def plot_different_distances(modelClass):
"""
Plot the distribution of different distances. Helps for construction of the network.
:param modelClass: A Network model that has PC elements.
:type modelClass: Either ThresholdModel or FairhallModel
"""
n = modelClass.p['rows'] * modelClass.p['cols']
list1 = []
list2 = []
for j in range(n):
list1.append(2 -
2 * exp(-((modelClass.PC.x[50] - modelClass.PC.x[j])**2 +
(modelClass.PC.y[50] - modelClass.PC.y[j])**2) /
((60 * meter)**2)))
list2.append((((modelClass.PC.x[50] - modelClass.PC.x[j])**2 +
(modelClass.PC.y[50] - modelClass.PC.y[j])**2) /
((2 * 15 * meter)**2)) / 10)
BasicFunctions.plot_distrib(list1, "2 - 2 * exp(-||n1 - n2||_2)")
BasicFunctions.plot_distrib(list2, "||n1 - n2||_2")
def plot_distance(modelClass):
"""
Plots the distance of all the pyramidal cell to a the neuron_idx one.
neuron_idx is an attribute of the class Model.
:param modelClass: A network model
:type modelClass: Either ThresholdModel or FairhallModel
"""
# Computes the distance of all PC cells to the neuron_idx and returns a list.
my_list = BasicFunctions.list_distance(modelClass.PC,
modelClass.neuron_idx)
# Plots the distribution of this list of distances.
BasicFunctions.plot_distrib(my_list, "distances")
# Marks the neuron_idx
plot(modelClass.PC.x[modelClass.neuron_idx] / meter,
modelClass.PC.y[modelClass.neuron_idx] / meter,
'o',
mfc='none',
label=str(modelClass.neuron_idx))
color = 'b'
# The alpha allows to "grade" the distance to the neuron cell.
for i in range(modelClass.p['rows'] * modelClass.p['cols']):
plot(modelClass.PC.x[i] / meter,
modelClass.PC.y[i] / meter,
color + '.',
alpha=my_list[i])
xlim(-10, modelClass.p['rows'])
ylim(0, modelClass.p['cols'])
xlabel('x')
ylabel('y', rotation='vertical')
axis('equal')
title("Distance plot")
show()
