1D sequence (signal) data resampling (python)

 import numpy as np

import matplotlib.pyplot as plt

from scipy.signal import resample

from scipy.interpolate import interp1d

def ResampleLinear1D(original, targetLen):

original = np.array(original, dtype=np.float)

index_arr = np.linspace(0, len(original)-1, num=targetLen, dtype=np.float)

index_floor = np.array(index_arr, dtype=np.int) #Round down

index_ceil = index_floor + 1

index_rem = index_arr - index_floor #Remain

val1 = original[index_floor]

val2 = original[index_ceil % len(original)]

interp = val1 * (1.0-index_rem) + val2 * index_rem

assert(len(interp) == targetLen)

return interp

if __name__=="__main__":

original = np.sin(np.arange(256)/10.0)

targetLen = 100

# Method 1: Use scipy interp1d (linear interpolation)

# This is the simplest conceptually as it just uses linear interpolation. Scipy

# also offers a range of other interpolation methods.

f = interp1d(np.arange(256), original, 'linear')

plt.plot(np.apply_along_axis(f, 0, np.linspace(0, 255, num=targetLen)))

# Method 2: Use numpy to do linear interpolation

# If you don't have scipy, you can do it in numpy with the above function

plt.plot(ResampleLinear1D(original, targetLen))

# Method 3: Use scipy's resample

# Converts the signal to frequency space (Fourier method), then back. This

# works efficiently on periodic functions but poorly on non-periodic functions.

plt.plot(resample(original, targetLen))

plt.show()