Datasets:
File size: 3,701 Bytes
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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.
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