{"categories":[{"label":"Front-Ends","url":"https://skillfed.io/packages/category/database-front-ends/2"}],"enrichment":{"capability":"h5grove provides building blocks for serving HDF5 file contents\u2014attributes, metadata, and data\u2014via web backends, handling common problems like external link resolution, dataset compression/slicing, and JSON-safe data encoding.","skillfed_tags":["hdf5","data-serving","scientific-data"],"use_cases":["Build a REST API to query and download slices of large HDF5 datasets stored on disk.","Serve scientific instrument data (common in synchrotron facilities) to web clients without reimplementing link resolution and compression logic.","Create a custom HDF5 viewer backend that handles external file references and encodes numeric data safely for JSON.","Integrate HDF5 file access into an existing FastAPI or Flask application with minimal boilerplate."],"what_it_does":"h5grove is a Python library for building backends that expose HDF5 file contents over the network. Rather than being a complete server, it provides reusable utilities that solve recurring problems in HDF5 serving: resolving external links, handling dataset compression and slicing, and encoding data consistently for JSON transport (including special handling for NaN and Infinity). It wraps h5py for file access and depends on numpy, orjson, tifffile, and typing-extensions.\n\nThe package is designed for developers building custom HDF5 backends, with optional integrations for FastAPI, Flask, and Tornado. You install the core package and add framework-specific extras only when needed. It's actively maintained, supports current Python versions (3.10+), and carries no known security vulnerabilities.","worth_installing":"Yes, if you are building a backend to serve HDF5 files and want to reuse battle-tested utilities for common problems (link resolution, compression, JSON encoding). No, if you only need to read HDF5 files in a local Python script\u2014h5py alone is sufficient. Yes-with-conditions if you plan to use the web framework integrations: verify that the FastAPI/Flask/Tornado extras match your framework version and production readiness expectations."},"id":"h5grove","links":{"html":"https://skillfed.io/packages/h5grove","md":"https://skillfed.io/packages/h5grove.md","pypi":"https://pypi.org/project/h5grove/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-14","license_spdx":null,"license_treatment":"permissive","name":"h5grove","python_support":"supports_current","summary":"Core utilities to serve HDF5 file contents"},"popularity":{"monthly_downloads":125674,"position":11807,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"4.0.0"}