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https://github.com/Findus23/collision-analyisis-and-interpolation.git
synced 2024-09-19 15:13:50 +02:00
50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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np.random.seed(15)
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fig, (ax1, ax2) = plt.subplots(2, 1, gridspec_kw={'height_ratios': [1, 20]})
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# Hide the right and top spines
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ax1.spines['right'].set_visible(False)
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ax1.spines['top'].set_visible(False)
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ax2.spines['right'].set_visible(False)
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ax2.spines['top'].set_visible(False)
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ax1.spines['left'].set_visible(False)
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ax1.yaxis.set_visible(False)
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ax1.set_ylim(0, 1)
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ax1.xaxis.set_ticks_position('bottom')
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points = np.sort(np.random.rand(20))
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print(points)
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testpoint = 0.4
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ax1.scatter(points, np.zeros_like(points) + 0.5)
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ax1.scatter(testpoint, 0.5, color="green")
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for i, val in enumerate(points):
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if val > testpoint:
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next_i = i
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prev_i = i - 1
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break
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Y = np.sin(points * 6)
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ax2.scatter(points, Y)
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ax2.scatter(points[prev_i], Y[prev_i], color="red")
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ax1.scatter(points[prev_i], 0.5, color="red")
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ax2.scatter(points[next_i], Y[next_i], color="red")
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ax1.scatter(points[next_i], 0.5, color="red")
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a, b = np.polyfit([points[prev_i], points[next_i]], [Y[prev_i], Y[next_i]], 1)
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print(a, b)
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linex = np.linspace(0.3, 0.6, 2)
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liney = b + a * linex
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testy = b + a * testpoint
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ax2.plot(linex, liney, color="lightgreen", zorder=-2)
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ax2.scatter(testpoint, testy, color="green")
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plt.savefig("../arbeit/images/vis1d.pdf")
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plt.show()
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