Should conversion from numpy arrays/matrices to lists and sage vectors/ matrices be implemented by a .sage() method on the numpy array/ matrix? This is more consistent with the behaviour interface objects.
diagonal_matrix(), matrix(), etc could then outsource the conversion steps to .sage(), rather than the numpy-specific conversions being separately implemented in each of the functions. .sage() would behave like this: sage: a = numpy.array([1,2,6.4], dtype='float') sage: type(a[0]) <type 'numpy.float64'> sage: a_s = a.sage() sage: type(a_s) <type 'list'> sage: a_s[0].parent() Real Field with 53 bits of precision sage: m = numpy.matrix([[1.,3,],[4.,5.6]],dtype='float') sage: m_s = m.sage() sage: m_s.parent() Full MatrixSpace of 2 by 2 dense matrices over Real Field with 53 bits of precision I don't know where .sage() would be defined though. -- To post to this group, send an email to sage-devel@googlegroups.com To unsubscribe from this group, send an email to sage-devel+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URL: http://www.sagemath.org
