SciPy定义了一些用于计算点集之间距离的有用函数。
函数scipy.spatial.distance.pdist计算给定集合中所有点对之间的距离：
import numpy as np
from scipy.spatial.distance import pdist, squareform
# Create the following array where each row is a point in 2D space:
# [[0 1]
# [1 0]
# [2 0]]
x = np.array([[0, 1], [1, 0], [2, 0]])
print(x)
# Compute the Euclidean distance between all rows of x.
# d[i, j] is the Euclidean distance between x[i, :] and x[j, :],
# and d is the following array:
# [[ 0. 1.41421356 2.23606798]
# [ 1.41421356 0. 1. ]
# [ 2.23606798 1. 0. ]]
d = squareform(pdist(x, 'euclidean'))
print(d)
Matplotlib
Matplotlib是一个绘图库。本节简要介绍 matplotlib.pyplot 模块，该模块提供了类似于MATLAB的绘图系统。
绘制
matplotlib中最重要的功能是plot，它允许你绘制2D数据的图像。这是一个简单的例子：
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)
plt.plot(x,y_sin)
plt.plot(x,y_cos)
plt.xlabel('x axis label')-横坐标
plt.ylabel('y axis label')-纵坐标
plt.title('Sine and Cosine')-标题
plt.legend(['Sine','Cosine'])-标识
plt.show()
子图
https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplot
你可以使用subplot函数在同一个图中绘制不同的东西。 这是一个例子：
import numpy as np
import matplotlib.pyplot as plt
# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)
# Set up a subplot grid that has height 2 and width 1,
# and set the first such subplot as active.
plt.subplot(2, 1, 1)-(2,1,2)一起在下方图中
# Make the first plot
plt.plot(x, y_sin)
plt.title('Sine')
# Set the second subplot as active, and make the second plot.
plt.subplot(2, 1, 2) -（2，1，1）一起在上方图中
plt.plot(x, y_cos)
plt.title('Cosine')
# Show the figure.
plt.show()
图片
你可以使用 imshow 函数来显示一张图片。 这是一个例子：
import numpy as np
from scipy.misc import imread, imresize
import matplotlib.pyplot as plt
img = imread('assets/cat.jpg')
img_tinted = img * [1, 0.95, 0.9]
# Show the original image
plt.subplot(1, 2, 1)
plt.imshow(img)
# Show the tinted image
plt.subplot(1, 2, 2)
# A slight gotcha with imshow is that it might give strange results
# if presented with data that is not uint8. To work around this, we
# explicitly cast the image to uint8 before displaying it.
plt.imshow(np.uint8(img_tinted))
plt.show()
