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Datasets:
fabiencasenave
/
ShapeNetCar_cgns

Tasks:
Graph Machine Learning
Tags:
physics learning
geometry learning
License:
Dataset card Files
xet
Community
ShapeNetCar_cgns
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---
task_categories:
- graph-ml
tags:
- physics learning
- geometry learning
license: cc-by-4.0
viewer: false
dataset_info:
  splits:
  - name: test
    num_bytes: 3326380
    num_examples: 10
  - name: train
    num_bytes: 3326380
    num_examples: 10
  download_size: 6652760
  dataset_size: 6652760
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test/*
  - split: train
    path: data/train/*
---
<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
legal:
  owner: NeuralOperator (https://zenodo.org/records/13993629)
  license: cc-by-4.0
data_production:
  physics: CFD
  type: simulation
  script: Converted to PLAID format for standardized access; no changes to data content.
num_samples:
  train: 10
  test: 10
storage_backend: cgns
plaid:
  version: 0.1.13.dev1+gb350f274a

```
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)
```

Plaid samples' features can be retrieved like the following:
```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 = pb_defs[0]

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.get_in_features_identifiers():
        plaid_sample.get_feature_by_path(path=path, time=t)
    for path in pb_def.get_out_features_identifiers():
        plaid_sample.get_feature_by_path(path=path, time=t)
```
This dataset was generated in [PLAID](https://plaid-lib.readthedocs.io/), we refer to this documentation for additional details on how to extract data from `sample` objects.