Core¶

sympify¶

sympy.core.sympify.sympify( a: int, *, strict: bool = False, ) → Integer[source]¶

sympy.core.sympify.sympify( a: float, *, strict: bool = False, ) → Float

sympy.core.sympify.sympify( a: Expr | complex, *, strict: bool = False, ) → Expr

sympy.core.sympify.sympify( a: Tbasic, *, strict: bool = False, ) → Tbasic

sympy.core.sympify.sympify( a: Any, *, strict: bool = False, ) → Basic

Converts an arbitrary expression to a type that can be used inside SymPy.

Parameters:

a :

any object defined in SymPy

standard numeric Python types: int, long, float, Decimal

strings (like "0.09", "2e-19" or 'sin(x)')

booleans, including None (will leave None unchanged)

dicts, lists, sets or tuples containing any of the above

convert_xor : bool, optional

If true, treats ^ as exponentiation. If False, treats ^ as XOR itself. Used only when input is a string.

locals : any object defined in SymPy, optional

In order to have strings be recognized it can be imported into a namespace dictionary and passed as locals.

strict : bool, optional

If the option strict is set to True, only the types for which an explicit conversion has been defined are converted. In the other cases, a SympifyError is raised.

rational : bool, optional

If True, converts floats into Rational. If False, it lets floats remain as it is. Used only when input is a string.

evaluate : bool, optional

If False, then arithmetic and operators will be converted into their SymPy equivalents. If True the expression will be evaluated and the result will be returned.

Explanation

It will convert Python ints into instances of Integer, floats into instances of Float, etc. It is also able to coerce symbolic expressions which inherit from Basic. This can be useful in cooperation with SAGE.

Warning

Note that this function uses eval, and thus shouldn’t be used on unsanitized input.

If the argument is already a type that SymPy understands, it will do nothing but return that value. This can be used at the beginning of a function to ensure you are working with the correct type.

Examples

>>> from sympy import sympify

>>> sympify(2).is_integer
True
>>> sympify(2).is_real
True

>>> sympify(2.0).is_real
True
>>> sympify("2.0").is_real
True
>>> sympify("2e-45").is_real
True

If the expression could not be converted, a SympifyError is raised.

>>> sympify("x***2")
Traceback (most recent call last):
...
SympifyError: SympifyError: "could not parse 'x***2'"

When attempting to parse non-Python syntax using sympify, it raises a SympifyError:

>>> sympify("2x+1")
Traceback (most recent call last):
...
SympifyError: Sympify of expression 'could not parse '2x+1'' failed

To parse non-Python syntax, use parse_expr from sympy.parsing.sympy_parser.

>>> from sympy.parsing.sympy_parser import parse_expr
>>> parse_expr("2x+1", transformations="all")
2*x + 1

For more details about transformations: see parse_expr()

Locals

The sympification happens with access to everything that is loaded by from sympy import *; anything used in a string that is not defined by that import will be converted to a symbol. In the following, the bitcount function is treated as a symbol and the O is interpreted as the Order object (used with series) and it raises an error when used improperly:

>>> s = 'bitcount(42)'
>>> sympify(s)
bitcount(42)
>>> sympify("O(x)")
O(x)
>>> sympify("O + 1")
Traceback (most recent call last):
...
TypeError: unbound method...

In order to have bitcount be recognized it can be imported into a namespace dictionary and passed as locals:

>>> ns = {}
>>> exec('from sympy.core.evalf import bitcount', ns)
>>> sympify(s, locals=ns)
6

In order to have the O interpreted as a Symbol, identify it as such in the namespace dictionary. This can be done in a variety of ways; all three of the following are possibilities:

>>> from sympy import Symbol
>>> ns["O"] = Symbol("O")  # method 1
>>> exec('from sympy.abc import O', ns)  # method 2
>>> ns.update(dict(O=Symbol("O")))  # method 3
>>> sympify("O + 1", locals=ns)
O + 1

If you want all single-letter and Greek-letter variables to be symbols then you can use the clashing-symbols dictionaries that have been defined there as private variables: _clash1 (single-letter variables), _clash2 (the multi-letter Greek names) or _clash (both single and multi-letter names that are defined in abc).

>>> from sympy.abc import _clash1
>>> set(_clash1)  # if this fails, see issue #23903
{'E', 'I', 'N', 'O', 'Q', 'S'}
>>> sympify('I & Q', _clash1)
I & Q

Strict

If the option strict is set to True, only the types for which an explicit conversion has been defined are converted. In the other cases, a SympifyError is raised.

>>> print(sympify(None))
None
>>> sympify(None, strict=True)
Traceback (most recent call last):
...
SympifyError: SympifyError: None

Deprecated since version 1.6: sympify(obj) automatically falls back to str(obj) when all other conversion methods fail, but this is deprecated. strict=True will disable this deprecated behavior. See The string fallback in sympify().

Evaluation

If the option evaluate is set to False, then arithmetic and operators will be converted into their SymPy equivalents and the evaluate=False option will be added. Nested Add or Mul will be denested first. This is done via an AST transformation that replaces operators with their SymPy equivalents, so if an operand redefines any of those operations, the redefined operators will not be used. If argument a is not a string, the mathematical expression is evaluated before being passed to sympify, so adding evaluate=False will still return the evaluated result of expression.

>>> sympify('2**2 / 3 + 5')
19/3
>>> sympify('2**2 / 3 + 5', evaluate=False)
2**2/3 + 5
>>> sympify('4/2+7', evaluate=True)
9
>>> sympify('4/2+7', evaluate=False)
4/2 + 7
>>> sympify(4/2+7, evaluate=False)
9.00000000000000

Extending

To extend sympify to convert custom objects (not derived from Basic), just define a _sympy_ method to your class. You can do that even to classes that you do not own by subclassing or adding the method at runtime.

>>> from sympy import Matrix
>>> class MyList1(object):
...     def __iter__(self):
...         yield 1
...         yield 2
...         return
...     def __getitem__(self, i): return list(self)[i]
...     def _sympy_(self): return Matrix(self)
>>> sympify(MyList1())
Matrix([
[1],
[2]])

If you do not have control over the class definition you could also use the converter global dictionary. The key is the class and the value is a function that takes a single argument and returns the desired SymPy object, e.g. converter[MyList] = lambda x: Matrix(x).

>>> class MyList2(object):   # XXX Do not do this if you control the class!
...     def __iter__(self):  #     Use _sympy_!
...         yield 1
...         yield 2
...         return
...     def __getitem__(self, i): return list(self)[i]
>>> from sympy.core.sympify import converter
>>> converter[MyList2] = lambda x: Matrix(x)
>>> sympify(MyList2())
Matrix([
[1],
[2]])

Notes

The keywords rational and convert_xor are only used when the input is a string.

Convert_xor

>>> sympify('x^y',convert_xor=True)
x**y
>>> sympify('x^y',convert_xor=False)
x ^ y

Rational

>>> sympify('0.1',rational=False)
0.1
>>> sympify('0.1',rational=True)
1/10

Sometimes autosimplification during sympification results in expressions that are very different in structure than what was entered. Until such autosimplification is no longer done, the kernS function might be of some use. In the example below you can see how an expression reduces to $-1$ by autosimplification, but does not do so when kernS is used.

>>> from sympy.core.sympify import kernS
>>> from sympy.abc import x
>>> -2*(-(-x + 1/x)/(x*(x - 1/x)**2) - 1/(x*(x - 1/x))) - 1
-1
>>> s = '-2*(-(-x + 1/x)/(x*(x - 1/x)**2) - 1/(x*(x - 1/x))) - 1'
>>> sympify(s)
-1
>>> kernS(s)
-2*(-(-x + 1/x)/(x*(x - 1/x)**2) - 1/(x*(x - 1/x))) - 1

class sympy.core.sympify.Tbasic¶

assumptions¶

This module contains the machinery handling assumptions. Do also consider the guide Assumptions.

All symbolic objects have assumption attributes that can be accessed via .is_<assumption name> attribute.

Assumptions determine certain properties of symbolic objects and can have 3 possible values: True, False, None. True is returned if the object has the property and False is returned if it does not or cannot (i.e. does not make sense):

>>> from sympy import I
>>> I.is_algebraic
True
>>> I.is_real
False
>>> I.is_prime
False

When the property cannot be determined (or when a method is not implemented) None will be returned. For example, a generic symbol, x, may or may not be positive so a value of None is returned for x.is_positive.

By default, all symbolic values are in the largest set in the given context without specifying the property. For example, a symbol that has a property being integer, is also real, complex, etc.

Here follows a list of possible assumption names:

commutative¶: object commutes with any other object with respect to multiplication operation. See [12].
complex¶: object can have only values from the set of complex numbers. See [13].
imaginary¶: object value is a number that can be written as a real number multiplied by the imaginary unit I. See [R112]. Please note that 0 is not considered to be an imaginary number, see issue #7649.
real¶: object can have only values from the set of real numbers.
extended_real¶: object can have only values from the set of real numbers, oo and -oo.
integer¶: object can have only values from the set of integers.
odd¶
even¶: object can have only values from the set of odd (even) integers [R111].
prime¶: object is a natural number greater than 1 that has no positive divisors other than 1 and itself. See [R115].
composite¶: object is a positive integer that has at least one positive divisor other than 1 or the number itself. See [R113].
zero¶: object has the value of 0.
nonzero¶: object is a real number that is not zero.
rational¶: object can have only values from the set of rationals.
algebraic¶: object can have only values from the set of algebraic numbers [11].
transcendental¶: object can have only values from the set of transcendental numbers [10].
irrational¶: object value cannot be represented exactly by Rational, see [R114].
finite¶
infinite¶: object absolute value is bounded (arbitrarily large). See [R116], [R117], [R118].
negative¶
nonnegative¶: object can have only negative (nonnegative) values [R110].
positive¶
nonpositive¶: object can have only positive (nonpositive) values.
extended_negative¶
extended_nonnegative¶
extended_positive¶
extended_nonpositive¶
extended_nonzero¶: as without the extended part, but also including infinity with corresponding sign, e.g., extended_positive includes oo
hermitian¶
antihermitian¶: object belongs to the field of Hermitian (antihermitian) operators.

Examples¶

>>> from sympy import Symbol
>>> x = Symbol('x', real=True); x
x
>>> x.is_real
True
>>> x.is_complex
True

Notes¶

The fully-resolved assumptions for any SymPy expression can be obtained as follows:

>>> from sympy.core.assumptions import assumptions
>>> x = Symbol('x',positive=True)
>>> assumptions(x + I)
{'commutative': True, 'complex': True, 'composite': False, 'even':
False, 'extended_negative': False, 'extended_nonnegative': False,
'extended_nonpositive': False, 'extended_nonzero': False,
'extended_positive': False, 'extended_real': False, 'finite': True,
'imaginary': False, 'infinite': False, 'integer': False, 'irrational':
False, 'negative': False, 'noninteger': False, 'nonnegative': False,
'nonpositive': False, 'nonzero': False, 'odd': False, 'positive':
False, 'prime': False, 'rational': False, 'real': False, 'zero':
False}

Developers Notes¶

The current (and possibly incomplete) values are stored in the obj._assumptions dictionary; queries to getter methods (with property decorators) or attributes of objects/classes will return values and update the dictionary.

>>> eq = x**2 + I
>>> eq._assumptions
{}
>>> eq.is_finite
True
>>> eq._assumptions
{'finite': True, 'infinite': False}

For a Symbol, there are two locations for assumptions that may be of interest. The assumptions0 attribute gives the full set of assumptions derived from a given set of initial assumptions. The latter assumptions are stored as Symbol._assumptions_orig

>>> Symbol('x', prime=True, even=True)._assumptions_orig
{'even': True, 'prime': True}

The _assumptions_orig are not necessarily canonical nor are they filtered in any way: they records the assumptions used to instantiate a Symbol and (for storage purposes) represent a more compact representation of the assumptions needed to recreate the full set in Symbol.assumptions0.

cache¶

sympy.core.cache.__cacheit(maxsize)[source]¶

caching decorator.

important: the result of cached function must be immutable

Examples

>>> from sympy import cacheit
>>> @cacheit
... def f(a, b):
...    return a+b

>>> @cacheit
... def f(a, b): # noqa: F811
...    return [a, b] # <-- WRONG, returns mutable object

to force cacheit to check returned results mutability and consistency, set environment variable SYMPY_USE_CACHE to ‘debug’

basic¶

class collections.abc.Mapping¶

class collections.abc.Iterable¶

class sympy.core.basic.Basic(*args)[source]¶

Base class for all SymPy objects.

Notes And Conventions

Always use .args, when accessing parameters of some instance:

>>> 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

Never use internal methods or variables (the ones prefixed with _):

>>> cot(x)._args    # do not use this, use cot(x).args instead
(x,)

By “SymPy object” we mean something that can be returned by sympify. But not all objects one encounters using SymPy are subclasses of Basic. For example, mutable objects are not:

>>> from sympy import Basic, Matrix, sympify
>>> A = Matrix([[1, 2], [3, 4]]).as_mutable()
>>> isinstance(A, Basic)
False

>>> B = sympify(A)
>>> isinstance(B, Basic)
True

property args: tuple[Basic, ...]¶

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. Do not override .args() from Basic (so that it is easy to change the interface in the future if needed).

as_content_primitive( radical=False, clear=True, )[source]¶: A stub to allow Basic args (like Tuple) to be skipped when computing the content and primitive components of an expression.

See also

sympy.core.expr.Expr.as_content_primitive

as_dummy() → Self[source]¶

Return the expression with any objects having structurally bound symbols replaced with unique, canonical symbols within the object in which they appear and having only the default assumption for commutativity being True. When applied to a symbol a new symbol having only the same commutativity will be returned.

Examples

>>> from sympy import Integral, Symbol
>>> from sympy.abc import x
>>> r = Symbol('r', real=True)
>>> Integral(r, (r, x)).as_dummy()
Integral(_0, (_0, x))
>>> _.variables[0].is_real is None
True
>>> r.as_dummy()
_r

Notes

Any object that has structurally bound variables should have a property, bound_symbols that returns those symbols appearing in the object.

property 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, 'extended_negative': False,
 'extended_nonnegative': True, 'extended_nonpositive': False,
 'extended_nonzero': True, 'extended_positive': True, 'extended_real':
 True, 'finite': True, 'hermitian': True, 'imaginary': False,
 'infinite': False, 'negative': False, 'nonnegative': True,
 'nonpositive': False, 'nonzero': True, 'positive': True, 'real':
 True, 'zero': False}

atoms() → set[Basic][source]¶

atoms( *types: Tbasic | type[Tbasic], ) → set[Tbasic]

Returns the atoms that form the current object.

By default, only objects that are truly atomic and cannot 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()
{1, 2, I, pi, x, y}

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

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

>>> (1 + x + 2*sin(y + I*pi)).atoms(Number)
{1, 2}

>>> (1 + x + 2*sin(y + I*pi)).atoms(Number, NumberSymbol)
{1, 2, pi}

>>> (1 + x + 2*sin(y + I*pi)).atoms(Number, NumberSymbol, I)
{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
{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))
{1}

>>> (1 + x + 2*sin(y + I*pi)).atoms(S(2))
{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)
{f(x), sin(y + I*pi)}
>>> (1 + f(x) + 2*sin(y + I*pi)).atoms(AppliedUndef)
{f(x)}

>>> (1 + x + 2*sin(y + I*pi)).atoms(Mul)
{I*pi, 2*sin(y + I*pi)}

property canonical_variables: dict[Basic, Symbol]¶

Return a dictionary mapping any variable defined in self.bound_symbols to Symbols that do not clash with any free symbols in the expression.

Examples

>>> from sympy import Lambda
>>> from sympy.abc import x
>>> Lambda(x, 2*x).canonical_variables
{x: _0}

classmethod class_key() → tuple[int, int, str][source]¶: Nice order of classes.

compare(other)[source]¶

Return -1, 0, 1 if the object is less than, equal, or greater than other in a canonical sense. Non-Basic are always greater than Basic. If both names of the classes being compared appear in the $ordering_of_classes$ then the ordering will depend on the appearance of the names there. If either does not appear in that list, then the comparison is based on the class name. If the names are the same then a comparison is made on the length of the hashable content. Items of the equal-lengthed contents are then successively compared using the same rules. If there is never a difference then 0 is returned.

Examples

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

count(query)[source]¶: Count the number of matching subexpressions.

count_ops(visual=False)[source]¶: Wrapper for count_ops that returns the operation count.

doit(**hints)[source]¶

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)[source]¶

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

find(query, group=False)[source]¶: Find all subexpressions matching a query.

property free_symbols: set[Basic]¶

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

Not all free symbols are Symbol (see examples)

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.

Examples

>>> from sympy import Derivative, Integral, IndexedBase
>>> from sympy.abc import x, y, n
>>> (x + 1).free_symbols
{x}
>>> Integral(x, y).free_symbols
{x, y}

Not all free symbols are actually symbols:

>>> IndexedBase('F')[0].free_symbols
{F, F[0]}

The symbols of differentiation are not included unless they appear in the expression being differentiated.

>>> Derivative(x + y, y).free_symbols
{x, y}
>>> Derivative(x, y).free_symbols
{x}
>>> Derivative(x, (y, n)).free_symbols
{n, x}

If you want to know if a symbol is in the variables of the Derivative you can do so as follows:

>>> Derivative(x, y).has_free(y)
True

classmethod fromiter(args, **assumptions)[source]¶

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)

property 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

has(*patterns)[source]¶

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 has is a structural algorithm with no knowledge of mathematics. Consider the following half-open interval:

>>> from sympy import Interval
>>> i = Interval.Lopen(0, 5); i
Interval.Lopen(0, 5)
>>> i.args
(0, 5, True, False)
>>> i.has(4)  # there is no "4" in the arguments
False
>>> i.has(0)  # there *is* a "0" in the arguments
True

Instead, use contains to determine whether a number is in the interval or not:

>>> i.contains(4)
True
>>> i.contains(0)
False

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_free(*patterns)[source]¶

Return True if self has object(s) x as a free expression else False.

Examples

>>> from sympy import Integral, Function
>>> from sympy.abc import x, y
>>> f = Function('f')
>>> g = Function('g')
>>> expr = Integral(f(x), (f(x), 1, g(y)))
>>> expr.free_symbols
{y}
>>> expr.has_free(g(y))
True
>>> expr.has_free(*(x, f(x)))
False

This works for subexpressions and types, too:

>>> expr.has_free(g)
True
>>> (x + y + 1).has_free(y + 1)
True

has_xfree(s: set[Basic])[source]¶

Return True if self has any of the patterns in s as a free argument, else False. This is like $Basic.has_free$ but this will only report exact argument matches.

Examples

>>> from sympy import Function
>>> from sympy.abc import x, y
>>> f = Function('f')
>>> f(x).has_xfree({f})
False
>>> f(x).has_xfree({f(x)})
True
>>> f(x + 1).has_xfree({x})
True
>>> f(x + 1).has_xfree({x + 1})
True
>>> f(x + y + 1).has_xfree({x + 1})
False

property 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_same(b, approx=None)[source]¶

Return True if a and b are structurally the same, else False. If $approx$ is supplied, it will be used to test whether two numbers are the same or not. By default, only numbers of the same type will compare equal, so S.Half != Float(0.5).

Examples

In SymPy (unlike Python) two numbers do not compare the same if they are not of the same type:

>>> from sympy import S
>>> 2.0 == S(2)
False
>>> 0.5 == S.Half
False

By supplying a function with which to compare two numbers, such differences can be ignored. e.g. $equal_valued$ will return True for decimal numbers having a denominator that is a power of 2, regardless of precision.

>>> from sympy import Float
>>> from sympy.core.numbers import equal_valued
>>> (S.Half/4).is_same(Float(0.125, 1), equal_valued)
True
>>> Float(1, 2).is_same(Float(1, 10), equal_valued)
True

But decimals without a power of 2 denominator will compare as not being the same.

>>> Float(0.1, 9).is_same(Float(0.1, 10), equal_valued)
False

But arbitrary differences can be ignored by supplying a function to test the equivalence of two numbers:

>>> import math
>>> Float(0.1, 9).is_same(Float(0.1, 10), math.isclose)
True

Other objects might compare the same even though types are not the same. This routine will only return True if two expressions are identical in terms of class types.

>>> from sympy import eye, Basic
>>> eye(1) == S(eye(1))  # mutable vs immutable
True
>>> Basic.is_same(eye(1), S(eye(1)))
False

match(pattern, old=False)[source]¶

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, Sum
>>> 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

Since match is purely structural expressions that are equivalent up to bound symbols will not match:

>>> print(Sum(x, (x, 1, 2)).match(Sum(y, (y, 1, p))))
None

An expression with bound symbols can be matched if the pattern uses a distinct Wild for each bound symbol:

>>> Sum(x, (x, 1, 2)).match(Sum(q, (q, 1, p)))
{p_: 2, q_: x}

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}

See also

matches: pattern.matches(expr) is the same as expr.match(pattern)
xreplace: exact structural replacement
replace: structural replacement with pattern matching
Wild: symbolic placeholders for expressions in pattern matching

matches(expr, repl_dict=None, old=False)[source]¶

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}

rcall(*args)[source]¶

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 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)[source]¶: See the refine function in sympy.assumptions

replace( query, value, map=False, simultaneous=True, exact=None, ) → Basic[source]¶

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 does not 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 None it will be set to True so the match will only succeed if all non-zero values are received for each Wild that appears in the match pattern. Setting this to False accepts a match of 0; while setting it True accepts all matches that have a 0 in them. See example below for cautions.

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).replace(Mul, Add)
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, b = map(Wild, 'ab')
>>> 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

Matching is exact by default when more than one Wild symbol is used: matching fails unless the match gives non-zero values for all Wild symbols:

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

When set to False, the results may be non-intuitive:

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

When matching a single symbol, $exact$ will default to True, but this may or may not be the behavior that is desired:

Here, we want $exact=False$:

>>> from sympy import Function
>>> f = Function('f')
>>> e = f(1) + f(0)
>>> q = f(a), lambda a: f(a + 1)
>>> e.replace(*q, exact=False)
f(1) + f(2)
>>> e.replace(*q, exact=True)
f(0) + f(2)

But here, the nature of matching makes selecting the right setting tricky:

>>> e = x**(1 + y)
>>> (x**(1 + y)).replace(x**(1 + a), lambda a: x**-a, exact=False)
x
>>> (x**(1 + y)).replace(x**(1 + a), lambda a: x**-a, exact=True)
x**(-x - y + 1)
>>> (x**y).replace(x**(1 + a), lambda a: x**-a, exact=False)
x
>>> (x**y).replace(x**(1 + a), lambda a: x**-a, exact=True)
x**(1 - y)

It is probably better to use a different form of the query that describes the target expression more precisely:

>>> (1 + x**(1 + y)).replace(
... lambda x: x.is_Pow and x.exp.is_Add and x.exp.args[0] == 1,
... lambda x: x.base**(1 - (x.exp - 1)))
...
x**(1 - y) + 1

See also

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, deep=True, **hints)[source]¶

Rewrite self using a defined rule.

Rewriting transforms an expression to another, which is mathematically equivalent but structurally different. For example you can rewrite trigonometric functions as complex exponentials or combinatorial functions as gamma function.

This method takes a pattern and a rule as positional arguments. pattern is optional parameter which defines the types of expressions that will be transformed. If it is not passed, all possible expressions will be rewritten. rule defines how the expression will be rewritten.

Parameters:

args : Expr

A rule, or pattern and rule. - pattern is a type or an iterable of types. - rule can be any object.

deep : bool, optional

If True, subexpressions are recursively transformed. Default is True.

Examples

If pattern is unspecified, all possible expressions are transformed.

>>> from sympy import cos, sin, exp, I
>>> from sympy.abc import x
>>> expr = cos(x) + I*sin(x)
>>> expr.rewrite(exp)
exp(I*x)

Pattern can be a type or an iterable of types.

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

Rewriting behavior can be implemented by defining _eval_rewrite() method.

>>> from sympy import Expr, sqrt, pi
>>> class MySin(Expr):
...     def _eval_rewrite(self, rule, args, **hints):
...         x, = args
...         if rule == cos:
...             return cos(pi/2 - x, evaluate=False)
...         if rule == sqrt:
...             return sqrt(1 - cos(x)**2)
>>> MySin(MySin(x)).rewrite(cos)
cos(-cos(-x + pi/2) + pi/2)
>>> MySin(x).rewrite(sqrt)
sqrt(1 - cos(x)**2)

Defining _eval_rewrite_as_[...]() method is supported for backwards compatibility reason. This may be removed in the future and using it is discouraged.

>>> class MySin(Expr):
...     def _eval_rewrite_as_cos(self, *args, **hints):
...         x, = args
...         return cos(pi/2 - x, evaluate=False)
>>> MySin(x).rewrite(cos)
cos(-x + pi/2)

simplify(

**kwargs,

) → Basic[source]¶: See the simplify function in sympy.simplify

sort_key(order=None)[source]¶

Return a sort key.

Examples

>>> from sympy import S, I

>>> sorted([S(1)/2, I, -I], key=lambda x: x.sort_key())
[1/2, -I, I]

>>> S("[x, 1/x, 1/x**2, x**2, x**(1/2), x**(1/4), x**(3/2)]")
[x, 1/x, x**(-2), x**2, sqrt(x), x**(1/4), x**(3/2)]
>>> sorted(_, key=lambda x: x.sort_key())
[x**(-2), 1/x, x**(1/4), sqrt(x), x, x**(3/2), x**2]

subs(

arg1: Mapping[Basic | complex, Basic | complex],

arg2: None = None,

**kwargs: Any,

) → Basic[source]¶

subs(

arg1: Iterable[tuple[Basic | complex, Basic | complex]],

arg2: None = None,

**kwargs: Any,

) → Basic

subs(

arg1: Basic | complex,

arg2: Basic | complex,

**kwargs: Any,

) → Basic

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

This has the added feature of not allowing subsequent substitutions to affect those already made:

>>> ((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.

See also

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
sympy.core.evalf.EvalfMixin.evalf: calculates the given formula to a desired level of precision

xreplace(rule)[source]¶

Replace occurrences of objects within the expression.

Parameters:: rule : dict-like

Expresses a replacement rule
Returns:: 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 does not 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),)

See also

replace: replacement capable of doing wildcard-like matching, parsing of match, and conditional replacements
subs: substitution of subexpressions as defined by the objects themselves.

class sympy.core.basic.Atom(*args)[source]¶

A parent class for atomic things. An atom is an expression with no subexpressions.

Examples

Symbol, Number, Rational, Integer, … But not: Add, Mul, Pow, …

singleton¶

class sympy.core.singleton.SingletonRegistry[source]¶

The registry for the singleton classes (accessible as S).

Explanation

This class serves as two separate things.

The first thing it is is the SingletonRegistry. Several classes in SymPy appear so often that they are singletonized, that is, using some metaprogramming they are made so that they can only be instantiated once (see the sympy.core.singleton.Singleton class for details). For instance, every time you create Integer(0), this will return the same instance, sympy.core.numbers.Zero. All singleton instances are attributes of the S object, so Integer(0) can also be accessed as S.Zero.

Singletonization offers two advantages: it saves memory, and it allows fast comparison. It saves memory because no matter how many times the singletonized objects appear in expressions in memory, they all point to the same single instance in memory. The fast comparison comes from the fact that you can use is to compare exact instances in Python (usually, you need to use == to compare things). is compares objects by memory address, and is very fast.

Examples

>>> from sympy import S, Integer
>>> a = Integer(0)
>>> a is S.Zero
True

For the most part, the fact that certain objects are singletonized is an implementation detail that users should not need to worry about. In SymPy library code, is comparison is often used for performance purposes The primary advantage of S for end users is the convenient access to certain instances that are otherwise difficult to type, like S.Half (instead of Rational(1, 2)).

When using is comparison, make sure the argument is sympified. For instance,

>>> x = 0
>>> x is S.Zero
False

This problem is not an issue when using ==, which is recommended for most use-cases:

>>> 0 == S.Zero
True

The second thing S is is a shortcut for sympy.core.sympify.sympify(). sympy.core.sympify.sympify() is the function that converts Python objects such as int(1) into SymPy objects such as Integer(1). It also converts the string form of an expression into a SymPy expression, like sympify("x**2") -> Symbol("x")**2. S(1) is the same thing as sympify(1) (basically, S.__call__ has been defined to call sympify).

This is for convenience, since S is a single letter. It’s mostly useful for defining rational numbers. Consider an expression like x + 1/2. If you enter this directly in Python, it will evaluate the 1/2 and give 0.5, because both arguments are ints (see also Two Final Notes: ^ and /). However, in SymPy, you usually want the quotient of two integers to give an exact rational number. The way Python’s evaluation works, at least one side of an operator needs to be a SymPy object for the SymPy evaluation to take over. You could write this as x + Rational(1, 2), but this is a lot more typing. A shorter version is x + S(1)/2. Since S(1) returns Integer(1), the division will return a Rational type, since it will call Integer.__truediv__, which knows how to return a Rational.

class sympy.core.singleton.Singleton(*args, **kwargs)[source]¶

Metaclass for singleton classes.

Explanation

A singleton class has only one instance which is returned every time the class is instantiated. Additionally, this instance can be accessed through the global registry object S as S.<class_name>.

Examples

>>> from sympy import S, Basic
>>> from sympy.core.singleton import Singleton
>>> class MySingleton(Basic, metaclass=Singleton):
...     pass
>>> Basic() is Basic()
False
>>> MySingleton() is MySingleton()
True
>>> S.MySingleton is MySingleton()
True

Notes

Instance creation is delayed until the first time the value is accessed. (SymPy versions before 1.0 would create the instance during class creation time, which would be prone to import cycles.)

expr¶

class sympy.core.expr.Expr(*args)[source]¶

Base class for algebraic expressions.

Explanation

Everything that requires arithmetic operations to be defined should subclass this class, instead of Basic (which should be used only for argument storage and expression manipulation, i.e. pattern matching, substitutions, etc).

If you want to override the comparisons of expressions: Should use _eval_is_ge for inequality, or _eval_is_eq, with multiple dispatch. _eval_is_ge return true if x >= y, false if x < y, and None if the two types are not comparable or the comparison is indeterminate

See also

sympy.core.basic.Basic

apart(x=None, **args)[source]¶: See the apart function in sympy.polys

args_cnc(cset=False, warn=True, split_1=True)[source]¶

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

Explanation

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 suppressed by setting warn to False.

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

Examples

>>> 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)
[{-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_coeff_Add( rational=False, ) → tuple['Number', Expr][source]¶: Efficiently extract the coefficient of a summation.

as_coeff_Mul( rational: bool = False, ) → tuple['Number', Expr][source]¶: Efficiently extract the coefficient of a product.

as_coeff_add( *deps, ) → tuple[Expr, tuple[Expr, ...]][source]¶

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 do not 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 do not 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_add()
(3, ())
>>> (3 + x).as_coeff_add()
(3, (x,))
>>> (3 + x + y).as_coeff_add(x)
(y + 3, (x,))
>>> (3 + y).as_coeff_add(x)
(y + 3, ())

as_coeff_exponent( x, ) → tuple[Expr, Expr][source]¶: c*x**e -> c,e where x can be any symbolic expression.

as_coeff_mul(

*deps,

**kwargs,

) → tuple[Expr, tuple[Expr, ...]][source]¶

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

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

args should be a tuple of all other factors 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 do not 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 do not 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: Expr, ) → Expr | None[source]¶

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)

See also

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.Poly.coeff_monomial: efficiently find the single coefficient of a monomial in Poly
sympy.polys.polytools.Poly.nth: like coeff_monomial but powers of monomial terms are used

as_coefficients_dict(*syms)[source]¶

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 symbols syms are provided, any multiplicative terms independent of them will be considered a coefficient and a regular dictionary of syms-dependent generators as keys and their corresponding coefficients as values will be returned.

Examples

>>> from sympy.abc import a, x, y
>>> (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}
>>> (3*a*x).as_coefficients_dict(x)
{x: 3*a}
>>> (3*a*x).as_coefficients_dict(y)
{1: 3*a*x}

as_content_primitive( radical=False, clear=True, )[source]¶

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 not 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)
>>> ((x*(1 + y) + 0.4*x*(3 + 3*y))**2).as_content_primitive()
(1, 4.84*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)[source]¶

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,

) → tuple[Expr, Expr][source]¶

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(mul=True) to change Add or Mul into Add
.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 either have terms that contain variables that are in deps, or be equal to 0 (when self is an Add) or 1 (when self is a Mul)
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

– self is an Add

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

as_leading_term(*symbols, logx=None, cdir=0)[source]¶

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() → tuple[Expr, Expr][source]¶

Return the numerator and the denominator of an expression.

expression -> a/b -> a, b

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

See also

normal: return a/b instead of (a, b)

as_ordered_factors(order=None)[source]¶: Return list of ordered factors (if Mul) else [self].

as_ordered_terms(order=None, data=False)[source]¶

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)[source]¶

Converts self to a polynomial or returns None.

Explanation

>>> 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()[source]¶

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.

See also

as_ordered_factors: An alternative for noncommutative applications, returning an ordered list of factors.
args_cnc: Similar to as_ordered_factors, but guarantees separation of commutative and noncommutative factors.

as_real_imag(

deep=True,

**hints,

) → tuple[Expr, Expr][source]¶

Performs complex expansion on ‘self’ and returns a tuple containing collected both real and imaginary parts. This method cannot 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()[source]¶: Transform an expression to a list of terms.

aseries(x=None, n=6, bound=0, hir=False)[source]¶

Asymptotic Series expansion of self. This is equivalent to self.series(x, oo, n).

Parameters:

self : Expression

The expression whose series is to be expanded.

x : Symbol

It is the variable of the expression to be calculated.

n : Value

The value used to represent the order in terms of x**n, up to which the series is to be expanded.

hir : Boolean

Set this parameter to be True to produce hierarchical series. It stops the recursion at an early level and may provide nicer and more useful results.

bound : Value, Integer

Use the bound parameter to give limit on rewriting coefficients in its normalised form.

Returns:

Expr

Asymptotic series expansion of the expression.

Examples

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

>>> e = sin(1/x + exp(-x)) - sin(1/x)

>>> e.aseries(x)
(1/(24*x**4) - 1/(2*x**2) + 1 + O(x**(-6), (x, oo)))*exp(-x)

>>> e.aseries(x, n=3, hir=True)
-exp(-2*x)*sin(1/x)/2 + exp(-x)*cos(1/x) + O(exp(-3*x), (x, oo))

>>> e = exp(exp(x)/(1 - 1/x))

>>> e.aseries(x)
exp(exp(x)/(1 - 1/x))

>>> e.aseries(x, bound=3) 
exp(exp(x)/x**2)*exp(exp(x)/x)*exp(-exp(x) + exp(x)/(1 - 1/x) - exp(x)/x - exp(x)/x**2)*exp(exp(x))

For rational expressions this method may return original expression without the Order term. >>> (1/x).aseries(x, n=8) 1/x

Notes

This algorithm is directly induced from the limit computational algorithm provided by Gruntz. It majorly uses the mrv and rewrite sub-routines. The overall idea of this algorithm is first to look for the most rapidly varying subexpression w of a given expression f and then expands f in a series in w. Then same thing is recursively done on the leading coefficient till we get constant coefficients.

If the most rapidly varying subexpression of a given expression f is f itself, the algorithm tries to find a normalised representation of the mrv set and rewrites f using this normalised representation.

If the expansion contains an order term, it will be either O(x ** (-n)) or O(w ** (-n)) where w belongs to the most rapidly varying expression of self.

See also

Expr.aseries: See the docstring of this function for complete details of this wrapper.

References

[R119]

Gruntz, Dominik. A new algorithm for computing asymptotic series. In: Proc. 1993 Int. Symp. Symbolic and Algebraic Computation. 1993. pp. 239-244.

[R120]

Gruntz thesis - p90

[R121]

https://en.wikipedia.org/wiki/Asymptotic_expansion

cancel(*gens, **args)[source]¶: See the cancel function in sympy.polys

coeff( x: Expr, n=1, right=False, _first=True, )[source]¶

Returns the coefficient from the term(s) containing x**n. If n is zero then all terms independent of x will be returned.

Explanation

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

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

See also

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.Poly.coeff_monomial: efficiently find the single coefficient of a monomial in Poly
sympy.polys.polytools.Poly.nth: like coeff_monomial but powers of monomial terms are used

collect( syms, func=None, evaluate=True, exact=False, distribute_order_term=True, )[source]¶: See the collect function in sympy.simplify

combsimp()[source]¶: See the combsimp function in sympy.simplify

compute_leading_term(x, logx=None)[source]¶

Deprecated function to compute the leading term of a series.

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

conjugate()[source]¶: Returns the complex conjugate of ‘self’.

could_extract_minus_sign() → bool[source]¶

Return True if self has -1 as a leading factor or has more literal negative signs than positive signs in a sum, otherwise False.

Examples

>>> from sympy.abc import x, y
>>> e = x - y
>>> {i.could_extract_minus_sign() for i in (e, -e)}
{False, True}

Though the y - x is considered like -(x - y), since it is in a product without a leading factor of -1, the result is false below:

>>> (x*(y - x)).could_extract_minus_sign()
False

To put something in canonical form wrt to sign, use $signsimp$:

>>> from sympy import signsimp
>>> signsimp(x*(y - x))
-x*(x - y)
>>> _.could_extract_minus_sign()
True

equals(other, failing_expression=False)[source]¶

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

Explanation

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.

expand(

deep=True,

modulus=None,

power_base=True,

power_exp=True,

mul=True,

log=True,

multinomial=True,

basic=True,

**hints,

)[source]¶

Expand an expression using hints.

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

property expr_free_symbols¶

Like free_symbols, but returns the free symbols only if they are contained in an expression node.

Examples

>>> from sympy.abc import x, y
>>> (x + y).expr_free_symbols 
{x, y}

If the expression is contained in a non-expression object, do not return the free symbols. Compare:

>>> from sympy import Tuple
>>> t = Tuple(x + y)
>>> t.expr_free_symbols 
set()
>>> t.free_symbols
{x, y}

extract_additively(c)[source]¶

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
>>> e.extract_additively(x + 1)
x + 2
>>> e.extract_additively(3*x)
>>> e.extract_additively(4)
>>> (y*(x + 1)).extract_additively(x + 1)
>>> ((x + 1)*(x + 2*y + 1) + 3).extract_additively(x + 1)
(x + 1)*(x + 2*y) + 3

extract_branch_factor(allow_half=False)[source]¶

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: Expr, ) → Expr | None[source]¶

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.

Examples

>>> 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)[source]¶: See the factor() function in sympy.polys.polytools

fourier_series(limits=None)[source]¶

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, )[source]¶

Compute formal power power series of self.

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

gammasimp()[source]¶: See the gammasimp function in sympy.simplify

getO() → Expr | None[source]¶: Returns the additive O(..) symbol if there is one, else None.

getn()[source]¶

Returns the order of the expression.

Explanation

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()

integrate(*args, **kwargs)[source]¶: See the integrate function in sympy.integrals

invert(g, *gens, **args)[source]¶: Return the multiplicative inverse of self mod g where self (and g) may be symbolic expressions).

See also

sympy.core.intfunc.mod_inverse, sympy.polys.polytools.invert

is_algebraic_expr(*syms)[source]¶

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

See also

is_rational_function

References

[R122]

https://en.wikipedia.org/wiki/Algebraic_expression

is_constant(*wrt, **flags)[source]¶

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

Explanation

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, a few 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.

3) finding out zeros of denominator expression with free_symbols. It will not be constant if there are zeros. It gives more negative answers for expression that are not constant.

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_meromorphic(x, a)[source]¶

This tests whether an expression is meromorphic as a function of the given symbol x at the point a.

This method is intended as a quick test that will return None if no decision can be made without simplification or more detailed analysis.

Examples

>>> from sympy import zoo, log, sin, sqrt
>>> from sympy.abc import x

>>> f = 1/x**2 + 1 - 2*x**3
>>> f.is_meromorphic(x, 0)
True
>>> f.is_meromorphic(x, 1)
True
>>> f.is_meromorphic(x, zoo)
True

>>> g = x**log(3)
>>> g.is_meromorphic(x, 0)
False
>>> g.is_meromorphic(x, 1)
True
>>> g.is_meromorphic(x, zoo)
False

>>> h = sin(1/x)*x**2
>>> h.is_meromorphic(x, 0)
False
>>> h.is_meromorphic(x, 1)
True
>>> h.is_meromorphic(x, zoo)
True

Multivalued functions are considered meromorphic when their branches are meromorphic. Thus most functions are meromorphic everywhere except at essential singularities and branch points. In particular, they will be meromorphic also on branch cuts except at their endpoints.

>>> log(x).is_meromorphic(x, -1)
True
>>> log(x).is_meromorphic(x, 0)
False
>>> sqrt(x).is_meromorphic(x, -1)
True
>>> sqrt(x).is_meromorphic(x, 0)
False

property is_number¶

Returns True if self has no free symbols and no undefined functions (AppliedUndef, to be precise). It will be faster than if not self.free_symbols, however, since is_number will fail as soon as it hits a free symbol or undefined function.

Examples

>>> from sympy import Function, Integral, cos, sin, pi
>>> from sympy.abc import x
>>> f = Function('f')

>>> x.is_number
False
>>> f(1).is_number
False
>>> (2*x).is_number
False
>>> (2 + Integral(2, x)).is_number
False
>>> (2 + Integral(2, (x, 1, 2))).is_number
True

Not all numbers are Numbers in the SymPy sense:

>>> pi.is_number, pi.is_Number
(True, False)

If something is a number it should evaluate to a number with real and imaginary parts that are Numbers; the result may not be comparable, however, since the real and/or imaginary part of the result may not have precision.

>>> cos(1).is_number and cos(1).is_comparable
True

>>> z = cos(1)**2 + sin(1)**2 - 1
>>> z.is_number
True
>>> z.is_comparable
False

See also

sympy.core.basic.Basic.is_comparable

is_polynomial(*syms)[source]¶

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, Function
>>> 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
>>> (2**x + 1).is_polynomial(2**x)
True
>>> f = Function('f')
>>> (f(x) + 1).is_polynomial(x)
False
>>> (f(x) + 1).is_polynomial(f(x))
True
>>> (1/f(x) + 1).is_polynomial(f(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

See also

as_numer_denom: return (a, b) instead of a/b

nseries( x=None, x0=0, n=6, dir='+', logx=None, cdir=0, )[source]¶

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 do not 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.

symbol¶

class sympy.core.symbol.Symbol(name, **assumptions)[source]¶

Symbol class is used to create symbolic variables.

Parameters:

AtomicExpr: variable name

Boolean: Assumption with a boolean value(True or False)

Explanation

Symbolic variables are placeholders for mathematical symbols that can represent numbers, constants, or any other mathematical entities and can be used in mathematical expressions and to perform symbolic computations.

Assumptions:

commutative = True positive = True real = True imaginary = True complex = True complete list of more assumptions- Predicates

You can override the default assumptions in the constructor.

Examples

>>> from sympy import Symbol
>>> x = Symbol("x", positive=True)
>>> x.is_positive
True
>>> x.is_negative
False

passing in greek letters:

>>> from sympy import Symbol
>>> alpha = Symbol('alpha')
>>> alpha 
α

Trailing digits are automatically treated like subscripts of what precedes them in the name. General format to add subscript to a symbol : <var_name> = Symbol('<symbol_name>_<subscript>')

>>> from sympy import Symbol
>>> alpha_i = Symbol('alpha_i')
>>> alpha_i 
αᵢ

class sympy.core.symbol.Wild(

name,

exclude=(),

properties=(),

**assumptions,

)[source]¶

A Wild symbol matches anything, or anything without whatever is explicitly excluded.

Parameters:

name : str

Name of the Wild instance.

exclude : iterable, optional

Instances in exclude will not be matched.

properties : iterable of functions, optional

Functions, each taking an expressions as input and returns a bool. All functions in properties need to return True in order for the Wild instance to match the expression.

Examples

>>> from sympy import Wild, WildFunction, cos, pi
>>> from sympy.abc import x, y, z
>>> a = Wild('a')
>>> x.match(a)
{a_: x}
>>> pi.match(a)
{a_: pi}
>>> (3*x**2).match(a*x)
{a_: 3*x}
>>> cos(x).match(a)
{a_: cos(x)}
>>> b = Wild('b', exclude=[x])
>>> (3*x**2).match(b*x)
>>> b.match(a)
{a_: b_}
>>> A = WildFunction('A')
>>> A.match(a)
{a_: A_}

Tips

When using Wild, be sure to use the exclude keyword to make the pattern more precise. Without the exclude pattern, you may get matches that are technically correct, but not what you wanted. For example, using the above without exclude:

>>> from sympy import symbols
>>> a, b = symbols('a b', cls=Wild)
>>> (2 + 3*y).match(a*x + b*y)
{a_: 2/x, b_: 3}

This is technically correct, because (2/x)*x + 3*y == 2 + 3*y, but you probably wanted it to not match at all. The issue is that you really did not want a and b to include x and y, and the exclude parameter lets you specify exactly this. With the exclude parameter, the pattern will not match.

>>> a = Wild('a', exclude=[x, y])
>>> b = Wild('b', exclude=[x, y])
>>> (2 + 3*y).match(a*x + b*y)

Exclude also helps remove ambiguity from matches.

>>> E = 2*x**3*y*z
>>> a, b = symbols('a b', cls=Wild)
>>> E.match(a*b)
{a_: 2*y*z, b_: x**3}
>>> a = Wild('a', exclude=[x, y])
>>> E.match(a*b)
{a_: z, b_: 2*x**3*y}
>>> a = Wild('a', exclude=[x, y, z])
>>> E.match(a*b)
{a_: 2, b_: x**3*y*z}

Wild also accepts a properties parameter:

>>> a = Wild('a', properties=[lambda k: k.is_Integer])
>>> E.match(a*b)
{a_: 2, b_: x**3*y*z}

class sympy.core.symbol.Dummy(name=None, dummy_index=None, **assumptions)[source]¶

Dummy symbols are each unique, even if they have the same name:

Examples

>>> from sympy import Dummy
>>> Dummy("x") == Dummy("x")
False

If a name is not supplied then a string value of an internal count will be used. This is useful when a temporary variable is needed and the name of the variable used in the expression is not important.

>>> Dummy() 
_Dummy_10

sympy.core.symbol.symbols(

names,

*,

cls=<class 'sympy.core.symbol.Symbol'>,

**args,

) → Any[source]¶

Transform strings into instances of Symbol class.

symbols() function returns a sequence of symbols with names taken from names argument, which can be a comma or whitespace delimited string, or a sequence of strings:

>>> from sympy import symbols, Function

>>> x, y, z = symbols('x,y,z')
>>> a, b, c = symbols('a b c')

The type of output is dependent on the properties of input arguments:

>>> symbols('x')
x
>>> symbols('x,')
(x,)
>>> symbols('x,y')
(x, y)
>>> symbols(('a', 'b', 'c'))
(a, b, c)
>>> symbols(['a', 'b', 'c'])
[a, b, c]
>>> symbols({'a', 'b', 'c'})
{a, b, c}

If an iterable container is needed for a single symbol, set the seq argument to True or terminate the symbol name with a comma:

>>> symbols('x', seq=True)
(x,)

To reduce typing, range syntax is supported to create indexed symbols. Ranges are indicated by a colon and the type of range is determined by the character to the right of the colon. If the character is a digit then all contiguous digits to the left are taken as the nonnegative starting value (or 0 if there is no digit left of the colon) and all contiguous digits to the right are taken as 1 greater than the ending value:

>>> symbols('x:10')
(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9)

>>> symbols('x5:10')
(x5, x6, x7, x8, x9)
>>> symbols('x5(:2)')
(x50, x51)

>>> symbols('x5:10,y:5')
(x5, x6, x7, x8, x9, y0, y1, y2, y3, y4)

>>> symbols(('x5:10', 'y:5'))
((x5, x6, x7, x8, x9), (y0, y1, y2, y3, y4))

If the character to the right of the colon is a letter, then the single letter to the left (or ‘a’ if there is none) is taken as the start and all characters in the lexicographic range through the letter to the right are used as the range:

>>> symbols('x:z')
(x, y, z)
>>> symbols('x:c')  # null range
()
>>> symbols('x(:c)')
(xa, xb, xc)

>>> symbols(':c')
(a, b, c)

>>> symbols('a:d, x:z')
(a, b, c, d, x, y, z)

>>> symbols(('a:d', 'x:z'))
((a, b, c, d), (x, y, z))

Multiple ranges are supported; contiguous numerical ranges should be separated by parentheses to disambiguate the ending number of one range from the starting number of the next:

>>> symbols('x:2(1:3)')
(x01, x02, x11, x12)
>>> symbols(':3:2')  # parsing is from left to right
(00, 01, 10, 11, 20, 21)

Only one pair of parentheses surrounding ranges are removed, so to include parentheses around ranges, double them. And to include spaces, commas, or colons, escape them with a backslash:

>>> symbols('x((a:b))')
(x(a), x(b))
>>> symbols(r'x(:1\,:2)')  # or r'x((:1)\,(:2))'
(x(0,0), x(0,1))

All newly created symbols have assumptions set according to args:

>>> a = symbols('a', integer=True)
>>> a.is_integer
True

>>> x, y, z = symbols('x,y,z', real=True)
>>> x.is_real and y.is_real and z.is_real
True

Despite its name, symbols() can create symbol-like objects like instances of Function or Wild classes. To achieve this, set cls keyword argument to the desired type:

>>> symbols('f,g,h', cls=Function)
(f, g, h)

>>> type(_[0])
<class 'sympy.core.function.UndefinedFunction'>

sympy.core.symbol.var(names, **args)[source]¶

Create symbols and inject them into the global namespace.

Explanation

This calls symbols() with the same arguments and puts the results into the global namespace. It’s recommended not to use var() in library code, where symbols() has to be used:

.. rubric:: Examples

>>> from sympy import var

>>> var('x')
x
>>> x # noqa: F821
x

>>> var('a,ab,abc')
(a, ab, abc)
>>> abc # noqa: F821
abc

>>> var('x,y', real=True)
(x, y)
>>> x.is_real and y.is_real # noqa: F821
True

See symbols() documentation for more details on what kinds of arguments can be passed to var().

intfunc¶

sympy.core.intfunc.num_digits(n, base=10)[source]¶

Return the number of digits needed to express n in give base.

Parameters:

n: integer

The number whose digits are counted.

b: integer

The base in which digits are computed.

Examples

>>> from sympy.core.intfunc import num_digits
>>> num_digits(10)
2
>>> num_digits(10, 2)  # 1010 -> 4 digits
4
>>> num_digits(-100, 16)  # -64 -> 2 digits
2

sympy.core.intfunc.trailing(n)[source]¶

Count the number of trailing zero digits in the binary representation of n, i.e. determine the largest power of 2 that divides n.

Examples

>>> from sympy import trailing
>>> trailing(128)
7
>>> trailing(63)
0

See also

sympy.ntheory.factor_.multiplicity

sympy.core.intfunc.ilcm(*args)[source]¶

Computes integer least common multiple.

Examples

>>> from sympy import ilcm
>>> ilcm(5, 10)
10
>>> ilcm(7, 3)
21
>>> ilcm(5, 10, 15)
30

sympy.core.intfunc.igcd(*args)¶

Computes nonnegative integer greatest common divisor.

Explanation

The algorithm is based on the well known Euclid’s algorithm [R123]. To improve speed, igcd() has its own caching mechanism. If you do not need the cache mechanism, using sympy.external.gmpy.gcd.

Examples

>>> from sympy import igcd
>>> igcd(2, 4)
2
>>> igcd(5, 10, 15)
5

References

[R123] (1,2)

https://en.wikipedia.org/wiki/Euclidean_algorithm

sympy.core.intfunc.igcd_lehmer(a, b)[source]¶

Computes greatest common divisor of two integers.

Explanation

Euclid’s algorithm for the computation of the greatest common divisor gcd(a, b) of two (positive) integers $a$ and $b$ is based on the division identity $$ a = q \times b + r$$, where the quotient $q$ and the remainder $r$ are integers and $0 \le r < b$. Then each common divisor of $a$ and $b$ divides $r$, and it follows that gcd(a, b) == gcd(b, r). The algorithm works by constructing the sequence r0, r1, r2, …, where r0 = a, r1 = b, and each rn is the remainder from the division of the two preceding elements.

In Python, q = a // b and r = a % b are obtained by the floor division and the remainder operations, respectively. These are the most expensive arithmetic operations, especially for large a and b.

Lehmer’s algorithm [R124] is based on the observation that the quotients qn = r(n-1) // rn are in general small integers even when a and b are very large. Hence the quotients can be usually determined from a relatively small number of most significant bits.

The efficiency of the algorithm is further enhanced by not computing each long remainder in Euclid’s sequence. The remainders are linear combinations of a and b with integer coefficients derived from the quotients. The coefficients can be computed as far as the quotients can be determined from the chosen most significant parts of a and b. Only then a new pair of consecutive remainders is computed and the algorithm starts anew with this pair.

References

[R124] (1,2)

https://en.wikipedia.org/wiki/Lehmer%27s_GCD_algorithm

sympy.core.intfunc.igcdex(a, b)[source]¶

Returns x, y, g such that g = x*a + y*b = gcd(a, b).

Examples

>>> from sympy.core.intfunc import igcdex
>>> igcdex(2, 3)
(-1, 1, 1)
>>> igcdex(10, 12)
(-1, 1, 2)

>>> x, y, g = igcdex(100, 2004)
>>> x, y, g
(-20, 1, 4)
>>> x*100 + y*2004
4

sympy.core.intfunc.isqrt(n)[source]¶

Return the largest integer less than or equal to $\sqrt{n}$.

Parameters:

n : non-negative integer

Returns:

int : $\left\lfloor\sqrt{n}\right\rfloor$

Raises:

ValueError

If n is negative.

TypeError

If n is of a type that cannot be compared to int. Therefore, a TypeError is raised for str, but not for float.

Examples

>>> from sympy.core.intfunc import isqrt
>>> isqrt(0)
0
>>> isqrt(9)
3
>>> isqrt(10)
3
>>> isqrt("30")
Traceback (most recent call last):
    ...
TypeError: '<' not supported between instances of 'str' and 'int'
>>> from sympy.core.numbers import Rational
>>> isqrt(Rational(-1, 2))
Traceback (most recent call last):
    ...
ValueError: n must be nonnegative

sympy.core.intfunc.integer_nthroot(y, n)[source]¶

Return a tuple containing x = floor(y**(1/n)) and a boolean indicating whether the result is exact (that is, whether x**n == y).

Examples

>>> from sympy import integer_nthroot
>>> integer_nthroot(16, 2)
(4, True)
>>> integer_nthroot(26, 2)
(5, False)

To simply determine if a number is a perfect square, the is_square function should be used:

>>> from sympy.ntheory.primetest import is_square
>>> is_square(26)
False

sympy.core.intfunc.integer_log(n, b)[source]¶

Returns (e, bool) where e is the largest nonnegative integer such that $|n| \geq |b^e|$ and bool is True if $n = b^e$.

Examples

>>> from sympy import integer_log
>>> integer_log(125, 5)
(3, True)
>>> integer_log(17, 9)
(1, False)

If the base is positive and the number negative the return value will always be the same except for 2:

>>> integer_log(-4, 2)
(2, False)
>>> integer_log(-16, 4)
(0, False)

When the base is negative, the returned value will only be True if the parity of the exponent is correct for the sign of the base:

>>> integer_log(4, -2)
(2, True)
>>> integer_log(8, -2)
(3, False)
>>> integer_log(-8, -2)
(3, True)
>>> integer_log(-4, -2)
(2, False)

sympy.core.intfunc.mod_inverse(a, m)[source]¶

Return the number $c$ such that, $a \times c = 1 \pmod{m}$ where $c$ has the same sign as $m$. If no such value exists, a ValueError is raised.

Examples

>>> from sympy import mod_inverse, S

Suppose we wish to find multiplicative inverse $x$ of 3 modulo 11. This is the same as finding $x$ such that $3x = 1 \pmod{11}$. One value of x that satisfies this congruence is 4. Because $3 \times 4 = 12$ and $12 = 1 \pmod{11}$. This is the value returned by mod_inverse:

>>> mod_inverse(3, 11)
4
>>> mod_inverse(-3, 11)
7

When there is a common factor between the numerators of $a$ and $m$ the inverse does not exist:

>>> mod_inverse(2, 4)
Traceback (most recent call last):
...
ValueError: inverse of 2 mod 4 does not exist

>>> mod_inverse(S(2)/7, S(5)/2)
7/2

References

[R125]

https://en.wikipedia.org/wiki/Modular_multiplicative_inverse

[R126]

https://en.wikipedia.org/wiki/Extended_Euclidean_algorithm

numbers¶

class sympy.core.numbers.Number(*obj)[source]¶

Represents atomic numbers in SymPy.

Explanation

Floating point numbers are represented by the Float class. Rational numbers (of any size) are represented by the Rational class. Integer numbers (of any size) are represented by the Integer class. Float and Rational are subclasses of Number; Integer is a subclass of Rational.

For example, 2/3 is represented as Rational(2, 3) which is a different object from the floating point number obtained with Python division 2/3. Even for numbers that are exactly represented in binary, there is a difference between how two forms, such as Rational(1, 2) and Float(0.5), are used in SymPy. The rational form is to be preferred in symbolic computations.

Other kinds of numbers, such as algebraic numbers sqrt(2) or complex numbers 3 + 4*I, are not instances of Number class as they are not atomic.

See also

Float, Integer, Rational

as_coeff_Add(rational=False)[source]¶: Efficiently extract the coefficient of a summation.

as_coeff_Mul(rational=False)[source]¶: Efficiently extract the coefficient of a product.

cofactors(other)[source]¶: Compute GCD and cofactors of $self$ and $other$.

gcd(other)[source]¶: Compute GCD of $self$ and $other$.

lcm(other)[source]¶: Compute LCM of $self$ and $other$.

class sympy.core.numbers.Float(num, dps=None, precision=None)[source]¶

Represent a floating-point number of arbitrary precision.

Examples

>>> from sympy import Float
>>> Float(3.5)
3.50000000000000
>>> Float(3)
3.00000000000000

Creating Floats from strings (and Python int and long types) will give a minimum precision of 15 digits, but the precision will automatically increase to capture all digits entered.

>>> Float(1)
1.00000000000000
>>> Float(10**20)
100000000000000000000.
>>> Float('1e20')
100000000000000000000.

However, floating-point numbers (Python float types) retain only 15 digits of precision:

>>> Float(1e20)
1.00000000000000e+20
>>> Float(1.23456789123456789)
1.23456789123457

It may be preferable to enter high-precision decimal numbers as strings:

>>> Float('1.23456789123456789')
1.23456789123456789

The desired number of digits can also be specified:

>>> Float('1e-3', 3)
0.00100
>>> Float(100, 4)
100.0

Float can automatically count significant figures if a null string is sent for the precision; spaces or underscores are also allowed. (Auto- counting is only allowed for strings, ints and longs).

>>> Float('123 456 789.123_456', '')
123456789.123456
>>> Float('12e-3', '')
0.012
>>> Float(3, '')
3.

If a number is written in scientific notation, only the digits before the exponent are considered significant if a decimal appears, otherwise the “e” signifies only how to move the decimal:

>>> Float('60.e2', '')  # 2 digits significant
6.0e+3
>>> Float('60e2', '')  # 4 digits significant
6000.
>>> Float('600e-2', '')  # 3 digits significant
6.00

Notes

Floats are inexact by their nature unless their value is a binary-exact value.

>>> approx, exact = Float(.1, 1), Float(.125, 1)

For calculation purposes, evalf needs to be able to change the precision but this will not increase the accuracy of the inexact value. The following is the most accurate 5-digit approximation of a value of 0.1 that had only 1 digit of precision:

>>> approx.evalf(5)
0.099609

By contrast, 0.125 is exact in binary (as it is in base 10) and so it can be passed to Float or evalf to obtain an arbitrary precision with matching accuracy:

>>> Float(exact, 5)
0.12500
>>> exact.evalf(20)
0.12500000000000000000

Trying to make a high-precision Float from a float is not disallowed, but one must keep in mind that the underlying float (not the apparent decimal value) is being obtained with high precision. For example, 0.3 does not have a finite binary representation. The closest rational is the fraction 5404319552844595/2**54. So if you try to obtain a Float of 0.3 to 20 digits of precision you will not see the same thing as 0.3 followed by 19 zeros:

>>> Float(0.3, 20)
0.29999999999999998890

If you want a 20-digit value of the decimal 0.3 (not the floating point approximation of 0.3) you should send the 0.3 as a string. The underlying representation is still binary but a higher precision than Python’s float is used:

>>> Float('0.3', 20)
0.30000000000000000000

Although you can increase the precision of an existing Float using Float it will not increase the accuracy – the underlying value is not changed:

>>> def show(f): # binary rep of Float
...     from sympy import Mul, Pow
...     s, m, e, b = f._mpf_
...     v = Mul(int(m), Pow(2, int(e), evaluate=False), evaluate=False)
...     print('%s at prec=%s' % (v, f._prec))
...
>>> t = Float('0.3', 3)
>>> show(t)
4915/2**14 at prec=13
>>> show(Float(t, 20)) # higher prec, not higher accuracy
4915/2**14 at prec=70
>>> show(Float(t, 2)) # lower prec
307/2**10 at prec=10

The same thing happens when evalf is used on a Float:

>>> show(t.evalf(20))
4915/2**14 at prec=70
>>> show(t.evalf(2))
307/2**10 at prec=10

Finally, Floats can be instantiated with an mpf tuple (n, c, p) to produce the number (-1)**n*c*2**p:

>>> n, c, p = 1, 5, 0
>>> (-1)**n*c*2**p
-5
>>> Float((1, 5, 0))
-5.00000000000000

An actual mpf tuple also contains the number of bits in c as the last element of the tuple:

>>> _._mpf_
(1, 5, 0, 3)

This is not needed for instantiation and is not the same thing as the precision. The mpf tuple and the precision are two separate quantities that Float tracks.

In SymPy, a Float is a number that can be computed with arbitrary precision. Although floating point ‘inf’ and ‘nan’ are not such numbers, Float can create these numbers:

>>> Float('-inf')
-oo
>>> _.is_Float
False

Zero in Float only has a single value. Values are not separate for positive and negative zeroes.

class sympy.core.numbers.Rational(p, q=None, gcd=None)[source]¶

Represents rational numbers (p/q) of any size.

Examples

>>> from sympy import Rational, nsimplify, S, pi
>>> Rational(1, 2)
1/2

Rational is unprejudiced in accepting input. If a float is passed, the underlying value of the binary representation will be returned:

>>> Rational(.5)
1/2
>>> Rational(.2)
3602879701896397/18014398509481984

If the simpler representation of the float is desired then consider limiting the denominator to the desired value or convert the float to a string (which is roughly equivalent to limiting the denominator to 10**12):

>>> Rational(str(.2))
1/5
>>> Rational(.2).limit_denominator(10**12)
1/5

An arbitrarily precise Rational is obtained when a string literal is passed:

>>> Rational("1.23")
123/100
>>> Rational('1e-2')
1/100
>>> Rational(".1")
1/10
>>> Rational('1e-2/3.2')
1/320

The conversion of other types of strings can be handled by the sympify() function, and conversion of floats to expressions or simple fractions can be handled with nsimplify:

>>> S('.[3]')  # repeating digits in brackets
1/3
>>> S('3**2/10')  # general expressions
9/10
>>> nsimplify(.3)  # numbers that have a simple form
3/10

But if the input does not reduce to a literal Rational, an error will be raised:

>>> Rational(pi)
Traceback (most recent call last):
...
TypeError: invalid input: pi

Low-level

Access numerator and denominator as .p and .q:

>>> r = Rational(3, 4)
>>> r
3/4
>>> r.p
3
>>> r.q
4

Note that p and q return integers (not SymPy Integers) so some care is needed when using them in expressions:

>>> r.p/r.q
0.75

If an unevaluated Rational is desired, gcd=1 can be passed and this will keep common divisors of the numerator and denominator from being eliminated. It is not possible, however, to leave a negative value in the denominator.

>>> Rational(2, 4, gcd=1)
2/4
>>> Rational(2, -4, gcd=1).q
4

as_coeff_Add(rational=False)[source]¶: Efficiently extract the coefficient of a summation.

as_coeff_Mul(rational=False)[source]¶: Efficiently extract the coefficient of a product.

as_content_primitive( radical=False, clear=True, )[source]¶

Return the tuple (R, self/R) where R is the positive Rational extracted from self.

Examples

>>> from sympy import S
>>> (S(-3)/2).as_content_primitive()
(3/2, -1)

See docstring of Expr.as_content_primitive for more examples.

factors( limit=None, use_trial=True, use_rho=False, use_pm1=False, verbose=False, visual=False, )[source]¶: A wrapper to factorint which return factors of self that are smaller than limit (or cheap to compute). Special methods of factoring are disabled by default so that only trial division is used.

limit_denominator( max_denominator=1000000, )[source]¶

Closest Rational to self with denominator at most max_denominator.

Examples

>>> from sympy import Rational
>>> Rational('3.141592653589793').limit_denominator(10)
22/7
>>> Rational('3.141592653589793').limit_denominator(100)
311/99

class sympy.core.numbers.Integer(i)[source]¶

Represents integer numbers of any size.

Examples

>>> from sympy import Integer
>>> Integer(3)
3

If a float or a rational is passed to Integer, the fractional part will be discarded; the effect is of rounding toward zero.

>>> Integer(3.8)
3
>>> Integer(-3.8)
-3

A string is acceptable input if it can be parsed as an integer:

>>> Integer("9" * 20)
99999999999999999999

It is rarely needed to explicitly instantiate an Integer, because Python integers are automatically converted to Integer when they are used in SymPy expressions.

class sympy.core.numbers.AlgebraicNumber(

expr,

coeffs=None,

alias=None,

**args,

)[source]¶

Class for representing algebraic numbers in SymPy.

Symbolically, an instance of this class represents an element $\alpha \in \mathbb{Q}(\theta) \hookrightarrow \mathbb{C}$. That is, the algebraic number $\alpha$ is represented as an element of a particular number field $\mathbb{Q}(\theta)$, with a particular embedding of this field into the complex numbers.

Formally, the primitive element $\theta$ is given by two data points: (1) its minimal polynomial (which defines $\mathbb{Q}(\theta)$), and (2) a particular complex number that is a root of this polynomial (which defines the embedding $\mathbb{Q}(\theta) \hookrightarrow \mathbb{C}$). Finally, the algebraic number $\alpha$ which we represent is then given by the coefficients of a polynomial in $\theta$.

static __new__(

cls,

expr,

coeffs=None,

alias=None,

**args,

)[source]¶

Construct a new algebraic number $\alpha$ belonging to a number field $k = \mathbb{Q}(\theta)$.

There are four instance attributes to be determined:

Attribute	Type/Meaning
`root`	`Expr` for $\theta$ as a complex number
`minpoly`	`Poly`, the minimal polynomial of $\theta$
`rep`	`DMP` giving $\alpha$ as poly in $\theta$
`alias`	`Symbol` for $\theta$, or `None`

See Parameters section for how they are determined.

Parameters:

expr : Expr, or pair $(m, r)$

There are three distinct modes of construction, depending on what is passed as expr.

(1) expr is an AlgebraicNumber: In this case we begin by copying all four instance attributes from expr. If coeffs were also given, we compose the two coeff polynomials (see below). If an alias was given, it overrides.

(2) expr is any other type of Expr: Then root will equal expr. Therefore it must express an algebraic quantity, and we will compute its minpoly.

(3) expr is an ordered pair $(m, r)$ giving the minpoly $m$, and a root $r$ thereof, which together define $\theta$. In this case $m$ may be either a univariate Poly or any Expr which represents the same, while $r$ must be some Expr representing a complex number that is a root of $m$, including both explicit expressions in radicals, and instances of ComplexRootOf or AlgebraicNumber.

coeffs : list, ANP, None, optional (default=None)

This defines rep, giving the algebraic number $\alpha$ as a polynomial in $\theta$.

If a list, the elements should be integers or rational numbers. If an ANP, we take its coefficients (using its to_list() method). If None, then the list of coefficients defaults to [1, 0], meaning that $\alpha = \theta$ is the primitive element of the field.

If expr was an AlgebraicNumber, let $g(x)$ be its rep polynomial, and let $f(x)$ be the polynomial defined by coeffs. Then self.rep will represent the composition $(f \circ g)(x)$.

alias : str, Symbol, None, optional (default=None)

This is a way to provide a name for the primitive element. We described several ways in which the expr argument can define the value of the primitive element, but none of these methods gave it a name. Here, for example, alias could be set as Symbol('theta'), in order to make this symbol appear when $\alpha$ is printed, or rendered as a polynomial, using the as_poly() method.

Examples

Recall that we are constructing an algebraic number as a field element $\alpha \in \mathbb{Q}(\theta)$.

>>> from sympy import AlgebraicNumber, sqrt, CRootOf, S
>>> from sympy.abc import x

Example (1): $\alpha = \theta = \sqrt{2}$

>>> a1 = AlgebraicNumber(sqrt(2))
>>> a1.minpoly_of_element().as_expr(x)
x**2 - 2
>>> a1.evalf(10)
1.414213562

Example (2): $\alpha = 3 \sqrt{2} - 5$, $\theta = \sqrt{2}$. We can either build on the last example:

>>> a2 = AlgebraicNumber(a1, [3, -5])
>>> a2.as_expr()
-5 + 3*sqrt(2)

or start from scratch:

>>> a2 = AlgebraicNumber(sqrt(2), [3, -5])
>>> a2.as_expr()
-5 + 3*sqrt(2)

Example (3): $\alpha = 6 \sqrt{2} - 11$, $\theta = \sqrt{2}$. Again we can build on the previous example, and we see that the coeff polys are composed:

>>> a3 = AlgebraicNumber(a2, [2, -1])
>>> a3.as_expr()
-11 + 6*sqrt(2)

reflecting the fact that $(2x - 1) \circ (3x - 5) = 6x - 11$.

Example (4): $\alpha = \sqrt{2}$, $\theta = \sqrt{2} + \sqrt{3}$. The easiest way is to use the to_number_field() function:

>>> from sympy import to_number_field
>>> a4 = to_number_field(sqrt(2), sqrt(2) + sqrt(3))
>>> a4.minpoly_of_element().as_expr(x)
x**2 - 2
>>> a4.to_root()
sqrt(2)
>>> a4.primitive_element()
sqrt(2) + sqrt(3)
>>> a4.coeffs()
[1/2, 0, -9/2, 0]

but if you already knew the right coefficients, you could construct it directly:

>>> a4 = AlgebraicNumber(sqrt(2) + sqrt(3), [S(1)/2, 0, S(-9)/2, 0])
>>> a4.to_root()
sqrt(2)
>>> a4.primitive_element()
sqrt(2) + sqrt(3)

Example (5): Construct the Golden Ratio as an element of the 5th cyclotomic field, supposing we already know its coefficients. This time we introduce the alias $\zeta$ for the primitive element of the field:

>>> from sympy import cyclotomic_poly
>>> from sympy.abc import zeta
>>> a5 = AlgebraicNumber(CRootOf(cyclotomic_poly(5), -1),
...                  [-1, -1, 0, 0], alias=zeta)
>>> a5.as_poly().as_expr()
-zeta**3 - zeta**2
>>> a5.evalf()
1.61803398874989

(The index -1 to CRootOf selects the complex root with the largest real and imaginary parts, which in this case is $\mathrm{e}^{2i\pi/5}$. See ComplexRootOf.)

Example (6): Building on the last example, construct the number $2 \phi \in \mathbb{Q}(\phi)$, where $\phi$ is the Golden Ratio:

>>> from sympy.abc import phi
>>> a6 = AlgebraicNumber(a5.to_root(), coeffs=[2, 0], alias=phi)
>>> a6.as_poly().as_expr()
2*phi
>>> a6.primitive_element().evalf()
1.61803398874989

Note that we needed to use a5.to_root(), since passing a5 as the first argument would have constructed the number $2 \phi$ as an element of the field $\mathbb{Q}(\zeta)$:

>>> a6_wrong = AlgebraicNumber(a5, coeffs=[2, 0])
>>> a6_wrong.as_poly().as_expr()
-2*zeta**3 - 2*zeta**2
>>> a6_wrong.primitive_element().evalf()
0.309016994374947 + 0.951056516295154*I

as_expr(x=None)[source]¶: Create a Basic expression from self.

as_poly(x=None)[source]¶: Create a Poly instance from self.

coeffs()[source]¶: Returns all SymPy coefficients of an algebraic number.

field_element(coeffs)[source]¶

Form another element of the same number field.

Parameters:: coeffs : list, ANP

Like the coeffs arg to the class constructor, defines the new element as a polynomial in the primitive element.

If a list, the elements should be integers or rational numbers. If an ANP, we take its coefficients (using its to_list() method).

Explanation

If we represent $\alpha \in \mathbb{Q}(\theta)$, form another element $\beta \in \mathbb{Q}(\theta)$ of the same number field.

Examples

>>> from sympy import AlgebraicNumber, sqrt
>>> a = AlgebraicNumber(sqrt(5), [-1, 1])
>>> b = a.field_element([3, 2])
>>> print(a)
1 - sqrt(5)
>>> print(b)
2 + 3*sqrt(5)
>>> print(b.primitive_element() == a.primitive_element())
True

See also

AlgebraicNumber

property is_aliased¶: Returns True if alias was set.

property is_primitive_element¶: Say whether this algebraic number $\alpha \in \mathbb{Q}(\theta)$ is equal to the primitive element $\theta$ for its field.

minpoly_of_element()[source]¶

Compute the minimal polynomial for this algebraic number.

Explanation

Recall that we represent an element $\alpha \in \mathbb{Q}(\theta)$. Our instance attribute self.minpoly is the minimal polynomial for our primitive element $\theta$. This method computes the minimal polynomial for $\alpha$.

native_coeffs()[source]¶: Returns all native coefficients of an algebraic number.

primitive_element()[source]¶

Get the primitive element $\theta$ for the number field $\mathbb{Q}(\theta)$ to which this algebraic number $\alpha$ belongs.

Returns:: AlgebraicNumber

to_algebraic_integer()[source]¶: Convert self to an algebraic integer.

to_primitive_element( radicals=True, )[source]¶

Convert self to an AlgebraicNumber instance that is equal to its own primitive element.

Parameters:: radicals : boolean, optional (default=True)

If True, then we will try to return an AlgebraicNumber whose root is an expression in radicals. If that is not possible (or if radicals is False), root will be a ComplexRootOf.
Returns:: AlgebraicNumber

Explanation

If we represent $\alpha \in \mathbb{Q}(\theta)$, $\alpha \neq \theta$, construct a new AlgebraicNumber that represents $\alpha \in \mathbb{Q}(\alpha)$.

Examples

>>> from sympy import sqrt, to_number_field
>>> from sympy.abc import x
>>> a = to_number_field(sqrt(2), sqrt(2) + sqrt(3))

The AlgebraicNumber a represents the number $\sqrt{2}$ in the field $\mathbb{Q}(\sqrt{2} + \sqrt{3})$. Rendering a as a polynomial,

>>> a.as_poly().as_expr(x)
x**3/2 - 9*x/2

reflects the fact that $\sqrt{2} = \theta^3/2 - 9 \theta/2$, where $\theta = \sqrt{2} + \sqrt{3}$.

a is not equal to its own primitive element. Its minpoly

>>> a.minpoly.as_poly().as_expr(x)
x**4 - 10*x**2 + 1

is that of $\theta$.

Converting to a primitive element,

>>> a_prim = a.to_primitive_element()
>>> a_prim.minpoly.as_poly().as_expr(x)
x**2 - 2

we obtain an AlgebraicNumber whose minpoly is that of the number itself.

See also

is_primitive_element

to_root( radicals=True, minpoly=None, )[source]¶

Convert to an Expr that is not an AlgebraicNumber, specifically, either a ComplexRootOf, or, optionally and where possible, an expression in radicals.

Parameters:

radicals : boolean, optional (default=True)

If True, then we will try to return the root as an expression in radicals. If that is not possible, we will return a ComplexRootOf.

minpoly : Poly

If the minimal polynomial for $self$ has been pre-computed, it can be passed in order to save time.

class sympy.core.numbers.NumberSymbol[source]¶

approximation(number_cls)[source]¶: Return an interval with number_cls endpoints that contains the value of NumberSymbol. If not implemented, then return None.

sympy.core.numbers.RealNumber[source]¶: alias of Float

sympy.core.numbers.seterr(divide=False)[source]¶

Should SymPy raise an exception on 0/0 or return a nan?

divide == True …. raise an exception divide == False … return nan

class sympy.core.numbers.Zero[source]¶

The number zero.

Zero is a singleton, and can be accessed by S.Zero

Examples

>>> from sympy import S, Integer
>>> Integer(0) is S.Zero
True
>>> 1/S.Zero
zoo

References

[R127]

https://en.wikipedia.org/wiki/Zero

class sympy.core.numbers.One[source]¶

The number one.

One is a singleton, and can be accessed by S.One.

Examples

>>> from sympy import S, Integer
>>> Integer(1) is S.One
True

References

[R128]

https://en.wikipedia.org/wiki/1_%28number%29

class sympy.core.numbers.NegativeOne[source]¶

The number negative one.

NegativeOne is a singleton, and can be accessed by S.NegativeOne.

Examples

>>> from sympy import S, Integer
>>> Integer(-1) is S.NegativeOne
True

See also

One

References

[R129]

https://en.wikipedia.org/wiki/%E2%88%921_%28number%29

class sympy.core.numbers.Half[source]¶

The rational number 1/2.

Half is a singleton, and can be accessed by S.Half.

Examples

>>> from sympy import S, Rational
>>> Rational(1, 2) is S.Half
True

References

[R130]

https://en.wikipedia.org/wiki/One_half

class sympy.core.numbers.NaN[source]¶

Not a Number.

Explanation

This serves as a place holder for numeric values that are indeterminate. Most operations on NaN, produce another NaN. Most indeterminate forms, such as 0/0 or oo - oo` produce NaN. Two exceptions are ``0**0 and oo**0, which all produce 1 (this is consistent with Python’s float).

NaN is loosely related to floating point nan, which is defined in the IEEE 754 floating point standard, and corresponds to the Python float('nan'). Differences are noted below.

NaN is mathematically not equal to anything else, even NaN itself. This explains the initially counter-intuitive results with Eq and == in the examples below.

NaN is not comparable so inequalities raise a TypeError. This is in contrast with floating point nan where all inequalities are false.

NaN is a singleton, and can be accessed by S.NaN, or can be imported as nan.

Examples

>>> from sympy import nan, S, oo, Eq
>>> nan is S.NaN
True
>>> oo - oo
nan
>>> nan + 1
nan
>>> Eq(nan, nan)   # mathematical equality
False
>>> nan == nan     # structural equality
True

References

[R131]

https://en.wikipedia.org/wiki/NaN

class sympy.core.numbers.Infinity[source]¶

Positive infinite quantity.

Explanation

In real analysis the symbol $\infty$ denotes an unbounded limit: $x\to\infty$ means that $x$ grows without bound.

Infinity is often used not only to define a limit but as a value in the affinely extended real number system. Points labeled $+\infty$ and $-\infty$ can be added to the topological space of the real numbers, producing the two-point compactification of the real numbers. Adding algebraic properties to this gives us the extended real numbers.

Infinity is a singleton, and can be accessed by S.Infinity, or can be imported as oo.

Examples

>>> from sympy import oo, exp, limit, Symbol
>>> 1 + oo
oo
>>> 42/oo
0
>>> x = Symbol('x')
>>> limit(exp(x), x, oo)
oo

See also

NegativeInfinity, NaN

References

[R132]

https://en.wikipedia.org/wiki/Infinity

class sympy.core.numbers.NegativeInfinity[source]¶

Negative infinite quantity.

NegativeInfinity is a singleton, and can be accessed by S.NegativeInfinity.

See also

Infinity

class sympy.core.numbers.ComplexInfinity[source]¶

Complex infinity.

Explanation

In complex analysis the symbol $\tilde\infty$, called “complex infinity”, represents a quantity with infinite magnitude, but undetermined complex phase.

ComplexInfinity is a singleton, and can be accessed by S.ComplexInfinity, or can be imported as zoo.

Examples

>>> from sympy import zoo
>>> zoo + 42
zoo
>>> 42/zoo
0
>>> zoo + zoo
nan
>>> zoo*zoo
zoo

See also

Infinity

class sympy.core.numbers.Exp1[source]¶

The $e$ constant.

Explanation

The transcendental number $e = 2.718281828\ldots$ is the base of the natural logarithm and of the exponential function, $e = \exp(1)$. Sometimes called Euler’s number or Napier’s constant.

Exp1 is a singleton, and can be accessed by S.Exp1, or can be imported as E.

Examples

>>> from sympy import exp, log, E
>>> E is exp(1)
True
>>> log(E)
1

References

[R133]

https://en.wikipedia.org/wiki/E_%28mathematical_constant%29

class sympy.core.numbers.ImaginaryUnit[source]¶

The imaginary unit, $i = \sqrt{-1}$.

I is a singleton, and can be accessed by S.I, or can be imported as I.

Examples

>>> from sympy import I, sqrt
>>> sqrt(-1)
I
>>> I*I
-1
>>> 1/I
-I

References

[R134]

https://en.wikipedia.org/wiki/Imaginary_unit

class sympy.core.numbers.Pi[source]¶

The $\pi$ constant.

Explanation

The transcendental number $\pi = 3.141592654\ldots$ represents the ratio of a circle’s circumference to its diameter, the area of the unit circle, the half-period of trigonometric functions, and many other things in mathematics.

Pi is a singleton, and can be accessed by S.Pi, or can be imported as pi.

Examples

>>> from sympy import S, pi, oo, sin, exp, integrate, Symbol
>>> S.Pi
pi
>>> pi > 3
True
>>> pi.is_irrational
True
>>> x = Symbol('x')
>>> sin(x + 2*pi)
sin(x)
>>> integrate(exp(-x**2), (x, -oo, oo))
sqrt(pi)

References

[R135]

https://en.wikipedia.org/wiki/Pi

class sympy.core.numbers.EulerGamma[source]¶

The Euler-Mascheroni constant.

Explanation

$\gamma = 0.5772157\ldots$ (also called Euler’s constant) is a mathematical constant recurring in analysis and number theory. It is defined as the limiting difference between the harmonic series and the natural logarithm:

\[\gamma = \lim\limits_{n\to\infty} \left(\sum\limits_{k=1}^n\frac{1}{k} - \ln n\right)\]

EulerGamma is a singleton, and can be accessed by S.EulerGamma.

Examples

>>> from sympy import S
>>> S.EulerGamma.is_irrational
>>> S.EulerGamma > 0
True
>>> S.EulerGamma > 1
False

References

[R136]

https://en.wikipedia.org/wiki/Euler%E2%80%93Mascheroni_constant

class sympy.core.numbers.Catalan[source]¶

Catalan’s constant.

Explanation

$G = 0.91596559\ldots$ is given by the infinite series

\[G = \sum_{k=0}^{\infty} \frac{(-1)^k}{(2k+1)^2}\]

Catalan is a singleton, and can be accessed by S.Catalan.

Examples

>>> from sympy import S
>>> S.Catalan.is_irrational
>>> S.Catalan > 0
True
>>> S.Catalan > 1
False

References

[R137]

https://en.wikipedia.org/wiki/Catalan%27s_constant

class sympy.core.numbers.GoldenRatio[source]¶

The golden ratio, $\phi$.

Explanation

$\phi = \frac{1 + \sqrt{5}}{2}$ is an algebraic number. Two quantities are in the golden ratio if their ratio is the same as the ratio of their sum to the larger of the two quantities, i.e. their maximum.

GoldenRatio is a singleton, and can be accessed by S.GoldenRatio.

Examples

>>> from sympy import S
>>> S.GoldenRatio > 1
True
>>> S.GoldenRatio.expand(func=True)
1/2 + sqrt(5)/2
>>> S.GoldenRatio.is_irrational
True

References

[R138]

https://en.wikipedia.org/wiki/Golden_ratio

class sympy.core.numbers.TribonacciConstant[source]¶

The tribonacci constant.

Explanation

The tribonacci numbers are like the Fibonacci numbers, but instead of starting with two predetermined terms, the sequence starts with three predetermined terms and each term afterwards is the sum of the preceding three terms.

The tribonacci constant is the ratio toward which adjacent tribonacci numbers tend. It is a root of the polynomial $x^3 - x^2 - x - 1 = 0$, and also satisfies the equation $x + x^{-3} = 2$.

TribonacciConstant is a singleton, and can be accessed by S.TribonacciConstant.

Examples

>>> from sympy import S
>>> S.TribonacciConstant > 1
True
>>> S.TribonacciConstant.expand(func=True)
1/3 + (19 - 3*sqrt(33))**(1/3)/3 + (3*sqrt(33) + 19)**(1/3)/3
>>> S.TribonacciConstant.is_irrational
True
>>> S.TribonacciConstant.n(20)
1.8392867552141611326

References

[R139]

https://en.wikipedia.org/wiki/Generalizations_of_Fibonacci_numbers#Tribonacci_numbers

sympy.core.numbers.mod_inverse(a, m)[source]¶

Return the number $c$ such that, $a \times c = 1 \pmod{m}$ where $c$ has the same sign as $m$. If no such value exists, a ValueError is raised.

Examples

>>> from sympy import mod_inverse, S

>>> mod_inverse(3, 11)
4
>>> mod_inverse(-3, 11)
7

When there is a common factor between the numerators of $a$ and $m$ the inverse does not exist:

>>> mod_inverse(2, 4)
Traceback (most recent call last):
...
ValueError: inverse of 2 mod 4 does not exist

>>> mod_inverse(S(2)/7, S(5)/2)
7/2

References

[R140]

https://en.wikipedia.org/wiki/Modular_multiplicative_inverse

[R141]

https://en.wikipedia.org/wiki/Extended_Euclidean_algorithm

sympy.core.numbers.equal_valued(x, y)[source]¶

Compare expressions treating plain floats as rationals.

Examples

>>> from sympy import S, symbols, Rational, Float
>>> from sympy.core.numbers import equal_valued
>>> equal_valued(1, 2)
False
>>> equal_valued(1, 1)
True

In SymPy expressions with Floats compare unequal to corresponding expressions with rationals:

>>> x = symbols('x')
>>> x**2 == x**2.0
False

However an individual Float compares equal to a Rational:

>>> Rational(1, 2) == Float(0.5)
False

In a future version of SymPy this might change so that Rational and Float compare unequal. This function provides the behavior currently expected of == so that it could still be used if the behavior of == were to change in future.

>>> equal_valued(1, 1.0) # Float vs Rational
True
>>> equal_valued(S(1).n(3), S(1).n(5)) # Floats of different precision
True

Explanation

In future SymPy verions Float and Rational might compare unequal and floats with different precisions might compare unequal. In that context a function is needed that can check if a number is equal to 1 or 0 etc. The idea is that instead of testing if x == 1: if we want to accept floats like 1.0 as well then the test can be written as if equal_valued(x, 1): or if equal_valued(x, 2):. Since this function is intended to be used in situations where one or both operands are expected to be concrete numbers like 1 or 0 the function does not recurse through the args of any compound expression to compare any nested floats.

References

[R142]

https://github.com/sympy/sympy/pull/20033

power¶

class sympy.core.power.Pow( b: Expr | complex, e: Expr | complex, evaluate=None, )[source]¶

Defines the expression x**y as “x raised to a power y”

Deprecated since version 1.7: Using arguments that aren’t subclasses of Expr in core operators (Mul, Add, and Pow) is deprecated. See Core operators no longer accept non-Expr args for details.

Singleton definitions involving (0, 1, -1, oo, -oo, I, -I):

expr	value	reason
z**0	1	Although arguments over 0**0 exist, see [2].
z**1	z
(-oo)**(-1)	0
(-1)**-1	-1
S.Zero**-1	zoo	This is not strictly true, as 0**-1 may be undefined, but is convenient in some contexts where the base is assumed to be positive.
1**-1	1
oo**-1	0
0**oo	0	Because for all complex numbers z near 0, z**oo -> 0.
0**-oo	zoo	This is not strictly true, as 0**oo may be oscillating between positive and negative values or rotating in the complex plane. It is convenient, however, when the base is positive.
1oo 1-oo	nan	Because there are various cases where lim(x(t),t)=1, lim(y(t),t)=oo (or -oo), but lim( x(t)**y(t), t) != 1. See [3].
b**zoo	nan	Because b**z has no limit as z -> zoo
(-1)oo (-1)(-oo)	nan	Because of oscillations in the limit.
oo**oo	oo
oo**-oo	0
(-oo)oo (-oo)-oo	nan
ooI (-oo)I	nan	ooe could probably be best thought of as the limit of xe for real x as x tends to oo. If e is I, then the limit does not exist and nan is used to indicate that.
oo(1+I) (-oo)(1+I)	zoo	If the real part of e is positive, then the limit of abs(x**e) is oo. So the limit value is zoo.
oo(-1+I) -oo(-1+I)	0	If the real part of e is negative, then the limit is 0.

Because symbolic computations are more flexible than floating point calculations and we prefer to never return an incorrect answer, we choose not to conform to all IEEE 754 conventions. This helps us avoid extra test-case code in the calculation of limits.

References

[R143]

https://en.wikipedia.org/wiki/Exponentiation

[R144]

https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero

[R145]

https://en.wikipedia.org/wiki/Indeterminate_forms

as_base_exp()[source]¶

Return base and exp of self.

Explanation

If base a Rational less than 1, then return 1/Rational, -exp. If this extra processing is not needed, the base and exp properties will give the raw arguments.

Examples

>>> from sympy import Pow, S
>>> p = Pow(S.Half, 2, evaluate=False)
>>> p.as_base_exp()
(2, -2)
>>> p.args
(1/2, 2)
>>> p.base, p.exp
(1/2, 2)

as_content_primitive( radical=False, clear=True, )[source]¶

Return the tuple (R, self/R) where R is the positive Rational extracted from self.

Examples

>>> from sympy import sqrt
>>> sqrt(4 + 4*sqrt(2)).as_content_primitive()
(2, sqrt(1 + sqrt(2)))
>>> sqrt(3 + 3*sqrt(2)).as_content_primitive()
(1, sqrt(3)*sqrt(1 + sqrt(2)))

>>> from sympy import expand_power_base, powsimp, Mul
>>> from sympy.abc import x, y

>>> ((2*x + 2)**2).as_content_primitive()
(4, (x + 1)**2)
>>> (4**((1 + y)/2)).as_content_primitive()
(2, 4**(y/2))
>>> (3**((1 + y)/2)).as_content_primitive()
(1, 3**((y + 1)/2))
>>> (3**((5 + y)/2)).as_content_primitive()
(9, 3**((y + 1)/2))
>>> eq = 3**(2 + 2*x)
>>> powsimp(eq) == eq
True
>>> eq.as_content_primitive()
(9, 3**(2*x))
>>> powsimp(Mul(*_))
3**(2*x + 2)

>>> eq = (2 + 2*x)**y
>>> s = expand_power_base(eq); s.is_Mul, s
(False, (2*x + 2)**y)
>>> eq.as_content_primitive()
(1, (2*(x + 1))**y)
>>> s = expand_power_base(_[1]); s.is_Mul, s
(True, 2**y*(x + 1)**y)

See docstring of Expr.as_content_primitive for more examples.

mul¶

class sympy.core.mul.Mul(*args, evaluate=None, _sympify=True)[source]¶

Expression representing multiplication operation for algebraic field.

Every argument of Mul() must be Expr. Infix operator * on most scalar objects in SymPy calls this class.

Another use of Mul() is to represent the structure of abstract multiplication so that its arguments can be substituted to return different class. Refer to examples section for this.

Mul() evaluates the argument unless evaluate=False is passed. The evaluation logic includes:

Flattening
Mul(x, Mul(y, z)) -> Mul(x, y, z)
Identity removing
Mul(x, 1, y) -> Mul(x, y)
Exponent collecting by .as_base_exp()
Mul(x, x**2) -> Pow(x, 3)
Term sorting
Mul(y, x, 2) -> Mul(2, x, y)

Since multiplication can be vector space operation, arguments may have the different sympy.core.kind.Kind(). Kind of the resulting object is automatically inferred.

Examples

>>> from sympy import Mul
>>> from sympy.abc import x, y
>>> Mul(x, 1)
x
>>> Mul(x, x)
x**2

If evaluate=False is passed, result is not evaluated.

>>> Mul(1, 2, evaluate=False)
1*2
>>> Mul(x, x, evaluate=False)
x*x

Mul() also represents the general structure of multiplication operation.

>>> from sympy import MatrixSymbol
>>> A = MatrixSymbol('A', 2,2)
>>> expr = Mul(x,y).subs({y:A})
>>> expr
x*A
>>> type(expr)
<class 'sympy.matrices.expressions.matmul.MatMul'>

See also

MatMul

as_coeff_Mul(rational=False)[source]¶: Efficiently extract the coefficient of a product.

as_content_primitive( radical=False, clear=True, )[source]¶

Return the tuple (R, self/R) where R is the positive Rational extracted from self.

Examples

>>> from sympy import sqrt
>>> (-3*sqrt(2)*(2 - 2*sqrt(2))).as_content_primitive()
(6, -sqrt(2)*(1 - sqrt(2)))

See docstring of Expr.as_content_primitive for more examples.

as_ordered_factors(order=None)[source]¶

Transform an expression into an ordered list of factors.

Examples

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

>>> (2*x*y*sin(x)*cos(x)).as_ordered_factors()
[2, x, y, sin(x), cos(x)]

as_two_terms()[source]¶

Return head and tail of self.

This is the most efficient way to get the head and tail of an expression.

if you want only the head, use self.args[0];
if you want to process the arguments of the tail then use self.as_coef_mul() which gives the head and a tuple containing the arguments of the tail when treated as a Mul.
if you want the coefficient when self is treated as an Add then use self.as_coeff_add()[0]

Examples

>>> from sympy.abc import x, y
>>> (3*x*y).as_two_terms()
(3, x*y)

classmethod flatten(seq)[source]¶

Return commutative, noncommutative and order arguments by combining related terms.

Notes

In an expression like a*b*c, Python process this through SymPy as Mul(Mul(a, b), c). This can have undesirable consequences.
- Sometimes terms are not combined as one would like: {c.f. https://github.com/sympy/sympy/issues/4596}
>>> from sympy import Mul, sqrt >>> from sympy.abc import x, y, z >>> 2*(x + 1) # this is the 2-arg Mul behavior 2*x + 2 >>> y*(x + 1)*2 2*y*(x + 1) >>> 2*(x + 1)*y # 2-arg result will be obtained first y*(2*x + 2) >>> Mul(2, x + 1, y) # all 3 args simultaneously processed 2*y*(x + 1) >>> 2*((x + 1)*y) # parentheses can control this behavior 2*y*(x + 1)
Powers with compound bases may not find a single base to combine with unless all arguments are processed at once. Post-processing may be necessary in such cases. {c.f. https://github.com/sympy/sympy/issues/5728}
>>> a = sqrt(x*sqrt(y)) >>> a**3 (x*sqrt(y))**(3/2) >>> Mul(a,a,a) (x*sqrt(y))**(3/2) >>> a*a*a x*sqrt(y)*sqrt(x*sqrt(y)) >>> _.subs(a.base, z).subs(z, a.base) (x*sqrt(y))**(3/2)
- If more than two terms are being multiplied then all the previous terms will be re-processed for each new argument. So if each of a, b and c were Mul expression, then a*b*c (or building up the product with *=) will process all the arguments of a and b twice: once when a*b is computed and again when c is multiplied.
  
  Using Mul(a, b, c) will process all arguments once.
The results of Mul are cached according to arguments, so flatten will only be called once for Mul(a, b, c). If you can structure a calculation so the arguments are most likely to be repeats then this can save time in computing the answer. For example, say you had a Mul, M, that you wished to divide by d[i] and multiply by n[i] and you suspect there are many repeats in n. It would be better to compute M*n[i]/d[i] rather than M/d[i]*n[i] since every time n[i] is a repeat, the product, M*n[i] will be returned without flattening – the cached value will be returned. If you divide by the d[i] first (and those are more unique than the n[i]) then that will create a new Mul, M/d[i] the args of which will be traversed again when it is multiplied by n[i].

{c.f. https://github.com/sympy/sympy/issues/5706}

This consideration is moot if the cache is turned off.

The validity of the above notes depends on the implementation details of Mul and flatten which may change at any time. Therefore, you should only consider them when your code is highly performance sensitive.

Removal of 1 from the sequence is already handled by AssocOp.__new__.

sympy.core.mul.prod(a, start=1)[source]¶

Return product of elements of a. Start with int 1 so if only: ints are included then an int result is returned.

Examples

>>> from sympy import prod, S
>>> prod(range(3))
0
>>> type(_) is int
True
>>> prod([S(2), 3])
6
>>> _.is_Integer
True

You can start the product at something other than 1:

>>> prod([1, 2], 3)
6

add¶

class sympy.core.add.Add(*args, evaluate=None, _sympify=True)[source]¶

Expression representing addition operation for algebraic group.

Every argument of Add() must be Expr. Infix operator + on most scalar objects in SymPy calls this class.

Another use of Add() is to represent the structure of abstract addition so that its arguments can be substituted to return different class. Refer to examples section for this.

Add() evaluates the argument unless evaluate=False is passed. The evaluation logic includes:

Flattening
Add(x, Add(y, z)) -> Add(x, y, z)
Identity removing
Add(x, 0, y) -> Add(x, y)
Coefficient collecting by .as_coeff_Mul()
Add(x, 2*x) -> Mul(3, x)
Term sorting
Add(y, x, 2) -> Add(2, x, y)

If no argument is passed, identity element 0 is returned. If single element is passed, that element is returned.

Note that Add(*args) is more efficient than sum(args) because it flattens the arguments. sum(a, b, c, ...) recursively adds the arguments as a + (b + (c + ...)), which has quadratic complexity. On the other hand, Add(a, b, c, d) does not assume nested structure, making the complexity linear.

Since addition is group operation, every argument should have the same sympy.core.kind.Kind().

Examples

>>> from sympy import Add, I
>>> from sympy.abc import x, y
>>> Add(x, 1)
x + 1
>>> Add(x, x)
2*x
>>> 2*x**2 + 3*x + I*y + 2*y + 2*x/5 + 1.0*y + 1
2*x**2 + 17*x/5 + 3.0*y + I*y + 1

If evaluate=False is passed, result is not evaluated.

>>> Add(1, 2, evaluate=False)
1 + 2
>>> Add(x, x, evaluate=False)
x + x

Add() also represents the general structure of addition operation.

>>> from sympy import MatrixSymbol
>>> A,B = MatrixSymbol('A', 2,2), MatrixSymbol('B', 2,2)
>>> expr = Add(x,y).subs({x:A, y:B})
>>> expr
A + B
>>> type(expr)
<class 'sympy.matrices.expressions.matadd.MatAdd'>

Note that the printers do not display in args order.

>>> Add(x, 1)
x + 1
>>> Add(x, 1).args
(1, x)

See also

MatAdd

as_coeff_Add( rational=False, deps=None, ) → tuple[Number, Expr][source]¶: Efficiently extract the coefficient of a summation.

as_coeff_add(*deps)[source]¶

Returns a tuple (coeff, args) where self is treated as an Add and coeff is the Number term and args is a tuple of all other terms.

Examples

>>> from sympy.abc import x
>>> (7 + 3*x).as_coeff_add()
(7, (3*x,))
>>> (7*x).as_coeff_add()
(0, (7*x,))

as_content_primitive( radical=False, clear=True, )[source]¶

Return the tuple (R, self/R) where R is the positive Rational extracted from self. If radical is True (default is False) then common radicals will be removed and included as a factor of the primitive expression.

Examples

>>> from sympy import sqrt
>>> (3 + 3*sqrt(2)).as_content_primitive()
(3, 1 + sqrt(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)))

See docstring of Expr.as_content_primitive for more examples.

as_numer_denom() → tuple[Expr, Expr][source]¶

Decomposes an expression to its numerator part and its denominator part.

Examples

>>> from sympy.abc import x, y, z
>>> (x*y/z).as_numer_denom()
(x*y, z)
>>> (x*(y + 1)/y**7).as_numer_denom()
(x*(y + 1), y**7)

See also

sympy.core.expr.Expr.as_numer_denom

as_real_imag(deep=True, **hints)[source]¶

Return a tuple representing a complex number.

Examples

>>> from sympy import I
>>> (7 + 9*I).as_real_imag()
(7, 9)
>>> ((1 + I)/(1 - I)).as_real_imag()
(0, 1)
>>> ((1 + 2*I)*(1 + 3*I)).as_real_imag()
(-5, 5)

as_two_terms()[source]¶

Return head and tail of self.

This is the most efficient way to get the head and tail of an expression.

if you want only the head, use self.args[0];
if you want to process the arguments of the tail then use self.as_coef_add() which gives the head and a tuple containing the arguments of the tail when treated as an Add.
if you want the coefficient when self is treated as a Mul then use self.as_coeff_mul()[0]

>>> from sympy.abc import x, y
>>> (3*x - 2*y + 5).as_two_terms()
(5, 3*x - 2*y)

extract_leading_order(symbols, point=None)[source]¶

Returns the leading term and its order.

Examples

>>> from sympy.abc import x
>>> (x + 1 + 1/x**5).extract_leading_order(x)
((x**(-5), O(x**(-5))),)
>>> (1 + x).extract_leading_order(x)
((1, O(1)),)
>>> (x + x**2).extract_leading_order(x)
((x, O(x)),)

classmethod flatten( seq: list[Expr], ) → tuple[list[Expr], list[Expr], None][source]¶

Takes the sequence “seq” of nested Adds and returns a flatten list.

Returns: (commutative_part, noncommutative_part, order_symbols)

Applies associativity, all terms are commutable with respect to addition.

NB: the removal of 0 is already handled by AssocOp.__new__

See also

sympy.core.mul.Mul.flatten

primitive()[source]¶

Return (R, self/R) where R` is the Rational GCD of self`.

R is collected only from the leading coefficient of each term.

Examples

>>> from sympy.abc import x, y

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

>>> (2*x/3 + 4*y/9).primitive()
(2/9, 3*x + 2*y)

>>> (2*x/3 + 4.2*y).primitive()
(1/3, 2*x + 12.6*y)

No subprocessing of term factors is performed:

>>> ((2 + 2*x)*x + 2).primitive()
(1, x*(2*x + 2) + 2)

Recursive processing can be done with the as_content_primitive() method:

>>> ((2 + 2*x)*x + 2).as_content_primitive()
(2, x*(x + 1) + 1)

See also: primitive() function in polytools.py

mod¶

class sympy.core.mod.Mod(p, q)[source]¶

Represents a modulo operation on symbolic expressions.

Parameters:

p : Expr

Dividend.

q : Expr

Divisor.

Notes

The convention used is the same as Python’s: the remainder always has the same sign as the divisor.

Many objects can be evaluated modulo n much faster than they can be evaluated directly (or at all). For this, evaluate=False is necessary to prevent eager evaluation:

>>> from sympy import binomial, factorial, Mod, Pow
>>> Mod(Pow(2, 10**16, evaluate=False), 97)
61
>>> Mod(factorial(10**9, evaluate=False), 10**9 + 9)
712524808
>>> Mod(binomial(10**18, 10**12, evaluate=False), (10**5 + 3)**2)
3744312326

Examples

>>> from sympy.abc import x, y
>>> x**2 % y
Mod(x**2, y)
>>> _.subs({x: 5, y: 6})
1

relational¶

class sympy.core.relational.Relational(lhs, rhs, rop=None, **assumptions)[source]¶

Base class for all relation types.

Parameters:: rop : str or None

Indicates what subclass to instantiate. Valid values can be found in the keys of Relational.ValidRelationOperator.

Explanation

Subclasses of Relational should generally be instantiated directly, but Relational can be instantiated with a valid rop value to dispatch to the appropriate subclass.

Examples

>>> from sympy import Rel
>>> from sympy.abc import x, y
>>> Rel(y, x + x**2, '==')
Eq(y, x**2 + x)

A relation’s type can be defined upon creation using rop. The relation type of an existing expression can be obtained using its rel_op property. Here is a table of all the relation types, along with their rop and rel_op values:

Relation	`rop`	`rel_op`
`Equality`	`==` or `eq` or `None`	`==`
`Unequality`	`!=` or `ne`	`!=`
`GreaterThan`	`>=` or `ge`	`>=`
`LessThan`	`<=` or `le`	`<=`
`StrictGreaterThan`	`>` or `gt`	`>`
`StrictLessThan`	`<` or `lt`	`<`

For example, setting rop to == produces an Equality relation, Eq(). So does setting rop to eq, or leaving rop unspecified. That is, the first three Rel() below all produce the same result. Using a rop from a different row in the table produces a different relation type. For example, the fourth Rel() below using lt for rop produces a StrictLessThan inequality:

>>> from sympy import Rel
>>> from sympy.abc import x, y
>>> Rel(y, x + x**2, '==')
    Eq(y, x**2 + x)
>>> Rel(y, x + x**2, 'eq')
    Eq(y, x**2 + x)
>>> Rel(y, x + x**2)
    Eq(y, x**2 + x)
>>> Rel(y, x + x**2, 'lt')
    y < x**2 + x

To obtain the relation type of an existing expression, get its rel_op property. For example, rel_op is == for the Equality relation above, and < for the strict less than inequality above:

>>> from sympy import Rel
>>> from sympy.abc import x, y
>>> my_equality = Rel(y, x + x**2, '==')
>>> my_equality.rel_op
    '=='
>>> my_inequality = Rel(y, x + x**2, 'lt')
>>> my_inequality.rel_op
    '<'

property canonical¶

Return a canonical form of the relational by putting a number on the rhs, canonically removing a sign or else ordering the args canonically. No other simplification is attempted.

Examples

>>> from sympy.abc import x, y
>>> x < 2
x < 2
>>> _.reversed.canonical
x < 2
>>> (-y < x).canonical
x > -y
>>> (-y > x).canonical
x < -y
>>> (-y < -x).canonical
x < y

The canonicalization is recursively applied:

>>> from sympy import Eq
>>> Eq(x < y, y > x).canonical
True

equals(other, failing_expression=False)[source]¶: Return True if the sides of the relationship are mathematically identical and the type of relationship is the same. If failing_expression is True, return the expression whose truth value was unknown.

property lhs¶: The left-hand side of the relation.

property negated¶

Return the negated relationship.

Examples

>>> from sympy import Eq
>>> from sympy.abc import x
>>> Eq(x, 1)
Eq(x, 1)
>>> _.negated
Ne(x, 1)
>>> x < 1
x < 1
>>> _.negated
x >= 1

Notes

This works more or less identical to ~/Not. The difference is that negated returns the relationship even if evaluate=False. Hence, this is useful in code when checking for e.g. negated relations to existing ones as it will not be affected by the $evaluate$ flag.

property reversed¶

Return the relationship with sides reversed.

Examples

>>> from sympy import Eq
>>> from sympy.abc import x
>>> Eq(x, 1)
Eq(x, 1)
>>> _.reversed
Eq(1, x)
>>> x < 1
x < 1
>>> _.reversed
1 > x

property reversedsign¶

Return the relationship with signs reversed.

Examples

>>> from sympy import Eq
>>> from sympy.abc import x
>>> Eq(x, 1)
Eq(x, 1)
>>> _.reversedsign
Eq(-x, -1)
>>> x < 1
x < 1
>>> _.reversedsign
-x > -1

property rhs¶: The right-hand side of the relation.

property strict¶

return the strict version of the inequality or self

Examples

>>> from sympy.abc import x
>>> (x <= 1).strict
x < 1
>>> _.strict
x < 1

property weak¶

return the non-strict version of the inequality or self

Examples

>>> from sympy.abc import x
>>> (x < 1).weak
x <= 1
>>> _.weak
x <= 1

sympy.core.relational.Rel[source]¶: alias of Relational

sympy.core.relational.Eq[source]¶: alias of Equality

sympy.core.relational.Ne[source]¶: alias of Unequality

sympy.core.relational.Lt[source]¶: alias of StrictLessThan

sympy.core.relational.Le[source]¶: alias of LessThan

sympy.core.relational.Gt[source]¶: alias of StrictGreaterThan

sympy.core.relational.Ge[source]¶: alias of GreaterThan

class sympy.core.relational.Equality(lhs, rhs, **options)[source]¶

An equal relation between two objects.

Explanation

Represents that two objects are equal. If they can be easily shown to be definitively equal (or unequal), this will reduce to True (or False). Otherwise, the relation is maintained as an unevaluated Equality object. Use the simplify function on this object for more nontrivial evaluation of the equality relation.

As usual, the keyword argument evaluate=False can be used to prevent any evaluation.

Examples

>>> from sympy import Eq, simplify, exp, cos
>>> from sympy.abc import x, y
>>> Eq(y, x + x**2)
Eq(y, x**2 + x)
>>> Eq(2, 5)
False
>>> Eq(2, 5, evaluate=False)
Eq(2, 5)
>>> _.doit()
False
>>> Eq(exp(x), exp(x).rewrite(cos))
Eq(exp(x), sinh(x) + cosh(x))
>>> simplify(_)
True

Notes

Python treats 1 and True (and 0 and False) as being equal; SymPy does not. And integer will always compare as unequal to a Boolean:

>>> Eq(True, 1), True == 1
(False, True)

This class is not the same as the == operator. The == operator tests for exact structural equality between two expressions; this class compares expressions mathematically.

If either object defines an _eval_Eq method, it can be used in place of the default algorithm. If lhs._eval_Eq(rhs) or rhs._eval_Eq(lhs) returns anything other than None, that return value will be substituted for the Equality. If None is returned by _eval_Eq, an Equality object will be created as usual.

Since this object is already an expression, it does not respond to the method as_expr if one tries to create $x - y$ from Eq(x, y). If eq = Eq(x, y) then write $eq.lhs - eq.rhs$ to get x - y.

Deprecated since version 1.5: Eq(expr) with a single argument is a shorthand for Eq(expr, 0), but this behavior is deprecated and will be removed in a future version of SymPy.

See also

sympy.logic.boolalg.Equivalent: for representing equality between two boolean expressions

as_poly(*gens, **kwargs)[source]¶

Returns lhs-rhs as a Poly

Examples

>>> from sympy import Eq
>>> from sympy.abc import x
>>> Eq(x**2, 1).as_poly(x)
Poly(x**2 - 1, x, domain='ZZ')

integrate(*args, **kwargs)[source]¶: See the integrate function in sympy.integrals

class sympy.core.relational.GreaterThan(lhs, rhs, **options)[source]¶

Class representations of inequalities.

Explanation

The *Than classes represent inequal relationships, where the left-hand side is generally bigger or smaller than the right-hand side. For example, the GreaterThan class represents an inequal relationship where the left-hand side is at least as big as the right side, if not bigger. In mathematical notation:

lhs $\ge$ rhs

In total, there are four *Than classes, to represent the four inequalities:

Class Name	Symbol
GreaterThan	`>=`
LessThan	`<=`
StrictGreaterThan	`>`
StrictLessThan	`<`

All classes take two arguments, lhs and rhs.

Signature Example	Math Equivalent
GreaterThan(lhs, rhs)	lhs $\ge$ rhs
LessThan(lhs, rhs)	lhs $\le$ rhs
StrictGreaterThan(lhs, rhs)	lhs $>$ rhs
StrictLessThan(lhs, rhs)	lhs $<$ rhs

In addition to the normal .lhs and .rhs of Relations, *Than inequality objects also have the .lts and .gts properties, which represent the “less than side” and “greater than side” of the operator. Use of .lts and .gts in an algorithm rather than .lhs and .rhs as an assumption of inequality direction will make more explicit the intent of a certain section of code, and will make it similarly more robust to client code changes:

>>> from sympy import GreaterThan, StrictGreaterThan
>>> from sympy import LessThan, StrictLessThan
>>> from sympy import And, Ge, Gt, Le, Lt, Rel, S
>>> from sympy.abc import x, y, z
>>> from sympy.core.relational import Relational

>>> e = GreaterThan(x, 1)
>>> e
x >= 1
>>> '%s >= %s is the same as %s <= %s' % (e.gts, e.lts, e.lts, e.gts)
'x >= 1 is the same as 1 <= x'

Examples

One generally does not instantiate these classes directly, but uses various convenience methods:

>>> for f in [Ge, Gt, Le, Lt]:  # convenience wrappers
...     print(f(x, 2))
x >= 2
x > 2
x <= 2
x < 2

Another option is to use the Python inequality operators (>=, >, <=, <) directly. Their main advantage over the Ge, Gt, Le, and Lt counterparts, is that one can write a more “mathematical looking” statement rather than littering the math with oddball function calls. However there are certain (minor) caveats of which to be aware (search for ‘gotcha’, below).

>>> x >= 2
x >= 2
>>> _ == Ge(x, 2)
True

However, it is also perfectly valid to instantiate a *Than class less succinctly and less conveniently:

>>> Rel(x, 1, ">")
x > 1
>>> Relational(x, 1, ">")
x > 1

>>> StrictGreaterThan(x, 1)
x > 1
>>> GreaterThan(x, 1)
x >= 1
>>> LessThan(x, 1)
x <= 1
>>> StrictLessThan(x, 1)
x < 1

Notes

There are a couple of “gotchas” to be aware of when using Python’s operators.

The first is that what your write is not always what you get:

>>> 1 < x
x > 1
Due to the order that Python parses a statement, it may not immediately find two objects comparable. When 1 < x is evaluated, Python recognizes that the number 1 is a native number and that x is not. Because a native Python number does not know how to compare itself with a SymPy object Python will try the reflective operation, x > 1 and that is the form that gets evaluated, hence returned.

If the order of the statement is important (for visual output to the console, perhaps), one can work around this annoyance in a couple ways:

“sympify” the literal before comparison
>>> S(1) < x
1 < x
(2) use one of the wrappers or less succinct methods described above
>>> Lt(1, x)
1 < x
>>> Relational(1, x, "<")
1 < x

The second gotcha involves writing equality tests between relationals when one or both sides of the test involve a literal relational:

>>> e = x < 1; e
x < 1
>>> e == e  # neither side is a literal
True
>>> e == x < 1  # expecting True, too
False
>>> e != x < 1  # expecting False
x < 1
>>> x < 1 != x < 1  # expecting False or the same thing as before
Traceback (most recent call last):
...
TypeError: cannot determine truth value of Relational

The solution for this case is to wrap literal relationals in parentheses:

>>> e == (x < 1)
True
>>> e != (x < 1)
False
>>> (x < 1) != (x < 1)
False

The third gotcha involves chained inequalities not involving == or !=. Occasionally, one may be tempted to write:

>>> e = x < y < z
Traceback (most recent call last):
...
TypeError: symbolic boolean expression has no truth value.
Due to an implementation detail or decision of Python [R146], there is no way for SymPy to create a chained inequality with that syntax so one must use And:
>>> e = And(x < y, y < z)
>>> type( e )
And
>>> e
(x < y) & (y < z)
Although this can also be done with the ‘&’ operator, it cannot be done with the ‘and’ operarator:
>>> (x < y) & (y < z)
(x < y) & (y < z)
>>> (x < y) and (y < z)
Traceback (most recent call last):
...
TypeError: cannot determine truth value of Relational

[R146]

This implementation detail is that Python provides no reliable method to determine that a chained inequality is being built. Chained comparison operators are evaluated pairwise, using “and” logic (see https://docs.python.org/3/reference/expressions.html#not-in). This is done in an efficient way, so that each object being compared is only evaluated once and the comparison can short-circuit. For example, 1 > 2 > 3 is evaluated by Python as (1 > 2) and (2 > 3). The and operator coerces each side into a bool, returning the object itself when it short-circuits. The bool of the –Than operators will raise TypeError on purpose, because SymPy cannot determine the mathematical ordering of symbolic expressions. Thus, if we were to compute x > y > z, with x, y, and z being Symbols, Python converts the statement (roughly) into these steps:

x > y > z

(x > y) and (y > z)

(GreaterThanObject) and (y > z)

(GreaterThanObject.__bool__()) and (y > z)

TypeError

Because of the and added at step 2, the statement gets turned into a weak ternary statement, and the first object’s __bool__ method will raise TypeError. Thus, creating a chained inequality is not possible.

In Python, there is no way to override the and operator, or to control how it short circuits, so it is impossible to make something like x > y > z work. There was a PEP to change this, PEP 335, but it was officially closed in March, 2012.

class sympy.core.relational.LessThan(lhs, rhs, **options)[source]¶

Class representations of inequalities.

Explanation

lhs $\ge$ rhs

In total, there are four *Than classes, to represent the four inequalities:

Class Name	Symbol
GreaterThan	`>=`
LessThan	`<=`
StrictGreaterThan	`>`
StrictLessThan	`<`

All classes take two arguments, lhs and rhs.

Signature Example	Math Equivalent
GreaterThan(lhs, rhs)	lhs $\ge$ rhs
LessThan(lhs, rhs)	lhs $\le$ rhs
StrictGreaterThan(lhs, rhs)	lhs $>$ rhs
StrictLessThan(lhs, rhs)	lhs $<$ rhs

>>> from sympy import GreaterThan, StrictGreaterThan
>>> from sympy import LessThan, StrictLessThan
>>> from sympy import And, Ge, Gt, Le, Lt, Rel, S
>>> from sympy.abc import x, y, z
>>> from sympy.core.relational import Relational

>>> e = GreaterThan(x, 1)
>>> e
x >= 1
>>> '%s >= %s is the same as %s <= %s' % (e.gts, e.lts, e.lts, e.gts)
'x >= 1 is the same as 1 <= x'

Examples

One generally does not instantiate these classes directly, but uses various convenience methods:

>>> for f in [Ge, Gt, Le, Lt]:  # convenience wrappers
...     print(f(x, 2))
x >= 2
x > 2
x <= 2
x < 2

>>> x >= 2
x >= 2
>>> _ == Ge(x, 2)
True

However, it is also perfectly valid to instantiate a *Than class less succinctly and less conveniently:

>>> Rel(x, 1, ">")
x > 1
>>> Relational(x, 1, ">")
x > 1

>>> StrictGreaterThan(x, 1)
x > 1
>>> GreaterThan(x, 1)
x >= 1
>>> LessThan(x, 1)
x <= 1
>>> StrictLessThan(x, 1)
x < 1

Notes

There are a couple of “gotchas” to be aware of when using Python’s operators.

The first is that what your write is not always what you get:

>>> 1 < x
x > 1
Due to the order that Python parses a statement, it may not immediately find two objects comparable. When 1 < x is evaluated, Python recognizes that the number 1 is a native number and that x is not. Because a native Python number does not know how to compare itself with a SymPy object Python will try the reflective operation, x > 1 and that is the form that gets evaluated, hence returned.

If the order of the statement is important (for visual output to the console, perhaps), one can work around this annoyance in a couple ways:

“sympify” the literal before comparison
>>> S(1) < x
1 < x
(2) use one of the wrappers or less succinct methods described above
>>> Lt(1, x)
1 < x
>>> Relational(1, x, "<")
1 < x

The second gotcha involves writing equality tests between relationals when one or both sides of the test involve a literal relational:

>>> e = x < 1; e
x < 1
>>> e == e  # neither side is a literal
True
>>> e == x < 1  # expecting True, too
False
>>> e != x < 1  # expecting False
x < 1
>>> x < 1 != x < 1  # expecting False or the same thing as before
Traceback (most recent call last):
...
TypeError: cannot determine truth value of Relational

The solution for this case is to wrap literal relationals in parentheses:

>>> e == (x < 1)
True
>>> e != (x < 1)
False
>>> (x < 1) != (x < 1)
False

The third gotcha involves chained inequalities not involving == or !=. Occasionally, one may be tempted to write:

>>> e = x < y < z
Traceback (most recent call last):
...
TypeError: symbolic boolean expression has no truth value.
Due to an implementation detail or decision of Python [R147], there is no way for SymPy to create a chained inequality with that syntax so one must use And:
>>> e = And(x < y, y < z)
>>> type( e )
And
>>> e
(x < y) & (y < z)
Although this can also be done with the ‘&’ operator, it cannot be done with the ‘and’ operarator:
>>> (x < y) & (y < z)
(x < y) & (y < z)
>>> (x < y) and (y < z)
Traceback (most recent call last):
...
TypeError: cannot determine truth value of Relational

[R147]

x > y > z

(x > y) and (y > z)

(GreaterThanObject) and (y > z)

(GreaterThanObject.__bool__()) and (y > z)

TypeError

In Python, there is no way to override the and operator, or to control how it short circuits, so it is impossible to make something like x > y > z work. There was a PEP to change this, PEP 335, but it was officially closed in March, 2012.

class sympy.core.relational.Unequality(lhs, rhs, **options)[source]¶

An unequal relation between two objects.

Explanation

Represents that two objects are not equal. If they can be shown to be definitively equal, this will reduce to False; if definitively unequal, this will reduce to True. Otherwise, the relation is maintained as an Unequality object.

Examples

>>> from sympy import Ne
>>> from sympy.abc import x, y
>>> Ne(y, x+x**2)
Ne(y, x**2 + x)

Notes

This class is not the same as the != operator. The != operator tests for exact structural equality between two expressions; this class compares expressions mathematically.

This class is effectively the inverse of Equality. As such, it uses the same algorithms, including any available $_eval_Eq$ methods.

See also

Equality

class sympy.core.relational.StrictGreaterThan(lhs, rhs, **options)[source]¶

Class representations of inequalities.

Explanation

lhs $\ge$ rhs

In total, there are four *Than classes, to represent the four inequalities:

Class Name	Symbol
GreaterThan	`>=`
LessThan	`<=`
StrictGreaterThan	`>`
StrictLessThan	`<`

All classes take two arguments, lhs and rhs.

Signature Example	Math Equivalent
GreaterThan(lhs, rhs)	lhs $\ge$ rhs
LessThan(lhs, rhs)	lhs $\le$ rhs
StrictGreaterThan(lhs, rhs)	lhs $>$ rhs
StrictLessThan(lhs, rhs)	lhs $<$ rhs

>>> from sympy import GreaterThan, StrictGreaterThan
>>> from sympy import LessThan, StrictLessThan
>>> from sympy import And, Ge, Gt, Le, Lt, Rel, S
>>> from sympy.abc import x, y, z
>>> from sympy.core.relational import Relational

>>> e = GreaterThan(x, 1)
>>> e
x >= 1
>>> '%s >= %s is the same as %s <= %s' % (e.gts, e.lts, e.lts, e.gts)
'x >= 1 is the same as 1 <= x'

Examples

One generally does not instantiate these classes directly, but uses various convenience methods:

>>> for f in [Ge, Gt, Le, Lt]:  # convenience wrappers
...     print(f(x, 2))
x >= 2
x > 2
x <= 2
x < 2

>>> x >= 2
x >= 2
>>> _ == Ge(x, 2)
True

However, it is also perfectly valid to instantiate a *Than class less succinctly and less conveniently:

>>> Rel(x, 1, ">")
x > 1
>>> Relational(x, 1, ">")
x > 1

>>> StrictGreaterThan(x, 1)
x > 1
>>> GreaterThan(x, 1)
x >= 1
>>> LessThan(x, 1)
x <= 1
>>> StrictLessThan(x, 1)
x < 1

Notes

There are a couple of “gotchas” to be aware of when using Python’s operators.

The first is that what your write is not always what you get:

>>> 1 < x
x > 1
Due to the order that Python parses a statement, it may not immediately find two objects comparable. When 1 < x is evaluated, Python recognizes that the number 1 is a native number and that x is not. Because a native Python number does not know how to compare itself with a SymPy object Python will try the reflective operation, x > 1 and that is the form that gets evaluated, hence returned.

If the order of the statement is important (for visual output to the console, perhaps), one can work around this annoyance in a couple ways:

“sympify” the literal before comparison
>>> S(1) < x
1 < x
(2) use one of the wrappers or less succinct methods described above
>>> Lt(1, x)
1 < x
>>> Relational(1, x, "<")
1 < x

The second gotcha involves writing equality tests between relationals when one or both sides of the test involve a literal relational:

>>> e = x < 1; e
x < 1
>>> e == e  # neither side is a literal
True
>>> e == x < 1  # expecting True, too
False
>>> e != x < 1  # expecting False
x < 1
>>> x < 1 != x < 1  # expecting False or the same thing as before
Traceback (most recent call last):
...
TypeError: cannot determine truth value of Relational

The solution for this case is to wrap literal relationals in parentheses:

>>> e == (x < 1)
True
>>> e != (x < 1)
False
>>> (x < 1) != (x < 1)
False

The third gotcha involves chained inequalities not involving == or !=. Occasionally, one may be tempted to write:

>>> e = x < y < z
Traceback (most recent call last):
...
TypeError: symbolic boolean expression has no truth value.
Due to an implementation detail or decision of Python [R148], there is no way for SymPy to create a chained inequality with that syntax so one must use And:
>>> e = And(x < y, y < z)
>>> type( e )
And
>>> e
(x < y) & (y < z)
Although this can also be done with the ‘&’ operator, it cannot be done with the ‘and’ operarator:
>>> (x < y) & (y < z)
(x < y) & (y < z)
>>> (x < y) and (y < z)
Traceback (most recent call last):
...
TypeError: cannot determine truth value of Relational

[R148]

x > y > z

(x > y) and (y > z)

(GreaterThanObject) and (y > z)

(GreaterThanObject.__bool__()) and (y > z)

TypeError

In Python, there is no way to override the and operator, or to control how it short circuits, so it is impossible to make something like x > y > z work. There was a PEP to change this, PEP 335, but it was officially closed in March, 2012.

class sympy.core.relational.StrictLessThan(lhs, rhs, **options)[source]¶

Class representations of inequalities.

Explanation

lhs $\ge$ rhs

In total, there are four *Than classes, to represent the four inequalities:

Class Name	Symbol
GreaterThan	`>=`
LessThan	`<=`
StrictGreaterThan	`>`
StrictLessThan	`<`

All classes take two arguments, lhs and rhs.

Signature Example	Math Equivalent
GreaterThan(lhs, rhs)	lhs $\ge$ rhs
LessThan(lhs, rhs)	lhs $\le$ rhs
StrictGreaterThan(lhs, rhs)	lhs $>$ rhs
StrictLessThan(lhs, rhs)	lhs $<$ rhs

>>> from sympy import GreaterThan, StrictGreaterThan
>>> from sympy import LessThan, StrictLessThan
>>> from sympy import And, Ge, Gt, Le, Lt, Rel, S
>>> from sympy.abc import x, y, z
>>> from sympy.core.relational import Relational

>>> e = GreaterThan(x, 1)
>>> e
x >= 1
>>> '%s >= %s is the same as %s <= %s' % (e.gts, e.lts, e.lts, e.gts)
'x >= 1 is the same as 1 <= x'

Examples

One generally does not instantiate these classes directly, but uses various convenience methods:

>>> for f in [Ge, Gt, Le, Lt]:  # convenience wrappers
...     print(f(x, 2))
x >= 2
x > 2
x <= 2
x < 2

>>> x >= 2
x >= 2
>>> _ == Ge(x, 2)
True

However, it is also perfectly valid to instantiate a *Than class less succinctly and less conveniently:

>>> Rel(x, 1, ">")
x > 1
>>> Relational(x, 1, ">")
x > 1

>>> StrictGreaterThan(x, 1)
x > 1
>>> GreaterThan(x, 1)
x >= 1
>>> LessThan(x, 1)
x <= 1
>>> StrictLessThan(x, 1)
x < 1

Notes

There are a couple of “gotchas” to be aware of when using Python’s operators.

The first is that what your write is not always what you get:

>>> 1 < x
x > 1
Due to the order that Python parses a statement, it may not immediately find two objects comparable. When 1 < x is evaluated, Python recognizes that the number 1 is a native number and that x is not. Because a native Python number does not know how to compare itself with a SymPy object Python will try the reflective operation, x > 1 and that is the form that gets evaluated, hence returned.

If the order of the statement is important (for visual output to the console, perhaps), one can work around this annoyance in a couple ways:

“sympify” the literal before comparison
>>> S(1) < x
1 < x
(2) use one of the wrappers or less succinct methods described above
>>> Lt(1, x)
1 < x
>>> Relational(1, x, "<")
1 < x

The second gotcha involves writing equality tests between relationals when one or both sides of the test involve a literal relational:

>>> e = x < 1; e
x < 1
>>> e == e  # neither side is a literal
True
>>> e == x < 1  # expecting True, too
False
>>> e != x < 1  # expecting False
x < 1
>>> x < 1 != x < 1  # expecting False or the same thing as before
Traceback (most recent call last):
...
TypeError: cannot determine truth value of Relational

The solution for this case is to wrap literal relationals in parentheses:

>>> e == (x < 1)
True
>>> e != (x < 1)
False
>>> (x < 1) != (x < 1)
False

The third gotcha involves chained inequalities not involving == or !=. Occasionally, one may be tempted to write:

>>> e = x < y < z
Traceback (most recent call last):
...
TypeError: symbolic boolean expression has no truth value.
Due to an implementation detail or decision of Python [R149], there is no way for SymPy to create a chained inequality with that syntax so one must use And:
>>> e = And(x < y, y < z)
>>> type( e )
And
>>> e
(x < y) & (y < z)
Although this can also be done with the ‘&’ operator, it cannot be done with the ‘and’ operarator:
>>> (x < y) & (y < z)
(x < y) & (y < z)
>>> (x < y) and (y < z)
Traceback (most recent call last):
...
TypeError: cannot determine truth value of Relational

[R149]

x > y > z

(x > y) and (y > z)

(GreaterThanObject) and (y > z)

(GreaterThanObject.__bool__()) and (y > z)

TypeError

In Python, there is no way to override the and operator, or to control how it short circuits, so it is impossible to make something like x > y > z work. There was a PEP to change this, PEP 335, but it was officially closed in March, 2012.

multidimensional¶

class sympy.core.multidimensional.vectorize(*mdargs)[source]¶

Generalizes a function taking scalars to accept multidimensional arguments.

Examples

>>> from sympy import vectorize, diff, sin, symbols, Function
>>> x, y, z = symbols('x y z')
>>> f, g, h = list(map(Function, 'fgh'))

>>> @vectorize(0)
... def vsin(x):
...     return sin(x)

>>> vsin([1, x, y])
[sin(1), sin(x), sin(y)]

>>> @vectorize(0, 1)
... def vdiff(f, y):
...     return diff(f, y)

>>> vdiff([f(x, y, z), g(x, y, z), h(x, y, z)], [x, y, z])
[[Derivative(f(x, y, z), x), Derivative(f(x, y, z), y), Derivative(f(x, y, z), z)], [Derivative(g(x, y, z), x), Derivative(g(x, y, z), y), Derivative(g(x, y, z), z)], [Derivative(h(x, y, z), x), Derivative(h(x, y, z), y), Derivative(h(x, y, z), z)]]

function¶

class sympy.core.function.Lambda(signature, expr)[source]¶

Lambda(x, expr) represents a lambda function similar to Python’s ‘lambda x: expr’. A function of several variables is written as Lambda((x, y, …), expr).

Examples

A simple example:

>>> from sympy import Lambda
>>> from sympy.abc import x
>>> f = Lambda(x, x**2)
>>> f(4)
16

For multivariate functions, use:

>>> from sympy.abc import y, z, t
>>> f2 = Lambda((x, y, z, t), x + y**z + t**z)
>>> f2(1, 2, 3, 4)
73

It is also possible to unpack tuple arguments:

>>> f = Lambda(((x, y), z), x + y + z)
>>> f((1, 2), 3)
6

A handy shortcut for lots of arguments:

>>> p = x, y, z
>>> f = Lambda(p, x + y*z)
>>> f(*p)
x + y*z

property bound_symbols¶: The variables used in the internal representation of the function

property expr¶: The return value of the function

property is_identity¶: Return True if this Lambda is an identity function.

property signature¶: The expected form of the arguments to be unpacked into variables

property variables¶: The variables used in the internal representation of the function

class sympy.core.function.WildFunction(*args)[source]¶

A WildFunction function matches any function (with its arguments).

Examples

>>> from sympy import WildFunction, Function, cos
>>> from sympy.abc import x, y
>>> F = WildFunction('F')
>>> f = Function('f')
>>> F.nargs
Naturals0
>>> x.match(F)
>>> F.match(F)
{F_: F_}
>>> f(x).match(F)
{F_: f(x)}
>>> cos(x).match(F)
{F_: cos(x)}
>>> f(x, y).match(F)
{F_: f(x, y)}

To match functions with a given number of arguments, set nargs to the desired value at instantiation:

>>> F = WildFunction('F', nargs=2)
>>> F.nargs
{2}
>>> f(x).match(F)
>>> f(x, y).match(F)
{F_: f(x, y)}

To match functions with a range of arguments, set nargs to a tuple containing the desired number of arguments, e.g. if nargs = (1, 2) then functions with 1 or 2 arguments will be matched.

>>> F = WildFunction('F', nargs=(1, 2))
>>> F.nargs
{1, 2}
>>> f(x).match(F)
{F_: f(x)}
>>> f(x, y).match(F)
{F_: f(x, y)}
>>> f(x, y, 1).match(F)

class sympy.core.function.Derivative(expr, *variables, **kwargs)[source]¶

Carries out differentiation of the given expression with respect to symbols.

Examples

>>> from sympy import Derivative, Function, symbols, Subs
>>> from sympy.abc import x, y
>>> f, g = symbols('f g', cls=Function)

>>> Derivative(x**2, x, evaluate=True)
2*x

Denesting of derivatives retains the ordering of variables:

>>> Derivative(Derivative(f(x, y), y), x)
Derivative(f(x, y), y, x)

Contiguously identical symbols are merged into a tuple giving the symbol and the count:

>>> Derivative(f(x), x, x, y, x)
Derivative(f(x), (x, 2), y, x)

If the derivative cannot be performed, and evaluate is True, the order of the variables of differentiation will be made canonical:

>>> Derivative(f(x, y), y, x, evaluate=True)
Derivative(f(x, y), x, y)

Derivatives with respect to undefined functions can be calculated:

>>> Derivative(f(x)**2, f(x), evaluate=True)
2*f(x)

Such derivatives will show up when the chain rule is used to evalulate a derivative:

>>> f(g(x)).diff(x)
Derivative(f(g(x)), g(x))*Derivative(g(x), x)

Substitution is used to represent derivatives of functions with arguments that are not symbols or functions:

>>> f(2*x + 3).diff(x) == 2*Subs(f(y).diff(y), y, 2*x + 3)
True

Notes

Simplification of high-order derivatives:

Because there can be a significant amount of simplification that can be done when multiple differentiations are performed, results will be automatically simplified in a fairly conservative fashion unless the keyword simplify is set to False.

>>> from sympy import sqrt, diff, Function, symbols
>>> from sympy.abc import x, y, z
>>> f, g = symbols('f,g', cls=Function)

>>> e = sqrt((x + 1)**2 + x)
>>> diff(e, (x, 5), simplify=False).count_ops()
136
>>> diff(e, (x, 5)).count_ops()
30

Ordering of variables:

If evaluate is set to True and the expression cannot be evaluated, the list of differentiation symbols will be sorted, that is, the expression is assumed to have continuous derivatives up to the order asked.

Derivative wrt non-Symbols:

For the most part, one may not differentiate wrt non-symbols. For example, we do not allow differentiation wrt $x*y$ because there are multiple ways of structurally defining where x*y appears in an expression: a very strict definition would make (x*y*z).diff(x*y) == 0. Derivatives wrt defined functions (like cos(x)) are not allowed, either:

>>> (x*y*z).diff(x*y)
Traceback (most recent call last):
...
ValueError: Can't calculate derivative wrt x*y.

To make it easier to work with variational calculus, however, derivatives wrt AppliedUndef and Derivatives are allowed. For example, in the Euler-Lagrange method one may write F(t, u, v) where u = f(t) and v = f’(t). These variables can be written explicitly as functions of time:

>>> from sympy.abc import t
>>> F = Function('F')
>>> U = f(t)
>>> V = U.diff(t)

The derivative wrt f(t) can be obtained directly:

>>> direct = F(t, U, V).diff(U)

When differentiation wrt a non-Symbol is attempted, the non-Symbol is temporarily converted to a Symbol while the differentiation is performed and the same answer is obtained:

>>> indirect = F(t, U, V).subs(U, x).diff(x).subs(x, U)
>>> assert direct == indirect

The implication of this non-symbol replacement is that all functions are treated as independent of other functions and the symbols are independent of the functions that contain them:

>>> x.diff(f(x))
0
>>> g(x).diff(f(x))
0

It also means that derivatives are assumed to depend only on the variables of differentiation, not on anything contained within the expression being differentiated:

>>> F = f(x)
>>> Fx = F.diff(x)
>>> Fx.diff(F)  # derivative depends on x, not F
0
>>> Fxx = Fx.diff(x)
>>> Fxx.diff(Fx)  # derivative depends on x, not Fx
0

The last example can be made explicit by showing the replacement of Fx in Fxx with y:

>>> Fxx.subs(Fx, y)
Derivative(y, x)
Since that in itself will evaluate to zero, differentiating wrt Fx will also be zero:
>>> _.doit()
0

Replacing undefined functions with concrete expressions

One must be careful to replace undefined functions with expressions that contain variables consistent with the function definition and the variables of differentiation or else insconsistent result will be obtained. Consider the following example:

>>> eq = f(x)*g(y)
>>> eq.subs(f(x), x*y).diff(x, y).doit()
y*Derivative(g(y), y) + g(y)
>>> eq.diff(x, y).subs(f(x), x*y).doit()
y*Derivative(g(y), y)

The results differ because $f(x)$ was replaced with an expression that involved both variables of differentiation. In the abstract case, differentiation of $f(x)$ by $y$ is 0; in the concrete case, the presence of $y$ made that derivative nonvanishing and produced the extra $g(y)$ term.

Defining differentiation for an object

An object must define ._eval_derivative(symbol) method that returns the differentiation result. This function only needs to consider the non-trivial case where expr contains symbol and it should call the diff() method internally (not _eval_derivative); Derivative should be the only one to call _eval_derivative.

Any class can allow derivatives to be taken with respect to itself (while indicating its scalar nature). See the docstring of Expr._diff_wrt.

See also

_sort_variable_count

property _diff_wrt¶

An expression may be differentiated wrt a Derivative if it is in elementary form.

Examples

>>> from sympy import Function, Derivative, cos
>>> from sympy.abc import x
>>> f = Function('f')

>>> Derivative(f(x), x)._diff_wrt
True
>>> Derivative(cos(x), x)._diff_wrt
False
>>> Derivative(x + 1, x)._diff_wrt
False

A Derivative might be an unevaluated form of what will not be a valid variable of differentiation if evaluated. For example,

>>> Derivative(f(f(x)), x).doit()
Derivative(f(x), x)*Derivative(f(f(x)), f(x))

Such an expression will present the same ambiguities as arise when dealing with any other product, like 2*x, so _diff_wrt is False:

>>> Derivative(f(f(x)), x)._diff_wrt
False

classmethod _sort_variable_count(vc)[source]¶

Sort (variable, count) pairs into canonical order while retaining order of variables that do not commute during differentiation:

symbols and functions commute with each other
derivatives commute with each other
a derivative does not commute with anything it contains
any other object is not allowed to commute if it has free symbols in common with another object

Examples

>>> from sympy import Derivative, Function, symbols
>>> vsort = Derivative._sort_variable_count
>>> x, y, z = symbols('x y z')
>>> f, g, h = symbols('f g h', cls=Function)

Contiguous items are collapsed into one pair:

>>> vsort([(x, 1), (x, 1)])
[(x, 2)]
>>> vsort([(y, 1), (f(x), 1), (y, 1), (f(x), 1)])
[(y, 2), (f(x), 2)]

Ordering is canonical.

>>> def vsort0(*v):
...     # docstring helper to
...     # change vi -> (vi, 0), sort, and return vi vals
...     return [i[0] for i in vsort([(i, 0) for i in v])]

>>> vsort0(y, x)
[x, y]
>>> vsort0(g(y), g(x), f(y))
[f(y), g(x), g(y)]

Symbols are sorted as far to the left as possible but never move to the left of a derivative having the same symbol in its variables; the same applies to AppliedUndef which are always sorted after Symbols:

>>> dfx = f(x).diff(x)
>>> assert vsort0(dfx, y) == [y, dfx]
>>> assert vsort0(dfx, x) == [dfx, x]

as_finite_difference( points=1, x0=None, wrt=None, )[source]¶

Expresses a Derivative instance as a finite difference.

Parameters:

points : sequence or coefficient, optional

If sequence: discrete values (length >= order+1) of the independent variable used for generating the finite difference weights. If it is a coefficient, it will be used as the step-size for generating an equidistant sequence of length order+1 centered around x0. Default: 1 (step-size 1)

x0 : number or Symbol, optional

the value of the independent variable (wrt) at which the derivative is to be approximated. Default: same as wrt.

wrt : Symbol, optional

“with respect to” the variable for which the (partial) derivative is to be approximated for. If not provided it is required that the derivative is ordinary. Default: None.

Examples

>>> from sympy import symbols, Function, exp, sqrt, Symbol
>>> x, h = symbols('x h')
>>> f = Function('f')
>>> f(x).diff(x).as_finite_difference()
-f(x - 1/2) + f(x + 1/2)

The default step size and number of points are 1 and order + 1 respectively. We can change the step size by passing a symbol as a parameter:

>>> f(x).diff(x).as_finite_difference(h)
-f(-h/2 + x)/h + f(h/2 + x)/h

We can also specify the discretized values to be used in a sequence:

>>> f(x).diff(x).as_finite_difference([x, x+h, x+2*h])
-3*f(x)/(2*h) + 2*f(h + x)/h - f(2*h + x)/(2*h)

The algorithm is not restricted to use equidistant spacing, nor do we need to make the approximation around x0, but we can get an expression estimating the derivative at an offset:

>>> e, sq2 = exp(1), sqrt(2)
>>> xl = [x-h, x+h, x+e*h]
>>> f(x).diff(x, 1).as_finite_difference(xl, x+h*sq2)  
2*h*((h + sqrt(2)*h)/(2*h) - (-sqrt(2)*h + h)/(2*h))*f(E*h + x)/...

To approximate Derivative around x0 using a non-equidistant spacing step, the algorithm supports assignment of undefined functions to points:

>>> dx = Function('dx')
>>> f(x).diff(x).as_finite_difference(points=dx(x), x0=x-h)
-f(-h + x - dx(-h + x)/2)/dx(-h + x) + f(-h + x + dx(-h + x)/2)/dx(-h + x)

Partial derivatives are also supported:

>>> y = Symbol('y')
>>> d2fdxdy=f(x,y).diff(x,y)
>>> d2fdxdy.as_finite_difference(wrt=x)
-Derivative(f(x - 1/2, y), y) + Derivative(f(x + 1/2, y), y)

We can apply as_finite_difference to Derivative instances in compound expressions using replace:

>>> (1 + 42**f(x).diff(x)).replace(lambda arg: arg.is_Derivative,
...     lambda arg: arg.as_finite_difference())
42**(-f(x - 1/2) + f(x + 1/2)) + 1

doit_numerically(z0)[source]¶

Evaluate the derivative at z numerically.

When we can represent derivatives at a point, this should be folded into the normal evalf. For now, we need a special method.

sympy.core.function.diff(f, *symbols, **kwargs)[source]¶

Differentiate f with respect to symbols.

Explanation

This is just a wrapper to unify .diff() and the Derivative class; its interface is similar to that of integrate(). You can use the same shortcuts for multiple variables as with Derivative. For example, diff(f(x), x, x, x) and diff(f(x), x, 3) both return the third derivative of f(x).

You can pass evaluate=False to get an unevaluated Derivative class. Note that if there are 0 symbols (such as diff(f(x), x, 0), then the result will be the function (the zeroth derivative), even if evaluate=False.

Examples

>>> from sympy import sin, cos, Function, diff
>>> from sympy.abc import x, y
>>> f = Function('f')

>>> diff(sin(x), x)
cos(x)
>>> diff(f(x), x, x, x)
Derivative(f(x), (x, 3))
>>> diff(f(x), x, 3)
Derivative(f(x), (x, 3))
>>> diff(sin(x)*cos(y), x, 2, y, 2)
sin(x)*cos(y)

>>> type(diff(sin(x), x))
cos
>>> type(diff(sin(x), x, evaluate=False))
<class 'sympy.core.function.Derivative'>
>>> type(diff(sin(x), x, 0))
sin
>>> type(diff(sin(x), x, 0, evaluate=False))
sin

>>> diff(sin(x))
cos(x)
>>> diff(sin(x*y))
Traceback (most recent call last):
...
ValueError: specify differentiation variables to differentiate sin(x*y)

Note that diff(sin(x)) syntax is meant only for convenience in interactive sessions and should be avoided in library code.

See also

Derivative

idiff: computes the derivative implicitly

References

[R150]

https://reference.wolfram.com/legacy/v5_2/Built-inFunctions/AlgebraicComputation/Calculus/D.html

class sympy.core.function.FunctionClass(*args, **kwargs)[source]¶

Base class for function classes. FunctionClass is a subclass of type.

Use Function(‘<function name>’ [ , signature ]) to create undefined function classes.

property nargs¶

Return a set of the allowed number of arguments for the function.

Examples

>>> from sympy import Function
>>> f = Function('f')

If the function can take any number of arguments, the set of whole numbers is returned:

>>> Function('f').nargs
Naturals0

If the function was initialized to accept one or more arguments, a corresponding set will be returned:

>>> Function('f', nargs=1).nargs
{1}
>>> Function('f', nargs=(2, 1)).nargs
{1, 2}

The undefined function, after application, also has the nargs attribute; the actual number of arguments is always available by checking the args attribute:

>>> f = Function('f')
>>> f(1).nargs
Naturals0
>>> len(f(1).args)
1

class sympy.core.function.Function(*args)[source]¶

Base class for applied mathematical functions.

It also serves as a constructor for undefined function classes.

See the Writing Custom Functions guide for details on how to subclass Function and what methods can be defined.

Examples

Undefined Functions

To create an undefined function, pass a string of the function name to Function.

>>> from sympy import Function, Symbol
>>> x = Symbol('x')
>>> f = Function('f')
>>> g = Function('g')(x)
>>> f
f
>>> f(x)
f(x)
>>> g
g(x)
>>> f(x).diff(x)
Derivative(f(x), x)
>>> g.diff(x)
Derivative(g(x), x)

Assumptions can be passed to Function the same as with a Symbol. Alternatively, you can use a Symbol with assumptions for the function name and the function will inherit the name and assumptions associated with the Symbol:

>>> f_real = Function('f', real=True)
>>> f_real(x).is_real
True
>>> f_real_inherit = Function(Symbol('f', real=True))
>>> f_real_inherit(x).is_real
True

Note that assumptions on a function are unrelated to the assumptions on the variables it is called on. If you want to add a relationship, subclass Function and define custom assumptions handler methods. See the Assumptions section of the Writing Custom Functions guide for more details.

Custom Function Subclasses

The Writing Custom Functions guide has several Complete Examples of how to subclass Function to create a custom function.

fdiff(argindex=1)[source]¶: Returns the first derivative of the function.

classmethod is_singular(a)[source]¶: Tests whether the argument is an essential singularity or a branch point, or the functions is non-holomorphic.

Note

Not all functions are the same

SymPy defines many functions (like cos and factorial). It also allows the user to create generic functions which act as argument holders. Such functions are created just like symbols:

>>> from sympy import Function, cos
>>> from sympy.abc import x
>>> f = Function('f')
>>> f(2) + f(x)
f(2) + f(x)

If you want to see which functions appear in an expression you can use the atoms method:

>>> e = (f(x) + cos(x) + 2)
>>> e.atoms(Function)
{f(x), cos(x)}

If you just want the function you defined, not SymPy functions, the thing to search for is AppliedUndef:

>>> from sympy.core.function import AppliedUndef
>>> e.atoms(AppliedUndef)
{f(x)}

class sympy.core.function.Subs(expr, variables, point, **assumptions)[source]¶

Represents unevaluated substitutions of an expression.

Subs(expr, x, x0) represents the expression resulting from substituting x with x0 in expr.

Parameters:

expr : Expr

An expression.

x : tuple, variable

A variable or list of distinct variables.

x0 : tuple or list of tuples

A point or list of evaluation points corresponding to those variables.

Examples

>>> from sympy import Subs, Function, sin, cos
>>> from sympy.abc import x, y, z
>>> f = Function('f')

Subs are created when a particular substitution cannot be made. The x in the derivative cannot be replaced with 0 because 0 is not a valid variables of differentiation:

>>> f(x).diff(x).subs(x, 0)
Subs(Derivative(f(x), x), x, 0)

Once f is known, the derivative and evaluation at 0 can be done:

>>> _.subs(f, sin).doit() == sin(x).diff(x).subs(x, 0) == cos(0)
True

Subs can also be created directly with one or more variables:

>>> Subs(f(x)*sin(y) + z, (x, y), (0, 1))
Subs(z + f(x)*sin(y), (x, y), (0, 1))
>>> _.doit()
z + f(0)*sin(1)

Notes

Subs objects are generally useful to represent unevaluated derivatives calculated at a point.

The variables may be expressions, but they are subjected to the limitations of subs(), so it is usually a good practice to use only symbols for variables, since in that case there can be no ambiguity.

There’s no automatic expansion - use the method .doit() to effect all possible substitutions of the object and also of objects inside the expression.

When evaluating derivatives at a point that is not a symbol, a Subs object is returned. One is also able to calculate derivatives of Subs objects - in this case the expression is always expanded (for the unevaluated form, use Derivative()).

In order to allow expressions to combine before doit is done, a representation of the Subs expression is used internally to make expressions that are superficially different compare the same:

>>> a, b = Subs(x, x, 0), Subs(y, y, 0)
>>> a + b
2*Subs(x, x, 0)

This can lead to unexpected consequences when using methods like $has$ that are cached:

>>> s = Subs(x, x, 0)
>>> s.has(x), s.has(y)
(True, False)
>>> ss = s.subs(x, y)
>>> ss.has(x), ss.has(y)
(True, False)
>>> s, ss
(Subs(x, x, 0), Subs(y, y, 0))

property bound_symbols¶: The variables to be evaluated

property expr¶: The expression on which the substitution operates

property point¶: The values for which the variables are to be substituted

property variables¶: The variables to be evaluated

sympy.core.function.expand(

e,

deep=True,

modulus=None,

power_base=True,

power_exp=True,

mul=True,

log=True,

multinomial=True,

basic=True,

**hints,

)[source]¶

Expand an expression using methods given as hints.

Explanation

Hints evaluated unless explicitly set to False are: basic, log, multinomial, mul, power_base, and power_exp The following hints are supported but not applied unless set to True: complex, func, and trig. In addition, the following meta-hints are supported by some or all of the other hints: frac, numer, denom, modulus, and force. deep is supported by all hints. Additionally, subclasses of Expr may define their own hints or meta-hints.

The basic hint is used for any special rewriting of an object that should be done automatically (along with the other hints like mul) when expand is called. This is a catch-all hint to handle any sort of expansion that may not be described by the existing hint names. To use this hint an object should override the _eval_expand_basic method. Objects may also define their own expand methods, which are not run by default. See the API section below.

If deep is set to True (the default), things like arguments of functions are recursively expanded. Use deep=False to only expand on the top level.

If the force hint is used, assumptions about variables will be ignored in making the expansion.

Hints

These hints are run by default

Mul

Distributes multiplication over addition:

>>> from sympy import cos, exp, sin
>>> from sympy.abc import x, y, z
>>> (y*(x + z)).expand(mul=True)
x*y + y*z

Multinomial

Expand (x + y + …)**n where n is a positive integer.

>>> ((x + y + z)**2).expand(multinomial=True)
x**2 + 2*x*y + 2*x*z + y**2 + 2*y*z + z**2

Power_exp

Expand addition in exponents into multiplied bases.

>>> exp(x + y).expand(power_exp=True)
exp(x)*exp(y)
>>> (2**(x + y)).expand(power_exp=True)
2**x*2**y

Power_base

Split powers of multiplied bases.

This only happens by default if assumptions allow, or if the force meta-hint is used:

>>> ((x*y)**z).expand(power_base=True)
(x*y)**z
>>> ((x*y)**z).expand(power_base=True, force=True)
x**z*y**z
>>> ((2*y)**z).expand(power_base=True)
2**z*y**z

Note that in some cases where this expansion always holds, SymPy performs it automatically:

>>> (x*y)**2
x**2*y**2

Log

Pull out power of an argument as a coefficient and split logs products into sums of logs.

Note that these only work if the arguments of the log function have the proper assumptions–the arguments must be positive and the exponents must be real–or else the force hint must be True:

>>> from sympy import log, symbols
>>> log(x**2*y).expand(log=True)
log(x**2*y)
>>> log(x**2*y).expand(log=True, force=True)
2*log(x) + log(y)
>>> x, y = symbols('x,y', positive=True)
>>> log(x**2*y).expand(log=True)
2*log(x) + log(y)

Basic

This hint is intended primarily as a way for custom subclasses to enable expansion by default.

These hints are not run by default:

Complex

Split an expression into real and imaginary parts.

>>> x, y = symbols('x,y')
>>> (x + y).expand(complex=True)
re(x) + re(y) + I*im(x) + I*im(y)
>>> cos(x).expand(complex=True)
-I*sin(re(x))*sinh(im(x)) + cos(re(x))*cosh(im(x))

Note that this is just a wrapper around as_real_imag(). Most objects that wish to redefine _eval_expand_complex() should consider redefining as_real_imag() instead.

Func

Expand other functions.

>>> from sympy import gamma
>>> gamma(x + 1).expand(func=True)
x*gamma(x)

Trig

Do trigonometric expansions.

>>> cos(x + y).expand(trig=True)
-sin(x)*sin(y) + cos(x)*cos(y)
>>> sin(2*x).expand(trig=True)
2*sin(x)*cos(x)

Note that the forms of sin(n*x) and cos(n*x) in terms of sin(x) and cos(x) are not unique, due to the identity $\sin^2(x) + \cos^2(x) = 1$. The current implementation uses the form obtained from Chebyshev polynomials, but this may change. See this MathWorld article for more information.

Notes

You can shut off unwanted methods:

>>> (exp(x + y)*(x + y)).expand()
x*exp(x)*exp(y) + y*exp(x)*exp(y)
>>> (exp(x + y)*(x + y)).expand(power_exp=False)
x*exp(x + y) + y*exp(x + y)
>>> (exp(x + y)*(x + y)).expand(mul=False)
(x + y)*exp(x)*exp(y)

Use deep=False to only expand on the top level:

>>> exp(x + exp(x + y)).expand()
exp(x)*exp(exp(x)*exp(y))
>>> exp(x + exp(x + y)).expand(deep=False)
exp(x)*exp(exp(x + y))

Hints are applied in an arbitrary, but consistent order (in the current implementation, they are applied in alphabetical order, except multinomial comes before mul, but this may change). Because of this, some hints may prevent expansion by other hints if they are applied first. For example, mul may distribute multiplications and prevent log and power_base from expanding them. Also, if mul is applied before multinomial`, the expression might not be fully distributed. The solution is to use the various ``expand_hint helper functions or to use hint=False to this function to finely control which hints are applied. Here are some examples:
```
>>> from sympy import expand, expand_mul, expand_power_base
>>> x, y, z = symbols('x,y,z', positive=True)

>>> expand(log(x*(y + z)))
log(x) + log(y + z)
```
Here, we see that log was applied before mul. To get the mul expanded form, either of the following will work:
```
>>> expand_mul(log(x*(y + z)))
log(x*y + x*z)
>>> expand(log(x*(y + z)), log=False)
log(x*y + x*z)
```
A similar thing can happen with the power_base hint:
```
>>> expand((x*(y + z))**x)
(x*y + x*z)**x
```
To get the power_base expanded form, either of the following will work:
```
>>> expand((x*(y + z))**x, mul=False)
x**x*(y + z)**x
>>> expand_power_base((x*(y + z))**x)
x**x*(y + z)**x

>>> expand((x + y)*y/x)
y + y**2/x
```
The parts of a rational expression can be targeted:
```
>>> expand((x + y)*y/x/(x + 1), frac=True)
(x*y + y**2)/(x**2 + x)
>>> expand((x + y)*y/x/(x + 1), numer=True)
(x*y + y**2)/(x*(x + 1))
>>> expand((x + y)*y/x/(x + 1), denom=True)
y*(x + y)/(x**2 + x)
```

The modulus meta-hint can be used to reduce the coefficients of an expression post-expansion:

>>> expand((3*x + 1)**2)
9*x**2 + 6*x + 1
>>> expand((3*x + 1)**2, modulus=5)
4*x**2 + x + 1

Either expand() the function or .expand() the method can be used. Both are equivalent:

>>> expand((x + 1)**2)
x**2 + 2*x + 1
>>> ((x + 1)**2).expand()
x**2 + 2*x + 1

Api

Objects can define their own expand hints by defining _eval_expand_hint(). The function should take the form:

def _eval_expand_hint(self, **hints):
    # Only apply the method to the top-level expression
    ...

See also the example below. Objects should define _eval_expand_hint() methods only if hint applies to that specific object. The generic _eval_expand_hint() method defined in Expr will handle the no-op case.

Each hint should be responsible for expanding that hint only. Furthermore, the expansion should be applied to the top-level expression only. expand() takes care of the recursion that happens when deep=True.

You should only call _eval_expand_hint() methods directly if you are 100% sure that the object has the method, as otherwise you are liable to get unexpected AttributeError``s. Note, again, that you do not need to recursively apply the hint to args of your object: this is handled automatically by ``expand(). _eval_expand_hint() should generally not be used at all outside of an _eval_expand_hint() method. If you want to apply a specific expansion from within another method, use the public expand() function, method, or expand_hint() functions.

In order for expand to work, objects must be rebuildable by their args, i.e., obj.func(*obj.args) == obj must hold.

Expand methods are passed **hints so that expand hints may use ‘metahints’–hints that control how different expand methods are applied. For example, the force=True hint described above that causes expand(log=True) to ignore assumptions is such a metahint. The deep meta-hint is handled exclusively by expand() and is not passed to _eval_expand_hint() methods.

Note that expansion hints should generally be methods that perform some kind of ‘expansion’. For hints that simply rewrite an expression, use the .rewrite() API.

Examples

>>> from sympy import Expr, sympify
>>> class MyClass(Expr):
...     def __new__(cls, *args):
...         args = sympify(args)
...         return Expr.__new__(cls, *args)
...
...     def _eval_expand_double(self, *, force=False, **hints):
...         '''
...         Doubles the args of MyClass.
...
...         If there more than four args, doubling is not performed,
...         unless force=True is also used (False by default).
...         '''
...         if not force and len(self.args) > 4:
...             return self
...         return self.func(*(self.args + self.args))
...
>>> a = MyClass(1, 2, MyClass(3, 4))
>>> a
MyClass(1, 2, MyClass(3, 4))
>>> a.expand(double=True)
MyClass(1, 2, MyClass(3, 4, 3, 4), 1, 2, MyClass(3, 4, 3, 4))
>>> a.expand(double=True, deep=False)
MyClass(1, 2, MyClass(3, 4), 1, 2, MyClass(3, 4))

>>> b = MyClass(1, 2, 3, 4, 5)
>>> b.expand(double=True)
MyClass(1, 2, 3, 4, 5)
>>> b.expand(double=True, force=True)
MyClass(1, 2, 3, 4, 5, 1, 2, 3, 4, 5)

class sympy.core.function.PoleError[source]¶

sympy.core.function.count_ops(expr, visual=False)[source]¶

Return a representation (integer or expression) of the operations in expr.

Parameters:

expr : Expr

If expr is an iterable, the sum of the op counts of the items will be returned.

visual : bool, optional

If False (default) then the sum of the coefficients of the visual expression will be returned. If True then the number of each type of operation is shown with the core class types (or their virtual equivalent) multiplied by the number of times they occur.

Examples

>>> from sympy.abc import a, b, x, y
>>> from sympy import sin, count_ops

Although there is not a SUB object, minus signs are interpreted as either negations or subtractions:

>>> (x - y).count_ops(visual=True)
SUB
>>> (-x).count_ops(visual=True)
NEG

Here, there are two Adds and a Pow:

>>> (1 + a + b**2).count_ops(visual=True)
2*ADD + POW

In the following, an Add, Mul, Pow and two functions:

>>> (sin(x)*x + sin(x)**2).count_ops(visual=True)
ADD + MUL + POW + 2*SIN

for a total of 5:

>>> (sin(x)*x + sin(x)**2).count_ops(visual=False)
5

Note that “what you type” is not always what you get. The expression 1/x/y is translated by sympy into 1/(x*y) so it gives a DIV and MUL rather than two DIVs:

>>> (1/x/y).count_ops(visual=True)
DIV + MUL

The visual option can be used to demonstrate the difference in operations for expressions in different forms. Here, the Horner representation is compared with the expanded form of a polynomial:

>>> eq=x*(1 + x*(2 + x*(3 + x)))
>>> count_ops(eq.expand(), visual=True) - count_ops(eq, visual=True)
-MUL + 3*POW

The count_ops function also handles iterables:

>>> count_ops([x, sin(x), None, True, x + 2], visual=False)
2
>>> count_ops([x, sin(x), None, True, x + 2], visual=True)
ADD + SIN
>>> count_ops({x: sin(x), x + 2: y + 1}, visual=True)
2*ADD + SIN

sympy.core.function.expand_mul(expr, deep=True)[source]¶

Wrapper around expand that only uses the mul hint. See the expand docstring for more information.

Examples

>>> from sympy import symbols, expand_mul, exp, log
>>> x, y = symbols('x,y', positive=True)
>>> expand_mul(exp(x+y)*(x+y)*log(x*y**2))
x*exp(x + y)*log(x*y**2) + y*exp(x + y)*log(x*y**2)

sympy.core.function.expand_log( expr, deep=True, force=False, factor=False, )[source]¶

Wrapper around expand that only uses the log hint. See the expand docstring for more information.

Examples

>>> from sympy import symbols, expand_log, exp, log
>>> x, y = symbols('x,y', positive=True)
>>> expand_log(exp(x+y)*(x+y)*log(x*y**2))
(x + y)*(log(x) + 2*log(y))*exp(x + y)

sympy.core.function.expand_func(expr, deep=True)[source]¶

Wrapper around expand that only uses the func hint. See the expand docstring for more information.

Examples

>>> from sympy import expand_func, gamma
>>> from sympy.abc import x
>>> expand_func(gamma(x + 2))
x*(x + 1)*gamma(x)

sympy.core.function.expand_trig(expr, deep=True)[source]¶

Wrapper around expand that only uses the trig hint. See the expand docstring for more information.

Examples

>>> from sympy import expand_trig, sin
>>> from sympy.abc import x, y
>>> expand_trig(sin(x+y)*(x+y))
(x + y)*(sin(x)*cos(y) + sin(y)*cos(x))

sympy.core.function.expand_complex(expr, deep=True)[source]¶

Wrapper around expand that only uses the complex hint. See the expand docstring for more information.

Examples

>>> from sympy import expand_complex, exp, sqrt, I
>>> from sympy.abc import z
>>> expand_complex(exp(z))
I*exp(re(z))*sin(im(z)) + exp(re(z))*cos(im(z))
>>> expand_complex(sqrt(I))
sqrt(2)/2 + sqrt(2)*I/2

See also

sympy.core.expr.Expr.as_real_imag

sympy.core.function.expand_multinomial(expr, deep=True)[source]¶

Wrapper around expand that only uses the multinomial hint. See the expand docstring for more information.

Examples

>>> from sympy import symbols, expand_multinomial, exp
>>> x, y = symbols('x y', positive=True)
>>> expand_multinomial((x + exp(x + 1))**2)
x**2 + 2*x*exp(x + 1) + exp(2*x + 2)

sympy.core.function.expand_power_exp(expr, deep=True)[source]¶

Wrapper around expand that only uses the power_exp hint.

See the expand docstring for more information.

Examples

>>> from sympy import expand_power_exp, Symbol
>>> from sympy.abc import x, y
>>> expand_power_exp(3**(y + 2))
9*3**y
>>> expand_power_exp(x**(y + 2))
x**(y + 2)

If x = 0 the value of the expression depends on the value of y; if the expression were expanded the result would be 0. So expansion is only done if x != 0:

>>> expand_power_exp(Symbol('x', zero=False)**(y + 2))
x**2*x**y

sympy.core.function.expand_power_base(expr, deep=True, force=False)[source]¶

Wrapper around expand that only uses the power_base hint.

A wrapper to expand(power_base=True) which separates a power with a base that is a Mul into a product of powers, without performing any other expansions, provided that assumptions about the power’s base and exponent allow.

deep=False (default is True) will only apply to the top-level expression.

force=True (default is False) will cause the expansion to ignore assumptions about the base and exponent. When False, the expansion will only happen if the base is non-negative or the exponent is an integer.

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

>>> (x*y)**2
x**2*y**2

>>> (2*x)**y
(2*x)**y
>>> expand_power_base(_)
2**y*x**y

>>> expand_power_base((x*y)**z)
(x*y)**z
>>> expand_power_base((x*y)**z, force=True)
x**z*y**z
>>> expand_power_base(sin((x*y)**z), deep=False)
sin((x*y)**z)
>>> expand_power_base(sin((x*y)**z), force=True)
sin(x**z*y**z)

>>> expand_power_base((2*sin(x))**y + (2*cos(x))**y)
2**y*sin(x)**y + 2**y*cos(x)**y

>>> expand_power_base((2*exp(y))**x)
2**x*exp(y)**x

>>> expand_power_base((2*cos(x))**y)
2**y*cos(x)**y

Notice that sums are left untouched. If this is not the desired behavior, apply full expand() to the expression:

>>> expand_power_base(((x+y)*z)**2)
z**2*(x + y)**2
>>> (((x+y)*z)**2).expand()
x**2*z**2 + 2*x*y*z**2 + y**2*z**2

>>> expand_power_base((2*y)**(1+z))
2**(z + 1)*y**(z + 1)
>>> ((2*y)**(1+z)).expand()
2*2**z*y**(z + 1)

The power that is unexpanded can be expanded safely when y != 0, otherwise different values might be obtained for the expression:

>>> prev = _

If we indicate that y is positive but then replace it with a value of 0 after expansion, the expression becomes 0:

>>> p = Symbol('p', positive=True)
>>> prev.subs(y, p).expand().subs(p, 0)
0

But if z = -1 the expression would not be zero:

>>> prev.subs(y, 0).subs(z, -1)
1

See also

expand

sympy.core.function.nfloat(expr, n=15, exponent=False, dkeys=False)[source]¶

Make all Rationals in expr Floats except those in exponents (unless the exponents flag is set to True) and those in undefined functions. When processing dictionaries, do not modify the keys unless dkeys=True.

Examples

>>> from sympy import nfloat, cos, pi, sqrt
>>> from sympy.abc import x, y
>>> nfloat(x**4 + x/2 + cos(pi/3) + 1 + sqrt(y))
x**4 + 0.5*x + sqrt(y) + 1.5
>>> nfloat(x**4 + sqrt(y), exponent=True)
x**4.0 + y**0.5

Container types are not modified:

>>> type(nfloat((1, 2))) is tuple
True

evalf¶

class sympy.core.evalf.EvalfMixin[source]¶

Mixin class adding evalf capability.

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

Evaluate the given formula to an accuracy of n digits.

Parameters:

subs : dict, optional

Substitute numerical values for symbols, e.g. subs={x:3, y:1+pi}. The substitutions must be given as a dictionary.

maxn : int, optional

Allow a maximum temporary working precision of maxn digits.

chop : bool or number, optional

Specifies how to replace tiny real or imaginary parts in subresults by exact zeros.

When True the chop value defaults to standard precision.

Otherwise the chop value is used to determine the magnitude of “small” for purposes of chopping.
>>> from sympy import N
>>> x = 1e-4
>>> N(x, chop=True)
0.000100000000000000
>>> N(x, chop=1e-5)
0.000100000000000000
>>> N(x, chop=1e-4)
0

strict : bool, optional

Raise PrecisionExhausted if any subresult fails to evaluate to full accuracy, given the available maxprec.

quad : str, optional

Choose algorithm for numerical quadrature. By default, tanh-sinh quadrature is used. For oscillatory integrals on an infinite interval, try quad='osc'.

verbose : bool, optional

Print debug information.

Notes

When Floats are naively substituted into an expression, precision errors may adversely affect the result. For example, adding 1e16 (a Float) to 1 will truncate to 1e16; if 1e16 is then subtracted, the result will be 0. That is exactly what happens in the following:

>>> from sympy.abc import x, y, z
>>> values = {x: 1e16, y: 1, z: 1e16}
>>> (x + y - z).subs(values)
0

Using the subs argument for evalf is the accurate way to evaluate such an expression:

>>> (x + y - z).evalf(subs=values)
1.00000000000000

n( n=15, subs=None, maxn=100, chop=False, strict=False, quad=None, verbose=False, )[source]¶

Evaluate the given formula to an accuracy of n digits.

Parameters:

subs : dict, optional

Substitute numerical values for symbols, e.g. subs={x:3, y:1+pi}. The substitutions must be given as a dictionary.

maxn : int, optional

Allow a maximum temporary working precision of maxn digits.

chop : bool or number, optional

Specifies how to replace tiny real or imaginary parts in subresults by exact zeros.

When True the chop value defaults to standard precision.

Otherwise the chop value is used to determine the magnitude of “small” for purposes of chopping.
>>> from sympy import N
>>> x = 1e-4
>>> N(x, chop=True)
0.000100000000000000
>>> N(x, chop=1e-5)
0.000100000000000000
>>> N(x, chop=1e-4)
0

strict : bool, optional

Raise PrecisionExhausted if any subresult fails to evaluate to full accuracy, given the available maxprec.

quad : str, optional

Choose algorithm for numerical quadrature. By default, tanh-sinh quadrature is used. For oscillatory integrals on an infinite interval, try quad='osc'.

verbose : bool, optional

Print debug information.

Notes

>>> from sympy.abc import x, y, z
>>> values = {x: 1e16, y: 1, z: 1e16}
>>> (x + y - z).subs(values)
0

Using the subs argument for evalf is the accurate way to evaluate such an expression:

>>> (x + y - z).evalf(subs=values)
1.00000000000000

class sympy.core.evalf.PrecisionExhausted[source]¶

sympy.core.evalf.N(x, n=15, **options)[source]¶

Calls x.evalf(n, **options).

Explanations

Both .n() and N() are equivalent to .evalf(); use the one that you like better. See also the docstring of .evalf() for information on the options.

Examples

>>> from sympy import Sum, oo, N
>>> from sympy.abc import k
>>> Sum(1/k**k, (k, 1, oo))
Sum(k**(-k), (k, 1, oo))
>>> N(_, 4)
1.291

containers¶

class sympy.core.containers.Tuple(*args, **kwargs)[source]¶

Wrapper around the builtin tuple object.

Parameters:: sympify : bool

If False, sympify is not called on args. This can be used for speedups for very large tuples where the elements are known to already be SymPy objects.

Explanation

The Tuple is a subclass of Basic, so that it works well in the SymPy framework. The wrapped tuple is available as self.args, but you can also access elements or slices with [:] syntax.

Examples

>>> from sympy import Tuple, symbols
>>> a, b, c, d = symbols('a b c d')
>>> Tuple(a, b, c)[1:]
(b, c)
>>> Tuple(a, b, c).subs(a, d)
(d, b, c)

index(value, start=None, stop=None)[source]¶: Searches and returns the first index of the value.

property kind¶

The kind of a Tuple instance.

The kind of a Tuple is always of TupleKind but parametrised by the number of elements and the kind of each element.

Examples

>>> from sympy import Tuple, Matrix
>>> Tuple(1, 2).kind
TupleKind(NumberKind, NumberKind)
>>> Tuple(Matrix([1, 2]), 1).kind
TupleKind(MatrixKind(NumberKind), NumberKind)
>>> Tuple(1, 2).kind.element_kind
(NumberKind, NumberKind)

tuple_count(value) → int[source]¶: Return number of occurrences of value.

class sympy.core.containers.TupleKind(*args)[source]¶

TupleKind is a subclass of Kind, which is used to define Kind of Tuple.

Parameters of TupleKind will be kinds of all the arguments in Tuples, for example

Parameters:: args : tuple(element_kind)

element_kind is kind of element. args is tuple of kinds of element

Examples

>>> from sympy import Tuple
>>> Tuple(1, 2).kind
TupleKind(NumberKind, NumberKind)
>>> Tuple(1, 2).kind.element_kind
(NumberKind, NumberKind)

class sympy.core.containers.Dict(*args)[source]¶

Wrapper around the builtin dict object.

Explanation

The Dict is a subclass of Basic, so that it works well in the SymPy framework. Because it is immutable, it may be included in sets, but its values must all be given at instantiation and cannot be changed afterwards. Otherwise it behaves identically to the Python dict.

Examples

>>> from sympy import Dict, Symbol

>>> D = Dict({1: 'one', 2: 'two'})
>>> for key in D:
...    if key == 1:
...        print('%s %s' % (key, D[key]))
1 one

The args are sympified so the 1 and 2 are Integers and the values are Symbols. Queries automatically sympify args so the following work:

>>> 1 in D
True
>>> D.has(Symbol('one')) # searches keys and values
True
>>> 'one' in D # not in the keys
False
>>> D[1]
one

get(key, default=None)[source]¶: Returns the value for key if the key is in the dictionary.

items()[source]¶: Returns a set-like object providing a view on dict’s items.

keys()[source]¶: Returns the list of the dict’s keys.

values()[source]¶: Returns the list of the dict’s values.

exprtools¶

sympy.core.exprtools.gcd_terms( terms, isprimitive=False, clear=True, fraction=True, )[source]¶

Compute the GCD of terms and put them together.

Parameters:

terms : Expr

Can be an expression or a non-Basic sequence of expressions which will be handled as though they are terms from a sum.

isprimitive : bool, optional

If isprimitive is True the _gcd_terms will not run the primitive method on the terms.

clear : bool, optional

It controls the removal of integers from the denominator of an Add expression. When True (default), all numerical denominator will be cleared; when False the denominators will be cleared only if all terms had numerical denominators other than 1.

fraction : bool, optional

When True (default), will put the expression over a common denominator.

Examples

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

>>> gcd_terms((x + 1)**2*y + (x + 1)*y**2)
y*(x + 1)*(x + y + 1)
>>> gcd_terms(x/2 + 1)
(x + 2)/2
>>> gcd_terms(x/2 + 1, clear=False)
x/2 + 1
>>> gcd_terms(x/2 + y/2, clear=False)
(x + y)/2
>>> gcd_terms(x/2 + 1/x)
(x**2 + 2)/(2*x)
>>> gcd_terms(x/2 + 1/x, fraction=False)
(x + 2/x)/2
>>> gcd_terms(x/2 + 1/x, fraction=False, clear=False)
x/2 + 1/x

>>> gcd_terms(x/2/y + 1/x/y)
(x**2 + 2)/(2*x*y)
>>> gcd_terms(x/2/y + 1/x/y, clear=False)
(x**2/2 + 1)/(x*y)
>>> gcd_terms(x/2/y + 1/x/y, clear=False, fraction=False)
(x/2 + 1/x)/y

The clear flag was ignored in this case because the returned expression was a rational expression, not a simple sum.

sympy.core.exprtools.factor_terms( expr: Expr | complex, radical=False, clear=False, fraction=False, sign=True, ) → Expr[source]¶

Remove common factors from terms in all arguments without changing the underlying structure of the expr. No expansion or simplification (and no processing of non-commutatives) is performed.

Parameters:

radical: bool, optional

If radical=True then a radical common to all terms will be factored out of any Add sub-expressions of the expr.

clear : bool, optional

If clear=False (default) then coefficients will not be separated from a single Add if they can be distributed to leave one or more terms with integer coefficients.

fraction : bool, optional

If fraction=True (default is False) then a common denominator will be constructed for the expression.

sign : bool, optional

If sign=True (default) then even if the only factor in common is a -1, it will be factored out of the expression.

Examples

>>> from sympy import factor_terms, Symbol
>>> from sympy.abc import x, y
>>> factor_terms(x + x*(2 + 4*y)**3)
x*(8*(2*y + 1)**3 + 1)
>>> A = Symbol('A', commutative=False)
>>> factor_terms(x*A + x*A + x*y*A)
x*(y*A + 2*A)

When clear is False, a rational will only be factored out of an Add expression if all terms of the Add have coefficients that are fractions:

>>> factor_terms(x/2 + 1, clear=False)
x/2 + 1
>>> factor_terms(x/2 + 1, clear=True)
(x + 2)/2

If a -1 is all that can be factored out, to not factor it out, the flag sign must be False:

>>> factor_terms(-x - y)
-(x + y)
>>> factor_terms(-x - y, sign=False)
-x - y
>>> factor_terms(-2*x - 2*y, sign=False)
-2*(x + y)

kind¶

class sympy.core.kind.Kind(*args)[source]¶

Base class for kinds.

Kind of the object represents the mathematical classification that the entity falls into. It is expected that functions and classes recognize and filter the argument by its kind.

Kind of every object must be carefully selected so that it shows the intention of design. Expressions may have different kind according to the kind of its arguments. For example, arguments of Add must have common kind since addition is group operator, and the resulting Add() has the same kind.

For the performance, each kind is as broad as possible and is not based on set theory. For example, NumberKind includes not only complex number but expression containing S.Infinity or S.NaN which are not strictly number.

Kind may have arguments as parameter. For example, MatrixKind() may be constructed with one element which represents the kind of its elements.

Kind behaves in singleton-like fashion. Same signature will return the same object.

sympy.core.kind.NumberKind¶: alias of NumberKind

sympy.core.kind.UndefinedKind¶: alias of UndefinedKind

sympy.core.kind.BooleanKind¶: alias of BooleanKind

Sorting¶

sympy.core.sorting.default_sort_key(item, order=None)[source]¶

Return a key that can be used for sorting.

The key has the structure:

(class_key, (len(args), args), exponent.sort_key(), coefficient)

This key is supplied by the sort_key routine of Basic objects when item is a Basic object or an object (other than a string) that sympifies to a Basic object. Otherwise, this function produces the key.

The order argument is passed along to the sort_key routine and is used to determine how the terms within an expression are ordered. (See examples below) order options are: ‘lex’, ‘grlex’, ‘grevlex’, and reversed values of the same (e.g. ‘rev-lex’). The default order value is None (which translates to ‘lex’).

Examples

>>> from sympy import S, I, default_sort_key, sin, cos, sqrt
>>> from sympy.core.function import UndefinedFunction
>>> from sympy.abc import x

The following are equivalent ways of getting the key for an object:

>>> x.sort_key() == default_sort_key(x)
True

Here are some examples of the key that is produced:

>>> default_sort_key(UndefinedFunction('f'))
((0, 0, 'UndefinedFunction'), (1, ('f',)), ((1, 0, 'Number'),
    (0, ()), (), 1), 1)
>>> default_sort_key('1')
((0, 0, 'str'), (1, ('1',)), ((1, 0, 'Number'), (0, ()), (), 1), 1)
>>> default_sort_key(S.One)
((1, 0, 'Number'), (0, ()), (), 1)
>>> default_sort_key(2)
((1, 0, 'Number'), (0, ()), (), 2)

While sort_key is a method only defined for SymPy objects, default_sort_key will accept anything as an argument so it is more robust as a sorting key. For the following, using key= lambda i: i.sort_key() would fail because 2 does not have a sort_key method; that’s why default_sort_key is used. Note, that it also handles sympification of non-string items likes ints:

>>> a = [2, I, -I]
>>> sorted(a, key=default_sort_key)
[2, -I, I]

The returned key can be used anywhere that a key can be specified for a function, e.g. sort, min, max, etc…:

>>> a.sort(key=default_sort_key); a[0]
2
>>> min(a, key=default_sort_key)
2

Notes

The key returned is useful for getting items into a canonical order that will be the same across platforms. It is not directly useful for sorting lists of expressions:

>>> a, b = x, 1/x

Since a has only 1 term, its value of sort_key is unaffected by order:

>>> a.sort_key() == a.sort_key('rev-lex')
True

If a and b are combined then the key will differ because there are terms that can be ordered:

>>> eq = a + b
>>> eq.sort_key() == eq.sort_key('rev-lex')
False
>>> eq.as_ordered_terms()
[x, 1/x]
>>> eq.as_ordered_terms('rev-lex')
[1/x, x]

But since the keys for each of these terms are independent of order’s value, they do not sort differently when they appear separately in a list:

>>> sorted(eq.args, key=default_sort_key)
[1/x, x]
>>> sorted(eq.args, key=lambda i: default_sort_key(i, order='rev-lex'))
[1/x, x]

The order of terms obtained when using these keys is the order that would be obtained if those terms were factors in a product.

Although it is useful for quickly putting expressions in canonical order, it does not sort expressions based on their complexity defined by the number of operations, power of variables and others:

>>> sorted([sin(x)*cos(x), sin(x)], key=default_sort_key)
[sin(x)*cos(x), sin(x)]
>>> sorted([x, x**2, sqrt(x), x**3], key=default_sort_key)
[sqrt(x), x, x**2, x**3]

sympy.core.sorting.ordered(seq, keys=None, default=True, warn=False)[source]¶

Return an iterator of the seq where keys are used to break ties in a conservative fashion: if, after applying a key, there are no ties then no other keys will be computed.

Two default keys will be applied if 1) keys are not provided or 2) the given keys do not resolve all ties (but only if default is True). The two keys are _nodes (which places smaller expressions before large) and default_sort_key which (if the sort_key for an object is defined properly) should resolve any ties. This strategy is similar to sorting done by Basic.compare, but differs in that ordered never makes a decision based on an objects name.

If warn is True then an error will be raised if there were no keys remaining to break ties. This can be used if it was expected that there should be no ties between items that are not identical.

Examples

>>> from sympy import ordered, count_ops
>>> from sympy.abc import x, y

The count_ops is not sufficient to break ties in this list and the first two items appear in their original order (i.e. the sorting is stable):

>>> list(ordered([y + 2, x + 2, x**2 + y + 3],
...    count_ops, default=False, warn=False))
...
[y + 2, x + 2, x**2 + y + 3]

The default_sort_key allows the tie to be broken:

>>> list(ordered([y + 2, x + 2, x**2 + y + 3]))
...
[x + 2, y + 2, x**2 + y + 3]

Here, sequences are sorted by length, then sum:

>>> seq, keys = [[[1, 2, 1], [0, 3, 1], [1, 1, 3], [2], [1]], [
...    lambda x: len(x),
...    lambda x: sum(x)]]
...
>>> list(ordered(seq, keys, default=False, warn=False))
[[1], [2], [1, 2, 1], [0, 3, 1], [1, 1, 3]]

If warn is True, an error will be raised if there were not enough keys to break ties:

>>> list(ordered(seq, keys, default=False, warn=True))
Traceback (most recent call last):
...
ValueError: not enough keys to break ties

Notes

The decorated sort is one of the fastest ways to sort a sequence for which special item comparison is desired: the sequence is decorated, sorted on the basis of the decoration (e.g. making all letters lower case) and then undecorated. If one wants to break ties for items that have the same decorated value, a second key can be used. But if the second key is expensive to compute then it is inefficient to decorate all items with both keys: only those items having identical first key values need to be decorated. This function applies keys successively only when needed to break ties. By yielding an iterator, use of the tie-breaker is delayed as long as possible.

This function is best used in cases when use of the first key is expected to be a good hashing function; if there are no unique hashes from application of a key, then that key should not have been used. The exception, however, is that even if there are many collisions, if the first group is small and one does not need to process all items in the list then time will not be wasted sorting what one was not interested in. For example, if one were looking for the minimum in a list and there were several criteria used to define the sort order, then this function would be good at returning that quickly if the first group of candidates is small relative to the number of items being processed.

Random¶

When you need to use random numbers in SymPy library code, import from here so there is only one generator working for SymPy. Imports from here should behave the same as if they were being imported from Python’s random module. But only the routines currently used in SymPy are included here. To use others import rng and access the method directly. For example, to capture the current state of the generator use rng.getstate().

There is intentionally no Random to import from here. If you want to control the state of the generator, import seed and call it with or without an argument to set the state.

Examples¶

>>> from sympy.core.random import random, seed
>>> assert random() < 1
>>> seed(1); a = random()
>>> b = random()
>>> seed(1); c = random()
>>> assert a == c
>>> assert a != b  # remote possibility this will fail

sympy.core.random.random_complex_number( a=2, b=-1, c=3, d=1, rational=False, tolerance=None, )[source]¶

Return a random complex number.

To reduce chance of hitting branch cuts or anything, we guarantee b <= Im z <= d, a <= Re z <= c

When rational is True, a rational approximation to a random number is obtained within specified tolerance, if any.

sympy.core.random.verify_numerically( f, g, z=None, tol=1e-06, a=2, b=-1, c=3, d=1, )[source]¶

Test numerically that f and g agree when evaluated in the argument z.

If z is None, all symbols will be tested. This routine does not test whether there are Floats present with precision higher than 15 digits so if there are, your results may not be what you expect due to round- off errors.

Examples

>>> from sympy import sin, cos
>>> from sympy.abc import x
>>> from sympy.core.random import verify_numerically as tn
>>> tn(sin(x)**2 + cos(x)**2, 1, x)
True

sympy.core.random.test_derivative_numerically( f, z, tol=1e-06, a=2, b=-1, c=3, d=1, )[source]¶

Test numerically that the symbolically computed derivative of f with respect to z is correct.

This routine does not test whether there are Floats present with precision higher than 15 digits so if there are, your results may not be what you expect due to round-off errors.

Examples

>>> from sympy import sin
>>> from sympy.abc import x
>>> from sympy.core.random import test_derivative_numerically as td
>>> td(sin(x), x)
True

sympy.core.random._randrange(seed=None)[source]¶

Return a randrange generator.

seed can be

None - return randomly seeded generator
int - return a generator seeded with the int
list - the values to be returned will be taken from the list in the order given; the provided list is not modified.

Examples

>>> from sympy.core.random import _randrange
>>> rr = _randrange()
>>> rr(1000) 
999
>>> rr = _randrange(3)
>>> rr(1000) 
238
>>> rr = _randrange([0, 5, 1, 3, 4])
>>> rr(3), rr(3)
(0, 1)

sympy.core.random._randint(seed=None)[source]¶

Return a randint generator.

seed can be

None - return randomly seeded generator
int - return a generator seeded with the int
list - the values to be returned will be taken from the list in the order given; the provided list is not modified.

Examples

>>> from sympy.core.random import _randint
>>> ri = _randint()
>>> ri(1, 1000) 
999
>>> ri = _randint(3)
>>> ri(1, 1000) 
238
>>> ri = _randint([0, 5, 1, 2, 4])
>>> ri(1, 3), ri(1, 3)
(1, 2)

Traversal¶

sympy.core.traversal.bottom_up(rv, F, atoms=False, nonbasic=False)[source]¶: Apply F to all expressions in an expression tree from the bottom up. If atoms is True, apply F even if there are no args; if nonbasic is True, try to apply F to non-Basic objects.

sympy.core.traversal.postorder_traversal(node, keys=None)[source]¶

Do a postorder traversal of a tree.

This generator recursively yields nodes that it has visited in a postorder fashion. That is, it descends through the tree depth-first to yield all of a node’s children’s postorder traversal before yielding the node itself.

Parameters:

node : SymPy expression

The expression to traverse.

keys : (default None) sort key(s)

The key(s) used to sort args of Basic objects. When None, args of Basic objects are processed in arbitrary order. If key is defined, it will be passed along to ordered() as the only key(s) to use to sort the arguments; if key is simply True then the default keys of ordered will be used (node count and default_sort_key).

Yields:

subtree : SymPy expression

All of the subtrees in the tree.

Examples

>>> from sympy import postorder_traversal
>>> from sympy.abc import w, x, y, z

The nodes are returned in the order that they are encountered unless key is given; simply passing key=True will guarantee that the traversal is unique.

>>> list(postorder_traversal(w + (x + y)*z)) 
[z, y, x, x + y, z*(x + y), w, w + z*(x + y)]
>>> list(postorder_traversal(w + (x + y)*z, keys=True))
[w, z, x, y, x + y, z*(x + y), w + z*(x + y)]

sympy.core.traversal.preorder_traversal(node, keys=None)[source]¶

Do a pre-order traversal of a tree.

This iterator recursively yields nodes that it has visited in a pre-order fashion. That is, it yields the current node then descends through the tree breadth-first to yield all of a node’s children’s pre-order traversal.

For an expression, the order of the traversal depends on the order of .args, which in many cases can be arbitrary.

Parameters:

node : SymPy expression

The expression to traverse.

keys : (default None) sort key(s)

The key(s) used to sort args of Basic objects. When None, args of Basic objects are processed in arbitrary order. If key is defined, it will be passed along to ordered() as the only key(s) to use to sort the arguments; if key is simply True then the default keys of ordered will be used.

Yields:

subtree : SymPy expression

All of the subtrees in the tree.

Examples

>>> from sympy import preorder_traversal, symbols
>>> x, y, z = symbols('x y z')

The nodes are returned in the order that they are encountered unless key is given; simply passing key=True will guarantee that the traversal is unique.

>>> list(preorder_traversal((x + y)*z, keys=None)) 
[z*(x + y), z, x + y, y, x]
>>> list(preorder_traversal((x + y)*z, keys=True))
[z*(x + y), z, x + y, x, y]

sympy.core.traversal.use(expr, func, level=0, args=(), kwargs={})[source]¶

Use func to transform expr at the given level.

Examples

>>> from sympy import use, expand
>>> from sympy.abc import x, y

>>> f = (x + y)**2*x + 1

>>> use(f, expand, level=2)
x*(x**2 + 2*x*y + y**2) + 1
>>> expand(f)
x**3 + 2*x**2*y + x*y**2 + 1

sympy.core.traversal.walk(e, *target)[source]¶

Iterate through the args that are the given types (target) and return a list of the args that were traversed; arguments that are not of the specified types are not traversed.

Examples

>>> from sympy.core.traversal import walk
>>> from sympy import Min, Max
>>> from sympy.abc import x, y, z
>>> list(walk(Min(x, Max(y, Min(1, z))), Min))
[Min(x, Max(y, Min(1, z)))]
>>> list(walk(Min(x, Max(y, Min(1, z))), Min, Max))
[Min(x, Max(y, Min(1, z))), Max(y, Min(1, z)), Min(1, z)]

See also

bottom_up

Core¶

sympify¶

assumptions¶

Examples¶

See Also¶

Notes¶

Developers Notes¶

References¶

cache¶

basic¶

singleton¶

expr¶

symbol¶

intfunc¶

numbers¶

power¶

mul¶

add¶

mod¶

relational¶

multidimensional¶

function¶

evalf¶

containers¶

exprtools¶

kind¶

Sorting¶

Random¶

Examples¶

Traversal¶

Attribute	Type/Meaning
`root`	`Expr` for \(\theta\) as a complex number
`minpoly`	`Poly`, the minimal polynomial of \(\theta\)
`rep`	`DMP` giving \(\alpha\) as poly in \(\theta\)
`alias`	`Symbol` for \(\theta\), or `None`

Signature Example	Math Equivalent
GreaterThan(lhs, rhs)	lhs \(\ge\) rhs
LessThan(lhs, rhs)	lhs \(\le\) rhs
StrictGreaterThan(lhs, rhs)	lhs \(>\) rhs
StrictLessThan(lhs, rhs)	lhs \(<\) rhs