Initial commit — multi-tenant filtering, port constraints, chart bbox
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#!/usr/bin/env python3
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"""
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Regenerate features.geojson for Bahia de Cartagena directly from the
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source CSVs, bypassing the GDAL S-57 round-trip that loses LITCHR/SIGPER/
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VALNMR for BOYLAT and CATCAM for BOYCAR.
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"""
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import csv
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import json
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from pathlib import Path
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CSV_DIR = Path(r"D:\Proyectos Software\QGISS57Converter\capas_ctg")
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OUT_FILE = Path(r"C:\AidsMonitoring\charts\BAHÍA_DE_CARTAGENA\features.geojson")
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META_FILE= Path(r"C:\AidsMonitoring\charts\BAHÍA_DE_CARTAGENA\meta.json")
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# ── S-57 attribute lookup tables ──────────────────────────────────────────────
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LITCHR_MAP = {
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1:"F", 2:"Fl", 3:"LFl", 4:"Q", 5:"VQ", 6:"UQ", 7:"Iso",
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8:"Oc", 9:"IQ", 10:"IVQ", 11:"IUQ", 12:"Mo", 13:"FFl",
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14:"FlLFl", 15:"OcFl", 16:"FLFl", 25:"Al.Oc", 26:"Al.LFl",
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27:"Al.Fl", 28:"Al.Grp",
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}
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COLOUR_MAP = {
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1:"W", 2:"K", 3:"R", 4:"G", 5:"B", 6:"Y",
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7:"Gy", 8:"Br", 9:"Amb", 10:"Vi", 11:"Or", 12:"Mg",
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}
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# CSV feat_type → canonical S-57 layer name
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FEAT_TYPE_TO_LAYER = {
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"BOYSPEC": "BOYSPP", # DIMAR CSV uses BOYSPEC; IHO S-57 is BOYSPP
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}
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# Layer → feature category
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CATEGORY_MAP = {
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"BOYLAT": "buoy", "BOYCAR": "buoy", "BOYISD": "buoy",
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"BOYSPP": "buoy", "BOYSAW": "buoy",
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"BCNLAT": "beacon", "BCNCAR": "beacon",
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"LIGHTS": "light",
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"LNDMRK": "landmark",
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}
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def _num(v):
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if v is None or str(v).strip() == "":
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return None
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try:
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return float(v)
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except Exception:
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return None
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def _int(v):
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n = _num(v)
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return int(n) if n is not None else None
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def _fmt(v):
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"""Format a number: drop trailing .0 for whole numbers."""
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try:
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f = float(v)
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return str(int(f)) if f == int(f) else str(f)
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except Exception:
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return str(v)
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def make_light_desc(litchr, siggrp, colour, sigper, valnmr):
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"""Build the compact light description string (e.g. 'Fl G 3s 3M')."""
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parts = []
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import re as _re2
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lc_int = _int(litchr)
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lc = LITCHR_MAP.get(lc_int, str(lc_int)) if lc_int is not None else ""
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sg_raw = str(siggrp or "").strip()
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sg_m = _re2.search(r'\d+', sg_raw)
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sg = int(sg_m.group()) if sg_m else None
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suffix = "+" if "+" in sg_raw else ""
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if sg is not None:
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lc = f"{lc}({sg}){suffix}"
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col_int = _int(colour)
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col_str = COLOUR_MAP.get(col_int, "") if col_int is not None else ""
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if lc:
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parts.append(f"{lc} {col_str}".strip())
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sp = _num(sigper)
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if sp is not None:
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parts.append(f"{_fmt(sp)}s")
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rng = _num(valnmr)
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if rng is not None:
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parts.append(f"{_fmt(rng)}M")
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return " ".join(parts)
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def infer_catcam(siggrp, name):
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"""
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Cardinal buoy direction from SIGGRP (most reliable).
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Q(9)+LFl = West, Q(6)+LFl = South, Q(3) = East, Q = North
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DIMAR name convention (fallback):
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SS/VS → S, SN/VN → N, SE → E, SO → W
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"""
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# Extract first integer from SIGGRP even if it looks like "(6)+" or "(9)+LFl"
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import re as _re
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sg_str = str(siggrp or "")
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m = _re.search(r'\d+', sg_str)
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sg = int(m.group()) if m else None
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if sg is not None:
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if sg == 9: return "W"
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if sg == 6: return "S"
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if sg == 3: return "E"
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return "N" # Q without group or Q(1) = North cardinal
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n = (name or "").upper()
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if any(x in n for x in ("SUR", " SS", "(SS)", " VS", "VS ")): return "S"
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if any(x in n for x in ("ESTE", " SE", "(SE)")): return "E"
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if any(x in n for x in ("OESTE", " SO", "(SO)", " SW", "(SW)")): return "W"
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if any(x in n for x in ("NORTE", " SN", "(SN)", " VN", "VN ")): return "N"
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return "N"
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def infer_catlam(colour):
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"""IALA B (Americas): green=port=1, red=stbd=2."""
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c = _int(colour)
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if c == 4: return 1 # green → port
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if c == 3: return 2 # red → starboard
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return None
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def classify(layer, props):
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catlam = props.get("catlam")
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catcam = props.get("catcam")
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if layer in ("BOYLAT", "BCNLAT"):
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if catlam == 1: return "LATERAL_PORT"
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if catlam == 2: return "LATERAL_STBD"
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if catlam == 3: return "LATERAL_PREF_STBD"
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if catlam == 4: return "LATERAL_PREF_PORT"
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return "LATERAL_UNKNOWN"
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if layer in ("BOYCAR", "BCNCAR"):
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if catcam == "N": return "CARDINAL_N"
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if catcam == "E": return "CARDINAL_E"
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if catcam == "S": return "CARDINAL_S"
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if catcam == "W": return "CARDINAL_W"
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return "CARDINAL_UNKNOWN"
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if layer in ("BOYISD", "BCNISD"): return "ISOLATED_DANGER"
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if layer == "BOYSAW": return "SAFE_WATER"
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if layer in ("BOYSPP", "BOYSPEC"): return "SPECIAL"
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if layer == "LIGHTS": return "LIGHT_POINT"
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if layer == "LNDMRK": return "LANDMARK"
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if layer.startswith("BCN"): return "BEACON_GENERIC"
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if layer.startswith("BOY"): return "BUOY_GENERIC"
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return "UNKNOWN"
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# ── Main loop ─────────────────────────────────────────────────────────────────
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features = []
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csv_files = sorted(CSV_DIR.glob("*.csv"))
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print(f"Processing {len(csv_files)} CSV files from {CSV_DIR}")
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for csv_file in csv_files:
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row_count = 0
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with open(csv_file, newline="", encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for row in reader:
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feat_type = (row.get("feat_type") or csv_file.stem).strip()
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layer = FEAT_TYPE_TO_LAYER.get(feat_type, feat_type)
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if not layer:
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continue
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lon = _num(row.get("lon"))
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lat = _num(row.get("lat"))
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if lon is None or lat is None:
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continue
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category = CATEGORY_MAP.get(layer, "buoy")
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litchr = row.get("LITCHR", "").strip()
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siggrp = row.get("SIGGRP", "").strip()
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colour = row.get("COLOUR", "").strip()
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sigper = row.get("SIGPER", "").strip()
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valnmr = row.get("VALNMR", "").strip()
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height = row.get("HEIGHT", "").strip()
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orient_raw = row.get("ORIENT", "").strip()
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name = row.get("OBJNAM", "").strip()
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inform = row.get("INFORM", "").strip()
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colour_int = _int(colour)
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colours = [colour_int] if colour_int is not None else []
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light_desc = make_light_desc(litchr, siggrp, colour, sigper, valnmr)
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props = {
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"layer": layer,
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"category": category,
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"name": name or None,
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"info": inform or None,
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"light_desc": light_desc or None,
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"range_nm": _num(valnmr),
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"height_m": _num(height),
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"colours": colours,
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"colour_code": colour_int,
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}
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# Layer-specific S-57 attributes
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if layer in ("BOYCAR", "BCNCAR"):
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# Prefer explicit CATCAM column (1=N,2=E,3=S,4=W); fall back to inference
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catcam_raw = row.get("CATCAM", "").strip()
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catcam_int = _int(catcam_raw)
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_CATCAM_INT = {1: "N", 2: "E", 3: "S", 4: "W"}
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if catcam_int in _CATCAM_INT:
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props["catcam"] = _CATCAM_INT[catcam_int]
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else:
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props["catcam"] = infer_catcam(siggrp, name)
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if layer in ("BOYLAT", "BCNLAT"):
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props["catlam"] = infer_catlam(colour)
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orient_num = _num(orient_raw)
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if orient_num is not None:
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props["orient"] = orient_num
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props["aid_type"] = classify(layer, props)
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# Strip None values (but keep empty lists like colours=[])
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props = {k: v for k, v in props.items() if v is not None}
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features.append({
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"type": "Feature",
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"geometry": {"type": "Point", "coordinates": [lon, lat]},
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"properties": props,
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})
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row_count += 1
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print(f" {csv_file.name}: {row_count} features")
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# ── Write features.geojson ────────────────────────────────────────────────────
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fc = {"type": "FeatureCollection", "features": features}
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OUT_FILE.parent.mkdir(parents=True, exist_ok=True)
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with open(OUT_FILE, "w", encoding="utf-8") as f:
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json.dump(fc, f, ensure_ascii=False, indent=2)
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print(f"\nWrote {len(features)} features to {OUT_FILE}")
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# ── Update meta.json ──────────────────────────────────────────────────────────
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if features:
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lons = [ft["geometry"]["coordinates"][0] for ft in features]
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lats = [ft["geometry"]["coordinates"][1] for ft in features]
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bbox = [min(lons), min(lats), max(lons), max(lats)]
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try:
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meta = json.loads(META_FILE.read_text(encoding="utf-8"))
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except Exception:
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meta = {}
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meta["feature_count"] = len(features)
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meta["bbox"] = bbox
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META_FILE.write_text(json.dumps(meta), encoding="utf-8")
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print(f"Updated meta.json: {len(features)} features, bbox={bbox}")
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# ── Quick QA: show light_desc for first 5 BOYLAT and all BOYCAR ──────────────
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print("\n── BOYLAT sample ──")
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for ft in [f for f in features if f["properties"].get("layer") == "BOYLAT"][:5]:
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p = ft["properties"]
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print(f" {p['name']:30s} {p.get('light_desc','(none)'):20s} "
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f"aid_type={p['aid_type']}")
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print("\n── BOYCAR (all) ──")
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for ft in [f for f in features if f["properties"].get("layer") == "BOYCAR"]:
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p = ft["properties"]
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print(f" {p['name']:35s} {p.get('light_desc','(none)'):20s} "
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f"catcam={p.get('catcam','?')} aid_type={p['aid_type']}")
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print("\n── LNDMRK (all) ──")
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for ft in [f for f in features if f["properties"].get("layer") == "LNDMRK"]:
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p = ft["properties"]
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print(f" {p['name']:35s} {p.get('light_desc','(none)'):22s} "
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f"height={p.get('height_m','?')}m range={p.get('range_nm','?')}NM")
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