AnsMath

class ansys.math.core.math.AnsMath(mapdl=None, **kwargs)

Provides the common class for abstract math objects.

Examples

Create an instance.

>>> import ansys.math.core.math as pymath
>>> mm = pymath.AnsMath()

Add vectors.

>>> v1 = mm.ones(10)
>>> v2 = mm.ones(10)
>>> v3 = v1 + v2

Multiply matrices (not yet available).

>>> v1 = mm.ones(10)
>>> m1 = mm.rand(10, 10)
>>> v2 = m1*v1

Methods

AnsMath.add(obj1, obj2)	Add two AnsMath vectors or matrices.
AnsMath.damp([dtype, name, fname, asarray])	Load the damping matrix from a FULL file.
AnsMath.dot(vec_a, vec_b)	Multiply two AnsMath vectors.
AnsMath.eigs(nev, k[, m, c, phi, algo, ...])	Solve an eigenproblem.
AnsMath.factorize(mat[, algo, inplace])	Factorize a matrix.
AnsMath.free([mat])	Delete AnsMath objects.
AnsMath.get_vec([dtype, name, fname, ...])	Load a vector from a FULL file.
AnsMath.load_matrix_from_file([dtype, name, ...])	Import a matrix from an existing FULL file.
AnsMath.mass([dtype, name, fname, asarray])	Load the mass matrix from a FULL file.
AnsMath.mat([nrow, ncol, dtype, init, name, ...])	Create a matrix.
AnsMath.matrix(matrix[, name, triu])	Send a SciPy matrix or NumPy array to MAPDL.
AnsMath.mgs(mat[, thresh])	Apply the Modified Gram-Schmidt (MGS) algorithm to a matrix.
AnsMath.norm(obj[, order])	Return the norm of an AnsMath object.
AnsMath.ones(nrow[, ncol, dtype, name, asarray])	Create a vector or a matrix where all values are ones.
AnsMath.rand(nrow[, ncol, dtype, name, asarray])	Create a vector or a matrix where all values are random.
AnsMath.rhs([dtype, name, fname, asarray])	Return the load vector from a FULL file.
AnsMath.set_vec(data[, name])	Push a NumPy array or a Python list to the MAPDL memory workspace.
AnsMath.sparse(mat[, thresh])	Sparsify an existing matrix based on a threshold value.
AnsMath.status()	Print the status of all AnsMath objects.
AnsMath.stiff([dtype, name, fname, asarray])	Load the stiffness matrix from a FULL file.
AnsMath.subtract(obj1, obj2)	Subtract two AnsMath vectors or matrices.
AnsMath.svd(mat[, thresh, sig, v])	Apply an SVD algorithm on a matrix.
AnsMath.vec([size, dtype, init, name, asarray])	Create a vector.
AnsMath.zeros(nrow[, ncol, dtype, name, asarray])	Create a vector or a matrix where all values are zeros.