# -*- coding: utf-8 -*- # LibreOffice Calc macro: NV_MASTER-Abgleich, Pandas+odf, Cache, Farben # Pfade: BASE_DIR muss auf das Verzeichnis zeigen, in dem NV_MASTER.ods + Makro liegen. # Speichern: /home/jarnold/.config/libreoffice/4/user/Scripts/python/NV Abgleich Makro/mapper_macro.py import os import re import json import traceback # UNO-Context wird zur Laufzeit zur Verfügung gestellt (XSCRIPTCONTEXT) # Third-party libs: pandas, odfpy, optional: spacy, rapidfuzz try: import pandas as pd PANDAS_AVAILABLE = True except Exception: PANDAS_AVAILABLE = False try: import spacy nlp = spacy.load("de_core_news_sm") SPACY_AVAILABLE = True except Exception: SPACY_AVAILABLE = False nlp = None try: from rapidfuzz import fuzz RAPIDFUZZ_AVAILABLE = True except Exception: RAPIDFUZZ_AVAILABLE = False from difflib import SequenceMatcher # ------------------------ # Konfiguration # ------------------------ BASE_DIR = "/home/jarnold/.config/libreoffice/4/user/Scripts/python/NV Abgleich Makro" NV_MASTER_PATH = os.path.join(BASE_DIR, "NV_MASTER.ods") LOG_FILE = os.path.join(BASE_DIR, "mapper_macro.log") CACHE_FILE = os.path.join(BASE_DIR, "mapper_cache.json") STOPWORDS = {"mit","ohne","der","die","das","ein","eine","und","zu","von","im","in","auf","an","als","bei","für","aus","dem","den","des","eines","einer"} CONF_THRESHOLD = 0.75 # Basis-Schwelle für Vorschläge # ------------------------ # Utilities: Logging & safe I/O # ------------------------ def log(msg): try: with open(LOG_FILE, "a", encoding="utf-8") as f: f.write(msg + "\n") except Exception: pass # ------------------------ # Cache laden # ------------------------ try: if os.path.exists(CACHE_FILE): with open(CACHE_FILE, "r", encoding="utf-8") as f: CACHE = json.load(f) else: CACHE = {} except Exception: CACHE = {} # ------------------------ # Text-Normalisierung & Lemma # ------------------------ def normalize_text(s): if not s: return "" s = str(s).strip().lower() s = re.sub(r"[\(\)\[\]\"'\\;:\?!,\.]", "", s) s = re.sub(r"\s+", " ", s) return s lemma_cache = {} def lemmatize_term(term): term_norm = normalize_text(term) if term_norm in lemma_cache: return lemma_cache[term_norm] if SPACY_AVAILABLE and nlp: try: doc = nlp(term_norm) lemma = " ".join([token.lemma_ for token in doc]) except Exception: lemma = term_norm else: lemma = term_norm lemma_cache[term_norm] = lemma return lemma # ------------------------ # NV_MASTER robust laden (pandas + odf) # ------------------------ def build_norm_index(nv_path): norm_dict = {} # normalized_name -> list of entries (Name, ID, Sheet) lemma_index = {} # lemma -> list of entries if not PANDAS_AVAILABLE: log("Pandas nicht verfügbar. NV_MASTER kann nicht zuverlässig gelesen werden.") return norm_dict, lemma_index try: sheets = pd.read_excel(nv_path, sheet_name=None, engine="odf") except Exception as e: log(f"Fehler beim Einlesen von NV_MASTER mit pandas: {e}") return norm_dict, lemma_index for sheet_name, df in sheets.items(): if str(sheet_name).strip().lower() == "master": continue # normalize columns names to find ID and Wort columns df = df.fillna("") # leere Zellen als "" cols = [str(c).strip().lower() for c in df.columns] # try to find columns id_col = None word_col = None for i, c in enumerate(cols): if "id" in c: id_col = df.columns[i] if "wort" in c or "vokabel" in c: word_col = df.columns[i] # fallback: if not found, try first/last if word_col is None and len(df.columns) >= 1: word_col = df.columns[-1] if id_col is None and len(df.columns) >= 1: id_col = df.columns[0] current_parent_id = None for _, row in df.iterrows(): id_val = str(row[id_col]).strip() if id_col in df.columns else "" word_val = str(row[word_col]).strip() if word_col in df.columns else "" # if row defines an ID, set as current parent if id_val: current_parent_id = id_val # skip empty word cells if not word_val: continue norm_name = normalize_text(word_val) lemma = lemmatize_term(word_val) entry = {"Name": word_val.strip(), "ID": current_parent_id or "", "Sheet": sheet_name} # add to norm_dict by normalized name (exact matching) norm_dict.setdefault(norm_name, []).append(entry) # add to lemma_index lemma_index.setdefault(lemma, []).append(entry) log(f"NV_MASTER geladen ({NV_MASTER_PATH}). Begriffe: {sum(len(v) for v in norm_dict.values())}") return norm_dict, lemma_index # ------------------------ # Matching: exakter Treffer, Lemma-Treffer, Fuzzy-Vorschläge # ------------------------ def fuzzy_score(a, b): if RAPIDFUZZ_AVAILABLE: try: return fuzz.token_set_ratio(a, b) / 100.0 except Exception: return 0.0 else: try: return SequenceMatcher(None, a.lower(), b.lower()).ratio() except Exception: return 0.0 def get_suggestions_for_term(term_lemma, norm_dict, lemma_index, top_n=None, threshold=CONF_THRESHOLD): # collect candidates from lemma_index keys and norm_dict keys candidates = [] # iterate over lemma_index keys for candidate names for key_lemma, entries in lemma_index.items(): score = fuzzy_score(term_lemma, key_lemma) if key_lemma.startswith(term_lemma): score = min(score + 0.1, 1.0) if score >= threshold: for e in entries: candidates.append((score, e["Name"], e["ID"])) # also check norm_dict keys (exact-normalized names) as additional candidates for norm_key, entries in norm_dict.items(): score = fuzzy_score(term_lemma, norm_key) if norm_key.startswith(term_lemma): score = min(score + 0.1, 1.0) if score >= threshold: for e in entries: candidates.append((score, e["Name"], e["ID"])) # sort by score descending candidates.sort(key=lambda t: t[0], reverse=True) # unique by (Name, ID) preserve score order seen = set() results = [] for score, name, id_ in candidates: key = (name, id_) if key in seen: continue seen.add(key) results.append({"score": score, "name": name, "id": id_}) # return all candidates (no limit) as "Name (ID)" return [f'{r["name"]} ({r["id"]})' if r["id"] else r["name"] for r in results] def map_term_with_indexes(term, norm_dict, lemma_index): term_norm = normalize_text(term) term_lemma = lemmatize_term(term) # cache lookup if term_lemma in CACHE: return CACHE[term_lemma]["hits"], CACHE[term_lemma]["suggestions"], CACHE[term_lemma]["ids"] hits = [] suggestions = [] ids = [] # 1) exact normalized name match if term_norm in norm_dict: for e in norm_dict[term_norm]: hits.append(e["Name"]) if e["ID"]: ids.append(e["ID"]) # 2) lemma match (if not already hits) if not hits and term_lemma in lemma_index: for e in lemma_index[term_lemma]: hits.append(e["Name"]) if e["ID"]: ids.append(e["ID"]) # 3) suggestions via fuzzy (always compute even if hits exist, but suggestions empty if exact) suggs = get_suggestions_for_term(term_lemma, norm_dict, lemma_index, top_n=None, threshold=CONF_THRESHOLD) # If there are exact hits, we still may present suggestions (user wanted unlimited), but suggestions are secondary suggestions = suggs # deduplicate lists preserving order def unique_preserve(seq): seen = set() out = [] for x in seq: if x not in seen: seen.add(x) out.append(x) return out hits = unique_preserve(hits) suggestions = unique_preserve(suggestions) ids = unique_preserve(ids) # cache result CACHE[term_lemma] = {"hits": hits, "suggestions": suggestions, "ids": ids} return hits, suggestions, ids # ------------------------ # Haupt-Makro # ------------------------ def run_mapper_macro(): try: # UNO doc/sheet doc = XSCRIPTCONTEXT.getDocument() sheet = doc.CurrentController.ActiveSheet cursor = sheet.createCursor() cursor.gotoStartOfUsedArea(False) cursor.gotoEndOfUsedArea(True) data_range = cursor.getRangeAddress() except Exception as e: log("Fehler: konnte Dokument/Sheet nicht öffnen: " + str(e)) return # find header row and Objektbeschreibung column (search first 5 rows) header_row = None objekt_col = None max_col = data_range.EndColumn for r in range(0, min(5, data_range.EndRow+1)): for c in range(0, max_col+1): try: val = str(sheet.getCellByPosition(c, r).String).strip().lower() except Exception: val = "" if val == "objektbeschreibung": header_row = r objekt_col = c break if objekt_col is not None: break if objekt_col is None: log("Spalte 'Objektbeschreibung' nicht gefunden. Abbruch.") return # determine or create result columns: search if exist anywhere; otherwise append at right end existing = {} for c in range(0, data_range.EndColumn+1): try: h = str(sheet.getCellByPosition(c, header_row).String).strip() except Exception: h = "" if h == "Norm_Treffer": existing["Norm_Treffer"] = c if h == "Norm_Vorschlag": existing["Norm_Vorschlag"] = c if h == "Norm_ID": existing["Norm_ID"] = c # append columns at right end if missing last_col = data_range.EndColumn if "Norm_Treffer" not in existing: last_col += 1 existing["Norm_Treffer"] = last_col try: sheet.getCellByPosition(last_col, header_row).String = "Norm_Treffer" except Exception: pass if "Norm_Vorschlag" not in existing: last_col += 1 existing["Norm_Vorschlag"] = last_col try: sheet.getCellByPosition(last_col, header_row).String = "Norm_Vorschlag" except Exception: pass if "Norm_ID" not in existing: last_col += 1 existing["Norm_ID"] = last_col try: sheet.getCellByPosition(last_col, header_row).String = "Norm_ID" except Exception: pass norm_tr_col = existing["Norm_Treffer"] norm_sug_col = existing["Norm_Vorschlag"] norm_id_col = existing["Norm_ID"] # Build norm indexes norm_dict, lemma_index = build_norm_index(NV_MASTER_PATH) if not norm_dict and not lemma_index: log("NV_MASTER leer oder nicht lesbar. Abbruch.") return # colors GREEN = 0xADFF2F YELLOW = 0xFFA500 RED = 0xCC0000 # iterate rows rows_processed = 0 for r in range(header_row + 1, data_range.EndRow + 1): try: cell = sheet.getCellByPosition(objekt_col, r) txt = str(cell.String).strip() if not txt: # clear any previous outputs? keep existing per spec; skip empty continue # tokenize: split by commas first, then whitespace; filter stopwords and pure numbers clauses = [c.strip() for c in re.split(r",", txt) if c.strip()] terms = [] for cl in clauses: parts = [p.strip() for p in re.split(r"\s+", cl) if p.strip()] for p in parts: if p.lower() in STOPWORDS: continue if re.fullmatch(r"\d+", p): continue terms.append(p) # for each term, get hits/suggestions/ids row_hits = [] row_sugs = [] row_ids = [] any_unmapped = False # at least one term without hit and without suggestion # We will record for each term for term in terms: hits, sugs, ids = map_term_with_indexes(term, norm_dict, lemma_index) if hits: row_hits.extend(hits) if sugs: row_sugs.extend(sugs) if ids: row_ids.extend(ids) if (not hits) and (not sugs): any_unmapped = True # deduplicate preserving order def uniq(seq): seen = set() out = [] for x in seq: if x not in seen: seen.add(x) out.append(x) return out row_hits = uniq(row_hits) row_sugs = uniq(row_sugs) row_ids = uniq(row_ids) # write outputs (unlimited lists, joined with " | ") try: sheet.getCellByPosition(norm_tr_col, r).String = " | ".join(row_hits) sheet.getCellByPosition(norm_sug_col, r).String = " | ".join(row_sugs) sheet.getCellByPosition(norm_id_col, r).String = " | ".join(row_ids) except Exception: pass # Coloring rules per new spec: # - Objektbeschreibung cell: RED if any_unmapped else no change (we do not color green/yellow here) # - Norm_Treffer cell: GREEN if all terms matched (i.e., terms non-empty and no term unmapped and at least one hit per term) # - Norm_Vorschlag cell: YELLOW if at least one suggestion exists # Determine "all matched": terms non-empty and every term has at least one hit (we approximated by checking any_unmapped and hits length) all_matched = False if terms: # all_matched if no term without hit and there is at least one hit overall if (not any_unmapped) and row_hits: all_matched = True # apply colors try: if any_unmapped: cell.CellBackColor = RED else: # clear red if previously set? We'll leave unchanged if not set. Optionally set to default 16777215 (white) pass # Norm_Treffer coloring tr_cell = sheet.getCellByPosition(norm_tr_col, r) if all_matched: tr_cell.CellBackColor = GREEN else: # clear color if needed -> set to white tr_cell.CellBackColor = 0xFFFFFF # Norm_Vorschlag coloring sug_cell = sheet.getCellByPosition(norm_sug_col, r) if row_sugs: sug_cell.CellBackColor = YELLOW else: sug_cell.CellBackColor = 0xFFFFFF except Exception: pass rows_processed += 1 except Exception as e: # continue processing other rows; log once log(f"Fehler in Zeile {r}: {e}") # persist cache try: with open(CACHE_FILE, "w", encoding="utf-8") as f: json.dump(CACHE, f, ensure_ascii=False, indent=2) except Exception: pass log(f"run_mapper_macro fertig. Zeilen verarbeitet: {rows_processed}") # Export for LO g_exportedScripts = (run_mapper_macro,)