Add TsFile (converted from dany-l-23/coupled-msd)

.gitattributes

@@ -58,3 +58,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+coupled_msd_test.tsfile filter=lfs diff=lfs merge=lfs -text
+coupled_msd_train.tsfile filter=lfs diff=lfs merge=lfs -text
+coupled_msd_validation.tsfile filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+pretty_name: "Coupled Mass-Spring-Damper (TsFile)"
+modality: timeseries
+authors: "dany-l-23"
+task_categories:
+- time-series-forecasting
+size_categories:
+- 1M<n<10M
+tags:
+- tsfile
+- timeseries
+- modality:timeseries
+- format:tsfile
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "coupled_msd_*.tsfile"
+---
+
+# Coupled Mass-Spring-Damper (TsFile)
+
+This dataset is an Apache TsFile conversion of
+[`dany-l-23/coupled-msd`](https://huggingface.co/datasets/dany-l-23/coupled-msd).
+
+Modalities: Time-series.
+
+## Overview
+
+- Input–output data of a coupled mass-spring-damper system with a nonlinear force profile (generated with `statesim`).
+- 200 simulation trajectories of length 7500, split 120 train / 20 validation / 60 test.
+- `u_1` is the input, `y_1` the output, and `x_1..x_8` the internal states.
+- The train/validation/test split is preserved as three tsfiles; each trajectory is a device via the `traj_id` TAG.
+
+- Converted observations: 1,500,000 rows across 3 TsFile file(s)
+- Source format: csv
+
+## TsFile schema
+
+- **Time** — source `t` (seconds), converted to INT64 milliseconds.
+
+| Column | Role | Type | Meaning |
+|---|---|---|---|
+| `Time` | TIME | INT64 (ms) | sample timestamp |
+| `traj_id` | TAG | STRING | trajectory id |
+| `u_1` | FIELD | FLOAT | input |
+| `y_1` | FIELD | FLOAT | output |
+| `x_1` | FIELD | FLOAT | state 1 |
+| `x_2` | FIELD | FLOAT | — |
+| `x_3` | FIELD | FLOAT | — |
+| `x_4` | FIELD | FLOAT | — |
+| `x_5` | FIELD | FLOAT | — |
+| `x_6` | FIELD | FLOAT | — |
+| `x_7` | FIELD | FLOAT | — |
+| `x_8` | FIELD | FLOAT | — |
+
+## Conversion notes
+
+- Split preserved: `coupled_msd_train.tsfile`, `coupled_msd_validation.tsfile`, `coupled_msd_test.tsfile`.
+- `t` consumed into Time; all 10 measurement/state columns kept as FLOAT FIELDs.
+
+## Source & license
+
+- Original dataset: https://huggingface.co/datasets/dany-l-23/coupled-msd
+- Author / publisher: dany-l-23
+- License: mit
+
+## Usage
+
+Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
+
+```python
+from pathlib import Path
+from tsfile import TsFileReader
+
+path = Path("coupled_msd_test.tsfile")
+with TsFileReader(str(path)) as reader:
+    schemas = reader.get_all_table_schemas()
+    print("tables:", list(schemas))
+    table_name = next(iter(schemas))
+    table = schemas[table_name]
+    columns = [column.get_column_name() for column in table.get_columns()]
+    print("columns:", columns)
+    field_names = [
+        column.get_column_name()
+        for column in table.get_columns()
+        if column.get_column_name() not in {"Time", "time"}
+    ]
+    if field_names:
+        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
+            batch = result.read_arrow_batch()
+            if batch is not None:
+                print(batch.to_pandas().head())
+```

coupled_msd_test.tsfile

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+size 17362455

coupled_msd_train.tsfile

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+size 34737973

coupled_msd_validation.tsfile

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