NumPy .npy files and memory-mapped storage interoperability

Data Persistence and Memory Mapping

NumPy provides the .npy binary format to persist array metadata and raw data. When dealing with datasets that exceed available system RAM, the mmap_mode parameter in np.load() allows the array to be mapped directly from disk, loading only the required segments into memory.

However, there is a design uncertainty regarding the behavior of memory-mapped views when the underlying filesystem undergoes modifications or when the file is accessed across different NumPy version headers. Ensuring data integrity during the restoration of these large-scale arrays requires a clear understanding of how the header versioning interacts with the memory-map interface.

• How does NumPy handle header version mismatches when a file is opened in mmap_mode?
• What is the expected behavior of a memory-mapped array if the underlying .npy file is modified by an external process during the session?