|
"""Dataset for the fluid cube |
|
|
|
More on: https://inductiva.ai/blog/article/fluid-cube-dataset |
|
|
|
""" |
|
import json |
|
|
|
import datasets |
|
import numpy as np |
|
|
|
_DESCRIPTION = 'https://inductiva.ai/blog/article/fluid-cube-dataset' |
|
|
|
_BASE_URL = 'https://storage.googleapis.com/fluid_cube/' |
|
|
|
|
|
class WindTunnel(datasets.GeneratorBasedBuilder): |
|
'''The FluidCube builder''' |
|
|
|
def __init__(self, version, **kwargs): |
|
super().__init__(**kwargs) |
|
self.bucket_url = _BASE_URL + f'{version}.tar.gz' |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features({ |
|
'block_position': [datasets.Value('float32')], |
|
'block_dimensions': [datasets.Value('float32')], |
|
'fluid_volume': |
|
datasets.Value('float32'), |
|
'block_velocity': [datasets.Value('float32')], |
|
'block_velocity_magnitude': |
|
datasets.Value('float32'), |
|
'kinematic_viscosity': |
|
datasets.Value('float32'), |
|
'density': |
|
datasets.Value('float32'), |
|
'tank_dimensions': [datasets.Value('float32')], |
|
'time_max': |
|
datasets.Value('float32'), |
|
'time_step': |
|
datasets.Value('float32'), |
|
'particle_radius': |
|
datasets.Value('float32'), |
|
'number_of_fluid_particles': |
|
datasets.Value('int32'), |
|
|
|
|
|
|
|
'simulation_time_steps': |
|
datasets.Sequence( |
|
datasets.Array2D(dtype='float64', shape=(None, 6))) |
|
})) |
|
|
|
def _split_generators(self, dl_manager): |
|
|
|
downloaded_dir = dl_manager.download(self.bucket_url) |
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
'json_files': dl_manager.iter_archive(downloaded_dir) |
|
}) |
|
] |
|
|
|
|
|
def _generate_examples(self, json_files): |
|
for id_, (_, json_file) in enumerate(json_files): |
|
bytes_data = json_file.read() |
|
data = json.loads(bytes_data) |
|
data['simulation_time_steps'] = [ |
|
np.transpose(a) for a in data['simulation_time_steps'] |
|
] |
|
yield id_, data |
|
|
