jangli 1.2.1

Data Object Mapping

Jangli

Scope

• Data Definition
• Data mapping

Convert json to python object

Convert json or dict to model object.

Supports

• dict
• json
• json dumps

from jangli.json_to_object import json_to_obj

data = '{"password": "123456", "id": 1, "name": "john"}'



class Student:
    def __init__(self):
        self.id = None
        self.name = None
        self.password = None


s = json_to_obj(data, Student)
print(s.name)

Convert json to python object with static variable

Convert json or dict to model object. Class containing static variables

Supports

• dict
• json
• json dumps

from jangli.json_to_object import json_to_obj

data_2 = '{"password": "123456", "id": 1, "name": "john", "school" : "SOHS"}'


class Student:
    school = None

    def __init__(self):
        self.id = None
        self.name = None
        self.password = None


s2 = json_to_obj(data_2, Student)
print(s2.school)

Custom object list

Create list of similar object. Pass a model which you want create a list. It only allows model objects which model passed whle creating list of objects.

from jangli.list_of_object import ListObject


class A:
    def __init__(self, b):
        self.b = b


lt = ListObject(A)
lt.append(A(7))
lt.insert(1, A(8))

print(lt)

Output : [<__main__.A object at 0x00CA3730>, <__main__.A object at 0x00CC6E10>]

Case Change to CamelCase

Converter snack case to camel case.

from jangli.case_type import CamelCase


@CamelCase
class NewClass:

    def __init__(self):
        self.a = 7
        self.b = "hi"
        self.c = True
        self._from = None


new = NewClass()

print(new.__dict__)

String of None to None

Change string of None to None,

EX :

String of None is : x = 'None'

After change : x = None

from jangli.checker.none_checker import NoneChecker

@NoneChecker
class A:

    def __init__(self):
        self.b = 8
        self.c = "None"
        self.d = True


print(A().__dict__)
>>> {'b': 8, 'c': None, 'd': True}

Re-Try Function

If a function failed one or many times, you can retry N no. of times just by passing retry_value = ?.

If retry_value = 1

A function will execute ones, mean while any error any occurring function will through exception.

@Retry(retry_value=1)
def x_fun():
    print("Function is executing ones")

If retry_value = 2

A function will execute twice if first execution fails else only ones.

@Retry(retry_value=2)
def x_fun():
    print("raise exception")
    raise Exception("Try twice")

If retry_value = 0

A function is disabled and could not execute the function.

@Retry(retry_value=0)
def x_fun():
    print("Function is disabled")

MIT License : Copyright (c) 2019 Abhimanyu Haralukallu