import sys from collections import defaultdict import pickle from xml.dom.minidom import Document import numpy as np from scipy.spatial import KDTree from interp.baker import run_baker import interp.grid.simplex import logging log = logging.getLogger("interp") MAX_SEARCH_COUNT = 256 class grid(object): def __init__(self, verts = None, q = None): """ verts = array of arrays (if passed in, will convert to numpy.array) [ [x0,y0], [x1,y1], ... ] q = array (1D) of physical values """ if verts != None: self.verts = np.array(verts) self.tree = KDTree(self.verts) if q != None: self.q = np.array(q) self.cells = {} self.cells_for_vert = defaultdict(list) def get_containing_simplex(self, X): if not self.cells: raise Exception("cell connectivity is not set up") # get closest point (dist, indicies) = self.tree.query(X, 2) closest_point = indicies[0] log.debug('X: %s' % X) log.debug('point index: %d' % closest_point) log.debug('actual point %s' % self.verts[closest_point]) log.debug('distance = %0.4f' % dist[0]) simplex = None checked_cells = [] cells_to_check = list(self.cells_for_vert[closest_point]) attempts = 0 while not simplex and cells_to_check: attempts += 1 if attempts > MAX_SEARCH_COUNT: raise Exception("Is the search becoming exhaustive? (%d attempts)" % attempts) cur_cell = cells_to_check.pop(0) checked_cells.append(cur_cell) if cur_cell.contains(X, self): simplex = cur_cell continue for neighbor in cur_cell.neighbors: if (neighbor not in checked_cells) and (neighbor not in cells_to_check): cells_to_check.append(neighbor) if not simplex: raise Exception('no containing simplex found') log.debug("simplex vert indicies: %s" % simplex.verts) R = self.create_mesh(simplex.verts) log.debug("R:\n%s", R) log.debug('total attempts before finding simplex: %d' % attempts) return R def create_mesh(self, indicies): """ this function takes a list of indicies, and then creates and returns a grid object (collection of verts and q). note: the input is indicies, the grid contains verts """ p = [self.verts[i] for i in indicies] q = [self.q[i] for i in indicies] return grid(p, q) def get_simplex_and_nearest_points(self, X, extra_points = 3): """ this returns two grid objects: R and S. R is a grid object that is supposedly a containing simplex around point X S is S_j from baker's paper : some verts from all point that are not the simplex """ simplex_size = self.dim + 1 log.debug("extra verts: %d" % extra_points) log.debug("simplex size: %d" % simplex_size) r_mesh = self.get_containing_simplex(X) # and some UNIQUE extra verts (dist, indicies) = self.tree.query(X, simplex_size + extra_points) log.debug("extra indicies: %s" % indicies) unique_indicies = [] for index in indicies: close_point_in_R = False for rvert in r_mesh.verts: if all(rvert == self.verts[index]): close_point_in_R = True break if not close_point_in_R: unique_indicies.append(index) else: log.debug('throwing out %s: %s' % (index, self.verts[index])) log.debug("indicies: %s" % indicies) log.debug("unique indicies: %s" % unique_indicies) s_mesh = self.create_mesh(unique_indicies) return (r_mesh, s_mesh) def run_baker(self, X, order = 2, extra_points = 3): (R, S) = self.get_simplex_and_nearest_points(X, extra_points) answer = run_baker(X, R, S, order) return answer def for_qhull_generator(self): """ this returns a generator that should be fed into qdelaunay """ yield str(len(self.verts[0])); yield '%d' % len(self.verts) for p in self.verts: yield "%f %f %f" % tuple(p) def for_qhull(self): """ this returns a single string that should be fed into qdelaunay """ r = '%d\n' % len(self.verts[0]) r += '%d\n' % len(self.verts) for p in self.verts: # r += "%f %f %f\n" % tuple(p) r += "%s\n" % " ".join("%f" % i for i in p) return r def __str__(self): r = '' assert( len(self.verts) == len(self.q) ) for c, i in enumerate(zip(self.verts, self.q)): r += "%d vert(%s): q(%0.4f)" % (c,i[0], i[1]) cell_str = ", ".join([str(f.name) for f in self.cells_for_vert[c]]) r += " cells: [%s]" % cell_str r += "\n" if self.cells: for v in self.cells.itervalues(): r += "%s\n" % v return r def normalize_q(self, new_max = 0.1): largest_number = np.max(np.abs(self.q)) self.q *= new_max/largest_number def dump_to_blender_files(self, pfile = '/tmp/points.p', cfile = '/tmp/cells.p'): if len(self.verts[0]) == 2: pickle.dump([(p[0], p[1], 0.0) for p in self.verts], open(pfile, 'w')) else: pickle.dump([(p[0], p[1], p[2]) for p in self.verts], open(pfile, 'w')) pickle.dump([f.verts for f in self.cells.itervalues()], open(cfile, 'w')) def get_xml(self): doc = Document() ps = doc.createElement("points") doc.appendChild(ps) for i in zip(self.verts, self.q): p = doc.createElement("point") p.setAttribute("x", str(i[0][0])) p.setAttribute('y', str(i[0][1])) p.setAttribute('z', str(i[0][2])) p.setAttribute('q', str(i[1] )) ps.appendChild(p) return doc def toxml(self): return self.get_xml().toxml() def toprettyxml(self): return self.get_xml().toprettyxml() class cell(object): def __init__(self, name): self.name = name self.verts = [] self.neighbors = [] def add_vert(self, v): """ v should be an index into grid.verts """ self.verts.append(v) def add_neighbor(self, n): """ reference to another cell object """ self.neighbors.append(n) def contains(self, X, G): """ X = point of interest G = corrensponding grid object (G.verts) because of the way i'm storing things, a cell simply stores indicies, and so one must pass in a reference to the grid object containing real verts. this simply calls grid.simplex.contains """ return interp.grid.simplex.contains(X, [G.verts[i] for i in self.verts]) def __str__(self): # neighbors = [str(i.name) for i in self.neighbors] return '' %\ ( self.name, self.verts, len(self.neighbors), # ", ".join(neighbors) ) __repr__ = __str__