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README.md

@@ -1,4 +1,11 @@
 ---
+license: cc-by-4.0
+task_categories:
+- graph-ml
+pretty_name: PDEBench 2D Shallow Water Equations
+tags:
+- physics learning
+- geometry learning
 dataset_info:
   features:
   - name: Base_2_2/Zone/CellData/water_height
@@ -17,3 +24,103 @@ configs:
   - split: train
     path: data/train-*
 ---
+```yaml
+legal:
+  owner: Takamoto, M et al. (https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2986)
+  license: cc-by-4.0
+data_production:
+  physics: Shallow Water Equations
+  type: simulation
+  script: Converted to PLAID format for standardized usage; no changes to data content.
+num_samples:
+  train: 1000
+storage_backend: hf_datasets
+plaid:
+  version: 0.1.12
+
+```
+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.
+
+### Dataset Sources
+
+- **Papers:**
+   - [arxiv](https://arxiv.org/pdf/2210.07182)