def block_collapse(expr):
"""Evaluates a block matrix expression
>>> from sympy import MatrixSymbol, BlockMatrix, symbols, \
Identity, Matrix, ZeroMatrix, block_collapse
>>> n,m,l = symbols('n m l')
>>> X = MatrixSymbol('X', n, n)
>>> Y = MatrixSymbol('Y', m ,m)
>>> Z = MatrixSymbol('Z', n, m)
>>> B = BlockMatrix([[X, Z], [ZeroMatrix(m, n), Y]])
>>> print B
[X, Z]
[0, Y]
>>> C = BlockMatrix([[Identity(n), Z]])
>>> print C
[I, Z]
>>> print block_collapse(C*B)
[X, Z + Z*Y]
"""
rule = canon(typed({MatAdd: do_one(bc_matadd, bc_block_plus_ident),
MatMul: do_one(bc_matmul, bc_dist),
BlockMatrix: bc_unpack}))
result = rule(expr)
try:
return result.doit()
except AttributeError:
return result
def block_collapse(expr):
"""Evaluates a block matrix expression
>>> from sympy import MatrixSymbol, BlockMatrix, symbols, \
Identity, Matrix, ZeroMatrix, block_collapse
>>> n,m,l = symbols('n m l')
>>> X = MatrixSymbol('X', n, n)
>>> Y = MatrixSymbol('Y', m ,m)
>>> Z = MatrixSymbol('Z', n, m)
>>> B = BlockMatrix([[X, Z], [ZeroMatrix(m, n), Y]])
>>> print(B)
Matrix([
[X, Z],
[0, Y]])
>>> C = BlockMatrix([[Identity(n), Z]])
>>> print(C)
Matrix([[I, Z]])
>>> print(block_collapse(C*B))
Matrix([[X, Z*Y + Z]])
"""
hasbm = lambda expr: isinstance(expr, MatrixExpr) and expr.has(BlockMatrix)
rule = exhaust(
bottom_up(exhaust(condition(hasbm, typed(
{MatAdd: do_one(bc_matadd, bc_block_plus_ident),
MatMul: do_one(bc_matmul, bc_dist),
Transpose: bc_transpose,
Inverse: bc_inverse,
BlockMatrix: do_one(bc_unpack, deblock)})))))
result = rule(expr)
try:
return result.doit()
except AttributeError:
return result
def block_collapse(expr):
"""Evaluates a block matrix expression
>>> from sympy import MatrixSymbol, BlockMatrix, symbols, \
Identity, Matrix, ZeroMatrix, block_collapse
>>> n,m,l = symbols('n m l')
>>> X = MatrixSymbol('X', n, n)
>>> Y = MatrixSymbol('Y', m ,m)
>>> Z = MatrixSymbol('Z', n, m)
>>> B = BlockMatrix([[X, Z], [ZeroMatrix(m, n), Y]])
>>> print(B)
Matrix([
[X, Z],
[0, Y]])
>>> C = BlockMatrix([[Identity(n), Z]])
>>> print(C)
Matrix([[I, Z]])
>>> print(block_collapse(C*B))
Matrix([[X, Z*Y + Z]])
"""
hasbm = lambda expr: isinstance(expr, MatrixExpr) and expr.has(BlockMatrix)
rule = exhaust(
bottom_up(exhaust(condition(hasbm, typed(
{MatAdd: do_one(bc_matadd, bc_block_plus_ident),
MatMul: do_one(bc_matmul, bc_dist),
Transpose: bc_transpose,
Inverse: bc_inverse,
BlockMatrix: do_one(bc_unpack, deblock)})))))
result = rule(expr)
try:
return result.doit()
except AttributeError:
return result
def block_collapse(expr):
"""Evaluates a block matrix expression
>>> from sympy import MatrixSymbol, BlockMatrix, symbols, \
Identity, Matrix, ZeroMatrix, block_collapse
>>> n,m,l = symbols('n m l')
>>> X = MatrixSymbol('X', n, n)
>>> Y = MatrixSymbol('Y', m ,m)
>>> Z = MatrixSymbol('Z', n, m)
>>> B = BlockMatrix([[X, Z], [ZeroMatrix(m, n), Y]])
>>> print B
[X, Z]
[0, Y]
>>> C = BlockMatrix([[Identity(n), Z]])
>>> print C
[I, Z]
>>> print block_collapse(C*B)
[X, Z + Z*Y]
"""
rule = canon(
typed({
MatAdd: do_one(bc_matadd, bc_block_plus_ident),
MatMul: do_one(bc_matmul, bc_dist),
BlockMatrix: bc_unpack
}))
result = rule(expr)
try:
return result.doit()
except AttributeError:
return result
def block_collapse(expr):
"""Evaluates a block matrix expression
>>> from sympy import MatrixSymbol, BlockMatrix, symbols, \
Identity, Matrix, ZeroMatrix, block_collapse
>>> n,m,l = symbols('n m l')
>>> X = MatrixSymbol('X', n, n)
>>> Y = MatrixSymbol('Y', m ,m)
>>> Z = MatrixSymbol('Z', n, m)
>>> B = BlockMatrix([[X, Z], [ZeroMatrix(m, n), Y]])
>>> print(B)
Matrix([
[X, Z],
[0, Y]])
>>> C = BlockMatrix([[Identity(n), Z]])
>>> print(C)
Matrix([[I, Z]])
>>> print(block_collapse(C*B))
Matrix([[X, Z + Z*Y]])
"""
from sympy.strategies.util import expr_fns
hasbm = lambda expr: isinstance(expr, MatrixExpr) and expr.has(BlockMatrix)
conditioned_rl = condition(
hasbm,
typed(
{MatAdd: do_one(bc_matadd, bc_block_plus_ident),
MatMul: do_one(bc_matmul, bc_dist),
MatPow: bc_matmul,
Transpose: bc_transpose,
Inverse: bc_inverse,
BlockMatrix: do_one(bc_unpack, deblock)}
)
)
rule = exhaust(
bottom_up(
exhaust(conditioned_rl),
fns=expr_fns
)
)
result = rule(expr)
doit = getattr(result, 'doit', None)
if doit is not None:
return doit()
else:
return result
if exp == 1:
args.append(base)
else:
args.append(base**exp)
exp = current_exp
base = current_base
if exp == 1:
args.append(base)
else:
args.append(base**exp)
return newmul(factor, *args)
rules = (any_zeros, remove_ids, xxinv, unpack, rm_id(lambda x: x == 1),
merge_explicit, factor_in_front, flatten, combine_powers)
canonicalize = exhaust(typed({MatMul: do_one(*rules)}))
def only_squares(*matrices):
"""factor matrices only if they are square"""
if matrices[0].rows != matrices[-1].cols:
raise RuntimeError("Invalid matrices being multiplied")
out = []
start = 0
for i, M in enumerate(matrices):
if M.cols == matrices[start].rows:
out.append(MatMul(*matrices[start:i+1]).doit())
start = i+1
return out
from sympy.assumptions.ask import ask, Q
""" Merge explicit MatrixBase arguments
>>> from sympy import MatrixSymbol, eye, Matrix, MatAdd, pprint
>>> from sympy.matrices.expressions.matadd import merge_explicit
>>> A = MatrixSymbol('A', 2, 2)
>>> B = eye(2)
>>> C = Matrix([[1, 2], [3, 4]])
>>> X = MatAdd(A, B, C)
>>> pprint(X)
[1 0] [1 2]
A + [ ] + [ ]
[0 1] [3 4]
>>> pprint(merge_explicit(X))
[2 2]
A + [ ]
[3 5]
"""
groups = sift(matadd.args, lambda arg: isinstance(arg, MatrixBase))
if len(groups[True]) > 1:
return MatAdd(*(groups[False] + [reduce(add, groups[True])]))
else:
return matadd
rules = (rm_id(lambda x: x == 0 or isinstance(x, ZeroMatrix)), unpack, flatten,
glom(matrix_of, factor_of,
combine), merge_explicit, sort(default_sort_key))
canonicalize = exhaust(
condition(lambda x: isinstance(x, MatAdd), do_one(*rules)))
def explicit_kronecker_product(kron):
# Make sure we have a sequence of Matrices
if not all(isinstance(m, MatrixBase) for m in kron.args):
return kron
return matrix_kronecker_product(*kron.args)
rules = (unpack,
explicit_kronecker_product,
flatten,
extract_commutative)
canonicalize = exhaust(condition(lambda x: isinstance(x, KroneckerProduct),
do_one(*rules)))
def _kronecker_dims_key(expr):
if isinstance(expr, KroneckerProduct):
return tuple(a.shape for a in expr.args)
else:
return (0,)
def kronecker_mat_add(expr):
from functools import reduce
args = sift(expr.args, _kronecker_dims_key)
nonkrons = args.pop((0,), None)
if not args:
return expr
return canonicalize(self)
def validate(*args):
if not all(arg.is_Matrix for arg in args):
raise TypeError("Mix of Matrix and Scalar symbols")
A = args[0]
for B in args[1:]:
if A.shape != B.shape:
raise ShapeError("Matrices %s and %s are not aligned" % (A, B))
rules = (unpack, flatten)
canonicalize = exhaust(
condition(lambda x: isinstance(x, HadamardProduct), do_one(*rules)))
def hadamard_power(base, exp):
base = sympify(base)
exp = sympify(exp)
if exp == 1:
return base
if not base.is_Matrix:
return base**exp
if exp.is_Matrix:
raise ValueError("cannot raise expression to a matrix")
return HadamardPower(base, exp)
class HadamardPower(MatrixExpr):
validate(*args)
return super(HadamardProduct, cls).__new__(cls, *args)
@property
def shape(self):
return self.args[0].shape
def _entry(self, i, j):
return Mul(*[arg._entry(i, j) for arg in self.args])
def _eval_transpose(self):
from sympy.matrices.expressions.transpose import transpose
return HadamardProduct(*list(map(transpose, self.args)))
def doit(self, **ignored):
return canonicalize(self)
def validate(*args):
if not all(arg.is_Matrix for arg in args):
raise TypeError("Mix of Matrix and Scalar symbols")
A = args[0]
for B in args[1:]:
if A.shape != B.shape:
raise ShapeError("Matrices %s and %s are not aligned" % (A, B))
rules = (unpack,
flatten)
canonicalize = exhaust(condition(lambda x: isinstance(x, HadamardProduct),
do_one(*rules)))
""" Remove Identities from a MatMul
This is a modified version of sympy.strategies.rm_id.
This is necesssary because MatMul may contain both MatrixExprs and Exprs
as args.
See Also
--------
sympy.strategies.rm_id
"""
# Separate Exprs from MatrixExprs in args
factor, mmul = mul.as_coeff_mmul()
# Apply standard rm_id for MatMuls
result = rm_id(lambda x: x.is_Identity is True)(mmul)
if result != mmul:
return newmul(factor, *result.args) # Recombine and return
else:
return mul
def factor_in_front(mul):
factor, matrices = mul.as_coeff_matrices()
if factor != 1:
return newmul(factor, *matrices)
return mul
rules = (any_zeros, remove_ids, xxinv, unpack, rm_id(lambda x: x == 1), factor_in_front, flatten)
canonicalize = exhaust(condition(lambda x: isinstance(x, MatMul), do_one(*rules)))
else:
return MatrixClass(matrix_expansion)
def explicit_kronecker_product(kron):
# Make sure we have a sequence of Matrices
if not all(isinstance(m, MatrixBase) for m in kron.args):
return kron
return matrix_kronecker_product(*kron.args)
rules = (unpack, explicit_kronecker_product, flatten, extract_commutative)
canonicalize = exhaust(
condition(lambda x: isinstance(x, KroneckerProduct), do_one(*rules)))
def _kronecker_dims_key(expr):
if isinstance(expr, KroneckerProduct):
return tuple(a.shape for a in expr.args)
else:
return (0, )
def kronecker_mat_add(expr):
from functools import reduce
args = sift(expr.args, _kronecker_dims_key)
nonkrons = args.pop((0, ), None)
if not args:
return expr
return [a.func(*a.args) for a in args]
# Adapted from sympy.matrices.expressions.matadd.py
groups = sift(vecadd.args, lambda arg: isinstance(arg, (Vector)))
if len(groups[True]) > 1:
return VecAdd(*(recreate_args(groups[False]) +
[reduce(add, recreate_args(groups[True]))]))
# return VecAdd(*(groups[False] + [reduce(add, groups[True])]))
else:
return vecadd
rules = (merge_explicit, sort(default_sort_key))
canonicalize = exhaust(
condition(lambda x: isinstance(x, VecAdd), do_one(*rules)))
class VecMul(VectorExpr, Mul):
""" A product of Vector expressions.
VecMul inherits from and operates like SymPy Mul.
"""
is_VecMul = True
is_commutative = True
def __new__(cls, *args, **kwargs):
evaluate = kwargs.get('evaluate', True)
print("VecMul __new__", args)
if not args:
