diff --git a/.gitignore b/.gitignore index 7137449..d855b3f 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,8 @@ # Generated build outputs. /out/ /soc/out/ + +# Python runtime caches and privacy-sensitive raw board captures. +__pycache__/ +*.py[cod] +/soc/software/pps_end_to_end_validation/hardware_logs/*.csv diff --git a/soc/software/pps_end_to_end_validation/README.md b/soc/software/pps_end_to_end_validation/README.md index 6a3a22e..638b686 100644 --- a/soc/software/pps_end_to_end_validation/README.md +++ b/soc/software/pps_end_to_end_validation/README.md @@ -93,11 +93,13 @@ Generated results are in `output/stability/`: capture, HDOP/VDOP/TDOP/GDOP, C/N0, pseudorange noise, clock sigma/drift, oscillator and timing-error inputs. -The real capture contains 989 GDOP values and 990 tracked-satellite values -(eight throughout), but no PPS phase/rate, ABS_SAMPLE, C/N0, TDOP/HDOP, -pseudorange residual, oscillator temperature, or trusted-edge/TIC samples. -The generated CSV labels its replacements as synthetic; it must not be read as -board telemetry. +The older fusion capture used by `stability_analysis.py` contains 989 GDOP +values and 990 tracked-satellite values (eight throughout), but no PPS +phase/rate, ABS_SAMPLE, C/N0, TDOP/HDOP, pseudorange residual, oscillator +temperature, or trusted-edge/TIC samples. The newer live-board workflow below +supplies many of those PPS status fields, while ABS_SAMPLE, REM_CODE_PHASE, +temperature, and trusted-edge timing remain unavailable. The stability CSV +labels replacements as synthetic; it must not be read as board telemetry. The current acquire, precision, and robust fixed-point poles are inside the unit circle at the 200 ms update rate, and the stress analysis observes no @@ -113,6 +115,53 @@ candidate. The generated report contains the current numerical comparison. The stability analysis does not alter loop constants. Production firmware now uses the current-RTL-compatible 180 ms epoch correction described below. +## Live board telemetry replay + +`hardware_replay.py` accepts the host-timestamped UART CSV produced by the +fusion capture utility. It extracts the firmware fields that actually drive +or describe PPS: raw/filtered WLS clock bias, innovation, Doppler datum drift, +TDOP/GDOP/HDOP/VDOP, clock sigma, C/N0, tracked/used satellites, velocity/FDE, +reference SVID, fractional capture correction, RTL phase residual, rate word, +integrator, lock/profile, pulse count, and commit sequence. + +Capture and replay from `soc/software`: + +```bash +python3 fusion/serial_capture.py \ + --port /dev/ttyUSB2 --baud 460800 --duration 300 \ + --output /tmp/genesys2_pps.csv + +python3 -m pps_end_to_end_validation.hardware_replay \ + --input /tmp/genesys2_pps.csv \ + --output-dir pps_end_to_end_validation/output/hardware_replay + +python3 -m unittest \ + pps_end_to_end_validation.test_hardware_replay +``` + +Raw UART files are ignored by Git because `[LOCATION]` records contain exact +coordinates. The replay output is normalized to PPS fields and deliberately +omits latitude/longitude. + +For an already-running board, replay imports the first logged locked +filter/PI snapshot. Starting the model from reset would erase the observed +integrator and create a false acquisition transient. When status is decimated, +commit sequence numbers expand the trace back to the nominal 200 ms cadence; +intermediate values are explicitly marked `interpolated=true`. + +The report separates three quantities that should not be conflated: + +- direct `hw_phase` statistics from the board; +- fixed-point PI reproduction and a telemetry-conditioned model replay; and +- inferred active phase obtained by integrating `rate_cmd` under a constant + oscillator assumption. + +Only the first two are directly constrained by firmware/RTL telemetry. The +third is useful for diagnosing slow wander but is not a trusted-generator +measurement. `hw_phase` itself is a 5 Hz commit residual, not the physical PPS +pin offset. A calibrated, common-clock TIC/scope capture is still required for +a maximum-20 ns output claim. + ## 20 ms diagnosis The current tracking wrapper shifts the current bit into `shift_reg_n`, while diff --git a/soc/software/pps_end_to_end_validation/hardware_replay.py b/soc/software/pps_end_to_end_validation/hardware_replay.py new file mode 100644 index 0000000..1e6048e --- /dev/null +++ b/soc/software/pps_end_to_end_validation/hardware_replay.py @@ -0,0 +1,1072 @@ +#!/usr/bin/env python3 +"""Parse live firmware PPS telemetry and replay it through the Python loop. + +The firmware status record is an observation of the already-running board. It +contains the WLS clock-bias path, Doppler datum predictor, quality selector and +RTL PI diagnostics, but it intentionally does not contain a trusted external +PPS edge. This module therefore reports two separate results: + +* direct statistics of the observed firmware/RTL state; and +* a telemetry-conditioned replay of the bit-accurate fixed-point loop model, + initialized from the captured locked PI/filter state. + +When ``GPS_PPS_STATUS_LOG_INTERVAL`` is greater than one, missing 200 ms solve +epochs are linearly interpolated. The replay reconstructs a self-consistent +reference trajectory from the logged filtered bias, PI residual and rate word. +That preserves measured low-frequency drift but is a lower bound for +unobserved high-frequency measurement noise. It is not an independent trusted +PPS measurement. +""" + +from __future__ import annotations + +import argparse +import csv +from dataclasses import asdict, dataclass, replace +import math +from pathlib import Path +import re +import statistics +from typing import Iterable + +from pps_python.adaptive_pps import ( + FixedPointAdaptivePps, + LoopProfile, + Observation, + PROFILES, + Profile, + Q32, + QualityMetrics, + SPEED_OF_LIGHT_MPS, + TRACKING_HZ, +) + + +UPDATE_PERIOD_S = 0.2 + + +PPS_RE = re.compile( + r"PPS profile=(?P\w+) mode=(?P\w+) " + r"locked=(?P\d+) tdop=(?P[-+\d.]+) " + r"gdop=(?P[-+\d.]+) " + r"clock_sigma=(?P[-+\d.]+) " + r"cn0=(?P[-+\d.]+)/(?P[-+\d.]+) " + r"innovation=(?P[-+\d.]+)m " + r"raw_bias=(?P[-+\d.]+)m " + r"filtered_bias=(?P[-+\d.]+)m " + r"clock_drift=(?P[-+\d.]+)mps " + r"bias_drift=(?P[-+\d.]+)mps " + r"predictor=(?P\d+) " + r"eskf_alpha=(?P[-+\d.]+) " + r"eskf_v=(?P[-+\d.]+)/" + r"(?P[-+\d.]+)/0x(?P[0-9a-fA-F]+) " + r"pps_v=(?P[-+\d.]+)/" + r"(?P[-+\d.]+)/0x(?P[0-9a-fA-F]+) " + r"ref_svid=(?P\d+) " + r"capture_corr=(?P[-+\d.]+)ns " + r"hw_phase=(?P[-+\d.]+)ns " + r"rate_cmd=(?P[-+\d.]+)ppm " + r"integ=(?P[-+\d.]+)ppm " + r"pulse=(?P\d+) seq=(?P\d+) " + r"status=0x(?P[0-9a-fA-F]+)" +) + +LOCATION_RE = re.compile( + r"source=gps .*?gdop=(?P[-+\d.]+) " + r"pdop=(?P[-+\d.]+) hdop=(?P[-+\d.]+) " + r"vdop=(?P[-+\d.]+) tdop=(?P[-+\d.]+) " + r"tracked=(?P\d+) used=(?P\d+) " + r"velocity_valid=(?P\d+) " + r"velocity_fde=(?P\d+) " + r"speed=(?P[-+\d.]+) " + r"velocity_rms=(?P[-+\d.]+)" +) + +TRACKED_RE = re.compile(r"tracked satellites=(?P\d+)") + + +@dataclass(frozen=True) +class GpsContext: + host_monotonic_s: float + tracked_satellites: int + used_satellites: int + hdop: float + vdop: float + velocity_valid: bool + velocity_fde_applied: bool + velocity_residual_rms_mps: float + + +@dataclass(frozen=True) +class FirmwarePpsSample: + host_monotonic_s: float + host_unix_s: float + time_s: float + profile: str + mode: str + locked: bool + tdop: float + gdop: float + hdop: float + vdop: float + clock_sigma_m: float + cn0_mean_dbhz: float + cn0_min_dbhz: float + tracked_satellites: int + used_satellites: int + innovation_m: float + raw_bias_m: float + filtered_bias_m: float + clock_drift_mps: float + bias_drift_mps: float + predictor_enabled: bool + eskf_alpha: float + eskf_speed_mps: float + eskf_velocity_rms_mps: float + eskf_fde_mask: int + pps_speed_mps: float + pps_velocity_rms_mps: float + pps_fde_mask: int + reference_svid: int + capture_correction_ns: float + hardware_phase_ns: float + rate_command_ppm: float + integrator_ppm: float + pulse_count: int + sequence: int + status: int + + +@dataclass(frozen=True) +class ObservedMetrics: + samples: int + duration_s: float + status_interval_median_s: float + sequence_step_median: float + locked_fraction: float + profile_transitions: int + reference_changes: int + hardware_phase_mean_ns: float + hardware_phase_centered_rms_ns: float + hardware_phase_centered_p95_ns: float + hardware_phase_centered_max_ns: float + hardware_phase_peak_to_peak_ns: float + hardware_phase_10s_wander_peak_to_peak_ns: float + sampled_phase_step_rms_ns: float + sampled_phase_step_p95_ns: float + sampled_phase_step_max_ns: float + hardware_phase_slope_ns_per_s: float + rate_command_median_ppm: float + rate_command_span_ppm: float + integrator_median_ppm: float + inferred_oscillator_ppm: float + inferred_active_phase_rms_ns: float + inferred_active_phase_peak_to_peak_ns: float + phase_rate_correlation: float + phase_innovation_correlation: float + pi_rate_reproduction_rms_ppb: float + pi_rate_reproduction_p95_ppb: float + pi_rate_reproduction_max_ppb: float + innovation_rms_m: float + innovation_p95_m: float + raw_bias_slope_mps: float + filtered_bias_slope_mps: float + tdop_median: float + gdop_median: float + clock_sigma_median_m: float + cn0_mean_median_dbhz: float + cn0_min_median_dbhz: float + tracked_median: float + used_median: float + pps_velocity_rms_median_mps: float + + +@dataclass(frozen=True) +class ReplayPoint: + time_s: float + source_sequence: int + interpolated: bool + raw_bias_m: float + observed_filtered_bias_m: float + modeled_filtered_bias_m: float + reconstructed_active_phase_ns: float + modeled_active_phase_ns: float + observed_hardware_phase_ns: float + modeled_loop_error_ns: float + observed_rate_command_ppm: float + modeled_rate_command_ppm: float + + +@dataclass(frozen=True) +class ReplayMetrics: + tuning: str + expanded_updates: int + interpolation_factor: float + first_lock_s: float + locked_fraction_after_warmup: float + unlocks: int + inferred_oscillator_ppm: float + modeled_edge_centered_rms_ns: float + modeled_edge_centered_p95_ns: float + modeled_edge_peak_to_peak_ns: float + modeled_period_rms_ns: float + modeled_period_p95_ns: float + modeled_period_max_ns: float + modeled_loop_residual_centered_rms_ns: float + modeled_loop_residual_centered_p95_ns: float + modeled_loop_residual_peak_to_peak_ns: float + logged_residual_reproduction_rms_ns: float + logged_residual_reproduction_p95_ns: float + filtered_bias_reproduction_rms_m: float + filtered_bias_reproduction_p95_m: float + + +def _percentile(values: Iterable[float], fraction: float) -> float: + ordered = sorted(values) + if not ordered: + return math.nan + position = (len(ordered) - 1) * fraction + lower = math.floor(position) + upper = math.ceil(position) + if lower == upper: + return ordered[lower] + weight = position - lower + return ordered[lower] * (1.0 - weight) + ordered[upper] * weight + + +def _rms(values: Iterable[float]) -> float: + items = list(values) + return ( + math.sqrt(statistics.mean(value * value for value in items)) + if items + else math.nan + ) + + +def _linear_fit(x: list[float], y: list[float]) -> tuple[float, float]: + if len(x) != len(y) or not x: + raise ValueError("linear fit requires equal non-empty inputs") + if len(x) == 1: + return y[0], 0.0 + mean_x = statistics.mean(x) + mean_y = statistics.mean(y) + denominator = sum((value - mean_x) ** 2 for value in x) + if denominator == 0.0: + return mean_y, 0.0 + slope = sum( + (x_value - mean_x) * (y_value - mean_y) + for x_value, y_value in zip(x, y) + ) / denominator + return mean_y - slope * mean_x, slope + + +def _correlation(first: list[float], second: list[float]) -> float: + if len(first) != len(second) or len(first) < 2: + return math.nan + first_mean = statistics.mean(first) + second_mean = statistics.mean(second) + first_centered = [value - first_mean for value in first] + second_centered = [value - second_mean for value in second] + denominator = math.sqrt( + sum(value * value for value in first_centered) + * sum(value * value for value in second_centered) + ) + if denominator == 0.0: + return math.nan + return sum( + first_value * second_value + for first_value, second_value in zip(first_centered, second_centered) + ) / denominator + + +def _profile(sample: FirmwarePpsSample) -> Profile: + try: + return Profile[sample.profile.upper()] + except KeyError: + return Profile.HOLDOVER + + +def _pi_rate_prediction_ppm(sample: FirmwarePpsSample) -> float: + """Recreate RTL ``integrator + Kp * phase`` from diagnostic fields.""" + profile = _profile(sample) + if profile == Profile.HOLDOVER or sample.mode != "tracking": + return math.nan + kp_q46, _, max_slew_q32 = PROFILES[profile].fixed_coefficients( + UPDATE_PERIOD_S + ) + phase_q32 = round( + sample.hardware_phase_ns * 1.0e-9 * TRACKING_HZ * Q32 + ) + integrator_q32 = round(sample.integrator_ppm * 1.0e-6 * Q32) + proportional_q32 = (phase_q32 * kp_q46) >> 46 + rate_q32 = max( + -max_slew_q32, + min(max_slew_q32, integrator_q32 + proportional_q32), + ) + return rate_q32 / Q32 * 1.0e6 + + +def _reconstructed_active_phase_ns( + samples: list[FirmwarePpsSample], oscillator_ppm: float +) -> list[float]: + """Integrate observed rate under a constant-oscillator assumption.""" + phase_ns = [0.0] + for first, second in zip(samples, samples[1:]): + elapsed_s = second.time_s - first.time_s + mean_command_ppm = 0.5 * ( + first.rate_command_ppm + second.rate_command_ppm + ) + phase_ns.append( + phase_ns[-1] + + (oscillator_ppm + mean_command_ppm) * elapsed_s * 1.0e3 + ) + return phase_ns + + +def _parse_float(group: dict[str, str], key: str) -> float: + return float(group[key]) + + +def _parse_int(group: dict[str, str], key: str) -> int: + return int(group[key]) + + +def load_firmware_telemetry(path: Path) -> list[FirmwarePpsSample]: + """Load host-timestamped UART CSV produced by ``serial_capture.py``.""" + pending: list[dict[str, object]] = [] + latest_location: GpsContext | None = None + latest_tracked = 0 + with path.open("r", encoding="utf-8", errors="replace", newline="") as source: + for row in csv.DictReader(source): + try: + host_monotonic_s = float(row["host_monotonic_s"]) + host_unix_s = float(row["host_unix_s"]) + line = row["uart_line"] + except (KeyError, TypeError, ValueError): + continue + + tracked_match = TRACKED_RE.search(line) + if tracked_match: + latest_tracked = int(tracked_match.group("tracked")) + + location_match = LOCATION_RE.search(line) + if location_match: + groups = location_match.groupdict() + latest_location = GpsContext( + host_monotonic_s, + int(groups["tracked"]), + int(groups["used"]), + float(groups["hdop"]), + float(groups["vdop"]), + bool(int(groups["velocity_valid"])), + bool(int(groups["velocity_fde"])), + float(groups["velocity_rms_mps"]), + ) + + pps_match = PPS_RE.search(line) + if not pps_match: + continue + groups = pps_match.groupdict() + context = latest_location + context_fresh = ( + context is not None + and host_monotonic_s - context.host_monotonic_s <= 1.0 + ) + pending.append( + { + "host_monotonic_s": host_monotonic_s, + "host_unix_s": host_unix_s, + "profile": groups["profile"], + "mode": groups["mode"], + "locked": bool(_parse_int(groups, "locked")), + "tdop": _parse_float(groups, "tdop"), + "gdop": _parse_float(groups, "gdop"), + "hdop": context.hdop if context_fresh else math.nan, + "vdop": context.vdop if context_fresh else math.nan, + "clock_sigma_m": _parse_float(groups, "clock_sigma_m"), + "cn0_mean_dbhz": _parse_float(groups, "cn0_mean_dbhz"), + "cn0_min_dbhz": _parse_float(groups, "cn0_min_dbhz"), + "tracked_satellites": ( + context.tracked_satellites + if context_fresh + else latest_tracked + ), + "used_satellites": ( + context.used_satellites if context_fresh else 0 + ), + "innovation_m": _parse_float(groups, "innovation_m"), + "raw_bias_m": _parse_float(groups, "raw_bias_m"), + "filtered_bias_m": _parse_float(groups, "filtered_bias_m"), + "clock_drift_mps": _parse_float(groups, "clock_drift_mps"), + "bias_drift_mps": _parse_float(groups, "bias_drift_mps"), + "predictor_enabled": bool(_parse_int(groups, "predictor")), + "eskf_alpha": _parse_float(groups, "eskf_alpha"), + "eskf_speed_mps": _parse_float(groups, "eskf_speed_mps"), + "eskf_velocity_rms_mps": _parse_float( + groups, "eskf_velocity_rms_mps" + ), + "eskf_fde_mask": int(groups["eskf_fde_mask"], 16), + "pps_speed_mps": _parse_float(groups, "pps_speed_mps"), + "pps_velocity_rms_mps": _parse_float( + groups, "pps_velocity_rms_mps" + ), + "pps_fde_mask": int(groups["pps_fde_mask"], 16), + "reference_svid": _parse_int(groups, "reference_svid"), + "capture_correction_ns": _parse_float( + groups, "capture_correction_ns" + ), + "hardware_phase_ns": _parse_float( + groups, "hardware_phase_ns" + ), + "rate_command_ppm": _parse_float(groups, "rate_command_ppm"), + "integrator_ppm": _parse_float(groups, "integrator_ppm"), + "pulse_count": _parse_int(groups, "pulse_count"), + "sequence": _parse_int(groups, "sequence"), + "status": int(groups["status"], 16), + } + ) + + if not pending: + return [] + first_sequence = int(pending[0]["sequence"]) + samples: list[FirmwarePpsSample] = [] + for fields in pending: + sequence = int(fields["sequence"]) + sequence_delta = (sequence - first_sequence) & 0xFFFF_FFFF + fields["time_s"] = sequence_delta * UPDATE_PERIOD_S + samples.append(FirmwarePpsSample(**fields)) + return samples + + +def observed_metrics(samples: list[FirmwarePpsSample]) -> ObservedMetrics: + if len(samples) < 2: + raise ValueError("at least two firmware PPS samples are required") + phase = [sample.hardware_phase_ns for sample in samples] + phase_center = statistics.median(phase) + centered_phase = [value - phase_center for value in phase] + phase_steps = [ + phase[index] - phase[index - 1] for index in range(1, len(phase)) + ] + host_intervals = [ + samples[index].host_monotonic_s - samples[index - 1].host_monotonic_s + for index in range(1, len(samples)) + ] + sequence_steps = [ + (samples[index].sequence - samples[index - 1].sequence) & 0xFFFF_FFFF + for index in range(1, len(samples)) + ] + _, phase_slope = _linear_fit( + [sample.time_s for sample in samples], phase + ) + _, raw_slope = _linear_fit( + [sample.time_s for sample in samples], + [sample.raw_bias_m for sample in samples], + ) + _, filtered_slope = _linear_fit( + [sample.time_s for sample in samples], + [sample.filtered_bias_m for sample in samples], + ) + profiles = [sample.profile for sample in samples] + references = [sample.reference_svid for sample in samples] + innovations = [sample.innovation_m for sample in samples] + rate = [sample.rate_command_ppm for sample in samples] + status_interval = statistics.median(host_intervals) + wander_window = max(1, round(10.0 / status_interval)) + phase_10s_means = [ + statistics.mean(centered_phase[index - wander_window + 1:index + 1]) + for index in range(wander_window - 1, len(centered_phase)) + ] + inferred_oscillator_ppm = -statistics.mean(rate) + inferred_active_phase = _reconstructed_active_phase_ns( + samples, inferred_oscillator_ppm + ) + active_center = statistics.median(inferred_active_phase) + centered_active = [ + value - active_center for value in inferred_active_phase + ] + pi_rate_errors_ppb = [ + (prediction - sample.rate_command_ppm) * 1.0e3 + for sample in samples + if math.isfinite( + prediction := _pi_rate_prediction_ppm(sample) + ) + ] + return ObservedMetrics( + len(samples), + samples[-1].time_s - samples[0].time_s, + statistics.median(host_intervals), + statistics.median(sequence_steps), + statistics.mean(sample.locked for sample in samples), + sum( + profiles[index] != profiles[index - 1] + for index in range(1, len(profiles)) + ), + sum( + references[index] != references[index - 1] + for index in range(1, len(references)) + ), + statistics.mean(phase), + _rms(centered_phase), + _percentile((abs(value) for value in centered_phase), 0.95), + max(abs(value) for value in centered_phase), + max(phase) - min(phase), + ( + max(phase_10s_means) - min(phase_10s_means) + if phase_10s_means else math.nan + ), + _rms(phase_steps), + _percentile((abs(value) for value in phase_steps), 0.95), + max(abs(value) for value in phase_steps), + phase_slope, + statistics.median(rate), + max(rate) - min(rate), + statistics.median(sample.integrator_ppm for sample in samples), + inferred_oscillator_ppm, + _rms(centered_active), + max(inferred_active_phase) - min(inferred_active_phase), + _correlation(phase, rate), + _correlation(phase, innovations), + _rms(pi_rate_errors_ppb), + _percentile((abs(value) for value in pi_rate_errors_ppb), 0.95), + max((abs(value) for value in pi_rate_errors_ppb), default=math.nan), + _rms(innovations), + _percentile((abs(value) for value in innovations), 0.95), + raw_slope, + filtered_slope, + statistics.median(sample.tdop for sample in samples), + statistics.median(sample.gdop for sample in samples), + statistics.median(sample.clock_sigma_m for sample in samples), + statistics.median(sample.cn0_mean_dbhz for sample in samples), + statistics.median(sample.cn0_min_dbhz for sample in samples), + statistics.median(sample.tracked_satellites for sample in samples), + statistics.median(sample.used_satellites for sample in samples), + statistics.median(sample.pps_velocity_rms_mps for sample in samples), + ) + + +def _lerp(first: float, second: float, fraction: float) -> float: + return first + (second - first) * fraction + + +def _quality(sample: FirmwarePpsSample) -> QualityMetrics: + # chi-square is not present in the level-1 status record. A finite value + # within the observed profile's accepted band preserves the firmware's + # already-reported robust/precision classification without pretending it + # was measured by this parser. + inferred_chi_square = 0.5 + solution_valid = sample.profile != "holdover" and sample.used_satellites >= 4 + return QualityMetrics( + sample.tdop, + sample.gdop, + sample.clock_sigma_m, + sample.cn0_mean_dbhz, + sample.cn0_min_dbhz, + inferred_chi_square, + sample.pps_velocity_rms_mps, + sample.used_satellites, + solution_valid=solution_valid, + residual_test_valid=solution_valid, + velocity_valid=solution_valid, + fde_applied=sample.pps_fde_mask != 0, + ) + + +def _interpolated_sample( + first: FirmwarePpsSample, + second: FirmwarePpsSample, + step: int, + total_steps: int, +) -> FirmwarePpsSample: + fraction = step / total_steps + fields = asdict(first) + linear_fields = ( + "host_monotonic_s", "host_unix_s", "tdop", "gdop", "hdop", "vdop", + "clock_sigma_m", "cn0_mean_dbhz", "cn0_min_dbhz", "innovation_m", + "raw_bias_m", "filtered_bias_m", "clock_drift_mps", "bias_drift_mps", + "eskf_alpha", "eskf_speed_mps", "eskf_velocity_rms_mps", + "pps_speed_mps", "pps_velocity_rms_mps", "capture_correction_ns", + "hardware_phase_ns", "rate_command_ppm", "integrator_ppm", + ) + for name in linear_fields: + fields[name] = _lerp( + float(getattr(first, name)), float(getattr(second, name)), fraction + ) + fields["sequence"] = (first.sequence + step) & 0xFFFF_FFFF + fields["time_s"] = first.time_s + step * UPDATE_PERIOD_S + fields["pulse_count"] = round( + _lerp(first.pulse_count, second.pulse_count, fraction) + ) + if fraction >= 0.5: + for name in ( + "profile", "mode", "locked", "tracked_satellites", + "used_satellites", "predictor_enabled", "eskf_fde_mask", + "pps_fde_mask", "reference_svid", "status", + ): + fields[name] = getattr(second, name) + return FirmwarePpsSample(**fields) + + +def expand_samples(samples: list[FirmwarePpsSample]) -> list[tuple[FirmwarePpsSample, bool]]: + if not samples: + return [] + expanded: list[tuple[FirmwarePpsSample, bool]] = [(samples[0], False)] + for first, second in zip(samples, samples[1:]): + steps = (second.sequence - first.sequence) & 0xFFFF_FFFF + if steps == 0 or steps > 10_000: + continue + for step in range(1, steps + 1): + expanded.append( + ( + second if step == steps else _interpolated_sample( + first, second, step, steps + ), + step != steps, + ) + ) + return expanded + + +def replay_firmware_telemetry( + samples: list[FirmwarePpsSample], + *, + warmup_s: float = 20.0, + tuning: str = "current", + profiles: dict[Profile, LoopProfile] | None = None, + software_drift_gain: float = 1.0 / 4.0, +) -> tuple[list[ReplayPoint], ReplayMetrics]: + if len(samples) < 2: + raise ValueError("at least two firmware PPS samples are required") + expanded = expand_samples(samples) + first = expanded[0][0] + first_profile = _profile(first) + if not first.locked or first_profile == Profile.HOLDOVER: + raise ValueError( + "hardware replay requires an initially locked tracking sample" + ) + + # The UART does not contain a direct oscillator measurement. Choosing + # the negative mean command is the unique constant-oscillator convention + # that gives zero net frequency over the capture. Integrating the logged + # command then reconstructs the board's relative active-phase trajectory. + oscillator_ppm = -statistics.mean( + sample.rate_command_ppm for sample in samples + ) + expanded_samples = [sample for sample, _ in expanded] + active_phase_ns = _reconstructed_active_phase_ns( + expanded_samples, oscillator_ppm + ) + reference_timing_s = [ + phase_ns * 1.0e-9 + sample.hardware_phase_ns * 1.0e-9 + for phase_ns, sample in zip(active_phase_ns, expanded_samples) + ] + # Firmware's filtered bias is the value used to form the PPS reference. + # Reversing that operation gives a self-consistent datum trajectory while + # retaining raw WLS bias as the actual software-filter input. + true_bias_m = [ + sample.filtered_bias_m + timing_s * SPEED_OF_LIGHT_MPS + for sample, timing_s in zip(expanded_samples, reference_timing_s) + ] + model = FixedPointAdaptivePps( + update_period_s=UPDATE_PERIOD_S, + oscillator_ppm=oscillator_ppm, + receiver_clock_bias_m=true_bias_m[0], + receiver_clock_drift_mps=0.0, + profiles=profiles, + software_drift_gain=software_drift_gain, + ) + model.seed_tracking_state( + time_s=first.time_s, + profile=first_profile, + phase_error_s=active_phase_ns[0] * 1.0e-9, + rate_command_ppm=first.rate_command_ppm, + integrator_ppm=first.integrator_ppm, + software_error_s=reference_timing_s[0], + software_bias_m=first.filtered_bias_m, + software_clock_drift_mps=first.bias_drift_mps, + software_previous_measurement_m=first.raw_bias_m, + reference_id=first.reference_svid, + ) + + points: list[ReplayPoint] = [ + ReplayPoint( + first.time_s, + first.sequence, + False, + first.raw_bias_m, + first.filtered_bias_m, + first.filtered_bias_m, + active_phase_ns[0], + active_phase_ns[0], + first.hardware_phase_ns, + first.hardware_phase_ns, + first.rate_command_ppm, + first.rate_command_ppm, + ) + ] + original_sequences = {sample.sequence for sample in samples} + observed_filtered = {sample.sequence: sample.filtered_bias_m for sample in samples} + for index, (sample, interpolated) in enumerate(expanded[1:], start=1): + observation = Observation( + sample.time_s, + (true_bias_m[index] - sample.raw_bias_m) / SPEED_OF_LIGHT_MPS, + sample.bias_drift_mps, + _quality(sample), + clock_drift_valid=sample.predictor_enabled, + reference_range_rate_mps=0.0, + reference_id=sample.reference_svid, + reference_datum_offset_m=( + true_bias_m[index] - true_bias_m[0] + ), + ) + model.update(observation) + modeled_loop_error_ns = model.trace.raw_loop_error_s[-1] * 1.0e9 + points.append( + ReplayPoint( + sample.time_s, + sample.sequence, + interpolated, + sample.raw_bias_m, + observed_filtered.get(sample.sequence, math.nan), + model.software_bias_m, + active_phase_ns[index], + model.trace.phase_error_s[-1] * 1.0e9, + sample.hardware_phase_ns if sample.sequence in original_sequences else math.nan, + modeled_loop_error_ns, + sample.rate_command_ppm if sample.sequence in original_sequences else math.nan, + model.trace.frequency_command_ppm[-1], + ) + ) + trace = model.finish(expanded[-1][0].time_s + UPDATE_PERIOD_S) + + edge_errors_ns = [ + error * 1.0e9 + for time_s, error in trace.integer_second_edge_errors(after_s=warmup_s) + ] + edge_center = ( + statistics.median(edge_errors_ns) if edge_errors_ns else math.nan + ) + centered_edges = [value - edge_center for value in edge_errors_ns] + periods = [ + edge_errors_ns[index] - edge_errors_ns[index - 1] + for index in range(1, len(edge_errors_ns)) + ] + locked_after_warmup = [ + locked + for time_s, locked in zip(trace.time_s, trace.locked) + if time_s >= warmup_s + ] + bias_errors = [ + point.modeled_filtered_bias_m - point.observed_filtered_bias_m + for point in points + if not point.interpolated + and point.time_s >= warmup_s + and math.isfinite(point.observed_filtered_bias_m) + ] + modeled_residuals = [ + point.modeled_loop_error_ns + for point in points + if point.time_s >= warmup_s + ] + modeled_residual_center = ( + statistics.median(modeled_residuals) + if modeled_residuals else math.nan + ) + centered_modeled_residuals = [ + value - modeled_residual_center for value in modeled_residuals + ] + residual_reproduction_errors = [ + point.modeled_loop_error_ns - point.observed_hardware_phase_ns + for point in points + if not point.interpolated + and point.time_s >= warmup_s + and math.isfinite(point.observed_hardware_phase_ns) + ] + metrics = ReplayMetrics( + tuning, + len(expanded), + len(expanded) / len(samples), + 0.0, + statistics.mean(locked_after_warmup) if locked_after_warmup else 0.0, + trace.unlock_events, + oscillator_ppm, + _rms(centered_edges), + _percentile((abs(value) for value in centered_edges), 0.95), + max(centered_edges) - min(centered_edges) if centered_edges else math.nan, + _rms(periods), + _percentile((abs(value) for value in periods), 0.95), + max((abs(value) for value in periods), default=math.nan), + _rms(centered_modeled_residuals), + _percentile( + (abs(value) for value in centered_modeled_residuals), 0.95 + ), + ( + max(centered_modeled_residuals) + - min(centered_modeled_residuals) + if centered_modeled_residuals else math.nan + ), + _rms(residual_reproduction_errors), + _percentile( + (abs(value) for value in residual_reproduction_errors), 0.95 + ), + _rms(bias_errors), + _percentile((abs(value) for value in bias_errors), 0.95), + ) + return points, metrics + + +def replay_tuning_sensitivity( + samples: list[FirmwarePpsSample], *, warmup_s: float = 20.0 +) -> list[ReplayMetrics]: + """Run small, explicitly non-production tuning counterfactuals.""" + _, current = replay_firmware_telemetry( + samples, warmup_s=warmup_s, tuning="current" + ) + guarded_profiles = dict(PROFILES) + guarded_profiles[Profile.ROBUST] = replace( + guarded_profiles[Profile.ROBUST], software_bias_gain=1.0 / 64.0 + ) + _, guarded = replay_firmware_telemetry( + samples, + warmup_s=warmup_s, + tuning="robust_bias_1_64", + profiles=guarded_profiles, + ) + _, faster_drift = replay_firmware_telemetry( + samples, + warmup_s=warmup_s, + tuning="drift_gain_1_2", + software_drift_gain=1.0 / 2.0, + ) + return [current, guarded, faster_drift] + + +def write_samples(path: Path, samples: list[FirmwarePpsSample]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as output: + writer = csv.DictWriter( + output, + fieldnames=list(FirmwarePpsSample.__annotations__), + lineterminator="\n", + ) + writer.writeheader() + for sample in samples: + writer.writerow(asdict(sample)) + + +def write_replay(path: Path, points: list[ReplayPoint]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as output: + writer = csv.DictWriter( + output, + fieldnames=list(ReplayPoint.__annotations__), + lineterminator="\n", + ) + writer.writeheader() + for point in points: + writer.writerow(asdict(point)) + + +def write_report( + path: Path, + source: Path, + samples: list[FirmwarePpsSample], + observed: ObservedMetrics, + replay: ReplayMetrics, + sensitivity: list[ReplayMetrics], +) -> None: + profiles = sorted({sample.profile for sample in samples}) + references = sorted({sample.reference_svid for sample in samples}) + lines = [ + "# Genesys2 live PPS telemetry replay", + "", + f"Source: `{source}`", + "", + "## Direct firmware/RTL observations", + "", + f"- Samples/duration: {observed.samples} / {observed.duration_s:.1f} s.", + f"- Status interval: {observed.status_interval_median_s:.3f} s; " + f"median sequence step {observed.sequence_step_median:.0f} " + f"({observed.sequence_step_median * UPDATE_PERIOD_S:.1f} s of 200 ms solves).", + f"- Profiles: {', '.join(profiles)}; locked fraction " + f"{observed.locked_fraction:.3f}; transitions " + f"{observed.profile_transitions}; reference SVIDs {references}.", + f"- TDOP/GDOP medians: {observed.tdop_median:.3f} / " + f"{observed.gdop_median:.3f}; clock sigma median " + f"{observed.clock_sigma_median_m:.2f} m.", + f"- C/N0 mean/min medians: {observed.cn0_mean_median_dbhz:.1f} / " + f"{observed.cn0_min_median_dbhz:.1f} dB-Hz; tracked/used medians " + f"{observed.tracked_median:.0f}/{observed.used_median:.0f}.", + f"- WLS innovation RMS/p95-absolute: {observed.innovation_rms_m:.2f} / " + f"{observed.innovation_p95_m:.2f} m.", + f"- Raw/filtered bias slopes: {observed.raw_bias_slope_mps:.3f} / " + f"{observed.filtered_bias_slope_mps:.3f} m/s; PPS velocity residual " + f"median {observed.pps_velocity_rms_median_mps:.3f} m/s.", + f"- Internal hardware phase mean: {observed.hardware_phase_mean_ns:.2f} ns; " + f"median-centered RMS/p95/max/p-p: " + f"{observed.hardware_phase_centered_rms_ns:.2f} / " + f"{observed.hardware_phase_centered_p95_ns:.2f} / " + f"{observed.hardware_phase_centered_max_ns:.2f} / " + f"{observed.hardware_phase_peak_to_peak_ns:.2f} ns.", + f"- Ten-second averaged phase wander p-p: " + f"{observed.hardware_phase_10s_wander_peak_to_peak_ns:.2f} ns.", + f"- Sampled phase-step RMS/p95/max: " + f"{observed.sampled_phase_step_rms_ns:.2f} / " + f"{observed.sampled_phase_step_p95_ns:.2f} / " + f"{observed.sampled_phase_step_max_ns:.2f} ns. These are at the status " + "interval, not consecutive physical 1 PPS edges.", + f"- Rate command median/span: {observed.rate_command_median_ppm:.4f} / " + f"{observed.rate_command_span_ppm:.4f} ppm; integrator median " + f"{observed.integrator_median_ppm:.4f} ppm.", + f"- Fixed-point PI rate reproduction RMS/p95/max: " + f"{observed.pi_rate_reproduction_rms_ppb:.3f} / " + f"{observed.pi_rate_reproduction_p95_ppb:.3f} / " + f"{observed.pi_rate_reproduction_max_ppb:.3f} ppb. This is the " + "difference between logged `rate_cmd` and `integrator + Kp*hw_phase`; " + "the remaining error is dominated by three-decimal UART rounding.", + f"- Phase correlation with rate command / WLS innovation: " + f"{observed.phase_rate_correlation:.3f} / " + f"{observed.phase_innovation_correlation:.3f}.", + f"- A constant oscillator inferred from zero net frequency is " + f"{observed.inferred_oscillator_ppm:.6f} ppm. Integrating the logged " + f"command under that assumption gives centered phase RMS/p-p " + f"{observed.inferred_active_phase_rms_ns:.2f} / " + f"{observed.inferred_active_phase_peak_to_peak_ns:.2f} ns.", + "", + "`hw_phase` is the RTL PI residual captured over APB. It is not a " + "trusted-generator time-interval measurement of the output pin, so " + "these values cannot certify physical PPS jitter or absolute offset.", + "", + "## Telemetry-conditioned end-to-end loop replay", + "", + "The replay imports the first locked phase/rate/integrator and software " + "filter snapshot instead of incorrectly resetting the board to an " + "acquisition state. It uses raw/filtered clock bias, Doppler datum " + "drift, TDOP/GDOP, clock sigma, C/N0, satellite count, FDE, reference " + "SVID and the production fixed-point coefficients. The reference " + "trajectory is reconstructed from filtered bias, the logged PI " + "residual and the integrated rate command.", + "", + f"- Expanded updates: {replay.expanded_updates}; interpolation factor " + f"{replay.interpolation_factor:.2f}.", + f"- Snapshot starts locked at {replay.first_lock_s:.2f} s; post-warmup locked " + f"fraction {replay.locked_fraction_after_warmup:.3f}; unlocks " + f"{replay.unlocks}.", + f"- Constant-oscillator convention: " + f"{replay.inferred_oscillator_ppm:.6f} ppm.", + f"- Modeled physical-grid edge centered RMS/p95/p-p: " + f"{replay.modeled_edge_centered_rms_ns:.2f} / " + f"{replay.modeled_edge_centered_p95_ns:.2f} / " + f"{replay.modeled_edge_peak_to_peak_ns:.2f} ns.", + f"- Modeled one-second period RMS/p95/max: " + f"{replay.modeled_period_rms_ns:.2f} / " + f"{replay.modeled_period_p95_ns:.2f} / " + f"{replay.modeled_period_max_ns:.2f} ns.", + f"- Modeled PI residual centered RMS/p95/p-p: " + f"{replay.modeled_loop_residual_centered_rms_ns:.2f} / " + f"{replay.modeled_loop_residual_centered_p95_ns:.2f} / " + f"{replay.modeled_loop_residual_peak_to_peak_ns:.2f} ns.", + f"- Logged-epoch PI residual reproduction RMS/p95 error: " + f"{replay.logged_residual_reproduction_rms_ns:.2f} / " + f"{replay.logged_residual_reproduction_p95_ns:.2f} ns.", + f"- Logged-epoch filtered-bias reproduction RMS/p95: " + f"{replay.filtered_bias_reproduction_rms_m:.2f} / " + f"{replay.filtered_bias_reproduction_p95_m:.2f} m.", + "", + "## Tuning sensitivity on the same trace", + "", + "| tuning | edge RMS (ns) | edge p-p (ns) | period max (ns) | " + "residual reproduction RMS (ns) | unlocks |", + "|---|---:|---:|---:|---:|---:|", + ] + for item in sensitivity: + lines.append( + f"| {item.tuning} | {item.modeled_edge_centered_rms_ns:.2f} | " + f"{item.modeled_edge_peak_to_peak_ns:.2f} | " + f"{item.modeled_period_max_ns:.2f} | " + f"{item.logged_residual_reproduction_rms_ns:.2f} | " + f"{item.unlocks} |" + ) + lines.extend([ + "", + "These counterfactuals reuse a reference trajectory reconstructed " + "from the current filter, and nine of every ten solve epochs are " + "interpolated. They are useful as a rejection test, not as evidence " + "to deploy new gains. On this capture neither `robust_bias_1_64` nor " + "`drift_gain_1_2` improves the current loop's absolute wander.", + "", + "## Limits and next measurement", + "", + "- Missing status epochs are linearly interpolated when sequence step " + "is greater than one. This preserves low-frequency wander but " + "understates unlogged 200 ms noise; it is not an exact firmware replay.", + "- The inferred oscillator and active phase assume one constant crystal " + "frequency chosen to make net phase slope zero over this capture. " + "Temperature telemetry was unavailable, so slow crystal motion is not " + "separable from the reconstructed reference.", + "- Level-1 telemetry does not expose ABS_SAMPLE, REM_CODE_PHASE, full " + "capture counter, chi-square ratio, pseudorange residual vector, board " + "temperature, or a trusted PPS edge timestamp. The bit/subframe part " + "of the end-to-end model remains synthetic for those fields.", + "- Set `GPS_PPS_STATUS_LOG_INTERVAL=1` for exact 200 ms input cadence. " + "For a max-20 ns pin claim, capture DUT and trusted PPS with a " + "common-clock TIC/scope after cable/output-delay calibration.", + "", + ]) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text("\n".join(lines), encoding="utf-8") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input", type=Path, required=True) + parser.add_argument( + "--output-dir", + type=Path, + default=Path(__file__).resolve().parent / "output/hardware_replay", + ) + parser.add_argument("--warmup", type=float, default=20.0) + parser.add_argument("--no-write", action="store_true") + return parser.parse_args() + + +def main() -> int: + args = parse_args() + samples = load_firmware_telemetry(args.input) + if len(samples) < 2: + raise SystemExit("input does not contain at least two firmware PPS records") + observed = observed_metrics(samples) + points, replay = replay_firmware_telemetry(samples, warmup_s=args.warmup) + sensitivity = replay_tuning_sensitivity( + samples, warmup_s=args.warmup + ) + print( + f"samples={observed.samples} duration={observed.duration_s:.1f}s " + f"locked={observed.locked_fraction:.3f} " + f"hw_phase_centered_rms={observed.hardware_phase_centered_rms_ns:.2f}ns " + f"hw_phase_p95={observed.hardware_phase_centered_p95_ns:.2f}ns " + f"hw_phase_pp={observed.hardware_phase_peak_to_peak_ns:.2f}ns" + ) + print( + f"replay_updates={replay.expanded_updates} " + f"model_edge_rms={replay.modeled_edge_centered_rms_ns:.2f}ns " + f"model_period_rms={replay.modeled_period_rms_ns:.2f}ns " + f"model_period_max={replay.modeled_period_max_ns:.2f}ns " + f"unlocks={replay.unlocks}" + ) + if not args.no_write: + write_samples(args.output_dir / "firmware_telemetry.csv", samples) + write_replay(args.output_dir / "model_replay.csv", points) + write_report( + args.output_dir / "hardware_replay_report.md", + args.input, + samples, + observed, + replay, + sensitivity, + ) + print(f"wrote {args.output_dir}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/soc/software/pps_end_to_end_validation/output/hardware_replay_20260728/firmware_telemetry.csv b/soc/software/pps_end_to_end_validation/output/hardware_replay_20260728/firmware_telemetry.csv new file mode 100644 index 0000000..50cc539 --- /dev/null +++ b/soc/software/pps_end_to_end_validation/output/hardware_replay_20260728/firmware_telemetry.csv @@ -0,0 +1,150 @@ +host_monotonic_s,host_unix_s,time_s,profile,mode,locked,tdop,gdop,hdop,vdop,clock_sigma_m,cn0_mean_dbhz,cn0_min_dbhz,tracked_satellites,used_satellites,innovation_m,raw_bias_m,filtered_bias_m,clock_drift_mps,bias_drift_mps,predictor_enabled,eskf_alpha,eskf_speed_mps,eskf_velocity_rms_mps,eskf_fde_mask,pps_speed_mps,pps_velocity_rms_mps,pps_fde_mask,reference_svid,capture_correction_ns,hardware_phase_ns,rate_command_ppm,integrator_ppm,pulse_count,sequence,status 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medians: 1.220 / 2.820; clock sigma median 11.24 m. +- C/N0 mean/min medians: 42.6 / 38.4 dB-Hz; tracked/used medians 6/5. +- WLS innovation RMS/p95-absolute: 10.47 / 19.66 m. +- Raw/filtered bias slopes: -90.020 / -90.025 m/s; PPS velocity residual median 0.079 m/s. +- Internal hardware phase mean: 155.37 ns; median-centered RMS/p95/max/p-p: 17.92 / 31.26 / 47.95 / 93.21 ns. +- Ten-second averaged phase wander p-p: 46.16 ns. +- Sampled phase-step RMS/p95/max: 22.84 / 44.13 / 56.38 ns. These are at the status interval, not consecutive physical 1 PPS edges. +- Rate command median/span: -1.5370 / 0.0140 ppm; integrator median -1.5600 ppm. +- Fixed-point PI rate reproduction RMS/p95/max: 0.430 / 0.852 / 0.921 ppb. This is the difference between logged `rate_cmd` and `integrator + Kp*hw_phase`; the remaining error is dominated by three-decimal UART rounding. +- Phase correlation with rate command / WLS innovation: 0.988 / 0.656. +- A constant oscillator inferred from zero net frequency is 1.536779 ppm. Integrating the logged command under that assumption gives centered phase RMS/p-p 23.65 / 92.48 ns. + +`hw_phase` is the RTL PI residual captured over APB. It is not a trusted-generator time-interval measurement of the output pin, so these values cannot certify physical PPS jitter or absolute offset. + +## Telemetry-conditioned end-to-end loop replay + +The replay imports the first locked phase/rate/integrator and software filter snapshot instead of incorrectly resetting the board to an acquisition state. It uses raw/filtered clock bias, Doppler datum drift, TDOP/GDOP, clock sigma, C/N0, satellite count, FDE, reference SVID and the production fixed-point coefficients. The reference trajectory is reconstructed from filtered bias, the logged PI residual and the integrated rate command. + +- Expanded updates: 1481; interpolation factor 9.94. +- Snapshot starts locked at 0.00 s; post-warmup locked fraction 1.000; unlocks 0. +- Constant-oscillator convention: 1.536779 ppm. +- Modeled physical-grid edge centered RMS/p95/p-p: 41.32 / 80.00 / 168.00 ns. +- Modeled one-second period RMS/p95/max: 5.51 / 8.00 / 16.00 ns. +- Modeled PI residual centered RMS/p95/p-p: 21.47 / 46.79 / 135.63 ns. +- Logged-epoch PI residual reproduction RMS/p95 error: 18.07 / 44.30 ns. +- Logged-epoch filtered-bias reproduction RMS/p95: 9.87 / 22.13 m. + +## Tuning sensitivity on the same trace + +| tuning | edge RMS (ns) | edge p-p (ns) | period max (ns) | residual reproduction RMS (ns) | unlocks | +|---|---:|---:|---:|---:|---:| +| current | 41.32 | 168.00 | 16.00 | 18.07 | 0 | +| robust_bias_1_64 | 57.06 | 216.00 | 16.00 | 24.53 | 0 | +| drift_gain_1_2 | 42.23 | 168.00 | 16.00 | 18.03 | 0 | + +These counterfactuals reuse a reference trajectory reconstructed from the current filter, and nine of every ten solve epochs are interpolated. They are useful as a rejection test, not as evidence to deploy new gains. On this capture neither `robust_bias_1_64` nor `drift_gain_1_2` improves the current loop's absolute wander. + +## Limits and next measurement + +- Missing status epochs are linearly interpolated when sequence step is greater than one. This preserves low-frequency wander but understates unlogged 200 ms noise; it is not an exact firmware replay. +- The inferred oscillator and active phase assume one constant crystal frequency chosen to make net phase slope zero over this capture. Temperature telemetry was unavailable, so slow crystal motion is not separable from the reconstructed reference. +- Level-1 telemetry does not expose ABS_SAMPLE, REM_CODE_PHASE, full capture counter, chi-square ratio, pseudorange residual vector, board temperature, or a trusted PPS edge timestamp. The bit/subframe part of the end-to-end model remains synthetic for those fields. +- Set `GPS_PPS_STATUS_LOG_INTERVAL=1` for exact 200 ms input cadence. 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+295.8,4519,True,666887.1669000001,nan,666896.1813409552,4.227248322169297,14.075362356379628,nan,120.89968405663966,nan,-1.5406403690576553 +296.0,4520,False,666863.837,666875.472,666874.5472175883,3.4429530201559366,9.946831544861197,124.56,121.14086356014013,-1.541,-1.5406403690576553 diff --git a/soc/software/pps_end_to_end_validation/output/stability/stability_report.md b/soc/software/pps_end_to_end_validation/output/stability/stability_report.md index 51d589d..efd8c62 100644 --- a/soc/software/pps_end_to_end_validation/output/stability/stability_report.md +++ b/soc/software/pps_end_to_end_validation/output/stability/stability_report.md @@ -9,13 +9,13 @@ This report is generated by `stability_analysis.py`. The absolute LNAV epoch pat - DUT minus trusted PPS was 32.01 ns. That remainder is the model's configured four-PCLK capture pipeline, not a measured board delay. - Production firmware and this analysis both define `POSTPONE_TOW` as 180 ms for the current RTL delivery semantics. -## Repository hardware data +## Hardware data used by this synthetic sweep - Real GPS capture: 989 GDOP samples and 990 tracked-satellite samples over about five minutes. - GDOP min/median/p95/max: 1.86 / 1.93 / 3.15 / 3.15. - Tracked satellites: 8..8 (median 8). - Horizontal scatter RMS/p95/max: 8.58 / 15.15 / 23.77 m. No surveyed truth was recorded. -- No real ABS_SAMPLE, REM_CODE_PHASE, TDOP/HDOP, C/N0, pseudorange residual, PPS phase/rate/integrator, temperature, or trusted-edge TIC samples exist in the repository. Those columns in the companion CSV are explicitly synthetic. +- The older fusion capture used by this sweep has no ABS_SAMPLE, REM_CODE_PHASE, TDOP/HDOP, C/N0, pseudorange residual, PPS phase/rate/integrator, temperature, or trusted-edge TIC samples. Those columns in the companion CSV are explicitly synthetic. Newer PPS status captures are analyzed separately by `hardware_replay.py` and are not silently mixed into this historical baseline. Synthetic timing uses the board-calibration values already encoded in the PPS tests: TDOP 1.50, GDOP 2.92, clock sigma 15.87 m (52.94 ns), C/N0 40.3/34.8 dB-Hz, drift noise 3.47 m/s, seven-satellite reference point, -291.75 m/s datum range rate, and 1.592 ppm local oscillator. Real GDOP and satellite count replace the geometry fields when available. diff --git a/soc/software/pps_end_to_end_validation/stability_analysis.py b/soc/software/pps_end_to_end_validation/stability_analysis.py index 00a130b..6a22ec3 100644 --- a/soc/software/pps_end_to_end_validation/stability_analysis.py +++ b/soc/software/pps_end_to_end_validation/stability_analysis.py @@ -600,7 +600,7 @@ def write_report( "- Production firmware and this analysis both define `POSTPONE_TOW` " "as 180 ms for the current RTL delivery semantics.", "", - "## Repository hardware data", + "## Hardware data used by this synthetic sweep", "", f"- Real GPS capture: {len(gdop)} GDOP samples and {len(tracked)} " "tracked-satellite samples over about five minutes.", @@ -613,10 +613,12 @@ def write_report( f"{_fmt(telemetry.horizontal_rms_m)} / " f"{_fmt(telemetry.horizontal_p95_m)} / " f"{_fmt(telemetry.horizontal_max_m)} m. No surveyed truth was recorded.", - "- No real ABS_SAMPLE, REM_CODE_PHASE, TDOP/HDOP, C/N0, pseudorange " - "residual, PPS phase/rate/integrator, temperature, or trusted-edge TIC " - "samples exist in the repository. Those columns in the companion CSV " - "are explicitly synthetic.", + "- The older fusion capture used by this sweep has no ABS_SAMPLE, " + "REM_CODE_PHASE, TDOP/HDOP, C/N0, pseudorange residual, PPS phase/rate/" + "integrator, temperature, or trusted-edge TIC samples. Those columns " + "in the companion CSV are explicitly synthetic. Newer PPS status " + "captures are analyzed separately by `hardware_replay.py` and are not " + "silently mixed into this historical baseline.", "", "Synthetic timing uses the board-calibration values already encoded " "in the PPS tests: TDOP 1.50, GDOP 2.92, clock sigma 15.87 m " diff --git a/soc/software/pps_end_to_end_validation/test_hardware_replay.py b/soc/software/pps_end_to_end_validation/test_hardware_replay.py new file mode 100644 index 0000000..5d29d7f --- /dev/null +++ b/soc/software/pps_end_to_end_validation/test_hardware_replay.py @@ -0,0 +1,113 @@ +from __future__ import annotations + +import csv +import math +from pathlib import Path +import tempfile +import unittest + +from pps_python.adaptive_pps import ( + PROFILES, + Profile, + Q32, + TRACKING_HZ, +) + +from pps_end_to_end_validation.hardware_replay import ( + UPDATE_PERIOD_S, + expand_samples, + load_firmware_telemetry, + observed_metrics, + replay_firmware_telemetry, + replay_tuning_sensitivity, +) + + +def _status_line(index: int) -> str: + phase_ns = 140.0 + 15.0 * math.sin(index * 0.7) + integrator_ppm = -1.560 + kp_q46, _, _ = PROFILES[Profile.ROBUST].fixed_coefficients( + UPDATE_PERIOD_S + ) + phase_q32 = round(phase_ns * 1.0e-9 * TRACKING_HZ * Q32) + proportional_q32 = (phase_q32 * kp_q46) >> 46 + rate_ppm = ( + round(integrator_ppm * 1.0e-6 * Q32) + proportional_q32 + ) / Q32 * 1.0e6 + raw_bias_m = 700_000.0 - 80.0 * (2.0 * index) + 4.0 * math.sin(index) + filtered_bias_m = 700_000.0 - 80.0 * (2.0 * index) + return ( + "[NAV] PPS profile=robust mode=tracking locked=1 " + "tdop=1.22 gdop=2.82 clock_sigma=11.24 cn0=42.6/38.4 " + f"innovation={raw_bias_m - filtered_bias_m:.2f}m " + f"raw_bias={raw_bias_m:.3f}m filtered_bias={filtered_bias_m:.3f}m " + "clock_drift=110.000mps bias_drift=-80.000mps predictor=1 " + "eskf_alpha=0.05 eskf_v=0.100/0.050/0x00 " + "pps_v=0.100/0.080/0x00 ref_svid=23 capture_corr=32.00ns " + f"hw_phase={phase_ns:.2f}ns rate_cmd={rate_ppm:.3f}ppm " + f"integ={integrator_ppm:.3f}ppm pulse={100 + 2 * index} " + f"seq={1000 + 10 * index} status=0x80f" + ) + + +def _write_capture(path: Path, count: int = 16) -> None: + location = ( + "[LOCATION] source=gps lat=0 lon=0 height=0 " + "gdop=2.82 pdop=2.54 hdop=1.81 vdop=1.78 tdop=1.22 " + "tracked=6 used=5 velocity_valid=1 velocity_fde=0 " + "speed=0.1 velocity_rms=0.08" + ) + with path.open("w", encoding="utf-8", newline="") as output: + writer = csv.writer(output, lineterminator="\n") + writer.writerow(("host_monotonic_s", "host_unix_s", "uart_line")) + for index in range(count): + host_s = 2.0 * index + writer.writerow((host_s - 0.01, 1000.0 + host_s, location)) + writer.writerow((host_s, 1000.01 + host_s, _status_line(index))) + + +class HardwareReplayTests(unittest.TestCase): + def setUp(self) -> None: + self.temporary = tempfile.TemporaryDirectory() + self.capture = Path(self.temporary.name) / "capture.csv" + _write_capture(self.capture) + + def tearDown(self) -> None: + self.temporary.cleanup() + + def test_uart_parser_and_sequence_expansion(self): + samples = load_firmware_telemetry(self.capture) + self.assertEqual(len(samples), 16) + self.assertEqual(samples[-1].time_s, 30.0) + self.assertEqual(samples[0].tracked_satellites, 6) + self.assertEqual(samples[0].used_satellites, 5) + expanded = expand_samples(samples) + self.assertEqual(len(expanded), 151) + self.assertEqual(sum(interpolated for _, interpolated in expanded), 135) + + def test_fixed_point_pi_and_checkpoint_replay(self): + samples = load_firmware_telemetry(self.capture) + observed = observed_metrics(samples) + self.assertEqual(observed.locked_fraction, 1.0) + self.assertLess(observed.pi_rate_reproduction_rms_ppb, 0.6) + points, replay = replay_firmware_telemetry( + samples, warmup_s=5.0 + ) + self.assertEqual(len(points), 151) + self.assertEqual(replay.unlocks, 0) + self.assertEqual(replay.locked_fraction_after_warmup, 1.0) + self.assertTrue(math.isfinite(replay.modeled_edge_centered_rms_ns)) + self.assertGreater(replay.modeled_edge_centered_rms_ns, 0.0) + + def test_tuning_sensitivity_keeps_candidates_separate(self): + samples = load_firmware_telemetry(self.capture) + results = replay_tuning_sensitivity(samples, warmup_s=5.0) + self.assertEqual( + [result.tuning for result in results], + ["current", "robust_bias_1_64", "drift_gain_1_2"], + ) + self.assertTrue(all(result.unlocks == 0 for result in results)) + + +if __name__ == "__main__": + unittest.main() diff --git a/soc/software/pps_python/adaptive_pps.py b/soc/software/pps_python/adaptive_pps.py index 65ab563..f2b1f4c 100644 --- a/soc/software/pps_python/adaptive_pps.py +++ b/soc/software/pps_python/adaptive_pps.py @@ -404,6 +404,88 @@ class FixedPointAdaptivePps: def _phase_q32_to_seconds(value: int) -> float: return value / (TRACKING_HZ * Q32) + def seed_tracking_state( + self, + *, + time_s: float, + profile: Profile, + phase_error_s: float, + rate_command_ppm: float, + integrator_ppm: float, + software_error_s: float, + software_bias_m: float, + software_clock_drift_mps: float, + software_previous_measurement_m: float, + reference_id: int, + ) -> None: + """Start a replay from a captured, already-locked board snapshot. + + Normal simulations deliberately start unaligned and exercise coarse + acquisition. Hardware logs, however, are commonly captured after the + board has been tracking for minutes. Replaying such a log from reset + introduces an artificial acquisition transient and clears the PI + integrator that was present on the board. This method imports only + state that the production diagnostic registers and firmware status + expose; subsequent updates still use the normal bit-accurate path. + + The method must be called before any observation has been recorded. + It is a model facility and has no firmware/RTL side effect. + """ + finite_values = ( + time_s, + phase_error_s, + rate_command_ppm, + integrator_ppm, + software_error_s, + software_bias_m, + software_clock_drift_mps, + software_previous_measurement_m, + ) + if self.trace.time_s: + raise ValueError("tracking state must be seeded before replay") + if not all(math.isfinite(value) for value in finite_values): + raise ValueError("tracking state values must be finite") + if time_s < 0.0: + raise ValueError("tracking state time must be non-negative") + if profile == Profile.HOLDOVER: + raise ValueError("a locked snapshot cannot use holdover profile") + + self.current_time_s = time_s + self.raw_tick_remainder = 0.0 + self.phase_q32 = self._seconds_to_phase_q32(phase_error_s) + self.profile = profile + self.selector.profile = profile + self.selector.candidate = profile + self.selector.candidate_updates = 0 + self.aligned = True + self.locked = True + self.lock_good_updates = 0 + self.reference_watchdog_holdover = False + self._select_loop_parameters() + self.integrator_q32 = self._clamp( + round(integrator_ppm * 1.0e-6 * Q32), self.max_slew_q32 + ) + self.rate_word_q32 = self._clamp( + round(rate_command_ppm * 1.0e-6 * Q32), self.max_slew_q32 + ) + + self.software_error_s = software_error_s + self.software_bias_m = software_bias_m + self.software_clock_drift_mps = software_clock_drift_mps + self.software_previous_measurement_m = ( + software_previous_measurement_m + ) + self.software_drift_anchor_time_s = time_s + self.software_drift_anchor_measurement_m = ( + software_previous_measurement_m + ) + self.software_drift_initialized = True + self.software_updates = max( + 1, self.parameters.software_settle_updates + ) + self.software_reference_id = reference_id + self.last_observation_time_s = time_s + def _apply_profile(self, profile: Profile) -> None: self.profile = profile self._select_loop_parameters()