Base stage

class txpipe.base_stage.PipelineStage(*args: Any, **kwargs: Any)

Bases: ceci.

This is the base pipeline stage for TXPipe, it is based on the ceci pipeline software.

__annotations__ = {'__sphinx_decorator_args__': typing.Tuple[typing.Any, ...]}

__call__(*args: Any, **kwargs: Any) → Any

Call self as a function.

__contains__(key: str) → bool

__dict__ = mappingproxy({'__module__': 'txpipe.base_stage', '__doc__': '\n    This is the base pipeline stage for TXPipe, it is based on the ceci pipeline software. \n    ', 'name': 'Error', 'inputs': [], 'outputs': [], 'config_options': {}, 'run': <function PipelineStage.run>, 'memory_report': <function PipelineStage.memory_report>, 'combined_iterators': <function PipelineStage.combined_iterators>, 'gather_provenance': <function PipelineStage.gather_provenance>, 'open_output': <function PipelineStage.open_output>, '__orig_bases__': (ceci.PipelineStage,), '__annotations__': {'__sphinx_decorator_args__': typing.Tuple[typing.Any, ...]}})

__display_name__ = 'ceci.PipelineStage'

__getattr__(key: str) → sphinx.ext.autodoc.mock._MockObject

__getitem__(key: Any) → sphinx.ext.autodoc.mock._MockObject

__init__(*args: Any, **kwargs: Any) → None

__iter__() → Iterator

__len__() → int

__module__ = 'txpipe.base_stage'

__mro_entries__(bases: Tuple) → Tuple

static __new__(cls, *args: Any, **kwargs: Any) → Any

__orig_bases__ = (ceci.PipelineStage,)

__repr__() → str

Return repr(self).

__sphinx_decorator_args__: Tuple[Any, ...] = ()

__sphinx_mock__ = True

__weakref__

list of weak references to the object (if defined)

combined_iterators(rows, *inputs, parallel=True)

config_options = {}

gather_provenance()

inputs = []

memory_report(tag=None)

Print a report about memory currently available on the node the process is running on.

Parameters

tag (str) – Additional info to print in the output line. Default is empty.

name = 'Error'

open_output(tag, wrapper=False, **kwargs)

Find and open an output file with the given tag, in write mode.

For general files this will simply return a standard python file object.

For specialized file types like FITS or HDF5 it will return a more specific object - see the types.py file for more info.

This is an extended version of the parent class method which also saves configuration information. Putting this here right now for testing.

outputs = []

run()