Source

# Builds the resume fonts: static Archivo and Newsreader with Latin, Latin Extended and Vietnamese,
# used by both the on-screen paper and the PDF, plus their advance widths for the layout.
# Kerning and ligatures are dropped so the browser, the layout and pdf-lib all measure text the same.
# Sources: the woff2 files of @fontsource/archivo@5.2.8 and @fontsource/newsreader@5.2.10 (OFL),
# unpacked with `npm pack`. Needs fonttools and brotli.
# Usage: python3 scripts/build-fonts.py <archivo package dir> <newsreader package dir>
import json, sys
from pathlib import Path
from fontTools.ttLib import TTFont
from fontTools.merge import Merger

archivo, newsreader = map(Path, sys.argv[1:3])
here = Path(__file__).resolve().parent.parent
FACES = {
    "sans": (archivo, "archivo", 400, "Archivo", "Regular"),
    "sansBold": (archivo, "archivo", 600, "Archivo", "SemiBold"),
    "serif": (newsreader, "newsreader", 500, "Newsreader", "Medium"),
}
metrics = {}
for key, (pkg, name, weight, family, style) in FACES.items():
    parts = []
    for subset in ("latin", "latin-ext", "vietnamese"):
        f = TTFont(pkg / "files" / f"{name}-{subset}-{weight}-normal.woff2")
        f.flavor = None
        for t in ("GSUB", "GPOS", "GDEF", "kern"):
            if t in f: del f[t]
        tmp = here / f".{key}-{subset}.ttf"
        f.save(tmp)
        parts.append(tmp)
    font = Merger().merge([str(p) for p in parts])
    for p in parts: p.unlink()
    names = font["name"]
    for nid, value in ((1, family), (2, style), (4, f"{family} {style}"), (6, f"{family}-{style}")):
        names.setName(value, nid, 3, 1, 0x409)
    out = here / "public" / "fonts" / f"resume-{key}.ttf"
    font.save(out)
    font = TTFont(out)
    cmap = font.getBestCmap()
    hmtx = font["hmtx"]
    metrics[key] = {
        "upm": font["head"].unitsPerEm,
        "widths": {str(cp): hmtx[g][0] for cp, g in sorted(cmap.items())},
    }
    print(key, out.name, out.stat().st_size, "bytes,", len(cmap), "characters")
(here / "src" / "fontMetrics.json").write_text(json.dumps(metrics, separators=(",", ":")))