import re
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

def _leggi_t_inf() -> float:
    report = ROOT / "start" / "report.md"
    if not report.exists():
        raise FileNotFoundError("start/report.md non trovato. Esegui prima 'Calcola T ambiente'.")
    m = re.search(r"media ponderata\*\*\s*\|\s*\*\*([0-9.]+)\s*°C",
                  report.read_text(encoding="utf-8"))
    if not m:
        raise ValueError("T∞ non trovata in start/report.md.")
    return float(m.group(1))

T_INF = _leggi_t_inf()  # temperatura ambiente [°C], letta da start/report.md
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=(10, 6))
ax.scatter(t_fit, T_fit,
           color="steelblue", s=60, edgecolors="white", linewidths=0.5,
           zorder=3, label="Dati raw (temp_obj)")
ax.plot(t_curve, T_curve,
        color="tomato", linewidth=2.5,
        label="Fit esponenziale")
ax.set_xlabel("Tempo [s]", fontsize=13)
ax.set_ylabel("Temperatura [°C]", fontsize=13)
ax.set_title(f"Induttore 2  —  Fit esponenziale  [{T0}–{T_END} s]", fontsize=14, fontweight="bold")
ax.tick_params(labelsize=11)
ax.legend(fontsize=11, framealpha=0.9)
ax.grid(True, which="both", alpha=0.3)
ax.minorticks_on()

eq_text = (f"$T(t) = {T_INF} + {A_fit:.2f}\\cdot e^{{-(t-{T0})/{tau_fit:.3f}}}$\n"
           f"$R^2 = {r2:.4f}$")
ax.text(0.97, 0.95, eq_text, transform=ax.transAxes,
        fontsize=11, verticalalignment="top", horizontalalignment="right",
        bbox=dict(boxstyle="round,pad=0.4", facecolor="white", alpha=0.8))

plt.tight_layout()
plt.savefig(Path(__file__).parent / "induttore2.png", dpi=200, 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.")