python - Function to compute 3D gradient with unevenly spaced sample locations -

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i have experimental observations in volume:

import numpy np  # observations not uniformly spaced  x = np.random.normal(0, 1, 10) y = np.random.normal(5, 2, 10) z = np.random.normal(10, 3, 10) xx, yy, zz = np.meshgrid(x, y, z, indexing='ij')  # fake temperatures @ coords tt = xx*2 + yy*2 + zz*2  # sample distances dx = np.diff(x) dy = np.diff(y) dz = np.diff(z)  grad = np.gradient(tt, [dx, dy, dz])  # returns error

this gives me error:

valueerror: operands not broadcast shapes (10,10,10) (3,9) (10,10,10).

edit: according @jay-kominek in comments below:

np.gradient won't work you, doesn't handle unevenly sampled data.

i've updated question. there function can can computation?

two things note: first, scalars single values, not arrays. second, signature of function numpy.gradient(f, *varargs, **kwargs). note * before varargs. means if varargs list, pass *varargs. or can provide elements of varargs separate arguments.

so, np.gradient wants single value distance along each dimension, like:

np.gradient(tt, np.diff(x)[0], np.diff(y)[0], np.diff(z)[0])

or:

distances = [np.diff(x)[0], np.diff(y)[0], np.diff(z)[0]] np.gradient(tt, *distances)

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