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Finalize MDFC follow-ups, docs, and diagnostics tooling
document deck summary DFC badges, exporter annotations, and per-face metadata across README/DOCKER/release notes record completion of all MDFC roadmap follow-ups and add the authoring guide for multi-face CSV entries wire in optional DFC_PER_FACE_SNAPSHOT env support, exporter regression tests, and diagnostics updates noted in the changelog
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matt
parent
6fefda714e
commit
88cf832bf2
46 changed files with 3292 additions and 86 deletions
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@ -8,15 +8,159 @@ Only import lightweight standard library modules here to avoid import cycles.
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"""
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from __future__ import annotations
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from typing import Dict, Iterable
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from typing import Any, Dict, Iterable, List
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import re
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import ast
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import random as _rand
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from functools import lru_cache
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from pathlib import Path
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import pandas as pd
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from . import builder_constants as bc
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import math
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from path_util import csv_dir
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COLOR_LETTERS = ['W', 'U', 'B', 'R', 'G']
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_MULTI_FACE_LAYOUTS = {
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"adventure",
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"aftermath",
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"augment",
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"flip",
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"host",
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"meld",
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"modal_dfc",
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"reversible_card",
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"split",
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"transform",
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}
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_SIDE_PRIORITY = {
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"": 0,
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"a": 0,
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"front": 0,
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"main": 0,
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"b": 1,
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"back": 1,
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"c": 2,
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}
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def _detect_produces_mana(text: str) -> bool:
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text = (text or "").lower()
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if not text:
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return False
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if 'add one mana of any color' in text or 'add one mana of any colour' in text:
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return True
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if 'add mana of any color' in text or 'add mana of any colour' in text:
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return True
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if 'mana of any one color' in text or 'any color of mana' in text:
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return True
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if 'add' in text:
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for sym in ('{w}', '{u}', '{b}', '{r}', '{g}', '{c}'):
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if sym in text:
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return True
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return False
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def _resolved_csv_dir(base_dir: str | None = None) -> str:
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try:
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if base_dir:
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return str(Path(base_dir).resolve())
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return str(Path(csv_dir()).resolve())
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except Exception:
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return base_dir or csv_dir()
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@lru_cache(maxsize=None)
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def _load_multi_face_land_map(base_dir: str) -> Dict[str, Dict[str, Any]]:
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"""Load mapping of multi-faced cards that have at least one land face."""
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try:
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base_path = Path(base_dir)
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csv_path = base_path / 'cards.csv'
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if not csv_path.exists():
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return {}
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usecols = ['name', 'layout', 'side', 'type', 'text', 'manaCost', 'manaValue', 'faceName']
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df = pd.read_csv(csv_path, usecols=usecols, low_memory=False)
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except Exception:
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return {}
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if df.empty or 'layout' not in df.columns or 'type' not in df.columns:
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return {}
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df['layout'] = df['layout'].fillna('').astype(str).str.lower()
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multi_df = df[df['layout'].isin(_MULTI_FACE_LAYOUTS)].copy()
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if multi_df.empty:
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return {}
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multi_df['type'] = multi_df['type'].fillna('').astype(str)
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multi_df['side'] = multi_df['side'].fillna('').astype(str)
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multi_df['text'] = multi_df['text'].fillna('').astype(str)
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land_rows = multi_df[multi_df['type'].str.contains('land', case=False, na=False)]
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if land_rows.empty:
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return {}
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mapping: Dict[str, Dict[str, Any]] = {}
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for name, group in land_rows.groupby('name', sort=False):
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faces: List[Dict[str, str]] = []
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seen: set[tuple[str, str, str]] = set()
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front_is_land = False
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layout_val = ''
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for _, row in group.iterrows():
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side_raw = str(row.get('side', '') or '').strip()
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side_key = side_raw.lower()
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if not side_key:
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side_key = 'a'
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type_val = str(row.get('type', '') or '')
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text_val = str(row.get('text', '') or '')
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mana_cost_val = str(row.get('manaCost', '') or '')
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mana_value_raw = row.get('manaValue', '')
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mana_value_val = None
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try:
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if mana_value_raw not in (None, ''):
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mana_value_val = float(mana_value_raw)
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if math.isnan(mana_value_val):
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mana_value_val = None
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except Exception:
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mana_value_val = None
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face_label = str(row.get('faceName', '') or row.get('name', '') or '')
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produces_mana = _detect_produces_mana(text_val)
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signature = (side_key, type_val, text_val)
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if signature in seen:
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continue
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seen.add(signature)
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faces.append({
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'face': face_label,
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'side': side_key,
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'type': type_val,
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'text': text_val,
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'mana_cost': mana_cost_val,
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'mana_value': mana_value_val,
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'produces_mana': produces_mana,
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'is_land': 'land' in type_val.lower(),
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'layout': str(row.get('layout', '') or ''),
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})
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if side_key in ('', 'a', 'front', 'main'):
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front_is_land = True
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layout_val = layout_val or str(row.get('layout', '') or '')
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if not faces:
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continue
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faces.sort(key=lambda face: _SIDE_PRIORITY.get(face.get('side', ''), 3))
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mapping[name] = {
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'faces': faces,
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'front_is_land': front_is_land,
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'layout': layout_val,
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}
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return mapping
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def multi_face_land_info(name: str, base_dir: str | None = None) -> Dict[str, Any]:
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return _load_multi_face_land_map(_resolved_csv_dir(base_dir)).get(name, {})
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def get_multi_face_land_faces(name: str, base_dir: str | None = None) -> List[Dict[str, str]]:
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entry = multi_face_land_info(name, base_dir)
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return list(entry.get('faces', []))
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def has_multi_face_land(name: str, base_dir: str | None = None) -> bool:
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entry = multi_face_land_info(name, base_dir)
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return bool(entry and entry.get('faces'))
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def parse_theme_tags(val) -> list[str]:
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@ -90,13 +234,49 @@ def compute_color_source_matrix(card_library: Dict[str, dict], full_df) -> Dict[
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nm = str(r.get('name', ''))
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if nm and nm not in lookup:
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lookup[nm] = r
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try:
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dfc_map = _load_multi_face_land_map(_resolved_csv_dir())
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except Exception:
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dfc_map = {}
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for name, entry in card_library.items():
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row = lookup.get(name, {})
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entry_type = str(entry.get('Card Type') or entry.get('Type') or '').lower()
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tline_full = str(row.get('type', row.get('type_line', '')) or '').lower()
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entry_type_raw = str(entry.get('Card Type') or entry.get('Type') or '')
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entry_type = entry_type_raw.lower()
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row_type_raw = ''
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if hasattr(row, 'get'):
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row_type_raw = row.get('type', row.get('type_line', '')) or ''
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tline_full = str(row_type_raw).lower()
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# Land or permanent that could produce mana via text
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is_land = ('land' in entry_type) or ('land' in tline_full)
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text_field = str(row.get('text', row.get('oracleText', '')) or '').lower()
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base_is_land = is_land
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text_field_raw = ''
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if hasattr(row, 'get'):
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text_field_raw = row.get('text', row.get('oracleText', '')) or ''
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if pd.isna(text_field_raw):
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text_field_raw = ''
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text_field_raw = str(text_field_raw)
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dfc_entry = dfc_map.get(name)
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if dfc_entry:
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faces = dfc_entry.get('faces', []) or []
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if faces:
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face_types: List[str] = []
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face_texts: List[str] = []
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for face in faces:
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type_val = str(face.get('type', '') or '')
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text_val = str(face.get('text', '') or '')
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if type_val:
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face_types.append(type_val)
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if text_val:
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face_texts.append(text_val)
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if face_types:
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joined_types = ' '.join(face_types)
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tline_full = (tline_full + ' ' + joined_types.lower()).strip()
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if face_texts:
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joined_text = ' '.join(face_texts)
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text_field_raw = (text_field_raw + ' ' + joined_text).strip()
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if face_types or face_texts:
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is_land = True
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text_field = text_field_raw.lower().replace('\n', ' ')
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# Skip obvious non-permanents (rituals etc.)
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if (not is_land) and ('instant' in entry_type or 'sorcery' in entry_type or 'instant' in tline_full or 'sorcery' in tline_full):
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continue
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@ -166,8 +346,13 @@ def compute_color_source_matrix(card_library: Dict[str, dict], full_df) -> Dict[
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col = mapping.get(base)
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if col:
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colors[col] = 1
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# Only include cards that produced at least one color
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if any(colors.values()):
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dfc_is_land = bool(dfc_entry and dfc_entry.get('faces'))
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if dfc_is_land:
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colors['_dfc_land'] = True
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if not (base_is_land or dfc_entry.get('front_is_land')):
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colors['_dfc_counts_as_extra'] = True
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produces_any_color = any(colors[c] for c in ('W', 'U', 'B', 'R', 'G', 'C'))
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if produces_any_color or colors.get('_dfc_land'):
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matrix[name] = colors
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return matrix
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@ -210,11 +395,15 @@ def compute_spell_pip_weights(card_library: Dict[str, dict], color_identity: Ite
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return {c: (pip_counts[c] / total_colored) for c in pip_counts}
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__all__ = [
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'compute_color_source_matrix',
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'compute_spell_pip_weights',
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'parse_theme_tags',
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'normalize_theme_list',
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'multi_face_land_info',
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'get_multi_face_land_faces',
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'has_multi_face_land',
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'detect_viable_multi_copy_archetypes',
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'prefer_owned_first',
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'compute_adjusted_target',
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