File size: 50,152 Bytes
33342cc b8c8277 82eb007 33342cc b268eec bb75255 b268eec 33342cc b268eec b8c8277 b268eec b8c8277 b268eec b8c8277 b268eec 8927925 b8c8277 8927925 b268eec 8927925 b8c8277 8927925 b268eec 82eb007 b268eec 8927925 b8c8277 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc 8927925 b268eec 8927925 b268eec 33342cc b268eec b8c8277 b268eec 33342cc b268eec 33342cc b8c8277 b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 82eb007 b268eec 82eb007 33342cc b268eec 82eb007 b268eec 82eb007 b268eec 82eb007 b268eec 82eb007 b268eec 33342cc 37d9a5e 33342cc 37d9a5e 8927925 33342cc 8927925 b268eec 33342cc b268eec 33342cc b268eec 33342cc 37d9a5e 33342cc b268eec b8c8277 b268eec 37d9a5e 33342cc 37d9a5e 33342cc 37d9a5e 33342cc 37d9a5e 33342cc 37d9a5e 33342cc 37d9a5e 33342cc 37d9a5e bb75255 37d9a5e bb75255 b268eec 8927925 b8c8277 8927925 37d9a5e 33342cc bb75255 33342cc 82eb007 33342cc bb75255 33342cc 82eb007 33342cc bb75255 82eb007 37d9a5e b268eec bb75255 b268eec bb75255 b268eec bb75255 37d9a5e 33342cc b8c8277 33342cc b8c8277 33342cc b8c8277 33342cc b8c8277 33342cc b8c8277 33342cc b8c8277 33342cc b8c8277 33342cc b8c8277 33342cc b8c8277 33342cc b8c8277 b268eec 33342cc b268eec 33342cc 82eb007 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec b8c8277 b268eec 8927925 b268eec b8c8277 8927925 b268eec 8927925 8ecb8eb 33342cc 37d9a5e 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc 37d9a5e b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc 82eb007 b268eec 33342cc 37d9a5e 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc 82eb007 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 123e11c b268eec 123e11c b268eec 123e11c b268eec 123e11c b9a77fd 123e11c 33342cc b268eec 33342cc b268eec 33342cc b268eec 8927925 b268eec bb75255 33342cc b268eec 33342cc bb75255 33342cc b268eec 82eb007 bb75255 8ecb8eb b268eec 8ecb8eb b268eec 82eb007 8ecb8eb 37d9a5e 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc bb75255 8927925 82eb007 37d9a5e 82eb007 bb75255 33342cc 82eb007 33342cc b268eec 33342cc 82eb007 33342cc bb75255 33342cc b268eec 33342cc 82eb007 33342cc b268eec 33342cc 82eb007 33342cc b268eec 82eb007 bb75255 8927925 33342cc 82eb007 33342cc b268eec b8c8277 8927925 b268eec 33342cc 8927925 33342cc b268eec b8c8277 b268eec b8c8277 b268eec 33342cc b268eec b8c8277 b268eec 82eb007 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b268eec 33342cc b8c8277 33342cc 653032a 0f6879b 653032a 0f6879b 653032a 0f6879b 653032a 0f6879b 653032a 0f6879b 653032a 0f6879b 653032a 0f6879b 653032a 0f6879b 653032a 0f6879b 653032a 0f6879b 653032a 0f6879b 653032a 0f6879b 33342cc 37d9a5e 33342cc 653032a 33342cc b268eec 33342cc bb75255 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 |
# engine/parser_rules.py
# ------------------------------------------------------------
# Rule-based core parser for microbiology descriptions.
#
# Stage 11F (Option A ranges + fixes) + 11H + 11I + 11J + 11L + 11M
# + NaCl + haemolysis symbol support + colony morphology tweaks.
#
# - Always store Growth Temperature as "low//high"
# • single: 37 → "37//37"
# • any two temps in text: min//max
# • ranges like "30–37 °C", "grows between 30 and 37 °C" → "30//37"
#
# - DNase robust parsing (DNase test / activity / production)
# - Non–spore-forming → Spore Formation = Negative (with early return)
# - "non-H2S producing" → H2S = Negative
# - Aerobic / Anaerobic including “aerobically / anaerobically”
#
# - NaCl tolerance phrases improved (>= 6% rule)
# • explicit positives require a growth/tolerance verb + % ≥ 6
# • explicit negatives ("no growth in NaCl", "does not grow in 7% NaCl",
# "NaCl sensitive", "not NaCl tolerant") override positives
# • ambiguous "in 6.5% NaCl" alone no longer auto-Positive
#
# - Colony morphology extraction, including:
# • "colonies are yellow, mucoid"
# • "colonies dry, white and irregular on nutrient agar"
# • "forming smooth, yellow-pigmented, opaque colonies"
# • "grey colonies", "large grey colonies" etc.
#
# - Sugars:
# • "<sugar> positive/negative"
# • "<sugar> is positive/negative"
# • "<sugar> fermenter" / "non-<sugar> fermenter"
# • "ferments X, Y but not Z"
# • grouped "does not ferment lactose and sucrose"
# (without nuking glucose in "but glucose positive")
# • global "non-fermenter" → all sugars Negative (Unknown-only)
# • "asaccharolytic" → all sugars Negative (Unknown-only)
# • "all other sugars negative" → all remaining sugars Negative
# (Unknown-only; no hard rewrite)
#
# - Core tests:
# • "<kw> positive/negative"
# • "positive for <kw>"
# • "<kw> is positive/negative"
# • "<kw> reaction is positive/negative"
# • "<kw> reaction positive/negative"
# • "<kw> test reaction is positive/negative"
# • "ONPG is negative" handled via core patterns
# • "H2S production is positive/negative"
# • "MR and VP negative/positive" → both set
# • grouped phrases like
# "gelatin and esculin hydrolysis negative"
# "lysine, ornithine and arginine negative"
# → all mentioned tests / sugars set to the given value
#
# - Decarboxylases:
# • "all decarboxylases negative/positive"
# → Lysine / Ornithine / Arginine dihydrolase set accordingly
# (Unknown-only; explicit values can override later)
#
# - Capsule / Motility:
# • "capsule present"/"capsule is present" → Capsule Positive
# • "capsule absent"/"capsule is absent"/"no capsule" → Capsule Negative
# • "encapsulated" / "capsulated" → Capsule Positive
# • "gliding/spreading/swarming motility" → Motility Positive
#
# - Gelatin / Esculin:
# • "gelatin positive/negative" → Gelatin Hydrolysis
# • "esculin positive/negative" → Esculin Hydrolysis
#
# - Shape:
# • "coccobacilli / coccobacillus" → Shape = Short Rods
# • (no 4F shape descriptor explosion; we keep existing logic)
#
# - Haemolysis:
# • alpha/beta/gamma haemolysis & haemolytic
# • now also supports α / β / γ symbols via normalisation
# ------------------------------------------------------------
from __future__ import annotations
import re
from typing import Dict, Any, List
UNKNOWN = "Unknown"
# ------------------------------------------------------------
# Core fields and sugar mapping
# ------------------------------------------------------------
# Sugar name → core DB column
SUGAR_FIELDS: Dict[str, str] = {
"glucose": "Glucose Fermentation",
"lactose": "Lactose Fermentation",
"sucrose": "Sucrose Fermentation",
"maltose": "Maltose Fermentation",
"mannitol": "Mannitol Fermentation",
"sorbitol": "Sorbitol Fermentation",
"xylose": "Xylose Fermentation",
"rhamnose": "Rhamnose Fermentation",
"arabinose": "Arabinose Fermentation",
"raffinose": "Raffinose Fermentation",
"trehalose": "Trehalose Fermentation",
"inositol": "Inositol Fermentation",
}
CORE_BOOL_FIELDS: Dict[str, List[str]] = {
# field: [keywords to recognise the test name]
"Catalase": ["catalase"],
"Oxidase": ["oxidase"],
"Indole": ["indole"],
"Urease": ["urease"],
"Citrate": ["citrate"],
# MR: include "mr"
"Methyl Red": ["methyl red", "mr test", "mr"],
"VP": ["voges-proskauer", "vp test", "vp"],
# H2S (includes H₂S → normalised to H2S in _clean_text)
"H2S": ["h2s", "hydrogen sulfide"],
# DNase: broaden patterns
"DNase": [
"dnase",
"dnase test",
"dnase activity",
"dnase production",
"dnaase",
"dna hydrolysis",
],
"ONPG": ["onpg"],
"Coagulase": ["coagulase"],
"Lipase Test": ["lipase"],
"Nitrate Reduction": ["nitrate reduction", "nitrate"],
"NaCl Tolerant (>=6%)": ["6% nacl", "7% nacl", "nacl tolerant"],
# Decarboxylases (also match plain amino acid words)
"Lysine Decarboxylase": ["lysine decarboxylase", "lysine decarb", "lysine"],
"Ornitihine Decarboxylase": ["ornithine decarboxylase", "ornithine decarb", "ornithine"],
"Arginine dihydrolase": ["arginine dihydrolase", "arginine decarboxylase", "arginine"],
# Gelatin / Esculin
"Gelatin Hydrolysis": ["gelatin hydrolysis", "gelatinase", "gelatin"],
"Esculin Hydrolysis": ["esculin hydrolysis", "esculin"],
}
# ------------------------------------------------------------
# Generic helpers
# ------------------------------------------------------------
def _clean_text(text: str) -> str:
"""
Normalise unicode oddities and collapse whitespace.
Also:
- strip degree symbols
- normalise subscript ₂ → 2 for H₂S
- normalise α/β/γ to alpha/beta/gamma for haemolysis patterns
"""
if not text:
return ""
s = text.replace("°", "").replace("º", "")
# normalise subscript 2 (H₂S → H2S)
s = s.replace("₂", "2")
# Greek letters for haemolysis and related descriptors
s = (
s.replace("α", "alpha")
.replace("β", "beta")
.replace("γ", "gamma")
)
# collapse whitespace
return " ".join(s.split())
def _norm(s: str) -> str:
return s.strip().lower()
def _set_if_stronger(parsed: Dict[str, str], field: str, value: str) -> None:
"""
Write value to parsed[field] if:
- field not present, or
- we are replacing Unknown with a concrete value
"""
if not value:
return
if field not in parsed or parsed[field] == UNKNOWN:
parsed[field] = value
def _value_from_pnv_token(token: str) -> str | None:
"""
Map a simple token to Positive / Negative / Variable.
"""
seg = _norm(token)
if seg in ["positive", "pos", "+"]:
return "Positive"
if seg in ["negative", "neg", "-"]:
return "Negative"
if seg in ["variable", "var", "v"]:
return "Variable"
return None
def _value_from_pnv_context(segment: str) -> str | None:
"""
Interpret a phrase as Positive / Negative / Variable.
Handles:
- "positive"
- "is positive"
- "+", "neg", etc.
"""
seg = _norm(segment)
# direct token first
val = _value_from_pnv_token(seg)
if val:
return val
# "... is positive"
m = re.search(r"\bis\s+(positive|negative|variable|pos|neg|\+|\-)\b", seg)
if m:
return _value_from_pnv_token(m.group(1))
return None
# ------------------------------------------------------------
# Gram stain and shape
# ------------------------------------------------------------
def _parse_gram_and_shape(text_lc: str, parsed: Dict[str, str]) -> None:
# Gram stain
if "gram-positive" in text_lc or "gram positive" in text_lc:
_set_if_stronger(parsed, "Gram Stain", "Positive")
elif "gram-negative" in text_lc or "gram negative" in text_lc:
_set_if_stronger(parsed, "Gram Stain", "Negative")
elif "gram variable" in text_lc:
_set_if_stronger(parsed, "Gram Stain", "Variable")
# Shape
# Prefer "short rods" / coccobacilli over generic rods
if "short rods" in text_lc:
_set_if_stronger(parsed, "Shape", "Short Rods")
# NEW: coccobacilli → Short Rods
if re.search(r"\bcoccobacill(?:us|i)\b", text_lc):
_set_if_stronger(parsed, "Shape", "Short Rods")
# Cocci and variants (diplococci, tetracocci, etc.)
if re.search(r"\bcocci\b", text_lc):
_set_if_stronger(parsed, "Shape", "Cocci")
if re.search(r"\b(diplococci|tetracocci|streptococci|staphylococci)\b", text_lc):
_set_if_stronger(parsed, "Shape", "Cocci")
# Rods / bacilli
if re.search(r"\brods?\b", text_lc) or "bacilli" in text_lc:
_set_if_stronger(parsed, "Shape", "Rods")
# Spiral
if "spiral" in text_lc or "spirochete" in text_lc:
_set_if_stronger(parsed, "Shape", "Spiral")
# ------------------------------------------------------------
# Haemolysis
# ------------------------------------------------------------
def _parse_haemolysis(text_lc: str, parsed: Dict[str, str]) -> None:
"""
Handle haemolysis phrasing:
- beta-haemolytic / beta hemolytic / beta-haemolysis / etc.
- alpha- / gamma- / non-haemolytic
- α / β / γ symbols are normalised to alpha/beta/gamma in _clean_text
"""
# Beta
if re.search(r"beta[- ]?(haemolytic|hemolytic|haemolysis|hemolysis)", text_lc):
_set_if_stronger(parsed, "Haemolysis Type", "Beta")
_set_if_stronger(parsed, "Haemolysis", "Positive")
# Alpha
if re.search(r"alpha[- ]?(haemolytic|hemolytic|haemolysis|hemolysis)", text_lc):
_set_if_stronger(parsed, "Haemolysis Type", "Alpha")
_set_if_stronger(parsed, "Haemolysis", "Positive")
# Gamma / non-haemolytic
if re.search(r"gamma[- ]?(haemolytic|hemolytic|haemolysis|hemolysis)", text_lc):
_set_if_stronger(parsed, "Haemolysis Type", "Gamma")
_set_if_stronger(parsed, "Haemolysis", "Negative")
if (
"non-haemolytic" in text_lc
or "non hemolytic" in text_lc
or "non-hemolytic" in text_lc
):
_set_if_stronger(parsed, "Haemolysis Type", "None")
_set_if_stronger(parsed, "Haemolysis", "Negative")
# Variable phrasing
if "variable haemolysis" in text_lc or "variable hemolysis" in text_lc:
_set_if_stronger(parsed, "Haemolysis Type", "Variable")
_set_if_stronger(parsed, "Haemolysis", "Variable")
# ------------------------------------------------------------
# Core enzyme / boolean tests
# ------------------------------------------------------------
def _parse_core_bool_tests(text_lc: str, parsed: Dict[str, str]) -> None:
"""
For each test in CORE_BOOL_FIELDS, look for patterns like:
- "catalase positive"
- "positive for catalase"
- "catalase is positive"
- "indole reaction is negative"
- "indole reaction negative"
- "indole test reaction is positive"
Plus:
- NaCl tolerance with % values
- Nitrate reduction text
- H2S production / non-production
- DNase coverage
- gelatinase / gelatin → Gelatin Hydrolysis
- esculin → Esculin Hydrolysis
- grouped MR/VP: "MR and VP negative"
- decarboxylase global phrases
- generic grouped phrases
"gelatin and esculin hydrolysis negative"
"lysine, ornithine and arginine negative"
"""
for field, keywords in CORE_BOOL_FIELDS.items():
for kw in keywords:
# 1) "... catalase positive"
m1 = re.search(
rf"{re.escape(kw)}[ \-]?"
r"(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m1:
val = _value_from_pnv_context(m1.group(1))
if val:
_set_if_stronger(parsed, field, val)
break
# 2) "positive for catalase"
m2 = re.search(
rf"(positive|negative|variable|pos|neg|\+|\-)\s+"
rf"(for\s+)?{re.escape(kw)}",
text_lc,
)
if m2:
val = _value_from_pnv_context(m2.group(1))
if val:
_set_if_stronger(parsed, field, val)
break
# 3) "<kw> is positive"
m3 = re.search(
rf"{re.escape(kw)}\s+is\s+"
r"(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m3:
val = _value_from_pnv_token(m3.group(1))
if val:
_set_if_stronger(parsed, field, val)
break
# 4) "<kw> reaction is positive/negative"
m4 = re.search(
rf"{re.escape(kw)}\s+reaction\s+is\s+"
r"(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m4:
val = _value_from_pnv_token(m4.group(1))
if val:
_set_if_stronger(parsed, field, val)
break
# 5) "<kw> reaction positive/negative"
m5 = re.search(
rf"{re.escape(kw)}\s+reaction\s+"
r"(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m5:
val = _value_from_pnv_token(m5.group(1))
if val:
_set_if_stronger(parsed, field, val)
break
# 6) "<kw> test reaction is positive"
m6 = re.search(
rf"{re.escape(kw)}\s+test\s+reaction\s+is\s+"
r"(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m6:
val = _value_from_pnv_token(m6.group(1))
if val:
_set_if_stronger(parsed, field, val)
break
# Special-case NaCl tolerance with explicit percentages
if field == "NaCl Tolerant (>=6%)":
# We scan the whole text for positive/negative NaCl evidence,
# then decide once per description. Negative has highest priority.
has_positive = False
has_negative = False
# --- Negative phrasing (highest priority) ---
# "does not grow in 7% NaCl", "doesn't grow at 10% NaCl"
if re.search(
r"does\s+(?:not|n't)\s+grow\s+(in|at)\s*\d+(?:\.\d+)?\s*%?\s*nacl",
text_lc,
):
has_negative = True
# "no growth in 6.5% NaCl", "no growth at 8% NaCl"
if re.search(
r"no\s+growth\s+(in|at)\s*\d+(?:\.\d+)?\s*%?\s*nacl",
text_lc,
):
has_negative = True
# "no growth in NaCl" (no explicit %)
if re.search(
r"no\s+growth\s+in\s+nacl",
text_lc,
):
has_negative = True
# "unable to grow in 7% NaCl", "unable to grow in NaCl"
if re.search(
r"unable\s+to\s+grow\s+(in|at)\s*(\d+(?:\.\d+)?\s*%?\s*)?nacl",
text_lc,
):
has_negative = True
# semantic negatives without explicit %
if re.search(r"cannot\s+tolerate\s+nacl", text_lc):
has_negative = True
if re.search(r"not\s+nacl\s+tolerant", text_lc):
has_negative = True
if re.search(r"nacl\s+sensitive", text_lc):
has_negative = True
if re.search(r"fails\s+to\s+grow\s+(in|at)\s*(\d+(?:\.\d+)?\s*%?\s*)?nacl", text_lc):
has_negative = True
if re.search(r"intolerant\s+to\s+nacl", text_lc):
has_negative = True
if re.search(r"no\s+tolerance\s+to\s+nacl", text_lc):
has_negative = True
if re.search(r"nacl\s+intolerance", text_lc):
has_negative = True
if re.search(r"no\s+growth\s+at\s+high\s+nacl", text_lc):
has_negative = True
# --- Positive phrasing (requires growth/tolerance verb + % ≥ 6) ---
# e.g. "grows in 6.5% NaCl", "growth occurs at 10% NaCl"
for m in re.finditer(
r"(grows|growth occurs|growth observed|able to grow|tolerates|tolerant)\s+"
r"(?:in|at|up to|to)\s*(\d+(?:\.\d+)?)\s*%?\s*nacl",
text_lc,
):
try:
conc = float(m.group(2))
if conc >= 6.0:
has_positive = True
except Exception:
pass
# e.g. "NaCl tolerant up to 10%", "NaCl tolerant to 8%"
for m in re.finditer(
r"nacl\s+tolerant\s+(?:to|up to)?\s*(\d+(?:\.\d+)?)\s*%?",
text_lc,
):
try:
conc = float(m.group(1))
if conc >= 6.0:
has_positive = True
except Exception:
pass
# Decide final value:
# Negative > Positive > Unknown
if has_negative:
# Negative explicitly overrides any previous value
parsed["NaCl Tolerant (>=6%)"] = "Negative"
elif has_positive:
_set_if_stronger(parsed, "NaCl Tolerant (>=6%)", "Positive")
# Nitrate: "reduces nitrate" / "does not reduce nitrate"
if re.search(r"reduces nitrate", text_lc):
_set_if_stronger(parsed, "Nitrate Reduction", "Positive")
if re.search(r"does (not|n't) reduce nitrate", text_lc):
_set_if_stronger(parsed, "Nitrate Reduction", "Negative")
# H2S: "produces H2S", "H2S production", "H2S production is positive"
if re.search(r"(produces|production of)\s+h2s", text_lc):
_set_if_stronger(parsed, "H2S", "Positive")
if re.search(r"h2s production\s+is\s+(positive|pos|\+)", text_lc):
_set_if_stronger(parsed, "H2S", "Positive")
if re.search(r"h2s production\s+is\s+(negative|neg|\-)", text_lc):
_set_if_stronger(parsed, "H2S", "Negative")
if (
re.search(r"does (not|n't) produce\s+h2s", text_lc)
or re.search(r"no h2s production", text_lc)
or re.search(r"non[- ]h2s producing", text_lc)
):
_set_if_stronger(parsed, "H2S", "Negative")
# --- DNase universal coverage ---
# Positive forms
if re.search(r"\bdnase(\s+test|\s+activity|\s+production)?\s*(positive|pos|\+)\b", text_lc):
_set_if_stronger(parsed, "DNase", "Positive")
if re.search(r"\b(positive|pos|\+)\s+dnase(\s+test|\s+activity|\s+production)?\b", text_lc):
_set_if_stronger(parsed, "DNase", "Positive")
# Negative forms
if re.search(r"\bdnase(\s+test|\s+activity|\s+production)?\s*(negative|neg|\-)\b", text_lc):
_set_if_stronger(parsed, "DNase", "Negative")
if re.search(r"\b(negative|neg|\-)\s+dnase(\s+test|\s+activity|\s+production)?\b", text_lc):
_set_if_stronger(parsed, "DNase", "Negative")
# non-DNase-producing
if re.search(r"\bnon[- ]?dnase[- ]?producing\b", text_lc):
_set_if_stronger(parsed, "DNase", "Negative")
# --- MR and VP grouped: "MR and VP negative" ---
mr_vp_pattern = re.compile(
r"\b("
r"mr(?: test)?|methyl red|"
r"vp(?: test)?|voges-proskauer"
r")\s*(?:test)?\s*(?:and|&)\s*( "
r"mr(?: test)?|methyl red|"
r"vp(?: test)?|voges-proskauer"
r")\s+"
r"(positive|negative|variable|pos|neg|\+|\-)"
)
for m in mr_vp_pattern.finditer(text_lc):
name1 = m.group(1)
name2 = m.group(2)
val = _value_from_pnv_token(m.group(3))
if not val:
continue
def _assign_mr_vp(name: str) -> None:
n = name.lower()
if "mr" in n or "methyl red" in n:
_set_if_stronger(parsed, "Methyl Red", val)
if "vp" in n or "voges" in n:
_set_if_stronger(parsed, "VP", val)
_assign_mr_vp(name1)
_assign_mr_vp(name2)
# --- Decarboxylases global "all decarboxylases negative/positive" ---
m_all_decarb = re.search(
r"all\s+decarboxylases?\s+(?:are\s+)?(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m_all_decarb:
val = _value_from_pnv_token(m_all_decarb.group(1))
if val:
for f in ("Lysine Decarboxylase", "Ornitihine Decarboxylase", "Arginine dihydrolase"):
_set_if_stronger(parsed, f, val)
# --- Generic grouped list logic for tests & sugars ---
#
# Handles things like:
# "gelatin and esculin hydrolysis negative"
# "lysine, ornithine and arginine negative"
# "indole, urease and citrate positive"
# "raffinose and inositol negative"
#
grouped_tests_pattern = re.compile(
r"([a-z0-9 ,/&\-]+?)\s+"
r"(?:hydrolysis|decarboxylases?|dihydrolases?|tests?|reactions?)?"
r"\s*(?:are\s+)?(positive|negative|variable|pos|neg|\+|\-)"
)
for m in grouped_tests_pattern.finditer(text_lc):
seg = m.group(1)
val = _value_from_pnv_token(m.group(2))
if not val:
continue
seg_lc = seg.lower()
# Quick filter: does this segment contain any known test/sugar keyword?
has_any = False
for _, keywords in CORE_BOOL_FIELDS.items():
if any(re.search(rf"\b{re.escape(kw)}\b", seg_lc) for kw in keywords):
has_any = True
break
if not has_any:
for sugar_key in SUGAR_FIELDS.keys():
if re.search(rf"\b{sugar_key}\b", seg_lc):
has_any = True
break
if not has_any:
continue # ignore segments unrelated to tests/sugars
# Apply to all matching core boolean tests
for field, keywords in CORE_BOOL_FIELDS.items():
for kw in keywords:
if re.search(rf"\b{re.escape(kw)}\b", seg_lc):
_set_if_stronger(parsed, field, val)
break
# Apply to all matching sugars
for sugar_key, field in SUGAR_FIELDS.items():
if re.search(rf"\b{sugar_key}\b", seg_lc):
_set_if_stronger(parsed, field, val)
# ------------------------------------------------------------
# Motility / Capsule / Spores
# ------------------------------------------------------------
def _parse_motility_capsule_spores(text_lc: str, parsed: Dict[str, str]) -> None:
# Motility
if (
re.search(r"\bmotile\b", text_lc)
and not re.search(r"\bnon[- ]?motile\b", text_lc)
and "nonmotile" not in text_lc
and "immotile" not in text_lc
):
_set_if_stronger(parsed, "Motility", "Positive")
if (
"non-motile" in text_lc
or "non motile" in text_lc
or "nonmotile" in text_lc
or "immotile" in text_lc
):
_set_if_stronger(parsed, "Motility", "Negative")
# Specific motility phrases: tumbling, swarming, corkscrew, gliding, spreading
if (
"tumbling motility" in text_lc
or "swarming motility" in text_lc
or "corkscrew motility" in text_lc
or re.search(r"\b(gliding|spreading)\s+motility\b", text_lc)
or ("swarming" in text_lc and "non-swarming" not in text_lc)
):
_set_if_stronger(parsed, "Motility", "Positive")
# Capsule (including "capsule positive/negative", present/absent)
if (
"capsulated" in text_lc
or "encapsulated" in text_lc
or "capsule present" in text_lc
or re.search(r"capsule\s+is\s+present", text_lc)
or re.search(r"capsule[ \-]?(positive|pos|\+)", text_lc)
):
_set_if_stronger(parsed, "Capsule", "Positive")
if (
"non-capsulated" in text_lc
or "no capsule" in text_lc
or "capsule absent" in text_lc
or re.search(r"capsule\s+is\s+absent", text_lc)
or re.search(r"capsule[ \-]?(negative|neg|\-)", text_lc)
):
_set_if_stronger(parsed, "Capsule", "Negative")
# Spore formation
# NEGATIVE FIRST with strict boundaries, then early-return
if (
re.search(r"\bnon[-\s]?spore[-\s]?forming\b", text_lc)
or "no spores" in text_lc
):
_set_if_stronger(parsed, "Spore Formation", "Negative")
return # prevent any positive overwrite
# POSITIVE (must not match the negative form)
if (
re.search(r"\bspore[-\s]?forming\b", text_lc)
or "forms spores" in text_lc
):
_set_if_stronger(parsed, "Spore Formation", "Positive")
# ------------------------------------------------------------
# Oxygen requirement
# ------------------------------------------------------------
def _parse_oxygen(text_lc: str, parsed: Dict[str, str]) -> None:
"""
Robust oxygen parsing:
- Handle facultative first
- Avoid "aerobic" accidentally matching inside "anaerobic"
- Include "aerobically" / "anaerobically"
"""
# Facultative first
if re.search(r"facultative(ly)? anaerob", text_lc):
_set_if_stronger(parsed, "Oxygen Requirement", "Facultative Anaerobe")
# Strict anaerobic (before aerobic)
if (
re.search(r"\bobligate anaerob", text_lc)
or (re.search(r"\banaerobic\b", text_lc) and "facultative" not in text_lc)
or re.search(r"\banaerobically\b", text_lc)
):
_set_if_stronger(parsed, "Oxygen Requirement", "Anaerobic")
# Now handle purely aerobic, avoiding "anaerobic"
if (
re.search(r"\bobligate aerobe\b", text_lc)
or (re.search(r"\baerobic\b", text_lc) and "anaerobic" not in text_lc)
or (
re.search(r"\baerobically\b", text_lc)
and "anaerobically" not in text_lc
)
):
_set_if_stronger(parsed, "Oxygen Requirement", "Aerobic")
if "microaerophilic" in text_lc or "microaerophile" in text_lc:
_set_if_stronger(parsed, "Oxygen Requirement", "Microaerophilic")
if "capnophilic" in text_lc or "co2" in text_lc:
_set_if_stronger(parsed, "Oxygen Requirement", "Capnophilic")
# ------------------------------------------------------------
# Growth temperature
# ------------------------------------------------------------
def _parse_growth_temperature(text_lc: str, parsed: Dict[str, str]) -> None:
"""
Look for explicit temperatures like "grows at 37 °C" or ranges like "4–45 °C".
We ALWAYS store as "low//high":
- true ranges: "4-45 °C" → "4//45"
- "grows between 30 and 37 °C" → "30//37"
- "grows at 30–37 °C" → "30//37"
- two temps in text: min//max (Option A)
- single temps: "37 °C" → "37//37"
"""
# 0) Explicit "between X and Y" ranges
between_pattern = re.compile(
r"between\s+(\d+)\s*(?:c|°c|degrees c|degrees celsius)?"
r"\s*(?:and|to|-)\s*(\d+)\s*(?:c|°c|degrees c|degrees celsius)?"
)
m_between = between_pattern.search(text_lc)
if m_between:
low = m_between.group(1)
high = m_between.group(2)
_set_if_stronger(parsed, "Growth Temperature", f"{low}//{high}")
return
# 1) Explicit ranges like "4-45 °C" or "10–40 °C"
range_pattern = re.compile(
r"(\d+)\s*[-–/]\s*(\d+)\s*(?:c|°c|degrees c|degrees celsius)"
)
m_range = range_pattern.search(text_lc)
if m_range:
low = m_range.group(1)
high = m_range.group(2)
_set_if_stronger(parsed, "Growth Temperature", f"{low}//{high}")
return
# 2) Any two explicit temps → min//max
temps = re.findall(r"(\d+)\s*(?:c|°c|degrees c|degrees celsius)", text_lc)
if len(temps) >= 2:
nums = [int(t) for t in temps]
low = min(nums)
high = max(nums)
_set_if_stronger(parsed, "Growth Temperature", f"{low}//{high}")
return
# 3) Single temps like "grows at 37 c"
single_pattern = re.compile(
r"(grows|growth|optimum|optimal)\s+(?:at\s+)?(\d+)\s*"
r"(?:c|°c|degrees c|degrees celsius)"
)
m_single = single_pattern.search(text_lc)
if m_single:
temp = m_single.group(2)
_set_if_stronger(parsed, "Growth Temperature", f"{temp}//{temp}")
return
# 4) Simplified: "grows at 37" (no explicit °C)
m_simple_num = re.search(r"grows at (\d+)\b", text_lc)
if m_simple_num:
temp = m_simple_num.group(1)
_set_if_stronger(parsed, "Growth Temperature", f"{temp}//{temp}")
return
# 5) Fallback: plain "37c" somewhere in the text
m_plain = re.search(
r"\b(\d+)\s*(?:c|°c|degrees c|degrees celsius)\b",
text_lc,
)
if m_plain:
temp = m_plain.group(1)
_set_if_stronger(parsed, "Growth Temperature", f"{temp}//{temp}")
# ------------------------------------------------------------
# Media grown on (coarse mapping)
# ------------------------------------------------------------
MEDIA_KEYWORDS = {
"Blood Agar": [
"blood agar",
"blood-agar",
],
"MacConkey Agar": [
"macconkey agar",
"mac conkey agar",
"macconkey",
],
"Chocolate Agar": [
"chocolate agar",
"chocolate-agar",
],
"Nutrient Agar": [
"nutrient agar",
"nutrient-agar",
"nut agar",
],
"XLD Agar": [
"xld agar",
"xld",
],
"TCBS Agar": [
"tcbs agar",
"tcbs",
],
"ALOA": [
"aloa agar",
"aloa",
],
"BCYE Agar": [
"bcye agar",
"bcye",
"Buffered Charcoal Yeast Extract Agar",
"buffered charcoal yeast extract agar"
],
"MRS Agar": [
"mrs agar",
],
"Mannitol Salt Agar": [
"msa agar",
"ms agar",
],
"Cycloserine Cefoxitin Fructose Agar": [
"ccfa agar",
"cycloserine cefoxitin fructose agar",
"ccf agar",
],
"Thayer Martin Agar": [
"thayer martin agar",
"tma agar",
"tma",
],
"Bordet-Gengou Agar": [
"bordet gengou agar",
],
"Cetrimide Agar": [
"cetrimide agar",
],
"Anaerobic Agar": [
"anaerobic agar",
],
"Anaerobic Blood Agar": [
"anaerobic blood agar",
],
"Hektoen Enteric Agar": [
"hektoen enteric agar",
"HK Agar",
"hk",
],
"Tryptic Soy Agar": [
"tryptic soy agar",
"t-soy agar",
"tsoy",
],
"Brucella Agar": [
"brucella agar",
],
"Charcoal Agar": [
"charcoal agar",
],
"Yeast Extract Mannitol Agar": [
"yeast extract mannitol agar",
],
"Sabouraud Agar": [
"sabouraud agar",
"sabouraud dextrose agar",
],
"BHI": [
"bhi",
"brain heart infusion agar",
"brain heart infusion",
],
"Columbia Blood Agar": [
"columbia blood agar",
"columbia agar",
"columbia",
],
"Lowenstein-Jensen Agar": [
"lowenstein-jensen agar",
"lowenstein jensen agar",
],
"BSK Medium": [
"bsk medium",
"bsk",
"bsk-ii medium",
"bsk-h medium",
],
"Ashby Agar": [
"ashby agar",
"ashby medium",
]
}
def _parse_media(text_lc: str, parsed: Dict[str, str]) -> None:
found_media: List[str] = []
for media_name, patterns in MEDIA_KEYWORDS.items():
for p in patterns:
if p in text_lc and media_name not in found_media:
found_media.append(media_name)
if found_media:
_set_if_stronger(parsed, "Media Grown On", "; ".join(found_media))
# ------------------------------------------------------------
# Sugar fermentation parsing
# ------------------------------------------------------------
def _parse_sugars(text_lc: str, parsed: Dict[str, str]) -> None:
"""
Handles patterns like:
- "glucose positive, mannitol negative"
- "ferments glucose, mannitol and sucrose but not lactose"
- "does not ferment lactose or sucrose"
- "non-lactose fermenter"
- "<sugar> fermenter" (positive unless "non-<sugar> fermenter")
- "<sugar> is positive/negative"
- "<sugar> fermentation is positive/negative"
- global non-fermenter phrases
- "asaccharolytic" → all sugars Negative (Unknown-only)
- "all other sugars negative" → remaining sugars Negative
"""
# 0) Simple "<sugar> positive/negative" and "<sugar> is positive"
for sugar_key, field in SUGAR_FIELDS.items():
# "glucose positive"
m_simple = re.search(
rf"{sugar_key}\s+(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m_simple:
val = _value_from_pnv_context(m_simple.group(1))
if val:
_set_if_stronger(parsed, field, val)
# "<sugar> is positive"
m_is = re.search(
rf"{sugar_key}\s+is\s+(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m_is:
val = _value_from_pnv_token(m_is.group(1))
if val:
_set_if_stronger(parsed, field, val)
# 0b) "<sugar> fermenter" vs "non-<sugar> fermenter"
for sugar_key, field in SUGAR_FIELDS.items():
# positive: "lactose fermenter"
if re.search(rf"\b{sugar_key}\s+fermenter\b", text_lc) and not re.search(
rf"\bnon[- ]{sugar_key}\s+fermenter\b", text_lc
):
_set_if_stronger(parsed, field, "Positive")
# negative: "non-lactose fermenter"
if re.search(rf"\bnon[- ]{sugar_key}\s+fermenter\b", text_lc):
_set_if_stronger(parsed, field, "Negative")
# 1) "ferments X, Y and Z but not A, B"
ferments_pattern = re.compile(r"ferments\s+([a-z0-9 ,;/&\-]+)")
for m in ferments_pattern.finditer(text_lc):
seg = m.group(1)
# Split positive vs negative part on "but not"
neg_split = re.split(r"\bbut not\b", seg, maxsplit=1)
pos_part = neg_split[0]
neg_part = neg_split[1] if len(neg_split) > 1 else ""
# Positive sugars from pos_part
for sugar_key, field in SUGAR_FIELDS.items():
if re.search(rf"\b{sugar_key}\b", pos_part):
_set_if_stronger(parsed, field, "Positive")
# Negative sugars from neg_part
for sugar_key, field in SUGAR_FIELDS.items():
if re.search(rf"\b{sugar_key}\b", neg_part):
_set_if_stronger(parsed, field, "Negative")
# 2) Grouped "does not ferment X, Y and Z" (stop at but/punctuation)
# Prevents glucose being accidentally marked negative in:
# "does not ferment lactose or sucrose, but glucose fermentation is positive"
grouped_neg_pattern = re.compile(
r"does\s+(?:not|n't)\s+ferment\s+([a-z0-9 ,;/&\-]+?)(?:\s+but\b|\.|;|,|$)"
)
for m in grouped_neg_pattern.finditer(text_lc):
seg = m.group(1)
for sugar_key, field in SUGAR_FIELDS.items():
if re.search(rf"\b{sugar_key}\b", seg):
_set_if_stronger(parsed, field, "Negative")
# 3) Single "does not ferment X"
for sugar_key, field in SUGAR_FIELDS.items():
if re.search(
rf"does\s+(?:not|n't)\s+ferment\s+{sugar_key}\b", text_lc
):
_set_if_stronger(parsed, field, "Negative")
# 4) "non-lactose fermenter" and similar
for sugar_key, field in SUGAR_FIELDS.items():
if re.search(
rf"non[- ]{sugar_key}\s+ferment(ing|er)?", text_lc
):
_set_if_stronger(parsed, field, "Negative")
# 5) "<sugar> fermentation positive/negative" + "is positive"
for sugar_key, field in SUGAR_FIELDS.items():
# "glucose fermentation positive"
m1 = re.search(
rf"{sugar_key}\s+fermentation[ \-]?"
r"(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m1:
val = _value_from_pnv_context(m1.group(1))
if val:
_set_if_stronger(parsed, field, val)
continue
# "positive for glucose fermentation"
m2 = re.search(
rf"(positive|negative|variable|pos|neg|\+|\-)\s+"
rf"(for\s+)?{sugar_key}\s+fermentation",
text_lc,
)
if m2:
val = _value_from_pnv_context(m2.group(1))
if val:
_set_if_stronger(parsed, field, val)
continue
# "<sugar> fermentation is positive/negative"
m3 = re.search(
rf"{sugar_key}\s+fermentation\s+is\s+"
r"(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m3:
val = _value_from_pnv_token(m3.group(1))
if val:
_set_if_stronger(parsed, field, val)
continue
# 6) Global non-fermenter phrases
# e.g. "non-fermenter", "does not ferment sugars"
# → set all sugars Negative *unless* already set by a more specific rule.
if (
re.search(
r"does\s+(?:not|n't)\s+ferment\s+(carbohydrates|sugars)", text_lc
)
or re.search(r"\bnon[- ]ferment(er|ing|ative)\b", text_lc)
):
for field in SUGAR_FIELDS.values():
if field not in parsed or parsed[field] == UNKNOWN:
_set_if_stronger(parsed, field, "Negative")
# 7) Asaccharolytic → all sugars Negative (Unknown-only)
if (
"asaccharolytic" in text_lc
or "non-saccharolytic" in text_lc
or "non saccharolytic" in text_lc
):
for field in SUGAR_FIELDS.values():
if field not in parsed or parsed[field] == UNKNOWN:
_set_if_stronger(parsed, field, "Negative")
# 8) "all other sugars negative/positive"
m_other = re.search(
r"all\s+other\s+sugars\s+(?:are\s+)?(positive|negative|variable|pos|neg|\+|\-)",
text_lc,
)
if m_other:
val = _value_from_pnv_token(m_other.group(1))
if val:
for field in SUGAR_FIELDS.values():
if field not in parsed or parsed[field] == UNKNOWN:
_set_if_stronger(parsed, field, val)
# ------------------------------------------------------------
# Colony morphology (coarse, optional)
# ------------------------------------------------------------
def _normalise_colony_desc(desc: str) -> str:
"""
Take a raw colony descriptor and normalise into:
"Smooth; Yellow; Opaque" etc.
Tweaks:
- Remove "-pigmented" → "yellow-pigmented" → "yellow"
- Treat "and" like a separator for parts
"""
# Remove "-pigmented" so "yellow-pigmented" → "yellow"
tmp = desc.replace("-pigmented", "")
# Normalise "and" to a comma so it acts like a separator
tmp = tmp.replace(" and ", ", ")
parts = [s.strip() for s in re.split(r"[;,]", tmp) if s.strip()]
pretty = "; ".join(p.capitalize() for p in parts)
return pretty
def _parse_colony(text_lc: str, parsed: Dict[str, str]) -> None:
"""
Very coarse mapping for colony morphology. We try:
- "colonies are yellow, mucoid"
- "colonies dry, white and irregular on nutrient agar"
- "forming smooth, yellow-pigmented, opaque colonies"
- "grey colonies", "large grey colonies" (no verb)
"""
# Pattern 1: "colonies are ..."
m = re.search(r"colon(y|ies)\s+(are|is)\s+([a-z0-9 ,;\-]+)", text_lc)
if m:
desc = m.group(3).strip()
if desc:
pretty = _normalise_colony_desc(desc)
if pretty:
_set_if_stronger(parsed, "Colony Morphology", pretty)
return
# Pattern 2: "colonies dry, white and irregular on nutrient agar"
m2 = re.search(
r"colonies\s+([a-z0-9 ,;\-]+?)(?:\s+on\b|\.|,)",
text_lc,
)
if m2:
desc = m2.group(1).strip()
if desc:
pretty = _normalise_colony_desc(desc)
if pretty:
_set_if_stronger(parsed, "Colony Morphology", pretty)
return
# Pattern 3: "forming green colonies", "forms mucoid colonies",
# "forming smooth, yellow-pigmented, opaque colonies"
m3 = re.search(
r"(forming|forms|produces)\s+([a-z0-9 ,;\-]+?)\s+colonies",
text_lc,
)
if m3:
desc = m3.group(2).strip()
if desc:
pretty = _normalise_colony_desc(desc)
if pretty:
_set_if_stronger(parsed, "Colony Morphology", pretty)
return
# Pattern 4: plain descriptor before "colonies" (e.g. "grey colonies",
# "large grey colonies") when none of the above match.
m4 = re.search(
r"\b([a-z0-9 ,;\-]+?)\s+colonies\b",
text_lc,
)
if m4:
desc = m4.group(1).strip()
if desc:
pretty = _normalise_colony_desc(desc)
if pretty:
_set_if_stronger(parsed, "Colony Morphology", pretty)
return
def _apply_patches(original_text: str, text_lc: str, parsed: Dict[str, str]) -> Dict[str, str]:
# ----------------------------------------------
# helper for P/N/V
# ----------------------------------------------
def _pnv(x: str) -> Optional[str]:
x = x.strip().lower()
if x in {"positive", "pos", "+", "strongly positive", "weakly positive"}:
return "Positive"
if x in {"negative", "neg", "-", "no"}:
return "Negative"
if x in {"variable", "var", "mixed"}:
return "Variable"
return None
# ============================================================
# NEW LOGIC: Haemolysis Type detection (alpha/beta/none)
# ============================================================
# alpha
m_alpha = re.search(r"(alpha|α)[-\s]*haemolysis", text_lc) or \
re.search(r"haemolysis type[: ]*(alpha|α)", text_lc)
if m_alpha:
if parsed.get("Haemolysis", UNKNOWN) == UNKNOWN:
parsed["Haemolysis"] = "Positive"
if parsed.get("Haemolysis Type", UNKNOWN) == UNKNOWN:
parsed["Haemolysis Type"] = "Alpha"
# beta
m_beta = re.search(r"(beta|β)[-\s]*haemolysis", text_lc) or \
re.search(r"haemolysis type[: ]*(beta|β)", text_lc)
if m_beta:
if parsed.get("Haemolysis", UNKNOWN) == UNKNOWN:
parsed["Haemolysis"] = "Positive"
if parsed.get("Haemolysis Type", UNKNOWN) == UNKNOWN:
parsed["Haemolysis Type"] = "Beta"
# gamma / none
m_gamma = re.search(r"(gamma|γ)[-\s]*haemolysis", text_lc)
m_none = re.search(r"(no haemolysis|non[- ]haemolytic|no hemolysis|non[- ]hemolytic)", text_lc)
if m_gamma or m_none:
if parsed.get("Haemolysis", UNKNOWN) == UNKNOWN:
parsed["Haemolysis"] = "Negative"
if parsed.get("Haemolysis Type", UNKNOWN) == UNKNOWN:
parsed["Haemolysis Type"] = "None"
# ============================================================
# ORIGINAL PATCH v1 LOGIC (fully preserved)
# ============================================================
# 1. Haemolysis: generic ± without type
m_h = re.search(r"haemolysis\s+(positive|negative|variable|pos|neg|\+|\-)", text_lc)
if m_h and "Haemolysis" not in parsed:
val = _pnv(m_h.group(1))
if val:
parsed["Haemolysis"] = val
if parsed.get("Haemolysis Type", UNKNOWN) == UNKNOWN and val == "Positive":
parsed["Haemolysis Type"] = "Unknown"
# 2. Motility: generic ±
m_mot = re.search(r"motility\s+(positive|negative|variable|pos|neg|\+|\-)", text_lc)
if m_mot and "Motility" not in parsed:
val = _pnv(m_mot.group(1))
if val:
parsed["Motility"] = val
# 3. Spore formation ±
m_sp = re.search(r"spore formation\s+(positive|negative|variable|pos|neg|\+|\-)", text_lc)
if m_sp and parsed.get("Spore Formation", UNKNOWN) == UNKNOWN:
val = _pnv(m_sp.group(1))
if val:
parsed["Spore Formation"] = val
# ============================================================
# FIXED NaCl tolerant logic (patch upgrade)
# ============================================================
if parsed.get("NaCl Tolerant (>=6%)", UNKNOWN) == UNKNOWN:
# direct p/n/v
m_nacl = re.search(
r"(?:nacl\s*(?:tolerant|tolerance)?|growth\s+in\s+6\%[\s]*nacl)"
r"\s*(positive|negative|variable|pos|neg|\+|\-)",
text_lc
)
if m_nacl:
val = _pnv(m_nacl.group(1))
if val:
parsed["NaCl Tolerant (>=6%)"] = val
# "no growth in 6% nacl"
if parsed.get("NaCl Tolerant (>=6%)", UNKNOWN) == UNKNOWN:
if re.search(r"no\s+growth\s+in\s+(?:>=)?\s*6\%?\s*nacl", text_lc):
parsed["NaCl Tolerant (>=6%)"] = "Negative"
# "grows in 6% nacl"
if parsed.get("NaCl Tolerant (>=6%)", UNKNOWN) == UNKNOWN:
if re.search(r"grows?\s+in\s+(?:>=)?\s*6\%?\s*nacl", text_lc):
parsed["NaCl Tolerant (>=6%)"] = "Positive"
# ============================================================
# Growth Temperature patterns (20/40, 20//40, 20 / 40)
# ============================================================
m_temp = re.search(r"\b(\d{1,3})\s*[/]{1,2}\s*(\d{1,3})\b", text_lc)
if m_temp and parsed.get("Growth Temperature", UNKNOWN) == UNKNOWN:
parsed["Growth Temperature"] = f"{m_temp.group(1)}//{m_temp.group(2)}"
# ============================================================
# Colony Morphology STRICT LIST extraction
# ============================================================
COLONY_TRIGGERS = [
"colony morphology",
"colonies are",
"colonies appear",
"colonies look",
"colony appearance",
"colony characteristics",
]
if any(t in text_lc for t in COLONY_TRIGGERS):
m_col = re.search(
r"(?:colony morphology|colonies are|colonies appear|colonies look|colony appearance|colony characteristics)"
r"[: ]+([a-z0-9 ,;/\-]+)",
text_lc
)
if m_col:
segment = m_col.group(1)
parts = [x.strip() for x in re.split(r"[;,/]", segment) if x.strip()]
clean_desc = [p.capitalize() for p in parts if len(p) > 1]
if clean_desc:
existing = parsed.get("Colony Morphology", "")
existing_list = [x.strip() for x in existing.split(";")] if existing else []
merged = []
for x in existing_list:
if x not in merged:
merged.append(x)
for x in clean_desc:
if x not in merged:
merged.append(x)
parsed["Colony Morphology"] = "; ".join(merged)
# ============================================================
# ORIGINAL MULTI-MEDIA PATCH (unchanged)
# ============================================================
if "media grown on" in text_lc or "grown on" in text_lc:
mm = re.search(r"(?:media\s+grown\s+on|grown\s+on)[: ]+([a-z0-9 ,;/\-]+)", text_lc)
if mm:
segment = mm.group(1)
raw_items = re.split(r"[;,]", segment)
raw_items = [x.strip() for x in raw_items if x.strip()]
detected_media = []
for item in raw_items:
for media_name, patterns in MEDIA_KEYWORDS.items():
for p in patterns:
if p in item and media_name not in detected_media:
detected_media.append(media_name)
if detected_media:
existing = parsed.get("Media Grown On", "")
existing_list = [x.strip() for x in existing.split(";")] if existing else []
merged = []
for m in existing_list:
if m not in merged:
merged.append(m)
for m in detected_media:
if m not in merged:
merged.append(m)
parsed["Media Grown On"] = "; ".join(merged)
return parsed
# ------------------------------------------------------------
# PUBLIC API
# ------------------------------------------------------------
def parse_text_rules(text: str) -> Dict[str, Any]:
"""
Main entry point for the rule-based core parser.
"""
original = text or ""
text_clean = _clean_text(original)
text_lc = text_clean.lower()
parsed: Dict[str, str] = {}
try:
_parse_gram_and_shape(text_lc, parsed)
_parse_haemolysis(text_lc, parsed)
_parse_core_bool_tests(text_lc, parsed)
_parse_motility_capsule_spores(text_lc, parsed)
_parse_oxygen(text_lc, parsed)
_parse_growth_temperature(text_lc, parsed)
_parse_media(text_lc, parsed)
_parse_sugars(text_lc, parsed)
_parse_colony(text_lc, parsed)
parsed = _apply_patches(original, text_lc, parsed)
return {
"parsed_fields": parsed,
"source": "rule_parser",
"raw": original,
}
except Exception as e:
# Fail-safe: never crash the app, just report an error
return {
"parsed_fields": parsed,
"source": "rule_parser",
"raw": original,
"error": f"{type(e).__name__}: {e}",
}
|