README.md

---
license: cc-by-4.0
task_categories:
- graph-ml
tags:
- physics learning
- geometry learning
dataset_info:
  features:
  - name: Base_2_3/Zone/Elements_TRI_3/ElementConnectivity
    list: int64
  - name: Base_2_3/Zone/GridCoordinates/CoordinateX
    list: float32
  - name: Base_2_3/Zone/GridCoordinates/CoordinateY
    list: float32
  - name: Base_2_3/Zone/GridCoordinates/CoordinateZ
    list: float32
  - name: Base_2_3/Zone/VertexFields/pressure
    list: float32
  splits:
  - name: train
    num_bytes: 114714000
    num_examples: 500
  - name: test
    num_bytes: 25466508
    num_examples: 111
  download_size: 140210510
  dataset_size: 140180508
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---
<p align='center'>
<img src='https://i.ibb.co/3mGHsHMk/Shape-Net-Car-samples.png' alt='https://i.ibb.co/3mGHsHMk/Shape-Net-Car-samples.png' width='1000'/>
</p>

```yaml
owner: NeuralOperator (https://zenodo.org/records/13993629)
license: cc-by-4.0
data_production:
  type: simulation
  physics: CFD
  script: Converted to PLAID format for standardized access; no changes to data content.
data_description: ExampleDescription
num_samples:
  train: 500
  test: 111
storage_backend: hf_datasets

```
This dataset was generated with [`plaid`](https://plaid-lib.readthedocs.io/), we refer to this documentation for additional details on how to extract data from `plaid_sample` objects.

The simplest way to use this dataset is to first download it:
```python
from plaid.storage import download_from_hub

repo_id = "channel/dataset"
local_folder = "downloaded_dataset"

download_from_hub(repo_id, local_folder)
```

Then, to iterate over the dataset and instantiate samples:
```python
from plaid.storage import init_from_disk

local_folder = "downloaded_dataset"
split_name = "train"

datasetdict, converterdict = init_from_disk(local_folder)

dataset = datasetdict[split]
converter = converterdict[split]

for i in range(len(dataset)):
    plaid_sample = converter.to_plaid(dataset, i)
```

It is possible to stream the data directly:
```python
from plaid.storage import init_streaming_from_hub

repo_id = "channel/dataset"

datasetdict, converterdict = init_streaming_from_hub(repo_id)

dataset = datasetdict[split]
converter = converterdict[split]

for sample_raw in dataset:
    plaid_sample = converter.sample_to_plaid(sample_raw)
```

Sample features can then be retrieved as follows:
```python
from plaid.storage import load_problem_definitions_from_disk
local_folder = "downloaded_dataset"
pb_defs = load_problem_definitions_from_disk(local_folder)

# or
from plaid.storage import load_problem_definitions_from_hub
repo_id = "channel/dataset"
pb_defs = load_problem_definitions_from_hub(repo_id)


pb_def = next(iter(pb_defs.values()))

plaid_sample = ... # use a method from above to instantiate a plaid sample

for t in plaid_sample.get_all_time_values():
    for path in pb_def.input_features:
        feature = plaid_sample.get_feature_by_path(path=path, time=t)
        ...
    for path in pb_def.output_features:
        feature = plaid_sample.get_feature_by_path(path=path, time=t)
        ...
```

For those familiar with HF's `datasets` library, raw data can be retrieved without using the `plaid` library:
```python
from datasets import load_dataset

repo_id = "channel/dataset"

datasetdict = load_dataset(repo_id)

for split_name, dataset in datasetdict.items():
    for raw_sample in dataset:
        for feat_name in dataset.column_names:
            feature = raw_sample[feat_name]
```
Notice that raw data refers to the variable features only, with a specific encoding for time variable features.