Add synthetic fixture and Cramer-Rao validation for xray linear prediction (refs #1) by jimsweeney · Pull Request #2 · NVIDIA/cuPhoton

jimsweeney · 2026-09-16T01:57:21Z

Adds a reproducible study of the damped-mode loss discussed in #1. Known synthetic modes and controlled noise let us measure recovery, parameter bias and scatter before changing the estimator's root filter.

cuphoton.xray.synthetic_validation generates the fixture, computes Cramer-Rao bounds from Fisher information using a numerical model Jacobian, and runs Monte Carlo sweeps. Frequency matching maximizes recovered modes before minimizing total frequency error; each fitted mode is used at most once. Truth and estimates use the same amplitude/phase convention.
Per-mode bias, standard deviation and RMSE are conditional on recovery. Bounds are unconditional and reported as standard deviation and variance. Trial counts distinguish successful fits, mode loss and estimator exceptions; the text report includes the exception count.
--distortion kind:amount supports chirp, Gaussian envelope, baseline drift, clipping and a single-sample glitch. The residual RMS divided by sigma provides a reconstruction diagnostic. Invalid noise scales, nonpositive trial/component counts and invalid distortion amounts fail before trials run. Singular Fisher information produces a validation error instead of a finite bound for unidentifiable modes.
linear-prediction-validate (lpv) prints text or strict JSON. --output-dir writes a versioned summary.json with configuration, runtime, results and relative artifact names, plus offline Bokeh HTML when the viz extra is installed. Source revision and tracked-file dirtiness are recorded only for a tracked source checkout.
The validation guide documents assumptions, schema and reproduction commands. XRay print helpers now live in _cli_output.py.

Historical representative validation result

Representative CPU result regenerated at d0670d2 with the default fixture and seed:

uv run --locked --extra viz cuphoton xray lpv --trials 300 \
  --snr-db 40,30,20,10,5 --output-dir /tmp/cuphoton-lpv

SNR (dB)	Successful / attempted trials	Weak mode (w = 0.9) loss rate	Weak frequency std / bound	Strong frequency std / bound
40	252 / 300	0.160	3.31	3.79
30	207 / 300	0.310	3.35	3.15
20	269 / 300	0.103	2.99	2.84
10	204 / 300	0.320	1.89	2.30
5	142 / 300	0.523	1.06	1.73

All five levels had zero estimator exceptions. The regenerated summary records the revision above and source_dirty: false; earlier comment attachments are from an older revision. Conditional finite-sample scatter can fall below the unconditional bound. The estimator and its root filter are unchanged.

Validation at 68327845 on public main 7f9a0669: make test-xray (392 passed, 33 skipped), make test-core (201 passed, 2 skipped), and make ci-lint passed. Two regressions reproduce zero-amplitude and duplicate-mode Fisher singularities. The rebase preserves main's expanded CLI surface and this PR's validation command. Production estimators and defaults are unchanged.

🤖 Generated with Claude Code

https://claude.ai/code/session_014wyj5iPSHmtR3vSTvJWKfw

jimsweeney · 2026-09-16T01:58:38Z

Representative summary.json from cuphoton xray lpv --trials 300 --snr-db 40,30,20,10,5 --output-dir <dir> at a9bb709 (CPU, WSL2 Ubuntu 24.04, Python 3.12). The Bokeh validation.html written next to it is not attachable here; a PNG rendering of the same summary follows.

summary.json

{
 "artifacts": {
  "figure": "validation.html",
  "summary": "summary.json"
 },
 "command": "linear-prediction-validate",
 "config": {
  "distortions": [
   "none"
  ],
  "estimators": [
   "cpu"
  ],
  "mode_matching": {
   "criterion": "a true mode is recovered when a fitted mode lies within the tolerance of its angular frequency; fitted modes are assigned nearest-first and each is used at most once",
   "tolerance_rad_per_time": 0.3
  },
  "model": "trace(t) = constant + sum_k amplitude_k exp(-decay_k t) cos(angular_frequency_k t + phase_k)",
  "n_components": 6,
  "noise": {
   "kind": "white Gaussian, independent per sample",
   "signal_rms": 0.6600738968812622,
   "snr_db": [
    40.0,
    30.0,
    20.0,
    10.0,
    5.0
   ],
   "snr_db_definition": "20 log10(rms of the mean-removed clean trace / sigma)"
  },
  "sampling": {
   "dt": 0.1,
   "duration": 9.5,
   "samples": 96,
   "start": 0.0
  },
  "seed": 20260914,
  "statistics": "bias, std (ddof 1) and rmse are computed over the recovered trials of each mode; crlb_std and crlb_variance are the unconditional Cramer-Rao bounds from the exact Fisher information of the model at the true parameters; the phase error is wrapped to [-pi, pi)",
  "trials_per_level": 300,
  "truth": {
   "constant": 0.15,
   "modes": [
    {
     "amplitude": 1.25,
     "angular_frequency": 2.4,
     "decay": 0.09,
     "phase": 0.3
    },
    {
     "amplitude": 0.45,
     "angular_frequency": 0.9,
     "decay": 0.03,
     "phase": -0.75
    }
   ]
  },
  "units": {
   "amplitude": "trace units",
   "angular_frequency": "rad / time unit",
   "decay": "1 / time unit",
   "phase": "rad",
   "time": "input time unit"
  }
 },
 "results": [
  {
   "any_mode_lost_rate": 0.16,
   "distortion": null,
   "estimator": "cpu",
   "modes": [
    {
     "amplitude": {
      "bias": -0.0057935345724696,
      "crlb_std": 0.0024871969853259394,
      "crlb_variance": 6.186148843814441e-06,
      "rmse": 0.024069170821429946,
      "std": 0.023400538995352594,
      "std_over_crlb_std": 9.408397940899734,
      "truth": 1.25
     },
     "angular_frequency": {
      "bias": -5.36680014263761e-05,
      "crlb_std": 0.000493864160666439,
      "crlb_variance": 2.439018091907663e-07,
      "rmse": 0.0018687880639754113,
      "std": 0.00187113845132492,
      "std_over_crlb_std": 3.788771488904833,
      "truth": 2.4
     },
     "decay": {
      "bias": -0.0004349684279657693,
      "crlb_std": 0.0004741038603883929,
      "crlb_variance": 2.2477447043517674e-07,
      "rmse": 0.003378229934641826,
      "std": 0.0033557079547503863,
      "std_over_crlb_std": 7.078001752614587,
      "truth": 0.09
     },
     "loss_rate": 0.0,
     "phase": {
      "bias": -0.0044971697363731505,
      "crlb_std": 0.0022066241972384043,
      "crlb_variance": 4.869190347838032e-06,
      "rmse": 0.020791967276599346,
      "std": 0.02033370517508434,
      "std_over_crlb_std": 9.214847367545422,
      "truth": 0.3
     },
     "recovered_trials": 300,
     "statistics_over": "recovered_trials"
    },
    {
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      "crlb_std": 0.0020154471692384332,
      "crlb_variance": 4.0620272919912134e-06,
      "rmse": 0.03910289390281825,
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      "std_over_crlb_std": 18.967102918504605,
      "truth": 0.45
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     "angular_frequency": {
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      "std_over_crlb_std": 3.3093352621804035,
      "truth": 0.9
     },
     "decay": {
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      "crlb_std": 0.000922954792228546,
      "crlb_variance": 8.518455484976385e-07,
      "rmse": 0.021909139824469605,
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     "recovered_trials": 252,
     "statistics_over": "recovered_trials"
    }
   ],
   "noise_sigma": 0.006600738968812621,
   "residual_rms_over_sigma_median": 1.8872992686925838,
   "snr_db": 40.0,
   "trials": {
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    "failed": 48,
    "successful": 252
   }
  },
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      "truth": 1.25
     },
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     "recovered_trials": 300,
     "statistics_over": "recovered_trials"
    },
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    }
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   "snr_db": 30.0,
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    }
   ],
   "noise_sigma": 0.3711868298957668,
   "residual_rms_over_sigma_median": 1.1210266208301034,
   "snr_db": 5.0,
   "trials": {
    "attempted": 300,
    "estimator_errors": 0,
    "failed": 158,
    "successful": 142
   }
  }
 ],
 "runtime": {
  "backend": "cpu",
  "device": "cpu",
  "dtype": "float64",
  "numpy_version": "2.4.6",
  "package_version": "0.1.2",
  "platform": "Linux-6.18.33.2-microsoft-standard-WSL2-x86_64-with-glibc2.39",
  "python_version": "3.12.3",
  "scipy_version": "1.18.0",
  "source_revision": "a9bb709d9e87e2e321b5022ebbf184feae8f2c44"
 },
 "schema_version": 1
}

jimsweeney · 2026-09-16T03:50:02Z

PNG rendering of the summary above: frequency and decay scatter against the bound, and the weak-mode loss rate, for the linear-prediction and refined estimators.

coderabbitai · 2026-09-24T02:45:42Z

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Adds a synthetic damped-mode validation workflow for linear prediction. The change includes validation statistics, summary and figure output, an lpv command, shared XRay text-output helpers, tests, and documentation.

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Synthetic linear prediction validation

Layer / File(s)	Summary
Trace generation and numerical primitives `src/cuphoton/xray/synthetic_validation.py`, `docs/xray/LINEAR-PREDICTION-VALIDATION.md`, `tests/xray/test_synthetic_validation.py`	Adds damped-mode trace generation, five distortion types, numerical Cramér–Rao bounds, and frequency-based mode matching. Tests cover fixture generation, noise, bounds, distortions, and matching.
Validation trials and statistics `src/cuphoton/xray/synthetic_validation.py`, `docs/xray/LINEAR-PREDICTION-VALIDATION.md`, `tests/xray/test_synthetic_validation.py`	Adds Monte Carlo trials, per-mode statistics, estimator-error and mode-loss accounting, and residual ratios. Tests cover statistics, invalid inputs, and estimator failures.
Summaries, provenance, and plots `src/cuphoton/xray/synthetic_validation.py`, `docs/xray/LINEAR-PREDICTION-VALIDATION.md`, `tests/xray/test_synthetic_validation.py`, `tests/xray/test_synthetic_validation_provenance.py`, `tests/xray/test_synthetic_validation_viz.py`	Adds versioned summaries with runtime and source metadata, JSON run output, and optional Bokeh figures. Tests cover serialization, provenance, and figure output.
Shared XRay text-output helpers `src/cuphoton/xray/_cli_output.py`, `src/cuphoton/xray/commands.py`	Adds shared printers for file probes, validation sweeps, model-order sweeps, and subspace benchmark batches. Existing command output delegates to the shared helpers.
Validation CLI and documentation `src/cuphoton/xray/commands.py`, `tests/xray/test_cli.py`, `tests/core/test_cli_contract.py`, `docs/xray/README.md`, `docs/xray/LINEAR-PREDICTION-VALIDATION.md`	Adds the `linear-prediction-validate` command and `lpv` alias, with text and JSON reports, optional run output, and input validation. Updates command-count expectations and links the guide from the XRay documentation.

Priority: ⬇️ Low

Merge Risk: 🔵 Low · up to d0670

Python callers may receive an unexpected exception for invalid modes, and readers may misinterpret the documented bound. Both issues are bounded and can be fixed without blocking the CLI workflow.

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Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
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tpn · 2026-09-25T00:36:46Z

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Inline comments:
In `@docs/xray/LINEAR-PREDICTION-VALIDATION.md`:
- Around line 50-53: Update the angular-frequency bound description in the
validation section to clarify that it matches the square root of the closed-form
variance, not the variance itself; keep the formula and existing comparison
details intact.

In `@src/cuphoton/xray/synthetic_validation.py`:
- Around line 221-228: Update the exception handler around the Fisher matrix
inversion in the Cramer-Rao bounds calculation to convert singular-matrix
failures from np.linalg.inv into ValueError, alongside FloatingPointError.
Adjust the error message to cover invalid modes as well as noise_sigma.

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docs/xray/LINEAR-PREDICTION-VALIDATION.md
docs/xray/README.md
src/cuphoton/xray/_cli_output.py
src/cuphoton/xray/commands.py
src/cuphoton/xray/synthetic_validation.py
tests/core/test_cli_contract.py
tests/xray/test_cli.py
tests/xray/test_synthetic_validation.py
tests/xray/test_synthetic_validation_provenance.py
tests/xray/test_synthetic_validation_viz.py

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Adds cuphoton.xray.synthetic_validation: a fixture of known damped modes (the synthetic_trace modes plus white Gaussian noise), Cramer-Rao bounds from the exact Fisher information of the same model, a Monte Carlo sweep that reports per-mode bias, standard deviation and rmse of every parameter against the bound together with the mode-loss rate, optional model-mismatch traces with a residual-to-noise ratio, and a schema_version 1 summary (config, runtime via runtime_metadata, results per condition with attempted/successful/failed trials, artifacts). The linear-prediction-validate (lpv) command runs the sweep and, with --output-dir, writes summary.json and a Bokeh figure outside the checkout. Documented in docs/xray/LINEAR-PREDICTION-VALIDATION.md. The sweep is the reproducer for the mode loss discussed in NVIDIA#1: a lightly damped mode whose fitted decay crosses zero under noise is dropped by the root filter. No default behaviour changes. Refs NVIDIA#1 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_014wyj5iPSHmtR3vSTvJWKfw Signed-off-by: James Sweeney <jmsweene@gmail.com>

Keep small-trial summaries valid JSON, integrate the requested chirp, and retain floating parameters for Fisher derivatives. Match nearby modes globally and reject incompatible combined sweep settings. Signed-off-by: Trent Nelson <trentn@nvidia.com>

Use the same amplitude and phase convention for truth, fitted modes, and summary metadata so equivalent modes have zero parameter error. Signed-off-by: Trent Nelson <trentn@nvidia.com>

Keep text formatting separate from command setup and execution. Signed-off-by: Trent Nelson <trentn@nvidia.com>

Reject invalid numerical inputs before trials and distinguish estimator exceptions from mode loss in the text report. Record revisions only for tracked source checkouts, together with tracked- file dirtiness. Embed plot resources for offline use without changing Bokeh output state. Clarify numerical Fisher bounds and conditional scatter, and anchor the Gaussian distortion envelope at the first sample. Signed-off-by: Trent Nelson <trentn@nvidia.com>

Report unidentifiable modes with the validation error context. Clarify that the frequency standard-deviation bound uses the square root of the closed-form variance. Signed-off-by: Trent Nelson <trentn@nvidia.com>

This was referenced Sep 16, 2026

Add experimental per-trace and batched mode refinement #4

Open

xray linear prediction: lightly damped modes are dropped by the decay >= 0 root filter under noise; proposal for a synthetic fixture with Cramer-Rao validation and per-GPU baselines #1

Open

tpn force-pushed the xray-lp-validate branch from c911ccb to 5d82f1b Compare September 24, 2026 02:45

tpn requested review from melo-gonzo and tpn as code owners September 24, 2026 02:45

tpn force-pushed the xray-lp-validate branch from 5d82f1b to d0670d2 Compare September 24, 2026 03:21

tpn mentioned this pull request Sep 24, 2026

Refresh or retire ROADMAP.md before the next public release #58

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coderabbitai Bot reviewed Sep 25, 2026

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Comment thread docs/xray/LINEAR-PREDICTION-VALIDATION.md Outdated

Comment thread src/cuphoton/xray/synthetic_validation.py

jimsweeney and others added 6 commits September 24, 2026 22:17

Normalize validation truth before comparing fitted modes

3e7fd5b

Use the same amplitude and phase convention for truth, fitted modes, and summary metadata so equivalent modes have zero parameter error. Signed-off-by: Trent Nelson <trentn@nvidia.com>

Move XRay command print helpers into a separate module

536b53c

Keep text formatting separate from command setup and execution. Signed-off-by: Trent Nelson <trentn@nvidia.com>

Reject singular Fisher information in XRay validation

6832784

Report unidentifiable modes with the validation error context. Clarify that the frequency standard-deviation bound uses the square root of the closed-form variance. Signed-off-by: Trent Nelson <trentn@nvidia.com>

tpn force-pushed the xray-lp-validate branch from d0670d2 to 6832784 Compare September 25, 2026 05:21

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Add synthetic fixture and Cramer-Rao validation for xray linear prediction (refs #1) - #2

Add synthetic fixture and Cramer-Rao validation for xray linear prediction (refs #1)#2
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