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#!/usr/bin/env python3
"""Create task-clustered confidence intervals for the P7 auto-zero study."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
KEY_CONDITIONS = (
"ideal_sample_hold",
"sample_noise_1",
"sample_gain_0.99",
"sample_gain_1.01",
"pedestal_0.1",
"pedestal_0.25",
"refresh_every_4",
"refresh_every_8",
"combined_mild",
"combined_strong",
"overclamp_plus_combined_mild",
)
def task_means(records: list[dict], value_key: str) -> np.ndarray:
task_indices = sorted({record["task_index"] for record in records})
return np.asarray([
np.mean([
record[value_key] for record in records
if record["task_index"] == task_index
])
for task_index in task_indices
])
def interval(
values: np.ndarray, bootstrap_indices: np.ndarray
) -> tuple[float, float]:
replicates = np.mean(values[bootstrap_indices], axis=1)
low, high = np.quantile(replicates, (0.025, 0.975))
return float(low), float(high)
def reference_by_condition(reference: dict, method: str) -> list[dict]:
return [{
"task_index": record["task_index"],
"device_seed": record["device_seed"],
"classification_error": (
record["methods"][method]["classification_error"]),
"zero_error": (
record["methods"][method]["classification_error"] == 0.0),
} for record in reference["records"]]
def paired_differences(
condition_records: list[dict], reference_records: list[dict]
) -> tuple[np.ndarray, np.ndarray]:
lookup = {
(record["task_index"], record["device_seed"]): record
for record in reference_records
}
error_records = []
zero_records = []
for record in condition_records:
reference = lookup[(record["task_index"], record["device_seed"])]
error_records.append({
"task_index": record["task_index"],
"difference": (
record["classification_error"]
- reference["classification_error"]),
})
zero_records.append({
"task_index": record["task_index"],
"difference": float(record["zero_error"])
- float(reference["zero_error"]),
})
return (
task_means(error_records, "difference"),
task_means(zero_records, "difference"),
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
"--input", type=Path,
default=Path(
"results/physical_bias/p7_grid_autozero_robustness.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/p7_grid_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_hardware_autozero_p7_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()
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