emoji: search the dictionary by prefix in the engine, not in the data
mkemoji wrote a row for every prefix of every alias, so that a query matched as it was typed; the trie is a prefix index already, and with a real emoji list those rows would be four times the aliases themselves. Now emoji.dict has one row per alias, trienode() names a key's node, and dictlookup walks the entries at and below it, the key's own first, up to Maxkouho. Trie children are appended rather than pushed, so the walk keeps the file's order and a bare digit still picks the superscript or subscript it always did. The hand-written symbol rows move to symbol.src; emoji.src is left to the emoji. mkemoji reads both by default, or the files it is given.
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@@ -1331,13 +1331,14 @@ engine_emoji_queries(struct ct *t)
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CT_CHECK(t, keystroke('e', Mctrl, &com));
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CT_CHECK(t, keystroke('a', 0, &com));
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checkstr(t, "raw query wins", "a", &search.text);
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CT_EQ_INT(t, 3, im.nkouho);
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CT_EQ_INT(t, 4, im.nkouho);
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checkstr(t, "raw candidate", "A", &im.kouho[0]);
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checkstr(t, "deduplicated candidate", "B", &im.kouho[1]);
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checkstr(t, "local candidate", "C", &im.kouho[2]);
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checkstr(t, "raw prefix candidate", "α", &im.kouho[2]);
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checkstr(t, "local candidate", "C", &im.kouho[3]);
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CT_CHECK(t, draindraw(&dc) > 0);
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checkstr(t, "prefix popup query", "a", &dc.pre);
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CT_EQ_INT(t, 3, dc.nkouho);
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CT_EQ_INT(t, 4, dc.nkouho);
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CT_CHECK(t, keystroke(Kesc, 0, &com));
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CT_CHECK(t, keystroke('e', Mctrl, &com));
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@@ -12,10 +12,8 @@ MKEMOJI = ROOT / "map" / "mkemoji"
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TIMEOUT = 10
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def generate(source=None):
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args = [sys.executable, "-B", str(MKEMOJI)]
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if source is not None:
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args.append(str(source))
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def generate(*sources):
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args = [sys.executable, "-B", str(MKEMOJI)] + [str(s) for s in sources]
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return subprocess.run(
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args, capture_output=True, text=True, check=False, timeout=TIMEOUT
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)
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@@ -37,27 +35,32 @@ class MkemojiTest(unittest.TestCase):
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result = generate()
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self.assertEqual(result.returncode, 0, result.stderr)
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data = table(result.stdout)
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self.assertEqual(data["^"].split()[:9], list("¹²³⁴⁵⁶⁷⁸⁹"))
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self.assertEqual(data["_"].split()[:9], list("₁₂₃₄₅₆₇₈₉"))
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self.assertEqual(data["<"].split()[2], "♥")
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keys = list(data)
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self.assertEqual([k for k in keys if k[:1] == "^"][:9],
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[f"^{d}" for d in range(1, 10)])
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self.assertEqual([k for k in keys if k[:1] == "_"][:9],
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[f"_{d}" for d in range(1, 10)])
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self.assertEqual([k for k in keys if k[:1] == "<"][2], "<3")
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self.assertEqual(data["^1"], "¹")
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self.assertEqual(data["_2"], "₂")
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self.assertEqual(data["<3"], "♥")
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self.assertEqual(data["<3"].split()[0], "♥")
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self.assertIn("😀", data["smile"].split())
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self.assertIn("😀", data["웃음"].split())
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def test_fold_normalize_and_exact_first(self):
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def test_fold_normalize_and_source_order(self):
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source = self.source(
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"β\tALPHABET\n"
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"β\tALPHABET\talpha\n"
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"α\talpha\n"
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"e\u0301\tE\u0301\n"
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"é\tÉ\n"
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"#\thash\n"
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)
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first = generate(source)
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second = generate(source)
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second = generate(source, source)
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self.assertEqual(first.returncode, 0, first.stderr)
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self.assertEqual(first.stdout, second.stdout)
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data = table(first.stdout)
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self.assertEqual(data["alpha"].split(), ["α", "β"])
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self.assertEqual(data["al"].split(), ["β", "α"])
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self.assertEqual(list(data), ["alphabet", "alpha", "é", "hash"])
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self.assertEqual(data["alpha"].split(), ["β", "α"])
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self.assertEqual(data["é"], "é")
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self.assertEqual(data["hash"], "#")
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