[Matrix-SIG] Casts and slices

[Christos Siopis]

Greetings, fellow NumPyers

First off, thanks to all the noble souls who created NumPy and
then shared it with the rest of us :) Having just changed from
an environment where unlimited IDL (in fact, PV WAVE) licenses
were available to a place where no IDL is available, I did
appreciate NumPy's availability :)

I have a question about what might be a possible inconsistency:

>>> b = reshape(ones(20, 'f'), (10,2))

>>> b[:,0] = b[:,1]   # This works fine: both sides are of the same type.

>>> b[:,0] = sqrt(b[:,1])  # This also works fine.

>>> b[:,0] = b[:,1]**3     # Should this not also work if sqrt works?
Traceback (innermost last):
  File "<stdin>", line 1, in ?
TypeError: Array can not be safely cast to required type

If the previous does not work because it attempts to cast a
double (right hand side) into a float, then should the following
not also produce an error, since each element of the list is
a Python double:

>>> b[:,0] = (b[:,1]**3).tolist()    # It works.

>>> b[:,0] = map(float, b[:,1]**3)   # It also works.

Since many operations have right hand sides which are doubles,
I was wondering if there is an easier way to assign results to
float array elements than using the tolist() method or map().

====

A second question: using take() one can access noncontiguous
elements of an array. Is there a way to also assign values to
noncontiguous elements? I mean something like:

b[(0,1,4)] = [1.2, 3.4, 5.3]


One last question is also coming under a different subject.

Thanks,
Christos Siopis