【704】Python绘制三维曲线
参考:用三维的视角理解二维世界
参考:Matplotlib - 3D Surface plot
参考:PLOT_SURFACE(AXES3D)方法:绘制3D图形
参考:python 3d图
import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D fig = plt.figure() ax = Axes3D(fig)

X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X ** 2 + Y ** 2)
# height value
Z = np.sin(R)
fig = plt.figure(figsize=(9, 6))
ax = fig.gca(projection='3d')
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'))
strike = np.arange(-4, 4, 0.25)
ttm = np.arange(-4, 4, 0.25)
strike, ttm = np.meshgrid(strike, ttm)
iv = np.sqrt(strike ** 2 + ttm ** 2)
# generate fake implied volatilities
fig = plt.figure(figsize=(9, 6))
ax = fig.gca(projection='3d')
'''同上面两行代码
fig = plt.figure(figsize=(12, 8))
ax = Axes3D(fig)
'''
surf = ax.plot_surface(strike, ttm, iv, rstride=2,
cstride=2, cmap=plt.get_cmap('rainbow'),
linewidth=0.5, antialiased=True)
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(12, 8))
ax = Axes3D(fig)
delta = 0.125
# 生成代表X轴数据的列表
x = np.arange(-3.0, 3.0, delta)
# 生成代表Y轴数据的列表
y = np.arange(-2.0, 2.0, delta)
# 对x、y数据执行网格化
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
# 计算Z轴数据(高度数据)
Z = (Z1 - Z2) * 2
# 绘制3D图形
ax.plot_surface(X, Y, Z,
rstride=1, # rstride(row)指定行的跨度
cstride=1, # cstride(column)指定列的跨度
cmap=plt.get_cmap('rainbow')) # 设置颜色映射
# 设置Z轴范围
ax.set_zlim(-2, 2)
# 设置标题
plt.title("3D图")
plt.show()

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.sans-serif']='Lisu'
plt.rcParams['axes.unicode_minus']=False
strike = np.linspace(50, 150, 24)
ttm = np.linspace(0.5, 2.5, 24)
strike, ttm = np.meshgrid(strike, ttm)
iv = (strike - 100) ** 2 / (100 * strike) / ttm
# generate fake implied volatilities
fig = plt.figure(figsize=(9, 6))
ax = fig.gca(projection='3d')
'''同上面两行代码
fig = plt.figure(figsize=(12, 8))
ax = Axes3D(fig)
'''
surf = ax.plot_surface(strike, ttm, iv, rstride=2,
cstride=2, cmap=plt.cm.coolwarm,
linewidth=0.5, antialiased=True)
ax.set_xlabel('strike')
ax.set_ylabel('time-to-maturity')
ax.set_zlabel('implied volatility')
fig.colorbar(surf, shrink=0.5, aspect=5)
#用三维的视角理解二维世界
#完美解释meshgrid函数,三维曲面,等高线
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#plt.rcParams['font.sans-serif']=['FangSong']# 用来正常显示中文标签
#plt.rcParams['axes.unicode_minus']=False# 用来正常显示负号
#meshgrid就是生成x1,y1所能代表的所有点的坐标矩阵
n=32
x1=np.linspace(-3,3,n)
y1=np.linspace(-3,3,n)
x, y = np.meshgrid(x1, y1)
#z = x - x
#z[0][0]=-1#变异值
#z[8][8]=1#变异值
#z = y - y 与z = x - x相同
z= np.power(x,2) + np.power(y,2)
fig=plt.figure()
ax = fig.add_subplot(111, projection='3d')
#ax.set_title('')
#三维曲面
ax.plot_surface(x,y,z,rstride=1,cstride=1,cmap=plt.get_cmap('rainbow'))
#等高线,其实就是投影,zdir代表了视角,offset表示离视角轴0点的距离
#z方向的等高线
ax.contourf(x,y,z,zdir='z',offset=-5,cmap=plt.get_cmap('rainbow'))
#x方向的等高线
#ax.contourf(x,y,z,zdir='x',offset=-5,cmap=plt.get_cmap('rainbow'))
#ax.contour(x,y,z,10,zdir='x',colors='black',linewidth=0.5)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
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