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Datasets:
THULab
/
ego4d_manipulation_v1

Tasks:
Time Series Forecasting
Modalities:
Tabular
Time-series
Formats:
parquet
Size:
1K - 10K
Tags:
tsfile
timeseries
time-series
format:tsfile
Libraries:
Datasets
Dask
Polars
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ego4d_manipulation_v1 / README.md
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wangdx25
Add TsFile (converted from mderry/ego4d-manipulation-v1)
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metadata 
task_categories:
  - time-series-forecasting
tags:
  - tsfile
  - timeseries
  - time-series
  - format:tsfile
pretty_name: ego4d_manipulation_v1
size_categories:
  - 10K<n<100K

ego4d_manipulation_v1 (TsFile)

Apache TsFile version of mderry/ego4d-manipulation-v1.

  • Records: 25,415

Schema (TsFile structure)

  • episode_index, task_index (TAG) — device dimension(s).
  • episode_index (FIELD).
  • task_index (FIELD).
  • frame_index (FIELD).
  • observation_images_ego (FIELD).
  • observation_state_0 (FIELD).
  • observation_state_1 (FIELD).
  • observation_state_2 (FIELD).
  • observation_state_3 (FIELD).
  • observation_state_4 (FIELD).
  • observation_state_5 (FIELD).
  • observation_state_6 (FIELD).
  • observation_state_7 (FIELD).
  • observation_state_8 (FIELD).
  • observation_state_9 (FIELD).
  • observation_state_10 (FIELD).
  • observation_state_11 (FIELD).
  • observation_state_12 (FIELD).
  • observation_state_13 (FIELD).
  • action_0 (FIELD).
  • action_1 (FIELD).
  • action_2 (FIELD).
  • action_3 (FIELD).
  • action_4 (FIELD).
  • action_5 (FIELD).
  • action_6 (FIELD).
  • action_7 (FIELD).
  • action_8 (FIELD).
  • action_9 (FIELD).
  • action_10 (FIELD).
  • action_11 (FIELD).
  • action_12 (FIELD).
  • action_13 (FIELD).

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("data/ego4d_manipulation_v1.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())

Source & license