如何在保留矩阵尺寸的同时序列化一个numpy数组? [英] How can I serialize a numpy array while preserving matrix dimensions?

问题描述

numpy.array.tostring似乎没有保留有关矩阵尺寸的信息(请参阅此问题)，要求用户执行以下操作:向numpy.array.reshape发出呼叫.

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.

是否有一种方法可以在保留此信息的同时将numpy数组序列化为JSON格式?

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

注意:数组可能包含整数，浮点数或布尔值.期望转置数组是合理的.

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

注释2:这样做是为了使numpy数组使用streamparse通过Storm拓扑传递，以防万一此类信息最终变得有意义.

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.

推荐答案

pickle.dumps 或 numpy.save 编码所有信息即使存在字节序问题，非连续数组或怪异的元组dtype，也需要重建任意NumPy数组.字节序问题可能是最重要的；您不希望array([1])突然变成array([16777216])，因为您是在大型字节序的计算机上加载了阵列.尽管save有其自身的优点，但pickle可能是更方便的选择，如

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.

pickle选项:

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

numpy.save使用二进制格式，它需要写入文件，但是您可以使用StringIO来解决该问题:

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

a = # any NumPy array
memfile = StringIO.StringIO()
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

并反序列化:

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

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