def frame_generation(index_iteration):
print(index_iteration);
figure.clf();
figure.suptitle("Hodgkin-Huxley Network (iteration: " + str(index_iteration) +")", fontsize = 18, fontweight = 'bold');
ax1 = figure.add_subplot(121);
ax2 = figure.add_subplot(122);
end_iteration = index_iteration;
if (end_iteration > len(dyn_peripheral)):
end_iteration = len(dyn_peripheral);
dynamic_length = 100;
begin_iteration = end_iteration - dynamic_length;
if (begin_iteration < 0):
begin_iteration = 0;
# Display output dynamic
xlim = [dyn_time[begin_iteration], dyn_time[begin_iteration + dynamic_length]];
visualizer = dynamic_visualizer(1, x_title="Time", y_title="V", x_lim=xlim, y_lim=ylim);
dyn_time_segment = [ dyn_time[i] for i in range(begin_iteration, end_iteration, 1) ];
dyn_peripheral_segment = [ dyn_peripheral[i] for i in range(begin_iteration, end_iteration, 1) ];
visualizer.append_dynamic(dyn_time_segment, dyn_peripheral_segment);
visualizer.show(ax1, False);
visualize_segmenetation(end_iteration, ax2, step);
return [ figure.gca() ];
def frame_generation(index_iteration):
print(index_iteration)
figure.clf()
figure.suptitle("Hodgkin-Huxley Network (iteration: " + str(index_iteration) +")", fontsize = 18, fontweight = 'bold')
ax1 = figure.add_subplot(121)
ax2 = figure.add_subplot(122)
end_iteration = index_iteration
if (end_iteration > len(dyn_peripheral)):
end_iteration = len(dyn_peripheral)
dynamic_length = 100
begin_iteration = end_iteration - dynamic_length
if begin_iteration < 0:
begin_iteration = 0
# Display output dynamic
xlim = [dyn_time[begin_iteration], dyn_time[begin_iteration + dynamic_length]]
visualizer = dynamic_visualizer(1, x_title="Time", y_title="V", x_lim=xlim, y_lim=ylim)
dyn_time_segment = [ dyn_time[i] for i in range(begin_iteration, end_iteration, 1) ]
dyn_peripheral_segment = [ dyn_peripheral[i] for i in range(begin_iteration, end_iteration, 1) ]
visualizer.append_dynamic(dyn_time_segment, dyn_peripheral_segment)
visualizer.show(ax1, False)
visualize_segmenetation(end_iteration, ax2, step)
return [ figure.gca() ]
def testVisualizeSeveralDynamicsOneCanvasNoCrash(self):
t1 = [0, 1, 2, 3, 4, 5, 6, 7];
y1 = [0, 0, 1, 2, 0, 1, 2, 0];
t2 = [0, 1, 2, 3, 4, 5, 6, 7];
y2 = [ [0, 0], [0, 0], [1, 0], [2, 1], [0, 2], [1, 0], [2, 1], [0, 2] ];
visualizer = dynamic_visualizer(1);
visualizer.append_dynamic(t1, y1);
visualizer.append_dynamics(t2, y2);
def testVisualizeSeveralDynamicsOneCanvasNoCrash(self):
t1 = [0, 1, 2, 3, 4, 5, 6, 7];
y1 = [0, 0, 1, 2, 0, 1, 2, 0];
t2 = [0, 1, 2, 3, 4, 5, 6, 7];
y2 = [ [0, 0], [0, 0], [1, 0], [2, 1], [0, 2], [1, 0], [2, 1], [0, 2] ];
visualizer = dynamic_visualizer(1);
visualizer.append_dynamic(t1, y1);
visualizer.append_dynamics(t2, y2);
def testVisualizeSeveralDynamicsSeveralCanvasesNoCrash(self):
t1 = [0, 1, 2, 3, 4, 5, 6, 7];
y1 = [0, 0, 1, 2, 0, 1, 2, 0];
t2 = [0, 1, 2, 3, 4, 5, 6, 7];
y2 = [ [0, 0], [0, 0], [1, 0], [2, 1], [0, 2], [1, 0], [2, 1], [0, 2] ];
visualizer = dynamic_visualizer(3);
visualizer.append_dynamic(t1, y1, canvas=0);
visualizer.append_dynamics(t2, y2, canvas=1, separate=True);
def testVisualizeSeveralDynamicsSeveralCanvasesNoCrash(self):
t1 = [0, 1, 2, 3, 4, 5, 6, 7];
y1 = [0, 0, 1, 2, 0, 1, 2, 0];
t2 = [0, 1, 2, 3, 4, 5, 6, 7];
y2 = [ [0, 0], [0, 0], [1, 0], [2, 1], [0, 2], [1, 0], [2, 1], [0, 2] ];
visualizer = dynamic_visualizer(3);
visualizer.append_dynamic(t1, y1, canvas=0);
visualizer.append_dynamics(t2, y2, canvas=1, separate=True);
def template_dynamic_hhn(num_osc, steps, time, stimulus = None, params = None, separate = False, ccore_flag = False):
net = hhn_network(num_osc, stimulus, params, ccore = ccore_flag);
(t, dyn_peripheral, dyn_central) = net.simulate(steps, time);
amount_canvases = 1;
if (isinstance(separate, list)):
amount_canvases = len(separate) + 2;
elif (separate is True):
amount_canvases = len(dyn_peripheral[0]) + 2;
visualizer = dynamic_visualizer(amount_canvases, x_title = "Time", y_title = "V", y_labels = False);
visualizer.append_dynamics(t, dyn_peripheral, 0, separate);
visualizer.append_dynamics(t, dyn_central, amount_canvases - 2, True);
visualizer.show();
def template_dynamic_hhn(num_osc, steps, time, stimulus = None, params = None, separate = False, ccore_flag = False):
net = hhn_network(num_osc, stimulus, params, ccore = ccore_flag);
(t, dyn_peripheral, dyn_central) = net.simulate(steps, time);
amount_canvases = 1;
if (isinstance(separate, list)):
amount_canvases = len(separate) + 2;
elif (separate is True):
amount_canvases = len(dyn_peripheral[0]) + 2;
visualizer = dynamic_visualizer(amount_canvases, x_title = "Time", y_title = "V", y_labels = False);
visualizer.append_dynamics(t, dyn_peripheral, 0, separate);
visualizer.append_dynamics(t, dyn_central, amount_canvases - 2, True);
visualizer.show();
def testVisualizeSeparateListNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7];
y = [ [0, 0], [0, 0], [1, 0], [2, 1], [0, 2], [1, 0], [2, 1], [0, 2] ];
visualizer = dynamic_visualizer(2);
visualizer.append_dynamics(t, y, canvas=0, separate=[ [0], [1] ]);
def testVisualizeMultipleDynamicNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7];
y = [ [0, 0], [0, 0], [1, 0], [2, 1], [0, 2], [1, 0], [2, 1], [0, 2] ];
visualizer = dynamic_visualizer(1);
visualizer.append_dynamics(t, y);
def testVisualizeSignleDynamicNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7];
y = [0, 0, 1, 2, 0, 1, 2, 0];
visualizer = dynamic_visualizer(1);
visualizer.append_dynamic(t, y);
def testVisualizeUnusedCanvasesNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7];
y = [0, 0, 1, 2, 0, 1, 2, 0];
visualizer = dynamic_visualizer(3);
visualizer.append_dynamic(t, y, canvas=0, color='red');
def testVisualizeSeparateSequenceNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7]
y = [[0, 0], [0, 0], [1, 0], [2, 1], [0, 2], [1, 0], [2, 1], [0, 2]]
visualizer = dynamic_visualizer(2)
visualizer.append_dynamics(t, y, canvas=0, separate=True)
def testVisualizeSeparateListNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7];
y = [ [0, 0], [0, 0], [1, 0], [2, 1], [0, 2], [1, 0], [2, 1], [0, 2] ];
visualizer = dynamic_visualizer(2);
visualizer.append_dynamics(t, y, canvas=0, separate=[ [0], [1] ]);
def testVisualizeMultipleDynamicNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7];
y = [ [0, 0], [0, 0], [1, 0], [2, 1], [0, 2], [1, 0], [2, 1], [0, 2] ];
visualizer = dynamic_visualizer(1);
visualizer.append_dynamics(t, y);
def testVisualizeSignleDynamicNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7];
y = [0, 0, 1, 2, 0, 1, 2, 0];
visualizer = dynamic_visualizer(1);
visualizer.append_dynamic(t, y);
def testVisualizeUnusedCanvasesNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7];
y = [0, 0, 1, 2, 0, 1, 2, 0];
visualizer = dynamic_visualizer(3);
visualizer.append_dynamic(t, y, canvas=0, color='red');
def testVisualizeDynamicWithColorNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7];
y = [0, 0, 1, 2, 0, 1, 2, 0];
visualizer = dynamic_visualizer(1);
visualizer.append_dynamic(t, y, canvas=0, color='red');
def testVisualizeDynamicWithColorNoCrash(self):
t = [0, 1, 2, 3, 4, 5, 6, 7];
y = [0, 0, 1, 2, 0, 1, 2, 0];
visualizer = dynamic_visualizer(1);
visualizer.append_dynamic(t, y, canvas=0, color='red');
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 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();
