import sys
import re
from   collections import defaultdict

import numpy as np
import scipy.spatial

from baker        import run_baker
from baker.tools  import exact_func, smberror
from simplex      import face
from smcqdelaunay import *



class grid(object):
  facet_re = re.compile(r'''
                            -\s+(?P<facet>f\d+).*?
                            vertices:\s(?P<verts>.*?)\n.*?
                            neighboring\s facets:\s+(?P<neigh>[\sf\d]*)
                         ''', re.S|re.X)

  point_re = re.compile(r'''
                            -\s+(?P<point>p\d+).*?
                            neighbors:\s+(?P<neigh>[\sf\d]*)
                         ''', re.S|re.X)

  vert_re  = re.compile(r'''
                            (p\d+)
                         ''', re.S|re.X)


  def __init__(self, points, q):
    """
      this thing eats two pre-constructed arrays of stuff:
      points = array of arrays (i will convert to numpy.array)
               [[x0,y0], [x1,y1], ...]
      q      = array (1D) of important values
    """

    self.points           = np.array(points)
    self.q                = np.array(q)
    self.tree             = scipy.spatial.KDTree(self.points)
    self.faces            = {}
    self.facets_for_point = defaultdict(list)


  def create_mesh(self, indicies):
    p = [self.points[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 the (a) containing simplex around point X
      S is S_j from baker's paper : some points from all point that are not the simplex
    """
    (dist, indicies) = self.tree.query(X, simplex_size + extra_points)


    # get the containing simplex
    r_mesh = self.create_mesh(indicies[:simplex_size])
    # and some extra points
    s_mesh = self.create_mesh(indicies[simplex_size:])

    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 points based on
      connectivity information, not just nearest-neighbor.
      in theory, this will work much better for situations like
      points near a short edge in a boundary layer cell where the
      nearest points would all be colinear

      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 points
    """
    if not self.faces:
      self.construct_connectivity()

    # get closest point
    (dist, indicies) = self.tree.query(X, 2)

    simplex = None
    for i in self.facets_for_point[indicies[0]]:
      if i.contains(X, self):
        simplex = i
        break

    if not simplex:
      raise AssertionError('no containing simplex found')

    R = self.create_mesh(simplex.verts)


    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):
    answer = None

    try:
      (R, S) = self.get_simplex_and_nearest_points(X)
      answer = run_baker(X, R, S)
    except smberror, e:
      print >> sys.stderr, "caught error: %s, trying with connectivity-based mesh" % e
      (R, S) = self.get_points_conn(X)
      answer = run_baker(X, R, S)

    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
    """
    qdelaunay_string = get_qdelaunay_dump_str(self)
    facet_to_facets = []
    for matcher in grid.facet_re.finditer(qdelaunay_string):
      d = matcher.groupdict()

      facet_name          = d['facet']
      verticies           = d['verts']
      neighboring_facets  = d['neigh']

      cur_face = face(facet_name)
      self.faces[facet_name] = cur_face

      for v in grid.vert_re.findall(verticies):
        vertex_index = int(v[1:])
        cur_face.add_vert(vertex_index)
        self.facets_for_point[vertex_index].append(cur_face)

      nghbrs = [(facet_name, i) for i in  neighboring_facets.split()]
      facet_to_facets.extend(nghbrs)

    for rel in facet_to_facets:
      if rel[1] in self.faces:
        self.faces[rel[0]].add_neighbor(self.faces[rel[1]])

    # for matcher in grid.point_re.finditer(qdelaunay_string):
    #   d = matcher.groupdict()

    #   point               = d['point']
    #   neighboring_facets  = d['neigh']

    #   self.facets_for_point[int(point[1:])] = [i for i in neighboring_facets.split() if i in self.faces]

  def __str__(self):
    r = ''
    assert( len(self.points) == len(self.q) )
    for c, i in enumerate(zip(self.points, self.q)):
      r += "%d %r: %0.4f" % (c,i[0], i[1])
      facet_str = ", ".join([f.name for f in self.facets_for_point[c]])
      r += "  faces: [%s]" % facet_str
      r += "\n"
    if self.faces:
      for v in self.faces.itervalues():
        r += "%s\n" % v
    return r