Module rio_tiler.models

rio-tiler models.

Variables

dtype_ranges

Functions

rescale_image

def rescale_image(
    data: numpy.ndarray,
    mask: numpy.ndarray,
    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 image data.

to_3d

def to_3d(
    data: numpy.ndarray
) -> numpy.ndarray

Makes sure we have a 3D array.

to_coordsbbox

def to_coordsbbox(
    bbox
) -> Union[rasterio.coords.BoundingBox, NoneType]

Convert bbox to CoordsBbox nameTuple.

Classes

BandStatistics

class BandStatistics(
    __pydantic_self__,
    **data: Any
)

Band statistics

Ancestors (in MRO)

• rio_tiler.models.RioTilerBaseModel
• pydantic.main.BaseModel
• pydantic.utils.Representation

Class variables

Config

Static methods

construct

def construct(
    _fields_set: Union[ForwardRef('SetStr'), NoneType] = None,
    **values: Any
) -> 'Model'

Creates a new model setting dict and 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

from_orm

def from_orm(
    obj: Any
) -> 'Model'

parse_file

def parse_file(
    path: Union[str, pathlib.Path],
    *,
    content_type: 'unicode' = None,
    encoding: 'unicode' = 'utf8',
    proto: pydantic.parse.Protocol = None,
    allow_pickle: bool = False
) -> 'Model'

parse_obj

def parse_obj(
    obj: Any
) -> 'Model'

parse_raw

def parse_raw(
    b: Union[str, bytes],
    *,
    content_type: 'unicode' = None,
    encoding: 'unicode' = 'utf8',
    proto: pydantic.parse.Protocol = None,
    allow_pickle: bool = False
) -> 'Model'

schema

def schema(
    by_alias: bool = True,
    ref_template: 'unicode' = '#/definitions/{model}'
) -> 'DictStrAny'

schema_json

def schema_json(
    *,
    by_alias: bool = True,
    ref_template: 'unicode' = '#/definitions/{model}',
    **dumps_kwargs: Any
) -> 'unicode'

update_forward_refs

def update_forward_refs(
    **localns: Any
) -> None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

validate

def validate(
    value: Any
) -> 'Model'

Methods

copy

def copy(
    self: 'Model',
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    update: Union[ForwardRef('DictStrAny'), NoneType] = None,
    deep: bool = False
) -> 'Model'

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

Name	Type	Description	Default
include	None	fields to include in new model	None
exclude	None	fields to exclude from new model, as with values this takes precedence over include	None
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

dict

def dict(
    self,
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    by_alias: bool = False,
    skip_defaults: Union[bool, NoneType] = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False
) -> 'DictStrAny'

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

json

def json(
    self,
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    by_alias: bool = False,
    skip_defaults: Union[bool, NoneType] = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    encoder: Union[Callable[[Any], Any], NoneType] = None,
    models_as_dict: bool = True,
    **dumps_kwargs: Any
) -> 'unicode'

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

Bounds

class Bounds(
    __pydantic_self__,
    **data: Any
)

Dataset Bounding box

Ancestors (in MRO)

• rio_tiler.models.RioTilerBaseModel
• pydantic.main.BaseModel
• pydantic.utils.Representation

Descendants

• rio_tiler.models.SpatialInfo

Class variables

Config

Static methods

construct

def construct(
    _fields_set: Union[ForwardRef('SetStr'), NoneType] = None,
    **values: Any
) -> 'Model'

Creates a new model setting dict and 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

from_orm

def from_orm(
    obj: Any
) -> 'Model'

parse_file

def parse_file(
    path: Union[str, pathlib.Path],
    *,
    content_type: 'unicode' = None,
    encoding: 'unicode' = 'utf8',
    proto: pydantic.parse.Protocol = None,
    allow_pickle: bool = False
) -> 'Model'

parse_obj

def parse_obj(
    obj: Any
) -> 'Model'

parse_raw

def parse_raw(
    b: Union[str, bytes],
    *,
    content_type: 'unicode' = None,
    encoding: 'unicode' = 'utf8',
    proto: pydantic.parse.Protocol = None,
    allow_pickle: bool = False
) -> 'Model'

schema

def schema(
    by_alias: bool = True,
    ref_template: 'unicode' = '#/definitions/{model}'
) -> 'DictStrAny'

schema_json

def schema_json(
    *,
    by_alias: bool = True,
    ref_template: 'unicode' = '#/definitions/{model}',
    **dumps_kwargs: Any
) -> 'unicode'

update_forward_refs

def update_forward_refs(
    **localns: Any
) -> None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

validate

def validate(
    value: Any
) -> 'Model'

Methods

copy

def copy(
    self: 'Model',
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    update: Union[ForwardRef('DictStrAny'), NoneType] = None,
    deep: bool = False
) -> 'Model'

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

Name	Type	Description	Default
include	None	fields to include in new model	None
exclude	None	fields to exclude from new model, as with values this takes precedence over include	None
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

dict

def dict(
    self,
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    by_alias: bool = False,
    skip_defaults: Union[bool, NoneType] = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False
) -> 'DictStrAny'

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

json

def json(
    self,
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    by_alias: bool = False,
    skip_defaults: Union[bool, NoneType] = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    encoder: Union[Callable[[Any], Any], NoneType] = None,
    models_as_dict: bool = True,
    **dumps_kwargs: Any
) -> 'unicode'

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

ImageData

class ImageData(
    data: numpy.ndarray,
    mask: numpy.ndarray = NOTHING,
    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
data	numpy.ndarray	pixel values.	None
mask	numpy.ndarray	rasterio mask 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.

height

Height of the data array.

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).

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: rasterio.enums.Resampling = '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
) -> Dict[str, rio_tiler.models.BandStatistics]

Return statistics from ImageData.

Info

class Info(
    __pydantic_self__,
    **data: Any
)

Dataset Info.

Ancestors (in MRO)

• rio_tiler.models.SpatialInfo
• rio_tiler.models.Bounds
• rio_tiler.models.RioTilerBaseModel
• pydantic.main.BaseModel
• pydantic.utils.Representation

Class variables

Config

Static methods

construct

def construct(
    _fields_set: Union[ForwardRef('SetStr'), NoneType] = None,
    **values: Any
) -> 'Model'

Creates a new model setting dict and 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

from_orm

def from_orm(
    obj: Any
) -> 'Model'

parse_file

def parse_file(
    path: Union[str, pathlib.Path],
    *,
    content_type: 'unicode' = None,
    encoding: 'unicode' = 'utf8',
    proto: pydantic.parse.Protocol = None,
    allow_pickle: bool = False
) -> 'Model'

parse_obj

def parse_obj(
    obj: Any
) -> 'Model'

parse_raw

def parse_raw(
    b: Union[str, bytes],
    *,
    content_type: 'unicode' = None,
    encoding: 'unicode' = 'utf8',
    proto: pydantic.parse.Protocol = None,
    allow_pickle: bool = False
) -> 'Model'

schema

def schema(
    by_alias: bool = True,
    ref_template: 'unicode' = '#/definitions/{model}'
) -> 'DictStrAny'

schema_json

def schema_json(
    *,
    by_alias: bool = True,
    ref_template: 'unicode' = '#/definitions/{model}',
    **dumps_kwargs: Any
) -> 'unicode'

update_forward_refs

def update_forward_refs(
    **localns: Any
) -> None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

validate

def validate(
    value: Any
) -> 'Model'

Methods

copy

def copy(
    self: 'Model',
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    update: Union[ForwardRef('DictStrAny'), NoneType] = None,
    deep: bool = False
) -> 'Model'

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

Name	Type	Description	Default
include	None	fields to include in new model	None
exclude	None	fields to exclude from new model, as with values this takes precedence over include	None
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

dict

def dict(
    self,
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    by_alias: bool = False,
    skip_defaults: Union[bool, NoneType] = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False
) -> 'DictStrAny'

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

json

def json(
    self,
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    by_alias: bool = False,
    skip_defaults: Union[bool, NoneType] = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    encoder: Union[Callable[[Any], Any], NoneType] = None,
    models_as_dict: bool = True,
    **dumps_kwargs: Any
) -> 'unicode'

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

NodataTypes

class NodataTypes(
    /,
    *args,
    **kwargs
)

rio-tiler Nodata types.

Ancestors (in MRO)

• builtins.str
• enum.Enum

Class variables

Alpha

Empty

Internal

Mask

Nodata

name

value

PointData

class PointData(
    data: numpy.ndarray,
    mask: numpy.ndarray = NOTHING,
    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
data	numpy.ndarray	pixel values.	None
mask	numpy.ndarray	rasterio mask 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.

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(
    __pydantic_self__,
    **data: Any
)

Base Model for rio-tiler models.

Ancestors (in MRO)

• pydantic.main.BaseModel
• pydantic.utils.Representation

Descendants

• rio_tiler.models.Bounds
• rio_tiler.models.BandStatistics

Class variables

Config

Static methods

construct

def construct(
    _fields_set: Union[ForwardRef('SetStr'), NoneType] = None,
    **values: Any
) -> 'Model'

Creates a new model setting dict and 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

from_orm

def from_orm(
    obj: Any
) -> 'Model'

parse_file

def parse_file(
    path: Union[str, pathlib.Path],
    *,
    content_type: 'unicode' = None,
    encoding: 'unicode' = 'utf8',
    proto: pydantic.parse.Protocol = None,
    allow_pickle: bool = False
) -> 'Model'

parse_obj

def parse_obj(
    obj: Any
) -> 'Model'

parse_raw

def parse_raw(
    b: Union[str, bytes],
    *,
    content_type: 'unicode' = None,
    encoding: 'unicode' = 'utf8',
    proto: pydantic.parse.Protocol = None,
    allow_pickle: bool = False
) -> 'Model'

schema

def schema(
    by_alias: bool = True,
    ref_template: 'unicode' = '#/definitions/{model}'
) -> 'DictStrAny'

schema_json

def schema_json(
    *,
    by_alias: bool = True,
    ref_template: 'unicode' = '#/definitions/{model}',
    **dumps_kwargs: Any
) -> 'unicode'

update_forward_refs

def update_forward_refs(
    **localns: Any
) -> None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

validate

def validate(
    value: Any
) -> 'Model'

Methods

copy

def copy(
    self: 'Model',
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    update: Union[ForwardRef('DictStrAny'), NoneType] = None,
    deep: bool = False
) -> 'Model'

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

Name	Type	Description	Default
include	None	fields to include in new model	None
exclude	None	fields to exclude from new model, as with values this takes precedence over include	None
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

dict

def dict(
    self,
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    by_alias: bool = False,
    skip_defaults: Union[bool, NoneType] = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False
) -> 'DictStrAny'

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

json

def json(
    self,
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    by_alias: bool = False,
    skip_defaults: Union[bool, NoneType] = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    encoder: Union[Callable[[Any], Any], NoneType] = None,
    models_as_dict: bool = True,
    **dumps_kwargs: Any
) -> 'unicode'

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

SpatialInfo

class SpatialInfo(
    __pydantic_self__,
    **data: Any
)

Dataset SpatialInfo

Ancestors (in MRO)

• rio_tiler.models.Bounds
• rio_tiler.models.RioTilerBaseModel
• pydantic.main.BaseModel
• pydantic.utils.Representation

Descendants

• rio_tiler.models.Info

Class variables

Config

Static methods

construct

def construct(
    _fields_set: Union[ForwardRef('SetStr'), NoneType] = None,
    **values: Any
) -> 'Model'

Creates a new model setting dict and 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

from_orm

def from_orm(
    obj: Any
) -> 'Model'

parse_file

def parse_file(
    path: Union[str, pathlib.Path],
    *,
    content_type: 'unicode' = None,
    encoding: 'unicode' = 'utf8',
    proto: pydantic.parse.Protocol = None,
    allow_pickle: bool = False
) -> 'Model'

parse_obj

def parse_obj(
    obj: Any
) -> 'Model'

parse_raw

def parse_raw(
    b: Union[str, bytes],
    *,
    content_type: 'unicode' = None,
    encoding: 'unicode' = 'utf8',
    proto: pydantic.parse.Protocol = None,
    allow_pickle: bool = False
) -> 'Model'

schema

def schema(
    by_alias: bool = True,
    ref_template: 'unicode' = '#/definitions/{model}'
) -> 'DictStrAny'

schema_json

def schema_json(
    *,
    by_alias: bool = True,
    ref_template: 'unicode' = '#/definitions/{model}',
    **dumps_kwargs: Any
) -> 'unicode'

update_forward_refs

def update_forward_refs(
    **localns: Any
) -> None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

validate

def validate(
    value: Any
) -> 'Model'

Methods

copy

def copy(
    self: 'Model',
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    update: Union[ForwardRef('DictStrAny'), NoneType] = None,
    deep: bool = False
) -> 'Model'

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

Name	Type	Description	Default
include	None	fields to include in new model	None
exclude	None	fields to exclude from new model, as with values this takes precedence over include	None
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

dict

def dict(
    self,
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    by_alias: bool = False,
    skip_defaults: Union[bool, NoneType] = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False
) -> 'DictStrAny'

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

json

def json(
    self,
    *,
    include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None,
    by_alias: bool = False,
    skip_defaults: Union[bool, NoneType] = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    encoder: Union[Callable[[Any], Any], NoneType] = None,
    models_as_dict: bool = True,
    **dumps_kwargs: Any
) -> 'unicode'

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().