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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. Behaves as if Config.extra = 'allow' was set since it adds all passed values

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 of
GenerateJsonSchema 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 class
Model 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/2.6/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/2.6/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: "Literal[('json', 'python')] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: 'bool' = True
) -> 'dict[str, Any]'

Usage docs: docs.pydantic.dev/2.6/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 list of fields to include in the output. None
exclude None A list of fields to exclude from the output. 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 Whether to log warnings when invalid fields are encountered. 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,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: 'bool' = True
) -> 'str'

Usage docs: docs.pydantic.dev/2.6/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
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 Whether to log warnings when invalid fields are encountered. 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. Behaves as if Config.extra = 'allow' was set since it adds all passed values

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 of
GenerateJsonSchema 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 class
Model 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/2.6/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/2.6/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: "Literal[('json', 'python')] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: 'bool' = True
) -> 'dict[str, Any]'

Usage docs: docs.pydantic.dev/2.6/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 list of fields to include in the output. None
exclude None A list of fields to exclude from the output. 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 Whether to log warnings when invalid fields are encountered. 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,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: 'bool' = True
) -> 'str'

Usage docs: docs.pydantic.dev/2.6/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
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 Whether to log warnings when invalid fields are encountered. 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. Behaves as if Config.extra = 'allow' was set since it adds all passed values

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 of
GenerateJsonSchema 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 class
Model 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/2.6/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/2.6/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: "Literal[('json', 'python')] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: 'bool' = True
) -> 'dict[str, Any]'

Usage docs: docs.pydantic.dev/2.6/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 list of fields to include in the output. None
exclude None A list of fields to exclude from the output. 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 Whether to log warnings when invalid fields are encountered. 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,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: 'bool' = True
) -> 'str'

Usage docs: docs.pydantic.dev/2.6/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
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 Whether to log warnings when invalid fields are encountered. 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. Behaves as if Config.extra = 'allow' was set since it adds all passed values

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 of
GenerateJsonSchema 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 class
Model 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/2.6/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/2.6/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: "Literal[('json', 'python')] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: 'bool' = True
) -> 'dict[str, Any]'

Usage docs: docs.pydantic.dev/2.6/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 list of fields to include in the output. None
exclude None A list of fields to exclude from the output. 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 Whether to log warnings when invalid fields are encountered. 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,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: 'bool' = True
) -> 'str'

Usage docs: docs.pydantic.dev/2.6/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
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 Whether to log warnings when invalid fields are encountered. 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. Behaves as if Config.extra = 'allow' was set since it adds all passed values

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 of
GenerateJsonSchema 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 class
Model 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/2.6/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/2.6/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: "Literal[('json', 'python')] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: 'bool' = True
) -> 'dict[str, Any]'

Usage docs: docs.pydantic.dev/2.6/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 list of fields to include in the output. None
exclude None A list of fields to exclude from the output. 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 Whether to log warnings when invalid fields are encountered. 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,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: 'bool' = True
) -> 'str'

Usage docs: docs.pydantic.dev/2.6/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
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 Whether to log warnings when invalid fields are encountered. 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.