Upload fluid_cube.py (#1)

- Upload fluid_cube.py (65795fc8da57c03608ab59d6747c0df9be808f23)

fluid_cube.py

@@ -0,0 +1,74 @@
+"""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'),
+                # Float64 because pyArrow is not capable of
+                # [Array2D(shape, float32)].
+                # https://github.com/huggingface/datasets/issues/5936
+                'simulation_time_steps':
+                    datasets.Sequence(
+                        datasets.Array2D(dtype='float64', shape=(None, 6)))
+            }))
+
+    def _split_generators(self, dl_manager):
+        # Download and extract the zip file in the bucket.
+        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)
+                })
+        ]
+
+    # pylint: disable=arguments-differ
+    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