尝试为数据绘制拟合余弦平方函数,但无法正确绘制
Trying to plot a fitted cosine sq function to data, but it won't plot properly
我已经按照我在网上找到的几个例子进行操作,但我仍然没有得到平滑的拟合曲线,我也不确定为什么。曲线应该主要跟随数据点,但是频率要高得多而且似乎不起作用,我不确定为什么。不知道还能说什么,我只是有点迷路..
import scipy as sp
import matplotlib.pyplot as plt
from scipy import linalg, optimize
X=sp.array([0.0,15.0,30.0,45.0,60.0,75.0,90.0,105.0,120.0,135.0,150.0,165.0,180.0,195.0,210.0,225.0,240.0,255.0,270.0,285.0,300.0,315.0,330.0,345.0,360.0])
Y=sp.array([196.3,282.0,337.0,347.0,312.0,240.0,152.0,69.0,15.3,1.1,33.0,105.4,195.4,195.4,286.0,345.0,362.0,256.0,162.0,75.0,14.9,1.3,35.1,105.2,194.9])
#DEBUG-------------------
print("Y len: ",len(Y))
print("X len: ",len(X))
#------------------------
def fit_func(x,a,c):
cosinesquare = a*sp.cos(x+c)**2
return cosinesquare
po, po_cov = sp.optimize.curve_fit(fit_func, X, Y, p0=[50,360])
#DEBUG------------------
print("po:",po)
print("po[0]: ",po[0])
print("po[1]: ",po[1])
#-----------------------
plt.scatter(X, Y, marker="x", label="Data")
plt.plot(X,fit_func(X, *po), label="Fitted func")
plt.xlabel("Angle of transmission axis")
plt.ylabel("voltage (mV)")
plt.grid()
plt.legend()
plt.show()
,
其他答案有效,但真正的问题是您需要将度数转换为弧度。您可以添加 sp.pi/180
以在您的函数中进行转换。
import scipy as sp
import matplotlib.pyplot as plt
from scipy import linalg, optimize
X=sp.array([0.0,15.0,30.0,45.0,60.0,75.0,90.0,105.0,120.0,135.0,150.0,165.0,180.0,195.0,210.0,225.0,240.0,255.0,270.0,285.0,300.0,315.0,330.0,345.0,360.0])
Y=sp.array([196.3,282.0,337.0,347.0,312.0,240.0,152.0,69.0,15.3,1.1,33.0,105.4,195.4,195.4,286.0,345.0,362.0,256.0,162.0,75.0,14.9,1.3,35.1,105.2,194.9])
#DEBUG-------------------
print("Y len: ",len(Y))
print("X len: ",len(X))
#------------------------
def fit_func(x,a,c):
cosinesquare = a*sp.cos((x+c)*sp.pi/180.)**2
return cosinesquare
po, po_cov = sp.optimize.curve_fit(fit_func, X, Y, p0=[50,360])
#DEBUG------------------
print("po:",po)
print("po[0]: ",po[0])
print("po[1]: ",po[1])
#-----------------------
plt.scatter(X, Y, marker="x", label="Data")
plt.plot(X,fit_func(X, *po), label="Fitted func")
plt.xlabel("Angle of transmission axis")
plt.ylabel("voltage (mV)")
plt.grid()
plt.legend()
plt.show()
我已经按照我在网上找到的几个例子进行操作,但我仍然没有得到平滑的拟合曲线,我也不确定为什么。曲线应该主要跟随数据点,但是频率要高得多而且似乎不起作用,我不确定为什么。不知道还能说什么,我只是有点迷路..
import scipy as sp
import matplotlib.pyplot as plt
from scipy import linalg, optimize
X=sp.array([0.0,15.0,30.0,45.0,60.0,75.0,90.0,105.0,120.0,135.0,150.0,165.0,180.0,195.0,210.0,225.0,240.0,255.0,270.0,285.0,300.0,315.0,330.0,345.0,360.0])
Y=sp.array([196.3,282.0,337.0,347.0,312.0,240.0,152.0,69.0,15.3,1.1,33.0,105.4,195.4,195.4,286.0,345.0,362.0,256.0,162.0,75.0,14.9,1.3,35.1,105.2,194.9])
#DEBUG-------------------
print("Y len: ",len(Y))
print("X len: ",len(X))
#------------------------
def fit_func(x,a,c):
cosinesquare = a*sp.cos(x+c)**2
return cosinesquare
po, po_cov = sp.optimize.curve_fit(fit_func, X, Y, p0=[50,360])
#DEBUG------------------
print("po:",po)
print("po[0]: ",po[0])
print("po[1]: ",po[1])
#-----------------------
plt.scatter(X, Y, marker="x", label="Data")
plt.plot(X,fit_func(X, *po), label="Fitted func")
plt.xlabel("Angle of transmission axis")
plt.ylabel("voltage (mV)")
plt.grid()
plt.legend()
plt.show()
,
其他答案有效,但真正的问题是您需要将度数转换为弧度。您可以添加 sp.pi/180
以在您的函数中进行转换。
import scipy as sp
import matplotlib.pyplot as plt
from scipy import linalg, optimize
X=sp.array([0.0,15.0,30.0,45.0,60.0,75.0,90.0,105.0,120.0,135.0,150.0,165.0,180.0,195.0,210.0,225.0,240.0,255.0,270.0,285.0,300.0,315.0,330.0,345.0,360.0])
Y=sp.array([196.3,282.0,337.0,347.0,312.0,240.0,152.0,69.0,15.3,1.1,33.0,105.4,195.4,195.4,286.0,345.0,362.0,256.0,162.0,75.0,14.9,1.3,35.1,105.2,194.9])
#DEBUG-------------------
print("Y len: ",len(Y))
print("X len: ",len(X))
#------------------------
def fit_func(x,a,c):
cosinesquare = a*sp.cos((x+c)*sp.pi/180.)**2
return cosinesquare
po, po_cov = sp.optimize.curve_fit(fit_func, X, Y, p0=[50,360])
#DEBUG------------------
print("po:",po)
print("po[0]: ",po[0])
print("po[1]: ",po[1])
#-----------------------
plt.scatter(X, Y, marker="x", label="Data")
plt.plot(X,fit_func(X, *po), label="Fitted func")
plt.xlabel("Angle of transmission axis")
plt.ylabel("voltage (mV)")
plt.grid()
plt.legend()
plt.show()