Python画三维图-----插值平滑数据

一、二维的插值方法：

原始数据（x，y）
先对横坐标x进行扩充数据量，采用linspace。【如下面例子，由7个值扩充到300个】
采用scipy.interpolate中的spline来对纵坐标数据y进行插值【也由7个扩充到300个】。
画图

import matplotlib.pyplot as plt
import numpy as np
#数据
T = np.array([6, 7, 8, 9, 10, 11, 12])
power = np.array([1.53E+03, 5.92E+02, 2.04E+02, 7.24E+01, 2.72E+01, 1.10E+01, 4.70E+00])

#插值
from scipy.interpolate import spline
xnew = np.linspace(T.min(),T.max(),300) #300 represents number of points to make between T.min and T.max
power_smooth = spline(T,power,xnew)
print(xnew.shape)  #（300，）
print(power_smooth.shape)  #（300，）
#画图
plt.plot(xnew,power_smooth)
plt.show()

二、三维平滑图---插值：

1、数据：

x = [0.1,0.2,……，0.9] (shape = (9)）

y = [0.1,0.2,……，0.9] （shape = (9))

z = 【81个数据】（shape = (81））

生成数据：

x = np.linspace(0.1,0.9,9)
y = np.linspace(0.1,0.9,9)
z = np.random.rand(81)

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2、将x和y进行扩充到想要的大小：

【两种方法：np.arange和np.linspace】

xnew = np.arange(0.1, 1, 0.03) 【shape=（31）】
ynew = np.arange(0.1, 1, 0.03) 【shape=（31）】

或者

xnew = np.linspace(0.1, 0.9, 31)

ynew = np.linspace(0.1, 0.9, 31)

3、对z进行插值：

采用 scipy.interpolate.interp2d函数进行插值。

x,y原数据：【x.shape=9，y.shape=9，z.shape=81】

　　f = interpolate.interp2d(x, y, z, kind='cubic')

x,y扩充数据：【xnew.shape=31，y.shape=31】

　　znew = f(xnew, ynew) 【得到的znew.shape = (31,31)】

　　znew为插值后的z

4、画图：

采用 from mpl_toolkits.mplot3d import Axes3D进行画三维图

Axes3D简单用法：

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

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比如采用plot_trisurf画三维图：plot_trisurf（x,y,z)

【plot_trisurf对数据要求是：x.shape = y.shape = z.shape，所以x和y的shape需要修改，采用np.meshgrid，且都为一维数据】

#修改x,y，z输入画图函数前的shape
xx1, yy1 = np.meshgrid(xnew, ynew)

newshape = (xx1.shape[0])*(xx1.shape[0])
y_input = xx1.reshape(newshape)
x_input = yy1.reshape(newshape)
z_input = znew.reshape(newshape)

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5、所有代码：

# 载入模块
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import pandas as pd
import seaborn as sns
from scipy import interpolate

#生成数据
x = np.linspace(0.1,0.9,9)
y = np.linspace(0.1,0.9,9)
z = np.random.rand(81)

#插值
# xx, yy = np.meshgrid(x, y)

f = interpolate.interp2d(x, y, z, kind='cubic')
xnew = np.arange(0.1, 1, 0.03)
ynew = np.arange(0.1, 1, 0.03)
znew = f(xnew, ynew)

#修改x,y，z输入画图函数前的shape
xx1, yy1 = np.meshgrid(xnew, ynew)
newshape = (xx1.shape[0])*(xx1.shape[0])
y_input = xx1.reshape(newshape)
x_input = yy1.reshape(newshape)
z_input = znew.reshape(newshape)

#画图
sns.set(style='white')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(x_input,y_input,z_input,cmap=cm.coolwarm)
plt.show()