```
In [1]:
```%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed

```
```

Here are the basic parameters we are going to use for this exercise:

```
In [2]:
```g = 9.81 # m/s^2
l = 0.5 # length of pendulum, in meters
tmax = 50. # seconds
t = np.linspace(0, tmax, int(100*tmax))

`derivs`

for usage with `scipy.integrate.odeint`

that computes the derivatives for the damped, driven harmonic oscillator. The solution vector at each time will be $\vec{y}(t) = (\theta(t),\omega(t))$.

```
In [3]:
```def derivs(y, t, a, b, omega0):
"""Compute the derivatives of the damped, driven pendulum.
Parameters
----------
y : ndarray
The solution vector at the current time t[i]: [theta[i],omega[i]].
t : float
The current time t[i].
a, b, omega0: float
The parameters in the differential equation.
Returns
-------
dy : ndarray
The vector of derviatives at t[i]: [dtheta[i],domega[i]].
"""
# YOUR CODE HERE
theta_t = y[0]
omega_t = y[1]
domega = -g/l * np.sin(theta_t) - a*omega_t - b*np.sin(omega0*t)
#It took me a long time to understand that theta and omega are the position and angular velocity in a sysytem,
#and that that's why you can set the derivative of position as omega.
dtheta = omega_t
dy = np.array([dtheta, domega])
return dy

```
In [4]:
```assert np.allclose(derivs(np.array([np.pi,1.0]), 0, 1.0, 1.0, 1.0), [1.,-1.])

The equations of motion for a simple pendulum of mass $m$, length $l$ are:

$$ \frac{d^2\theta}{dt^2} = \frac{-g}{\ell}\sin\theta $$When a damping and periodic driving force are added the resulting system has much richer and interesting dynamics:

$$ \frac{d^2\theta}{dt^2} = \frac{-g}{\ell}\sin\theta - a \omega - b \sin(\omega_0 t) $$In this equation:

- $a$ governs the strength of the damping.
- $b$ governs the strength of the driving force.
- $\omega_0$ is the angular frequency of the driving force.

When $a=0$ and $b=0$, the energy/mass is conserved:

$$E/m =g\ell(1-\cos(\theta)) + \frac{1}{2}\ell^2\omega^2$$```
In [5]:
```def energy(y):
"""Compute the energy for the state array y.
The state array y can have two forms:
1. It could be an ndim=1 array of np.array([theta,omega]) at a single time.
2. It could be an ndim=2 array where each row is the [theta,omega] at single
time.
Parameters
----------
y : ndarray, list, tuple
A solution vector
Returns
-------
E/m : float (ndim=1) or ndarray (ndim=2)
The energy per mass.
"""
# YOUR CODE HERE
if y.ndim==1:
theta = y[0]
omega = y[1]
elif y.ndim==2:
theta = y[:,0]
omega = y[:,1]
return g*l*(1-np.cos(theta)) + 0.5*(l**2)*omega**2
### END SOLUTION

```
In [6]:
```assert np.allclose(energy(np.array([np.pi,0])),g)
assert np.allclose(energy(np.ones((10,2))), np.ones(10)*energy(np.array([1,1])))

Use the above functions to integrate the simple pendulum for the case where it starts at rest pointing vertically upwards. In this case, it should remain at rest with constant energy.

- Integrate the equations of motion.
- Plot $E/m$ versus time.
- Plot $\theta(t)$ and $\omega(t)$ versus time.
- Tune the
`atol`

and`rtol`

arguments of`odeint`

until $E/m$, $\theta(t)$ and $\omega(t)$ are constant.

Anytime you have a differential equation with a a conserved quantity, it is critical to make sure the numerical solutions conserve that quantity as well. This also gives you an opportunity to find other bugs in your code. The default error tolerances (`atol`

and `rtol`

) used by `odeint`

are not sufficiently small for this problem. Start by trying `atol=1e-3`

, `rtol=1e-2`

and then decrease each by an order of magnitude until your solutions are stable.

```
In [7]:
```# YOUR CODE HERE
thetai = np.pi #exactly vertical
omegai = 0. #starts at rest
ic = np.array([thetai, omegai])
y = odeint(derivs, ic, t, args=(0.0,0.0,0.0), atol=1e-6, rtol=1e-5)

```
In [8]:
```# YOUR CODE HERE
plt.plot(t, energy(y))
plt.xlabel('$t$')
plt.ylabel('$E/m$')
plt.title('Energy per mass versus time');

```
```

```
In [9]:
```# YOUR CODE HERE
plt.plot(t, y[:,0], label='$\\theta(t)$')
plt.plot(t, y[:,1], label='$\omega(t)$')
plt.xlabel('$t$')
plt.ylabel('Solution')
plt.title('State variables versus time')
plt.legend(loc='best');

```
```

```
In [ ]:
```assert True # leave this to grade the two plots and their tuning of atol, rtol.

Write a `plot_pendulum`

function that integrates the damped, driven pendulum differential equation for a particular set of parameters $[a,b,\omega_0]$.

- Use the initial conditions $\theta(0)=-\pi + 0.1$ and $\omega=0$.
- Decrease your
`atol`

and`rtol`

even futher and make sure your solutions have converged. - Make a parametric plot of $[\theta(t),\omega(t)]$ versus time.
- Use the plot limits $\theta \in [-2 \pi,2 \pi]$ and $\theta \in [-10,10]$
- Label your axes and customize your plot to make it beautiful and effective.

```
In [10]:
```def plot_pendulum(a=0.0, b=0.0, omega0=0.0):
"""Integrate the damped, driven pendulum and make a phase plot of the solution."""
# YOUR CODE HERE
thetai = -np.pi+0.1
omegai = 0.0
ic = np.array([thetai, omegai])
y = odeint(derivs, ic, t, args=(a,b,omega0), atol=1e-10, rtol=1e-9)
plt.plot(y[:,0], y[:,1])
plt.xlim(-2.0*np.pi,2.0*np.pi)
plt.ylim(-10,10)
plt.xlabel('$\\theta(t)$')
plt.ylabel('$\omega(t)$')

Here is an example of the output of your `plot_pendulum`

function that should show a decaying spiral.

```
In [11]:
```plot_pendulum(0.5, 0.0, 0.0)

```
```

Use `interact`

to explore the `plot_pendulum`

function with:

`a`

: a float slider over the interval $[0.0,1.0]$ with steps of $0.1$.`b`

: a float slider over the interval $[0.0,10.0]$ with steps of $0.1$.`omega0`

: a float slider over the interval $[0.0,10.0]$ with steps of $0.1$.

```
In [12]:
```# YOUR CODE HERE
interact(plot_pendulum, a=(0.0,1.0,0.1), b=(0.0,10.0,0.1), omega0=(0.0,10.0,0.1));

```
```

Use your interactive plot to explore the behavior of the damped, driven pendulum by varying the values of $a$, $b$ and $\omega_0$.

- First start by increasing $a$ with $b=0$ and $\omega_0=0$.
- Then fix $a$ at a non-zero value and start to increase $b$ and $\omega_0$.

Describe the different *classes* of behaviors you observe below.