107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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from pathlib import Path
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# --- Dati ---
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ROOT = Path(__file__).parent.parent
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df = pd.read_csv(ROOT / "data.csv")
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df["time_s"] = df["time since start [ms]"] / 1000.0
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T_INF = 22.99 # temperatura ambiente [°C]
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T0 = 115.0 # inizio intervallo [s]
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T_END = 133.7 # fine intervallo [s] — ultimo campione nel dataset
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W0_START = 115.9 # inizio zona esclusa [s] (None = nessuna esclusione)
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W0_END = 117.2 # fine zona esclusa [s]
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mask = (df["time_s"] >= T0) & (df["time_s"] <= T_END)
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t_fit = df.loc[mask, "time_s"].values
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T_fit = df.loc[mask, "temp_obj IR [C]"].values
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sigma = np.where(
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(t_fit >= W0_START) & (t_fit <= W0_END),
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1e10,
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1.0
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)
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# --- Modello: esponenziale di Newton ---
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def modello(t, A, tau):
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return T_INF + A * np.exp(-(t - T0) / tau)
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A0 = T_fit[0] - T_INF
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tau0 = 5.0
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popt, pcov = curve_fit(
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modello, t_fit, T_fit,
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p0=[A0, tau0],
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sigma=sigma, absolute_sigma=True,
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method="trf",
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bounds=([-np.inf, 0.01], [np.inf, np.inf])
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)
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A_fit, tau_fit = popt
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perr = np.sqrt(np.diag(pcov))
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# --- R² (solo punti con peso pieno) ---
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mask_clean = (t_fit < W0_START) | (t_fit > W0_END)
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T_pred = modello(t_fit[mask_clean], *popt)
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ss_res = np.sum((T_fit[mask_clean] - T_pred) ** 2)
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ss_tot = np.sum((T_fit[mask_clean] - T_fit[mask_clean].mean()) ** 2)
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r2 = 1 - ss_res / ss_tot
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print(f"A = {A_fit:.4f} +/- {perr[0]:.4f} °C")
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print(f"tau = {tau_fit:.4f} +/- {perr[1]:.4f} s")
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print(f"R² = {r2:.6f}")
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print(f"\nEquazione: T(t) = {T_INF} + {A_fit:.4f} * exp(-(t - {T0}) / {tau_fit:.4f})")
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# --- Curva continua per il plot ---
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t_curve = np.linspace(T0, T_END, 500)
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T_curve = modello(t_curve, *popt)
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# --- Plot ---
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fig, ax = plt.subplots(figsize=(10, 6))
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ax.scatter(t_fit, T_fit,
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color="steelblue", s=60, edgecolors="white", linewidths=0.5,
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zorder=3, label="Dati raw (temp_obj)")
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ax.axvspan(W0_START, W0_END, color="orange", alpha=0.25,
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label=f"Zona esclusa [{W0_START}–{W0_END} s]")
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ax.plot(t_curve, T_curve,
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color="tomato", linewidth=2.5,
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label="Fit esponenziale")
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ax.set_xlabel("Tempo [s]", fontsize=13)
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ax.set_ylabel("Temperatura [°C]", fontsize=13)
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ax.set_title(f"Induttore 3 — Fit esponenziale [{T0}–{T_END} s]", fontsize=14, fontweight="bold")
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ax.tick_params(labelsize=11)
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ax.legend(fontsize=11, framealpha=0.9)
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ax.grid(True, which="both", alpha=0.3)
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ax.minorticks_on()
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eq_text = (f"$T(t) = {T_INF} + {A_fit:.2f}\\cdot e^{{-(t-{T0})/{tau_fit:.3f}}}$\n"
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f"$R^2 = {r2:.4f}$")
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ax.text(0.97, 0.95, eq_text, transform=ax.transAxes,
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fontsize=11, verticalalignment="top", horizontalalignment="right",
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bbox=dict(boxstyle="round,pad=0.4", facecolor="white", alpha=0.8))
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plt.tight_layout()
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plt.savefig(Path(__file__).parent / "induttore3.png", dpi=200, bbox_inches="tight")
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plt.show()
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# --- Report ---
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report = Path(__file__).parent / "report3.md"
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with open(report, "w", encoding="utf-8") as f:
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f.write("# Report Induttore 3\n\n")
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f.write("## Intervallo di campionamento\n\n")
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f.write(f"| Parametro | Valore |\n|---|---|\n")
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f.write(f"| Inizio | {T0} s |\n")
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f.write(f"| Fine | {T_END} s |\n")
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f.write(f"| Numero di punti | {len(t_fit)} |\n")
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f.write(f"| Zona esclusa | {W0_START}–{W0_END} s |\n\n")
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f.write("## Equazione trovata\n\n")
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f.write(f"$$T(t) = {T_INF} + {A_fit:.4f} \\cdot e^{{-(t - {T0}) / {tau_fit:.4f}}}$$\n\n")
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f.write("| Parametro | Valore | Incertezza (1σ) |\n|---|---|---|\n")
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f.write(f"| A | {A_fit:.4f} °C | +/- {perr[0]:.4f} °C |\n")
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f.write(f"| τ | {tau_fit:.4f} s | +/- {perr[1]:.4f} s |\n\n")
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f.write("## Qualità del fit\n\n")
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f.write(f"| R² | {r2:.6f} |\n|---|---|\n")
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print("report3.md generato.")
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