Module rio_tiler.models¶
rio-tiler models.
Variables¶
WGS84_CRS
dtype_ranges
Functions¶
masked_and_3d¶
def masked_and_3d(
array: numpy.ndarray
) -> numpy.ma.core.MaskedArray
Makes sure we have a 3D array and mask
rescale_image¶
def rescale_image(
array: numpy.ma.core.MaskedArray,
in_range: Sequence[Tuple[Union[float, int], Union[float, int]]],
out_range: Sequence[Tuple[Union[float, int], Union[float, int]]] = ((0, 255),),
out_dtype: Union[str, numpy.number] = 'uint8'
) -> numpy.ma.core.MaskedArray
Rescale image data in-place.
to_coordsbbox¶
def to_coordsbbox(
bbox
) -> Union[rasterio.coords.BoundingBox, NoneType]
Convert bbox to CoordsBbox nameTuple.
to_masked¶
def to_masked(
array: numpy.ndarray
) -> numpy.ma.core.MaskedArray
Makes sure we have a MaskedArray.
Classes¶
BandStatistics¶
class BandStatistics(
/,
**data: 'Any'
)
Band statistics
Ancestors (in MRO)¶
- rio_tiler.models.RioTilerBaseModel
- pydantic.main.BaseModel
Class variables¶
model_computed_fields
model_config
model_fields
Static methods¶
construct¶
def construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Model'
from_orm¶
def from_orm(
obj: 'Any'
) -> 'Model'
model_construct¶
def model_construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Model'
Creates a new instance of the Model
class with validated data.
Creates a new model setting __dict__
and __pydantic_fields_set__
from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Note
model_construct()
generally respects the model_config.extra
setting on the provided model.
That is, if model_config.extra == 'allow'
, then all extra passed values are added to the model instance's __dict__
and __pydantic_extra__
fields. If model_config.extra == 'ignore'
(the default), then all extra passed values are ignored.
Because no validation is performed with a call to model_construct()
, having model_config.extra == 'forbid'
does not result in
an error if extra values are passed, but they will be ignored.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
_fields_set | None | The set of field names accepted for the Model instance. | None |
values | None | Trusted or pre-validated data dictionary. | None |
Returns:
Type | Description |
---|---|
None | A new instance of the Model class with validated data. |
model_json_schema¶
def model_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
Generates a JSON schema for a model class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
by_alias | None | Whether to use attribute aliases or not. | None |
ref_template | None | The reference template. | None |
schema_generator | None | To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema with your desired modifications |
None |
mode | None | The mode in which to generate the schema. | None |
Returns:
Type | Description |
---|---|
None | The JSON schema for the given model class. |
model_parametrized_name¶
def model_parametrized_name(
params: 'tuple[type[Any], ...]'
) -> 'str'
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
params | None | Tuple of types of the class. Given a generic classModel with 2 type variables and a concrete model Model[str, int] ,the value (str, int) would be passed to params . |
None |
Returns:
Type | Description |
---|---|
None | String representing the new class where params are passed to cls as type variables. |
Raises:
Type | Description |
---|---|
TypeError | Raised when trying to generate concrete names for non-generic models. |
model_rebuild¶
def model_rebuild(
*,
force: 'bool' = False,
raise_errors: 'bool' = True,
_parent_namespace_depth: 'int' = 2,
_types_namespace: 'dict[str, Any] | None' = None
) -> 'bool | None'
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
force | None | Whether to force the rebuilding of the model schema, defaults to False . |
None |
raise_errors | None | Whether to raise errors, defaults to True . |
None |
_parent_namespace_depth | None | The depth level of the parent namespace, defaults to 2. | None |
_types_namespace | None | The types namespace, defaults to None . |
None |
Returns:
Type | Description |
---|---|
None | Returns None if the schema is already "complete" and rebuilding was not required.If rebuilding was required, returns True if rebuilding was successful, otherwise False . |
model_validate¶
def model_validate(
obj: 'Any',
*,
strict: 'bool | None' = None,
from_attributes: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Validate a pydantic model instance.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj | None | The object to validate. | None |
strict | None | Whether to enforce types strictly. | None |
from_attributes | None | Whether to extract data from object attributes. | None |
context | None | Additional context to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated model instance. |
Raises:
Type | Description |
---|---|
ValidationError | If the object could not be validated. |
model_validate_json¶
def model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Usage docs: docs.pydantic.dev/2.7/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
json_data | None | The JSON data to validate. | None |
strict | None | Whether to enforce types strictly. | None |
context | None | Extra variables to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated Pydantic model. |
Raises:
Type | Description |
---|---|
ValueError | If json_data is not a JSON string. |
model_validate_strings¶
def model_validate_strings(
obj: 'Any',
*,
strict: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Validate the given object contains string data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj | None | The object contains string data to validate. | None |
strict | None | Whether to enforce types strictly. | None |
context | None | Extra variables to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated Pydantic model. |
parse_file¶
def parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Model'
parse_obj¶
def parse_obj(
obj: 'Any'
) -> 'Model'
parse_raw¶
def parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Model'
schema¶
def schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}'
) -> 'typing.Dict[str, Any]'
schema_json¶
def schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
update_forward_refs¶
def update_forward_refs(
**localns: 'Any'
) -> 'None'
validate¶
def validate(
value: 'Any'
) -> 'Model'
Instance variables¶
model_extra
Get extra fields set during validation.
model_fields_set
Returns the set of fields that have been explicitly set on this model instance.
Methods¶
copy¶
def copy(
self: 'Model',
*,
include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
update: 'typing.Dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Model'
Returns a copy of the model.
Deprecated
This method is now deprecated; use model_copy
instead.
If you need include
or exclude
, use:
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
include | None | Optional set or mapping specifying which fields to include in the copied model. | None |
exclude | None | Optional set or mapping specifying which fields to exclude in the copied model. | None |
update | None | Optional dictionary of field-value pairs to override field values in the copied model. | None |
deep | None | If True, the values of fields that are Pydantic models will be deep-copied. | None |
Returns:
Type | Description |
---|---|
None | A copy of the model with included, excluded and updated fields as specified. |
dict¶
def dict(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'typing.Dict[str, Any]'
json¶
def json(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'typing.Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
model_copy¶
def model_copy(
self: 'Model',
*,
update: 'dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Model'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update | None | Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. |
None |
deep | None | Set to True to make a deep copy of the model. |
None |
Returns:
Type | Description |
---|---|
None | New model instance. |
model_dump¶
def model_dump(
self,
*,
mode: "typing_extensions.Literal[('json', 'python')] | str" = 'python',
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'dict[str, Any] | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal[('none', 'warn', 'error')]" = True,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
mode | None | The mode in which to_python should run.If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. |
None |
include | None | A set of fields to include in the output. | None |
exclude | None | A set of fields to exclude from the output. | None |
context | None | Additional context to pass to the serializer. | None |
by_alias | None | Whether to use the field's alias in the dictionary key if defined. | None |
exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
exclude_none | None | Whether to exclude fields that have a value of None . |
None |
round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError ][pydantic_core.PydanticSerializationError]. |
None |
serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
Type | Description |
---|---|
None | A dictionary representation of the model. |
model_dump_json¶
def model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'dict[str, Any] | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal[('none', 'warn', 'error')]" = True,
serialize_as_any: 'bool' = False
) -> 'str'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's to_json
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
indent | None | Indentation to use in the JSON output. If None is passed, the output will be compact. | None |
include | None | Field(s) to include in the JSON output. | None |
exclude | None | Field(s) to exclude from the JSON output. | None |
context | None | Additional context to pass to the serializer. | None |
by_alias | None | Whether to serialize using field aliases. | None |
exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
exclude_none | None | Whether to exclude fields that have a value of None . |
None |
round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError ][pydantic_core.PydanticSerializationError]. |
None |
serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
Type | Description |
---|---|
None | A JSON string representation of the model. |
model_post_init¶
def model_post_init(
self,
_BaseModel__context: 'Any'
) -> 'None'
Override this method to perform additional initialization after __init__
and model_construct
.
This is useful if you want to do some validation that requires the entire model to be initialized.
Bounds¶
class Bounds(
/,
**data: 'Any'
)
Dataset Bounding box
Ancestors (in MRO)¶
- rio_tiler.models.RioTilerBaseModel
- pydantic.main.BaseModel
Descendants¶
- rio_tiler.models.SpatialInfo
Class variables¶
model_computed_fields
model_config
model_fields
Static methods¶
construct¶
def construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Model'
from_orm¶
def from_orm(
obj: 'Any'
) -> 'Model'
model_construct¶
def model_construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Model'
Creates a new instance of the Model
class with validated data.
Creates a new model setting __dict__
and __pydantic_fields_set__
from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Note
model_construct()
generally respects the model_config.extra
setting on the provided model.
That is, if model_config.extra == 'allow'
, then all extra passed values are added to the model instance's __dict__
and __pydantic_extra__
fields. If model_config.extra == 'ignore'
(the default), then all extra passed values are ignored.
Because no validation is performed with a call to model_construct()
, having model_config.extra == 'forbid'
does not result in
an error if extra values are passed, but they will be ignored.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
_fields_set | None | The set of field names accepted for the Model instance. | None |
values | None | Trusted or pre-validated data dictionary. | None |
Returns:
Type | Description |
---|---|
None | A new instance of the Model class with validated data. |
model_json_schema¶
def model_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
Generates a JSON schema for a model class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
by_alias | None | Whether to use attribute aliases or not. | None |
ref_template | None | The reference template. | None |
schema_generator | None | To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema with your desired modifications |
None |
mode | None | The mode in which to generate the schema. | None |
Returns:
Type | Description |
---|---|
None | The JSON schema for the given model class. |
model_parametrized_name¶
def model_parametrized_name(
params: 'tuple[type[Any], ...]'
) -> 'str'
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
params | None | Tuple of types of the class. Given a generic classModel with 2 type variables and a concrete model Model[str, int] ,the value (str, int) would be passed to params . |
None |
Returns:
Type | Description |
---|---|
None | String representing the new class where params are passed to cls as type variables. |
Raises:
Type | Description |
---|---|
TypeError | Raised when trying to generate concrete names for non-generic models. |
model_rebuild¶
def model_rebuild(
*,
force: 'bool' = False,
raise_errors: 'bool' = True,
_parent_namespace_depth: 'int' = 2,
_types_namespace: 'dict[str, Any] | None' = None
) -> 'bool | None'
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
force | None | Whether to force the rebuilding of the model schema, defaults to False . |
None |
raise_errors | None | Whether to raise errors, defaults to True . |
None |
_parent_namespace_depth | None | The depth level of the parent namespace, defaults to 2. | None |
_types_namespace | None | The types namespace, defaults to None . |
None |
Returns:
Type | Description |
---|---|
None | Returns None if the schema is already "complete" and rebuilding was not required.If rebuilding was required, returns True if rebuilding was successful, otherwise False . |
model_validate¶
def model_validate(
obj: 'Any',
*,
strict: 'bool | None' = None,
from_attributes: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Validate a pydantic model instance.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj | None | The object to validate. | None |
strict | None | Whether to enforce types strictly. | None |
from_attributes | None | Whether to extract data from object attributes. | None |
context | None | Additional context to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated model instance. |
Raises:
Type | Description |
---|---|
ValidationError | If the object could not be validated. |
model_validate_json¶
def model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Usage docs: docs.pydantic.dev/2.7/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
json_data | None | The JSON data to validate. | None |
strict | None | Whether to enforce types strictly. | None |
context | None | Extra variables to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated Pydantic model. |
Raises:
Type | Description |
---|---|
ValueError | If json_data is not a JSON string. |
model_validate_strings¶
def model_validate_strings(
obj: 'Any',
*,
strict: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Validate the given object contains string data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj | None | The object contains string data to validate. | None |
strict | None | Whether to enforce types strictly. | None |
context | None | Extra variables to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated Pydantic model. |
parse_file¶
def parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Model'
parse_obj¶
def parse_obj(
obj: 'Any'
) -> 'Model'
parse_raw¶
def parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Model'
schema¶
def schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}'
) -> 'typing.Dict[str, Any]'
schema_json¶
def schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
update_forward_refs¶
def update_forward_refs(
**localns: 'Any'
) -> 'None'
validate¶
def validate(
value: 'Any'
) -> 'Model'
Instance variables¶
model_extra
Get extra fields set during validation.
model_fields_set
Returns the set of fields that have been explicitly set on this model instance.
Methods¶
copy¶
def copy(
self: 'Model',
*,
include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
update: 'typing.Dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Model'
Returns a copy of the model.
Deprecated
This method is now deprecated; use model_copy
instead.
If you need include
or exclude
, use:
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
include | None | Optional set or mapping specifying which fields to include in the copied model. | None |
exclude | None | Optional set or mapping specifying which fields to exclude in the copied model. | None |
update | None | Optional dictionary of field-value pairs to override field values in the copied model. | None |
deep | None | If True, the values of fields that are Pydantic models will be deep-copied. | None |
Returns:
Type | Description |
---|---|
None | A copy of the model with included, excluded and updated fields as specified. |
dict¶
def dict(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'typing.Dict[str, Any]'
json¶
def json(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'typing.Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
model_copy¶
def model_copy(
self: 'Model',
*,
update: 'dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Model'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update | None | Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. |
None |
deep | None | Set to True to make a deep copy of the model. |
None |
Returns:
Type | Description |
---|---|
None | New model instance. |
model_dump¶
def model_dump(
self,
*,
mode: "typing_extensions.Literal[('json', 'python')] | str" = 'python',
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'dict[str, Any] | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal[('none', 'warn', 'error')]" = True,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
mode | None | The mode in which to_python should run.If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. |
None |
include | None | A set of fields to include in the output. | None |
exclude | None | A set of fields to exclude from the output. | None |
context | None | Additional context to pass to the serializer. | None |
by_alias | None | Whether to use the field's alias in the dictionary key if defined. | None |
exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
exclude_none | None | Whether to exclude fields that have a value of None . |
None |
round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError ][pydantic_core.PydanticSerializationError]. |
None |
serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
Type | Description |
---|---|
None | A dictionary representation of the model. |
model_dump_json¶
def model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'dict[str, Any] | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal[('none', 'warn', 'error')]" = True,
serialize_as_any: 'bool' = False
) -> 'str'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's to_json
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
indent | None | Indentation to use in the JSON output. If None is passed, the output will be compact. | None |
include | None | Field(s) to include in the JSON output. | None |
exclude | None | Field(s) to exclude from the JSON output. | None |
context | None | Additional context to pass to the serializer. | None |
by_alias | None | Whether to serialize using field aliases. | None |
exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
exclude_none | None | Whether to exclude fields that have a value of None . |
None |
round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError ][pydantic_core.PydanticSerializationError]. |
None |
serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
Type | Description |
---|---|
None | A JSON string representation of the model. |
model_post_init¶
def model_post_init(
self,
_BaseModel__context: 'Any'
) -> 'None'
Override this method to perform additional initialization after __init__
and model_construct
.
This is useful if you want to do some validation that requires the entire model to be initialized.
ImageData¶
class ImageData(
array: numpy.ndarray,
cutline_mask: Union[numpy.ndarray, NoneType] = None,
*,
assets: Union[List, NoneType] = None,
bounds=None,
crs: Union[rasterio.crs.CRS, NoneType] = None,
metadata: Union[Dict, NoneType] = NOTHING,
band_names: List[str] = NOTHING,
dataset_statistics: Union[Sequence[Tuple[float, float]], NoneType] = None
)
Image Data class.
Attributes¶
Name | Type | Description | Default |
---|---|---|---|
array | numpy.ma.MaskedArray | image values. | None |
assets | list | list of assets used to construct the data values. | None |
bounds | BoundingBox | bounding box of the data. | None |
crs | rasterio.crs.CRS | Coordinates Reference System of the bounds. | None |
metadata | dict | Additional metadata. Defaults to {} . |
{} |
band_names | list | name of each band. Defaults to ["1", "2", "3"] for 3 bands image. |
["1", "2", "3"] for 3 bands image |
dataset_statistics | list | dataset statistics [(min, max), (min, max)] |
None |
Static methods¶
create_from_list¶
def create_from_list(
data: Sequence[ForwardRef('ImageData')]
) -> 'ImageData'
Create ImageData from a sequence of ImageData objects.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data | sequence | sequence of ImageData. | None |
from_array¶
def from_array(
arr: numpy.ndarray
) -> 'ImageData'
Create ImageData from a numpy array.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
arr | numpy.ndarray | Numpy array or Numpy masked array. | None |
from_bytes¶
def from_bytes(
data: bytes
) -> 'ImageData'
Create ImageData from bytes.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data | bytes | raster dataset as bytes. | None |
Instance variables¶
count
Number of band.
data
Return data part of the masked array.
height
Height of the data array.
mask
Return Mask in form of rasterio dataset mask.
transform
Returns the affine transform.
width
Width of the data array.
Methods¶
apply_color_formula¶
def apply_color_formula(
self,
color_formula: Union[str, NoneType]
)
Apply color-operations formula in place.
apply_colormap¶
def apply_colormap(
self,
colormap: Union[Dict[int, Tuple[int, int, int, int]], Sequence[Tuple[Tuple[Union[float, int], Union[float, int]], Tuple[int, int, int, int]]]]
) -> 'ImageData'
Apply colormap to the image data.
apply_expression¶
def apply_expression(
self,
expression: str
) -> 'ImageData'
Apply expression to the image data.
as_masked¶
def as_masked(
self
) -> numpy.ma.core.MaskedArray
return a numpy masked array.
clip¶
def clip(
self,
bbox: Tuple[float, float, float, float]
) -> 'ImageData'
Clip data and mask to a bbox.
data_as_image¶
def data_as_image(
self
) -> numpy.ndarray
Return the data array reshaped into an image processing/visualization software friendly order.
(bands, rows, columns) -> (rows, columns, bands).
get_coverage_array¶
def get_coverage_array(
self,
shape: Dict,
shape_crs: rasterio.crs.CRS = CRS.from_epsg(4326),
cover_scale: int = 10
) -> numpy.ndarray[typing.Any, numpy.dtype[numpy.floating]]
Post-process image data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
in_range | tuple | input min/max bounds value to rescale from. | None |
out_dtype | str | output datatype after rescaling. Defaults to uint8 . |
uint8 |
color_formula | str | color-ops formula (see: vincentsarago/color-ops). | None |
cover_scale | int | Scale used when generating coverage estimates of each raster cell by vector feature. Coverage is generated by rasterizing the feature at a finer resolution than the raster then using a summation to aggregate to the raster resolution and dividing by the square of cover_scale to get coverage value for each cell. Increasing cover_scale will increase the accuracy of coverage values; three orders magnitude finer resolution (cover_scale=1000) is usually enough to get coverage estimates with <1% error in individual edge cells coverage estimates, though much smaller values (e.g., cover_scale=10) are often sufficient (<10% error) and require less memory. |
None |
Returns:
Type | Description |
---|---|
numpy.array | percent coverage. |
post_process¶
def post_process(
self,
in_range: Union[Sequence[Tuple[Union[float, int], Union[float, int]]], NoneType] = None,
out_dtype: Union[str, numpy.number] = 'uint8',
color_formula: Union[str, NoneType] = None,
**kwargs: Any
) -> 'ImageData'
Post-process image data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
in_range | tuple | input min/max bounds value to rescale from. | None |
out_dtype | str | output datatype after rescaling. Defaults to uint8 . |
uint8 |
color_formula | str | color-ops formula (see: vincentsarago/color-ops). | None |
kwargs | optional | keyword arguments to forward to rio_tiler.utils.linear_rescale . |
None |
Returns:
Type | Description |
---|---|
ImageData | new ImageData object with the updated data. |
render¶
def render(
self,
add_mask: bool = True,
img_format: str = 'PNG',
colormap: Union[Dict[int, Tuple[int, int, int, int]], Sequence[Tuple[Tuple[Union[float, int], Union[float, int]], Tuple[int, int, int, int]]], NoneType] = None,
**kwargs
) -> bytes
Render data to image blob.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
add_mask | bool | add mask to output image. Defaults to True . |
True |
img_format | str | output image format. Defaults to PNG . |
PNG |
colormap | dict or sequence | RGBA Color Table dictionary or sequence. | None |
kwargs | optional | keyword arguments to forward to rio_tiler.utils.render . |
None |
Returns:
Type | Description |
---|---|
bytes | image. |
rescale¶
def rescale(
self,
in_range: Sequence[Tuple[Union[float, int], Union[float, int]]],
out_range: Sequence[Tuple[Union[float, int], Union[float, int]]] = ((0, 255),),
out_dtype: Union[str, numpy.number] = 'uint8'
)
Rescale data in place.
resize¶
def resize(
self,
height: int,
width: int,
resampling_method: Literal['nearest', 'bilinear', 'cubic', 'cubic_spline', 'lanczos', 'average', 'mode', 'gauss', 'rms'] = 'nearest'
) -> 'ImageData'
Resize data and mask.
statistics¶
def statistics(
self,
categorical: bool = False,
categories: Union[List[float], NoneType] = None,
percentiles: Union[List[int], NoneType] = None,
hist_options: Union[Dict, NoneType] = None,
coverage: Union[numpy.ndarray, NoneType] = None
) -> Dict[str, rio_tiler.models.BandStatistics]
Return statistics from ImageData.
Info¶
class Info(
/,
**data: 'Any'
)
Dataset Info.
Ancestors (in MRO)¶
- rio_tiler.models.SpatialInfo
- rio_tiler.models.Bounds
- rio_tiler.models.RioTilerBaseModel
- pydantic.main.BaseModel
Class variables¶
model_computed_fields
model_config
model_fields
Static methods¶
construct¶
def construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Model'
from_orm¶
def from_orm(
obj: 'Any'
) -> 'Model'
model_construct¶
def model_construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Model'
Creates a new instance of the Model
class with validated data.
Creates a new model setting __dict__
and __pydantic_fields_set__
from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Note
model_construct()
generally respects the model_config.extra
setting on the provided model.
That is, if model_config.extra == 'allow'
, then all extra passed values are added to the model instance's __dict__
and __pydantic_extra__
fields. If model_config.extra == 'ignore'
(the default), then all extra passed values are ignored.
Because no validation is performed with a call to model_construct()
, having model_config.extra == 'forbid'
does not result in
an error if extra values are passed, but they will be ignored.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
_fields_set | None | The set of field names accepted for the Model instance. | None |
values | None | Trusted or pre-validated data dictionary. | None |
Returns:
Type | Description |
---|---|
None | A new instance of the Model class with validated data. |
model_json_schema¶
def model_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
Generates a JSON schema for a model class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
by_alias | None | Whether to use attribute aliases or not. | None |
ref_template | None | The reference template. | None |
schema_generator | None | To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema with your desired modifications |
None |
mode | None | The mode in which to generate the schema. | None |
Returns:
Type | Description |
---|---|
None | The JSON schema for the given model class. |
model_parametrized_name¶
def model_parametrized_name(
params: 'tuple[type[Any], ...]'
) -> 'str'
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
params | None | Tuple of types of the class. Given a generic classModel with 2 type variables and a concrete model Model[str, int] ,the value (str, int) would be passed to params . |
None |
Returns:
Type | Description |
---|---|
None | String representing the new class where params are passed to cls as type variables. |
Raises:
Type | Description |
---|---|
TypeError | Raised when trying to generate concrete names for non-generic models. |
model_rebuild¶
def model_rebuild(
*,
force: 'bool' = False,
raise_errors: 'bool' = True,
_parent_namespace_depth: 'int' = 2,
_types_namespace: 'dict[str, Any] | None' = None
) -> 'bool | None'
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
force | None | Whether to force the rebuilding of the model schema, defaults to False . |
None |
raise_errors | None | Whether to raise errors, defaults to True . |
None |
_parent_namespace_depth | None | The depth level of the parent namespace, defaults to 2. | None |
_types_namespace | None | The types namespace, defaults to None . |
None |
Returns:
Type | Description |
---|---|
None | Returns None if the schema is already "complete" and rebuilding was not required.If rebuilding was required, returns True if rebuilding was successful, otherwise False . |
model_validate¶
def model_validate(
obj: 'Any',
*,
strict: 'bool | None' = None,
from_attributes: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Validate a pydantic model instance.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj | None | The object to validate. | None |
strict | None | Whether to enforce types strictly. | None |
from_attributes | None | Whether to extract data from object attributes. | None |
context | None | Additional context to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated model instance. |
Raises:
Type | Description |
---|---|
ValidationError | If the object could not be validated. |
model_validate_json¶
def model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Usage docs: docs.pydantic.dev/2.7/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
json_data | None | The JSON data to validate. | None |
strict | None | Whether to enforce types strictly. | None |
context | None | Extra variables to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated Pydantic model. |
Raises:
Type | Description |
---|---|
ValueError | If json_data is not a JSON string. |
model_validate_strings¶
def model_validate_strings(
obj: 'Any',
*,
strict: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Validate the given object contains string data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj | None | The object contains string data to validate. | None |
strict | None | Whether to enforce types strictly. | None |
context | None | Extra variables to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated Pydantic model. |
parse_file¶
def parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Model'
parse_obj¶
def parse_obj(
obj: 'Any'
) -> 'Model'
parse_raw¶
def parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Model'
schema¶
def schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}'
) -> 'typing.Dict[str, Any]'
schema_json¶
def schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
update_forward_refs¶
def update_forward_refs(
**localns: 'Any'
) -> 'None'
validate¶
def validate(
value: 'Any'
) -> 'Model'
Instance variables¶
model_extra
Get extra fields set during validation.
model_fields_set
Returns the set of fields that have been explicitly set on this model instance.
Methods¶
copy¶
def copy(
self: 'Model',
*,
include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
update: 'typing.Dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Model'
Returns a copy of the model.
Deprecated
This method is now deprecated; use model_copy
instead.
If you need include
or exclude
, use:
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
include | None | Optional set or mapping specifying which fields to include in the copied model. | None |
exclude | None | Optional set or mapping specifying which fields to exclude in the copied model. | None |
update | None | Optional dictionary of field-value pairs to override field values in the copied model. | None |
deep | None | If True, the values of fields that are Pydantic models will be deep-copied. | None |
Returns:
Type | Description |
---|---|
None | A copy of the model with included, excluded and updated fields as specified. |
dict¶
def dict(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'typing.Dict[str, Any]'
json¶
def json(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'typing.Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
model_copy¶
def model_copy(
self: 'Model',
*,
update: 'dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Model'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update | None | Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. |
None |
deep | None | Set to True to make a deep copy of the model. |
None |
Returns:
Type | Description |
---|---|
None | New model instance. |
model_dump¶
def model_dump(
self,
*,
mode: "typing_extensions.Literal[('json', 'python')] | str" = 'python',
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'dict[str, Any] | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal[('none', 'warn', 'error')]" = True,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
mode | None | The mode in which to_python should run.If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. |
None |
include | None | A set of fields to include in the output. | None |
exclude | None | A set of fields to exclude from the output. | None |
context | None | Additional context to pass to the serializer. | None |
by_alias | None | Whether to use the field's alias in the dictionary key if defined. | None |
exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
exclude_none | None | Whether to exclude fields that have a value of None . |
None |
round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError ][pydantic_core.PydanticSerializationError]. |
None |
serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
Type | Description |
---|---|
None | A dictionary representation of the model. |
model_dump_json¶
def model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'dict[str, Any] | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal[('none', 'warn', 'error')]" = True,
serialize_as_any: 'bool' = False
) -> 'str'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's to_json
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
indent | None | Indentation to use in the JSON output. If None is passed, the output will be compact. | None |
include | None | Field(s) to include in the JSON output. | None |
exclude | None | Field(s) to exclude from the JSON output. | None |
context | None | Additional context to pass to the serializer. | None |
by_alias | None | Whether to serialize using field aliases. | None |
exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
exclude_none | None | Whether to exclude fields that have a value of None . |
None |
round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError ][pydantic_core.PydanticSerializationError]. |
None |
serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
Type | Description |
---|---|
None | A JSON string representation of the model. |
model_post_init¶
def model_post_init(
self,
_BaseModel__context: 'Any'
) -> 'None'
Override this method to perform additional initialization after __init__
and model_construct
.
This is useful if you want to do some validation that requires the entire model to be initialized.
PointData¶
class PointData(
array: numpy.ndarray,
*,
band_names: List[str] = NOTHING,
coordinates: Union[Tuple[float, float], NoneType] = None,
crs: Union[rasterio.crs.CRS, NoneType] = None,
assets: Union[List, NoneType] = None,
metadata: Union[Dict, NoneType] = NOTHING
)
Point Data class.
Attributes¶
Name | Type | Description | Default |
---|---|---|---|
array | numpy.ma.MaskedArray | pixel values. | None |
band_names | list | name of each band. Defaults to ["1", "2", "3"] for 3 bands image. |
["1", "2", "3"] for 3 bands image |
coordinates | tuple | Point's coordinates. | None |
crs | rasterio.crs.CRS | Coordinates Reference System of the bounds. | None |
assets | list | list of assets used to construct the data values. | None |
metadata | dict | Additional metadata. Defaults to {} . |
{} |
Static methods¶
create_from_list¶
def create_from_list(
data: Sequence[ForwardRef('PointData')]
)
Create PointData from a sequence of PointsData objects.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data | sequence | sequence of PointData. | None |
Instance variables¶
count
Number of band.
data
Return data part of the masked array.
mask
Return Mask in form of rasterio dataset mask.
Methods¶
apply_expression¶
def apply_expression(
self,
expression: str
) -> 'PointData'
Apply expression to the image data.
as_masked¶
def as_masked(
self
) -> numpy.ma.core.MaskedArray
return a numpy masked array.
RioTilerBaseModel¶
class RioTilerBaseModel(
/,
**data: 'Any'
)
Provides dictionary access for pydantic models, for backwards compatability.
Ancestors (in MRO)¶
- pydantic.main.BaseModel
Descendants¶
- rio_tiler.models.Bounds
- rio_tiler.models.BandStatistics
Class variables¶
model_computed_fields
model_config
model_fields
Static methods¶
construct¶
def construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Model'
from_orm¶
def from_orm(
obj: 'Any'
) -> 'Model'
model_construct¶
def model_construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Model'
Creates a new instance of the Model
class with validated data.
Creates a new model setting __dict__
and __pydantic_fields_set__
from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Note
model_construct()
generally respects the model_config.extra
setting on the provided model.
That is, if model_config.extra == 'allow'
, then all extra passed values are added to the model instance's __dict__
and __pydantic_extra__
fields. If model_config.extra == 'ignore'
(the default), then all extra passed values are ignored.
Because no validation is performed with a call to model_construct()
, having model_config.extra == 'forbid'
does not result in
an error if extra values are passed, but they will be ignored.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
_fields_set | None | The set of field names accepted for the Model instance. | None |
values | None | Trusted or pre-validated data dictionary. | None |
Returns:
Type | Description |
---|---|
None | A new instance of the Model class with validated data. |
model_json_schema¶
def model_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
Generates a JSON schema for a model class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
by_alias | None | Whether to use attribute aliases or not. | None |
ref_template | None | The reference template. | None |
schema_generator | None | To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema with your desired modifications |
None |
mode | None | The mode in which to generate the schema. | None |
Returns:
Type | Description |
---|---|
None | The JSON schema for the given model class. |
model_parametrized_name¶
def model_parametrized_name(
params: 'tuple[type[Any], ...]'
) -> 'str'
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
params | None | Tuple of types of the class. Given a generic classModel with 2 type variables and a concrete model Model[str, int] ,the value (str, int) would be passed to params . |
None |
Returns:
Type | Description |
---|---|
None | String representing the new class where params are passed to cls as type variables. |
Raises:
Type | Description |
---|---|
TypeError | Raised when trying to generate concrete names for non-generic models. |
model_rebuild¶
def model_rebuild(
*,
force: 'bool' = False,
raise_errors: 'bool' = True,
_parent_namespace_depth: 'int' = 2,
_types_namespace: 'dict[str, Any] | None' = None
) -> 'bool | None'
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
force | None | Whether to force the rebuilding of the model schema, defaults to False . |
None |
raise_errors | None | Whether to raise errors, defaults to True . |
None |
_parent_namespace_depth | None | The depth level of the parent namespace, defaults to 2. | None |
_types_namespace | None | The types namespace, defaults to None . |
None |
Returns:
Type | Description |
---|---|
None | Returns None if the schema is already "complete" and rebuilding was not required.If rebuilding was required, returns True if rebuilding was successful, otherwise False . |
model_validate¶
def model_validate(
obj: 'Any',
*,
strict: 'bool | None' = None,
from_attributes: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Validate a pydantic model instance.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj | None | The object to validate. | None |
strict | None | Whether to enforce types strictly. | None |
from_attributes | None | Whether to extract data from object attributes. | None |
context | None | Additional context to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated model instance. |
Raises:
Type | Description |
---|---|
ValidationError | If the object could not be validated. |
model_validate_json¶
def model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Usage docs: docs.pydantic.dev/2.7/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
json_data | None | The JSON data to validate. | None |
strict | None | Whether to enforce types strictly. | None |
context | None | Extra variables to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated Pydantic model. |
Raises:
Type | Description |
---|---|
ValueError | If json_data is not a JSON string. |
model_validate_strings¶
def model_validate_strings(
obj: 'Any',
*,
strict: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Validate the given object contains string data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj | None | The object contains string data to validate. | None |
strict | None | Whether to enforce types strictly. | None |
context | None | Extra variables to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated Pydantic model. |
parse_file¶
def parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Model'
parse_obj¶
def parse_obj(
obj: 'Any'
) -> 'Model'
parse_raw¶
def parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Model'
schema¶
def schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}'
) -> 'typing.Dict[str, Any]'
schema_json¶
def schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
update_forward_refs¶
def update_forward_refs(
**localns: 'Any'
) -> 'None'
validate¶
def validate(
value: 'Any'
) -> 'Model'
Instance variables¶
model_extra
Get extra fields set during validation.
model_fields_set
Returns the set of fields that have been explicitly set on this model instance.
Methods¶
copy¶
def copy(
self: 'Model',
*,
include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
update: 'typing.Dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Model'
Returns a copy of the model.
Deprecated
This method is now deprecated; use model_copy
instead.
If you need include
or exclude
, use:
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
include | None | Optional set or mapping specifying which fields to include in the copied model. | None |
exclude | None | Optional set or mapping specifying which fields to exclude in the copied model. | None |
update | None | Optional dictionary of field-value pairs to override field values in the copied model. | None |
deep | None | If True, the values of fields that are Pydantic models will be deep-copied. | None |
Returns:
Type | Description |
---|---|
None | A copy of the model with included, excluded and updated fields as specified. |
dict¶
def dict(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'typing.Dict[str, Any]'
json¶
def json(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'typing.Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
model_copy¶
def model_copy(
self: 'Model',
*,
update: 'dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Model'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update | None | Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. |
None |
deep | None | Set to True to make a deep copy of the model. |
None |
Returns:
Type | Description |
---|---|
None | New model instance. |
model_dump¶
def model_dump(
self,
*,
mode: "typing_extensions.Literal[('json', 'python')] | str" = 'python',
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'dict[str, Any] | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal[('none', 'warn', 'error')]" = True,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
mode | None | The mode in which to_python should run.If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. |
None |
include | None | A set of fields to include in the output. | None |
exclude | None | A set of fields to exclude from the output. | None |
context | None | Additional context to pass to the serializer. | None |
by_alias | None | Whether to use the field's alias in the dictionary key if defined. | None |
exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
exclude_none | None | Whether to exclude fields that have a value of None . |
None |
round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError ][pydantic_core.PydanticSerializationError]. |
None |
serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
Type | Description |
---|---|
None | A dictionary representation of the model. |
model_dump_json¶
def model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'dict[str, Any] | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal[('none', 'warn', 'error')]" = True,
serialize_as_any: 'bool' = False
) -> 'str'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's to_json
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
indent | None | Indentation to use in the JSON output. If None is passed, the output will be compact. | None |
include | None | Field(s) to include in the JSON output. | None |
exclude | None | Field(s) to exclude from the JSON output. | None |
context | None | Additional context to pass to the serializer. | None |
by_alias | None | Whether to serialize using field aliases. | None |
exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
exclude_none | None | Whether to exclude fields that have a value of None . |
None |
round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError ][pydantic_core.PydanticSerializationError]. |
None |
serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
Type | Description |
---|---|
None | A JSON string representation of the model. |
model_post_init¶
def model_post_init(
self,
_BaseModel__context: 'Any'
) -> 'None'
Override this method to perform additional initialization after __init__
and model_construct
.
This is useful if you want to do some validation that requires the entire model to be initialized.
SpatialInfo¶
class SpatialInfo(
/,
**data: 'Any'
)
Dataset SpatialInfo
Ancestors (in MRO)¶
- rio_tiler.models.Bounds
- rio_tiler.models.RioTilerBaseModel
- pydantic.main.BaseModel
Descendants¶
- rio_tiler.models.Info
Class variables¶
model_computed_fields
model_config
model_fields
Static methods¶
construct¶
def construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Model'
from_orm¶
def from_orm(
obj: 'Any'
) -> 'Model'
model_construct¶
def model_construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Model'
Creates a new instance of the Model
class with validated data.
Creates a new model setting __dict__
and __pydantic_fields_set__
from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Note
model_construct()
generally respects the model_config.extra
setting on the provided model.
That is, if model_config.extra == 'allow'
, then all extra passed values are added to the model instance's __dict__
and __pydantic_extra__
fields. If model_config.extra == 'ignore'
(the default), then all extra passed values are ignored.
Because no validation is performed with a call to model_construct()
, having model_config.extra == 'forbid'
does not result in
an error if extra values are passed, but they will be ignored.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
_fields_set | None | The set of field names accepted for the Model instance. | None |
values | None | Trusted or pre-validated data dictionary. | None |
Returns:
Type | Description |
---|---|
None | A new instance of the Model class with validated data. |
model_json_schema¶
def model_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
Generates a JSON schema for a model class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
by_alias | None | Whether to use attribute aliases or not. | None |
ref_template | None | The reference template. | None |
schema_generator | None | To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema with your desired modifications |
None |
mode | None | The mode in which to generate the schema. | None |
Returns:
Type | Description |
---|---|
None | The JSON schema for the given model class. |
model_parametrized_name¶
def model_parametrized_name(
params: 'tuple[type[Any], ...]'
) -> 'str'
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
params | None | Tuple of types of the class. Given a generic classModel with 2 type variables and a concrete model Model[str, int] ,the value (str, int) would be passed to params . |
None |
Returns:
Type | Description |
---|---|
None | String representing the new class where params are passed to cls as type variables. |
Raises:
Type | Description |
---|---|
TypeError | Raised when trying to generate concrete names for non-generic models. |
model_rebuild¶
def model_rebuild(
*,
force: 'bool' = False,
raise_errors: 'bool' = True,
_parent_namespace_depth: 'int' = 2,
_types_namespace: 'dict[str, Any] | None' = None
) -> 'bool | None'
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
force | None | Whether to force the rebuilding of the model schema, defaults to False . |
None |
raise_errors | None | Whether to raise errors, defaults to True . |
None |
_parent_namespace_depth | None | The depth level of the parent namespace, defaults to 2. | None |
_types_namespace | None | The types namespace, defaults to None . |
None |
Returns:
Type | Description |
---|---|
None | Returns None if the schema is already "complete" and rebuilding was not required.If rebuilding was required, returns True if rebuilding was successful, otherwise False . |
model_validate¶
def model_validate(
obj: 'Any',
*,
strict: 'bool | None' = None,
from_attributes: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Validate a pydantic model instance.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj | None | The object to validate. | None |
strict | None | Whether to enforce types strictly. | None |
from_attributes | None | Whether to extract data from object attributes. | None |
context | None | Additional context to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated model instance. |
Raises:
Type | Description |
---|---|
ValidationError | If the object could not be validated. |
model_validate_json¶
def model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Usage docs: docs.pydantic.dev/2.7/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
json_data | None | The JSON data to validate. | None |
strict | None | Whether to enforce types strictly. | None |
context | None | Extra variables to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated Pydantic model. |
Raises:
Type | Description |
---|---|
ValueError | If json_data is not a JSON string. |
model_validate_strings¶
def model_validate_strings(
obj: 'Any',
*,
strict: 'bool | None' = None,
context: 'dict[str, Any] | None' = None
) -> 'Model'
Validate the given object contains string data against the Pydantic model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
obj | None | The object contains string data to validate. | None |
strict | None | Whether to enforce types strictly. | None |
context | None | Extra variables to pass to the validator. | None |
Returns:
Type | Description |
---|---|
None | The validated Pydantic model. |
parse_file¶
def parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Model'
parse_obj¶
def parse_obj(
obj: 'Any'
) -> 'Model'
parse_raw¶
def parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Model'
schema¶
def schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}'
) -> 'typing.Dict[str, Any]'
schema_json¶
def schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
update_forward_refs¶
def update_forward_refs(
**localns: 'Any'
) -> 'None'
validate¶
def validate(
value: 'Any'
) -> 'Model'
Instance variables¶
model_extra
Get extra fields set during validation.
model_fields_set
Returns the set of fields that have been explicitly set on this model instance.
Methods¶
copy¶
def copy(
self: 'Model',
*,
include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
update: 'typing.Dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Model'
Returns a copy of the model.
Deprecated
This method is now deprecated; use model_copy
instead.
If you need include
or exclude
, use:
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
include | None | Optional set or mapping specifying which fields to include in the copied model. | None |
exclude | None | Optional set or mapping specifying which fields to exclude in the copied model. | None |
update | None | Optional dictionary of field-value pairs to override field values in the copied model. | None |
deep | None | If True, the values of fields that are Pydantic models will be deep-copied. | None |
Returns:
Type | Description |
---|---|
None | A copy of the model with included, excluded and updated fields as specified. |
dict¶
def dict(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'typing.Dict[str, Any]'
json¶
def json(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'typing.Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
model_copy¶
def model_copy(
self: 'Model',
*,
update: 'dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Model'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update | None | Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. |
None |
deep | None | Set to True to make a deep copy of the model. |
None |
Returns:
Type | Description |
---|---|
None | New model instance. |
model_dump¶
def model_dump(
self,
*,
mode: "typing_extensions.Literal[('json', 'python')] | str" = 'python',
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'dict[str, Any] | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal[('none', 'warn', 'error')]" = True,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
mode | None | The mode in which to_python should run.If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. |
None |
include | None | A set of fields to include in the output. | None |
exclude | None | A set of fields to exclude from the output. | None |
context | None | Additional context to pass to the serializer. | None |
by_alias | None | Whether to use the field's alias in the dictionary key if defined. | None |
exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
exclude_none | None | Whether to exclude fields that have a value of None . |
None |
round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError ][pydantic_core.PydanticSerializationError]. |
None |
serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
Type | Description |
---|---|
None | A dictionary representation of the model. |
model_dump_json¶
def model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'dict[str, Any] | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal[('none', 'warn', 'error')]" = True,
serialize_as_any: 'bool' = False
) -> 'str'
Usage docs: docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's to_json
method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
indent | None | Indentation to use in the JSON output. If None is passed, the output will be compact. | None |
include | None | Field(s) to include in the JSON output. | None |
exclude | None | Field(s) to exclude from the JSON output. | None |
context | None | Additional context to pass to the serializer. | None |
by_alias | None | Whether to serialize using field aliases. | None |
exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
exclude_none | None | Whether to exclude fields that have a value of None . |
None |
round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError ][pydantic_core.PydanticSerializationError]. |
None |
serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
Type | Description |
---|---|
None | A JSON string representation of the model. |
model_post_init¶
def model_post_init(
self,
_BaseModel__context: 'Any'
) -> 'None'
Override this method to perform additional initialization after __init__
and model_construct
.
This is useful if you want to do some validation that requires the entire model to be initialized.