CHANGELOG.md

@@ -43,5 +43,6 @@ replacement, and each removed path, column or flag.
   `first`, `last`, `prefix` and `sex`, and `person.femalefirst` and
   `person.malefirst` are no longer paths.
 - `sv_SE.personnummer` and `sv_SE.samordningsnummer`, Skatteverket's test series
-  over the sex the render drew, in place of `sv_SE.ssn`; `en_US.ssn` in the ranges
-  the SSA assigns, and `en_US.itin`.
+  from a `sv_SE.birth-number` table under `sex`, in place of `sv_SE.ssn`; `en_US.ssn`
+  in the ranges the SSA assigns, and `en_US.itin`. `en_US.person.prefix` no longer
+  draws `Mr`, `Mrs`, `Ms` or `Miss`, which could contradict the record's `sex`.

DATA-LICENSES.md

@@ -14,7 +14,7 @@ Every shipped dataset, its source, its licence and the attribution it asks for.
 | `sv_SE/first-name.tsv`, `last-name.tsv` | [SCB](https://www.scb.se/) names with at least two bearers, 31 December 2022 | CC0 1.0 | "Källa: SCB" | `data-import/names-se.py` |
 | `en_US/first-name.tsv` | [SSA](https://www.ssa.gov/oact/babynames/) baby names, births 1930 to 2020, through [hackerb9/ssa-baby-names](https://github.com/hackerb9/ssa-baby-names) | public domain | none required | `data-import/names-us.py` |
 | `en_US/last-name.tsv` | Census Bureau surnames occurring 100 or more times, 2010 | public domain | none required | `data-import/names-us.py` |
-| `sv_SE/sex.tsv`, `en_US/sex.tsv` | curated (Skatteverket's test birth numbers are facts) | — | — | — |
+| `sv_SE/sex.tsv`, `sv_SE/birth-number.tsv`, `en_US/sex.tsv` | curated (Skatteverket's test birth numbers are facts) | — | — | — |
 | `misc/country.tsv` | [datasets/country-codes](https://github.com/datasets/country-codes) | [PDDL 1.0](https://opendatacommons.org/licenses/pddl/1-0/) | none required | `data-import/country.py` |
 | `misc/currency.tsv` | [datasets/currency-codes](https://github.com/datasets/currency-codes); symbols from [Unicode CLDR](https://github.com/unicode-org/cldr) `en.xml` and `root.xml` | PDDL 1.0; [Unicode License v3](https://www.unicode.org/license.txt) | CLDR: "Copyright © 1991-2025 Unicode, Inc. Unicode and the Unicode Logo are registered trademarks of Unicode, Inc. in the United States and other countries." | `data-import/currency.py` |
 | `misc/httpstatus.tsv` | curated (IANA HTTP status codes are facts) | — | — | — |

README.md

@@ -235,8 +235,9 @@ SSA and the Census Bureau. `first-name` links to `sex`, so `sv_SE.sex[f].first-n
 draws a woman's name, and a name both sexes carry is a row under each, so
 `en_US.sex[m].first-name[Taylor]` names the one a `first-name[Taylor]` alone cannot.
 `person` reads one draw of the three, so its `first` and `sex` columns agree, and so
-does a `personnummer` in the same render: its birth number is Skatteverket's test
-series, 238 for a woman and 239 for a man, which no real person is ever given.
+does a `personnummer` in the same render: its birth number, `sv_SE.birth-number`
+under `sex`, is Skatteverket's test series, 238 for a woman and 239 for a man, which no
+real person is ever given.
 
 A `geo` folder holds one tree per country under its alpha-2 code: five
 [linked tables](#linked-tables) named alike, and an `address` record over one
@@ -536,8 +537,9 @@ Renders e.g. `811218-2389`. A layout is Go's: the reference time `Mon Jan 2
 15:04:05 MST 2006` spelled as the output should look, quoted, since a layout may
 carry the comma that separates arguments, with English names. Every second
 between the two days is reachable, so a layout with a clock draws the time too.
-Rejected at `New`: a bound that is no calendar date, or not before the other; an
-unquoted layout, naming the quoted one; a layout naming no field, which is text;
+The quotes delimit a layout outside a selector only, so `[O'Fallon]` in an
+argument stays a name. Rejected at `New`: a bound that is no calendar date, or not
+before the other; an unquoted layout, naming the quoted one; a layout naming no field, which is text;
 and for `time` a layout naming a date field, naming `date`. `{seq()}` spans `Fake`
 calls and `repeat`, resets with a new generator, and is the natural primary key for
 the SQL example above.
@@ -1078,8 +1080,10 @@ renamed or retyped line is a major.
 - **The Swedish ids draw Skatteverket's test series.** A Luhn-valid personnummer
   over a random birth number may be a living person's; 238 and 239 after any date
     are blocked from assignment, so the shipped `personnummer` and
-  `samordningsnummer` use those, read from the `sex` table's `birth-number`
-  column so the number and the name agree on sex.
+  `samordningsnummer` use those. They sit in a `birth-number` table under `sex`
+  rather than as a column of it: the render's shared draw of the family is what
+  makes the number and the name agree on sex, and `sex` stays one shape across
+  locales instead of collecting every sex-keyed id fact.
 - **The US given names come from a mirror of the SSA file.** ssa.gov refuses a
   client outside the US, so `names-us.py` reads a GitHub copy that ends at 2020,
   which a count over the births since 1930 barely feels; `--names` takes the

data-import/country.py

@@ -9,6 +9,7 @@ import io
 import re
 from pathlib import Path
 
+import source
 import tsv
 
 SOURCE = "https://raw.githubusercontent.com/datasets/country-codes/main/data/country-codes.csv"
@@ -58,7 +59,7 @@ def main():
     p.add_argument("--source", default=SOURCE)
     p.add_argument("--out", default=str(OUT))
     a = p.parse_args()
-    table = rows(tsv.fetch(a.source, a.cache, "country-codes.csv").decode("utf-8"))
+    table = rows(source.fetch(a.source, a.cache, "country-codes.csv").decode("utf-8"))
     tsv.write(a.out, COLUMNS, sorted(table, key=lambda r: r["alpha2"]))
 
 

data-import/currency.py

@@ -11,6 +11,7 @@ import io
 import xml.etree.ElementTree as ET
 from pathlib import Path
 
+import source
 import tsv
 
 SOURCE = "https://raw.githubusercontent.com/datasets/currency-codes/main/data/codes-all.csv"
@@ -58,8 +59,8 @@ def main():
     p.add_argument("--symbols", nargs="+", default=SYMBOLS)
     p.add_argument("--out", default=str(OUT))
     a = p.parse_args()
-    symbol = symbols(tsv.fetch(s, a.cache, Path(s).name).decode("utf-8") for s in a.symbols)
-    table = rows(tsv.fetch(a.source, a.cache, "codes-all.csv").decode("utf-8"), symbol)
+    symbol = symbols(source.fetch(s, a.cache, Path(s).name).decode("utf-8") for s in a.symbols)
+    table = rows(source.fetch(a.source, a.cache, "codes-all.csv").decode("utf-8"), symbol)
     tsv.write(a.out, COLUMNS, sorted(table, key=lambda r: r["code"]))
 
 

data-import/geo-se.py

@@ -16,6 +16,8 @@ import urllib.request
 import zipfile
 from pathlib import Path
 
+import source
+import xlsx
 import tsv
 
 CODES = "https://www.scb.se/contentassets/7a89e48960f741e08918e489ea36354a/kommunlankod-2026.xlsx"
@@ -39,7 +41,7 @@ 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")):
+    for cells in xlsx.rows(source.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()
@@ -48,12 +50,12 @@ def scb_codes(cache):
 
 def scb_population(cache):
     body = json.dumps(POPULATION_QUERY).encode()
-    data = tsv.fetch(POPULATION, cache, "befolkning.json", data=body, headers={"Content-Type": "application/json"})
+    data = source.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")
+    text = source.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"])))
@@ -61,7 +63,7 @@ def scb_tatorter(cache):
 
 
 def geonames(cache):
-    z = zipfile.ZipFile(io.BytesIO(tsv.fetch(POSTAL_CODES, cache, "SE.zip", magic=b"PK")))
+    z = zipfile.ZipFile(io.BytesIO(source.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")

data-import/geo-us.py

@@ -14,6 +14,7 @@ import sys
 import zipfile
 from pathlib import Path
 
+import source
 import tsv
 
 GAZETTEER = "https://www2.census.gov/geo/docs/maps-data/data/gazetteer/2026_Gazetteer/2026_Gaz_{}_national.zip"
@@ -56,14 +57,14 @@ def text(data):
 
 
 def gazetteer(cache, kind):
-    z = zipfile.ZipFile(io.BytesIO(tsv.fetch(GAZETTEER.format(kind), cache, f"gaz_{kind}.zip", magic=b"PK")))
+    z = zipfile.ZipFile(io.BytesIO(source.fetch(GAZETTEER.format(kind), cache, f"gaz_{kind}.zip", magic=b"PK")))
     rows = text(z.read(z.namelist()[0])).splitlines()
     header = [h.strip() for h in rows[0].split("|")]
     return [dict(zip(header, (c.strip() for c in row.split("|")))) for row in rows[1:]]
 
 
 def csv_rows(cache, url, name):
-    return list(csv.DictReader(io.StringIO(text(tsv.fetch(url, cache, name)))))
+    return list(csv.DictReader(io.StringIO(text(source.fetch(url, cache, name)))))
 
 
 def dbf_rows(data, wanted):
@@ -89,7 +90,7 @@ def dbf_rows(data, wanted):
 
 
 def tiger_zip(cache, kind, county):
-    return tsv.fetch(TIGER.format(kind.upper(), county, kind), cache, f"tl_{county}_{kind}.zip", magic=b"PK")
+    return source.fetch(TIGER.format(kind.upper(), county, kind), cache, f"tl_{county}_{kind}.zip", magic=b"PK")
 
 
 def tiger(cache, kind, county, wanted):
@@ -130,7 +131,7 @@ def localities(cache, min_population, counties):
 def postal_codes(cache, localities):
     """Each ZCTA whose largest part inside an incorporated place lies in a shipped place."""
     parts = {}
-    for r in csv.DictReader(io.StringIO(text(tsv.fetch(ZCTA_PLACE, cache, "zcta-place.txt"))), delimiter="|"):
+    for r in csv.DictReader(io.StringIO(text(source.fetch(ZCTA_PLACE, cache, "zcta-place.txt"))), delimiter="|"):
         if r["GEOID_ZCTA5_20"] and r["GEOID_PLACE_20"] and not r["NAMELSAD_PLACE_20"].endswith(" CDP"):
             parts.setdefault(r["GEOID_ZCTA5_20"], []).append((int(r["AREALAND_PART"]), r["GEOID_PLACE_20"]))
     largest = {zcta: max(p)[1] for zcta, p in parts.items()}

data-import/names-se.py

@@ -9,6 +9,8 @@ import argparse
 import re
 from pathlib import Path
 
+import source
+import xlsx
 import tsv
 
 SOURCE = "https://www.scb.se/contentassets/9fe7dbb460994c72b835163dbc491ef9/namn-med-minst-tva-barare-31-december-2022.xlsx"
@@ -40,9 +42,9 @@ def main():
     p.add_argument("--out", default=str(OUT))
     p.add_argument("--source", default=SOURCE)
     a = p.parse_args()
-    data = tsv.fetch(a.source, a.cache, "scb-namn-2022.xlsx", magic=b"PK")
-    first = [{"name": cased(n), "sex": sex, "count": c} for sex, sheet in SHEETS.items() for n, c in counted(tsv.xlsx_rows(data, sheet), a.first)]
-    last = [{"name": cased(n), "count": c} for n, c in counted(tsv.xlsx_rows(data, SURNAMES), a.last)]
+    data = source.fetch(a.source, a.cache, "scb-namn-2022.xlsx", magic=b"PK")
+    first = [{"name": cased(n), "sex": sex, "count": c} for sex, sheet in SHEETS.items() for n, c in counted(xlsx.rows(data, sheet), a.first)]
+    last = [{"name": cased(n), "count": c} for n, c in counted(xlsx.rows(data, SURNAMES), a.last)]
     out = Path(a.out)
     tsv.write(out / "first-name.tsv", ["name", "sex", "count"], sorted(first, key=lambda r: (r["name"], r["sex"])))
     tsv.write(out / "last-name.tsv", ["name", "count"], sorted(last, key=lambda r: r["name"]))

data-import/names-us.py

@@ -15,6 +15,7 @@ import re
 import zipfile
 from pathlib import Path
 
+import source
 import tsv
 
 NAMES = "https://raw.githubusercontent.com/hackerb9/ssa-baby-names/master/alldata.txt"
@@ -24,7 +25,7 @@ CACHE = Path(__file__).resolve().parent / "cache"
 MC = re.compile(r"^Mc([a-z])")
 
 
-def csv_rows(data, member):
+def csv_or_zip_rows(data, member):
     """The rows of a CSV, or of every member of a zip named like member, name,sex,count[,year]."""
     if data.startswith(b"PK"):
         z = zipfile.ZipFile(io.BytesIO(data))
@@ -39,7 +40,7 @@ def csv_rows(data, member):
 
 def given(data, from_year):
     counts = collections.Counter()
-    for r in csv_rows(data, r"yob\d{4}\.txt"):
+    for r in csv_or_zip_rows(data, r"yob\d{4}\.txt"):
         if len(r) >= 4 and r[3].isdigit() and int(r[3]) >= from_year:
             counts[(r[0], r[1].lower())] += int(r[2])
     return counts
@@ -60,12 +61,12 @@ def main():
     p.add_argument("--out", default=str(OUT))
     p.add_argument("--surnames", default=SURNAMES)
     a = p.parse_args()
-    counts = given(tsv.fetch(a.names, a.cache, "ssa-names.txt"), a.from_year)
+    counts = given(source.fetch(a.names, a.cache, "ssa-names.txt"), a.from_year)
     first = []
     for sex in ("f", "m"):
         top = sorted(((n, c) for (n, s), c in counts.items() if s == sex), key=lambda n: (-n[1], n[0]))[:a.first]
         first += [{"name": n, "sex": sex, "count": c} for n, c in top]
-    rows = csv_rows(tsv.fetch(a.surnames, a.cache, "census-surnames-2010.zip"), r"Names_2010Census\.csv")
+    rows = csv_or_zip_rows(source.fetch(a.surnames, a.cache, "census-surnames-2010.zip"), r"Names_2010Census\.csv")
     last = [{"name": surname(r[0]), "count": int(r[2])} for r in rows if len(r) >= 3 and r[2].isdigit() and r[0].isalpha()]
     last = sorted(last, key=lambda r: (-r["count"], r["name"]))[:a.last]
     out = Path(a.out)

data-import/source.py

@@ -0,0 +1,28 @@
+"""A source fetched once into the cache."""
+import re
+import sys
+import time
+import urllib.request
+from pathlib import Path
+
+
+def fetch(source, cache, name, magic=b"", data=None, headers=None):
+    """The bytes of a URL, downloaded into cache/name once, or of a local file."""
+    if not re.match(r"^https?://", source):
+        return Path(source).read_bytes()
+    path = Path(cache) / name
+    for attempt in range(1, 6):
+        if path.exists():
+            return path.read_bytes()
+        req = urllib.request.Request(source, data=data, headers={"User-Agent": "fejkdata data-import", **(headers or {})})
+        try:
+            with urllib.request.urlopen(req, timeout=600) as r:
+                body = r.read()
+        except OSError:
+            body = b""
+        if body and body.startswith(magic) and b"Request Rejected" not in body[:512]:
+            path.parent.mkdir(parents=True, exist_ok=True)
+            path.write_bytes(body)
+        elif attempt < 5:
+            time.sleep(10 * attempt)
+    sys.exit(f"{source}: no valid download in 5 attempts")

data-import/tsv.py

@@ -1,52 +1,8 @@
-"""A source fetched once into the cache, and a table written as the loader admits it."""
-import io
+"""A table written as the loader admits it."""
 import re
 import sys
-import time
-import urllib.request
-import xml.etree.ElementTree as ET
-import zipfile
 from pathlib import Path
 
-XLSX_NS = {"m": "http://schemas.openxmlformats.org/spreadsheetml/2006/main", "r": "http://schemas.openxmlformats.org/officeDocument/2006/relationships"}
-
-
-def fetch(source, cache, name, magic=b"", data=None, headers=None):
-    """The bytes of a URL, downloaded into cache/name once, or of a local file."""
-    if not re.match(r"^https?://", source):
-        return Path(source).read_bytes()
-    path = Path(cache) / name
-    for attempt in range(1, 6):
-        if path.exists():
-            return path.read_bytes()
-        req = urllib.request.Request(source, data=data, headers={"User-Agent": "fejkdata data-import", **(headers or {})})
-        try:
-            with urllib.request.urlopen(req, timeout=600) as r:
-                body = r.read()
-        except OSError:
-            body = b""
-        if body and body.startswith(magic) and b"Request Rejected" not in body[:512]:
-            path.parent.mkdir(parents=True, exist_ok=True)
-            path.write_bytes(body)
-        elif attempt < 5:
-            time.sleep(10 * attempt)
-    sys.exit(f"{source}: no valid download in 5 attempts")
-
-
-def xlsx_rows(data, sheet=None):
-    """The rows of an xlsx sheet named sheet, the first sheet by default, each a list of cell texts."""
-    z = zipfile.ZipFile(io.BytesIO(data))
-    strings = ["".join(t.text or "" for t in si.iter("{%s}t" % XLSX_NS["m"])) for si in ET.fromstring(z.read("xl/sharedStrings.xml")).findall("m:si", XLSX_NS)]
-    rels = {r.get("Id"): r.get("Target") for r in ET.fromstring(z.read("xl/_rels/workbook.xml.rels"))}
-    sheets = {s.get("name"): rels[s.get("{%s}id" % XLSX_NS["r"])] for s in ET.fromstring(z.read("xl/workbook.xml")).iter("{%s}sheet" % XLSX_NS["m"])}
-    target = sheets[sheet] if sheet else next(iter(sheets.values()))
-    for row in ET.fromstring(z.read("xl/" + target)).findall(".//m:row", XLSX_NS):
-        cells = []
-        for c in row.findall("m:c", XLSX_NS):
-            v = c.find("m:v", XLSX_NS)
-            cells.append("" if v is None else strings[int(v.text)] if c.get("t") == "s" else v.text)
-        yield cells
-
 
 def write(path, columns, rows):
     """Write the rows as a TSV; every cell must be non-empty and free of tabs, newlines and braces."""

data-import/xlsx.py

@@ -0,0 +1,21 @@
+"""The rows of an xlsx sheet."""
+import io
+import xml.etree.ElementTree as ET
+import zipfile
+
+NS = {"m": "http://schemas.openxmlformats.org/spreadsheetml/2006/main", "r": "http://schemas.openxmlformats.org/officeDocument/2006/relationships"}
+
+
+def rows(data, sheet=None):
+    """The rows of an xlsx sheet named sheet, the first sheet by default, each a list of cell texts."""
+    z = zipfile.ZipFile(io.BytesIO(data))
+    strings = ["".join(t.text or "" for t in si.iter("{%s}t" % NS["m"])) for si in ET.fromstring(z.read("xl/sharedStrings.xml")).findall("m:si", NS)]
+    rels = {r.get("Id"): r.get("Target") for r in ET.fromstring(z.read("xl/_rels/workbook.xml.rels"))}
+    sheets = {s.get("name"): rels[s.get("{%s}id" % NS["r"])] for s in ET.fromstring(z.read("xl/workbook.xml")).iter("{%s}sheet" % NS["m"])}
+    target = sheets[sheet] if sheet else next(iter(sheets.values()))
+    for row in ET.fromstring(z.read("xl/" + target)).findall(".//m:row", NS):
+        cells = []
+        for c in row.findall("m:c", NS):
+            v = c.find("m:v", NS)
+            cells.append("" if v is None else strings[int(v.text)] if c.get("t") == "s" else v.text)
+        yield cells

data/en_US/person.json

@@ -2,6 +2,6 @@
   "format": "{prefix}{first} {last}",
   "first": "{.first-name.name}",
   "last": "{.last-name.name}",
-  "prefix": ["", { "format": "{title} ", "title": ["Dr", "Miss", "Mr", "Mrs", "Ms", "Mx", "Prof"], "weight": 0.1 }],
+  "prefix": ["", { "format": "{title} ", "title": ["Dr", "Mx", "Prof"], "weight": 0.1 }],
   "sex": "{.sex.name}"
 }

data/sv_SE/birth-number.json

@@ -0,0 +1 @@
+{ "format": "{number}", "rows": "birth-number.tsv", "parent": "sex" }

data/sv_SE/birth-number.tsv

@@ -0,0 +1,3 @@
+sex	number
+f	238
+m	239

data/sv_SE/personnummer.json

@@ -1 +1 @@
-"{date(1930-01-01,2025-12-31,'060102')}-{.sex.birth-number}{luhn()}"
+"{date(1930-01-01,2025-12-31,'060102')}-{.birth-number.number}{luhn()}"

data/sv_SE/samordningsnummer.json

@@ -1 +1 @@
-"{date(1930-01-01,2025-12-31,'0601')}{int(61,88)}-{.sex.birth-number}{luhn()}"
+"{date(1930-01-01,2025-12-31,'0601')}{int(61,88)}-{.birth-number.number}{luhn()}"

data/sv_SE/sex.tsv

@@ -1,3 +1,3 @@
-code	name	birth-number
-f	kvinna	238
-m	man	239
+code	name
+f	kvinna
+m	man

todo.md

@@ -97,7 +97,7 @@ ids. Shape: T = table, t = template, c = choice.
 | Category | Shape | Source | Licence |
 |---|---|---|---|
 | `person` first (female, male, generic), middle, last, weighted | T | SCB 2022 whole-population xlsx, Skatteverket 2026 surnames | CC0, "Källa: SCB" |
-| `person` title, gender, birthdate, age, blood type weighted | t | geblod.nu distribution | facts |
+| `person` title, sex, birthdate, age, blood type weighted | t | geblod.nu distribution | facts |
 | `personnummer`, `samordningsnummer` | t | Skatteverket test series: date + 238/239, Luhn | CC0 |
 | `organisationsnummer` by form, `vat` | t | Bolagsverket group digits, Luhn, `SE…01` | facts |
 | `company` name patterns, legal form weighted | t | Bolagsverket registrations 2025 | CC BY 2.5 SE |