davideandClaude Sonnet 4.6
6a38284e1d
Aggiunge _leggi_t_inf() in app1/2/3.py che legge T∞ dal report generato da calcola_t_amb.py. Solleva errore esplicito se il file non esiste o il valore non è trovato. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
import re
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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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def _leggi_t_inf() -> float:
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report = ROOT / "start" / "report.md"
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if not report.exists():
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raise FileNotFoundError("start/report.md non trovato. Esegui prima 'Calcola T ambiente'.")
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m = re.search(r"media ponderata\*\*\s*\|\s*\*\*([0-9.]+)\s*°C",
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report.read_text(encoding="utf-8"))
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if not m:
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raise ValueError("T∞ non trovata in start/report.md.")
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return float(m.group(1))
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T_INF = _leggi_t_inf() # temperatura ambiente [°C], letta da start/report.md
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T0 = 110.81 # inizio intervallo [s]
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T_END = 111.56 # fine intervallo [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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# --- Modello: esponenziale di Newton (raffreddamento/riscaldamento) ---
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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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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 ---
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T_pred = modello(t_fit, *popt)
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ss_res = np.sum((T_fit - T_pred) ** 2)
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ss_tot = np.sum((T_fit - T_fit.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.plot(t_curve, T_curve,
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color="tomato", linewidth=2.5,
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label=f"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 1 — 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 / "induttore1.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 / "report1.md"
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with open(report, "w", encoding="utf-8") as f:
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f.write("# Report Induttore 1\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\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("report1.md generato.")
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