Add more sample points to data

Question

Given some data of shape 20x45, where each row is a separate data set, say 20 different sine curves with 45 data points each, how would I go about getting the same data, but with shape 20x100?

In other words, I have some data A of shape 20x45, and some data B of length 20x100, and I would like to have A be of shape 20x100 so I can compare them better.

This is for Python and Numpy/Scipy.

I assume it can be done with splines, so I am looking for a simple example, maybe just 2x10 to 2x20 or something, where each row is just a line, to demonstrate the solution.

Thanks!

Answer 1

Ubuntu beat me to it while I was typing this example, but his example just uses linear interpolation, which can be more easily done with numpy.interpolate... (The difference is only a keyword argument in scipy.interpolate.interp1d, however).

I figured I'd include my example, as it shows using scipy.interpolate.interp1d with a cubic spline...

import numpy as np
import scipy as sp
import scipy.interpolate
import matplotlib.pyplot as plt

# Generate some random data
y = (np.random.random(10) - 0.5).cumsum()
x = np.arange(y.size)

# Interpolate the data using a cubic spline to "new_length" samples
new_length = 50
new_x = np.linspace(x.min(), x.max(), new_length)
new_y = sp.interpolate.interp1d(x, y, kind='cubic')(new_x)

# Plot the results
plt.figure()
plt.subplot(2,1,1)
plt.plot(x, y, 'bo-')
plt.title('Using 1D Cubic Spline Interpolation')

plt.subplot(2,1,2)
plt.plot(new_x, new_y, 'ro-')

plt.show()

Answer 2

One way would be to use scipy.interpolate.interp1d:

import scipy as sp
import scipy.interpolate
import numpy as np
x=np.linspace(0,2*np.pi,45)
y=np.zeros((2,45))
y[0,:]=sp.sin(x)
y[1,:]=sp.sin(2*x)
f=sp.interpolate.interp1d(x,y)
y2=f(np.linspace(0,2*np.pi,100))

If your data is fairly dense, it may not be necessary to use higher order interpolation.