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README.md

@@ -82,53 +82,109 @@ configs:
   - split: OOD
     path: data/OOD-*
 ---
-![image/png](https://i.ibb.co/MDqsmb5H/Logo-Tensile2d-2-consolas-100.png)
-![image/png](https://i.ibb.co/Js062hF/preview.png)
+<p align='center'>
+<img src='https://i.ibb.co/MDqsmb5H/Logo-Tensile2d-2-consolas-100.png' alt='https://i.ibb.co/MDqsmb5H/Logo-Tensile2d-2-consolas-100.png' width='1000'/>
+<img src='https://i.ibb.co/Js062hF/preview.png' alt='https://i.ibb.co/Js062hF/preview.png' width='1000'/>
+</p>
+
 ```yaml
+legal:
+  owner: Safran
+  license: cc-by-sa-4.0
 data_production:
+  type: simulation
   physics: 2D quasistatic non-linear structural mechanics, small deformations, plane
     strain
-  type: simulation
-legal:
-  license: CC-BY-SA
-  owner: Safran
+num_samples:
+  train: 500
+  test: 200
+  OOD: 2
+storage_backend: hf_datasets
 plaid:
-  version: 0.1.10.dev114+gcbd3fd46f.d20251014
+  version: 0.1.11.dev21+g94f13b9c8
 
 ```
-Example of commands:
+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 datasets import load_dataset
-from plaid.bridges import huggingface_bridge
-
-repo_id = "chanel/dataset"
-pb_def_name = "pb_def_name" #`pb_def_name` is to choose from the repo `problem_definitions` folder
-
-# Load the dataset
-hf_datasetdict = load_dataset(repo_id)
-
-# Load addition required data
-flat_cst, key_mappings = huggingface_bridge.load_tree_struct_from_hub(repo_id)
-pb_def = huggingface_bridge.load_problem_definition_from_hub(repo_id, pb_def_name)
-
-# Efficient reconstruction of plaid samples
-for split_name, hf_dataset in hf_datasetdict.items():
-    for i in range(len(hf_dataset)):
-        sample = huggingface_bridge.to_plaid_sample(
-            hf_dataset,
-            i,
-            flat_cst[split_name],
-            key_mappings["cgns_types"],
-        )
-
-# Extract input and output features from samples:
-for t in sample.get_all_mesh_times():
+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)):
+    raw_sample = dataset[i]
+    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():
-        sample.get_feature_by_path(path=path, time=t)
+        plaid_sample.get_feature_by_path(path=path, time=t)
     for path in pb_def.get_out_features_identifiers():
-        sample.get_feature_by_path(path=path, time=t)
+        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]
 ```
-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.
+Notice that raw data refers to the variable features only, with a specific encoding for time variable features.
 
 ### Dataset Sources