ODEintWarning: Excess work done on this call (perhaps wrong Dfun type)

I was messing around with some numerical integration functions. I wrote an arbitrary differential equation to test my understanding, the code is as follows:

import numpy as np

from scipy.integrate import odeint

intT = np.array([0,1,2,3,4,6,8,10,13,16,19,20,21,22,27,40,80])

def dydt(y,t):
    dydt = np.random.choice([0,1], 1)[0]
    return dydt

yInt = odeint(dydt, 0, intT)
print(yInt)

Outputs:

[[0.00000000e+000]
[0.00000000e+000]
[0.00000000e+000]
[0.00000000e+000]
[0.00000000e+000]
[0.00000000e+000]
[0.00000000e+000]
[2.21317648e-001]
[3.22131765e+000]
[3.62733355e+000]
[0.00000000e+000]
[2.42092166e-322]
[2.70777855e-316]
[2.81469831e-316]
[0.00000000e+000]
[0.00000000e+000]
[7.11454530e-322]]

/usr/lib/python3/dist-packages/scipy/integrate/odepack.py:236: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.
warnings.warn(warning_msg, ODEintWarning)

This gave me very weird results, and that was not what I was expecting. If I change return value of dydt into 1, then this simple integration surely just works properly. What I was actually trying to do is to apply a predetermined window function to my differential equation, this code above is a simplified version I would imagine. But I just don’t get what is wrong here. I wonder if you have any thoughts.

Thanks!

Thanks to @Lutz Lehmann. I was trying to integrate some breathing function, who spikes every 100 time intervals. I had another shot as follows, this doesn’t work either. And I notice this seems to be very tricky, if I change my window function and differential equation, sometimes it works, sometimes it doesn’t. I kind of understand the smoothness issue, but I just wonder if there is a way to handle this problem. I initially had a numerical integration script by myself, but it works way slower, that’s why I turned to the scipy integrate library.

intT = (np.arange(1, 1000) + 0.1)

def arbWindow(t):
    w = np.exp(1/t - t/10)
    return w
    
def dydt(y,t):
    dydt = arbWindow(t%100) - 0.01*y**2
    return dydt

yInt = odeint(dydt, 0, intT)