Bolo组件

### 概述
`Bolo`是一个python类，它可以用来在不同的系统之间传递对象。

示例：
```
data = [1,[2,3], datetime.now()]
r = Bolo.encode(data)
print(r.json())
# 可以将r.json()的结果发送给其它服务
rr = Bolo.from_json(r.json())
# 通过decode()可以获得原始数据，类型也都是正确的
print(rr.decode())
print(r.json() == rr.json())
```
支持的类型：
 - `int/float/str`
 - `list/tuple/dict`
 - `date/datetime`
 - `pandas.DataFrame`

### 用途
在不同的python服务之间传递数据，其实可以使用`pickle`，但是这不利于日志的记录，因为`pickle`产生的对象是不可阅读的，日志记录下来也没法阅读。
因此`Bolo`可以替代`pickle`，用于不同服务之间的数据传递，便于日志记录。

### 待解决的问题
1. 数据膨胀的厉害，json原生支持的数据其实不需要再封装了

### 源码
```
import pandas
from datetime import date, datetime

class Bolo(object):

def __init__(self, data, _type, _datetime_format="%Y-%m-%d %H:%M:%S", _date_format="%Y-%m-%d"):
        if isinstance(data, list):
            data2 = []
            for d in data:
                if isinstance(d, dict) and "_type" in d:
                    data2.append(Bolo(**d))
                else:
                    data2.append(d)
        elif isinstance(data, dict) and "_type" in data:
            data2 = Bolo(**data)
        elif isinstance(data, dict):
            data2 = {}
            for key, val in data.items():
                data2[key] = Bolo(**val)
        elif isinstance(data, (str, int, float)):
            data2 = data
        else:
            raise TypeError(f"invalid data with type={type(data)}")
        self.data = data2
        self._type = _type
        self._date_format = _date_format
        self._datetime_format = _datetime_format
    
    def decode(self):
        result = None
        if self._type == "pandas.core.frame.DataFrame":
            result = pandas.read_json(self.data)
        elif self._type == "datetime.datetime":
            result = datetime.strptime(self.data, self._datetime_format)
        elif self._type == "datetime.date":
            result = datetime.strptime(self.data, self._date_format)
        elif isinstance(self.data, list):
            result = [d.decode() if isinstance(d, Bolo) else d for d in self.data]
        elif isinstance(self.data, dict):
            result = {}
            for key, val in self.data.items():
                result[key] = val.decode() if isinstance(val, Bolo) else val
        else:
            result = self.data
        return result

@staticmethod
    def encode(obj, _datetime_format="%Y-%m-%d %H:%M:%S", _date_format="%Y-%m-%d"):
        data, _type = None, None
        if isinstance(obj, (int, float, str)):
            data = obj
        elif isinstance(obj, (list, tuple)):
            data = [Bolo.encode(item, _datetime_format=_datetime_format, _date_format=_date_format) for item in obj]
        elif isinstance(obj, dict):
            data = {}
            for key, val in obj.items():
                data[key] = Bolo.encode(val, _datetime_format=_datetime_format, _date_format=_date_format)
        elif isinstance(obj, datetime):
            data = obj.strftime(_datetime_format)
            _type = "datetime.datetime"
        elif isinstance(obj, date):
            data = obj.strftime(_date_format)
            _type = "datetime.date"
        elif isinstance(obj, pandas.DataFrame):
            data = obj.to_json()
            _type = "pandas.core.frame.DataFrame"
        return Bolo(data=data, _type=_type)

def json(self):
        r = None
        if isinstance(self.data, list):
            r = [d.json() if isinstance(d, Bolo) else d for d in self.data]
        elif isinstance(self.data, dict):
            r = {}
            for key, val in self.data.items():
                r[key] = val.json() if isinstance(val, Bolo) else val
        else:
            r = self.data
        result = {
            "data": r,
            "_type": self._type,
            "_date_format": self._date_format,
            "_datetime_format": self._datetime_format,
        }
        return result

@staticmethod
    def from_json(data: dict):
        if "_type" not in data:
            raise ValueError("invalid data")
        return Bolo(**data)

if __name__ == "__main__":
    data0 = 1
    data1 = [1,2,3]
    data2 = [1,[2,3]]
    data3 = [1,[2,3], datetime.now()]
    data4 = {"name":"gaojian"}
    data5 = pandas.DataFrame([[1, 1, 2], [2, 3,4], [3, 5, 6]], columns=["id", "name", "age"])
    # r0 = Bolo.encode(data0)
    # r1 = Bolo.encode(data1)
    # r2 = Bolo.encode(data2)
    # r3 = Bolo.encode(data3)
    # r4 = Bolo.encode(data4)
    r5 = Bolo.encode(data5)
    # print(r0, r0.data, r0.decode())
    # print(r1, r1.data)
    # print(r1.decode())
    # print(r2.decode())
    # print(r3.decode())
    # print(r4.decode())
    # print(r5.decode())
    # print(r1.json())
    # print(r2.json())
    # print(r3.json())
    # print(r4.json())
    print(r5.json())

# rr1 = Bolo.from_json(r1.json())
    # print(rr1.json())

# rr3 = Bolo.from_json(r3.json())
    # print(rr3.json())
    # print(rr3.decode())

rr5 = Bolo.from_json(r5.json())
    print(rr5.json())
    print(r5.json() == rr5.json())
    print(rr5.decode())
```