smbinterp/interp/grid/__init__.py

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, 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 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 '<cell %s: verts: %s neighbor count: %s>' %\
           (
             self.name,
             self.verts,
             len(self.neighbors),
             # ", ".join(neighbors)
           )

  __repr__ = __str__