def extend_front(front, seed, constellation):
# new_pos =front.xyz + np.array([L_NORM,0,2.0])
# new_front = prepare_next_front(front,new_pos,set_radius=1.0)
# return [new_front]
# repelled by a different neuron, get information
other_entities = get_entity("cell_type_1", constellation)
target = np.array([200, 30, 200])
if not len(other_entities) == 0:
dir_to_entity = gradient_to(front,
other_entities,
strength=10.0,
decay_factor=0.15,
cutoff=2.5,
what="nearest")
if np.allclose(dir_to_entity, np.array([0.0, 0.0, 0.0])):
new_pos = front.xyz + \
normalize_length(target-front.xyz,L_NORM ) + \
normalize_length(unit_sample_on_sphere(),L_NORM/1.0 ) # was 10
new_front = prepare_next_front(front, new_pos, set_radius=1.0)
print("[debug] To_left.py:: still far")
else:
new_pos = front.xyz + normalize_length(
-1 * dir_to_entity + unit_sample_on_sphere(), L_NORM)
new_front = prepare_next_front(front, new_pos, set_radius=1.0)
print("[debug] To_left.py:: potential intercept avoided")
else: # during the first extension cycle, no distal info is known locally
new_pos = front.xyz + normalize_length(target - front.xyz, L_NORM)
new_front = prepare_next_front(front, new_pos, set_radius=1.0)
return [new_front]
def extend_front(front, seed, constellation):
if front.order == 0: # soma
new_fronts = []
for i in range(2):
rnd_dir = unit_sample_on_sphere()
rnd_dir[2] = np.abs(rnd_dir[2])
new_pos = normalize_length(rnd_dir, 25)
new_pos = front.xyz + new_pos
new_front = prepare_next_front(front,
new_pos,
set_radius=3.0,
add_order=1)
new_fronts.append(new_front)
return new_fronts
else:
rnd_dir = unit_sample_on_sphere()
other_entities = get_entity("pia", constellation)
#print ("pia entities: ", other_entities)
dir_to_pia = direction_to(front, other_entities, what="nearest")
new_pos = front.xyz + normalize_length(dir_to_pia, 10)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.5,
add_order=False)
return [new_front]
def extend_front(front,seed,constellation) :
# new_pos =front.xyz + np.array([L_NORM,0,2.0])
# new_front = prepare_next_front(front,new_pos,set_radius=1.0)
# return [new_front]
# repelled by a different neuron, get information
other_entities = get_entity("cell_type_1",constellation)
target = np.array([200,30,200])
if not len(other_entities) == 0:
dir_to_entity = gradient_to(front,other_entities,strength=10.0,decay_factor=0.15,cutoff=2.5,what="nearest")
if np.allclose(dir_to_entity,np.array([0.0,0.0,0.0])):
new_pos = front.xyz + \
normalize_length(target-front.xyz,L_NORM ) + \
normalize_length(unit_sample_on_sphere(),L_NORM/1.0 ) # was 10
new_front = prepare_next_front(front,new_pos,set_radius=1.0)
print "[debug] To_left.py:: still far"
else:
new_pos = front.xyz + normalize_length(-1*dir_to_entity + unit_sample_on_sphere(),L_NORM)
new_front = prepare_next_front(front,new_pos,set_radius=1.0)
print "[debug] To_left.py:: potential intercept avoided"
else: # during the first extension cycle, no distal info is known locally
new_pos =front.xyz + normalize_length(target-front.xyz,L_NORM )
new_front = prepare_next_front(front,new_pos,set_radius=1.0)
return [new_front]
def extend_front(front, seed, constellation):
if front.order == 0: # this is the soma
new_fronts = []
for i in range(np.random.randint(8, 17)):
rnd_dir = unit_sample_on_sphere()
new_pos = front.xyz + normalize_length(rnd_dir, L_NORM)
new_front = prepare_next_front(front,
new_pos,
set_radius=8.0,
add_order=True)
new_front.swc_type = 2
new_fronts.append(new_front)
return new_fronts
else:
# follow a simple branching rule in all other cases
bif_prob = 0.6 / (front.order * 2.5)
if front.order > 5:
bif_prob = 0.03
if np.random.random() > bif_prob: # continue a front
# random component
rnd_dir = unit_sample_on_sphere()
# unit vector of current heading
heading = front.xyz - front.parent.xyz
# soma-tropism, sample direction away from the soma
soma_dir = -1.0 * normalize_length(
direction_to(front, [front.soma_pos], what="nearest"), 0.4)
# combine all infliences on the new direction of growth
new_dir = normalize_length(heading, 1.0) + soma_dir + rnd_dir
new_pos = front.xyz + normalize_length(new_dir, L_NORM)
if front.radius <= 0.3:
new_front = prepare_next_front(front,
new_pos,
set_radius=0.3,
add_order=False)
else:
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.9,
add_order=False)
# stop growth
if np.random.random() < 0.06 and front.path_length >= 600:
return []
return [new_front]
else: # branch a front, generate two child fronts
new_fronts = []
for i in range(2):
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(heading, 1.5) + rnd_dir
new_pos = front.xyz + normalize_length(new_dir, L_NORM)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.7,
add_order=True)
new_fronts.append(new_front)
return new_fronts
def extend_front(front,seed,constellation) :
if front.order == 0 : # soma
new_fronts = []
for i in range(3):
rnd_dir = unit_sample_on_sphere()
rnd_dir[2] = np.abs(rnd_dir[2])
new_pos = normalize_length(rnd_dir,15)
new_pos = front.xyz + new_pos
new_front = prepare_next_front(front,new_pos,set_radius=3.0,add_order=1)
new_fronts.append(new_front)
return new_fronts
else :
if np.random.random() < 0.1 :
new_fronts = []
other_entities = get_entity("pia",constellation)
for i in range(2):
rnd_dir = unit_sample_on_sphere()
if len(other_entities) == 0:
rnd_dir = unit_sample_on_sphere()
rnd_dir[2] = np.abs(rnd_dir[2])
new_pos = normalize_length(rnd_dir,15)
new_pos = front.xyz + new_pos
new_front = prepare_next_front(front,new_pos,set_radius=3.0,add_order=1)
new_fronts.append(new_front)
else:
dir_to_pia = direction_to(front,other_entities,what="nearest")
dir_to_pia = normalize_length(dir_to_pia,2.0) + rnd_dir
new_pos = front.xyz + normalize_length(dir_to_pia,10)
new_front = prepare_next_front(front,new_pos,radius_factor=0.5,add_order=True)
new_fronts.append(new_front)
return new_fronts
else :
rnd_dir = unit_sample_on_sphere()
other_entities = get_entity("pia",constellation)
gradient_to_pia = gradient_to(front,other_entities,1.0,0.0001,what='nearest')
# or go directly to the pia
# gradient_to_pia = direction_to(front,other_entities,what='nearest')
pia_interaction = gradient_to_pia + rnd_dir
new_dir = normalize_length(pia_interaction,1.5) #+ rnd_dir
new_pos = front.xyz + normalize_length(new_dir,10)
new_front = prepare_next_front(front,new_pos,radius_factor=0.95)
if new_pos[2] > 190 :
return None
else:
return [new_front]
def extend_basal_front(front, constellation):
bif_prob = 0.78 / (2.0 * front.order + 1.0) # was2.5 and 0.8
bif_prob = bif_prob if front.order < 6 else bif_prob / 1.8
bif_prob = 0.0 if front.order >= 6 else bif_prob
if front.order >= 2 and np.random.random() < 0.07: # wa 00.6
front.no_branch = True
if np.random.random() > bif_prob or \
front.no_branch:
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
soma_dir = -1.0 * normalize_length(
direction_to(front, [front.soma_pos], what="nearest"), 0.8)
eigen_entities = get_eigen_entity(front, constellation)
eigen_dir = np.array([0, 0, 0])
eigen_dir = -1.0 * gradient_to(front,
eigen_entities,
5.0 / (front.order),
0.05,
what="nearest",
cutoff=0.2)
new_dir = normalize_length(heading, 1.0) + soma_dir + normalize_length(
rnd_dir, 1.2) + eigen_dir
new_pos = front.xyz + normalize_length(new_dir, L_NORM)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=False)
term_prob = 0.015 * front.order if front.order <= 6 else 0.3
if np.random.random() < term_prob:
return []
return [new_front]
else:
new_fronts = []
for i in range(2):
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(heading, 1.5) + rnd_dir
new_pos = front.xyz + normalize_length(new_dir, L_NORM)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=True)
new_fronts.append(new_front)
return new_fronts
def extend_front(front,seed,constellation) :
if front.order == 0:
new_fronts = []
for i in range(5):
rnd_dir = unit_sample_on_sphere()
new_pos = normalize_length(rnd_dir,L_NORM*5)
new_pos = front.xyz + new_pos
new_front = prepare_next_front(front,new_pos,set_radius=1.0,add_order=True)
new_fronts.append(new_front)
return new_fronts
else:
rnd_dir = unit_sample_on_sphere()
new_pos = normalize_length(rnd_dir,L_NORM)
new_pos = front.xyz + new_pos
new_front = prepare_next_front(front,new_pos,set_radius=1.0)
return [new_front]
def extend_front(front, seed, constellation):
""" Grow away from self-contained stretches of neurites.
"""
eigen_entities = get_eigen_entity(front, constellation)
if len(eigen_entities) == 0:
if front.order == 0:
rnd_dir = unit_sample_on_sphere()
new_pos = front.xyz + np.array([26, 0, 0])
new_front = prepare_next_front(front,
new_pos,
set_radius=1.0,
add_order=True)
return [new_front]
else:
new_pos1 = front.xyz + np.array([5, 5, 0])
new_front1 = prepare_next_front(front,
new_pos1,
set_radius=1.0,
add_order=True)
new_pos2 = front.xyz + np.array([5, -5, 0])
new_front2 = prepare_next_front(front,
new_pos2,
set_radius=1.0,
add_order=True)
return [new_front1, new_front2]
else:
if np.random.random() > 0.2:
rnd_dir = unit_sample_on_sphere()
dir_away = -1.0 * direction_to(
front, eigen_entities, what="nearest")
dir_away = normalize_length(dir_away, 4) + rnd_dir
new_pos = front.xyz + normalize_length(dir_away, L_NORM)
new_front = prepare_next_front(front, new_pos, set_radius=1.0)
return [new_front]
else:
daughters = []
for i in range(2):
rnd_dir = unit_sample_on_sphere()
dir_away = -1.0 * direction_to(
front, eigen_entities, what="nearest")
dir_away = normalize_length(dir_away, 4.0) + rnd_dir
new_pos = front.xyz + normalize_length(dir_away, L_NORM)
new_front = prepare_next_front(front, new_pos, set_radius=1.0)
daughters.append(new_front)
return daughters
def extend_front(front,seed,constellation) :
if front.order == 0 : # soma
new_fronts = []
for i in range(2):
rnd_dir = unit_sample_on_sphere()
rnd_dir[2] = np.abs(rnd_dir[2])
new_pos = normalize_length(rnd_dir,25)
new_pos = front.xyz + new_pos
new_front = prepare_next_front(front,new_pos,set_radius=3.0,add_order=1)
new_fronts.append(new_front)
return new_fronts
else :
rnd_dir = unit_sample_on_sphere()
other_entities = get_entity("pia",constellation)
#print "pia entities: ", other_entities
dir_to_pia = direction_to(front,other_entities,what="nearest")
new_pos = front.xyz + normalize_length(dir_to_pia,10)
new_front = prepare_next_front(front,new_pos,radius_factor=0.5,add_order=False)
return [new_front]
def extend_front(front, seed, constellation):
if front.order == 0:
new_fronts = []
for i in range(5):
rnd_dir = unit_sample_on_sphere()
new_pos = normalize_length(rnd_dir, L_NORM * 5)
new_pos = front.xyz + new_pos
new_front = prepare_next_front(front,
new_pos,
set_radius=1.0,
add_order=True)
new_fronts.append(new_front)
return new_fronts
else:
rnd_dir = unit_sample_on_sphere()
new_pos = normalize_length(rnd_dir, L_NORM)
new_pos = front.xyz + new_pos
new_front = prepare_next_front(front, new_pos, set_radius=1.0)
return [new_front]
def extend_front(front,seed,constellation) :
""" follow gradient to a_point Attractor.
The stronger the attraction becomes, the straighter the path
to the attractor
"""
other_entities = get_entity("a_point",constellation)
rnd_dir = unit_sample_on_sphere()
dir_to_entity = gradient_to(front,other_entities,1.7,0.01,what="nearest")
dir_to_entity = dir_to_entity + 0.5*rnd_dir
new_pos = front.xyz + normalize_length(dir_to_entity,L_NORM)
new_front = prepare_next_front(front,new_pos,set_radius=1.0)
return [new_front]
def create_basal_branches(front, constellation):
new_fronts = []
for i in range(np.random.randint(5, 11)):
rnd_dir = unit_sample_on_sphere()
rnd_dir[2] = -1.0 * np.abs(
rnd_dir[2]) # try to grow "downwards"/deeper
new_pos = front.xyz + normalize_length(rnd_dir, L_NORM)
new_front = prepare_next_front(front,
new_pos,
set_radius=0.3,
add_order=True)
new_front.swc_type = 3
new_front.no_branch = False
new_fronts.append(new_front)
return new_fronts
def extend_front(front,seed,constellation) :
# attract by a different neuron, get information
other_entities = get_entity("substance_x",constellation)
# fetch the case that no such entities exists
if len(other_entities) == 0 :
new_pos =front.xyz + np.array([L_NORM,0,0])
else :
rnd_dir = unit_sample_on_sphere()
dir_to_entity = direction_to(front,other_entities,what="nearest")
dir_to_entity = normalize_length(dir_to_entity,1.0) + rnd_dir*0.3
new_pos = front.xyz + normalize_length(dir_to_entity,L_NORM)
new_front = prepare_next_front(front,new_pos,set_radius=1.5)
return [new_front]
def extend_front(front, seed, constellation):
# attract by a different neuron, get information
other_entities = get_entity("substance_x", constellation)
# fetch the case that no such entities exists
if len(other_entities) == 0:
new_pos = front.xyz + np.array([L_NORM, 0, 0])
else:
rnd_dir = unit_sample_on_sphere()
dir_to_entity = direction_to(front, other_entities, what="nearest")
dir_to_entity = normalize_length(dir_to_entity, 1.0) + rnd_dir * 0.3
new_pos = front.xyz + normalize_length(dir_to_entity, L_NORM)
new_front = prepare_next_front(front, new_pos, set_radius=1.5)
return [new_front]
def extend_apical_front(front, constellation):
pia = get_entity("pia", constellation)
dir_to_pia = direction_to(front, pia, what="nearest")
grad_to_pia = gradient_to(front,
pia,
5.0,
0.015,
cutoff=0.3,
what="nearest")
if np.sqrt(sum((grad_to_pia)**2)) >= 3.0:
print "too close to pia!!! +++++++++++++++++++"
#time.sleep(1)
return []
if (front.layer == 4 or front.layer == 5) and not front.oblique:
# make an oblique dendrite with some probability (and extend the main branch)
oblique_prob = 0.3 if front.layer == 4 else 0.12 # maybe L5 -> 0.0
if front.order >= 15:
oblique_prob = 0.0
if np.random.random() < oblique_prob:
new_fronts = []
# orthogonal direction to parent branch: HOWTO?
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(heading,0.5) + \
normalize_length(rnd_dir,1.0)
new_pos = front.xyz + normalize_length(new_dir, APICAL_NORM)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=True)
new_front.oblique = True
#new_front.swc_type=8
new_front.oblique_start = front # for directional information
# if front.layer == 4 and np.random.random() < 0.005:
# new_front.oblique=False
# and extend
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(dir_to_pia,1.5) + \
normalize_length(heading,0.) + \
normalize_length(rnd_dir,.5)
new_pos = front.xyz + normalize_length(new_dir, APICAL_MAIN)
new_front2 = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=True)
#new_front2.swc_type = 4 if front.layer == 5 else 5
return [new_front, new_front2]
else:
# only extend
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(dir_to_pia,1.5) + \
normalize_length(heading,0.) + \
normalize_length(rnd_dir,.5)
new_pos = front.xyz + normalize_length(new_dir, APICAL_MAIN)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=False)
#new_front.swc_type = 4 if front.layer == 5 else 5
return [new_front]
if front.oblique:
# extend or terminate the oblique
if front.path_length - front.oblique_start.path_length > 100 and np.random.random(
) < 0.1:
return []
else:
rnd_dir = unit_sample_on_sphere()
eigen_entities = get_eigen_entity(front, constellation)
eigen_dir = np.array([0, 0, 0])
# fairly strong self-repulsion, away from the main trunk...
eigen_dir = -1.5 * gradient_to(
front, eigen_entities, 5.0, 0.03, what="nearest", cutoff=0.2)
# or away from the trunk, front.oblique_start
dir_from_trunk = -1.0 * (front.oblique_start.xyz - front.xyz)
dir_from_trunk = normalize_length(dir_from_trunk, 1.0)
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(heading,1.0) + \
normalize_length(rnd_dir,0.5) + \
dir_from_trunk
#normalize_length(dir_to_pia,0.1)
#eigen_dir # + \
# normalize_length(dir_to_pia,0.4)
if front.layer == 3:
new_dir = new_dir + -1.0 * normalize_length(dir_to_pia, 0.5)
# if front.layer == 5 :
# new_dir = new_dir + normalize_length(dir_to_pia,0.5)
new_pos = front.xyz + normalize_length(new_dir, APICAL_NORM)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=False)
#new_front.swc_type=8
# make "normal, not oblique"
return [new_front]
if front.layer >= 3: #== 3:
# keep track of the order to limit the number of bifurcations in this layer
if not hasattr(front, 'start_order_L3') and front.layer == 3:
# this is the first time, store the current order
front.start_order_L3 = front.order
# bifurcate or extend
bif_prob = 0.08
if front.order - front.start_order_L3 >= 3:
bif_prob = 0
if np.random.random() < bif_prob:
#bifurcate
new_fronts = []
first_vec = None
attempts = 0
for i in range(2):
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(dir_to_pia,0.5) + \
normalize_length(heading,0.5) + \
normalize_length(rnd_dir,1.0)
new_pos = front.xyz + normalize_length(new_dir, APICAL_NORM)
# make sure the bif amplitude reaches a minimum...
if first_vec == None:
first_vec = normalize_length(new_dir, APICAL_NORM)
else:
angle_amp = _amp_angle(
first_vec, normalize_length(new_dir, APICAL_NORM))
while angle_amp <= MIN_AMP_ANGLE and attempts <= MAX_ANGLE_ATTEMPTS:
attempts = attempts + 1
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(dir_to_pia,0.5) + \
normalize_length(heading,0.5) + \
normalize_length(rnd_dir,1.0)
new_pos = front.xyz + normalize_length(
new_dir, APICAL_NORM)
angle_amp = _amp_angle(
first_vec, normalize_length(new_dir, APICAL_NORM))
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=True)
new_front.tufted = True
# new_front.swc_type = 5
new_fronts.append(new_front)
return new_fronts
else:
# simply extend
rnd_dir = unit_sample_on_sphere()
eigen_entities = get_eigen_entity(front, constellation)
eigen_dir = np.array([0, 0, 0])
eigen_dir = -1.0 * gradient_to(front,
eigen_entities,
front.repulsion_l3,
0.015,
what="nearest",
cutoff=0.2)
gradient_from_pia = -1.0 * gradient_to(
front, pia, 5 * 0.7, 0.01, what="nearest", cutoff=0.2)
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(heading,1.0) + \
normalize_length(rnd_dir,0.3) + \
eigen_dir + \
normalize_length(dir_to_pia,1.)+ \
gradient_from_pia
new_pos = front.xyz + normalize_length(new_dir, APICAL_NORM)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=False)
# new_front.swc_type = 6
# some probability to terminate
if front.layer == 3 and np.random.random(
) < 0.01 and front.order - front.start_order_L3 >= 3:
return []
else:
return [new_front]
#return [new_front]
if front.layer == 2:
# keep track of the order to limit the number of bifurcations in this layer
if not hasattr(front, 'start_order_L2'):
# this is the first time, store the current order
front.start_order_L2 = front.order
# bifurcate or extend
bif_prob = 0.1
if front.order - front.start_order_L3 <= 2:
bif_prob = 0.4
rrr = np.random.random()
if rrr <= 0.5:
if front.order - front.start_order_L2 >= 3:
bif_prob = 0
else:
if front.order - front.start_order_L2 >= 4:
bif_prob = 0
if np.random.random() < bif_prob:
#bifurcate
new_fronts = []
first_vec = None
attempts = 0
for i in range(2):
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(dir_to_pia,0.5) + \
normalize_length(heading,0.5) + \
normalize_length(rnd_dir,1.0)
new_pos = front.xyz + normalize_length(new_dir, APICAL_NORM)
# make sure the bif amplitude reaches a minimum...
if first_vec == None:
first_vec = normalize_length(new_dir, APICAL_NORM)
else:
angle_amp = _amp_angle(
first_vec, normalize_length(new_dir, APICAL_NORM))
while angle_amp <= MIN_AMP_ANGLE and attempts <= MAX_ANGLE_ATTEMPTS:
attempts = attempts + 1
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(dir_to_pia,0.5) + \
normalize_length(heading,0.5) + \
normalize_length(rnd_dir,1.0)
new_pos = front.xyz + normalize_length(
new_dir, APICAL_NORM)
angle_amp = _amp_angle(
first_vec, normalize_length(new_dir, APICAL_NORM))
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=True)
new_front.tufted = True
# new_front.swc_type = 7
new_fronts.append(new_front)
return new_fronts
else:
# simply extend
rnd_dir = unit_sample_on_sphere()
eigen_entities = get_eigen_entity(front, constellation)
eigen_dir = np.array([0, 0, 0])
eigen_dir = -1.0 * gradient_to(front,
eigen_entities,
front.repulsion_l2,
0.015,
what="nearest",
cutoff=0.2)
gradient_from_pia = -1.0 * gradient_to(
front, pia, 4.0 * 0.7, 0.01, what="nearest", cutoff=0.2)
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(heading,1.0) + \
normalize_length(rnd_dir,0.3) + \
eigen_dir + \
normalize_length(dir_to_pia,1.)+ \
gradient_from_pia
new_pos = front.xyz + normalize_length(new_dir, APICAL_NORM)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=False)
# front.swc_type = 7
# some probability to terminate
if np.random.random(
) < 0.03: #* (front.order - front.start_order_L2):
return []
else:
return [new_front]
if front.layer == 1:
# keep track of the order to limit the number of bifurcations in this layer
if not hasattr(front, 'start_order_L1'):
# this is the first time, store the current order
front.start_order_L1 = front.order
# bifurcate or extend
bif_prob = 0.08
if front.order - front.start_order_L3 <= 4:
bif_prob = 0.3
if front.order - front.start_order_L1 >= 1:
bif_prob = 0
if np.random.random() < bif_prob:
#bifurcate
new_fronts = []
first_vec = None
attempts = 0
for i in range(2):
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(dir_to_pia,0.5) + \
normalize_length(heading,0.5) + \
normalize_length(rnd_dir,1.0)
new_pos = front.xyz + normalize_length(new_dir, APICAL_NORM)
# make sure the bif amplitude reaches a minimum...
if first_vec == None:
print "first_vec == None, new_dir: ", new_dir
first_vec = normalize_length(new_dir, APICAL_NORM)
else:
print "first_vec: ", first_vec, " , other: ", normalize_length(
new_dir, APICAL_NORM)
angle_amp = _amp_angle(
first_vec, normalize_length(new_dir, APICAL_NORM))
while angle_amp <= MIN_AMP_ANGLE and attempts <= MAX_ANGLE_ATTEMPTS:
attempts = attempts + 1
rnd_dir = unit_sample_on_sphere()
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(dir_to_pia,0.5) + \
normalize_length(heading,0.5) + \
normalize_length(rnd_dir,1.0)
new_pos = front.xyz + normalize_length(
new_dir, APICAL_NORM)
angle_amp = _amp_angle(
first_vec, normalize_length(new_dir, APICAL_NORM))
print "angle_amp=", angle_amp, ", attemps: ", attempts
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=True)
new_front.tufted = True
#new_front.swc_type = 8
new_fronts.append(new_front)
return new_fronts
else:
# simply extend
rnd_dir = unit_sample_on_sphere()
eigen_entities = get_eigen_entity(front, constellation)
eigen_dir = np.array([0, 0, 0])
eigen_dir = -1.0 * gradient_to(front,
eigen_entities,
front.repulsion_l1,
0.015,
what="nearest",
cutoff=0.2)
gradient_from_pia = 0 * gradient_to(
front, pia, 3.0 * 0.7, 0.01, what="nearest",
cutoff=0.2) # 2014-05-12
heading = front.xyz - front.parent.xyz
new_dir = normalize_length(heading,1.0) + \
normalize_length(rnd_dir,0.3) + \
eigen_dir + \
normalize_length(dir_to_pia,1.)+ \
gradient_from_pia
new_pos = front.xyz + normalize_length(new_dir, APICAL_NORM)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.98,
add_order=False)
#new_front.swc_type = 8
if np.random.random() < 0.06:
return []
else:
return [new_front]
return [new_front]
def extend_front(front, seed, constellation):
if front.order == 0: # soma
new_fronts = []
for i in range(3):
rnd_dir = unit_sample_on_sphere()
rnd_dir[2] = np.abs(rnd_dir[2])
new_pos = normalize_length(rnd_dir, 15)
new_pos = front.xyz + new_pos
new_front = prepare_next_front(front,
new_pos,
set_radius=3.0,
add_order=1)
new_fronts.append(new_front)
return new_fronts
else:
if np.random.random() < 0.1:
new_fronts = []
other_entities = get_entity("pia", constellation)
for i in range(2):
rnd_dir = unit_sample_on_sphere()
if len(other_entities) == 0:
rnd_dir = unit_sample_on_sphere()
rnd_dir[2] = np.abs(rnd_dir[2])
new_pos = normalize_length(rnd_dir, 15)
new_pos = front.xyz + new_pos
new_front = prepare_next_front(front,
new_pos,
set_radius=3.0,
add_order=1)
new_fronts.append(new_front)
else:
dir_to_pia = direction_to(front,
other_entities,
what="nearest")
dir_to_pia = normalize_length(dir_to_pia, 2.0) + rnd_dir
new_pos = front.xyz + normalize_length(dir_to_pia, 10)
new_front = prepare_next_front(front,
new_pos,
radius_factor=0.5,
add_order=True)
new_fronts.append(new_front)
return new_fronts
else:
rnd_dir = unit_sample_on_sphere()
other_entities = get_entity("pia", constellation)
gradient_to_pia = gradient_to(front,
other_entities,
1.0,
0.0001,
what='nearest')
# or go directly to the pia
# gradient_to_pia = direction_to(front,other_entities,what='nearest')
pia_interaction = gradient_to_pia + rnd_dir
new_dir = normalize_length(pia_interaction, 1.5) #+ rnd_dir
new_pos = front.xyz + normalize_length(new_dir, 10)
new_front = prepare_next_front(front, new_pos, radius_factor=0.95)
if new_pos[2] > 190:
return None
else:
return [new_front]