Transformers Guide

This page explains how to use transformers in your project.

Overview

Transformers convert message payloads from one shape to another before model evaluation or interface publication.

All transformer modules follow the BaseTransformer contract and expose:

• handler(pv_name, value) to receive incoming struct data

• transform() to produce output values

Available Transformers

• SimpleTransformer

• CAImageTransfomer

• PassThroughTransformer

• CompoundTransformer

Transformer Configs

Module	Description	YAML reference
SimpleTransformer	Scalar/array formula transform from input symbols to output variables	See sample below
CAImageTransformer	Reconstruct image arrays from flattened channel + X + Y inputs	See sample below
PassThroughTransformer	Relabel and forward values without numeric transformation	See sample below
CompoundTransformer	Run multiple transformers in one module	See sample below

Metadata Propagation

Transformer outputs may be either plain values or structured payloads with value and additional fields:

• PassThroughTransformer preserves non-value fields (for example alarm, timestamp, metadata) when they are present in input structs.

• SimpleTransformer preserves non-value fields only for direct-symbol formulas (for example OUT = IN). Computed formulas emit value-only output.

• TransformerObserver forwards structured outputs unchanged and wraps plain outputs as {"value": ...}.

This makes direct pass-through paths suitable for carrying alarm payloads across transformer stages.

SimpleTransformer Sample Configuration

modules:
  input_transformer:
    name: "input_transformer"
    type: "transformer.SimpleTransformer"
    pub: "model_input"
    sub:
      - "system_input"
    module_args: None
    config:
      symbols:
        - "LUME:MLFLOW:TEST_B"
        - "LUME:MLFLOW:TEST_A"
      variables:
        x2:
          formula: "LUME:MLFLOW:TEST_B"
        x1:
          formula: "LUME:MLFLOW:TEST_A"

CAImageTransformer Sample Configuration

modules:
  image_transformer:
    name: "image_transformer"
    type: "transformer.CAImageTransformer"
    pub: "model_input"
    sub:
      - "update"
    module_args: None
    config:
      variables:
        img_1:
          img_ch: "MY_TEST_CA"
          img_x_ch: "MY_TEST_CA_X"
          img_y_ch: "MY_TEST_CA_Y"
        img_2:
          img_ch: "MY_TEST_C2"
          img_x_ch: "MY_TEST_CA_X2"
          img_y_ch: "MY_TEST_CA_Y2"

PassThroughTransformer Sample Configuration

modules:
  output_transformer:
    name: "output_transformer"
    type: "transformer.PassThroughTransformer"
    pub: "system_output"
    sub:
      - "model_output"
    module_args: None
    config:
      variables:
        LUME:MLFLOW:TEST_IMAGE: "y_img"

CompoundTransformer Sample Configuration

Caution

This module may be deprecated in the future because the pub-sub model can replace most compound-transformer use cases.

modules:
  compound_transformer:
    name: "compound_transformer"
    type: "transformer.CompoundTransformer"
    pub: "model_input"
    sub:
      - "update"
    module_args: None
    config:
      transformers:
        transformer_1:
          type: "SimpleTransformer"
          config:
            symbols:
              - "MY_TEST_A"
              - "MY_TEST_B"
            variables:
              x2:
                formula: "MY_TEST_A*2"
              x1:
                formula: "MY_TEST_B+MY_TEST_A"
        transformer_2:
          type: "CAImageTransformer"
          config:
            variables:
              img_1:
                img_ch: "MY_TEST_CA"
                img_x_ch: "MY_TEST_CA_X"
                img_y_ch: "MY_TEST_CA_Y"
              img_2:
                img_ch: "MY_TEST_C2"
                img_x_ch: "MY_TEST_CA_X2"
                img_y_ch: "MY_TEST_CA_Y2"