AFL.double_agent.PipelineOp module#

class AFL.double_agent.PipelineOp.PipelineOp(name: str | None | List[str] = None, input_variable: str | None | List[str] = None, output_variable: str | None | List[str] = None, input_prefix: str | None | List[str] = None, output_prefix: str | None | List[str] = None)#

Bases: ABC

Abstract base class for data processors. All operations in AFL.double_agent should inherit PipelineOpBase.

Parameters:

  • name (Optional[str] | List[str]) – The name to use when added to a Pipeline. This name is used when calling Pipeline.search()

  • input_variable (Optional[str] | List[str]) – The name of the xarray.Dataset data variable to extract from the input dataset

  • output_variable (Optional[str] | List[str]) – The name of the variable to be inserted into the xarray.Dataset by this PipelineOp

  • input_prefix (Optional[str] | List[str]) – Prefix for input variables when using pattern matching

  • output_prefix (Optional[str] | List[str]) – Prefix for output variables when using pattern matching

add_to_dataset(dataset, copy_dataset=True)#

Adds (xarray) data in output dictionary to provided xarray dataset

add_to_tiled(tiled_data)#

Adds data in output dictionary to provided tiled catalogue

abstract calculate(dataset: Dataset) → Self#
copy() → Self#
plot(sample_dim: str = 'sample', **mpl_kwargs) → Figure#

Plots the output of the PipelineOp.

This method attempts to guess how to plot the data produced by the operation.