import matplotlib.pyplot as plt
import numpy as np


# creates horizontal bar plots based on the classification results

def horizontal_bar_plot(index, confidence, label):
    
    plt.rcParams["font.family"] = "Arial"

    fig, ax = plt.subplots()
    fig.set_size_inches(8, 0.9)
    
    ax.barh([0], [1], color='lightgray', edgecolor='black', linewidth=3)  

    if index == 0:
        i = 0.1
    elif index == 1:
        i = 0.5
    else:
        i = 0.9
    
    plt.title(label, fontsize=16, fontweight='light', color='black', loc='center')
    ax.scatter([i], [0], color='#00509b', marker="s", s=2000, edgecolors='black', linewidth=2)
    #ax.text(i, 0, f"C={confidence:.2f}", fontsize=12, color='white', ha='center', va='center')

    ax.set_xlim(-0.2, 1.2)  # Adjust x-limits
    ax.set_ylim(-0.5, 0.5)
    ax.set_xticks([])
    ax.set_yticks([])

    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)

    return fig


def find_max_index(array):
    max_index = np.argmax(array)
    return max_index, array[max_index]

def check_classification(array):
    feed = array[0:3]
    depth = array[3:6]
    condition = array[6:9]

    feed_index, feed = find_max_index(feed)
    fig_feed = horizontal_bar_plot(feed_index, feed, "Vorschub")

    depth_index, depth = find_max_index(depth)
    fig_depth = horizontal_bar_plot(depth_index, depth, "Schnitttiefe")

    condition_index, condition = find_max_index(condition)
    fig_condition = horizontal_bar_plot(condition_index, condition, "Verschleiß")

    return fig_feed, fig_depth, fig_condition