source: inundation/ga/storm_surge/pyvolution/mesh.py @ 1192

"""Classes implementing general 2D geometrical mesh of triangles.

   Ole Nielsen, Stephen Roberts, Duncan Gray, Christopher Zoppou
   Geoscience Australia, 2004
"""

from general_mesh import General_mesh

class Mesh(General_mesh):
    """Collection of triangular elements (purely geometric)

    A triangular element is defined in terms of three vertex ids,
    ordered counter clock-wise,
    each corresponding to a given coordinate set.
    Vertices from different elements can point to the same
    coordinate set.

    Coordinate sets are implemented as an N x 2 Numeric array containing
    x and y coordinates.


    To instantiate:
       Mesh(coordinates, triangles)

    where

      coordinates is either a list of 2-tuples or an Mx2 Numeric array of
      floats representing all x, y coordinates in the mesh.

      triangles is either a list of 3-tuples or an Nx3 Numeric array of
      integers representing indices of all vertices in the mesh.
      Each vertex is identified by its index i in [0, M-1].


    Example:
        a = [0.0, 0.0]
        b = [0.0, 2.0]
        c = [2.0,0.0]
        e = [2.0, 2.0]

        points = [a, b, c, e]
        triangles = [ [1,0,2], [1,2,3] ]   #bac, bce
        mesh = Mesh(points, triangles)

        #creates two triangles: bac and bce


    Mesh takes the optional third argument boundary which is a
    dictionary mapping from (element_id, edge_id) to boundary tag.
    The default value is None which will assign the default_boundary_tag
    as specified in config.py to all boundary edges.
    """

    #FIXME: Maybe rename coordinates to points (as in a poly file)
    #But keep 'vertex_coordinates'

    #FIXME: Put in check for angles less than a set minimum

    def __init__(self, coordinates, triangles, boundary = None,
                 tagged_elements = None, geo_reference = None,
                 use_incribed_circle = False):
        """
        Build triangles from x,y coordinates (sequence of 2-tuples or
        Mx2 Numeric array of floats) and triangles (sequence of 3-tuples
        or Nx3 Numeric array of non-negative integers).
        """

        from Numeric import array, zeros, Int, Float, maximum, sqrt, sum

        General_mesh.__init__(self, coordinates, triangles, geo_reference)

        N = self.number_of_elements

        #Allocate space for geometric quantities
        self.centroid_coordinates = zeros((N, 2), Float)

        self.radii = zeros(N, Float)

        self.neighbours = zeros((N, 3), Int)
        self.neighbour_edges = zeros((N, 3), Int)
        self.number_of_boundaries = zeros(N, Int)
        self.surrogate_neighbours = zeros((N, 3), Int)

        #Get x,y coordinates for all triangles and store
        V = self.vertex_coordinates

        #Initialise each triangle
        for i in range(N):
            #if i % (N/10) == 0: print '(%d/%d)' %(i, N)

            x0 = V[i, 0]; y0 = V[i, 1]
            x1 = V[i, 2]; y1 = V[i, 3]
            x2 = V[i, 4]; y2 = V[i, 5]

            #Compute centroid
            centroid = array([(x0 + x1 + x2)/3, (y0 + y1 + y2)/3])
            self.centroid_coordinates[i] = centroid

#OLD
            if use_incribed_circle == False:
            #Midpoints
                m0 = array([(x1 + x2)/2, (y1 + y2)/2])
                m1 = array([(x0 + x2)/2, (y0 + y2)/2])
                m2 = array([(x1 + x0)/2, (y1 + y0)/2])

                #The radius is the distance from the centroid of
                #a triangle to the midpoint of the side of the triangle
                #closest to the centroid
                d0 = sqrt(sum( (centroid-m0)**2 ))
                d1 = sqrt(sum( (centroid-m1)**2 ))
                d2 = sqrt(sum( (centroid-m2)**2 ))

                self.radii[i] = min(d0, d1, d2)

#NEW
            else:
                from math import sqrt
                a = sqrt((x0-x1)**2+(y0-y1)**2)
                b = sqrt((x1-x2)**2+(y1-y2)**2)
                c = sqrt((x2-x0)**2+(y2-y0)**2)

                self.radii[i]=self.areas[i]/(2*(a+b+c)) 

            #Initialise Neighbours (-1 means that it is a boundary neighbour)
            self.neighbours[i, :] = [-1, -1, -1]

            #Initialise edge ids of neighbours
            #In case of boundaries this slot is not used
            self.neighbour_edges[i, :] = [-1, -1, -1]


        #Build neighbour structure
        self.build_neighbour_structure()

        #Build surrogate neighbour structure
        self.build_surrogate_neighbour_structure()

        #Build boundary dictionary mapping (id, edge) to symbolic tags
        self.build_boundary_dictionary(boundary)

        #Build tagged element  dictionary mapping (tag) to array of elements
        self.build_tagged_elements_dictionary(tagged_elements)

        #Update boundary indices FIXME: OBSOLETE
        #self.build_boundary_structure()

        #FIXME check integrity?

    def __repr__(self):
        return 'Mesh: %d triangles, %d elements, %d boundary segments'\
               %(self.coordinates.shape[0], len(self), len(self.boundary))


    def set_to_incribed_circle(self,safety_factor = 1):
        #FIXME phase out eventually
        from math import sqrt
        N = self.number_of_elements
        V = self.vertex_coordinates

        #initialising min and max ratio
        i=0
        old_rad = self.radii[i]
        x0 = V[i, 0]; y0 = V[i, 1]
        x1 = V[i, 2]; y1 = V[i, 3]
        x2 = V[i, 4]; y2 = V[i, 5]
        a = sqrt((x0-x1)**2+(y0-y1)**2)
        b = sqrt((x1-x2)**2+(y1-y2)**2)
        c = sqrt((x2-x0)**2+(y2-y0)**2)
        ratio = old_rad/self.radii[i]
        max_ratio = ratio
        min_ratio = ratio

        for i in range(N):
            old_rad = self.radii[i]
            x0 = V[i, 0]; y0 = V[i, 1]
            x1 = V[i, 2]; y1 = V[i, 3]
            x2 = V[i, 4]; y2 = V[i, 5]
            a = sqrt((x0-x1)**2+(y0-y1)**2)
            b = sqrt((x1-x2)**2+(y1-y2)**2)
            c = sqrt((x2-x0)**2+(y2-y0)**2)
            self.radii[i]=self.areas[i]/(2*(a+b+c))*safety_factor
            ratio = old_rad/self.radii[i]
            if ratio >= max_ratio: max_ratio = ratio
            if ratio <= min_ratio: min_ratio = ratio
        return max_ratio,min_ratio

    def build_neighbour_structure(self):
        """Update all registered triangles to point to their neighbours.

        Also, keep a tally of the number of boundaries for each triangle

        Postconditions:
          neighbours and neighbour_edges is populated
          number_of_boundaries integer array is defined.
        """

        #Step 1:
        #Build dictionary mapping from segments (2-tuple of points)
        #to left hand side edge (facing neighbouring triangle)

        N = self.number_of_elements
        neighbourdict = {}
        for i in range(N):

            #Register all segments as keys mapping to current triangle
            #and segment id
            a = self.triangles[i, 0]
            b = self.triangles[i, 1]
            c = self.triangles[i, 2]
            if neighbourdict.has_key((a,b)):
                    msg = "Edge 2 of triangle %d is duplicating edge %d of triangle %d.\n" %(i,neighbourdict[a,b][1],neighbourdict[a,b][0])
                    raise msg
            if neighbourdict.has_key((b,c)):
                    msg = "Edge 0 of triangle %d is duplicating edge %d of triangle %d.\n" %(i,neighbourdict[b,c][1],neighbourdict[b,c][0])
                    raise msg
            if neighbourdict.has_key((c,a)):
                    msg = "Edge 1 of triangle %d is duplicating edge %d of triangle %d.\n" %(i,neighbourdict[c,a][1],neighbourdict[c,a][0])
                    raise msg

            neighbourdict[a,b] = (i, 2) #(id, edge)
            neighbourdict[b,c] = (i, 0) #(id, edge)
            neighbourdict[c,a] = (i, 1) #(id, edge)


        #Step 2:
        #Go through triangles again, but this time
        #reverse direction of segments and lookup neighbours.
        for i in range(N):
            a = self.triangles[i, 0]
            b = self.triangles[i, 1]
            c = self.triangles[i, 2]

            self.number_of_boundaries[i] = 3
            if neighbourdict.has_key((b,a)):
                self.neighbours[i, 2] = neighbourdict[b,a][0]
                self.neighbour_edges[i, 2] = neighbourdict[b,a][1]
                self.number_of_boundaries[i] -= 1

            if neighbourdict.has_key((c,b)):
                self.neighbours[i, 0] = neighbourdict[c,b][0]
                self.neighbour_edges[i, 0] = neighbourdict[c,b][1]
                self.number_of_boundaries[i] -= 1

            if neighbourdict.has_key((a,c)):
                self.neighbours[i, 1] = neighbourdict[a,c][0]
                self.neighbour_edges[i, 1] = neighbourdict[a,c][1]
                self.number_of_boundaries[i] -= 1


    def build_surrogate_neighbour_structure(self):
        """Build structure where each triangle edge points to its neighbours
        if they exist. Otherwise point to the triangle itself.

        The surrogate neighbour structure is useful for computing gradients
        based on centroid values of neighbours.

        Precondition: Neighbour structure is defined
        Postcondition:
          Surrogate neighbour structure is defined:
          surrogate_neighbours: i0, i1, i2 where all i_k >= 0 point to
          triangles.

        """

        N = self.number_of_elements
        for i in range(N):
            #Find all neighbouring volumes that are not boundaries
            for k in range(3):
                if self.neighbours[i, k] < 0:
                    self.surrogate_neighbours[i, k] = i #Point this triangle
                else:
                    self.surrogate_neighbours[i, k] = self.neighbours[i, k]



    def build_boundary_dictionary(self, boundary = None):
        """Build or check the dictionary of boundary tags.
         self.boundary is a dictionary of tags,
         keyed by volume id and edge:
         { (id, edge): tag, ... }

         Postconditions:
            self.boundary is defined.
        """

        from config import default_boundary_tag

        if boundary is None:
            boundary = {}
            for vol_id in range(self.number_of_elements):
                for edge_id in range(0, 3):
                    if self.neighbours[vol_id, edge_id] < 0:
                        boundary[(vol_id, edge_id)] = default_boundary_tag
        else:
            #Check that all keys in given boundary exist
            for vol_id, edge_id in boundary.keys():
                msg = 'Segment (%d, %d) does not exist' %(vol_id, edge_id)
                a, b = self.neighbours.shape
                assert vol_id < a and edge_id < b, msg

                #FIXME: This assert violates internal boundaries (delete it)
                #msg = 'Segment (%d, %d) is not a boundary' %(vol_id, edge_id)
                #assert self.neighbours[vol_id, edge_id] < 0, msg

            #Check that all boundary segments are assigned a tag
            for vol_id in range(self.number_of_elements):
                for edge_id in range(0, 3):
                    if self.neighbours[vol_id, edge_id] < 0:
                        if not boundary.has_key( (vol_id, edge_id) ):
                            msg = 'WARNING: Given boundary does not contain '
                            msg += 'tags for edge (%d, %d). '\
                                   %(vol_id, edge_id)
                            msg += 'Assigning default tag (%s).'\
                                   %default_boundary_tag

                            #FIXME: Print only as per verbosity
                            #print msg

                            #FIXME: Make this situation an error in the future
                            #and make another function which will
                            #enable default boundary-tags where
                            #tags a not specified
                            boundary[ (vol_id, edge_id) ] =\
                                      default_boundary_tag



        self.boundary = boundary


    def build_tagged_elements_dictionary(self, tagged_elements = None):
        """Build the dictionary of element tags.
         self.tagged_elements is a dictionary of element arrays,
         keyed by tag:
         { (tag): [e1, e2, e3..] }

         Postconditions:
            self.element_tag is defined
        """
        from Numeric import array, Int

        if tagged_elements is None:
            tagged_elements = {}
        else:
            #Check that all keys in given boundary exist
            for tag in tagged_elements.keys():
                tagged_elements[tag] = array(tagged_elements[tag]).astype(Int)

                msg = 'Not all elements exist. '
                assert max(tagged_elements[tag]) < self.number_of_elements, msg
        #print "tagged_elements", tagged_elements
        self.tagged_elements = tagged_elements

    def build_boundary_structure(self):
        """Traverse boundary and
        enumerate neighbour indices from -1 and
        counting down.

        Precondition:
            self.boundary is defined.
        Post condition:
            neighbour array has unique negative indices for boundary
            boundary_segments array imposes an ordering on segments
            (not otherwise available from the dictionary)

        Note: If a segment is listed in the boundary dictionary
        it *will* become a boundary - even if there is a neighbouring triangle.
        This would be the case for internal boundaries
        """

        #FIXME: Now Obsolete - maybe use some comments from here in
        #domain.set_boundary

        if self.boundary is None:
            msg = 'Boundary dictionary must be defined before '
            msg += 'building boundary structure'
            raise msg


        self.boundary_segments = self.boundary.keys()
        self.boundary_segments.sort()

        index = -1
        for id, edge in self.boundary_segments:

            #FIXME: One would detect internal boundaries as follows
            #if self.neighbours[id, edge] > -1:
            #    print 'Internal boundary'

            self.neighbours[id, edge] = index
            index -= 1


    def get_boundary_tags(self):
        """Return list of available boundary tags
        """

        tags = {}
        for v in self.boundary.values():
            tags[v] = 1

        return tags.keys()


    def get_boundary_polygon(self):
        """Return bounding polygon as a list of points
        """
        from Numeric import allclose, sqrt, array, minimum, maximum



        V = self.get_vertex_coordinates()
        segments = {}

        #pmin = (min(self.coordinates[:,0]), min(self.coordinates[:,1]))
        #pmax = (max(self.coordinates[:,0]), max(self.coordinates[:,1]))

        #FIXME:Can this be written more compactly, e.g.
        #using minimum and maximium?
        pmin = array( [min(self.coordinates[:,0]),
                       min(self.coordinates[:,1]) ] )

        pmax = array( [max(self.coordinates[:,0]),
                       max(self.coordinates[:,1]) ] )

        mindist = sqrt(sum( (pmax-pmin)**2 ))
        for i, edge_id in self.boundary.keys():
            #Find vertex ids for boundary segment
            if edge_id == 0: a = 1; b = 2
            if edge_id == 1: a = 2; b = 0
            if edge_id == 2: a = 0; b = 1

            A = tuple(self.get_vertex_coordinate(i, a))
            B = tuple(self.get_vertex_coordinate(i, b))

            #Take the point closest to pmin as starting point
            #Note: Could be arbitrary, but nice to have
            #a unique way of selecting
            dist_A = sqrt(sum( (A-pmin)**2 ))
            dist_B = sqrt(sum( (B-pmin)**2 ))

            #Find minimal point
            if dist_A < mindist:
                mindist = dist_A
                p0 = A
            if dist_B < mindist:
                mindist = dist_B
                p0 = B


            if p0 is None:
                raise 'Weird'
                p0 = A #We need a starting point (FIXME)

                print 'A', A
                print 'B', B
                print 'pmin', pmin
                print

            segments[A] = B


        #Start with smallest point and follow boundary (counter clock wise)
        polygon = [p0]
        while len(polygon) < len(self.boundary):
            p1 = segments[p0]
            polygon.append(p1)
            p0 = p1

        return polygon


    def check_integrity(self):
        """Check that triangles are internally consistent e.g.
        that area corresponds to edgelengths, that vertices
        are arranged in a counter-clockwise order, etc etc
        Neighbour structure will be checked by class Mesh
        """

        from config import epsilon
        from math import pi
        from util import anglediff

        N = self.number_of_elements

        #Get x,y coordinates for all vertices for all triangles
        V = self.get_vertex_coordinates()

        #Check each triangle
        for i in range(N):
            x0 = V[i, 0]; y0 = V[i, 1]
            x1 = V[i, 2]; y1 = V[i, 3]
            x2 = V[i, 4]; y2 = V[i, 5]

            #Check that area hasn't been compromised
            area = self.areas[i]
            ref = abs((x1*y0-x0*y1)+(x2*y1-x1*y2)+(x0*y2-x2*y0))/2
            msg = 'Wrong area for vertex coordinates: %f %f %f %f %f %f'\
                  %(x0,y0,x1,y1,x2,y2)
            assert abs((area - ref)/area) < epsilon, msg

            #Check that points are arranged in counter clock-wise order
            v0 = [x1-x0, y1-y0]
            v1 = [x2-x1, y2-y1]
            v2 = [x0-x2, y0-y2]

            a0 = anglediff(v1, v0)
            a1 = anglediff(v2, v1)
            a2 = anglediff(v0, v2)

            msg = '''Vertices (%s,%s), (%s,%s), (%s,%s) are not arranged
            in counter clockwise order''' %(x0, y0, x1, y1, x2, y2)
            assert a0 < pi and a1 < pi and a2 < pi, msg

            #Check that normals are orthogonal to edge vectors
            #Note that normal[k] lies opposite vertex k

            normal0 = self.normals[i, 0:2]
            normal1 = self.normals[i, 2:4]
            normal2 = self.normals[i, 4:6]

            for u, v in [ (v0, normal2), (v1, normal0), (v2, normal1) ]:
                assert u[0]*v[0] + u[1]*v[1] < epsilon


        #Check that all vertices have been registered
        for v_id, v in enumerate(self.vertexlist):

            msg = 'Vertex %s does not belong to an element.'
            #assert v is not None, msg
            if v is None:
                print msg%v_id

        #Check integrity of neighbour structure
        for i in range(N):
            for v in self.triangles[i, :]:
                #Check that all vertices have been registered
                assert self.vertexlist[v] is not None

                #Check that this triangle is listed with at least one vertex
                assert (i, 0) in self.vertexlist[v] or\
                       (i, 1) in self.vertexlist[v] or\
                       (i, 2) in self.vertexlist[v]



            #Check neighbour structure
            for k, neighbour_id in enumerate(self.neighbours[i,:]):

                #Assert that my neighbour's neighbour is me
                #Boundaries need not fulfill this
                if neighbour_id >= 0:
                    edge = self.neighbour_edges[i, k]
                    msg = 'Triangle %d has neighbour %d but it does not point back. \n' %(i,neighbour_id)
                    msg += 'Only points to (%s)' %(self.neighbours[neighbour_id,:])
                    assert self.neighbours[neighbour_id, edge] == i ,msg



        #Check that all boundaries have
        # unique, consecutive, negative indices

        #L = len(self.boundary)
        #for i in range(L):
        #    id, edge = self.boundary_segments[i]
        #    assert self.neighbours[id, edge] == -i-1


        #NOTE: This assert doesn't hold true if there are internal boundaries
        #FIXME: Look into this further.
        #FIXME (Ole): In pyvolution mark 3 this is OK again
        #NOTE: No longer works because neighbour structure is modified by
        #      domain set_boundary.
        #for id, edge in self.boundary:
        #    assert self.neighbours[id,edge] < 0
        #
        #NOTE (Ole): I reckon this was resolved late 2004?
        #
        #See domain.set_boundary


    def get_centroid_coordinates(self):
        """Return all centroid coordinates.
        Return all centroid coordinates for all triangles as an Nx2 array
        (ordered as x0, y0 for each triangle)
        """
        return self.centroid_coordinates