moved teh grid code into the grid class
by next friday i need to have implemented a way to select between normal kdtree point lookups, and nearest-neighbor lookups. Hopefully i'll have that done tonight.
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@ -4,9 +4,7 @@ import sys
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from optparse import OptionParser
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import numpy as np
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import scipy.spatial
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import baker
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from tools import rms, exact_func
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import grid
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@ -72,15 +70,13 @@ if __name__ == '__main__':
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help = "verbosity")
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(options, args) = parser.parse_args()
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print >> sys.stderr, "options: %s, args: %s" % (options, args)
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(mesh_source, mesh_dest) = get_mesh(options.source_total, options.destination_total, options.structured)
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tree = scipy.spatial.KDTree(mesh_source.points)
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open(options.output, 'w').write(mesh_source.for_qhull())
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if options.verbose:
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print >> sys.stderr, "options: %s, args: %s" % (options, args)
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print >> sys.stderr, "wrote source mesh output to %s" % options.output
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errors = []
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@ -88,20 +84,7 @@ if __name__ == '__main__':
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for X in mesh_dest.points:
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(dist, indicies) = tree.query(X, 3 + options.extra)
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# get the containing simplex
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R = [mesh_source.points[i] for i in indicies[:3] ]
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Rq = [mesh_source.q[i] for i in indicies[:3] ]
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r_mesh = grid.grid(R, Rq)
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# and some extra points
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S = [mesh_source.points[i] for i in indicies[3:] ]
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Sq = [mesh_source.q[i] for i in indicies[3:] ]
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s_mesh = grid.grid(S, Sq)
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answer = baker.run_baker(X, r_mesh, s_mesh, options.verbose)
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answer = mesh_source.run_baker(X)
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if answer['a'] == None:
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errors.append(0)
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@ -114,9 +97,9 @@ if __name__ == '__main__':
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if options.verbose:
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print "current point : %s" % X
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print "exact : %0.4f" % exact
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print "qlin : %0.4f" % answer['lin']
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print "qlin : %0.4f" % answer['qlin']
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print "q_final : %0.4f" % answer['final']
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print "qlinerr : %1.4f" % (exact - anser['lin'],)
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print "qlinerr : %1.4f" % (exact - answer['qlin'],)
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print "q_final_err : %0.4f" % (exact - answer['final'],)
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cur_error = np.abs(answer['final'] - exact)
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errors.append(cur_error)
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@ -1,4 +1,3 @@
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from grid import exact_func
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import numpy as np
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import sys
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@ -93,16 +92,16 @@ def qlinear_3D(X, R, q):
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qlin = sum([q_i * phi_i for q_i, phi_i in zip(q, phis)])
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return qlin
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def run_baker(X, R, S, verbose = False):
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def run_baker(X, R, S):
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"""
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This is the main function to call to get an interpolation to X from the tree
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This is the main function to call to get an interpolation to X from the input meshes
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X -- the destination point (2D)
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X = [0,0]
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g -- the grid object
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R = Simplex
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tree -- the kdtree search object (built from the g mesh)
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S = extra points
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"""
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28
lib/grid.py
28
lib/grid.py
@ -7,6 +7,7 @@ from collections import defaultdict
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import numpy as np
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import scipy.spatial
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from baker import run_baker
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from tools import exact_func
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from smcqdelaunay import *
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@ -58,9 +59,36 @@ class grid(object):
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self.points = np.array(points)
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self.q = np.array(q)
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self.tree = scipy.spatial.KDTree(self.points)
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self.faces = {}
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self.facets_for_point = defaultdict(list)
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def get_simplex_and_nearest_points(self, X, extra_points = 3, simplex_size = 3):
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"""
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this returns two grid objects: R and S.
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R is a grid object that is the (a) containing simplex around point X
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S is S_j from baker's paper : some points from all point that are not the simplex
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"""
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(dist, indicies) = self.tree.query(X, 3 + extra_points)
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# get the containing simplex
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R = [self.points[i] for i in indicies[:simplex_size] ]
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Rq = [self.q[i] for i in indicies[:simplex_size] ]
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r_mesh = grid(R, Rq)
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# and some extra points
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S = [self.points[i] for i in indicies[simplex_size:] ]
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Sq = [self.q[i] for i in indicies[simplex_size:] ]
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s_mesh = grid(S, Sq)
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return (r_mesh, s_mesh)
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def run_baker(self, X):
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(R, S) = self.get_simplex_and_nearest_points(X)
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return run_baker(X, R, S)
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def construct_connectivity(self):
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"""
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a call to this method prepares the internal connectivity structure.
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