def MatMul_elements(matrix_predicate, scalar_predicate, expr, assumptions):
d = sift(expr.args, lambda x: isinstance(x, MatrixExpr))
factors, matrices = d[False], d[True]
return fuzzy_and([
test_closed_group(Basic(*factors), assumptions, scalar_predicate),
test_closed_group(Basic(*matrices), assumptions, matrix_predicate)
])
def test_Pow_Expr_args():
x = Symbol('x')
bases = [Basic(), Poly(x, x), FiniteSet(x)]
for base in bases:
# The cache can mess with the stacklevel test
with warns(SymPyDeprecationWarning, test_stacklevel=False):
Pow(base, S.One)
def test_issue_3001():
x = Symbol('x')
y = Symbol('y')
assert x**1.0 == x
assert x == x**1.0
assert True != x**1.0
assert x**1.0 is not True
assert x is not True
assert x*y == (x*y)**1.0
assert (x**1.0)**1.0 == x
assert (x**1.0)**2.0 == x**2
b = Basic()
assert Pow(b, 1.0, evaluate=False) == b
# if the following gets distributed as a Mul (x**1.0*y**1.0 then
# __eq__ methods could be added to Symbol and Pow to detect the
# power-of-1.0 case.
assert ((x*y)**1.0).func is Pow
def test_issue_6100_12942_4473():
x = Symbol("x")
y = Symbol("y")
assert x**1.0 != x
assert x != x**1.0
assert True != x**1.0
assert x**1.0 is not True
assert x is not True
assert x * y != (x * y)**1.0
# Pow != Symbol
assert (x**1.0)**1.0 != x
assert (x**1.0)**2.0 != x**2
b = Basic()
assert Pow(b, 1.0, evaluate=False) != b
# if the following gets distributed as a Mul (x**1.0*y**1.0 then
# __eq__ methods could be added to Symbol and Pow to detect the
# power-of-1.0 case.
assert ((x * y)**1.0).func is Pow
def is_shape_numeric(self):
"""
Test if the array is shape-numeric which means there is no symbolic
dimension.
Examples
========
>>> from sympy.tensor.array import ArrayComprehension
>>> from sympy import symbols
>>> i, j, k = symbols('i j k')
>>> a = ArrayComprehension(10*i + j, (i, 1, 4), (j, 1, 3))
>>> a.is_shape_numeric
True
>>> b = ArrayComprehension(10*i + j, (i, 1, 4), (j, 1, k+3))
>>> b.is_shape_numeric
False
"""
for _, inf, sup in self._limits:
if Basic(inf, sup).atoms(Symbol):
return False
return True
def is_numeric(self):
"""
Return True if the expanded array is numeric, which means that there is not
symbolic dimension.
Examples
========
>>> from sympy.tensor.array import ArrayComprehension
>>> from sympy.abc import i, j, k
>>> a = ArrayComprehension(10*i + j, (i, 1, 4), (j, 1, 3))
>>> a.is_numeric
True
>>> b = ArrayComprehension(10*i + j, (i, 1, 4), (j, 1, k+3))
>>> b.is_numeric
False
"""
for var, inf, sup in self._limits:
if Basic(inf, sup).atoms(Symbol):
return False
return True
def test_Pow_Expr_args():
x = Symbol('x')
bases = [Basic(), Poly(x, x), FiniteSet(x)]
for base in bases:
with warns_deprecated_sympy():
Pow(base, S.One)
