使用 simplejson 序列化 numpy 数组的最有效方法是什么？
问问题
37654 次
9 回答
80
为了保持 dtype 和维度，请尝试以下操作：
import base64
import json
import numpy as np
class NumpyEncoder(json.JSONEncoder):
def default(self, obj):
"""If input object is an ndarray it will be converted into a dict
holding dtype, shape and the data, base64 encoded.
"""
if isinstance(obj, np.ndarray):
if obj.flags['C_CONTIGUOUS']:
obj_data = obj.data
else:
cont_obj = np.ascontiguousarray(obj)
assert(cont_obj.flags['C_CONTIGUOUS'])
obj_data = cont_obj.data
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
super(NumpyEncoder, self).default(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
expected = np.arange(100, dtype=np.float)
dumped = json.dumps(expected, cls=NumpyEncoder)
result = json.loads(dumped, object_hook=json_numpy_obj_hook)
# None of the following assertions will be broken.
assert result.dtype == expected.dtype, "Wrong Type"
assert result.shape == expected.shape, "Wrong Shape"
assert np.allclose(expected, result), "Wrong Values"
于 2014-06-23T21:46:30.270 回答
28
我会使用simplejson.dumps(somearray.tolist())最方便的方法（如果我仍在使用simplejson，这意味着被 Python 2.5 或更早版本卡住了；2.6 及更高版本有一个json以相同方式工作的标准库模块，所以我当然会使用它如果使用中的 Python 版本支持它；-)。
为了提高效率，您可以继承json.JSONEncoder（在json；我不知道旧版本是否simplejson已经提供了这种自定义可能性），并且在方法中，通过将它们变成列表或元组default的特殊情况实例“只是numpy.array及时”。不过，我有点怀疑，通过这种方法，您是否会在性能方面获得足够的收益来证明这种努力是合理的。
于 2010-08-15T20:00:28.987 回答
17
我发现这个 json 子类代码用于序列化字典中的一维 numpy 数组。我试过了，它对我有用。
class NumpyAwareJSONEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, numpy.ndarray) and obj.ndim == 1:
return obj.tolist()
return json.JSONEncoder.default(self, obj)
我的字典是“结果”。这是我写入文件“data.json”的方式：
j=json.dumps(results,cls=NumpyAwareJSONEncoder)
f=open("data.json","w")
f.write(j)
f.close()
于 2012-04-28T20:51:58.803 回答
12
这显示了如何从一维 NumPy 数组转换为 JSON 并返回到数组：
try:
import json
except ImportError:
import simplejson as json
import numpy as np
def arr2json(arr):
return json.dumps(arr.tolist())
def json2arr(astr,dtype):
return np.fromiter(json.loads(astr),dtype)
arr=np.arange(10)
astr=arr2json(arr)
print(repr(astr))
# '[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]'
dt=np.int32
arr=json2arr(astr,dt)
print(repr(arr))
# array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
基于tlausch 的回答，这是一种对 NumPy 数组进行 JSON 编码的方法，同时保留任何 NumPy 数组的形状和 dtype ——包括具有复杂 dtype 的数组。
class NDArrayEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray):
output = io.BytesIO()
np.savez_compressed(output, obj=obj)
return {'b64npz' : base64.b64encode(output.getvalue())}
return json.JSONEncoder.default(self, obj)
def ndarray_decoder(dct):
if isinstance(dct, dict) and 'b64npz' in dct:
output = io.BytesIO(base64.b64decode(dct['b64npz']))
output.seek(0)
return np.load(output)['obj']
return dct
# Make expected non-contiguous structured array:
expected = np.arange(10)[::2]
expected = expected.view('<i4,<f4')
dumped = json.dumps(expected, cls=NDArrayEncoder)
result = json.loads(dumped, object_hook=ndarray_decoder)
assert result.dtype == expected.dtype, "Wrong Type"
assert result.shape == expected.shape, "Wrong Shape"
assert np.array_equal(expected, result), "Wrong Values"
于 2010-08-15T20:22:30.080 回答
4
我刚刚发现了 tlausch 对这个问题的回答，并意识到它为我的问题提供了几乎正确的答案，但至少对我而言，它在 Python 3.5 中不起作用，因为有几个错误：1 - 无限递归 2 - 数据保存为无
因为我还不能直接评论原始答案，所以这是我的版本：
import base64
import json
import numpy as np
class NumpyEncoder(json.JSONEncoder):
def default(self, obj):
"""If input object is an ndarray it will be converted into a dict
holding dtype, shape and the data, base64 encoded.
"""
if isinstance(obj, np.ndarray):
if obj.flags['C_CONTIGUOUS']:
obj_data = obj.data
else:
cont_obj = np.ascontiguousarray(obj)
assert(cont_obj.flags['C_CONTIGUOUS'])
obj_data = cont_obj.data
data_b64 = base64.b64encode(obj_data)
return dict(__ndarray__= data_b64.decode('utf-8'),
dtype=str(obj.dtype),
shape=obj.shape)
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
expected = np.arange(100, dtype=np.float)
dumped = json.dumps(expected, cls=NumpyEncoder)
result = json.loads(dumped, object_hook=json_numpy_obj_hook)
# None of the following assertions will be broken.
assert result.dtype == expected.dtype, "Wrong Type"
assert result.shape == expected.shape, "Wrong Shape"
assert np.allclose(expected, result), "Wrong Values"
于 2017-06-27T08:52:11.860 回答
3
如果你想将 Russ 的方法应用于 n 维 numpy 数组，你可以试试这个
class NumpyAwareJSONEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, numpy.ndarray):
if obj.ndim == 1:
return obj.tolist()
else:
return [self.default(obj[i]) for i in range(obj.shape[0])]
return json.JSONEncoder.default(self, obj)
这将简单地将 n 维数组转换为深度为“n”的列表列表。要将此类列表转换回 numpy 数组，my_nparray = numpy.array(my_list)无论列表“深度”如何，都将起作用。
于 2014-12-16T19:34:38.070 回答
2
改进 Russ 的答案，我还将包括np.generic 标量：
class NumpyAwareJSONEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray) and obj.ndim == 1:
return obj.tolist()
elif isinstance(obj, np.generic):
return obj.item()
return json.JSONEncoder.default(self, obj)
于 2014-01-24T01:44:39.817 回答
2
您也可以通过以这种方式传入的函数来回答这个json.dumps问题：
json.dumps(np.array([1, 2, 3]), default=json_numpy_serializer)
和
import numpy as np
def json_numpy_serialzer(o):
""" Serialize numpy types for json
Parameters:
o (object): any python object which fails to be serialized by json
Example:
>>> import json
>>> a = np.array([1, 2, 3])
>>> json.dumps(a, default=json_numpy_serializer)
"""
numpy_types = (
np.bool_,
# np.bytes_, -- python `bytes` class is not json serializable
# np.complex64, -- python `complex` class is not json serializable
# np.complex128, -- python `complex` class is not json serializable
# np.complex256, -- special handling below
# np.datetime64, -- python `datetime.datetime` class is not json serializable
np.float16,
np.float32,
np.float64,
# np.float128, -- special handling below
np.int8,
np.int16,
np.int32,
np.int64,
# np.object_ -- should already be evaluated as python native
np.str_,
np.timedelta64,
np.uint8,
np.uint16,
np.uint32,
np.uint64,
np.void,
)
if isinstance(o, np.ndarray):
return o.tolist()
elif isinstance(o, numpy_types):
return o.item()
elif isinstance(o, np.float128):
return o.astype(np.float64).item()
# elif isinstance(o, np.complex256): -- no python native for np.complex256
# return o.astype(np.complex128).item() -- python `complex` class is not json serializable
else:
raise TypeError("{} of type {} is not JSON serializable".format(repr(o), type(o)))
验证：
need_addition_json_handeling = (
np.bytes_,
np.complex64,
np.complex128,
np.complex256,
np.datetime64,
np.float128,
)
numpy_types = tuple(set(np.typeDict.values()))
for numpy_type in numpy_types:
print(numpy_type)
if numpy_type == np.void:
# complex dtypes evaluate as np.void, e.g.
numpy_type = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
elif numpy_type in need_addition_json_handeling:
print('python native can not be json serialized')
continue
a = np.ones(1, dtype=nptype)
json.dumps(a, default=json_numpy_serialzer)
于 2016-09-14T13:10:08.107 回答
1
一种快速但不是真正最佳的方法是使用Pandas：
import pandas as pd
pd.Series(your_array).to_json(orient='values')
于 2017-06-26T01:54:56.303 回答