The matplotlib library is specially used to develop 2D charts (including 3D charts), with outstanding advantages:
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It is extremely simple to use.
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Visualize data in a progressive, interactive way.
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Expressions and text are typeset using LaTeX.
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Strong control over image elements.
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It can output various formats such as PNG, PDF, SVG and EPS.
Install
conda install matplotlibor
pip install matplotlibmatplotlib architecture
One of the main tasks of matplotlib is to provide a set of functions and tools for representing and manipulating graphical objects (primary objects) and its internal objects. It not only handles graphics, but also provides event handling tools and the ability to add animation effects to graphics. With these additional features, matplotlib can generate interactive graphs of events triggered by keyboard key presses or mouse movements.
Logically speaking, the overall architecture of matplotlib is 3 layers, with one-way communication between layers:
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Scripting layer.
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Artist layer.
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Backend layer.
1. Basic usage of matplotlib
import numpy as npimport matplotlib.pyplot as pltx = np.linspace(-np.pi, np.pi, 30) # 在区间内生成30个等差数y = np.sin(x)print('x = ', x)print('y = ', y)
output:
x = [-3.14159265 -2.92493109 -2.70826953 -2.49160797 -2.2749464 -2.05828484-1.84162328 -1.62496172 -1.40830016 -1.19163859 -0.97497703 -0.75831547-0.54165391 -0.32499234 -0.10833078 0.10833078 0.32499234 0.541653910.75831547 0.97497703 1.19163859 1.40830016 1.62496172 1.841623282.05828484 2.2749464 2.49160797 2.70826953 2.92493109 3.14159265]y = [-1.22464680e-16 -2.14970440e-01 -4.19889102e-01 -6.05174215e-01-7.62162055e-01 -8.83512044e-01 -9.63549993e-01 -9.98533414e-01-9.86826523e-01 -9.28976720e-01 -8.27688998e-01 -6.87699459e-01-5.15553857e-01 -3.19301530e-01 -1.08119018e-01 1.08119018e-013.19301530e-01 5.15553857e-01 6.87699459e-01 8.27688998e-019.28976720e-01 9.86826523e-01 9.98533414e-01 9.63549993e-018.83512044e-01 7.62162055e-01 6.05174215e-01 4.19889102e-012.14970440e-01 1.22464680e-16]
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draw a curve
plt.figure() # 创建一个新的窗口plt.plot(x, y) # 画一个x与y相关的曲线plt.show()# 显示图像
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Draw multiple curves and add axes and labels
import numpy as npimport matplotlib.pyplot as pltx = np.linspace(-np.pi, np.pi, 100) # 在区间内生成21个等差数y = np.sin(x)linear_y = 0.2 * x + 0.1plt.figure(figsize = (8, 6)) # 自定义窗口的大小plt.plot(x, y)plt.plot(x, linear_y, color = "red", linestyle = '--') # 自定义颜色和表示方式plt.title('y = sin(x) and y = 0.2x + 0.1') # 定义该曲线的标题plt.xlabel('x') # 定义横轴标签plt.ylabel('y') # 定义纵轴标签plt.show()
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Specify the coordinate range and set the axis scale
import numpy as npimport matplotlib.pyplot as pltx = np.linspace(-np.pi, np.pi, 100) # 在区间内生成21个等差数y = np.sin(x)linear_y = 0.2 * x + 0.1plt.figure(figsize = (8, 6)) # 自定义窗口的大小plt.plot(x, y)plt.plot(x, linear_y, color = "red", linestyle = '--') # 自定义颜色和表示方式plt.title('y = sin(x) and y = 0.2x + 0.1') # 定义该曲线的标题plt.xlabel('x') # 定义横轴标签plt.ylabel('y') # 定义纵轴标签plt.xlim(-np.pi, np.pi)plt.ylim(-1, 1)# 重新设置x轴的刻度# plt.xticks(np.linspace(-np.pi, np.pi, 5))x_value_range = np.linspace(-np.pi, np.pi, 5)x_value_strs = [r'$\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$']plt.xticks(x_value_range, x_value_strs)plt.show() # 显示图像
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Define the axes with the origin at the center
import numpy as npimport matplotlib.pyplot as pltx = np.linspace(-np.pi, np.pi, 100)y = np.sin(x)linear_y = 0.2 * x + 0.1plt.figure(figsize = (8, 6))plt.plot(x, y)plt.plot(x, linear_y, color = "red", linestyle = '--')plt.title('y = sin(x) and y = 0.2x + 0.1')plt.xlabel('x')plt.ylabel('y')plt.xlim(-np.pi, np.pi)plt.ylim(-1, 1)# plt.xticks(np.linspace(-np.pi, np.pi, 5))x_value_range = np.linspace(-np.pi, np.pi, 5)x_value_strs = [r'$\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$']plt.xticks(x_value_range, x_value_strs)ax = plt.gca() # 获取坐标轴ax.spines['right'].set_color('none') # 隐藏上方和右方的坐标轴ax.spines['top'].set_color('none')# 设置左方和下方坐标轴的位置ax.spines['bottom'].set_position(('data', 0)) # 将下方的坐标轴设置到y = 0的位置ax.spines['left'].set_position(('data', 0)) # 将左方的坐标轴设置到 x = 0 的位置plt.show() # 显示图像
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legend legend
Replace the axis labels with the xticks() and yticks() functions, passing in two columns of values for each function. The first list stores the positions of the ticks, and the second list stores the labels of the ticks. import numpy as np
import matplotlib.pyplot as pltx = np.linspace(-np.pi, np.pi, 100)y = np.sin(x)linear_y = 0.2 * x + 0.1plt.figure(figsize = (8, 6))# 为曲线加上标签plt.plot(x, y, label = "y = sin(x)")plt.plot(x, linear_y, color = "red", linestyle = '--', label = 'y = 0.2x + 0.1')plt.title('y = sin(x) and y = 0.2x + 0.1')plt.xlabel('x')plt.ylabel('y')plt.xlim(-np.pi, np.pi)plt.ylim(-1, 1)# plt.xticks(np.linspace(-np.pi, np.pi, 5))x_value_range = np.linspace(-np.pi, np.pi, 5)x_value_strs = [r'$\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$']plt.xticks(x_value_range, x_value_strs)ax = plt.gca()ax.spines['right'].set_color('none')ax.spines['top'].set_color('none')ax.spines['bottom'].set_position(('data', 0))ax.spines['left'].set_position(('data', 0))# 将曲线的信息标识出来plt.legend(loc = 'lower right', fontsize = 12)plt.show()
Optional settings for the loc parameter in the legend method
| location string | position number | position statement |
|---|---|---|
| ‘best’ | 0 | Best location |
| ‘upper right’ | 1 | top right |
| ‘upper left’ | 2 | top left |
| ‘lower left’ | 3 | bottom left |
| ‘lower right’ | 4 | bottom right |
| ‘right’ | 5 | Right |
| ‘center left’ | 6 | Vertically Centered Left |
| ‘center right’ | 7 | Right vertical center |
| ‘lower center’ | 8 | Bottom horizontally centered |
| ‘upper center’ | 9 | Top horizontally centered |
| ‘center’ | 10 | The middle |
2. Histogram
Method used: plt.bar
import numpy as npimport matplotlib.pyplot as pltplt.figure(figsize = (16, 12))x = np.array([1, 2, 3, 4, 5, 6, 7, 8])y = np.array([3, 5, 7, 6, 2, 6, 10, 15])plt.plot(x, y, 'r', lw = 5) # 指定线的颜色和宽度x = np.array([1, 2, 3, 4, 5, 6, 7, 8])y = np.array([13, 25, 17, 36, 21, 16, 10, 15])plt.bar(x, y, 0.2, alpha = 1, color='b') # 生成柱状图，指明图的宽度，透明度和颜色plt.show()
Sometimes the histogram will appear on both sides of the x-axis for easy comparison. The code is implemented as follows:
import numpy as npimport matplotlib.pyplot as pltplt.figure(figsize = (16, 12))n = 12x = np.arange(n) # 按顺序生成从12以内的数字y1 = (1 - x / float(n)) * np.random.uniform(0.5, 1.0, n)y2 = (1 - x / float(n)) * np.random.uniform(0.5, 1.0, n)# 设置柱状图的颜色以及边界颜色#+y表示在x轴的上方 -y表示在x轴的下方plt.bar(x, +y1, facecolor = '#9999ff', edgecolor = 'white')plt.bar(x, -y2, facecolor = '#ff9999', edgecolor = 'white')plt.xlim(-0.5, n) # 设置x轴的范围，plt.xticks(()) # 可以通过设置刻度为空，消除刻度plt.ylim(-1.25, 1.25) # 设置y轴的范围plt.yticks(())# plt.text()在图像中写入文本，设置位置，设置文本，ha设置水平方向对其方式，va设置垂直方向对齐方式for x1, y in zip(x, y2):plt.text(x1, -y - 0.05, '%.2f' % y, ha = 'center', va = 'top')for x1, y in zip(x, y1):plt.text(x1, y + 0.05, '%.2f' % y, ha = 'center', va = 'bottom')plt.show()
3. Scatter plot
import numpy as npimport matplotlib.pyplot as pltN = 50x = np.random.rand(N)y = np.random.rand(N)colors = np.random.rand(N)area = np.pi * (15 * np.random.rand(N))**2plt.scatter(x, y, s = area,c = colors, alpha = 0.8)plt.show()
Four, contour map
import matplotlib.pyplot as pltimport numpy as npdef f(x, y):return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)n = 256x = np.linspace(-3, 3, n)y = np.linspace(-3, 3, n)X, Y = np.meshgrid(x, y) # 生成网格坐标 将x轴与y轴正方形区域的点全部获取line_num = 10 # 等高线的数量plt.figure(figsize = (16, 12))#contour 生成等高线的函数#前俩个参数表示点的坐标，第三个参数表示等成等高线的函数，第四个参数表示生成多少个等高线C = plt.contour(X, Y, f(X, Y), line_num, colors = 'black', linewidths = 0.5) # 设置颜色和线段的宽度plt.clabel(C, inline = True, fontsize = 12) # 得到每条等高线确切的值# 填充颜色, cmap 表示以什么方式填充，hot表示填充热量的颜色plt.contourf(X, Y, f(X, Y), line_num, alpha = 0.75, cmap = plt.cm.hot)plt.show()
Five, processing pictures
import matplotlib.pyplot as pltimport matplotlib.image as mpimg # 导入处理图片的库import matplotlib.cm as cm # 导入处理颜色的库colormapplt.figure(figsize = (16, 12))img = mpimg.imread('image/fuli.jpg')# 读取图片print(img) # numpy数据print(img.shape) #plt.imshow(img, cmap = 'hot')plt.colorbar() # 得到颜色多对应的数值plt.show()
[[[ 11 23 63][ 12 24 64][ 1 13 55]...[ 1 12 42][ 1 12 42][ 1 12 42]][[ 19 31 71][ 3 15 55][ 0 10 52]...[ 0 11 39][ 0 11 39][ 0 11 39]][[ 22 34 74][ 3 15 55][ 7 19 61]...[ 0 11 39][ 0 11 39][ 0 11 39]]...[[ 84 125 217][ 80 121 213][ 78 118 214]...[ 58 90 191][ 54 86 187][ 53 85 186]][[ 84 124 220][ 79 119 215][ 78 117 218]...[ 55 87 188][ 55 87 188][ 55 87 188]][[ 83 121 220][ 80 118 219][ 83 120 224]...[ 56 88 189][ 58 90 191][ 59 91 192]]](728, 516, 3)
Use numpy matrix to get pictures
import matplotlib.pyplot as pltimport matplotlib.cm as cm # 导入处理颜色的库colormapimport numpy as npsize = 8# 得到一个8*8数值在(0, 1)之间的矩阵a = np.linspace(0, 1, size ** 2).reshape(size, size)plt.figure(figsize = (16, 12))plt.imshow(a)plt.show()
6. 3D map
import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D # 导入Axes3D对象fig = plt.figure(figsize = (16, 12))ax = fig.add_subplot(111, projection = '3d') # 得到3d图像x = np.arange(-4, 4, 0.25)y = np.arange(-4, 4, 0.25)X, Y = np.meshgrid(x, y) # 生成网格Z = np.sqrt(X ** 2 + Y ** 2)# 画曲面图 # 行和列对应的跨度 # 设置颜色ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = plt.get_cmap('rainbow'))plt.show()
The above is the data visualization of matplotlib based on test data. Combined with the data in the actual project, the code can be slightly modified to have impressive effects.