Metadata-Version: 2.1
Name: resguard
Version: 0.10
Summary: UNKNOWN
Home-page: UNKNOWN
Author: Daniel Hilst Selli
Author-email: danielhilst@gmail.com
License: Apache-2.0
Description: [![Build Status](https://travis-ci.org/dhilst/resguard.svg?branch={branch})](https://travis-ci.org/dhilst/resguard)
        
        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.
        
        ```python
        >>> @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:
        
        ```json
        	{
        		"_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
        
        ```python
        >>> 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:
        
        ```python
        >>> 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. Expected one of (_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
        ```python
        >>> type_ = res.json()['type']
        >>> type_, type(type_)
        ('cat', <class 'str'>)
        
        ```
        
        This may happen again and again. Some APIs are freaking crazy, they may add
        some fields in some responses and another not. If you pass
        `,ignore_unknows=True)` to `parse_dc` it will not raise type errors if an
        unexpected field arrives. If you want this behavior by default you can memoise
        `parse_dc` as
        
        ```python
        from functools import partial
        parse_dc = partial(parse_dc, ignore_unknows=True)
        ```
        
        So let's update our `Fact` definition
        
        ```python
        >>> @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
        
        ```python
        >>> parse_dc(Fact, res.json())
        Traceback (most recent call last):
        ...
        TypeError:  in dataclass Fact while trying to construct value from datetime...
        
        ```
        
        Know it stopped in `updatedAt: datetime`..  Well, for a dataclass field `F: T`
        and value `V`, parse_dc will call `T(V)` at somepoint. So is important that
        construtors can work with data that came from the response, but also we don't want
        to lose typechecking. The solution here is to wrap the problematic object in a
        class and override its constructor. I override `__new__` instead of `__init__`
        here because I want to control the returned object not how it's initializated.
        
        ```python
        >>> class datetimestr(datetime):
        ...     def __new__(cls, datestr):
        ...         return datetime.strptime(datestr, "%Y-%m-%dT%H:%M:%S.%fZ")
        
        >>> @dataclass
        ... class Fact:
        ...     _id: str
        ...     __v: int
        ...     updatedAt: datetimestr
        ...     createdAt: datetimestr
        ...     deleted: bool
        ...     source: str
        ...     used: bool
        ...     type: str
        ...     user: Optional[str]
        ...     text: str
        ...
        >>> parse_dc(Fact, res.json())
        Traceback (most recent call last):
        ...
        TypeError: Unknow field status for Fact. ...
        
        ```
        
        Oh no, another missing field. While I write this tutorial, we just got
        two outdated fields... this is why the library is called resguard. It's
        an abbreviation for response guard, it guard you from dealing to bad responses
        all over your code... Back to the tutorial, let't see what we have in `status`
        field:
        
        ```python
        >>> res.json()['status']
        {'verified': True, 'sentCount': 1}
        
        ```
        
        So it's a dictionary, let's create another dataclass to represent this
        and update our `Facts` dataclass to include status field
        
        ```python
        >>> @dataclass
        ... class Status:
        ...     verified: bool
        ...     sentCount: int
        
        >>> @dataclass
        ... class Fact:
        ...     _id: str
        ...     __v: int
        ...     updatedAt: datetimestr
        ...     createdAt: datetimestr
        ...     deleted: bool
        ...     source: str
        ...     used: bool
        ...     type: str
        ...     user: str
        ...     text: str
        ...     status: Status # <- we added this
        
        ```
        
        Here we go again
        
        ```python
        >>> parse_dc(Fact, res.json())
        Fact(_id=..., _Fact__v=..., updatedAt=..., createdAt=..., deleted=..., source=..., used=..., type='cat', user=..., text=..., status=Status(verified=True, sentCount=1))
        
        ```
        
        And it worked!
        
        To avoid the class boilerplate is possible to use the `@create_base`
        decorator, it takes a function and replaces it by a class which is subclass of
        the sole argument
        
        So you can replace this
        
        ```python
        >>> class datetimestr(datetime):
        ...     def __new__(cls, datestr):
        ...         return datetime.strptime(datestr, "%Y-%m-%dT%H:%M:%S.%fZ")
        
        ```
        
        by this
        ```python
        >>> @create_base(datetime)
        ... def datetimestr(s):
        ...     return datetime.strptime(s, r"%d/%m/%Y")
        
        ```
        
        Since datetimestr is a subtype of datetime it typechecks for 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
        
        ```python
        >>> 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)
        ```shell
        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(cls: resguard.Dataclass, data: dict, ignore_unknows=False) -> resguard.Dataclass
        
        Given an arbitrary dataclass and a dict this function will
        recursively parse the data, checking data types against cls
        dataclass.
        
        It raises TypeError if something goes wrong. It tries to improve
        the common errors by reraising then with better messages.
        
        First declare some dataclasses that define the data that you want
        to parse.
        ```python
        >>> @dataclass
        ... class Foo:
        ...     name: Literal[0, 1]
        ...
        >>> @dataclass
        ... class Bar:
        ...     l: List[int]
        ...     foo: Dict[str, int]
        ...     Foo: Foo
        ...     age: Optional[int] = None
        
        ```
        
        Now suppose that you get this data from a network response. I'm
        expecting it to be plain json parsed to dicts, lists and so on,
        but no objects, just decoded json:
        
        ```python
        >>> data = {"foo": {"bar": 1}, "l": [], "Foo": {"name": 1}}
        
        ```
        
        Hmm, it seems to match, lets try to parse this
        ```python
        >>> parse_dc(Bar, data)
        Bar(l=[], foo={'bar': 1}, Foo=Foo(name=1), age=None)
        
        ```
        
        You can see that it creates nested dataclasses too, cool. But this
        was easy, this was the happy path, what about the not so happy path.
        Let's change data, and see how parse_dc handle errors
        ```python
        >>> data["badkey"] = "bad things"
        >>> parse_dc(Bar, data)
        Traceback (most recent call last):                                           
        ...
        TypeError: Unknow field badkey for Bar. Expected one of (l,foo,Foo,age)
        
        ```
        
        Hmm... interesting... It knows that badkey is not in Bar dataclass
        definition and it shows what are the expected keys. Let's try another thing
        
        ```python
        >>> @dataclass
        ... class Foo:
        ...     foo: int
        >>> parse_dc(Foo, {"foo": "an string"})
        Traceback (most recent call last):
        ...
        TypeError: in dataclass Foo, 'an string' is not int: invalid literal for int() with base 10: 'an string'
        
        ```
        
        So I passed a bad (by little margin) value in "foo" key. It expects an int
        and received an string. The "invalid literal ..." part is from an error that
        raises when parce_dc tries to pass "an string" to int(), it handles the error
        and reraise as TypeError. The idea is that calle should catch TypeError.
        
        Now if it uses the dataclass field type as constructor, this works
        ```python
        >>> @dataclass
        ... class Foo:
        ...     foo: str 
        >>> parse_dc(Foo, {"foo": 1}).foo
        '1'
        
        ```
        
        It works because str(1) just .. works .. So think about str as the Any type
        from typing module. Almost anything can be encoded as string, so take care
        of yours, since they point to holes on type checking, but provide a nice
        generic system
        
        # create_base(base)
        
        A function decorator. It replace the function by a class
        which call the decorated function in its new method, for
        example
        
        ```python
        >>> 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
        
        ```python
        >>> unpack_union(Optional[str])
        <class 'str'>
        
        >>> unpack_union(List[str])
        <class 'str'>
        
        ```
        
        It respect concrete types
        ```python
        >>> unpack_union(int)
        <class 'int'>
        
        ```
        
        If the input is a literal, it returns itself. Literals are types
        and values at same time, like enums
        ```python
        >>> 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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