Flumpy doesn't gracefully handle non-contiguous arrays

[ndevenish]

>>> data = np.zeros((256,256,256))
>>> flumpy.from_numpy(data)[0:1,1:255,1:255]
double([[[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]])
>>> flumpy.from_numpy(data[0:1,1:255,1:255])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'numpy.ndarray' object has no attribute 'obj'

This should either throw an explicit error, or try to safely derive the originating object and apply that via array_family focus (in this case, a focus on data[0:1,1:255,1:255].base)

[ndevenish]

Apparently I've confused the array_family focus and origin features. A combination of origin and array sizes appears like it might do what we want to map to (with caveats....) https://github.com/cctbx/cctbx_project/blob/c989249e67daf3f790f28586adc292c2c8114f77/scitbx/array_family/accessors/flex_grid.h#L384-L399

[ndevenish]

No, this appears not possible - although you can set the origin of a flex.grid you can't change the presented width of the data - so it'd only work with data sliced off the front, and flex_grid isn't very amenable to subclassing. Can still make it have a better error though.