Dimension

class dynode.config.dimension.Dimension(*, name: Annotated[str, BeforeValidator(func=_verify_name, json_schema_input_type=PydanticUndefined)], bins: List[Bin])

Bases: BaseModel

A dimension of an compartment.

__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

Attributes

idx	Dimension idxs for indexing the bins within this dimension.
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
name
bins

name: Annotated[str, BeforeValidator(func=_verify_name, json_schema_input_type=PydanticUndefined)]

bins: List[Bin]

property idx

Dimension idxs for indexing the bins within this dimension.

classmethod _check_bins_same_type(bins) → Self

Assert all bins are of same type and bins is not empty.

classmethod _check_bin_names_unique(bins: list[Bin]) → list[Bin]

classmethod _validate_discretized_int_bins_sorted(bins: list[Bin]) → list[Bin]

Assert that DiscretizedPositiveIntBin do not overlap and sorts them lowest to highest.

classmethod _validate_no_gaps_discretized_int_bins(bins: list[Bin]) → list[Bin]

Validate that dimensions of DiscretizedPositiveIntBin have no gaps.

_abc_impl = <_abc._abc_data object>

model_config: ClassVar[ConfigDict] = {}

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