Sage Reference Manual: Matrices and Spaces of Matrices - Mirrors

Sage Reference Manual: Matrices and Spaces of Matrices, Release 6.1.1
sage: A.new_matrix()
[0.000000000000000 0.000000000000000 0.000000000000000]
[0.000000000000000 0.000000000000000 0.000000000000000]
sage: A.new_matrix().parent()
Full MatrixSpace of 2 by 3 dense matrices over Real Field with 53 bits of precision
numpy(dtype=None)
Return the Numpy matrix associated to this matrix.
INPUT:
•dtype - The desired data-type for the array. If not given, then the type will be determined as the
minimum type required to hold the objects in the sequence.
EXAMPLES:
sage: a = matrix(3,range(12))
sage: a.numpy()
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
sage: a.numpy(’f’)
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
sage: a.numpy(’d’)
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]])
sage: a.numpy(’B’)
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]], dtype=uint8)
Type numpy.typecodes for a list of the possible typecodes:
sage: import numpy
sage: sorted(numpy.typecodes.items())
[(’All’, ’bhilqpBHILQPefdgFDGSUVOMm’), (’AllFloat’, ’efdgFDG’), (’AllInteger’, ’bBhHiIlLqQp
Alternatively, numpy automatically calls this function (via the magic __array__() method) to convert
Sage matrices to numpy arrays:
sage: import numpy
sage: b=numpy.array(a); b
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
sage: b.dtype
dtype(’int32’) # 32-bit
dtype(’int64’) # 64-bit
sage: b.shape
(3, 4)
row(i, from_list=False)
Return the i‘th row of this matrix as a vector.
This row is a dense vector if and only if the matrix is a dense matrix.
INPUT:
108 Chapter 6. Base class for matrices, part 1