Primarily: fixed the gmsh module to use integer indices
also did some pep8/pyflakes cleanup
This commit is contained in:
Stephen M. McQuay
parent
0ae558f660
commit
1af176a6e0
@ -164,7 +164,6 @@ def interpolate(X, R, R_q, S=None, S_q=None, order=2):
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elif order not in xrange(2, 11):
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raise Exception('unsupported order "%d" for baker method' % order)
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return Answer(qlin=qlin, error=error_term, final=final, abc=abc)
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@ -13,5 +13,7 @@ def save_history(historyPath=historyPath):
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if os.path.exists(historyPath):
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readline.read_history_file(historyPath)
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rl = rlcompleter
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atexit.register(save_history)
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del os, atexit, readline, rlcompleter, save_history, historyPath
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del os, atexit, readline, rl, save_history, historyPath
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@ -16,7 +16,18 @@ class grid(object):
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pass
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def get_simplex_extra_points(self, X, extra_points=8):
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pass
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# I need two things: find_simplex, and self.simplices
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simplex_id = self.find_simplex(X)
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simplex_verts_ids = set(self.simplices[simplex_id])
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distances, kdt_ids \
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= self.tree.query(X, extra_points + len(simplex_verts_ids))
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kdt_ids = set(kdt_ids)
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simplex_ids = list(simplex_verts_ids)
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extra_points_ids = list(kdt_ids - simplex_verts_ids)
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return simplex_ids, extra_points_ids
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def interpolate(self, X, order=2, extra_points=3):
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r, s = self.get_simplex_extra_points(X, extra_points=extra_points)
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@ -25,27 +36,16 @@ class grid(object):
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def dump_to_blender_files(self,
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pfile='/tmp/points.p', cfile='/tmp/cells.p'):
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if len(self.verts[0]) == 2:
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pickle.dump([(p[0], p[1], 0.0) for p in self.verts],
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if len(self.points[0]) == 2:
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pickle.dump([(p[0], p[1], 0.0) for p in self.points.tolist()],
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open(pfile, 'w'))
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else:
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pickle.dump([(p[0], p[1], p[2]) for p in self.verts],
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pickle.dump([(p[0], p[1], p[2]) for p in self.points.tolist()],
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open(pfile, 'w'))
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pickle.dump([f.verts for f in self.cells.itervalues()],
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pickle.dump([face for face in self.simplices.tolist()],
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open(cfile, 'w'))
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def get_simplex_extra_points(X, points, triangulation, kdtree, extra_points=8):
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simplex_id = triangulation.find_simplex(X)
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simplex_verts_ids = set(triangulation.vertices[simplex_id])
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distances, kdt_ids = kdtree.query(X, extra_points + len(simplex_verts_ids))
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kdt_ids = set(kdt_ids)
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simplex_ids = list(simplex_verts_ids)
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extra_points_ids = list(kdt_ids - simplex_verts_ids)
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return simplex_ids, extra_points_ids
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def contains(X, R):
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"""
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@ -1,7 +1,6 @@
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import scipy.spatial
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from interp.grid import grid as basegrid
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from interp.grid import get_simplex_extra_points
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class dgrid(basegrid):
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@ -9,8 +8,8 @@ class dgrid(basegrid):
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self.points = points
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self.values = values
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self.triangulation = scipy.spatial.Delaunay(points)
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self.kdtree = scipy.spatial.KDTree(points)
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self.simplices = self.triangulation.vertices
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self.tree = scipy.spatial.KDTree(points)
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def get_simplex_extra_points(self, X, extra_points=8):
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return get_simplex_extra_points(X, self.points, self.triangulation,
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self.kdtree, extra_points=extra_points)
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def find_simplex(self, X):
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return self.triangulation.find_simplex(X)
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@ -3,44 +3,34 @@ from itertools import combinations
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import numpy as np
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from scipy.spatial import KDTree
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from interp.grid import grid
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from interp.grid import cell
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import logging
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log = logging.getLogger('interp')
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from interp.grid import grid, contains
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THREE_NODE_TRIANGLE = 2
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FOUR_NODE_TET = 4
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EDGES_FOR_FACE_CONNECTIVITY = 2
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EDGES_FOR_VOLUME_CONNECTIVITY = 3
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MAX_SEARCH_COUNT = 256
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class ggrid(grid):
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def __init__(self, filename, dimension = 3):
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def __init__(self, filename, values=None, dimension=3):
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"""
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construct an interp.grid.grid-compliant grid
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object out of a {2,3}D gmsh file
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"""
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self.dim = dimension
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log.debug("dimension: %d", self.dim)
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self.values = None
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gmsh_file = open(filename, 'r')
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gmsh_file.readline() # $MeshFormat
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gmsh_file.readline()
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fmt = gmsh_file.readline()
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self.version, self.file_type, self.data_size = fmt.split()
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gmsh_file.readline() # $EndMeshFormat
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gmsh_file.readline() # $Nodes
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node_count = int(gmsh_file.readline())
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self.verts = np.empty((node_count, dimension))
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self.points = np.empty((node_count, dimension), dtype=np.float64)
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self.q = np.empty(node_count)
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for i in xrange(node_count):
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@ -48,54 +38,84 @@ class ggrid(grid):
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(index, x, y, z) = cur_line.split()
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index = int(index) - 1
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self.verts[i][0] = float(x)
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self.verts[i][1] = float(y)
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self.points[i][0] = float(x)
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self.points[i][1] = float(y)
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if self.dim == 3:
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self.verts[i][2] = float(z)
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self.points[i][2] = float(z)
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self.tree = KDTree(self.verts)
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# initialize rest of structures about to be populated (cells,
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# cells_for_vert)
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grid.__init__(self)
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self.tree = KDTree(self.points)
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gmsh_file.readline() # $EndNodes
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gmsh_file.readline() # $Elements
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# temporary dict used to compute cell connectivity
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neighbors = {}
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simplices = []
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self.point_to_simplex = [[] for i in xrange(len(self.points))]
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simplex_counter = 0
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element_count = int(gmsh_file.readline())
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for i in xrange(element_count):
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for simplex_id in xrange(element_count):
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cur_line = gmsh_file.readline()
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cur_line = cur_line.split()
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cur_cell_index, node_type, rest = (int(cur_line[0]),
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simplex_index, node_type, rest = (int(cur_line[0]),
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int(cur_line[1]),
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[int(j) for j in cur_line[2:]])
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if (node_type == THREE_NODE_TRIANGLE and self.dim == 2) \
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or (node_type == FOUR_NODE_TET and self.dim == 3):
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points_for_cur_cell = [i-1 for i in rest[rest[0]+1:]]
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points_for_simplex = [i - 1 for i in rest[rest[0] + 1:]]
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for point in points_for_simplex:
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self.point_to_simplex[point].append(simplex_counter)
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simplices.append(points_for_simplex)
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simplex_counter += 1
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cur_cell = cell(cur_cell_index)
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for cur_point in points_for_cur_cell:
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self.cells_for_vert[cur_point].append(cur_cell)
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cur_cell.verts = points_for_cur_cell
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self.cells[cur_cell_index] = cur_cell
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edges = [tuple(sorted(i)) for i in combinations(points_for_cur_cell, self.dim)]
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self.simplex_to_simplex = [[] for i in xrange(len(simplices))]
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for simplex in xrange(len(simplices)):
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edges = [tuple(sorted(i)) for i in \
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combinations(simplices[simplex], self.dim)]
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for edge in edges:
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if edge in neighbors:
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neighbors[edge].append(cur_cell_index)
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neighbors[edge].append(simplex)
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else:
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neighbors[edge] = [cur_cell_index]
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neighbors[edge] = [simplex]
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for k, v in neighbors.iteritems():
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if len(v) > 1:
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self.cells[v[0]].add_neighbor(self.cells[v[1]])
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self.cells[v[1]].add_neighbor(self.cells[v[0]])
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self.simplex_to_simplex[v[0]].append(v[1])
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self.simplex_to_simplex[v[1]].append(v[0])
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self.simplices = np.array(simplices, dtype=np.int32)
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def find_simplex(self, X, max_search_count=MAX_SEARCH_COUNT):
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# get closest point
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(dist, indicies) = self.tree.query(X, 2)
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closest_point = indicies[0]
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simplex = None
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checked_cells = []
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cells_to_check = list(self.point_to_simplex[closest_point])
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attempts = 0
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while not simplex and cells_to_check:
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cur_cell = cells_to_check.pop(0)
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checked_cells.append(cur_cell)
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R = self.points[self.simplices[cur_cell]]
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if contains(X, R):
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simplex = cur_cell
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continue
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attempts += 1
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if attempts >= max_search_count:
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raise Exception("Is the search becoming exhaustive?"
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" (%dth attempt)" % attempts)
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for neighbor in self.simplex_to_simplex[cur_cell]:
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if (neighbor not in checked_cells) \
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and (neighbor not in cells_to_check):
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cells_to_check.append(neighbor)
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if not simplex:
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raise Exception('no containing simplex found')
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return simplex
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@ -45,8 +45,9 @@ def baker_exact_3D(X):
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np.sin(z * np.pi / 2.0)), 2)
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return answer
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def exact_me(X, f):
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a = np.array([f(i) for i in X])
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def exact_me(points, f):
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a = np.array([f(i) for i in points])
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return a
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@ -71,10 +72,12 @@ def improved_answer(answer, exact):
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else:
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return False
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def identical_points(a, b):
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return all(set(j[i] for j in a) \
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== set(j[i] for j in b) for i in xrange(len(a[0])))
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def improved(qlin, err, final, exact):
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if np.abs(final - exact) <= np.abs(qlin - exact):
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return True
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@ -75,7 +75,6 @@ class Test(unittest.TestCase):
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R, R_q = (self.all_points[:size_of_simplex],
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self.q[:size_of_simplex])
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answer = baker.interpolate(self.X, R, R_q)
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good_answer = Answer(qlin=0.5, final=None, error=None, abc={})
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self.assertEqual(answer, good_answer)
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@ -367,7 +367,7 @@ points = np.array([[ 0.11428445, 0.28754022, 0.21450866],
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[0.20235818, 0.51695988, 0.64702214],
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[0.26292108, 0.63850411, 0.41783859],
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[0.3334563, 0.03273507, 0.88846568],
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[ 0.84896155, 0.3335999 , 0.50311305],
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[0.84896155, 0.333599, 0.50311305],
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[0.56448553, 0.45171843, 0.50840164],
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[0.91613533, 0.53890878, 0.56899432],
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[0.62234921, 0.62566902, 0.60233196],
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@ -467,7 +467,7 @@ points = np.array([[ 0.11428445, 0.28754022, 0.21450866],
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[0.27525872, 0.23174335, 0.515169],
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[0.64357232, 0.55353622, 0.34310174],
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[0.99969868, 0.84022261, 0.6835174],
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[ 0.6637306 , 0.50890979, 0.22126423],
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[0.663730, 0.50890979, 0.22126423],
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[0.17609186, 0.40599717, 0.21219534],
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[0.41194042, 0.81643934, 0.5071791],
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[0.79273471, 0.43139493, 0.97951786],
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@ -3,7 +3,6 @@
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import unittest
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from interp.baker import interpolate
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from interp.grid import grid
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from interp.grid import contains
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