hdf5

Is there an analysis speed or memory usage advantage to using HDF5 for large array storage (instead of flat binary files)?

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HDF5 Advantages: Organization, flexibility, interoperability Some of the main advantages of HDF5 are its hierarchical structure (similar to folders/files), optional arbitrary metadata stored with each item, and its flexibility (e.g. compression). This organizational structure and metadata storage may sound trivial, but it’s very useful in practice. Another advantage of HDF is that the datasets can … Read more

How to read HDF5 files in Python

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Read HDF5 import h5py filename = “file.hdf5” with h5py.File(filename, “r”) as f: # Print all root level object names (aka keys) # these can be group or dataset names print(“Keys: %s” % f.keys()) # get first object name/key; may or may NOT be a group a_group_key = list(f.keys())[0] # get the object type for a_group_key: … Read more

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