pyanno4rt.datahub

Datahub module.

This module aims to provide methods and classes to centralize and distribute information units within each treatment plan.

Overview

Classes
`Datahub`	Central data storage and management hub class.

Classes

class pyanno4rt.datahub.Datahub(*args)[source]

Central data storage and management hub class.

This class provides a singleton datahub for centralizing the information units generated across one or multiple treatment plans, e.g. dictionaries with CT and segmentation data, to efficiently manage and distribute them.

Parameters:: *args (tuple) – Tuple with optional (non-keyworded) parameters. The element args[0] refers to the treatment plan label, while args[1] is a Logger object and args[2] is an InputChecker object. Only required for (re-)instantiating a datahub.

instances

Dictionary with pairs of treatment plan labels and associated Datahub objects.

Type:: dict

label

Label of the current active treatment plan instance.

Type:: None or str

input_checker

The object used to approve the input dictionaries.

Type:: None or object of class InputChecker

logger

The object used to print and store logging messages.

Type:: None or object of class Logger

computed_tomography

Dictionary with information on the CT images.

Type:: None or dict

segmentation

Dictionary with information on the segmented structures.

Type:: None or dict

plan_configuration

Dictionary with information on the plan configuration.

Type:: None or dict

dose_information

Dictionary with information on the dose grid.

Type:: None or dict

optimization

Dictionary with information on the fluence optimization.

Type:: None or dict

datasets

Dictionary with pairs of model labels and associated external datasets used for model fitting. Each dataset is a dictionary itself, holding information on the raw data and the features/labels.

Type:: None or dict

feature_maps

Dictionary with pairs of model labels and associated feature maps. Each feature map holds links between the features from the respective dataset, the segments, and the definitions from the feature catalogue.

Type:: None or dict

model_instances

Dictionary with pairs of model labels and associated model instances, i.e., the prediction model, the model configuration dictionary, and the model hyperparameters obtained from hyperparameter tuning.

Type:: None or dict

model_inspections

Dictionary with pairs of model labels and associated model inspectors. Each inspector holds information on the inspection measures calculated.

Type:: None or dict

model_evaluations

Dictionary with pairs of model labels and associated model evaluators. Each evaluator holds information on the evaluation measures calculated.

Type:: None or dict

dose_histogram

Dictionary with information on the cumulative or differential dose-volume histogram for each segmented structure.

Type:: None or dict

dosimetrics

Dictionary with information on the dosimetrics for each segmented structure.

Type:: None or dict