---
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())
```