Package to Encode/Decode some common file formats to json
Available via pip install znjson
In comparison to pickle this allows having readable json files combined with some
serialized data.
import numpy as np
import json
import znjson
data = json.dumps(
obj={"data_np": np.arange(2), "data": [x for x in range(10)]},
cls=znjson.ZnEncoder,
indent=4
)
_ = json.loads(data, cls=znjson.ZnDecoder)The resulting *.json file is partially readable and looks like this:
{
"data_np": {
"_type": "np.ndarray_small",
"value": [
0,
1
]
},
"data": [
0,
1,
2,
3,
4
]
}ZnJSON allows you to easily add custom converters.
Let's write a serializer for datetime.datetime.
from znjson import ConverterBase
from datetime import datetime
class DatetimeConverter(ConverterBase):
"""Encode/Decode datetime objects
Attributes
----------
level: int
Priority of this converter over others.
A higher level will be used first, if there
are multiple converters available
representation: str
An unique identifier for this converter.
instance:
Used to select the correct converter.
This should fulfill isinstance(other, self.instance)
or __eq__ should be overwritten.
"""
level = 100
representation = "datetime"
instance = datetime
def encode(self, obj: datetime) -> str:
"""Convert the datetime object to str / isoformat"""
return obj.isoformat()
def decode(self, value: str) -> datetime:
"""Create datetime object from str / isoformat"""
return datetime.fromisoformat(value)This allows us to use this new serializer:
znjson.config.register(DatetimeConverter) # we need to register the new converter first
json_string = json.dumps(dt, cls=znjson.ZnEncoder, indent=4)
json.loads(json_string, cls=znjson.ZnDecoder)and will result in
{
"_type": "datetime",
"value": "2022-03-11T09:47:35.280331"
}If you don't want to register your converter to be used everywhere, simply use:
json_string = json.dumps(dt, cls=znjson.ZnEncoder.from_converters(DatetimeConverter))