def factory(type, parameters, mass):
if type == "random":
return Random_thinning_algorithm.Random_thinning_algorithm(
parameters.reduction_percent)
if type == "number_conservative":
return Number_conservative_thinning_algorithm.Number_conservative_thinning_algorithm(
parameters.reduction_percent)
if type == "energy_conservative":
return Energy_conservative_thinning_algorithm.Energy_conservative_thinning_algorithm(
parameters.reduction_percent, mass)
if type == "kmeans":
divisions = [16, 40]
return k_means_clustering_algorithm.K_means_clustering_algorithm(
parameters.reduction_percent, parameters.max_iterations,
parameters.tolerance, divisions)
if type == "kmeans_avg":
divisions = [32, 60]
return k_means_merge_average_algorithm.K_means_merge_average_algorithm(
parameters.reduction_percent, parameters.max_iterations,
parameters.tolerance, divisions)
if type == "voronoi":
return Voronoi_algorithm.VoronoiMergingAlgorithm(
parameters.tolerance)
if type == "voronoi_prob":
voronoi_parameters = Voronoi_probabilistic_algorithm.Voronoi_probabilistic_algorithm_parameters\
(parameters.reduction_percent, parameters.ratio_left_particles)
return Voronoi_probabilistic_algorithm.Voronoi_probabilistic_algorithm(
voronoi_parameters)
if type == "leveling":
return Leveling_thinning_algorithm.Leveling_thinning_algorithm(
parameters.leveling_coefficient)
if type == "vranic": pass
assert 0, "Bad type_algoritm: " + type
def random_thinning_algorithm(hdf_file_name, hdf_file_reduction_name,
reduction_percent):
parameters = Random_thinning_algorithm.Random_thinning_algorithm_parameters(
reduction_percent)
base_reduction_function(hdf_file_name, hdf_file_reduction_name, "random",
parameters)
parser.add_argument("-leveling_coefficient", metavar='leveling_coefficient', type=float,
help="leveling_coefficient")
parser.add_argument("-k_means_subdivision", metavar='leveling_coefficient', type=list,
help="leveling_coefficient")
args = parser.parse_args()
if args.algorithm == 'voronoi':
tolerance = [args.momentum_tol, args.momentum_pos]
parameters = Voronoi_algorithm.VoronoiMergingAlgorithmParameters(tolerance)
base_reduction_function(args.hdf, args.hdf_re, "voronoi", parameters)
elif args.algorithm == 'random':
parameters = Random_thinning_algorithm.Random_thinning_algorithm_parameters(args.reduction_percent)
base_reduction_function(args.hdf, args.hdf_re, "random", parameters)
elif args.algorithm == 'number_conservative':
parameters = Number_conservative_thinning_algorithm.Number_conservative_thinning_algorithm_parameters(args.reduction_percent)
base_reduction_function(args.hdf, args.hdf_re, "number_conservative", parameters)
elif args.algorithm == 'energy_conservative':
parameters = Energy_conservative_thinning_algorithm.Energy_conservative_thinning_algorithm_parameters(args.reduction_percent)
base_reduction_function(args.hdf, args.hdf_re, "energy_conservative", parameters)
elif args.algorithm == 'kmeans':
parameters = k_means_clustering_algorithm.K_means_clustering_algorithm_parameters(args.reduction_percent)
base_reduction_function(args.hdf, args.hdf_re, "kmeans", parameters)
elif args.algorithm == 'kmeans_avg':
args = parser.parse_args()
if args.algorithm == 'voronoi':
tolerance = [args.momentum_tol, args.position_lol]
parameters = Voronoi_algorithm.VoronoiMergingAlgorithmParameters(tolerance)
voronoi_algorithm(args.hdf, args.hdf_re, args.momentum_tol, args.position_lol, args.iteration)
elif args.algorithm == 'voronoi_prob':
divide_particles = 20
parameters = Voronoi_probabilistic_algorithm.Voronoi_probabilistic_algorithm_parameters(1. - args.ratio_deleted_particles, divide_particles)
base_reduction_function(args.hdf, args.hdf_re, "voronoi_prob", parameters, args.iteration)
elif args.algorithm == 'random':
parameters = Random_thinning_algorithm.Random_thinning_algorithm_parameters(args.ratio_deleted_particles)
base_reduction_function(args.hdf, args.hdf_re, "random", parameters, args.iteration)
elif args.algorithm == 'number_conservative':
parameters = Number_conservative_thinning_algorithm.Number_conservative_thinning_algorithm_parameters(args.ratio_deleted_particles)
base_reduction_function(args.hdf, args.hdf_re, "number_conservative", parameters, args.iteration)
elif args.algorithm == 'energy_conservative':
parameters = Energy_conservative_thinning_algorithm.Energy_conservative_thinning_algorithm_parameters(args.ratio_deleted_particles)
base_reduction_function(args.hdf, args.hdf_re, "energy_conservative", parameters, args.iteration)
elif args.algorithm == 'kmeans':
parameters = k_means_clustering_algorithm.K_means_clustering_algorithm_parameters(args.ratio_deleted_particles)
base_reduction_function(args.hdf, args.hdf_re, "kmeans", parameters, args.iteration)
elif args.algorithm == 'kmeans_avg':