Delete mapper_macro_2.1.py
This commit is contained in:
parent
ff8588d1ec
commit
cee7602ffb
@ -1,365 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# LibreOffice Calc macro: NV_MASTER-Abgleich, Pandas+odf, Cache, Farben
|
||||
# Speicherort: /home/jarnold/.config/libreoffice/4/user/Scripts/python/Vokabular_Abgleich_Makro/mapper_macro_2.1.py
|
||||
|
||||
import os
|
||||
import re
|
||||
import json
|
||||
import traceback
|
||||
|
||||
# UNO-Context wird zur Laufzeit zur Verfügung gestellt (XSCRIPTCONTEXT)
|
||||
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/Vokabular_Abgleich_Makro"
|
||||
NV_MASTER_PATH = os.path.join(BASE_DIR, "NV_MASTER.ods")
|
||||
LOG_FILE = os.path.join(BASE_DIR, "mapper_macro_2.1.log")
|
||||
CACHE_FILE = os.path.join(BASE_DIR, "mapper_cache_2.1.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
|
||||
df = df.fillna("")
|
||||
cols = [str(c).strip().lower() for c in df.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]
|
||||
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 id_val:
|
||||
current_parent_id = id_val
|
||||
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}
|
||||
norm_dict.setdefault(norm_name, []).append(entry)
|
||||
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, threshold=CONF_THRESHOLD):
|
||||
candidates = []
|
||||
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"]))
|
||||
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"]))
|
||||
candidates.sort(key=lambda t: t[0], reverse=True)
|
||||
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 [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)
|
||||
if term_lemma in CACHE:
|
||||
return CACHE[term_lemma]["hits"], CACHE[term_lemma]["suggestions"]
|
||||
|
||||
hits = []
|
||||
suggestions = []
|
||||
|
||||
if term_norm in norm_dict:
|
||||
for e in norm_dict[term_norm]:
|
||||
hits.append(f'{e["Name"]} ({e["ID"]})' if e["ID"] else e["Name"])
|
||||
|
||||
if not hits and term_lemma in lemma_index:
|
||||
for e in lemma_index[term_lemma]:
|
||||
hits.append(f'{e["Name"]} ({e["ID"]})' if e["ID"] else e["Name"])
|
||||
|
||||
if not hits:
|
||||
suggestions = get_suggestions_for_term(term_lemma, norm_dict, lemma_index)
|
||||
|
||||
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)
|
||||
|
||||
CACHE[term_lemma] = {"hits": hits, "suggestions": suggestions}
|
||||
return hits, suggestions
|
||||
|
||||
# ------------------------
|
||||
# Haupt-Makro
|
||||
# ------------------------
|
||||
def run_mapper_macro():
|
||||
try:
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
# Prüfen/Anlegen der Ergebnis-Spalten
|
||||
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
|
||||
|
||||
last_col = data_range.EndColumn
|
||||
if "Norm_Treffer" not in existing:
|
||||
last_col += 1
|
||||
existing["Norm_Treffer"] = last_col
|
||||
sheet.getCellByPosition(last_col, header_row).String = "Norm_Treffer"
|
||||
if "Norm_Vorschlag" not in existing:
|
||||
last_col += 1
|
||||
existing["Norm_Vorschlag"] = last_col
|
||||
sheet.getCellByPosition(last_col, header_row).String = "Norm_Vorschlag"
|
||||
|
||||
norm_tr_col = existing["Norm_Treffer"]
|
||||
norm_sug_col = existing["Norm_Vorschlag"]
|
||||
|
||||
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
|
||||
|
||||
GREEN = 0xADFF2F
|
||||
YELLOW = 0xFFA500
|
||||
RED = 0xCC0000
|
||||
WHITE = 0xFFFFFF
|
||||
|
||||
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:
|
||||
continue
|
||||
|
||||
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)
|
||||
|
||||
row_hits = []
|
||||
row_sugs = []
|
||||
unmapped_terms = []
|
||||
|
||||
for term in terms:
|
||||
hits, sugs = map_term_with_indexes(term, norm_dict, lemma_index)
|
||||
if hits:
|
||||
row_hits.extend(hits)
|
||||
else:
|
||||
unmapped_terms.append(term)
|
||||
if sugs:
|
||||
row_sugs.extend(sugs)
|
||||
|
||||
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)
|
||||
|
||||
# Farb-Logik für Objektbeschreibung
|
||||
if terms and not unmapped_terms and row_hits:
|
||||
cell.CellBackColor = GREEN
|
||||
row_sugs = []
|
||||
elif row_hits:
|
||||
cell.CellBackColor = YELLOW
|
||||
else:
|
||||
cell.CellBackColor = RED
|
||||
|
||||
# Ergebniszellen
|
||||
tr_cell = sheet.getCellByPosition(norm_tr_col, r)
|
||||
tr_cell.String = " | ".join(row_hits)
|
||||
tr_cell.CellBackColor = GREEN if row_hits else WHITE
|
||||
|
||||
sug_cell = sheet.getCellByPosition(norm_sug_col, r)
|
||||
sug_cell.String = " | ".join(row_sugs)
|
||||
sug_cell.CellBackColor = YELLOW if row_sugs else WHITE
|
||||
|
||||
rows_processed += 1
|
||||
|
||||
except Exception as e:
|
||||
log(f"Fehler in Zeile {r}: {e}\n{traceback.format_exc()}")
|
||||
|
||||
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 für LibreOffice
|
||||
g_exportedScripts = (run_mapper_macro,)
|
||||
Loading…
x
Reference in New Issue
Block a user