def test_derivatives_issue_4757():
x = Symbol('x', real=True)
y = Symbol('y', imaginary=True)
f = Function('f')
assert re(f(x)).diff(x) == re(f(x).diff(x))
assert im(f(x)).diff(x) == im(f(x).diff(x))
assert re(f(y)).diff(y) == -I * im(f(y).diff(y))
assert im(f(y)).diff(y) == -I * re(f(y).diff(y))
assert Abs(f(x)).diff(x).subs(f(x), 1 + I * x).doit() == x / sqrt(1 + x**2)
assert arg(f(x)).diff(x).subs(f(x),
1 + I * x**2).doit() == 2 * x / (1 + x**4)
assert Abs(f(y)).diff(y).subs(f(y), 1 + y).doit() == -y / sqrt(1 - y**2)
assert arg(f(y)).diff(y).subs(f(y), I + y**2).doit() == 2 * y / (1 + y**4)
def as_real_imag(self, deep=True, **hints):
"""
Returns this function as a complex coordinate.
Examples
========
>>> from sympy import I, log
>>> from sympy.abc import x
>>> log(x).as_real_imag()
(log(Abs(x)), arg(x))
>>> log(I).as_real_imag()
(0, pi/2)
>>> log(1 + I).as_real_imag()
(log(sqrt(2)), pi/4)
>>> log(I*x).as_real_imag()
(log(Abs(x)), arg(I*x))
"""
from sympy.functions.elementary.complexes import (Abs, arg)
sarg = self.args[0]
if deep:
sarg = self.args[0].expand(deep, **hints)
abs = Abs(sarg)
if abs == sarg:
return self, S.Zero
arg = arg(sarg)
if hints.get('log', False): # Expand the log
hints['complex'] = False
return (log(abs).expand(deep, **hints), arg)
else:
return log(abs), arg
def test_tensorflow_complexes():
if not tensorflow:
skip("tensorflow not installed")
func1 = lambdify(x, re(x), modules="tensorflow")
func2 = lambdify(x, im(x), modules="tensorflow")
func3 = lambdify(x, Abs(x), modules="tensorflow")
func4 = lambdify(x, arg(x), modules="tensorflow")
with tensorflow.compat.v1.Session() as s:
# For versions before
# https://github.com/tensorflow/tensorflow/issues/30029
# resolved, using python numeric types may not work
a = tensorflow.constant(1 + 2j)
assert func1(a).eval(session=s) == 1
assert func2(a).eval(session=s) == 2
tensorflow_result = func3(a).eval(session=s)
sympy_result = Abs(1 + 2j).evalf()
assert abs(tensorflow_result - sympy_result) < 10**-6
tensorflow_result = func4(a).eval(session=s)
sympy_result = arg(1 + 2j).evalf()
assert abs(tensorflow_result - sympy_result) < 10**-6
def is_eq(lhs, rhs, assumptions=None):
"""
Fuzzy bool representing mathematical equality between *lhs* and *rhs*.
Parameters
==========
lhs : Expr
The left-hand side of the expression, must be sympified.
rhs : Expr
The right-hand side of the expression, must be sympified.
assumptions: Boolean, optional
Assumptions taken to evaluate the equality.
Returns
=======
``True`` if *lhs* is equal to *rhs*, ``False`` is *lhs* is not equal to *rhs*,
and ``None`` if the comparison between *lhs* and *rhs* is indeterminate.
Explanation
===========
This function is intended to give a relatively fast determination and
deliberately does not attempt slow calculations that might help in
obtaining a determination of True or False in more difficult cases.
:func:`~.is_neq` calls this function to return its value, so supporting
new type with this function will ensure correct behavior for ``is_neq``
as well.
Examples
========
>>> from sympy import Q, S
>>> from sympy.core.relational import is_eq, is_neq
>>> from sympy.abc import x
>>> is_eq(S(0), S(0))
True
>>> is_neq(S(0), S(0))
False
>>> is_eq(S(0), S(2))
False
>>> is_neq(S(0), S(2))
True
Assumptions can be passed to evaluate the equality which is otherwise
indeterminate.
>>> print(is_eq(x, S(0)))
None
>>> is_eq(x, S(0), assumptions=Q.zero(x))
True
New types can be supported by dispatching to ``_eval_is_eq``.
>>> from sympy import Basic, sympify
>>> from sympy.multipledispatch import dispatch
>>> class MyBasic(Basic):
... def __new__(cls, arg):
... return Basic.__new__(cls, sympify(arg))
... @property
... def value(self):
... return self.args[0]
...
>>> @dispatch(MyBasic, MyBasic)
... def _eval_is_eq(a, b):
... return is_eq(a.value, b.value)
...
>>> a = MyBasic(1)
>>> b = MyBasic(1)
>>> is_eq(a, b)
True
>>> is_neq(a, b)
False
"""
from sympy.assumptions.wrapper import (AssumptionsWrapper, is_infinite,
is_extended_real)
from sympy.core.add import Add
from sympy.functions.elementary.complexes import arg
from sympy.simplify.simplify import clear_coefficients
from sympy.utilities.iterables import sift
# here, _eval_Eq is only called for backwards compatibility
# new code should use is_eq with multiple dispatch as
# outlined in the docstring
for side1, side2 in (lhs, rhs), (rhs, lhs):
eval_func = getattr(side1, '_eval_Eq', None)
if eval_func is not None:
retval = eval_func(side2)
if retval is not None:
return retval
retval = _eval_is_eq(lhs, rhs)
if retval is not None:
return retval
if dispatch(type(lhs), type(rhs)) != dispatch(type(rhs), type(lhs)):
retval = _eval_is_eq(rhs, lhs)
if retval is not None:
return retval
# retval is still None, so go through the equality logic
# If expressions have the same structure, they must be equal.
if lhs == rhs:
return True # e.g. True == True
elif all(isinstance(i, BooleanAtom) for i in (rhs, lhs)):
return False # True != False
elif not (lhs.is_Symbol or rhs.is_Symbol) and (isinstance(lhs, Boolean) !=
isinstance(rhs, Boolean)):
return False # only Booleans can equal Booleans
_lhs = AssumptionsWrapper(lhs, assumptions)
_rhs = AssumptionsWrapper(rhs, assumptions)
if _lhs.is_infinite or _rhs.is_infinite:
if fuzzy_xor([_lhs.is_infinite, _rhs.is_infinite]):
return False
if fuzzy_xor([_lhs.is_extended_real, _rhs.is_extended_real]):
return False
if fuzzy_and([_lhs.is_extended_real, _rhs.is_extended_real]):
return fuzzy_xor([
_lhs.is_extended_positive,
fuzzy_not(_rhs.is_extended_positive)
])
# Try to split real/imaginary parts and equate them
I = S.ImaginaryUnit
def split_real_imag(expr):
real_imag = lambda t: (
'real' if is_extended_real(t, assumptions) else 'imag'
if is_extended_real(I * t, assumptions) else None)
return sift(Add.make_args(expr), real_imag)
lhs_ri = split_real_imag(lhs)
if not lhs_ri[None]:
rhs_ri = split_real_imag(rhs)
if not rhs_ri[None]:
eq_real = is_eq(Add(*lhs_ri['real']), Add(*rhs_ri['real']),
assumptions)
eq_imag = is_eq(I * Add(*lhs_ri['imag']),
I * Add(*rhs_ri['imag']), assumptions)
return fuzzy_and(map(fuzzy_bool, [eq_real, eq_imag]))
# Compare e.g. zoo with 1+I*oo by comparing args
arglhs = arg(lhs)
argrhs = arg(rhs)
# Guard against Eq(nan, nan) -> Falsesymp
if not (arglhs == S.NaN and argrhs == S.NaN):
return fuzzy_bool(is_eq(arglhs, argrhs, assumptions))
if all(isinstance(i, Expr) for i in (lhs, rhs)):
# see if the difference evaluates
dif = lhs - rhs
_dif = AssumptionsWrapper(dif, assumptions)
z = _dif.is_zero
if z is not None:
if z is False and _dif.is_commutative: # issue 10728
return False
if z:
return True
n2 = _n2(lhs, rhs)
if n2 is not None:
return _sympify(n2 == 0)
# see if the ratio evaluates
n, d = dif.as_numer_denom()
rv = None
_n = AssumptionsWrapper(n, assumptions)
_d = AssumptionsWrapper(d, assumptions)
if _n.is_zero:
rv = _d.is_nonzero
elif _n.is_finite:
if _d.is_infinite:
rv = True
elif _n.is_zero is False:
rv = _d.is_infinite
if rv is None:
# if the condition that makes the denominator
# infinite does not make the original expression
# True then False can be returned
l, r = clear_coefficients(d, S.Infinity)
args = [_.subs(l, r) for _ in (lhs, rhs)]
if args != [lhs, rhs]:
rv = fuzzy_bool(is_eq(*args, assumptions))
if rv is True:
rv = None
elif any(is_infinite(a, assumptions) for a in Add.make_args(n)):
# (inf or nan)/x != 0
rv = False
if rv is not None:
return rv
def test_meijerint():
from sympy.core.function import expand
from sympy.core.symbol import symbols
from sympy.functions.elementary.complexes import arg
s, t, mu = symbols('s t mu', real=True)
assert integrate(
meijerg([], [], [0], [], s * t) *
meijerg([], [], [mu / 2], [-mu / 2], t**2 / 4),
(t, 0, oo)).is_Piecewise
s = symbols('s', positive=True)
assert integrate(x**s*meijerg([[], []], [[0], []], x), (x, 0, oo)) == \
gamma(s + 1)
assert integrate(x**s * meijerg([[], []], [[0], []], x), (x, 0, oo),
meijerg=True) == gamma(s + 1)
assert isinstance(
integrate(x**s * meijerg([[], []], [[0], []], x), (x, 0, oo),
meijerg=False), Integral)
assert meijerint_indefinite(exp(x), x) == exp(x)
# TODO what simplifications should be done automatically?
# This tests "extra case" for antecedents_1.
a, b = symbols('a b', positive=True)
assert simplify(meijerint_definite(x**a, x, 0, b)[0]) == \
b**(a + 1)/(a + 1)
# This tests various conditions and expansions:
assert meijerint_definite((x + 1)**3 * exp(-x), x, 0, oo) == (16, True)
# Again, how about simplifications?
sigma, mu = symbols('sigma mu', positive=True)
i, c = meijerint_definite(exp(-((x - mu) / (2 * sigma))**2), x, 0, oo)
assert simplify(i) == sqrt(pi) * sigma * (2 - erfc(mu / (2 * sigma)))
assert c == True
i, _ = meijerint_definite(exp(-mu * x) * exp(sigma * x), x, 0, oo)
# TODO it would be nice to test the condition
assert simplify(i) == 1 / (mu - sigma)
# Test substitutions to change limits
assert meijerint_definite(exp(x), x, -oo, 2) == (exp(2), True)
# Note: causes a NaN in _check_antecedents
assert expand(meijerint_definite(exp(x), x, 0, I)[0]) == exp(I) - 1
assert expand(meijerint_definite(exp(-x), x, 0, x)[0]) == \
1 - exp(-exp(I*arg(x))*abs(x))
# Test -oo to oo
assert meijerint_definite(exp(-x**2), x, -oo, oo) == (sqrt(pi), True)
assert meijerint_definite(exp(-abs(x)), x, -oo, oo) == (2, True)
assert meijerint_definite(exp(-(2*x - 3)**2), x, -oo, oo) == \
(sqrt(pi)/2, True)
assert meijerint_definite(exp(-abs(2 * x - 3)), x, -oo, oo) == (1, True)
assert meijerint_definite(
exp(-((x - mu) / sigma)**2 / 2) / sqrt(2 * pi * sigma**2), x, -oo,
oo) == (1, True)
assert meijerint_definite(sinc(x)**2, x, -oo, oo) == (pi, True)
# Test one of the extra conditions for 2 g-functinos
assert meijerint_definite(exp(-x) * sin(x), x, 0, oo) == (S.Half, True)
# Test a bug
def res(n):
return (1 / (1 + x**2)).diff(x, n).subs(x, 1) * (-1)**n
for n in range(6):
assert integrate(exp(-x)*sin(x)*x**n, (x, 0, oo), meijerg=True) == \
res(n)
# This used to test trigexpand... now it is done by linear substitution
assert simplify(integrate(exp(-x) * sin(x + a), (x, 0, oo),
meijerg=True)) == sqrt(2) * sin(a + pi / 4) / 2
# Test the condition 14 from prudnikov.
# (This is besselj*besselj in disguise, to stop the product from being
# recognised in the tables.)
a, b, s = symbols('a b s')
from sympy.functions.elementary.complexes import re
assert meijerint_definite(
meijerg([], [], [a / 2], [-a / 2], x / 4) *
meijerg([], [], [b / 2], [-b / 2], x / 4) * x**(s - 1), x, 0,
oo) == ((4 * 2**(2 * s - 2) * gamma(-2 * s + 1) *
gamma(a / 2 + b / 2 + s) /
(gamma(-a / 2 + b / 2 - s + 1) *
gamma(a / 2 - b / 2 - s + 1) * gamma(a / 2 + b / 2 - s + 1)),
(re(s) < 1) & (re(s) < S(1) / 2) &
(re(a) / 2 + re(b) / 2 + re(s) > 0)))
# test a bug
assert integrate(sin(x**a)*sin(x**b), (x, 0, oo), meijerg=True) == \
Integral(sin(x**a)*sin(x**b), (x, 0, oo))
# test better hyperexpand
assert integrate(exp(-x**2)*log(x), (x, 0, oo), meijerg=True) == \
(sqrt(pi)*polygamma(0, S.Half)/4).expand()
# Test hyperexpand bug.
from sympy.functions.special.gamma_functions import lowergamma
n = symbols('n', integer=True)
assert simplify(integrate(exp(-x)*x**n, x, meijerg=True)) == \
lowergamma(n + 1, x)
# Test a bug with argument 1/x
alpha = symbols('alpha', positive=True)
assert meijerint_definite((2 - x)**alpha*sin(alpha/x), x, 0, 2) == \
(sqrt(pi)*alpha*gamma(alpha + 1)*meijerg(((), (alpha/2 + S.Half,
alpha/2 + 1)), ((0, 0, S.Half), (Rational(-1, 2),)), alpha**2/16)/4, True)
# test a bug related to 3016
a, s = symbols('a s', positive=True)
assert simplify(integrate(x**s*exp(-a*x**2), (x, -oo, oo))) == \
a**(-s/2 - S.Half)*((-1)**s + 1)*gamma(s/2 + S.Half)/2
def test_sympy__functions__elementary__complexes__arg():
from sympy.functions.elementary.complexes import arg
assert _test_args(arg(x))
def test_arg():
x = Symbol('x', complex = True)
assert refine(arg(x), Q.positive(x)) == 0
assert refine(arg(x), Q.negative(x)) == pi
def bode_phase_evalf(system, point):
expr = system.to_expr()
_w = Dummy("w", real=True)
w_expr = expr.subs({system.var: I * _w})
return arg(w_expr).subs({_w: point}).evalf()
def is_eq(lhs, rhs):
"""
Fuzzy bool representing mathematical equality between lhs and rhs.
Parameters
==========
lhs: Expr
The left-hand side of the expression, must be sympified.
rhs: Expr
The right-hand side of the expression, must be sympified.
Returns
=======
True if lhs is equal to rhs, false is lhs is not equal to rhs, and
None if the comparison between lhs and rhs is indeterminate.
Explanation
===========
This function is intended to give a relatively fast determination and deliberately does not attempt slow
calculations that might help in obtaining a determination of True or False in more difficult cases.
InEquality classes, such as Lt, Gt, etc. Use one of is_ge, is_le, etc.
To implement comparisons with ``Gt(a, b)`` or ``a > b`` etc for an ``Expr`` subclass
it is only necessary to define a dispatcher method for ``_eval_is_ge`` like
>>> from sympy.core.relational import is_eq
>>> from sympy.core.relational import is_neq
>>> from sympy import S, Basic, Eq, sympify
>>> from sympy.abc import x
>>> from sympy.multipledispatch import dispatch
>>> class MyBasic(Basic):
... def __new__(cls, arg):
... return Basic.__new__(cls, sympify(arg))
... @property
... def value(self):
... return self.args[0]
...
>>> @dispatch(MyBasic, MyBasic)
... def _eval_is_eq(a, b):
... return is_eq(a.value, b.value)
...
>>> a = MyBasic(1)
>>> b = MyBasic(1)
>>> a == b
True
>>> Eq(a, b)
True
>>> a != b
False
>>> is_eq(a, b)
True
Examples
========
>>> is_eq(S(0), S(0))
True
>>> Eq(0, 0)
True
>>> is_neq(S(0), S(0))
False
>>> is_eq(S(0), S(2))
False
>>> Eq(0, 2)
False
>>> is_neq(S(0), S(2))
True
>>> is_eq(S(0), x)
>>> Eq(S(0), x)
Eq(0, x)
"""
from sympy.core.add import Add
from sympy.functions.elementary.complexes import arg
from sympy.simplify.simplify import clear_coefficients
from sympy.utilities.iterables import sift
# here, _eval_Eq is only called for backwards compatibility
# new code should use is_eq with multiple dispatch as
# outlined in the docstring
for side1, side2 in (lhs, rhs), (rhs, lhs):
eval_func = getattr(side1, '_eval_Eq', None)
if eval_func is not None:
retval = eval_func(side2)
if retval is not None:
return retval
retval = _eval_is_eq(lhs, rhs)
if retval is not None:
return retval
if dispatch(type(lhs), type(rhs)) != dispatch(type(rhs), type(lhs)):
retval = _eval_is_eq(rhs, lhs)
if retval is not None:
return retval
# retval is still None, so go through the equality logic
# If expressions have the same structure, they must be equal.
if lhs == rhs:
return True # e.g. True == True
elif all(isinstance(i, BooleanAtom) for i in (rhs, lhs)):
return False # True != False
elif not (lhs.is_Symbol or rhs.is_Symbol) and (isinstance(lhs, Boolean) !=
isinstance(rhs, Boolean)):
return False # only Booleans can equal Booleans
if lhs.is_infinite or rhs.is_infinite:
if fuzzy_xor([lhs.is_infinite, rhs.is_infinite]):
return False
if fuzzy_xor([lhs.is_extended_real, rhs.is_extended_real]):
return False
if fuzzy_and([lhs.is_extended_real, rhs.is_extended_real]):
return fuzzy_xor([
lhs.is_extended_positive,
fuzzy_not(rhs.is_extended_positive)
])
# Try to split real/imaginary parts and equate them
I = S.ImaginaryUnit
def split_real_imag(expr):
real_imag = lambda t: ('real' if t.is_extended_real else 'imag'
if (I * t).is_extended_real else None)
return sift(Add.make_args(expr), real_imag)
lhs_ri = split_real_imag(lhs)
if not lhs_ri[None]:
rhs_ri = split_real_imag(rhs)
if not rhs_ri[None]:
eq_real = Eq(Add(*lhs_ri['real']), Add(*rhs_ri['real']))
eq_imag = Eq(I * Add(*lhs_ri['imag']),
I * Add(*rhs_ri['imag']))
return fuzzy_and(map(fuzzy_bool, [eq_real, eq_imag]))
# Compare e.g. zoo with 1+I*oo by comparing args
arglhs = arg(lhs)
argrhs = arg(rhs)
# Guard against Eq(nan, nan) -> Falsesymp
if not (arglhs == S.NaN and argrhs == S.NaN):
return fuzzy_bool(Eq(arglhs, argrhs))
if all(isinstance(i, Expr) for i in (lhs, rhs)):
# see if the difference evaluates
dif = lhs - rhs
z = dif.is_zero
if z is not None:
if z is False and dif.is_commutative: # issue 10728
return False
if z:
return True
n2 = _n2(lhs, rhs)
if n2 is not None:
return _sympify(n2 == 0)
# see if the ratio evaluates
n, d = dif.as_numer_denom()
rv = None
if n.is_zero:
rv = d.is_nonzero
elif n.is_finite:
if d.is_infinite:
rv = True
elif n.is_zero is False:
rv = d.is_infinite
if rv is None:
# if the condition that makes the denominator
# infinite does not make the original expression
# True then False can be returned
l, r = clear_coefficients(d, S.Infinity)
args = [_.subs(l, r) for _ in (lhs, rhs)]
if args != [lhs, rhs]:
rv = fuzzy_bool(Eq(*args))
if rv is True:
rv = None
elif any(a.is_infinite for a in Add.make_args(n)):
# (inf or nan)/x != 0
rv = False
if rv is not None:
return rv
def test_sympy__functions__elementary__complexes__arg():
from sympy.functions.elementary.complexes import arg
assert _test_args(arg(x))
def __new__(cls, lhs, rhs=None, **options):
from sympy.core.add import Add
from sympy.core.containers import Tuple
from sympy.core.logic import fuzzy_bool, fuzzy_xor, fuzzy_and, fuzzy_not
from sympy.core.expr import _n2
from sympy.functions.elementary.complexes import arg
from sympy.simplify.simplify import clear_coefficients
from sympy.utilities.iterables import sift
if rhs is None:
SymPyDeprecationWarning(feature="Eq(expr) with rhs default to 0",
useinstead="Eq(expr, 0)",
issue=16587,
deprecated_since_version="1.5").warn()
rhs = 0
lhs = _sympify(lhs)
rhs = _sympify(rhs)
evaluate = options.pop('evaluate', global_parameters.evaluate)
if evaluate:
# If one expression has an _eval_Eq, return its results.
if hasattr(lhs, '_eval_Eq'):
r = lhs._eval_Eq(rhs)
if r is not None:
return r
if hasattr(rhs, '_eval_Eq'):
r = rhs._eval_Eq(lhs)
if r is not None:
return r
# If expressions have the same structure, they must be equal.
if lhs == rhs:
return S.true # e.g. True == True
elif all(isinstance(i, BooleanAtom) for i in (rhs, lhs)):
return S.false # True != False
elif not (lhs.is_Symbol or rhs.is_Symbol) and (isinstance(
lhs, Boolean) != isinstance(rhs, Boolean)):
return S.false # only Booleans can equal Booleans
if lhs.is_infinite or rhs.is_infinite:
if fuzzy_xor([lhs.is_infinite, rhs.is_infinite]):
return S.false
if fuzzy_xor([lhs.is_extended_real, rhs.is_extended_real]):
return S.false
if fuzzy_and([lhs.is_extended_real, rhs.is_extended_real]):
r = fuzzy_xor([
lhs.is_extended_positive,
fuzzy_not(rhs.is_extended_positive)
])
return S(r)
# Try to split real/imaginary parts and equate them
I = S.ImaginaryUnit
def split_real_imag(expr):
real_imag = lambda t: ('real'
if t.is_extended_real else 'imag' if
(I * t).is_extended_real else None)
return sift(Add.make_args(expr), real_imag)
lhs_ri = split_real_imag(lhs)
if not lhs_ri[None]:
rhs_ri = split_real_imag(rhs)
if not rhs_ri[None]:
eq_real = Eq(Add(*lhs_ri['real']),
Add(*rhs_ri['real']))
eq_imag = Eq(I * Add(*lhs_ri['imag']),
I * Add(*rhs_ri['imag']))
res = fuzzy_and(map(fuzzy_bool, [eq_real, eq_imag]))
if res is not None:
return S(res)
# Compare e.g. zoo with 1+I*oo by comparing args
arglhs = arg(lhs)
argrhs = arg(rhs)
# Guard against Eq(nan, nan) -> False
if not (arglhs == S.NaN and argrhs == S.NaN):
res = fuzzy_bool(Eq(arglhs, argrhs))
if res is not None:
return S(res)
return Relational.__new__(cls, lhs, rhs, **options)
if all(isinstance(i, Expr) for i in (lhs, rhs)):
# see if the difference evaluates
dif = lhs - rhs
z = dif.is_zero
if z is not None:
if z is False and dif.is_commutative: # issue 10728
return S.false
if z:
return S.true
# evaluate numerically if possible
n2 = _n2(lhs, rhs)
if n2 is not None:
return _sympify(n2 == 0)
# see if the ratio evaluates
n, d = dif.as_numer_denom()
rv = None
if n.is_zero:
rv = d.is_nonzero
elif n.is_finite:
if d.is_infinite:
rv = S.true
elif n.is_zero is False:
rv = d.is_infinite
if rv is None:
# if the condition that makes the denominator
# infinite does not make the original expression
# True then False can be returned
l, r = clear_coefficients(d, S.Infinity)
args = [_.subs(l, r) for _ in (lhs, rhs)]
if args != [lhs, rhs]:
rv = fuzzy_bool(Eq(*args))
if rv is True:
rv = None
elif any(a.is_infinite for a in Add.make_args(n)):
# (inf or nan)/x != 0
rv = S.false
if rv is not None:
return _sympify(rv)
return Relational.__new__(cls, lhs, rhs, **options)
def test_issue_4035():
x = Symbol('x')
assert Abs(x).expand(trig=True) == Abs(x)
assert sign(x).expand(trig=True) == sign(x)
assert arg(x).expand(trig=True) == arg(x)
def test_arg_rewrite():
assert arg(1 + I) == atan2(1, 1)
x = Symbol('x', real=True)
y = Symbol('y', real=True)
assert arg(x + I * y).rewrite(atan2) == atan2(y, x)
def test_arg():
assert arg(0) is nan
assert arg(1) == 0
assert arg(-1) == pi
assert arg(I) == pi / 2
assert arg(-I) == -pi / 2
assert arg(1 + I) == pi / 4
assert arg(-1 + I) == pi * Rational(3, 4)
assert arg(1 - I) == -pi / 4
assert arg(exp_polar(4 * pi * I)) == 4 * pi
assert arg(exp_polar(-7 * pi * I)) == -7 * pi
assert arg(exp_polar(5 - 3 * pi * I / 4)) == pi * Rational(-3, 4)
f = Function('f')
assert not arg(f(0) + I * f(1)).atoms(re)
# check nesting
x = Symbol('x')
assert arg(arg(arg(x))) is not S.NaN
assert arg(arg(arg(arg(x)))) is S.NaN
r = Symbol('r', extended_real=True)
assert arg(arg(r)) is not S.NaN
assert arg(arg(arg(r))) is S.NaN
p = Function('p', extended_positive=True)
assert arg(p(x)) == 0
assert arg((3 + I) * p(x)) == arg(3 + I)
p = Symbol('p', positive=True)
assert arg(p) == 0
assert arg(p * I) == pi / 2
n = Symbol('n', negative=True)
assert arg(n) == pi
assert arg(n * I) == -pi / 2
x = Symbol('x')
assert conjugate(arg(x)) == arg(x)
e = p + I * p**2
assert arg(e) == arg(1 + p * I)
# make sure sign doesn't swap
e = -2 * p + 4 * I * p**2
assert arg(e) == arg(-1 + 2 * p * I)
# make sure sign isn't lost
x = symbols('x', real=True) # could be zero
e = x + I * x
assert arg(e) == arg(x * (1 + I))
assert arg(e / p) == arg(x * (1 + I))
e = p * cos(p) + I * log(p) * exp(p)
assert arg(e).args[0] == e
# keep it simple -- let the user do more advanced cancellation
e = (p + 1) + I * (p**2 - 1)
assert arg(e).args[0] == e
f = Function('f')
e = 2 * x * (f(0) - 1) - 2 * x * f(0)
assert arg(e) == arg(-2 * x)
assert arg(f(0)).func == arg and arg(f(0)).args == (f(0), )
