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 media ponderata [°C]
T0    = 117.5   # inizio finestra di fit [s]

mask = df["time_s"] >= T0
t_fit = df.loc[mask, "time_s"].values
T_fit = df.loc[mask, "temp_obj IR [C]"].values

# --- Modello (t0 fisso) ---
def modello(t, A, tau):
    return T_INF + A * np.exp(-(t - T0) / tau)

# Stima iniziale: A dal primo punto, tau arbitrario
A0  = T_fit[0] - T_INF
tau0 = 20.0

popt, pcov = curve_fit(
    modello, t_fit, T_fit,
    p0=[A0, tau0],
    method="trf",
    bounds=([0, 0.1], [np.inf, np.inf])
)
A_fit, tau_fit = popt

# --- R² ---
T_pred   = modello(t_fit, *popt)
ss_res   = np.sum((T_fit - T_pred) ** 2)
ss_tot   = np.sum((T_fit - T_fit.mean()) ** 2)
r2       = 1 - ss_res / ss_tot

print(f"A   = {A_fit:.4f} °C")
print(f"tau = {tau_fit:.4f} s")
print(f"R²  = {r2:.6f}")

# --- Curva continua per il plot ---
t_curve = np.linspace(T0, df["time_s"].max(), 500)
T_curve = modello(t_curve, *popt)

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

df_plot = df[df["time_s"] >= 115]
ax.plot(df_plot["time_s"], df_plot["temp_obj IR [C]"],
        color="steelblue", linewidth=0.8, label="Dati raw (temp_obj)")
ax.plot(t_curve, T_curve,
        color="tomato", linewidth=2, linestyle="--",
        label=f"Fit: $T_{{\\infty}}$ + {A_fit:.2f}·exp(-(t-{T0})/{tau_fit:.1f})")
ax.axvline(T0, color="gray", linewidth=0.8, linestyle=":")
ax.text(T0 + 0.5, ax.get_ylim()[0], f"t₀ = {T0} s", color="gray", fontsize=8, va="bottom")

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

plt.tight_layout()
plt.savefig("fit_raffreddamento_2tratto.png", dpi=150, bbox_inches="tight")
plt.show()