import sys import numpy as np from functools import wraps import itertools import interp import logging log = logging.getLogger('interp') def get_phis(X, R): """ The get_phis function is used to get barycentric coordonites for a point on a triangle or tetrahedron: in 2D: X -- the destination point (2D) X = [0,0] r -- the three points that make up the containing triangular simplex (2D) r = [[-1, -1], [0, 2], [1, -1]] this will return [0.333, 0.333, 0.333] in 3D: X -- the destination point (3D) X = [0,0,0] R -- the four points that make up the containing simplex, tetrahedron (3D) R = [ [0.0, 0.0, 1.0], [0.94280904333606508, 0.0, -0.3333333283722672], [-0.47140452166803232, 0.81649658244673617, -0.3333333283722672], [-0.47140452166803298, -0.81649658244673584, -0.3333333283722672], ] this will return [0.25, 0.25, 0.25, 0.25] """ # baker: eq 7 # TODO: perhaps also test len(R[0]) .. ? if len(X) == 2: log.debug("running 2D") A = np.array([ [ 1, 1, 1], [R[0][0], R[1][0], R[2][0]], [R[0][1], R[1][1], R[2][1]], ]) b = np.array([ 1, X[0], X[1] ]) elif len(X) == 3: log.debug("running 3D") A = np.array([ [ 1, 1, 1, 1 ], [R[0][0], R[1][0], R[2][0], R[3][0]], [R[0][1], R[1][1], R[2][1], R[3][1]], [R[0][2], R[1][2], R[2][2], R[3][2]], ]) b = np.array([ 1, X[0], X[1], X[2] ]) else: raise Exception("inapropriate demension on X") try: phi = np.linalg.solve(A,b) except np.linalg.LinAlgError as e: msg = "calculation of phis yielded a linearly dependant system (%s)" % e log.error(msg) # raise Exception(msg) phi = np.dot(np.linalg.pinv(A), b) log.debug("phi: %s", phi) return phi def qlinear(X, R): """ this calculates the linear portion of q from R to X also, this is baker eq 3 X = destination point R = a inter.grid object; must have R.points and R.q """ phis = get_phis(X, R.verts) qlin = np.sum([q_i * phi_i for q_i, phi_i in zip(R.q, phis)]) log.debug("phis: %s", phis) log.debug("qlin: %s", qlin) return phis, qlin def get_error(phi, R, S, order = 2): #TODO: change the equation names in the comments B = [] # baker eq 9 w = [] # baker eq 11 cur_pattern = pattern(len(phi), order) log.info("pattern: %s" % cur_pattern) for (s,q) in zip(S.verts, S.q): cur_phi, cur_qlin = qlinear(s, R) l = [] for i in cur_pattern: cur_sum = cur_phi[i[0]] for j in i[1:]: cur_sum *= cur_phi[j] l.append(cur_sum) B.append(l) w.append(q - cur_qlin) log.info("B: %s" % B) log.info("w: %s" % w) B = np.array(B) w = np.array(w) A = np.dot(B.T, B) b = np.dot(B.T, w) # baker solve eq 10 try: abc = np.linalg.solve(A,b) except np.linalg.LinAlgError as e: log.error("linear calculation went bad, resorting to np.linalg.pinv: %s" % e) abc = np.dot(np.linalg.pinv(A), b) error_term = 0.0 for (a, i) in zip(abc, cur_pattern): cur_sum = a for j in i: cur_sum *= phi[j] error_term += cur_sum log.debug("error_term: %s" % error_term) return error_term, abc def run_baker(X, R, S, order=2): """ This is the main function to call to get an interpolation to X from the input meshes X -- the destination point (2D) X = [0,0] R = Simplex S = extra points """ log.debug("order = %d" % order) log.debug("extra points = %d" % len(S.verts)) answer = { 'qlin': None, 'error': None, 'final': None, } # calculate values only for the simplex triangle phi, qlin = qlinear(X, R) if order == 1: answer['qlin'] = qlin answer['final'] = qlin return answer elif order in xrange(2,11): error_term, abc = get_error(phi, R, S, order) else: raise Exception('unsupported order "%d" for baker method' % order) q_final = qlin + error_term answer['qlin' ] = qlin answer['error'] = error_term answer['final'] = q_final answer['abc' ] = abc log.debug(answer) return answer def memoize(f): """ for more information on what I'm doing here, please read: http://en.wikipedia.org/wiki/Memoize """ cache = {} @wraps(f) def memf(simplex_size, nu): x = (simplex_size, nu) if x not in cache: log.debug("adding to cache: %s", x) cache[x] = f(simplex_size, nu) return cache[x] return memf @memoize def pattern(simplex_size, nu): """ This function returns the pattern requisite to compose the error approximation function, and the matrix B. """ log.debug("pattern: simplex: %d, order: %d" % (simplex_size, nu)) r = [] for i in itertools.product(xrange(simplex_size), repeat = nu): if len(set(i)) !=1: r.append(tuple(sorted(i))) unique_r = list(set(r)) return unique_r if __name__ == '__main__': print len(pattern(3, 2)), pattern(3, 2) print len(pattern(4, 2)), pattern(4, 2) print len(pattern(3, 3)), pattern(3, 3) print len(pattern(4, 3)), pattern(4, 3) print len(pattern(3, 4)), pattern(3, 4) print len(pattern(4, 4)), pattern(4, 4) print len(pattern(3, 2)), pattern(3, 2) print len(pattern(4, 2)), pattern(4, 2) print len(pattern(3, 3)), pattern(3, 3) print len(pattern(4, 3)), pattern(4, 3) print len(pattern(3, 4)), pattern(3, 4) print len(pattern(4, 4)), pattern(4, 4) print len(pattern(3, 2)), pattern(3, 2) print len(pattern(4, 2)), pattern(4, 2) print len(pattern(3, 3)), pattern(3, 3) print len(pattern(4, 3)), pattern(4, 3) print len(pattern(3, 4)), pattern(3, 4) print len(pattern(4, 4)), pattern(4, 4) print len(pattern(3, 2)), pattern(3, 2) print len(pattern(4, 2)), pattern(4, 2) print len(pattern(3, 3)), pattern(3, 3) print len(pattern(4, 3)), pattern(4, 3) print len(pattern(3, 4)), pattern(3, 4) print len(pattern(4, 4)), pattern(4, 4)