import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

# --- Dati ---
df = pd.read_csv("data.csv")
df["time_s"] = df["time since start [ms]"] / 1000.0

T_INF        = 22.99   # temperatura ambiente [°C]
T_START      = 115.0   # inizio finestra di fit [s]
T1           = 115.0   # riferimento 1° esponenziale (fisso)
T2           = 117.5   # riferimento 2° esponenziale (fisso)
W_ZERO_START = 115.9
W_ZERO_END   = 117.2

mask  = df["time_s"] >= T_START
t_fit = df.loc[mask, "time_s"].values
T_fit = df.loc[mask, "temp_obj IR [C]"].values

# Pesi espliciti: w=0 nella zona di transizione
sigma = np.where(
    (t_fit >= W_ZERO_START) & (t_fit <= W_ZERO_END),
    1e10,
    1.0
)

# --- Modello doppio esponenziale ---
def modello(t, A1, tau1, A2, tau2):
    return (T_INF
            + A1 * np.exp(-(t - T1) / tau1)
            + A2 * np.exp(-(t - T2) / tau2))

# Stime iniziali dai fit singoli
p0     = [194.51, 13.17, 154.94, 17.12]
bounds = ([0, 0.1, 0, 0.1], [np.inf, np.inf, np.inf, np.inf])

popt, pcov = curve_fit(
    modello, t_fit, T_fit,
    p0=p0,
    sigma=sigma,
    absolute_sigma=True,
    method="trf",
    bounds=bounds
)
A1_fit, tau1_fit, A2_fit, tau2_fit = popt
perr = np.sqrt(np.diag(pcov))

# --- R² (solo punti con peso pieno) ---
mask_w   = (t_fit < W_ZERO_START) | (t_fit > W_ZERO_END)
T_pred_w = modello(t_fit[mask_w], *popt)
ss_res   = np.sum((T_fit[mask_w] - T_pred_w) ** 2)
ss_tot   = np.sum((T_fit[mask_w] - T_fit[mask_w].mean()) ** 2)
r2       = 1 - ss_res / ss_tot

print(f"A1   = {A1_fit:.4f} ± {perr[0]:.4f} °C")
print(f"tau1 = {tau1_fit:.4f} ± {perr[1]:.4f} s")
print(f"A2   = {A2_fit:.4f} ± {perr[2]:.4f} °C")
print(f"tau2 = {tau2_fit:.4f} ± {perr[3]:.4f} s")
print(f"R²   = {r2:.6f}  (punti con peso pieno)")

# --- Curve per il plot ---
t_curve  = np.linspace(T_START, df["time_s"].max(), 1000)
T_tot    = modello(t_curve, *popt)
T_exp1   = T_INF + A1_fit * np.exp(-(t_curve - T1) / tau1_fit)
T_exp2   = T_INF + A2_fit * np.exp(-(t_curve - T2) / tau2_fit)

# --- Plot ---
fig, ax = plt.subplots(figsize=(12, 5))

ax.plot(t_fit, T_fit,
        color="steelblue", linewidth=0.8, label="Dati raw (temp_obj)")
ax.axvspan(W_ZERO_START, W_ZERO_END,
           color="orange", alpha=0.25, label=f"Zona esclusa [{W_ZERO_START}–{W_ZERO_END} s]")
ax.plot(t_curve, T_exp1,
        color="tomato", linewidth=1.2, linestyle=":",
        label=rf"$T_\infty + A_1 e^{{-(t-{T1})/\tau_1}}$  ($A_1$={A1_fit:.1f}, $\tau_1$={tau1_fit:.2f} s)")
ax.plot(t_curve, T_exp2,
        color="seagreen", linewidth=1.2, linestyle=":",
        label=rf"$T_\infty + A_2 e^{{-(t-{T2})/\tau_2}}$  ($A_2$={A2_fit:.1f}, $\tau_2$={tau2_fit:.2f} s)")
ax.plot(t_curve, T_tot,
        color="purple", linewidth=2, linestyle="--",
        label="Somma (fit totale)")

ax.set_xlabel("Tempo [s]")
ax.set_ylabel("Temperatura [°C]")
ax.set_title(f"Fit doppio esponenziale  |  R² = {r2:.4f}")
ax.legend(fontsize=8)
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig("fit_doppio_esponenziale.png", dpi=150, bbox_inches="tight")
plt.show()
