Source code for sfepy.discrete.simplex_cubature

"""
by Andreas Kloeckner.
"""
from __future__ import division

from __future__ import absolute_import
import numpy as nm
import six
from six.moves import range
from functools import reduce

[docs]def generate_decreasing_nonnegative_tuples_summing_to(n, length, min=0, max=None): if length == 0: yield () elif length == 1: if n <= max: #print "MX", n, max yield (n,) else: return else: if max is None or n < max: max = n for i in range(min, max+1): #print "SIG", sig, i for remainder in generate_decreasing_nonnegative_tuples_summing_to( n-i, length-1, min, i): yield (i,) + remainder
[docs]def generate_permutations(original): """ Generate all permutations of the list original'. Nicked from http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/252178 """ if len(original) <=1: yield original else: for perm in generate_permutations(original[1:]): for i in range(len(perm)+1): #nb str[0:1] works in both string and list contexts yield perm[:i] + original[0:1] + perm[i:]
[docs]def generate_unique_permutations(original): """ Generate all unique permutations of the list original'. """ had_those = set() for perm in generate_permutations(original): if perm not in had_those: had_those.add(perm) yield perm
[docs]def wandering_element(length, wanderer=1, landscape=0): for i in range(length): yield i*(landscape,) + (wanderer,) + (length-1-i)*(landscape,)
[docs]def factorial(n): from operator import mul assert n == int(n) return reduce(mul, (i for i in range(1,n+1)), 1)
def _extended_euclidean(q, r): """ Return a tuple (p, a, b) such that p = aq + br, where p is the greatest common divisor. """ # see [Davenport], Appendix, p. 214 if abs(q) < abs(r): p, a, b = _extended_euclidean(r, q) return p, b, a Q = 1, 0 R = 0, 1 while r: quot, t = divmod(q, r) T = Q - quot*R, Q - quot*R q, r = r, t Q, R = R, T return q, Q, Q def _gcd(q, r): return _extended_euclidean(q, r) def _simplify_fraction(a_b): (a, b) = a_b gcd = _gcd(a,b) return (a//gcd, b//gcd)
[docs]def get_simplex_cubature(order, dimension): """ Cubature on an M{n}-simplex. cf. A. Grundmann and H.M. Moeller, Invariant integration formulas for the n-simplex by combinatorial methods, SIAM J. Numer. Anal. 15 (1978), 282--290. This cubature rule has both negative and positive weights. It is exact for polynomials up to order :math:2s+1, where :math:s is given as *order*. The integration domain is the unit simplex .. math:: T_n := \{(x_1, \dots, x_n): x_i \ge -1, \sum_i x_i \le -1\} """ s = order n = dimension d = exact_to = 2*s+1 points_to_weights = {} for i in range(s+1): weight = (-1)**i * 2**(-2*s) \ * (d + n-2*i)**d \ / factorial(i) \ / factorial(d+n-i) for t in generate_decreasing_nonnegative_tuples_summing_to(s-i, n+1): for beta in generate_unique_permutations(t): denominator = d+n-2*i point = tuple( _simplify_fraction((2*beta_i+1, denominator)) for beta_i in beta) points_to_weights[point] = points_to_weights.get(point, 0) \ + weight from operator import add vertices = [-1 * nm.ones((n,))] \ + [nm.array(x) for x in wandering_element(n, landscape=-1, wanderer=1)] pos_points = [] pos_weights = [] neg_points = [] neg_weights = [] dim_factor = 2**n for p, w in six.iteritems(points_to_weights): real_p = reduce(add, (a/b*v for (a,b),v in zip(p, vertices))) if w > 0: pos_points.append(real_p) pos_weights.append(dim_factor*w) else: neg_points.append(real_p) neg_weights.append(dim_factor*w) points = nm.array(pos_points + neg_points) weights = nm.array(pos_weights + neg_weights) return points, weights, exact_to