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v1

lesion_calculation record, v1 — canonical aliases.

Consumers and the record registry import from here and never touch the inner module names, mirroring steps/*/v1/__init__.py.

Module​

Submodules​

Classes​

ORM​

class ORM(**kwargs):

SQLAlchemy model for the lesion_calculation cache table.

One row per (task_hash, file_id): the per-group lesion metrics computed for a scan from pathology model predictions — area, volume, per-lesion detail and fovea-relative geometry. Every one of those is a deterministic function of the scan, the pathology output and the config (which folds into task_hash) — NOT of the project — so project_id is provenance only and excluded from the key. The metrics live in the schemaless metrics_json blob (LesionMetrics.groups, one nested record per configured group), so adding a metric needs no schema version.

A simple constructor that allows initialization from kwargs.

Sets attributes on the constructed instance using the names and values in kwargs.

Only keys that are present as attributes of the instance's class are allowed. These could be, for example, any mapped columns or relationships.

Ancestors​

Variables​

  • error : Union[sqlalchemy.orm.attributes.InstrumentedAttribute[+_T_co], +_T_co]
  • file_id : Union[sqlalchemy.orm.attributes.InstrumentedAttribute[+_T_co], +_T_co]
  • last_accessed_at : Union[sqlalchemy.orm.attributes.InstrumentedAttribute[+_T_co], +_T_co]
  • metrics_json : Union[sqlalchemy.orm.attributes.InstrumentedAttribute[+_T_co], +_T_co]
  • processed_at : Union[sqlalchemy.orm.attributes.InstrumentedAttribute[+_T_co], +_T_co]
  • project_id : Union[sqlalchemy.orm.attributes.InstrumentedAttribute[+_T_co], +_T_co]
  • run_id : Union[sqlalchemy.orm.attributes.InstrumentedAttribute[+_T_co], +_T_co]
  • tags : Union[sqlalchemy.orm.attributes.InstrumentedAttribute[+_T_co], +_T_co]
  • task_hash : Union[sqlalchemy.orm.attributes.InstrumentedAttribute[+_T_co], +_T_co]

Record​

class Record(**data: Any):

Record of a single row from the lesion_calculation table.

Exactly one of metrics_json (successful calculation) or error (missing-data reason or calculation_error:...) is populated for a row.

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.

Variables​

  • static error : str | None
  • static file_id : str
  • static metrics_json : dict[str, typing.Any] | None
  • static model_config
  • static project_id : str | None
  • static run_id : str | None
  • static task_hash : str