ericmjl ericmjl - 1 year ago 150
Python Question

Interpolating a numpy array to fit another array

Say I have

of shape
(1, n)
. I have new
of shape
(1, n±x)
, where x is some positive integer much smaller than
. I would like to squeeze or stretch
such that it is of the same length as
. How might this be done, using the SciPy stack?

Here's an example of what I'm trying to accomplish.

# Stretch arr2 to arr1's shape while "filling in" interpolated value
arr1 = np.array([1, 5, 2, 3, 7, 2, 1])
arr2 = np.array([1, 5, 2, 3, 7, 1])
> np.array([1, 5, 2, 3, 6.x, 2.x 1]) # of shape (arr1.shape)

As another example:

# Squeeze arr2 to arr1's shape while placing interpolated value.
arr1 = np.array([1, 5, 2, 3, 7, 2, 1])
arr2 = np.array([1, 5, 2, 3, 4, 7, 2, 1])
> np.array([1, 5, 2, 3.x, 7.x, 2.x, 1]) # of shape (arr1.shape)

Answer Source

You can implement this simple compression or stretching of your data using scipy.interpolate.interp1d. I'm not saying it necessarily makes sense (it makes a huge difference what kind of interpolation you're using, and you'll generally only get a reasonable result if you can correctly guess the behaviour of the underlying function), but you can do it.

The idea is to interpolate your original array over its indices as x values, then perform interpolation with a sparser x mesh, while keeping its end points the same. So essentially you have to do a continuum approximation to your discrete data, and resample that at the necessary points:

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

arr_ref = np.array([1, 5, 2, 3, 7, 1])  # shape (6,), reference
arr1 = np.array([1, 5, 2, 3, 7, 2, 1])  # shape (7,), to "compress"
arr2 = np.array([1, 5, 2, 7, 1])        # shape (5,), to "stretch"
arr1_interp = interp.interp1d(np.arange(arr1.size),arr1)
arr1_compress = arr1_interp(np.linspace(0,arr1.size-1,arr_ref.size))
arr2_interp = interp.interp1d(np.arange(arr2.size),arr2)
arr2_stretch = arr2_interp(np.linspace(0,arr2.size-1,arr_ref.size))

# plot the examples, assuming same x_min, x_max for all data
xmin,xmax = 0,1
fig,(ax1,ax2) = plt.subplots(ncols=2)

The resulting plot:


In the plots, blue circles are the original data points, and red squares are the interpolated ones (these overlap at the boundaries). As you can see, what I called compressing and stretching is actually upsampling and downsampling of an underlying (linear, by default) function. This is why I said you must be very careful with interpolation: you can get very wrong results if your expectations don't match your data.

Recommended from our users: Dynamic Network Monitoring from WhatsUp Gold from IPSwitch. Free Download