Disable HF viewer and add TsFile data-preview section (viewer: false)

README.md

@@ -16,6 +16,7 @@ tags:
 - tsfile
 task_categories:
 - time-series-forecasting
+viewer: false
 ---
 
 # SpatialEpiBench TsFile
@@ -26,6 +27,45 @@ This repository contains a TsFile conversion of the Hugging Face dataset
 The description below separates information taken from the original dataset
 card from the changes made during this TsFile conversion.
 
+## ⚠️ About the Dataset Viewer
+
+**The Hugging Face dataset viewer is disabled for this repository on purpose.**
+
+The actual data lives in **11 `.tsfile` files** (Apache IoTDB TsFile, a binary
+time-series format). The HF viewer does **not** support `.tsfile`, so it cannot
+render the real data. The only files the viewer *could* auto-preview are the
+sidecar `.csv` files (`adjacency/*_adj.csv`, `column_mapping.csv`,
+`conversion_summary.csv`) — but those are **static graph/metadata tables, not
+the time-series data**. Showing them would misrepresent this dataset as having
+no timestamps, so the viewer is turned off (`viewer: false`).
+
+Each `.tsfile` does contain a proper time axis. Below is a real preview read
+back from `AUcase/AUcase.tsfile` (millisecond `Time` plus regional case counts;
+only 4 of 8 regions shown for width):
+
+| Time (epoch ms) | Time (UTC) | australian_capital_territory | new_south_wales | queensland | victoria |
+|---:|---|---:|---:|---:|---:|
+| 1585699200000 | 2020-04-01 | 4 | 150 | 38 | 51 |
+| 1585785600000 | 2020-04-02 | 3 | 116 | 54 | 68 |
+| 1585872000000 | 2020-04-03 | 4 | 91 | 38 | 49 |
+| 1585958400000 | 2020-04-04 | 2 | 104 | 27 | 30 |
+| 1586044800000 | 2020-04-05 | 3 | 87 | 7 | 20 |
+
+To read a `.tsfile`, use the TsFile SDK (Python or Java):
+
+```python
+from tsfile import TsFileReader, ColumnCategory
+
+reader = TsFileReader("AUcase/AUcase.tsfile")
+schemas = reader.get_all_table_schemas()
+table = next(iter(schemas))                      # e.g. "aucase"
+fields = [c.get_column_name() for c in schemas[table].get_columns()
+          if c.get_category() in (ColumnCategory.FIELD, ColumnCategory.TAG)]
+with reader.query_table(table, fields, batch_size=65536) as rs:
+    batch = rs.read_arrow_batch()                # Arrow batch; includes the `time` column
+    print(batch.to_pandas().head())
+```
+
 ## Original Dataset Information
 
 Original dataset: <https://huggingface.co/datasets/ruiqil/SpatialEpiBench>