This is rather a contribution than a question since I will answer my own question. However, I am still interested in how the community would solve this problem. So feel free to answer.
So when I was playing around with QT in Python (i.e., PySide6) and it's Volumerendering capabilities I noticed some problems when setting my data array. Long story short: I didn't know (and if it is stated somwhere in the QT documentation at all) that the provided texture has to be of a shape where each dimension is a power of two.
Thus, I wanted to rescale my array to a shape which fulfills this criteria.
Calculating this shape with numpy is easy:
new_shape = numpy.power(2, numpy.ceil(numpy.log2(old_shape))).astype(int)
Now the only problem left is to rescale my array with shape old_shape to the new array with shape new_shape and properly interpolate the values.
And since I am usually only interested in some sort of generic approaches (who knows what this might be good for and for whom in the future), the following question did arise:
How to resize an arbitrary Numpy NDArray of shape old_shape to a Numpy NDArray of shape new shape with proper interpolation?
I tried using scipy RegularGridInterpolator to rescale my array and it actually worked.
I used scipy's RegularGridInterpolator to interpolate my array.
Other interpolators should work as well.
def resample_array_to_shape(array: np.array, new_shape, method="linear"):
# generate points for each entry in the array
entries = [np.arange(s) for s in array.shape]
# the value for each point corresponds to its value in the original array
interp = RegularGridInterpolator(entries, array, method=method)
# new entries
new_entries = [np.linspace(0, array.shape[i] - 1, new_shape[i]) for i in range(len(array.shape))]
# use 'ij' indexing to avoid swapping axes
new_grid = np.meshgrid(*new_entries, indexing='ij')
# interpolate and return
return interp(tuple(new_grid)).astype(array.dtype)