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()
.