src.toolbox.steps.base_step#

This module defines the base class for pipeline steps and configurations.

Attributes#

REGISTERED_STEPS

Registry of explicitly registered step classes.

Classes#

Base class for pipeline steps with config-mirroring support.

Functions#

register_step(cls)

Decorator to mark a step class for inclusion in the pipeline.

Module Contents#

src.toolbox.steps.base_step.REGISTERED_STEPS[source]#: Registry of explicitly registered step classes.

src.toolbox.steps.base_step.register_step(cls)[source]#: Decorator to mark a step class for inclusion in the pipeline.

class src.toolbox.steps.base_step.BaseStep(name, parameters=None, diagnostics=False, context=None)[source]#

Bases: toolbox.utils.config_mirror.ConfigMirrorMixin

Base class for pipeline steps with config-mirroring support. Every concrete subclass (registered via @register_step) inherits this.

name[source]#

parameters[source]#

diagnostics = False[source]#

context[source]#

logger[source]#

abstract run()[source]#: To be implemented by subclasses.

generate_diagnostics()[source]#: Hook for diagnostics (optional).

log(message)[source]#: Log an info-level message with step name prefix.

log_warn(message, warning_type=UserWarning)[source]#: Log a warning-level message with step name prefix.

check_data()[source]#: Check for data in context for transformer steps.

update_parameters(**kwargs)[source]#: Update parameter values both in attributes and in private store. Example:

self.update_parameters(file_path=’newfile.nc’, add_meta=False)

generate_config()[source]#: Return this step’s config dict (suitable for saving to YAML).

save_config(path: str | None = None)[source]#: Save this step’s config to YAML (for standalone debugging).