Continuing and repeating a study

For a study with synthetic variants and a saved hybrid plan, continue directly with materialization:

from hybrid_sample_generator.configuration.root import load_config_file
from hybrid_sample_generator.pipeline.hybrid_data_generator import HybridDataGenerator

config = load_config_file("results/study-01/configuration.json")
generator = HybridDataGenerator(config)
generator.materialize_hybrid_samples()

To build a new plan from existing synthetic variants, call generator.plan_hybrid_samples() before materialization. To regenerate synthetic variants with a saved generator, set config.training.trial_selection to "best", "last", or a concrete nonnegative trial ID and call generator.load_generator() followed by generator.generate_synthetic_anomalies().

Repeating a phase has the following effects:

Phase	Effect on existing results
ingest_dataset()	Replaces all originals and removes real/synthetic anomalies, hybrids, placements and matching-cache entries.
extract_anomalies()	Replaces real anomalies and removes synthetic anomalies, hybrids, placements and matching-cache entries.
generate_synthetic_anomalies()	Replaces synthetic variants and removes hybrids and placements; the real-ROI matching cache remains available.
plan_hybrid_samples()	Replaces all hybrid and placement records, including generated statuses and their artifact references; retains and updates the matching cache.
materialize_hybrid_samples()	Processes every stored hybrid, including already generated or failed ones, and rewrites its generated outputs.

These resets remove database records; old array files can remain on disk without repository references. Repeating a phase is not an incremental append or an automatic skip of completed work. After changing generated data, rerun evaluation to replace its previous CSV results.