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resguard
This module provides function for parsing response data, based on dataclass defined schemas.
The user define arbitrary schema using dataclass. One dataclass can refer to others to represent nested structures.
>>> @dataclass
... class Foo:
... pass
>>> @dataclass
... class Bar:
... foo: Foo
While made with parsing json decoded data from REST responses in mind, the approach is pretty generic and may work for other use cases.
So suppose that you're in charging to do another API client.. if you started doing this once you know that you'll gonna work with JSON and that JSON become plain dicts and lists in python, it's easy to lose the track of these objects and start to spread KeyError and IndexError handlers all over the codebase.
It became usual to me to write representation of the response data as objects and instantiating these objects, and with objects I can have some type checking, mutch better than with dicts... and can track what the fields
But writing ad-hoc classes and parsers from dict -> myobject became boring too.. so I created this! Much more declarative and type checking friendly
So let's write an API to cat facts, we can find the docs here https://alexwohlbruck.github.io/cat-facts/docs/endpoints/facts.html
We're implementing the /facts/random endpoint. The documentation said that it will respond like this:
{
"_id": "591f9894d369931519ce358f",
"__v": 0,
"text": "A female cat will be pregnant for approximately 9 weeks - between 62 and 65 days from conception to delivery.",
"updatedAt": "2018-01-04T01:10:54.673Z",
"deleted": false,
"source": "api",
"used": false
}
So is a list of facts, a fact can be defined like this
>>> from datetime import datetime
>>> @dataclass
... class Fact:
... _id: str
... __v: int
... text: str
... updatedAt: datetime
... deleted: bool
... source: str
... used: bool
... user: Optional[str]
To parse a respone you call parse_dc
, where dc
stands for dataclass. You
call it with the dataclass and the response data:
>>> import requests as r
>>> url = "https://cat-fact.herokuapp.com"
>>> res = r.get(f"{url}/facts/random")
>>> parse_dc(Fact, res.json())
Traceback (most recent call last):
...
TypeError: Unknow field type for Fact(_id,_Fact__v,text,updatedAt,deleted,source,used,user)
You may notice that I put a user: Optional[str]
on the Fact
definition too.
This is how you express optional fields, that may or may not be present on
response. Missing optinal fields become None
in dataclass
What happens here is that the documentation is outdated, there are a type field
that was not expected in response. parse_dc
raise a TypeError if anything
goes out of rails. Let's see in response what we have in type
field
>>> type_ = res.json()['type']
>>> type_, type(type_)
('cat', <class 'str'>)
We do not want that our software breaks because the API put a brand new
field in the response. You can ignore unknow fields by passing strict=False
to parse_dc
. If you want this by default you can memoise the parse_dc like
below:
>>> from functools import partial
>>> parse_dc = partial(parse_dc, strict=False)
So let's update our Fact
definition
>>> @dataclass
... class Fact:
... _id: str
... __v: int
... text: str
... updatedAt: datetime
... deleted: bool
... source: str
... used: bool
... user: Optional[str]
... type: str # <- we added this
And parse again. This time it works, but it's doesn't properly initialize the dataclasses fields. Well, dataclass don't do runtime type checking.
>>> dc = parse_dc(Fact, res.json())
>>> dc
Fact(...)
>>> type(dc.updatedAt)
<class 'str'>
If you pass it a string, it doens't matter if the field type says datetime,
constructor will put the string there and it's done. But the standard library
provides a way to handle this. You need to provide an __post_init__
method.
It will not receive any arguments and it.s called by constructor after
initializing self.
>>> @dataclass
... class Fact:
... _id: str
... __v: int
... text: str
... updatedAt: datetime
... deleted: bool
... source: str
... used: bool
... user: Optional[str]
... type: str
...
... def __post_init__(self):
... if isinstance(self.updatedAt, str):
... self.updatedAt = datetime.strptime(self.updatedAt, "%Y-%m-%dT%H:%M:%S.%fZ")
>>> dc = parse_dc(Fact, res.json())
>>> dc
Fact(...)
>>> type(dc.updatedAt)
<class 'datetime.datetime'>
Now what if we want go to the oposite direction, given somejson, construct
a dataclass. Well resguard can be invoked as curl something | python -m resguard fromjson
and it will output a dataclass definition for that JSON.
The type inference is pretty simple, but it is already better than writing all that dataclasses by hand. Let's see it in action
>>> print(print_dc(fromjson("Root", '{"foo": "foo", "bar": { "bar": "bar" }}')))
@dataclass
class bar:
bar: str
<BLANKLINE>
<BLANKLINE>
@dataclass
class Root:
foo: str
bar: bar
<BLANKLINE>
To use it from command line (much simpler)
curl -s https://cat-fact.herokuapp.com/facts/random | python -m resguard fromjson
@dataclass
class status:
verified: bool
sentCount: int
@dataclass
class Root:
used: bool
source: str
type: str
deleted: bool
_id: str
__v: int
text: str
updatedAt: str
createdAt: str
status: status
user: str
That's it, check below for function docs
parse_dc(dc, data, strict=True)
Build tree of dataclasses initialized with data
It don't type checks, just instantiate the dataclasses recursively. Just note that dataclass don't check at runtime too, so, this doesn't typecheck but it works at runtime
from dataclasses import dataclass, asdict @dataclass ... class Foo: ... foo: str ... __bar: str asdict(Foo(foo=1, _Foo__bar=1)) {'foo': 1, '_Foo__bar': 1}
But mypy will detect the foo=1
there.
Let's parse something :-)
>>> from enum import Enum
>>> FooEnum = Enum("FooEnum", "foo bar")
>>>
>>> @dataclass
... class Bar:
... bar: str
>>>
>>> @dataclass
... class Foo:
... foo: str
... bar: Bar
>>> parse_dc(Foo, {"foo": "foo", "num": 1, "bar": {"bar": "bar"}})
Foo(foo='foo', bar=Bar(bar='bar'))
>>> from datetime import datetime
>>> @dataclass
... class Date:
... d: datetime
>>> Date(d="20010101T00:00Z").d
20010101T00:00Z
>>> @dataclass
... class Date:
... d: datetime
... def __post_init__(self):
... if isinstance(self.d, str):
... self.d = datetime.strptime("%Y%m%dT%H%MZ")
>>> Date(d="20010101T00:00Z").d
create_base(base)
A function decorator. It replace the function by a class which call the decorated function in its new method, for example
>>> from datetime import datetime
>>> @create_base(datetime)
... def date_br(s):
... return datetime.strptime(s, r"%d/%m/%Y")
>>> issubclass(date_br, datetime)
True
>>> date_br("01/01/2001")
datetime.datetime(2001, 1, 1, 0, 0)
unpack_union(union: Union[~T, Any, NoneType]) -> ~T
Takes an Unin and return another union with the same arguments as input, but with None and Any filtered
>>> unpack_union(Optional[str])
<class 'str'>
>>> unpack_union(List[str])
<class 'str'>
It respect concrete types
>>> unpack_union(int)
<class 'int'>
If the input is a literal, it returns itself. Literals are types and values at same time, like enums
>>> unpack_union(1)
1
>>> unpack_union([1,2])
[1, 2]
Dataclass(*args, **kwds)
Dataclass static type https://stackoverflow.com/a/55240861/652528
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