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| -rw-r--r-- | experiments/summarize_correlated_autozero_p9.py | 124 |
1 files changed, 124 insertions, 0 deletions
diff --git a/experiments/summarize_correlated_autozero_p9.py b/experiments/summarize_correlated_autozero_p9.py new file mode 100644 index 0000000..7132f05 --- /dev/null +++ b/experiments/summarize_correlated_autozero_p9.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python3 +"""Task-clustered confidence intervals for correlated auto-zero sampling.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +import sys + +import numpy as np + +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +from summarize_physical_autozero_p7 import ( # noqa: E402 + interval, + paired_differences, + reference_by_condition, + task_means, +) + + +KEY_CONDITIONS = ( + "ideal_cds", + "common_pedestal_10", + "pedestal_mismatch_0.05", + "pedestal_mismatch_0.1", + "pedestal_mismatch_0.25", + "gain_mismatch_0.01", + "gain_mismatch_0.05", + "sample_noise_1", + "refresh_every_4", + "combined_mild", + "combined_mild_refresh4", + "combined_strong", + "overclamp_plus_combined_mild", +) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument( + "--input", type=Path, + default=Path( + "results/physical_bias/p9_grid_correlated_autozero.json")) + parser.add_argument( + "--reference", type=Path, + default=Path( + "results/physical_bias/p5_full_grid_bias_crossover.json")) + parser.add_argument( + "--output", type=Path, + default=Path( + "results/physical_bias/p9_grid_correlated_autozero_key_results.json")) + parser.add_argument("--bootstrap-replicates", type=int, default=20_000) + parser.add_argument("--seed", type=int, default=20260829) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + report = json.loads(args.input.read_text()) + reference = json.loads(args.reference.read_text()) + task_count = report["protocol"]["task_count"] + rng = np.random.default_rng(args.seed) + bootstrap_indices = rng.integers( + 0, task_count, size=(args.bootstrap_replicates, task_count)) + reference_records = { + method: reference_by_condition(reference, method) + for method in ("raw", "overclamp") + } + results = {} + for condition in KEY_CONDITIONS: + selected = [ + record for record in report["records"] + if record["condition"] == condition + ] + error_task_means = task_means(selected, "classification_error") + zero_task_means = task_means(selected, "zero_error") + comparisons = {} + for method in ("raw", "overclamp"): + error_difference, zero_difference = paired_differences( + selected, reference_records[method]) + comparisons[method] = { + "mean_classification_error_difference": float(np.mean( + error_difference)), + "classification_error_difference_95ci": interval( + error_difference, bootstrap_indices), + "zero_error_fraction_difference": float(np.mean( + zero_difference)), + "zero_error_fraction_difference_95ci": interval( + zero_difference, bootstrap_indices), + } + results[condition] = { + "trials": len(selected), + "task_clusters": task_count, + "mean_classification_error": float(np.mean(error_task_means)), + "mean_classification_error_95ci": interval( + error_task_means, bootstrap_indices), + "zero_error_fraction": float(np.mean(zero_task_means)), + "zero_error_fraction_95ci": interval( + zero_task_means, bootstrap_indices), + "comparisons": comparisons, + } + output = { + "analysis": "physical_grid_correlated_autozero_p9_key_statistics", + "source": str(args.input), + "reference": str(args.reference), + "bootstrap": { + "unit": "task; four device draws are averaged within each task", + "task_clusters": task_count, + "replicates": args.bootstrap_replicates, + "seed": args.seed, + "interval": "percentile 95%", + }, + "results": results, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(output, indent=2) + "\n") + print(json.dumps(results, indent=2)) + print(f"wrote {args.output}") + + +if __name__ == "__main__": + main() |