import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path

folder = Path(__file__).parent
files = sorted(folder.glob("*_ultimo_ciclo.csv"))

# categorical palette (light mode), fixed order
colors = ["#2a78d6", "#1baf7a", "#eda100", "#e34948"]

combined = {}
series = []

for f in files:
    df = pd.read_csv(f, sep=";", decimal=",")
    label = f.stem.replace(" soluzione (-1.6_1.2", "").replace(")_ultimo_ciclo", "").replace("_ultimo_ciclo", "")
    label = label.replace("AL7 10 mgml", "").strip()
    if not label.lower().startswith("scan"):
        label = "scan " + label.split("scan")[-1].strip() if "scan" in label else label
    c = df.iloc[:, 2]  # column C: Potential normalized/V
    e = df.iloc[:, 4]  # column E: last column
    combined[f"{label} - Potential normalized/V"] = c.reset_index(drop=True)
    combined[f"{label} - {df.columns[4]}"] = e.reset_index(drop=True)
    series.append((label, c, e))

out_df = pd.concat(combined, axis=1)
out_csv = folder / "combined_C_E.csv"
out_df.to_csv(out_csv, sep=";", decimal=",", index=False)
print(f"Saved {out_csv}")

fig, ax = plt.subplots(figsize=(9, 6), dpi=150)
fig.patch.set_facecolor("#fcfcfb")
ax.set_facecolor("#fcfcfb")

for i, (label, c, e) in enumerate(series):
    ax.plot(c, e, label=label, color=colors[i % len(colors)], linewidth=2)

ax.set_xlabel("Potential normalized / V", color="#0b0b0b")
ax.set_ylabel("Current / sqrt(scan rate) / scan rate", color="#0b0b0b")
ax.set_title("Confronto cicli - Potenziale normalizzato vs ultima colonna", color="#0b0b0b")
ax.tick_params(colors="#52514e")
ax.grid(True, color="#e1e0d9", linewidth=0.8)
for spine in ax.spines.values():
    spine.set_color("#c3c2b7")
legend = ax.legend(frameon=False, labelcolor="#0b0b0b")

out_png = folder / "combined_plot.png"
fig.savefig(out_png, facecolor=fig.get_facecolor())
print(f"Saved {out_png}")