# Interact Exercise 3

## Imports



In [1]:

%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np




In [2]:

from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display




:0: FutureWarning: IPython widgets are experimental and may change in the future.



# Using interact for animation with data

A soliton is a constant velocity wave that maintains its shape as it propagates. They arise from non-linear wave equations, such has the Korteweg–de Vries equation, which has the following analytical solution:

$$\phi(x,t) = \frac{1}{2} c \mathrm{sech}^2 \left[ \frac{\sqrt{c}}{2} \left(x - ct - a \right) \right]$$

The constant c is the velocity and the constant a is the initial location of the soliton.

Define soliton(x, t, c, a) function that computes the value of the soliton wave for the given arguments. Your function should work when the postion x or t are NumPy arrays, in which case it should return a NumPy array itself.



In [3]:

from math import sqrt




In [4]:

def soliton(x, t, c, a):
# make x and t arrays (if not already)
if type(t) != np.ndarray:
t = np.array([t])
if type(x) != np.ndarray:
x = np.array([x])

#set "xct" to x-ct
#because x and t are both arrays, use the sum of meshgid to "add" them without worrying about array size
#this will givs us a 1D array if T or X is a single element array
xct = sum(np.meshgrid(-1*c*t, x))

#set a to be the same array size as xct
a = a*np.ones_like(xct)

# z is the array that represents the bracketed expression
z = (sqrt(c))/2*(xct-a)

# sech = 2/e**z + e**-z use np.exp to input each element
sech2z = (2/(np.exp(z) + np.exp(-z)))**2




In [5]:

assert np.allclose(soliton(np.array([0]),0.0,1.0,0.0), np.array([0.5]))



To create an animation of a soliton propagating in time, we are going to precompute the soliton data and store it in a 2d array. To set this up, we create the following variables and arrays:



In [6]:

tmin = 0.0
tmax = 10.0
tpoints = 100
t = np.linspace(tmin, tmax, tpoints)

xmin = 0.0
xmax = 10.0
xpoints = 200
x = np.linspace(xmin, xmax, xpoints)

c = 1.0
a = 0.0



Compute a 2d NumPy array called phi:

• It should have a dtype of float.
• It should have a shape of (xpoints, tpoints).
• phi[i,j] should contain the value $\phi(x[i],t[j])$.


In [7]:

phi = soliton(x, t, c, a)
phi.shape




Out[7]:

(200, 100)




In [8]:

assert phi.shape==(xpoints, tpoints)
assert phi.ndim==2
assert phi.dtype==np.dtype(float)
assert phi[0,0]==soliton(x[0],t[0],c,a)



Write a plot_soliton_data(i) function that plots the soliton wave $\phi(x, t[i])$. Customize your plot to make it effective and beautiful.



In [9]:

def plot_soliton_data(i=0):
#set the t to vary with [i]
#note t is set at 0 (i=0) so this plot is a graph of Phi v X with t=0
plt.plot(x, soliton(x, t[i], c, a))

#Graph styling
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.xlabel("X", fontsize = 14)
plt.ylabel(r'$\phi$ (x, %s)' %(t[i]), fontsize = 16)
plt.title("Korteweg de Vries Equation", fontsize = 16)




In [10]:

plot_soliton_data(0)







In [11]:

assert True # leave this for grading the plot_soliton_data function



Use interact to animate the plot_soliton_data function versus time.



In [12]:

interact(plot_soliton_data, i=(0,99));







In [45]:

assert True # leave this for grading the interact with plot_soliton_data cell




In [ ]: