import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

def analyze_paper_breakdown(lengths, voltages, ed_standard_values):
    lengths = np.array(lengths)
    voltages = np.array(voltages)
    ed_real = voltages / lengths

    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8), sharex=True)

    colors = ['red', 'green', 'purple', 'orange', 'brown', 'cyan', 'magenta', 'olive']
    if len(ed_standard_values) > len(colors):
        import itertools
        colors = list(itertools.islice(itertools.cycle(colors), len(ed_standard_values)))

    # Feste rote Farbe
    red_color = 'red'

    ax1.plot(lengths, voltages, label="U (gemessen)", marker='o', linestyle='-', color='blue')

    for i, ed_standard in enumerate(ed_standard_values):
        u_erwartung = lengths * ed_standard
        ax1.plot(lengths, u_erwartung, label=f"U (Erwartung, {ed_standard} kV/mm)", marker='x', linestyle='--', color=colors[i])

        if ed_standard == 5:
            deviation = u_erwartung - voltages
            mask = (ed_real >= 4) & (ed_real <= 6)
            for k in np.where(mask)[0]:
                x_vals = lengths[k:k+2]
                if len(x_vals) == 2:
                    if deviation[k] > 0:
                        ax1.fill_between(x_vals, voltages[k:k+2], u_erwartung[k:k+2], color=red_color, alpha=0.5, zorder=5)
                    elif deviation[k] < 0:
                        ax1.fill_between(x_vals, u_erwartung[k:k+2], voltages[k:k+2], color=red_color, alpha=0.5, zorder=5)
            #Füllen im unteren Graphen
            ed_5 = np.full_like(lengths, 5) # Erzeugt ein Array mit dem Wert 5
            for k in np.where(mask)[0]:
                x_vals = lengths[k:k+2]
                if len(x_vals) == 2:
                    if ed_real[k] > 5:
                        ax2.fill_between(x_vals, ed_5[k:k+2], ed_real[k:k+2], color=red_color, alpha=0.5, zorder=5)
                    elif ed_real[k] < 5:
                        ax2.fill_between(x_vals, ed_real[k:k+2],ed_5[k:k+2], color=red_color, alpha=0.5, zorder=5)



    ax1.set_ylabel("Spannung U [kV]")
    #ax1.set_title("Durchschlageigenschaften in Abhängigkeit von Materialstärke")

    handles1, labels1 = ax1.get_legend_handles_labels()
    by_label = dict(zip(labels1, handles1))
    ax1.legend(by_label.values(), by_label.keys(), loc='upper left')

    ax1.grid(True)

    ax2.plot(lengths, ed_real, label="ED (gemessen)", marker='s', color='blue', linestyle='-')
    for i, ed_standard in enumerate(ed_standard_values):
        ax2.plot(lengths, np.full_like(lengths, ed_standard), label=f"ED (Erwartung, {ed_standard} kV/mm)", linestyle='--', color=colors[i])

    ax2.set_xlabel("Materialstärke l [mm]")
    ax2.set_ylabel("Durchschlagsfeldstärke E [kV/mm]")
    ax2.legend(loc='lower left')
    ax2.grid(True)

    fig.tight_layout()
    plt.show()
    return fig

# Beispieldaten
papier_lengths = [2, 3, 4, 5]
papier_voltages = [11, 15.1, 16.6, 21.6]
ed_standard_values = [4, 5, 6]

# Analyse und Anzeige
fig = analyze_paper_breakdown(papier_lengths, papier_voltages, ed_standard_values)

papier_ed_real = np.array(papier_voltages) / np.array(papier_lengths)
print(f"Gemessene Durchschlagsfeldstärken (ED-Real): {papier_ed_real} kV/mm")