Share the steps every country's geo import takes in geo.py, so geo-se.py and geo-us.py hold only their own rules

data-import/geo-se.py

@@ -3,12 +3,10 @@
 
     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
@@ -16,6 +14,7 @@ import urllib.request
 import zipfile
 from pathlib import Path
 
+import geo
 import source
 import tsv
 import xlsx
@@ -34,8 +33,6 @@ TATORTER = "https://geodata.scb.se/geoserver/stat/wfs?service=WFS&version=2.0.0&
 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"
 # This script's reading of the "exceptions in big cities" in docs/research/research-geo-se.md, Postal codes → Structure.
 ONE_POSITION = {"Stockholm", "Göteborg", "Malmö"}
@@ -106,34 +103,6 @@ def nvdb_segments(cache, key):
         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. A postort's own prefix is three digits where one three-digit prefix starts at
     least half its codes, else two. Outside ONE_POSITION's cities, the digit after that prefix is 0 or 1 for boxes and 8
@@ -181,11 +150,10 @@ def localities(codes, tatorter, municipalities, population):
         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]
+        point = geo.centroid(rows)
-        if municipality is None or not kept or not with_point or not well_cased(name):
+        if municipality is None or not kept or point is None or not well_cased(name):
             continue
-        lat = sum(r["lat"] for r in with_point) / len(with_point)
+        lat, lon = 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
@@ -193,37 +161,29 @@ def localities(codes, tatorter, municipalities, population):
 
 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)
+    nearest = geo.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)
+    return geo.top_streets(count, per_locality, "segments")
-    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 = geo.parser(__doc__, "SE")
-    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, out = geo.directories(a)
-    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}
+    places = geo.with_streets(places, 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}")

data-import/geo-us.py

@@ -3,7 +3,6 @@
 
     data-import/geo-us.py [--cache DIR] [--min-population N] [--streets-per-locality N] [--out DIR]
 """
-import argparse
 import collections
 import concurrent.futures
 import csv
@@ -12,8 +11,8 @@ import re
 import struct
 import sys
 import zipfile
-from pathlib import Path
 
+import geo
 import source
 import tsv
 
@@ -24,8 +23,6 @@ COUNTIES = POPULATION.format("counties/totals/co-est2025-alldata.csv")
 PLACES = POPULATION.format("cities/totals/sub-est2025.csv")
 ZCTA_PLACE = "https://www2.census.gov/geo/docs/maps-data/data/rel2020/zcta520/tab20_zcta520_place20_natl.txt"
 TIGER = "https://www2.census.gov/geo/tiger/TIGER2025/{0}/tl_2025_{1}_{2}.zip"
-OUT = Path(__file__).resolve().parent.parent / "data" / "geo" / "US"
-CACHE = Path(__file__).resolve().parent / "cache"
 ESTIMATE = "POPESTIMATE2025"
 CDP = "57"
 HIGHWAY = re.compile(r"\b(I- |Hwy |Highway |Loop |Rte |Route |Rd )\d")
@@ -153,23 +150,14 @@ def streets(cache, counties, locality_of_zcta, per_locality):
             if r["PAFLAG"] == "P" and r["FULLNAME"] and not HIGHWAY.search(r["FULLNAME"]):
                 for z in zips.get(r["TLID"], ()):
                     count[(locality_of_zcta[z], r["FULLNAME"])] += 1
-    of = collections.defaultdict(list)
+    return geo.top_streets(count, per_locality, "addresses"), addresses
-    for (locality, name), n in count.items():
-        of[locality].append((n, name))
-    named = {locality: [{"name": name, "locality": locality, "addresses": n} for n, name in sorted(ranked, key=lambda s: (-s[0], s[1]))[:per_locality]] for locality, ranked in of.items()}
-    return named, addresses
 
 
 def main():
-    p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    p = geo.parser(__doc__, "US")
-    p.add_argument("--cache", default=str(CACHE))
     p.add_argument("--min-population", type=int, default=25000)
-    p.add_argument("--out", default=str(OUT))
-    p.add_argument("--streets-per-locality", type=int, default=10)
     a = p.parse_args()
-    cache, out = Path(a.cache), Path(a.out)
+    cache, out = geo.directories(a)
-    cache.mkdir(parents=True, exist_ok=True)
-    out.mkdir(parents=True, exist_ok=True)
 
     state_population = {r["STATE"]: r[ESTIMATE] for r in csv_rows(cache, STATES, "nst-est2025.csv") if r["SUMLEV"] == "040"}
     regions = {r["USPS"]: {"abbr": r["USPS"], "code": r["GEOID"], "name": r["NAME"], "population": state_population[r["GEOID"]], "timezone": TIMEZONES[r["USPS"]]} for r in gazetteer(cache, "state")}
@@ -185,9 +173,7 @@ def main():
     places = localities(cache, a.min_population, counties)
     locality_of_zcta = postal_codes(cache, places)
     named, addresses = streets(cache, sorted({l["municipality"] for l in places.values()}), locality_of_zcta, a.streets_per_locality)
-    for geoid in [l for l in places if l not in named]:
+    places = geo.with_streets(places, named)
-        print(f"{geoid} {places[geoid]['name']}: no streets, dropped", file=sys.stderr)
-        del places[geoid]
     kept_counties = {l["municipality"] for l in places.values()}
     kept_regions = {counties[c]["region"] for c in kept_counties}
 

data-import/geo.py

@@ -0,0 +1,80 @@
+"""The steps every country's geo import takes."""
+import argparse
+import collections
+import math
+import sys
+from pathlib import Path
+
+DATA = Path(__file__).resolve().parent.parent / "data" / "geo"
+CACHE = Path(__file__).resolve().parent / "cache"
+
+
+def parser(doc, country):
+    """The options every country's import takes; a script adds its own before parse_args."""
+    p = argparse.ArgumentParser(description=doc.splitlines()[0])
+    p.add_argument("--cache", default=str(CACHE))
+    p.add_argument("--out", default=str(DATA / country))
+    p.add_argument("--streets-per-locality", type=int, default=10)
+    return p
+
+
+def directories(args):
+    """The cache and out directories of the parsed options, both created."""
+    cache, out = Path(args.cache), Path(args.out)
+    cache.mkdir(parents=True, exist_ok=True)
+    out.mkdir(parents=True, exist_ok=True)
+    return cache, out
+
+
+def centroid(rows):
+    """The mean lat and lon of the rows with a point, or None where no row has one."""
+    points = [r for r in rows if r["lat"] is not None]
+    if not points:
+        return None
+    return sum(r["lat"] for r in points) / len(points), sum(r["lon"] for r in points) / len(points)
+
+
+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 top_streets(count, per_locality, column):
+    """Per locality, the per_locality street names counted most in count, keyed (locality, name), ties by name; each
+    row holds its count in column."""
+    of = collections.defaultdict(list)
+    for (locality, name), n in count.items():
+        of[locality].append((n, name))
+    return {locality: [{"name": name, "locality": locality, column: n} for n, name in sorted(named, key=lambda s: (-s[0], s[1]))[:per_locality]] for locality, named in of.items()}
+
+
+def with_streets(places, named):
+    """The places named holds streets for; each other one is logged as dropped."""
+    for key, place in places.items():
+        if key not in named:
+            label = key if key == place["name"] else f"{key} {place['name']}"
+            print(f"{label}: no streets, dropped", file=sys.stderr)
+    return {key: place for key, place in places.items() if key in named}

todo.md

@@ -77,7 +77,6 @@
 | 144 | 0.1.0 | decision | **Run the nine-seat comprehension panel and the scoring run again once items 130 to 143 have merged, and file what the panel names.** | 1 | 3 | 3 | 9 | 3 | 20.4 |
 | 91 | 0.1.0 |  | **Accept a path through a name as a `calc` operand, so `{/misc.coordinate as c}{c}: {calc(c.lat * 60)}` computes from the coordinate `{c}` prints.** | 3 | 4 | 6 | 7 | 4.1, 5.3 | 19.9 |
 | 89 | 0.1.0 |  | **Fail `New` without `WithSeed`, naming the option, and seed the CLI from the system.** | 3 | 3 | 6 | 7 | 10.1 | 16.0 |
-| 138 | 0.1.0 |  | **Split `geo-se.py` into the steps every country's import takes and the Swedish rules, and share the steps with `geo-us.py`, so a new country's script holds only its own rules.** | 2 | 3 | 3 | 6 | 2.1, 3.2 | 14.6 |
 | 127 | 0.1.0 |  | **Give a Swedish street only postal codes whose delivery digit means street or rural delivery, dropping codes whose delivery digit is 9, or record why they stay.** | 3 | 3 | 4 | 6 | 4 | 14.4 |
 | 110 | 0.1.0 |  | **Refuse an empty segment in a path `Fake`, `FakeRecord` and the CLI take, as a reference does: `sv_SE.person.` reports `path has an empty segment`.** | 1 | 1 | 3 | 6 | 6 | 13.8 |
 | 79 | 0.1.0 | decision | **Beside a `--data-path`, load in `New` the user's categories, the shipped ones they override or read, and the shipped ones whose reads reach an overridden one; load the rest on first reach.** | 6 | 6 | 7 | 8 | 13 | 12.9 |
@@ -271,10 +270,6 @@ A calc operand parses as a bare identifier, so `{calc(c.lat * 60)}` fails with `
 
 Goal 10.1 has the caller supply the seed, and today `New` without `WithSeed` seeds itself. This item ships in 0.1.0 beside item 77, so `New` breaks once before anyone depends on it. Mark `WithSeed` required in the Library section's options table. Its tests then stop swapping `randomBytes`, as `fejkdata_test.go` does today, which fails every `New` in the package while that test runs.
 
-### 138. Split `geo-se.py` into the steps every country's import takes and the Swedish rules, and share the steps with `geo-us.py`, so a new country's script holds only its own rules.
-
-Needed by item 61. The extender seat of the 2026-10-07 panel, reading for item 61, could not tell in `localities` and its helpers which steps a Norwegian script keeps; `geo-us.py` copies the per-locality street ranking of `streets` in `geo-se.py`.
-
 ### 127. Give a Swedish street only postal codes whose delivery digit means street or rural delivery, dropping codes whose delivery digit is 9, or record why they stay.
 
 `docs/research/research-geo-se.md` reads delivery digit 0 or 1 as boxes, 2–4 and 6–7 as street, 5 as rural, 8 as reply mail and 9 as competitions or temporary, with exceptions in big cities. `street_delivery` drops 0, 1 and 8 and keeps 9. The panel of 2026-10-05 found it; whether a shipped street carries a 9 code is unchecked.