td-ts-compressor 0.1.9

Python helpers for the ts_compress_to Teradata time-series table operator.

td-ts-compressor

Python helpers for the ts_compress_to Teradata Vantage table operator.

This package is intentionally small: it bundles the ts_compress_to C table operator source and builds Teradata SQL for compression, feature engineering, installation, and TD_PLOT workflows. It does not include repository notebooks, generated images, local datasets, or project-level documentation.

Install

uv pip install td-ts-compressor

For local development from this repository:

uv pip install -e ".[dev]"

Install the optional Teradata interface when you want methods to return teradataml.DataFrame objects or execute SQL directly:

uv pip install -e ".[teradataml,dev]"

Compress A Series Table

from td_ts_compressor import (
    CompressorSpec,
    TeradataTimeSeriesCompressor,
    visual_fidelity,
)

compressor = TeradataTimeSeriesCompressor(
    CompressorSpec(
        id_columns=["mac_code"],
        time_column="seq_no",
        value_column="generator_speed",
        time_type="numeric",
    ),
    operator_database="utility_db",
)

compressed = compressor.compress(
    data_database="analytics",
    table_name="input_wind_plateau_full_csv",
    parameters=visual_fidelity(width_px=1024, height_px=768),
)

Pass as_dataframe=False to receive SQL instead of a teradataml.DataFrame. operator_database is where the table operator function mapping is installed. Source tables or views can live elsewhere and are addressed with data_database plus table_name. You can also pass input_query=... or dataframe=... for a SQL query or teradataml.DataFrame source.

Install The Table Operator

Generate installation SQL for a target database:

install_sql = compressor.install_table_operator_sql(
    target_database="utility_db",
)

Or execute the installation through teradataml.execute_sql:

compressor.install_table_operator(
    target_database="utility_db",
)

Pass c_source_path=... only when you want to install a modified local C source file instead of the C source bundled with the package.

Build Features

from td_ts_compressor import analytical_points

features = compressor.features(
    data_database="analytics",
    table_name="input_wind_plateau_full_csv",
    parameters=analytical_points(auto_log_drop=1.5),
    top_k_distances=3,
)

Generated feature SQL includes point counts, compression ratio, reduction percentage, selected-distance aggregates, and optional distance ranks.

Enable TD_PLOT On Large Tables

Materialize a compressed plotting table first, then call TD_PLOT on that smaller table.

create_plot_table_sql = compressor.materialize_for_td_plot_sql(
    data_database="analytics",
    table_name="input_wind_plateau_full_csv",
    output_schema_name="analytics",
    output_table="plot_generator_speed_compressed",
)

plot_sql = compressor.td_plot_sql(
    schema_name="analytics",
    table_name="plot_generator_speed_compressed",
    title="Generator speed compressed",
)

Package Contents

Distributed artifacts include only:

• td_ts_compressor Python modules
• bundled ts_compress_to.c operator source
• package-specific documentation
• package tests in the source distribution
• package metadata

They exclude notebooks, generated plots, local data, SQL scripts, and the broader repository documentation.

Publishing

Publishing is configured through GitHub Actions and PyPI Trusted Publishing. See package_docs/publishing.md in the source distribution for the release process.