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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
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