#!/usr/bin/env python 
# -*-coding:utf-8 -*- 
import numpy as np 
from scipy import interpolate 
import pylab as pl 
 
x=np.linspace(0,10,11) 
#x=[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.] 
y=np.sin(x) 
xnew=np.linspace(0,10,101) 
pl.plot(x,y,"ro") 
 
for kind in ["nearest","zero","slinear","quadratic","cubic"]:#插值方式 
 #"nearest","zero"为阶梯插值 
 #slinear 线性插值 
 #"quadratic","cubic" 为2阶、3阶B样条曲线插值 
 f=interpolate.interp1d(x,y,kind=kind) 
 # ‘slinear', ‘quadratic' and ‘cubic' refer to a spline interpolation of first, second or third order) 
 ynew=f(xnew) 
 pl.plot(xnew,ynew,label=str(kind)) 
pl.legend(loc="lower right") 
pl.show() 

# -*- coding: utf-8 -*- 
""" 
演示二维插值。 
""" 
import numpy as np 
from scipy import interpolate 
import pylab as pl 
import matplotlib as mpl 
 
def func(x, y): 
 return (x+y)*np.exp(-5.0*(x**2 + y**2)) 
 
# X-Y轴分为15*15的网格 
y,x= np.mgrid[-1:1:15j, -1:1:15j] 
 
fvals = func(x,y) # 计算每个网格点上的函数值 15*15的值 
print len(fvals[0]) 
 
#三次样条二维插值 
newfunc = interpolate.interp2d(x, y, fvals, kind='cubic') 
 
# 计算100*100的网格上的插值 
xnew = np.linspace(-1,1,100)#x 
ynew = np.linspace(-1,1,100)#y 
fnew = newfunc(xnew, ynew)#仅仅是y值 100*100的值 
 
# 绘图 
# 为了更明显地比较插值前后的区别，使用关键字参数interpolation='nearest' 
# 关闭imshow()内置的插值运算。 
pl.subplot(121) 
im1=pl.imshow(fvals, extent=[-1,1,-1,1], cmap=mpl.cm.hot, interpolation='nearest', origin="lower")#pl.cm.jet 
#extent=[-1,1,-1,1]为x,y范围 favals为 
pl.colorbar(im1) 
 
pl.subplot(122) 
im2=pl.imshow(fnew, extent=[-1,1,-1,1], cmap=mpl.cm.hot, interpolation='nearest', origin="lower") 
pl.colorbar(im2) 
 
pl.show() 

# -*- coding: utf-8 -*- 
""" 
演示二维插值。 
""" 
# -*- coding: utf-8 -*- 
import numpy as np 
from mpl_toolkits.mplot3d import Axes3D 
import matplotlib as mpl 
from scipy import interpolate 
import matplotlib.cm as cm 
import matplotlib.pyplot as plt 
 
def func(x, y): 
 return (x+y)*np.exp(-5.0*(x**2 + y**2)) 
 
# X-Y轴分为20*20的网格 
x = np.linspace(-1, 1, 20) 
y = np.linspace(-1,1,20) 
x, y = np.meshgrid(x, y)#20*20的网格数据 
 
fvals = func(x,y) # 计算每个网格点上的函数值 15*15的值 
 
fig = plt.figure(figsize=(9, 6)) 
#Draw sub-graph1 
ax=plt.subplot(1, 2, 1,projection = '3d') 
surf = ax.plot_surface(x, y, fvals, rstride=2, cstride=2, cmap=cm.coolwarm,linewidth=0.5, antialiased=True) 
ax.set_xlabel('x') 
ax.set_ylabel('y') 
ax.set_zlabel('f(x, y)') 
plt.colorbar(surf, shrink=0.5, aspect=5)#标注 
 
#二维插值 
newfunc = interpolate.interp2d(x, y, fvals, kind='cubic')#newfunc为一个函数 
 
# 计算100*100的网格上的插值 
xnew = np.linspace(-1,1,100)#x 
ynew = np.linspace(-1,1,100)#y 
fnew = newfunc(xnew, ynew)#仅仅是y值 100*100的值 np.shape(fnew) is 100*100 
xnew, ynew = np.meshgrid(xnew, ynew) 
ax2=plt.subplot(1, 2, 2,projection = '3d') 
surf2 = ax2.plot_surface(xnew, ynew, fnew, rstride=2, cstride=2, cmap=cm.coolwarm,linewidth=0.5, antialiased=True) 
ax2.set_xlabel('xnew') 
ax2.set_ylabel('ynew') 
ax2.set_zlabel('fnew(x, y)') 
plt.colorbar(surf2, shrink=0.5, aspect=5)#标注 
 
plt.show() 

from scipy.interpolate import interp2d
df_rotormap = pd.read_csv('filepath',header = None) #读取Cp表
x = np.array(df_rotormap.iloc[:,0].dropna()) #Cp表的X坐标是TSR
y = np.array(df_rotormap.iloc[:,1]) #Cp表的Y坐标是pitch angle
z = np.array(df_rotormap.iloc[:,2:]) #Cp表的具体值，y行x列

rho = 1.225 #kg/m3
s = (141/2)**2*np.pi #m2
df_cal['TSR'] = df_cal['发电机转速（PDM1）']/148*141*np.pi/60/df_cal['风速']

func_new = interp2d(x,y,z,kind = 'linear') #定义二维表插值函数，选择线性插值

cp_list = []
for i in range(df_cal.shape[0]):
 cp = float(func_new(df_cal['TSR'][i],df_cal['1号桨叶角度'][i])) #输入X,Y坐标， 输出插值计算的Cp
 cp_list.append(cp)

df_cal['cp'] = cp_list #把Cp放回到Dataframe中去

df_cal['air_power'] = 0.5*rho*s*df_cal['风速']**3*df_cal['cp']