Consultoría Alcaraz Salazar
a718592291
- Frontend React (SKEEN Brand) con Vite, TypeScript, Tailwind - Frontend Homenest (versión alternativa) - Módulos Odoo 17 custom (citas, pacientes, monedero, pagos, ventas, inventario, whatsapp) - WACRM fork (Next.js 16 + Supabase) - Hermes + Bridge + Skills (Qwen3.6 via Nan Builders) - Scripts de migración y operación - Documentación extensiva en docs/
122 lines
3.8 KiB
Python
122 lines
3.8 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""Limpieza de nombres de pacientes (reversible).
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Pasos:
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1) Respaldo CSV id -> nombre original.
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2) Normaliza: control chars, frases marcador (no usar exp...), barras, espacios, signos.
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3) Title Case con particulas en minuscula (de, del, de la, los, las, y, etc.).
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4) Dry-run por defecto; con --apply actualiza solo las filas que cambian.
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"""
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import csv
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import os
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import re
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import sys
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import unicodedata
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from datetime import datetime
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import psycopg2
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DSN = "host=localhost dbname=skeen_odoo user=skeen password=skeen_dev_2026"
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PARTICULAS = {
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'de', 'del', 'la', 'las', 'los', 'y', 'e', 'da', 'do', 'das', 'dos',
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'di', 'van', 'von', 'der', 'den', 'san', 'santa', 'mc',
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}
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# Frases marcador inyectadas por el personal dentro del nombre
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MARKER_RE = re.compile(
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r'\bno\s*usar(\s+(este\s+)?exp(?:ediente)?)?\b[!.,;:/\\-]*',
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re.IGNORECASE,
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)
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ERROR_RE = re.compile(r'\berror\b[!.,;:/\\-]*', re.IGNORECASE)
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SLASHES_RE = re.compile(r'[/\\]+')
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MULTISPACE_RE = re.compile(r'\s{2,}')
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LEAD_TRAIL_JUNK_RE = re.compile(r'^[\s.\-_,;:]+|[\s.\-_,;:]+$')
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CONTROL_RE = re.compile(r'[\x00-\x1f\x7f]')
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def cap_token(tok, first):
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low = tok.lower()
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if not first and low in PARTICULAS:
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return low
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# Mantener siglas cortas (iniciales) en mayuscula: J, GPE -> J, Gpe
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if len(tok) <= 1:
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return tok.upper()
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return low.capitalize()
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def clean_name(raw):
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if raw is None:
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return raw
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s = CONTROL_RE.sub('', raw)
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s = SLASHES_RE.sub(' ', s) # barras -> espacio
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s = MARKER_RE.sub(' ', s) # quita "no usar exp..."
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s = ERROR_RE.sub(' ', s) # quita "ERROR" suelto
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s = s.replace('.', ' ') # puntos (iniciales, GPE.) -> espacio
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s = MULTISPACE_RE.sub(' ', s)
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s = LEAD_TRAIL_JUNK_RE.sub('', s).strip()
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s = MULTISPACE_RE.sub(' ', s)
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if not s:
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return s
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toks = s.split(' ')
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toks = [cap_token(t, i == 0) for i, t in enumerate(toks) if t]
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return ' '.join(toks)
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def main():
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apply = '--apply' in sys.argv
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conn = psycopg2.connect(DSN)
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conn.autocommit = False
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cur = conn.cursor()
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cur.execute("SELECT id, name FROM res_partner WHERE is_patient ORDER BY id")
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rows = cur.fetchall()
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changes = []
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for rid, name in rows:
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new = clean_name(name)
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if new != name:
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changes.append((rid, name, new))
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ts = datetime.now().strftime('%Y%m%d_%H%M%S')
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backup_path = f'/root/migracion/datos/name_backup_{ts}.csv'
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with open(backup_path, 'w', newline='', encoding='utf-8') as f:
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w = csv.writer(f)
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w.writerow(['id', 'name_original'])
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for rid, name, _ in changes:
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w.writerow([rid, name])
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print(f'Total pacientes: {len(rows)}')
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print(f'A modificar: {len(changes)}')
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print(f'Sin cambios: {len(rows) - len(changes)}')
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print(f'Respaldo: {backup_path}')
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print()
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print('--- Ejemplos antes -> despues (30) ---')
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shown = 0
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# prioriza los que tenian marcador o signos
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def score(c):
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n = c[1] or ''
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return (('no usar' in n.lower()) * 10) + (('/' in n) * 5) + ((' ' in n) * 2)
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for rid, old, new in sorted(changes, key=score, reverse=True)[:30]:
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print(f' [{rid}] "{old}" -> "{new}"')
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shown += 1
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print(f' ({shown} ejemplos)')
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if not apply:
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print('\nDRY-RUN: no se aplicaron cambios. Ejecuta con --apply para aplicar.')
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conn.rollback()
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cur.close(); conn.close()
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return
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updated = 0
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for rid, old, new in changes:
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cur.execute("UPDATE res_partner SET name = %s WHERE id = %s", (new, rid))
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updated += 1
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conn.commit()
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print(f'\nAPLICADO: {updated} nombres actualizados.')
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cur.close(); conn.close()
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if __name__ == '__main__':
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main()
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