aggiunge analisi induttore3 (t=115.0–133.7 s) con zona esclusa [115.9–117.2 s]

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

induttore3/app3.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+from pathlib import Path
+
+# --- Dati ---
+ROOT = Path(__file__).parent.parent
+df = pd.read_csv(ROOT / "data.csv")
+df["time_s"] = df["time since start [ms]"] / 1000.0
+
+T_INF    = 22.99   # temperatura ambiente [°C]
+T0       = 115.0   # inizio intervallo [s]
+T_END    = 133.7   # fine intervallo [s] — ultimo campione nel dataset
+W0_START = 115.9   # inizio zona esclusa [s]  (None = nessuna esclusione)
+W0_END   = 117.2   # fine zona esclusa [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
+
+sigma = np.where(
+    (t_fit >= W0_START) & (t_fit <= W0_END),
+    1e10,
+    1.0
+)
+
+# --- Modello: esponenziale di Newton ---
+def modello(t, A, tau):
+    return T_INF + A * np.exp(-(t - T0) / tau)
+
+A0   = T_fit[0] - T_INF
+tau0 = 5.0
+
+popt, pcov = curve_fit(
+    modello, t_fit, T_fit,
+    p0=[A0, tau0],
+    sigma=sigma, absolute_sigma=True,
+    method="trf",
+    bounds=([-np.inf, 0.01], [np.inf, np.inf])
+)
+A_fit, tau_fit = popt
+perr = np.sqrt(np.diag(pcov))
+
+# --- R² (solo punti con peso pieno) ---
+mask_clean = (t_fit < W0_START) | (t_fit > W0_END)
+T_pred = modello(t_fit[mask_clean], *popt)
+ss_res = np.sum((T_fit[mask_clean] - T_pred) ** 2)
+ss_tot = np.sum((T_fit[mask_clean] - T_fit[mask_clean].mean()) ** 2)
+r2 = 1 - ss_res / ss_tot
+
+print(f"A   = {A_fit:.4f} +/- {perr[0]:.4f} °C")
+print(f"tau = {tau_fit:.4f} +/- {perr[1]:.4f} s")
+print(f"R²  = {r2:.6f}")
+print(f"\nEquazione: T(t) = {T_INF} + {A_fit:.4f} * exp(-(t - {T0}) / {tau_fit:.4f})")
+
+# --- 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=(8, 5))
+ax.scatter(t_fit, T_fit,
+           color="steelblue", s=20, zorder=3, label="Dati raw (temp_obj)")
+ax.axvspan(W0_START, W0_END, color="orange", alpha=0.25,
+           label=f"Zona esclusa [{W0_START}–{W0_END} s]")
+ax.plot(t_curve, T_curve,
+        color="tomato", linewidth=2,
+        label=f"Fit: {T_INF} + {A_fit:.2f}·exp(-(t-{T0})/{tau_fit:.3f})")
+ax.set_xlabel("Tempo [s]")
+ax.set_ylabel("Temperatura [°C]")
+ax.set_title(f"Fit esponenziale  [{T0}–{T_END} s]  |  R² = {r2:.4f}")
+ax.legend()
+ax.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.savefig(Path(__file__).parent / "induttore3.png", dpi=150, bbox_inches="tight")
+plt.show()
+
+# --- Report ---
+report = Path(__file__).parent / "report3.md"
+with open(report, "w", encoding="utf-8") as f:
+    f.write("# Report Induttore 3\n\n")
+    f.write("## Intervallo di campionamento\n\n")
+    f.write(f"| Parametro | Valore |\n|---|---|\n")
+    f.write(f"| Inizio | {T0} s |\n")
+    f.write(f"| Fine | {T_END} s |\n")
+    f.write(f"| Numero di punti | {len(t_fit)} |\n")
+    f.write(f"| Zona esclusa | {W0_START}–{W0_END} s |\n\n")
+    f.write("## Equazione trovata\n\n")
+    f.write(f"$$T(t) = {T_INF} + {A_fit:.4f} \\cdot e^{{-(t - {T0}) / {tau_fit:.4f}}}$$\n\n")
+    f.write("| Parametro | Valore | Incertezza (1σ) |\n|---|---|---|\n")
+    f.write(f"| A | {A_fit:.4f} °C | +/- {perr[0]:.4f} °C |\n")
+    f.write(f"| τ | {tau_fit:.4f} s | +/- {perr[1]:.4f} s |\n\n")
+    f.write("## Qualità del fit\n\n")
+    f.write(f"| R² | {r2:.6f} |\n|---|---|\n")
+print("report3.md generato.")

induttore3/report3.md

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+# Report Induttore 3
+
+## Intervallo di campionamento
+
+| Parametro | Valore |
+|---|---|
+| Inizio | 115.0 s |
+| Fine | 133.7 s |
+| Numero di punti | 125 |
+| Zona esclusa | 115.9–117.2 s |
+
+## Equazione trovata
+
+$$T(t) = 22.99 + 185.1847 \cdot e^{-(t - 115.0) / 16.2654}$$
+
+| Parametro | Valore | Incertezza (1σ) |
+|---|---|---|
+| A | 185.1847 °C | +/- 0.2706 °C |
+| τ | 16.2654 s | +/- 0.0464 s |
+
+## Qualità del fit
+
+| R² | 0.993827 |
+|---|---|