【编程语言】Python-Matplotlib

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0 引言

如果想用Python来取代Matlab，至少有一个库是必须要学习的，那就是matplotlib。数学建模的时候，C++出数据，但是出不了图的“窘困”还历历在目。为了一站式完成出数据和出图。痛下决心学习一下matplotlib。

1 基本功能

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import matplotlib.pyplot as plt
import numpy as np
# generate some data for x and y
x = np.linspace(-3,3,1000)
y = 2*x+1
y_ = x**2
plt.figure(num=2, figsize=(8,6))  # create figure and set number/size
l1 = plt.plot(x, y, label='linear') # plot
l2 = plt.plot(x, y_, color='red', linewidth=1.2, linestyle='--', label='non-linear')
plt.xlim(-1,2)  # axis x limitation
plt.ylim(-2,4)
plt.xlabel(r'$time:\ \tau$')  # label
plt.ylabel(r'$d_a$')
plt.title(r'$Bisc\ Figure\ Demo$') # title
plt.legend(labels=['best linear'], loc='lower right') # legend
plt.yticks([2],[r'$thresh$'])  # add tick for axis
# gca = 'get current axis'
ax = plt.gca()
ax.spines['right'].set_color('none')  # remove right and top axis
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom') 
ax.yaxis.set_ticks_position('left')
ax.spines['bottom'].set_position(('data', 0))  # set bottom and left axis position
ax.spines['left'].set_position(('data', 0))
x0 = 1
y0 = 2*x0 + 1
plt.scatter(x0, y0, s=50, color='b')   # scatter a point 
plt.plot([x0,x0],[y0,0], 'k--', lw=2.5) # draw a line
# annotation method one
plt.annotate(r'$2x+1=%s$'%y0,xy=(x0,y0),xycoords='data',xytext=(+30,-30),textcoords='offset points', fontsize = 16, arrowprops=dict(arrowstyle='->',connectionstyle='arc3,rad=0.2'))
# method two
plt.text(-0.7,3, r'$This\ is\ the\ some\ text.\mu\ \sigma_i\ \alpha^2$',
        fontdict={'size':16, 'color':'r'})
# make axis index or tick more clear 
for label in ax.get_xticklabels()+ax.get_yticklabels():
    label.set_fontsize(12)
    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.7))
plt.show()

2 其他图像类型

2.1 Scatter

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import matplotlib.pyplot as plt
import numpy as np
n = 1024    # data size
X = np.random.normal(0, 1, n) # 每一个点的X值
Y = np.random.normal(0, 1, n) # 每一个点的Y值
T = np.arctan2(Y,X) # for color value
plt.scatter(X, Y, s=75, c=T, alpha=.5)
plt.xlim(-1.5, 1.5)
plt.xticks(())  # ignore xticks
plt.ylim(-1.5, 1.5)
plt.yticks(())  # ignore yticks
plt.show()

2.2 Bar

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import matplotlib.pyplot as plt
import numpy as np
n = 12
X = np.arange(n)
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)
plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')
for x, y in zip(X, Y1):
    # ha: horizontal alignment
    # va: vertical alignment
    plt.text(x + 0.4, y + 0.05, '%.2f' % y, ha='center', va='bottom')
for x, y in zip(X, Y2):
    # ha: horizontal alignment
    # va: vertical alignment
    plt.text(x + 0.4, -y - 0.05, '-%.2f' % y, ha='center', va='top')
plt.xlim(-.5, n)
plt.xticks(())
plt.ylim(-1.25, 1.25)
plt.yticks(())
plt.show()

2.3 Contours

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import matplotlib.pyplot as plt
import numpy as np
def f(x,y):
    # the height function
    return (1 - x / 2 + x**5 + y**3) * np.exp(-x**2 -y**2)
n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)
X,Y = np.meshgrid(x, y)
# use plt.contourf to filling contours
# X, Y and value for (X,Y) point
plt.contourf(X, Y, f(X, Y), 8, alpha=.75, cmap=plt.cm.hot)
# use plt.contour to add contour lines
C = plt.contour(X, Y, f(X, Y), 8, colors='black', linewidth=.5)
plt.clabel(C, inline=True, fontsize=10)
plt.xticks(())
plt.yticks(())
plt.show()

2.4 Image

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import matplotlib.pyplot as plt
import numpy as np
a = np.array([0.313660827978, 0.365348418405, 0.423733120134,
              0.365348418405, 0.439599930621, 0.525083754405,
              0.423733120134, 0.525083754405, 0.651536351379]).reshape(3,3)
plt.imshow(a, interpolation='nearest', cmap='bone', origin='lower')
plt.colorbar(shrink=.92)
plt.xticks(())
plt.yticks(())
plt.show()

2.5 3D

文章开头那张图的感觉，有木有？

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import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
# X, Y value
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)    # x-y 平面的网格
R = np.sqrt(X ** 2 + Y ** 2)
# height value
Z = np.sin(R)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.get_cmap('rainbow'))
ax.contourf(X, Y, Z, zdir='x', offset=-4, cmap=plt.get_cmap('rainbow'))
ax.contourf(X, Y, Z, zdir='y', offset=4, cmap=plt.get_cmap('rainbow'))
plt.show()

3 多图合并与显示

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import matplotlib.pyplot as plt
plt.figure()
plt.subplot(2,2,1)
plt.plot([0,1],[0,1])
plt.subplot(2,2,2)
plt.plot([0,1],[0,2])
plt.subplot(223)
plt.plot([0,1],[0,3])
plt.subplot(224)
plt.plot([0,1],[0,4])
plt.show()  # 展示

更复杂的多图排列

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import matplotlib.pyplot as plt
plt.figure()
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)
ax1.plot([1, 2], [1, 2])    # 画小图
ax1.set_title('ax1_title')  # 设置小图的标题
ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=2)
ax3 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)
ax4 = plt.subplot2grid((3, 3), (2, 0))
ax5 = plt.subplot2grid((3, 3), (2, 1))
ax4.scatter([1, 2], [3, 4])
ax4.set_xlabel('ax4_x')
ax4.set_ylabel('ax4_y')
plt.show()

图中图

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# 导入pyplot模块
import matplotlib.pyplot as plt
# 初始化figure
fig = plt.figure()
# 创建数据
x = [1, 2, 3, 4, 5, 6, 7]
y = [1, 3, 4, 2, 5, 8, 6]
left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
ax1 = fig.add_axes([left, bottom, width, height])
ax1.plot(x, y, 'r')
ax1.set_xlabel('x')
ax1.set_ylabel('y')
ax1.set_title('title')
left, bottom, width, height = 0.2, 0.6, 0.25, 0.25
ax2 = fig.add_axes([left, bottom, width, height])
ax2.plot(y, x, 'b')
ax2.set_xlabel('x')
ax2.set_ylabel('y')
ax2.set_title('title inside 1')
plt.axes([0.6, 0.2, 0.25, 0.25])
plt.plot(y, y, 'g') # 注意对y进行了逆序处理
plt.xlabel('x')
plt.ylabel('y')
plt.title('title inside 2')
plt.show()

次坐标轴

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import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0, 10, 0.1)
y1 = 0.05 * x**2
y2 = -1 * y1
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(x, y1, 'g-')   # green, solid line
ax1.set_xlabel('X data')
ax1.set_ylabel('Y1 data', color='g')
ax2.plot(x, y2, 'b-') # blue
ax2.set_ylabel('Y2 data', color='b')
plt.show()

4 Animation 动画

暂时不讨论

参考文献

[1] 利用Python进行数据分析
[2] Morvan matplotlib lecture
[3] http://matplotlib.org/gallery.html

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