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Quickstart

This Quickstart gives you two ways to run the pipeline end-to-end:

  1. CLI path – run spectralbridge-pipeline from a terminal
  2. Notebook path – run the pipeline inside Jupyter

Both produce the same ENVI, Parquet, and QA artifacts.

Install

Install from PyPI:

pip install spectralbridge

Ray is included in the standard dependency set. The spectralbridge[full] extra remains available as an alias for existing automation and currently resolves to the same dependency set.

Upgrading from older versions? Legacy imports and CLI aliases still work, but the Quickstart uses spectralbridge imports and spectralbridge-* entry points.

1. CLI path

Use the CLI if you run jobs on your laptop, server, or HPC cluster.

Run a NEON flight line

Choose an output directory:

BASE=output_quickstart
mkdir -p "$BASE"

Run the pipeline:

spectralbridge-pipeline \
  --base-folder "$BASE" \
  --site-code NIWO \
  --year-month 2023-08 \
  --product-code DP1.30006.001 \
  --flight-lines NEON_D13_NIWO_DP1_L020-1_20230815_directional_reflectance \
  --max-workers 2 \
  --engine thread

The first time this runs, it will:

  • download NEON HDF5 tiles
  • export ENVI cubes
  • apply topographic + BRDF correction
  • convolve to Landsat-style reflectance
  • write Parquet tables
  • produce QA PNG, PDF, and JSON summaries

If rerun, completed stages are skipped safely.

Inspect QA files (example on macOS)

open $BASE/NEON_D13_NIWO_DP1_L020-1_20230815_directional_reflectance/*_qa.png
open $BASE/NEON_D13_NIWO_DP1_L020-1_20230815_directional_reflectance/*_qa.pdf

2. Notebook path (Jupyter)

Use this if you want an interactive, reproducible workflow.

Run the pipeline from Python

In a notebook cell:

from spectralbridge import go_forth_and_multiply

base = "output_quickstart_py"

go_forth_and_multiply(
    base_folder=base,
    site_code="NIWO",
    year_month="2023-08",
    product_code="DP1.30006.001",
    flight_lines=["NEON_D13_NIWO_DP1_L020-1_20230815_directional_reflectance"],
    max_workers=2,
    engine="thread",
)

Preview the merged Parquet table

import duckdb, os

fl = "NEON_D13_NIWO_DP1_L020-1_20230815_directional_reflectance"
merged = os.path.join(base, fl, f"{fl}_merged_pixel_extraction.parquet")

duckdb.query(f"SELECT * FROM '{merged}' LIMIT 5").df()

For a more complete notebook-style walkthrough, see:
Usage → Jupyter notebook example

Next steps