Dense vectors using a NumPy backend.¶
AUTHORS:
- Jason Grout, Oct 2008: switch to numpy backend, factored out - Vector_double_denseclass
- Josh Kantor 
- William Stein 
- class sage.modules.vector_numpy_dense.Vector_numpy_dense[source]¶
- Bases: - FreeModuleElement- Base class for vectors implemented using numpy arrays. - This class cannot be instantiated on its own. The numpy vector creation depends on several variables that are set in the subclasses. - EXAMPLES: - sage: v = vector(RDF, [1,2,3,4]); v (1.0, 2.0, 3.0, 4.0) sage: v*v 30.0 - >>> from sage.all import * >>> v = vector(RDF, [Integer(1),Integer(2),Integer(3),Integer(4)]); v (1.0, 2.0, 3.0, 4.0) >>> v*v 30.0 - numpy(dtype=None)[source]¶
- Return numpy array corresponding to this vector. - INPUT: - dtype– if specified, the numpy dtype of the returned array
 - EXAMPLES: - sage: v = vector(CDF,4,range(4)) sage: v.numpy() array([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]) sage: v = vector(CDF,0) sage: v.numpy() array([], dtype=complex128) sage: v = vector(RDF,4,range(4)) sage: v.numpy() array([0., 1., 2., 3.]) sage: v = vector(RDF,0) sage: v.numpy() array([], dtype=float64) - >>> from sage.all import * >>> v = vector(CDF,Integer(4),range(Integer(4))) >>> v.numpy() array([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]) >>> v = vector(CDF,Integer(0)) >>> v.numpy() array([], dtype=complex128) >>> v = vector(RDF,Integer(4),range(Integer(4))) >>> v.numpy() array([0., 1., 2., 3.]) >>> v = vector(RDF,Integer(0)) >>> v.numpy() array([], dtype=float64) - A numpy dtype may be requested manually: - sage: import numpy sage: v = vector(CDF, 3, range(3)) sage: v.numpy() array([0.+0.j, 1.+0.j, 2.+0.j]) sage: v.numpy(dtype=numpy.float64) array([0., 1., 2.]) sage: v.numpy(dtype=numpy.float32) array([0., 1., 2.], dtype=float32) - >>> from sage.all import * >>> import numpy >>> v = vector(CDF, Integer(3), range(Integer(3))) >>> v.numpy() array([0.+0.j, 1.+0.j, 2.+0.j]) >>> v.numpy(dtype=numpy.float64) array([0., 1., 2.]) >>> v.numpy(dtype=numpy.float32) array([0., 1., 2.], dtype=float32)