spectral-library
spectral-library is a Python package and CLI for:
- preparing stable row-aligned mapping runtimes from SIAC-style hyperspectral exports
- mapping source-sensor reflectance to target sensors
- reconstructing VNIR, SWIR, or full
400-2500 nmspectra with retrieval-based spectral mapping
Project identity:
- distribution:
spectral-library - import package:
spectral_library - public CLI:
spectral-library - internal maintainer CLI:
spectral-library-internal - current version:
0.6.1 - license:
MITfor repository software and repository-authored docs; referenced or redistributed third-party datasets, metadata, and derived artifacts may remain subject to upstream terms
Documentation:
- published site: https://marcyin.github.io/spectral_library/
- getting started: docs/mapping_quickstart.md
- theory: docs/theory.md
- official sensor examples: docs/official_sensor_examples.md
- CLI reference: docs/cli_reference.md
- Python API reference: docs/python_api_reference.md
- prepared runtime contract: docs/prepared_runtime_contract.md
- security and provenance: docs/security_provenance.md
- release process: docs/release_process.md
Install
Install the public package in Python 3.9 to 3.14:
python3 -m pip install .
Optional extras:
- docs site:
python3 -m pip install ".[docs]" - SciPy
cKDTreeKNN backend:python3 -m pip install ".[knn]" - FAISS backend:
python3 -m pip install ".[knn-faiss]" - PyNNDescent backend:
python3 -m pip install ".[knn-pynndescent]" - ScaNN backend:
python3 -m pip install ".[knn-scann]" - all ANN extras:
python3 -m pip install ".[knn-all]" - internal build and example-regeneration tools:
python3 -m pip install ".[internal-build]"
Quickstart
Download a pre-built runtime (77,125 spectra, MODIS/Sentinel-2/Landsat 8/9):
spectral-library download-prepared-library \
--output-root build/mapping_runtime
Or build your own from a SIAC-style export:
spectral-library build-mapping-library \
--siac-root build/siac_library \
--srf-root path/to/srfs \
--source-sensor SENSOR_A \
--source-sensor SENSOR_B \
--knn-index-backend faiss \
--output-root build/mapping_runtime
Validate it:
spectral-library validate-prepared-library \
--prepared-root build/mapping_runtime
Map one sample:
spectral-library map-reflectance \
--source-sensor SENSOR_A \
--target-sensor SENSOR_B \
--input path/to/query.csv \
--output-mode target_sensor \
--neighbor-estimator simplex_mixture \
--knn-backend faiss \
--output path/to/mapped.csv
Map a batch and write diagnostics:
spectral-library map-reflectance-batch \
--source-sensor SENSOR_A \
--target-sensor SENSOR_B \
--input path/to/query_batch.csv \
--output-mode target_sensor \
--neighbor-estimator simplex_mixture \
--knn-backend faiss \
--diagnostics-output path/to/diagnostics.json \
--neighbor-review-output path/to/neighbor_review.csv \
--output path/to/mapped_batch.csv
If you omit --prepared-root, the mapping CLI downloads the package-matched
published runtime into the user cache on first use. Pass --prepared-root when
you want to use a custom or locally built runtime instead.
Benchmark a prepared runtime:
python3 scripts/run_full_library_benchmarks.py \
--prepared-root build/mapping_runtime \
--source-sensor SENSOR_A \
--source-sensor SENSOR_B \
--neighbor-estimator simplex_mixture \
--knn-backend numpy \
--knn-backend faiss \
--max-test-rows 200 \
--output-root build/benchmark_outputs
For CSV layouts, SRF JSON shape, batch self-exclusion, diagnostics, confidence policy, and Python examples, start with docs/mapping_quickstart.md.
Repository Layout
The repository is now organized around four package areas:
spectral_library.mappingpublic mapping runtime, prepared-runtime build, and retrieval enginespectral_library.distributionruntime download helpers used bydownload-prepared-libraryspectral_library.sourcesmaintainer-oriented source manifests, fetchers, fetch batching, and catalog assemblyspectral_library.normalizationmaintainer-oriented normalization, coverage filtering, quality plots, and SIAC package export
Compatibility wrappers keep older flat root modules like batch.py,
build_db.py, normalize.py, and runtime_download.py importable while the
implementation lives in the new package layout.
What The Package Ships
- Stable public Python API for
prepare_mapping_library(...),build_mapping_library(...),build_mapping_runtime(...),coerce_sensor_input(...),validate_prepared_library(...), andSpectralMapper - Public CLI for runtime build, validation, single-sample mapping, batch mapping, and benchmarking
- Prepared runtime format with manifest, checksums, sensor schema, and optional persisted ANN indexes
- Exact
numpyKNN plus optionalscipy_ckdtree,faiss,pynndescent, andscannbackends - Multiple retrieval estimators including
mean,distance_weighted_mean, andsimplex_mixture - Rich diagnostics with neighbor identities, distances, weights, source-band fits, and confidence routing
Official Sensor Examples
The repository includes reproducible cross-sensor examples built from official:
- NASA MODIS response functions
- ESA Sentinel-2A response functions
- USGS Landsat 8 and Landsat 9 response functions
Those examples live in examples/official_mapping/README.md and are documented in docs/official_sensor_examples.md.
Important:
- the official example bundle expects the previously composed full SIAC library recorded in its manifest
- the docs example is a held-out evaluation against that full library, not a vendored miniature retrieval database
- Sentinel-2 uses
B08for the semanticnirband in the published example set
Production Readiness
The repository includes the production-release work needed to ship the package:
- package build and smoke-test workflows
- GitHub Pages docs deployment
- dependency review,
pip-audit, and Python CodeQL - weekly Dependabot update PRs
- pinned GitHub Action SHAs across workflows
- SBOM generation and provenance attestations in the release workflow
- scheduled and manual full-library benchmark workflows
See docs/security_provenance.md, SECURITY.md, and RELEASE.md.
Maintainers
Internal build and source-ingest material is intentionally separated from the public mapping package. If you are maintaining the full library build pipeline, start here:
- internal overview: docs/internal_overview.md
- internal build pipeline: docs/internal_build_pipeline.md
- release notes: docs/releases/0.6.1.md
1 maintainer
Metadata
1 maintainer
Release files for spectral-library 0.6.3
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