pycsamt.pipeline._base#

Base class for pyCSAMT pipeline objects.

PipelineBase is the common ancestor for Pipeline (and any future pipeline variant). It provides three groups of functionality that are domain-specific to the pipeline subsystem and would not fit naturally into PyCSAMTObject (which targets lightweight config/style objects).

Why not inherit PyCSAMTObject?#

PyCSAMTObject gives generic __repr__, to_dict, and summary built for dataclass-style config holders. Pipeline needs a specialized scikit-learn-style repr and its own serialization contract (to_yaml, to_json, to_py). Inheriting from two incompatible repr contracts creates confusion; a clean separation is better.

Groups provided#

Registry access (static / class methods) Explore what steps are available without constructing a pipeline.
Export (instance methods) to_py(path) — new format that produces an editable Python script (YAML and JSON are on Pipeline itself).
Scaffold (class method) scaffold(path, fmt, preset, name) — generate a ready-to-edit starter config in YAML, JSON, or Python.

Classes

PipelineBase()

Base class for pyCSAMT pipeline objects.

class pycsamt.pipeline._base.PipelineBase[source]#

Bases: object

Base class for pyCSAMT pipeline objects.

Inherit from this to gain registry introspection, to_py export, and scaffold template generation. Pipeline inherits this.

static available_steps(category=None)[source]#

Return all registered StepSpec objects.

Parameters:: category (str | None) – When supplied, restrict to steps in that category (e.g. "noise_removal", "frequency").
Return type:: list

Examples

>>> Pipeline.available_steps()
>>> Pipeline.available_steps("static_shift")

static available_categories()[source]#

Return a sorted list of step category names.

Examples

>>> Pipeline.available_categories()
['dimensionality', 'frequency', 'noise_removal', ...]

Return type:: list[str]

static step_info(code_or_name)[source]#

Return a formatted info block for a single step.

Parameters:: code_or_name (str) – Registry code ("NR001") or snake-case name ("notch_powerline").
Return type:: str

Examples

>>> print(Pipeline.step_info("NR001"))
>>> print(Pipeline.step_info("correct_ss_ama"))

classmethod catalogue(category=None)[source]#

Return a full formatted catalogue of all available steps.

Parameters:: category (str | None) – Restrict output to one category, or None for all.
Return type:: str

Examples

>>> print(Pipeline.catalogue())
>>> print(Pipeline.catalogue("tensor"))

to_py(path=None)[source]#

Serialize this pipeline to a Python config script.

Produces a pipeline_config dict in a clean, readable .py file that can be edited and reloaded with from_py(). Steps are grouped by category and commented with their human-readable labels.

Parameters:: path (str | Path | None) – If given, write the output to this file path.
Returns:: The generated Python source as a string (always returned, whether or not path is provided).
Return type:: str

Examples

>>> src = pipe.to_py()
>>> print(src)
>>> pipe.to_py("config/my_workflow.py")

classmethod scaffold(path=None, *, fmt='yaml', preset=None, name='my_workflow', outdir='pipe_results')[source]#

Generate a ready-to-edit starter pipeline config.

The scaffold contains: - Active steps from preset (or a sensible default set) - All other available steps commented out with descriptions

Parameters:

path (str | Path | None) – Write the output to this file. Extension is inferred from fmt if the path has none.
fmt (str) – Output format: "yaml" (default), "json", or "py".
preset (str | None) – Name of a built-in preset to use as the active step set (e.g. "basic_qc", "full_processing"). When None a minimal basic_qc-style set is used.
name (str) – Pipeline name written into the config.
outdir (str) – Default output directory written into the config.

Returns:

The generated config content as a string.

Return type:

str

Examples

>>> print(Pipeline.scaffold())
>>> Pipeline.scaffold("config/starter.yaml", preset="full_processing")
>>> Pipeline.scaffold("config/starter.py",   fmt="py")