import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parent))
import parse_log as P

df_s, df_e = P.parse_log(str(P.ROOT / "21092025.txt"))
df, ph = P.build_physical(df_s, df_e)
# variante PNG para inspeccion visual (las figuras del articulo son PDF)
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import pandas as pd  # noqa: E402

orig = P.ARTICULO / "figs"
P.ARTICULO_FIGS = P.ARTICULO / "resultados_21092025"


def save_png(fig, name):
    fig.savefig((P.ROOT / "articulo" / "resultados_21092025" / name))
    plt.close(fig)


# --- Fig 1 (PNG preview) ---
t = df["timestamp"]
lit_mask = (df["G_POA"] > 50.0).values
cmds = df_e[df_e["kind"] == "cmd"]
fig, ax1 = plt.subplots(figsize=(7.2, 3.4), dpi=110)
ax1.plot(t, df["P_CPV"] * 1000, color="tab:red", lw=1.0, label="$P_{CPV}$")
ax1.plot(t, df["P_ref"] * 1000, color="k", ls="--", lw=1.0, label="$P_{ref}$")
ax1.fill_between(t, df["P_CPV"] * 1000, df["P_ref"] * 1000,
                 where=lit_mask, color="tab:red", alpha=0.12)
ax2 = ax1.twinx()
ax2.plot(t, df["G_POA"], color="tab:orange", lw=0.8, alpha=0.85)
for _, r in cmds.iterrows():
    if int(r["cmd"]) != 2:
        continue  # solo "Track solar" (igual que la figura del articulo)
    ax1.axvline(r["timestamp"], color="tab:blue", ls=":", lw=1.0, alpha=0.8)
fig.tight_layout()
save_png(fig, "preview_power.png")

fig, axes = plt.subplots(2, 1, figsize=(7.2, 4.2), dpi=110, sharex=True)
for ax, col in ((axes[0], "x"), (axes[1], "y")):
    ax.plot(t, df[col], color="tab:blue", lw=0.9)
    tc = df["tgt_x" if col == "x" else "tgt_y"]
    ax.step(t, tc, where="post", color="k", ls="--", lw=1.0)
    for _, r in cmds.iterrows():
        ax.axvline(r["timestamp"], color="tab:blue", ls=":", lw=0.8, alpha=0.7)
fig.tight_layout()
save_png(fig, "preview_position.png")

fig, ax1 = plt.subplots(figsize=(7.2, 3.2), dpi=110)
ax1.plot(t, df["er"], color="tab:purple", lw=0.7, alpha=0.9)
ax2 = ax1.twinx()
ax2.semilogy(t, df["dC"].clip(lower=1), color="tab:red", lw=0.5, alpha=0.7)
at = df_e[df_e["kind"] == "at_rec"]
for _, r in at.iterrows():
    ax1.axvline(r["timestamp"], color="k", ls=":", lw=1.0)
fig.tight_layout()
save_png(fig, "preview_finetune.png")

rec = [r for _, r in cmds.iterrows() if int(r["cmd"]) in (2, 5)]
fig, axes = plt.subplots(1, min(2, len(rec)), figsize=(7.2, 2.9), dpi=110)
axes = np.atleast_1d(axes)
for ax, r in zip(axes, rec[:2]):
    t0 = r["timestamp"]
    w = df[(df["timestamp"] >= t0 - pd.Timedelta(minutes=1)) &
           (df["timestamp"] <= t0 + pd.Timedelta(minutes=20))]
    ax.plot(w["timestamp"], w["P_CPV"] * 1000, color="tab:red", lw=1.0)
    ax.plot(w["timestamp"], w["P_ref"] * 1000, color="k", ls="--", lw=1.0)
    ax.plot(w["timestamp"], 0.95 * w["P_ref"] * 1000, color="tab:green", ls=":", lw=1.0)
    ax.axvline(t0, color="tab:blue", ls=":", lw=1.0)
    ax.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M:%S"))
fig.tight_layout()
save_png(fig, "preview_recovery.png")
print("previews guardados en articulo/resultados_21092025")