# data_serialize
This library allows reading and writing binary to string
## Installation
```pip3 install data_serialize```
## serialize and deserialize
```python
#
import data_serialize
import numpy as np
def test_feature():
val1 = data_serialize.Int64List(value=[1, 2, 3] * 20)
val2 = data_serialize.FloatList(value=[1, 2, 3] * 20)
val3 = data_serialize.BytesList(value=[b'The china', b'boy'])
featrue = data_serialize.Features(feature=
{
"item_0": data_serialize.Feature(int64_list=val1),
"item_1": data_serialize.Feature(float_list=val2),
"item_2": data_serialize.Feature(bytes_list=val3)
}
)
example = data_serialize.Example(features=featrue)
# 序列化
serialize = example.SerializeToString()
print(serialize)
# 反序列化
example = data_serialize.Example()
example.ParseFromString(serialize)
print(example)
def test_numpyobject():
a = np.random.randint(0, 21128, size=(10,), dtype=np.int64)
b = np.random.rand(3, 4)
c = np.asarray(b'The china')
val1 = data_serialize.NumpyObject(
header='',
dtype=str(a.dtype),
shape=list(a.shape),
int64=a.reshape((-1,)).tolist(),
)
val2 = data_serialize.NumpyObject(
header='',
dtype=str(b.dtype),
shape=list(b.shape),
float64=b.reshape((-1,)).tolist(),
)
val3 = data_serialize.NumpyObject(
header='',
dtype=str(c.dtype),
shape=list(c.shape),
bytes=c.tobytes(),
)
example = data_serialize.NumpyObjectMap(numpyobjects={
"item_0": val1,
"item_1": val2,
"item_2": val3}
)
# 序列化
serialize = example.SerializeToString()
print(serialize)
# 反序列化
example = data_serialize.NumpyObjectMap()
example.ParseFromString(serialize)
print(example)
test_feature()
test_numpyobject()
```
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"description": "# data_serialize\n\nThis library allows reading and writing binary to string \n\n## Installation\n\n```pip3 install data_serialize```\n\n\n## serialize and deserialize\n\n```python\n#\nimport data_serialize\nimport numpy as np\n\n\ndef test_feature():\n val1 = data_serialize.Int64List(value=[1, 2, 3] * 20)\n val2 = data_serialize.FloatList(value=[1, 2, 3] * 20)\n val3 = data_serialize.BytesList(value=[b'The china', b'boy'])\n\n featrue = data_serialize.Features(feature=\n {\n \"item_0\": data_serialize.Feature(int64_list=val1),\n \"item_1\": data_serialize.Feature(float_list=val2),\n \"item_2\": data_serialize.Feature(bytes_list=val3)\n }\n )\n\n example = data_serialize.Example(features=featrue)\n\n # \u5e8f\u5217\u5316\n serialize = example.SerializeToString()\n print(serialize)\n\n # \u53cd\u5e8f\u5217\u5316\n example = data_serialize.Example()\n example.ParseFromString(serialize)\n print(example)\n\n\ndef test_numpyobject():\n a = np.random.randint(0, 21128, size=(10,), dtype=np.int64)\n b = np.random.rand(3, 4)\n c = np.asarray(b'The china')\n\n val1 = data_serialize.NumpyObject(\n header='',\n dtype=str(a.dtype),\n shape=list(a.shape),\n int64=a.reshape((-1,)).tolist(),\n )\n val2 = data_serialize.NumpyObject(\n header='',\n dtype=str(b.dtype),\n shape=list(b.shape),\n float64=b.reshape((-1,)).tolist(),\n )\n val3 = data_serialize.NumpyObject(\n header='',\n dtype=str(c.dtype),\n shape=list(c.shape),\n bytes=c.tobytes(),\n )\n\n example = data_serialize.NumpyObjectMap(numpyobjects={\n \"item_0\": val1,\n \"item_1\": val2,\n \"item_2\": val3}\n )\n # \u5e8f\u5217\u5316\n serialize = example.SerializeToString()\n print(serialize)\n\n # \u53cd\u5e8f\u5217\u5316\n example = data_serialize.NumpyObjectMap()\n example.ParseFromString(serialize)\n print(example)\n\n\ntest_feature()\n\ntest_numpyobject()\n\n\n```\n\n\n\n",
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