Ajoute page statistiques avec graphiques et onglets de période
- Nouvelle page /stats avec histogrammes de distribution par créneau horaire (entrée/sortie matin et après-midi) - Camemberts animés de conformité aux directives (≤09:00, ≥12:00, ≤14:00, ≥17:00) globale, par slot et par période (mois, sem. N-1) - Onglets Tout / 1 an / 3 mois / 1 mois : filtrage côté client avec recalcul μ, σ, n et conformité en temps réel - Graphique des soldes hebdomadaires (barres animées, toutes semaines sur l'axe X en rotation -45°, semaines partielles distinctes) - Clic sur un graphique → modal plein écran avec axe X détaillé (15 min) - Légende σ / n / directive / moyenne sur chaque histogramme - Journées incomplètes exclues des stats ; heures dues retirées du solde - Lien "Statistiques" dans la nav Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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app/stats.py
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app/stats.py
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"""Statistics computation across all recorded weeks."""
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import statistics as _st
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from datetime import datetime, timedelta, date as _date
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from models import load_week_pointages, load_conges, list_all_weeks, load_config
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from calcul import hhmm_to_minutes, minutes_to_hhmm, heures_travaillees, heures_dues
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def _week_dates(year: int, week: int) -> list[str]:
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first = datetime.strptime(f"{year}-W{week:02d}-1", "%G-W%V-%u").date()
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return [(first + timedelta(days=i)).strftime("%Y-%m-%d") for i in range(5)]
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def _to_hhmm(minutes_float: float) -> str:
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m = round(minutes_float)
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return f"{m // 60:02d}:{m % 60:02d}"
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def _day_compliant(p: dict, mc: bool, ac: bool) -> bool | None:
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if mc and ac:
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return None
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matin_ok = mc or (bool(p.get("matin_entree")) and bool(p.get("matin_sortie")))
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aprem_ok = ac or (bool(p.get("aprem_entree")) and bool(p.get("aprem_sortie")))
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if not (matin_ok and aprem_ok):
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return None
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if not mc:
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if p.get("matin_entree") and hhmm_to_minutes(p["matin_entree"]) > 9 * 60:
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return False
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if p.get("matin_sortie") and hhmm_to_minutes(p["matin_sortie"]) < 12 * 60:
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return False
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if not ac:
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if p.get("aprem_entree") and hhmm_to_minutes(p["aprem_entree"]) > 14 * 60:
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return False
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if p.get("aprem_sortie") and hhmm_to_minutes(p["aprem_sortie"]) < 17 * 60:
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return False
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return True
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def _compliance_over(all_days: list[tuple], cg_set: set) -> dict:
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n = n_ok = 0
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for date, p in all_days:
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mc = (date, "matin") in cg_set or (date, "jour") in cg_set
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ac = (date, "aprem") in cg_set or (date, "jour") in cg_set
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result = _day_compliant(p, mc, ac)
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if result is None:
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continue
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n += 1
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if result:
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n_ok += 1
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return {"pct": round(100 * n_ok / n) if n else None, "n": n, "n_ok": n_ok}
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def compute_all_stats() -> dict:
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all_weeks = sorted(list_all_weeks())
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conges = load_conges()
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h_jour = int(float(load_config().get("heures_jour", 7.8)) * 60)
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cg_set = {(c["date"], c["type"]) for c in conges}
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today = _date.today()
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prev_week_date = today - timedelta(weeks=1)
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prev_iso = prev_week_date.isocalendar()
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prev_week_dates = set(_week_dates(prev_iso.year, prev_iso.week))
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current_ym = (today.year, today.month)
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SLOTS = ["matin_entree", "matin_sortie", "aprem_entree", "aprem_sortie"]
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# Store (value, date) pairs per slot
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slot_pairs: dict[str, list[tuple[int, str]]] = {s: [] for s in SLOTS}
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comp_n = {s: 0 for s in SLOTS}
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comp_ok = {s: 0 for s in SLOTS}
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TARGETS = {
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"matin_entree": ("le", 9 * 60),
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"matin_sortie": ("ge", 12 * 60),
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"aprem_entree": ("le", 14 * 60),
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"aprem_sortie": ("ge", 17 * 60),
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}
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weekly_balances = []
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all_days: list[tuple] = []
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month_days: list[tuple] = []
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prev_week_day_list: list[tuple] = []
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for year, week in all_weeks:
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raw = load_week_pointages(year, week)
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if not raw:
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continue
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dates = _week_dates(year, week)
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p_by_date = {p["date"]: p for p in raw}
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week_delta = 0
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week_has_incomplete = False
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for date in dates:
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p = p_by_date.get(date, {
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"date": date, "matin_entree": None, "matin_sortie": None,
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"aprem_entree": None, "aprem_sortie": None,
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})
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mc = (date, "matin") in cg_set or (date, "jour") in cg_set
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ac = (date, "aprem") in cg_set or (date, "jour") in cg_set
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matin_filled = mc or (bool(p.get("matin_entree")) and bool(p.get("matin_sortie")))
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aprem_filled = ac or (bool(p.get("aprem_entree")) and bool(p.get("aprem_sortie")))
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all_days.append((date, p))
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d = datetime.strptime(date, "%Y-%m-%d").date()
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if (d.year, d.month) == current_ym:
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month_days.append((date, p))
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if date in prev_week_dates:
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prev_week_day_list.append((date, p))
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if not (matin_filled and aprem_filled):
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week_has_incomplete = True
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continue
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trav = heures_travaillees(p)
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du = heures_dues(date, conges, h_jour)
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week_delta += trav - du
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if not mc:
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for slot in ("matin_entree", "matin_sortie"):
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if p.get(slot):
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v = hhmm_to_minutes(p[slot])
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slot_pairs[slot].append((v, date))
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op, thr = TARGETS[slot]
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comp_n[slot] += 1
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comp_ok[slot] += 1 if (v <= thr if op == "le" else v >= thr) else 0
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if not ac:
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for slot in ("aprem_entree", "aprem_sortie"):
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if p.get(slot):
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v = hhmm_to_minutes(p[slot])
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slot_pairs[slot].append((v, date))
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op, thr = TARGETS[slot]
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comp_n[slot] += 1
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comp_ok[slot] += 1 if (v <= thr if op == "le" else v >= thr) else 0
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weekly_balances.append({
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"year": year, "week": week,
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"label": f"S{week:02d}",
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"date": dates[0], # Monday — used for period filtering
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"delta_min": week_delta,
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"delta": minutes_to_hhmm(week_delta),
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"partial": week_has_incomplete,
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})
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time_stats = {}
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for slot, pairs in slot_pairs.items():
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if len(pairs) < 2:
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time_stats[slot] = None
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continue
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vals = [v for v, _ in pairs]
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mean = _st.mean(vals)
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sigma = _st.stdev(vals)
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time_stats[slot] = {
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"mean": mean, "sigma": sigma,
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"mean_hhmm": _to_hhmm(mean),
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"sigma_min": round(sigma),
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"n": len(vals),
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"dated_values": [{"v": v, "d": d} for v, d in pairs],
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}
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compliance = {
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slot: (round(100 * comp_ok[slot] / comp_n[slot]) if comp_n[slot] else None)
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for slot in comp_n
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}
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rates = [v for v in compliance.values() if v is not None]
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overall_slot = round(sum(rates) / len(rates)) if rates else None
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n_jours = max(len(slot_pairs["matin_entree"]), len(slot_pairs["aprem_entree"]))
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deltas = [b["delta_min"] for b in weekly_balances]
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avg_delta_min = round(_st.mean(deltas)) if deltas else 0
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comp_globale = _compliance_over(all_days, cg_set)
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comp_mois = _compliance_over(month_days, cg_set)
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comp_sem_n1 = _compliance_over(prev_week_day_list, cg_set)
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return {
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"time_stats": time_stats,
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"compliance": compliance,
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"overall_compliance": overall_slot,
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"weekly_balances": weekly_balances,
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"total_weeks": len(weekly_balances),
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"n_jours": n_jours,
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"avg_delta": minutes_to_hhmm(avg_delta_min),
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"avg_delta_min": avg_delta_min,
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"comp_globale": comp_globale,
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"comp_mois": comp_mois,
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"comp_sem_n1": comp_sem_n1,
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"mois_label": today.strftime("%B %Y"),
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"prev_week_label": f"S{prev_iso.week:02d} / {prev_iso.year}",
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"today": today.strftime("%Y-%m-%d"),
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}
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