def compare_weight_coordinates(first_hdf_file_name, second_hdf_file_name):
first_hdf_file = h5py.File(first_hdf_file_name, 'a')
second_hdf_file = h5py.File(second_hdf_file_name, 'a')
particles_name_first = read_hdf_file.get_particles_name(first_hdf_file)
particles_groups_first = read_hdf_file.ParticlesGroups(
particles_name_first)
first_hdf_file.visititems(particles_groups_first)
particles_name_second = read_hdf_file.get_particles_name(second_hdf_file)
particles_groups_second = read_hdf_file.ParticlesGroups(
particles_name_second)
second_hdf_file.visititems(particles_groups_second)
size_groups = len(particles_groups_second.particles_groups)
for i in range(0, size_groups):
weight_first, positions_first = get_dataset_values(
particles_groups_first.particles_groups[i], 'position')
weight_first, momentum_first = get_dataset_values(
particles_groups_first.particles_groups[i], 'momentum')
weight_second, positions_second = get_dataset_values(
particles_groups_second.particles_groups[i], 'position')
weight_second, momentum_second = get_dataset_values(
particles_groups_second.particles_groups[i], 'momentum')
count_weight_difference(weight_first, positions_first, weight_second,
positions_second)
count_weight_difference(weight_first, momentum_first, weight_second,
momentum_second)
def get_particles_groups(hdf_file, hdf_file_reduction_name):
hdf_file_reduction = h5py.File(hdf_file_reduction_name, 'a')
particles_name = read_hdf_file.get_particles_name(hdf_file_reduction)
particles_collect = read_hdf_file.ParticlesGroups(particles_name)
hdf_file.visititems(particles_collect)
return particles_collect, hdf_file_reduction
def base_reading_function(hdf_file_name):
hdf_file = h5py.File(hdf_file_name, 'a')
particles_name = read_hdf_file.get_particles_name(hdf_file)
particles_collect = read_hdf_file.ParticlesGroups(particles_name)
print(particles_collect)
hdf_file.visititems(particles_collect)
for group in particles_collect.particles_groups:
print('name group ' + str(group.name))
get_particles_sizes(hdf_file, group)
def base_corparation(first_hdf_file_name, second_hdf_file_name, csv_file_name,
file_indetifacator):
first_hdf_file = h5py.File(first_hdf_file_name, 'a')
second_hdf_file = h5py.File(second_hdf_file_name, 'a')
particles_name_first = read_hdf_file.get_particles_name(first_hdf_file)
particles_groups_first = read_hdf_file.ParticlesGroups(
particles_name_first)
first_hdf_file.visititems(particles_groups_first)
particles_name_second = read_hdf_file.get_particles_name(second_hdf_file)
particles_groups_second = read_hdf_file.ParticlesGroups(
particles_name_second)
second_hdf_file.visititems(particles_groups_second)
idx = first_hdf_file_name.rfind('/')
name_of_file = first_hdf_file_name[idx + 1:len(first_hdf_file_name) - 3]
for i in range(0, len(particles_groups_first.particles_groups)):
substr = particles_groups_first.particles_groups[i].name
print('name group ' + str(substr))
name_of_group = substr[substr.rfind('/') + 1:len(substr)]
absolute_coordinates_first, dimensions_first, weights_first =\
read_group_values(particles_groups_first, i)
if len(absolute_coordinates_first) == 0:
return
absolute_coordinates_second, dimensions_second, weights_second =\
read_group_values(particles_groups_second, i)
kernel_coords_first_x, kernel_coords_first_y, kernel_coords_first_z =\
compute_1d_kernels(0, dimensions_first.dimension_position, absolute_coordinates_first, weights_first)
kernel_coords_second_x, kernel_coords_second_y, kernel_coords_second_z =\
compute_1d_kernels(0, dimensions_second.dimension_position, absolute_coordinates_second, weights_second)
iteration_name = name_of_group + "coords"
row_coordinates = \
compute_1_d_stats_metrics(kernel_coords_first_x, kernel_coords_first_y, kernel_coords_first_z,
kernel_coords_second_x, kernel_coords_second_y, kernel_coords_second_z, name_of_file, iteration_name)
write_values_into_csv_file(row_coordinates, csv_file_name)
######
momentum_idx = dimensions_first.dimension_position + dimensions_first.dimension_momentum
kernel_coords_first_x, kernel_coords_first_y, kernel_coords_first_z = \
compute_1d_kernels(dimensions_first.dimension_position, momentum_idx, absolute_coordinates_first, weights_first)
kernel_coords_second_x, kernel_coords_second_y, kernel_coords_second_z = \
compute_1d_kernels(dimensions_first.dimension_position, momentum_idx, absolute_coordinates_second, weights_second)
iteration_name = name_of_group + "momentum"
row_momentum = \
compute_1_d_stats_metrics(kernel_coords_first_x, kernel_coords_first_y, kernel_coords_first_z,
kernel_coords_second_x, kernel_coords_second_y, kernel_coords_second_z,
name_of_file, iteration_name)
write_values_into_csv_file(row_momentum, csv_file_name)
sorted_momentum = create_sorted_momentum_list(data)
cells = create_momentum_cells(sorted_momentum,
self.parameters.tolerance_momentum,
x_segment, y_segment, z_segment)
data, weights = recount_cells(data, weigths, cells, mass)
return data, weights
if __name__ == "__main__":
hdf_file_name = '/home/kseniia/Documents/k_means_measure/data00000355.h5'
hdf_file_reduction_name = '/home/kseniia/Documents/k_means_measure/data00000355_thinning.h5'
copyfile(hdf_file_name, hdf_file_reduction_name)
hdf_file = h5py.File(hdf_file_name, 'a')
hdf_file_reduction = h5py.File(hdf_file_reduction_name, 'a')
particles_name = read_hdf_file.get_particles_name(hdf_file_reduction)
particles_collect = read_hdf_file.ParticlesGroups(particles_name)
hdf_file.visititems(particles_collect)
tolerance_parsent = 1.9e-23
dimension = 3
type_particles = 'mass'
parameters = Vranic_merging_algorithm_parameters(tolerance_parsent,
dimension, type_particles)
group = particles_collect.particles_groups[0]
for group in particles_collect.particles_groups:
data, weights, dimensions\
= read_hdf_file.read_points_group(group)
algorithm = Vranic_merging_algorithm(parameters)
data = np.array(data)