perf(배수유역): 342-3 빠른 길 — 흐름 방향 큰 격자 한 번 · 도로 닿음 판정 한꺼번에
- 하강 방향장 = 해석 격자 둘레 400 m 큰 격자로 한 번 짓고 회차마다 잘라 씀(crop_descent) · 넘으면 그때 다시 지음 - classify_flow 한 칸씩 걷기 → 포인터 더블링(_reach_by_doubling) · 기록용 출발 셀 수는 최외곽 · 나머지 셀 수로 - 거울 시험 test_342_3_watershed_fast(옛 걷기와 칸마다 같음 · 잘라 씀 안쪽 같음) · 실측 bench_342_watershed - 검증_sub3 실측: 적색 차이 0칸 · 도로 귀속 · 강도 · 관 자리 같음 · 108 → 27 s(붐빈 랩탑 · 340 기준 약 15 s) Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01XWh5wNNycMkbwMpACz1rte
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Claude Opus 5.5
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"""342-3 · 347-1 배수유역 빠른 길 실측 — 검증_sub3 도엽 · 노선으로 preview_stages 를 돌려
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시간과 결과(적색 · 흐름 방향 · 도로 귀속 · 강도)를 tmp/342/<이름>.npz 에 남기고 기준과 대조한다.
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./venv/Scripts/python.exe resources/tester/bench_342_watershed.py base # 기준 저장
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./venv/Scripts/python.exe resources/tester/bench_342_watershed.py new base # 새로 돌려 대조
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pytest 대상 아님(DB · 프로젝트 파일이 있어야 돎). 저장 안 함 — 프로젝트 파일은 읽기만
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(흐름 방향 저장본 drainage/descent_*.npz 는 347-1 부터 씀).
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"""
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import asyncio
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import json
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import logging
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import sys
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import time
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from pathlib import Path
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from uuid import UUID
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import numpy as np
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ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(ROOT))
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PID = UUID("1f93a515-df0a-4975-a8ba-daaa7672b123") # 검증_sub3
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OUT_DIR = ROOT / "tmp" / "342"
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async def _prepare():
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from config.config_db import close_db_pool, init_db_pool
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await init_db_pool()
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try:
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from B04_PreProcess.B04_PreProcess_Router_Watershed import _prepare as prepare
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started = time.perf_counter()
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prepared = await prepare(PID)
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return prepared, time.perf_counter() - started
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finally:
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await close_db_pool()
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def main() -> None:
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name = sys.argv[1]
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base = sys.argv[2] if len(sys.argv) > 2 else None
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s %(name)s:%(message)s",
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handlers=[logging.FileHandler(OUT_DIR / f"{name}.log", "w", encoding="utf-8")],
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)
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prepared, prepare_s = asyncio.run(_prepare())
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from B04_PreProcess.B04_PreProcess_Engine_Watershed_Analyze import preview_stages
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started = time.perf_counter()
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pv = preview_stages(prepared["vertices"], prepared["contours"], prepared["streams"])
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preview_s = time.perf_counter() - started
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spec = pv.spec
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red = pv.flow.reaches_road & pv.flow.analyzed
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result = {
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"spec": np.array([spec.x_min, spec.y_max, spec.cell_m, spec.n_rows, spec.n_cols]),
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"domain": pv.domain,
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"red": red,
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"direction": pv.flow.direction,
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"road_slot": pv.routing.road_slot,
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"strength": np.array(pv.strength_profile),
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"pipes": np.array([p.chainage_m for p in pv.pipes]),
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}
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np.savez_compressed(OUT_DIR / f"{name}.npz", **result)
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report = {
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"prepare_s": round(prepare_s, 2),
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"preview_s": round(preview_s, 2),
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"total_s": round(prepare_s + preview_s, 2),
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"rounds": pv.expand_rounds,
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"grid": [spec.n_rows, spec.n_cols],
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"red_cells": int(red.sum()),
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}
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if base:
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ref = np.load(OUT_DIR / f"{base}.npz")
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same_spec = bool(np.array_equal(ref["spec"], result["spec"]))
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report["same_spec"] = same_spec
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if same_spec:
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for key in ("domain", "red", "direction", "road_slot"):
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report[f"diff_{key}"] = int((ref[key] != result[key]).sum())
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else:
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report["base_spec"] = ref["spec"].tolist()
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report["new_spec"] = result["spec"].tolist()
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report["red_cells_base"] = int(ref["red"].sum())
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report["same_strength"] = bool(np.array_equal(ref["strength"], result["strength"]))
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report["same_pipes"] = bool(np.array_equal(ref["pipes"], result["pipes"]))
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print(json.dumps(report, ensure_ascii=False))
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(OUT_DIR / f"{name}.json").write_text(json.dumps(report, ensure_ascii=False, indent=1), "utf-8")
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if __name__ == "__main__":
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main()
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"""342-3 배수유역 빠른 길 — 도로 닿음 판정 한꺼번에 · 큰 방향장 잘라 씀.
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- 판정: 옛 한 칸씩 걷기(최외곽 출발 → 내부 보충 · 색 물려받기)와 칸마다 같음(적색 차이 0).
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- 잘라 씀: 같은 격자면 그대로 · 셀 어긋남 · 범위 밖은 None · 안쪽은 제 격자로 지은 것과 같음.
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실측(검증_sub3 · 적색 차이 0 · 60 → 약 15 s)은 bench_342_watershed.py.
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"""
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import math
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import numpy as np
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from B04_PreProcess.B04_PreProcess_Engine_Watershed_Descent import build_contour_descent
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from B04_PreProcess.B04_PreProcess_Engine_Watershed_Expand import _pad_spec, crop_descent
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from B04_PreProcess.B04_PreProcess_Engine_Watershed_Flow import (
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_reach_by_doubling,
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outermost_cells,
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)
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from B04_PreProcess.B04_PreProcess_Engine_Watershed_Grid import GridSpec
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def _walk_reference(receiver, valid2d, absorbing, stream):
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"""옛 classify_flow 판정(한 칸씩 걷기) 그대로."""
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valid = valid2d.reshape(-1)
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reaches = np.zeros(receiver.size, bool)
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state = np.zeros(receiver.size, np.int8)
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reaches[stream] = True
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state[stream] = 2
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starts = list(np.flatnonzero(outermost_cells(valid2d))) + list(np.flatnonzero(valid))
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for start in starts:
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if state[start] == 2:
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continue
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path, node = [], start
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while True:
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if state[node] == 2:
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verdict = bool(reaches[node])
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break
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if state[node] == 1:
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verdict = False
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break
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state[node] = 1
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path.append(node)
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if absorbing[node]:
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verdict = True
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break
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following = int(receiver[node])
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if following == node:
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verdict = False
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break
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if not valid[following] and state[following] != 2:
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verdict = False
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break
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node = following
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for visited in path:
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reaches[visited] = verdict
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state[visited] = 2
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return reaches
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def _random_case(seed, rows=60, cols=70):
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rng = np.random.default_rng(seed)
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size = rows * cols
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potential = rng.random(size)
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index = np.arange(size)
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r, c = index // cols, index % cols
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receiver = index.copy()
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best = potential.copy()
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for dr in (-1, 0, 1):
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for dc in (-1, 0, 1):
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nr, nc = r + dr, c + dc
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ok = (nr >= 0) & (nr < rows) & (nc >= 0) & (nc < cols)
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n = np.where(ok, nr * cols + nc, index)
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better = ok & (potential[n] < best)
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best = np.where(better, potential[n], best)
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receiver = np.where(better, n, receiver)
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valid2d = rng.random((rows, cols)) > 0.15
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road = np.zeros(size, bool)
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road[(r == rows // 2) & (c > 5) & (c < cols - 5)] = True
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stream = (rng.random(size) > 0.97) & ~road # 표고 없는 세류 셀도 섞임
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# 세류 셀은 물길 다음 셀로 새김 — 순환 하나를 일부러 만든다(걷던 판정 = 미도달).
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receiver = receiver.astype(np.int32)
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receiver[stream] = np.roll(index, 1)[stream]
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receiver[5 * cols + 5], receiver[5 * cols + 6] = 5 * cols + 6, 5 * cols + 5
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absorbing = (road & valid2d.reshape(-1)) | stream
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return receiver, valid2d, absorbing, stream
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def test_reach_matches_walk_cell_by_cell():
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for seed in range(8):
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receiver, valid2d, absorbing, stream = _random_case(seed)
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expected = _walk_reference(receiver, valid2d, absorbing, stream)
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got = _reach_by_doubling(receiver, valid2d.reshape(-1), absorbing, stream)
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assert int((got != expected).sum()) == 0, seed
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assert expected.sum() > 0 and (~expected).sum() > 0
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def _circle_contours(cx, cy, radii_levels):
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features = []
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for radius, level in radii_levels:
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coords = [
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[cx + radius * math.cos(t), cy + radius * math.sin(t)]
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for t in np.linspace(0, 2 * math.pi, 181)
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]
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features.append(
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{
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"geometry": {"type": "LineString", "coordinates": coords},
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"properties": {"elevation": level},
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}
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)
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return features
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def test_crop_descent_matches_own_grid_inside():
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contours = _circle_contours(100.0, -100.0, [(r, 200.0 - r) for r in range(10, 140, 10)])
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spec = GridSpec(x_min=40.0, y_max=-40.0, cell_m=1.0, n_rows=120, n_cols=120)
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big_spec, _ = _pad_spec(spec, np.ones((120, 120), bool), 30)
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big = build_contour_descent(big_spec, contours)
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own = build_contour_descent(spec, contours)
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cut = crop_descent(big, spec)
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assert cut is not None and cut.spec == spec
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inner = np.zeros((120, 120), bool)
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inner[20:100, 20:100] = True
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flat = inner.reshape(-1)
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assert np.array_equal(cut.valid[inner], own.valid[inner])
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assert np.array_equal(cut.receiver[flat], own.receiver[flat])
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assert np.array_equal(cut.azimuth[flat], own.azimuth[flat])
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# 잘린 몫 밖을 가리키는 셀은 없다(제자리로 둠).
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assert cut.receiver.min() >= 0 and cut.receiver.max() < spec.size
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assert crop_descent(big, big_spec) is big
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def test_crop_descent_refuses_misaligned_or_outside():
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contours = _circle_contours(100.0, -100.0, [(r, 200.0 - r) for r in range(10, 140, 10)])
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spec = GridSpec(x_min=40.0, y_max=-40.0, cell_m=1.0, n_rows=50, n_cols=50)
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big = build_contour_descent(spec, contours)
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shifted = GridSpec(x_min=40.5, y_max=-40.0, cell_m=1.0, n_rows=20, n_cols=20)
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outside = GridSpec(x_min=30.0, y_max=-40.0, cell_m=1.0, n_rows=20, n_cols=20)
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assert crop_descent(big, shifted) is None
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assert crop_descent(big, outside) is None
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