Checkpoint CM95 PBF production code and docs for Linux migration
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kaotei
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cm95_karatsu_presence_diagnostic_v2.py
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304
cm95_karatsu_presence_diagnostic_v2.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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CM95 唐津 AOI Presence Diagnostic v2
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只读诊断：
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1) chart_cell bbox 是否覆盖唐津 z10/881/410；
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2) chart_feature bbox 是否命中；
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3) 实际 Point/Line/Area geometry 是否通过 MBRIntersects 命中；
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4) Sounding point 是否命中；
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5) 输出离唐津中心最近的 Cell / Feature，帮助判断 Dataset 1 是否根本没有唐津细节数据。
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不修改数据库。
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"""
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from __future__ import annotations
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import argparse
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import getpass
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import json
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import math
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from pathlib import Path
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from datetime import datetime
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import pymysql
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from pymysql.cursors import DictCursor
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LON_MIN = 129.7265625
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LAT_MIN = 33.4314413355753
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LON_MAX = 130.078125
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LAT_MAX = 33.7243396617476
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CENTER_LON = (LON_MIN + LON_MAX) / 2
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CENTER_LAT = (LAT_MIN + LAT_MAX) / 2
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def args():
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p = argparse.ArgumentParser()
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p.add_argument("--host", default="127.0.0.1")
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p.add_argument("--port", type=int, default=3306)
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p.add_argument("--user", default="root")
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p.add_argument("--password")
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p.add_argument("--database", default="cm93_chart")
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p.add_argument("--dataset-id", type=int, default=1)
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p.add_argument("--output", default="cm95_karatsu_presence_diagnostic_v2.json")
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return p.parse_args()
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def fetch(cur, sql, params=()):
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cur.execute(sql, params)
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return list(cur.fetchall())
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def scalar(cur, sql, params=()):
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cur.execute(sql, params)
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r = cur.fetchone()
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return None if not r else next(iter(r.values()))
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def main():
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a = args()
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if a.password is None:
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a.password = getpass.getpass("MySQL password: ")
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conn = pymysql.connect(
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host=a.host, port=a.port, user=a.user, password=a.password,
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database=a.database, charset="utf8mb4",
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cursorclass=DictCursor, autocommit=False,
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connect_timeout=20, read_timeout=900,
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)
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report = {
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"version": "v2",
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"generated_at": datetime.now().isoformat(timespec="seconds"),
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"dataset_id": a.dataset_id,
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"target": {
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"tile": "z10/881/410",
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"bbox": [LON_MIN, LAT_MIN, LON_MAX, LAT_MAX],
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"center": [CENTER_LON, CENTER_LAT],
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},
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"read_only": True,
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}
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poly_wkt = (
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f"POLYGON(({LON_MIN} {LAT_MIN},"
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f"{LON_MAX} {LAT_MIN},"
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f"{LON_MAX} {LAT_MAX},"
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f"{LON_MIN} {LAT_MAX},"
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f"{LON_MIN} {LAT_MIN}))"
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)
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try:
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with conn.cursor() as cur:
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try:
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cur.execute("SET SESSION TRANSACTION READ ONLY")
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cur.execute("START TRANSACTION READ ONLY")
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except Exception:
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cur.execute("START TRANSACTION")
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report["dataset"] = fetch(cur, """
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SELECT id,name,source_type,source_root,parser_version,normalized_srid
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FROM chart_dataset WHERE id=%s
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""", (a.dataset_id,))
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report["global_feature_bbox"] = fetch(cur, """
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SELECT
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MIN(bbox_min_lon) min_lon,
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MIN(bbox_min_lat) min_lat,
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MAX(bbox_max_lon) max_lon,
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MAX(bbox_max_lat) max_lat,
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SUM(bbox_min_lon IS NOT NULL) bbox_rows,
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COUNT(*) total_rows
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FROM chart_feature
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WHERE dataset_id=%s
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""", (a.dataset_id,))
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report["global_cell_bbox"] = fetch(cur, """
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SELECT
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MIN(bbox_min_lon) min_lon,
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MIN(bbox_min_lat) min_lat,
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MAX(bbox_max_lon) max_lon,
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MAX(bbox_max_lat) max_lat,
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SUM(bbox_min_lon IS NOT NULL) bbox_rows,
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COUNT(*) total_rows
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FROM chart_cell
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WHERE dataset_id=%s
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""", (a.dataset_id,))
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bbox_params = (
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a.dataset_id,
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LON_MIN, LON_MAX, LAT_MIN, LAT_MAX,
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)
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report["cell_bbox_overlap"] = fetch(cur, """
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SELECT id,source_relative_path,scale_code,native_scale,
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bbox_min_lon,bbox_min_lat,bbox_max_lon,bbox_max_lat,
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feature_count
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FROM chart_cell
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WHERE dataset_id=%s
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AND bbox_max_lon >= %s
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AND bbox_min_lon <= %s
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AND bbox_max_lat >= %s
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AND bbox_min_lat <= %s
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ORDER BY native_scale,source_relative_path
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""", bbox_params)
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report["feature_bbox_count"] = scalar(cur, """
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SELECT COUNT(*)
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FROM chart_feature
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WHERE dataset_id=%s
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AND bbox_max_lon >= %s
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AND bbox_min_lon <= %s
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AND bbox_max_lat >= %s
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AND bbox_min_lat <= %s
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""", bbox_params)
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# Direct actual geometry checks. These bypass chart_feature bbox metadata.
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direct = {}
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for kind, table in [
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("point", "chart_point_geometry"),
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("line", "chart_line_geometry"),
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("area", "chart_area_geometry"),
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]:
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try:
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direct[kind] = scalar(cur, f"""
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SELECT COUNT(*)
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FROM {table} g
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JOIN chart_feature f ON f.id=g.feature_id
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WHERE f.dataset_id=%s
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AND MBRIntersects(
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g.geom,
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ST_GeomFromText(%s,4326)
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)
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""", (a.dataset_id, poly_wkt))
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except Exception as e:
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direct[kind] = {"error": str(e)}
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report["direct_geometry_mbr_counts"] = direct
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try:
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report["sounding_bbox_count"] = scalar(cur, """
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SELECT COUNT(*)
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FROM chart_sounding_point s
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JOIN chart_feature f ON f.id=s.feature_id
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WHERE f.dataset_id=%s
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AND ST_X(s.geom) BETWEEN %s AND %s
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AND ST_Y(s.geom) BETWEEN %s AND %s
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""", (
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a.dataset_id,
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LON_MIN, LON_MAX, LAT_MIN, LAT_MAX,
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))
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except Exception as e:
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report["sounding_bbox_count"] = {"error": str(e)}
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# Nearest cell by bbox center, excluding pathological world-scale cells when possible.
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report["nearest_cells"] = fetch(cur, """
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SELECT id,source_relative_path,scale_code,native_scale,
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bbox_min_lon,bbox_min_lat,bbox_max_lon,bbox_max_lat,
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feature_count,
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SQRT(
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POW(((bbox_min_lon+bbox_max_lon)/2)-%s,2) +
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POW(((bbox_min_lat+bbox_max_lat)/2)-%s,2)
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) AS center_distance_deg
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FROM chart_cell
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WHERE dataset_id=%s
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AND bbox_min_lon IS NOT NULL
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AND bbox_min_lat IS NOT NULL
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AND bbox_max_lon IS NOT NULL
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AND bbox_max_lat IS NOT NULL
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AND (bbox_max_lon-bbox_min_lon) < 20
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AND (bbox_max_lat-bbox_min_lat) < 20
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ORDER BY center_distance_deg ASC
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LIMIT 30
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""", (CENTER_LON, CENTER_LAT, a.dataset_id))
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report["nearest_features"] = fetch(cur, """
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SELECT f.id,f.cell_id,c.source_relative_path,
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f.source_record_index,f.object_class_id,f.object_class,
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f.geometry_type,f.scale_code,f.native_scale,
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f.bbox_min_lon,f.bbox_min_lat,f.bbox_max_lon,f.bbox_max_lat,
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SQRT(
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POW(((f.bbox_min_lon+f.bbox_max_lon)/2)-%s,2) +
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POW(((f.bbox_min_lat+f.bbox_max_lat)/2)-%s,2)
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) AS center_distance_deg
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FROM chart_feature f
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JOIN chart_cell c ON c.id=f.cell_id
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WHERE f.dataset_id=%s
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AND f.bbox_min_lon IS NOT NULL
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AND f.bbox_min_lat IS NOT NULL
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AND f.bbox_max_lon IS NOT NULL
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AND f.bbox_max_lat IS NOT NULL
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ORDER BY center_distance_deg ASC
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LIMIT 50
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""", (CENTER_LON, CENTER_LAT, a.dataset_id))
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# Coarse 2-degree spatial occupancy by feature bbox center.
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report["coarse_feature_grid"] = fetch(cur, """
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SELECT
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FLOOR(((bbox_min_lon+bbox_max_lon)/2)/2)*2 AS lon_bin,
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FLOOR(((bbox_min_lat+bbox_max_lat)/2)/2)*2 AS lat_bin,
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COUNT(*) AS feature_count
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FROM chart_feature
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WHERE dataset_id=%s
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AND bbox_min_lon IS NOT NULL
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AND bbox_min_lat IS NOT NULL
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AND bbox_max_lon IS NOT NULL
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AND bbox_max_lat IS NOT NULL
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AND ((bbox_min_lon+bbox_max_lon)/2) BETWEEN 120 AND 160
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AND ((bbox_min_lat+bbox_max_lat)/2) BETWEEN 20 AND 50
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GROUP BY lon_bin,lat_bin
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ORDER BY lat_bin,lon_bin
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""", (a.dataset_id,))
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conn.rollback()
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finally:
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conn.close()
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# Derive diagnosis.
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fcnt = report.get("feature_bbox_count")
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d = report.get("direct_geometry_mbr_counts", {})
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direct_sum = 0
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direct_errors = []
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for k, v in d.items():
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if isinstance(v, int):
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direct_sum += v
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else:
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direct_errors.append((k, v))
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if isinstance(fcnt, int) and fcnt > 0:
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diagnosis = "AOI_PRESENT_BY_FEATURE_BBOX"
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elif direct_sum > 0:
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diagnosis = "AOI_PRESENT_IN_GEOMETRY_BUT_FEATURE_BBOX_QUERY_FAILED"
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else:
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diagnosis = "NO_CM95_GEOMETRY_FOUND_IN_KARATSU_AOI"
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report["diagnosis"] = diagnosis
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report["direct_geometry_total"] = direct_sum
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report["direct_geometry_errors"] = direct_errors
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out = Path(a.output).resolve()
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out.write_text(json.dumps(report, ensure_ascii=False, indent=2, default=str), encoding="utf-8")
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print("CM95 唐津 AOI Presence Diagnostic v2")
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print("=" * 72)
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print("Target bbox :", [LON_MIN, LAT_MIN, LON_MAX, LAT_MAX])
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print("Cell bbox overlap :", len(report["cell_bbox_overlap"]))
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print("Feature bbox count:", report["feature_bbox_count"])
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print("Direct geometry :", report["direct_geometry_mbr_counts"])
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print("Soundings :", report["sounding_bbox_count"])
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print("Diagnosis :", diagnosis)
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print("")
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print("Nearest cells:")
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for r in report["nearest_cells"][:10]:
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print(
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f" {r['source_relative_path']} scale={r['native_scale']} "
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f"bbox=({r['bbox_min_lon']},{r['bbox_min_lat']},"
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f"{r['bbox_max_lon']},{r['bbox_max_lat']}) "
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f"dist={float(r['center_distance_deg']):.3f}°"
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)
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print("")
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print("Output:", out)
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print("把 JSON 上传给 ChatGPT。")
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if __name__ == "__main__":
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main()
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