def scales(g):
""" Compute the scale of each vertex inside the graph.
One solution: If we have several edges of decomposition, we follow the deeper.
The root is a real node or a false one?
TODO: specification
"""
edge_type = g.edge_property("edge_type").copy()
root = g.root
has_decomposition = [
eid for eid in g.out_edges(root) if edge_type.get(eid) == '/'
]
scale0 = 0
_scale = {root: scale0}
if not has_decomposition:
empty_vertex = True
for name in g.vertex_property_names():
if g.root in g.vertex_property(name):
empty_vertex = False
break
if empty_vertex:
root_edge = g.out_edges(root).next()
edge_type[root_edge] = '/'
for v in breadth_first_search(g, root):
parents = list(g.in_neighbors(v))
edges = list(g.in_edges(v))
if not parents:
continue
decompositions = [eid for eid in edges if edge_type.get(eid) == '/']
if decompositions:
eid_max = None
scale_max = 0
for eid in decompositions:
s = _scale[g.source(eid)]
if s > scale_max:
scale_max = s
eid_max = eid
scale = scale_max + 1
else:
scale = max(_scale.get(pid, 0) for pid in parents)
_scale[v] = scale
return _scale
def scales(g):
""" Compute the scale of each vertex inside the graph.
One solution: If we have several edges of decomposition, we follow the deeper.
The root is a real node or a false one?
TODO: specification
"""
edge_type = g.edge_property("edge_type").copy()
root = g.root
has_decomposition = [eid for eid in g.out_edges(root) if edge_type.get(eid) == '/']
scale0 = 0
_scale = {root : scale0}
if not has_decomposition:
empty_vertex = True
for name in g.vertex_property_names():
if g.root in g.vertex_property(name):
empty_vertex = False
break
if empty_vertex:
root_edge = g.out_edges(root).next()
edge_type[root_edge] = '/'
for v in breadth_first_search(g, root):
parents = list(g.in_neighbors(v))
edges = list(g.in_edges(v))
if not parents:
continue
decompositions = [eid for eid in edges if edge_type.get(eid) == '/']
if decompositions:
eid_max = None
scale_max = 0
for eid in decompositions:
s = _scale[g.source(eid)]
if s > scale_max:
scale_max = s
eid_max = eid
scale = scale_max + 1
else:
scale = max(_scale.get(pid,0) for pid in parents)
_scale[v] = scale
return _scale
def traverse2(self, vid):
from openalea.container.traversal.graph import breadth_first_search
g = self._graph
edge_type = g.edge_property("edge_type")
transform = g.vertex_property("transform")
local_turtles = g.vertex_property("turtle_state")
transfos = {g.root: pgl.Matrix4()}
# CPL
turtles = {g.root: TurtleState()}
def parent(vid):
for eid in g.in_edges(vid):
if edge_type[eid] in ['<', '+', '/']:
return g.source(eid)
return vid
def update_turtle(v, ts):
local_turtle = local_turtles.get(v, TurtleState())
global_turtle = ts.combine(local_turtle)
return global_turtle
for v in breadth_first_search(g, vid):
pid = parent(v)
if pid == v and v != g.root:
print "ERRRORRRR"
print v
continue
# print "v",v
# print "parent(v)", parent(v)
# print "transfos", transfos
#m = transfos[parent(v)]
m = transfos.get(pid)
# CPL
ts = turtles.get(pid, TurtleState())
gt = global_turtle = update_turtle(v, ts)
local_t = transform.get(v)
if local_t == self.FUNCTIONAL:
# Get the functional shape to compute the transformation and geometry
# with the turtle state
local_t = self.f_shape(v, global_turtle)
# print "every m : ", m
# Transform the current shape with the stack of transfos m from the root.
# Store the result in the graph.
self._local2global(v, m, global_turtle.color)
# print "local_t : ", local_t
if local_t == -1:
m = adjust_lu(m)
elif local_t == -2:
#RV and RG
local_m = grotation(m, gt.tropism)
m = m * local_m
elif local_t == -3:
# RD
local_m = directionalTropism(m, gt.tropism_direction, gt.tropism)
m = m * local_m
elif local_t == -4:
# RO
local_m = orthogonalTropism(m, gt.tropism_direction, gt.tropism)
m = m * local_m
elif local_t == -5:
#RP and RN
local_m = positionalTropism(m, gt.tropism_target, gt.tropism)
m = m * local_m
elif local_t:
if local_t.getColumn(3) != Vector4(0, 0, 0, 1):
m = m * local_t
else:
m = m * local_t
else:
# print m
pass
transfos[v] = m
turtles[v] = global_turtle
def traverse2(self, vid):
from openalea.container.traversal.graph import breadth_first_search
g = self._graph
edge_type = g.edge_property("edge_type")
transform = g.vertex_property("transform")
local_turtles = g.vertex_property("turtle_state")
transfos = {g.root: pgl.Matrix4()}
# CPL
turtles = {g.root: TurtleState()}
def parent(vid):
for eid in g.in_edges(vid):
if edge_type[eid] in ['<', '+', '/']:
return g.source(eid)
return vid
def update_turtle(v, ts):
local_turtle = local_turtles.get(v, TurtleState())
global_turtle = ts.combine(local_turtle)
return global_turtle
for v in breadth_first_search(g, vid):
pid = parent(v)
if pid == v and v != g.root:
print "ERRRORRRR"
print v
continue
# print "v",v
# print "parent(v)", parent(v)
# print "transfos", transfos
#m = transfos[parent(v)]
m = transfos.get(pid)
# CPL
ts = turtles.get(pid, TurtleState())
gt = global_turtle = update_turtle(v, ts)
local_t = transform.get(v)
if local_t == self.FUNCTIONAL:
# Get the functional shape to compute the transformation and geometry
# with the turtle state
local_t = self.f_shape(v, global_turtle)
# print "every m : ", m
# Transform the current shape with the stack of transfos m from the root.
# Store the result in the graph.
self._local2global(v, m, global_turtle.color)
# print "local_t : ", local_t
if local_t == -1:
m = adjust_lu(m)
elif local_t == -2:
#RV and RG
local_m = grotation(m, gt.tropism)
m = m * local_m
elif local_t == -3:
# RD
local_m = directionalTropism(m, gt.tropism_direction, gt.tropism)
m = m * local_m
elif local_t == -4:
# RO
local_m = orthogonalTropism(m, gt.tropism_direction, gt.tropism)
m = m * local_m
elif local_t == -5:
#RP and RN
local_m = positionalTropism(m, gt.tropism_target, gt.tropism)
m = m * local_m
elif local_t:
if local_t.getColumn(3) != Vector4(0, 0, 0, 1):
m = m * local_t
else:
m = m * local_t
else:
# print m
pass
transfos[v] = m
turtles[v] = global_turtle