import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

OUT = os.path.join("repport", "images")
os.makedirs(OUT, exist_ok=True)

# Supprime l'ancienne figure règle 3 si elle existe encore
STALE = os.path.join(OUT, "regle3_vieillissement.png")
if os.path.exists(STALE):
    os.remove(STALE)
    print(f"Ancien fichier supprimé : {STALE}")

def save_fig(fig, name):
    path = os.path.join(OUT, f"{name}.png")
    fig.savefig(path, dpi=300, bbox_inches="tight")
    plt.close(fig)
    print(f"OK -> {path}")

# --- Chargement ---
df = pd.read_csv("rabbits.csv", skipinitialspace=True)
df.columns = df.columns.str.strip()
df = df.loc[:, ~df.columns.str.startswith("Unnamed")]
cols = df.columns.drop("Month")
df[cols] = df[cols].astype("float64")   # effectifs > int64 max

m       = df["Month"].values.astype(int)
litters = df["Litters"].values
births  = (df["Kitten Male (age 1)"] + df["Kitten Female (age 1)"]).values
adult_1 = (df["Male (age 1)"] + df["Female (age 1)"]).values
adult_f = df.filter(regex=r"^Female \(age").sum(axis=1).values

km  = df[[f"Kitten Male (age {i})"   for i in range(1, 9)]].values
kf  = df[[f"Kitten Female (age {i})" for i in range(1, 9)]].values
kit = km + kf                                     # (N, 8), âges en mois

# Buckets adultes etanches (age 1 exclu : il recoit la maturation), ages 2..16 ans
ages = list(range(2, 17))
ad = df[[f"Male (age {i})" for i in ages] +
        [f"Female (age {i})" for i in ages]].values   # (N, 30)

# --- Controle d'integrite : saut du ratio naissances/portees ? ---
ratio_g = births[1:] / np.maximum(litters[:-1], 1)
jumps = np.where(np.abs(np.diff(ratio_g)) > 0.75)[0]
if jumps.size:
    print(f"⚠️  CSV incoherent : le ratio naissances/portees saute autour du mois "
          f"{m[jumps[0] + 1]} — verifie le regime de gestation !")

# === Regle 1 : gestation a 50 %/mois ===
fig, ax = plt.subplots(figsize=(9, 5))
ax.plot(m, litters / np.maximum(adult_f, 1), lw=1, color="tab:blue")
ax.axhline(0.5, color="red", ls="--", label="attendu : 0.50")
ax.set_ylim(0, 1)
ax.set_xlabel("Mois"); ax.set_ylabel("Litters / femelles adultes")
ax.set_title("Règle 1 — Taux de gestation"); ax.legend(); ax.grid(alpha=0.3)
fig.tight_layout(); save_fig(fig, "regle1_gestation")

# === Regle 2 : 3 a 6 bebes par gestation ===
fig, ax = plt.subplots(figsize=(9, 5))
ax.plot(m[1:], ratio_g, lw=1, color="tab:purple")
ax.axhspan(3, 6, color="green", alpha=0.15, label="zone [3, 6]")
ax.axhline(4.5, color="red", ls="--", label="moyenne uniforme : 4.5")
ax.set_ylim(0, 8)
ax.set_xlabel("Mois"); ax.set_ylabel("Nouveaux-nés / portée")
ax.set_title("Règle 2 — Bébés par gestation"); ax.legend(); ax.grid(alpha=0.3)
fig.tight_layout(); save_fig(fig, "regle2_portees")

# === Regle 2bis : distribution des portees ===
fig, ax = plt.subplots(figsize=(9, 5))
valid = ratio_g[(ratio_g > 1) & (ratio_g < 12)]
ax.hist(valid, bins=60, color="tab:purple", alpha=0.8)
ax.axvline(4.5, color="red", ls="--", label="4.5 attendu")
ax.set_xlabel("Bébés / portée"); ax.set_ylabel("Occurrences")
ax.set_title("Règle 2 — Distribution des portées"); ax.legend(); ax.grid(alpha=0.3)
fig.tight_layout(); save_fig(fig, "regle2_distribution")

# === Regle 3 : maturation 5-8 mois (mesure seule, sans prediction) ===
fig, ax = plt.subplots(figsize=(9, 5))
ok3 = (m % 12 != 1) & (m >= 14) & (adult_1 > 100)
ax.plot(m[ok3], adult_1[ok3], lw=1.5, color="tab:green", label="Adultes âge 1 (mesuré)")
ax.set_yscale("log")
ax.set_xlabel("Mois"); ax.set_ylabel("Effectifs (log)")
ax.set_title("Règle 3 — Maturation entre 5 et 8 mois"); ax.legend(); ax.grid(alpha=0.3)
fig.tight_layout(); save_fig(fig, "regle3_maturation")

# === Regle 5 : survie des kittens ~95 %/mois ===
fig, ax = plt.subplots(figsize=(9, 5))
surv = kit[1:, 1:] / np.maximum(kit[:-1, :-1], 1)   # (N-1, 7)
mask = kit[:-1, :-1] > 100                          # (N-1, 7)
xs = np.repeat(m[1:, None], 7, axis=1)              # (N-1, 7)
ax.plot(xs[mask], surv[mask], ".", ms=2, alpha=0.2, color="tab:cyan")
ax.axhline(0.95, color="red", ls="--", label="attendu : 0.95")
ax.set_ylim(0.8, 1.05)
ax.set_xlabel("Mois"); ax.set_ylabel("Taux de survie mensuel")
ax.set_title("Règle 5 — Survie des kittens (5 %/mois)"); ax.legend(); ax.grid(alpha=0.3)
fig.tight_layout(); save_fig(fig, "regle5_survie_kittens")

# === Regle 4 : mortalite adultes — 40 %/an, +10 pts/an apres 10 ans ===
trans1 = (m[1:] % 12 == 1)                                 # (N-1,) : mois cible = bascule
s_reg  = ad[1:] / np.maximum(ad[:-1], 1)                   # (N-1, 30) mois reguliers
s_diag = np.full_like(s_reg, np.nan)
s_diag[trans1, 1:] = ad[1:][trans1, 1:] / np.maximum(ad[:-1][trans1, :-1], 1)
s_meas = np.where(trans1[:, None], s_diag, s_reg)          # (N-1, 30)

d_annuel  = np.array([0.40 + 0.10 * max(0, a - 10) for a in ages])
s_attendu = 1 - d_annuel / 12

fig, ax = plt.subplots(figsize=(10, 5.5))
for j, a in enumerate(ages):
    ok = (ad[:-1, j] > 100) & ~np.isnan(s_meas[:, j]) & \
         (s_meas[:, j] > 0.5) & (s_meas[:, j] < 1.02)
    ax.plot(m[1:][ok], s_meas[ok, j], ".", ms=2.5, alpha=0.45, label=f"âge {a} ans")
for a, s in zip(ages, s_attendu):
    ax.axhline(s, color="black", ls=":", lw=0.7, alpha=0.6)
    ax.text(1.01, s, f"{s:.4f}", transform=ax.get_yaxis_transform(),
            va="center", fontsize=7, color="black")
ax.set_ylim(0.88, 1.0)
ax.set_xlabel("Mois"); ax.set_ylabel("Taux de survie mensuel par bucket d'âge")
ax.set_title("Règle 4 — 40 %/an jusqu'à 10 ans, +10 pts/an ensuite (appliqué /12 par mois)")
ax.legend(ncol=2, fontsize=8, loc="lower left"); ax.grid(alpha=0.3)
fig.tight_layout(); save_fig(fig, "regle4_mortalite_adultes")

print("\nTaux de survie mensuels attendus (règle 4) :")
for a, d, s in zip(ages, d_annuel, s_attendu):
    print(f"  âge {a:2d} ans : {100*d:5.1f} %/an  ->  survie mensuelle {s:.5f}")

print("\nToutes les images sont dans", OUT)