fabiencasenave/2D_profile

data_production:
  physics: 2D stationary RANS
  simulator: elsA
  type: simulation
legal:
  license: CC-by-SA 4.0
  owner: Safran
plaid:
  version: 0.1.10.dev114+gcbd3fd46f.d20251014

Example of commands:

from datasets import load_dataset
from plaid.bridges import huggingface_bridge

repo_id = "chanel/dataset"
pb_def_name = "pb_def_name" #`pb_def_name` is to choose from the repo `problem_definitions` folder

# Load the dataset
hf_datasetdict = load_dataset(repo_id)

# Load addition required data
flat_cst, key_mappings = huggingface_bridge.load_tree_struct_from_hub(repo_id)
pb_def = huggingface_bridge.load_problem_definition_from_hub(repo_id, pb_def_name)

# Efficient reconstruction of plaid samples
for split_name, hf_dataset in hf_datasetdict.items():
    for i in range(len(hf_dataset)):
        sample = huggingface_bridge.to_plaid_sample(
            hf_dataset,
            i,
            flat_cst[split_name],
            key_mappings["cgns_types"],
        )

# Extract input and output features from samples:
for t in sample.get_all_mesh_times():
    for path in pb_def.get_in_features_identifiers():
        sample.get_feature_by_path(path=path, time=t)
    for path in pb_def.get_out_features_identifiers():
        sample.get_feature_by_path(path=path, time=t)

This dataset was generated in PLAID, we refer to this documentation for additional details on how to extract data from sample objects.

Dataset Sources

• Papers:
  • arxiv