vipr_reflectometry.reflectorch.postprocess.data_collectors package

Submodules

vipr_reflectometry.reflectorch.postprocess.data_collectors.batch_visualizations module

Batch visualization creation for multi-spectrum reflectometry analysis.

This module provides functions for creating visualizations that analyze trends and patterns across multiple spectra in a batch.

vipr_reflectometry.reflectorch.postprocess.data_collectors.batch_visualizations.analyze_batch_statistics(spectra_data)

Analyze statistics across all spectra in a batch.

vipr_reflectometry.reflectorch.postprocess.data_collectors.batch_visualizations.create_parameter_comparison_diagrams(app, spectra_data, parameter_names)

Create multiple trend diagrams for different parameters.

vipr_reflectometry.reflectorch.postprocess.data_collectors.batch_visualizations.create_parameter_trend_diagram(app, spectra_data, parameter_name)

Create a scatter plot showing parameter trend across multiple spectra.

vipr_reflectometry.reflectorch.postprocess.data_collectors.collector module

Main ReflectorchDataCollector orchestration class.

This module contains the main ReflectorchDataCollector class that coordinates all reflectorch-specific data collection activities and integrates with the generic VIPR DataCollector system.

class vipr_reflectometry.reflectorch.postprocess.data_collectors.collector.ReflectorchDataCollector(app)

Bases: object

Batch-aware UI data collector for reflectorch results.

collect_prediction_results(app, data=None, result=None)

Collect prediction results for UI visualization - unified batch handling.

vipr_reflectometry.reflectorch.postprocess.data_collectors.data_extraction module

Data extraction utilities for reflectorch prediction results.

This module provides pure functions for extracting and transforming data from prediction results and DataSet objects in a reflectorch-specific context.

vipr_reflectometry.reflectorch.postprocess.data_collectors.data_extraction.detect_batch_size(prediction_data: dict, app) → int

Smart batch size detection from multiple sources.

vipr_reflectometry.reflectorch.postprocess.data_collectors.data_extraction.extract_first_errors(dataset)

Extract first spectrum’s error bars from batch DataSet.

vipr_reflectometry.reflectorch.postprocess.data_collectors.data_extraction.extract_first_result(result_array)

Extract first result from batch prediction results.

vipr_reflectometry.reflectorch.postprocess.data_collectors.data_extraction.extract_first_spectrum(dataset)

Extract first spectrum from batch DataSet for visualization.

vipr_reflectometry.reflectorch.postprocess.data_collectors.data_extraction.extract_prediction_for_spectrum(prediction_data: dict, spectrum_index: int) → dict

Extract prediction data for one spectrum - handles batch format.

vipr_reflectometry.reflectorch.postprocess.data_collectors.data_extraction.extract_spectrum_dataset(dataset: DataSet, spectrum_index: int) → DataSet

Extract single spectrum DataSet - handles both single and batch.

vipr_reflectometry.reflectorch.postprocess.data_collectors.data_extraction.get_parameter_with_unit(param_name)

Add appropriate units to parameter names based on parameter type.

vipr_reflectometry.reflectorch.postprocess.data_collectors.data_extraction.get_timestamp() → str

Get current timestamp.

vipr_reflectometry.reflectorch.postprocess.data_collectors.spectrum_visualizations module

Spectrum visualization creation for individual reflectometry spectra.

This module provides the SpectrumVisualizer class that creates tables, diagrams, and images for individual spectrum data using the generic DataCollector builders.

class vipr_reflectometry.reflectorch.postprocess.data_collectors.spectrum_visualizations.SpectrumVisualizer(app)

Bases: object

Creates visualizations for individual reflectometry spectra.

add_chi2_contributions_diagram_for_spectrum(dc: DataCollector, spectrum_dataset: DataSet, spectrum_prediction: dict, spectrum_index: int)

Add Chi² contributions diagram for single spectrum.

add_parameter_table_for_spectrum(dc: DataCollector, spectrum_prediction: dict, spectrum_index: int)

Add parameter table for single spectrum.

add_reflectivity_diagram_for_spectrum(dc: DataCollector, spectrum_dataset: DataSet, spectrum_prediction: dict, spectrum_index: int)

Add reflectivity curve diagram for single spectrum.

add_reflectivity_image_for_spectrum(dc: DataCollector, spectrum_dataset: DataSet, spectrum_prediction: dict, spectrum_index: int)

Add reflectivity image for single spectrum, with standalone plot script export.

add_residuals_diagram_for_spectrum(dc: DataCollector, spectrum_dataset: DataSet, spectrum_prediction: dict, spectrum_index: int)

Add residuals diagram for single spectrum.

Displays normalized residuals following reflectometry conventions: - If σ (uncertainty) available: Δ/σ = (y_exp - y_pred) / σ - If no σ available: Δ/y_exp = (y_exp - y_pred) / y_exp

add_sld_diagram_for_spectrum(dc: DataCollector, spectrum_prediction: dict, spectrum_index: int)

Add SLD profile diagram for single spectrum.

vipr_reflectometry.reflectorch.postprocess.data_collectors.statistics module

Statistical calculations for reflectorch goodness-of-fit metrics.

This module provides pure functions for calculating comprehensive statistical metrics including Chi², RSS, MSE, RMSE, and MAE for reflectometry fits.

vipr_reflectometry.reflectorch.postprocess.data_collectors.statistics.calculate_curve_statistics(experimental, predicted, error_bars, n_points)

Calculate all statistical metrics for a single curve.

vipr_reflectometry.reflectorch.postprocess.data_collectors.statistics.calculate_spectrum_goodness_of_fit_stats(experimental_curve, spectrum_prediction, error_bars)

Calculate goodness-of-fit statistics for single spectrum.

vipr_reflectometry.reflectorch.postprocess.data_collectors.streaming_handler module

Streaming trend handling for reflectometry real-time analysis.

This module provides functions for caching parameter values across streaming updates and creating trend visualizations over time.

vipr_reflectometry.reflectorch.postprocess.data_collectors.streaming_handler.cleanup_streaming_cache_if_needed(app, consumer_id)

Clean up streaming cache when a new scan is detected.

vipr_reflectometry.reflectorch.postprocess.data_collectors.streaming_handler.create_streaming_trend(app, cached_data, param_name, include_predicted=True, polished_fallback_to_predicted=True)

Create trend diagram from cached parameter data.

vipr_reflectometry.reflectorch.postprocess.data_collectors.streaming_handler.get_streaming_statistics(app, consumer_id)

Get statistics about cached streaming data.

Simple streaming trend handling using table data.

vipr_reflectometry.reflectorch.postprocess.data_collectors.streaming_handler.initialize_streaming_cache(app, consumer_id)

Initialize streaming cache for a new consumer.

Module contents

Reflectorch Data Collectors Package

This package provides a modular data collection system for reflectorch-specific UI visualization data. It extends the generic VIPR DataCollector with reflectometry-specific functionality.

class vipr_reflectometry.reflectorch.postprocess.data_collectors.ReflectorchDataCollector(app)

Bases: object

Batch-aware UI data collector for reflectorch results.

collect_prediction_results(app, data=None, result=None)

Collect prediction results for UI visualization - unified batch handling.