{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"pylerc encodes and decodes raster image data using the LERC (Limited Error Raster Compression) format, supporting lossless and lossy compression with user-defined maximum error per pixel.","skillfed_tags":["raster-compression","geospatial","numpy-integration"],"use_cases":["Compress geospatial satellite or aerial imagery tiles for efficient storage and transmission with bounded error.","Encode scientific raster data (e.g., elevation models, climate grids) where lossy compression with user-defined error tolerance is acceptable.","Decode LERC-compressed blobs received from remote APIs or read from disk into numpy arrays for analysis.","Handle multi-band imagery (e.g., RGB, hyperspectral) with per-band noData values and mixed valid/invalid pixels.","Benchmark or migrate existing LERC 3.0 workflows to the newer masked-array API for cleaner mask handling."],"what_it_does":"pylerc is a Python binding for LERC, an open-source raster compression format designed for rapid encoding and decoding of image tiles with user-controlled maximum compression error. It works with numpy arrays in 2D, 3D, or 4D shapes, supporting any pixel type from byte to double, and allows marking pixels as invalid via masks, noData values, or NaN. The package offers two main APIs: one using numpy masked arrays (encode_ma/decode_ma) and another using separate data and mask arrays (encode_4D/decode_4D), giving flexibility depending on whether all pixels are valid or sparse masking is needed.\n\nVersion 4.2.0 introduces stricter size limits for safety: input data is capped at 2 GB per band, compressed output at 2 GB per band, and total blob size at 4 GB across all bands. The package maintains backward compatibility with LERC 3.0 functions, though mixed valid/invalid cases at the same pixel for multi-band data require the newer API. It is actively maintained, supports current Python versions (3.11\u20133.14), and carries no known security vulnerabilities.","worth_installing":"Yes. pylerc is actively maintained, carries no known vulnerabilities, uses a permissive Apache 2 license, and offers a clean numpy-integrated API for a specialized but well-defined task. Install it if you work with raster imagery and need bounded-error compression; the medium install friction is typical for packages with compiled wheels. Not necessary for general image compression or if your raster format is already standardized in your pipeline."},"id":"pylerc","links":{"html":"https://skillfed.io/packages/pylerc","md":"https://skillfed.io/packages/pylerc.md","pypi":"https://pypi.org/project/pylerc/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-23","license_spdx":null,"license_treatment":"permissive","name":"pylerc","python_support":"supports_current","summary":"Limited Error Raster Compression"},"popularity":{"monthly_downloads":173034,"position":10319,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"4.2.0"}