Json Encoder AND Decoder for complex numpy arrays

Question

I'm trying to JSON encode a complex numpy array, and I've found a utility from astropy (http://astropy.readthedocs.org/en/latest/_modules/astropy/utils/misc.html#JsonCustomEncoder) for this purpose:

import numpy as np

class JsonCustomEncoder(json.JSONEncoder):
    """ <cropped for brevity> """
    def default(self, obj):
        if isinstance(obj, (np.ndarray, np.number)):
            return obj.tolist()
        elif isinstance(obj, (complex, np.complex)):
            return [obj.real, obj.imag]
        elif isinstance(obj, set):
            return list(obj)
        elif isinstance(obj, bytes):  # pragma: py3
            return obj.decode()
        return json.JSONEncoder.default(self, obj)

This works well for a complex numpy array:

test = {'some_key':np.array([1+1j,2+5j, 3-4j])}

As dumping yields:

encoded = json.dumps(test, cls=JsonCustomEncoder)
print encoded
>>> {"some key": [[1.0, 1.0], [2.0, 5.0], [3.0, -4.0]]}

The problem is, I don't a way to read this back into a complex array automatically. For example:

json.loads(encoded)
>>> {"some_key": [[1.0, 1.0], [2.0, 5.0], [3.0, -4.0]]}

Can you guys help me figure out the way to overwrite loads/decoding so that it infers that this must be a complex array? I.E. Instead of a list of 2-element items, it should just put these back into a complex array. The JsonCustomDecoder doesn't have a default() method to overwrite, and the docs on encoding have too much jargon for me.

Answer 1

Here is my final solution that was adapted from hpaulj's answer, and his answer to this thread: https://stackoverflow.com/a/24375113/901925

This will encode/decode arrays that are nested to arbitrary depth in nested dictionaries, of any datatype.

import base64
import json
import numpy as np

class NumpyEncoder(json.JSONEncoder):
    def default(self, obj):
        """
        if input object is a ndarray it will be converted into a dict holding dtype, shape and the data base64 encoded
        """
        if isinstance(obj, np.ndarray):
            data_b64 = base64.b64encode(obj.data)
            return dict(__ndarray__=data_b64,
                        dtype=str(obj.dtype),
                        shape=obj.shape)
        # Let the base class default method raise the TypeError
        return json.JSONEncoder(self, obj)


def json_numpy_obj_hook(dct):
    """
    Decodes a previously encoded numpy ndarray
    with proper shape and dtype
    :param dct: (dict) json encoded ndarray
    :return: (ndarray) if input was an encoded ndarray
    """
    if isinstance(dct, dict) and '__ndarray__' in dct:
        data = base64.b64decode(dct['__ndarray__'])
        return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])
    return dct

# Overload dump/load to default use this behavior.
def dumps(*args, **kwargs):
    kwargs.setdefault('cls', NumpyEncoder)
    return json.dumps(*args, **kwargs)

def loads(*args, **kwargs):
    kwargs.setdefault('object_hook', json_numpy_obj_hook)    
    return json.loads(*args, **kwargs)

def dump(*args, **kwargs):
    kwargs.setdefault('cls', NumpyEncoder)
    return json.dump(*args, **kwargs)

def load(*args, **kwargs):
    kwargs.setdefault('object_hook', json_numpy_obj_hook)
    return json.load(*args, **kwargs)

if __name__ == '__main__':

    data = np.arange(3, dtype=np.complex)

    one_level = {'level1': data, 'foo':'bar'}
    two_level = {'level2': one_level}

    dumped = dumps(two_level)
    result = loads(dumped)

    print '\noriginal data', data
    print '\nnested dict of dict complex array', two_level
    print '\ndecoded nested data', result

Which yields output:

original data [ 0.+0.j  1.+0.j  2.+0.j]

nested dict of dict complex array {'level2': {'level1': array([ 0.+0.j,  1.+0.j,  2.+0.j]), 'foo': 'bar'}}

decoded nested data {u'level2': {u'level1': array([ 0.+0.j,  1.+0.j,  2.+0.j]), u'foo': u'bar'}}

Answer 2

Try traitschema https://traitschema.readthedocs.io/en/latest/

"Create serializable, type-checked schema using traits and Numpy. A typical use case involves saving several Numpy arrays of varying shape and type."

See to_json()

"This uses a custom JSON encoder to handle numpy arrays but could conceivably lose precision. If this is important, please consider serializing in HDF5 format instead"