# Source code for sympy.calculus.finite_diff

"""
Finite difference weights
=========================

This module implements an algorithm for efficient generation of finite
difference weights for ordinary differentials of functions for
derivatives from 0 (interpolation) up to arbitrary order.

The core algorithm is provided in the finite difference weight generating
function (finite_diff_weights), and two convenience functions are provided
for:

- estimating a derivative (or interpolate) directly from a series of points
is also provided (apply_finite_diff).
- making a finite difference approximation of a Derivative instance
(as_finite_diff).

"""

from sympy import S
from sympy.core.compatibility import iterable

[docs]def finite_diff_weights(order, x_list, x0):
"""
Calculates the finite difference weights for an arbitrarily
spaced one-dimensional grid (x_list) for derivatives at 'x0'
of order 0, 1, ..., up to 'order' using a recursive formula.

Parameters
==========

order : int
Up to what derivative order weights should be calculated.
0 corresponds to interpolation.
x_list: sequence
Strictly monotonically increasing sequence of values for
the independent variable.
x0: Number or Symbol
At what value of the independent variable the finite difference
weights should be generated.

Returns
=======

list
A list of sublists, each corresponding to coefficients for
increasing derivative order, and each containing lists of
coefficients for increasing accuracy.

Examples
========

>>> from sympy import S
>>> from sympy.calculus import finite_diff_weights
>>> finite_diff_weights(1, [-S(1)/2, S(1)/2, S(3)/2, S(5)/2], 0)
[[[1, 0, 0, 0],
[1/2, 1/2, 0, 0],
[3/8, 3/4, -1/8, 0],
[5/16, 15/16, -5/16, 1/16]],
[[0, 0, 0, 0], [-1, 1, 0, 0], [-1, 1, 0, 0], [-23/24, 7/8, 1/8, -1/24]]]

the result is two subslists, the first is for the 0:th derivative
(interpolation) and the second for the first derivative (we gave
1 as the parameter of order so this is why we get no list for
a higher order derivative). Each sublist contains the most accurate
formula in the end (all points used).

Beware of the offset in the lower accuracy formulae when looking at a
centered difference:

>>> from sympy import S
>>> from sympy.calculus import finite_diff_weights
>>> finite_diff_weights(1, [-S(5)/2, -S(3)/2, -S(1)/2, S(1)/2,
...    S(3)/2, S(5)/2], 0) #doctest: +NORMALIZE_WHITESPACE
[[[1, 0, 0, 0, 0, 0],
[-3/2, 5/2, 0, 0, 0, 0],
[3/8, -5/4, 15/8, 0, 0, 0],
[1/16, -5/16, 15/16, 5/16, 0, 0],
[3/128, -5/32, 45/64, 15/32, -5/128, 0],
[3/256, -25/256, 75/128, 75/128, -25/256, 3/256]],
[[0, 0, 0, 0, 0, 0],
[-1, 1, 0, 0, 0, 0],
[1, -3, 2, 0, 0, 0],
[1/24, -1/8, -7/8, 23/24, 0, 0],
[0, 1/24, -9/8, 9/8, -1/24, 0],
[-3/640, 25/384, -75/64, 75/64, -25/384, 3/640]]]

The capability to generate weights at arbitrary points can be
used e.g. to minimize Runge's phenomenon by using Chebyshev nodes:

>>> from sympy import cos, symbols, pi, simplify
>>> from sympy.calculus import finite_diff_weights
>>> N, (h, x) = 4, symbols('h x')
>>> x_list = [x+h*cos(i*pi/(N)) for i in range(N,-1,-1)] # chebyshev nodes
>>> print(x_list)
[-h + x, -sqrt(2)*h/2 + x, x, sqrt(2)*h/2 + x, h + x]
>>> mycoeffs = finite_diff_weights(1, x_list, 0)[1][4]
>>> [simplify(c) for c in  mycoeffs] #doctest: +NORMALIZE_WHITESPACE
[(h**3/2 + h**2*x - 3*h*x**2 - 4*x**3)/h**4,
(-sqrt(2)*h**3 - 4*h**2*x + 3*sqrt(2)*h*x**2 + 8*x**3)/h**4,
6*x/h**2 - 8*x**3/h**4,
(sqrt(2)*h**3 - 4*h**2*x - 3*sqrt(2)*h*x**2 + 8*x**3)/h**4,
(-h**3/2 + h**2*x + 3*h*x**2 - 4*x**3)/h**4]

Notes
=====

If weights for a finite difference approximation
of the 3rd order derivative is wanted, weights for 0th, 1st
fewer and fewer of the parameters. This is something one can
take advantage of to save computational cost.

========

sympy.calculus.finite_diff.apply_finite_diff

References
==========

.. [1] Generation of Finite Difference Formulas on Arbitrarily Spaced
Grids, Bengt Fornberg; Mathematics of computation; 51; 184;
(1988); 699-706; doi:10.1090/S0025-5718-1988-0935077-0

"""
# The notation below closely corresponds to the one used in the paper.
if order < 0:
raise ValueError("Negative derivative order illegal.")
if int(order) != order:
raise ValueError("Non-integer order illegal")
M = order
N = len(x_list) - 1
delta = [[[0 for nu in range(N+1)] for n in range(N+1)] for
m in range(M+1)]
delta[0][0][0] = S(1)
c1 = S(1)
for n in range(1, N+1):
c2 = S(1)
for nu in range(0, n):
c3 = x_list[n]-x_list[nu]
c2 = c2 * c3
if n <= M:
delta[n][n-1][nu] = 0
for m in range(0, min(n, M)+1):
delta[m][n][nu] = (x_list[n]-x0)*delta[m][n-1][nu] -\
m*delta[m-1][n-1][nu]
delta[m][n][nu] /= c3
for m in range(0, min(n, M)+1):
delta[m][n][n] = c1/c2*(m*delta[m-1][n-1][n-1] -
(x_list[n-1]-x0)*delta[m][n-1][n-1])
c1 = c2
return delta

[docs]def apply_finite_diff(order, x_list, y_list, x0):
"""
Calculates the finite difference approximation of
the derivative of requested order at x0 from points
provided in x_list and y_list.

Parameters
==========

order: int
order of derivative to approximate. 0 corresponds to interpolation.
x_list: sequence
Strictly monotonically increasing sequence of values for
the independent variable.
y_list: sequence
The function value at corresponding values for the independent
variable in x_list.
x0: Number or Symbol
At what value of the independent variable the derivative should be
evaluated.

Returns
=======

The finite difference expression approximating the requested
derivative order at x0.

Examples
========

>>> from sympy.calculus import apply_finite_diff
>>> cube = lambda arg: (1.0*arg)**3
>>> xlist = range(-3,3+1)
>>> apply_finite_diff(2, xlist, map(cube, xlist), 2) - 12 # doctest: +SKIP
-3.55271367880050e-15

we see that the example above only contain rounding errors.
apply_finite_diff can also be used on more abstract objects:

>>> from sympy import IndexedBase, Idx
>>> from sympy.calculus import apply_finite_diff
>>> x, y = map(IndexedBase, 'xy')
>>> i = Idx('i')
>>> x_list, y_list = zip(*[(x[i+j], y[i+j]) for j in range(-1,2)])
>>> apply_finite_diff(1, x_list, y_list, x[i])
(-1 + (x[i + 1] - x[i])/(-x[i - 1] + x[i]))*y[i]/(x[i + 1] - x[i]) + \
(-x[i - 1] + x[i])*y[i + 1]/((-x[i - 1] + x[i + 1])*(x[i + 1] - x[i])) - \
(x[i + 1] - x[i])*y[i - 1]/((-x[i - 1] + x[i + 1])*(-x[i - 1] + x[i]))

Notes
=====

Order = 0 corresponds to interpolation.
Only supply so many points you think makes sense
to around x0 when extracting the derivative (the function
need to be well behaved within that region). Also beware
of Runge's phenomenon.

========

sympy.calculus.finite_diff.finite_diff_weights

References
==========

Fortran 90 implementation with Python interface for numerics: finitediff_

.. _finitediff: https://github.com/bjodah/finitediff

"""

# In the original paper the following holds for the notation:
# M = order
# N = len(x_list) - 1

N = len(x_list) - 1
if len(x_list) != len(y_list):
raise ValueError("x_list and y_list not equal in length.")

delta = finite_diff_weights(order, x_list, x0)

derivative = 0
for nu in range(0, len(x_list)):
derivative += delta[order][N][nu]*y_list[nu]
return derivative

[docs]def as_finite_diff(derivative, points=1, x0=None, wrt=None):
"""
Returns an approximation of a derivative of a function in
the form of a finite difference formula. The expression is a
weighted sum of the function at a number of discrete values of
(one of) the independent variable(s).

Parameters
==========

derivative: a Derivative instance (needs to have an variables
and expr attribute).

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, as_finite_diff
>>> x, h = symbols('x h')
>>> f = Function('f')
>>> as_finite_diff(f(x).diff(x))
-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:

>>> as_finite_diff(f(x).diff(x), h)
-f(-h/2 + x)/h + f(h/2 + x)/h

We can also specify the discretized values to be used in a sequence:

>>> as_finite_diff(f(x).diff(x), [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]
>>> as_finite_diff(f(x).diff(x, 1), xl, x+h*sq2)
2*h*((h + sqrt(2)*h)/(2*h) - (-sqrt(2)*h + h)/(2*h))*f(E*h + x)/\
((-h + E*h)*(h + E*h)) + (-(-sqrt(2)*h + h)/(2*h) - \
(-sqrt(2)*h + E*h)/(2*h))*f(-h + x)/(h + E*h) + \
(-(h + sqrt(2)*h)/(2*h) + (-sqrt(2)*h + E*h)/(2*h))*f(h + x)/(-h + E*h)

Partial derivatives are also supported:

>>> y = Symbol('y')
>>> d2fdxdy=f(x,y).diff(x,y)
>>> as_finite_diff(d2fdxdy, wrt=x)
-f(x - 1/2, y) + f(x + 1/2, y)

========

sympy.calculus.finite_diff.apply_finite_diff
sympy.calculus.finite_diff.finite_diff_weights

"""
if wrt is None:
wrt = derivative.variables[0]
# we need Derivative to be univariate to guess wrt
if any(v != wrt for v in derivative.variables):
raise ValueError('if the function is not univariate' +
' then wrt must be given')

order = derivative.variables.count(wrt)

if x0 is None:
x0 = wrt

if not iterable(points):
# points is simply the step-size, let's make it a
# equidistant sequence centered around x0
if order % 2 == 0:
# even order => odd number of points, grid point included
points = [x0 + points*i for i
in range(-order//2, order//2 + 1)]
else:
# odd order => even number of points, half-way wrt grid point
points = [x0 + points*i/S(2) for i
in range(-order, order + 1, 2)]

if len(points) < order+1:
raise ValueError("Too few points for order %d" % order)
return apply_finite_diff(order, points, [
derivative.expr.subs({wrt: x}) for x in points], x0)