import numpy

import src.dimensions as D


# Genetic Algorithm methods

def local_fitness(field, x, y, plants_case):
    soil_value = 0
    if field[x][y].field_type == "soil":
        soil_value = 1
    else:
        soil_value = 0.5

    if plants_case[x][y] == "":
        plant_value = 0
    elif plants_case[x][y] == "w":
        plant_value = 1
    elif plants_case[x][y] == "p":
        plant_value = 2
    elif plants_case[x][y] == "s":
        plant_value = 3
    else:
        plant_value = 1

    neighbour_bonus = 1
    if x - 1 >= 0:
        if plants_case[x][y] == plants_case[x - 1][y]:
            neighbour_bonus += 1
    if x + 1 < D.GSIZE:
        if plants_case[x][y] == plants_case[x + 1][y]:
            neighbour_bonus += 1
    if y - 1 >= 0:
        if plants_case[x][y] == plants_case[x][y - 1]:
            neighbour_bonus += 1
    if y + 1 < D.GSIZE:
        if plants_case[x][y] == plants_case[x][y + 1]:
            neighbour_bonus += 1

    # TODO * multiculture_bonus
    local_fitness_value = (soil_value + plant_value) * (0.5 * neighbour_bonus + 1)
    return local_fitness_value


def population_fitness(population_text, field, population_size):
    # Calculating the fitness value of each solution in the current population.
    # The fitness function calulates the sum of products between each input and its corresponding weight.
    fitness = []

    for k in range(population_size):
        population_values_single = []
        population_values_single_row = []
        fitness_row = []

        for i in range(0, D.GSIZE):
            for j in range(0, D.GSIZE):
                population_values_single_row.append(local_fitness(field, i, j, population_text))
            population_values_single.append(population_values_single_row)

        for i in range(D.GSIZE):
            fitness_row.append(sum(population_values_single[i]))
        fitness = sum(fitness_row)
    return fitness


def crossover(parents, offspring_size):
    offspring = numpy.empty(offspring_size)
    # The point at which crossover takes place between two parents. Usually, it is at the center.
    crossover_point = numpy.uint8(offspring_size[1] / 2)

    for k in range(offspring_size[0]):
        # Index of the first parent to mate.
        parent1_idx = k % parents.shape[0]
        # Index of the second parent to mate.
        parent2_idx = (k + 1) % parents.shape[0]
        # The new offspring will have its first half of its genes taken from the first parent.
        offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point]
        # The new offspring will have its second half of its genes taken from the second parent.
        offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]
    return offspring


def mutation(offspring_crossover, num_mutations=1):
    mutations_counter = numpy.uint8(offspring_crossover.shape[1] / num_mutations)
    # Mutation changes a number of genes as defined by the num_mutations argument. The changes are random.
    for idx in range(offspring_crossover.shape[0]):
        gene_idx = mutations_counter - 1
        for mutation_num in range(num_mutations):
            # The random value to be added to the gene.
            random_value = numpy.random.uniform(-1.0, 1.0, 1)
            offspring_crossover[idx, gene_idx] = offspring_crossover[idx, gene_idx] + random_value
            gene_idx = gene_idx + mutations_counter
    return offspring_crossover