Custom validator registration

I think that it would be interesting to push upstream the abiility that was added in jupyter-incubator's traittypes: registration of custom validators (non-cross) with a chaining `valid` method.

``` python
from traitlets import HasTraits, TraitError
from traittypes import Array

def shape(*dimensions):
def validator(trait, value):
if value.shape != dimensions:
raise TraitError('Expected an of shape %s and got and array with shape %s' % (dimensions, value.shape))
else:
return value
return validator

class Foo(HasTraits):
bar = Array(np.identity(2)).valid(shape(2, 2))
foo = Foo()

foo.bar = [1, 2] # Should raise a TraitError
```

In the case of the numpy array trait type, there are many possibilities that would be arguably natural but would be very likely bloating the TraitType specialization if implemented in the class.

Examples:
- only accepting attributes with a certain shape
- only accepting values that have less than a certain number of elements
- bounds on the dimensionality of the array
- requirements on dtypes (be a subdtype of a certain type, or exactly a certain dtype)
- squeezing dimensions en length 1, as we do in bqplot

Review the custom-validator behavior in jupyter-incubator's traittypes and the issue's Array examples first. Done means agreeing on the registration and chaining API, defining its validation semantics, and covering the demonstrated shape failure and related array constraints with tests.

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