def template_clustering(file,
radius,
order,
show_dyn=False,
show_conn=False,
show_clusters=True,
ena_conn_weight=False,
ccore_flag=True,
tolerance=0.1):
sample = read_sample(file)
network = syncnet(sample,
radius,
enable_conn_weight=ena_conn_weight,
ccore=ccore_flag)
(ticks, analyser) = timedcall(network.process, order, solve_type.FAST,
show_dyn)
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n")
if (show_dyn == True):
sync_visualizer.show_output_dynamic(analyser)
sync_visualizer.animate(analyser)
#sync_visualizer.animate_output_dynamic(analyser);
#sync_visualizer.animate_correlation_matrix(analyser, colormap = 'hsv');
if ((show_conn == True) and (ccore_flag == False)):
network.show_network()
if (show_clusters == True):
clusters = analyser.allocate_clusters(tolerance)
print("amout of clusters: ", len(clusters))
draw_clusters(sample, clusters)
def templateVisualizerNoFailures(self, size, velocity, ccore_flag):
net = sync_network(size, ccore = ccore_flag);
output_dynamic = net.simulate_dynamic(solution = solve_type.FAST, collect_dynamic = True);
sync_visualizer.animate_correlation_matrix(output_dynamic, velocity);
sync_visualizer.animate_output_dynamic(output_dynamic, velocity);
sync_visualizer.show_correlation_matrix(output_dynamic);
sync_visualizer.show_output_dynamic(output_dynamic);
def show_first_layer_dynamic(analyser):
"""!
@brief Shows output dynamic of the first layer.
@param[in] analyser (syncsegm_analyser): Analyser of output dynamic of the 'syncsegm' oscillatory network.
"""
sync_visualizer.show_output_dynamic(analyser.get_first_layer_analyser());
def show_first_layer_dynamic(analyser):
"""!
@brief Shows output dynamic of the first layer.
@param[in] analyser (syncsegm_analyser): Analyser of output dynamic of the 'syncsegm' oscillatory network.
"""
sync_visualizer.show_output_dynamic(analyser.get_first_layer_analyser());
def templateVisualizerNoFailures(self, size, velocity, ccore_flag):
net = sync_network(size, ccore=ccore_flag)
output_dynamic = net.simulate_dynamic(solution=solve_type.FAST,
collect_dynamic=True)
sync_visualizer.animate(output_dynamic)
sync_visualizer.animate_correlation_matrix(output_dynamic, velocity)
sync_visualizer.animate_output_dynamic(output_dynamic, velocity)
sync_visualizer.show_correlation_matrix(output_dynamic)
sync_visualizer.show_output_dynamic(output_dynamic)
def template_clustering(file, number_clusters, arg_order = 0.999, arg_collect_dynamic = True, ccore_flag = False):
sample = read_sample(file);
network = hsyncnet(sample, number_clusters, ccore = ccore_flag);
analyser = network.process(arg_order, collect_dynamic = arg_collect_dynamic);
clusters = analyser.allocate_clusters();
if (arg_collect_dynamic == True):
sync_visualizer.show_output_dynamic(analyser);
draw_clusters(sample, clusters);
def template_dynamic_sync(num_osc, k = 1, sim_arg = None, conn = conn_type.ALL_TO_ALL, type_solution = solve_type.FAST, collect_dyn = True, ccore_flag = False):
network = sync_network(num_osc, k, type_conn = conn, ccore = ccore_flag);
if (sim_arg is not None):
sync_output_dynamic = network.simulate(sim_arg[0], sim_arg[1], solution = type_solution, collect_dynamic = collect_dyn);
else:
sync_output_dynamic = network.simulate_dynamic(collect_dynamic = collect_dyn, solution = type_solution);
sync_visualizer.show_output_dynamic(sync_output_dynamic);
sync_visualizer.animate_output_dynamic(sync_output_dynamic);
sync_visualizer.animate_correlation_matrix(sync_output_dynamic);
return network;
def template_dynamic_sync(num_osc, k = 1, sim_arg = None, conn = conn_type.ALL_TO_ALL, type_solution = solve_type.FAST, collect_dyn = True, ccore_flag = False):
network = sync_network(num_osc, k, type_conn = conn, ccore = ccore_flag);
if (sim_arg is not None):
sync_output_dynamic = network.simulate(sim_arg[0], sim_arg[1], solution = type_solution, collect_dynamic = collect_dyn);
else:
sync_output_dynamic = network.simulate_dynamic(collect_dynamic = collect_dyn, solution = type_solution);
sync_visualizer.show_output_dynamic(sync_output_dynamic);
sync_visualizer.animate_output_dynamic(sync_output_dynamic);
sync_visualizer.animate_correlation_matrix(sync_output_dynamic);
return network;
def template_clustering(file, number_clusters, arg_order = 0.999, arg_collect_dynamic = True, ccore_flag = False):
sample = read_sample(file);
network = hsyncnet(sample, number_clusters, initial_neighbors = int(len(sample) * 0.15), osc_initial_phases = initial_type.EQUIPARTITION, ccore = ccore_flag);
(ticks, analyser) = timedcall(network.process, arg_order, solve_type.FAST, arg_collect_dynamic);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
clusters = analyser.allocate_clusters();
if (arg_collect_dynamic == True):
sync_visualizer.show_output_dynamic(analyser);
draw_clusters(sample, clusters);
def template_clustering(file, number_clusters, arg_order = 0.999, arg_collect_dynamic = True, ccore_flag = False):
sample = read_sample(file);
network = hsyncnet(sample, number_clusters, initial_neighbors = int(len(sample) * 0.15), osc_initial_phases = initial_type.EQUIPARTITION, ccore = ccore_flag);
(ticks, analyser) = timedcall(network.process, arg_order, solve_type.FAST, arg_collect_dynamic);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
clusters = analyser.allocate_clusters();
if (arg_collect_dynamic == True):
sync_visualizer.show_output_dynamic(analyser);
draw_clusters(sample, clusters);
def templateVisualizerNoFailures(size, velocity, ccore_flag):
net = sync_network(size, ccore = ccore_flag);
output_dynamic = net.simulate_dynamic(solution = solve_type.FAST, collect_dynamic = True);
sync_visualizer.animate(output_dynamic);
sync_visualizer.animate_correlation_matrix(output_dynamic, velocity);
sync_visualizer.animate_output_dynamic(output_dynamic, velocity);
sync_visualizer.animate_phase_matrix(output_dynamic, 1, size, velocity);
sync_visualizer.show_correlation_matrix(output_dynamic);
sync_visualizer.show_output_dynamic(output_dynamic);
sync_visualizer.show_phase_matrix(output_dynamic, 1, size);
sync_visualizer.show_order_parameter(output_dynamic);
sync_visualizer.show_local_order_parameter(output_dynamic, net);
def template_clustering(file,
number_clusters,
arg_order=0.999,
arg_collect_dynamic=True,
ccore_flag=False):
sample = read_sample(file)
network = hsyncnet(sample, number_clusters, ccore=ccore_flag)
analyser = network.process(arg_order, collect_dynamic=arg_collect_dynamic)
clusters = analyser.allocate_clusters()
if (arg_collect_dynamic == True):
sync_visualizer.show_output_dynamic(analyser)
draw_clusters(sample, clusters)
def template_clustering(file, radius, order, show_dyn = False, show_conn = False, show_clusters = True, ena_conn_weight = False, ccore_flag = True, tolerance = 0.1):
sample = read_sample(file);
network = syncnet(sample, radius, enable_conn_weight = ena_conn_weight, ccore = ccore_flag);
(ticks, analyser) = timedcall(network.process, order, solve_type.FAST, show_dyn);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
if (show_dyn == True):
sync_visualizer.show_output_dynamic(analyser);
sync_visualizer.animate_output_dynamic(analyser);
if ( (show_conn == True) and (ccore_flag == False) ):
network.show_network();
if (show_clusters == True):
clusters = analyser.allocate_clusters(tolerance);
draw_clusters(sample, clusters);
def template_clustering(file, radius, order, show_dyn = False, show_conn = False, show_clusters = True, ena_conn_weight = False, ccore_flag = True, tolerance = 0.1):
sample = read_sample(file)
syncnet_instance = syncnet(sample, radius, enable_conn_weight = ena_conn_weight, ccore = ccore_flag)
(ticks, analyser) = timedcall(syncnet_instance.process, order, solve_type.FAST, show_dyn)
print("Sample: ", file, "\t\tExecution time: ", ticks)
if show_dyn == True:
sync_visualizer.show_output_dynamic(analyser)
sync_visualizer.animate(analyser)
sync_visualizer.show_local_order_parameter(analyser, syncnet_instance)
#sync_visualizer.animate_output_dynamic(analyser);
#sync_visualizer.animate_correlation_matrix(analyser, colormap = 'hsv')
if show_conn == True:
syncnet_instance.show_network()
if show_clusters == True:
clusters = analyser.allocate_clusters(tolerance)
print("Amount of allocated clusters: ", len(clusters))
draw_clusters(sample, clusters)
print("----------------------------\n")
return (sample, clusters)
def template_clustering(file, radius, order, show_dyn = False, show_conn = False, show_clusters = True, ena_conn_weight = False, ccore_flag = True, tolerance = 0.1):
sample = read_sample(file)
syncnet_instance = syncnet(sample, radius, enable_conn_weight = ena_conn_weight, ccore = ccore_flag)
(ticks, analyser) = timedcall(syncnet_instance.process, order, solve_type.FAST, show_dyn)
print("Sample: ", file, "\t\tExecution time: ", ticks)
if show_dyn == True:
sync_visualizer.show_output_dynamic(analyser)
sync_visualizer.animate(analyser)
sync_visualizer.show_local_order_parameter(analyser, syncnet_instance)
#sync_visualizer.animate_output_dynamic(analyser);
#sync_visualizer.animate_correlation_matrix(analyser, colormap = 'hsv')
if show_conn == True:
syncnet_instance.show_network()
if show_clusters == True:
clusters = analyser.allocate_clusters(tolerance)
print("Amount of allocated clusters: ", len(clusters))
draw_clusters(sample, clusters)
print("----------------------------\n")
return (sample, clusters)
def template_segmentation_image(source, color_radius, object_radius, noise_size, show_dyn):
data = read_image(source);
print("Pixel dimension: ", len(data[0]));
network = syncnet(data, color_radius, ccore = True);
print("Network has been created");
(ticks, analyser) = timedcall(network.process, 0.9995, solve_type.FAST, show_dyn);
print("Sample: ", source, "\t\tExecution time: ", ticks, "\n");
if (show_dyn is True):
sync_visualizer.show_output_dynamic(analyser);
clusters = analyser.allocate_clusters();
real_clusters = [cluster for cluster in clusters if len(cluster) > noise_size];
draw_image_mask_segments(source, real_clusters);
if (object_radius is None):
return;
# continue analysis
pointer_image = Image.open(source);
image_size = pointer_image.size;
object_colored_clusters = [];
object_colored_dynamics = [];
total_dyn = [];
for cluster in clusters:
coordinates = [];
for index in cluster:
y = floor(index / image_size[0]);
x = index - y * image_size[0];
coordinates.append([x, y]);
print(coordinates);
# perform clustering analysis of the colored objects
if (network is not None):
del network;
network = None;
if (len(coordinates) < noise_size):
continue;
network = syncnet(coordinates, object_radius, ccore = True);
analyser = network.process(0.999, solve_type.FAST, show_dyn);
if (show_dyn is True):
object_colored_dynamics.append( (analyser.time, analyser.output) );
object_clusters = analyser.allocate_clusters();
# decode it
real_description_clusters = [];
for object_cluster in object_clusters:
real_description = [];
for index_object in object_cluster:
real_description.append(cluster[index_object]);
real_description_clusters.append(real_description);
if (len(real_description) > noise_size):
object_colored_clusters.append(real_description);
# draw_image_mask_segments(source, [ cluster ]);
# draw_image_mask_segments(source, real_description_clusters);
draw_image_mask_segments(source, object_colored_clusters);
if (show_dyn is True):
draw_dynamics_set(object_colored_dynamics, None, None, None, [0, 2 * 3.14], False, False);
def template_segmentation_image(source, color_radius, object_radius, noise_size, show_dyn):
data = read_image(source);
print("Pixel dimension: ", len(data[0]));
network = syncnet(data, color_radius, ccore = True);
print("Network has been created");
(ticks, analyser) = timedcall(network.process, 0.9995, solve_type.FAST, show_dyn);
print("Sample: ", source, "\t\tExecution time: ", ticks, "\n");
if (show_dyn is True):
sync_visualizer.show_output_dynamic(analyser);
clusters = analyser.allocate_clusters();
real_clusters = [cluster for cluster in clusters if len(cluster) > noise_size];
draw_image_mask_segments(source, real_clusters);
if (object_radius is None):
return;
# continue analysis
pointer_image = Image.open(source);
image_size = pointer_image.size;
object_colored_clusters = [];
object_colored_dynamics = [];
total_dyn = [];
for cluster in clusters:
coordinates = [];
for index in cluster:
y = floor(index / image_size[0]);
x = index - y * image_size[0];
coordinates.append([x, y]);
print(coordinates);
# perform clustering analysis of the colored objects
if (network is not None):
del network;
network = None;
if (len(coordinates) < noise_size):
continue;
network = syncnet(coordinates, object_radius, ccore = True);
analyser = network.process(0.999, solve_type.FAST, show_dyn);
if (show_dyn is True):
object_colored_dynamics.append( (analyser.time, analyser.output) );
object_clusters = analyser.allocate_clusters();
# decode it
real_description_clusters = [];
for object_cluster in object_clusters:
real_description = [];
for index_object in object_cluster:
real_description.append(cluster[index_object]);
real_description_clusters.append(real_description);
if (len(real_description) > noise_size):
object_colored_clusters.append(real_description);
# draw_image_mask_segments(source, [ cluster ]);
# draw_image_mask_segments(source, real_description_clusters);
draw_image_mask_segments(source, object_colored_clusters);
if (show_dyn is True):
draw_dynamics_set(object_colored_dynamics, None, None, None, [0, 2 * 3.14], False, False);
