hdary-85

import pandas as pd
import numpy as np
from math import ceil
from datetime import datetime

# =======================
# Configuration par défaut
# =======================
SLIDING_WEEKS = 8            # Fenêtre glissante (en semaines) pour recalculer les volumes "observés"
RARE_WEEKLY_THRESHOLD = 1.0  # En dessous de 1 dossier/semaine en moyenne => "rare"

hdary-85

import re
import pandas as pd
from typing import Optional, List

# --- Réseaux & utilitaires ---
CARD_PATTERNS = {
    "Visa": re.compile(r"^4\d{12}(?:\d{3})?(?:\d{3})?$"),
    "Mastercard": re.compile(r"^(5[1-5]\d{14}|2(?:2[2-9]\d{12}|[3-6]\d{13}|7[01]\d{12}|720\d{12}))$"),
    "American Express": re.compile(r"^3[47]\d{13}$"),
    "Discover": re.compile(r"^(?:6011\d{12}|65\d{14}|64[4-9]\d{13}|622(?:12[6-9]|1[3-9]\d|[2-8]\d{2}|9(?:[01]\d|2[0-5]))\d{10})$"),

hdary-85

import pandas as pd
from collections import defaultdict, deque

# -------------------------------------------------------------------
# PRÉREQUIS : tu dois déjà avoir ces 3 DataFrames (de ta 1re partie) :
# nodes(doc_id, node_id, tag, parent_id, depth, path)
# attrs(doc_id, node_id, attr, value)
# texts(doc_id, node_id, text)
# -------------------------------------------------------------------

hdary-85

import pandas as pd
from collections import defaultdict, deque

# -------------------------------------------------------------------
# PRÉREQUIS : tu dois déjà avoir ces 3 DataFrames (de ta 1re partie) :
# nodes(doc_id, node_id, tag, parent_id, depth, path)
# attrs(doc_id, node_id, attr, value)
# texts(doc_id, node_id, text)
# -------------------------------------------------------------------

hdary-85

import io
from xml.etree.ElementTree import iterparse
import pandas as pd
from collections import defaultdict
from typing import Dict, List, Optional, Iterable, Set

# ===================== Helpers =====================
def _local(tag: str) -> str:
    return tag.split('}', 1)[-1] if '}' in tag else tag

hdary-85

# 1) Parser n’importe quel XML
dfs = parse_xml_to_tables("ton_fichier.xml")
nodes, attrs, texts, id_index, refs = dfs["nodes"], dfs["attrs"], dfs["texts"], dfs["id_index"], dfs["refs"]

# 2) Construire l’index des enfants pour naviguer dans l’arbre
children_idx = build_children_index(nodes)

# 3) Ex.: récupérer tous les "subjects" (peu importe le nom exact, on matche au regex)
subjects = nodes[nodes["tag"].str.contains(r"(?i)subject")]  # tu peux ajuster le motif

hdary-85

import re
from xml.etree.ElementTree import iterparse
import pandas as pd
from collections import defaultdict, deque
from typing import Dict, List, Optional, Iterable, Set

# ---------- helpers ----------
def local(tag: str) -> str:
    return tag.split('}', 1)[-1] if '}' in tag else tag

hdary-85

TERME (FR)	ANGLAIS	ESPAGNOL	MANDARIN (pinyin)	PENDJABI (translit.)
ELEPHANT	elephant	elefante	daxiang	hathi
IVOIRE	ivory	marfil	xiangya	daant
RHINOCEROS	rhino	rinoceronte	xiniu	gainda
CORNE	horn	cuerno	jiao	singh
TIGRE	tiger	tigre	laohu	bagh
LION	lion	leon	shizi	sher
LEOPARD	leopard	leopardo	baozi	tendua
PANTHERE	panther	pantera	heibao	kali bagh
JAGUAR	jaguar	jaguar	meibao	jaguar

hdary-85

df.loc[df["colonne_condition"].str.contains("CDD", na=False) & df["colonne_cible"].isna(), "colonne_cible"] = "CDD"

hdary-85

# app.py
# ------------------------------------------------------------------------------
# LANCER :  python app.py
# DEPENDANCE :  pip install flask
# ------------------------------------------------------------------------------
from flask import Flask, render_template, jsonify, request, Response
from threading import Thread, Lock
from datetime import datetime
import time, json, uuid, webbrowser
import tkinter as tk