How can I serialize a numpy array while preserving matrix dimensions?

Question

numpy.array.tostring doesn't seem to preserve information about matrix dimensions (see this question), requiring the user to issue a call to numpy.array.reshape.

Is there a way to serialize a numpy array to JSON format while preserving this information?

Note: The arrays may contain ints, floats or bools. It's reasonable to expect a transposed array.

Note 2: this is being done with the intent of passing the numpy array through a Storm topology using streamparse, in case such information ends up being relevant.

Answer 1

pickle.dumps or numpy.save encode all the information needed to reconstruct an arbitrary NumPy array, even in the presence of endianness issues, non-contiguous arrays, or weird tuple dtypes. Endianness issues are probably the most important; you don't want array([1]) to suddenly become array([16777216]) because you loaded your array on a big-endian machine. pickle is probably the more convenient option, though save has its own benefits, given in the npy format rationale.

The pickle option:

import pickle
a = # some NumPy array
serialized = pickle.dumps(a, protocol=0) # protocol 0 is printable ASCII
deserialized_a = pickle.loads(serialized)

numpy.save uses a binary format, and it needs to write to a file, but you can get around that with io.BytesIO:

a = # any NumPy array
memfile = io.BytesIO()
numpy.save(memfile, a)
memfile.seek(0)
serialized = json.dumps(memfile.read().decode('latin-1'))
# latin-1 maps byte n to unicode code point n

And to deserialize:

memfile = io.BytesIO()
memfile.write(json.loads(serialized).encode('latin-1'))
memfile.seek(0)
a = numpy.load(memfile)