add interpolations graphics for thesis

visualisation.py

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+import matplotlib.pyplot as plt
+import numpy as np
+# from scipy.spatial import voronoi_plot_2d, Delaunay
+from mpl_toolkits.mplot3d import Axes3D
+from scipy.spatial.qhull import Delaunay
+from matplotlib.axes import Axes
+
+np.random.seed(10)
+
+points = np.random.rand(100, 2)
+
+testpoint = (0.7, 0.7)
+fig1 = plt.figure()
+ax = fig1.add_subplot()  # type:Axes
+fig2 = plt.figure()
+ax2 = fig2.add_subplot()  # type:Axes
+fig3=plt.figure()
+ax3d = fig3.add_subplot(projection='3d')  # type:Axes3D
+
+# vor = Voronoi(grid)
+#
+#
+# fig = voronoi_plot_2d(vor)
+tri = Delaunay(points)
+print(type(tri))
+ax.scatter(points[:, 0], points[:, 1])
+ax2.scatter(points[:, 0], points[:, 1])
+#
+center_tri = tri.find_simplex(np.array([testpoint]))[0]
+print(center_tri)
+print(tri.simplices[center_tri])
+
+ax.scatter(*testpoint, color="green", zorder=2)
+ax2.scatter(*testpoint, color="green", zorder=2)
+close_points = []
+for dot in tri.simplices[center_tri]:
+    point = tri.points[dot]
+    z = (point[0] - 0.5) ** 2 + (point[1] - 0.5) ** 2
+    close_points.append([point[0], point[1], z])
+    print(point)
+    print("test")
+    ax2.scatter(point[0], point[1], color="red", zorder=2)
+    ax3d.scatter(point[0], point[1], z, color="red", zorder=2)
+ax2.triplot(points[:, 0], points[:, 1], tri.simplices.copy())
+# plt.show()
+# plt.close()
+close_points = np.asarray(close_points)
+z = (points[:, 0] - 0.5) ** 2 + (points[:, 1] - 0.5) ** 2
+
+ax3d.scatter(points[:, 0], points[:, 1], z)
+
+p1, p2, p3 = close_points
+
+v1 = p3 - p1
+v2 = p2 - p1
+
+print(v1, v2)
+
+# the cross product is a vector normal to the plane
+cp = np.cross(v1, v2)
+print(cp)
+a, b, c = cp
+
+# This evaluates a * x3 + b * y3 + c * z3 which equals d
+d = np.dot(cp, p3)
+
+x = np.linspace(0.5, .9, 5)
+y = np.linspace(0.5, .9, 5)
+X, Y = np.meshgrid(x, y)
+
+Z = (d - a * X - b * Y) / c
+
+z_center = (d - a * testpoint[0] - b * testpoint[1]) / c
+
+ax3d.scatter(*testpoint, z_center, color="green")
+
+ax3d.plot_surface(X, Y, Z, color="lightgreen", alpha=0.4)
+
+
+fig1.savefig("../arbeit/images/vis2d1.pdf")
+fig2.savefig("../arbeit/images/vis2d2.pdf")
+fig3.savefig("../arbeit/images/vis2d3.pdf")
+plt.show()

visualisation1d.py

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