Metadata-Version: 2.1
Name: mashumaro
Version: 1.13
Summary: Fast serialization framework on top of dataclasses
Home-page: https://github.com/Fatal1ty/mashumaro
Author: Alexander Tikhonov
Author-email: random.gauss@gmail.com
License: Apache License, Version 2.0
Description: # mashumaro (マシュマロ)
        
        > **mashumaro** is a fast and well tested serialization framework on top of dataclasses.
        
        [![Build Status](https://travis-ci.org/Fatal1ty/mashumaro.svg?branch=master)](https://travis-ci.org/Fatal1ty/mashumaro)
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        [![Latest Version](https://img.shields.io/pypi/v/mashumaro.svg)](https://pypi.python.org/pypi/mashumaro)
        [![Python Version](https://img.shields.io/pypi/pyversions/mashumaro.svg)](https://pypi.python.org/pypi/mashumaro)
        [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
        
        
        When using dataclasses, you often need to dump and load objects according to
        the described scheme.
        This framework not only adds this ability to serialize in different formats,
        but also makes **serialization rapidly**.
        
        Table of contents
        --------------------------------------------------------------------------------
        * [Installation](#installation)
        * [Supported serialization formats](#supported-serialization-formats)
        * [Supported field types](#supported-field-types)
        * [Usage example](#usage-example)
        * [How does it work?](#how-does-it-work)
        * [API](#api)
        * [User defined classes](#user-defined-classes)
        
        Installation
        --------------------------------------------------------------------------------
        
        Use pip to install:
        ```shell
        $ pip install mashumaro
        ```
        
        Supported serialization formats
        --------------------------------------------------------------------------------
        
        This framework adds methods for dumping to and loading from the
        following formats:
        
        * plain dict
        * json
        * yaml
        * msgpack
        
        Plain dict can be useful when you need to pass a dict object to a
        third-party library, such as a client for MongoDB.
        
        Supported field types
        --------------------------------------------------------------------------------
        
        There is support for generic types from the standard *typing* module:
        * List
        * Tuple
        * Set
        * FrozenSet
        * Deque
        * Dict
        * Mapping
        * MutableMapping
        * ChainMap
        * Sequence
        
        for special primitives from the *typing* module:
        * Optional
        * Any
        
        for enumerations based on classes from the standard *enum* module:
        * Enum
        * IntEnum
        * Flag
        * IntFlag
        
        for common built-in types:
        * int
        * float
        * bool
        * str
        * bytes
        * bytearray
        
        for built-in datetime oriented types:
        * datetime
        * date
        * time
        * timedelta
        * timezone
        
        for other less popular built-in types:
        * uuid.UUID
        * decimal.Decimal
        * fractions.Fraction
        * os.PathLike (loads to Path)
        
        for specific types like *NoneType*, nested dataclasses itself and
        even [user defined classes](#user-defined-classes).
        
        
        Usage example
        --------------------------------------------------------------------------------
        
        ```python
        from enum import Enum
        from typing import Set
        from dataclasses import dataclass
        from mashumaro import DataClassJSONMixin
        
        class PetType(Enum):
            CAT = 'CAT'
            MOUSE = 'MOUSE'
        
        @dataclass(unsafe_hash=True)
        class Pet(DataClassJSONMixin):
            name: str
            age: int
            pet_type: PetType
        
        @dataclass
        class Person(DataClassJSONMixin):
            first_name: str
            second_name: str
            age: int
            pets: Set[Pet]
        
        
        tom = Pet(name='Tom', age=5, pet_type=PetType.CAT)
        jerry = Pet(name='Jerry', age=3, pet_type=PetType.MOUSE)
        john = Person(first_name='John', second_name='Smith', age=18, pets={tom, jerry})
        
        dump = john.to_json()
        person = Person.from_json(dump)
        # person == john
        
        Pet.from_json('{"name": "Tom", "age": 5, "pet_type": "CAT"}')
        # Pet(name='Tom', age=5, pet_type=<PetType.CAT: 'CAT'>)
        ```
        
        How does it work?
        --------------------------------------------------------------------------------
        
        This framework works by taking the schema of the data and generating a
        specific parser and builder for exactly that schema.
        This is much faster than inspection of field types on every call of parsing or
        building at runtime.
        
        API
        --------------------------------------------------------------------------------
        
        Mashumaro provides a couple of mixins for each format.
        
        #### `DataClassDictMixin.to_dict(use_bytes: bool, use_enum: bool, use_datetime: bool)`
        
        Make a dictionary from dataclass object based on the dataclass schema provided.
        Options include:
        ```python
        use_bytes: False     # False - convert bytes/bytearray objects to base64 encoded string, True - keep untouched
        use_enum: False      # False - convert enum objects to enum values, True - keep untouched
        use_datetime: False  # False - convert datetime oriented objects to ISO 8601 formatted string, True - keep untouched
        ```
        
        #### `DataClassDictMixin.from_dict(data: Mapping, use_bytes: bool, use_enum: bool, use_datetime: bool)`
        
        Make a new object from dict object based on the dataclass schema provided.
        Options include:
        ```python
        use_bytes: False     # False - load bytes/bytearray objects from base64 encoded string, True - keep untouched
        use_enum: False      # False - load enum objects from enum values, True - keep untouched
        use_datetime: False  # False - load datetime oriented objects from ISO 8601 formatted string, True - keep untouched
        ```
        
        #### `DataClassJSONMixin.to_json(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)`
        
        Make a JSON formatted string from dataclass object based on the dataclass
        schema provided. Options include:
        ```
        encoder        # function called for json encoding, defaults to json.dumps
        dict_params    # dictionary of parameter values passed underhood to `to_dict` function
        encoder_kwargs # keyword arguments for encoder function
        ```
        
        #### `DataClassJSONMixin.from_json(data: Union[str, bytes, bytearray], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)`
        
        Make a new object from JSON formatted string based on the dataclass schema
        provided. Options include:
        ```
        decoder        # function called for json decoding, defaults to json.loads
        dict_params    # dictionary of parameter values passed underhood to `from_dict` function
        decoder_kwargs # keyword arguments for decoder function
        ```
        
        #### `DataClassMessagePackMixin.to_msgpack(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)`
        
        Make a MessagePack formatted bytes object from dataclass object based on the
        dataclass schema provided. Options include:
        ```
        encoder        # function called for MessagePack encoding, defaults to msgpack.packb
        dict_params    # dictionary of parameter values passed underhood to `to_dict` function
        encoder_kwargs # keyword arguments for encoder function
        ```
        
        #### `DataClassMessagePackMixin.from_msgpack(data: Union[str, bytes, bytearray], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)`
        
        Make a new object from MessagePack formatted data based on the
        dataclass schema provided. Options include:
        ```
        decoder        # function called for MessagePack decoding, defaults to msgpack.unpackb
        dict_params    # dictionary of parameter values passed underhood to `from_dict` function
        decoder_kwargs # keyword arguments for decoder function
        ```
        
        #### `DataClassYAMLMixin.to_yaml(encoder: Optional[Encoder], dict_params: Optional[Mapping], **encoder_kwargs)`
        
        Make an YAML formatted bytes object from dataclass object based on the
        dataclass schema provided. Options include:
        ```
        encoder        # function called for YAML encoding, defaults to yaml.dump
        dict_params    # dictionary of parameter values passed underhood to `to_dict` function
        encoder_kwargs # keyword arguments for encoder function
        ```
        
        #### `DataClassYAMLMixin.from_yaml(data: Union[str, bytes], decoder: Optional[Decoder], dict_params: Optional[Mapping], **decoder_kwargs)`
        
        Make a new object from YAML formatted data based on the
        dataclass schema provided. Options include:
        ```
        decoder        # function called for YAML decoding, defaults to yaml.safe_load
        dict_params    # dictionary of parameter values passed underhood to `from_dict` function
        decoder_kwargs # keyword arguments for decoder function
        ```
        
        User defined classes
        --------------------------------------------------------------------------------
        
        You can define and use custom classes with *mashumaro*. There are two options
        for customization. The first one is useful when you already have the separate
        custom class and you want to serialize instances of it with *mashumaro*.
        All what you need is to implement *SerializableType* interface:
        
        ```python
        from typing import Dict
        from datetime import datetime
        from dataclasses import dataclass
        from mashumaro import DataClassDictMixin
        from mashumaro.types import SerializableType
        
        class DateTime(datetime, SerializableType):
            def _serialize(self) -> Dict[str, int]:
                return {
                    "year": self.year,
                    "month": self.month,
                    "day": self.day,
                    "hour": self.hour,
                    "minute": self.minute,
                    "second": self.second,
                }
        
            @classmethod
            def _deserialize(cls, value: Dict[str, int]) -> 'DateTime':
                return DateTime(
                    year=value['year'],
                    month=value['month'],
                    day=value['day'],
                    hour=value['hour'],
                    minute=value['minute'],
                    second=value['second'],
                )
        
        
        @dataclass
        class Holiday(DataClassDictMixin):
            when: DateTime = DateTime.now()
        
        
        new_year = Holiday(when=DateTime(2019, 1, 1, 12))
        dictionary = new_year.to_dict()
        # {'x': {'year': 2019, 'month': 1, 'day': 1, 'hour': 0, 'minute': 0, 'second': 0}}
        assert Holiday.from_dict(dictionary) == new_year
        ```
        
        The second option is useful when you want to change the serialization behaviour
        for a class depending on some defined parameters. For this case you can create
        the special class implementing *SerializationStrategy* interface:
        
        ```python
        from datetime import datetime
        from dataclasses import dataclass
        from mashumaro import DataClassDictMixin
        from mashumaro.types import SerializationStrategy
        
        class FormattedDateTime(SerializationStrategy):
            def __init__(self, fmt):
                self.fmt = fmt
        
            def _serialize(self, value: datetime) -> str:
                return value.strftime(self.fmt)
        
            def _deserialize(self, value: str) -> datetime:
                return datetime.strptime(value, self.fmt)
        
        
        @dataclass
        class DateTimeFormats(DataClassDictMixin):
            short: FormattedDateTime(fmt='%d%m%Y%H%M%S') = datetime.now()
            verbose: FormattedDateTime(fmt='%A %B %d, %Y, %H:%M:%S') = datetime.now()
        
        
        formats = DateTimeFormats(
            short=datetime(2019, 1, 1, 12),
            verbose=datetime(2019, 1, 1, 12),
        )
        dictionary = formats.to_dict()
        # {'short': '01012019120000', 'verbose': 'Tuesday January 01, 2019, 12:00:00'}
        assert DateTimeFormats.from_dict(dictionary) == formats
        ```
        
        TODO
        --------------------------------------------------------------------------------
        
        * write benchmarks
        * add optional validation
        * add Union support (try to match types on each call)
        * write custom useful types such as URL, Email etc
        * write documentation
        
Platform: all
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Development Status :: 5 - Production/Stable
Requires-Python: >=3.6
Description-Content-Type: text/markdown
