不打印出 z 和 theta 的值

如何解决不打印出 z 和 theta 的值

我正在使用四阶 Runge-Kutta 方法求解两个耦合常微分方程。由于应用此方法，我无法打印 z 的值。源代码如下供参考。请帮助我通过打印 z 和 theta 的更新值来修复此代码。谢谢。

#Import neeeded modules
import numpy as np
import matplotlib.pyplot as plt

#Input parameters
k = 5 #longitudinal torsional constant
delta = 10**-3 #longitudinal torsional constant
I = 10**-4 #Rotational Inertia
eps = 10**-2 #coupling constant
m = 0.5


#Time Step
#Setting time array for graph visualization
dt = 0.002 #Time Step
tStop = 0.30 #Maximum time for graph visualization derived from Kinematics
t = np.arange(0.,tStop+dt,dt) #Array of time
z = np.zeros(len(t))
dz = np.zeros(len(t))
theta = np.zeros(len(t))
dtheta = np.zeros(len(t))

#Functions that include the equations of motion
def dYdt(t,u):
    z,dz,theta,dtheta = u
    ddz = -(k*z+0.5*eps*theta)/m
    ddtheta = -(delta*theta+0.5*eps*z)/I
    return np.array([dz,ddz,dtheta,ddtheta])

def rk4(t,u,dt):
    for i in range(len(t)-1):
     # runge_kutta
        k1 = dYdt(t[i],u[i])
        k2 = dYdt(t[i] + dt/2,u[i] + dt/2 * k1)
        k3 = dYdt(t[i] + dt/2,u[i] + dt/2 * k2)
        k4 = dYdt(t[i] + dt,u[i] + dt * k3)
        u.append(u[i] + (k1 + 2*k2 + 2*k3 + k4) * dt / 6)
        #Unpacking
        z,dtheta = np.asarray(u).T

print(z)

这是我用来想出源代码的运动方程。 Coupled ODEs

解决方法

这就是我认为您想要的，但我不知道要传递给 u 的参数是什么。另外，z,dz,theta,dtheta = np.asarray(u).T 是一维向量，所以我不知道您期望 import numpy as np import matplotlib.pyplot as plt #Input parameters k = 5 #longitudinal torsional constant delta = 10**-3 #longitudinal torsional constant I = 10**-4 #Rotational Inertia eps = 10**-2 #Coupling constant m = 0.5 #Time Step #Setting time array for graph visualization dt = 0.002 #Time Step tStop = 0.30 #Maximum time for graph visualization derived from Kinematics t = np.arange(0.,tStop+dt,dt) #Array of time #Functions that include the equations of motion def dYdt(t,u): z,dtheta = u ddz = -(k*z+0.5*eps*theta)/m ddtheta = -(delta*theta+0.5*eps*z)/I return np.array([dz,ddz,dtheta,ddtheta]) def rk4(t,u,dt): for i in range(len(t)-1): # runge_kutta k1 = dYdt(t[i],u[i]) k2 = dYdt(t[i] + dt/2,u[i] + dt/2 * k1) k3 = dYdt(t[i] + dt/2,u[i] + dt/2 * k2) k4 = dYdt(t[i] + dt,u[i] + dt * k3) u.append(u[i] + (k1 + 2*k2 + 2*k3 + k4) * dt / 6) #Unpacking return np.asarray(u).T z,dtheta = rk4(t,dt) print(z) 做什么。因此，此代码不会运行，但会向您展示一个潜在的设计。

# Dropping the duplicates in order to get the unique dealer list

dealer_df = df_s[["DealerID","LAT","LNG"]].drop_duplicates()
dealer_df = dealer_df.set_index("DealerID")

from sklearn.neighbors import KNeighborsClassifier

# Instantiating with n_neighbors as 1 and weights as "distance"

knn = KNeighborsClassifier(n_neighbors=1,weights="distance",n_jobs=-1)
knn.fit(dealer_df.values,dealer_df.index)

df_c["Nearest Dealer"] = knn.predict(df_c[["LAT","LNG"]].values)