Skip to main content

Hat JSON library

Project description

This library is part of Hat Open project - open-source framework of tools and libraries for developing applications used for remote monitoring, control and management of intelligent electronic devices such as IoT devices, PLCs, industrial automation or home automation systems.

Development of Hat Open and associated repositories is sponsored by Končar - Power Plant and Electric Traction Engineering Inc. (Končar KET - https://www.koncar-ket.hr).

For more information see:

About

JSON data manipulation and validation functions.

  • type definitions:

    Array: typing.Type = typing.List['Data']
    Object: typing.Type = typing.Dict[str, 'Data']
    Data: typing.Type = typing.Union[None, bool, int, float, str, Array, Object]
    """JSON data type identifier."""
    
    Format = enum.Enum('Format', ['JSON', 'YAML'])
    """Encoding format"""
    
    Path: typing.Type = typing.Union[int, str, typing.List['Path']]
    """Data path"""
  • hat.json.equals and hat.json.flatten:

    def equals(a: Data,
               b: Data
               ) -> bool:
        """Equality comparison of json serializable data.
    
        Tests for equality of data according to JSON format. Notably, ``bool``
        values are not considered equal to numeric values in any case. This is
        different from default equality comparison, which considers `False`
        equal to `0` and `0.0`; and `True` equal to `1` and `1.0`.
    
        Example::
    
            assert equals(0, 0.0) is True
            assert equals({'a': 1, 'b': 2}, {'b': 2, 'a': 1}) is True
            assert equals(1, True) is False
    
        """
    
    def flatten(data: Data
                ) -> typing.Iterable[Data]:
        """Flatten JSON data
    
        If `data` is array, this generator recursively yields result of
        `flatten` call with each element of input list. For other `Data` types,
        input data is yielded.
    
        Example::
    
            data = [1, [], [2], {'a': [3]}]
            result = [1, 2, {'a': [3]}]
            assert list(flatten(data)) == result
    
        """
  • hat.json.get and hat.json.set_:

    def get(data: Data,
            path: Path,
            default: typing.Optional[Data] = None
            ) -> Data:
        """Get data element referenced by path
    
        Example::
    
            data = {'a': [1, 2, [3, 4]]}
            path = ['a', 2, 0]
            assert get(data, path) == 3
    
            data = [1, 2, 3]
            assert get(data, 0) == 1
            assert get(data, 5) is None
    
        """
    
    def set_(data: Data,
             path: Path,
             value: Data
             ) -> Data:
        """Create new data by setting data path element value
    
        Example::
    
            data = [1, {'a': 2, 'b': 3}, 4]
            path = [1, 'b']
            result = set_(data, path, 5)
            assert result == [1, {'a': 2, 'b': 5}, 4]
            assert result is not data
    
            data = [1, 2, 3]
            result = set_(data, 4, 4)
            assert result == [1, 2, 3, None, 4]
    
        """
  • hat.json.diff and hat.json.patch:

    def diff(src: Data,
             dst: Data
             ) -> Data:
        """Generate JSON Patch diff.
    
        Example::
    
            src = [1, {'a': 2}, 3]
            dst = [1, {'a': 4}, 3]
            result = diff(src, dst)
            assert result == [{'op': 'replace', 'path': '/1/a', 'value': 4}]
    
        """
    
    def patch(data: Data,
              diff: Data
              ) -> Data:
        """Apply JSON Patch diff.
    
        Example::
    
            data = [1, {'a': 2}, 3]
            d = [{'op': 'replace', 'path': '/1/a', 'value': 4}]
            result = patch(data, d)
            assert result == [1, {'a': 4}, 3]
    
        """
  • hat.json.encode and hat.json.decode:

    def encode(data: Data,
               format: Format = Format.JSON,
               indent: typing.Optional[int] = None
               ) -> str:
        """Encode JSON data.
    
        Args:
            data: JSON data
            format: encoding format
            indent: indentation size
    
        """
    
    def decode(data_str: str,
               format: Format = Format.JSON
               ) -> Data:
        """Decode JSON data.
    
        Args:
            data_str: encoded JSON data
            format: encoding format
    
        """
  • hat.json.encode_file and hat.json.decode_file:

    def encode_file(data: Data,
                    path: pathlib.PurePath,
                    format: typing.Optional[Format] = None,
                    indent: typing.Optional[int] = 4):
        """Encode JSON data to file.
    
        If `format` is ``None``, encoding format is derived from path suffix.
    
        Args:
            data: JSON data
            path: file path
            format: encoding format
            indent: indentation size
    
        """
    
    def decode_file(path: pathlib.PurePath,
                    format: typing.Optional[Format] = None
                    ) -> Data:
        """Decode JSON data from file.
    
        If `format` is ``None``, encoding format is derived from path suffix.
    
        Args:
            path: file path
            format: encoding format
    
        """
  • hat.json.SchemaRepository:

    class SchemaRepository:
        """JSON Schema repository.
    
        A repository that holds json schemas and enables validation against
        them.
    
        Repository can be initialized with multiple arguments, which can be
        instances of ``pathlib.PurePath``, ``Data`` or ``SchemaRepository``.
    
        If an argument is of type ``pathlib.PurePath``, and path points to file
        with a suffix '.json', '.yml' or '.yaml', json serializable data is
        decoded from the file. Otherwise, it is assumed that path points to a
        directory, which is recursively searched for json and yaml files. All
        decoded schemas are added to the repository. If a schema with the same
        `id` was previosly added, an exception is raised.
    
        If an argument is of type ``Data``, it should be a json serializable
        data representation of a JSON schema. If a schema with the same `id`
        was previosly added, an exception is raised.
    
        If an argument is of type ``SchemaRepository``, its schemas are added
        to the new repository. Previously added schemas with the same `id` are
        replaced.
    
        """
    
        def __init__(self, *args: typing.Union[pathlib.PurePath,
                                               Data,
                                               'SchemaRepository']): ...
    
        def validate(self,
                     schema_id: str,
                     data: Data):
            """Validate data against JSON schema.
    
            Args:
                schema_id: JSON schema identifier
                data: data to be validated
    
            Raises:
                jsonschema.ValidationError
    
            """
    
        def to_json(self) -> Data:
            """Export repository content as json serializable data.
    
            Entire repository content is exported as json serializable data.
            New repository can be created from the exported content by using
            :meth:`SchemaRepository.from_json`.
    
            """
    
        @staticmethod
        def from_json(data: typing.Union[pathlib.PurePath,
                                         Data]
                      ) -> 'SchemaRepository':
            """Create new repository from content exported as json serializable
            data.
    
            Creates a new repository from content of another repository that was
            exported by using :meth:`SchemaRepository.to_json`.
    
            Args:
                data: repository data
    
            """

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Built Distribution

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page