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"""Adds NumPy array support to msgpack.
msgpack is good for (de)serializing data over a network for multiple reasons:
- msgpack is secure (as opposed to pickle/dill/etc which allow for arbitrary code execution)
- msgpack is widely used and has good cross-language support
- msgpack does not require a schema (as opposed to protobuf/flatbuffers/etc) which is convenient in dynamically typed
languages like Python and JavaScript
- msgpack is fast and efficient (as opposed to readable formats like JSON/YAML/etc); I found that msgpack was ~4x faster
than pickle for serializing large arrays using the below strategy
The code below is adapted from GitHub - lebedov/msgpack-numpy: Serialize numpy arrays using msgpack. The reason not to use that library directly is
that it falls back to pickle for object arrays.
"""
import functools
import msgpack
import numpy as np
def pack_array(obj):
if (isinstance(obj, (np.ndarray, np.generic))) and obj.dtype.kind in ("V", "O", "c"):
raise ValueError(f"Unsupported dtype: {obj.dtype}")
if isinstance(obj, np.ndarray):
return {
b"__ndarray__": True,
b"data": obj.tobytes(),
b"dtype": obj.dtype.str,
b"shape": obj.shape,
}
if isinstance(obj, np.generic):
return {
b"__npgeneric__": True,
b"data": obj.item(),
b"dtype": obj.dtype.str,
}
return obj
def unpack_array(obj):
if b"__ndarray__" in obj:
return np.ndarray(buffer=obj[b"data"], dtype=np.dtype(obj[b"dtype"]), shape=obj[b"shape"])
if b"__npgeneric__" in obj:
return np.dtype(obj[b"dtype"]).type(obj[b"data"])
return obj
Packer = functools.partial(msgpack.Packer, default=pack_array)
packb = functools.partial(msgpack.packb, default=pack_array)
Unpacker = functools.partial(msgpack.Unpacker, object_hook=unpack_array)
unpackb = functools.partial(msgpack.unpackb, object_hook=unpack_array)
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