import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint

def f(y,t,A):
    return -A*y

A    = 0.3   # The A constant in the ODE
t0   = 0.    # Starting time
tend = 10.   # End time
nt   = 10    # Nr of time steps
t    = np.linspace(t0,tend,nt)
y0   = 2.    # Intial condition
sol  = odeint(f,y0,t,args=(A,))
y    = sol[:,0].T
yana = y[0]*np.exp(-A*t)
plt.figure()
plt.plot(t,yana,label='Analytic')
plt.plot(t,y,'o',label='Scipy odeint')
plt.xlabel('t')
plt.ylabel('y(t)')
plt.legend()
plt.savefig('fig_ode_scipy_1_1.pdf')
plt.show()