Matplotlib 绘图空间分隔数据数组

Question

假设每条线包含单个点的 x 和 y 坐标，那么格式化此数组数据的最简单方法是什么，以便我可以迭代连接图形上的点？

import matplotlib.pyplot as plt
(...)

<class 'numpy.ndarray'>
[-22.58343371   7.97162262]
[-49.08400669 -28.64111278]
[-71.47754547 -25.78248676]
[-46.27120899 -21.72541444]
[ 43.6158669  109.61815799]
[-22.58343371   7.97162262]

(...)

plt.plot(x, y, color='orange')

抱歉，由于 Quang Hoang 的评论，以下几乎是完整的代码（顺序由另一个算法计算）。这显然是众所周知的 tsp 问题解决方案的一部分。当然，重点是根据列表中的正确顺序连接点，在这种情况下为：(0, 2, 1, 3, 4, 0)。

from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np

n = 5
order = (0, 2, 1, 3, 4, 0)

distances = [[0, 39, 22, 59, 54, 33, 57, 32, 89, 73, 29, 46],
             [39, 0, 20, 20, 81, 8, 49, 64, 63, 84, 10, 61],
             [22, 20, 0, 39, 74, 18, 60, 44, 71, 73, 11, 46],
             [59, 20, 39, 0, 93, 27, 51, 81, 48, 80, 30, 69],
             [54, 81, 74, 93, 0, 73, 43, 56, 104, 76, 76, 77],
             [33, 8, 18, 27, 73, 0, 45, 61, 71, 88, 8, 63],
             [57, 49, 60, 51, 43, 45, 0, 85, 88, 115, 52, 103],
             [32, 64, 44, 81, 56, 61, 85, 0, 74, 43, 55, 23],
             [89, 63, 71, 48, 104, 71, 88, 74, 0, 38, 69, 51],
             [73, 84, 73, 80, 76, 88, 115, 43, 38, 0, 81, 28],
             [29, 10, 11, 30, 76, 8, 52, 55, 69, 81, 0, 55],
             [46, 61, 46, 69, 77, 63, 103, 23, 51, 28, 55, 0]]

pca = PCA(n_components=2)
coord = pca.fit_transform(distances[:n])

plt.scatter(coord[:,0], coord[:,1])

for i in coord:
    x = np.where(coord == i)
    plt.annotate((x[0][0]) ,i, color='red')

for j in order:
    print(coord[j])
    plt.plot(coord[:,0], coord[:,1], color='orange')
plt.show()

Answer 1

有时，经过几个小时后，事情变得比实际更困难：) 非常感谢 Kapocsi！下面的工作解决方案：

from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np

n = 5
order = [0, 2, 1, 3, 4, 0]

distances = [[0, 39, 22, 59, 54, 33, 57, 32, 89, 73, 29, 46],
             [39, 0, 20, 20, 81, 8, 49, 64, 63, 84, 10, 61],
             [22, 20, 0, 39, 74, 18, 60, 44, 71, 73, 11, 46],
             [59, 20, 39, 0, 93, 27, 51, 81, 48, 80, 30, 69],
             [54, 81, 74, 93, 0, 73, 43, 56, 104, 76, 76, 77],
             [33, 8, 18, 27, 73, 0, 45, 61, 71, 88, 8, 63]]

pca = PCA(n_components=2)
coord = pca.fit_transform(distances[:n])

plt.scatter(coord[:,0], coord[:,1])

for i in coord:
    x = np.where(coord == i)
    plt.annotate((x[0][0]) ,i, color='red')

x = coord[order]
print(x)

plt.plot(x[:, 0], x[:, 1], color='orange', marker='o', linestyle='-')
plt.show()

Answer 2

您可能需要对数据进行排序以获得所需的可视化效果：

import numpy as np
import matplotlib.pyplot as plt


a = np.array([[-22.58343371,   7.97162262],
              [-49.08400669, -28.64111278],
              [-71.47754547, -25.78248676],
              [-46.27120899, -21.72541444],
              [ 43.6158669 , 109.61815799],
              [-22.58343371,   7.97162262]])


order = a[:, 0].argsort()
plt.plot(a[order, 0], a[order, 1], color='orange', marker='o', linestyle='-')
plt.show()

如果你不排序，你会得到这样的结果：

import numpy as np
import matplotlib.pyplot as plt


a = np.array([[-22.58343371,   7.97162262],
              [-49.08400669, -28.64111278],
              [-71.47754547, -25.78248676],
              [-46.27120899, -21.72541444],
              [ 43.6158669 , 109.61815799],
              [-22.58343371,   7.97162262]])

plt.plot(a[:, 0], a[:, 1], color='orange', marker='o', linestyle='-')
plt.show()