gdsfactory.cross_section.Transition

gdsfactory.cross_section.Transition#

class gdsfactory.cross_section.Transition(*, sections: tuple[Section, ...] = None, components_along_path: tuple[ComponentAlongPath, ...] = None, radius: float | None = None, radius_min: float | None = None, bbox_layers: Iterable[tuple[int, int] | str] | None = None, bbox_offsets: tuple[float, ...] | None = None, cross_section1: CrossSection | str | dict[str, Any] | Callable[[...], CrossSection], cross_section2: CrossSection | str | dict[str, Any] | Callable[[...], CrossSection], width_type: Literal['sine', 'linear', 'parabolic'] | Callable = 'sine')[source]#

Waveguide information to extrude a path between two CrossSection.

cladding_layers follow path shape

Parameters:
  • cross_section1 – input cross_section.

  • cross_section2 – output cross_section.

  • width_type – sine or linear. Sets the type of width transition used if widths are different between the two input CrossSections.

__init__(**data: Any) None#

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

add_bbox(component[, top, bottom, right, left])

Add bounding box layers to a component.

append_sections(sections)

Append sections to the cross_section.

construct([_fields_set])

copy([width, layer, width_function, ...])

Returns copy of the cross_section with new parameters.

dict(*[, include, exclude, by_alias, ...])

from_orm(obj)

get_xmin_xmax()

Returns the min and max extent of the cross_section across all sections.

json(*[, include, exclude, by_alias, ...])

mirror()

Returns a mirrored copy of the cross_section.

model_construct([_fields_set])

Creates a new instance of the Model class with validated data.

model_copy(*[, update, deep])

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy

model_dump(*[, mode, include, exclude, ...])

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump

model_dump_json(*[, indent, include, ...])

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json

model_json_schema([by_alias, ref_template, ...])

Generates a JSON schema for a model class.

model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

model_post_init(_BaseModel__context)

Override this method to perform additional initialization after __init__ and model_construct.

model_rebuild(*[, force, raise_errors, ...])

Try to rebuild the pydantic-core schema for the model.

model_validate(obj, *[, strict, ...])

Validate a pydantic model instance.

model_validate_json(json_data, *[, strict, ...])

Usage docs: https://docs.pydantic.dev/2.7/concepts/json/#json-parsing

model_validate_strings(obj, *[, strict, context])

Validate the given object contains string data against the Pydantic model.

parse_file(path, *[, content_type, ...])

parse_obj(obj)

parse_raw(b, *[, content_type, encoding, ...])

schema([by_alias, ref_template])

schema_json(*[, by_alias, ref_template])

serialize_width(width_type)

update_forward_refs(**localns)

validate(value)

validate_radius(radius[, error_type])

Attributes

layer

model_computed_fields

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_extra

Get extra fields set during validation.

model_fields

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

name

width

cross_section1

cross_section2

width_type

sections

components_along_path

radius

radius_min

bbox_layers

bbox_offsets