galgebra.ga

Geometric Algebra (inherits Metric)

Members

class galgebra.ga.Ga(bases, **kwargs)[source]

The vector space (basis, metric, derivatives of basis vectors) is defined by the base class Metric.

The instanciating the class Ga constructs the geometric algebra of the vector space defined by the metric.

The construction includes the multivector bases, multiplication tables or functions for the geometric (*), inner (|), outer (^) products, plus the left (<) and right (>) contractions. The geometric derivative operator and any required connections for the derivative are also calculated.

Except for the geometric product in the case of a non-orthogonal set of basis vectors all products and connections (if needed) are calculated when needed and place in dictionaries (lists of tuples) to be used when needed. This greatly speeds up evaluations of multivector expressions over previous versions of this code since the products of multivector bases and connection are not calculated unless they are actually needed in the current calculation.

Only instantiate the Ga class via the Mv class or any use of enhanced printing (text or latex) will cause the bases and multiplication table entries to be incorrectly labeled .

Inherited from Metric class

galgebra.metric.Metric.g
galgebra.metric.Metric.g_inv
galgebra.metric.Metric.norm
galgebra.metric.Metric.coords
galgebra.metric.Metric.is_ortho
galgebra.metric.Metric.connect_flg
galgebra.metric.Metric.basis
galgebra.metric.Metric.r_symbols
galgebra.metric.Metric.n
galgebra.metric.Metric.n_range
galgebra.metric.Metric.de

Basis, basis bases, and basis blades data structures

indexes

Index list for multivector bases and blades by grade (tuple of tuples). Tuple so that indexes can be used to index dictionaries.

bases

List of bases (non-commutative sympy symbols) by grade. Only created for non-orthogonal basis vectors.

blades

List of basis blades (non-commutative sympy symbols) by grade. For orthogonal basis vectors the same as bases.

coord_vec

Linear combination of coordinates and basis vectors. For example in orthogonal 3D \(x*e_x+y*e_y+z*e_z\).

blades_to_indexes_dict

Map basis blades to index tuples (dictionary).

indexes_to_blades_dict

Map index tuples to basis blades (dictionary).

bases_to_indexes_dict

Map basis bases to index tuples (dictionary).

indexes_to_bases_dict

Map index tuples to basis bases (dictionary).

Multiplication tables data structures

Keys in all multiplication tables (*, ^, |, <, >) are always symbol1*symbol2. The correct operation is known by the context (name) of the relevant list or dictionary). These dictionaries are lazy, meaning they may be empty until an attempt is made to index them.

mul_table_dict

Geometric products of basis blades as a lazy_dict, {base1*base2: Expansion of base1*base2,...}

wedge_table_dict

Outer products of basis blades as a lazy_dict, ``{base1*base2: Expansion of base1^base2,…}`

dot_table_dict

Hestenes inner products of basis blades as a lazy_dict, {base1*base2: Expansion of base1|base2,...}

left_contract_table_dict

Left contraction of basis blades as a lazy_dict, {base1*base2: Expansion of base1<base2,...}

right_contract_table_dict

Right contraction of basis blades as a lazy_dict, {base1*base2: Expansion of base1>base2,...}

Reciprocal basis data structures

r_symbols

Reciprocal basis vector symbols (list of non-commutative sympy variables)

r_basis

List of reciprocal basis vectors expanded as linear combination of basis vector symbols.

r_basis_dict

Dictionary to map reciprocal basis symbols to reciprocal basis expanded in terms of basis symbols {reciprocal basis symbol: linear combination of basis symbols, ...}

r_basis_mv

List of reciprocal basis vectors in terms of basis multivectors (elements of list can be used in multivector expressions.)

Derivative data structures

de

Derivatives of basis functions. Two dimensional list. First entry is differentiating coordinate index. Second entry is basis vector index. Quantities are linear combinations of basis vector symbols.

Pdop_identity

Partial differential operator identity (operates on multivector function to return function).

Pdiffs

Dictionary of partial differential operators (operates on multivector functions) for each coordinate \(\{x: \partial_{x}, ...\}\)

sPds

Dictionary of scalar partial differential operators (operates on scalar functions) for each coordinate \(\{x: \partial_{x}, ...\}\)

grad

Geometric derivative operator from left. grad*F returns multivector derivative, F*grad returns differential operator.

rgrad

Geometric derivative operator from right. rgrad*F returns differential operator, F*rgrad returns multivector derivative.

Other members

dot_mode

Controls the behavior of dot()

value dot aliases
'|' hestenes_dot()
'<' left_contract()
'>' right_contract()
Christoffel_symbols(mode=1)

mode = 1 Christoffel symbols of the first kind mode = 2 Christoffel symbols of the second kind

bases_dict(prefix=None)[source]

returns a dictionary mapping basis element names to their MultiVector instances, optionally for specific grades

if you are lazy, you might do this to populate your namespace with the variables of a given layout.

>>> locals().update(ga.bases())
blade_derivation(blade, ib)[source]

Calculate derivatives of basis blade ‘blade’ using derivative of basis vectors calculated by metric. ‘ib’ is the index of the coordinate the derivation is with respect to or the coordinate symbol. These are requried for the calculation of the geometric derivatives in curvilinear coordinates or for more general manifolds.

‘blade_derivation’ caches the results in a dictionary, self._dbases, so that the derivation for a given blade and coordinate is never calculated more that once.

Note that the return value is not a multivector, but linear combination of basis blade symbols.

static build(*args, **kwargs)[source]

Static method to instantiate geometric algebra and return geometric algebra, basis vectors, and grad operator as a tuple.

connection(rbase, key_base, mode, left)[source]

Compute required multivector connections of the form (Einstein summation convention) \(e^{j}*(D_{j}e_{i_{1}...i_{r}})\) and \((D_{j}e_{i_{1}...i_{r}})*e^{j}\) where \(*\) could be *, ^, |, <, or > depending upon the mode, and \(e^{j}\) are reciprocal basis vectors.

dop(*args, **kwargs)[source]

Instanciate and return a multivector differential operator for this, ‘self’, geometric algebra.

dot(A, B)[source]

Inner product |, <, or >.

The dot_mode attribute determines which of these is used.

static dot_orthogonal(V1, V2, g=None)

Returns the dot product of two vectors in an orthogonal coordinate system. V1 and V2 are lists of sympy expressions. g is a list of constants that gives the signature of the vector space to allow for non-euclidian vector spaces.

This function is only used to form the dot product of vectors in the embedding space of a vector manifold or in the case where the basis vectors are explicitly defined by vector fields in the embedding space.

A g of None is for a Euclidian embedding space.

static dual_mode(mode='I+')[source]

Sets mode of multivector dual function for all geometric algebras in users program.

If Ga.dual_mode(mode) not called the default mode is 'I+'.

mode return value
+I I*self
-I -I*self
I+ self*I
I- -self*I
+Iinv Iinv*self
-Iinv -Iinv*self
Iinv+ self*Iinv
Iinv- -self*Iinv
er_blade(er, blade, mode='*', left=True)[source]

Product (*, ^, |, <, >) of reciprocal basis vector ‘er’ and basis blade ‘blade’ needed for application of derivatives to multivectors. left is ‘True’ means ‘er’ is multiplying ‘blade’ on the left, ‘False’ is for ‘er’ multiplying ‘blade’ on the right. Symbolically for left geometric product:

\[e^{j}*(e_{i_{1}}\wedge ...\wedge e_{i_{r}})\]
grad_sqr(A, grad_sqr_mode, mode, left)[source]

Calculate \((grad *_{1} grad) *_{2} A\) or \(A *_{2} (grad *_{1} grad)\) where grad_sqr_mode = \(*_{1}\) = *, ^, or | and mode = \(*_{2}\) = *, ^, or |.

grade_decomposition(A)[source]

Returns dictionary with grades as keys of grades of A. For example if A is a rotor the dictionary keys would be 0 and 2. For a vector the single key would be 1. Note A can be input as a multivector or an multivector object (sympy expression). If A is a multivector the dictionary entries are multivectors. If A is a sympy expression (in this case a linear combination of non-commutative symbols) the dictionary entries are sympy expressions.

hestenes_dot(A, B)[source]

compute the hestenes dot product, \(A \bullet B\)

left_contract(A, B)[source]

compute the left contraction, \(A \rfloor B\)

lt(*args, **kwargs)[source]

Instanciate and return a linear transformation for this, ‘self’, geometric algebra.

metric_symbols_list(s=None)

rows of metric tensor are separated by “,” and elements of each row separated by ” “. If the input is a single row it is assummed that the metric tensor is diagonal.

Output is a square matrix.

mv(root=None, *args, **kwargs)[source]

Instanciate and return a multivector for this, ‘self’, geometric algebra.

mvr(norm=True)[source]

Returns tumple of reciprocal basis vectors. If norm=True or basis vectors are orthogonal the reciprocal basis is normalized in the sense that

\[{i}\cdot e^{j} = \delta_{i}^{j}.\]

If the basis is not orthogonal and norm=False then

\[e_{i}\cdot e^{j} = I^{2}\delta_{i}^{j}.\]
pDiff(A, coord)[source]

Compute partial derivative of multivector function ‘A’ with respect to coordinate ‘coord’.

reduce_basis(blst)[source]

Repetitively applies reduce_basis_loop to blst product representation until normal form is realized for non-orthogonal basis

static reduce_basis_loop(g, blst)[source]

blst is a list of integers \([i_{1},...,i_{r}]\) representing the geometric product of r basis vectors \(a_{{i_1}}*...*a_{{i_r}}\). reduce_basis_loop() searches along the list \([i_{1},...,i_{r}]\) untill it finds \(i_{j} = i_{j+1}\) and in this case contracts the list, or if \(i_{j} > i_{j+1}\) it revises the list (\(\sim i_{j}\) means remove \(i_{j}\) from the list)

  • Case 1: If \(i_{j} = i_{j+1}\), return \(a_{i_{j}}^2\) and \([i_{1},..,\sim i_{j},\sim i_{j+1},...,i_{r}]\)
  • Case 2: If \(i_{j} > i_{j+1}\), return \(a_{i_{j}}.a_{i_{j+1}}\), \([i_{1},..,\sim i_{j},\sim i_{j+1},...,i_{r}]\), and \([i_{1},..,i_{j+1},i_{j},...,i_{r}]\)
remove_scalar_part(A)[source]

Return non-commutative part (sympy object) of A.obj.

right_contract(A, B)[source]

compute the right contraction, \(A \lfloor B\)

sm(*args, **kwargs)[source]

Instanciate and return a submanifold for this geometric algebra. See Sm for instantiation inputs.

split_multivector(A)[source]

Split multivector \(A\) into commutative part \(a\) and non-commutative part \(A'\) so that \(A = a+A'\)

class galgebra.ga.Sm(*args, **kwargs)[source]

Submanifold is a geometric algebra defined on a submanifold of a base geometric algebra defined on a manifold. The submanifold is defined by a mapping from the coordinates of the base manifold to the coordinates of the submanifold. The inputs required to define the submanifold are:

Parameters:
  • u

    (args[0]) The coordinate map defining the submanifold which is a list of functions of coordinates of the base manifold in terms of the coordinates of the submanifold. for example if the manifold is a unit sphere then - u = [sin(u)*cos(v),sin(u)*sin(v),cos(u)].

    Alternatively (args[0]) is a parametric vector function of the basis vectors of the base manifold. The coefficients of the bases are functions of the coordinates (args[1]). In this case we would call the submanifold a “vector” manifold and additional characteristics of the manifold can be calculated since we have given an explicit embedding of the manifold in the base manifold.

  • coords – (args[1]) The coordinate list for the submanifold, for example [u, v].

Notes

See ‘init_slots’ for possible other inputs. The ‘Ga’ member function ‘sm’ can be used to instantiate the submanifold via (o3d is the base manifold):

coords = (u,v) = symbols(',v',real=True)
sm_example = o3d.sm([sin(u)*cos(v),sin(u)*sin(v),cos(u)],coords)

(eu,ev) = sm_example.mv()
sm_grad = sm_example.grad
Christoffel_symbols(mode=1)

mode = 1 Christoffel symbols of the first kind mode = 2 Christoffel symbols of the second kind

bases_dict(prefix=None)

returns a dictionary mapping basis element names to their MultiVector instances, optionally for specific grades

if you are lazy, you might do this to populate your namespace with the variables of a given layout.

>>> locals().update(ga.bases())
blade_derivation(blade, ib)

Calculate derivatives of basis blade ‘blade’ using derivative of basis vectors calculated by metric. ‘ib’ is the index of the coordinate the derivation is with respect to or the coordinate symbol. These are requried for the calculation of the geometric derivatives in curvilinear coordinates or for more general manifolds.

‘blade_derivation’ caches the results in a dictionary, self._dbases, so that the derivation for a given blade and coordinate is never calculated more that once.

Note that the return value is not a multivector, but linear combination of basis blade symbols.

static build(*args, **kwargs)

Static method to instantiate geometric algebra and return geometric algebra, basis vectors, and grad operator as a tuple.

connection(rbase, key_base, mode, left)

Compute required multivector connections of the form (Einstein summation convention) \(e^{j}*(D_{j}e_{i_{1}...i_{r}})\) and \((D_{j}e_{i_{1}...i_{r}})*e^{j}\) where \(*\) could be *, ^, |, <, or > depending upon the mode, and \(e^{j}\) are reciprocal basis vectors.

dop(*args, **kwargs)

Instanciate and return a multivector differential operator for this, ‘self’, geometric algebra.

dot(A, B)

Inner product |, <, or >.

The dot_mode attribute determines which of these is used.

static dot_orthogonal(V1, V2, g=None)

Returns the dot product of two vectors in an orthogonal coordinate system. V1 and V2 are lists of sympy expressions. g is a list of constants that gives the signature of the vector space to allow for non-euclidian vector spaces.

This function is only used to form the dot product of vectors in the embedding space of a vector manifold or in the case where the basis vectors are explicitly defined by vector fields in the embedding space.

A g of None is for a Euclidian embedding space.

static dual_mode(mode='I+')

Sets mode of multivector dual function for all geometric algebras in users program.

If Ga.dual_mode(mode) not called the default mode is 'I+'.

mode return value
+I I*self
-I -I*self
I+ self*I
I- -self*I
+Iinv Iinv*self
-Iinv -Iinv*self
Iinv+ self*Iinv
Iinv- -self*Iinv
er_blade(er, blade, mode='*', left=True)

Product (*, ^, |, <, >) of reciprocal basis vector ‘er’ and basis blade ‘blade’ needed for application of derivatives to multivectors. left is ‘True’ means ‘er’ is multiplying ‘blade’ on the left, ‘False’ is for ‘er’ multiplying ‘blade’ on the right. Symbolically for left geometric product:

\[e^{j}*(e_{i_{1}}\wedge ...\wedge e_{i_{r}})\]
grad_sqr(A, grad_sqr_mode, mode, left)

Calculate \((grad *_{1} grad) *_{2} A\) or \(A *_{2} (grad *_{1} grad)\) where grad_sqr_mode = \(*_{1}\) = *, ^, or | and mode = \(*_{2}\) = *, ^, or |.

grade_decomposition(A)

Returns dictionary with grades as keys of grades of A. For example if A is a rotor the dictionary keys would be 0 and 2. For a vector the single key would be 1. Note A can be input as a multivector or an multivector object (sympy expression). If A is a multivector the dictionary entries are multivectors. If A is a sympy expression (in this case a linear combination of non-commutative symbols) the dictionary entries are sympy expressions.

hestenes_dot(A, B)

compute the hestenes dot product, \(A \bullet B\)

left_contract(A, B)

compute the left contraction, \(A \rfloor B\)

lt(*args, **kwargs)

Instanciate and return a linear transformation for this, ‘self’, geometric algebra.

metric_symbols_list(s=None)

rows of metric tensor are separated by “,” and elements of each row separated by ” “. If the input is a single row it is assummed that the metric tensor is diagonal.

Output is a square matrix.

mv(root=None, *args, **kwargs)

Instanciate and return a multivector for this, ‘self’, geometric algebra.

mvr(norm=True)

Returns tumple of reciprocal basis vectors. If norm=True or basis vectors are orthogonal the reciprocal basis is normalized in the sense that

\[{i}\cdot e^{j} = \delta_{i}^{j}.\]

If the basis is not orthogonal and norm=False then

\[e_{i}\cdot e^{j} = I^{2}\delta_{i}^{j}.\]
pDiff(A, coord)

Compute partial derivative of multivector function ‘A’ with respect to coordinate ‘coord’.

reduce_basis(blst)

Repetitively applies reduce_basis_loop to blst product representation until normal form is realized for non-orthogonal basis

static reduce_basis_loop(g, blst)

blst is a list of integers \([i_{1},...,i_{r}]\) representing the geometric product of r basis vectors \(a_{{i_1}}*...*a_{{i_r}}\). reduce_basis_loop() searches along the list \([i_{1},...,i_{r}]\) untill it finds \(i_{j} = i_{j+1}\) and in this case contracts the list, or if \(i_{j} > i_{j+1}\) it revises the list (\(\sim i_{j}\) means remove \(i_{j}\) from the list)

  • Case 1: If \(i_{j} = i_{j+1}\), return \(a_{i_{j}}^2\) and \([i_{1},..,\sim i_{j},\sim i_{j+1},...,i_{r}]\)
  • Case 2: If \(i_{j} > i_{j+1}\), return \(a_{i_{j}}.a_{i_{j+1}}\), \([i_{1},..,\sim i_{j},\sim i_{j+1},...,i_{r}]\), and \([i_{1},..,i_{j+1},i_{j},...,i_{r}]\)
remove_scalar_part(A)

Return non-commutative part (sympy object) of A.obj.

right_contract(A, B)

compute the right contraction, \(A \lfloor B\)

sm(*args, **kwargs)

Instanciate and return a submanifold for this geometric algebra. See Sm for instantiation inputs.

split_multivector(A)

Split multivector \(A\) into commutative part \(a\) and non-commutative part \(A'\) so that \(A = a+A'\)

class galgebra.ga.lazy_dict(d, f_value)[source]

A dictionary that creates missing entries on the fly.

When the dictionary is indexed and the key used is not one of the existing keys, self.f_value(key) is called to evaluate the key. The result is then added to the dictionary so that self.f_value is not used to evaluate the same key again.

Parameters:
  • d – Arguments to pass on to the dict constructor, typically a regular dictionary
  • f_value (function) – The function to call to generate a value for a given key
clear() → None. Remove all items from D.
copy() → a shallow copy of D
fromkeys()

Create a new dictionary with keys from iterable and values set to value.

get()

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised

popitem() → (k, v), remove and return some (key, value) pair as a

2-tuple; but raise KeyError if D is empty.

setdefault()

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
galgebra.ga.nc_subs(expr, base_keys, base_values=None)[source]

See if expr contains nc keys in base_keys and substitute corresponding value in base_values for nc key. This was written since standard sympy subs was very slow in performing this operation for non-commutative keys for long lists of keys.

galgebra.ga.update_and_substitute(expr1, expr2, mul_dict)[source]

Linear expand expr1 and expr2 to get (summation convention):

expr1 = coefs1[i] * bases1[i]
expr2 = coefs2[j] * bases2[j]

where coefs1 and coefs2 are lists of are commutative expressions and bases1 and bases2 are lists of bases for the geometric algebra.

Then evaluate:

expr = coefs1[i] * coefs2[j] * mul_dict[bases1[i], bases2[j]]

where mul_dict[bases1[i], bases2[j]] contains the appropriate product of bases1[i]*bases2[j] as a linear combination of scalars and bases of the geometric algebra.