Interpolation Exercise 1



In [1]:

    
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np



In [2]:

    
from scipy.interpolate import interp2d
from scipy.interpolate import interp1d

2D trajectory interpolation

The file trajectory.npz contains 3 Numpy arrays that describe a 2d trajectory of a particle as a function of time:

t which has discrete values of time t[i].
x which has values of the x position at those times: x[i] = x(t[i]).
x which has values of the y position at those times: y[i] = y(t[i]).

Load those arrays into this notebook and save them as variables x, y and t:



In [3]:

    
with np.load('trajectory.npz') as data:
    t = data['t']
    x = data['x']
    y = data['y']



In [4]:

    
assert isinstance(x, np.ndarray) and len(x)==40
assert isinstance(y, np.ndarray) and len(y)==40
assert isinstance(t, np.ndarray) and len(t)==40

Use these arrays to create interpolated functions $x(t)$ and $y(t)$. Then use those functions to create the following arrays:

newt which has 200 points between $\{t_{min},t_{max}\}$.
newx which has the interpolated values of $x(t)$ at those times.
newy which has the interpolated values of $y(t)$ at those times.



In [5]:

    
newt = np.linspace(t.min(),t.max(),200)
fx = interp1d(t,x,kind = 'cubic')
fy = interp1d(t,y,kind = 'cubic')
newx = fx(newt)
newy = fy(newt)

len(newx)









    Out[5]:





200



In [6]:

    
assert newt[0]==t.min()
assert newt[-1]==t.max()
assert len(newt)==200
assert len(newx)==200
assert len(newy)==200

Make a parametric plot of $\{x(t),y(t)\}$ that shows the interpolated values and the original points:

For the interpolated points, use a solid line.
For the original points, use circles of a different color and no line.
Customize you plot to make it effective and beautiful.



In [16]:

    
ax = plt.gca()
plt.plot(newx,newy, color = 'r')
plt.plot(x,y, 'bo')

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
plt.title('Trajectory')
plt.xlabel('Distance')
plt.ylabel('Height')



In [8]:

    
assert True # leave this to grade the trajectory plot



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