mirror of
https://github.com/ruvnet/RuView
synced 2026-06-20 12:03:19 +00:00
ruv
78916d8455
Benchmarks (`python/bench/`, pytest-benchmark — opt-in via --benchmark-only): | Hot path | Mean | Ops/sec | % of 100 Hz budget | |---|---|---|---| | BfldFrame HT20 1×1×52 | 800 ns | 1.25 Mops | 0.008% | | BfldFrame HE20 2×1×242 | 1.3 μs | 750 kops | 0.013% | | BfldFrame HE80 2×1×996 | 4.2 μs | 236 kops | 0.042% | | BfldFrame HE160 2×2×1992 | 14 μs | 71 kops | 0.14% | | BfldFrame.feedback_matrix() | 2.8 μs | 352 kops | — | | WS edge_vitals decode | 7.4 μs | 134 kops | 0.074% | | WS pose_data decode (3 persons) | 23 μs | 42 kops | 0.24% | | BreathingExtractor.extract() 56sc | 28 μs | 35 kops | 0.28% | | BreathingExtractor.extract() 114sc | 44 μs | 23 kops | 0.44% | | BreathingExtractor.extract() 242sc | 79 μs | 13 kops | 0.79% | | HeartRateExtractor.extract() 56sc | 105 μs | 9.5 kops | 1.05% | All hot paths well under the 100 Hz ESP32 frame budget (10 ms). Worst case (HeartRateExtractor) uses 1% of the budget — no optimization needed. Scaling on n_subcarriers is sub-quadratic (56→242 = 4.3× input, 2.8× time) — catches future O(n²) regressions. Security & robustness tests (`tests/test_security.py`, +27 tests): - WS decoder: rejects non-object roots cleanly, survives 1 MB string values, handles non-ASCII node IDs, survives deeply-nested JSON (Python's json.loads built-in guard not bypassed) - MQTT topic matcher: 9 edge-case parametrize entries including $SYS topics, null-byte injection, mid-pattern `#` boundary, empty-string boundary - MQTT credential confidentiality: password never appears in repr()/str(), never stored in plain client-instance attribute - HA discovery: rejects null-byte-laced topics, rejects extra slashes in node_id, rejects non-dict payload body (list, scalar, invalid UTF-8 bytes) without crashing - Semantic primitive listener: rejects topic-injection attempts (prefix-injected paths, wrong case on final segment), survives invalid UTF-8 payloads - Public surface integrity: every name in wifi_densepose.__all__ AND wifi_densepose.client.__all__ resolves — catches accidental re-export breakage between phases - Multi-handler MQTT exception isolation: a crashing handler in the middle of the registered list doesn't stop later handlers from firing Test count: 156 → 183 (+27). All passing. Bench results steady-state confirm no Rust-binding-layer optimization is needed before the v2.0.0 publish. Refs: docs/adr/ADR-117-pip-wifi-densepose-modernization.md Refs: #785 Co-Authored-By: claude-flow <ruv@ruv.net>
112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
"""ADR-117 hardening sweep — Benchmarks for the P3.5 numpy bridge
|
||
and the P4 WS decoder.
|
||
|
||
The numpy bridge is the most-likely candidate for a hidden allocation
|
||
hot-spot: every `BfldFrame.from_compressed_feedback()` call copies the
|
||
ndarray into a Vec<Complex64>. Confirm the per-frame cost is
|
||
acceptable for the BFR cadence the AP emits (typically a few
|
||
hundred per second, not thousands).
|
||
|
||
The WS decoder runs once per frame the sensing-server emits. At
|
||
worst-case ~100 Hz × number-of-subscribers, the decoder budget is
|
||
tight; make sure dataclass construction doesn't dominate.
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import json
|
||
|
||
import numpy as np
|
||
import pytest
|
||
|
||
from wifi_densepose import BfldFrame, BfldKind
|
||
|
||
|
||
@pytest.mark.parametrize("kind,shape", [
|
||
(BfldKind.UncompressedHT20, (1, 1, 52)),
|
||
(BfldKind.CompressedHE20, (2, 1, 242)),
|
||
(BfldKind.CompressedHE80, (2, 1, 996)),
|
||
(BfldKind.CompressedHE160, (2, 2, 1992)),
|
||
])
|
||
def test_bfld_from_compressed_feedback(benchmark, kind: BfldKind, shape: tuple[int, int, int]) -> None:
|
||
rng = np.random.default_rng(seed=42)
|
||
fb = (rng.standard_normal(shape) + 1j * rng.standard_normal(shape)).astype(np.complex128)
|
||
|
||
def _build():
|
||
return BfldFrame.from_compressed_feedback(
|
||
timestamp_ms=0,
|
||
sounding_index=0,
|
||
sta_mac="aa:bb:cc:dd:ee:ff",
|
||
kind=kind,
|
||
feedback_matrix=fb,
|
||
)
|
||
|
||
benchmark(_build)
|
||
|
||
|
||
def test_bfld_feedback_matrix_roundtrip(benchmark) -> None:
|
||
"""How expensive is the numpy-out round-trip? Used by clients
|
||
that want to do further analysis in numpy after constructing
|
||
the frame."""
|
||
rng = np.random.default_rng(seed=42)
|
||
fb = (rng.standard_normal((2, 1, 996)) + 1j * rng.standard_normal((2, 1, 996))).astype(np.complex128)
|
||
frame = BfldFrame.from_compressed_feedback(
|
||
timestamp_ms=0,
|
||
sounding_index=0,
|
||
sta_mac="aa:bb:cc:dd:ee:ff",
|
||
kind=BfldKind.CompressedHE80,
|
||
feedback_matrix=fb,
|
||
)
|
||
benchmark(frame.feedback_matrix)
|
||
|
||
|
||
# ─── WS decoder ──────────────────────────────────────────────────────
|
||
|
||
|
||
_EDGE_VITALS_FRAME = json.dumps({
|
||
"type": "edge_vitals",
|
||
"node_id": "bench-node",
|
||
"presence": True,
|
||
"fall_detected": False,
|
||
"motion": 0.34,
|
||
"breathing_rate_bpm": 14.2,
|
||
"heartrate_bpm": 72.5,
|
||
"n_persons": 1,
|
||
"motion_energy": 0.04,
|
||
"presence_score": 0.91,
|
||
"rssi": -42.0,
|
||
})
|
||
|
||
|
||
def test_ws_decoder_edge_vitals(benchmark) -> None:
|
||
from wifi_densepose.client.ws import _decode
|
||
|
||
def _decode_one():
|
||
return _decode(_EDGE_VITALS_FRAME)
|
||
|
||
benchmark(_decode_one)
|
||
|
||
|
||
_POSE_FRAME = json.dumps({
|
||
"type": "pose_data",
|
||
"node_id": "bench-node",
|
||
"timestamp": 1700000000.5,
|
||
"persons": [
|
||
{"id": i, "keypoints": [[0.5, 0.5, 0.9] for _ in range(17)]}
|
||
for i in range(3)
|
||
],
|
||
"confidence": 0.85,
|
||
})
|
||
|
||
|
||
def test_ws_decoder_pose_data(benchmark) -> None:
|
||
"""The pose_data frame is typically the largest one the server
|
||
emits — bench it separately so a future blob-size regression
|
||
in the persons array is visible."""
|
||
from wifi_densepose.client.ws import _decode
|
||
|
||
def _decode_one():
|
||
return _decode(_POSE_FRAME)
|
||
|
||
benchmark(_decode_one)
|