Added a delaunay triangulating grid that uses scipy.spatial faculties
This commit is contained in:
Stephen M. McQuay
parent
2f6c87dd4f
commit
fcb2bf2931
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@ -1,184 +1,27 @@
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from collections import defaultdict
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import pickle
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import pickle
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from xml.dom.minidom import Document
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import numpy as np
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from scipy.spatial import KDTree
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from interp.baker import interpolate
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from interp.baker import interpolate
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from interp.baker import get_phis
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from interp.baker import get_phis
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import interp
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import interp
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import logging
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log = logging.getLogger("interp")
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MAX_SEARCH_COUNT = 256
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TOL = 1e-8
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__version__ = interp.__version__
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__version__ = interp.__version__
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class grid(object):
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class grid(object):
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def __init__(self, verts=None, q=None):
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def __init__(self):
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"""
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"""
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verts = array of arrays (if passed in, will convert to numpy.array)
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Child classes should populate at a minimum the points and values
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[
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arrays, and a method for getting a simplex and extra points.
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[x0,y0 <, z0>],
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[x1,y1 <, z1>],
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...
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]
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q = array (1D) of physical values
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"""
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"""
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pass
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if verts != None:
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def get_simplex_extra_points(self, X, extra_points=8):
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self.verts = np.array(verts)
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pass
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self.tree = KDTree(self.verts)
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if q != None:
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self.q = np.array(q)
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self.cells = {}
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self.cells_for_vert = defaultdict(list)
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def get_containing_simplex(self, X):
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if not self.cells:
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raise Exception("cell connectivity is not set up")
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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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log.debug('X: %s' % X)
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log.debug('point index: %d' % closest_point)
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log.debug('actual point %s' % self.verts[closest_point])
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log.debug('distance = %0.4f' % dist[0])
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simplex = None
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checked_cells = []
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cells_to_check = list(self.cells_for_vert[closest_point])
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attempts = 0
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while not simplex and cells_to_check:
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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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'(%d attempts)" % attempts)
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cur_cell = cells_to_check.pop(0)
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checked_cells.append(cur_cell)
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if cur_cell.contains(X, self):
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simplex = cur_cell
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continue
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for neighbor in cur_cell.neighbors:
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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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log.debug("simplex vert indicies: %s" % simplex.verts)
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R = self.create_mesh(simplex.verts)
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log.debug("R:\n%s", R)
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log.debug('total attempts before finding simplex: %d' % attempts)
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return R
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def create_mesh(self, indicies):
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"""
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this function takes a list of indicies, and then creates and
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returns a grid object (collection of verts and q).
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note: the input is indicies, the grid contains verts
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"""
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return grid(self.verts[indicies], self.q[indicies])
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def get_simplex_and_nearest_points(self, X, extra_points=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 a containing simplex around point X
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S : some verts from all points that are not the simplex
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"""
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simplex_size = self.dim + 1
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log.debug("extra verts: %d" % extra_points)
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log.debug("simplex size: %d" % simplex_size)
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r_mesh = self.get_containing_simplex(X)
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# and some UNIQUE extra verts
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(dist, indicies) = self.tree.query(X, simplex_size + extra_points)
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log.debug("extra indicies: %s" % indicies)
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unique_indicies = []
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for index in indicies:
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close_point_in_R = False
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for rvert in r_mesh.verts:
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if all(rvert == self.verts[index]):
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close_point_in_R = True
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break
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if not close_point_in_R:
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unique_indicies.append(index)
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else:
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log.debug('throwing out %s: %s' % (index, self.verts[index]))
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log.debug("indicies: %s" % indicies)
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log.debug("unique indicies: %s" % unique_indicies)
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s_mesh = self.create_mesh(unique_indicies)
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return (r_mesh, s_mesh)
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def interpolate(self, X, order=2, extra_points=3):
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def interpolate(self, X, order=2, extra_points=3):
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(R, S) = self.get_simplex_and_nearest_points(X, extra_points)
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r, s = self.get_simplex_extra_points(X, extra_points=extra_points)
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answer = interpolate(X, R, S, order)
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return interpolate(X, self.points[r], self.values[r],
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return answer
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self.points[s], self.values[s], order=order)
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def for_qhull_generator(self):
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"""
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this returns a generator that should be fed into qdelaunay
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"""
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yield str(len(self.verts[0]))
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yield '%d' % len(self.verts)
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for p in self.verts:
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yield "%f %f %f" % tuple(p)
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def for_qhull(self):
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"""
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this returns a single string that should be fed into qdelaunay
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"""
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r = '%d\n' % len(self.verts[0])
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r += '%d\n' % len(self.verts)
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for p in self.verts:
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# r += "%f %f %f\n" % tuple(p)
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r += "%s\n" % " ".join("%f" % i for i in p)
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return r
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def __str__(self):
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r = ''
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assert(len(self.verts) == len(self.q))
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for c, i in enumerate(zip(self.verts, self.q)):
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r += "%d vert(%s): q(%0.4f)" % (c, i[0], i[1])
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cell_str = ", ".join([str(f.name) for f in self.cells_for_vert[c]])
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r += " cells: [%s]" % cell_str
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r += "\n"
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if self.cells:
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for v in self.cells.itervalues():
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r += "%s\n" % v
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return r
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def normalize_q(self, new_max=0.1):
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largest_number = np.max(np.abs(self.q))
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self.q *= new_max / largest_number
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def dump_to_blender_files(self,
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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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pfile='/tmp/points.p', cfile='/tmp/cells.p'):
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@ -192,81 +35,13 @@ class grid(object):
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pickle.dump([f.verts for f in self.cells.itervalues()],
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pickle.dump([f.verts for f in self.cells.itervalues()],
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open(cfile, 'w'))
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open(cfile, 'w'))
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def get_xml(self):
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doc = Document()
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ps = doc.createElement("points")
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doc.appendChild(ps)
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for i in zip(self.verts, self.q):
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p = doc.createElement("point")
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p.setAttribute("x", str(i[0][0]))
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p.setAttribute('y', str(i[0][1]))
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p.setAttribute('z', str(i[0][2]))
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p.setAttribute('q', str(i[1]))
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ps.appendChild(p)
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return doc
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def toxml(self):
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return self.get_xml().toxml()
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def toprettyxml(self):
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return self.get_xml().toprettyxml()
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class cell(object):
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def __init__(self, name):
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self.name = name
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self.verts = []
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self.neighbors = []
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def add_vert(self, v):
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"""
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v should be an index into grid.verts
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"""
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self.verts.append(v)
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def add_neighbor(self, n):
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"""
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reference to another cell object
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"""
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self.neighbors.append(n)
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def contains(self, X, G):
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"""
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X = point of interest
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G = corrensponding grid object (G.verts)
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because of the way i'm storing things, a cell simply stores
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indicies, and so one must pass in a reference to the grid object
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containing real verts.
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this simply calls grid.simplex.contains
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"""
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return contains(X, [G.verts[i] for i in self.verts])
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def __str__(self):
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# neighbors = [str(i.name) for i in self.neighbors]
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return '<cell %s: verts: %s neighbor count: %s>' %\
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(
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self.name,
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self.verts,
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len(self.neighbors),
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# ", ".join(neighbors)
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)
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__repr__ = __str__
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def contains(X, R):
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def contains(X, R):
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"""
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"""
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tests if X (point) is in R
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tests if X (point) is in R
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R is a simplex, represented by a list of n-degree coordinates
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R is a simplex, represented by a list of N-degree coordinates
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"""
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"""
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phis = get_phis(X, R)
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phis = get_phis(X, R)
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any_negatives = any(map(lambda x: x < 0, phis))
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r = True
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return not any_negatives
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if [i for i in phis if i < 0.0 - TOL]:
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r = False
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return r
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@ -1,86 +1,27 @@
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import re
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import scipy.spatial
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import logging
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log = logging.getLogger("interp")
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from interp.grid import grid as basegrid
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from interp.grid import grid as basegrid, cell
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from subprocess import Popen, PIPE
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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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def get_qdelaunay_dump(g):
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distances, kdt_ids = kdtree.query(X, extra_points + len(simplex_verts_ids))
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"""
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kdt_ids = set(kdt_ids)
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pass in interp.grid g, and get back lines from a qhull triangulation:
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qdelaunay Qt f
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simplex_ids = list(simplex_verts_ids)
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"""
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extra_points_ids = list(kdt_ids - simplex_verts_ids)
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cmd = 'qdelaunay Qt f'
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p = Popen(cmd.split(), bufsize=1, stdin=PIPE, stdout=PIPE)
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so, se = p.communicate(g.for_qhull())
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for i in so.splitlines():
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yield i
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def get_qdelaunay_dump_str(g):
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return simplex_ids, extra_points_ids
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return "\n".join(get_qdelaunay_dump(g))
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def get_index_only(g):
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cmd = 'qdelaunay Qt i'
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p = Popen(cmd.split(), bufsize=1, stdin=PIPE, stdout=PIPE)
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so, se = p.communicate(g.for_qhull())
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for i in so.splitlines():
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yield i
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def get_index_only_str(g):
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return "\n".join(get_index_only(g))
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class dgrid(basegrid):
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class dgrid(basegrid):
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cell_re = re.compile(r'''
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def __init__(self, points, values):
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-\s+(?P<cell>f\d+).*?
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self.points = points
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vertices:\s(?P<verts>.*?)\n.*?
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self.values = values
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neighboring\s facets:\s+(?P<neigh>[\sf\d]*)
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self.triangulation = scipy.spatial.Delaunay(points)
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''', re.S|re.X)
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self.kdtree = scipy.spatial.KDTree(points)
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vert_re = re.compile(r'''
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def get_simplex_extra_points(self, X, extra_points=8):
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(p\d+)
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return get_simplex_extra_points(X, self.points, self.triangulation,
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''', re.S|re.X)
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self.kdtree, extra_points=extra_points)
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def __init__(self, verts, q = None):
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self.dim = len(verts[0])
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basegrid.__init__(self, verts,q)
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self.construct_connectivity()
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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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"""
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log.info('start')
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qdelaunay_string = get_qdelaunay_dump_str(self)
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with open('/tmp/qdel.out', 'w') as of:
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of.write(qdelaunay_string)
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cell_to_cells = []
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for matcher in dgrid.cell_re.finditer(qdelaunay_string):
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d = matcher.groupdict()
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cell_name = d['cell']
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verticies = d['verts']
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neighboring_cells = d['neigh']
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cur_cell = cell(cell_name)
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self.cells[cell_name] = cur_cell
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for v in dgrid.vert_re.findall(verticies):
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vertex_index = int(v[1:])
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cur_cell.add_vert(vertex_index)
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self.cells_for_vert[vertex_index].append(cur_cell)
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nghbrs = [(cell_name, i) for i in neighboring_cells.split()]
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cell_to_cells.extend(nghbrs)
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log.debug(cell_to_cells)
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for rel in cell_to_cells:
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if rel[1] in self.cells:
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self.cells[rel[0]].add_neighbor(self.cells[rel[1]])
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log.debug(self.cells)
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log.info('end')
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