0,0,0,0,0.3,1.71,0,0.1,0,0,0.1,0,1.01,0.3,0.5,0,0,0,2.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.042,0,0.014,0,0,4.325,63,545,0 0,0,0,0,1.11,0,0,0,0,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.11,0,0,0,0,0,1.11,0,0,0,0,1.11,0,0,0,2.22,0,0,0,0,0,0,0,0.363,0,0.181,0,0,1.285,4,27,0 0,0,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0.336,0,0,1.555,4,42,0 0.07,0,0.07,0,0,0.07,0,0,0,0,0.14,1.04,0.14,0.07,0,0,0.52,0,0,0,0,0,0.22,0,2.23,0.07,0,0,0,0,0,0,0,0,0,0.22,0.14,0,0.07,0,0,0,0.07,0,0,0,0,0,0.111,0.151,0.01,0,0.02,0,2.25,24,720,0 0,0.27,0,0,0,0,0,0,0,0,0,1.94,0,0,0,0,0.27,0,1.39,0,0,0,0,0,0.83,0.55,0,0,0,0.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0.128,0,0,0,0,0,1.197,6,109,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,4.34,0,8.69,0,0,0,0,0,0,0,0,0,0.636,1.273,0,0,0,0,3.5,24,35,0 1.06,0,0,0,1.06,0,0,0,0,0,0,1.06,0,0,0,0,0,0,1.06,0,1.06,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0.386,0,0,0,0,1.705,6,29,0 0,0,0,0,3.44,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.574,0,0,0,0,1.714,4,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0.8,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0,0,0,1.428,5,50,0 0,0,0,0,0,0,0,0,0,0,0,0.55,0.55,0,0,0,0,0,1.65,0,0.55,0,0,0,1.1,0.55,0,0,0,0.55,0.55,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0.087,0,0,0,0,0,1.657,8,58,0 0,0,0,0,0,0,0,0,1.16,0,0,1.16,1.16,0,0,0,0,0,1.16,0,1.16,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,3,12,0 0,0,0,0,1.85,0,0,0,0,0,0,1.85,1.85,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.714,4,12,0 0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0 0,0,0,0,0,0.17,0,0,0,0,0,0.52,0.17,0,0,0,0.69,0,0,0,0.17,0,0,0,1.04,0,0,0,0.34,0.34,0,0,0,0,0,1.04,0,0,0,0.17,0,0,0,0.52,0,0,0,0,0,0.055,0,0,0,0,1.685,7,204,0 0,0,0,0,1.61,0,0,0,0,0,0,0.8,0.8,0,0,0.8,0,0,0.8,0,0,0,0,0,1.61,1.61,0,0,0,0,0,0,0,0,0,0,0.8,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0.144,0,0,0,1.913,13,44,0 0,0,0,0,2.04,0,0,0,0,0,0,1.02,1.02,0,0,1.02,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.642,4,23,0 0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,0,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.411,0,0,0,0,1.866,10,112,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.4,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,1.2,0,0,0,1.2,0,0,0,1.2,0,0,0,0,0,0.446,0,0,0,0,2.166,11,39,0 0,0,0.28,0,0.28,0,0,0,0,0,0,0.85,0,0,0,0,0,0,0.28,0,0,0,0,0,1.7,0,0,0.56,0,0,0,0,0,0,0.56,2.55,0.28,0,0.28,0,0,0,0,0.28,0,0,0,0,0.223,0.074,0,0,0,0,1.958,55,190,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.333,4,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,1.88,0,1.88,0,0,0,0,0,1.88,0,0,0,0,0,3.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.366,0,0,0,0,1.307,3,17,0 0,0,0.5,0,0,0,0,0.5,0,0,0,0.5,0,0,0,0.5,0,0,0.5,0,0,0,0,0,0.5,1,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.062,0,0.188,0,0,3.461,47,180,0 0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.43,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.041,26,73,0 0,0,0.36,0,0,0.73,0,0,0,0,0,1.46,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0.049,0,0.049,0,0,1.919,54,167,0 0,0,0,0,0,0,0,0.42,0,0,0,1.28,0.42,0,0,0,0.42,0,0,0,0,0,0,0,2.57,0,0,0,0.14,0,0,0,0.14,0,0,0.28,0.28,0.14,0,0,0,0,0,0,0,0,0,0.14,0.08,0.242,0,0,0.04,0,2.275,20,421,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.6,3,8,0 0,0,0.5,0,0.5,0,0,0,0,0.5,0,1.01,0,0,0,0,0.5,1.01,2.03,0,3.04,0,0,0,1.52,0.5,1.01,1.01,0,1.01,0,0,0,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.551,0,0.055,0,0,4.275,45,248,0 0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.888,13,35,0 0,0,1.31,0,0,0,0,1.31,0,0,0,0,0,0,0,0,0,0,3.94,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0.279,0,0.139,0,0,2.13,15,49,0 0,0,2.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.27,0,0,0,0,0.404,0,0.404,0,0,2.076,15,27,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.862,0,0.862,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0.507,0,0,0,0,1.133,3,17,0 0,0,0.65,0,0.65,0,0,0,0,0,0,0.65,0,0,0,0,0.65,0,0,0,0,0,0,0,0.65,3.26,0,0,0,0.65,0,0,0,0,0,0,0.65,0,0.65,0,0,0,0.65,0,0.65,0,0,0,0.093,0,0,0.093,0,0,1.705,17,87,0 0,0,0,0,0,0,0,2.63,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0.37,0,0.75,0,0.37,0,0.75,1.12,0,0,0,0,0.063,0,0,0,2.023,14,85,0 0,0,0,0,0,0,0,3.97,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.32,0,0,0,1.98,0,0,0,0.66,1.98,0,0,0.11,0.11,0,0,0,0,2.857,19,120,0 0,0,0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,10,0 0,0,0,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0.092,0,0,0,0,1.568,9,69,0 0.46,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0.92,0,0.46,0,0,0,0.92,0,0,0,0,0,0,0,0,0,0.46,0,0.92,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0,0,0,1.51,10,74,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,3,6,0 0,0,0,0,0,0,0,0.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.83,0.41,0,0.41,0.41,0,0,0,0,0,0.41,0.41,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0.158,0,0,0,0,1.969,13,130,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.85,0,0,0,0,0,4.27,3.41,2.56,0.85,0.85,0.85,0.85,0.85,0,0.85,0.85,0.85,0.85,0,0.85,0.85,0,0,0.85,0,0.85,0,0,0,0,0.278,0.139,0,0,0,2.138,12,77,0 0,0,0,0,0.67,0,0,0,0,0,0,2.01,0,0,0,0,0,0,1.34,0.67,1.34,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0.67,0,0,0,0.117,0.117,0,0,0,0,1.222,5,33,0 0,0.25,0,0,0,0.25,0,0.5,0,0,0,1.01,0,0,0.25,0,0,0.25,0.25,0,0,0,0,0,0.5,0.25,0,0,0,0.25,0,0,0.25,0,0,0.25,0,0,0,0,0,0.25,0,0,0,0.25,0,0,0,0.073,0,0,0,0,1.545,7,136,0 0,0,1.33,0,1.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.33,0,0,0,8,8,0,0,0,0,0,0,0,0,0,0,1.33,4,1.33,0,0,4,0,0,0,0,0,0,0.865,0,0.216,0,0,0,1.647,12,28,0 0,0.04,0.23,0,0.09,0,0,0.04,0.04,0.04,0.04,0.74,0,0,0,0.13,0.04,0.04,0.93,0,0.65,0,0,0,1.49,0.32,0,0.23,0,0.18,0.18,0,0,0,0.23,0,0.32,0,0.04,0.04,0,0.18,0,0.13,0,0,0,0.04,0.027,0.184,0,0.047,0.061,0,1.686,20,1184,0 0,0,3.22,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.45,0,0,0,6.45,0,0,0,0,0,0,0,0,0,0,0,0,1,1,8,0 0,0,0.1,0,0.2,0.1,0,0,0,0,0,2.04,0.2,0.1,0,0,0.81,0,0,0,0,0,0.2,0,2.75,0,0,0,0,0,0,0,0,0,0,0.3,0.3,0,0,0,0,0,0,0,0,0,0,0,0.03,0.091,0,0,0,0,2.161,27,575,0 0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,1.36,0,2.73,0,0,0,0,0,0,0,1.36,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0.475,0,0,0,0,3.478,11,80,0 0,0,0,0,0,0,0,0,0,0,0,1.11,0,0,0,0,0,0,0,0,0,0,0,0,4.44,1.66,0,1.11,0,0,0,0,0,0,1.11,0,0.55,0,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,2.018,12,107,0 0,0,0.31,0,1.04,0.1,0,0,0,0,0,0.1,0,0,0,0,0,0,0.2,0,0,0,0,0,0.41,0.2,0.52,0.2,0.2,0.2,0.2,0.2,0.41,0.2,0.2,0.2,0.1,1.57,0.1,0.2,0,0.41,0.1,0.1,0.1,0,0,0.1,0.067,0.523,0.016,0,0.016,0.033,2.232,47,393,0 0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0,1.4,5,14,0 0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,1.333,3,16,0 0,0,0.28,0,0.84,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0.28,0,0,0,0,0,1.4,0.84,0,0,0,0.84,0,0,0,0,0,0,0.56,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0,0,0,0,1.426,7,97,0 0.55,0,0,0,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.11,0.55,1.66,0.55,0.55,0.55,0.55,0.55,0,0.55,0.55,0.55,0.55,0,0.55,0.55,0,0,0.55,0,0.55,0,0,0,0,0.367,0.091,0,0,0,2.117,12,108,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0.86,2.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0.295,0,0,0,0,3.26,42,75,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0,0,0.218,0.218,0,0.054,0,0,2.16,9,108,0 0,0,0.78,0,0,0,0,0,0,0,0,0,0.78,0,0,0,0,0,0,0,0.78,0,0,0,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.78,0,0,0,0,0.401,0,0.133,0,0,1.565,4,36,0 0,0,0,0,6.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,2,3,0 0,0,0.71,0,0.71,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0,0,0,0,0.055,0,0.055,0,0,15.333,54,138,0 0,0,0.82,0,0.82,0,0,0,0,0,0,0.82,0,0,0,0,0.82,0,0,0,0,0,0,0,0,1.65,0,0,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.82,0,0,0,0,0,0,0.119,0,0,1.272,6,42,0 0,0,0,0,0,0,0,0,0,2.43,0,2.43,0,0,0,0,0,0,0,0,2.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.43,0,0,0,0,0,0,5.3,40,53,0 0,0,0,0,3.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.96,0,1.96,1.96,0,0,0,0,0,0,0.348,0,0,1.312,4,21,0 0,0,0.52,0,1.04,0,0,0,0,0,0,1.04,0,0,0,0,0,0,0.52,0,0.52,0,0,0,1.83,1.57,0.52,0.26,0.26,0.26,0.26,0.26,1.3,0.26,0.26,0.26,0.26,0,0.26,0.26,0,0.78,0.26,0.26,0.78,0,0,0.52,0.136,0.182,0.091,0,0.045,0,1.823,13,155,0 0,0,0.62,0,0.62,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0.62,0,0.62,0,0,0,1.57,1.57,0.31,0,0,0,0,0,1.57,0,0,0,0.31,0,0.31,0,0,0.94,0,0,0.62,0,0,0.62,0.164,0.109,0.109,0,0.054,0,1.671,13,107,0 0,0,0.31,0,0,0,0,0,0,0,0,0.63,0,0,0,0,0,0.31,0,0,0.31,0,0,0,0.63,0.63,0,0.63,0,0.63,0,0,0,0,0.31,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0.588,0,0,0,0,3.183,55,191,0 0,0,0.11,0,0.11,0,0,0,0,0,0.11,1.02,0,0,0,0,0,0.11,0.11,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0.22,0,0.22,0,0.11,0.11,0,0.34,0,0,0,1.02,0,0,0.049,0.149,0,0,0,0,1.637,18,511,0 0,0,0.71,0,0.71,0,0,0,0,0,0,1.43,0,0,0,0,0,0,0.71,0,0.71,0,0,0,0,0,0.35,0,0,0,0,0,1.79,0,0,0,0,0,0,0,0,0.71,0,0,0.71,0,0,0.71,0,0.125,0.062,0,0.062,0,1.574,6,85,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,53,56,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,1.75,3,7,0 0,0,0.1,0,0,0,0,0.1,0,0,0.31,0.52,0.1,0,0,0.1,0.1,0,0.1,0,0,0,0.1,0,3.14,0,0,0,0,0,0,0,0,0,0,0.52,0.31,0,0,0.1,0,0,0,0,0,0,0,0.1,0.079,0.142,0,0,0.063,0,2.542,26,605,0 0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0.7,0,1.4,0,1.4,0,0,0,0,0,0.7,0,0,0,0.7,0,0,0,0,0,0,0,0,2.11,0,0,0,0,0,0,0,0,0,0.267,0.066,0,0,0,17.904,200,376,0 0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.866,6,28,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,3.63,1.81,0,0,0,3.63,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.227,11,49,0 0,0,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0.62,0,0,1.88,0,0.62,0,0,0,1.25,0.62,0,0,0,0,0,0,0,0,0,0,1.25,0,1.25,0,0,0,1.25,0,0,0,0,0,0.895,0.179,0.358,0,0,0,1.712,13,149,0 0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,1.25,4,15,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0.8,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0.8,0,0,0,0,0,0,0,0.265,0,1.347,3,31,0 0,0,0.1,0,0,0,0,0.1,0,0,0.2,0.41,0.1,0,0,0.1,0.1,0,0.1,0,0,0,0.1,0,3.02,0,0,0,0,0,0,0,0,0,0,0.52,0.31,0,0,0.1,0,0,0,0,0,0,0,0.1,0.074,0.134,0,0,0.059,0,2.529,26,597,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,6.89,3.44,0,0,0,3.44,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.16,11,54,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.65,2.32,2.32,2.32,2.32,2.32,2.32,2.32,0,2.32,2.32,2.32,0,0,0,2.32,0,0,0,0,0,2.32,0,0,0,0.692,0,0,0,0,3.312,11,53,0 0,0,0,0,0,0,0,0,0,1.57,0,4.72,0,0,0,0,0,0,1.57,0,0,0,0,0,0.78,0.78,0,1.57,0,0,0,0,0,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0.268,0,0,0,0,2.885,11,101,0 0,0,2.56,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.227,0,0,0,0,1.647,7,28,0 0,0,0,0,0,0,0,0,0,0,0,1.22,0,0,0,0,0,0.61,0,0,0,0,0,0,0.61,0.61,0,1.22,0,0,0,0,0.61,0,0.61,0,0.61,0,0,0,0,0,0,0.61,0,0.61,0,0,0,0.412,0,0,0,0,2.206,19,128,0 0,0.16,0.32,0,0.16,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,1.13,0,0,0,0,0,0,0.8,0,0,0,1.29,0,0,0,0.32,0,0,0,0,1.61,0,0,0.184,0.394,0.131,0,0,0,3.666,20,506,0 1.12,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.12,0,0,0,0,0,0,0.204,0,0,1.214,3,34,0 0.19,0,0.59,0,0,0,0,0,0,0.39,0,0,0,0,0,0,0,0,2.59,0,0.39,0,0,0,0.79,0.39,0.59,0.39,0.39,0.39,0.39,0.39,0,0.39,0.39,0.39,0.19,0,0,0.39,0,0,0.19,0,1.19,0,0,0,0.093,0.657,0.062,0,0,0.062,2.156,13,207,0 0,0,0.87,0,0,0,0,0,0,2.63,0.87,0.87,0,0,0,0,0,0,1.75,0,0,0,0,0,1.75,0.87,2.63,0.87,0.87,0.87,0.87,0.87,0,0.87,0.87,0.87,0.87,0,0.87,0.87,0,0,0.87,0,0.87,0,0,0,0.139,0.976,0,0.139,0,0,1.767,12,76,0 0,0,0.6,0,0,0,0,3.04,0,0,0,0.6,0,0,0,0.6,0,0,0.6,0,1.21,0,0,0,1.21,1.82,0,0.6,0,0.6,0,0,0,0,0.6,0.6,1.21,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0.077,0,0,3.277,33,177,0 0,0,0,0,0,0,0,0,0,0.82,0,0.82,0,0,0,0,0,0,1.65,0,0.82,0,0,0,0,1.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.82,0,0,0,0,0.122,0,0,0,0,2.111,19,76,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,6.38,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.722,7,31,0 0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,2.2,0,0.73,0,0,0,0.73,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.111,0.223,0,1.76,6,88,0 0,0,0,0,0.87,0,0,0,0,0,1.31,0.43,0,0,0,1.75,0,1.31,2.63,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0.361,0.18,0,1.72,6,86,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.285,3,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,1.16,0,0,0,0,0.391,0,0,0,0,1.384,4,18,0 0,0,0.47,0,0.95,0.47,0,0,0,0,0,0.47,0,0,0,0,0,0,0.95,0,0,0,0,0,0,0.47,0.47,0,0,0,0,0,0,0,0,0,0,0.95,0,0,0,0.47,0,0,0,0,0,0,0,0.073,0,0,0,0,1.884,8,98,0 0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,0,3.5,0,3.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0.325,0,0,0,0.651,0,1.125,3,18,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,1,1,8,0 0,0,0,0,0.81,0,0,0,0,1.22,0,0.4,0,0,0,0,0,0,0.4,0,0.4,0,0,0,4.08,4.08,0,0,0,1.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.754,8,93,0 0,0,0,0,0.88,0,0,0,0,2.65,0.88,0,0,0,0,0,0,0,1.76,0,0,0,0,0,1.76,0.88,1.76,0.88,0.88,0.88,0.88,0.88,0,0.88,0.88,0.88,0.88,0,0.88,0.88,0,0,0.88,0,2.65,0,0,0,0.142,0.855,0,0.285,0,0,1.777,12,80,0 0,0,0,0,0,0,0,0.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.83,0.41,0,0.41,0.41,0,0,0,0,0,0.41,0.41,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0.159,0,0,0,0,1.848,13,122,0 0,0,0.51,0,0.51,0,0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,2.07,2.07,0,0,0,0,0,0,0,0,0,0,1.55,0,0,0,0,0.51,0,0,0,0,0,0.51,0.165,0.497,0,0.082,0,0,3.525,20,208,0 0,0,0,0,0,0,0,0,0.13,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,1.38,1.52,0,0,0,0,0,0,1.38,0,0,0,1.25,0,0.27,0,0.69,0,0,0,0,2.63,0.27,0,0.125,0.438,0.146,0,0,0,3.657,35,534,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.65,2.32,2.32,2.32,2.32,2.32,2.32,2.32,0,2.32,2.32,2.32,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0.757,0,0,0,0,2.5,11,50,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,3.5,3.5,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.846,11,48,0 0,0,0,0,0.28,0,0,0,0,0,0,0.57,0,0,0,0,0,0.85,0,0,0,0,0,0,5.14,4,2.28,1.14,0.28,1.14,1.14,0.28,0.57,0.28,1.14,1.14,0.28,0,0,0.28,0,0,0.28,0,0.57,0,0,0,0.064,0.292,0.194,0.097,0,0.097,2.291,12,307,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.83,5.5,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.91,0,0,0.91,0,0.175,0,0,0,0,1,1,18,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,3.33,0,0,0,0,0,0,1,1,6,0 0,0.19,0.59,0,0.19,0,0,0,0,0.59,0.39,0.19,0,0.19,0,0,0,0.79,2.79,0,1.99,0,0,0,1.79,0.19,0.39,0.19,0,0,0.59,0.19,0.79,0.19,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.203,0.018,0.018,0,0,3.716,47,472,0 0,0,0,0,1.15,0.28,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.072,0,0,0,0,1.517,8,88,0 0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,1.29,3.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.625,6,26,0 0,0,0,0,0.14,0,0,0,0,0,0,1.75,0,0,0,0,0,0,0.29,0,0,0,0,0,0.14,0,0,0.29,0,0.14,0,0,0.14,0,0.14,0,0.14,0.14,0,0,0,0,0,0.29,0,0.14,0,0,0,0.064,0,0.021,0,0,1.715,11,187,0 0,0,0,0,1.28,0,0,0,0,2.56,0,0.64,0,0,0,0,0,0,1.92,0,0.64,0,0,0,0.64,0.64,0,0,0,1.92,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.536,8,63,0 0,0.22,0.22,0,0.45,0,0.22,0,0,1.82,0,0.68,0,0,0,0.68,0.22,0,2.05,0.45,1.59,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,0,0,0,0,0.101,0,0.135,0.067,0,2.5,27,210,0 0,0,0,0,0,0,0,0,0,1.44,0,0,0,0,0,0,0,0,1.44,0,1.44,0,0,0,2.89,1.44,4.34,1.44,1.44,1.44,1.44,1.44,0,1.44,1.44,1.44,0,0,0,1.44,0,0,0,0,1.44,0,0,0,0,0.417,0,0,0,0,2.166,11,39,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,0,0,3.57,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.406,7,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.846,17,76,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.846,0,0,0,0,0,6.333,17,19,0 0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0.083,0,0,0,0,6.096,21,189,0 0.24,0,0.24,0,0.24,0,0,0,0,0,0,0,0,0,0,0,0.24,0.24,0.24,0,0,0,0,0.24,0.98,0.73,0,0.49,0,0.24,0,0,0,0,0.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.831,13,152,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,2.22,2.22,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.374,0,0,0,1.583,8,19,0 0,0.25,0.5,0,0,0,0,0,0,0.5,0,0.63,0,0,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.018,0.129,0.092,0.018,0,0,8.021,66,746,0 0,0,1.16,0,1.16,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,1.16,0,0.368,0,0.184,0,0,2.833,11,51,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.5,4,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.888,8,35,0 0,0,0,0,0,0.4,0,0,0.4,0.4,0,0,0,0,0.4,0,0,0,1.22,1.22,0.4,0,0,0,0,0.4,0.4,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0.4,0,0,0,0.065,0,0,0,0,1.84,8,81,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,5,6,0 0,0,0,0,0,0,0,0,0,0,0,2.08,0,0,0,0,1.04,0,0,0,0,0,0,0,1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.478,0,0,0,0,3.291,21,79,0 0.17,0,0.26,0,0.08,0.08,0,0.08,0.08,0.08,0.17,0.17,0.08,0,0,0.08,0.26,0,1.75,0,1.14,0,0,0,1.93,0.52,0,0.17,0,0,0.26,0,0.17,0,0.26,0.08,0.79,0,0,0,0,0,0,0,0.08,0,0,0,0,0.063,0,0.038,0,0,1.66,20,646,0 0,0.18,0.72,0,0.18,0,0,0,0,0,0,0.54,0,0,0,0,0,0.18,0.9,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0.18,0.54,0,0,0,0.177,0.059,0.148,0.029,0,1.6,18,256,0 2,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,2,0,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.888,29,53,0 0,0,0.11,0,0.22,0.11,0,0,0,0,0,0.99,0.11,0.11,0,0,0.22,0,0,0,0,0,0.11,0,3.21,0.11,0,0,0.33,0,0,0,0.11,0,0,0.88,0.44,0,0.11,0,0,0,0.11,0,0,0,0,0,0.044,0.149,0.014,0,0,0,2.419,27,559,0 0,0,0.33,0,0.33,0,0,0,0,0,0,0.33,0,0,0,0,0,0,1.01,0,0.67,0,0,0,1.35,1.01,0.67,0.33,0.33,0.33,0.33,0.33,0.33,0.33,0.33,0.33,0.33,0,0.33,0.33,0,0,0.33,0,1.35,0,0,0,0,0.175,0.058,0,0,0,2.068,12,120,0 0,0,0.59,0,0.59,0,0,0,0,0,0,0.59,0,0,0,0,0,0,0.59,0,0.59,0,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0.105,0,0,0,0,1.826,8,42,0 0,0,0.3,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0.91,0,0.3,0,0,0,2.44,0.61,0,0,0,0,0,0,0,0,0,0,0.3,1.52,0,0,0,0,0.61,1.22,0,0,0,0,0.301,0.043,0.043,0,0.086,0,2.161,19,227,0 0.4,0,0.81,0,0,0.4,0,0,0,0,0,0.81,0,0,0,0,0,0,1.63,0,0.4,0,0,0,0,0,0.81,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0.4,0,0,0,0,0.071,0,0,0,0,1.156,3,59,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,33.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0.16,0,0,0,0,0,0,0,0,0.76,0.028,0,0,0,3.989,33,738,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0.4,0,0.4,0,0,0,0,0,0,0,0,1.22,0,0,0,0.4,0.4,0,0.81,0,0,0,0,0.81,0,0,0.4,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0.199,0,0,0,0,2.386,11,105,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.47,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,1.785,6,25,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.186,0,0,0,3.677,28,114,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.44,2.22,2.22,2.22,2.22,2.22,2.22,2.22,0,2.22,2.22,2.22,0,0,0,2.22,0,0,0,0,0,0,0,0,0,0.735,0,0,0,0,2.45,11,49,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.428,4,10,0 0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0,1.07,1.07,2.15,2.15,0,0,0,0,0,0,0,1.07,1.07,0,1.07,0,0,0,1.07,0,2.15,0,0,0,0,0.326,0,0,0,0,2.7,12,108,0 0,0,1.14,0,0,0,0,0,0,0,0,2.29,0,0,0,0,0,0,1.14,0,0,0,0,0,0,0,1.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.209,0,0,0,0,1.833,5,22,0 0.08,0,0.16,0,0,0.08,0,0.08,0.73,0,0,0.24,0,0,0,0,0,0,0.32,0,0.16,0,0,0,0.49,0.57,0.08,0,0,0,0,0,0.57,0,0,0,0.16,0,0,0,0,0,0,0,0,0,0,0,0.126,0.172,0.057,0,0.022,0,3.212,44,665,0 0.12,0,0.12,0,0.12,0,0,0,1.11,0,0,0.37,0,0,0,0,0,0,0.49,0,0.24,0,0,0,0.62,0.74,0.12,0,0,0,0,0,0.49,0,0,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0.083,0.167,0.033,0,0.033,0,3.211,32,485,0 0.06,0,0.06,0,0,0,0,0,0.61,0,0,0.2,0,0,0,0,0,0.06,0.27,0,0.2,0,0,0,0.75,0.81,0.06,0,0,0,0,0,0.27,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0.173,0.183,0.048,0,0.019,0,2.738,36,827,0 0.08,0,0.08,0,0,0,0,0,0.77,0,0,0.25,0,0,0,0,0,0.08,0.34,0,0.25,0,0,0,0.77,0.86,0.08,0,0,0,0,0,0.25,0,0,0,0.43,0,0,0.17,0,0,0,0,0,0,0,0,0.098,0.16,0.037,0,0.024,0,2.634,36,598,0 0.07,0.03,0.18,0,0.1,0.03,0,0,0.4,0,0,0.1,0,0,0,0,0,0.03,0.14,0,0.1,0,0,0,0.47,0.5,0.03,0,0,0,0,0,0.76,0,0,0,0.32,0,0,0,0.07,0,0,0,0,0,0,0,0.188,0.148,0.035,0,0.01,0,3.233,66,1387,0 0,0,0,0,0,0,0,0,0,0,0,3.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0.232,0.116,0,0,0,0,1.976,9,83,0 0.23,0,0.47,0,0,0,0.23,0,0,0.47,0,0,0,0,0,0,0,0,1.17,0,0.23,0,0,0,1.64,0.7,0.7,1.17,0.23,0.23,0.23,0.23,0,0.23,0.23,0.7,0.47,0,0.23,0.23,0,0,0.47,0,0.7,0,0,0,0,0.237,0,0,0,0,2.42,12,334,0 0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.18,1.45,0,1.45,0,0,0,0,0,0,0,0.72,0.72,0,0.72,0,0,0,0.72,0,0.72,0,0,0,0,0.467,0.116,0,0,0,2.431,12,124,0 0,0,0,0,0,0,0,0,0,0,0,0.54,0.54,0,0,0,0,0,1.09,0,0,0,0,0,0.54,0.54,0.54,0.54,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0.102,0.308,0,0,0,0,1.4,10,77,0 2.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0.465,0,0,0,0,1.25,3,10,0 0,0,0,0,0,0,0,0,0,0,0,2.23,0,0,0,0,0,0,0.74,0,0,0,0,0.74,0,0.74,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0.124,0,0,2.333,31,77,0 0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,1.61,0,0,0,0,0,1.61,0.8,2.41,0.8,0.8,0.8,0.8,0.8,0,0.8,0.8,0.8,0.8,0,0,0.8,0,0,0.8,0,0.8,0,0,0,0.122,0.366,0,0,0,0,1.853,13,76,0 0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,2.77,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.228,0,0,0,0,1,1,12,0 0.04,0.08,0.15,0,0.04,0.04,0,0.04,0.04,0.08,0,0.41,0.06,0,0,0,0.06,0.15,0.6,0,0.34,0,0.02,0,0,0,0,0,0.02,0,0,0,1.67,0,0,0.19,0.82,0.02,0.04,0,0.02,0.02,0.08,0.02,0,0.26,0.04,0.54,0.005,0.213,0.002,0.031,0.039,0.008,2.246,54,3003,0 0,0,0.86,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,2.6,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,0.167,0,0,1.5,4,24,0 0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.217,0.29,0,0,0,4.461,28,290,0 0,0,0,0,0,0,0,0,0,1.86,0,1.24,0,0,0,0,0,0,0,0,0,0,0,0,2.48,1.24,1.24,1.86,0.62,0.62,0.62,0.62,0,0.62,0.62,1.24,0,0,0.62,0.62,0,0,0.62,0,0.62,0,0,0,0.189,0.757,0,0,0,0,2.63,16,171,0 0,0,0,0,0,3.44,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,6.89,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.52,0,0,0,0,1,1,7,0 0,0,0.87,0,0,0.14,0,0,0,0,0.14,1.46,0.14,0,0,0.14,0.58,0.43,0.14,0,0.43,0,0,0,1.9,0.58,0,0.29,0.14,0,0,0,0,0,0.29,0,0.29,0,0,0.14,0,0.43,0.14,0,0.14,0,0,0.29,0.019,0.019,0.019,0,0,0,2.174,35,461,0 0,0,0.74,0,0,0,0,0,0,0.74,0,0,0.37,0.74,0,0,0.37,0,0.37,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.11,0,0,0,0,0,0,0.37,0,0,0,0,0,0.245,0,0,0,0,4.666,64,196,0 0,2.35,0,0,3.52,1.17,0,1.17,0,4.7,0,0,0,0,0,1.17,0,0,1.17,0,1.17,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0.192,0,0,0,0,1,1,14,0 0,0.17,0,0,0.17,0,0,0.35,0,0,0,0.88,0,0,0,0,1.95,0,0.17,0,0,0,0,0,0.35,0.17,0,0,0,0.17,0,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0.256,0,0,0,0,2.097,14,237,0 0,0,0,0,0,0,0,0,0,0.62,0.31,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0.93,0.62,0,0.93,0,0,0,0,0,0,0.31,0,0.93,0,0,0,0.93,0,0.31,0,0,0.62,0,1.86,0,0.122,0.122,0,0.214,0,2.904,20,363,0 0,0,0,0,0,0,0,1.78,0,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,1.444,5,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,1.44,0,0,0,0,0,0,1.44,0,0,0,1.6,0,0,0,2.56,0,0,0,0,3.52,0,0,0.208,0.671,0.092,0,0,0,4.122,20,540,0 0,0,1.81,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,3.63,0,0,0,0,0,0,0,0,0.849,0,0,0,2.294,8,39,0 0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,1.928,15,54,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,1.88,0,0,0,0,0,0,0.647,0,0,0,0,2.8,18,42,0 0,0,2.08,0,0,0,0,0,0,0,0,2.08,0,2.08,0,0,0,0,2.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,11,0 0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.335,0,0,0,4.133,26,124,0 0.09,0,0.36,0,0,0,0,0.09,0,0,0.18,1.01,0.18,0,0,0,0.64,0,0,0,0,0,0,0,2.49,0,0,0,0,0,0,0,0,0,0,0.09,0.18,0,0,0,0,0,0,0,0,0,0,0,0.131,0.209,0,0,0.039,0,2.278,24,629,0 0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0.77,0,0,0,0,0,0,0,0,1.55,0.77,0.77,0.77,0.77,0.77,0.77,0.77,0,0.77,0.77,0.77,0,0,0,0.77,0,0,0,0,0,0,0,0,0,0.376,0.125,0,0,0,2.4,11,48,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,2.094,26,111,0 0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0,0,0.17,0,0,0,0,0,1.41,1.59,0,0,0,0,0,0,0.17,0,0,0,2.83,0,0,0,2.83,0,0,0,0,3,0,0.17,0.271,0.753,0.12,0,0,0,4.84,20,576,0 0,0,0,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.38,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,2.111,6,19,0 0,0,0.49,0,0.49,0.49,0,0,0,0.49,0,2.94,0,0,0,0,0,0,0.98,0,0,0,0,0,1.47,0.98,0,0.98,0.49,0,0,0,0.49,0,0,0.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0.166,0,0,0,0,2.234,11,105,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,6.38,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.666,6,30,0 0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,1.16,0,0,0,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0.196,0.393,0,0,0,0,1.058,2,18,0 0,0,0,0,0.47,0,0,0,0,0,0,0.47,0,0,0,0,1.9,0,0,0,0,0,0,0,1.9,0.95,0,0,0,1.42,0,0,0,0,0,0.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0.217,0,0,0,0,1.677,5,99,0 0,0,0,0,0,0,0,0,0,0,0,4.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.333,0,0,1.666,4,25,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,8.333,0,0,2,3,8,0 0,0,0.28,0,0.28,0,0,0,0,0,0,0.84,0,0,0,0,0,0,0.28,0,0,0,0,0,1.69,0,0,0.56,0,0,0,0,0,0,0.56,2.54,0.28,0,0.28,0,0,0,0,0.28,0,0,0,0,0.217,0.072,0,0,0,0,1.948,55,191,0 0,0,0,0,0.32,0,0,0,0.32,0.96,0,1.29,0,0,0.32,0.32,0,0,1.29,0,0,0,0,0,0.64,0.64,0,0,0.32,0,0,0,0,0,0,0.32,0.64,0,0.32,0,0,0,0.32,1.29,0.32,0,0,0,0,0.145,0.048,0,0,0,1.967,18,120,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0.632,0,0,1,1,4,0 0.33,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0.33,2.01,0,0.33,0,0,0,1.34,1,1.34,0.33,0.33,0.33,0.33,0.33,1.34,0.33,0.33,0.33,0.33,0,0.33,0.33,0,0,0.33,0,0.33,0,0,0,0,0.296,0.059,0,0,0,1.742,12,122,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.46,0,1.23,0,0,0,0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.062,2,17,0 0,0,1,0,0,0,0,0,0,0.25,0.25,0.5,0,0,0,0,0,0,1,0,0.75,0,0,0,1,0.5,0,0,0.5,0.25,0,0,0,0,0,0,0.75,0,0,0,0,0,0,0,0,0,0,1,0.457,0.294,0,0,0,0,4.379,208,508,0 0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.207,0.207,0,0,0,0,1.466,4,22,0 0.54,0,0,0,0,0.27,0,0,0,0,0,0,0.54,0,0,0,0,0,3.79,0,0.54,0,0,0,0.27,0,0,0,0,0,0.54,0,0,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.433,0,0,0.078,0,1.859,18,106,0 0.09,0,0.57,0,0,0.09,0,0,0,0,0.09,1.33,0.19,0,0,0.09,0.38,0.28,0.38,0,0.19,0,0,0,4.37,0.57,0.19,0.28,0.19,0.09,0.09,0.09,0,0.09,0.28,0.09,0.19,0,0,0.19,0,0.28,0.09,0,0.28,0,0,0.19,0.21,0.052,0.013,0,0,0,2.731,34,885,0 0,0.17,0,0,0.17,0,0,0.35,0,0,0,0.88,0,0,0,0,1.95,0,0.17,0,0,0,0,0,0.35,0.17,0,0,0,0.17,0,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0.256,0,0,0,0,2.053,13,232,0 0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,1.17,0,1.17,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.817,0,0,0,0,1.64,5,146,0 0,0,0,0,0,0,0,0,0,0,0,1.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.578,5,60,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.476,0,0,0,0,1.285,3,18,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.95,0,0,0,0,0,0.95,0,0,0,0,0,0,0,0.95,0,0,0,0,0,0,0,0,0,0,1.9,0,0,0,0,0.263,0.394,0,0,0,0,2.142,5,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.222,2,11,0 0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.342,0,0,1.2,2,12,0 0,0,0.87,0,0,0.17,0,0,0,0,0.17,1.74,0.17,0,0,0.17,0.69,0.52,0.17,0,0.17,0,0,0,1.21,0.52,0,0.34,0.17,0,0,0,0,0,0.34,0,0.17,0,0,0.17,0,0.52,0,0,0.17,0,0,0.34,0.022,0.022,0,0,0,0,1.601,11,277,0 0.06,0,0.18,0,0.12,0.12,0,0,0.06,0.18,0,0.55,0.06,0,0,0.06,0.12,0.06,0.93,0.06,1.05,0,0,0,0.93,0.43,0,0,0,0.18,0.18,0,0,0,0.31,0,0.49,0,0,0.06,0,0,0,0.12,0,0,0,0.24,0,0.182,0,0.1,0.109,0,2.062,21,1056,0 0,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0,0,2.53,1.26,0,1.26,0,1.26,1.26,0,0,0,1.26,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0.149,0,0.149,0,0,1.423,10,37,0 0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,1.61,0,0,0.8,0,0.8,0,0,0,0.8,0,0,0,0,0,0.8,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.089,0,0,0,0,2.405,28,89,0 0,0.85,0.42,0,0,0,0,1.28,0,0,0,0.42,0,0,0,0,0,0.42,1.28,0,0,0,0,0,2.14,1.28,0,0.42,0,0.42,0.42,0,0,0,0.42,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0.112,0,0.056,0,0,1.602,14,125,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0.44,0,0,0,0,0,0,0.44,0.44,0,0.88,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0.119,0,0,0,0,1.842,15,105,0 0,0,0.51,0,0.17,0.17,0,0,0,0,0,0,0.17,0,0,0,0,0,1.19,0,1.02,0,0,0,2.9,0,0,0,0,0,0.34,0,0,0,0,0,0.34,0,0,0,0,0,0.17,0,0,0,0,0,0.026,0.156,0,0.078,0,0,1.748,13,299,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.166,2,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.166,2,7,0 0,0.22,0,0,0.22,0,0,0.22,0,0.45,0,0.22,0,1.59,0,0,0.22,0,1.36,0,0,0,0,0,0.68,0,0.22,0,0,0,0.22,0,0,0,0.22,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0.053,0,0,0,0,4.964,152,705,0 0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.6,0,0,0,0,0,2.4,1.6,0,0.8,0,0,0,0,1.6,0,0.8,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0.371,0.123,0,0,0,2.44,10,61,0 0,0,1.09,0,1.09,0,0,0,0,0,0,1.09,0,0,0,0,0,0,3.29,0,0,0,0,0,0,0,1.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.434,0.217,0,0,0,0,1,1,18,0 0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0.485,0,0,3.444,15,31,0 0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0.74,0.74,0,1.48,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0.257,0,0,0,0,2.638,11,95,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.545,6,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0.87,0,0,0,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.87,0,0,0,0.327,0.327,0,0,0,0,1.3,3,26,0 0,0,0,0,0,0,0,0,0,0,0,4.22,0,0,0,0,0,0,0,0,1.4,0,0,0,0,2.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.81,0,0,0,0,0,0,0,0,0,0,0,0,3.153,38,82,0 0,0,0,0,0,0,0,4.23,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0.84,0,1.69,0,0.84,0,0.84,1.69,0,0,0,0,0.126,0,0,0,1.605,12,61,0 0,0,0,0,0,0,0,4.68,0,0,0,0,0,0,0,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.153,3,15,0 0.07,0,0.23,0,0.15,0,0,0.07,0,0.07,0.15,1.84,0.07,0,0,0,0.15,0,0.23,0.23,0,0,0.23,0,2.61,0,0,0,0,0,0,0,0,0,0,0.07,0.07,0.07,0,0,0,0,0,0.15,0,0,0,0,0.011,0.143,0,0,0.044,0,2.442,26,591,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,4,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0.343,0,0.171,0,0,0,1.725,13,69,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0.33,0.33,0,0,0,0,0,1,0,0.33,0,0,0,8.69,4.68,0,0,0,0.33,0.33,0,0,0,0,0,0.66,0,0.33,0,1.33,0,0,0,0,0,0,0,1.001,0,0,0,0,0,2.701,20,181,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,8,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.31,0,0,0,0,0,9.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.875,12,46,0 0,0,0,0,0.92,0,0,0,0,0,0,0.92,0,0,0,0,0,0,0.92,0,0.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,7,33,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,1.72,0,0,0,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,1.72,0,0,1.72,0,0,1.72,0,0,0,0,0,0,1.2,4,18,0 0,0,0.66,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,1.98,0,0.66,0,0,0,0.99,0.66,0.66,0.99,0.33,0.33,0.33,0.33,0,0.33,0.33,0.66,0.33,0,0,0.33,0,0,0.33,0,0.33,0,0,0,0,0.282,0,0,0,0,2.238,13,188,0 0,0,0.38,0,0.38,0,0,0,0,0,0,1.15,0,0,0,0,0,0,0,0,0.38,0,0,0,0.38,0.38,0,0,1.93,0,0,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0.129,0,0,0,0,1.8,5,108,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.72,0,0,0,0,0,6.89,3.44,0,0,0,3.44,0,0,0,0,1.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.16,11,54,0 0,0,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.12,0,0,0,0,0,0,0,0,0,0,0,0,4.368,52,83,0 0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,0,0,0,0,0,1.8,0,0.9,0,0,0,0,0,0,0.281,0,0,1.551,13,76,0 0,0,0.13,0,0.2,0,0,0,0,0,0,0.6,0.06,0,0,0.13,0,0,0.73,0.06,0.73,0,0,0,1.6,0.33,0,0.13,0,0,0.26,0,0,0,0.33,0.13,0.4,0,0,0,0,0,0,0,0.13,0.06,0,0.2,0,0.208,0,0.028,0.075,0,2.068,29,871,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.43,0,0,0,0,0,4.87,4.87,0,2.43,0,0,0,0,0,0,2.43,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0.182,0.365,0,0,0,0,2.25,10,63,0 0,0,0.4,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0.4,0,0.4,0,0,0,1.2,0.8,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.099,0,0.049,0,0,2.288,9,135,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.79,1.79,0,0.89,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0.136,0,0,0,0,1.988,24,179,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.29,2.19,0,3.29,0,0,0,0,0,0,1.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.155,0,0,0,0,2.862,15,83,0 0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0.666,0,0,0,0,2.111,7,19,0 0.19,0,0,0,0,0,0,0,0,0.59,0,0.19,0.19,0,0,0,0,0.19,0.59,0,0.19,0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0.127,0.095,0,0,0.031,0,1.411,7,120,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,4.16,4.16,4.16,4.16,4.16,4.16,4.16,0,4.16,4.16,4.16,0,0,0,4.16,0,0,0,0,0,0,0,0,0,1.176,0,0,0,0,3.444,11,31,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.69,4.34,4.34,4.34,4.34,4.34,4.34,4.34,0,4.34,4.34,4.34,0,0,0,4.34,0,0,0,0,0,0,0,0,0,1.19,0,0,0,0,3.333,11,30,0 0,0,0,0,0,0,0,0,0,0,0,0,1.09,0,0,0,0,0,1.63,0,0.54,0,0,0,1.09,0.54,0.54,0.54,0.54,0.54,0.54,0.54,0,0.54,0.54,0.54,0,0,0,0.54,0,0,0,0,0,0,0,0,0,0.17,0,0,0,0,1.373,11,169,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,2,5,0 0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,2.9,0,0,0,0,0,1.74,1.16,1.16,1.74,0.58,1.16,0.58,0.58,0,0.58,0.58,1.16,0.58,0,0.58,0.58,0,0,0.58,0,0.58,0,0,0,0,0.379,0,0,0,0,2.222,12,140,0 0,0,0,0,0,0,0,0,0,0.67,0,0.67,0.67,0,0,0,0,0,2.68,0,0,0,0,0,2.68,1.34,2.01,0.67,0.67,0.67,0.67,0.67,0,0.67,0.67,0.67,0.67,0,0.67,0.67,0,0,0.67,0,1.34,0,0,0,0.107,0.537,0,0,0,0,2.604,17,112,0 0.34,0,0.34,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0,2.41,0,1.03,0,0,0,2.06,1.03,1.03,0.68,0,0.68,0,0,0,0,0.68,0,1.03,0,0,0,0,0,0.34,0,0.68,0.34,0,0,0.116,0.292,0.058,0,0,0,2.333,15,182,0 0,0,1.2,0,0,0,0,0,0,0,0,2.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0.666,1.111,0.222,0,0,2.826,8,65,0 0.08,0,0.16,0,0,0.08,0,0.08,0.08,0,0.16,0.74,0.57,0.16,0,0,0.41,0,0,0,0,0,0.24,0,3.3,0,0,0,0,0,0,0,0,0,0,0.24,0.24,0,0,0,0,0,0,0,0,0,0,0,0.199,0.105,0,0,0.023,0,1.878,24,740,0 0.89,0,0,0,0.89,0.89,0,0,0,0,0,0,0,0,0,0,0,0,2.67,0,1.78,0,0,0,1.78,0.89,1.78,0.89,0,0.89,0,0,0,0,0.89,0,0.89,0,0,0,0,0,0,0,0.89,0,0,0,0.149,0.298,0,0,0,0,2.259,15,61,0 0,0,0,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,2.63,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.208,10,53,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.25,10,13,0 0,0,0,0,0.32,0,0,0,0,0,0,0.32,0,0,0,0,0,0.32,0.64,0,0.32,0,0,0,1.28,1.28,0.64,0.32,0.32,0.32,0.32,0.32,0.64,0.32,0.32,0.32,0.96,0,0.32,0.32,0,0,0.64,0.32,0.32,0.64,0,0,0,0.094,0.047,0.094,0,0,1.919,13,167,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,1.53,0.76,2.3,0.76,0.76,0.76,0.76,0.76,0,0.76,0.76,0.76,0.76,0,0.76,0.76,0,0,0.76,0,0.76,0,0,0,0,0.339,0,0.339,0,0,1.813,12,78,0 0,0,0,0,0,0,0,0,0,0,0,1.6,0,0,0,0,0,0,0,1.6,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0.136,0,0.273,0,0,2.588,29,88,0 0.51,0,0.51,0,1.53,0.51,0,0,0,0,0,0.51,0,0,0,0,0,0,3.58,0,0,0,0,0,2.56,0,2.05,0.51,0.51,2.05,0.51,0.51,0,0.51,0.51,1.02,0,0,0,0.51,0,0,0,0,1.02,0.51,0,0,0,0.27,0,0,0,0,1.983,24,121,0 0,0,0,0,0.51,0,0,0,0,0,0,0.51,0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0.51,0,0,0,0,0,0,0.51,0,1.03,0,0,0,0,0,0,0,0,1.681,11,74,0 0,0,1.05,0,0,0,0,0,0,0,0,1.05,0,0,0,0,0,0,0,0,0,0,0,0,4.21,3.15,0,0,0,0,0,0,1.05,0,0,0,0,0,1.05,0,0,2.1,1.05,0,0,0,0,0,0.169,0,0.679,0,0,0,2.096,12,65,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.282,0,0,1,1,8,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.44,2.22,0,2.22,0,0,0,0,0,4.44,0,0,0,0,0,0,0,0,0,2.22,0,2.22,0,0,0,2.22,0,4.44,0,0,0,0,0,0,0,0,0,1.947,12,37,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.69,4.34,4.34,4.34,4.34,4.34,4.34,4.34,0,4.34,4.34,4.34,0,0,0,4.34,0,0,0,0,0,0,0,0,0,1.111,0,0,0,0,3.1,11,31,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.5,9,11,0 0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.85,0,0,0,0.398,0,0,0,0.199,3.055,11,55,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,5.93,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0,0.84,0,0.84,0,0,0,0,0,0,0,0,1.285,4,36,0 0.34,0,0,0,0,0,0,0,0,0,0,0.69,0,0,0,0,0,0,3.12,0,0.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0.432,0,0,0,0,1.526,11,87,0 0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0.287,0,0.287,0,0,1.076,2,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0,0,0,3.26,0,0,1,1,5,0 0,0,0.9,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,3.63,0,0.9,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.45,0.45,0,0,0,0.155,0,0.077,0,0,1.545,15,68,0 0,0,1.4,0,0,0,0,0,0,0,0,1.4,0,0,0,0,0,0,1.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.81,0,0,0,0,1.4,0,0,0,0,0,0.497,0,0,1.722,10,31,0 0.26,0,0.52,0,0.52,0,0,0,0,0.26,0,0.26,0,0,0,0,0,0.26,1.31,0,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0.52,0.26,0,0,0.047,0.047,0,0.047,0,0,1.081,3,53,0 0,0,0.27,0,0,0.27,0,0,0,0,0,0.27,1.39,0,0,0.27,0,0.27,2.79,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0.27,0,0,0.051,0,0,0,0,0,1.195,6,55,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,0,0,0,0.202,0,0,0,0,1,1,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.45,0,3.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,1.125,2,9,0 0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,1.94,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0.255,0,0,0,0.127,2.344,11,68,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,2.32,0,0,0,0,0,0,0,0,1.666,5,25,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,2.15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.15,0,0,0,0,1.07,0,0,0,0.197,0,0,0,0,2.315,7,44,0 0,0,0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,1.73,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0.86,0,0.86,0,0,0,0.152,0,0.457,0,0,1.192,3,31,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.44,0,0,0,1.44,1.44,0,0,0,0,0,0.247,0,0,1.684,5,32,0 0,0,0,0,0,0.34,0,0,0,0,0,0.69,0,0,0,0,0,0,4.19,0,1.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0.34,0.34,0,0,0,0,0,0,0,0,1.206,5,70,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0.174,0,0,0,0,1.222,4,22,0 0,0,0.49,0,0,0.49,0,0,0,0,0,0.99,0,0,0,0,0,0,2.47,0,0.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0.093,0,0.093,0,0,1.275,4,51,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.943,0,0.943,0,0,2.166,5,13,0 0,0,0,0,0.96,0.48,0,0,0.48,0,0.48,0.48,0,0,0,1.44,0,1.92,0.96,0,1.44,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0.666,0,0,4.437,27,142,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,2.01,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0.26,0,1.592,5,43,0 0,0,0.59,0,0.19,0,0,0,0,0,0,0.39,0.19,0,0,0.19,0.19,0.19,2.19,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0.19,0,0,0,0.232,0,0,0.038,0,1.129,4,96,0 3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0.645,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,1.724,0,0,1,1,6,0 0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,0,0,0,0,0,0,1.16,1.16,0,0,0,0,0,0.578,0,0,1.36,5,34,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0.684,0,0,0,0,1.125,2,9,0 0,0,0,0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,2.31,0,2.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,0,0,0,0,0,1.73,0.57,0,0,0,0,0,0,0,0,1.645,5,51,0 0.54,0,0,0,0,0,0,0,0,0,0,2.18,0.54,0,0,0,0,0,3.82,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0,1.09,0,0,0,0,0.294,0,0.392,0,0,1.829,7,75,0 0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,1.5,4,24,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.19,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.06,0,0,0,1.06,1.06,0,0,0,0,0,0.398,0,0,1.181,5,26,0 0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,1.94,0,2.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0.105,0.105,0,0,0,1,1,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.89,0,0.44,0,0,0,0,0,1.34,2.69,0,0,0,0,0,0,0,0,2.362,15,137,0 0,0,0,0,0,0,0,0,0,0,0,3.84,0,0,0,0,0,0,5.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,1.92,0,0,0,0,0,0,0,0,1.166,3,14,0 0,0,0.67,0,0,0,0,0,0,0,0,1.34,0,0,0,0,0,0,4.69,0,1.34,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0.493,0,0,0,0,1.24,3,31,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.4,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0.613,0,0,1,1,8,0 0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,4.16,0,0,0,0,0,0,0,0,1,1,9,0 0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1.428,3,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,2.12,0,0,0,0.344,0,0,0,0,1.4,5,14,0 0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,2.85,2.85,0,0,0,0.473,0,2.843,0,0,1.294,5,22,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,3.57,3.57,0,0,0,0.564,0,0,0,0,1.454,5,16,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,3.33,0,0,0,0.537,0,1.075,0,0,1.2,3,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0.28,0,0,0,0,0,0.86,1.72,0,0,0,0,0,0,0,0,2.557,16,179,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,4.08,0,0.68,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0.68,0,0,1.36,0.68,0,0,0,0.38,0,0,0,0,1.607,6,45,0 0.49,0,0.49,0,0.49,0,0,0,0,0,0,0.99,0,0,0,0,0,0,0.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,0,0.99,0.49,0,0,0,0,0,0.091,0,0,1.214,5,51,0 0,0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,0,0,0,0,1.21,0,0,0,0,0.212,0,0,0,0,1.406,5,45,0 0,0,0,0,0,0,0,0,0,0,0,2.38,0,0,0,0,0,1.19,2.38,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0.395,0,0,0.197,0,1.428,4,30,0 0,0,0,0,0,0,0,0,0,0,0,3.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.438,0,0,0,0,1,1,9,0 0,0,0,0,0,0,0,0,0,0,0,1.81,0,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0.159,0,0,0.159,0,1.515,5,50,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,5,0,0,0,0,0,1.438,0,0,1,1,7,0 0.08,0,0.17,0,0,0.08,0,0,0.08,0,0,0,0.08,0,0,0,0,0.08,4.19,0,1.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,0,0,0.031,0.078,0,0.078,0,0,1.114,9,272,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,2.85,0,0,0,0,0,0,0,0,1.111,3,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,2.17,0,0,0,0.743,0,0.371,0,0.371,1.714,11,24,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,1.142,2,8,0 1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.31,1.31,0,0,0,0,0,0,0,0,1.25,3,30,0 0,0,0,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0,0,5.04,0,0.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.84,0,0,0,0,0.143,0,0.143,0,0,1.37,4,37,0 0,0,0,0,0,0,0,0,0,0,0,1.86,0,0,0,0,0,0,1.86,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0.165,0,0,1.238,4,26,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0.704,0,0,1,1,10,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,1,1,11,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,1.04,0,0,0,0,0,0,1.04,0,0,0,0,0,0,0,0,5.2,0,0,0,0,0,1.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.08,0,0,0,0,0.211,0,0.422,0,0,1.16,4,29,0 0,0,0,0,0,0,0,0,0,0,0,1.53,0,0,0,0,0,0,4.61,0,0,0,0,0,0,0,0,0,1.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.222,5,20,0 0,0,0,0,0.79,0.79,0,0,0,0,0,0,0,0,0,0,0,0,3.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0.79,0,0,0,0,0,0,0,0,0,1.076,2,28,0 0.13,0,0.41,0,0,0,0,0.27,0,0,0.27,1.93,0.13,0,0,0,0,0.27,1.65,0,0.13,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0,0,0,0,0,0,0.82,0,0,0.13,0,0.023,0.046,0.164,0,0,1.279,11,183,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0,0.61,0,0,0,0,0.118,0,0,0.118,0,1.59,5,35,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,2.666,7,24,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0,0,0.546,0,0,1.75,7,14,0 0,0,0,0,0,0,0,0,0,0.95,0,0,0,0,0,0,0,0,2.85,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.95,0,0,0,0.172,0.172,0,0,0,0,1.263,5,24,0 0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,2.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,1.069,0,0,1,1,13,0 0,0,0.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,7.88,0,0,0.109,0,0,0.054,0,0,1.786,14,134,0 0,0,0,0,0,0.6,0,0,0,0.6,0,0.6,0.6,0,0,0,0,0,3.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0.6,3.04,0,0,0.094,0,0,0.094,0.189,0,1.976,15,83,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.89,0,0,0,0.188,0,0.564,0,0,1,1,14,0 0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0.182,0.182,0,0,0,0,1,1,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0,1.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0,0,0,0,0.47,0.47,1.91,0,0,0,0.076,0,0.076,0,0,1.833,12,77,0 0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,0,0,3.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.87,1.75,0,0,0,0,0,0.259,0,0,1.681,12,37,0 0.66,0.66,0.66,0,0,0,0,0,0,1.33,0,0,0,0,0,0.66,0,0,3.33,0,2.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.117,0,0,2.487,17,97,0 0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,2.413,15,70,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,1,1,8,0 0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,4.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0,0,0,0.68,3.42,0,0,0,0,0,0.109,0.218,0,1.897,15,74,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.12,0,0,0,0,0,0.248,0,0,1.1,2,11,0 0,0,0,0,0,0,0,0,0,1.44,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.89,0,0,0,0,0,0.954,0,0,9.125,63,73,0 0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,4.1,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.45,0.91,0,0,0,0.219,0,0,0,0,1.225,5,49,0 0,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.08,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,4.08,0,0,0.226,0,0,0,0,0,1,1,8,0 0,0,0,0,0,0,0,0,0,2.55,0,0,0,0,0,0,0,0,3.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,1.02,0,0,0,0.253,0,0.169,0.169,0,1.677,7,52,0 0,0,0,0,0,0,0,0,0,0.84,0,0.84,0,0,0,0,0,0,2.54,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0.134,0,0,0,0,1.285,5,27,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.81,0,1.16,0,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0.163,0,0.49,0,0,2.125,7,34,0 0,0,0.35,0,0.35,0,0,0,0.35,0,0,0,0,0,0,0,0,0,1.4,0,3.5,1.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0.35,0,0,0,0.65,0,0,0,0.05,2.483,17,226,0 0,0,0.52,0,0,1.04,0,0,0,0.52,0,1.57,0,0,0,0,0,0,3.66,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.04,0,0,0,0,0,0.09,0,0,1.466,6,44,0 0,0,0,0,0,0,0,0,0,1.02,0,0,1.02,0,0,0,0,0,4.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,0,0,0.147,0,0,1.333,4,24,0 0.63,0.63,0,0,0,0,0,0,0,0.63,0,0,0,0,0.63,0,0,0,4.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.27,1.91,0,0,0,0.204,0,0.102,0,0,1.361,4,49,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,0,1.25,2.5,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.176,55,71,0 0.1,0.72,0.62,0,0.62,0.1,0.2,0.2,0,0,0.1,0.51,0,0,0,0,0,0.82,3.61,0,0.93,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0.41,0,0,0,0.122,0,0.157,0,0,2.213,29,425,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.25,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,1.47,0,0,0,1.066,0,0.213,0,0,1.333,3,36,0 0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0.246,0,0,0,0.246,0,1.363,4,30,0 0,0,0,0,0,0,0,0,0,0,0,0,1.13,0,0,0,0,0,1.13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.13,0,0,0,0.634,0,0.211,0,0.211,0,1.347,4,31,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.253,0.253,0,0,0,2.352,17,40,0 0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.17,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,4,16,0 0.34,0,0.69,0,0,0,0,0,0,0,0,0.69,0,0,0,0,0,0,2.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.69,0,1.39,0,0.34,0,0,0,0.374,0,0,0,0,1.775,5,71,0 0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,0,1.454,5,32,0 0.9,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,3.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,1.8,0.9,0,0,0,0,0,0,0,0,0,1.727,5,19,0 0,0,0.4,0,0,0,0,0,0.4,0.4,0,0,0,0,0,0,0,0.4,1.63,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0.81,0,0,0,0,3.68,0,0,0.139,0,0,0.069,0,0,2.525,15,101,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,0,0,0,0.9,4.5,0,0,0.145,0,0,0,0,0,2.638,20,124,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,2.263,0,0,0,0,3.149,9,1310,0 0,0,0,0,0.66,0,0,0,0,0,0,0.66,0,0,0,0,0.66,0,3.33,0,0,0,0,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0,0.254,0,0,0,0,1.458,7,35,0 1.08,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,0,1.523,5,32,0 0,0,0,0,0,0,0,0,0,0.44,0,0,0.44,0,0,0,0,0,3.53,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0.44,0,0,0,0,0,0,0,0,2.063,47,97,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0,0,0,1.06,2.65,0,0,0,0.322,0,0,0,0.129,2.6,18,182,0 0,0.78,1.56,0,0,0,0,0,0,0,0,0.78,0,0,0,0,0,1.56,5.46,0,3.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.137,0,0.275,0,0,1.625,9,39,0 0,0,0,0,0,1.63,0,0,0,0,0,0,0.81,0,0,0,0,0,3.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0.81,2.45,0,0,0,0,0,0,0,0,2.829,47,116,0 0,0,0.55,0,0,0,0,0,0,0,0,0.55,0.55,0,0,0,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0,0,0,0.087,0,0,0,0,2.54,47,94,0 0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0,2.53,0,1.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,0,0,0,0,0,0,0,0,4.352,47,74,0 0,0,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0,3.75,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,1.87,0,0,0,0,0,0,0,0,2.704,47,119,0 0,0,0.81,0,0.27,0,0,0,0,0.27,0,0.27,0.27,0,0,0,0,0,2.16,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0.27,0,0,0.045,0.091,0,0.045,0,0,2.078,47,106,0 0,0,0.78,0,0,0.78,0,0,0,0.78,0,0,0.78,0,0,0,0,0,1.56,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.78,0,0,0,0.12,0,0.12,0,0,2.862,47,83,0 0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,2.94,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,4.312,47,69,0 0,0,0,0,0,0,0,0,0,0.54,0,0,0.54,0,0,0,0,0,5.43,0,1.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,1.63,0.54,0,0,0,0.083,0,0,0,0,2.827,47,82,0 0,0,0,0,0,0.33,0,0,0,0,0,0,0.82,0.16,0,0,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.49,0.16,0,0,0.019,0.039,0,0.059,0,0,1.632,47,191,0 0,0,0,0,0,0.65,0,0,0,0,0,0,0.65,0,0,0,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,0,0,0,2.555,47,92,0 0,0,0.43,0,0,0,0,0,0,0,0,3.94,0,0,0,0,0,0,2.63,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.314,5,46,0 0,0,0.5,0,0,0.5,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.5,0,0,0,0,0,0,0,0,2.527,47,91,0 0,0,0,0,0,0,0,0,0,0,0,0,1.09,0,0,0,0,0,2.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.09,0,0,0,0,0,0,0,0,3.304,47,76,0 0.32,0,0.16,0,0,0,0,0,0,0,0,1.29,0.48,0,0,0.16,0,0,2.43,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0.48,0.16,0,0,0,0,0,0.082,0,0,1.704,47,167,0 0.43,0,1.31,0,0,0.43,0,0,0,0,0,0,0.87,0,0,0,0,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,2.137,47,109,0 0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,4.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,3.391,47,78,0 0,0,0.67,0,0,0,0,0,0,0,0,1.01,0.33,0,0,0,0,0,1.35,0,0.33,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0.33,0,0,0,0,0,0.174,0,0,2.071,47,116,0 0.15,0,0.15,0,0,0,0,0,0.07,0,0,0.07,0.15,0,0,0.07,0,0.07,3.6,0,1.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15,0,0,0,0.013,0.123,0,0.082,0,0,1.111,9,328,0 0.09,0,0.54,0,0,0.09,0,0,0.09,0,0,0.09,0.09,0,0,0.09,0,0,0.09,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0.017,0,0.034,0,0,1.429,47,306,0 0,0,0.38,0,0.19,0.29,0,0,0,0,0,0,0.87,0,0,0.09,0,0,0.19,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0,0,0.09,0,0,0,0,0,0,0,0,1.508,47,187,0 0,0,0.09,0,0,0,0,0,0,0,0,0.47,0.66,0,0,0.09,0,0,1.23,0,0.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0.09,0,0,0,0.033,0,0,0,0,1.536,47,192,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.68,0,1.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0.471,0,0,1.033,2,31,0 0,0,1.57,0,0.22,0.22,0,0,0,0,0,0,0.22,0,0,0,0,0,2.02,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0.89,0,0,0,0,0.091,0,0.045,0,0,1.276,16,97,0 0,0,0.66,0,0,0.66,0,0,0,0,0,0.66,0,0,0,0,0,0,1.66,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0.33,0,0,0,0,0,0,0,0,0,1.142,4,56,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0,3.103,51,90,0 0,0,0,0,0,0,0,0,0,0.86,0,1.72,0.86,0,0,0,0,0,2.58,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0.321,0,0.214,0,0,3.956,51,91,0 0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,3.84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,1.28,0,0,0,0,0,0,0,0,3.772,51,83,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.57,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.78,0.78,0.78,0,0,0,0,0,0,0,0,2.848,51,94,0 0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,0,0,4.05,51,81,0 0,0,0,0,0,0,0,0,0,0.75,0,0,0,0,0,0,0,0,2.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.75,0,0,0,0,0,0,0,0,3.333,51,90,0 0,0,0.25,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,2.05,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.76,0.25,0,0,0,0.094,0,0.047,0,0,1.884,51,147,0 0,0,0.48,0,0.32,0.16,0,0,0.32,0,0,0,0.16,0,0,0,0,0,2.26,0,0.48,0,0,0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0.16,0,0,0,0.086,0,0.057,0,0,1.698,51,158,0 0,0,1.88,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0,2.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.94,0,0,0,0,0,0,0.756,0,0,1,1,22,0 0.38,0,1.16,0,0,0,0,0,0,0,0,1.16,0,0,0,0.77,0,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.77,0.38,0,0,0,0,0,0,0,0,2,51,114,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,4.368,51,83,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,2.01,2.68,0,0,0,0.102,0,0,0,0,3.4,51,119,0 0,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0,0,1.52,2.29,0,0,0,0.139,0,0,0,0,2.29,16,71,0 0,0,0.53,0,0,0.53,0,0,0,0.53,0,0,0.53,0,0,0,0,0,2.15,0,0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.07,0,0,0,0,0.101,0,0,0,0,1.857,16,52,0 2.32,0,0,0,0,0.77,0,0,0,0,0,0.77,0,0,0,0,0,0,4.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.55,0,0,0,0,0,0,0.159,0,0,1.346,4,35,0 0,0,0,0,0,0,0,0,0,1.43,0,0,0,0,0,0,0,0,2.15,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.71,0.71,0,0,0,0,0,0,0,0,2.939,51,97,0 0,0,0,0,0.64,1.29,0,0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,1.29,0,0,0,0,0,1.29,1.94,0,0,0,0,0,0.188,0,0,2.686,51,137,0 0,0,0.27,0,0,0,0,0,0.27,0.55,0,0,0,0,0,0,0,0,3.3,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0.27,0,0,0,0,0,0.048,0,0,1.873,47,118,0 0,0,1.39,0,0,0,0,0,0,0,0,0.34,0,0,0,1.04,0,0,4.52,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.04,0.34,0,0,0,0.122,0,0,0,0,1.963,47,108,0 0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,1.8,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.817,0,0,1.857,15,39,0 0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,4.117,47,70,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.92,0.92,0,0,0,0,0,0,0.857,0,2.918,47,108,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.06,0,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,1.06,0,0,0,0.14,0,0,0,0,2.625,47,84,0 0.7,0,0.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.41,1.41,0,0,0,0,0,0.105,0,0,2.342,47,89,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,3.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,1.03,0,0,0,0,0,0,0,0,2.843,47,91,0 0,0,0,0,0,0,0,0,0,0,0,1.53,0.76,0,0,0,0,0,3.07,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.444,6,26,0 0,0,0.91,0,0,0,0,0,0,0,0,0.91,0.91,0,0,0,0,0,5.5,0,0.91,0,0,1.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.91,0.91,0,0,0,0,0,0.13,0,0,2.457,47,86,0 0,0,0,0,0,0,0,0,0,0,0,0.83,0,0,0,0,0,0,3.33,0,0.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.83,0,0,0,0.83,0,0,0,0.12,0,0,0,0,3.137,47,91,0 0,0,1.17,0,0,0,0,0,0,1.17,0,2.35,1.17,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0.361,0,0.361,0.18,0,1.652,4,38,0 0,0,0,0,0,0,0,0,0,0.96,0,0,0.96,0,0,0,0,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.96,0,0,2.88,0,0,0,0,0.327,0,0.327,0.327,0,1.482,4,43,0 0,0,0,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0.78,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.277,0,0.263,0,0,1.047,2,22,0 0,0,1.17,0,1.17,0,0,0,0,0,0,3.52,0,0,0,0,0,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.35,0,0,2.35,0,0,0,0,0.192,0,1.156,0.192,0,1.7,6,34,0 0,0,1.17,0,0,0,0,0,0,0,0,2.35,0.78,0,0,0,0,0,3.13,0,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0.284,0,0.284,0.213,0.071,1.565,12,72,0 0,0,1.5,0,0.75,0,0,0,0,0,0,0.75,1.5,0,0,0.75,0,0,1.5,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.75,0,0,0,0,0.147,0,0.441,0,0,2,6,54,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.331,0,0.331,0,0,1.714,4,24,0 0,0,0,0,0,0,0,0,0,0,0,1.88,0,0,0,0,0,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0.33,0,0,1.769,4,23,0 0.36,0,0.36,0,0.36,0,0,0,0,0,0,0.72,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0.36,0,1.08,0.72,0,0,0.124,0,0.062,0.062,0,0,1.414,13,116,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,1.75,5.26,0,1.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.238,0,0,4.375,55,70,0 0,0,0.39,0,0.39,0.39,0,0,0,0,0,0,0.39,0,0,0.39,0,0.39,1.17,0,0.78,0,0.39,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0,0,0,0.065,0.065,0.261,0.065,0,2.89,55,159,0 0,0,0,0,0,0,0,0,0,0,0,1.31,0,0,0,1.31,0,1.31,0,0,3.94,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0.194,0,0,0,5.2,55,104,0 0,0,1.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0.68,0,0,0,0.113,0,0,0,0,1.315,4,25,0 0,0,0.71,0,0,0,0,0,0,0,0,0.71,0,0,0,0,0,0,2.15,0,0.71,0,0,0,0,0,0,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.342,0,0,1,1,31,0 0,0,0.9,0,0,0,0,0,0,0.45,0,0,0,0,0,0.45,0,0.45,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0.45,0.45,0,0,0.056,0.227,0,0.056,0,0.056,5.8,70,290,0 0,0,1.25,0,0.62,0,0,0,0,0,0,1.25,0,0,0,1.88,0,0,4.4,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.327,0,0,0.109,0.109,1.705,9,58,0 0.31,0,0.31,0,0,0,0,0,0,0,0,0.31,0.31,0,0,0,0,0,2.84,0,0.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.059,0,0.709,0,0,1.119,4,47,0 0,0,0.21,0,0.21,0,0,0.21,0,0,0,0,0,0,0,0,0,0,1.94,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.079,0,0.039,0.119,0,0.039,1.086,3,101,0 0,0,1.85,0,0,0,0,0,0,1.85,0,1.85,1.85,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.692,0,0,1.727,5,19,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.216,0,0,1,1,18,0 0,0,0.35,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,2.47,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0.7,0,0,0,0,0.064,0,0.324,0,0,1.12,3,56,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.653,0,0,1.666,5,10,0 0,0,0.58,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,1.76,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.108,0.108,0.432,0,0,1,1,35,0 0.28,0,0.28,0,0.57,0,0,0,0,0,0,0.28,0,0,0,0,0,0,2.87,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.154,0,0.308,0,0,1.148,4,54,0 0,0,0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,1.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.63,0,0,0,0,0,0.103,0,0.62,0,0,1,1,26,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,8,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.757,0,0,1.222,4,22,0 0.39,0,0.13,0,0.13,0,0,0,0.13,0,0.13,0.13,0,0,0,0.13,0,0,3.85,0,1.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.066,0,0,0,0.022,1.514,21,159,0 0,0.49,0,0,0,0,0,0,0,0,0,0.49,0,0,0,0,0,0,2.94,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.241,0,0,0,0.08,1.77,21,85,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,0,1.66,3.33,0,0,0,0.8,0,0,0,0,1.5,4,33,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.87,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.478,0,0,0,0,1.333,4,28,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,0,0,0,0,0,0,0,1.4,4,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,1.69,0,0,0,0,0,0,0,0,0,1.071,2,15,0 0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,1.25,0,3.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0.715,0,0,0,0,1.411,4,24,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.63,0,2.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.523,0,0,0,0,1.6,4,16,0 0,0.52,0.52,0,0,1.57,0,0,0,0,0,0,0,0,0,0.52,0,0.52,1.04,0,0.52,0,0,0,0,0,0,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0.52,0,0.52,0,0,0,0,0,0.087,0,0.175,0,0,1.093,3,35,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.94,0,2.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,1.92,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,21,42,0 0,0,0.19,0,0,0,0,0,0,0,0,0.79,0,0,0,0.39,0,0,0.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0,0.19,1.19,0,0,0,0,0,0.029,0,0,1.131,11,155,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.69,0,0,0,0,0,0,9.575,0,0,1.387,5,43,0 0.28,0,0.28,0,0,0,0,0,0,0,0,0.28,0.28,0,0,0.28,0,0.28,1.97,0,0,0,0,0.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.28,0,0,0,0.103,0,5.054,0,0,1.403,18,80,0 0,0,0.73,0,0.36,0.36,0,0,0,0,0,0,0,0,0,0,0,0,2.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.387,0,0,1.131,4,69,0 0,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,2.43,0,0.97,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0.45,0,0,1.138,4,41,0 0,0,0,0,0,0.61,0,0,0,0,0,0.61,0,0,0,0,0,0,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0,0,0,0,0,1,1,35,0 0,0.35,0.35,0,0,0.35,0,0,0,0.35,0,0.71,0,0,0,0,0,0,3.58,0,1.07,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0,0,0,0,0,0,0.12,0.06,0,0,0,1.787,11,118,0 0,0,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,2.59,0,2.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,1.5,5,51,0 0,0,0.51,0,0,0.51,0,0,0,0,0,0,0.51,0,0,0.51,0,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.087,0,0,1.218,6,39,0 0,0.38,0.38,0,0,0.38,0,0,0,0.38,0,0.77,0,0,0,0,0,0,3.5,0,1.16,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.131,0.065,0,0,0,1.843,11,118,0 0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,2.608,14,60,0 0.76,0,0,0,0,0.76,0,0,0,0,0,0,0,0,0,0,0,0,3.07,0,3.07,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.253,0,0.253,0,0,2.172,9,63,0 0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0,0,1.69,0,0,1.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.69,0,0,0,0,0,0,0.278,0,0,1.777,4,32,0 0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,3.33,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0.558,0,0,0,0,1,1,6,0 1.47,1.47,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.391,21,55,0 0,0.87,0.87,0,0,0,0,0,0,0.87,0,0.87,0,0,0,0,0,0,3.5,0,0.87,0,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.87,0,0,0,0,0,0,0,0.138,0,2.136,21,47,0 0,3.03,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,2.769,21,36,0 0,1.08,0,0,0,0,0,0,0,1.08,0,3.26,0,0,0,0,0,0,5.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0.169,0,0,2.052,21,39,0 0,2.7,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,8.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,2.538,21,33,0 0.58,0,0,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,2.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0.58,0,0,0.58,1.16,0,0,0,0.165,0,0.082,0,1.403,2.674,17,115,0 0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,0,0,0,0,1.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,1.285,3,18,0 0,1.28,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,5.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,1.28,1.28,0,0,0,0,0,0,0,0,0,2.105,21,40,0 0,0.36,0.36,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,1.47,0,0.36,8.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.121,0,0,0.063,0,0.507,7.326,43,359,0 0,0.42,0.21,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,1.26,0,0.21,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,0,0.42,0,0,4.385,0,0,0.071,0,0.503,6.822,43,614,0 0,0.36,0,0,0.36,0,0,0,0,0.36,0,0.36,0,0,0,0,0,0,1.08,0,0,7.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.972,0,0,0.063,0,0.504,6.423,43,334,0 0,0.44,0,0,0.44,0,0,0,0,0.44,0,0.44,0,0,0,0,0,0,0.44,0,0,8.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.367,0,0,0.074,0,0.592,7.288,43,328,0 0,0.41,0,0,0,0,0,0,0,0.41,0,0.41,0,0,0,0,0,0,0.41,0,0,8.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.096,0,0,0.07,0,0.776,7.531,43,354,0 0,1.35,1.35,0,0,0,0,0,0,1.35,0,0,0,0,0,0,0,0,2.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.35,0,0,0,0,0.221,0,0,0,0,2.222,21,40,0 0,1.38,1.38,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,9.72,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,0,2.052,21,39,0 0,2.12,0,0,0,0,0,0,0,2.12,0,2.12,0,0,0,0,0,0,6.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,0,0,0,0,0,0,0,0,2.692,21,35,0 0.35,0.35,0,0,0,0,0,0,0,0.35,0,0.35,0,0,0,0,0,0,1.42,0,0,11.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.672,0,0,0.06,0,0.481,7.464,43,418,0 0,0,0,0,0,0,0,0,0,0,0,1.01,0,0,0,1.01,0,0,2.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.327,0,0,1.263,6,24,0 0,0.36,0,0,0,0,0,0,0,0.73,0,0,0,0,0,0,0,0,1.46,0,0.36,10.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.838,0,0,0.062,0,0.503,6.912,43,394,0 0,1.42,0,0,0,0,0,0,0,1.42,0,0,0,0,0,0,0,0,4.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.42,1.42,0,0,0,0,0,0,0,0,3.555,21,96,0 0,1.78,0,0,0,0,0,0,0,1.78,0,3.57,0,0,0,0,0,0,8.92,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,1.78,0,0,0,0,0,0,0,0,0,2.388,21,43,0 0.36,0,0.73,0,0,0,0,0,0,0.73,0,0.73,0,0,0,0,0,0,3.3,0,0,0,0,0,0.73,1.1,0,0.73,0.36,0.36,0,0,0,0,0.36,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0.231,0,0,0,0,2.482,16,144,0 1.49,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,4.47,0,1.49,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,1.933,8,29,0 0,0,0,0,0,0,0,0,0,0.69,0,2.09,0,0,0,0,0,0,4.19,0,0.69,0,0,0,1.39,3.49,0,1.39,0.69,0.69,0,0,0,0,0.69,1.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0.281,0,0,0.093,0,2.744,12,129,0 0.16,0,0.32,0,0,0.16,0,0,0,0.16,0,1.44,0,0,0,0.16,0,0,3.21,0,0.96,0,0,0,0.16,0.16,0,0,0.16,0.16,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0.64,0,0,0,0.32,0.185,0.318,0,0.079,0,0.053,1.695,36,290,0 0,0,0,0,0,0,0,0,0,1.02,0,1.02,0,0,0,0,0,0,5.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,0,0,0.309,0.154,0,0.154,0,0,3.304,48,76,0 0,0,2.32,0,0,0,0,0,0,2.32,0,0,0,0,1.16,0,0,0,2.32,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.16,0,1.16,0,0,0,0,0.204,0,0,0,0,1.75,11,35,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0.72,0.72,0,2.17,0,0,0,0,0,0,1.44,0,0,0,0,0,0,0,0,0.72,0,0,0.72,0,0,0.204,0,0.306,0.102,0,2.538,22,99,0 0,0.56,0,0,0,0,0,0,0,0,0,0.56,0,0,0,0,0,0,2.27,0,0,0,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.56,0.56,0,0,0,0,0.099,0,0,0,0.099,1.035,2,29,0 0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,4.05,0,2.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0.67,0,0,0,0,0.679,0,0,0,0,1.636,6,72,0 0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.307,5,17,0 0,0.8,0,0,0.6,0,0,0.2,0,0.2,0,0,0,0,0,1.8,0,2.2,1.8,0,2.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0.2,0,0,0,0,0,0.06,0,0,2.533,43,228,0 0,0.37,0.37,0,0.09,0.09,0,0.37,0,0,0,0.28,0.28,0,0,0.84,0.09,0.56,2.72,0,2.16,0,0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0,0,0,0,0,0,0,0.18,0,0,0,0,0.056,0,0.142,0.071,0.014,1.934,19,383,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,1.571,3,11,0 2.27,0,0,0,0,0,0,0,0,2.27,0,0,0,0,0,0,0,0,2.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.27,0,0,0,0,0,2.27,0,0,0,0,0,0,0,0,0,1.2,3,12,0 4,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.168,0,0.168,0,0,1.459,10,54,0 0,0,0,0,0.48,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.084,9,123,0 0,0,0.37,0,1.13,0,0,0,0,0.75,0,1.13,0,0,0,0,0,0,2.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0,0,0,0,0,0,1.264,4,43,0 0,0,1.98,0,0.99,0,0,0,0,0,0,1.98,0,0,0,0,0,0,4.95,0,0.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.99,0,0,1.98,0,0,0,0,0,0,0,0,0,0,0,0,1.222,4,22,0 0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0.5,2,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.271,0,0,1.057,2,37,0 0,0,0,0,0,0,0,0,0,0.88,0,0,0,0,0,0,0,0,1.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.88,0,0.88,0,0,0,0,0,0,1.76,0,0,0,0.157,0,0.157,0,0,2,15,84,0 0,0,0.51,0,0.17,0,0,0,0,0,0,0.34,0,0,0,0,0,0,2.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.064,0,0.064,0,0,3.587,55,226,0 0,0,0.46,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0,0,3.7,0,0,0,0,0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0.094,0,0.473,0,0,2.5,24,40,0 0,0,0.36,0,0.09,0,0,0,0,0,0,0,0.09,0,0,0,0,0.18,4.24,0,1.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,0.99,0,0,0.072,0.116,0,0.188,0,0,1.302,9,297,0 0,0,3.61,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,3.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0.12,0,0.12,1.96,9,49,0 0,0.82,0,0,0,0,0,0,0,1.24,0,0,0,0,0,0,0,0,1.65,0,0,9.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.149,0,0,0.07,0,0.562,7.416,43,356,0 0,0,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,0,0,2.77,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,0.438,0,0,1.214,3,17,0 0,9.52,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,0,0,0,0,0,1,1,10,0 0,0.27,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,1.94,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.23,0,0,0.048,0,0.482,5.802,43,412,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.63,0,0,0,0,0,0,0,0,0,1,1,12,0 0,0,0.71,0,0,0,0,0,0,0,0,0.71,0.71,0,0,0,0,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.121,0,0.243,0,0,1,1,31,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.04,0,0,9.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.187,0,0,0.141,0,0.425,6.51,43,319,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,6.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.43,0,0,0,0,0,0,0,0,0.43,0,0,3.885,0,0,0.073,0,0.439,5.754,43,328,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,2.24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,3.024,0.059,0,0.059,0,0.237,5.016,43,311,0 0,0,0.22,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,1.11,0,0.22,7.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0,3.125,0,0,0.24,0,0.28,5.397,43,448,0 0,0,0,0,0,0.42,0,0,0,0,0,0.84,0,0,0,0,0,0,2.1,0,0,6.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0,4.123,0,0,0.073,0,0.441,6.186,43,266,0 0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,6.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.63,0,0,0,0,0,0,0,0,0,1.333,4,20,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0,1.076,3,28,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.37,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.116,0,1.419,5,44,0 0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,9,0 0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,1,1,9,0 0,0,0,0,0,0,0,0,0,0,0,0.76,0,0,0,0,0,0.76,1.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.133,0,0.266,0,0,1,1,23,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12.19,0,4.87,0,0,9.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,0,3.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,3.33,0,0,0,0,0,0,0,0,0,1.142,3,16,0 0,0,0,0,0,0,0,0,0,0,0.24,0.72,0.24,0,0,0,0.24,0,0.72,0.24,2.16,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,0.447,0,0.122,0.285,0,3.714,19,286,0 0,0,0.91,0,0.3,0,0,0,0,0,0,0.3,0.3,0,0,0,0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0.3,0,0,0,0,0,0,0,0,1.505,14,128,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0.208,0,0,2.655,15,77,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,10,0 0,1.25,0,0,0,0,0,0,0,0,0,2.81,0,0,0,0,0,1.56,0.93,0,0.31,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0.164,0,0.109,0.054,0,2.193,18,136,0 0,0.22,0,0,0.22,0,0,0,0,0,0,1.36,0,0,0,0,0,1.59,0.91,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.326,0,0.285,0,0,2.043,31,141,0 0.51,0,0.51,0,1.53,0,0,0,0,0.51,0,0.51,0,0,0,0,0,0,1.02,0,0,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0.51,0,0,0.079,0,0,0,0,1.442,8,75,0 0,0,0.34,0,0.34,0,0,0,0,0,0,1.37,1.37,0,0,0,0,0.34,2.74,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0.232,0,0.406,0,0,1.425,6,77,0 0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.377,0,0,1,1,33,0 0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0.232,0,0,1.296,8,35,0 0,0,2.12,0,1.06,0,0,0,0,0,0,2.12,0,0,0,0,0,0,5.31,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.06,0,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,1.238,4,26,0 0.26,0,0.26,0,0.52,0,0,0,0,0.26,0,0.26,0,0,0,0.26,0,0,1.31,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.038,0,0.038,1.541,12,202,0 0,0,0,0,0,0,0,0,0,0,0,0.69,0,0,0,0.69,0,0,2.79,0,0.69,0,0,0,2.09,0,0,0,0,1.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.69,1.39,0,0,0,0.221,0,0,0,0,2.184,12,83,0 0,0,0,0,0.54,0,0,0,0,0,0.54,1.09,0,0,0,0,0,0,3.82,0,0,0,0,0,2.18,2.18,0,0.54,0,1.09,0,0,0,0,0.54,0,0,0,0,0,0,0,0.54,0,0.54,0,0,0,0,0.087,0,0,0,0,3.533,34,159,0 0,0,0,0,0,0,0,0,0,1.25,0,1.25,0,0,0,0,0,0,2.5,0,1.25,0,0,0,1.25,1.25,0,0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,1.25,1.25,0,0,0,0,0,0,0.204,0,2.45,15,49,0 0,0,0.55,0,0,0,0,0,0.55,0,0,0,0.55,0,0,0.55,0,0.55,0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.093,0,0.563,0,0,2.928,55,82,0 0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0,0.54,0.54,0,1.63,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,0.54,0,0,0,0,0,0.407,0,0,2.038,14,53,0 0,0,2.27,0,0,0,0,0,0,0,0,2.27,0,0,0,1.13,0,1.13,2.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.13,0,0,0,0.143,0,0,0,0,8.761,77,184,0 0,0,0,0,0,0,0,0,0,0,0,0.92,0,0,0,0,0,0.92,3.7,0,0.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.92,1.85,0,0,0,0.295,0,0,0,0,2.535,12,71,0 0,0,0,0,0,0,0,0,0,0,0,0.99,0,0,0,0,0,0,5.94,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.99,0,0,0,0.191,0,0,0.766,0,0,1,1,18,0 0.12,0,0.12,0,0,0,0,0,0,0,0.12,0.38,0,0,0,0,0.12,0,1.78,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0,0,0,0.25,0,0,0.12,0.63,0,0,0.018,0.074,0,0.055,0,0.018,3.08,63,419,0 0.11,0,0.33,0,0,0,0,0,0,0.11,0,0.45,0,0,0,0.11,0.11,0,2.81,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0,0,0,0,0.22,0,0,0.33,0.56,0,0,0.017,0.136,0,0.051,0,0.017,2.944,63,427,0 0,0.6,0,0,0.6,0,0,0,0,2.43,0,0.6,0,0,0,0,0,0,1.82,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,1.82,0,0,0,0.271,0,0,0,0.09,6.09,71,201,0 0,0.6,0,0,0.6,0,0,0,0,2.43,0,0.6,0,0,0,0,0,0,1.82,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,1.82,0,0,0,0.271,0,0,0,0.09,6.09,71,201,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.63,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.625,3,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.28,0,7.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,2.34,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0.33,0,0,0,0.06,0,0.302,0,0,1.562,14,100,0 0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.2,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.1,0,0,0,0,0,0,0.633,0,0,1.386,11,61,0 0,0,0,0,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,7.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.156,0,0.312,0,0,1.08,2,27,0 0,0,0.26,0,0.52,0,0,0,0,0,0,0,0,0,0,0,0,0.52,1.56,0,1.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.52,0,0,0,0.26,0,0,0,0,0.26,0,0,0,0.753,0.113,0,0.037,0.037,0,1.797,20,169,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0.34,0,0,0.68,0,0.68,0,0,0.34,0.34,0,0,0,0,0.34,0,1.36,3.42,0,2.73,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0.34,0,0,0,0,0,0.048,0.048,0,1.405,15,97,0 0,0,0.59,0,0.29,0.59,0.59,0.29,0,0.29,0.29,0,0,0,0,0,0,0.89,3.58,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0.29,0,0,0.088,0,0,0.044,0.132,0,1.592,15,121,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0.675,0,0,0,0,1,1,4,0 0.06,0,0.32,0,0,0,0,0,0,0.06,0,0.06,0.06,0,0,0,0,0.06,2.79,0,1.1,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0.06,0.19,0,0,0.317,0.035,0,0.093,0,0,1.11,9,261,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,7.69,0,0,0,0.775,0,0,0,0,1,1,5,0 0,0,0.6,0,0,0,0,0,0,0,0,0.43,0.08,0,0,0,0,0,3.02,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0,0,0.51,0,0,0,0,0.083,0,0.099,0,0,1.329,18,214,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.56,0,0,0,0,0,0,0,0,7.69,0,0,0,0.395,0,0,0,0,3,18,39,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.88,0,0,0,0,0,0,0,0,1,1,7,0 0,1.57,1.18,0,0,0,0,0,0,2.36,0,0.78,0,0,0,0,0,0,0.39,0,0,6.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.151,0.203,0,0.271,0,0.067,5.689,30,330,0 0,0,0,0,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.42,0,0,0,0,0.267,0,0,0,0,1,1,17,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,1.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.82,0,0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.21,0,0,0,0,0.371,0,0,0,0,1.967,13,61,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21.42,0,0,0,0,0,0,0,0,0,1.125,2,9,0 0,2.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.47,0,1.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.154,0,0.773,0,0,1,1,17,0 0,0,0.21,0,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,1.95,0,0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.131,0.175,0,0,0,0,1,1,68,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,3,0 0,0,1.01,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,5.05,0,2.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.187,0,0,1.166,3,21,0 0,0,0.81,0,0,0,0,0,0,0,0,3.25,0,0,0,0,0,0,4.06,0,1.62,0,0,0,0.81,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0.134,0,0,1.366,5,41,0 0,0,1.81,0,0,0,0,0,0,0,0,0.9,0.9,0,0,0,0,0,4.54,0,2.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.164,0,0,1.391,8,32,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.71,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,2.125,5,17,0 1.39,0,2.09,0,0,0,0,0,0,0,0,6.29,0,0,0,0.69,0,0,4.19,0.69,0.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.69,0,0,0,0,0,0,0,0,0,0,0,0,0.254,0,0,2,13,64,0 0.97,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0.48,0,0,2.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.48,0,0,0.48,0.97,0,0,0,0.15,0,0,0,0.075,3.367,21,165,0 0.15,0,0.63,0,0.07,0.11,0,0,0,0.03,0,0.07,0.37,0,0,0,0.03,0.03,1.16,0,0.22,0,0,0,0.03,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0.03,0,0,0.22,0.03,0,0,0.014,0.05,0,0.014,0,0,1.111,7,389,0 0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.473,0,0,1.687,5,27,0 0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,26,0 0,0,2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.214,4,17,0 4.34,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.75,4,14,0 0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.645,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0.344,0,0.344,0.172,0,2.166,11,39,0 0,0,1.66,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0.83,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.151,0,0,1.518,8,41,0 0,1.08,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.181,0,0,0,0,1.612,11,50,0 0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0.266,0,0.533,0,0,4.5,31,63,0 0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0,0.19,0,0.19,0,0,0,0,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0,0,22.05,0,0,0.135,0.339,0.067,0,0,0,4.13,81,285,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.17,0,3.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,1.58,0,0,0,0,0,0,0,0,1,1,12,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.934,0,0,0,0,3,9,18,0 0,0.36,0.36,0,0,0.36,0,0.73,0,0.36,0.36,1.46,0,0,0,0.36,0,2.56,2.93,0,0.36,0,0,0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.36,0,0,0,0,0,0,0,0,0,0.123,0,2.854,68,157,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0.112,0,0,0.903,0,2.285,14,80,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,1,1,4,0 0,0,1.72,0,0,0,0,0,0,0,0,2.58,0,0,0,0,0,0,2.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0.86,2.58,0,0,0.86,0,0,0,0,0.303,0,0.91,0,0,2.171,9,76,0 0,0,0,0,0,0,0,0,0,0,0,1.78,0,0,0,0,0,0,3.57,0,1.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.78,0,0,1.78,0,0,0,0,0,0,1.194,0,0,2.23,8,29,0 0,0,0,0.31,0.94,0,0,0.31,0,0.63,0,1.26,0,0,0,0,0,0,0.94,0,1.26,0,0,0,0,0,0,0.63,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0.037,0,0.074,0,0,3.904,39,246,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.571,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0.79,0,0.79,0,0,0,0,0,0.79,1.58,0,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.79,0,0,0,0,0,0,0,1.58,0,0,0,0.135,0.405,0,0.27,0,0,1.608,13,37,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.225,0,0,1,1,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.32,0,0,0,0,0,0,0.763,0,2.181,6,24,0 0,0.15,0.3,0,0.15,0,0,0,0,0,0,1.38,0.15,0,0,0,0.15,0,2.6,0,1.68,0,0.15,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0.3,0,0,0,0,0,0,0,0.61,2.91,0,0,0.023,0.093,0,0.069,0,0,2.05,23,326,0 0.32,0.32,0.32,0,0,0,0,0,0,0,0,1.29,0.32,0,0,0,0,0,2.92,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0.32,0,0,0,0,0,1.29,0,0,0,0.058,0.174,0,0.291,0,0,1.833,15,121,0 0,0,1.18,0,0.16,0,0,0,0,0.16,0,0.16,0.16,0,0,0,0.16,0,2.88,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0.031,0.374,0,0.561,0,0,1.462,10,136,0 0,0,1.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.94,0,1.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.656,0,0.656,0,0,1.488,5,67,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.571,5,11,0 0.13,0,0.13,0,0.27,0.27,0,0,0,0,0,0.41,0.27,0,0,0,0,0,1.25,0,0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.13,0,0,0,0,0,0.27,0.13,0,0,0,0.294,0,0.514,0,0,1.409,17,172,0 0,0.16,0.49,0,0,0.16,0,0,0,0.49,0,0.16,0.32,0,0,0,0,0,1.3,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0,0,0,0,0,0,0,0.16,0.16,0,0,0,0.119,0,0.149,0,0,2.178,107,244,0 0,3.36,1.92,0,0,0,0,0,0,4.32,0,1.44,0,0,0,0,0,0,0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.695,0,0.347,0,0,6.137,107,178,0 0,0,0.21,0,0,0,0,0,0,0.21,0.21,0,0.42,0,0,0,0,0,0,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,0,0,0,0.058,0,0,0,0,1.203,8,195,0 0,0,0.23,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0.23,1.4,0,0,0,0.064,0,0.161,0,0,1.065,7,146,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,4.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.22,2.22,0,0,0,0,0,0,0,0,1.75,5,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.01,0,1.01,5.05,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,55,60,0 0,0,0,0,0,0,0,0,0.58,0,0,1.16,0,0,0,0,0,0.58,1.75,0,1.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0,0,0,0.58,0,0,0,0.282,0,0.376,0,0,1.702,16,80,0 0.99,0,0.99,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.98,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.98,2.97,0,0,0,0,0,0.186,0,0,1.937,15,62,0 0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0.74,0,0,1.49,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,4.47,0,0,0,0.124,0,0,0,0,1.966,15,59,0 0.71,0,0.71,0,0,0,0,0,0,0.71,0,1.43,0,0,0,1.43,0,0,1.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.43,0,0,0,0,0,0,0,0,1.032,2,32,0 0,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0,0,0,0,0,0,0,9.52,0,0,0,0,0,0,0,0,2.074,15,56,0 0,0,1.01,0,0,1.01,0,0,0,1.01,0,0,0,0,0,0,0,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.01,0,0,0,0,0,1.01,3.03,0,0,0,0,0,0.475,0,0,1.576,15,41,0 0,0,0,0,0,0,0,0,0,0.33,0,0,0.33,0,0,0,0,0,2,0,0.33,0,0,0.33,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0,0,4.33,0,0,0,0.112,0,0.224,0.224,0,1.542,15,108,0 0,1.62,0.54,0,0,0,0,0,0,0.54,0,1.62,0,0,0,0,0,0,1.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0.484,0,0,0,0,1.769,27,69,0 0,0,0,0,0,0,0,0,0,11.11,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0 0.59,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.19,0,0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.59,1.19,0,0,0,0.212,0,0.212,0,0.106,1.7,11,68,0 0,0.32,0.96,0,0,0,0,0,0,0.64,0,1.28,0,0,0,0,0,0,3.52,0,1.6,0,0,0,0.96,1.6,0,0,0,0.64,0,0,0,0,0,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0.064,0,0.128,0,0,1.653,5,86,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,2.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0.81,0,0,0.81,0.81,0,0,0,0,0,0,0,0,1.684,5,64,0 0,0,0,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,1.85,0,0.61,0,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,1.23,0,0,1.23,1.85,0,0,0,0.098,0,0.098,0,0,1.627,15,70,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,1,1,5,0 0,0,0.41,0,0,0,0,0,0,0.41,0,1.25,0,0,0,0,0,0,2.91,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.184,0,0,0,0,1.538,10,40,0 0.4,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0.81,1.22,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.22,0,0,0,0,0.223,0,0,0,0.055,4.75,70,266,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0.38,2.31,0,0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,0,0,0,0.216,0,0.162,0,0.054,5.07,70,289,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0.27,0.55,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.55,0.27,0,0,0,0.122,0.081,0,0,0.04,3.891,70,323,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,3.03,3.03,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.153,55,67,0 0,0,1.13,0,0.37,0,0,0,0,0,0,0,0,0,0,0.37,0,0.37,1.13,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.145,0,0.436,0,0,1.792,55,147,0 0,0,2.06,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,4.12,0,1.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.03,0,0,0,0,0,0,0,0,1,1,16,0 0,0.31,0.31,0,0,0,0,0,0,0.31,0,0,0.31,0,0,0.63,0,0.31,4.73,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.63,0,0,0,0,0.228,0,0.045,0,0.045,8.117,97,414,0 0,0,0.4,0,0,0.4,0,0,0,0,0,0,0,0,0,0.4,0,0.4,0.4,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0.4,0.4,0,0,0,0.323,0.053,0,0,0.053,5.263,70,300,0 0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0,0.44,0,0.44,0.44,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0.44,0.44,0,0,0,0.175,0.058,0,0,0.058,8.478,122,390,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.41,0,0.41,1.23,0,0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.82,0,0,0,0,0.229,0,0.114,0,0.057,5.196,70,265,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0.72,2.18,0,0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0.298,0,0.198,0,0.099,4,59,128,0 0,0,0.59,0,0,0,0,0,0,0.29,0,0.59,0,0,0,0.29,0,0.29,1.47,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0.039,0.235,0,0.471,0,0.039,3.659,70,333,0 0,0.13,0.66,0,0,0,0,0,0,0.13,0,0.13,0,0,0,0.26,0,0.13,2.65,0,0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0,0,0,0.26,0,0,0,0.019,0.367,0,0.193,0,0.038,3.122,70,559,0 0,0,0.92,0,0,0,0,0,0,0,0,0,0.61,0,0,0.3,0,0.3,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.044,0.222,0,0.178,0,0.044,4.757,70,314,0 0,0,0.74,0,0,0,0,0,0,0,0,0.24,0,0,0,0.49,0,0.49,2.71,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,0,0,0,0,0.49,0,0,0,0.036,0.147,0,0.147,0,0,2.587,55,282,0 0,0,0.74,0,0,0,0,0,0,0,0,0.24,0,0,0,0.49,0,0.49,2.71,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,0,0,0,0,0.49,0,0,0,0.036,0.147,0,0.147,0,0,2.587,55,282,0 0,0,0,0,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0.43,2.19,0,0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.87,0.87,0.43,0,0,0,0.079,0,0,0,0,1.292,5,53,0 0,0,0.74,0,0,0,0,0,0,0,0,0.24,0,0,0,0.49,0,0.49,2.71,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24,0,0,0,0,0,0,0,0.49,0,0,0,0.036,0.147,0,0.147,0,0,2.587,55,282,0 0,0,0,0,0,0,0,0,0,0,0,0.61,0.61,0,0,0.61,0,0.3,3.09,0,0.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0,0,0,0,0.179,0,0.448,0,0,5.277,70,285,0 0,0.28,0.42,0,0,0,0,0,0,0,0,0.28,0,0,0,0.14,0,0.14,0.14,0,0.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,0.14,0.14,0,0,0,0,0.132,0,0.022,0,0,2.621,70,422,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0.44,0,0,0.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0,0,0,0,0.88,0,0,0,0.178,0.059,0,0,0.059,7.046,70,303,0 0,0,0.08,0,0,0.17,0,0,0,0,0,0.17,0,0,0,0.08,0,0.08,0.17,0,0.25,0,0,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0,0,0,0.08,0,0,0,0,0.59,0,0,0,0.075,0,0.012,0.012,0,2.057,70,605,0 0,0,0.68,0,0.68,0,0,0,0,0,0,0,0.34,0,0,0,0,0.34,1.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0,0,0.173,0,0.463,0,0,1.538,11,80,0 0,0,0,0,0,0,0,0,0,2.11,0,0,0.7,0,0,0.7,0,0.7,2.11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.7,0,0.7,0.7,0,0,0,0,0,0.336,0,0,2.97,68,101,0 0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,3.84,0,0,0,0,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,1.28,0,0,0,0,0,0,0,0,0,1.428,2,10,0 0,0,0.62,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,3.41,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0.62,0,0,0,1.24,0,0,0,0,0.112,0,0.225,0,0,1.866,4,28,0 0.3,0,0.3,0,0,0,0,0,0,0,0,0.3,0.6,0,0,0,0,0,3.03,0,1.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,1.21,0,0,0,0.055,0.11,0,0.055,0,0,1.947,7,74,0 0.12,0,0.12,0,0,0.25,0,0,0,0,0,0.12,0.25,0,0,0.12,0,0,2.19,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0.64,0.25,0.12,0,0,0,0.093,0,0.023,0,0,1.247,5,131,0 0,0,0,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,1.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0,0,0,0.116,0,0.232,0,0,1.551,6,45,0 0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,2.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0.74,0,0,0,0,0.276,0,0.552,0,0,2.666,16,72,0 0,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,0,2.12,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,13.333,73,160,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.77,2.77,0,0,0,0,0,0,0,0,0,1,1,8,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.12,3.12,0,0,0,0,0,0,0.467,0,0,1,1,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,0,1.6,4,8,0 0.25,0,0.51,0,0,0.25,0,0,0,0.12,0,0,0.25,0,0,0.25,0.25,0.38,1.78,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,4.34,0,0,0.019,0.019,0,0,0.038,0,1.642,17,207,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.23,0,0,0,0,0,0,19.131,0,0,13.25,48,53,0 0.16,0.16,0.16,0,0.83,0.16,0,0.16,0,0,0,0.5,0.16,0,0,0,0,0,2.34,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.16,0.33,0.16,0,0,0.087,0.058,0,0,0,0,1.901,16,135,0 0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.9,0,0.95,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.95,0.95,0,0,0,0.144,0,5.78,0,0,2.13,15,49,0 0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,4.81,0,3.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,1.3,3,13,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0,0,0,32.478,0,0,1.666,3,5,0 0,0,1.2,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,6.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.4,0,1.2,0,0,0,1.2,1.2,0,0,0.197,0,0,7.707,0,0,3.4,15,51,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,0,0,0,0,0,5.76,0,0,0,0.336,0,0,0,0,2.352,15,40,0 0,0,2.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.05,0,1.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.17,0,1.17,0,0,0,1.17,1.17,0,0,0,0,0,0,0,0,3,15,45,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.704,0,0,0,0,1.75,3,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.33,0,0,0,0,0,1.66,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0,0,0,5,0,0,0,0.554,0,0,0,0,2.294,15,39,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.45,0,0,0,0,0,1.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.5,2,3,0 0,0,0.44,0,0,0.44,0,0,0,0,0,0,0.44,0,0,0,0,0,2.67,0,0.89,0,0,0,0.89,0,0,0,0,0,0,0,0,0,0,0.44,0,0,0,0,0.44,0,0,0,0.44,0,0,0,0,0.074,0,0.149,0,0,1.115,2,29,0 1.42,0,0,0,0,0,0,0,0,0,0,4.28,0,0,0,0,0,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.28,0,0,0,0,0,0,0,0,0.35,0,0.175,0,0,1.826,7,42,0 0.76,0,0.76,0,0,0.38,0,0,0,0,0,1.15,0.38,0,0,0,0,0,2.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,0,2.69,0,0,0,0.38,0.38,0,0,0,0.18,0,0.54,0,0,2.285,15,144,0 0.26,0,0,0,0,0.26,0,0,0,0,0,0,0.26,0,0,0,0,0,2.66,0,0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.53,0,0,0.26,0.53,0.26,0,0,0,0.046,0,0,0,0,1.222,5,77,0 0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,8.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0.94,0,0,0,0,0,0,2.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.94,0,0,0,1.88,0,0,0,0.94,8.49,0,0,0,0.267,0,0,0,0,2.241,15,65,0 0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.684,0,0.684,0,0,1,1,1,0 0,0,0.37,0,0,0,0,0,0,0,0,0.37,0.37,0,0,0,0,0,3.33,0,0.37,0,0,0.37,1.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0.067,0,0.135,0.135,0,1.437,4,23,0 0,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0.57,0,0,3.17,0,0.28,0,0,0,0.57,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0.196,0,0.049,0.147,0,1.1,2,55,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.25,0,4.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.359,0.359,0,0,0,0,1,1,1,0 1.88,0,0,0,0,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.168,0,0.112,0,0.056,2.933,23,311,0 0,0.11,0.11,0,0.34,0,0,0,0.11,0.69,0.34,0.23,0.11,0,0,0,0,0.11,0.81,0,0.46,0,0.34,0,0,0,0,0,0,0,0,0,0,0,0,0.11,0.23,0,0.11,0,0,0,0,0,0,0.92,0,0,0.017,0.153,0,0.017,0.068,0.017,3.441,35,499,0 0.08,0.08,0.61,0,0,0,0,0,0,0.43,0,0,0.08,0,0,0,0,0.08,0.87,0,0.08,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.26,0,0,0,0,0.08,0,0,0,0.78,0,0,0.027,0.208,0.013,0.027,0,0,4.696,124,1315,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,1.47,0,0,0,0.335,0,0,0,0.167,2.652,11,61,0 0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,4.8,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0.8,0,0,0,0,0,0,0,0,1,1,18,0 0,0.62,0.62,0,0,0,0,0,0,1.24,0,0,0,0,0,0.62,0,0.62,0,0,3.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.24,0,1.86,0,0,0,0,0,0,1.24,0,0,0,0.384,0,0.288,0,0.096,6,116,294,0 0.39,0,0.98,0,0,0.19,0,0,0,0,0,0.58,0.19,0,0,0.78,0,0.39,5.09,0,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19,0,0,0.39,0,0.19,0,0,0.239,0,0.444,0,0,1.626,8,122,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0.127,0,0,0,0,0,1.137,3,33,0 0.35,0,0.71,0,0,0,0,0,0,0.35,0,0.71,0,0,0,0,0,0,7.47,0,1.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0,0.067,0,0,0,0,1,1,40,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,4.38,0,0.58,0,0,0,0,0,0,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0.055,0.167,0,0,0,0,1.122,3,55,0 0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,2.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.62,0,0,0,0,0.62,0.62,0,0,0,0,0.356,0,0.594,0,0,2.125,16,34,0 0,0,1.09,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.29,0,0,0,0.191,0,0,0,0,3,15,51,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,3.176,15,54,0 0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,0,4.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.36,0,0,0,0,0,1.36,1.36,0,0,0,0,0,0.234,0,0,2.076,15,27,0 0,0,0,0,0,0,0,0,0,0,0,0.95,0,0,0,0,0,0,3.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.95,0,0,0,0,0,0,0,0,2.85,0,0,0,0,0,0.175,0,0,3.125,15,50,0 0,0,0,0,0.35,0.35,0,0,0,0,0,0,0,0,0,0,0,0,1.79,0,0.71,0,0,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0,0,0,0.064,0,0,0,0,1.27,8,61,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.17,0,0,0,0,0,3.17,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,1.58,1.58,0,0,0,0,0,0,0,0,2.071,14,29,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,4.83,0,0,0,0,0,0,0,0,3.117,15,53,0 0,0,0,0,0,0.74,0,0,0,0,0,0.74,1.49,0,0,0,0,0,1.49,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,1.36,3,34,0 0.78,0,0,0,0,0,0,0,0,0,0,0.78,0,0,0,0,0,0,2.36,0,0.78,0,0,0,0,0.78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.78,0,0,0,0,0,0,0,0,0,0,1.875,8,30,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.84,0,1.28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,0,1.28,0,0,0,0,0,0,1.548,0,0,3.222,14,58,0 0,0,0,0,0,0,0,0,0,0,0,2.38,0,0,0,0,0,0,2.38,0,2.38,0,0,0,2.38,2.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.404,0,0.809,0.809,0,3,11,27,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.08,0,0,0,0,0,0,0,0,6.25,0,0,0,0,0,0,0,0,3.125,15,50,0 0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.22,0,0,0,0,0,0.64,0,0,0,0,0,0,0,0,0,0,0,0.64,0,0.64,0,0,0,0,0,1.29,2.58,0,0,0,0.348,0,1.16,0,0,3.121,15,103,0 0,0,0,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.74,0,0,0,0,0,0,0,0,2.22,0,0,0,0,0,0,0.277,0,2.72,15,68,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.01,0,0,0,0,0,0,0,0,5.05,0,0,0,0,0,0,0,0,3.043,15,70,0 0.23,0,0,0,0,0.11,0,0,0,0.11,0,0.11,0.11,0,0,0,0,0.23,2.15,0,0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.23,0,0,0,0,0,0,0.71,0.11,0,0,0,0.126,0,0.021,0,0,1.198,5,145,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.85,0,0.42,0,0,0,0,0,0.85,3.84,0,0,0,0,0,0,0,0,2.769,15,180,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.63,0,0.31,0,0,0,0,0,0.95,2.22,0,0,0,0,0,0,0,0,2.603,16,164,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,2.45,0,0,0,0.306,0,0,0.46,0.153,3.173,15,73,0 0,0,0,0,0,0,0,0,0,0,0,0,1.08,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.17,0,0,0,0,0,0,0,0,7.6,0,0,0,0,0,0,0,0,3.387,15,105,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.66,0,0,0,0,0,0,1.66,0,5,0,0,0,0,0,0,0,0,3.125,15,50,0 0.88,0,0,0,0,0,0,0,0,1.76,0,0,0,0,0,0,0,0,1.76,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.88,0,0,0,0,0.88,0,0,0.88,1.76,0,0,0,0.125,0,0.125,0,0,1.681,5,37,0 0,0,0,0,0,0,0,0,0,0,0,0.86,0.86,0,0,0,0,0,2.58,0,0.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0,0.86,0,0,0,0,0,0,0.152,0,0,2.166,14,52,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.91,0,0,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0.45,0,0,0,0,0,0,0,0,16.7,0,0,0,0.066,0,0,0,0,2.284,19,329,0 0,0.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39,0.98,0.19,0.98,0,0,0,0.19,0,0,0,0,0.19,0,0,0,0,0,0,0.39,0,0,0,0,0,0,0,0.19,15.35,0,0,0.086,0,0,0.028,0,0,3.377,15,537,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,1,1,9,0 0,2.01,0,0,0,0,0,0,0,2.68,0,0.67,0,0,0,0,0,0,4.02,0,3.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,0,0,0,0,0,0,2.01,0,0,0.112,0.112,0,0.112,0,0,2.484,15,82,0 0.09,0,0.48,0,0,0.29,0,0,0,0.09,0,0,0.19,0,0,0.09,0.19,0.58,1.35,0,0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.38,3.1,0,0,0.015,0.03,0,0,0.046,0,1.722,17,267,0 0,0,0,0,0,0,0,0,0,0,0,0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.63,0,0,0,0,0,0,0,0.63,13.37,0,0,0,0.158,0,0,0.079,0.079,1.719,15,98,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.13,0,0,0,0,0,0,0,0,0,1.13,0,0,0,0.136,0,0,0,0.409,1.837,12,68,0 0.42,0,0.42,0,0.21,0,0,0,0,0,0,0.21,0,0,0,0,0,0,1.91,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0,0,0,0.04,0.04,0,0,0,0,2,3,14,0 0,0,0,0,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,3.73,0,0.37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.11,0,0,0,0,0.066,0,0.066,0,0,1.555,4,14,0 0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,4.29,0,2.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.66,0,0,0,0,0.058,0,0,0,0,1.153,3,15,0 0,0,0.4,0,0.2,0.1,0,0,0,0,0,0.1,0.2,0,0,0,0,0,1.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0.055,0,0.018,0,0,1.666,4,25,0 0,0,0.36,0,0.12,0.24,0,0,0,0.24,0,0,0.24,0,0,0,0,0,1.58,0,0.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12,0,0.12,0.24,0,0,0,0.067,0.022,0,0,0,1.433,12,76,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.72,0,0,0,0,0,0,0,0,0,0.123,1.75,4,21,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.33,0,1.86,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0.46,0,0.46,0,0,0,0.082,0,0,0,0,1.117,3,38,0 0,0,0,0,0,0,0,0,0,5.26,0,0,0,0,0,0,0,0,5.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.666,3,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,5.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.26,2.53,0,0,0,0,0.263,0,0,0,0,2,5,32,0 0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,1.92,0,0,3.84,0,1.92,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.92,0,0,0,1.92,1.92,0,0,0,0,0,0,0,0,1.611,5,29,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,9,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5.55,0,0,0,0,0,0,0,0,0,0,0,0,1.375,4,11,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,5.333,18,32,0 0,0,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,1.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.746,0,0,0,0,1.687,4,27,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,0,0,0,0,3.03,0,0,3.03,3.03,0,0,0,0,0,0,0,0,1.47,5,25,0 0,0,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,3.7,0,7.4,0,0,0,0,0,0,0,3.7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4,8,0 0,0,0.42,0,0,0,0,0,0,0,0,0,0.21,0,0,0.21,0,0.21,2.14,0,0.42,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.21,0.21,0,0,0.42,0.21,0,0,0,0.078,0.039,0.039,0,0,1.292,6,106,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.272,4,25,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.16,0,0,0,0,0,0,0,0,0,0,1.666,3,10,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.93,0,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.93,0,0,0,0,0,0,0.93,0.93,0.93,0,0,0,0.163,0,0,0,0,1.911,15,65,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0.8,0.8,0.8,0,0,0,0.149,0,0,0,0,1.9,15,57,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.25,2,5,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.333,5,7,0 0,0,0.97,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,2.91,0,0.97,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.97,0,0,0,0,0,0,0,0,0,0,1.714,6,12,0 0,0,0,0,0,0.8,0,0,0.8,0,0,0,0,0,0,0,0,0.8,1.6,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0.8,0,0,0.294,0,0,0,0,1.166,2,14,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,4,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.47,0.47,0,0,0,0.252,0.168,0.168,0,0,1.228,5,43,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.66,0,0,0,0.334,0,0,0,0,3.333,18,60,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16.66,0,0,0,0,0,0,0,0,0,0,2,3,4,0 0.33,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0,0,0,0,0.66,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.33,0,0,0.99,0.33,0,0,0,0.175,0.058,0.116,0,0,1.271,5,75,0 0.17,0,0.68,0,0.34,0.34,0,0,0,0,0,0,0,0,0,0,0,0.34,4.8,0,1.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0.17,0,0,0,0.032,0,0.065,0,0,1.189,5,69,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.77,0,0,0,0,0,0,0,0,1,1,10,0 0.69,0,0,0,0.69,0,0,0,0,0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.69,0,0,1.38,0,0,1.38,1.38,0,0,0,0.302,0,0,0,0.1,2.447,15,93,0 0.16,0,0.32,0,0.1,0.1,0,0,0,0,0,0.21,0.96,0,0,0.05,0.05,0,0.64,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0.025,0.017,0.008,0,0.008,0.008,1.318,12,244,0 0,0,0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.11,4.45,0,0.83,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0.27,0,0,0,0.052,0,0,0,0,1.2,4,54,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.12,0,0,0,0,0,0,0,0,1,1,7,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10.63,0,2.12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2.12,2.12,0,0,0.374,0,0,0,0,0,1,1,7,0 0,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.06,0,2.04,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.02,0,0,0,0,1.02,0,0,0,0.55,0,0,0,0,1.333,5,28,0 0.54,0,0.54,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,4.39,0,1.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.54,0,1.09,0,0,0,0,0.097,0,0,0,1.512,11,59,0 0,0,0.37,0,0.28,0.28,0,0,0.09,0,0,0.18,0.28,0,0,0,0,0.46,2.71,0,0.93,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.56,0.09,0.09,0,0,0.017,0,0,0,0,1.024,3,128,0 0,0,0,0,0,0,0,0,0,0,0.6,0,0,0,0,0,0,0,1.82,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6,1.21,0,0,0.112,0,0,0,0,0,1.617,11,55,0 0,0,0.45,0,0.45,0,0,0,0,0,0,0,0.22,0,0,0,0,0,1.35,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,0.22,0.22,0,0,0,0,0,0,0,0,1.13,3,78,0 0.14,0,0.14,0,0,0.56,0,0,0,0,0,0.14,0,0,0,0,0,0.28,2.41,0,0.14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.42,0,0,0,0.7,0.14,0,0,0,0.053,0,0,0,0,1.136,5,108,0 0.67,0,0,0,0.67,0,0,0,0,0,0,0,0,0,0,0,0,0,1.34,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.67,0,0,1.34,0,0,2.01,1.34,0,0,0,0.29,0,0,0,0.096,2.432,15,90,0 0.25,0,0.5,0,0.25,0,0,0,0,0,0,0.5,0,0,0,0,0,0.75,6.28,0,0.75,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.25,0,0,0.048,0,0,0,0,0,1,1,42,0 0,0,0,0,0,0,0,0,0,0,0,0.5,1.01,0,0,0.5,0,0.5,2.53,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0.5,0.5,0,0,0,0.087,0,0,0.087,0,1.225,3,38,0 0,0,0.46,0,0.23,0.23,0,0,0,0,0,0,0,0,0,0.23,0,0,1.63,0,0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.46,0,0,0,0,0.23,0,0,0,0.082,0,0.082,0,0,1.256,5,98,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.4,0,0,0,0.254,0,0,0,0,1,1,13,0 0,0,0.18,0,0.18,0.18,0,0,0,0,0,0,0,0,0,0,0,0,2.06,0,0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0.56,0.37,0,0,0.033,0.033,0,0.099,0,0,1.489,11,137,0 0.29,0,0.29,0,0,0,0,0,0,0.29,0,0.29,0.59,0,0,0.29,0,0,3.86,0,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29,0,0,0,0.107,0,0,0,0,1.22,6,61,0 0,0,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,1.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.38,2.77,0,0,0,0.213,0,0,0,0,1.72,11,43,0 0,0,0,0,0,0,0,0,0,0,0,0.37,0.37,0,0,0,0,0,1.49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.37,0,0,0,0,0.37,0,0,0,1.11,0.37,0,0,0,0.131,0,0,0,0,1.488,5,64,0 0,0,1.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.61,0,2.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0,0,0,0,0,0,1.2,3,24,0 0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.8,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0.4,0,0,0,0,0.145,0,0,0,1.372,5,70,0 0.27,0.05,0.1,0,0,0,0,0,0,0,0,0.48,0,0,0,0,0,0.1,0.97,0,0.1,3.47,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.27,0,0,0,0,0,0,0,0,0.76,0,0,0.607,0.064,0.036,0.055,0,0.202,3.766,43,1789,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0,4.76,0,0,0,0,0,0,0,0,1.571,5,11,0 0,0,0,0,0,0.51,0,0,0,0,0,0,0,0,0,0,0,0.51,3.06,0,1.02,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.51,0,0,0,0.091,0,0.091,0,0,1.586,4,46,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.89,0.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.89,0,0,0,0,0,0,0,0,1.266,3,19,0 0,0,1.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,1.85,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.61,0.61,0,0,1.23,0.61,0,0,0,0,0.406,0,0,0,1.666,13,70,0 0,0,0.45,0,0,0.22,0,0,0,0,0,0,0.45,0,0,0,0,0,1.83,0,0.45,0,0,0,0,0,0,0,0.22,0,0,0,0,0,0,0,0,0,0,0,0.68,0,0,0.45,0.22,0.22,0,0,0,0.082,0,0.041,0,0,1.5,7,123,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9.52,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.76,0,0,0,0.625,0,0,0,0,1.375,4,11,0 0,0,0,0,0.36,0,0,0,0,0,0,3.3,0,0,0,0,0.36,0.36,1.47,0,0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.47,0,0,0,0,0,0,0,0,0,0,0.112,0,0,0,0.056,1.793,21,174,0 0,0,0,0,0,0,0,0,0,0,0,0.71,0.71,0,0,0,0,0,0.71,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.71,0,0,0,0.125,0,0,0.125,0,1.272,4,28,0 0,0,3.03,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.03,3.03,0,0,0,0,0,0,0,0,1.111,2,10,0 0,0,0,0,0.54,0,0,0,0,0,0,0.54,0,0,0,0,0,0,0.54,0,0.54,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.09,0,0.54,0,0,0,0,0,0,0,0,1,1,22,0 0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0.58,0,0,2.9,0,0.58,0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.58,0,0,0,0.185,0,0,0,0.092,2.468,11,79,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6.89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.44,0,0,0,0,0,0,0,0,1,1,8,0 0,0,1.25,0,2.5,0,0,0,0,0,0,0,0.62,0,0,0,0,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.62,0,0,1.25,0.62,0.62,0,0,0,0.111,0,0,0,0,1.285,4,27,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7.69,0,0,0,0,0,1.052,0,0,1,1,6,0 0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,6.45,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.61,0,0,0,0.63,0,0,0,0,1.727,5,19,0 0,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.59,3.57,0,1.19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.59,0,0,0,0,0,0,0,0,1,1,24,0 0.31,0,0.62,0,0,0.31,0,0,0,0,0,1.88,0,0,0,0,0,0,0.62,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.31,0.31,0.31,0,0,0,0.232,0,0,0,0,1.142,3,88,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0.353,0,0,1.555,4,14,0 0.3,0,0.3,0,0,0,0,0,0,0,0,1.8,0.3,0,0,0,0,0.9,1.5,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.2,0,0,0.102,0.718,0,0,0,0,1.404,6,118,0 0.96,0,0,0,0.32,0,0,0,0,0,0,0.32,0,0,0,0,0,0,1.93,0,0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.32,0,0.32,0,0,0,0.057,0,0,0,0,1.147,5,78,0 0,0,0.65,0,0,0,0,0,0,0,0,0,0.65,0,0,0,0,0,4.6,0,0.65,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.97,0.65,0,0,0,0,0,0.125,0,0,1.25,5,40,0 deap-1.0.1/examples/gp/spambase.py0000644000076500000240000000762012320777200017252 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import random import operator import csv import itertools import numpy from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp # Read the spam list features and put it in a list of lists. # The dataset is from http://archive.ics.uci.edu/ml/datasets/Spambase # This example is a copy of the OpenBEAGLE example : # http://beagle.gel.ulaval.ca/refmanual/beagle/html/d2/dbe/group__Spambase.html spamReader = csv.reader(open("spambase.csv")) spam = list(list(float(elem) for elem in row) for row in spamReader) # defined a new primitive set for strongly typed GP pset = gp.PrimitiveSetTyped("MAIN", itertools.repeat(float, 57), bool, "IN") # boolean operators pset.addPrimitive(operator.and_, [bool, bool], bool) pset.addPrimitive(operator.or_, [bool, bool], bool) pset.addPrimitive(operator.not_, [bool], bool) # floating point operators # Define a safe division function def safeDiv(left, right): try: return left / right except ZeroDivisionError: return 0 pset.addPrimitive(operator.add, [float,float], float) pset.addPrimitive(operator.sub, [float,float], float) pset.addPrimitive(operator.mul, [float,float], float) pset.addPrimitive(safeDiv, [float,float], float) # logic operators # Define a new if-then-else function def if_then_else(input, output1, output2): if input: return output1 else: return output2 pset.addPrimitive(operator.lt, [float, float], bool) pset.addPrimitive(operator.eq, [float, float], bool) pset.addPrimitive(if_then_else, [bool, float, float], float) # terminals pset.addEphemeralConstant("rand100", lambda: random.random() * 100, float) pset.addTerminal(False, bool) pset.addTerminal(True, bool) creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("expr", gp.genHalfAndHalf, pset=pset, type_=pset.ret, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("compile", gp.compile, pset=pset) def evalSpambase(individual): # Transform the tree expression in a callable function func = toolbox.compile(expr=individual) # Randomly sample 400 mails in the spam database spam_samp = random.sample(spam, 400) # Evaluate the sum of correctly identified mail as spam result = sum(bool(func(*mail[:57])) is bool(mail[57]) for mail in spam_samp) return result, toolbox.register("evaluate", evalSpambase) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("mate", gp.cxOnePoint) toolbox.register("expr_mut", gp.genFull, min_=0, max_=2) toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset) def main(): random.seed(10) pop = toolbox.population(n=100) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) algorithms.eaSimple(pop, toolbox, 0.5, 0.2, 40, stats, halloffame=hof) return pop, stats, hof if __name__ == "__main__": main() deap-1.0.1/examples/gp/symbreg.py0000644000076500000240000000612012320777200017121 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import operator import math import random import numpy from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp # Define new functions def safeDiv(left, right): try: return left / right except ZeroDivisionError: return 0 pset = gp.PrimitiveSet("MAIN", 1) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.sub, 2) pset.addPrimitive(operator.mul, 2) pset.addPrimitive(safeDiv, 2) pset.addPrimitive(operator.neg, 1) pset.addPrimitive(math.cos, 1) pset.addPrimitive(math.sin, 1) pset.addEphemeralConstant("rand101", lambda: random.randint(-1,1)) pset.renameArguments(ARG0='x') creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("compile", gp.compile, pset=pset) def evalSymbReg(individual, points): # Transform the tree expression in a callable function func = toolbox.compile(expr=individual) # Evaluate the mean squared error between the expression # and the real function : x**4 + x**3 + x**2 + x sqerrors = ((func(x) - x**4 - x**3 - x**2 - x)**2 for x in points) return math.fsum(sqerrors) / len(points), toolbox.register("evaluate", evalSymbReg, points=[x/10. for x in range(-10,10)]) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("mate", gp.cxOnePoint) toolbox.register("expr_mut", gp.genFull, min_=0, max_=2) toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset) def main(): random.seed(318) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats_fit = tools.Statistics(lambda ind: ind.fitness.values) stats_size = tools.Statistics(len) mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size) mstats.register("avg", numpy.mean) mstats.register("std", numpy.std) mstats.register("min", numpy.min) mstats.register("max", numpy.max) pop, log = algorithms.eaSimple(pop, toolbox, 0.5, 0.1, 40, stats=mstats, halloffame=hof, verbose=True) # print log return pop, log, hof if __name__ == "__main__": main() deap-1.0.1/examples/gp/symbreg_numpy.py0000644000076500000240000000626212320777200020360 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import operator import math import random import numpy from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp # Define new functions def safeDiv(left, right): with numpy.errstate(divide='ignore',invalid='ignore'): x = numpy.divide(left, right) if isinstance(x, numpy.ndarray): x[numpy.isinf(x)] = 0 x[numpy.isnan(x)] = 0 elif numpy.isinf(x) or numpy.isnan(x): x = 0 return x pset = gp.PrimitiveSet("MAIN", 1) pset.addPrimitive(numpy.add, 2, name="vadd") pset.addPrimitive(numpy.subtract, 2, name="vsub") pset.addPrimitive(numpy.multiply, 2, name="vmul") pset.addPrimitive(safeDiv, 2) pset.addPrimitive(numpy.negative, 1, name="vneg") pset.addPrimitive(numpy.cos, 1, name="vcos") pset.addPrimitive(numpy.sin, 1, name="vsin") pset.addEphemeralConstant("rand101", lambda: random.randint(-1,1)) pset.renameArguments(ARG0='x') creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("compile", gp.compile, pset=pset) samples = numpy.linspace(-1, 1, 10000) values = samples**4 + samples**3 + samples**2 + samples def evalSymbReg(individual): # Transform the tree expression in a callable function func = toolbox.compile(expr=individual) # Evaluate the sum of squared difference between the expression # and the real function values : x**4 + x**3 + x**2 + x diff = numpy.sum((func(samples) - values)**2) return diff, toolbox.register("evaluate", evalSymbReg) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("mate", gp.cxOnePoint) toolbox.register("expr_mut", gp.genFull, min_=0, max_=2) toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr_mut, pset=pset) def main(): random.seed(318) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) algorithms.eaSimple(pop, toolbox, 0.5, 0.1, 40, stats, halloffame=hof) return pop, stats, hof if __name__ == "__main__": main() deap-1.0.1/examples/pso/0000755000076500000240000000000012321001644015264 5ustar felixstaff00000000000000deap-1.0.1/examples/pso/basic.py0000644000076500000240000000633212301410325016721 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import operator import random import numpy from deap import base from deap import benchmarks from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Particle", list, fitness=creator.FitnessMax, speed=list, smin=None, smax=None, best=None) def generate(size, pmin, pmax, smin, smax): part = creator.Particle(random.uniform(pmin, pmax) for _ in range(size)) part.speed = [random.uniform(smin, smax) for _ in range(size)] part.smin = smin part.smax = smax return part def updateParticle(part, best, phi1, phi2): u1 = (random.uniform(0, phi1) for _ in range(len(part))) u2 = (random.uniform(0, phi2) for _ in range(len(part))) v_u1 = map(operator.mul, u1, map(operator.sub, part.best, part)) v_u2 = map(operator.mul, u2, map(operator.sub, best, part)) part.speed = list(map(operator.add, part.speed, map(operator.add, v_u1, v_u2))) for i, speed in enumerate(part.speed): if speed < part.smin: part.speed[i] = part.smin elif speed > part.smax: part.speed[i] = part.smax part[:] = list(map(operator.add, part, part.speed)) toolbox = base.Toolbox() toolbox.register("particle", generate, size=2, pmin=-6, pmax=6, smin=-3, smax=3) toolbox.register("population", tools.initRepeat, list, toolbox.particle) toolbox.register("update", updateParticle, phi1=2.0, phi2=2.0) toolbox.register("evaluate", benchmarks.h1) def main(): pop = toolbox.population(n=5) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = ["gen", "evals"] + stats.fields GEN = 1000 best = None for g in range(GEN): for part in pop: part.fitness.values = toolbox.evaluate(part) if not part.best or part.best.fitness < part.fitness: part.best = creator.Particle(part) part.best.fitness.values = part.fitness.values if not best or best.fitness < part.fitness: best = creator.Particle(part) best.fitness.values = part.fitness.values for part in pop: toolbox.update(part, best) # Gather all the fitnesses in one list and print the stats logbook.record(gen=g, evals=len(pop), **stats.compile(pop)) print(logbook.stream) return pop, logbook, best if __name__ == "__main__": main() deap-1.0.1/examples/pso/basic_numpy.py0000644000076500000240000000604612301410325020153 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import operator import random import numpy from deap import base from deap import benchmarks from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Particle", numpy.ndarray, fitness=creator.FitnessMax, speed=list, smin=None, smax=None, best=None) def generate(size, pmin, pmax, smin, smax): part = creator.Particle(numpy.random.uniform(pmin, pmax, size)) part.speed = numpy.random.uniform(smin, smax, size) part.smin = smin part.smax = smax return part def updateParticle(part, best, phi1, phi2): u1 = numpy.random.uniform(0, phi1, len(part)) u2 = numpy.random.uniform(0, phi2, len(part)) v_u1 = u1 * (part.best - part) v_u2 = u2 * (best - part) part.speed += v_u1 + v_u2 for i, speed in enumerate(part.speed): if speed < part.smin: part.speed[i] = part.smin elif speed > part.smax: part.speed[i] = part.smax part += part.speed toolbox = base.Toolbox() toolbox.register("particle", generate, size=2, pmin=-6, pmax=6, smin=-3, smax=3) toolbox.register("population", tools.initRepeat, list, toolbox.particle) toolbox.register("update", updateParticle, phi1=2.0, phi2=2.0) toolbox.register("evaluate", benchmarks.h1) def main(): pop = toolbox.population(n=5) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = ["gen", "evals"] + stats.fields GEN = 1000 best = None for g in range(GEN): for part in pop: part.fitness.values = toolbox.evaluate(part) if part.best is None or part.best.fitness < part.fitness: part.best = creator.Particle(part) part.best.fitness.values = part.fitness.values if best is None or best.fitness < part.fitness: best = creator.Particle(part) best.fitness.values = part.fitness.values for part in pop: toolbox.update(part, best) # Gather all the fitnesses in one list and print the stats logbook.record(gen=g, evals=len(pop), **stats.compile(pop)) print(logbook.stream) return pop, logbook, best if __name__ == "__main__": main() deap-1.0.1/examples/pso/multiswarm.py0000644000076500000240000002237112301410325020045 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """Implementation of the Multiswarm Particle Swarm Optimization algorithm as presented in *Blackwell, Branke, and Li, 2008, Particle Swarms for Dynamic Optimization Problems.* """ import itertools import math import operator import random import numpy try: from itertools import imap except: # Python 3 nothing to do pass else: map = imap from deap import base from deap.benchmarks import movingpeaks from deap import creator from deap import tools scenario = movingpeaks.SCENARIO_2 NDIM = 5 BOUNDS = [scenario["min_coord"], scenario["max_coord"]] mpb = movingpeaks.MovingPeaks(dim=NDIM, **scenario) creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Particle", list, fitness=creator.FitnessMax, speed=list, best=None, bestfit=creator.FitnessMax) creator.create("Swarm", list, best=None, bestfit=creator.FitnessMax) def generate(pclass, dim, pmin, pmax, smin, smax): part = pclass(random.uniform(pmin, pmax) for _ in range(dim)) part.speed = [random.uniform(smin, smax) for _ in range(dim)] return part def convertQuantum(swarm, rcloud, centre, dist): dim = len(swarm[0]) for part in swarm: position = [random.gauss(0, 1) for _ in range(dim)] dist = math.sqrt(sum(x**2 for x in position)) if dist == "gaussian": u = abs(random.gauss(0, 1.0/3.0)) part[:] = [(rcloud * x * u**(1.0/dim) / dist) + c for x, c in zip(position, centre)] elif dist == "uvd": u = random.random() part[:] = [(rcloud * x * u**(1.0/dim) / dist) + c for x, c in zip(position, centre)] elif dist == "nuvd": u = abs(random.gauss(0, 1.0/3.0)) part[:] = [(rcloud * x * u / dist) + c for x, c in zip(position, centre)] del part.fitness.values del part.bestfit.values part.best = None return swarm def updateParticle(part, best, chi, c): ce1 = (c * random.uniform(0, 1) for _ in range(len(part))) ce2 = (c * random.uniform(0, 1) for _ in range(len(part))) ce1_p = map(operator.mul, ce1, map(operator.sub, best, part)) ce2_g = map(operator.mul, ce2, map(operator.sub, part.best, part)) a = map(operator.sub, map(operator.mul, itertools.repeat(chi), map(operator.add, ce1_p, ce2_g)), map(operator.mul, itertools.repeat(1 - chi), part.speed)) part.speed = list(map(operator.add, part.speed, a)) part[:] = list(map(operator.add, part, part.speed)) toolbox = base.Toolbox() toolbox.register("particle", generate, creator.Particle, dim=NDIM, pmin=BOUNDS[0], pmax=BOUNDS[1], smin=-(BOUNDS[1] - BOUNDS[0])/2.0, smax=(BOUNDS[1] - BOUNDS[0])/2.0) toolbox.register("swarm", tools.initRepeat, creator.Swarm, toolbox.particle) toolbox.register("update", updateParticle, chi=0.729843788, c=2.05) toolbox.register("convert", convertQuantum, dist="nuvd") toolbox.register("evaluate", mpb) def main(verbose=True): NSWARMS = 1 NPARTICLES = 5 NEXCESS = 3 RCLOUD = 0.5 # 0.5 times the move severity stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "nswarm", "evals", "error", "offline_error", "avg", "max" # Generate the initial population population = [toolbox.swarm(n=NPARTICLES) for _ in range(NSWARMS)] # Evaluate each particle for swarm in population: for part in swarm: part.fitness.values = toolbox.evaluate(part) # Update swarm's attractors personal best and global best if not part.best or part.fitness > part.bestfit: part.best = toolbox.clone(part[:]) # Get the position part.bestfit.values = part.fitness.values # Get the fitness if not swarm.best or part.fitness > swarm.bestfit: swarm.best = toolbox.clone(part[:]) # Get the position swarm.bestfit.values = part.fitness.values # Get the fitness record = stats.compile(itertools.chain(*population)) logbook.record(gen=0, evals=mpb.nevals, nswarm=len(population), error=mpb.currentError(), offline_error=mpb.offlineError(), **record) if verbose: print(logbook.stream) generation = 1 while mpb.nevals < 5e5: # Check for convergence rexcl = (BOUNDS[1] - BOUNDS[0]) / (2 * len(population)**(1.0/NDIM)) not_converged = 0 worst_swarm_idx = None worst_swarm = None for i, swarm in enumerate(population): # Compute the diameter of the swarm for p1, p2 in itertools.combinations(swarm, 2): d = math.sqrt(sum((x1 - x2)**2. for x1, x2 in zip(p1, p2))) if d > 2*rexcl: not_converged += 1 # Search for the worst swarm according to its global best if not worst_swarm or swarm.bestfit < worst_swarm.bestfit: worst_swarm_idx = i worst_swarm = swarm break # If all swarms have converged, add a swarm if not_converged == 0: population.append(toolbox.swarm(n=NPARTICLES)) # If too many swarms are roaming, remove the worst swarm elif not_converged > NEXCESS: population.pop(worst_swarm_idx) # Update and evaluate the swarm for swarm in population: # Check for change if swarm.best and toolbox.evaluate(swarm.best) != swarm.bestfit.values: # Convert particles to quantum particles swarm[:] = toolbox.convert(swarm, rcloud=RCLOUD, centre=swarm.best) swarm.best = None del swarm.bestfit.values for part in swarm: # Not necessary to update if it is a new swarm # or a swarm just converted to quantum if swarm.best and part.best: toolbox.update(part, swarm.best) part.fitness.values = toolbox.evaluate(part) # Update swarm's attractors personal best and global best if not part.best or part.fitness > part.bestfit: part.best = toolbox.clone(part[:]) part.bestfit.values = part.fitness.values if not swarm.best or part.fitness > swarm.bestfit: swarm.best = toolbox.clone(part[:]) swarm.bestfit.values = part.fitness.values record = stats.compile(itertools.chain(*population)) logbook.record(gen=generation, evals=mpb.nevals, nswarm=len(population), error=mpb.currentError(), offline_error=mpb.offlineError(), **record) if verbose: print(logbook.stream) # Apply exclusion reinit_swarms = set() for s1, s2 in itertools.combinations(range(len(population)), 2): # Swarms must have a best and not already be set to reinitialize if population[s1].best and population[s2].best and not (s1 in reinit_swarms or s2 in reinit_swarms): dist = 0 for x1, x2 in zip(population[s1].best, population[s2].best): dist += (x1 - x2)**2. dist = math.sqrt(dist) if dist < rexcl: if population[s1].bestfit <= population[s2].bestfit: reinit_swarms.add(s1) else: reinit_swarms.add(s2) # Reinitialize and evaluate swarms for s in reinit_swarms: population[s] = toolbox.swarm(n=NPARTICLES) for part in population[s]: part.fitness.values = toolbox.evaluate(part) # Update swarm's attractors personal best and global best if not part.best or part.fitness > part.bestfit: part.best = toolbox.clone(part[:]) part.bestfit.values = part.fitness.values if not population[s].best or part.fitness > population[s].bestfit: population[s].best = toolbox.clone(part[:]) population[s].bestfit.values = part.fitness.values generation += 1 if __name__ == "__main__": main() deap-1.0.1/examples/pso/speciation.py0000644000076500000240000001502012301410325017770 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """Implementation of the Speciation Particle Swarm Optimization algorithm as presented in *Li, Blackwell, and Branke, 2006, Particle Swarm with Speciation and Adaptation in a Dynamic Environment.* """ import itertools import math import operator import random import numpy try: from itertools import imap except: # Python 3 nothing to do pass else: map = imap from deap import base from deap.benchmarks import movingpeaks from deap import creator from deap import tools scenario = movingpeaks.SCENARIO_2 NDIM = 5 BOUNDS = [scenario["min_coord"], scenario["max_coord"]] mpb = movingpeaks.MovingPeaks(dim=NDIM, **scenario) creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Particle", list, fitness=creator.FitnessMax, speed=list, best=None, bestfit=creator.FitnessMax) def generate(pclass, dim, pmin, pmax, smin, smax): part = pclass(random.uniform(pmin, pmax) for _ in range(dim)) part.speed = [random.uniform(smin, smax) for _ in range(dim)] return part def convert_quantum(swarm, rcloud, centre): dim = len(swarm[0]) for part in swarm: position = [random.gauss(0, 1) for _ in range(dim)] dist = math.sqrt(sum(x**2 for x in position)) # Gaussian distribution # u = abs(random.gauss(0, 1.0/3.0)) # part[:] = [(rcloud * x * u**(1.0/dim) / dist) + c for x, c in zip(position, centre)] # UVD distribution # u = random.random() # part[:] = [(rcloud * x * u**(1.0/dim) / dist) + c for x, c in zip(position, centre)] # NUVD distribution u = abs(random.gauss(0, 1.0/3.0)) part[:] = [(rcloud * x * u / dist) + c for x, c in zip(position, centre)] del part.fitness.values del part.bestfit.values part.best = None return swarm def updateParticle(part, best, chi, c): ce1 = (c*random.uniform(0, 1) for _ in range(len(part))) ce2 = (c*random.uniform(0, 1) for _ in range(len(part))) ce1_p = map(operator.mul, ce1, map(operator.sub, best, part)) ce2_g = map(operator.mul, ce2, map(operator.sub, part.best, part)) a = map(operator.sub, map(operator.mul, itertools.repeat(chi), map(operator.add, ce1_p, ce2_g)), map(operator.mul, itertools.repeat(1-chi), part.speed)) part.speed = list(map(operator.add, part.speed, a)) part[:] = list(map(operator.add, part, part.speed)) toolbox = base.Toolbox() toolbox.register("particle", generate, creator.Particle, dim=NDIM, pmin=BOUNDS[0], pmax=BOUNDS[1], smin=-(BOUNDS[1] - BOUNDS[0])/2.0, smax=(BOUNDS[1] - BOUNDS[0])/2.0) toolbox.register("swarm", tools.initRepeat, list, toolbox.particle) toolbox.register("update", updateParticle, chi=0.729843788, c=2.05) toolbox.register("convert", convert_quantum) toolbox.register("evaluate", mpb) def main(verbose=True): NPARTICLES = 100 RS = (BOUNDS[1] - BOUNDS[0]) / (50**(1.0/NDIM)) # between 1/20 and 1/10 of the domain's range PMAX = 10 RCLOUD = 1.0 # 0.5 times the move severity stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) logbook = tools.Logbook() logbook.header = "gen", "nswarm", "evals", "error", "offline_error", "avg", "max" swarm = toolbox.swarm(n=NPARTICLES) generation = 0 while mpb.nevals < 5e5: # Evaluate each particle in the swarm for part in swarm: part.fitness.values = toolbox.evaluate(part) if not part.best or part.bestfit < part.fitness: part.best = toolbox.clone(part[:]) # Get the position part.bestfit.values = part.fitness.values # Get the fitness # Sort swarm into species, best individual comes first sorted_swarm = sorted(swarm, key=lambda ind: ind.bestfit, reverse=True) species = [] while sorted_swarm: found = False for s in species: dist = math.sqrt(sum((x1 - x2)**2 for x1, x2 in zip(sorted_swarm[0].best, s[0].best))) if dist <= RS: found = True s.append(sorted_swarm[0]) break if not found: species.append([sorted_swarm[0]]) sorted_swarm.pop(0) record = stats.compile(swarm) logbook.record(gen=generation, evals=mpb.nevals, nswarm=len(species), error=mpb.currentError(), offline_error=mpb.offlineError(), **record) if verbose: print(logbook.stream) # Detect change if any(s[0].bestfit.values != toolbox.evaluate(s[0].best) for s in species): # Convert particles to quantum particles for s in species: s[:] = toolbox.convert(s, rcloud=RCLOUD, centre=s[0].best) else: # Replace exceeding particles in a species with new particles for s in species: if len(s) > PMAX: n = len(s) - PMAX del s[PMAX:] s.extend(toolbox.swarm(n=n)) # Update particles that have not been reinitialized for s in species[:-1]: for part in s[:PMAX]: toolbox.update(part, s[0].best) del part.fitness.values # Return all but the worst species' updated particles to the swarm # The worst species is replaced by new particles swarm = list(itertools.chain(toolbox.swarm(n=len(species[-1])), *species[:-1])) generation += 1 if __name__ == '__main__': main() deap-1.0.1/examples/speed.txt0000644000076500000240000000074312320777200016337 0ustar felixstaff00000000000000ga/evosn ga/knapsack ga/evoknn_jmlr ga/evoknn ga/kursawefct ga/onemax_multidemic ga/onemax ga/onemax_numpy ga/onemax_short ga/tsp ga/nsga2 ga/onemax_mp ga/onemax_island ga/nqueens gp/adf_symbreg gp/ant gp/parity gp/spambase gp/symbreg gp/symbreg_numpy es/fctmin es/onefifth es/cma_minfct es/cma_1+l_minfct es/cma_bipop coev/hillis coev/symbreg coev/coop_evol coev/coop_gen coev/coop_niche coev/coop_adapt de/basic de/sphere eda/fctmin eda/pbil pso/basic pso/speciation pso/multiswarmdeap-1.0.1/INSTALL.txt0000644000076500000240000000123412301410325014512 0ustar felixstaff00000000000000================================ UNIX based platforms and Windows ================================ In order to install DEAP from sources, change directory to the root of deap and type in : $ python setup.py install This will try to install deap into your package directory, you might need permissions to write to this directory. ======= Options ======= Prefix ++++++ You might want to install this software somewhere else by addind the prefix options to the installation. $ python setup.py install --prefix=somewhere/else Other +++++ Other basic options are provided by the building tools of Python, see http://docs.python.org/install/ for more information. deap-1.0.1/LICENSE.txt0000644000076500000240000001672512117373622014516 0ustar felixstaff00000000000000 GNU LESSER GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. This version of the GNU Lesser General Public License incorporates the terms and conditions of version 3 of the GNU General Public License, supplemented by the additional permissions listed below. 0. Additional Definitions. As used herein, "this License" refers to version 3 of the GNU Lesser General Public License, and the "GNU GPL" refers to version 3 of the GNU General Public License. "The Library" refers to a covered work governed by this License, other than an Application or a Combined Work as defined below. An "Application" is any work that makes use of an interface provided by the Library, but which is not otherwise based on the Library. Defining a subclass of a class defined by the Library is deemed a mode of using an interface provided by the Library. A "Combined Work" is a work produced by combining or linking an Application with the Library. The particular version of the Library with which the Combined Work was made is also called the "Linked Version". The "Minimal Corresponding Source" for a Combined Work means the Corresponding Source for the Combined Work, excluding any source code for portions of the Combined Work that, considered in isolation, are based on the Application, and not on the Linked Version. The "Corresponding Application Code" for a Combined Work means the object code and/or source code for the Application, including any data and utility programs needed for reproducing the Combined Work from the Application, but excluding the System Libraries of the Combined Work. 1. Exception to Section 3 of the GNU GPL. You may convey a covered work under sections 3 and 4 of this License without being bound by section 3 of the GNU GPL. 2. Conveying Modified Versions. If you modify a copy of the Library, and, in your modifications, a facility refers to a function or data to be supplied by an Application that uses the facility (other than as an argument passed when the facility is invoked), then you may convey a copy of the modified version: a) under this License, provided that you make a good faith effort to ensure that, in the event an Application does not supply the function or data, the facility still operates, and performs whatever part of its purpose remains meaningful, or b) under the GNU GPL, with none of the additional permissions of this License applicable to that copy. 3. Object Code Incorporating Material from Library Header Files. The object code form of an Application may incorporate material from a header file that is part of the Library. You may convey such object code under terms of your choice, provided that, if the incorporated material is not limited to numerical parameters, data structure layouts and accessors, or small macros, inline functions and templates (ten or fewer lines in length), you do both of the following: a) Give prominent notice with each copy of the object code that the Library is used in it and that the Library and its use are covered by this License. b) Accompany the object code with a copy of the GNU GPL and this license document. 4. Combined Works. You may convey a Combined Work under terms of your choice that, taken together, effectively do not restrict modification of the portions of the Library contained in the Combined Work and reverse engineering for debugging such modifications, if you also do each of the following: a) Give prominent notice with each copy of the Combined Work that the Library is used in it and that the Library and its use are covered by this License. b) Accompany the Combined Work with a copy of the GNU GPL and this license document. c) For a Combined Work that displays copyright notices during execution, include the copyright notice for the Library among these notices, as well as a reference directing the user to the copies of the GNU GPL and this license document. d) Do one of the following: 0) Convey the Minimal Corresponding Source under the terms of this License, and the Corresponding Application Code in a form suitable for, and under terms that permit, the user to recombine or relink the Application with a modified version of the Linked Version to produce a modified Combined Work, in the manner specified by section 6 of the GNU GPL for conveying Corresponding Source. 1) Use a suitable shared library mechanism for linking with the Library. A suitable mechanism is one that (a) uses at run time a copy of the Library already present on the user's computer system, and (b) will operate properly with a modified version of the Library that is interface-compatible with the Linked Version. e) Provide Installation Information, but only if you would otherwise be required to provide such information under section 6 of the GNU GPL, and only to the extent that such information is necessary to install and execute a modified version of the Combined Work produced by recombining or relinking the Application with a modified version of the Linked Version. (If you use option 4d0, the Installation Information must accompany the Minimal Corresponding Source and Corresponding Application Code. If you use option 4d1, you must provide the Installation Information in the manner specified by section 6 of the GNU GPL for conveying Corresponding Source.) 5. Combined Libraries. You may place library facilities that are a work based on the Library side by side in a single library together with other library facilities that are not Applications and are not covered by this License, and convey such a combined library under terms of your choice, if you do both of the following: a) Accompany the combined library with a copy of the same work based on the Library, uncombined with any other library facilities, conveyed under the terms of this License. b) Give prominent notice with the combined library that part of it is a work based on the Library, and explaining where to find the accompanying uncombined form of the same work. 6. Revised Versions of the GNU Lesser General Public License. The Free Software Foundation may publish revised and/or new versions of the GNU Lesser General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Library as you received it specifies that a certain numbered version of the GNU Lesser General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that published version or of any later version published by the Free Software Foundation. If the Library as you received it does not specify a version number of the GNU Lesser General Public License, you may choose any version of the GNU Lesser General Public License ever published by the Free Software Foundation. If the Library as you received it specifies that a proxy can decide whether future versions of the GNU Lesser General Public License shall apply, that proxy's public statement of acceptance of any version is permanent authorization for you to choose that version for the Library. deap-1.0.1/PKG-INFO0000644000076500000240000000270712321001644013750 0ustar felixstaff00000000000000Metadata-Version: 1.1 Name: deap Version: 1.0.1 Summary: Distributed Evolutionary Algorithms in Python Home-page: http://deap.googlecode.com Author: deap Development Team Author-email: deap-users@googlegroups.com License: LGPL Description: DEAP stands for Distributed Evolutionary Algorithm in Python, it is dedicated to people who wish to learn how to use evolutionary algorithms and to those who wish to rediscover evolutionary algorithms. DEAP is the proof that evolutionary algorithms do **not** need to be neither complex or complicated. DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanism such as multiprocessing and SCOOP (http://scoop.googlecode.com/). Keywords: evolutionary algorithms,genetic algorithms,genetic programming,cma-es,ga,gp,es,pso Platform: any Classifier: Development Status :: 5 - Production/Stable Classifier: Intended Audience :: Developers Classifier: Intended Audience :: Education Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL) Classifier: Programming Language :: Python Classifier: Programming Language :: Python :: 3 Classifier: Topic :: Scientific/Engineering Classifier: Topic :: Software Development deap-1.0.1/README.txt0000644000076500000240000000112612301410325014341 0ustar felixstaff00000000000000DEAP stands for Distributed Evolutionary Algorithm in Python, it is dedicated to people who wish to learn how to use evolutionary algorithms and to those who wish to rediscover evolutionary algorithms. DEAP is the proof that evolutionary algorithms do **not** need to be neither complex or complicated. DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanism such as multiprocessing and SCOOP (http://scoop.googlecode.com/). deap-1.0.1/setup.py0000644000076500000240000000243212320777200014367 0ustar felixstaff00000000000000#!/usr/bin/env python import sys from distutils.core import setup try: from distutils.command.build_py import build_py_2to3 as build_py except ImportError: from distutils.command.build_py import build_py import deap setup(name='deap', version=deap.__revision__, description='Distributed Evolutionary Algorithms in Python', long_description=open('README.txt').read(), author='deap Development Team', author_email='deap-users@googlegroups.com', url='http://deap.googlecode.com', packages=['deap', 'deap.tools', 'deap.benchmarks', 'deap.tests'], platforms=['any'], keywords=['evolutionary algorithms','genetic algorithms','genetic programming','cma-es','ga','gp','es','pso'], license='LGPL', classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Intended Audience :: Education', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Topic :: Scientific/Engineering', 'Topic :: Software Development', ], ext_modules = [], cmdclass = {'build_py': build_py} )