pulizia

fit_raffreddamento_1tratto.py

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+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     = 115.0  # inizio finestra di fit [s]
+T_END  = 115.9  # fine finestra di fit [s]
+
+mask = (df["time_s"] >= T0) & (df["time_s"] <= T_END)
+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)
+
+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, T_END, 500)
+T_curve = modello(t_curve, *popt)
+
+# --- Plot ---
+fig, ax = plt.subplots(figsize=(12, 5))
+
+df_plot = df[(df["time_s"] >= T0) & (df["time_s"] <= T_END)]
+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.set_xlabel("Tempo [s]")
+ax.set_ylabel("Temperatura [°C]")
+ax.set_title(f"Fit 1° tratto [{T0}–{T_END} s]  |  R² = {r2:.4f}")
+ax.legend()
+ax.grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.savefig("fit_raffreddamento_1tratto.png", dpi=150, bbox_inches="tight")
+plt.show()