#!/usr/bin/env python3
"""Rebuild data/geo/SE/*.tsv from SCB (CC0), GeoNames (CC BY 4.0) and Trafikverket NVDB (CC0).

    TRAFIKVERKET_API_KEY=… data-import/geo-se.py [--key-file FILE] [--cache DIR] [--streets-per-locality N] [--out DIR]
"""
import argparse
import collections
import csv
import io
import json
import math
import os
import re
import sys
import urllib.request
import zipfile
from pathlib import Path

import tsv

CODES = "https://www.scb.se/contentassets/7a89e48960f741e08918e489ea36354a/kommunlankod-2026.xlsx"
POPULATION = "https://api.scb.se/OV0104/v1/doris/sv/ssd/START/BE/BE0101/BE0101A/BefolkningNy"
POPULATION_QUERY = {
    "query": [
        {"code": "Region", "selection": {"filter": "all", "values": ["*"]}},
        {"code": "ContentsCode", "selection": {"filter": "item", "values": ["BE0101N1"]}},
        {"code": "Tid", "selection": {"filter": "top", "values": ["1"]}},
    ],
    "response": {"format": "json"},
}
TATORTER = "https://geodata.scb.se/geoserver/stat/wfs?service=WFS&version=2.0.0&request=GetFeature&typeNames=stat:Tatorter_2023&outputFormat=csv&propertyName=tatort,kommun,bef"
POSTAL_CODES = "https://download.geonames.org/export/zip/SE.zip"
NVDB = "https://api.trafikinfo.trafikverket.se/v2/data.json"
NVDB_PAGE = 50000
OUT = Path(__file__).resolve().parent.parent / "data" / "geo" / "SE"
CACHE = Path(__file__).resolve().parent / "cache"
TIMEZONE = "Europe/Stockholm"
ONE_POSITION = {"Stockholm", "Göteborg", "Malmö"}
UNMATCHED_POPULATION = 200
def scb_codes(cache):
    regions, municipalities = {}, {}
    for cells in tsv.xlsx_rows(tsv.fetch(CODES, cache, "kommunlankod.xlsx", magic=b"PK")):
        if len(cells) < 2 or not re.fullmatch(r"\d{2}|\d{4}", cells[0]):
            continue
        (regions if len(cells[0]) == 2 else municipalities)[cells[0]] = cells[1].strip()
    return regions, municipalities


def scb_population(cache):
    body = json.dumps(POPULATION_QUERY).encode()
    data = tsv.fetch(POPULATION, cache, "befolkning.json", data=body, headers={"Content-Type": "application/json"})
    return {row["key"][0]: row["values"][0] for row in json.loads(data.decode("utf-8-sig"))["data"]}


def scb_tatorter(cache):
    text = tsv.fetch(TATORTER, cache, "tatorter.csv").decode("utf-8")
    by_name = collections.defaultdict(list)
    for r in csv.DictReader(io.StringIO(text)):
        by_name[r["tatort"]].append((r["kommun"], int(r["bef"])))
    return by_name


def geonames(cache):
    z = zipfile.ZipFile(io.BytesIO(tsv.fetch(POSTAL_CODES, cache, "SE.zip", magic=b"PK")))
    rows = []
    for line in z.read("SE.txt").decode("utf-8").splitlines():
        f = line.split("\t")
        lat, lon = (float(f[9]), float(f[10])) if f[9] and f[10] else (None, None)
        rows.append({"code": f[1], "locality": f[2], "municipality": f[6], "lat": lat, "lon": lon})
    return rows


def nvdb_segments(cache, key):
    path = cache / "nvdb-gatunamn.tsv"
    if not path.exists():
        with open(path.with_suffix(".part"), "w", encoding="utf-8") as out:
            change = "0"
            while True:
                query = (
                    f'<REQUEST><LOGIN authenticationkey="{key}"/>'
                    f'<QUERY objecttype="Gatunamn" namespace="vägdata.nvdb_dk_o" schemaversion="1.0" limit="{NVDB_PAGE}" changeid="{change}">'
                    "<FILTER><EQ name=\"Deleted\" value=\"false\"/></FILTER>"
                    "<INCLUDE>Namn</INCLUDE><INCLUDE>Geometry.WKT-WGS84-3D</INCLUDE></QUERY></REQUEST>"
                )
                req = urllib.request.Request(NVDB, data=query.encode(), headers={"Content-Type": "text/xml"})
                with urllib.request.urlopen(req, timeout=600) as r:
                    result = json.load(r)["RESPONSE"]["RESULT"][0]
                rows = result.get("Gatunamn", [])
                for row in rows:
                    m = re.match(r"LINESTRING Z \(([-\d.]+) ([-\d.]+) ", row.get("Geometry", {}).get("WKT-WGS84-3D", ""))
                    name = " ".join(row.get("Namn", "").split())
                    if m and name:
                        out.write(f"{name}\t{m.group(1)}\t{m.group(2)}\n")
                change = result["INFO"]["LASTCHANGEID"]
                if len(rows) < NVDB_PAGE:
                    break
        path.with_suffix(".part").rename(path)
    for line in path.read_text(encoding="utf-8").splitlines():
        name, lon, lat = line.split("\t")
        yield name, float(lat), float(lon)


class Nearest:
    """Nearest point by an equirectangular distance, over a degree grid."""

    def __init__(self, points, cell=0.05):
        self.cell = cell
        self.grid = collections.defaultdict(list)
        for lat, lon, value in points:
            self.grid[(int(lat // cell), int(lon // cell))].append((lat, lon, value))

    def find(self, lat, lon):
        ci, cj = int(lat // self.cell), int(lon // self.cell)
        best, best_d = None, math.inf
        ring = 0
        while ring < 400:
            for i in range(ci - ring, ci + ring + 1):
                for j in range(cj - ring, cj + ring + 1):
                    if max(abs(i - ci), abs(j - cj)) != ring:
                        continue
                    for plat, plon, value in self.grid.get((i, j), ()):
                        d = (plat - lat) ** 2 + ((plon - lon) * math.cos(math.radians(lat))) ** 2
                        if d < best_d:
                            best, best_d = value, d
            if best is not None and math.sqrt(best_d) < ring * self.cell * math.cos(math.radians(lat)):
                return best
            ring += 1
        return best


def street_delivery(name, codes):
    """The codes delivered to a street: the digit after the postort's own prefix says box, company or reply."""
    if name in ONE_POSITION:
        return [c for c in codes if c[1] != "0"]
    largest = collections.Counter(c[:3] for c in codes).most_common(1)[0][1]
    length = 3 if largest * 2 >= len(codes) else 2
    return [c for c in codes if c[length] not in "018"]


def municipality_of(name, rows, tatorter, municipalities):
    named = [code for code, n in municipalities.items() if n == name]
    if named:
        return named[0], "kommun"
    voted = collections.Counter(r["municipality"] for r in rows if r["municipality"] in municipalities)
    matches = tatorter.get(name, [])
    if matches:
        in_vote = [m for m in matches if voted and m[0] == voted.most_common(1)[0][0]]
        return max(in_vote or matches, key=lambda m: m[1])[0], "tatort"
    if voted:
        return voted.most_common(1)[0][0], "codes"
    return None, "unplaced"


def population_of(name, municipality, tatorter, municipalities, population):
    matches = [m for m in tatorter.get(name, []) if m[0] == municipality]
    if matches:
        return str(max(m[1] for m in matches))
    return population[municipality] if municipalities[municipality] == name else str(UNMATCHED_POPULATION)


def well_cased(name):
    return all(part[:1].isupper() and (len(part) == 1 or not part.isupper()) for part in re.split(r"[ -]", name))


def localities(codes, tatorter, municipalities, population):
    """Each postort with its municipality, weight, centroid and street-delivery codes."""
    by_locality = collections.defaultdict(list)
    for r in codes:
        by_locality[r["locality"]].append(r)
    out, how = {}, collections.Counter()
    for name, rows in by_locality.items():
        municipality, method = municipality_of(name, rows, tatorter, municipalities)
        how[method] += 1
        kept = street_delivery(name, [r["code"].replace(" ", "") for r in rows])
        with_point = [r for r in rows if r["lat"] is not None]
        if municipality is None or not kept or not with_point or not well_cased(name):
            continue
        lat = sum(r["lat"] for r in with_point) / len(with_point)
        lon = sum(r["lon"] for r in with_point) / len(with_point)
        out[name] = {"name": name, "municipality": municipality, "population": population_of(name, municipality, tatorter, municipalities, population), "lat": f"{lat:.4f}", "lon": f"{lon:.4f}", "codes": kept}
    print(f"municipality by {dict(how)}; {len(by_locality) - len(out)} postorter dropped", file=sys.stderr)
    return out


def streets(segments, codes, localities, per_locality):
    """The names with most segments per locality, each segment at its nearest code centroid."""
    nearest = Nearest((r["lat"], r["lon"], r["locality"]) for r in codes if r["lat"] is not None and r["locality"] in localities)
    count = collections.Counter()
    for name, lat, lon in segments:
        if name[0].isalpha():
            count[(nearest.find(lat, lon), name)] += 1
    of = collections.defaultdict(list)
    for (locality, name), n in count.items():
        of[locality].append((n, name))
    return {locality: [{"name": name, "locality": locality, "segments": n} for n, name in sorted(named, key=lambda s: (-s[0], s[1]))[:per_locality]] for locality, named in of.items()}


def main():
    p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
    p.add_argument("--cache", default=str(CACHE))
    p.add_argument("--key-file", help="file holding the Trafikverket API key; TRAFIKVERKET_API_KEY otherwise")
    p.add_argument("--out", default=str(OUT))
    p.add_argument("--streets-per-locality", type=int, default=10)
    a = p.parse_args()
    key = Path(a.key_file).read_text().strip() if a.key_file else os.environ.get("TRAFIKVERKET_API_KEY")
    if not key:
        sys.exit("set TRAFIKVERKET_API_KEY or pass --key-file")
    cache, out = Path(a.cache), Path(a.out)
    cache.mkdir(parents=True, exist_ok=True)
    out.mkdir(parents=True, exist_ok=True)

    regions, municipalities = scb_codes(cache)
    population = scb_population(cache)
    codes = geonames(cache)
    places = localities(codes, scb_tatorter(cache), municipalities, population)
    named = streets(nvdb_segments(cache, key), codes, places, a.streets_per_locality)
    places = {name: l for name, l in places.items() if name in named}
    empty = sorted(m for m in municipalities if not any(l["municipality"] == m for l in places.values()))
    if empty:
        sys.exit(f"municipalities without a locality: {empty}")

    tsv.write(out / "region.tsv", ["code", "name", "population", "timezone"], [{"code": c, "name": n, "population": population[c], "timezone": TIMEZONE} for c, n in sorted(regions.items())])
    tsv.write(out / "municipality.tsv", ["code", "name", "region", "population"], [{"code": c, "name": n, "region": c[:2], "population": population[c]} for c, n in sorted(municipalities.items())])
    tsv.write(out / "locality.tsv", ["name", "municipality", "population", "lat", "lon"], [l for _, l in sorted(places.items())])
    tsv.write(out / "postal-code.tsv", ["code", "locality"], sorted(({"code": f"{c[:3]} {c[3:]}", "locality": l["name"]} for l in places.values() for c in l["codes"]), key=lambda r: r["code"]))
    tsv.write(out / "street.tsv", ["name", "locality", "segments"], [s for locality in sorted(named) for s in named[locality]])


if __name__ == "__main__":
    main()