def calc_bearing(uh, vh):
""" Calculate velocity bearing from North
"""
#FIXME (Ole): I reckon we should refactor this one to use
# the function angle() in utilities/numerical_tools
#
# It will be a simple matter of
# * converting from radians to degrees
# * moving the reference direction from [1,0] to North
# * changing from counter clockwise to clocwise.
# if indeterminate, just return
if uh==0 and vh==0:
return NAN
return degrees(angle([uh, vh], [0, -1]))
def calc_bearing(uh, vh):
""" Calculate velocity bearing from North
"""
#FIXME (Ole): I reckon we should refactor this one to use
# the function angle() in utilities/numerical_tools
#
# It will be a simple matter of
# * converting from radians to degrees
# * moving the reference direction from [1,0] to North
# * changing from counter clockwise to clocwise.
# if indeterminate, just return
if uh == 0 and vh == 0:
return NAN
return degrees(angle([uh, vh], [0, -1]))
def get_boundary_polygon(self, verbose=False):
"""Return bounding polygon for mesh (counter clockwise)
Using the mesh boundary, derive a bounding polygon for this mesh.
If multiple vertex values are present (vertices stored uniquely),
the algorithm will select the path that contains the entire mesh.
All points are in absolute UTM coordinates
"""
from anuga.utilities.numerical_tools import angle, ensure_numeric
# Get mesh extent
xmin, xmax, ymin, ymax = self.get_extent(absolute=True)
pmin = ensure_numeric([xmin, ymin])
pmax = ensure_numeric([xmax, ymax])
# Assemble dictionary of boundary segments and choose starting point
segments = {}
inverse_segments = {}
p0 = None
# Start value across entire mesh
mindist = num.sqrt(num.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 = self.get_vertex_coordinate(i, a, absolute=True) # Start
B = self.get_vertex_coordinate(i, b, absolute=True) # End
# Take the point closest to pmin as starting point
# Note: Could be arbitrary, but nice to have
# a unique way of selecting
dist_A = num.sqrt(num.sum((A-pmin)**2))
dist_B = num.sqrt(num.sum((B-pmin)**2))
# Find lower leftmost point
if dist_A < mindist:
mindist = dist_A
p0 = A
if dist_B < mindist:
mindist = dist_B
p0 = B
# Sanity check
if p0 is None:
msg = 'Impossible: p0 is None!?'
raise Exception(msg)
# Register potential paths from A to B
if not segments.has_key(tuple(A)):
segments[tuple(A)] = [] # Empty list for candidate points
segments[tuple(A)].append(B)
# Start with smallest point and follow boundary (counter clock wise)
polygon = [list(p0)]# Storage for final boundary polygon
point_registry = {} # Keep track of storage to avoid multiple runs
# around boundary. This will only be the case if
# there are more than one candidate.
# FIXME (Ole): Perhaps we can do away with polygon
# and use only point_registry to save space.
point_registry[tuple(p0)] = 0
while len(point_registry) < len(self.boundary):
candidate_list = segments[tuple(p0)]
if len(candidate_list) > 1:
# Multiple points detected (this will be the case for meshes
# with duplicate points as those used for discontinuous
# triangles with vertices stored uniquely).
# Take the candidate that is furthest to the clockwise
# direction, as that will follow the boundary.
#
# This will also be the case for pathological triangles
# that have no neighbours.
if verbose:
log.critical('Point %s has multiple candidates: %s'
% (str(p0), candidate_list))
# Check that previous are not in candidate list
#for p in candidate_list:
# assert not allclose(p0, p)
# Choose vector against which all angles will be measured
if len(polygon) > 1:
v_prev = p0 - polygon[-2] # Vector that leads to p0
# from previous point
else:
# FIXME (Ole): What do we do if the first point has
# multiple candidates?
# Being the lower left corner, perhaps we can use the
# vector [1, 0], but I really don't know if this is
# completely watertight.
v_prev = [1.0, 0.0]
# Choose candidate with minimum angle
minimum_angle = 2*pi
for pc in candidate_list:
vc = pc-p0 # Candidate vector (from p0 to candidate pt)
# Angle between each candidate and the previous vector
# in [-pi, pi]
ac = angle(vc, v_prev)
if ac > pi:
# Give preference to angles on the right hand side
# of v_prev
ac = ac-2*pi
# Take the minimal angle corresponding to the
# rightmost vector
if ac < minimum_angle:
minimum_angle = ac
p1 = pc # Best candidate
if verbose is True:
log.critical(' Best candidate %s, angle %f'
% (p1, minimum_angle*180/pi))
else:
p1 = candidate_list[0]
if point_registry.has_key(tuple(p1)):
# We have reached a point already visited.
if num.allclose(p1, polygon[0]):
# If it is the initial point, the polygon is complete.
if verbose is True:
log.critical(' Stop criterion fulfilled at point %s'
% str(p1))
log.critical(str(polygon))
# We have completed the boundary polygon - yeehaa
break
else:
# The point already visited is not the initial point
# This would be a pathological triangle, but the
# algorithm must be able to deal with this
pass
else:
# We are still finding new points on the boundary
point_registry[tuple(p1)] = len(point_registry)
polygon.append(list(p1)) # De-numeric each point :-)
p0 = p1
return polygon