I am having trouble with the following:

I would like to write an ndarray subclass and enforce shape (-1,3) for any new instance of this subclass, whichever way it comes about- explicit constructor, view casting or from template.

I've tried loads of things but none seem to work. 
I reckon I haven't fully grasped the underlying process. Any help is much appreciated!

```
import numpy as np

class test(np.ndarray):
def __new__(cls, *args, **kwargs):
    return np.ndarray.__new__(cls, *args, **kwargs)

def __array_finalize__(self, obj):

#        self.resize(-1,3)
#        self.reshape(-1,3)
#        self=self.reshape(-1,3)
        np.reshape(self,(-1,3))

a=np.array([1,2,3])
b=a.view(test)
c=test(a)
d=a.reshape(-1,3)
print '+++++++'
print a.shape,a
print '+++++++'
print b.shape,b
print '+++++++'
print c.shape,c
print '+++++++'
print d.shape,d

```

To clarify what I am trying to do:

I have vector fields which I want to treat generically as 3D, hence the (:,3) shape and (-1,3) shape resizing. I am looking for a pure object oriented solution to implement essentially a few additional methods to complement what already comes with NumPy. 

For example, I’ve started writing some stuff purely with ndarrays, but the code would be much more readable if I could just write 

```
normalizedVector = ndarray.view(my3DVectorClass).normalize()

```

rather than 

```
normalizedVector = ndarray / ( sum(ndarray**2, axis=1)**0.5 )

```

My problems with the second:

* I would like to be able to not have to worry about whether or not I am asking for the normalized version of a shape (3,) or (:,3) array.
* I would like to be able to use pure linear algebra terminology inside the classes method implementations and not have to bother with indexing and error/dimension checking within method definitions

I guess you could argue to just work with instances of my3DVectorClass exclusively but I would then have to do the reverse view casting when using all of the SciPy machinery, since they expect ndarray if I'm not mistaken, which would make these parts of the code a little bloated.

If I might have my logic wrong somehow I am grateful for suggestions. I’m still very much on the learning curve for both OOP and SciPy/NumPy.

Thanks a lot already!

Markus