from __future__ import print_function, division
from sympy.core import S, sympify
from sympy.core.compatibility import range
from sympy.functions import Piecewise, piecewise_fold
from sympy.sets.sets import Interval
def _add_splines(c, b1, d, b2):
"""Construct c*b1 + d*b2."""
if b1 == S.Zero or c == S.Zero:
rv = piecewise_fold(d*b2)
elif b2 == S.Zero or d == S.Zero:
rv = piecewise_fold(c*b1)
else:
new_args = []
n_intervals = len(b1.args)
if n_intervals != len(b2.args):
raise ValueError("Args of b1 and b2 are not equal")
new_args.append((c*b1.args[0].expr, b1.args[0].cond))
for i in range(1, n_intervals - 1):
new_args.append((
c*b1.args[i].expr + d*b2.args[i - 1].expr,
b1.args[i].cond
))
new_args.append((d*b2.args[-2].expr, b2.args[-2].cond))
new_args.append(b2.args[-1])
rv = Piecewise(*new_args)
return rv.expand()
[docs]def bspline_basis(d, knots, n, x, close=True):
"""The `n`-th B-spline at `x` of degree `d` with knots.
B-Splines are piecewise polynomials of degree `d` [1]_. They are defined on
a set of knots, which is a sequence of integers or floats.
The 0th degree splines have a value of one on a single interval:
>>> from sympy import bspline_basis
>>> from sympy.abc import x
>>> d = 0
>>> knots = range(5)
>>> bspline_basis(d, knots, 0, x)
Piecewise((1, And(x <= 1, x >= 0)), (0, True))
For a given ``(d, knots)`` there are ``len(knots)-d-1`` B-splines defined, that
are indexed by ``n`` (starting at 0).
Here is an example of a cubic B-spline:
>>> bspline_basis(3, range(5), 0, x)
Piecewise((x**3/6, And(x < 1, x >= 0)),
(-x**3/2 + 2*x**2 - 2*x + 2/3, And(x < 2, x >= 1)),
(x**3/2 - 4*x**2 + 10*x - 22/3, And(x < 3, x >= 2)),
(-x**3/6 + 2*x**2 - 8*x + 32/3, And(x <= 4, x >= 3)),
(0, True))
By repeating knot points, you can introduce discontinuities in the
B-splines and their derivatives:
>>> d = 1
>>> knots = [0,0,2,3,4]
>>> bspline_basis(d, knots, 0, x)
Piecewise((-x/2 + 1, And(x <= 2, x >= 0)), (0, True))
It is quite time consuming to construct and evaluate B-splines. If you
need to evaluate a B-splines many times, it is best to lambdify them
first:
>>> from sympy import lambdify
>>> d = 3
>>> knots = range(10)
>>> b0 = bspline_basis(d, knots, 0, x)
>>> f = lambdify(x, b0)
>>> y = f(0.5)
See Also
========
bsplines_basis_set
References
==========
.. [1] http://en.wikipedia.org/wiki/B-spline
"""
knots = [sympify(k) for k in knots]
d = int(d)
n = int(n)
n_knots = len(knots)
n_intervals = n_knots - 1
if n + d + 1 > n_intervals:
raise ValueError('n + d + 1 must not exceed len(knots) - 1')
if d == 0:
result = Piecewise(
(S.One, Interval(knots[n], knots[n + 1], False,
not close).contains(x)),
(0, True)
)
elif d > 0:
denom = knots[n + d + 1] - knots[n + 1]
if denom != S.Zero:
B = (knots[n + d + 1] - x)/denom
b2 = bspline_basis(d - 1, knots, n + 1, x, close)
else:
b2 = B = S.Zero
denom = knots[n + d] - knots[n]
if denom != S.Zero:
A = (x - knots[n])/denom
b1 = bspline_basis(
d - 1, knots, n, x, close and (B == S.Zero or b2 == S.Zero))
else:
b1 = A = S.Zero
result = _add_splines(A, b1, B, b2)
else:
raise ValueError('degree must be non-negative: %r' % n)
return result
[docs]def bspline_basis_set(d, knots, x):
"""Return the ``len(knots)-d-1`` B-splines at ``x`` of degree ``d`` with ``knots``.
This function returns a list of Piecewise polynomials that are the
``len(knots)-d-1`` B-splines of degree ``d`` for the given knots. This function
calls ``bspline_basis(d, knots, n, x)`` for different values of ``n``.
Examples
========
>>> from sympy import bspline_basis_set
>>> from sympy.abc import x
>>> d = 2
>>> knots = range(5)
>>> splines = bspline_basis_set(d, knots, x)
>>> splines
[Piecewise((x**2/2, And(x < 1, x >= 0)),
(-x**2 + 3*x - 3/2, And(x < 2, x >= 1)),
(x**2/2 - 3*x + 9/2, And(x <= 3, x >= 2)),
(0, True)),
Piecewise((x**2/2 - x + 1/2, And(x < 2, x >= 1)),
(-x**2 + 5*x - 11/2, And(x < 3, x >= 2)),
(x**2/2 - 4*x + 8, And(x <= 4, x >= 3)),
(0, True))]
See Also
========
bsplines_basis
"""
n_splines = len(knots) - d - 1
return [bspline_basis(d, knots, i, x) for i in range(n_splines)]
