|
|
|
|
| """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) |
|
|
|
|
|
|
|
|
