Datasets:
File size: 3,684 Bytes
2c7c4d7 d80da59 2c7c4d7 61f1b15 2c7c4d7 61f1b15 2c7c4d7 61f1b15 2c7c4d7 61f1b15 2c7c4d7 daf484f 2c7c4d7 daf484f 2c7c4d7 d80da59 2ed4519 d80da59 1735a89 d80da59 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
---
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: 2294280
num_examples: 10
- name: test
num_bytes: 2294280
num_examples: 10
download_size: 2231859
dataset_size: 4588560
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
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: hf_datasets
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)
```
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.
|