gdsfactory.technology.DerivedLayer

class gdsfactory.technology.DerivedLayer(*, sizings_xoffsets: Sequence[int] = (0,), sizings_yoffsets: Sequence[int] = (0,), sizings_modes: Sequence[int] = (2,), layer1: DerivedLayer | LogicalLayer, layer2: DerivedLayer | LogicalLayer, operation: Literal['and', '&', 'or', '|', 'xor', '^', 'not', '-'])

Physical “derived layer”, resulting from a combination of GDS design layers. Can be used by renderers and simulators.

Overloads operators for simpler expressions.

input_layer1

primary layer comprising the derived layer. Can be a GDS design layer (kf.kcell.LayerEnum , tuple[int, int]), or another derived layer.

input_layer2

secondary layer comprising the derived layer. Can be a GDS design layer (kf.kcell.LayerEnum , tuple[int, int]), or another derived layer.

operation

operation to perform between layer1 and layer2. One of “and”, “or”, “xor”, or “not” or associated symbols.

Type:

Literal[‘and’, ‘&’, ‘or’, ‘|’, ‘xor’, ‘^’, ‘not’, ‘-’]

__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.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_orm(obj)
get_shapes(component)	Return the shapes of the component argument corresponding to this layer.
get_symbol()
json(*[, include, exclude, by_alias, ...])
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/2.10/concepts/serialization/#model_copy
model_dump(*[, mode, include, exclude, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump
model_dump_json(*[, indent, include, ...])	Usage docs: https://docs.pydantic.dev/2.10/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/2.10/concepts/json/#json-parsing
model_validate_strings(obj, *[, strict, context])	Validate the given object with 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])
sized(xoffset[, yoffset, mode])	Accumulates a list of sizing operations for the layer by the provided offset (in dbu).
update_forward_refs(**localns)
validate(value)

Attributes

keyword_to_symbol
model_computed_fields
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
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
symbol_to_keyword
layer1
layer2
operation
sizings_xoffsets
sizings_yoffsets
sizings_modes