THULab/phenology_normal_hawaii

Phenology-Normal-Hawaii (TsFile)

This dataset is an Apache TsFile conversion of `imageomics/phenology-normal-hawaii`.

Modalities: Time-series.

Overview

• —Vegetation color-index time series (GCC / RCC) from the PUUM (Pu'u Maka'ala) site, Hawaii, for fine-grained phenological analysis.
• —Extracted from NEON PhenoCam images; daily curves with gcc_mean, gcc_50, rcc_*, midday_* etc.
• —Each monitoring site is a device identified by the site TAG.

• —Converted observations: 10,945 rows across 1 TsFile file(s)
• —Source format: csv

TsFile schema

• —Time — source date (datetime), converted to INT64 milliseconds.

Conversion notes

• —site (from the source file name) is a TAG so each site is a separate device.
• —Flag columns (snow_flag, outlierflag_*) dropped as per-sample quality flags, not measurements.

Source & license

• —Original dataset: https://huggingface.co/datasets/imageomics/phenology-normal-hawaii
• —Author / publisher: imageomics
• —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("phenology_normal_hawaii.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())