# yt.units.unit_object module¶

A class that represents a unit symbol.

exception yt.units.unit_object.InvalidUnitOperation[source]

Bases: Exception

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class yt.units.unit_object.Unit[source]

Bases: sympy.core.expr.Expr

A symbolic unit, using sympy functionality. We only add “dimensions” so that sympy understands relations between different units.

adjoint()
apart(x=None, **args)

See the apart function in sympy.polys

args

Returns a tuple of arguments of ‘self’.

Examples

>>> from sympy import cot
>>> from sympy.abc import x, y

>>> cot(x).args
(x,)

>>> cot(x).args[0]
x

>>> (x*y).args
(x, y)

>>> (x*y).args[1]
y


Notes

Never use self._args, always use self.args. Only use _args in __new__ when creating a new function. Don’t override .args() from Basic (so that it’s easy to change the interface in the future if needed).

args_cnc(cset=False, warn=True, split_1=True)

Return [commutative factors, non-commutative factors] of self.

self is treated as a Mul and the ordering of the factors is maintained. If cset is True the commutative factors will be returned in a set. If there were repeated factors (as may happen with an unevaluated Mul) then an error will be raised unless it is explicitly supressed by setting warn to False.

Note: -1 is always separated from a Number unless split_1 is False.

>>> from sympy import symbols, oo
>>> A, B = symbols('A B', commutative=0)
>>> x, y = symbols('x y')
>>> (-2*x*y).args_cnc()
[[-1, 2, x, y], []]
>>> (-2.5*x).args_cnc()
[[-1, 2.5, x], []]
>>> (-2*x*A*B*y).args_cnc()
[[-1, 2, x, y], [A, B]]
>>> (-2*x*A*B*y).args_cnc(split_1=False)
[[-2, x, y], [A, B]]
>>> (-2*x*y).args_cnc(cset=True)
[set([-1, 2, x, y]), []]


The arg is always treated as a Mul:

>>> (-2 + x + A).args_cnc()
[[], [x - 2 + A]]
>>> (-oo).args_cnc() # -oo is a singleton
[[-1, oo], []]

as_base_exp()
as_coeff_Add()

Efficiently extract the coefficient of a summation.

as_coeff_Mul(rational=False)

Efficiently extract the coefficient of a product.

as_coeff_add(*deps)

Return the tuple (c, args) where self is written as an Add, a.

c should be a Rational added to any terms of the Add that are independent of deps.

args should be a tuple of all other terms of a; args is empty if self is a Number or if self is independent of deps (when given).

This should be used when you don’t know if self is an Add or not but you want to treat self as an Add or if you want to process the individual arguments of the tail of self as an Add.

• if you know self is an Add and want only the head, use self.args[0];
• if you don’t want to process the arguments of the tail but need the tail then use self.as_two_terms() which gives the head and tail.
• if you want to split self into an independent and dependent parts use self.as_independent(*deps)
>>> from sympy import S
>>> from sympy.abc import x, y
(3, ())
(3, (x,))
>>> (3 + x + y).as_coeff_add(x)
(y + 3, (x,))
(y + 3, ())

as_coeff_exponent(x)

c*x**e -> c,e where x can be any symbolic expression.

as_coeff_mul(*deps, **kwargs)

Return the tuple (c, args) where self is written as a Mul, m.

c should be a Rational multiplied by any terms of the Mul that are independent of deps.

args should be a tuple of all other terms of m; args is empty if self is a Number or if self is independent of deps (when given).

This should be used when you don’t know if self is a Mul or not but you want to treat self as a Mul or if you want to process the individual arguments of the tail of self as a Mul.

• if you know self is a Mul and want only the head, use self.args[0];
• if you don’t want to process the arguments of the tail but need the tail then use self.as_two_terms() which gives the head and tail;
• if you want to split self into an independent and dependent parts use self.as_independent(*deps)
>>> from sympy import S
>>> from sympy.abc import x, y
>>> (S(3)).as_coeff_mul()
(3, ())
>>> (3*x*y).as_coeff_mul()
(3, (x, y))
>>> (3*x*y).as_coeff_mul(x)
(3*y, (x,))
>>> (3*y).as_coeff_mul(x)
(3*y, ())

as_coefficient(expr)

Extracts symbolic coefficient at the given expression. In other words, this functions separates ‘self’ into the product of ‘expr’ and ‘expr’-free coefficient. If such separation is not possible it will return None.

Examples

>>> from sympy import E, pi, sin, I, Poly
>>> from sympy.abc import x

>>> E.as_coefficient(E)
1
>>> (2*E).as_coefficient(E)
2
>>> (2*sin(E)*E).as_coefficient(E)


Two terms have E in them so a sum is returned. (If one were desiring the coefficient of the term exactly matching E then the constant from the returned expression could be selected. Or, for greater precision, a method of Poly can be used to indicate the desired term from which the coefficient is desired.)

>>> (2*E + x*E).as_coefficient(E)
x + 2
>>> _.args[0]  # just want the exact match
2
>>> p = Poly(2*E + x*E); p
Poly(x*E + 2*E, x, E, domain='ZZ')
>>> p.coeff_monomial(E)
2
>>> p.nth(0, 1)
2


Since the following cannot be written as a product containing E as a factor, None is returned. (If the coefficient 2*x is desired then the coeff method should be used.)

>>> (2*E*x + x).as_coefficient(E)
>>> (2*E*x + x).coeff(E)
2*x

>>> (E*(x + 1) + x).as_coefficient(E)

>>> (2*pi*I).as_coefficient(pi*I)
2
>>> (2*I).as_coefficient(pi*I)


coeff()
return sum of terms have a given factor
as_coeff_Add()
separate the additive constant from an expression
as_coeff_Mul()
separate the multiplicative constant from an expression
as_independent()
separate x-dependent terms/factors from others
sympy.polys.polytools.coeff_monomial()
efficiently find the single coefficient of a monomial in Poly
sympy.polys.polytools.nth()
like coeff_monomial but powers of monomial terms are used
as_coefficients_dict()

Return a dictionary mapping terms to their Rational coefficient. Since the dictionary is a defaultdict, inquiries about terms which were not present will return a coefficient of 0. If an expression is not an Add it is considered to have a single term.

Examples

>>> from sympy.abc import a, x
>>> (3*x + a*x + 4).as_coefficients_dict()
{1: 4, x: 3, a*x: 1}
>>> _[a]
0
>>> (3*a*x).as_coefficients_dict()
{a*x: 3}

as_content_primitive(radical=False, clear=True)

This method should recursively remove a Rational from all arguments and return that (content) and the new self (primitive). The content should always be positive and Mul(*foo.as_content_primitive()) == foo. The primitive need no be in canonical form and should try to preserve the underlying structure if possible (i.e. expand_mul should not be applied to self).

Examples

>>> from sympy import sqrt
>>> from sympy.abc import x, y, z

>>> eq = 2 + 2*x + 2*y*(3 + 3*y)


The as_content_primitive function is recursive and retains structure:

>>> eq.as_content_primitive()
(2, x + 3*y*(y + 1) + 1)


Integer powers will have Rationals extracted from the base:

>>> ((2 + 6*x)**2).as_content_primitive()
(4, (3*x + 1)**2)
>>> ((2 + 6*x)**(2*y)).as_content_primitive()
(1, (2*(3*x + 1))**(2*y))


Terms may end up joining once their as_content_primitives are added:

>>> ((5*(x*(1 + y)) + 2*x*(3 + 3*y))).as_content_primitive()
(11, x*(y + 1))
>>> ((3*(x*(1 + y)) + 2*x*(3 + 3*y))).as_content_primitive()
(9, x*(y + 1))
>>> ((3*(z*(1 + y)) + 2.0*x*(3 + 3*y))).as_content_primitive()
(1, 6.0*x*(y + 1) + 3*z*(y + 1))
>>> ((5*(x*(1 + y)) + 2*x*(3 + 3*y))**2).as_content_primitive()
(121, x**2*(y + 1)**2)
>>> ((5*(x*(1 + y)) + 2.0*x*(3 + 3*y))**2).as_content_primitive()
(1, 121.0*x**2*(y + 1)**2)


Radical content can also be factored out of the primitive:

>>> (2*sqrt(2) + 4*sqrt(10)).as_content_primitive(radical=True)
(2, sqrt(2)*(1 + 2*sqrt(5)))


If clear=False (default is True) then content will not be removed from an Add if it can be distributed to leave one or more terms with integer coefficients.

>>> (x/2 + y).as_content_primitive()
(1/2, x + 2*y)
>>> (x/2 + y).as_content_primitive(clear=False)
(1, x/2 + y)

as_expr(*gens)

Convert a polynomial to a SymPy expression.

Examples

>>> from sympy import sin
>>> from sympy.abc import x, y

>>> f = (x**2 + x*y).as_poly(x, y)
>>> f.as_expr()
x**2 + x*y

>>> sin(x).as_expr()
sin(x)

as_independent(*deps, **hint)

A mostly naive separation of a Mul or Add into arguments that are not are dependent on deps. To obtain as complete a separation of variables as possible, use a separation method first, e.g.:

• separatevars() to change Mul, Add and Pow (including exp) into Mul
• .expand(log=True) to change log expr into an Add

The only non-naive thing that is done here is to respect noncommutative ordering of variables and to always return (0, 0) for self of zero regardless of hints.

For nonzero self, the returned tuple (i, d) has the following interpretation:

• i will has no variable that appears in deps
• d will be 1 or else have terms that contain variables that are in deps
• if self is an Add then self = i + d
• if self is a Mul then self = i*d
• otherwise (self, S.One) or (S.One, self) is returned.

To force the expression to be treated as an Add, use the hint as_Add=True

Examples

>>> from sympy import sin, cos, exp
>>> from sympy.abc import x, y, z

>>> (x + x*y).as_independent(x)
(0, x*y + x)
>>> (x + x*y).as_independent(y)
(x, x*y)
>>> (2*x*sin(x) + y + x + z).as_independent(x)
(y + z, 2*x*sin(x) + x)
>>> (2*x*sin(x) + y + x + z).as_independent(x, y)
(z, 2*x*sin(x) + x + y)


– self is a Mul

>>> (x*sin(x)*cos(y)).as_independent(x)
(cos(y), x*sin(x))


non-commutative terms cannot always be separated out when self is a Mul

>>> from sympy import symbols
>>> n1, n2, n3 = symbols('n1 n2 n3', commutative=False)
>>> (n1 + n1*n2).as_independent(n2)
(n1, n1*n2)
>>> (n2*n1 + n1*n2).as_independent(n2)
(0, n1*n2 + n2*n1)
>>> (n1*n2*n3).as_independent(n1)
(1, n1*n2*n3)
>>> (n1*n2*n3).as_independent(n2)
(n1, n2*n3)
>>> ((x-n1)*(x-y)).as_independent(x)
(1, (x - y)*(x - n1))


– self is anything else:

>>> (sin(x)).as_independent(x)
(1, sin(x))
>>> (sin(x)).as_independent(y)
(sin(x), 1)
>>> exp(x+y).as_independent(x)
(1, exp(x + y))


– force self to be treated as an Add:

>>> (3*x).as_independent(x, as_Add=True)
(0, 3*x)


– force self to be treated as a Mul:

>>> (3+x).as_independent(x, as_Add=False)
(1, x + 3)
(1, x - 3)


Note how the below differs from the above in making the constant on the dep term positive.

>>> (y*(-3+x)).as_independent(x)
(y, x - 3)

– use .as_independent() for true independence testing instead
of .has(). The former considers only symbols in the free symbols while the latter considers all symbols
>>> from sympy import Integral
>>> I = Integral(x, (x, 1, 2))
>>> I.has(x)
True
>>> x in I.free_symbols
False
>>> I.as_independent(x) == (I, 1)
True
>>> (I + x).as_independent(x) == (I, x)
True


Note: when trying to get independent terms, a separation method might need to be used first. In this case, it is important to keep track of what you send to this routine so you know how to interpret the returned values

>>> from sympy import separatevars, log
>>> separatevars(exp(x+y)).as_independent(x)
(exp(y), exp(x))
>>> (x + x*y).as_independent(y)
(x, x*y)
>>> separatevars(x + x*y).as_independent(y)
(x, y + 1)
>>> (x*(1 + y)).as_independent(y)
(x, y + 1)
>>> (x*(1 + y)).expand(mul=True).as_independent(y)
(x, x*y)
>>> a, b=symbols('a b', positive=True)
>>> (log(a*b).expand(log=True)).as_independent(b)
(log(a), log(b))


separatevars(), expand(), Add.as_two_terms(), Mul.as_two_terms(), as_coeff_add(), as_coeff_mul()

as_leading_term(*symbols)

Returns the leading (nonzero) term of the series expansion of self.

The _eval_as_leading_term routines are used to do this, and they must always return a non-zero value.

Examples

>>> from sympy.abc import x
>>> (1 + x + x**2).as_leading_term(x)
1
>>> (1/x**2 + x + x**2).as_leading_term(x)
x**(-2)

as_numer_denom()

expression -> a/b -> a, b

This is just a stub that should be defined by an object’s class methods to get anything else.

normal()
return a/b instead of a, b
as_ordered_factors(order=None)

Return list of ordered factors (if Mul) else [self].

as_ordered_terms(order=None, data=False)

Transform an expression to an ordered list of terms.

Examples

>>> from sympy import sin, cos
>>> from sympy.abc import x

>>> (sin(x)**2*cos(x) + sin(x)**2 + 1).as_ordered_terms()
[sin(x)**2*cos(x), sin(x)**2, 1]

as_poly(*gens, **args)

Converts self to a polynomial or returns None.

>>> from sympy import sin
>>> from sympy.abc import x, y

>>> print((x**2 + x*y).as_poly())
Poly(x**2 + x*y, x, y, domain='ZZ')

>>> print((x**2 + x*y).as_poly(x, y))
Poly(x**2 + x*y, x, y, domain='ZZ')

>>> print((x**2 + sin(y)).as_poly(x, y))
None

as_powers_dict()

Return self as a dictionary of factors with each factor being treated as a power. The keys are the bases of the factors and the values, the corresponding exponents. The resulting dictionary should be used with caution if the expression is a Mul and contains non- commutative factors since the order that they appeared will be lost in the dictionary.

as_real_imag(deep=True, **hints)

Performs complex expansion on ‘self’ and returns a tuple containing collected both real and imaginary parts. This method can’t be confused with re() and im() functions, which does not perform complex expansion at evaluation.

However it is possible to expand both re() and im() functions and get exactly the same results as with a single call to this function.

>>> from sympy import symbols, I

>>> x, y = symbols('x,y', real=True)

>>> (x + y*I).as_real_imag()
(x, y)

>>> from sympy.abc import z, w

>>> (z + w*I).as_real_imag()
(re(z) - im(w), re(w) + im(z))

as_terms()

Transform an expression to a list of terms.

assumptions0

Return object type assumptions.

For example:

Symbol(‘x’, real=True) Symbol(‘x’, integer=True)

are different objects. In other words, besides Python type (Symbol in this case), the initial assumptions are also forming their typeinfo.

Examples

>>> from sympy import Symbol
>>> from sympy.abc import x
>>> x.assumptions0
{'commutative': True}
>>> x = Symbol("x", positive=True)
>>> x.assumptions0
{'commutative': True, 'complex': True, 'hermitian': True,
'imaginary': False, 'negative': False, 'nonnegative': True,
'nonpositive': False, 'nonzero': True, 'positive': True, 'real': True,
'zero': False}

atoms(*types)

Returns the atoms that form the current object.

By default, only objects that are truly atomic and can’t be divided into smaller pieces are returned: symbols, numbers, and number symbols like I and pi. It is possible to request atoms of any type, however, as demonstrated below.

Examples

>>> from sympy import I, pi, sin
>>> from sympy.abc import x, y
>>> (1 + x + 2*sin(y + I*pi)).atoms()
set([1, 2, I, pi, x, y])


If one or more types are given, the results will contain only those types of atoms.

Examples

>>> from sympy import Number, NumberSymbol, Symbol
>>> (1 + x + 2*sin(y + I*pi)).atoms(Symbol)
set([x, y])

>>> (1 + x + 2*sin(y + I*pi)).atoms(Number)
set([1, 2])

>>> (1 + x + 2*sin(y + I*pi)).atoms(Number, NumberSymbol)
set([1, 2, pi])

>>> (1 + x + 2*sin(y + I*pi)).atoms(Number, NumberSymbol, I)
set([1, 2, I, pi])


Note that I (imaginary unit) and zoo (complex infinity) are special types of number symbols and are not part of the NumberSymbol class.

The type can be given implicitly, too:

>>> (1 + x + 2*sin(y + I*pi)).atoms(x) # x is a Symbol
set([x, y])


Be careful to check your assumptions when using the implicit option since S(1).is_Integer = True but type(S(1)) is One, a special type of sympy atom, while type(S(2)) is type Integer and will find all integers in an expression:

>>> from sympy import S
>>> (1 + x + 2*sin(y + I*pi)).atoms(S(1))
set([1])

>>> (1 + x + 2*sin(y + I*pi)).atoms(S(2))
set([1, 2])


Finally, arguments to atoms() can select more than atomic atoms: any sympy type (loaded in core/__init__.py) can be listed as an argument and those types of “atoms” as found in scanning the arguments of the expression recursively:

>>> from sympy import Function, Mul
>>> from sympy.core.function import AppliedUndef
>>> f = Function('f')
>>> (1 + f(x) + 2*sin(y + I*pi)).atoms(Function)
set([f(x), sin(y + I*pi)])
>>> (1 + f(x) + 2*sin(y + I*pi)).atoms(AppliedUndef)
set([f(x)])

>>> (1 + x + 2*sin(y + I*pi)).atoms(Mul)
set([I*pi, 2*sin(y + I*pi)])

base_offset
base_value
cancel(*gens, **args)

See the cancel function in sympy.polys

canonical_variables

Return a dictionary mapping any variable defined in self.variables as underscore-suffixed numbers corresponding to their position in self.variables. Enough underscores are added to ensure that there will be no clash with existing free symbols.

Examples

>>> from sympy import Lambda
>>> from sympy.abc import x
>>> Lambda(x, 2*x).canonical_variables
{x: 0_}

class_key()

Nice order of classes.

coeff(x, n=1, right=False)

Returns the coefficient from the term(s) containing x**n or None. If n is zero then all terms independent of x will be returned.

When x is noncommutative, the coeff to the left (default) or right of x can be returned. The keyword ‘right’ is ignored when x is commutative.

as_coefficient()
separate the expression into a coefficient and factor
as_coeff_Add()
separate the additive constant from an expression
as_coeff_Mul()
separate the multiplicative constant from an expression
as_independent()
separate x-dependent terms/factors from others
sympy.polys.polytools.coeff_monomial()
efficiently find the single coefficient of a monomial in Poly
sympy.polys.polytools.nth()
like coeff_monomial but powers of monomial terms are used

Examples

>>> from sympy import symbols
>>> from sympy.abc import x, y, z


You can select terms that have an explicit negative in front of them:

>>> (-x + 2*y).coeff(-1)
x
>>> (x - 2*y).coeff(-1)
2*y


You can select terms with no Rational coefficient:

>>> (x + 2*y).coeff(1)
x
>>> (3 + 2*x + 4*x**2).coeff(1)
0


You can select terms independent of x by making n=0; in this case expr.as_independent(x)[0] is returned (and 0 will be returned instead of None):

>>> (3 + 2*x + 4*x**2).coeff(x, 0)
3
>>> eq = ((x + 1)**3).expand() + 1
>>> eq
x**3 + 3*x**2 + 3*x + 2
>>> [eq.coeff(x, i) for i in reversed(range(4))]
[1, 3, 3, 2]
>>> eq -= 2
>>> [eq.coeff(x, i) for i in reversed(range(4))]
[1, 3, 3, 0]


You can select terms that have a numerical term in front of them:

>>> (-x - 2*y).coeff(2)
-y
>>> from sympy import sqrt
>>> (x + sqrt(2)*x).coeff(sqrt(2))
x


The matching is exact:

>>> (3 + 2*x + 4*x**2).coeff(x)
2
>>> (3 + 2*x + 4*x**2).coeff(x**2)
4
>>> (3 + 2*x + 4*x**2).coeff(x**3)
0
>>> (z*(x + y)**2).coeff((x + y)**2)
z
>>> (z*(x + y)**2).coeff(x + y)
0


In addition, no factoring is done, so 1 + z*(1 + y) is not obtained from the following:

>>> (x + z*(x + x*y)).coeff(x)
1


If such factoring is desired, factor_terms can be used first:

>>> from sympy import factor_terms
>>> factor_terms(x + z*(x + x*y)).coeff(x)
z*(y + 1) + 1

>>> n, m, o = symbols('n m o', commutative=False)
>>> n.coeff(n)
1
>>> (3*n).coeff(n)
3
>>> (n*m + m*n*m).coeff(n) # = (1 + m)*n*m
1 + m
>>> (n*m + m*n*m).coeff(n, right=True) # = (1 + m)*n*m
m


If there is more than one possible coefficient 0 is returned:

>>> (n*m + m*n).coeff(n)
0


If there is only one possible coefficient, it is returned:

>>> (n*m + x*m*n).coeff(m*n)
x
>>> (n*m + x*m*n).coeff(m*n, right=1)
1

collect(syms, func=None, evaluate=True, exact=False, distribute_order_term=True)

See the collect function in sympy.simplify

combsimp()

See the combsimp function in sympy.simplify

compare(other)

Return -1, 0, 1 if the object is smaller, equal, or greater than other.

Not in the mathematical sense. If the object is of a different type from the “other” then their classes are ordered according to the sorted_classes list.

Examples

>>> from sympy.abc import x, y
>>> x.compare(y)
-1
>>> x.compare(x)
0
>>> y.compare(x)
1

compute_leading_term(x, logx=None)

as_leading_term is only allowed for results of .series() This is a wrapper to compute a series first.

conjugate()
copy()[source]
could_extract_minus_sign()

Canonical way to choose an element in the set {e, -e} where e is any expression. If the canonical element is e, we have e.could_extract_minus_sign() == True, else e.could_extract_minus_sign() == False.

For any expression, the set {e.could_extract_minus_sign(), (-e).could_extract_minus_sign()} must be {True, False}.

>>> from sympy.abc import x, y
>>> (x-y).could_extract_minus_sign() != (y-x).could_extract_minus_sign()
True

count(query)

Count the number of matching subexpressions.

count_ops(visual=None)

wrapper for count_ops that returns the operation count.

default_assumptions = {'commutative': True, 'positive': True, 'nonzero': True, 'zero': False, 'nonpositive': False, 'nonnegative': True, 'hermitian': True, 'real': True, 'imaginary': False, 'negative': False, 'complex': True}
diff(*symbols, **assumptions)
dimensions
doit(**hints)

Evaluate objects that are not evaluated by default like limits, integrals, sums and products. All objects of this kind will be evaluated recursively, unless some species were excluded via ‘hints’ or unless the ‘deep’ hint was set to ‘False’.

>>> from sympy import Integral
>>> from sympy.abc import x

>>> 2*Integral(x, x)
2*Integral(x, x)

>>> (2*Integral(x, x)).doit()
x**2

>>> (2*Integral(x, x)).doit(deep=False)
2*Integral(x, x)

dummy_eq(other, symbol=None)

Compare two expressions and handle dummy symbols.

Examples

>>> from sympy import Dummy
>>> from sympy.abc import x, y

>>> u = Dummy('u')

>>> (u**2 + 1).dummy_eq(x**2 + 1)
True
>>> (u**2 + 1) == (x**2 + 1)
False

>>> (u**2 + y).dummy_eq(x**2 + y, x)
True
>>> (u**2 + y).dummy_eq(x**2 + y, y)
False

equals(other, failing_expression=False)

Return True if self == other, False if it doesn’t, or None. If failing_expression is True then the expression which did not simplify to a 0 will be returned instead of None.

If self is a Number (or complex number) that is not zero, then the result is False.

If self is a number and has not evaluated to zero, evalf will be used to test whether the expression evaluates to zero. If it does so and the result has significance (i.e. the precision is either -1, for a Rational result, or is greater than 1) then the evalf value will be used to return True or False.

evalf(n=15, subs=None, maxn=100, chop=False, strict=False, quad=None, verbose=False)

Evaluate the given formula to an accuracy of n digits. Optional keyword arguments:

subs=<dict>
Substitute numerical values for symbols, e.g. subs={x:3, y:1+pi}. The substitutions must be given as a dictionary.
maxn=<integer>
Allow a maximum temporary working precision of maxn digits (default=100)
chop=<bool>
Replace tiny real or imaginary parts in subresults by exact zeros (default=False)
strict=<bool>
Raise PrecisionExhausted if any subresult fails to evaluate to full accuracy, given the available maxprec (default=False)
Choose algorithm for numerical quadrature. By default, tanh-sinh quadrature is used. For oscillatory integrals on an infinite interval, try quad=’osc’.
verbose=<bool>
Print debug information (default=False)
expand(deep=True, modulus=None, power_base=True, power_exp=True, mul=True, log=True, multinomial=True, basic=True, **hints)

Expand an expression using hints.

See the docstring of the expand() function in sympy.core.function for more information.

expr
extract_additively(c)

Return self - c if it’s possible to subtract c from self and make all matching coefficients move towards zero, else return None.

Examples

>>> from sympy.abc import x, y
>>> e = 2*x + 3
x + 2
>>> (y*(x + 1)).extract_additively(x + 1)
>>> ((x + 1)*(x + 2*y + 1) + 3).extract_additively(x + 1)
(x + 1)*(x + 2*y) + 3


Sometimes auto-expansion will return a less simplified result than desired; gcd_terms might be used in such cases:

>>> from sympy import gcd_terms
>>> (4*x*(y + 1) + y).extract_additively(x)
4*x*(y + 1) + x*(4*y + 3) - x*(4*y + 4) + y
>>> gcd_terms(_)
x*(4*y + 3) + y

extract_branch_factor(allow_half=False)

Try to write self as exp_polar(2*pi*I*n)*z in a nice way. Return (z, n).

>>> from sympy import exp_polar, I, pi
>>> from sympy.abc import x, y
>>> exp_polar(I*pi).extract_branch_factor()
(exp_polar(I*pi), 0)
>>> exp_polar(2*I*pi).extract_branch_factor()
(1, 1)
>>> exp_polar(-pi*I).extract_branch_factor()
(exp_polar(I*pi), -1)
>>> exp_polar(3*pi*I + x).extract_branch_factor()
(exp_polar(x + I*pi), 1)
>>> (y*exp_polar(-5*pi*I)*exp_polar(3*pi*I + 2*pi*x)).extract_branch_factor()
(y*exp_polar(2*pi*x), -1)
>>> exp_polar(-I*pi/2).extract_branch_factor()
(exp_polar(-I*pi/2), 0)


If allow_half is True, also extract exp_polar(I*pi):

>>> exp_polar(I*pi).extract_branch_factor(allow_half=True)
(1, 1/2)
>>> exp_polar(2*I*pi).extract_branch_factor(allow_half=True)
(1, 1)
>>> exp_polar(3*I*pi).extract_branch_factor(allow_half=True)
(1, 3/2)
>>> exp_polar(-I*pi).extract_branch_factor(allow_half=True)
(1, -1/2)

extract_multiplicatively(c)

Return None if it’s not possible to make self in the form c * something in a nice way, i.e. preserving the properties of arguments of self.

>>> from sympy import symbols, Rational

>>> x, y = symbols('x,y', real=True)

>>> ((x*y)**3).extract_multiplicatively(x**2 * y)
x*y**2

>>> ((x*y)**3).extract_multiplicatively(x**4 * y)

>>> (2*x).extract_multiplicatively(2)
x

>>> (2*x).extract_multiplicatively(3)

>>> (Rational(1, 2)*x).extract_multiplicatively(3)
x/6

factor(*gens, **args)

See the factor() function in sympy.polys.polytools

find(query, group=False)

Find all subexpressions matching a query.

fourier_series(limits=None)

Compute fourier sine/cosine series of self.

See the docstring of the fourier_series() in sympy.series.fourier for more information.

fps(x=None, x0=0, dir=1, hyper=True, order=4, rational=True, full=False)

Compute formal power power series of self.

See the docstring of the fps() function in sympy.series.formal for more information.

free_symbols

Return from the atoms of self those which are free symbols.

For most expressions, all symbols are free symbols. For some classes this is not true. e.g. Integrals use Symbols for the dummy variables which are bound variables, so Integral has a method to return all symbols except those. Derivative keeps track of symbols with respect to which it will perform a derivative; those are bound variables, too, so it has its own free_symbols method.

Any other method that uses bound variables should implement a free_symbols method.

fromiter(args, **assumptions)

Create a new object from an iterable.

This is a convenience function that allows one to create objects from any iterable, without having to convert to a list or tuple first.

Examples

>>> from sympy import Tuple
>>> Tuple.fromiter(i for i in range(5))
(0, 1, 2, 3, 4)

func

The top-level function in an expression.

The following should hold for all objects:

>> x == x.func(*x.args)


Examples

>>> from sympy.abc import x
>>> a = 2*x
>>> a.func
<class 'sympy.core.mul.Mul'>
>>> a.args
(2, x)
>>> a.func(*a.args)
2*x
>>> a == a.func(*a.args)
True

getO()

Returns the additive O(..) symbol if there is one, else None.

get_base_equivalent(unit_system='cgs')[source]

Create and return dimensionally-equivalent units in a specified base.

get_cgs_equivalent()[source]

Create and return dimensionally-equivalent cgs units.

get_conversion_factor(other_units)[source]
get_mks_equivalent()[source]

Create and return dimensionally-equivalent mks units.

getn()

Returns the order of the expression.

The order is determined either from the O(...) term. If there is no O(...) term, it returns None.

Examples

>>> from sympy import O
>>> from sympy.abc import x
>>> (1 + x + O(x**2)).getn()
2
>>> (1 + x).getn()

has(*patterns)

Test whether any subexpression matches any of the patterns.

Examples

>>> from sympy import sin
>>> from sympy.abc import x, y, z
>>> (x**2 + sin(x*y)).has(z)
False
>>> (x**2 + sin(x*y)).has(x, y, z)
True
>>> x.has(x)
True


Note that expr.has(*patterns) is exactly equivalent to any(expr.has(p) for p in patterns). In particular, False is returned when the list of patterns is empty.

>>> x.has()
False

has_equivalent(equiv)[source]

Check to see if this unit object as an equivalent unit in equiv.

integrate(*args, **kwargs)

See the integrate function in sympy.integrals

invert(g, *gens, **args)

Return the multiplicative inverse of self mod g where self (and g) may be symbolic expressions).

sympy.core.numbers.mod_inverse(), sympy.polys.polytools.invert()

is_Add = False
is_AlgebraicNumber = False
is_Atom = False
is_Boolean = False
is_Derivative = False
is_Dummy = False
is_Equality = False
is_Float = False
is_Function = False
is_Integer = False
is_Matrix = False
is_Mul = False
is_Not = False
is_Number = False
is_NumberSymbol = False
is_Order = False
is_Piecewise = False
is_Point = False
is_Poly = False
is_Pow = False
is_Rational = False
is_Relational = False
is_Symbol = False
is_Vector = False
is_Wild = False
is_algebraic
is_algebraic_expr(*syms)

This tests whether a given expression is algebraic or not, in the given symbols, syms. When syms is not given, all free symbols will be used. The rational function does not have to be in expanded or in any kind of canonical form.

This function returns False for expressions that are “algebraic expressions” with symbolic exponents. This is a simple extension to the is_rational_function, including rational exponentiation.

Examples

>>> from sympy import Symbol, sqrt
>>> x = Symbol('x', real=True)
>>> sqrt(1 + x).is_rational_function()
False
>>> sqrt(1 + x).is_algebraic_expr()
True


This function does not attempt any nontrivial simplifications that may result in an expression that does not appear to be an algebraic expression to become one.

>>> from sympy import exp, factor
>>> a = sqrt(exp(x)**2 + 2*exp(x) + 1)/(exp(x) + 1)
>>> a.is_algebraic_expr(x)
False
>>> factor(a).is_algebraic_expr()
True


References

is_antihermitian
is_atomic
is_code_unit
is_commutative = True
is_comparable

Return True if self can be computed to a real number (or already is a real number) with precision, else False.

Examples

>>> from sympy import exp_polar, pi, I
>>> (I*exp_polar(I*pi/2)).is_comparable
True
>>> (I*exp_polar(I*pi*2)).is_comparable
False


A False result does not mean that self cannot be rewritten into a form that would be comparable. For example, the difference computed below is zero but without simplification it does not evaluate to a zero with precision:

>>> e = 2**pi*(1 + 2**pi)
>>> dif = e - e.expand()
>>> dif.is_comparable
False
>>> dif.n(2)._prec
1

is_complex = True
is_composite
is_constant(*wrt, **flags)

Return True if self is constant, False if not, or None if the constancy could not be determined conclusively.

If an expression has no free symbols then it is a constant. If there are free symbols it is possible that the expression is a constant, perhaps (but not necessarily) zero. To test such expressions, two strategies are tried:

1) numerical evaluation at two random points. If two such evaluations give two different values and the values have a precision greater than 1 then self is not constant. If the evaluations agree or could not be obtained with any precision, no decision is made. The numerical testing is done only if wrt is different than the free symbols.

2) differentiation with respect to variables in ‘wrt’ (or all free symbols if omitted) to see if the expression is constant or not. This will not always lead to an expression that is zero even though an expression is constant (see added test in test_expr.py). If all derivatives are zero then self is constant with respect to the given symbols.

If neither evaluation nor differentiation can prove the expression is constant, None is returned unless two numerical values happened to be the same and the flag failing_number is True – in that case the numerical value will be returned.

If flag simplify=False is passed, self will not be simplified; the default is True since self should be simplified before testing.

Examples

>>> from sympy import cos, sin, Sum, S, pi
>>> from sympy.abc import a, n, x, y
>>> x.is_constant()
False
>>> S(2).is_constant()
True
>>> Sum(x, (x, 1, 10)).is_constant()
True
>>> Sum(x, (x, 1, n)).is_constant()
False
>>> Sum(x, (x, 1, n)).is_constant(y)
True
>>> Sum(x, (x, 1, n)).is_constant(n)
False
>>> Sum(x, (x, 1, n)).is_constant(x)
True
>>> eq = a*cos(x)**2 + a*sin(x)**2 - a
>>> eq.is_constant()
True
>>> eq.subs({x: pi, a: 2}) == eq.subs({x: pi, a: 3}) == 0
True

>>> (0**x).is_constant()
False
>>> x.is_constant()
False
>>> (x**x).is_constant()
False
>>> one = cos(x)**2 + sin(x)**2
>>> one.is_constant()
True
>>> ((one - 1)**(x + 1)).is_constant() in (True, False) # could be 0 or 1
True

is_dimensionless
is_even
is_finite
is_hermitian = True
is_hypergeometric(k)
is_imaginary = False
is_infinite
is_integer
is_irrational
is_negative = False
is_noninteger
is_nonnegative = True
is_nonpositive = False
is_nonzero = True
is_number = False
is_odd
is_polar
is_polynomial(*syms)

Return True if self is a polynomial in syms and False otherwise.

This checks if self is an exact polynomial in syms. This function returns False for expressions that are “polynomials” with symbolic exponents. Thus, you should be able to apply polynomial algorithms to expressions for which this returns True, and Poly(expr, *syms) should work if and only if expr.is_polynomial(*syms) returns True. The polynomial does not have to be in expanded form. If no symbols are given, all free symbols in the expression will be used.

This is not part of the assumptions system. You cannot do Symbol(‘z’, polynomial=True).

Examples

>>> from sympy import Symbol
>>> x = Symbol('x')
>>> ((x**2 + 1)**4).is_polynomial(x)
True
>>> ((x**2 + 1)**4).is_polynomial()
True
>>> (2**x + 1).is_polynomial(x)
False

>>> n = Symbol('n', nonnegative=True, integer=True)
>>> (x**n + 1).is_polynomial(x)
False


This function does not attempt any nontrivial simplifications that may result in an expression that does not appear to be a polynomial to become one.

>>> from sympy import sqrt, factor, cancel
>>> y = Symbol('y', positive=True)
>>> a = sqrt(y**2 + 2*y + 1)
>>> a.is_polynomial(y)
False
>>> factor(a)
y + 1
>>> factor(a).is_polynomial(y)
True

>>> b = (y**2 + 2*y + 1)/(y + 1)
>>> b.is_polynomial(y)
False
>>> cancel(b)
y + 1
>>> cancel(b).is_polynomial(y)
True


is_positive = True
is_prime
is_rational
is_rational_function(*syms)

Test whether function is a ratio of two polynomials in the given symbols, syms. When syms is not given, all free symbols will be used. The rational function does not have to be in expanded or in any kind of canonical form.

This function returns False for expressions that are “rational functions” with symbolic exponents. Thus, you should be able to call .as_numer_denom() and apply polynomial algorithms to the result for expressions for which this returns True.

This is not part of the assumptions system. You cannot do Symbol(‘z’, rational_function=True).

Examples

>>> from sympy import Symbol, sin
>>> from sympy.abc import x, y

>>> (x/y).is_rational_function()
True

>>> (x**2).is_rational_function()
True

>>> (x/sin(y)).is_rational_function(y)
False

>>> n = Symbol('n', integer=True)
>>> (x**n + 1).is_rational_function(x)
False


This function does not attempt any nontrivial simplifications that may result in an expression that does not appear to be a rational function to become one.

>>> from sympy import sqrt, factor
>>> y = Symbol('y', positive=True)
>>> a = sqrt(y**2 + 2*y + 1)/y
>>> a.is_rational_function(y)
False
>>> factor(a)
(y + 1)/y
>>> factor(a).is_rational_function(y)
True


is_real = True
is_transcendental
is_zero = False
latex_repr
latex_representation()[source]
leadterm(x)

Returns the leading term a*x**b as a tuple (a, b).

Examples

>>> from sympy.abc import x
(1, 0)
(1, -2)

limit(x, xlim, dir='+')

Compute limit x->xlim.

list_equivalencies()[source]

Lists the possible equivalencies associated with this unit object

lseries(x=None, x0=0, dir='+', logx=None)

Wrapper for series yielding an iterator of the terms of the series.

Note: an infinite series will yield an infinite iterator. The following, for exaxmple, will never terminate. It will just keep printing terms of the sin(x) series:

for term in sin(x).lseries(x):
print term


The advantage of lseries() over nseries() is that many times you are just interested in the next term in the series (i.e. the first term for example), but you don’t know how many you should ask for in nseries() using the “n” parameter.

match(pattern, old=False)

Pattern matching.

Wild symbols match all.

Return None when expression (self) does not match with pattern. Otherwise return a dictionary such that:

pattern.xreplace(self.match(pattern)) == self


Examples

>>> from sympy import Wild
>>> from sympy.abc import x, y
>>> p = Wild("p")
>>> q = Wild("q")
>>> r = Wild("r")
>>> e = (x+y)**(x+y)
>>> e.match(p**p)
{p_: x + y}
>>> e.match(p**q)
{p_: x + y, q_: x + y}
>>> e = (2*x)**2
>>> e.match(p*q**r)
{p_: 4, q_: x, r_: 2}
>>> (p*q**r).xreplace(e.match(p*q**r))
4*x**2


The old flag will give the old-style pattern matching where expressions and patterns are essentially solved to give the match. Both of the following give None unless old=True:

>>> (x - 2).match(p - x, old=True)
{p_: 2*x - 2}
>>> (2/x).match(p*x, old=True)
{p_: 2/x**2}

matches(expr, repl_dict={}, old=False)

Helper method for match() that looks for a match between Wild symbols in self and expressions in expr.

Examples

>>> from sympy import symbols, Wild, Basic
>>> a, b, c = symbols('a b c')
>>> x = Wild('x')
>>> Basic(a + x, x).matches(Basic(a + b, c)) is None
True
>>> Basic(a + x, x).matches(Basic(a + b + c, b + c))
{x_: b + c}

n(n=15, subs=None, maxn=100, chop=False, strict=False, quad=None, verbose=False)

Evaluate the given formula to an accuracy of n digits. Optional keyword arguments:

subs=<dict>
Substitute numerical values for symbols, e.g. subs={x:3, y:1+pi}. The substitutions must be given as a dictionary.
maxn=<integer>
Allow a maximum temporary working precision of maxn digits (default=100)
chop=<bool>
Replace tiny real or imaginary parts in subresults by exact zeros (default=False)
strict=<bool>
Raise PrecisionExhausted if any subresult fails to evaluate to full accuracy, given the available maxprec (default=False)
Choose algorithm for numerical quadrature. By default, tanh-sinh quadrature is used. For oscillatory integrals on an infinite interval, try quad=’osc’.
verbose=<bool>
Print debug information (default=False)
normal()
nseries(x=None, x0=0, n=6, dir='+', logx=None)

Wrapper to _eval_nseries if assumptions allow, else to series.

If x is given, x0 is 0, dir=’+’, and self has x, then _eval_nseries is called. This calculates “n” terms in the innermost expressions and then builds up the final series just by “cross-multiplying” everything out.

The optional logx parameter can be used to replace any log(x) in the returned series with a symbolic value to avoid evaluating log(x) at 0. A symbol to use in place of log(x) should be provided.

Advantage – it’s fast, because we don’t have to determine how many terms we need to calculate in advance.

Disadvantage – you may end up with less terms than you may have expected, but the O(x**n) term appended will always be correct and so the result, though perhaps shorter, will also be correct.

If any of those assumptions is not met, this is treated like a wrapper to series which will try harder to return the correct number of terms.

Examples

>>> from sympy import sin, log, Symbol
>>> from sympy.abc import x, y
>>> sin(x).nseries(x, 0, 6)
x - x**3/6 + x**5/120 + O(x**6)
>>> log(x+1).nseries(x, 0, 5)
x - x**2/2 + x**3/3 - x**4/4 + O(x**5)


Handling of the logx parameter — in the following example the expansion fails since sin does not have an asymptotic expansion at -oo (the limit of log(x) as x approaches 0):

>>> e = sin(log(x))
>>> e.nseries(x, 0, 6)
Traceback (most recent call last):
...
PoleError: ...
...
>>> logx = Symbol('logx')
>>> e.nseries(x, 0, 6, logx=logx)
sin(logx)


In the following example, the expansion works but gives only an Order term unless the logx parameter is used:

>>> e = x**y
>>> e.nseries(x, 0, 2)
O(log(x)**2)
>>> e.nseries(x, 0, 2, logx=logx)
exp(logx*y)

nsimplify(constants=[], tolerance=None, full=False)

See the nsimplify function in sympy.simplify

powsimp(deep=False, combine='all')

See the powsimp function in sympy.simplify

primitive()

Return the positive Rational that can be extracted non-recursively from every term of self (i.e., self is treated like an Add). This is like the as_coeff_Mul() method but primitive always extracts a positive Rational (never a negative or a Float).

Examples

>>> from sympy.abc import x
>>> (3*(x + 1)**2).primitive()
(3, (x + 1)**2)
>>> a = (6*x + 2); a.primitive()
(2, 3*x + 1)
>>> b = (x/2 + 3); b.primitive()
(1/2, x + 6)
>>> (a*b).primitive() == (1, a*b)
True

radsimp()

See the radsimp function in sympy.simplify

ratsimp()

See the ratsimp function in sympy.simplify

rcall(*args)

Apply on the argument recursively through the expression tree.

This method is used to simulate a common abuse of notation for operators. For instance in SymPy the the following will not work:

(x+Lambda(y, 2*y))(z) == x+2*z,

however you can use

>>> from sympy import Lambda
>>> from sympy.abc import x, y, z
>>> (x + Lambda(y, 2*y)).rcall(z)
x + 2*z

refine(assumption=True)

See the refine function in sympy.assumptions

registry
removeO()

Removes the additive O(..) symbol if there is one

replace(query, value, map=False, simultaneous=True, exact=False)

Replace matching subexpressions of self with value.

If map = True then also return the mapping {old: new} where old was a sub-expression found with query and new is the replacement value for it. If the expression itself doesn’t match the query, then the returned value will be self.xreplace(map) otherwise it should be self.subs(ordered(map.items())).

Traverses an expression tree and performs replacement of matching subexpressions from the bottom to the top of the tree. The default approach is to do the replacement in a simultaneous fashion so changes made are targeted only once. If this is not desired or causes problems, simultaneous can be set to False. In addition, if an expression containing more than one Wild symbol is being used to match subexpressions and the exact flag is True, then the match will only succeed if non-zero values are received for each Wild that appears in the match pattern.

The list of possible combinations of queries and replacement values is listed below:

Examples

Initial setup

>>> from sympy import log, sin, cos, tan, Wild, Mul, Add
>>> from sympy.abc import x, y
>>> f = log(sin(x)) + tan(sin(x**2))

1.1. type -> type

obj.replace(type, newtype)

When object of type type is found, replace it with the result of passing its argument(s) to newtype.

>>> f.replace(sin, cos)
log(cos(x)) + tan(cos(x**2))
>>> sin(x).replace(sin, cos, map=True)
(cos(x), {sin(x): cos(x)})
x + y

1.2. type -> func

obj.replace(type, func)

When object of type type is found, apply func to its argument(s). func must be written to handle the number of arguments of type.

>>> f.replace(sin, lambda arg: sin(2*arg))
log(sin(2*x)) + tan(sin(2*x**2))
>>> (x*y).replace(Mul, lambda *args: sin(2*Mul(*args)))
sin(2*x*y)

2.1. pattern -> expr

obj.replace(pattern(wild), expr(wild))

Replace subexpressions matching pattern with the expression written in terms of the Wild symbols in pattern.

>>> a = Wild('a')
>>> f.replace(sin(a), tan(a))
log(tan(x)) + tan(tan(x**2))
>>> f.replace(sin(a), tan(a/2))
log(tan(x/2)) + tan(tan(x**2/2))
>>> f.replace(sin(a), a)
log(x) + tan(x**2)
>>> (x*y).replace(a*x, a)
y


When the default value of False is used with patterns that have more than one Wild symbol, non-intuitive results may be obtained:

>>> b = Wild('b')
>>> (2*x).replace(a*x + b, b - a)
2/x


For this reason, the exact option can be used to make the replacement only when the match gives non-zero values for all Wild symbols:

>>> (2*x + y).replace(a*x + b, b - a, exact=True)
y - 2
>>> (2*x).replace(a*x + b, b - a, exact=True)
2*x

2.2. pattern -> func

obj.replace(pattern(wild), lambda wild: expr(wild))

All behavior is the same as in 2.1 but now a function in terms of pattern variables is used rather than an expression:

>>> f.replace(sin(a), lambda a: sin(2*a))
log(sin(2*x)) + tan(sin(2*x**2))

3.1. func -> func

obj.replace(filter, func)

Replace subexpression e with func(e) if filter(e) is True.

>>> g = 2*sin(x**3)
>>> g.replace(lambda expr: expr.is_Number, lambda expr: expr**2)
4*sin(x**9)


The expression itself is also targeted by the query but is done in such a fashion that changes are not made twice.

>>> e = x*(x*y + 1)
>>> e.replace(lambda x: x.is_Mul, lambda x: 2*x)
2*x*(2*x*y + 1)


subs()
substitution of subexpressions as defined by the objects themselves.
xreplace()
exact node replacement in expr tree; also capable of using matching rules
rewrite(*args, **hints)

Rewrite functions in terms of other functions.

Rewrites expression containing applications of functions of one kind in terms of functions of different kind. For example you can rewrite trigonometric functions as complex exponentials or combinatorial functions as gamma function.

As a pattern this function accepts a list of functions to to rewrite (instances of DefinedFunction class). As rule you can use string or a destination function instance (in this case rewrite() will use the str() function).

There is also the possibility to pass hints on how to rewrite the given expressions. For now there is only one such hint defined called ‘deep’. When ‘deep’ is set to False it will forbid functions to rewrite their contents.

Examples

>>> from sympy import sin, exp
>>> from sympy.abc import x


Unspecified pattern:

>>> sin(x).rewrite(exp)
-I*(exp(I*x) - exp(-I*x))/2


Pattern as a single function:

>>> sin(x).rewrite(sin, exp)
-I*(exp(I*x) - exp(-I*x))/2


Pattern as a list of functions:

>>> sin(x).rewrite([sin, ], exp)
-I*(exp(I*x) - exp(-I*x))/2

round(p=0)

Return x rounded to the given decimal place.

If a complex number would results, apply round to the real and imaginary components of the number.

Examples

>>> from sympy import pi, E, I, S, Add, Mul, Number
>>> S(10.5).round()
11.
>>> pi.round()
3.
>>> pi.round(2)
3.14
>>> (2*pi + E*I).round()
6. + 3.*I


The round method has a chopping effect:

>>> (2*pi + I/10).round()
6.
>>> (pi/10 + 2*I).round()
2.*I
>>> (pi/10 + E*I).round(2)
0.31 + 2.72*I


Notes

Do not confuse the Python builtin function, round, with the SymPy method of the same name. The former always returns a float (or raises an error if applied to a complex value) while the latter returns either a Number or a complex number:

>>> isinstance(round(S(123), -2), Number)
False
>>> isinstance(S(123).round(-2), Number)
True
>>> isinstance((3*I).round(), Mul)
True
True

same_dimensions_as(other_unit)[source]

Test if dimensions are the same.

separate(deep=False, force=False)

See the separate function in sympy.simplify

series(x=None, x0=0, n=6, dir='+', logx=None)

Series expansion of “self” around x = x0 yielding either terms of the series one by one (the lazy series given when n=None), else all the terms at once when n != None.

Returns the series expansion of “self” around the point x = x0 with respect to x up to O((x - x0)**n, x, x0) (default n is 6).

If x=None and self is univariate, the univariate symbol will be supplied, otherwise an error will be raised.

>>> from sympy import cos, exp
>>> from sympy.abc import x, y
>>> cos(x).series()
1 - x**2/2 + x**4/24 + O(x**6)
>>> cos(x).series(n=4)
1 - x**2/2 + O(x**4)
>>> cos(x).series(x, x0=1, n=2)
cos(1) - (x - 1)*sin(1) + O((x - 1)**2, (x, 1))
>>> e = cos(x + exp(y))
>>> e.series(y, n=2)
cos(x + 1) - y*sin(x + 1) + O(y**2)
>>> e.series(x, n=2)
cos(exp(y)) - x*sin(exp(y)) + O(x**2)


If n=None then a generator of the series terms will be returned.

>>> term=cos(x).series(n=None)
>>> [next(term) for i in range(2)]
[1, -x**2/2]


For dir=+ (default) the series is calculated from the right and for dir=- the series from the left. For smooth functions this flag will not alter the results.

>>> abs(x).series(dir="+")
x
>>> abs(x).series(dir="-")
-x

simplify(ratio=1.7, measure=None)

See the simplify function in sympy.simplify

sort_key(order=None)
subs(*args, **kwargs)

Substitutes old for new in an expression after sympifying args.

args is either:
• two arguments, e.g. foo.subs(old, new)
• one iterable argument, e.g. foo.subs(iterable). The iterable may be
o an iterable container with (old, new) pairs. In this case the
replacements are processed in the order given with successive patterns possibly affecting replacements already made.
o a dict or set whose key/value items correspond to old/new pairs.
In this case the old/new pairs will be sorted by op count and in case of a tie, by number of args and the default_sort_key. The resulting sorted list is then processed as an iterable container (see previous).

If the keyword simultaneous is True, the subexpressions will not be evaluated until all the substitutions have been made.

Examples

>>> from sympy import pi, exp, limit, oo
>>> from sympy.abc import x, y
>>> (1 + x*y).subs(x, pi)
pi*y + 1
>>> (1 + x*y).subs({x:pi, y:2})
1 + 2*pi
>>> (1 + x*y).subs([(x, pi), (y, 2)])
1 + 2*pi
>>> reps = [(y, x**2), (x, 2)]
>>> (x + y).subs(reps)
6
>>> (x + y).subs(reversed(reps))
x**2 + 2

>>> (x**2 + x**4).subs(x**2, y)
y**2 + y


To replace only the x**2 but not the x**4, use xreplace:

>>> (x**2 + x**4).xreplace({x**2: y})
x**4 + y


To delay evaluation until all substitutions have been made, set the keyword simultaneous to True:

>>> (x/y).subs([(x, 0), (y, 0)])
0
>>> (x/y).subs([(x, 0), (y, 0)], simultaneous=True)
nan


>>> ((x + y)/y).subs({x + y: y, y: x + y})
1
>>> ((x + y)/y).subs({x + y: y, y: x + y}, simultaneous=True)
y/(x + y)


In order to obtain a canonical result, unordered iterables are sorted by count_op length, number of arguments and by the default_sort_key to break any ties. All other iterables are left unsorted.

>>> from sympy import sqrt, sin, cos
>>> from sympy.abc import a, b, c, d, e

>>> A = (sqrt(sin(2*x)), a)
>>> B = (sin(2*x), b)
>>> C = (cos(2*x), c)
>>> D = (x, d)
>>> E = (exp(x), e)

>>> expr = sqrt(sin(2*x))*sin(exp(x)*x)*cos(2*x) + sin(2*x)

>>> expr.subs(dict([A, B, C, D, E]))
a*c*sin(d*e) + b


The resulting expression represents a literal replacement of the old arguments with the new arguments. This may not reflect the limiting behavior of the expression:

>>> (x**3 - 3*x).subs({x: oo})
nan

>>> limit(x**3 - 3*x, x, oo)
oo


If the substitution will be followed by numerical evaluation, it is better to pass the substitution to evalf as

>>> (1/x).evalf(subs={x: 3.0}, n=21)
0.333333333333333333333


rather than

>>> (1/x).subs({x: 3.0}).evalf(21)
0.333333333333333314830


as the former will ensure that the desired level of precision is obtained.

replace()
replacement capable of doing wildcard-like matching, parsing of match, and conditional replacements
xreplace()
exact node replacement in expr tree; also capable of using matching rules
evalf()
calculates the given formula to a desired level of precision
taylor_term(n, x, *previous_terms)

General method for the taylor term.

This method is slow, because it differentiates n-times. Subclasses can redefine it to make it faster by using the “previous_terms”.

together(*args, **kwargs)

See the together function in sympy.polys

transpose()
trigsimp(**args)

See the trigsimp function in sympy.simplify

xreplace(rule)

Replace occurrences of objects within the expression.

Parameters: rule (dict-like) – Expresses a replacement rule xreplace the result of the replacement

Examples

>>> from sympy import symbols, pi, exp
>>> x, y, z = symbols('x y z')
>>> (1 + x*y).xreplace({x: pi})
pi*y + 1
>>> (1 + x*y).xreplace({x: pi, y: 2})
1 + 2*pi


Replacements occur only if an entire node in the expression tree is matched:

>>> (x*y + z).xreplace({x*y: pi})
z + pi
>>> (x*y*z).xreplace({x*y: pi})
x*y*z
>>> (2*x).xreplace({2*x: y, x: z})
y
>>> (2*2*x).xreplace({2*x: y, x: z})
4*z
>>> (x + y + 2).xreplace({x + y: 2})
x + y + 2
>>> (x + 2 + exp(x + 2)).xreplace({x + 2: y})
x + exp(y) + 2


xreplace doesn’t differentiate between free and bound symbols. In the following, subs(x, y) would not change x since it is a bound symbol, but xreplace does:

>>> from sympy import Integral
>>> Integral(x, (x, 1, 2*x)).xreplace({x: y})
Integral(y, (y, 1, 2*y))


Trying to replace x with an expression raises an error:

>>> Integral(x, (x, 1, 2*x)).xreplace({x: 2*y})
ValueError: Invalid limits given: ((2*y, 1, 4*y),)


replace()
replacement capable of doing wildcard-like matching, parsing of match, and conditional replacements
subs()
substitution of subexpressions as defined by the objects themselves.
yt.units.unit_object.auto_positive_symbol(tokens, local_dict, global_dict)[source]

Inserts calls to Symbol for undefined variables. Passes in positive=True as a keyword argument. Adapted from sympy.sympy.parsing.sympy_parser.auto_symbol

yt.units.unit_object.define_unit(symbol, value, tex_repr=None, offset=None, prefixable=False)[source]

Define a new unit and add it to the default unit registry.

Parameters: symbol (string) – The symbol for the new unit. value (tuple or YTQuantity) – The definition of the new unit in terms of some other units. For example, one would define a new “mph” unit with (1.0, “mile/hr”) tex_repr (string, optional) – The LaTeX representation of the new unit. If one is not supplied, it will be generated automatically based on the symbol string. offset (float, optional) – The default offset for the unit. If not set, an offset of 0 is assumed. prefixable (boolean, optional) – Whether or not the new unit can use SI prefixes. Default: False

Examples

>>> yt.define_unit("mph", (1.0, "mile/hr"))
>>> two_weeks = YTQuantity(14.0, "days")
>>> yt.define_unit("fortnight", two_weeks)

yt.units.unit_object.get_conversion_factor(old_units, new_units)[source]

Get the conversion factor between two units of equivalent dimensions. This is the number you multiply data by to convert from values in old_units to values in new_units.

Parameters: old_units (str or Unit object) – The current units. new_units (str or Unit object) – The units we want. conversion_factor (float) – old_units / new_units offset (float or None) – Offset between the old unit and new unit.
yt.units.unit_object.get_latex_representation(expr, registry)[source]
yt.units.unit_object.iskeyword()

x.__contains__(y) <==> y in x.

yt.units.unit_object.validate_dimensions(dimensions)[source]