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from __future__ import annotations
from typing import TYPE_CHECKING
from sympy.simplify import simplify as simp, trigsimp as tsimp # type: ignore
from sympy.core.decorators import call_highest_priority, _sympifyit
from sympy.core.assumptions import StdFactKB
from sympy.core.function import diff as df
from sympy.integrals.integrals import Integral
from sympy.polys.polytools import factor as fctr
from sympy.core import S, Add, Mul
from sympy.core.expr import Expr
if TYPE_CHECKING:
from sympy.vector.vector import BaseVector
class BasisDependent(Expr):
"""
Super class containing functionality common to vectors and
dyadics.
Named so because the representation of these quantities in
sympy.vector is dependent on the basis they are expressed in.
"""
zero: BasisDependentZero
@call_highest_priority('__radd__')
def __add__(self, other):
return self._add_func(self, other)
@call_highest_priority('__add__')
def __radd__(self, other):
return self._add_func(other, self)
@call_highest_priority('__rsub__')
def __sub__(self, other):
return self._add_func(self, -other)
@call_highest_priority('__sub__')
def __rsub__(self, other):
return self._add_func(other, -self)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__rmul__')
def __mul__(self, other):
return self._mul_func(self, other)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__mul__')
def __rmul__(self, other):
return self._mul_func(other, self)
def __neg__(self):
return self._mul_func(S.NegativeOne, self)
@_sympifyit('other', NotImplemented)
@call_highest_priority('__rtruediv__')
def __truediv__(self, other):
return self._div_helper(other)
@call_highest_priority('__truediv__')
def __rtruediv__(self, other):
return TypeError("Invalid divisor for division")
def evalf(self, n=15, subs=None, maxn=100, chop=False, strict=False, quad=None, verbose=False):
"""
Implements the SymPy evalf routine for this quantity.
evalf's documentation
=====================
"""
options = {'subs':subs, 'maxn':maxn, 'chop':chop, 'strict':strict,
'quad':quad, 'verbose':verbose}
vec = self.zero
for k, v in self.components.items():
vec += v.evalf(n, **options) * k
return vec
evalf.__doc__ += Expr.evalf.__doc__ # type: ignore
n = evalf
def simplify(self, **kwargs):
"""
Implements the SymPy simplify routine for this quantity.
simplify's documentation
========================
"""
simp_components = [simp(v, **kwargs) * k for
k, v in self.components.items()]
return self._add_func(*simp_components)
simplify.__doc__ += simp.__doc__ # type: ignore
def trigsimp(self, **opts):
"""
Implements the SymPy trigsimp routine, for this quantity.
trigsimp's documentation
========================
"""
trig_components = [tsimp(v, **opts) * k for
k, v in self.components.items()]
return self._add_func(*trig_components)
trigsimp.__doc__ += tsimp.__doc__ # type: ignore
def _eval_simplify(self, **kwargs):
return self.simplify(**kwargs)
def _eval_trigsimp(self, **opts):
return self.trigsimp(**opts)
def _eval_derivative(self, wrt):
return self.diff(wrt)
def _eval_Integral(self, *symbols, **assumptions):
integral_components = [Integral(v, *symbols, **assumptions) * k
for k, v in self.components.items()]
return self._add_func(*integral_components)
def as_numer_denom(self):
"""
Returns the expression as a tuple wrt the following
transformation -
expression -> a/b -> a, b
"""
return self, S.One
def factor(self, *args, **kwargs):
"""
Implements the SymPy factor routine, on the scalar parts
of a basis-dependent expression.
factor's documentation
========================
"""
fctr_components = [fctr(v, *args, **kwargs) * k for
k, v in self.components.items()]
return self._add_func(*fctr_components)
factor.__doc__ += fctr.__doc__ # type: ignore
def as_coeff_Mul(self, rational=False):
"""Efficiently extract the coefficient of a product."""
return (S.One, self)
def as_coeff_add(self, *deps):
"""Efficiently extract the coefficient of a summation."""
l = [x * self.components[x] for x in self.components]
return 0, tuple(l)
def diff(self, *args, **kwargs):
"""
Implements the SymPy diff routine, for vectors.
diff's documentation
========================
"""
for x in args:
if isinstance(x, BasisDependent):
raise TypeError("Invalid arg for differentiation")
diff_components = [df(v, *args, **kwargs) * k for
k, v in self.components.items()]
return self._add_func(*diff_components)
diff.__doc__ += df.__doc__ # type: ignore
def doit(self, **hints):
"""Calls .doit() on each term in the Dyadic"""
doit_components = [self.components[x].doit(**hints) * x
for x in self.components]
return self._add_func(*doit_components)
class BasisDependentAdd(BasisDependent, Add):
"""
Denotes sum of basis dependent quantities such that they cannot
be expressed as base or Mul instances.
"""
def __new__(cls, *args, **options):
components = {}
# Check each arg and simultaneously learn the components
for i, arg in enumerate(args):
if not isinstance(arg, cls._expr_type):
if isinstance(arg, Mul):
arg = cls._mul_func(*(arg.args))
elif isinstance(arg, Add):
arg = cls._add_func(*(arg.args))
else:
raise TypeError(str(arg) +
" cannot be interpreted correctly")
# If argument is zero, ignore
if arg == cls.zero:
continue
# Else, update components accordingly
if hasattr(arg, "components"):
for x in arg.components:
components[x] = components.get(x, 0) + arg.components[x]
temp = list(components.keys())
for x in temp:
if components[x] == 0:
del components[x]
# Handle case of zero vector
if len(components) == 0:
return cls.zero
# Build object
newargs = [x * components[x] for x in components]
obj = super().__new__(cls, *newargs, **options)
if isinstance(obj, Mul):
return cls._mul_func(*obj.args)
assumptions = {'commutative': True}
obj._assumptions = StdFactKB(assumptions)
obj._components = components
obj._sys = (list(components.keys()))[0]._sys
return obj
class BasisDependentMul(BasisDependent, Mul):
"""
Denotes product of base- basis dependent quantity with a scalar.
"""
def __new__(cls, *args, **options):
from sympy.vector import Cross, Dot, Curl, Gradient
count = 0
measure_number = S.One
zeroflag = False
extra_args = []
# Determine the component and check arguments
# Also keep a count to ensure two vectors aren't
# being multiplied
for arg in args:
if isinstance(arg, cls._zero_func):
count += 1
zeroflag = True
elif arg == S.Zero:
zeroflag = True
elif isinstance(arg, (cls._base_func, cls._mul_func)):
count += 1
expr = arg._base_instance
measure_number *= arg._measure_number
elif isinstance(arg, cls._add_func):
count += 1
expr = arg
elif isinstance(arg, (Cross, Dot, Curl, Gradient)):
extra_args.append(arg)
else:
measure_number *= arg
# Make sure incompatible types weren't multiplied
if count > 1:
raise ValueError("Invalid multiplication")
elif count == 0:
return Mul(*args, **options)
# Handle zero vector case
if zeroflag:
return cls.zero
# If one of the args was a VectorAdd, return an
# appropriate VectorAdd instance
if isinstance(expr, cls._add_func):
newargs = [cls._mul_func(measure_number, x) for
x in expr.args]
return cls._add_func(*newargs)
obj = super().__new__(cls, measure_number,
expr._base_instance,
*extra_args,
**options)
if isinstance(obj, Add):
return cls._add_func(*obj.args)
obj._base_instance = expr._base_instance
obj._measure_number = measure_number
assumptions = {'commutative': True}
obj._assumptions = StdFactKB(assumptions)
obj._components = {expr._base_instance: measure_number}
obj._sys = expr._base_instance._sys
return obj
def _sympystr(self, printer):
measure_str = printer._print(self._measure_number)
if ('(' in measure_str or '-' in measure_str or
'+' in measure_str):
measure_str = '(' + measure_str + ')'
return measure_str + '*' + printer._print(self._base_instance)
class BasisDependentZero(BasisDependent):
"""
Class to denote a zero basis dependent instance.
"""
components: dict['BaseVector', Expr] = {}
_latex_form: str
def __new__(cls):
obj = super().__new__(cls)
# Pre-compute a specific hash value for the zero vector
# Use the same one always
obj._hash = (S.Zero, cls).__hash__()
return obj
def __hash__(self):
return self._hash
@call_highest_priority('__req__')
def __eq__(self, other):
return isinstance(other, self._zero_func)
__req__ = __eq__
@call_highest_priority('__radd__')
def __add__(self, other):
if isinstance(other, self._expr_type):
return other
else:
raise TypeError("Invalid argument types for addition")
@call_highest_priority('__add__')
def __radd__(self, other):
if isinstance(other, self._expr_type):
return other
else:
raise TypeError("Invalid argument types for addition")
@call_highest_priority('__rsub__')
def __sub__(self, other):
if isinstance(other, self._expr_type):
return -other
else:
raise TypeError("Invalid argument types for subtraction")
@call_highest_priority('__sub__')
def __rsub__(self, other):
if isinstance(other, self._expr_type):
return other
else:
raise TypeError("Invalid argument types for subtraction")
def __neg__(self):
return self
def normalize(self):
"""
Returns the normalized version of this vector.
"""
return self
def _sympystr(self, printer):
return '0'