v1.0.2

Improvements

• Tightened CLI validation for file types, output paths, and download/write concurrency options.
• Improved FASTA input handling for uppercase suffixes and cleaner missing-file errors.
• Surfaced structure write failures from worker threads instead of allowing them to be ignored.
• Reduced URL batching memory use by chunking iterables directly.
• Added benchmarks versus curl download of the AlphaFold tarball.

1.0.1

Changes

• No changes

v1.0.0

• Switched the project to a uv-native workflow with uv_build, dependency groups, and a refreshed lockfile.
• Modernized pyproject.toml metadata and tool configuration for the current package layout.
• Refactored the CLI implementation with stronger typing, centralized constants, and a canonical alphafold_file_url helper while keeping backward compatibility for the older misspelled helper name.
• Added explicit typing exports in affetch.__init__ and affetch.__version__.
• Replaced the placeholder test module with executable smoke tests for core parsing and file-writing behavior.
• Rewrote the README, contributing guide, and MkDocs site to focus on real install, usage, development, and reference workflows.

v0.0.1

Release Summary for AlphaFoldFetch

AlphaFoldFetch is a new command-line tool designed to simplify downloading AlphaFold protein structure predictions using UniProt IDs or UniProt-formatted FASTA files. It provides researchers with easy access to structural predictions for specific proteomes or sets of proteins that may not be available on AlphaFold’s bulk download page.

Key Features

• Input Options: Supports both UniProt IDs and UniProt-formatted FASTA files as inputs, making it adaptable for single or batch protein queries.
• Download Customization: Allows users to specify file type (.pdb or .cif), file compression (.gz option), AlphaFold model version (v1-4), and optimized download parameters to handle large-scale queries.
• Efficient Download Management: Users can adjust parameters for the number of simultaneous downloads (--n-sync) and concurrent file writes (--n-save) to optimize performance on larger datasets.