Thank you very much for your replies. xarray is perfect though I'm not sure
what overhead I'm paying to get the following:
import numpy as np
import xarray as xr
data = xr.DataArray(np.random.randn(2, 3, 4), dims=("x", "y", "z"))
data.transpose('z', 'y', 'x').shape
data.transpose('y', 'z', 'x').s
Thanks for your replies.
In retrospect, I realise that using the shape will not be helpful for a cubic
array i.e. the permutations of (10, 10, 10) are all (10, 10, 10)! However, the
problem remains. Let me try to explain.
Short version
The problem boils down to the meaning of axis indices as a
Hellos,
I would like to propose `numpy.ndarray.permute_shape()` method
to predictably permute the shape of an ndarray. In my opinion, the current
alternatives (`swapaxes`, `transform`, `moveaxes` and friends) are
counterintuitive and rely on referring to the axis indices. It would be
abundantly
