From 988f9686e720139e86ed99eac866b50c2dc69e50 Mon Sep 17 00:00:00 2001 From: onur Date: Tue, 28 Jul 2026 21:41:41 +0300 Subject: [PATCH] test(pps): analyze loop stability and telemetry --- .../pps_end_to_end_validation/README.md | 42 + .../output/stability/parameter_sweep.csv | 25 + .../output/stability/stability_report.md | 70 + .../output/stability/synthetic_telemetry.csv | 1501 +++++++++++++++++ .../stability_analysis.py | 865 ++++++++++ .../test_stability_analysis.py | 84 + soc/software/pps_python/adaptive_pps.py | 79 +- 7 files changed, 2648 insertions(+), 18 deletions(-) create mode 100644 soc/software/pps_end_to_end_validation/output/stability/parameter_sweep.csv create mode 100644 soc/software/pps_end_to_end_validation/output/stability/stability_report.md create mode 100644 soc/software/pps_end_to_end_validation/output/stability/synthetic_telemetry.csv create mode 100644 soc/software/pps_end_to_end_validation/stability_analysis.py create mode 100644 soc/software/pps_end_to_end_validation/test_stability_analysis.py diff --git a/soc/software/pps_end_to_end_validation/README.md b/soc/software/pps_end_to_end_validation/README.md index 2a6a9bf..f1d4425 100644 --- a/soc/software/pps_end_to_end_validation/README.md +++ b/soc/software/pps_end_to_end_validation/README.md @@ -70,6 +70,48 @@ python3 -m pps_end_to_end_validation.pps_end_to_end \ --trusted-output-delay-ns 35 ``` +## Closed-loop stability and dynamic tuning + +The bit-level command above isolates the absolute epoch convention. The +companion stability analysis connects that result to the fixed-point +firmware/RTL PI model, replays the real GDOP and tracked-satellite history in +`fusion/logs/gps_live_5min_20260713.csv`, and synthesizes only the telemetry +that the repository did not capture: + +```bash +python3 -m pps_end_to_end_validation.stability_analysis +python3 -m unittest \ + pps_end_to_end_validation/test_stability_analysis.py +``` + +Generated results are in `output/stability/`: + +- `stability_report.md`: real-data inventory, discrete poles, absolute phase + wander, period jitter, dynamic parameter sweep, and recommendations; +- `parameter_sweep.csv`: machine-readable results for every scenario/tuning; +- `synthetic_telemetry.csv`: 5 Hz ABS_SAMPLE, REM_CODE_PHASE, corrected 125 MHz + 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 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 +unlock. It also exposes an important metrics issue: cycle-to-cycle PPS period +jitter can remain below 20 ns while absolute phase moves slowly through tens +of nanoseconds. A physical 20 ns maximum therefore requires a common-clock +time-interval-counter capture and cable/output-delay calibration; a p95 period +jitter assertion or synthetic run cannot certify it. + +The sweep includes the current coefficients, quieter/faster precision loops, +a robust-noise guard, a larger Doppler-drift gain, and a quality-scheduled +candidate. The generated report contains the current numerical comparison. +No production constants are changed by the analysis. + ## 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/output/stability/parameter_sweep.csv b/soc/software/pps_end_to_end_validation/output/stability/parameter_sweep.csv new file mode 100644 index 0000000..78caeb5 --- /dev/null +++ b/soc/software/pps_end_to_end_validation/output/stability/parameter_sweep.csv @@ -0,0 +1,25 @@ +scenario,tuning,runs,lock_p95_s,absolute_rms_median_ns,absolute_max_p95_ns,absolute_peak_to_peak_median_ns,period_rms_median_ns,period_p95_median_ns,period_max_worst_ns,wander_10s_peak_to_peak_median_ns,profile_transitions_total,unlocks_total +real_geometry_board_noise,current,24,10.770000000000001,10.881176406988356,32.0,48.00000000000001,3.867084378182096,8.0,8.000000000000002,36.8,0,0 +real_geometry_board_noise,robust_noise_guard,24,10.770000000000001,10.881176406988356,32.0,48.00000000000001,3.867084378182096,8.0,8.000000000000002,36.8,0,0 +real_geometry_board_noise,quiet_precision,24,10.770000000000001,6.311779001549132,16.000000000000004,24.0,2.7197398634103065,7.999999999999999,8.000000000000005,20.0,0,0 +real_geometry_board_noise,fast_precision,24,10.770000000000001,11.482151620012546,40.0,56.0,4.4110037618184545,8.0,16.0,40.0,0,0 +real_geometry_board_noise,drift_gain_1_2,24,10.770000000000001,9.830551108590587,32.0,40.0,3.761748764716249,8.0,8.000000000000002,33.6,0,0 +real_geometry_board_noise,quality_scheduled_drift,24,10.770000000000001,9.830551108590587,32.0,40.0,3.761748764716249,8.0,8.000000000000002,33.6,0,0 +scope_like_multipath,current,24,10.770000000000001,22.943340809908015,62.79999999999998,88.0,4.727734084565672,8.000000000000002,16.0,80.80000000000001,0,0 +scope_like_multipath,robust_noise_guard,24,10.770000000000001,22.943340809908015,62.79999999999998,88.0,4.727734084565672,8.000000000000002,16.0,80.80000000000001,0,0 +scope_like_multipath,quiet_precision,24,10.770000000000001,17.147532219212493,40.0,63.99999999999999,3.7617487647162484,8.000000000000004,8.000000000000005,58.4,0,0 +scope_like_multipath,fast_precision,24,10.770000000000001,22.40714058089752,56.00000000000001,92.0,5.040539175116031,8.000000000000002,16.0,78.0,0,0 +scope_like_multipath,drift_gain_1_2,24,10.8,21.354092092763203,62.79999999999998,88.0,4.465388951979694,8.0,16.0,74.80000000000001,0,0 +scope_like_multipath,quality_scheduled_drift,24,10.8,21.354092092763203,62.79999999999998,88.0,4.465388951979694,8.0,16.0,74.80000000000001,0,0 +quality_transitions,current,24,10.770000000000001,18.351896116449183,96.0,80.0,4.4110037618184545,8.0,23.99999999999999,73.20000000000002,72,0 +quality_transitions,robust_noise_guard,24,10.770000000000001,17.481868755279223,80.0,80.0,4.374396022586886,8.0,16.00000000000001,67.6,72,0 +quality_transitions,quiet_precision,24,10.770000000000001,10.467771592924619,56.0,47.99999999999999,3.0800577533580347,7.999999999999999,16.0,42.0,72,0 +quality_transitions,fast_precision,24,10.770000000000001,19.145424296464835,88.00000000000001,88.0,4.861884148700504,8.0,24.000000000000004,73.6,72,0 +quality_transitions,drift_gain_1_2,24,10.770000000000001,16.291766736261422,83.39999999999999,76.0,4.089452052592408,8.0,16.000000000000004,63.599999999999994,72,0 +quality_transitions,quality_scheduled_drift,24,10.770000000000001,16.766578539927025,83.39999999999999,80.0,4.224793965697071,8.0,16.00000000000001,67.6,72,0 +thermal_0p05ppm,current,24,10.770000000000001,39.2873108676108,102.79999999999998,136.0,5.727479384554999,8.000000000000005,24.0,124.0,0,0 +thermal_0p05ppm,robust_noise_guard,24,10.770000000000001,39.2873108676108,102.79999999999998,136.0,5.727479384554999,8.000000000000005,24.0,124.0,0,0 +thermal_0p05ppm,quiet_precision,24,10.770000000000001,210.25812707207118,416.0,648.0,13.000191801484194,24.000000000000004,32.0,632.4,0,0 +thermal_0p05ppm,fast_precision,24,10.770000000000001,21.850277755888733,63.39999999999999,96.0,5.82476895848467,8.000000000000002,16.000000000000004,77.60000000000001,0,0 +thermal_0p05ppm,drift_gain_1_2,24,10.770000000000001,38.40828815100511,96.0,136.0,5.656836393570529,8.000000000000005,16.000000000000004,122.0,0,0 +thermal_0p05ppm,quality_scheduled_drift,24,10.770000000000001,38.40828815100511,96.0,136.0,5.656836393570529,8.000000000000005,16.000000000000004,122.0,0,0 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 new file mode 100644 index 0000000..f60d88b --- /dev/null +++ b/soc/software/pps_end_to_end_validation/output/stability/stability_report.md @@ -0,0 +1,70 @@ +# PPS stability analysis: current RTL + 180 ms firmware model + +This report is generated by `stability_analysis.py`. The absolute LNAV epoch path and the stochastic control loop are evaluated separately so a 20 ms semantic error cannot be hidden by loop noise. + +## Absolute epoch result + +- Current RTL delivered the validated subframe after 9 following bits. +- With 180 ms postponement, reference error was -0.000032 ms. +- DUT minus trusted PPS was 32.01 ns. That remainder is the model's configured four-PCLK capture pipeline, not a measured board delay. +- The production source still defines `POSTPONE_TOW` as 160 ms; this analysis uses the requested 180 ms candidate configuration. + +## Repository hardware data + +- 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. + +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. + +## Current-loop results + +`period max` is the worst |edge[n]-edge[n-1]| across all seeds. `absolute p-p` and `10 s wander p-p` expose the slow left/right motion that period RMS hides. + +| scenario | abs RMS median (ns) | abs p-p median (ns) | 10 s wander p-p (ns) | period RMS (ns) | period max (ns) | unlocks | +|---|---:|---:|---:|---:|---:|---:| +| real_geometry_board_noise | 10.88 | 48.00 | 36.80 | 3.87 | 8.00 | 0 | +| scope_like_multipath | 22.94 | 88.00 | 80.80 | 4.73 | 16.00 | 0 | +| quality_transitions | 18.35 | 80.00 | 73.20 | 4.41 | 24.00 | 0 | +| thermal_0p05ppm | 39.29 | 136.00 | 124.00 | 5.73 | 24.00 | 0 | + +The loop does not numerically oscillate or lose lock in these runs. The scope-like symptom is low-frequency phase wander: it can be tens of nanoseconds while one-second period jitter remains below 20 ns. Therefore a `p95 period jitter < 20 ns` assertion is not sufficient for an absolute ±20 ns requirement. + +## Linear stability + +The fixed-point coefficients give the following discrete poles at the 200 ms navigation update rate: + +| profile | natural frequency | damping | poles | max magnitude | +|---|---:|---:|---|---:| +| acquire | 0.120 Hz | 0.90 | 0.852913+0.033242j, 0.852913-0.033242j | 0.853561 | +| precision | 0.035 Hz | 0.90 | 0.959449+0.017031j, 0.959449-0.017031j | 0.959600 | +| robust | 0.012 Hz | 1.00 | 0.986668+0.000000j, 0.982945+0.000000j | 0.986668 | + +All magnitudes are below one. Profile changes preserve the PI integrator, and the RTL snapshots Kp/Ki/max-slew/lock limits together with the next commit, so the current acquire/precision/robust switching is structurally sound. Stable individual modes do not prove arbitrary-switching stability, but the asymmetric 8-update upgrade / 3-update downgrade hysteresis and the stress runs show no coefficient chatter or unlock. + +## Dynamic parameter sweep + +| tuning | worst period max (ns) | median abs RMS over scenarios (ns) | transitions | unlocks | +|---|---:|---:|---:|---:| +| current | 24.00 | 20.65 | 72 | 0 | +| robust_noise_guard | 24.00 | 20.21 | 72 | 0 | +| quiet_precision | 32.00 | 13.81 | 72 | 0 | +| fast_precision | 24.00 | 20.50 | 72 | 0 | +| drift_gain_1_2 | 16.00 | 18.82 | 72 | 0 | +| quality_scheduled_drift | 16.00 | 19.06 | 72 | 0 | + +The safest small candidate is `robust_noise_guard`: retain the current 0.035 Hz precision loop and 0.012 Hz robust loop, but reduce only the robust-profile WLS residual gain from 1/32 to 1/64. In the synthetic quality-transition case its worst period step changed from 24.00 to 16.00 ns. This is a candidate for hardware A/B testing, not enough evidence for a production constant change. + +The best synthetic all-scenario period result is `quality_scheduled_drift`: use drift gain 1/2 only with a precision-quality Doppler solution, fall back to the current 1/4 gain in robust quality, and combine that with the 1/64 robust bias gain. Its worst period step was 16.00 ns with no unlocks. It is the most meaningful dynamic A/B candidate, but the repository has no real drift-covariance or TIC samples with which to validate the threshold. + +Narrowing precision to 0.012 Hz improves stationary noise but lags even the explicit 0.05 ppm/120 s synthetic oscillator variation. Widening it improves oscillator tracking but passes more WLS/multipath motion. A geometry-only bandwidth scheduler cannot resolve that tradeoff. + +## Recommended architecture update + +1. Keep current acquire/precision/robust PI profiles until real TIC and oscillator-temperature data are available. Do not tune from scope appearance alone. +2. Log the existing firmware status line at every solve during validation: ABS_SAMPLE, REM_CODE_PHASE, capture sequence/counter, TDOP/HDOP/GDOP, C/N0, pseudorange residual RMS, raw/filtered bias, innovation, bias drift, hardware phase, rate, integrator, profile, lock, FPGA/oscillator temperature, DUT edge and trusted edge. +3. A/B test the simulated scheduled filter first: precision drift gain 1/2, robust drift gain 1/4, precision bias gain 1/32, and robust bias gain 1/64. Longer term, use estimated phase/drift covariance to interpolate those software gains within bounds. Keep RTL PI bandwidth changes hysteretic and preserve its integrator as today. +4. If temperature/frequency acceleration is the main source, add oscillator temperature feed-forward or a phase/frequency/frequency-drift estimator. Switching PI bandwidth alone either passes WLS wander or lags the oscillator. +5. Define acceptance separately: calibrated mean offset, absolute centered error/p-p wander, one-second period error, holdover, and re-lock. A physical max-20 ns claim requires a common-clock TIC run after cable/output-delay calibration; this synthetic test cannot certify it. diff --git a/soc/software/pps_end_to_end_validation/output/stability/synthetic_telemetry.csv b/soc/software/pps_end_to_end_validation/output/stability/synthetic_telemetry.csv new file mode 100644 index 0000000..cbfc90b --- /dev/null +++ b/soc/software/pps_end_to_end_validation/output/stability/synthetic_telemetry.csv @@ -0,0 +1,1501 @@ +scenario,time_s,abs_sample,rem_code_phase,corrected_abs_sample,capture_counter_125,used_satellites,capture_correction_ns,gdop,hdop,vdop,tdop,cn0_mean_dbhz,cn0_min_dbhz,pseudorange_noise_rms_m,clock_bias_sigma_m,clock_drift_error_mps,timing_error_ns,oscillator_ppm,quality_profile 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new file mode 100644 index 0000000..a1cb4ef --- /dev/null +++ b/soc/software/pps_end_to_end_validation/stability_analysis.py @@ -0,0 +1,865 @@ +#!/usr/bin/env python3 +"""End-to-end PPS epoch, telemetry, and closed-loop stability analysis. + +The bit-level model proves the current-RTL/180 ms epoch convention. This +module then drives the fixed-point firmware/RTL loop model with the telemetry +that is present in the repository and deterministic synthetic replacements for +the timing fields that were not captured on the board. +""" + +from __future__ import annotations + +import argparse +import csv +from dataclasses import dataclass, replace +import math +from pathlib import Path +import random +import re +import statistics +from typing import Iterable + +from pps_python.adaptive_pps import ( + ACQUIRE_PROFILE, + PROFILES, + FixedPointAdaptivePps, + LoopProfile, + Observation, + Profile, + QualityMetrics, + TRACKING_HZ, + classify_quality, +) + +from .pps_end_to_end import DecoderMode, EndToEndConfig, run_end_to_end + + +RF_HZ = 16_367_981.0 +CODE_FREQ_HZ = 1_023_000.0 +SPEED_OF_LIGHT_MPS = 299_792_458.0 +UPDATE_PERIOD_S = 0.2 +BOARD_CLOCK_SIGMA_M = 15.87 +BOARD_TDOP = 1.50 +BOARD_GDOP = 2.92 +BOARD_CN0_MEAN_DBHZ = 40.3 +BOARD_CN0_MIN_DBHZ = 34.8 +BOARD_DRIFT_SIGMA_MPS = 3.47 +BOARD_OSCILLATOR_PPM = 1.592 +BOARD_REFERENCE_RANGE_RATE_MPS = -291.75 + + +@dataclass(frozen=True) +class RealTelemetry: + gdop_time_s: tuple[float, ...] + gdop: tuple[float, ...] + tracked_time_s: tuple[float, ...] + tracked: tuple[int, ...] + horizontal_rms_m: float + horizontal_p95_m: float + horizontal_max_m: float + + def gdop_at(self, time_s: float) -> float: + return _linear_sample(self.gdop_time_s, self.gdop, time_s) + + def tracked_at(self, time_s: float) -> int: + if not self.tracked: + return 8 + index = _previous_index(self.tracked_time_s, time_s) + return self.tracked[index] + + +@dataclass(frozen=True) +class Scenario: + name: str + multipath: bool = False + geometry_transitions: bool = False + thermal_amplitude_ppm: float = 0.0 + thermal_period_s: float = 120.0 + + +@dataclass(frozen=True) +class Tuning: + name: str + profiles: dict[Profile, LoopProfile] + acquire: LoopProfile + drift_gain: float + robust_drift_gain: float | None = None + + +@dataclass(frozen=True) +class SyntheticEpoch: + scenario: str + time_s: float + abs_sample: int + rem_code_phase: int + corrected_abs_sample: float + capture_counter_125: int + capture_correction_ns: float + used_satellites: int + gdop: float + hdop: float + vdop: float + tdop: float + cn0_mean_dbhz: float + cn0_min_dbhz: float + pseudorange_noise_rms_m: float + clock_bias_sigma_m: float + clock_drift_error_mps: float + timing_error_ns: float + oscillator_ppm: float + quality_profile: str + observation: Observation + + +@dataclass(frozen=True) +class RunMetrics: + first_lock_s: float + absolute_rms_ns: float + absolute_p95_ns: float + absolute_max_ns: float + absolute_peak_to_peak_ns: float + period_rms_ns: float + period_p95_ns: float + period_max_ns: float + wander_10s_peak_to_peak_ns: float + profile_transitions: int + unlocks: int + + +@dataclass(frozen=True) +class Aggregate: + scenario: str + tuning: str + runs: int + lock_p95_s: float + absolute_rms_median_ns: float + absolute_max_p95_ns: float + absolute_peak_to_peak_median_ns: float + period_rms_median_ns: float + period_p95_median_ns: float + period_max_worst_ns: float + wander_10s_peak_to_peak_median_ns: float + profile_transitions_total: int + unlocks_total: int + + +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 _previous_index(times: tuple[float, ...], time_s: float) -> int: + if not times: + return 0 + low = 0 + high = len(times) + while low < high: + middle = (low + high) // 2 + if times[middle] <= time_s: + low = middle + 1 + else: + high = middle + return max(0, low - 1) + + +def _linear_sample( + times: tuple[float, ...], values: tuple[float, ...], time_s: float +) -> float: + if not values: + return 1.93 + if time_s <= times[0]: + return values[0] + if time_s >= times[-1]: + return values[-1] + lower = _previous_index(times, time_s) + upper = min(lower + 1, len(times) - 1) + span = times[upper] - times[lower] + if span <= 0.0: + return values[lower] + fraction = (time_s - times[lower]) / span + return values[lower] + fraction * (values[upper] - values[lower]) + + +def load_real_telemetry(software_dir: Path) -> RealTelemetry: + """Load the only real GPS capture and its horizontal replay output.""" + log_path = software_dir / "fusion/logs/gps_live_5min_20260713.csv" + comparison_path = ( + software_dir + / "fusion/output/model_compare_5min_20260713/model_comparison.csv" + ) + gdop_time: list[float] = [] + gdop: list[float] = [] + tracked_time: list[float] = [] + tracked: list[int] = [] + if log_path.exists(): + with log_path.open( + "r", encoding="utf-8", errors="replace", newline="" + ) as source: + for row in csv.reader(source): + if len(row) < 3: + continue + try: + time_s = float(row[0]) + except ValueError: + continue + match = re.search(r"GDOP=([0-9.]+)", row[2]) + if match: + gdop_time.append(time_s) + gdop.append(float(match.group(1))) + match = re.search(r"tracked satellites=(\d+)", row[2]) + if match: + tracked_time.append(time_s) + tracked.append(int(match.group(1))) + + radii: list[float] = [] + if comparison_path.exists(): + with comparison_path.open("r", encoding="utf-8", newline="") as source: + for row in csv.DictReader(source): + try: + radii.append( + math.hypot(float(row["gps_e_m"]), float(row["gps_n_m"])) + ) + except (KeyError, TypeError, ValueError): + continue + horizontal_rms = ( + math.sqrt(statistics.mean(value * value for value in radii)) + if radii + else math.nan + ) + return RealTelemetry( + tuple(gdop_time), + tuple(gdop), + tuple(tracked_time), + tuple(tracked), + horizontal_rms, + _percentile(radii, 0.95), + max(radii, default=math.nan), + ) + + +def scenario_matrix() -> tuple[Scenario, ...]: + return ( + Scenario("real_geometry_board_noise"), + Scenario("scope_like_multipath", multipath=True), + Scenario("quality_transitions", geometry_transitions=True), + Scenario("thermal_0p05ppm", thermal_amplitude_ppm=0.05), + ) + + +def tuning_matrix() -> tuple[Tuning, ...]: + current = dict(PROFILES) + guarded = dict(PROFILES) + guarded[Profile.ROBUST] = replace( + guarded[Profile.ROBUST], software_bias_gain=1.0 / 64.0 + ) + quiet = dict(guarded) + quiet[Profile.PRECISION] = replace( + quiet[Profile.PRECISION], + natural_frequency_hz=0.012, + damping=1.0, + software_bias_gain=1.0 / 16.0, + ) + fast = dict(guarded) + fast[Profile.PRECISION] = replace( + fast[Profile.PRECISION], natural_frequency_hz=0.050, damping=1.0 + ) + return ( + Tuning("current", current, ACQUIRE_PROFILE, 1.0 / 4.0), + Tuning("robust_noise_guard", guarded, ACQUIRE_PROFILE, 1.0 / 4.0), + Tuning("quiet_precision", quiet, ACQUIRE_PROFILE, 1.0 / 4.0), + Tuning("fast_precision", fast, ACQUIRE_PROFILE, 1.0 / 4.0), + Tuning("drift_gain_1_2", guarded, ACQUIRE_PROFILE, 1.0 / 2.0), + Tuning( + "quality_scheduled_drift", + guarded, + ACQUIRE_PROFILE, + 1.0 / 2.0, + 1.0 / 4.0, + ), + ) + + +def _weak_geometry(time_s: float) -> bool: + return (80.0 <= time_s < 110.0) or (180.0 <= time_s < 205.0) + + +def synthesize_epochs( + telemetry: RealTelemetry, + scenario: Scenario, + *, + duration_s: float, + seed: int, +) -> list[SyntheticEpoch]: + """Combine real GDOP/satellite count with documented board noise.""" + rng = random.Random(seed) + result: list[SyntheticEpoch] = [] + count = round(duration_s / UPDATE_PERIOD_S) + rf_initial = (1 << 32) - round(10.25 * RF_HZ) - 1000 + for update in range(count): + time_s = update * UPDATE_PERIOD_S + gdop = telemetry.gdop_at(time_s) + satellites = max(4, min(8, telemetry.tracked_at(time_s))) + tdop = gdop * (BOARD_TDOP / BOARD_GDOP) + pdop = math.sqrt(max(0.0, gdop * gdop - tdop * tdop)) + vdop = 0.65 * pdop + hdop = math.sqrt(max(0.0, pdop * pdop - vdop * vdop)) + cn0_mean = ( + BOARD_CN0_MEAN_DBHZ + + 1.2 * math.sin(2.0 * math.pi * time_s / 93.0) + + rng.gauss(0.0, 0.35) + ) + cn0_min = cn0_mean - ( + BOARD_CN0_MEAN_DBHZ - BOARD_CN0_MIN_DBHZ + ) + chi_square = 0.35 + velocity_residual = 0.23 + drift_sigma = BOARD_DRIFT_SIGMA_MPS + if scenario.geometry_transitions and _weak_geometry(time_s): + gdop = 8.0 + tdop = 5.0 + hdop = 4.3 + vdop = math.sqrt(max(0.0, gdop * gdop - tdop * tdop - hdop * hdop)) + satellites = 5 + cn0_mean = 27.0 + cn0_min = 18.0 + chi_square = 0.85 + velocity_residual = 1.8 + clock_sigma = 65.0 + drift_sigma = 5.0 + uere_sigma = clock_sigma / tdop + else: + uere_sigma = ( + (BOARD_CLOCK_SIGMA_M / BOARD_TDOP) + * 10.0 ** ((BOARD_CN0_MEAN_DBHZ - cn0_mean) / 20.0) + ) + clock_sigma = tdop * uere_sigma + + pseudorange_errors = [ + rng.gauss(0.0, uere_sigma) for _ in range(satellites) + ] + pseudorange_rms = math.sqrt( + statistics.mean(value * value for value in pseudorange_errors) + ) + long_omega = 2.0 * math.pi / 75.0 + short_omega = 2.0 * math.pi / 19.0 + true_datum_drift = ( + 5.0 * math.sin(long_omega * time_s) + + 2.0 * math.sin(short_omega * time_s) + ) + true_datum_offset = ( + 5.0 * (1.0 - math.cos(long_omega * time_s)) / long_omega + + 2.0 * (1.0 - math.cos(short_omega * time_s)) / short_omega + ) + timing_error_s = rng.gauss(0.0, clock_sigma / SPEED_OF_LIGHT_MPS) + if scenario.multipath: + timing_error_s += ( + 25.0e-9 * math.sin(2.0 * math.pi * time_s / 67.0) + + 15.0e-9 + * math.sin(2.0 * math.pi * time_s / 29.0 + 0.8) + ) + drift_error = true_datum_drift + rng.gauss(0.0, drift_sigma) + oscillator_ppm = BOARD_OSCILLATOR_PPM + ( + scenario.thermal_amplitude_ppm + * math.sin(2.0 * math.pi * time_s / scenario.thermal_period_s) + ) + quality = QualityMetrics( + tdop, + gdop, + clock_sigma, + cn0_mean, + cn0_min, + chi_square, + velocity_residual, + satellites, + ) + observation = Observation( + time_s, + timing_error_s, + drift_error, + quality, + True, + BOARD_REFERENCE_RANGE_RATE_MPS, + 1, + true_datum_offset, + ) + + continuous_abs = rf_initial + time_s * RF_HZ + raw_unwrapped = math.ceil(continuous_abs - 1.0e-12) + fractional_sample = raw_unwrapped - continuous_abs + rem_code_phase = round(fractional_sample * CODE_FREQ_HZ) + abs_sample = raw_unwrapped & 0xFFFF_FFFF + corrected_abs = abs_sample - rem_code_phase / CODE_FREQ_HZ + raw_capture_counter = math.floor( + time_s * TRACKING_HZ * (1.0 + oscillator_ppm * 1.0e-6) + ) + capture_correction_ticks = round( + (rem_code_phase / CODE_FREQ_HZ) * (TRACKING_HZ / RF_HZ) + ) + capture_counter = raw_capture_counter - capture_correction_ticks + result.append( + SyntheticEpoch( + scenario.name, + time_s, + abs_sample, + rem_code_phase, + corrected_abs, + capture_counter, + capture_correction_ticks / TRACKING_HZ * 1.0e9, + satellites, + gdop, + hdop, + vdop, + tdop, + cn0_mean, + cn0_min, + pseudorange_rms, + clock_sigma, + drift_error, + timing_error_s * 1.0e9, + oscillator_ppm, + classify_quality(quality).name.lower(), + observation, + ) + ) + return result + + +def _run_loop(epochs: list[SyntheticEpoch], tuning: Tuning): + model = FixedPointAdaptivePps( + oscillator_ppm=BOARD_OSCILLATOR_PPM, + profiles=tuning.profiles, + acquire_profile=tuning.acquire, + software_drift_gain=tuning.drift_gain, + ) + for epoch in epochs: + # Update at a solve boundary; the selected value applies to the + # preceding synthetic interval, whose endpoint is this observation. + model.oscillator_fraction = epoch.oscillator_ppm * 1.0e-6 + if tuning.robust_drift_gain is not None: + target = classify_quality(epoch.observation.quality) + model.software_drift_gain = ( + tuning.robust_drift_gain + if target != Profile.PRECISION + else tuning.drift_gain + ) + model.update(epoch.observation) + finish_s = epochs[-1].time_s + UPDATE_PERIOD_S if epochs else 0.0 + return model.finish(finish_s) + + +def measure_trace(trace, *, after_s: float) -> RunMetrics: + edge_errors = [ + error for _, error in trace.integer_second_edge_errors(after_s=after_s) + ] + if len(edge_errors) < 12: + raise RuntimeError("not enough locked integer-second PPS edges") + center = statistics.median(edge_errors) + centered = [value - center for value in edge_errors] + periods = [ + edge_errors[index] - edge_errors[index - 1] + for index in range(1, len(edge_errors)) + ] + moving_10s = [ + statistics.mean(centered[max(0, index - 9) : index + 1]) + for index in range(len(centered)) + ] + profiles = [ + profile + for time_s, profile in zip(trace.time_s, trace.profile) + if time_s >= after_s + ] + profile_transitions = sum( + profiles[index] != profiles[index - 1] + for index in range(1, len(profiles)) + ) + return RunMetrics( + trace.first_lock_time() if trace.first_lock_time() is not None else math.inf, + math.sqrt(statistics.mean(value * value for value in centered)) * 1.0e9, + _percentile((abs(value) for value in centered), 0.95) * 1.0e9, + max(abs(value) for value in centered) * 1.0e9, + (max(centered) - min(centered)) * 1.0e9, + math.sqrt(statistics.mean(value * value for value in periods)) * 1.0e9, + _percentile((abs(value) for value in periods), 0.95) * 1.0e9, + max(abs(value) for value in periods) * 1.0e9, + (max(moving_10s) - min(moving_10s)) * 1.0e9, + profile_transitions, + trace.unlock_events, + ) + + +def aggregate( + scenario: Scenario, tuning: Tuning, runs: list[RunMetrics] +) -> Aggregate: + return Aggregate( + scenario.name, + tuning.name, + len(runs), + _percentile((run.first_lock_s for run in runs), 0.95), + statistics.median(run.absolute_rms_ns for run in runs), + _percentile((run.absolute_max_ns for run in runs), 0.95), + statistics.median(run.absolute_peak_to_peak_ns for run in runs), + statistics.median(run.period_rms_ns for run in runs), + statistics.median(run.period_p95_ns for run in runs), + max(run.period_max_ns for run in runs), + statistics.median(run.wander_10s_peak_to_peak_ns for run in runs), + sum(run.profile_transitions for run in runs), + sum(run.unlocks for run in runs), + ) + + +def discrete_poles(profile: LoopProfile) -> tuple[complex, complex]: + """Poles of the exact sample/commit ordering, before slew saturation.""" + kp_q46, ki_q46, _ = profile.fixed_coefficients(UPDATE_PERIOD_S) + kp = kp_q46 / (1 << 46) + ki = ki_q46 / (1 << 46) + ticks = UPDATE_PERIOD_S * TRACKING_HZ + a = 1.0 - ticks * (kp + ki) + b = ticks + c = -ki + d = 1.0 + trace = a + d + determinant = a * d - b * c + discriminant = complex(trace * trace - 4.0 * determinant, 0.0) ** 0.5 + return (trace + discriminant) / 2.0, (trace - discriminant) / 2.0 + + +def write_telemetry(path: Path, epochs: list[SyntheticEpoch]) -> None: + fields = [ + "scenario", "time_s", "abs_sample", "rem_code_phase", + "corrected_abs_sample", "capture_counter_125", "used_satellites", + "capture_correction_ns", + "gdop", "hdop", "vdop", "tdop", "cn0_mean_dbhz", + "cn0_min_dbhz", "pseudorange_noise_rms_m", "clock_bias_sigma_m", + "clock_drift_error_mps", "timing_error_ns", "oscillator_ppm", + "quality_profile", + ] + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as output: + writer = csv.DictWriter( + output, fieldnames=fields, lineterminator="\n" + ) + writer.writeheader() + for epoch in epochs: + writer.writerow({field: getattr(epoch, field) for field in fields}) + + +def write_sweep(path: Path, results: list[Aggregate]) -> 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(Aggregate.__annotations__), + lineterminator="\n", + ) + writer.writeheader() + for item in results: + writer.writerow(item.__dict__) + + +def _fmt(value: float) -> str: + return "n/a" if not math.isfinite(value) else f"{value:.2f}" + + +def write_report( + path: Path, + telemetry: RealTelemetry, + results: list[Aggregate], + epoch_result, +) -> None: + gdop = telemetry.gdop + tracked = telemetry.tracked + current = [item for item in results if item.tuning == "current"] + guarded = [ + item for item in results if item.tuning == "robust_noise_guard" + ] + lines = [ + "# PPS stability analysis: current RTL + 180 ms firmware model", + "", + "This report is generated by `stability_analysis.py`. The absolute " + "LNAV epoch path and the stochastic control loop are evaluated " + "separately so a 20 ms semantic error cannot be hidden by loop noise.", + "", + "## Absolute epoch result", + "", + f"- Current RTL delivered the validated subframe after " + f"{epoch_result.first_subframe_readout_bits} following bits.", + f"- With 180 ms postponement, reference error was " + f"{epoch_result.first_reference_error_ms:.6f} ms.", + f"- DUT minus trusted PPS was " + f"{epoch_result.pps.edges[0].dut_minus_trusted_s * 1.0e9:.2f} ns. " + "That remainder is the model's configured four-PCLK capture pipeline, " + "not a measured board delay.", + "- The production source still defines `POSTPONE_TOW` as 160 ms; this " + "analysis uses the requested 180 ms candidate configuration.", + "", + "## Repository hardware data", + "", + f"- Real GPS capture: {len(gdop)} GDOP samples and {len(tracked)} " + "tracked-satellite samples over about five minutes.", + f"- GDOP min/median/p95/max: {_fmt(min(gdop))} / " + f"{_fmt(statistics.median(gdop))} / {_fmt(_percentile(gdop, 0.95))} / " + f"{_fmt(max(gdop))}.", + f"- Tracked satellites: {min(tracked)}..{max(tracked)} " + f"(median {statistics.median(tracked):.0f}).", + f"- Horizontal scatter RMS/p95/max: " + 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.", + "", + "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.", + "", + "## Current-loop results", + "", + "`period max` is the worst |edge[n]-edge[n-1]| across all seeds. " + "`absolute p-p` and `10 s wander p-p` expose the slow left/right " + "motion that period RMS hides.", + "", + "| scenario | abs RMS median (ns) | abs p-p median (ns) | " + "10 s wander p-p (ns) | period RMS (ns) | period max (ns) | unlocks |", + "|---|---:|---:|---:|---:|---:|---:|", + ] + for item in current: + lines.append( + f"| {item.scenario} | {item.absolute_rms_median_ns:.2f} | " + f"{item.absolute_peak_to_peak_median_ns:.2f} | " + f"{item.wander_10s_peak_to_peak_median_ns:.2f} | " + f"{item.period_rms_median_ns:.2f} | " + f"{item.period_max_worst_ns:.2f} | {item.unlocks_total} |" + ) + lines.extend( + [ + "", + "The loop does not numerically oscillate or lose lock in these " + "runs. The scope-like symptom is low-frequency phase wander: it " + "can be tens of nanoseconds while one-second period jitter remains " + "below 20 ns. Therefore a `p95 period jitter < 20 ns` assertion is " + "not sufficient for an absolute ±20 ns requirement.", + "", + "## Linear stability", + "", + "The fixed-point coefficients give the following discrete poles " + "at the 200 ms navigation update rate:", + "", + "| profile | natural frequency | damping | poles | max magnitude |", + "|---|---:|---:|---|---:|", + ] + ) + profile_rows = [("acquire", ACQUIRE_PROFILE)] + [ + (name, PROFILES[profile]) + for name, profile in ( + ("precision", Profile.PRECISION), + ("robust", Profile.ROBUST), + ) + ] + for name, profile in profile_rows: + poles = discrete_poles(profile) + pole_text = ", ".join( + f"{pole.real:.6f}{pole.imag:+.6f}j" for pole in poles + ) + lines.append( + f"| {name} | {profile.natural_frequency_hz:.3f} Hz | " + f"{profile.damping:.2f} | {pole_text} | " + f"{max(abs(pole) for pole in poles):.6f} |" + ) + lines.extend( + [ + "", + "All magnitudes are below one. Profile changes preserve the PI " + "integrator, and the RTL snapshots Kp/Ki/max-slew/lock limits " + "together with the next commit, so the current acquire/precision/" + "robust switching is structurally sound. Stable individual modes " + "do not prove arbitrary-switching stability, but the asymmetric " + "8-update upgrade / 3-update downgrade hysteresis and the stress " + "runs show no coefficient chatter or unlock.", + "", + "## Dynamic parameter sweep", + "", + "| tuning | worst period max (ns) | median abs RMS over scenarios " + "(ns) | transitions | unlocks |", + "|---|---:|---:|---:|---:|", + ] + ) + for tuning in tuning_matrix(): + subset = [item for item in results if item.tuning == tuning.name] + median_scenario_rms = statistics.median( + item.absolute_rms_median_ns for item in subset + ) + lines.append( + f"| {tuning.name} | " + f"{max(item.period_max_worst_ns for item in subset):.2f} | " + f"{median_scenario_rms:.2f} | " + f"{sum(item.profile_transitions_total for item in subset)} | " + f"{sum(item.unlocks_total for item in subset)} |" + ) + current_transition = next( + item for item in current if item.scenario == "quality_transitions" + ) + guarded_transition = next( + item for item in guarded if item.scenario == "quality_transitions" + ) + scheduled = [ + item for item in results + if item.tuning == "quality_scheduled_drift" + ] + lines.extend( + [ + "", + "The safest small candidate is `robust_noise_guard`: retain the " + "current 0.035 Hz precision loop and 0.012 Hz robust loop, but " + "reduce only the robust-profile WLS residual gain from 1/32 to " + "1/64. In the synthetic quality-transition case its worst period " + f"step changed from {current_transition.period_max_worst_ns:.2f} " + f"to {guarded_transition.period_max_worst_ns:.2f} ns. This is a " + "candidate for hardware A/B testing, not enough evidence for a " + "production constant change.", + "", + "The best synthetic all-scenario period result is " + "`quality_scheduled_drift`: use drift gain 1/2 only with a " + "precision-quality Doppler solution, fall back to the current 1/4 " + "gain in robust quality, and combine that with the 1/64 robust " + "bias gain. Its worst period step was " + f"{max(item.period_max_worst_ns for item in scheduled):.2f} ns " + "with no unlocks. It is the most meaningful dynamic A/B candidate, " + "but the repository has no real drift-covariance or TIC samples " + "with which to validate the threshold.", + "", + "Narrowing precision to 0.012 Hz improves stationary noise but " + "lags even the explicit 0.05 ppm/120 s synthetic oscillator " + "variation. Widening it improves oscillator tracking but passes " + "more WLS/multipath motion. A geometry-only bandwidth scheduler " + "cannot resolve that tradeoff.", + "", + "## Recommended architecture update", + "", + "1. Keep current acquire/precision/robust PI profiles until real " + "TIC and oscillator-temperature data are available. Do not tune " + "from scope appearance alone.", + "2. Log the existing firmware status line at every solve during " + "validation: ABS_SAMPLE, REM_CODE_PHASE, capture sequence/counter, " + "TDOP/HDOP/GDOP, C/N0, pseudorange residual RMS, raw/filtered bias, " + "innovation, bias drift, hardware phase, rate, integrator, profile, " + "lock, FPGA/oscillator temperature, DUT edge and trusted edge.", + "3. A/B test the simulated scheduled filter first: precision " + "drift gain 1/2, robust drift gain 1/4, precision bias gain 1/32, " + "and robust bias gain 1/64. Longer term, use estimated phase/drift " + "covariance to interpolate those software gains within bounds. " + "Keep RTL PI bandwidth changes hysteretic and preserve its " + "integrator as today.", + "4. If temperature/frequency acceleration is the main source, add " + "oscillator temperature feed-forward or a phase/frequency/frequency-" + "drift estimator. Switching PI bandwidth alone either passes WLS " + "wander or lags the oscillator.", + "5. Define acceptance separately: calibrated mean offset, absolute " + "centered error/p-p wander, one-second period error, holdover, and " + "re-lock. A physical max-20 ns claim requires a common-clock TIC " + "run after cable/output-delay calibration; this synthetic test " + "cannot certify it.", + "", + ] + ) + 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("--duration", type=float, default=300.0) + parser.add_argument("--seeds", type=int, default=24) + parser.add_argument( + "--output-dir", + type=Path, + default=Path(__file__).resolve().parent / "output/stability", + ) + parser.add_argument("--no-write", action="store_true") + return parser.parse_args() + + +def main() -> int: + args = parse_args() + if args.duration < 120.0 or args.seeds < 1: + raise SystemExit("duration must be >= 120 s and seeds must be positive") + software_dir = Path(__file__).resolve().parents[1] + telemetry = load_real_telemetry(software_dir) + if not telemetry.gdop or not telemetry.tracked: + raise SystemExit("real GPS GDOP/satellite capture was not found") + + epoch_result = run_end_to_end( + EndToEndConfig( + decoder_mode=DecoderMode.CURRENT_RTL, + postpone_tow_s=0.180, + ) + ) + analysis_start = max(60.0, args.duration / 3.0) + results: list[Aggregate] = [] + telemetry_epochs: list[SyntheticEpoch] = [] + for scenario in scenario_matrix(): + seed_epochs = [ + synthesize_epochs( + telemetry, + scenario, + duration_s=args.duration, + seed=seed, + ) + for seed in range(1, args.seeds + 1) + ] + if scenario.name == "scope_like_multipath": + telemetry_epochs = seed_epochs[0] + for tuning in tuning_matrix(): + runs = [ + measure_trace( + _run_loop(epochs, tuning), after_s=analysis_start + ) + for epochs in seed_epochs + ] + results.append(aggregate(scenario, tuning, runs)) + + print( + "scenario tuning abs_rms " + "abs_pp wander10 period_rms period_max unlock" + ) + for item in results: + print( + f"{item.scenario:27s} {item.tuning:22s} " + f"{item.absolute_rms_median_ns:7.2f} " + f"{item.absolute_peak_to_peak_median_ns:7.2f} " + f"{item.wander_10s_peak_to_peak_median_ns:8.2f} " + f"{item.period_rms_median_ns:10.2f} " + f"{item.period_max_worst_ns:10.2f} " + f"{item.unlocks_total:6d}" + ) + print( + "current RTL + 180 ms: DUT-trusted = " + f"{epoch_result.pps.edges[0].dut_minus_trusted_s * 1.0e9:.2f} ns" + ) + if not args.no_write: + write_telemetry(args.output_dir / "synthetic_telemetry.csv", telemetry_epochs) + write_sweep(args.output_dir / "parameter_sweep.csv", results) + write_report( + args.output_dir / "stability_report.md", + telemetry, + results, + epoch_result, + ) + print(f"wrote {args.output_dir}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/soc/software/pps_end_to_end_validation/test_stability_analysis.py b/soc/software/pps_end_to_end_validation/test_stability_analysis.py new file mode 100644 index 0000000..8adf4ea --- /dev/null +++ b/soc/software/pps_end_to_end_validation/test_stability_analysis.py @@ -0,0 +1,84 @@ +from __future__ import annotations + +import math +from pathlib import Path +import unittest + +from pps_python.adaptive_pps import ACQUIRE_PROFILE, PROFILES, Profile + +from pps_end_to_end_validation.stability_analysis import ( + discrete_poles, + load_real_telemetry, + measure_trace, + scenario_matrix, + synthesize_epochs, + tuning_matrix, + _run_loop, +) + + +SOFTWARE_DIR = Path(__file__).resolve().parents[1] + + +class StabilityAnalysisTests(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.telemetry = load_real_telemetry(SOFTWARE_DIR) + + def test_repository_capture_is_loaded(self): + self.assertEqual(len(self.telemetry.gdop), 989) + self.assertEqual(len(self.telemetry.tracked), 990) + self.assertEqual(set(self.telemetry.tracked), {8}) + self.assertAlmostEqual(min(self.telemetry.gdop), 1.8593929921) + self.assertAlmostEqual(max(self.telemetry.gdop), 3.1537716348) + self.assertGreater(self.telemetry.horizontal_rms_m, 8.0) + self.assertLess(self.telemetry.horizontal_rms_m, 9.0) + + def test_fixed_point_profiles_have_inside_unit_circle_poles(self): + for profile in ( + ACQUIRE_PROFILE, + PROFILES[Profile.PRECISION], + PROFILES[Profile.ROBUST], + ): + poles = discrete_poles(profile) + self.assertTrue(all(abs(pole) < 1.0 for pole in poles)) + + def test_synthetic_telemetry_exercises_wrap_and_physical_fields(self): + epochs = synthesize_epochs( + self.telemetry, + scenario_matrix()[0], + duration_s=120.0, + seed=1, + ) + self.assertEqual(len(epochs), 600) + self.assertTrue(any(epochs[i].abs_sample < epochs[i - 1].abs_sample + for i in range(1, len(epochs)))) + self.assertTrue(all(0 <= epoch.rem_code_phase <= 1_023_000 + for epoch in epochs)) + self.assertTrue(all(epoch.used_satellites == 8 for epoch in epochs)) + self.assertTrue(all(math.isfinite(epoch.corrected_abs_sample) + for epoch in epochs)) + self.assertTrue(all(epoch.capture_correction_ns >= 0.0 + for epoch in epochs)) + + def test_current_loop_locks_without_unlock_in_board_scenario(self): + epochs = synthesize_epochs( + self.telemetry, + scenario_matrix()[0], + duration_s=120.0, + seed=4, + ) + trace = _run_loop(epochs, tuning_matrix()[0]) + metrics = measure_trace(trace, after_s=60.0) + self.assertLess(metrics.first_lock_s, 15.0) + self.assertEqual(metrics.unlocks, 0) + self.assertLessEqual(metrics.period_max_ns, 16.0 + 1.0e-9) + # Slow absolute motion is deliberately measured separately. + self.assertGreater( + metrics.absolute_peak_to_peak_ns, + metrics.period_max_ns, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/soc/software/pps_python/adaptive_pps.py b/soc/software/pps_python/adaptive_pps.py index 477b4ea..65ab563 100644 --- a/soc/software/pps_python/adaptive_pps.py +++ b/soc/software/pps_python/adaptive_pps.py @@ -11,7 +11,7 @@ from __future__ import annotations from dataclasses import dataclass from enum import IntEnum import math -from typing import Iterable +from typing import Iterable, Mapping TRACKING_HZ = 125_000_000 @@ -242,17 +242,7 @@ class AdaptiveTrace: def one_second_edge_jitter_rms(self, *, after_s: float = 0.0) -> float: """Return 1 s PPS-edge jitter including the 125 MHz output grid.""" - selected = { - round(time_s, 9): ( - round(-phase_s * TRACKING_HZ) / TRACKING_HZ - ) - for time_s, phase_s, locked in zip( - self.time_s, self.phase_error_s, self.locked - ) - if locked - and time_s >= after_s - and abs(time_s - round(time_s)) < 1.0e-9 - } + selected = dict(self.integer_second_edge_errors(after_s=after_s)) differences = [ edge_error - selected[round(time_s - 1.0, 9)] for time_s, edge_error in selected.items() @@ -264,6 +254,31 @@ class AdaptiveTrace: sum(value * value for value in differences) / len(differences) ) + def integer_second_edge_errors( + self, + *, + after_s: float = 0.0, + locked_only: bool = True, + ) -> list[tuple[float, float]]: + """Return physical PPS edge error samples on the 125 MHz grid. + + Period jitter alone hides low-frequency motion. Exposing the signed + edge error lets stability analyses report absolute RMS, peak-to-peak + wander and moving-average wander alongside cycle-to-cycle jitter. + """ + return [ + ( + round(time_s, 9), + round(-phase_s * TRACKING_HZ) / TRACKING_HZ, + ) + for time_s, phase_s, locked in zip( + self.time_s, self.phase_error_s, self.locked + ) + if (locked or not locked_only) + and time_s >= after_s + and abs(time_s - round(time_s)) < 1.0e-9 + ] + def phase_rms(self, *, after_s: float = 0.0) -> float: values = [ phase_s @@ -319,6 +334,10 @@ class FixedPointAdaptivePps: software_filter: bool = True, receiver_clock_bias_m: float = 1200.0, receiver_clock_drift_mps: float = 475.0, + profiles: Mapping[Profile, LoopProfile] | None = None, + acquire_profile: LoopProfile | None = None, + software_drift_gain: float = 1.0 / 4.0, + software_fallback_drift_gain: float = 1.0 / 16.0, ) -> None: self.update_period_s = update_period_s self.oscillator_fraction = oscillator_ppm * 1.0e-6 @@ -326,9 +345,24 @@ class FixedPointAdaptivePps: self.software_filter = software_filter self.receiver_clock_bias_m = receiver_clock_bias_m self.receiver_clock_drift_mps = receiver_clock_drift_mps + self.profiles = dict(PROFILES if profiles is None else profiles) + missing_profiles = set(Profile) - self.profiles.keys() + if missing_profiles: + raise ValueError(f"missing loop profiles: {sorted(missing_profiles)}") + self.acquire_profile = ( + ACQUIRE_PROFILE if acquire_profile is None else acquire_profile + ) + if not 0.0 < software_drift_gain <= 1.0: + raise ValueError("software_drift_gain must be in (0, 1]") + if not 0.0 < software_fallback_drift_gain <= 1.0: + raise ValueError( + "software_fallback_drift_gain must be in (0, 1]" + ) + self.software_drift_gain = software_drift_gain + self.software_fallback_drift_gain = software_fallback_drift_gain self.selector = QualitySelector() self.profile = Profile.HOLDOVER if adaptive else Profile.PRECISION - self.parameters = PROFILES[self.profile] + self.parameters = self.profiles[self.profile] self.kp_q46, self.ki_q46, self.max_slew_q32 = ( self.parameters.fixed_coefficients(update_period_s) ) @@ -376,10 +410,10 @@ class FixedPointAdaptivePps: def _select_loop_parameters(self) -> None: parameters = ( - ACQUIRE_PROFILE + self.acquire_profile if self.adaptive and self.profile != Profile.HOLDOVER and not self.locked - else PROFILES[self.profile] + else self.profiles[self.profile] ) if parameters == self.parameters: return @@ -541,15 +575,16 @@ class FixedPointAdaptivePps: + self.parameters.software_bias_gain * innovation_m ) if observation.clock_drift_valid: - self.software_clock_drift_mps += 0.25 * ( + self.software_clock_drift_mps += self.software_drift_gain * ( measured_drift_mps - self.software_clock_drift_mps ) else: if self.software_updates < self.parameters.software_settle_updates: self.software_clock_drift_mps = fallback_drift_mps else: - self.software_clock_drift_mps += (1.0 / 16.0) * ( - fallback_drift_mps - self.software_clock_drift_mps + self.software_clock_drift_mps += ( + self.software_fallback_drift_gain * + (fallback_drift_mps - self.software_clock_drift_mps) ) self.software_previous_measurement_m = measured_bias_m self.software_error_s = -( @@ -705,6 +740,10 @@ def run_observations( software_filter: bool = True, receiver_clock_bias_m: float = 1200.0, receiver_clock_drift_mps: float = 475.0, + profiles: Mapping[Profile, LoopProfile] | None = None, + acquire_profile: LoopProfile | None = None, + software_drift_gain: float = 1.0 / 4.0, + software_fallback_drift_gain: float = 1.0 / 16.0, ) -> AdaptiveTrace: model = FixedPointAdaptivePps( oscillator_ppm=oscillator_ppm, @@ -712,6 +751,10 @@ def run_observations( software_filter=software_filter, receiver_clock_bias_m=receiver_clock_bias_m, receiver_clock_drift_mps=receiver_clock_drift_mps, + profiles=profiles, + acquire_profile=acquire_profile, + software_drift_gain=software_drift_gain, + software_fallback_drift_gain=software_fallback_drift_gain, ) for observation in observations: model.update(observation)