import re
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


def parse_wit_log(file_path):
    """Parsea el archivo de log del WiT-CPV y extrae lecturas temporales."""
    pattern = re.compile(
        r"\[(?P<time>\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2})\] "
        r"X:(?P<X>-?\d+),Y:(?P<Y>-?\d+),C:(?P<C>\d+),V:(?P<V>\d+),P:(?P<P>\d+),S:(?P<S>\d+)"
    )

    data = []
    with open(file_path, "r", encoding="utf-8") as f:
        for line in f:
            match = pattern.search(line)
            if match:
                entry = match.groupdict()
                data.append(
                    {
                        "timestamp": pd.to_datetime(entry["time"]),
                        "X_steps": int(entry["X"]),
                        "Y_steps": int(entry["Y"]),
                        "C_raw": int(entry["C"]),  # Isc raw ADC counts
                        "V_raw": int(entry["V"]),  # Voc raw ADC counts
                        "P_raw": int(entry["P"]),  # Irradiance raw ADC counts
                        "S_elapsed": int(entry["S"]),
                    }
                )

    df = pd.DataFrame(data)
    if df.empty:
        raise ValueError("No se encontraron registros de mediciones en el archivo de log.")

    # -------------------------------------------------------------------------
    # Conversiones a Unidades Físicas (ADS122C04: 24-bit, Vref = 2.048V)
    # -------------------------------------------------------------------------
    ADC_MAX = 8388607.0  # 2^23 - 1
    VREF_MV = 2048.0  # mV

    # 1. Irradiancia Global en el Plano del Arreglo (G_POA in W/m2)
    # Pira V2: (raw / ADC_MAX) * VREF_MV / 2.0
    df["G_POA"] = (df["P_raw"] / ADC_MAX) * (VREF_MV / 2.0)

    # 2. Voltaje de Circuito Abierto (Voc in Volts)
    # Divisor 10k/10k (factor x2) -> (raw / ADC_MAX) * 2.048V * 2.0
    df["Voc"] = (df["V_raw"] / ADC_MAX) * 2.048 * 2.0

    # 3. Corriente de Cortocircuito (Isc in Amperes)
    # V_shunt (mV) = (raw / ADC_MAX) * VREF_MV / Gain_AD8418 (20x)
    # Isc (A) = V_shunt / R_shunt (75 mOhm = 0.075 Ohm)
    df["V_shunt_mV"] = (df["C_raw"] / ADC_MAX) * (VREF_MV / 20.0)
    df["Isc"] = (df["V_shunt_mV"] / 1000.0) / 0.075

    # 4. Potencia Eléctrica Generada Estimada (P_CPV in Watts)
    df["P_CPV"] = df["Voc"] * df["Isc"] * 0.82 #FILL FACTOR AL FINAL DE 0.82

    # Diferencial de tiempo en horas (dt)
    df["dt_hours"] = df["timestamp"].diff().dt.total_seconds() / 3600.0
    df["dt_hours"] = df["dt_hours"].fillna(0)

    return df


def calculate_energy_yield_metrics(df):
    """Calcula las métricas de Aprovechamiento Energético (E, H, E_1kWh/m2)."""
    # Integración Trapezoidal para mayor precisión
    # E: Energía Eléctrica Producida (Wh)
    E_Wh = np.trapz(df["P_CPV"], x=df["timestamp"].astype("int64") / 1e9) / 3600.0

    # H: Recurso Solar Recibido en el Plano (Wh/m2)
    H_Wh_m2 = np.trapz(df["G_POA"], x=df["timestamp"].astype("int64") / 1e9) / 3600.0

    # H convertido a kWh/m2
    H_kWh_m2 = H_Wh_m2 / 1000.0

    # E_1kWh/m2: Energía producida normalizada por cada 1 kWh/m2 de recurso solar
    E_normalized = E_Wh / H_kWh_m2 if H_kWh_m2 > 0 else 0.0

    metrics = {
        "E_generada_Wh": E_Wh,
        "H_irradiacion_Wh_m2": H_Wh_m2,
        "H_irradiacion_kWh_m2": H_kWh_m2,
        "E_normalizada_Wh_per_kWh_m2": E_normalized,
    }

    return metrics


def plot_energy_profile(df):
    """Genera las gráficas de potencia e irradiancia temporal."""
    fig, ax1 = plt.subplots(figsize=(10, 5))

    color = "tab:red"
    ax1.set_xlabel("Hora del Día")
    ax1.set_ylabel("Potencia CPV (W)", color=color)
    ax1.plot(df["timestamp"], df["P_CPV"], color=color, linewidth=1.5, label="P_CPV (W)")
    ax1.tick_params(axis="y", labelcolor=color)
    ax1.grid(True, linestyle="--", alpha=0.6)

    ax2 = ax1.twinx()
    color = "tab:orange"
    ax2.set_ylabel("Irradiancia G_POA (W/m²)", color=color)
    ax2.plot(
        df["timestamp"],
        df["G_POA"],
        color=color,
        linestyle=":",
        linewidth=1.5,
        label="G_POA (W/m²)",
    )
    ax2.tick_params(axis="y", labelcolor=color)

    plt.title("Perfil de Potencia Generada e Irradiancia Incidente")
    fig.tight_layout()
    plt.show()


# =============================================================================
# EJECUCIÓN DEL SCRIPT
# =============================================================================
if __name__ == "__main__":
    file_path = "23092025.txt"  # Ruta a tu archivo de log

    try:
        df_log = parse_wit_log(file_path)
        metrics = calculate_energy_yield_metrics(df_log)

        print("==================================================")
        print("   MÉTRICAS DE APROVECHAMIENTO ENERGÉTICO        ")
        print("==================================================")
        print(f"Energía Generada (E):          {metrics['E_generada_Wh']:.4f} Wh")
        print(f"Recurso Solar Incidente (H):   {metrics['H_irradiacion_kWh_m2']:.4f} kWh/m²")
        print(
            f"Aprovechamiento (E_1kWh/m²):   {metrics['E_normalizada_Wh_per_kWh_m2']:.4f} Wh / (kWh/m²)"
        )
        print("==================================================")

        # Opcional: Graficar el perfil
        plot_energy_profile(df_log)

    except Exception as e:
        print(f"Error procesando el archivo: {e}")