Data Selection and Filtering

The data selection and filtering components provide builders for operators that control which data is included in the fringe fitting process based on various selection criteria and quality metrics.

MHO_DataSelectionBuilder

Class	MHO_DataSelectionBuilder
Primary Functionality	Builds a data selection operator
Key Features	Constructs and initializes data selection operator Acts on fringe data for selection operations Uses MHO_Tokenizer for processing Inherits from MHO_OperatorBuilder

The MHO_DataSelectionBuilder class builds a data selection operator that controls which data is included in the fringe fitting process. This operator can apply various selection criteria to filter data based on time ranges, frequency ranges, polarization products, or other data characteristics.

The builder uses the MHO_Tokenizer for processing selection criteria and provides flexible data selection capabilities for fringe fitting operations.

MHO_MinWeightBuilder

Class	MHO_MinWeightBuilder
Primary Functionality	Builds MHO_MinWeight operator for weight-based filtering
Key Features	Constructs and adds MHO_MinWeight operator to toolbox Cuts data with weight less than threshold Implements quality-based data filtering Returns bool indicating successful construction

The MHO_MinWeightBuilder class builds a minimum weight operator that filters data based on weight thresholds. This operator removes data points that have weights below a specified minimum value, effectively filtering out low-quality or unreliable data from the fringe fitting process.

The builder provides boolean feedback on the success of the operator construction and is essential for maintaining data quality in VLBI processing pipelines.