366 lines
12 KiB
Python
366 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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# LibreOffice Calc macro: NV_MASTER-Abgleich, Pandas+odf, Cache, Farben
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# Speicherort: /home/jarnold/.config/libreoffice/4/user/Scripts/python/Vokabular_Abgleich_Makro/mapper_macro_2.1.py
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import os
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import re
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import json
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import traceback
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# UNO-Context wird zur Laufzeit zur Verfügung gestellt (XSCRIPTCONTEXT)
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try:
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import pandas as pd
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PANDAS_AVAILABLE = True
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except Exception:
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PANDAS_AVAILABLE = False
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try:
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import spacy
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nlp = spacy.load("de_core_news_sm")
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SPACY_AVAILABLE = True
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except Exception:
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SPACY_AVAILABLE = False
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nlp = None
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try:
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from rapidfuzz import fuzz
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RAPIDFUZZ_AVAILABLE = True
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except Exception:
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RAPIDFUZZ_AVAILABLE = False
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from difflib import SequenceMatcher
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# ------------------------
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# Konfiguration
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# ------------------------
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BASE_DIR = "/home/jarnold/.config/libreoffice/4/user/Scripts/python/Vokabular_Abgleich_Makro"
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NV_MASTER_PATH = os.path.join(BASE_DIR, "NV_MASTER.ods")
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LOG_FILE = os.path.join(BASE_DIR, "mapper_macro_2.1.log")
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CACHE_FILE = os.path.join(BASE_DIR, "mapper_cache_2.1.json")
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STOPWORDS = {"mit","ohne","der","die","das","ein","eine","und","zu","von","im","in","auf","an","als","bei","für","aus","dem","den","des","eines","einer"}
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CONF_THRESHOLD = 0.75 # Basis-Schwelle für Vorschläge
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# ------------------------
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# Utilities: Logging & safe I/O
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# ------------------------
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def log(msg):
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try:
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with open(LOG_FILE, "a", encoding="utf-8") as f:
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f.write(msg + "\n")
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except Exception:
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pass
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# ------------------------
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# Cache laden
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# ------------------------
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try:
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if os.path.exists(CACHE_FILE):
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with open(CACHE_FILE, "r", encoding="utf-8") as f:
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CACHE = json.load(f)
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else:
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CACHE = {}
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except Exception:
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CACHE = {}
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# ------------------------
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# Text-Normalisierung & Lemma
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# ------------------------
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def normalize_text(s):
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if not s:
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return ""
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s = str(s).strip().lower()
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s = re.sub(r"[\(\)\[\]\"'\\;:\?!,\.]", "", s)
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s = re.sub(r"\s+", " ", s)
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return s
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lemma_cache = {}
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def lemmatize_term(term):
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term_norm = normalize_text(term)
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if term_norm in lemma_cache:
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return lemma_cache[term_norm]
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if SPACY_AVAILABLE and nlp:
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try:
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doc = nlp(term_norm)
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lemma = " ".join([token.lemma_ for token in doc])
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except Exception:
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lemma = term_norm
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else:
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lemma = term_norm
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lemma_cache[term_norm] = lemma
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return lemma
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# ------------------------
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# NV_MASTER robust laden (pandas + odf)
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# ------------------------
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def build_norm_index(nv_path):
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norm_dict = {} # normalized_name -> list of entries (Name, ID, Sheet)
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lemma_index = {} # lemma -> list of entries
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if not PANDAS_AVAILABLE:
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log("Pandas nicht verfügbar. NV_MASTER kann nicht zuverlässig gelesen werden.")
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return norm_dict, lemma_index
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try:
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sheets = pd.read_excel(nv_path, sheet_name=None, engine="odf")
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except Exception as e:
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log(f"Fehler beim Einlesen von NV_MASTER mit pandas: {e}")
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return norm_dict, lemma_index
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for sheet_name, df in sheets.items():
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if str(sheet_name).strip().lower() == "master":
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continue
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df = df.fillna("")
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cols = [str(c).strip().lower() for c in df.columns]
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id_col = None
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word_col = None
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for i, c in enumerate(cols):
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if "id" in c:
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id_col = df.columns[i]
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if "wort" in c or "vokabel" in c:
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word_col = df.columns[i]
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if word_col is None and len(df.columns) >= 1:
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word_col = df.columns[-1]
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if id_col is None and len(df.columns) >= 1:
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id_col = df.columns[0]
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current_parent_id = None
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for _, row in df.iterrows():
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id_val = str(row[id_col]).strip() if id_col in df.columns else ""
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word_val = str(row[word_col]).strip() if word_col in df.columns else ""
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if id_val:
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current_parent_id = id_val
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if not word_val:
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continue
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norm_name = normalize_text(word_val)
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lemma = lemmatize_term(word_val)
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entry = {"Name": word_val.strip(), "ID": current_parent_id or "", "Sheet": sheet_name}
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norm_dict.setdefault(norm_name, []).append(entry)
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lemma_index.setdefault(lemma, []).append(entry)
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log(f"NV_MASTER geladen ({NV_MASTER_PATH}). Begriffe: {sum(len(v) for v in norm_dict.values())}")
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return norm_dict, lemma_index
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# ------------------------
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# Matching: exakter Treffer, Lemma-Treffer, Fuzzy-Vorschläge
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# ------------------------
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def fuzzy_score(a, b):
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if RAPIDFUZZ_AVAILABLE:
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try:
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return fuzz.token_set_ratio(a, b) / 100.0
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except Exception:
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return 0.0
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else:
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try:
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return SequenceMatcher(None, a.lower(), b.lower()).ratio()
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except Exception:
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return 0.0
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def get_suggestions_for_term(term_lemma, norm_dict, lemma_index, threshold=CONF_THRESHOLD):
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candidates = []
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for key_lemma, entries in lemma_index.items():
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score = fuzzy_score(term_lemma, key_lemma)
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if key_lemma.startswith(term_lemma):
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score = min(score + 0.1, 1.0)
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if score >= threshold:
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for e in entries:
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candidates.append((score, e["Name"], e["ID"]))
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for norm_key, entries in norm_dict.items():
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score = fuzzy_score(term_lemma, norm_key)
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if norm_key.startswith(term_lemma):
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score = min(score + 0.1, 1.0)
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if score >= threshold:
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for e in entries:
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candidates.append((score, e["Name"], e["ID"]))
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candidates.sort(key=lambda t: t[0], reverse=True)
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seen = set()
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results = []
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for score, name, id_ in candidates:
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key = (name, id_)
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if key in seen:
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continue
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seen.add(key)
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results.append({"score": score, "name": name, "id": id_})
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return [f'{r["name"]} ({r["id"]})' if r["id"] else r["name"] for r in results]
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def map_term_with_indexes(term, norm_dict, lemma_index):
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term_norm = normalize_text(term)
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term_lemma = lemmatize_term(term)
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if term_lemma in CACHE:
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return CACHE[term_lemma]["hits"], CACHE[term_lemma]["suggestions"]
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hits = []
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suggestions = []
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if term_norm in norm_dict:
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for e in norm_dict[term_norm]:
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hits.append(f'{e["Name"]} ({e["ID"]})' if e["ID"] else e["Name"])
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if not hits and term_lemma in lemma_index:
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for e in lemma_index[term_lemma]:
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hits.append(f'{e["Name"]} ({e["ID"]})' if e["ID"] else e["Name"])
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if not hits:
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suggestions = get_suggestions_for_term(term_lemma, norm_dict, lemma_index)
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def unique_preserve(seq):
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seen = set()
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out = []
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for x in seq:
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if x not in seen:
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seen.add(x)
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out.append(x)
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return out
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hits = unique_preserve(hits)
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suggestions = unique_preserve(suggestions)
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CACHE[term_lemma] = {"hits": hits, "suggestions": suggestions}
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return hits, suggestions
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# ------------------------
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# Haupt-Makro
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# ------------------------
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def run_mapper_macro():
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try:
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doc = XSCRIPTCONTEXT.getDocument()
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sheet = doc.CurrentController.ActiveSheet
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cursor = sheet.createCursor()
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cursor.gotoStartOfUsedArea(False)
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cursor.gotoEndOfUsedArea(True)
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data_range = cursor.getRangeAddress()
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except Exception as e:
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log("Fehler: konnte Dokument/Sheet nicht öffnen: " + str(e))
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return
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header_row = None
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objekt_col = None
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max_col = data_range.EndColumn
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for r in range(0, min(5, data_range.EndRow+1)):
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for c in range(0, max_col+1):
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try:
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val = str(sheet.getCellByPosition(c, r).String).strip().lower()
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except Exception:
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val = ""
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if val == "objektbeschreibung":
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header_row = r
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objekt_col = c
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break
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if objekt_col is not None:
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break
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if objekt_col is None:
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log("Spalte 'Objektbeschreibung' nicht gefunden. Abbruch.")
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return
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# Prüfen/Anlegen der Ergebnis-Spalten
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existing = {}
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for c in range(0, data_range.EndColumn+1):
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try:
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h = str(sheet.getCellByPosition(c, header_row).String).strip()
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except Exception:
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h = ""
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if h == "Norm_Treffer":
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existing["Norm_Treffer"] = c
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if h == "Norm_Vorschlag":
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existing["Norm_Vorschlag"] = c
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last_col = data_range.EndColumn
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if "Norm_Treffer" not in existing:
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last_col += 1
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existing["Norm_Treffer"] = last_col
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sheet.getCellByPosition(last_col, header_row).String = "Norm_Treffer"
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if "Norm_Vorschlag" not in existing:
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last_col += 1
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existing["Norm_Vorschlag"] = last_col
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sheet.getCellByPosition(last_col, header_row).String = "Norm_Vorschlag"
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norm_tr_col = existing["Norm_Treffer"]
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norm_sug_col = existing["Norm_Vorschlag"]
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norm_dict, lemma_index = build_norm_index(NV_MASTER_PATH)
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if not norm_dict and not lemma_index:
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log("NV_MASTER leer oder nicht lesbar. Abbruch.")
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return
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GREEN = 0xADFF2F
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YELLOW = 0xFFA500
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RED = 0xCC0000
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WHITE = 0xFFFFFF
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rows_processed = 0
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for r in range(header_row + 1, data_range.EndRow + 1):
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try:
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cell = sheet.getCellByPosition(objekt_col, r)
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txt = str(cell.String).strip()
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if not txt:
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continue
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clauses = [c.strip() for c in re.split(r",", txt) if c.strip()]
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terms = []
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for cl in clauses:
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parts = [p.strip() for p in re.split(r"\s+", cl) if p.strip()]
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for p in parts:
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if p.lower() in STOPWORDS:
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continue
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if re.fullmatch(r"\d+", p):
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continue
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terms.append(p)
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row_hits = []
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row_sugs = []
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unmapped_terms = []
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for term in terms:
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hits, sugs = map_term_with_indexes(term, norm_dict, lemma_index)
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if hits:
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row_hits.extend(hits)
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else:
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unmapped_terms.append(term)
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if sugs:
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row_sugs.extend(sugs)
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def uniq(seq):
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seen = set()
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out = []
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for x in seq:
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if x not in seen:
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seen.add(x)
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out.append(x)
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return out
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row_hits = uniq(row_hits)
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row_sugs = uniq(row_sugs)
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# Farb-Logik für Objektbeschreibung
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if terms and not unmapped_terms and row_hits:
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cell.CellBackColor = GREEN
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row_sugs = []
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elif row_hits:
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cell.CellBackColor = YELLOW
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else:
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cell.CellBackColor = RED
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# Ergebniszellen
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tr_cell = sheet.getCellByPosition(norm_tr_col, r)
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tr_cell.String = " | ".join(row_hits)
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tr_cell.CellBackColor = GREEN if row_hits else WHITE
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sug_cell = sheet.getCellByPosition(norm_sug_col, r)
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sug_cell.String = " | ".join(row_sugs)
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sug_cell.CellBackColor = YELLOW if row_sugs else WHITE
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rows_processed += 1
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except Exception as e:
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log(f"Fehler in Zeile {r}: {e}\n{traceback.format_exc()}")
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try:
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with open(CACHE_FILE, "w", encoding="utf-8") as f:
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json.dump(CACHE, f, ensure_ascii=False, indent=2)
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except Exception:
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pass
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log(f"run_mapper_macro fertig. Zeilen verarbeitet: {rows_processed}")
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# Export für LibreOffice
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g_exportedScripts = (run_mapper_macro,)
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