import sys import re from collections import defaultdict from xml.dom.minidom import Document import numpy as np from scipy.spatial import KDTree from interp.baker import run_baker from interp.tools import log from interp.grid.simplex import cell, contains from interp.grid.smcqdelaunay import * 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: log.debug("found q") self.q = np.array(q) else: log.debug("no 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 > 20: # raise Exception("probably recursing to many times") 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('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, simplex_size = 3): """ this returns two grid objects: R and S. R is a grid object that is supposedly a containing simplex around point X (TODO: it tends not to be) S is S_j from baker's paper : some verts from all point that are not the simplex """ 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, extra_points = 3, order = 2): (R, S) = self.get_simplex_and_nearest_points(X, extra_points) answer = run_baker(X, R, S, order) return answer 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 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 delaunay_grid(grid): cell_re = re.compile(r''' -\s+(?Pf\d+).*? vertices:\s(?P.*?)\n.*? neighboring\s cells:\s+(?P[\sf\d]*) ''', re.S|re.X) point_re = re.compile(r''' -\s+(?Pp\d+).*? neighbors:\s+(?P[\sf\d]*) ''', re.S|re.X) vert_re = re.compile(r''' (p\d+) ''', re.S|re.X) def __init__(self, verts, q): grid.__init__(self, verts,q) def get_containing_simplex(self, X): if not self.cells: self.construct_connectivity() # 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 = self.cells_for_vert[closest_point] attempts = 0 while not simplex and cells_to_check: attempts += 1 # if attempts > 20: # raise Exception("probably recursing to many times") 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') R = self.create_mesh(simplex.verts) log.debug('total attempts before finding simplex: %d' % attempts) return R def get_simplex_and_nearest_points(self, X, extra_points = 3, simplex_size = 3): """ this returns two grid objects: R and S. R is a grid object that is supposedly a containing simplex around point X (it tends not to be) S is S_j from baker's paper : some verts from all point that are not the simplex """ log.debug("extra verts: %d" % extra_points) log.debug("simplex size: %d" % simplex_size) r_mesh = self.get_containing_simplex(X) # log.debug("R:\n%s" % r_mesh) # and some UNIQUE extra verts (dist, indicies) = self.tree.query(X, simplex_size + extra_points) unique_indicies = [] for index in indicies: if self.verts[index] not in r_mesh.verts: unique_indicies.append(index) log.debug("indicies: %s" % ",".join([str(i) for i in indicies])) log.debug("indicies: %s" % ",".join([str(i) for i in unique_indicies])) s_mesh = self.create_mesh(unique_indicies)# indicies[simplex_size:]) # TODO: eventually remove this test: for point in s_mesh.verts: if point in r_mesh.verts: log.error("ERROR") log.error("\n%s\nin\n%s" % (point, r_mesh)) raise Exception("repeating point S and R") return (r_mesh, s_mesh) def get_points_conn(self, X): """ this returns two grid objects: R and S. this function differes from the get_simplex_and_nearest_points function in that it builds up the extra verts based on connectivity information, not just nearest-neighbor. in theory, this will work much better for situations like verts near a short edge in a boundary layer cell where the nearest verts would all be colinear also, it guarantees that we find a containing simplex R is a grid object that is the (a) containing simplex around point X S is a connectivity-based nearest-neighbor lookup, limited to 3 extra verts """ if not self.cells: self.construct_connectivity() # get closest point (dist, indicies) = self.tree.query(X, 2) simplex = None for cell in self.cells_for_vert[indicies[0]]: if cell.contains(X, self): simplex = cell break if not simplex: raise AssertionError('no containing simplex found') # self.create_mesh(simplex.verts) R = self.get_containing_simplex(X) s = [] for c,i in enumerate(simplex.neighbors): s.extend([guy for guy in i.verts if not guy in simplex.verts]) S = self.create_mesh(s) return R, S def run_baker(self, X, extra_points = 3, order = 2): answer = None try: (R, S) = self.get_simplex_and_nearest_points(X) if not contains(X, R.verts): raise Exception("run_baker with get_simplex_and_nearest_points returned non-containing simplex") answer = run_baker(X, R, S, order) except Exception, e: log.error("caught error: %s, trying with connectivity-based mesh" % e) (R, S) = self.get_points_conn(X) answer = run_baker(X, R, S, order) return answer def construct_connectivity(self): """ a call to this method prepares the internal connectivity structure. this is part of the __init__ for a rect_grid, but can be called from any grid object """ log.debug('start') qdelaunay_string = get_qdelaunay_dump_str(self) # log.debug(qdelaunay_string) cell_to_cells = [] for matcher in delaunay_grid.cell_re.finditer(qdelaunay_string): d = matcher.groupdict() cell_name = d['cell'] verticies = d['verts'] neighboring_cells = d['neigh'] cur_cell = cell(cell_name) self.cells[cell_name] = cur_cell for v in grid.vert_re.findall(verticies): vertex_index = int(v[1:]) cur_cell.add_vert(vertex_index) self.cells_for_vert[vertex_index].append(cur_cell) nghbrs = [(cell_name, i) for i in neighboring_cells.split()] cell_to_cells.extend(nghbrs) log.debug(cell_to_cells) for rel in cell_to_cells: if rel[1] in self.cells: self.cells[rel[0]].add_neighbor(self.cells[rel[1]]) log.debug(self.cells) log.debug('end')