def radial(center, vertex, b):
#Sammelt Numerische Werte aus Variables-Objekt
pForward=singleton.radialpForward
pTurn=singleton.radialpTurn
lMin=singleton.radiallMin
lMax=singleton.radiallMax
#Berechnet Radialvector und Vektor des letzten Weges zu diesem Punkt
radialvector=vertex.coords-center
previous_vector=np.array(vertex.coords-vertex.neighbours[len(vertex.neighbours)-1].coords)
previous_vector=previous_vector/np.linalg.norm(previous_vector)
suggested_vertices=[]
weiter=False
#Berechnet, ob der Vektor eher Radial oder Tangential verlaeuft
alpha=case_differentiation(previous_vector, radialvector)
previous_vector=rotate(alpha, previous_vector)
#Geradeaus
v=random.uniform(lMin, lMax)*previous_vector
random_number=random.randint(0, 100)
if random_number<=pForward:
k=Vertex(vertex.coords+v)
suggested_vertices.append(k)
#Rechts
v=random.uniform(lMin, lMax)*previous_vector
random_number=random.randint(0, 100)
if random_number<=pTurn*b*b:
k=Vertex(vertex.coords+rotate(90, v))
suggested_vertices.append(k)
weiter=True
#Links
v=random.uniform(lMin, lMax)*previous_vector
random_number=random.randint(0, 100)
if random_number<=pTurn*b*b:
k=Vertex(vertex.coords-rotate(90, v))
suggested_vertices.append(k)
weiter=True
#Seed!
if not weiter:
vertex.seed=True
singleton.global_lists.vertex_queue.append([vertex, 0])
return suggested_vertices
def radial(center, vertex, b):
#Sammelt Numerische Werte aus Variables-Objekt
pForward = singleton.radialpForward
pTurn = singleton.radialpTurn
lMin = singleton.radiallMin
lMax = singleton.radiallMax
#Berechnet Radialvector und Vektor des letzten Weges zu diesem Punkt
radialvector = vertex.coords - center
previous_vector = np.array(vertex.coords -
vertex.neighbours[len(vertex.neighbours) -
1].coords)
previous_vector = previous_vector / np.linalg.norm(previous_vector)
suggested_vertices = []
weiter = False
#Berechnet, ob der Vektor eher Radial oder Tangential verlaeuft
alpha = case_differentiation(previous_vector, radialvector)
previous_vector = rotate(alpha, previous_vector)
#Geradeaus
v = random.uniform(lMin, lMax) * previous_vector
random_number = random.randint(0, 100)
if random_number <= pForward:
k = Vertex(vertex.coords + v)
suggested_vertices.append(k)
#Rechts
v = random.uniform(lMin, lMax) * previous_vector
random_number = random.randint(0, 100)
if random_number <= pTurn * b * b:
k = Vertex(vertex.coords + rotate(90, v))
suggested_vertices.append(k)
weiter = True
#Links
v = random.uniform(lMin, lMax) * previous_vector
random_number = random.randint(0, 100)
if random_number <= pTurn * b * b:
k = Vertex(vertex.coords - rotate(90, v))
suggested_vertices.append(k)
weiter = True
#Seed!
if not weiter:
vertex.seed = True
singleton.global_lists.vertex_queue.append([vertex, 0])
return suggested_vertices
def organic(vertex, b):
# Sammelt Numerische Werte aus Variables-Objekt
pForward = singleton.organicpForward
pTurn = singleton.organicpTurn
lMin = singleton.organiclMin
lMax = singleton.organiclMax
suggested_vertices = []
weiter = False
# Berechnet den Vektor des letzten Weges zu diesem Punkt
previous_vector = np.array(vertex.coords -
vertex.neighbours[len(vertex.neighbours) -
1].coords)
previous_vector = previous_vector / np.linalg.norm(previous_vector)
# Geradeaus
v = random.uniform(lMin, lMax) * previous_vector
random_number = random.randint(0, 100)
if random_number <= pForward:
k = Vertex(vertex.coords + rotate(np.random.uniform(-30, 30), v))
suggested_vertices.append(k)
# Rechts
v = random.uniform(lMin, lMax) * previous_vector
random_number = random.randint(0, 100)
if random_number <= b * pTurn:
k = Vertex(vertex.coords + rotate(np.random.uniform(-120, -60), v))
suggested_vertices.append(k)
weiter = True
# Links
v = random.uniform(lMin, lMax) * previous_vector
random_number = random.randint(0, 100)
if random_number <= b * pTurn:
k = Vertex(vertex.coords + rotate(np.random.uniform(60, 120), v))
suggested_vertices.append(k)
weiter = True
# Seed!
if not weiter:
vertex.seed = True
singleton.global_lists.vertex_queue.append([vertex, 0])
return suggested_vertices
def organic(vertex,b): #Sammelt Numerische Werte aus Variables-Objekt pForward=singleton.organicpForward pTurn=singleton.organicpTurn lMin=singleton.organiclMin lMax=singleton.organiclMax suggested_vertices=[] weiter=False #Berechnet den Vektor des letzten Weges zu diesem Punkt previous_vector=np.array(vertex.coords-vertex.neighbours[len(vertex.neighbours)-1].coords) previous_vector=previous_vector/np.linalg.norm(previous_vector) #Geradeaus v=random.uniform(lMin,lMax)*previous_vector random_number=random.randint(0,100) if random_number<=pForward: k=Vertex(vertex.coords+rotate(np.random.uniform(-30,30),v)) suggested_vertices.append(k) #Rechts v=random.uniform(lMin,lMax)*previous_vector random_number=random.randint(0,100) if random_number<=b*pTurn: k=Vertex(vertex.coords+rotate(np.random.uniform(-120,-60),v)) suggested_vertices.append(k) weiter=True #Links v=random.uniform(lMin,lMax)*previous_vector random_number=random.randint(0,100) if random_number<=b*pTurn: k=Vertex(vertex.coords+rotate(np.random.uniform(60,120),v)) suggested_vertices.append(k) weiter=True #Seed! if not weiter: vertex.seed=True singleton.global_lists.vertex_queue.append([vertex, 0]) return suggested_vertices
def seed(vertex,b): pSeed=singleton.pSeed lMin=singleton.seedlMin lMax=singleton.seedlMax suggested_vertices=[] l=len(vertex.neighbours) v1=rotate(90,vertex.neighbours[0].coords-vertex.coords) v2=None if l==1: v2=v1 elif l==2: v2=rotate(90,vertex.neighbours[1].coords-vertex.coords)*-1 else: return [] v1=v1/np.linalg.norm(v1) v2=v2/np.linalg.norm(v2) #Rechts if b*b*pSeed>np.random.randint(0,100): l=np.random.uniform(lMin,lMax) k=np.random.uniform(0,1) coords=((1-k)*v1+k*v2)*l k=Vertex(vertex.coords+coords) k.minor_road=True suggested_vertices.append(k) v1=v1*-1 v2=v2*-1 #Links if b*b*pSeed>np.random.randint(0,100): l=np.random.uniform(lMin,lMax) k=np.random.uniform(0,1) coords=((1-k)*v1+k*v2)*l k=Vertex(vertex.coords+coords) k.minor_road=True suggested_vertices.append(k) return suggested_vertices
def seed(vertex, b):
pSeed = singleton.pSeed
lMin = singleton.seedlMin
lMax = singleton.seedlMax
suggested_vertices = []
l = len(vertex.neighbours)
v1 = rotate(90, vertex.neighbours[0].coords - vertex.coords)
v2 = None
if l == 1:
v2 = v1
elif l == 2:
v2 = rotate(90, vertex.neighbours[1].coords - vertex.coords) * -1
else:
return []
v1 = v1 / np.linalg.norm(v1)
v2 = v2 / np.linalg.norm(v2)
#Rechts
if b * b * pSeed > np.random.randint(0, 100):
l = np.random.uniform(lMin, lMax)
k = np.random.uniform(0, 1)
coords = ((1 - k) * v1 + k * v2) * l
k = Vertex(vertex.coords + coords)
k.minor_road = True
suggested_vertices.append(k)
v1 = v1 * -1
v2 = v2 * -1
#Links
if b * b * pSeed > np.random.randint(0, 100):
l = np.random.uniform(lMin, lMax)
k = np.random.uniform(0, 1)
coords = ((1 - k) * v1 + k * v2) * l
k = Vertex(vertex.coords + coords)
k.minor_road = True
suggested_vertices.append(k)
return suggested_vertices