def six_oscillators_mix_3_stimulated():
params = hhn_parameters()
params.deltah = 400
stimulus = [0, 0, 25, 25, 47, 47]
template_dynamic_hhn(6, 1200, 600, stimulus, params, separate=True, ccore_flag=False)
template_dynamic_hhn(6, 2400, 600, stimulus, params, separate=True, ccore_flag=True)
def six_oscillators_mix_3_stimulated():
params = hhn_parameters();
params.deltah = 400;
stimulus = [0, 0, 25, 25, 47, 47];
template_dynamic_hhn(6, 1200, 600, stimulus, params, separate=True, ccore_flag=False);
template_dynamic_hhn(6, 2400, 600, stimulus, params, separate=True, ccore_flag=True);
def ten_oscillators_stimulated_partial_sync():
params = hhn_parameters()
params.w1 = 0.1
params.w2 = 5.0
params.w3 = 0
template_dynamic_hhn(10, 750, 200, [25, 25, 25, 25, 25, 11, 11, 11, 11, 11], params, separate_representation=True)
def two_ensembles_80_oscillators():
params = hhn_parameters()
params.deltah = 650
params.w1 = 0.1
params.w2 = 9.0
params.w3 = 5.0
params.threshold = -10
expected_ensembles = []
stimulus = []
base_stimulus = 10
step_stimulus = 10
amount_ensebles = 4
region_size = 20
for i in range(amount_ensebles):
expected_ensembles += [[
i for i in range(region_size * i, region_size * i + region_size)
]]
stimulus += [base_stimulus + step_stimulus * i] * region_size
template_dynamic_hhn(len(stimulus),
4000,
1000,
stimulus,
params,
separate=expected_ensembles,
ccore_flag=True)
def ten_oscillators_stimulated_sync():
params = hhn_parameters();
params.w1 = 0.1;
params.w2 = 0.0;
params.w3 = 0;
template_dynamic_hhn(10, 750, 100, [25, 25, 25, 25, 25, 27, 27, 27, 27, 27], params, separate_representation = True);
def ten_oscillators_stimulated_partial_sync():
params = hhn_parameters();
params.w1 = 0.1;
params.w2 = 5.0;
params.w3 = 0;
template_dynamic_hhn(10, 750, 200, [25, 25, 25, 25, 25, 11, 11, 11, 11, 11], params, separate_representation = True);
def three_sync_ensembles():
params = hhn_parameters();
params.deltah = 400;
stimulus = [25, 26, 25, 25, 26, 45, 46, 45, 44, 45, 65, 65, 65, 64, 66];
separate = [ [0, 1, 2, 3, 4], [5, 6, 7, 8, 9], [10, 11, 12, 13, 14] ];
num_osc = len(stimulus);
template_dynamic_hhn(num_osc, 2400, 600, stimulus, params, separate=separate, ccore_flag=True);
def three_sync_ensembles():
params = hhn_parameters();
params.deltah = 400;
stimulus = [25, 26, 25, 25, 26, 45, 46, 45, 44, 45, 65, 65, 65, 64, 66];
separate = [ [0, 1, 2, 3, 4], [5, 6, 7, 8, 9], [10, 11, 12, 13, 14] ];
num_osc = len(stimulus);
template_dynamic_hhn(num_osc, 2400, 600, stimulus, params, separate=separate, ccore_flag=True);
def ten_oscillators_mix_stimulated():
params = hhn_parameters()
params.deltah = 400
template_dynamic_hhn(6,
1200,
600, [0, 0, 25, 25, 47, 47],
params,
separate_representation=True)
def ten_oscillators_stimulated_partial_sync():
params = hhn_parameters();
params.w1 = 0.1;
params.w2 = 5.0;
params.w3 = 0;
stimulus = [25, 25, 25, 25, 25, 11, 11, 11, 11, 11];
template_dynamic_hhn(10, 750, 200, stimulus, params, separate=True, ccore_flag=False);
template_dynamic_hhn(10, 750, 200, stimulus, params, separate=True, ccore_flag=True);
def ten_oscillators_stimulated_partial_sync():
params = hhn_parameters();
params.w1 = 0.1;
params.w2 = 5.0;
params.w3 = 0;
stimulus = [25, 25, 25, 25, 25, 11, 11, 11, 11, 11];
template_dynamic_hhn(10, 750, 200, stimulus, params, separate=True, ccore_flag=False);
template_dynamic_hhn(10, 750, 200, stimulus, params, separate=True, ccore_flag=True);
def ten_oscillators_stimulated_sync():
params = hhn_parameters()
params.w1 = 0.1
params.w2 = 0.0
params.w3 = 0
stumulus = [25, 25, 25, 25, 25, 27, 27, 27, 27, 27]
template_dynamic_hhn(10, 750, 100, stumulus, params, separate=True, ccore_flag=False)
template_dynamic_hhn(10, 750, 100, stumulus, params, separate=True, ccore_flag=True)
def ten_oscillators_stimulated_sync():
params = hhn_parameters()
params.w1 = 0.1
params.w2 = 0.0
params.w3 = 0
template_dynamic_hhn(10,
750,
100, [25, 25, 25, 25, 25, 27, 27, 27, 27, 27],
params,
separate_representation=True)
def two_ensembles_80_oscillators():
params = hhn_parameters();
params.deltah = 650;
params.w1 = 0.1;
params.w2 = 9.0;
params.w3 = 5.0;
params.threshold = -10;
expected_ensembles = [];
stimulus = [];
base_stimulus = 10;
step_stimulus = 10;
amount_ensebles = 4;
region_size = 20;
for i in range(amount_ensebles):
expected_ensembles += [ [i for i in range(region_size * i, region_size * i + region_size)] ];
stimulus += [ base_stimulus + step_stimulus * i ] * region_size;
template_dynamic_hhn(len(stimulus), 4000, 1000, stimulus, params, separate=expected_ensembles, ccore_flag=True);
def six_oscillators_mix_2_stimulated():
params = hhn_parameters();
params.deltah = 400;
template_dynamic_hhn(6, 1200, 600, [25, 25, 25, 47, 47, 47], params, separate_representation = True);
def template_image_segmentation(image_file, steps, time, dynamic_file_prefix):
image = read_image(image_file);
stimulus = rgb2gray(image);
params = hhn_parameters();
params.deltah = 650;
params.w1 = 0.1;
params.w2 = 9.0;
params.w3 = 5.0;
params.threshold = -10;
stimulus = [255.0 - pixel for pixel in stimulus];
divider = max(stimulus) / 50.0;
stimulus = [int(pixel / divider) for pixel in stimulus];
t, dyn_peripheral, dyn_central = None, None, None;
if ( not os.path.exists(dynamic_file_prefix + 'dynamic_time.txt') or
not os.path.exists(dynamic_file_prefix + 'dynamic_peripheral.txt') or
not os.path.exists(dynamic_file_prefix + 'dynamic_dyn_central.txt') ):
print("File with output dynamic is not found - simulation will be performed - it may take some time, be patient.");
net = hhn_network(len(stimulus), stimulus, params, ccore=True);
(t, dyn_peripheral, dyn_central) = net.simulate(steps, time);
print("Store dynamic to save time for simulation next time.");
with open(dynamic_file_prefix + 'dynamic_time.txt', 'wb') as file_descriptor:
pickle.dump(t, file_descriptor);
with open(dynamic_file_prefix + 'dynamic_peripheral.txt', 'wb') as file_descriptor:
pickle.dump(dyn_peripheral, file_descriptor);
with open(dynamic_file_prefix + 'dynamic_dyn_central.txt', 'wb') as file_descriptor:
pickle.dump(dyn_central, file_descriptor);
else:
print("Load output dynamic from file.");
with open (dynamic_file_prefix + 'dynamic_time.txt', 'rb') as file_descriptor:
t = pickle.load(file_descriptor);
with open (dynamic_file_prefix + 'dynamic_peripheral.txt', 'rb') as file_descriptor:
dyn_peripheral = pickle.load(file_descriptor);
with open (dynamic_file_prefix + 'dynamic_dyn_central.txt', 'rb') as file_descriptor:
dyn_central = pickle.load(file_descriptor);
animate_segmentation(t, dyn_peripheral, image_file, 200);
# just for checking correctness of results - let's use classical algorithm
if (False):
dbscan_instance = dbscan(image, 3, 4, True);
dbscan_instance.process();
trustable_clusters = dbscan_instance.get_clusters();
amount_canvases = len(trustable_clusters) + 2;
visualizer = dynamic_visualizer(amount_canvases, x_title = "Time", y_title = "V", y_labels = False);
visualizer.append_dynamics(t, dyn_peripheral, 0, trustable_clusters);
visualizer.append_dynamics(t, dyn_central, amount_canvases - 2, True);
visualizer.show();
def ten_oscillators_mix_stimulated():
params = hhn_parameters()
params.deltah = 400
template_dynamic_hhn(6, 1200, 600, [0, 0, 25, 25, 47, 47], params, separate_representation=True)
def template_image_segmentation(image_file, steps, time, dynamic_file_prefix):
image = read_image(image_file)
stimulus = rgb2gray(image)
params = hhn_parameters()
params.deltah = 650
params.w1 = 0.1
params.w2 = 9.0
params.w3 = 5.0
params.threshold = -10
stimulus = [255.0 - pixel for pixel in stimulus]
divider = max(stimulus) / 50.0
stimulus = [int(pixel / divider) for pixel in stimulus]
t, dyn_peripheral, dyn_central = None, None, None
if (not os.path.exists(dynamic_file_prefix + 'dynamic_time.txt') or
not os.path.exists(dynamic_file_prefix + 'dynamic_peripheral.txt')
or not os.path.exists(dynamic_file_prefix +
'dynamic_dyn_central.txt')):
print(
"File with output dynamic is not found - simulation will be performed - it may take some time, be patient."
)
net = hhn_network(len(stimulus), stimulus, params, ccore=True)
(t, dyn_peripheral, dyn_central) = net.simulate(steps, time)
print("Store dynamic to save time for simulation next time.")
with open(dynamic_file_prefix + 'dynamic_time.txt',
'wb') as file_descriptor:
pickle.dump(t, file_descriptor)
with open(dynamic_file_prefix + 'dynamic_peripheral.txt',
'wb') as file_descriptor:
pickle.dump(dyn_peripheral, file_descriptor)
with open(dynamic_file_prefix + 'dynamic_dyn_central.txt',
'wb') as file_descriptor:
pickle.dump(dyn_central, file_descriptor)
else:
print("Load output dynamic from file.")
with open(dynamic_file_prefix + 'dynamic_time.txt',
'rb') as file_descriptor:
t = pickle.load(file_descriptor)
with open(dynamic_file_prefix + 'dynamic_peripheral.txt',
'rb') as file_descriptor:
dyn_peripheral = pickle.load(file_descriptor)
with open(dynamic_file_prefix + 'dynamic_dyn_central.txt',
'rb') as file_descriptor:
dyn_central = pickle.load(file_descriptor)
animate_segmentation(t, dyn_peripheral, image_file, 200)
# just for checking correctness of results - let's use classical algorithm
if (False):
dbscan_instance = dbscan(image, 3, 4, True)
dbscan_instance.process()
trustable_clusters = dbscan_instance.get_clusters()
amount_canvases = len(trustable_clusters) + 2
visualizer = dynamic_visualizer(amount_canvases,
x_title="Time",
y_title="V",
y_labels=False)
visualizer.append_dynamics(t, dyn_peripheral, 0, trustable_clusters)
visualizer.append_dynamics(t, dyn_central, amount_canvases - 2, True)
visualizer.show()
def six_oscillators_mix_2_stimulated():
params = hhn_parameters();
params.deltah = 400;
template_dynamic_hhn(6, 1200, 600, [25, 25, 25, 47, 47, 47], params, separate_representation = True);
