def saveUVDistanceForPost(filename, name, p_index, c_index):
""" Saves connection length data but for incoming connections of a given
post-synaptic neuron,
name and p_index therefore also refer to the post-synaptic layer """
object = bpy.data.objects[name]
uvs = getUVs(object, p_index)
distances = []
axon_lengths = []
number_particles = len(object.particle_systems[p_index].particles)
for i, p in enumerate(object.particle_systems[p_index].particles):
logger.info('%i / %i' % (i, number_particles))
pre_neurons, synapses = model.getPreIndicesOfPostIndex(c_index, i)
distance = []
axon_length = []
for j in range(len(pre_neurons)):
distance.append(model.CONNECTION_RESULTS[c_index]['d'][pre_neurons[j]][synapses[j]])
axon_length.append(pam.computeDistance_PreToSynapse(c_index, pre_neurons[j], [synapses[j]])[0])
distances.append(np.mean(distance))
axon_lengths.append(np.mean(axon_length))
f = open(filename, 'w')
writer = csv.writer(
f,
delimiter=";",
quoting=csv.QUOTE_NONNUMERIC
)
for i, uv in enumerate(uvs):
#for d in distances[i]:
writer.writerow([uv[0], uv[1], distances[i], axon_lengths[i]])
f.close()
def saveUVDistanceForPost(filename, name, p_index, c_index):
""" Saves connection length data but for incoming connections of a given
post-synaptic neuron,
name and p_index therefore also refer to the post-synaptic layer """
object = bpy.data.objects[name]
uvs = getUVs(object, p_index)
distances = []
axon_lengths = []
number_particles = len(object.particle_systems[p_index].particles)
for i, p in enumerate(object.particle_systems[p_index].particles):
logger.info('%i / %i' % (i, number_particles))
pre_neurons, synapses = model.getPreIndicesOfPostIndex(c_index, i)
distance = []
axon_length = []
for j in range(len(pre_neurons)):
distance.append(model.CONNECTION_RESULTS[c_index]['d'][
pre_neurons[j]][synapses[j]])
axon_length.append(
pam.computeDistance_PreToSynapse(c_index, pre_neurons[j],
[synapses[j]])[0])
distances.append(np.mean(distance))
axon_lengths.append(np.mean(axon_length))
f = open(filename, 'w')
writer = csv.writer(f, delimiter=";", quoting=csv.QUOTE_NONNUMERIC)
for i, uv in enumerate(uvs):
#for d in distances[i]:
writer.writerow([uv[0], uv[1], distances[i], axon_lengths[i]])
f.close()
def colorizeMapping(mapping_id):
""" Colorizes pre layer for a given mapping """
pre = model.CONNECTIONS[mapping_id][0][0]
post = model.CONNECTIONS[mapping_id][0][-1]
#neuron = bpy.data.objects['Neuron_Sphere']
distances_pre = getDistancesPerParticle(
model.CONNECTION_RESULTS[mapping_id]['d'])
pre_indices = getParticleIndicesForVertices(pre, 0)
for i, p_ind in enumerate(pre_indices):
if distances_pre[p_ind] == 0:
pv.visualizePoint(
pre.particle_systems[0].particles[p_ind].location)
distances_post = []
post_indices = getParticleIndicesForVertices(post, 0)
for i, p in enumerate(post.particle_systems[0].particles):
pre_neurons, synapses = model.getPreIndicesOfPostIndex(mapping_id, i)
distance = []
for j in range(len(pre_neurons)):
distance.append(model.CONNECTION_RESULTS[mapping_id]['d'][
pre_neurons[j]][synapses[j]])
mean = np.mean(distance)
if np.isnan(mean):
distances_post.append(0)
else:
distances_post.append(mean)
for i, p_ind in enumerate(post_indices):
if distances_post[p_ind] == 0:
pv.visualizePoint(
post.particle_systems[0].particles[p_ind].location)
print(distances_post)
print(type(distances_pre))
print(type(distances_post))
mean_d = np.mean(list(distances_pre) + distances_post)
min_percent = np.min(list(distances_pre) + distances_post) / mean_d
max_percent = np.max(list(distances_pre) + distances_post) / mean_d
distances_pre = distances_pre / mean_d
distances_post = np.array(distances_post) / mean_d
colorizeLayer(pre, np.take(distances_pre, pre_indices),
[min_percent, max_percent])
colorizeLayer(post, np.take(distances_post, post_indices),
[min_percent, max_percent])
def colorizeMapping(mapping_id):
""" Colorizes pre layer for a given mapping """
pre = model.CONNECTIONS[mapping_id][0][0]
post = model.CONNECTIONS[mapping_id][0][-1]
#neuron = bpy.data.objects['Neuron_Sphere']
distances_pre = getDistancesPerParticle(model.CONNECTION_RESULTS[mapping_id]['d'])
pre_indices = getParticleIndicesForVertices(pre, 0)
for i, p_ind in enumerate(pre_indices):
if distances_pre[p_ind] == 0:
pv.visualizePoint(pre.particle_systems[0].particles[p_ind].location)
distances_post = []
post_indices = getParticleIndicesForVertices(post, 0)
for i, p in enumerate(post.particle_systems[0].particles):
pre_neurons, synapses = model.getPreIndicesOfPostIndex(mapping_id, i)
distance = []
for j in range(len(pre_neurons)):
distance.append(model.CONNECTION_RESULTS[mapping_id]['d'][pre_neurons[j]][synapses[j]])
mean = np.mean(distance)
if np.isnan(mean):
distances_post.append(0)
else:
distances_post.append(mean)
for i, p_ind in enumerate(post_indices):
if distances_post[p_ind] == 0:
pv.visualizePoint(post.particle_systems[0].particles[p_ind].location)
print(distances_post)
print(type(distances_pre))
print(type(distances_post))
mean_d = np.mean(list(distances_pre) + distances_post)
min_percent = np.min(list(distances_pre) + distances_post) / mean_d
max_percent = np.max(list(distances_pre) + distances_post) / mean_d
distances_pre = distances_pre / mean_d
distances_post = np.array(distances_post) / mean_d
colorizeLayer(pre, np.take(distances_pre, pre_indices), [min_percent, max_percent])
colorizeLayer(post, np.take(distances_post, post_indices), [min_percent, max_percent])
