def __new__(cls, expr, condition=None, **kwargs):
expr = _sympify(expr)
if not kwargs.pop('evaluate', global_evaluate[0]):
if condition is None:
obj = Expr.__new__(cls, expr)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, expr, condition)
obj._condition = condition
return obj
if not expr.has(RandomSymbol):
return expr
if condition is not None:
condition = _sympify(condition)
if isinstance(expr, Add):
return Add(*[Expectation(a, condition=condition) for a in expr.args])
elif isinstance(expr, Mul):
rv = []
nonrv = []
for a in expr.args:
if isinstance(a, RandomSymbol) or a.has(RandomSymbol):
rv.append(a)
else:
nonrv.append(a)
return Mul(*nonrv)*Expectation(Mul(*rv), condition=condition, evaluate=False)
else:
if condition is None:
obj = Expr.__new__(cls, expr)
else:
obj = Expr.__new__(cls, expr, condition)
obj._condition = condition
return obj
def __new__(cls, *args, **kwargs):
from sympy.tensor.array import NDimArray, tensorproduct, Array
from sympy import MatrixBase, MatrixExpr
from sympy.strategies import flatten
args = [sympify(arg) for arg in args]
evaluate = kwargs.get("evaluate", global_evaluate[0])
if not evaluate:
obj = Expr.__new__(cls, *args)
return obj
arrays = []
other = []
scalar = S.One
for arg in args:
if isinstance(arg, (Iterable, MatrixBase, NDimArray)):
arrays.append(Array(arg))
elif isinstance(arg, (MatrixExpr,)):
other.append(arg)
else:
scalar *= arg
coeff = scalar*tensorproduct(*arrays)
if len(other) == 0:
return coeff
if coeff != 1:
newargs = [coeff] + other
else:
newargs = other
obj = Expr.__new__(cls, *newargs, **kwargs)
return flatten(obj)
def __new__(cls, *args):
""" Construct a Trace object.
Parameters
==========
args = sympy expression
"""
expr = args[0]
indices = Tuple(*args[1]) if len(args) == 2 else Tuple()
if isinstance(expr, Matrix):
return expr.trace()
elif hasattr(expr, 'trace') and callable(t.x):
#for any objects that have trace() defined e.g numpy
return expr.trace()
elif isinstance(expr, Add):
return Add(*[Tr(arg, indices) for arg in expr.args])
elif isinstance(expr, Mul):
c_part, nc_part = expr.args_cnc()
if len(nc_part) == 0:
return Mul(*c_part)
else:
nc_part_ordered = _cycle_permute(_rearrange_args(nc_part))
return Mul(*c_part) * Expr.__new__(cls, Mul(*nc_part_ordered), indices )
elif isinstance(expr, Pow):
if (_is_scalar(expr.args[0]) and
_is_scalar(expr.args[1])):
return expr
else:
return Expr.__new__(cls, expr, indices)
else:
if (_is_scalar(expr)):
return expr
return Expr.__new__(cls, expr, indices)
def __new__(cls, *args, **hints):
if not len(args) == 6:
raise ValueError('6 parameters expected, got %s' % args)
evaluate = hints.get('evaluate', False)
if evaluate:
return Expr.__new__(cls, *args)._eval_wignerd()
return Expr.__new__(cls, *args, **{'evaluate': False})
def __new__(cls, *args, **kwargs):
from sympy.tensor.array import NDimArray, tensorproduct, Array
from sympy import MatrixBase, MatrixExpr
from sympy.strategies import flatten
args = [sympify(arg) for arg in args]
evaluate = kwargs.get("evaluate", global_parameters.evaluate)
if not evaluate:
obj = Expr.__new__(cls, *args)
return obj
arrays = []
other = []
scalar = S.One
for arg in args:
if isinstance(arg, (Iterable, MatrixBase, NDimArray)):
arrays.append(Array(arg))
elif isinstance(arg, (MatrixExpr, )):
other.append(arg)
else:
scalar *= arg
coeff = scalar * tensorproduct(*arrays)
if len(other) == 0:
return coeff
if coeff != 1:
newargs = [coeff] + other
else:
newargs = other
obj = Expr.__new__(cls, *newargs, **kwargs)
return flatten(obj)
def _call_super_constructor(cls, arg1, arg2, condition):
if condition is not None:
obj = Expr.__new__(cls, arg1, arg2, condition)
else:
obj = Expr.__new__(cls, arg1, arg2)
obj._condition = condition
return obj
def _call_super_constructor(cls, arg1, arg2, condition):
if condition is not None:
obj = Expr.__new__(cls, arg1, arg2, condition)
else:
obj = Expr.__new__(cls, arg1, arg2)
obj._condition = condition
return obj
def __new__(cls, *args):
""" Construct a Trace object.
"""
expr = args[0]
indices = args[1] if len(args) == 2 else -1 # -1 indicates full trace
if isinstance(expr, Matrix):
return expr.trace()
elif hasattr(expr, "trace") and callable(t.x):
# for any objects that have trace() defined e.g numpy
return expr.trace()
elif isinstance(expr, Add):
return Add(*[Tr(arg, indices) for arg in expr.args])
elif isinstance(expr, Mul):
c_part, nc_part = expr.args_cnc()
if len(nc_part) == 0:
return Mul(*c_part)
else:
# cyclic permute nc_part for canonical ordering
nc_part_ordered = _cycle_permute(nc_part)
return Mul(*c_part) * Expr.__new__(cls, Mul(*nc_part_ordered), indices)
elif isinstance(expr, Pow):
if _is_scalar(expr.args[0]) and _is_scalar(expr.args[1]):
return expr
else:
return Expr.__new__(cls, expr, indices)
else:
if _is_scalar(expr):
return expr
return Expr.__new__(cls, expr, indices)
def __new__(cls, arg1, arg2, condition=None, **kwargs):
arg1 = _sympify(arg1)
arg2 = _sympify(arg2)
if not kwargs.pop('evaluate', global_evaluate[0]):
if condition is None:
obj = Expr.__new__(cls, arg1, arg2)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, arg1, arg2, condition)
obj._condition = condition
return obj
if condition is not None:
condition = _sympify(condition)
if arg1 == arg2:
return Variance(arg1, condition)
if not arg1.has(RandomSymbol):
return S.Zero
if not arg2.has(RandomSymbol):
return S.Zero
arg1, arg2 = sorted([arg1, arg2], key=default_sort_key)
if isinstance(arg1, RandomSymbol) and isinstance(arg2, RandomSymbol):
return Expr.__new__(cls, arg1, arg2)
coeff_rv_list1 = cls._expand_single_argument(arg1.expand())
coeff_rv_list2 = cls._expand_single_argument(arg2.expand())
addends = [a*b*Covariance(*sorted([r1, r2], key=default_sort_key), evaluate=False)
for (a, r1) in coeff_rv_list1 for (b, r2) in coeff_rv_list2]
return Add(*addends)
def __new__(cls, *args, **hints):
if not len(args) == 6:
raise ValueError("6 parameters expected, got %s" % args)
args = sympify(args)
evaluate = hints.get("evaluate", False)
if evaluate:
return Expr.__new__(cls, *args)._eval_wignerd()
return Expr.__new__(cls, *args, **{"evaluate": False})
def __new__(cls, *args, **hints):
if not len(args) == 6:
raise ValueError('6 parameters expected, got %s' % args)
args = sympify(args)
evaluate = hints.get('evaluate', False)
if evaluate:
return Expr.__new__(cls, *args)._eval_wignerd()
return Expr.__new__(cls, *args, **{'evaluate': False})
def __new__(cls, arg, condition=None, **kwargs):
arg = _sympify(arg)
if condition is None:
obj = Expr.__new__(cls, arg)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, arg, condition)
obj._condition = condition
return obj
def __new__(cls, arg, condition=None, **kwargs):
arg = _sympify(arg)
if condition is None:
obj = Expr.__new__(cls, arg)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, arg, condition)
obj._condition = condition
return obj
def __new__(cls, prob, condition=None, **kwargs):
prob = _sympify(prob)
if condition is None:
obj = Expr.__new__(cls, prob)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, prob, condition)
obj._condition = condition
return obj
def __new__(cls, *args, **options):
assert len(args) == 1
if isinstance(args[0], Transpose):
return args[0].arg
elif isinstance(args[0], Add):
return Add(*[Transpose(a) for a in args[0].args])
elif isinstance(args[0], Mul):
coeffs = [a for a in args[0].args if a.is_commutative]
args = [a for a in args[0].args if not a.is_commutative]
return Mul(*coeffs) * Expr.__new__(cls, Mul(*args))
return Expr.__new__(cls, *args)
def __new__(cls, expr, condition=None, **kwargs):
expr = _sympify(expr)
if condition is None:
if not expr.has(RandomSymbol):
return expr
obj = Expr.__new__(cls, expr)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, expr, condition)
obj._condition = condition
return obj
def __new__(cls, expr, condition=None, **kwargs):
expr = _sympify(expr)
if condition is None:
if not expr.has(RandomSymbol):
return expr
obj = Expr.__new__(cls, expr)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, expr, condition)
obj._condition = condition
return obj
def __new__(cls, *args):
""" Construct a Trace object.
Parameters
==========
args = sympy expression
indices = tuple/list if indices, optional
"""
# expect no indices,int or a tuple/list/Tuple
if (len(args) == 2):
if not isinstance(args[1], (list, Tuple, tuple)):
indices = Tuple(args[1])
else:
indices = Tuple(*args[1])
expr = args[0]
elif (len(args) == 1):
indices = Tuple()
expr = args[0]
else:
raise ValueError("Arguments to Tr should be of form"
"(expr[, [indices]])")
if isinstance(expr, Matrix):
return expr.trace()
elif hasattr(expr, 'trace') and callable(expr.trace):
#for any objects that have trace() defined e.g numpy
return expr.trace()
elif isinstance(expr, Add):
return Add(*[Tr(arg, indices) for arg in expr.args])
elif isinstance(expr, Mul):
c_part, nc_part = expr.args_cnc()
if len(nc_part) == 0:
return Mul(*c_part)
else:
obj = Expr.__new__(cls, Mul(*nc_part), indices )
#this check is needed to prevent cached instances
#being returned even if len(c_part)==0
return Mul(*c_part)*obj if len(c_part)>0 else obj
elif isinstance(expr, Pow):
if (_is_scalar(expr.args[0]) and
_is_scalar(expr.args[1])):
return expr
else:
return Expr.__new__(cls, expr, indices)
else:
if (_is_scalar(expr)):
return expr
return Expr.__new__(cls, expr, indices)
def __new__(cls, arg, condition=None, **kwargs):
arg = _sympify(arg)
if arg.is_Matrix:
from sympy.stats.symbolic_multivariate_probability import VarianceMatrix
return VarianceMatrix(arg, condition)
if condition is None:
obj = Expr.__new__(cls, arg)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, arg, condition)
obj._condition = condition
return obj
def __new__(cls, expr, condition=None):
expr = _sympify(expr)
if condition is None:
if not is_random(expr):
return expr
obj = Expr.__new__(cls, expr)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, expr, condition)
obj._shape = expr.shape
obj._condition = condition
return obj
def __new__(cls, expr, condition=None, **kwargs):
expr = _sympify(expr)
if expr.is_Matrix:
from sympy.stats.symbolic_multivariate_probability import ExpectationMatrix
return ExpectationMatrix(expr, condition)
if condition is None:
if not is_random(expr):
return expr
obj = Expr.__new__(cls, expr)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, expr, condition)
obj._condition = condition
return obj
def __new__(cls, arg1, arg2, condition=None, **kwargs):
arg1 = _sympify(arg1)
arg2 = _sympify(arg2)
if kwargs.pop('evaluate', global_evaluate[0]):
arg1, arg2 = sorted([arg1, arg2], key=default_sort_key)
if condition is None:
obj = Expr.__new__(cls, arg1, arg2)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, arg1, arg2, condition)
obj._condition = condition
return obj
def __new__(cls, arg1, arg2, condition=None, **kwargs):
arg1 = _sympify(arg1)
arg2 = _sympify(arg2)
if kwargs.pop('evaluate', global_evaluate[0]):
arg1, arg2 = sorted([arg1, arg2], key=default_sort_key)
if condition is None:
obj = Expr.__new__(cls, arg1, arg2)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, arg1, arg2, condition)
obj._condition = condition
return obj
def _eval_adjoint(self):
obj = Expr._eval_adjoint(self)
if obj is None:
obj = Expr.__new__(Dagger, self)
if isinstance(obj, QExpr):
obj.hilbert_space = self.hilbert_space
return obj
def _eval_adjoint(self):
obj = Expr._eval_adjoint(self)
if obj is None:
obj = Expr.__new__(Dagger, self)
if isinstance(obj, QExpr):
obj.hilbert_space = self.hilbert_space
return obj
def __new__(cls, factor=1, unit=None, abbrev='', **assumptions):
if not isinstance(factor, str):
factor = sympify(factor)
# if the given unit is a number (because of some operations) and
# the factor is represented as a number, then return a number
if ((unit is None or isinstance(unit, (Number, numbers.Real)))
and isinstance(factor, (Number, numbers.Real))):
return factor * (unit or 1)
#TODO: if factor is of the form "1 m", parse the factor and the unit
if isinstance(factor, (Number, numbers.Real)):
unit = cls.qsimplify(unit)
if isinstance(unit, Quantity):
unit = unit.as_unit
if not isinstance(unit, Unit):
raise TypeError("'unit' should be a Unit instance; %s found"
% type(unit))
else:
raise NotImplementedError
obj = Expr.__new__(cls, factor, unit, **assumptions)
obj.factor, obj.unit = factor, unit
obj._abbrev = abbrev
return obj
def __new__(cls, X, n, c=0, condition=None, **kwargs):
X = _sympify(X)
n = _sympify(n)
c = _sympify(c)
if condition is not None:
condition = _sympify(condition)
return Expr.__new__(cls, X, n, c, condition)
def __new__(cls, arg, **old_assumptions):
# Return the dagger of a sympy Matrix immediately.
if isinstance(arg, (Matrix, numpy_ndarray, scipy_sparse_matrix)):
return matrix_dagger(arg)
arg = sympify(arg)
r = cls.eval(arg)
if isinstance(r, Expr):
return r
#make unevaluated dagger commutative or non-commutative depending on arg
if arg.is_commutative:
obj = Expr.__new__(cls, arg, **{'commutative':True})
else:
obj = Expr.__new__(cls, arg, **{'commutative':False})
if isinstance(obj, QExpr):
obj.hilbert_space = arg.hilbert_space
return obj
def __new__(cls, arg, condition=None):
arg = _sympify(arg)
if 1 not in arg.shape:
raise ShapeError("Expression is not a vector")
shape = (arg.shape[0], arg.shape[0]) if arg.shape[1] == 1 else (arg.shape[1], arg.shape[1])
if condition:
obj = Expr.__new__(cls, arg, condition)
else:
obj = Expr.__new__(cls, arg)
obj._shape = shape
obj._condition = condition
return obj
def __new__(cls, bra, ket):
if not isinstance(ket, KetBase):
raise TypeError('KetBase subclass expected, got: %r' % ket)
if not isinstance(bra, BraBase):
raise TypeError('BraBase subclass expected, got: %r' % ket)
obj = Expr.__new__(cls, bra, ket)
return obj
def __new__(cls, before, variable, after):
if not variable.is_symbol:
variable = Symbol(variable)
return Expr.__new__(cls,
sympify(before),
variable,
sympify(after, {variable.name: variable}))
def __new__(cls, bra, ket):
if not isinstance(ket, KetBase):
raise TypeError('KetBase subclass expected, got: %r' % ket)
if not isinstance(bra, BraBase):
raise TypeError('BraBase subclass expected, got: %r' % ket)
obj = Expr.__new__(cls, bra, ket)
return obj
def __new__(cls, expr1, expr2):
expr1 = sympify(expr1)
expr2 = sympify(expr2)
check = lambda x: isinstance(x, (VectorExpr, Vector))
if not (check(expr1) and check(expr2) and \
expr1.is_Vector and expr2.is_Vector):
raise TypeError(
"Both side of the cross-operator must be vectors\n" +
"\t Left: " + str(expr1.func) + ", " + str(expr1.is_Vector) +
", {}\n".format(expr1) + "\t Right: " + str(expr2.func) +
", " + str(expr2.is_Vector) + ", {}\n".format(expr2))
if expr1 == VectorZero() or expr2 == VectorZero():
return VectorZero()
if (isinstance(expr1, Vector) and isinstance(expr2, Vector)
and expr1 == expr2):
# TODO: At this point I'm dealing with unevaluated cross product.
# is it better to return VectorZero()?
return expr1.zero
if expr1 == expr2 and isinstance(expr1, Nabla):
raise TypeError(
"Cross product of two nabla operators not supported.")
if isinstance(expr2, Nabla):
raise TypeError(
"To compute the curl, nabla operator must be the first argument.\n"
+
"To write the advection operator, use the class Advection.\n" +
"\t Left: " + str(expr1.func) + "{}\n".format(expr1) +
"\t Right: " + str(expr2.func) + "{}\n".format(expr2))
if expr1 == expr2:
return VectorZero()
obj = Expr.__new__(cls, expr1, expr2)
return obj
def __new__(cls, bra, ket, **old_assumptions):
if not isinstance(ket, KetBase):
raise TypeError('KetBase subclass expected, got: %r' % ket)
if not isinstance(bra, BraBase):
raise TypeError('BraBase subclass expected, got: %r' % ket)
obj = Expr.__new__(cls, *(bra, ket), **{'commutative': True})
return obj
def __new__(cls, *args, **old_assumptions):
"""Construct a new quantum object.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the
quantum object. For a state, this will be its symbol or its
set of quantum numbers.
Examples
========
>>> from sympy.physics.quantum.qexpr import QExpr
>>> q = QExpr(0)
>>> q
0
>>> q.label
(0,)
>>> q.hilbert_space
H
>>> q.args
(0,)
>>> q.is_commutative
False
"""
# First compute args and call Expr.__new__ to create the instance
args = cls._eval_args(args)
inst = Expr.__new__(cls, *args, **{'commutative':False})
# Now set the slots on the instance
inst.hilbert_space = cls._eval_hilbert_space(args)
return inst
def __new__(cls, bra, ket, **old_assumptions):
if not isinstance(ket, KetBase):
raise TypeError('KetBase subclass expected, got: %r' % ket)
if not isinstance(bra, BraBase):
raise TypeError('BraBase subclass expected, got: %r' % ket)
obj = Expr.__new__(cls, *(bra, ket), **{'commutative':True})
return obj
def __new__(cls, arg):
if isinstance(arg, LiteralFloat):
return LiteralFloat(arg, precision = cls._precision)
elif isinstance(arg, LiteralInteger):
return LiteralFloat(arg.p, precision = cls._precision)
else:
return Expr.__new__(cls, arg)
def __new__(cls, *args, **old_assumptions):
"""Construct a new quantum object.
Parameters
==========
args : tuple
The list of numbers or parameters that uniquely specify the
quantum object. For a state, this will be its symbol or its
set of quantum numbers.
Examples
========
>>> from sympy.physics.quantum.qexpr import QExpr
>>> q = QExpr(0)
>>> q
0
>>> q.label
(0,)
>>> q.hilbert_space
H
>>> q.args
(0,)
>>> q.is_commutative
False
"""
# First compute args and call Expr.__new__ to create the instance
args = cls._eval_args(args)
inst = Expr.__new__(cls, *args, **{'commutative':False})
# Now set the slots on the instance
inst.hilbert_space = cls._eval_hilbert_space(args)
return inst
def __new__(cls, prob, given):
assert prob.is_random, prob
assert given.is_random, given
prob = _sympify(prob)
given = _sympify(given)
if prob.is_Conditioned:
return Expr.__new__(cls, prob.lhs, prob.rhs & given)
if prob.is_And:
if given.is_And:
if given._argset & prob._argset:
prob = prob.func(*prob._argset - given._argset)
else:
if given in prob._argset:
prob = prob.func(*prob._argset - {given})
return Expr.__new__(cls, prob, given)
def __new__(cls, symbol, *shape):
if isinstance(symbol, str):
symbol = Symbol(symbol)
# symbol = _sympify(symbol)
shape = map(_sympify, shape)
obj = Expr.__new__(cls, symbol, *shape)
return obj
def __new__(cls, arg, **old_assumptions):
# Return the dagger of a sympy Matrix immediately.
if isinstance(arg, (Matrix, numpy_ndarray, scipy_sparse_matrix)):
return matrix_dagger(arg)
arg = sympify(arg)
r = cls.eval(arg)
if isinstance(r, Expr):
return r
#make unevaluated dagger commutative or non-commutative depending on arg
if arg.is_commutative:
obj = Expr.__new__(cls, arg, **{'commutative':True})
else:
obj = Expr.__new__(cls, arg, **{'commutative':False})
if isinstance(obj, QExpr):
obj.hilbert_space = arg.hilbert_space
return obj
def __new__(cls, arg0, arg1=LiteralFloat(0)):
if isinstance(arg0, Literal) and isinstance(arg1, Literal):
real_part = 0
imag_part = 0
# Collect real and imag part from first argument
if isinstance(arg0, LiteralComplex):
real_part += arg0.real.python_value
imag_part += arg0.imag.python_value
else:
real_part += arg0.python_value
# Collect real and imag part from second argument
if isinstance(arg1, LiteralComplex):
real_part -= arg1.imag.python_value
imag_part += arg1.real.python_value
else:
imag_part += arg1.python_value
return LiteralComplex(real_part, imag_part, precision = cls._precision)
# Split arguments depending on their type to ensure that the arguments are
# either a complex and LiteralFloat(0) or 2 floats
from .operators import PyccelAdd, PyccelMul
if arg0.dtype is NativeComplex() and arg1.dtype is NativeComplex():
# both args are complex
return PyccelAdd(arg0, PyccelMul(arg1, LiteralImaginaryUnit()))
return Expr.__new__(cls, arg0, arg1)
def __new__(cls, *args):
if not len(args) in [1, 2]:
raise ValueError('1 or 2 parameters expected, got %s' % str(args))
if len(args) == 1:
args = (args[0], Integer(0))
if len(args) == 2:
args = (args[0], Integer(args[1]))
return Expr.__new__(cls, *args)
def __new__(cls, *args):
if not len(args) in [1, 2]:
raise ValueError('1 or 2 parameters expected, got %s' % str(args))
if len(args) == 1:
args = (args[0], Integer(0))
if len(args) == 2:
args = (args[0], Integer(args[1]))
return Expr.__new__(cls, *args)
def __new__(cls, p, q=None, prec=15):
rat = Rational.__new__(cls, p, q)
if isinstance(rat, (Integer, Infinity)):
return rat
obj = Expr.__new__(cls)
obj.p = rat.p
obj.q = rat.q
obj.prec = prec
return obj
def __new__(cls, arg1, arg2, condition=None, **kwargs):
arg1 = _sympify(arg1)
arg2 = _sympify(arg2)
if arg1.is_Matrix or arg2.is_Matrix:
from sympy.stats.symbolic_multivariate_probability import CrossCovarianceMatrix
return CrossCovarianceMatrix(arg1, arg2, condition)
if kwargs.pop('evaluate', global_parameters.evaluate):
arg1, arg2 = sorted([arg1, arg2], key=default_sort_key)
if condition is None:
obj = Expr.__new__(cls, arg1, arg2)
else:
condition = _sympify(condition)
obj = Expr.__new__(cls, arg1, arg2, condition)
obj._condition = condition
return obj
def __new__(cls, arg1, arg2, condition=None):
arg1 = _sympify(arg1)
arg2 = _sympify(arg2)
if (1 not in arg1.shape) or (1 not in arg2.shape) or (arg1.shape[1] != arg2.shape[1]):
raise ShapeError("Expression is not a vector")
shape = (arg1.shape[0], arg2.shape[0]) if arg1.shape[1] == 1 and arg2.shape[1] == 1 \
else (1, 1)
if condition:
obj = Expr.__new__(cls, arg1, arg2, condition)
else:
obj = Expr.__new__(cls, arg1, arg2)
obj._shape = shape
obj._condition = condition
return obj
def __new__(cls, *args, **old_assumptions):
from sympy.physics.quantum.state import KetBase, BraBase
if len(args) != 2:
raise ValueError('2 parameters expected, got %d' % len(args))
ket_expr = expand(args[0])
bra_expr = expand(args[1])
if (isinstance(ket_expr, (KetBase, Mul)) and
isinstance(bra_expr, (BraBase, Mul))):
ket_c, kets = ket_expr.args_cnc()
bra_c, bras = bra_expr.args_cnc()
if len(kets) != 1 or not isinstance(kets[0], KetBase):
raise TypeError('KetBase subclass expected'
', got: %r' % Mul(*kets))
if len(bras) != 1 or not isinstance(bras[0], BraBase):
raise TypeError('BraBase subclass expected'
', got: %r' % Mul(*bras))
if not kets[0].dual_class() == bras[0].__class__:
raise TypeError(
'ket and bra are not dual classes: %r, %r' %
(kets[0].__class__, bras[0].__class__)
)
# TODO: make sure the hilbert spaces of the bra and ket are
# compatible
obj = Expr.__new__(cls, *(kets[0], bras[0]), **old_assumptions)
obj.hilbert_space = kets[0].hilbert_space
return Mul(*(ket_c + bra_c)) * obj
op_terms = []
if isinstance(ket_expr, Add) and isinstance(bra_expr, Add):
for ket_term in ket_expr.args:
for bra_term in bra_expr.args:
op_terms.append(OuterProduct(ket_term, bra_term,
**old_assumptions))
elif isinstance(ket_expr, Add):
for ket_term in ket_expr.args:
op_terms.append(OuterProduct(ket_term, bra_expr,
**old_assumptions))
elif isinstance(bra_expr, Add):
for bra_term in bra_expr.args:
op_terms.append(OuterProduct(ket_expr, bra_term,
**old_assumptions))
else:
raise TypeError(
'Expected ket and bra expression, got: %r, %r' %
(ket_expr, bra_expr)
)
return Add(*op_terms)
def __new__(cls, *args, **hints):
if not len(args) in [2, 3]:
raise ValueError('2 or 3 parameters expected, got %s' % args)
if len(args) == 2:
args = (args[0], args[1], Integer(0))
if len(args) == 3:
args = (args[0], args[1], Integer(args[2]))
return Expr.__new__(cls, *args)
def __new__(cls, *args, **assumptions):
if isinstance(args[0], (Matrix, numpy_ndarray, scipy_sparse_matrix)):
return matrix_tensor_product(*args)
c_part, new_args = cls.flatten(sympify(args))
c_part = Mul(*c_part)
if len(new_args) == 0:
return c_part
elif len(new_args) == 1:
return c_part*new_args[0]
else:
tp = Expr.__new__(cls, *new_args, **{'commutative': False})
return c_part*tp
def __new__(cls, arg, **old_assumptions):
# Return the dagger of a sympy Matrix immediately.
if isinstance(arg, (Matrix, numpy_ndarray, scipy_sparse_matrix)):
return matrix_dagger(arg)
arg = sympify(arg)
r = cls.eval(arg)
if isinstance(r, Expr):
return r
obj = Expr.__new__(cls, arg)
if isinstance(obj, QExpr):
obj.hilbert_space = arg.hilbert_space
return obj
def _new_rawargs(cls, hilbert_space, *args):
"""Create new instance of this class with hilbert_space and args.
This is used to bypass the more complex logic in the ``__new__``
method in cases where you already have the exact ``hilbert_space``
and ``args``. This should be used when you are positive these
arguments are valid, in their final, proper form and want to optimize
the creation of the object.
"""
obj = Expr.__new__(cls, *args, **{'commutative':False})
obj.hilbert_space = hilbert_space
return obj
def __new__(cls, expr, coeffs=Tuple(), alias=None, **args):
"""Construct a new algebraic number. """
expr = sympify(expr)
if isinstance(expr, (tuple, Tuple)):
minpoly, root = expr
if not minpoly.is_Poly:
minpoly = Poly(minpoly)
elif expr.is_AlgebraicNumber:
minpoly, root = expr.minpoly, expr.root
else:
minpoly, root = minimal_polynomial(
expr, args.get('gen'), polys=True), expr
dom = minpoly.get_domain()
if coeffs != Tuple():
if not isinstance(coeffs, ANP):
rep = DMP.from_sympy_list(sympify(coeffs), 0, dom)
scoeffs = Tuple(*coeffs)
else:
rep = DMP.from_list(coeffs.to_list(), 0, dom)
scoeffs = Tuple(*coeffs.to_list())
if rep.degree() >= minpoly.degree():
rep = rep.rem(minpoly.rep)
sargs = (root, scoeffs)
else:
rep = DMP.from_list([1, 0], 0, dom)
if ask(Q.negative(root)):
rep = -rep
sargs = (root, coeffs)
if alias is not None:
if not isinstance(alias, Symbol):
alias = Symbol(alias)
sargs = sargs + (alias,)
obj = Expr.__new__(cls, *sargs)
obj.rep = rep
obj.root = root
obj.alias = alias
obj.minpoly = minpoly
return obj
def __new__(cls, *args):
""" Construct a Trace object.
Parameters
==========
args = sympy expression
"""
expr = args[0]
indices = Tuple(*args[1]) if len(args) == 2 else Tuple()
if isinstance(expr, Matrix):
return expr.trace()
elif hasattr(expr, 'trace') and callable(expr.trace):
#for any objects that have trace() defined e.g numpy
return expr.trace()
elif isinstance(expr, Add):
return Add(*[Tr(arg, indices) for arg in expr.args])
elif isinstance(expr, Mul):
c_part, nc_part = expr.args_cnc()
if len(nc_part) == 0:
return Mul(*c_part)
else:
obj = Expr.__new__(cls, Mul(*nc_part), indices )
#this check is needed to prevent cached instances
#being returned even if len(c_part)==0
return Mul(*c_part)*obj if len(c_part)>0 else obj
elif isinstance(expr, Pow):
if (_is_scalar(expr.args[0]) and
_is_scalar(expr.args[1])):
return expr
else:
return Expr.__new__(cls, expr, indices)
else:
if (_is_scalar(expr)):
return expr
return Expr.__new__(cls, expr, indices)
def __new__(cls, wavelen, z, **kwargs):
wavelen, z = map(sympify, (wavelen, z))
inst = Expr.__new__(cls, wavelen, z)
inst.wavelen = wavelen
inst.z = z
if len(kwargs) != 1:
raise ValueError('Constructor expects exactly one named argument.')
elif 'z_r' in kwargs:
inst.z_r = sympify(kwargs['z_r'])
elif 'w' in kwargs:
inst.z_r = waist2rayleigh(sympify(kwargs['w']), wavelen)
else:
raise ValueError('The constructor needs named argument w or z_r')
return inst
def __new__(cls, name, abbrev, exponent, base=sympify(10)):
name = sympify(name)
abbrev = sympify(abbrev)
exponent = sympify(exponent)
base = sympify(base)
obj = Expr.__new__(cls, name, abbrev, exponent, base)
obj._name = name
obj._abbrev = abbrev
obj._scale_factor = base**exponent
obj._exponent = exponent
obj._base = base
return obj
