Add bundled-bestiary mechanism for shipping creatures with the app
D&D creatures listed in data/bestiary/dnd-bundled.json are now merged into the search index and pre-loaded into creatureMap, so they appear alongside 5etools creatures with no "Load source" step. Source codes are derived from the JSON itself (each creature carries source + sourceDisplayName), so adding a new book is a pure data change. Bundled sources are excluded from getAllSourceCodes() so bulk-import skips them, and they never appear in the source manager (which only lists cached sources). Includes a reference extractor (scripts/extract-great-labors.py) for the 5.5e revised stat-block format and a /bundle-bestiary skill that future agents can follow to add monsters from other PDF books. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Lukas
@@ -0,0 +1,561 @@
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#!/usr/bin/env python3
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"""Extract D&D 5.5e stat blocks from The Great Labors PDF.
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Usage:
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python3 scripts/extract-great-labors.py <path-to-pdf>
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Reads pages 163-199 (Appendix B: Monsters) and emits
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data/bestiary/dnd-bundled.json in the Creature[] shape from
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packages/domain/src/creature-types.ts.
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Requires: PyPDF2 (pip install PyPDF2)
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"""
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import json
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import os
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import re
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import sys
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from pathlib import Path
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from PyPDF2 import PdfReader
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# --- Constants ---
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SOURCE_CODE = "TGL"
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SOURCE_DISPLAY = "The Great Labors"
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PAGE_START = 163 # 1-indexed
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PAGE_END = 199
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SIZE_RE = r"(Tiny|Small|Medium|Large|Huge|Gargantuan)"
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TYPE_PIECE = r"[A-Za-z][A-Za-z\- ]*?"
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ALIGN_PIECE = r"[A-Za-z][A-Za-z ()]*?"
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HEADER_RE = re.compile(
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rf"^{SIZE_RE}\s+({TYPE_PIECE}(?:\s+\([^)]+\))?),\s+({ALIGN_PIECE})\s*$"
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)
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AC_RE = re.compile(r"^AC\s+(\d+)\s+Initiative\s+([+\-–]\s*\d+|[+\-–]?\d+)")
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HP_RE = re.compile(r"^HP\s+(\d+)\s*\(([^)]+)\)")
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SPEED_RE = re.compile(r"^Speed\s+(.+?)\s*$")
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ABILITY_ROW_RE = re.compile(
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r"^(Str|Dex|Con|Int|Wis|Cha)\s+(\d+)\s*([+\-–]?\s*\d+)\s+([+\-–]?\s*\d+)\s+"
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r"(Str|Dex|Con|Int|Wis|Cha)\s+(\d+)\s*([+\-–]?\s*\d+)\s+([+\-–]?\s*\d+)\s+"
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r"(Str|Dex|Con|Int|Wis|Cha)\s+(\d+)\s*([+\-–]?\s*\d+)\s+([+\-–]?\s*\d+)\s*$"
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)
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CR_RE = re.compile(
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r"^Challenge\s+([\d/]+)\s*\(([\d,]+)\s*XP;\s*PB\s+\+(\d+)\)"
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)
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SECTION_HEADERS = ("Traits", "Actions", "Bonus Actions", "Reactions",
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"Legendary Actions", "Mythic Actions")
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# Page running header like "166APPENDIX B � MONSTERS..." -- marks the
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# transition from stat-block content into prose on the next page.
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RUNNING_HEADER_RE = re.compile(r"^\d+APPENDIX B\b")
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# Condition / status-word false positives that the title-case entry regex
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# would otherwise mistake for a new entry name. These names commonly end a
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# sentence inside an entry's body (e.g. "...while it is Bloodied.").
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NAME_FALSE_POSITIVES = {
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"Bloodied", "Restrained", "Grappled", "Charmed", "Frightened",
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"Prone", "Incapacitated", "Stunned", "Paralyzed", "Petrified",
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"Poisoned", "Blinded", "Deafened", "Invisible", "Unconscious",
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"Exhaustion", "Surprised", "Furious",
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"Failure", "Success", "Trigger", "Response", "Hit", "Miss",
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"Habitat", "Treasure", "Bonus Actions", "Reactions", "Traits", "Actions",
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"Disadvantage", "Advantage",
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}
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# --- Helpers ---
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def norm_dash(s: str) -> str:
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return s.replace("–", "-").replace("—", "-").replace("−", "-")
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def proficiency_bonus(cr_str: str) -> int:
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if "/" in cr_str:
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n, d = cr_str.split("/")
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cr = int(n) / int(d)
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else:
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cr = int(cr_str)
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if cr <= 4:
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return 2
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if cr <= 8:
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return 3
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if cr <= 12:
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return 4
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if cr <= 16:
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return 5
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if cr <= 20:
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return 6
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if cr <= 24:
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return 7
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if cr <= 28:
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return 8
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return 9
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def make_creature_id(source: str, name: str) -> str:
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slug = re.sub(r"[^a-z0-9]+", "-", name.lower()).strip("-")
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return f"{source.lower()}:{slug}"
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def parse_passive_perception(senses_text: str) -> int | None:
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# The PDF sometimes renders multi-digit values with a kerning space
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# (e.g. "Passive Perception 1 1" meaning 11). Collapse those.
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m = re.search(r"Passive Perception\s+(\d(?:\s*\d)*)\s*$", senses_text)
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if not m:
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m = re.search(r"Passive Perception\s+(\d+)", senses_text)
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return int(m.group(1).replace(" ", "")) if m else None
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# --- Page extraction ---
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def extract_pages(pdf_path: Path) -> str:
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reader = PdfReader(str(pdf_path))
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parts = []
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for i in range(PAGE_START - 1, PAGE_END):
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parts.append(reader.pages[i].extract_text())
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return "\n".join(parts)
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# --- Block splitting ---
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def find_stat_block_starts(lines: list[str]) -> list[int]:
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starts = []
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for i, line in enumerate(lines):
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if AC_RE.match(line.strip()):
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header_idx = None
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for j in range(i - 1, max(-1, i - 5), -1):
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if HEADER_RE.match(lines[j].strip()):
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header_idx = j
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break
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if header_idx is None:
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continue
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name_idx = header_idx - 1
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if name_idx >= 0 and lines[name_idx].strip():
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starts.append(name_idx)
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return starts
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SECTION_HEADER_SMUSH_RE = re.compile(
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r"^(?P<body>.+?)\.(?P<hdr>Actions|Bonus Actions|Reactions|Legendary Actions|Traits)\s*$"
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)
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def block_for(lines: list[str], start: int, next_start: int | None) -> list[str]:
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"""Build the line list for one stat block.
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Drops page markers and everything from the first running-header line
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onward (which marks the transition to a new prose page). Splits PDF
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smush lines like "...plants.Actions" into two lines so section header
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detection works.
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"""
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end = next_start if next_start is not None else len(lines)
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out: list[str] = []
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for ln in lines[start:end]:
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if ln.startswith("===PAGE"):
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continue
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if RUNNING_HEADER_RE.match(ln.strip()):
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break
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m = SECTION_HEADER_SMUSH_RE.match(ln.strip())
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if m:
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out.append(m.group("body") + ".")
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out.append(m.group("hdr"))
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else:
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out.append(ln)
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return out
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# --- Vitals parsing ---
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def parse_header(block: list[str]) -> dict:
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name = block[0].strip()
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header = block[1].strip()
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m = HEADER_RE.match(header)
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if not m:
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raise ValueError(f"Bad header for {name!r}: {header!r}")
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size, ctype, alignment = m.group(1), m.group(2).strip(), m.group(3).strip()
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return {"name": name, "size": size, "type": ctype, "alignment": alignment}
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def parse_ac(line: str) -> int:
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m = AC_RE.match(line.strip())
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if not m:
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raise ValueError(f"Bad AC line: {line!r}")
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return int(m.group(1))
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def parse_hp(line: str) -> dict:
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m = HP_RE.match(line.strip())
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if not m:
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raise ValueError(f"Bad HP line: {line!r}")
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return {"average": int(m.group(1)), "formula": m.group(2).strip()}
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def parse_speed(line: str) -> str:
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m = SPEED_RE.match(line.strip())
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if not m:
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raise ValueError(f"Bad Speed line: {line!r}")
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speed = m.group(1).rstrip(".").strip()
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# Normalize "30 ft" → "30 ft." to match 5etools adapter output style.
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speed = re.sub(r"(\d+)\s+ft\b\.?", r"\1 ft.", speed)
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return speed
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def parse_abilities(row1: str, row2: str) -> dict:
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out = {}
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for row in (row1, row2):
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m = ABILITY_ROW_RE.match(row.strip())
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if not m:
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raise ValueError(f"Bad ability row: {row!r}")
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for off in (0, 4, 8):
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ab = m.group(off + 1).lower()
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score = int(m.group(off + 2))
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out[ab] = score
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return out
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# --- Meta lines ---
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META_KEYS = ("Skills", "Saving Throws", "Resistances", "Immunities",
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"Vulnerabilities", "Senses", "Languages", "Gear")
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def is_meta_start(line: str) -> str | None:
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for key in META_KEYS:
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if line.startswith(key + " ") or line.startswith(key + " "):
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return key
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return None
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def parse_meta(lines: list[str], start: int) -> tuple[dict, int]:
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meta: dict[str, str] = {}
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i = start
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current_key: str | None = None
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current_val_parts: list[str] = []
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def flush() -> None:
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nonlocal current_key, current_val_parts
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if current_key is not None:
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meta[current_key] = " ".join(p.strip() for p in current_val_parts).strip()
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current_key = None
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current_val_parts = []
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while i < len(lines):
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line = lines[i].strip()
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if not line:
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i += 1
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continue
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if line.startswith("Challenge "):
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flush()
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return meta, i
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key = is_meta_start(line)
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if key:
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flush()
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current_key = key
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current_val_parts.append(line[len(key):].strip())
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elif current_key is not None:
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current_val_parts.append(line)
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i += 1
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flush()
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return meta, i
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# --- Section discovery ---
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def find_section_starts(block: list[str], start_idx: int) -> list[tuple[str, int]]:
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starts = []
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for i in range(start_idx, len(block)):
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ln = block[i].strip()
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if ln in SECTION_HEADERS:
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starts.append((ln, i))
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return starts
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def collect_section_lines(block: list[str], start: int, end: int) -> list[str]:
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"""Collect the raw lines for one section (between header indices)."""
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out: list[str] = []
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for line in block[start:end]:
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if not line.strip():
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continue
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out.append(line.rstrip())
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return out
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def join_section_text(lines: list[str]) -> str:
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"""Join section lines into a single text blob, repairing wrap hyphens."""
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text = " ".join(line.strip() for line in lines if line.strip())
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text = re.sub(r"\s+", " ", text)
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# Repair "civi -li zation" → "civilization" (PDF column-wrap hyphens).
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text = re.sub(r"(\w)\s*-\s+(\w)", r"\1\2", text)
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return text.strip()
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# --- Entry splitting ---
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# Entry name: title-case phrase, where each "word" is either a Capitalized
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# word, a lowercase connector (of/the/and/or/in/at/on/to/with/from), a roman
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# numeral, etc. Optionally followed by parenthesized modifier.
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ENTRY_NAME_INNER = (
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r"[A-Z][A-Za-z'’]*"
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r"(?:[ \-](?:[A-Z][A-Za-z'’]*|of|the|and|or|in|at|on|to|with|from))*"
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r"(?:\s*\([^)]+\))?"
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)
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# An entry boundary occurs at the start of the joined section text, or
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# immediately after a sentence-ending punctuation. The PDF sometimes drops
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# the space between the period and the new entry name, so `\s*` is fine.
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ENTRY_BOUNDARY = re.compile(
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rf"(?:^|(?<=[\.\?\!]))\s*(?P<name>{ENTRY_NAME_INNER})\.\s+(?=[A-Z“\"(])"
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)
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# Trim attribution quotes / page-header bleed-through from entry bodies.
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PROSE_TAIL_PATTERNS = (
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# Em-dash attribution: " —Chondrus, Priest of Lutheria"
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re.compile(r"\s+—\s*[A-Z][^—]*$"),
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# Smushed section header at end ("...plants.Actions").
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re.compile(
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r"\.\s*(?:Actions|Bonus\s+Actions|Reactions|Legendary\s+Actions|Traits)\s*$"
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),
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# Curated prose subheadings / phrase markers that follow stat blocks in
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# this book. PDF reflow often merges prose onto the same logical line
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# as the last action body, so the leading whitespace is optional.
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re.compile(
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r"\.?\s*(?:Random Trapped Creature|Maenad Bacchanal|The Phalanx Formation"
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r"|Reinforced Portal|TRAPPED|HUNGER FOR|PURSUIT OF|RITUAL|MyTHIC|BRON"
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r"|GOlDEN|NyMPH|MARBlE|KElEDONE|SOlDIER|MINOTAUR|SATyRS|GOATlING|EMPUS"
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r"|ANARCH|GyGAN|CERBERUS|WHITE STAG|STORM|FEy|VOlKAN).*",
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re.DOTALL,
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),
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# Specific prose sentence-starts observed leaking in.
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re.compile(
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r"\.(?:will gleefully|Some report that|Storm Dory|This magic weapon"
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r"|Thylean soldiers|Some claim|These leaders).*",
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re.DOTALL,
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),
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# All-caps run of 3+ uppercase letters in a word, then a space, then
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# another word with 3+ uppercase letters (PDF small-caps section header
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# like "BRON zE STRATEGOS", "MyTHIC BEAST", "GOlDEN RAM").
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re.compile(r"(?<=[\.\s])[A-Z]{2}\w*\s+[\w ]{0,12}[A-Z]{3}[A-Z\w ]*"),
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)
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def trim_prose_tail(body: str) -> str:
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out = body
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for pat in PROSE_TAIL_PATTERNS:
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m = pat.search(out)
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if m:
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out = out[:m.start()].rstrip().rstrip(".") + "."
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return out.strip()
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def is_valid_entry_name(name: str) -> bool:
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"""Filter false-positive matches that aren't really entry names."""
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if name in NAME_FALSE_POSITIVES:
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return False
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# Single short capitalized word that's a common condition or noun is
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# usually a false positive when followed by a period. Real entry names
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# almost always have either multiple words or a parenthesized modifier.
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bare = re.sub(r"\s*\([^)]+\)\s*", "", name).strip()
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if bare in NAME_FALSE_POSITIVES:
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return False
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return True
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def split_text_into_entries(text: str) -> list[tuple[str, str]]:
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"""Split section text into (name, body) entries by scanning for entry-name
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boundaries (start-of-text or after a sentence period)."""
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matches: list[tuple[int, int, str]] = []
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for m in ENTRY_BOUNDARY.finditer(text):
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name = m.group("name").strip()
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if is_valid_entry_name(name):
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matches.append((m.start(), m.end(), name))
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if not matches:
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return []
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entries: list[tuple[str, str]] = []
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for i, (_, body_start, name) in enumerate(matches):
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body_end = matches[i + 1][0] if i + 1 < len(matches) else len(text)
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body = text[body_start:body_end].strip()
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entries.append((name, body))
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return entries
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def parse_section_traits(lines: list[str]) -> list[dict]:
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text = join_section_text(lines)
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entries = split_text_into_entries(text)
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out = []
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for name, body in entries:
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body = trim_prose_tail(body)
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if body or name:
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out.append({"name": name,
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"segments": [{"type": "text", "value": body}]})
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return out
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def parse_legendary(lines: list[str], creature_name: str) -> dict | None:
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"""Parse the Legendary Actions section. Text before the first entry whose
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body contains action vocabulary forms the preamble.
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"""
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text = join_section_text(lines)
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all_matches: list[tuple[int, int, str]] = []
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for m in ENTRY_BOUNDARY.finditer(text):
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name = m.group("name").strip()
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if is_valid_entry_name(name):
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all_matches.append((m.start(), m.end(), name))
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action_anchors = ("Saving Throw", "Attack Roll", "Trigger", "Recharge",
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"Melee", "Ranged", "Constitution", "Dexterity",
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"Strength", "Intelligence", "Wisdom", "Charisma")
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first_action_idx = None
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for i, (_, body_start, _) in enumerate(all_matches):
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body_end = all_matches[i + 1][0] if i + 1 < len(all_matches) else len(text)
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body_head = text[body_start:min(body_end, body_start + 100)]
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if any(a in body_head for a in action_anchors):
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first_action_idx = i
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break
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if first_action_idx is None:
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return None
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preamble = text[:all_matches[first_action_idx][0]].strip()
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if not preamble:
|
||||
preamble = f"{creature_name} can take Legendary Actions."
|
||||
entries = []
|
||||
for i in range(first_action_idx, len(all_matches)):
|
||||
_, body_start, name = all_matches[i]
|
||||
body_end = all_matches[i + 1][0] if i + 1 < len(all_matches) else len(text)
|
||||
body = text[body_start:body_end].strip()
|
||||
entries.append((name, body))
|
||||
if not entries:
|
||||
return None
|
||||
return {
|
||||
"preamble": preamble,
|
||||
"entries": [
|
||||
{"name": name,
|
||||
"segments": [{"type": "text", "value": trim_prose_tail(body)}]}
|
||||
for name, body in entries if body
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
# --- Top-level parse ---
|
||||
|
||||
|
||||
def parse_block(block: list[str]) -> dict:
|
||||
head = parse_header(block)
|
||||
ac = parse_ac(block[2])
|
||||
hp = parse_hp(block[3])
|
||||
speed = parse_speed(block[4])
|
||||
if not block[5].strip().startswith("MOD"):
|
||||
raise ValueError(f"Expected MOD header, got: {block[5]!r}")
|
||||
abilities = parse_abilities(block[6], block[7])
|
||||
|
||||
meta, ch_idx = parse_meta(block, 8)
|
||||
cr_match = CR_RE.match(block[ch_idx].strip())
|
||||
if not cr_match:
|
||||
raise ValueError(f"Bad Challenge line: {block[ch_idx]!r}")
|
||||
cr_str = cr_match.group(1)
|
||||
|
||||
section_starts = find_section_starts(block, ch_idx + 1)
|
||||
sections: dict[str, list[str]] = {}
|
||||
for i, (name, idx) in enumerate(section_starts):
|
||||
end = section_starts[i + 1][1] if i + 1 < len(section_starts) else len(block)
|
||||
sections[name] = collect_section_lines(block, idx + 1, end)
|
||||
|
||||
creature: dict = {
|
||||
"id": make_creature_id(SOURCE_CODE, head["name"]),
|
||||
"name": head["name"],
|
||||
"source": SOURCE_CODE,
|
||||
"sourceDisplayName": SOURCE_DISPLAY,
|
||||
"size": head["size"],
|
||||
"type": head["type"],
|
||||
"alignment": head["alignment"],
|
||||
"ac": ac,
|
||||
"hp": hp,
|
||||
"speed": speed,
|
||||
"abilities": abilities,
|
||||
"cr": cr_str,
|
||||
"initiativeProficiency": 0,
|
||||
"proficiencyBonus": proficiency_bonus(cr_str),
|
||||
"passive": parse_passive_perception(meta.get("Senses", "")) or 10,
|
||||
}
|
||||
|
||||
if "Saving Throws" in meta:
|
||||
creature["savingThrows"] = meta["Saving Throws"]
|
||||
if "Skills" in meta:
|
||||
creature["skills"] = meta["Skills"]
|
||||
if "Resistances" in meta:
|
||||
creature["resist"] = meta["Resistances"]
|
||||
if "Immunities" in meta:
|
||||
creature["immune"] = meta["Immunities"]
|
||||
if "Vulnerabilities" in meta:
|
||||
creature["vulnerable"] = meta["Vulnerabilities"]
|
||||
if "Senses" in meta:
|
||||
senses = re.sub(r"[;,]?\s*Passive Perception\s+\d+\s*$", "", meta["Senses"])
|
||||
senses = senses.strip().rstrip(";").strip()
|
||||
if senses:
|
||||
creature["senses"] = senses
|
||||
if "Languages" in meta:
|
||||
creature["languages"] = meta["Languages"]
|
||||
|
||||
if "Traits" in sections:
|
||||
creature["traits"] = parse_section_traits(sections["Traits"])
|
||||
if "Actions" in sections:
|
||||
creature["actions"] = parse_section_traits(sections["Actions"])
|
||||
if "Bonus Actions" in sections:
|
||||
creature["bonusActions"] = parse_section_traits(sections["Bonus Actions"])
|
||||
if "Reactions" in sections:
|
||||
creature["reactions"] = parse_section_traits(sections["Reactions"])
|
||||
if "Legendary Actions" in sections:
|
||||
leg = parse_legendary(sections["Legendary Actions"], head["name"])
|
||||
if leg:
|
||||
creature["legendaryActions"] = leg
|
||||
|
||||
return creature
|
||||
|
||||
|
||||
def main() -> int:
|
||||
if len(sys.argv) != 2:
|
||||
print("Usage: python3 extract-great-labors.py <path-to-pdf>",
|
||||
file=sys.stderr)
|
||||
return 1
|
||||
pdf_path = Path(os.path.expanduser(sys.argv[1]))
|
||||
if not pdf_path.exists():
|
||||
print(f"PDF not found: {pdf_path}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
text = extract_pages(pdf_path)
|
||||
lines = text.split("\n")
|
||||
|
||||
starts = find_stat_block_starts(lines)
|
||||
print(f"Detected {len(starts)} stat blocks", file=sys.stderr)
|
||||
|
||||
creatures = []
|
||||
failures = []
|
||||
for i, s in enumerate(starts):
|
||||
next_s = starts[i + 1] if i + 1 < len(starts) else None
|
||||
block = block_for(lines, s, next_s)
|
||||
try:
|
||||
creatures.append(parse_block(block))
|
||||
except Exception as e:
|
||||
failures.append((block[0] if block else "<empty>", str(e)))
|
||||
|
||||
if failures:
|
||||
print(f"\n{len(failures)} parse failures:", file=sys.stderr)
|
||||
for name, err in failures:
|
||||
print(f" - {name}: {err}", file=sys.stderr)
|
||||
|
||||
out_path = Path(__file__).resolve().parent.parent / "data" / "bestiary" / "dnd-bundled.json"
|
||||
out_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with out_path.open("w") as f:
|
||||
json.dump(creatures, f, indent="\t", ensure_ascii=False)
|
||||
f.write("\n")
|
||||
print(f"Wrote {len(creatures)} creatures to {out_path}", file=sys.stderr)
|
||||
return 0 if not failures else 2
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in New Issue
Block a user