371 lines
11 KiB
Python
371 lines
11 KiB
Python
import sys
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import re
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from collections import defaultdict
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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 run_baker
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from interp.tools import log
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from interp.grid.simplex import cell, contains
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from interp.grid.smcqdelaunay import *
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class grid(object):
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def __init__(self, verts = None, q = None):
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"""
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verts = array of arrays (if passed in, will convert to numpy.array)
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[
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[x0,y0],
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[x1,y1], ...
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]
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q = array (1D) of physical values
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"""
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if verts != None:
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self.verts = np.array(verts)
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self.tree = KDTree(self.verts)
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if q != None:
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log.debug("found q")
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self.q = np.array(q)
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else:
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log.debug("no 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 > 20:
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# raise Exception("probably recursing to many times")
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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) 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('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
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and 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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p = [self.verts[i] for i in indicies]
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q = [self.q[i] for i in indicies]
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return grid(p, q)
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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 supposedly a containing simplex
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around point X (TODO: it tends not to be)
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S is S_j from baker's paper : some verts from all point that are not the simplex
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"""
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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 run_baker(self, X, extra_points = 3, order = 2):
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(R, S) = self.get_simplex_and_nearest_points(X, extra_points)
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answer = run_baker(X, R, S, order)
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return answer
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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 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 delaunay_grid(grid):
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cell_re = re.compile(r'''
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-\s+(?P<cell>f\d+).*?
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vertices:\s(?P<verts>.*?)\n.*?
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neighboring\s cells:\s+(?P<neigh>[\sf\d]*)
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''', re.S|re.X)
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point_re = re.compile(r'''
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-\s+(?P<point>p\d+).*?
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neighbors:\s+(?P<neigh>[\sf\d]*)
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''', re.S|re.X)
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vert_re = re.compile(r'''
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(p\d+)
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''', re.S|re.X)
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def __init__(self, verts, q):
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grid.__init__(self, verts,q)
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def get_containing_simplex(self, X):
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if not self.cells:
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self.construct_connectivity()
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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 = 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 > 20:
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# raise Exception("probably recursing to many times")
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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) 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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R = self.create_mesh(simplex.verts)
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log.debug('total attempts before finding simplex: %d' % attempts)
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return R
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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 supposedly a containing simplex
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around point X (it tends not to be)
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S is S_j from baker's paper : some verts from all point that are not the simplex
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"""
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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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# log.debug("R:\n%s" % r_mesh)
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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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unique_indicies = []
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for index in indicies:
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if self.verts[index] not in r_mesh.verts:
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unique_indicies.append(index)
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log.debug("indicies: %s" % ",".join([str(i) for i in indicies]))
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log.debug("indicies: %s" % ",".join([str(i) for i in unique_indicies]))
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s_mesh = self.create_mesh(unique_indicies)# indicies[simplex_size:])
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# TODO: eventually remove this test:
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for point in s_mesh.verts:
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if point in r_mesh.verts:
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log.error("ERROR")
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log.error("\n%s\nin\n%s" % (point, r_mesh))
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raise Exception("repeating point S and R")
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return (r_mesh, s_mesh)
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def get_points_conn(self, X):
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"""
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this returns two grid objects: R and S.
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this function differes from the get_simplex_and_nearest_points
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function in that it builds up the extra verts based on
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connectivity information, not just nearest-neighbor.
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in theory, this will work much better for situations like
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verts near a short edge in a boundary layer cell where the
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nearest verts would all be colinear
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also, it guarantees that we find a containing simplex
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R is a grid object that is the (a) containing simplex around point X
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S is a connectivity-based nearest-neighbor lookup, limited to 3 extra verts
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"""
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if not self.cells:
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self.construct_connectivity()
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# get closest point
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(dist, indicies) = self.tree.query(X, 2)
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simplex = None
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for cell in self.cells_for_vert[indicies[0]]:
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if cell.contains(X, self):
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simplex = cell
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break
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if not simplex:
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raise AssertionError('no containing simplex found')
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# self.create_mesh(simplex.verts)
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R = self.get_containing_simplex(X)
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s = []
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for c,i in enumerate(simplex.neighbors):
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s.extend([guy for guy in i.verts if not guy in simplex.verts])
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S = self.create_mesh(s)
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return R, S
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def run_baker(self, X, extra_points = 3, order = 2):
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answer = None
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try:
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(R, S) = self.get_simplex_and_nearest_points(X)
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if not contains(X, R.verts):
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raise Exception("run_baker with get_simplex_and_nearest_points returned non-containing simplex")
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answer = run_baker(X, R, S, order)
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except Exception, e:
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log.error("caught error: %s, trying with connectivity-based mesh" % e)
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(R, S) = self.get_points_conn(X)
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answer = run_baker(X, R, S, order)
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return answer
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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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this is part of the __init__ for a rect_grid, but can be called from any grid object
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"""
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log.debug('start')
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qdelaunay_string = get_qdelaunay_dump_str(self)
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# log.debug(qdelaunay_string)
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cell_to_cells = []
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for matcher in delaunay_grid.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 grid.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.debug('end')
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