添加全国三层生成与港名查FPC工具
This commit is contained in:
OpenAI Codex
485
coastline/export_navgrid_mysql_assets.py
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485
coastline/export_navgrid_mysql_assets.py
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@@ -0,0 +1,485 @@
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#!/usr/bin/env python3
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from __future__ import annotations
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import argparse
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import datetime as dt
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import json
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import math
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from pathlib import Path
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import pymysql
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DB_NAME = "navsea_japan_coast_grid"
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DB_USER = "root"
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DB_PASSWORD = "2chi9ks2"
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DB_HOST = "localhost"
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DB_SOCKET = "/tmp/mysql.sock"
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DEFAULT_OUT_DIR = "src/pbf/coastline-mysql/japan_national"
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DENSITY_OVERVIEW_FILE = "density_overview_grid.geojson"
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CELL_SIZES = {
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"coast_200m": 200.0,
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"fish_port_20m": 20.0,
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"hazard_50m": 50.0,
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}
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RADIUS = 6378137.0
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MAX_MERCATOR_LAT = 85.0511287798066
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def mysql_connect(database: str, *, cursorclass=pymysql.cursors.Cursor):
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kwargs = {
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"host": DB_HOST,
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"user": DB_USER,
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"password": DB_PASSWORD,
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"database": database,
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"charset": "utf8mb4",
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"cursorclass": cursorclass,
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}
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if DB_SOCKET:
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kwargs["unix_socket"] = DB_SOCKET
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return pymysql.connect(**kwargs)
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def rect_geometry(min_lon: float, min_lat: float, max_lon: float, max_lat: float) -> dict:
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return {
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"type": "Polygon",
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"coordinates": [
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[
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[min_lon, min_lat],
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[max_lon, min_lat],
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[max_lon, max_lat],
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[min_lon, max_lat],
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[min_lon, min_lat],
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]
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],
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}
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def lon_from_mercator(x: float) -> float:
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return math.degrees(x / RADIUS)
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def lat_from_mercator(y: float) -> float:
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return math.degrees(2.0 * math.atan(math.exp(y / RADIUS)) - math.pi / 2.0)
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def mercator_bbox_to_lonlat(minx: float, miny: float, maxx: float, maxy: float) -> tuple[float, float, float, float]:
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return (
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lon_from_mercator(minx),
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lat_from_mercator(miny),
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lon_from_mercator(maxx),
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lat_from_mercator(maxy),
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)
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def normalize_bbox(layer_name: str, min_lon: float, min_lat: float, max_lon: float, max_lat: float) -> tuple[float, float, float, float]:
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if layer_name == "coast_200m" and (
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abs(min_lon) > 180.0 or abs(max_lon) > 180.0 or abs(min_lat) > 90.0 or abs(max_lat) > 90.0
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):
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return mercator_bbox_to_lonlat(min_lon, min_lat, max_lon, max_lat)
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return min_lon, min_lat, max_lon, max_lat
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def fetch_stats(conn, where_sql: str, params: tuple) -> tuple[int, list[float] | None]:
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sql = f"""
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SELECT COUNT(*), MIN(min_lon), MIN(min_lat), MAX(max_lon), MAX(max_lat)
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FROM navsea_grid_cell
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WHERE {where_sql}
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"""
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with conn.cursor() as cur:
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cur.execute(sql, params)
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row = cur.fetchone()
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count = int(row[0] or 0)
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if count == 0:
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return 0, None
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min_lon, min_lat, max_lon, max_lat = normalize_bbox(
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params[0] if params else "",
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float(row[1]),
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float(row[2]),
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float(row[3]),
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float(row[4]),
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)
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return count, [min_lon, min_lat, max_lon, max_lat]
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def export_layer(
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conn,
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*,
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path: Path,
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where_sql: str,
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params: tuple,
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) -> int:
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path.parent.mkdir(parents=True, exist_ok=True)
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sql = f"""
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SELECT layer_name, cell_id, row_idx, col_idx, cell_size_m, state_name, class_name,
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source_name, min_lon, min_lat, max_lon, max_lat
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FROM navsea_grid_cell
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WHERE {where_sql}
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ORDER BY row_idx, col_idx
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"""
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count = 0
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with conn.cursor(pymysql.cursors.SSCursor) as cur, path.open("w", encoding="utf-8") as fh:
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cur.execute(sql, params)
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fh.write('{"type":"FeatureCollection","features":[\n')
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first = True
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for row in cur:
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(
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layer_name,
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cell_id,
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row_idx,
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col_idx,
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cell_size_m,
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state_name,
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class_name,
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source_name,
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min_lon,
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min_lat,
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max_lon,
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max_lat,
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) = row
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min_lon, min_lat, max_lon, max_lat = normalize_bbox(
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row[0],
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float(min_lon),
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float(min_lat),
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float(max_lon),
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float(max_lat),
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)
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feature = {
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"type": "Feature",
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"properties": {
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"layer_name": layer_name,
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"cell_id": cell_id,
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"row": int(row_idx),
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"col": int(col_idx),
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"cell_size_m": float(cell_size_m),
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"state_name": state_name,
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"class_name": class_name,
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"source_name": source_name,
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},
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"geometry": rect_geometry(min_lon, min_lat, max_lon, max_lat),
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}
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if not first:
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fh.write(",\n")
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fh.write(json.dumps(feature, ensure_ascii=False))
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first = False
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count += 1
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fh.write("\n]}\n")
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return count
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def _range_for_overlap(min_value: float, max_value: float, fine_size_m: float) -> tuple[int, int]:
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start = int(math.floor(min_value / fine_size_m))
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end = int(math.floor((max_value - 1e-9) / fine_size_m))
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return start, end
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def _overlaps_finer_cells(row_idx: int, col_idx: int, coarse_size_m: float, fine_size_m: float, fine_keys: set[tuple[int, int]]) -> bool:
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min_x = col_idx * coarse_size_m
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min_y = row_idx * coarse_size_m
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max_x = min_x + coarse_size_m
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max_y = min_y + coarse_size_m
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row_start, row_end = _range_for_overlap(min_y, max_y, fine_size_m)
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col_start, col_end = _range_for_overlap(min_x, max_x, fine_size_m)
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for fine_row in range(row_start, row_end + 1):
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for fine_col in range(col_start, col_end + 1):
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if (fine_row, fine_col) in fine_keys:
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return True
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return False
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def export_density_overview(conn, *, path: Path) -> tuple[int, dict]:
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path.parent.mkdir(parents=True, exist_ok=True)
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kept_counts = {
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"20": 0,
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"50": 0,
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"200": 0,
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}
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kept_bbox = [float("inf"), float("inf"), float("-inf"), float("-inf")]
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keys_20: set[tuple[int, int]] = set()
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keys_50: set[tuple[int, int]] = set()
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def write_feature(fh, feature: dict, first: bool) -> bool:
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if not first:
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fh.write(",\n")
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fh.write(json.dumps(feature, ensure_ascii=False))
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return False
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with conn.cursor(pymysql.cursors.SSCursor) as cur, path.open("w", encoding="utf-8") as fh:
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fh.write('{"type":"FeatureCollection","features":[\n')
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first = True
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# 20m: always keep
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cur.execute(
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"""
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SELECT layer_name, cell_id, row_idx, col_idx, cell_size_m, state_name, class_name,
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source_name, min_lon, min_lat, max_lon, max_lat
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FROM navsea_grid_cell
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WHERE layer_name=%s
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ORDER BY row_idx, col_idx
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""",
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("fish_port_20m",),
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)
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for row in cur:
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(
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layer_name,
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cell_id,
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row_idx,
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col_idx,
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cell_size_m,
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state_name,
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class_name,
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source_name,
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min_lon,
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min_lat,
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max_lon,
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max_lat,
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) = row
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keys_20.add((int(row_idx), int(col_idx)))
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kept_counts["20"] += 1
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min_lon, min_lat, max_lon, max_lat = normalize_bbox(
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layer_name,
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float(min_lon),
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float(min_lat),
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float(max_lon),
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float(max_lat),
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)
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kept_bbox[0] = min(kept_bbox[0], min_lon)
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kept_bbox[1] = min(kept_bbox[1], min_lat)
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kept_bbox[2] = max(kept_bbox[2], max_lon)
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kept_bbox[3] = max(kept_bbox[3], max_lat)
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feature = {
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"type": "Feature",
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"properties": {
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"layer_name": layer_name,
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"cell_id": cell_id,
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"row": int(row_idx),
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"col": int(col_idx),
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"cell_size_m": float(cell_size_m),
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"state_name": state_name,
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"class_name": class_name,
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"source_name": source_name,
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"density_key": "20",
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"density_level": 3,
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"density_name": "高密度",
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},
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"geometry": rect_geometry(min_lon, min_lat, max_lon, max_lat),
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}
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first = write_feature(fh, feature, first)
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# 50m: keep only when no 20m cell overlaps this area
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cur.execute(
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"""
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SELECT layer_name, cell_id, row_idx, col_idx, cell_size_m, state_name, class_name,
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source_name, min_lon, min_lat, max_lon, max_lat
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FROM navsea_grid_cell
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WHERE layer_name=%s
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ORDER BY row_idx, col_idx
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""",
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("hazard_50m",),
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)
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for row in cur:
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(
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layer_name,
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cell_id,
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row_idx,
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col_idx,
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cell_size_m,
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state_name,
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class_name,
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source_name,
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min_lon,
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min_lat,
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max_lon,
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max_lat,
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) = row
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row_idx_i = int(row_idx)
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col_idx_i = int(col_idx)
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if _overlaps_finer_cells(row_idx_i, col_idx_i, CELL_SIZES["hazard_50m"], CELL_SIZES["fish_port_20m"], keys_20):
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continue
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keys_50.add((row_idx_i, col_idx_i))
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kept_counts["50"] += 1
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min_lon, min_lat, max_lon, max_lat = normalize_bbox(
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layer_name,
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float(min_lon),
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float(min_lat),
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float(max_lon),
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float(max_lat),
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)
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kept_bbox[0] = min(kept_bbox[0], min_lon)
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kept_bbox[1] = min(kept_bbox[1], min_lat)
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kept_bbox[2] = max(kept_bbox[2], max_lon)
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kept_bbox[3] = max(kept_bbox[3], max_lat)
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feature = {
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"type": "Feature",
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"properties": {
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"layer_name": layer_name,
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"cell_id": cell_id,
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"row": row_idx_i,
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"col": col_idx_i,
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"cell_size_m": float(cell_size_m),
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"state_name": state_name,
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"class_name": class_name,
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"source_name": source_name,
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"density_key": "50",
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"density_level": 2,
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"density_name": "中密度",
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},
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"geometry": rect_geometry(min_lon, min_lat, max_lon, max_lat),
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}
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first = write_feature(fh, feature, first)
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# 200m: keep only when neither 20m nor 50m cell overlaps this area
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cur.execute(
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"""
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SELECT layer_name, cell_id, row_idx, col_idx, cell_size_m, state_name, class_name,
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source_name, min_lon, min_lat, max_lon, max_lat
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FROM navsea_grid_cell
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WHERE layer_name=%s
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ORDER BY row_idx, col_idx
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""",
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("coast_200m",),
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)
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for row in cur:
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(
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layer_name,
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cell_id,
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row_idx,
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col_idx,
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cell_size_m,
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state_name,
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class_name,
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source_name,
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min_lon,
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min_lat,
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max_lon,
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max_lat,
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) = row
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row_idx_i = int(row_idx)
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col_idx_i = int(col_idx)
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if _overlaps_finer_cells(row_idx_i, col_idx_i, CELL_SIZES["coast_200m"], CELL_SIZES["fish_port_20m"], keys_20):
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continue
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if _overlaps_finer_cells(row_idx_i, col_idx_i, CELL_SIZES["coast_200m"], CELL_SIZES["hazard_50m"], keys_50):
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continue
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kept_counts["200"] += 1
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min_lon, min_lat, max_lon, max_lat = normalize_bbox(
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layer_name,
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float(min_lon),
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float(min_lat),
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float(max_lon),
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float(max_lat),
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)
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kept_bbox[0] = min(kept_bbox[0], min_lon)
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kept_bbox[1] = min(kept_bbox[1], min_lat)
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kept_bbox[2] = max(kept_bbox[2], max_lon)
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kept_bbox[3] = max(kept_bbox[3], max_lat)
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feature = {
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"type": "Feature",
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"properties": {
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"layer_name": layer_name,
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"cell_id": cell_id,
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"row": row_idx_i,
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"col": col_idx_i,
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"cell_size_m": float(cell_size_m),
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"state_name": state_name,
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"class_name": class_name,
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"source_name": source_name,
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"density_key": "200",
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"density_level": 1,
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"density_name": "低密度",
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},
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"geometry": rect_geometry(min_lon, min_lat, max_lon, max_lat),
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}
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first = write_feature(fh, feature, first)
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fh.write("\n]}\n")
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total = kept_counts["20"] + kept_counts["50"] + kept_counts["200"]
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if total == 0:
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raise RuntimeError("density overview export produced no features")
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summary = {
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"count": total,
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"by_density": kept_counts,
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"bbox": None if kept_bbox[0] == float("inf") else kept_bbox,
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}
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return total, summary
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def main() -> None:
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parser = argparse.ArgumentParser(description="Export NavSea MySQL grid layers to static GeoJSON assets")
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parser.add_argument("--db-name", default=DB_NAME)
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parser.add_argument("--out-dir", default=DEFAULT_OUT_DIR)
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args = parser.parse_args()
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project_root = Path(__file__).resolve().parent.parent
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out_dir = Path(args.out_dir)
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if not out_dir.is_absolute():
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out_dir = project_root / out_dir
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out_dir.mkdir(parents=True, exist_ok=True)
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layers = {
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"coast_200m": {
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"file": "coast_200m_grid.geojson",
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"where": "layer_name=%s",
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"params": ("coast_200m",),
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"cell_size_m": 200.0,
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},
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"fish_port_20m": {
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"file": "fish_port_20m_grid.geojson",
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"where": "layer_name=%s",
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"params": ("fish_port_20m",),
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"cell_size_m": 20.0,
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},
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"hazard_50m": {
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"file": "hazard_50m_grid.geojson",
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"where": "layer_name=%s",
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"params": ("hazard_50m",),
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"cell_size_m": 50.0,
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||||
},
|
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}
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manifest_layers: dict[str, dict] = {}
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conn = mysql_connect(args.db_name)
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try:
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for layer_name, spec in layers.items():
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out_path = out_dir / spec["file"]
|
||||
count = export_layer(conn, path=out_path, where_sql=spec["where"], params=spec["params"])
|
||||
stat_count, bbox = fetch_stats(conn, spec["where"], spec["params"])
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||||
if count != stat_count:
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raise RuntimeError(f"{layer_name} export count mismatch: {count} != {stat_count}")
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||||
manifest_layers[layer_name] = {
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||||
"count": count,
|
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"bbox_lonlat": bbox,
|
||||
"cell_size_m": spec["cell_size_m"],
|
||||
"export_file": str(out_path.relative_to(project_root)),
|
||||
}
|
||||
print(f"{layer_name}: {count} -> {out_path}")
|
||||
|
||||
density_path = out_dir / DENSITY_OVERVIEW_FILE
|
||||
density_count, density_summary = export_density_overview(conn, path=density_path)
|
||||
manifest_layers["density_overview"] = {
|
||||
"count": density_count,
|
||||
"bbox_lonlat": density_summary["bbox"],
|
||||
"cell_size_m": None,
|
||||
"export_file": str(density_path.relative_to(project_root)),
|
||||
"by_density": density_summary["by_density"],
|
||||
}
|
||||
print(f"density_overview: {density_count} -> {density_path}")
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
manifest = {
|
||||
"database": args.db_name,
|
||||
"generated_at": dt.datetime.now().isoformat(timespec="seconds"),
|
||||
"layers": manifest_layers,
|
||||
}
|
||||
manifest_path = out_dir / "manifest.json"
|
||||
manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
print(f"manifest: {manifest_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user