使用 Matplotlib 的实时图形不显示绘图

Question

我正在研究一个物理问题，并试图通过在每 4 个计算步骤之后进行绘图来“实时”观看模拟。如果相关，我正在使用 Runge-Kutta（4 阶）方法进行区分。

当我运行代码时（应该运行代码大约 10/4 秒，但我想加快速度，虽然我不知道除了只显示一些帧之外如何），我可以看到轴在移动，但情节中没有显示任何内容。

我已将我的代码简化为只生成随机数而不是实际的函数，这样我就可以共享尽可能接近我实际拥有的代码。

import numpy as np
import matplotlib.pyplot as plt
 
# Parameters
time = 10  # maximum time for the simulation
h = 0.1  # step size
steps = int(time/h)  # number of steps
order = 4  # two second order equations
 
ICs = [0, 0, 0, 0.25, 0]  # intial conditions; t0, x1, x1dot, x2, x2dot
 
# Initializing vars array
vars = np.empty(shape=(order,steps))  # each row is another var, i.e. x1,x2,...
 
# Set initial conditions for each var
for i in range(order):
    vars[i][0] = ICs[i+1]

t = np.empty(steps)
t[0] = ICs[0]

K = np.zeros(shape=(4,len(vars))) # Each row is k1, k2, k3, k4 for each var


# ODE function
def ODE():
    dx1dt = np.random.randint(-1000,1000)
    dv1dt = np.random.randint(-1000,1000)
    dx2dt = np.random.randint(-1000,1000)
    dv2dt = np.random.randint(-1000,1000)
    
    return(np.array([dx1dt, dv1dt, dx2dt, dv2dt]))

# Loop calculates each var value using RK 4th order method
for i in range(steps-1):
    K[0] = ODE()
    K[1] = ODE()
    K[2] = ODE()
    K[3] = ODE()
    
    vars[:,i+1] = vars[:,i] + 1/6 * (K[0] + 2 * K[1] + 2 * K[2] + K[3])
    
    t[i+1] = t[i] + h
    
    # Plotting every fourth calculation
    if (i%4 == 0):
        plt.cla()
        plt.plot(t[i], vars[0,i], label='x1')
        
        plt.title(f'Title (stepsize: {h})')
        plt.xlabel('time [s]')
        plt.ylabel('position [m]')
        
        plt.legend(loc=1)
        plt.tight_layout()
        plt.pause(0.01)
 
plt.tight_layout()
 
plt.show()

感谢您的帮助。

Answer 1

您可以使用matplotlib's 交互模式plt.ion() 来启动交互绘图。然后调用plt.show()显示窗口，并使用plt.gcf().canvas.draw更新它，如下所示：

import numpy as np
import matplotlib.pyplot as plt
  
# Parameters
time = 10  # maximum time for the simulation
h = 0.1  # step size
steps = int(time/h)  # number of steps
order = 4  # two second order equations
 
ICs = [0, 0, 0, 0.25, 0]  # intial conditions; t0, x1, x1dot, x2, x2dot
 
# Initializing vars array
vars = np.empty(shape=(order,steps))  # each row is another var, i.e. x1,x2,...
 
# Set initial conditions for each var
for i in range(order):
    vars[i][0] = ICs[i+1]

t = np.empty(steps)
t[0] = ICs[0]

K = np.zeros(shape=(4,len(vars))) # Each row is k1, k2, k3, k4 for each var
fig,ax=plt.subplots()
plt.ion() # set interactive mode on 
plt.show() # open display window

# ODE function
def ODE():
    dx1dt = np.random.randint(-1000,1000)
    dv1dt = np.random.randint(-1000,1000)
    dx2dt = np.random.randint(-1000,1000)
    dv2dt = np.random.randint(-1000,1000)
    
    return(np.array([dx1dt, dv1dt, dx2dt, dv2dt]))

# Loop calculates each var value using RK 4th order method
for i in range(steps-1):
    K[0] = ODE()
    K[1] = ODE()
    K[2] = ODE()
    K[3] = ODE()
    
    vars[:,i+1] = vars[:,i] + 1/6 * (K[0] + 2 * K[1] + 2 * K[2] + K[3])
    
    t[i+1] = t[i] + h
    
    # Plotting every fourth calculation
    if (i%4 == 0):
        #plt.cla()
        plt.plot(t[:i], vars[0,:i],color='black')
        
        plt.title(f'Title (stepsize: {h})')
        plt.xlabel('time [s]')
        plt.ylabel('position [m]')
        

        plt.tight_layout()
        plt.gcf().canvas.draw() #update display window
        plt.pause(0.01)
        
plt.tight_layout()

Answer 2

它可能不起作用，因为您的 plt.show() 不在 if 子句中。