In [1]:

    

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


    

In [2]:

    

from scipy.interpolate import interp1d


    

In [3]:

    

x= np.linspace(0,4*np.pi,10)


    

In [4]:

    

f=np.sin(x)


    

In [5]:

    

plt.plot(x, f, marker='o')


    

    
Out[5]:





[<matplotlib.lines.Line2D at 0x7ffb0fdf24a8>]






    

In [6]:

    

sin_approx= interp1d(x, f, kind='cubic')


    

In [7]:

    

newx = np.linspace(0,4*np.pi,100)
newf = sin_approx(newx)


    

In [8]:

    

plt.plot(x, f, marker='o', linestyle='')
plt.plot(newx, newf, marker='.')


    

    
Out[8]:





[<matplotlib.lines.Line2D at 0x7ffb0fdb7630>]






    

In [9]:

    

plt.plot(newx,np.abs(np.sin(newx)-sin_approx(newx)))


    

    
Out[9]:





[<matplotlib.lines.Line2D at 0x7ffb0fc95048>]






    

In [10]:

    

x=4*np.pi*np.random.rand(15)
f=np.sin(x)


    

In [11]:

    

newx=np.linspace(np.min(x),np.max(x),100)
newf=sin_approx(newx)


    

In [12]:

    

plt.plot(x, f, marker='o', linestyle='', label='original data')
plt.plot(newx, newf, marker='.', label='interpolated');


    

In [13]:

    

from scipy.interpolate import interp2d


    

In [14]:

    

def wave2d(x,y):
    return np.sin(2*np.pi*x)*np.sin(3*np.pi*y)


    

In [ ]:

    

x=np.linspace