Processing Model
Contents
Processing Model¶
A TOAST workflow usually consists of loading or simulating different
types of data into one or more [Observation]{.title-ref} objects (See
section observations{.interpreted-text role=”ref”}) inside an overall
[Data]{.title-ref} instance. The workflow classes that populate and
manipulate these Data objects are called “Operators”.
Operators¶
An operator class inherits from the [toast.ops.Operator]{.title-ref} class, and has several key characteristics:
Each Operator is configured used the traitlets package . This allows easy configuration of an operator at construction time or afterwards, and allows modular documentation of parameters, parameter checking, and construction from parameter files.
Operators can be called repeatedly on subsets of data (both observations and detectors) with the [exec()]{.title-ref} method.
Operators have a [finalize()]{.title-ref} method that performs any final calculations or other steps after all timestream data has been processed.
toast.ops.Operator
For details about specific operators see the relevant sections
(simulation-operators{.interpreted-text role=”ref”},
reduction-operators{.interpreted-text role=”ref”},
utility-operators{.interpreted-text role=”ref”}).
Pipeline Operator¶
Although one can run a single operator on the whole dataset before running the next operator, a common processing paradigm is to run a sequence of operations on one detector at a time or on sets of detectors.