smbinterp/interp/baker/__init__.py
2011-05-04 20:24:35 -06:00

225 lines
5.4 KiB
Python

import sys
import numpy as np
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):
B = [] # baker eq 9
w = [] # baker eq 11
p = pattern(order, len(phi), offset = -1)
log.info("pattern: %s" % p)
for (s,q) in zip(S.verts, S.q):
cur_phi, cur_qlin = qlinear(s, R)
l = []
for i in p:
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, p):
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
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 _boxings(n, k):
"""\
source for this function:
http://old.nabble.com/Simple-combinatorics-with-Numpy-td20086915.html
http://old.nabble.com/Re:-Simple-combinatorics-with-Numpy-p20099736.html
"""
seq, i = [n] * k + [0], k
while i:
yield tuple(seq[i] - seq[i+1] for i in xrange(k))
i = seq.index(0) - 1
seq[i:k] = [seq[i] - 1] * (k-i)
def _samples_ur(items, k, offset = 0):
"""Returns k unordered samples (with replacement) from items."""
n = len(items)
for sample in _boxings(k, n):
selections = [[items[i]]*count for i,count in enumerate(sample)]
yield tuple([x + offset for sel in selections for x in sel])
def memoize(f):
"""
I only cache on power and phicount; I figure that one should stick to a
particular offset throughout one's codebase.
"""
cache = {}
def memf(*x, **kargs):
if x not in cache:
cache[x] = f(*x, **kargs)
return cache[x]
return memf
@memoize
def pattern(power, phicount, offset = 0):
log.debug("(power = %s, phicount = %s)" % (power, phicount))
r = []
for i in _samples_ur(range(1, phicount + 1), power, offset):
if not len(set(i)) == 1:
r.append(i)
return r