piscal-processor 0.1.1

PISCAL CSV/Parquet processing: convert, export, and schema utilities for curves and measurements.

piscal-processor

PISCAL CSV/Parquet processing: convert curves to Parquet, export to CSV/TSV, and use standard schemas. PISCAL is used at LeafWeb.org.

Install from PyPI, a local clone, or a Git URL (see below).

Install

From PyPI (when published):

pip install piscal-processor

Optional S3 support (e.g. for s3a:// paths):

pip install piscal-processor[s3]

From a local clone (development):

pip install -e /path/to/piscal-processor

From a Git URL (CI or private install): use a personal access token or SSH:

pip install "piscal-processor @ git+https://github.com/kolpacksoftware/piscal-processor.git@main"
# or
pip install "piscal-processor @ git+ssh://[e-mail]/kolpacksoftware/piscal-processor.git@main"

You can pin a branch (@main), tag (@v0.1.0), or commit (@abc1234).

CLI

Convert PISCAL CSV files to Parquet (metadata + measurements):

piscal-processor /path/to/csv_dir --output-dir parquet_output

Options: --no-discover-pathway-subdirs, --source-pathway, --metadata-name, --measurements-name. Input can be a local path or s3a:// URI.

Export measurement Parquet to CSV or TSV:

piscal-processor-export curve_measurements.parquet -o out.tsv --format tsv --columns curve_id,AnetCO2,PARi

Validate that one or more CSV files are in PISCAL format:

piscal-processor-check-format tests/fixtures/sampleinput.csv

Library

from piscal_processor import (
    convert_curves,
    export_curves,
    get_backend,
    is_piscal_csv,
)

# Convert CSVs to DataFrames (or write Parquet via converter.normalize_and_write_parquet)
backend = get_backend("/path/to/csv_dir")
meta_df, meas_df = convert_curves("/path/to/csv_dir", backend, source_pathway="C3")

# Export measurements to CSV/TSV
export_curves(meas_df, "out.tsv", columns=["curve_id", "AnetCO2", "PARi"], format="tsv")

# Quickly validate that a single CSV file looks like a PISCAL/Leafweb curve file
ok = is_piscal_csv("/path/to/file.csv")
print(ok)

Schema constants and parser helpers are also available:

from piscal_processor import STANDARD_MEASUREMENT_COLUMNS, STANDARD_METADATA_COLUMNS
from piscal_processor.parser import parse_csv_line, parse_key_value_section

Tests

pip install -e ".[dev]"
pytest

Documentation

• docs/csv_structure.md: CSV file structure (header, site/parameter blocks, measurement table).
• docs/inputformat.txt: PISCAL input file specification (official format description).