Yves Biener
7581966c88
The final API will be derived from these scripts into a different repository, which then only holds the corresponding functions that provide the corresponding functionalities described in the associated master thesis.
41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
import matplotlib.pyplot as plt
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from matplotlib.collections import LineCollection
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import numpy as np
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b = 0.7
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c0 = 0.2
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m = 0.85
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d_curve = np.linspace(0.3, 0.9, 500)
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C_curve = np.piecewise(
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d_curve,
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[d_curve <= b, d_curve > b],
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[lambda x: m * x + c0, lambda x: m * b + c0]
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)
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fig, ax = plt.subplots(figsize=(15, 12))
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ax.set_xlabel('Distance to Bounding-Box')
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ax.set_ylabel('Centrality Value')
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ex = [0.5, 0.7, 0.5, 0.5]
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ey = [m*b + c0, m*b + c0, m*0.5 + c0, m*b + c0]
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ch = LineCollection([np.column_stack([ex, ey])], colors=['r'], linewidths=0.5)
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ax.add_collection(ch)
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ax.annotate("$𝛿$", xy=(0.48, 0.71), xytext=(0.48, 0.71), fontsize=18)
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ax.scatter([0.5, 0.8, 0.85], [0.5, 0.75, 0.9], color='w', s=1)
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ax.plot(d_curve, C_curve, color='k', linewidth=0.8)
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ax.annotate("$x$", xy=(0.5, m*0.5 + c0 - 0.01), xytext=(0.5, m*0.5 + c0 - 0.01), fontsize=10)
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ax.annotate("$b$", xy=(b, m*b + c0 + 0.005), xytext=(b, m*b + c0 + 0.005), fontsize=10)
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# ax.annotate("$f(x) = m*x + c_0$", xy=(0.32, 0.58), xytext=(0.32, 0.58), fontsize=10)
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# ax.annotate("$g(x) = m*b + c_0$", xy=(0.75, 0.8), xytext=(0.75, 0.8), fontsize=10)
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ax.axis('off')
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# ax.get_xaxis().set_visible(False)
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# ax.get_yaxis().set_visible(False)
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ax.set_aspect('equal')
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fig.savefig("model_correction_visualization.svg", format='svg', bbox_inches='tight', pad_inches=0)
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