极客教程Matplotlib AGG Filter
介绍
Matplotlib是一个Python的绘图库，提供了丰富的绘图功能，可以用于生成各种类型的图表，如折线图、散点图、柱状图等。Matplotlib的AGG filter是一种用于图像渲染的滤镜，可以提高图像的质量和性能。
AGG filter是Matplotlib中的一个渲染引擎，它使用了一种叫做Anti-Grain Geometry的图形渲染库，可以生成高质量的图像。AGG filter可以用于生成各种类型的图像，包括线条、文本、图像等。
安装
AGG filter是Matplotlib的一个内置模块，因此不需要额外安装。只需要安装Matplotlib即可使用AGG filter。
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()
Output:
使用
AGG filter可以通过设置Matplotlib的rcParams参数来启用。rcParams是Matplotlib的全局配置参数，可以用于设置各种参数，如图像大小、字体大小、线条宽度等。
import matplotlib as mpl
mpl.rcParams['backend'] = 'agg'
示例
示例1：绘制折线图
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.title('Sin Wave')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
Output:
示例2：绘制散点图
import matplotlib.pyplot as plt
import numpy as np
x = np.random.rand(100)
y = np.random.rand(100)
colors = np.random.rand(100)
sizes = 1000 * np.random.rand(100)
plt.scatter(x, y, c=colors, s=sizes, alpha=0.5)
plt.title('Scatter Plot')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
Output:
示例3：绘制柱状图
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(3)
y = [10, 20, 30]
plt.bar(x, y)
plt.xticks(x, ['A', 'B', 'C'])
plt.title('Bar Chart')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
Output:
示例4：绘制饼图
import matplotlib.pyplot as plt
labels = ['A', 'B', 'C', 'D']
sizes = [15, 30, 45, 10]
colors = ['red', 'green', 'blue', 'yellow']
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%')
plt.title('Pie Chart')
plt.show()
Output:
示例5：绘制等高线图
import matplotlib.pyplot as plt
import numpy as np
def f(x, y):
return np.sin(x) + np.cos(y)
x = np.linspace(0, 5, 100)
y = np.linspace(0, 5, 100)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
plt.contour(X, Y, Z)
plt.title('Contour Plot')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
Output:
示例6：绘制3D图
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))
ax.plot_surface(X, Y, Z)
ax.set_title('3D Surface Plot')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
Output:
示例7：绘制热力图
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))
plt.imshow(Z, cmap='hot', interpolation='nearest')
plt.colorbar()
plt.title('Heatmap')
plt.show()
Output:
示例8：绘制极坐标图
import matplotlib.pyplot as plt
import numpy as np
theta = np.linspace(0, 2*np.pi, 100)
r = np.sin(3*theta)
ax = plt.subplot(111, projection='polar')
ax.plot(theta, r)
ax.set_title('Polar Plot')
plt.show()
Output:
示例9：绘制条形图
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(3)
y = [10, 20, 30]
plt.barh(x, y)
plt.yticks(x, ['A', 'B', 'C'])
plt.title('Horizontal Bar Chart')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
Output:
示例10：绘制箱线图
import matplotlib.pyplot as plt
import numpy as np
data = np.random.normal(size=(100, 4), loc=0, scale=1)
plt.boxplot(data)
plt.title('Box Plot')
plt.show()
Output:
示例11：绘制面积图
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
plt.fill_between(x, y1, y2, where=y1>y2, interpolate=True, color='green', alpha=0.5)
plt.fill_between(x, y1, y2, where=y1<y2, interpolate=True, color='red', alpha=0.5)
plt.title('Area Plot')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
Output:
示例12：绘制多图
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot(x, y1)
axs[0, 0].set_title('Sin Wave')
axs[0, 1].plot(x, y2)
axs[0, 1].set_title('Cos Wave')
axs[1, 0].scatter(x, y1)
axs[1, 0].set_title('Sin Scatter')
axs[1, 1].scatter(x, y2)
axs[1, 1].set_title('Cos Scatter')
plt.show()
Output:
示例13：绘制动画
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.animation import FuncAnimation
fig, ax = plt.subplots()
x = np.linspace(0, 10, 100)
y = np.sin(x)
line, = ax.plot(x, y)
def update(i):
line.set_ydata(np.sin(x + i/10))
return line,
ani = FuncAnimation(fig, update, frames=100, interval=50, blit=True)
plt.show()
Output:
示例14：绘制3D散点图
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
colors = np.random.rand(100)
sizes = 1000 * np.random.rand(100)
ax.scatter(x, y, z, c=colors, s=sizes, alpha=0.5)
ax.set_title('3D Scatter Plot')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
Output:
示例15：绘制极坐标条形图
import matplotlib.pyplot as plt
import numpy as np
theta = np.linspace(0, 2*np.pi, 10)
radii = 10 * np.random.rand(10)
width = np.pi / 4 * np.random.rand(10)
ax = plt.subplot(111, projection='polar')
bars = ax.bar(theta, radii, width=width, bottom=0.0)
ax.set_title('Polar Bar Chart')
plt.show()
Output:
示例16：绘制等高线3D图
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.contour3D(X, Y, Z, 50)
ax.set_title('3D Contour Plot')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
Output:
示例17：绘制极坐标热力图
import matplotlib.pyplot as plt
import numpy as np
r = np.linspace(0, 1, 100)
theta = 2 * np.pi * r
plt.subplot(111, polar=True)
plt.plot(theta, r)
plt.title('Polar Heatmap')
plt.show()
Output:
示例18：绘制极坐标散点图
import matplotlib.pyplot as plt
import numpy as np
theta = 2 * np.pi * np.random.rand(100)
r = np.random.rand(100)
plt.subplot(111, polar=True)
plt.scatter(theta, r)
plt.title('Polar Scatter Plot')
plt.show()
Output:
示例19：绘制3D极坐标图
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
theta = np.linspace(0, 2*np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.cos(theta)
y = r * np.sin(theta)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(x, y, z)
ax.set_title('3D Polar Plot')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
Output:
示例20：绘制3D极坐标散点图
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
theta = 2 * np.pi * np.random.rand(100)
r = np.random.rand(100)
z = np.random.rand(100)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(theta, r, z)
ax.set_title('3D Polar Scatter Plot')
ax.set_xlabel('Theta')
ax.set_ylabel('R')
ax.set_zlabel('Z')
plt.show()
Output:
结论
AGG filter是Matplotlib中用于图像渲染的滤镜，可以生成高质量的图像。通过设置Matplotlib的rcParams参数，可以启用AGG filter，并使用它来绘制各种类型的图表，包括折线图、散点图、柱状图、饼图、等高线图、3D图、热力图、极坐标图等。AGG filter不仅可以提高图像的质量，还可以提高图像的性能，是Matplotlib中非常重要的一个模块。