### Navigation

- index
- modules |
- next |
- previous |
- Symbolic Calculus »

# Symbolic Expressions

Symbolic Expressions

RELATIONAL EXPRESSIONS:

We create a relational expression:

    sage: x = var('x')
    sage: eqn = (x-1)^2 <= x^2 - 2*x + 3
    sage: eqn.subs(x == 5)
    16 <= 18

Notice that squaring the relation squares both sides.

    sage: eqn^2
    (x - 1)^4 <= (x^2 - 2*x + 3)^2
    sage: eqn.expand()
    x^2 - 2*x + 1 <= x^2 - 2*x + 3

The can transform a true relational into a false one:

    sage: eqn = SR(-5) < SR(-3); eqn
    -5 < -3
    sage: bool(eqn)
    True
    sage: eqn^2
    25 < 9
    sage: bool(eqn^2)
    False

We can do arithmetic with relationals:

    sage: e = x+1 <= x-2
    sage: e + 2
    x + 3 <= x
    sage: e - 1
    x <= x - 3
    sage: e*(-1)
    -x - 1 <= -x + 2
    sage: (-2)*e
    -2*x - 2 <= -2*x + 4
    sage: e*5
    5*x + 5 <= 5*x - 10
    sage: e/5
    1/5*x + 1/5 <= 1/5*x - 2/5
    sage: 5/e
    5/(x + 1) <= 5/(x - 2)
    sage: e/(-2)
    -1/2*x - 1/2 <= -1/2*x + 1
    sage: -2/e
    -2/(x + 1) <= -2/(x - 2)

We can even add together two relations, so long as the operators are the same:

    sage: (x^3 + x <= x - 17)  + (-x <= x - 10)
    x^3 <= 2*x - 27

Here they are not:

    sage: (x^3 + x <= x - 17)  + (-x >= x - 10)
    Traceback (most recent call last):
    ...
    TypeError: incompatible relations

ARBITRARY SAGE ELEMENTS:

You can work symbolically with any Sage data type. This can lead to nonsense if the data type is strange, e.g., an element of a finite field (at present).

We mix Singular variables with symbolic variables:

    sage: R.<u,v> = QQ[]
    sage: var('a,b,c')
    (a, b, c)
    sage: expand((u + v + a + b + c)^2)
    a^2 + 2*a*b + b^2 + 2*a*c + 2*b*c + c^2 + 2*a*u + 2*b*u + 2*c*u + u^2 + 2*a*v + 2*b*v + 2*c*v + 2*u*v + v^2

TESTS:

Test Jacobian on Pynac expressions. (trac ticket #5546)

    sage: var('x,y')
    (x, y)
    sage: f = x + y
    sage: jacobian(f, [x,y])
    [1 1]

Test if matrices work (trac ticket #5546)

    sage: var('x,y,z')
    (x, y, z)
    sage: M = matrix(2,2,[x,y,z,x])
    sage: v = vector([x,y])
    sage: M * v
    (x^2 + y^2, x*y + x*z)
    sage: v*M
    (x^2 + y*z, 2*x*y)

Test if comparison bugs from trac ticket #6256 are fixed:

    sage: t = exp(sqrt(x)); u = 1/t
    sage: t*u
    1
    sage: t + u
    e^(-sqrt(x)) + e^sqrt(x)
    sage: t
    e^sqrt(x)

Test if trac ticket #9947 is fixed:

    sage: real_part(1+2*(sqrt(2)+1)*(sqrt(2)-1))
    3
    sage: a=(sqrt(4*(sqrt(3) - 5)*(sqrt(3) + 5) + 48) + 4*sqrt(3))/ (sqrt(3) + 5)
    sage: a.real_part()
    4*sqrt(3)/(sqrt(3) + 5)
    sage: a.imag_part()
    sqrt(abs(4*(sqrt(3) + 5)*(sqrt(3) - 5) + 48))/(sqrt(3) + 5)

class `sage.symbolic.expression.``Expression`

  Bases: `sage.structure.element.CommutativeRingElement`

  Nearly all expressions are created by calling new_Expression_from_*, but we need to make sure this at least does not leave self._gobj uninitialized and segfault.

  TESTS:

      sage: sage.symbolic.expression.Expression(SR)
      0
      sage: sage.symbolic.expression.Expression(SR, 5)
      5

  We test subclassing `Expression`:

      sage: from sage.symbolic.expression import Expression
      sage: class exp_sub(Expression): pass
      sage: f = function('f')
      sage: t = f(x)
      sage: u = exp_sub(SR, t)
      sage: u.operator()
      f

  `N`(prec=None, digits=None, algorithm=None)

    Return a numerical approximation this symbolic expression as either a real or complex number with at least the requested number of bits or digits of precision.

    EXAMPLES:

        sage: sin(x).subs(x=5).n()
        -0.958924274663138
        sage: sin(x).subs(x=5).n(100)
        -0.95892427466313846889315440616
        sage: sin(x).subs(x=5).n(digits=50)
        -0.95892427466313846889315440615599397335246154396460
        sage: zeta(x).subs(x=2).numerical_approx(digits=50)
        1.6449340668482264364724151666460251892189499012068

        sage: cos(3).numerical_approx(200)
        -0.98999249660044545727157279473126130239367909661558832881409
        sage: numerical_approx(cos(3),200)
        -0.98999249660044545727157279473126130239367909661558832881409
        sage: numerical_approx(cos(3), digits=10)
        -0.9899924966
        sage: (i + 1).numerical_approx(32)
        1.00000000 + 1.00000000*I
        sage: (pi + e + sqrt(2)).numerical_approx(100)
        7.2740880444219335226246195788

    TESTS:

    We test the evaluation of different infinities available in Pynac:

        sage: t = x - oo; t
        -Infinity
        sage: t.n()
        -infinity
        sage: t = x + oo; t
        +Infinity
        sage: t.n()
        +infinity
        sage: t = x - unsigned_infinity; t
        Infinity
        sage: t.n()
        Traceback (most recent call last):
        ...
        ValueError: can only convert signed infinity to RR

    Some expressions cannot be evaluated numerically:

        sage: n(sin(x))
        Traceback (most recent call last):
        ...
        TypeError: cannot evaluate symbolic expression numerically
        sage: a = var('a')
        sage: (x^2 + 2*x + 2).subs(x=a).n()
        Traceback (most recent call last):
        ...
        TypeError: cannot evaluate symbolic expression numerically

    Make sure we’ve rounded up log(10,2) enough to guarantee sufficient precision (trac ticket #10164):

        sage: ks = 4*10**5, 10**6
        sage: all(len(str(e.n(digits=k)))-1 >= k for k in ks)
        True

  `Order`(hold=False)

    Return the order of the expression, as in big oh notation.

    OUTPUT:

    A symbolic expression.

    EXAMPLES:

        sage: n = var('n')
        sage: t = (17*n^3).Order(); t
        Order(n^3)
        sage: t.derivative(n)
        Order(n^2)

    To prevent automatic evaluation use the `hold` argument:

        sage: (17*n^3).Order(hold=True)
        Order(17*n^3)

  `abs`(hold=False)

    Return the absolute value of this expression.

    EXAMPLES:

        sage: var('x, y')
        (x, y)
        sage: (x+y).abs()
        abs(x + y)

    Using the `hold` parameter it is possible to prevent automatic evaluation:

        sage: SR(-5).abs(hold=True)
        abs(-5)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(-5).abs(hold=True); a.simplify()
        5

    TESTS:

    From trac ticket #7557:

        sage: var('y', domain='real')
        y
        sage: abs(exp(1.1*y*I)).simplify()
        1
        sage: var('y', domain='complex') # reset the domain for other tests
        y

  `add`(hold=False, *args)

    Return the sum of the current expression and the given arguments.

    To prevent automatic evaluation use the `hold` argument.

    EXAMPLES:

        sage: x.add(x)
        2*x
        sage: x.add(x, hold=True)
        x + x
        sage: x.add(x, (2+x), hold=True)
        (x + 2) + x + x
        sage: x.add(x, (2+x), x, hold=True)
        (x + 2) + x + x + x
        sage: x.add(x, (2+x), x, 2*x, hold=True)
        (x + 2) + 2*x + x + x + x

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = x.add(x, hold=True); a.simplify()
        2*x

  `add_to_both_sides`(x)

    Return a relation obtained by adding x to both sides of this relation.

    EXAMPLES:

        sage: var('x y z')
        (x, y, z)
        sage: eqn = x^2 + y^2 + z^2 <= 1
        sage: eqn.add_to_both_sides(-z^2)
        x^2 + y^2 <= -z^2 + 1
        sage: eqn.add_to_both_sides(I)
        x^2 + y^2 + z^2 + I <= (I + 1)

  `arccos`(hold=False)

    Return the arc cosine of self.

    EXAMPLES:

        sage: x.arccos()
        arccos(x)
        sage: SR(1).arccos()
        0
        sage: SR(1/2).arccos()
        1/3*pi
        sage: SR(0.4).arccos()
        1.15927948072741
        sage: plot(lambda x: SR(x).arccos(), -1,1)
        Graphics object consisting of 1 graphics primitive

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(1).arccos(hold=True)
        arccos(1)

    This also works using functional notation:

        sage: arccos(1,hold=True)
        arccos(1)
        sage: arccos(1)
        0

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(1).arccos(hold=True); a.simplify()
        0

    TESTS:

        sage: SR(oo).arccos()
        Traceback (most recent call last):
        ...
        RuntimeError: arccos_eval(): arccos(infinity) encountered
        sage: SR(-oo).arccos()
        Traceback (most recent call last):
        ...
        RuntimeError: arccos_eval(): arccos(infinity) encountered
        sage: SR(unsigned_infinity).arccos()
        Infinity

  `arccosh`(hold=False)

    Return the inverse hyperbolic cosine of self.

    EXAMPLES:

        sage: x.arccosh()
        arccosh(x)
        sage: SR(0).arccosh()
        1/2*I*pi
        sage: SR(1/2).arccosh()
        arccosh(1/2)
        sage: SR(CDF(1/2)).arccosh()  # rel tol 1e-15
        1.0471975511965976*I
        sage: maxima('acosh(0.5)')
        1.04719755119659...*%i

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(-1).arccosh()
        I*pi
        sage: SR(-1).arccosh(hold=True)
        arccosh(-1)

    This also works using functional notation:

        sage: arccosh(-1,hold=True)
        arccosh(-1)
        sage: arccosh(-1)
        I*pi

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(-1).arccosh(hold=True); a.simplify()
        I*pi

    TESTS:

        sage: SR(oo).arccosh()
        +Infinity
        sage: SR(-oo).arccosh()
        +Infinity
        sage: SR(unsigned_infinity).arccosh()
        +Infinity

  `arcsin`(hold=False)

    Return the arcsin of x, i.e., the number y between -pi and pi such that sin(y) == x.

    EXAMPLES:

        sage: x.arcsin()
        arcsin(x)
        sage: SR(0.5).arcsin()
        0.523598775598299
        sage: SR(0.999).arcsin()
        1.52607123962616
        sage: SR(1/3).arcsin()
        arcsin(1/3)
        sage: SR(-1/3).arcsin()
        -arcsin(1/3)

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(0).arcsin()
        0
        sage: SR(0).arcsin(hold=True)
        arcsin(0)

    This also works using functional notation:

        sage: arcsin(0,hold=True)
        arcsin(0)
        sage: arcsin(0)
        0

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(0).arcsin(hold=True); a.simplify()
        0

    TESTS:

        sage: SR(oo).arcsin()
        Traceback (most recent call last):
        ...
        RuntimeError: arcsin_eval(): arcsin(infinity) encountered
        sage: SR(-oo).arcsin()
        Traceback (most recent call last):
        ...
        RuntimeError: arcsin_eval(): arcsin(infinity) encountered
        sage: SR(unsigned_infinity).arcsin()
        Infinity

  `arcsinh`(hold=False)

    Return the inverse hyperbolic sine of self.

    EXAMPLES:

        sage: x.arcsinh()
        arcsinh(x)
        sage: SR(0).arcsinh()
        0
        sage: SR(1).arcsinh()
        arcsinh(1)
        sage: SR(1.0).arcsinh()
        0.881373587019543
        sage: maxima('asinh(2.0)')
        1.4436354751788...

    Sage automatically applies certain identities:

        sage: SR(3/2).arcsinh().cosh()
        1/2*sqrt(13)

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(-2).arcsinh()
        -arcsinh(2)
        sage: SR(-2).arcsinh(hold=True)
        arcsinh(-2)

    This also works using functional notation:

        sage: arcsinh(-2,hold=True)
        arcsinh(-2)
        sage: arcsinh(-2)
        -arcsinh(2)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(-2).arcsinh(hold=True); a.simplify()
        -arcsinh(2)

    TESTS:

        sage: SR(oo).arcsinh()
        +Infinity
        sage: SR(-oo).arcsinh()
        -Infinity
        sage: SR(unsigned_infinity).arcsinh()
        Infinity

  `arctan`(hold=False)

    Return the arc tangent of self.

    EXAMPLES:

        sage: x = var('x')
        sage: x.arctan()
        arctan(x)
        sage: SR(1).arctan()
        1/4*pi
        sage: SR(1/2).arctan()
        arctan(1/2)
        sage: SR(0.5).arctan()
        0.463647609000806
        sage: plot(lambda x: SR(x).arctan(), -20,20)
        Graphics object consisting of 1 graphics primitive

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(1).arctan(hold=True)
        arctan(1)

    This also works using functional notation:

        sage: arctan(1,hold=True)
        arctan(1)
        sage: arctan(1)
        1/4*pi

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(1).arctan(hold=True); a.simplify()
        1/4*pi

    TESTS:

        sage: SR(oo).arctan()
        1/2*pi
        sage: SR(-oo).arctan()
        -1/2*pi
        sage: SR(unsigned_infinity).arctan()
        Traceback (most recent call last):
        ...
        RuntimeError: arctan_eval(): arctan(unsigned_infinity) encountered

  `arctan2`(x, hold=False)

    Return the inverse of the 2-variable tan function on self and x.

    EXAMPLES:

        sage: var('x,y')
        (x, y)
        sage: x.arctan2(y)
        arctan2(x, y)
        sage: SR(1/2).arctan2(1/2)
        1/4*pi
        sage: maxima.eval('atan2(1/2,1/2)')
        '%pi/4'

        sage: SR(-0.7).arctan2(SR(-0.6))
        -2.27942259892257

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(1/2).arctan2(1/2, hold=True)
        arctan2(1/2, 1/2)

    This also works using functional notation:

        sage: arctan2(1,2,hold=True)
        arctan2(1, 2)
        sage: arctan2(1,2)
        arctan(1/2)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(1/2).arctan2(1/2, hold=True); a.simplify()
        1/4*pi

    TESTS:

    We compare a bunch of different evaluation points between Sage and Maxima:

        sage: float(SR(0.7).arctan2(0.6))
        0.8621700546672264
        sage: maxima('atan2(0.7,0.6)')
        0.8621700546672264
        sage: float(SR(0.7).arctan2(-0.6))
        2.279422598922567
        sage: maxima('atan2(0.7,-0.6)')
        2.279422598922567
        sage: float(SR(-0.7).arctan2(0.6))
        -0.8621700546672264
        sage: maxima('atan2(-0.7,0.6)')
        -0.8621700546672264
        sage: float(SR(-0.7).arctan2(-0.6))
        -2.279422598922567
        sage: maxima('atan2(-0.7,-0.6)')
        -2.279422598922567
        sage: float(SR(0).arctan2(-0.6))
        3.141592653589793
        sage: maxima('atan2(0,-0.6)')
        3.141592653589793
        sage: float(SR(0).arctan2(0.6))
        0.0
        sage: maxima('atan2(0,0.6)')
        0.0
        sage: SR(0).arctan2(0) # see trac ticket #11423
        Traceback (most recent call last):
        ...
        RuntimeError: arctan2_eval(): arctan2(0,0) encountered
        sage: SR(I).arctan2(1)
        arctan2(I, 1)
        sage: SR(CDF(0,1)).arctan2(1)
        arctan2(1.0*I, 1)
        sage: SR(1).arctan2(CDF(0,1))
        arctan2(1, 1.0*I)

        sage: arctan2(0,oo)
        0
        sage: SR(oo).arctan2(oo)
        1/4*pi
        sage: SR(oo).arctan2(0)
        1/2*pi
        sage: SR(-oo).arctan2(0)
        -1/2*pi
        sage: SR(-oo).arctan2(-2)
        pi
        sage: SR(unsigned_infinity).arctan2(2)
        Traceback (most recent call last):
        ...
        RuntimeError: arctan2_eval(): arctan2(x, unsigned_infinity) encountered
        sage: SR(2).arctan2(oo)
        1/2*pi
        sage: SR(2).arctan2(-oo)
        -1/2*pi
        sage: SR(2).arctan2(SR(unsigned_infinity))
        Traceback (most recent call last):
        ...
        RuntimeError: arctan2_eval(): arctan2(unsigned_infinity, x) encountered

  `arctanh`(hold=False)

    Return the inverse hyperbolic tangent of self.

    EXAMPLES:

        sage: x.arctanh()
        arctanh(x)
        sage: SR(0).arctanh()
        0
        sage: SR(1/2).arctanh()
        arctanh(1/2)
        sage: SR(0.5).arctanh()
        0.549306144334055
        sage: SR(0.5).arctanh().tanh()
        0.500000000000000
        sage: maxima('atanh(0.5)')  # abs tol 2e-16
        0.5493061443340548

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(-1/2).arctanh()
        -arctanh(1/2)
        sage: SR(-1/2).arctanh(hold=True)
        arctanh(-1/2)

    This also works using functional notation:

        sage: arctanh(-1/2,hold=True)
        arctanh(-1/2)
        sage: arctanh(-1/2)
        -arctanh(1/2)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(-1/2).arctanh(hold=True); a.simplify()
        -arctanh(1/2)

    TESTS:

        sage: SR(1).arctanh()
        +Infinity
        sage: SR(-1).arctanh()
        -Infinity

        sage: SR(oo).arctanh()
        -1/2*I*pi
        sage: SR(-oo).arctanh()
        1/2*I*pi
        sage: SR(unsigned_infinity).arctanh()
        Traceback (most recent call last):
        ...
        RuntimeError: arctanh_eval(): arctanh(unsigned_infinity) encountered

  `args`()

    EXAMPLES:

        sage: x,y = var('x,y')
        sage: f = x + y
        sage: f.arguments()
        (x, y)

        sage: g = f.function(x)
        sage: g.arguments()
        (x,)

  `arguments`()

    EXAMPLES:

        sage: x,y = var('x,y')
        sage: f = x + y
        sage: f.arguments()
        (x, y)

        sage: g = f.function(x)
        sage: g.arguments()
        (x,)

  `assume`()

    Assume that this equation holds. This is relevant for symbolic integration, among other things.

    EXAMPLES: We call the assume method to assume that \(x>2\):

        sage: (x > 2).assume()

    Bool returns True below if the inequality is definitely known to be True.

        sage: bool(x > 0)
        True
        sage: bool(x < 0)
        False

    This may or may not be True, so bool returns False:

        sage: bool(x > 3)
        False

    If you make inconsistent or meaningless assumptions, Sage will let you know:

        sage: forget()
        sage: assume(x<0)
        sage: assume(x>0)
        Traceback (most recent call last):
        ...
        ValueError: Assumption is inconsistent
        sage: assumptions()
        [x < 0]
        sage: forget()

    TESTS:

        sage: v,c = var('v,c')
        sage: assume(c != 0)
        sage: integral((1+v^2/c^2)^3/(1-v^2/c^2)^(3/2),v)
        83/8*v/sqrt(-v^2/c^2 + 1) - 17/8*v^3/(c^2*sqrt(-v^2/c^2 + 1)) - 1/4*v^5/(c^4*sqrt(-v^2/c^2 + 1)) - 75/8*arcsin(v/(c^2*sqrt(c^(-2))))/sqrt(c^(-2))
        sage: forget()

  `binomial`(k, hold=False)

    Return binomial coefficient “self choose k”.

    OUTPUT:

    A symbolic expression.

    EXAMPLES:

        sage: var('x, y')
        (x, y)
        sage: SR(5).binomial(SR(3))
        10
        sage: x.binomial(SR(3))
        1/6*(x - 1)*(x - 2)*x
        sage: x.binomial(y)
        binomial(x, y)

    To prevent automatic evaluation use the `hold` argument:

        sage: x.binomial(3, hold=True)
        binomial(x, 3)
        sage: SR(5).binomial(3, hold=True)
        binomial(5, 3)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(5).binomial(3, hold=True); a.simplify()
        10

    The `hold` parameter is also supported in functional notation:

        sage: binomial(5,3, hold=True)
        binomial(5, 3)

    TESTS:

    Check if we handle zero correctly (trac ticket #8561):

        sage: x.binomial(0)
        1
        sage: SR(0).binomial(0)
        1

  `coeff`(s, n=1)

    Return the coefficient of \(s^n\) in this symbolic expression.

    INPUT:

    - `s` - expression
    - `n` - integer, default 1

    OUTPUT:

    A symbolic expression. The coefficient of \(s^n\).

    Sometimes it may be necessary to expand or factor first, since this is not done automatically.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)
        sage: f = 100 + a*x + x^3*sin(x*y) + x*y + x/y + 2*sin(x*y)/x; f
        x^3*sin(x*y) + a*x + x*y + x/y + 2*sin(x*y)/x + 100
        sage: f.collect(x)
        x^3*sin(x*y) + (a + y + 1/y)*x + 2*sin(x*y)/x + 100
        sage: f.coefficient(x,0)
        100
        sage: f.coefficient(x,-1)
        2*sin(x*y)
        sage: f.coefficient(x,1)
        a + y + 1/y
        sage: f.coefficient(x,2)
        0
        sage: f.coefficient(x,3)
        sin(x*y)
        sage: f.coefficient(x^3)
        sin(x*y)
        sage: f.coefficient(sin(x*y))
        x^3 + 2/x
        sage: f.collect(sin(x*y))
        a*x + x*y + (x^3 + 2/x)*sin(x*y) + x/y + 100

        sage: var('a, x, y, z')
        (a, x, y, z)
        sage: f = (a*sqrt(2))*x^2 + sin(y)*x^(1/2) + z^z
        sage: f.coefficient(sin(y))
        sqrt(x)
        sage: f.coefficient(x^2)
        sqrt(2)*a
        sage: f.coefficient(x^(1/2))
        sin(y)
        sage: f.coefficient(1)
        0
        sage: f.coefficient(x, 0)
        sqrt(x)*sin(y) + z^z

    TESTS:

    Check if trac ticket #9505 is fixed:

        sage: var('x,y,z')
        (x, y, z)
        sage: f = x*y*z^2
        sage: f.coeff(x*y)
        z^2
        sage: f.coeff(x*y, 2)
        Traceback (most recent call last):
        ...
        TypeError: n != 1 only allowed for s being a variable

  `coefficient`(s, n=1)

    Return the coefficient of \(s^n\) in this symbolic expression.

    INPUT:

    - `s` - expression
    - `n` - integer, default 1

    OUTPUT:

    A symbolic expression. The coefficient of \(s^n\).

    Sometimes it may be necessary to expand or factor first, since this is not done automatically.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)
        sage: f = 100 + a*x + x^3*sin(x*y) + x*y + x/y + 2*sin(x*y)/x; f
        x^3*sin(x*y) + a*x + x*y + x/y + 2*sin(x*y)/x + 100
        sage: f.collect(x)
        x^3*sin(x*y) + (a + y + 1/y)*x + 2*sin(x*y)/x + 100
        sage: f.coefficient(x,0)
        100
        sage: f.coefficient(x,-1)
        2*sin(x*y)
        sage: f.coefficient(x,1)
        a + y + 1/y
        sage: f.coefficient(x,2)
        0
        sage: f.coefficient(x,3)
        sin(x*y)
        sage: f.coefficient(x^3)
        sin(x*y)
        sage: f.coefficient(sin(x*y))
        x^3 + 2/x
        sage: f.collect(sin(x*y))
        a*x + x*y + (x^3 + 2/x)*sin(x*y) + x/y + 100

        sage: var('a, x, y, z')
        (a, x, y, z)
        sage: f = (a*sqrt(2))*x^2 + sin(y)*x^(1/2) + z^z
        sage: f.coefficient(sin(y))
        sqrt(x)
        sage: f.coefficient(x^2)
        sqrt(2)*a
        sage: f.coefficient(x^(1/2))
        sin(y)
        sage: f.coefficient(1)
        0
        sage: f.coefficient(x, 0)
        sqrt(x)*sin(y) + z^z

    TESTS:

    Check if trac ticket #9505 is fixed:

        sage: var('x,y,z')
        (x, y, z)
        sage: f = x*y*z^2
        sage: f.coeff(x*y)
        z^2
        sage: f.coeff(x*y, 2)
        Traceback (most recent call last):
        ...
        TypeError: n != 1 only allowed for s being a variable

  `coefficients`(x=None)

    Return the coefficients of this symbolic expression as a polynomial in x.

    INPUT:

    - `x` – optional variable.

    OUTPUT:

    A list of pairs `(expr, n)`, where `expr` is a symbolic expression and `n` is a power.

    EXAMPLES:

        sage: var('x, y, a')
        (x, y, a)
        sage: p = x^3 - (x-3)*(x^2+x) + 1
        sage: p.coefficients()
        [[1, 0], [3, 1], [2, 2]]
        sage: p = expand((x-a*sqrt(2))^2 + x + 1); p
        -2*sqrt(2)*a*x + 2*a^2 + x^2 + x + 1
        sage: p.coefficients(a)
        [[x^2 + x + 1, 0], [-2*sqrt(2)*x, 1], [2, 2]]
        sage: p.coefficients(x)
        [[2*a^2 + 1, 0], [-2*sqrt(2)*a + 1, 1], [1, 2]]

    A polynomial with wacky exponents:

        sage: p = (17/3*a)*x^(3/2) + x*y + 1/x + x^x
        sage: p.coefficients(x)
        [[1, -1], [x^x, 0], [y, 1], [17/3*a, 3/2]]

  `coeffs`(x=None)

    Return the coefficients of this symbolic expression as a polynomial in x.

    INPUT:

    - `x` – optional variable.

    OUTPUT:

    A list of pairs `(expr, n)`, where `expr` is a symbolic expression and `n` is a power.

    EXAMPLES:

        sage: var('x, y, a')
        (x, y, a)
        sage: p = x^3 - (x-3)*(x^2+x) + 1
        sage: p.coefficients()
        [[1, 0], [3, 1], [2, 2]]
        sage: p = expand((x-a*sqrt(2))^2 + x + 1); p
        -2*sqrt(2)*a*x + 2*a^2 + x^2 + x + 1
        sage: p.coefficients(a)
        [[x^2 + x + 1, 0], [-2*sqrt(2)*x, 1], [2, 2]]
        sage: p.coefficients(x)
        [[2*a^2 + 1, 0], [-2*sqrt(2)*a + 1, 1], [1, 2]]

    A polynomial with wacky exponents:

        sage: p = (17/3*a)*x^(3/2) + x*y + 1/x + x^x
        sage: p.coefficients(x)
        [[1, -1], [x^x, 0], [y, 1], [17/3*a, 3/2]]

  `collect`(s)

    Collect the coefficients of `s` into a group.

    INPUT:

    - `s` – the symbol whose coefficients will be collected.

    OUTPUT:

    A new expression, equivalent to the original one, with the coefficients of `s` grouped.

    Note

    The expression is not expanded or factored before the grouping takes place. For best results, call `expand()` on the expression before `collect()`.

    EXAMPLES:

    In the first term of \(f\), \(x\) has a coefficient of \(4y\). In the second term, \(x\) has a coefficient of \(z\). Therefore, if we collect those coefficients, \(x\) will have a coefficient of \(4y+z\):

        sage: x,y,z = var('x,y,z')
        sage: f = 4*x*y + x*z + 20*y^2 + 21*y*z + 4*z^2 + x^2*y^2*z^2
        sage: f.collect(x)
        x^2*y^2*z^2 + x*(4*y + z) + 20*y^2 + 21*y*z + 4*z^2

    Here we do the same thing for \(y\) and \(z\); however, note that we do not factor the \(y^{2}\) and \(z^{2}\) terms before collecting coefficients:

        sage: f.collect(y)
        (x^2*z^2 + 20)*y^2 + (4*x + 21*z)*y + x*z + 4*z^2
        sage: f.collect(z)
        (x^2*y^2 + 4)*z^2 + 4*x*y + 20*y^2 + (x + 21*y)*z

    Sometimes, we do have to call `expand()` on the expression first to achieve the desired result:

        sage: f = (x + y)*(x - z)
        sage: f.collect(x)
        x^2 + x*y - x*z - y*z
        sage: f.expand().collect(x)
        x^2 + x*(y - z) - y*z

    TESTS:

    The output should be equivalent to the input:

        sage: polynomials = QQ['x']
        sage: f = SR(polynomials.random_element())
        sage: g = f.collect(x)
        sage: bool(f == g)
        True

    If `s` is not present in the given expression, the expression should not be modified. The variable \(z\) will not be present in \(f\) below since \(f\) is a random polynomial of maximum degree 10 in \(x\) and \(y\):

        sage: z = var('z')
        sage: polynomials = QQ['x,y']
        sage: f = SR(polynomials.random_element(10))
        sage: g = f.collect(z)
        sage: bool(str(f) == str(g))
        True

    Check if trac ticket #9046 is fixed:

        sage: var('a b x y z')
        (a, b, x, y, z)
        sage: p = -a*x^3 - a*x*y^2 + 2*b*x^2*y + 2*y^3 + x^2*z + y^2*z + x^2 + y^2 + a*x
        sage: p.collect(x)
        -a*x^3 + (2*b*y + z + 1)*x^2 + 2*y^3 + y^2*z - (a*y^2 - a)*x + y^2

  `collect_common_factors`()

    This function does not perform a full factorization but only looks for factors which are already explicitly present.

    Polynomials can often be brought into a more compact form by collecting common factors from the terms of sums. This is accomplished by this function.

    EXAMPLES:

        sage: var('x')
        x
        sage: (x/(x^2 + x)).collect_common_factors()
        1/(x + 1)

        sage: var('a,b,c,x,y')
        (a, b, c, x, y)
        sage: (a*x+a*y).collect_common_factors()
        a*(x + y)
        sage: (a*x^2+2*a*x*y+a*y^2).collect_common_factors()
        (x^2 + 2*x*y + y^2)*a
        sage: (a*(b*(a+c)*x+b*((a+c)*x+(a+c)*y)*y)).collect_common_factors()
        ((x + y)*y + x)*(a + c)*a*b

  `combine`()

    Return a simplified version of this symbolic expression by combining all terms with the same denominator into a single term.

    EXAMPLES:

        sage: var('x, y, a, b, c')
        (x, y, a, b, c)
        sage: f = x*(x-1)/(x^2 - 7) + y^2/(x^2-7) + 1/(x+1) + b/a + c/a; f
        (x - 1)*x/(x^2 - 7) + y^2/(x^2 - 7) + b/a + c/a + 1/(x + 1)
        sage: f.combine()
        ((x - 1)*x + y^2)/(x^2 - 7) + (b + c)/a + 1/(x + 1)

  `conjugate`(hold=False)

    Return the complex conjugate of this symbolic expression.

    EXAMPLES:

        sage: a = 1 + 2*I
        sage: a.conjugate()
        -2*I + 1
        sage: a = sqrt(2) + 3^(1/3)*I; a
        sqrt(2) + I*3^(1/3)
        sage: a.conjugate()
        sqrt(2) - I*3^(1/3)

        sage: SR(CDF.0).conjugate()
        -1.0*I
        sage: x.conjugate()
        conjugate(x)
        sage: SR(RDF(1.5)).conjugate()
        1.5
        sage: SR(float(1.5)).conjugate()
        1.5
        sage: SR(I).conjugate()
        -I
        sage: ( 1+I  + (2-3*I)*x).conjugate()
        (3*I + 2)*conjugate(x) - I + 1

    Using the `hold` parameter it is possible to prevent automatic evaluation:

        sage: SR(I).conjugate(hold=True)
        conjugate(I)

    This also works in functional notation:

        sage: conjugate(I)
        -I
        sage: conjugate(I,hold=True)
        conjugate(I)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(I).conjugate(hold=True); a.simplify()
        -I

  `content`(s)

    Return the content of this expression when considered as a polynomial in `s`.

    See also `unit()`, `primitive_part()`, and `unit_content_primitive()`.

    INPUT:

    - `s` – a symbolic expression.

    OUTPUT:

    The content part of a polynomial as a symbolic expression. It is defined as the gcd of the coefficients.

    Warning

    The expression is considered to be a univariate polynomial in `s`. The output is different from the `content()` method provided by multivariate polynomial rings in Sage.

    EXAMPLES:

        sage: (2*x+4).content(x)
        2
        sage: (2*x+1).content(x)
        1
        sage: (2*x+1/2).content(x)
        1/2
        sage: var('y')
        y
        sage: (2*x + 4*sin(y)).content(sin(y))
        2

  `contradicts`(soln)

    Return `True` if this relation is violated by the given variable assignment(s).

    EXAMPLES:

        sage: (x<3).contradicts(x==0)
        False
        sage: (x<3).contradicts(x==3)
        True
        sage: (x<=3).contradicts(x==3)
        False
        sage: y = var('y')
        sage: (x<y).contradicts(x==30)
        False
        sage: (x<y).contradicts({x: 30, y: 20})
        True

  `convert`(target=None)

    Call the convert function in the units package. For symbolic variables that are not units, this function just returns the variable.

    INPUT:

    - `self` – the symbolic expression converting from
    - `target` – (default None) the symbolic expression converting to

    OUTPUT:

    A symbolic expression.

    EXAMPLES:

        sage: units.length.foot.convert()
        381/1250*meter
        sage: units.mass.kilogram.convert(units.mass.pound)
        100000000/45359237*pound

    We do not get anything new by converting an ordinary symbolic variable:

        sage: a = var('a')
        sage: a - a.convert()
        0

    Raises ValueError if self and target are not convertible:

        sage: units.mass.kilogram.convert(units.length.foot)
        Traceback (most recent call last):
        ...
        ValueError: Incompatible units
        sage: (units.length.meter^2).convert(units.length.foot)
        Traceback (most recent call last):
        ...
        ValueError: Incompatible units

    Recognizes derived unit relationships to base units and other derived units:

        sage: (units.length.foot/units.time.second^2).convert(units.acceleration.galileo)
        762/25*galileo
        sage: (units.mass.kilogram*units.length.meter/units.time.second^2).convert(units.force.newton)
        newton
        sage: (units.length.foot^3).convert(units.area.acre*units.length.inch)
        1/3630*(acre*inch)
        sage: (units.charge.coulomb).convert(units.current.ampere*units.time.second)
        (ampere*second)
        sage: (units.pressure.pascal*units.si_prefixes.kilo).convert(units.pressure.pounds_per_square_inch)
        1290320000000/8896443230521*pounds_per_square_inch

    For decimal answers multiply by 1.0:

        sage: (units.pressure.pascal*units.si_prefixes.kilo).convert(units.pressure.pounds_per_square_inch)*1.0
        0.145037737730209*pounds_per_square_inch

    Converting temperatures works as well:

        sage: s = 68*units.temperature.fahrenheit
        sage: s.convert(units.temperature.celsius)
        20*celsius
        sage: s.convert()
        293.150000000000*kelvin

    Trying to multiply temperatures by another unit then converting raises a ValueError:

        sage: wrong = 50*units.temperature.celsius*units.length.foot
        sage: wrong.convert()
        Traceback (most recent call last):
        ...
        ValueError: Cannot convert

  `cos`(hold=False)

    Return the cosine of self.

    EXAMPLES:

        sage: var('x, y')
        (x, y)
        sage: cos(x^2 + y^2)
        cos(x^2 + y^2)
        sage: cos(sage.symbolic.constants.pi)
        -1
        sage: cos(SR(1))
        cos(1)
        sage: cos(SR(RealField(150)(1)))
        0.54030230586813971740093660744297660373231042

    In order to get a numeric approximation use .n():

        sage: SR(RR(1)).cos().n()
        0.540302305868140
        sage: SR(float(1)).cos().n()
        0.540302305868140

    To prevent automatic evaluation use the `hold` argument:

        sage: pi.cos()
        -1
        sage: pi.cos(hold=True)
        cos(pi)

    This also works using functional notation:

        sage: cos(pi,hold=True)
        cos(pi)
        sage: cos(pi)
        -1

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = pi.cos(hold=True); a.simplify()
        -1

    TESTS:

        sage: SR(oo).cos()
        Traceback (most recent call last):
        ...
        RuntimeError: cos_eval(): cos(infinity) encountered
        sage: SR(-oo).cos()
        Traceback (most recent call last):
        ...
        RuntimeError: cos_eval(): cos(infinity) encountered
        sage: SR(unsigned_infinity).cos()
        Traceback (most recent call last):
        ...
        RuntimeError: cos_eval(): cos(infinity) encountered

  `cosh`(hold=False)

    Return cosh of self.

    We have \(\cosh(x) = (e^{x} + e^{-x})/2\).

    EXAMPLES:

        sage: x.cosh()
        cosh(x)
        sage: SR(1).cosh()
        cosh(1)
        sage: SR(0).cosh()
        1
        sage: SR(1.0).cosh()
        1.54308063481524
        sage: maxima('cosh(1.0)')
        1.54308063481524...
        sage: SR(1.00000000000000000000000000).cosh()
        1.5430806348152437784779056
        sage: SR(RIF(1)).cosh()
        1.543080634815244?

    To prevent automatic evaluation use the `hold` argument:

        sage: arcsinh(x).cosh()
        sqrt(x^2 + 1)
        sage: arcsinh(x).cosh(hold=True)
        cosh(arcsinh(x))

    This also works using functional notation:

        sage: cosh(arcsinh(x),hold=True)
        cosh(arcsinh(x))
        sage: cosh(arcsinh(x))
        sqrt(x^2 + 1)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = arcsinh(x).cosh(hold=True); a.simplify()
        sqrt(x^2 + 1)

    TESTS:

        sage: SR(oo).cosh()
        +Infinity
        sage: SR(-oo).cosh()
        +Infinity
        sage: SR(unsigned_infinity).cosh()
        Traceback (most recent call last):
        ...
        RuntimeError: cosh_eval(): cosh(unsigned_infinity) encountered

  `csgn`(hold=False)

    Return the sign of self, which is -1 if self < 0, 0 if self == 0, and 1 if self > 0, or unevaluated when self is a nonconstant symbolic expression.

    If self is not real, return the complex half-plane (left or right) in which the number lies. If self is pure imaginary, return the sign of the imaginary part of self.

    EXAMPLES:

        sage: x = var('x')
        sage: SR(-2).csgn()
        -1
        sage: SR(0.0).csgn()
        0
        sage: SR(10).csgn()
        1
        sage: x.csgn()
        csgn(x)
        sage: SR(CDF.0).csgn()
        1
        sage: SR(I).csgn()
        1
        sage: SR(-I).csgn()
        -1
        sage: SR(1+I).csgn()
        1
        sage: SR(1-I).csgn()
        1
        sage: SR(-1+I).csgn()
        -1
        sage: SR(-1-I).csgn()
        -1

    Using the `hold` parameter it is possible to prevent automatic evaluation:

        sage: SR(I).csgn(hold=True)
        csgn(I)

  `default_variable`()

    Return the default variable, which is by definition the first variable in self, or \(x\) is there are no variables in self. The result is cached.

    EXAMPLES:

        sage: sqrt(2).default_variable()
        x
        sage: x, theta, a = var('x, theta, a')
        sage: f = x^2 + theta^3 - a^x
        sage: f.default_variable()
        a

    Note that this is the first variable, not the first argument:

        sage: f(theta, a, x) = a + theta^3
        sage: f.default_variable()
        a
        sage: f.variables()
        (a, theta)
        sage: f.arguments()
        (theta, a, x)

  `degree`(s)

    Return the exponent of the highest nonnegative power of s in self.

    OUTPUT:

    An integer `>= 0`.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)
        sage: f = 100 + a*x + x^3*sin(x*y) + x*y + x/y^10 + 2*sin(x*y)/x; f
        x^3*sin(x*y) + a*x + x*y + 2*sin(x*y)/x + x/y^10 + 100
        sage: f.degree(x)
        3
        sage: f.degree(y)
        1
        sage: f.degree(sin(x*y))
        1
        sage: (x^-3+y).degree(x)
        0

  `denominator`(normalize=True)

    Return the denominator of this symbolic expression

    INPUT:

    - `normalize` – (default: `True`) a boolean.

    If `normalize` is `True`, the expression is first normalized to have it as a fraction before getting the denominator.

    If `normalize` is `False`, the expression is kept and if it is not a quotient, then this will just return 1.

    See also

    `normalize()`, `numerator()`, `numerator_denominator()`, `combine()`

    EXAMPLES:

        sage: x, y, z, theta = var('x, y, z, theta')
        sage: f = (sqrt(x) + sqrt(y) + sqrt(z))/(x^10 - y^10 - sqrt(theta))
        sage: f.numerator()
        sqrt(x) + sqrt(y) + sqrt(z)
        sage: f.denominator()
        x^10 - y^10 - sqrt(theta)

        sage: f.numerator(normalize=False)
        (sqrt(x) + sqrt(y) + sqrt(z))
        sage: f.denominator(normalize=False)
        x^10 - y^10 - sqrt(theta)

        sage: y = var('y')
        sage: g = x + y/(x + 2); g
        x + y/(x + 2)
        sage: g.numerator(normalize=False)
        x + y/(x + 2)
        sage: g.denominator(normalize=False)
        1

    TESTS:

        sage: ((x+y)^2/(x-y)^3*x^3).denominator(normalize=False)
        (x - y)^3
        sage: ((x+y)^2*x^3).denominator(normalize=False)
        1
        sage: (y/x^3).denominator(normalize=False)
        x^3
        sage: t = y/x^3/(x+y)^(1/2); t
        y/(sqrt(x + y)*x^3)
        sage: t.denominator(normalize=False)
        sqrt(x + y)*x^3
        sage: (1/x^3).denominator(normalize=False)
        x^3
        sage: (x^3).denominator(normalize=False)
        1
        sage: (y*x^sin(x)).denominator(normalize=False)
        Traceback (most recent call last):
        ...
        TypeError: self is not a rational expression

  `derivative`(*args)

    Return the derivative of this expressions with respect to the variables supplied in args.

    Multiple variables and iteration counts may be supplied; see documentation for the global `derivative()` function for more details.

    See also

    This is implemented in the \(_derivative\) method (see the source code).

    EXAMPLES:

        sage: var("x y")
        (x, y)
        sage: t = (x^2+y)^2
        sage: t.derivative(x)
        4*(x^2 + y)*x
        sage: t.derivative(x, 2)
        12*x^2 + 4*y
        sage: t.derivative(x, 2, y)
        4
        sage: t.derivative(y)
        2*x^2 + 2*y

    If the function depends on only one variable, you may omit the variable. Giving just a number (for the order of the derivative) also works:

        sage: f(x) = x^3 + sin(x)
        sage: f.derivative()
        x |--> 3*x^2 + cos(x)
        sage: f.derivative(2)
        x |--> 6*x - sin(x)

        sage: t = sin(x+y^2)*tan(x*y)
        sage: t.derivative(x)
        (tan(x*y)^2 + 1)*y*sin(y^2 + x) + cos(y^2 + x)*tan(x*y)
        sage: t.derivative(y)
        (tan(x*y)^2 + 1)*x*sin(y^2 + x) + 2*y*cos(y^2 + x)*tan(x*y)

        sage: h = sin(x)/cos(x)
        sage: derivative(h,x,x,x)
        8*sin(x)^2/cos(x)^2 + 6*sin(x)^4/cos(x)^4 + 2
        sage: derivative(h,x,3)
        8*sin(x)^2/cos(x)^2 + 6*sin(x)^4/cos(x)^4 + 2

        sage: var('x, y')
        (x, y)
        sage: u = (sin(x) + cos(y))*(cos(x) - sin(y))
        sage: derivative(u,x,y)
        -cos(x)*cos(y) + sin(x)*sin(y)
        sage: f = ((x^2+1)/(x^2-1))^(1/4)
        sage: g = derivative(f, x); g # this is a complex expression
        -1/2*((x^2 + 1)*x/(x^2 - 1)^2 - x/(x^2 - 1))/((x^2 + 1)/(x^2 - 1))^(3/4)
        sage: g.factor()
        -x/((x + 1)^2*(x - 1)^2*((x^2 + 1)/(x^2 - 1))^(3/4))

        sage: y = var('y')
        sage: f = y^(sin(x))
        sage: derivative(f, x)
        y^sin(x)*cos(x)*log(y)

        sage: g(x) = sqrt(5-2*x)
        sage: g_3 = derivative(g, x, 3); g_3(2)
        -3

        sage: f = x*e^(-x)
        sage: derivative(f, 100)
        x*e^(-x) - 100*e^(-x)

        sage: g = 1/(sqrt((x^2-1)*(x+5)^6))
        sage: derivative(g, x)
        -((x + 5)^6*x + 3*(x^2 - 1)*(x + 5)^5)/((x^2 - 1)*(x + 5)^6)^(3/2)

    TESTS:

        sage: t.derivative()
        Traceback (most recent call last):
        ...
        ValueError: No differentiation variable specified.

  `diff`(*args)

    Return the derivative of this expressions with respect to the variables supplied in args.

    Multiple variables and iteration counts may be supplied; see documentation for the global `derivative()` function for more details.

    See also

    This is implemented in the \(_derivative\) method (see the source code).

    EXAMPLES:

        sage: var("x y")
        (x, y)
        sage: t = (x^2+y)^2
        sage: t.derivative(x)
        4*(x^2 + y)*x
        sage: t.derivative(x, 2)
        12*x^2 + 4*y
        sage: t.derivative(x, 2, y)
        4
        sage: t.derivative(y)
        2*x^2 + 2*y

    If the function depends on only one variable, you may omit the variable. Giving just a number (for the order of the derivative) also works:

        sage: f(x) = x^3 + sin(x)
        sage: f.derivative()
        x |--> 3*x^2 + cos(x)
        sage: f.derivative(2)
        x |--> 6*x - sin(x)

        sage: t = sin(x+y^2)*tan(x*y)
        sage: t.derivative(x)
        (tan(x*y)^2 + 1)*y*sin(y^2 + x) + cos(y^2 + x)*tan(x*y)
        sage: t.derivative(y)
        (tan(x*y)^2 + 1)*x*sin(y^2 + x) + 2*y*cos(y^2 + x)*tan(x*y)

        sage: h = sin(x)/cos(x)
        sage: derivative(h,x,x,x)
        8*sin(x)^2/cos(x)^2 + 6*sin(x)^4/cos(x)^4 + 2
        sage: derivative(h,x,3)
        8*sin(x)^2/cos(x)^2 + 6*sin(x)^4/cos(x)^4 + 2

        sage: var('x, y')
        (x, y)
        sage: u = (sin(x) + cos(y))*(cos(x) - sin(y))
        sage: derivative(u,x,y)
        -cos(x)*cos(y) + sin(x)*sin(y)
        sage: f = ((x^2+1)/(x^2-1))^(1/4)
        sage: g = derivative(f, x); g # this is a complex expression
        -1/2*((x^2 + 1)*x/(x^2 - 1)^2 - x/(x^2 - 1))/((x^2 + 1)/(x^2 - 1))^(3/4)
        sage: g.factor()
        -x/((x + 1)^2*(x - 1)^2*((x^2 + 1)/(x^2 - 1))^(3/4))

        sage: y = var('y')
        sage: f = y^(sin(x))
        sage: derivative(f, x)
        y^sin(x)*cos(x)*log(y)

        sage: g(x) = sqrt(5-2*x)
        sage: g_3 = derivative(g, x, 3); g_3(2)
        -3

        sage: f = x*e^(-x)
        sage: derivative(f, 100)
        x*e^(-x) - 100*e^(-x)

        sage: g = 1/(sqrt((x^2-1)*(x+5)^6))
        sage: derivative(g, x)
        -((x + 5)^6*x + 3*(x^2 - 1)*(x + 5)^5)/((x^2 - 1)*(x + 5)^6)^(3/2)

    TESTS:

        sage: t.derivative()
        Traceback (most recent call last):
        ...
        ValueError: No differentiation variable specified.

  `differentiate`(*args)

    Return the derivative of this expressions with respect to the variables supplied in args.

    Multiple variables and iteration counts may be supplied; see documentation for the global `derivative()` function for more details.

    See also

    This is implemented in the \(_derivative\) method (see the source code).

    EXAMPLES:

        sage: var("x y")
        (x, y)
        sage: t = (x^2+y)^2
        sage: t.derivative(x)
        4*(x^2 + y)*x
        sage: t.derivative(x, 2)
        12*x^2 + 4*y
        sage: t.derivative(x, 2, y)
        4
        sage: t.derivative(y)
        2*x^2 + 2*y

    If the function depends on only one variable, you may omit the variable. Giving just a number (for the order of the derivative) also works:

        sage: f(x) = x^3 + sin(x)
        sage: f.derivative()
        x |--> 3*x^2 + cos(x)
        sage: f.derivative(2)
        x |--> 6*x - sin(x)

        sage: t = sin(x+y^2)*tan(x*y)
        sage: t.derivative(x)
        (tan(x*y)^2 + 1)*y*sin(y^2 + x) + cos(y^2 + x)*tan(x*y)
        sage: t.derivative(y)
        (tan(x*y)^2 + 1)*x*sin(y^2 + x) + 2*y*cos(y^2 + x)*tan(x*y)

        sage: h = sin(x)/cos(x)
        sage: derivative(h,x,x,x)
        8*sin(x)^2/cos(x)^2 + 6*sin(x)^4/cos(x)^4 + 2
        sage: derivative(h,x,3)
        8*sin(x)^2/cos(x)^2 + 6*sin(x)^4/cos(x)^4 + 2

        sage: var('x, y')
        (x, y)
        sage: u = (sin(x) + cos(y))*(cos(x) - sin(y))
        sage: derivative(u,x,y)
        -cos(x)*cos(y) + sin(x)*sin(y)
        sage: f = ((x^2+1)/(x^2-1))^(1/4)
        sage: g = derivative(f, x); g # this is a complex expression
        -1/2*((x^2 + 1)*x/(x^2 - 1)^2 - x/(x^2 - 1))/((x^2 + 1)/(x^2 - 1))^(3/4)
        sage: g.factor()
        -x/((x + 1)^2*(x - 1)^2*((x^2 + 1)/(x^2 - 1))^(3/4))

        sage: y = var('y')
        sage: f = y^(sin(x))
        sage: derivative(f, x)
        y^sin(x)*cos(x)*log(y)

        sage: g(x) = sqrt(5-2*x)
        sage: g_3 = derivative(g, x, 3); g_3(2)
        -3

        sage: f = x*e^(-x)
        sage: derivative(f, 100)
        x*e^(-x) - 100*e^(-x)

        sage: g = 1/(sqrt((x^2-1)*(x+5)^6))
        sage: derivative(g, x)
        -((x + 5)^6*x + 3*(x^2 - 1)*(x + 5)^5)/((x^2 - 1)*(x + 5)^6)^(3/2)

    TESTS:

        sage: t.derivative()
        Traceback (most recent call last):
        ...
        ValueError: No differentiation variable specified.

  `divide_both_sides`(x, checksign=None)

    Return a relation obtained by dividing both sides of this relation by x.

    Note

    The checksign keyword argument is currently ignored and is included for backward compatibility reasons only.

    EXAMPLES:

        sage: theta = var('theta')
        sage: eqn =   (x^3 + theta < sin(x*theta))
        sage: eqn.divide_both_sides(theta, checksign=False)
        (x^3 + theta)/theta < sin(theta*x)/theta
        sage: eqn.divide_both_sides(theta)
        (x^3 + theta)/theta < sin(theta*x)/theta
        sage: eqn/theta
        (x^3 + theta)/theta < sin(theta*x)/theta

  `exp`(hold=False)

    Return exponential function of self, i.e., e to the power of self.

    EXAMPLES:

        sage: x.exp()
        e^x
        sage: SR(0).exp()
        1
        sage: SR(1/2).exp()
        e^(1/2)
        sage: SR(0.5).exp()
        1.64872127070013
        sage: math.exp(0.5)
        1.6487212707001282

        sage: SR(0.5).exp().log()
        0.500000000000000
        sage: (pi*I).exp()
        -1

    To prevent automatic evaluation use the `hold` argument:

        sage: (pi*I).exp(hold=True)
        e^(I*pi)

    This also works using functional notation:

        sage: exp(I*pi,hold=True)
        e^(I*pi)
        sage: exp(I*pi)
        -1

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = (pi*I).exp(hold=True); a.simplify()
        -1

    TESTS:

    Test if trac ticket #6377 is fixed:

        sage: SR(oo).exp()
        +Infinity
        sage: SR(-oo).exp()
        0
        sage: SR(unsigned_infinity).exp()
        Traceback (most recent call last):
        ...
        RuntimeError: exp_eval(): exp^(unsigned_infinity) encountered

  `exp_simplify`()

    Simplify this symbolic expression, which can contain logs, exponentials, and radicals, by trying to convert it into a canonical form over a large class of expressions and a given ordering of variables.

    Warning

    As shown in the examples below, a canonical form is not always returned, i.e., two mathematically identical expressions might be simplified to different expressions.

    ALGORITHM:

    This uses the Maxima `radcan()` command. From the Maxima documentation: “All functionally equivalent forms are mapped into a unique form. For a somewhat larger class of expressions, produces a regular form. Two equivalent expressions in this class do not necessarily have the same appearance, but their difference can be simplified by radcan to zero. For some expressions radcan is quite time consuming. This is the cost of exploring certain relationships among the components of the expression for simplifications based on factoring and partial fraction expansions of exponents.”

    Note

    `radical_simplify()`, `simplify_radical()`, `exp_simplify()`, `simplify_exp()` are all the same.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)

        sage: f = log(x*y)
        sage: f.simplify_radical()
        log(x) + log(y)

        sage: f = log(8)/log(2)
        sage: f.simplify_radical()
        3

        sage: f = (log(x+x^2)-log(x))^a/log(1+x)^(a/2)
        sage: f.simplify_radical()
        log(x + 1)^(1/2*a)

        sage: f = (e^x-1)/(1+e^(x/2))
        sage: f.simplify_exp()
        e^(1/2*x) - 1

    The example below shows two expressions e1 and e2 which are “simplified” to different expressions, while their difference is “simplified” to zero, thus `simplify_radical` does not return a canonical form, except maybe for 0.

        sage: e1 = 1/(sqrt(5)+sqrt(2))
        sage: e2 = (sqrt(5)-sqrt(2))/3
        sage: e1.simplify_radical()
        1/(sqrt(5) + sqrt(2))
        sage: e2.simplify_radical()
        1/3*sqrt(5) - 1/3*sqrt(2)
        sage: (e1-e2).simplify_radical()
        0

    TESTS:

    This tests that trac ticket #11668 has been fixed (by trac ticket #12780):

        sage: a,b = var('a b')
        sage: A = abs((a+I*b))^2
        sage: A.simplify_radical()
        abs(a + I*b)^2
        sage: imag(A)
        0
        sage: imag(A.simplify_radical())
        0

  `expand`(side=None)

    Expand this symbolic expression. Products of sums and exponentiated sums are multiplied out, numerators of rational expressions which are sums are split into their respective terms, and multiplications are distributed over addition at all levels.

    EXAMPLES:

    We expand the expression \((x-y)^5\) using both method and functional notation.

        sage: x,y = var('x,y')
        sage: a = (x-y)^5
        sage: a.expand()
        x^5 - 5*x^4*y + 10*x^3*y^2 - 10*x^2*y^3 + 5*x*y^4 - y^5
        sage: expand(a)
        x^5 - 5*x^4*y + 10*x^3*y^2 - 10*x^2*y^3 + 5*x*y^4 - y^5

    We expand some other expressions:

        sage: expand((x-1)^3/(y-1))
        x^3/(y - 1) - 3*x^2/(y - 1) + 3*x/(y - 1) - 1/(y - 1)
        sage: expand((x+sin((x+y)^2))^2)
        x^2 + 2*x*sin((x + y)^2) + sin((x + y)^2)^2

    We can expand individual sides of a relation:

        sage: a = (16*x-13)^2 == (3*x+5)^2/2
        sage: a.expand()
        256*x^2 - 416*x + 169 == 9/2*x^2 + 15*x + 25/2
        sage: a.expand('left')
        256*x^2 - 416*x + 169 == 1/2*(3*x + 5)^2
        sage: a.expand('right')
        (16*x - 13)^2 == 9/2*x^2 + 15*x + 25/2

    TESTS:

        sage: var('x,y')
        (x, y)
        sage: ((x + (2/3)*y)^3).expand()
        x^3 + 2*x^2*y + 4/3*x*y^2 + 8/27*y^3
        sage: expand( (x*sin(x) - cos(y)/x)^2 )
        x^2*sin(x)^2 - 2*cos(y)*sin(x) + cos(y)^2/x^2
        sage: f = (x-y)*(x+y); f
        (x + y)*(x - y)
        sage: f.expand()
        x^2 - y^2

  `expand_log`(algorithm='products')

    Simplify symbolic expression, which can contain logs.

    Expands logarithms of powers, logarithms of products and logarithms of quotients. The option `algorithm` specifies which expression types should be expanded.

    INPUT:

    - `self` - expression to be simplified

    - `algorithm` - (default: ‘products’) optional, governs which expression is expanded. Possible values are

      - ‘nothing’ (no expansion),
      - ‘powers’ (log(a^r) is expanded),
      - ‘products’ (like ‘powers’ and also log(a*b) are expanded),
      - ‘all’ (all possible expansion).

      See also examples below.

    DETAILS: This uses the Maxima simplifier and sets `logexpand` option for this simplifier. From the Maxima documentation: “Logexpand:true causes log(a^b) to become b*log(a). If it is set to all, log(a*b) will also simplify to log(a)+log(b). If it is set to super, then log(a/b) will also simplify to log(a)-log(b) for rational numbers a/b, a#1. (log(1/b), for integer b, always simplifies.) If it is set to false, all of these simplifications will be turned off. “

    ALIAS: `log_expand()` and `expand_log()` are the same

    EXAMPLES:

    By default powers and products (and quotients) are expanded, but not quotients of integers:

        sage: (log(3/4*x^pi)).log_expand()
        pi*log(x) + log(3/4)

    To expand also log(3/4) use `algorithm='all'`:

        sage: (log(3/4*x^pi)).log_expand('all')
        pi*log(x) - log(4) + log(3)

    To expand only the power use `algorithm='powers'`.:

        sage: (log(x^6)).log_expand('powers')
        6*log(x)

    The expression `log((3*x)^6)` is not expanded with `algorithm='powers'`, since it is converted into product first:

        sage: (log((3*x)^6)).log_expand('powers')
        log(729*x^6)

    This shows that the option `algorithm` from the previous call has no influence to future calls (we changed some default Maxima flag, and have to ensure that this flag has been restored):

        sage: (log(3/4*x^pi)).log_expand()
        pi*log(x) + log(3/4)

        sage: (log(3/4*x^pi)).log_expand('all')
        pi*log(x) - log(4) + log(3)

        sage: (log(3/4*x^pi)).log_expand()
        pi*log(x) + log(3/4)

    TESTS:

    Most of these log expansions only make sense over the reals. So, we should set the Maxima `domain` variable to ‘real’ before we call out to Maxima. When we return, however, we should set the `domain` back to what it was, rather than assuming that it was ‘complex’. See trac ticket #12780:

        sage: from sage.calculus.calculus import maxima
        sage: maxima('domain: real;')
        real
        sage: x.expand_log()
        x
        sage: maxima('domain;')
        real
        sage: maxima('domain: complex;')
        complex

    AUTHORS:

    - Robert Marik (11-2009)
  `expand_rational`(side=None)

    Expand this symbolic expression. Products of sums and exponentiated sums are multiplied out, numerators of rational expressions which are sums are split into their respective terms, and multiplications are distributed over addition at all levels.

    EXAMPLES:

    We expand the expression \((x-y)^5\) using both method and functional notation.

        sage: x,y = var('x,y')
        sage: a = (x-y)^5
        sage: a.expand()
        x^5 - 5*x^4*y + 10*x^3*y^2 - 10*x^2*y^3 + 5*x*y^4 - y^5
        sage: expand(a)
        x^5 - 5*x^4*y + 10*x^3*y^2 - 10*x^2*y^3 + 5*x*y^4 - y^5

    We expand some other expressions:

        sage: expand((x-1)^3/(y-1))
        x^3/(y - 1) - 3*x^2/(y - 1) + 3*x/(y - 1) - 1/(y - 1)
        sage: expand((x+sin((x+y)^2))^2)
        x^2 + 2*x*sin((x + y)^2) + sin((x + y)^2)^2

    We can expand individual sides of a relation:

        sage: a = (16*x-13)^2 == (3*x+5)^2/2
        sage: a.expand()
        256*x^2 - 416*x + 169 == 9/2*x^2 + 15*x + 25/2
        sage: a.expand('left')
        256*x^2 - 416*x + 169 == 1/2*(3*x + 5)^2
        sage: a.expand('right')
        (16*x - 13)^2 == 9/2*x^2 + 15*x + 25/2

    TESTS:

        sage: var('x,y')
        (x, y)
        sage: ((x + (2/3)*y)^3).expand()
        x^3 + 2*x^2*y + 4/3*x*y^2 + 8/27*y^3
        sage: expand( (x*sin(x) - cos(y)/x)^2 )
        x^2*sin(x)^2 - 2*cos(y)*sin(x) + cos(y)^2/x^2
        sage: f = (x-y)*(x+y); f
        (x + y)*(x - y)
        sage: f.expand()
        x^2 - y^2

  `expand_trig`(full=False, half_angles=False, plus=True, times=True)

    Expand trigonometric and hyperbolic functions of sums of angles and of multiple angles occurring in self. For best results, self should already be expanded.

    INPUT:

    - `full` - (default: False) To enhance user control of simplification, this function expands only one level at a time by default, expanding sums of angles or multiple angles. To obtain full expansion into sines and cosines immediately, set the optional parameter full to True.
    - `half_angles` - (default: False) If True, causes half-angles to be simplified away.
    - `plus` - (default: True) Controls the sum rule; expansion of sums (e.g. ‘sin(x + y)’) will take place only if plus is True.
    - `times` - (default: True) Controls the product rule, expansion of products (e.g. sin(2*x)) will take place only if times is True.

    OUTPUT:

    A symbolic expression.

    EXAMPLES:

        sage: sin(5*x).expand_trig()
        5*cos(x)^4*sin(x) - 10*cos(x)^2*sin(x)^3 + sin(x)^5
        sage: cos(2*x + var('y')).expand_trig()
        cos(2*x)*cos(y) - sin(2*x)*sin(y)

    We illustrate various options to this function:

        sage: f = sin(sin(3*cos(2*x))*x)
        sage: f.expand_trig()
        sin((3*cos(cos(2*x))^2*sin(cos(2*x)) - sin(cos(2*x))^3)*x)
        sage: f.expand_trig(full=True)
        sin((3*(cos(cos(x)^2)*cos(sin(x)^2) + sin(cos(x)^2)*sin(sin(x)^2))^2*(cos(sin(x)^2)*sin(cos(x)^2) - cos(cos(x)^2)*sin(sin(x)^2)) - (cos(sin(x)^2)*sin(cos(x)^2) - cos(cos(x)^2)*sin(sin(x)^2))^3)*x)
        sage: sin(2*x).expand_trig(times=False)
        sin(2*x)
        sage: sin(2*x).expand_trig(times=True)
        2*cos(x)*sin(x)
        sage: sin(2 + x).expand_trig(plus=False)
        sin(x + 2)
        sage: sin(2 + x).expand_trig(plus=True)
        cos(x)*sin(2) + cos(2)*sin(x)
        sage: sin(x/2).expand_trig(half_angles=False)
        sin(1/2*x)
        sage: sin(x/2).expand_trig(half_angles=True)
        (-1)^floor(1/2*x/pi)*sqrt(-1/2*cos(x) + 1/2)

    ALIASES:

    `trig_expand()` and `expand_trig()` are the same

  `factor`(dontfactor=[])

    Factor the expression, containing any number of variables or functions, into factors irreducible over the integers.

    INPUT:

    - `self` - a symbolic expression
    - `dontfactor` - list (default: []), a list of variables with respect to which factoring is not to occur. Factoring also will not take place with respect to any variables which are less important (using the variable ordering assumed for CRE form) than those on the ‘dontfactor’ list.

    EXAMPLES:

        sage: x,y,z = var('x, y, z')
        sage: (x^3-y^3).factor()
        (x^2 + x*y + y^2)*(x - y)
        sage: factor(-8*y - 4*x + z^2*(2*y + x))
        (x + 2*y)*(z + 2)*(z - 2)
        sage: f = -1 - 2*x - x^2 + y^2 + 2*x*y^2 + x^2*y^2
        sage: F = factor(f/(36*(1 + 2*y + y^2)), dontfactor=[x]); F
        1/36*(x^2 + 2*x + 1)*(y - 1)/(y + 1)

    If you are factoring a polynomial with rational coefficients (and dontfactor is empty) the factorization is done using Singular instead of Maxima, so the following is very fast instead of dreadfully slow:

        sage: var('x,y')
        (x, y)
        sage: (x^99 + y^99).factor()
        (x^60 + x^57*y^3 - x^51*y^9 - x^48*y^12 + x^42*y^18 + x^39*y^21 -
        x^33*y^27 - x^30*y^30 - x^27*y^33 + x^21*y^39 + x^18*y^42 -
        x^12*y^48 - x^9*y^51 + x^3*y^57 + y^60)*(x^20 + x^19*y -
        x^17*y^3 - x^16*y^4 + x^14*y^6 + x^13*y^7 - x^11*y^9 -
        x^10*y^10 - x^9*y^11 + x^7*y^13 + x^6*y^14 - x^4*y^16 -
        x^3*y^17 + x*y^19 + y^20)*(x^10 - x^9*y + x^8*y^2 - x^7*y^3 +
        x^6*y^4 - x^5*y^5 + x^4*y^6 - x^3*y^7 + x^2*y^8 - x*y^9 +
        y^10)*(x^6 - x^3*y^3 + y^6)*(x^2 - x*y + y^2)*(x + y)

  `factor_list`(dontfactor=[])

    Return a list of the factors of self, as computed by the factor command.

    INPUT:

    - `self` - a symbolic expression
    - `dontfactor` - see docs for `factor()`

    Note

    If you already have a factored expression and just want to get at the individual factors, use the \(_factor_list\) method instead.

    EXAMPLES:

        sage: var('x, y, z')
        (x, y, z)
        sage: f = x^3-y^3
        sage: f.factor()
        (x^2 + x*y + y^2)*(x - y)

    Notice that the -1 factor is separated out:

        sage: f.factor_list()
        [(x^2 + x*y + y^2, 1), (x - y, 1)]

    We factor a fairly straightforward expression:

        sage: factor(-8*y - 4*x + z^2*(2*y + x)).factor_list()
        [(x + 2*y, 1), (z + 2, 1), (z - 2, 1)]

    A more complicated example:

        sage: var('x, u, v')
        (x, u, v)
        sage: f = expand((2*u*v^2-v^2-4*u^3)^2 * (-u)^3 * (x-sin(x))^3)
        sage: f.factor()
        -(4*u^3 - 2*u*v^2 + v^2)^2*u^3*(x - sin(x))^3
        sage: g = f.factor_list(); g
        [(4*u^3 - 2*u*v^2 + v^2, 2), (u, 3), (x - sin(x), 3), (-1, 1)]

    This function also works for quotients:

        sage: f = -1 - 2*x - x^2 + y^2 + 2*x*y^2 + x^2*y^2
        sage: g = f/(36*(1 + 2*y + y^2)); g
        1/36*(x^2*y^2 + 2*x*y^2 - x^2 + y^2 - 2*x - 1)/(y^2 + 2*y + 1)
        sage: g.factor(dontfactor=[x])
        1/36*(x^2 + 2*x + 1)*(y - 1)/(y + 1)
        sage: g.factor_list(dontfactor=[x])
        [(x^2 + 2*x + 1, 1), (y + 1, -1), (y - 1, 1), (1/36, 1)]

    This example also illustrates that the exponents do not have to be integers:

        sage: f = x^(2*sin(x)) * (x-1)^(sqrt(2)*x); f
        (x - 1)^(sqrt(2)*x)*x^(2*sin(x))
        sage: f.factor_list()
        [(x - 1, sqrt(2)*x), (x, 2*sin(x))]

  `factorial`(hold=False)

    Return the factorial of self.

    OUTPUT:

    A symbolic expression.

    EXAMPLES:

        sage: var('x, y')
        (x, y)
        sage: SR(5).factorial()
        120
        sage: x.factorial()
        factorial(x)
        sage: (x^2+y^3).factorial()
        factorial(y^3 + x^2)

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(5).factorial(hold=True)
        factorial(5)

    This also works using functional notation:

        sage: factorial(5,hold=True)
        factorial(5)
        sage: factorial(5)
        120

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(5).factorial(hold=True); a.simplify()
        120

  `factorial_simplify`()

    Simplify by combining expressions with factorials, and by expanding binomials into factorials.

    ALIAS: factorial_simplify and simplify_factorial are the same

    EXAMPLES:

    Some examples are relatively clear:

        sage: var('n,k')
        (n, k)
        sage: f = factorial(n+1)/factorial(n); f
        factorial(n + 1)/factorial(n)
        sage: f.simplify_factorial()
        n + 1

        sage: f = factorial(n)*(n+1); f
        (n + 1)*factorial(n)
        sage: simplify(f)
        (n + 1)*factorial(n)
        sage: f.simplify_factorial()
        factorial(n + 1)

        sage: f = binomial(n, k)*factorial(k)*factorial(n-k); f
        binomial(n, k)*factorial(k)*factorial(-k + n)
        sage: f.simplify_factorial()
        factorial(n)

    A more complicated example, which needs further processing:

        sage: f = factorial(x)/factorial(x-2)/2 + factorial(x+1)/factorial(x)/2; f
        1/2*factorial(x + 1)/factorial(x) + 1/2*factorial(x)/factorial(x - 2)
        sage: g = f.simplify_factorial(); g
        1/2*(x - 1)*x + 1/2*x + 1/2
        sage: g.simplify_rational()
        1/2*x^2 + 1/2

    TESTS:

    Check that the problem with applying \(full_simplify()\) to gamma functions (trac ticket #9240) has been fixed:

        sage: gamma(1/3)
        gamma(1/3)
        sage: gamma(1/3).full_simplify()
        gamma(1/3)
        sage: gamma(4/3)
        gamma(4/3)
        sage: gamma(4/3).full_simplify()
        1/3*gamma(1/3)

  `find`(pattern)

    Find all occurrences of the given pattern in this expression.

    Note that once a subexpression matches the pattern, the search does not extend to subexpressions of it.

    EXAMPLES:

        sage: var('x,y,z,a,b')
        (x, y, z, a, b)
        sage: w0 = SR.wild(0); w1 = SR.wild(1)

        sage: (sin(x)*sin(y)).find(sin(w0))
        [sin(y), sin(x)]

        sage: ((sin(x)+sin(y))*(a+b)).expand().find(sin(w0))
        [sin(y), sin(x)]

        sage: (1+x+x^2+x^3).find(x)
        [x]
        sage: (1+x+x^2+x^3).find(x^w0)
        [x^2, x^3]

        sage: (1+x+x^2+x^3).find(y)
        []

        # subexpressions of a match are not listed
        sage: ((x^y)^z).find(w0^w1)
        [(x^y)^z]

  `find_local_maximum`(a, b, var=None, tol=1.48e-08, maxfun=500)

    Numerically find a local maximum of the expression `self` on the interval [a,b] (or [b,a]) along with the point at which the maximum is attained.

    See the documentation for `find_local_minimum()` for more details.

    EXAMPLES:

        sage: f = x*cos(x)
        sage: f.find_local_maximum(0,5)
        (0.5610963381910451, 0.8603335890...)
        sage: f.find_local_maximum(0,5, tol=0.1, maxfun=10)
        (0.561090323458081..., 0.857926501456...)

  `find_local_minimum`(a, b, var=None, tol=1.48e-08, maxfun=500)

    Numerically find a local minimum of the expression `self` on the interval [a,b] (or [b,a]) and the point at which it attains that minimum. Note that `self` must be a function of (at most) one variable.

    INPUT:

    - `var` - variable (default: first variable in self)
    - `a,b` - endpoints of interval on which to minimize self.
    - `tol` - the convergence tolerance
    - `maxfun` - maximum function evaluations

    OUTPUT:

    A tuple `(minval, x)`, where

    - `minval` – float. The minimum value that self takes on in the interval `[a,b]`.
    - `x` – float. The point at which self takes on the minimum value.

    EXAMPLES:

        sage: f = x*cos(x)
        sage: f.find_local_minimum(1, 5)
        (-3.288371395590..., 3.4256184695...)
        sage: f.find_local_minimum(1, 5, tol=1e-3)
        (-3.288371361890..., 3.4257507903...)
        sage: f.find_local_minimum(1, 5, tol=1e-2, maxfun=10)
        (-3.288370845983..., 3.4250840220...)
        sage: show(f.plot(0, 20))
        sage: f.find_local_minimum(1, 15)
        (-9.477294259479..., 9.5293344109...)

    ALGORITHM:

    Uses `sage.numerical.optimize.find_local_minimum()`.

    AUTHORS:

    - William Stein (2007-12-07)
  `find_root`(a, b, var=None, xtol=1e-12, rtol=4.5e-16, maxiter=100, full_output=False)

    Numerically find a root of self on the closed interval [a,b] (or [b,a]) if possible, where self is a function in the one variable. Note: this function only works in fixed (machine) precision, it is not possible to get arbitrary precision approximations with it.

    INPUT:

    - `a, b` - endpoints of the interval
    - `var` - optional variable
    - `xtol, rtol` - the routine converges when a root is known to lie within xtol of the value return. Should be >= 0. The routine modifies this to take into account the relative precision of doubles.
    - `maxiter` - integer; if convergence is not achieved in maxiter iterations, an error is raised. Must be >= 0.
    - `full_output` - bool (default: False), if True, also return object that contains information about convergence.

    EXAMPLES:

    Note that in this example both f(-2) and f(3) are positive, yet we still find a root in that interval:

        sage: f = x^2 - 1
        sage: f.find_root(-2, 3)
        1.0
        sage: f.find_root(-2, 3, x)
        1.0
        sage: z, result = f.find_root(-2, 3, full_output=True)
        sage: result.converged
        True
        sage: result.flag
        'converged'
        sage: result.function_calls
        11
        sage: result.iterations
        10
        sage: result.root
        1.0

    More examples:

        sage: (sin(x) + exp(x)).find_root(-10, 10)
        -0.588532743981862...
        sage: sin(x).find_root(-1,1)
        0.0
        sage: (1/tan(x)).find_root(3,3.5)
        3.1415926535...

    An example with a square root:

        sage: f = 1 + x + sqrt(x+2); f.find_root(-2,10)
        -1.618033988749895

    Some examples that Ted Kosan came up with:

        sage: t = var('t')
        sage: v = 0.004*(9600*e^(-(1200*t)) - 2400*e^(-(300*t)))
        sage: v.find_root(0, 0.002)
        0.001540327067911417...

    With this expression, we can see there is a zero very close to the origin:

        sage: a = .004*(8*e^(-(300*t)) - 8*e^(-(1200*t)))*(720000*e^(-(300*t)) - 11520000*e^(-(1200*t))) +.004*(9600*e^(-(1200*t)) - 2400*e^(-(300*t)))^2
        sage: show(plot(a, 0, .002), xmin=0, xmax=.002)

    It is easy to approximate with `find_root`:

        sage: a.find_root(0,0.002)
        0.0004110514049349...

    Using solve takes more effort, and even then gives only a solution with free (integer) variables:

        sage: a.solve(t)
        []
        sage: b = a.simplify_radical(); b
        -23040*(-2.0*e^(1800*t) + 25.0*e^(900*t) - 32.0)*e^(-2400*t)
        sage: b.solve(t)
        []
        sage: b.solve(t, to_poly_solve=True)
        [t == 1/450*I*pi*z... + 1/900*log(-3/4*sqrt(41) + 25/4),
         t == 1/450*I*pi*z... + 1/900*log(3/4*sqrt(41) + 25/4)]
        sage: n(1/900*log(-3/4*sqrt(41) + 25/4))
        0.000411051404934985

    We illustrate that root finding is only implemented in one dimension:

        sage: x, y = var('x,y')
        sage: (x-y).find_root(-2,2)
        Traceback (most recent call last):
        ...
        NotImplementedError: root finding currently only implemented in 1 dimension.

    TESTS:

    Test the special case that failed for the first attempt to fix trac ticket #3980:

        sage: t = var('t')
        sage: find_root(1/t - x,0,2)
        Traceback (most recent call last):
        ...
        NotImplementedError: root finding currently only implemented in 1 dimension.

  `forget`()

    Forget the given constraint.

    EXAMPLES:

        sage: var('x,y')
        (x, y)
        sage: forget()
        sage: assume(x>0, y < 2)
        sage: assumptions()
        [x > 0, y < 2]
        sage: forget(y < 2)
        sage: assumptions()
        [x > 0]

    TESTS:

    Check if trac ticket #7507 is fixed:

        sage: forget()
        sage: n = var('n')
        sage: foo=sin((-1)*n*pi)
        sage: foo.simplify()
        -sin(pi*n)
        sage: assume(n, 'odd')
        sage: assumptions()
        [n is odd]
        sage: foo.simplify()
        0
        sage: forget(n, 'odd')
        sage: assumptions()
        []
        sage: foo.simplify()
        -sin(pi*n)

  `full_simplify`()

    Apply simplify_factorial, simplify_trig, simplify_rational, simplify_log, and again simplify_rational to self (in that order).

    ALIAS: simplify_full and full_simplify are the same.

    EXAMPLES:

        sage: f = sin(x)^2 + cos(x)^2
        sage: f.simplify_full()
        1

        sage: f = sin(x/(x^2 + x))
        sage: f.simplify_full()
        sin(1/(x + 1))

        sage: var('n,k')
        (n, k)
        sage: f = binomial(n,k)*factorial(k)*factorial(n-k)
        sage: f.simplify_full()
        factorial(n)

    TESTS:

    There are two square roots of \($(x + 1)^2$\), so this should not be simplified to \($x + 1$\), trac ticket #12737:

        sage: f = sqrt((x + 1)^2)
        sage: f.simplify_full()
        sqrt(x^2 + 2*x + 1)

    The imaginary part of an expression should not change under simplification; trac ticket #11934:

        sage: f = sqrt(-8*(4*sqrt(2) - 7)*x^4 + 16*(3*sqrt(2) - 5)*x^3)
        sage: original = f.imag_part()
        sage: simplified = f.full_simplify().imag_part()
        sage: original - simplified
        0

    The invalid simplification from trac ticket #12322 should not occur after trac ticket #12737:

        sage: t = var('t')
        sage: assume(t, 'complex')
        sage: assumptions()
        [t is complex]
        sage: f = (1/2)*log(2*t) + (1/2)*log(1/t)
        sage: f.simplify_full()
        1/2*log(2*t) - 1/2*log(t)

  `function`(*args)

    Return a callable symbolic expression with the given variables.

    EXAMPLES:

    We will use several symbolic variables in the examples below:

        sage: var('x, y, z, t, a, w, n')
        (x, y, z, t, a, w, n)

        sage: u = sin(x) + x*cos(y)
        sage: g = u.function(x,y)
        sage: g(x,y)
        x*cos(y) + sin(x)
        sage: g(t,z)
        t*cos(z) + sin(t)
        sage: g(x^2, x^y)
        x^2*cos(x^y) + sin(x^2)

        sage: f = (x^2 + sin(a*w)).function(a,x,w); f
        (a, x, w) |--> x^2 + sin(a*w)
        sage: f(1,2,3)
        sin(3) + 4

    Using the `function()` method we can obtain the above function \(f\), but viewed as a function of different variables:

        sage: h = f.function(w,a); h
        (w, a) |--> x^2 + sin(a*w)

    This notation also works:

        sage: h(w,a) = f
        sage: h
        (w, a) |--> x^2 + sin(a*w)

    You can even make a symbolic expression \(f\) into a function by writing `f(x,y) = f`:

        sage: f = x^n + y^n; f
        x^n + y^n
        sage: f(x,y) = f
        sage: f
        (x, y) |--> x^n + y^n
        sage: f(2,3)
        3^n + 2^n

  `gamma`(hold=False)

    Return the Gamma function evaluated at self.

    EXAMPLES:

        sage: x = var('x')
        sage: x.gamma()
        gamma(x)
        sage: SR(2).gamma()
        1
        sage: SR(10).gamma()
        362880
        sage: SR(10.0r).gamma()  # For ARM: rel tol 2e-15
        362880.0
        sage: SR(CDF(1,1)).gamma()
        0.49801566811835607 - 0.15494982830181067*I

        sage: gp('gamma(1+I)')
        0.4980156681183560427136911175 - 0.1549498283018106851249551305*I # 32-bit
        0.49801566811835604271369111746219809195 - 0.15494982830181068512495513048388660520*I # 64-bit

    We plot the familiar plot of this log-convex function:

        sage: plot(gamma(x), -6,4).show(ymin=-3,ymax=3)

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(1/2).gamma()
        sqrt(pi)
        sage: SR(1/2).gamma(hold=True)
        gamma(1/2)

    This also works using functional notation:

        sage: gamma(1/2,hold=True)
        gamma(1/2)
        sage: gamma(1/2)
        sqrt(pi)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(1/2).gamma(hold=True); a.simplify()
        sqrt(pi)

  `gcd`(b)

    Return the gcd of self and b, which must be integers or polynomials over the rational numbers.

    TODO: I tried the massive gcd from http://trac.sagemath.org/sage_trac/ticket/694 on Ginac dies after about 10 seconds. Singular easily does that GCD now. Since Ginac only handles poly gcd over QQ, we should change ginac itself to use Singular.

    EXAMPLES:

        sage: var('x,y')
        (x, y)
        sage: SR(10).gcd(SR(15))
        5
        sage: (x^3 - 1).gcd(x-1)
        x - 1
        sage: (x^3 - 1).gcd(x^2+x+1)
        x^2 + x + 1
        sage: (x^3 - sage.symbolic.constants.pi).gcd(x-sage.symbolic.constants.pi)
        Traceback (most recent call last):
        ...
        ValueError: gcd: arguments must be polynomials over the rationals
        sage: gcd(x^3 - y^3, x-y)
        -x + y
        sage: gcd(x^100-y^100, x^10-y^10)
        -x^10 + y^10
        sage: gcd(expand( (x^2+17*x+3/7*y)*(x^5 - 17*y + 2/3) ), expand((x^13+17*x+3/7*y)*(x^5 - 17*y + 2/3)) )
        1/7*x^5 - 17/7*y + 2/21

  `gradient`(variables=None)

    Compute the gradient of a symbolic function.

    This function returns a vector whose components are the derivatives of the original function with respect to the arguments of the original function. Alternatively, you can specify the variables as a list.

    EXAMPLES:

        sage: x,y = var('x y')
        sage: f = x^2+y^2
        sage: f.gradient()
        (2*x, 2*y)
        sage: g(x,y) = x^2+y^2
        sage: g.gradient()
        (x, y) |--> (2*x, 2*y)
        sage: n = var('n')
        sage: f(x,y) = x^n+y^n
        sage: f.gradient()
        (x, y) |--> (n*x^(n - 1), n*y^(n - 1))
        sage: f.gradient([y,x])
        (x, y) |--> (n*y^(n - 1), n*x^(n - 1))

  `has`(pattern)

    EXAMPLES:

        sage: var('x,y,a'); w0 = SR.wild(); w1 = SR.wild()
        (x, y, a)
        sage: (x*sin(x + y + 2*a)).has(y)
        True

    Here “x+y” is not a subexpression of “x+y+2*a” (which has the subexpressions “x”, “y” and “2*a”):

        sage: (x*sin(x + y + 2*a)).has(x+y)
        False
        sage: (x*sin(x + y + 2*a)).has(x + y + w0)
        True

    The following fails because “2*(x+y)” automatically gets converted to “2*x+2*y” of which “x+y” is not a subexpression:

        sage: (x*sin(2*(x+y) + 2*a)).has(x+y)
        False

    Although x^1==x and x^0==1, neither “x” nor “1” are actually of the form “x^something”:

        sage: (x+1).has(x^w0)
        False

    Here is another possible pitfall, where the first expression matches because the term “-x” has the form “(-1)*x” in GiNaC. To check whether a polynomial contains a linear term you should use the coeff() function instead.

        sage: (4*x^2 - x + 3).has(w0*x)
        True
        sage: (4*x^2 + x + 3).has(w0*x)
        False
        sage: (4*x^2 + x + 3).has(x)
        True
        sage: (4*x^2 - x + 3).coeff(x,1)
        -1
        sage: (4*x^2 + x + 3).coeff(x,1)
        1

  `hessian`()

    Compute the hessian of a function. This returns a matrix components are the 2nd partial derivatives of the original function.

    EXAMPLES:

        sage: x,y = var('x y')
        sage: f = x^2+y^2
        sage: f.hessian()
        [2 0]
        [0 2]
        sage: g(x,y) = x^2+y^2
        sage: g.hessian()
        [(x, y) |--> 2 (x, y) |--> 0]
        [(x, y) |--> 0 (x, y) |--> 2]

  `hypergeometric_simplify`(algorithm='maxima')

    Simplify an expression containing hypergeometric functions.

    INPUT:

    - `algorithm` – (default: `'maxima'`) the algorithm to use for for simplification. Implemented are `'maxima'`, which uses Maxima’s `hgfred` function, and `'sage'`, which uses an algorithm implemented in the hypergeometric module

    ALIAS: `hypergeometric_simplify()` and `simplify_hypergeometric()` are the same

    EXAMPLES:

        sage: hypergeometric((5, 4), (4, 1, 2, 3),
        ....:                x).simplify_hypergeometric()
        1/144*x^2*hypergeometric((), (3, 4), x) +...
        1/3*x*hypergeometric((), (2, 3), x) + hypergeometric((), (1, 2), x)
        sage: (2*hypergeometric((), (), x)).simplify_hypergeometric()
        2*e^x
        sage: (nest(lambda y: hypergeometric([y], [1], x), 3, 1)
        ....:  .simplify_hypergeometric())
        laguerre(-laguerre(-e^x, x), x)
        sage: (nest(lambda y: hypergeometric([y], [1], x), 3, 1)
        ....:  .simplify_hypergeometric(algorithm='sage'))
        hypergeometric((hypergeometric((e^x,), (1,), x),), (1,), x)

  `imag`(hold=False)

    Return the imaginary part of this symbolic expression.

    EXAMPLES:

        sage: sqrt(-2).imag_part()
        sqrt(2)

    We simplify \(\ln(\exp(z))\) to \(z\). This should only be for \(-\pi<{\rm Im}(z)<=\pi\), but Maxima does not have a symbolic imaginary part function, so we cannot use `assume` to assume that first:

        sage: z = var('z')
        sage: f = log(exp(z))
        sage: f
        log(e^z)
        sage: f.simplify()
        z
        sage: forget()

    A more symbolic example:

        sage: var('a, b')
        (a, b)
        sage: f = log(a + b*I)
        sage: f.imag_part()
        arctan2(imag_part(a) + real_part(b), -imag_part(b) + real_part(a))

    Using the `hold` parameter it is possible to prevent automatic evaluation:

        sage: I.imag_part()
        1
        sage: I.imag_part(hold=True)
        imag_part(I)

    This also works using functional notation:

        sage: imag_part(I,hold=True)
        imag_part(I)
        sage: imag_part(I)
        1

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = I.imag_part(hold=True); a.simplify()
        1

    TESTS:

        sage: x = var('x')
        sage: x.imag_part()
        imag_part(x)
        sage: SR(2+3*I).imag_part()
        3
        sage: SR(CC(2,3)).imag_part()
        3.00000000000000
        sage: SR(CDF(2,3)).imag_part()
        3.0

  `imag_part`(hold=False)

    Return the imaginary part of this symbolic expression.

    EXAMPLES:

        sage: sqrt(-2).imag_part()
        sqrt(2)

    We simplify \(\ln(\exp(z))\) to \(z\). This should only be for \(-\pi<{\rm Im}(z)<=\pi\), but Maxima does not have a symbolic imaginary part function, so we cannot use `assume` to assume that first:

        sage: z = var('z')
        sage: f = log(exp(z))
        sage: f
        log(e^z)
        sage: f.simplify()
        z
        sage: forget()

    A more symbolic example:

        sage: var('a, b')
        (a, b)
        sage: f = log(a + b*I)
        sage: f.imag_part()
        arctan2(imag_part(a) + real_part(b), -imag_part(b) + real_part(a))

    Using the `hold` parameter it is possible to prevent automatic evaluation:

        sage: I.imag_part()
        1
        sage: I.imag_part(hold=True)
        imag_part(I)

    This also works using functional notation:

        sage: imag_part(I,hold=True)
        imag_part(I)
        sage: imag_part(I)
        1

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = I.imag_part(hold=True); a.simplify()
        1

    TESTS:

        sage: x = var('x')
        sage: x.imag_part()
        imag_part(x)
        sage: SR(2+3*I).imag_part()
        3
        sage: SR(CC(2,3)).imag_part()
        3.00000000000000
        sage: SR(CDF(2,3)).imag_part()
        3.0

  `integral`(*args, **kwds)

    Compute the integral of self. Please see `sage.symbolic.integration.integral.integrate()` for more details.

    EXAMPLES:

        sage: sin(x).integral(x,0,3)
        -cos(3) + 1
        sage: sin(x).integral(x)
        -cos(x)

    TESTS:

    We check that trac ticket #12438 is resolved:

        sage: f(x) = x; f
        x |--> x
        sage: integral(f, x)
        x |--> 1/2*x^2
        sage: integral(f, x, 0, 1)
        1/2

        sage: f(x, y) = x + y
        sage: f
        (x, y) |--> x + y
        sage: integral(f, y, 0, 1)
        x |--> x + 1/2
        sage: integral(f, x, 0, 1)
        y |--> y + 1/2
        sage: _(3)
        7/2
        sage: var("z")
        z
        sage: integral(f, z, 0, 2)
        (x, y) |--> 2*x + 2*y
        sage: integral(f, z)
        (x, y) |--> (x + y)*z

  `integrate`(*args, **kwds)

    Compute the integral of self. Please see `sage.symbolic.integration.integral.integrate()` for more details.

    EXAMPLES:

        sage: sin(x).integral(x,0,3)
        -cos(3) + 1
        sage: sin(x).integral(x)
        -cos(x)

    TESTS:

    We check that trac ticket #12438 is resolved:

        sage: f(x) = x; f
        x |--> x
        sage: integral(f, x)
        x |--> 1/2*x^2
        sage: integral(f, x, 0, 1)
        1/2

        sage: f(x, y) = x + y
        sage: f
        (x, y) |--> x + y
        sage: integral(f, y, 0, 1)
        x |--> x + 1/2
        sage: integral(f, x, 0, 1)
        y |--> y + 1/2
        sage: _(3)
        7/2
        sage: var("z")
        z
        sage: integral(f, z, 0, 2)
        (x, y) |--> 2*x + 2*y
        sage: integral(f, z)
        (x, y) |--> (x + y)*z

  `inverse_laplace`(t, s)

    Return inverse Laplace transform of self. See `sage.calculus.calculus.inverse_laplace`

    EXAMPLES:

        sage: var('w, m')
        (w, m)
        sage: f = (1/(w^2+10)).inverse_laplace(w, m); f
        1/10*sqrt(10)*sin(sqrt(10)*m)

  `is_constant`()

    Return True if this symbolic expression is a constant.

    This function is intended to provide an interface to query the internal representation of the expression. In this sense, the word `constant` does not reflect the mathematical properties of the expression. Expressions which have no variables may return `False`.

    EXAMPLES:

        sage: pi.is_constant()
        True
        sage: x.is_constant()
        False
        sage: SR(1).is_constant()
        False

    Note that the complex I is not a constant:

        sage: I.is_constant()
        False
        sage: I.is_numeric()
        True

  `is_infinity`()

    Return True if self is an infinite expression.

    EXAMPLES:

        sage: SR(oo).is_infinity()
        True
        sage: x.is_infinity()
        False

  `is_integer`()

    Return True if this expression is known to be an integer.

    EXAMPLES:

        sage: SR(5).is_integer()
        True

  `is_negative`()

    Return True if this expression is known to be negative.

    EXAMPLES:

        sage: SR(-5).is_negative()
        True

    Check if we can correctly deduce negativity of mul objects:

        sage: t0 = SR.symbol("t0", domain='positive')
        sage: t0.is_negative()
        False
        sage: (-t0).is_negative()
        True
        sage: (-pi).is_negative()
        True

  `is_negative_infinity`()

    Return True if self is a negative infinite expression.

    EXAMPLES:

        sage: SR(oo).is_negative_infinity()
        False
        sage: SR(-oo).is_negative_infinity()
        True
        sage: x.is_negative_infinity()
        False

  `is_numeric`()

    Return True if this expression only consists of a numeric object.

    EXAMPLES:

        sage: SR(1).is_numeric()
        True
        sage: x.is_numeric()
        False
        sage: pi.is_numeric()
        False

  `is_polynomial`(var)

    Return True if self is a polynomial in the given variable.

    EXAMPLES:

        sage: var('x,y,z')
        (x, y, z)
        sage: t = x^2 + y; t
        x^2 + y
        sage: t.is_polynomial(x)
        True
        sage: t.is_polynomial(y)
        True
        sage: t.is_polynomial(z)
        True

        sage: t = sin(x) + y; t
        y + sin(x)
        sage: t.is_polynomial(x)
        False
        sage: t.is_polynomial(y)
        True
        sage: t.is_polynomial(sin(x))
        True

    TESTS:

    Check if we can handle derivatives. trac ticket #6523:

        sage: f(x) = function('f',x)
        sage: f(x).diff(x).is_zero()
        False

    Check if trac ticket #11352 is fixed:

        sage: el = -1/2*(2*x^2 - sqrt(2*x - 1)*sqrt(2*x + 1) - 1)
        sage: el.is_polynomial(x)
        False

  `is_positive`()

    Return True if this expression is known to be positive.

    EXAMPLES:

        sage: t0 = SR.symbol("t0", domain='positive')
        sage: t0.is_positive()
        True
        sage: t0.is_negative()
        False
        sage: t0.is_real()
        True
        sage: t1 = SR.symbol("t1", domain='positive')
        sage: (t0*t1).is_positive()
        True
        sage: (t0 + t1).is_positive()
        True
        sage: (t0*x).is_positive()
        False

  `is_positive_infinity`()

    Return True if self is a positive infinite expression.

    EXAMPLES:

        sage: SR(oo).is_positive_infinity()
        True
        sage: SR(-oo).is_positive_infinity()
        False
        sage: x.is_infinity()
        False

  `is_real`()

    Return True if this expression is known to be a real number.

    EXAMPLES:

        sage: t0 = SR.symbol("t0", domain='real')
        sage: t0.is_real()
        True
        sage: t0.is_positive()
        False
        sage: t1 = SR.symbol("t1", domain='positive')
        sage: (t0+t1).is_real()
        True
        sage: (t0+x).is_real()
        False
        sage: (t0*t1).is_real()
        True
        sage: (t0*x).is_real()
        False

    The following is real, but we cannot deduce that.:

        sage: (x*x.conjugate()).is_real()
        False

  `is_relational`()

    Return True if self is a relational expression.

    EXAMPLES:

        sage: x = var('x')
        sage: eqn = (x-1)^2 == x^2 - 2*x + 3
        sage: eqn.is_relational()
        True
        sage: sin(x).is_relational()
        False

  `is_series`()

    Return True if `self` is a series.

    Series are special kinds of symbolic expressions that are constructed via the `series()` method. They usually have an `Order()` term unless the series representation is exact, see `is_terminating_series()`.

    OUTPUT:

    Boolean. Whether `self` is a series symbolic expression. Usually, this means that it was constructed by the `series()` method.

    Returns `False` if only a subexpression of the symbolic expression is a series.

    EXAMPLES:

        sage: SR(5).is_series()
        False
        sage: var('x')
        x
        sage: x.is_series()
        False
        sage: exp(x).is_series()
        False
        sage: exp(x).series(x,10).is_series()
        True

    Laurent series are series, too:

        sage: laurent_series = (cos(x)/x).series(x, 5)
        sage: laurent_series
        1*x^(-1) + (-1/2)*x + 1/24*x^3 + Order(x^5)
        sage: laurent_series.is_series()
        True

    Something only containing a series as a subexpression is not a series:

        sage: sum_expr = 1 + exp(x).series(x,5); sum_expr
        (1 + 1*x + 1/2*x^2 + 1/6*x^3 + 1/24*x^4 + Order(x^5)) + 1
        sage: sum_expr.is_series()
        False

  `is_symbol`()

    Return True if this symbolic expression consists of only a symbol, i.e., a symbolic variable.

    EXAMPLES:

        sage: x.is_symbol()
        True
        sage: var('y')
        y
        sage: y.is_symbol()
        True
        sage: (x*y).is_symbol()
        False
        sage: pi.is_symbol()
        False

        sage: ((x*y)/y).is_symbol()
        True
        sage: (x^y).is_symbol()
        False

  `is_terminating_series`()

    Return True if `self` is a series without order term.

    A series is terminating if it can be represented exactly, without requiring an order term. See also `is_series()` for general series.

    OUTPUT:

    Boolean. Whether `self` was constructed by `series()` and has no order term.

    EXAMPLES:

        sage: (x^5+x^2+1).series(x,10)
        1 + 1*x^2 + 1*x^5
        sage: (x^5+x^2+1).series(x,10).is_series()
        True
        sage: (x^5+x^2+1).series(x,10).is_terminating_series()
        True
        sage: SR(5).is_terminating_series()
        False
        sage: var('x')
        x
        sage: x.is_terminating_series()
        False
        sage: exp(x).series(x,10).is_terminating_series()
        False

  `is_trivial_zero`()

    Check if this expression is trivially equal to zero without any simplification.

    This method is intended to be used in library code where trying to obtain a mathematically correct result by applying potentially expensive rewrite rules is not desirable.

    EXAMPLES:

        sage: SR(0).is_trivial_zero()
        True
        sage: SR(0.0).is_trivial_zero()
        True
        sage: SR(float(0.0)).is_trivial_zero()
        True

        sage: (SR(1)/2^1000).is_trivial_zero()
        False
        sage: SR(1./2^10000).is_trivial_zero()
        False

    The `is_zero()` method is more capable:

        sage: t = pi + (pi - 1)*pi - pi^2
        sage: t.is_trivial_zero()
        False
        sage: t.is_zero()
        True
        sage: u = sin(x)^2 + cos(x)^2 - 1
        sage: u.is_trivial_zero()
        False
        sage: u.is_zero()
        True

  `is_unit`()

    Return True if this expression is a unit of the symbolic ring.

    EXAMPLES:

        sage: SR(1).is_unit()
        True
        sage: SR(-1).is_unit()
        True
        sage: SR(0).is_unit()
        False

  `iterator`()

    Return an iterator over the operands of this expression.

    EXAMPLES:

        sage: x,y,z = var('x,y,z')
        sage: list((x+y+z).iterator())
        [x, y, z]
        sage: list((x*y*z).iterator())
        [x, y, z]
        sage: list((x^y*z*(x+y)).iterator())
        [x + y, x^y, z]

    Note that symbols, constants and numeric objects do not have operands, so the iterator function raises an error in these cases:

        sage: x.iterator()
        Traceback (most recent call last):
        ...
        ValueError: expressions containing only a numeric coefficient, constant or symbol have no operands
        sage: pi.iterator()
        Traceback (most recent call last):
        ...
        ValueError: expressions containing only a numeric coefficient, constant or symbol have no operands
        sage: SR(5).iterator()
        Traceback (most recent call last):
        ...
        ValueError: expressions containing only a numeric coefficient, constant or symbol have no operands

  `laplace`(t, s)

    Return Laplace transform of self. See `sage.calculus.calculus.laplace`

    EXAMPLES:

        sage: var('x,s,z')
        (x, s, z)
        sage: (z + exp(x)).laplace(x, s)
        z/s + 1/(s - 1)

  `lcm`(b)

    Return the lcm of self and b, which must be integers or polynomials over the rational numbers. This is computed from the gcd of self and b implicitly from the relation self * b = gcd(self, b) * lcm(self, b).

    Note

    In agreement with the convention in use for integers, if self * b == 0, then gcd(self, b) == max(self, b) and lcm(self, b) == 0.

    EXAMPLES:

        sage: var('x,y')
        (x, y)
        sage: SR(10).lcm(SR(15))
        30
        sage: (x^3 - 1).lcm(x-1)
        x^3 - 1
        sage: (x^3 - 1).lcm(x^2+x+1)
        x^3 - 1
        sage: (x^3 - sage.symbolic.constants.pi).lcm(x-sage.symbolic.constants.pi)
        Traceback (most recent call last):
        ...
        ValueError: lcm: arguments must be polynomials over the rationals
        sage: lcm(x^3 - y^3, x-y)
        -x^3 + y^3
        sage: lcm(x^100-y^100, x^10-y^10)
        -x^100 + y^100
        sage: lcm(expand( (x^2+17*x+3/7*y)*(x^5 - 17*y + 2/3) ), expand((x^13+17*x+3/7*y)*(x^5 - 17*y + 2/3)) )
         1/21*(21*x^18 - 357*x^13*y + 14*x^13 + 357*x^6 + 9*x^5*y -
                 6069*x*y - 153*y^2 + 238*x + 6*y)*(21*x^7 + 357*x^6 +
                         9*x^5*y - 357*x^2*y + 14*x^2 - 6069*x*y -
                         153*y^2 + 238*x + 6*y)/(3*x^5 - 51*y + 2)

    TESTS:

    Verify that x * y = gcd(x,y) * lcm(x,y):

        sage: x, y = var('x,y')
        sage: LRs = [(SR(10), SR(15)), (x^3-1, x-1), (x^3-y^3, x-y), (x^3-1, x^2+x+1), (SR(0), x-y)]
        sage: all((L.gcd(R) * L.lcm(R)) == L*R for L, R in LRs)
        True

    Make sure that the convention for what to do with the 0 is being respected:

        sage: gcd(x, SR(0)), lcm(x, SR(0))
        (x, 0)
        sage: gcd(SR(0), SR(0)), lcm(SR(0), SR(0))
        (0, 0)

  `leading_coeff`(s)

    Return the leading coefficient of s in self.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)
        sage: f = 100 + a*x + x^3*sin(x*y) + x*y + x/y + 2*sin(x*y)/x; f
        x^3*sin(x*y) + a*x + x*y + x/y + 2*sin(x*y)/x + 100
        sage: f.leading_coefficient(x)
        sin(x*y)
        sage: f.leading_coefficient(y)
        x
        sage: f.leading_coefficient(sin(x*y))
        x^3 + 2/x

  `leading_coefficient`(s)

    Return the leading coefficient of s in self.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)
        sage: f = 100 + a*x + x^3*sin(x*y) + x*y + x/y + 2*sin(x*y)/x; f
        x^3*sin(x*y) + a*x + x*y + x/y + 2*sin(x*y)/x + 100
        sage: f.leading_coefficient(x)
        sin(x*y)
        sage: f.leading_coefficient(y)
        x
        sage: f.leading_coefficient(sin(x*y))
        x^3 + 2/x

  `left`()

    If self is a relational expression, return the left hand side of the relation. Otherwise, raise a ValueError.

    EXAMPLES:

        sage: x = var('x')
        sage: eqn = (x-1)^2 == x^2 - 2*x + 3
        sage: eqn.left_hand_side()
        (x - 1)^2
        sage: eqn.lhs()
        (x - 1)^2
        sage: eqn.left()
        (x - 1)^2

  `left_hand_side`()

    If self is a relational expression, return the left hand side of the relation. Otherwise, raise a ValueError.

    EXAMPLES:

        sage: x = var('x')
        sage: eqn = (x-1)^2 == x^2 - 2*x + 3
        sage: eqn.left_hand_side()
        (x - 1)^2
        sage: eqn.lhs()
        (x - 1)^2
        sage: eqn.left()
        (x - 1)^2

  `lgamma`(hold=False)

    This method is deprecated, please use the `.log_gamma()` function instead.

    Log gamma function evaluated at self.

    EXAMPLES:

        sage: x.lgamma()
        doctest:...: DeprecationWarning: The lgamma() function is deprecated. Use log_gamma() instead.
        See http://trac.sagemath.org/6992 for details.
        log_gamma(x)

  `lhs`()

    If self is a relational expression, return the left hand side of the relation. Otherwise, raise a ValueError.

    EXAMPLES:

        sage: x = var('x')
        sage: eqn = (x-1)^2 == x^2 - 2*x + 3
        sage: eqn.left_hand_side()
        (x - 1)^2
        sage: eqn.lhs()
        (x - 1)^2
        sage: eqn.left()
        (x - 1)^2

  `limit`(*args, **kwds)

    Return a symbolic limit. See `sage.calculus.calculus.limit`

    EXAMPLES:

        sage: (sin(x)/x).limit(x=0)
        1

  `log`(b=None, hold=False)

    Return the logarithm of self.

    EXAMPLES:

        sage: x, y = var('x, y')
        sage: x.log()
        log(x)
        sage: (x^y + y^x).log()
        log(x^y + y^x)
        sage: SR(0).log()
        -Infinity
        sage: SR(-1).log()
        I*pi
        sage: SR(1).log()
        0
        sage: SR(1/2).log()
        log(1/2)
        sage: SR(0.5).log()
        -0.693147180559945
        sage: SR(0.5).log().exp()
        0.500000000000000
        sage: math.log(0.5)
        -0.6931471805599453
        sage: plot(lambda x: SR(x).log(), 0.1,10)
        Graphics object consisting of 1 graphics primitive

    To prevent automatic evaluation use the `hold` argument:

        sage: I.log()
        1/2*I*pi
        sage: I.log(hold=True)
        log(I)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = I.log(hold=True); a.simplify()
        1/2*I*pi

    The `hold` parameter also works in functional notation:

        sage: log(-1,hold=True)
        log(-1)
        sage: log(-1)
        I*pi

    TESTS:

        sage: SR(oo).log()
        +Infinity
        sage: SR(-oo).log()
        +Infinity
        sage: SR(unsigned_infinity).log()
        +Infinity

  `log_expand`(algorithm='products')

    Simplify symbolic expression, which can contain logs.

    Expands logarithms of powers, logarithms of products and logarithms of quotients. The option `algorithm` specifies which expression types should be expanded.

    INPUT:

    - `self` - expression to be simplified

    - `algorithm` - (default: ‘products’) optional, governs which expression is expanded. Possible values are

      - ‘nothing’ (no expansion),
      - ‘powers’ (log(a^r) is expanded),
      - ‘products’ (like ‘powers’ and also log(a*b) are expanded),
      - ‘all’ (all possible expansion).

      See also examples below.

    DETAILS: This uses the Maxima simplifier and sets `logexpand` option for this simplifier. From the Maxima documentation: “Logexpand:true causes log(a^b) to become b*log(a). If it is set to all, log(a*b) will also simplify to log(a)+log(b). If it is set to super, then log(a/b) will also simplify to log(a)-log(b) for rational numbers a/b, a#1. (log(1/b), for integer b, always simplifies.) If it is set to false, all of these simplifications will be turned off. “

    ALIAS: `log_expand()` and `expand_log()` are the same

    EXAMPLES:

    By default powers and products (and quotients) are expanded, but not quotients of integers:

        sage: (log(3/4*x^pi)).log_expand()
        pi*log(x) + log(3/4)

    To expand also log(3/4) use `algorithm='all'`:

        sage: (log(3/4*x^pi)).log_expand('all')
        pi*log(x) - log(4) + log(3)

    To expand only the power use `algorithm='powers'`.:

        sage: (log(x^6)).log_expand('powers')
        6*log(x)

    The expression `log((3*x)^6)` is not expanded with `algorithm='powers'`, since it is converted into product first:

        sage: (log((3*x)^6)).log_expand('powers')
        log(729*x^6)

    This shows that the option `algorithm` from the previous call has no influence to future calls (we changed some default Maxima flag, and have to ensure that this flag has been restored):

        sage: (log(3/4*x^pi)).log_expand()
        pi*log(x) + log(3/4)

        sage: (log(3/4*x^pi)).log_expand('all')
        pi*log(x) - log(4) + log(3)

        sage: (log(3/4*x^pi)).log_expand()
        pi*log(x) + log(3/4)

    TESTS:

    Most of these log expansions only make sense over the reals. So, we should set the Maxima `domain` variable to ‘real’ before we call out to Maxima. When we return, however, we should set the `domain` back to what it was, rather than assuming that it was ‘complex’. See trac ticket #12780:

        sage: from sage.calculus.calculus import maxima
        sage: maxima('domain: real;')
        real
        sage: x.expand_log()
        x
        sage: maxima('domain;')
        real
        sage: maxima('domain: complex;')
        complex

    AUTHORS:

    - Robert Marik (11-2009)
  `log_gamma`(hold=False)

    Return the log gamma function evaluated at self. This is the logarithm of gamma of self, where gamma is a complex function such that \(gamma(n)\) equals \(factorial(n-1)\).

    EXAMPLES:

        sage: x = var('x')
        sage: x.log_gamma()
        log_gamma(x)
        sage: SR(2).log_gamma()
        0
        sage: SR(5).log_gamma()
        log(24)
        sage: a = SR(5).log_gamma(); a.n()
        3.17805383034795
        sage: SR(5-1).factorial().log()
        log(24)
        sage: set_verbose(-1); plot(lambda x: SR(x).log_gamma(), -7,8, plot_points=1000).show()
        sage: math.exp(0.5)
        1.6487212707001282
        sage: plot(lambda x: (SR(x).exp() - SR(-x).exp())/2 - SR(x).sinh(), -1, 1)
        Graphics object consisting of 1 graphics primitive

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(5).log_gamma(hold=True)
        log_gamma(5)

    To evaluate again, currently we must use numerical evaluation via `n()`:

        sage: a = SR(5).log_gamma(hold=True); a.n()
        3.17805383034795

  `log_simplify`(algorithm=None)

    Simplify symbolic expression, which can contain logs.

    Recursively scans the expression self, transforming subexpressions of the form \(a1 \log(b1) + a2 \log(b2) + c\) into \(\log( b1^{a1} b2^{a2} ) + c\) and simplifies inside logarithm. User can specify, which conditions must satisfy \(a1\) and \(a2\) to use this transformation in optional parameter `algorithm`.

    INPUT:

    - `self` - expression to be simplified

    - `algorithm` - (default: None) optional, governs the condition on \(a1\) and \(a2\) which must be satisfied to contract expression \(a1 \log(b1) + a2 \log(b2)\). Values are

      - None (use Maxima default, integers),
      - ‘one’ (1 and -1),
      - ‘ratios’ (integers and fractions of integers),
      - ‘constants’ (constants),
      - ‘all’ (all expressions).

      See also examples below.

    DETAILS: This uses the Maxima logcontract() command. From the Maxima documentation: “Recursively scans the expression expr, transforming subexpressions of the form a1*log(b1) + a2*log(b2) + c into log(ratsimp(b1^a1 * b2^a2)) + c. The user can control which coefficients are contracted by setting the option logconcoeffp to the name of a predicate function of one argument. E.g. if you like to generate SQRTs, you can do logconcoeffp:’logconfun` logconfun(m):=featurep(m,integer) or ratnump(m)` . Then logcontract(1/2*log(x)); will give log(sqrt(x)).”

    ALIAS: `log_simplify()` and `simplify_log()` are the same

    EXAMPLES:

        sage: x,y,t=var('x y t')

    Only two first terms are contracted in the following example , the logarithm with coefficient 1/2 is not contracted:

        sage: f = log(x)+2*log(y)+1/2*log(t)
        sage: f.simplify_log()
        log(x*y^2) + 1/2*log(t)

    To contract all terms in previous example use option `algorithm`:

        sage: f.simplify_log(algorithm='ratios')
        log(sqrt(t)*x*y^2)

    This shows that the option `algorithm` from the previous call has no influence to future calls (we changed some default Maxima flag, and have to ensure that this flag has been restored):

        sage: f.simplify_log('one')
        1/2*log(t) + log(x) + 2*log(y)

        sage: f.simplify_log('ratios')
        log(sqrt(t)*x*y^2)

        sage: f.simplify_log()
        log(x*y^2) + 1/2*log(t)

    To contract terms with no coefficient (more precisely, with coefficients 1 and -1) use option `algorithm`:

        sage: f = log(x)+2*log(y)-log(t)
        sage: f.simplify_log('one')
        2*log(y) + log(x/t)

        sage: f = log(x)+log(y)-1/3*log((x+1))
        sage: f.simplify_log()
        log(x*y) - 1/3*log(x + 1)

        sage: f.simplify_log('ratios')
        log(x*y/(x + 1)^(1/3))

    \(\pi\) is irrational number, to contract logarithms in the following example we have to put `algorithm` to `constants` or `all`:

        sage: f = log(x)+log(y)-pi*log((x+1))
        sage: f.simplify_log('constants')
        log(x*y/(x + 1)^pi)

    x*log(9) is contracted only if `algorithm` is `all`:

        sage: (x*log(9)).simplify_log()
        x*log(9)
        sage: (x*log(9)).simplify_log('all')
        log(9^x)

    TESTS:

    This shows that the issue at trac ticket #7334 is fixed. Maxima intentionally keeps the expression inside the log factored:

        sage: log_expr = (log(sqrt(2)-1)+log(sqrt(2)+1))
        sage: log_expr.simplify_log('all')
        log((sqrt(2) + 1)*(sqrt(2) - 1))
        sage: _.simplify_rational()
        0
        sage: log_expr.simplify_full()   # applies both simplify_log and simplify_rational
        0

    We should use the current simplification domain rather than set it to ‘real’ explicitly (trac ticket #12780):

        sage: f = sqrt(x^2)
        sage: f.simplify_log()
        sqrt(x^2)
        sage: from sage.calculus.calculus import maxima
        sage: maxima('domain: real;')
        real
        sage: f.simplify_log()
        abs(x)
        sage: maxima('domain: complex;')
        complex

    AUTHORS:

    - Robert Marik (11-2009)
  `low_degree`(s)

    Return the exponent of the lowest nonpositive power of s in self.

    OUTPUT:

    An integer `<= 0`.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)
        sage: f = 100 + a*x + x^3*sin(x*y) + x*y + x/y^10 + 2*sin(x*y)/x; f
        x^3*sin(x*y) + a*x + x*y + 2*sin(x*y)/x + x/y^10 + 100
        sage: f.low_degree(x)
        -1
        sage: f.low_degree(y)
        -10
        sage: f.low_degree(sin(x*y))
        0
        sage: (x^3+y).low_degree(x)
        0

  `match`(pattern)

    Check if self matches the given pattern.

    INPUT:

    - `pattern` – a symbolic expression, possibly containing wildcards to match for

    OUTPUT:

    One of

    `None` if there is no match, or a dictionary mapping the wildcards to the matching values if a match was found. Note that the dictionary is empty if there were no wildcards in the given pattern.

    See also http://www.ginac.de/tutorial/Pattern-matching-and-advanced-substitutions.html

    EXAMPLES:

        sage: var('x,y,z,a,b,c,d,f,g')
        (x, y, z, a, b, c, d, f, g)
        sage: w0 = SR.wild(0); w1 = SR.wild(1); w2 = SR.wild(2)
        sage: ((x+y)^a).match((x+y)^a)  # no wildcards, so empty dict
        {}
        sage: print ((x+y)^a).match((x+y)^b)
        None
        sage: t = ((x+y)^a).match(w0^w1)
        sage: t[w0], t[w1]
        (x + y, a)
        sage: print ((x+y)^a).match(w0^w0)
        None
        sage: ((x+y)^(x+y)).match(w0^w0)
        {$0: x + y}
        sage: t = ((a+b)*(a+c)).match((a+w0)*(a+w1))
        sage: t[w0], t[w1]
        (c, b)
        sage: ((a+b)*(a+c)).match((w0+b)*(w0+c))
        {$0: a}
        sage: t = ((a+b)*(a+c)).match((w0+w1)*(w0+w2))
        sage: t[w0], t[w1], t[w2]
        (a, c, b)
        sage: print ((a+b)*(a+c)).match((w0+w1)*(w1+w2))
        None
        sage: t = (a*(x+y)+a*z+b).match(a*w0+w1)
        sage: t[w0], t[w1]
        (x + y, a*z + b)
        sage: print (a+b+c+d+f+g).match(c)
        None
        sage: (a+b+c+d+f+g).has(c)
        True
        sage: (a+b+c+d+f+g).match(c+w0)
        {$0: a + b + d + f + g}
        sage: (a+b+c+d+f+g).match(c+g+w0)
        {$0: a + b + d + f}
        sage: (a+b).match(a+b+w0)
        {$0: 0}
        sage: print (a*b^2).match(a^w0*b^w1)
        None
        sage: (a*b^2).match(a*b^w1)
        {$1: 2}
        sage: (x*x.arctan2(x^2)).match(w0*w0.arctan2(w0^2))
        {$0: x}

    Beware that behind-the-scenes simplification can lead to surprising results in matching:

        sage: print (x+x).match(w0+w1)
        None
        sage: t = x+x; t
        2*x
        sage: t.operator()
        <built-in function mul>

    Since asking to match w0+w1 looks for an addition operator, there is no match.

  `maxima_methods`()

    Provide easy access to maxima methods, converting the result to a Sage expression automatically.

    EXAMPLES:

        sage: t = log(sqrt(2) - 1) + log(sqrt(2) + 1); t
        log(sqrt(2) + 1) + log(sqrt(2) - 1)
        sage: res = t.maxima_methods().logcontract(); res
        log((sqrt(2) + 1)*(sqrt(2) - 1))
        sage: type(res)
        <type 'sage.symbolic.expression.Expression'>

  `minpoly`(*args, **kwds)

    Return the minimal polynomial of this symbolic expression.

    EXAMPLES:

        sage: golden_ratio.minpoly()
        x^2 - x - 1

  `mul`(hold=False, *args)

    Return the product of the current expression and the given arguments.

    To prevent automatic evaluation use the `hold` argument.

    EXAMPLES:

        sage: x.mul(x)
        x^2
        sage: x.mul(x, hold=True)
        x*x
        sage: x.mul(x, (2+x), hold=True)
        (x + 2)*x*x
        sage: x.mul(x, (2+x), x, hold=True)
        (x + 2)*x*x*x
        sage: x.mul(x, (2+x), x, 2*x, hold=True)
        (2*x)*(x + 2)*x*x*x

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = x.mul(x, hold=True); a.simplify()
        x^2

  `multiply_both_sides`(x, checksign=None)

    Return a relation obtained by multiplying both sides of this relation by x.

    Note

    The checksign keyword argument is currently ignored and is included for backward compatibility reasons only.

    EXAMPLES:

        sage: var('x,y'); f = x + 3 < y - 2
        (x, y)
        sage: f.multiply_both_sides(7)
        7*x + 21 < 7*y - 14
        sage: f.multiply_both_sides(-1/2)
        -1/2*x - 3/2 < -1/2*y + 1
        sage: f*(-2/3)
        -2/3*x - 2 < -2/3*y + 4/3
        sage: f*(-pi)
        -pi*(x + 3) < -pi*(y - 2)

    Since the direction of the inequality never changes when doing arithmetic with equations, you can multiply or divide the equation by a quantity with unknown sign:

        sage: f*(1+I)
        (I + 1)*x + 3*I + 3 < (I + 1)*y - 2*I - 2
        sage: f = sqrt(2) + x == y^3
        sage: f.multiply_both_sides(I)
        I*x + I*sqrt(2) == I*y^3
        sage: f.multiply_both_sides(-1)
        -x - sqrt(2) == -y^3

    Note that the direction of the following inequalities is not reversed:

        sage: (x^3 + 1 > 2*sqrt(3)) * (-1)
        -x^3 - 1 > -2*sqrt(3)
        sage: (x^3 + 1 >= 2*sqrt(3)) * (-1)
        -x^3 - 1 >= -2*sqrt(3)
        sage: (x^3 + 1 <= 2*sqrt(3)) * (-1)
        -x^3 - 1 <= -2*sqrt(3)

  `n`(prec=None, digits=None, algorithm=None)

    Return a numerical approximation this symbolic expression as either a real or complex number with at least the requested number of bits or digits of precision.

    EXAMPLES:

        sage: sin(x).subs(x=5).n()
        -0.958924274663138
        sage: sin(x).subs(x=5).n(100)
        -0.95892427466313846889315440616
        sage: sin(x).subs(x=5).n(digits=50)
        -0.95892427466313846889315440615599397335246154396460
        sage: zeta(x).subs(x=2).numerical_approx(digits=50)
        1.6449340668482264364724151666460251892189499012068

        sage: cos(3).numerical_approx(200)
        -0.98999249660044545727157279473126130239367909661558832881409
        sage: numerical_approx(cos(3),200)
        -0.98999249660044545727157279473126130239367909661558832881409
        sage: numerical_approx(cos(3), digits=10)
        -0.9899924966
        sage: (i + 1).numerical_approx(32)
        1.00000000 + 1.00000000*I
        sage: (pi + e + sqrt(2)).numerical_approx(100)
        7.2740880444219335226246195788

    TESTS:

    We test the evaluation of different infinities available in Pynac:

        sage: t = x - oo; t
        -Infinity
        sage: t.n()
        -infinity
        sage: t = x + oo; t
        +Infinity
        sage: t.n()
        +infinity
        sage: t = x - unsigned_infinity; t
        Infinity
        sage: t.n()
        Traceback (most recent call last):
        ...
        ValueError: can only convert signed infinity to RR

    Some expressions cannot be evaluated numerically:

        sage: n(sin(x))
        Traceback (most recent call last):
        ...
        TypeError: cannot evaluate symbolic expression numerically
        sage: a = var('a')
        sage: (x^2 + 2*x + 2).subs(x=a).n()
        Traceback (most recent call last):
        ...
        TypeError: cannot evaluate symbolic expression numerically

    Make sure we’ve rounded up log(10,2) enough to guarantee sufficient precision (trac ticket #10164):

        sage: ks = 4*10**5, 10**6
        sage: all(len(str(e.n(digits=k)))-1 >= k for k in ks)
        True

  `negation`()

    Return the negated version of self, that is the relation that is False iff self is True.

    EXAMPLES:

        sage: (x < 5).negation()
        x >= 5
        sage: (x == sin(3)).negation()
        x != sin(3)
        sage: (2*x >= sqrt(2)).negation()
        2*x < sqrt(2)

  `nintegral`(*args, **kwds)

    Compute the numerical integral of self. Please see `sage.calculus.calculus.nintegral` for more details.

    EXAMPLES:

        sage: sin(x).nintegral(x,0,3)
        (1.989992496600..., 2.209335488557...e-14, 21, 0)

  `nintegrate`(*args, **kwds)

    Compute the numerical integral of self. Please see `sage.calculus.calculus.nintegral` for more details.

    EXAMPLES:

        sage: sin(x).nintegral(x,0,3)
        (1.989992496600..., 2.209335488557...e-14, 21, 0)

  `nops`()

    Return the number of arguments of this expression.

    EXAMPLES:

        sage: var('a,b,c,x,y')
        (a, b, c, x, y)
        sage: a.number_of_operands()
        0
        sage: (a^2 + b^2 + (x+y)^2).number_of_operands()
        3
        sage: (a^2).number_of_operands()
        2
        sage: (a*b^2*c).number_of_operands()
        3

  `norm`()

    Return the complex norm of this symbolic expression, i.e., the expression times its complex conjugate. If \(c = a + bi\) is a complex number, then the norm of \(c\) is defined as the product of \(c\) and its complex conjugate

    \[\text{norm}(c) = \text{norm}(a + bi) = c \cdot \overline{c} = a^2 + b^2.\]

    The norm of a complex number is different from its absolute value. The absolute value of a complex number is defined to be the square root of its norm. A typical use of the complex norm is in the integral domain \(\ZZ[i]\) of Gaussian integers, where the norm of each Gaussian integer \(c = a + bi\) is defined as its complex norm.

    See also

    - `sage.misc.functional.norm()`

    EXAMPLES:

        sage: a = 1 + 2*I
        sage: a.norm()
        5
        sage: a = sqrt(2) + 3^(1/3)*I; a
        sqrt(2) + I*3^(1/3)
        sage: a.norm()
        3^(2/3) + 2
        sage: CDF(a).norm()
        4.080083823051...
        sage: CDF(a.norm())
        4.080083823051904

  `normalize`()

    Return this expression normalized as a fraction

    EXAMPLES:

        sage: var('x, y, a, b, c')
        (x, y, a, b, c)
        sage: g = x + y/(x + 2)
        sage: g.normalize()
        (x^2 + 2*x + y)/(x + 2)

        sage: f = x*(x-1)/(x^2 - 7) + y^2/(x^2-7) + 1/(x+1) + b/a + c/a
        sage: f.normalize()
        (a*x^3 + b*x^3 + c*x^3 + a*x*y^2 + a*x^2 + b*x^2 + c*x^2 +
                a*y^2 - a*x - 7*b*x - 7*c*x - 7*a - 7*b - 7*c)/((x^2 -
                    7)*a*(x + 1))

    ALGORITHM: Uses GiNaC.

  `number_of_arguments`()

    EXAMPLES:

        sage: x,y = var('x,y')
        sage: f = x + y
        sage: f.number_of_arguments()
        2

        sage: g = f.function(x)
        sage: g.number_of_arguments()
        1

        sage: x,y,z = var('x,y,z')
        sage: (x+y).number_of_arguments()
        2
        sage: (x+1).number_of_arguments()
        1
        sage: (sin(x)+1).number_of_arguments()
        1
        sage: (sin(z)+x+y).number_of_arguments()
        3
        sage: (sin(x+y)).number_of_arguments()
        2

        sage: ( 2^(8/9) - 2^(1/9) )(x-1)
        Traceback (most recent call last):
        ...
        ValueError: the number of arguments must be less than or equal to 0

  `number_of_operands`()

    Return the number of arguments of this expression.

    EXAMPLES:

        sage: var('a,b,c,x,y')
        (a, b, c, x, y)
        sage: a.number_of_operands()
        0
        sage: (a^2 + b^2 + (x+y)^2).number_of_operands()
        3
        sage: (a^2).number_of_operands()
        2
        sage: (a*b^2*c).number_of_operands()
        3

  `numerator`(normalize=True)

    Return the numerator of this symbolic expression

    INPUT:

    - `normalize` – (default: `True`) a boolean.

    If `normalize` is `True`, the expression is first normalized to have it as a fraction before getting the numerator.

    If `normalize` is `False`, the expression is kept and if it is not a quotient, then this will return the expression itself.

    See also

    `normalize()`, `denominator()`, `numerator_denominator()`, `combine()`

    EXAMPLES:

        sage: a, x, y = var('a,x,y')
        sage: f = x*(x-a)/((x^2 - y)*(x-a)); f
        x/(x^2 - y)
        sage: f.numerator()
        x
        sage: f.denominator()
        x^2 - y
        sage: f.numerator(normalize=False)
        x
        sage: f.denominator(normalize=False)
        x^2 - y

        sage: y = var('y')
        sage: g = x + y/(x + 2); g
        x + y/(x + 2)
        sage: g.numerator()
        x^2 + 2*x + y
        sage: g.denominator()
        x + 2
        sage: g.numerator(normalize=False)
        x + y/(x + 2)
        sage: g.denominator(normalize=False)
        1

    TESTS:

        sage: ((x+y)^2/(x-y)^3*x^3).numerator(normalize=False)
        (x + y)^2*x^3
        sage: ((x+y)^2*x^3).numerator(normalize=False)
        (x + y)^2*x^3
        sage: (y/x^3).numerator(normalize=False)
        y
        sage: t = y/x^3/(x+y)^(1/2); t
        y/(sqrt(x + y)*x^3)
        sage: t.numerator(normalize=False)
        y
        sage: (1/x^3).numerator(normalize=False)
        1
        sage: (x^3).numerator(normalize=False)
        x^3
        sage: (y*x^sin(x)).numerator(normalize=False)
        Traceback (most recent call last):
        ...
        TypeError: self is not a rational expression

  `numerator_denominator`(normalize=True)

    Return the numerator and the denominator of this symbolic expression

    INPUT:

    - `normalize` – (default: `True`) a boolean.

    If `normalize` is `True`, the expression is first normalized to have it as a fraction before getting the numerator and denominator.

    If `normalize` is `False`, the expression is kept and if it is not a quotient, then this will return the expression itself together with 1.

    See also

    `normalize()`, `numerator()`, `denominator()`, `combine()`

    EXAMPLE:

        sage: x, y, a = var("x y a")
        sage: ((x+y)^2/(x-y)^3*x^3).numerator_denominator()
        ((x + y)^2*x^3, (x - y)^3)

        sage: ((x+y)^2/(x-y)^3*x^3).numerator_denominator(False)
        ((x + y)^2*x^3, (x - y)^3)

        sage: g = x + y/(x + 2)
        sage: g.numerator_denominator()
        (x^2 + 2*x + y, x + 2)
        sage: g.numerator_denominator(normalize=False)
        (x + y/(x + 2), 1)

        sage: g = x^2*(x + 2)
        sage: g.numerator_denominator()
        ((x + 2)*x^2, 1)
        sage: g.numerator_denominator(normalize=False)
        ((x + 2)*x^2, 1)

    TESTS:

        sage: ((x+y)^2/(x-y)^3*x^3).numerator_denominator(normalize=False)
        ((x + y)^2*x^3, (x - y)^3)
        sage: ((x+y)^2*x^3).numerator_denominator(normalize=False)
        ((x + y)^2*x^3, 1)
        sage: (y/x^3).numerator_denominator(normalize=False)
        (y, x^3)
        sage: t = y/x^3/(x+y)^(1/2); t
        y/(sqrt(x + y)*x^3)
        sage: t.numerator_denominator(normalize=False)
        (y, sqrt(x + y)*x^3)
        sage: (1/x^3).numerator_denominator(normalize=False)
        (1, x^3)
        sage: (x^3).numerator_denominator(normalize=False)
        (x^3, 1)
        sage: (y*x^sin(x)).numerator_denominator(normalize=False)
        Traceback (most recent call last):
        ...
        TypeError: self is not a rational expression

  `numerical_approx`(prec=None, digits=None, algorithm=None)

    Return a numerical approximation this symbolic expression as either a real or complex number with at least the requested number of bits or digits of precision.

    EXAMPLES:

        sage: sin(x).subs(x=5).n()
        -0.958924274663138
        sage: sin(x).subs(x=5).n(100)
        -0.95892427466313846889315440616
        sage: sin(x).subs(x=5).n(digits=50)
        -0.95892427466313846889315440615599397335246154396460
        sage: zeta(x).subs(x=2).numerical_approx(digits=50)
        1.6449340668482264364724151666460251892189499012068

        sage: cos(3).numerical_approx(200)
        -0.98999249660044545727157279473126130239367909661558832881409
        sage: numerical_approx(cos(3),200)
        -0.98999249660044545727157279473126130239367909661558832881409
        sage: numerical_approx(cos(3), digits=10)
        -0.9899924966
        sage: (i + 1).numerical_approx(32)
        1.00000000 + 1.00000000*I
        sage: (pi + e + sqrt(2)).numerical_approx(100)
        7.2740880444219335226246195788

    TESTS:

    We test the evaluation of different infinities available in Pynac:

        sage: t = x - oo; t
        -Infinity
        sage: t.n()
        -infinity
        sage: t = x + oo; t
        +Infinity
        sage: t.n()
        +infinity
        sage: t = x - unsigned_infinity; t
        Infinity
        sage: t.n()
        Traceback (most recent call last):
        ...
        ValueError: can only convert signed infinity to RR

    Some expressions cannot be evaluated numerically:

        sage: n(sin(x))
        Traceback (most recent call last):
        ...
        TypeError: cannot evaluate symbolic expression numerically
        sage: a = var('a')
        sage: (x^2 + 2*x + 2).subs(x=a).n()
        Traceback (most recent call last):
        ...
        TypeError: cannot evaluate symbolic expression numerically

    Make sure we’ve rounded up log(10,2) enough to guarantee sufficient precision (trac ticket #10164):

        sage: ks = 4*10**5, 10**6
        sage: all(len(str(e.n(digits=k)))-1 >= k for k in ks)
        True

  `operands`()

    Return a list containing the operands of this expression.

    EXAMPLES:

        sage: var('a,b,c,x,y')
        (a, b, c, x, y)
        sage: (a^2 + b^2 + (x+y)^2).operands()
        [a^2, b^2, (x + y)^2]
        sage: (a^2).operands()
        [a, 2]
        sage: (a*b^2*c).operands()
        [a, b^2, c]

  `operator`()

    Return the topmost operator in this expression.

    EXAMPLES:

        sage: x,y,z = var('x,y,z')
        sage: (x+y).operator()
        <built-in function add>
        sage: (x^y).operator()
        <built-in function pow>
        sage: (x^y * z).operator()
        <built-in function mul>
        sage: (x < y).operator()
        <built-in function lt>

        sage: abs(x).operator()
        abs
        sage: r = gamma(x).operator(); type(r)
        <class 'sage.functions.other.Function_gamma'>

        sage: psi = function('psi', nargs=1)
        sage: psi(x).operator()
        psi

        sage: r = psi(x).operator()
        sage: r == psi
        True

        sage: f = function('f', nargs=1, conjugate_func=lambda self, x: 2*x)
        sage: nf = f(x).operator()
        sage: nf(x).conjugate()
        2*x

        sage: f = function('f')
        sage: a = f(x).diff(x); a
        D[0](f)(x)
        sage: a.operator()
        D[0](f)

    TESTS:

        sage: (x <= y).operator()
        <built-in function le>
        sage: (x == y).operator()
        <built-in function eq>
        sage: (x != y).operator()
        <built-in function ne>
        sage: (x > y).operator()
        <built-in function gt>
        sage: (x >= y).operator()
        <built-in function ge>
        sage: SR._force_pyobject( (x, x + 1, x + 2) ).operator()
        <type 'tuple'>

  `partial_fraction`(var=None)

    Return the partial fraction expansion of `self` with respect to the given variable.

    INPUT:

    - `var` - variable name or string (default: first variable)

    OUTPUT:

    A symbolic expression.

    EXAMPLES:

        sage: f = x^2/(x+1)^3
        sage: f.partial_fraction()
        1/(x + 1) - 2/(x + 1)^2 + 1/(x + 1)^3
        sage: f.partial_fraction()
        1/(x + 1) - 2/(x + 1)^2 + 1/(x + 1)^3

    Notice that the first variable in the expression is used by default:

        sage: y = var('y')
        sage: f = y^2/(y+1)^3
        sage: f.partial_fraction()
        1/(y + 1) - 2/(y + 1)^2 + 1/(y + 1)^3

        sage: f = y^2/(y+1)^3 + x/(x-1)^3
        sage: f.partial_fraction()
        y^2/(y^3 + 3*y^2 + 3*y + 1) + 1/(x - 1)^2 + 1/(x - 1)^3

    You can explicitly specify which variable is used:

        sage: f.partial_fraction(y)
        x/(x^3 - 3*x^2 + 3*x - 1) + 1/(y + 1) - 2/(y + 1)^2 + 1/(y + 1)^3

  `plot`(*args, **kwds)

    Plot a symbolic expression. All arguments are passed onto the standard plot command.

    EXAMPLES:

    This displays a straight line:

        sage: sin(2).plot((x,0,3))
        Graphics object consisting of 1 graphics primitive

    This draws a red oscillatory curve:

        sage: sin(x^2).plot((x,0,2*pi), rgbcolor=(1,0,0))
        Graphics object consisting of 1 graphics primitive

    Another plot using the variable theta:

        sage: var('theta')
        theta
        sage: (cos(theta) - erf(theta)).plot((theta,-2*pi,2*pi))
        Graphics object consisting of 1 graphics primitive

    A very thick green plot with a frame:

        sage: sin(x).plot((x,-4*pi, 4*pi), thickness=20, rgbcolor=(0,0.7,0)).show(frame=True)

    You can embed 2d plots in 3d space as follows:

        sage: plot(sin(x^2), (x,-pi, pi), thickness=2).plot3d(z = 1)
        Graphics3d Object

    A more complicated family:

        sage: G = sum([plot(sin(n*x), (x,-2*pi, 2*pi)).plot3d(z=n) for n in [0,0.1,..1]])
        sage: G.show(frame_aspect_ratio=[1,1,1/2])  # long time (5s on sage.math, 2012)

    A plot involving the floor function:

        sage: plot(1.0 - x * floor(1/x), (x,0.00001,1.0))
        Graphics object consisting of 1 graphics primitive

    Sage used to allow symbolic functions with “no arguments”; this no longer works:

        sage: plot(2*sin, -4, 4)
        Traceback (most recent call last):
        ...
        TypeError: unsupported operand parent(s) for '*': 'Integer Ring' and '<class 'sage.functions.trig.Function_sin'>'

    You should evaluate the function first:

        sage: plot(2*sin(x), -4, 4)
        Graphics object consisting of 1 graphics primitive

    TESTS:

        sage: f(x) = x*(1 - x)
        sage: plot(f,0,1)
        Graphics object consisting of 1 graphics primitive

  `poly`(x=None)

    Express this symbolic expression as a polynomial in x. If this is not a polynomial in x, then some coefficients may be functions of x.

    Warning

    This is different from `polynomial()` which returns a Sage polynomial over a given base ring.

    EXAMPLES:

        sage: var('a, x')
        (a, x)
        sage: p = expand((x-a*sqrt(2))^2 + x + 1); p
        -2*sqrt(2)*a*x + 2*a^2 + x^2 + x + 1
        sage: p.poly(a)
        -2*sqrt(2)*a*x + 2*a^2 + x^2 + x + 1
        sage: bool(p.poly(a) == (x-a*sqrt(2))^2 + x + 1)
        True
        sage: p.poly(x)
        2*a^2 - (2*sqrt(2)*a - 1)*x + x^2 + 1

  `polynomial`(base_ring=None, ring=None)

    Return this symbolic expression as an algebraic polynomial over the given base ring, if possible.

    The point of this function is that it converts purely symbolic polynomials into optimised algebraic polynomials over a given base ring.

    You can specify either the base ring (`base_ring`) you want the output polynomial to be over, or you can specify the full polynomial ring (`ring`) you want the output polynomial to be an element of.

    INPUT:

    - `base_ring` - (optional) the base ring for the polynomial
    - `ring` - (optional) the parent for the polynomial

    Warning

    This is different from `poly()` which is used to rewrite self as a polynomial in terms of one of the variables.

    EXAMPLES:

        sage: f = x^2 -2/3*x + 1
        sage: f.polynomial(QQ)
        x^2 - 2/3*x + 1
        sage: f.polynomial(GF(19))
        x^2 + 12*x + 1

    Polynomials can be useful for getting the coefficients of an expression:

        sage: g = 6*x^2 - 5
        sage: g.coefficients()
        [[-5, 0], [6, 2]]
        sage: g.polynomial(QQ).list()
        [-5, 0, 6]
        sage: g.polynomial(QQ).dict()
        {0: -5, 2: 6}

        sage: f = x^2*e + x + pi/e
        sage: f.polynomial(RDF)
        2.718281828459045*x^2 + x + 1.1557273497909217
        sage: g = f.polynomial(RR); g
        2.71828182845905*x^2 + x + 1.15572734979092
        sage: g.parent()
        Univariate Polynomial Ring in x over Real Field with 53 bits of precision
        sage: f.polynomial(RealField(100))
        2.7182818284590452353602874714*x^2 + x + 1.1557273497909217179100931833
        sage: f.polynomial(CDF)
        2.718281828459045*x^2 + x + 1.1557273497909217
        sage: f.polynomial(CC)
        2.71828182845905*x^2 + x + 1.15572734979092

    We coerce a multivariate polynomial with complex symbolic coefficients:

        sage: x, y, n = var('x, y, n')
        sage: f = pi^3*x - y^2*e - I; f
        pi^3*x - y^2*e - I
        sage: f.polynomial(CDF)
        (-2.71828182846)*y^2 + 31.0062766803*x - 1.0*I
        sage: f.polynomial(CC)
        (-2.71828182845905)*y^2 + 31.0062766802998*x - 1.00000000000000*I
        sage: f.polynomial(ComplexField(70))
        (-2.7182818284590452354)*y^2 + 31.006276680299820175*x - 1.0000000000000000000*I

    Another polynomial:

        sage: f = sum((e*I)^n*x^n for n in range(5)); f
        x^4*e^4 - I*x^3*e^3 - x^2*e^2 + I*x*e + 1
        sage: f.polynomial(CDF)
        54.598150033144236*x^4 - 20.085536923187668*I*x^3 - 7.38905609893065*x^2 + 2.718281828459045*I*x + 1.0
        sage: f.polynomial(CC)
        54.5981500331442*x^4 - 20.0855369231877*I*x^3 - 7.38905609893065*x^2 + 2.71828182845905*I*x + 1.00000000000000

    A multivariate polynomial over a finite field:

        sage: f = (3*x^5 - 5*y^5)^7; f
        (3*x^5 - 5*y^5)^7
        sage: g = f.polynomial(GF(7)); g
        3*x^35 + 2*y^35
        sage: parent(g)
        Multivariate Polynomial Ring in x, y over Finite Field of size 7

    We check to make sure constants are converted appropriately:

        sage: (pi*x).polynomial(SR)
        pi*x

    Using the `ring` parameter, you can also create polynomials rings over the symbolic ring where only certain variables are considered generators of the polynomial ring and the others are considered “constants”:

        sage: a, x, y = var('a,x,y')
        sage: f = a*x^10*y+3*x
        sage: B = f.polynomial(ring=SR['x,y'])
        sage: B.coefficients()
        [a, 3]

  `power`(exp, hold=False)

    Return the current expression to the power `exp`.

    To prevent automatic evaluation use the `hold` argument.

    EXAMPLES:

        sage: (x^2).power(2)
        x^4
        sage: (x^2).power(2, hold=True)
        (x^2)^2

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = (x^2).power(2, hold=True); a.simplify()
        x^4

  `power_series`(base_ring)

    Return algebraic power series associated to this symbolic expression, which must be a polynomial in one variable, with coefficients coercible to the base ring.

    The power series is truncated one more than the degree.

    EXAMPLES:

        sage: theta = var('theta')
        sage: f = theta^3 + (1/3)*theta - 17/3
        sage: g = f.power_series(QQ); g
        -17/3 + 1/3*theta + theta^3 + O(theta^4)
        sage: g^3
        -4913/27 + 289/9*theta - 17/9*theta^2 + 2602/27*theta^3 + O(theta^4)
        sage: g.parent()
        Power Series Ring in theta over Rational Field

  `primitive_part`(s)

    Return the primitive polynomial of this expression when considered as a polynomial in `s`.

    See also `unit()`, `content()`, and `unit_content_primitive()`.

    INPUT:

    - `s` – a symbolic expression.

    OUTPUT:

    The primitive polynomial as a symbolic expression. It is defined as the quotient by the `unit()` and `content()` parts (with respect to the variable `s`).

    EXAMPLES:

        sage: (2*x+4).primitive_part(x)
        x + 2
        sage: (2*x+1).primitive_part(x)
        2*x + 1
        sage: (2*x+1/2).primitive_part(x)
        4*x + 1
        sage: var('y')
        y
        sage: (2*x + 4*sin(y)).primitive_part(sin(y))
        x + 2*sin(y)

  `pyobject`()

    Get the underlying Python object.

    OUTPUT:

    The Python object corresponding to this expression, assuming this expression is a single numerical value or an infinity representable in Python. Otherwise, a `TypeError` is raised.

    EXAMPLES:

        sage: var('x')
        x
        sage: b = -17/3
        sage: a = SR(b)
        sage: a.pyobject()
        -17/3
        sage: a.pyobject() is b
        True

    TESTS:

        sage: SR(oo).pyobject()
        +Infinity
        sage: SR(-oo).pyobject()
        -Infinity
        sage: SR(unsigned_infinity).pyobject()
        Infinity
        sage: SR(I*oo).pyobject()
        Traceback (most recent call last):
        ...
        TypeError: Python infinity cannot have complex phase.

  `radical_simplify`()

    Simplify this symbolic expression, which can contain logs, exponentials, and radicals, by trying to convert it into a canonical form over a large class of expressions and a given ordering of variables.

    Warning

    As shown in the examples below, a canonical form is not always returned, i.e., two mathematically identical expressions might be simplified to different expressions.

    ALGORITHM:

    This uses the Maxima `radcan()` command. From the Maxima documentation: “All functionally equivalent forms are mapped into a unique form. For a somewhat larger class of expressions, produces a regular form. Two equivalent expressions in this class do not necessarily have the same appearance, but their difference can be simplified by radcan to zero. For some expressions radcan is quite time consuming. This is the cost of exploring certain relationships among the components of the expression for simplifications based on factoring and partial fraction expansions of exponents.”

    Note

    `radical_simplify()`, `simplify_radical()`, `exp_simplify()`, `simplify_exp()` are all the same.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)

        sage: f = log(x*y)
        sage: f.simplify_radical()
        log(x) + log(y)

        sage: f = log(8)/log(2)
        sage: f.simplify_radical()
        3

        sage: f = (log(x+x^2)-log(x))^a/log(1+x)^(a/2)
        sage: f.simplify_radical()
        log(x + 1)^(1/2*a)

        sage: f = (e^x-1)/(1+e^(x/2))
        sage: f.simplify_exp()
        e^(1/2*x) - 1

    The example below shows two expressions e1 and e2 which are “simplified” to different expressions, while their difference is “simplified” to zero, thus `simplify_radical` does not return a canonical form, except maybe for 0.

        sage: e1 = 1/(sqrt(5)+sqrt(2))
        sage: e2 = (sqrt(5)-sqrt(2))/3
        sage: e1.simplify_radical()
        1/(sqrt(5) + sqrt(2))
        sage: e2.simplify_radical()
        1/3*sqrt(5) - 1/3*sqrt(2)
        sage: (e1-e2).simplify_radical()
        0

    TESTS:

    This tests that trac ticket #11668 has been fixed (by trac ticket #12780):

        sage: a,b = var('a b')
        sage: A = abs((a+I*b))^2
        sage: A.simplify_radical()
        abs(a + I*b)^2
        sage: imag(A)
        0
        sage: imag(A.simplify_radical())
        0

  `rational_expand`(side=None)

    Expand this symbolic expression. Products of sums and exponentiated sums are multiplied out, numerators of rational expressions which are sums are split into their respective terms, and multiplications are distributed over addition at all levels.

    EXAMPLES:

    We expand the expression \((x-y)^5\) using both method and functional notation.

        sage: x,y = var('x,y')
        sage: a = (x-y)^5
        sage: a.expand()
        x^5 - 5*x^4*y + 10*x^3*y^2 - 10*x^2*y^3 + 5*x*y^4 - y^5
        sage: expand(a)
        x^5 - 5*x^4*y + 10*x^3*y^2 - 10*x^2*y^3 + 5*x*y^4 - y^5

    We expand some other expressions:

        sage: expand((x-1)^3/(y-1))
        x^3/(y - 1) - 3*x^2/(y - 1) + 3*x/(y - 1) - 1/(y - 1)
        sage: expand((x+sin((x+y)^2))^2)
        x^2 + 2*x*sin((x + y)^2) + sin((x + y)^2)^2

    We can expand individual sides of a relation:

        sage: a = (16*x-13)^2 == (3*x+5)^2/2
        sage: a.expand()
        256*x^2 - 416*x + 169 == 9/2*x^2 + 15*x + 25/2
        sage: a.expand('left')
        256*x^2 - 416*x + 169 == 1/2*(3*x + 5)^2
        sage: a.expand('right')
        (16*x - 13)^2 == 9/2*x^2 + 15*x + 25/2

    TESTS:

        sage: var('x,y')
        (x, y)
        sage: ((x + (2/3)*y)^3).expand()
        x^3 + 2*x^2*y + 4/3*x*y^2 + 8/27*y^3
        sage: expand( (x*sin(x) - cos(y)/x)^2 )
        x^2*sin(x)^2 - 2*cos(y)*sin(x) + cos(y)^2/x^2
        sage: f = (x-y)*(x+y); f
        (x + y)*(x - y)
        sage: f.expand()
        x^2 - y^2

  `rational_simplify`(algorithm='full', map=False)

    Simplify rational expressions.

    INPUT:

    - `self` - symbolic expression
    - `algorithm` - (default: ‘full’) string which switches the algorithm for simplifications. Possible values are
      - ‘simple’ (simplify rational functions into quotient of two polynomials),
      - ‘full’ (apply repeatedly, if necessary)
      - ‘noexpand’ (convert to commmon denominator and add)
    - `map` - (default: False) if True, the result is an expression whose leading operator is the same as that of the expression `self` but whose subparts are the results of applying simplification rules to the corresponding subparts of the expressions.

    ALIAS: `rational_simplify()` and `simplify_rational()` are the same

    DETAILS: We call Maxima functions ratsimp, fullratsimp and xthru. If each part of the expression has to be simplified separately, we use Maxima function map.

    EXAMPLES:

        sage: f = sin(x/(x^2 + x))
        sage: f
        sin(x/(x^2 + x))
        sage: f.simplify_rational()
        sin(1/(x + 1))

        sage: f = ((x - 1)^(3/2) - (x + 1)*sqrt(x - 1))/sqrt((x - 1)*(x + 1)); f
        -((x + 1)*sqrt(x - 1) - (x - 1)^(3/2))/sqrt((x + 1)*(x - 1))
        sage: f.simplify_rational()
        -2*sqrt(x - 1)/sqrt(x^2 - 1)

    With `map=True` each term in a sum is simplified separately and thus the resuls are shorter for functions which are combination of rational and nonrational funtions. In the following example, we use this option if we want not to combine logarithm and the rational function into one fraction:

        sage: f=(x^2-1)/(x+1)-ln(x)/(x+2)
        sage: f.simplify_rational()
        (x^2 + x - log(x) - 2)/(x + 2)
        sage: f.simplify_rational(map=True)
        x - log(x)/(x + 2) - 1

    Here is an example from the Maxima documentation of where `algorithm='simple'` produces an (possibly useful) intermediate step:

        sage: y = var('y')
        sage: g = (x^(y/2) + 1)^2*(x^(y/2) - 1)^2/(x^y - 1)
        sage: g.simplify_rational(algorithm='simple')
        (x^(2*y) - 2*x^y + 1)/(x^y - 1)
        sage: g.simplify_rational()
        x^y - 1

    With option `algorithm='noexpand'` we only convert to common denominators and add. No expansion of products is performed:

        sage: f=1/(x+1)+x/(x+2)^2
        sage: f.simplify_rational()
        (2*x^2 + 5*x + 4)/(x^3 + 5*x^2 + 8*x + 4)
        sage: f.simplify_rational(algorithm='noexpand')
        ((x + 2)^2 + (x + 1)*x)/((x + 2)^2*(x + 1))

  `real`(hold=False)

    Return the real part of this symbolic expression.

    EXAMPLES:

        sage: x = var('x')
        sage: x.real_part()
        real_part(x)
        sage: SR(2+3*I).real_part()
        2
        sage: SR(CDF(2,3)).real_part()
        2.0
        sage: SR(CC(2,3)).real_part()
        2.00000000000000

        sage: f = log(x)
        sage: f.real_part()
        log(abs(x))

    Using the `hold` parameter it is possible to prevent automatic evaluation:

        sage: SR(2).real_part()
        2
        sage: SR(2).real_part(hold=True)
        real_part(2)

    This also works using functional notation:

        sage: real_part(I,hold=True)
        real_part(I)
        sage: real_part(I)
        0

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(2).real_part(hold=True); a.simplify()
        2

  `real_part`(hold=False)

    Return the real part of this symbolic expression.

    EXAMPLES:

        sage: x = var('x')
        sage: x.real_part()
        real_part(x)
        sage: SR(2+3*I).real_part()
        2
        sage: SR(CDF(2,3)).real_part()
        2.0
        sage: SR(CC(2,3)).real_part()
        2.00000000000000

        sage: f = log(x)
        sage: f.real_part()
        log(abs(x))

    Using the `hold` parameter it is possible to prevent automatic evaluation:

        sage: SR(2).real_part()
        2
        sage: SR(2).real_part(hold=True)
        real_part(2)

    This also works using functional notation:

        sage: real_part(I,hold=True)
        real_part(I)
        sage: real_part(I)
        0

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(2).real_part(hold=True); a.simplify()
        2

  `rectform`()

    Convert this symbolic expression to rectangular form; that is, the form \(a + bi\) where \(a\) and \(b\) are real numbers and \(i\) is the imaginary unit.

    Note

    The name “rectangular” comes from the fact that, in the complex plane, \(a\) and \(bi\) are perpendicular.

    INPUT:

    - `self` – the expression to convert.

    OUTPUT:

    A new expression, equivalent to the original, but expressed in the form \(a + bi\).

    ALGORITHM:

    We call Maxima’s `rectform()` and return the result unmodified.

    EXAMPLES:

    The exponential form of \(\sin(x)\):

        sage: f = (e^(I*x) - e^(-I*x)) / (2*I)
        sage: f.rectform()
        sin(x)

    And \(\cos(x)\):

        sage: f = (e^(I*x) + e^(-I*x)) / 2
        sage: f.rectform()
        cos(x)

    In some cases, this will simplify the given expression. For example, here, \(e^{ik\pi}\), \(\sin(k\pi)=0\) should cancel leaving only \(\cos(k\pi)\) which can then be simplified:

        sage: k = var('k')
        sage: assume(k, 'integer')
        sage: f = e^(I*pi*k)
        sage: f.rectform()
        (-1)^k

    However, in general, the resulting expression may be more complicated than the original:

        sage: f = e^(I*x)
        sage: f.rectform()
        cos(x) + I*sin(x)

    TESTS:

    If the expression is already in rectangular form, it should be left alone:

        sage: a,b = var('a,b')
        sage: assume((a, 'real'), (b, 'real'))
        sage: f = a + b*I
        sage: f.rectform()
        a + I*b
        sage: forget()

    We can check with specific real numbers:

        sage: a = RR.random_element()
        sage: b = RR.random_element()
        sage: f = a + b*I
        sage: bool(f.rectform() == a + b*I)
        True

    If we decompose a complex number into its real and imaginary parts, they should correspond to the real and imaginary terms of the rectangular form:

        sage: z = CC.random_element()
        sage: a = z.real_part()
        sage: b = z.imag_part()
        sage: bool(SR(z).rectform() == a + b*I)
        True

  `reduce_trig`(var=None)

    Combine products and powers of trigonometric and hyperbolic sin’s and cos’s of x into those of multiples of x. It also tries to eliminate these functions when they occur in denominators.

    INPUT:

    - `self` - a symbolic expression
    - `var` - (default: None) the variable which is used for these transformations. If not specified, all variables are used.

    OUTPUT:

    A symbolic expression.

    EXAMPLES:

        sage: y=var('y')
        sage: f=sin(x)*cos(x)^3+sin(y)^2
        sage: f.reduce_trig()
        -1/2*cos(2*y) + 1/8*sin(4*x) + 1/4*sin(2*x) + 1/2

    To reduce only the expressions involving x we use optional parameter:

        sage: f.reduce_trig(x)
        sin(y)^2 + 1/8*sin(4*x) + 1/4*sin(2*x)

    ALIASES: `trig_reduce()` and `reduce_trig()` are the same

  `residue`(symbol)

    Calculate the residue of `self` with respect to `symbol`.

    INPUT:

    - `symbol` - a symbolic variable or symbolic equality such as `x == 5`. If an equality is given, the expansion is around the value on the right hand side of the equality, otherwise at `0`.

    OUTPUT:

    The residue of `self`.

    Say, `symbol` is `x == a`, then this function calculates the residue of `self` at \(x=a\), i.e., the coefficient of \(1/(x-a)\) of the series expansion of `self` around \(a\).

    EXAMPLES:

        sage: (1/x).residue(x == 0)
        1
        sage: (1/x).residue(x == oo)
        -1
        sage: (1/x^2).residue(x == 0)
        0
        sage: (1/sin(x)).residue(x == 0)
        1
        sage: var('q, n, z')
        (q, n, z)
        sage: (-z^(-n-1)/(1-z/q)^2).residue(z == q).simplify_full()
        (n + 1)/q^n
        sage: var('s')
        s
        sage: zeta(s).residue(s == 1) # not tested - #15846
        1

    TESTS:

        sage: (exp(x)/sin(x)^4).residue(x == 0)
        5/6

  `rhs`()

    If self is a relational expression, return the right hand side of the relation. Otherwise, raise a ValueError.

    EXAMPLES:

        sage: x = var('x')
        sage: eqn = (x-1)^2 <= x^2 - 2*x + 3
        sage: eqn.right_hand_side()
        x^2 - 2*x + 3
        sage: eqn.rhs()
        x^2 - 2*x + 3
        sage: eqn.right()
        x^2 - 2*x + 3

  `right`()

    If self is a relational expression, return the right hand side of the relation. Otherwise, raise a ValueError.

    EXAMPLES:

        sage: x = var('x')
        sage: eqn = (x-1)^2 <= x^2 - 2*x + 3
        sage: eqn.right_hand_side()
        x^2 - 2*x + 3
        sage: eqn.rhs()
        x^2 - 2*x + 3
        sage: eqn.right()
        x^2 - 2*x + 3

  `right_hand_side`()

    If self is a relational expression, return the right hand side of the relation. Otherwise, raise a ValueError.

    EXAMPLES:

        sage: x = var('x')
        sage: eqn = (x-1)^2 <= x^2 - 2*x + 3
        sage: eqn.right_hand_side()
        x^2 - 2*x + 3
        sage: eqn.rhs()
        x^2 - 2*x + 3
        sage: eqn.right()
        x^2 - 2*x + 3

  `roots`(x=None, explicit_solutions=True, multiplicities=True, ring=None)

    Return roots of `self` that can be found exactly, possibly with multiplicities. Not all roots are guaranteed to be found.

    Warning

    This is not a numerical solver - use `find_root` to solve for self == 0 numerically on an interval.

    INPUT:

    - `x` - variable to view the function in terms of (use default variable if not given)
    - `explicit_solutions` - bool (default True); require that roots be explicit rather than implicit
    - `multiplicities` - bool (default True); when True, return multiplicities
    - `ring` - a ring (default None): if not None, convert self to a polynomial over ring and find roots over ring

    OUTPUT:

    A list of pairs `(root, multiplicity)` or list of roots.

    If there are infinitely many roots, e.g., a function like \(\sin(x)\), only one is returned.

    EXAMPLES:

        sage: var('x, a')
        (x, a)

    A simple example:

        sage: ((x^2-1)^2).roots()
        [(-1, 2), (1, 2)]
        sage: ((x^2-1)^2).roots(multiplicities=False)
        [-1, 1]

    A complicated example:

        sage: f = expand((x^2 - 1)^3*(x^2 + 1)*(x-a)); f
        -a*x^8 + x^9 + 2*a*x^6 - 2*x^7 - 2*a*x^2 + 2*x^3 + a - x

    The default variable is \(a\), since it is the first in alphabetical order:

        sage: f.roots()
        [(x, 1)]

    As a polynomial in \(a\), \(x\) is indeed a root:

        sage: f.poly(a)
        x^9 - 2*x^7 + 2*x^3 - (x^8 - 2*x^6 + 2*x^2 - 1)*a - x
        sage: f(a=x)
        0

    The roots in terms of \(x\) are what we expect:

        sage: f.roots(x)
        [(a, 1), (-I, 1), (I, 1), (1, 3), (-1, 3)]

    Only one root of \(\sin(x) = 0\) is given:

        sage: f = sin(x)
        sage: f.roots(x)
        [(0, 1)]

    Note

    It is possible to solve a greater variety of equations using `solve()` and the keyword `to_poly_solve`, but only at the price of possibly encountering approximate solutions. See documentation for f.solve for more details.

    We derive the roots of a general quadratic polynomial:

        sage: var('a,b,c,x')
        (a, b, c, x)
        sage: (a*x^2 + b*x + c).roots(x)
        [(-1/2*(b + sqrt(b^2 - 4*a*c))/a, 1), (-1/2*(b - sqrt(b^2 - 4*a*c))/a, 1)]

    By default, all the roots are required to be explicit rather than implicit. To get implicit roots, pass `explicit_solutions=False` to `.roots()`

        sage: var('x')
        x
        sage: f = x^(1/9) + (2^(8/9) - 2^(1/9))*(x - 1) - x^(8/9)
        sage: f.roots()
        Traceback (most recent call last):
        ...
        RuntimeError: no explicit roots found
        sage: f.roots(explicit_solutions=False)
        [((2^(8/9) + x^(8/9) - 2^(1/9) - x^(1/9))/(2^(8/9) - 2^(1/9)), 1)]

    Another example, but involving a degree 5 poly whose roots do not get computed explicitly:

        sage: f = x^5 + x^3 + 17*x + 1
        sage: f.roots()
        Traceback (most recent call last):
        ...
        RuntimeError: no explicit roots found
        sage: f.roots(explicit_solutions=False)
        [(x^5 + x^3 + 17*x + 1, 1)]
        sage: f.roots(explicit_solutions=False, multiplicities=False)
        [x^5 + x^3 + 17*x + 1]

    Now let us find some roots over different rings:

        sage: f.roots(ring=CC)
        [(-0.0588115223184..., 1), (-1.331099917875... - 1.52241655183732*I, 1), (-1.331099917875... + 1.52241655183732*I, 1), (1.36050567903502 - 1.51880872209965*I, 1), (1.36050567903502 + 1.51880872209965*I, 1)]
        sage: (2.5*f).roots(ring=RR)
        [(-0.058811522318449..., 1)]
        sage: f.roots(ring=CC, multiplicities=False)
        [-0.05881152231844..., -1.331099917875... - 1.52241655183732*I, -1.331099917875... + 1.52241655183732*I, 1.36050567903502 - 1.51880872209965*I, 1.36050567903502 + 1.51880872209965*I]
        sage: f.roots(ring=QQ)
        []
        sage: f.roots(ring=QQbar, multiplicities=False)
        [-0.05881152231844944?, -1.331099917875796? - 1.522416551837318?*I, -1.331099917875796? + 1.522416551837318?*I, 1.360505679035020? - 1.518808722099650?*I, 1.360505679035020? + 1.518808722099650?*I]

    Root finding over finite fields:

        sage: f.roots(ring=GF(7^2, 'a'))
        [(3, 1), (4*a + 6, 2), (3*a + 3, 2)]

    TESTS:

        sage: (sqrt(3) * f).roots(ring=QQ)
        Traceback (most recent call last):
        ...
        TypeError: unable to convert sqrt(3) to a rational

    Check if trac ticket #9538 is fixed:

        sage: var('f6,f5,f4,x')
        (f6, f5, f4, x)
        sage: e=15*f6*x^2 + 5*f5*x + f4
        sage: res = e.roots(x); res
        [(-1/30*(5*f5 + sqrt(25*f5^2 - 60*f4*f6))/f6, 1), (-1/30*(5*f5 - sqrt(25*f5^2 - 60*f4*f6))/f6, 1)]
        sage: e.subs(x=res[0][0]).is_zero()
        True

  `round`()

    Round this expression to the nearest integer.

    EXAMPLES:

        sage: u = sqrt(43203735824841025516773866131535024)
        sage: u.round()
        207855083711803945
        sage: t = sqrt(Integer('1'*1000)).round(); print str(t)[-10:]
        3333333333
        sage: (-sqrt(110)).round()
        -10
        sage: (-sqrt(115)).round()
        -11
        sage: (sqrt(-3)).round()
        Traceback (most recent call last):
        ...
        ValueError: could not convert sqrt(-3) to a real number

  `series`(symbol, order)

    Return the power series expansion of self in terms of the given variable to the given order.

    INPUT:

    - `symbol` - a symbolic variable or symbolic equality such as `x == 5`; if an equality is given, the expansion is around the value on the right hand side of the equality
    - `order` - an integer

    OUTPUT:

    A power series.

    To truncate the power series and obtain a normal expression, use the `truncate()` command.

    EXAMPLES:

    We expand a polynomial in \(x\) about 0, about \(1\), and also truncate it back to a polynomial:

        sage: var('x,y')
        (x, y)
        sage: f = (x^3 - sin(y)*x^2 - 5*x + 3); f
        x^3 - x^2*sin(y) - 5*x + 3
        sage: g = f.series(x, 4); g
        3 + (-5)*x + (-sin(y))*x^2 + 1*x^3
        sage: g.truncate()
        x^3 - x^2*sin(y) - 5*x + 3
        sage: g = f.series(x==1, 4); g
        (-sin(y) - 1) + (-2*sin(y) - 2)*(x - 1) + (-sin(y) + 3)*(x - 1)^2 + 1*(x - 1)^3
        sage: h = g.truncate(); h
        (x - 1)^3 - (x - 1)^2*(sin(y) - 3) - 2*(x - 1)*(sin(y) + 1) - sin(y) - 1
        sage: h.expand()
        x^3 - x^2*sin(y) - 5*x + 3

    We computer another series expansion of an analytic function:

        sage: f = sin(x)/x^2
        sage: f.series(x,7)
        1*x^(-1) + (-1/6)*x + 1/120*x^3 + (-1/5040)*x^5 + Order(x^7)
        sage: f.series(x==1,3)
        (sin(1)) + (cos(1) - 2*sin(1))*(x - 1) + (-2*cos(1) + 5/2*sin(1))*(x - 1)^2 + Order((x - 1)^3)
        sage: f.series(x==1,3).truncate().expand()
        -2*x^2*cos(1) + 5/2*x^2*sin(1) + 5*x*cos(1) - 7*x*sin(1) - 3*cos(1) + 11/2*sin(1)

    Following the GiNaC tutorial, we use John Machin’s amazing formula \(\pi = 16 \tan^{-1}(1/5) - 4 \tan^{-1}(1/239)\) to compute digits of \(\pi\). We expand the arc tangent around 0 and insert the fractions 1/5 and 1/239.

        sage: x = var('x')
        sage: f = atan(x).series(x, 10); f
        1*x + (-1/3)*x^3 + 1/5*x^5 + (-1/7)*x^7 + 1/9*x^9 + Order(x^10)
        sage: float(16*f.subs(x==1/5) - 4*f.subs(x==1/239))
        3.1415926824043994

    TESTS:

    Check if trac ticket #8943 is fixed:

        sage: ((1+arctan(x))**(1/x)).series(x==0, 3)
        (e) + (-1/2*e)*x + (1/8*e)*x^2 + Order(x^3)

  `show`()

    Show this symbolic expression, i.e., typeset it nicely.

    EXAMPLES:

        sage: (x^2 + 1).show()
        x^{2}  + 1

  `simplify`()

    Return a simplified version of this symbolic expression.

    Note

    Currently, this just sends the expression to Maxima and converts it back to Sage.

    See also

    `simplify_full()`, `simplify_trig()`, `simplify_rational()`, `simplify_radical()`, `simplify_factorial()`, `simplify_log()`

    EXAMPLES:

        sage: a = var('a'); f = x*sin(2)/(x^a); f
        x*sin(2)/x^a
        sage: f.simplify()
        x^(-a + 1)*sin(2)

    TESTS:

    Check that trac ticket #14637 is fixed:

        sage: assume(x > 0, x < pi/2)
        sage: acos(cos(x)).simplify()
        x
        sage: forget()

  `simplify_exp`()

    Simplify this symbolic expression, which can contain logs, exponentials, and radicals, by trying to convert it into a canonical form over a large class of expressions and a given ordering of variables.

    Warning

    As shown in the examples below, a canonical form is not always returned, i.e., two mathematically identical expressions might be simplified to different expressions.

    ALGORITHM:

    This uses the Maxima `radcan()` command. From the Maxima documentation: “All functionally equivalent forms are mapped into a unique form. For a somewhat larger class of expressions, produces a regular form. Two equivalent expressions in this class do not necessarily have the same appearance, but their difference can be simplified by radcan to zero. For some expressions radcan is quite time consuming. This is the cost of exploring certain relationships among the components of the expression for simplifications based on factoring and partial fraction expansions of exponents.”

    Note

    `radical_simplify()`, `simplify_radical()`, `exp_simplify()`, `simplify_exp()` are all the same.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)

        sage: f = log(x*y)
        sage: f.simplify_radical()
        log(x) + log(y)

        sage: f = log(8)/log(2)
        sage: f.simplify_radical()
        3

        sage: f = (log(x+x^2)-log(x))^a/log(1+x)^(a/2)
        sage: f.simplify_radical()
        log(x + 1)^(1/2*a)

        sage: f = (e^x-1)/(1+e^(x/2))
        sage: f.simplify_exp()
        e^(1/2*x) - 1

    The example below shows two expressions e1 and e2 which are “simplified” to different expressions, while their difference is “simplified” to zero, thus `simplify_radical` does not return a canonical form, except maybe for 0.

        sage: e1 = 1/(sqrt(5)+sqrt(2))
        sage: e2 = (sqrt(5)-sqrt(2))/3
        sage: e1.simplify_radical()
        1/(sqrt(5) + sqrt(2))
        sage: e2.simplify_radical()
        1/3*sqrt(5) - 1/3*sqrt(2)
        sage: (e1-e2).simplify_radical()
        0

    TESTS:

    This tests that trac ticket #11668 has been fixed (by trac ticket #12780):

        sage: a,b = var('a b')
        sage: A = abs((a+I*b))^2
        sage: A.simplify_radical()
        abs(a + I*b)^2
        sage: imag(A)
        0
        sage: imag(A.simplify_radical())
        0

  `simplify_factorial`()

    Simplify by combining expressions with factorials, and by expanding binomials into factorials.

    ALIAS: factorial_simplify and simplify_factorial are the same

    EXAMPLES:

    Some examples are relatively clear:

        sage: var('n,k')
        (n, k)
        sage: f = factorial(n+1)/factorial(n); f
        factorial(n + 1)/factorial(n)
        sage: f.simplify_factorial()
        n + 1

        sage: f = factorial(n)*(n+1); f
        (n + 1)*factorial(n)
        sage: simplify(f)
        (n + 1)*factorial(n)
        sage: f.simplify_factorial()
        factorial(n + 1)

        sage: f = binomial(n, k)*factorial(k)*factorial(n-k); f
        binomial(n, k)*factorial(k)*factorial(-k + n)
        sage: f.simplify_factorial()
        factorial(n)

    A more complicated example, which needs further processing:

        sage: f = factorial(x)/factorial(x-2)/2 + factorial(x+1)/factorial(x)/2; f
        1/2*factorial(x + 1)/factorial(x) + 1/2*factorial(x)/factorial(x - 2)
        sage: g = f.simplify_factorial(); g
        1/2*(x - 1)*x + 1/2*x + 1/2
        sage: g.simplify_rational()
        1/2*x^2 + 1/2

    TESTS:

    Check that the problem with applying \(full_simplify()\) to gamma functions (trac ticket #9240) has been fixed:

        sage: gamma(1/3)
        gamma(1/3)
        sage: gamma(1/3).full_simplify()
        gamma(1/3)
        sage: gamma(4/3)
        gamma(4/3)
        sage: gamma(4/3).full_simplify()
        1/3*gamma(1/3)

  `simplify_full`()

    Apply simplify_factorial, simplify_trig, simplify_rational, simplify_log, and again simplify_rational to self (in that order).

    ALIAS: simplify_full and full_simplify are the same.

    EXAMPLES:

        sage: f = sin(x)^2 + cos(x)^2
        sage: f.simplify_full()
        1

        sage: f = sin(x/(x^2 + x))
        sage: f.simplify_full()
        sin(1/(x + 1))

        sage: var('n,k')
        (n, k)
        sage: f = binomial(n,k)*factorial(k)*factorial(n-k)
        sage: f.simplify_full()
        factorial(n)

    TESTS:

    There are two square roots of \($(x + 1)^2$\), so this should not be simplified to \($x + 1$\), trac ticket #12737:

        sage: f = sqrt((x + 1)^2)
        sage: f.simplify_full()
        sqrt(x^2 + 2*x + 1)

    The imaginary part of an expression should not change under simplification; trac ticket #11934:

        sage: f = sqrt(-8*(4*sqrt(2) - 7)*x^4 + 16*(3*sqrt(2) - 5)*x^3)
        sage: original = f.imag_part()
        sage: simplified = f.full_simplify().imag_part()
        sage: original - simplified
        0

    The invalid simplification from trac ticket #12322 should not occur after trac ticket #12737:

        sage: t = var('t')
        sage: assume(t, 'complex')
        sage: assumptions()
        [t is complex]
        sage: f = (1/2)*log(2*t) + (1/2)*log(1/t)
        sage: f.simplify_full()
        1/2*log(2*t) - 1/2*log(t)

  `simplify_hypergeometric`(algorithm='maxima')

    Simplify an expression containing hypergeometric functions.

    INPUT:

    - `algorithm` – (default: `'maxima'`) the algorithm to use for for simplification. Implemented are `'maxima'`, which uses Maxima’s `hgfred` function, and `'sage'`, which uses an algorithm implemented in the hypergeometric module

    ALIAS: `hypergeometric_simplify()` and `simplify_hypergeometric()` are the same

    EXAMPLES:

        sage: hypergeometric((5, 4), (4, 1, 2, 3),
        ....:                x).simplify_hypergeometric()
        1/144*x^2*hypergeometric((), (3, 4), x) +...
        1/3*x*hypergeometric((), (2, 3), x) + hypergeometric((), (1, 2), x)
        sage: (2*hypergeometric((), (), x)).simplify_hypergeometric()
        2*e^x
        sage: (nest(lambda y: hypergeometric([y], [1], x), 3, 1)
        ....:  .simplify_hypergeometric())
        laguerre(-laguerre(-e^x, x), x)
        sage: (nest(lambda y: hypergeometric([y], [1], x), 3, 1)
        ....:  .simplify_hypergeometric(algorithm='sage'))
        hypergeometric((hypergeometric((e^x,), (1,), x),), (1,), x)

  `simplify_log`(algorithm=None)

    Simplify symbolic expression, which can contain logs.

    Recursively scans the expression self, transforming subexpressions of the form \(a1 \log(b1) + a2 \log(b2) + c\) into \(\log( b1^{a1} b2^{a2} ) + c\) and simplifies inside logarithm. User can specify, which conditions must satisfy \(a1\) and \(a2\) to use this transformation in optional parameter `algorithm`.

    INPUT:

    - `self` - expression to be simplified

    - `algorithm` - (default: None) optional, governs the condition on \(a1\) and \(a2\) which must be satisfied to contract expression \(a1 \log(b1) + a2 \log(b2)\). Values are

      - None (use Maxima default, integers),
      - ‘one’ (1 and -1),
      - ‘ratios’ (integers and fractions of integers),
      - ‘constants’ (constants),
      - ‘all’ (all expressions).

      See also examples below.

    DETAILS: This uses the Maxima logcontract() command. From the Maxima documentation: “Recursively scans the expression expr, transforming subexpressions of the form a1*log(b1) + a2*log(b2) + c into log(ratsimp(b1^a1 * b2^a2)) + c. The user can control which coefficients are contracted by setting the option logconcoeffp to the name of a predicate function of one argument. E.g. if you like to generate SQRTs, you can do logconcoeffp:’logconfun` logconfun(m):=featurep(m,integer) or ratnump(m)` . Then logcontract(1/2*log(x)); will give log(sqrt(x)).”

    ALIAS: `log_simplify()` and `simplify_log()` are the same

    EXAMPLES:

        sage: x,y,t=var('x y t')

    Only two first terms are contracted in the following example , the logarithm with coefficient 1/2 is not contracted:

        sage: f = log(x)+2*log(y)+1/2*log(t)
        sage: f.simplify_log()
        log(x*y^2) + 1/2*log(t)

    To contract all terms in previous example use option `algorithm`:

        sage: f.simplify_log(algorithm='ratios')
        log(sqrt(t)*x*y^2)

    This shows that the option `algorithm` from the previous call has no influence to future calls (we changed some default Maxima flag, and have to ensure that this flag has been restored):

        sage: f.simplify_log('one')
        1/2*log(t) + log(x) + 2*log(y)

        sage: f.simplify_log('ratios')
        log(sqrt(t)*x*y^2)

        sage: f.simplify_log()
        log(x*y^2) + 1/2*log(t)

    To contract terms with no coefficient (more precisely, with coefficients 1 and -1) use option `algorithm`:

        sage: f = log(x)+2*log(y)-log(t)
        sage: f.simplify_log('one')
        2*log(y) + log(x/t)

        sage: f = log(x)+log(y)-1/3*log((x+1))
        sage: f.simplify_log()
        log(x*y) - 1/3*log(x + 1)

        sage: f.simplify_log('ratios')
        log(x*y/(x + 1)^(1/3))

    \(\pi\) is irrational number, to contract logarithms in the following example we have to put `algorithm` to `constants` or `all`:

        sage: f = log(x)+log(y)-pi*log((x+1))
        sage: f.simplify_log('constants')
        log(x*y/(x + 1)^pi)

    x*log(9) is contracted only if `algorithm` is `all`:

        sage: (x*log(9)).simplify_log()
        x*log(9)
        sage: (x*log(9)).simplify_log('all')
        log(9^x)

    TESTS:

    This shows that the issue at trac ticket #7334 is fixed. Maxima intentionally keeps the expression inside the log factored:

        sage: log_expr = (log(sqrt(2)-1)+log(sqrt(2)+1))
        sage: log_expr.simplify_log('all')
        log((sqrt(2) + 1)*(sqrt(2) - 1))
        sage: _.simplify_rational()
        0
        sage: log_expr.simplify_full()   # applies both simplify_log and simplify_rational
        0

    We should use the current simplification domain rather than set it to ‘real’ explicitly (trac ticket #12780):

        sage: f = sqrt(x^2)
        sage: f.simplify_log()
        sqrt(x^2)
        sage: from sage.calculus.calculus import maxima
        sage: maxima('domain: real;')
        real
        sage: f.simplify_log()
        abs(x)
        sage: maxima('domain: complex;')
        complex

    AUTHORS:

    - Robert Marik (11-2009)
  `simplify_radical`()

    Simplify this symbolic expression, which can contain logs, exponentials, and radicals, by trying to convert it into a canonical form over a large class of expressions and a given ordering of variables.

    Warning

    As shown in the examples below, a canonical form is not always returned, i.e., two mathematically identical expressions might be simplified to different expressions.

    ALGORITHM:

    This uses the Maxima `radcan()` command. From the Maxima documentation: “All functionally equivalent forms are mapped into a unique form. For a somewhat larger class of expressions, produces a regular form. Two equivalent expressions in this class do not necessarily have the same appearance, but their difference can be simplified by radcan to zero. For some expressions radcan is quite time consuming. This is the cost of exploring certain relationships among the components of the expression for simplifications based on factoring and partial fraction expansions of exponents.”

    Note

    `radical_simplify()`, `simplify_radical()`, `exp_simplify()`, `simplify_exp()` are all the same.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)

        sage: f = log(x*y)
        sage: f.simplify_radical()
        log(x) + log(y)

        sage: f = log(8)/log(2)
        sage: f.simplify_radical()
        3

        sage: f = (log(x+x^2)-log(x))^a/log(1+x)^(a/2)
        sage: f.simplify_radical()
        log(x + 1)^(1/2*a)

        sage: f = (e^x-1)/(1+e^(x/2))
        sage: f.simplify_exp()
        e^(1/2*x) - 1

    The example below shows two expressions e1 and e2 which are “simplified” to different expressions, while their difference is “simplified” to zero, thus `simplify_radical` does not return a canonical form, except maybe for 0.

        sage: e1 = 1/(sqrt(5)+sqrt(2))
        sage: e2 = (sqrt(5)-sqrt(2))/3
        sage: e1.simplify_radical()
        1/(sqrt(5) + sqrt(2))
        sage: e2.simplify_radical()
        1/3*sqrt(5) - 1/3*sqrt(2)
        sage: (e1-e2).simplify_radical()
        0

    TESTS:

    This tests that trac ticket #11668 has been fixed (by trac ticket #12780):

        sage: a,b = var('a b')
        sage: A = abs((a+I*b))^2
        sage: A.simplify_radical()
        abs(a + I*b)^2
        sage: imag(A)
        0
        sage: imag(A.simplify_radical())
        0

  `simplify_rational`(algorithm='full', map=False)

    Simplify rational expressions.

    INPUT:

    - `self` - symbolic expression
    - `algorithm` - (default: ‘full’) string which switches the algorithm for simplifications. Possible values are
      - ‘simple’ (simplify rational functions into quotient of two polynomials),
      - ‘full’ (apply repeatedly, if necessary)
      - ‘noexpand’ (convert to commmon denominator and add)
    - `map` - (default: False) if True, the result is an expression whose leading operator is the same as that of the expression `self` but whose subparts are the results of applying simplification rules to the corresponding subparts of the expressions.

    ALIAS: `rational_simplify()` and `simplify_rational()` are the same

    DETAILS: We call Maxima functions ratsimp, fullratsimp and xthru. If each part of the expression has to be simplified separately, we use Maxima function map.

    EXAMPLES:

        sage: f = sin(x/(x^2 + x))
        sage: f
        sin(x/(x^2 + x))
        sage: f.simplify_rational()
        sin(1/(x + 1))

        sage: f = ((x - 1)^(3/2) - (x + 1)*sqrt(x - 1))/sqrt((x - 1)*(x + 1)); f
        -((x + 1)*sqrt(x - 1) - (x - 1)^(3/2))/sqrt((x + 1)*(x - 1))
        sage: f.simplify_rational()
        -2*sqrt(x - 1)/sqrt(x^2 - 1)

    With `map=True` each term in a sum is simplified separately and thus the resuls are shorter for functions which are combination of rational and nonrational funtions. In the following example, we use this option if we want not to combine logarithm and the rational function into one fraction:

        sage: f=(x^2-1)/(x+1)-ln(x)/(x+2)
        sage: f.simplify_rational()
        (x^2 + x - log(x) - 2)/(x + 2)
        sage: f.simplify_rational(map=True)
        x - log(x)/(x + 2) - 1

    Here is an example from the Maxima documentation of where `algorithm='simple'` produces an (possibly useful) intermediate step:

        sage: y = var('y')
        sage: g = (x^(y/2) + 1)^2*(x^(y/2) - 1)^2/(x^y - 1)
        sage: g.simplify_rational(algorithm='simple')
        (x^(2*y) - 2*x^y + 1)/(x^y - 1)
        sage: g.simplify_rational()
        x^y - 1

    With option `algorithm='noexpand'` we only convert to common denominators and add. No expansion of products is performed:

        sage: f=1/(x+1)+x/(x+2)^2
        sage: f.simplify_rational()
        (2*x^2 + 5*x + 4)/(x^3 + 5*x^2 + 8*x + 4)
        sage: f.simplify_rational(algorithm='noexpand')
        ((x + 2)^2 + (x + 1)*x)/((x + 2)^2*(x + 1))

  `simplify_rectform`(complexity_measure='string_length')

    Attempt to simplify this expression by expressing it in the form \(a + bi\) where both \(a\) and \(b\) are real. This transformation is generally not a simplification, so we use the given `complexity_measure` to discard non-simplifications.

    INPUT:

    - `self` – the expression to simplify.
    - `complexity_measure` – (default: `sage.symbolic.complexity_measures.string_length`) a function taking a symbolic expression as an argument and returning a measure of that expressions complexity. If `None` is supplied, the simplification will be performed regardless of the result.

    OUTPUT:

    If the transformation produces a simpler expression (according to `complexity_measure`) then that simpler expression is returned. Otherwise, the original expression is returned.

    ALGORITHM:

    We first call `rectform()` on the given expression. Then, the supplied complexity measure is used to determine whether or not the result is simpler than the original expression.

    EXAMPLES:

    The exponential form of \(\tan(x)\):

        sage: f = ( e^(I*x) - e^(-I*x) ) / ( I*e^(I*x) + I*e^(-I*x) )
        sage: f.simplify_rectform()
        sin(x)/cos(x)

    This should not be expanded with Euler’s formula since the resulting expression is longer when considered as a string, and the default `complexity_measure` uses string length to determine which expression is simpler:

        sage: f = e^(I*x)
        sage: f.simplify_rectform()
        e^(I*x)

    However, if we pass `None` as our complexity measure, it is:

        sage: f = e^(I*x)
        sage: f.simplify_rectform(complexity_measure = None)
        cos(x) + I*sin(x)

    TESTS:

    When given `None`, we should always call `rectform()` and return the result:

        sage: polynomials = QQ['x']
        sage: f = SR(polynomials.random_element())
        sage: g = f.simplify_rectform(complexity_measure = None)
        sage: bool(g == f.rectform())
        True

  `simplify_trig`(expand=True)

    Optionally expand and then employ identities such as \(\sin(x)^2 + \cos(x)^2 = 1\), \(\cosh(x)^2 - \sinh(x)^2 = 1\), \(\sin(x)\csc(x) = 1\), or \(\tanh(x)=\sinh(x)/\cosh(x)\) to simplify expressions containing tan, sec, etc., to sin, cos, sinh, cosh.

    INPUT:

    - `self` - symbolic expression
    - `expand` - (default:True) if True, expands trigonometric and hyperbolic functions of sums of angles and of multiple angles occurring in `self` first. For best results, `self` should be expanded. See also `expand_trig()` to get more controls on this expansion.

    ALIAS: `trig_simplify()` and `simplify_trig()` are the same

    EXAMPLES:

        sage: f = sin(x)^2 + cos(x)^2; f
        cos(x)^2 + sin(x)^2
        sage: f.simplify()
        cos(x)^2 + sin(x)^2
        sage: f.simplify_trig()
        1
        sage: h = sin(x)*csc(x)
        sage: h.simplify_trig()
        1
        sage: k = tanh(x)*cosh(2*x)
        sage: k.simplify_trig()
        (2*sinh(x)^3 + sinh(x))/cosh(x)

    In some cases we do not want to expand:

        sage: f=tan(3*x)
        sage: f.simplify_trig()
        (4*cos(x)^2 - 1)*sin(x)/(4*cos(x)^3 - 3*cos(x))
        sage: f.simplify_trig(False)
        sin(3*x)/cos(3*x)

  `sin`(hold=False)

    EXAMPLES:

        sage: var('x, y')
        (x, y)
        sage: sin(x^2 + y^2)
        sin(x^2 + y^2)
        sage: sin(sage.symbolic.constants.pi)
        0
        sage: sin(SR(1))
        sin(1)
        sage: sin(SR(RealField(150)(1)))
        0.84147098480789650665250232163029899962256306

    Using the `hold` parameter it is possible to prevent automatic evaluation:

        sage: SR(0).sin()
        0
        sage: SR(0).sin(hold=True)
        sin(0)

    This also works using functional notation:

        sage: sin(0,hold=True)
        sin(0)
        sage: sin(0)
        0

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(0).sin(hold=True); a.simplify()
        0

    TESTS:

        sage: SR(oo).sin()
        Traceback (most recent call last):
        ...
        RuntimeError: sin_eval(): sin(infinity) encountered
        sage: SR(-oo).sin()
        Traceback (most recent call last):
        ...
        RuntimeError: sin_eval(): sin(infinity) encountered
        sage: SR(unsigned_infinity).sin()
        Traceback (most recent call last):
        ...
        RuntimeError: sin_eval(): sin(infinity) encountered

  `sinh`(hold=False)

    Return sinh of self.

    We have \(\sinh(x) = (e^{x} - e^{-x})/2\).

    EXAMPLES:

        sage: x.sinh()
        sinh(x)
        sage: SR(1).sinh()
        sinh(1)
        sage: SR(0).sinh()
        0
        sage: SR(1.0).sinh()
        1.17520119364380
        sage: maxima('sinh(1.0)')
        1.17520119364380...

        sinh(1.0000000000000000000000000)
        sage: SR(1).sinh().n(90)
        1.1752011936438014568823819
        sage: SR(RIF(1)).sinh()
        1.175201193643802?

    To prevent automatic evaluation use the `hold` argument:

        sage: arccosh(x).sinh()
        sqrt(x + 1)*sqrt(x - 1)
        sage: arccosh(x).sinh(hold=True)
        sinh(arccosh(x))

    This also works using functional notation:

        sage: sinh(arccosh(x),hold=True)
        sinh(arccosh(x))
        sage: sinh(arccosh(x))
        sqrt(x + 1)*sqrt(x - 1)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = arccosh(x).sinh(hold=True); a.simplify()
        sqrt(x + 1)*sqrt(x - 1)

    TESTS:

        sage: SR(oo).sinh()
        +Infinity
        sage: SR(-oo).sinh()
        -Infinity
        sage: SR(unsigned_infinity).sinh()
        Traceback (most recent call last):
        ...
        RuntimeError: sinh_eval(): sinh(unsigned_infinity) encountered

  `solve`(x, multiplicities=False, solution_dict=False, explicit_solutions=False, to_poly_solve=False)

    Analytically solve the equation `self == 0` or a univariate inequality for the variable \(x\).

    Warning

    This is not a numerical solver - use `find_root` to solve for self == 0 numerically on an interval.

    INPUT:

    - `x` - variable(s) to solve for
    - `multiplicities` - bool (default: False); if True, return corresponding multiplicities. This keyword is incompatible with `to_poly_solve=True` and does not make any sense when solving an inequality.
    - `solution_dict` - bool (default: False); if True or non-zero, return a list of dictionaries containing solutions. Not used when solving an inequality.
    - `explicit_solutions` - bool (default: False); require that all roots be explicit rather than implicit. Not used when solving an inequality.
    - `to_poly_solve` - bool (default: False) or string; use Maxima’s `to_poly_solver` package to search for more possible solutions, but possibly encounter approximate solutions. This keyword is incompatible with `multiplicities=True` and is not used when solving an inequality. Setting `to_poly_solve` to `'force'` omits Maxima’s solve command (useful when some solutions of trigonometric equations are lost).

    EXAMPLES:

        sage: z = var('z')
        sage: (z^5 - 1).solve(z)
        [z == e^(2/5*I*pi), z == e^(4/5*I*pi), z == e^(-4/5*I*pi), z == e^(-2/5*I*pi), z == 1]

        sage: solve((z^3-1)^3, z, multiplicities=True)
        ([z == 1/2*I*sqrt(3) - 1/2, z == -1/2*I*sqrt(3) - 1/2, z == 1], [3, 3, 3])

    A simple example to show the use of the keyword `multiplicities`:

        sage: ((x^2-1)^2).solve(x)
        [x == -1, x == 1]
        sage: ((x^2-1)^2).solve(x,multiplicities=True)
        ([x == -1, x == 1], [2, 2])
        sage: ((x^2-1)^2).solve(x,multiplicities=True,to_poly_solve=True)
        Traceback (most recent call last):
        ...
        NotImplementedError: to_poly_solve does not return multiplicities

    Here is how the `explicit_solutions` keyword functions:

        sage: solve(sin(x)==x,x)
        [x == sin(x)]
        sage: solve(sin(x)==x,x,explicit_solutions=True)
        []
        sage: solve(x*sin(x)==x^2,x)
        [x == 0, x == sin(x)]
        sage: solve(x*sin(x)==x^2,x,explicit_solutions=True)
        [x == 0]

    The following examples show the use of the keyword `to_poly_solve`:

        sage: solve(abs(1-abs(1-x)) == 10, x)
        [abs(abs(x - 1) - 1) == 10]
        sage: solve(abs(1-abs(1-x)) == 10, x, to_poly_solve=True)
        [x == -10, x == 12]

        sage: var('Q')
        Q
        sage: solve(Q*sqrt(Q^2 + 2) - 1, Q)
        [Q == 1/sqrt(Q^2 + 2)]
        sage: solve(Q*sqrt(Q^2 + 2) - 1, Q, to_poly_solve=True)
        [Q == 1/sqrt(-sqrt(2) + 1), Q == 1/sqrt(sqrt(2) + 1)]

    In some cases there may be infinitely many solutions indexed by a dummy variable. If it begins with `z`, it is implicitly assumed to be an integer, a real if with `r`, and so on:

        sage: solve( sin(x)==cos(x), x, to_poly_solve=True)
        [x == 1/4*pi + pi*z...]

    An effort is made to only return solutions that satisfy the current assumptions:

        sage: solve(x^2==4, x)
        [x == -2, x == 2]
        sage: assume(x<0)
        sage: solve(x^2==4, x)
        [x == -2]
        sage: solve((x^2-4)^2 == 0, x, multiplicities=True)
        ([x == -2], [2])
        sage: solve(x^2==2, x)
        [x == -sqrt(2)]
        sage: assume(x, 'rational')
        sage: solve(x^2 == 2, x)
        []
        sage: solve(x^2==2-z, x)
        [x == -sqrt(-z + 2)]
        sage: solve((x-z)^2==2, x)
        [x == z - sqrt(2), x == z + sqrt(2)]

    There is still room for improvement:

        sage: assume(x, 'integer')
        sage: assume(z, 'integer')
        sage: solve((x-z)^2==2, x)
        [x == z - sqrt(2), x == z + sqrt(2)]

        sage: forget()

    In some cases it may be worthwhile to directly use `to_poly_solve` if one suspects some answers are being missed:

        sage: solve(cos(x)==0, x)
        [x == 1/2*pi]
        sage: solve(cos(x)==0, x, to_poly_solve=True)
        [x == 1/2*pi]
        sage: solve(cos(x)==0, x, to_poly_solve='force')
        [x == 1/2*pi + pi*z77]

    The same may also apply if a returned unsolved expression has a denominator, but the original one did not:

        sage: solve(cos(x) * sin(x) == 1/2, x, to_poly_solve=True)
        [sin(x) == 1/2/cos(x)]
        sage: solve(cos(x) * sin(x) == 1/2, x, to_poly_solve=True, explicit_solutions=True)
        [x == 1/4*pi + pi*z...]
        sage: solve(cos(x) * sin(x) == 1/2, x, to_poly_solve='force')
        [x == 1/4*pi + pi*z...]

    We can also solve for several variables:

        sage: var('b, c')
        (b, c)
        sage: solve((b-1)*(c-1), [b,c])
        [[b == 1, c == r4], [b == r5, c == 1]]

    Some basic inequalities can be also solved:

        sage: x,y=var('x,y'); (ln(x)-ln(y)>0).solve(x)
        [[log(x) - log(y) > 0]]

        sage: x,y=var('x,y'); (ln(x)>ln(y)).solve(x) # not tested - output depends on system
        [[0 < y, y < x, 0 < x]]
        [[y < x, 0 < y]]

    TESTS:

    trac ticket #7325 (solving inequalities):

        sage: (x^2>1).solve(x)
        [[x < -1], [x > 1]]

    Catch error message from Maxima:

        sage: solve(acot(x),x)
        []

        sage: solve(acot(x),x,to_poly_solve=True)
        []

    trac ticket #7491 fixed:

        sage: y=var('y')
        sage: solve(y==y,y)
        [y == r1]
        sage: solve(y==y,y,multiplicities=True)
        ([y == r1], [])

        sage: from sage.symbolic.assumptions import GenericDeclaration
        sage: GenericDeclaration(x, 'rational').assume()
        sage: solve(x^2 == 2, x)
        []
        sage: forget()

    trac ticket #8390 fixed:

        sage: solve(sin(x)==1/2,x)
        [x == 1/6*pi]

        sage: solve(sin(x)==1/2,x,to_poly_solve=True)
        [x == 1/6*pi]

        sage: solve(sin(x)==1/2, x, to_poly_solve='force')
        [x == 1/6*pi + 2*pi*z..., x == 5/6*pi + 2*pi*z...]

    trac ticket #11618 fixed:

        sage: g(x)=0
        sage: solve(g(x)==0,x,solution_dict=True)
        [{x: r1}]

    trac ticket #13286 fixed:

        sage: solve([x-4], [x])
        [x == 4]

    trac ticket #13645: fixed:

        sage: x.solve((1,2))
        Traceback (most recent call last):
        ...
        TypeError: (1, 2) are not valid variables.

    trac ticket #17128: fixed:

        sage: var('x,y')
        (x, y)
        sage: f = x+y
        sage: sol = f.solve([x, y], solution_dict=True)
        sage: sol[0].get(x) + sol[0].get(y)
        0

    trac ticket #16651 fixed:

        sage: (x^7-x-1).solve(x, to_poly_solve=True)     # abs tol 1e-6
        [x == 1.11277569705,
         x == (-0.363623519329 - 0.952561195261*I),
         x == (0.617093477784 - 0.900864951949*I),
         x == (-0.809857800594 - 0.262869645851*I),
         x == (-0.809857800594 + 0.262869645851*I),
         x == (0.617093477784 + 0.900864951949*I),
         x == (-0.363623519329 + 0.952561195261*I)]

  `sqrt`(hold=False)

    Return the square root of this expression

    EXAMPLES:

        sage: var('x, y')
        (x, y)
        sage: SR(2).sqrt()
        sqrt(2)
        sage: (x^2+y^2).sqrt()
        sqrt(x^2 + y^2)
        sage: (x^2).sqrt()
        sqrt(x^2)

    Using the `hold` parameter it is possible to prevent automatic evaluation:

        sage: SR(4).sqrt()
        2
        sage: SR(4).sqrt(hold=True)
        sqrt(4)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(4).sqrt(hold=True); a.simplify()
        2

    To use this parameter in functional notation, you must coerce to the symbolic ring:

        sage: sqrt(SR(4),hold=True)
        sqrt(4)
        sage: sqrt(4,hold=True)
        Traceback (most recent call last):
        ...
        TypeError: _do_sqrt() got an unexpected keyword argument 'hold'

  `step`(hold=False)

    Return the value of the Heaviside step function, which is 0 for negative x, 1/2 for 0, and 1 for positive x.

    EXAMPLES:

        sage: x = var('x')
        sage: SR(1.5).step()
        1
        sage: SR(0).step()
        1/2
        sage: SR(-1/2).step()
        0
        sage: SR(float(-1)).step()
        0

    Using the `hold` parameter it is possible to prevent automatic evaluation:

        sage: SR(2).step()
        1
        sage: SR(2).step(hold=True)
        step(2)

  `subs`(in_dict=None, **kwds)

    EXAMPLES:

        sage: var('x,y,z,a,b,c,d,f,g')
        (x, y, z, a, b, c, d, f, g)
        sage: w0 = SR.wild(0); w1 = SR.wild(1)
        sage: t = a^2 + b^2 + (x+y)^3

        # substitute with keyword arguments (works only with symbols)
        sage: t.subs(a=c)
        (x + y)^3 + b^2 + c^2

        # substitute with a dictionary argument
        sage: t.subs({a^2: c})
        (x + y)^3 + b^2 + c

        sage: t.subs({w0^2: w0^3})
        a^3 + b^3 + (x + y)^3

        # substitute with a relational expression
        sage: t.subs(w0^2 == w0^3)
        a^3 + b^3 + (x + y)^3

        sage: t.subs(w0==w0^2)
        (x^2 + y^2)^18 + a^16 + b^16

        # more than one keyword argument is accepted
        sage: t.subs(a=b, b=c)
        (x + y)^3 + b^2 + c^2

        # using keyword arguments with a dictionary is allowed
        sage: t.subs({a:b}, b=c)
        (x + y)^3 + b^2 + c^2

        # in this case keyword arguments override the dictionary
        sage: t.subs({a:b}, a=c)
        (x + y)^3 + b^2 + c^2

        sage: t.subs({a:b, b:c})
        (x + y)^3 + b^2 + c^2

    TESTS:

        sage: # no arguments return the same expression
        sage: t.subs()
        (x + y)^3 + a^2 + b^2

        # similarly for an empty dictionary argument
        sage: t.subs({})
        (x + y)^3 + a^2 + b^2

        # non keyword or dictionary argument returns error
        sage: t.subs(5)
        Traceback (most recent call last):
        ...
        TypeError: subs takes either a set of keyword arguments, a dictionary, or a symbolic relational expression

        # substitutions with infinity
        sage: (x/y).subs(y=oo)
        0
        sage: (x/y).subs(x=oo)
        Traceback (most recent call last):
        ...
        RuntimeError: indeterminate expression: infinity * f(x) encountered.
        sage: (x*y).subs(x=oo)
        Traceback (most recent call last):
        ...
        RuntimeError: indeterminate expression: infinity * f(x) encountered.
        sage: (x^y).subs(x=oo)
        Traceback (most recent call last):
        ...
        ValueError: power::eval(): pow(Infinity, f(x)) is not defined.
        sage: (x^y).subs(y=oo)
        Traceback (most recent call last):
        ...
        ValueError: power::eval(): pow(f(x), infinity) is not defined.
        sage: (x+y).subs(x=oo)
        +Infinity
        sage: (x-y).subs(y=oo)
        -Infinity
        sage: gamma(x).subs(x=-1)
        Infinity
        sage: 1/gamma(x).subs(x=-1)
        0

    Verify that this operation does not modify the passed dictionary (trac ticket #6622):

        sage: var('v t')
        (v, t)
        sage: f = v*t
        sage: D = {v: 2}
        sage: f(D, t=3)
        6
        sage: D
        {v: 2}

    Check if trac ticket #9891 is fixed:

        sage: exp(x).subs(x=log(x))
        x

    Check if trac ticket #13587 is fixed:

        sage: t = tan(x)^2 - tan(x)
        sage: t.subs(x=pi/2)
        Infinity
        sage: u = gamma(x) - gamma(x-1)
        sage: u.subs(x=-1)
        Infinity

  `subs_expr`(*equations)

    Given a dictionary of key:value pairs, substitute all occurrences of key for value in self. The substitutions can also be given as a number of symbolic equalities key == value; see the examples.

    Warning

    This is a formal pattern substitution, which may or may not have any mathematical meaning. The exact rules used at present in Sage are determined by Maxima’s subst command. Sometimes patterns are not replaced even though one would think they should be - see examples below.

    EXAMPLES:

        sage: f = x^2 + 1
        sage: f.subs_expr(x^2 == x)
        x + 1

        sage: var('x,y,z'); f = x^3 + y^2 + z
        (x, y, z)
        sage: f.subs_expr(x^3 == y^2, z == 1)
        2*y^2 + 1

    Or the same thing giving the substitutions as a dictionary:

        sage: f.subs_expr({x^3:y^2, z:1})
        2*y^2 + 1

        sage: f = x^2 + x^4
        sage: f.subs_expr(x^2 == x)
        x^4 + x
        sage: f = cos(x^2) + sin(x^2)
        sage: f.subs_expr(x^2 == x)
        cos(x) + sin(x)

        sage: f(x,y,t) = cos(x) + sin(y) + x^2 + y^2 + t
        sage: f.subs_expr(y^2 == t)
        (x, y, t) |--> x^2 + 2*t + cos(x) + sin(y)

    The following seems really weird, but it is what Maple does:

        sage: f.subs_expr(x^2 + y^2 == t)
        (x, y, t) |--> x^2 + y^2 + t + cos(x) + sin(y)
        sage: maple.eval('subs(x^2 + y^2 = t, cos(x) + sin(y) + x^2 + y^2 + t)')          # optional - maple
        'cos(x)+sin(y)+x^2+y^2+t'
        sage: maxima.quit()
        sage: maxima.eval('cos(x) + sin(y) + x^2 + y^2 + t, x^2 + y^2 = t')
        'sin(y)+y^2+cos(x)+x^2+t'

    Actually Mathematica does something that makes more sense:

        sage: mathematica.eval('Cos[x] + Sin[y] + x^2 + y^2 + t /. x^2 + y^2 -> t')       # optional - mathematica
        2 t + Cos[x] + Sin[y]

  `substitute`(in_dict=None, **kwds)

    EXAMPLES:

        sage: var('x,y,z,a,b,c,d,f,g')
        (x, y, z, a, b, c, d, f, g)
        sage: w0 = SR.wild(0); w1 = SR.wild(1)
        sage: t = a^2 + b^2 + (x+y)^3

        # substitute with keyword arguments (works only with symbols)
        sage: t.subs(a=c)
        (x + y)^3 + b^2 + c^2

        # substitute with a dictionary argument
        sage: t.subs({a^2: c})
        (x + y)^3 + b^2 + c

        sage: t.subs({w0^2: w0^3})
        a^3 + b^3 + (x + y)^3

        # substitute with a relational expression
        sage: t.subs(w0^2 == w0^3)
        a^3 + b^3 + (x + y)^3

        sage: t.subs(w0==w0^2)
        (x^2 + y^2)^18 + a^16 + b^16

        # more than one keyword argument is accepted
        sage: t.subs(a=b, b=c)
        (x + y)^3 + b^2 + c^2

        # using keyword arguments with a dictionary is allowed
        sage: t.subs({a:b}, b=c)
        (x + y)^3 + b^2 + c^2

        # in this case keyword arguments override the dictionary
        sage: t.subs({a:b}, a=c)
        (x + y)^3 + b^2 + c^2

        sage: t.subs({a:b, b:c})
        (x + y)^3 + b^2 + c^2

    TESTS:

        sage: # no arguments return the same expression
        sage: t.subs()
        (x + y)^3 + a^2 + b^2

        # similarly for an empty dictionary argument
        sage: t.subs({})
        (x + y)^3 + a^2 + b^2

        # non keyword or dictionary argument returns error
        sage: t.subs(5)
        Traceback (most recent call last):
        ...
        TypeError: subs takes either a set of keyword arguments, a dictionary, or a symbolic relational expression

        # substitutions with infinity
        sage: (x/y).subs(y=oo)
        0
        sage: (x/y).subs(x=oo)
        Traceback (most recent call last):
        ...
        RuntimeError: indeterminate expression: infinity * f(x) encountered.
        sage: (x*y).subs(x=oo)
        Traceback (most recent call last):
        ...
        RuntimeError: indeterminate expression: infinity * f(x) encountered.
        sage: (x^y).subs(x=oo)
        Traceback (most recent call last):
        ...
        ValueError: power::eval(): pow(Infinity, f(x)) is not defined.
        sage: (x^y).subs(y=oo)
        Traceback (most recent call last):
        ...
        ValueError: power::eval(): pow(f(x), infinity) is not defined.
        sage: (x+y).subs(x=oo)
        +Infinity
        sage: (x-y).subs(y=oo)
        -Infinity
        sage: gamma(x).subs(x=-1)
        Infinity
        sage: 1/gamma(x).subs(x=-1)
        0

    Verify that this operation does not modify the passed dictionary (trac ticket #6622):

        sage: var('v t')
        (v, t)
        sage: f = v*t
        sage: D = {v: 2}
        sage: f(D, t=3)
        6
        sage: D
        {v: 2}

    Check if trac ticket #9891 is fixed:

        sage: exp(x).subs(x=log(x))
        x

    Check if trac ticket #13587 is fixed:

        sage: t = tan(x)^2 - tan(x)
        sage: t.subs(x=pi/2)
        Infinity
        sage: u = gamma(x) - gamma(x-1)
        sage: u.subs(x=-1)
        Infinity

  `substitute_expression`(*equations)

    Given a dictionary of key:value pairs, substitute all occurrences of key for value in self. The substitutions can also be given as a number of symbolic equalities key == value; see the examples.

    Warning

    This is a formal pattern substitution, which may or may not have any mathematical meaning. The exact rules used at present in Sage are determined by Maxima’s subst command. Sometimes patterns are not replaced even though one would think they should be - see examples below.

    EXAMPLES:

        sage: f = x^2 + 1
        sage: f.subs_expr(x^2 == x)
        x + 1

        sage: var('x,y,z'); f = x^3 + y^2 + z
        (x, y, z)
        sage: f.subs_expr(x^3 == y^2, z == 1)
        2*y^2 + 1

    Or the same thing giving the substitutions as a dictionary:

        sage: f.subs_expr({x^3:y^2, z:1})
        2*y^2 + 1

        sage: f = x^2 + x^4
        sage: f.subs_expr(x^2 == x)
        x^4 + x
        sage: f = cos(x^2) + sin(x^2)
        sage: f.subs_expr(x^2 == x)
        cos(x) + sin(x)

        sage: f(x,y,t) = cos(x) + sin(y) + x^2 + y^2 + t
        sage: f.subs_expr(y^2 == t)
        (x, y, t) |--> x^2 + 2*t + cos(x) + sin(y)

    The following seems really weird, but it is what Maple does:

        sage: f.subs_expr(x^2 + y^2 == t)
        (x, y, t) |--> x^2 + y^2 + t + cos(x) + sin(y)
        sage: maple.eval('subs(x^2 + y^2 = t, cos(x) + sin(y) + x^2 + y^2 + t)')          # optional - maple
        'cos(x)+sin(y)+x^2+y^2+t'
        sage: maxima.quit()
        sage: maxima.eval('cos(x) + sin(y) + x^2 + y^2 + t, x^2 + y^2 = t')
        'sin(y)+y^2+cos(x)+x^2+t'

    Actually Mathematica does something that makes more sense:

        sage: mathematica.eval('Cos[x] + Sin[y] + x^2 + y^2 + t /. x^2 + y^2 -> t')       # optional - mathematica
        2 t + Cos[x] + Sin[y]

  `substitute_function`(original, new)

    Return this symbolic expressions all occurrences of the function original replaced with the function new.

    EXAMPLES:

        sage: x,y = var('x,y')
        sage: foo = function('foo'); bar = function('bar')
        sage: f = foo(x) + 1/foo(pi*y)
        sage: f.substitute_function(foo, bar)
        1/bar(pi*y) + bar(x)

  `subtract_from_both_sides`(x)

    Return a relation obtained by subtracting x from both sides of this relation.

    EXAMPLES:

        sage: eqn = x*sin(x)*sqrt(3) + sqrt(2) > cos(sin(x))
        sage: eqn.subtract_from_both_sides(sqrt(2))
        sqrt(3)*x*sin(x) > -sqrt(2) + cos(sin(x))
        sage: eqn.subtract_from_both_sides(cos(sin(x)))
        sqrt(3)*x*sin(x) + sqrt(2) - cos(sin(x)) > 0

  `sum`(*args, **kwds)

    Return the symbolic sum \(\sum_{v = a}^b self\)

    with respect to the variable \(v\) with endpoints \(a\) and \(b\).

    INPUT:

    - `v` - a variable or variable name

    - `a` - lower endpoint of the sum

    - `b` - upper endpoint of the sum

    - `algorithm` - (default: `'maxima'`) one of

      > - `'maxima'` - use Maxima (the default)
      > - `'maple'` - (optional) use Maple
      > - `'mathematica'` - (optional) use Mathematica
      > - `'giac'` - (optional) use Giac

    EXAMPLES:

        sage: k, n = var('k,n')
        sage: k.sum(k, 1, n).factor()
        1/2*(n + 1)*n

        sage: (1/k^4).sum(k, 1, oo)
        1/90*pi^4

        sage: (1/k^5).sum(k, 1, oo)
        zeta(5)

    Warning

    This function only works with symbolic expressions. To sum any other objects like list elements or function return values, please use python summation, see http://docs.python.org/library/functions.html#sum

    In particular, this does not work:

        sage: n = var('n')
        sage: list=[1,2,3,4,5]
        sage: sum(list[n],n,0,3)
        Traceback (most recent call last):
        ...
        TypeError: unable to convert x (=n) to an integer

    Use python `sum()` instead:

        sage: sum(list[n] for n in range(4))
        10

    Also, only a limited number of functions are recognized in symbolic sums:

        sage: sum(valuation(n,2),n,1,5)
        Traceback (most recent call last):
        ...
        AttributeError: 'sage.symbolic.expression.Expression' object has no attribute 'valuation'

    Again, use python `sum()`:

        sage: sum(valuation(n+1,2) for n in range(5))
        3

    (now back to the Sage `sum` examples)

    A well known binomial identity:

        sage: assume(n>=0)
        sage: binomial(n,k).sum(k, 0, n)
        2^n

    And some truncations thereof:

        sage: binomial(n,k).sum(k,1,n)
        2^n - 1
        sage: binomial(n,k).sum(k,2,n)
        2^n - n - 1
        sage: binomial(n,k).sum(k,0,n-1)
        2^n - 1
        sage: binomial(n,k).sum(k,1,n-1)
        2^n - 2

    The binomial theorem:

        sage: x, y = var('x, y')
        sage: (binomial(n,k) * x^k * y^(n-k)).sum(k, 0, n)
        (x + y)^n

        sage: (k * binomial(n, k)).sum(k, 1, n)
        2^(n - 1)*n

        sage: ((-1)^k*binomial(n,k)).sum(k, 0, n)
        0

        sage: (2^(-k)/(k*(k+1))).sum(k, 1, oo)
        -log(2) + 1

    Summing a hypergeometric term:

        sage: (binomial(n, k) * factorial(k) / factorial(n+1+k)).sum(k, 0, n)
        1/2*sqrt(pi)/factorial(n + 1/2)

    We check a well known identity:

        sage: bool((k^3).sum(k, 1, n) == k.sum(k, 1, n)^2)
        True

    A geometric sum:

        sage: a, q = var('a, q')
        sage: (a*q^k).sum(k, 0, n)
        (a*q^(n + 1) - a)/(q - 1)

    The geometric series:

        sage: assume(abs(q) < 1)
        sage: (a*q^k).sum(k, 0, oo)
        -a/(q - 1)

    A divergent geometric series. Do not forget to forget your assumptions:

        sage: forget()
        sage: assume(q > 1)
        sage: (a*q^k).sum(k, 0, oo)
        Traceback (most recent call last):
        ...
        ValueError: Sum is divergent.

    This summation only Mathematica can perform:

        sage: (1/(1+k^2)).sum(k, -oo, oo, algorithm = 'mathematica')     # optional - mathematica
        pi*coth(pi)

    Use Giac to perform this summation:

        sage: (sum(1/(1+k^2), k, -oo, oo, algorithm = 'giac')).factor()       # optional - giac
        (e^(2*pi) + 1)*pi/((e^pi - 1)*(e^pi + 1))

    Use Maple as a backend for summation:

        sage: (binomial(n,k)*x^k).sum(k, 0, n, algorithm = 'maple')      # optional - maple
        (x + 1)^n

    Check that the sum in trac ticket #10682 is done right:

        sage: sum(binomial(n,k)*k^2, k, 2, n)
        1/4*(n^2 + n)*2^n - n

    Note

    1. Sage can currently only understand a subset of the output of Maxima, Maple and Mathematica, so even if the chosen backend can perform the summation the result might not be convertable into a Sage expression.
  `tan`(hold=False)

    EXAMPLES:

        sage: var('x, y')
        (x, y)
        sage: tan(x^2 + y^2)
        tan(x^2 + y^2)
        sage: tan(sage.symbolic.constants.pi/2)
        Infinity
        sage: tan(SR(1))
        tan(1)
        sage: tan(SR(RealField(150)(1)))
        1.5574077246549022305069748074583601730872508

    To prevent automatic evaluation use the `hold` argument:

        sage: (pi/12).tan()
        -sqrt(3) + 2
        sage: (pi/12).tan(hold=True)
        tan(1/12*pi)

    This also works using functional notation:

        sage: tan(pi/12,hold=True)
        tan(1/12*pi)
        sage: tan(pi/12)
        -sqrt(3) + 2

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = (pi/12).tan(hold=True); a.simplify()
        -sqrt(3) + 2

    TESTS:

        sage: SR(oo).tan()
        Traceback (most recent call last):
        ...
        RuntimeError: tan_eval(): tan(infinity) encountered
        sage: SR(-oo).tan()
        Traceback (most recent call last):
        ...
        RuntimeError: tan_eval(): tan(infinity) encountered
        sage: SR(unsigned_infinity).tan()
        Traceback (most recent call last):
        ...
        RuntimeError: tan_eval(): tan(infinity) encountered

  `tanh`(hold=False)

    Return tanh of self.

    We have \(\tanh(x) = \sinh(x) / \cosh(x)\).

    EXAMPLES:

        sage: x.tanh()
        tanh(x)
        sage: SR(1).tanh()
        tanh(1)
        sage: SR(0).tanh()
        0
        sage: SR(1.0).tanh()
        0.761594155955765
        sage: maxima('tanh(1.0)')
        0.7615941559557649
        sage: plot(lambda x: SR(x).tanh(), -1, 1)
        Graphics object consisting of 1 graphics primitive

    To prevent automatic evaluation use the `hold` argument:

        sage: arcsinh(x).tanh()
        x/sqrt(x^2 + 1)
        sage: arcsinh(x).tanh(hold=True)
        tanh(arcsinh(x))

    This also works using functional notation:

        sage: tanh(arcsinh(x),hold=True)
        tanh(arcsinh(x))
        sage: tanh(arcsinh(x))
        x/sqrt(x^2 + 1)

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = arcsinh(x).tanh(hold=True); a.simplify()
        x/sqrt(x^2 + 1)

    TESTS:

        sage: SR(oo).tanh()
        1
        sage: SR(-oo).tanh()
        -1
        sage: SR(unsigned_infinity).tanh()
        Traceback (most recent call last):
        ...
        RuntimeError: tanh_eval(): tanh(unsigned_infinity) encountered

  `taylor`(*args)

    Expand this symbolic expression in a truncated Taylor or Laurent series in the variable \(v\) around the point \(a\), containing terms through \((x - a)^n\). Functions in more variables is also supported.

    INPUT:

    - `*args` - the following notation is supported
      - `x, a, n` - variable, point, degree
      - `(x, a), (y, b), n` - variables with points, degree of polynomial

    EXAMPLES:

        sage: var('a, x, z')
        (a, x, z)
        sage: taylor(a*log(z), z, 2, 3)
        1/24*a*(z - 2)^3 - 1/8*a*(z - 2)^2 + 1/2*a*(z - 2) + a*log(2)

        sage: taylor(sqrt (sin(x) + a*x + 1), x, 0, 3)
        1/48*(3*a^3 + 9*a^2 + 9*a - 1)*x^3 - 1/8*(a^2 + 2*a + 1)*x^2 + 1/2*(a + 1)*x + 1

        sage: taylor (sqrt (x + 1), x, 0, 5)
        7/256*x^5 - 5/128*x^4 + 1/16*x^3 - 1/8*x^2 + 1/2*x + 1

        sage: taylor (1/log (x + 1), x, 0, 3)
        -19/720*x^3 + 1/24*x^2 - 1/12*x + 1/x + 1/2

        sage: taylor (cos(x) - sec(x), x, 0, 5)
        -1/6*x^4 - x^2

        sage: taylor ((cos(x) - sec(x))^3, x, 0, 9)
        -1/2*x^8 - x^6

        sage: taylor (1/(cos(x) - sec(x))^3, x, 0, 5)
        -15377/7983360*x^4 - 6767/604800*x^2 + 11/120/x^2 + 1/2/x^4 - 1/x^6 - 347/15120

    TESTS:

    Check that ticket trac ticket #7472 is fixed (Taylor polynomial in more variables):

        sage: x,y=var('x y'); taylor(x*y^3,(x,1),(y,1),4)
        (x - 1)*(y - 1)^3 + 3*(x - 1)*(y - 1)^2 + (y - 1)^3 + 3*(x - 1)*(y - 1) + 3*(y - 1)^2 + x + 3*y - 3
        sage: expand(_)
        x*y^3

  `test_relation`(ntests=20, domain=None, proof=True)

    Test this relation at several random values, attempting to find a contradiction. If this relation has no variables, it will also test this relation after casting into the domain.

    Because the interval fields never return false positives, we can be assured that if True or False is returned (and proof is False) then the answer is correct.

    INPUT:

    - `ntests` – (default `20`) the number of iterations to run
    - `domain` – (optional) the domain from which to draw the random values defaults to `CIF` for equality testing and `RIF` for order testing
    - `proof` – (default `True`) if `False` and the domain is an interval field, regard overlapping (potentially equal) intervals as equal, and return `True` if all tests succeeded.

    OUTPUT:

    Boolean or `NotImplemented`, meaning

    - `True` – this relation holds in the domain and has no variables.
    - `False` – a contradiction was found.
    - `NotImplemented` – no contradiction found.

    EXAMPLES:

        sage: (3 < pi).test_relation()
        True
        sage: (0 >= pi).test_relation()
        False
        sage: (exp(pi) - pi).n()
        19.9990999791895
        sage: (exp(pi) - pi == 20).test_relation()
        False
        sage: (sin(x)^2 + cos(x)^2 == 1).test_relation()
        NotImplemented
        sage: (sin(x)^2 + cos(x)^2 == 1).test_relation(proof=False)
        True
        sage: (x == 1).test_relation()
        False
        sage: var('x,y')
        (x, y)
        sage: (x < y).test_relation()
        False

    TESTS:

        sage: all_relations = [op for name, op in sorted(operator.__dict__.items()) if len(name) == 2]
        sage: all_relations
        [<built-in function eq>, <built-in function ge>, <built-in function gt>, <built-in function le>, <built-in function lt>, <built-in function ne>]
        sage: [op(3, pi).test_relation() for op in all_relations]
        [False, False, False, True, True, True]
        sage: [op(pi, pi).test_relation() for op in all_relations]
        [True, True, False, True, False, False]

        sage: s = 'some_very_long_variable_name_which_will_definitely_collide_if_we_use_a_reasonable_length_bound_for_a_hash_that_respects_lexicographic_order'
        sage: t1, t2 = var(','.join([s+'1',s+'2']))
        sage: (t1 == t2).test_relation()
        False
        sage: (cot(pi + x) == 0).test_relation()
        NotImplemented

  `trailing_coeff`(s)

    Return the trailing coefficient of s in self, i.e., the coefficient of the smallest power of s in self.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)
        sage: f = 100 + a*x + x^3*sin(x*y) + x*y + x/y + 2*sin(x*y)/x; f
        x^3*sin(x*y) + a*x + x*y + x/y + 2*sin(x*y)/x + 100
        sage: f.trailing_coefficient(x)
        2*sin(x*y)
        sage: f.trailing_coefficient(y)
        x
        sage: f.trailing_coefficient(sin(x*y))
        a*x + x*y + x/y + 100

  `trailing_coefficient`(s)

    Return the trailing coefficient of s in self, i.e., the coefficient of the smallest power of s in self.

    EXAMPLES:

        sage: var('x,y,a')
        (x, y, a)
        sage: f = 100 + a*x + x^3*sin(x*y) + x*y + x/y + 2*sin(x*y)/x; f
        x^3*sin(x*y) + a*x + x*y + x/y + 2*sin(x*y)/x + 100
        sage: f.trailing_coefficient(x)
        2*sin(x*y)
        sage: f.trailing_coefficient(y)
        x
        sage: f.trailing_coefficient(sin(x*y))
        a*x + x*y + x/y + 100

  `trig_expand`(full=False, half_angles=False, plus=True, times=True)

    Expand trigonometric and hyperbolic functions of sums of angles and of multiple angles occurring in self. For best results, self should already be expanded.

    INPUT:

    - `full` - (default: False) To enhance user control of simplification, this function expands only one level at a time by default, expanding sums of angles or multiple angles. To obtain full expansion into sines and cosines immediately, set the optional parameter full to True.
    - `half_angles` - (default: False) If True, causes half-angles to be simplified away.
    - `plus` - (default: True) Controls the sum rule; expansion of sums (e.g. ‘sin(x + y)’) will take place only if plus is True.
    - `times` - (default: True) Controls the product rule, expansion of products (e.g. sin(2*x)) will take place only if times is True.

    OUTPUT:

    A symbolic expression.

    EXAMPLES:

        sage: sin(5*x).expand_trig()
        5*cos(x)^4*sin(x) - 10*cos(x)^2*sin(x)^3 + sin(x)^5
        sage: cos(2*x + var('y')).expand_trig()
        cos(2*x)*cos(y) - sin(2*x)*sin(y)

    We illustrate various options to this function:

        sage: f = sin(sin(3*cos(2*x))*x)
        sage: f.expand_trig()
        sin((3*cos(cos(2*x))^2*sin(cos(2*x)) - sin(cos(2*x))^3)*x)
        sage: f.expand_trig(full=True)
        sin((3*(cos(cos(x)^2)*cos(sin(x)^2) + sin(cos(x)^2)*sin(sin(x)^2))^2*(cos(sin(x)^2)*sin(cos(x)^2) - cos(cos(x)^2)*sin(sin(x)^2)) - (cos(sin(x)^2)*sin(cos(x)^2) - cos(cos(x)^2)*sin(sin(x)^2))^3)*x)
        sage: sin(2*x).expand_trig(times=False)
        sin(2*x)
        sage: sin(2*x).expand_trig(times=True)
        2*cos(x)*sin(x)
        sage: sin(2 + x).expand_trig(plus=False)
        sin(x + 2)
        sage: sin(2 + x).expand_trig(plus=True)
        cos(x)*sin(2) + cos(2)*sin(x)
        sage: sin(x/2).expand_trig(half_angles=False)
        sin(1/2*x)
        sage: sin(x/2).expand_trig(half_angles=True)
        (-1)^floor(1/2*x/pi)*sqrt(-1/2*cos(x) + 1/2)

    ALIASES:

    `trig_expand()` and `expand_trig()` are the same

  `trig_reduce`(var=None)

    Combine products and powers of trigonometric and hyperbolic sin’s and cos’s of x into those of multiples of x. It also tries to eliminate these functions when they occur in denominators.

    INPUT:

    - `self` - a symbolic expression
    - `var` - (default: None) the variable which is used for these transformations. If not specified, all variables are used.

    OUTPUT:

    A symbolic expression.

    EXAMPLES:

        sage: y=var('y')
        sage: f=sin(x)*cos(x)^3+sin(y)^2
        sage: f.reduce_trig()
        -1/2*cos(2*y) + 1/8*sin(4*x) + 1/4*sin(2*x) + 1/2

    To reduce only the expressions involving x we use optional parameter:

        sage: f.reduce_trig(x)
        sin(y)^2 + 1/8*sin(4*x) + 1/4*sin(2*x)

    ALIASES: `trig_reduce()` and `reduce_trig()` are the same

  `trig_simplify`(expand=True)

    Optionally expand and then employ identities such as \(\sin(x)^2 + \cos(x)^2 = 1\), \(\cosh(x)^2 - \sinh(x)^2 = 1\), \(\sin(x)\csc(x) = 1\), or \(\tanh(x)=\sinh(x)/\cosh(x)\) to simplify expressions containing tan, sec, etc., to sin, cos, sinh, cosh.

    INPUT:

    - `self` - symbolic expression
    - `expand` - (default:True) if True, expands trigonometric and hyperbolic functions of sums of angles and of multiple angles occurring in `self` first. For best results, `self` should be expanded. See also `expand_trig()` to get more controls on this expansion.

    ALIAS: `trig_simplify()` and `simplify_trig()` are the same

    EXAMPLES:

        sage: f = sin(x)^2 + cos(x)^2; f
        cos(x)^2 + sin(x)^2
        sage: f.simplify()
        cos(x)^2 + sin(x)^2
        sage: f.simplify_trig()
        1
        sage: h = sin(x)*csc(x)
        sage: h.simplify_trig()
        1
        sage: k = tanh(x)*cosh(2*x)
        sage: k.simplify_trig()
        (2*sinh(x)^3 + sinh(x))/cosh(x)

    In some cases we do not want to expand:

        sage: f=tan(3*x)
        sage: f.simplify_trig()
        (4*cos(x)^2 - 1)*sin(x)/(4*cos(x)^3 - 3*cos(x))
        sage: f.simplify_trig(False)
        sin(3*x)/cos(3*x)

  `truncate`()

    Given a power series or expression, return the corresponding expression without the big oh.

    INPUT:

    - `self` – a series as output by the `series()` command.

    OUTPUT:

    A symbolic expression.

    EXAMPLES:

        sage: f = sin(x)/x^2
        sage: f.truncate()
        sin(x)/x^2
        sage: f.series(x,7)
        1*x^(-1) + (-1/6)*x + 1/120*x^3 + (-1/5040)*x^5 + Order(x^7)
        sage: f.series(x,7).truncate()
        -1/5040*x^5 + 1/120*x^3 - 1/6*x + 1/x
        sage: f.series(x==1,3).truncate().expand()
        -2*x^2*cos(1) + 5/2*x^2*sin(1) + 5*x*cos(1) - 7*x*sin(1) - 3*cos(1) + 11/2*sin(1)

  `unit`(s)

    Return the unit of this expression when considered as a polynomial in `s`.

    See also `content()`, `primitive_part()`, and `unit_content_primitive()`.

    INPUT:

    - `s` – a symbolic expression.

    OUTPUT:

    The unit part of a polynomial as a symbolic expression. It is defined as the sign of the leading coefficient.

    EXAMPLES:

        sage: (2*x+4).unit(x)
        1
        sage: (-2*x+1).unit(x)
        -1
        sage: (2*x+1/2).unit(x)
        1
        sage: var('y')
        y
        sage: (2*x - 4*sin(y)).unit(sin(y))
        -1

  `unit_content_primitive`(s)

    Return the factorization into unit, content, and primitive part.

    INPUT:

    - `s` – a symbolic expression, usually a symbolic variable. The whole symbolic expression `self` will be considered as a univariate polynomial in `s`.

    OUTPUT:

    A triple (unit, content, primitive polynomial)` containing the `unit`, `content`, and `primitive polynomial`. Their product equals `self`.

    EXAMPLES:

        sage: var('x,y')
        (x, y)
        sage: ex = 9*x^3*y+3*y
        sage: ex.unit_content_primitive(x)
        (1, 3*y, 3*x^3 + 1)
        sage: ex.unit_content_primitive(y)
        (1, 9*x^3 + 3, y)

  `variables`()

    Return sorted tuple of variables that occur in this expression.

    EXAMPLES:

        sage: (x,y,z) = var('x,y,z')
        sage: (x+y).variables()
        (x, y)
        sage: (2*x).variables()
        (x,)
        sage: (x^y).variables()
        (x, y)
        sage: sin(x+y^z).variables()
        (x, y, z)

  `zeta`(hold=False)

    EXAMPLES:

        sage: x, y = var('x, y')
        sage: (x/y).zeta()
        zeta(x/y)
        sage: SR(2).zeta()
        1/6*pi^2
        sage: SR(3).zeta()
        zeta(3)
        sage: SR(CDF(0,1)).zeta()  # abs tol 1e-16
        0.003300223685324103 - 0.4181554491413217*I
        sage: CDF(0,1).zeta()  # abs tol 1e-16
        0.003300223685324103 - 0.4181554491413217*I
        sage: plot(lambda x: SR(x).zeta(), -10,10).show(ymin=-3,ymax=3)

    To prevent automatic evaluation use the `hold` argument:

        sage: SR(2).zeta(hold=True)
        zeta(2)

    This also works using functional notation:

        sage: zeta(2,hold=True)
        zeta(2)
        sage: zeta(2)
        1/6*pi^2

    To then evaluate again, we currently must use Maxima via `simplify()`:

        sage: a = SR(2).zeta(hold=True); a.simplify()
        1/6*pi^2

    TESTS:

        sage: t = SR(1).zeta(); t
        Infinity

class `sage.symbolic.expression.``ExpressionIterator`

  Bases: `object`

  x.__init__(...) initializes x; see help(type(x)) for signature

  `next`()

    x.next() -> the next value, or raise StopIteration

`sage.symbolic.expression.``is_Expression`(x)

  Return True if x is a symbolic Expression.

  EXAMPLES:

      sage: from sage.symbolic.expression import is_Expression
      sage: is_Expression(x)
      True
      sage: is_Expression(2)
      False
      sage: is_Expression(SR(2))
      True

`sage.symbolic.expression.``is_SymbolicEquation`(x)

  Return True if x is a symbolic equation.

  EXAMPLES:

  The following two examples are symbolic equations:

      sage: from sage.symbolic.expression import is_SymbolicEquation
      sage: is_SymbolicEquation(sin(x) == x)
      True
      sage: is_SymbolicEquation(sin(x) < x)
      True
      sage: is_SymbolicEquation(x)
      False

  This is not, since `2==3` evaluates to the boolean `False`:

      sage: is_SymbolicEquation(2 == 3)
      False

  However here since both 2 and 3 are coerced to be symbolic, we obtain a symbolic equation:

      sage: is_SymbolicEquation(SR(2) == SR(3))
      True

### Navigation

- index
- modules |
- next |
- previous |
- Symbolic Calculus »