numericalderivative.FunctionWithArguments

class numericalderivative.FunctionWithArguments(function, args=None)

Evaluate a function with extra arguments.

Parameters:

functionfunction

The function to differentiate.

argslist

A list of optional arguments that the function takes as inputs. By default, there is no extra argument and calling sequence of the function must be y = function(x). If there are extra arguments, then the calling sequence of the function must be y = function(x, arg1, arg2, ...) where arg1, arg2, ..., are the items in the args list.

verbosebool, optional

Set to True to print intermediate messages.

Methods

__call__(x)	Evaluates the function at point x
get_args()	Return the extra arguments
get_function()	Return the function
get_number_of_evaluations()	Returns the number of function evaluations.

Returns:

None.

See also

FiniteDifferenceFormula, SteplemanWinarsky, GillMurraySaundersWright, DumontetVignes

Examples

Define and evaluate a function with arguments.

>>> import numericalderivative as nd
>>>
>>> def scaled_exp_with_2_args(x, alpha, beta):
>>>     return beta * np.exp(-x / alpha)
>>>
>>> alpha = 1.e6
>>> beta = 2.0
>>> args = [alpha, beta]
>>> function = nd.FunctionWithArguments(
...     scaled_exp_with_2_args, args
>>> )
>>> y = function(x)
>>> for i in range(10):
>>>     y = function(x)
>>> counter = function.get_number_of_evaluations()

__init__(function, args=None)

Methods

__init__(function[, args])
get_args()	Return the extra arguments
get_function()	Return the function
get_number_of_evaluations()	Returns the number of function evaluations.

get_args()

Return the extra arguments

Returns:

argslist

The arguments

get_function()

Return the function

Returns:

functionfunction

The function

get_number_of_evaluations()

Returns the number of function evaluations.

Returns:

number_of_evaluationsint

The number of function evaluations.