Files
fejkdata/data-import/geo.py
T

94 lines
4.3 KiB
Python

"""The steps a country's geo import may share with another's."""
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, **own):
"""A parser for the options every country's import takes, plus the script's own: each keyword names one option, with
"_" standing for "-"."""
options = {"cache": {"default": str(CACHE)}, "out": {"default": str(DATA / country)}, "streets_per_locality": {"type": int, "default": 10}, **own}
p = argparse.ArgumentParser(description=doc.splitlines()[0])
for name in sorted(options):
p.add_argument("--" + name.replace("_", "-"), **options[name])
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):
"""count maps (locality, street name) to a count. Returns, per locality, rows for the per_locality names counted
most, ties broken by name; each row holds its count under 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 fence(regions, municipalities, localities, streets):
"""Meet the loader's fence that every parent row has a child row: drop each locality streets holds no rows for, then
each municipality and region left without one, logging each drop. regions and municipalities map a code to a row,
and a municipality row names its region under "region". localities map a key to a locality row: name, municipality,
population, lat and lon, besides the columns its country adds. streets maps a locality's key to its street rows.
Returns the kept regions, municipalities and localities."""
localities = kept(localities, streets, "streets")
municipalities = kept(municipalities, {l["municipality"] for l in localities.values()}, "localities")
regions = kept(regions, {m["region"] for m in municipalities.values()}, "municipalities")
return regions, municipalities, localities
def kept(parents, children, kind):
"""The parents whose key children holds; logs each other parent as dropped."""
for key, parent in parents.items():
if key not in children:
label = key if key == parent["name"] else f"{key} {parent['name']}"
print(f"{label}: no {kind}, dropped", file=sys.stderr)
return {key: parent for key, parent in parents.items() if key in children}