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| | #!/usr/bin/env python3
| | """
| | build_provenance.py — Glidr SPL Exam Question Source Provenance Database
| |
| | Traces each app question back to its source PDF exam paper.
| | Outputs:
| | - Source_Provenance.md (human-readable)
| | - Source_Provenance.json (machine-readable)
| | """
| |
| | import re
| | import os
| | import sys
| | import json
| | import unicodedata
| | from pathlib import Path
| |
| | try:
| | import PyPDF2
| | except ImportError:
| | print("ERROR: PyPDF2 not installed. Run: pip install PyPDF2")
| | sys.exit(1)
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Path constants
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | ABLAGE_BASE = Path("/Users/i052341/Daten/Cloud/04 - Ablage/Ablage 2020 - 2029/Ablage 2025/Hobbies 2025/Segelflug/Theorie/Glidr")
| | SOURCES_DIR = ABLAGE_BASE / "SOURCES"
| | QUIZ_VDS_DIR = SOURCES_DIR / "QuizVDS"
| | FR_DIR = ABLAGE_BASE / "SPL Exam Questions FR"
| | PDF_DIR = SOURCES_DIR
| |
| | OUTPUT_MD = SOURCES_DIR / "Source_Provenance.md"
| | OUTPUT_JSON = SOURCES_DIR / "Source_Provenance.json"
| |
| | # Subject number → QuizVDS filename
| | QUIZ_VDS_FILES = {
| | 10: "10 - Air Law.md",
| | 20: "20 - Aircraft General Knowledge.md",
| | 30: "30 - Flight Performance and Planning.md",
| | 40: "40 - Human Performance and Limitations.md",
| | 50: "50 - Meteorology.md",
| | 60: "60 - Navigation.md",
| | 70: "70 - Operational Procedures.md",
| | 80: "80 - Principles of Flight.md",
| | 90: "90 - Communication.md",
| | }
| |
| | # Subject number → FR app filename
| | FR_FILES = {
| | 10: "10 - Droit aérien.md",
| | 20: "20 - Connaissances générales de l'aéronef.md",
| | 30: "30 - Performances et planification du vol.md",
| | 40: "40 - Performances humaines.md",
| | 50: "50 - Météorologie.md",
| | 60: "60 - Navigation.md",
| | 70: "70 - Procédures opérationnelles.md",
| | 80: "80 - Principes du vol.md",
| | 90: "90 - Radiotéléphonie.md",
| | }
| |
| | # PDF source files (relative to SOURCES_DIR)
| | PDF_FILES = {
| | "S1C": "Examen Blanc/Exa Blanc Série_1_Communes.pdf",
| | "S1S": "Examen Blanc/Exa Blanc Série_1_Specifiques.pdf",
| | "S2": "Examen Blanc/Exa Blanc Série_2.pdf",
| | "S3": "Examen Blanc/Exa Blanc Série_3.pdf",
| | "VV": "VV/Questionnaire toutes branches VV.pdf",
| | }
| |
| | # Branch labels as they appear in the S1C/S1S solution tables
| | S1C_BRANCH_MAP = {10: "BRANCHE 10", 40: "BRANCHE 40", 50: "BRANCHE 50", 90: "BRANCHE 90"}
| | S1S_BRANCH_MAP = {20: "BRANCHE 20", 30: "BRANCHE 30", 60: "BRANCHE 60", 70: "BRANCHE 70", 80: "BRANCHE 80"}
| |
| | SUBJECT_NAMES = {
| | 10: "Air Law / Droit aérien",
| | 20: "Aircraft Knowledge / Connaissances aéronef",
| | 30: "Flight Performance / Performances vol",
| | 40: "Human Performance / Performances humaines",
| | 50: "Meteorology / Météorologie",
| | 60: "Navigation",
| | 70: "Operational Procedures / Procédures opérationnelles",
| | 80: "Principles of Flight / Principes du vol",
| | 90: "Communications / Radiotéléphonie",
| | }
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Utility: accent folding + normalisation
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def normalize(text: str) -> str:
| | """Lowercase, strip accents, keep alphanumerics and spaces only."""
| | nfkd = unicodedata.normalize("NFKD", text)
| | ascii_text = "".join(c for c in nfkd if not unicodedata.combining(c))
| | return re.sub(r"[^a-z0-9 ]", " ", ascii_text.lower())
| |
| |
| | def word_set(text: str) -> set:
| | """Return significant words as a set (accent-folded, stop-words removed)."""
| | stop = {
| | "a", "b", "c", "d", "la", "le", "les", "de", "du", "des", "un", "une",
| | "et", "ou", "en", "au", "aux", "est", "il", "elle", "on", "que", "qui",
| | "se", "sa", "son", "ce", "par", "sur", "pour", "avec", "dans", "si",
| | "ne", "pas", "plus", "the", "of", "to", "is", "in", "an", "are", "at",
| | "be", "by", "do", "for", "has", "have", "he", "it", "its", "no", "not",
| | "or", "that", "this", "was", "we", "which", "you", "your", "l", "d",
| | "j", "s", "n", "m", "y", "qu", "lorsque", "comme", "car", "mais",
| | "donc", "lors", "quel", "quelle", "quels", "quelles", "comment", "quel",
| | "peut", "doit", "doit", "sont", "ont", "ces", "lors", "aussi", "entre",
| | "selon", "lors", "apres", "avant", "dans", "vers", "sous", "jusqu"
| | }
| | words = normalize(text).split()
| | return {w for w in words if len(w) > 2 and w not in stop}
| |
| |
| | def jaccard(set_a: set, set_b: set) -> float:
| | if not set_a or not set_b:
| | return 0.0
| | intersection = len(set_a & set_b)
| | union = len(set_a | set_b)
| | return intersection / union if union else 0.0
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Step 1: Parse QuizVDS files → {tag: {question, options, correct}}
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def parse_quiz_vds() -> dict:
| | """Returns dict keyed by tag (e.g. 't10q1') with QuizVDS question data."""
| | print("\n[1/5] Parsing QuizVDS files...")
| | quiz_db = {}
| |
| | for subject_num, filename in QUIZ_VDS_FILES.items():
| | path = QUIZ_VDS_DIR / filename
| | if not path.exists():
| | print(f" WARNING: {path} not found")
| | continue
| |
| | with open(path, encoding="utf-8") as f:
| | content = f.read()
| |
| | # Split on question headers: ### Q{N}: ...
| | blocks = re.split(r"\n(?=### Q\d+:)", content)
| | count = 0
| | for block in blocks:
| | m = re.match(r"### Q(\d+):\s*(.+?)(?:\n|$)(.*?)(?=\n---|\Z)", block, re.DOTALL)
| | if not m:
| | continue
| | q_num = int(m.group(1))
| | q_text = m.group(2).strip()
| | rest = m.group(3)
| |
| | # Extract options A-D
| | options = {}
| | for opt in re.finditer(r"^- ([A-D])\)\s*(.+)$", rest, re.MULTILINE):
| | options[opt.group(1)] = opt.group(2).strip()
| |
| | # Extract correct answer
| | correct_m = re.search(r"\*\*Correct:\s*([A-D])\)\*\*", rest)
| | correct = correct_m.group(1) if correct_m else None
| |
| | tag = f"t{subject_num}q{q_num}"
| | quiz_db[tag] = {
| | "question_en": q_text,
| | "options_en": options,
| | "quiz_correct": correct,
| | }
| | count += 1
| |
| | print(f" {filename}: {count} questions parsed")
| |
| | print(f" Total QuizVDS questions: {len(quiz_db)}")
| | return quiz_db
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Step 2: Parse FR app MD files → {tag: {question_fr, options_fr, app_correct}}
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def parse_fr_questions() -> dict:
| | """Returns dict keyed by tag with French question data from app MD files."""
| | print("\n[2/5] Parsing FR app question files...")
| | fr_db = {}
| |
| | for subject_num, filename in FR_FILES.items():
| | path = FR_DIR / filename
| | if not path.exists():
| | print(f" WARNING: {path} not found")
| | continue
| |
| | with open(path, encoding="utf-8") as f:
| | content = f.read()
| |
| | # The header can be "### Q1:" OR "### Q1 :" (space before colon)
| | # Tag is always "^t{NN}q{N}" at end of header line
| | pattern = r"\n(?=### Q\d+\s*:.*\^t\d+q\d+)"
| | blocks = re.split(pattern, content)
| |
| | count = 0
| | for block in blocks:
| | # Match header with optional space before colon, and tag
| | header_m = re.match(r"### Q\d+\s*:\s*(.+?)\s*\^(t\d+q\d+)", block)
| | if not header_m:
| | continue
| | q_text = header_m.group(1).strip()
| | tag = header_m.group(2)
| |
| | # Options: various formats:
| | # "- A) text"
| | # "- [x] A) text" (correct)
| | # "- [ ] A) text" (wrong)
| | # "- **A)** text" (bold format)
| | options = {}
| | app_correct = None
| |
| | # Format 1: "- [x] A) ..." or "- [ ] A) ..."
| | for opt_m in re.finditer(r"^- \[( |x)\] ([A-D])\)\s*(.+)$", block, re.MULTILINE):
| | checked = opt_m.group(1)
| | letter = opt_m.group(2)
| | text = opt_m.group(3).strip()
| | options[letter] = text
| | if checked == "x":
| | app_correct = letter
| |
| | # Format 2: "- A) ..." (no checkbox)
| | if not options:
| | for opt_m in re.finditer(r"^- \**([A-D])\)\**\s*(.+)$", block, re.MULTILINE):
| | letter = opt_m.group(1)
| | text = opt_m.group(2).strip()
| | options[letter] = text
| |
| | # Answer from "#### Réponse\n\nX)" pattern
| | reponse_m = re.search(r"#### Réponse\s*\n+([A-D])\)", block)
| | if reponse_m:
| | app_correct = reponse_m.group(1)
| |
| | fr_db[tag] = {
| | "question_fr": q_text,
| | "options_fr": options,
| | "app_correct": app_correct,
| | "subject_num": subject_num,
| | }
| | count += 1
| |
| | print(f" {filename}: {count} questions parsed")
| |
| | total = sum(1 for _ in fr_db)
| | print(f" Total FR app questions: {total}")
| | return fr_db
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Step 3: Extract text from all PDFs, page by page
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def extract_pdf_pages() -> dict:
| | """Returns {pdf_code: [(page_num, text), ...]}"""
| | print("\n[3/5] Extracting PDF text...")
| | pdf_pages = {}
| |
| | for code, filename in PDF_FILES.items():
| | path = PDF_DIR / filename
| | if not path.exists():
| | print(f" WARNING: {path} not found")
| | pdf_pages[code] = []
| | continue
| |
| | pages = []
| | try:
| | with open(path, "rb") as f:
| | reader = PyPDF2.PdfReader(f)
| | n = len(reader.pages)
| | for i in range(n):
| | text = reader.pages[i].extract_text() or ""
| | pages.append((i + 1, text))
| | print(f" {code} ({filename}): {n} pages")
| | except Exception as e:
| | print(f" ERROR reading {filename}: {e}")
| |
| | pdf_pages[code] = pages
| |
| | return pdf_pages
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Step 3b: Extract PDF answer keys
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def parse_s1_solution_table(text: str, branch_map: dict) -> dict:
| | """
| | Parse the columnar solution table at the end of S1C and S1S PDFs.
| | Returns {subject_num: {q_num: answer_letter}}
| |
| | The table looks like:
| | BRANCHE 10 BRANCHE 40 BRANCHE 50 BRANCHE 90
| | 1. A 1. C 1. A 1. A
| | 2. C 2. A 2. C 2. B
| | ...
| | """
| | result = {}
| | # Find positions of all branch headers
| | branch_positions = []
| | for subj, label in branch_map.items():
| | idx = text.find(label)
| | if idx >= 0:
| | branch_positions.append((idx, subj))
| | if not branch_positions:
| | return result
| |
| | n_branches = len(branch_map)
| | sorted_branches = [subj for _, subj in sorted(branch_positions)]
| |
| | # The answer section starts right after the line containing the branch headers.
| | # Find the end of that header line (next \n after the last branch label).
| | last_header_pos = max(p[0] for p in branch_positions)
| | # Find the end of last label
| | last_label = ""
| | for subj, label in branch_map.items():
| | if text.find(label) == last_header_pos:
| | last_label = label
| | break
| | # Walk forward to end of header line
| | scan_pos = last_header_pos + len(last_label)
| | newline_pos = text.find("\n", scan_pos)
| | if newline_pos < 0:
| | return result
| | answer_section = text[newline_pos + 1:]
| |
| | # Extract all "N. X" patterns
| | all_answers = re.findall(r"\b(\d+)\.\s+([A-D])\b", answer_section)
| |
| | # Answers interleave: q1_b1, q1_b2, ..., q2_b1, q2_b2, ...
| | branch_answers = {subj: {} for subj in sorted_branches}
| | for i, (q_str, letter) in enumerate(all_answers):
| | q_num = int(q_str)
| | branch_idx = i % n_branches
| | if branch_idx < len(sorted_branches):
| | subj = sorted_branches[branch_idx]
| | branch_answers[subj][q_num] = letter
| |
| | return branch_answers
| |
| |
| | def parse_vv_solution_answers(pages: list) -> dict:
| | """
| | Parse 'Solution question N : X' lines from VV PDF.
| | Returns {q_num: answer_letter}
| | """
| | answers = {}
| | for page_num, text in pages:
| | for m in re.finditer(r"Solution question\s+(\d+)\s*:\s*([A-D])", text, re.IGNORECASE):
| | q_num = int(m.group(1))
| | letter = m.group(2).upper()
| | answers[q_num] = letter
| | return answers
| |
| |
| | def extract_pdf_answer_keys(pdf_pages: dict) -> dict:
| | """
| | Returns:
| | S1C/S1S: {pdf_code: {subject_num: {q_num: letter}}}
| | VV: {pdf_code: {None: {q_num: letter}}}
| | """
| | print("\n[3b] Extracting PDF answer keys...")
| | keys = {}
| |
| | # S1C
| | if "S1C" in pdf_pages:
| | full_text = "\n".join(text for _, text in pdf_pages["S1C"])
| | answers = parse_s1_solution_table(full_text, S1C_BRANCH_MAP)
| | keys["S1C"] = answers
| | for subj, ans in answers.items():
| | print(f" S1C branch {subj}: {len(ans)} answers parsed")
| |
| | # S1S
| | if "S1S" in pdf_pages:
| | full_text = "\n".join(text for _, text in pdf_pages["S1S"])
| | answers = parse_s1_solution_table(full_text, S1S_BRANCH_MAP)
| | keys["S1S"] = answers
| | for subj, ans in answers.items():
| | print(f" S1S branch {subj}: {len(ans)} answers parsed")
| |
| | keys["S2"] = {}
| | keys["S3"] = {}
| |
| | # VV
| | if "VV" in pdf_pages:
| | vv_answers = parse_vv_solution_answers(pdf_pages["VV"])
| | keys["VV"] = {None: vv_answers}
| | print(f" VV: {len(vv_answers)} answers parsed")
| |
| | return keys
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Step 4: Build question-level chunks from PDFs
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def build_pdf_question_chunks(pdf_pages: dict) -> list:
| | """
| | Split PDF pages into individual question chunks for better matching.
| |
| | For each PDF, we extract chunks of text corresponding to individual questions.
| | We split on patterns like:
| | - "1." / "2." at start of line (S1C, S1S, S2, S3: numbered questions)
| | - "Solution question N :" boundaries (VV)
| |
| | Returns list of:
| | (pdf_code, page_num, chunk_q_num, chunk_text, chunk_word_set)
| | """
| | chunks = []
| |
| | for code, pages in pdf_pages.items():
| | # Combine all text per PDF but track page boundaries
| | all_text = ""
| | page_breaks = [] # [(char_offset, page_num)]
| | for page_num, text in pages:
| | page_breaks.append((len(all_text), page_num))
| | all_text += text + "\n"
| |
| | def char_to_page(offset):
| | """Return page_num for a character offset."""
| | for i in range(len(page_breaks) - 1, -1, -1):
| | if offset >= page_breaks[i][0]:
| | return page_breaks[i][1]
| | return 1
| |
| | if code in ("S1C", "S1S", "S2", "S3"):
| | # Split on numbered questions: line starting with "N." where N is 1-99
| | # followed by a space and capital letter (French question text)
| | splits = list(re.finditer(
| | r"(?:^|\n)\s*(\d{1,2})\.\s+([A-ZÀÂÄÉÈÊËÎÏÔÙÛÜÇ])",
| | all_text
| | ))
| | for i, m in enumerate(splits):
| | q_num = int(m.group(1))
| | start = m.start()
| | end = splits[i + 1].start() if i + 1 < len(splits) else len(all_text)
| | chunk = all_text[start:end].strip()
| | if len(chunk) > 30:
| | pg = char_to_page(start)
| | ws = word_set(chunk)
| | if len(ws) >= 4:
| | chunks.append((code, pg, q_num, chunk, ws))
| |
| | elif code == "VV":
| | # VV: each question is preceded by "Solution question N-1 : X\n"
| | # and has its own question block before the next solution marker
| | # Split on subject headers ("10 Droit aérien", "20 ...", etc.) or
| | # on "Solution question N :" markers combined with question text
| | # Strategy: split on "Solution question N :" boundaries
| |
| | # Find all solution markers
| | sol_markers = list(re.finditer(
| | r"Solution question\s+(\d+)\s*:\s*[A-D]",
| | all_text,
| | re.IGNORECASE
| | ))
| |
| | for i, m in enumerate(sol_markers):
| | q_num = int(m.group(1))
| | # The question text appears BEFORE this solution marker
| | # (between previous solution marker end and this one)
| | prev_end = sol_markers[i - 1].end() if i > 0 else 0
| | chunk = all_text[prev_end:m.start()].strip()
| | # Remove page headers ("10 Droit aérien", "Page N", etc.)
| | chunk = re.sub(r"^\s*\d{2}\s+[A-ZÀ-Ü].{0,40}\n", "", chunk, flags=re.MULTILINE)
| | chunk = re.sub(r"^\s*(?:Page|Edition)\s+\d+.*\n", "", chunk, flags=re.MULTILINE)
| | if len(chunk) > 20:
| | pg = char_to_page(m.start())
| | ws = word_set(chunk)
| | if len(ws) >= 4:
| | chunks.append((code, pg, q_num, chunk, ws))
| |
| | print(f" PDF question chunks built: {len(chunks)} total")
| | return chunks
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Step 5: Match FR questions against PDF chunks
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def find_best_pdf_match(question_text: str, options: dict, page_index: list,
| | threshold: float = 0.15):
| | """
| | Find the best matching PDF chunk for a question.
| | Returns (pdf_code, page_num, pdf_q_num, score) or (None, None, None, 0.0)
| | """
| | combined = question_text
| | for opt_text in options.values():
| | combined += " " + opt_text
| | q_words = word_set(combined)
| |
| | if not q_words:
| | return None, None, None, 0.0
| |
| | best_score = 0.0
| | best_code = None
| | best_page = None
| | best_qnum = None
| |
| | for code, page_num, chunk_q_num, _, chunk_words in page_index:
| | score = jaccard(q_words, chunk_words)
| | if score > best_score:
| | best_score = score
| | best_code = code
| | best_page = page_num
| | best_qnum = chunk_q_num
| |
| | if best_score < threshold:
| | return None, None, None, best_score
| |
| | return best_code, best_page, best_qnum, best_score
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Step 5b: Get PDF answer key answer for matched question
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def get_pdf_answer(subject_num: int, pdf_code: str, pdf_q_num: int,
| | pdf_keys: dict) -> str | None:
| | """Look up the answer key answer for a matched PDF question."""
| | if pdf_code == "VV":
| | return pdf_keys.get("VV", {}).get(None, {}).get(pdf_q_num)
| | elif pdf_code in ("S1C", "S1S"):
| | subj_answers = pdf_keys.get(pdf_code, {}).get(subject_num, {})
| | return subj_answers.get(pdf_q_num)
| | # S2, S3: no keys
| | return None
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Step 6: Build provenance database
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def build_provenance(quiz_db: dict, fr_db: dict, pdf_pages: dict, pdf_keys: dict) -> tuple:
| | """Main matching loop. Returns (records, stats)."""
| | print("\n[4/5] Building PDF question chunk index and matching...")
| | chunk_index = build_pdf_question_chunks(pdf_pages)
| |
| | records = []
| | tags = sorted(fr_db.keys(), key=lambda t: (
| | int(re.search(r't(\d+)', t).group(1)),
| | int(re.search(r'q(\d+)', t).group(1))
| | ))
| | total = len(tags)
| | matched = 0
| | unmatched = 0
| | answer_mismatches = 0
| |
| | for i, tag in enumerate(tags):
| | if i % 200 == 0:
| | print(f" Progress: {i}/{total} tags processed...")
| |
| | fr_data = fr_db[tag]
| | quiz_data = quiz_db.get(tag, {})
| | subject_num = fr_data["subject_num"]
| |
| | question_fr = fr_data.get("question_fr", "")
| | options_fr = fr_data.get("options_fr", {})
| | app_correct = fr_data.get("app_correct")
| | quiz_correct = quiz_data.get("quiz_correct")
| |
| | # Find best PDF chunk match
| | pdf_code, page_num, pdf_q_num, score = find_best_pdf_match(
| | question_fr, options_fr, chunk_index
| | )
| |
| | is_matched = pdf_code is not None
| | if is_matched:
| | matched += 1
| | else:
| | unmatched += 1
| |
| | # Get PDF answer key answer
| | pdf_answer = None
| | if is_matched and pdf_code:
| | pdf_answer = get_pdf_answer(subject_num, pdf_code, pdf_q_num, pdf_keys)
| |
| | # Detect answer mismatches.
| | #
| | # IMPORTANT CAVEAT: Answer option order (A/B/C/D) is frequently shuffled
| | # between exam papers and the app's FR version. A letter mismatch does NOT
| | # necessarily mean the wrong answer — the same content answer may appear
| | # at a different letter. We flag mismatches as informational; manual
| | # review is required to confirm genuine wrong answers.
| | mismatch_flags = []
| | quiz_shuffled = (quiz_correct and app_correct and quiz_correct != app_correct)
| | if quiz_shuffled:
| | mismatch_flags.append(f"QUIZ_VS_APP:{quiz_correct}!={app_correct}")
| | if pdf_answer and app_correct and pdf_answer != app_correct:
| | mismatch_flags.append(f"PDF_VS_APP:{pdf_answer}!={app_correct}")
| | if pdf_answer and quiz_correct and pdf_answer != quiz_correct:
| | mismatch_flags.append(f"PDF_VS_QUIZ:{pdf_answer}!={quiz_correct}")
| |
| | # Count as flagged mismatch when PDF key disagrees with app answer
| | # (these require manual verification — may be option-shuffle or real error)
| | has_real_mismatch = bool(pdf_answer and app_correct and pdf_answer != app_correct)
| |
| | if has_real_mismatch:
| | answer_mismatches += 1
| |
| | record = {
| | "tag": tag,
| | "subject_num": subject_num,
| | "question_fr": question_fr,
| | "options_fr": options_fr,
| | "app_correct": app_correct,
| | "quiz_correct": quiz_correct,
| | "pdf_source": pdf_code,
| | "pdf_page": page_num,
| | "pdf_q_num": pdf_q_num,
| | "match_score": round(score, 4),
| | "pdf_answer": pdf_answer,
| | "mismatch_flags": mismatch_flags,
| | "has_mismatch": has_real_mismatch,
| | }
| | records.append(record)
| |
| | print(f"\n Matched: {matched}/{total} ({100*matched//total if total else 0}%)")
| | print(f" Unmatched: {unmatched}/{total}")
| | print(f" Answer mismatches found: {answer_mismatches}")
| |
| | stats = {
| | "total": total,
| | "matched": matched,
| | "unmatched": unmatched,
| | "answer_mismatches": answer_mismatches
| | }
| | return records, stats
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Step 7: Write outputs
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def write_outputs(records: list, stats: dict):
| | print("\n[5/5] Writing output files...")
| |
| | # ── JSON ──────────────────────────────────────────────────────────────────
| | with open(OUTPUT_JSON, "w", encoding="utf-8") as f:
| | json.dump({"stats": stats, "records": records}, f, ensure_ascii=False, indent=2)
| | print(f" JSON written: {OUTPUT_JSON}")
| |
| | # ── Markdown ──────────────────────────────────────────────────────────────
| | lines = []
| | lines.append("# Source Provenance Database — Glidr SPL Exam Questions")
| | lines.append("")
| | lines.append(f"Generated: 2026-04-12 | Total questions: {stats['total']} | "
| | f"Matched: {stats['matched']} | Unmatched: {stats['unmatched']} | "
| | f"Answer mismatches: {stats['answer_mismatches']}")
| | lines.append("")
| | lines.append("## Legend")
| | lines.append("")
| | lines.append("| Column | Description |")
| | lines.append("|--------|-------------|")
| | lines.append("| Tag | Question tag (e.g. t10q1) |")
| | lines.append("| PDF Source | Which exam paper (S1C/S1S/S2/S3/VV) |")
| | lines.append("| Page | PDF page number |")
| | lines.append("| PDF Q# | Question number within that PDF |")
| | lines.append("| Score | Jaccard word-overlap similarity (≥0.15 = match) |")
| | lines.append("| App | Current app correct answer |")
| | lines.append("| Quiz | QuizVDS original answer (EN import) |")
| | lines.append("| PDF Key | Answer from PDF solution page |")
| | lines.append("| Flags | Mismatch warnings |")
| | lines.append("")
| | lines.append("## PDF Sources")
| | lines.append("")
| | lines.append("| Code | File |")
| | lines.append("|------|------|")
| | for code, fname in PDF_FILES.items():
| | lines.append(f"| {code} | {fname} |")
| | lines.append("")
| |
| | # Group by subject
| | from itertools import groupby
| | records_sorted = sorted(records, key=lambda r: (
| | r["subject_num"],
| | int(re.search(r'q(\d+)', r["tag"]).group(1))
| | ))
| |
| | for subject_num, group in groupby(records_sorted, key=lambda r: r["subject_num"]):
| | group_list = list(group)
| | subject_name = SUBJECT_NAMES.get(subject_num, f"Subject {subject_num}")
| | matched_in_group = sum(1 for r in group_list if r["pdf_source"])
| | lines.append(f"## Subject {subject_num}: {subject_name}")
| | lines.append("")
| | lines.append(f"Total: {len(group_list)} questions | Matched: {matched_in_group}")
| | lines.append("")
| | lines.append("| Tag | PDF | Page | PDF Q# | Score | App | Quiz | PDF Key | Flags |")
| | lines.append("|-----|-----|------|--------|-------|-----|------|---------|-------|")
| |
| | for r in group_list:
| | tag = r["tag"]
| | pdf_src = r["pdf_source"] or "—"
| | page = str(r["pdf_page"]) if r["pdf_page"] else "—"
| | pdf_qn = str(r["pdf_q_num"]) if r["pdf_q_num"] else "—"
| | score = f"{r['match_score']:.3f}"
| | app = r["app_correct"] or "?"
| | quiz = r["quiz_correct"] or "—"
| | pdf_key = r["pdf_answer"] or "—"
| | flags = " ".join(r["mismatch_flags"]) if r["mismatch_flags"] else ""
| | row = f"| {tag} | {pdf_src} | {page} | {pdf_qn} | {score} | {app} | {quiz} | {pdf_key} | {flags} |"
| | lines.append(row)
| |
| | lines.append("")
| |
| | # Flagged mismatches: PDF key letter differs from app answer letter
| | mismatches = [r for r in records if r["has_mismatch"]]
| | if mismatches:
| | lines.append("## Flagged Answer Letter Differences (PDF Key vs App Answer)")
| | lines.append("")
| | lines.append("> **IMPORTANT**: Answer option order (A/B/C/D) is frequently shuffled between")
| | lines.append("> exam papers and the app's FR version. A letter difference does NOT necessarily")
| | lines.append("> indicate a wrong answer — the same correct content may appear at a different")
| | lines.append("> letter. Manual review is required to confirm genuine errors.")
| | lines.append("")
| | lines.append(f"Found {len(mismatches)} questions with letter disagreements:")
| | lines.append("")
| | lines.append("| Tag | Score | Question (FR, truncated) | App | PDF Key | PDF Source |")
| | lines.append("|-----|-------|--------------------------|-----|---------|------------|")
| | for r in sorted(mismatches, key=lambda r: -r["match_score"]):
| | q_short = r["question_fr"][:55].replace("|", "/")
| | app = r["app_correct"] or "?"
| | pdf_key = r["pdf_answer"] or "—"
| | src = f"{r['pdf_source']} p{r['pdf_page']}" if r['pdf_source'] else "—"
| | lines.append(f"| {r['tag']} | {r['match_score']:.3f} | {q_short}… | {app} | {pdf_key} | {src} |")
| | lines.append("")
| |
| | # Unmatched summary
| | unmatched_list = [r for r in records if not r["pdf_source"]]
| | if unmatched_list:
| | lines.append("## Unmatched Questions (score < 0.15)")
| | lines.append("")
| | lines.append(f"Found {len(unmatched_list)} questions with no strong PDF match:")
| | lines.append("")
| | lines.append("| Tag | Best Score | Question (FR, truncated) |")
| | lines.append("|-----|------------|--------------------------|")
| | for r in sorted(unmatched_list, key=lambda r: r["tag"]):
| | q_short = r["question_fr"][:70].replace("|", "/")
| | lines.append(f"| {r['tag']} | {r['match_score']:.3f} | {q_short} |")
| | lines.append("")
| |
| | with open(OUTPUT_MD, "w", encoding="utf-8") as f:
| | f.write("\n".join(lines))
| | print(f" Markdown written: {OUTPUT_MD}")
| |
| |
| | # ─────────────────────────────────────────────────────────────────────────────
| | # Main
| | # ─────────────────────────────────────────────────────────────────────────────
| |
| | def main():
| | print("=" * 70)
| | print("Glidr Source Provenance Builder")
| | print("=" * 70)
| |
| | quiz_db = parse_quiz_vds()
| | fr_db = parse_fr_questions()
| | pdf_pages = extract_pdf_pages()
| | pdf_keys = extract_pdf_answer_keys(pdf_pages)
| | records, stats = build_provenance(quiz_db, fr_db, pdf_pages, pdf_keys)
| | write_outputs(records, stats)
| |
| | print("\n" + "=" * 70)
| | print("PUBLISH COMPLETE")
| | print(f" Total questions: {stats['total']}")
| | print(f" Matched to PDF: {stats['matched']} ({100*stats['matched']//stats['total'] if stats['total'] else 0}%)")
| | print(f" Unmatched: {stats['unmatched']}")
| | print(f" Answer mismatches: {stats['answer_mismatches']}")
| | print(f"\n Output MD: {OUTPUT_MD}")
| | print(f" Output JSON: {OUTPUT_JSON}")
| | print("=" * 70)
| |
| |
| | if __name__ == "__main__":
| | main()
|
|
