aggiunge analisi induttore2 (t=112.02–113.95 s)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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davideandClaude Sonnet 4.6 committed 2026-05-14 09:46:27 +02:00
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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 = 112.02 # inizio intervallo [s]
T_END = 113.95 # fine intervallo [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: 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],
method="trf",
bounds=([-np.inf, 0.01], [np.inf, np.inf])
)
A_fit, tau_fit = popt
perr = np.sqrt(np.diag(pcov))
# --- 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} +/- {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.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 / "induttore2.png", dpi=150, bbox_inches="tight")
plt.show()
# --- Report ---
report = Path(__file__).parent / "report2.md"
with open(report, "w", encoding="utf-8") as f:
f.write("# Report Induttore 2\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\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("report2.md generato.")
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# Report Induttore 2
## Intervallo di campionamento
| Parametro | Valore |
|---|---|
| Inizio | 112.02 s |
| Fine | 113.95 s |
| Numero di punti | 12 |
## Equazione trovata
$$T(t) = 22.99 + 158.3430 \cdot e^{-(t - 112.02) / 13.2844}$$
| Parametro | Valore | Incertezza (1σ) |
|---|---|---|
| A | 158.3430 °C | +/- 0.2088 °C |
| τ | 13.2844 s | +/- 0.2183 s |
## Qualità del fit
| R² | 0.997314 |
|---|---|