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chili-2.1.0


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توضیحات

Chili is serialisation library. It can serialise/deserialise almost any object.
ویژگی مقدار
سیستم عامل -
نام فایل chili-2.1.0
نام chili
نسخه کتابخانه 2.1.0
نگهدارنده []
ایمیل نگهدارنده []
نویسنده Dawid Kraczkowski
ایمیل نویسنده dawid.kraczkowski@gmail.com
آدرس صفحه اصلی https://github.com/kodemore/chili
آدرس اینترنتی https://pypi.org/project/chili/
مجوز MIT
# Chili [![PyPI version](https://badge.fury.io/py/chili.svg)](https://pypi.org/project/chili) [![codecov](https://codecov.io/gh/kodemore/chili/branch/main/graph/badge.svg?token=TCG7SRQFD5)](https://codecov.io/gh/kodemore/chili) [![CI](https://github.com/kodemore/chili/actions/workflows/main.yaml/badge.svg?branch=main)](https://github.com/kodemore/chili/actions/workflows/main.yaml) [![Release](https://github.com/kodemore/chili/actions/workflows/release.yml/badge.svg)](https://github.com/kodemore/chili/actions/workflows/release.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) Chili is an extensible library which provides a simple and efficient way to encode and decode complex Python objects to and from their dictionary representation. It offers complete coverage for the `typing` package; including generics, and supports custom types, allowing you to extend the library to handle your specific needs. With support for nested data structures, default values, forward references, and data mapping and transformation, Chili is designed to be both easy to use and powerful enough to handle complex data structures. > Note: Chili is not a validation library, although it ensures the type integrity. # Installation To install the library, simply use pip: ```shell pip install chili ``` or poetry: ```shell poetry add chili ``` # Usage The library provides three main classes for encoding and decoding objects, `chili.Encoder` and `chili.Decoder`, and `chili.Serializer`, which combines both functionalities. Functional interface is also provided through `chili.encode` and `chili.decode` functions. Additionally, library by default supports json serialization and deserialization, so you can use `chili.JsonDecoder`, and `chili.JsonDecoder`, and `chili.JsonSerializer` or its functional replacement `chili.json_encode` and `chili.json_decode` to serialize and deserialize objects to and from json. ## Defining encodable/decodable properties To define the properties of a class that should be encoded and decoded, you need to define them with type annotations. The `@encodable`, `@decodable`, or `@serializable` decorator should also be used to mark the class as encodable/decodable or serializable. > Note: Dataclasses are supported automatically, so you don't need to use the decorator. ```python from chili import encodable @encodable class Pet: name: str age: int breed: str def __init__(self, name: str, age: int, breed: str): self.name = name self.age = age self.breed = breed ``` ## Encoding To encode an object, you need to create an instance of the `chili.Encoder` class, and then call the `encode()` method, passing the object to be encoded as an argument. > Note: The `chili.Encoder` class is a generic class, and you need to pass the type of the object to be encoded as a type argument. ```python from chili import Encoder encoder = Encoder[Pet]() my_pet = Pet("Max", 3, "Golden Retriever") encoded = encoder.encode(my_pet) assert encoded == {"name": "Max", "age": 3, "breed": "Golden Retriever"} ``` ## Decoding To decode an object, you need to create an instance of the `chili.Decoder` class, and then call the `decode()` method, passing the dictionary to be decoded as an argument. > Note: The `chili.Decoder` class is a generic class, and you need to pass the type of the object to be decoded as a type argument. ```python from chili import Decoder decoder = Decoder[Pet]() data = {"name": "Max", "age": 3, "breed": "Golden Retriever"} decoded = decoder.decode(data) assert isinstance(decoded, Pet) ``` ## Missing Properties If a property is not present in the dictionary when decoding, the `chili.Decoder` class will not fill in the property value, unless there is a default value defined in the type annotation. Similarly, if a property is not defined on the class, the `chili.Encoder` class will hide the property in the resulting dictionary. ## Using Default Values To provide default values for class properties that are not present in the encoded dictionary, you can define the properties with an equal sign and the default value. For example: ```python from typing import List from chili import Decoder, decodable @decodable class Book: name: str author: str isbn: str = "1234567890" tags: List[str] = [] book_data = {"name": "The Hobbit", "author": "J.R.R. Tolkien"} decoder = Decoder[Book]() book = decoder.decode(book_data) assert book.tags == [] assert book.isbn == "1234567890" ``` > Note: When using default values with mutable objects, such as lists or dictionaries, be aware that the default value is shared among all instances of the class that do not have that property defined in the encoded dictionary. However, if the default value is empty (e.g. `[]` for a list, `{}` for a dictionary), it is not shared among instances. ## Custom Type Encoders You can also specify custom type encoders by defining a class that implements the `chili.TypeEncoder` protocol and passing it as a dictionary to the `encoders` argument of the Encoder constructor. ```python from chili import Encoder, TypeEncoder class MyCustomEncoder(TypeEncoder): def encode(self, value: MyCustomType) -> str: return value.encode() type_encoders = {MyCustomType: MyCustomEncoder()} encoder = Encoder[Pet](encoders=type_encoders) ``` ## Custom Type Decoders You can also specify custom type decoders by defining a class that implements the `chili.TypeDecoder` protocol and passing it as a dictionary to the `decoders` argument of the Decoder constructor. ```python from chili import Decoder, TypeDecoder class MyCustomDecoder(TypeDecoder): def decode(self, value: str) -> MyCustomType: return MyCustomType.decode(value) type_decoders = {MyCustomType: MyCustomDecoder()} decoder = Decoder[Pet](decoders=type_decoders) ``` ## Convenient Functions The library also provides convenient functions for encoding and decoding objects. The `chili.encode` function takes an object and an optional type hint and returns a dictionary. The `chili.decode` function takes a dictionary, a type hint, and returns an object. ```python from chili import encode, decode my_pet = Pet("Max", 3, "Golden Retriever") encoded = encode(my_pet) decoded = decode(encoded, Pet) ``` > To specify custom type encoders and decoders, you can pass them as keyword arguments to the `chili.encode` and `chili.decode` functions. ## Serialization If your object is both encodable and decodable, you can use the `@serializable` decorator to mark it as such. You can then use the `chili.Serializer` class to encode and decode objects. ```python from chili import Serializer, serializable @serializable class Pet: name: str age: int breed: str def __init__(self, name: str, age: int, breed: str): self.name = name self.age = age self.breed = breed my_pet = Pet("Max", 3, "Golden Retriever") serializer = Serializer[Pet]() encoded = serializer.encode(my_pet) decoded = serializer.decode(encoded) ``` > Note: that you should only use the `@serializable` decorator for objects that are both encodable and decodable. ## JSON Serialization The library also provides classes for encoding and decoding objects to and from JSON formats. The `chili.JsonEncoder` and `chili.JsonDecoder` classes provide JSON serialization. ```python from chili import JsonEncoder, JsonDecoder, JsonSerializer # JSON Serialization encoder = JsonEncoder[Pet]() decoder = JsonDecoder[Pet]() serializer = JsonSerializer[Pet]() my_pet = Pet("Max", 3, "Golden Retriever") encoded = encoder.encode(my_pet) decoded = decoder.decode(encoded) ``` The `encoded` value will be a json string: ```json {"name": "Max", "age": 3, "breed": "Golden Retriever"} ``` The `decoded` value will be an instance of a Pet object. > Functional interface is also available through the `chili.json_encode`, `chili.json_decode` functions. ## Mapping Mapping allows you to remap keys, apply functions to the values, and even change the structure of the input dictionary. This is particularly useful when you need to convert data from one format to another, such as when interacting with different APIs or data sources that use different naming conventions. ### Simple mapping Here's an example of how to use the `chili.Mapper` class from the library with a Pet class: ```python from chili import Mapper # Create a Mapper instance with the specified scheme mapper = Mapper({ "pet_name": "name", "pet_age": "age", "pet_tags": { "tag_name": "tag", "tag_type": "type", }, }) data = { "pet_name": "Max", "pet_age": 3, "pet_tags": [ {"tag_name": "cute", "tag_type": "description"}, {"tag_name": "furry", "tag_type": "description"}, ], } # Apply the mapping to your input data mapped_data = mapper.map(data) print(mapped_data) ``` The `mapped_data` output would be: ```python { "name": "Max", "age": 3, "pet_tags": [ {"tag": "cute", "type": "description"}, {"tag": "furry", "type": "description"}, ], } ``` ### Using KeyScheme `KeyScheme` can be used to define mapping rules for nested structures more explicitly. It allows you to specify both the old key and the nested mapping scheme in a single, concise object. This can be particularly useful when you want to map a nested structure but need to maintain clarity in your mapping scheme. Here's an example of how to use `chili.KeyScheme` with the `chili.Mapper` class: ```python from chili import Mapper, KeyScheme # Create a Mapper instance with the specified scheme mapper = Mapper({ "pet_name": "name", "pet_age": "age", "pet_tags": KeyScheme("tags", { "tag_name": "tag", "tag_type": "type", }), }) pet_dict = { "pet_name": "Max", "pet_age": 3, "pet_tags": [ {"tag_name": "cute", "tag_type": "description"}, {"tag_name": "furry", "tag_type": "description"}, ], } # Apply the mapping to your input data mapped_data = mapper.map(pet_dict) print(mapped_data) ``` The `mapped_data` output would be: ```python { "name": "Max", "age": 3, "tags": [ {"tag": "cute", "type": "description"}, {"tag": "furry", "type": "description"}, ], } ``` ### Using wildcards in mapping The `chili.Mapper` supports using `...` (Ellipsis) as a wildcard for keys that you want to include in the mapping but do not want to explicitly define. This can be useful when you want to map all keys in the input data, or when you want to map specific keys and leave the remaining keys unchanged. You can use a lambda function with the `...` wildcard to apply a transformation to the keys or values that match the wildcard. Here's an example of how to use the `...` wildcard with the `chili.Mapper` class: ```python from chili import Mapper # Create a Mapper instance with the specified scheme containing a wildcard ... mapper = Mapper({ "pet_name": "name", "pet_age": "age", ...: lambda k, v: (f"extra_{k}", v.upper() if isinstance(v, str) else v), }) pet_dict = { "pet_name": "Max", "pet_age": 3, "pet_color": "white", "pet_breed": "Golden Retriever", "pet_tags": [ {"tag": "cute", "type": "description"}, {"tag": "furry", "type": "description"}, ], } # Apply the mapping to your input data mapped_data = mapper.map(pet_dict) print(mapped_data) ``` The `mapped_data` output would be: ```python { "pet_name": "Fluffy", "pet_age": 3, "extra_color": "WHITE", "extra_breed": "POODLE", "extra_tags": [ { "tag": "cute", "type": "description", }, { "tag": "furry", "type": "description", }, ], } ``` ## Error handling The library raises errors if an invalid type is passed to the Encoder or Decoder, or if an invalid dictionary is passed to the Decoder. ```python from chili import Encoder, EncoderError, Decoder, DecoderError # Invalid Type encoder = Encoder[MyInvalidType]() # Raises EncoderError.invalid_type decoder = Decoder[MyInvalidType]() # Raises DecoderError.invalid_type # Invalid Dictionary decoder = Decoder[Pet]() invalid_data = {"name": "Max", "age": "three", "breed": "Golden Retriever"} decoded = decoder.decode(invalid_data) # Raises DecoderError.invalid_input ``` ## Supported types The following section lists all the data types supported by the library and explains how they are decoded and encoded. The supported data types include built-in Python types like `bool`, `dict`, `float`, `int`, `list`, `set`, `str`, and `tuple`, as well as more complex types like `collections.namedtuple`, `collections.deque`, `collections.OrderedDict`, `datetime.date`, `datetime.datetime`, `datetime.time`, `datetime.timedelta`, `decimal.Decimal`, `enum.Enum`, `enum.IntEnum`, and various types defined in the typing module. ### Simple types Simple type are handled by a ProxyEncoder and ProxyDecoder. These types are decoded and encoded by casting the value to the specified type. > For more details please refer to [chili.encoder.ProxyEncoder](chili/encoder.py#L50) and [chili.decoder.ProxyDecoder](chili/decoder.py#L65). #### `bool` Passed value is automatically cast to a boolean with python's built-in `bool` type during decoding and encoding process. #### `int` Passed value is automatically cast to an int with python's built-in `int` type during decoding and encoding process. #### `float` Passed value is automatically cast to float with python's built-in `float` type during decoding and encoding process. #### `str` Passed value is automatically cast to string with python's built-in `str` during encoding and decoding process. #### `set` Passed value is automatically cast to either `set` during decoding process or `list` during encoding process. #### `frozenset` Passed value is automatically cast to either `frozenset` during decoding process or `list` during encoding process. #### `list` Passed value is automatically cast to list with python's built-in `list` during encoding and decoding process. #### `tuple` Passed value is automatically cast either to `tuple` during decoding process or to `list` during encoding process. #### `dict` Passed value is automatically cast to dict with python's built-in `dict` during encoding and decoding process. ### Complex types Complex types are handled by corresponding Encoder and Decoder classes. #### `collections.namedtuple` Passed value is automatically cast to either `collections.namedtuple` during decoding process or `list` during encoding process. > Please refer to [chili.encoder.NamedTupleEncoder](chili/encoder.py#L226) and [chili.decoder.NamedTupleDecoder](chili/decoder.py#L307) for more details. #### `collections.deque` Passed value is automatically cast to either `collections.deque` during decoding process or `list` during encoding process. > Please refer to [chili.encoder.DequeEncoder](chili/encoder.py#L187) and [chili.decoder.DequeDecoder](chili/decoder.py#L268) for more details. #### `collections.OrderedDict` Passed value is automatically cast to either `collections.OrderedDict` during decoding process or `list` where each item is a `list` of two elements corresponding to `key` and `value`, during encoding process. #### `datetime.date` Passed value must be valid ISO-8601 date string, then it is automatically hydrated to an instance of `datetime.date` class and extracted to ISO-8601 format compatible string. #### `datetime.datetime` Passed value must be valid ISO-8601 date time string, then it is automatically hydrated to an instance of `datetime.datetime` class and extracted to ISO-8601 format compatible string. #### `datetime.time` Passed value must be valid ISO-8601 time string, then it is automatically hydrated to an instance of `datetime.time` class and extracted to ISO-8601 format compatible string. #### `datetime.timedelta` Passed value must be valid ISO-8601 duration string, then it is automatically hydrated to an instance of `datetime.timedelta` class and extracted to ISO-8601 format compatible string. #### `decimal.Decimal` Passed value must be a string containing valid decimal number representation, for more please read python's manual about [`decimal.Decimal`](https://docs.python.org/3/library/decimal.html#decimal.Decimal), on extraction value is extracted back to string. #### `enum.Enum` Supports hydration of all instances of `enum.Enum` subclasses as long as value can be assigned to one of the members defined in the specified `enum.Enum` subclass. During extraction the value is extracted to value of the enum member. #### `enum.IntEnum` Same as `enum.Enum`. ### Typing module support #### `typing.Any` Passed value is unchanged during hydration and extraction process. #### `typing.AnyStr` Same as `str` #### `typing.Deque` Same as `collection.dequeue` with one exception, if subtype is defined, eg `typing.Deque[int]` each item inside queue is hydrated accordingly to subtype. #### `typing.Dict` Same as `dict` with exception that keys and values are respectively hydrated and extracted to match annotated type. #### `typing.FrozenSet` Same as `frozenset` with exception that values of a frozen set are respectively hydrated and extracted to match annotated type. #### `typing.List` Same as `list` with exception that values of a list are respectively hydrated and extracted to match annotated type. #### `typing.NamedTuple` Same as `namedtuple`. #### `typing.Set` Same as `set` with exception that values of a set are respectively hydrated and extracted to match annotated type. #### `typing.Tuple` Same as `tuple` with exception that values of a set are respectively hydrated and extracted to match annotated types. Ellipsis operator (`...`) is also supported. #### `typing.TypedDict` Same as `dict` but values of a dict are respectively hydrated and extracted to match annotated types. #### `typing.Generic` Only parametrised generic classes are supported, dataclasses that extends other Generic classes without parametrisation will fail. #### `typing.Optional` Optional types can carry additional `None` value which chili's hydration process will respect, so for example if your type is `typing.Optional[int]` `None` value is not hydrated to `int`. #### `typing.Union` Limited support for Unions.


نیازمندی

مقدار نام
>=4.2,<5.0 typing-extensions
==0.2.0 gaffe


زبان مورد نیاز

مقدار نام
>=3.8,<4.0 Python


نحوه نصب


نصب پکیج whl chili-2.1.0:

    pip install chili-2.1.0.whl


نصب پکیج tar.gz chili-2.1.0:

    pip install chili-2.1.0.tar.gz