python - np.roll vs scipy.interpolation.shift--discrepancy for integer shift values

Question

I wrote some code to shift an array, and was trying to generalize it to handle non-integer shifts using the "shift" function in scipy.ndimage. The data is circular and so the result should wrap around, exactly as the np.roll command does it.

However, scipy.ndimage.shift does not appear to wrap integer shifts properly. The following code snippet shows the discrepancy:

import numpy as np
import scipy.ndimage as sciim
import matplotlib.pyplot as plt 

def shiftfunc(data, amt):
    return sciim.interpolation.shift(data, amt, mode='wrap', order = 3)

if __name__ == "__main__":
    xvals = np.arange(100)*1.0

    yvals = np.sin(xvals*0.1)

    rollshift   = np.roll(yvals, 2)

    interpshift = shiftfunc(yvals, 2)

    plt.plot(xvals, rollshift, label = 'np.roll', alpha = 0.5)
    plt.plot(xvals, interpshift, label = 'interpolation.shift', alpha = 0.5)
    plt.legend()
    plt.show()

It can be seen that the first couple of values are highly discrepant, while the rest are fine. I suspect this is an implementation error of the prefiltering and interpolation operation when using the wrap option. A way around this would be to modify shiftfunc to revert to np.roll when the shift value is an integer, but this is unsatisfying.

Am I missing something obvious here?

Is there a way to make ndimage.shift coincide with np.roll?

Answer 1

我不认为移位功能有什么问题。当您使用滚动时，您需要切掉一个额外的元素以进行公平比较。请看下面的代码。

import numpy as np
import scipy.ndimage as sciim
import matplotlib.pyplot as plt 


def shiftfunc(data, amt):
    return sciim.interpolation.shift(data, amt, mode='wrap', order = 3)

def rollfunc(data,amt):
    rollshift   = np.roll(yvals, amt)
    # Here I remove one element (first one before rollshift) from the array 
    return np.concatenate((rollshift[:amt], rollshift[amt+1:]))

if __name__ == "__main__":
    shift_by = 5
    xvals = np.linspace(0,2*np.pi,20)
    yvals = np.sin(xvals)
    rollshift   = rollfunc(yvals, shift_by)
    interpshift = shiftfunc(yvals,shift_by)
    plt.plot(xvals, yvals, label = 'original', alpha = 0.5)
    plt.plot(xvals[1:], rollshift, label = 'np.roll', alpha = 0.5,marker='s')
    plt.plot(xvals, interpshift, label = 'interpolation.shift', alpha = 0.5,marker='o') 
    plt.legend()
    plt.show()

结果是