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see.py
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see.py
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import matplotlib.pyplot as plt
import math
from scipy.spatial import Voronoi, voronoi_plot_2d
def extractPath(path = None, firing_history = None):
x_positions = []
y_positions = []
if firing_history != None:
for t in range(len(firing_history)):
x, y = firing_history[t][1]
x_positions.append(x)
y_positions.append(y)
if path != None:
for t in range(len(path)):
x, y = path[t][0]
x_positions.append(x)
y_positions.append(y)
return(x_positions, y_positions)
def path(path=None, firing_history=None, path_x = None, path_y = None):
if (path_x != None and path_y != None):
x_positions = path_x
y_positions = path_y
elif (path != None):
x_positions, y_positions = extractPath(path=path)
elif (firing_history != None):
x_positions, y_positions = extractPath(firing_history=firing_history)
plt.plot(x_positions, y_positions)
plt.show()
def firing(firing_history):
x_positions = []
y_positions = []
firing = []
for t in range(len(firing_history)):
x, y = firing_history[t][1]
x_positions.append(x)
y_positions.append(y)
firing.append(firing_history[t][2])
plt.scatter(x_positions, y_positions, c=firing)
plt.show()
def firing_voronoi(positions, firing, title=""):
"""
Plot firing rate as voronoi regions.
"""
# compute voronoi regions
vor = Voronoi(points=positions)
# plot voronoi regions without borders
voronoi_plot_2d(vor, show_vertices=False, show_points=False, line_width=0.0)
# map firing rate to colors
mapper = plt.cm.ScalarMappable(cmap=plt.cm.viridis)
colors = mapper.to_rgba(firing, norm=True)
# color voronoi region by firing rate
for point_region, color in zip(vor.point_region, colors):
# exclude regions with infinite vertices
if -1 not in vor.regions[point_region]:
polygon = [vor.vertices[i] for i in vor.regions[point_region]] # get vertices of region
plt.fill(*zip(*polygon), facecolor=color, edgecolor=color, linewidth=0.1)
# set lims to position range
plt.xlim(positions[:, 0].min(), positions[:, 0].max())
plt.ylim(positions[:, 1].min(), positions[:, 1].max())
plt.title(title)
plt.gcf().set_dpi(300)
plt.gca().set_aspect('equal')
plt.gcf().set_constrained_layout(True)
plt.show()
def oscillatorActivitySpatially(firing_history, oscillator_history):
x_positions = []
y_positions = []
oscillator_activity = []
for t in range(len(firing_history)):
x, y = firing_history[t][1]
x_positions.append(x)
y_positions.append(y)
oscillator_activity.append(oscillator_history[t])
plt.scatter(x_positions, y_positions, c=oscillator_activity)
plt.show()
def oscillatorActivityTemporally(oscillator_history):
t = range(len(oscillator_history))
plt.plot(t, oscillator_history)
plt.show()
def all(grid_cell):
firing_history = grid_cell.firing_history
somatic_activity = grid_cell.soma.activity_history
n_dendrites = len(grid_cell.dendrites)
x_positions = []
y_positions = []
firing = []
for t in range(len(firing_history)):
x, y = firing_history[t][1]
x_positions.append(x)
y_positions.append(y)
firing.append(firing_history[t][2])
fig, axs = plt.subplots(math.ceil((n_dendrites + 2) / 2), 2)
axs[0, 0].scatter(x_positions, y_positions, c=firing)
axs[0, 0].set_title('Firing')
axs[0, 1].scatter(x_positions, y_positions, c=somatic_activity)
axs[0, 1].set_title('Somatic Activity')
row = 1
column = 0
for dendrite in range(n_dendrites):
dendrite_activity = grid_cell.dendrites[dendrite].activity_history
axs[row, column].scatter(x_positions, y_positions, c=dendrite_activity)
axs[row, column].set_title('Dendritic Activity')
row = math.ceil((dendrite + 2) / 2)
column += 1
column = column % 2
fig.tight_layout()
plt.show()