Functions#

All functions support the methods documented below, inherited from sympy.core.function.Function.

class sympy.core.function.Function(*args)[source]

Base class for applied mathematical functions.

It also serves as a constructor for undefined function classes.

Examples

First example shows how to use Function as a constructor for undefined function classes:

>>> 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, and if function is initialized with a Symbol, the function inherits 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 variable it is called on. If you want to add a relationship, subclass Function and define the appropriate _eval_is_assumption methods.

In the following example Function is used as a base class for my_func that represents a mathematical function my_func. Suppose that it is well known, that my_func(0) is 1 and my_func at infinity goes to 0, so we want those two simplifications to occur automatically. Suppose also that my_func(x) is real exactly when x is real. Here is an implementation that honours those requirements:

>>> from sympy import Function, S, oo, I, sin
>>> class my_func(Function):
...
...     @classmethod
...     def eval(cls, x):
...         if x.is_Number:
...             if x.is_zero:
...                 return S.One
...             elif x is S.Infinity:
...                 return S.Zero
...
...     def _eval_is_real(self):
...         return self.args[0].is_real
...
>>> x = S('x')
>>> my_func(0) + sin(0)
1
>>> my_func(oo)
0
>>> my_func(3.54).n() # Not yet implemented for my_func.
my_func(3.54)
>>> my_func(I).is_real
False

In order for my_func to become useful, several other methods would need to be implemented. See source code of some of the already implemented functions for more complete examples.

Also, if the function can take more than one argument, then nargs must be defined, e.g. if my_func can take one or two arguments then,

>>> class my_func(Function):
...     nargs = (1, 2)
...
>>>

as_base_exp()[source]: Returns the method as the 2-tuple (base, exponent).

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.

Functions#

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