Dysts Chaotic Dynamical-System Trajectories (TsFile)

This dataset is the Apache TsFile conversion of williamgilpin/dysts, a collection of simulated trajectories from named chaotic and nonlinear dynamical systems. See the source papers arXiv:2110.05266 and arXiv:2303.08011.

Modalities: Time-series.

Overview

• Source revision: 5d305abb3c9010e36ca6a3e6c967b72932f6cca9
• Source files: coarse/*.csv, medium/*.csv, and fine/*.csv
• Trajectories/files: 352 total (117 coarse, 118 medium, 117 fine)
• Points per trajectory: 10,000
• Converted rows: 3,520,000 total (1,170,000 coarse, 1,180,000 medium, 1,170,000 fine)
• License: cc-by-4.0

Each CSV contains a time column and a variable number of numeric channels. The source channel names are commonly x0, x1, ...; the leading whitespace is normalized during conversion. Different systems may have different channel counts, so each output table uses a union schema.

TsFile files and schema

The converter emits one logical table per source resolution. Each logical table is split into two TsFile shards by the import tool:

File	Table	Rows	Size (bytes)
dysts_coarse_1.tsfile	dysts_coarse	1,048,576	29,477,332
dysts_coarse_2.tsfile	dysts_coarse	121,424	3,581,515
dysts_medium_1.tsfile	dysts_medium	1,048,576	30,225,699
dysts_medium_2.tsfile	dysts_medium	131,424	3,893,846
dysts_fine_1.tsfile	dysts_fine	1,048,576	30,972,948
dysts_fine_2.tsfile	dysts_fine	121,424	3,527,556

Every table has the following common columns:

Column	Role	Type	Meaning
Time	TIME	INT64 (ms)	Rounded source time * 1000; source values run from 0 to 9,999
granularity	TAG	STRING	coarse, medium, or fine
system	TAG	STRING	System name parsed from the source filename
x0 through x9	FIELD	DOUBLE	Numeric trajectory channels; unavailable channels are null

The actual number of populated channel fields depends on the system. The union schema keeps the maximum observed channel names (x0 through x9) in each resolution table.

Conversion notes

• Source time is interpreted as seconds and converted to integer-millisecond Time by rounding. It is not retained as a second field.
• The filename suffix (_coarse, _medium, _fine) supplies the granularity TAG; the remaining filename stem supplies system.
• Channel names are trimmed and sanitized to TsFile-safe identifiers. Numeric values are copied without scaling. Missing channels in a system are null; no values are imputed and no trajectory rows are dropped.
• Files are processed deterministically and sorted by granularity, system, and Time. Each source system has unique (system, Time) keys within its resolution.

Read example

from tsfile import TsFileReader

path = "dysts_coarse_1.tsfile"
with TsFileReader(path) as reader:
    with reader.query_table(
        "dysts_coarse",
        ["x0", "x1", "x2"],
        batch_size=4096,
    ) as result:
        batch = result.read_arrow_batch()
        if batch is not None:
            print(batch.to_pandas().head())

Source & license

• Original dataset: https://huggingface.co/datasets/williamgilpin/dysts
• Author / publisher: William Gilpin
• Papers: https://arxiv.org/abs/2110.05266 and https://arxiv.org/abs/2303.08011
• License: CC-BY-4.0

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("dysts_coarse_1.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())