hoops_ai.storage.convert_storage

hoops_ai.storage.convert_storage(source_handler, dest_handler, verbose=False)

Generic converter that works with ANY DataStorage implementation.

This universal converter supports all storage types including: - OptStorage / ZarrStorage - Compressed binary format - JsonStorageHandler - Human-readable JSON format - MemoryStorage - In-memory storage (no files) - Custom implementations - Any class inheriting from DataStorage

The converter uses only the base DataStorage interface methods: - get_keys() or load_metadata() to discover data keys - load_data() to read data arrays - save_data() to write data arrays - save_metadata() to copy metadata

This makes it completely storage-agnostic and extensible.

Common Use Cases: - OptStorage → JSON: Decompress for inspection/debugging - JSON → OptStorage: Compress for performance/storage - MemoryStorage → OptStorage: Persist in-memory data to disk - OptStorage → MemoryStorage: Load encoded data for ML training

Parameters:

  • source_handler (DataStorage) – Source storage to read from (any type).

  • dest_handler (DataStorage) – Destination storage to write to (any type).

  • verbose (bool) – Print progress messages. Default False.

Returns:

None

Raises:

RuntimeError – If no data keys found or conversion fails.

Return type:

None

Examples

>>> # OptStorage → JSON (decompression)
>>> opt = OptStorage("flows/data_mining/abc123")
>>> json_storage = JsonStorageHandler("flows/json/abc123")
>>> convert_storage(opt, json_storage)

>>> # MemoryStorage → OptStorage (persist to disk)
>>> mem = MemoryStorage()
>>> mem.save_data("faces/positions", np.array([[0,0,0], [1,1,1]]))
>>> opt = OptStorage("output/saved_data")
>>> convert_storage(mem, opt)

>>> # JSON → MemoryStorage (load for processing)
>>> json_storage = JsonStorageHandler("data/abc123")
>>> mem = MemoryStorage()
>>> convert_storage(json_storage, mem)