serialize (10)

deserialize

deserialize(data: str, fmt: Literal['yaml', 'json', 'ini', 'toml'], **kwargs: Any) -> Any

Serialize given json-like object to given format.

Example

Jinja call:

{{ "[abc.def]
value = 1" | deserialize("toml") }}

Result: {'abc': {'def': {'value': 1}}}

DocStrings

Parameters:

Name	Type	Description	Default
data	str	The data to deserialize	required
fmt	SerializeFormatStr	The serialization format	required
kwargs	Any	Keyword arguments passed to the loader function	{}

Source code in src/jinjarope/serializefilters.py

def deserialize(data: str, fmt: SerializeFormatStr, **kwargs: Any) -> Any:
    """Serialize given json-like object to given format.

    Args:
        data: The data to deserialize
        fmt: The serialization format
        kwargs: Keyword arguments passed to the loader function
    """
    match fmt:
        case "yaml":
            import yamling

            return yamling.load_yaml(data, **kwargs)
        case "json":
            return json.loads(data, **kwargs)
        case "ini":
            return load_ini(data, **kwargs)
        case "toml":
            import tomllib

            return tomllib.loads(data, **kwargs)
        case _:
            raise TypeError(fmt)

dictsort

dictsort(value: Mapping[~K, ~V], case_sensitive: bool = False, by: 'te.Literal["key", "value"]' = 'key', reverse: bool = False) -> List[Tuple[~K, ~V]]

Sort a dict and yield (key, value) pairs. Python dicts may not

Example

Jinja call:

{{ {"b": 1, "a": 2} | dictsort }}

Result: [('a', 2), ('b', 1)]

Example

Jinja call:

{{ {"b": 1, "a": 2} | dictsort(by="value") }}

Result: [('b', 1), ('a', 2)]

DocStrings

Source code in .venv/lib/python3.12/site-packages/jinja2/filters.py

def do_dictsort(
    value: t.Mapping[K, V],
    case_sensitive: bool = False,
    by: 'te.Literal["key", "value"]' = "key",
    reverse: bool = False,
) -> t.List[t.Tuple[K, V]]:
    """Sort a dict and yield (key, value) pairs. Python dicts may not
    be in the order you want to display them in, so sort them first.

    .. sourcecode:: jinja

        {% for key, value in mydict|dictsort %}
            sort the dict by key, case insensitive

        {% for key, value in mydict|dictsort(reverse=true) %}
            sort the dict by key, case insensitive, reverse order

        {% for key, value in mydict|dictsort(true) %}
            sort the dict by key, case sensitive

        {% for key, value in mydict|dictsort(false, 'value') %}
            sort the dict by value, case insensitive
    """
    if by == "key":
        pos = 0
    elif by == "value":
        pos = 1
    else:
        raise FilterArgumentError('You can only sort by either "key" or "value"')

    def sort_func(item: t.Tuple[t.Any, t.Any]) -> t.Any:
        value = item[pos]

        if not case_sensitive:
            value = ignore_case(value)

        return value

    return sorted(value.items(), key=sort_func, reverse=reverse)

dig

dig(data: dict, *sections: str, keep_path: bool = False, dig_yaml_lists: bool = True) -> Any

Try to get data with given section path from a dict-list structure.

Example

Jinja call:

{{ {"section1": {"section2": {"section3": "Hello, World"} } } | dig("section1", "section2") }}

Result: {'section3': 'Hello, World'}

Example

Jinja call:

{{ {"section1": {"section2": {"section3": "Hello, World"} } } | dig("section1", "section2", keep_path=True) }}

Result: {'section1': {'section2': {'section3': 'Hello, World'}}}

DocStrings

Parameters:

Name	Type	Description	Default
data	dict	The data to dig into	required
sections	str	Sections to dig into	()
keep_path	bool	Return result with original nesting	False
dig_yaml_lists	bool	Also dig into single-key->value pairs, as often found in yaml.	True

Source code in src/jinjarope/serializefilters.py

def dig(
    data: dict,
    *sections: str,
    keep_path: bool = False,
    dig_yaml_lists: bool = True,
) -> Any:
    """Try to get data with given section path from a dict-list structure.

    If a list is encountered and dig_yaml_lists is true, treat it like a list of
    {"identifier", {subdict}} items, as used in MkDocs config for
    plugins & extensions.
    If Key path does not exist, return None.

    Args:
        data: The data to dig into
        sections: Sections to dig into
        keep_path: Return result with original nesting
        dig_yaml_lists: Also dig into single-key->value pairs, as often found in yaml.
    """
    for i in sections:
        if isinstance(data, dict):
            if child := data.get(i):
                data = child
            else:
                return None
        elif dig_yaml_lists and isinstance(data, list):
            # this part is for yaml-style listitems
            for idx in data:
                if i in idx and isinstance(idx, dict):
                    data = idx[i]
                    break
                if isinstance(idx, str) and idx == i:
                    data = idx
                    break
            else:
                return None
    if not keep_path:
        return data
    result: dict[str, dict] = {}
    new = result
    for sect in sections:
        result[sect] = data if sect == sections[-1] else {}
        result = result[sect]
    return new

dump_json

dump_json(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)

Serialize obj to a JSON formatted str.

Example

Jinja call:

{{ {'a': 'b'} | dump_json }}

Result: {"a": "b"}

DocStrings

Source code in json/__init__.py

def dumps(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True,
        allow_nan=True, cls=None, indent=None, separators=None,
        default=None, sort_keys=False, **kw):
    """Serialize ``obj`` to a JSON formatted ``str``.

    If ``skipkeys`` is true then ``dict`` keys that are not basic types
    (``str``, ``int``, ``float``, ``bool``, ``None``) will be skipped
    instead of raising a ``TypeError``.

    If ``ensure_ascii`` is false, then the return value can contain non-ASCII
    characters if they appear in strings contained in ``obj``. Otherwise, all
    such characters are escaped in JSON strings.

    If ``check_circular`` is false, then the circular reference check
    for container types will be skipped and a circular reference will
    result in an ``RecursionError`` (or worse).

    If ``allow_nan`` is false, then it will be a ``ValueError`` to
    serialize out of range ``float`` values (``nan``, ``inf``, ``-inf``) in
    strict compliance of the JSON specification, instead of using the
    JavaScript equivalents (``NaN``, ``Infinity``, ``-Infinity``).

    If ``indent`` is a non-negative integer, then JSON array elements and
    object members will be pretty-printed with that indent level. An indent
    level of 0 will only insert newlines. ``None`` is the most compact
    representation.

    If specified, ``separators`` should be an ``(item_separator, key_separator)``
    tuple.  The default is ``(', ', ': ')`` if *indent* is ``None`` and
    ``(',', ': ')`` otherwise.  To get the most compact JSON representation,
    you should specify ``(',', ':')`` to eliminate whitespace.

    ``default(obj)`` is a function that should return a serializable version
    of obj or raise TypeError. The default simply raises TypeError.

    If *sort_keys* is true (default: ``False``), then the output of
    dictionaries will be sorted by key.

    To use a custom ``JSONEncoder`` subclass (e.g. one that overrides the
    ``.default()`` method to serialize additional types), specify it with
    the ``cls`` kwarg; otherwise ``JSONEncoder`` is used.

    """
    # cached encoder
    if (not skipkeys and ensure_ascii and
        check_circular and allow_nan and
        cls is None and indent is None and separators is None and
        default is None and not sort_keys and not kw):
        return _default_encoder.encode(obj)
    if cls is None:
        cls = JSONEncoder
    return cls(
        skipkeys=skipkeys, ensure_ascii=ensure_ascii,
        check_circular=check_circular, allow_nan=allow_nan, indent=indent,
        separators=separators, default=default, sort_keys=sort_keys,
        **kw).encode(obj)

items

items(value: Union[Mapping[~K, ~V], jinja2.runtime.Undefined]) -> Iterator[Tuple[~K, ~V]]

Return an iterator over the (key, value) items of a mapping.

Example

Jinja call:

{{ {"a": "b"} | items }}

Result: <generator object do_items at 0x7f828052f780>

Example

Jinja call:

{{ {"a": {"b": "c"}} | items | list }}

Result: [('a', {'b': 'c'})]

DocStrings

Source code in .venv/lib/python3.12/site-packages/jinja2/filters.py

def do_items(value: t.Union[t.Mapping[K, V], Undefined]) -> t.Iterator[t.Tuple[K, V]]:
    """Return an iterator over the ``(key, value)`` items of a mapping.

    ``x|items`` is the same as ``x.items()``, except if ``x`` is
    undefined an empty iterator is returned.

    This filter is useful if you expect the template to be rendered with
    an implementation of Jinja in another programming language that does
    not have a ``.items()`` method on its mapping type.

    .. code-block:: html+jinja

        <dl>
        {% for key, value in my_dict|items %}
            <dt>{{ key }}
            <dd>{{ value }}
        {% endfor %}
        </dl>

    .. versionadded:: 3.1
    """
    if isinstance(value, Undefined):
        return

    if not isinstance(value, abc.Mapping):
        raise TypeError("Can only get item pairs from a mapping.")

    yield from value.items()

load_json

load_json(s, *, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)

Deserialize s (a str, bytes or bytearray instance

Example

Jinja call:

{{ "[1, 2]" | load_json }}

Result: [1, 2]

DocStrings

Source code in json/__init__.py

def loads(s, *, cls=None, object_hook=None, parse_float=None,
        parse_int=None, parse_constant=None, object_pairs_hook=None, **kw):
    """Deserialize ``s`` (a ``str``, ``bytes`` or ``bytearray`` instance
    containing a JSON document) to a Python object.

    ``object_hook`` is an optional function that will be called with the
    result of any object literal decode (a ``dict``). The return value of
    ``object_hook`` will be used instead of the ``dict``. This feature
    can be used to implement custom decoders (e.g. JSON-RPC class hinting).

    ``object_pairs_hook`` is an optional function that will be called with the
    result of any object literal decoded with an ordered list of pairs.  The
    return value of ``object_pairs_hook`` will be used instead of the ``dict``.
    This feature can be used to implement custom decoders.  If ``object_hook``
    is also defined, the ``object_pairs_hook`` takes priority.

    ``parse_float``, if specified, will be called with the string
    of every JSON float to be decoded. By default this is equivalent to
    float(num_str). This can be used to use another datatype or parser
    for JSON floats (e.g. decimal.Decimal).

    ``parse_int``, if specified, will be called with the string
    of every JSON int to be decoded. By default this is equivalent to
    int(num_str). This can be used to use another datatype or parser
    for JSON integers (e.g. float).

    ``parse_constant``, if specified, will be called with one of the
    following strings: -Infinity, Infinity, NaN.
    This can be used to raise an exception if invalid JSON numbers
    are encountered.

    To use a custom ``JSONDecoder`` subclass, specify it with the ``cls``
    kwarg; otherwise ``JSONDecoder`` is used.
    """
    if isinstance(s, str):
        if s.startswith('\ufeff'):
            raise JSONDecodeError("Unexpected UTF-8 BOM (decode using utf-8-sig)",
                                  s, 0)
    else:
        if not isinstance(s, (bytes, bytearray)):
            raise TypeError(f'the JSON object must be str, bytes or bytearray, '
                            f'not {s.__class__.__name__}')
        s = s.decode(detect_encoding(s), 'surrogatepass')

    if (cls is None and object_hook is None and
            parse_int is None and parse_float is None and
            parse_constant is None and object_pairs_hook is None and not kw):
        return _default_decoder.decode(s)
    if cls is None:
        cls = JSONDecoder
    if object_hook is not None:
        kw['object_hook'] = object_hook
    if object_pairs_hook is not None:
        kw['object_pairs_hook'] = object_pairs_hook
    if parse_float is not None:
        kw['parse_float'] = parse_float
    if parse_int is not None:
        kw['parse_int'] = parse_int
    if parse_constant is not None:
        kw['parse_constant'] = parse_constant
    return cls(**kw).decode(s)

load_toml

load_toml(s: str, /, *, parse_float: Callable[[str], Any] = <class 'float'>) -> dict[str, typing.Any]

Parse TOML from a string.

Example

Jinja call:

{{ "[abc]
b=1" | load_toml }}

Result: {'abc': {'b': 1}}

DocStrings

Source code in tomllib/_parser.py

def loads(s: str, /, *, parse_float: ParseFloat = float) -> dict[str, Any]:  # noqa: C901
    """Parse TOML from a string."""

    # The spec allows converting "\r\n" to "\n", even in string
    # literals. Let's do so to simplify parsing.
    src = s.replace("\r\n", "\n")
    pos = 0
    out = Output(NestedDict(), Flags())
    header: Key = ()
    parse_float = make_safe_parse_float(parse_float)

    # Parse one statement at a time
    # (typically means one line in TOML source)
    while True:
        # 1. Skip line leading whitespace
        pos = skip_chars(src, pos, TOML_WS)

        # 2. Parse rules. Expect one of the following:
        #    - end of file
        #    - end of line
        #    - comment
        #    - key/value pair
        #    - append dict to list (and move to its namespace)
        #    - create dict (and move to its namespace)
        # Skip trailing whitespace when applicable.
        try:
            char = src[pos]
        except IndexError:
            break
        if char == "\n":
            pos += 1
            continue
        if char in KEY_INITIAL_CHARS:
            pos = key_value_rule(src, pos, out, header, parse_float)
            pos = skip_chars(src, pos, TOML_WS)
        elif char == "[":
            try:
                second_char: str | None = src[pos + 1]
            except IndexError:
                second_char = None
            out.flags.finalize_pending()
            if second_char == "[":
                pos, header = create_list_rule(src, pos, out)
            else:
                pos, header = create_dict_rule(src, pos, out)
            pos = skip_chars(src, pos, TOML_WS)
        elif char != "#":
            raise suffixed_err(src, pos, "Invalid statement")

        # 3. Skip comment
        pos = skip_comment(src, pos)

        # 4. Expect end of line or end of file
        try:
            char = src[pos]
        except IndexError:
            break
        if char != "\n":
            raise suffixed_err(
                src, pos, "Expected newline or end of document after a statement"
            )
        pos += 1

    return out.data.dict

merge

merge(target: 'list | dict', *source: 'list | dict', deepcopy: 'bool' = False, mergers: 'dict[type, Callable[[Any, Any, Any], Any]] | None' = None) -> 'list | dict'

Merge given data structures using mergers provided.

Example

Jinja call:

{{ {"a": {"b": 1} } | merge({"a": {"c": 2} }) }}

Result: {'a': {'b': 1, 'c': 2}}

DocStrings

Parameters:

Name	Type	Description	Default
target	list | dict	Data structure to merge into	required
source	list | dict	Data structures to merge into target	()
deepcopy	bool	Whether to deepcopy the target	False
mergers	dict[type, Callable[[Any, Any, Any], Any]] | None	Mergers with strategies for each type (default: additive)	None

Source code in src/jinjarope/serializefilters.py

def merge(
    target: list | dict,
    *source: list | dict,
    deepcopy: bool = False,
    mergers: dict[type, Callable[[Any, Any, Any], Any]] | None = None,
) -> list | dict:
    """Merge given data structures using mergers provided.

    Args:
        target: Data structure to merge into
        source:  Data structures to merge into target
        deepcopy: Whether to deepcopy the target
        mergers: Mergers with strategies for each type (default: additive)
    """
    import copy

    if deepcopy:
        target = copy.deepcopy(target)
    context = deepmerge.DeepMerger(mergers)
    for s in source:
        target = context.merge(s, target)
    return target

serialize

serialize(data: Any, fmt: Literal['yaml', 'json', 'ini', 'toml'], **kwargs: Any) -> str

Serialize given json-like object to given format.

Example

Jinja call:

{{ {"a": "b"} | serialize("toml") }}

Result: a = "b"

DocStrings

Parameters:

Name	Type	Description	Default
data	Any	The data to serialize	required
fmt	SerializeFormatStr	The serialization format	required
kwargs	Any	Keyword arguments passed to the dumper function	{}

Source code in src/jinjarope/serializefilters.py

def serialize(data: Any, fmt: SerializeFormatStr, **kwargs: Any) -> str:
    """Serialize given json-like object to given format.

    Args:
        data: The data to serialize
        fmt: The serialization format
        kwargs: Keyword arguments passed to the dumper function
    """
    match fmt:
        case "yaml":
            import yamling

            return yamling.dump_yaml(data, **kwargs)
        case "json":
            return json.dumps(data, indent=4, **kwargs)
        case "ini":
            config = configparser.ConfigParser(**kwargs)
            config.read_dict(data)
            file = io.StringIO()
            with file as fp:
                config.write(fp)
                return file.getvalue()
        case "toml" if isinstance(data, dict):
            import tomli_w

            return tomli_w.dumps(data, **kwargs)
        case _:
            raise TypeError(fmt)

tojson

tojson(eval_ctx: 'EvalContext', value: Any, indent: Optional[int] = None) -> markupsafe.Markup

Serialize an object to a string of JSON, and mark it safe to

Example

Jinja call:

{{ "[1, 2]" | tojson }}

Result: "[1, 2]"

Example

Jinja call:

{{ {"nested": {"data": [1,2]}} | tojson(indent=2) }}

Result: { "nested": { "data": [ 1, 2 ] } }

DocStrings

Source code in .venv/lib/python3.12/site-packages/jinja2/filters.py

@pass_eval_context
def do_tojson(
    eval_ctx: "EvalContext", value: t.Any, indent: t.Optional[int] = None
) -> Markup:
    """Serialize an object to a string of JSON, and mark it safe to
    render in HTML. This filter is only for use in HTML documents.

    The returned string is safe to render in HTML documents and
    ``<script>`` tags. The exception is in HTML attributes that are
    double quoted; either use single quotes or the ``|forceescape``
    filter.

    :param value: The object to serialize to JSON.
    :param indent: The ``indent`` parameter passed to ``dumps``, for
        pretty-printing the value.

    .. versionadded:: 2.9
    """
    policies = eval_ctx.environment.policies
    dumps = policies["json.dumps_function"]
    kwargs = policies["json.dumps_kwargs"]

    if indent is not None:
        kwargs = kwargs.copy()
        kwargs["indent"] = indent

    return htmlsafe_json_dumps(value, dumps=dumps, **kwargs)