galgebra.lt¶
Multivector Linear Transformation
Members¶

galgebra.lt.
Matrix_to_dictionary
(mat_rep, basis)[source]¶ Convert matrix representation of linear transformation to dictionary

galgebra.lt.
Dictionary_to_Matrix
(dict_rep, ga)[source]¶ Convert dictionary representation of linear transformation to matrix

class
galgebra.lt.
Lt
(*args, ga, f=False, mode='g')[source]¶ Bases:
galgebra.printer.GaPrintable
A Linear Transformation
Except for the spinor representation the linear transformation is stored as a dictionary with basis vector keys and vector values
self.lt_dict
so that a is a vector \(a = a^{i}e_{i}\) then\[\mathtt{self(}a\mathtt{)} = a^{i} * \mathtt{self.lt\_dict[}e_{i}\mathtt{]}.\]For the spinor representation the linear transformation is stored as the even multivector
self.R
so that if a is a vector:self(a) = self.R * a * self.R.rev().

lt_dict
¶ the keys are the basis symbols, \(e_i\), and the dictionary entries are the object vector images (linear combination of sympy noncommutative basis symbols) of the keys so that if
L
is the linear transformation then:L(e_i) = self.Ga.mv(L.lt_dict[e_i])
 Type
 Parameters

det
() → sympy.core.expr.Expr[source]¶ Returns the determinant (a scalar) of the linear transformation, \(L\), defined by \({{\det}\lp {L} \rp }I = {{L}\lp {I} \rp }\).

tr
() → sympy.core.expr.Expr[source]¶ Returns the trace (a scalar) of the linear transformation, \(L\), defined by \({{\operatorname{tr}}\lp {L} \rp }=\nabla_{a}\cdot{{L}\lp {a} \rp }\) where \(a\) is a vector in the tangent space.

adj
() → galgebra.lt.Lt[source]¶ Returns the adjoint (a linear transformation) of the linear transformation, \(L\), defined by \(a\cdot{{L}\lp {b} \rp } = b\cdot{{\bar{L}}\lp {a} \rp }\) where \(a\) and \(b\) are any two vectors in the tangent space and \(\bar{L}\) is the adjoint of \(L\).

matrix
() → sympy.matrices.dense.MutableDenseMatrix[source]¶ Returns the matrix representation of the linear transformation, \(L\), defined by \({{L}\lp {{{\eb}}_{i}} \rp } = L_{ij}{{\eb}}_{j}\) where \(L_{ij}\) is the matrix representation.


class
galgebra.lt.
Mlt
(f, Ga, nargs=None, fct=False)[source]¶ Bases:
galgebra.printer.GaPrintable
A multilinear transformation (mlt) is a multilinear multivector function of a list of vectors (
*args
) \(F(v_1,...,v_r)\) where for any argument slot \(j\) we have (\(a\) is a scalar and \(u_j\) a vector)\[\begin{split}F(v_1,...,a*v_j,...,v_r) &= a*F(v_1,...,v_j,...,v_r) \\ F(v_1,...,v_j+u_j,...,v_r) &= F(v_1,...,v_j,...,v_r) + F(v_1,...,u_j,...,v_r).\end{split}\]If F and G are two
Mlt
s with the same number of argument slots then the sum is\[(F+G)F(v_1,...,v_r) = F(v_1,...,v_r) + G(v_1,...,v_r).\]If \(F\) and \(G\) are two
Mlt
s with \(r\) and \(s\) argument slots then their product is\[(F*G)(v_1,...,v_r,...,v_{r+s}) = F(v_1,...,v_r)*G(v_{r+1},...,v_{r+s}),\]where \(*\) is any of the multivector multiplicative operations. The derivative of a
Mlt
with is defined as the directional derivative with respect to the coordinate vector (we assume \(F\) is implicitely a function of the coordinates)\[F(v_1,...,v_r;v_{r+1}) = (v_{r+1} \bullet \nabla)F(v_1,...,v_j,...,v_r).\]The contraction of a
Mlt
between slots \(j\) and \(k\) is defined as the geometric derivative of \(F\) with respect to slot \(k\) and the inner geometric derivative with respect to slot \(j\) (this gives the standard tensor definition of contraction for the case that \(F\) is a scalar function)\[\begin{split}\operatorname{Contract}(i,j,F) &= \nabla_i \bullet (\nabla_j F(v_1,...,v_i,...,v_j,...,v_r)) \\ &= \nabla_j \bullet (\nabla_i F(v_1,...,v_i,...,v_j,...,v_r)).\end{split}\]This returns a
Mlt
with slot \(i\) and \(j\) removed.
Fmt
(lcnt=1, title=None) → galgebra.printer.GaPrintable[source]¶ Set format for printing of Tensors
 Parameters
lcnt – Number of components per line
Notes
Usage for tensor T example is:
T.fmt('2', 'T')
output is:
print 'T = '+str(A)
with two components per line. Works for both standard printing and for latex.

pdiff
(slot: int)[source]¶ Returns gradient of tensor,
T
, with respect to slot vector.For example if the tensor is \({{T}\lp {a_{1},a_{2}} \rp }\) then
T.pdiff(2)
is \(\nabla_{a_{2}}T\). SinceT
is a scalar function,T.pdiff(2)
is a vector function.

contract
(slot1: int, slot2: int)[source]¶ Returns contraction of tensor between
slot1
andslot2
whereslot1
is the index of the first vector argument andslot2
is the index of the second vector argument of the tensor.For example if we have a rank two tensor,
T(a1, a2)
, thenT.contract(1, 2)
is the contraction ofT
. For this case since there are only two slots, there can only be one contraction.

cderiv
()[source]¶ Returns covariant derivative of tensor field.
If
T
is a tensor of rank \(k\) thenT.cderiv()
is a tensor of rank \(k+1\). The operation performed is defined in section Multilinear Functions.
