%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

"""
Explanation: Ordinary Differential Equations Exercise 3
Imports
End of explanation
"""


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))

"""
Explanation: Damped, driven nonlinear pendulum
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:
$$E/m =g\ell(1-\cos(\theta)) + \frac{1}{2}\ell^2\omega^2$$
Basic setup
Here are the basic parameters we are going to use for this exercise:
End of explanation
"""


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]].
    """
    theta = y[0]
    dtheta = y[1]
    dw = -(g/l)*np.sin(theta) - a*dtheta - b*np.sin(omega0*t)
    return [dtheta,dw]

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

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.
    """
    theta = y[0]
    omega = y[1]
    if y.ndim == 1:
        theta = y[0]
        omega = y[1]
        EperM = g*l*(1-np.cos(theta))+.5*(l**2)*omega**2
        return EperM
    if y.ndim == 2:
        theta = y[:,0]
        omega = y[:,1]
        EperM = g*l*(1-np.cos(theta))+.5*(l**2)*omega**2
        return EperM

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])))

"""
Explanation: Write a function 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 $\vec{y}(t) = (\theta(t),\omega(t))$.
End of explanation
"""


y0 = [np.pi,0]
a = 0
b = 0
omega0 = 0
soln = odeint(derivs,y0,t,args=(a,b,omega0),atol=1e-5, rtol=1e-4)

theta = soln[:,0]
omega = soln[:,1]

plt.plot(t,energy(soln));
plt.title('Energy per Mass vs time');

plt.plot(t,theta,label='$\Theta(t)$');
plt.title('Theta and Omega vs Time');
plt.ylim((-np.pi,2*np.pi));
plt.plot(t,omega,label='$\omega(t)$');
plt.legend();
plt.xlabel('Time');
plt.ylabel('Omega,Theta');

assert True # leave this to grade the two plots and their tuning of atol, rtol.

"""
Explanation: Simple pendulum
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.
End of explanation
"""


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."""
    y0 = [-np.pi + .1,0]
    soln = odeint(derivs,y0,t,args=(a,b,omega0),atol=1e-5, rtol=1e-4)
    theta = soln[:,0]
    omega = soln[:,1]
    plt.figure(figsize=(10,6))
    plt.plot(theta,omega)
    
    plt.title('Pendlum Motion')
    plt.xlabel('Theta')
    plt.ylabel('Omega')

"""
Explanation: Damped pendulum
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.
End of explanation
"""


plot_pendulum(0.5, 0.0, 0.0)

"""
Explanation: Here is an example of the output of your plot_pendulum function that should show a decaying spiral.
End of explanation
"""


interact(plot_pendulum,a=(0.0,1.0,.1),b=(0.0,10.0,.1),omega0=(0.0,10.0,.1));

"""
Explanation: 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 $0.1$.
End of explanation
"""


