| --- |
| pretty_name: "Dysts Chaotic Dynamical-System Trajectories (TsFile)" |
| modality: timeseries |
| license: cc-by-4.0 |
| authors: "William Gilpin" |
| task_categories: |
| - time-series-forecasting |
| size_categories: |
| - 1M<n<10M |
| tags: |
| - tsfile |
| - timeseries |
| - modality:timeseries |
| - format:tsfile |
| - dynamical-systems |
| - chaotic-systems |
| - simulation |
| configs: |
| - config_name: coarse |
| data_files: |
| - split: coarse |
| path: "dysts_coarse_*.tsfile" |
| - config_name: medium |
| data_files: |
| - split: medium |
| path: "dysts_medium_*.tsfile" |
| - config_name: fine |
| data_files: |
| - split: fine |
| path: "dysts_fine_*.tsfile" |
| --- |
| |
| # Dysts Chaotic Dynamical-System Trajectories (TsFile) |
|
|
| This dataset is the Apache TsFile conversion of |
| [`williamgilpin/dysts`](https://huggingface.co/datasets/williamgilpin/dysts), |
| a collection of simulated trajectories from named chaotic and nonlinear |
| dynamical systems. See the source papers [arXiv:2110.05266](https://arxiv.org/abs/2110.05266) |
| and [arXiv:2303.08011](https://arxiv.org/abs/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 |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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()) |
| ``` |
|
|
